2022/10/20 09:57:02 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0] CUDA available: True numpy_random_seed: 168664542 GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/petrelfs/share/cuda-11.3 NVCC: Cuda compilation tools, release 11.3, V11.3.109 GCC: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44) PyTorch: 1.12.0+cu113 PyTorch compiling details: PyTorch built with: - GCC 9.3 - 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 v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.3 - 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 8.2.1 - Built with CuDNN 8.3.2 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/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 -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.13.0+cu113 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 ------------------------------------------------------------ 2022/10/20 09:57:04 - 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=10, max_keep_ckpts=1, save_best='coco/AP', rule='greater'), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='PoseVisualizationHook', enable=False)) custom_hooks = [dict(type='SyncBuffersHook')] env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='PoseLocalVisualizer', vis_backends=[dict(type='LocalVisBackend')], name='visualizer') log_processor = dict( type='LogProcessor', window_size=50, by_epoch=True, num_digits=6) log_level = 'INFO' load_from = None resume = False file_client_args = dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' })) train_cfg = dict(by_epoch=True, max_epochs=210, val_interval=10) val_cfg = dict() test_cfg = dict() optim_wrapper = dict(optimizer=dict(type='Adam', lr=0.0005)) param_scheduler = [ dict( type='LinearLR', begin=0, end=500, start_factor=0.001, by_epoch=False), dict( type='MultiStepLR', begin=0, end=210, milestones=[170, 200], gamma=0.1, by_epoch=True) ] auto_scale_lr = dict(base_batch_size=512) codec = dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) model = dict( type='TopdownPoseEstimator', data_preprocessor=dict( type='PoseDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True), backbone=dict( type='ResNetV1d', depth=101, init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnet101_v1d')), head=dict( type='HeatmapHead', in_channels=2048, out_channels=17, loss=dict(type='KeypointMSELoss', use_target_weight=True), decoder=dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)), test_cfg=dict(flip_test=True, flip_mode='heatmap', shift_heatmap=True)) dataset_type = 'CocoDataset' data_mode = 'topdown' data_root = 'data/coco/' train_pipeline = [ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict(type='RandomBBoxTransform'), dict(type='TopdownAffine', input_size=(192, 256)), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)), dict(type='PackPoseInputs') ] val_pipeline = [ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ] train_dataloader = dict( batch_size=64, num_workers=2, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_train2017.json', data_prefix=dict(img='train2017/'), pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict(type='RandomBBoxTransform'), dict(type='TopdownAffine', input_size=(192, 256)), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)), dict(type='PackPoseInputs') ])) val_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ])) test_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ])) val_evaluator = dict( type='CocoMetric', ann_file='data/coco/annotations/person_keypoints_val2017.json') test_evaluator = dict( type='CocoMetric', ann_file='data/coco/annotations/person_keypoints_val2017.json') launcher = 'slurm' work_dir = 'work_dirs/20221020/resnetv1d101_256' 2022/10/20 09:57:45 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "data sampler" registry tree. As a workaround, the current "data sampler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/20 09:57:45 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "optimizer wrapper constructor" registry tree. As a workaround, the current "optimizer wrapper constructor" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/20 09:57:45 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "optimizer" registry tree. As a workaround, the current "optimizer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/20 09:57:45 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "optim_wrapper" registry tree. As a workaround, the current "optim_wrapper" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/20 09:57:45 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/20 09:57:45 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/20 09:57:45 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/20 09:57:45 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/20 09:57:50 - 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. 2022/10/20 09:57:52 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/20 09:58:10 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/20 09:58:10 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. Name of parameter - Initialization information backbone.stem.0.conv.weight - torch.Size([32, 3, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.stem.0.bn.weight - torch.Size([32]): PretrainedInit: load from mmcls://resnet101_v1d backbone.stem.0.bn.bias - torch.Size([32]): PretrainedInit: load from mmcls://resnet101_v1d backbone.stem.1.conv.weight - torch.Size([32, 32, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.stem.1.bn.weight - torch.Size([32]): PretrainedInit: load from mmcls://resnet101_v1d backbone.stem.1.bn.bias - torch.Size([32]): PretrainedInit: load from mmcls://resnet101_v1d backbone.stem.2.conv.weight - torch.Size([64, 32, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.stem.2.bn.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet101_v1d backbone.stem.2.bn.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.0.bn1.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.0.bn1.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.0.bn2.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.0.bn2.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.0.bn3.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.0.bn3.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.0.downsample.1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.0.downsample.1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.1.bn1.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.1.bn1.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.1.bn2.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.1.bn2.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.1.bn3.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.1.bn3.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.2.bn1.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.2.bn1.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.2.bn2.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.2.bn2.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.2.bn3.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer1.2.bn3.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.0.bn1.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.0.bn1.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.0.bn2.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.0.bn2.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.0.bn3.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.0.bn3.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.0.downsample.1.weight - torch.Size([512, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.0.downsample.2.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.0.downsample.2.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.1.bn1.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.1.bn1.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.1.bn2.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.1.bn2.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.1.bn3.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.1.bn3.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.2.bn1.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.2.bn1.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.2.bn2.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.2.bn2.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.2.bn3.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.2.bn3.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.3.bn1.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.3.bn1.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.3.bn2.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.3.bn2.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.3.bn3.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer2.3.bn3.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.0.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.0.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.0.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.0.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.0.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.0.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.0.downsample.1.weight - torch.Size([1024, 512, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.0.downsample.2.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.0.downsample.2.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.1.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.1.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.1.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.1.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.1.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.1.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.2.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.2.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.2.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.2.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.2.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.2.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.3.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.3.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.3.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.3.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.3.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.3.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.4.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.4.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.4.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.4.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.4.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.4.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.5.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.5.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.5.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.5.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.5.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.5.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.6.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.6.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.6.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.6.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.6.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.6.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.6.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.6.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.6.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.7.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.7.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.7.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.7.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.7.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.7.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.7.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.7.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.7.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.8.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.8.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.8.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.8.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.8.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.8.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.8.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.8.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.8.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.9.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.9.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.9.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.9.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.9.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.9.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.9.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.9.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.9.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.10.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.10.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.10.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.10.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.10.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.10.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.10.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.10.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.10.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.11.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.11.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.11.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.11.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.11.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.11.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.11.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.11.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.11.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.12.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.12.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.12.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.12.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.12.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.12.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.12.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.12.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.12.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.13.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.13.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.13.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.13.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.13.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.13.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.13.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.13.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.13.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.14.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.14.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.14.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.14.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.14.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.14.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.14.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.14.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.14.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.15.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.15.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.15.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.15.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.15.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.15.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.15.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.15.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.15.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.16.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.16.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.16.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.16.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.16.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.16.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.16.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.16.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.16.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.17.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.17.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.17.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.17.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.17.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.17.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.17.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.17.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.17.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.18.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.18.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.18.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.18.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.18.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.18.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.18.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.18.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.18.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.19.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.19.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.19.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.19.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.19.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.19.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.19.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.19.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.19.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.20.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.20.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.20.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.20.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.20.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.20.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.20.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.20.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.20.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.21.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.21.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.21.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.21.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.21.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.21.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.21.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.21.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.21.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.22.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.22.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.22.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.22.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.22.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.22.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.22.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.22.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer3.22.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.0.bn1.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.0.bn1.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.0.bn2.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.0.bn2.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.0.bn3.weight - torch.Size([2048]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.0.bn3.bias - torch.Size([2048]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.0.downsample.1.weight - torch.Size([2048, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.0.downsample.2.weight - torch.Size([2048]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.0.downsample.2.bias - torch.Size([2048]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.1.bn1.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.1.bn1.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.1.bn2.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.1.bn2.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.1.bn3.weight - torch.Size([2048]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.1.bn3.bias - torch.Size([2048]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.2.bn1.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.2.bn1.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.2.bn2.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.2.bn2.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.2.bn3.weight - torch.Size([2048]): PretrainedInit: load from mmcls://resnet101_v1d backbone.layer4.2.bn3.bias - torch.Size([2048]): PretrainedInit: load from mmcls://resnet101_v1d head.deconv_layers.0.weight - torch.Size([2048, 256, 4, 4]): NormalInit: mean=0, std=0.001, bias=0 head.deconv_layers.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_layers.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_layers.3.weight - torch.Size([256, 256, 4, 4]): NormalInit: mean=0, std=0.001, bias=0 head.deconv_layers.4.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_layers.4.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_layers.6.weight - torch.Size([256, 256, 4, 4]): NormalInit: mean=0, std=0.001, bias=0 head.deconv_layers.7.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_layers.7.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.final_layer.weight - torch.Size([17, 256, 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 2022/10/20 09:58:10 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221020/resnetv1d101_256 by HardDiskBackend. 2022/10/20 09:58:36 - mmengine - INFO - Epoch(train) [1][50/293] lr: 4.954910e-05 eta: 8:43:38 time: 0.511029 data_time: 0.163696 memory: 9999 loss_kpt: 0.002166 acc_pose: 0.191579 loss: 0.002166 2022/10/20 09:58:53 - mmengine - INFO - Epoch(train) [1][100/293] lr: 9.959920e-05 eta: 7:20:09 time: 0.348784 data_time: 0.076195 memory: 9999 loss_kpt: 0.001762 acc_pose: 0.351446 loss: 0.001762 2022/10/20 09:59:11 - mmengine - INFO - Epoch(train) [1][150/293] lr: 1.496493e-04 eta: 6:58:45 time: 0.368223 data_time: 0.077066 memory: 9999 loss_kpt: 0.001466 acc_pose: 0.513167 loss: 0.001466 2022/10/20 09:59:29 - mmengine - INFO - Epoch(train) [1][200/293] lr: 1.996994e-04 eta: 6:43:48 time: 0.352201 data_time: 0.075564 memory: 9999 loss_kpt: 0.001336 acc_pose: 0.546140 loss: 0.001336 2022/10/20 09:59:47 - mmengine - INFO - Epoch(train) [1][250/293] lr: 2.497495e-04 eta: 6:37:01 time: 0.363426 data_time: 0.068531 memory: 9999 loss_kpt: 0.001227 acc_pose: 0.565042 loss: 0.001227 2022/10/20 10:00:02 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:00:21 - mmengine - INFO - Epoch(train) [2][50/293] lr: 3.428427e-04 eta: 5:44:29 time: 0.373691 data_time: 0.079154 memory: 9999 loss_kpt: 0.001162 acc_pose: 0.607159 loss: 0.001162 2022/10/20 10:00:39 - mmengine - INFO - Epoch(train) [2][100/293] lr: 3.928928e-04 eta: 5:48:02 time: 0.367407 data_time: 0.071740 memory: 9999 loss_kpt: 0.001148 acc_pose: 0.575409 loss: 0.001148 2022/10/20 10:00:57 - mmengine - INFO - Epoch(train) [2][150/293] lr: 4.429429e-04 eta: 5:49:00 time: 0.352415 data_time: 0.061345 memory: 9999 loss_kpt: 0.001124 acc_pose: 0.646929 loss: 0.001124 2022/10/20 10:01:14 - mmengine - INFO - Epoch(train) [2][200/293] lr: 4.929930e-04 eta: 5:48:58 time: 0.345200 data_time: 0.060689 memory: 9999 loss_kpt: 0.001124 acc_pose: 0.641738 loss: 0.001124 2022/10/20 10:01:32 - mmengine - INFO - Epoch(train) [2][250/293] lr: 5.000000e-04 eta: 5:49:22 time: 0.350476 data_time: 0.065543 memory: 9999 loss_kpt: 0.001085 acc_pose: 0.650581 loss: 0.001085 2022/10/20 10:01:47 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:02:05 - mmengine - INFO - Epoch(train) [3][50/293] lr: 5.000000e-04 eta: 5:26:27 time: 0.358831 data_time: 0.080279 memory: 9999 loss_kpt: 0.001042 acc_pose: 0.611395 loss: 0.001042 2022/10/20 10:02:23 - mmengine - INFO - Epoch(train) [3][100/293] lr: 5.000000e-04 eta: 5:28:31 time: 0.353126 data_time: 0.063356 memory: 9999 loss_kpt: 0.001037 acc_pose: 0.647225 loss: 0.001037 2022/10/20 10:02:41 - mmengine - INFO - Epoch(train) [3][150/293] lr: 5.000000e-04 eta: 5:31:12 time: 0.366850 data_time: 0.119652 memory: 9999 loss_kpt: 0.001016 acc_pose: 0.676945 loss: 0.001016 2022/10/20 10:02:58 - mmengine - INFO - Epoch(train) [3][200/293] lr: 5.000000e-04 eta: 5:32:10 time: 0.346212 data_time: 0.093129 memory: 9999 loss_kpt: 0.001019 acc_pose: 0.648653 loss: 0.001019 2022/10/20 10:03:16 - mmengine - INFO - Epoch(train) [3][250/293] lr: 5.000000e-04 eta: 5:33:44 time: 0.358532 data_time: 0.065644 memory: 9999 loss_kpt: 0.001030 acc_pose: 0.698162 loss: 0.001030 2022/10/20 10:03:31 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:03:50 - mmengine - INFO - Epoch(train) [4][50/293] lr: 5.000000e-04 eta: 5:20:06 time: 0.372142 data_time: 0.072394 memory: 9999 loss_kpt: 0.000980 acc_pose: 0.697667 loss: 0.000980 2022/10/20 10:04:08 - mmengine - INFO - Epoch(train) [4][100/293] lr: 5.000000e-04 eta: 5:21:57 time: 0.358060 data_time: 0.078466 memory: 9999 loss_kpt: 0.000962 acc_pose: 0.617899 loss: 0.000962 2022/10/20 10:04:15 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:04:25 - mmengine - INFO - Epoch(train) [4][150/293] lr: 5.000000e-04 eta: 5:23:21 time: 0.352966 data_time: 0.068741 memory: 9999 loss_kpt: 0.000986 acc_pose: 0.667651 loss: 0.000986 2022/10/20 10:04:43 - mmengine - INFO - Epoch(train) [4][200/293] lr: 5.000000e-04 eta: 5:24:31 time: 0.351509 data_time: 0.069445 memory: 9999 loss_kpt: 0.000963 acc_pose: 0.691475 loss: 0.000963 2022/10/20 10:05:00 - mmengine - INFO - Epoch(train) [4][250/293] lr: 5.000000e-04 eta: 5:25:18 time: 0.345575 data_time: 0.068164 memory: 9999 loss_kpt: 0.000978 acc_pose: 0.705055 loss: 0.000978 2022/10/20 10:05:15 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:05:34 - mmengine - INFO - Epoch(train) [5][50/293] lr: 5.000000e-04 eta: 5:15:36 time: 0.377507 data_time: 0.086797 memory: 9999 loss_kpt: 0.000941 acc_pose: 0.705483 loss: 0.000941 2022/10/20 10:05:52 - mmengine - INFO - Epoch(train) [5][100/293] lr: 5.000000e-04 eta: 5:17:16 time: 0.362572 data_time: 0.078084 memory: 9999 loss_kpt: 0.000940 acc_pose: 0.706940 loss: 0.000940 2022/10/20 10:06:09 - mmengine - INFO - Epoch(train) [5][150/293] lr: 5.000000e-04 eta: 5:18:01 time: 0.342949 data_time: 0.068853 memory: 9999 loss_kpt: 0.000942 acc_pose: 0.705036 loss: 0.000942 2022/10/20 10:06:27 - mmengine - INFO - Epoch(train) [5][200/293] lr: 5.000000e-04 eta: 5:19:02 time: 0.351656 data_time: 0.060423 memory: 9999 loss_kpt: 0.000935 acc_pose: 0.750296 loss: 0.000935 2022/10/20 10:06:44 - mmengine - INFO - Epoch(train) [5][250/293] lr: 5.000000e-04 eta: 5:19:41 time: 0.344274 data_time: 0.065122 memory: 9999 loss_kpt: 0.000943 acc_pose: 0.738019 loss: 0.000943 2022/10/20 10:06:59 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:07:18 - mmengine - INFO - Epoch(train) [6][50/293] lr: 5.000000e-04 eta: 5:11:59 time: 0.375483 data_time: 0.085304 memory: 9999 loss_kpt: 0.000937 acc_pose: 0.668575 loss: 0.000937 2022/10/20 10:07:35 - mmengine - INFO - Epoch(train) [6][100/293] lr: 5.000000e-04 eta: 5:12:50 time: 0.346573 data_time: 0.067428 memory: 9999 loss_kpt: 0.000913 acc_pose: 0.714030 loss: 0.000913 2022/10/20 10:08:09 - mmengine - INFO - Epoch(train) [6][150/293] lr: 5.000000e-04 eta: 5:23:46 time: 0.675223 data_time: 0.077155 memory: 9999 loss_kpt: 0.000919 acc_pose: 0.667908 loss: 0.000919 2022/10/20 10:08:37 - mmengine - INFO - Epoch(train) [6][200/293] lr: 5.000000e-04 eta: 5:30:45 time: 0.566025 data_time: 0.065682 memory: 9999 loss_kpt: 0.000921 acc_pose: 0.734429 loss: 0.000921 2022/10/20 10:08:55 - mmengine - INFO - Epoch(train) [6][250/293] lr: 5.000000e-04 eta: 5:31:14 time: 0.357689 data_time: 0.057586 memory: 9999 loss_kpt: 0.000894 acc_pose: 0.808963 loss: 0.000894 2022/10/20 10:09:10 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:09:29 - mmengine - INFO - Epoch(train) [7][50/293] lr: 5.000000e-04 eta: 5:24:07 time: 0.378319 data_time: 0.095785 memory: 9999 loss_kpt: 0.000893 acc_pose: 0.715181 loss: 0.000893 2022/10/20 10:09:46 - mmengine - INFO - Epoch(train) [7][100/293] lr: 5.000000e-04 eta: 5:24:17 time: 0.341761 data_time: 0.061630 memory: 9999 loss_kpt: 0.000890 acc_pose: 0.762446 loss: 0.000890 2022/10/20 10:10:04 - mmengine - INFO - Epoch(train) [7][150/293] lr: 5.000000e-04 eta: 5:24:52 time: 0.358856 data_time: 0.066551 memory: 9999 loss_kpt: 0.000884 acc_pose: 0.722307 loss: 0.000884 2022/10/20 10:10:21 - mmengine - INFO - Epoch(train) [7][200/293] lr: 5.000000e-04 eta: 5:25:01 time: 0.343975 data_time: 0.063615 memory: 9999 loss_kpt: 0.000896 acc_pose: 0.724538 loss: 0.000896 2022/10/20 10:10:36 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:10:40 - mmengine - INFO - Epoch(train) [7][250/293] lr: 5.000000e-04 eta: 5:25:47 time: 0.369279 data_time: 0.080392 memory: 9999 loss_kpt: 0.000884 acc_pose: 0.728651 loss: 0.000884 2022/10/20 10:10:54 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:11:13 - mmengine - INFO - Epoch(train) [8][50/293] lr: 5.000000e-04 eta: 5:19:34 time: 0.368836 data_time: 0.095500 memory: 9999 loss_kpt: 0.000863 acc_pose: 0.663396 loss: 0.000863 2022/10/20 10:11:30 - mmengine - INFO - Epoch(train) [8][100/293] lr: 5.000000e-04 eta: 5:19:48 time: 0.344768 data_time: 0.063247 memory: 9999 loss_kpt: 0.000880 acc_pose: 0.710420 loss: 0.000880 2022/10/20 10:11:47 - mmengine - INFO - Epoch(train) [8][150/293] lr: 5.000000e-04 eta: 5:20:11 time: 0.351484 data_time: 0.064039 memory: 9999 loss_kpt: 0.000888 acc_pose: 0.748931 loss: 0.000888 