2022/09/12 19:46:39 - 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: 2047989241 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 19:46:40 - 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=(288, 384), heatmap_size=(72, 96), sigma=3) 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=(288, 384), heatmap_size=(72, 96), sigma=3)), 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=(288, 384), use_udp=True), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='UDPHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3)), 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=(288, 384), 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=(288, 384), use_udp=True), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='UDPHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3)), 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=(288, 384), 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=(288, 384), 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_384_v1/' 2022/09/12 19:47:15 - 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 19:47:15 - 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 19:47:15 - 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 19:47:15 - 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 19:47:15 - 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 19:47:15 - 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 19:47:15 - 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 19:47:15 - 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 19:47:19 - 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 19:47:22 - 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 19:47:23 - 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 19:47:23 - 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 19:47:36 - mmengine - INFO - Checkpoints will be saved to /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_384_v1 by HardDiskBackend. 2022/09/12 19:49:16 - mmengine - INFO - Epoch(train) [1][50/586] lr: 4.954910e-05 eta: 2 days, 20:00:47 time: 1.990469 data_time: 0.296855 memory: 15239 loss_kpt: 0.002230 acc_pose: 0.112594 loss: 0.002230 2022/09/12 19:50:12 - mmengine - INFO - Epoch(train) [1][100/586] lr: 9.959920e-05 eta: 2 days, 5:09:42 time: 1.122460 data_time: 0.192369 memory: 15239 loss_kpt: 0.001836 acc_pose: 0.393606 loss: 0.001836 2022/09/12 19:51:00 - mmengine - INFO - Epoch(train) [1][150/586] lr: 1.496493e-04 eta: 1 day, 22:20:33 time: 0.959151 data_time: 0.273642 memory: 15239 loss_kpt: 0.001484 acc_pose: 0.541453 loss: 0.001484 2022/09/12 19:51:45 - mmengine - INFO - Epoch(train) [1][200/586] lr: 1.996994e-04 eta: 1 day, 18:28:14 time: 0.905778 data_time: 0.114292 memory: 15239 loss_kpt: 0.001322 acc_pose: 0.516271 loss: 0.001322 2022/09/12 19:52:25 - mmengine - INFO - Epoch(train) [1][250/586] lr: 2.497495e-04 eta: 1 day, 15:25:15 time: 0.799980 data_time: 0.064506 memory: 15239 loss_kpt: 0.001253 acc_pose: 0.595996 loss: 0.001253 2022/09/12 19:53:03 - mmengine - INFO - Epoch(train) [1][300/586] lr: 2.997996e-04 eta: 1 day, 13:07:20 time: 0.753976 data_time: 0.029286 memory: 15239 loss_kpt: 0.001235 acc_pose: 0.623905 loss: 0.001235 2022/09/12 19:53:36 - mmengine - INFO - Epoch(train) [1][350/586] lr: 3.498497e-04 eta: 1 day, 11:02:28 time: 0.664343 data_time: 0.065771 memory: 15239 loss_kpt: 0.001172 acc_pose: 0.533297 loss: 0.001172 2022/09/12 19:54:07 - mmengine - INFO - Epoch(train) [1][400/586] lr: 3.998998e-04 eta: 1 day, 9:14:13 time: 0.607775 data_time: 0.106708 memory: 15239 loss_kpt: 0.001154 acc_pose: 0.636663 loss: 0.001154 2022/09/12 19:54:34 - mmengine - INFO - Epoch(train) [1][450/586] lr: 4.499499e-04 eta: 1 day, 7:34:23 time: 0.539374 data_time: 0.037463 memory: 15239 loss_kpt: 0.001127 acc_pose: 0.741888 loss: 0.001127 2022/09/12 19:54:58 - mmengine - INFO - Epoch(train) [1][500/586] lr: 5.000000e-04 eta: 1 day, 6:04:14 time: 0.489499 data_time: 0.031905 memory: 15239 loss_kpt: 0.001120 acc_pose: 0.638662 loss: 0.001120 2022/09/12 19:55:22 - mmengine - INFO - Epoch(train) [1][550/586] lr: 5.000000e-04 eta: 1 day, 4:48:01 time: 0.476581 data_time: 0.032250 memory: 15239 loss_kpt: 0.001105 acc_pose: 0.641643 loss: 0.001105 2022/09/12 19:55:43 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 19:55:43 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/09/12 19:56:14 - mmengine - INFO - Epoch(train) [2][50/586] lr: 5.000000e-04 eta: 1 day, 2:09:11 time: 0.473017 data_time: 0.036313 memory: 15239 loss_kpt: 0.001080 acc_pose: 0.602747 loss: 0.001080 2022/09/12 19:56:37 - mmengine - INFO - Epoch(train) [2][100/586] lr: 5.000000e-04 eta: 1 day, 1:22:49 time: 0.461522 data_time: 0.025501 memory: 15239 loss_kpt: 0.001067 acc_pose: 0.635732 loss: 0.001067 2022/09/12 19:57:01 - mmengine - INFO - Epoch(train) [2][150/586] lr: 5.000000e-04 eta: 1 day, 0:43:34 time: 0.467771 data_time: 0.026983 memory: 15239 loss_kpt: 0.001054 acc_pose: 0.653157 loss: 0.001054 2022/09/12 19:57:24 - mmengine - INFO - Epoch(train) [2][200/586] lr: 5.000000e-04 eta: 1 day, 0:09:15 time: 0.467685 data_time: 0.025907 memory: 15239 loss_kpt: 0.001005 acc_pose: 0.656750 loss: 0.001005 2022/09/12 19:57:47 - mmengine - INFO - Epoch(train) [2][250/586] lr: 5.000000e-04 eta: 23:37:57 time: 0.459023 data_time: 0.028378 memory: 15239 loss_kpt: 0.000997 acc_pose: 0.628622 loss: 0.000997 2022/09/12 19:58:10 - mmengine - INFO - Epoch(train) [2][300/586] lr: 5.000000e-04 eta: 23:10:06 time: 0.458863 data_time: 0.025918 memory: 15239 loss_kpt: 0.001001 acc_pose: 0.696609 loss: 0.001001 2022/09/12 19:58:33 - mmengine - INFO - Epoch(train) [2][350/586] lr: 5.000000e-04 eta: 22:46:02 time: 0.466524 data_time: 0.026181 memory: 15239 loss_kpt: 0.000990 acc_pose: 0.694329 loss: 0.000990 2022/09/12 19:58:56 - mmengine - INFO - Epoch(train) [2][400/586] lr: 5.000000e-04 eta: 22:23:31 time: 0.458235 data_time: 0.025404 memory: 15239 loss_kpt: 0.000972 acc_pose: 0.623246 loss: 0.000972 2022/09/12 19:59:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 19:59:19 - mmengine - INFO - Epoch(train) [2][450/586] lr: 5.000000e-04 eta: 22:03:32 time: 0.462417 data_time: 0.026513 memory: 15239 loss_kpt: 0.000936 acc_pose: 0.723761 loss: 0.000936 2022/09/12 19:59:43 - mmengine - INFO - Epoch(train) [2][500/586] lr: 5.000000e-04 eta: 21:45:52 time: 0.467809 data_time: 0.025914 memory: 15239 loss_kpt: 0.000916 acc_pose: 0.707481 loss: 0.000916 2022/09/12 20:00:06 - mmengine - INFO - Epoch(train) [2][550/586] lr: 5.000000e-04 eta: 21:29:13 time: 0.462156 data_time: 0.029693 memory: 15239 loss_kpt: 0.000936 acc_pose: 0.739677 loss: 0.000936 2022/09/12 20:00:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:00:22 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/09/12 20:00:53 - mmengine - INFO - Epoch(train) [3][50/586] lr: 5.000000e-04 eta: 20:38:13 time: 0.488397 data_time: 0.035041 memory: 15239 loss_kpt: 0.000930 acc_pose: 0.699546 loss: 0.000930 2022/09/12 20:01:16 - mmengine - INFO - Epoch(train) [3][100/586] lr: 5.000000e-04 eta: 20:25:31 time: 0.456998 data_time: 0.026098 memory: 15239 loss_kpt: 0.000898 acc_pose: 0.706847 loss: 0.000898 2022/09/12 20:01:39 - mmengine - INFO - Epoch(train) [3][150/586] lr: 5.000000e-04 eta: 20:14:14 time: 0.463240 data_time: 0.025114 memory: 15239 loss_kpt: 0.000931 acc_pose: 0.726971 loss: 0.000931 2022/09/12 20:02:03 - mmengine - INFO - Epoch(train) [3][200/586] lr: 5.000000e-04 eta: 20:04:21 time: 0.471600 data_time: 0.026112 memory: 15239 loss_kpt: 0.000932 acc_pose: 0.734015 loss: 0.000932 2022/09/12 20:02:26 - mmengine - INFO - Epoch(train) [3][250/586] lr: 5.000000e-04 eta: 19:54:00 time: 0.455565 data_time: 0.025828 memory: 15239 loss_kpt: 0.000919 acc_pose: 0.705847 loss: 0.000919 2022/09/12 20:02:49 - mmengine - INFO - Epoch(train) [3][300/586] lr: 5.000000e-04 eta: 19:45:00 time: 0.465404 data_time: 0.025175 memory: 15239 loss_kpt: 0.000915 acc_pose: 0.712201 loss: 0.000915 2022/09/12 20:03:13 - mmengine - INFO - Epoch(train) [3][350/586] lr: 5.000000e-04 eta: 19:37:08 time: 0.473871 data_time: 0.025838 memory: 15239 loss_kpt: 0.000893 acc_pose: 0.634127 loss: 0.000893 2022/09/12 20:03:36 - mmengine - INFO - Epoch(train) [3][400/586] lr: 5.000000e-04 eta: 19:28:58 time: 0.461630 data_time: 0.025898 memory: 15239 loss_kpt: 0.000879 acc_pose: 0.682289 loss: 0.000879 2022/09/12 20:03:59 - mmengine - INFO - Epoch(train) [3][450/586] lr: 5.000000e-04 eta: 19:21:16 time: 0.461630 data_time: 0.026360 memory: 15239 loss_kpt: 0.000901 acc_pose: 0.741955 loss: 0.000901 2022/09/12 20:04:23 - mmengine - INFO - Epoch(train) [3][500/586] lr: 5.000000e-04 eta: 19:14:51 time: 0.475606 data_time: 0.026406 memory: 15239 loss_kpt: 0.000886 acc_pose: 0.644506 loss: 0.000886 2022/09/12 20:04:46 - mmengine - INFO - Epoch(train) [3][550/586] lr: 5.000000e-04 eta: 19:07:59 time: 0.462172 data_time: 0.025900 memory: 15239 loss_kpt: 0.000881 acc_pose: 0.771877 loss: 0.000881 2022/09/12 20:05:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:05:02 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/09/12 20:05:33 - mmengine - INFO - Epoch(train) [4][50/586] lr: 5.000000e-04 eta: 18:39:19 time: 0.477822 data_time: 0.029820 memory: 15239 loss_kpt: 0.000857 acc_pose: 0.697547 loss: 0.000857 2022/09/12 20:05:56 - mmengine - INFO - Epoch(train) [4][100/586] lr: 5.000000e-04 eta: 18:33:44 time: 0.459857 data_time: 0.025534 memory: 15239 loss_kpt: 0.000849 acc_pose: 0.675930 loss: 0.000849 2022/09/12 20:06:19 - mmengine - INFO - Epoch(train) [4][150/586] lr: 5.000000e-04 eta: 18:28:49 time: 0.467067 data_time: 0.025994 memory: 15239 loss_kpt: 0.000882 acc_pose: 0.676266 loss: 0.000882 2022/09/12 20:06:43 - mmengine - INFO - Epoch(train) [4][200/586] lr: 5.000000e-04 eta: 18:24:19 time: 0.470785 data_time: 0.025984 memory: 15239 loss_kpt: 0.000854 acc_pose: 0.702478 loss: 0.000854 2022/09/12 20:07:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:07:06 - mmengine - INFO - Epoch(train) [4][250/586] lr: 5.000000e-04 eta: 18:19:26 time: 0.458974 data_time: 0.025908 memory: 15239 loss_kpt: 0.000883 acc_pose: 0.750834 loss: 0.000883 2022/09/12 20:07:29 - mmengine - INFO - Epoch(train) [4][300/586] lr: 5.000000e-04 eta: 18:15:04 time: 0.464886 data_time: 0.025802 memory: 15239 loss_kpt: 0.000835 acc_pose: 0.744442 loss: 0.000835 2022/09/12 20:07:52 - mmengine - INFO - Epoch(train) [4][350/586] lr: 5.000000e-04 eta: 18:11:08 time: 0.470342 data_time: 0.025520 memory: 15239 loss_kpt: 0.000848 acc_pose: 0.663495 loss: 0.000848 2022/09/12 20:08:16 - mmengine - INFO - Epoch(train) [4][400/586] lr: 5.000000e-04 eta: 18:07:11 time: 0.466134 data_time: 0.026826 memory: 15239 loss_kpt: 0.000861 acc_pose: 0.704227 loss: 0.000861 2022/09/12 20:08:39 - mmengine - INFO - Epoch(train) [4][450/586] lr: 5.000000e-04 eta: 18:03:27 time: 0.467789 data_time: 0.028578 memory: 15239 loss_kpt: 0.000868 acc_pose: 0.708546 loss: 0.000868 2022/09/12 20:09:03 - mmengine - INFO - Epoch(train) [4][500/586] lr: 5.000000e-04 eta: 17:59:57 time: 0.469248 data_time: 0.025192 memory: 15239 loss_kpt: 0.000846 acc_pose: 0.760221 loss: 0.000846 2022/09/12 20:09:26 - mmengine - INFO - Epoch(train) [4][550/586] lr: 5.000000e-04 eta: 17:56:11 time: 0.460505 data_time: 0.026001 memory: 15239 loss_kpt: 0.000841 acc_pose: 0.730222 loss: 0.000841 2022/09/12 20:09:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:09:42 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/09/12 20:10:12 - mmengine - INFO - Epoch(train) [5][50/586] lr: 5.000000e-04 eta: 17:36:47 time: 0.475844 data_time: 0.030190 memory: 15239 loss_kpt: 0.000833 acc_pose: 0.796812 loss: 0.000833 2022/09/12 20:10:36 - mmengine - INFO - Epoch(train) [5][100/586] lr: 5.000000e-04 eta: 17:33:48 time: 0.463691 data_time: 0.025277 memory: 15239 loss_kpt: 0.000841 acc_pose: 0.741080 loss: 0.000841 2022/09/12 20:10:59 - mmengine - INFO - Epoch(train) [5][150/586] lr: 5.000000e-04 eta: 17:30:56 time: 0.463859 data_time: 0.026272 memory: 15239 loss_kpt: 0.000833 acc_pose: 0.702909 loss: 0.000833 2022/09/12 20:11:23 - mmengine - INFO - Epoch(train) [5][200/586] lr: 5.000000e-04 eta: 17:28:34 time: 0.474254 data_time: 0.026542 memory: 15239 loss_kpt: 0.000809 acc_pose: 0.810234 loss: 0.000809 2022/09/12 20:11:46 - mmengine - INFO - Epoch(train) [5][250/586] lr: 5.000000e-04 eta: 17:25:44 time: 0.460053 data_time: 0.025499 memory: 15239 loss_kpt: 0.000806 acc_pose: 0.753180 loss: 0.000806 2022/09/12 20:12:09 - mmengine - INFO - Epoch(train) [5][300/586] lr: 5.000000e-04 eta: 17:23:11 time: 0.465097 data_time: 0.025314 memory: 15239 loss_kpt: 0.000814 acc_pose: 0.774957 loss: 0.000814 2022/09/12 20:12:33 - mmengine - INFO - Epoch(train) [5][350/586] lr: 5.000000e-04 eta: 17:21:02 time: 0.473667 data_time: 0.025604 memory: 15239 loss_kpt: 0.000818 acc_pose: 0.689038 loss: 0.000818 2022/09/12 20:12:56 - mmengine - INFO - Epoch(train) [5][400/586] lr: 5.000000e-04 eta: 17:18:37 time: 0.464424 data_time: 0.030169 memory: 15239 loss_kpt: 0.000805 acc_pose: 0.722402 loss: 0.000805 2022/09/12 20:13:19 - mmengine - INFO - Epoch(train) [5][450/586] lr: 5.000000e-04 eta: 17:16:14 time: 0.463526 data_time: 0.025431 memory: 15239 loss_kpt: 0.000796 acc_pose: 0.773069 loss: 0.000796 2022/09/12 20:13:42 - mmengine - INFO - Epoch(train) [5][500/586] lr: 5.000000e-04 eta: 17:14:10 time: 0.470785 data_time: 0.025250 memory: 15239 loss_kpt: 0.000795 acc_pose: 0.807246 loss: 0.000795 2022/09/12 20:14:05 - mmengine - INFO - Epoch(train) [5][550/586] lr: 5.000000e-04 eta: 17:11:50 time: 0.460923 data_time: 0.029741 memory: 15239 loss_kpt: 0.000814 acc_pose: 0.791543 loss: 0.000814 2022/09/12 20:14:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:14:22 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/09/12 20:14:55 - mmengine - INFO - Epoch(train) [6][50/586] lr: 5.000000e-04 eta: 16:57:24 time: 0.478678 data_time: 0.032807 memory: 15239 loss_kpt: 0.000806 acc_pose: 0.818932 loss: 0.000806 2022/09/12 20:15:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:15:19 - mmengine - INFO - Epoch(train) [6][100/586] lr: 5.000000e-04 eta: 16:55:40 time: 0.468725 data_time: 0.027978 memory: 15239 loss_kpt: 0.000806 acc_pose: 0.730260 loss: 0.000806 2022/09/12 20:15:43 - mmengine - INFO - Epoch(train) [6][150/586] lr: 5.000000e-04 eta: 16:54:12 time: 0.475202 data_time: 0.028138 memory: 15239 loss_kpt: 0.000812 acc_pose: 0.832853 loss: 0.000812 2022/09/12 20:16:06 - mmengine - INFO - Epoch(train) [6][200/586] lr: 5.000000e-04 eta: 16:52:29 time: 0.467080 data_time: 0.027856 memory: 15239 loss_kpt: 0.000805 acc_pose: 0.740316 loss: 0.000805 2022/09/12 20:16:29 - mmengine - INFO - Epoch(train) [6][250/586] lr: 5.000000e-04 eta: 16:50:53 time: 0.468985 data_time: 0.027352 memory: 15239 loss_kpt: 0.000809 acc_pose: 0.757752 loss: 0.000809 2022/09/12 20:16:53 - mmengine - INFO - Epoch(train) [6][300/586] lr: 5.000000e-04 eta: 16:49:20 time: 0.469252 data_time: 0.026486 memory: 15239 loss_kpt: 0.000804 acc_pose: 0.745898 loss: 0.000804 2022/09/12 20:17:16 - mmengine - INFO - Epoch(train) [6][350/586] lr: 5.000000e-04 eta: 16:47:42 time: 0.465666 data_time: 0.025628 memory: 15239 loss_kpt: 0.000796 acc_pose: 0.787685 loss: 0.000796 2022/09/12 20:17:39 - mmengine - INFO - Epoch(train) [6][400/586] lr: 5.000000e-04 eta: 16:46:05 time: 0.464583 data_time: 0.025221 memory: 15239 loss_kpt: 0.000784 acc_pose: 0.731695 loss: 0.000784 2022/09/12 20:18:03 - mmengine - INFO - Epoch(train) [6][450/586] lr: 5.000000e-04 eta: 16:44:32 time: 0.466184 data_time: 0.025672 memory: 15239 loss_kpt: 0.000796 acc_pose: 0.692307 loss: 0.000796 2022/09/12 20:18:26 - mmengine - INFO - Epoch(train) [6][500/586] lr: 5.000000e-04 eta: 16:43:04 time: 0.467536 data_time: 0.026896 memory: 15239 loss_kpt: 0.000793 acc_pose: 0.817296 loss: 0.000793 2022/09/12 20:18:50 - mmengine - INFO - Epoch(train) [6][550/586] lr: 5.000000e-04 eta: 16:41:42 time: 0.469681 data_time: 0.030982 memory: 15239 loss_kpt: 0.000761 acc_pose: 0.702262 loss: 0.000761 2022/09/12 20:19:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:19:07 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/09/12 20:19:37 - mmengine - INFO - Epoch(train) [7][50/586] lr: 5.000000e-04 eta: 16:30:02 time: 0.472635 data_time: 0.043208 memory: 15239 loss_kpt: 0.000802 acc_pose: 0.770111 loss: 0.000802 2022/09/12 20:20:01 - mmengine - INFO - Epoch(train) [7][100/586] lr: 5.000000e-04 eta: 16:28:47 time: 0.466645 data_time: 0.031492 memory: 15239 loss_kpt: 0.000778 acc_pose: 0.828562 loss: 0.000778 2022/09/12 20:20:24 - mmengine - INFO - Epoch(train) [7][150/586] lr: 5.000000e-04 eta: 16:27:38 time: 0.469878 data_time: 0.032465 memory: 15239 loss_kpt: 0.000769 acc_pose: 0.752528 loss: 0.000769 2022/09/12 20:20:47 - mmengine - INFO - Epoch(train) [7][200/586] lr: 5.000000e-04 eta: 16:26:25 time: 0.466197 data_time: 0.032501 memory: 15239 loss_kpt: 0.000770 acc_pose: 0.767338 loss: 0.000770 2022/09/12 20:21:11 - mmengine - INFO - Epoch(train) [7][250/586] lr: 5.000000e-04 eta: 16:25:27 time: 0.474799 data_time: 0.031923 memory: 15239 loss_kpt: 0.000770 acc_pose: 0.794955 loss: 0.000770 2022/09/12 20:21:35 - mmengine - INFO - Epoch(train) [7][300/586] lr: 5.000000e-04 eta: 16:24:22 time: 0.469837 data_time: 0.026901 memory: 15239 loss_kpt: 0.000781 acc_pose: 0.648367 loss: 0.000781 2022/09/12 20:21:58 - mmengine - INFO - Epoch(train) [7][350/586] lr: 5.000000e-04 eta: 16:23:09 time: 0.464021 data_time: 0.026482 memory: 15239 loss_kpt: 0.000759 acc_pose: 0.698804 loss: 0.000759 2022/09/12 20:22:21 - mmengine - INFO - Epoch(train) [7][400/586] lr: 5.000000e-04 eta: 16:22:06 time: 0.470070 data_time: 0.030668 memory: 15239 loss_kpt: 0.000776 acc_pose: 0.789588 loss: 0.000776 2022/09/12 20:22:45 - mmengine - INFO - Epoch(train) [7][450/586] lr: 5.000000e-04 eta: 16:21:00 time: 0.466876 data_time: 0.026725 memory: 15239 loss_kpt: 0.000775 acc_pose: 0.808656 loss: 0.000775 2022/09/12 20:23:00 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:23:08 - mmengine - INFO - Epoch(train) [7][500/586] lr: 5.000000e-04 eta: 16:19:49 time: 0.463129 data_time: 0.026725 memory: 15239 loss_kpt: 0.000780 acc_pose: 0.692146 loss: 0.000780 2022/09/12 20:23:31 - mmengine - INFO - Epoch(train) [7][550/586] lr: 5.000000e-04 eta: 16:18:46 time: 0.467774 data_time: 0.025776 memory: 15239 loss_kpt: 0.000754 acc_pose: 0.748346 loss: 0.000754 2022/09/12 20:23:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:23:48 - mmengine - INFO - Saving checkpoint at 7 epochs 2022/09/12 20:24:19 - mmengine - INFO - Epoch(train) [8][50/586] lr: 5.000000e-04 eta: 16:09:20 time: 0.483005 data_time: 0.040291 memory: 15239 loss_kpt: 0.000764 acc_pose: 0.816689 loss: 0.000764 2022/09/12 20:24:42 - mmengine - INFO - Epoch(train) [8][100/586] lr: 5.000000e-04 eta: 16:08:28 time: 0.470079 data_time: 0.035777 memory: 15239 loss_kpt: 0.000777 acc_pose: 0.714552 loss: 0.000777 2022/09/12 20:25:06 - mmengine - INFO - Epoch(train) [8][150/586] lr: 5.000000e-04 eta: 16:07:44 time: 0.474793 data_time: 0.027058 memory: 15239 loss_kpt: 0.000761 acc_pose: 0.794061 loss: 0.000761 2022/09/12 20:25:29 - mmengine - INFO - Epoch(train) [8][200/586] lr: 5.000000e-04 eta: 16:06:53 time: 0.469389 data_time: 0.026414 memory: 15239 loss_kpt: 0.000782 acc_pose: 0.697698 loss: 0.000782 2022/09/12 20:25:53 - mmengine - INFO - Epoch(train) [8][250/586] lr: 5.000000e-04 eta: 16:06:12 time: 0.476614 data_time: 0.026461 memory: 15239 loss_kpt: 0.000763 acc_pose: 0.769648 loss: 0.000763 2022/09/12 20:26:17 - mmengine - INFO - Epoch(train) [8][300/586] lr: 5.000000e-04 eta: 16:05:40 time: 0.482464 data_time: 0.025957 memory: 15239 loss_kpt: 0.000738 acc_pose: 0.832852 loss: 0.000738 2022/09/12 20:26:41 - mmengine - INFO - Epoch(train) [8][350/586] lr: 5.000000e-04 eta: 16:04:42 time: 0.462935 data_time: 0.026647 memory: 15239 loss_kpt: 0.000774 acc_pose: 0.800517 loss: 0.000774 2022/09/12 20:27:04 - mmengine - INFO - Epoch(train) [8][400/586] lr: 5.000000e-04 eta: 16:03:47 time: 0.464511 data_time: 0.026479 memory: 15239 loss_kpt: 0.000768 acc_pose: 0.767641 loss: 0.000768 2022/09/12 20:27:28 - mmengine - INFO - Epoch(train) [8][450/586] lr: 5.000000e-04 eta: 16:03:11 time: 0.479153 data_time: 0.032128 memory: 15239 loss_kpt: 0.000782 acc_pose: 0.762502 loss: 0.000782 2022/09/12 20:27:51 - mmengine - INFO - Epoch(train) [8][500/586] lr: 5.000000e-04 eta: 16:02:26 time: 0.471683 data_time: 0.026891 memory: 15239 loss_kpt: 0.000748 acc_pose: 0.743910 loss: 0.000748 2022/09/12 20:28:15 - mmengine - INFO - Epoch(train) [8][550/586] lr: 5.000000e-04 eta: 16:01:34 time: 0.465847 data_time: 0.026906 memory: 15239 loss_kpt: 0.000766 acc_pose: 0.797459 loss: 0.000766 2022/09/12 20:28:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:28:31 - mmengine - INFO - Saving checkpoint at 8 epochs 2022/09/12 20:29:02 - mmengine - INFO - Epoch(train) [9][50/586] lr: 5.000000e-04 eta: 15:53:21 time: 0.476470 data_time: 0.035095 memory: 15239 loss_kpt: 0.000757 acc_pose: 0.753945 loss: 0.000757 2022/09/12 20:29:25 - mmengine - INFO - Epoch(train) [9][100/586] lr: 5.000000e-04 eta: 15:52:40 time: 0.470115 data_time: 0.025480 memory: 15239 loss_kpt: 0.000745 acc_pose: 0.762278 loss: 0.000745 2022/09/12 20:29:49 - mmengine - INFO - Epoch(train) [9][150/586] lr: 5.000000e-04 eta: 15:52:09 time: 0.477828 data_time: 0.025269 memory: 15239 loss_kpt: 0.000765 acc_pose: 0.736662 loss: 0.000765 2022/09/12 20:30:13 - mmengine - INFO - Epoch(train) [9][200/586] lr: 5.000000e-04 eta: 15:51:29 time: 0.469575 data_time: 0.025944 memory: 15239 loss_kpt: 0.000786 acc_pose: 0.838865 loss: 0.000786 2022/09/12 20:30:36 - mmengine - INFO - Epoch(train) [9][250/586] lr: 5.000000e-04 eta: 15:50:41 time: 0.463202 data_time: 0.025793 memory: 15239 loss_kpt: 0.000748 acc_pose: 0.801915 loss: 0.000748 2022/09/12 20:30:59 - mmengine - INFO - Epoch(train) [9][300/586] lr: 5.000000e-04 eta: 15:49:52 time: 0.461898 data_time: 0.026610 memory: 15239 loss_kpt: 0.000770 acc_pose: 0.739531 loss: 0.000770 2022/09/12 20:31:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:31:22 - mmengine - INFO - Epoch(train) [9][350/586] lr: 5.000000e-04 eta: 15:49:08 time: 0.466088 data_time: 0.024945 memory: 15239 loss_kpt: 0.000764 acc_pose: 0.744746 loss: 0.000764 2022/09/12 20:31:45 - mmengine - INFO - Epoch(train) [9][400/586] lr: 5.000000e-04 eta: 15:48:22 time: 0.463560 data_time: 0.029042 memory: 15239 loss_kpt: 0.000740 acc_pose: 0.770150 loss: 0.000740 2022/09/12 20:32:08 - mmengine - INFO - Epoch(train) [9][450/586] lr: 5.000000e-04 eta: 15:47:34 time: 0.461792 data_time: 0.025062 memory: 15239 loss_kpt: 0.000755 acc_pose: 0.783731 loss: 0.000755 2022/09/12 20:32:32 - mmengine - INFO - Epoch(train) [9][500/586] lr: 5.000000e-04 eta: 15:46:55 time: 0.468811 data_time: 0.025733 memory: 15239 loss_kpt: 0.000729 acc_pose: 0.725079 loss: 0.000729 2022/09/12 20:32:55 - mmengine - INFO - Epoch(train) [9][550/586] lr: 5.000000e-04 eta: 15:46:11 time: 0.464763 data_time: 0.025258 memory: 15239 loss_kpt: 0.000753 acc_pose: 0.720591 loss: 0.000753 2022/09/12 20:33:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:33:12 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/09/12 20:33:43 - mmengine - INFO - Epoch(train) [10][50/586] lr: 5.000000e-04 eta: 15:39:09 time: 0.483980 data_time: 0.036192 memory: 15239 loss_kpt: 0.000729 acc_pose: 0.825554 loss: 0.000729 2022/09/12 20:34:06 - mmengine - INFO - Epoch(train) [10][100/586] lr: 5.000000e-04 eta: 15:38:32 time: 0.466635 data_time: 0.029131 memory: 15239 loss_kpt: 0.000758 acc_pose: 0.730471 loss: 0.000758 2022/09/12 20:34:29 - mmengine - INFO - Epoch(train) [10][150/586] lr: 5.000000e-04 eta: 15:37:58 time: 0.469360 data_time: 0.029878 memory: 15239 loss_kpt: 0.000743 acc_pose: 0.776404 loss: 0.000743 2022/09/12 20:34:53 - mmengine - INFO - Epoch(train) [10][200/586] lr: 5.000000e-04 eta: 15:37:23 time: 0.467918 data_time: 0.025140 memory: 15239 loss_kpt: 0.000733 acc_pose: 0.750162 loss: 0.000733 2022/09/12 20:35:16 - mmengine - INFO - Epoch(train) [10][250/586] lr: 5.000000e-04 eta: 15:36:40 time: 0.461063 data_time: 0.025256 memory: 15239 loss_kpt: 0.000747 acc_pose: 0.771112 loss: 0.000747 2022/09/12 20:35:40 - mmengine - INFO - Epoch(train) [10][300/586] lr: 5.000000e-04 eta: 15:36:18 time: 0.479710 data_time: 0.026622 memory: 15239 loss_kpt: 0.000748 acc_pose: 0.771716 loss: 0.000748 2022/09/12 20:36:03 - mmengine - INFO - Epoch(train) [10][350/586] lr: 5.000000e-04 eta: 15:35:50 time: 0.473986 data_time: 0.026286 memory: 15239 loss_kpt: 0.000726 acc_pose: 0.821114 loss: 0.000726 2022/09/12 20:36:26 - mmengine - INFO - Epoch(train) [10][400/586] lr: 5.000000e-04 eta: 15:35:07 time: 0.460283 data_time: 0.026410 memory: 15239 loss_kpt: 0.000735 acc_pose: 0.815098 loss: 0.000735 2022/09/12 20:36:50 - mmengine - INFO - Epoch(train) [10][450/586] lr: 5.000000e-04 eta: 15:34:38 time: 0.472502 data_time: 0.030471 memory: 15239 loss_kpt: 0.000732 acc_pose: 0.786381 loss: 0.000732 2022/09/12 20:37:14 - mmengine - INFO - Epoch(train) [10][500/586] lr: 5.000000e-04 eta: 15:34:07 time: 0.470597 data_time: 0.025819 memory: 15239 loss_kpt: 0.000736 acc_pose: 0.716345 loss: 0.000736 2022/09/12 20:37:37 - mmengine - INFO - Epoch(train) [10][550/586] lr: 5.000000e-04 eta: 15:33:22 time: 0.457710 data_time: 0.025780 memory: 15239 loss_kpt: 0.000737 acc_pose: 0.811674 loss: 0.000737 2022/09/12 20:37:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:37:53 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/09/12 20:38:16 - mmengine - INFO - Epoch(val) [10][50/407] eta: 0:01:43 time: 0.288937 data_time: 0.077851 memory: 15239 2022/09/12 20:38:29 - mmengine - INFO - Epoch(val) [10][100/407] eta: 0:01:19 time: 0.258516 data_time: 0.047410 memory: 2064 2022/09/12 20:38:41 - mmengine - INFO - Epoch(val) [10][150/407] eta: 0:01:04 time: 0.252429 data_time: 0.043474 memory: 2064 2022/09/12 20:38:54 - mmengine - INFO - Epoch(val) [10][200/407] eta: 0:00:54 time: 0.261633 data_time: 0.050585 memory: 2064 2022/09/12 20:39:07 - mmengine - INFO - Epoch(val) [10][250/407] eta: 0:00:40 time: 0.261036 data_time: 0.050501 memory: 2064 2022/09/12 20:39:19 - mmengine - INFO - Epoch(val) [10][300/407] eta: 0:00:25 time: 0.236509 data_time: 0.026399 memory: 2064 2022/09/12 20:39:31 - mmengine - INFO - Epoch(val) [10][350/407] eta: 0:00:13 time: 0.241302 data_time: 0.031715 memory: 2064 2022/09/12 20:39:43 - mmengine - INFO - Epoch(val) [10][400/407] eta: 0:00:01 time: 0.237204 data_time: 0.026712 memory: 2064 2022/09/12 20:40:20 - mmengine - INFO - Evaluating CocoMetric... 2022/09/12 20:40:34 - mmengine - INFO - Epoch(val) [10][407/407] coco/AP: 0.694409 coco/AP .5: 0.881742 coco/AP .75: 0.757662 coco/AP (M): 0.655017 coco/AP (L): 0.767703 coco/AR: 0.749858 coco/AR .5: 0.920812 coco/AR .75: 0.809509 coco/AR (M): 0.703633 coco/AR (L): 0.816202 2022/09/12 20:40:38 - mmengine - INFO - The best checkpoint with 0.6944 coco/AP at 10 epoch is saved to best_coco/AP_epoch_10.pth. 2022/09/12 20:41:01 - mmengine - INFO - Epoch(train) [11][50/586] lr: 5.000000e-04 eta: 15:26:47 time: 0.463910 data_time: 0.032271 memory: 15239 loss_kpt: 0.000718 acc_pose: 0.759645 loss: 0.000718 2022/09/12 20:41:24 - mmengine - INFO - Epoch(train) [11][100/586] lr: 5.000000e-04 eta: 15:26:15 time: 0.466174 data_time: 0.034806 memory: 15239 loss_kpt: 0.000724 acc_pose: 0.855650 loss: 0.000724 2022/09/12 20:41:43 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:41:48 - mmengine - INFO - Epoch(train) [11][150/586] lr: 5.000000e-04 eta: 15:25:46 time: 0.469199 data_time: 0.032052 memory: 15239 loss_kpt: 0.000703 acc_pose: 0.832269 loss: 0.000703 2022/09/12 20:42:11 - mmengine - INFO - Epoch(train) [11][200/586] lr: 5.000000e-04 eta: 15:25:10 time: 0.461702 data_time: 0.025858 memory: 15239 loss_kpt: 0.000719 acc_pose: 0.742721 loss: 0.000719 2022/09/12 20:42:34 - mmengine - INFO - Epoch(train) [11][250/586] lr: 5.000000e-04 eta: 15:24:36 time: 0.464607 data_time: 0.025729 memory: 15239 loss_kpt: 0.000718 acc_pose: 0.771635 loss: 0.000718 2022/09/12 20:42:57 - mmengine - INFO - Epoch(train) [11][300/586] lr: 5.000000e-04 eta: 15:24:05 time: 0.465683 data_time: 0.029918 memory: 15239 loss_kpt: 0.000722 acc_pose: 0.774866 loss: 0.000722 2022/09/12 20:43:21 - mmengine - INFO - Epoch(train) [11][350/586] lr: 5.000000e-04 eta: 15:23:30 time: 0.463329 data_time: 0.025902 memory: 15239 loss_kpt: 0.000734 acc_pose: 0.764331 loss: 0.000734 2022/09/12 20:43:44 - mmengine - INFO - Epoch(train) [11][400/586] lr: 5.000000e-04 eta: 15:23:02 time: 0.469327 data_time: 0.026939 memory: 15239 loss_kpt: 0.000727 acc_pose: 0.779516 loss: 0.000727 2022/09/12 20:44:07 - mmengine - INFO - Epoch(train) [11][450/586] lr: 5.000000e-04 eta: 15:22:30 time: 0.464448 data_time: 0.025605 memory: 15239 loss_kpt: 0.000715 acc_pose: 0.828526 loss: 0.000715 2022/09/12 20:44:31 - mmengine - INFO - Epoch(train) [11][500/586] lr: 5.000000e-04 eta: 15:22:02 time: 0.469732 data_time: 0.026694 memory: 15239 loss_kpt: 0.000717 acc_pose: 0.873009 loss: 0.000717 2022/09/12 20:44:54 - mmengine - INFO - Epoch(train) [11][550/586] lr: 5.000000e-04 eta: 15:21:31 time: 0.466039 data_time: 0.026726 memory: 15239 loss_kpt: 0.000750 acc_pose: 0.690317 loss: 0.000750 2022/09/12 20:45:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:45:11 - mmengine - INFO - Saving checkpoint at 11 epochs 2022/09/12 20:45:41 - mmengine - INFO - Epoch(train) [12][50/586] lr: 5.000000e-04 eta: 15:15:45 time: 0.474749 data_time: 0.037179 memory: 15239 loss_kpt: 0.000747 acc_pose: 0.781004 loss: 0.000747 2022/09/12 20:46:04 - mmengine - INFO - Epoch(train) [12][100/586] lr: 5.000000e-04 eta: 15:15:19 time: 0.469058 data_time: 0.026159 memory: 15239 loss_kpt: 0.000724 acc_pose: 0.740075 loss: 0.000724 2022/09/12 20:46:28 - mmengine - INFO - Epoch(train) [12][150/586] lr: 5.000000e-04 eta: 15:14:53 time: 0.468865 data_time: 0.025577 memory: 15239 loss_kpt: 0.000713 acc_pose: 0.824140 loss: 0.000713 2022/09/12 20:46:51 - mmengine - INFO - Epoch(train) [12][200/586] lr: 5.000000e-04 eta: 15:14:27 time: 0.467932 data_time: 0.026297 memory: 15239 loss_kpt: 0.000715 acc_pose: 0.795498 loss: 0.000715 2022/09/12 20:47:15 - mmengine - INFO - Epoch(train) [12][250/586] lr: 5.000000e-04 eta: 15:14:03 time: 0.470724 data_time: 0.025337 memory: 15239 loss_kpt: 0.000716 acc_pose: 0.769990 loss: 0.000716 2022/09/12 20:47:39 - mmengine - INFO - Epoch(train) [12][300/586] lr: 5.000000e-04 eta: 15:13:42 time: 0.474297 data_time: 0.025371 memory: 15239 loss_kpt: 0.000702 acc_pose: 0.835817 loss: 0.000702 2022/09/12 20:48:02 - mmengine - INFO - Epoch(train) [12][350/586] lr: 5.000000e-04 eta: 15:13:10 time: 0.462330 data_time: 0.028552 memory: 15239 loss_kpt: 0.000725 acc_pose: 0.797011 loss: 0.000725 2022/09/12 20:48:25 - mmengine - INFO - Epoch(train) [12][400/586] lr: 5.000000e-04 eta: 15:12:42 time: 0.466233 data_time: 0.025989 memory: 15239 loss_kpt: 0.000737 acc_pose: 0.759129 loss: 0.000737 2022/09/12 20:48:48 - mmengine - INFO - Epoch(train) [12][450/586] lr: 5.000000e-04 eta: 15:12:08 time: 0.458721 data_time: 0.025116 memory: 15239 loss_kpt: 0.000713 acc_pose: 0.820005 loss: 0.000713 2022/09/12 20:49:11 - mmengine - INFO - Epoch(train) [12][500/586] lr: 5.000000e-04 eta: 15:11:37 time: 0.461720 data_time: 0.025786 memory: 15239 loss_kpt: 0.000734 acc_pose: 0.727239 loss: 0.000734 2022/09/12 20:49:35 - mmengine - INFO - Epoch(train) [12][550/586] lr: 5.000000e-04 eta: 15:11:13 time: 0.470778 data_time: 0.025488 memory: 15239 loss_kpt: 0.000723 acc_pose: 0.818118 loss: 0.000723 2022/09/12 20:49:36 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:49:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:49:51 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/09/12 20:50:21 - mmengine - INFO - Epoch(train) [13][50/586] lr: 5.000000e-04 eta: 15:06:00 time: 0.476780 data_time: 0.036210 memory: 15239 loss_kpt: 0.000710 acc_pose: 0.751608 loss: 0.000710 2022/09/12 20:50:45 - mmengine - INFO - Epoch(train) [13][100/586] lr: 5.000000e-04 eta: 15:05:38 time: 0.471148 data_time: 0.025501 memory: 15239 loss_kpt: 0.000719 acc_pose: 0.780741 loss: 0.000719 2022/09/12 20:51:08 - mmengine - INFO - Epoch(train) [13][150/586] lr: 5.000000e-04 eta: 15:05:10 time: 0.463357 data_time: 0.025862 memory: 15239 loss_kpt: 0.000708 acc_pose: 0.816186 loss: 0.000708 2022/09/12 20:51:32 - mmengine - INFO - Epoch(train) [13][200/586] lr: 5.000000e-04 eta: 15:04:46 time: 0.467352 data_time: 0.025152 memory: 15239 loss_kpt: 0.000731 acc_pose: 0.734531 loss: 0.000731 2022/09/12 20:51:55 - mmengine - INFO - Epoch(train) [13][250/586] lr: 5.000000e-04 eta: 15:04:18 time: 0.462586 data_time: 0.026191 memory: 15239 loss_kpt: 0.000715 acc_pose: 0.812177 loss: 0.000715 2022/09/12 20:52:18 - mmengine - INFO - Epoch(train) [13][300/586] lr: 5.000000e-04 eta: 15:03:50 time: 0.463888 data_time: 0.025926 memory: 15239 loss_kpt: 0.000723 acc_pose: 0.732726 loss: 0.000723 2022/09/12 20:52:41 - mmengine - INFO - Epoch(train) [13][350/586] lr: 5.000000e-04 eta: 15:03:26 time: 0.467482 data_time: 0.026290 memory: 15239 loss_kpt: 0.000719 acc_pose: 0.792084 loss: 0.000719 2022/09/12 20:53:05 - mmengine - INFO - Epoch(train) [13][400/586] lr: 5.000000e-04 eta: 15:03:02 time: 0.468193 data_time: 0.025508 memory: 15239 loss_kpt: 0.000716 acc_pose: 0.744441 loss: 0.000716 2022/09/12 20:53:28 - mmengine - INFO - Epoch(train) [13][450/586] lr: 5.000000e-04 eta: 15:02:33 time: 0.461386 data_time: 0.030647 memory: 15239 loss_kpt: 0.000710 acc_pose: 0.866797 loss: 0.000710 2022/09/12 20:53:51 - mmengine - INFO - Epoch(train) [13][500/586] lr: 5.000000e-04 eta: 15:02:11 time: 0.470069 data_time: 0.025961 memory: 15239 loss_kpt: 0.000699 acc_pose: 0.857935 loss: 0.000699 2022/09/12 20:54:14 - mmengine - INFO - Epoch(train) [13][550/586] lr: 5.000000e-04 eta: 15:01:45 time: 0.464919 data_time: 0.028190 memory: 15239 loss_kpt: 0.000707 acc_pose: 0.746477 loss: 0.000707 2022/09/12 20:54:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:54:31 - mmengine - INFO - Saving checkpoint at 13 epochs 2022/09/12 20:55:02 - mmengine - INFO - Epoch(train) [14][50/586] lr: 5.000000e-04 eta: 14:57:04 time: 0.486134 data_time: 0.039128 memory: 15239 loss_kpt: 0.000691 acc_pose: 0.718238 loss: 0.000691 2022/09/12 20:55:25 - mmengine - INFO - Epoch(train) [14][100/586] lr: 5.000000e-04 eta: 14:56:42 time: 0.468380 data_time: 0.033036 memory: 15239 loss_kpt: 0.000710 acc_pose: 0.756660 loss: 0.000710 2022/09/12 20:55:49 - mmengine - INFO - Epoch(train) [14][150/586] lr: 5.000000e-04 eta: 14:56:20 time: 0.468158 data_time: 0.027298 memory: 15239 loss_kpt: 0.000677 acc_pose: 0.831356 loss: 0.000677 2022/09/12 20:56:12 - mmengine - INFO - Epoch(train) [14][200/586] lr: 5.000000e-04 eta: 14:56:01 time: 0.472175 data_time: 0.027387 memory: 15239 loss_kpt: 0.000722 acc_pose: 0.768279 loss: 0.000722 2022/09/12 20:56:36 - mmengine - INFO - Epoch(train) [14][250/586] lr: 5.000000e-04 eta: 14:55:36 time: 0.464554 data_time: 0.026111 memory: 15239 loss_kpt: 0.000689 acc_pose: 0.810828 loss: 0.000689 2022/09/12 20:56:59 - mmengine - INFO - Epoch(train) [14][300/586] lr: 5.000000e-04 eta: 14:55:10 time: 0.462749 data_time: 0.025644 memory: 15239 loss_kpt: 0.000680 acc_pose: 0.857276 loss: 0.000680 2022/09/12 20:57:22 - mmengine - INFO - Epoch(train) [14][350/586] lr: 5.000000e-04 eta: 14:54:51 time: 0.471653 data_time: 0.025227 memory: 15239 loss_kpt: 0.000699 acc_pose: 0.767390 loss: 0.000699 2022/09/12 20:57:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:57:45 - mmengine - INFO - Epoch(train) [14][400/586] lr: 5.000000e-04 eta: 14:54:24 time: 0.462122 data_time: 0.025517 memory: 15239 loss_kpt: 0.000698 acc_pose: 0.728302 loss: 0.000698 2022/09/12 20:58:09 - mmengine - INFO - Epoch(train) [14][450/586] lr: 5.000000e-04 eta: 14:54:04 time: 0.470404 data_time: 0.027520 memory: 15239 loss_kpt: 0.000708 acc_pose: 0.825609 loss: 0.000708 2022/09/12 20:58:33 - mmengine - INFO - Epoch(train) [14][500/586] lr: 5.000000e-04 eta: 14:53:45 time: 0.473328 data_time: 0.025698 memory: 15239 loss_kpt: 0.000717 acc_pose: 0.670575 loss: 0.000717 2022/09/12 20:58:56 - mmengine - INFO - Epoch(train) [14][550/586] lr: 5.000000e-04 eta: 14:53:19 time: 0.463161 data_time: 0.026062 memory: 15239 loss_kpt: 0.000703 acc_pose: 0.825635 loss: 0.000703 2022/09/12 20:59:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 20:59:12 - mmengine - INFO - Saving checkpoint at 14 epochs 2022/09/12 20:59:45 - mmengine - INFO - Epoch(train) [15][50/586] lr: 5.000000e-04 eta: 14:48:58 time: 0.484581 data_time: 0.029842 memory: 15239 loss_kpt: 0.000696 acc_pose: 0.771293 loss: 0.000696 2022/09/12 21:00:08 - mmengine - INFO - Epoch(train) [15][100/586] lr: 5.000000e-04 eta: 14:48:38 time: 0.469155 data_time: 0.026381 memory: 15239 loss_kpt: 0.000711 acc_pose: 0.690230 loss: 0.000711 2022/09/12 21:00:32 - mmengine - INFO - Epoch(train) [15][150/586] lr: 5.000000e-04 eta: 14:48:21 time: 0.474181 data_time: 0.029777 memory: 15239 loss_kpt: 0.000701 acc_pose: 0.799914 loss: 0.000701 2022/09/12 21:00:56 - mmengine - INFO - Epoch(train) [15][200/586] lr: 5.000000e-04 eta: 14:48:00 time: 0.467357 data_time: 0.026340 memory: 15239 loss_kpt: 0.000689 acc_pose: 0.830736 loss: 0.000689 2022/09/12 21:01:19 - mmengine - INFO - Epoch(train) [15][250/586] lr: 5.000000e-04 eta: 14:47:35 time: 0.461664 data_time: 0.025038 memory: 15239 loss_kpt: 0.000694 acc_pose: 0.752484 loss: 0.000694 2022/09/12 21:01:42 - mmengine - INFO - Epoch(train) [15][300/586] lr: 5.000000e-04 eta: 14:47:15 time: 0.470418 data_time: 0.026533 memory: 15239 loss_kpt: 0.000683 acc_pose: 0.877197 loss: 0.000683 2022/09/12 21:02:06 - mmengine - INFO - Epoch(train) [15][350/586] lr: 5.000000e-04 eta: 14:46:56 time: 0.470149 data_time: 0.025510 memory: 15239 loss_kpt: 0.000693 acc_pose: 0.768383 loss: 0.000693 2022/09/12 21:02:29 - mmengine - INFO - Epoch(train) [15][400/586] lr: 5.000000e-04 eta: 14:46:33 time: 0.466011 data_time: 0.027231 memory: 15239 loss_kpt: 0.000687 acc_pose: 0.809947 loss: 0.000687 2022/09/12 21:02:53 - mmengine - INFO - Epoch(train) [15][450/586] lr: 5.000000e-04 eta: 14:46:17 time: 0.475485 data_time: 0.025984 memory: 15239 loss_kpt: 0.000693 acc_pose: 0.792172 loss: 0.000693 2022/09/12 21:03:16 - mmengine - INFO - Epoch(train) [15][500/586] lr: 5.000000e-04 eta: 14:45:56 time: 0.468409 data_time: 0.026080 memory: 15239 loss_kpt: 0.000693 acc_pose: 0.723512 loss: 0.000693 2022/09/12 21:03:40 - mmengine - INFO - Epoch(train) [15][550/586] lr: 5.000000e-04 eta: 14:45:35 time: 0.467369 data_time: 0.026778 memory: 15239 loss_kpt: 0.000691 acc_pose: 0.680137 loss: 0.000691 2022/09/12 21:03:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:03:56 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/09/12 21:04:27 - mmengine - INFO - Epoch(train) [16][50/586] lr: 5.000000e-04 eta: 14:41:32 time: 0.485263 data_time: 0.036905 memory: 15239 loss_kpt: 0.000703 acc_pose: 0.737820 loss: 0.000703 2022/09/12 21:04:51 - mmengine - INFO - Epoch(train) [16][100/586] lr: 5.000000e-04 eta: 14:41:10 time: 0.464792 data_time: 0.025162 memory: 15239 loss_kpt: 0.000709 acc_pose: 0.833937 loss: 0.000709 2022/09/12 21:05:14 - mmengine - INFO - Epoch(train) [16][150/586] lr: 5.000000e-04 eta: 14:40:49 time: 0.466439 data_time: 0.026215 memory: 15239 loss_kpt: 0.000699 acc_pose: 0.854474 loss: 0.000699 2022/09/12 21:05:38 - mmengine - INFO - Epoch(train) [16][200/586] lr: 5.000000e-04 eta: 14:40:31 time: 0.471037 data_time: 0.025902 memory: 15239 loss_kpt: 0.000672 acc_pose: 0.715377 loss: 0.000672 2022/09/12 21:05:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:06:01 - mmengine - INFO - Epoch(train) [16][250/586] lr: 5.000000e-04 eta: 14:40:05 time: 0.459859 data_time: 0.029390 memory: 15239 loss_kpt: 0.000685 acc_pose: 0.805231 loss: 0.000685 2022/09/12 21:06:24 - mmengine - INFO - Epoch(train) [16][300/586] lr: 5.000000e-04 eta: 14:39:49 time: 0.473744 data_time: 0.026496 memory: 15239 loss_kpt: 0.000673 acc_pose: 0.820685 loss: 0.000673 2022/09/12 21:06:48 - mmengine - INFO - Epoch(train) [16][350/586] lr: 5.000000e-04 eta: 14:39:30 time: 0.470099 data_time: 0.025946 memory: 15239 loss_kpt: 0.000702 acc_pose: 0.825175 loss: 0.000702 2022/09/12 21:07:11 - mmengine - INFO - Epoch(train) [16][400/586] lr: 5.000000e-04 eta: 14:39:09 time: 0.467408 data_time: 0.031096 memory: 15239 loss_kpt: 0.000714 acc_pose: 0.733481 loss: 0.000714 2022/09/12 21:07:35 - mmengine - INFO - Epoch(train) [16][450/586] lr: 5.000000e-04 eta: 14:38:51 time: 0.470895 data_time: 0.026451 memory: 15239 loss_kpt: 0.000690 acc_pose: 0.814670 loss: 0.000690 2022/09/12 21:07:58 - mmengine - INFO - Epoch(train) [16][500/586] lr: 5.000000e-04 eta: 14:38:29 time: 0.465162 data_time: 0.026311 memory: 15239 loss_kpt: 0.000687 acc_pose: 0.816538 loss: 0.000687 2022/09/12 21:08:21 - mmengine - INFO - Epoch(train) [16][550/586] lr: 5.000000e-04 eta: 14:38:06 time: 0.463870 data_time: 0.026335 memory: 15239 loss_kpt: 0.000675 acc_pose: 0.753188 loss: 0.000675 2022/09/12 21:08:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:08:38 - mmengine - INFO - Saving checkpoint at 16 epochs 2022/09/12 21:09:09 - mmengine - INFO - Epoch(train) [17][50/586] lr: 5.000000e-04 eta: 14:34:16 time: 0.480777 data_time: 0.029884 memory: 15239 loss_kpt: 0.000673 acc_pose: 0.841644 loss: 0.000673 2022/09/12 21:09:32 - mmengine - INFO - Epoch(train) [17][100/586] lr: 5.000000e-04 eta: 14:33:51 time: 0.459630 data_time: 0.024923 memory: 15239 loss_kpt: 0.000703 acc_pose: 0.759596 loss: 0.000703 2022/09/12 21:09:56 - mmengine - INFO - Epoch(train) [17][150/586] lr: 5.000000e-04 eta: 14:33:36 time: 0.474753 data_time: 0.031830 memory: 15239 loss_kpt: 0.000685 acc_pose: 0.813653 loss: 0.000685 2022/09/12 21:10:19 - mmengine - INFO - Epoch(train) [17][200/586] lr: 5.000000e-04 eta: 14:33:15 time: 0.464951 data_time: 0.025184 memory: 15239 loss_kpt: 0.000669 acc_pose: 0.702946 loss: 0.000669 2022/09/12 21:10:42 - mmengine - INFO - Epoch(train) [17][250/586] lr: 5.000000e-04 eta: 14:32:55 time: 0.467331 data_time: 0.026219 memory: 15239 loss_kpt: 0.000672 acc_pose: 0.795021 loss: 0.000672 2022/09/12 21:11:06 - mmengine - INFO - Epoch(train) [17][300/586] lr: 5.000000e-04 eta: 14:32:40 time: 0.474742 data_time: 0.029531 memory: 15239 loss_kpt: 0.000659 acc_pose: 0.828546 loss: 0.000659 2022/09/12 21:11:29 - mmengine - INFO - Epoch(train) [17][350/586] lr: 5.000000e-04 eta: 14:32:21 time: 0.468337 data_time: 0.026578 memory: 15239 loss_kpt: 0.000676 acc_pose: 0.718790 loss: 0.000676 2022/09/12 21:11:52 - mmengine - INFO - Epoch(train) [17][400/586] lr: 5.000000e-04 eta: 14:31:57 time: 0.460873 data_time: 0.026883 memory: 15239 loss_kpt: 0.000687 acc_pose: 0.845047 loss: 0.000687 2022/09/12 21:12:16 - mmengine - INFO - Epoch(train) [17][450/586] lr: 5.000000e-04 eta: 14:31:40 time: 0.472181 data_time: 0.025443 memory: 15239 loss_kpt: 0.000686 acc_pose: 0.771042 loss: 0.000686 2022/09/12 21:12:39 - mmengine - INFO - Epoch(train) [17][500/586] lr: 5.000000e-04 eta: 14:31:17 time: 0.462977 data_time: 0.025117 memory: 15239 loss_kpt: 0.000680 acc_pose: 0.812140 loss: 0.000680 2022/09/12 21:13:02 - mmengine - INFO - Epoch(train) [17][550/586] lr: 5.000000e-04 eta: 14:30:54 time: 0.462192 data_time: 0.025816 memory: 15239 loss_kpt: 0.000679 acc_pose: 0.804915 loss: 0.000679 2022/09/12 21:13:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:13:19 - mmengine - INFO - Saving checkpoint at 17 epochs 2022/09/12 21:13:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:13:49 - mmengine - INFO - Epoch(train) [18][50/586] lr: 5.000000e-04 eta: 14:27:12 time: 0.470834 data_time: 0.030220 memory: 15239 loss_kpt: 0.000688 acc_pose: 0.834358 loss: 0.000688 2022/09/12 21:14:13 - mmengine - INFO - Epoch(train) [18][100/586] lr: 5.000000e-04 eta: 14:26:52 time: 0.465819 data_time: 0.025121 memory: 15239 loss_kpt: 0.000680 acc_pose: 0.831510 loss: 0.000680 2022/09/12 21:14:36 - mmengine - INFO - Epoch(train) [18][150/586] lr: 5.000000e-04 eta: 14:26:36 time: 0.472270 data_time: 0.026010 memory: 15239 loss_kpt: 0.000664 acc_pose: 0.812778 loss: 0.000664 2022/09/12 21:15:00 - mmengine - INFO - Epoch(train) [18][200/586] lr: 5.000000e-04 eta: 14:26:15 time: 0.463646 data_time: 0.026508 memory: 15239 loss_kpt: 0.000659 acc_pose: 0.760754 loss: 0.000659 2022/09/12 21:15:23 - mmengine - INFO - Epoch(train) [18][250/586] lr: 5.000000e-04 eta: 14:25:58 time: 0.472724 data_time: 0.025133 memory: 15239 loss_kpt: 0.000675 acc_pose: 0.778620 loss: 0.000675 2022/09/12 21:15:47 - mmengine - INFO - Epoch(train) [18][300/586] lr: 5.000000e-04 eta: 14:25:39 time: 0.467441 data_time: 0.025478 memory: 15239 loss_kpt: 0.000684 acc_pose: 0.770734 loss: 0.000684 2022/09/12 21:16:10 - mmengine - INFO - Epoch(train) [18][350/586] lr: 5.000000e-04 eta: 14:25:23 time: 0.472276 data_time: 0.026043 memory: 15239 loss_kpt: 0.000673 acc_pose: 0.760909 loss: 0.000673 2022/09/12 21:16:33 - mmengine - INFO - Epoch(train) [18][400/586] lr: 5.000000e-04 eta: 14:25:02 time: 0.464199 data_time: 0.028656 memory: 15239 loss_kpt: 0.000662 acc_pose: 0.847817 loss: 0.000662 2022/09/12 21:16:57 - mmengine - INFO - Epoch(train) [18][450/586] lr: 5.000000e-04 eta: 14:24:45 time: 0.471852 data_time: 0.027305 memory: 15239 loss_kpt: 0.000655 acc_pose: 0.737188 loss: 0.000655 2022/09/12 21:17:21 - mmengine - INFO - Epoch(train) [18][500/586] lr: 5.000000e-04 eta: 14:24:28 time: 0.472046 data_time: 0.025751 memory: 15239 loss_kpt: 0.000689 acc_pose: 0.775008 loss: 0.000689 2022/09/12 21:17:44 - mmengine - INFO - Epoch(train) [18][550/586] lr: 5.000000e-04 eta: 14:24:07 time: 0.465257 data_time: 0.030232 memory: 15239 loss_kpt: 0.000694 acc_pose: 0.763333 loss: 0.000694 2022/09/12 21:18:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:18:01 - mmengine - INFO - Saving checkpoint at 18 epochs 2022/09/12 21:18:31 - mmengine - INFO - Epoch(train) [19][50/586] lr: 5.000000e-04 eta: 14:20:38 time: 0.472531 data_time: 0.038736 memory: 15239 loss_kpt: 0.000681 acc_pose: 0.799105 loss: 0.000681 2022/09/12 21:18:55 - mmengine - INFO - Epoch(train) [19][100/586] lr: 5.000000e-04 eta: 14:20:19 time: 0.466216 data_time: 0.030218 memory: 15239 loss_kpt: 0.000688 acc_pose: 0.731622 loss: 0.000688 2022/09/12 21:19:18 - mmengine - INFO - Epoch(train) [19][150/586] lr: 5.000000e-04 eta: 14:19:59 time: 0.466050 data_time: 0.031796 memory: 15239 loss_kpt: 0.000670 acc_pose: 0.854452 loss: 0.000670 2022/09/12 21:19:42 - mmengine - INFO - Epoch(train) [19][200/586] lr: 5.000000e-04 eta: 14:19:43 time: 0.472282 data_time: 0.035442 memory: 15239 loss_kpt: 0.000670 acc_pose: 0.794610 loss: 0.000670 2022/09/12 21:20:05 - mmengine - INFO - Epoch(train) [19][250/586] lr: 5.000000e-04 eta: 14:19:25 time: 0.468860 data_time: 0.040951 memory: 15239 loss_kpt: 0.000705 acc_pose: 0.816477 loss: 0.000705 2022/09/12 21:20:28 - mmengine - INFO - Epoch(train) [19][300/586] lr: 5.000000e-04 eta: 14:19:05 time: 0.464957 data_time: 0.031081 memory: 15239 loss_kpt: 0.000676 acc_pose: 0.622646 loss: 0.000676 2022/09/12 21:20:52 - mmengine - INFO - Epoch(train) [19][350/586] lr: 5.000000e-04 eta: 14:18:47 time: 0.468534 data_time: 0.031275 memory: 15239 loss_kpt: 0.000688 acc_pose: 0.779252 loss: 0.000688 2022/09/12 21:21:15 - mmengine - INFO - Epoch(train) [19][400/586] lr: 5.000000e-04 eta: 14:18:28 time: 0.466675 data_time: 0.031637 memory: 15239 loss_kpt: 0.000679 acc_pose: 0.826604 loss: 0.000679 2022/09/12 21:21:38 - mmengine - INFO - Epoch(train) [19][450/586] lr: 5.000000e-04 eta: 14:18:07 time: 0.464658 data_time: 0.033203 memory: 15239 loss_kpt: 0.000670 acc_pose: 0.720174 loss: 0.000670 2022/09/12 21:21:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:22:02 - mmengine - INFO - Epoch(train) [19][500/586] lr: 5.000000e-04 eta: 14:17:52 time: 0.474794 data_time: 0.028985 memory: 15239 loss_kpt: 0.000681 acc_pose: 0.763011 loss: 0.000681 2022/09/12 21:22:25 - mmengine - INFO - Epoch(train) [19][550/586] lr: 5.000000e-04 eta: 14:17:31 time: 0.463268 data_time: 0.025810 memory: 15239 loss_kpt: 0.000673 acc_pose: 0.782323 loss: 0.000673 2022/09/12 21:22:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:22:42 - mmengine - INFO - Saving checkpoint at 19 epochs 2022/09/12 21:23:12 - mmengine - INFO - Epoch(train) [20][50/586] lr: 5.000000e-04 eta: 14:14:16 time: 0.480073 data_time: 0.036735 memory: 15239 loss_kpt: 0.000653 acc_pose: 0.868227 loss: 0.000653 2022/09/12 21:23:35 - mmengine - INFO - Epoch(train) [20][100/586] lr: 5.000000e-04 eta: 14:13:55 time: 0.462220 data_time: 0.028921 memory: 15239 loss_kpt: 0.000667 acc_pose: 0.797748 loss: 0.000667 2022/09/12 21:23:59 - mmengine - INFO - Epoch(train) [20][150/586] lr: 5.000000e-04 eta: 14:13:36 time: 0.464941 data_time: 0.025823 memory: 15239 loss_kpt: 0.000674 acc_pose: 0.790920 loss: 0.000674 2022/09/12 21:24:22 - mmengine - INFO - Epoch(train) [20][200/586] lr: 5.000000e-04 eta: 14:13:19 time: 0.471244 data_time: 0.025161 memory: 15239 loss_kpt: 0.000675 acc_pose: 0.798202 loss: 0.000675 2022/09/12 21:24:46 - mmengine - INFO - Epoch(train) [20][250/586] lr: 5.000000e-04 eta: 14:13:03 time: 0.470504 data_time: 0.025695 memory: 15239 loss_kpt: 0.000688 acc_pose: 0.808111 loss: 0.000688 2022/09/12 21:25:09 - mmengine - INFO - Epoch(train) [20][300/586] lr: 5.000000e-04 eta: 14:12:41 time: 0.461827 data_time: 0.026497 memory: 15239 loss_kpt: 0.000654 acc_pose: 0.797057 loss: 0.000654 2022/09/12 21:25:33 - mmengine - INFO - Epoch(train) [20][350/586] lr: 5.000000e-04 eta: 14:12:26 time: 0.474380 data_time: 0.029674 memory: 15239 loss_kpt: 0.000694 acc_pose: 0.808183 loss: 0.000694 2022/09/12 21:25:56 - mmengine - INFO - Epoch(train) [20][400/586] lr: 5.000000e-04 eta: 14:12:07 time: 0.465428 data_time: 0.026690 memory: 15239 loss_kpt: 0.000645 acc_pose: 0.790504 loss: 0.000645 2022/09/12 21:26:19 - mmengine - INFO - Epoch(train) [20][450/586] lr: 5.000000e-04 eta: 14:11:46 time: 0.463810 data_time: 0.026576 memory: 15239 loss_kpt: 0.000649 acc_pose: 0.811750 loss: 0.000649 2022/09/12 21:26:42 - mmengine - INFO - Epoch(train) [20][500/586] lr: 5.000000e-04 eta: 14:11:27 time: 0.465789 data_time: 0.026452 memory: 15239 loss_kpt: 0.000670 acc_pose: 0.837950 loss: 0.000670 2022/09/12 21:27:06 - mmengine - INFO - Epoch(train) [20][550/586] lr: 5.000000e-04 eta: 14:11:09 time: 0.468331 data_time: 0.030573 memory: 15239 loss_kpt: 0.000663 acc_pose: 0.807513 loss: 0.000663 2022/09/12 21:27:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:27:22 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/09/12 21:27:41 - mmengine - INFO - Epoch(val) [20][50/407] eta: 0:01:20 time: 0.226404 data_time: 0.014046 memory: 15239 2022/09/12 21:27:52 - mmengine - INFO - Epoch(val) [20][100/407] eta: 0:01:06 time: 0.218017 data_time: 0.009357 memory: 2064 2022/09/12 21:28:03 - mmengine - INFO - Epoch(val) [20][150/407] eta: 0:00:56 time: 0.218294 data_time: 0.008643 memory: 2064 2022/09/12 21:28:14 - mmengine - INFO - Epoch(val) [20][200/407] eta: 0:00:45 time: 0.220285 data_time: 0.008557 memory: 2064 2022/09/12 21:28:25 - mmengine - INFO - Epoch(val) [20][250/407] eta: 0:00:34 time: 0.219269 data_time: 0.008968 memory: 2064 2022/09/12 21:28:36 - mmengine - INFO - Epoch(val) [20][300/407] eta: 0:00:23 time: 0.220832 data_time: 0.011822 memory: 2064 2022/09/12 21:28:47 - mmengine - INFO - Epoch(val) [20][350/407] eta: 0:00:12 time: 0.222584 data_time: 0.008595 memory: 2064 2022/09/12 21:28:57 - mmengine - INFO - Epoch(val) [20][400/407] eta: 0:00:01 time: 0.216181 data_time: 0.008002 memory: 2064 2022/09/12 21:29:33 - mmengine - INFO - Evaluating CocoMetric... 2022/09/12 21:29:47 - mmengine - INFO - Epoch(val) [20][407/407] coco/AP: 0.724554 coco/AP .5: 0.890101 coco/AP .75: 0.790207 coco/AP (M): 0.683438 coco/AP (L): 0.797913 coco/AR: 0.776464 coco/AR .5: 0.929628 coco/AR .75: 0.836272 coco/AR (M): 0.729118 coco/AR (L): 0.844221 2022/09/12 21:29:47 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_384_v1/best_coco/AP_epoch_10.pth is removed 2022/09/12 21:29:51 - mmengine - INFO - The best checkpoint with 0.7246 coco/AP at 20 epoch is saved to best_coco/AP_epoch_20.pth. 2022/09/12 21:30:15 - mmengine - INFO - Epoch(train) [21][50/586] lr: 5.000000e-04 eta: 14:08:02 time: 0.476264 data_time: 0.035880 memory: 15239 loss_kpt: 0.000669 acc_pose: 0.867244 loss: 0.000669 2022/09/12 21:30:38 - mmengine - INFO - Epoch(train) [21][100/586] lr: 5.000000e-04 eta: 14:07:42 time: 0.464168 data_time: 0.029596 memory: 15239 loss_kpt: 0.000627 acc_pose: 0.826728 loss: 0.000627 2022/09/12 21:31:02 - mmengine - INFO - Epoch(train) [21][150/586] lr: 5.000000e-04 eta: 14:07:24 time: 0.468021 data_time: 0.031701 memory: 15239 loss_kpt: 0.000686 acc_pose: 0.837734 loss: 0.000686 2022/09/12 21:31:25 - mmengine - INFO - Epoch(train) [21][200/586] lr: 5.000000e-04 eta: 14:07:05 time: 0.465014 data_time: 0.025033 memory: 15239 loss_kpt: 0.000658 acc_pose: 0.860317 loss: 0.000658 2022/09/12 21:31:48 - mmengine - INFO - Epoch(train) [21][250/586] lr: 5.000000e-04 eta: 14:06:47 time: 0.467168 data_time: 0.025737 memory: 15239 loss_kpt: 0.000656 acc_pose: 0.753401 loss: 0.000656 2022/09/12 21:32:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:32:11 - mmengine - INFO - Epoch(train) [21][300/586] lr: 5.000000e-04 eta: 14:06:27 time: 0.463602 data_time: 0.029786 memory: 15239 loss_kpt: 0.000682 acc_pose: 0.829891 loss: 0.000682 2022/09/12 21:32:35 - mmengine - INFO - Epoch(train) [21][350/586] lr: 5.000000e-04 eta: 14:06:07 time: 0.463808 data_time: 0.025581 memory: 15239 loss_kpt: 0.000668 acc_pose: 0.802714 loss: 0.000668 2022/09/12 21:32:58 - mmengine - INFO - Epoch(train) [21][400/586] lr: 5.000000e-04 eta: 14:05:46 time: 0.462391 data_time: 0.025587 memory: 15239 loss_kpt: 0.000662 acc_pose: 0.797411 loss: 0.000662 2022/09/12 21:33:21 - mmengine - INFO - Epoch(train) [21][450/586] lr: 5.000000e-04 eta: 14:05:28 time: 0.468123 data_time: 0.025780 memory: 15239 loss_kpt: 0.000661 acc_pose: 0.769713 loss: 0.000661 2022/09/12 21:33:44 - mmengine - INFO - Epoch(train) [21][500/586] lr: 5.000000e-04 eta: 14:05:08 time: 0.464570 data_time: 0.025669 memory: 15239 loss_kpt: 0.000647 acc_pose: 0.838627 loss: 0.000647 2022/09/12 21:34:07 - mmengine - INFO - Epoch(train) [21][550/586] lr: 5.000000e-04 eta: 14:04:47 time: 0.460427 data_time: 0.025861 memory: 15239 loss_kpt: 0.000671 acc_pose: 0.822826 loss: 0.000671 2022/09/12 21:34:24 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:34:24 - mmengine - INFO - Saving checkpoint at 21 epochs 2022/09/12 21:34:55 - mmengine - INFO - Epoch(train) [22][50/586] lr: 5.000000e-04 eta: 14:01:48 time: 0.474698 data_time: 0.033772 memory: 15239 loss_kpt: 0.000649 acc_pose: 0.832166 loss: 0.000649 2022/09/12 21:35:18 - mmengine - INFO - Epoch(train) [22][100/586] lr: 5.000000e-04 eta: 14:01:29 time: 0.464429 data_time: 0.025805 memory: 15239 loss_kpt: 0.000665 acc_pose: 0.818679 loss: 0.000665 2022/09/12 21:35:41 - mmengine - INFO - Epoch(train) [22][150/586] lr: 5.000000e-04 eta: 14:01:11 time: 0.467169 data_time: 0.025477 memory: 15239 loss_kpt: 0.000667 acc_pose: 0.792598 loss: 0.000667 2022/09/12 21:36:05 - mmengine - INFO - Epoch(train) [22][200/586] lr: 5.000000e-04 eta: 14:00:52 time: 0.465620 data_time: 0.027207 memory: 15239 loss_kpt: 0.000650 acc_pose: 0.782664 loss: 0.000650 2022/09/12 21:36:28 - mmengine - INFO - Epoch(train) [22][250/586] lr: 5.000000e-04 eta: 14:00:34 time: 0.467177 data_time: 0.029312 memory: 15239 loss_kpt: 0.000660 acc_pose: 0.813401 loss: 0.000660 2022/09/12 21:36:51 - mmengine - INFO - Epoch(train) [22][300/586] lr: 5.000000e-04 eta: 14:00:14 time: 0.462548 data_time: 0.025123 memory: 15239 loss_kpt: 0.000670 acc_pose: 0.792148 loss: 0.000670 2022/09/12 21:37:14 - mmengine - INFO - Epoch(train) [22][350/586] lr: 5.000000e-04 eta: 13:59:55 time: 0.465132 data_time: 0.027173 memory: 15239 loss_kpt: 0.000670 acc_pose: 0.771836 loss: 0.000670 2022/09/12 21:37:38 - mmengine - INFO - Epoch(train) [22][400/586] lr: 5.000000e-04 eta: 13:59:38 time: 0.471641 data_time: 0.027580 memory: 15239 loss_kpt: 0.000679 acc_pose: 0.819710 loss: 0.000679 2022/09/12 21:38:01 - mmengine - INFO - Epoch(train) [22][450/586] lr: 5.000000e-04 eta: 13:59:18 time: 0.462332 data_time: 0.025697 memory: 15239 loss_kpt: 0.000667 acc_pose: 0.759465 loss: 0.000667 2022/09/12 21:38:25 - mmengine - INFO - Epoch(train) [22][500/586] lr: 5.000000e-04 eta: 13:59:01 time: 0.470323 data_time: 0.026150 memory: 15239 loss_kpt: 0.000648 acc_pose: 0.866672 loss: 0.000648 2022/09/12 21:38:48 - mmengine - INFO - Epoch(train) [22][550/586] lr: 5.000000e-04 eta: 13:58:41 time: 0.463857 data_time: 0.030117 memory: 15239 loss_kpt: 0.000667 acc_pose: 0.763133 loss: 0.000667 2022/09/12 21:39:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:39:05 - mmengine - INFO - Saving checkpoint at 22 epochs 2022/09/12 21:39:35 - mmengine - INFO - Epoch(train) [23][50/586] lr: 5.000000e-04 eta: 13:55:49 time: 0.473045 data_time: 0.030250 memory: 15239 loss_kpt: 0.000634 acc_pose: 0.769261 loss: 0.000634 2022/09/12 21:39:58 - mmengine - INFO - Epoch(train) [23][100/586] lr: 5.000000e-04 eta: 13:55:30 time: 0.463618 data_time: 0.026043 memory: 15239 loss_kpt: 0.000669 acc_pose: 0.871728 loss: 0.000669 2022/09/12 21:40:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:40:22 - mmengine - INFO - Epoch(train) [23][150/586] lr: 5.000000e-04 eta: 13:55:17 time: 0.477429 data_time: 0.028709 memory: 15239 loss_kpt: 0.000662 acc_pose: 0.784615 loss: 0.000662 2022/09/12 21:40:46 - mmengine - INFO - Epoch(train) [23][200/586] lr: 5.000000e-04 eta: 13:55:00 time: 0.470658 data_time: 0.026454 memory: 15239 loss_kpt: 0.000659 acc_pose: 0.704755 loss: 0.000659 2022/09/12 21:41:09 - mmengine - INFO - Epoch(train) [23][250/586] lr: 5.000000e-04 eta: 13:54:40 time: 0.462842 data_time: 0.025550 memory: 15239 loss_kpt: 0.000652 acc_pose: 0.778195 loss: 0.000652 2022/09/12 21:41:33 - mmengine - INFO - Epoch(train) [23][300/586] lr: 5.000000e-04 eta: 13:54:27 time: 0.477069 data_time: 0.030083 memory: 15239 loss_kpt: 0.000666 acc_pose: 0.832172 loss: 0.000666 2022/09/12 21:41:56 - mmengine - INFO - Epoch(train) [23][350/586] lr: 5.000000e-04 eta: 13:54:08 time: 0.464998 data_time: 0.025909 memory: 15239 loss_kpt: 0.000644 acc_pose: 0.817911 loss: 0.000644 2022/09/12 21:42:19 - mmengine - INFO - Epoch(train) [23][400/586] lr: 5.000000e-04 eta: 13:53:49 time: 0.464894 data_time: 0.025568 memory: 15239 loss_kpt: 0.000639 acc_pose: 0.808908 loss: 0.000639 2022/09/12 21:42:43 - mmengine - INFO - Epoch(train) [23][450/586] lr: 5.000000e-04 eta: 13:53:32 time: 0.469972 data_time: 0.026266 memory: 15239 loss_kpt: 0.000652 acc_pose: 0.711535 loss: 0.000652 2022/09/12 21:43:06 - mmengine - INFO - Epoch(train) [23][500/586] lr: 5.000000e-04 eta: 13:53:14 time: 0.467330 data_time: 0.024814 memory: 15239 loss_kpt: 0.000642 acc_pose: 0.815982 loss: 0.000642 2022/09/12 21:43:30 - mmengine - INFO - Epoch(train) [23][550/586] lr: 5.000000e-04 eta: 13:52:57 time: 0.471544 data_time: 0.026821 memory: 15239 loss_kpt: 0.000635 acc_pose: 0.825004 loss: 0.000635 2022/09/12 21:43:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:43:47 - mmengine - INFO - Saving checkpoint at 23 epochs 2022/09/12 21:44:18 - mmengine - INFO - Epoch(train) [24][50/586] lr: 5.000000e-04 eta: 13:50:14 time: 0.477812 data_time: 0.033979 memory: 15239 loss_kpt: 0.000643 acc_pose: 0.870560 loss: 0.000643 2022/09/12 21:44:41 - mmengine - INFO - Epoch(train) [24][100/586] lr: 5.000000e-04 eta: 13:49:55 time: 0.464458 data_time: 0.026136 memory: 15239 loss_kpt: 0.000641 acc_pose: 0.787573 loss: 0.000641 2022/09/12 21:45:05 - mmengine - INFO - Epoch(train) [24][150/586] lr: 5.000000e-04 eta: 13:49:41 time: 0.476724 data_time: 0.025496 memory: 15239 loss_kpt: 0.000640 acc_pose: 0.773466 loss: 0.000640 2022/09/12 21:45:28 - mmengine - INFO - Epoch(train) [24][200/586] lr: 5.000000e-04 eta: 13:49:25 time: 0.470668 data_time: 0.025939 memory: 15239 loss_kpt: 0.000665 acc_pose: 0.835108 loss: 0.000665 2022/09/12 21:45:52 - mmengine - INFO - Epoch(train) [24][250/586] lr: 5.000000e-04 eta: 13:49:06 time: 0.465635 data_time: 0.029601 memory: 15239 loss_kpt: 0.000653 acc_pose: 0.733911 loss: 0.000653 2022/09/12 21:46:15 - mmengine - INFO - Epoch(train) [24][300/586] lr: 5.000000e-04 eta: 13:48:47 time: 0.463777 data_time: 0.026266 memory: 15239 loss_kpt: 0.000636 acc_pose: 0.834564 loss: 0.000636 2022/09/12 21:46:39 - mmengine - INFO - Epoch(train) [24][350/586] lr: 5.000000e-04 eta: 13:48:32 time: 0.475674 data_time: 0.025446 memory: 15239 loss_kpt: 0.000645 acc_pose: 0.765945 loss: 0.000645 2022/09/12 21:47:02 - mmengine - INFO - Epoch(train) [24][400/586] lr: 5.000000e-04 eta: 13:48:14 time: 0.465568 data_time: 0.025706 memory: 15239 loss_kpt: 0.000656 acc_pose: 0.789839 loss: 0.000656 2022/09/12 21:47:25 - mmengine - INFO - Epoch(train) [24][450/586] lr: 5.000000e-04 eta: 13:47:55 time: 0.464993 data_time: 0.025557 memory: 15239 loss_kpt: 0.000627 acc_pose: 0.785791 loss: 0.000627 2022/09/12 21:47:49 - mmengine - INFO - Epoch(train) [24][500/586] lr: 5.000000e-04 eta: 13:47:39 time: 0.474385 data_time: 0.026249 memory: 15239 loss_kpt: 0.000650 acc_pose: 0.787571 loss: 0.000650 2022/09/12 21:47:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:48:12 - mmengine - INFO - Epoch(train) [24][550/586] lr: 5.000000e-04 eta: 13:47:22 time: 0.469523 data_time: 0.032688 memory: 15239 loss_kpt: 0.000657 acc_pose: 0.799490 loss: 0.000657 2022/09/12 21:48:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:48:29 - mmengine - INFO - Saving checkpoint at 24 epochs 2022/09/12 21:49:00 - mmengine - INFO - Epoch(train) [25][50/586] lr: 5.000000e-04 eta: 13:44:50 time: 0.489904 data_time: 0.038462 memory: 15239 loss_kpt: 0.000650 acc_pose: 0.773791 loss: 0.000650 2022/09/12 21:49:24 - mmengine - INFO - Epoch(train) [25][100/586] lr: 5.000000e-04 eta: 13:44:34 time: 0.472521 data_time: 0.028550 memory: 15239 loss_kpt: 0.000635 acc_pose: 0.788709 loss: 0.000635 2022/09/12 21:49:47 - mmengine - INFO - Epoch(train) [25][150/586] lr: 5.000000e-04 eta: 13:44:19 time: 0.473848 data_time: 0.033459 memory: 15239 loss_kpt: 0.000647 acc_pose: 0.851250 loss: 0.000647 2022/09/12 21:50:11 - mmengine - INFO - Epoch(train) [25][200/586] lr: 5.000000e-04 eta: 13:44:03 time: 0.471687 data_time: 0.027663 memory: 15239 loss_kpt: 0.000630 acc_pose: 0.789649 loss: 0.000630 2022/09/12 21:50:35 - mmengine - INFO - Epoch(train) [25][250/586] lr: 5.000000e-04 eta: 13:43:49 time: 0.477587 data_time: 0.029076 memory: 15239 loss_kpt: 0.000643 acc_pose: 0.795733 loss: 0.000643 2022/09/12 21:50:58 - mmengine - INFO - Epoch(train) [25][300/586] lr: 5.000000e-04 eta: 13:43:33 time: 0.473635 data_time: 0.027601 memory: 15239 loss_kpt: 0.000655 acc_pose: 0.782405 loss: 0.000655 2022/09/12 21:51:21 - mmengine - INFO - Epoch(train) [25][350/586] lr: 5.000000e-04 eta: 13:43:13 time: 0.459823 data_time: 0.026266 memory: 15239 loss_kpt: 0.000631 acc_pose: 0.820089 loss: 0.000631 2022/09/12 21:51:45 - mmengine - INFO - Epoch(train) [25][400/586] lr: 5.000000e-04 eta: 13:42:55 time: 0.467865 data_time: 0.025818 memory: 15239 loss_kpt: 0.000647 acc_pose: 0.836012 loss: 0.000647 2022/09/12 21:52:08 - mmengine - INFO - Epoch(train) [25][450/586] lr: 5.000000e-04 eta: 13:42:39 time: 0.471594 data_time: 0.027092 memory: 15239 loss_kpt: 0.000646 acc_pose: 0.848387 loss: 0.000646 2022/09/12 21:52:32 - mmengine - INFO - Epoch(train) [25][500/586] lr: 5.000000e-04 eta: 13:42:19 time: 0.463781 data_time: 0.026524 memory: 15239 loss_kpt: 0.000642 acc_pose: 0.818912 loss: 0.000642 2022/09/12 21:52:55 - mmengine - INFO - Epoch(train) [25][550/586] lr: 5.000000e-04 eta: 13:42:03 time: 0.472557 data_time: 0.031028 memory: 15239 loss_kpt: 0.000661 acc_pose: 0.792847 loss: 0.000661 2022/09/12 21:53:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:53:12 - mmengine - INFO - Saving checkpoint at 25 epochs 2022/09/12 21:53:42 - mmengine - INFO - Epoch(train) [26][50/586] lr: 5.000000e-04 eta: 13:39:28 time: 0.467595 data_time: 0.032178 memory: 15239 loss_kpt: 0.000654 acc_pose: 0.809062 loss: 0.000654 2022/09/12 21:54:05 - mmengine - INFO - Epoch(train) [26][100/586] lr: 5.000000e-04 eta: 13:39:08 time: 0.461688 data_time: 0.025849 memory: 15239 loss_kpt: 0.000647 acc_pose: 0.815852 loss: 0.000647 2022/09/12 21:54:29 - mmengine - INFO - Epoch(train) [26][150/586] lr: 5.000000e-04 eta: 13:38:51 time: 0.469496 data_time: 0.026835 memory: 15239 loss_kpt: 0.000640 acc_pose: 0.841386 loss: 0.000640 2022/09/12 21:54:52 - mmengine - INFO - Epoch(train) [26][200/586] lr: 5.000000e-04 eta: 13:38:33 time: 0.466233 data_time: 0.030141 memory: 15239 loss_kpt: 0.000658 acc_pose: 0.805438 loss: 0.000658 2022/09/12 21:55:15 - mmengine - INFO - Epoch(train) [26][250/586] lr: 5.000000e-04 eta: 13:38:14 time: 0.464324 data_time: 0.025808 memory: 15239 loss_kpt: 0.000656 acc_pose: 0.888163 loss: 0.000656 2022/09/12 21:55:39 - mmengine - INFO - Epoch(train) [26][300/586] lr: 5.000000e-04 eta: 13:37:57 time: 0.468743 data_time: 0.026133 memory: 15239 loss_kpt: 0.000627 acc_pose: 0.793143 loss: 0.000627 2022/09/12 21:56:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:56:02 - mmengine - INFO - Epoch(train) [26][350/586] lr: 5.000000e-04 eta: 13:37:40 time: 0.470290 data_time: 0.031109 memory: 15239 loss_kpt: 0.000660 acc_pose: 0.803471 loss: 0.000660 2022/09/12 21:56:25 - mmengine - INFO - Epoch(train) [26][400/586] lr: 5.000000e-04 eta: 13:37:19 time: 0.459883 data_time: 0.025928 memory: 15239 loss_kpt: 0.000633 acc_pose: 0.743654 loss: 0.000633 2022/09/12 21:56:48 - mmengine - INFO - Epoch(train) [26][450/586] lr: 5.000000e-04 eta: 13:37:01 time: 0.465459 data_time: 0.027456 memory: 15239 loss_kpt: 0.000653 acc_pose: 0.837629 loss: 0.000653 2022/09/12 21:57:12 - mmengine - INFO - Epoch(train) [26][500/586] lr: 5.000000e-04 eta: 13:36:43 time: 0.468162 data_time: 0.026868 memory: 15239 loss_kpt: 0.000657 acc_pose: 0.857458 loss: 0.000657 2022/09/12 21:57:35 - mmengine - INFO - Epoch(train) [26][550/586] lr: 5.000000e-04 eta: 13:36:22 time: 0.459387 data_time: 0.026561 memory: 15239 loss_kpt: 0.000664 acc_pose: 0.821881 loss: 0.000664 2022/09/12 21:57:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 21:57:52 - mmengine - INFO - Saving checkpoint at 26 epochs 2022/09/12 21:58:25 - mmengine - INFO - Epoch(train) [27][50/586] lr: 5.000000e-04 eta: 13:33:57 time: 0.479137 data_time: 0.034716 memory: 15239 loss_kpt: 0.000638 acc_pose: 0.872808 loss: 0.000638 2022/09/12 21:58:49 - mmengine - INFO - Epoch(train) [27][100/586] lr: 5.000000e-04 eta: 13:33:40 time: 0.470434 data_time: 0.029533 memory: 15239 loss_kpt: 0.000643 acc_pose: 0.811041 loss: 0.000643 2022/09/12 21:59:12 - mmengine - INFO - Epoch(train) [27][150/586] lr: 5.000000e-04 eta: 13:33:25 time: 0.473251 data_time: 0.027057 memory: 15239 loss_kpt: 0.000653 acc_pose: 0.781056 loss: 0.000653 2022/09/12 21:59:35 - mmengine - INFO - Epoch(train) [27][200/586] lr: 5.000000e-04 eta: 13:33:05 time: 0.462482 data_time: 0.025267 memory: 15239 loss_kpt: 0.000650 acc_pose: 0.838282 loss: 0.000650 2022/09/12 21:59:58 - mmengine - INFO - Epoch(train) [27][250/586] lr: 5.000000e-04 eta: 13:32:45 time: 0.461131 data_time: 0.030050 memory: 15239 loss_kpt: 0.000640 acc_pose: 0.779334 loss: 0.000640 2022/09/12 22:00:22 - mmengine - INFO - Epoch(train) [27][300/586] lr: 5.000000e-04 eta: 13:32:29 time: 0.472869 data_time: 0.026323 memory: 15239 loss_kpt: 0.000646 acc_pose: 0.787653 loss: 0.000646 2022/09/12 22:00:45 - mmengine - INFO - Epoch(train) [27][350/586] lr: 5.000000e-04 eta: 13:32:11 time: 0.465692 data_time: 0.025951 memory: 15239 loss_kpt: 0.000644 acc_pose: 0.777119 loss: 0.000644 2022/09/12 22:01:08 - mmengine - INFO - Epoch(train) [27][400/586] lr: 5.000000e-04 eta: 13:31:51 time: 0.461517 data_time: 0.029770 memory: 15239 loss_kpt: 0.000651 acc_pose: 0.883279 loss: 0.000651 2022/09/12 22:01:32 - mmengine - INFO - Epoch(train) [27][450/586] lr: 5.000000e-04 eta: 13:31:33 time: 0.467760 data_time: 0.025914 memory: 15239 loss_kpt: 0.000632 acc_pose: 0.815944 loss: 0.000632 2022/09/12 22:01:55 - mmengine - INFO - Epoch(train) [27][500/586] lr: 5.000000e-04 eta: 13:31:16 time: 0.468689 data_time: 0.026380 memory: 15239 loss_kpt: 0.000649 acc_pose: 0.811989 loss: 0.000649 2022/09/12 22:02:18 - mmengine - INFO - Epoch(train) [27][550/586] lr: 5.000000e-04 eta: 13:30:55 time: 0.460189 data_time: 0.030323 memory: 15239 loss_kpt: 0.000653 acc_pose: 0.775453 loss: 0.000653 2022/09/12 22:02:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:02:35 - mmengine - INFO - Saving checkpoint at 27 epochs 2022/09/12 22:03:05 - mmengine - INFO - Epoch(train) [28][50/586] lr: 5.000000e-04 eta: 13:28:36 time: 0.482472 data_time: 0.029890 memory: 15239 loss_kpt: 0.000636 acc_pose: 0.801678 loss: 0.000636 2022/09/12 22:03:29 - mmengine - INFO - Epoch(train) [28][100/586] lr: 5.000000e-04 eta: 13:28:16 time: 0.461725 data_time: 0.026826 memory: 15239 loss_kpt: 0.000636 acc_pose: 0.860487 loss: 0.000636 2022/09/12 22:03:52 - mmengine - INFO - Epoch(train) [28][150/586] lr: 5.000000e-04 eta: 13:28:00 time: 0.470806 data_time: 0.026495 memory: 15239 loss_kpt: 0.000611 acc_pose: 0.778265 loss: 0.000611 2022/09/12 22:04:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:04:15 - mmengine - INFO - Epoch(train) [28][200/586] lr: 5.000000e-04 eta: 13:27:41 time: 0.464241 data_time: 0.025761 memory: 15239 loss_kpt: 0.000646 acc_pose: 0.824809 loss: 0.000646 2022/09/12 22:04:38 - mmengine - INFO - Epoch(train) [28][250/586] lr: 5.000000e-04 eta: 13:27:22 time: 0.462943 data_time: 0.026229 memory: 15239 loss_kpt: 0.000635 acc_pose: 0.773244 loss: 0.000635 2022/09/12 22:05:02 - mmengine - INFO - Epoch(train) [28][300/586] lr: 5.000000e-04 eta: 13:27:06 time: 0.473291 data_time: 0.025133 memory: 15239 loss_kpt: 0.000608 acc_pose: 0.773528 loss: 0.000608 2022/09/12 22:05:25 - mmengine - INFO - Epoch(train) [28][350/586] lr: 5.000000e-04 eta: 13:26:46 time: 0.462085 data_time: 0.028808 memory: 15239 loss_kpt: 0.000619 acc_pose: 0.809419 loss: 0.000619 2022/09/12 22:05:48 - mmengine - INFO - Epoch(train) [28][400/586] lr: 5.000000e-04 eta: 13:26:26 time: 0.461012 data_time: 0.026087 memory: 15239 loss_kpt: 0.000620 acc_pose: 0.869859 loss: 0.000620 2022/09/12 22:06:12 - mmengine - INFO - Epoch(train) [28][450/586] lr: 5.000000e-04 eta: 13:26:09 time: 0.470231 data_time: 0.026687 memory: 15239 loss_kpt: 0.000656 acc_pose: 0.834168 loss: 0.000656 2022/09/12 22:06:35 - mmengine - INFO - Epoch(train) [28][500/586] lr: 5.000000e-04 eta: 13:25:52 time: 0.470280 data_time: 0.025982 memory: 15239 loss_kpt: 0.000653 acc_pose: 0.844535 loss: 0.000653 2022/09/12 22:06:58 - mmengine - INFO - Epoch(train) [28][550/586] lr: 5.000000e-04 eta: 13:25:32 time: 0.459489 data_time: 0.024761 memory: 15239 loss_kpt: 0.000644 acc_pose: 0.781026 loss: 0.000644 2022/09/12 22:07:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:07:15 - mmengine - INFO - Saving checkpoint at 28 epochs 2022/09/12 22:07:45 - mmengine - INFO - Epoch(train) [29][50/586] lr: 5.000000e-04 eta: 13:23:12 time: 0.468605 data_time: 0.030593 memory: 15239 loss_kpt: 0.000631 acc_pose: 0.849372 loss: 0.000631 2022/09/12 22:08:09 - mmengine - INFO - Epoch(train) [29][100/586] lr: 5.000000e-04 eta: 13:22:53 time: 0.464245 data_time: 0.026549 memory: 15239 loss_kpt: 0.000619 acc_pose: 0.843461 loss: 0.000619 2022/09/12 22:08:32 - mmengine - INFO - Epoch(train) [29][150/586] lr: 5.000000e-04 eta: 13:22:36 time: 0.468645 data_time: 0.029474 memory: 15239 loss_kpt: 0.000628 acc_pose: 0.833666 loss: 0.000628 2022/09/12 22:08:55 - mmengine - INFO - Epoch(train) [29][200/586] lr: 5.000000e-04 eta: 13:22:18 time: 0.464574 data_time: 0.026056 memory: 15239 loss_kpt: 0.000632 acc_pose: 0.834056 loss: 0.000632 2022/09/12 22:09:19 - mmengine - INFO - Epoch(train) [29][250/586] lr: 5.000000e-04 eta: 13:21:59 time: 0.464968 data_time: 0.026784 memory: 15239 loss_kpt: 0.000635 acc_pose: 0.783273 loss: 0.000635 2022/09/12 22:09:42 - mmengine - INFO - Epoch(train) [29][300/586] lr: 5.000000e-04 eta: 13:21:42 time: 0.469489 data_time: 0.025312 memory: 15239 loss_kpt: 0.000616 acc_pose: 0.806524 loss: 0.000616 2022/09/12 22:10:06 - mmengine - INFO - Epoch(train) [29][350/586] lr: 5.000000e-04 eta: 13:21:25 time: 0.469894 data_time: 0.025748 memory: 15239 loss_kpt: 0.000638 acc_pose: 0.820342 loss: 0.000638 2022/09/12 22:10:29 - mmengine - INFO - Epoch(train) [29][400/586] lr: 5.000000e-04 eta: 13:21:06 time: 0.462921 data_time: 0.027148 memory: 15239 loss_kpt: 0.000646 acc_pose: 0.807780 loss: 0.000646 2022/09/12 22:10:52 - mmengine - INFO - Epoch(train) [29][450/586] lr: 5.000000e-04 eta: 13:20:48 time: 0.468747 data_time: 0.025273 memory: 15239 loss_kpt: 0.000653 acc_pose: 0.794055 loss: 0.000653 2022/09/12 22:11:16 - mmengine - INFO - Epoch(train) [29][500/586] lr: 5.000000e-04 eta: 13:20:31 time: 0.469002 data_time: 0.025963 memory: 15239 loss_kpt: 0.000664 acc_pose: 0.853964 loss: 0.000664 2022/09/12 22:11:39 - mmengine - INFO - Epoch(train) [29][550/586] lr: 5.000000e-04 eta: 13:20:10 time: 0.457868 data_time: 0.026690 memory: 15239 loss_kpt: 0.000639 acc_pose: 0.816319 loss: 0.000639 2022/09/12 22:11:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:11:55 - mmengine - INFO - Saving checkpoint at 29 epochs 2022/09/12 22:12:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:12:26 - mmengine - INFO - Epoch(train) [30][50/586] lr: 5.000000e-04 eta: 13:17:57 time: 0.476871 data_time: 0.030160 memory: 15239 loss_kpt: 0.000639 acc_pose: 0.841957 loss: 0.000639 2022/09/12 22:12:50 - mmengine - INFO - Epoch(train) [30][100/586] lr: 5.000000e-04 eta: 13:17:41 time: 0.471402 data_time: 0.025561 memory: 15239 loss_kpt: 0.000614 acc_pose: 0.743005 loss: 0.000614 2022/09/12 22:13:14 - mmengine - INFO - Epoch(train) [30][150/586] lr: 5.000000e-04 eta: 13:17:27 time: 0.479111 data_time: 0.025842 memory: 15239 loss_kpt: 0.000623 acc_pose: 0.874323 loss: 0.000623 2022/09/12 22:13:36 - mmengine - INFO - Epoch(train) [30][200/586] lr: 5.000000e-04 eta: 13:17:05 time: 0.455506 data_time: 0.026315 memory: 15239 loss_kpt: 0.000631 acc_pose: 0.784709 loss: 0.000631 2022/09/12 22:14:00 - mmengine - INFO - Epoch(train) [30][250/586] lr: 5.000000e-04 eta: 13:16:47 time: 0.467389 data_time: 0.029800 memory: 15239 loss_kpt: 0.000634 acc_pose: 0.778091 loss: 0.000634 2022/09/12 22:14:23 - mmengine - INFO - Epoch(train) [30][300/586] lr: 5.000000e-04 eta: 13:16:32 time: 0.474513 data_time: 0.026484 memory: 15239 loss_kpt: 0.000616 acc_pose: 0.796936 loss: 0.000616 2022/09/12 22:14:47 - mmengine - INFO - Epoch(train) [30][350/586] lr: 5.000000e-04 eta: 13:16:12 time: 0.461211 data_time: 0.026406 memory: 15239 loss_kpt: 0.000652 acc_pose: 0.789240 loss: 0.000652 2022/09/12 22:15:10 - mmengine - INFO - Epoch(train) [30][400/586] lr: 5.000000e-04 eta: 13:15:54 time: 0.466641 data_time: 0.029444 memory: 15239 loss_kpt: 0.000631 acc_pose: 0.842783 loss: 0.000631 2022/09/12 22:15:33 - mmengine - INFO - Epoch(train) [30][450/586] lr: 5.000000e-04 eta: 13:15:37 time: 0.472347 data_time: 0.027628 memory: 15239 loss_kpt: 0.000625 acc_pose: 0.818185 loss: 0.000625 2022/09/12 22:15:57 - mmengine - INFO - Epoch(train) [30][500/586] lr: 5.000000e-04 eta: 13:15:20 time: 0.468511 data_time: 0.027075 memory: 15239 loss_kpt: 0.000622 acc_pose: 0.816457 loss: 0.000622 2022/09/12 22:16:20 - mmengine - INFO - Epoch(train) [30][550/586] lr: 5.000000e-04 eta: 13:15:01 time: 0.464084 data_time: 0.030756 memory: 15239 loss_kpt: 0.000635 acc_pose: 0.860682 loss: 0.000635 2022/09/12 22:16:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:16:37 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/09/12 22:16:56 - mmengine - INFO - Epoch(val) [30][50/407] eta: 0:01:23 time: 0.232620 data_time: 0.016808 memory: 15239 2022/09/12 22:17:07 - mmengine - INFO - Epoch(val) [30][100/407] eta: 0:01:07 time: 0.218951 data_time: 0.008612 memory: 2064 2022/09/12 22:17:18 - mmengine - INFO - Epoch(val) [30][150/407] eta: 0:00:56 time: 0.219805 data_time: 0.008643 memory: 2064 2022/09/12 22:17:29 - mmengine - INFO - Epoch(val) [30][200/407] eta: 0:00:45 time: 0.219823 data_time: 0.008828 memory: 2064 2022/09/12 22:17:40 - mmengine - INFO - Epoch(val) [30][250/407] eta: 0:00:34 time: 0.219355 data_time: 0.008492 memory: 2064 2022/09/12 22:17:51 - mmengine - INFO - Epoch(val) [30][300/407] eta: 0:00:23 time: 0.218507 data_time: 0.008126 memory: 2064 2022/09/12 22:18:02 - mmengine - INFO - Epoch(val) [30][350/407] eta: 0:00:12 time: 0.218238 data_time: 0.009572 memory: 2064 2022/09/12 22:18:13 - mmengine - INFO - Epoch(val) [30][400/407] eta: 0:00:01 time: 0.216591 data_time: 0.008406 memory: 2064 2022/09/12 22:18:48 - mmengine - INFO - Evaluating CocoMetric... 2022/09/12 22:19:02 - mmengine - INFO - Epoch(val) [30][407/407] coco/AP: 0.736178 coco/AP .5: 0.895566 coco/AP .75: 0.796159 coco/AP (M): 0.695916 coco/AP (L): 0.807220 coco/AR: 0.786004 coco/AR .5: 0.931518 coco/AR .75: 0.840523 coco/AR (M): 0.740809 coco/AR (L): 0.851022 2022/09/12 22:19:02 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_384_v1/best_coco/AP_epoch_20.pth is removed 2022/09/12 22:19:06 - mmengine - INFO - The best checkpoint with 0.7362 coco/AP at 30 epoch is saved to best_coco/AP_epoch_30.pth. 2022/09/12 22:19:29 - mmengine - INFO - Epoch(train) [31][50/586] lr: 5.000000e-04 eta: 13:12:50 time: 0.471564 data_time: 0.030388 memory: 15239 loss_kpt: 0.000634 acc_pose: 0.837777 loss: 0.000634 2022/09/12 22:19:52 - mmengine - INFO - Epoch(train) [31][100/586] lr: 5.000000e-04 eta: 13:12:32 time: 0.464935 data_time: 0.025515 memory: 15239 loss_kpt: 0.000634 acc_pose: 0.826430 loss: 0.000634 2022/09/12 22:20:16 - mmengine - INFO - Epoch(train) [31][150/586] lr: 5.000000e-04 eta: 13:12:17 time: 0.475391 data_time: 0.025203 memory: 15239 loss_kpt: 0.000601 acc_pose: 0.817625 loss: 0.000601 2022/09/12 22:20:39 - mmengine - INFO - Epoch(train) [31][200/586] lr: 5.000000e-04 eta: 13:11:58 time: 0.465730 data_time: 0.026818 memory: 15239 loss_kpt: 0.000612 acc_pose: 0.855963 loss: 0.000612 2022/09/12 22:21:03 - mmengine - INFO - Epoch(train) [31][250/586] lr: 5.000000e-04 eta: 13:11:40 time: 0.465586 data_time: 0.025305 memory: 15239 loss_kpt: 0.000626 acc_pose: 0.789064 loss: 0.000626 2022/09/12 22:21:26 - mmengine - INFO - Epoch(train) [31][300/586] lr: 5.000000e-04 eta: 13:11:24 time: 0.475592 data_time: 0.026682 memory: 15239 loss_kpt: 0.000622 acc_pose: 0.815999 loss: 0.000622 2022/09/12 22:21:49 - mmengine - INFO - Epoch(train) [31][350/586] lr: 5.000000e-04 eta: 13:11:04 time: 0.458518 data_time: 0.026754 memory: 15239 loss_kpt: 0.000614 acc_pose: 0.793819 loss: 0.000614 2022/09/12 22:22:13 - mmengine - INFO - Epoch(train) [31][400/586] lr: 5.000000e-04 eta: 13:10:48 time: 0.475015 data_time: 0.030236 memory: 15239 loss_kpt: 0.000635 acc_pose: 0.812631 loss: 0.000635 2022/09/12 22:22:23 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:22:37 - mmengine - INFO - Epoch(train) [31][450/586] lr: 5.000000e-04 eta: 13:10:31 time: 0.468391 data_time: 0.026257 memory: 15239 loss_kpt: 0.000627 acc_pose: 0.830163 loss: 0.000627 2022/09/12 22:23:00 - mmengine - INFO - Epoch(train) [31][500/586] lr: 5.000000e-04 eta: 13:10:13 time: 0.467135 data_time: 0.027184 memory: 15239 loss_kpt: 0.000625 acc_pose: 0.812703 loss: 0.000625 2022/09/12 22:23:23 - mmengine - INFO - Epoch(train) [31][550/586] lr: 5.000000e-04 eta: 13:09:53 time: 0.463934 data_time: 0.025444 memory: 15239 loss_kpt: 0.000627 acc_pose: 0.797474 loss: 0.000627 2022/09/12 22:23:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:23:40 - mmengine - INFO - Saving checkpoint at 31 epochs 2022/09/12 22:24:10 - mmengine - INFO - Epoch(train) [32][50/586] lr: 5.000000e-04 eta: 13:07:48 time: 0.476229 data_time: 0.032576 memory: 15239 loss_kpt: 0.000631 acc_pose: 0.836284 loss: 0.000631 2022/09/12 22:24:33 - mmengine - INFO - Epoch(train) [32][100/586] lr: 5.000000e-04 eta: 13:07:29 time: 0.461269 data_time: 0.025526 memory: 15239 loss_kpt: 0.000619 acc_pose: 0.857243 loss: 0.000619 2022/09/12 22:24:57 - mmengine - INFO - Epoch(train) [32][150/586] lr: 5.000000e-04 eta: 13:07:12 time: 0.471086 data_time: 0.026272 memory: 15239 loss_kpt: 0.000619 acc_pose: 0.847049 loss: 0.000619 2022/09/12 22:25:21 - mmengine - INFO - Epoch(train) [32][200/586] lr: 5.000000e-04 eta: 13:06:57 time: 0.476432 data_time: 0.025639 memory: 15239 loss_kpt: 0.000626 acc_pose: 0.664032 loss: 0.000626 2022/09/12 22:25:44 - mmengine - INFO - Epoch(train) [32][250/586] lr: 5.000000e-04 eta: 13:06:36 time: 0.459166 data_time: 0.025600 memory: 15239 loss_kpt: 0.000605 acc_pose: 0.838234 loss: 0.000605 2022/09/12 22:26:08 - mmengine - INFO - Epoch(train) [32][300/586] lr: 5.000000e-04 eta: 13:06:23 time: 0.482090 data_time: 0.027171 memory: 15239 loss_kpt: 0.000620 acc_pose: 0.818860 loss: 0.000620 2022/09/12 22:26:31 - mmengine - INFO - Epoch(train) [32][350/586] lr: 5.000000e-04 eta: 13:06:04 time: 0.465312 data_time: 0.029731 memory: 15239 loss_kpt: 0.000625 acc_pose: 0.814789 loss: 0.000625 2022/09/12 22:26:55 - mmengine - INFO - Epoch(train) [32][400/586] lr: 5.000000e-04 eta: 13:05:47 time: 0.471021 data_time: 0.025858 memory: 15239 loss_kpt: 0.000627 acc_pose: 0.796139 loss: 0.000627 2022/09/12 22:27:18 - mmengine - INFO - Epoch(train) [32][450/586] lr: 5.000000e-04 eta: 13:05:29 time: 0.466523 data_time: 0.025385 memory: 15239 loss_kpt: 0.000634 acc_pose: 0.795873 loss: 0.000634 2022/09/12 22:27:41 - mmengine - INFO - Epoch(train) [32][500/586] lr: 5.000000e-04 eta: 13:05:09 time: 0.460314 data_time: 0.026268 memory: 15239 loss_kpt: 0.000633 acc_pose: 0.814931 loss: 0.000633 2022/09/12 22:28:05 - mmengine - INFO - Epoch(train) [32][550/586] lr: 5.000000e-04 eta: 13:04:53 time: 0.474464 data_time: 0.027052 memory: 15239 loss_kpt: 0.000643 acc_pose: 0.810611 loss: 0.000643 2022/09/12 22:28:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:28:21 - mmengine - INFO - Saving checkpoint at 32 epochs 2022/09/12 22:28:54 - mmengine - INFO - Epoch(train) [33][50/586] lr: 5.000000e-04 eta: 13:02:49 time: 0.470452 data_time: 0.035484 memory: 15239 loss_kpt: 0.000632 acc_pose: 0.811245 loss: 0.000632 2022/09/12 22:29:18 - mmengine - INFO - Epoch(train) [33][100/586] lr: 5.000000e-04 eta: 13:02:34 time: 0.478421 data_time: 0.030085 memory: 15239 loss_kpt: 0.000629 acc_pose: 0.857035 loss: 0.000629 2022/09/12 22:29:41 - mmengine - INFO - Epoch(train) [33][150/586] lr: 5.000000e-04 eta: 13:02:16 time: 0.465499 data_time: 0.026640 memory: 15239 loss_kpt: 0.000613 acc_pose: 0.874936 loss: 0.000613 2022/09/12 22:30:04 - mmengine - INFO - Epoch(train) [33][200/586] lr: 5.000000e-04 eta: 13:01:56 time: 0.462338 data_time: 0.026374 memory: 15239 loss_kpt: 0.000608 acc_pose: 0.768721 loss: 0.000608 2022/09/12 22:30:27 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:30:28 - mmengine - INFO - Epoch(train) [33][250/586] lr: 5.000000e-04 eta: 13:01:39 time: 0.468624 data_time: 0.031191 memory: 15239 loss_kpt: 0.000619 acc_pose: 0.832815 loss: 0.000619 2022/09/12 22:30:51 - mmengine - INFO - Epoch(train) [33][300/586] lr: 5.000000e-04 eta: 13:01:21 time: 0.466713 data_time: 0.025344 memory: 15239 loss_kpt: 0.000603 acc_pose: 0.917231 loss: 0.000603 2022/09/12 22:31:14 - mmengine - INFO - Epoch(train) [33][350/586] lr: 5.000000e-04 eta: 13:01:01 time: 0.462718 data_time: 0.026763 memory: 15239 loss_kpt: 0.000618 acc_pose: 0.799799 loss: 0.000618 2022/09/12 22:31:38 - mmengine - INFO - Epoch(train) [33][400/586] lr: 5.000000e-04 eta: 13:00:43 time: 0.464310 data_time: 0.025965 memory: 15239 loss_kpt: 0.000620 acc_pose: 0.830796 loss: 0.000620 2022/09/12 22:32:01 - mmengine - INFO - Epoch(train) [33][450/586] lr: 5.000000e-04 eta: 13:00:26 time: 0.473900 data_time: 0.029608 memory: 15239 loss_kpt: 0.000614 acc_pose: 0.838333 loss: 0.000614 2022/09/12 22:32:24 - mmengine - INFO - Epoch(train) [33][500/586] lr: 5.000000e-04 eta: 13:00:06 time: 0.459758 data_time: 0.026199 memory: 15239 loss_kpt: 0.000623 acc_pose: 0.799593 loss: 0.000623 2022/09/12 22:32:48 - mmengine - INFO - Epoch(train) [33][550/586] lr: 5.000000e-04 eta: 12:59:48 time: 0.466148 data_time: 0.025618 memory: 15239 loss_kpt: 0.000624 acc_pose: 0.738088 loss: 0.000624 2022/09/12 22:33:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:33:04 - mmengine - INFO - Saving checkpoint at 33 epochs 2022/09/12 22:33:35 - mmengine - INFO - Epoch(train) [34][50/586] lr: 5.000000e-04 eta: 12:57:49 time: 0.474974 data_time: 0.035122 memory: 15239 loss_kpt: 0.000620 acc_pose: 0.821758 loss: 0.000620 2022/09/12 22:33:59 - mmengine - INFO - Epoch(train) [34][100/586] lr: 5.000000e-04 eta: 12:57:31 time: 0.469929 data_time: 0.025065 memory: 15239 loss_kpt: 0.000627 acc_pose: 0.852947 loss: 0.000627 2022/09/12 22:34:22 - mmengine - INFO - Epoch(train) [34][150/586] lr: 5.000000e-04 eta: 12:57:14 time: 0.468210 data_time: 0.027093 memory: 15239 loss_kpt: 0.000631 acc_pose: 0.809882 loss: 0.000631 2022/09/12 22:34:45 - mmengine - INFO - Epoch(train) [34][200/586] lr: 5.000000e-04 eta: 12:56:55 time: 0.463726 data_time: 0.026337 memory: 15239 loss_kpt: 0.000628 acc_pose: 0.823842 loss: 0.000628 2022/09/12 22:35:09 - mmengine - INFO - Epoch(train) [34][250/586] lr: 5.000000e-04 eta: 12:56:37 time: 0.467525 data_time: 0.029584 memory: 15239 loss_kpt: 0.000641 acc_pose: 0.831595 loss: 0.000641 2022/09/12 22:35:33 - mmengine - INFO - Epoch(train) [34][300/586] lr: 5.000000e-04 eta: 12:56:21 time: 0.475957 data_time: 0.026438 memory: 15239 loss_kpt: 0.000626 acc_pose: 0.851255 loss: 0.000626 2022/09/12 22:35:56 - mmengine - INFO - Epoch(train) [34][350/586] lr: 5.000000e-04 eta: 12:56:05 time: 0.475246 data_time: 0.029530 memory: 15239 loss_kpt: 0.000615 acc_pose: 0.812638 loss: 0.000615 2022/09/12 22:36:20 - mmengine - INFO - Epoch(train) [34][400/586] lr: 5.000000e-04 eta: 12:55:46 time: 0.465388 data_time: 0.025666 memory: 15239 loss_kpt: 0.000601 acc_pose: 0.680328 loss: 0.000601 2022/09/12 22:36:43 - mmengine - INFO - Epoch(train) [34][450/586] lr: 5.000000e-04 eta: 12:55:27 time: 0.464580 data_time: 0.025514 memory: 15239 loss_kpt: 0.000618 acc_pose: 0.815927 loss: 0.000618 2022/09/12 22:37:06 - mmengine - INFO - Epoch(train) [34][500/586] lr: 5.000000e-04 eta: 12:55:10 time: 0.470210 data_time: 0.030129 memory: 15239 loss_kpt: 0.000613 acc_pose: 0.818209 loss: 0.000613 2022/09/12 22:37:30 - mmengine - INFO - Epoch(train) [34][550/586] lr: 5.000000e-04 eta: 12:54:52 time: 0.469028 data_time: 0.025031 memory: 15239 loss_kpt: 0.000626 acc_pose: 0.847525 loss: 0.000626 2022/09/12 22:37:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:37:46 - mmengine - INFO - Saving checkpoint at 34 epochs 2022/09/12 22:38:17 - mmengine - INFO - Epoch(train) [35][50/586] lr: 5.000000e-04 eta: 12:52:58 time: 0.482672 data_time: 0.033030 memory: 15239 loss_kpt: 0.000616 acc_pose: 0.773540 loss: 0.000616 2022/09/12 22:38:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:38:41 - mmengine - INFO - Epoch(train) [35][100/586] lr: 5.000000e-04 eta: 12:52:43 time: 0.478121 data_time: 0.032135 memory: 15239 loss_kpt: 0.000636 acc_pose: 0.810088 loss: 0.000636 2022/09/12 22:39:05 - mmengine - INFO - Epoch(train) [35][150/586] lr: 5.000000e-04 eta: 12:52:27 time: 0.475463 data_time: 0.032180 memory: 15239 loss_kpt: 0.000596 acc_pose: 0.738987 loss: 0.000596 2022/09/12 22:39:29 - mmengine - INFO - Epoch(train) [35][200/586] lr: 5.000000e-04 eta: 12:52:11 time: 0.474792 data_time: 0.028104 memory: 15239 loss_kpt: 0.000610 acc_pose: 0.811791 loss: 0.000610 2022/09/12 22:39:52 - mmengine - INFO - Epoch(train) [35][250/586] lr: 5.000000e-04 eta: 12:51:53 time: 0.470052 data_time: 0.028332 memory: 15239 loss_kpt: 0.000628 acc_pose: 0.732217 loss: 0.000628 2022/09/12 22:40:15 - mmengine - INFO - Epoch(train) [35][300/586] lr: 5.000000e-04 eta: 12:51:34 time: 0.463430 data_time: 0.030364 memory: 15239 loss_kpt: 0.000617 acc_pose: 0.838705 loss: 0.000617 2022/09/12 22:40:39 - mmengine - INFO - Epoch(train) [35][350/586] lr: 5.000000e-04 eta: 12:51:17 time: 0.470395 data_time: 0.039117 memory: 15239 loss_kpt: 0.000616 acc_pose: 0.848540 loss: 0.000616 2022/09/12 22:41:02 - mmengine - INFO - Epoch(train) [35][400/586] lr: 5.000000e-04 eta: 12:50:58 time: 0.465022 data_time: 0.025605 memory: 15239 loss_kpt: 0.000634 acc_pose: 0.784937 loss: 0.000634 2022/09/12 22:41:25 - mmengine - INFO - Epoch(train) [35][450/586] lr: 5.000000e-04 eta: 12:50:38 time: 0.461724 data_time: 0.025909 memory: 15239 loss_kpt: 0.000615 acc_pose: 0.791777 loss: 0.000615 2022/09/12 22:41:49 - mmengine - INFO - Epoch(train) [35][500/586] lr: 5.000000e-04 eta: 12:50:25 time: 0.484887 data_time: 0.031224 memory: 15239 loss_kpt: 0.000637 acc_pose: 0.815645 loss: 0.000637 2022/09/12 22:42:12 - mmengine - INFO - Epoch(train) [35][550/586] lr: 5.000000e-04 eta: 12:50:04 time: 0.459252 data_time: 0.026304 memory: 15239 loss_kpt: 0.000640 acc_pose: 0.830672 loss: 0.000640 2022/09/12 22:42:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:42:29 - mmengine - INFO - Saving checkpoint at 35 epochs 2022/09/12 22:42:59 - mmengine - INFO - Epoch(train) [36][50/586] lr: 5.000000e-04 eta: 12:48:11 time: 0.475689 data_time: 0.035448 memory: 15239 loss_kpt: 0.000621 acc_pose: 0.886682 loss: 0.000621 2022/09/12 22:43:22 - mmengine - INFO - Epoch(train) [36][100/586] lr: 5.000000e-04 eta: 12:47:50 time: 0.456052 data_time: 0.031886 memory: 15239 loss_kpt: 0.000619 acc_pose: 0.768669 loss: 0.000619 2022/09/12 22:43:46 - mmengine - INFO - Epoch(train) [36][150/586] lr: 5.000000e-04 eta: 12:47:32 time: 0.466617 data_time: 0.030575 memory: 15239 loss_kpt: 0.000616 acc_pose: 0.870804 loss: 0.000616 2022/09/12 22:44:09 - mmengine - INFO - Epoch(train) [36][200/586] lr: 5.000000e-04 eta: 12:47:13 time: 0.466288 data_time: 0.031155 memory: 15239 loss_kpt: 0.000624 acc_pose: 0.910398 loss: 0.000624 2022/09/12 22:44:33 - mmengine - INFO - Epoch(train) [36][250/586] lr: 5.000000e-04 eta: 12:46:57 time: 0.473331 data_time: 0.037225 memory: 15239 loss_kpt: 0.000622 acc_pose: 0.823342 loss: 0.000622 2022/09/12 22:44:56 - mmengine - INFO - Epoch(train) [36][300/586] lr: 5.000000e-04 eta: 12:46:41 time: 0.476793 data_time: 0.031649 memory: 15239 loss_kpt: 0.000597 acc_pose: 0.795498 loss: 0.000597 2022/09/12 22:45:20 - mmengine - INFO - Epoch(train) [36][350/586] lr: 5.000000e-04 eta: 12:46:23 time: 0.468354 data_time: 0.031601 memory: 15239 loss_kpt: 0.000608 acc_pose: 0.816923 loss: 0.000608 2022/09/12 22:45:43 - mmengine - INFO - Epoch(train) [36][400/586] lr: 5.000000e-04 eta: 12:46:06 time: 0.471167 data_time: 0.030090 memory: 15239 loss_kpt: 0.000611 acc_pose: 0.855659 loss: 0.000611 2022/09/12 22:46:07 - mmengine - INFO - Epoch(train) [36][450/586] lr: 5.000000e-04 eta: 12:45:46 time: 0.463353 data_time: 0.025434 memory: 15239 loss_kpt: 0.000626 acc_pose: 0.831421 loss: 0.000626 2022/09/12 22:46:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:46:31 - mmengine - INFO - Epoch(train) [36][500/586] lr: 5.000000e-04 eta: 12:45:31 time: 0.477713 data_time: 0.031203 memory: 15239 loss_kpt: 0.000620 acc_pose: 0.833416 loss: 0.000620 2022/09/12 22:46:54 - mmengine - INFO - Epoch(train) [36][550/586] lr: 5.000000e-04 eta: 12:45:12 time: 0.465659 data_time: 0.025941 memory: 15239 loss_kpt: 0.000631 acc_pose: 0.884816 loss: 0.000631 2022/09/12 22:47:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:47:10 - mmengine - INFO - Saving checkpoint at 36 epochs 2022/09/12 22:47:42 - mmengine - INFO - Epoch(train) [37][50/586] lr: 5.000000e-04 eta: 12:43:22 time: 0.479272 data_time: 0.037588 memory: 15239 loss_kpt: 0.000616 acc_pose: 0.851985 loss: 0.000616 2022/09/12 22:48:05 - mmengine - INFO - Epoch(train) [37][100/586] lr: 5.000000e-04 eta: 12:43:02 time: 0.460509 data_time: 0.029303 memory: 15239 loss_kpt: 0.000599 acc_pose: 0.870827 loss: 0.000599 2022/09/12 22:48:29 - mmengine - INFO - Epoch(train) [37][150/586] lr: 5.000000e-04 eta: 12:42:46 time: 0.475490 data_time: 0.029464 memory: 15239 loss_kpt: 0.000601 acc_pose: 0.787099 loss: 0.000601 2022/09/12 22:48:52 - mmengine - INFO - Epoch(train) [37][200/586] lr: 5.000000e-04 eta: 12:42:29 time: 0.471626 data_time: 0.025308 memory: 15239 loss_kpt: 0.000588 acc_pose: 0.805441 loss: 0.000588 2022/09/12 22:49:15 - mmengine - INFO - Epoch(train) [37][250/586] lr: 5.000000e-04 eta: 12:42:10 time: 0.465733 data_time: 0.025413 memory: 15239 loss_kpt: 0.000594 acc_pose: 0.815864 loss: 0.000594 2022/09/12 22:49:39 - mmengine - INFO - Epoch(train) [37][300/586] lr: 5.000000e-04 eta: 12:41:54 time: 0.476495 data_time: 0.027175 memory: 15239 loss_kpt: 0.000625 acc_pose: 0.796512 loss: 0.000625 2022/09/12 22:50:03 - mmengine - INFO - Epoch(train) [37][350/586] lr: 5.000000e-04 eta: 12:41:36 time: 0.467537 data_time: 0.025310 memory: 15239 loss_kpt: 0.000613 acc_pose: 0.827740 loss: 0.000613 2022/09/12 22:50:26 - mmengine - INFO - Epoch(train) [37][400/586] lr: 5.000000e-04 eta: 12:41:20 time: 0.476070 data_time: 0.026093 memory: 15239 loss_kpt: 0.000622 acc_pose: 0.794776 loss: 0.000622 2022/09/12 22:50:50 - mmengine - INFO - Epoch(train) [37][450/586] lr: 5.000000e-04 eta: 12:41:01 time: 0.468159 data_time: 0.026931 memory: 15239 loss_kpt: 0.000622 acc_pose: 0.822005 loss: 0.000622 2022/09/12 22:51:13 - mmengine - INFO - Epoch(train) [37][500/586] lr: 5.000000e-04 eta: 12:40:44 time: 0.472213 data_time: 0.026040 memory: 15239 loss_kpt: 0.000618 acc_pose: 0.805163 loss: 0.000618 2022/09/12 22:51:37 - mmengine - INFO - Epoch(train) [37][550/586] lr: 5.000000e-04 eta: 12:40:26 time: 0.468847 data_time: 0.031497 memory: 15239 loss_kpt: 0.000615 acc_pose: 0.796759 loss: 0.000615 2022/09/12 22:51:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:51:53 - mmengine - INFO - Saving checkpoint at 37 epochs 2022/09/12 22:52:24 - mmengine - INFO - Epoch(train) [38][50/586] lr: 5.000000e-04 eta: 12:38:41 time: 0.487133 data_time: 0.041835 memory: 15239 loss_kpt: 0.000609 acc_pose: 0.800470 loss: 0.000609 2022/09/12 22:52:48 - mmengine - INFO - Epoch(train) [38][100/586] lr: 5.000000e-04 eta: 12:38:25 time: 0.477590 data_time: 0.028852 memory: 15239 loss_kpt: 0.000616 acc_pose: 0.851531 loss: 0.000616 2022/09/12 22:53:11 - mmengine - INFO - Epoch(train) [38][150/586] lr: 5.000000e-04 eta: 12:38:07 time: 0.468318 data_time: 0.029802 memory: 15239 loss_kpt: 0.000601 acc_pose: 0.842600 loss: 0.000601 2022/09/12 22:53:35 - mmengine - INFO - Epoch(train) [38][200/586] lr: 5.000000e-04 eta: 12:37:48 time: 0.465939 data_time: 0.030851 memory: 15239 loss_kpt: 0.000614 acc_pose: 0.790174 loss: 0.000614 2022/09/12 22:53:58 - mmengine - INFO - Epoch(train) [38][250/586] lr: 5.000000e-04 eta: 12:37:31 time: 0.474223 data_time: 0.037406 memory: 15239 loss_kpt: 0.000608 acc_pose: 0.804150 loss: 0.000608 2022/09/12 22:54:22 - mmengine - INFO - Epoch(train) [38][300/586] lr: 5.000000e-04 eta: 12:37:12 time: 0.464049 data_time: 0.031837 memory: 15239 loss_kpt: 0.000617 acc_pose: 0.790772 loss: 0.000617 2022/09/12 22:54:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:54:45 - mmengine - INFO - Epoch(train) [38][350/586] lr: 5.000000e-04 eta: 12:36:53 time: 0.462472 data_time: 0.030992 memory: 15239 loss_kpt: 0.000584 acc_pose: 0.817111 loss: 0.000584 2022/09/12 22:55:09 - mmengine - INFO - Epoch(train) [38][400/586] lr: 5.000000e-04 eta: 12:36:36 time: 0.474106 data_time: 0.032220 memory: 15239 loss_kpt: 0.000616 acc_pose: 0.789148 loss: 0.000616 2022/09/12 22:55:32 - mmengine - INFO - Epoch(train) [38][450/586] lr: 5.000000e-04 eta: 12:36:17 time: 0.466438 data_time: 0.030712 memory: 15239 loss_kpt: 0.000593 acc_pose: 0.822509 loss: 0.000593 2022/09/12 22:55:56 - mmengine - INFO - Epoch(train) [38][500/586] lr: 5.000000e-04 eta: 12:36:00 time: 0.473412 data_time: 0.027167 memory: 15239 loss_kpt: 0.000600 acc_pose: 0.807907 loss: 0.000600 2022/09/12 22:56:19 - mmengine - INFO - Epoch(train) [38][550/586] lr: 5.000000e-04 eta: 12:35:43 time: 0.471589 data_time: 0.027715 memory: 15239 loss_kpt: 0.000606 acc_pose: 0.816585 loss: 0.000606 2022/09/12 22:56:36 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 22:56:36 - mmengine - INFO - Saving checkpoint at 38 epochs 2022/09/12 22:57:07 - mmengine - INFO - Epoch(train) [39][50/586] lr: 5.000000e-04 eta: 12:33:59 time: 0.484009 data_time: 0.031392 memory: 15239 loss_kpt: 0.000597 acc_pose: 0.818088 loss: 0.000597 2022/09/12 22:57:30 - mmengine - INFO - Epoch(train) [39][100/586] lr: 5.000000e-04 eta: 12:33:39 time: 0.463531 data_time: 0.024898 memory: 15239 loss_kpt: 0.000615 acc_pose: 0.805759 loss: 0.000615 2022/09/12 22:57:53 - mmengine - INFO - Epoch(train) [39][150/586] lr: 5.000000e-04 eta: 12:33:22 time: 0.471530 data_time: 0.024950 memory: 15239 loss_kpt: 0.000622 acc_pose: 0.812382 loss: 0.000622 2022/09/12 22:58:17 - mmengine - INFO - Epoch(train) [39][200/586] lr: 5.000000e-04 eta: 12:33:04 time: 0.469162 data_time: 0.026687 memory: 15239 loss_kpt: 0.000602 acc_pose: 0.863731 loss: 0.000602 2022/09/12 22:58:40 - mmengine - INFO - Epoch(train) [39][250/586] lr: 5.000000e-04 eta: 12:32:45 time: 0.465353 data_time: 0.024916 memory: 15239 loss_kpt: 0.000592 acc_pose: 0.764084 loss: 0.000592 2022/09/12 22:59:04 - mmengine - INFO - Epoch(train) [39][300/586] lr: 5.000000e-04 eta: 12:32:28 time: 0.472487 data_time: 0.028670 memory: 15239 loss_kpt: 0.000622 acc_pose: 0.797323 loss: 0.000622 2022/09/12 22:59:27 - mmengine - INFO - Epoch(train) [39][350/586] lr: 5.000000e-04 eta: 12:32:09 time: 0.464415 data_time: 0.025691 memory: 15239 loss_kpt: 0.000617 acc_pose: 0.836265 loss: 0.000617 2022/09/12 22:59:51 - mmengine - INFO - Epoch(train) [39][400/586] lr: 5.000000e-04 eta: 12:31:51 time: 0.470335 data_time: 0.025437 memory: 15239 loss_kpt: 0.000607 acc_pose: 0.803972 loss: 0.000607 2022/09/12 23:00:14 - mmengine - INFO - Epoch(train) [39][450/586] lr: 5.000000e-04 eta: 12:31:33 time: 0.469245 data_time: 0.025506 memory: 15239 loss_kpt: 0.000637 acc_pose: 0.768302 loss: 0.000637 2022/09/12 23:00:37 - mmengine - INFO - Epoch(train) [39][500/586] lr: 5.000000e-04 eta: 12:31:14 time: 0.466456 data_time: 0.026243 memory: 15239 loss_kpt: 0.000596 acc_pose: 0.822533 loss: 0.000596 2022/09/12 23:01:00 - mmengine - INFO - Epoch(train) [39][550/586] lr: 5.000000e-04 eta: 12:30:55 time: 0.462158 data_time: 0.025941 memory: 15239 loss_kpt: 0.000625 acc_pose: 0.797680 loss: 0.000625 2022/09/12 23:01:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:01:17 - mmengine - INFO - Saving checkpoint at 39 epochs 2022/09/12 23:01:48 - mmengine - INFO - Epoch(train) [40][50/586] lr: 5.000000e-04 eta: 12:29:12 time: 0.480889 data_time: 0.039374 memory: 15239 loss_kpt: 0.000605 acc_pose: 0.853148 loss: 0.000605 2022/09/12 23:02:11 - mmengine - INFO - Epoch(train) [40][100/586] lr: 5.000000e-04 eta: 12:28:53 time: 0.464017 data_time: 0.025891 memory: 15239 loss_kpt: 0.000583 acc_pose: 0.828831 loss: 0.000583 2022/09/12 23:02:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:02:35 - mmengine - INFO - Epoch(train) [40][150/586] lr: 5.000000e-04 eta: 12:28:34 time: 0.465763 data_time: 0.026138 memory: 15239 loss_kpt: 0.000590 acc_pose: 0.837012 loss: 0.000590 2022/09/12 23:02:58 - mmengine - INFO - Epoch(train) [40][200/586] lr: 5.000000e-04 eta: 12:28:16 time: 0.467430 data_time: 0.026631 memory: 15239 loss_kpt: 0.000607 acc_pose: 0.852294 loss: 0.000607 2022/09/12 23:03:22 - mmengine - INFO - Epoch(train) [40][250/586] lr: 5.000000e-04 eta: 12:28:00 time: 0.480357 data_time: 0.031709 memory: 15239 loss_kpt: 0.000627 acc_pose: 0.822515 loss: 0.000627 2022/09/12 23:03:45 - mmengine - INFO - Epoch(train) [40][300/586] lr: 5.000000e-04 eta: 12:27:40 time: 0.461465 data_time: 0.026249 memory: 15239 loss_kpt: 0.000605 acc_pose: 0.811664 loss: 0.000605 2022/09/12 23:04:09 - mmengine - INFO - Epoch(train) [40][350/586] lr: 5.000000e-04 eta: 12:27:23 time: 0.474944 data_time: 0.026548 memory: 15239 loss_kpt: 0.000617 acc_pose: 0.770606 loss: 0.000617 2022/09/12 23:04:32 - mmengine - INFO - Epoch(train) [40][400/586] lr: 5.000000e-04 eta: 12:27:04 time: 0.465708 data_time: 0.028847 memory: 15239 loss_kpt: 0.000595 acc_pose: 0.812065 loss: 0.000595 2022/09/12 23:04:55 - mmengine - INFO - Epoch(train) [40][450/586] lr: 5.000000e-04 eta: 12:26:45 time: 0.463221 data_time: 0.027327 memory: 15239 loss_kpt: 0.000602 acc_pose: 0.804085 loss: 0.000602 2022/09/12 23:05:19 - mmengine - INFO - Epoch(train) [40][500/586] lr: 5.000000e-04 eta: 12:26:27 time: 0.472047 data_time: 0.025423 memory: 15239 loss_kpt: 0.000608 acc_pose: 0.817859 loss: 0.000608 2022/09/12 23:05:42 - mmengine - INFO - Epoch(train) [40][550/586] lr: 5.000000e-04 eta: 12:26:09 time: 0.468349 data_time: 0.030277 memory: 15239 loss_kpt: 0.000613 acc_pose: 0.798263 loss: 0.000613 2022/09/12 23:05:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:05:59 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/09/12 23:06:17 - mmengine - INFO - Epoch(val) [40][50/407] eta: 0:01:20 time: 0.225499 data_time: 0.014423 memory: 15239 2022/09/12 23:06:28 - mmengine - INFO - Epoch(val) [40][100/407] eta: 0:01:07 time: 0.218379 data_time: 0.008319 memory: 2064 2022/09/12 23:06:39 - mmengine - INFO - Epoch(val) [40][150/407] eta: 0:00:56 time: 0.218331 data_time: 0.008241 memory: 2064 2022/09/12 23:06:50 - mmengine - INFO - Epoch(val) [40][200/407] eta: 0:00:45 time: 0.218753 data_time: 0.008885 memory: 2064 2022/09/12 23:07:01 - mmengine - INFO - Epoch(val) [40][250/407] eta: 0:00:34 time: 0.218562 data_time: 0.008889 memory: 2064 2022/09/12 23:07:12 - mmengine - INFO - Epoch(val) [40][300/407] eta: 0:00:23 time: 0.218958 data_time: 0.008850 memory: 2064 2022/09/12 23:07:22 - mmengine - INFO - Epoch(val) [40][350/407] eta: 0:00:12 time: 0.217971 data_time: 0.008398 memory: 2064 2022/09/12 23:07:33 - mmengine - INFO - Epoch(val) [40][400/407] eta: 0:00:01 time: 0.217570 data_time: 0.008345 memory: 2064 2022/09/12 23:08:09 - mmengine - INFO - Evaluating CocoMetric... 2022/09/12 23:08:23 - mmengine - INFO - Epoch(val) [40][407/407] coco/AP: 0.745934 coco/AP .5: 0.896699 coco/AP .75: 0.809202 coco/AP (M): 0.706858 coco/AP (L): 0.815981 coco/AR: 0.793844 coco/AR .5: 0.932147 coco/AR .75: 0.851385 coco/AR (M): 0.750123 coco/AR (L): 0.857860 2022/09/12 23:08:23 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_384_v1/best_coco/AP_epoch_30.pth is removed 2022/09/12 23:08:27 - mmengine - INFO - The best checkpoint with 0.7459 coco/AP at 40 epoch is saved to best_coco/AP_epoch_40.pth. 2022/09/12 23:08:51 - mmengine - INFO - Epoch(train) [41][50/586] lr: 5.000000e-04 eta: 12:24:26 time: 0.468185 data_time: 0.030815 memory: 15239 loss_kpt: 0.000601 acc_pose: 0.809788 loss: 0.000601 2022/09/12 23:09:14 - mmengine - INFO - Epoch(train) [41][100/586] lr: 5.000000e-04 eta: 12:24:08 time: 0.472038 data_time: 0.025674 memory: 15239 loss_kpt: 0.000597 acc_pose: 0.852231 loss: 0.000597 2022/09/12 23:09:38 - mmengine - INFO - Epoch(train) [41][150/586] lr: 5.000000e-04 eta: 12:23:50 time: 0.466953 data_time: 0.026364 memory: 15239 loss_kpt: 0.000593 acc_pose: 0.816504 loss: 0.000593 2022/09/12 23:10:01 - mmengine - INFO - Epoch(train) [41][200/586] lr: 5.000000e-04 eta: 12:23:31 time: 0.464300 data_time: 0.025695 memory: 15239 loss_kpt: 0.000603 acc_pose: 0.793060 loss: 0.000603 2022/09/12 23:10:25 - mmengine - INFO - Epoch(train) [41][250/586] lr: 5.000000e-04 eta: 12:23:13 time: 0.473035 data_time: 0.026588 memory: 15239 loss_kpt: 0.000602 acc_pose: 0.816935 loss: 0.000602 2022/09/12 23:10:48 - mmengine - INFO - Epoch(train) [41][300/586] lr: 5.000000e-04 eta: 12:22:55 time: 0.469167 data_time: 0.025165 memory: 15239 loss_kpt: 0.000594 acc_pose: 0.736784 loss: 0.000594 2022/09/12 23:11:12 - mmengine - INFO - Epoch(train) [41][350/586] lr: 5.000000e-04 eta: 12:22:37 time: 0.470849 data_time: 0.026059 memory: 15239 loss_kpt: 0.000617 acc_pose: 0.841506 loss: 0.000617 2022/09/12 23:11:35 - mmengine - INFO - Epoch(train) [41][400/586] lr: 5.000000e-04 eta: 12:22:20 time: 0.472122 data_time: 0.025753 memory: 15239 loss_kpt: 0.000615 acc_pose: 0.817741 loss: 0.000615 2022/09/12 23:11:59 - mmengine - INFO - Epoch(train) [41][450/586] lr: 5.000000e-04 eta: 12:22:02 time: 0.470997 data_time: 0.025581 memory: 15239 loss_kpt: 0.000613 acc_pose: 0.684496 loss: 0.000613 2022/09/12 23:12:23 - mmengine - INFO - Epoch(train) [41][500/586] lr: 5.000000e-04 eta: 12:21:45 time: 0.477045 data_time: 0.025232 memory: 15239 loss_kpt: 0.000606 acc_pose: 0.849824 loss: 0.000606 2022/09/12 23:12:46 - mmengine - INFO - Epoch(train) [41][550/586] lr: 5.000000e-04 eta: 12:21:26 time: 0.465790 data_time: 0.028985 memory: 15239 loss_kpt: 0.000607 acc_pose: 0.836243 loss: 0.000607 2022/09/12 23:12:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:13:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:13:03 - mmengine - INFO - Saving checkpoint at 41 epochs 2022/09/12 23:13:33 - mmengine - INFO - Epoch(train) [42][50/586] lr: 5.000000e-04 eta: 12:19:47 time: 0.477344 data_time: 0.035992 memory: 15239 loss_kpt: 0.000574 acc_pose: 0.891905 loss: 0.000574 2022/09/12 23:13:56 - mmengine - INFO - Epoch(train) [42][100/586] lr: 5.000000e-04 eta: 12:19:28 time: 0.464492 data_time: 0.025815 memory: 15239 loss_kpt: 0.000616 acc_pose: 0.807978 loss: 0.000616 2022/09/12 23:14:19 - mmengine - INFO - Epoch(train) [42][150/586] lr: 5.000000e-04 eta: 12:19:08 time: 0.460478 data_time: 0.025000 memory: 15239 loss_kpt: 0.000596 acc_pose: 0.876245 loss: 0.000596 2022/09/12 23:14:43 - mmengine - INFO - Epoch(train) [42][200/586] lr: 5.000000e-04 eta: 12:18:50 time: 0.469815 data_time: 0.025964 memory: 15239 loss_kpt: 0.000606 acc_pose: 0.832725 loss: 0.000606 2022/09/12 23:15:06 - mmengine - INFO - Epoch(train) [42][250/586] lr: 5.000000e-04 eta: 12:18:32 time: 0.469658 data_time: 0.025662 memory: 15239 loss_kpt: 0.000579 acc_pose: 0.828769 loss: 0.000579 2022/09/12 23:15:30 - mmengine - INFO - Epoch(train) [42][300/586] lr: 5.000000e-04 eta: 12:18:13 time: 0.465777 data_time: 0.026498 memory: 15239 loss_kpt: 0.000590 acc_pose: 0.812761 loss: 0.000590 2022/09/12 23:15:53 - mmengine - INFO - Epoch(train) [42][350/586] lr: 5.000000e-04 eta: 12:17:55 time: 0.472731 data_time: 0.025812 memory: 15239 loss_kpt: 0.000605 acc_pose: 0.874445 loss: 0.000605 2022/09/12 23:16:17 - mmengine - INFO - Epoch(train) [42][400/586] lr: 5.000000e-04 eta: 12:17:37 time: 0.469205 data_time: 0.026216 memory: 15239 loss_kpt: 0.000585 acc_pose: 0.814968 loss: 0.000585 2022/09/12 23:16:40 - mmengine - INFO - Epoch(train) [42][450/586] lr: 5.000000e-04 eta: 12:17:17 time: 0.461266 data_time: 0.027652 memory: 15239 loss_kpt: 0.000595 acc_pose: 0.867432 loss: 0.000595 2022/09/12 23:17:04 - mmengine - INFO - Epoch(train) [42][500/586] lr: 5.000000e-04 eta: 12:16:59 time: 0.472874 data_time: 0.031449 memory: 15239 loss_kpt: 0.000614 acc_pose: 0.795620 loss: 0.000614 2022/09/12 23:17:27 - mmengine - INFO - Epoch(train) [42][550/586] lr: 5.000000e-04 eta: 12:16:40 time: 0.461140 data_time: 0.026205 memory: 15239 loss_kpt: 0.000611 acc_pose: 0.876120 loss: 0.000611 2022/09/12 23:17:43 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:17:43 - mmengine - INFO - Saving checkpoint at 42 epochs 2022/09/12 23:18:14 - mmengine - INFO - Epoch(train) [43][50/586] lr: 5.000000e-04 eta: 12:15:01 time: 0.471911 data_time: 0.033342 memory: 15239 loss_kpt: 0.000606 acc_pose: 0.878393 loss: 0.000606 2022/09/12 23:18:37 - mmengine - INFO - Epoch(train) [43][100/586] lr: 5.000000e-04 eta: 12:14:44 time: 0.474158 data_time: 0.029000 memory: 15239 loss_kpt: 0.000599 acc_pose: 0.830515 loss: 0.000599 2022/09/12 23:19:00 - mmengine - INFO - Epoch(train) [43][150/586] lr: 5.000000e-04 eta: 12:14:24 time: 0.462507 data_time: 0.030182 memory: 15239 loss_kpt: 0.000587 acc_pose: 0.848586 loss: 0.000587 2022/09/12 23:19:24 - mmengine - INFO - Epoch(train) [43][200/586] lr: 5.000000e-04 eta: 12:14:08 time: 0.480067 data_time: 0.033929 memory: 15239 loss_kpt: 0.000605 acc_pose: 0.798729 loss: 0.000605 2022/09/12 23:19:48 - mmengine - INFO - Epoch(train) [43][250/586] lr: 5.000000e-04 eta: 12:13:50 time: 0.471160 data_time: 0.037413 memory: 15239 loss_kpt: 0.000604 acc_pose: 0.868991 loss: 0.000604 2022/09/12 23:20:11 - mmengine - INFO - Epoch(train) [43][300/586] lr: 5.000000e-04 eta: 12:13:32 time: 0.470797 data_time: 0.029673 memory: 15239 loss_kpt: 0.000625 acc_pose: 0.864999 loss: 0.000625 2022/09/12 23:20:35 - mmengine - INFO - Epoch(train) [43][350/586] lr: 5.000000e-04 eta: 12:13:15 time: 0.475095 data_time: 0.026297 memory: 15239 loss_kpt: 0.000593 acc_pose: 0.790014 loss: 0.000593 2022/09/12 23:20:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:20:59 - mmengine - INFO - Epoch(train) [43][400/586] lr: 5.000000e-04 eta: 12:12:56 time: 0.465166 data_time: 0.025036 memory: 15239 loss_kpt: 0.000581 acc_pose: 0.831671 loss: 0.000581 2022/09/12 23:21:22 - mmengine - INFO - Epoch(train) [43][450/586] lr: 5.000000e-04 eta: 12:12:37 time: 0.467543 data_time: 0.026827 memory: 15239 loss_kpt: 0.000600 acc_pose: 0.868543 loss: 0.000600 2022/09/12 23:21:45 - mmengine - INFO - Epoch(train) [43][500/586] lr: 5.000000e-04 eta: 12:12:18 time: 0.464619 data_time: 0.029768 memory: 15239 loss_kpt: 0.000580 acc_pose: 0.791182 loss: 0.000580 2022/09/12 23:22:09 - mmengine - INFO - Epoch(train) [43][550/586] lr: 5.000000e-04 eta: 12:11:59 time: 0.469472 data_time: 0.026604 memory: 15239 loss_kpt: 0.000622 acc_pose: 0.718577 loss: 0.000622 2022/09/12 23:22:25 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:22:25 - mmengine - INFO - Saving checkpoint at 43 epochs 2022/09/12 23:22:56 - mmengine - INFO - Epoch(train) [44][50/586] lr: 5.000000e-04 eta: 12:10:26 time: 0.487323 data_time: 0.037076 memory: 15239 loss_kpt: 0.000599 acc_pose: 0.769900 loss: 0.000599 2022/09/12 23:23:20 - mmengine - INFO - Epoch(train) [44][100/586] lr: 5.000000e-04 eta: 12:10:07 time: 0.468244 data_time: 0.027149 memory: 15239 loss_kpt: 0.000595 acc_pose: 0.826414 loss: 0.000595 2022/09/12 23:23:43 - mmengine - INFO - Epoch(train) [44][150/586] lr: 5.000000e-04 eta: 12:09:48 time: 0.466414 data_time: 0.030332 memory: 15239 loss_kpt: 0.000605 acc_pose: 0.849092 loss: 0.000605 2022/09/12 23:24:06 - mmengine - INFO - Epoch(train) [44][200/586] lr: 5.000000e-04 eta: 12:09:30 time: 0.467474 data_time: 0.026440 memory: 15239 loss_kpt: 0.000611 acc_pose: 0.804359 loss: 0.000611 2022/09/12 23:24:30 - mmengine - INFO - Epoch(train) [44][250/586] lr: 5.000000e-04 eta: 12:09:11 time: 0.466298 data_time: 0.024936 memory: 15239 loss_kpt: 0.000599 acc_pose: 0.787596 loss: 0.000599 2022/09/12 23:24:53 - mmengine - INFO - Epoch(train) [44][300/586] lr: 5.000000e-04 eta: 12:08:53 time: 0.472370 data_time: 0.027275 memory: 15239 loss_kpt: 0.000601 acc_pose: 0.829296 loss: 0.000601 2022/09/12 23:25:17 - mmengine - INFO - Epoch(train) [44][350/586] lr: 5.000000e-04 eta: 12:08:36 time: 0.473843 data_time: 0.028076 memory: 15239 loss_kpt: 0.000588 acc_pose: 0.791431 loss: 0.000588 2022/09/12 23:25:40 - mmengine - INFO - Epoch(train) [44][400/586] lr: 5.000000e-04 eta: 12:08:16 time: 0.466001 data_time: 0.025984 memory: 15239 loss_kpt: 0.000620 acc_pose: 0.818945 loss: 0.000620 2022/09/12 23:26:04 - mmengine - INFO - Epoch(train) [44][450/586] lr: 5.000000e-04 eta: 12:07:59 time: 0.472030 data_time: 0.028753 memory: 15239 loss_kpt: 0.000624 acc_pose: 0.886765 loss: 0.000624 2022/09/12 23:26:27 - mmengine - INFO - Epoch(train) [44][500/586] lr: 5.000000e-04 eta: 12:07:39 time: 0.464973 data_time: 0.027286 memory: 15239 loss_kpt: 0.000616 acc_pose: 0.754152 loss: 0.000616 2022/09/12 23:26:51 - mmengine - INFO - Epoch(train) [44][550/586] lr: 5.000000e-04 eta: 12:07:21 time: 0.471913 data_time: 0.025595 memory: 15239 loss_kpt: 0.000611 acc_pose: 0.752679 loss: 0.000611 2022/09/12 23:27:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:27:08 - mmengine - INFO - Saving checkpoint at 44 epochs 2022/09/12 23:27:38 - mmengine - INFO - Epoch(train) [45][50/586] lr: 5.000000e-04 eta: 12:05:47 time: 0.473492 data_time: 0.034773 memory: 15239 loss_kpt: 0.000612 acc_pose: 0.830553 loss: 0.000612 2022/09/12 23:28:02 - mmengine - INFO - Epoch(train) [45][100/586] lr: 5.000000e-04 eta: 12:05:29 time: 0.473858 data_time: 0.024753 memory: 15239 loss_kpt: 0.000609 acc_pose: 0.788357 loss: 0.000609 2022/09/12 23:28:25 - mmengine - INFO - Epoch(train) [45][150/586] lr: 5.000000e-04 eta: 12:05:09 time: 0.460933 data_time: 0.026656 memory: 15239 loss_kpt: 0.000592 acc_pose: 0.793051 loss: 0.000592 2022/09/12 23:28:48 - mmengine - INFO - Epoch(train) [45][200/586] lr: 5.000000e-04 eta: 12:04:52 time: 0.474794 data_time: 0.025627 memory: 15239 loss_kpt: 0.000591 acc_pose: 0.841545 loss: 0.000591 2022/09/12 23:28:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:29:12 - mmengine - INFO - Epoch(train) [45][250/586] lr: 5.000000e-04 eta: 12:04:33 time: 0.467444 data_time: 0.025481 memory: 15239 loss_kpt: 0.000573 acc_pose: 0.808446 loss: 0.000573 2022/09/12 23:29:35 - mmengine - INFO - Epoch(train) [45][300/586] lr: 5.000000e-04 eta: 12:04:13 time: 0.460592 data_time: 0.025351 memory: 15239 loss_kpt: 0.000583 acc_pose: 0.853315 loss: 0.000583 2022/09/12 23:29:58 - mmengine - INFO - Epoch(train) [45][350/586] lr: 5.000000e-04 eta: 12:03:55 time: 0.471296 data_time: 0.025104 memory: 15239 loss_kpt: 0.000605 acc_pose: 0.897953 loss: 0.000605 2022/09/12 23:30:22 - mmengine - INFO - Epoch(train) [45][400/586] lr: 5.000000e-04 eta: 12:03:38 time: 0.478975 data_time: 0.026043 memory: 15239 loss_kpt: 0.000608 acc_pose: 0.831401 loss: 0.000608 2022/09/12 23:30:45 - mmengine - INFO - Epoch(train) [45][450/586] lr: 5.000000e-04 eta: 12:03:18 time: 0.458414 data_time: 0.026141 memory: 15239 loss_kpt: 0.000598 acc_pose: 0.872354 loss: 0.000598 2022/09/12 23:31:09 - mmengine - INFO - Epoch(train) [45][500/586] lr: 5.000000e-04 eta: 12:02:59 time: 0.469120 data_time: 0.026281 memory: 15239 loss_kpt: 0.000590 acc_pose: 0.787239 loss: 0.000590 2022/09/12 23:31:32 - mmengine - INFO - Epoch(train) [45][550/586] lr: 5.000000e-04 eta: 12:02:39 time: 0.461111 data_time: 0.025312 memory: 15239 loss_kpt: 0.000605 acc_pose: 0.849745 loss: 0.000605 2022/09/12 23:31:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:31:48 - mmengine - INFO - Saving checkpoint at 45 epochs 2022/09/12 23:32:20 - mmengine - INFO - Epoch(train) [46][50/586] lr: 5.000000e-04 eta: 12:01:09 time: 0.487684 data_time: 0.043923 memory: 15239 loss_kpt: 0.000592 acc_pose: 0.703155 loss: 0.000592 2022/09/12 23:32:43 - mmengine - INFO - Epoch(train) [46][100/586] lr: 5.000000e-04 eta: 12:00:50 time: 0.468750 data_time: 0.034451 memory: 15239 loss_kpt: 0.000580 acc_pose: 0.780249 loss: 0.000580 2022/09/12 23:33:06 - mmengine - INFO - Epoch(train) [46][150/586] lr: 5.000000e-04 eta: 12:00:31 time: 0.466299 data_time: 0.028459 memory: 15239 loss_kpt: 0.000598 acc_pose: 0.845310 loss: 0.000598 2022/09/12 23:33:30 - mmengine - INFO - Epoch(train) [46][200/586] lr: 5.000000e-04 eta: 12:00:13 time: 0.469506 data_time: 0.026493 memory: 15239 loss_kpt: 0.000606 acc_pose: 0.858744 loss: 0.000606 2022/09/12 23:33:54 - mmengine - INFO - Epoch(train) [46][250/586] lr: 5.000000e-04 eta: 11:59:56 time: 0.477387 data_time: 0.025656 memory: 15239 loss_kpt: 0.000597 acc_pose: 0.795533 loss: 0.000597 2022/09/12 23:34:17 - mmengine - INFO - Epoch(train) [46][300/586] lr: 5.000000e-04 eta: 11:59:36 time: 0.463874 data_time: 0.030383 memory: 15239 loss_kpt: 0.000582 acc_pose: 0.820912 loss: 0.000582 2022/09/12 23:34:41 - mmengine - INFO - Epoch(train) [46][350/586] lr: 5.000000e-04 eta: 11:59:18 time: 0.473199 data_time: 0.025962 memory: 15239 loss_kpt: 0.000604 acc_pose: 0.743888 loss: 0.000604 2022/09/12 23:35:04 - mmengine - INFO - Epoch(train) [46][400/586] lr: 5.000000e-04 eta: 11:58:59 time: 0.464352 data_time: 0.024805 memory: 15239 loss_kpt: 0.000590 acc_pose: 0.766250 loss: 0.000590 2022/09/12 23:35:27 - mmengine - INFO - Epoch(train) [46][450/586] lr: 5.000000e-04 eta: 11:58:39 time: 0.460943 data_time: 0.026740 memory: 15239 loss_kpt: 0.000609 acc_pose: 0.887201 loss: 0.000609 2022/09/12 23:35:51 - mmengine - INFO - Epoch(train) [46][500/586] lr: 5.000000e-04 eta: 11:58:22 time: 0.482846 data_time: 0.026297 memory: 15239 loss_kpt: 0.000586 acc_pose: 0.759448 loss: 0.000586 2022/09/12 23:36:14 - mmengine - INFO - Epoch(train) [46][550/586] lr: 5.000000e-04 eta: 11:58:02 time: 0.460373 data_time: 0.025638 memory: 15239 loss_kpt: 0.000577 acc_pose: 0.740936 loss: 0.000577 2022/09/12 23:36:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:36:31 - mmengine - INFO - Saving checkpoint at 46 epochs 2022/09/12 23:36:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:37:02 - mmengine - INFO - Epoch(train) [47][50/586] lr: 5.000000e-04 eta: 11:56:32 time: 0.481592 data_time: 0.031688 memory: 15239 loss_kpt: 0.000587 acc_pose: 0.826313 loss: 0.000587 2022/09/12 23:37:25 - mmengine - INFO - Epoch(train) [47][100/586] lr: 5.000000e-04 eta: 11:56:14 time: 0.469681 data_time: 0.030476 memory: 15239 loss_kpt: 0.000597 acc_pose: 0.833941 loss: 0.000597 2022/09/12 23:37:49 - mmengine - INFO - Epoch(train) [47][150/586] lr: 5.000000e-04 eta: 11:55:56 time: 0.472141 data_time: 0.025944 memory: 15239 loss_kpt: 0.000582 acc_pose: 0.763474 loss: 0.000582 2022/09/12 23:38:12 - mmengine - INFO - Epoch(train) [47][200/586] lr: 5.000000e-04 eta: 11:55:37 time: 0.469963 data_time: 0.026089 memory: 15239 loss_kpt: 0.000608 acc_pose: 0.798917 loss: 0.000608 2022/09/12 23:38:35 - mmengine - INFO - Epoch(train) [47][250/586] lr: 5.000000e-04 eta: 11:55:18 time: 0.463842 data_time: 0.027050 memory: 15239 loss_kpt: 0.000602 acc_pose: 0.832672 loss: 0.000602 2022/09/12 23:38:59 - mmengine - INFO - Epoch(train) [47][300/586] lr: 5.000000e-04 eta: 11:54:59 time: 0.469027 data_time: 0.027081 memory: 15239 loss_kpt: 0.000585 acc_pose: 0.871682 loss: 0.000585 2022/09/12 23:39:22 - mmengine - INFO - Epoch(train) [47][350/586] lr: 5.000000e-04 eta: 11:54:41 time: 0.469631 data_time: 0.026266 memory: 15239 loss_kpt: 0.000589 acc_pose: 0.859566 loss: 0.000589 2022/09/12 23:39:46 - mmengine - INFO - Epoch(train) [47][400/586] lr: 5.000000e-04 eta: 11:54:22 time: 0.468015 data_time: 0.025033 memory: 15239 loss_kpt: 0.000595 acc_pose: 0.781259 loss: 0.000595 2022/09/12 23:40:09 - mmengine - INFO - Epoch(train) [47][450/586] lr: 5.000000e-04 eta: 11:54:02 time: 0.462826 data_time: 0.025935 memory: 15239 loss_kpt: 0.000623 acc_pose: 0.805701 loss: 0.000623 2022/09/12 23:40:33 - mmengine - INFO - Epoch(train) [47][500/586] lr: 5.000000e-04 eta: 11:53:44 time: 0.473465 data_time: 0.027164 memory: 15239 loss_kpt: 0.000577 acc_pose: 0.734291 loss: 0.000577 2022/09/12 23:40:56 - mmengine - INFO - Epoch(train) [47][550/586] lr: 5.000000e-04 eta: 11:53:24 time: 0.460608 data_time: 0.030391 memory: 15239 loss_kpt: 0.000613 acc_pose: 0.779332 loss: 0.000613 2022/09/12 23:41:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:41:12 - mmengine - INFO - Saving checkpoint at 47 epochs 2022/09/12 23:41:43 - mmengine - INFO - Epoch(train) [48][50/586] lr: 5.000000e-04 eta: 11:51:56 time: 0.483719 data_time: 0.039443 memory: 15239 loss_kpt: 0.000602 acc_pose: 0.812123 loss: 0.000602 2022/09/12 23:42:06 - mmengine - INFO - Epoch(train) [48][100/586] lr: 5.000000e-04 eta: 11:51:36 time: 0.462511 data_time: 0.033076 memory: 15239 loss_kpt: 0.000582 acc_pose: 0.834648 loss: 0.000582 2022/09/12 23:42:30 - mmengine - INFO - Epoch(train) [48][150/586] lr: 5.000000e-04 eta: 11:51:17 time: 0.468782 data_time: 0.034535 memory: 15239 loss_kpt: 0.000571 acc_pose: 0.885085 loss: 0.000571 2022/09/12 23:42:53 - mmengine - INFO - Epoch(train) [48][200/586] lr: 5.000000e-04 eta: 11:51:00 time: 0.475752 data_time: 0.026702 memory: 15239 loss_kpt: 0.000582 acc_pose: 0.749230 loss: 0.000582 2022/09/12 23:43:16 - mmengine - INFO - Epoch(train) [48][250/586] lr: 5.000000e-04 eta: 11:50:39 time: 0.459771 data_time: 0.025404 memory: 15239 loss_kpt: 0.000588 acc_pose: 0.878869 loss: 0.000588 2022/09/12 23:43:40 - mmengine - INFO - Epoch(train) [48][300/586] lr: 5.000000e-04 eta: 11:50:21 time: 0.470898 data_time: 0.027194 memory: 15239 loss_kpt: 0.000571 acc_pose: 0.803583 loss: 0.000571 2022/09/12 23:44:03 - mmengine - INFO - Epoch(train) [48][350/586] lr: 5.000000e-04 eta: 11:50:02 time: 0.466154 data_time: 0.025899 memory: 15239 loss_kpt: 0.000596 acc_pose: 0.787239 loss: 0.000596 2022/09/12 23:44:27 - mmengine - INFO - Epoch(train) [48][400/586] lr: 5.000000e-04 eta: 11:49:43 time: 0.467126 data_time: 0.026783 memory: 15239 loss_kpt: 0.000589 acc_pose: 0.859182 loss: 0.000589 2022/09/12 23:44:50 - mmengine - INFO - Epoch(train) [48][450/586] lr: 5.000000e-04 eta: 11:49:23 time: 0.466168 data_time: 0.025461 memory: 15239 loss_kpt: 0.000599 acc_pose: 0.837584 loss: 0.000599 2022/09/12 23:44:54 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:45:13 - mmengine - INFO - Epoch(train) [48][500/586] lr: 5.000000e-04 eta: 11:49:04 time: 0.462033 data_time: 0.025880 memory: 15239 loss_kpt: 0.000571 acc_pose: 0.870895 loss: 0.000571 2022/09/12 23:45:36 - mmengine - INFO - Epoch(train) [48][550/586] lr: 5.000000e-04 eta: 11:48:44 time: 0.464480 data_time: 0.025860 memory: 15239 loss_kpt: 0.000571 acc_pose: 0.821638 loss: 0.000571 2022/09/12 23:45:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:45:53 - mmengine - INFO - Saving checkpoint at 48 epochs 2022/09/12 23:46:24 - mmengine - INFO - Epoch(train) [49][50/586] lr: 5.000000e-04 eta: 11:47:16 time: 0.474778 data_time: 0.033179 memory: 15239 loss_kpt: 0.000618 acc_pose: 0.795484 loss: 0.000618 2022/09/12 23:46:47 - mmengine - INFO - Epoch(train) [49][100/586] lr: 5.000000e-04 eta: 11:46:56 time: 0.465821 data_time: 0.031774 memory: 15239 loss_kpt: 0.000584 acc_pose: 0.769864 loss: 0.000584 2022/09/12 23:47:10 - mmengine - INFO - Epoch(train) [49][150/586] lr: 5.000000e-04 eta: 11:46:38 time: 0.470191 data_time: 0.029350 memory: 15239 loss_kpt: 0.000595 acc_pose: 0.859389 loss: 0.000595 2022/09/12 23:47:34 - mmengine - INFO - Epoch(train) [49][200/586] lr: 5.000000e-04 eta: 11:46:19 time: 0.465247 data_time: 0.025914 memory: 15239 loss_kpt: 0.000578 acc_pose: 0.822835 loss: 0.000578 2022/09/12 23:47:57 - mmengine - INFO - Epoch(train) [49][250/586] lr: 5.000000e-04 eta: 11:46:00 time: 0.468870 data_time: 0.025837 memory: 15239 loss_kpt: 0.000587 acc_pose: 0.828033 loss: 0.000587 2022/09/12 23:48:20 - mmengine - INFO - Epoch(train) [49][300/586] lr: 5.000000e-04 eta: 11:45:41 time: 0.466835 data_time: 0.025102 memory: 15239 loss_kpt: 0.000590 acc_pose: 0.790846 loss: 0.000590 2022/09/12 23:48:44 - mmengine - INFO - Epoch(train) [49][350/586] lr: 5.000000e-04 eta: 11:45:21 time: 0.463798 data_time: 0.025710 memory: 15239 loss_kpt: 0.000593 acc_pose: 0.818492 loss: 0.000593 2022/09/12 23:49:07 - mmengine - INFO - Epoch(train) [49][400/586] lr: 5.000000e-04 eta: 11:45:03 time: 0.473262 data_time: 0.025955 memory: 15239 loss_kpt: 0.000601 acc_pose: 0.863822 loss: 0.000601 2022/09/12 23:49:31 - mmengine - INFO - Epoch(train) [49][450/586] lr: 5.000000e-04 eta: 11:44:44 time: 0.466583 data_time: 0.026359 memory: 15239 loss_kpt: 0.000602 acc_pose: 0.833583 loss: 0.000602 2022/09/12 23:49:54 - mmengine - INFO - Epoch(train) [49][500/586] lr: 5.000000e-04 eta: 11:44:23 time: 0.460287 data_time: 0.026192 memory: 15239 loss_kpt: 0.000592 acc_pose: 0.885954 loss: 0.000592 2022/09/12 23:50:17 - mmengine - INFO - Epoch(train) [49][550/586] lr: 5.000000e-04 eta: 11:44:05 time: 0.470249 data_time: 0.029831 memory: 15239 loss_kpt: 0.000591 acc_pose: 0.843976 loss: 0.000591 2022/09/12 23:50:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:50:34 - mmengine - INFO - Saving checkpoint at 49 epochs 2022/09/12 23:51:04 - mmengine - INFO - Epoch(train) [50][50/586] lr: 5.000000e-04 eta: 11:42:37 time: 0.470321 data_time: 0.033035 memory: 15239 loss_kpt: 0.000599 acc_pose: 0.855532 loss: 0.000599 2022/09/12 23:51:27 - mmengine - INFO - Epoch(train) [50][100/586] lr: 5.000000e-04 eta: 11:42:17 time: 0.461390 data_time: 0.026056 memory: 15239 loss_kpt: 0.000590 acc_pose: 0.841241 loss: 0.000590 2022/09/12 23:51:51 - mmengine - INFO - Epoch(train) [50][150/586] lr: 5.000000e-04 eta: 11:41:59 time: 0.474430 data_time: 0.029314 memory: 15239 loss_kpt: 0.000588 acc_pose: 0.854309 loss: 0.000588 2022/09/12 23:52:14 - mmengine - INFO - Epoch(train) [50][200/586] lr: 5.000000e-04 eta: 11:41:40 time: 0.464082 data_time: 0.026136 memory: 15239 loss_kpt: 0.000606 acc_pose: 0.840945 loss: 0.000606 2022/09/12 23:52:38 - mmengine - INFO - Epoch(train) [50][250/586] lr: 5.000000e-04 eta: 11:41:20 time: 0.466731 data_time: 0.026969 memory: 15239 loss_kpt: 0.000577 acc_pose: 0.890114 loss: 0.000577 2022/09/12 23:52:54 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:53:01 - mmengine - INFO - Epoch(train) [50][300/586] lr: 5.000000e-04 eta: 11:41:01 time: 0.465446 data_time: 0.029767 memory: 15239 loss_kpt: 0.000600 acc_pose: 0.864825 loss: 0.000600 2022/09/12 23:53:24 - mmengine - INFO - Epoch(train) [50][350/586] lr: 5.000000e-04 eta: 11:40:42 time: 0.468150 data_time: 0.026193 memory: 15239 loss_kpt: 0.000580 acc_pose: 0.732004 loss: 0.000580 2022/09/12 23:53:47 - mmengine - INFO - Epoch(train) [50][400/586] lr: 5.000000e-04 eta: 11:40:23 time: 0.464511 data_time: 0.026217 memory: 15239 loss_kpt: 0.000616 acc_pose: 0.875254 loss: 0.000616 2022/09/12 23:54:11 - mmengine - INFO - Epoch(train) [50][450/586] lr: 5.000000e-04 eta: 11:40:05 time: 0.477330 data_time: 0.027292 memory: 15239 loss_kpt: 0.000580 acc_pose: 0.835459 loss: 0.000580 2022/09/12 23:54:35 - mmengine - INFO - Epoch(train) [50][500/586] lr: 5.000000e-04 eta: 11:39:45 time: 0.464322 data_time: 0.026219 memory: 15239 loss_kpt: 0.000585 acc_pose: 0.898855 loss: 0.000585 2022/09/12 23:54:58 - mmengine - INFO - Epoch(train) [50][550/586] lr: 5.000000e-04 eta: 11:39:25 time: 0.462540 data_time: 0.028516 memory: 15239 loss_kpt: 0.000576 acc_pose: 0.804760 loss: 0.000576 2022/09/12 23:55:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/12 23:55:15 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/09/12 23:55:33 - mmengine - INFO - Epoch(val) [50][50/407] eta: 0:01:20 time: 0.225441 data_time: 0.014044 memory: 15239 2022/09/12 23:55:44 - mmengine - INFO - Epoch(val) [50][100/407] eta: 0:01:07 time: 0.219699 data_time: 0.008616 memory: 2064 2022/09/12 23:55:55 - mmengine - INFO - Epoch(val) [50][150/407] eta: 0:00:56 time: 0.218629 data_time: 0.008575 memory: 2064 2022/09/12 23:56:06 - mmengine - INFO - Epoch(val) [50][200/407] eta: 0:00:46 time: 0.224207 data_time: 0.008230 memory: 2064 2022/09/12 23:56:17 - mmengine - INFO - Epoch(val) [50][250/407] eta: 0:00:34 time: 0.216654 data_time: 0.008249 memory: 2064 2022/09/12 23:56:28 - mmengine - INFO - Epoch(val) [50][300/407] eta: 0:00:23 time: 0.219144 data_time: 0.008457 memory: 2064 2022/09/12 23:56:39 - mmengine - INFO - Epoch(val) [50][350/407] eta: 0:00:12 time: 0.218014 data_time: 0.008183 memory: 2064 2022/09/12 23:56:50 - mmengine - INFO - Epoch(val) [50][400/407] eta: 0:00:01 time: 0.216293 data_time: 0.007885 memory: 2064 2022/09/12 23:57:26 - mmengine - INFO - Evaluating CocoMetric... 