2022/10/20 10:12:05 - mmengine - INFO - Epoch(train) [8][200/293] lr: 5.000000e-04 eta: 5:20:27 time: 0.348354 data_time: 0.067916 memory: 9999 loss_kpt: 0.000881 acc_pose: 0.680349 loss: 0.000881 2022/10/20 10:12:43 - mmengine - INFO - Epoch(train) [8][250/293] lr: 5.000000e-04 eta: 5:29:33 time: 0.761490 data_time: 0.176358 memory: 9999 loss_kpt: 0.000880 acc_pose: 0.800289 loss: 0.000880 2022/10/20 10:13:06 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:13:25 - mmengine - INFO - Epoch(train) [9][50/293] lr: 5.000000e-04 eta: 5:23:51 time: 0.369241 data_time: 0.079535 memory: 9999 loss_kpt: 0.000862 acc_pose: 0.746352 loss: 0.000862 2022/10/20 10:13:42 - mmengine - INFO - Epoch(train) [9][100/293] lr: 5.000000e-04 eta: 5:23:54 time: 0.344290 data_time: 0.065770 memory: 9999 loss_kpt: 0.000851 acc_pose: 0.688873 loss: 0.000851 2022/10/20 10:14:00 - mmengine - INFO - Epoch(train) [9][150/293] lr: 5.000000e-04 eta: 5:24:10 time: 0.356997 data_time: 0.066159 memory: 9999 loss_kpt: 0.000833 acc_pose: 0.776576 loss: 0.000833 2022/10/20 10:14:18 - mmengine - INFO - Epoch(train) [9][200/293] lr: 5.000000e-04 eta: 5:24:18 time: 0.349951 data_time: 0.066775 memory: 9999 loss_kpt: 0.000853 acc_pose: 0.752969 loss: 0.000853 2022/10/20 10:14:35 - mmengine - INFO - Epoch(train) [9][250/293] lr: 5.000000e-04 eta: 5:24:20 time: 0.346835 data_time: 0.067071 memory: 9999 loss_kpt: 0.000865 acc_pose: 0.730717 loss: 0.000865 2022/10/20 10:14:51 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:15:11 - mmengine - INFO - Epoch(train) [10][50/293] lr: 5.000000e-04 eta: 5:19:49 time: 0.394296 data_time: 0.081375 memory: 9999 loss_kpt: 0.000837 acc_pose: 0.743832 loss: 0.000837 2022/10/20 10:15:28 - mmengine - INFO - Epoch(train) [10][100/293] lr: 5.000000e-04 eta: 5:19:59 time: 0.350990 data_time: 0.073532 memory: 9999 loss_kpt: 0.000843 acc_pose: 0.768376 loss: 0.000843 2022/10/20 10:15:47 - mmengine - INFO - Epoch(train) [10][150/293] lr: 5.000000e-04 eta: 5:20:19 time: 0.361012 data_time: 0.062555 memory: 9999 loss_kpt: 0.000847 acc_pose: 0.759465 loss: 0.000847 2022/10/20 10:16:04 - mmengine - INFO - Epoch(train) [10][200/293] lr: 5.000000e-04 eta: 5:20:26 time: 0.349284 data_time: 0.068663 memory: 9999 loss_kpt: 0.000837 acc_pose: 0.762871 loss: 0.000837 2022/10/20 10:16:22 - mmengine - INFO - Epoch(train) [10][250/293] lr: 5.000000e-04 eta: 5:20:47 time: 0.364138 data_time: 0.068583 memory: 9999 loss_kpt: 0.000852 acc_pose: 0.690564 loss: 0.000852 2022/10/20 10:16:36 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:16:36 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/10/20 10:17:12 - mmengine - INFO - Epoch(val) [10][50/407] eta: 0:03:47 time: 0.637729 data_time: 0.574259 memory: 9999 2022/10/20 10:17:23 - mmengine - INFO - Epoch(val) [10][100/407] eta: 0:01:12 time: 0.237505 data_time: 0.174232 memory: 1378 2022/10/20 10:17:33 - mmengine - INFO - Epoch(val) [10][150/407] eta: 0:00:50 time: 0.197692 data_time: 0.133350 memory: 1378 2022/10/20 10:17:39 - mmengine - INFO - Epoch(val) [10][200/407] eta: 0:00:24 time: 0.120596 data_time: 0.056524 memory: 1378 2022/10/20 10:17:45 - mmengine - INFO - Epoch(val) [10][250/407] eta: 0:00:18 time: 0.118176 data_time: 0.050673 memory: 1378 2022/10/20 10:17:51 - mmengine - INFO - Epoch(val) [10][300/407] eta: 0:00:12 time: 0.114341 data_time: 0.048787 memory: 1378 2022/10/20 10:17:57 - mmengine - INFO - Epoch(val) [10][350/407] eta: 0:00:06 time: 0.117990 data_time: 0.049344 memory: 1378 2022/10/20 10:18:02 - mmengine - INFO - Epoch(val) [10][400/407] eta: 0:00:00 time: 0.102068 data_time: 0.038677 memory: 1378 2022/10/20 10:18:38 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 10:18:51 - mmengine - INFO - Epoch(val) [10][407/407] coco/AP: 0.636014 coco/AP .5: 0.864709 coco/AP .75: 0.708773 coco/AP (M): 0.599374 coco/AP (L): 0.702632 coco/AR: 0.699386 coco/AR .5: 0.908375 coco/AR .75: 0.767160 coco/AR (M): 0.655804 coco/AR (L): 0.761130 2022/10/20 10:18:54 - mmengine - INFO - The best checkpoint with 0.6360 coco/AP at 10 epoch is saved to best_coco/AP_epoch_10.pth. 2022/10/20 10:19:12 - mmengine - INFO - Epoch(train) [11][50/293] lr: 5.000000e-04 eta: 5:16:17 time: 0.366744 data_time: 0.087426 memory: 9999 loss_kpt: 0.000829 acc_pose: 0.746610 loss: 0.000829 2022/10/20 10:19:19 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:19:30 - mmengine - INFO - Epoch(train) [11][100/293] lr: 5.000000e-04 eta: 5:16:37 time: 0.362076 data_time: 0.076445 memory: 9999 loss_kpt: 0.000821 acc_pose: 0.760189 loss: 0.000821 2022/10/20 10:19:47 - mmengine - INFO - Epoch(train) [11][150/293] lr: 5.000000e-04 eta: 5:16:43 time: 0.348027 data_time: 0.071236 memory: 9999 loss_kpt: 0.000821 acc_pose: 0.759905 loss: 0.000821 2022/10/20 10:20:05 - mmengine - INFO - Epoch(train) [11][200/293] lr: 5.000000e-04 eta: 5:16:57 time: 0.357070 data_time: 0.069652 memory: 9999 loss_kpt: 0.000847 acc_pose: 0.697851 loss: 0.000847 2022/10/20 10:20:23 - mmengine - INFO - Epoch(train) [11][250/293] lr: 5.000000e-04 eta: 5:17:00 time: 0.347400 data_time: 0.068437 memory: 9999 loss_kpt: 0.000815 acc_pose: 0.747527 loss: 0.000815 2022/10/20 10:20:38 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:20:56 - mmengine - INFO - Epoch(train) [12][50/293] lr: 5.000000e-04 eta: 5:13:03 time: 0.373768 data_time: 0.082538 memory: 9999 loss_kpt: 0.000830 acc_pose: 0.736195 loss: 0.000830 2022/10/20 10:21:19 - mmengine - INFO - Epoch(train) [12][100/293] lr: 5.000000e-04 eta: 5:14:34 time: 0.445060 data_time: 0.068409 memory: 9999 loss_kpt: 0.000814 acc_pose: 0.731502 loss: 0.000814 2022/10/20 10:21:53 - mmengine - INFO - Epoch(train) [12][150/293] lr: 5.000000e-04 eta: 5:19:39 time: 0.696151 data_time: 0.076299 memory: 9999 loss_kpt: 0.000824 acc_pose: 0.784431 loss: 0.000824 2022/10/20 10:22:16 - mmengine - INFO - Epoch(train) [12][200/293] lr: 5.000000e-04 eta: 5:21:08 time: 0.454494 data_time: 0.088765 memory: 9999 loss_kpt: 0.000839 acc_pose: 0.754063 loss: 0.000839 2022/10/20 10:22:34 - mmengine - INFO - Epoch(train) [12][250/293] lr: 5.000000e-04 eta: 5:21:14 time: 0.358858 data_time: 0.073103 memory: 9999 loss_kpt: 0.000805 acc_pose: 0.776849 loss: 0.000805 2022/10/20 10:22:49 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:23:08 - mmengine - INFO - Epoch(train) [13][50/293] lr: 5.000000e-04 eta: 5:17:25 time: 0.373664 data_time: 0.076755 memory: 9999 loss_kpt: 0.000809 acc_pose: 0.749511 loss: 0.000809 2022/10/20 10:23:26 - mmengine - INFO - Epoch(train) [13][100/293] lr: 5.000000e-04 eta: 5:17:31 time: 0.356820 data_time: 0.073137 memory: 9999 loss_kpt: 0.000807 acc_pose: 0.700624 loss: 0.000807 2022/10/20 10:23:43 - mmengine - INFO - Epoch(train) [13][150/293] lr: 5.000000e-04 eta: 5:17:31 time: 0.349436 data_time: 0.076643 memory: 9999 loss_kpt: 0.000825 acc_pose: 0.768667 loss: 0.000825 2022/10/20 10:24:01 - mmengine - INFO - Epoch(train) [13][200/293] lr: 5.000000e-04 eta: 5:17:32 time: 0.351608 data_time: 0.082849 memory: 9999 loss_kpt: 0.000809 acc_pose: 0.684197 loss: 0.000809 2022/10/20 10:24:19 - mmengine - INFO - Epoch(train) [13][250/293] lr: 5.000000e-04 eta: 5:17:37 time: 0.357160 data_time: 0.068311 memory: 9999 loss_kpt: 0.000828 acc_pose: 0.734616 loss: 0.000828 2022/10/20 10:24:34 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:24:52 - mmengine - INFO - Epoch(train) [14][50/293] lr: 5.000000e-04 eta: 5:14:04 time: 0.369843 data_time: 0.086763 memory: 9999 loss_kpt: 0.000794 acc_pose: 0.785079 loss: 0.000794 2022/10/20 10:25:10 - mmengine - INFO - Epoch(train) [14][100/293] lr: 5.000000e-04 eta: 5:14:06 time: 0.351900 data_time: 0.067997 memory: 9999 loss_kpt: 0.000812 acc_pose: 0.699328 loss: 0.000812 2022/10/20 10:25:28 - mmengine - INFO - Epoch(train) [14][150/293] lr: 5.000000e-04 eta: 5:14:06 time: 0.349630 data_time: 0.068775 memory: 9999 loss_kpt: 0.000797 acc_pose: 0.804311 loss: 0.000797 2022/10/20 10:25:44 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:25:54 - mmengine - INFO - Epoch(train) [14][200/293] lr: 5.000000e-04 eta: 5:16:21 time: 0.538669 data_time: 0.090219 memory: 9999 loss_kpt: 0.000811 acc_pose: 0.778991 loss: 0.000811 2022/10/20 10:26:30 - mmengine - INFO - Epoch(train) [14][250/293] lr: 5.000000e-04 eta: 5:20:37 time: 0.714284 data_time: 0.068808 memory: 9999 loss_kpt: 0.000814 acc_pose: 0.704593 loss: 0.000814 2022/10/20 10:26:48 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:27:06 - mmengine - INFO - Epoch(train) [15][50/293] lr: 5.000000e-04 eta: 5:17:07 time: 0.364519 data_time: 0.087090 memory: 9999 loss_kpt: 0.000801 acc_pose: 0.744686 loss: 0.000801 2022/10/20 10:27:24 - mmengine - INFO - Epoch(train) [15][100/293] lr: 5.000000e-04 eta: 5:17:12 time: 0.363387 data_time: 0.071344 memory: 9999 loss_kpt: 0.000800 acc_pose: 0.756802 loss: 0.000800 2022/10/20 10:27:43 - mmengine - INFO - Epoch(train) [15][150/293] lr: 5.000000e-04 eta: 5:17:17 time: 0.363875 data_time: 0.084035 memory: 9999 loss_kpt: 0.000807 acc_pose: 0.799279 loss: 0.000807 2022/10/20 10:28:00 - mmengine - INFO - Epoch(train) [15][200/293] lr: 5.000000e-04 eta: 5:17:14 time: 0.352814 data_time: 0.072528 memory: 9999 loss_kpt: 0.000802 acc_pose: 0.790416 loss: 0.000802 2022/10/20 10:28:18 - mmengine - INFO - Epoch(train) [15][250/293] lr: 5.000000e-04 eta: 5:17:08 time: 0.348848 data_time: 0.067189 memory: 9999 loss_kpt: 0.000782 acc_pose: 0.756064 loss: 0.000782 2022/10/20 10:28:33 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:28:52 - mmengine - INFO - Epoch(train) [16][50/293] lr: 5.000000e-04 eta: 5:14:01 time: 0.375172 data_time: 0.078323 memory: 9999 loss_kpt: 0.000812 acc_pose: 0.726888 loss: 0.000812 2022/10/20 10:29:09 - mmengine - INFO - Epoch(train) [16][100/293] lr: 5.000000e-04 eta: 5:13:57 time: 0.349479 data_time: 0.062920 memory: 9999 loss_kpt: 0.000782 acc_pose: 0.772362 loss: 0.000782 2022/10/20 10:29:27 - mmengine - INFO - Epoch(train) [16][150/293] lr: 5.000000e-04 eta: 5:13:57 time: 0.357454 data_time: 0.067396 memory: 9999 loss_kpt: 0.000811 acc_pose: 0.754983 loss: 0.000811 2022/10/20 10:29:45 - mmengine - INFO - Epoch(train) [16][200/293] lr: 5.000000e-04 eta: 5:13:56 time: 0.354954 data_time: 0.076909 memory: 9999 loss_kpt: 0.000787 acc_pose: 0.796798 loss: 0.000787 2022/10/20 10:30:03 - mmengine - INFO - Epoch(train) [16][250/293] lr: 5.000000e-04 eta: 5:14:03 time: 0.370223 data_time: 0.070299 memory: 9999 loss_kpt: 0.000785 acc_pose: 0.760762 loss: 0.000785 2022/10/20 10:30:30 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:31:05 - mmengine - INFO - Epoch(train) [17][50/293] lr: 5.000000e-04 eta: 5:14:31 time: 0.713510 data_time: 0.100859 memory: 9999 loss_kpt: 0.000779 acc_pose: 0.790753 loss: 0.000779 2022/10/20 10:31:23 - mmengine - INFO - Epoch(train) [17][100/293] lr: 5.000000e-04 eta: 5:14:22 time: 0.345920 data_time: 0.069410 memory: 9999 loss_kpt: 0.000797 acc_pose: 0.767567 loss: 0.000797 2022/10/20 10:31:41 - mmengine - INFO - Epoch(train) [17][150/293] lr: 5.000000e-04 eta: 5:14:20 time: 0.356657 data_time: 0.071144 memory: 9999 loss_kpt: 0.000783 acc_pose: 0.757128 loss: 0.000783 2022/10/20 10:31:58 - mmengine - INFO - Epoch(train) [17][200/293] lr: 5.000000e-04 eta: 5:14:14 time: 0.351465 data_time: 0.072760 memory: 9999 loss_kpt: 0.000794 acc_pose: 0.780306 loss: 0.000794 2022/10/20 10:32:16 - mmengine - INFO - Epoch(train) [17][250/293] lr: 5.000000e-04 eta: 5:14:16 time: 0.366054 data_time: 0.072177 memory: 9999 loss_kpt: 0.000778 acc_pose: 0.785076 loss: 0.000778 2022/10/20 10:32:32 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:32:40 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:32:50 - mmengine - INFO - Epoch(train) [18][50/293] lr: 5.000000e-04 eta: 5:11:23 time: 0.365619 data_time: 0.085751 memory: 9999 loss_kpt: 0.000765 acc_pose: 0.800835 loss: 0.000765 2022/10/20 10:33:09 - mmengine - INFO - Epoch(train) [18][100/293] lr: 5.000000e-04 eta: 5:11:28 time: 0.369680 data_time: 0.085338 memory: 9999 loss_kpt: 0.000799 acc_pose: 0.798044 loss: 0.000799 2022/10/20 10:33:27 - mmengine - INFO - Epoch(train) [18][150/293] lr: 5.000000e-04 eta: 5:11:23 time: 0.352626 data_time: 0.068427 memory: 9999 loss_kpt: 0.000784 acc_pose: 0.807288 loss: 0.000784 2022/10/20 10:33:44 - mmengine - INFO - Epoch(train) [18][200/293] lr: 5.000000e-04 eta: 5:11:15 time: 0.347537 data_time: 0.073977 memory: 9999 loss_kpt: 0.000784 acc_pose: 0.803858 loss: 0.000784 2022/10/20 10:34:02 - mmengine - INFO - Epoch(train) [18][250/293] lr: 5.000000e-04 eta: 5:11:13 time: 0.357370 data_time: 0.079904 memory: 9999 loss_kpt: 0.000768 acc_pose: 0.797341 loss: 0.000768 2022/10/20 10:34:17 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:34:35 - mmengine - INFO - Epoch(train) [19][50/293] lr: 5.000000e-04 eta: 5:08:31 time: 0.368290 data_time: 0.093968 memory: 9999 loss_kpt: 0.000772 acc_pose: 0.807322 loss: 0.000772 2022/10/20 10:35:11 - mmengine - INFO - Epoch(train) [19][100/293] lr: 5.000000e-04 eta: 5:11:33 time: 0.710484 data_time: 0.067866 memory: 9999 loss_kpt: 0.000783 acc_pose: 0.760287 loss: 0.000783 2022/10/20 10:35:39 - mmengine - INFO - Epoch(train) [19][150/293] lr: 5.000000e-04 eta: 5:13:17 time: 0.564482 data_time: 0.066981 memory: 9999 loss_kpt: 0.000777 acc_pose: 0.748692 loss: 0.000777 2022/10/20 10:35:57 - mmengine - INFO - Epoch(train) [19][200/293] lr: 5.000000e-04 eta: 5:13:11 time: 0.356089 data_time: 0.068043 memory: 9999 loss_kpt: 0.000768 acc_pose: 0.804935 loss: 0.000768 2022/10/20 10:36:15 - mmengine - INFO - Epoch(train) [19][250/293] lr: 5.000000e-04 eta: 5:13:07 time: 0.360838 data_time: 0.076201 memory: 9999 loss_kpt: 0.000759 acc_pose: 0.833489 loss: 0.000759 2022/10/20 10:36:30 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:36:48 - mmengine - INFO - Epoch(train) [20][50/293] lr: 5.000000e-04 eta: 5:10:30 time: 0.370968 data_time: 0.082694 memory: 9999 loss_kpt: 0.000770 acc_pose: 0.801607 loss: 0.000770 2022/10/20 10:37:06 - mmengine - INFO - Epoch(train) [20][100/293] lr: 5.000000e-04 eta: 5:10:22 time: 0.351754 data_time: 0.079298 memory: 9999 loss_kpt: 0.000756 acc_pose: 0.826426 loss: 0.000756 2022/10/20 10:37:24 - mmengine - INFO - Epoch(train) [20][150/293] lr: 5.000000e-04 eta: 5:10:16 time: 0.353929 data_time: 0.065547 memory: 9999 loss_kpt: 0.000772 acc_pose: 0.796164 loss: 0.000772 2022/10/20 10:37:42 - mmengine - INFO - Epoch(train) [20][200/293] lr: 5.000000e-04 eta: 5:10:12 time: 0.360065 data_time: 0.068656 memory: 9999 loss_kpt: 0.000776 acc_pose: 0.760637 loss: 0.000776 2022/10/20 10:38:00 - mmengine - INFO - Epoch(train) [20][250/293] lr: 5.000000e-04 eta: 5:10:09 time: 0.362575 data_time: 0.072316 memory: 9999 loss_kpt: 0.000768 acc_pose: 0.743747 loss: 0.000768 2022/10/20 10:38:15 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:38:15 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/10/20 10:38:25 - mmengine - INFO - Epoch(val) [20][50/407] eta: 0:00:43 time: 0.122624 data_time: 0.055385 memory: 9999 2022/10/20 10:38:30 - mmengine - INFO - Epoch(val) [20][100/407] eta: 0:00:34 time: 0.112602 data_time: 0.045823 memory: 1378 2022/10/20 10:38:36 - mmengine - INFO - Epoch(val) [20][150/407] eta: 0:00:29 time: 0.114478 data_time: 0.047629 memory: 1378 2022/10/20 10:38:42 - mmengine - INFO - Epoch(val) [20][200/407] eta: 0:00:24 time: 0.117105 data_time: 0.050438 memory: 1378 2022/10/20 10:38:48 - mmengine - INFO - Epoch(val) [20][250/407] eta: 0:00:19 time: 0.122277 data_time: 0.056090 memory: 1378 2022/10/20 10:38:54 - mmengine - INFO - Epoch(val) [20][300/407] eta: 0:00:12 time: 0.113274 data_time: 0.045442 memory: 1378 2022/10/20 10:39:00 - mmengine - INFO - Epoch(val) [20][350/407] eta: 0:00:06 time: 0.118032 data_time: 0.050617 memory: 1378 2022/10/20 10:39:05 - mmengine - INFO - Epoch(val) [20][400/407] eta: 0:00:00 time: 0.108678 data_time: 0.044765 memory: 1378 2022/10/20 10:39:39 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 10:39:53 - mmengine - INFO - Epoch(val) [20][407/407] coco/AP: 0.667637 coco/AP .5: 0.880530 coco/AP .75: 0.745336 coco/AP (M): 0.630043 coco/AP (L): 0.734938 coco/AR: 0.729802 coco/AR .5: 0.921914 coco/AR .75: 0.800850 coco/AR (M): 0.685987 coco/AR (L): 0.791787 2022/10/20 10:39:53 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221020/resnetv1d101_256/best_coco/AP_epoch_10.pth is removed 2022/10/20 10:39:55 - mmengine - INFO - The best checkpoint with 0.6676 coco/AP at 20 epoch is saved to best_coco/AP_epoch_20.pth. 2022/10/20 10:40:14 - mmengine - INFO - Epoch(train) [21][50/293] lr: 5.000000e-04 eta: 5:07:45 time: 0.381575 data_time: 0.090115 memory: 9999 loss_kpt: 0.000765 acc_pose: 0.732467 loss: 0.000765 2022/10/20 10:40:32 - mmengine - INFO - Epoch(train) [21][100/293] lr: 5.000000e-04 eta: 5:07:40 time: 0.357449 data_time: 0.063543 memory: 9999 loss_kpt: 0.000758 acc_pose: 0.798867 loss: 0.000758 2022/10/20 10:40:46 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:40:49 - mmengine - INFO - Epoch(train) [21][150/293] lr: 5.000000e-04 eta: 5:07:29 time: 0.342810 data_time: 0.071094 memory: 9999 loss_kpt: 0.000752 acc_pose: 0.797418 loss: 0.000752 2022/10/20 10:41:08 - mmengine - INFO - Epoch(train) [21][200/293] lr: 5.000000e-04 eta: 5:07:28 time: 0.368119 data_time: 0.076891 memory: 9999 loss_kpt: 0.000754 acc_pose: 0.763637 loss: 0.000754 2022/10/20 10:41:25 - mmengine - INFO - Epoch(train) [21][250/293] lr: 5.000000e-04 eta: 5:07:22 time: 0.354453 data_time: 0.069402 memory: 9999 loss_kpt: 0.000761 acc_pose: 0.791994 loss: 0.000761 2022/10/20 10:41:40 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:41:59 - mmengine - INFO - Epoch(train) [22][50/293] lr: 5.000000e-04 eta: 5:05:01 time: 0.374012 data_time: 0.078943 memory: 9999 loss_kpt: 0.000755 acc_pose: 0.802618 loss: 0.000755 2022/10/20 10:42:17 - mmengine - INFO - Epoch(train) [22][100/293] lr: 5.000000e-04 eta: 5:04:57 time: 0.358370 data_time: 0.070639 memory: 9999 loss_kpt: 0.000757 acc_pose: 0.753222 loss: 0.000757 2022/10/20 10:42:35 - mmengine - INFO - Epoch(train) [22][150/293] lr: 5.000000e-04 eta: 5:04:53 time: 0.359656 data_time: 0.064348 memory: 9999 loss_kpt: 0.000753 acc_pose: 0.776078 loss: 0.000753 2022/10/20 10:42:53 - mmengine - INFO - Epoch(train) [22][200/293] lr: 5.000000e-04 eta: 5:04:49 time: 0.360340 data_time: 0.074322 memory: 9999 loss_kpt: 0.000775 acc_pose: 0.760489 loss: 0.000775 2022/10/20 10:43:11 - mmengine - INFO - Epoch(train) [22][250/293] lr: 5.000000e-04 eta: 5:04:43 time: 0.355391 data_time: 0.063305 memory: 9999 loss_kpt: 0.000753 acc_pose: 0.763174 loss: 0.000753 2022/10/20 10:43:26 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:43:44 - mmengine - INFO - Epoch(train) [23][50/293] lr: 5.000000e-04 eta: 5:02:23 time: 0.359646 data_time: 0.082212 memory: 9999 loss_kpt: 0.000772 acc_pose: 0.752849 loss: 0.000772 2022/10/20 10:44:02 - mmengine - INFO - Epoch(train) [23][100/293] lr: 5.000000e-04 eta: 5:02:14 time: 0.347834 data_time: 0.062267 memory: 9999 loss_kpt: 0.000750 acc_pose: 0.800415 loss: 0.000750 2022/10/20 10:44:19 - mmengine - INFO - Epoch(train) [23][150/293] lr: 5.000000e-04 eta: 5:02:05 time: 0.346705 data_time: 0.063795 memory: 9999 loss_kpt: 0.000738 acc_pose: 0.773121 loss: 0.000738 2022/10/20 10:44:38 - mmengine - INFO - Epoch(train) [23][200/293] lr: 5.000000e-04 eta: 5:02:06 time: 0.373488 data_time: 0.071643 memory: 9999 loss_kpt: 0.000745 acc_pose: 0.782898 loss: 0.000745 2022/10/20 10:44:55 - mmengine - INFO - Epoch(train) [23][250/293] lr: 5.000000e-04 eta: 5:01:55 time: 0.343337 data_time: 0.068431 memory: 9999 loss_kpt: 0.000743 acc_pose: 0.768003 loss: 0.000743 2022/10/20 10:45:10 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:45:28 - mmengine - INFO - Epoch(train) [24][50/293] lr: 5.000000e-04 eta: 4:59:43 time: 0.364401 data_time: 0.083385 memory: 9999 loss_kpt: 0.000737 acc_pose: 0.751779 loss: 0.000737 2022/10/20 10:45:46 - mmengine - INFO - Epoch(train) [24][100/293] lr: 5.000000e-04 eta: 4:59:38 time: 0.355427 data_time: 0.064994 memory: 9999 loss_kpt: 0.000759 acc_pose: 0.805647 loss: 0.000759 2022/10/20 10:46:04 - mmengine - INFO - Epoch(train) [24][150/293] lr: 5.000000e-04 eta: 4:59:36 time: 0.364956 data_time: 0.089283 memory: 9999 loss_kpt: 0.000755 acc_pose: 0.787631 loss: 0.000755 2022/10/20 10:46:22 - mmengine - INFO - Epoch(train) [24][200/293] lr: 5.000000e-04 eta: 4:59:29 time: 0.354752 data_time: 0.070285 memory: 9999 loss_kpt: 0.000741 acc_pose: 0.790230 loss: 0.000741 2022/10/20 10:46:40 - mmengine - INFO - Epoch(train) [24][250/293] lr: 5.000000e-04 eta: 4:59:24 time: 0.357693 data_time: 0.081204 memory: 9999 loss_kpt: 0.000743 acc_pose: 0.824001 loss: 0.000743 2022/10/20 10:46:44 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:46:55 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:47:14 - mmengine - INFO - Epoch(train) [25][50/293] lr: 5.000000e-04 eta: 4:57:21 time: 0.372050 data_time: 0.100807 memory: 9999 loss_kpt: 0.000730 acc_pose: 0.838735 loss: 0.000730 2022/10/20 10:47:31 - mmengine - INFO - Epoch(train) [25][100/293] lr: 5.000000e-04 eta: 4:57:12 time: 0.345865 data_time: 0.071451 memory: 9999 loss_kpt: 0.000739 acc_pose: 0.735722 loss: 0.000739 2022/10/20 10:47:49 - mmengine - INFO - Epoch(train) [25][150/293] lr: 5.000000e-04 eta: 4:57:04 time: 0.349975 data_time: 0.069466 memory: 9999 loss_kpt: 0.000756 acc_pose: 0.790317 loss: 0.000756 2022/10/20 10:48:07 - mmengine - INFO - Epoch(train) [25][200/293] lr: 5.000000e-04 eta: 4:56:58 time: 0.356315 data_time: 0.067517 memory: 9999 loss_kpt: 0.000746 acc_pose: 0.781091 loss: 0.000746 2022/10/20 10:48:24 - mmengine - INFO - Epoch(train) [25][250/293] lr: 5.000000e-04 eta: 4:56:52 time: 0.356466 data_time: 0.066778 memory: 9999 loss_kpt: 0.000750 acc_pose: 0.785021 loss: 0.000750 2022/10/20 10:48:40 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:48:59 - mmengine - INFO - Epoch(train) [26][50/293] lr: 5.000000e-04 eta: 4:54:56 time: 0.379476 data_time: 0.080013 memory: 9999 loss_kpt: 0.000743 acc_pose: 0.768038 loss: 0.000743 2022/10/20 10:49:17 - mmengine - INFO - Epoch(train) [26][100/293] lr: 5.000000e-04 eta: 4:54:54 time: 0.363743 data_time: 0.069394 memory: 9999 loss_kpt: 0.000738 acc_pose: 0.807945 loss: 0.000738 2022/10/20 10:49:35 - mmengine - INFO - Epoch(train) [26][150/293] lr: 5.000000e-04 eta: 4:54:53 time: 0.370804 data_time: 0.066460 memory: 9999 loss_kpt: 0.000735 acc_pose: 0.812384 loss: 0.000735 2022/10/20 10:49:54 - mmengine - INFO - Epoch(train) [26][200/293] lr: 5.000000e-04 eta: 4:54:50 time: 0.364764 data_time: 0.072748 memory: 9999 loss_kpt: 0.000744 acc_pose: 0.837978 loss: 0.000744 2022/10/20 10:50:11 - mmengine - INFO - Epoch(train) [26][250/293] lr: 5.000000e-04 eta: 4:54:44 time: 0.356698 data_time: 0.076600 memory: 9999 loss_kpt: 0.000739 acc_pose: 0.804908 loss: 0.000739 2022/10/20 10:50:26 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:50:45 - mmengine - INFO - Epoch(train) [27][50/293] lr: 5.000000e-04 eta: 4:52:51 time: 0.373877 data_time: 0.084975 memory: 9999 loss_kpt: 0.000738 acc_pose: 0.809378 loss: 0.000738 2022/10/20 10:51:03 - mmengine - INFO - Epoch(train) [27][100/293] lr: 5.000000e-04 eta: 4:52:48 time: 0.365588 data_time: 0.065748 memory: 9999 loss_kpt: 0.000735 acc_pose: 0.752309 loss: 0.000735 2022/10/20 10:51:21 - mmengine - INFO - Epoch(train) [27][150/293] lr: 5.000000e-04 eta: 4:52:38 time: 0.343959 data_time: 0.075926 memory: 9999 loss_kpt: 0.000741 acc_pose: 0.773942 loss: 0.000741 2022/10/20 10:51:38 - mmengine - INFO - Epoch(train) [27][200/293] lr: 5.000000e-04 eta: 4:52:32 time: 0.357315 data_time: 0.065910 memory: 9999 loss_kpt: 0.000731 acc_pose: 0.802272 loss: 0.000731 2022/10/20 10:51:56 - mmengine - INFO - Epoch(train) [27][250/293] lr: 5.000000e-04 eta: 4:52:23 time: 0.348810 data_time: 0.069242 memory: 9999 loss_kpt: 0.000727 acc_pose: 0.848184 loss: 0.000727 2022/10/20 10:52:11 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:52:29 - mmengine - INFO - Epoch(train) [28][50/293] lr: 5.000000e-04 eta: 4:50:31 time: 0.365477 data_time: 0.082252 memory: 9999 loss_kpt: 0.000749 acc_pose: 0.769204 loss: 0.000749 2022/10/20 10:52:43 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:52:46 - mmengine - INFO - Epoch(train) [28][100/293] lr: 5.000000e-04 eta: 4:50:23 time: 0.347972 data_time: 0.068381 memory: 9999 loss_kpt: 0.000743 acc_pose: 0.775261 loss: 0.000743 2022/10/20 10:53:04 - mmengine - INFO - Epoch(train) [28][150/293] lr: 5.000000e-04 eta: 4:50:18 time: 0.359470 data_time: 0.073321 memory: 9999 loss_kpt: 0.000732 acc_pose: 0.754829 loss: 0.000732 2022/10/20 10:53:23 - mmengine - INFO - Epoch(train) [28][200/293] lr: 5.000000e-04 eta: 4:50:14 time: 0.364020 data_time: 0.069125 memory: 9999 loss_kpt: 0.000734 acc_pose: 0.760443 loss: 0.000734 2022/10/20 10:53:40 - mmengine - INFO - Epoch(train) [28][250/293] lr: 5.000000e-04 eta: 4:50:07 time: 0.354690 data_time: 0.070476 memory: 9999 loss_kpt: 0.000734 acc_pose: 0.766393 loss: 0.000734 2022/10/20 10:53:55 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:54:14 - mmengine - INFO - Epoch(train) [29][50/293] lr: 5.000000e-04 eta: 4:48:20 time: 0.369841 data_time: 0.088093 memory: 9999 loss_kpt: 0.000719 acc_pose: 0.809318 loss: 0.000719 2022/10/20 10:54:31 - mmengine - INFO - Epoch(train) [29][100/293] lr: 5.000000e-04 eta: 4:48:12 time: 0.349727 data_time: 0.068798 memory: 9999 loss_kpt: 0.000739 acc_pose: 0.783639 loss: 0.000739 2022/10/20 10:54:49 - mmengine - INFO - Epoch(train) [29][150/293] lr: 5.000000e-04 eta: 4:48:06 time: 0.356092 data_time: 0.083140 memory: 9999 loss_kpt: 0.000733 acc_pose: 0.756129 loss: 0.000733 2022/10/20 10:55:07 - mmengine - INFO - Epoch(train) [29][200/293] lr: 5.000000e-04 eta: 4:47:58 time: 0.351711 data_time: 0.069273 memory: 9999 loss_kpt: 0.000733 acc_pose: 0.825832 loss: 0.000733 2022/10/20 10:55:25 - mmengine - INFO - Epoch(train) [29][250/293] lr: 5.000000e-04 eta: 4:47:53 time: 0.360592 data_time: 0.066692 memory: 9999 loss_kpt: 0.000735 acc_pose: 0.779975 loss: 0.000735 2022/10/20 10:55:39 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:55:58 - mmengine - INFO - Epoch(train) [30][50/293] lr: 5.000000e-04 eta: 4:46:11 time: 0.374559 data_time: 0.089008 memory: 9999 loss_kpt: 0.000722 acc_pose: 0.793387 loss: 0.000722 2022/10/20 10:56:16 - mmengine - INFO - Epoch(train) [30][100/293] lr: 5.000000e-04 eta: 4:46:06 time: 0.360115 data_time: 0.075529 memory: 9999 loss_kpt: 0.000736 acc_pose: 0.826038 loss: 0.000736 2022/10/20 10:56:34 - mmengine - INFO - Epoch(train) [30][150/293] lr: 5.000000e-04 eta: 4:46:02 time: 0.365065 data_time: 0.072807 memory: 9999 loss_kpt: 0.000721 acc_pose: 0.777224 loss: 0.000721 2022/10/20 10:56:52 - mmengine - INFO - Epoch(train) [30][200/293] lr: 5.000000e-04 eta: 4:45:56 time: 0.357326 data_time: 0.071394 memory: 9999 loss_kpt: 0.000722 acc_pose: 0.819945 loss: 0.000722 2022/10/20 10:57:10 - mmengine - INFO - Epoch(train) [30][250/293] lr: 5.000000e-04 eta: 4:45:50 time: 0.359661 data_time: 0.068635 memory: 9999 loss_kpt: 0.000721 acc_pose: 0.728001 loss: 0.000721 2022/10/20 10:57:25 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 10:57:25 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/10/20 10:57:35 - mmengine - INFO - Epoch(val) [30][50/407] eta: 0:00:44 time: 0.124926 data_time: 0.057264 memory: 9999 2022/10/20 10:57:41 - mmengine - INFO - Epoch(val) [30][100/407] eta: 0:00:34 time: 0.113069 data_time: 0.046051 memory: 1378 2022/10/20 10:57:47 - mmengine - INFO - Epoch(val) [30][150/407] eta: 0:00:30 time: 0.117659 data_time: 0.051060 memory: 1378 2022/10/20 10:57:53 - mmengine - INFO - Epoch(val) [30][200/407] eta: 0:00:24 time: 0.118578 data_time: 0.051644 memory: 1378 2022/10/20 10:57:59 - mmengine - INFO - Epoch(val) [30][250/407] eta: 0:00:17 time: 0.112893 data_time: 0.045289 memory: 1378 2022/10/20 10:58:05 - mmengine - INFO - Epoch(val) [30][300/407] eta: 0:00:12 time: 0.120431 data_time: 0.054162 memory: 1378 2022/10/20 10:58:10 - mmengine - INFO - Epoch(val) [30][350/407] eta: 0:00:06 time: 0.116346 data_time: 0.049194 memory: 1378 2022/10/20 10:58:16 - mmengine - INFO - Epoch(val) [30][400/407] eta: 0:00:00 time: 0.115957 data_time: 0.051512 memory: 1378 2022/10/20 10:58:51 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 10:59:05 - mmengine - INFO - Epoch(val) [30][407/407] coco/AP: 0.687689 coco/AP .5: 0.886504 coco/AP .75: 0.763347 coco/AP (M): 0.652825 coco/AP (L): 0.751136 coco/AR: 0.746678 coco/AR .5: 0.926165 coco/AR .75: 0.814704 coco/AR (M): 0.705053 coco/AR (L): 0.806615 2022/10/20 10:59:05 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221020/resnetv1d101_256/best_coco/AP_epoch_20.pth is removed 2022/10/20 10:59:07 - mmengine - INFO - The best checkpoint with 0.6877 coco/AP at 30 epoch is saved to best_coco/AP_epoch_30.pth. 2022/10/20 10:59:26 - mmengine - INFO - Epoch(train) [31][50/293] lr: 5.000000e-04 eta: 4:44:10 time: 0.371582 data_time: 0.093638 memory: 9999 loss_kpt: 0.000718 acc_pose: 0.807508 loss: 0.000718 2022/10/20 10:59:44 - mmengine - INFO - Epoch(train) [31][100/293] lr: 5.000000e-04 eta: 4:44:02 time: 0.349266 data_time: 0.065397 memory: 9999 loss_kpt: 0.000727 acc_pose: 0.824215 loss: 0.000727 2022/10/20 11:00:01 - mmengine - INFO - Epoch(train) [31][150/293] lr: 5.000000e-04 eta: 4:43:52 time: 0.345908 data_time: 0.067621 memory: 9999 loss_kpt: 0.000718 acc_pose: 0.823799 loss: 0.000718 2022/10/20 11:00:19 - mmengine - INFO - Epoch(train) [31][200/293] lr: 5.000000e-04 eta: 4:43:47 time: 0.363044 data_time: 0.072454 memory: 9999 loss_kpt: 0.000722 acc_pose: 0.775864 loss: 0.000722 2022/10/20 11:00:23 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:00:37 - mmengine - INFO - Epoch(train) [31][250/293] lr: 5.000000e-04 eta: 4:43:39 time: 0.352079 data_time: 0.070284 memory: 9999 loss_kpt: 0.000733 acc_pose: 0.768642 loss: 0.000733 2022/10/20 11:00:52 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:01:10 - mmengine - INFO - Epoch(train) [32][50/293] lr: 5.000000e-04 eta: 4:42:04 time: 0.374306 data_time: 0.084221 memory: 9999 loss_kpt: 0.000711 acc_pose: 0.809522 loss: 0.000711 2022/10/20 11:01:28 - mmengine - INFO - Epoch(train) [32][100/293] lr: 5.000000e-04 eta: 4:41:55 time: 0.347911 data_time: 0.077771 memory: 9999 loss_kpt: 0.000713 acc_pose: 0.780854 loss: 0.000713 2022/10/20 11:01:45 - mmengine - INFO - Epoch(train) [32][150/293] lr: 5.000000e-04 eta: 4:41:47 time: 0.355151 data_time: 0.098625 memory: 9999 loss_kpt: 0.000720 acc_pose: 0.820856 loss: 0.000720 2022/10/20 11:02:03 - mmengine - INFO - Epoch(train) [32][200/293] lr: 5.000000e-04 eta: 4:41:37 time: 0.342220 data_time: 0.066156 memory: 9999 loss_kpt: 0.000742 acc_pose: 0.789290 loss: 0.000742 2022/10/20 11:02:20 - mmengine - INFO - Epoch(train) [32][250/293] lr: 5.000000e-04 eta: 4:41:29 time: 0.355302 data_time: 0.070768 memory: 9999 loss_kpt: 0.000739 acc_pose: 0.773513 loss: 0.000739 2022/10/20 11:02:35 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:02:54 - mmengine - INFO - Epoch(train) [33][50/293] lr: 5.000000e-04 eta: 4:39:55 time: 0.369491 data_time: 0.081844 memory: 9999 loss_kpt: 0.000717 acc_pose: 0.786064 loss: 0.000717 2022/10/20 11:03:12 - mmengine - INFO - Epoch(train) [33][100/293] lr: 5.000000e-04 eta: 4:39:51 time: 0.364644 data_time: 0.080594 memory: 9999 loss_kpt: 0.000728 acc_pose: 0.817257 loss: 0.000728 2022/10/20 11:03:29 - mmengine - INFO - Epoch(train) [33][150/293] lr: 5.000000e-04 eta: 4:39:41 time: 0.346353 data_time: 0.062625 memory: 9999 loss_kpt: 0.000724 acc_pose: 0.786484 loss: 0.000724 2022/10/20 11:03:47 - mmengine - INFO - Epoch(train) [33][200/293] lr: 5.000000e-04 eta: 4:39:31 time: 0.346052 data_time: 0.067815 memory: 9999 loss_kpt: 0.000722 acc_pose: 0.829702 loss: 0.000722 2022/10/20 11:04:04 - mmengine - INFO - Epoch(train) [33][250/293] lr: 5.000000e-04 eta: 4:39:21 time: 0.346277 data_time: 0.070444 memory: 9999 loss_kpt: 0.000704 acc_pose: 0.727904 loss: 0.000704 2022/10/20 11:04:19 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:04:37 - mmengine - INFO - Epoch(train) [34][50/293] lr: 5.000000e-04 eta: 4:37:49 time: 0.365660 data_time: 0.084456 memory: 9999 loss_kpt: 0.000698 acc_pose: 0.817301 loss: 0.000698 2022/10/20 11:04:55 - mmengine - INFO - Epoch(train) [34][100/293] lr: 5.000000e-04 eta: 4:37:38 time: 0.340844 data_time: 0.069736 memory: 9999 loss_kpt: 0.000706 acc_pose: 0.765764 loss: 0.000706 2022/10/20 11:05:12 - mmengine - INFO - Epoch(train) [34][150/293] lr: 5.000000e-04 eta: 4:37:28 time: 0.347740 data_time: 0.068055 memory: 9999 loss_kpt: 0.000733 acc_pose: 0.809158 loss: 0.000733 2022/10/20 11:05:30 - mmengine - INFO - Epoch(train) [34][200/293] lr: 5.000000e-04 eta: 4:37:20 time: 0.353237 data_time: 0.063788 memory: 9999 loss_kpt: 0.000721 acc_pose: 0.813260 loss: 0.000721 2022/10/20 11:05:47 - mmengine - INFO - Epoch(train) [34][250/293] lr: 5.000000e-04 eta: 4:37:14 time: 0.358016 data_time: 0.068705 memory: 9999 loss_kpt: 0.000723 acc_pose: 0.779739 loss: 0.000723 2022/10/20 11:06:03 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:06:17 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:06:22 - mmengine - INFO - Epoch(train) [35][50/293] lr: 5.000000e-04 eta: 4:35:47 time: 0.380745 data_time: 0.079320 memory: 9999 loss_kpt: 0.000720 acc_pose: 0.829832 loss: 0.000720 2022/10/20 11:06:40 - mmengine - INFO - Epoch(train) [35][100/293] lr: 5.000000e-04 eta: 4:35:42 time: 0.363221 data_time: 0.062399 memory: 9999 loss_kpt: 0.000713 acc_pose: 0.852974 loss: 0.000713 2022/10/20 11:06:57 - mmengine - INFO - Epoch(train) [35][150/293] lr: 5.000000e-04 eta: 4:35:32 time: 0.346479 data_time: 0.061701 memory: 9999 loss_kpt: 0.000707 acc_pose: 0.779111 loss: 0.000707 2022/10/20 11:07:15 - mmengine - INFO - Epoch(train) [35][200/293] lr: 5.000000e-04 eta: 4:35:26 time: 0.359244 data_time: 0.093092 memory: 9999 loss_kpt: 0.000719 acc_pose: 0.739388 loss: 0.000719 2022/10/20 11:07:33 - mmengine - INFO - Epoch(train) [35][250/293] lr: 5.000000e-04 eta: 4:35:18 time: 0.352715 data_time: 0.073735 memory: 9999 loss_kpt: 0.000720 acc_pose: 0.781967 loss: 0.000720 2022/10/20 11:07:48 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:08:07 - mmengine - INFO - Epoch(train) [36][50/293] lr: 5.000000e-04 eta: 4:33:54 time: 0.382546 data_time: 0.088625 memory: 9999 loss_kpt: 0.000714 acc_pose: 0.694324 loss: 0.000714 2022/10/20 11:08:24 - mmengine - INFO - Epoch(train) [36][100/293] lr: 5.000000e-04 eta: 4:33:44 time: 0.345016 data_time: 0.069321 memory: 9999 loss_kpt: 0.000718 acc_pose: 0.808420 loss: 0.000718 2022/10/20 11:08:42 - mmengine - INFO - Epoch(train) [36][150/293] lr: 5.000000e-04 eta: 4:33:34 time: 0.345754 data_time: 0.067090 memory: 9999 loss_kpt: 0.000710 acc_pose: 0.779796 loss: 0.000710 2022/10/20 11:08:59 - mmengine - INFO - Epoch(train) [36][200/293] lr: 5.000000e-04 eta: 4:33:24 time: 0.346771 data_time: 0.068385 memory: 9999 loss_kpt: 0.000706 acc_pose: 0.806919 loss: 0.000706 2022/10/20 11:09:16 - mmengine - INFO - Epoch(train) [36][250/293] lr: 5.000000e-04 eta: 4:33:15 time: 0.347871 data_time: 0.061097 memory: 9999 loss_kpt: 0.000697 acc_pose: 0.818954 loss: 0.000697 2022/10/20 11:09:31 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:09:50 - mmengine - INFO - Epoch(train) [37][50/293] lr: 5.000000e-04 eta: 4:31:51 time: 0.373883 data_time: 0.079476 memory: 9999 loss_kpt: 0.000684 acc_pose: 0.812097 loss: 0.000684 2022/10/20 11:10:07 - mmengine - INFO - Epoch(train) [37][100/293] lr: 5.000000e-04 eta: 4:31:41 time: 0.345714 data_time: 0.072148 memory: 9999 loss_kpt: 0.000698 acc_pose: 0.801616 loss: 0.000698 2022/10/20 11:10:24 - mmengine - INFO - Epoch(train) [37][150/293] lr: 5.000000e-04 eta: 4:31:32 time: 0.348153 data_time: 0.064995 memory: 9999 loss_kpt: 0.000704 acc_pose: 0.778199 loss: 0.000704 2022/10/20 11:10:42 - mmengine - INFO - Epoch(train) [37][200/293] lr: 5.000000e-04 eta: 4:31:24 time: 0.357519 data_time: 0.073993 memory: 9999 loss_kpt: 0.000704 acc_pose: 0.826211 loss: 0.000704 2022/10/20 11:11:00 - mmengine - INFO - Epoch(train) [37][250/293] lr: 5.000000e-04 eta: 4:31:14 time: 0.347207 data_time: 0.080738 memory: 9999 loss_kpt: 0.000718 acc_pose: 0.806220 loss: 0.000718 2022/10/20 11:11:15 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:11:33 - mmengine - INFO - Epoch(train) [38][50/293] lr: 5.000000e-04 eta: 4:29:51 time: 0.364161 data_time: 0.088402 memory: 9999 loss_kpt: 0.000706 acc_pose: 0.799539 loss: 0.000706 2022/10/20 11:11:51 - mmengine - INFO - Epoch(train) [38][100/293] lr: 5.000000e-04 eta: 4:29:44 time: 0.362350 data_time: 0.072934 memory: 9999 loss_kpt: 0.000710 acc_pose: 0.832884 loss: 0.000710 2022/10/20 11:12:09 - mmengine - INFO - Epoch(train) [38][150/293] lr: 5.000000e-04 eta: 4:29:36 time: 0.352803 data_time: 0.072752 memory: 9999 loss_kpt: 0.000712 acc_pose: 0.747879 loss: 0.000712 2022/10/20 11:12:11 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:12:26 - mmengine - INFO - Epoch(train) [38][200/293] lr: 5.000000e-04 eta: 4:29:27 time: 0.350235 data_time: 0.071129 memory: 9999 loss_kpt: 0.000701 acc_pose: 0.792292 loss: 0.000701 2022/10/20 11:12:44 - mmengine - INFO - Epoch(train) [38][250/293] lr: 5.000000e-04 eta: 4:29:19 time: 0.356431 data_time: 0.071308 memory: 9999 loss_kpt: 0.000704 acc_pose: 0.755852 loss: 0.000704 2022/10/20 11:12:59 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:13:17 - mmengine - INFO - Epoch(train) [39][50/293] lr: 5.000000e-04 eta: 4:27:58 time: 0.366891 data_time: 0.089275 memory: 9999 loss_kpt: 0.000702 acc_pose: 0.751946 loss: 0.000702 2022/10/20 11:13:35 - mmengine - INFO - Epoch(train) [39][100/293] lr: 5.000000e-04 eta: 4:27:51 time: 0.360148 data_time: 0.071332 memory: 9999 loss_kpt: 0.000693 acc_pose: 0.823200 loss: 0.000693 2022/10/20 11:13:53 - mmengine - INFO - Epoch(train) [39][150/293] lr: 5.000000e-04 eta: 4:27:43 time: 0.354161 data_time: 0.080260 memory: 9999 loss_kpt: 0.000723 acc_pose: 0.775791 loss: 0.000723 2022/10/20 11:14:10 - mmengine - INFO - Epoch(train) [39][200/293] lr: 5.000000e-04 eta: 4:27:34 time: 0.352161 data_time: 0.076066 memory: 9999 loss_kpt: 0.000700 acc_pose: 0.802708 loss: 0.000700 2022/10/20 11:14:28 - mmengine - INFO - Epoch(train) [39][250/293] lr: 5.000000e-04 eta: 4:27:28 time: 0.364336 data_time: 0.065882 memory: 9999 loss_kpt: 0.000693 acc_pose: 0.830537 loss: 0.000693 2022/10/20 11:14:44 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:15:02 - mmengine - INFO - Epoch(train) [40][50/293] lr: 5.000000e-04 eta: 4:26:09 time: 0.372452 data_time: 0.084910 memory: 9999 loss_kpt: 0.000699 acc_pose: 0.797326 loss: 0.000699 2022/10/20 11:15:20 - mmengine - INFO - Epoch(train) [40][100/293] lr: 5.000000e-04 eta: 4:26:02 time: 0.359750 data_time: 0.068006 memory: 9999 loss_kpt: 0.000688 acc_pose: 0.794870 loss: 0.000688 2022/10/20 11:15:38 - mmengine - INFO - Epoch(train) [40][150/293] lr: 5.000000e-04 eta: 4:25:55 time: 0.361400 data_time: 0.067594 memory: 9999 loss_kpt: 0.000689 acc_pose: 0.826148 loss: 0.000689 2022/10/20 11:15:56 - mmengine - INFO - Epoch(train) [40][200/293] lr: 5.000000e-04 eta: 4:25:47 time: 0.355450 data_time: 0.072124 memory: 9999 loss_kpt: 0.000699 acc_pose: 0.837296 loss: 0.000699 2022/10/20 11:16:14 - mmengine - INFO - Epoch(train) [40][250/293] lr: 5.000000e-04 eta: 4:25:38 time: 0.352929 data_time: 0.068594 memory: 9999 loss_kpt: 0.000691 acc_pose: 0.777043 loss: 0.000691 2022/10/20 11:16:29 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:16:29 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/10/20 11:16:39 - mmengine - INFO - Epoch(val) [40][50/407] eta: 0:00:46 time: 0.128921 data_time: 0.060072 memory: 9999 2022/10/20 11:16:45 - mmengine - INFO - Epoch(val) [40][100/407] eta: 0:00:37 time: 0.121303 data_time: 0.054360 memory: 1378 2022/10/20 11:16:51 - mmengine - INFO - Epoch(val) [40][150/407] eta: 0:00:31 time: 0.123936 data_time: 0.057261 memory: 1378 2022/10/20 11:16:57 - mmengine - INFO - Epoch(val) [40][200/407] eta: 0:00:24 time: 0.116275 data_time: 0.049869 memory: 1378 2022/10/20 11:17:03 - mmengine - INFO - Epoch(val) [40][250/407] eta: 0:00:18 time: 0.115505 data_time: 0.048405 memory: 1378 2022/10/20 11:17:08 - mmengine - INFO - Epoch(val) [40][300/407] eta: 0:00:12 time: 0.116075 data_time: 0.049768 memory: 1378 2022/10/20 11:17:14 - mmengine - INFO - Epoch(val) [40][350/407] eta: 0:00:06 time: 0.119485 data_time: 0.051799 memory: 1378 2022/10/20 11:17:20 - mmengine - INFO - Epoch(val) [40][400/407] eta: 0:00:00 time: 0.104691 data_time: 0.042239 memory: 1378 2022/10/20 11:17:55 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 11:18:09 - mmengine - INFO - Epoch(val) [40][407/407] coco/AP: 0.697855 coco/AP .5: 0.889658 coco/AP .75: 0.774851 coco/AP (M): 0.661305 coco/AP (L): 0.762972 coco/AR: 0.755416 coco/AR .5: 0.928841 coco/AR .75: 0.824307 coco/AR (M): 0.712456 coco/AR (L): 0.817094 2022/10/20 11:18:09 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221020/resnetv1d101_256/best_coco/AP_epoch_30.pth is removed 2022/10/20 11:18:11 - mmengine - INFO - The best checkpoint with 0.6979 coco/AP at 40 epoch is saved to best_coco/AP_epoch_40.pth. 2022/10/20 11:18:29 - mmengine - INFO - Epoch(train) [41][50/293] lr: 5.000000e-04 eta: 4:24:21 time: 0.372356 data_time: 0.084721 memory: 9999 loss_kpt: 0.000700 acc_pose: 0.804852 loss: 0.000700 2022/10/20 11:18:47 - mmengine - INFO - Epoch(train) [41][100/293] lr: 5.000000e-04 eta: 4:24:12 time: 0.347146 data_time: 0.070163 memory: 9999 loss_kpt: 0.000703 acc_pose: 0.805588 loss: 0.000703 2022/10/20 11:19:05 - mmengine - INFO - Epoch(train) [41][150/293] lr: 5.000000e-04 eta: 4:24:04 time: 0.360503 data_time: 0.064540 memory: 9999 loss_kpt: 0.000699 acc_pose: 0.832825 loss: 0.000699 2022/10/20 11:19:23 - mmengine - INFO - Epoch(train) [41][200/293] lr: 5.000000e-04 eta: 4:23:59 time: 0.368682 data_time: 0.064159 memory: 9999 loss_kpt: 0.000703 acc_pose: 0.839855 loss: 0.000703 2022/10/20 11:19:41 - mmengine - INFO - Epoch(train) [41][250/293] lr: 5.000000e-04 eta: 4:23:49 time: 0.349592 data_time: 0.072401 memory: 9999 loss_kpt: 0.000714 acc_pose: 0.841088 loss: 0.000714 2022/10/20 11:19:51 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:19:56 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:20:15 - mmengine - INFO - Epoch(train) [42][50/293] lr: 5.000000e-04 eta: 4:22:36 time: 0.382739 data_time: 0.079137 memory: 9999 loss_kpt: 0.000688 acc_pose: 0.831373 loss: 0.000688 2022/10/20 11:20:33 - mmengine - INFO - Epoch(train) [42][100/293] lr: 5.000000e-04 eta: 4:22:30 time: 0.369458 data_time: 0.076678 memory: 9999 loss_kpt: 0.000704 acc_pose: 0.810388 loss: 0.000704 2022/10/20 11:20:51 - mmengine - INFO - Epoch(train) [42][150/293] lr: 5.000000e-04 eta: 4:22:21 time: 0.353101 data_time: 0.070961 memory: 9999 loss_kpt: 0.000690 acc_pose: 0.813470 loss: 0.000690 2022/10/20 11:21:08 - mmengine - INFO - Epoch(train) [42][200/293] lr: 5.000000e-04 eta: 4:22:12 time: 0.349095 data_time: 0.066348 memory: 9999 loss_kpt: 0.000699 acc_pose: 0.779181 loss: 0.000699 2022/10/20 11:21:26 - mmengine - INFO - Epoch(train) [42][250/293] lr: 5.000000e-04 eta: 4:22:03 time: 0.354987 data_time: 0.068677 memory: 9999 loss_kpt: 0.000706 acc_pose: 0.788718 loss: 0.000706 2022/10/20 11:21:41 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:21:59 - mmengine - INFO - Epoch(train) [43][50/293] lr: 5.000000e-04 eta: 4:20:47 time: 0.363593 data_time: 0.091000 memory: 9999 loss_kpt: 0.000700 acc_pose: 0.844281 loss: 0.000700 2022/10/20 11:22:17 - mmengine - INFO - Epoch(train) [43][100/293] lr: 5.000000e-04 eta: 4:20:40 time: 0.360688 data_time: 0.071516 memory: 9999 loss_kpt: 0.000700 acc_pose: 0.769397 loss: 0.000700 2022/10/20 11:22:35 - mmengine - INFO - Epoch(train) [43][150/293] lr: 5.000000e-04 eta: 4:20:32 time: 0.359306 data_time: 0.069990 memory: 9999 loss_kpt: 0.000690 acc_pose: 0.820011 loss: 0.000690 2022/10/20 11:22:53 - mmengine - INFO - Epoch(train) [43][200/293] lr: 5.000000e-04 eta: 4:20:24 time: 0.358109 data_time: 0.071538 memory: 9999 loss_kpt: 0.000669 acc_pose: 0.823339 loss: 0.000669 2022/10/20 11:23:12 - mmengine - INFO - Epoch(train) [43][250/293] lr: 5.000000e-04 eta: 4:20:19 time: 0.372851 data_time: 0.074616 memory: 9999 loss_kpt: 0.000687 acc_pose: 0.844421 loss: 0.000687 2022/10/20 11:23:27 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:23:45 - mmengine - INFO - Epoch(train) [44][50/293] lr: 5.000000e-04 eta: 4:19:04 time: 0.358952 data_time: 0.083643 memory: 9999 loss_kpt: 0.000688 acc_pose: 0.860965 loss: 0.000688 2022/10/20 11:24:03 - mmengine - INFO - Epoch(train) [44][100/293] lr: 5.000000e-04 eta: 4:18:55 time: 0.357614 data_time: 0.068101 memory: 9999 loss_kpt: 0.000700 acc_pose: 0.820563 loss: 0.000700 2022/10/20 11:24:20 - mmengine - INFO - Epoch(train) [44][150/293] lr: 5.000000e-04 eta: 4:18:46 time: 0.352124 data_time: 0.076918 memory: 9999 loss_kpt: 0.000702 acc_pose: 0.835735 loss: 0.000702 2022/10/20 11:24:38 - mmengine - INFO - Epoch(train) [44][200/293] lr: 5.000000e-04 eta: 4:18:36 time: 0.349646 data_time: 0.067046 memory: 9999 loss_kpt: 0.000684 acc_pose: 0.827695 loss: 0.000684 2022/10/20 11:24:55 - mmengine - INFO - Epoch(train) [44][250/293] lr: 5.000000e-04 eta: 4:18:26 time: 0.347528 data_time: 0.070580 memory: 9999 loss_kpt: 0.000695 acc_pose: 0.778098 loss: 0.000695 2022/10/20 11:25:10 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:25:29 - mmengine - INFO - Epoch(train) [45][50/293] lr: 5.000000e-04 eta: 4:17:13 time: 0.364329 data_time: 0.089833 memory: 9999 loss_kpt: 0.000691 acc_pose: 0.815238 loss: 0.000691 2022/10/20 11:25:46 - mmengine - INFO - Epoch(train) [45][100/293] lr: 5.000000e-04 eta: 4:17:02 time: 0.344867 data_time: 0.067761 memory: 9999 loss_kpt: 0.000682 acc_pose: 0.778084 loss: 0.000682 2022/10/20 11:25:49 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:26:04 - mmengine - INFO - Epoch(train) [45][150/293] lr: 5.000000e-04 eta: 4:16:56 time: 0.367309 data_time: 0.069789 memory: 9999 loss_kpt: 0.000691 acc_pose: 0.817941 loss: 0.000691 2022/10/20 11:26:22 - mmengine - INFO - Epoch(train) [45][200/293] lr: 5.000000e-04 eta: 4:16:45 time: 0.348810 data_time: 0.070461 memory: 9999 loss_kpt: 0.000702 acc_pose: 0.757429 loss: 0.000702 2022/10/20 11:26:40 - mmengine - INFO - Epoch(train) [45][250/293] lr: 5.000000e-04 eta: 4:16:38 time: 0.362816 data_time: 0.073235 memory: 9999 loss_kpt: 0.000686 acc_pose: 0.796458 loss: 0.000686 2022/10/20 11:26:55 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:27:13 - mmengine - INFO - Epoch(train) [46][50/293] lr: 5.000000e-04 eta: 4:15:26 time: 0.360157 data_time: 0.100614 memory: 9999 loss_kpt: 0.000678 acc_pose: 0.860155 loss: 0.000678 2022/10/20 11:27:31 - mmengine - INFO - Epoch(train) [46][100/293] lr: 5.000000e-04 eta: 4:15:18 time: 0.362419 data_time: 0.069080 memory: 9999 loss_kpt: 0.000682 acc_pose: 0.820496 loss: 0.000682 2022/10/20 11:27:49 - mmengine - INFO - Epoch(train) [46][150/293] lr: 5.000000e-04 eta: 4:15:09 time: 0.355631 data_time: 0.068408 memory: 9999 loss_kpt: 0.000696 acc_pose: 0.796831 loss: 0.000696 2022/10/20 11:28:06 - mmengine - INFO - Epoch(train) [46][200/293] lr: 5.000000e-04 eta: 4:14:59 time: 0.346655 data_time: 0.065703 memory: 9999 loss_kpt: 0.000699 acc_pose: 0.809636 loss: 0.000699 2022/10/20 11:28:23 - mmengine - INFO - Epoch(train) [46][250/293] lr: 5.000000e-04 eta: 4:14:48 time: 0.344853 data_time: 0.069146 memory: 9999 loss_kpt: 0.000685 acc_pose: 0.824596 loss: 0.000685 2022/10/20 11:28:38 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:28:57 - mmengine - INFO - Epoch(train) [47][50/293] lr: 5.000000e-04 eta: 4:13:38 time: 0.366529 data_time: 0.081255 memory: 9999 loss_kpt: 0.000677 acc_pose: 0.821610 loss: 0.000677 2022/10/20 11:29:14 - mmengine - INFO - Epoch(train) [47][100/293] lr: 5.000000e-04 eta: 4:13:29 time: 0.353466 data_time: 0.064301 memory: 9999 loss_kpt: 0.000679 acc_pose: 0.787843 loss: 0.000679 2022/10/20 11:29:32 - mmengine - INFO - Epoch(train) [47][150/293] lr: 5.000000e-04 eta: 4:13:20 time: 0.357090 data_time: 0.061593 memory: 9999 loss_kpt: 0.000682 acc_pose: 0.793269 loss: 0.000682 2022/10/20 11:29:50 - mmengine - INFO - Epoch(train) [47][200/293] lr: 5.000000e-04 eta: 4:13:10 time: 0.353850 data_time: 0.071008 memory: 9999 loss_kpt: 0.000695 acc_pose: 0.806089 loss: 0.000695 2022/10/20 11:30:07 - mmengine - INFO - Epoch(train) [47][250/293] lr: 5.000000e-04 eta: 4:13:00 time: 0.351935 data_time: 0.078309 memory: 9999 loss_kpt: 0.000691 acc_pose: 0.765699 loss: 0.000691 2022/10/20 11:30:22 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:30:42 - mmengine - INFO - Epoch(train) [48][50/293] lr: 5.000000e-04 eta: 4:11:57 time: 0.391806 data_time: 0.088623 memory: 9999 loss_kpt: 0.000697 acc_pose: 0.745962 loss: 0.000697 2022/10/20 11:31:00 - mmengine - INFO - Epoch(train) [48][100/293] lr: 5.000000e-04 eta: 4:11:47 time: 0.353616 data_time: 0.077476 memory: 9999 loss_kpt: 0.000669 acc_pose: 0.817395 loss: 0.000669 2022/10/20 11:31:18 - mmengine - INFO - Epoch(train) [48][150/293] lr: 5.000000e-04 eta: 4:11:40 time: 0.370762 data_time: 0.073456 memory: 9999 loss_kpt: 0.000675 acc_pose: 0.797094 loss: 0.000675 2022/10/20 11:31:35 - mmengine - INFO - Epoch(train) [48][200/293] lr: 5.000000e-04 eta: 4:11:30 time: 0.346255 data_time: 0.069089 memory: 9999 loss_kpt: 0.000656 acc_pose: 0.781021 loss: 0.000656 2022/10/20 11:31:45 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:31:53 - mmengine - INFO - Epoch(train) [48][250/293] lr: 5.000000e-04 eta: 4:11:19 time: 0.348878 data_time: 0.065171 memory: 9999 loss_kpt: 0.000688 acc_pose: 0.835544 loss: 0.000688 2022/10/20 11:32:08 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:32:26 - mmengine - INFO - Epoch(train) [49][50/293] lr: 5.000000e-04 eta: 4:10:12 time: 0.367650 data_time: 0.088399 memory: 9999 loss_kpt: 0.000678 acc_pose: 0.843768 loss: 0.000678 2022/10/20 11:32:44 - mmengine - INFO - Epoch(train) [49][100/293] lr: 5.000000e-04 eta: 4:10:03 time: 0.359373 data_time: 0.070374 memory: 9999 loss_kpt: 0.000685 acc_pose: 0.798413 loss: 0.000685 2022/10/20 11:33:02 - mmengine - INFO - Epoch(train) [49][150/293] lr: 5.000000e-04 eta: 4:09:54 time: 0.356268 data_time: 0.068548 memory: 9999 loss_kpt: 0.000680 acc_pose: 0.808490 loss: 0.000680 2022/10/20 11:33:19 - mmengine - INFO - Epoch(train) [49][200/293] lr: 5.000000e-04 eta: 4:09:43 time: 0.343872 data_time: 0.068160 memory: 9999 loss_kpt: 0.000668 acc_pose: 0.809704 loss: 0.000668 2022/10/20 11:33:37 - mmengine - INFO - Epoch(train) [49][250/293] lr: 5.000000e-04 eta: 4:09:33 time: 0.353772 data_time: 0.070665 memory: 9999 loss_kpt: 0.000679 acc_pose: 0.808061 loss: 0.000679 2022/10/20 11:33:52 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:34:11 - mmengine - INFO - Epoch(train) [50][50/293] lr: 5.000000e-04 eta: 4:08:27 time: 0.366535 data_time: 0.093014 memory: 9999 loss_kpt: 0.000679 acc_pose: 0.842027 loss: 0.000679 2022/10/20 11:34:29 - mmengine - INFO - Epoch(train) [50][100/293] lr: 5.000000e-04 eta: 4:08:18 time: 0.358357 data_time: 0.071140 memory: 9999 loss_kpt: 0.000669 acc_pose: 0.788547 loss: 0.000669 2022/10/20 11:34:46 - mmengine - INFO - Epoch(train) [50][150/293] lr: 5.000000e-04 eta: 4:08:07 time: 0.348983 data_time: 0.071441 memory: 9999 loss_kpt: 0.000676 acc_pose: 0.821706 loss: 0.000676 2022/10/20 11:35:04 - mmengine - INFO - Epoch(train) [50][200/293] lr: 5.000000e-04 eta: 4:07:59 time: 0.365059 data_time: 0.069065 memory: 9999 loss_kpt: 0.000673 acc_pose: 0.827744 loss: 0.000673 2022/10/20 11:35:22 - mmengine - INFO - Epoch(train) [50][250/293] lr: 5.000000e-04 eta: 4:07:49 time: 0.352806 data_time: 0.068046 memory: 9999 loss_kpt: 0.000662 acc_pose: 0.809396 loss: 0.000662 2022/10/20 11:35:37 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:35:37 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/10/20 11:35:47 - mmengine - INFO - Epoch(val) [50][50/407] eta: 0:00:44 time: 0.123813 data_time: 0.055777 memory: 9999 2022/10/20 11:35:53 - mmengine - INFO - Epoch(val) [50][100/407] eta: 0:00:35 time: 0.114545 data_time: 0.048889 memory: 1378 2022/10/20 11:35:59 - mmengine - INFO - Epoch(val) [50][150/407] eta: 0:00:30 time: 0.119310 data_time: 0.051215 memory: 1378 2022/10/20 11:36:05 - mmengine - INFO - Epoch(val) [50][200/407] eta: 0:00:25 time: 0.121113 data_time: 0.054962 memory: 1378 2022/10/20 11:36:11 - mmengine - INFO - Epoch(val) [50][250/407] eta: 0:00:19 time: 0.121219 data_time: 0.054630 memory: 1378 2022/10/20 11:36:16 - mmengine - INFO - Epoch(val) [50][300/407] eta: 0:00:11 time: 0.108553 data_time: 0.040922 memory: 1378 2022/10/20 11:36:22 - mmengine - INFO - Epoch(val) [50][350/407] eta: 0:00:06 time: 0.119946 data_time: 0.053360 memory: 1378 2022/10/20 11:36:28 - mmengine - INFO - Epoch(val) [50][400/407] eta: 0:00:00 time: 0.107179 data_time: 0.043415 memory: 1378 2022/10/20 11:37:03 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 11:37:16 - mmengine - INFO - Epoch(val) [50][407/407] coco/AP: 0.702743 coco/AP .5: 0.890326 coco/AP .75: 0.782510 coco/AP (M): 0.666030 coco/AP (L): 0.768136 coco/AR: 0.758832 coco/AR .5: 0.929628 coco/AR .75: 0.830447 coco/AR (M): 0.716389 coco/AR (L): 0.820104 2022/10/20 11:37:16 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221020/resnetv1d101_256/best_coco/AP_epoch_40.pth is removed 2022/10/20 11:37:18 - mmengine - INFO - The best checkpoint with 0.7027 coco/AP at 50 epoch is saved to best_coco/AP_epoch_50.pth. 2022/10/20 11:37:36 - mmengine - INFO - Epoch(train) [51][50/293] lr: 5.000000e-04 eta: 4:06:42 time: 0.352220 data_time: 0.078330 memory: 9999 loss_kpt: 0.000658 acc_pose: 0.810983 loss: 0.000658 2022/10/20 11:37:53 - mmengine - INFO - Epoch(train) [51][100/293] lr: 5.000000e-04 eta: 4:06:32 time: 0.351090 data_time: 0.066938 memory: 9999 loss_kpt: 0.000684 acc_pose: 0.838740 loss: 0.000684 2022/10/20 11:38:11 - mmengine - INFO - Epoch(train) [51][150/293] lr: 5.000000e-04 eta: 4:06:22 time: 0.354800 data_time: 0.079515 memory: 9999 loss_kpt: 0.000684 acc_pose: 0.818934 loss: 0.000684 2022/10/20 11:38:29 - mmengine - INFO - Epoch(train) [51][200/293] lr: 5.000000e-04 eta: 4:06:13 time: 0.357117 data_time: 0.069834 memory: 9999 loss_kpt: 0.000665 acc_pose: 0.833177 loss: 0.000665 2022/10/20 11:38:47 - mmengine - INFO - Epoch(train) [51][250/293] lr: 5.000000e-04 eta: 4:06:02 time: 0.352134 data_time: 0.071558 memory: 9999 loss_kpt: 0.000668 acc_pose: 0.798853 loss: 0.000668 2022/10/20 11:39:02 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:39:20 - mmengine - INFO - Epoch(train) [52][50/293] lr: 5.000000e-04 eta: 4:04:59 time: 0.372496 data_time: 0.084720 memory: 9999 loss_kpt: 0.000685 acc_pose: 0.744059 loss: 0.000685 2022/10/20 11:39:23 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:39:38 - mmengine - INFO - Epoch(train) [52][100/293] lr: 5.000000e-04 eta: 4:04:50 time: 0.358071 data_time: 0.066170 memory: 9999 loss_kpt: 0.000678 acc_pose: 0.810993 loss: 0.000678 2022/10/20 11:39:57 - mmengine - INFO - Epoch(train) [52][150/293] lr: 5.000000e-04 eta: 4:04:42 time: 0.366289 data_time: 0.064650 memory: 9999 loss_kpt: 0.000675 acc_pose: 0.857019 loss: 0.000675 2022/10/20 11:40:14 - mmengine - INFO - Epoch(train) [52][200/293] lr: 5.000000e-04 eta: 4:04:31 time: 0.345760 data_time: 0.066978 memory: 9999 loss_kpt: 0.000668 acc_pose: 0.792648 loss: 0.000668 2022/10/20 11:40:32 - mmengine - INFO - Epoch(train) [52][250/293] lr: 5.000000e-04 eta: 4:04:22 time: 0.363127 data_time: 0.070102 memory: 9999 loss_kpt: 0.000659 acc_pose: 0.805763 loss: 0.000659 2022/10/20 11:40:47 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:41:05 - mmengine - INFO - Epoch(train) [53][50/293] lr: 5.000000e-04 eta: 4:03:18 time: 0.358039 data_time: 0.081714 memory: 9999 loss_kpt: 0.000676 acc_pose: 0.851288 loss: 0.000676 2022/10/20 11:41:23 - mmengine - INFO - Epoch(train) [53][100/293] lr: 5.000000e-04 eta: 4:03:10 time: 0.366243 data_time: 0.073038 memory: 9999 loss_kpt: 0.000667 acc_pose: 0.775324 loss: 0.000667 2022/10/20 11:41:41 - mmengine - INFO - Epoch(train) [53][150/293] lr: 5.000000e-04 eta: 4:02:58 time: 0.345613 data_time: 0.072409 memory: 9999 loss_kpt: 0.000667 acc_pose: 0.793188 loss: 0.000667 2022/10/20 11:41:59 - mmengine - INFO - Epoch(train) [53][200/293] lr: 5.000000e-04 eta: 4:02:49 time: 0.361329 data_time: 0.068957 memory: 9999 loss_kpt: 0.000674 acc_pose: 0.834235 loss: 0.000674 2022/10/20 11:42:16 - mmengine - INFO - Epoch(train) [53][250/293] lr: 5.000000e-04 eta: 4:02:39 time: 0.352933 data_time: 0.065785 memory: 9999 loss_kpt: 0.000666 acc_pose: 0.823860 loss: 0.000666 2022/10/20 11:42:31 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:42:51 - mmengine - INFO - Epoch(train) [54][50/293] lr: 5.000000e-04 eta: 4:01:40 time: 0.385636 data_time: 0.085542 memory: 9999 loss_kpt: 0.000673 acc_pose: 0.848784 loss: 0.000673 2022/10/20 11:43:08 - mmengine - INFO - Epoch(train) [54][100/293] lr: 5.000000e-04 eta: 4:01:28 time: 0.343014 data_time: 0.070082 memory: 9999 loss_kpt: 0.000685 acc_pose: 0.838323 loss: 0.000685 2022/10/20 11:43:25 - mmengine - INFO - Epoch(train) [54][150/293] lr: 5.000000e-04 eta: 4:01:17 time: 0.345784 data_time: 0.061287 memory: 9999 loss_kpt: 0.000689 acc_pose: 0.791160 loss: 0.000689 2022/10/20 11:43:43 - mmengine - INFO - Epoch(train) [54][200/293] lr: 5.000000e-04 eta: 4:01:08 time: 0.367252 data_time: 0.075689 memory: 9999 loss_kpt: 0.000680 acc_pose: 0.777856 loss: 0.000680 2022/10/20 11:44:01 - mmengine - INFO - Epoch(train) [54][250/293] lr: 5.000000e-04 eta: 4:00:58 time: 0.351805 data_time: 0.066573 memory: 9999 loss_kpt: 0.000673 acc_pose: 0.807767 loss: 0.000673 2022/10/20 11:44:16 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:44:35 - mmengine - INFO - Epoch(train) [55][50/293] lr: 5.000000e-04 eta: 3:59:56 time: 0.364559 data_time: 0.086513 memory: 9999 loss_kpt: 0.000664 acc_pose: 0.841195 loss: 0.000664 2022/10/20 11:44:52 - mmengine - INFO - Epoch(train) [55][100/293] lr: 5.000000e-04 eta: 3:59:45 time: 0.344815 data_time: 0.065511 memory: 9999 loss_kpt: 0.000679 acc_pose: 0.821703 loss: 0.000679 2022/10/20 11:45:10 - mmengine - INFO - Epoch(train) [55][150/293] lr: 5.000000e-04 eta: 3:59:35 time: 0.359337 data_time: 0.060931 memory: 9999 loss_kpt: 0.000668 acc_pose: 0.861969 loss: 0.000668 2022/10/20 11:45:19 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:45:27 - mmengine - INFO - Epoch(train) [55][200/293] lr: 5.000000e-04 eta: 3:59:23 time: 0.340059 data_time: 0.065598 memory: 9999 loss_kpt: 0.000672 acc_pose: 0.804710 loss: 0.000672 2022/10/20 11:45:44 - mmengine - INFO - Epoch(train) [55][250/293] lr: 5.000000e-04 eta: 3:59:12 time: 0.350879 data_time: 0.064830 memory: 9999 loss_kpt: 0.000662 acc_pose: 0.832171 loss: 0.000662 2022/10/20 11:46:00 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:46:18 - mmengine - INFO - Epoch(train) [56][50/293] lr: 5.000000e-04 eta: 3:58:13 time: 0.372678 data_time: 0.084345 memory: 9999 loss_kpt: 0.000658 acc_pose: 0.780792 loss: 0.000658 2022/10/20 11:46:36 - mmengine - INFO - Epoch(train) [56][100/293] lr: 5.000000e-04 eta: 3:58:03 time: 0.358616 data_time: 0.071746 memory: 9999 loss_kpt: 0.000666 acc_pose: 0.831950 loss: 0.000666 2022/10/20 11:46:53 - mmengine - INFO - Epoch(train) [56][150/293] lr: 5.000000e-04 eta: 3:57:50 time: 0.337519 data_time: 0.072137 memory: 9999 loss_kpt: 0.000665 acc_pose: 0.808315 loss: 0.000665 2022/10/20 11:47:11 - mmengine - INFO - Epoch(train) [56][200/293] lr: 5.000000e-04 eta: 3:57:40 time: 0.351963 data_time: 0.071439 memory: 9999 loss_kpt: 0.000678 acc_pose: 0.784587 loss: 0.000678 2022/10/20 11:47:28 - mmengine - INFO - Epoch(train) [56][250/293] lr: 5.000000e-04 eta: 3:57:28 time: 0.345299 data_time: 0.069155 memory: 9999 loss_kpt: 0.000667 acc_pose: 0.809489 loss: 0.000667 2022/10/20 11:47:42 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:48:01 - mmengine - INFO - Epoch(train) [57][50/293] lr: 5.000000e-04 eta: 3:56:30 time: 0.374048 data_time: 0.080269 memory: 9999 loss_kpt: 0.000672 acc_pose: 0.805203 loss: 0.000672 2022/10/20 11:48:19 - mmengine - INFO - Epoch(train) [57][100/293] lr: 5.000000e-04 eta: 3:56:18 time: 0.348930 data_time: 0.070289 memory: 9999 loss_kpt: 0.000669 acc_pose: 0.873335 loss: 0.000669 