2022/09/12 23:57:39 - mmengine - INFO - Epoch(val) [50][407/407] coco/AP: 0.748411 coco/AP .5: 0.897329 coco/AP .75: 0.812105 coco/AP (M): 0.707689 coco/AP (L): 0.822583 coco/AR: 0.799024 coco/AR .5: 0.935768 coco/AR .75: 0.855636 coco/AR (M): 0.752062 coco/AR (L): 0.866184 2022/09/12 23:57:39 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_384_v1/best_coco/AP_epoch_40.pth is removed 2022/09/12 23:57:43 - mmengine - INFO - The best checkpoint with 0.7484 coco/AP at 50 epoch is saved to best_coco/AP_epoch_50.pth. 2022/09/12 23:58:07 - mmengine - INFO - Epoch(train) [51][50/586] lr: 5.000000e-04 eta: 11:37:59 time: 0.471606 data_time: 0.029662 memory: 15239 loss_kpt: 0.000579 acc_pose: 0.800566 loss: 0.000579 2022/09/12 23:58:30 - mmengine - INFO - Epoch(train) [51][100/586] lr: 5.000000e-04 eta: 11:37:40 time: 0.466194 data_time: 0.027317 memory: 15239 loss_kpt: 0.000581 acc_pose: 0.838518 loss: 0.000581 2022/09/12 23:58:53 - mmengine - INFO - Epoch(train) [51][150/586] lr: 5.000000e-04 eta: 11:37:21 time: 0.469026 data_time: 0.025987 memory: 15239 loss_kpt: 0.000582 acc_pose: 0.741893 loss: 0.000582 2022/09/12 23:59:17 - mmengine - INFO - Epoch(train) [51][200/586] lr: 5.000000e-04 eta: 11:37:02 time: 0.468441 data_time: 0.025772 memory: 15239 loss_kpt: 0.000585 acc_pose: 0.789709 loss: 0.000585 2022/09/12 23:59:40 - mmengine - INFO - Epoch(train) [51][250/586] lr: 5.000000e-04 eta: 11:36:42 time: 0.458846 data_time: 0.025695 memory: 15239 loss_kpt: 0.000584 acc_pose: 0.843332 loss: 0.000584 2022/09/13 00:00:07 - mmengine - INFO - Epoch(train) [51][300/586] lr: 5.000000e-04 eta: 11:36:34 time: 0.536622 data_time: 0.028372 memory: 15239 loss_kpt: 0.000600 acc_pose: 0.851150 loss: 0.000600 2022/09/13 00:00:30 - mmengine - INFO - Epoch(train) [51][350/586] lr: 5.000000e-04 eta: 11:36:15 time: 0.467803 data_time: 0.027638 memory: 15239 loss_kpt: 0.000585 acc_pose: 0.888740 loss: 0.000585 2022/09/13 00:00:54 - mmengine - INFO - Epoch(train) [51][400/586] lr: 5.000000e-04 eta: 11:35:56 time: 0.470841 data_time: 0.025455 memory: 15239 loss_kpt: 0.000607 acc_pose: 0.816460 loss: 0.000607 2022/09/13 00:01:17 - mmengine - INFO - Epoch(train) [51][450/586] lr: 5.000000e-04 eta: 11:35:37 time: 0.472009 data_time: 0.026093 memory: 15239 loss_kpt: 0.000589 acc_pose: 0.885960 loss: 0.000589 2022/09/13 00:01:40 - mmengine - INFO - Epoch(train) [51][500/586] lr: 5.000000e-04 eta: 11:35:18 time: 0.463800 data_time: 0.030564 memory: 15239 loss_kpt: 0.000605 acc_pose: 0.826461 loss: 0.000605 2022/09/13 00:02:04 - mmengine - INFO - Epoch(train) [51][550/586] lr: 5.000000e-04 eta: 11:34:58 time: 0.465939 data_time: 0.026604 memory: 15239 loss_kpt: 0.000589 acc_pose: 0.816177 loss: 0.000589 2022/09/13 00:02:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:02:21 - mmengine - INFO - Saving checkpoint at 51 epochs 2022/09/13 00:02:51 - mmengine - INFO - Epoch(train) [52][50/586] lr: 5.000000e-04 eta: 11:33:33 time: 0.467946 data_time: 0.032869 memory: 15239 loss_kpt: 0.000585 acc_pose: 0.904046 loss: 0.000585 2022/09/13 00:03:14 - mmengine - INFO - Epoch(train) [52][100/586] lr: 5.000000e-04 eta: 11:33:13 time: 0.462363 data_time: 0.025808 memory: 15239 loss_kpt: 0.000590 acc_pose: 0.769243 loss: 0.000590 2022/09/13 00:03:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:03:38 - mmengine - INFO - Epoch(train) [52][150/586] lr: 5.000000e-04 eta: 11:32:55 time: 0.475407 data_time: 0.025684 memory: 15239 loss_kpt: 0.000591 acc_pose: 0.835208 loss: 0.000591 2022/09/13 00:04:01 - mmengine - INFO - Epoch(train) [52][200/586] lr: 5.000000e-04 eta: 11:32:35 time: 0.464655 data_time: 0.025689 memory: 15239 loss_kpt: 0.000584 acc_pose: 0.802756 loss: 0.000584 2022/09/13 00:04:24 - mmengine - INFO - Epoch(train) [52][250/586] lr: 5.000000e-04 eta: 11:32:16 time: 0.465492 data_time: 0.027066 memory: 15239 loss_kpt: 0.000573 acc_pose: 0.886640 loss: 0.000573 2022/09/13 00:04:48 - mmengine - INFO - Epoch(train) [52][300/586] lr: 5.000000e-04 eta: 11:31:57 time: 0.469470 data_time: 0.030826 memory: 15239 loss_kpt: 0.000574 acc_pose: 0.840402 loss: 0.000574 2022/09/13 00:05:11 - mmengine - INFO - Epoch(train) [52][350/586] lr: 5.000000e-04 eta: 11:31:38 time: 0.469731 data_time: 0.025300 memory: 15239 loss_kpt: 0.000592 acc_pose: 0.856964 loss: 0.000592 2022/09/13 00:05:34 - mmengine - INFO - Epoch(train) [52][400/586] lr: 5.000000e-04 eta: 11:31:17 time: 0.457263 data_time: 0.025817 memory: 15239 loss_kpt: 0.000584 acc_pose: 0.847105 loss: 0.000584 2022/09/13 00:05:58 - mmengine - INFO - Epoch(train) [52][450/586] lr: 5.000000e-04 eta: 11:30:59 time: 0.472992 data_time: 0.029442 memory: 15239 loss_kpt: 0.000592 acc_pose: 0.816282 loss: 0.000592 2022/09/13 00:06:21 - mmengine - INFO - Epoch(train) [52][500/586] lr: 5.000000e-04 eta: 11:30:40 time: 0.471738 data_time: 0.026937 memory: 15239 loss_kpt: 0.000580 acc_pose: 0.876245 loss: 0.000580 2022/09/13 00:06:44 - mmengine - INFO - Epoch(train) [52][550/586] lr: 5.000000e-04 eta: 11:30:19 time: 0.456896 data_time: 0.025488 memory: 15239 loss_kpt: 0.000591 acc_pose: 0.859947 loss: 0.000591 2022/09/13 00:07:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:07:01 - mmengine - INFO - Saving checkpoint at 52 epochs 2022/09/13 00:07:36 - mmengine - INFO - Epoch(train) [53][50/586] lr: 5.000000e-04 eta: 11:28:56 time: 0.474231 data_time: 0.041945 memory: 15239 loss_kpt: 0.000601 acc_pose: 0.932689 loss: 0.000601 2022/09/13 00:07:59 - mmengine - INFO - Epoch(train) [53][100/586] lr: 5.000000e-04 eta: 11:28:37 time: 0.469218 data_time: 0.033196 memory: 15239 loss_kpt: 0.000572 acc_pose: 0.898346 loss: 0.000572 2022/09/13 00:08:23 - mmengine - INFO - Epoch(train) [53][150/586] lr: 5.000000e-04 eta: 11:28:20 time: 0.477531 data_time: 0.026405 memory: 15239 loss_kpt: 0.000591 acc_pose: 0.865110 loss: 0.000591 2022/09/13 00:08:47 - mmengine - INFO - Epoch(train) [53][200/586] lr: 5.000000e-04 eta: 11:28:02 time: 0.478375 data_time: 0.026160 memory: 15239 loss_kpt: 0.000580 acc_pose: 0.833758 loss: 0.000580 2022/09/13 00:09:10 - mmengine - INFO - Epoch(train) [53][250/586] lr: 5.000000e-04 eta: 11:27:42 time: 0.464823 data_time: 0.027032 memory: 15239 loss_kpt: 0.000578 acc_pose: 0.842811 loss: 0.000578 2022/09/13 00:09:34 - mmengine - INFO - Epoch(train) [53][300/586] lr: 5.000000e-04 eta: 11:27:23 time: 0.466986 data_time: 0.027257 memory: 15239 loss_kpt: 0.000582 acc_pose: 0.861276 loss: 0.000582 2022/09/13 00:09:57 - mmengine - INFO - Epoch(train) [53][350/586] lr: 5.000000e-04 eta: 11:27:05 time: 0.473795 data_time: 0.026946 memory: 15239 loss_kpt: 0.000566 acc_pose: 0.832741 loss: 0.000566 2022/09/13 00:10:21 - mmengine - INFO - Epoch(train) [53][400/586] lr: 5.000000e-04 eta: 11:26:45 time: 0.465748 data_time: 0.025873 memory: 15239 loss_kpt: 0.000588 acc_pose: 0.831793 loss: 0.000588 2022/09/13 00:10:45 - mmengine - INFO - Epoch(train) [53][450/586] lr: 5.000000e-04 eta: 11:26:27 time: 0.475224 data_time: 0.025316 memory: 15239 loss_kpt: 0.000593 acc_pose: 0.781063 loss: 0.000593 2022/09/13 00:11:08 - mmengine - INFO - Epoch(train) [53][500/586] lr: 5.000000e-04 eta: 11:26:08 time: 0.467862 data_time: 0.029852 memory: 15239 loss_kpt: 0.000576 acc_pose: 0.801315 loss: 0.000576 2022/09/13 00:11:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:11:31 - mmengine - INFO - Epoch(train) [53][550/586] lr: 5.000000e-04 eta: 11:25:48 time: 0.466630 data_time: 0.025313 memory: 15239 loss_kpt: 0.000567 acc_pose: 0.815305 loss: 0.000567 2022/09/13 00:11:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:11:48 - mmengine - INFO - Saving checkpoint at 53 epochs 2022/09/13 00:12:18 - mmengine - INFO - Epoch(train) [54][50/586] lr: 5.000000e-04 eta: 11:24:26 time: 0.470923 data_time: 0.031923 memory: 15239 loss_kpt: 0.000602 acc_pose: 0.786396 loss: 0.000602 2022/09/13 00:12:42 - mmengine - INFO - Epoch(train) [54][100/586] lr: 5.000000e-04 eta: 11:24:07 time: 0.467233 data_time: 0.025181 memory: 15239 loss_kpt: 0.000577 acc_pose: 0.815659 loss: 0.000577 2022/09/13 00:13:05 - mmengine - INFO - Epoch(train) [54][150/586] lr: 5.000000e-04 eta: 11:23:48 time: 0.473049 data_time: 0.026794 memory: 15239 loss_kpt: 0.000601 acc_pose: 0.809772 loss: 0.000601 2022/09/13 00:13:29 - mmengine - INFO - Epoch(train) [54][200/586] lr: 5.000000e-04 eta: 11:23:30 time: 0.474731 data_time: 0.027429 memory: 15239 loss_kpt: 0.000595 acc_pose: 0.826758 loss: 0.000595 2022/09/13 00:13:52 - mmengine - INFO - Epoch(train) [54][250/586] lr: 5.000000e-04 eta: 11:23:11 time: 0.468422 data_time: 0.026880 memory: 15239 loss_kpt: 0.000594 acc_pose: 0.886624 loss: 0.000594 2022/09/13 00:14:16 - mmengine - INFO - Epoch(train) [54][300/586] lr: 5.000000e-04 eta: 11:22:51 time: 0.467069 data_time: 0.026027 memory: 15239 loss_kpt: 0.000573 acc_pose: 0.760758 loss: 0.000573 2022/09/13 00:14:39 - mmengine - INFO - Epoch(train) [54][350/586] lr: 5.000000e-04 eta: 11:22:31 time: 0.460513 data_time: 0.027993 memory: 15239 loss_kpt: 0.000563 acc_pose: 0.873067 loss: 0.000563 2022/09/13 00:15:03 - mmengine - INFO - Epoch(train) [54][400/586] lr: 5.000000e-04 eta: 11:22:13 time: 0.477948 data_time: 0.026087 memory: 15239 loss_kpt: 0.000592 acc_pose: 0.855424 loss: 0.000592 2022/09/13 00:15:26 - mmengine - INFO - Epoch(train) [54][450/586] lr: 5.000000e-04 eta: 11:21:54 time: 0.471329 data_time: 0.031255 memory: 15239 loss_kpt: 0.000571 acc_pose: 0.868654 loss: 0.000571 2022/09/13 00:15:50 - mmengine - INFO - Epoch(train) [54][500/586] lr: 5.000000e-04 eta: 11:21:35 time: 0.466127 data_time: 0.025892 memory: 15239 loss_kpt: 0.000571 acc_pose: 0.846613 loss: 0.000571 2022/09/13 00:16:13 - mmengine - INFO - Epoch(train) [54][550/586] lr: 5.000000e-04 eta: 11:21:16 time: 0.468832 data_time: 0.026035 memory: 15239 loss_kpt: 0.000578 acc_pose: 0.842612 loss: 0.000578 2022/09/13 00:16:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:16:30 - mmengine - INFO - Saving checkpoint at 54 epochs 2022/09/13 00:17:00 - mmengine - INFO - Epoch(train) [55][50/586] lr: 5.000000e-04 eta: 11:19:54 time: 0.471570 data_time: 0.035992 memory: 15239 loss_kpt: 0.000591 acc_pose: 0.854212 loss: 0.000591 2022/09/13 00:17:24 - mmengine - INFO - Epoch(train) [55][100/586] lr: 5.000000e-04 eta: 11:19:36 time: 0.476360 data_time: 0.032672 memory: 15239 loss_kpt: 0.000581 acc_pose: 0.849224 loss: 0.000581 2022/09/13 00:17:47 - mmengine - INFO - Epoch(train) [55][150/586] lr: 5.000000e-04 eta: 11:19:16 time: 0.460966 data_time: 0.030697 memory: 15239 loss_kpt: 0.000576 acc_pose: 0.784576 loss: 0.000576 2022/09/13 00:18:11 - mmengine - INFO - Epoch(train) [55][200/586] lr: 5.000000e-04 eta: 11:18:58 time: 0.476744 data_time: 0.033914 memory: 15239 loss_kpt: 0.000566 acc_pose: 0.849064 loss: 0.000566 2022/09/13 00:18:35 - mmengine - INFO - Epoch(train) [55][250/586] lr: 5.000000e-04 eta: 11:18:40 time: 0.477132 data_time: 0.030899 memory: 15239 loss_kpt: 0.000575 acc_pose: 0.781106 loss: 0.000575 2022/09/13 00:18:58 - mmengine - INFO - Epoch(train) [55][300/586] lr: 5.000000e-04 eta: 11:18:22 time: 0.473989 data_time: 0.038698 memory: 15239 loss_kpt: 0.000588 acc_pose: 0.853343 loss: 0.000588 2022/09/13 00:19:22 - mmengine - INFO - Epoch(train) [55][350/586] lr: 5.000000e-04 eta: 11:18:03 time: 0.471256 data_time: 0.031374 memory: 15239 loss_kpt: 0.000593 acc_pose: 0.816846 loss: 0.000593 2022/09/13 00:19:25 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:19:45 - mmengine - INFO - Epoch(train) [55][400/586] lr: 5.000000e-04 eta: 11:17:43 time: 0.464671 data_time: 0.033206 memory: 15239 loss_kpt: 0.000589 acc_pose: 0.794820 loss: 0.000589 2022/09/13 00:20:09 - mmengine - INFO - Epoch(train) [55][450/586] lr: 5.000000e-04 eta: 11:17:24 time: 0.467364 data_time: 0.026114 memory: 15239 loss_kpt: 0.000573 acc_pose: 0.826950 loss: 0.000573 2022/09/13 00:20:32 - mmengine - INFO - Epoch(train) [55][500/586] lr: 5.000000e-04 eta: 11:17:04 time: 0.468648 data_time: 0.027013 memory: 15239 loss_kpt: 0.000590 acc_pose: 0.840332 loss: 0.000590 2022/09/13 00:20:56 - mmengine - INFO - Epoch(train) [55][550/586] lr: 5.000000e-04 eta: 11:16:46 time: 0.472661 data_time: 0.025936 memory: 15239 loss_kpt: 0.000572 acc_pose: 0.885848 loss: 0.000572 2022/09/13 00:21:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:21:12 - mmengine - INFO - Saving checkpoint at 55 epochs 2022/09/13 00:21:43 - mmengine - INFO - Epoch(train) [56][50/586] lr: 5.000000e-04 eta: 11:15:26 time: 0.475594 data_time: 0.035149 memory: 15239 loss_kpt: 0.000591 acc_pose: 0.786319 loss: 0.000591 2022/09/13 00:22:06 - mmengine - INFO - Epoch(train) [56][100/586] lr: 5.000000e-04 eta: 11:15:07 time: 0.467353 data_time: 0.030046 memory: 15239 loss_kpt: 0.000592 acc_pose: 0.878011 loss: 0.000592 2022/09/13 00:22:30 - mmengine - INFO - Epoch(train) [56][150/586] lr: 5.000000e-04 eta: 11:14:48 time: 0.470749 data_time: 0.034090 memory: 15239 loss_kpt: 0.000592 acc_pose: 0.916939 loss: 0.000592 2022/09/13 00:22:53 - mmengine - INFO - Epoch(train) [56][200/586] lr: 5.000000e-04 eta: 11:14:29 time: 0.470649 data_time: 0.026748 memory: 15239 loss_kpt: 0.000587 acc_pose: 0.843497 loss: 0.000587 2022/09/13 00:23:17 - mmengine - INFO - Epoch(train) [56][250/586] lr: 5.000000e-04 eta: 11:14:10 time: 0.470083 data_time: 0.024956 memory: 15239 loss_kpt: 0.000577 acc_pose: 0.814582 loss: 0.000577 2022/09/13 00:23:40 - mmengine - INFO - Epoch(train) [56][300/586] lr: 5.000000e-04 eta: 11:13:51 time: 0.471941 data_time: 0.031471 memory: 15239 loss_kpt: 0.000598 acc_pose: 0.830680 loss: 0.000598 2022/09/13 00:24:04 - mmengine - INFO - Epoch(train) [56][350/586] lr: 5.000000e-04 eta: 11:13:32 time: 0.467401 data_time: 0.027459 memory: 15239 loss_kpt: 0.000580 acc_pose: 0.816351 loss: 0.000580 2022/09/13 00:24:27 - mmengine - INFO - Epoch(train) [56][400/586] lr: 5.000000e-04 eta: 11:13:12 time: 0.463901 data_time: 0.026460 memory: 15239 loss_kpt: 0.000574 acc_pose: 0.816996 loss: 0.000574 2022/09/13 00:24:50 - mmengine - INFO - Epoch(train) [56][450/586] lr: 5.000000e-04 eta: 11:12:52 time: 0.468880 data_time: 0.028737 memory: 15239 loss_kpt: 0.000569 acc_pose: 0.864651 loss: 0.000569 2022/09/13 00:25:14 - mmengine - INFO - Epoch(train) [56][500/586] lr: 5.000000e-04 eta: 11:12:33 time: 0.468449 data_time: 0.027224 memory: 15239 loss_kpt: 0.000587 acc_pose: 0.840014 loss: 0.000587 2022/09/13 00:25:37 - mmengine - INFO - Epoch(train) [56][550/586] lr: 5.000000e-04 eta: 11:12:15 time: 0.475313 data_time: 0.027133 memory: 15239 loss_kpt: 0.000576 acc_pose: 0.789262 loss: 0.000576 2022/09/13 00:25:54 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:25:54 - mmengine - INFO - Saving checkpoint at 56 epochs 2022/09/13 00:26:29 - mmengine - INFO - Epoch(train) [57][50/586] lr: 5.000000e-04 eta: 11:10:57 time: 0.484791 data_time: 0.035857 memory: 15239 loss_kpt: 0.000562 acc_pose: 0.771706 loss: 0.000562 2022/09/13 00:26:52 - mmengine - INFO - Epoch(train) [57][100/586] lr: 5.000000e-04 eta: 11:10:37 time: 0.464258 data_time: 0.026688 memory: 15239 loss_kpt: 0.000561 acc_pose: 0.834650 loss: 0.000561 2022/09/13 00:27:15 - mmengine - INFO - Epoch(train) [57][150/586] lr: 5.000000e-04 eta: 11:10:18 time: 0.467499 data_time: 0.026279 memory: 15239 loss_kpt: 0.000578 acc_pose: 0.871530 loss: 0.000578 2022/09/13 00:27:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:27:39 - mmengine - INFO - Epoch(train) [57][200/586] lr: 5.000000e-04 eta: 11:09:59 time: 0.474789 data_time: 0.026289 memory: 15239 loss_kpt: 0.000582 acc_pose: 0.776358 loss: 0.000582 2022/09/13 00:28:02 - mmengine - INFO - Epoch(train) [57][250/586] lr: 5.000000e-04 eta: 11:09:40 time: 0.469909 data_time: 0.025475 memory: 15239 loss_kpt: 0.000580 acc_pose: 0.858921 loss: 0.000580 2022/09/13 00:28:27 - mmengine - INFO - Epoch(train) [57][300/586] lr: 5.000000e-04 eta: 11:09:24 time: 0.486731 data_time: 0.026505 memory: 15239 loss_kpt: 0.000560 acc_pose: 0.839973 loss: 0.000560 2022/09/13 00:28:50 - mmengine - INFO - Epoch(train) [57][350/586] lr: 5.000000e-04 eta: 11:09:04 time: 0.467633 data_time: 0.029047 memory: 15239 loss_kpt: 0.000588 acc_pose: 0.833749 loss: 0.000588 2022/09/13 00:29:14 - mmengine - INFO - Epoch(train) [57][400/586] lr: 5.000000e-04 eta: 11:08:46 time: 0.481172 data_time: 0.025815 memory: 15239 loss_kpt: 0.000576 acc_pose: 0.752792 loss: 0.000576 2022/09/13 00:29:38 - mmengine - INFO - Epoch(train) [57][450/586] lr: 5.000000e-04 eta: 11:08:28 time: 0.472341 data_time: 0.026339 memory: 15239 loss_kpt: 0.000573 acc_pose: 0.862156 loss: 0.000573 2022/09/13 00:30:01 - mmengine - INFO - Epoch(train) [57][500/586] lr: 5.000000e-04 eta: 11:08:08 time: 0.469968 data_time: 0.026785 memory: 15239 loss_kpt: 0.000570 acc_pose: 0.789475 loss: 0.000570 2022/09/13 00:30:25 - mmengine - INFO - Epoch(train) [57][550/586] lr: 5.000000e-04 eta: 11:07:49 time: 0.468789 data_time: 0.025593 memory: 15239 loss_kpt: 0.000574 acc_pose: 0.893866 loss: 0.000574 2022/09/13 00:30:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:30:41 - mmengine - INFO - Saving checkpoint at 57 epochs 2022/09/13 00:31:13 - mmengine - INFO - Epoch(train) [58][50/586] lr: 5.000000e-04 eta: 11:06:33 time: 0.486731 data_time: 0.043218 memory: 15239 loss_kpt: 0.000596 acc_pose: 0.860046 loss: 0.000596 2022/09/13 00:31:36 - mmengine - INFO - Epoch(train) [58][100/586] lr: 5.000000e-04 eta: 11:06:14 time: 0.473442 data_time: 0.029711 memory: 15239 loss_kpt: 0.000604 acc_pose: 0.822230 loss: 0.000604 2022/09/13 00:32:00 - mmengine - INFO - Epoch(train) [58][150/586] lr: 5.000000e-04 eta: 11:05:55 time: 0.470055 data_time: 0.030159 memory: 15239 loss_kpt: 0.000568 acc_pose: 0.866819 loss: 0.000568 2022/09/13 00:32:24 - mmengine - INFO - Epoch(train) [58][200/586] lr: 5.000000e-04 eta: 11:05:36 time: 0.471615 data_time: 0.025433 memory: 15239 loss_kpt: 0.000578 acc_pose: 0.836430 loss: 0.000578 2022/09/13 00:32:47 - mmengine - INFO - Epoch(train) [58][250/586] lr: 5.000000e-04 eta: 11:05:17 time: 0.471117 data_time: 0.025815 memory: 15239 loss_kpt: 0.000584 acc_pose: 0.882029 loss: 0.000584 2022/09/13 00:33:11 - mmengine - INFO - Epoch(train) [58][300/586] lr: 5.000000e-04 eta: 11:04:59 time: 0.482109 data_time: 0.025070 memory: 15239 loss_kpt: 0.000576 acc_pose: 0.801138 loss: 0.000576 2022/09/13 00:33:34 - mmengine - INFO - Epoch(train) [58][350/586] lr: 5.000000e-04 eta: 11:04:38 time: 0.456430 data_time: 0.025769 memory: 15239 loss_kpt: 0.000564 acc_pose: 0.823709 loss: 0.000564 2022/09/13 00:33:58 - mmengine - INFO - Epoch(train) [58][400/586] lr: 5.000000e-04 eta: 11:04:19 time: 0.471065 data_time: 0.026806 memory: 15239 loss_kpt: 0.000571 acc_pose: 0.885364 loss: 0.000571 2022/09/13 00:34:21 - mmengine - INFO - Epoch(train) [58][450/586] lr: 5.000000e-04 eta: 11:04:00 time: 0.472491 data_time: 0.025410 memory: 15239 loss_kpt: 0.000580 acc_pose: 0.805015 loss: 0.000580 2022/09/13 00:34:44 - mmengine - INFO - Epoch(train) [58][500/586] lr: 5.000000e-04 eta: 11:03:40 time: 0.461782 data_time: 0.025964 memory: 15239 loss_kpt: 0.000574 acc_pose: 0.825511 loss: 0.000574 2022/09/13 00:35:08 - mmengine - INFO - Epoch(train) [58][550/586] lr: 5.000000e-04 eta: 11:03:20 time: 0.463219 data_time: 0.025777 memory: 15239 loss_kpt: 0.000592 acc_pose: 0.876297 loss: 0.000592 2022/09/13 00:35:24 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:35:24 - mmengine - INFO - Saving checkpoint at 58 epochs 2022/09/13 00:35:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:35:55 - mmengine - INFO - Epoch(train) [59][50/586] lr: 5.000000e-04 eta: 11:02:05 time: 0.487126 data_time: 0.036309 memory: 15239 loss_kpt: 0.000571 acc_pose: 0.770348 loss: 0.000571 2022/09/13 00:36:19 - mmengine - INFO - Epoch(train) [59][100/586] lr: 5.000000e-04 eta: 11:01:46 time: 0.472758 data_time: 0.027282 memory: 15239 loss_kpt: 0.000588 acc_pose: 0.862727 loss: 0.000588 2022/09/13 00:36:42 - mmengine - INFO - Epoch(train) [59][150/586] lr: 5.000000e-04 eta: 11:01:26 time: 0.467776 data_time: 0.025460 memory: 15239 loss_kpt: 0.000563 acc_pose: 0.837466 loss: 0.000563 2022/09/13 00:37:06 - mmengine - INFO - Epoch(train) [59][200/586] lr: 5.000000e-04 eta: 11:01:07 time: 0.465626 data_time: 0.031115 memory: 15239 loss_kpt: 0.000575 acc_pose: 0.869171 loss: 0.000575 2022/09/13 00:37:29 - mmengine - INFO - Epoch(train) [59][250/586] lr: 5.000000e-04 eta: 11:00:47 time: 0.467058 data_time: 0.026446 memory: 15239 loss_kpt: 0.000582 acc_pose: 0.856436 loss: 0.000582 2022/09/13 00:37:52 - mmengine - INFO - Epoch(train) [59][300/586] lr: 5.000000e-04 eta: 11:00:27 time: 0.466985 data_time: 0.026068 memory: 15239 loss_kpt: 0.000565 acc_pose: 0.917629 loss: 0.000565 2022/09/13 00:38:16 - mmengine - INFO - Epoch(train) [59][350/586] lr: 5.000000e-04 eta: 11:00:07 time: 0.465767 data_time: 0.026058 memory: 15239 loss_kpt: 0.000580 acc_pose: 0.834045 loss: 0.000580 2022/09/13 00:38:39 - mmengine - INFO - Epoch(train) [59][400/586] lr: 5.000000e-04 eta: 10:59:47 time: 0.464723 data_time: 0.026171 memory: 15239 loss_kpt: 0.000563 acc_pose: 0.829769 loss: 0.000563 2022/09/13 00:39:02 - mmengine - INFO - Epoch(train) [59][450/586] lr: 5.000000e-04 eta: 10:59:28 time: 0.467671 data_time: 0.025777 memory: 15239 loss_kpt: 0.000573 acc_pose: 0.842592 loss: 0.000573 2022/09/13 00:39:26 - mmengine - INFO - Epoch(train) [59][500/586] lr: 5.000000e-04 eta: 10:59:08 time: 0.468831 data_time: 0.031403 memory: 15239 loss_kpt: 0.000577 acc_pose: 0.868238 loss: 0.000577 2022/09/13 00:39:49 - mmengine - INFO - Epoch(train) [59][550/586] lr: 5.000000e-04 eta: 10:58:49 time: 0.470374 data_time: 0.026477 memory: 15239 loss_kpt: 0.000572 acc_pose: 0.820283 loss: 0.000572 2022/09/13 00:40:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:40:06 - mmengine - INFO - Saving checkpoint at 59 epochs 2022/09/13 00:40:37 - mmengine - INFO - Epoch(train) [60][50/586] lr: 5.000000e-04 eta: 10:57:34 time: 0.477957 data_time: 0.034884 memory: 15239 loss_kpt: 0.000580 acc_pose: 0.907723 loss: 0.000580 2022/09/13 00:41:00 - mmengine - INFO - Epoch(train) [60][100/586] lr: 5.000000e-04 eta: 10:57:15 time: 0.471818 data_time: 0.031804 memory: 15239 loss_kpt: 0.000579 acc_pose: 0.826135 loss: 0.000579 2022/09/13 00:41:24 - mmengine - INFO - Epoch(train) [60][150/586] lr: 5.000000e-04 eta: 10:56:55 time: 0.467877 data_time: 0.026511 memory: 15239 loss_kpt: 0.000578 acc_pose: 0.817669 loss: 0.000578 2022/09/13 00:41:47 - mmengine - INFO - Epoch(train) [60][200/586] lr: 5.000000e-04 eta: 10:56:35 time: 0.465396 data_time: 0.026275 memory: 15239 loss_kpt: 0.000580 acc_pose: 0.864705 loss: 0.000580 2022/09/13 00:42:10 - mmengine - INFO - Epoch(train) [60][250/586] lr: 5.000000e-04 eta: 10:56:15 time: 0.462814 data_time: 0.031662 memory: 15239 loss_kpt: 0.000568 acc_pose: 0.853336 loss: 0.000568 2022/09/13 00:42:34 - mmengine - INFO - Epoch(train) [60][300/586] lr: 5.000000e-04 eta: 10:55:56 time: 0.473337 data_time: 0.026791 memory: 15239 loss_kpt: 0.000560 acc_pose: 0.848240 loss: 0.000560 2022/09/13 00:42:57 - mmengine - INFO - Epoch(train) [60][350/586] lr: 5.000000e-04 eta: 10:55:36 time: 0.462225 data_time: 0.026230 memory: 15239 loss_kpt: 0.000579 acc_pose: 0.770006 loss: 0.000579 2022/09/13 00:43:21 - mmengine - INFO - Epoch(train) [60][400/586] lr: 5.000000e-04 eta: 10:55:17 time: 0.477022 data_time: 0.026114 memory: 15239 loss_kpt: 0.000578 acc_pose: 0.866284 loss: 0.000578 2022/09/13 00:43:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:43:44 - mmengine - INFO - Epoch(train) [60][450/586] lr: 5.000000e-04 eta: 10:54:58 time: 0.466761 data_time: 0.026543 memory: 15239 loss_kpt: 0.000586 acc_pose: 0.803777 loss: 0.000586 2022/09/13 00:44:07 - mmengine - INFO - Epoch(train) [60][500/586] lr: 5.000000e-04 eta: 10:54:38 time: 0.464312 data_time: 0.025619 memory: 15239 loss_kpt: 0.000568 acc_pose: 0.876464 loss: 0.000568 2022/09/13 00:44:31 - mmengine - INFO - Epoch(train) [60][550/586] lr: 5.000000e-04 eta: 10:54:19 time: 0.473994 data_time: 0.030860 memory: 15239 loss_kpt: 0.000573 acc_pose: 0.781426 loss: 0.000573 2022/09/13 00:44:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:44:48 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/09/13 00:45:06 - mmengine - INFO - Epoch(val) [60][50/407] eta: 0:01:21 time: 0.228910 data_time: 0.014942 memory: 15239 2022/09/13 00:45:17 - mmengine - INFO - Epoch(val) [60][100/407] eta: 0:01:08 time: 0.222769 data_time: 0.008244 memory: 2064 2022/09/13 00:45:28 - mmengine - INFO - Epoch(val) [60][150/407] eta: 0:00:56 time: 0.218051 data_time: 0.008106 memory: 2064 2022/09/13 00:45:39 - mmengine - INFO - Epoch(val) [60][200/407] eta: 0:00:45 time: 0.220505 data_time: 0.008409 memory: 2064 2022/09/13 00:45:50 - mmengine - INFO - Epoch(val) [60][250/407] eta: 0:00:34 time: 0.217341 data_time: 0.008387 memory: 2064 2022/09/13 00:46:01 - mmengine - INFO - Epoch(val) [60][300/407] eta: 0:00:23 time: 0.217838 data_time: 0.008235 memory: 2064 2022/09/13 00:46:12 - mmengine - INFO - Epoch(val) [60][350/407] eta: 0:00:12 time: 0.218159 data_time: 0.008912 memory: 2064 2022/09/13 00:46:23 - mmengine - INFO - Epoch(val) [60][400/407] eta: 0:00:01 time: 0.218880 data_time: 0.008573 memory: 2064 2022/09/13 00:46:59 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 00:47:13 - mmengine - INFO - Epoch(val) [60][407/407] coco/AP: 0.752066 coco/AP .5: 0.900975 coco/AP .75: 0.815848 coco/AP (M): 0.712097 coco/AP (L): 0.824268 coco/AR: 0.801259 coco/AR .5: 0.938130 coco/AR .75: 0.858942 coco/AR (M): 0.756487 coco/AR (L): 0.865961 2022/09/13 00:47:13 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_384_v1/best_coco/AP_epoch_50.pth is removed 2022/09/13 00:47:16 - mmengine - INFO - The best checkpoint with 0.7521 coco/AP at 60 epoch is saved to best_coco/AP_epoch_60.pth. 2022/09/13 00:47:40 - mmengine - INFO - Epoch(train) [61][50/586] lr: 5.000000e-04 eta: 10:53:04 time: 0.477669 data_time: 0.029876 memory: 15239 loss_kpt: 0.000589 acc_pose: 0.804678 loss: 0.000589 2022/09/13 00:48:04 - mmengine - INFO - Epoch(train) [61][100/586] lr: 5.000000e-04 eta: 10:52:45 time: 0.470536 data_time: 0.025201 memory: 15239 loss_kpt: 0.000574 acc_pose: 0.804274 loss: 0.000574 2022/09/13 00:48:28 - mmengine - INFO - Epoch(train) [61][150/586] lr: 5.000000e-04 eta: 10:52:26 time: 0.476671 data_time: 0.030377 memory: 15239 loss_kpt: 0.000580 acc_pose: 0.816030 loss: 0.000580 2022/09/13 00:48:51 - mmengine - INFO - Epoch(train) [61][200/586] lr: 5.000000e-04 eta: 10:52:08 time: 0.477640 data_time: 0.025168 memory: 15239 loss_kpt: 0.000562 acc_pose: 0.861370 loss: 0.000562 2022/09/13 00:49:14 - mmengine - INFO - Epoch(train) [61][250/586] lr: 5.000000e-04 eta: 10:51:47 time: 0.460736 data_time: 0.025625 memory: 15239 loss_kpt: 0.000563 acc_pose: 0.873923 loss: 0.000563 2022/09/13 00:49:38 - mmengine - INFO - Epoch(train) [61][300/586] lr: 5.000000e-04 eta: 10:51:28 time: 0.472774 data_time: 0.029665 memory: 15239 loss_kpt: 0.000579 acc_pose: 0.869330 loss: 0.000579 2022/09/13 00:50:02 - mmengine - INFO - Epoch(train) [61][350/586] lr: 5.000000e-04 eta: 10:51:09 time: 0.469587 data_time: 0.026237 memory: 15239 loss_kpt: 0.000571 acc_pose: 0.823696 loss: 0.000571 2022/09/13 00:50:25 - mmengine - INFO - Epoch(train) [61][400/586] lr: 5.000000e-04 eta: 10:50:49 time: 0.464064 data_time: 0.027683 memory: 15239 loss_kpt: 0.000574 acc_pose: 0.881142 loss: 0.000574 2022/09/13 00:50:49 - mmengine - INFO - Epoch(train) [61][450/586] lr: 5.000000e-04 eta: 10:50:30 time: 0.476241 data_time: 0.026917 memory: 15239 loss_kpt: 0.000566 acc_pose: 0.770813 loss: 0.000566 2022/09/13 00:51:12 - mmengine - INFO - Epoch(train) [61][500/586] lr: 5.000000e-04 eta: 10:50:10 time: 0.464297 data_time: 0.026424 memory: 15239 loss_kpt: 0.000542 acc_pose: 0.816404 loss: 0.000542 2022/09/13 00:51:35 - mmengine - INFO - Epoch(train) [61][550/586] lr: 5.000000e-04 eta: 10:49:50 time: 0.468238 data_time: 0.027379 memory: 15239 loss_kpt: 0.000568 acc_pose: 0.843586 loss: 0.000568 2022/09/13 00:51:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:51:52 - mmengine - INFO - Saving checkpoint at 61 epochs 2022/09/13 00:52:24 - mmengine - INFO - Epoch(train) [62][50/586] lr: 5.000000e-04 eta: 10:48:38 time: 0.487817 data_time: 0.034980 memory: 15239 loss_kpt: 0.000572 acc_pose: 0.831227 loss: 0.000572 2022/09/13 00:52:47 - mmengine - INFO - Epoch(train) [62][100/586] lr: 5.000000e-04 eta: 10:48:19 time: 0.471823 data_time: 0.032105 memory: 15239 loss_kpt: 0.000569 acc_pose: 0.794099 loss: 0.000569 2022/09/13 00:53:11 - mmengine - INFO - Epoch(train) [62][150/586] lr: 5.000000e-04 eta: 10:47:59 time: 0.468681 data_time: 0.026241 memory: 15239 loss_kpt: 0.000586 acc_pose: 0.861089 loss: 0.000586 2022/09/13 00:53:34 - mmengine - INFO - Epoch(train) [62][200/586] lr: 5.000000e-04 eta: 10:47:40 time: 0.469200 data_time: 0.026308 memory: 15239 loss_kpt: 0.000573 acc_pose: 0.860680 loss: 0.000573 2022/09/13 00:53:57 - mmengine - INFO - Epoch(train) [62][250/586] lr: 5.000000e-04 eta: 10:47:20 time: 0.464323 data_time: 0.025444 memory: 15239 loss_kpt: 0.000588 acc_pose: 0.815932 loss: 0.000588 2022/09/13 00:53:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:54:21 - mmengine - INFO - Epoch(train) [62][300/586] lr: 5.000000e-04 eta: 10:47:00 time: 0.470334 data_time: 0.030521 memory: 15239 loss_kpt: 0.000571 acc_pose: 0.847118 loss: 0.000571 2022/09/13 00:54:44 - mmengine - INFO - Epoch(train) [62][350/586] lr: 5.000000e-04 eta: 10:46:41 time: 0.470212 data_time: 0.027997 memory: 15239 loss_kpt: 0.000590 acc_pose: 0.777645 loss: 0.000590 2022/09/13 00:55:08 - mmengine - INFO - Epoch(train) [62][400/586] lr: 5.000000e-04 eta: 10:46:21 time: 0.467416 data_time: 0.029407 memory: 15239 loss_kpt: 0.000574 acc_pose: 0.760145 loss: 0.000574 2022/09/13 00:55:31 - mmengine - INFO - Epoch(train) [62][450/586] lr: 5.000000e-04 eta: 10:46:00 time: 0.459465 data_time: 0.026724 memory: 15239 loss_kpt: 0.000576 acc_pose: 0.811312 loss: 0.000576 2022/09/13 00:55:54 - mmengine - INFO - Epoch(train) [62][500/586] lr: 5.000000e-04 eta: 10:45:41 time: 0.472531 data_time: 0.028164 memory: 15239 loss_kpt: 0.000565 acc_pose: 0.821323 loss: 0.000565 2022/09/13 00:56:18 - mmengine - INFO - Epoch(train) [62][550/586] lr: 5.000000e-04 eta: 10:45:22 time: 0.468806 data_time: 0.026966 memory: 15239 loss_kpt: 0.000572 acc_pose: 0.833793 loss: 0.000572 2022/09/13 00:56:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 00:56:35 - mmengine - INFO - Saving checkpoint at 62 epochs 2022/09/13 00:57:07 - mmengine - INFO - Epoch(train) [63][50/586] lr: 5.000000e-04 eta: 10:44:09 time: 0.477129 data_time: 0.033667 memory: 15239 loss_kpt: 0.000553 acc_pose: 0.803677 loss: 0.000553 2022/09/13 00:57:30 - mmengine - INFO - Epoch(train) [63][100/586] lr: 5.000000e-04 eta: 10:43:49 time: 0.468614 data_time: 0.026435 memory: 15239 loss_kpt: 0.000576 acc_pose: 0.821973 loss: 0.000576 2022/09/13 00:57:53 - mmengine - INFO - Epoch(train) [63][150/586] lr: 5.000000e-04 eta: 10:43:29 time: 0.463909 data_time: 0.026550 memory: 15239 loss_kpt: 0.000562 acc_pose: 0.779180 loss: 0.000562 2022/09/13 00:58:17 - mmengine - INFO - Epoch(train) [63][200/586] lr: 5.000000e-04 eta: 10:43:10 time: 0.477517 data_time: 0.030334 memory: 15239 loss_kpt: 0.000573 acc_pose: 0.893578 loss: 0.000573 2022/09/13 00:58:41 - mmengine - INFO - Epoch(train) [63][250/586] lr: 5.000000e-04 eta: 10:42:51 time: 0.467202 data_time: 0.027098 memory: 15239 loss_kpt: 0.000578 acc_pose: 0.766585 loss: 0.000578 2022/09/13 00:59:04 - mmengine - INFO - Epoch(train) [63][300/586] lr: 5.000000e-04 eta: 10:42:31 time: 0.468275 data_time: 0.025610 memory: 15239 loss_kpt: 0.000569 acc_pose: 0.826291 loss: 0.000569 2022/09/13 00:59:28 - mmengine - INFO - Epoch(train) [63][350/586] lr: 5.000000e-04 eta: 10:42:11 time: 0.467187 data_time: 0.026988 memory: 15239 loss_kpt: 0.000586 acc_pose: 0.876623 loss: 0.000586 2022/09/13 00:59:51 - mmengine - INFO - Epoch(train) [63][400/586] lr: 5.000000e-04 eta: 10:41:51 time: 0.468624 data_time: 0.025980 memory: 15239 loss_kpt: 0.000541 acc_pose: 0.858991 loss: 0.000541 2022/09/13 01:00:14 - mmengine - INFO - Epoch(train) [63][450/586] lr: 5.000000e-04 eta: 10:41:31 time: 0.461574 data_time: 0.026431 memory: 15239 loss_kpt: 0.000580 acc_pose: 0.847358 loss: 0.000580 2022/09/13 01:00:38 - mmengine - INFO - Epoch(train) [63][500/586] lr: 5.000000e-04 eta: 10:41:12 time: 0.474062 data_time: 0.026741 memory: 15239 loss_kpt: 0.000570 acc_pose: 0.858461 loss: 0.000570 2022/09/13 01:01:01 - mmengine - INFO - Epoch(train) [63][550/586] lr: 5.000000e-04 eta: 10:40:52 time: 0.470102 data_time: 0.027726 memory: 15239 loss_kpt: 0.000581 acc_pose: 0.808726 loss: 0.000581 2022/09/13 01:01:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:01:18 - mmengine - INFO - Saving checkpoint at 63 epochs 2022/09/13 01:01:50 - mmengine - INFO - Epoch(train) [64][50/586] lr: 5.000000e-04 eta: 10:39:41 time: 0.487021 data_time: 0.038165 memory: 15239 loss_kpt: 0.000568 acc_pose: 0.857604 loss: 0.000568 2022/09/13 01:02:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:02:14 - mmengine - INFO - Epoch(train) [64][100/586] lr: 5.000000e-04 eta: 10:39:22 time: 0.473671 data_time: 0.031223 memory: 15239 loss_kpt: 0.000584 acc_pose: 0.765376 loss: 0.000584 2022/09/13 01:02:37 - mmengine - INFO - Epoch(train) [64][150/586] lr: 5.000000e-04 eta: 10:39:01 time: 0.457412 data_time: 0.026273 memory: 15239 loss_kpt: 0.000563 acc_pose: 0.816948 loss: 0.000563 2022/09/13 01:03:01 - mmengine - INFO - Epoch(train) [64][200/586] lr: 5.000000e-04 eta: 10:38:43 time: 0.479330 data_time: 0.025599 memory: 15239 loss_kpt: 0.000579 acc_pose: 0.863198 loss: 0.000579 2022/09/13 01:03:24 - mmengine - INFO - Epoch(train) [64][250/586] lr: 5.000000e-04 eta: 10:38:24 time: 0.473193 data_time: 0.032154 memory: 15239 loss_kpt: 0.000560 acc_pose: 0.842428 loss: 0.000560 2022/09/13 01:03:48 - mmengine - INFO - Epoch(train) [64][300/586] lr: 5.000000e-04 eta: 10:38:04 time: 0.467198 data_time: 0.025671 memory: 15239 loss_kpt: 0.000574 acc_pose: 0.838636 loss: 0.000574 2022/09/13 01:04:11 - mmengine - INFO - Epoch(train) [64][350/586] lr: 5.000000e-04 eta: 10:37:45 time: 0.475959 data_time: 0.026460 memory: 15239 loss_kpt: 0.000566 acc_pose: 0.809490 loss: 0.000566 2022/09/13 01:04:35 - mmengine - INFO - Epoch(train) [64][400/586] lr: 5.000000e-04 eta: 10:37:26 time: 0.473481 data_time: 0.031713 memory: 15239 loss_kpt: 0.000602 acc_pose: 0.832523 loss: 0.000602 2022/09/13 01:04:58 - mmengine - INFO - Epoch(train) [64][450/586] lr: 5.000000e-04 eta: 10:37:05 time: 0.462477 data_time: 0.025299 memory: 15239 loss_kpt: 0.000583 acc_pose: 0.803062 loss: 0.000583 2022/09/13 01:05:22 - mmengine - INFO - Epoch(train) [64][500/586] lr: 5.000000e-04 eta: 10:36:46 time: 0.468045 data_time: 0.026616 memory: 15239 loss_kpt: 0.000569 acc_pose: 0.765599 loss: 0.000569 2022/09/13 01:05:45 - mmengine - INFO - Epoch(train) [64][550/586] lr: 5.000000e-04 eta: 10:36:26 time: 0.469265 data_time: 0.030529 memory: 15239 loss_kpt: 0.000572 acc_pose: 0.825200 loss: 0.000572 2022/09/13 01:06:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:06:02 - mmengine - INFO - Saving checkpoint at 64 epochs 2022/09/13 01:06:32 - mmengine - INFO - Epoch(train) [65][50/586] lr: 5.000000e-04 eta: 10:35:16 time: 0.486597 data_time: 0.035106 memory: 15239 loss_kpt: 0.000601 acc_pose: 0.864091 loss: 0.000601 2022/09/13 01:06:56 - mmengine - INFO - Epoch(train) [65][100/586] lr: 5.000000e-04 eta: 10:34:56 time: 0.466435 data_time: 0.027221 memory: 15239 loss_kpt: 0.000565 acc_pose: 0.804459 loss: 0.000565 2022/09/13 01:07:19 - mmengine - INFO - Epoch(train) [65][150/586] lr: 5.000000e-04 eta: 10:34:35 time: 0.461673 data_time: 0.026120 memory: 15239 loss_kpt: 0.000567 acc_pose: 0.844702 loss: 0.000567 2022/09/13 01:07:43 - mmengine - INFO - Epoch(train) [65][200/586] lr: 5.000000e-04 eta: 10:34:16 time: 0.474933 data_time: 0.026677 memory: 15239 loss_kpt: 0.000580 acc_pose: 0.849410 loss: 0.000580 2022/09/13 01:08:06 - mmengine - INFO - Epoch(train) [65][250/586] lr: 5.000000e-04 eta: 10:33:56 time: 0.463356 data_time: 0.026699 memory: 15239 loss_kpt: 0.000573 acc_pose: 0.801880 loss: 0.000573 2022/09/13 01:08:29 - mmengine - INFO - Epoch(train) [65][300/586] lr: 5.000000e-04 eta: 10:33:36 time: 0.468540 data_time: 0.026186 memory: 15239 loss_kpt: 0.000580 acc_pose: 0.792502 loss: 0.000580 2022/09/13 01:08:53 - mmengine - INFO - Epoch(train) [65][350/586] lr: 5.000000e-04 eta: 10:33:17 time: 0.473190 data_time: 0.030149 memory: 15239 loss_kpt: 0.000576 acc_pose: 0.843858 loss: 0.000576 2022/09/13 01:09:16 - mmengine - INFO - Epoch(train) [65][400/586] lr: 5.000000e-04 eta: 10:32:57 time: 0.467730 data_time: 0.027331 memory: 15239 loss_kpt: 0.000560 acc_pose: 0.811636 loss: 0.000560 2022/09/13 01:09:39 - mmengine - INFO - Epoch(train) [65][450/586] lr: 5.000000e-04 eta: 10:32:37 time: 0.462232 data_time: 0.027297 memory: 15239 loss_kpt: 0.000560 acc_pose: 0.868498 loss: 0.000560 2022/09/13 01:10:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:10:03 - mmengine - INFO - Epoch(train) [65][500/586] lr: 5.000000e-04 eta: 10:32:18 time: 0.475884 data_time: 0.030550 memory: 15239 loss_kpt: 0.000577 acc_pose: 0.855572 loss: 0.000577 2022/09/13 01:10:27 - mmengine - INFO - Epoch(train) [65][550/586] lr: 5.000000e-04 eta: 10:31:58 time: 0.467402 data_time: 0.026705 memory: 15239 loss_kpt: 0.000577 acc_pose: 0.840044 loss: 0.000577 2022/09/13 01:10:43 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:10:43 - mmengine - INFO - Saving checkpoint at 65 epochs 2022/09/13 01:11:14 - mmengine - INFO - Epoch(train) [66][50/586] lr: 5.000000e-04 eta: 10:30:49 time: 0.490871 data_time: 0.030269 memory: 15239 loss_kpt: 0.000546 acc_pose: 0.913046 loss: 0.000546 2022/09/13 01:11:37 - mmengine - INFO - Epoch(train) [66][100/586] lr: 5.000000e-04 eta: 10:30:28 time: 0.463172 data_time: 0.026998 memory: 15239 loss_kpt: 0.000571 acc_pose: 0.828094 loss: 0.000571 2022/09/13 01:12:01 - mmengine - INFO - Epoch(train) [66][150/586] lr: 5.000000e-04 eta: 10:30:09 time: 0.472563 data_time: 0.026539 memory: 15239 loss_kpt: 0.000565 acc_pose: 0.754911 loss: 0.000565 2022/09/13 01:12:24 - mmengine - INFO - Epoch(train) [66][200/586] lr: 5.000000e-04 eta: 10:29:49 time: 0.466290 data_time: 0.026895 memory: 15239 loss_kpt: 0.000591 acc_pose: 0.877521 loss: 0.000591 2022/09/13 01:12:48 - mmengine - INFO - Epoch(train) [66][250/586] lr: 5.000000e-04 eta: 10:29:29 time: 0.467222 data_time: 0.025039 memory: 15239 loss_kpt: 0.000557 acc_pose: 0.847189 loss: 0.000557 2022/09/13 01:13:11 - mmengine - INFO - Epoch(train) [66][300/586] lr: 5.000000e-04 eta: 10:29:10 time: 0.470835 data_time: 0.030737 memory: 15239 loss_kpt: 0.000590 acc_pose: 0.899010 loss: 0.000590 2022/09/13 01:13:35 - mmengine - INFO - Epoch(train) [66][350/586] lr: 5.000000e-04 eta: 10:28:50 time: 0.470474 data_time: 0.025530 memory: 15239 loss_kpt: 0.000547 acc_pose: 0.858730 loss: 0.000547 2022/09/13 01:13:58 - mmengine - INFO - Epoch(train) [66][400/586] lr: 5.000000e-04 eta: 10:28:30 time: 0.466546 data_time: 0.026724 memory: 15239 loss_kpt: 0.000565 acc_pose: 0.807802 loss: 0.000565 2022/09/13 01:14:22 - mmengine - INFO - Epoch(train) [66][450/586] lr: 5.000000e-04 eta: 10:28:10 time: 0.467849 data_time: 0.026046 memory: 15239 loss_kpt: 0.000562 acc_pose: 0.801714 loss: 0.000562 2022/09/13 01:14:45 - mmengine - INFO - Epoch(train) [66][500/586] lr: 5.000000e-04 eta: 10:27:50 time: 0.469532 data_time: 0.025359 memory: 15239 loss_kpt: 0.000557 acc_pose: 0.869746 loss: 0.000557 2022/09/13 01:15:08 - mmengine - INFO - Epoch(train) [66][550/586] lr: 5.000000e-04 eta: 10:27:30 time: 0.468091 data_time: 0.026373 memory: 15239 loss_kpt: 0.000584 acc_pose: 0.842583 loss: 0.000584 2022/09/13 01:15:25 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:15:25 - mmengine - INFO - Saving checkpoint at 66 epochs 2022/09/13 01:15:56 - mmengine - INFO - Epoch(train) [67][50/586] lr: 5.000000e-04 eta: 10:26:21 time: 0.479350 data_time: 0.031567 memory: 15239 loss_kpt: 0.000573 acc_pose: 0.852601 loss: 0.000573 2022/09/13 01:16:20 - mmengine - INFO - Epoch(train) [67][100/586] lr: 5.000000e-04 eta: 10:26:01 time: 0.469201 data_time: 0.030146 memory: 15239 loss_kpt: 0.000557 acc_pose: 0.882289 loss: 0.000557 2022/09/13 01:16:43 - mmengine - INFO - Epoch(train) [67][150/586] lr: 5.000000e-04 eta: 10:25:41 time: 0.470485 data_time: 0.025980 memory: 15239 loss_kpt: 0.000562 acc_pose: 0.813160 loss: 0.000562 2022/09/13 01:17:07 - mmengine - INFO - Epoch(train) [67][200/586] lr: 5.000000e-04 eta: 10:25:22 time: 0.471952 data_time: 0.026975 memory: 15239 loss_kpt: 0.000565 acc_pose: 0.755074 loss: 0.000565 2022/09/13 01:17:30 - mmengine - INFO - Epoch(train) [67][250/586] lr: 5.000000e-04 eta: 10:25:02 time: 0.462909 data_time: 0.028699 memory: 15239 loss_kpt: 0.000572 acc_pose: 0.765052 loss: 0.000572 2022/09/13 01:17:53 - mmengine - INFO - Epoch(train) [67][300/586] lr: 5.000000e-04 eta: 10:24:42 time: 0.471637 data_time: 0.025396 memory: 15239 loss_kpt: 0.000550 acc_pose: 0.825093 loss: 0.000550 2022/09/13 01:18:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:18:17 - mmengine - INFO - Epoch(train) [67][350/586] lr: 5.000000e-04 eta: 10:24:22 time: 0.465737 data_time: 0.026487 memory: 15239 loss_kpt: 0.000552 acc_pose: 0.879263 loss: 0.000552 2022/09/13 01:18:40 - mmengine - INFO - Epoch(train) [67][400/586] lr: 5.000000e-04 eta: 10:24:03 time: 0.472865 data_time: 0.030029 memory: 15239 loss_kpt: 0.000563 acc_pose: 0.863811 loss: 0.000563 2022/09/13 01:19:04 - mmengine - INFO - Epoch(train) [67][450/586] lr: 5.000000e-04 eta: 10:23:42 time: 0.464666 data_time: 0.026300 memory: 15239 loss_kpt: 0.000588 acc_pose: 0.846052 loss: 0.000588 2022/09/13 01:19:27 - mmengine - INFO - Epoch(train) [67][500/586] lr: 5.000000e-04 eta: 10:23:23 time: 0.469430 data_time: 0.026019 memory: 15239 loss_kpt: 0.000559 acc_pose: 0.917235 loss: 0.000559 2022/09/13 01:19:50 - mmengine - INFO - Epoch(train) [67][550/586] lr: 5.000000e-04 eta: 10:23:02 time: 0.464085 data_time: 0.025750 memory: 15239 loss_kpt: 0.000561 acc_pose: 0.884367 loss: 0.000561 2022/09/13 01:20:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:20:07 - mmengine - INFO - Saving checkpoint at 67 epochs 2022/09/13 01:20:38 - mmengine - INFO - Epoch(train) [68][50/586] lr: 5.000000e-04 eta: 10:21:53 time: 0.475004 data_time: 0.035549 memory: 15239 loss_kpt: 0.000563 acc_pose: 0.862030 loss: 0.000563 2022/09/13 01:21:01 - mmengine - INFO - Epoch(train) [68][100/586] lr: 5.000000e-04 eta: 10:21:33 time: 0.468926 data_time: 0.026504 memory: 15239 loss_kpt: 0.000564 acc_pose: 0.822664 loss: 0.000564 2022/09/13 01:21:24 - mmengine - INFO - Epoch(train) [68][150/586] lr: 5.000000e-04 eta: 10:21:13 time: 0.465558 data_time: 0.025096 memory: 15239 loss_kpt: 0.000555 acc_pose: 0.852761 loss: 0.000555 2022/09/13 01:21:48 - mmengine - INFO - Epoch(train) [68][200/586] lr: 5.000000e-04 eta: 10:20:54 time: 0.474524 data_time: 0.025707 memory: 15239 loss_kpt: 0.000562 acc_pose: 0.820557 loss: 0.000562 2022/09/13 01:22:12 - mmengine - INFO - Epoch(train) [68][250/586] lr: 5.000000e-04 eta: 10:20:33 time: 0.466278 data_time: 0.026813 memory: 15239 loss_kpt: 0.000561 acc_pose: 0.851507 loss: 0.000561 2022/09/13 01:22:35 - mmengine - INFO - Epoch(train) [68][300/586] lr: 5.000000e-04 eta: 10:20:13 time: 0.466372 data_time: 0.026544 memory: 15239 loss_kpt: 0.000548 acc_pose: 0.858586 loss: 0.000548 2022/09/13 01:22:58 - mmengine - INFO - Epoch(train) [68][350/586] lr: 5.000000e-04 eta: 10:19:53 time: 0.467211 data_time: 0.029941 memory: 15239 loss_kpt: 0.000575 acc_pose: 0.899775 loss: 0.000575 2022/09/13 01:23:22 - mmengine - INFO - Epoch(train) [68][400/586] lr: 5.000000e-04 eta: 10:19:34 time: 0.471886 data_time: 0.026057 memory: 15239 loss_kpt: 0.000560 acc_pose: 0.792397 loss: 0.000560 2022/09/13 01:23:45 - mmengine - INFO - Epoch(train) [68][450/586] lr: 5.000000e-04 eta: 10:19:13 time: 0.464538 data_time: 0.026543 memory: 15239 loss_kpt: 0.000557 acc_pose: 0.824697 loss: 0.000557 2022/09/13 01:24:09 - mmengine - INFO - Epoch(train) [68][500/586] lr: 5.000000e-04 eta: 10:18:54 time: 0.468742 data_time: 0.030105 memory: 15239 loss_kpt: 0.000578 acc_pose: 0.921568 loss: 0.000578 2022/09/13 01:24:32 - mmengine - INFO - Epoch(train) [68][550/586] lr: 5.000000e-04 eta: 10:18:34 time: 0.468417 data_time: 0.026790 memory: 15239 loss_kpt: 0.000584 acc_pose: 0.873047 loss: 0.000584 2022/09/13 01:24:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:24:49 - mmengine - INFO - Saving checkpoint at 68 epochs 2022/09/13 01:25:19 - mmengine - INFO - Epoch(train) [69][50/586] lr: 5.000000e-04 eta: 10:17:24 time: 0.470020 data_time: 0.031547 memory: 15239 loss_kpt: 0.000569 acc_pose: 0.867710 loss: 0.000569 2022/09/13 01:25:43 - mmengine - INFO - Epoch(train) [69][100/586] lr: 5.000000e-04 eta: 10:17:06 time: 0.480048 data_time: 0.026415 memory: 15239 loss_kpt: 0.000576 acc_pose: 0.818957 loss: 0.000576 2022/09/13 01:26:06 - mmengine - INFO - Epoch(train) [69][150/586] lr: 5.000000e-04 eta: 10:16:46 time: 0.466049 data_time: 0.026346 memory: 15239 loss_kpt: 0.000569 acc_pose: 0.791476 loss: 0.000569 2022/09/13 01:26:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:26:29 - mmengine - INFO - Epoch(train) [69][200/586] lr: 5.000000e-04 eta: 10:16:25 time: 0.459224 data_time: 0.026246 memory: 15239 loss_kpt: 0.000559 acc_pose: 0.843780 loss: 0.000559 2022/09/13 01:26:53 - mmengine - INFO - Epoch(train) [69][250/586] lr: 5.000000e-04 eta: 10:16:05 time: 0.474403 data_time: 0.025815 memory: 15239 loss_kpt: 0.000551 acc_pose: 0.788052 loss: 0.000551 2022/09/13 01:27:17 - mmengine - INFO - Epoch(train) [69][300/586] lr: 5.000000e-04 eta: 10:15:46 time: 0.471000 data_time: 0.028237 memory: 15239 loss_kpt: 0.000569 acc_pose: 0.876346 loss: 0.000569 2022/09/13 01:27:40 - mmengine - INFO - Epoch(train) [69][350/586] lr: 5.000000e-04 eta: 10:15:25 time: 0.463242 data_time: 0.026429 memory: 15239 loss_kpt: 0.000565 acc_pose: 0.785111 loss: 0.000565 2022/09/13 01:28:03 - mmengine - INFO - Epoch(train) [69][400/586] lr: 5.000000e-04 eta: 10:15:06 time: 0.475209 data_time: 0.026655 memory: 15239 loss_kpt: 0.000563 acc_pose: 0.864748 loss: 0.000563 2022/09/13 01:28:27 - mmengine - INFO - Epoch(train) [69][450/586] lr: 5.000000e-04 eta: 10:14:47 time: 0.474497 data_time: 0.025770 memory: 15239 loss_kpt: 0.000562 acc_pose: 0.812631 loss: 0.000562 2022/09/13 01:28:50 - mmengine - INFO - Epoch(train) [69][500/586] lr: 5.000000e-04 eta: 10:14:26 time: 0.463605 data_time: 0.026823 memory: 15239 loss_kpt: 0.000576 acc_pose: 0.816384 loss: 0.000576 2022/09/13 01:29:14 - mmengine - INFO - Epoch(train) [69][550/586] lr: 5.000000e-04 eta: 10:14:07 time: 0.474889 data_time: 0.025806 memory: 15239 loss_kpt: 0.000562 acc_pose: 0.848600 loss: 0.000562 2022/09/13 01:29:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:29:31 - mmengine - INFO - Saving checkpoint at 69 epochs 2022/09/13 01:30:01 - mmengine - INFO - Epoch(train) [70][50/586] lr: 5.000000e-04 eta: 10:12:59 time: 0.479076 data_time: 0.039168 memory: 15239 loss_kpt: 0.000549 acc_pose: 0.775830 loss: 0.000549 2022/09/13 01:30:26 - mmengine - INFO - Epoch(train) [70][100/586] lr: 5.000000e-04 eta: 10:12:41 time: 0.483048 data_time: 0.033466 memory: 15239 loss_kpt: 0.000553 acc_pose: 0.877040 loss: 0.000553 2022/09/13 01:30:49 - mmengine - INFO - Epoch(train) [70][150/586] lr: 5.000000e-04 eta: 10:12:21 time: 0.468120 data_time: 0.026234 memory: 15239 loss_kpt: 0.000572 acc_pose: 0.843625 loss: 0.000572 2022/09/13 01:31:13 - mmengine - INFO - Epoch(train) [70][200/586] lr: 5.000000e-04 eta: 10:12:01 time: 0.469520 data_time: 0.030387 memory: 15239 loss_kpt: 0.000572 acc_pose: 0.865643 loss: 0.000572 2022/09/13 01:31:36 - mmengine - INFO - Epoch(train) [70][250/586] lr: 5.000000e-04 eta: 10:11:41 time: 0.470037 data_time: 0.025731 memory: 15239 loss_kpt: 0.000582 acc_pose: 0.801630 loss: 0.000582 2022/09/13 01:31:59 - mmengine - INFO - Epoch(train) [70][300/586] lr: 5.000000e-04 eta: 10:11:21 time: 0.467210 data_time: 0.026832 memory: 15239 loss_kpt: 0.000569 acc_pose: 0.736405 loss: 0.000569 2022/09/13 01:32:23 - mmengine - INFO - Epoch(train) [70][350/586] lr: 5.000000e-04 eta: 10:11:02 time: 0.471119 data_time: 0.025816 memory: 15239 loss_kpt: 0.000560 acc_pose: 0.794345 loss: 0.000560 2022/09/13 01:32:47 - mmengine - INFO - Epoch(train) [70][400/586] lr: 5.000000e-04 eta: 10:10:42 time: 0.475668 data_time: 0.025754 memory: 15239 loss_kpt: 0.000546 acc_pose: 0.824548 loss: 0.000546 2022/09/13 01:33:10 - mmengine - INFO - Epoch(train) [70][450/586] lr: 5.000000e-04 eta: 10:10:22 time: 0.470127 data_time: 0.026233 memory: 15239 loss_kpt: 0.000553 acc_pose: 0.771411 loss: 0.000553 2022/09/13 01:33:34 - mmengine - INFO - Epoch(train) [70][500/586] lr: 5.000000e-04 eta: 10:10:03 time: 0.468949 data_time: 0.030053 memory: 15239 loss_kpt: 0.000549 acc_pose: 0.849149 loss: 0.000549 2022/09/13 01:33:57 - mmengine - INFO - Epoch(train) [70][550/586] lr: 5.000000e-04 eta: 10:09:43 time: 0.468566 data_time: 0.026929 memory: 15239 loss_kpt: 0.000561 acc_pose: 0.878275 loss: 0.000561 2022/09/13 01:34:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:34:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:34:14 - mmengine - INFO - Saving checkpoint at 70 epochs 2022/09/13 01:34:33 - mmengine - INFO - Epoch(val) [70][50/407] eta: 0:01:20 time: 0.225257 data_time: 0.013736 memory: 15239 2022/09/13 01:34:44 - mmengine - INFO - Epoch(val) [70][100/407] eta: 0:01:07 time: 0.220217 data_time: 0.009784 memory: 2064 2022/09/13 01:34:55 - mmengine - INFO - Epoch(val) [70][150/407] eta: 0:00:56 time: 0.220933 data_time: 0.010061 memory: 2064 2022/09/13 01:35:06 - mmengine - INFO - Epoch(val) [70][200/407] eta: 0:00:45 time: 0.219073 data_time: 0.008482 memory: 2064 2022/09/13 01:35:17 - mmengine - INFO - Epoch(val) [70][250/407] eta: 0:00:35 time: 0.226003 data_time: 0.011907 memory: 2064 2022/09/13 01:35:28 - mmengine - INFO - Epoch(val) [70][300/407] eta: 0:00:23 time: 0.219006 data_time: 0.008679 memory: 2064 2022/09/13 01:35:39 - mmengine - INFO - Epoch(val) [70][350/407] eta: 0:00:12 time: 0.221623 data_time: 0.011833 memory: 2064 2022/09/13 01:35:50 - mmengine - INFO - Epoch(val) [70][400/407] eta: 0:00:01 time: 0.216688 data_time: 0.007978 memory: 2064 2022/09/13 01:36:26 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 01:36:40 - mmengine - INFO - Epoch(val) [70][407/407] coco/AP: 0.755350 coco/AP .5: 0.901800 coco/AP .75: 0.820720 coco/AP (M): 0.715031 coco/AP (L): 0.825683 coco/AR: 0.803275 coco/AR .5: 0.937657 coco/AR .75: 0.861776 coco/AR (M): 0.758782 coco/AR (L): 0.867521 2022/09/13 01:36:40 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_384_v1/best_coco/AP_epoch_60.pth is removed 2022/09/13 01:36:44 - mmengine - INFO - The best checkpoint with 0.7554 coco/AP at 70 epoch is saved to best_coco/AP_epoch_70.pth. 2022/09/13 01:37:08 - mmengine - INFO - Epoch(train) [71][50/586] lr: 5.000000e-04 eta: 10:08:35 time: 0.475847 data_time: 0.030210 memory: 15239 loss_kpt: 0.000593 acc_pose: 0.856725 loss: 0.000593 2022/09/13 01:37:32 - mmengine - INFO - Epoch(train) [71][100/586] lr: 5.000000e-04 eta: 10:08:16 time: 0.478215 data_time: 0.025482 memory: 15239 loss_kpt: 0.000557 acc_pose: 0.790755 loss: 0.000557 2022/09/13 01:37:56 - mmengine - INFO - Epoch(train) [71][150/586] lr: 5.000000e-04 eta: 10:07:57 time: 0.474630 data_time: 0.025467 memory: 15239 loss_kpt: 0.000578 acc_pose: 0.854691 loss: 0.000578 2022/09/13 01:38:19 - mmengine - INFO - Epoch(train) [71][200/586] lr: 5.000000e-04 eta: 10:07:37 time: 0.470931 data_time: 0.029266 memory: 15239 loss_kpt: 0.000557 acc_pose: 0.882399 loss: 0.000557 2022/09/13 01:38:43 - mmengine - INFO - Epoch(train) [71][250/586] lr: 5.000000e-04 eta: 10:07:17 time: 0.465971 data_time: 0.025587 memory: 15239 loss_kpt: 0.000563 acc_pose: 0.808162 loss: 0.000563 2022/09/13 01:39:06 - mmengine - INFO - Epoch(train) [71][300/586] lr: 5.000000e-04 eta: 10:06:57 time: 0.474673 data_time: 0.026391 memory: 15239 loss_kpt: 0.000566 acc_pose: 0.833202 loss: 0.000566 2022/09/13 01:39:29 - mmengine - INFO - Epoch(train) [71][350/586] lr: 5.000000e-04 eta: 10:06:37 time: 0.464190 data_time: 0.025563 memory: 15239 loss_kpt: 0.000554 acc_pose: 0.787401 loss: 0.000554 2022/09/13 01:39:53 - mmengine - INFO - Epoch(train) [71][400/586] lr: 5.000000e-04 eta: 10:06:17 time: 0.470828 data_time: 0.026674 memory: 15239 loss_kpt: 0.000568 acc_pose: 0.753153 loss: 0.000568 2022/09/13 01:40:16 - mmengine - INFO - Epoch(train) [71][450/586] lr: 5.000000e-04 eta: 10:05:57 time: 0.466895 data_time: 0.027244 memory: 15239 loss_kpt: 0.000564 acc_pose: 0.833990 loss: 0.000564 2022/09/13 01:40:40 - mmengine - INFO - Epoch(train) [71][500/586] lr: 5.000000e-04 eta: 10:05:38 time: 0.474197 data_time: 0.029845 memory: 15239 loss_kpt: 0.000560 acc_pose: 0.848560 loss: 0.000560 2022/09/13 01:41:03 - mmengine - INFO - Epoch(train) [71][550/586] lr: 5.000000e-04 eta: 10:05:17 time: 0.464065 data_time: 0.025771 memory: 15239 loss_kpt: 0.000557 acc_pose: 0.860167 loss: 0.000557 2022/09/13 01:41:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:41:20 - mmengine - INFO - Saving checkpoint at 71 epochs 2022/09/13 01:41:52 - mmengine - INFO - Epoch(train) [72][50/586] lr: 5.000000e-04 eta: 10:04:10 time: 0.471193 data_time: 0.037140 memory: 15239 loss_kpt: 0.000543 acc_pose: 0.829829 loss: 0.000543 2022/09/13 01:42:16 - mmengine - INFO - Epoch(train) [72][100/586] lr: 5.000000e-04 eta: 10:03:51 time: 0.475460 data_time: 0.030757 memory: 15239 loss_kpt: 0.000553 acc_pose: 0.861822 loss: 0.000553 2022/09/13 01:42:40 - mmengine - INFO - Epoch(train) [72][150/586] lr: 5.000000e-04 eta: 10:03:31 time: 0.470412 data_time: 0.027253 memory: 15239 loss_kpt: 0.000564 acc_pose: 0.899979 loss: 0.000564 2022/09/13 01:43:03 - mmengine - INFO - Epoch(train) [72][200/586] lr: 5.000000e-04 eta: 10:03:10 time: 0.462218 data_time: 0.025889 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.872184 loss: 0.000529 2022/09/13 01:43:27 - mmengine - INFO - Epoch(train) [72][250/586] lr: 5.000000e-04 eta: 10:02:51 time: 0.476386 data_time: 0.026803 memory: 15239 loss_kpt: 0.000551 acc_pose: 0.825583 loss: 0.000551 2022/09/13 01:43:50 - mmengine - INFO - Epoch(train) [72][300/586] lr: 5.000000e-04 eta: 10:02:31 time: 0.468100 data_time: 0.030749 memory: 15239 loss_kpt: 0.000555 acc_pose: 0.872900 loss: 0.000555 2022/09/13 01:44:13 - mmengine - INFO - Epoch(train) [72][350/586] lr: 5.000000e-04 eta: 10:02:11 time: 0.468077 data_time: 0.025556 memory: 15239 loss_kpt: 0.000560 acc_pose: 0.842117 loss: 0.000560 2022/09/13 01:44:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:44:37 - mmengine - INFO - Epoch(train) [72][400/586] lr: 5.000000e-04 eta: 10:01:51 time: 0.471163 data_time: 0.027201 memory: 15239 loss_kpt: 0.000576 acc_pose: 0.771201 loss: 0.000576 2022/09/13 01:45:00 - mmengine - INFO - Epoch(train) [72][450/586] lr: 5.000000e-04 eta: 10:01:31 time: 0.469210 data_time: 0.030472 memory: 15239 loss_kpt: 0.000549 acc_pose: 0.865541 loss: 0.000549 2022/09/13 01:45:24 - mmengine - INFO - Epoch(train) [72][500/586] lr: 5.000000e-04 eta: 10:01:12 time: 0.478234 data_time: 0.026641 memory: 15239 loss_kpt: 0.000560 acc_pose: 0.844226 loss: 0.000560 2022/09/13 01:45:48 - mmengine - INFO - Epoch(train) [72][550/586] lr: 5.000000e-04 eta: 10:00:52 time: 0.470463 data_time: 0.025284 memory: 15239 loss_kpt: 0.000570 acc_pose: 0.834306 loss: 0.000570 2022/09/13 01:46:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:46:05 - mmengine - INFO - Saving checkpoint at 72 epochs 2022/09/13 01:46:35 - mmengine - INFO - Epoch(train) [73][50/586] lr: 5.000000e-04 eta: 9:59:45 time: 0.473464 data_time: 0.032134 memory: 15239 loss_kpt: 0.000545 acc_pose: 0.762791 loss: 0.000545 2022/09/13 01:46:59 - mmengine - INFO - Epoch(train) [73][100/586] lr: 5.000000e-04 eta: 9:59:25 time: 0.468876 data_time: 0.030666 memory: 15239 loss_kpt: 0.000544 acc_pose: 0.859934 loss: 0.000544 2022/09/13 01:47:22 - mmengine - INFO - Epoch(train) [73][150/586] lr: 5.000000e-04 eta: 9:59:06 time: 0.472315 data_time: 0.026195 memory: 15239 loss_kpt: 0.000549 acc_pose: 0.812880 loss: 0.000549 2022/09/13 01:47:46 - mmengine - INFO - Epoch(train) [73][200/586] lr: 5.000000e-04 eta: 9:58:45 time: 0.466112 data_time: 0.027207 memory: 15239 loss_kpt: 0.000581 acc_pose: 0.874771 loss: 0.000581 2022/09/13 01:48:09 - mmengine - INFO - Epoch(train) [73][250/586] lr: 5.000000e-04 eta: 9:58:26 time: 0.473523 data_time: 0.031247 memory: 15239 loss_kpt: 0.000567 acc_pose: 0.818944 loss: 0.000567 2022/09/13 01:48:33 - mmengine - INFO - Epoch(train) [73][300/586] lr: 5.000000e-04 eta: 9:58:06 time: 0.473966 data_time: 0.026521 memory: 15239 loss_kpt: 0.000566 acc_pose: 0.885148 loss: 0.000566 2022/09/13 01:48:56 - mmengine - INFO - Epoch(train) [73][350/586] lr: 5.000000e-04 eta: 9:57:46 time: 0.464314 data_time: 0.027115 memory: 15239 loss_kpt: 0.000568 acc_pose: 0.837487 loss: 0.000568 2022/09/13 01:49:20 - mmengine - INFO - Epoch(train) [73][400/586] lr: 5.000000e-04 eta: 9:57:26 time: 0.468723 data_time: 0.030360 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.883359 loss: 0.000537 2022/09/13 01:49:43 - mmengine - INFO - Epoch(train) [73][450/586] lr: 5.000000e-04 eta: 9:57:06 time: 0.470046 data_time: 0.025367 memory: 15239 loss_kpt: 0.000559 acc_pose: 0.842213 loss: 0.000559 2022/09/13 01:50:07 - mmengine - INFO - Epoch(train) [73][500/586] lr: 5.000000e-04 eta: 9:56:46 time: 0.470786 data_time: 0.026467 memory: 15239 loss_kpt: 0.000568 acc_pose: 0.861111 loss: 0.000568 2022/09/13 01:50:30 - mmengine - INFO - Epoch(train) [73][550/586] lr: 5.000000e-04 eta: 9:56:26 time: 0.467688 data_time: 0.029519 memory: 15239 loss_kpt: 0.000555 acc_pose: 0.810617 loss: 0.000555 2022/09/13 01:50:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:50:47 - mmengine - INFO - Saving checkpoint at 73 epochs 2022/09/13 01:51:18 - mmengine - INFO - Epoch(train) [74][50/586] lr: 5.000000e-04 eta: 9:55:21 time: 0.486353 data_time: 0.035710 memory: 15239 loss_kpt: 0.000557 acc_pose: 0.885504 loss: 0.000557 2022/09/13 01:51:41 - mmengine - INFO - Epoch(train) [74][100/586] lr: 5.000000e-04 eta: 9:55:02 time: 0.473327 data_time: 0.030069 memory: 15239 loss_kpt: 0.000555 acc_pose: 0.861488 loss: 0.000555 2022/09/13 01:52:05 - mmengine - INFO - Epoch(train) [74][150/586] lr: 5.000000e-04 eta: 9:54:42 time: 0.470060 data_time: 0.035392 memory: 15239 loss_kpt: 0.000562 acc_pose: 0.884898 loss: 0.000562 2022/09/13 01:52:29 - mmengine - INFO - Epoch(train) [74][200/586] lr: 5.000000e-04 eta: 9:54:22 time: 0.475616 data_time: 0.025884 memory: 15239 loss_kpt: 0.000549 acc_pose: 0.873649 loss: 0.000549 2022/09/13 01:52:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:52:52 - mmengine - INFO - Epoch(train) [74][250/586] lr: 5.000000e-04 eta: 9:54:02 time: 0.468300 data_time: 0.025953 memory: 15239 loss_kpt: 0.000534 acc_pose: 0.855265 loss: 0.000534 2022/09/13 01:53:16 - mmengine - INFO - Epoch(train) [74][300/586] lr: 5.000000e-04 eta: 9:53:42 time: 0.469903 data_time: 0.028927 memory: 15239 loss_kpt: 0.000569 acc_pose: 0.825341 loss: 0.000569 2022/09/13 01:53:39 - mmengine - INFO - Epoch(train) [74][350/586] lr: 5.000000e-04 eta: 9:53:22 time: 0.472642 data_time: 0.026034 memory: 15239 loss_kpt: 0.000563 acc_pose: 0.873658 loss: 0.000563 2022/09/13 01:54:03 - mmengine - INFO - Epoch(train) [74][400/586] lr: 5.000000e-04 eta: 9:53:03 time: 0.474087 data_time: 0.025279 memory: 15239 loss_kpt: 0.000563 acc_pose: 0.792789 loss: 0.000563 2022/09/13 01:54:26 - mmengine - INFO - Epoch(train) [74][450/586] lr: 5.000000e-04 eta: 9:52:42 time: 0.461382 data_time: 0.026323 memory: 15239 loss_kpt: 0.000560 acc_pose: 0.872478 loss: 0.000560 2022/09/13 01:54:50 - mmengine - INFO - Epoch(train) [74][500/586] lr: 5.000000e-04 eta: 9:52:23 time: 0.476749 data_time: 0.029089 memory: 15239 loss_kpt: 0.000531 acc_pose: 0.873117 loss: 0.000531 2022/09/13 01:55:13 - mmengine - INFO - Epoch(train) [74][550/586] lr: 5.000000e-04 eta: 9:52:03 time: 0.470272 data_time: 0.025618 memory: 15239 loss_kpt: 0.000565 acc_pose: 0.892559 loss: 0.000565 2022/09/13 01:55:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 01:55:30 - mmengine - INFO - Saving checkpoint at 74 epochs 2022/09/13 01:56:01 - mmengine - INFO - Epoch(train) [75][50/586] lr: 5.000000e-04 eta: 9:50:58 time: 0.476634 data_time: 0.033091 memory: 15239 loss_kpt: 0.000585 acc_pose: 0.842038 loss: 0.000585 2022/09/13 01:56:24 - mmengine - INFO - Epoch(train) [75][100/586] lr: 5.000000e-04 eta: 9:50:38 time: 0.469313 data_time: 0.030866 memory: 15239 loss_kpt: 0.000555 acc_pose: 0.898383 loss: 0.000555 2022/09/13 01:56:47 - mmengine - INFO - Epoch(train) [75][150/586] lr: 5.000000e-04 eta: 9:50:17 time: 0.464208 data_time: 0.026864 memory: 15239 loss_kpt: 0.000540 acc_pose: 0.821235 loss: 0.000540 2022/09/13 01:57:11 - mmengine - INFO - Epoch(train) [75][200/586] lr: 5.000000e-04 eta: 9:49:58 time: 0.476045 data_time: 0.027000 memory: 15239 loss_kpt: 0.000560 acc_pose: 0.892727 loss: 0.000560 2022/09/13 01:57:34 - mmengine - INFO - Epoch(train) [75][250/586] lr: 5.000000e-04 eta: 9:49:37 time: 0.468846 data_time: 0.026962 memory: 15239 loss_kpt: 0.000547 acc_pose: 0.854841 loss: 0.000547 2022/09/13 01:57:58 - mmengine - INFO - Epoch(train) [75][300/586] lr: 5.000000e-04 eta: 9:49:17 time: 0.469702 data_time: 0.026019 memory: 15239 loss_kpt: 0.000555 acc_pose: 0.862414 loss: 0.000555 2022/09/13 01:58:22 - mmengine - INFO - Epoch(train) [75][350/586] lr: 5.000000e-04 eta: 9:48:58 time: 0.476384 data_time: 0.027104 memory: 15239 loss_kpt: 0.000561 acc_pose: 0.875168 loss: 0.000561 2022/09/13 01:58:45 - mmengine - INFO - Epoch(train) [75][400/586] lr: 5.000000e-04 eta: 9:48:38 time: 0.472407 data_time: 0.032812 memory: 15239 loss_kpt: 0.000566 acc_pose: 0.785136 loss: 0.000566 2022/09/13 01:59:09 - mmengine - INFO - Epoch(train) [75][450/586] lr: 5.000000e-04 eta: 9:48:18 time: 0.468050 data_time: 0.027499 memory: 15239 loss_kpt: 0.000561 acc_pose: 0.821331 loss: 0.000561 2022/09/13 01:59:32 - mmengine - INFO - Epoch(train) [75][500/586] lr: 5.000000e-04 eta: 9:47:58 time: 0.470768 data_time: 0.026706 memory: 15239 loss_kpt: 0.000565 acc_pose: 0.880777 loss: 0.000565 2022/09/13 01:59:56 - mmengine - INFO - Epoch(train) [75][550/586] lr: 5.000000e-04 eta: 9:47:38 time: 0.467030 data_time: 0.026661 memory: 15239 loss_kpt: 0.000556 acc_pose: 0.787730 loss: 0.000556 2022/09/13 02:00:13 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:00:13 - mmengine - INFO - Saving checkpoint at 75 epochs 2022/09/13 02:00:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:00:45 - mmengine - INFO - Epoch(train) [76][50/586] lr: 5.000000e-04 eta: 9:46:36 time: 0.509668 data_time: 0.049677 memory: 15239 loss_kpt: 0.000565 acc_pose: 0.881533 loss: 0.000565 2022/09/13 02:01:09 - mmengine - INFO - Epoch(train) [76][100/586] lr: 5.000000e-04 eta: 9:46:16 time: 0.470200 data_time: 0.029224 memory: 15239 loss_kpt: 0.000550 acc_pose: 0.867411 loss: 0.000550 2022/09/13 02:01:32 - mmengine - INFO - Epoch(train) [76][150/586] lr: 5.000000e-04 eta: 9:45:56 time: 0.471279 data_time: 0.036033 memory: 15239 loss_kpt: 0.000548 acc_pose: 0.901764 loss: 0.000548 2022/09/13 02:01:56 - mmengine - INFO - Epoch(train) [76][200/586] lr: 5.000000e-04 eta: 9:45:36 time: 0.466492 data_time: 0.030635 memory: 15239 loss_kpt: 0.000566 acc_pose: 0.856735 loss: 0.000566 2022/09/13 02:02:19 - mmengine - INFO - Epoch(train) [76][250/586] lr: 5.000000e-04 eta: 9:45:16 time: 0.473855 data_time: 0.033500 memory: 15239 loss_kpt: 0.000556 acc_pose: 0.829853 loss: 0.000556 2022/09/13 02:02:43 - mmengine - INFO - Epoch(train) [76][300/586] lr: 5.000000e-04 eta: 9:44:56 time: 0.469113 data_time: 0.031575 memory: 15239 loss_kpt: 0.000571 acc_pose: 0.894396 loss: 0.000571 2022/09/13 02:03:06 - mmengine - INFO - Epoch(train) [76][350/586] lr: 5.000000e-04 eta: 9:44:36 time: 0.473777 data_time: 0.031942 memory: 15239 loss_kpt: 0.000591 acc_pose: 0.878550 loss: 0.000591 2022/09/13 02:03:30 - mmengine - INFO - Epoch(train) [76][400/586] lr: 5.000000e-04 eta: 9:44:16 time: 0.467960 data_time: 0.032308 memory: 15239 loss_kpt: 0.000571 acc_pose: 0.788119 loss: 0.000571 2022/09/13 02:03:53 - mmengine - INFO - Epoch(train) [76][450/586] lr: 5.000000e-04 eta: 9:43:56 time: 0.464519 data_time: 0.026820 memory: 15239 loss_kpt: 0.000548 acc_pose: 0.903468 loss: 0.000548 2022/09/13 02:04:17 - mmengine - INFO - Epoch(train) [76][500/586] lr: 5.000000e-04 eta: 9:43:36 time: 0.470948 data_time: 0.026704 memory: 15239 loss_kpt: 0.000562 acc_pose: 0.889239 loss: 0.000562 2022/09/13 02:04:40 - mmengine - INFO - Epoch(train) [76][550/586] lr: 5.000000e-04 eta: 9:43:16 time: 0.474880 data_time: 0.026035 memory: 15239 loss_kpt: 0.000565 acc_pose: 0.815374 loss: 0.000565 2022/09/13 02:04:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:04:57 - mmengine - INFO - Saving checkpoint at 76 epochs 2022/09/13 02:05:28 - mmengine - INFO - Epoch(train) [77][50/586] lr: 5.000000e-04 eta: 9:42:12 time: 0.476323 data_time: 0.033500 memory: 15239 loss_kpt: 0.000569 acc_pose: 0.830238 loss: 0.000569 2022/09/13 02:05:52 - mmengine - INFO - Epoch(train) [77][100/586] lr: 5.000000e-04 eta: 9:41:53 time: 0.478398 data_time: 0.033500 memory: 15239 loss_kpt: 0.000538 acc_pose: 0.864764 loss: 0.000538 2022/09/13 02:06:15 - mmengine - INFO - Epoch(train) [77][150/586] lr: 5.000000e-04 eta: 9:41:32 time: 0.461468 data_time: 0.027020 memory: 15239 loss_kpt: 0.000556 acc_pose: 0.896952 loss: 0.000556 2022/09/13 02:06:38 - mmengine - INFO - Epoch(train) [77][200/586] lr: 5.000000e-04 eta: 9:41:12 time: 0.474005 data_time: 0.025900 memory: 15239 loss_kpt: 0.000566 acc_pose: 0.865080 loss: 0.000566 2022/09/13 02:07:02 - mmengine - INFO - Epoch(train) [77][250/586] lr: 5.000000e-04 eta: 9:40:52 time: 0.466524 data_time: 0.029488 memory: 15239 loss_kpt: 0.000559 acc_pose: 0.822271 loss: 0.000559 2022/09/13 02:07:25 - mmengine - INFO - Epoch(train) [77][300/586] lr: 5.000000e-04 eta: 9:40:31 time: 0.462646 data_time: 0.026818 memory: 15239 loss_kpt: 0.000562 acc_pose: 0.847123 loss: 0.000562 2022/09/13 02:07:48 - mmengine - INFO - Epoch(train) [77][350/586] lr: 5.000000e-04 eta: 9:40:11 time: 0.471510 data_time: 0.026672 memory: 15239 loss_kpt: 0.000562 acc_pose: 0.868014 loss: 0.000562 2022/09/13 02:08:12 - mmengine - INFO - Epoch(train) [77][400/586] lr: 5.000000e-04 eta: 9:39:51 time: 0.472601 data_time: 0.026264 memory: 15239 loss_kpt: 0.000555 acc_pose: 0.842405 loss: 0.000555 2022/09/13 02:08:35 - mmengine - INFO - Epoch(train) [77][450/586] lr: 5.000000e-04 eta: 9:39:30 time: 0.460667 data_time: 0.026485 memory: 15239 loss_kpt: 0.000584 acc_pose: 0.825015 loss: 0.000584 2022/09/13 02:08:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:08:59 - mmengine - INFO - Epoch(train) [77][500/586] lr: 5.000000e-04 eta: 9:39:11 time: 0.483682 data_time: 0.027010 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.884504 loss: 0.000529 2022/09/13 02:09:23 - mmengine - INFO - Epoch(train) [77][550/586] lr: 5.000000e-04 eta: 9:38:51 time: 0.474623 data_time: 0.026392 memory: 15239 loss_kpt: 0.000565 acc_pose: 0.865874 loss: 0.000565 2022/09/13 02:09:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:09:40 - mmengine - INFO - Saving checkpoint at 77 epochs 2022/09/13 02:10:10 - mmengine - INFO - Epoch(train) [78][50/586] lr: 5.000000e-04 eta: 9:37:48 time: 0.471873 data_time: 0.035158 memory: 15239 loss_kpt: 0.000532 acc_pose: 0.822895 loss: 0.000532 2022/09/13 02:10:34 - mmengine - INFO - Epoch(train) [78][100/586] lr: 5.000000e-04 eta: 9:37:28 time: 0.475438 data_time: 0.028466 memory: 15239 loss_kpt: 0.000558 acc_pose: 0.843400 loss: 0.000558 2022/09/13 02:10:57 - mmengine - INFO - Epoch(train) [78][150/586] lr: 5.000000e-04 eta: 9:37:08 time: 0.466099 data_time: 0.033539 memory: 15239 loss_kpt: 0.000579 acc_pose: 0.845782 loss: 0.000579 2022/09/13 02:11:21 - mmengine - INFO - Epoch(train) [78][200/586] lr: 5.000000e-04 eta: 9:36:48 time: 0.477970 data_time: 0.025571 memory: 15239 loss_kpt: 0.000557 acc_pose: 0.874620 loss: 0.000557 2022/09/13 02:11:44 - mmengine - INFO - Epoch(train) [78][250/586] lr: 5.000000e-04 eta: 9:36:28 time: 0.465382 data_time: 0.026479 memory: 15239 loss_kpt: 0.000565 acc_pose: 0.839144 loss: 0.000565 2022/09/13 02:12:08 - mmengine - INFO - Epoch(train) [78][300/586] lr: 5.000000e-04 eta: 9:36:07 time: 0.466595 data_time: 0.030616 memory: 15239 loss_kpt: 0.000547 acc_pose: 0.845366 loss: 0.000547 2022/09/13 02:12:31 - mmengine - INFO - Epoch(train) [78][350/586] lr: 5.000000e-04 eta: 9:35:47 time: 0.469879 data_time: 0.025831 memory: 15239 loss_kpt: 0.000545 acc_pose: 0.876389 loss: 0.000545 2022/09/13 02:12:54 - mmengine - INFO - Epoch(train) [78][400/586] lr: 5.000000e-04 eta: 9:35:26 time: 0.464383 data_time: 0.025839 memory: 15239 loss_kpt: 0.000550 acc_pose: 0.861507 loss: 0.000550 2022/09/13 02:13:18 - mmengine - INFO - Epoch(train) [78][450/586] lr: 5.000000e-04 eta: 9:35:06 time: 0.470518 data_time: 0.030968 memory: 15239 loss_kpt: 0.000564 acc_pose: 0.750984 loss: 0.000564 2022/09/13 02:13:41 - mmengine - INFO - Epoch(train) [78][500/586] lr: 5.000000e-04 eta: 9:34:46 time: 0.466868 data_time: 0.026682 memory: 15239 loss_kpt: 0.000551 acc_pose: 0.830387 loss: 0.000551 2022/09/13 02:14:05 - mmengine - INFO - Epoch(train) [78][550/586] lr: 5.000000e-04 eta: 9:34:26 time: 0.470211 data_time: 0.029576 memory: 15239 loss_kpt: 0.000560 acc_pose: 0.877188 loss: 0.000560 2022/09/13 02:14:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:14:22 - mmengine - INFO - Saving checkpoint at 78 epochs 2022/09/13 02:14:52 - mmengine - INFO - Epoch(train) [79][50/586] lr: 5.000000e-04 eta: 9:33:23 time: 0.477319 data_time: 0.035850 memory: 15239 loss_kpt: 0.000555 acc_pose: 0.868737 loss: 0.000555 2022/09/13 02:15:16 - mmengine - INFO - Epoch(train) [79][100/586] lr: 5.000000e-04 eta: 9:33:03 time: 0.465137 data_time: 0.029067 memory: 15239 loss_kpt: 0.000552 acc_pose: 0.833750 loss: 0.000552 2022/09/13 02:15:39 - mmengine - INFO - Epoch(train) [79][150/586] lr: 5.000000e-04 eta: 9:32:42 time: 0.469966 data_time: 0.035010 memory: 15239 loss_kpt: 0.000546 acc_pose: 0.824741 loss: 0.000546 2022/09/13 02:16:03 - mmengine - INFO - Epoch(train) [79][200/586] lr: 5.000000e-04 eta: 9:32:22 time: 0.469532 data_time: 0.029358 memory: 15239 loss_kpt: 0.000565 acc_pose: 0.853864 loss: 0.000565 2022/09/13 02:16:26 - mmengine - INFO - Epoch(train) [79][250/586] lr: 5.000000e-04 eta: 9:32:01 time: 0.462044 data_time: 0.028356 memory: 15239 loss_kpt: 0.000550 acc_pose: 0.881486 loss: 0.000550 2022/09/13 02:16:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:16:49 - mmengine - INFO - Epoch(train) [79][300/586] lr: 5.000000e-04 eta: 9:31:41 time: 0.471428 data_time: 0.034262 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.856639 loss: 0.000537 2022/09/13 02:17:13 - mmengine - INFO - Epoch(train) [79][350/586] lr: 5.000000e-04 eta: 9:31:21 time: 0.473262 data_time: 0.031550 memory: 15239 loss_kpt: 0.000535 acc_pose: 0.857709 loss: 0.000535 2022/09/13 02:17:36 - mmengine - INFO - Epoch(train) [79][400/586] lr: 5.000000e-04 eta: 9:31:01 time: 0.467374 data_time: 0.026256 memory: 15239 loss_kpt: 0.000544 acc_pose: 0.869110 loss: 0.000544 2022/09/13 02:18:00 - mmengine - INFO - Epoch(train) [79][450/586] lr: 5.000000e-04 eta: 9:30:41 time: 0.470409 data_time: 0.026268 memory: 15239 loss_kpt: 0.000555 acc_pose: 0.875024 loss: 0.000555 2022/09/13 02:18:23 - mmengine - INFO - Epoch(train) [79][500/586] lr: 5.000000e-04 eta: 9:30:21 time: 0.470929 data_time: 0.027323 memory: 15239 loss_kpt: 0.000572 acc_pose: 0.810421 loss: 0.000572 2022/09/13 02:18:47 - mmengine - INFO - Epoch(train) [79][550/586] lr: 5.000000e-04 eta: 9:30:01 time: 0.477092 data_time: 0.026565 memory: 15239 loss_kpt: 0.000560 acc_pose: 0.783887 loss: 0.000560 2022/09/13 02:19:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:19:04 - mmengine - INFO - Saving checkpoint at 79 epochs 2022/09/13 02:19:35 - mmengine - INFO - Epoch(train) [80][50/586] lr: 5.000000e-04 eta: 9:29:00 time: 0.487945 data_time: 0.035137 memory: 15239 loss_kpt: 0.000553 acc_pose: 0.851851 loss: 0.000553 2022/09/13 02:19:59 - mmengine - INFO - Epoch(train) [80][100/586] lr: 