2022/10/20 11:48:36 - mmengine - INFO - Epoch(train) [57][150/293] lr: 5.000000e-04 eta: 3:56:07 time: 0.349860 data_time: 0.067679 memory: 9999 loss_kpt: 0.000662 acc_pose: 0.839382 loss: 0.000662 2022/10/20 11:48:54 - mmengine - INFO - Epoch(train) [57][200/293] lr: 5.000000e-04 eta: 3:55:57 time: 0.351696 data_time: 0.071324 memory: 9999 loss_kpt: 0.000674 acc_pose: 0.814358 loss: 0.000674 2022/10/20 11:49:12 - mmengine - INFO - Epoch(train) [57][250/293] lr: 5.000000e-04 eta: 3:55:47 time: 0.360005 data_time: 0.068051 memory: 9999 loss_kpt: 0.000660 acc_pose: 0.786741 loss: 0.000660 2022/10/20 11:49:26 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:49:45 - mmengine - INFO - Epoch(train) [58][50/293] lr: 5.000000e-04 eta: 3:54:48 time: 0.367243 data_time: 0.083843 memory: 9999 loss_kpt: 0.000668 acc_pose: 0.832175 loss: 0.000668 2022/10/20 11:50:03 - mmengine - INFO - Epoch(train) [58][100/293] lr: 5.000000e-04 eta: 3:54:39 time: 0.366357 data_time: 0.064264 memory: 9999 loss_kpt: 0.000664 acc_pose: 0.822621 loss: 0.000664 2022/10/20 11:50:21 - mmengine - INFO - Epoch(train) [58][150/293] lr: 5.000000e-04 eta: 3:54:30 time: 0.363480 data_time: 0.063180 memory: 9999 loss_kpt: 0.000669 acc_pose: 0.842455 loss: 0.000669 2022/10/20 11:50:39 - mmengine - INFO - Epoch(train) [58][200/293] lr: 5.000000e-04 eta: 3:54:19 time: 0.350695 data_time: 0.071702 memory: 9999 loss_kpt: 0.000664 acc_pose: 0.852049 loss: 0.000664 2022/10/20 11:50:57 - mmengine - INFO - Epoch(train) [58][250/293] lr: 5.000000e-04 eta: 3:54:10 time: 0.366433 data_time: 0.071936 memory: 9999 loss_kpt: 0.000663 acc_pose: 0.783086 loss: 0.000663 2022/10/20 11:51:12 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:51:15 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:51:31 - mmengine - INFO - Epoch(train) [59][50/293] lr: 5.000000e-04 eta: 3:53:14 time: 0.378584 data_time: 0.090439 memory: 9999 loss_kpt: 0.000655 acc_pose: 0.812282 loss: 0.000655 2022/10/20 11:51:49 - mmengine - INFO - Epoch(train) [59][100/293] lr: 5.000000e-04 eta: 3:53:03 time: 0.353552 data_time: 0.065835 memory: 9999 loss_kpt: 0.000645 acc_pose: 0.848596 loss: 0.000645 2022/10/20 11:52:06 - mmengine - INFO - Epoch(train) [59][150/293] lr: 5.000000e-04 eta: 3:52:53 time: 0.356166 data_time: 0.065682 memory: 9999 loss_kpt: 0.000664 acc_pose: 0.822257 loss: 0.000664 2022/10/20 11:52:24 - mmengine - INFO - Epoch(train) [59][200/293] lr: 5.000000e-04 eta: 3:52:42 time: 0.353178 data_time: 0.071093 memory: 9999 loss_kpt: 0.000675 acc_pose: 0.824832 loss: 0.000675 2022/10/20 11:52:41 - mmengine - INFO - Epoch(train) [59][250/293] lr: 5.000000e-04 eta: 3:52:30 time: 0.344020 data_time: 0.069014 memory: 9999 loss_kpt: 0.000658 acc_pose: 0.810614 loss: 0.000658 2022/10/20 11:52:56 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:53:15 - mmengine - INFO - Epoch(train) [60][50/293] lr: 5.000000e-04 eta: 3:51:34 time: 0.379444 data_time: 0.080727 memory: 9999 loss_kpt: 0.000648 acc_pose: 0.812058 loss: 0.000648 2022/10/20 11:53:33 - mmengine - INFO - Epoch(train) [60][100/293] lr: 5.000000e-04 eta: 3:51:24 time: 0.357672 data_time: 0.078290 memory: 9999 loss_kpt: 0.000659 acc_pose: 0.799370 loss: 0.000659 2022/10/20 11:53:51 - mmengine - INFO - Epoch(train) [60][150/293] lr: 5.000000e-04 eta: 3:51:14 time: 0.359429 data_time: 0.083802 memory: 9999 loss_kpt: 0.000669 acc_pose: 0.813715 loss: 0.000669 2022/10/20 11:54:09 - mmengine - INFO - Epoch(train) [60][200/293] lr: 5.000000e-04 eta: 3:51:04 time: 0.359901 data_time: 0.084595 memory: 9999 loss_kpt: 0.000652 acc_pose: 0.791917 loss: 0.000652 2022/10/20 11:54:27 - mmengine - INFO - Epoch(train) [60][250/293] lr: 5.000000e-04 eta: 3:50:53 time: 0.352572 data_time: 0.070971 memory: 9999 loss_kpt: 0.000673 acc_pose: 0.818827 loss: 0.000673 2022/10/20 11:54:41 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:54:41 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/10/20 11:54:51 - mmengine - INFO - Epoch(val) [60][50/407] eta: 0:00:46 time: 0.129621 data_time: 0.061810 memory: 9999 2022/10/20 11:54:57 - mmengine - INFO - Epoch(val) [60][100/407] eta: 0:00:34 time: 0.113462 data_time: 0.047718 memory: 1378 2022/10/20 11:55:03 - mmengine - INFO - Epoch(val) [60][150/407] eta: 0:00:30 time: 0.117544 data_time: 0.046744 memory: 1378 2022/10/20 11:55:09 - mmengine - INFO - Epoch(val) [60][200/407] eta: 0:00:24 time: 0.118189 data_time: 0.051373 memory: 1378 2022/10/20 11:55:15 - mmengine - INFO - Epoch(val) [60][250/407] eta: 0:00:17 time: 0.114410 data_time: 0.049216 memory: 1378 2022/10/20 11:55:21 - mmengine - INFO - Epoch(val) [60][300/407] eta: 0:00:13 time: 0.123538 data_time: 0.056857 memory: 1378 2022/10/20 11:55:26 - mmengine - INFO - Epoch(val) [60][350/407] eta: 0:00:06 time: 0.110930 data_time: 0.044363 memory: 1378 2022/10/20 11:55:32 - mmengine - INFO - Epoch(val) [60][400/407] eta: 0:00:00 time: 0.107223 data_time: 0.044175 memory: 1378 2022/10/20 11:56:07 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 11:56:20 - mmengine - INFO - Epoch(val) [60][407/407] coco/AP: 0.708879 coco/AP .5: 0.895037 coco/AP .75: 0.785610 coco/AP (M): 0.670393 coco/AP (L): 0.776106 coco/AR: 0.764310 coco/AR .5: 0.931518 coco/AR .75: 0.833596 coco/AR (M): 0.719639 coco/AR (L): 0.827982 2022/10/20 11:56:20 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221020/resnetv1d101_256/best_coco/AP_epoch_50.pth is removed 2022/10/20 11:56:23 - mmengine - INFO - The best checkpoint with 0.7089 coco/AP at 60 epoch is saved to best_coco/AP_epoch_60.pth. 2022/10/20 11:56:41 - mmengine - INFO - Epoch(train) [61][50/293] lr: 5.000000e-04 eta: 3:49:56 time: 0.366009 data_time: 0.074567 memory: 9999 loss_kpt: 0.000665 acc_pose: 0.813275 loss: 0.000665 2022/10/20 11:56:59 - mmengine - INFO - Epoch(train) [61][100/293] lr: 5.000000e-04 eta: 3:49:46 time: 0.355794 data_time: 0.075311 memory: 9999 loss_kpt: 0.000665 acc_pose: 0.824993 loss: 0.000665 2022/10/20 11:57:17 - mmengine - INFO - Epoch(train) [61][150/293] lr: 5.000000e-04 eta: 3:49:35 time: 0.356023 data_time: 0.070283 memory: 9999 loss_kpt: 0.000656 acc_pose: 0.845885 loss: 0.000656 2022/10/20 11:57:34 - mmengine - INFO - Epoch(train) [61][200/293] lr: 5.000000e-04 eta: 3:49:24 time: 0.352099 data_time: 0.068169 memory: 9999 loss_kpt: 0.000666 acc_pose: 0.811971 loss: 0.000666 2022/10/20 11:57:52 - mmengine - INFO - Epoch(train) [61][250/293] lr: 5.000000e-04 eta: 3:49:13 time: 0.353770 data_time: 0.075920 memory: 9999 loss_kpt: 0.000655 acc_pose: 0.820559 loss: 0.000655 2022/10/20 11:58:07 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:58:26 - mmengine - INFO - Epoch(train) [62][50/293] lr: 5.000000e-04 eta: 3:48:18 time: 0.370815 data_time: 0.088014 memory: 9999 loss_kpt: 0.000651 acc_pose: 0.817135 loss: 0.000651 2022/10/20 11:58:43 - mmengine - INFO - Epoch(train) [62][100/293] lr: 5.000000e-04 eta: 3:48:07 time: 0.354307 data_time: 0.067435 memory: 9999 loss_kpt: 0.000651 acc_pose: 0.779601 loss: 0.000651 2022/10/20 11:58:53 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 11:59:01 - mmengine - INFO - Epoch(train) [62][150/293] lr: 5.000000e-04 eta: 3:47:57 time: 0.363931 data_time: 0.066806 memory: 9999 loss_kpt: 0.000658 acc_pose: 0.746164 loss: 0.000658 2022/10/20 11:59:19 - mmengine - INFO - Epoch(train) [62][200/293] lr: 5.000000e-04 eta: 3:47:46 time: 0.349597 data_time: 0.068322 memory: 9999 loss_kpt: 0.000647 acc_pose: 0.788932 loss: 0.000647 2022/10/20 11:59:37 - mmengine - INFO - Epoch(train) [62][250/293] lr: 5.000000e-04 eta: 3:47:35 time: 0.360288 data_time: 0.079382 memory: 9999 loss_kpt: 0.000648 acc_pose: 0.854582 loss: 0.000648 2022/10/20 11:59:52 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:00:11 - mmengine - INFO - Epoch(train) [63][50/293] lr: 5.000000e-04 eta: 3:46:42 time: 0.378899 data_time: 0.093133 memory: 9999 loss_kpt: 0.000652 acc_pose: 0.871593 loss: 0.000652 2022/10/20 12:00:29 - mmengine - INFO - Epoch(train) [63][100/293] lr: 5.000000e-04 eta: 3:46:31 time: 0.352085 data_time: 0.068820 memory: 9999 loss_kpt: 0.000658 acc_pose: 0.825926 loss: 0.000658 2022/10/20 12:00:46 - mmengine - INFO - Epoch(train) [63][150/293] lr: 5.000000e-04 eta: 3:46:18 time: 0.338891 data_time: 0.065458 memory: 9999 loss_kpt: 0.000673 acc_pose: 0.758130 loss: 0.000673 2022/10/20 12:01:04 - mmengine - INFO - Epoch(train) [63][200/293] lr: 5.000000e-04 eta: 3:46:07 time: 0.357771 data_time: 0.071075 memory: 9999 loss_kpt: 0.000654 acc_pose: 0.802378 loss: 0.000654 2022/10/20 12:01:21 - mmengine - INFO - Epoch(train) [63][250/293] lr: 5.000000e-04 eta: 3:45:56 time: 0.355579 data_time: 0.068306 memory: 9999 loss_kpt: 0.000645 acc_pose: 0.822426 loss: 0.000645 2022/10/20 12:01:36 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:01:54 - mmengine - INFO - Epoch(train) [64][50/293] lr: 5.000000e-04 eta: 3:45:02 time: 0.370403 data_time: 0.084335 memory: 9999 loss_kpt: 0.000654 acc_pose: 0.821086 loss: 0.000654 2022/10/20 12:02:12 - mmengine - INFO - Epoch(train) [64][100/293] lr: 5.000000e-04 eta: 3:44:50 time: 0.347067 data_time: 0.069022 memory: 9999 loss_kpt: 0.000663 acc_pose: 0.866818 loss: 0.000663 2022/10/20 12:02:29 - mmengine - INFO - Epoch(train) [64][150/293] lr: 5.000000e-04 eta: 3:44:39 time: 0.353385 data_time: 0.080070 memory: 9999 loss_kpt: 0.000656 acc_pose: 0.818075 loss: 0.000656 2022/10/20 12:02:47 - mmengine - INFO - Epoch(train) [64][200/293] lr: 5.000000e-04 eta: 3:44:29 time: 0.358833 data_time: 0.100501 memory: 9999 loss_kpt: 0.000662 acc_pose: 0.847914 loss: 0.000662 2022/10/20 12:03:05 - mmengine - INFO - Epoch(train) [64][250/293] lr: 5.000000e-04 eta: 3:44:17 time: 0.354115 data_time: 0.064588 memory: 9999 loss_kpt: 0.000663 acc_pose: 0.844690 loss: 0.000663 2022/10/20 12:03:20 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:03:39 - mmengine - INFO - Epoch(train) [65][50/293] lr: 5.000000e-04 eta: 3:43:23 time: 0.366224 data_time: 0.083284 memory: 9999 loss_kpt: 0.000645 acc_pose: 0.810204 loss: 0.000645 2022/10/20 12:03:57 - mmengine - INFO - Epoch(train) [65][100/293] lr: 5.000000e-04 eta: 3:43:14 time: 0.367534 data_time: 0.069932 memory: 9999 loss_kpt: 0.000657 acc_pose: 0.803428 loss: 0.000657 2022/10/20 12:04:15 - mmengine - INFO - Epoch(train) [65][150/293] lr: 5.000000e-04 eta: 3:43:04 time: 0.364882 data_time: 0.074891 memory: 9999 loss_kpt: 0.000645 acc_pose: 0.803545 loss: 0.000645 2022/10/20 12:04:33 - mmengine - INFO - Epoch(train) [65][200/293] lr: 5.000000e-04 eta: 3:42:53 time: 0.357738 data_time: 0.075012 memory: 9999 loss_kpt: 0.000666 acc_pose: 0.857457 loss: 0.000666 2022/10/20 12:04:50 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:04:51 - mmengine - INFO - Epoch(train) [65][250/293] lr: 5.000000e-04 eta: 3:42:42 time: 0.351811 data_time: 0.080293 memory: 9999 loss_kpt: 0.000640 acc_pose: 0.829830 loss: 0.000640 2022/10/20 12:05:06 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:05:25 - mmengine - INFO - Epoch(train) [66][50/293] lr: 5.000000e-04 eta: 3:41:49 time: 0.371849 data_time: 0.081838 memory: 9999 loss_kpt: 0.000645 acc_pose: 0.776925 loss: 0.000645 2022/10/20 12:05:43 - mmengine - INFO - Epoch(train) [66][100/293] lr: 5.000000e-04 eta: 3:41:38 time: 0.356358 data_time: 0.071875 memory: 9999 loss_kpt: 0.000648 acc_pose: 0.844599 loss: 0.000648 2022/10/20 12:06:00 - mmengine - INFO - Epoch(train) [66][150/293] lr: 5.000000e-04 eta: 3:41:27 time: 0.354782 data_time: 0.074834 memory: 9999 loss_kpt: 0.000643 acc_pose: 0.766388 loss: 0.000643 2022/10/20 12:06:18 - mmengine - INFO - Epoch(train) [66][200/293] lr: 5.000000e-04 eta: 3:41:16 time: 0.357612 data_time: 0.072171 memory: 9999 loss_kpt: 0.000650 acc_pose: 0.821960 loss: 0.000650 2022/10/20 12:06:35 - mmengine - INFO - Epoch(train) [66][250/293] lr: 5.000000e-04 eta: 3:41:02 time: 0.334195 data_time: 0.062366 memory: 9999 loss_kpt: 0.000649 acc_pose: 0.845708 loss: 0.000649 2022/10/20 12:06:50 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:07:08 - mmengine - INFO - Epoch(train) [67][50/293] lr: 5.000000e-04 eta: 3:40:10 time: 0.371224 data_time: 0.091561 memory: 9999 loss_kpt: 0.000646 acc_pose: 0.830864 loss: 0.000646 2022/10/20 12:07:26 - mmengine - INFO - Epoch(train) [67][100/293] lr: 5.000000e-04 eta: 3:39:59 time: 0.355140 data_time: 0.069563 memory: 9999 loss_kpt: 0.000650 acc_pose: 0.836161 loss: 0.000650 2022/10/20 12:07:44 - mmengine - INFO - Epoch(train) [67][150/293] lr: 5.000000e-04 eta: 3:39:48 time: 0.360686 data_time: 0.071256 memory: 9999 loss_kpt: 0.000660 acc_pose: 0.766569 loss: 0.000660 2022/10/20 12:08:02 - mmengine - INFO - Epoch(train) [67][200/293] lr: 5.000000e-04 eta: 3:39:36 time: 0.348386 data_time: 0.067109 memory: 9999 loss_kpt: 0.000623 acc_pose: 0.847497 loss: 0.000623 2022/10/20 12:08:19 - mmengine - INFO - Epoch(train) [67][250/293] lr: 5.000000e-04 eta: 3:39:24 time: 0.347482 data_time: 0.080388 memory: 9999 loss_kpt: 0.000649 acc_pose: 0.851619 loss: 0.000649 2022/10/20 12:08:34 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:08:53 - mmengine - INFO - Epoch(train) [68][50/293] lr: 5.000000e-04 eta: 3:38:33 time: 0.378676 data_time: 0.095708 memory: 9999 loss_kpt: 0.000643 acc_pose: 0.782938 loss: 0.000643 2022/10/20 12:09:11 - mmengine - INFO - Epoch(train) [68][100/293] lr: 5.000000e-04 eta: 3:38:21 time: 0.349539 data_time: 0.071268 memory: 9999 loss_kpt: 0.000648 acc_pose: 0.877902 loss: 0.000648 2022/10/20 12:09:28 - mmengine - INFO - Epoch(train) [68][150/293] lr: 5.000000e-04 eta: 3:38:10 time: 0.357016 data_time: 0.072528 memory: 9999 loss_kpt: 0.000663 acc_pose: 0.836337 loss: 0.000663 2022/10/20 12:09:46 - mmengine - INFO - Epoch(train) [68][200/293] lr: 5.000000e-04 eta: 3:37:59 time: 0.353905 data_time: 0.064815 memory: 9999 loss_kpt: 0.000656 acc_pose: 0.797888 loss: 0.000656 2022/10/20 12:10:04 - mmengine - INFO - Epoch(train) [68][250/293] lr: 5.000000e-04 eta: 3:37:48 time: 0.358703 data_time: 0.076626 memory: 9999 loss_kpt: 0.000646 acc_pose: 0.869499 loss: 0.000646 2022/10/20 12:10:19 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:10:37 - mmengine - INFO - Epoch(train) [69][50/293] lr: 5.000000e-04 eta: 3:36:56 time: 0.365749 data_time: 0.086042 memory: 9999 loss_kpt: 0.000637 acc_pose: 0.809529 loss: 0.000637 2022/10/20 12:10:46 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:10:54 - mmengine - INFO - Epoch(train) [69][100/293] lr: 5.000000e-04 eta: 3:36:43 time: 0.337804 data_time: 0.071814 memory: 9999 loss_kpt: 0.000636 acc_pose: 0.835898 loss: 0.000636 2022/10/20 12:11:12 - mmengine - INFO - Epoch(train) [69][150/293] lr: 5.000000e-04 eta: 3:36:32 time: 0.359040 data_time: 0.075939 memory: 9999 loss_kpt: 0.000652 acc_pose: 0.844783 loss: 0.000652 2022/10/20 12:11:30 - mmengine - INFO - Epoch(train) [69][200/293] lr: 5.000000e-04 eta: 3:36:20 time: 0.347756 data_time: 0.073237 memory: 9999 loss_kpt: 0.000640 acc_pose: 0.852153 loss: 0.000640 2022/10/20 12:11:48 - mmengine - INFO - Epoch(train) [69][250/293] lr: 5.000000e-04 eta: 3:36:09 time: 0.361498 data_time: 0.076609 memory: 9999 loss_kpt: 0.000658 acc_pose: 0.840860 loss: 0.000658 2022/10/20 12:12:03 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:12:21 - mmengine - INFO - Epoch(train) [70][50/293] lr: 5.000000e-04 eta: 3:35:18 time: 0.368570 data_time: 0.083944 memory: 9999 loss_kpt: 0.000638 acc_pose: 0.803342 loss: 0.000638 2022/10/20 12:12:39 - mmengine - INFO - Epoch(train) [70][100/293] lr: 5.000000e-04 eta: 3:35:07 time: 0.361902 data_time: 0.066265 memory: 9999 loss_kpt: 0.000637 acc_pose: 0.771012 loss: 0.000637 2022/10/20 12:12:57 - mmengine - INFO - Epoch(train) [70][150/293] lr: 5.000000e-04 eta: 3:34:55 time: 0.351592 data_time: 0.067613 memory: 9999 loss_kpt: 0.000645 acc_pose: 0.805558 loss: 0.000645 2022/10/20 12:13:14 - mmengine - INFO - Epoch(train) [70][200/293] lr: 5.000000e-04 eta: 3:34:44 time: 0.355039 data_time: 0.069792 memory: 9999 loss_kpt: 0.000654 acc_pose: 0.845077 loss: 0.000654 2022/10/20 12:13:32 - mmengine - INFO - Epoch(train) [70][250/293] lr: 5.000000e-04 eta: 3:34:32 time: 0.350580 data_time: 0.074328 memory: 9999 loss_kpt: 0.000656 acc_pose: 0.804509 loss: 0.000656 2022/10/20 12:13:47 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:13:47 - mmengine - INFO - Saving checkpoint at 70 epochs 2022/10/20 12:13:57 - mmengine - INFO - Epoch(val) [70][50/407] eta: 0:00:42 time: 0.119086 data_time: 0.050742 memory: 9999 2022/10/20 12:14:02 - mmengine - INFO - Epoch(val) [70][100/407] eta: 0:00:35 time: 0.115599 data_time: 0.047873 memory: 1378 2022/10/20 12:14:08 - mmengine - INFO - Epoch(val) [70][150/407] eta: 0:00:30 time: 0.118283 data_time: 0.052120 memory: 1378 2022/10/20 12:14:14 - mmengine - INFO - Epoch(val) [70][200/407] eta: 0:00:24 time: 0.117605 data_time: 0.051141 memory: 1378 2022/10/20 12:14:20 - mmengine - INFO - Epoch(val) [70][250/407] eta: 0:00:18 time: 0.118074 data_time: 0.050351 memory: 1378 2022/10/20 12:14:26 - mmengine - INFO - Epoch(val) [70][300/407] eta: 0:00:13 time: 0.121902 data_time: 0.055437 memory: 1378 2022/10/20 12:14:32 - mmengine - INFO - Epoch(val) [70][350/407] eta: 0:00:06 time: 0.116984 data_time: 0.051353 memory: 1378 2022/10/20 12:14:38 - mmengine - INFO - Epoch(val) [70][400/407] eta: 0:00:00 time: 0.111215 data_time: 0.047016 memory: 1378 2022/10/20 12:15:12 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 12:15:26 - mmengine - INFO - Epoch(val) [70][407/407] coco/AP: 0.711442 coco/AP .5: 0.895136 coco/AP .75: 0.790367 coco/AP (M): 0.674409 coco/AP (L): 0.776852 coco/AR: 0.765775 coco/AR .5: 0.931203 coco/AR .75: 0.834068 coco/AR (M): 0.723190 coco/AR (L): 0.827648 2022/10/20 12:15:26 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221020/resnetv1d101_256/best_coco/AP_epoch_60.pth is removed 2022/10/20 12:15:28 - mmengine - INFO - The best checkpoint with 0.7114 coco/AP at 70 epoch is saved to best_coco/AP_epoch_70.pth. 2022/10/20 12:15:47 - mmengine - INFO - Epoch(train) [71][50/293] lr: 5.000000e-04 eta: 3:33:41 time: 0.366455 data_time: 0.105469 memory: 9999 loss_kpt: 0.000652 acc_pose: 0.858550 loss: 0.000652 2022/10/20 12:16:05 - mmengine - INFO - Epoch(train) [71][100/293] lr: 5.000000e-04 eta: 3:33:31 time: 0.362603 data_time: 0.069911 memory: 9999 loss_kpt: 0.000639 acc_pose: 0.845336 loss: 0.000639 2022/10/20 12:16:22 - mmengine - INFO - Epoch(train) [71][150/293] lr: 5.000000e-04 eta: 3:33:19 time: 0.351775 data_time: 0.068657 memory: 9999 loss_kpt: 0.000638 acc_pose: 0.821690 loss: 0.000638 2022/10/20 12:16:40 - mmengine - INFO - Epoch(train) [71][200/293] lr: 5.000000e-04 eta: 3:33:08 time: 0.361391 data_time: 0.068334 memory: 9999 loss_kpt: 0.000654 acc_pose: 0.846161 loss: 0.000654 2022/10/20 12:16:58 - mmengine - INFO - Epoch(train) [71][250/293] lr: 5.000000e-04 eta: 3:32:56 time: 0.353139 data_time: 0.069199 memory: 9999 loss_kpt: 0.000638 acc_pose: 0.836325 loss: 0.000638 2022/10/20 12:17:12 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:17:31 - mmengine - INFO - Epoch(train) [72][50/293] lr: 5.000000e-04 eta: 3:32:06 time: 0.363774 data_time: 0.077152 memory: 9999 loss_kpt: 0.000646 acc_pose: 0.813926 loss: 0.000646 2022/10/20 12:17:48 - mmengine - INFO - Epoch(train) [72][100/293] lr: 5.000000e-04 eta: 3:31:54 time: 0.352965 data_time: 0.069758 memory: 9999 loss_kpt: 0.000644 acc_pose: 0.832366 loss: 0.000644 2022/10/20 12:18:06 - mmengine - INFO - Epoch(train) [72][150/293] lr: 5.000000e-04 eta: 3:31:43 time: 0.358776 data_time: 0.072554 memory: 9999 loss_kpt: 0.000643 acc_pose: 0.819985 loss: 0.000643 2022/10/20 12:18:23 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:18:24 - mmengine - INFO - Epoch(train) [72][200/293] lr: 5.000000e-04 eta: 3:31:31 time: 0.355281 data_time: 0.072861 memory: 9999 loss_kpt: 0.000645 acc_pose: 0.794989 loss: 0.000645 2022/10/20 12:18:42 - mmengine - INFO - Epoch(train) [72][250/293] lr: 5.000000e-04 eta: 3:31:20 time: 0.362636 data_time: 0.076365 memory: 9999 loss_kpt: 0.000656 acc_pose: 0.849385 loss: 0.000656 2022/10/20 12:18:58 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:19:16 - mmengine - INFO - Epoch(train) [73][50/293] lr: 5.000000e-04 eta: 3:30:30 time: 0.362438 data_time: 0.077889 memory: 9999 loss_kpt: 0.000643 acc_pose: 0.836296 loss: 0.000643 2022/10/20 12:19:34 - mmengine - INFO - Epoch(train) [73][100/293] lr: 5.000000e-04 eta: 3:30:18 time: 0.355646 data_time: 0.072442 memory: 9999 loss_kpt: 0.000646 acc_pose: 0.829502 loss: 0.000646 2022/10/20 12:19:51 - mmengine - INFO - Epoch(train) [73][150/293] lr: 5.000000e-04 eta: 3:30:06 time: 0.350416 data_time: 0.067998 memory: 9999 loss_kpt: 0.000656 acc_pose: 0.817112 loss: 0.000656 2022/10/20 12:20:09 - mmengine - INFO - Epoch(train) [73][200/293] lr: 5.000000e-04 eta: 3:29:55 time: 0.353713 data_time: 0.079342 memory: 9999 loss_kpt: 0.000644 acc_pose: 0.814751 loss: 0.000644 2022/10/20 12:20:27 - mmengine - INFO - Epoch(train) [73][250/293] lr: 5.000000e-04 eta: 3:29:43 time: 0.355879 data_time: 0.091635 memory: 9999 loss_kpt: 0.000662 acc_pose: 0.859038 loss: 0.000662 2022/10/20 12:20:41 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:21:00 - mmengine - INFO - Epoch(train) [74][50/293] lr: 5.000000e-04 eta: 3:28:54 time: 0.374005 data_time: 0.081390 memory: 9999 loss_kpt: 0.000646 acc_pose: 0.857724 loss: 0.000646 2022/10/20 12:21:17 - mmengine - INFO - Epoch(train) [74][100/293] lr: 5.000000e-04 eta: 3:28:42 time: 0.349696 data_time: 0.067172 memory: 9999 loss_kpt: 0.000640 acc_pose: 0.797481 loss: 0.000640 2022/10/20 12:21:36 - mmengine - INFO - Epoch(train) [74][150/293] lr: 5.000000e-04 eta: 3:28:32 time: 0.371747 data_time: 0.067681 memory: 9999 loss_kpt: 0.000642 acc_pose: 0.844776 loss: 0.000642 2022/10/20 12:21:54 - mmengine - INFO - Epoch(train) [74][200/293] lr: 5.000000e-04 eta: 3:28:21 time: 0.359974 data_time: 0.063527 memory: 9999 loss_kpt: 0.000642 acc_pose: 0.796006 loss: 0.000642 2022/10/20 12:22:12 - mmengine - INFO - Epoch(train) [74][250/293] lr: 5.000000e-04 eta: 3:28:09 time: 0.355470 data_time: 0.073606 memory: 9999 loss_kpt: 0.000644 acc_pose: 0.869673 loss: 0.000644 2022/10/20 12:22:27 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:22:45 - mmengine - INFO - Epoch(train) [75][50/293] lr: 5.000000e-04 eta: 3:27:20 time: 0.364418 data_time: 0.087436 memory: 9999 loss_kpt: 0.000630 acc_pose: 0.871311 loss: 0.000630 2022/10/20 12:23:03 - mmengine - INFO - Epoch(train) [75][100/293] lr: 5.000000e-04 eta: 3:27:08 time: 0.353783 data_time: 0.068169 memory: 9999 loss_kpt: 0.000659 acc_pose: 0.848931 loss: 0.000659 2022/10/20 12:23:20 - mmengine - INFO - Epoch(train) [75][150/293] lr: 5.000000e-04 eta: 3:26:56 time: 0.350635 data_time: 0.068678 memory: 9999 loss_kpt: 0.000630 acc_pose: 0.841797 loss: 0.000630 2022/10/20 12:23:38 - mmengine - INFO - Epoch(train) [75][200/293] lr: 5.000000e-04 eta: 3:26:44 time: 0.352512 data_time: 0.067058 memory: 9999 loss_kpt: 0.000637 acc_pose: 0.835761 loss: 0.000637 2022/10/20 12:23:56 - mmengine - INFO - Epoch(train) [75][250/293] lr: 5.000000e-04 eta: 3:26:32 time: 0.354129 data_time: 0.074852 memory: 9999 loss_kpt: 0.000629 acc_pose: 0.804121 loss: 0.000629 2022/10/20 12:24:11 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:24:20 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:24:30 - mmengine - INFO - Epoch(train) [76][50/293] lr: 5.000000e-04 eta: 3:25:45 time: 0.378596 data_time: 0.083353 memory: 9999 loss_kpt: 0.000647 acc_pose: 0.828004 loss: 0.000647 2022/10/20 12:24:47 - mmengine - INFO - Epoch(train) [76][100/293] lr: 5.000000e-04 eta: 3:25:33 time: 0.355272 data_time: 0.073493 memory: 9999 loss_kpt: 0.000636 acc_pose: 0.824362 loss: 0.000636 2022/10/20 12:25:06 - mmengine - INFO - Epoch(train) [76][150/293] lr: 5.000000e-04 eta: 3:25:22 time: 0.364844 data_time: 0.072311 memory: 9999 loss_kpt: 0.000625 acc_pose: 0.838416 loss: 0.000625 2022/10/20 12:25:24 - mmengine - INFO - Epoch(train) [76][200/293] lr: 5.000000e-04 eta: 3:25:10 time: 0.360229 data_time: 0.081832 memory: 9999 loss_kpt: 0.000619 acc_pose: 0.884835 loss: 0.000619 2022/10/20 12:25:41 - mmengine - INFO - Epoch(train) [76][250/293] lr: 5.000000e-04 eta: 3:24:58 time: 0.345906 data_time: 0.069465 memory: 9999 loss_kpt: 0.000639 acc_pose: 0.816483 loss: 0.000639 2022/10/20 12:25:56 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:26:14 - mmengine - INFO - Epoch(train) [77][50/293] lr: 5.000000e-04 eta: 3:24:11 time: 0.377161 data_time: 0.078598 memory: 9999 loss_kpt: 0.000630 acc_pose: 0.861112 loss: 0.000630 2022/10/20 12:26:32 - mmengine - INFO - Epoch(train) [77][100/293] lr: 5.000000e-04 eta: 3:23:58 time: 0.350425 data_time: 0.068546 memory: 9999 loss_kpt: 0.000626 acc_pose: 0.846117 loss: 0.000626 2022/10/20 12:26:50 - mmengine - INFO - Epoch(train) [77][150/293] lr: 5.000000e-04 eta: 3:23:47 time: 0.360077 data_time: 0.072533 memory: 9999 loss_kpt: 0.000636 acc_pose: 0.822508 loss: 0.000636 2022/10/20 12:27:08 - mmengine - INFO - Epoch(train) [77][200/293] lr: 5.000000e-04 eta: 3:23:35 time: 0.361823 data_time: 0.067680 memory: 9999 loss_kpt: 0.000637 acc_pose: 0.819965 loss: 0.000637 2022/10/20 12:27:26 - mmengine - INFO - Epoch(train) [77][250/293] lr: 5.000000e-04 eta: 3:23:23 time: 0.348894 data_time: 0.074911 memory: 9999 loss_kpt: 0.000638 acc_pose: 0.794859 loss: 0.000638 2022/10/20 12:27:41 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:28:00 - mmengine - INFO - Epoch(train) [78][50/293] lr: 5.000000e-04 eta: 3:22:36 time: 0.369998 data_time: 0.093437 memory: 9999 loss_kpt: 0.000624 acc_pose: 0.835365 loss: 0.000624 2022/10/20 12:28:17 - mmengine - INFO - Epoch(train) [78][100/293] lr: 5.000000e-04 eta: 3:22:23 time: 0.351830 data_time: 0.069458 memory: 9999 loss_kpt: 0.000633 acc_pose: 0.836737 loss: 0.000633 2022/10/20 12:28:35 - mmengine - INFO - Epoch(train) [78][150/293] lr: 5.000000e-04 eta: 3:22:11 time: 0.348014 data_time: 0.063852 memory: 9999 loss_kpt: 0.000631 acc_pose: 0.837303 loss: 0.000631 2022/10/20 12:28:53 - mmengine - INFO - Epoch(train) [78][200/293] lr: 5.000000e-04 eta: 3:22:00 time: 0.364719 data_time: 0.071991 memory: 9999 loss_kpt: 0.000634 acc_pose: 0.846776 loss: 0.000634 2022/10/20 12:29:11 - mmengine - INFO - Epoch(train) [78][250/293] lr: 5.000000e-04 eta: 3:21:48 time: 0.355618 data_time: 0.072229 memory: 9999 loss_kpt: 0.000635 acc_pose: 0.843417 loss: 0.000635 2022/10/20 12:29:25 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:29:45 - mmengine - INFO - Epoch(train) [79][50/293] lr: 5.000000e-04 eta: 3:21:02 time: 0.388001 data_time: 0.080271 memory: 9999 loss_kpt: 0.000622 acc_pose: 0.860518 loss: 0.000622 2022/10/20 12:30:02 - mmengine - INFO - Epoch(train) [79][100/293] lr: 5.000000e-04 eta: 3:20:50 time: 0.355630 data_time: 0.069110 memory: 9999 loss_kpt: 0.000639 acc_pose: 0.837715 loss: 0.000639 2022/10/20 12:30:19 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:30:20 - mmengine - INFO - Epoch(train) [79][150/293] lr: 5.000000e-04 eta: 3:20:39 time: 0.359000 data_time: 0.067466 memory: 9999 loss_kpt: 0.000642 acc_pose: 0.802608 loss: 0.000642 2022/10/20 12:30:38 - mmengine - INFO - Epoch(train) [79][200/293] lr: 5.000000e-04 eta: 3:20:26 time: 0.350662 data_time: 0.076046 memory: 9999 loss_kpt: 0.000631 acc_pose: 0.850970 loss: 0.000631 2022/10/20 12:30:56 - mmengine - INFO - Epoch(train) [79][250/293] lr: 5.000000e-04 eta: 3:20:15 time: 0.364310 data_time: 0.073272 memory: 9999 loss_kpt: 0.000631 acc_pose: 0.855021 loss: 0.000631 2022/10/20 12:31:12 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:31:30 - mmengine - INFO - Epoch(train) [80][50/293] lr: 5.000000e-04 eta: 3:19:29 time: 0.374293 data_time: 0.085752 memory: 9999 loss_kpt: 0.000632 acc_pose: 0.832563 loss: 0.000632 2022/10/20 12:31:48 - mmengine - INFO - Epoch(train) [80][100/293] lr: 5.000000e-04 eta: 3:19:17 time: 0.359384 data_time: 0.076938 memory: 9999 loss_kpt: 0.000635 acc_pose: 0.843264 loss: 0.000635 2022/10/20 12:32:06 - mmengine - INFO - Epoch(train) [80][150/293] lr: 5.000000e-04 eta: 3:19:04 time: 0.349135 data_time: 0.073081 memory: 9999 loss_kpt: 0.000621 acc_pose: 0.851942 loss: 0.000621 2022/10/20 12:32:24 - mmengine - INFO - Epoch(train) [80][200/293] lr: 5.000000e-04 eta: 3:18:53 time: 0.367530 data_time: 0.070143 memory: 9999 loss_kpt: 0.000634 acc_pose: 0.846126 loss: 0.000634 2022/10/20 12:32:43 - mmengine - INFO - Epoch(train) [80][250/293] lr: 5.000000e-04 eta: 3:18:42 time: 0.367127 data_time: 0.074982 memory: 9999 loss_kpt: 0.000634 acc_pose: 0.813968 loss: 0.000634 2022/10/20 12:32:58 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:32:58 - mmengine - INFO - Saving checkpoint at 80 epochs 2022/10/20 12:33:07 - mmengine - INFO - Epoch(val) [80][50/407] eta: 0:00:42 time: 0.118827 data_time: 0.052101 memory: 9999 2022/10/20 12:33:13 - mmengine - INFO - Epoch(val) [80][100/407] eta: 0:00:35 time: 0.117043 data_time: 0.049946 memory: 1378 2022/10/20 12:33:19 - mmengine - INFO - Epoch(val) [80][150/407] eta: 0:00:29 time: 0.115745 data_time: 0.049460 memory: 1378 2022/10/20 12:33:25 - mmengine - INFO - Epoch(val) [80][200/407] eta: 0:00:23 time: 0.112848 data_time: 0.046005 memory: 1378 2022/10/20 12:33:31 - mmengine - INFO - Epoch(val) [80][250/407] eta: 0:00:20 time: 0.128829 data_time: 0.061772 memory: 1378 2022/10/20 12:33:37 - mmengine - INFO - Epoch(val) [80][300/407] eta: 0:00:12 time: 0.114144 data_time: 0.047230 memory: 1378 2022/10/20 12:33:43 - mmengine - INFO - Epoch(val) [80][350/407] eta: 0:00:07 time: 0.124968 data_time: 0.057973 memory: 1378 2022/10/20 12:33:48 - mmengine - INFO - Epoch(val) [80][400/407] eta: 0:00:00 time: 0.101498 data_time: 0.037973 memory: 1378 2022/10/20 12:34:24 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 12:34:37 - mmengine - INFO - Epoch(val) [80][407/407] coco/AP: 0.715515 coco/AP .5: 0.896934 coco/AP .75: 0.789957 coco/AP (M): 0.676641 coco/AP (L): 0.784941 coco/AR: 0.771080 coco/AR .5: 0.934037 coco/AR .75: 0.838319 coco/AR (M): 0.725212 coco/AR (L): 0.837124 2022/10/20 12:34:37 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221020/resnetv1d101_256/best_coco/AP_epoch_70.pth is removed 2022/10/20 12:34:40 - mmengine - INFO - The best checkpoint with 0.7155 coco/AP at 80 epoch is saved to best_coco/AP_epoch_80.pth. 2022/10/20 12:34:58 - mmengine - INFO - Epoch(train) [81][50/293] lr: 5.000000e-04 eta: 3:17:55 time: 0.361425 data_time: 0.099061 memory: 9999 loss_kpt: 0.000629 acc_pose: 0.860956 loss: 0.000629 2022/10/20 12:35:15 - mmengine - INFO - Epoch(train) [81][100/293] lr: 5.000000e-04 eta: 3:17:43 time: 0.353751 data_time: 0.071558 memory: 9999 loss_kpt: 0.000620 acc_pose: 0.841020 loss: 0.000620 2022/10/20 12:35:33 - mmengine - INFO - Epoch(train) [81][150/293] lr: 5.000000e-04 eta: 3:17:30 time: 0.352503 data_time: 0.078830 memory: 9999 loss_kpt: 0.000642 acc_pose: 0.844824 loss: 0.000642 2022/10/20 12:35:50 - mmengine - INFO - Epoch(train) [81][200/293] lr: 5.000000e-04 eta: 3:17:18 time: 0.347232 data_time: 0.061093 memory: 9999 loss_kpt: 0.000635 acc_pose: 0.816422 loss: 0.000635 2022/10/20 12:36:08 - mmengine - INFO - Epoch(train) [81][250/293] lr: 5.000000e-04 eta: 3:17:06 time: 0.358512 data_time: 0.066835 memory: 9999 loss_kpt: 0.000643 acc_pose: 0.841754 loss: 0.000643 2022/10/20 12:36:24 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:36:43 - mmengine - INFO - Epoch(train) [82][50/293] lr: 5.000000e-04 eta: 3:16:20 time: 0.372200 data_time: 0.091320 memory: 9999 loss_kpt: 0.000624 acc_pose: 0.796531 loss: 0.000624 2022/10/20 12:37:00 - mmengine - INFO - Epoch(train) [82][100/293] lr: 5.000000e-04 eta: 3:16:07 time: 0.343827 data_time: 0.067215 memory: 9999 loss_kpt: 0.000628 acc_pose: 0.807450 loss: 0.000628 2022/10/20 12:37:17 - mmengine - INFO - Epoch(train) [82][150/293] lr: 5.000000e-04 eta: 3:15:54 time: 0.343656 data_time: 0.063233 memory: 9999 loss_kpt: 0.000634 acc_pose: 0.842975 loss: 0.000634 2022/10/20 12:37:35 - mmengine - INFO - Epoch(train) [82][200/293] lr: 5.000000e-04 eta: 3:15:41 time: 0.347039 data_time: 0.075895 memory: 9999 loss_kpt: 0.000639 acc_pose: 0.817100 loss: 0.000639 2022/10/20 12:37:52 - mmengine - INFO - Epoch(train) [82][250/293] lr: 5.000000e-04 eta: 3:15:28 time: 0.351354 data_time: 0.073169 memory: 9999 loss_kpt: 0.000635 acc_pose: 0.838229 loss: 0.000635 2022/10/20 12:37:58 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:38:07 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:38:26 - mmengine - INFO - Epoch(train) [83][50/293] lr: 5.000000e-04 eta: 3:14:44 time: 0.381096 data_time: 0.090694 memory: 9999 loss_kpt: 0.000627 acc_pose: 0.794325 loss: 0.000627 2022/10/20 12:38:44 - mmengine - INFO - Epoch(train) [83][100/293] lr: 5.000000e-04 eta: 3:14:32 time: 0.363212 data_time: 0.069789 memory: 9999 loss_kpt: 0.000645 acc_pose: 0.820869 loss: 0.000645 2022/10/20 12:39:02 - mmengine - INFO - Epoch(train) [83][150/293] lr: 5.000000e-04 eta: 3:14:19 time: 0.350048 data_time: 0.068620 memory: 9999 loss_kpt: 0.000613 acc_pose: 0.825577 loss: 0.000613 2022/10/20 12:39:19 - mmengine - INFO - Epoch(train) [83][200/293] lr: 5.000000e-04 eta: 3:14:07 time: 0.358478 data_time: 0.091481 memory: 9999 loss_kpt: 0.000631 acc_pose: 0.832258 loss: 0.000631 2022/10/20 12:39:37 - mmengine - INFO - Epoch(train) [83][250/293] lr: 5.000000e-04 eta: 3:13:55 time: 0.360032 data_time: 0.081281 memory: 9999 loss_kpt: 0.000633 acc_pose: 0.820479 loss: 0.000633 2022/10/20 12:39:53 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:40:12 - mmengine - INFO - Epoch(train) [84][50/293] lr: 5.000000e-04 eta: 3:13:11 time: 0.379849 data_time: 0.097922 memory: 9999 loss_kpt: 0.000631 acc_pose: 0.819592 loss: 0.000631 2022/10/20 12:40:30 - mmengine - INFO - Epoch(train) [84][100/293] lr: 5.000000e-04 eta: 3:12:59 time: 0.359235 data_time: 0.075964 memory: 9999 loss_kpt: 0.000628 acc_pose: 0.795760 loss: 0.000628 2022/10/20 12:40:47 - mmengine - INFO - Epoch(train) [84][150/293] lr: 5.000000e-04 eta: 3:12:46 time: 0.352089 data_time: 0.072942 memory: 9999 loss_kpt: 0.000630 acc_pose: 0.855563 loss: 0.000630 2022/10/20 12:41:05 - mmengine - INFO - Epoch(train) [84][200/293] lr: 5.000000e-04 eta: 3:12:34 time: 0.349360 data_time: 0.068640 memory: 9999 loss_kpt: 0.000623 acc_pose: 0.842323 loss: 0.000623 2022/10/20 12:41:23 - mmengine - INFO - Epoch(train) [84][250/293] lr: 5.000000e-04 eta: 3:12:21 time: 0.356820 data_time: 0.065519 memory: 9999 loss_kpt: 0.000632 acc_pose: 0.811048 loss: 0.000632 2022/10/20 12:41:38 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:41:56 - mmengine - INFO - Epoch(train) [85][50/293] lr: 5.000000e-04 eta: 3:11:37 time: 0.372029 data_time: 0.090131 memory: 9999 loss_kpt: 0.000631 acc_pose: 0.774852 loss: 0.000631 2022/10/20 12:42:14 - mmengine - INFO - Epoch(train) [85][100/293] lr: 5.000000e-04 eta: 3:11:24 time: 0.354880 data_time: 0.070023 memory: 9999 loss_kpt: 0.000621 acc_pose: 0.804782 loss: 0.000621 2022/10/20 12:42:31 - mmengine - INFO - Epoch(train) [85][150/293] lr: 5.000000e-04 eta: 3:11:11 time: 0.343211 data_time: 0.071389 memory: 9999 loss_kpt: 0.000635 acc_pose: 0.808159 loss: 0.000635 2022/10/20 12:42:50 - mmengine - INFO - Epoch(train) [85][200/293] lr: 5.000000e-04 eta: 3:10:59 time: 0.364848 data_time: 0.099182 memory: 9999 loss_kpt: 0.000634 acc_pose: 0.847948 loss: 0.000634 2022/10/20 12:43:07 - mmengine - INFO - Epoch(train) [85][250/293] lr: 5.000000e-04 eta: 3:10:47 time: 0.357151 data_time: 0.073841 memory: 9999 loss_kpt: 0.000620 acc_pose: 0.842094 loss: 0.000620 2022/10/20 12:43:22 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:43:41 - mmengine - INFO - Epoch(train) [86][50/293] lr: 5.000000e-04 eta: 3:10:03 time: 0.375242 data_time: 0.085260 memory: 9999 loss_kpt: 0.000618 acc_pose: 0.816263 loss: 0.000618 2022/10/20 12:43:57 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:43:59 - mmengine - INFO - Epoch(train) [86][100/293] lr: 5.000000e-04 eta: 3:09:51 time: 0.357375 data_time: 0.069268 memory: 9999 loss_kpt: 0.000626 acc_pose: 0.788024 loss: 0.000626 2022/10/20 12:44:17 - mmengine - INFO - Epoch(train) [86][150/293] lr: 5.000000e-04 eta: 3:09:39 time: 0.362428 data_time: 0.071167 memory: 9999 loss_kpt: 0.000628 acc_pose: 0.802643 loss: 0.000628 2022/10/20 12:44:34 - mmengine - INFO - Epoch(train) [86][200/293] lr: 5.000000e-04 eta: 3:09:26 time: 0.348917 data_time: 0.069343 memory: 9999 loss_kpt: 0.000625 acc_pose: 0.842824 loss: 0.000625 2022/10/20 12:44:52 - mmengine - INFO - Epoch(train) [86][250/293] lr: 5.000000e-04 eta: 3:09:13 time: 0.353402 data_time: 0.076577 memory: 9999 loss_kpt: 0.000640 acc_pose: 0.853987 loss: 0.000640 2022/10/20 12:45:07 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:45:26 - mmengine - INFO - Epoch(train) [87][50/293] lr: 5.000000e-04 eta: 3:08:30 time: 0.383927 data_time: 0.087239 memory: 9999 loss_kpt: 0.000624 acc_pose: 0.772071 loss: 0.000624 2022/10/20 12:45:43 - mmengine - INFO - Epoch(train) [87][100/293] lr: 5.000000e-04 eta: 3:08:17 time: 0.339647 data_time: 0.074745 memory: 9999 loss_kpt: 0.000627 acc_pose: 0.839414 loss: 0.000627 2022/10/20 12:46:00 - mmengine - INFO - Epoch(train) [87][150/293] lr: 5.000000e-04 eta: 3:08:04 time: 0.351255 data_time: 0.073097 memory: 9999 loss_kpt: 0.000616 acc_pose: 0.855273 loss: 0.000616 2022/10/20 12:46:18 - mmengine - INFO - Epoch(train) [87][200/293] lr: 5.000000e-04 eta: 3:07:52 time: 0.362384 data_time: 0.077115 memory: 9999 loss_kpt: 0.000638 acc_pose: 0.794772 loss: 0.000638 2022/10/20 12:46:36 - mmengine - INFO - Epoch(train) [87][250/293] lr: 5.000000e-04 eta: 3:07:39 time: 0.346785 data_time: 0.069773 memory: 9999 loss_kpt: 0.000618 acc_pose: 0.890581 loss: 0.000618 2022/10/20 12:46:51 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:47:09 - mmengine - INFO - Epoch(train) [88][50/293] lr: 5.000000e-04 eta: 3:06:54 time: 0.361325 data_time: 0.084866 memory: 9999 loss_kpt: 0.000627 acc_pose: 0.819417 loss: 0.000627 2022/10/20 12:47:27 - mmengine - INFO - Epoch(train) [88][100/293] lr: 5.000000e-04 eta: 3:06:42 time: 0.361138 data_time: 0.070015 memory: 9999 loss_kpt: 0.000624 acc_pose: 0.851703 loss: 0.000624 2022/10/20 12:47:44 - mmengine - INFO - Epoch(train) [88][150/293] lr: 5.000000e-04 eta: 3:06:29 time: 0.346251 data_time: 0.070883 memory: 9999 loss_kpt: 0.000623 acc_pose: 0.823366 loss: 0.000623 2022/10/20 12:48:02 - mmengine - INFO - Epoch(train) [88][200/293] lr: 5.000000e-04 eta: 3:06:17 time: 0.359718 data_time: 0.073099 memory: 9999 loss_kpt: 0.000621 acc_pose: 0.810467 loss: 0.000621 2022/10/20 12:48:20 - mmengine - INFO - Epoch(train) [88][250/293] lr: 5.000000e-04 eta: 3:06:04 time: 0.358274 data_time: 0.068394 memory: 9999 loss_kpt: 0.000630 acc_pose: 0.847399 loss: 0.000630 2022/10/20 12:48:35 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:48:54 - mmengine - INFO - Epoch(train) [89][50/293] lr: 5.000000e-04 eta: 3:05:21 time: 0.374862 data_time: 0.080136 memory: 9999 loss_kpt: 0.000620 acc_pose: 0.817126 loss: 0.000620 2022/10/20 12:49:11 - mmengine - INFO - Epoch(train) [89][100/293] lr: 5.000000e-04 eta: 3:05:08 time: 0.346904 data_time: 0.072465 memory: 9999 loss_kpt: 0.000619 acc_pose: 0.830884 loss: 0.000619 2022/10/20 12:49:29 - mmengine - INFO - Epoch(train) [89][150/293] lr: 5.000000e-04 eta: 3:04:56 time: 0.360243 data_time: 0.072026 memory: 9999 loss_kpt: 0.000638 acc_pose: 0.826407 loss: 0.000638 2022/10/20 12:49:47 - mmengine - INFO - Epoch(train) [89][200/293] lr: 5.000000e-04 eta: 3:04:42 time: 0.343008 data_time: 0.069308 memory: 9999 loss_kpt: 0.000620 acc_pose: 0.850645 loss: 0.000620 2022/10/20 12:49:52 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:50:04 - mmengine - INFO - Epoch(train) [89][250/293] lr: 5.000000e-04 eta: 3:04:29 time: 0.349322 data_time: 0.065508 memory: 9999 loss_kpt: 0.000627 acc_pose: 0.865819 loss: 0.000627 2022/10/20 12:50:19 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:50:38 - mmengine - INFO - Epoch(train) [90][50/293] lr: 5.000000e-04 eta: 3:03:47 time: 0.378719 data_time: 0.081832 memory: 9999 loss_kpt: 0.000625 acc_pose: 0.797621 loss: 0.000625 2022/10/20 12:50:55 - mmengine - INFO - Epoch(train) [90][100/293] lr: 5.000000e-04 eta: 3:03:34 time: 0.348037 data_time: 0.064864 memory: 9999 loss_kpt: 0.000603 acc_pose: 0.839890 loss: 0.000603 2022/10/20 12:51:12 - mmengine - INFO - Epoch(train) [90][150/293] lr: 5.000000e-04 eta: 3:03:20 time: 0.345297 data_time: 0.068940 memory: 9999 loss_kpt: 0.000627 acc_pose: 0.840555 loss: 0.000627 2022/10/20 12:51:30 - mmengine - INFO - Epoch(train) [90][200/293] lr: 5.000000e-04 eta: 3:03:07 time: 0.353361 data_time: 0.067486 memory: 9999 loss_kpt: 0.000623 acc_pose: 0.845287 loss: 0.000623 2022/10/20 12:51:48 - mmengine - INFO - Epoch(train) [90][250/293] lr: 5.000000e-04 eta: 3:02:55 time: 0.362378 data_time: 0.076102 memory: 9999 loss_kpt: 0.000627 acc_pose: 0.863368 loss: 0.000627 2022/10/20 12:52:03 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:52:03 - mmengine - INFO - Saving checkpoint at 90 epochs 2022/10/20 12:52:13 - mmengine - INFO - Epoch(val) [90][50/407] eta: 0:00:44 time: 0.123974 data_time: 0.056048 memory: 9999 2022/10/20 12:52:19 - mmengine - INFO - Epoch(val) [90][100/407] eta: 0:00:35 time: 0.115681 data_time: 0.049176 memory: 1378 2022/10/20 12:52:24 - mmengine - INFO - Epoch(val) [90][150/407] eta: 0:00:29 time: 0.116707 data_time: 0.050312 memory: 1378 2022/10/20 12:52:30 - mmengine - INFO - Epoch(val) [90][200/407] eta: 0:00:23 time: 0.113349 data_time: 0.045648 memory: 1378 2022/10/20 12:52:36 - mmengine - INFO - Epoch(val) [90][250/407] eta: 0:00:18 time: 0.116327 data_time: 0.050436 memory: 1378 2022/10/20 12:52:42 - mmengine - INFO - Epoch(val) [90][300/407] eta: 0:00:12 time: 0.114789 data_time: 0.047830 memory: 1378 2022/10/20 12:52:47 - mmengine - INFO - Epoch(val) [90][350/407] eta: 0:00:06 time: 0.116119 data_time: 0.048061 memory: 1378 2022/10/20 12:52:53 - mmengine - INFO - Epoch(val) [90][400/407] eta: 0:00:00 time: 0.104583 data_time: 0.036952 memory: 1378 2022/10/20 12:53:28 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 12:53:41 - mmengine - INFO - Epoch(val) [90][407/407] coco/AP: 0.717738 coco/AP .5: 0.898108 coco/AP .75: 0.795209 coco/AP (M): 0.681049 coco/AP (L): 0.783407 coco/AR: 0.773520 coco/AR .5: 0.936083 coco/AR .75: 0.841782 coco/AR (M): 0.731248 coco/AR (L): 0.834411 2022/10/20 12:53:41 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221020/resnetv1d101_256/best_coco/AP_epoch_80.pth is removed 2022/10/20 12:53:44 - mmengine - INFO - The best checkpoint with 0.7177 coco/AP at 90 epoch is saved to best_coco/AP_epoch_90.pth. 2022/10/20 12:54:01 - mmengine - INFO - Epoch(train) [91][50/293] lr: 5.000000e-04 eta: 3:02:11 time: 0.356267 data_time: 0.081022 memory: 9999 loss_kpt: 0.000622 acc_pose: 0.858883 loss: 0.000622 2022/10/20 12:54:19 - mmengine - INFO - Epoch(train) [91][100/293] lr: 5.000000e-04 eta: 3:01:58 time: 0.346854 data_time: 0.065541 memory: 9999 loss_kpt: 0.000625 acc_pose: 0.839056 loss: 0.000625 2022/10/20 12:54:38 - mmengine - INFO - Epoch(train) [91][150/293] lr: 5.000000e-04 eta: 3:01:47 time: 0.376160 data_time: 0.076536 memory: 9999 loss_kpt: 0.000623 acc_pose: 0.809326 loss: 0.000623 2022/10/20 12:54:55 - mmengine - INFO - Epoch(train) [91][200/293] lr: 5.000000e-04 eta: 3:01:34 time: 0.348598 data_time: 0.070622 memory: 9999 loss_kpt: 0.000632 acc_pose: 0.838742 loss: 0.000632 2022/10/20 12:55:13 - mmengine - INFO - Epoch(train) [91][250/293] lr: 5.000000e-04 eta: 3:01:22 time: 0.362508 data_time: 0.071777 memory: 9999 loss_kpt: 0.000628 acc_pose: 0.820171 loss: 0.000628 2022/10/20 12:55:28 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:55:47 - mmengine - INFO - Epoch(train) [92][50/293] lr: 5.000000e-04 eta: 3:00:39 time: 0.370277 data_time: 0.085752 memory: 9999 loss_kpt: 0.000603 acc_pose: 0.856684 loss: 0.000603 2022/10/20 12:56:05 - mmengine - INFO - Epoch(train) [92][100/293] lr: 5.000000e-04 eta: 3:00:27 time: 0.363262 data_time: 0.072790 memory: 9999 loss_kpt: 0.000615 acc_pose: 0.819896 loss: 0.000615 2022/10/20 12:56:23 - mmengine - INFO - Epoch(train) [92][150/293] lr: 5.000000e-04 eta: 3:00:14 time: 0.355122 data_time: 0.067301 memory: 9999 loss_kpt: 0.000623 acc_pose: 0.842529 loss: 0.000623 2022/10/20 12:56:40 - mmengine - INFO - Epoch(train) [92][200/293] lr: 5.000000e-04 eta: 3:00:01 time: 0.354643 data_time: 0.073298 memory: 9999 loss_kpt: 0.000622 acc_pose: 0.831589 loss: 0.000622 2022/10/20 12:56:58 - mmengine - INFO - Epoch(train) [92][250/293] lr: 5.000000e-04 eta: 2:59:48 time: 0.348825 data_time: 0.073213 memory: 9999 loss_kpt: 0.000621 acc_pose: 0.819132 loss: 0.000621 2022/10/20 12:57:12 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:57:29 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:57:31 - mmengine - INFO - Epoch(train) [93][50/293] lr: 5.000000e-04 eta: 2:59:06 time: 0.375981 data_time: 0.089654 memory: 9999 loss_kpt: 0.000620 acc_pose: 0.845236 loss: 0.000620 2022/10/20 12:57:49 - mmengine - INFO - Epoch(train) [93][100/293] lr: 5.000000e-04 eta: 2:58:53 time: 0.349962 data_time: 0.065482 memory: 9999 loss_kpt: 0.000625 acc_pose: 0.844802 loss: 0.000625 2022/10/20 12:58:07 - mmengine - INFO - Epoch(train) [93][150/293] lr: 5.000000e-04 eta: 2:58:40 time: 0.354990 data_time: 0.068684 memory: 9999 loss_kpt: 0.000618 acc_pose: 0.822417 loss: 0.000618 2022/10/20 12:58:24 - mmengine - INFO - Epoch(train) [93][200/293] lr: 5.000000e-04 eta: 2:58:27 time: 0.351766 data_time: 0.069366 memory: 9999 loss_kpt: 0.000616 acc_pose: 0.838445 loss: 0.000616 2022/10/20 12:58:42 - mmengine - INFO - Epoch(train) [93][250/293] lr: 5.000000e-04 eta: 2:58:14 time: 0.359257 data_time: 0.071343 memory: 9999 loss_kpt: 0.000624 acc_pose: 0.834895 loss: 0.000624 2022/10/20 12:58:57 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 12:59:16 - mmengine - INFO - Epoch(train) [94][50/293] lr: 5.000000e-04 eta: 2:57:32 time: 0.370708 data_time: 0.083734 memory: 9999 loss_kpt: 0.000630 acc_pose: 0.809192 loss: 0.000630 2022/10/20 12:59:33 - mmengine - INFO - Epoch(train) [94][100/293] lr: 5.000000e-04 eta: 2:57:19 time: 0.352721 data_time: 0.071184 memory: 9999 loss_kpt: 0.000620 acc_pose: 0.808729 loss: 0.000620 2022/10/20 12:59:51 - mmengine - INFO - Epoch(train) [94][150/293] lr: 5.000000e-04 eta: 2:57:07 time: 0.353590 data_time: 0.072981 memory: 9999 loss_kpt: 0.000603 acc_pose: 0.871723 loss: 0.000603 2022/10/20 13:00:09 - mmengine - INFO - Epoch(train) [94][200/293] lr: 5.000000e-04 eta: 2:56:54 time: 0.354305 data_time: 0.069717 memory: 9999 loss_kpt: 0.000616 acc_pose: 0.879201 loss: 0.000616 2022/10/20 13:00:27 - mmengine - INFO - Epoch(train) [94][250/293] lr: 5.000000e-04 eta: 2:56:41 time: 0.359240 data_time: 0.065054 memory: 9999 loss_kpt: 0.000611 acc_pose: 0.840615 loss: 0.000611 2022/10/20 13:00:42 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:01:00 - mmengine - INFO - Epoch(train) [95][50/293] lr: 5.000000e-04 eta: 2:55:59 time: 0.373162 data_time: 0.095657 memory: 9999 loss_kpt: 0.000623 acc_pose: 0.848484 loss: 0.000623 2022/10/20 13:01:18 - mmengine - INFO - Epoch(train) [95][100/293] lr: 5.000000e-04 eta: 2:55:46 time: 0.353319 data_time: 0.084605 memory: 9999 loss_kpt: 0.000623 acc_pose: 0.799839 loss: 0.000623 2022/10/20 13:01:36 - mmengine - INFO - Epoch(train) [95][150/293] lr: 5.000000e-04 eta: 2:55:33 time: 0.352223 data_time: 0.069954 memory: 9999 loss_kpt: 0.000603 acc_pose: 0.858599 loss: 0.000603 2022/10/20 13:01:54 - mmengine - INFO - Epoch(train) [95][200/293] lr: 5.000000e-04 eta: 2:55:21 time: 0.359981 data_time: 0.073072 memory: 9999 loss_kpt: 0.000629 acc_pose: 0.831359 loss: 0.000629 2022/10/20 13:02:12 - mmengine - INFO - Epoch(train) [95][250/293] lr: 5.000000e-04 eta: 2:55:08 time: 0.357341 data_time: 0.072400 memory: 9999 loss_kpt: 0.000623 acc_pose: 0.867612 loss: 0.000623 2022/10/20 13:02:27 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:02:45 - mmengine - INFO - Epoch(train) [96][50/293] lr: 5.000000e-04 eta: 2:54:26 time: 0.368777 data_time: 0.081112 memory: 9999 loss_kpt: 0.000609 acc_pose: 0.841164 loss: 0.000609 2022/10/20 13:03:03 - mmengine - INFO - Epoch(train) [96][100/293] lr: 5.000000e-04 eta: 2:54:13 time: 0.354784 data_time: 0.069538 memory: 9999 loss_kpt: 0.000621 acc_pose: 0.875192 loss: 0.000621 2022/10/20 13:03:20 - mmengine - INFO - Epoch(train) [96][150/293] lr: 5.000000e-04 eta: 2:54:00 time: 0.343434 data_time: 0.073452 memory: 9999 loss_kpt: 0.000612 acc_pose: 0.864052 loss: 0.000612 2022/10/20 13:03:25 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:03:37 - mmengine - INFO - Epoch(train) [96][200/293] lr: 5.000000e-04 eta: 2:53:46 time: 0.347635 data_time: 0.072268 memory: 9999 loss_kpt: 0.000611 acc_pose: 0.817940 loss: 0.000611 2022/10/20 13:03:56 - mmengine - INFO - Epoch(train) [96][250/293] lr: 5.000000e-04 eta: 2:53:34 time: 0.362538 data_time: 0.070342 memory: 9999 loss_kpt: 0.000630 acc_pose: 0.758719 loss: 0.000630 2022/10/20 13:04:11 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:04:30 - mmengine - INFO - Epoch(train) [97][50/293] lr: 5.000000e-04 eta: 2:52:53 time: 0.379609 data_time: 0.089749 memory: 9999 loss_kpt: 0.000612 acc_pose: 0.818718 loss: 0.000612 2022/10/20 13:04:48 - mmengine - INFO - Epoch(train) [97][100/293] lr: 5.000000e-04 eta: 2:52:40 time: 0.362014 data_time: 0.070569 memory: 9999 loss_kpt: 0.000613 acc_pose: 0.811113 loss: 0.000613 2022/10/20 13:05:05 - mmengine - INFO - Epoch(train) [97][150/293] lr: 5.000000e-04 eta: 2:52:27 time: 0.347657 data_time: 0.070603 memory: 9999 loss_kpt: 0.000608 acc_pose: 0.862625 loss: 0.000608 2022/10/20 13:05:23 - mmengine - INFO - Epoch(train) [97][200/293] lr: 5.000000e-04 eta: 2:52:14 time: 0.353929 data_time: 0.068852 memory: 9999 loss_kpt: 0.000624 acc_pose: 0.826880 loss: 0.000624 2022/10/20 13:05:40 - mmengine - INFO - Epoch(train) [97][250/293] lr: 5.000000e-04 eta: 2:52:00 time: 0.345346 data_time: 0.070227 memory: 9999 loss_kpt: 0.000621 acc_pose: 0.852796 loss: 0.000621 2022/10/20 13:05:56 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:06:14 - mmengine - INFO - Epoch(train) [98][50/293] lr: 5.000000e-04 eta: 2:51:19 time: 0.367661 data_time: 0.083089 memory: 9999 loss_kpt: 0.000619 acc_pose: 0.817373 loss: 0.000619 2022/10/20 13:06:34 - mmengine - INFO - Epoch(train) [98][100/293] lr: 5.000000e-04 eta: 2:51:09 time: 0.398222 data_time: 0.064910 memory: 9999 loss_kpt: 0.000614 acc_pose: 0.772341 loss: 0.000614 2022/10/20 13:06:53 - mmengine - INFO - Epoch(train) [98][150/293] lr: 5.000000e-04 eta: 2:50:57 time: 0.374491 data_time: 0.070466 memory: 9999 loss_kpt: 0.000629 acc_pose: 0.818977 loss: 0.000629 2022/10/20 13:07:12 - mmengine - INFO - Epoch(train) [98][200/293] lr: 5.000000e-04 eta: 2:50:46 time: 0.387190 data_time: 0.069720 memory: 9999 loss_kpt: 0.000623 acc_pose: 0.796697 loss: 0.000623 2022/10/20 13:07:31 - mmengine - INFO - Epoch(train) [98][250/293] lr: 5.000000e-04 eta: 2:50:33 time: 0.369941 data_time: 0.068442 memory: 9999 loss_kpt: 0.000619 acc_pose: 0.808481 loss: 0.000619 2022/10/20 13:07:46 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:08:05 - mmengine - INFO - Epoch(train) [99][50/293] lr: 5.000000e-04 eta: 2:49:53 time: 0.384024 data_time: 0.079693 memory: 9999 loss_kpt: 0.000620 acc_pose: 0.823919 loss: 0.000620 2022/10/20 13:08:23 - mmengine - INFO - Epoch(train) [99][100/293] lr: 5.000000e-04 eta: 2:49:41 time: 0.362291 data_time: 0.074795 memory: 9999 loss_kpt: 0.000617 acc_pose: 0.835075 loss: 0.000617 2022/10/20 13:08:41 - mmengine - INFO - Epoch(train) [99][150/293] lr: 5.000000e-04 eta: 2:49:28 time: 0.354337 data_time: 0.069194 memory: 9999 loss_kpt: 0.000616 acc_pose: 0.864643 loss: 0.000616 2022/10/20 13:08:59 - mmengine - INFO - Epoch(train) [99][200/293] lr: 5.000000e-04 eta: 2:49:15 time: 0.356655 data_time: 0.070662 memory: 9999 loss_kpt: 0.000612 acc_pose: 0.848794 loss: 0.000612 2022/10/20 13:09:17 - mmengine - INFO - Epoch(train) [99][250/293] lr: 5.000000e-04 eta: 2:49:02 time: 0.356460 data_time: 0.065956 memory: 9999 loss_kpt: 0.000608 acc_pose: 0.846391 loss: 0.000608 2022/10/20 13:09:29 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:09:31 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:09:50 - mmengine - INFO - Epoch(train) [100][50/293] lr: 5.000000e-04 eta: 2:48:21 time: 0.370245 data_time: 0.078285 memory: 9999 loss_kpt: 0.000614 acc_pose: 0.795760 loss: 0.000614 2022/10/20 13:10:08 - mmengine - INFO - Epoch(train) [100][100/293] lr: 5.000000e-04 eta: 2:48:08 time: 0.358286 data_time: 0.087231 memory: 9999 loss_kpt: 0.000614 acc_pose: 0.792916 loss: 0.000614 2022/10/20 13:10:26 - mmengine - INFO - Epoch(train) [100][150/293] lr: 5.000000e-04 eta: 2:47:55 time: 0.361109 data_time: 0.070786 memory: 9999 loss_kpt: 0.000606 acc_pose: 0.859412 loss: 0.000606 2022/10/20 13:10:43 - mmengine - INFO - Epoch(train) [100][200/293] lr: 5.000000e-04 eta: 2:47:41 time: 0.342724 data_time: 0.063830 memory: 9999 loss_kpt: 0.000617 acc_pose: 0.767926 loss: 0.000617 2022/10/20 13:11:00 - mmengine - INFO - Epoch(train) [100][250/293] lr: 5.000000e-04 eta: 2:47:28 time: 0.345944 data_time: 0.073618 memory: 9999 loss_kpt: 0.000614 acc_pose: 0.779714 loss: 0.000614 2022/10/20 13:11:15 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:11:15 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/10/20 13:11:25 - mmengine - INFO - Epoch(val) [100][50/407] eta: 0:00:44 time: 0.123538 data_time: 0.055158 memory: 9999 2022/10/20 13:11:31 - mmengine - INFO - Epoch(val) [100][100/407] eta: 0:00:36 time: 0.117833 data_time: 0.050043 memory: 1378 2022/10/20 13:11:37 - mmengine - INFO - Epoch(val) [100][150/407] eta: 0:00:29 time: 0.116023 data_time: 0.050941 memory: 1378 2022/10/20 13:11:42 - mmengine - INFO - Epoch(val) [100][200/407] eta: 0:00:22 time: 0.110127 data_time: 0.045014 memory: 1378 2022/10/20 13:11:48 - mmengine - INFO - Epoch(val) [100][250/407] eta: 0:00:19 time: 0.126195 data_time: 0.060455 memory: 1378 2022/10/20 13:11:54 - mmengine - INFO - Epoch(val) [100][300/407] eta: 0:00:12 time: 0.112185 data_time: 0.040496 memory: 1378 2022/10/20 13:12:00 - mmengine - INFO - Epoch(val) [100][350/407] eta: 0:00:06 time: 0.113608 data_time: 0.047618 memory: 1378 2022/10/20 13:12:05 - mmengine - INFO - Epoch(val) [100][400/407] eta: 0:00:00 time: 0.110586 data_time: 0.046696 memory: 1378 2022/10/20 13:12:41 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 13:12:54 - mmengine - INFO - Epoch(val) [100][407/407] coco/AP: 0.714624 coco/AP .5: 0.895190 coco/AP .75: 0.790737 coco/AP (M): 0.675319 coco/AP (L): 0.782867 coco/AR: 0.771001 coco/AR .5: 0.934351 coco/AR .75: 0.839578 coco/AR (M): 0.726004 coco/AR (L): 0.835377 2022/10/20 13:13:12 - mmengine - INFO - Epoch(train) [101][50/293] lr: 5.000000e-04 eta: 2:46:47 time: 0.361045 data_time: 0.078242 memory: 9999 loss_kpt: 0.000610 acc_pose: 0.853192 loss: 0.000610 2022/10/20 13:13:30 - mmengine - INFO - Epoch(train) [101][100/293] lr: 5.000000e-04 eta: 2:46:34 time: 0.355293 data_time: 0.069779 memory: 9999 loss_kpt: 0.000603 acc_pose: 0.852304 loss: 0.000603 2022/10/20 13:13:47 - mmengine - INFO - Epoch(train) [101][150/293] lr: 5.000000e-04 eta: 2:46:20 time: 0.343194 data_time: 0.072684 memory: 9999 loss_kpt: 0.000608 acc_pose: 0.835306 loss: 0.000608 2022/10/20 13:14:05 - mmengine - INFO - Epoch(train) [101][200/293] lr: 5.000000e-04 eta: 2:46:06 time: 0.349760 data_time: 0.069541 memory: 9999 loss_kpt: 0.000619 acc_pose: 0.828889 loss: 0.000619 2022/10/20 13:14:22 - mmengine - INFO - Epoch(train) [101][250/293] lr: 5.000000e-04 eta: 2:45:53 time: 0.348225 data_time: 0.070665 memory: 9999 loss_kpt: 0.000608 acc_pose: 0.820585 loss: 0.000608 2022/10/20 13:14:37 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:14:55 - mmengine - INFO - Epoch(train) [102][50/293] lr: 5.000000e-04 eta: 2:45:12 time: 0.358262 data_time: 0.086698 memory: 9999 loss_kpt: 0.000620 acc_pose: 0.837483 loss: 0.000620 2022/10/20 13:15:12 - mmengine - INFO - Epoch(train) [102][100/293] lr: 5.000000e-04 eta: 2:44:58 time: 0.341908 data_time: 0.075247 memory: 9999 loss_kpt: 0.000616 acc_pose: 0.840264 loss: 0.000616 2022/10/20 13:15:30 - mmengine - INFO - Epoch(train) [102][150/293] lr: 5.000000e-04 eta: 2:44:45 time: 0.347948 data_time: 0.073322 memory: 9999 loss_kpt: 0.000606 acc_pose: 0.833743 loss: 0.000606 2022/10/20 13:15:47 - mmengine - INFO - Epoch(train) [102][200/293] lr: 5.000000e-04 eta: 2:44:31 time: 0.345781 data_time: 0.067536 memory: 9999 loss_kpt: 0.000609 acc_pose: 0.870975 loss: 0.000609 2022/10/20 13:16:05 - mmengine - INFO - Epoch(train) [102][250/293] lr: 5.000000e-04 eta: 2:44:18 time: 0.359172 data_time: 0.070501 memory: 9999 loss_kpt: 0.000620 acc_pose: 0.861972 loss: 0.000620 2022/10/20 13:16:20 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:16:38 - mmengine - INFO - Epoch(train) [103][50/293] lr: 5.000000e-04 eta: 2:43:38 time: 0.364654 data_time: 0.084773 memory: 9999 loss_kpt: 0.000600 acc_pose: 0.830294 loss: 0.000600 2022/10/20 13:16:55 - mmengine - INFO - Epoch(train) [103][100/293] lr: 5.000000e-04 eta: 2:43:24 time: 0.341022 data_time: 0.070652 memory: 9999 loss_kpt: 0.000622 acc_pose: 0.824514 loss: 0.000622 2022/10/20 13:17:00 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:17:13 - mmengine - INFO - Epoch(train) [103][150/293] lr: 5.000000e-04 eta: 2:43:11 time: 0.356623 data_time: 0.065537 memory: 9999 loss_kpt: 0.000619 acc_pose: 0.782704 loss: 0.000619 2022/10/20 13:17:30 - mmengine - INFO - Epoch(train) [103][200/293] lr: 5.000000e-04 eta: 2:42:57 time: 0.350764 data_time: 0.068943 memory: 9999 loss_kpt: 0.000615 acc_pose: 0.843100 loss: 0.000615 2022/10/20 13:17:48 - mmengine - INFO - Epoch(train) [103][250/293] lr: 5.000000e-04 eta: 2:42:44 time: 0.352411 data_time: 0.067300 memory: 9999 loss_kpt: 0.000600 acc_pose: 0.846025 loss: 0.000600 2022/10/20 13:18:03 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:18:22 - mmengine - INFO - Epoch(train) [104][50/293] lr: 5.000000e-04 eta: 2:42:04 time: 0.364958 data_time: 0.094158 memory: 9999 loss_kpt: 0.000613 acc_pose: 0.870486 loss: 0.000613 2022/10/20 13:18:39 - mmengine - INFO - Epoch(train) [104][100/293] lr: 5.000000e-04 eta: 2:41:51 time: 0.355998 data_time: 0.073370 memory: 9999 loss_kpt: 0.000601 acc_pose: 0.864426 loss: 0.000601 2022/10/20 13:18:57 - mmengine - INFO - Epoch(train) [104][150/293] lr: 5.000000e-04 eta: 2:41:37 time: 0.352301 data_time: 0.065389 memory: 9999 loss_kpt: 0.000601 acc_pose: 0.807647 loss: 0.000601 2022/10/20 13:19:14 - mmengine - INFO - Epoch(train) [104][200/293] lr: 5.000000e-04 eta: 2:41:23 time: 0.341467 data_time: 0.067664 memory: 9999 loss_kpt: 0.000612 acc_pose: 0.879548 loss: 0.000612 2022/10/20 13:19:32 - mmengine - INFO - Epoch(train) [104][250/293] lr: 5.000000e-04 eta: 2:41:10 time: 0.362973 data_time: 0.075022 memory: 9999 loss_kpt: 0.000610 acc_pose: 0.874736 loss: 0.000610 2022/10/20 13:19:47 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:20:05 - mmengine - INFO - Epoch(train) [105][50/293] lr: 5.000000e-04 eta: 2:40:31 time: 0.366169 data_time: 0.078268 memory: 9999 loss_kpt: 0.000620 acc_pose: 0.812273 loss: 0.000620 2022/10/20 13:20:23 - mmengine - INFO - Epoch(train) [105][100/293] lr: 5.000000e-04 eta: 2:40:18 time: 0.358577 data_time: 0.066347 memory: 9999 loss_kpt: 0.000609 acc_pose: 0.884408 loss: 0.000609 2022/10/20 13:20:41 - mmengine - INFO - Epoch(train) [105][150/293] lr: 5.000000e-04 eta: 2:40:04 time: 0.350233 data_time: 0.067178 memory: 9999 loss_kpt: 0.000612 acc_pose: 0.840990 loss: 0.000612 2022/10/20 13:20:58 - mmengine - INFO - Epoch(train) [105][200/293] lr: 5.000000e-04 eta: 2:39:50 time: 0.344615 data_time: 0.065352 memory: 9999 loss_kpt: 0.000609 acc_pose: 0.846405 loss: 0.000609 2022/10/20 13:21:16 - mmengine - INFO - Epoch(train) [105][250/293] lr: 5.000000e-04 eta: 2:39:37 time: 0.359789 data_time: 0.072473 memory: 9999 loss_kpt: 0.000623 acc_pose: 0.824508 loss: 0.000623 2022/10/20 13:21:31 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:21:49 - mmengine - INFO - Epoch(train) [106][50/293] lr: 5.000000e-04 eta: 2:38:58 time: 0.369315 data_time: 0.081830 memory: 9999 loss_kpt: 0.000605 acc_pose: 0.838273 loss: 0.000605 2022/10/20 13:22:07 - mmengine - INFO - Epoch(train) [106][100/293] lr: 