5.000000e-04 eta: 9:28:40 time: 0.474905 data_time: 0.035420 memory: 15239 loss_kpt: 0.000557 acc_pose: 0.884796 loss: 0.000557 2022/09/13 02:20:23 - mmengine - INFO - Epoch(train) [80][150/586] lr: 5.000000e-04 eta: 9:28:20 time: 0.467330 data_time: 0.029951 memory: 15239 loss_kpt: 0.000540 acc_pose: 0.878175 loss: 0.000540 2022/09/13 02:20:46 - mmengine - INFO - Epoch(train) [80][200/586] lr: 5.000000e-04 eta: 9:27:59 time: 0.470973 data_time: 0.028899 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.864838 loss: 0.000536 2022/09/13 02:21:10 - mmengine - INFO - Epoch(train) [80][250/586] lr: 5.000000e-04 eta: 9:27:40 time: 0.476223 data_time: 0.026301 memory: 15239 loss_kpt: 0.000550 acc_pose: 0.838253 loss: 0.000550 2022/09/13 02:21:33 - mmengine - INFO - Epoch(train) [80][300/586] lr: 5.000000e-04 eta: 9:27:19 time: 0.470122 data_time: 0.030235 memory: 15239 loss_kpt: 0.000559 acc_pose: 0.838898 loss: 0.000559 2022/09/13 02:21:57 - mmengine - INFO - Epoch(train) [80][350/586] lr: 5.000000e-04 eta: 9:26:59 time: 0.471873 data_time: 0.027049 memory: 15239 loss_kpt: 0.000551 acc_pose: 0.857388 loss: 0.000551 2022/09/13 02:22:20 - mmengine - INFO - Epoch(train) [80][400/586] lr: 5.000000e-04 eta: 9:26:39 time: 0.464392 data_time: 0.025655 memory: 15239 loss_kpt: 0.000543 acc_pose: 0.862451 loss: 0.000543 2022/09/13 02:22:44 - mmengine - INFO - Epoch(train) [80][450/586] lr: 5.000000e-04 eta: 9:26:18 time: 0.466846 data_time: 0.026377 memory: 15239 loss_kpt: 0.000554 acc_pose: 0.861076 loss: 0.000554 2022/09/13 02:23:07 - mmengine - INFO - Epoch(train) [80][500/586] lr: 5.000000e-04 eta: 9:25:58 time: 0.473954 data_time: 0.027778 memory: 15239 loss_kpt: 0.000547 acc_pose: 0.860024 loss: 0.000547 2022/09/13 02:23:31 - mmengine - INFO - Epoch(train) [80][550/586] lr: 5.000000e-04 eta: 9:25:39 time: 0.476660 data_time: 0.027311 memory: 15239 loss_kpt: 0.000551 acc_pose: 0.872977 loss: 0.000551 2022/09/13 02:23:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:23:48 - mmengine - INFO - Saving checkpoint at 80 epochs 2022/09/13 02:24:07 - mmengine - INFO - Epoch(val) [80][50/407] eta: 0:01:21 time: 0.229381 data_time: 0.014161 memory: 15239 2022/09/13 02:24:18 - mmengine - INFO - Epoch(val) [80][100/407] eta: 0:01:09 time: 0.226710 data_time: 0.009195 memory: 2064 2022/09/13 02:24:29 - mmengine - INFO - Epoch(val) [80][150/407] eta: 0:00:56 time: 0.219264 data_time: 0.008679 memory: 2064 2022/09/13 02:24:40 - mmengine - INFO - Epoch(val) [80][200/407] eta: 0:00:45 time: 0.220706 data_time: 0.008763 memory: 2064 2022/09/13 02:24:51 - mmengine - INFO - Epoch(val) [80][250/407] eta: 0:00:34 time: 0.218848 data_time: 0.008566 memory: 2064 2022/09/13 02:25:02 - mmengine - INFO - Epoch(val) [80][300/407] eta: 0:00:23 time: 0.220810 data_time: 0.009116 memory: 2064 2022/09/13 02:25:13 - mmengine - INFO - Epoch(val) [80][350/407] eta: 0:00:12 time: 0.220054 data_time: 0.008628 memory: 2064 2022/09/13 02:25:24 - mmengine - INFO - Epoch(val) [80][400/407] eta: 0:00:01 time: 0.218970 data_time: 0.008559 memory: 2064 2022/09/13 02:26:01 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 02:26:15 - mmengine - INFO - Epoch(val) [80][407/407] coco/AP: 0.760632 coco/AP .5: 0.907036 coco/AP .75: 0.821152 coco/AP (M): 0.720030 coco/AP (L): 0.830871 coco/AR: 0.807683 coco/AR .5: 0.941278 coco/AR .75: 0.863035 coco/AR (M): 0.764026 coco/AR (L): 0.871200 2022/09/13 02:26:15 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_384_v1/best_coco/AP_epoch_70.pth is removed 2022/09/13 02:26:19 - mmengine - INFO - The best checkpoint with 0.7606 coco/AP at 80 epoch is saved to best_coco/AP_epoch_80.pth. 2022/09/13 02:26:42 - mmengine - INFO - Epoch(train) [81][50/586] lr: 5.000000e-04 eta: 9:24:36 time: 0.469048 data_time: 0.031134 memory: 15239 loss_kpt: 0.000546 acc_pose: 0.854622 loss: 0.000546 2022/09/13 02:27:05 - mmengine - INFO - Epoch(train) [81][100/586] lr: 5.000000e-04 eta: 9:24:15 time: 0.462542 data_time: 0.026570 memory: 15239 loss_kpt: 0.000554 acc_pose: 0.857077 loss: 0.000554 2022/09/13 02:27:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:27:28 - mmengine - INFO - Epoch(train) [81][150/586] lr: 5.000000e-04 eta: 9:23:55 time: 0.465760 data_time: 0.027530 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.840970 loss: 0.000536 2022/09/13 02:27:52 - mmengine - INFO - Epoch(train) [81][200/586] lr: 5.000000e-04 eta: 9:23:35 time: 0.470877 data_time: 0.026792 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.906215 loss: 0.000529 2022/09/13 02:28:15 - mmengine - INFO - Epoch(train) [81][250/586] lr: 5.000000e-04 eta: 9:23:14 time: 0.469054 data_time: 0.026546 memory: 15239 loss_kpt: 0.000552 acc_pose: 0.791716 loss: 0.000552 2022/09/13 02:28:39 - mmengine - INFO - Epoch(train) [81][300/586] lr: 5.000000e-04 eta: 9:22:54 time: 0.463587 data_time: 0.025890 memory: 15239 loss_kpt: 0.000569 acc_pose: 0.841792 loss: 0.000569 2022/09/13 02:29:02 - mmengine - INFO - Epoch(train) [81][350/586] lr: 5.000000e-04 eta: 9:22:33 time: 0.469611 data_time: 0.027606 memory: 15239 loss_kpt: 0.000547 acc_pose: 0.878543 loss: 0.000547 2022/09/13 02:29:26 - mmengine - INFO - Epoch(train) [81][400/586] lr: 5.000000e-04 eta: 9:22:13 time: 0.468377 data_time: 0.027562 memory: 15239 loss_kpt: 0.000549 acc_pose: 0.885451 loss: 0.000549 2022/09/13 02:29:49 - mmengine - INFO - Epoch(train) [81][450/586] lr: 5.000000e-04 eta: 9:21:52 time: 0.465440 data_time: 0.027862 memory: 15239 loss_kpt: 0.000545 acc_pose: 0.741811 loss: 0.000545 2022/09/13 02:30:13 - mmengine - INFO - Epoch(train) [81][500/586] lr: 5.000000e-04 eta: 9:21:32 time: 0.475803 data_time: 0.031182 memory: 15239 loss_kpt: 0.000540 acc_pose: 0.821524 loss: 0.000540 2022/09/13 02:30:36 - mmengine - INFO - Epoch(train) [81][550/586] lr: 5.000000e-04 eta: 9:21:12 time: 0.467199 data_time: 0.027705 memory: 15239 loss_kpt: 0.000554 acc_pose: 0.853025 loss: 0.000554 2022/09/13 02:30:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:30:53 - mmengine - INFO - Saving checkpoint at 81 epochs 2022/09/13 02:31:24 - mmengine - INFO - Epoch(train) [82][50/586] lr: 5.000000e-04 eta: 9:20:12 time: 0.490105 data_time: 0.036993 memory: 15239 loss_kpt: 0.000561 acc_pose: 0.854697 loss: 0.000561 2022/09/13 02:31:48 - mmengine - INFO - Epoch(train) [82][100/586] lr: 5.000000e-04 eta: 9:19:52 time: 0.475958 data_time: 0.025637 memory: 15239 loss_kpt: 0.000548 acc_pose: 0.839033 loss: 0.000548 2022/09/13 02:32:12 - mmengine - INFO - Epoch(train) [82][150/586] lr: 5.000000e-04 eta: 9:19:32 time: 0.476144 data_time: 0.032741 memory: 15239 loss_kpt: 0.000533 acc_pose: 0.866005 loss: 0.000533 2022/09/13 02:32:35 - mmengine - INFO - Epoch(train) [82][200/586] lr: 5.000000e-04 eta: 9:19:12 time: 0.469293 data_time: 0.027802 memory: 15239 loss_kpt: 0.000552 acc_pose: 0.898062 loss: 0.000552 2022/09/13 02:32:59 - mmengine - INFO - Epoch(train) [82][250/586] lr: 5.000000e-04 eta: 9:18:51 time: 0.466572 data_time: 0.028491 memory: 15239 loss_kpt: 0.000556 acc_pose: 0.831408 loss: 0.000556 2022/09/13 02:33:22 - mmengine - INFO - Epoch(train) [82][300/586] lr: 5.000000e-04 eta: 9:18:31 time: 0.471719 data_time: 0.028290 memory: 15239 loss_kpt: 0.000540 acc_pose: 0.816913 loss: 0.000540 2022/09/13 02:33:46 - mmengine - INFO - Epoch(train) [82][350/586] lr: 5.000000e-04 eta: 9:18:12 time: 0.482466 data_time: 0.027331 memory: 15239 loss_kpt: 0.000545 acc_pose: 0.864272 loss: 0.000545 2022/09/13 02:34:10 - mmengine - INFO - Epoch(train) [82][400/586] lr: 5.000000e-04 eta: 9:17:52 time: 0.470962 data_time: 0.029663 memory: 15239 loss_kpt: 0.000549 acc_pose: 0.802873 loss: 0.000549 2022/09/13 02:34:34 - mmengine - INFO - Epoch(train) [82][450/586] lr: 5.000000e-04 eta: 9:17:32 time: 0.475691 data_time: 0.027401 memory: 15239 loss_kpt: 0.000524 acc_pose: 0.833676 loss: 0.000524 2022/09/13 02:34:57 - mmengine - INFO - Epoch(train) [82][500/586] lr: 5.000000e-04 eta: 9:17:11 time: 0.467285 data_time: 0.026312 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.900113 loss: 0.000536 2022/09/13 02:35:13 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:35:20 - mmengine - INFO - Epoch(train) [82][550/586] lr: 5.000000e-04 eta: 9:16:51 time: 0.465762 data_time: 0.025638 memory: 15239 loss_kpt: 0.000560 acc_pose: 0.861826 loss: 0.000560 2022/09/13 02:35:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:35:37 - mmengine - INFO - Saving checkpoint at 82 epochs 2022/09/13 02:36:08 - mmengine - INFO - Epoch(train) [83][50/586] lr: 5.000000e-04 eta: 9:15:50 time: 0.481157 data_time: 0.034630 memory: 15239 loss_kpt: 0.000547 acc_pose: 0.817387 loss: 0.000547 2022/09/13 02:36:31 - mmengine - INFO - Epoch(train) [83][100/586] lr: 5.000000e-04 eta: 9:15:30 time: 0.466983 data_time: 0.030750 memory: 15239 loss_kpt: 0.000544 acc_pose: 0.820404 loss: 0.000544 2022/09/13 02:36:55 - mmengine - INFO - Epoch(train) [83][150/586] lr: 5.000000e-04 eta: 9:15:09 time: 0.467628 data_time: 0.026748 memory: 15239 loss_kpt: 0.000567 acc_pose: 0.853047 loss: 0.000567 2022/09/13 02:37:18 - mmengine - INFO - Epoch(train) [83][200/586] lr: 5.000000e-04 eta: 9:14:48 time: 0.465003 data_time: 0.026356 memory: 15239 loss_kpt: 0.000552 acc_pose: 0.881795 loss: 0.000552 2022/09/13 02:37:42 - mmengine - INFO - Epoch(train) [83][250/586] lr: 5.000000e-04 eta: 9:14:28 time: 0.472598 data_time: 0.031140 memory: 15239 loss_kpt: 0.000549 acc_pose: 0.895117 loss: 0.000549 2022/09/13 02:38:05 - mmengine - INFO - Epoch(train) [83][300/586] lr: 5.000000e-04 eta: 9:14:08 time: 0.470604 data_time: 0.026883 memory: 15239 loss_kpt: 0.000554 acc_pose: 0.880542 loss: 0.000554 2022/09/13 02:38:29 - mmengine - INFO - Epoch(train) [83][350/586] lr: 5.000000e-04 eta: 9:13:48 time: 0.469663 data_time: 0.031958 memory: 15239 loss_kpt: 0.000550 acc_pose: 0.848112 loss: 0.000550 2022/09/13 02:38:52 - mmengine - INFO - Epoch(train) [83][400/586] lr: 5.000000e-04 eta: 9:13:27 time: 0.468400 data_time: 0.025777 memory: 15239 loss_kpt: 0.000578 acc_pose: 0.860152 loss: 0.000578 2022/09/13 02:39:15 - mmengine - INFO - Epoch(train) [83][450/586] lr: 5.000000e-04 eta: 9:13:07 time: 0.467534 data_time: 0.025842 memory: 15239 loss_kpt: 0.000557 acc_pose: 0.802340 loss: 0.000557 2022/09/13 02:39:39 - mmengine - INFO - Epoch(train) [83][500/586] lr: 5.000000e-04 eta: 9:12:46 time: 0.469333 data_time: 0.030257 memory: 15239 loss_kpt: 0.000564 acc_pose: 0.877402 loss: 0.000564 2022/09/13 02:40:02 - mmengine - INFO - Epoch(train) [83][550/586] lr: 5.000000e-04 eta: 9:12:26 time: 0.470608 data_time: 0.027259 memory: 15239 loss_kpt: 0.000547 acc_pose: 0.861021 loss: 0.000547 2022/09/13 02:40:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:40:19 - mmengine - INFO - Saving checkpoint at 83 epochs 2022/09/13 02:40:50 - mmengine - INFO - Epoch(train) [84][50/586] lr: 5.000000e-04 eta: 9:11:25 time: 0.474650 data_time: 0.036907 memory: 15239 loss_kpt: 0.000532 acc_pose: 0.824091 loss: 0.000532 2022/09/13 02:41:13 - mmengine - INFO - Epoch(train) [84][100/586] lr: 5.000000e-04 eta: 9:11:05 time: 0.467370 data_time: 0.031557 memory: 15239 loss_kpt: 0.000562 acc_pose: 0.765001 loss: 0.000562 2022/09/13 02:41:37 - mmengine - INFO - Epoch(train) [84][150/586] lr: 5.000000e-04 eta: 9:10:45 time: 0.477589 data_time: 0.036884 memory: 15239 loss_kpt: 0.000545 acc_pose: 0.851792 loss: 0.000545 2022/09/13 02:42:00 - mmengine - INFO - Epoch(train) [84][200/586] lr: 5.000000e-04 eta: 9:10:24 time: 0.463568 data_time: 0.027540 memory: 15239 loss_kpt: 0.000539 acc_pose: 0.825423 loss: 0.000539 2022/09/13 02:42:24 - mmengine - INFO - Epoch(train) [84][250/586] lr: 5.000000e-04 eta: 9:10:04 time: 0.466193 data_time: 0.027517 memory: 15239 loss_kpt: 0.000540 acc_pose: 0.923251 loss: 0.000540 2022/09/13 02:42:47 - mmengine - INFO - Epoch(train) [84][300/586] lr: 5.000000e-04 eta: 9:09:44 time: 0.473766 data_time: 0.031871 memory: 15239 loss_kpt: 0.000549 acc_pose: 0.813406 loss: 0.000549 2022/09/13 02:43:10 - mmengine - INFO - Epoch(train) [84][350/586] lr: 5.000000e-04 eta: 9:09:22 time: 0.459154 data_time: 0.027426 memory: 15239 loss_kpt: 0.000544 acc_pose: 0.842815 loss: 0.000544 2022/09/13 02:43:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:43:34 - mmengine - INFO - Epoch(train) [84][400/586] lr: 5.000000e-04 eta: 9:09:03 time: 0.480449 data_time: 0.028005 memory: 15239 loss_kpt: 0.000528 acc_pose: 0.841382 loss: 0.000528 2022/09/13 02:43:58 - mmengine - INFO - Epoch(train) [84][450/586] lr: 5.000000e-04 eta: 9:08:42 time: 0.469540 data_time: 0.026599 memory: 15239 loss_kpt: 0.000530 acc_pose: 0.840907 loss: 0.000530 2022/09/13 02:44:21 - mmengine - INFO - Epoch(train) [84][500/586] lr: 5.000000e-04 eta: 9:08:21 time: 0.462261 data_time: 0.026388 memory: 15239 loss_kpt: 0.000531 acc_pose: 0.810389 loss: 0.000531 2022/09/13 02:44:44 - mmengine - INFO - Epoch(train) [84][550/586] lr: 5.000000e-04 eta: 9:08:01 time: 0.467082 data_time: 0.025931 memory: 15239 loss_kpt: 0.000543 acc_pose: 0.864559 loss: 0.000543 2022/09/13 02:45:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:45:01 - mmengine - INFO - Saving checkpoint at 84 epochs 2022/09/13 02:45:33 - mmengine - INFO - Epoch(train) [85][50/586] lr: 5.000000e-04 eta: 9:07:02 time: 0.485332 data_time: 0.035771 memory: 15239 loss_kpt: 0.000549 acc_pose: 0.786657 loss: 0.000549 2022/09/13 02:45:56 - mmengine - INFO - Epoch(train) [85][100/586] lr: 5.000000e-04 eta: 9:06:41 time: 0.470971 data_time: 0.029663 memory: 15239 loss_kpt: 0.000565 acc_pose: 0.851715 loss: 0.000565 2022/09/13 02:46:19 - mmengine - INFO - Epoch(train) [85][150/586] lr: 5.000000e-04 eta: 9:06:21 time: 0.465339 data_time: 0.028881 memory: 15239 loss_kpt: 0.000554 acc_pose: 0.770453 loss: 0.000554 2022/09/13 02:46:43 - mmengine - INFO - Epoch(train) [85][200/586] lr: 5.000000e-04 eta: 9:06:00 time: 0.468236 data_time: 0.030443 memory: 15239 loss_kpt: 0.000556 acc_pose: 0.764264 loss: 0.000556 2022/09/13 02:47:06 - mmengine - INFO - Epoch(train) [85][250/586] lr: 5.000000e-04 eta: 9:05:40 time: 0.472036 data_time: 0.027288 memory: 15239 loss_kpt: 0.000554 acc_pose: 0.847780 loss: 0.000554 2022/09/13 02:47:30 - mmengine - INFO - Epoch(train) [85][300/586] lr: 5.000000e-04 eta: 9:05:20 time: 0.471306 data_time: 0.026291 memory: 15239 loss_kpt: 0.000541 acc_pose: 0.881165 loss: 0.000541 2022/09/13 02:47:53 - mmengine - INFO - Epoch(train) [85][350/586] lr: 5.000000e-04 eta: 9:04:59 time: 0.461394 data_time: 0.027645 memory: 15239 loss_kpt: 0.000545 acc_pose: 0.831240 loss: 0.000545 2022/09/13 02:48:17 - mmengine - INFO - Epoch(train) [85][400/586] lr: 5.000000e-04 eta: 9:04:39 time: 0.476879 data_time: 0.028194 memory: 15239 loss_kpt: 0.000539 acc_pose: 0.807703 loss: 0.000539 2022/09/13 02:48:40 - mmengine - INFO - Epoch(train) [85][450/586] lr: 5.000000e-04 eta: 9:04:18 time: 0.467224 data_time: 0.025930 memory: 15239 loss_kpt: 0.000556 acc_pose: 0.897229 loss: 0.000556 2022/09/13 02:49:03 - mmengine - INFO - Epoch(train) [85][500/586] lr: 5.000000e-04 eta: 9:03:57 time: 0.461588 data_time: 0.030635 memory: 15239 loss_kpt: 0.000554 acc_pose: 0.843198 loss: 0.000554 2022/09/13 02:49:27 - mmengine - INFO - Epoch(train) [85][550/586] lr: 5.000000e-04 eta: 9:03:37 time: 0.476618 data_time: 0.026593 memory: 15239 loss_kpt: 0.000560 acc_pose: 0.847854 loss: 0.000560 2022/09/13 02:49:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:49:44 - mmengine - INFO - Saving checkpoint at 85 epochs 2022/09/13 02:50:14 - mmengine - INFO - Epoch(train) [86][50/586] lr: 5.000000e-04 eta: 9:02:37 time: 0.471015 data_time: 0.033350 memory: 15239 loss_kpt: 0.000563 acc_pose: 0.893048 loss: 0.000563 2022/09/13 02:50:38 - mmengine - INFO - Epoch(train) [86][100/586] lr: 5.000000e-04 eta: 9:02:17 time: 0.476879 data_time: 0.033837 memory: 15239 loss_kpt: 0.000540 acc_pose: 0.832033 loss: 0.000540 2022/09/13 02:51:02 - mmengine - INFO - Epoch(train) [86][150/586] lr: 5.000000e-04 eta: 9:01:57 time: 0.469030 data_time: 0.027481 memory: 15239 loss_kpt: 0.000534 acc_pose: 0.882936 loss: 0.000534 2022/09/13 02:51:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:51:25 - mmengine - INFO - Epoch(train) [86][200/586] lr: 5.000000e-04 eta: 9:01:36 time: 0.468423 data_time: 0.027525 memory: 15239 loss_kpt: 0.000545 acc_pose: 0.885131 loss: 0.000545 2022/09/13 02:51:49 - mmengine - INFO - Epoch(train) [86][250/586] lr: 5.000000e-04 eta: 9:01:16 time: 0.471238 data_time: 0.025929 memory: 15239 loss_kpt: 0.000565 acc_pose: 0.863212 loss: 0.000565 2022/09/13 02:52:12 - mmengine - INFO - Epoch(train) [86][300/586] lr: 5.000000e-04 eta: 9:00:56 time: 0.469447 data_time: 0.026475 memory: 15239 loss_kpt: 0.000549 acc_pose: 0.829527 loss: 0.000549 2022/09/13 02:52:35 - mmengine - INFO - Epoch(train) [86][350/586] lr: 5.000000e-04 eta: 9:00:35 time: 0.461125 data_time: 0.026457 memory: 15239 loss_kpt: 0.000544 acc_pose: 0.877095 loss: 0.000544 2022/09/13 02:52:59 - mmengine - INFO - Epoch(train) [86][400/586] lr: 5.000000e-04 eta: 9:00:15 time: 0.477313 data_time: 0.028479 memory: 15239 loss_kpt: 0.000555 acc_pose: 0.860167 loss: 0.000555 2022/09/13 02:53:22 - mmengine - INFO - Epoch(train) [86][450/586] lr: 5.000000e-04 eta: 8:59:54 time: 0.466874 data_time: 0.026603 memory: 15239 loss_kpt: 0.000542 acc_pose: 0.862756 loss: 0.000542 2022/09/13 02:53:46 - mmengine - INFO - Epoch(train) [86][500/586] lr: 5.000000e-04 eta: 8:59:33 time: 0.462524 data_time: 0.025966 memory: 15239 loss_kpt: 0.000555 acc_pose: 0.912663 loss: 0.000555 2022/09/13 02:54:09 - mmengine - INFO - Epoch(train) [86][550/586] lr: 5.000000e-04 eta: 8:59:13 time: 0.473491 data_time: 0.030281 memory: 15239 loss_kpt: 0.000538 acc_pose: 0.825172 loss: 0.000538 2022/09/13 02:54:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:54:26 - mmengine - INFO - Saving checkpoint at 86 epochs 2022/09/13 02:54:57 - mmengine - INFO - Epoch(train) [87][50/586] lr: 5.000000e-04 eta: 8:58:14 time: 0.475454 data_time: 0.033227 memory: 15239 loss_kpt: 0.000524 acc_pose: 0.885630 loss: 0.000524 2022/09/13 02:55:21 - mmengine - INFO - Epoch(train) [87][100/586] lr: 5.000000e-04 eta: 8:57:54 time: 0.474075 data_time: 0.027021 memory: 15239 loss_kpt: 0.000540 acc_pose: 0.905797 loss: 0.000540 2022/09/13 02:55:44 - mmengine - INFO - Epoch(train) [87][150/586] lr: 5.000000e-04 eta: 8:57:34 time: 0.477427 data_time: 0.026640 memory: 15239 loss_kpt: 0.000553 acc_pose: 0.888778 loss: 0.000553 2022/09/13 02:56:08 - mmengine - INFO - Epoch(train) [87][200/586] lr: 5.000000e-04 eta: 8:57:13 time: 0.465538 data_time: 0.026256 memory: 15239 loss_kpt: 0.000559 acc_pose: 0.888486 loss: 0.000559 2022/09/13 02:56:31 - mmengine - INFO - Epoch(train) [87][250/586] lr: 5.000000e-04 eta: 8:56:52 time: 0.468491 data_time: 0.027434 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.782596 loss: 0.000537 2022/09/13 02:56:55 - mmengine - INFO - Epoch(train) [87][300/586] lr: 5.000000e-04 eta: 8:56:32 time: 0.474084 data_time: 0.031107 memory: 15239 loss_kpt: 0.000552 acc_pose: 0.866327 loss: 0.000552 2022/09/13 02:57:18 - mmengine - INFO - Epoch(train) [87][350/586] lr: 5.000000e-04 eta: 8:56:11 time: 0.464220 data_time: 0.025443 memory: 15239 loss_kpt: 0.000552 acc_pose: 0.869900 loss: 0.000552 2022/09/13 02:57:41 - mmengine - INFO - Epoch(train) [87][400/586] lr: 5.000000e-04 eta: 8:55:51 time: 0.468463 data_time: 0.025227 memory: 15239 loss_kpt: 0.000547 acc_pose: 0.884904 loss: 0.000547 2022/09/13 02:58:05 - mmengine - INFO - Epoch(train) [87][450/586] lr: 5.000000e-04 eta: 8:55:30 time: 0.470378 data_time: 0.026801 memory: 15239 loss_kpt: 0.000548 acc_pose: 0.836772 loss: 0.000548 2022/09/13 02:58:28 - mmengine - INFO - Epoch(train) [87][500/586] lr: 5.000000e-04 eta: 8:55:09 time: 0.461541 data_time: 0.025253 memory: 15239 loss_kpt: 0.000535 acc_pose: 0.825934 loss: 0.000535 2022/09/13 02:58:52 - mmengine - INFO - Epoch(train) [87][550/586] lr: 5.000000e-04 eta: 8:54:49 time: 0.475386 data_time: 0.026821 memory: 15239 loss_kpt: 0.000541 acc_pose: 0.831264 loss: 0.000541 2022/09/13 02:59:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:59:09 - mmengine - INFO - Saving checkpoint at 87 epochs 2022/09/13 02:59:25 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 02:59:39 - mmengine - INFO - Epoch(train) [88][50/586] lr: 5.000000e-04 eta: 8:53:51 time: 0.479422 data_time: 0.034728 memory: 15239 loss_kpt: 0.000555 acc_pose: 0.865740 loss: 0.000555 2022/09/13 03:00:03 - mmengine - INFO - Epoch(train) [88][100/586] lr: 5.000000e-04 eta: 8:53:30 time: 0.472030 data_time: 0.025779 memory: 15239 loss_kpt: 0.000532 acc_pose: 0.881299 loss: 0.000532 2022/09/13 03:00:27 - mmengine - INFO - Epoch(train) [88][150/586] lr: 5.000000e-04 eta: 8:53:10 time: 0.476440 data_time: 0.026289 memory: 15239 loss_kpt: 0.000558 acc_pose: 0.807197 loss: 0.000558 2022/09/13 03:00:50 - mmengine - INFO - Epoch(train) [88][200/586] lr: 5.000000e-04 eta: 8:52:50 time: 0.466614 data_time: 0.025735 memory: 15239 loss_kpt: 0.000556 acc_pose: 0.806462 loss: 0.000556 2022/09/13 03:01:14 - mmengine - INFO - Epoch(train) [88][250/586] lr: 5.000000e-04 eta: 8:52:30 time: 0.475135 data_time: 0.026037 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.869349 loss: 0.000537 2022/09/13 03:01:37 - mmengine - INFO - Epoch(train) [88][300/586] lr: 5.000000e-04 eta: 8:52:09 time: 0.466602 data_time: 0.025956 memory: 15239 loss_kpt: 0.000542 acc_pose: 0.792952 loss: 0.000542 2022/09/13 03:02:00 - mmengine - INFO - Epoch(train) [88][350/586] lr: 5.000000e-04 eta: 8:51:48 time: 0.460889 data_time: 0.025655 memory: 15239 loss_kpt: 0.000546 acc_pose: 0.857612 loss: 0.000546 2022/09/13 03:02:24 - mmengine - INFO - Epoch(train) [88][400/586] lr: 5.000000e-04 eta: 8:51:28 time: 0.477237 data_time: 0.029406 memory: 15239 loss_kpt: 0.000522 acc_pose: 0.833907 loss: 0.000522 2022/09/13 03:02:48 - mmengine - INFO - Epoch(train) [88][450/586] lr: 5.000000e-04 eta: 8:51:07 time: 0.466924 data_time: 0.026083 memory: 15239 loss_kpt: 0.000541 acc_pose: 0.917986 loss: 0.000541 2022/09/13 03:03:11 - mmengine - INFO - Epoch(train) [88][500/586] lr: 5.000000e-04 eta: 8:50:46 time: 0.463479 data_time: 0.027216 memory: 15239 loss_kpt: 0.000540 acc_pose: 0.807639 loss: 0.000540 2022/09/13 03:03:35 - mmengine - INFO - Epoch(train) [88][550/586] lr: 5.000000e-04 eta: 8:50:26 time: 0.479157 data_time: 0.027594 memory: 15239 loss_kpt: 0.000538 acc_pose: 0.826822 loss: 0.000538 2022/09/13 03:03:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:03:51 - mmengine - INFO - Saving checkpoint at 88 epochs 2022/09/13 03:04:22 - mmengine - INFO - Epoch(train) [89][50/586] lr: 5.000000e-04 eta: 8:49:28 time: 0.474807 data_time: 0.030946 memory: 15239 loss_kpt: 0.000546 acc_pose: 0.823814 loss: 0.000546 2022/09/13 03:04:46 - mmengine - INFO - Epoch(train) [89][100/586] lr: 5.000000e-04 eta: 8:49:07 time: 0.472067 data_time: 0.025818 memory: 15239 loss_kpt: 0.000542 acc_pose: 0.786628 loss: 0.000542 2022/09/13 03:05:09 - mmengine - INFO - Epoch(train) [89][150/586] lr: 5.000000e-04 eta: 8:48:47 time: 0.471350 data_time: 0.025704 memory: 15239 loss_kpt: 0.000547 acc_pose: 0.862994 loss: 0.000547 2022/09/13 03:05:33 - mmengine - INFO - Epoch(train) [89][200/586] lr: 5.000000e-04 eta: 8:48:27 time: 0.473001 data_time: 0.026594 memory: 15239 loss_kpt: 0.000539 acc_pose: 0.775549 loss: 0.000539 2022/09/13 03:05:56 - mmengine - INFO - Epoch(train) [89][250/586] lr: 5.000000e-04 eta: 8:48:06 time: 0.468563 data_time: 0.027086 memory: 15239 loss_kpt: 0.000533 acc_pose: 0.852053 loss: 0.000533 2022/09/13 03:06:20 - mmengine - INFO - Epoch(train) [89][300/586] lr: 5.000000e-04 eta: 8:47:46 time: 0.470296 data_time: 0.026224 memory: 15239 loss_kpt: 0.000533 acc_pose: 0.863826 loss: 0.000533 2022/09/13 03:06:43 - mmengine - INFO - Epoch(train) [89][350/586] lr: 5.000000e-04 eta: 8:47:25 time: 0.474143 data_time: 0.030800 memory: 15239 loss_kpt: 0.000547 acc_pose: 0.853986 loss: 0.000547 2022/09/13 03:07:07 - mmengine - INFO - Epoch(train) [89][400/586] lr: 5.000000e-04 eta: 8:47:05 time: 0.467722 data_time: 0.026841 memory: 15239 loss_kpt: 0.000553 acc_pose: 0.839751 loss: 0.000553 2022/09/13 03:07:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:07:30 - mmengine - INFO - Epoch(train) [89][450/586] lr: 5.000000e-04 eta: 8:46:44 time: 0.464801 data_time: 0.026641 memory: 15239 loss_kpt: 0.000532 acc_pose: 0.805008 loss: 0.000532 2022/09/13 03:07:54 - mmengine - INFO - Epoch(train) [89][500/586] lr: 5.000000e-04 eta: 8:46:23 time: 0.469386 data_time: 0.025471 memory: 15239 loss_kpt: 0.000549 acc_pose: 0.839325 loss: 0.000549 2022/09/13 03:08:17 - mmengine - INFO - Epoch(train) [89][550/586] lr: 5.000000e-04 eta: 8:46:03 time: 0.475334 data_time: 0.027489 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.858829 loss: 0.000536 2022/09/13 03:08:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:08:34 - mmengine - INFO - Saving checkpoint at 89 epochs 2022/09/13 03:09:05 - mmengine - INFO - Epoch(train) [90][50/586] lr: 5.000000e-04 eta: 8:45:05 time: 0.474171 data_time: 0.034521 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.887607 loss: 0.000536 2022/09/13 03:09:29 - mmengine - INFO - Epoch(train) [90][100/586] lr: 5.000000e-04 eta: 8:44:45 time: 0.471758 data_time: 0.034190 memory: 15239 loss_kpt: 0.000533 acc_pose: 0.818373 loss: 0.000533 2022/09/13 03:09:53 - mmengine - INFO - Epoch(train) [90][150/586] lr: 5.000000e-04 eta: 8:44:24 time: 0.470801 data_time: 0.028527 memory: 15239 loss_kpt: 0.000540 acc_pose: 0.876453 loss: 0.000540 2022/09/13 03:10:16 - mmengine - INFO - Epoch(train) [90][200/586] lr: 5.000000e-04 eta: 8:44:04 time: 0.474061 data_time: 0.025357 memory: 15239 loss_kpt: 0.000533 acc_pose: 0.876438 loss: 0.000533 2022/09/13 03:10:40 - mmengine - INFO - Epoch(train) [90][250/586] lr: 5.000000e-04 eta: 8:43:43 time: 0.468699 data_time: 0.025407 memory: 15239 loss_kpt: 0.000556 acc_pose: 0.858248 loss: 0.000556 2022/09/13 03:11:03 - mmengine - INFO - Epoch(train) [90][300/586] lr: 5.000000e-04 eta: 8:43:23 time: 0.471558 data_time: 0.025505 memory: 15239 loss_kpt: 0.000540 acc_pose: 0.836720 loss: 0.000540 2022/09/13 03:11:26 - mmengine - INFO - Epoch(train) [90][350/586] lr: 5.000000e-04 eta: 8:43:02 time: 0.461729 data_time: 0.026169 memory: 15239 loss_kpt: 0.000518 acc_pose: 0.912408 loss: 0.000518 2022/09/13 03:11:50 - mmengine - INFO - Epoch(train) [90][400/586] lr: 5.000000e-04 eta: 8:42:41 time: 0.463856 data_time: 0.025989 memory: 15239 loss_kpt: 0.000532 acc_pose: 0.911904 loss: 0.000532 2022/09/13 03:12:13 - mmengine - INFO - Epoch(train) [90][450/586] lr: 5.000000e-04 eta: 8:42:20 time: 0.470613 data_time: 0.026445 memory: 15239 loss_kpt: 0.000560 acc_pose: 0.748623 loss: 0.000560 2022/09/13 03:12:36 - mmengine - INFO - Epoch(train) [90][500/586] lr: 5.000000e-04 eta: 8:42:00 time: 0.464891 data_time: 0.026326 memory: 15239 loss_kpt: 0.000546 acc_pose: 0.885044 loss: 0.000546 2022/09/13 03:13:00 - mmengine - INFO - Epoch(train) [90][550/586] lr: 5.000000e-04 eta: 8:41:39 time: 0.475445 data_time: 0.026330 memory: 15239 loss_kpt: 0.000558 acc_pose: 0.887769 loss: 0.000558 2022/09/13 03:13:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:13:17 - mmengine - INFO - Saving checkpoint at 90 epochs 2022/09/13 03:13:36 - mmengine - INFO - Epoch(val) [90][50/407] eta: 0:01:21 time: 0.227217 data_time: 0.014520 memory: 15239 2022/09/13 03:13:47 - mmengine - INFO - Epoch(val) [90][100/407] eta: 0:01:08 time: 0.224423 data_time: 0.012143 memory: 2064 2022/09/13 03:13:58 - mmengine - INFO - Epoch(val) [90][150/407] eta: 0:00:56 time: 0.220116 data_time: 0.008263 memory: 2064 2022/09/13 03:14:09 - mmengine - INFO - Epoch(val) [90][200/407] eta: 0:00:46 time: 0.223587 data_time: 0.012814 memory: 2064 2022/09/13 03:14:20 - mmengine - INFO - Epoch(val) [90][250/407] eta: 0:00:34 time: 0.219133 data_time: 0.008543 memory: 2064 2022/09/13 03:14:31 - mmengine - INFO - Epoch(val) [90][300/407] eta: 0:00:24 time: 0.225024 data_time: 0.015331 memory: 2064 2022/09/13 03:14:42 - mmengine - INFO - Epoch(val) [90][350/407] eta: 0:00:12 time: 0.218255 data_time: 0.008310 memory: 2064 2022/09/13 03:14:53 - mmengine - INFO - Epoch(val) [90][400/407] eta: 0:00:01 time: 0.221736 data_time: 0.010408 memory: 2064 2022/09/13 03:15:30 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 03:15:44 - mmengine - INFO - Epoch(val) [90][407/407] coco/AP: 0.760148 coco/AP .5: 0.902287 coco/AP .75: 0.822691 coco/AP (M): 0.719685 coco/AP (L): 0.832294 coco/AR: 0.808375 coco/AR .5: 0.939547 coco/AR .75: 0.865239 coco/AR (M): 0.763944 coco/AR (L): 0.873096 2022/09/13 03:16:08 - mmengine - INFO - Epoch(train) [91][50/586] lr: 5.000000e-04 eta: 8:40:42 time: 0.483162 data_time: 0.036412 memory: 15239 loss_kpt: 0.000532 acc_pose: 0.803435 loss: 0.000532 2022/09/13 03:16:32 - mmengine - INFO - Epoch(train) [91][100/586] lr: 5.000000e-04 eta: 8:40:22 time: 0.472186 data_time: 0.025836 memory: 15239 loss_kpt: 0.000557 acc_pose: 0.841415 loss: 0.000557 2022/09/13 03:16:56 - mmengine - INFO - Epoch(train) [91][150/586] lr: 5.000000e-04 eta: 8:40:02 time: 0.474933 data_time: 0.027401 memory: 15239 loss_kpt: 0.000521 acc_pose: 0.774298 loss: 0.000521 2022/09/13 03:17:19 - mmengine - INFO - Epoch(train) [91][200/586] lr: 5.000000e-04 eta: 8:39:41 time: 0.468837 data_time: 0.025497 memory: 15239 loss_kpt: 0.000541 acc_pose: 0.829156 loss: 0.000541 2022/09/13 03:17:43 - mmengine - INFO - Epoch(train) [91][250/586] lr: 5.000000e-04 eta: 8:39:21 time: 0.473865 data_time: 0.026576 memory: 15239 loss_kpt: 0.000546 acc_pose: 0.868402 loss: 0.000546 2022/09/13 03:17:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:18:07 - mmengine - INFO - Epoch(train) [91][300/586] lr: 5.000000e-04 eta: 8:39:00 time: 0.474769 data_time: 0.030370 memory: 15239 loss_kpt: 0.000549 acc_pose: 0.829085 loss: 0.000549 2022/09/13 03:18:30 - mmengine - INFO - Epoch(train) [91][350/586] lr: 5.000000e-04 eta: 8:38:39 time: 0.462397 data_time: 0.025947 memory: 15239 loss_kpt: 0.000551 acc_pose: 0.850551 loss: 0.000551 2022/09/13 03:18:54 - mmengine - INFO - Epoch(train) [91][400/586] lr: 5.000000e-04 eta: 8:38:19 time: 0.476587 data_time: 0.025941 memory: 15239 loss_kpt: 0.000539 acc_pose: 0.876971 loss: 0.000539 2022/09/13 03:19:17 - mmengine - INFO - Epoch(train) [91][450/586] lr: 5.000000e-04 eta: 8:37:59 time: 0.471048 data_time: 0.026225 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.822601 loss: 0.000529 2022/09/13 03:19:40 - mmengine - INFO - Epoch(train) [91][500/586] lr: 5.000000e-04 eta: 8:37:37 time: 0.459783 data_time: 0.025889 memory: 15239 loss_kpt: 0.000543 acc_pose: 0.836262 loss: 0.000543 2022/09/13 03:20:04 - mmengine - INFO - Epoch(train) [91][550/586] lr: 5.000000e-04 eta: 8:37:17 time: 0.472383 data_time: 0.026082 memory: 15239 loss_kpt: 0.000549 acc_pose: 0.920022 loss: 0.000549 2022/09/13 03:20:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:20:20 - mmengine - INFO - Saving checkpoint at 91 epochs 2022/09/13 03:20:51 - mmengine - INFO - Epoch(train) [92][50/586] lr: 5.000000e-04 eta: 8:36:20 time: 0.476267 data_time: 0.038266 memory: 15239 loss_kpt: 0.000541 acc_pose: 0.877405 loss: 0.000541 2022/09/13 03:21:15 - mmengine - INFO - Epoch(train) [92][100/586] lr: 5.000000e-04 eta: 8:36:00 time: 0.473244 data_time: 0.030288 memory: 15239 loss_kpt: 0.000545 acc_pose: 0.868529 loss: 0.000545 2022/09/13 03:21:38 - mmengine - INFO - Epoch(train) [92][150/586] lr: 5.000000e-04 eta: 8:35:39 time: 0.471028 data_time: 0.030729 memory: 15239 loss_kpt: 0.000538 acc_pose: 0.847878 loss: 0.000538 2022/09/13 03:22:02 - mmengine - INFO - Epoch(train) [92][200/586] lr: 5.000000e-04 eta: 8:35:19 time: 0.473501 data_time: 0.033140 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.806727 loss: 0.000537 2022/09/13 03:22:25 - mmengine - INFO - Epoch(train) [92][250/586] lr: 5.000000e-04 eta: 8:34:58 time: 0.468430 data_time: 0.026029 memory: 15239 loss_kpt: 0.000517 acc_pose: 0.861496 loss: 0.000517 2022/09/13 03:22:49 - mmengine - INFO - Epoch(train) [92][300/586] lr: 5.000000e-04 eta: 8:34:37 time: 0.464783 data_time: 0.026576 memory: 15239 loss_kpt: 0.000542 acc_pose: 0.885141 loss: 0.000542 2022/09/13 03:23:12 - mmengine - INFO - Epoch(train) [92][350/586] lr: 5.000000e-04 eta: 8:34:16 time: 0.468121 data_time: 0.026039 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.767323 loss: 0.000537 2022/09/13 03:23:35 - mmengine - INFO - Epoch(train) [92][400/586] lr: 5.000000e-04 eta: 8:33:56 time: 0.470042 data_time: 0.025862 memory: 15239 loss_kpt: 0.000551 acc_pose: 0.833934 loss: 0.000551 2022/09/13 03:23:59 - mmengine - INFO - Epoch(train) [92][450/586] lr: 5.000000e-04 eta: 8:33:35 time: 0.462024 data_time: 0.025356 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.885324 loss: 0.000537 2022/09/13 03:24:22 - mmengine - INFO - Epoch(train) [92][500/586] lr: 5.000000e-04 eta: 8:33:14 time: 0.469879 data_time: 0.030121 memory: 15239 loss_kpt: 0.000539 acc_pose: 0.858404 loss: 0.000539 2022/09/13 03:24:46 - mmengine - INFO - Epoch(train) [92][550/586] lr: 5.000000e-04 eta: 8:32:54 time: 0.475062 data_time: 0.026674 memory: 15239 loss_kpt: 0.000521 acc_pose: 0.881791 loss: 0.000521 2022/09/13 03:25:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:25:03 - mmengine - INFO - Saving checkpoint at 92 epochs 2022/09/13 03:25:33 - mmengine - INFO - Epoch(train) [93][50/586] lr: 5.000000e-04 eta: 8:31:57 time: 0.473417 data_time: 0.032490 memory: 15239 loss_kpt: 0.000532 acc_pose: 0.869306 loss: 0.000532 2022/09/13 03:25:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:25:57 - mmengine - INFO - Epoch(train) [93][100/586] lr: 5.000000e-04 eta: 8:31:36 time: 0.469673 data_time: 0.027502 memory: 15239 loss_kpt: 0.000535 acc_pose: 0.872832 loss: 0.000535 2022/09/13 03:26:20 - mmengine - INFO - Epoch(train) [93][150/586] lr: 5.000000e-04 eta: 8:31:15 time: 0.466040 data_time: 0.030486 memory: 15239 loss_kpt: 0.000553 acc_pose: 0.830464 loss: 0.000553 2022/09/13 03:26:43 - mmengine - INFO - Epoch(train) [93][200/586] lr: 5.000000e-04 eta: 8:30:55 time: 0.468081 data_time: 0.026890 memory: 15239 loss_kpt: 0.000530 acc_pose: 0.871444 loss: 0.000530 2022/09/13 03:27:07 - mmengine - INFO - Epoch(train) [93][250/586] lr: 5.000000e-04 eta: 8:30:34 time: 0.471671 data_time: 0.027151 memory: 15239 loss_kpt: 0.000531 acc_pose: 0.857034 loss: 0.000531 2022/09/13 03:27:31 - mmengine - INFO - Epoch(train) [93][300/586] lr: 5.000000e-04 eta: 8:30:14 time: 0.471240 data_time: 0.026733 memory: 15239 loss_kpt: 0.000549 acc_pose: 0.819486 loss: 0.000549 2022/09/13 03:27:54 - mmengine - INFO - Epoch(train) [93][350/586] lr: 5.000000e-04 eta: 8:29:53 time: 0.466653 data_time: 0.026469 memory: 15239 loss_kpt: 0.000550 acc_pose: 0.897687 loss: 0.000550 2022/09/13 03:28:17 - mmengine - INFO - Epoch(train) [93][400/586] lr: 5.000000e-04 eta: 8:29:32 time: 0.464131 data_time: 0.026112 memory: 15239 loss_kpt: 0.000556 acc_pose: 0.854167 loss: 0.000556 2022/09/13 03:28:41 - mmengine - INFO - Epoch(train) [93][450/586] lr: 5.000000e-04 eta: 8:29:11 time: 0.472871 data_time: 0.025812 memory: 15239 loss_kpt: 0.000527 acc_pose: 0.843058 loss: 0.000527 2022/09/13 03:29:04 - mmengine - INFO - Epoch(train) [93][500/586] lr: 5.000000e-04 eta: 8:28:50 time: 0.464450 data_time: 0.026741 memory: 15239 loss_kpt: 0.000538 acc_pose: 0.881057 loss: 0.000538 2022/09/13 03:29:28 - mmengine - INFO - Epoch(train) [93][550/586] lr: 5.000000e-04 eta: 8:28:30 time: 0.476134 data_time: 0.028239 memory: 15239 loss_kpt: 0.000548 acc_pose: 0.828900 loss: 0.000548 2022/09/13 03:29:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:29:45 - mmengine - INFO - Saving checkpoint at 93 epochs 2022/09/13 03:30:15 - mmengine - INFO - Epoch(train) [94][50/586] lr: 5.000000e-04 eta: 8:27:33 time: 0.474088 data_time: 0.035063 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.856889 loss: 0.000529 2022/09/13 03:30:38 - mmengine - INFO - Epoch(train) [94][100/586] lr: 5.000000e-04 eta: 8:27:13 time: 0.471342 data_time: 0.036605 memory: 15239 loss_kpt: 0.000546 acc_pose: 0.861503 loss: 0.000546 2022/09/13 03:31:02 - mmengine - INFO - Epoch(train) [94][150/586] lr: 5.000000e-04 eta: 8:26:53 time: 0.477067 data_time: 0.032307 memory: 15239 loss_kpt: 0.000543 acc_pose: 0.915941 loss: 0.000543 2022/09/13 03:31:26 - mmengine - INFO - Epoch(train) [94][200/586] lr: 5.000000e-04 eta: 8:26:32 time: 0.468542 data_time: 0.031811 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.940499 loss: 0.000529 2022/09/13 03:31:49 - mmengine - INFO - Epoch(train) [94][250/586] lr: 5.000000e-04 eta: 8:26:11 time: 0.471259 data_time: 0.026405 memory: 15239 loss_kpt: 0.000533 acc_pose: 0.825914 loss: 0.000533 2022/09/13 03:32:13 - mmengine - INFO - Epoch(train) [94][300/586] lr: 5.000000e-04 eta: 8:25:51 time: 0.469685 data_time: 0.025517 memory: 15239 loss_kpt: 0.000543 acc_pose: 0.892059 loss: 0.000543 2022/09/13 03:32:36 - mmengine - INFO - Epoch(train) [94][350/586] lr: 5.000000e-04 eta: 8:25:30 time: 0.468706 data_time: 0.026199 memory: 15239 loss_kpt: 0.000535 acc_pose: 0.832206 loss: 0.000535 2022/09/13 03:33:00 - mmengine - INFO - Epoch(train) [94][400/586] lr: 5.000000e-04 eta: 8:25:09 time: 0.468639 data_time: 0.025850 memory: 15239 loss_kpt: 0.000541 acc_pose: 0.866579 loss: 0.000541 2022/09/13 03:33:23 - mmengine - INFO - Epoch(train) [94][450/586] lr: 5.000000e-04 eta: 8:24:49 time: 0.471271 data_time: 0.027466 memory: 15239 loss_kpt: 0.000530 acc_pose: 0.842248 loss: 0.000530 2022/09/13 03:33:47 - mmengine - INFO - Epoch(train) [94][500/586] lr: 5.000000e-04 eta: 8:24:28 time: 0.468727 data_time: 0.032199 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.764118 loss: 0.000537 2022/09/13 03:33:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:34:10 - mmengine - INFO - Epoch(train) [94][550/586] lr: 5.000000e-04 eta: 8:24:07 time: 0.469901 data_time: 0.026669 memory: 15239 loss_kpt: 0.000539 acc_pose: 0.847377 loss: 0.000539 2022/09/13 03:34:27 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:34:27 - mmengine - INFO - Saving checkpoint at 94 epochs 2022/09/13 03:34:58 - mmengine - INFO - Epoch(train) [95][50/586] lr: 5.000000e-04 eta: 8:23:11 time: 0.478284 data_time: 0.030511 memory: 15239 loss_kpt: 0.000542 acc_pose: 0.920465 loss: 0.000542 2022/09/13 03:35:21 - mmengine - INFO - Epoch(train) [95][100/586] lr: 5.000000e-04 eta: 8:22:50 time: 0.463058 data_time: 0.026724 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.865947 loss: 0.000536 2022/09/13 03:35:45 - mmengine - INFO - Epoch(train) [95][150/586] lr: 5.000000e-04 eta: 8:22:30 time: 0.473141 data_time: 0.031056 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.904444 loss: 0.000537 2022/09/13 03:36:08 - mmengine - INFO - Epoch(train) [95][200/586] lr: 5.000000e-04 eta: 8:22:09 time: 0.471094 data_time: 0.027541 memory: 15239 loss_kpt: 0.000534 acc_pose: 0.862312 loss: 0.000534 2022/09/13 03:36:31 - mmengine - INFO - Epoch(train) [95][250/586] lr: 5.000000e-04 eta: 8:21:48 time: 0.462967 data_time: 0.026390 memory: 15239 loss_kpt: 0.000525 acc_pose: 0.897571 loss: 0.000525 2022/09/13 03:36:55 - mmengine - INFO - Epoch(train) [95][300/586] lr: 5.000000e-04 eta: 8:21:28 time: 0.472139 data_time: 0.027153 memory: 15239 loss_kpt: 0.000559 acc_pose: 0.893706 loss: 0.000559 2022/09/13 03:37:19 - mmengine - INFO - Epoch(train) [95][350/586] lr: 5.000000e-04 eta: 8:21:07 time: 0.471247 data_time: 0.026746 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.840400 loss: 0.000529 2022/09/13 03:37:42 - mmengine - INFO - Epoch(train) [95][400/586] lr: 5.000000e-04 eta: 8:20:46 time: 0.464882 data_time: 0.026055 memory: 15239 loss_kpt: 0.000534 acc_pose: 0.889560 loss: 0.000534 2022/09/13 03:38:05 - mmengine - INFO - Epoch(train) [95][450/586] lr: 5.000000e-04 eta: 8:20:25 time: 0.472783 data_time: 0.026534 memory: 15239 loss_kpt: 0.000534 acc_pose: 0.877556 loss: 0.000534 2022/09/13 03:38:29 - mmengine - INFO - Epoch(train) [95][500/586] lr: 5.000000e-04 eta: 8:20:05 time: 0.468017 data_time: 0.027057 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.895641 loss: 0.000537 2022/09/13 03:38:53 - mmengine - INFO - Epoch(train) [95][550/586] lr: 5.000000e-04 eta: 8:19:44 time: 0.474984 data_time: 0.027496 memory: 15239 loss_kpt: 0.000551 acc_pose: 0.829506 loss: 0.000551 2022/09/13 03:39:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:39:10 - mmengine - INFO - Saving checkpoint at 95 epochs 2022/09/13 03:39:42 - mmengine - INFO - Epoch(train) [96][50/586] lr: 5.000000e-04 eta: 8:18:49 time: 0.484320 data_time: 0.039120 memory: 15239 loss_kpt: 0.000556 acc_pose: 0.847001 loss: 0.000556 2022/09/13 03:40:05 - mmengine - INFO - Epoch(train) [96][100/586] lr: 5.000000e-04 eta: 8:18:28 time: 0.471259 data_time: 0.026905 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.815853 loss: 0.000536 2022/09/13 03:40:29 - mmengine - INFO - Epoch(train) [96][150/586] lr: 5.000000e-04 eta: 8:18:08 time: 0.471014 data_time: 0.026925 memory: 15239 loss_kpt: 0.000531 acc_pose: 0.893108 loss: 0.000531 2022/09/13 03:40:52 - mmengine - INFO - Epoch(train) [96][200/586] lr: 5.000000e-04 eta: 8:17:47 time: 0.466959 data_time: 0.026637 memory: 15239 loss_kpt: 0.000518 acc_pose: 0.878184 loss: 0.000518 2022/09/13 03:41:16 - mmengine - INFO - Epoch(train) [96][250/586] lr: 5.000000e-04 eta: 8:17:26 time: 0.469807 data_time: 0.026568 memory: 15239 loss_kpt: 0.000533 acc_pose: 0.901383 loss: 0.000533 2022/09/13 03:41:40 - mmengine - INFO - Epoch(train) [96][300/586] lr: 5.000000e-04 eta: 8:17:06 time: 0.472814 data_time: 0.027856 memory: 15239 loss_kpt: 0.000542 acc_pose: 0.823419 loss: 0.000542 2022/09/13 03:41:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:42:03 - mmengine - INFO - Epoch(train) [96][350/586] lr: 5.000000e-04 eta: 8:16:45 time: 0.464450 data_time: 0.026545 memory: 15239 loss_kpt: 0.000538 acc_pose: 0.785499 loss: 0.000538 2022/09/13 03:42:26 - mmengine - INFO - Epoch(train) [96][400/586] lr: 5.000000e-04 eta: 8:16:24 time: 0.472509 data_time: 0.026509 memory: 15239 loss_kpt: 0.000544 acc_pose: 0.876288 loss: 0.000544 2022/09/13 03:42:50 - mmengine - INFO - Epoch(train) [96][450/586] lr: 5.000000e-04 eta: 8:16:03 time: 0.468782 data_time: 0.026197 memory: 15239 loss_kpt: 0.000530 acc_pose: 0.859128 loss: 0.000530 2022/09/13 03:43:13 - mmengine - INFO - Epoch(train) [96][500/586] lr: 5.000000e-04 eta: 8:15:42 time: 0.464324 data_time: 0.027519 memory: 15239 loss_kpt: 0.000531 acc_pose: 0.793222 loss: 0.000531 2022/09/13 03:43:37 - mmengine - INFO - Epoch(train) [96][550/586] lr: 5.000000e-04 eta: 8:15:22 time: 0.471113 data_time: 0.027580 memory: 15239 loss_kpt: 0.000557 acc_pose: 0.875391 loss: 0.000557 2022/09/13 03:43:54 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:43:54 - mmengine - INFO - Saving checkpoint at 96 epochs 2022/09/13 03:44:25 - mmengine - INFO - Epoch(train) [97][50/586] lr: 5.000000e-04 eta: 8:14:26 time: 0.477663 data_time: 0.037960 memory: 15239 loss_kpt: 0.000534 acc_pose: 0.920458 loss: 0.000534 2022/09/13 03:44:48 - mmengine - INFO - Epoch(train) [97][100/586] lr: 5.000000e-04 eta: 8:14:06 time: 0.472061 data_time: 0.033125 memory: 15239 loss_kpt: 0.000522 acc_pose: 0.800657 loss: 0.000522 2022/09/13 03:45:13 - mmengine - INFO - Epoch(train) [97][150/586] lr: 5.000000e-04 eta: 8:13:46 time: 0.485217 data_time: 0.029362 memory: 15239 loss_kpt: 0.000521 acc_pose: 0.844944 loss: 0.000521 2022/09/13 03:45:36 - mmengine - INFO - Epoch(train) [97][200/586] lr: 5.000000e-04 eta: 8:13:25 time: 0.467472 data_time: 0.027184 memory: 15239 loss_kpt: 0.000542 acc_pose: 0.790395 loss: 0.000542 2022/09/13 03:46:00 - mmengine - INFO - Epoch(train) [97][250/586] lr: 5.000000e-04 eta: 8:13:05 time: 0.474925 data_time: 0.027842 memory: 15239 loss_kpt: 0.000542 acc_pose: 0.796766 loss: 0.000542 2022/09/13 03:46:23 - mmengine - INFO - Epoch(train) [97][300/586] lr: 5.000000e-04 eta: 8:12:44 time: 0.471457 data_time: 0.029243 memory: 15239 loss_kpt: 0.000540 acc_pose: 0.804963 loss: 0.000540 2022/09/13 03:46:47 - mmengine - INFO - Epoch(train) [97][350/586] lr: 5.000000e-04 eta: 8:12:23 time: 0.465066 data_time: 0.025902 memory: 15239 loss_kpt: 0.000557 acc_pose: 0.860518 loss: 0.000557 2022/09/13 03:47:10 - mmengine - INFO - Epoch(train) [97][400/586] lr: 5.000000e-04 eta: 8:12:02 time: 0.469001 data_time: 0.026641 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.849323 loss: 0.000537 2022/09/13 03:47:34 - mmengine - INFO - Epoch(train) [97][450/586] lr: 5.000000e-04 eta: 8:11:41 time: 0.468484 data_time: 0.026141 memory: 15239 loss_kpt: 0.000544 acc_pose: 0.859022 loss: 0.000544 2022/09/13 03:47:57 - mmengine - INFO - Epoch(train) [97][500/586] lr: 5.000000e-04 eta: 8:11:20 time: 0.465019 data_time: 0.026058 memory: 15239 loss_kpt: 0.000534 acc_pose: 0.854019 loss: 0.000534 2022/09/13 03:48:21 - mmengine - INFO - Epoch(train) [97][550/586] lr: 5.000000e-04 eta: 8:11:00 time: 0.474978 data_time: 0.027852 memory: 15239 loss_kpt: 0.000547 acc_pose: 0.854542 loss: 0.000547 2022/09/13 03:48:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:48:37 - mmengine - INFO - Saving checkpoint at 97 epochs 2022/09/13 03:49:08 - mmengine - INFO - Epoch(train) [98][50/586] lr: 5.000000e-04 eta: 8:10:05 time: 0.475481 data_time: 0.031058 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.871266 loss: 0.000526 2022/09/13 03:49:32 - mmengine - INFO - Epoch(train) [98][100/586] lr: 5.000000e-04 eta: 8:09:44 time: 0.469861 data_time: 0.030020 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.922367 loss: 0.000523 2022/09/13 03:49:56 - mmengine - INFO - Epoch(train) [98][150/586] lr: 5.000000e-04 eta: 8:09:24 time: 0.475224 data_time: 0.027047 memory: 15239 loss_kpt: 0.000534 acc_pose: 0.800638 loss: 0.000534 2022/09/13 03:49:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:50:19 - mmengine - INFO - Epoch(train) [98][200/586] lr: 5.000000e-04 eta: 8:09:03 time: 0.465372 data_time: 0.025741 memory: 15239 loss_kpt: 0.000534 acc_pose: 0.842742 loss: 0.000534 2022/09/13 03:50:42 - mmengine - INFO - Epoch(train) [98][250/586] lr: 5.000000e-04 eta: 8:08:41 time: 0.462702 data_time: 0.029589 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.839134 loss: 0.000523 2022/09/13 03:51:05 - mmengine - INFO - Epoch(train) [98][300/586] lr: 5.000000e-04 eta: 8:08:21 time: 0.468302 data_time: 0.026400 memory: 15239 loss_kpt: 0.000539 acc_pose: 0.808888 loss: 0.000539 2022/09/13 03:51:29 - mmengine - INFO - Epoch(train) [98][350/586] lr: 5.000000e-04 eta: 8:07:59 time: 0.462418 data_time: 0.026939 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.834745 loss: 0.000526 2022/09/13 03:51:52 - mmengine - INFO - Epoch(train) [98][400/586] lr: 5.000000e-04 eta: 8:07:38 time: 0.467912 data_time: 0.027199 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.872512 loss: 0.000536 2022/09/13 03:52:16 - mmengine - INFO - Epoch(train) [98][450/586] lr: 5.000000e-04 eta: 8:07:18 time: 0.471583 data_time: 0.025877 memory: 15239 loss_kpt: 0.000545 acc_pose: 0.902307 loss: 0.000545 2022/09/13 03:52:39 - mmengine - INFO - Epoch(train) [98][500/586] lr: 5.000000e-04 eta: 8:06:57 time: 0.466135 data_time: 0.026444 memory: 15239 loss_kpt: 0.000531 acc_pose: 0.864367 loss: 0.000531 2022/09/13 03:53:02 - mmengine - INFO - Epoch(train) [98][550/586] lr: 5.000000e-04 eta: 8:06:36 time: 0.468615 data_time: 0.027242 memory: 15239 loss_kpt: 0.000550 acc_pose: 0.884821 loss: 0.000550 2022/09/13 03:53:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:53:19 - mmengine - INFO - Saving checkpoint at 98 epochs 2022/09/13 03:53:50 - mmengine - INFO - Epoch(train) [99][50/586] lr: 5.000000e-04 eta: 8:05:41 time: 0.473133 data_time: 0.030129 memory: 15239 loss_kpt: 0.000553 acc_pose: 0.807072 loss: 0.000553 2022/09/13 03:54:13 - mmengine - INFO - Epoch(train) [99][100/586] lr: 5.000000e-04 eta: 8:05:20 time: 0.470985 data_time: 0.026988 memory: 15239 loss_kpt: 0.000534 acc_pose: 0.855863 loss: 0.000534 2022/09/13 03:54:37 - mmengine - INFO - Epoch(train) [99][150/586] lr: 5.000000e-04 eta: 8:05:00 time: 0.476099 data_time: 0.025666 memory: 15239 loss_kpt: 0.000542 acc_pose: 0.864908 loss: 0.000542 2022/09/13 03:55:00 - mmengine - INFO - Epoch(train) [99][200/586] lr: 5.000000e-04 eta: 8:04:39 time: 0.467257 data_time: 0.026014 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.861814 loss: 0.000526 2022/09/13 03:55:24 - mmengine - INFO - Epoch(train) [99][250/586] lr: 5.000000e-04 eta: 8:04:18 time: 0.465016 data_time: 0.026760 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.866538 loss: 0.000536 2022/09/13 03:55:47 - mmengine - INFO - Epoch(train) [99][300/586] lr: 5.000000e-04 eta: 8:03:57 time: 0.471798 data_time: 0.027702 memory: 15239 loss_kpt: 0.000517 acc_pose: 0.898033 loss: 0.000517 2022/09/13 03:56:11 - mmengine - INFO - Epoch(train) [99][350/586] lr: 5.000000e-04 eta: 8:03:36 time: 0.465838 data_time: 0.026957 memory: 15239 loss_kpt: 0.000542 acc_pose: 0.842688 loss: 0.000542 2022/09/13 03:56:34 - mmengine - INFO - Epoch(train) [99][400/586] lr: 5.000000e-04 eta: 8:03:15 time: 0.465754 data_time: 0.025852 memory: 15239 loss_kpt: 0.000538 acc_pose: 0.837850 loss: 0.000538 2022/09/13 03:56:57 - mmengine - INFO - Epoch(train) [99][450/586] lr: 5.000000e-04 eta: 8:02:55 time: 0.470739 data_time: 0.026893 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.772171 loss: 0.000523 2022/09/13 03:57:21 - mmengine - INFO - Epoch(train) [99][500/586] lr: 5.000000e-04 eta: 8:02:34 time: 0.471710 data_time: 0.026647 memory: 15239 loss_kpt: 0.000522 acc_pose: 0.851957 loss: 0.000522 2022/09/13 03:57:44 - mmengine - INFO - Epoch(train) [99][550/586] lr: 5.000000e-04 eta: 8:02:13 time: 0.465614 data_time: 0.026572 memory: 15239 loss_kpt: 0.000540 acc_pose: 0.837631 loss: 0.000540 2022/09/13 03:57:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:58:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 03:58:01 - mmengine - INFO - Saving checkpoint at 99 epochs 2022/09/13 03:58:32 - mmengine - INFO - Epoch(train) [100][50/586] lr: 5.000000e-04 eta: 8:01:18 time: 0.473020 data_time: 0.034063 memory: 15239 loss_kpt: 0.000546 acc_pose: 0.840489 loss: 0.000546 2022/09/13 03:58:55 - mmengine - INFO - Epoch(train) [100][100/586] lr: 5.000000e-04 eta: 8:00:57 time: 0.468129 data_time: 0.026919 memory: 15239 loss_kpt: 0.000532 acc_pose: 0.889784 loss: 0.000532 2022/09/13 03:59:19 - mmengine - INFO - Epoch(train) [100][150/586] lr: 5.000000e-04 eta: 8:00:37 time: 0.479814 data_time: 0.029663 memory: 15239 loss_kpt: 0.000535 acc_pose: 0.758267 loss: 0.000535 2022/09/13 03:59:43 - mmengine - INFO - Epoch(train) [100][200/586] lr: 5.000000e-04 eta: 8:00:16 time: 0.463395 data_time: 0.026850 memory: 15239 loss_kpt: 0.000527 acc_pose: 0.857659 loss: 0.000527 2022/09/13 04:00:06 - mmengine - INFO - Epoch(train) [100][250/586] lr: 5.000000e-04 eta: 7:59:55 time: 0.473437 data_time: 0.026597 memory: 15239 loss_kpt: 0.000524 acc_pose: 0.849051 loss: 0.000524 2022/09/13 04:00:30 - mmengine - INFO - Epoch(train) [100][300/586] lr: 5.000000e-04 eta: 7:59:35 time: 0.471769 data_time: 0.027317 memory: 15239 loss_kpt: 0.000542 acc_pose: 0.854207 loss: 0.000542 2022/09/13 04:00:53 - mmengine - INFO - Epoch(train) [100][350/586] lr: 5.000000e-04 eta: 7:59:14 time: 0.467950 data_time: 0.026478 memory: 15239 loss_kpt: 0.000518 acc_pose: 0.833878 loss: 0.000518 2022/09/13 04:01:17 - mmengine - INFO - Epoch(train) [100][400/586] lr: 5.000000e-04 eta: 7:58:53 time: 0.470761 data_time: 0.026256 memory: 15239 loss_kpt: 0.000531 acc_pose: 0.813595 loss: 0.000531 2022/09/13 04:01:40 - mmengine - INFO - Epoch(train) [100][450/586] lr: 5.000000e-04 eta: 7:58:32 time: 0.467166 data_time: 0.026219 memory: 15239 loss_kpt: 0.000539 acc_pose: 0.798400 loss: 0.000539 2022/09/13 04:02:03 - mmengine - INFO - Epoch(train) [100][500/586] lr: 5.000000e-04 eta: 7:58:11 time: 0.466682 data_time: 0.026003 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.866649 loss: 0.000536 2022/09/13 04:02:27 - mmengine - INFO - Epoch(train) [100][550/586] lr: 5.000000e-04 eta: 7:57:50 time: 0.469595 data_time: 0.026902 memory: 15239 loss_kpt: 0.000534 acc_pose: 0.860293 loss: 0.000534 2022/09/13 04:02:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:02:44 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/09/13 04:03:03 - mmengine - INFO - Epoch(val) [100][50/407] eta: 0:01:21 time: 0.229417 data_time: 0.016079 memory: 15239 2022/09/13 04:03:14 - mmengine - INFO - Epoch(val) [100][100/407] eta: 0:01:07 time: 0.219323 data_time: 0.008231 memory: 2064 2022/09/13 04:03:25 - mmengine - INFO - Epoch(val) [100][150/407] eta: 0:00:56 time: 0.218497 data_time: 0.008576 memory: 2064 2022/09/13 04:03:36 - mmengine - INFO - Epoch(val) [100][200/407] eta: 0:00:46 time: 0.223774 data_time: 0.011640 memory: 2064 2022/09/13 04:03:47 - mmengine - INFO - Epoch(val) [100][250/407] eta: 0:00:34 time: 0.218123 data_time: 0.008225 memory: 2064 2022/09/13 04:03:58 - mmengine - INFO - Epoch(val) [100][300/407] eta: 0:00:23 time: 0.221416 data_time: 0.008454 memory: 2064 2022/09/13 04:04:09 - mmengine - INFO - Epoch(val) [100][350/407] eta: 0:00:12 time: 0.221959 data_time: 0.009730 memory: 2064 2022/09/13 04:04:20 - mmengine - INFO - Epoch(val) [100][400/407] eta: 0:00:01 time: 0.218136 data_time: 0.008481 memory: 2064 2022/09/13 04:04:57 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 04:05:11 - mmengine - INFO - Epoch(val) [100][407/407] coco/AP: 0.760725 coco/AP .5: 0.905625 coco/AP .75: 0.824536 coco/AP (M): 0.718865 coco/AP (L): 0.834769 coco/AR: 0.809934 coco/AR .5: 0.943010 coco/AR .75: 0.866656 coco/AR (M): 0.764682 coco/AR (L): 0.875845 2022/09/13 04:05:11 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_384_v1/best_coco/AP_epoch_80.pth is removed 2022/09/13 04:05:15 - mmengine - INFO - The best checkpoint with 0.7607 coco/AP at 100 epoch is saved to best_coco/AP_epoch_100.pth. 2022/09/13 04:05:38 - mmengine - INFO - Epoch(train) [101][50/586] lr: 5.000000e-04 eta: 7:56:56 time: 0.470746 data_time: 0.035567 memory: 15239 loss_kpt: 0.000542 acc_pose: 0.846517 loss: 0.000542 2022/09/13 04:06:02 - mmengine - INFO - Epoch(train) [101][100/586] lr: 5.000000e-04 eta: 7:56:35 time: 0.472655 data_time: 0.031748 memory: 15239 loss_kpt: 0.000539 acc_pose: 0.813805 loss: 0.000539 2022/09/13 04:06:26 - mmengine - INFO - Epoch(train) [101][150/586] lr: 5.000000e-04 eta: 7:56:14 time: 0.469824 data_time: 0.031910 memory: 15239 loss_kpt: 0.000566 acc_pose: 0.814472 loss: 0.000566 2022/09/13 04:06:49 - mmengine - INFO - Epoch(train) [101][200/586] lr: 5.000000e-04 eta: 7:55:53 time: 0.469233 data_time: 0.032617 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.884883 loss: 0.000526 2022/09/13 04:07:13 - mmengine - INFO - Epoch(train) [101][250/586] lr: 5.000000e-04 eta: 7:55:33 time: 0.472963 data_time: 0.035220 memory: 15239 loss_kpt: 0.000540 acc_pose: 0.877873 loss: 0.000540 2022/09/13 04:07:36 - mmengine - INFO - Epoch(train) [101][300/586] lr: 5.000000e-04 eta: 7:55:12 time: 0.465518 data_time: 0.032943 memory: 15239 loss_kpt: 0.000527 acc_pose: 0.767243 loss: 0.000527 2022/09/13 04:08:00 - mmengine - INFO - Epoch(train) [101][350/586] lr: 5.000000e-04 eta: 7:54:51 time: 0.473294 data_time: 0.025861 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.836067 loss: 0.000537 2022/09/13 04:08:23 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:08:23 - mmengine - INFO - Epoch(train) [101][400/586] lr: 5.000000e-04 eta: 7:54:30 time: 0.469589 data_time: 0.029780 memory: 15239 loss_kpt: 0.000541 acc_pose: 0.834632 loss: 0.000541 2022/09/13 04:08:46 - mmengine - INFO - Epoch(train) [101][450/586] lr: 5.000000e-04 eta: 7:54:09 time: 0.466057 data_time: 0.026089 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.850180 loss: 0.000523 2022/09/13 04:09:10 - mmengine - INFO - Epoch(train) [101][500/586] lr: 5.000000e-04 eta: 7:53:49 time: 0.477975 data_time: 0.028207 memory: 15239 loss_kpt: 0.000510 acc_pose: 0.867625 loss: 0.000510 2022/09/13 04:09:34 - mmengine - INFO - Epoch(train) [101][550/586] lr: 5.000000e-04 eta: 7:53:28 time: 0.470734 data_time: 0.029986 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.828075 loss: 0.000536 2022/09/13 04:09:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:09:50 - mmengine - INFO - Saving checkpoint at 101 epochs 2022/09/13 04:10:21 - mmengine - INFO - Epoch(train) [102][50/586] lr: 5.000000e-04 eta: 7:52:35 time: 0.483595 data_time: 0.031958 memory: 15239 loss_kpt: 0.000522 acc_pose: 0.864420 loss: 0.000522 2022/09/13 04:10:45 - mmengine - INFO - Epoch(train) [102][100/586] lr: 5.000000e-04 eta: 7:52:14 time: 0.478672 data_time: 0.025945 memory: 15239 loss_kpt: 0.000539 acc_pose: 0.912770 loss: 0.000539 2022/09/13 04:11:09 - mmengine - INFO - Epoch(train) [102][150/586] lr: 5.000000e-04 eta: 7:51:53 time: 0.468372 data_time: 0.026133 memory: 15239 loss_kpt: 0.000542 acc_pose: 0.834949 loss: 0.000542 2022/09/13 04:11:32 - mmengine - INFO - Epoch(train) [102][200/586] lr: 5.000000e-04 eta: 7:51:33 time: 0.471374 data_time: 0.031864 memory: 15239 loss_kpt: 0.000541 acc_pose: 0.873850 loss: 0.000541 2022/09/13 04:11:56 - mmengine - INFO - Epoch(train) [102][250/586] lr: 5.000000e-04 eta: 7:51:12 time: 0.468538 data_time: 0.026120 memory: 15239 loss_kpt: 0.000540 acc_pose: 0.865010 loss: 0.000540 2022/09/13 04:12:19 - mmengine - INFO - Epoch(train) [102][300/586] lr: 5.000000e-04 eta: 7:50:51 time: 0.471116 data_time: 0.030393 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.876369 loss: 0.000523 2022/09/13 04:12:43 - mmengine - INFO - Epoch(train) [102][350/586] lr: 5.000000e-04 eta: 7:50:30 time: 0.468427 data_time: 0.027032 memory: 15239 loss_kpt: 0.000541 acc_pose: 0.804028 loss: 0.000541 2022/09/13 04:13:06 - mmengine - INFO - Epoch(train) [102][400/586] lr: 5.000000e-04 eta: 7:50:09 time: 0.468756 data_time: 0.027534 memory: 15239 loss_kpt: 0.000533 acc_pose: 0.850946 loss: 0.000533 2022/09/13 04:13:30 - mmengine - INFO - Epoch(train) [102][450/586] lr: 5.000000e-04 eta: 7:49:48 time: 0.466897 data_time: 0.026649 memory: 15239 loss_kpt: 0.000525 acc_pose: 0.808899 loss: 0.000525 2022/09/13 04:13:53 - mmengine - INFO - Epoch(train) [102][500/586] lr: 5.000000e-04 eta: 7:49:27 time: 0.469921 data_time: 0.028185 memory: 15239 loss_kpt: 0.000528 acc_pose: 0.882371 loss: 0.000528 2022/09/13 04:14:16 - mmengine - INFO - Epoch(train) [102][550/586] lr: 5.000000e-04 eta: 7:49:06 time: 0.467614 data_time: 0.027787 memory: 15239 loss_kpt: 0.000531 acc_pose: 0.882307 loss: 0.000531 2022/09/13 04:14:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:14:33 - mmengine - INFO - Saving checkpoint at 102 epochs 2022/09/13 04:15:06 - mmengine - INFO - Epoch(train) [103][50/586] lr: 5.000000e-04 eta: 7:48:13 time: 0.480902 data_time: 0.035954 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.871434 loss: 0.000529 2022/09/13 04:15:29 - mmengine - INFO - Epoch(train) [103][100/586] lr: 5.000000e-04 eta: 7:47:52 time: 0.470112 data_time: 0.026240 memory: 15239 loss_kpt: 0.000517 acc_pose: 0.807170 loss: 0.000517 2022/09/13 04:15:53 - mmengine - INFO - Epoch(train) [103][150/586] lr: 5.000000e-04 eta: 7:47:31 time: 0.469063 data_time: 0.026936 memory: 15239 loss_kpt: 0.000528 acc_pose: 0.879529 loss: 0.000528 2022/09/13 04:16:16 - mmengine - INFO - Epoch(train) [103][200/586] lr: 5.000000e-04 eta: 7:47:10 time: 0.471224 data_time: 0.026408 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.864300 loss: 0.000537 2022/09/13 04:16:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:16:40 - mmengine - INFO - Epoch(train) [103][250/586] lr: 5.000000e-04 eta: 7:46:49 time: 0.467751 data_time: 0.026085 memory: 15239 loss_kpt: 0.000516 acc_pose: 0.864670 loss: 0.000516 2022/09/13 04:17:03 - mmengine - INFO - Epoch(train) [103][300/586] lr: 5.000000e-04 eta: 7:46:28 time: 0.471020 data_time: 0.026895 memory: 15239 loss_kpt: 0.000531 acc_pose: 0.825166 loss: 0.000531 2022/09/13 04:17:27 - mmengine - INFO - Epoch(train) [103][350/586] lr: 5.000000e-04 eta: 7:46:08 time: 0.468292 data_time: 0.026597 memory: 15239 loss_kpt: 0.000532 acc_pose: 0.821348 loss: 0.000532 2022/09/13 04:17:50 - mmengine - INFO - Epoch(train) [103][400/586] lr: 5.000000e-04 eta: 7:45:47 time: 0.468510 data_time: 0.027393 memory: 15239 loss_kpt: 0.000525 acc_pose: 0.874628 loss: 0.000525 2022/09/13 04:18:14 - mmengine - INFO - Epoch(train) [103][450/586] lr: 5.000000e-04 eta: 7:45:26 time: 0.471570 data_time: 0.028845 memory: 15239 loss_kpt: 0.000533 acc_pose: 0.791334 loss: 0.000533 2022/09/13 04:18:37 - mmengine - INFO - Epoch(train) [103][500/586] lr: 5.000000e-04 eta: 7:45:05 time: 0.464176 data_time: 0.026576 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.833450 loss: 0.000537 2022/09/13 04:19:00 - mmengine - INFO - Epoch(train) [103][550/586] lr: 5.000000e-04 eta: 7:44:44 time: 0.470182 data_time: 0.030329 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.813124 loss: 0.000513 2022/09/13 04:19:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:19:17 - mmengine - INFO - Saving checkpoint at 103 epochs 2022/09/13 04:19:49 - mmengine - INFO - Epoch(train) [104][50/586] lr: 5.000000e-04 eta: 7:43:51 time: 0.480158 data_time: 0.037033 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.873720 loss: 0.000526 2022/09/13 04:20:12 - mmengine - INFO - Epoch(train) [104][100/586] lr: 5.000000e-04 eta: 7:43:30 time: 0.471894 data_time: 0.029306 memory: 15239 loss_kpt: 0.000528 acc_pose: 0.845118 loss: 0.000528 2022/09/13 04:20:36 - mmengine - INFO - Epoch(train) [104][150/586] lr: 5.000000e-04 eta: 7:43:09 time: 0.471825 data_time: 0.026096 memory: 15239 loss_kpt: 0.000548 acc_pose: 0.928973 loss: 0.000548 2022/09/13 04:20:59 - mmengine - INFO - Epoch(train) [104][200/586] lr: 5.000000e-04 eta: 7:42:48 time: 0.470781 data_time: 0.026235 memory: 15239 loss_kpt: 0.000542 acc_pose: 0.844233 loss: 0.000542 2022/09/13 04:21:22 - mmengine - INFO - Epoch(train) [104][250/586] lr: 5.000000e-04 eta: 7:42:27 time: 0.463421 data_time: 0.025691 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.834812 loss: 0.000523 2022/09/13 04:21:46 - mmengine - INFO - Epoch(train) [104][300/586] lr: 5.000000e-04 eta: 7:42:06 time: 0.472569 data_time: 0.027097 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.851068 loss: 0.000537 2022/09/13 04:22:10 - mmengine - INFO - Epoch(train) [104][350/586] lr: 5.000000e-04 eta: 7:41:45 time: 0.471170 data_time: 0.025786 memory: 15239 loss_kpt: 0.000532 acc_pose: 0.838911 loss: 0.000532 2022/09/13 04:22:33 - mmengine - INFO - Epoch(train) [104][400/586] lr: 5.000000e-04 eta: 7:41:24 time: 0.465200 data_time: 0.027917 memory: 15239 loss_kpt: 0.000535 acc_pose: 0.872415 loss: 0.000535 2022/09/13 04:22:57 - mmengine - INFO - Epoch(train) [104][450/586] lr: 5.000000e-04 eta: 7:41:04 time: 0.474783 data_time: 0.030424 memory: 15239 loss_kpt: 0.000535 acc_pose: 0.833712 loss: 0.000535 2022/09/13 04:23:20 - mmengine - INFO - Epoch(train) [104][500/586] lr: 5.000000e-04 eta: 7:40:43 time: 0.465494 data_time: 0.027255 memory: 15239 loss_kpt: 0.000527 acc_pose: 0.789239 loss: 0.000527 2022/09/13 04:23:43 - mmengine - INFO - Epoch(train) [104][550/586] lr: 5.000000e-04 eta: 7:40:22 time: 0.468628 data_time: 0.027386 memory: 15239 loss_kpt: 0.000525 acc_pose: 0.822330 loss: 0.000525 2022/09/13 04:24:00 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:24:00 - mmengine - INFO - Saving checkpoint at 104 epochs 2022/09/13 04:24:31 - mmengine - INFO - Epoch(train) [105][50/586] lr: 5.000000e-04 eta: 7:39:29 time: 0.488389 data_time: 0.030576 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.890910 loss: 0.000526 2022/09/13 04:24:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:24:55 - mmengine - INFO - Epoch(train) [105][100/586] lr: 5.000000e-04 eta: 7:39:08 time: 0.471977 data_time: 0.026241 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.831735 loss: 0.000523 2022/09/13 04:25:19 - mmengine - INFO - Epoch(train) [105][150/586] lr: 5.000000e-04 eta: 7:38:48 time: 0.478444 data_time: 0.025815 memory: 15239 loss_kpt: 0.000518 acc_pose: 0.797530 loss: 0.000518 2022/09/13 04:25:42 - mmengine - INFO - Epoch(train) [105][200/586] lr: 5.000000e-04 eta: 7:38:27 time: 0.466316 data_time: 0.025322 memory: 15239 loss_kpt: 0.000550 acc_pose: 0.872473 loss: 0.000550 2022/09/13 04:26:06 - mmengine - INFO - Epoch(train) [105][250/586] lr: 5.000000e-04 eta: 7:38:06 time: 0.472380 data_time: 0.030733 memory: 15239 loss_kpt: 0.000533 acc_pose: 0.854213 loss: 0.000533 2022/09/13 04:26:30 - mmengine - INFO - Epoch(train) [105][300/586] lr: 5.000000e-04 eta: 7:37:45 time: 0.471486 data_time: 0.026394 memory: 15239 loss_kpt: 0.000538 acc_pose: 0.911303 loss: 0.000538 2022/09/13 04:26:53 - mmengine - INFO - Epoch(train) [105][350/586] lr: 5.000000e-04 eta: 7:37:24 time: 0.463566 data_time: 0.026131 memory: 15239 loss_kpt: 0.000519 acc_pose: 0.854098 loss: 0.000519 2022/09/13 04:27:16 - mmengine - INFO - Epoch(train) [105][400/586] lr: 5.000000e-04 eta: 7:37:03 time: 0.468313 data_time: 0.026839 memory: 15239 loss_kpt: 0.000530 acc_pose: 0.905971 loss: 0.000530 2022/09/13 04:27:40 - mmengine - INFO - Epoch(train) [105][450/586] lr: 5.000000e-04 eta: 7:36:42 time: 0.472979 data_time: 0.029377 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.831337 loss: 0.000526 2022/09/13 04:28:03 - mmengine - INFO - Epoch(train) [105][500/586] lr: 5.000000e-04 eta: 7:36:21 time: 0.469307 data_time: 0.025540 memory: 15239 loss_kpt: 0.000539 acc_pose: 0.862154 loss: 0.000539 2022/09/13 04:28:27 - mmengine - INFO - Epoch(train) [105][550/586] lr: 5.000000e-04 eta: 7:36:00 time: 0.465687 data_time: 0.026312 memory: 15239 loss_kpt: 0.000546 acc_pose: 0.801592 loss: 0.000546 2022/09/13 04:28:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:28:44 - mmengine - INFO - Saving checkpoint at 105 epochs 2022/09/13 04:29:14 - mmengine - INFO - Epoch(train) [106][50/586] lr: 5.000000e-04 eta: 7:35:08 time: 0.478172 data_time: 0.031191 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.823996 loss: 0.000515 2022/09/13 04:29:38 - mmengine - INFO - Epoch(train) [106][100/586] lr: 5.000000e-04 eta: 7:34:47 time: 0.467236 data_time: 0.025416 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.872441 loss: 0.000537 2022/09/13 04:30:01 - mmengine - INFO - Epoch(train) [106][150/586] lr: 5.000000e-04 eta: 7:34:26 time: 0.467550 data_time: 0.025370 memory: 15239 loss_kpt: 0.000535 acc_pose: 0.835142 loss: 0.000535 2022/09/13 04:30:25 - mmengine - INFO - Epoch(train) [106][200/586] lr: 5.000000e-04 eta: 7:34:05 time: 