5.000000e-04 eta: 2:38:44 time: 0.354328 data_time: 0.071466 memory: 9999 loss_kpt: 0.000596 acc_pose: 0.884358 loss: 0.000596 2022/10/20 13:22:25 - mmengine - INFO - Epoch(train) [106][150/293] lr: 5.000000e-04 eta: 2:38:31 time: 0.359534 data_time: 0.074869 memory: 9999 loss_kpt: 0.000616 acc_pose: 0.866836 loss: 0.000616 2022/10/20 13:22:43 - mmengine - INFO - Epoch(train) [106][200/293] lr: 5.000000e-04 eta: 2:38:18 time: 0.355881 data_time: 0.065432 memory: 9999 loss_kpt: 0.000601 acc_pose: 0.843494 loss: 0.000601 2022/10/20 13:22:55 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:23:00 - mmengine - INFO - Epoch(train) [106][250/293] lr: 5.000000e-04 eta: 2:38:04 time: 0.350887 data_time: 0.063560 memory: 9999 loss_kpt: 0.000611 acc_pose: 0.829333 loss: 0.000611 2022/10/20 13:23:15 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:23:34 - mmengine - INFO - Epoch(train) [107][50/293] lr: 5.000000e-04 eta: 2:37:25 time: 0.369944 data_time: 0.081301 memory: 9999 loss_kpt: 0.000610 acc_pose: 0.871792 loss: 0.000610 2022/10/20 13:23:52 - mmengine - INFO - Epoch(train) [107][100/293] lr: 5.000000e-04 eta: 2:37:12 time: 0.360113 data_time: 0.070152 memory: 9999 loss_kpt: 0.000612 acc_pose: 0.861655 loss: 0.000612 2022/10/20 13:24:09 - mmengine - INFO - Epoch(train) [107][150/293] lr: 5.000000e-04 eta: 2:36:59 time: 0.352789 data_time: 0.073361 memory: 9999 loss_kpt: 0.000615 acc_pose: 0.788745 loss: 0.000615 2022/10/20 13:24:27 - mmengine - INFO - Epoch(train) [107][200/293] lr: 5.000000e-04 eta: 2:36:46 time: 0.360280 data_time: 0.068169 memory: 9999 loss_kpt: 0.000605 acc_pose: 0.828248 loss: 0.000605 2022/10/20 13:24:45 - mmengine - INFO - Epoch(train) [107][250/293] lr: 5.000000e-04 eta: 2:36:32 time: 0.359816 data_time: 0.080046 memory: 9999 loss_kpt: 0.000614 acc_pose: 0.884014 loss: 0.000614 2022/10/20 13:25:00 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:25:18 - mmengine - INFO - Epoch(train) [108][50/293] lr: 5.000000e-04 eta: 2:35:53 time: 0.357573 data_time: 0.081529 memory: 9999 loss_kpt: 0.000603 acc_pose: 0.872704 loss: 0.000603 2022/10/20 13:25:35 - mmengine - INFO - Epoch(train) [108][100/293] lr: 5.000000e-04 eta: 2:35:40 time: 0.356334 data_time: 0.068188 memory: 9999 loss_kpt: 0.000612 acc_pose: 0.841740 loss: 0.000612 2022/10/20 13:25:53 - mmengine - INFO - Epoch(train) [108][150/293] lr: 5.000000e-04 eta: 2:35:26 time: 0.350128 data_time: 0.071139 memory: 9999 loss_kpt: 0.000626 acc_pose: 0.843813 loss: 0.000626 2022/10/20 13:26:10 - mmengine - INFO - Epoch(train) [108][200/293] lr: 5.000000e-04 eta: 2:35:12 time: 0.343811 data_time: 0.067734 memory: 9999 loss_kpt: 0.000606 acc_pose: 0.863436 loss: 0.000606 2022/10/20 13:26:27 - mmengine - INFO - Epoch(train) [108][250/293] lr: 5.000000e-04 eta: 2:34:58 time: 0.343546 data_time: 0.066484 memory: 9999 loss_kpt: 0.000604 acc_pose: 0.890768 loss: 0.000604 2022/10/20 13:26:43 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:27:02 - mmengine - INFO - Epoch(train) [109][50/293] lr: 5.000000e-04 eta: 2:34:20 time: 0.373118 data_time: 0.095321 memory: 9999 loss_kpt: 0.000617 acc_pose: 0.824539 loss: 0.000617 2022/10/20 13:27:19 - mmengine - INFO - Epoch(train) [109][100/293] lr: 5.000000e-04 eta: 2:34:06 time: 0.352919 data_time: 0.068626 memory: 9999 loss_kpt: 0.000599 acc_pose: 0.826796 loss: 0.000599 2022/10/20 13:27:37 - mmengine - INFO - Epoch(train) [109][150/293] lr: 5.000000e-04 eta: 2:33:53 time: 0.355125 data_time: 0.067388 memory: 9999 loss_kpt: 0.000589 acc_pose: 0.876902 loss: 0.000589 2022/10/20 13:27:55 - mmengine - INFO - Epoch(train) [109][200/293] lr: 5.000000e-04 eta: 2:33:39 time: 0.353813 data_time: 0.077869 memory: 9999 loss_kpt: 0.000605 acc_pose: 0.857930 loss: 0.000605 2022/10/20 13:28:12 - mmengine - INFO - Epoch(train) [109][250/293] lr: 5.000000e-04 eta: 2:33:25 time: 0.346669 data_time: 0.068759 memory: 9999 loss_kpt: 0.000595 acc_pose: 0.854458 loss: 0.000595 2022/10/20 13:28:27 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:28:45 - mmengine - INFO - Epoch(train) [110][50/293] lr: 5.000000e-04 eta: 2:32:46 time: 0.361608 data_time: 0.083211 memory: 9999 loss_kpt: 0.000589 acc_pose: 0.836772 loss: 0.000589 2022/10/20 13:28:50 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:29:03 - mmengine - INFO - Epoch(train) [110][100/293] lr: 5.000000e-04 eta: 2:32:33 time: 0.352352 data_time: 0.073920 memory: 9999 loss_kpt: 0.000595 acc_pose: 0.840561 loss: 0.000595 2022/10/20 13:29:21 - mmengine - INFO - Epoch(train) [110][150/293] lr: 5.000000e-04 eta: 2:32:20 time: 0.361242 data_time: 0.066819 memory: 9999 loss_kpt: 0.000601 acc_pose: 0.848631 loss: 0.000601 2022/10/20 13:29:38 - mmengine - INFO - Epoch(train) [110][200/293] lr: 5.000000e-04 eta: 2:32:06 time: 0.343864 data_time: 0.065265 memory: 9999 loss_kpt: 0.000603 acc_pose: 0.865132 loss: 0.000603 2022/10/20 13:29:56 - mmengine - INFO - Epoch(train) [110][250/293] lr: 5.000000e-04 eta: 2:31:52 time: 0.353183 data_time: 0.071591 memory: 9999 loss_kpt: 0.000607 acc_pose: 0.885800 loss: 0.000607 2022/10/20 13:30:10 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:30:10 - mmengine - INFO - Saving checkpoint at 110 epochs 2022/10/20 13:30:20 - mmengine - INFO - Epoch(val) [110][50/407] eta: 0:00:43 time: 0.122385 data_time: 0.056717 memory: 9999 2022/10/20 13:30:26 - mmengine - INFO - Epoch(val) [110][100/407] eta: 0:00:33 time: 0.109927 data_time: 0.042259 memory: 1378 2022/10/20 13:30:31 - mmengine - INFO - Epoch(val) [110][150/407] eta: 0:00:28 time: 0.112212 data_time: 0.046272 memory: 1378 2022/10/20 13:30:37 - mmengine - INFO - Epoch(val) [110][200/407] eta: 0:00:23 time: 0.113858 data_time: 0.046862 memory: 1378 2022/10/20 13:30:43 - mmengine - INFO - Epoch(val) [110][250/407] eta: 0:00:18 time: 0.115198 data_time: 0.045021 memory: 1378 2022/10/20 13:30:49 - mmengine - INFO - Epoch(val) [110][300/407] eta: 0:00:12 time: 0.117367 data_time: 0.050432 memory: 1378 2022/10/20 13:30:55 - mmengine - INFO - Epoch(val) [110][350/407] eta: 0:00:07 time: 0.123277 data_time: 0.055098 memory: 1378 2022/10/20 13:31:00 - mmengine - INFO - Epoch(val) [110][400/407] eta: 0:00:00 time: 0.110204 data_time: 0.047413 memory: 1378 2022/10/20 13:31:35 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 13:31:49 - mmengine - INFO - Epoch(val) [110][407/407] coco/AP: 0.720797 coco/AP .5: 0.899113 coco/AP .75: 0.797620 coco/AP (M): 0.684615 coco/AP (L): 0.786850 coco/AR: 0.775693 coco/AR .5: 0.937815 coco/AR .75: 0.842254 coco/AR (M): 0.731822 coco/AR (L): 0.838870 2022/10/20 13:31:49 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221020/resnetv1d101_256/best_coco/AP_epoch_90.pth is removed 2022/10/20 13:31:52 - mmengine - INFO - The best checkpoint with 0.7208 coco/AP at 110 epoch is saved to best_coco/AP_epoch_110.pth. 2022/10/20 13:32:10 - mmengine - INFO - Epoch(train) [111][50/293] lr: 5.000000e-04 eta: 2:31:14 time: 0.365276 data_time: 0.111734 memory: 9999 loss_kpt: 0.000592 acc_pose: 0.830777 loss: 0.000592 2022/10/20 13:32:27 - mmengine - INFO - Epoch(train) [111][100/293] lr: 5.000000e-04 eta: 2:31:00 time: 0.344473 data_time: 0.067373 memory: 9999 loss_kpt: 0.000613 acc_pose: 0.844988 loss: 0.000613 2022/10/20 13:32:45 - mmengine - INFO - Epoch(train) [111][150/293] lr: 5.000000e-04 eta: 2:30:46 time: 0.349690 data_time: 0.077523 memory: 9999 loss_kpt: 0.000603 acc_pose: 0.840098 loss: 0.000603 2022/10/20 13:33:01 - mmengine - INFO - Epoch(train) [111][200/293] lr: 5.000000e-04 eta: 2:30:31 time: 0.335636 data_time: 0.063915 memory: 9999 loss_kpt: 0.000597 acc_pose: 0.875691 loss: 0.000597 2022/10/20 13:33:19 - mmengine - INFO - Epoch(train) [111][250/293] lr: 5.000000e-04 eta: 2:30:18 time: 0.362151 data_time: 0.062988 memory: 9999 loss_kpt: 0.000604 acc_pose: 0.797164 loss: 0.000604 2022/10/20 13:33:34 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:33:53 - mmengine - INFO - Epoch(train) [112][50/293] lr: 5.000000e-04 eta: 2:29:40 time: 0.375263 data_time: 0.084942 memory: 9999 loss_kpt: 0.000600 acc_pose: 0.832425 loss: 0.000600 2022/10/20 13:34:11 - mmengine - INFO - Epoch(train) [112][100/293] lr: 5.000000e-04 eta: 2:29:27 time: 0.356782 data_time: 0.074120 memory: 9999 loss_kpt: 0.000603 acc_pose: 0.852496 loss: 0.000603 2022/10/20 13:34:28 - mmengine - INFO - Epoch(train) [112][150/293] lr: 5.000000e-04 eta: 2:29:13 time: 0.350370 data_time: 0.067555 memory: 9999 loss_kpt: 0.000615 acc_pose: 0.836729 loss: 0.000615 2022/10/20 13:34:45 - mmengine - INFO - Epoch(train) [112][200/293] lr: 5.000000e-04 eta: 2:28:59 time: 0.345094 data_time: 0.067918 memory: 9999 loss_kpt: 0.000599 acc_pose: 0.821929 loss: 0.000599 2022/10/20 13:35:03 - mmengine - INFO - Epoch(train) [112][250/293] lr: 5.000000e-04 eta: 2:28:46 time: 0.359706 data_time: 0.066740 memory: 9999 loss_kpt: 0.000595 acc_pose: 0.867700 loss: 0.000595 2022/10/20 13:35:18 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:35:36 - mmengine - INFO - Epoch(train) [113][50/293] lr: 5.000000e-04 eta: 2:28:07 time: 0.354496 data_time: 0.078470 memory: 9999 loss_kpt: 0.000605 acc_pose: 0.849050 loss: 0.000605 2022/10/20 13:35:53 - mmengine - INFO - Epoch(train) [113][100/293] lr: 5.000000e-04 eta: 2:27:54 time: 0.349768 data_time: 0.068738 memory: 9999 loss_kpt: 0.000619 acc_pose: 0.887933 loss: 0.000619 2022/10/20 13:36:11 - mmengine - INFO - Epoch(train) [113][150/293] lr: 5.000000e-04 eta: 2:27:40 time: 0.350838 data_time: 0.076931 memory: 9999 loss_kpt: 0.000601 acc_pose: 0.841974 loss: 0.000601 2022/10/20 13:36:23 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:36:28 - mmengine - INFO - Epoch(train) [113][200/293] lr: 5.000000e-04 eta: 2:27:26 time: 0.351435 data_time: 0.076118 memory: 9999 loss_kpt: 0.000602 acc_pose: 0.893148 loss: 0.000602 2022/10/20 13:36:46 - mmengine - INFO - Epoch(train) [113][250/293] lr: 5.000000e-04 eta: 2:27:12 time: 0.353765 data_time: 0.069237 memory: 9999 loss_kpt: 0.000587 acc_pose: 0.830583 loss: 0.000587 2022/10/20 13:37:01 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:37:20 - mmengine - INFO - Epoch(train) [114][50/293] lr: 5.000000e-04 eta: 2:26:35 time: 0.374763 data_time: 0.086773 memory: 9999 loss_kpt: 0.000598 acc_pose: 0.837132 loss: 0.000598 2022/10/20 13:37:37 - mmengine - INFO - Epoch(train) [114][100/293] lr: 5.000000e-04 eta: 2:26:21 time: 0.342694 data_time: 0.068550 memory: 9999 loss_kpt: 0.000614 acc_pose: 0.818818 loss: 0.000614 2022/10/20 13:37:55 - mmengine - INFO - Epoch(train) [114][150/293] lr: 5.000000e-04 eta: 2:26:07 time: 0.354145 data_time: 0.064902 memory: 9999 loss_kpt: 0.000596 acc_pose: 0.845688 loss: 0.000596 2022/10/20 13:38:12 - mmengine - INFO - Epoch(train) [114][200/293] lr: 5.000000e-04 eta: 2:25:53 time: 0.349470 data_time: 0.069710 memory: 9999 loss_kpt: 0.000593 acc_pose: 0.840211 loss: 0.000593 2022/10/20 13:38:29 - mmengine - INFO - Epoch(train) [114][250/293] lr: 5.000000e-04 eta: 2:25:39 time: 0.346740 data_time: 0.067499 memory: 9999 loss_kpt: 0.000596 acc_pose: 0.842845 loss: 0.000596 2022/10/20 13:38:44 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:39:03 - mmengine - INFO - Epoch(train) [115][50/293] lr: 5.000000e-04 eta: 2:25:02 time: 0.370718 data_time: 0.105705 memory: 9999 loss_kpt: 0.000591 acc_pose: 0.866852 loss: 0.000591 2022/10/20 13:39:20 - mmengine - INFO - Epoch(train) [115][100/293] lr: 5.000000e-04 eta: 2:24:48 time: 0.345502 data_time: 0.067873 memory: 9999 loss_kpt: 0.000603 acc_pose: 0.851538 loss: 0.000603 2022/10/20 13:39:37 - mmengine - INFO - Epoch(train) [115][150/293] lr: 5.000000e-04 eta: 2:24:34 time: 0.341296 data_time: 0.069709 memory: 9999 loss_kpt: 0.000598 acc_pose: 0.811698 loss: 0.000598 2022/10/20 13:39:55 - mmengine - INFO - Epoch(train) [115][200/293] lr: 5.000000e-04 eta: 2:24:20 time: 0.361260 data_time: 0.078998 memory: 9999 loss_kpt: 0.000596 acc_pose: 0.837773 loss: 0.000596 2022/10/20 13:40:13 - mmengine - INFO - Epoch(train) [115][250/293] lr: 5.000000e-04 eta: 2:24:07 time: 0.355415 data_time: 0.082683 memory: 9999 loss_kpt: 0.000606 acc_pose: 0.831564 loss: 0.000606 2022/10/20 13:40:28 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:40:47 - mmengine - INFO - Epoch(train) [116][50/293] lr: 5.000000e-04 eta: 2:23:30 time: 0.379333 data_time: 0.085267 memory: 9999 loss_kpt: 0.000596 acc_pose: 0.852168 loss: 0.000596 2022/10/20 13:41:05 - mmengine - INFO - Epoch(train) [116][100/293] lr: 5.000000e-04 eta: 2:23:16 time: 0.344461 data_time: 0.070313 memory: 9999 loss_kpt: 0.000583 acc_pose: 0.837869 loss: 0.000583 2022/10/20 13:41:22 - mmengine - INFO - Epoch(train) [116][150/293] lr: 5.000000e-04 eta: 2:23:02 time: 0.355349 data_time: 0.074618 memory: 9999 loss_kpt: 0.000601 acc_pose: 0.842115 loss: 0.000601 2022/10/20 13:41:40 - mmengine - INFO - Epoch(train) [116][200/293] lr: 5.000000e-04 eta: 2:22:48 time: 0.342441 data_time: 0.065091 memory: 9999 loss_kpt: 0.000601 acc_pose: 0.899508 loss: 0.000601 2022/10/20 13:41:57 - mmengine - INFO - Epoch(train) [116][250/293] lr: 5.000000e-04 eta: 2:22:34 time: 0.357657 data_time: 0.065782 memory: 9999 loss_kpt: 0.000599 acc_pose: 0.855663 loss: 0.000599 2022/10/20 13:42:12 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:42:18 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:42:31 - mmengine - INFO - Epoch(train) [117][50/293] lr: 5.000000e-04 eta: 2:21:57 time: 0.379700 data_time: 0.087725 memory: 9999 loss_kpt: 0.000599 acc_pose: 0.867103 loss: 0.000599 2022/10/20 13:42:49 - mmengine - INFO - Epoch(train) [117][100/293] lr: 5.000000e-04 eta: 2:21:44 time: 0.360229 data_time: 0.074766 memory: 9999 loss_kpt: 0.000599 acc_pose: 0.847044 loss: 0.000599 2022/10/20 13:43:07 - mmengine - INFO - Epoch(train) [117][150/293] lr: 5.000000e-04 eta: 2:21:30 time: 0.351960 data_time: 0.070296 memory: 9999 loss_kpt: 0.000603 acc_pose: 0.843085 loss: 0.000603 2022/10/20 13:43:25 - mmengine - INFO - Epoch(train) [117][200/293] lr: 5.000000e-04 eta: 2:21:16 time: 0.349800 data_time: 0.070248 memory: 9999 loss_kpt: 0.000601 acc_pose: 0.868706 loss: 0.000601 2022/10/20 13:43:42 - mmengine - INFO - Epoch(train) [117][250/293] lr: 5.000000e-04 eta: 2:21:02 time: 0.352038 data_time: 0.066621 memory: 9999 loss_kpt: 0.000602 acc_pose: 0.877779 loss: 0.000602 2022/10/20 13:43:57 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:44:15 - mmengine - INFO - Epoch(train) [118][50/293] lr: 5.000000e-04 eta: 2:20:25 time: 0.368832 data_time: 0.078747 memory: 9999 loss_kpt: 0.000595 acc_pose: 0.826287 loss: 0.000595 2022/10/20 13:44:33 - mmengine - INFO - Epoch(train) [118][100/293] lr: 5.000000e-04 eta: 2:20:12 time: 0.358504 data_time: 0.086793 memory: 9999 loss_kpt: 0.000597 acc_pose: 0.851055 loss: 0.000597 2022/10/20 13:44:50 - mmengine - INFO - Epoch(train) [118][150/293] lr: 5.000000e-04 eta: 2:19:58 time: 0.346591 data_time: 0.068136 memory: 9999 loss_kpt: 0.000597 acc_pose: 0.834904 loss: 0.000597 2022/10/20 13:45:08 - mmengine - INFO - Epoch(train) [118][200/293] lr: 5.000000e-04 eta: 2:19:44 time: 0.349647 data_time: 0.069963 memory: 9999 loss_kpt: 0.000605 acc_pose: 0.850241 loss: 0.000605 2022/10/20 13:45:25 - mmengine - INFO - Epoch(train) [118][250/293] lr: 5.000000e-04 eta: 2:19:30 time: 0.350357 data_time: 0.073449 memory: 9999 loss_kpt: 0.000607 acc_pose: 0.767282 loss: 0.000607 2022/10/20 13:45:40 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:45:58 - mmengine - INFO - Epoch(train) [119][50/293] lr: 5.000000e-04 eta: 2:18:53 time: 0.360736 data_time: 0.087364 memory: 9999 loss_kpt: 0.000589 acc_pose: 0.847071 loss: 0.000589 2022/10/20 13:46:16 - mmengine - INFO - Epoch(train) [119][100/293] lr: 5.000000e-04 eta: 2:18:39 time: 0.356388 data_time: 0.068685 memory: 9999 loss_kpt: 0.000596 acc_pose: 0.814835 loss: 0.000596 2022/10/20 13:46:33 - mmengine - INFO - Epoch(train) [119][150/293] lr: 5.000000e-04 eta: 2:18:25 time: 0.349934 data_time: 0.065616 memory: 9999 loss_kpt: 0.000607 acc_pose: 0.885056 loss: 0.000607 2022/10/20 13:46:51 - mmengine - INFO - Epoch(train) [119][200/293] lr: 5.000000e-04 eta: 2:18:11 time: 0.358342 data_time: 0.067770 memory: 9999 loss_kpt: 0.000597 acc_pose: 0.796748 loss: 0.000597 2022/10/20 13:47:09 - mmengine - INFO - Epoch(train) [119][250/293] lr: 5.000000e-04 eta: 2:17:58 time: 0.353377 data_time: 0.072929 memory: 9999 loss_kpt: 0.000597 acc_pose: 0.803864 loss: 0.000597 2022/10/20 13:47:24 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:47:42 - mmengine - INFO - Epoch(train) [120][50/293] lr: 5.000000e-04 eta: 2:17:21 time: 0.377122 data_time: 0.081619 memory: 9999 loss_kpt: 0.000599 acc_pose: 0.875888 loss: 0.000599 2022/10/20 13:48:00 - mmengine - INFO - Epoch(train) [120][100/293] lr: 5.000000e-04 eta: 2:17:07 time: 0.343226 data_time: 0.064496 memory: 9999 loss_kpt: 0.000606 acc_pose: 0.823376 loss: 0.000606 2022/10/20 13:48:11 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:48:17 - mmengine - INFO - Epoch(train) [120][150/293] lr: 5.000000e-04 eta: 2:16:53 time: 0.354766 data_time: 0.072753 memory: 9999 loss_kpt: 0.000590 acc_pose: 0.844698 loss: 0.000590 2022/10/20 13:48:35 - mmengine - INFO - Epoch(train) [120][200/293] lr: 5.000000e-04 eta: 2:16:39 time: 0.343523 data_time: 0.064563 memory: 9999 loss_kpt: 0.000592 acc_pose: 0.860166 loss: 0.000592 2022/10/20 13:48:52 - mmengine - INFO - Epoch(train) [120][250/293] lr: 5.000000e-04 eta: 2:16:25 time: 0.346312 data_time: 0.072444 memory: 9999 loss_kpt: 0.000603 acc_pose: 0.839747 loss: 0.000603 2022/10/20 13:49:07 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:49:07 - mmengine - INFO - Saving checkpoint at 120 epochs 2022/10/20 13:49:16 - mmengine - INFO - Epoch(val) [120][50/407] eta: 0:00:44 time: 0.123759 data_time: 0.054033 memory: 9999 2022/10/20 13:49:22 - mmengine - INFO - Epoch(val) [120][100/407] eta: 0:00:34 time: 0.112769 data_time: 0.045549 memory: 1378 2022/10/20 13:49:28 - mmengine - INFO - Epoch(val) [120][150/407] eta: 0:00:29 time: 0.114732 data_time: 0.042885 memory: 1378 2022/10/20 13:49:34 - mmengine - INFO - Epoch(val) [120][200/407] eta: 0:00:23 time: 0.114659 data_time: 0.048385 memory: 1378 2022/10/20 13:49:40 - mmengine - INFO - Epoch(val) [120][250/407] eta: 0:00:19 time: 0.123559 data_time: 0.056719 memory: 1378 2022/10/20 13:49:45 - mmengine - INFO - Epoch(val) [120][300/407] eta: 0:00:12 time: 0.113187 data_time: 0.045918 memory: 1378 2022/10/20 13:49:51 - mmengine - INFO - Epoch(val) [120][350/407] eta: 0:00:06 time: 0.114029 data_time: 0.046949 memory: 1378 2022/10/20 13:49:56 - mmengine - INFO - Epoch(val) [120][400/407] eta: 0:00:00 time: 0.103584 data_time: 0.040187 memory: 1378 2022/10/20 13:50:31 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 13:50:45 - mmengine - INFO - Epoch(val) [120][407/407] coco/AP: 0.720977 coco/AP .5: 0.897018 coco/AP .75: 0.796660 coco/AP (M): 0.684307 coco/AP (L): 0.788230 coco/AR: 0.775913 coco/AR .5: 0.935768 coco/AR .75: 0.843199 coco/AR (M): 0.732286 coco/AR (L): 0.838164 2022/10/20 13:50:45 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221020/resnetv1d101_256/best_coco/AP_epoch_110.pth is removed 2022/10/20 13:50:47 - mmengine - INFO - The best checkpoint with 0.7210 coco/AP at 120 epoch is saved to best_coco/AP_epoch_120.pth. 2022/10/20 13:51:05 - mmengine - INFO - Epoch(train) [121][50/293] lr: 5.000000e-04 eta: 2:15:48 time: 0.358003 data_time: 0.083999 memory: 9999 loss_kpt: 0.000594 acc_pose: 0.863888 loss: 0.000594 2022/10/20 13:51:23 - mmengine - INFO - Epoch(train) [121][100/293] lr: 5.000000e-04 eta: 2:15:34 time: 0.350820 data_time: 0.065154 memory: 9999 loss_kpt: 0.000599 acc_pose: 0.825527 loss: 0.000599 2022/10/20 13:51:41 - mmengine - INFO - Epoch(train) [121][150/293] lr: 5.000000e-04 eta: 2:15:20 time: 0.357328 data_time: 0.069575 memory: 9999 loss_kpt: 0.000612 acc_pose: 0.877308 loss: 0.000612 2022/10/20 13:51:58 - mmengine - INFO - Epoch(train) [121][200/293] lr: 5.000000e-04 eta: 2:15:06 time: 0.354471 data_time: 0.067180 memory: 9999 loss_kpt: 0.000597 acc_pose: 0.794111 loss: 0.000597 2022/10/20 13:52:15 - mmengine - INFO - Epoch(train) [121][250/293] lr: 5.000000e-04 eta: 2:14:52 time: 0.340173 data_time: 0.065954 memory: 9999 loss_kpt: 0.000595 acc_pose: 0.789653 loss: 0.000595 2022/10/20 13:52:30 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:52:48 - mmengine - INFO - Epoch(train) [122][50/293] lr: 5.000000e-04 eta: 2:14:15 time: 0.363441 data_time: 0.079974 memory: 9999 loss_kpt: 0.000589 acc_pose: 0.861715 loss: 0.000589 2022/10/20 13:53:07 - mmengine - INFO - Epoch(train) [122][100/293] lr: 5.000000e-04 eta: 2:14:02 time: 0.361789 data_time: 0.068968 memory: 9999 loss_kpt: 0.000605 acc_pose: 0.849102 loss: 0.000605 2022/10/20 13:53:24 - mmengine - INFO - Epoch(train) [122][150/293] lr: 5.000000e-04 eta: 2:13:48 time: 0.349171 data_time: 0.062060 memory: 9999 loss_kpt: 0.000596 acc_pose: 0.840442 loss: 0.000596 2022/10/20 13:53:42 - mmengine - INFO - Epoch(train) [122][200/293] lr: 5.000000e-04 eta: 2:13:34 time: 0.356115 data_time: 0.065898 memory: 9999 loss_kpt: 0.000591 acc_pose: 0.797375 loss: 0.000591 2022/10/20 13:53:59 - mmengine - INFO - Epoch(train) [122][250/293] lr: 5.000000e-04 eta: 2:13:20 time: 0.343185 data_time: 0.075782 memory: 9999 loss_kpt: 0.000579 acc_pose: 0.867546 loss: 0.000579 2022/10/20 13:54:14 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:54:32 - mmengine - INFO - Epoch(train) [123][50/293] lr: 5.000000e-04 eta: 2:12:44 time: 0.372047 data_time: 0.077303 memory: 9999 loss_kpt: 0.000604 acc_pose: 0.868407 loss: 0.000604 2022/10/20 13:54:52 - mmengine - INFO - Epoch(train) [123][100/293] lr: 5.000000e-04 eta: 2:12:31 time: 0.381753 data_time: 0.072619 memory: 9999 loss_kpt: 0.000595 acc_pose: 0.812945 loss: 0.000595 2022/10/20 13:55:10 - mmengine - INFO - Epoch(train) [123][150/293] lr: 5.000000e-04 eta: 2:12:17 time: 0.366589 data_time: 0.071174 memory: 9999 loss_kpt: 0.000591 acc_pose: 0.871068 loss: 0.000591 2022/10/20 13:55:28 - mmengine - INFO - Epoch(train) [123][200/293] lr: 5.000000e-04 eta: 2:12:03 time: 0.356759 data_time: 0.072693 memory: 9999 loss_kpt: 0.000606 acc_pose: 0.854916 loss: 0.000606 2022/10/20 13:55:45 - mmengine - INFO - Epoch(train) [123][250/293] lr: 5.000000e-04 eta: 2:11:49 time: 0.351174 data_time: 0.081794 memory: 9999 loss_kpt: 0.000591 acc_pose: 0.801118 loss: 0.000591 2022/10/20 13:55:47 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:56:00 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:56:19 - mmengine - INFO - Epoch(train) [124][50/293] lr: 5.000000e-04 eta: 2:11:14 time: 0.385002 data_time: 0.080007 memory: 9999 loss_kpt: 0.000580 acc_pose: 0.807980 loss: 0.000580 2022/10/20 13:56:36 - mmengine - INFO - Epoch(train) [124][100/293] lr: 5.000000e-04 eta: 2:11:00 time: 0.343223 data_time: 0.070636 memory: 9999 loss_kpt: 0.000592 acc_pose: 0.810410 loss: 0.000592 2022/10/20 13:56:54 - mmengine - INFO - Epoch(train) [124][150/293] lr: 5.000000e-04 eta: 2:10:46 time: 0.353158 data_time: 0.074919 memory: 9999 loss_kpt: 0.000593 acc_pose: 0.829979 loss: 0.000593 2022/10/20 13:57:12 - mmengine - INFO - Epoch(train) [124][200/293] lr: 5.000000e-04 eta: 2:10:32 time: 0.355480 data_time: 0.069290 memory: 9999 loss_kpt: 0.000587 acc_pose: 0.864050 loss: 0.000587 2022/10/20 13:57:29 - mmengine - INFO - Epoch(train) [124][250/293] lr: 5.000000e-04 eta: 2:10:18 time: 0.349206 data_time: 0.074360 memory: 9999 loss_kpt: 0.000596 acc_pose: 0.808302 loss: 0.000596 2022/10/20 13:57:44 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:58:03 - mmengine - INFO - Epoch(train) [125][50/293] lr: 5.000000e-04 eta: 2:09:42 time: 0.374605 data_time: 0.080442 memory: 9999 loss_kpt: 0.000601 acc_pose: 0.844282 loss: 0.000601 2022/10/20 13:58:21 - mmengine - INFO - Epoch(train) [125][100/293] lr: 5.000000e-04 eta: 2:09:28 time: 0.365224 data_time: 0.074556 memory: 9999 loss_kpt: 0.000586 acc_pose: 0.823262 loss: 0.000586 2022/10/20 13:58:40 - mmengine - INFO - Epoch(train) [125][150/293] lr: 5.000000e-04 eta: 2:09:15 time: 0.363231 data_time: 0.067678 memory: 9999 loss_kpt: 0.000597 acc_pose: 0.879106 loss: 0.000597 2022/10/20 13:58:57 - mmengine - INFO - Epoch(train) [125][200/293] lr: 5.000000e-04 eta: 2:09:00 time: 0.349076 data_time: 0.075208 memory: 9999 loss_kpt: 0.000600 acc_pose: 0.845959 loss: 0.000600 2022/10/20 13:59:15 - mmengine - INFO - Epoch(train) [125][250/293] lr: 5.000000e-04 eta: 2:08:47 time: 0.354791 data_time: 0.069346 memory: 9999 loss_kpt: 0.000599 acc_pose: 0.816659 loss: 0.000599 2022/10/20 13:59:30 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 13:59:49 - mmengine - INFO - Epoch(train) [126][50/293] lr: 5.000000e-04 eta: 2:08:11 time: 0.384395 data_time: 0.088256 memory: 9999 loss_kpt: 0.000578 acc_pose: 0.799449 loss: 0.000578 2022/10/20 14:00:07 - mmengine - INFO - Epoch(train) [126][100/293] lr: 5.000000e-04 eta: 2:07:57 time: 0.356261 data_time: 0.076186 memory: 9999 loss_kpt: 0.000584 acc_pose: 0.805857 loss: 0.000584 2022/10/20 14:00:25 - mmengine - INFO - Epoch(train) [126][150/293] lr: 5.000000e-04 eta: 2:07:44 time: 0.367018 data_time: 0.065350 memory: 9999 loss_kpt: 0.000590 acc_pose: 0.847030 loss: 0.000590 2022/10/20 14:00:43 - mmengine - INFO - Epoch(train) [126][200/293] lr: 5.000000e-04 eta: 2:07:30 time: 0.351575 data_time: 0.071546 memory: 9999 loss_kpt: 0.000591 acc_pose: 0.844265 loss: 0.000591 2022/10/20 14:01:01 - mmengine - INFO - Epoch(train) [126][250/293] lr: 5.000000e-04 eta: 2:07:16 time: 0.354280 data_time: 0.074195 memory: 9999 loss_kpt: 0.000584 acc_pose: 0.861629 loss: 0.000584 2022/10/20 14:01:16 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:01:35 - mmengine - INFO - Epoch(train) [127][50/293] lr: 5.000000e-04 eta: 2:06:40 time: 0.366887 data_time: 0.091171 memory: 9999 loss_kpt: 0.000593 acc_pose: 0.807701 loss: 0.000593 2022/10/20 14:01:46 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:01:52 - mmengine - INFO - Epoch(train) [127][100/293] lr: 5.000000e-04 eta: 2:06:26 time: 0.348504 data_time: 0.069652 memory: 9999 loss_kpt: 0.000594 acc_pose: 0.858903 loss: 0.000594 2022/10/20 14:02:10 - mmengine - INFO - Epoch(train) [127][150/293] lr: 5.000000e-04 eta: 2:06:12 time: 0.348529 data_time: 0.074681 memory: 9999 loss_kpt: 0.000593 acc_pose: 0.843406 loss: 0.000593 2022/10/20 14:02:27 - mmengine - INFO - Epoch(train) [127][200/293] lr: 5.000000e-04 eta: 2:05:57 time: 0.344454 data_time: 0.071792 memory: 9999 loss_kpt: 0.000593 acc_pose: 0.844899 loss: 0.000593 2022/10/20 14:02:44 - mmengine - INFO - Epoch(train) [127][250/293] lr: 5.000000e-04 eta: 2:05:43 time: 0.353074 data_time: 0.076496 memory: 9999 loss_kpt: 0.000593 acc_pose: 0.875272 loss: 0.000593 2022/10/20 14:02:59 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:03:18 - mmengine - INFO - Epoch(train) [128][50/293] lr: 5.000000e-04 eta: 2:05:08 time: 0.389574 data_time: 0.088700 memory: 9999 loss_kpt: 0.000582 acc_pose: 0.833305 loss: 0.000582 2022/10/20 14:03:36 - mmengine - INFO - Epoch(train) [128][100/293] lr: 5.000000e-04 eta: 2:04:54 time: 0.355314 data_time: 0.065809 memory: 9999 loss_kpt: 0.000579 acc_pose: 0.798252 loss: 0.000579 2022/10/20 14:03:54 - mmengine - INFO - Epoch(train) [128][150/293] lr: 5.000000e-04 eta: 2:04:40 time: 0.352201 data_time: 0.064408 memory: 9999 loss_kpt: 0.000597 acc_pose: 0.864429 loss: 0.000597 2022/10/20 14:04:12 - mmengine - INFO - Epoch(train) [128][200/293] lr: 5.000000e-04 eta: 2:04:27 time: 0.369887 data_time: 0.069778 memory: 9999 loss_kpt: 0.000601 acc_pose: 0.851241 loss: 0.000601 2022/10/20 14:04:30 - mmengine - INFO - Epoch(train) [128][250/293] lr: 5.000000e-04 eta: 2:04:13 time: 0.349179 data_time: 0.077618 memory: 9999 loss_kpt: 0.000594 acc_pose: 0.852859 loss: 0.000594 2022/10/20 14:04:45 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:05:05 - mmengine - INFO - Epoch(train) [129][50/293] lr: 5.000000e-04 eta: 2:03:38 time: 0.399282 data_time: 0.083980 memory: 9999 loss_kpt: 0.000585 acc_pose: 0.844286 loss: 0.000585 2022/10/20 14:05:23 - mmengine - INFO - Epoch(train) [129][100/293] lr: 5.000000e-04 eta: 2:03:24 time: 0.356017 data_time: 0.067518 memory: 9999 loss_kpt: 0.000594 acc_pose: 0.813782 loss: 0.000594 2022/10/20 14:05:41 - mmengine - INFO - Epoch(train) [129][150/293] lr: 5.000000e-04 eta: 2:03:11 time: 0.368807 data_time: 0.079645 memory: 9999 loss_kpt: 0.000589 acc_pose: 0.869774 loss: 0.000589 2022/10/20 14:06:00 - mmengine - INFO - Epoch(train) [129][200/293] lr: 5.000000e-04 eta: 2:02:57 time: 0.366828 data_time: 0.062642 memory: 9999 loss_kpt: 0.000587 acc_pose: 0.857923 loss: 0.000587 2022/10/20 14:06:17 - mmengine - INFO - Epoch(train) [129][250/293] lr: 5.000000e-04 eta: 2:02:43 time: 0.351916 data_time: 0.064260 memory: 9999 loss_kpt: 0.000580 acc_pose: 0.865560 loss: 0.000580 2022/10/20 14:06:32 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:06:51 - mmengine - INFO - Epoch(train) [130][50/293] lr: 5.000000e-04 eta: 2:02:08 time: 0.381670 data_time: 0.085674 memory: 9999 loss_kpt: 0.000600 acc_pose: 0.889676 loss: 0.000600 2022/10/20 14:07:10 - mmengine - INFO - Epoch(train) [130][100/293] lr: 5.000000e-04 eta: 2:01:54 time: 0.372837 data_time: 0.075398 memory: 9999 loss_kpt: 0.000581 acc_pose: 0.835489 loss: 0.000581 2022/10/20 14:07:28 - mmengine - INFO - Epoch(train) [130][150/293] lr: 5.000000e-04 eta: 2:01:41 time: 0.375217 data_time: 0.068695 memory: 9999 loss_kpt: 0.000583 acc_pose: 0.876806 loss: 0.000583 2022/10/20 14:07:47 - mmengine - INFO - Epoch(train) [130][200/293] lr: 5.000000e-04 eta: 2:01:27 time: 0.370701 data_time: 0.071183 memory: 9999 loss_kpt: 0.000581 acc_pose: 0.830686 loss: 0.000581 2022/10/20 14:07:48 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:08:05 - mmengine - INFO - Epoch(train) [130][250/293] lr: 5.000000e-04 eta: 2:01:13 time: 0.361593 data_time: 0.075195 memory: 9999 loss_kpt: 0.000582 acc_pose: 0.820911 loss: 0.000582 2022/10/20 14:08:20 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:08:20 - mmengine - INFO - Saving checkpoint at 130 epochs 2022/10/20 14:08:30 - mmengine - INFO - Epoch(val) [130][50/407] eta: 0:00:46 time: 0.129552 data_time: 0.058912 memory: 9999 