0.470286 data_time: 0.032183 memory: 15239 loss_kpt: 0.000535 acc_pose: 0.874222 loss: 0.000535 2022/09/13 04:30:48 - mmengine - INFO - Epoch(train) [106][250/586] lr: 5.000000e-04 eta: 7:33:43 time: 0.465242 data_time: 0.026288 memory: 15239 loss_kpt: 0.000535 acc_pose: 0.841257 loss: 0.000535 2022/09/13 04:31:12 - mmengine - INFO - Epoch(train) [106][300/586] lr: 5.000000e-04 eta: 7:33:23 time: 0.470569 data_time: 0.026064 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.839411 loss: 0.000536 2022/09/13 04:31:35 - mmengine - INFO - Epoch(train) [106][350/586] lr: 5.000000e-04 eta: 7:33:02 time: 0.469499 data_time: 0.026762 memory: 15239 loss_kpt: 0.000549 acc_pose: 0.782471 loss: 0.000549 2022/09/13 04:31:58 - mmengine - INFO - Epoch(train) [106][400/586] lr: 5.000000e-04 eta: 7:32:40 time: 0.466334 data_time: 0.027134 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.796606 loss: 0.000536 2022/09/13 04:32:22 - mmengine - INFO - Epoch(train) [106][450/586] lr: 5.000000e-04 eta: 7:32:20 time: 0.474875 data_time: 0.026828 memory: 15239 loss_kpt: 0.000549 acc_pose: 0.880967 loss: 0.000549 2022/09/13 04:32:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:32:46 - mmengine - INFO - Epoch(train) [106][500/586] lr: 5.000000e-04 eta: 7:31:59 time: 0.471844 data_time: 0.030234 memory: 15239 loss_kpt: 0.000516 acc_pose: 0.845567 loss: 0.000516 2022/09/13 04:33:09 - mmengine - INFO - Epoch(train) [106][550/586] lr: 5.000000e-04 eta: 7:31:38 time: 0.463726 data_time: 0.026791 memory: 15239 loss_kpt: 0.000528 acc_pose: 0.854101 loss: 0.000528 2022/09/13 04:33:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:33:26 - mmengine - INFO - Saving checkpoint at 106 epochs 2022/09/13 04:33:57 - mmengine - INFO - Epoch(train) [107][50/586] lr: 5.000000e-04 eta: 7:30:46 time: 0.481883 data_time: 0.033932 memory: 15239 loss_kpt: 0.000524 acc_pose: 0.836707 loss: 0.000524 2022/09/13 04:34:20 - mmengine - INFO - Epoch(train) [107][100/586] lr: 5.000000e-04 eta: 7:30:25 time: 0.468161 data_time: 0.029166 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.888858 loss: 0.000529 2022/09/13 04:34:44 - mmengine - INFO - Epoch(train) [107][150/586] lr: 5.000000e-04 eta: 7:30:04 time: 0.473636 data_time: 0.034248 memory: 15239 loss_kpt: 0.000525 acc_pose: 0.765355 loss: 0.000525 2022/09/13 04:35:07 - mmengine - INFO - Epoch(train) [107][200/586] lr: 5.000000e-04 eta: 7:29:43 time: 0.464775 data_time: 0.031307 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.856196 loss: 0.000526 2022/09/13 04:35:30 - mmengine - INFO - Epoch(train) [107][250/586] lr: 5.000000e-04 eta: 7:29:21 time: 0.465098 data_time: 0.030779 memory: 15239 loss_kpt: 0.000541 acc_pose: 0.861719 loss: 0.000541 2022/09/13 04:35:54 - mmengine - INFO - Epoch(train) [107][300/586] lr: 5.000000e-04 eta: 7:29:01 time: 0.473299 data_time: 0.033627 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.871546 loss: 0.000523 2022/09/13 04:36:18 - mmengine - INFO - Epoch(train) [107][350/586] lr: 5.000000e-04 eta: 7:28:40 time: 0.468687 data_time: 0.031004 memory: 15239 loss_kpt: 0.000521 acc_pose: 0.876892 loss: 0.000521 2022/09/13 04:36:41 - mmengine - INFO - Epoch(train) [107][400/586] lr: 5.000000e-04 eta: 7:28:18 time: 0.463014 data_time: 0.029391 memory: 15239 loss_kpt: 0.000510 acc_pose: 0.755317 loss: 0.000510 2022/09/13 04:37:04 - mmengine - INFO - Epoch(train) [107][450/586] lr: 5.000000e-04 eta: 7:27:57 time: 0.468243 data_time: 0.033695 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.838125 loss: 0.000536 2022/09/13 04:37:27 - mmengine - INFO - Epoch(train) [107][500/586] lr: 5.000000e-04 eta: 7:27:36 time: 0.466169 data_time: 0.035189 memory: 15239 loss_kpt: 0.000530 acc_pose: 0.874218 loss: 0.000530 2022/09/13 04:37:51 - mmengine - INFO - Epoch(train) [107][550/586] lr: 5.000000e-04 eta: 7:27:15 time: 0.466986 data_time: 0.030161 memory: 15239 loss_kpt: 0.000535 acc_pose: 0.845027 loss: 0.000535 2022/09/13 04:38:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:38:08 - mmengine - INFO - Saving checkpoint at 107 epochs 2022/09/13 04:38:38 - mmengine - INFO - Epoch(train) [108][50/586] lr: 5.000000e-04 eta: 7:26:23 time: 0.478599 data_time: 0.031771 memory: 15239 loss_kpt: 0.000533 acc_pose: 0.923293 loss: 0.000533 2022/09/13 04:39:02 - mmengine - INFO - Epoch(train) [108][100/586] lr: 5.000000e-04 eta: 7:26:02 time: 0.468377 data_time: 0.026834 memory: 15239 loss_kpt: 0.000519 acc_pose: 0.908754 loss: 0.000519 2022/09/13 04:39:25 - mmengine - INFO - Epoch(train) [108][150/586] lr: 5.000000e-04 eta: 7:25:41 time: 0.469443 data_time: 0.027661 memory: 15239 loss_kpt: 0.000528 acc_pose: 0.848820 loss: 0.000528 2022/09/13 04:39:49 - mmengine - INFO - Epoch(train) [108][200/586] lr: 5.000000e-04 eta: 7:25:20 time: 0.467294 data_time: 0.026810 memory: 15239 loss_kpt: 0.000525 acc_pose: 0.821345 loss: 0.000525 2022/09/13 04:40:12 - mmengine - INFO - Epoch(train) [108][250/586] lr: 5.000000e-04 eta: 7:24:59 time: 0.464366 data_time: 0.026697 memory: 15239 loss_kpt: 0.000540 acc_pose: 0.832390 loss: 0.000540 2022/09/13 04:40:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:40:36 - mmengine - INFO - Epoch(train) [108][300/586] lr: 5.000000e-04 eta: 7:24:38 time: 0.472029 data_time: 0.028236 memory: 15239 loss_kpt: 0.000517 acc_pose: 0.813723 loss: 0.000517 2022/09/13 04:40:59 - mmengine - INFO - Epoch(train) [108][350/586] lr: 5.000000e-04 eta: 7:24:17 time: 0.471401 data_time: 0.028162 memory: 15239 loss_kpt: 0.000542 acc_pose: 0.846244 loss: 0.000542 2022/09/13 04:41:23 - mmengine - INFO - Epoch(train) [108][400/586] lr: 5.000000e-04 eta: 7:23:56 time: 0.471582 data_time: 0.030324 memory: 15239 loss_kpt: 0.000538 acc_pose: 0.887859 loss: 0.000538 2022/09/13 04:41:46 - mmengine - INFO - Epoch(train) [108][450/586] lr: 5.000000e-04 eta: 7:23:35 time: 0.473092 data_time: 0.026072 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.905247 loss: 0.000529 2022/09/13 04:42:10 - mmengine - INFO - Epoch(train) [108][500/586] lr: 5.000000e-04 eta: 7:23:14 time: 0.465172 data_time: 0.026970 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.807648 loss: 0.000523 2022/09/13 04:42:33 - mmengine - INFO - Epoch(train) [108][550/586] lr: 5.000000e-04 eta: 7:22:53 time: 0.466430 data_time: 0.026196 memory: 15239 loss_kpt: 0.000533 acc_pose: 0.887900 loss: 0.000533 2022/09/13 04:42:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:42:50 - mmengine - INFO - Saving checkpoint at 108 epochs 2022/09/13 04:43:21 - mmengine - INFO - Epoch(train) [109][50/586] lr: 5.000000e-04 eta: 7:22:01 time: 0.477809 data_time: 0.034779 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.824937 loss: 0.000526 2022/09/13 04:43:44 - mmengine - INFO - Epoch(train) [109][100/586] lr: 5.000000e-04 eta: 7:21:40 time: 0.468361 data_time: 0.025087 memory: 15239 loss_kpt: 0.000531 acc_pose: 0.863342 loss: 0.000531 2022/09/13 04:44:08 - mmengine - INFO - Epoch(train) [109][150/586] lr: 5.000000e-04 eta: 7:21:19 time: 0.468961 data_time: 0.025517 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.898767 loss: 0.000503 2022/09/13 04:44:31 - mmengine - INFO - Epoch(train) [109][200/586] lr: 5.000000e-04 eta: 7:20:58 time: 0.470153 data_time: 0.032528 memory: 15239 loss_kpt: 0.000533 acc_pose: 0.827709 loss: 0.000533 2022/09/13 04:44:54 - mmengine - INFO - Epoch(train) [109][250/586] lr: 5.000000e-04 eta: 7:20:37 time: 0.465284 data_time: 0.027183 memory: 15239 loss_kpt: 0.000527 acc_pose: 0.862944 loss: 0.000527 2022/09/13 04:45:18 - mmengine - INFO - Epoch(train) [109][300/586] lr: 5.000000e-04 eta: 7:20:16 time: 0.469562 data_time: 0.025419 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.864203 loss: 0.000526 2022/09/13 04:45:41 - mmengine - INFO - Epoch(train) [109][350/586] lr: 5.000000e-04 eta: 7:19:54 time: 0.465784 data_time: 0.026517 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.911937 loss: 0.000536 2022/09/13 04:46:04 - mmengine - INFO - Epoch(train) [109][400/586] lr: 5.000000e-04 eta: 7:19:33 time: 0.468011 data_time: 0.027529 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.830590 loss: 0.000529 2022/09/13 04:46:28 - mmengine - INFO - Epoch(train) [109][450/586] lr: 5.000000e-04 eta: 7:19:13 time: 0.476848 data_time: 0.026488 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.856254 loss: 0.000504 2022/09/13 04:46:52 - mmengine - INFO - Epoch(train) [109][500/586] lr: 5.000000e-04 eta: 7:18:51 time: 0.466767 data_time: 0.027624 memory: 15239 loss_kpt: 0.000524 acc_pose: 0.763025 loss: 0.000524 2022/09/13 04:47:15 - mmengine - INFO - Epoch(train) [109][550/586] lr: 5.000000e-04 eta: 7:18:30 time: 0.465694 data_time: 0.026024 memory: 15239 loss_kpt: 0.000532 acc_pose: 0.865376 loss: 0.000532 2022/09/13 04:47:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:47:32 - mmengine - INFO - Saving checkpoint at 109 epochs 2022/09/13 04:48:06 - mmengine - INFO - Epoch(train) [110][50/586] lr: 5.000000e-04 eta: 7:17:39 time: 0.474100 data_time: 0.033706 memory: 15239 loss_kpt: 0.000527 acc_pose: 0.823239 loss: 0.000527 2022/09/13 04:48:30 - mmengine - INFO - Epoch(train) [110][100/586] lr: 5.000000e-04 eta: 7:17:18 time: 0.471347 data_time: 0.026032 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.835027 loss: 0.000504 2022/09/13 04:48:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:48:54 - mmengine - INFO - Epoch(train) [110][150/586] lr: 5.000000e-04 eta: 7:16:57 time: 0.477106 data_time: 0.025820 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.802954 loss: 0.000523 2022/09/13 04:49:17 - mmengine - INFO - Epoch(train) [110][200/586] lr: 5.000000e-04 eta: 7:16:36 time: 0.462481 data_time: 0.027182 memory: 15239 loss_kpt: 0.000524 acc_pose: 0.901337 loss: 0.000524 2022/09/13 04:49:40 - mmengine - INFO - Epoch(train) [110][250/586] lr: 5.000000e-04 eta: 7:16:15 time: 0.469582 data_time: 0.025913 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.835619 loss: 0.000536 2022/09/13 04:50:04 - mmengine - INFO - Epoch(train) [110][300/586] lr: 5.000000e-04 eta: 7:15:54 time: 0.473842 data_time: 0.032304 memory: 15239 loss_kpt: 0.000517 acc_pose: 0.906893 loss: 0.000517 2022/09/13 04:50:27 - mmengine - INFO - Epoch(train) [110][350/586] lr: 5.000000e-04 eta: 7:15:33 time: 0.467179 data_time: 0.027462 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.873968 loss: 0.000526 2022/09/13 04:50:51 - mmengine - INFO - Epoch(train) [110][400/586] lr: 5.000000e-04 eta: 7:15:12 time: 0.469888 data_time: 0.025436 memory: 15239 loss_kpt: 0.000522 acc_pose: 0.833064 loss: 0.000522 2022/09/13 04:51:15 - mmengine - INFO - Epoch(train) [110][450/586] lr: 5.000000e-04 eta: 7:14:51 time: 0.474136 data_time: 0.027035 memory: 15239 loss_kpt: 0.000521 acc_pose: 0.871460 loss: 0.000521 2022/09/13 04:51:38 - mmengine - INFO - Epoch(train) [110][500/586] lr: 5.000000e-04 eta: 7:14:29 time: 0.464404 data_time: 0.025966 memory: 15239 loss_kpt: 0.000532 acc_pose: 0.898000 loss: 0.000532 2022/09/13 04:52:01 - mmengine - INFO - Epoch(train) [110][550/586] lr: 5.000000e-04 eta: 7:14:08 time: 0.468031 data_time: 0.027538 memory: 15239 loss_kpt: 0.000521 acc_pose: 0.875734 loss: 0.000521 2022/09/13 04:52:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:52:18 - mmengine - INFO - Saving checkpoint at 110 epochs 2022/09/13 04:52:37 - mmengine - INFO - Epoch(val) [110][50/407] eta: 0:01:22 time: 0.230341 data_time: 0.015914 memory: 15239 2022/09/13 04:52:48 - mmengine - INFO - Epoch(val) [110][100/407] eta: 0:01:07 time: 0.220772 data_time: 0.008939 memory: 2064 2022/09/13 04:52:59 - mmengine - INFO - Epoch(val) [110][150/407] eta: 0:00:57 time: 0.223368 data_time: 0.011785 memory: 2064 2022/09/13 04:53:10 - mmengine - INFO - Epoch(val) [110][200/407] eta: 0:00:45 time: 0.219652 data_time: 0.008381 memory: 2064 2022/09/13 04:53:21 - mmengine - INFO - Epoch(val) [110][250/407] eta: 0:00:35 time: 0.225857 data_time: 0.013035 memory: 2064 2022/09/13 04:53:32 - mmengine - INFO - Epoch(val) [110][300/407] eta: 0:00:23 time: 0.217805 data_time: 0.008447 memory: 2064 2022/09/13 04:53:43 - mmengine - INFO - Epoch(val) [110][350/407] eta: 0:00:12 time: 0.223961 data_time: 0.013806 memory: 2064 2022/09/13 04:53:54 - mmengine - INFO - Epoch(val) [110][400/407] eta: 0:00:01 time: 0.217180 data_time: 0.008161 memory: 2064 2022/09/13 04:54:31 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 04:54:45 - mmengine - INFO - Epoch(val) [110][407/407] coco/AP: 0.761362 coco/AP .5: 0.906763 coco/AP .75: 0.822689 coco/AP (M): 0.719375 coco/AP (L): 0.836441 coco/AR: 0.810312 coco/AR .5: 0.943640 coco/AR .75: 0.864452 coco/AR (M): 0.763152 coco/AR (L): 0.878670 2022/09/13 04:54:45 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_384_v1/best_coco/AP_epoch_100.pth is removed 2022/09/13 04:54:49 - mmengine - INFO - The best checkpoint with 0.7614 coco/AP at 110 epoch is saved to best_coco/AP_epoch_110.pth. 2022/09/13 04:55:13 - mmengine - INFO - Epoch(train) [111][50/586] lr: 5.000000e-04 eta: 7:13:17 time: 0.475527 data_time: 0.034352 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.801217 loss: 0.000523 2022/09/13 04:55:37 - mmengine - INFO - Epoch(train) [111][100/586] lr: 5.000000e-04 eta: 7:12:56 time: 0.479926 data_time: 0.026519 memory: 15239 loss_kpt: 0.000531 acc_pose: 0.843798 loss: 0.000531 2022/09/13 04:56:01 - mmengine - INFO - Epoch(train) [111][150/586] lr: 5.000000e-04 eta: 7:12:36 time: 0.474258 data_time: 0.026014 memory: 15239 loss_kpt: 0.000519 acc_pose: 0.870118 loss: 0.000519 2022/09/13 04:56:24 - mmengine - INFO - Epoch(train) [111][200/586] lr: 5.000000e-04 eta: 7:12:14 time: 0.461932 data_time: 0.026195 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.852209 loss: 0.000526 2022/09/13 04:56:48 - mmengine - INFO - Epoch(train) [111][250/586] lr: 5.000000e-04 eta: 7:11:53 time: 0.471089 data_time: 0.025386 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.889311 loss: 0.000513 2022/09/13 04:57:11 - mmengine - INFO - Epoch(train) [111][300/586] lr: 5.000000e-04 eta: 7:11:32 time: 0.465673 data_time: 0.026679 memory: 15239 loss_kpt: 0.000521 acc_pose: 0.856645 loss: 0.000521 2022/09/13 04:57:34 - mmengine - INFO - Epoch(train) [111][350/586] lr: 5.000000e-04 eta: 7:11:11 time: 0.469231 data_time: 0.026737 memory: 15239 loss_kpt: 0.000534 acc_pose: 0.863541 loss: 0.000534 2022/09/13 04:57:58 - mmengine - INFO - Epoch(train) [111][400/586] lr: 5.000000e-04 eta: 7:10:50 time: 0.468366 data_time: 0.026129 memory: 15239 loss_kpt: 0.000524 acc_pose: 0.850515 loss: 0.000524 2022/09/13 04:58:21 - mmengine - INFO - Epoch(train) [111][450/586] lr: 5.000000e-04 eta: 7:10:29 time: 0.474120 data_time: 0.026148 memory: 15239 loss_kpt: 0.000535 acc_pose: 0.861031 loss: 0.000535 2022/09/13 04:58:45 - mmengine - INFO - Epoch(train) [111][500/586] lr: 5.000000e-04 eta: 7:10:08 time: 0.468596 data_time: 0.026033 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.876741 loss: 0.000523 2022/09/13 04:59:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:59:09 - mmengine - INFO - Epoch(train) [111][550/586] lr: 5.000000e-04 eta: 7:09:47 time: 0.472618 data_time: 0.026823 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.841190 loss: 0.000529 2022/09/13 04:59:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 04:59:26 - mmengine - INFO - Saving checkpoint at 111 epochs 2022/09/13 04:59:56 - mmengine - INFO - Epoch(train) [112][50/586] lr: 5.000000e-04 eta: 7:08:56 time: 0.474552 data_time: 0.031221 memory: 15239 loss_kpt: 0.000530 acc_pose: 0.855425 loss: 0.000530 2022/09/13 05:00:20 - mmengine - INFO - Epoch(train) [112][100/586] lr: 5.000000e-04 eta: 7:08:35 time: 0.473310 data_time: 0.028867 memory: 15239 loss_kpt: 0.000525 acc_pose: 0.891677 loss: 0.000525 2022/09/13 05:00:43 - mmengine - INFO - Epoch(train) [112][150/586] lr: 5.000000e-04 eta: 7:08:14 time: 0.469067 data_time: 0.026426 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.831569 loss: 0.000523 2022/09/13 05:01:07 - mmengine - INFO - Epoch(train) [112][200/586] lr: 5.000000e-04 eta: 7:07:53 time: 0.467522 data_time: 0.027090 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.877183 loss: 0.000512 2022/09/13 05:01:30 - mmengine - INFO - Epoch(train) [112][250/586] lr: 5.000000e-04 eta: 7:07:31 time: 0.462663 data_time: 0.026458 memory: 15239 loss_kpt: 0.000546 acc_pose: 0.852830 loss: 0.000546 2022/09/13 05:01:54 - mmengine - INFO - Epoch(train) [112][300/586] lr: 5.000000e-04 eta: 7:07:10 time: 0.474444 data_time: 0.030696 memory: 15239 loss_kpt: 0.000510 acc_pose: 0.895690 loss: 0.000510 2022/09/13 05:02:17 - mmengine - INFO - Epoch(train) [112][350/586] lr: 5.000000e-04 eta: 7:06:49 time: 0.468675 data_time: 0.028699 memory: 15239 loss_kpt: 0.000520 acc_pose: 0.866843 loss: 0.000520 2022/09/13 05:02:41 - mmengine - INFO - Epoch(train) [112][400/586] lr: 5.000000e-04 eta: 7:06:28 time: 0.469254 data_time: 0.027249 memory: 15239 loss_kpt: 0.000528 acc_pose: 0.829281 loss: 0.000528 2022/09/13 05:03:04 - mmengine - INFO - Epoch(train) [112][450/586] lr: 5.000000e-04 eta: 7:06:07 time: 0.477727 data_time: 0.026886 memory: 15239 loss_kpt: 0.000518 acc_pose: 0.915910 loss: 0.000518 2022/09/13 05:03:28 - mmengine - INFO - Epoch(train) [112][500/586] lr: 5.000000e-04 eta: 7:05:46 time: 0.469795 data_time: 0.030356 memory: 15239 loss_kpt: 0.000525 acc_pose: 0.871080 loss: 0.000525 2022/09/13 05:03:52 - mmengine - INFO - Epoch(train) [112][550/586] lr: 5.000000e-04 eta: 7:05:25 time: 0.475057 data_time: 0.027356 memory: 15239 loss_kpt: 0.000525 acc_pose: 0.810061 loss: 0.000525 2022/09/13 05:04:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:04:08 - mmengine - INFO - Saving checkpoint at 112 epochs 2022/09/13 05:04:41 - mmengine - INFO - Epoch(train) [113][50/586] lr: 5.000000e-04 eta: 7:04:35 time: 0.480940 data_time: 0.037489 memory: 15239 loss_kpt: 0.000530 acc_pose: 0.856039 loss: 0.000530 2022/09/13 05:05:05 - mmengine - INFO - Epoch(train) [113][100/586] lr: 5.000000e-04 eta: 7:04:14 time: 0.475607 data_time: 0.027812 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.873472 loss: 0.000537 2022/09/13 05:05:28 - mmengine - INFO - Epoch(train) [113][150/586] lr: 5.000000e-04 eta: 7:03:53 time: 0.468610 data_time: 0.026562 memory: 15239 loss_kpt: 0.000527 acc_pose: 0.871661 loss: 0.000527 2022/09/13 05:05:52 - mmengine - INFO - Epoch(train) [113][200/586] lr: 5.000000e-04 eta: 7:03:32 time: 0.469939 data_time: 0.026499 memory: 15239 loss_kpt: 0.000516 acc_pose: 0.892347 loss: 0.000516 2022/09/13 05:06:16 - mmengine - INFO - Epoch(train) [113][250/586] lr: 5.000000e-04 eta: 7:03:11 time: 0.481667 data_time: 0.032028 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.894108 loss: 0.000500 2022/09/13 05:06:39 - mmengine - INFO - Epoch(train) [113][300/586] lr: 5.000000e-04 eta: 7:02:50 time: 0.468622 data_time: 0.026985 memory: 15239 loss_kpt: 0.000516 acc_pose: 0.892978 loss: 0.000516 2022/09/13 05:07:03 - mmengine - INFO - Epoch(train) [113][350/586] lr: 5.000000e-04 eta: 7:02:29 time: 0.467890 data_time: 0.027160 memory: 15239 loss_kpt: 0.000538 acc_pose: 0.780306 loss: 0.000538 2022/09/13 05:07:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:07:26 - mmengine - INFO - Epoch(train) [113][400/586] lr: 5.000000e-04 eta: 7:02:08 time: 0.470832 data_time: 0.029763 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.801816 loss: 0.000512 2022/09/13 05:07:50 - mmengine - INFO - Epoch(train) [113][450/586] lr: 5.000000e-04 eta: 7:01:47 time: 0.468189 data_time: 0.026219 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.840227 loss: 0.000523 2022/09/13 05:08:13 - mmengine - INFO - Epoch(train) [113][500/586] lr: 5.000000e-04 eta: 7:01:26 time: 0.469894 data_time: 0.026765 memory: 15239 loss_kpt: 0.000531 acc_pose: 0.856553 loss: 0.000531 2022/09/13 05:08:37 - mmengine - INFO - Epoch(train) [113][550/586] lr: 5.000000e-04 eta: 7:01:05 time: 0.476679 data_time: 0.030208 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.841321 loss: 0.000503 2022/09/13 05:08:54 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:08:54 - mmengine - INFO - Saving checkpoint at 113 epochs 2022/09/13 05:09:24 - mmengine - INFO - Epoch(train) [114][50/586] lr: 5.000000e-04 eta: 7:00:14 time: 0.476218 data_time: 0.035284 memory: 15239 loss_kpt: 0.000518 acc_pose: 0.824963 loss: 0.000518 2022/09/13 05:09:48 - mmengine - INFO - Epoch(train) [114][100/586] lr: 5.000000e-04 eta: 6:59:54 time: 0.477247 data_time: 0.029597 memory: 15239 loss_kpt: 0.000524 acc_pose: 0.812180 loss: 0.000524 2022/09/13 05:10:11 - mmengine - INFO - Epoch(train) [114][150/586] lr: 5.000000e-04 eta: 6:59:32 time: 0.462954 data_time: 0.026937 memory: 15239 loss_kpt: 0.000527 acc_pose: 0.857910 loss: 0.000527 2022/09/13 05:10:35 - mmengine - INFO - Epoch(train) [114][200/586] lr: 5.000000e-04 eta: 6:59:11 time: 0.469300 data_time: 0.029313 memory: 15239 loss_kpt: 0.000528 acc_pose: 0.869910 loss: 0.000528 2022/09/13 05:10:58 - mmengine - INFO - Epoch(train) [114][250/586] lr: 5.000000e-04 eta: 6:58:50 time: 0.468056 data_time: 0.026628 memory: 15239 loss_kpt: 0.000516 acc_pose: 0.789958 loss: 0.000516 2022/09/13 05:11:22 - mmengine - INFO - Epoch(train) [114][300/586] lr: 5.000000e-04 eta: 6:58:29 time: 0.467484 data_time: 0.027747 memory: 15239 loss_kpt: 0.000511 acc_pose: 0.867153 loss: 0.000511 2022/09/13 05:11:46 - mmengine - INFO - Epoch(train) [114][350/586] lr: 5.000000e-04 eta: 6:58:08 time: 0.478592 data_time: 0.030740 memory: 15239 loss_kpt: 0.000525 acc_pose: 0.866970 loss: 0.000525 2022/09/13 05:12:09 - mmengine - INFO - Epoch(train) [114][400/586] lr: 5.000000e-04 eta: 6:57:47 time: 0.468874 data_time: 0.028234 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.901751 loss: 0.000529 2022/09/13 05:12:32 - mmengine - INFO - Epoch(train) [114][450/586] lr: 5.000000e-04 eta: 6:57:26 time: 0.468250 data_time: 0.027706 memory: 15239 loss_kpt: 0.000524 acc_pose: 0.849979 loss: 0.000524 2022/09/13 05:12:56 - mmengine - INFO - Epoch(train) [114][500/586] lr: 5.000000e-04 eta: 6:57:04 time: 0.467452 data_time: 0.029349 memory: 15239 loss_kpt: 0.000530 acc_pose: 0.845526 loss: 0.000530 2022/09/13 05:13:20 - mmengine - INFO - Epoch(train) [114][550/586] lr: 5.000000e-04 eta: 6:56:43 time: 0.475483 data_time: 0.026548 memory: 15239 loss_kpt: 0.000553 acc_pose: 0.850980 loss: 0.000553 2022/09/13 05:13:36 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:13:36 - mmengine - INFO - Saving checkpoint at 114 epochs 2022/09/13 05:14:13 - mmengine - INFO - Epoch(train) [115][50/586] lr: 5.000000e-04 eta: 6:55:53 time: 0.475241 data_time: 0.032566 memory: 15239 loss_kpt: 0.000506 acc_pose: 0.872524 loss: 0.000506 2022/09/13 05:14:37 - mmengine - INFO - Epoch(train) [115][100/586] lr: 5.000000e-04 eta: 6:55:32 time: 0.475473 data_time: 0.027650 memory: 15239 loss_kpt: 0.000507 acc_pose: 0.910339 loss: 0.000507 2022/09/13 05:15:01 - mmengine - INFO - Epoch(train) [115][150/586] lr: 5.000000e-04 eta: 6:55:11 time: 0.473831 data_time: 0.030185 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.849958 loss: 0.000515 2022/09/13 05:15:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:15:24 - mmengine - INFO - Epoch(train) [115][200/586] lr: 5.000000e-04 eta: 6:54:50 time: 0.465112 data_time: 0.026486 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.848934 loss: 0.000526 2022/09/13 05:15:47 - mmengine - INFO - Epoch(train) [115][250/586] lr: 5.000000e-04 eta: 6:54:29 time: 0.466604 data_time: 0.025677 memory: 15239 loss_kpt: 0.000527 acc_pose: 0.801859 loss: 0.000527 2022/09/13 05:16:11 - mmengine - INFO - Epoch(train) [115][300/586] lr: 5.000000e-04 eta: 6:54:08 time: 0.471667 data_time: 0.029990 memory: 15239 loss_kpt: 0.000548 acc_pose: 0.798950 loss: 0.000548 2022/09/13 05:16:34 - mmengine - INFO - Epoch(train) [115][350/586] lr: 5.000000e-04 eta: 6:53:46 time: 0.465255 data_time: 0.026651 memory: 15239 loss_kpt: 0.000533 acc_pose: 0.829356 loss: 0.000533 2022/09/13 05:16:58 - mmengine - INFO - Epoch(train) [115][400/586] lr: 5.000000e-04 eta: 6:53:25 time: 0.470416 data_time: 0.025618 memory: 15239 loss_kpt: 0.000524 acc_pose: 0.844159 loss: 0.000524 2022/09/13 05:17:21 - mmengine - INFO - Epoch(train) [115][450/586] lr: 5.000000e-04 eta: 6:53:04 time: 0.472395 data_time: 0.025549 memory: 15239 loss_kpt: 0.000511 acc_pose: 0.840931 loss: 0.000511 2022/09/13 05:17:45 - mmengine - INFO - Epoch(train) [115][500/586] lr: 5.000000e-04 eta: 6:52:43 time: 0.469558 data_time: 0.025835 memory: 15239 loss_kpt: 0.000508 acc_pose: 0.866265 loss: 0.000508 2022/09/13 05:18:08 - mmengine - INFO - Epoch(train) [115][550/586] lr: 5.000000e-04 eta: 6:52:22 time: 0.467224 data_time: 0.026875 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.850182 loss: 0.000529 2022/09/13 05:18:25 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:18:25 - mmengine - INFO - Saving checkpoint at 115 epochs 2022/09/13 05:18:56 - mmengine - INFO - Epoch(train) [116][50/586] lr: 5.000000e-04 eta: 6:51:32 time: 0.477353 data_time: 0.034173 memory: 15239 loss_kpt: 0.000534 acc_pose: 0.897878 loss: 0.000534 2022/09/13 05:19:20 - mmengine - INFO - Epoch(train) [116][100/586] lr: 5.000000e-04 eta: 6:51:11 time: 0.476366 data_time: 0.026508 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.814378 loss: 0.000529 2022/09/13 05:19:43 - mmengine - INFO - Epoch(train) [116][150/586] lr: 5.000000e-04 eta: 6:50:50 time: 0.466864 data_time: 0.025908 memory: 15239 loss_kpt: 0.000532 acc_pose: 0.890952 loss: 0.000532 2022/09/13 05:20:06 - mmengine - INFO - Epoch(train) [116][200/586] lr: 5.000000e-04 eta: 6:50:28 time: 0.467450 data_time: 0.031449 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.800571 loss: 0.000523 2022/09/13 05:20:30 - mmengine - INFO - Epoch(train) [116][250/586] lr: 5.000000e-04 eta: 6:50:07 time: 0.466550 data_time: 0.026686 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.868472 loss: 0.000526 2022/09/13 05:20:53 - mmengine - INFO - Epoch(train) [116][300/586] lr: 5.000000e-04 eta: 6:49:46 time: 0.469188 data_time: 0.025928 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.886733 loss: 0.000515 2022/09/13 05:21:16 - mmengine - INFO - Epoch(train) [116][350/586] lr: 5.000000e-04 eta: 6:49:25 time: 0.464475 data_time: 0.026687 memory: 15239 loss_kpt: 0.000528 acc_pose: 0.859974 loss: 0.000528 2022/09/13 05:21:40 - mmengine - INFO - Epoch(train) [116][400/586] lr: 5.000000e-04 eta: 6:49:04 time: 0.478056 data_time: 0.031123 memory: 15239 loss_kpt: 0.000511 acc_pose: 0.859526 loss: 0.000511 2022/09/13 05:22:04 - mmengine - INFO - Epoch(train) [116][450/586] lr: 5.000000e-04 eta: 6:48:42 time: 0.466520 data_time: 0.026895 memory: 15239 loss_kpt: 0.000516 acc_pose: 0.826653 loss: 0.000516 2022/09/13 05:22:27 - mmengine - INFO - Epoch(train) [116][500/586] lr: 5.000000e-04 eta: 6:48:21 time: 0.470439 data_time: 0.026918 memory: 15239 loss_kpt: 0.000530 acc_pose: 0.901785 loss: 0.000530 2022/09/13 05:22:51 - mmengine - INFO - Epoch(train) [116][550/586] lr: 5.000000e-04 eta: 6:48:01 time: 0.483423 data_time: 0.027914 memory: 15239 loss_kpt: 0.000519 acc_pose: 0.871015 loss: 0.000519 2022/09/13 05:23:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:23:08 - mmengine - INFO - Saving checkpoint at 116 epochs 2022/09/13 05:23:27 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:23:40 - mmengine - INFO - Epoch(train) [117][50/586] lr: 5.000000e-04 eta: 6:47:11 time: 0.485993 data_time: 0.034838 memory: 15239 loss_kpt: 0.000524 acc_pose: 0.890380 loss: 0.000524 2022/09/13 05:24:03 - mmengine - INFO - Epoch(train) [117][100/586] lr: 5.000000e-04 eta: 6:46:50 time: 0.473496 data_time: 0.029991 memory: 15239 loss_kpt: 0.000540 acc_pose: 0.829752 loss: 0.000540 2022/09/13 05:24:27 - mmengine - INFO - Epoch(train) [117][150/586] lr: 5.000000e-04 eta: 6:46:30 time: 0.481866 data_time: 0.030513 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.875987 loss: 0.000513 2022/09/13 05:24:51 - mmengine - INFO - Epoch(train) [117][200/586] lr: 5.000000e-04 eta: 6:46:08 time: 0.468034 data_time: 0.027317 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.854597 loss: 0.000529 2022/09/13 05:25:15 - mmengine - INFO - Epoch(train) [117][250/586] lr: 5.000000e-04 eta: 6:45:47 time: 0.477056 data_time: 0.030669 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.907772 loss: 0.000536 2022/09/13 05:25:38 - mmengine - INFO - Epoch(train) [117][300/586] lr: 5.000000e-04 eta: 6:45:26 time: 0.465543 data_time: 0.027650 memory: 15239 loss_kpt: 0.000518 acc_pose: 0.865279 loss: 0.000518 2022/09/13 05:26:02 - mmengine - INFO - Epoch(train) [117][350/586] lr: 5.000000e-04 eta: 6:45:05 time: 0.480321 data_time: 0.027058 memory: 15239 loss_kpt: 0.000534 acc_pose: 0.804087 loss: 0.000534 2022/09/13 05:26:26 - mmengine - INFO - Epoch(train) [117][400/586] lr: 5.000000e-04 eta: 6:44:44 time: 0.474092 data_time: 0.026598 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.897901 loss: 0.000537 2022/09/13 05:26:49 - mmengine - INFO - Epoch(train) [117][450/586] lr: 5.000000e-04 eta: 6:44:23 time: 0.467007 data_time: 0.026333 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.893758 loss: 0.000513 2022/09/13 05:27:13 - mmengine - INFO - Epoch(train) [117][500/586] lr: 5.000000e-04 eta: 6:44:02 time: 0.474918 data_time: 0.025820 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.808227 loss: 0.000536 2022/09/13 05:27:36 - mmengine - INFO - Epoch(train) [117][550/586] lr: 5.000000e-04 eta: 6:43:41 time: 0.471237 data_time: 0.031490 memory: 15239 loss_kpt: 0.000514 acc_pose: 0.874265 loss: 0.000514 2022/09/13 05:27:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:27:53 - mmengine - INFO - Saving checkpoint at 117 epochs 2022/09/13 05:28:25 - mmengine - INFO - Epoch(train) [118][50/586] lr: 5.000000e-04 eta: 6:42:52 time: 0.483440 data_time: 0.043007 memory: 15239 loss_kpt: 0.000527 acc_pose: 0.804965 loss: 0.000527 2022/09/13 05:28:48 - mmengine - INFO - Epoch(train) [118][100/586] lr: 5.000000e-04 eta: 6:42:30 time: 0.460297 data_time: 0.025800 memory: 15239 loss_kpt: 0.000517 acc_pose: 0.850669 loss: 0.000517 2022/09/13 05:29:11 - mmengine - INFO - Epoch(train) [118][150/586] lr: 5.000000e-04 eta: 6:42:09 time: 0.463066 data_time: 0.026578 memory: 15239 loss_kpt: 0.000535 acc_pose: 0.822183 loss: 0.000535 2022/09/13 05:29:35 - mmengine - INFO - Epoch(train) [118][200/586] lr: 5.000000e-04 eta: 6:41:48 time: 0.480018 data_time: 0.026413 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.868513 loss: 0.000529 2022/09/13 05:29:59 - mmengine - INFO - Epoch(train) [118][250/586] lr: 5.000000e-04 eta: 6:41:27 time: 0.465833 data_time: 0.027253 memory: 15239 loss_kpt: 0.000510 acc_pose: 0.871484 loss: 0.000510 2022/09/13 05:30:22 - mmengine - INFO - Epoch(train) [118][300/586] lr: 5.000000e-04 eta: 6:41:06 time: 0.474174 data_time: 0.030184 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.889204 loss: 0.000512 2022/09/13 05:30:46 - mmengine - INFO - Epoch(train) [118][350/586] lr: 5.000000e-04 eta: 6:40:44 time: 0.471082 data_time: 0.025637 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.880350 loss: 0.000526 2022/09/13 05:31:09 - mmengine - INFO - Epoch(train) [118][400/586] lr: 5.000000e-04 eta: 6:40:23 time: 0.466312 data_time: 0.025857 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.870035 loss: 0.000515 2022/09/13 05:31:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:31:33 - mmengine - INFO - Epoch(train) [118][450/586] lr: 5.000000e-04 eta: 6:40:02 time: 0.476741 data_time: 0.027457 memory: 15239 loss_kpt: 0.000549 acc_pose: 0.883718 loss: 0.000549 2022/09/13 05:31:57 - mmengine - INFO - Epoch(train) [118][500/586] lr: 5.000000e-04 eta: 6:39:41 time: 0.472496 data_time: 0.027606 memory: 15239 loss_kpt: 0.000518 acc_pose: 0.894705 loss: 0.000518 2022/09/13 05:32:21 - mmengine - INFO - Epoch(train) [118][550/586] lr: 5.000000e-04 eta: 6:39:20 time: 0.475782 data_time: 0.028580 memory: 15239 loss_kpt: 0.000522 acc_pose: 0.806640 loss: 0.000522 2022/09/13 05:32:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:32:38 - mmengine - INFO - Saving checkpoint at 118 epochs 2022/09/13 05:33:08 - mmengine - INFO - Epoch(train) [119][50/586] lr: 5.000000e-04 eta: 6:38:31 time: 0.474794 data_time: 0.033354 memory: 15239 loss_kpt: 0.000528 acc_pose: 0.888074 loss: 0.000528 2022/09/13 05:33:32 - mmengine - INFO - Epoch(train) [119][100/586] lr: 5.000000e-04 eta: 6:38:10 time: 0.473926 data_time: 0.030077 memory: 15239 loss_kpt: 0.000525 acc_pose: 0.839634 loss: 0.000525 2022/09/13 05:33:56 - mmengine - INFO - Epoch(train) [119][150/586] lr: 5.000000e-04 eta: 6:37:48 time: 0.466810 data_time: 0.026926 memory: 15239 loss_kpt: 0.000511 acc_pose: 0.792801 loss: 0.000511 2022/09/13 05:34:19 - mmengine - INFO - Epoch(train) [119][200/586] lr: 5.000000e-04 eta: 6:37:27 time: 0.470427 data_time: 0.027096 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.850081 loss: 0.000512 2022/09/13 05:34:42 - mmengine - INFO - Epoch(train) [119][250/586] lr: 5.000000e-04 eta: 6:37:06 time: 0.468749 data_time: 0.027167 memory: 15239 loss_kpt: 0.000528 acc_pose: 0.854731 loss: 0.000528 2022/09/13 05:35:06 - mmengine - INFO - Epoch(train) [119][300/586] lr: 5.000000e-04 eta: 6:36:45 time: 0.469974 data_time: 0.027805 memory: 15239 loss_kpt: 0.000525 acc_pose: 0.833029 loss: 0.000525 2022/09/13 05:35:30 - mmengine - INFO - Epoch(train) [119][350/586] lr: 5.000000e-04 eta: 6:36:23 time: 0.469784 data_time: 0.027681 memory: 15239 loss_kpt: 0.000510 acc_pose: 0.886889 loss: 0.000510 2022/09/13 05:35:53 - mmengine - INFO - Epoch(train) [119][400/586] lr: 5.000000e-04 eta: 6:36:02 time: 0.462398 data_time: 0.031093 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.822310 loss: 0.000503 2022/09/13 05:36:16 - mmengine - INFO - Epoch(train) [119][450/586] lr: 5.000000e-04 eta: 6:35:41 time: 0.468264 data_time: 0.027045 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.864682 loss: 0.000529 2022/09/13 05:36:40 - mmengine - INFO - Epoch(train) [119][500/586] lr: 5.000000e-04 eta: 6:35:20 time: 0.473124 data_time: 0.026395 memory: 15239 loss_kpt: 0.000530 acc_pose: 0.771059 loss: 0.000530 2022/09/13 05:37:03 - mmengine - INFO - Epoch(train) [119][550/586] lr: 5.000000e-04 eta: 6:34:59 time: 0.473199 data_time: 0.026546 memory: 15239 loss_kpt: 0.000534 acc_pose: 0.864092 loss: 0.000534 2022/09/13 05:37:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:37:20 - mmengine - INFO - Saving checkpoint at 119 epochs 2022/09/13 05:37:51 - mmengine - INFO - Epoch(train) [120][50/586] lr: 5.000000e-04 eta: 6:34:10 time: 0.481246 data_time: 0.035330 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.855923 loss: 0.000523 2022/09/13 05:38:15 - mmengine - INFO - Epoch(train) [120][100/586] lr: 5.000000e-04 eta: 6:33:49 time: 0.484886 data_time: 0.030267 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.835243 loss: 0.000537 2022/09/13 05:38:39 - mmengine - INFO - Epoch(train) [120][150/586] lr: 5.000000e-04 eta: 6:33:28 time: 0.478148 data_time: 0.033636 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.857194 loss: 0.000515 2022/09/13 05:39:03 - mmengine - INFO - Epoch(train) [120][200/586] lr: 5.000000e-04 eta: 6:33:07 time: 0.479363 data_time: 0.029087 memory: 15239 loss_kpt: 0.000507 acc_pose: 0.883295 loss: 0.000507 2022/09/13 05:39:27 - mmengine - INFO - Epoch(train) [120][250/586] lr: 5.000000e-04 eta: 6:32:46 time: 0.476916 data_time: 0.028449 memory: 15239 loss_kpt: 0.000530 acc_pose: 0.837021 loss: 0.000530 2022/09/13 05:39:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:39:51 - mmengine - INFO - Epoch(train) [120][300/586] lr: 5.000000e-04 eta: 6:32:25 time: 0.469103 data_time: 0.027684 memory: 15239 loss_kpt: 0.000514 acc_pose: 0.869866 loss: 0.000514 2022/09/13 05:40:14 - mmengine - INFO - Epoch(train) [120][350/586] lr: 5.000000e-04 eta: 6:32:04 time: 0.468711 data_time: 0.027134 memory: 15239 loss_kpt: 0.000524 acc_pose: 0.797866 loss: 0.000524 2022/09/13 05:40:37 - mmengine - INFO - Epoch(train) [120][400/586] lr: 5.000000e-04 eta: 6:31:42 time: 0.467416 data_time: 0.027084 memory: 15239 loss_kpt: 0.000525 acc_pose: 0.824700 loss: 0.000525 2022/09/13 05:41:01 - mmengine - INFO - Epoch(train) [120][450/586] lr: 5.000000e-04 eta: 6:31:21 time: 0.473061 data_time: 0.028141 memory: 15239 loss_kpt: 0.000518 acc_pose: 0.835508 loss: 0.000518 2022/09/13 05:41:25 - mmengine - INFO - Epoch(train) [120][500/586] lr: 5.000000e-04 eta: 6:31:00 time: 0.471687 data_time: 0.026791 memory: 15239 loss_kpt: 0.000539 acc_pose: 0.799495 loss: 0.000539 2022/09/13 05:41:49 - mmengine - INFO - Epoch(train) [120][550/586] lr: 5.000000e-04 eta: 6:30:39 time: 0.480169 data_time: 0.027714 memory: 15239 loss_kpt: 0.000537 acc_pose: 0.834732 loss: 0.000537 2022/09/13 05:42:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:42:06 - mmengine - INFO - Saving checkpoint at 120 epochs 2022/09/13 05:42:24 - mmengine - INFO - Epoch(val) [120][50/407] eta: 0:01:21 time: 0.228594 data_time: 0.014024 memory: 15239 2022/09/13 05:42:35 - mmengine - INFO - Epoch(val) [120][100/407] eta: 0:01:08 time: 0.222657 data_time: 0.008183 memory: 2064 2022/09/13 05:42:46 - mmengine - INFO - Epoch(val) [120][150/407] eta: 0:00:56 time: 0.218174 data_time: 0.008293 memory: 2064 2022/09/13 05:42:57 - mmengine - INFO - Epoch(val) [120][200/407] eta: 0:00:45 time: 0.218220 data_time: 0.008233 memory: 2064 2022/09/13 05:43:08 - mmengine - INFO - Epoch(val) [120][250/407] eta: 0:00:34 time: 0.220796 data_time: 0.008300 memory: 2064 2022/09/13 05:43:19 - mmengine - INFO - Epoch(val) [120][300/407] eta: 0:00:23 time: 0.218353 data_time: 0.008277 memory: 2064 2022/09/13 05:43:30 - mmengine - INFO - Epoch(val) [120][350/407] eta: 0:00:12 time: 0.219371 data_time: 0.008818 memory: 2064 2022/09/13 05:43:41 - mmengine - INFO - Epoch(val) [120][400/407] eta: 0:00:01 time: 0.217624 data_time: 0.008087 memory: 2064 2022/09/13 05:44:18 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 05:44:32 - mmengine - INFO - Epoch(val) [120][407/407] coco/AP: 0.764292 coco/AP .5: 0.908880 coco/AP .75: 0.825774 coco/AP (M): 0.723468 coco/AP (L): 0.835481 coco/AR: 0.813020 coco/AR .5: 0.944270 coco/AR .75: 0.868073 coco/AR (M): 0.768424 coco/AR (L): 0.877369 2022/09/13 05:44:32 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_384_v1/best_coco/AP_epoch_110.pth is removed 2022/09/13 05:44:36 - mmengine - INFO - The best checkpoint with 0.7643 coco/AP at 120 epoch is saved to best_coco/AP_epoch_120.pth. 2022/09/13 05:44:59 - mmengine - INFO - Epoch(train) [121][50/586] lr: 5.000000e-04 eta: 6:29:50 time: 0.473201 data_time: 0.031766 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.869371 loss: 0.000503 2022/09/13 05:45:23 - mmengine - INFO - Epoch(train) [121][100/586] lr: 5.000000e-04 eta: 6:29:29 time: 0.473967 data_time: 0.027294 memory: 15239 loss_kpt: 0.000516 acc_pose: 0.877745 loss: 0.000516 2022/09/13 05:45:46 - mmengine - INFO - Epoch(train) [121][150/586] lr: 5.000000e-04 eta: 6:29:08 time: 0.467872 data_time: 0.027431 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.898998 loss: 0.000503 2022/09/13 05:46:10 - mmengine - INFO - Epoch(train) [121][200/586] lr: 5.000000e-04 eta: 6:28:46 time: 0.466011 data_time: 0.027006 memory: 15239 loss_kpt: 0.000518 acc_pose: 0.863114 loss: 0.000518 2022/09/13 05:46:34 - mmengine - INFO - Epoch(train) [121][250/586] lr: 5.000000e-04 eta: 6:28:25 time: 0.478027 data_time: 0.028132 memory: 15239 loss_kpt: 0.000527 acc_pose: 0.804189 loss: 0.000527 2022/09/13 05:46:57 - mmengine - INFO - Epoch(train) [121][300/586] lr: 5.000000e-04 eta: 6:28:04 time: 0.465686 data_time: 0.030288 memory: 15239 loss_kpt: 0.000520 acc_pose: 0.819525 loss: 0.000520 2022/09/13 05:47:20 - mmengine - INFO - Epoch(train) [121][350/586] lr: 5.000000e-04 eta: 6:27:43 time: 0.465505 data_time: 0.026817 memory: 15239 loss_kpt: 0.000517 acc_pose: 0.775670 loss: 0.000517 2022/09/13 05:47:44 - mmengine - INFO - Epoch(train) [121][400/586] lr: 5.000000e-04 eta: 6:27:22 time: 0.474285 data_time: 0.027675 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.866569 loss: 0.000529 2022/09/13 05:48:08 - mmengine - INFO - Epoch(train) [121][450/586] lr: 5.000000e-04 eta: 6:27:00 time: 0.470538 data_time: 0.027033 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.823150 loss: 0.000500 2022/09/13 05:48:31 - mmengine - INFO - Epoch(train) [121][500/586] lr: 5.000000e-04 eta: 6:26:39 time: 0.468757 data_time: 0.027866 memory: 15239 loss_kpt: 0.000521 acc_pose: 0.839059 loss: 0.000521 2022/09/13 05:48:55 - mmengine - INFO - Epoch(train) [121][550/586] lr: 5.000000e-04 eta: 6:26:18 time: 0.478241 data_time: 0.027280 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.841819 loss: 0.000503 2022/09/13 05:49:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:49:12 - mmengine - INFO - Saving checkpoint at 121 epochs 2022/09/13 05:49:42 - mmengine - INFO - Epoch(train) [122][50/586] lr: 5.000000e-04 eta: 6:25:30 time: 0.485051 data_time: 0.044756 memory: 15239 loss_kpt: 0.000520 acc_pose: 0.899820 loss: 0.000520 2022/09/13 05:50:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:50:07 - mmengine - INFO - Epoch(train) [122][100/586] lr: 5.000000e-04 eta: 6:25:09 time: 0.484121 data_time: 0.026864 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.885631 loss: 0.000497 2022/09/13 05:50:30 - mmengine - INFO - Epoch(train) [122][150/586] lr: 5.000000e-04 eta: 6:24:47 time: 0.461581 data_time: 0.025901 memory: 15239 loss_kpt: 0.000517 acc_pose: 0.864665 loss: 0.000517 2022/09/13 05:50:53 - mmengine - INFO - Epoch(train) [122][200/586] lr: 5.000000e-04 eta: 6:24:26 time: 0.471662 data_time: 0.026476 memory: 15239 loss_kpt: 0.000528 acc_pose: 0.819733 loss: 0.000528 2022/09/13 05:51:17 - mmengine - INFO - Epoch(train) [122][250/586] lr: 5.000000e-04 eta: 6:24:05 time: 0.474724 data_time: 0.026924 memory: 15239 loss_kpt: 0.000528 acc_pose: 0.817015 loss: 0.000528 2022/09/13 05:51:41 - mmengine - INFO - Epoch(train) [122][300/586] lr: 5.000000e-04 eta: 6:23:44 time: 0.469528 data_time: 0.026559 memory: 15239 loss_kpt: 0.000520 acc_pose: 0.826041 loss: 0.000520 2022/09/13 05:52:04 - mmengine - INFO - Epoch(train) [122][350/586] lr: 5.000000e-04 eta: 6:23:23 time: 0.467886 data_time: 0.026940 memory: 15239 loss_kpt: 0.000511 acc_pose: 0.849332 loss: 0.000511 2022/09/13 05:52:28 - mmengine - INFO - Epoch(train) [122][400/586] lr: 5.000000e-04 eta: 6:23:01 time: 0.471545 data_time: 0.029942 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.876368 loss: 0.000509 2022/09/13 05:52:51 - mmengine - INFO - Epoch(train) [122][450/586] lr: 5.000000e-04 eta: 6:22:40 time: 0.469835 data_time: 0.026278 memory: 15239 loss_kpt: 0.000520 acc_pose: 0.834191 loss: 0.000520 2022/09/13 05:53:15 - mmengine - INFO - Epoch(train) [122][500/586] lr: 5.000000e-04 eta: 6:22:19 time: 0.470233 data_time: 0.027611 memory: 15239 loss_kpt: 0.000517 acc_pose: 0.861398 loss: 0.000517 2022/09/13 05:53:39 - mmengine - INFO - Epoch(train) [122][550/586] lr: 5.000000e-04 eta: 6:21:58 time: 0.479459 data_time: 0.030849 memory: 15239 loss_kpt: 0.000531 acc_pose: 0.870631 loss: 0.000531 2022/09/13 05:53:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:53:55 - mmengine - INFO - Saving checkpoint at 122 epochs 2022/09/13 05:54:26 - mmengine - INFO - Epoch(train) [123][50/586] lr: 5.000000e-04 eta: 6:21:09 time: 0.479437 data_time: 0.037399 memory: 15239 loss_kpt: 0.000521 acc_pose: 0.870409 loss: 0.000521 2022/09/13 05:54:50 - mmengine - INFO - Epoch(train) [123][100/586] lr: 5.000000e-04 eta: 6:20:48 time: 0.474479 data_time: 0.029146 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.877291 loss: 0.000504 2022/09/13 05:55:14 - mmengine - INFO - Epoch(train) [123][150/586] lr: 5.000000e-04 eta: 6:20:27 time: 0.471992 data_time: 0.033998 memory: 15239 loss_kpt: 0.000531 acc_pose: 0.887840 loss: 0.000531 2022/09/13 05:55:37 - mmengine - INFO - Epoch(train) [123][200/586] lr: 5.000000e-04 eta: 6:20:06 time: 0.474749 data_time: 0.030767 memory: 15239 loss_kpt: 0.000519 acc_pose: 0.877358 loss: 0.000519 2022/09/13 05:56:01 - mmengine - INFO - Epoch(train) [123][250/586] lr: 5.000000e-04 eta: 6:19:45 time: 0.471214 data_time: 0.032318 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.876049 loss: 0.000515 2022/09/13 05:56:25 - mmengine - INFO - Epoch(train) [123][300/586] lr: 5.000000e-04 eta: 6:19:24 time: 0.472294 data_time: 0.026660 memory: 15239 loss_kpt: 0.000511 acc_pose: 0.867975 loss: 0.000511 2022/09/13 05:56:48 - mmengine - INFO - Epoch(train) [123][350/586] lr: 5.000000e-04 eta: 6:19:02 time: 0.465338 data_time: 0.026916 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.856744 loss: 0.000513 2022/09/13 05:57:12 - mmengine - INFO - Epoch(train) [123][400/586] lr: 5.000000e-04 eta: 6:18:41 time: 0.473775 data_time: 0.034290 memory: 15239 loss_kpt: 0.000524 acc_pose: 0.847958 loss: 0.000524 2022/09/13 05:57:35 - mmengine - INFO - Epoch(train) [123][450/586] lr: 5.000000e-04 eta: 6:18:20 time: 0.470635 data_time: 0.027469 memory: 15239 loss_kpt: 0.000496 acc_pose: 0.855452 loss: 0.000496 2022/09/13 05:57:58 - mmengine - INFO - Epoch(train) [123][500/586] lr: 5.000000e-04 eta: 6:17:58 time: 0.465664 data_time: 0.027469 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.870126 loss: 0.000512 2022/09/13 05:58:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:58:22 - mmengine - INFO - Epoch(train) [123][550/586] lr: 5.000000e-04 eta: 6:17:37 time: 0.470938 data_time: 0.027366 memory: 15239 loss_kpt: 0.000517 acc_pose: 0.838014 loss: 0.000517 2022/09/13 05:58:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 05:58:39 - mmengine - INFO - Saving checkpoint at 123 epochs 2022/09/13 05:59:09 - mmengine - INFO - Epoch(train) [124][50/586] lr: 5.000000e-04 eta: 6:16:49 time: 0.479483 data_time: 0.041533 memory: 15239 loss_kpt: 0.000511 acc_pose: 0.907126 loss: 0.000511 2022/09/13 05:59:33 - mmengine - INFO - Epoch(train) [124][100/586] lr: 5.000000e-04 eta: 6:16:28 time: 0.470117 data_time: 0.033898 memory: 15239 loss_kpt: 0.000507 acc_pose: 0.831237 loss: 0.000507 2022/09/13 05:59:56 - mmengine - INFO - Epoch(train) [124][150/586] lr: 5.000000e-04 eta: 6:16:06 time: 0.471747 data_time: 0.034055 memory: 15239 loss_kpt: 0.000507 acc_pose: 0.857966 loss: 0.000507 2022/09/13 06:00:22 - mmengine - INFO - Epoch(train) [124][200/586] lr: 5.000000e-04 eta: 6:15:47 time: 0.518712 data_time: 0.034683 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.822987 loss: 0.000504 2022/09/13 06:00:46 - mmengine - INFO - Epoch(train) [124][250/586] lr: 5.000000e-04 eta: 6:15:25 time: 0.467085 data_time: 0.035613 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.842842 loss: 0.000505 2022/09/13 06:01:10 - mmengine - INFO - Epoch(train) [124][300/586] lr: 5.000000e-04 eta: 6:15:04 time: 0.478129 data_time: 0.026230 memory: 15239 loss_kpt: 0.000517 acc_pose: 0.882637 loss: 0.000517 2022/09/13 06:01:33 - mmengine - INFO - Epoch(train) [124][350/586] lr: 5.000000e-04 eta: 6:14:43 time: 0.469699 data_time: 0.026157 memory: 15239 loss_kpt: 0.000517 acc_pose: 0.908813 loss: 0.000517 2022/09/13 06:01:56 - mmengine - INFO - Epoch(train) [124][400/586] lr: 5.000000e-04 eta: 6:14:22 time: 0.462249 data_time: 0.025543 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.880588 loss: 0.000526 2022/09/13 06:02:20 - mmengine - INFO - Epoch(train) [124][450/586] lr: 5.000000e-04 eta: 6:14:00 time: 0.471711 data_time: 0.030700 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.786442 loss: 0.000526 2022/09/13 06:02:43 - mmengine - INFO - Epoch(train) [124][500/586] lr: 5.000000e-04 eta: 6:13:39 time: 0.471178 data_time: 0.026145 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.873711 loss: 0.000504 2022/09/13 06:03:06 - mmengine - INFO - Epoch(train) [124][550/586] lr: 5.000000e-04 eta: 6:13:17 time: 0.460005 data_time: 0.026379 memory: 15239 loss_kpt: 0.000514 acc_pose: 0.891519 loss: 0.000514 2022/09/13 06:03:24 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:03:24 - mmengine - INFO - Saving checkpoint at 124 epochs 2022/09/13 06:03:54 - mmengine - INFO - Epoch(train) [125][50/586] lr: 5.000000e-04 eta: 6:12:29 time: 0.475821 data_time: 0.035822 memory: 15239 loss_kpt: 0.000520 acc_pose: 0.916835 loss: 0.000520 2022/09/13 06:04:18 - mmengine - INFO - Epoch(train) [125][100/586] lr: 5.000000e-04 eta: 6:12:08 time: 0.464769 data_time: 0.026047 memory: 15239 loss_kpt: 0.000519 acc_pose: 0.904635 loss: 0.000519 2022/09/13 06:04:41 - mmengine - INFO - Epoch(train) [125][150/586] lr: 5.000000e-04 eta: 6:11:47 time: 0.476400 data_time: 0.026855 memory: 15239 loss_kpt: 0.000522 acc_pose: 0.890069 loss: 0.000522 2022/09/13 06:05:04 - mmengine - INFO - Epoch(train) [125][200/586] lr: 5.000000e-04 eta: 6:11:25 time: 0.460702 data_time: 0.025392 memory: 15239 loss_kpt: 0.000522 acc_pose: 0.869407 loss: 0.000522 2022/09/13 06:05:28 - mmengine - INFO - Epoch(train) [125][250/586] lr: 5.000000e-04 eta: 6:11:04 time: 0.469062 data_time: 0.030538 memory: 15239 loss_kpt: 0.000502 acc_pose: 0.866493 loss: 0.000502 2022/09/13 06:05:52 - mmengine - INFO - Epoch(train) [125][300/586] lr: 5.000000e-04 eta: 6:10:42 time: 0.472369 data_time: 0.026422 memory: 15239 loss_kpt: 0.000521 acc_pose: 0.904781 loss: 0.000521 2022/09/13 06:06:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:06:15 - mmengine - INFO - Epoch(train) [125][350/586] lr: 5.000000e-04 eta: 6:10:21 time: 0.470758 data_time: 0.030551 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.862468 loss: 0.000515 2022/09/13 06:06:38 - mmengine - INFO - Epoch(train) [125][400/586] lr: 5.000000e-04 eta: 6:10:00 time: 0.464030 data_time: 0.026644 memory: 15239 loss_kpt: 0.000514 acc_pose: 0.849492 loss: 0.000514 2022/09/13 06:07:02 - mmengine - INFO - Epoch(train) [125][450/586] lr: 5.000000e-04 eta: 6:09:38 time: 0.466584 data_time: 0.025880 memory: 15239 loss_kpt: 0.000517 acc_pose: 0.920072 loss: 0.000517 2022/09/13 06:07:25 - mmengine - INFO - Epoch(train) [125][500/586] lr: 5.000000e-04 eta: 6:09:17 time: 0.471992 data_time: 0.031819 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.860603 loss: 0.000513 2022/09/13 06:07:49 - mmengine - INFO - Epoch(train) [125][550/586] lr: 5.000000e-04 eta: 6:08:56 time: 0.466368 data_time: 0.026049 memory: 15239 loss_kpt: 0.000531 acc_pose: 0.804890 loss: 0.000531 2022/09/13 06:08:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:08:06 - mmengine - INFO - Saving checkpoint at 125 epochs 2022/09/13 06:08:37 - mmengine - INFO - Epoch(train) [126][50/586] lr: 5.000000e-04 eta: 6:08:08 time: 0.478459 data_time: 0.034983 memory: 15239 loss_kpt: 0.000530 acc_pose: 0.912141 loss: 0.000530 2022/09/13 06:09:00 - mmengine - INFO - Epoch(train) [126][100/586] lr: 5.000000e-04 eta: 6:07:46 time: 0.466578 data_time: 0.030834 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.864368 loss: 0.000509 2022/09/13 06:09:24 - mmengine - INFO - Epoch(train) [126][150/586] lr: 5.000000e-04 eta: 6:07:25 time: 0.468072 data_time: 0.035684 memory: 15239 loss_kpt: 0.000542 acc_pose: 0.803887 loss: 0.000542 2022/09/13 06:09:47 - mmengine - INFO - Epoch(train) [126][200/586] lr: 5.000000e-04 eta: 6:07:03 time: 0.464141 data_time: 0.032217 memory: 15239 loss_kpt: 0.000539 acc_pose: 0.854673 loss: 0.000539 2022/09/13 06:10:10 - mmengine - INFO - Epoch(train) [126][250/586] lr: 5.000000e-04 eta: 6:06:42 time: 0.471825 data_time: 0.030664 memory: 15239 loss_kpt: 0.000520 acc_pose: 0.841220 loss: 0.000520 2022/09/13 06:10:34 - mmengine - INFO - Epoch(train) [126][300/586] lr: 5.000000e-04 eta: 6:06:21 time: 0.472554 data_time: 0.031518 memory: 15239 loss_kpt: 0.000507 acc_pose: 0.812980 loss: 0.000507 2022/09/13 06:10:58 - mmengine - INFO - Epoch(train) [126][350/586] lr: 5.000000e-04 eta: 6:06:00 time: 0.473298 data_time: 0.035591 memory: 15239 loss_kpt: 0.000528 acc_pose: 0.801654 loss: 0.000528 2022/09/13 06:11:21 - mmengine - INFO - Epoch(train) [126][400/586] lr: 5.000000e-04 eta: 6:05:38 time: 0.469690 data_time: 0.032514 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.902205 loss: 0.000515 2022/09/13 06:11:45 - mmengine - INFO - Epoch(train) [126][450/586] lr: 5.000000e-04 eta: 6:05:17 time: 0.475157 data_time: 0.031415 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.856705 loss: 0.000526 2022/09/13 06:12:08 - mmengine - INFO - Epoch(train) [126][500/586] lr: 5.000000e-04 eta: 6:04:56 time: 0.464659 data_time: 0.025307 memory: 15239 loss_kpt: 0.000508 acc_pose: 0.815067 loss: 0.000508 2022/09/13 06:12:32 - mmengine - INFO - Epoch(train) [126][550/586] lr: 5.000000e-04 eta: 6:04:34 time: 0.467866 data_time: 0.026723 memory: 15239 loss_kpt: 0.000511 acc_pose: 0.821442 loss: 0.000511 2022/09/13 06:12:49 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:12:49 - mmengine - INFO - Saving checkpoint at 126 epochs 2022/09/13 06:13:19 - mmengine - INFO - Epoch(train) [127][50/586] lr: 5.000000e-04 eta: 6:03:46 time: 0.471508 data_time: 0.035900 memory: 15239 loss_kpt: 0.000510 acc_pose: 0.827708 loss: 0.000510 2022/09/13 06:13:43 - mmengine - INFO - Epoch(train) [127][100/586] lr: 5.000000e-04 eta: 6:03:25 time: 0.472631 data_time: 0.026154 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.885796 loss: 0.000513 2022/09/13 06:14:06 - mmengine - INFO - Epoch(train) [127][150/586] lr: 5.000000e-04 eta: 6:03:04 time: 0.467489 data_time: 0.025983 memory: 15239 loss_kpt: 0.000495 acc_pose: 0.847363 loss: 0.000495 2022/09/13 06:14:13 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:14:30 - mmengine - INFO - Epoch(train) [127][200/586] lr: 5.000000e-04 eta: 6:02:42 time: 0.469009 data_time: 0.026500 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.898590 loss: 0.000515 2022/09/13 06:14:54 - mmengine - INFO - Epoch(train) [127][250/586] lr: 5.000000e-04 eta: 6:02:21 time: 0.475403 data_time: 0.026148 memory: 15239 loss_kpt: 0.000527 acc_pose: 0.889400 loss: 0.000527 2022/09/13 06:15:17 - mmengine - INFO - Epoch(train) [127][300/586] lr: 5.000000e-04 eta: 6:02:00 time: 0.465635 data_time: 0.026163 memory: 15239 loss_kpt: 0.000502 acc_pose: 0.898394 loss: 0.000502 2022/09/13 06:15:41 - mmengine - INFO - Epoch(train) [127][350/586] lr: 5.000000e-04 eta: 6:01:39 time: 0.476345 data_time: 0.026081 memory: 15239 loss_kpt: 0.000514 acc_pose: 0.861742 loss: 0.000514 2022/09/13 06:16:05 - mmengine - INFO - Epoch(train) [127][400/586] lr: 5.000000e-04 eta: 6:01:17 time: 0.475492 data_time: 0.030423 memory: 15239 loss_kpt: 0.000518 acc_pose: 0.837490 loss: 0.000518 2022/09/13 06:16:28 - mmengine - INFO - Epoch(train) [127][450/586] lr: 5.000000e-04 eta: 6:00:56 time: 0.466473 data_time: 0.026303 memory: 15239 loss_kpt: 0.000506 acc_pose: 0.880918 loss: 0.000506 2022/09/13 06:16:52 - mmengine - INFO - Epoch(train) [127][500/586] lr: 5.000000e-04 eta: 6:00:35 time: 0.482547 data_time: 0.026944 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.868175 loss: 0.000513 2022/09/13 06:17:16 - mmengine - INFO - Epoch(train) [127][550/586] lr: 5.000000e-04 eta: 6:00:14 time: 0.473886 data_time: 0.031632 memory: 15239 loss_kpt: 0.000518 acc_pose: 0.895593 loss: 0.000518 2022/09/13 06:17:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:17:33 - mmengine - INFO - Saving checkpoint at 127 epochs 2022/09/13 06:18:04 - mmengine - INFO - Epoch(train) [128][50/586] lr: 5.000000e-04 eta: 5:59:27 time: 0.486225 data_time: 0.035169 memory: 15239 loss_kpt: 0.000501 acc_pose: 0.850616 loss: 0.000501 2022/09/13 06:18:28 - mmengine - INFO - Epoch(train) [128][100/586] lr: 5.000000e-04 eta: 5:59:05 time: 0.474390 data_time: 0.033892 memory: 15239 loss_kpt: 0.000519 acc_pose: 0.831285 loss: 0.000519 2022/09/13 06:18:51 - mmengine - INFO - Epoch(train) [128][150/586] lr: 5.000000e-04 eta: 5:58:44 time: 0.473358 data_time: 0.031577 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.901437 loss: 0.000503 2022/09/13 06:19:15 - mmengine - INFO - Epoch(train) [128][200/586] lr: 5.000000e-04 eta: 5:58:22 time: 0.462772 data_time: 0.026624 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.908056 loss: 0.000503 2022/09/13 06:19:39 - mmengine - INFO - Epoch(train) [128][250/586] lr: 5.000000e-04 eta: 5:58:02 time: 0.484983 data_time: 0.026124 memory: 15239 loss_kpt: 0.000508 acc_pose: 0.886746 loss: 0.000508 2022/09/13 06:20:03 - mmengine - INFO - Epoch(train) [128][300/586] lr: 5.000000e-04 eta: 5:57:41 time: 0.477323 data_time: 0.026704 memory: 15239 loss_kpt: 0.000530 acc_pose: 0.858670 loss: 0.000530 2022/09/13 06:20:26 - mmengine - INFO - Epoch(train) [128][350/586] lr: 5.000000e-04 eta: 5:57:19 time: 0.469846 data_time: 0.026553 memory: 15239 loss_kpt: 0.000510 acc_pose: 0.890123 loss: 0.000510 2022/09/13 06:20:50 - mmengine - INFO - Epoch(train) [128][400/586] lr: 5.000000e-04 eta: 5:56:58 time: 0.473049 data_time: 0.025859 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.860968 loss: 0.000503 2022/09/13 06:21:14 - mmengine - INFO - Epoch(train) [128][450/586] lr: 5.000000e-04 eta: 5:56:37 time: 0.476526 data_time: 0.029283 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.849803 loss: 0.000509 2022/09/13 06:21:38 - mmengine - INFO - Epoch(train) [128][500/586] lr: 5.000000e-04 eta: 5:56:16 time: 0.477093 data_time: 0.027157 memory: 15239 loss_kpt: 0.000522 acc_pose: 0.870903 loss: 0.000522 2022/09/13 06:22:01 - mmengine - INFO - Epoch(train) [128][550/586] lr: 5.000000e-04 eta: 5:55:54 time: 0.472338 data_time: 0.026087 memory: 15239 loss_kpt: 0.000528 acc_pose: 0.783597 loss: 0.000528 2022/09/13 06:22:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:22:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:22:18 - mmengine - INFO - Saving checkpoint at 128 epochs 2022/09/13 06:22:49 - mmengine - INFO - Epoch(train) [129][50/586] lr: 5.000000e-04 eta: 5:55:07 time: 0.473356 data_time: 0.031070 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.865574 loss: 0.000497 2022/09/13 06:23:13 - mmengine - INFO - Epoch(train) [129][100/586] lr: 5.000000e-04 eta: 5:54:46 time: 0.480186 data_time: 0.030635 memory: 15239 loss_kpt: 0.000518 acc_pose: 0.864107 loss: 0.000518 2022/09/13 06:23:36 - mmengine - INFO - Epoch(train) [129][150/586] lr: 5.000000e-04 eta: 5:54:24 time: 0.462688 data_time: 0.026952 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.845271 loss: 0.000509 2022/09/13 06:23:59 - mmengine - INFO - Epoch(train) [129][200/586] lr: 5.000000e-04 eta: 5:54:03 time: 0.467211 data_time: 0.026151 memory: 15239 loss_kpt: 0.000520 acc_pose: 0.843519 loss: 0.000520 2022/09/13 06:24:23 - mmengine - INFO - Epoch(train) [129][250/586] lr: 5.000000e-04 eta: 5:53:42 time: 0.477719 data_time: 0.031627 memory: 15239 loss_kpt: 0.000490 acc_pose: 0.748901 loss: 0.000490 2022/09/13 06:24:46 - mmengine - INFO - Epoch(train) [129][300/586] lr: 5.000000e-04 eta: 5:53:20 time: 0.463604 data_time: 0.027023 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.902168 loss: 0.000515 2022/09/13 06:25:10 - mmengine - INFO - Epoch(train) [129][350/586] lr: 5.000000e-04 eta: 5:52:59 time: 0.469490 data_time: 0.026924 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.917565 loss: 0.000523 2022/09/13 06:25:34 - mmengine - INFO - Epoch(train) [129][400/586] lr: 5.000000e-04 eta: 5:52:38 time: 0.482622 data_time: 0.031087 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.902880 loss: 0.000503 2022/09/13 06:25:57 - mmengine - INFO - Epoch(train) [129][450/586] lr: 5.000000e-04 eta: 5:52:16 time: 0.469362 data_time: 0.026671 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.876282 loss: 0.000515 2022/09/13 06:26:21 - mmengine - INFO - Epoch(train) [129][500/586] lr: 5.000000e-04 eta: 5:51:55 time: 0.467672 data_time: 0.029789 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.853269 loss: 0.000504 2022/09/13 06:26:45 - mmengine - INFO - Epoch(train) [129][550/586] lr: 5.000000e-04 eta: 5:51:34 time: 0.477116 data_time: 0.026589 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.912671 loss: 0.000512 2022/09/13 06:27:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:27:02 - mmengine - INFO - Saving checkpoint at 129 epochs 2022/09/13 06:27:32 - mmengine - INFO - Epoch(train) [130][50/586] lr: 5.000000e-04 eta: 5:50:46 time: 0.472533 data_time: 0.031060 memory: 15239 loss_kpt: 0.000514 acc_pose: 0.813136 loss: 0.000514 2022/09/13 06:27:56 - mmengine - INFO - Epoch(train) [130][100/586] lr: 5.000000e-04 eta: 5:50:25 time: 0.480358 data_time: 0.026824 memory: 15239 loss_kpt: 0.000521 acc_pose: 0.859829 loss: 0.000521 2022/09/13 06:28:20 - mmengine - INFO - Epoch(train) [130][150/586] lr: 5.000000e-04 eta: 5:50:04 time: 0.468371 data_time: 0.025727 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.836434 loss: 0.000509 2022/09/13 06:28:43 - mmengine - INFO - Epoch(train) [130][200/586] lr: 5.000000e-04 eta: 5:49:42 time: 0.466012 data_time: 0.025918 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.857538 loss: 0.000505 2022/09/13 06:29:07 - mmengine - INFO - Epoch(train) [130][250/586] lr: 5.000000e-04 eta: 5:49:21 time: 0.476358 data_time: 0.026599 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.881723 loss: 0.000523 2022/09/13 06:29:30 - mmengine - INFO - Epoch(train) [130][300/586] lr: 5.000000e-04 eta: 5:49:00 time: 0.466535 data_time: 0.026603 memory: 15239 loss_kpt: 0.000494 acc_pose: 0.854832 loss: 0.000494 2022/09/13 06:29:53 - mmengine - INFO - Epoch(train) [130][350/586] lr: 5.000000e-04 eta: 5:48:38 time: 0.465676 data_time: 0.026989 memory: 15239 loss_kpt: 0.000519 acc_pose: 0.865368 loss: 0.000519 2022/09/13 06:30:17 - mmengine - INFO - Epoch(train) [130][400/586] lr: 5.000000e-04 eta: 5:48:17 time: 0.476489 data_time: 0.026386 memory: 15239 loss_kpt: 0.000499 acc_pose: 0.898965 loss: 0.000499 2022/09/13 06:30:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:30:41 - mmengine - INFO - Epoch(train) [130][450/586] lr: 5.000000e-04 eta: 5:47:55 time: 0.465869 data_time: 0.026453 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.883966 loss: 0.000515 2022/09/13 06:31:04 - mmengine - INFO - Epoch(train) [130][500/586] lr: 5.000000e-04 eta: 5:47:34 time: 0.469459 data_time: 0.025637 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.869309 loss: 0.000503 2022/09/13 06:31:28 - mmengine - INFO - Epoch(train) [130][550/586] lr: 5.000000e-04 eta: 5:47:13 time: 0.469674 data_time: 0.027566 memory: 15239 loss_kpt: 0.000533 acc_pose: 0.868422 loss: 0.000533 2022/09/13 06:31:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:31:44 - mmengine - INFO - Saving checkpoint at 130 epochs 2022/09/13 06:32:03 - mmengine - INFO - Epoch(val) [130][50/407] eta: 0:01:20 time: 0.224738 data_time: 0.013810 memory: 15239 2022/09/13 06:32:14 - mmengine - INFO - Epoch(val) [130][100/407] eta: 0:01:06 time: 0.218203 data_time: 0.008380 memory: 2064 2022/09/13 06:32:25 - mmengine - INFO - Epoch(val) [130][150/407] eta: 0:00:56 time: 0.220174 data_time: 0.008263 memory: 2064 2022/09/13 06:32:36 - mmengine - INFO - Epoch(val) [130][200/407] eta: 0:00:46 time: 0.222585 data_time: 0.012483 memory: 2064 2022/09/13 06:32:47 - mmengine - INFO - Epoch(val) [130][250/407] eta: 0:00:34 time: 0.220258 data_time: 0.009082 memory: 2064 2022/09/13 06:32:58 - mmengine - INFO - Epoch(val) [130][300/407] eta: 0:00:23 time: 0.217726 data_time: 0.008382 memory: 2064 2022/09/13 06:33:09 - mmengine - INFO - Epoch(val) [130][350/407] eta: 0:00:12 time: 0.219931 data_time: 0.008621 memory: 2064 2022/09/13 06:33:20 - mmengine - INFO - Epoch(val) [130][400/407] eta: 0:00:01 time: 0.216042 data_time: 0.008163 memory: 2064 2022/09/13 06:33:56 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 06:34:10 - mmengine - INFO - Epoch(val) [130][407/407] coco/AP: 0.760481 coco/AP .5: 0.903923 coco/AP .75: 0.822981 coco/AP (M): 0.720996 coco/AP (L): 0.832580 coco/AR: 0.809147 coco/AR .5: 0.940649 coco/AR .75: 0.865397 coco/AR (M): 0.763562 coco/AR (L): 0.875697 2022/09/13 06:34:34 - mmengine - INFO - Epoch(train) [131][50/586] lr: 5.000000e-04 eta: 5:46:26 time: 0.483414 data_time: 0.031942 memory: 15239 loss_kpt: 0.000516 acc_pose: 0.825666 loss: 0.000516 2022/09/13 06:34:58 - mmengine - INFO - Epoch(train) [131][100/586] lr: 5.000000e-04 eta: 5:46:05 time: 0.479048 data_time: 0.026004 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.924516 loss: 0.000513 2022/09/13 06:35:21 - mmengine - INFO - Epoch(train) [131][150/586] lr: 5.000000e-04 eta: 5:45:43 time: 0.466346 data_time: 0.027375 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.880953 loss: 0.000513 2022/09/13 06:35:44 - mmengine - INFO - Epoch(train) [131][200/586] lr: 5.000000e-04 eta: 5:45:22 time: 0.465857 data_time: 0.027182 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.825152 loss: 0.000526 2022/09/13 06:36:08 - mmengine - INFO - Epoch(train) [131][250/586] lr: 5.000000e-04 eta: 5:45:00 time: 0.478680 data_time: 0.026942 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.895018 loss: 0.000523 2022/09/13 06:36:32 - mmengine - INFO - Epoch(train) [131][300/586] lr: 5.000000e-04 eta: 5:44:39 time: 0.471478 data_time: 0.028053 memory: 15239 loss_kpt: 0.000518 acc_pose: 0.752957 loss: 0.000518 2022/09/13 06:36:56 - mmengine - INFO - Epoch(train) [131][350/586] lr: 5.000000e-04 eta: 5:44:18 time: 0.471175 data_time: 0.026358 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.938728 loss: 0.000505 2022/09/13 06:37:19 - mmengine - INFO - Epoch(train) [131][400/586] lr: 5.000000e-04 eta: 5:43:56 time: 0.469992 data_time: 0.027393 memory: 15239 loss_kpt: 0.000506 acc_pose: 0.820479 loss: 0.000506 2022/09/13 06:37:42 - mmengine - INFO - Epoch(train) [131][450/586] lr: 5.000000e-04 eta: 5:43:35 time: 0.465357 data_time: 0.025781 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.902548 loss: 0.000504 2022/09/13 06:38:06 - mmengine - INFO - Epoch(train) [131][500/586] lr: 5.000000e-04 eta: 5:43:14 time: 0.476768 data_time: 0.025795 memory: 15239 loss_kpt: 0.000523 acc_pose: 0.873698 loss: 0.000523 2022/09/13 06:38:30 - mmengine - INFO - Epoch(train) [131][550/586] lr: 5.000000e-04 eta: 5:42:52 time: 0.470697 data_time: 0.031500 memory: 15239 loss_kpt: 0.000528 acc_pose: 0.845269 loss: 0.000528 2022/09/13 06:38:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:38:47 - mmengine - INFO - Saving checkpoint at 131 epochs 2022/09/13 06:39:17 - mmengine - INFO - Epoch(train) [132][50/586] lr: 5.000000e-04 eta: 5:42:05 time: 0.471940 data_time: 0.031339 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.853972 loss: 0.000513 2022/09/13 06:39:41 - mmengine - INFO - Epoch(train) [132][100/586] lr: 5.000000e-04 eta: 5:41:44 time: 0.471648 data_time: 0.026191 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.908690 loss: 0.000509 2022/09/13 06:40:04 - mmengine - INFO - Epoch(train) [132][150/586] lr: 5.000000e-04 eta: 5:41:22 time: 0.469810 data_time: 0.026430 memory: 15239 loss_kpt: 0.000518 acc_pose: 0.827069 loss: 0.000518 2022/09/13 06:40:28 - mmengine - INFO - Epoch(train) [132][200/586] lr: 5.000000e-04 eta: 5:41:01 time: 0.468241 data_time: 0.032457 memory: 15239 loss_kpt: 0.000530 acc_pose: 0.902626 loss: 0.000530 2022/09/13 06:40:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:40:51 - mmengine - INFO - Epoch(train) [132][250/586] lr: 5.000000e-04 eta: 5:40:40 time: 0.475455 data_time: 0.026273 memory: 15239 loss_kpt: 0.000535 acc_pose: 0.892763 loss: 0.000535 2022/09/13 06:41:15 - mmengine - INFO - Epoch(train) [132][300/586] lr: 5.000000e-04 eta: 5:40:18 time: 0.464338 data_time: 0.027036 memory: 15239 loss_kpt: 0.000511 acc_pose: 0.879149 loss: 0.000511 2022/09/13 06:41:38 - mmengine - INFO - Epoch(train) [132][350/586] lr: 5.000000e-04 eta: 5:39:57 time: 0.474586 data_time: 0.027225 memory: 15239 loss_kpt: 0.000536 acc_pose: 0.897869 loss: 0.000536 2022/09/13 06:42:02 - mmengine - INFO - Epoch(train) [132][400/586] lr: 5.000000e-04 eta: 5:39:35 time: 0.471710 data_time: 0.027405 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.859989 loss: 0.000503 2022/09/13 06:42:25 - mmengine - INFO - Epoch(train) [132][450/586] lr: 5.000000e-04 eta: 5:39:14 time: 0.467074 data_time: 0.028161 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.855873 loss: 0.000526 2022/09/13 06:42:49 - mmengine - INFO - Epoch(train) [132][500/586] lr: 5.000000e-04 eta: 5:38:53 time: 0.479098 data_time: 0.027985 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.855401 loss: 0.000526 2022/09/13 06:43:13 - mmengine - INFO - Epoch(train) [132][550/586] lr: 5.000000e-04 eta: 5:38:31 time: 0.471799 data_time: 0.028122 memory: 15239 loss_kpt: 0.000516 acc_pose: 0.893967 loss: 0.000516 2022/09/13 06:43:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:43:29 - mmengine - INFO - Saving checkpoint at 132 epochs 2022/09/13 06:44:01 - mmengine - INFO - Epoch(train) [133][50/586] lr: 5.000000e-04 eta: 5:37:45 time: 0.481959 data_time: 0.039782 memory: 15239 loss_kpt: 0.000525 acc_pose: 0.890024 loss: 0.000525 2022/09/13 06:44:24 - mmengine - INFO - Epoch(train) [133][100/586] lr: 5.000000e-04 eta: 5:37:24 time: 0.473775 data_time: 0.026271 memory: 15239 loss_kpt: 0.000525 acc_pose: 0.886077 loss: 0.000525 2022/09/13 06:44:48 - mmengine - INFO - Epoch(train) [133][150/586] lr: 5.000000e-04 eta: 5:37:02 time: 0.469318 data_time: 0.026599 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.850614 loss: 0.000505 2022/09/13 06:45:11 - mmengine - INFO - Epoch(train) [133][200/586] lr: 5.000000e-04 eta: 5:36:41 time: 0.469663 data_time: 0.028807 memory: 15239 loss_kpt: 0.000499 acc_pose: 0.891995 loss: 0.000499 2022/09/13 06:45:35 - mmengine - INFO - Epoch(train) [133][250/586] lr: 5.000000e-04 eta: 5:36:20 time: 0.478972 data_time: 0.032230 memory: 15239 loss_kpt: 0.000514 acc_pose: 0.801022 loss: 0.000514 2022/09/13 06:45:58 - mmengine - INFO - Epoch(train) [133][300/586] lr: 5.000000e-04 eta: 5:35:58 time: 0.463411 data_time: 0.026697 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.842173 loss: 0.000513 2022/09/13 06:46:22 - mmengine - INFO - Epoch(train) [133][350/586] lr: 5.000000e-04 eta: 5:35:37 time: 0.471976 data_time: 0.026694 memory: 15239 loss_kpt: 0.000520 acc_pose: 0.855253 loss: 0.000520 2022/09/13 06:46:46 - mmengine - INFO - Epoch(train) [133][400/586] lr: 5.000000e-04 eta: 5:35:15 time: 0.474217 data_time: 0.031940 memory: 15239 loss_kpt: 0.000499 acc_pose: 0.806236 loss: 0.000499 2022/09/13 06:47:09 - mmengine - INFO - Epoch(train) [133][450/586] lr: 5.000000e-04 eta: 5:34:54 time: 0.464257 data_time: 0.027776 memory: 15239 loss_kpt: 0.000510 acc_pose: 0.902494 loss: 0.000510 2022/09/13 06:47:33 - mmengine - INFO - Epoch(train) [133][500/586] lr: 5.000000e-04 eta: 5:34:32 time: 0.474594 data_time: 0.025997 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.846212 loss: 0.000515 2022/09/13 06:47:56 - mmengine - INFO - Epoch(train) [133][550/586] lr: 5.000000e-04 eta: 5:34:11 time: 0.464826 data_time: 0.025960 memory: 15239 loss_kpt: 0.000511 acc_pose: 0.881446 loss: 0.000511 2022/09/13 06:48:13 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:48:13 - mmengine - INFO - Saving checkpoint at 133 epochs 2022/09/13 06:48:44 - mmengine - INFO - Epoch(train) [134][50/586] lr: 5.000000e-04 eta: 5:33:24 time: 0.479982 data_time: 0.038618 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.811102 loss: 0.000503 2022/09/13 06:48:49 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:49:07 - mmengine - INFO - Epoch(train) [134][100/586] lr: 5.000000e-04 eta: 5:33:03 time: 0.468981 data_time: 0.031857 memory: 15239 loss_kpt: 0.000514 acc_pose: 0.842596 loss: 0.000514 2022/09/13 06:49:30 - mmengine - INFO - Epoch(train) [134][150/586] lr: 5.000000e-04 eta: 5:32:41 time: 0.468824 data_time: 0.034724 memory: 15239 loss_kpt: 0.000496 acc_pose: 0.902365 loss: 0.000496 2022/09/13 06:49:54 - mmengine - INFO - Epoch(train) [134][200/586] lr: 5.000000e-04 eta: 5:32:20 time: 0.465205 data_time: 0.031981 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.840120 loss: 0.000513 2022/09/13 06:50:17 - mmengine - INFO - Epoch(train) [134][250/586] lr: 5.000000e-04 eta: 5:31:59 time: 0.475180 data_time: 0.027957 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.863411 loss: 0.000515 2022/09/13 06:50:41 - mmengine - INFO - Epoch(train) [134][300/586] lr: 5.000000e-04 eta: 5:31:37 time: 0.464494 data_time: 0.025891 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.856312 loss: 0.000509 2022/09/13 06:51:04 - mmengine - INFO - Epoch(train) [134][350/586] lr: 5.000000e-04 eta: 5:31:16 time: 0.472750 data_time: 0.027154 memory: 15239 loss_kpt: 0.000514 acc_pose: 0.758809 loss: 0.000514 2022/09/13 06:51:28 - mmengine - INFO - Epoch(train) [134][400/586] lr: 5.000000e-04 eta: 5:30:54 time: 0.468109 data_time: 0.026796 memory: 15239 loss_kpt: 0.000506 acc_pose: 0.847162 loss: 0.000506 2022/09/13 06:51:51 - mmengine - INFO - Epoch(train) [134][450/586] lr: 5.000000e-04 eta: 5:30:33 time: 0.469542 data_time: 0.026588 memory: 15239 loss_kpt: 0.000495 acc_pose: 0.903324 loss: 0.000495 2022/09/13 06:52:15 - mmengine - INFO - Epoch(train) [134][500/586] lr: 5.000000e-04 eta: 5:30:11 time: 0.472109 data_time: 0.026812 memory: 15239 loss_kpt: 0.000517 acc_pose: 0.845695 loss: 0.000517 2022/09/13 06:52:38 - mmengine - INFO - Epoch(train) [134][550/586] lr: 5.000000e-04 eta: 5:29:50 time: 0.468224 data_time: 0.026569 memory: 15239 loss_kpt: 0.000507 acc_pose: 0.863493 loss: 0.000507 2022/09/13 06:52:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:52:55 - mmengine - INFO - Saving checkpoint at 134 epochs 2022/09/13 06:53:26 - mmengine - INFO - Epoch(train) [135][50/586] lr: 5.000000e-04 eta: 5:29:04 time: 0.484452 data_time: 0.038498 memory: 15239 loss_kpt: 0.000506 acc_pose: 0.902493 loss: 0.000506 2022/09/13 06:53:50 - mmengine - INFO - Epoch(train) [135][100/586] lr: 5.000000e-04 eta: 5:28:42 time: 0.477018 data_time: 0.033679 memory: 15239 loss_kpt: 0.000526 acc_pose: 0.868262 loss: 0.000526 2022/09/13 06:54:13 - mmengine - INFO - Epoch(train) [135][150/586] lr: 5.000000e-04 eta: 5:28:21 time: 0.461104 data_time: 0.026652 memory: 15239 loss_kpt: 0.000502 acc_pose: 0.794819 loss: 0.000502 2022/09/13 06:54:37 - mmengine - INFO - Epoch(train) [135][200/586] lr: 5.000000e-04 eta: 5:27:59 time: 0.477788 data_time: 0.029958 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.853799 loss: 0.000498 2022/09/13 06:55:01 - mmengine - INFO - Epoch(train) [135][250/586] lr: 5.000000e-04 eta: 5:27:38 time: 0.468318 data_time: 0.026643 memory: 15239 loss_kpt: 0.000514 acc_pose: 0.875120 loss: 0.000514 2022/09/13 06:55:24 - mmengine - INFO - Epoch(train) [135][300/586] lr: 5.000000e-04 eta: 5:27:16 time: 0.462329 data_time: 0.026832 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.884924 loss: 0.000505 2022/09/13 06:55:48 - mmengine - INFO - Epoch(train) [135][350/586] lr: 5.000000e-04 eta: 5:26:55 time: 0.476881 data_time: 0.027635 memory: 15239 loss_kpt: 0.000507 acc_pose: 0.864797 loss: 0.000507 2022/09/13 06:56:11 - mmengine - INFO - Epoch(train) [135][400/586] lr: 5.000000e-04 eta: 5:26:33 time: 0.470659 data_time: 0.026930 memory: 15239 loss_kpt: 0.000506 acc_pose: 0.821066 loss: 0.000506 2022/09/13 06:56:35 - mmengine - INFO - Epoch(train) [135][450/586] lr: 5.000000e-04 eta: 5:26:12 time: 0.473582 data_time: 0.025976 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.863141 loss: 0.000498 2022/09/13 06:56:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:56:58 - mmengine - INFO - Epoch(train) [135][500/586] lr: 5.000000e-04 eta: 5:25:51 time: 0.468520 data_time: 0.030906 memory: 15239 loss_kpt: 0.000508 acc_pose: 0.861059 loss: 0.000508 2022/09/13 06:57:22 - mmengine - INFO - Epoch(train) [135][550/586] lr: 5.000000e-04 eta: 5:25:29 time: 0.471106 data_time: 0.026188 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.846763 loss: 0.000512 2022/09/13 06:57:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 06:57:39 - mmengine - INFO - Saving checkpoint at 135 epochs 2022/09/13 06:58:10 - mmengine - INFO - Epoch(train) [136][50/586] lr: 5.000000e-04 eta: 5:24:43 time: 0.483039 data_time: 0.035268 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.857307 loss: 0.000503 2022/09/13 06:58:34 - mmengine - INFO - Epoch(train) [136][100/586] lr: 5.000000e-04 eta: 5:24:22 time: 0.475802 data_time: 0.028704 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.899622 loss: 0.000509 2022/09/13 06:58:57 - mmengine - INFO - Epoch(train) [136][150/586] lr: 5.000000e-04 eta: 5:24:00 time: 0.468071 data_time: 0.030841 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.871771 loss: 0.000515 2022/09/13 06:59:21 - mmengine - INFO - Epoch(train) [136][200/586] lr: 5.000000e-04 eta: 5:23:39 time: 0.473991 data_time: 0.026361 memory: 15239 loss_kpt: 0.000527 acc_pose: 0.856932 loss: 0.000527 2022/09/13 06:59:44 - mmengine - INFO - Epoch(train) [136][250/586] lr: 5.000000e-04 eta: 5:23:17 time: 0.466080 data_time: 0.026453 memory: 15239 loss_kpt: 0.000502 acc_pose: 0.845543 loss: 0.000502 2022/09/13 07:00:08 - mmengine - INFO - Epoch(train) [136][300/586] lr: 5.000000e-04 eta: 5:22:56 time: 0.475121 data_time: 0.027885 memory: 15239 loss_kpt: 0.000511 acc_pose: 0.878074 loss: 0.000511 2022/09/13 07:00:32 - mmengine - INFO - Epoch(train) [136][350/586] lr: 5.000000e-04 eta: 5:22:35 time: 0.474435 data_time: 0.026683 memory: 15239 loss_kpt: 0.000532 acc_pose: 0.839668 loss: 0.000532 2022/09/13 07:00:55 - mmengine - INFO - Epoch(train) [136][400/586] lr: 5.000000e-04 eta: 5:22:13 time: 0.467712 data_time: 0.026838 memory: 15239 loss_kpt: 0.000514 acc_pose: 0.853128 loss: 0.000514 2022/09/13 07:01:19 - mmengine - INFO - Epoch(train) [136][450/586] lr: 5.000000e-04 eta: 5:21:52 time: 0.474149 data_time: 0.026761 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.908224 loss: 0.000497 2022/09/13 07:01:42 - mmengine - INFO - Epoch(train) [136][500/586] lr: 5.000000e-04 eta: 5:21:30 time: 0.465938 data_time: 0.027033 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.817341 loss: 0.000503 2022/09/13 07:02:06 - mmengine - INFO - Epoch(train) [136][550/586] lr: 5.000000e-04 eta: 5:21:09 time: 0.470557 data_time: 0.027017 memory: 15239 loss_kpt: 0.000516 acc_pose: 0.864166 loss: 0.000516 2022/09/13 07:02:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:02:22 - mmengine - INFO - Saving checkpoint at 136 epochs 2022/09/13 07:02:53 - mmengine - INFO - Epoch(train) [137][50/586] lr: 5.000000e-04 eta: 5:20:23 time: 0.479401 data_time: 0.033829 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.877503 loss: 0.000509 2022/09/13 07:03:17 - mmengine - INFO - Epoch(train) [137][100/586] lr: 5.000000e-04 eta: 5:20:01 time: 0.465423 data_time: 0.027157 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.859223 loss: 0.000505 2022/09/13 07:03:40 - mmengine - INFO - Epoch(train) [137][150/586] lr: 5.000000e-04 eta: 5:19:40 time: 0.467174 data_time: 0.025857 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.839641 loss: 0.000498 2022/09/13 07:04:04 - mmengine - INFO - Epoch(train) [137][200/586] lr: 5.000000e-04 eta: 5:19:18 time: 0.471742 data_time: 0.026388 memory: 15239 loss_kpt: 0.000508 acc_pose: 0.849978 loss: 0.000508 2022/09/13 07:04:27 - mmengine - INFO - Epoch(train) [137][250/586] lr: 5.000000e-04 eta: 5:18:57 time: 0.467252 data_time: 0.027627 memory: 15239 loss_kpt: 0.000514 acc_pose: 0.780432 loss: 0.000514 2022/09/13 07:04:51 - mmengine - INFO - Epoch(train) [137][300/586] lr: 5.000000e-04 eta: 5:18:35 time: 0.469872 data_time: 0.027500 memory: 15239 loss_kpt: 0.000494 acc_pose: 0.893625 loss: 0.000494 2022/09/13 07:04:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:05:14 - mmengine - INFO - Epoch(train) [137][350/586] lr: 5.000000e-04 eta: 5:18:14 time: 0.472643 data_time: 0.027816 memory: 15239 loss_kpt: 0.000521 acc_pose: 0.877835 loss: 0.000521 2022/09/13 07:05:38 - mmengine - INFO - Epoch(train) [137][400/586] lr: 5.000000e-04 eta: 5:17:52 time: 0.469520 data_time: 0.027037 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.868811 loss: 0.000512 2022/09/13 07:06:01 - mmengine - INFO - Epoch(train) [137][450/586] lr: 5.000000e-04 eta: 5:17:31 time: 0.463900 data_time: 0.026564 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.826916 loss: 0.000512 2022/09/13 07:06:25 - mmengine - INFO - Epoch(train) [137][500/586] lr: 5.000000e-04 eta: 5:17:09 time: 0.475326 data_time: 0.030020 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.845521 loss: 0.000512 2022/09/13 07:06:48 - mmengine - INFO - Epoch(train) [137][550/586] lr: 5.000000e-04 eta: 5:16:48 time: 0.464034 data_time: 0.026464 memory: 15239 loss_kpt: 0.000506 acc_pose: 0.880128 loss: 0.000506 2022/09/13 07:07:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:07:04 - mmengine - INFO - Saving checkpoint at 137 epochs 2022/09/13 07:07:36 - mmengine - INFO - Epoch(train) [138][50/586] lr: 5.000000e-04 eta: 5:16:02 time: 0.484965 data_time: 0.032714 memory: 15239 loss_kpt: 0.000507 acc_pose: 0.878616 loss: 0.000507 2022/09/13 07:07:59 - mmengine - INFO - Epoch(train) [138][100/586] lr: 5.000000e-04 eta: 5:15:40 time: 0.465195 data_time: 0.027178 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.896126 loss: 0.000515 2022/09/13 07:08:23 - mmengine - INFO - Epoch(train) [138][150/586] lr: 5.000000e-04 eta: 5:15:19 time: 0.472413 data_time: 0.029861 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.833857 loss: 0.000512 2022/09/13 07:08:46 - mmengine - INFO - Epoch(train) [138][200/586] lr: 5.000000e-04 eta: 5:14:57 time: 0.468856 data_time: 0.025786 memory: 15239 loss_kpt: 0.000516 acc_pose: 0.848876 loss: 0.000516 2022/09/13 07:09:09 - mmengine - INFO - Epoch(train) [138][250/586] lr: 5.000000e-04 eta: 5:14:36 time: 0.467071 data_time: 0.026471 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.831279 loss: 0.000515 2022/09/13 07:09:33 - mmengine - INFO - Epoch(train) [138][300/586] lr: 5.000000e-04 eta: 5:14:14 time: 0.469155 data_time: 0.026193 memory: 15239 loss_kpt: 0.000507 acc_pose: 0.764611 loss: 0.000507 2022/09/13 07:09:56 - mmengine - INFO - Epoch(train) [138][350/586] lr: 5.000000e-04 eta: 5:13:53 time: 0.471488 data_time: 0.025847 memory: 15239 loss_kpt: 0.000522 acc_pose: 0.903082 loss: 0.000522 2022/09/13 07:10:20 - mmengine - INFO - Epoch(train) [138][400/586] lr: 5.000000e-04 eta: 5:13:31 time: 0.469183 data_time: 0.026532 memory: 15239 loss_kpt: 0.000510 acc_pose: 0.839012 loss: 0.000510 2022/09/13 07:10:44 - mmengine - INFO - Epoch(train) [138][450/586] lr: 5.000000e-04 eta: 5:13:10 time: 0.472031 data_time: 0.026996 memory: 15239 loss_kpt: 0.000518 acc_pose: 0.828861 loss: 0.000518 2022/09/13 07:11:07 - mmengine - INFO - Epoch(train) [138][500/586] lr: 5.000000e-04 eta: 5:12:48 time: 0.468833 data_time: 0.025737 memory: 15239 loss_kpt: 0.000510 acc_pose: 0.793028 loss: 0.000510 2022/09/13 07:11:30 - mmengine - INFO - Epoch(train) [138][550/586] lr: 5.000000e-04 eta: 5:12:27 time: 0.467838 data_time: 0.027585 memory: 15239 loss_kpt: 0.000508 acc_pose: 0.868437 loss: 0.000508 2022/09/13 07:11:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:11:47 - mmengine - INFO - Saving checkpoint at 138 epochs 2022/09/13 07:12:18 - mmengine - INFO - Epoch(train) [139][50/586] lr: 5.000000e-04 eta: 5:11:41 time: 0.473116 data_time: 0.030987 memory: 15239 loss_kpt: 0.000490 acc_pose: 0.902317 loss: 0.000490 2022/09/13 07:12:42 - mmengine - INFO - Epoch(train) [139][100/586] lr: 5.000000e-04 eta: 5:11:20 time: 0.474201 data_time: 0.026484 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.907714 loss: 0.000509 2022/09/13 07:12:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:13:05 - mmengine - INFO - Epoch(train) [139][150/586] lr: 5.000000e-04 eta: 5:10:58 time: 0.464343 data_time: 0.027835 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.840580 loss: 0.000500 2022/09/13 07:13:29 - mmengine - INFO - Epoch(train) [139][200/586] lr: 5.000000e-04 eta: 5:10:37 time: 0.474144 data_time: 0.026439 memory: 15239 loss_kpt: 0.000517 acc_pose: 0.864416 loss: 0.000517 2022/09/13 07:13:52 - mmengine - INFO - Epoch(train) [139][250/586] lr: 5.000000e-04 eta: 5:10:15 time: 0.462866 data_time: 0.026502 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.860627 loss: 0.000498 2022/09/13 07:14:15 - mmengine - INFO - Epoch(train) [139][300/586] lr: 5.000000e-04 eta: 5:09:53 time: 0.469743 data_time: 0.025847 memory: 15239 loss_kpt: 0.000511 acc_pose: 0.845560 loss: 0.000511 2022/09/13 07:14:39 - mmengine - INFO - Epoch(train) [139][350/586] lr: 5.000000e-04 eta: 5:09:32 time: 0.472464 data_time: 0.026961 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.895248 loss: 0.000515 2022/09/13 07:15:03 - mmengine - INFO - Epoch(train) [139][400/586] lr: 5.000000e-04 eta: 5:09:10 time: 0.473147 data_time: 0.027132 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.873186 loss: 0.000515 2022/09/13 07:15:26 - mmengine - INFO - Epoch(train) [139][450/586] lr: 5.000000e-04 eta: 5:08:49 time: 0.462887 data_time: 0.026311 memory: 15239 loss_kpt: 0.000516 acc_pose: 0.894043 loss: 0.000516 2022/09/13 07:15:49 - mmengine - INFO - Epoch(train) [139][500/586] lr: 5.000000e-04 eta: 5:08:27 time: 0.468629 data_time: 0.026036 memory: 15239 loss_kpt: 0.000502 acc_pose: 0.794711 loss: 0.000502 2022/09/13 07:16:13 - mmengine - INFO - Epoch(train) [139][550/586] lr: 5.000000e-04 eta: 5:08:06 time: 0.474100 data_time: 0.026665 memory: 15239 loss_kpt: 0.000517 acc_pose: 0.897145 loss: 0.000517 2022/09/13 07:16:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:16:30 - mmengine - INFO - Saving checkpoint at 139 epochs 2022/09/13 07:17:01 - mmengine - INFO - Epoch(train) [140][50/586] lr: 5.000000e-04 eta: 5:07:20 time: 0.477357 data_time: 0.040391 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.908700 loss: 0.000500 2022/09/13 07:17:25 - mmengine - INFO - Epoch(train) [140][100/586] lr: 5.000000e-04 eta: 5:06:59 time: 0.476506 data_time: 0.029225 memory: 15239 loss_kpt: 0.000502 acc_pose: 0.863713 loss: 0.000502 2022/09/13 07:17:49 - mmengine - INFO - Epoch(train) [140][150/586] lr: 5.000000e-04 eta: 5:06:37 time: 0.469202 data_time: 0.030306 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.831889 loss: 0.000513 2022/09/13 07:18:13 - mmengine - INFO - Epoch(train) [140][200/586] lr: 5.000000e-04 eta: 5:06:16 time: 0.480737 data_time: 0.028839 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.883118 loss: 0.000513 2022/09/13 07:18:36 - mmengine - INFO - Epoch(train) [140][250/586] lr: 5.000000e-04 eta: 5:05:54 time: 0.467078 data_time: 0.029154 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.873789 loss: 0.000513 2022/09/13 07:19:00 - mmengine - INFO - Epoch(train) [140][300/586] lr: 5.000000e-04 eta: 5:05:33 time: 0.477147 data_time: 0.029132 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.879870 loss: 0.000509 2022/09/13 07:19:23 - mmengine - INFO - Epoch(train) [140][350/586] lr: 5.000000e-04 eta: 5:05:11 time: 0.465996 data_time: 0.026074 memory: 15239 loss_kpt: 0.000495 acc_pose: 0.891469 loss: 0.000495 2022/09/13 07:19:47 - mmengine - INFO - Epoch(train) [140][400/586] lr: 5.000000e-04 eta: 5:04:50 time: 0.471055 data_time: 0.027386 memory: 15239 loss_kpt: 0.000506 acc_pose: 0.883333 loss: 0.000506 2022/09/13 07:20:10 - mmengine - INFO - Epoch(train) [140][450/586] lr: 5.000000e-04 eta: 5:04:28 time: 0.469130 data_time: 0.026633 memory: 15239 loss_kpt: 0.000527 acc_pose: 0.877876 loss: 0.000527 2022/09/13 07:20:34 - mmengine - INFO - Epoch(train) [140][500/586] lr: 5.000000e-04 eta: 5:04:07 time: 0.468373 data_time: 0.026490 memory: 15239 loss_kpt: 0.000499 acc_pose: 0.856310 loss: 0.000499 2022/09/13 07:20:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:20:57 - mmengine - INFO - Epoch(train) [140][550/586] lr: 5.000000e-04 eta: 5:03:45 time: 0.467815 data_time: 0.030473 memory: 15239 loss_kpt: 0.000493 acc_pose: 0.807021 loss: 0.000493 2022/09/13 07:21:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:21:14 - mmengine - INFO - Saving checkpoint at 140 epochs 2022/09/13 07:21:32 - mmengine - INFO - Epoch(val) [140][50/407] eta: 0:01:22 time: 0.231577 data_time: 0.017114 memory: 15239 2022/09/13 07:21:43 - mmengine - INFO - Epoch(val) [140][100/407] eta: 0:01:07 time: 0.220421 data_time: 0.008811 memory: 2064 2022/09/13 07:21:54 - mmengine - INFO - Epoch(val) [140][150/407] eta: 0:00:56 time: 0.218531 data_time: 0.008667 memory: 2064 2022/09/13 07:22:05 - mmengine - INFO - Epoch(val) [140][200/407] eta: 0:00:45 time: 0.221046 data_time: 0.008830 memory: 2064 2022/09/13 07:22:16 - mmengine - INFO - Epoch(val) [140][250/407] eta: 0:00:34 time: 0.218554 data_time: 0.008705 memory: 2064 2022/09/13 07:22:27 - mmengine - INFO - Epoch(val) [140][300/407] eta: 0:00:23 time: 0.218724 data_time: 0.008414 memory: 2064 2022/09/13 07:22:38 - mmengine - INFO - Epoch(val) [140][350/407] eta: 0:00:12 time: 0.219463 data_time: 0.008519 memory: 2064 2022/09/13 07:22:49 - mmengine - INFO - Epoch(val) [140][400/407] eta: 0:00:01 time: 0.219218 data_time: 0.010601 memory: 2064 2022/09/13 07:23:26 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 07:23:39 - mmengine - INFO - Epoch(val) [140][407/407] coco/AP: 0.762501 coco/AP .5: 0.907244 coco/AP .75: 0.824469 coco/AP (M): 0.721876 coco/AP (L): 0.835460 coco/AR: 0.811792 coco/AR .5: 0.943797 coco/AR .75: 0.867286 coco/AR (M): 0.766239 coco/AR (L): 0.877220 2022/09/13 07:24:04 - mmengine - INFO - Epoch(train) [141][50/586] lr: 5.000000e-04 eta: 5:03:00 time: 0.485872 data_time: 0.031951 memory: 15239 loss_kpt: 0.000495 acc_pose: 0.894746 loss: 0.000495 2022/09/13 07:24:27 - mmengine - INFO - Epoch(train) [141][100/586] lr: 5.000000e-04 eta: 5:02:38 time: 0.461943 data_time: 0.026777 memory: 15239 loss_kpt: 0.000514 acc_pose: 0.835870 loss: 0.000514 2022/09/13 07:24:51 - mmengine - INFO - Epoch(train) [141][150/586] lr: 5.000000e-04 eta: 5:02:17 time: 0.479558 data_time: 0.027491 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.877757 loss: 0.000500 2022/09/13 07:25:14 - mmengine - INFO - Epoch(train) [141][200/586] lr: 5.000000e-04 eta: 5:01:56 time: 0.471770 data_time: 0.027961 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.893182 loss: 0.000509 2022/09/13 07:25:38 - mmengine - INFO - Epoch(train) [141][250/586] lr: 5.000000e-04 eta: 5:01:34 time: 0.469185 data_time: 0.027286 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.890083 loss: 0.000529 2022/09/13 07:26:01 - mmengine - INFO - Epoch(train) [141][300/586] lr: 5.000000e-04 eta: 5:01:13 time: 0.472104 data_time: 0.028216 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.880765 loss: 0.000497 2022/09/13 07:26:25 - mmengine - INFO - Epoch(train) [141][350/586] lr: 5.000000e-04 eta: 5:00:51 time: 0.473028 data_time: 0.026197 memory: 15239 loss_kpt: 0.000518 acc_pose: 0.841003 loss: 0.000518 2022/09/13 07:26:49 - mmengine - INFO - Epoch(train) [141][400/586] lr: 5.000000e-04 eta: 5:00:29 time: 0.467036 data_time: 0.027058 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.858297 loss: 0.000505 2022/09/13 07:27:12 - mmengine - INFO - Epoch(train) [141][450/586] lr: 5.000000e-04 eta: 5:00:08 time: 0.474486 data_time: 0.030758 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.884138 loss: 0.000512 2022/09/13 07:27:36 - mmengine - INFO - Epoch(train) [141][500/586] lr: 5.000000e-04 eta: 4:59:46 time: 0.470514 data_time: 0.025500 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.876517 loss: 0.000515 2022/09/13 07:27:59 - mmengine - INFO - Epoch(train) [141][550/586] lr: 5.000000e-04 eta: 4:59:25 time: 0.464893 data_time: 0.025686 memory: 15239 loss_kpt: 0.000511 acc_pose: 0.850375 loss: 0.000511 2022/09/13 07:28:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:28:16 - mmengine - INFO - Saving checkpoint at 141 epochs 2022/09/13 07:28:46 - mmengine - INFO - Epoch(train) [142][50/586] lr: 5.000000e-04 eta: 4:58:40 time: 0.479404 data_time: 0.035532 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.887702 loss: 0.000503 2022/09/13 07:29:10 - mmengine - INFO - Epoch(train) [142][100/586] lr: 5.000000e-04 eta: 4:58:18 time: 0.477165 data_time: 0.028798 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.866090 loss: 0.000529 2022/09/13 07:29:34 - mmengine - INFO - Epoch(train) [142][150/586] lr: 5.000000e-04 eta: 4:57:57 time: 0.477698 data_time: 0.029313 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.880691 loss: 0.000498 2022/09/13 07:29:58 - mmengine - INFO - Epoch(train) [142][200/586] lr: 5.000000e-04 eta: 4:57:35 time: 0.468107 data_time: 0.030524 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.808647 loss: 0.000512 2022/09/13 07:30:21 - mmengine - INFO - Epoch(train) [142][250/586] lr: 5.000000e-04 eta: 4:57:14 time: 0.470176 data_time: 0.026619 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.833622 loss: 0.000497 2022/09/13 07:30:45 - mmengine - INFO - Epoch(train) [142][300/586] lr: 5.000000e-04 eta: 4:56:52 time: 0.474097 data_time: 0.027348 memory: 15239 loss_kpt: 0.000495 acc_pose: 0.852572 loss: 0.000495 2022/09/13 07:31:08 - mmengine - INFO - Epoch(train) [142][350/586] lr: 5.000000e-04 eta: 4:56:31 time: 0.469537 data_time: 0.027448 memory: 15239 loss_kpt: 0.000502 acc_pose: 0.878316 loss: 0.000502 2022/09/13 07:31:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:31:32 - mmengine - INFO - Epoch(train) [142][400/586] lr: 5.000000e-04 eta: 4:56:09 time: 0.466419 data_time: 0.026485 memory: 15239 loss_kpt: 0.000511 acc_pose: 0.869993 loss: 0.000511 2022/09/13 07:31:55 - mmengine - INFO - Epoch(train) [142][450/586] lr: 5.000000e-04 eta: 4:55:48 time: 0.474596 data_time: 0.026116 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.880257 loss: 0.000505 2022/09/13 07:32:19 - mmengine - INFO - Epoch(train) [142][500/586] lr: 5.000000e-04 eta: 4:55:26 time: 0.473535 data_time: 0.026350 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.876036 loss: 0.000505 2022/09/13 07:32:43 - mmengine - INFO - Epoch(train) [142][550/586] lr: 5.000000e-04 eta: 4:55:05 time: 0.469085 data_time: 0.027801 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.869783 loss: 0.000505 2022/09/13 07:32:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:33:00 - mmengine - INFO - Saving checkpoint at 142 epochs 2022/09/13 07:33:30 - mmengine - INFO - Epoch(train) [143][50/586] lr: 5.000000e-04 eta: 4:54:20 time: 0.481444 data_time: 0.039046 memory: 15239 loss_kpt: 0.000495 acc_pose: 0.778612 loss: 0.000495 2022/09/13 07:33:54 - mmengine - INFO - Epoch(train) [143][100/586] lr: 5.000000e-04 eta: 4:53:58 time: 0.477755 data_time: 0.030088 memory: 15239 loss_kpt: 0.000507 acc_pose: 0.870039 loss: 0.000507 2022/09/13 07:34:18 - mmengine - INFO - Epoch(train) [143][150/586] lr: 5.000000e-04 eta: 4:53:37 time: 0.476292 data_time: 0.028472 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.889953 loss: 0.000529 2022/09/13 07:34:42 - mmengine - INFO - Epoch(train) [143][200/586] lr: 5.000000e-04 eta: 4:53:16 time: 0.483017 data_time: 0.037783 memory: 15239 loss_kpt: 0.000520 acc_pose: 0.929260 loss: 0.000520 2022/09/13 07:35:06 - mmengine - INFO - Epoch(train) [143][250/586] lr: 5.000000e-04 eta: 4:52:54 time: 0.466689 data_time: 0.028873 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.821179 loss: 0.000515 2022/09/13 07:35:29 - mmengine - INFO - Epoch(train) [143][300/586] lr: 5.000000e-04 eta: 4:52:32 time: 0.472667 data_time: 0.029363 memory: 15239 loss_kpt: 0.000507 acc_pose: 0.864290 loss: 0.000507 2022/09/13 07:35:54 - mmengine - INFO - Epoch(train) [143][350/586] lr: 5.000000e-04 eta: 4:52:11 time: 0.484711 data_time: 0.030015 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.886956 loss: 0.000503 2022/09/13 07:36:17 - mmengine - INFO - Epoch(train) [143][400/586] lr: 5.000000e-04 eta: 4:51:50 time: 0.468954 data_time: 0.031519 memory: 15239 loss_kpt: 0.000502 acc_pose: 0.840089 loss: 0.000502 2022/09/13 07:36:41 - mmengine - INFO - Epoch(train) [143][450/586] lr: 5.000000e-04 eta: 4:51:28 time: 0.482974 data_time: 0.034267 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.872095 loss: 0.000503 2022/09/13 07:37:05 - mmengine - INFO - Epoch(train) [143][500/586] lr: 5.000000e-04 eta: 4:51:07 time: 0.474724 data_time: 0.031184 memory: 15239 loss_kpt: 0.000514 acc_pose: 0.853732 loss: 0.000514 2022/09/13 07:37:28 - mmengine - INFO - Epoch(train) [143][550/586] lr: 5.000000e-04 eta: 4:50:45 time: 0.467102 data_time: 0.034847 memory: 15239 loss_kpt: 0.000507 acc_pose: 0.927140 loss: 0.000507 2022/09/13 07:37:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:37:45 - mmengine - INFO - Saving checkpoint at 143 epochs 2022/09/13 07:38:16 - mmengine - INFO - Epoch(train) [144][50/586] lr: 5.000000e-04 eta: 4:50:00 time: 0.479629 data_time: 0.040163 memory: 15239 loss_kpt: 0.000496 acc_pose: 0.835869 loss: 0.000496 2022/09/13 07:38:39 - mmengine - INFO - Epoch(train) [144][100/586] lr: 5.000000e-04 eta: 4:49:39 time: 0.467941 data_time: 0.032439 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.862315 loss: 0.000509 2022/09/13 07:39:03 - mmengine - INFO - Epoch(train) [144][150/586] lr: 5.000000e-04 eta: 4:49:17 time: 0.475804 data_time: 0.032184 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.878291 loss: 0.000500 2022/09/13 07:39:27 - mmengine - INFO - Epoch(train) [144][200/586] lr: 5.000000e-04 eta: 4:48:56 time: 0.467776 data_time: 0.031241 memory: 15239 loss_kpt: 0.000499 acc_pose: 0.900994 loss: 0.000499 2022/09/13 07:39:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:39:51 - mmengine - INFO - Epoch(train) [144][250/586] lr: 5.000000e-04 eta: 4:48:34 time: 0.479675 data_time: 0.035086 memory: 15239 loss_kpt: 0.000510 acc_pose: 0.846963 loss: 0.000510 2022/09/13 07:40:14 - mmengine - INFO - Epoch(train) [144][300/586] lr: 5.000000e-04 eta: 4:48:13 time: 0.465451 data_time: 0.030065 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.893415 loss: 0.000512 2022/09/13 07:40:38 - mmengine - INFO - Epoch(train) [144][350/586] lr: 5.000000e-04 eta: 4:47:51 time: 0.474051 data_time: 0.025536 memory: 15239 loss_kpt: 0.000494 acc_pose: 0.881540 loss: 0.000494 2022/09/13 07:41:01 - mmengine - INFO - Epoch(train) [144][400/586] lr: 5.000000e-04 eta: 4:47:30 time: 0.466599 data_time: 0.025910 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.801021 loss: 0.000504 2022/09/13 07:41:24 - mmengine - INFO - Epoch(train) [144][450/586] lr: 5.000000e-04 eta: 4:47:08 time: 0.466225 data_time: 0.026188 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.809018 loss: 0.000509 2022/09/13 07:41:48 - mmengine - INFO - Epoch(train) [144][500/586] lr: 5.000000e-04 eta: 4:46:46 time: 0.471080 data_time: 0.026572 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.850950 loss: 0.000503 2022/09/13 07:42:11 - mmengine - INFO - Epoch(train) [144][550/586] lr: 5.000000e-04 eta: 4:46:25 time: 0.465343 data_time: 0.029430 memory: 15239 loss_kpt: 0.000502 acc_pose: 0.800406 loss: 0.000502 2022/09/13 07:42:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:42:28 - mmengine - INFO - Saving checkpoint at 144 epochs 2022/09/13 07:42:59 - mmengine - INFO - Epoch(train) [145][50/586] lr: 5.000000e-04 eta: 4:45:40 time: 0.483090 data_time: 0.033083 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.875661 loss: 0.000497 2022/09/13 07:43:23 - mmengine - INFO - Epoch(train) [145][100/586] lr: 5.000000e-04 eta: 4:45:19 time: 0.478804 data_time: 0.027538 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.915044 loss: 0.000498 2022/09/13 07:43:47 - mmengine - INFO - Epoch(train) [145][150/586] lr: 5.000000e-04 eta: 4:44:57 time: 0.463144 data_time: 0.027392 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.812431 loss: 0.000505 2022/09/13 07:44:10 - mmengine - INFO - Epoch(train) [145][200/586] lr: 5.000000e-04 eta: 4:44:35 time: 0.474527 data_time: 0.025892 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.854510 loss: 0.000509 2022/09/13 07:44:34 - mmengine - INFO - Epoch(train) [145][250/586] lr: 5.000000e-04 eta: 4:44:14 time: 0.468148 data_time: 0.028341 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.858768 loss: 0.000509 2022/09/13 07:44:57 - mmengine - INFO - Epoch(train) [145][300/586] lr: 5.000000e-04 eta: 4:43:52 time: 0.469459 data_time: 0.028107 memory: 15239 loss_kpt: 0.000521 acc_pose: 0.893267 loss: 0.000521 2022/09/13 07:45:21 - mmengine - INFO - Epoch(train) [145][350/586] lr: 5.000000e-04 eta: 4:43:31 time: 0.475434 data_time: 0.026067 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.856926 loss: 0.000512 2022/09/13 07:45:44 - mmengine - INFO - Epoch(train) [145][400/586] lr: 5.000000e-04 eta: 4:43:09 time: 0.464293 data_time: 0.026550 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.863399 loss: 0.000504 2022/09/13 07:46:08 - mmengine - INFO - Epoch(train) [145][450/586] lr: 5.000000e-04 eta: 4:42:47 time: 0.470391 data_time: 0.026042 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.869168 loss: 0.000505 2022/09/13 07:46:31 - mmengine - INFO - Epoch(train) [145][500/586] lr: 5.000000e-04 eta: 4:42:26 time: 0.471366 data_time: 0.026703 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.877334 loss: 0.000512 2022/09/13 07:46:55 - mmengine - INFO - Epoch(train) [145][550/586] lr: 5.000000e-04 eta: 4:42:04 time: 0.470097 data_time: 0.027039 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.818966 loss: 0.000500 2022/09/13 07:47:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:47:11 - mmengine - INFO - Saving checkpoint at 145 epochs 2022/09/13 07:47:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:47:42 - mmengine - INFO - Epoch(train) [146][50/586] lr: 5.000000e-04 eta: 4:41:20 time: 0.479798 data_time: 0.030873 memory: 15239 loss_kpt: 0.000492 acc_pose: 0.880773 loss: 0.000492 2022/09/13 07:48:06 - mmengine - INFO - Epoch(train) [146][100/586] lr: 5.000000e-04 eta: 4:40:58 time: 0.479008 data_time: 0.026285 memory: 15239 loss_kpt: 0.000499 acc_pose: 0.851605 loss: 0.000499 2022/09/13 07:48:30 - mmengine - INFO - Epoch(train) [146][150/586] lr: 5.000000e-04 eta: 4:40:37 time: 0.467076 data_time: 0.027028 memory: 15239 loss_kpt: 0.000508 acc_pose: 0.863849 loss: 0.000508 2022/09/13 07:48:53 - mmengine - INFO - Epoch(train) [146][200/586] lr: 5.000000e-04 eta: 4:40:15 time: 0.469563 data_time: 0.027766 memory: 15239 loss_kpt: 0.000488 acc_pose: 0.832877 loss: 0.000488 2022/09/13 07:49:17 - mmengine - INFO - Epoch(train) [146][250/586] lr: 5.000000e-04 eta: 4:39:53 time: 0.473301 data_time: 0.026249 memory: 15239 loss_kpt: 0.000490 acc_pose: 0.891921 loss: 0.000490 2022/09/13 07:49:40 - mmengine - INFO - Epoch(train) [146][300/586] lr: 5.000000e-04 eta: 4:39:32 time: 0.469481 data_time: 0.026811 memory: 15239 loss_kpt: 0.000491 acc_pose: 0.913172 loss: 0.000491 2022/09/13 07:50:04 - mmengine - INFO - Epoch(train) [146][350/586] lr: 5.000000e-04 eta: 4:39:10 time: 0.475728 data_time: 0.029003 memory: 15239 loss_kpt: 0.000519 acc_pose: 0.857148 loss: 0.000519 2022/09/13 07:50:27 - mmengine - INFO - Epoch(train) [146][400/586] lr: 5.000000e-04 eta: 4:38:49 time: 0.469069 data_time: 0.026256 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.851211 loss: 0.000498 2022/09/13 07:50:51 - mmengine - INFO - Epoch(train) [146][450/586] lr: 5.000000e-04 eta: 4:38:27 time: 0.467132 data_time: 0.025917 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.850076 loss: 0.000515 2022/09/13 07:51:14 - mmengine - INFO - Epoch(train) [146][500/586] lr: 5.000000e-04 eta: 4:38:05 time: 0.470839 data_time: 0.027873 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.833739 loss: 0.000504 2022/09/13 07:51:38 - mmengine - INFO - Epoch(train) [146][550/586] lr: 5.000000e-04 eta: 4:37:44 time: 0.471313 data_time: 0.028025 memory: 15239 loss_kpt: 0.000502 acc_pose: 0.884150 loss: 0.000502 2022/09/13 07:51:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:51:55 - mmengine - INFO - Saving checkpoint at 146 epochs 2022/09/13 07:52:26 - mmengine - INFO - Epoch(train) [147][50/586] lr: 5.000000e-04 eta: 4:36:59 time: 0.479462 data_time: 0.042646 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.871691 loss: 0.000509 2022/09/13 07:52:50 - mmengine - INFO - Epoch(train) [147][100/586] lr: 5.000000e-04 eta: 4:36:38 time: 0.477996 data_time: 0.027463 memory: 15239 loss_kpt: 0.000506 acc_pose: 0.860059 loss: 0.000506 2022/09/13 07:53:13 - mmengine - INFO - Epoch(train) [147][150/586] lr: 5.000000e-04 eta: 4:36:16 time: 0.466603 data_time: 0.027204 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.848789 loss: 0.000509 2022/09/13 07:53:37 - mmengine - INFO - Epoch(train) [147][200/586] lr: 5.000000e-04 eta: 4:35:55 time: 0.466729 data_time: 0.027004 memory: 15239 loss_kpt: 0.000499 acc_pose: 0.842615 loss: 0.000499 2022/09/13 07:54:01 - mmengine - INFO - Epoch(train) [147][250/586] lr: 5.000000e-04 eta: 4:35:33 time: 0.478373 data_time: 0.027003 memory: 15239 loss_kpt: 0.000493 acc_pose: 0.855099 loss: 0.000493 2022/09/13 07:54:24 - mmengine - INFO - Epoch(train) [147][300/586] lr: 5.000000e-04 eta: 4:35:11 time: 0.464618 data_time: 0.026522 memory: 15239 loss_kpt: 0.000494 acc_pose: 0.869264 loss: 0.000494 2022/09/13 07:54:48 - mmengine - INFO - Epoch(train) [147][350/586] lr: 5.000000e-04 eta: 4:34:50 time: 0.475070 data_time: 0.026403 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.856574 loss: 0.000500 2022/09/13 07:55:11 - mmengine - INFO - Epoch(train) [147][400/586] lr: 5.000000e-04 eta: 4:34:28 time: 0.466322 data_time: 0.026589 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.881473 loss: 0.000513 2022/09/13 07:55:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:55:34 - mmengine - INFO - Epoch(train) [147][450/586] lr: 5.000000e-04 eta: 4:34:06 time: 0.469081 data_time: 0.026909 memory: 15239 loss_kpt: 0.000518 acc_pose: 0.865418 loss: 0.000518 2022/09/13 07:55:58 - mmengine - INFO - Epoch(train) [147][500/586] lr: 5.000000e-04 eta: 4:33:45 time: 0.467474 data_time: 0.028587 memory: 15239 loss_kpt: 0.000495 acc_pose: 0.850092 loss: 0.000495 2022/09/13 07:56:21 - mmengine - INFO - Epoch(train) [147][550/586] lr: 5.000000e-04 eta: 4:33:23 time: 0.468611 data_time: 0.026957 memory: 15239 loss_kpt: 0.000485 acc_pose: 0.867162 loss: 0.000485 2022/09/13 07:56:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 07:56:38 - mmengine - INFO - Saving checkpoint at 147 epochs 2022/09/13 07:57:09 - mmengine - INFO - Epoch(train) [148][50/586] lr: 5.000000e-04 eta: 4:32:39 time: 0.470122 data_time: 0.034735 memory: 15239 loss_kpt: 0.000496 acc_pose: 0.798254 loss: 0.000496 2022/09/13 07:57:33 - mmengine - INFO - Epoch(train) [148][100/586] lr: 5.000000e-04 eta: 4:32:17 time: 0.473207 data_time: 0.025974 memory: 15239 loss_kpt: 0.000502 acc_pose: 0.876569 loss: 0.000502 2022/09/13 07:57:56 - mmengine - INFO - Epoch(train) [148][150/586] lr: 5.000000e-04 eta: 4:31:56 time: 0.469827 data_time: 0.030062 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.887547 loss: 0.000497 2022/09/13 07:58:19 - mmengine - INFO - Epoch(train) [148][200/586] lr: 5.000000e-04 eta: 4:31:34 time: 0.465635 data_time: 0.028616 memory: 15239 loss_kpt: 0.000491 acc_pose: 0.860399 loss: 0.000491 2022/09/13 07:58:43 - mmengine - INFO - Epoch(train) [148][250/586] lr: 5.000000e-04 eta: 4:31:12 time: 0.468996 data_time: 0.026599 memory: 15239 loss_kpt: 0.000529 acc_pose: 0.809663 loss: 0.000529 2022/09/13 07:59:07 - mmengine - INFO - Epoch(train) [148][300/586] lr: 5.000000e-04 eta: 4:30:51 time: 0.476093 data_time: 0.026792 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.868594 loss: 0.000500 2022/09/13 07:59:30 - mmengine - INFO - Epoch(train) [148][350/586] lr: 5.000000e-04 eta: 4:30:29 time: 0.471353 data_time: 0.027890 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.803453 loss: 0.000497 2022/09/13 07:59:54 - mmengine - INFO - Epoch(train) [148][400/586] lr: 5.000000e-04 eta: 4:30:07 time: 0.471220 data_time: 0.027108 memory: 15239 loss_kpt: 0.000514 acc_pose: 0.833090 loss: 0.000514 2022/09/13 08:00:17 - mmengine - INFO - Epoch(train) [148][450/586] lr: 5.000000e-04 eta: 4:29:46 time: 0.471335 data_time: 0.026996 memory: 15239 loss_kpt: 0.000494 acc_pose: 0.901797 loss: 0.000494 2022/09/13 08:00:41 - mmengine - INFO - Epoch(train) [148][500/586] lr: 5.000000e-04 eta: 4:29:24 time: 0.464724 data_time: 0.027340 memory: 15239 loss_kpt: 0.000490 acc_pose: 0.849551 loss: 0.000490 2022/09/13 08:01:04 - mmengine - INFO - Epoch(train) [148][550/586] lr: 5.000000e-04 eta: 4:29:02 time: 0.473023 data_time: 0.026521 memory: 15239 loss_kpt: 0.000485 acc_pose: 0.852348 loss: 0.000485 2022/09/13 08:01:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:01:21 - mmengine - INFO - Saving checkpoint at 148 epochs 2022/09/13 08:01:52 - mmengine - INFO - Epoch(train) [149][50/586] lr: 5.000000e-04 eta: 4:28:18 time: 0.483023 data_time: 0.037605 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.797180 loss: 0.000512 2022/09/13 08:02:16 - mmengine - INFO - Epoch(train) [149][100/586] lr: 5.000000e-04 eta: 4:27:57 time: 0.471481 data_time: 0.026524 memory: 15239 loss_kpt: 0.000494 acc_pose: 0.865532 loss: 0.000494 2022/09/13 08:02:39 - mmengine - INFO - Epoch(train) [149][150/586] lr: 5.000000e-04 eta: 4:27:35 time: 0.473273 data_time: 0.026311 memory: 15239 loss_kpt: 0.000489 acc_pose: 0.927486 loss: 0.000489 2022/09/13 08:03:03 - mmengine - INFO - Epoch(train) [149][200/586] lr: 5.000000e-04 eta: 4:27:14 time: 0.467546 data_time: 0.026180 memory: 15239 loss_kpt: 0.000502 acc_pose: 0.843792 loss: 0.000502 2022/09/13 08:03:26 - mmengine - INFO - Epoch(train) [149][250/586] lr: 5.000000e-04 eta: 4:26:52 time: 0.470226 data_time: 0.027775 memory: 15239 loss_kpt: 0.000502 acc_pose: 0.879740 loss: 0.000502 2022/09/13 08:03:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:03:50 - mmengine - INFO - Epoch(train) [149][300/586] lr: 5.000000e-04 eta: 4:26:30 time: 0.475853 data_time: 0.026670 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.860500 loss: 0.000512 2022/09/13 08:04:13 - mmengine - INFO - Epoch(train) [149][350/586] lr: 5.000000e-04 eta: 4:26:09 time: 0.464150 data_time: 0.026406 memory: 15239 loss_kpt: 0.000515 acc_pose: 0.877835 loss: 0.000515 2022/09/13 08:04:37 - mmengine - INFO - Epoch(train) [149][400/586] lr: 5.000000e-04 eta: 4:25:47 time: 0.476289 data_time: 0.027229 memory: 15239 loss_kpt: 0.000516 acc_pose: 0.843144 loss: 0.000516 2022/09/13 08:05:00 - mmengine - INFO - Epoch(train) [149][450/586] lr: 5.000000e-04 eta: 4:25:25 time: 0.466808 data_time: 0.026852 memory: 15239 loss_kpt: 0.000494 acc_pose: 0.895879 loss: 0.000494 2022/09/13 08:05:24 - mmengine - INFO - Epoch(train) [149][500/586] lr: 5.000000e-04 eta: 4:25:04 time: 0.470001 data_time: 0.026373 memory: 15239 loss_kpt: 0.000506 acc_pose: 0.843143 loss: 0.000506 2022/09/13 08:05:48 - mmengine - INFO - Epoch(train) [149][550/586] lr: 5.000000e-04 eta: 4:24:42 time: 0.472340 data_time: 0.031386 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.867384 loss: 0.000497 2022/09/13 08:06:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:06:04 - mmengine - INFO - Saving checkpoint at 149 epochs 2022/09/13 08:06:36 - mmengine - INFO - Epoch(train) [150][50/586] lr: 5.000000e-04 eta: 4:23:58 time: 0.483997 data_time: 0.048193 memory: 15239 loss_kpt: 0.000532 acc_pose: 0.895422 loss: 0.000532 2022/09/13 08:07:00 - mmengine - INFO - Epoch(train) [150][100/586] lr: 5.000000e-04 eta: 4:23:37 time: 0.473796 data_time: 0.030041 memory: 15239 loss_kpt: 0.000495 acc_pose: 0.816546 loss: 0.000495 2022/09/13 08:07:23 - mmengine - INFO - Epoch(train) [150][150/586] lr: 5.000000e-04 eta: 4:23:15 time: 0.469390 data_time: 0.031776 memory: 15239 loss_kpt: 0.000491 acc_pose: 0.848812 loss: 0.000491 2022/09/13 08:07:46 - mmengine - INFO - Epoch(train) [150][200/586] lr: 5.000000e-04 eta: 4:22:53 time: 0.463094 data_time: 0.026998 memory: 15239 loss_kpt: 0.000501 acc_pose: 0.861067 loss: 0.000501 2022/09/13 08:08:10 - mmengine - INFO - Epoch(train) [150][250/586] lr: 5.000000e-04 eta: 4:22:32 time: 0.468527 data_time: 0.027237 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.904061 loss: 0.000504 2022/09/13 08:08:34 - mmengine - INFO - Epoch(train) [150][300/586] lr: 5.000000e-04 eta: 4:22:10 time: 0.476304 data_time: 0.026390 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.864824 loss: 0.000505 2022/09/13 08:08:57 - mmengine - INFO - Epoch(train) [150][350/586] lr: 5.000000e-04 eta: 4:21:48 time: 0.461299 data_time: 0.026536 memory: 15239 loss_kpt: 0.000499 acc_pose: 0.868034 loss: 0.000499 2022/09/13 08:09:20 - mmengine - INFO - Epoch(train) [150][400/586] lr: 5.000000e-04 eta: 4:21:27 time: 0.470871 data_time: 0.027199 memory: 15239 loss_kpt: 0.000487 acc_pose: 0.906436 loss: 0.000487 2022/09/13 08:09:44 - mmengine - INFO - Epoch(train) [150][450/586] lr: 5.000000e-04 eta: 4:21:05 time: 0.472408 data_time: 0.026173 memory: 15239 loss_kpt: 0.000516 acc_pose: 0.878523 loss: 0.000516 2022/09/13 08:10:07 - mmengine - INFO - Epoch(train) [150][500/586] lr: 5.000000e-04 eta: 4:20:43 time: 0.467419 data_time: 0.026423 memory: 15239 loss_kpt: 0.000506 acc_pose: 0.816460 loss: 0.000506 2022/09/13 08:10:31 - mmengine - INFO - Epoch(train) [150][550/586] lr: 5.000000e-04 eta: 4:20:21 time: 0.468519 data_time: 0.027172 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.913680 loss: 0.000500 2022/09/13 08:10:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:10:48 - mmengine - INFO - Saving checkpoint at 150 epochs 2022/09/13 08:11:08 - mmengine - INFO - Epoch(val) [150][50/407] eta: 0:01:21 time: 0.229519 data_time: 0.015247 memory: 15239 2022/09/13 08:11:19 - mmengine - INFO - Epoch(val) [150][100/407] eta: 0:01:07 time: 0.219353 data_time: 0.008663 memory: 2064 2022/09/13 08:11:30 - mmengine - INFO - Epoch(val) [150][150/407] eta: 0:00:58 time: 0.229179 data_time: 0.008883 memory: 2064 2022/09/13 08:11:41 - mmengine - INFO - Epoch(val) [150][200/407] eta: 0:00:45 time: 0.220916 data_time: 0.009084 memory: 2064 2022/09/13 08:11:52 - mmengine - INFO - Epoch(val) [150][250/407] eta: 0:00:34 time: 0.220174 data_time: 0.008730 memory: 2064 2022/09/13 08:12:03 - mmengine - INFO - Epoch(val) [150][300/407] eta: 0:00:23 time: 0.219570 data_time: 0.008559 memory: 2064 2022/09/13 08:12:14 - mmengine - INFO - Epoch(val) [150][350/407] eta: 0:00:12 time: 0.219182 data_time: 0.008687 memory: 2064 2022/09/13 08:12:25 - mmengine - INFO - Epoch(val) [150][400/407] eta: 0:00:01 time: 0.217794 data_time: 0.008642 memory: 2064 2022/09/13 08:13:02 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 08:13:16 - mmengine - INFO - Epoch(val) [150][407/407] coco/AP: 0.763360 coco/AP .5: 0.905937 coco/AP .75: 0.825185 coco/AP (M): 0.723143 coco/AP (L): 0.835477 coco/AR: 0.812059 coco/AR .5: 0.942695 coco/AR .75: 0.866499 coco/AR (M): 0.767441 coco/AR (L): 0.876440 2022/09/13 08:13:40 - mmengine - INFO - Epoch(train) [151][50/586] lr: 5.000000e-04 eta: 4:19:38 time: 0.478047 data_time: 0.031815 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.885110 loss: 0.000498 2022/09/13 08:14:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:14:04 - mmengine - INFO - Epoch(train) [151][100/586] lr: 5.000000e-04 eta: 4:19:16 time: 0.466840 data_time: 0.025897 memory: 15239 loss_kpt: 0.000496 acc_pose: 0.834689 loss: 0.000496 2022/09/13 08:14:27 - mmengine - INFO - Epoch(train) [151][150/586] lr: 5.000000e-04 eta: 4:18:54 time: 0.475841 data_time: 0.027307 memory: 15239 loss_kpt: 0.000492 acc_pose: 0.757393 loss: 0.000492 2022/09/13 08:14:51 - mmengine - INFO - Epoch(train) [151][200/586] lr: 5.000000e-04 eta: 4:18:33 time: 0.465328 data_time: 0.027120 memory: 15239 loss_kpt: 0.000508 acc_pose: 0.851945 loss: 0.000508 2022/09/13 08:15:14 - mmengine - INFO - Epoch(train) [151][250/586] lr: 5.000000e-04 eta: 4:18:11 time: 0.471749 data_time: 0.025869 memory: 15239 loss_kpt: 0.000487 acc_pose: 0.840493 loss: 0.000487 2022/09/13 08:15:38 - mmengine - INFO - Epoch(train) [151][300/586] lr: 5.000000e-04 eta: 4:17:49 time: 0.476517 data_time: 0.026922 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.831371 loss: 0.000497 2022/09/13 08:16:02 - mmengine - INFO - Epoch(train) [151][350/586] lr: 5.000000e-04 eta: 4:17:28 time: 0.470444 data_time: 0.026380 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.858028 loss: 0.000497 2022/09/13 08:16:25 - mmengine - INFO - Epoch(train) [151][400/586] lr: 5.000000e-04 eta: 4:17:06 time: 0.476798 data_time: 0.031208 memory: 15239 loss_kpt: 0.000514 acc_pose: 0.926624 loss: 0.000514 2022/09/13 08:16:49 - mmengine - INFO - Epoch(train) [151][450/586] lr: 5.000000e-04 eta: 4:16:44 time: 0.467018 data_time: 0.026833 memory: 15239 loss_kpt: 0.000496 acc_pose: 0.843698 loss: 0.000496 2022/09/13 08:17:12 - mmengine - INFO - Epoch(train) [151][500/586] lr: 5.000000e-04 eta: 4:16:23 time: 0.471519 data_time: 0.026863 memory: 15239 loss_kpt: 0.000520 acc_pose: 0.827131 loss: 0.000520 2022/09/13 08:17:36 - mmengine - INFO - Epoch(train) [151][550/586] lr: 5.000000e-04 eta: 4:16:01 time: 0.471070 data_time: 0.029405 memory: 15239 loss_kpt: 0.000479 acc_pose: 0.891481 loss: 0.000479 2022/09/13 08:17:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:17:53 - mmengine - INFO - Saving checkpoint at 151 epochs 2022/09/13 08:18:24 - mmengine - INFO - Epoch(train) [152][50/586] lr: 5.000000e-04 eta: 4:15:18 time: 0.483817 data_time: 0.036869 memory: 15239 loss_kpt: 0.000485 acc_pose: 0.884022 loss: 0.000485 2022/09/13 08:18:47 - mmengine - INFO - Epoch(train) [152][100/586] lr: 5.000000e-04 eta: 4:14:56 time: 0.472027 data_time: 0.031589 memory: 15239 loss_kpt: 0.000488 acc_pose: 0.863884 loss: 0.000488 2022/09/13 08:19:11 - mmengine - INFO - Epoch(train) [152][150/586] lr: 5.000000e-04 eta: 4:14:34 time: 0.476276 data_time: 0.026448 memory: 15239 loss_kpt: 0.000496 acc_pose: 0.887226 loss: 0.000496 2022/09/13 08:19:35 - mmengine - INFO - Epoch(train) [152][200/586] lr: 5.000000e-04 eta: 4:14:13 time: 0.467728 data_time: 0.027406 memory: 15239 loss_kpt: 0.000488 acc_pose: 0.894052 loss: 0.000488 2022/09/13 08:19:58 - mmengine - INFO - Epoch(train) [152][250/586] lr: 5.000000e-04 eta: 4:13:51 time: 0.465908 data_time: 0.026916 memory: 15239 loss_kpt: 0.000492 acc_pose: 0.874707 loss: 0.000492 2022/09/13 08:20:22 - mmengine - INFO - Epoch(train) [152][300/586] lr: 5.000000e-04 eta: 4:13:29 time: 0.472010 data_time: 0.027293 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.851161 loss: 0.000504 2022/09/13 08:20:45 - mmengine - INFO - Epoch(train) [152][350/586] lr: 5.000000e-04 eta: 4:13:08 time: 0.476945 data_time: 0.027468 memory: 15239 loss_kpt: 0.000489 acc_pose: 0.813599 loss: 0.000489 2022/09/13 08:21:09 - mmengine - INFO - Epoch(train) [152][400/586] lr: 5.000000e-04 eta: 4:12:46 time: 0.476563 data_time: 0.028618 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.913964 loss: 0.000505 2022/09/13 08:21:33 - mmengine - INFO - Epoch(train) [152][450/586] lr: 5.000000e-04 eta: 4:12:24 time: 0.472760 data_time: 0.029134 memory: 15239 loss_kpt: 0.000525 acc_pose: 0.861092 loss: 0.000525 2022/09/13 08:21:57 - mmengine - INFO - Epoch(train) [152][500/586] lr: 5.000000e-04 eta: 4:12:03 time: 0.472846 data_time: 0.026070 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.860435 loss: 0.000509 2022/09/13 08:22:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:22:20 - mmengine - INFO - Epoch(train) [152][550/586] lr: 5.000000e-04 eta: 4:11:41 time: 0.469199 data_time: 0.026427 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.851658 loss: 0.000505 2022/09/13 08:22:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:22:37 - mmengine - INFO - Saving checkpoint at 152 epochs 2022/09/13 08:23:08 - mmengine - INFO - Epoch(train) [153][50/586] lr: 5.000000e-04 eta: 4:10:58 time: 0.481537 data_time: 0.042357 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.860205 loss: 0.000500 2022/09/13 08:23:32 - mmengine - INFO - Epoch(train) [153][100/586] lr: 5.000000e-04 eta: 4:10:36 time: 0.477338 data_time: 0.026339 memory: 15239 loss_kpt: 0.000493 acc_pose: 0.822782 loss: 0.000493 2022/09/13 08:23:55 - mmengine - INFO - Epoch(train) [153][150/586] lr: 5.000000e-04 eta: 4:10:14 time: 0.472687 data_time: 0.027865 memory: 15239 loss_kpt: 0.000488 acc_pose: 0.891204 loss: 0.000488 2022/09/13 08:24:19 - mmengine - INFO - Epoch(train) [153][200/586] lr: 5.000000e-04 eta: 4:09:53 time: 0.468713 data_time: 0.026871 memory: 15239 loss_kpt: 0.000493 acc_pose: 0.871417 loss: 0.000493 2022/09/13 08:24:43 - mmengine - INFO - Epoch(train) [153][250/586] lr: 5.000000e-04 eta: 4:09:31 time: 0.473197 data_time: 0.030667 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.826085 loss: 0.000513 2022/09/13 08:25:06 - mmengine - INFO - Epoch(train) [153][300/586] lr: 5.000000e-04 eta: 4:09:09 time: 0.471221 data_time: 0.025805 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.859272 loss: 0.000500 2022/09/13 08:25:29 - mmengine - INFO - Epoch(train) [153][350/586] lr: 5.000000e-04 eta: 4:08:48 time: 0.464710 data_time: 0.025720 memory: 15239 loss_kpt: 0.000516 acc_pose: 0.845067 loss: 0.000516 2022/09/13 08:25:53 - mmengine - INFO - Epoch(train) [153][400/586] lr: 5.000000e-04 eta: 4:08:26 time: 0.474315 data_time: 0.026052 memory: 15239 loss_kpt: 0.000514 acc_pose: 0.838741 loss: 0.000514 2022/09/13 08:26:17 - mmengine - INFO - Epoch(train) [153][450/586] lr: 5.000000e-04 eta: 4:08:04 time: 0.468216 data_time: 0.026768 memory: 15239 loss_kpt: 0.000499 acc_pose: 0.837026 loss: 0.000499 2022/09/13 08:26:40 - mmengine - INFO - Epoch(train) [153][500/586] lr: 5.000000e-04 eta: 4:07:43 time: 0.467829 data_time: 0.026539 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.832778 loss: 0.000500 2022/09/13 08:27:03 - mmengine - INFO - Epoch(train) [153][550/586] lr: 5.000000e-04 eta: 4:07:21 time: 0.466239 data_time: 0.026791 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.887988 loss: 0.000500 2022/09/13 08:27:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:27:20 - mmengine - INFO - Saving checkpoint at 153 epochs 2022/09/13 08:27:51 - mmengine - INFO - Epoch(train) [154][50/586] lr: 5.000000e-04 eta: 4:06:37 time: 0.480935 data_time: 0.032704 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.827933 loss: 0.000512 2022/09/13 08:28:15 - mmengine - INFO - Epoch(train) [154][100/586] lr: 5.000000e-04 eta: 4:06:16 time: 0.467983 data_time: 0.027390 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.821146 loss: 0.000500 2022/09/13 08:28:38 - mmengine - INFO - Epoch(train) [154][150/586] lr: 5.000000e-04 eta: 4:05:54 time: 0.472770 data_time: 0.026585 memory: 15239 loss_kpt: 0.000491 acc_pose: 0.848714 loss: 0.000491 2022/09/13 08:29:02 - mmengine - INFO - Epoch(train) [154][200/586] lr: 5.000000e-04 eta: 4:05:32 time: 0.472453 data_time: 0.027475 memory: 15239 loss_kpt: 0.000538 acc_pose: 0.870383 loss: 0.000538 2022/09/13 08:29:26 - mmengine - INFO - Epoch(train) [154][250/586] lr: 5.000000e-04 eta: 4:05:11 time: 0.476292 data_time: 0.028183 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.818042 loss: 0.000504 2022/09/13 08:29:49 - mmengine - INFO - Epoch(train) [154][300/586] lr: 5.000000e-04 eta: 4:04:49 time: 0.465770 data_time: 0.027165 memory: 15239 loss_kpt: 0.000488 acc_pose: 0.866384 loss: 0.000488 2022/09/13 08:30:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:30:13 - mmengine - INFO - Epoch(train) [154][350/586] lr: 5.000000e-04 eta: 4:04:27 time: 0.477711 data_time: 0.027048 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.871300 loss: 0.000503 2022/09/13 08:30:37 - mmengine - INFO - Epoch(train) [154][400/586] lr: 5.000000e-04 eta: 4:04:06 time: 0.470980 data_time: 0.027074 memory: 15239 loss_kpt: 0.000492 acc_pose: 0.898333 loss: 0.000492 2022/09/13 08:31:01 - mmengine - INFO - Epoch(train) [154][450/586] lr: 5.000000e-04 eta: 4:03:44 time: 0.483241 data_time: 0.026242 memory: 15239 loss_kpt: 0.000494 acc_pose: 0.906421 loss: 0.000494 2022/09/13 08:31:24 - mmengine - INFO - Epoch(train) [154][500/586] lr: 5.000000e-04 eta: 4:03:22 time: 0.468589 data_time: 0.027416 memory: 15239 loss_kpt: 0.000494 acc_pose: 0.895486 loss: 0.000494 2022/09/13 08:31:48 - mmengine - INFO - Epoch(train) [154][550/586] lr: 5.000000e-04 eta: 4:03:01 time: 0.473073 data_time: 0.027858 memory: 15239 loss_kpt: 0.000489 acc_pose: 0.878099 loss: 0.000489 2022/09/13 08:32:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:32:05 - mmengine - INFO - Saving checkpoint at 154 epochs 2022/09/13 08:32:35 - mmengine - INFO - Epoch(train) [155][50/586] lr: 5.000000e-04 eta: 4:02:17 time: 0.474648 data_time: 0.032394 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.840063 loss: 0.000500 2022/09/13 08:32:59 - mmengine - INFO - Epoch(train) [155][100/586] lr: 5.000000e-04 eta: 4:01:56 time: 0.471338 data_time: 0.026836 memory: 15239 loss_kpt: 0.000501 acc_pose: 0.880743 loss: 0.000501 2022/09/13 08:33:22 - mmengine - INFO - Epoch(train) [155][150/586] lr: 5.000000e-04 eta: 4:01:34 time: 0.469352 data_time: 0.030195 memory: 15239 loss_kpt: 0.000510 acc_pose: 0.807331 loss: 0.000510 2022/09/13 08:33:46 - mmengine - INFO - Epoch(train) [155][200/586] lr: 5.000000e-04 eta: 4:01:12 time: 0.469749 data_time: 0.026803 memory: 15239 loss_kpt: 0.000492 acc_pose: 0.825539 loss: 0.000492 2022/09/13 08:34:09 - mmengine - INFO - Epoch(train) [155][250/586] lr: 5.000000e-04 eta: 4:00:51 time: 0.469962 data_time: 0.026639 memory: 15239 loss_kpt: 0.000507 acc_pose: 0.798358 loss: 0.000507 2022/09/13 08:34:33 - mmengine - INFO - Epoch(train) [155][300/586] lr: 5.000000e-04 eta: 4:00:29 time: 0.467570 data_time: 0.027021 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.895915 loss: 0.000498 2022/09/13 08:34:56 - mmengine - INFO - Epoch(train) [155][350/586] lr: 5.000000e-04 eta: 4:00:07 time: 0.468165 data_time: 0.026529 memory: 15239 loss_kpt: 0.000495 acc_pose: 0.830802 loss: 0.000495 2022/09/13 08:35:20 - mmengine - INFO - Epoch(train) [155][400/586] lr: 5.000000e-04 eta: 3:59:45 time: 0.472419 data_time: 0.026571 memory: 15239 loss_kpt: 0.000508 acc_pose: 0.875151 loss: 0.000508 2022/09/13 08:35:43 - mmengine - INFO - Epoch(train) [155][450/586] lr: 5.000000e-04 eta: 3:59:24 time: 0.466557 data_time: 0.026641 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.819857 loss: 0.000500 2022/09/13 08:36:07 - mmengine - INFO - Epoch(train) [155][500/586] lr: 5.000000e-04 eta: 3:59:02 time: 0.472757 data_time: 0.026983 memory: 15239 loss_kpt: 0.000496 acc_pose: 0.857071 loss: 0.000496 2022/09/13 08:36:31 - mmengine - INFO - Epoch(train) [155][550/586] lr: 5.000000e-04 eta: 3:58:40 time: 0.476040 data_time: 0.028434 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.846559 loss: 0.000500 2022/09/13 08:36:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:36:47 - mmengine - INFO - Saving checkpoint at 155 epochs 2022/09/13 08:37:19 - mmengine - INFO - Epoch(train) [156][50/586] lr: 5.000000e-04 eta: 3:57:57 time: 0.481034 data_time: 0.035778 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.859388 loss: 0.000504 2022/09/13 08:37:42 - mmengine - INFO - Epoch(train) [156][100/586] lr: 5.000000e-04 eta: 3:57:36 time: 0.475412 data_time: 0.033878 memory: 15239 loss_kpt: 0.000489 acc_pose: 0.884546 loss: 0.000489 2022/09/13 08:38:06 - mmengine - INFO - Epoch(train) [156][150/586] lr: 5.000000e-04 eta: 3:57:14 time: 0.478146 data_time: 0.026911 memory: 15239 loss_kpt: 0.000499 acc_pose: 0.879077 loss: 0.000499 2022/09/13 08:38:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:38:30 - mmengine - INFO - Epoch(train) [156][200/586] lr: 5.000000e-04 eta: 3:56:52 time: 0.470174 data_time: 0.026428 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.847602 loss: 0.000505 2022/09/13 08:38:53 - mmengine - INFO - Epoch(train) [156][250/586] lr: 5.000000e-04 eta: 3:56:30 time: 0.470547 data_time: 0.026651 memory: 15239 loss_kpt: 0.000485 acc_pose: 0.855039 loss: 0.000485 2022/09/13 08:39:17 - mmengine - INFO - Epoch(train) [156][300/586] lr: 5.000000e-04 eta: 3:56:09 time: 0.471154 data_time: 0.027386 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.915448 loss: 0.000509 2022/09/13 08:39:41 - mmengine - INFO - Epoch(train) [156][350/586] lr: 5.000000e-04 eta: 3:55:47 time: 0.471887 data_time: 0.030418 memory: 15239 loss_kpt: 0.000506 acc_pose: 0.893254 loss: 0.000506 2022/09/13 08:40:04 - mmengine - INFO - Epoch(train) [156][400/586] lr: 5.000000e-04 eta: 3:55:25 time: 0.465544 data_time: 0.026445 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.821532 loss: 0.000498 2022/09/13 08:40:28 - mmengine - INFO - Epoch(train) [156][450/586] lr: 5.000000e-04 eta: 3:55:04 time: 0.472930 data_time: 0.026755 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.864661 loss: 0.000505 2022/09/13 08:40:51 - mmengine - INFO - Epoch(train) [156][500/586] lr: 5.000000e-04 eta: 3:54:42 time: 0.464059 data_time: 0.026135 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.865444 loss: 0.000512 2022/09/13 08:41:14 - mmengine - INFO - Epoch(train) [156][550/586] lr: 5.000000e-04 eta: 3:54:20 time: 0.472014 data_time: 0.026497 memory: 15239 loss_kpt: 0.000499 acc_pose: 0.913026 loss: 0.000499 2022/09/13 08:41:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:41:31 - mmengine - INFO - Saving checkpoint at 156 epochs 2022/09/13 08:42:02 - mmengine - INFO - Epoch(train) [157][50/586] lr: 5.000000e-04 eta: 3:53:37 time: 0.479120 data_time: 0.035895 memory: 15239 loss_kpt: 0.000506 acc_pose: 0.907450 loss: 0.000506 2022/09/13 08:42:26 - mmengine - INFO - Epoch(train) [157][100/586] lr: 5.000000e-04 eta: 3:53:15 time: 0.472246 data_time: 0.026642 memory: 15239 loss_kpt: 0.000502 acc_pose: 0.862020 loss: 0.000502 2022/09/13 08:42:49 - mmengine - INFO - Epoch(train) [157][150/586] lr: 5.000000e-04 eta: 3:52:54 time: 0.472386 data_time: 0.026213 memory: 15239 loss_kpt: 0.000489 acc_pose: 0.861744 loss: 0.000489 2022/09/13 08:43:13 - mmengine - INFO - Epoch(train) [157][200/586] lr: 5.000000e-04 eta: 3:52:32 time: 0.476770 data_time: 0.027565 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.871803 loss: 0.000497 2022/09/13 08:43:37 - mmengine - INFO - Epoch(train) [157][250/586] lr: 5.000000e-04 eta: 3:52:10 time: 0.468502 data_time: 0.026982 memory: 15239 loss_kpt: 0.000492 acc_pose: 0.883158 loss: 0.000492 2022/09/13 08:44:00 - mmengine - INFO - Epoch(train) [157][300/586] lr: 5.000000e-04 eta: 3:51:48 time: 0.470050 data_time: 0.030119 memory: 15239 loss_kpt: 0.000485 acc_pose: 0.834120 loss: 0.000485 2022/09/13 08:44:24 - mmengine - INFO - Epoch(train) [157][350/586] lr: 5.000000e-04 eta: 3:51:27 time: 0.469223 data_time: 0.026961 memory: 15239 loss_kpt: 0.000486 acc_pose: 0.832667 loss: 0.000486 2022/09/13 08:44:47 - mmengine - INFO - Epoch(train) [157][400/586] lr: 5.000000e-04 eta: 3:51:05 time: 0.468902 data_time: 0.028169 memory: 15239 loss_kpt: 0.000491 acc_pose: 0.902478 loss: 0.000491 2022/09/13 08:45:11 - mmengine - INFO - Epoch(train) [157][450/586] lr: 5.000000e-04 eta: 3:50:43 time: 0.479843 data_time: 0.026284 memory: 15239 loss_kpt: 0.000506 acc_pose: 0.877900 loss: 0.000506 2022/09/13 08:45:34 - mmengine - INFO - Epoch(train) [157][500/586] lr: 5.000000e-04 eta: 3:50:22 time: 0.464117 data_time: 0.028550 memory: 15239 loss_kpt: 0.000496 acc_pose: 0.887522 loss: 0.000496 2022/09/13 08:45:59 - mmengine - INFO - Epoch(train) [157][550/586] lr: 5.000000e-04 eta: 3:50:00 time: 0.482087 data_time: 0.028712 memory: 15239 loss_kpt: 0.000508 acc_pose: 0.934856 loss: 0.000508 2022/09/13 08:46:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:46:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:46:15 - mmengine - INFO - Saving checkpoint at 157 epochs 2022/09/13 08:46:46 - mmengine - INFO - Epoch(train) [158][50/586] lr: 5.000000e-04 eta: 3:49:17 time: 0.477508 data_time: 0.036127 memory: 15239 loss_kpt: 0.000490 acc_pose: 0.864055 loss: 0.000490 2022/09/13 08:47:09 - mmengine - INFO - Epoch(train) [158][100/586] lr: 5.000000e-04 eta: 3:48:55 time: 0.473538 data_time: 0.031795 memory: 15239 loss_kpt: 0.000499 acc_pose: 0.879988 loss: 0.000499 2022/09/13 08:47:33 - mmengine - INFO - Epoch(train) [158][150/586] lr: 5.000000e-04 eta: 3:48:34 time: 0.467554 data_time: 0.027293 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.904184 loss: 0.000509 2022/09/13 08:47:57 - mmengine - INFO - Epoch(train) [158][200/586] lr: 5.000000e-04 eta: 3:48:12 time: 0.476484 data_time: 0.027507 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.827257 loss: 0.000505 2022/09/13 08:48:20 - mmengine - INFO - Epoch(train) [158][250/586] lr: 5.000000e-04 eta: 3:47:50 time: 0.471859 data_time: 0.028232 memory: 15239 loss_kpt: 0.000492 acc_pose: 0.808366 loss: 0.000492 2022/09/13 08:48:44 - mmengine - INFO - Epoch(train) [158][300/586] lr: 5.000000e-04 eta: 3:47:28 time: 0.467487 data_time: 0.027775 memory: 15239 loss_kpt: 0.000488 acc_pose: 0.848895 loss: 0.000488 2022/09/13 08:49:07 - mmengine - INFO - Epoch(train) [158][350/586] lr: 5.000000e-04 eta: 3:47:07 time: 0.475474 data_time: 0.027138 memory: 15239 loss_kpt: 0.000491 acc_pose: 0.887603 loss: 0.000491 2022/09/13 08:49:31 - mmengine - INFO - Epoch(train) [158][400/586] lr: 5.000000e-04 eta: 3:46:45 time: 0.465724 data_time: 0.027141 memory: 15239 loss_kpt: 0.000501 acc_pose: 0.870710 loss: 0.000501 2022/09/13 08:49:54 - mmengine - INFO - Epoch(train) [158][450/586] lr: 5.000000e-04 eta: 3:46:23 time: 0.469389 data_time: 