2022/10/20 14:08:36 - mmengine - INFO - Epoch(val) [130][100/407] eta: 0:00:37 time: 0.122347 data_time: 0.056529 memory: 1378 2022/10/20 14:08:42 - mmengine - INFO - Epoch(val) [130][150/407] eta: 0:00:30 time: 0.118214 data_time: 0.051273 memory: 1378 2022/10/20 14:08:48 - mmengine - INFO - Epoch(val) [130][200/407] eta: 0:00:23 time: 0.113397 data_time: 0.046307 memory: 1378 2022/10/20 14:08:54 - mmengine - INFO - Epoch(val) [130][250/407] eta: 0:00:18 time: 0.118038 data_time: 0.052275 memory: 1378 2022/10/20 14:09:00 - mmengine - INFO - Epoch(val) [130][300/407] eta: 0:00:13 time: 0.126521 data_time: 0.059652 memory: 1378 2022/10/20 14:09:06 - mmengine - INFO - Epoch(val) [130][350/407] eta: 0:00:06 time: 0.113184 data_time: 0.048202 memory: 1378 2022/10/20 14:09:11 - mmengine - INFO - Epoch(val) [130][400/407] eta: 0:00:00 time: 0.107579 data_time: 0.044370 memory: 1378 2022/10/20 14:09:46 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 14:10:00 - mmengine - INFO - Epoch(val) [130][407/407] coco/AP: 0.723117 coco/AP .5: 0.899357 coco/AP .75: 0.801617 coco/AP (M): 0.684593 coco/AP (L): 0.790667 coco/AR: 0.778558 coco/AR .5: 0.937185 coco/AR .75: 0.848552 coco/AR (M): 0.734526 coco/AR (L): 0.841620 2022/10/20 14:10:00 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221020/resnetv1d101_256/best_coco/AP_epoch_120.pth is removed 2022/10/20 14:10:02 - mmengine - INFO - The best checkpoint with 0.7231 coco/AP at 130 epoch is saved to best_coco/AP_epoch_130.pth. 2022/10/20 14:10:21 - mmengine - INFO - Epoch(train) [131][50/293] lr: 5.000000e-04 eta: 2:00:38 time: 0.369400 data_time: 0.095940 memory: 9999 loss_kpt: 0.000591 acc_pose: 0.833067 loss: 0.000591 2022/10/20 14:10:39 - mmengine - INFO - Epoch(train) [131][100/293] lr: 5.000000e-04 eta: 2:00:24 time: 0.359177 data_time: 0.065670 memory: 9999 loss_kpt: 0.000583 acc_pose: 0.839276 loss: 0.000583 2022/10/20 14:10:57 - mmengine - INFO - Epoch(train) [131][150/293] lr: 5.000000e-04 eta: 2:00:11 time: 0.373602 data_time: 0.076501 memory: 9999 loss_kpt: 0.000583 acc_pose: 0.847920 loss: 0.000583 2022/10/20 14:11:17 - mmengine - INFO - Epoch(train) [131][200/293] lr: 5.000000e-04 eta: 1:59:58 time: 0.389392 data_time: 0.076425 memory: 9999 loss_kpt: 0.000581 acc_pose: 0.869427 loss: 0.000581 2022/10/20 14:11:35 - mmengine - INFO - Epoch(train) [131][250/293] lr: 5.000000e-04 eta: 1:59:44 time: 0.356658 data_time: 0.070486 memory: 9999 loss_kpt: 0.000580 acc_pose: 0.856691 loss: 0.000580 2022/10/20 14:11:52 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:12:11 - mmengine - INFO - Epoch(train) [132][50/293] lr: 5.000000e-04 eta: 1:59:09 time: 0.382958 data_time: 0.079106 memory: 9999 loss_kpt: 0.000584 acc_pose: 0.854534 loss: 0.000584 2022/10/20 14:12:30 - mmengine - INFO - Epoch(train) [132][100/293] lr: 5.000000e-04 eta: 1:58:56 time: 0.374355 data_time: 0.081206 memory: 9999 loss_kpt: 0.000597 acc_pose: 0.830147 loss: 0.000597 2022/10/20 14:12:48 - mmengine - INFO - Epoch(train) [132][150/293] lr: 5.000000e-04 eta: 1:58:41 time: 0.357354 data_time: 0.067465 memory: 9999 loss_kpt: 0.000584 acc_pose: 0.888936 loss: 0.000584 2022/10/20 14:13:05 - mmengine - INFO - Epoch(train) [132][200/293] lr: 5.000000e-04 eta: 1:58:27 time: 0.355857 data_time: 0.071703 memory: 9999 loss_kpt: 0.000568 acc_pose: 0.861556 loss: 0.000568 2022/10/20 14:13:23 - mmengine - INFO - Epoch(train) [132][250/293] lr: 5.000000e-04 eta: 1:58:13 time: 0.360197 data_time: 0.072204 memory: 9999 loss_kpt: 0.000585 acc_pose: 0.820268 loss: 0.000585 2022/10/20 14:13:39 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:13:59 - mmengine - INFO - Epoch(train) [133][50/293] lr: 5.000000e-04 eta: 1:57:39 time: 0.387641 data_time: 0.081567 memory: 9999 loss_kpt: 0.000600 acc_pose: 0.838955 loss: 0.000600 2022/10/20 14:14:17 - mmengine - INFO - Epoch(train) [133][100/293] lr: 5.000000e-04 eta: 1:57:25 time: 0.362413 data_time: 0.071259 memory: 9999 loss_kpt: 0.000577 acc_pose: 0.883150 loss: 0.000577 2022/10/20 14:14:36 - mmengine - INFO - Epoch(train) [133][150/293] lr: 5.000000e-04 eta: 1:57:12 time: 0.375017 data_time: 0.077120 memory: 9999 loss_kpt: 0.000594 acc_pose: 0.866265 loss: 0.000594 2022/10/20 14:14:54 - mmengine - INFO - Epoch(train) [133][200/293] lr: 5.000000e-04 eta: 1:56:58 time: 0.362978 data_time: 0.061780 memory: 9999 loss_kpt: 0.000582 acc_pose: 0.806994 loss: 0.000582 2022/10/20 14:15:12 - mmengine - INFO - Epoch(train) [133][250/293] lr: 5.000000e-04 eta: 1:56:43 time: 0.357862 data_time: 0.071681 memory: 9999 loss_kpt: 0.000589 acc_pose: 0.883520 loss: 0.000589 2022/10/20 14:15:27 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:15:39 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:15:45 - mmengine - INFO - Epoch(train) [134][50/293] lr: 5.000000e-04 eta: 1:56:09 time: 0.368482 data_time: 0.085119 memory: 9999 loss_kpt: 0.000594 acc_pose: 0.873596 loss: 0.000594 2022/10/20 14:16:04 - mmengine - INFO - Epoch(train) [134][100/293] lr: 5.000000e-04 eta: 1:55:55 time: 0.370950 data_time: 0.085871 memory: 9999 loss_kpt: 0.000575 acc_pose: 0.853237 loss: 0.000575 2022/10/20 14:16:23 - mmengine - INFO - Epoch(train) [134][150/293] lr: 5.000000e-04 eta: 1:55:42 time: 0.381760 data_time: 0.068172 memory: 9999 loss_kpt: 0.000580 acc_pose: 0.859740 loss: 0.000580 2022/10/20 14:16:41 - mmengine - INFO - Epoch(train) [134][200/293] lr: 5.000000e-04 eta: 1:55:28 time: 0.361563 data_time: 0.070597 memory: 9999 loss_kpt: 0.000580 acc_pose: 0.825151 loss: 0.000580 2022/10/20 14:16:59 - mmengine - INFO - Epoch(train) [134][250/293] lr: 5.000000e-04 eta: 1:55:14 time: 0.365757 data_time: 0.079268 memory: 9999 loss_kpt: 0.000590 acc_pose: 0.856775 loss: 0.000590 2022/10/20 14:17:14 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:17:33 - mmengine - INFO - Epoch(train) [135][50/293] lr: 5.000000e-04 eta: 1:54:39 time: 0.370487 data_time: 0.082112 memory: 9999 loss_kpt: 0.000586 acc_pose: 0.850119 loss: 0.000586 2022/10/20 14:17:51 - mmengine - INFO - Epoch(train) [135][100/293] lr: 5.000000e-04 eta: 1:54:25 time: 0.368491 data_time: 0.065820 memory: 9999 loss_kpt: 0.000581 acc_pose: 0.873240 loss: 0.000581 2022/10/20 14:18:10 - mmengine - INFO - Epoch(train) [135][150/293] lr: 5.000000e-04 eta: 1:54:11 time: 0.365630 data_time: 0.067567 memory: 9999 loss_kpt: 0.000581 acc_pose: 0.840674 loss: 0.000581 2022/10/20 14:18:28 - mmengine - INFO - Epoch(train) [135][200/293] lr: 5.000000e-04 eta: 1:53:57 time: 0.360448 data_time: 0.069008 memory: 9999 loss_kpt: 0.000588 acc_pose: 0.884710 loss: 0.000588 2022/10/20 14:18:46 - mmengine - INFO - Epoch(train) [135][250/293] lr: 5.000000e-04 eta: 1:53:43 time: 0.364700 data_time: 0.075468 memory: 9999 loss_kpt: 0.000584 acc_pose: 0.847711 loss: 0.000584 2022/10/20 14:19:01 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:19:20 - mmengine - INFO - Epoch(train) [136][50/293] lr: 5.000000e-04 eta: 1:53:09 time: 0.374427 data_time: 0.096516 memory: 9999 loss_kpt: 0.000572 acc_pose: 0.888678 loss: 0.000572 2022/10/20 14:19:39 - mmengine - INFO - Epoch(train) [136][100/293] lr: 5.000000e-04 eta: 1:52:55 time: 0.374707 data_time: 0.068381 memory: 9999 loss_kpt: 0.000594 acc_pose: 0.900331 loss: 0.000594 2022/10/20 14:19:57 - mmengine - INFO - Epoch(train) [136][150/293] lr: 5.000000e-04 eta: 1:52:41 time: 0.378001 data_time: 0.067962 memory: 9999 loss_kpt: 0.000583 acc_pose: 0.855964 loss: 0.000583 2022/10/20 14:20:16 - mmengine - INFO - Epoch(train) [136][200/293] lr: 5.000000e-04 eta: 1:52:28 time: 0.369949 data_time: 0.074998 memory: 9999 loss_kpt: 0.000567 acc_pose: 0.860636 loss: 0.000567 2022/10/20 14:20:33 - mmengine - INFO - Epoch(train) [136][250/293] lr: 5.000000e-04 eta: 1:52:13 time: 0.349594 data_time: 0.074446 memory: 9999 loss_kpt: 0.000589 acc_pose: 0.790315 loss: 0.000589 2022/10/20 14:20:49 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:21:07 - mmengine - INFO - Epoch(train) [137][50/293] lr: 5.000000e-04 eta: 1:51:39 time: 0.366438 data_time: 0.083146 memory: 9999 loss_kpt: 0.000568 acc_pose: 0.854260 loss: 0.000568 2022/10/20 14:21:25 - mmengine - INFO - Epoch(train) [137][100/293] lr: 5.000000e-04 eta: 1:51:25 time: 0.359567 data_time: 0.082982 memory: 9999 loss_kpt: 0.000593 acc_pose: 0.854299 loss: 0.000593 2022/10/20 14:21:42 - mmengine - INFO - Epoch(train) [137][150/293] lr: 5.000000e-04 eta: 1:51:10 time: 0.348905 data_time: 0.065858 memory: 9999 loss_kpt: 0.000578 acc_pose: 0.848862 loss: 0.000578 2022/10/20 14:21:43 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:22:01 - mmengine - INFO - Epoch(train) [137][200/293] lr: 5.000000e-04 eta: 1:50:56 time: 0.370689 data_time: 0.059539 memory: 9999 loss_kpt: 0.000583 acc_pose: 0.839246 loss: 0.000583 2022/10/20 14:22:19 - mmengine - INFO - Epoch(train) [137][250/293] lr: 5.000000e-04 eta: 1:50:42 time: 0.361684 data_time: 0.070855 memory: 9999 loss_kpt: 0.000581 acc_pose: 0.884214 loss: 0.000581 2022/10/20 14:22:34 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:22:52 - mmengine - INFO - Epoch(train) [138][50/293] lr: 5.000000e-04 eta: 1:50:08 time: 0.358447 data_time: 0.081366 memory: 9999 loss_kpt: 0.000580 acc_pose: 0.826605 loss: 0.000580 2022/10/20 14:23:10 - mmengine - INFO - Epoch(train) [138][100/293] lr: 5.000000e-04 eta: 1:49:53 time: 0.357775 data_time: 0.061836 memory: 9999 loss_kpt: 0.000579 acc_pose: 0.904363 loss: 0.000579 2022/10/20 14:23:28 - mmengine - INFO - Epoch(train) [138][150/293] lr: 5.000000e-04 eta: 1:49:39 time: 0.353434 data_time: 0.062033 memory: 9999 loss_kpt: 0.000578 acc_pose: 0.875246 loss: 0.000578 2022/10/20 14:23:46 - mmengine - INFO - Epoch(train) [138][200/293] lr: 5.000000e-04 eta: 1:49:25 time: 0.356992 data_time: 0.065652 memory: 9999 loss_kpt: 0.000578 acc_pose: 0.810944 loss: 0.000578 2022/10/20 14:24:03 - mmengine - INFO - Epoch(train) [138][250/293] lr: 5.000000e-04 eta: 1:49:11 time: 0.357046 data_time: 0.070733 memory: 9999 loss_kpt: 0.000587 acc_pose: 0.850286 loss: 0.000587 2022/10/20 14:24:19 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:24:38 - mmengine - INFO - Epoch(train) [139][50/293] lr: 5.000000e-04 eta: 1:48:37 time: 0.383861 data_time: 0.093810 memory: 9999 loss_kpt: 0.000578 acc_pose: 0.901572 loss: 0.000578 2022/10/20 14:24:56 - mmengine - INFO - Epoch(train) [139][100/293] lr: 5.000000e-04 eta: 1:48:22 time: 0.355528 data_time: 0.068631 memory: 9999 loss_kpt: 0.000578 acc_pose: 0.835704 loss: 0.000578 2022/10/20 14:25:14 - mmengine - INFO - Epoch(train) [139][150/293] lr: 5.000000e-04 eta: 1:48:08 time: 0.363967 data_time: 0.067308 memory: 9999 loss_kpt: 0.000575 acc_pose: 0.866293 loss: 0.000575 2022/10/20 14:25:32 - mmengine - INFO - Epoch(train) [139][200/293] lr: 5.000000e-04 eta: 1:47:54 time: 0.364105 data_time: 0.076264 memory: 9999 loss_kpt: 0.000573 acc_pose: 0.883221 loss: 0.000573 2022/10/20 14:25:50 - mmengine - INFO - Epoch(train) [139][250/293] lr: 5.000000e-04 eta: 1:47:40 time: 0.359063 data_time: 0.066925 memory: 9999 loss_kpt: 0.000579 acc_pose: 0.884972 loss: 0.000579 2022/10/20 14:26:06 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:26:25 - mmengine - INFO - Epoch(train) [140][50/293] lr: 5.000000e-04 eta: 1:47:06 time: 0.375816 data_time: 0.089787 memory: 9999 loss_kpt: 0.000583 acc_pose: 0.858623 loss: 0.000583 2022/10/20 14:26:42 - mmengine - INFO - Epoch(train) [140][100/293] lr: 5.000000e-04 eta: 1:46:52 time: 0.349931 data_time: 0.070695 memory: 9999 loss_kpt: 0.000577 acc_pose: 0.851825 loss: 0.000577 2022/10/20 14:27:01 - mmengine - INFO - Epoch(train) [140][150/293] lr: 5.000000e-04 eta: 1:46:37 time: 0.362612 data_time: 0.072559 memory: 9999 loss_kpt: 0.000585 acc_pose: 0.792349 loss: 0.000585 2022/10/20 14:27:19 - mmengine - INFO - Epoch(train) [140][200/293] lr: 5.000000e-04 eta: 1:46:24 time: 0.372002 data_time: 0.072197 memory: 9999 loss_kpt: 0.000570 acc_pose: 0.833295 loss: 0.000570 2022/10/20 14:27:37 - mmengine - INFO - Epoch(train) [140][250/293] lr: 5.000000e-04 eta: 1:46:09 time: 0.351533 data_time: 0.084224 memory: 9999 loss_kpt: 0.000583 acc_pose: 0.837667 loss: 0.000583 2022/10/20 14:27:45 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:27:52 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:27:52 - mmengine - INFO - Saving checkpoint at 140 epochs 2022/10/20 14:28:02 - mmengine - INFO - Epoch(val) [140][50/407] eta: 0:00:43 time: 0.120636 data_time: 0.053840 memory: 9999 2022/10/20 14:28:07 - mmengine - INFO - Epoch(val) [140][100/407] eta: 0:00:34 time: 0.111862 data_time: 0.045949 memory: 1378 2022/10/20 14:28:13 - mmengine - INFO - Epoch(val) [140][150/407] eta: 0:00:29 time: 0.114462 data_time: 0.047262 memory: 1378 2022/10/20 14:28:19 - mmengine - INFO - Epoch(val) [140][200/407] eta: 0:00:24 time: 0.117304 data_time: 0.050897 memory: 1378 2022/10/20 14:28:25 - mmengine - INFO - Epoch(val) [140][250/407] eta: 0:00:17 time: 0.112979 data_time: 0.046864 memory: 1378 2022/10/20 14:28:31 - mmengine - INFO - Epoch(val) [140][300/407] eta: 0:00:12 time: 0.117724 data_time: 0.050754 memory: 1378 2022/10/20 14:28:36 - mmengine - INFO - Epoch(val) [140][350/407] eta: 0:00:06 time: 0.116686 data_time: 0.050092 memory: 1378 2022/10/20 14:28:42 - mmengine - INFO - Epoch(val) [140][400/407] eta: 0:00:00 time: 0.108521 data_time: 0.043840 memory: 1378 2022/10/20 14:29:17 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 14:29:31 - mmengine - INFO - Epoch(val) [140][407/407] coco/AP: 0.723377 coco/AP .5: 0.899149 coco/AP .75: 0.797493 coco/AP (M): 0.685751 coco/AP (L): 0.790639 coco/AR: 0.778857 coco/AR .5: 0.936870 coco/AR .75: 0.844301 coco/AR (M): 0.734827 coco/AR (L): 0.841843 2022/10/20 14:29:31 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221020/resnetv1d101_256/best_coco/AP_epoch_130.pth is removed 2022/10/20 14:29:33 - mmengine - INFO - The best checkpoint with 0.7234 coco/AP at 140 epoch is saved to best_coco/AP_epoch_140.pth. 2022/10/20 14:29:51 - mmengine - INFO - Epoch(train) [141][50/293] lr: 5.000000e-04 eta: 1:45:35 time: 0.366644 data_time: 0.086437 memory: 9999 loss_kpt: 0.000580 acc_pose: 0.850481 loss: 0.000580 2022/10/20 14:30:10 - mmengine - INFO - Epoch(train) [141][100/293] lr: 5.000000e-04 eta: 1:45:21 time: 0.375218 data_time: 0.074321 memory: 9999 loss_kpt: 0.000571 acc_pose: 0.880035 loss: 0.000571 2022/10/20 14:30:28 - mmengine - INFO - Epoch(train) [141][150/293] lr: 5.000000e-04 eta: 1:45:07 time: 0.365194 data_time: 0.071142 memory: 9999 loss_kpt: 0.000584 acc_pose: 0.872728 loss: 0.000584 2022/10/20 14:30:46 - mmengine - INFO - Epoch(train) [141][200/293] lr: 5.000000e-04 eta: 1:44:53 time: 0.352803 data_time: 0.069948 memory: 9999 loss_kpt: 0.000576 acc_pose: 0.896177 loss: 0.000576 2022/10/20 14:31:04 - mmengine - INFO - Epoch(train) [141][250/293] lr: 5.000000e-04 eta: 1:44:38 time: 0.361521 data_time: 0.068646 memory: 9999 loss_kpt: 0.000566 acc_pose: 0.858702 loss: 0.000566 2022/10/20 14:31:19 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:31:38 - mmengine - INFO - Epoch(train) [142][50/293] lr: 5.000000e-04 eta: 1:44:05 time: 0.381490 data_time: 0.087519 memory: 9999 loss_kpt: 0.000561 acc_pose: 0.869566 loss: 0.000561 2022/10/20 14:31:56 - mmengine - INFO - Epoch(train) [142][100/293] lr: 5.000000e-04 eta: 1:43:50 time: 0.345209 data_time: 0.064790 memory: 9999 loss_kpt: 0.000588 acc_pose: 0.856573 loss: 0.000588 2022/10/20 14:32:14 - mmengine - INFO - Epoch(train) [142][150/293] lr: 5.000000e-04 eta: 1:43:36 time: 0.360674 data_time: 0.067787 memory: 9999 loss_kpt: 0.000581 acc_pose: 0.839060 loss: 0.000581 2022/10/20 14:32:32 - mmengine - INFO - Epoch(train) [142][200/293] lr: 5.000000e-04 eta: 1:43:22 time: 0.373668 data_time: 0.072003 memory: 9999 loss_kpt: 0.000585 acc_pose: 0.855983 loss: 0.000585 2022/10/20 14:32:50 - mmengine - INFO - Epoch(train) [142][250/293] lr: 5.000000e-04 eta: 1:43:07 time: 0.350403 data_time: 0.070254 memory: 9999 loss_kpt: 0.000579 acc_pose: 0.865460 loss: 0.000579 2022/10/20 14:33:05 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:33:24 - mmengine - INFO - Epoch(train) [143][50/293] lr: 5.000000e-04 eta: 1:42:34 time: 0.394934 data_time: 0.094750 memory: 9999 loss_kpt: 0.000577 acc_pose: 0.852418 loss: 0.000577 2022/10/20 14:33:43 - mmengine - INFO - Epoch(train) [143][100/293] lr: 5.000000e-04 eta: 1:42:20 time: 0.362307 data_time: 0.063906 memory: 9999 loss_kpt: 0.000570 acc_pose: 0.755810 loss: 0.000570 2022/10/20 14:34:02 - mmengine - INFO - Epoch(train) [143][150/293] lr: 5.000000e-04 eta: 1:42:06 time: 0.378878 data_time: 0.067636 memory: 9999 loss_kpt: 0.000580 acc_pose: 0.868452 loss: 0.000580 2022/10/20 14:34:20 - mmengine - INFO - Epoch(train) [143][200/293] lr: 5.000000e-04 eta: 1:41:52 time: 0.367731 data_time: 0.078088 memory: 9999 loss_kpt: 0.000584 acc_pose: 0.859689 loss: 0.000584 2022/10/20 14:34:38 - mmengine - INFO - Epoch(train) [143][250/293] lr: 5.000000e-04 eta: 1:41:38 time: 0.370214 data_time: 0.065883 memory: 9999 loss_kpt: 0.000572 acc_pose: 0.864417 loss: 0.000572 2022/10/20 14:34:53 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:35:12 - mmengine - INFO - Epoch(train) [144][50/293] lr: 5.000000e-04 eta: 1:41:04 time: 0.374213 data_time: 0.082251 memory: 9999 loss_kpt: 0.000564 acc_pose: 0.873774 loss: 0.000564 2022/10/20 14:35:30 - mmengine - INFO - Epoch(train) [144][100/293] lr: 5.000000e-04 eta: 1:40:50 time: 0.366974 data_time: 0.073891 memory: 9999 loss_kpt: 0.000577 acc_pose: 0.859975 loss: 0.000577 2022/10/20 14:35:31 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:35:48 - mmengine - INFO - Epoch(train) [144][150/293] lr: 5.000000e-04 eta: 1:40:36 time: 0.357456 data_time: 0.080651 memory: 9999 loss_kpt: 0.000572 acc_pose: 0.862295 loss: 0.000572 2022/10/20 14:36:06 - mmengine - INFO - Epoch(train) [144][200/293] lr: 5.000000e-04 eta: 1:40:22 time: 0.360478 data_time: 0.077434 memory: 9999 loss_kpt: 0.000570 acc_pose: 0.864184 loss: 0.000570 2022/10/20 14:36:25 - mmengine - INFO - Epoch(train) [144][250/293] lr: 5.000000e-04 eta: 1:40:07 time: 0.362695 data_time: 0.072069 memory: 9999 loss_kpt: 0.000575 acc_pose: 0.843114 loss: 0.000575 2022/10/20 14:36:39 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:36:58 - mmengine - INFO - Epoch(train) [145][50/293] lr: 5.000000e-04 eta: 1:39:34 time: 0.366954 data_time: 0.078015 memory: 9999 loss_kpt: 0.000573 acc_pose: 0.855067 loss: 0.000573 2022/10/20 14:37:16 - mmengine - INFO - Epoch(train) [145][100/293] lr: 5.000000e-04 eta: 1:39:20 time: 0.366595 data_time: 0.063325 memory: 9999 loss_kpt: 0.000571 acc_pose: 0.903028 loss: 0.000571 2022/10/20 14:37:35 - mmengine - INFO - Epoch(train) [145][150/293] lr: 5.000000e-04 eta: 1:39:06 time: 0.374616 data_time: 0.082017 memory: 9999 loss_kpt: 0.000584 acc_pose: 0.837386 loss: 0.000584 2022/10/20 14:37:52 - mmengine - INFO - Epoch(train) [145][200/293] lr: 5.000000e-04 eta: 1:38:51 time: 0.349116 data_time: 0.064311 memory: 9999 loss_kpt: 0.000570 acc_pose: 0.881944 loss: 0.000570 2022/10/20 14:38:11 - mmengine - INFO - Epoch(train) [145][250/293] lr: 5.000000e-04 eta: 1:38:37 time: 0.361917 data_time: 0.074299 memory: 9999 loss_kpt: 0.000569 acc_pose: 0.854141 loss: 0.000569 2022/10/20 14:38:27 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:38:45 - mmengine - INFO - Epoch(train) [146][50/293] lr: 5.000000e-04 eta: 1:38:03 time: 0.375401 data_time: 0.087870 memory: 9999 loss_kpt: 0.000581 acc_pose: 0.892933 loss: 0.000581 2022/10/20 14:39:03 - mmengine - INFO - Epoch(train) [146][100/293] lr: 5.000000e-04 eta: 1:37:49 time: 0.354455 data_time: 0.086104 memory: 9999 loss_kpt: 0.000561 acc_pose: 0.888891 loss: 0.000561 2022/10/20 14:39:22 - mmengine - INFO - Epoch(train) [146][150/293] lr: 5.000000e-04 eta: 1:37:35 time: 0.371525 data_time: 0.078485 memory: 9999 loss_kpt: 0.000575 acc_pose: 0.852913 loss: 0.000575 2022/10/20 14:39:40 - mmengine - INFO - Epoch(train) [146][200/293] lr: 5.000000e-04 eta: 1:37:20 time: 0.355477 data_time: 0.066845 memory: 9999 loss_kpt: 0.000584 acc_pose: 0.841176 loss: 0.000584 2022/10/20 14:39:59 - mmengine - INFO - Epoch(train) [146][250/293] lr: 5.000000e-04 eta: 1:37:06 time: 0.380854 data_time: 0.085368 memory: 9999 loss_kpt: 0.000588 acc_pose: 0.860760 loss: 0.000588 2022/10/20 14:40:13 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:40:32 - mmengine - INFO - Epoch(train) [147][50/293] lr: 5.000000e-04 eta: 1:36:33 time: 0.375321 data_time: 0.086948 memory: 9999 loss_kpt: 0.000583 acc_pose: 0.875034 loss: 0.000583 2022/10/20 14:40:50 - mmengine - INFO - Epoch(train) [147][100/293] lr: 5.000000e-04 eta: 1:36:19 time: 0.364909 data_time: 0.069925 memory: 9999 loss_kpt: 0.000572 acc_pose: 0.849819 loss: 0.000572 2022/10/20 14:41:08 - mmengine - INFO - Epoch(train) [147][150/293] lr: 5.000000e-04 eta: 1:36:04 time: 0.366348 data_time: 0.067092 memory: 9999 loss_kpt: 0.000573 acc_pose: 0.834809 loss: 0.000573 2022/10/20 14:41:26 - mmengine - INFO - Epoch(train) [147][200/293] lr: 5.000000e-04 eta: 1:35:50 time: 0.349532 data_time: 0.072028 memory: 9999 loss_kpt: 0.000570 acc_pose: 0.887470 loss: 0.000570 2022/10/20 14:41:33 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:41:43 - mmengine - INFO - Epoch(train) [147][250/293] lr: 5.000000e-04 eta: 1:35:35 time: 0.340933 data_time: 0.071563 memory: 9999 loss_kpt: 0.000572 acc_pose: 0.865966 loss: 0.000572 2022/10/20 14:41:58 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:42:17 - mmengine - INFO - Epoch(train) [148][50/293] lr: 5.000000e-04 eta: 1:35:02 time: 0.375884 data_time: 0.092816 memory: 9999 loss_kpt: 0.000575 acc_pose: 0.885212 loss: 0.000575 2022/10/20 14:42:35 - mmengine - INFO - Epoch(train) [148][100/293] lr: 5.000000e-04 eta: 1:34:47 time: 0.362251 data_time: 0.070194 memory: 9999 loss_kpt: 0.000570 acc_pose: 0.822863 loss: 0.000570 2022/10/20 14:42:54 - mmengine - INFO - Epoch(train) [148][150/293] lr: 5.000000e-04 eta: 1:34:33 time: 0.367581 data_time: 0.062837 memory: 9999 loss_kpt: 0.000575 acc_pose: 0.872202 loss: 0.000575 2022/10/20 14:43:13 - mmengine - INFO - Epoch(train) [148][200/293] lr: 5.000000e-04 eta: 1:34:19 time: 0.380969 data_time: 0.068146 memory: 9999 loss_kpt: 0.000580 acc_pose: 0.867974 loss: 0.000580 2022/10/20 14:43:31 - mmengine - INFO - Epoch(train) [148][250/293] lr: 5.000000e-04 eta: 1:34:05 time: 0.361878 data_time: 0.067220 memory: 9999 loss_kpt: 0.000571 acc_pose: 0.866803 loss: 0.000571 2022/10/20 14:43:46 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:44:05 - mmengine - INFO - Epoch(train) [149][50/293] lr: 5.000000e-04 eta: 1:33:32 time: 0.371009 data_time: 0.084124 memory: 9999 loss_kpt: 0.000573 acc_pose: 0.894652 loss: 0.000573 2022/10/20 14:44:22 - mmengine - INFO - Epoch(train) [149][100/293] lr: 5.000000e-04 eta: 1:33:17 time: 0.349766 data_time: 0.074146 memory: 9999 loss_kpt: 0.000576 acc_pose: 0.893907 loss: 0.000576 2022/10/20 14:44:41 - mmengine - INFO - Epoch(train) [149][150/293] lr: 5.000000e-04 eta: 1:33:03 time: 0.369809 data_time: 0.075102 memory: 9999 loss_kpt: 0.000571 acc_pose: 0.779072 loss: 0.000571 2022/10/20 14:44:59 - mmengine - INFO - Epoch(train) [149][200/293] lr: 5.000000e-04 eta: 1:32:48 time: 0.356587 data_time: 0.073550 memory: 9999 loss_kpt: 0.000566 acc_pose: 0.859627 loss: 0.000566 2022/10/20 14:45:18 - mmengine - INFO - Epoch(train) [149][250/293] lr: 5.000000e-04 eta: 1:32:34 time: 0.385157 data_time: 0.083165 memory: 9999 loss_kpt: 0.000573 acc_pose: 0.843609 loss: 0.000573 2022/10/20 14:45:33 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:45:52 - mmengine - INFO - Epoch(train) [150][50/293] lr: 5.000000e-04 eta: 1:32:02 time: 0.383788 data_time: 0.089182 memory: 9999 loss_kpt: 0.000577 acc_pose: 0.856498 loss: 0.000577 2022/10/20 14:46:10 - mmengine - INFO - Epoch(train) [150][100/293] lr: 5.000000e-04 eta: 1:31:47 time: 0.360535 data_time: 0.081128 memory: 9999 loss_kpt: 0.000570 acc_pose: 0.855537 loss: 0.000570 2022/10/20 14:46:28 - mmengine - INFO - Epoch(train) [150][150/293] lr: 5.000000e-04 eta: 1:31:33 time: 0.365147 data_time: 0.073883 memory: 9999 loss_kpt: 0.000584 acc_pose: 0.839022 loss: 0.000584 2022/10/20 14:46:47 - mmengine - INFO - Epoch(train) [150][200/293] lr: 5.000000e-04 eta: 1:31:18 time: 0.370483 data_time: 0.066204 memory: 9999 loss_kpt: 0.000562 acc_pose: 0.872176 loss: 0.000562 2022/10/20 14:47:04 - mmengine - INFO - Epoch(train) [150][250/293] lr: 5.000000e-04 eta: 1:31:04 time: 0.350334 data_time: 0.074701 memory: 9999 loss_kpt: 0.000572 acc_pose: 0.867849 loss: 0.000572 2022/10/20 14:47:20 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:47:20 - mmengine - INFO - Saving checkpoint at 150 epochs 2022/10/20 14:47:29 - mmengine - INFO - Epoch(val) [150][50/407] eta: 0:00:41 time: 0.117014 data_time: 0.049864 memory: 9999 2022/10/20 14:47:35 - mmengine - INFO - Epoch(val) [150][100/407] eta: 0:00:34 time: 0.113165 data_time: 0.046322 memory: 1378 2022/10/20 14:47:41 - mmengine - INFO - Epoch(val) [150][150/407] eta: 0:00:29 time: 0.115707 data_time: 0.049423 memory: 1378 2022/10/20 14:47:46 - mmengine - INFO - Epoch(val) [150][200/407] eta: 0:00:23 time: 0.113992 data_time: 0.047473 memory: 1378 2022/10/20 14:47:52 - mmengine - INFO - Epoch(val) [150][250/407] eta: 0:00:18 time: 0.119674 data_time: 0.053065 memory: 1378 2022/10/20 14:47:58 - mmengine - INFO - Epoch(val) [150][300/407] eta: 0:00:12 time: 0.115430 data_time: 0.050482 memory: 1378 2022/10/20 14:48:04 - mmengine - INFO - Epoch(val) [150][350/407] eta: 0:00:06 time: 0.117375 data_time: 0.051187 memory: 1378 2022/10/20 14:48:09 - mmengine - INFO - Epoch(val) [150][400/407] eta: 0:00:00 time: 0.109933 data_time: 0.045857 memory: 1378 2022/10/20 14:48:45 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 14:48:59 - mmengine - INFO - Epoch(val) [150][407/407] coco/AP: 0.722821 coco/AP .5: 0.896609 coco/AP .75: 0.801369 coco/AP (M): 0.685852 coco/AP (L): 0.789579 coco/AR: 0.778275 coco/AR .5: 0.934194 coco/AR .75: 0.848552 coco/AR (M): 0.734171 coco/AR (L): 0.841100 2022/10/20 14:49:17 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:49:17 - mmengine - INFO - Epoch(train) [151][50/293] lr: 5.000000e-04 eta: 1:30:31 time: 0.378240 data_time: 0.083093 memory: 9999 loss_kpt: 0.000570 acc_pose: 0.884753 loss: 0.000570 2022/10/20 14:49:36 - mmengine - INFO - Epoch(train) [151][100/293] lr: 5.000000e-04 eta: 1:30:17 time: 0.366757 data_time: 0.066200 memory: 9999 loss_kpt: 0.000574 acc_pose: 0.876521 loss: 0.000574 2022/10/20 14:49:53 - mmengine - INFO - Epoch(train) [151][150/293] lr: 5.000000e-04 eta: 1:30:02 time: 0.351116 data_time: 0.071027 memory: 9999 loss_kpt: 0.000585 acc_pose: 0.852362 loss: 0.000585 2022/10/20 14:50:12 - mmengine - INFO - Epoch(train) [151][200/293] lr: 5.000000e-04 eta: 1:29:48 time: 0.371971 data_time: 0.076060 memory: 9999 loss_kpt: 0.000585 acc_pose: 0.835666 loss: 0.000585 2022/10/20 14:50:31 - mmengine - INFO - Epoch(train) [151][250/293] lr: 5.000000e-04 eta: 1:29:34 time: 0.382657 data_time: 0.077499 memory: 9999 loss_kpt: 0.000567 acc_pose: 0.839605 loss: 0.000567 2022/10/20 14:50:47 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:51:06 - mmengine - INFO - Epoch(train) [152][50/293] lr: 5.000000e-04 eta: 1:29:01 time: 0.378450 data_time: 0.078723 memory: 9999 loss_kpt: 0.000571 acc_pose: 0.789116 loss: 0.000571 2022/10/20 14:51:24 - mmengine - INFO - Epoch(train) [152][100/293] lr: 5.000000e-04 eta: 1:28:46 time: 0.357845 data_time: 0.073775 memory: 9999 loss_kpt: 0.000569 acc_pose: 0.798083 loss: 0.000569 2022/10/20 14:51:42 - mmengine - INFO - Epoch(train) [152][150/293] lr: 5.000000e-04 eta: 1:28:32 time: 0.377003 data_time: 0.072888 memory: 9999 loss_kpt: 0.000571 acc_pose: 0.833913 loss: 0.000571 2022/10/20 14:52:01 - mmengine - INFO - Epoch(train) [152][200/293] lr: 5.000000e-04 eta: 1:28:18 time: 0.365167 data_time: 0.069088 memory: 9999 loss_kpt: 0.000575 acc_pose: 0.843755 loss: 0.000575 2022/10/20 14:52:18 - mmengine - INFO - Epoch(train) [152][250/293] lr: 5.000000e-04 eta: 1:28:03 time: 0.355915 data_time: 0.072199 memory: 9999 loss_kpt: 0.000569 acc_pose: 0.856390 loss: 0.000569 2022/10/20 14:52:34 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:52:54 - mmengine - INFO - Epoch(train) [153][50/293] lr: 5.000000e-04 eta: 1:27:31 time: 0.394105 data_time: 0.085219 memory: 9999 loss_kpt: 0.000563 acc_pose: 0.876290 loss: 0.000563 2022/10/20 14:53:11 - mmengine - INFO - Epoch(train) [153][100/293] lr: 5.000000e-04 eta: 1:27:16 time: 0.346280 data_time: 0.065797 memory: 9999 loss_kpt: 0.000567 acc_pose: 0.876942 loss: 0.000567 2022/10/20 14:53:30 - mmengine - INFO - Epoch(train) [153][150/293] lr: 5.000000e-04 eta: 1:27:02 time: 0.377201 data_time: 0.070944 memory: 9999 loss_kpt: 0.000580 acc_pose: 0.837514 loss: 0.000580 2022/10/20 14:53:49 - mmengine - INFO - Epoch(train) [153][200/293] lr: 5.000000e-04 eta: 1:26:47 time: 0.381776 data_time: 0.068584 memory: 9999 loss_kpt: 0.000584 acc_pose: 0.827390 loss: 0.000584 2022/10/20 14:54:08 - mmengine - INFO - Epoch(train) [153][250/293] lr: 5.000000e-04 eta: 1:26:33 time: 0.363240 data_time: 0.070747 memory: 9999 loss_kpt: 0.000573 acc_pose: 0.856772 loss: 0.000573 2022/10/20 14:54:23 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:54:42 - mmengine - INFO - Epoch(train) [154][50/293] lr: 5.000000e-04 eta: 1:26:01 time: 0.384008 data_time: 0.084358 memory: 9999 loss_kpt: 0.000571 acc_pose: 0.805438 loss: 0.000571 2022/10/20 14:55:01 - mmengine - INFO - Epoch(train) [154][100/293] lr: 5.000000e-04 eta: 1:25:46 time: 0.366292 data_time: 0.075594 memory: 9999 loss_kpt: 0.000564 acc_pose: 0.835147 loss: 0.000564 2022/10/20 14:55:19 - mmengine - INFO - Epoch(train) [154][150/293] lr: 5.000000e-04 eta: 1:25:32 time: 0.365262 data_time: 0.081373 memory: 9999 loss_kpt: 0.000563 acc_pose: 0.869783 loss: 0.000563 2022/10/20 14:55:26 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:55:37 - mmengine - INFO - Epoch(train) [154][200/293] lr: 5.000000e-04 eta: 1:25:17 time: 0.354996 data_time: 0.064515 memory: 9999 loss_kpt: 0.000561 acc_pose: 0.812887 loss: 0.000561 2022/10/20 14:55:55 - mmengine - INFO - Epoch(train) [154][250/293] lr: 5.000000e-04 