0.028092 memory: 15239 loss_kpt: 0.000519 acc_pose: 0.891773 loss: 0.000519 2022/09/13 08:50:18 - mmengine - INFO - Epoch(train) [158][500/586] lr: 5.000000e-04 eta: 3:46:02 time: 0.480403 data_time: 0.027453 memory: 15239 loss_kpt: 0.000508 acc_pose: 0.828097 loss: 0.000508 2022/09/13 08:50:42 - mmengine - INFO - Epoch(train) [158][550/586] lr: 5.000000e-04 eta: 3:45:40 time: 0.471065 data_time: 0.028442 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.854952 loss: 0.000497 2022/09/13 08:50:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:50:59 - mmengine - INFO - Saving checkpoint at 158 epochs 2022/09/13 08:51:30 - mmengine - INFO - Epoch(train) [159][50/586] lr: 5.000000e-04 eta: 3:44:57 time: 0.492483 data_time: 0.037055 memory: 15239 loss_kpt: 0.000517 acc_pose: 0.880805 loss: 0.000517 2022/09/13 08:51:54 - mmengine - INFO - Epoch(train) [159][100/586] lr: 5.000000e-04 eta: 3:44:36 time: 0.480967 data_time: 0.032099 memory: 15239 loss_kpt: 0.000487 acc_pose: 0.864303 loss: 0.000487 2022/09/13 08:52:18 - mmengine - INFO - Epoch(train) [159][150/586] lr: 5.000000e-04 eta: 3:44:14 time: 0.478612 data_time: 0.034180 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.905049 loss: 0.000500 2022/09/13 08:52:43 - mmengine - INFO - Epoch(train) [159][200/586] lr: 5.000000e-04 eta: 3:43:52 time: 0.482775 data_time: 0.032115 memory: 15239 loss_kpt: 0.000517 acc_pose: 0.884269 loss: 0.000517 2022/09/13 08:53:06 - mmengine - INFO - Epoch(train) [159][250/586] lr: 5.000000e-04 eta: 3:43:31 time: 0.477512 data_time: 0.035369 memory: 15239 loss_kpt: 0.000491 acc_pose: 0.842401 loss: 0.000491 2022/09/13 08:53:30 - mmengine - INFO - Epoch(train) [159][300/586] lr: 5.000000e-04 eta: 3:43:09 time: 0.466572 data_time: 0.032950 memory: 15239 loss_kpt: 0.000491 acc_pose: 0.870510 loss: 0.000491 2022/09/13 08:53:54 - mmengine - INFO - Epoch(train) [159][350/586] lr: 5.000000e-04 eta: 3:42:47 time: 0.483350 data_time: 0.032011 memory: 15239 loss_kpt: 0.000484 acc_pose: 0.878700 loss: 0.000484 2022/09/13 08:54:18 - mmengine - INFO - Epoch(train) [159][400/586] lr: 5.000000e-04 eta: 3:42:26 time: 0.477809 data_time: 0.030810 memory: 15239 loss_kpt: 0.000489 acc_pose: 0.807978 loss: 0.000489 2022/09/13 08:54:24 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:54:41 - mmengine - INFO - Epoch(train) [159][450/586] lr: 5.000000e-04 eta: 3:42:04 time: 0.472070 data_time: 0.027361 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.886820 loss: 0.000500 2022/09/13 08:55:06 - mmengine - INFO - Epoch(train) [159][500/586] lr: 5.000000e-04 eta: 3:41:43 time: 0.481668 data_time: 0.027274 memory: 15239 loss_kpt: 0.000493 acc_pose: 0.895514 loss: 0.000493 2022/09/13 08:55:29 - mmengine - INFO - Epoch(train) [159][550/586] lr: 5.000000e-04 eta: 3:41:21 time: 0.469578 data_time: 0.026901 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.897532 loss: 0.000497 2022/09/13 08:55:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 08:55:46 - mmengine - INFO - Saving checkpoint at 159 epochs 2022/09/13 08:56:17 - mmengine - INFO - Epoch(train) [160][50/586] lr: 5.000000e-04 eta: 3:40:38 time: 0.475189 data_time: 0.034277 memory: 15239 loss_kpt: 0.000495 acc_pose: 0.930191 loss: 0.000495 2022/09/13 08:56:41 - mmengine - INFO - Epoch(train) [160][100/586] lr: 5.000000e-04 eta: 3:40:16 time: 0.471918 data_time: 0.026760 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.849668 loss: 0.000500 2022/09/13 08:57:04 - mmengine - INFO - Epoch(train) [160][150/586] lr: 5.000000e-04 eta: 3:39:54 time: 0.462277 data_time: 0.026582 memory: 15239 loss_kpt: 0.000496 acc_pose: 0.879173 loss: 0.000496 2022/09/13 08:57:27 - mmengine - INFO - Epoch(train) [160][200/586] lr: 5.000000e-04 eta: 3:39:33 time: 0.469559 data_time: 0.027440 memory: 15239 loss_kpt: 0.000495 acc_pose: 0.781624 loss: 0.000495 2022/09/13 08:57:51 - mmengine - INFO - Epoch(train) [160][250/586] lr: 5.000000e-04 eta: 3:39:11 time: 0.478494 data_time: 0.028767 memory: 15239 loss_kpt: 0.000499 acc_pose: 0.896956 loss: 0.000499 2022/09/13 08:58:15 - mmengine - INFO - Epoch(train) [160][300/586] lr: 5.000000e-04 eta: 3:38:49 time: 0.471350 data_time: 0.031502 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.894199 loss: 0.000504 2022/09/13 08:58:39 - mmengine - INFO - Epoch(train) [160][350/586] lr: 5.000000e-04 eta: 3:38:27 time: 0.476511 data_time: 0.026805 memory: 15239 loss_kpt: 0.000477 acc_pose: 0.884680 loss: 0.000477 2022/09/13 08:59:02 - mmengine - INFO - Epoch(train) [160][400/586] lr: 5.000000e-04 eta: 3:38:06 time: 0.465116 data_time: 0.027279 memory: 15239 loss_kpt: 0.000501 acc_pose: 0.889514 loss: 0.000501 2022/09/13 08:59:25 - mmengine - INFO - Epoch(train) [160][450/586] lr: 5.000000e-04 eta: 3:37:44 time: 0.469931 data_time: 0.027812 memory: 15239 loss_kpt: 0.000522 acc_pose: 0.806435 loss: 0.000522 2022/09/13 08:59:49 - mmengine - INFO - Epoch(train) [160][500/586] lr: 5.000000e-04 eta: 3:37:22 time: 0.475822 data_time: 0.027160 memory: 15239 loss_kpt: 0.000492 acc_pose: 0.851885 loss: 0.000492 2022/09/13 09:00:13 - mmengine - INFO - Epoch(train) [160][550/586] lr: 5.000000e-04 eta: 3:37:00 time: 0.466233 data_time: 0.027100 memory: 15239 loss_kpt: 0.000501 acc_pose: 0.880435 loss: 0.000501 2022/09/13 09:00:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:00:30 - mmengine - INFO - Saving checkpoint at 160 epochs 2022/09/13 09:00:48 - mmengine - INFO - Epoch(val) [160][50/407] eta: 0:01:21 time: 0.228151 data_time: 0.014043 memory: 15239 2022/09/13 09:00:59 - mmengine - INFO - Epoch(val) [160][100/407] eta: 0:01:07 time: 0.219093 data_time: 0.008752 memory: 2064 2022/09/13 09:01:10 - mmengine - INFO - Epoch(val) [160][150/407] eta: 0:00:56 time: 0.219071 data_time: 0.008562 memory: 2064 2022/09/13 09:01:22 - mmengine - INFO - Epoch(val) [160][200/407] eta: 0:00:46 time: 0.226299 data_time: 0.008731 memory: 2064 2022/09/13 09:01:33 - mmengine - INFO - Epoch(val) [160][250/407] eta: 0:00:34 time: 0.219074 data_time: 0.008331 memory: 2064 2022/09/13 09:01:44 - mmengine - INFO - Epoch(val) [160][300/407] eta: 0:00:23 time: 0.219756 data_time: 0.009079 memory: 2064 2022/09/13 09:01:55 - mmengine - INFO - Epoch(val) [160][350/407] eta: 0:00:12 time: 0.218538 data_time: 0.008594 memory: 2064 2022/09/13 09:02:06 - mmengine - INFO - Epoch(val) [160][400/407] eta: 0:00:01 time: 0.219755 data_time: 0.008200 memory: 2064 2022/09/13 09:02:43 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 09:02:57 - mmengine - INFO - Epoch(val) [160][407/407] coco/AP: 0.764946 coco/AP .5: 0.908596 coco/AP .75: 0.825074 coco/AP (M): 0.722457 coco/AP (L): 0.837707 coco/AR: 0.812563 coco/AR .5: 0.943168 coco/AR .75: 0.866814 coco/AR (M): 0.766594 coco/AR (L): 0.879116 2022/09/13 09:02:58 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_384_v1/best_coco/AP_epoch_120.pth is removed 2022/09/13 09:03:02 - mmengine - INFO - The best checkpoint with 0.7649 coco/AP at 160 epoch is saved to best_coco/AP_epoch_160.pth. 2022/09/13 09:03:26 - mmengine - INFO - Epoch(train) [161][50/586] lr: 5.000000e-04 eta: 3:36:18 time: 0.483499 data_time: 0.034625 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.773526 loss: 0.000505 2022/09/13 09:03:49 - mmengine - INFO - Epoch(train) [161][100/586] lr: 5.000000e-04 eta: 3:35:56 time: 0.473755 data_time: 0.030933 memory: 15239 loss_kpt: 0.000508 acc_pose: 0.883627 loss: 0.000508 2022/09/13 09:04:14 - mmengine - INFO - Epoch(train) [161][150/586] lr: 5.000000e-04 eta: 3:35:35 time: 0.483696 data_time: 0.038176 memory: 15239 loss_kpt: 0.000496 acc_pose: 0.871949 loss: 0.000496 2022/09/13 09:04:37 - mmengine - INFO - Epoch(train) [161][200/586] lr: 5.000000e-04 eta: 3:35:13 time: 0.467209 data_time: 0.026828 memory: 15239 loss_kpt: 0.000482 acc_pose: 0.909593 loss: 0.000482 2022/09/13 09:04:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:05:01 - mmengine - INFO - Epoch(train) [161][250/586] lr: 5.000000e-04 eta: 3:34:51 time: 0.484596 data_time: 0.029912 memory: 15239 loss_kpt: 0.000514 acc_pose: 0.780530 loss: 0.000514 2022/09/13 09:05:25 - mmengine - INFO - Epoch(train) [161][300/586] lr: 5.000000e-04 eta: 3:34:30 time: 0.480857 data_time: 0.032392 memory: 15239 loss_kpt: 0.000494 acc_pose: 0.873784 loss: 0.000494 2022/09/13 09:05:49 - mmengine - INFO - Epoch(train) [161][350/586] lr: 5.000000e-04 eta: 3:34:08 time: 0.475437 data_time: 0.028043 memory: 15239 loss_kpt: 0.000501 acc_pose: 0.854005 loss: 0.000501 2022/09/13 09:06:13 - mmengine - INFO - Epoch(train) [161][400/586] lr: 5.000000e-04 eta: 3:33:46 time: 0.475893 data_time: 0.026489 memory: 15239 loss_kpt: 0.000492 acc_pose: 0.877026 loss: 0.000492 2022/09/13 09:06:37 - mmengine - INFO - Epoch(train) [161][450/586] lr: 5.000000e-04 eta: 3:33:24 time: 0.472750 data_time: 0.031816 memory: 15239 loss_kpt: 0.000494 acc_pose: 0.844973 loss: 0.000494 2022/09/13 09:07:01 - mmengine - INFO - Epoch(train) [161][500/586] lr: 5.000000e-04 eta: 3:33:03 time: 0.479760 data_time: 0.028449 memory: 15239 loss_kpt: 0.000486 acc_pose: 0.865019 loss: 0.000486 2022/09/13 09:07:24 - mmengine - INFO - Epoch(train) [161][550/586] lr: 5.000000e-04 eta: 3:32:41 time: 0.471286 data_time: 0.027782 memory: 15239 loss_kpt: 0.000473 acc_pose: 0.873373 loss: 0.000473 2022/09/13 09:07:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:07:42 - mmengine - INFO - Saving checkpoint at 161 epochs 2022/09/13 09:08:12 - mmengine - INFO - Epoch(train) [162][50/586] lr: 5.000000e-04 eta: 3:31:58 time: 0.474925 data_time: 0.030906 memory: 15239 loss_kpt: 0.000506 acc_pose: 0.888299 loss: 0.000506 2022/09/13 09:08:36 - mmengine - INFO - Epoch(train) [162][100/586] lr: 5.000000e-04 eta: 3:31:37 time: 0.480594 data_time: 0.030390 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.889927 loss: 0.000505 2022/09/13 09:08:59 - mmengine - INFO - Epoch(train) [162][150/586] lr: 5.000000e-04 eta: 3:31:15 time: 0.465873 data_time: 0.026202 memory: 15239 loss_kpt: 0.000491 acc_pose: 0.848729 loss: 0.000491 2022/09/13 09:09:23 - mmengine - INFO - Epoch(train) [162][200/586] lr: 5.000000e-04 eta: 3:30:53 time: 0.469957 data_time: 0.027795 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.870977 loss: 0.000498 2022/09/13 09:09:47 - mmengine - INFO - Epoch(train) [162][250/586] lr: 5.000000e-04 eta: 3:30:31 time: 0.476388 data_time: 0.030006 memory: 15239 loss_kpt: 0.000495 acc_pose: 0.879116 loss: 0.000495 2022/09/13 09:10:10 - mmengine - INFO - Epoch(train) [162][300/586] lr: 5.000000e-04 eta: 3:30:10 time: 0.476303 data_time: 0.027117 memory: 15239 loss_kpt: 0.000486 acc_pose: 0.848168 loss: 0.000486 2022/09/13 09:10:34 - mmengine - INFO - Epoch(train) [162][350/586] lr: 5.000000e-04 eta: 3:29:48 time: 0.471241 data_time: 0.027164 memory: 15239 loss_kpt: 0.000513 acc_pose: 0.918817 loss: 0.000513 2022/09/13 09:10:58 - mmengine - INFO - Epoch(train) [162][400/586] lr: 5.000000e-04 eta: 3:29:26 time: 0.471105 data_time: 0.030097 memory: 15239 loss_kpt: 0.000481 acc_pose: 0.933108 loss: 0.000481 2022/09/13 09:11:21 - mmengine - INFO - Epoch(train) [162][450/586] lr: 5.000000e-04 eta: 3:29:04 time: 0.471829 data_time: 0.027866 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.824771 loss: 0.000504 2022/09/13 09:11:45 - mmengine - INFO - Epoch(train) [162][500/586] lr: 5.000000e-04 eta: 3:28:43 time: 0.480199 data_time: 0.027718 memory: 15239 loss_kpt: 0.000508 acc_pose: 0.880118 loss: 0.000508 2022/09/13 09:12:09 - mmengine - INFO - Epoch(train) [162][550/586] lr: 5.000000e-04 eta: 3:28:21 time: 0.475428 data_time: 0.030566 memory: 15239 loss_kpt: 0.000490 acc_pose: 0.900602 loss: 0.000490 2022/09/13 09:12:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:12:26 - mmengine - INFO - Saving checkpoint at 162 epochs 2022/09/13 09:12:57 - mmengine - INFO - Epoch(train) [163][50/586] lr: 5.000000e-04 eta: 3:27:39 time: 0.492628 data_time: 0.039376 memory: 15239 loss_kpt: 0.000484 acc_pose: 0.861646 loss: 0.000484 2022/09/13 09:13:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:13:21 - mmengine - INFO - Epoch(train) [163][100/586] lr: 5.000000e-04 eta: 3:27:17 time: 0.469827 data_time: 0.026388 memory: 15239 loss_kpt: 0.000508 acc_pose: 0.835478 loss: 0.000508 2022/09/13 09:13:45 - mmengine - INFO - Epoch(train) [163][150/586] lr: 5.000000e-04 eta: 3:26:55 time: 0.479547 data_time: 0.026965 memory: 15239 loss_kpt: 0.000486 acc_pose: 0.826219 loss: 0.000486 2022/09/13 09:14:09 - mmengine - INFO - Epoch(train) [163][200/586] lr: 5.000000e-04 eta: 3:26:34 time: 0.476232 data_time: 0.026925 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.878601 loss: 0.000498 2022/09/13 09:14:32 - mmengine - INFO - Epoch(train) [163][250/586] lr: 5.000000e-04 eta: 3:26:12 time: 0.466149 data_time: 0.028798 memory: 15239 loss_kpt: 0.000488 acc_pose: 0.853944 loss: 0.000488 2022/09/13 09:14:56 - mmengine - INFO - Epoch(train) [163][300/586] lr: 5.000000e-04 eta: 3:25:50 time: 0.472395 data_time: 0.027077 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.923982 loss: 0.000497 2022/09/13 09:15:19 - mmengine - INFO - Epoch(train) [163][350/586] lr: 5.000000e-04 eta: 3:25:28 time: 0.472348 data_time: 0.030417 memory: 15239 loss_kpt: 0.000495 acc_pose: 0.881575 loss: 0.000495 2022/09/13 09:15:43 - mmengine - INFO - Epoch(train) [163][400/586] lr: 5.000000e-04 eta: 3:25:06 time: 0.472916 data_time: 0.027792 memory: 15239 loss_kpt: 0.000483 acc_pose: 0.882633 loss: 0.000483 2022/09/13 09:16:07 - mmengine - INFO - Epoch(train) [163][450/586] lr: 5.000000e-04 eta: 3:24:45 time: 0.474716 data_time: 0.026747 memory: 15239 loss_kpt: 0.000507 acc_pose: 0.888840 loss: 0.000507 2022/09/13 09:16:30 - mmengine - INFO - Epoch(train) [163][500/586] lr: 5.000000e-04 eta: 3:24:23 time: 0.470349 data_time: 0.027455 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.869400 loss: 0.000500 2022/09/13 09:16:54 - mmengine - INFO - Epoch(train) [163][550/586] lr: 5.000000e-04 eta: 3:24:01 time: 0.468080 data_time: 0.026954 memory: 15239 loss_kpt: 0.000486 acc_pose: 0.853238 loss: 0.000486 2022/09/13 09:17:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:17:10 - mmengine - INFO - Saving checkpoint at 163 epochs 2022/09/13 09:17:41 - mmengine - INFO - Epoch(train) [164][50/586] lr: 5.000000e-04 eta: 3:23:19 time: 0.475557 data_time: 0.034503 memory: 15239 loss_kpt: 0.000495 acc_pose: 0.927681 loss: 0.000495 2022/09/13 09:18:05 - mmengine - INFO - Epoch(train) [164][100/586] lr: 5.000000e-04 eta: 3:22:57 time: 0.480330 data_time: 0.026858 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.880577 loss: 0.000498 2022/09/13 09:18:29 - mmengine - INFO - Epoch(train) [164][150/586] lr: 5.000000e-04 eta: 3:22:35 time: 0.480119 data_time: 0.027080 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.826797 loss: 0.000503 2022/09/13 09:18:53 - mmengine - INFO - Epoch(train) [164][200/586] lr: 5.000000e-04 eta: 3:22:14 time: 0.479493 data_time: 0.028360 memory: 15239 loss_kpt: 0.000506 acc_pose: 0.891135 loss: 0.000506 2022/09/13 09:19:17 - mmengine - INFO - Epoch(train) [164][250/586] lr: 5.000000e-04 eta: 3:21:52 time: 0.470225 data_time: 0.031029 memory: 15239 loss_kpt: 0.000495 acc_pose: 0.852671 loss: 0.000495 2022/09/13 09:19:40 - mmengine - INFO - Epoch(train) [164][300/586] lr: 5.000000e-04 eta: 3:21:30 time: 0.470480 data_time: 0.026611 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.844268 loss: 0.000498 2022/09/13 09:20:04 - mmengine - INFO - Epoch(train) [164][350/586] lr: 5.000000e-04 eta: 3:21:08 time: 0.470300 data_time: 0.026593 memory: 15239 loss_kpt: 0.000480 acc_pose: 0.850991 loss: 0.000480 2022/09/13 09:20:27 - mmengine - INFO - Epoch(train) [164][400/586] lr: 5.000000e-04 eta: 3:20:46 time: 0.469789 data_time: 0.027606 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.834072 loss: 0.000505 2022/09/13 09:20:51 - mmengine - INFO - Epoch(train) [164][450/586] lr: 5.000000e-04 eta: 3:20:25 time: 0.476602 data_time: 0.027297 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.853811 loss: 0.000497 2022/09/13 09:21:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:21:15 - mmengine - INFO - Epoch(train) [164][500/586] lr: 5.000000e-04 eta: 3:20:03 time: 0.467908 data_time: 0.027837 memory: 15239 loss_kpt: 0.000487 acc_pose: 0.872011 loss: 0.000487 2022/09/13 09:21:38 - mmengine - INFO - Epoch(train) [164][550/586] lr: 5.000000e-04 eta: 3:19:41 time: 0.468914 data_time: 0.026565 memory: 15239 loss_kpt: 0.000506 acc_pose: 0.870116 loss: 0.000506 2022/09/13 09:21:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:21:55 - mmengine - INFO - Saving checkpoint at 164 epochs 2022/09/13 09:22:26 - mmengine - INFO - Epoch(train) [165][50/586] lr: 5.000000e-04 eta: 3:18:59 time: 0.477067 data_time: 0.038806 memory: 15239 loss_kpt: 0.000501 acc_pose: 0.900730 loss: 0.000501 2022/09/13 09:22:49 - mmengine - INFO - Epoch(train) [165][100/586] lr: 5.000000e-04 eta: 3:18:37 time: 0.466205 data_time: 0.032505 memory: 15239 loss_kpt: 0.000476 acc_pose: 0.894522 loss: 0.000476 2022/09/13 09:23:13 - mmengine - INFO - Epoch(train) [165][150/586] lr: 5.000000e-04 eta: 3:18:15 time: 0.480768 data_time: 0.034029 memory: 15239 loss_kpt: 0.000491 acc_pose: 0.898245 loss: 0.000491 2022/09/13 09:23:37 - mmengine - INFO - Epoch(train) [165][200/586] lr: 5.000000e-04 eta: 3:17:53 time: 0.467683 data_time: 0.032635 memory: 15239 loss_kpt: 0.000485 acc_pose: 0.874084 loss: 0.000485 2022/09/13 09:24:00 - mmengine - INFO - Epoch(train) [165][250/586] lr: 5.000000e-04 eta: 3:17:32 time: 0.472942 data_time: 0.039086 memory: 15239 loss_kpt: 0.000506 acc_pose: 0.825440 loss: 0.000506 2022/09/13 09:24:25 - mmengine - INFO - Epoch(train) [165][300/586] lr: 5.000000e-04 eta: 3:17:10 time: 0.485213 data_time: 0.031728 memory: 15239 loss_kpt: 0.000485 acc_pose: 0.868038 loss: 0.000485 2022/09/13 09:24:48 - mmengine - INFO - Epoch(train) [165][350/586] lr: 5.000000e-04 eta: 3:16:48 time: 0.474243 data_time: 0.027883 memory: 15239 loss_kpt: 0.000480 acc_pose: 0.903594 loss: 0.000480 2022/09/13 09:25:12 - mmengine - INFO - Epoch(train) [165][400/586] lr: 5.000000e-04 eta: 3:16:26 time: 0.470260 data_time: 0.026488 memory: 15239 loss_kpt: 0.000487 acc_pose: 0.874096 loss: 0.000487 2022/09/13 09:25:36 - mmengine - INFO - Epoch(train) [165][450/586] lr: 5.000000e-04 eta: 3:16:05 time: 0.477100 data_time: 0.026343 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.853774 loss: 0.000504 2022/09/13 09:25:59 - mmengine - INFO - Epoch(train) [165][500/586] lr: 5.000000e-04 eta: 3:15:43 time: 0.466628 data_time: 0.027560 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.872809 loss: 0.000503 2022/09/13 09:26:22 - mmengine - INFO - Epoch(train) [165][550/586] lr: 5.000000e-04 eta: 3:15:21 time: 0.467077 data_time: 0.031689 memory: 15239 loss_kpt: 0.000494 acc_pose: 0.872405 loss: 0.000494 2022/09/13 09:26:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:26:39 - mmengine - INFO - Saving checkpoint at 165 epochs 2022/09/13 09:27:11 - mmengine - INFO - Epoch(train) [166][50/586] lr: 5.000000e-04 eta: 3:14:39 time: 0.482638 data_time: 0.035640 memory: 15239 loss_kpt: 0.000491 acc_pose: 0.883719 loss: 0.000491 2022/09/13 09:27:33 - mmengine - INFO - Epoch(train) [166][100/586] lr: 5.000000e-04 eta: 3:14:17 time: 0.459531 data_time: 0.031549 memory: 15239 loss_kpt: 0.000484 acc_pose: 0.845718 loss: 0.000484 2022/09/13 09:27:57 - mmengine - INFO - Epoch(train) [166][150/586] lr: 5.000000e-04 eta: 3:13:55 time: 0.468594 data_time: 0.032626 memory: 15239 loss_kpt: 0.000486 acc_pose: 0.876913 loss: 0.000486 2022/09/13 09:28:20 - mmengine - INFO - Epoch(train) [166][200/586] lr: 5.000000e-04 eta: 3:13:33 time: 0.469068 data_time: 0.031998 memory: 15239 loss_kpt: 0.000491 acc_pose: 0.890549 loss: 0.000491 2022/09/13 09:28:44 - mmengine - INFO - Epoch(train) [166][250/586] lr: 5.000000e-04 eta: 3:13:11 time: 0.472099 data_time: 0.036037 memory: 15239 loss_kpt: 0.000492 acc_pose: 0.863726 loss: 0.000492 2022/09/13 09:29:08 - mmengine - INFO - Epoch(train) [166][300/586] lr: 5.000000e-04 eta: 3:12:50 time: 0.469055 data_time: 0.026703 memory: 15239 loss_kpt: 0.000471 acc_pose: 0.916973 loss: 0.000471 2022/09/13 09:29:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:29:31 - mmengine - INFO - Epoch(train) [166][350/586] lr: 5.000000e-04 eta: 3:12:28 time: 0.477154 data_time: 0.026649 memory: 15239 loss_kpt: 0.000505 acc_pose: 0.900315 loss: 0.000505 2022/09/13 09:29:55 - mmengine - INFO - Epoch(train) [166][400/586] lr: 5.000000e-04 eta: 3:12:06 time: 0.466222 data_time: 0.032995 memory: 15239 loss_kpt: 0.000492 acc_pose: 0.814516 loss: 0.000492 2022/09/13 09:30:18 - mmengine - INFO - Epoch(train) [166][450/586] lr: 5.000000e-04 eta: 3:11:44 time: 0.466796 data_time: 0.026994 memory: 15239 loss_kpt: 0.000512 acc_pose: 0.800294 loss: 0.000512 2022/09/13 09:30:42 - mmengine - INFO - Epoch(train) [166][500/586] lr: 5.000000e-04 eta: 3:11:22 time: 0.475496 data_time: 0.027556 memory: 15239 loss_kpt: 0.000501 acc_pose: 0.835638 loss: 0.000501 2022/09/13 09:31:06 - mmengine - INFO - Epoch(train) [166][550/586] lr: 5.000000e-04 eta: 3:11:01 time: 0.474909 data_time: 0.027877 memory: 15239 loss_kpt: 0.000484 acc_pose: 0.858403 loss: 0.000484 2022/09/13 09:31:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:31:22 - mmengine - INFO - Saving checkpoint at 166 epochs 2022/09/13 09:31:53 - mmengine - INFO - Epoch(train) [167][50/586] lr: 5.000000e-04 eta: 3:10:19 time: 0.479690 data_time: 0.031233 memory: 15239 loss_kpt: 0.000496 acc_pose: 0.870557 loss: 0.000496 2022/09/13 09:32:16 - mmengine - INFO - Epoch(train) [167][100/586] lr: 5.000000e-04 eta: 3:09:57 time: 0.469229 data_time: 0.026567 memory: 15239 loss_kpt: 0.000493 acc_pose: 0.867066 loss: 0.000493 2022/09/13 09:32:40 - mmengine - INFO - Epoch(train) [167][150/586] lr: 5.000000e-04 eta: 3:09:35 time: 0.474816 data_time: 0.026127 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.869298 loss: 0.000498 2022/09/13 09:33:04 - mmengine - INFO - Epoch(train) [167][200/586] lr: 5.000000e-04 eta: 3:09:13 time: 0.471122 data_time: 0.031998 memory: 15239 loss_kpt: 0.000490 acc_pose: 0.860294 loss: 0.000490 2022/09/13 09:33:28 - mmengine - INFO - Epoch(train) [167][250/586] lr: 5.000000e-04 eta: 3:08:51 time: 0.480877 data_time: 0.026097 memory: 15239 loss_kpt: 0.000481 acc_pose: 0.833534 loss: 0.000481 2022/09/13 09:33:52 - mmengine - INFO - Epoch(train) [167][300/586] lr: 5.000000e-04 eta: 3:08:30 time: 0.472495 data_time: 0.026512 memory: 15239 loss_kpt: 0.000492 acc_pose: 0.846458 loss: 0.000492 2022/09/13 09:34:15 - mmengine - INFO - Epoch(train) [167][350/586] lr: 5.000000e-04 eta: 3:08:08 time: 0.470169 data_time: 0.027107 memory: 15239 loss_kpt: 0.000493 acc_pose: 0.897615 loss: 0.000493 2022/09/13 09:34:39 - mmengine - INFO - Epoch(train) [167][400/586] lr: 5.000000e-04 eta: 3:07:46 time: 0.477982 data_time: 0.026575 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.814380 loss: 0.000497 2022/09/13 09:35:02 - mmengine - INFO - Epoch(train) [167][450/586] lr: 5.000000e-04 eta: 3:07:24 time: 0.467750 data_time: 0.026872 memory: 15239 loss_kpt: 0.000485 acc_pose: 0.880836 loss: 0.000485 2022/09/13 09:35:26 - mmengine - INFO - Epoch(train) [167][500/586] lr: 5.000000e-04 eta: 3:07:02 time: 0.469693 data_time: 0.031074 memory: 15239 loss_kpt: 0.000480 acc_pose: 0.878083 loss: 0.000480 2022/09/13 09:35:50 - mmengine - INFO - Epoch(train) [167][550/586] lr: 5.000000e-04 eta: 3:06:41 time: 0.474067 data_time: 0.026282 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.868464 loss: 0.000504 2022/09/13 09:36:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:36:06 - mmengine - INFO - Saving checkpoint at 167 epochs 2022/09/13 09:36:37 - mmengine - INFO - Epoch(train) [168][50/586] lr: 5.000000e-04 eta: 3:05:59 time: 0.474837 data_time: 0.035834 memory: 15239 loss_kpt: 0.000503 acc_pose: 0.832756 loss: 0.000503 2022/09/13 09:37:01 - mmengine - INFO - Epoch(train) [168][100/586] lr: 5.000000e-04 eta: 3:05:37 time: 0.473604 data_time: 0.028643 memory: 15239 loss_kpt: 0.000472 acc_pose: 0.904258 loss: 0.000472 2022/09/13 09:37:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:37:24 - mmengine - INFO - Epoch(train) [168][150/586] lr: 5.000000e-04 eta: 3:05:15 time: 0.466949 data_time: 0.028582 memory: 15239 loss_kpt: 0.000496 acc_pose: 0.924303 loss: 0.000496 2022/09/13 09:37:48 - mmengine - INFO - Epoch(train) [168][200/586] lr: 5.000000e-04 eta: 3:04:53 time: 0.472447 data_time: 0.027562 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.871266 loss: 0.000498 2022/09/13 09:38:11 - mmengine - INFO - Epoch(train) [168][250/586] lr: 5.000000e-04 eta: 3:04:31 time: 0.476297 data_time: 0.028153 memory: 15239 loss_kpt: 0.000489 acc_pose: 0.902130 loss: 0.000489 2022/09/13 09:38:35 - mmengine - INFO - Epoch(train) [168][300/586] lr: 5.000000e-04 eta: 3:04:10 time: 0.470786 data_time: 0.030521 memory: 15239 loss_kpt: 0.000495 acc_pose: 0.909212 loss: 0.000495 2022/09/13 09:38:58 - mmengine - INFO - Epoch(train) [168][350/586] lr: 5.000000e-04 eta: 3:03:48 time: 0.469652 data_time: 0.027194 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.836347 loss: 0.000497 2022/09/13 09:39:22 - mmengine - INFO - Epoch(train) [168][400/586] lr: 5.000000e-04 eta: 3:03:26 time: 0.476880 data_time: 0.027834 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.901809 loss: 0.000498 2022/09/13 09:39:46 - mmengine - INFO - Epoch(train) [168][450/586] lr: 5.000000e-04 eta: 3:03:04 time: 0.467995 data_time: 0.027194 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.827459 loss: 0.000498 2022/09/13 09:40:09 - mmengine - INFO - Epoch(train) [168][500/586] lr: 5.000000e-04 eta: 3:02:42 time: 0.471884 data_time: 0.027196 memory: 15239 loss_kpt: 0.000494 acc_pose: 0.861183 loss: 0.000494 2022/09/13 09:40:33 - mmengine - INFO - Epoch(train) [168][550/586] lr: 5.000000e-04 eta: 3:02:20 time: 0.469951 data_time: 0.027628 memory: 15239 loss_kpt: 0.000502 acc_pose: 0.920178 loss: 0.000502 2022/09/13 09:40:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:40:50 - mmengine - INFO - Saving checkpoint at 168 epochs 2022/09/13 09:41:21 - mmengine - INFO - Epoch(train) [169][50/586] lr: 5.000000e-04 eta: 3:01:39 time: 0.483265 data_time: 0.031265 memory: 15239 loss_kpt: 0.000496 acc_pose: 0.863967 loss: 0.000496 2022/09/13 09:41:45 - mmengine - INFO - Epoch(train) [169][100/586] lr: 5.000000e-04 eta: 3:01:17 time: 0.480239 data_time: 0.031258 memory: 15239 loss_kpt: 0.000484 acc_pose: 0.864804 loss: 0.000484 2022/09/13 09:42:08 - mmengine - INFO - Epoch(train) [169][150/586] lr: 5.000000e-04 eta: 3:00:55 time: 0.461256 data_time: 0.026957 memory: 15239 loss_kpt: 0.000504 acc_pose: 0.940234 loss: 0.000504 2022/09/13 09:42:32 - mmengine - INFO - Epoch(train) [169][200/586] lr: 5.000000e-04 eta: 3:00:33 time: 0.472176 data_time: 0.028056 memory: 15239 loss_kpt: 0.000478 acc_pose: 0.860884 loss: 0.000478 2022/09/13 09:42:56 - mmengine - INFO - Epoch(train) [169][250/586] lr: 5.000000e-04 eta: 3:00:11 time: 0.478831 data_time: 0.030500 memory: 15239 loss_kpt: 0.000488 acc_pose: 0.857854 loss: 0.000488 2022/09/13 09:43:19 - mmengine - INFO - Epoch(train) [169][300/586] lr: 5.000000e-04 eta: 2:59:49 time: 0.470181 data_time: 0.029234 memory: 15239 loss_kpt: 0.000495 acc_pose: 0.870199 loss: 0.000495 2022/09/13 09:43:43 - mmengine - INFO - Epoch(train) [169][350/586] lr: 5.000000e-04 eta: 2:59:28 time: 0.476104 data_time: 0.027060 memory: 15239 loss_kpt: 0.000482 acc_pose: 0.851667 loss: 0.000482 2022/09/13 09:44:07 - mmengine - INFO - Epoch(train) [169][400/586] lr: 5.000000e-04 eta: 2:59:06 time: 0.480420 data_time: 0.027650 memory: 15239 loss_kpt: 0.000462 acc_pose: 0.907480 loss: 0.000462 2022/09/13 09:44:30 - mmengine - INFO - Epoch(train) [169][450/586] lr: 5.000000e-04 eta: 2:58:44 time: 0.466776 data_time: 0.027379 memory: 15239 loss_kpt: 0.000494 acc_pose: 0.866086 loss: 0.000494 2022/09/13 09:44:54 - mmengine - INFO - Epoch(train) [169][500/586] lr: 5.000000e-04 eta: 2:58:22 time: 0.469290 data_time: 0.027046 memory: 15239 loss_kpt: 0.000496 acc_pose: 0.895954 loss: 0.000496 2022/09/13 09:45:18 - mmengine - INFO - Epoch(train) [169][550/586] lr: 5.000000e-04 eta: 2:58:00 time: 0.478566 data_time: 0.031132 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.861565 loss: 0.000497 2022/09/13 09:45:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:45:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:45:34 - mmengine - INFO - Saving checkpoint at 169 epochs 2022/09/13 09:46:05 - mmengine - INFO - Epoch(train) [170][50/586] lr: 5.000000e-04 eta: 2:57:19 time: 0.477031 data_time: 0.034318 memory: 15239 loss_kpt: 0.000489 acc_pose: 0.921640 loss: 0.000489 2022/09/13 09:46:29 - mmengine - INFO - Epoch(train) [170][100/586] lr: 5.000000e-04 eta: 2:56:57 time: 0.475924 data_time: 0.028432 memory: 15239 loss_kpt: 0.000489 acc_pose: 0.848922 loss: 0.000489 2022/09/13 09:46:53 - mmengine - INFO - Epoch(train) [170][150/586] lr: 5.000000e-04 eta: 2:56:35 time: 0.472136 data_time: 0.028548 memory: 15239 loss_kpt: 0.000497 acc_pose: 0.898019 loss: 0.000497 2022/09/13 09:47:16 - mmengine - INFO - Epoch(train) [170][200/586] lr: 5.000000e-04 eta: 2:56:13 time: 0.468346 data_time: 0.027426 memory: 15239 loss_kpt: 0.000485 acc_pose: 0.901424 loss: 0.000485 2022/09/13 09:47:40 - mmengine - INFO - Epoch(train) [170][250/586] lr: 5.000000e-04 eta: 2:55:51 time: 0.471497 data_time: 0.027258 memory: 15239 loss_kpt: 0.000500 acc_pose: 0.841493 loss: 0.000500 2022/09/13 09:48:03 - mmengine - INFO - Epoch(train) [170][300/586] lr: 5.000000e-04 eta: 2:55:29 time: 0.470750 data_time: 0.027731 memory: 15239 loss_kpt: 0.000501 acc_pose: 0.823545 loss: 0.000501 2022/09/13 09:48:27 - mmengine - INFO - Epoch(train) [170][350/586] lr: 5.000000e-04 eta: 2:55:08 time: 0.474086 data_time: 0.028161 memory: 15239 loss_kpt: 0.000486 acc_pose: 0.899022 loss: 0.000486 2022/09/13 09:48:51 - mmengine - INFO - Epoch(train) [170][400/586] lr: 5.000000e-04 eta: 2:54:46 time: 0.475091 data_time: 0.027657 memory: 15239 loss_kpt: 0.000498 acc_pose: 0.867288 loss: 0.000498 2022/09/13 09:49:14 - mmengine - INFO - Epoch(train) [170][450/586] lr: 5.000000e-04 eta: 2:54:24 time: 0.467057 data_time: 0.027471 memory: 15239 loss_kpt: 0.000509 acc_pose: 0.885775 loss: 0.000509 2022/09/13 09:49:38 - mmengine - INFO - Epoch(train) [170][500/586] lr: 5.000000e-04 eta: 2:54:02 time: 0.475801 data_time: 0.030442 memory: 15239 loss_kpt: 0.000495 acc_pose: 0.883026 loss: 0.000495 2022/09/13 09:50:01 - mmengine - INFO - Epoch(train) [170][550/586] lr: 5.000000e-04 eta: 2:53:40 time: 0.469352 data_time: 0.027940 memory: 15239 loss_kpt: 0.000473 acc_pose: 0.937460 loss: 0.000473 2022/09/13 09:50:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:50:18 - mmengine - INFO - Saving checkpoint at 170 epochs 2022/09/13 09:50:37 - mmengine - INFO - Epoch(val) [170][50/407] eta: 0:01:23 time: 0.233245 data_time: 0.018756 memory: 15239 2022/09/13 09:50:48 - mmengine - INFO - Epoch(val) [170][100/407] eta: 0:01:07 time: 0.218850 data_time: 0.008672 memory: 2064 2022/09/13 09:50:59 - mmengine - INFO - Epoch(val) [170][150/407] eta: 0:00:56 time: 0.220344 data_time: 0.008687 memory: 2064 2022/09/13 09:51:10 - mmengine - INFO - Epoch(val) [170][200/407] eta: 0:00:45 time: 0.219677 data_time: 0.008985 memory: 2064 2022/09/13 09:51:21 - mmengine - INFO - Epoch(val) [170][250/407] eta: 0:00:35 time: 0.225235 data_time: 0.012033 memory: 2064 2022/09/13 09:51:32 - mmengine - INFO - Epoch(val) [170][300/407] eta: 0:00:23 time: 0.223436 data_time: 0.012519 memory: 2064 2022/09/13 09:51:43 - mmengine - INFO - Epoch(val) [170][350/407] eta: 0:00:12 time: 0.219575 data_time: 0.008329 memory: 2064 2022/09/13 09:51:54 - mmengine - INFO - Epoch(val) [170][400/407] eta: 0:00:01 time: 0.217147 data_time: 0.008433 memory: 2064 2022/09/13 09:52:31 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 09:52:45 - mmengine - INFO - Epoch(val) [170][407/407] coco/AP: 0.765861 coco/AP .5: 0.905970 coco/AP .75: 0.826175 coco/AP (M): 0.724184 coco/AP (L): 0.839541 coco/AR: 0.814484 coco/AR .5: 0.942223 coco/AR .75: 0.869332 coco/AR (M): 0.769161 coco/AR (L): 0.880528 2022/09/13 09:52:45 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_384_v1/best_coco/AP_epoch_160.pth is removed 2022/09/13 09:52:49 - mmengine - INFO - The best checkpoint with 0.7659 coco/AP at 170 epoch is saved to best_coco/AP_epoch_170.pth. 2022/09/13 09:53:14 - mmengine - INFO - Epoch(train) [171][50/586] lr: 5.000000e-05 eta: 2:52:59 time: 0.485727 data_time: 0.038333 memory: 15239 loss_kpt: 0.000496 acc_pose: 0.868912 loss: 0.000496 2022/09/13 09:53:37 - mmengine - INFO - Epoch(train) [171][100/586] lr: 5.000000e-05 eta: 2:52:37 time: 0.472443 data_time: 0.031550 memory: 15239 loss_kpt: 0.000465 acc_pose: 0.891701 loss: 0.000465 2022/09/13 09:54:01 - mmengine - INFO - Epoch(train) [171][150/586] lr: 5.000000e-05 eta: 2:52:15 time: 0.473682 data_time: 0.028406 memory: 15239 loss_kpt: 0.000485 acc_pose: 0.871692 loss: 0.000485 2022/09/13 09:54:25 - mmengine - INFO - Epoch(train) [171][200/586] lr: 5.000000e-05 eta: 2:51:53 time: 0.474850 data_time: 0.027336 memory: 15239 loss_kpt: 0.000494 acc_pose: 0.885916 loss: 0.000494 2022/09/13 09:54:48 - mmengine - INFO - Epoch(train) [171][250/586] lr: 5.000000e-05 eta: 2:51:31 time: 0.467003 data_time: 0.028024 memory: 15239 loss_kpt: 0.000489 acc_pose: 0.808781 loss: 0.000489 2022/09/13 09:55:11 - mmengine - INFO - Epoch(train) [171][300/586] lr: 5.000000e-05 eta: 2:51:09 time: 0.467318 data_time: 0.027816 memory: 15239 loss_kpt: 0.000477 acc_pose: 0.881573 loss: 0.000477 2022/09/13 09:55:35 - mmengine - INFO - Epoch(train) [171][350/586] lr: 5.000000e-05 eta: 2:50:48 time: 0.473088 data_time: 0.026664 memory: 15239 loss_kpt: 0.000475 acc_pose: 0.851814 loss: 0.000475 2022/09/13 09:55:49 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:55:58 - mmengine - INFO - Epoch(train) [171][400/586] lr: 5.000000e-05 eta: 2:50:26 time: 0.467311 data_time: 0.027578 memory: 15239 loss_kpt: 0.000478 acc_pose: 0.866775 loss: 0.000478 2022/09/13 09:56:22 - mmengine - INFO - Epoch(train) [171][450/586] lr: 5.000000e-05 eta: 2:50:04 time: 0.470356 data_time: 0.029820 memory: 15239 loss_kpt: 0.000480 acc_pose: 0.818672 loss: 0.000480 2022/09/13 09:56:45 - mmengine - INFO - Epoch(train) [171][500/586] lr: 5.000000e-05 eta: 2:49:42 time: 0.469108 data_time: 0.026580 memory: 15239 loss_kpt: 0.000464 acc_pose: 0.881450 loss: 0.000464 2022/09/13 09:57:09 - mmengine - INFO - Epoch(train) [171][550/586] lr: 5.000000e-05 eta: 2:49:20 time: 0.465843 data_time: 0.026420 memory: 15239 loss_kpt: 0.000472 acc_pose: 0.892175 loss: 0.000472 2022/09/13 09:57:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 09:57:26 - mmengine - INFO - Saving checkpoint at 171 epochs 2022/09/13 09:57:57 - mmengine - INFO - Epoch(train) [172][50/586] lr: 5.000000e-05 eta: 2:48:39 time: 0.492958 data_time: 0.041561 memory: 15239 loss_kpt: 0.000464 acc_pose: 0.855531 loss: 0.000464 2022/09/13 09:58:21 - mmengine - INFO - Epoch(train) [172][100/586] lr: 5.000000e-05 eta: 2:48:17 time: 0.471078 data_time: 0.026685 memory: 15239 loss_kpt: 0.000482 acc_pose: 0.839094 loss: 0.000482 2022/09/13 09:58:45 - mmengine - INFO - Epoch(train) [172][150/586] lr: 5.000000e-05 eta: 2:47:55 time: 0.484424 data_time: 0.027854 memory: 15239 loss_kpt: 0.000473 acc_pose: 0.864587 loss: 0.000473 2022/09/13 09:59:09 - mmengine - INFO - Epoch(train) [172][200/586] lr: 5.000000e-05 eta: 2:47:33 time: 0.477724 data_time: 0.031424 memory: 15239 loss_kpt: 0.000455 acc_pose: 0.908782 loss: 0.000455 2022/09/13 09:59:32 - mmengine - INFO - Epoch(train) [172][250/586] lr: 5.000000e-05 eta: 2:47:11 time: 0.469501 data_time: 0.026438 memory: 15239 loss_kpt: 0.000470 acc_pose: 0.901524 loss: 0.000470 2022/09/13 09:59:56 - mmengine - INFO - Epoch(train) [172][300/586] lr: 5.000000e-05 eta: 2:46:50 time: 0.483390 data_time: 0.031496 memory: 15239 loss_kpt: 0.000470 acc_pose: 0.844798 loss: 0.000470 2022/09/13 10:00:20 - mmengine - INFO - Epoch(train) [172][350/586] lr: 5.000000e-05 eta: 2:46:28 time: 0.478052 data_time: 0.031776 memory: 15239 loss_kpt: 0.000482 acc_pose: 0.901655 loss: 0.000482 2022/09/13 10:00:44 - mmengine - INFO - Epoch(train) [172][400/586] lr: 5.000000e-05 eta: 2:46:06 time: 0.478338 data_time: 0.027497 memory: 15239 loss_kpt: 0.000463 acc_pose: 0.905540 loss: 0.000463 2022/09/13 10:01:08 - mmengine - INFO - Epoch(train) [172][450/586] lr: 5.000000e-05 eta: 2:45:44 time: 0.469292 data_time: 0.027087 memory: 15239 loss_kpt: 0.000479 acc_pose: 0.906281 loss: 0.000479 2022/09/13 10:01:31 - mmengine - INFO - Epoch(train) [172][500/586] lr: 5.000000e-05 eta: 2:45:22 time: 0.470031 data_time: 0.027771 memory: 15239 loss_kpt: 0.000481 acc_pose: 0.848373 loss: 0.000481 2022/09/13 10:01:55 - mmengine - INFO - Epoch(train) [172][550/586] lr: 5.000000e-05 eta: 2:45:00 time: 0.473296 data_time: 0.026840 memory: 15239 loss_kpt: 0.000489 acc_pose: 0.921710 loss: 0.000489 2022/09/13 10:02:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:02:12 - mmengine - INFO - Saving checkpoint at 172 epochs 2022/09/13 10:02:42 - mmengine - INFO - Epoch(train) [173][50/586] lr: 5.000000e-05 eta: 2:44:19 time: 0.472726 data_time: 0.038487 memory: 15239 loss_kpt: 0.000481 acc_pose: 0.870766 loss: 0.000481 2022/09/13 10:03:06 - mmengine - INFO - Epoch(train) [173][100/586] lr: 5.000000e-05 eta: 2:43:57 time: 0.478000 data_time: 0.027310 memory: 15239 loss_kpt: 0.000475 acc_pose: 0.916316 loss: 0.000475 2022/09/13 10:03:30 - mmengine - INFO - Epoch(train) [173][150/586] lr: 5.000000e-05 eta: 2:43:35 time: 0.470575 data_time: 0.026776 memory: 15239 loss_kpt: 0.000465 acc_pose: 0.885139 loss: 0.000465 2022/09/13 10:03:53 - mmengine - INFO - Epoch(train) [173][200/586] lr: 5.000000e-05 eta: 2:43:13 time: 0.467842 data_time: 0.030454 memory: 15239 loss_kpt: 0.000476 acc_pose: 0.868726 loss: 0.000476 2022/09/13 10:03:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:04:17 - mmengine - INFO - Epoch(train) [173][250/586] lr: 5.000000e-05 eta: 2:42:51 time: 0.477610 data_time: 0.027437 memory: 15239 loss_kpt: 0.000462 acc_pose: 0.865711 loss: 0.000462 2022/09/13 10:04:41 - mmengine - INFO - Epoch(train) [173][300/586] lr: 5.000000e-05 eta: 2:42:30 time: 0.469545 data_time: 0.026758 memory: 15239 loss_kpt: 0.000477 acc_pose: 0.840307 loss: 0.000477 2022/09/13 10:05:04 - mmengine - INFO - Epoch(train) [173][350/586] lr: 5.000000e-05 eta: 2:42:08 time: 0.473387 data_time: 0.026857 memory: 15239 loss_kpt: 0.000472 acc_pose: 0.916599 loss: 0.000472 2022/09/13 10:05:28 - mmengine - INFO - Epoch(train) [173][400/586] lr: 5.000000e-05 eta: 2:41:46 time: 0.471979 data_time: 0.028739 memory: 15239 loss_kpt: 0.000459 acc_pose: 0.906457 loss: 0.000459 2022/09/13 10:05:51 - mmengine - INFO - Epoch(train) [173][450/586] lr: 5.000000e-05 eta: 2:41:24 time: 0.468211 data_time: 0.026667 memory: 15239 loss_kpt: 0.000469 acc_pose: 0.905552 loss: 0.000469 2022/09/13 10:06:15 - mmengine - INFO - Epoch(train) [173][500/586] lr: 5.000000e-05 eta: 2:41:02 time: 0.471966 data_time: 0.027325 memory: 15239 loss_kpt: 0.000466 acc_pose: 0.849351 loss: 0.000466 2022/09/13 10:06:39 - mmengine - INFO - Epoch(train) [173][550/586] lr: 5.000000e-05 eta: 2:40:40 time: 0.468795 data_time: 0.027573 memory: 15239 loss_kpt: 0.000457 acc_pose: 0.892658 loss: 0.000457 2022/09/13 10:06:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:06:55 - mmengine - INFO - Saving checkpoint at 173 epochs 2022/09/13 10:07:26 - mmengine - INFO - Epoch(train) [174][50/586] lr: 5.000000e-05 eta: 2:39:59 time: 0.476542 data_time: 0.033757 memory: 15239 loss_kpt: 0.000450 acc_pose: 0.881326 loss: 0.000450 2022/09/13 10:07:50 - mmengine - INFO - Epoch(train) [174][100/586] lr: 5.000000e-05 eta: 2:39:37 time: 0.482584 data_time: 0.034838 memory: 15239 loss_kpt: 0.000469 acc_pose: 0.889045 loss: 0.000469 2022/09/13 10:08:14 - mmengine - INFO - Epoch(train) [174][150/586] lr: 5.000000e-05 eta: 2:39:15 time: 0.466754 data_time: 0.028358 memory: 15239 loss_kpt: 0.000491 acc_pose: 0.864567 loss: 0.000491 2022/09/13 10:08:37 - mmengine - INFO - Epoch(train) [174][200/586] lr: 5.000000e-05 eta: 2:38:53 time: 0.464867 data_time: 0.027377 memory: 15239 loss_kpt: 0.000464 acc_pose: 0.911360 loss: 0.000464 2022/09/13 10:09:01 - mmengine - INFO - Epoch(train) [174][250/586] lr: 5.000000e-05 eta: 2:38:31 time: 0.481642 data_time: 0.029448 memory: 15239 loss_kpt: 0.000466 acc_pose: 0.888804 loss: 0.000466 2022/09/13 10:09:24 - mmengine - INFO - Epoch(train) [174][300/586] lr: 5.000000e-05 eta: 2:38:09 time: 0.464851 data_time: 0.026988 memory: 15239 loss_kpt: 0.000470 acc_pose: 0.831791 loss: 0.000470 2022/09/13 10:09:48 - mmengine - INFO - Epoch(train) [174][350/586] lr: 5.000000e-05 eta: 2:37:47 time: 0.470327 data_time: 0.028485 memory: 15239 loss_kpt: 0.000457 acc_pose: 0.949691 loss: 0.000457 2022/09/13 10:10:12 - mmengine - INFO - Epoch(train) [174][400/586] lr: 5.000000e-05 eta: 2:37:26 time: 0.480254 data_time: 0.031913 memory: 15239 loss_kpt: 0.000476 acc_pose: 0.881610 loss: 0.000476 2022/09/13 10:10:35 - mmengine - INFO - Epoch(train) [174][450/586] lr: 5.000000e-05 eta: 2:37:04 time: 0.467448 data_time: 0.026518 memory: 15239 loss_kpt: 0.000469 acc_pose: 0.868364 loss: 0.000469 2022/09/13 10:10:58 - mmengine - INFO - Epoch(train) [174][500/586] lr: 5.000000e-05 eta: 2:36:42 time: 0.463150 data_time: 0.027911 memory: 15239 loss_kpt: 0.000466 acc_pose: 0.818609 loss: 0.000466 2022/09/13 10:11:22 - mmengine - INFO - Epoch(train) [174][550/586] lr: 5.000000e-05 eta: 2:36:20 time: 0.474622 data_time: 0.026722 memory: 15239 loss_kpt: 0.000455 acc_pose: 0.842731 loss: 0.000455 2022/09/13 10:11:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:11:39 - mmengine - INFO - Saving checkpoint at 174 epochs 2022/09/13 10:12:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:12:11 - mmengine - INFO - Epoch(train) [175][50/586] lr: 5.000000e-05 eta: 2:35:39 time: 0.475564 data_time: 0.035662 memory: 15239 loss_kpt: 0.000456 acc_pose: 0.891585 loss: 0.000456 2022/09/13 10:12:35 - mmengine - INFO - Epoch(train) [175][100/586] lr: 5.000000e-05 eta: 2:35:17 time: 0.478282 data_time: 0.027128 memory: 15239 loss_kpt: 0.000463 acc_pose: 0.907679 loss: 0.000463 2022/09/13 10:12:59 - mmengine - INFO - Epoch(train) [175][150/586] lr: 5.000000e-05 eta: 2:34:55 time: 0.474752 data_time: 0.028480 memory: 15239 loss_kpt: 0.000471 acc_pose: 0.858913 loss: 0.000471 2022/09/13 10:13:22 - mmengine - INFO - Epoch(train) [175][200/586] lr: 5.000000e-05 eta: 2:34:33 time: 0.468924 data_time: 0.027037 memory: 15239 loss_kpt: 0.000478 acc_pose: 0.858959 loss: 0.000478 2022/09/13 10:13:46 - mmengine - INFO - Epoch(train) [175][250/586] lr: 5.000000e-05 eta: 2:34:11 time: 0.481413 data_time: 0.027824 memory: 15239 loss_kpt: 0.000459 acc_pose: 0.853320 loss: 0.000459 2022/09/13 10:14:10 - mmengine - INFO - Epoch(train) [175][300/586] lr: 5.000000e-05 eta: 2:33:49 time: 0.470559 data_time: 0.027493 memory: 15239 loss_kpt: 0.000451 acc_pose: 0.902335 loss: 0.000451 2022/09/13 10:14:33 - mmengine - INFO - Epoch(train) [175][350/586] lr: 5.000000e-05 eta: 2:33:27 time: 0.469947 data_time: 0.031372 memory: 15239 loss_kpt: 0.000468 acc_pose: 0.827785 loss: 0.000468 2022/09/13 10:14:57 - mmengine - INFO - Epoch(train) [175][400/586] lr: 5.000000e-05 eta: 2:33:05 time: 0.474282 data_time: 0.027017 memory: 15239 loss_kpt: 0.000463 acc_pose: 0.909249 loss: 0.000463 2022/09/13 10:15:21 - mmengine - INFO - Epoch(train) [175][450/586] lr: 5.000000e-05 eta: 2:32:44 time: 0.477489 data_time: 0.027248 memory: 15239 loss_kpt: 0.000465 acc_pose: 0.905701 loss: 0.000465 2022/09/13 10:15:44 - mmengine - INFO - Epoch(train) [175][500/586] lr: 5.000000e-05 eta: 2:32:22 time: 0.464382 data_time: 0.028551 memory: 15239 loss_kpt: 0.000461 acc_pose: 0.837683 loss: 0.000461 2022/09/13 10:16:08 - mmengine - INFO - Epoch(train) [175][550/586] lr: 5.000000e-05 eta: 2:32:00 time: 0.476701 data_time: 0.028803 memory: 15239 loss_kpt: 0.000456 acc_pose: 0.910223 loss: 0.000456 2022/09/13 10:16:25 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:16:25 - mmengine - INFO - Saving checkpoint at 175 epochs 2022/09/13 10:16:56 - mmengine - INFO - Epoch(train) [176][50/586] lr: 5.000000e-05 eta: 2:31:19 time: 0.473323 data_time: 0.044049 memory: 15239 loss_kpt: 0.000458 acc_pose: 0.884907 loss: 0.000458 2022/09/13 10:17:19 - mmengine - INFO - Epoch(train) [176][100/586] lr: 5.000000e-05 eta: 2:30:57 time: 0.466985 data_time: 0.030297 memory: 15239 loss_kpt: 0.000477 acc_pose: 0.885324 loss: 0.000477 2022/09/13 10:17:43 - mmengine - INFO - Epoch(train) [176][150/586] lr: 5.000000e-05 eta: 2:30:35 time: 0.475397 data_time: 0.030761 memory: 15239 loss_kpt: 0.000463 acc_pose: 0.903646 loss: 0.000463 2022/09/13 10:18:06 - mmengine - INFO - Epoch(train) [176][200/586] lr: 5.000000e-05 eta: 2:30:13 time: 0.468716 data_time: 0.031681 memory: 15239 loss_kpt: 0.000443 acc_pose: 0.873545 loss: 0.000443 2022/09/13 10:18:30 - mmengine - INFO - Epoch(train) [176][250/586] lr: 5.000000e-05 eta: 2:29:51 time: 0.481492 data_time: 0.034681 memory: 15239 loss_kpt: 0.000460 acc_pose: 0.891655 loss: 0.000460 2022/09/13 10:18:54 - mmengine - INFO - Epoch(train) [176][300/586] lr: 5.000000e-05 eta: 2:29:29 time: 0.464151 data_time: 0.032556 memory: 15239 loss_kpt: 0.000468 acc_pose: 0.831125 loss: 0.000468 2022/09/13 10:19:17 - mmengine - INFO - Epoch(train) [176][350/586] lr: 5.000000e-05 eta: 2:29:07 time: 0.468837 data_time: 0.027512 memory: 15239 loss_kpt: 0.000462 acc_pose: 0.851455 loss: 0.000462 2022/09/13 10:19:41 - mmengine - INFO - Epoch(train) [176][400/586] lr: 5.000000e-05 eta: 2:28:45 time: 0.477745 data_time: 0.027426 memory: 15239 loss_kpt: 0.000469 acc_pose: 0.857366 loss: 0.000469 2022/09/13 10:20:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:20:04 - mmengine - INFO - Epoch(train) [176][450/586] lr: 5.000000e-05 eta: 2:28:23 time: 0.468630 data_time: 0.027400 memory: 15239 loss_kpt: 0.000454 acc_pose: 0.853493 loss: 0.000454 2022/09/13 10:20:28 - mmengine - INFO - Epoch(train) [176][500/586] lr: 5.000000e-05 eta: 2:28:01 time: 0.467557 data_time: 0.028542 memory: 15239 loss_kpt: 0.000446 acc_pose: 0.866095 loss: 0.000446 2022/09/13 10:20:52 - mmengine - INFO - Epoch(train) [176][550/586] lr: 5.000000e-05 eta: 2:27:40 time: 0.475891 data_time: 0.027789 memory: 15239 loss_kpt: 0.000462 acc_pose: 0.949944 loss: 0.000462 2022/09/13 10:21:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:21:08 - mmengine - INFO - Saving checkpoint at 176 epochs 2022/09/13 10:21:40 - mmengine - INFO - Epoch(train) [177][50/586] lr: 5.000000e-05 eta: 2:26:59 time: 0.484868 data_time: 0.037617 memory: 15239 loss_kpt: 0.000460 acc_pose: 0.832477 loss: 0.000460 2022/09/13 10:22:04 - mmengine - INFO - Epoch(train) [177][100/586] lr: 5.000000e-05 eta: 2:26:37 time: 0.478469 data_time: 0.027475 memory: 15239 loss_kpt: 0.000470 acc_pose: 0.841555 loss: 0.000470 2022/09/13 10:22:28 - mmengine - INFO - Epoch(train) [177][150/586] lr: 5.000000e-05 eta: 2:26:15 time: 0.470375 data_time: 0.028301 memory: 15239 loss_kpt: 0.000474 acc_pose: 0.845390 loss: 0.000474 2022/09/13 10:22:51 - mmengine - INFO - Epoch(train) [177][200/586] lr: 5.000000e-05 eta: 2:25:53 time: 0.461213 data_time: 0.026941 memory: 15239 loss_kpt: 0.000467 acc_pose: 0.875606 loss: 0.000467 2022/09/13 10:23:15 - mmengine - INFO - Epoch(train) [177][250/586] lr: 5.000000e-05 eta: 2:25:31 time: 0.485409 data_time: 0.027165 memory: 15239 loss_kpt: 0.000459 acc_pose: 0.915912 loss: 0.000459 2022/09/13 10:23:39 - mmengine - INFO - Epoch(train) [177][300/586] lr: 5.000000e-05 eta: 2:25:09 time: 0.470457 data_time: 0.032841 memory: 15239 loss_kpt: 0.000479 acc_pose: 0.790321 loss: 0.000479 2022/09/13 10:24:02 - mmengine - INFO - Epoch(train) [177][350/586] lr: 5.000000e-05 eta: 2:24:47 time: 0.465468 data_time: 0.026746 memory: 15239 loss_kpt: 0.000442 acc_pose: 0.877184 loss: 0.000442 2022/09/13 10:24:26 - mmengine - INFO - Epoch(train) [177][400/586] lr: 5.000000e-05 eta: 2:24:25 time: 0.477498 data_time: 0.027823 memory: 15239 loss_kpt: 0.000466 acc_pose: 0.878312 loss: 0.000466 2022/09/13 10:24:49 - mmengine - INFO - Epoch(train) [177][450/586] lr: 5.000000e-05 eta: 2:24:03 time: 0.468093 data_time: 0.027785 memory: 15239 loss_kpt: 0.000453 acc_pose: 0.900462 loss: 0.000453 2022/09/13 10:25:13 - mmengine - INFO - Epoch(train) [177][500/586] lr: 5.000000e-05 eta: 2:23:41 time: 0.465898 data_time: 0.028857 memory: 15239 loss_kpt: 0.000470 acc_pose: 0.841312 loss: 0.000470 2022/09/13 10:25:36 - mmengine - INFO - Epoch(train) [177][550/586] lr: 5.000000e-05 eta: 2:23:19 time: 0.477588 data_time: 0.027054 memory: 15239 loss_kpt: 0.000466 acc_pose: 0.876156 loss: 0.000466 2022/09/13 10:25:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:25:53 - mmengine - INFO - Saving checkpoint at 177 epochs 2022/09/13 10:26:24 - mmengine - INFO - Epoch(train) [178][50/586] lr: 5.000000e-05 eta: 2:22:39 time: 0.477782 data_time: 0.043492 memory: 15239 loss_kpt: 0.000462 acc_pose: 0.899352 loss: 0.000462 2022/09/13 10:26:48 - mmengine - INFO - Epoch(train) [178][100/586] lr: 5.000000e-05 eta: 2:22:17 time: 0.486022 data_time: 0.031231 memory: 15239 loss_kpt: 0.000463 acc_pose: 0.852939 loss: 0.000463 2022/09/13 10:27:12 - mmengine - INFO - Epoch(train) [178][150/586] lr: 5.000000e-05 eta: 2:21:55 time: 0.467418 data_time: 0.032355 memory: 15239 loss_kpt: 0.000457 acc_pose: 0.914942 loss: 0.000457 2022/09/13 10:27:36 - mmengine - INFO - Epoch(train) [178][200/586] lr: 5.000000e-05 eta: 2:21:33 time: 0.474818 data_time: 0.037402 memory: 15239 loss_kpt: 0.000453 acc_pose: 0.873876 loss: 0.000453 2022/09/13 10:27:59 - mmengine - INFO - Epoch(train) [178][250/586] lr: 5.000000e-05 eta: 2:21:11 time: 0.472694 data_time: 0.032997 memory: 15239 loss_kpt: 0.000456 acc_pose: 0.939548 loss: 0.000456 2022/09/13 10:28:13 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:28:23 - mmengine - INFO - Epoch(train) [178][300/586] lr: 5.000000e-05 eta: 2:20:49 time: 0.473366 data_time: 0.034725 memory: 15239 loss_kpt: 0.000468 acc_pose: 0.860368 loss: 0.000468 2022/09/13 10:28:47 - mmengine - INFO - Epoch(train) [178][350/586] lr: 5.000000e-05 eta: 2:20:27 time: 0.478849 data_time: 0.035272 memory: 15239 loss_kpt: 0.000471 acc_pose: 0.812442 loss: 0.000471 2022/09/13 10:29:11 - mmengine - INFO - Epoch(train) [178][400/586] lr: 5.000000e-05 eta: 2:20:05 time: 0.474938 data_time: 0.034695 memory: 15239 loss_kpt: 0.000473 acc_pose: 0.893498 loss: 0.000473 2022/09/13 10:29:34 - mmengine - INFO - Epoch(train) [178][450/586] lr: 5.000000e-05 eta: 2:19:43 time: 0.473534 data_time: 0.033117 memory: 15239 loss_kpt: 0.000477 acc_pose: 0.891591 loss: 0.000477 2022/09/13 10:29:58 - mmengine - INFO - Epoch(train) [178][500/586] lr: 5.000000e-05 eta: 2:19:21 time: 0.477447 data_time: 0.036273 memory: 15239 loss_kpt: 0.000461 acc_pose: 0.881995 loss: 0.000461 2022/09/13 10:30:22 - mmengine - INFO - Epoch(train) [178][550/586] lr: 5.000000e-05 eta: 2:19:00 time: 0.472607 data_time: 0.036265 memory: 15239 loss_kpt: 0.000474 acc_pose: 0.795812 loss: 0.000474 2022/09/13 10:30:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:30:38 - mmengine - INFO - Saving checkpoint at 178 epochs 2022/09/13 10:31:10 - mmengine - INFO - Epoch(train) [179][50/586] lr: 5.000000e-05 eta: 2:18:19 time: 0.483595 data_time: 0.033019 memory: 15239 loss_kpt: 0.000458 acc_pose: 0.929154 loss: 0.000458 2022/09/13 10:31:34 - mmengine - INFO - Epoch(train) [179][100/586] lr: 5.000000e-05 eta: 2:17:57 time: 0.473284 data_time: 0.027920 memory: 15239 loss_kpt: 0.000461 acc_pose: 0.864316 loss: 0.000461 2022/09/13 10:31:57 - mmengine - INFO - Epoch(train) [179][150/586] lr: 5.000000e-05 eta: 2:17:35 time: 0.472973 data_time: 0.027394 memory: 15239 loss_kpt: 0.000445 acc_pose: 0.897771 loss: 0.000445 2022/09/13 10:32:21 - mmengine - INFO - Epoch(train) [179][200/586] lr: 5.000000e-05 eta: 2:17:13 time: 0.474371 data_time: 0.027806 memory: 15239 loss_kpt: 0.000448 acc_pose: 0.876318 loss: 0.000448 2022/09/13 10:32:45 - mmengine - INFO - Epoch(train) [179][250/586] lr: 5.000000e-05 eta: 2:16:51 time: 0.472544 data_time: 0.028003 memory: 15239 loss_kpt: 0.000460 acc_pose: 0.800504 loss: 0.000460 2022/09/13 10:33:08 - mmengine - INFO - Epoch(train) [179][300/586] lr: 5.000000e-05 eta: 2:16:29 time: 0.475067 data_time: 0.034782 memory: 15239 loss_kpt: 0.000459 acc_pose: 0.914668 loss: 0.000459 2022/09/13 10:33:32 - mmengine - INFO - Epoch(train) [179][350/586] lr: 5.000000e-05 eta: 2:16:07 time: 0.469245 data_time: 0.028552 memory: 15239 loss_kpt: 0.000471 acc_pose: 0.910016 loss: 0.000471 2022/09/13 10:33:56 - mmengine - INFO - Epoch(train) [179][400/586] lr: 5.000000e-05 eta: 2:15:45 time: 0.478149 data_time: 0.028120 memory: 15239 loss_kpt: 0.000449 acc_pose: 0.895997 loss: 0.000449 2022/09/13 10:34:19 - mmengine - INFO - Epoch(train) [179][450/586] lr: 5.000000e-05 eta: 2:15:23 time: 0.473672 data_time: 0.028757 memory: 15239 loss_kpt: 0.000453 acc_pose: 0.835666 loss: 0.000453 2022/09/13 10:34:44 - mmengine - INFO - Epoch(train) [179][500/586] lr: 5.000000e-05 eta: 2:15:02 time: 0.483822 data_time: 0.034925 memory: 15239 loss_kpt: 0.000458 acc_pose: 0.884872 loss: 0.000458 2022/09/13 10:35:07 - mmengine - INFO - Epoch(train) [179][550/586] lr: 5.000000e-05 eta: 2:14:40 time: 0.471541 data_time: 0.035287 memory: 15239 loss_kpt: 0.000445 acc_pose: 0.915342 loss: 0.000445 2022/09/13 10:35:24 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:35:24 - mmengine - INFO - Saving checkpoint at 179 epochs 2022/09/13 10:35:55 - mmengine - INFO - Epoch(train) [180][50/586] lr: 5.000000e-05 eta: 2:13:59 time: 0.487696 data_time: 0.044892 memory: 15239 loss_kpt: 0.000458 acc_pose: 0.881666 loss: 0.000458 2022/09/13 10:36:20 - mmengine - INFO - Epoch(train) [180][100/586] lr: 5.000000e-05 eta: 2:13:37 time: 0.486661 data_time: 0.030823 memory: 15239 loss_kpt: 0.000459 acc_pose: 0.844028 loss: 0.000459 2022/09/13 10:36:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:36:43 - mmengine - INFO - Epoch(train) [180][150/586] lr: 5.000000e-05 eta: 2:13:15 time: 0.474390 data_time: 0.031110 memory: 15239 loss_kpt: 0.000458 acc_pose: 0.895668 loss: 0.000458 2022/09/13 10:37:07 - mmengine - INFO - Epoch(train) [180][200/586] lr: 5.000000e-05 eta: 2:12:53 time: 0.473930 data_time: 0.032771 memory: 15239 loss_kpt: 0.000449 acc_pose: 0.875022 loss: 0.000449 2022/09/13 10:37:31 - mmengine - INFO - Epoch(train) [180][250/586] lr: 5.000000e-05 eta: 2:12:31 time: 0.472629 data_time: 0.028004 memory: 15239 loss_kpt: 0.000448 acc_pose: 0.862642 loss: 0.000448 2022/09/13 10:37:54 - mmengine - INFO - Epoch(train) [180][300/586] lr: 5.000000e-05 eta: 2:12:09 time: 0.464289 data_time: 0.026913 memory: 15239 loss_kpt: 0.000456 acc_pose: 0.859409 loss: 0.000456 2022/09/13 10:38:18 - mmengine - INFO - Epoch(train) [180][350/586] lr: 5.000000e-05 eta: 2:11:48 time: 0.477665 data_time: 0.027811 memory: 15239 loss_kpt: 0.000451 acc_pose: 0.921977 loss: 0.000451 2022/09/13 10:38:41 - mmengine - INFO - Epoch(train) [180][400/586] lr: 5.000000e-05 eta: 2:11:26 time: 0.464030 data_time: 0.026945 memory: 15239 loss_kpt: 0.000471 acc_pose: 0.892508 loss: 0.000471 2022/09/13 10:39:05 - mmengine - INFO - Epoch(train) [180][450/586] lr: 5.000000e-05 eta: 2:11:04 time: 0.473855 data_time: 0.027229 memory: 15239 loss_kpt: 0.000471 acc_pose: 0.914437 loss: 0.000471 2022/09/13 10:39:28 - mmengine - INFO - Epoch(train) [180][500/586] lr: 5.000000e-05 eta: 2:10:42 time: 0.470041 data_time: 0.026821 memory: 15239 loss_kpt: 0.000471 acc_pose: 0.905937 loss: 0.000471 2022/09/13 10:39:52 - mmengine - INFO - Epoch(train) [180][550/586] lr: 5.000000e-05 eta: 2:10:20 time: 0.465735 data_time: 0.027074 memory: 15239 loss_kpt: 0.000459 acc_pose: 0.888209 loss: 0.000459 2022/09/13 10:40:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:40:08 - mmengine - INFO - Saving checkpoint at 180 epochs 2022/09/13 10:40:27 - mmengine - INFO - Epoch(val) [180][50/407] eta: 0:01:24 time: 0.235884 data_time: 0.020686 memory: 15239 2022/09/13 10:40:38 - mmengine - INFO - Epoch(val) [180][100/407] eta: 0:01:08 time: 0.222213 data_time: 0.008577 memory: 2064 2022/09/13 10:40:49 - mmengine - INFO - Epoch(val) [180][150/407] eta: 0:00:56 time: 0.219830 data_time: 0.008912 memory: 2064 2022/09/13 10:41:00 - mmengine - INFO - Epoch(val) [180][200/407] eta: 0:00:45 time: 0.218449 data_time: 0.008190 memory: 2064 2022/09/13 10:41:11 - mmengine - INFO - Epoch(val) [180][250/407] eta: 0:00:34 time: 0.221494 data_time: 0.008856 memory: 2064 2022/09/13 10:41:22 - mmengine - INFO - Epoch(val) [180][300/407] eta: 0:00:23 time: 0.219072 data_time: 0.008699 memory: 2064 2022/09/13 10:41:33 - mmengine - INFO - Epoch(val) [180][350/407] eta: 0:00:12 time: 0.219821 data_time: 0.008433 memory: 2064 2022/09/13 10:41:44 - mmengine - INFO - Epoch(val) [180][400/407] eta: 0:00:01 time: 0.217378 data_time: 0.008214 memory: 2064 2022/09/13 10:42:21 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 10:42:35 - mmengine - INFO - Epoch(val) [180][407/407] coco/AP: 0.770732 coco/AP .5: 0.909448 coco/AP .75: 0.835853 coco/AP (M): 0.730766 coco/AP (L): 0.842113 coco/AR: 0.819742 coco/AR .5: 0.945372 coco/AR .75: 0.878149 coco/AR (M): 0.775444 coco/AR (L): 0.883798 2022/09/13 10:42:35 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_384_v1/best_coco/AP_epoch_170.pth is removed 2022/09/13 10:42:39 - mmengine - INFO - The best checkpoint with 0.7707 coco/AP at 180 epoch is saved to best_coco/AP_epoch_180.pth. 2022/09/13 10:43:03 - mmengine - INFO - Epoch(train) [181][50/586] lr: 5.000000e-05 eta: 2:09:39 time: 0.480618 data_time: 0.035729 memory: 15239 loss_kpt: 0.000433 acc_pose: 0.928774 loss: 0.000433 2022/09/13 10:43:27 - mmengine - INFO - Epoch(train) [181][100/586] lr: 5.000000e-05 eta: 2:09:17 time: 0.482321 data_time: 0.027371 memory: 15239 loss_kpt: 0.000457 acc_pose: 0.925858 loss: 0.000457 2022/09/13 10:43:51 - mmengine - INFO - Epoch(train) [181][150/586] lr: 5.000000e-05 eta: 2:08:55 time: 0.478640 data_time: 0.027449 memory: 15239 loss_kpt: 0.000467 acc_pose: 0.865982 loss: 0.000467 2022/09/13 10:44:15 - mmengine - INFO - Epoch(train) [181][200/586] lr: 5.000000e-05 eta: 2:08:33 time: 0.478205 data_time: 0.027467 memory: 15239 loss_kpt: 0.000453 acc_pose: 0.876946 loss: 0.000453 2022/09/13 10:44:38 - mmengine - INFO - Epoch(train) [181][250/586] lr: 5.000000e-05 eta: 2:08:11 time: 0.467878 data_time: 0.026719 memory: 15239 loss_kpt: 0.000457 acc_pose: 0.882672 loss: 0.000457 2022/09/13 10:45:02 - mmengine - INFO - Epoch(train) [181][300/586] lr: 5.000000e-05 eta: 2:07:49 time: 0.474825 data_time: 0.026637 memory: 15239 loss_kpt: 0.000447 acc_pose: 0.872928 loss: 0.000447 2022/09/13 10:45:26 - mmengine - INFO - Epoch(train) [181][350/586] lr: 5.000000e-05 eta: 2:07:27 time: 0.474047 data_time: 0.030001 memory: 15239 loss_kpt: 0.000439 acc_pose: 0.890328 loss: 0.000439 2022/09/13 10:45:50 - mmengine - INFO - Epoch(train) [181][400/586] lr: 5.000000e-05 eta: 2:07:06 time: 0.481636 data_time: 0.026453 memory: 15239 loss_kpt: 0.000465 acc_pose: 0.900192 loss: 0.000465 2022/09/13 10:46:14 - mmengine - INFO - Epoch(train) [181][450/586] lr: 5.000000e-05 eta: 2:06:44 time: 0.475994 data_time: 0.027295 memory: 15239 loss_kpt: 0.000463 acc_pose: 0.933902 loss: 0.000463 2022/09/13 10:46:38 - mmengine - INFO - Epoch(train) [181][500/586] lr: 5.000000e-05 eta: 2:06:22 time: 0.475676 data_time: 0.033452 memory: 15239 loss_kpt: 0.000456 acc_pose: 0.861689 loss: 0.000456 2022/09/13 10:46:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:47:01 - mmengine - INFO - Epoch(train) [181][550/586] lr: 5.000000e-05 eta: 2:06:00 time: 0.471977 data_time: 0.028915 memory: 15239 loss_kpt: 0.000450 acc_pose: 0.910325 loss: 0.000450 2022/09/13 10:47:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:47:18 - mmengine - INFO - Saving checkpoint at 181 epochs 2022/09/13 10:47:49 - mmengine - INFO - Epoch(train) [182][50/586] lr: 5.000000e-05 eta: 2:05:19 time: 0.478202 data_time: 0.031198 memory: 15239 loss_kpt: 0.000459 acc_pose: 0.930464 loss: 0.000459 2022/09/13 10:48:13 - mmengine - INFO - Epoch(train) [182][100/586] lr: 5.000000e-05 eta: 2:04:57 time: 0.473512 data_time: 0.027715 memory: 15239 loss_kpt: 0.000448 acc_pose: 0.805032 loss: 0.000448 2022/09/13 10:48:36 - mmengine - INFO - Epoch(train) [182][150/586] lr: 5.000000e-05 eta: 2:04:35 time: 0.465955 data_time: 0.032111 memory: 15239 loss_kpt: 0.000440 acc_pose: 0.820742 loss: 0.000440 2022/09/13 10:49:00 - mmengine - INFO - Epoch(train) [182][200/586] lr: 5.000000e-05 eta: 2:04:13 time: 0.475835 data_time: 0.028358 memory: 15239 loss_kpt: 0.000462 acc_pose: 0.902960 loss: 0.000462 2022/09/13 10:49:24 - mmengine - INFO - Epoch(train) [182][250/586] lr: 5.000000e-05 eta: 2:03:51 time: 0.474624 data_time: 0.027457 memory: 15239 loss_kpt: 0.000463 acc_pose: 0.906939 loss: 0.000463 2022/09/13 10:49:47 - mmengine - INFO - Epoch(train) [182][300/586] lr: 5.000000e-05 eta: 2:03:29 time: 0.470176 data_time: 0.031268 memory: 15239 loss_kpt: 0.000457 acc_pose: 0.896801 loss: 0.000457 2022/09/13 10:50:12 - mmengine - INFO - Epoch(train) [182][350/586] lr: 5.000000e-05 eta: 2:03:08 time: 0.480870 data_time: 0.028333 memory: 15239 loss_kpt: 0.000463 acc_pose: 0.823130 loss: 0.000463 2022/09/13 10:50:35 - mmengine - INFO - Epoch(train) [182][400/586] lr: 5.000000e-05 eta: 2:02:46 time: 0.469726 data_time: 0.028263 memory: 15239 loss_kpt: 0.000459 acc_pose: 0.895035 loss: 0.000459 2022/09/13 10:50:59 - mmengine - INFO - Epoch(train) [182][450/586] lr: 5.000000e-05 eta: 2:02:24 time: 0.470130 data_time: 0.027425 memory: 15239 loss_kpt: 0.000457 acc_pose: 0.904142 loss: 0.000457 2022/09/13 10:51:22 - mmengine - INFO - Epoch(train) [182][500/586] lr: 5.000000e-05 eta: 2:02:02 time: 0.470186 data_time: 0.026849 memory: 15239 loss_kpt: 0.000468 acc_pose: 0.894471 loss: 0.000468 2022/09/13 10:51:46 - mmengine - INFO - Epoch(train) [182][550/586] lr: 5.000000e-05 eta: 2:01:40 time: 0.476592 data_time: 0.027587 memory: 15239 loss_kpt: 0.000473 acc_pose: 0.890627 loss: 0.000473 2022/09/13 10:52:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:52:03 - mmengine - INFO - Saving checkpoint at 182 epochs 2022/09/13 10:52:35 - mmengine - INFO - Epoch(train) [183][50/586] lr: 5.000000e-05 eta: 2:00:59 time: 0.483158 data_time: 0.036483 memory: 15239 loss_kpt: 0.000457 acc_pose: 0.931343 loss: 0.000457 2022/09/13 10:52:59 - mmengine - INFO - Epoch(train) [183][100/586] lr: 5.000000e-05 eta: 2:00:37 time: 0.479485 data_time: 0.031122 memory: 15239 loss_kpt: 0.000443 acc_pose: 0.871279 loss: 0.000443 2022/09/13 10:53:22 - mmengine - INFO - Epoch(train) [183][150/586] lr: 5.000000e-05 eta: 2:00:15 time: 0.474174 data_time: 0.030489 memory: 15239 loss_kpt: 0.000449 acc_pose: 0.870740 loss: 0.000449 2022/09/13 10:53:46 - mmengine - INFO - Epoch(train) [183][200/586] lr: 5.000000e-05 eta: 1:59:53 time: 0.468770 data_time: 0.035313 memory: 15239 loss_kpt: 0.000460 acc_pose: 0.895605 loss: 0.000460 2022/09/13 10:54:10 - mmengine - INFO - Epoch(train) [183][250/586] lr: 5.000000e-05 eta: 1:59:31 time: 0.477401 data_time: 0.033391 memory: 15239 loss_kpt: 0.000455 acc_pose: 0.889249 loss: 0.000455 2022/09/13 10:54:34 - mmengine - INFO - Epoch(train) [183][300/586] lr: 5.000000e-05 eta: 1:59:10 time: 0.480712 data_time: 0.027436 memory: 15239 loss_kpt: 0.000457 acc_pose: 0.903001 loss: 0.000457 2022/09/13 10:54:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:54:57 - mmengine - INFO - Epoch(train) [183][350/586] lr: 5.000000e-05 eta: 1:58:48 time: 0.470254 data_time: 0.027585 memory: 15239 loss_kpt: 0.000465 acc_pose: 0.848580 loss: 0.000465 2022/09/13 10:55:21 - mmengine - INFO - Epoch(train) [183][400/586] lr: 5.000000e-05 eta: 1:58:26 time: 0.472065 data_time: 0.027614 memory: 15239 loss_kpt: 0.000461 acc_pose: 0.882521 loss: 0.000461 2022/09/13 10:55:45 - mmengine - INFO - Epoch(train) [183][450/586] lr: 5.000000e-05 eta: 1:58:04 time: 0.469992 data_time: 0.027507 memory: 15239 loss_kpt: 0.000462 acc_pose: 0.858867 loss: 0.000462 2022/09/13 10:56:08 - mmengine - INFO - Epoch(train) [183][500/586] lr: 5.000000e-05 eta: 1:57:42 time: 0.469486 data_time: 0.027705 memory: 15239 loss_kpt: 0.000474 acc_pose: 0.878166 loss: 0.000474 2022/09/13 10:56:32 - mmengine - INFO - Epoch(train) [183][550/586] lr: 5.000000e-05 eta: 1:57:20 time: 0.470713 data_time: 0.027016 memory: 15239 loss_kpt: 0.000464 acc_pose: 0.881613 loss: 0.000464 2022/09/13 10:56:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 10:56:48 - mmengine - INFO - Saving checkpoint at 183 epochs 2022/09/13 10:57:19 - mmengine - INFO - Epoch(train) [184][50/586] lr: 5.000000e-05 eta: 1:56:39 time: 0.484010 data_time: 0.037605 memory: 15239 loss_kpt: 0.000462 acc_pose: 0.873538 loss: 0.000462 2022/09/13 10:57:43 - mmengine - INFO - Epoch(train) [184][100/586] lr: 5.000000e-05 eta: 1:56:17 time: 0.472217 data_time: 0.027308 memory: 15239 loss_kpt: 0.000458 acc_pose: 0.913585 loss: 0.000458 2022/09/13 10:58:06 - mmengine - INFO - Epoch(train) [184][150/586] lr: 5.000000e-05 eta: 1:55:55 time: 0.466820 data_time: 0.027228 memory: 15239 loss_kpt: 0.000467 acc_pose: 0.866246 loss: 0.000467 2022/09/13 10:58:30 - mmengine - INFO - Epoch(train) [184][200/586] lr: 5.000000e-05 eta: 1:55:33 time: 0.475823 data_time: 0.028258 memory: 15239 loss_kpt: 0.000450 acc_pose: 0.893647 loss: 0.000450 2022/09/13 10:58:54 - mmengine - INFO - Epoch(train) [184][250/586] lr: 5.000000e-05 eta: 1:55:11 time: 0.471879 data_time: 0.028712 memory: 15239 loss_kpt: 0.000445 acc_pose: 0.884436 loss: 0.000445 2022/09/13 10:59:17 - mmengine - INFO - Epoch(train) [184][300/586] lr: 5.000000e-05 eta: 1:54:49 time: 0.464776 data_time: 0.027139 memory: 15239 loss_kpt: 0.000458 acc_pose: 0.909369 loss: 0.000458 2022/09/13 10:59:41 - mmengine - INFO - Epoch(train) [184][350/586] lr: 5.000000e-05 eta: 1:54:27 time: 0.482848 data_time: 0.030756 memory: 15239 loss_kpt: 0.000441 acc_pose: 0.875500 loss: 0.000441 2022/09/13 11:00:05 - mmengine - INFO - Epoch(train) [184][400/586] lr: 5.000000e-05 eta: 1:54:05 time: 0.470956 data_time: 0.027457 memory: 15239 loss_kpt: 0.000454 acc_pose: 0.911405 loss: 0.000454 2022/09/13 11:00:28 - mmengine - INFO - Epoch(train) [184][450/586] lr: 5.000000e-05 eta: 1:53:43 time: 0.471913 data_time: 0.027616 memory: 15239 loss_kpt: 0.000431 acc_pose: 0.906355 loss: 0.000431 2022/09/13 11:00:53 - mmengine - INFO - Epoch(train) [184][500/586] lr: 5.000000e-05 eta: 1:53:22 time: 0.482936 data_time: 0.028606 memory: 15239 loss_kpt: 0.000438 acc_pose: 0.926353 loss: 0.000438 2022/09/13 11:01:16 - mmengine - INFO - Epoch(train) [184][550/586] lr: 5.000000e-05 eta: 1:52:59 time: 0.467131 data_time: 0.027254 memory: 15239 loss_kpt: 0.000468 acc_pose: 0.908964 loss: 0.000468 2022/09/13 11:01:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:01:33 - mmengine - INFO - Saving checkpoint at 184 epochs 2022/09/13 11:02:05 - mmengine - INFO - Epoch(train) [185][50/586] lr: 5.000000e-05 eta: 1:52:19 time: 0.482500 data_time: 0.045932 memory: 15239 loss_kpt: 0.000450 acc_pose: 0.855598 loss: 0.000450 2022/09/13 11:02:29 - mmengine - INFO - Epoch(train) [185][100/586] lr: 5.000000e-05 eta: 1:51:57 time: 0.475372 data_time: 0.027420 memory: 15239 loss_kpt: 0.000469 acc_pose: 0.861340 loss: 0.000469 2022/09/13 11:02:53 - mmengine - INFO - Epoch(train) [185][150/586] lr: 5.000000e-05 eta: 1:51:35 time: 0.472886 data_time: 0.028037 memory: 15239 loss_kpt: 0.000444 acc_pose: 0.872644 loss: 0.000444 2022/09/13 11:03:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:03:17 - mmengine - INFO - Epoch(train) [185][200/586] lr: 5.000000e-05 eta: 1:51:13 time: 0.480481 data_time: 0.027725 memory: 15239 loss_kpt: 0.000449 acc_pose: 0.891618 loss: 0.000449 2022/09/13 11:03:40 - mmengine - INFO - Epoch(train) [185][250/586] lr: 5.000000e-05 eta: 1:50:51 time: 0.461829 data_time: 0.028068 memory: 15239 loss_kpt: 0.000446 acc_pose: 0.896508 loss: 0.000446 2022/09/13 11:04:03 - mmengine - INFO - Epoch(train) [185][300/586] lr: 5.000000e-05 eta: 1:50:29 time: 0.470621 data_time: 0.026660 memory: 15239 loss_kpt: 0.000439 acc_pose: 0.888163 loss: 0.000439 2022/09/13 11:04:27 - mmengine - INFO - Epoch(train) [185][350/586] lr: 5.000000e-05 eta: 1:50:07 time: 0.474489 data_time: 0.026651 memory: 15239 loss_kpt: 0.000448 acc_pose: 0.950606 loss: 0.000448 2022/09/13 11:04:50 - mmengine - INFO - Epoch(train) [185][400/586] lr: 5.000000e-05 eta: 1:49:45 time: 0.467738 data_time: 0.027235 memory: 15239 loss_kpt: 0.000461 acc_pose: 0.882862 loss: 0.000461 2022/09/13 11:05:14 - mmengine - INFO - Epoch(train) [185][450/586] lr: 5.000000e-05 eta: 1:49:23 time: 0.469198 data_time: 0.028123 memory: 15239 loss_kpt: 0.000469 acc_pose: 0.882373 loss: 0.000469 2022/09/13 11:05:38 - mmengine - INFO - Epoch(train) [185][500/586] lr: 5.000000e-05 eta: 1:49:01 time: 0.476032 data_time: 0.028986 memory: 15239 loss_kpt: 0.000441 acc_pose: 0.889589 loss: 0.000441 2022/09/13 11:06:01 - mmengine - INFO - Epoch(train) [185][550/586] lr: 5.000000e-05 eta: 1:48:39 time: 0.473623 data_time: 0.027094 memory: 15239 loss_kpt: 0.000476 acc_pose: 0.915851 loss: 0.000476 2022/09/13 11:06:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:06:18 - mmengine - INFO - Saving checkpoint at 185 epochs 2022/09/13 11:06:49 - mmengine - INFO - Epoch(train) [186][50/586] lr: 5.000000e-05 eta: 1:47:59 time: 0.479090 data_time: 0.040282 memory: 15239 loss_kpt: 0.000465 acc_pose: 0.856749 loss: 0.000465 2022/09/13 11:07:13 - mmengine - INFO - Epoch(train) [186][100/586] lr: 5.000000e-05 eta: 1:47:37 time: 0.479163 data_time: 0.033708 memory: 15239 loss_kpt: 0.000440 acc_pose: 0.889196 loss: 0.000440 2022/09/13 11:07:36 - mmengine - INFO - Epoch(train) [186][150/586] lr: 5.000000e-05 eta: 1:47:15 time: 0.467621 data_time: 0.031583 memory: 15239 loss_kpt: 0.000461 acc_pose: 0.818601 loss: 0.000461 2022/09/13 11:08:00 - mmengine - INFO - Epoch(train) [186][200/586] lr: 5.000000e-05 eta: 1:46:53 time: 0.480050 data_time: 0.035011 memory: 15239 loss_kpt: 0.000445 acc_pose: 0.863207 loss: 0.000445 2022/09/13 11:08:24 - mmengine - INFO - Epoch(train) [186][250/586] lr: 5.000000e-05 eta: 1:46:31 time: 0.481611 data_time: 0.038286 memory: 15239 loss_kpt: 0.000438 acc_pose: 0.906714 loss: 0.000438 2022/09/13 11:08:49 - mmengine - INFO - Epoch(train) [186][300/586] lr: 5.000000e-05 eta: 1:46:09 time: 0.483189 data_time: 0.035189 memory: 15239 loss_kpt: 0.000461 acc_pose: 0.797995 loss: 0.000461 2022/09/13 11:09:12 - mmengine - INFO - Epoch(train) [186][350/586] lr: 5.000000e-05 eta: 1:45:47 time: 0.469886 data_time: 0.029709 memory: 15239 loss_kpt: 0.000443 acc_pose: 0.888236 loss: 0.000443 2022/09/13 11:09:36 - mmengine - INFO - Epoch(train) [186][400/586] lr: 5.000000e-05 eta: 1:45:25 time: 0.472891 data_time: 0.027090 memory: 15239 loss_kpt: 0.000457 acc_pose: 0.878828 loss: 0.000457 2022/09/13 11:09:59 - mmengine - INFO - Epoch(train) [186][450/586] lr: 5.000000e-05 eta: 1:45:03 time: 0.470252 data_time: 0.027025 memory: 15239 loss_kpt: 0.000452 acc_pose: 0.842437 loss: 0.000452 2022/09/13 11:10:23 - mmengine - INFO - Epoch(train) [186][500/586] lr: 5.000000e-05 eta: 1:44:41 time: 0.466662 data_time: 0.028673 memory: 15239 loss_kpt: 0.000441 acc_pose: 0.858281 loss: 0.000441 2022/09/13 11:10:46 - mmengine - INFO - Epoch(train) [186][550/586] lr: 5.000000e-05 eta: 1:44:19 time: 0.475576 data_time: 0.027670 memory: 15239 loss_kpt: 0.000457 acc_pose: 0.772503 loss: 0.000457 2022/09/13 11:11:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:11:03 - mmengine - INFO - Saving checkpoint at 186 epochs 2022/09/13 11:11:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:11:36 - mmengine - INFO - Epoch(train) [187][50/586] lr: 5.000000e-05 eta: 1:43:39 time: 0.494062 data_time: 0.047492 memory: 15239 loss_kpt: 0.000443 acc_pose: 0.879974 loss: 0.000443 2022/09/13 11:12:00 - mmengine - INFO - Epoch(train) [187][100/586] lr: 5.000000e-05 eta: 1:43:17 time: 0.478946 data_time: 0.029194 memory: 15239 loss_kpt: 0.000462 acc_pose: 0.887096 loss: 0.000462 2022/09/13 11:12:23 - mmengine - INFO - Epoch(train) [187][150/586] lr: 5.000000e-05 eta: 1:42:55 time: 0.465240 data_time: 0.027868 memory: 15239 loss_kpt: 0.000454 acc_pose: 0.897077 loss: 0.000454 2022/09/13 11:12:47 - mmengine - INFO - Epoch(train) [187][200/586] lr: 5.000000e-05 eta: 1:42:33 time: 0.478930 data_time: 0.033075 memory: 15239 loss_kpt: 0.000448 acc_pose: 0.875740 loss: 0.000448 2022/09/13 11:13:11 - mmengine - INFO - Epoch(train) [187][250/586] lr: 5.000000e-05 eta: 1:42:11 time: 0.477960 data_time: 0.029029 memory: 15239 loss_kpt: 0.000446 acc_pose: 0.925573 loss: 0.000446 2022/09/13 11:13:35 - mmengine - INFO - Epoch(train) [187][300/586] lr: 5.000000e-05 eta: 1:41:49 time: 0.468664 data_time: 0.027049 memory: 15239 loss_kpt: 0.000442 acc_pose: 0.932409 loss: 0.000442 2022/09/13 11:13:58 - mmengine - INFO - Epoch(train) [187][350/586] lr: 5.000000e-05 eta: 1:41:27 time: 0.474432 data_time: 0.028044 memory: 15239 loss_kpt: 0.000441 acc_pose: 0.884724 loss: 0.000441 2022/09/13 11:14:22 - mmengine - INFO - Epoch(train) [187][400/586] lr: 5.000000e-05 eta: 1:41:05 time: 0.473451 data_time: 0.027462 memory: 15239 loss_kpt: 0.000440 acc_pose: 0.919955 loss: 0.000440 2022/09/13 11:14:46 - mmengine - INFO - Epoch(train) [187][450/586] lr: 5.000000e-05 eta: 1:40:43 time: 0.471298 data_time: 0.028616 memory: 15239 loss_kpt: 0.000451 acc_pose: 0.882332 loss: 0.000451 2022/09/13 11:15:09 - mmengine - INFO - Epoch(train) [187][500/586] lr: 5.000000e-05 eta: 1:40:21 time: 0.475979 data_time: 0.031235 memory: 15239 loss_kpt: 0.000470 acc_pose: 0.872128 loss: 0.000470 2022/09/13 11:15:34 - mmengine - INFO - Epoch(train) [187][550/586] lr: 5.000000e-05 eta: 1:39:59 time: 0.484416 data_time: 0.028289 memory: 15239 loss_kpt: 0.000450 acc_pose: 0.874922 loss: 0.000450 2022/09/13 11:15:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:15:51 - mmengine - INFO - Saving checkpoint at 187 epochs 2022/09/13 11:16:22 - mmengine - INFO - Epoch(train) [188][50/586] lr: 5.000000e-05 eta: 1:39:20 time: 0.483299 data_time: 0.036075 memory: 15239 loss_kpt: 0.000440 acc_pose: 0.855723 loss: 0.000440 2022/09/13 11:16:45 - mmengine - INFO - Epoch(train) [188][100/586] lr: 5.000000e-05 eta: 1:38:57 time: 0.469158 data_time: 0.031926 memory: 15239 loss_kpt: 0.000444 acc_pose: 0.904459 loss: 0.000444 2022/09/13 11:17:09 - mmengine - INFO - Epoch(train) [188][150/586] lr: 5.000000e-05 eta: 1:38:36 time: 0.484109 data_time: 0.034512 memory: 15239 loss_kpt: 0.000462 acc_pose: 0.881737 loss: 0.000462 2022/09/13 11:17:33 - mmengine - INFO - Epoch(train) [188][200/586] lr: 5.000000e-05 eta: 1:38:13 time: 0.465983 data_time: 0.031751 memory: 15239 loss_kpt: 0.000440 acc_pose: 0.888140 loss: 0.000440 2022/09/13 11:17:57 - mmengine - INFO - Epoch(train) [188][250/586] lr: 5.000000e-05 eta: 1:37:52 time: 0.481515 data_time: 0.033276 memory: 15239 loss_kpt: 0.000458 acc_pose: 0.869032 loss: 0.000458 2022/09/13 11:18:21 - mmengine - INFO - Epoch(train) [188][300/586] lr: 5.000000e-05 eta: 1:37:30 time: 0.478281 data_time: 0.030432 memory: 15239 loss_kpt: 0.000466 acc_pose: 0.830400 loss: 0.000466 2022/09/13 11:18:44 - mmengine - INFO - Epoch(train) [188][350/586] lr: 5.000000e-05 eta: 1:37:07 time: 0.465212 data_time: 0.028337 memory: 15239 loss_kpt: 0.000454 acc_pose: 0.843200 loss: 0.000454 2022/09/13 11:19:08 - mmengine - INFO - Epoch(train) [188][400/586] lr: 5.000000e-05 eta: 1:36:45 time: 0.472882 data_time: 0.027032 memory: 15239 loss_kpt: 0.000449 acc_pose: 0.930824 loss: 0.000449 2022/09/13 11:19:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:19:31 - mmengine - INFO - Epoch(train) [188][450/586] lr: 5.000000e-05 eta: 1:36:23 time: 0.473607 data_time: 0.028058 memory: 15239 loss_kpt: 0.000442 acc_pose: 0.913531 loss: 0.000442 2022/09/13 11:19:55 - mmengine - INFO - Epoch(train) [188][500/586] lr: 5.000000e-05 eta: 1:36:01 time: 0.470330 data_time: 0.027919 memory: 15239 loss_kpt: 0.000457 acc_pose: 0.867325 loss: 0.000457 2022/09/13 11:20:19 - mmengine - INFO - Epoch(train) [188][550/586] lr: 5.000000e-05 eta: 1:35:39 time: 0.475666 data_time: 0.032648 memory: 15239 loss_kpt: 0.000455 acc_pose: 0.866731 loss: 0.000455 2022/09/13 11:20:36 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:20:36 - mmengine - INFO - Saving checkpoint at 188 epochs 2022/09/13 11:21:08 - mmengine - INFO - Epoch(train) [189][50/586] lr: 5.000000e-05 eta: 1:34:59 time: 0.477305 data_time: 0.032755 memory: 15239 loss_kpt: 0.000445 acc_pose: 0.893335 loss: 0.000445 2022/09/13 11:21:31 - mmengine - INFO - Epoch(train) [189][100/586] lr: 5.000000e-05 eta: 1:34:37 time: 0.471809 data_time: 0.028353 memory: 15239 loss_kpt: 0.000440 acc_pose: 0.896580 loss: 0.000440 2022/09/13 11:21:55 - mmengine - INFO - Epoch(train) [189][150/586] lr: 5.000000e-05 eta: 1:34:15 time: 0.476830 data_time: 0.027541 memory: 15239 loss_kpt: 0.000451 acc_pose: 0.820526 loss: 0.000451 2022/09/13 11:22:19 - mmengine - INFO - Epoch(train) [189][200/586] lr: 5.000000e-05 eta: 1:33:53 time: 0.479213 data_time: 0.027764 memory: 15239 loss_kpt: 0.000464 acc_pose: 0.864378 loss: 0.000464 2022/09/13 11:22:42 - mmengine - INFO - Epoch(train) [189][250/586] lr: 5.000000e-05 eta: 1:33:31 time: 0.463301 data_time: 0.026643 memory: 15239 loss_kpt: 0.000444 acc_pose: 0.920982 loss: 0.000444 2022/09/13 11:23:06 - mmengine - INFO - Epoch(train) [189][300/586] lr: 5.000000e-05 eta: 1:33:09 time: 0.476459 data_time: 0.027919 memory: 15239 loss_kpt: 0.000443 acc_pose: 0.903951 loss: 0.000443 2022/09/13 11:23:30 - mmengine - INFO - Epoch(train) [189][350/586] lr: 5.000000e-05 eta: 1:32:47 time: 0.479251 data_time: 0.032517 memory: 15239 loss_kpt: 0.000456 acc_pose: 0.899313 loss: 0.000456 2022/09/13 11:23:53 - mmengine - INFO - Epoch(train) [189][400/586] lr: 5.000000e-05 eta: 1:32:25 time: 0.463964 data_time: 0.027486 memory: 15239 loss_kpt: 0.000466 acc_pose: 0.851442 loss: 0.000466 2022/09/13 11:24:18 - mmengine - INFO - Epoch(train) [189][450/586] lr: 5.000000e-05 eta: 1:32:03 time: 0.487677 data_time: 0.027773 memory: 15239 loss_kpt: 0.000451 acc_pose: 0.903225 loss: 0.000451 2022/09/13 11:24:41 - mmengine - INFO - Epoch(train) [189][500/586] lr: 5.000000e-05 eta: 1:31:41 time: 0.470780 data_time: 0.027876 memory: 15239 loss_kpt: 0.000442 acc_pose: 0.840926 loss: 0.000442 2022/09/13 11:25:05 - mmengine - INFO - Epoch(train) [189][550/586] lr: 5.000000e-05 eta: 1:31:19 time: 0.468029 data_time: 0.028507 memory: 15239 loss_kpt: 0.000446 acc_pose: 0.898056 loss: 0.000446 2022/09/13 11:25:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:25:22 - mmengine - INFO - Saving checkpoint at 189 epochs 2022/09/13 11:25:53 - mmengine - INFO - Epoch(train) [190][50/586] lr: 5.000000e-05 eta: 1:30:39 time: 0.480670 data_time: 0.035908 memory: 15239 loss_kpt: 0.000463 acc_pose: 0.863735 loss: 0.000463 2022/09/13 11:26:16 - mmengine - INFO - Epoch(train) [190][100/586] lr: 5.000000e-05 eta: 1:30:17 time: 0.470163 data_time: 0.031555 memory: 15239 loss_kpt: 0.000449 acc_pose: 0.835735 loss: 0.000449 2022/09/13 11:26:40 - mmengine - INFO - Epoch(train) [190][150/586] lr: 5.000000e-05 eta: 1:29:55 time: 0.473495 data_time: 0.026959 memory: 15239 loss_kpt: 0.000451 acc_pose: 0.875768 loss: 0.000451 2022/09/13 11:27:04 - mmengine - INFO - Epoch(train) [190][200/586] lr: 5.000000e-05 eta: 1:29:33 time: 0.477395 data_time: 0.028171 memory: 15239 loss_kpt: 0.000458 acc_pose: 0.893161 loss: 0.000458 2022/09/13 11:27:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:27:28 - mmengine - INFO - Epoch(train) [190][250/586] lr: 5.000000e-05 eta: 1:29:11 time: 0.471689 data_time: 0.035583 memory: 15239 loss_kpt: 0.000462 acc_pose: 0.845330 loss: 0.000462 2022/09/13 11:27:52 - mmengine - INFO - Epoch(train) [190][300/586] lr: 5.000000e-05 eta: 1:28:49 time: 0.478218 data_time: 0.026693 memory: 15239 loss_kpt: 0.000439 acc_pose: 0.937926 loss: 0.000439 2022/09/13 11:28:15 - mmengine - INFO - Epoch(train) [190][350/586] lr: 5.000000e-05 eta: 1:28:27 time: 0.471793 data_time: 0.028083 memory: 15239 loss_kpt: 0.000467 acc_pose: 0.845223 loss: 0.000467 2022/09/13 11:28:38 - mmengine - INFO - Epoch(train) [190][400/586] lr: 5.000000e-05 eta: 1:28:05 time: 0.462901 data_time: 0.027824 memory: 15239 loss_kpt: 0.000453 acc_pose: 0.883645 loss: 0.000453 2022/09/13 11:29:02 - mmengine - INFO - Epoch(train) [190][450/586] lr: 5.000000e-05 eta: 1:27:43 time: 0.481888 data_time: 0.032323 memory: 15239 loss_kpt: 0.000465 acc_pose: 0.912159 loss: 0.000465 2022/09/13 11:29:26 - mmengine - INFO - Epoch(train) [190][500/586] lr: 5.000000e-05 eta: 1:27:21 time: 0.470153 data_time: 0.028167 memory: 15239 loss_kpt: 0.000453 acc_pose: 0.892577 loss: 0.000453 2022/09/13 11:29:49 - mmengine - INFO - Epoch(train) [190][550/586] lr: 5.000000e-05 eta: 1:26:59 time: 0.462597 data_time: 0.028049 memory: 15239 loss_kpt: 0.000446 acc_pose: 0.895358 loss: 0.000446 2022/09/13 11:30:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:30:06 - mmengine - INFO - Saving checkpoint at 190 epochs 2022/09/13 11:30:25 - mmengine - INFO - Epoch(val) [190][50/407] eta: 0:01:21 time: 0.227682 data_time: 0.014130 memory: 15239 2022/09/13 11:30:36 - mmengine - INFO - Epoch(val) [190][100/407] eta: 0:01:08 time: 0.221906 data_time: 0.011899 memory: 2064 2022/09/13 11:30:47 - mmengine - INFO - Epoch(val) [190][150/407] eta: 0:00:57 time: 0.222717 data_time: 0.012776 memory: 2064 2022/09/13 11:30:58 - mmengine - INFO - Epoch(val) [190][200/407] eta: 0:00:45 time: 0.218758 data_time: 0.008211 memory: 2064 2022/09/13 11:31:09 - mmengine - INFO - Epoch(val) [190][250/407] eta: 0:00:34 time: 0.218370 data_time: 0.008704 memory: 2064 2022/09/13 11:31:20 - mmengine - INFO - Epoch(val) [190][300/407] eta: 0:00:23 time: 0.219949 data_time: 0.008691 memory: 2064 2022/09/13 11:31:31 - mmengine - INFO - Epoch(val) [190][350/407] eta: 0:00:12 time: 0.218998 data_time: 0.008433 memory: 2064 2022/09/13 11:31:42 - mmengine - INFO - Epoch(val) [190][400/407] eta: 0:00:01 time: 0.219950 data_time: 0.010305 memory: 2064 2022/09/13 11:32:19 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 11:32:33 - mmengine - INFO - Epoch(val) [190][407/407] coco/AP: 0.772473 coco/AP .5: 0.909295 coco/AP .75: 0.834841 coco/AP (M): 0.732447 coco/AP (L): 0.843342 coco/AR: 0.820466 coco/AR .5: 0.944742 coco/AR .75: 0.875157 coco/AR (M): 0.776536 coco/AR (L): 0.884281 2022/09/13 11:32:33 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_384_v1/best_coco/AP_epoch_180.pth is removed 2022/09/13 11:32:37 - mmengine - INFO - The best checkpoint with 0.7725 coco/AP at 190 epoch is saved to best_coco/AP_epoch_190.pth. 2022/09/13 11:33:01 - mmengine - INFO - Epoch(train) [191][50/586] lr: 5.000000e-05 eta: 1:26:19 time: 0.477693 data_time: 0.037888 memory: 15239 loss_kpt: 0.000447 acc_pose: 0.909468 loss: 0.000447 2022/09/13 11:33:25 - mmengine - INFO - Epoch(train) [191][100/586] lr: 5.000000e-05 eta: 1:25:57 time: 0.473007 data_time: 0.028414 memory: 15239 loss_kpt: 0.000447 acc_pose: 0.855512 loss: 0.000447 2022/09/13 11:33:49 - mmengine - INFO - Epoch(train) [191][150/586] lr: 5.000000e-05 eta: 1:25:35 time: 0.481929 data_time: 0.028311 memory: 15239 loss_kpt: 0.000466 acc_pose: 0.849771 loss: 0.000466 2022/09/13 11:34:12 - mmengine - INFO - Epoch(train) [191][200/586] lr: 5.000000e-05 eta: 1:25:13 time: 0.476521 data_time: 0.027135 memory: 15239 loss_kpt: 0.000456 acc_pose: 0.920717 loss: 0.000456 2022/09/13 11:34:36 - mmengine - INFO - Epoch(train) [191][250/586] lr: 5.000000e-05 eta: 1:24:51 time: 0.462415 data_time: 0.029018 memory: 15239 loss_kpt: 0.000456 acc_pose: 0.868262 loss: 0.000456 2022/09/13 11:35:00 - mmengine - INFO - Epoch(train) [191][300/586] lr: 5.000000e-05 eta: 1:24:29 time: 0.481070 data_time: 0.028000 memory: 15239 loss_kpt: 0.000461 acc_pose: 0.811034 loss: 0.000461 2022/09/13 11:35:23 - mmengine - INFO - Epoch(train) [191][350/586] lr: 5.000000e-05 eta: 1:24:07 time: 0.466814 data_time: 0.027685 memory: 15239 loss_kpt: 0.000455 acc_pose: 0.886305 loss: 0.000455 2022/09/13 11:35:47 - mmengine - INFO - Epoch(train) [191][400/586] lr: 5.000000e-05 eta: 1:23:45 time: 0.470758 data_time: 0.028559 memory: 15239 loss_kpt: 0.000441 acc_pose: 0.884911 loss: 0.000441 2022/09/13 11:36:11 - mmengine - INFO - Epoch(train) [191][450/586] lr: 5.000000e-05 eta: 1:23:23 time: 0.478307 data_time: 0.027237 memory: 15239 loss_kpt: 0.000444 acc_pose: 0.923380 loss: 0.000444 2022/09/13 11:36:34 - mmengine - INFO - Epoch(train) [191][500/586] lr: 5.000000e-05 eta: 1:23:01 time: 0.468520 data_time: 0.028808 memory: 15239 loss_kpt: 0.000440 acc_pose: 0.866967 loss: 0.000440 2022/09/13 11:36:57 - mmengine - INFO - Epoch(train) [191][550/586] lr: 5.000000e-05 eta: 1:22:39 time: 0.463320 data_time: 0.027601 memory: 15239 loss_kpt: 0.000462 acc_pose: 0.858973 loss: 0.000462 2022/09/13 11:37:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:37:14 - mmengine - INFO - Saving checkpoint at 191 epochs 2022/09/13 11:37:45 - mmengine - INFO - Epoch(train) [192][50/586] lr: 5.000000e-05 eta: 1:21:59 time: 0.473293 data_time: 0.032085 memory: 15239 loss_kpt: 0.000434 acc_pose: 0.909594 loss: 0.000434 2022/09/13 11:37:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:38:09 - mmengine - INFO - Epoch(train) [192][100/586] lr: 5.000000e-05 eta: 1:21:37 time: 0.480626 data_time: 0.031660 memory: 15239 loss_kpt: 0.000459 acc_pose: 0.868525 loss: 0.000459 2022/09/13 11:38:33 - mmengine - INFO - Epoch(train) [192][150/586] lr: 5.000000e-05 eta: 1:21:15 time: 0.471676 data_time: 0.027096 memory: 15239 loss_kpt: 0.000435 acc_pose: 0.914077 loss: 0.000435 2022/09/13 11:38:56 - mmengine - INFO - Epoch(train) [192][200/586] lr: 5.000000e-05 eta: 1:20:53 time: 0.467107 data_time: 0.027612 memory: 15239 loss_kpt: 0.000467 acc_pose: 0.883484 loss: 0.000467 2022/09/13 11:39:21 - mmengine - INFO - Epoch(train) [192][250/586] lr: 5.000000e-05 eta: 1:20:31 time: 0.490626 data_time: 0.033427 memory: 15239 loss_kpt: 0.000464 acc_pose: 0.906053 loss: 0.000464 2022/09/13 11:39:45 - mmengine - INFO - Epoch(train) [192][300/586] lr: 5.000000e-05 eta: 1:20:09 time: 0.473695 data_time: 0.026967 memory: 15239 loss_kpt: 0.000451 acc_pose: 0.908221 loss: 0.000451 2022/09/13 11:40:08 - mmengine - INFO - Epoch(train) [192][350/586] lr: 5.000000e-05 eta: 1:19:47 time: 0.472713 data_time: 0.026876 memory: 15239 loss_kpt: 0.000462 acc_pose: 0.843660 loss: 0.000462 2022/09/13 11:40:32 - mmengine - INFO - Epoch(train) [192][400/586] lr: 5.000000e-05 eta: 1:19:25 time: 0.471756 data_time: 0.034031 memory: 15239 loss_kpt: 0.000445 acc_pose: 0.883215 loss: 0.000445 2022/09/13 11:40:56 - mmengine - INFO - Epoch(train) [192][450/586] lr: 5.000000e-05 eta: 1:19:03 time: 0.481275 data_time: 0.028716 memory: 15239 loss_kpt: 0.000448 acc_pose: 0.901316 loss: 0.000448 2022/09/13 11:41:20 - mmengine - INFO - Epoch(train) [192][500/586] lr: 5.000000e-05 eta: 1:18:41 time: 0.472885 data_time: 0.027654 memory: 15239 loss_kpt: 0.000458 acc_pose: 0.875733 loss: 0.000458 2022/09/13 11:41:44 - mmengine - INFO - Epoch(train) [192][550/586] lr: 5.000000e-05 eta: 1:18:19 time: 0.478033 data_time: 0.034090 memory: 15239 loss_kpt: 0.000440 acc_pose: 0.898096 loss: 0.000440 2022/09/13 11:42:00 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:42:00 - mmengine - INFO - Saving checkpoint at 192 epochs 2022/09/13 11:42:32 - mmengine - INFO - Epoch(train) [193][50/586] lr: 5.000000e-05 eta: 1:17:39 time: 0.488587 data_time: 0.044148 memory: 15239 loss_kpt: 0.000456 acc_pose: 0.884863 loss: 0.000456 2022/09/13 11:42:56 - mmengine - INFO - Epoch(train) [193][100/586] lr: 5.000000e-05 eta: 1:17:17 time: 0.479392 data_time: 0.032564 memory: 15239 loss_kpt: 0.000451 acc_pose: 0.855709 loss: 0.000451 2022/09/13 11:43:20 - mmengine - INFO - Epoch(train) [193][150/586] lr: 5.000000e-05 eta: 1:16:55 time: 0.485538 data_time: 0.026393 memory: 15239 loss_kpt: 0.000445 acc_pose: 0.890055 loss: 0.000445 2022/09/13 11:43:44 - mmengine - INFO - Epoch(train) [193][200/586] lr: 5.000000e-05 eta: 1:16:33 time: 0.470505 data_time: 0.026654 memory: 15239 loss_kpt: 0.000449 acc_pose: 0.864765 loss: 0.000449 2022/09/13 11:44:07 - mmengine - INFO - Epoch(train) [193][250/586] lr: 5.000000e-05 eta: 1:16:11 time: 0.475509 data_time: 0.027742 memory: 15239 loss_kpt: 0.000446 acc_pose: 0.871592 loss: 0.000446 2022/09/13 11:44:31 - mmengine - INFO - Epoch(train) [193][300/586] lr: 5.000000e-05 eta: 1:15:49 time: 0.476676 data_time: 0.031410 memory: 15239 loss_kpt: 0.000452 acc_pose: 0.895690 loss: 0.000452 2022/09/13 11:44:55 - mmengine - INFO - Epoch(train) [193][350/586] lr: 5.000000e-05 eta: 1:15:27 time: 0.481097 data_time: 0.028206 memory: 15239 loss_kpt: 0.000446 acc_pose: 0.941900 loss: 0.000446 2022/09/13 11:45:19 - mmengine - INFO - Epoch(train) [193][400/586] lr: 5.000000e-05 eta: 1:15:05 time: 0.472935 data_time: 0.026723 memory: 15239 loss_kpt: 0.000449 acc_pose: 0.870799 loss: 0.000449 2022/09/13 11:45:43 - mmengine - INFO - Epoch(train) [193][450/586] lr: 5.000000e-05 eta: 1:14:43 time: 0.481318 data_time: 0.027363 memory: 15239 loss_kpt: 0.000448 acc_pose: 0.832928 loss: 0.000448 2022/09/13 11:46:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:46:07 - mmengine - INFO - Epoch(train) [193][500/586] lr: 5.000000e-05 eta: 1:14:21 time: 0.468848 data_time: 0.027450 memory: 15239 loss_kpt: 0.000445 acc_pose: 0.909218 loss: 0.000445 2022/09/13 11:46:30 - mmengine - INFO - Epoch(train) [193][550/586] lr: 5.000000e-05 eta: 1:13:59 time: 0.471129 data_time: 0.026543 memory: 15239 loss_kpt: 0.000443 acc_pose: 0.919527 loss: 0.000443 2022/09/13 11:46:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:46:47 - mmengine - INFO - Saving checkpoint at 193 epochs 2022/09/13 11:47:18 - mmengine - INFO - Epoch(train) [194][50/586] lr: 5.000000e-05 eta: 1:13:19 time: 0.478580 data_time: 0.031098 memory: 15239 loss_kpt: 0.000447 acc_pose: 0.868213 loss: 0.000447 2022/09/13 11:47:42 - mmengine - INFO - Epoch(train) [194][100/586] lr: 5.000000e-05 eta: 1:12:57 time: 0.473901 data_time: 0.031883 memory: 15239 loss_kpt: 0.000475 acc_pose: 0.830341 loss: 0.000475 2022/09/13 11:48:05 - mmengine - INFO - Epoch(train) [194][150/586] lr: 5.000000e-05 eta: 1:12:35 time: 0.472795 data_time: 0.027285 memory: 15239 loss_kpt: 0.000467 acc_pose: 0.883352 loss: 0.000467 2022/09/13 11:48:29 - mmengine - INFO - Epoch(train) [194][200/586] lr: 5.000000e-05 eta: 1:12:13 time: 0.474641 data_time: 0.027408 memory: 15239 loss_kpt: 0.000435 acc_pose: 0.903228 loss: 0.000435 2022/09/13 11:48:53 - mmengine - INFO - Epoch(train) [194][250/586] lr: 5.000000e-05 eta: 1:11:51 time: 0.479254 data_time: 0.027117 memory: 15239 loss_kpt: 0.000457 acc_pose: 0.865254 loss: 0.000457 2022/09/13 11:49:17 - mmengine - INFO - Epoch(train) [194][300/586] lr: 5.000000e-05 eta: 1:11:29 time: 0.474066 data_time: 0.029426 memory: 15239 loss_kpt: 0.000441 acc_pose: 0.906291 loss: 0.000441 2022/09/13 11:49:40 - mmengine - INFO - Epoch(train) [194][350/586] lr: 5.000000e-05 eta: 1:11:07 time: 0.472438 data_time: 0.028096 memory: 15239 loss_kpt: 0.000442 acc_pose: 0.806060 loss: 0.000442 2022/09/13 11:50:04 - mmengine - INFO - Epoch(train) [194][400/586] lr: 5.000000e-05 eta: 1:10:45 time: 0.477226 data_time: 0.031198 memory: 15239 loss_kpt: 0.000447 acc_pose: 0.910198 loss: 0.000447 2022/09/13 11:50:28 - mmengine - INFO - Epoch(train) [194][450/586] lr: 5.000000e-05 eta: 1:10:23 time: 0.472251 data_time: 0.028126 memory: 15239 loss_kpt: 0.000446 acc_pose: 0.829226 loss: 0.000446 2022/09/13 11:50:52 - mmengine - INFO - Epoch(train) [194][500/586] lr: 5.000000e-05 eta: 1:10:01 time: 0.479857 data_time: 0.028232 memory: 15239 loss_kpt: 0.000464 acc_pose: 0.816211 loss: 0.000464 2022/09/13 11:51:16 - mmengine - INFO - Epoch(train) [194][550/586] lr: 5.000000e-05 eta: 1:09:39 time: 0.478528 data_time: 0.032354 memory: 15239 loss_kpt: 0.000445 acc_pose: 0.827896 loss: 0.000445 2022/09/13 11:51:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:51:32 - mmengine - INFO - Saving checkpoint at 194 epochs 2022/09/13 11:52:04 - mmengine - INFO - Epoch(train) [195][50/586] lr: 5.000000e-05 eta: 1:08:59 time: 0.487194 data_time: 0.039303 memory: 15239 loss_kpt: 0.000448 acc_pose: 0.908244 loss: 0.000448 2022/09/13 11:52:27 - mmengine - INFO - Epoch(train) [195][100/586] lr: 5.000000e-05 eta: 1:08:37 time: 0.469494 data_time: 0.033227 memory: 15239 loss_kpt: 0.000450 acc_pose: 0.920221 loss: 0.000450 2022/09/13 11:52:51 - mmengine - INFO - Epoch(train) [195][150/586] lr: 5.000000e-05 eta: 1:08:15 time: 0.478645 data_time: 0.036247 memory: 15239 loss_kpt: 0.000449 acc_pose: 0.867065 loss: 0.000449 2022/09/13 11:53:15 - mmengine - INFO - Epoch(train) [195][200/586] lr: 5.000000e-05 eta: 1:07:53 time: 0.470522 data_time: 0.030136 memory: 15239 loss_kpt: 0.000446 acc_pose: 0.860242 loss: 0.000446 2022/09/13 11:53:38 - mmengine - INFO - Epoch(train) [195][250/586] lr: 5.000000e-05 eta: 1:07:31 time: 0.470993 data_time: 0.028563 memory: 15239 loss_kpt: 0.000457 acc_pose: 0.918044 loss: 0.000457 2022/09/13 11:54:02 - mmengine - INFO - Epoch(train) [195][300/586] lr: 5.000000e-05 eta: 1:07:09 time: 0.476067 data_time: 0.027301 memory: 15239 loss_kpt: 0.000439 acc_pose: 0.893133 loss: 0.000439 2022/09/13 11:54:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:54:25 - mmengine - INFO - Epoch(train) [195][350/586] lr: 5.000000e-05 eta: 1:06:47 time: 0.470694 data_time: 0.028160 memory: 15239 loss_kpt: 0.000440 acc_pose: 0.904249 loss: 0.000440 2022/09/13 11:54:49 - mmengine - INFO - Epoch(train) [195][400/586] lr: 5.000000e-05 eta: 1:06:25 time: 0.475928 data_time: 0.028285 memory: 15239 loss_kpt: 0.000442 acc_pose: 0.901750 loss: 0.000442 2022/09/13 11:55:13 - mmengine - INFO - Epoch(train) [195][450/586] lr: 5.000000e-05 eta: 1:06:03 time: 0.473996 data_time: 0.031580 memory: 15239 loss_kpt: 0.000447 acc_pose: 0.852400 loss: 0.000447 2022/09/13 11:55:37 - mmengine - INFO - Epoch(train) [195][500/586] lr: 5.000000e-05 eta: 1:05:41 time: 0.471243 data_time: 0.028364 memory: 15239 loss_kpt: 0.000447 acc_pose: 0.839200 loss: 0.000447 2022/09/13 11:56:01 - mmengine - INFO - Epoch(train) [195][550/586] lr: 5.000000e-05 eta: 1:05:19 time: 0.479371 data_time: 0.027640 memory: 15239 loss_kpt: 0.000434 acc_pose: 0.875866 loss: 0.000434 2022/09/13 11:56:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 11:56:18 - mmengine - INFO - Saving checkpoint at 195 epochs 2022/09/13 11:56:48 - mmengine - INFO - Epoch(train) [196][50/586] lr: 5.000000e-05 eta: 1:04:39 time: 0.477389 data_time: 0.031906 memory: 15239 loss_kpt: 0.000431 acc_pose: 0.900321 loss: 0.000431 2022/09/13 11:57:12 - mmengine - INFO - Epoch(train) [196][100/586] lr: 5.000000e-05 eta: 1:04:17 time: 0.477859 data_time: 0.027511 memory: 15239 loss_kpt: 0.000446 acc_pose: 0.910116 loss: 0.000446 2022/09/13 11:57:36 - mmengine - INFO - Epoch(train) [196][150/586] lr: 5.000000e-05 eta: 1:03:55 time: 0.483725 data_time: 0.028180 memory: 15239 loss_kpt: 0.000449 acc_pose: 0.897529 loss: 0.000449 2022/09/13 11:58:00 - mmengine - INFO - Epoch(train) [196][200/586] lr: 5.000000e-05 eta: 1:03:33 time: 0.474082 data_time: 0.027507 memory: 15239 loss_kpt: 0.000453 acc_pose: 0.915396 loss: 0.000453 2022/09/13 11:58:24 - mmengine - INFO - Epoch(train) [196][250/586] lr: 5.000000e-05 eta: 1:03:11 time: 0.472465 data_time: 0.028155 memory: 15239 loss_kpt: 0.000449 acc_pose: 0.845299 loss: 0.000449 2022/09/13 11:58:47 - mmengine - INFO - Epoch(train) [196][300/586] lr: 5.000000e-05 eta: 1:02:49 time: 0.472133 data_time: 0.028625 memory: 15239 loss_kpt: 0.000441 acc_pose: 0.904242 loss: 0.000441 2022/09/13 11:59:11 - mmengine - INFO - Epoch(train) [196][350/586] lr: 5.000000e-05 eta: 1:02:27 time: 0.470679 data_time: 0.027359 memory: 15239 loss_kpt: 0.000462 acc_pose: 0.912518 loss: 0.000462 2022/09/13 11:59:35 - mmengine - INFO - Epoch(train) [196][400/586] lr: 5.000000e-05 eta: 1:02:05 time: 0.475976 data_time: 0.030673 memory: 15239 loss_kpt: 0.000450 acc_pose: 0.847358 loss: 0.000450 2022/09/13 11:59:58 - mmengine - INFO - Epoch(train) [196][450/586] lr: 5.000000e-05 eta: 1:01:43 time: 0.465511 data_time: 0.028272 memory: 15239 loss_kpt: 0.000450 acc_pose: 0.888518 loss: 0.000450 2022/09/13 12:00:26 - mmengine - INFO - Epoch(train) [196][500/586] lr: 5.000000e-05 eta: 1:01:21 time: 0.553923 data_time: 0.037689 memory: 15239 loss_kpt: 0.000445 acc_pose: 0.958101 loss: 0.000445 2022/09/13 12:00:49 - mmengine - INFO - Epoch(train) [196][550/586] lr: 5.000000e-05 eta: 1:00:59 time: 0.472997 data_time: 0.031983 memory: 15239 loss_kpt: 0.000465 acc_pose: 0.924569 loss: 0.000465 2022/09/13 12:01:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:01:06 - mmengine - INFO - Saving checkpoint at 196 epochs 2022/09/13 12:01:37 - mmengine - INFO - Epoch(train) [197][50/586] lr: 5.000000e-05 eta: 1:00:20 time: 0.479340 data_time: 0.032903 memory: 15239 loss_kpt: 0.000438 acc_pose: 0.903865 loss: 0.000438 2022/09/13 12:02:00 - mmengine - INFO - Epoch(train) [197][100/586] lr: 5.000000e-05 eta: 0:59:57 time: 0.466010 data_time: 0.025785 memory: 15239 loss_kpt: 0.000433 acc_pose: 0.909690 loss: 0.000433 2022/09/13 12:02:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:02:24 - mmengine - INFO - Epoch(train) [197][150/586] lr: 5.000000e-05 eta: 0:59:35 time: 0.469227 data_time: 0.026792 memory: 15239 loss_kpt: 0.000456 acc_pose: 0.894880 loss: 0.000456 2022/09/13 12:02:48 - mmengine - INFO - Epoch(train) [197][200/586] lr: 5.000000e-05 eta: 0:59:13 time: 0.481789 data_time: 0.027163 memory: 15239 loss_kpt: 0.000441 acc_pose: 0.908548 loss: 0.000441 2022/09/13 12:03:11 - mmengine - INFO - Epoch(train) [197][250/586] lr: 5.000000e-05 eta: 0:58:51 time: 0.465548 data_time: 0.026925 memory: 15239 loss_kpt: 0.000455 acc_pose: 0.848618 loss: 0.000455 2022/09/13 12:03:35 - mmengine - INFO - Epoch(train) [197][300/586] lr: 5.000000e-05 eta: 0:58:29 time: 0.470855 data_time: 0.026185 memory: 15239 loss_kpt: 0.000446 acc_pose: 0.842163 loss: 0.000446 2022/09/13 12:03:59 - mmengine - INFO - Epoch(train) [197][350/586] lr: 5.000000e-05 eta: 0:58:07 time: 0.481650 data_time: 0.026528 memory: 15239 loss_kpt: 0.000464 acc_pose: 0.875547 loss: 0.000464 2022/09/13 12:04:22 - mmengine - INFO - Epoch(train) [197][400/586] lr: 5.000000e-05 eta: 0:57:45 time: 0.466344 data_time: 0.026983 memory: 15239 loss_kpt: 0.000447 acc_pose: 0.851303 loss: 0.000447 2022/09/13 12:04:46 - mmengine - INFO - Epoch(train) [197][450/586] lr: 5.000000e-05 eta: 0:57:23 time: 0.467829 data_time: 0.027301 memory: 15239 loss_kpt: 0.000457 acc_pose: 0.849384 loss: 0.000457 2022/09/13 12:05:10 - mmengine - INFO - Epoch(train) [197][500/586] lr: 5.000000e-05 eta: 0:57:01 time: 0.482680 data_time: 0.028338 memory: 15239 loss_kpt: 0.000443 acc_pose: 0.898143 loss: 0.000443 2022/09/13 12:05:33 - mmengine - INFO - Epoch(train) [197][550/586] lr: 5.000000e-05 eta: 0:56:39 time: 0.470257 data_time: 0.026712 memory: 15239 loss_kpt: 0.000451 acc_pose: 0.859431 loss: 0.000451 2022/09/13 12:05:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:05:50 - mmengine - INFO - Saving checkpoint at 197 epochs 2022/09/13 12:06:21 - mmengine - INFO - Epoch(train) [198][50/586] lr: 5.000000e-05 eta: 0:55:59 time: 0.488453 data_time: 0.035363 memory: 15239 loss_kpt: 0.000463 acc_pose: 0.911291 loss: 0.000463 2022/09/13 12:06:45 - mmengine - INFO - Epoch(train) [198][100/586] lr: 5.000000e-05 eta: 0:55:37 time: 0.477044 data_time: 0.027218 memory: 15239 loss_kpt: 0.000445 acc_pose: 0.929577 loss: 0.000445 2022/09/13 12:07:08 - mmengine - INFO - Epoch(train) [198][150/586] lr: 5.000000e-05 eta: 0:55:15 time: 0.462260 data_time: 0.028002 memory: 15239 loss_kpt: 0.000441 acc_pose: 0.939562 loss: 0.000441 2022/09/13 12:07:32 - mmengine - INFO - Epoch(train) [198][200/586] lr: 5.000000e-05 eta: 0:54:53 time: 0.480533 data_time: 0.028770 memory: 15239 loss_kpt: 0.000467 acc_pose: 0.888461 loss: 0.000467 2022/09/13 12:07:56 - mmengine - INFO - Epoch(train) [198][250/586] lr: 5.000000e-05 eta: 0:54:31 time: 0.469275 data_time: 0.026362 memory: 15239 loss_kpt: 0.000464 acc_pose: 0.861503 loss: 0.000464 2022/09/13 12:08:19 - mmengine - INFO - Epoch(train) [198][300/586] lr: 5.000000e-05 eta: 0:54:09 time: 0.468394 data_time: 0.027438 memory: 15239 loss_kpt: 0.000440 acc_pose: 0.921602 loss: 0.000440 2022/09/13 12:08:43 - mmengine - INFO - Epoch(train) [198][350/586] lr: 5.000000e-05 eta: 0:53:47 time: 0.477827 data_time: 0.031946 memory: 15239 loss_kpt: 0.000471 acc_pose: 0.837510 loss: 0.000471 2022/09/13 12:09:06 - mmengine - INFO - Epoch(train) [198][400/586] lr: 5.000000e-05 eta: 0:53:25 time: 0.470663 data_time: 0.026874 memory: 15239 loss_kpt: 0.000457 acc_pose: 0.822651 loss: 0.000457 2022/09/13 12:09:30 - mmengine - INFO - Epoch(train) [198][450/586] lr: 5.000000e-05 eta: 0:53:03 time: 0.466254 data_time: 0.026658 memory: 15239 loss_kpt: 0.000454 acc_pose: 0.955772 loss: 0.000454 2022/09/13 12:09:54 - mmengine - INFO - Epoch(train) [198][500/586] lr: 5.000000e-05 eta: 0:52:40 time: 0.476651 data_time: 0.026876 memory: 15239 loss_kpt: 0.000452 acc_pose: 0.911528 loss: 0.000452 2022/09/13 12:10:17 - mmengine - INFO - Epoch(train) [198][550/586] lr: 5.000000e-05 eta: 0:52:18 time: 0.469238 data_time: 0.027165 memory: 15239 loss_kpt: 0.000458 acc_pose: 0.891795 loss: 0.000458 2022/09/13 12:10:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:10:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:10:34 - mmengine - INFO - Saving checkpoint at 198 epochs 2022/09/13 12:11:10 - mmengine - INFO - Epoch(train) [199][50/586] lr: 5.000000e-05 eta: 0:51:39 time: 0.491935 data_time: 0.038811 memory: 15239 loss_kpt: 0.000441 acc_pose: 0.898866 loss: 0.000441 2022/09/13 12:11:33 - mmengine - INFO - Epoch(train) [199][100/586] lr: 5.000000e-05 eta: 0:51:17 time: 0.466435 data_time: 0.026575 memory: 15239 loss_kpt: 0.000460 acc_pose: 0.879344 loss: 0.000460 2022/09/13 12:11:57 - mmengine - INFO - Epoch(train) [199][150/586] lr: 5.000000e-05 eta: 0:50:55 time: 0.467271 data_time: 0.030133 memory: 15239 loss_kpt: 0.000441 acc_pose: 0.836302 loss: 0.000441 2022/09/13 12:12:21 - mmengine - INFO - Epoch(train) [199][200/586] lr: 5.000000e-05 eta: 0:50:33 time: 0.478456 data_time: 0.027486 memory: 15239 loss_kpt: 0.000438 acc_pose: 0.855338 loss: 0.000438 2022/09/13 12:12:44 - mmengine - INFO - Epoch(train) [199][250/586] lr: 5.000000e-05 eta: 0:50:11 time: 0.465050 data_time: 0.026067 memory: 15239 loss_kpt: 0.000448 acc_pose: 0.837649 loss: 0.000448 2022/09/13 12:13:07 - mmengine - INFO - Epoch(train) [199][300/586] lr: 5.000000e-05 eta: 0:49:49 time: 0.471446 data_time: 0.026683 memory: 15239 loss_kpt: 0.000454 acc_pose: 0.831189 loss: 0.000454 2022/09/13 12:13:31 - mmengine - INFO - Epoch(train) [199][350/586] lr: 5.000000e-05 eta: 0:49:27 time: 0.471139 data_time: 0.026312 memory: 15239 loss_kpt: 0.000444 acc_pose: 0.882152 loss: 0.000444 2022/09/13 12:13:55 - mmengine - INFO - Epoch(train) [199][400/586] lr: 5.000000e-05 eta: 0:49:04 time: 0.471412 data_time: 0.027083 memory: 15239 loss_kpt: 0.000439 acc_pose: 0.862647 loss: 0.000439 2022/09/13 12:14:18 - mmengine - INFO - Epoch(train) [199][450/586] lr: 5.000000e-05 eta: 0:48:42 time: 0.466055 data_time: 0.030495 memory: 15239 loss_kpt: 0.000455 acc_pose: 0.926961 loss: 0.000455 2022/09/13 12:14:42 - mmengine - INFO - Epoch(train) [199][500/586] lr: 5.000000e-05 eta: 0:48:20 time: 0.475858 data_time: 0.027997 memory: 15239 loss_kpt: 0.000443 acc_pose: 0.910055 loss: 0.000443 2022/09/13 12:15:05 - mmengine - INFO - Epoch(train) [199][550/586] lr: 5.000000e-05 eta: 0:47:58 time: 0.470408 data_time: 0.027404 memory: 15239 loss_kpt: 0.000443 acc_pose: 0.866811 loss: 0.000443 2022/09/13 12:15:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:15:22 - mmengine - INFO - Saving checkpoint at 199 epochs 2022/09/13 12:15:56 - mmengine - INFO - Epoch(train) [200][50/586] lr: 5.000000e-05 eta: 0:47:19 time: 0.485622 data_time: 0.042297 memory: 15239 loss_kpt: 0.000440 acc_pose: 0.901912 loss: 0.000440 2022/09/13 12:16:20 - mmengine - INFO - Epoch(train) [200][100/586] lr: 5.000000e-05 eta: 0:46:57 time: 0.473403 data_time: 0.031146 memory: 15239 loss_kpt: 0.000434 acc_pose: 0.894538 loss: 0.000434 2022/09/13 12:16:43 - mmengine - INFO - Epoch(train) [200][150/586] lr: 5.000000e-05 eta: 0:46:35 time: 0.467351 data_time: 0.029927 memory: 15239 loss_kpt: 0.000461 acc_pose: 0.881523 loss: 0.000461 2022/09/13 12:17:07 - mmengine - INFO - Epoch(train) [200][200/586] lr: 5.000000e-05 eta: 0:46:13 time: 0.471731 data_time: 0.034504 memory: 15239 loss_kpt: 0.000464 acc_pose: 0.874999 loss: 0.000464 2022/09/13 12:17:30 - mmengine - INFO - Epoch(train) [200][250/586] lr: 5.000000e-05 eta: 0:45:51 time: 0.469403 data_time: 0.027242 memory: 15239 loss_kpt: 0.000459 acc_pose: 0.835563 loss: 0.000459 2022/09/13 12:17:54 - mmengine - INFO - Epoch(train) [200][300/586] lr: 5.000000e-05 eta: 0:45:28 time: 0.473185 data_time: 0.027599 memory: 15239 loss_kpt: 0.000454 acc_pose: 0.911104 loss: 0.000454 2022/09/13 12:18:18 - mmengine - INFO - Epoch(train) [200][350/586] lr: 5.000000e-05 eta: 0:45:06 time: 0.478190 data_time: 0.027846 memory: 15239 loss_kpt: 0.000446 acc_pose: 0.895120 loss: 0.000446 2022/09/13 12:18:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:18:42 - mmengine - INFO - Epoch(train) [200][400/586] lr: 5.000000e-05 eta: 0:44:44 time: 0.472795 data_time: 0.028013 memory: 15239 loss_kpt: 0.000449 acc_pose: 0.870281 loss: 0.000449 2022/09/13 12:19:05 - mmengine - INFO - Epoch(train) [200][450/586] lr: 5.000000e-05 eta: 0:44:22 time: 0.472407 data_time: 0.027337 memory: 15239 loss_kpt: 0.000464 acc_pose: 0.938153 loss: 0.000464 2022/09/13 12:19:29 - mmengine - INFO - Epoch(train) [200][500/586] lr: 5.000000e-05 eta: 0:44:00 time: 0.466747 data_time: 0.027365 memory: 15239 loss_kpt: 0.000438 acc_pose: 0.870405 loss: 0.000438 2022/09/13 12:19:52 - mmengine - INFO - Epoch(train) [200][550/586] lr: 5.000000e-05 eta: 0:43:38 time: 0.471795 data_time: 0.027539 memory: 15239 loss_kpt: 0.000448 acc_pose: 0.888424 loss: 0.000448 2022/09/13 12:20:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:20:09 - mmengine - INFO - Saving checkpoint at 200 epochs 2022/09/13 12:20:27 - mmengine - INFO - Epoch(val) [200][50/407] eta: 0:01:21 time: 0.227433 data_time: 0.014117 memory: 15239 2022/09/13 12:20:38 - mmengine - INFO - Epoch(val) [200][100/407] eta: 0:01:07 time: 0.218308 data_time: 0.008379 memory: 2064 2022/09/13 12:20:49 - mmengine - INFO - Epoch(val) [200][150/407] eta: 0:00:56 time: 0.219250 data_time: 0.008232 memory: 2064 2022/09/13 12:21:00 - mmengine - INFO - Epoch(val) [200][200/407] eta: 0:00:45 time: 0.220032 data_time: 0.008938 memory: 2064 2022/09/13 12:21:11 - mmengine - INFO - Epoch(val) [200][250/407] eta: 0:00:34 time: 0.219065 data_time: 0.008472 memory: 2064 2022/09/13 12:21:22 - mmengine - INFO - Epoch(val) [200][300/407] eta: 0:00:23 time: 0.217441 data_time: 0.008023 memory: 2064 2022/09/13 12:21:33 - mmengine - INFO - Epoch(val) [200][350/407] eta: 0:00:12 time: 0.218242 data_time: 0.008193 memory: 2064 2022/09/13 12:21:44 - mmengine - INFO - Epoch(val) [200][400/407] eta: 0:00:01 time: 0.222409 data_time: 0.013367 memory: 2064 2022/09/13 12:22:21 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 12:22:34 - mmengine - INFO - Epoch(val) [200][407/407] coco/AP: 0.771942 coco/AP .5: 0.910182 coco/AP .75: 0.835017 coco/AP (M): 0.731812 coco/AP (L): 0.843062 coco/AR: 0.820246 coco/AR .5: 0.946316 coco/AR .75: 0.876417 coco/AR (M): 0.776673 coco/AR (L): 0.883798 2022/09/13 12:22:59 - mmengine - INFO - Epoch(train) [201][50/586] lr: 5.000000e-06 eta: 0:42:59 time: 0.488093 data_time: 0.035852 memory: 15239 loss_kpt: 0.000448 acc_pose: 0.826450 loss: 0.000448 2022/09/13 12:23:23 - mmengine - INFO - Epoch(train) [201][100/586] lr: 5.000000e-06 eta: 0:42:37 time: 0.491280 data_time: 0.029163 memory: 15239 loss_kpt: 0.000455 acc_pose: 0.905058 loss: 0.000455 2022/09/13 12:23:47 - mmengine - INFO - Epoch(train) [201][150/586] lr: 5.000000e-06 eta: 0:42:15 time: 0.476924 data_time: 0.027702 memory: 15239 loss_kpt: 0.000451 acc_pose: 0.883253 loss: 0.000451 2022/09/13 12:24:11 - mmengine - INFO - Epoch(train) [201][200/586] lr: 5.000000e-06 eta: 0:41:53 time: 0.479239 data_time: 0.027275 memory: 15239 loss_kpt: 0.000442 acc_pose: 0.838914 loss: 0.000442 2022/09/13 12:24:35 - mmengine - INFO - Epoch(train) [201][250/586] lr: 5.000000e-06 eta: 0:41:30 time: 0.479818 data_time: 0.027133 memory: 15239 loss_kpt: 0.000441 acc_pose: 0.868983 loss: 0.000441 2022/09/13 12:24:59 - mmengine - INFO - Epoch(train) [201][300/586] lr: 5.000000e-06 eta: 0:41:08 time: 0.468945 data_time: 0.027678 memory: 15239 loss_kpt: 0.000437 acc_pose: 0.902723 loss: 0.000437 2022/09/13 12:25:22 - mmengine - INFO - Epoch(train) [201][350/586] lr: 5.000000e-06 eta: 0:40:46 time: 0.464621 data_time: 0.027628 memory: 15239 loss_kpt: 0.000435 acc_pose: 0.932575 loss: 0.000435 2022/09/13 12:25:45 - mmengine - INFO - Epoch(train) [201][400/586] lr: 5.000000e-06 eta: 0:40:24 time: 0.471666 data_time: 0.026359 memory: 15239 loss_kpt: 0.000459 acc_pose: 0.886954 loss: 0.000459 2022/09/13 12:26:09 - mmengine - INFO - Epoch(train) [201][450/586] lr: 5.000000e-06 eta: 0:40:02 time: 0.469301 data_time: 0.026824 memory: 15239 loss_kpt: 0.000456 acc_pose: 0.846721 loss: 0.000456 2022/09/13 12:26:32 - mmengine - INFO - Epoch(train) [201][500/586] lr: 5.000000e-06 eta: 0:39:40 time: 0.469663 data_time: 0.027802 memory: 15239 loss_kpt: 0.000438 acc_pose: 0.880455 loss: 0.000438 2022/09/13 12:26:56 - mmengine - INFO - Epoch(train) [201][550/586] lr: 5.000000e-06 eta: 0:39:18 time: 0.472257 data_time: 0.026947 memory: 15239 loss_kpt: 0.000453 acc_pose: 0.856096 loss: 0.000453 2022/09/13 12:27:13 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:27:13 - mmengine - INFO - Saving checkpoint at 201 epochs 2022/09/13 12:27:44 - mmengine - INFO - Epoch(train) [202][50/586] lr: 5.000000e-06 eta: 0:38:39 time: 0.485197 data_time: 0.032225 memory: 15239 loss_kpt: 0.000445 acc_pose: 0.912101 loss: 0.000445 2022/09/13 12:28:07 - mmengine - INFO - Epoch(train) [202][100/586] lr: 5.000000e-06 eta: 0:38:17 time: 0.471342 data_time: 0.027860 memory: 15239 loss_kpt: 0.000447 acc_pose: 0.916773 loss: 0.000447 2022/09/13 12:28:31 - mmengine - INFO - Epoch(train) [202][150/586] lr: 5.000000e-06 eta: 0:37:55 time: 0.473807 data_time: 0.027966 memory: 15239 loss_kpt: 0.000458 acc_pose: 0.868243 loss: 0.000458 2022/09/13 12:28:55 - mmengine - INFO - Epoch(train) [202][200/586] lr: 5.000000e-06 eta: 0:37:32 time: 0.474386 data_time: 0.027264 memory: 15239 loss_kpt: 0.000442 acc_pose: 0.915004 loss: 0.000442 2022/09/13 12:29:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:29:18 - mmengine - INFO - Epoch(train) [202][250/586] lr: 5.000000e-06 eta: 0:37:10 time: 0.471624 data_time: 0.026958 memory: 15239 loss_kpt: 0.000442 acc_pose: 0.866812 loss: 0.000442 2022/09/13 12:29:42 - mmengine - INFO - Epoch(train) [202][300/586] lr: 5.000000e-06 eta: 0:36:48 time: 0.466462 data_time: 0.027559 memory: 15239 loss_kpt: 0.000432 acc_pose: 0.922607 loss: 0.000432 2022/09/13 12:30:06 - mmengine - INFO - Epoch(train) [202][350/586] lr: 5.000000e-06 eta: 0:36:26 time: 0.475326 data_time: 0.026822 memory: 15239 loss_kpt: 0.000454 acc_pose: 0.926750 loss: 0.000454 2022/09/13 12:30:29 - mmengine - INFO - Epoch(train) [202][400/586] lr: 5.000000e-06 eta: 0:36:04 time: 0.473975 data_time: 0.026811 memory: 15239 loss_kpt: 0.000445 acc_pose: 0.876984 loss: 0.000445 2022/09/13 12:30:52 - mmengine - INFO - Epoch(train) [202][450/586] lr: 5.000000e-06 eta: 0:35:42 time: 0.462860 data_time: 0.026071 memory: 15239 loss_kpt: 0.000460 acc_pose: 0.857792 loss: 0.000460 2022/09/13 12:31:16 - mmengine - INFO - Epoch(train) [202][500/586] lr: 5.000000e-06 eta: 0:35:20 time: 0.470928 data_time: 0.027041 memory: 15239 loss_kpt: 0.000462 acc_pose: 0.893065 loss: 0.000462 2022/09/13 12:31:40 - mmengine - INFO - Epoch(train) [202][550/586] lr: 5.000000e-06 eta: 0:34:57 time: 0.472784 data_time: 0.027567 memory: 15239 loss_kpt: 0.000452 acc_pose: 0.840992 loss: 0.000452 2022/09/13 12:31:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:31:57 - mmengine - INFO - Saving checkpoint at 202 epochs 2022/09/13 12:32:28 - mmengine - INFO - Epoch(train) [203][50/586] lr: 5.000000e-06 eta: 0:34:19 time: 0.474989 data_time: 0.036075 memory: 15239 loss_kpt: 0.000450 acc_pose: 0.850494 loss: 0.000450 2022/09/13 12:32:52 - mmengine - INFO - Epoch(train) [203][100/586] lr: 5.000000e-06 eta: 0:33:56 time: 0.475030 data_time: 0.035453 memory: 15239 loss_kpt: 0.000440 acc_pose: 0.900062 loss: 0.000440 2022/09/13 12:33:15 - mmengine - INFO - Epoch(train) [203][150/586] lr: 5.000000e-06 eta: 0:33:34 time: 0.475362 data_time: 0.034847 memory: 15239 loss_kpt: 0.000440 acc_pose: 0.888623 loss: 0.000440 2022/09/13 12:33:39 - mmengine - INFO - Epoch(train) [203][200/586] lr: 5.000000e-06 eta: 0:33:12 time: 0.469876 data_time: 0.032724 memory: 15239 loss_kpt: 0.000446 acc_pose: 0.936694 loss: 0.000446 2022/09/13 12:34:03 - mmengine - INFO - Epoch(train) [203][250/586] lr: 5.000000e-06 eta: 0:32:50 time: 0.471648 data_time: 0.027132 memory: 15239 loss_kpt: 0.000445 acc_pose: 0.883511 loss: 0.000445 2022/09/13 12:34:26 - mmengine - INFO - Epoch(train) [203][300/586] lr: 5.000000e-06 eta: 0:32:28 time: 0.470281 data_time: 0.027271 memory: 15239 loss_kpt: 0.000438 acc_pose: 0.922786 loss: 0.000438 2022/09/13 12:34:49 - mmengine - INFO - Epoch(train) [203][350/586] lr: 5.000000e-06 eta: 0:32:06 time: 0.466984 data_time: 0.027411 memory: 15239 loss_kpt: 0.000433 acc_pose: 0.889717 loss: 0.000433 2022/09/13 12:35:13 - mmengine - INFO - Epoch(train) [203][400/586] lr: 5.000000e-06 eta: 0:31:44 time: 0.474484 data_time: 0.032020 memory: 15239 loss_kpt: 0.000447 acc_pose: 0.880185 loss: 0.000447 2022/09/13 12:35:37 - mmengine - INFO - Epoch(train) [203][450/586] lr: 5.000000e-06 eta: 0:31:21 time: 0.466730 data_time: 0.027864 memory: 15239 loss_kpt: 0.000451 acc_pose: 0.894629 loss: 0.000451 2022/09/13 12:36:00 - mmengine - INFO - Epoch(train) [203][500/586] lr: 5.000000e-06 eta: 0:30:59 time: 0.471801 data_time: 0.027753 memory: 15239 loss_kpt: 0.000457 acc_pose: 0.840098 loss: 0.000457 2022/09/13 12:36:24 - mmengine - INFO - Epoch(train) [203][550/586] lr: 5.000000e-06 eta: 0:30:37 time: 0.472295 data_time: 0.032504 memory: 15239 loss_kpt: 0.000435 acc_pose: 0.859439 loss: 0.000435 2022/09/13 12:36:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:36:40 - mmengine - INFO - Saving checkpoint at 203 epochs 2022/09/13 12:37:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:37:12 - mmengine - INFO - Epoch(train) [204][50/586] lr: 5.000000e-06 eta: 0:29:58 time: 0.481723 data_time: 0.036231 memory: 15239 loss_kpt: 0.000438 acc_pose: 0.878578 loss: 0.000438 2022/09/13 12:37:36 - mmengine - INFO - Epoch(train) [204][100/586] lr: 5.000000e-06 eta: 0:29:36 time: 0.474254 data_time: 0.032676 memory: 15239 loss_kpt: 0.000444 acc_pose: 0.906958 loss: 0.000444 2022/09/13 12:37:59 - mmengine - INFO - Epoch(train) [204][150/586] lr: 5.000000e-06 eta: 0:29:14 time: 0.469821 data_time: 0.039262 memory: 15239 loss_kpt: 0.000462 acc_pose: 0.882564 loss: 0.000462 2022/09/13 12:38:23 - mmengine - INFO - Epoch(train) [204][200/586] lr: 5.000000e-06 eta: 0:28:52 time: 0.475808 data_time: 0.036549 memory: 15239 loss_kpt: 0.000463 acc_pose: 0.911385 loss: 0.000463 2022/09/13 12:38:47 - mmengine - INFO - Epoch(train) [204][250/586] lr: 5.000000e-06 eta: 0:28:30 time: 0.472738 data_time: 0.026813 memory: 15239 loss_kpt: 0.000436 acc_pose: 0.877398 loss: 0.000436 2022/09/13 12:39:10 - mmengine - INFO - Epoch(train) [204][300/586] lr: 5.000000e-06 eta: 0:28:08 time: 0.470245 data_time: 0.028275 memory: 15239 loss_kpt: 0.000455 acc_pose: 0.865861 loss: 0.000455 2022/09/13 12:39:34 - mmengine - INFO - Epoch(train) [204][350/586] lr: 5.000000e-06 eta: 0:27:46 time: 0.471454 data_time: 0.026272 memory: 15239 loss_kpt: 0.000426 acc_pose: 0.901255 loss: 0.000426 2022/09/13 12:39:57 - mmengine - INFO - Epoch(train) [204][400/586] lr: 5.000000e-06 eta: 0:27:23 time: 0.472401 data_time: 0.027546 memory: 15239 loss_kpt: 0.000452 acc_pose: 0.892866 loss: 0.000452 2022/09/13 12:40:21 - mmengine - INFO - Epoch(train) [204][450/586] lr: 5.000000e-06 eta: 0:27:01 time: 0.475878 data_time: 0.028376 memory: 15239 loss_kpt: 0.000461 acc_pose: 0.830115 loss: 0.000461 2022/09/13 12:40:45 - mmengine - INFO - Epoch(train) [204][500/586] lr: 5.000000e-06 eta: 0:26:39 time: 0.468441 data_time: 0.027553 memory: 15239 loss_kpt: 0.000447 acc_pose: 0.872212 loss: 0.000447 2022/09/13 12:41:09 - mmengine - INFO - Epoch(train) [204][550/586] lr: 5.000000e-06 eta: 0:26:17 time: 0.475108 data_time: 0.030407 memory: 15239 loss_kpt: 0.000447 acc_pose: 0.882073 loss: 0.000447 2022/09/13 12:41:25 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:41:25 - mmengine - INFO - Saving checkpoint at 204 epochs 2022/09/13 12:41:58 - mmengine - INFO - Epoch(train) [205][50/586] lr: 5.000000e-06 eta: 0:25:38 time: 0.490773 data_time: 0.039869 memory: 15239 loss_kpt: 0.000445 acc_pose: 0.936661 loss: 0.000445 2022/09/13 12:42:21 - mmengine - INFO - Epoch(train) [205][100/586] lr: 5.000000e-06 eta: 0:25:16 time: 0.470599 data_time: 0.032277 memory: 15239 loss_kpt: 0.000435 acc_pose: 0.888637 loss: 0.000435 2022/09/13 12:42:45 - mmengine - INFO - Epoch(train) [205][150/586] lr: 5.000000e-06 eta: 0:24:54 time: 0.472848 data_time: 0.036707 memory: 15239 loss_kpt: 0.000438 acc_pose: 0.880316 loss: 0.000438 2022/09/13 12:43:09 - mmengine - INFO - Epoch(train) [205][200/586] lr: 5.000000e-06 eta: 0:24:32 time: 0.478430 data_time: 0.031911 memory: 15239 loss_kpt: 0.000475 acc_pose: 0.895252 loss: 0.000475 2022/09/13 12:43:33 - mmengine - INFO - Epoch(train) [205][250/586] lr: 5.000000e-06 eta: 0:24:10 time: 0.478949 data_time: 0.031643 memory: 15239 loss_kpt: 0.000451 acc_pose: 0.834121 loss: 0.000451 2022/09/13 12:43:56 - mmengine - INFO - Epoch(train) [205][300/586] lr: 5.000000e-06 eta: 0:23:48 time: 0.466600 data_time: 0.033571 memory: 15239 loss_kpt: 0.000443 acc_pose: 0.916267 loss: 0.000443 2022/09/13 12:44:20 - mmengine - INFO - Epoch(train) [205][350/586] lr: 5.000000e-06 eta: 0:23:25 time: 0.472312 data_time: 0.027340 memory: 15239 loss_kpt: 0.000445 acc_pose: 0.911701 loss: 0.000445 2022/09/13 12:44:44 - mmengine - INFO - Epoch(train) [205][400/586] lr: 5.000000e-06 eta: 0:23:03 time: 0.475586 data_time: 0.027492 memory: 15239 loss_kpt: 0.000439 acc_pose: 0.889856 loss: 0.000439 2022/09/13 12:45:08 - mmengine - INFO - Epoch(train) [205][450/586] lr: 5.000000e-06 eta: 0:22:41 time: 0.478488 data_time: 0.027747 memory: 15239 loss_kpt: 0.000429 acc_pose: 0.901162 loss: 0.000429 2022/09/13 12:45:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:45:31 - mmengine - INFO - Epoch(train) [205][500/586] lr: 5.000000e-06 eta: 0:22:19 time: 0.467366 data_time: 0.027398 memory: 15239 loss_kpt: 0.000459 acc_pose: 0.846279 loss: 0.000459 2022/09/13 12:45:55 - mmengine - INFO - Epoch(train) [205][550/586] lr: 5.000000e-06 eta: 0:21:57 time: 0.477823 data_time: 0.029342 memory: 15239 loss_kpt: 0.000453 acc_pose: 0.918683 loss: 0.000453 2022/09/13 12:46:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:46:12 - mmengine - INFO - Saving checkpoint at 205 epochs 2022/09/13 12:46:43 - mmengine - INFO - Epoch(train) [206][50/586] lr: 5.000000e-06 eta: 0:21:18 time: 0.482312 data_time: 0.043389 memory: 15239 loss_kpt: 0.000445 acc_pose: 0.916860 loss: 0.000445 2022/09/13 12:47:07 - mmengine - INFO - Epoch(train) [206][100/586] lr: 5.000000e-06 eta: 0:20:56 time: 0.479404 data_time: 0.032037 memory: 15239 loss_kpt: 0.000447 acc_pose: 0.898034 loss: 0.000447 2022/09/13 12:47:30 - mmengine - INFO - Epoch(train) [206][150/586] lr: 5.000000e-06 eta: 0:20:34 time: 0.471673 data_time: 0.034860 memory: 15239 loss_kpt: 0.000442 acc_pose: 0.892764 loss: 0.000442 2022/09/13 12:47:54 - mmengine - INFO - Epoch(train) [206][200/586] lr: 5.000000e-06 eta: 0:20:12 time: 0.476835 data_time: 0.031405 memory: 15239 loss_kpt: 0.000450 acc_pose: 0.956880 loss: 0.000450 2022/09/13 12:48:18 - mmengine - INFO - Epoch(train) [206][250/586] lr: 5.000000e-06 eta: 0:19:49 time: 0.471554 data_time: 0.028119 memory: 15239 loss_kpt: 0.000439 acc_pose: 0.886195 loss: 0.000439 2022/09/13 12:48:42 - mmengine - INFO - Epoch(train) [206][300/586] lr: 5.000000e-06 eta: 0:19:27 time: 0.474103 data_time: 0.027090 memory: 15239 loss_kpt: 0.000446 acc_pose: 0.907711 loss: 0.000446 2022/09/13 12:49:06 - mmengine - INFO - Epoch(train) [206][350/586] lr: 5.000000e-06 eta: 0:19:05 time: 0.477340 data_time: 0.026742 memory: 15239 loss_kpt: 0.000459 acc_pose: 0.890180 loss: 0.000459 2022/09/13 12:49:29 - mmengine - INFO - Epoch(train) [206][400/586] lr: 5.000000e-06 eta: 0:18:43 time: 0.478205 data_time: 0.030575 memory: 15239 loss_kpt: 0.000449 acc_pose: 0.852542 loss: 0.000449 2022/09/13 12:49:53 - mmengine - INFO - Epoch(train) [206][450/586] lr: 5.000000e-06 eta: 0:18:21 time: 0.470433 data_time: 0.026542 memory: 15239 loss_kpt: 0.000444 acc_pose: 0.876414 loss: 0.000444 2022/09/13 12:50:17 - mmengine - INFO - Epoch(train) [206][500/586] lr: 5.000000e-06 eta: 0:17:59 time: 0.474284 data_time: 0.027257 memory: 15239 loss_kpt: 0.000440 acc_pose: 0.911365 loss: 0.000440 2022/09/13 12:50:41 - mmengine - INFO - Epoch(train) [206][550/586] lr: 5.000000e-06 eta: 0:17:36 time: 0.477577 data_time: 0.029006 memory: 15239 loss_kpt: 0.000439 acc_pose: 0.869258 loss: 0.000439 2022/09/13 12:50:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:50:57 - mmengine - INFO - Saving checkpoint at 206 epochs 2022/09/13 12:51:29 - mmengine - INFO - Epoch(train) [207][50/586] lr: 5.000000e-06 eta: 0:16:58 time: 0.483791 data_time: 0.041679 memory: 15239 loss_kpt: 0.000446 acc_pose: 0.931764 loss: 0.000446 2022/09/13 12:51:53 - mmengine - INFO - Epoch(train) [207][100/586] lr: 5.000000e-06 eta: 0:16:36 time: 0.479974 data_time: 0.036317 memory: 15239 loss_kpt: 0.000451 acc_pose: 0.885396 loss: 0.000451 2022/09/13 12:52:16 - mmengine - INFO - Epoch(train) [207][150/586] lr: 5.000000e-06 eta: 0:16:14 time: 0.475650 data_time: 0.031404 memory: 15239 loss_kpt: 0.000441 acc_pose: 0.883012 loss: 0.000441 2022/09/13 12:52:41 - mmengine - INFO - Epoch(train) [207][200/586] lr: 5.000000e-06 eta: 0:15:52 time: 0.494890 data_time: 0.029533 memory: 15239 loss_kpt: 0.000443 acc_pose: 0.914505 loss: 0.000443 2022/09/13 12:53:05 - mmengine - INFO - Epoch(train) [207][250/586] lr: 5.000000e-06 eta: 0:15:29 time: 0.481985 data_time: 0.033619 memory: 15239 loss_kpt: 0.000448 acc_pose: 0.916439 loss: 0.000448 2022/09/13 12:53:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:53:29 - mmengine - INFO - Epoch(train) [207][300/586] lr: 5.000000e-06 eta: 0:15:07 time: 0.476888 data_time: 0.031092 memory: 15239 loss_kpt: 0.000447 acc_pose: 0.895767 loss: 0.000447 2022/09/13 12:53:53 - mmengine - INFO - Epoch(train) [207][350/586] lr: 5.000000e-06 eta: 0:14:45 time: 0.467489 data_time: 0.027009 memory: 15239 loss_kpt: 0.000446 acc_pose: 0.915480 loss: 0.000446 2022/09/13 12:54:16 - mmengine - INFO - Epoch(train) [207][400/586] lr: 5.000000e-06 eta: 0:14:23 time: 0.473569 data_time: 0.027690 memory: 15239 loss_kpt: 0.000465 acc_pose: 0.895036 loss: 0.000465 2022/09/13 12:54:40 - mmengine - INFO - Epoch(train) [207][450/586] lr: 5.000000e-06 eta: 0:14:01 time: 0.474873 data_time: 0.027932 memory: 15239 loss_kpt: 0.000449 acc_pose: 0.897354 loss: 0.000449 2022/09/13 12:55:04 - mmengine - INFO - Epoch(train) [207][500/586] lr: 5.000000e-06 eta: 0:13:38 time: 0.475751 data_time: 0.027194 memory: 15239 loss_kpt: 0.000449 acc_pose: 0.802267 loss: 0.000449 2022/09/13 12:55:27 - mmengine - INFO - Epoch(train) [207][550/586] lr: 5.000000e-06 eta: 0:13:16 time: 0.470355 data_time: 0.029323 memory: 15239 loss_kpt: 0.000451 acc_pose: 0.863898 loss: 0.000451 2022/09/13 12:55:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 12:55:45 - mmengine - INFO - Saving checkpoint at 207 epochs 2022/09/13 12:56:16 - mmengine - INFO - Epoch(train) [208][50/586] lr: 5.000000e-06 eta: 0:12:38 time: 0.487889 data_time: 0.036728 memory: 15239 loss_kpt: 0.000450 acc_pose: 0.875624 loss: 0.000450 2022/09/13 12:56:40 - mmengine - INFO - Epoch(train) [208][100/586] lr: 5.000000e-06 eta: 0:12:16 time: 0.478046 data_time: 0.030270 memory: 15239 loss_kpt: 0.000436 acc_pose: 0.898060 loss: 0.000436 2022/09/13 12:57:03 - mmengine - INFO - Epoch(train) [208][150/586] lr: 5.000000e-06 eta: 0:11:54 time: 0.471293 data_time: 0.027071 memory: 15239 loss_kpt: 0.000430 acc_pose: 0.931694 loss: 0.000430 2022/09/13 12:57:27 - mmengine - INFO - Epoch(train) [208][200/586] lr: 5.000000e-06 eta: 0:11:31 time: 0.473047 data_time: 0.032249 memory: 15239 loss_kpt: 0.000443 acc_pose: 0.924292 loss: 0.000443 2022/09/13 12:57:50 - mmengine - INFO - Epoch(train) [208][250/586] lr: 5.000000e-06 eta: 0:11:09 time: 0.467910 data_time: 0.026415 memory: 15239 loss_kpt: 0.000475 acc_pose: 0.904454 loss: 0.000475 2022/09/13 12:58:14 - mmengine - INFO - Epoch(train) [208][300/586] lr: 5.000000e-06 eta: 0:10:47 time: 0.478691 data_time: 0.027833 memory: 15239 loss_kpt: 0.000439 acc_pose: 0.927159 loss: 0.000439 2022/09/13 12:58:39 - mmengine - INFO - Epoch(train) [208][350/586] lr: 5.000000e-06 eta: 0:10:25 time: 0.483528 data_time: 0.032130 memory: 15239 loss_kpt: 0.000447 acc_pose: 0.882693 loss: 0.000447 2022/09/13 12:59:02 - mmengine - INFO - Epoch(train) [208][400/586] lr: 5.000000e-06 eta: 0:10:03 time: 0.468040 data_time: 0.028159 memory: 15239 loss_kpt: 0.000453 acc_pose: 0.905373 loss: 0.000453 2022/09/13 12:59:26 - mmengine - INFO - Epoch(train) [208][450/586] lr: 5.000000e-06 eta: 0:09:40 time: 0.472787 data_time: 0.026776 memory: 15239 loss_kpt: 0.000435 acc_pose: 0.881361 loss: 0.000435 2022/09/13 12:59:50 - mmengine - INFO - Epoch(train) [208][500/586] lr: 5.000000e-06 eta: 0:09:18 time: 0.476248 data_time: 0.027491 memory: 15239 loss_kpt: 0.000456 acc_pose: 0.916840 loss: 0.000456 2022/09/13 13:00:13 - mmengine - INFO - Epoch(train) [208][550/586] lr: 5.000000e-06 eta: 0:08:56 time: 0.464295 data_time: 0.027659 memory: 15239 loss_kpt: 0.000439 acc_pose: 0.891729 loss: 0.000439 2022/09/13 13:00:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 13:00:29 - mmengine - INFO - Saving checkpoint at 208 epochs 2022/09/13 13:01:02 - mmengine - INFO - Epoch(train) [209][50/586] lr: 5.000000e-06 eta: 0:08:18 time: 0.479474 data_time: 0.031930 memory: 15239 loss_kpt: 0.000433 acc_pose: 0.894956 loss: 0.000433 2022/09/13 13:01:26 - mmengine - INFO - Epoch(train) [209][100/586] lr: 5.000000e-06 eta: 0:07:55 time: 0.468604 data_time: 0.032549 memory: 15239 loss_kpt: 0.000450 acc_pose: 0.871819 loss: 0.000450 2022/09/13 13:01:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 13:01:50 - mmengine - INFO - Epoch(train) [209][150/586] lr: 5.000000e-06 eta: 0:07:33 time: 0.472489 data_time: 0.027280 memory: 15239 loss_kpt: 0.000458 acc_pose: 0.919201 loss: 0.000458 2022/09/13 13:02:13 - mmengine - INFO - Epoch(train) [209][200/586] lr: 5.000000e-06 eta: 0:07:11 time: 0.468544 data_time: 0.027306 memory: 15239 loss_kpt: 0.000456 acc_pose: 0.866711 loss: 0.000456 2022/09/13 13:02:37 - mmengine - INFO - Epoch(train) [209][250/586] lr: 5.000000e-06 eta: 0:06:49 time: 0.475436 data_time: 0.030352 memory: 15239 loss_kpt: 0.000459 acc_pose: 0.805295 loss: 0.000459 2022/09/13 13:03:00 - mmengine - INFO - Epoch(train) [209][300/586] lr: 5.000000e-06 eta: 0:06:27 time: 0.470552 data_time: 0.027181 memory: 15239 loss_kpt: 0.000452 acc_pose: 0.892569 loss: 0.000452 2022/09/13 13:03:24 - mmengine - INFO - Epoch(train) [209][350/586] lr: 5.000000e-06 eta: 0:06:05 time: 0.476649 data_time: 0.026476 memory: 15239 loss_kpt: 0.000454 acc_pose: 0.874612 loss: 0.000454 2022/09/13 13:03:48 - mmengine - INFO - Epoch(train) [209][400/586] lr: 5.000000e-06 eta: 0:05:42 time: 0.473543 data_time: 0.031801 memory: 15239 loss_kpt: 0.000455 acc_pose: 0.873090 loss: 0.000455 2022/09/13 13:04:12 - mmengine - INFO - Epoch(train) [209][450/586] lr: 5.000000e-06 eta: 0:05:20 time: 0.474040 data_time: 0.027720 memory: 15239 loss_kpt: 0.000448 acc_pose: 0.872808 loss: 0.000448 2022/09/13 13:04:35 - mmengine - INFO - Epoch(train) [209][500/586] lr: 5.000000e-06 eta: 0:04:58 time: 0.473055 data_time: 0.027328 memory: 15239 loss_kpt: 0.000446 acc_pose: 0.878111 loss: 0.000446 2022/09/13 13:04:59 - mmengine - INFO - Epoch(train) [209][550/586] lr: 5.000000e-06 eta: 0:04:36 time: 0.474062 data_time: 0.029687 memory: 15239 loss_kpt: 0.000442 acc_pose: 0.907062 loss: 0.000442 2022/09/13 13:05:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 13:05:16 - mmengine - INFO - Saving checkpoint at 209 epochs 2022/09/13 13:05:47 - mmengine - INFO - Epoch(train) [210][50/586] lr: 5.000000e-06 eta: 0:03:57 time: 0.478890 data_time: 0.042558 memory: 15239 loss_kpt: 0.000470 acc_pose: 0.898592 loss: 0.000470 2022/09/13 13:06:11 - mmengine - INFO - Epoch(train) [210][100/586] lr: 5.000000e-06 eta: 0:03:35 time: 0.474213 data_time: 0.032997 memory: 15239 loss_kpt: 0.000452 acc_pose: 0.908772 loss: 0.000452 2022/09/13 13:06:34 - mmengine - INFO - Epoch(train) [210][150/586] lr: 5.000000e-06 eta: 0:03:13 time: 0.472285 data_time: 0.030524 memory: 15239 loss_kpt: 0.000429 acc_pose: 0.901548 loss: 0.000429 2022/09/13 13:06:58 - mmengine - INFO - Epoch(train) [210][200/586] lr: 5.000000e-06 eta: 0:02:51 time: 0.467393 data_time: 0.027778 memory: 15239 loss_kpt: 0.000454 acc_pose: 0.870162 loss: 0.000454 2022/09/13 13:07:21 - mmengine - INFO - Epoch(train) [210][250/586] lr: 5.000000e-06 eta: 0:02:29 time: 0.475361 data_time: 0.027100 memory: 15239 loss_kpt: 0.000437 acc_pose: 0.892759 loss: 0.000437 2022/09/13 13:07:45 - mmengine - INFO - Epoch(train) [210][300/586] lr: 5.000000e-06 eta: 0:02:07 time: 0.470904 data_time: 0.030769 memory: 15239 loss_kpt: 0.000436 acc_pose: 0.915357 loss: 0.000436 2022/09/13 13:08:09 - mmengine - INFO - Epoch(train) [210][350/586] lr: 5.000000e-06 eta: 0:01:44 time: 0.472851 data_time: 0.026878 memory: 15239 loss_kpt: 0.000451 acc_pose: 0.855926 loss: 0.000451 2022/09/13 13:08:32 - mmengine - INFO - Epoch(train) [210][400/586] lr: 5.000000e-06 eta: 0:01:22 time: 0.472973 data_time: 0.026214 memory: 15239 loss_kpt: 0.000439 acc_pose: 0.816578 loss: 0.000439 2022/09/13 13:08:56 - mmengine - INFO - Epoch(train) [210][450/586] lr: 5.000000e-06 eta: 0:01:00 time: 0.474834 data_time: 0.030263 memory: 15239 loss_kpt: 0.000436 acc_pose: 0.884556 loss: 0.000436 2022/09/13 13:09:20 - mmengine - INFO - Epoch(train) [210][500/586] lr: 5.000000e-06 eta: 0:00:38 time: 0.470391 data_time: 0.028169 memory: 15239 loss_kpt: 0.000442 acc_pose: 0.850128 loss: 0.000442 2022/09/13 13:09:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 13:09:43 - mmengine - INFO - Epoch(train) [210][550/586] lr: 5.000000e-06 eta: 0:00:15 time: 0.473896 data_time: 0.027344 memory: 15239 loss_kpt: 0.000444 acc_pose: 0.855088 loss: 0.000444 2022/09/13 13:10:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220912_194629 2022/09/13 13:10:01 - mmengine - INFO - Saving checkpoint at 210 epochs 2022/09/13 13:10:19 - mmengine - INFO - Epoch(val) [210][50/407] eta: 0:01:20 time: 0.225045 data_time: 0.013732 memory: 15239 2022/09/13 13:10:30 - mmengine - INFO - Epoch(val) [210][100/407] eta: 0:01:07 time: 0.218268 data_time: 0.008821 memory: 2064 2022/09/13 13:10:41 - mmengine - INFO - Epoch(val) [210][150/407] eta: 0:00:57 time: 0.222244 data_time: 0.010168 memory: 2064 2022/09/13 13:10:52 - mmengine - INFO - Epoch(val) [210][200/407] eta: 0:00:46 time: 0.222855 data_time: 0.008746 memory: 2064 2022/09/13 13:11:03 - mmengine - INFO - Epoch(val) [210][250/407] eta: 0:00:34 time: 0.219337 data_time: 0.008897 memory: 2064 2022/09/13 13:11:14 - mmengine - INFO - Epoch(val) [210][300/407] eta: 0:00:23 time: 0.217862 data_time: 0.008184 memory: 2064 2022/09/13 13:11:25 - mmengine - INFO - Epoch(val) [210][350/407] eta: 0:00:12 time: 0.219147 data_time: 0.008633 memory: 2064 2022/09/13 13:11:36 - mmengine - INFO - Epoch(val) [210][400/407] eta: 0:00:01 time: 0.217174 data_time: 0.008724 memory: 2064 2022/09/13 13:12:12 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 13:12:26 - mmengine - INFO - Epoch(val) [210][407/407] coco/AP: 0.772821 coco/AP .5: 0.910543 coco/AP .75: 0.835954 coco/AP (M): 0.732353 coco/AP (L): 0.844858 coco/AR: 0.821080 coco/AR .5: 0.946159 coco/AR .75: 0.877204 coco/AR (M): 0.776892 coco/AR (L): 0.884913 2022/09/13 13:12:26 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_384_v1/best_coco/AP_epoch_190.pth is removed 2022/09/13 13:12:30 - mmengine - INFO - The best checkpoint with 0.7728 coco/AP at 210 epoch is saved to best_coco/AP_epoch_210.pth.