eta: 1:25:02 time: 0.361435 data_time: 0.074078 memory: 9999 loss_kpt: 0.000573 acc_pose: 0.894681 loss: 0.000573 2022/10/20 14:56:10 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:56:29 - mmengine - INFO - Epoch(train) [155][50/293] lr: 5.000000e-04 eta: 1:24:30 time: 0.381746 data_time: 0.095887 memory: 9999 loss_kpt: 0.000558 acc_pose: 0.882752 loss: 0.000558 2022/10/20 14:56:47 - mmengine - INFO - Epoch(train) [155][100/293] lr: 5.000000e-04 eta: 1:24:15 time: 0.359431 data_time: 0.066679 memory: 9999 loss_kpt: 0.000572 acc_pose: 0.878499 loss: 0.000572 2022/10/20 14:57:05 - mmengine - INFO - Epoch(train) [155][150/293] lr: 5.000000e-04 eta: 1:24:01 time: 0.359903 data_time: 0.072440 memory: 9999 loss_kpt: 0.000580 acc_pose: 0.844483 loss: 0.000580 2022/10/20 14:57:23 - mmengine - INFO - Epoch(train) [155][200/293] lr: 5.000000e-04 eta: 1:23:46 time: 0.346342 data_time: 0.064888 memory: 9999 loss_kpt: 0.000561 acc_pose: 0.854822 loss: 0.000561 2022/10/20 14:57:40 - mmengine - INFO - Epoch(train) [155][250/293] lr: 5.000000e-04 eta: 1:23:31 time: 0.355506 data_time: 0.080658 memory: 9999 loss_kpt: 0.000559 acc_pose: 0.871273 loss: 0.000559 2022/10/20 14:57:56 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 14:58:23 - mmengine - INFO - Epoch(train) [156][50/293] lr: 5.000000e-04 eta: 1:23:02 time: 0.539371 data_time: 0.096209 memory: 9999 loss_kpt: 0.000562 acc_pose: 0.842336 loss: 0.000562 2022/10/20 14:58:48 - mmengine - INFO - Epoch(train) [156][100/293] lr: 5.000000e-04 eta: 1:22:49 time: 0.497828 data_time: 0.072531 memory: 9999 loss_kpt: 0.000560 acc_pose: 0.844129 loss: 0.000560 2022/10/20 14:59:08 - mmengine - INFO - Epoch(train) [156][150/293] lr: 5.000000e-04 eta: 1:22:35 time: 0.395570 data_time: 0.073896 memory: 9999 loss_kpt: 0.000574 acc_pose: 0.845073 loss: 0.000574 2022/10/20 14:59:27 - mmengine - INFO - Epoch(train) [156][200/293] lr: 5.000000e-04 eta: 1:22:21 time: 0.393709 data_time: 0.076227 memory: 9999 loss_kpt: 0.000567 acc_pose: 0.837342 loss: 0.000567 2022/10/20 14:59:46 - mmengine - INFO - Epoch(train) [156][250/293] lr: 5.000000e-04 eta: 1:22:07 time: 0.368228 data_time: 0.069334 memory: 9999 loss_kpt: 0.000562 acc_pose: 0.867715 loss: 0.000562 2022/10/20 15:00:03 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:00:23 - mmengine - INFO - Epoch(train) [157][50/293] lr: 5.000000e-04 eta: 1:21:35 time: 0.391774 data_time: 0.098426 memory: 9999 loss_kpt: 0.000567 acc_pose: 0.877627 loss: 0.000567 2022/10/20 15:00:41 - mmengine - INFO - Epoch(train) [157][100/293] lr: 5.000000e-04 eta: 1:21:20 time: 0.361076 data_time: 0.072355 memory: 9999 loss_kpt: 0.000579 acc_pose: 0.828993 loss: 0.000579 2022/10/20 15:00:59 - mmengine - INFO - Epoch(train) [157][150/293] lr: 5.000000e-04 eta: 1:21:06 time: 0.364087 data_time: 0.065527 memory: 9999 loss_kpt: 0.000575 acc_pose: 0.851477 loss: 0.000575 2022/10/20 15:01:17 - mmengine - INFO - Epoch(train) [157][200/293] lr: 5.000000e-04 eta: 1:20:51 time: 0.358431 data_time: 0.070760 memory: 9999 loss_kpt: 0.000567 acc_pose: 0.857549 loss: 0.000567 2022/10/20 15:01:35 - mmengine - INFO - Epoch(train) [157][250/293] lr: 5.000000e-04 eta: 1:20:36 time: 0.360122 data_time: 0.067438 memory: 9999 loss_kpt: 0.000576 acc_pose: 0.899889 loss: 0.000576 2022/10/20 15:01:50 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:01:50 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:02:09 - mmengine - INFO - Epoch(train) [158][50/293] lr: 5.000000e-04 eta: 1:20:04 time: 0.383462 data_time: 0.092081 memory: 9999 loss_kpt: 0.000578 acc_pose: 0.875161 loss: 0.000578 2022/10/20 15:02:28 - mmengine - INFO - Epoch(train) [158][100/293] lr: 5.000000e-04 eta: 1:19:50 time: 0.383922 data_time: 0.066527 memory: 9999 loss_kpt: 0.000567 acc_pose: 0.882137 loss: 0.000567 2022/10/20 15:02:46 - mmengine - INFO - Epoch(train) [158][150/293] lr: 5.000000e-04 eta: 1:19:35 time: 0.352277 data_time: 0.072540 memory: 9999 loss_kpt: 0.000568 acc_pose: 0.845629 loss: 0.000568 2022/10/20 15:03:03 - mmengine - INFO - Epoch(train) [158][200/293] lr: 5.000000e-04 eta: 1:19:20 time: 0.344144 data_time: 0.079857 memory: 9999 loss_kpt: 0.000565 acc_pose: 0.807083 loss: 0.000565 2022/10/20 15:03:22 - mmengine - INFO - Epoch(train) [158][250/293] lr: 5.000000e-04 eta: 1:19:05 time: 0.376823 data_time: 0.072295 memory: 9999 loss_kpt: 0.000570 acc_pose: 0.866031 loss: 0.000570 2022/10/20 15:03:38 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:03:57 - mmengine - INFO - Epoch(train) [159][50/293] lr: 5.000000e-04 eta: 1:18:33 time: 0.381032 data_time: 0.078078 memory: 9999 loss_kpt: 0.000563 acc_pose: 0.800525 loss: 0.000563 2022/10/20 15:04:14 - mmengine - INFO - Epoch(train) [159][100/293] lr: 5.000000e-04 eta: 1:18:18 time: 0.349248 data_time: 0.069303 memory: 9999 loss_kpt: 0.000577 acc_pose: 0.842483 loss: 0.000577 2022/10/20 15:04:32 - mmengine - INFO - Epoch(train) [159][150/293] lr: 5.000000e-04 eta: 1:18:04 time: 0.356873 data_time: 0.064791 memory: 9999 loss_kpt: 0.000567 acc_pose: 0.797742 loss: 0.000567 2022/10/20 15:04:50 - mmengine - INFO - Epoch(train) [159][200/293] lr: 5.000000e-04 eta: 1:17:49 time: 0.369275 data_time: 0.072794 memory: 9999 loss_kpt: 0.000562 acc_pose: 0.854292 loss: 0.000562 2022/10/20 15:05:08 - mmengine - INFO - Epoch(train) [159][250/293] lr: 5.000000e-04 eta: 1:17:34 time: 0.359877 data_time: 0.076694 memory: 9999 loss_kpt: 0.000575 acc_pose: 0.800963 loss: 0.000575 2022/10/20 15:05:24 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:05:44 - mmengine - INFO - Epoch(train) [160][50/293] lr: 5.000000e-04 eta: 1:17:03 time: 0.397574 data_time: 0.079646 memory: 9999 loss_kpt: 0.000550 acc_pose: 0.853326 loss: 0.000550 2022/10/20 15:06:02 - mmengine - INFO - Epoch(train) [160][100/293] lr: 5.000000e-04 eta: 1:16:48 time: 0.371592 data_time: 0.075616 memory: 9999 loss_kpt: 0.000563 acc_pose: 0.868889 loss: 0.000563 2022/10/20 15:06:20 - mmengine - INFO - Epoch(train) [160][150/293] lr: 5.000000e-04 eta: 1:16:33 time: 0.352581 data_time: 0.090806 memory: 9999 loss_kpt: 0.000564 acc_pose: 0.912501 loss: 0.000564 2022/10/20 15:06:38 - mmengine - INFO - Epoch(train) [160][200/293] lr: 5.000000e-04 eta: 1:16:19 time: 0.366194 data_time: 0.068842 memory: 9999 loss_kpt: 0.000557 acc_pose: 0.859843 loss: 0.000557 2022/10/20 15:06:56 - mmengine - INFO - Epoch(train) [160][250/293] lr: 5.000000e-04 eta: 1:16:04 time: 0.360388 data_time: 0.065148 memory: 9999 loss_kpt: 0.000565 acc_pose: 0.864018 loss: 0.000565 2022/10/20 15:07:12 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:07:12 - mmengine - INFO - Saving checkpoint at 160 epochs 2022/10/20 15:07:21 - mmengine - INFO - Epoch(val) [160][50/407] eta: 0:00:42 time: 0.118306 data_time: 0.052114 memory: 9999 2022/10/20 15:07:27 - mmengine - INFO - Epoch(val) [160][100/407] eta: 0:00:36 time: 0.118735 data_time: 0.053258 memory: 1378 2022/10/20 15:07:33 - mmengine - INFO - Epoch(val) [160][150/407] eta: 0:00:28 time: 0.111800 data_time: 0.045745 memory: 1378 2022/10/20 15:07:38 - mmengine - INFO - Epoch(val) [160][200/407] eta: 0:00:22 time: 0.109565 data_time: 0.043602 memory: 1378 2022/10/20 15:07:44 - mmengine - INFO - Epoch(val) [160][250/407] eta: 0:00:18 time: 0.119437 data_time: 0.054429 memory: 1378 2022/10/20 15:07:51 - mmengine - INFO - Epoch(val) [160][300/407] eta: 0:00:14 time: 0.133845 data_time: 0.063911 memory: 1378 2022/10/20 15:07:56 - mmengine - INFO - Epoch(val) [160][350/407] eta: 0:00:06 time: 0.109101 data_time: 0.042522 memory: 1378 2022/10/20 15:08:02 - mmengine - INFO - Epoch(val) [160][400/407] eta: 0:00:00 time: 0.119837 data_time: 0.053469 memory: 1378 2022/10/20 15:08:39 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 15:08:52 - mmengine - INFO - Epoch(val) [160][407/407] coco/AP: 0.723529 coco/AP .5: 0.896741 coco/AP .75: 0.800265 coco/AP (M): 0.686932 coco/AP (L): 0.790735 coco/AR: 0.778463 coco/AR .5: 0.936555 coco/AR .75: 0.847292 coco/AR (M): 0.733789 coco/AR (L): 0.842809 2022/10/20 15:08:52 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221020/resnetv1d101_256/best_coco/AP_epoch_140.pth is removed 2022/10/20 15:08:55 - mmengine - INFO - The best checkpoint with 0.7235 coco/AP at 160 epoch is saved to best_coco/AP_epoch_160.pth. 2022/10/20 15:09:13 - mmengine - INFO - Epoch(train) [161][50/293] lr: 5.000000e-04 eta: 1:15:32 time: 0.370768 data_time: 0.090633 memory: 9999 loss_kpt: 0.000561 acc_pose: 0.881233 loss: 0.000561 2022/10/20 15:09:31 - mmengine - INFO - Epoch(train) [161][100/293] lr: 5.000000e-04 eta: 1:15:17 time: 0.356984 data_time: 0.071222 memory: 9999 loss_kpt: 0.000570 acc_pose: 0.841732 loss: 0.000570 2022/10/20 15:09:38 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:09:49 - mmengine - INFO - Epoch(train) [161][150/293] lr: 5.000000e-04 eta: 1:15:02 time: 0.352186 data_time: 0.063467 memory: 9999 loss_kpt: 0.000556 acc_pose: 0.889977 loss: 0.000556 2022/10/20 15:10:06 - mmengine - INFO - Epoch(train) [161][200/293] lr: 5.000000e-04 eta: 1:14:47 time: 0.351445 data_time: 0.070553 memory: 9999 loss_kpt: 0.000558 acc_pose: 0.863167 loss: 0.000558 2022/10/20 15:10:24 - mmengine - INFO - Epoch(train) [161][250/293] lr: 5.000000e-04 eta: 1:14:32 time: 0.360007 data_time: 0.069242 memory: 9999 loss_kpt: 0.000556 acc_pose: 0.826850 loss: 0.000556 2022/10/20 15:10:39 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:10:58 - mmengine - INFO - Epoch(train) [162][50/293] lr: 5.000000e-04 eta: 1:14:00 time: 0.369954 data_time: 0.082414 memory: 9999 loss_kpt: 0.000565 acc_pose: 0.839504 loss: 0.000565 2022/10/20 15:11:16 - mmengine - INFO - Epoch(train) [162][100/293] lr: 5.000000e-04 eta: 1:13:46 time: 0.362868 data_time: 0.071419 memory: 9999 loss_kpt: 0.000555 acc_pose: 0.876147 loss: 0.000555 2022/10/20 15:11:33 - mmengine - INFO - Epoch(train) [162][150/293] lr: 5.000000e-04 eta: 1:13:31 time: 0.349668 data_time: 0.071575 memory: 9999 loss_kpt: 0.000561 acc_pose: 0.886644 loss: 0.000561 2022/10/20 15:11:52 - mmengine - INFO - Epoch(train) [162][200/293] lr: 5.000000e-04 eta: 1:13:16 time: 0.367235 data_time: 0.074250 memory: 9999 loss_kpt: 0.000557 acc_pose: 0.830967 loss: 0.000557 2022/10/20 15:12:09 - mmengine - INFO - Epoch(train) [162][250/293] lr: 5.000000e-04 eta: 1:13:01 time: 0.346140 data_time: 0.064866 memory: 9999 loss_kpt: 0.000567 acc_pose: 0.854689 loss: 0.000567 2022/10/20 15:12:24 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:12:43 - mmengine - INFO - Epoch(train) [163][50/293] lr: 5.000000e-04 eta: 1:12:29 time: 0.370791 data_time: 0.083704 memory: 9999 loss_kpt: 0.000549 acc_pose: 0.851743 loss: 0.000549 2022/10/20 15:13:00 - mmengine - INFO - Epoch(train) [163][100/293] lr: 5.000000e-04 eta: 1:12:14 time: 0.347804 data_time: 0.064671 memory: 9999 loss_kpt: 0.000563 acc_pose: 0.883164 loss: 0.000563 2022/10/20 15:13:18 - mmengine - INFO - Epoch(train) [163][150/293] lr: 5.000000e-04 eta: 1:11:59 time: 0.368938 data_time: 0.068080 memory: 9999 loss_kpt: 0.000565 acc_pose: 0.859895 loss: 0.000565 2022/10/20 15:13:37 - mmengine - INFO - Epoch(train) [163][200/293] lr: 5.000000e-04 eta: 1:11:45 time: 0.371270 data_time: 0.077580 memory: 9999 loss_kpt: 0.000561 acc_pose: 0.880839 loss: 0.000561 2022/10/20 15:13:56 - mmengine - INFO - Epoch(train) [163][250/293] lr: 5.000000e-04 eta: 1:11:30 time: 0.377678 data_time: 0.070816 memory: 9999 loss_kpt: 0.000581 acc_pose: 0.879224 loss: 0.000581 2022/10/20 15:14:12 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:14:32 - mmengine - INFO - Epoch(train) [164][50/293] lr: 5.000000e-04 eta: 1:10:59 time: 0.386156 data_time: 0.078538 memory: 9999 loss_kpt: 0.000574 acc_pose: 0.867845 loss: 0.000574 2022/10/20 15:14:50 - mmengine - INFO - Epoch(train) [164][100/293] lr: 5.000000e-04 eta: 1:10:44 time: 0.358832 data_time: 0.082589 memory: 9999 loss_kpt: 0.000567 acc_pose: 0.886259 loss: 0.000567 2022/10/20 15:15:08 - mmengine - INFO - Epoch(train) [164][150/293] lr: 5.000000e-04 eta: 1:10:29 time: 0.357478 data_time: 0.070238 memory: 9999 loss_kpt: 0.000561 acc_pose: 0.859921 loss: 0.000561 2022/10/20 15:15:26 - mmengine - INFO - Epoch(train) [164][200/293] lr: 5.000000e-04 eta: 1:10:14 time: 0.374035 data_time: 0.067981 memory: 9999 loss_kpt: 0.000562 acc_pose: 0.914050 loss: 0.000562 2022/10/20 15:15:41 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:15:44 - mmengine - INFO - Epoch(train) [164][250/293] lr: 5.000000e-04 eta: 1:09:59 time: 0.360358 data_time: 0.072270 memory: 9999 loss_kpt: 0.000577 acc_pose: 0.855531 loss: 0.000577 2022/10/20 15:16:00 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:16:19 - mmengine - INFO - Epoch(train) [165][50/293] lr: 5.000000e-04 eta: 1:09:28 time: 0.378548 data_time: 0.083341 memory: 9999 loss_kpt: 0.000557 acc_pose: 0.889460 loss: 0.000557 2022/10/20 15:16:36 - mmengine - INFO - Epoch(train) [165][100/293] lr: 5.000000e-04 eta: 1:09:13 time: 0.355007 data_time: 0.067754 memory: 9999 loss_kpt: 0.000563 acc_pose: 0.874601 loss: 0.000563 2022/10/20 15:16:54 - mmengine - INFO - Epoch(train) [165][150/293] lr: 5.000000e-04 eta: 1:08:58 time: 0.352374 data_time: 0.080499 memory: 9999 loss_kpt: 0.000559 acc_pose: 0.858386 loss: 0.000559 2022/10/20 15:17:12 - mmengine - INFO - Epoch(train) [165][200/293] lr: 5.000000e-04 eta: 1:08:43 time: 0.360788 data_time: 0.074116 memory: 9999 loss_kpt: 0.000565 acc_pose: 0.876663 loss: 0.000565 2022/10/20 15:17:30 - mmengine - INFO - Epoch(train) [165][250/293] lr: 5.000000e-04 eta: 1:08:28 time: 0.348725 data_time: 0.062657 memory: 9999 loss_kpt: 0.000557 acc_pose: 0.869436 loss: 0.000557 2022/10/20 15:17:44 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:18:03 - mmengine - INFO - Epoch(train) [166][50/293] lr: 5.000000e-04 eta: 1:07:56 time: 0.376055 data_time: 0.083561 memory: 9999 loss_kpt: 0.000570 acc_pose: 0.870081 loss: 0.000570 2022/10/20 15:18:21 - mmengine - INFO - Epoch(train) [166][100/293] lr: 5.000000e-04 eta: 1:07:42 time: 0.353787 data_time: 0.075734 memory: 9999 loss_kpt: 0.000561 acc_pose: 0.856436 loss: 0.000561 2022/10/20 15:18:38 - mmengine - INFO - Epoch(train) [166][150/293] lr: 5.000000e-04 eta: 1:07:27 time: 0.349077 data_time: 0.075255 memory: 9999 loss_kpt: 0.000554 acc_pose: 0.857684 loss: 0.000554 2022/10/20 15:18:56 - mmengine - INFO - Epoch(train) [166][200/293] lr: 5.000000e-04 eta: 1:07:12 time: 0.351162 data_time: 0.069976 memory: 9999 loss_kpt: 0.000566 acc_pose: 0.879456 loss: 0.000566 2022/10/20 15:19:13 - mmengine - INFO - Epoch(train) [166][250/293] lr: 5.000000e-04 eta: 1:06:57 time: 0.353831 data_time: 0.072076 memory: 9999 loss_kpt: 0.000561 acc_pose: 0.817951 loss: 0.000561 2022/10/20 15:19:29 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:19:47 - mmengine - INFO - Epoch(train) [167][50/293] lr: 5.000000e-04 eta: 1:06:25 time: 0.358541 data_time: 0.082566 memory: 9999 loss_kpt: 0.000568 acc_pose: 0.856969 loss: 0.000568 2022/10/20 15:20:04 - mmengine - INFO - Epoch(train) [167][100/293] lr: 5.000000e-04 eta: 1:06:10 time: 0.352183 data_time: 0.066354 memory: 9999 loss_kpt: 0.000563 acc_pose: 0.850464 loss: 0.000563 2022/10/20 15:20:21 - mmengine - INFO - Epoch(train) [167][150/293] lr: 5.000000e-04 eta: 1:05:55 time: 0.339416 data_time: 0.072221 memory: 9999 loss_kpt: 0.000554 acc_pose: 0.885436 loss: 0.000554 2022/10/20 15:20:39 - mmengine - INFO - Epoch(train) [167][200/293] lr: 5.000000e-04 eta: 1:05:40 time: 0.353331 data_time: 0.068515 memory: 9999 loss_kpt: 0.000562 acc_pose: 0.867375 loss: 0.000562 2022/10/20 15:20:57 - mmengine - INFO - Epoch(train) [167][250/293] lr: 5.000000e-04 eta: 1:05:25 time: 0.365762 data_time: 0.071532 memory: 9999 loss_kpt: 0.000564 acc_pose: 0.815403 loss: 0.000564 2022/10/20 15:21:13 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:21:32 - mmengine - INFO - Epoch(train) [168][50/293] lr: 5.000000e-04 eta: 1:04:54 time: 0.377177 data_time: 0.089598 memory: 9999 loss_kpt: 0.000573 acc_pose: 0.843922 loss: 0.000573 2022/10/20 15:21:38 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:21:49 - mmengine - INFO - Epoch(train) [168][100/293] lr: 5.000000e-04 eta: 1:04:38 time: 0.345298 data_time: 0.064444 memory: 9999 loss_kpt: 0.000561 acc_pose: 0.814194 loss: 0.000561 2022/10/20 15:22:06 - mmengine - INFO - Epoch(train) [168][150/293] lr: 5.000000e-04 eta: 1:04:23 time: 0.344311 data_time: 0.066891 memory: 9999 loss_kpt: 0.000561 acc_pose: 0.861976 loss: 0.000561 2022/10/20 15:22:24 - mmengine - INFO - Epoch(train) [168][200/293] lr: 5.000000e-04 eta: 1:04:08 time: 0.361499 data_time: 0.080361 memory: 9999 loss_kpt: 0.000570 acc_pose: 0.807076 loss: 0.000570 2022/10/20 15:22:42 - mmengine - INFO - Epoch(train) [168][250/293] lr: 5.000000e-04 eta: 1:03:54 time: 0.355487 data_time: 0.068826 memory: 9999 loss_kpt: 0.000569 acc_pose: 0.834965 loss: 0.000569 2022/10/20 15:22:57 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:23:16 - mmengine - INFO - Epoch(train) [169][50/293] lr: 5.000000e-04 eta: 1:03:22 time: 0.368595 data_time: 0.096534 memory: 9999 loss_kpt: 0.000557 acc_pose: 0.850541 loss: 0.000557 2022/10/20 15:23:33 - mmengine - INFO - Epoch(train) [169][100/293] lr: 5.000000e-04 eta: 1:03:07 time: 0.343937 data_time: 0.064892 memory: 9999 loss_kpt: 0.000569 acc_pose: 0.863779 loss: 0.000569 2022/10/20 15:23:51 - mmengine - INFO - Epoch(train) [169][150/293] lr: 5.000000e-04 eta: 1:02:52 time: 0.357602 data_time: 0.078426 memory: 9999 loss_kpt: 0.000563 acc_pose: 0.866902 loss: 0.000563 2022/10/20 15:24:09 - mmengine - INFO - Epoch(train) [169][200/293] lr: 5.000000e-04 eta: 1:02:37 time: 0.372395 data_time: 0.062948 memory: 9999 loss_kpt: 0.000555 acc_pose: 0.904114 loss: 0.000555 2022/10/20 15:24:28 - mmengine - INFO - Epoch(train) [169][250/293] lr: 5.000000e-04 eta: 1:02:22 time: 0.368816 data_time: 0.074420 memory: 9999 loss_kpt: 0.000573 acc_pose: 0.860877 loss: 0.000573 2022/10/20 15:24:42 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:25:02 - mmengine - INFO - Epoch(train) [170][50/293] lr: 5.000000e-04 eta: 1:01:51 time: 0.384194 data_time: 0.085080 memory: 9999 loss_kpt: 0.000547 acc_pose: 0.855465 loss: 0.000547 2022/10/20 15:25:20 - mmengine - INFO - Epoch(train) [170][100/293] lr: 5.000000e-04 eta: 1:01:36 time: 0.363527 data_time: 0.068534 memory: 9999 loss_kpt: 0.000550 acc_pose: 0.828557 loss: 0.000550 2022/10/20 15:25:37 - mmengine - INFO - Epoch(train) [170][150/293] lr: 5.000000e-04 eta: 1:01:21 time: 0.346271 data_time: 0.071466 memory: 9999 loss_kpt: 0.000565 acc_pose: 0.836853 loss: 0.000565 2022/10/20 15:25:55 - mmengine - INFO - Epoch(train) [170][200/293] lr: 5.000000e-04 eta: 1:01:06 time: 0.355878 data_time: 0.066071 memory: 9999 loss_kpt: 0.000551 acc_pose: 0.878537 loss: 0.000551 2022/10/20 15:26:13 - mmengine - INFO - Epoch(train) [170][250/293] lr: 5.000000e-04 eta: 1:00:51 time: 0.356085 data_time: 0.069238 memory: 9999 loss_kpt: 0.000560 acc_pose: 0.811956 loss: 0.000560 2022/10/20 15:26:28 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:26:28 - mmengine - INFO - Saving checkpoint at 170 epochs 2022/10/20 15:26:38 - mmengine - INFO - Epoch(val) [170][50/407] eta: 0:00:46 time: 0.129127 data_time: 0.061456 memory: 9999 2022/10/20 15:26:44 - mmengine - INFO - Epoch(val) [170][100/407] eta: 0:00:34 time: 0.113677 data_time: 0.048628 memory: 1378 2022/10/20 15:26:49 - mmengine - INFO - Epoch(val) [170][150/407] eta: 0:00:28 time: 0.111905 data_time: 0.044732 memory: 1378 2022/10/20 15:26:55 - mmengine - INFO - Epoch(val) [170][200/407] eta: 0:00:23 time: 0.111565 data_time: 0.044827 memory: 1378 2022/10/20 15:27:01 - mmengine - INFO - Epoch(val) [170][250/407] eta: 0:00:17 time: 0.112907 data_time: 0.045973 memory: 1378 2022/10/20 15:27:06 - mmengine - INFO - Epoch(val) [170][300/407] eta: 0:00:11 time: 0.106242 data_time: 0.042077 memory: 1378 2022/10/20 15:27:19 - mmengine - INFO - Epoch(val) [170][350/407] eta: 0:00:15 time: 0.271861 data_time: 0.207602 memory: 1378 2022/10/20 15:27:25 - mmengine - INFO - Epoch(val) [170][400/407] eta: 0:00:00 time: 0.110086 data_time: 0.044014 memory: 1378 2022/10/20 15:28:01 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 15:28:15 - mmengine - INFO - Epoch(val) [170][407/407] coco/AP: 0.725111 coco/AP .5: 0.898040 coco/AP .75: 0.800914 coco/AP (M): 0.687813 coco/AP (L): 0.791484 coco/AR: 0.778306 coco/AR .5: 0.934666 coco/AR .75: 0.845875 coco/AR (M): 0.734827 coco/AR (L): 0.841137 2022/10/20 15:28:15 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221020/resnetv1d101_256/best_coco/AP_epoch_160.pth is removed 2022/10/20 15:28:17 - mmengine - INFO - The best checkpoint with 0.7251 coco/AP at 170 epoch is saved to best_coco/AP_epoch_170.pth. 2022/10/20 15:28:35 - mmengine - INFO - Epoch(train) [171][50/293] lr: 5.000000e-05 eta: 1:00:20 time: 0.356284 data_time: 0.083916 memory: 9999 loss_kpt: 0.000558 acc_pose: 0.869230 loss: 0.000558 2022/10/20 15:28:52 - mmengine - INFO - Epoch(train) [171][100/293] lr: 5.000000e-05 eta: 1:00:05 time: 0.351179 data_time: 0.070026 memory: 9999 loss_kpt: 0.000552 acc_pose: 0.906299 loss: 0.000552 2022/10/20 15:29:10 - mmengine - INFO - Epoch(train) [171][150/293] lr: 5.000000e-05 eta: 0:59:50 time: 0.347701 data_time: 0.070463 memory: 9999 loss_kpt: 0.000551 acc_pose: 0.920090 loss: 0.000551 2022/10/20 15:29:24 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:29:27 - mmengine - INFO - Epoch(train) [171][200/293] lr: 5.000000e-05 eta: 0:59:35 time: 0.350391 data_time: 0.069516 memory: 9999 loss_kpt: 0.000539 acc_pose: 0.868671 loss: 0.000539 2022/10/20 15:29:44 - mmengine - INFO - Epoch(train) [171][250/293] lr: 5.000000e-05 eta: 0:59:20 time: 0.337346 data_time: 0.071050 memory: 9999 loss_kpt: 0.000546 acc_pose: 0.907080 loss: 0.000546 2022/10/20 15:29:59 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:30:17 - mmengine - INFO - Epoch(train) [172][50/293] lr: 5.000000e-05 eta: 0:58:48 time: 0.366380 data_time: 0.079393 memory: 9999 loss_kpt: 0.000531 acc_pose: 0.874091 loss: 0.000531 2022/10/20 15:30:34 - mmengine - INFO - Epoch(train) [172][100/293] lr: 5.000000e-05 eta: 0:58:33 time: 0.334820 data_time: 0.062893 memory: 9999 loss_kpt: 0.000529 acc_pose: 0.848720 loss: 0.000529 2022/10/20 15:30:52 - mmengine - INFO - Epoch(train) [172][150/293] lr: 5.000000e-05 eta: 0:58:18 time: 0.356736 data_time: 0.077721 memory: 9999 loss_kpt: 0.000532 acc_pose: 0.899592 loss: 0.000532 2022/10/20 15:31:09 - mmengine - INFO - Epoch(train) [172][200/293] lr: 5.000000e-05 eta: 0:58:03 time: 0.349518 data_time: 0.071669 memory: 9999 loss_kpt: 0.000534 acc_pose: 0.877108 loss: 0.000534 2022/10/20 15:31:27 - mmengine - INFO - Epoch(train) [172][250/293] lr: 5.000000e-05 eta: 0:57:48 time: 0.349011 data_time: 0.076655 memory: 9999 loss_kpt: 0.000533 acc_pose: 0.877805 loss: 0.000533 2022/10/20 15:31:42 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:32:00 - mmengine - INFO - Epoch(train) [173][50/293] lr: 5.000000e-05 eta: 0:57:17 time: 0.369676 data_time: 0.084942 memory: 9999 loss_kpt: 0.000530 acc_pose: 0.885473 loss: 0.000530 2022/10/20 15:32:18 - mmengine - INFO - Epoch(train) [173][100/293] lr: 5.000000e-05 eta: 0:57:02 time: 0.356981 data_time: 0.072470 memory: 9999 loss_kpt: 0.000532 acc_pose: 0.882247 loss: 0.000532 2022/10/20 15:32:36 - mmengine - INFO - Epoch(train) [173][150/293] lr: 5.000000e-05 eta: 0:56:47 time: 0.348660 data_time: 0.069225 memory: 9999 loss_kpt: 0.000523 acc_pose: 0.881793 loss: 0.000523 2022/10/20 15:32:53 - mmengine - INFO - Epoch(train) [173][200/293] lr: 5.000000e-05 eta: 0:56:31 time: 0.339126 data_time: 0.069460 memory: 9999 loss_kpt: 0.000534 acc_pose: 0.878627 loss: 0.000534 2022/10/20 15:33:10 - mmengine - INFO - Epoch(train) [173][250/293] lr: 5.000000e-05 eta: 0:56:16 time: 0.338798 data_time: 0.070925 memory: 9999 loss_kpt: 0.000532 acc_pose: 0.872102 loss: 0.000532 2022/10/20 15:33:24 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:33:42 - mmengine - INFO - Epoch(train) [174][50/293] lr: 5.000000e-05 eta: 0:55:45 time: 0.354999 data_time: 0.075302 memory: 9999 loss_kpt: 0.000526 acc_pose: 0.893746 loss: 0.000526 2022/10/20 15:33:58 - mmengine - INFO - Epoch(train) [174][100/293] lr: 5.000000e-05 eta: 0:55:30 time: 0.330105 data_time: 0.066775 memory: 9999 loss_kpt: 0.000526 acc_pose: 0.888286 loss: 0.000526 2022/10/20 15:34:16 - mmengine - INFO - Epoch(train) [174][150/293] lr: 5.000000e-05 eta: 0:55:15 time: 0.351713 data_time: 0.069867 memory: 9999 loss_kpt: 0.000532 acc_pose: 0.877580 loss: 0.000532 2022/10/20 15:34:33 - mmengine - INFO - Epoch(train) [174][200/293] lr: 5.000000e-05 eta: 0:55:00 time: 0.344151 data_time: 0.060799 memory: 9999 loss_kpt: 0.000529 acc_pose: 0.860283 loss: 0.000529 2022/10/20 15:34:50 - mmengine - INFO - Epoch(train) [174][250/293] lr: 5.000000e-05 eta: 0:54:44 time: 0.335157 data_time: 0.064718 memory: 9999 loss_kpt: 0.000526 acc_pose: 0.862407 loss: 0.000526 2022/10/20 15:35:05 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:35:12 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:35:23 - mmengine - INFO - Epoch(train) [175][50/293] lr: 5.000000e-05 eta: 0:54:13 time: 0.363356 data_time: 0.076513 memory: 9999 loss_kpt: 0.000530 acc_pose: 0.869774 loss: 0.000530 2022/10/20 15:35:40 - mmengine - INFO - Epoch(train) [175][100/293] lr: 5.000000e-05 eta: 0:53:58 time: 0.339550 data_time: 0.063883 memory: 9999 loss_kpt: 0.000528 acc_pose: 0.875092 loss: 0.000528 2022/10/20 15:35:57 - mmengine - INFO - Epoch(train) [175][150/293] lr: 5.000000e-05 eta: 0:53:43 time: 0.345688 data_time: 0.078409 memory: 9999 loss_kpt: 0.000521 acc_pose: 0.879209 loss: 0.000521 2022/10/20 15:36:15 - mmengine - INFO - Epoch(train) [175][200/293] lr: 5.000000e-05 eta: 0:53:28 time: 0.352203 data_time: 0.068320 memory: 9999 loss_kpt: 0.000526 acc_pose: 0.851373 loss: 0.000526 2022/10/20 15:36:32 - mmengine - INFO - Epoch(train) [175][250/293] lr: 5.000000e-05 eta: 0:53:13 time: 0.351330 data_time: 0.072177 memory: 9999 loss_kpt: 0.000520 acc_pose: 0.893220 loss: 0.000520 2022/10/20 15:36:47 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:37:05 - mmengine - INFO - Epoch(train) [176][50/293] lr: 5.000000e-05 eta: 0:52:42 time: 0.362629 data_time: 0.081891 memory: 9999 loss_kpt: 0.000523 acc_pose: 0.871626 loss: 0.000523 2022/10/20 15:37:23 - mmengine - INFO - Epoch(train) [176][100/293] lr: 5.000000e-05 eta: 0:52:27 time: 0.350116 data_time: 0.077262 memory: 9999 loss_kpt: 0.000522 acc_pose: 0.866926 loss: 0.000522 2022/10/20 15:37:41 - mmengine - INFO - Epoch(train) [176][150/293] lr: 5.000000e-05 eta: 0:52:12 time: 0.362396 data_time: 0.061337 memory: 9999 loss_kpt: 0.000535 acc_pose: 0.867465 loss: 0.000535 2022/10/20 15:37:58 - mmengine - INFO - Epoch(train) [176][200/293] lr: 5.000000e-05 eta: 0:51:57 time: 0.340102 data_time: 0.067983 memory: 9999 loss_kpt: 0.000516 acc_pose: 0.899499 loss: 0.000516 2022/10/20 15:38:15 - mmengine - INFO - Epoch(train) [176][250/293] lr: 5.000000e-05 eta: 0:51:41 time: 0.346556 data_time: 0.071653 memory: 9999 loss_kpt: 0.000526 acc_pose: 0.887032 loss: 0.000526 2022/10/20 15:38:31 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:38:49 - mmengine - INFO - Epoch(train) [177][50/293] lr: 5.000000e-05 eta: 0:51:10 time: 0.356425 data_time: 0.079267 memory: 9999 loss_kpt: 0.000526 acc_pose: 0.853753 loss: 0.000526 2022/10/20 15:39:07 - mmengine - INFO - Epoch(train) [177][100/293] lr: 5.000000e-05 eta: 0:50:55 time: 0.357360 data_time: 0.063340 memory: 9999 loss_kpt: 0.000504 acc_pose: 0.894905 loss: 0.000504 2022/10/20 15:39:24 - mmengine - INFO - Epoch(train) [177][150/293] lr: 5.000000e-05 eta: 0:50:40 time: 0.343202 data_time: 0.069469 memory: 9999 loss_kpt: 0.000524 acc_pose: 0.842875 loss: 0.000524 2022/10/20 15:39:42 - mmengine - INFO - Epoch(train) [177][200/293] lr: 5.000000e-05 eta: 0:50:25 time: 0.356134 data_time: 0.061319 memory: 9999 loss_kpt: 0.000522 acc_pose: 0.850230 loss: 0.000522 2022/10/20 15:39:58 - mmengine - INFO - Epoch(train) [177][250/293] lr: 5.000000e-05 eta: 0:50:10 time: 0.338077 data_time: 0.060091 memory: 9999 loss_kpt: 0.000528 acc_pose: 0.888550 loss: 0.000528 2022/10/20 15:40:13 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:40:31 - mmengine - INFO - Epoch(train) [178][50/293] lr: 5.000000e-05 eta: 0:49:39 time: 0.353206 data_time: 0.083866 memory: 9999 loss_kpt: 0.000527 acc_pose: 0.834547 loss: 0.000527 2022/10/20 15:40:48 - mmengine - INFO - Epoch(train) [178][100/293] lr: 5.000000e-05 eta: 0:49:24 time: 0.354690 data_time: 0.080656 memory: 9999 loss_kpt: 0.000517 acc_pose: 0.873870 loss: 0.000517 2022/10/20 15:41:01 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:41:05 - mmengine - INFO - Epoch(train) [178][150/293] lr: 5.000000e-05 eta: 0:49:09 time: 0.333756 data_time: 0.068052 memory: 9999 loss_kpt: 0.000528 acc_pose: 0.864584 loss: 0.000528 2022/10/20 15:41:22 - mmengine - INFO - Epoch(train) [178][200/293] lr: 5.000000e-05 eta: 0:48:54 time: 0.345675 data_time: 0.070825 memory: 9999 loss_kpt: 0.000524 acc_pose: 0.902516 loss: 0.000524 2022/10/20 15:41:40 - mmengine - INFO - Epoch(train) [178][250/293] lr: 5.000000e-05 eta: 0:48:38 time: 0.348838 data_time: 0.076478 memory: 9999 loss_kpt: 0.000513 acc_pose: 0.893551 loss: 0.000513 2022/10/20 15:41:54 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:42:13 - mmengine - INFO - Epoch(train) [179][50/293] lr: 5.000000e-05 eta: 0:48:08 time: 0.367150 data_time: 0.078855 memory: 9999 loss_kpt: 0.000523 acc_pose: 0.808665 loss: 0.000523 2022/10/20 15:42:30 - mmengine - INFO - Epoch(train) [179][100/293] lr: 5.000000e-05 eta: 0:47:53 time: 0.353421 data_time: 0.068403 memory: 9999 loss_kpt: 0.000516 acc_pose: 0.909604 loss: 0.000516 2022/10/20 15:42:48 - mmengine - INFO - Epoch(train) [179][150/293] lr: 5.000000e-05 eta: 0:47:37 time: 0.344246 data_time: 0.067223 memory: 9999 loss_kpt: 0.000522 acc_pose: 0.866539 loss: 0.000522 2022/10/20 15:43:05 - mmengine - INFO - Epoch(train) [179][200/293] lr: 5.000000e-05 eta: 0:47:22 time: 0.339819 data_time: 0.068825 memory: 9999 loss_kpt: 0.000515 acc_pose: 0.876498 loss: 0.000515 2022/10/20 15:43:22 - mmengine - INFO - Epoch(train) [179][250/293] lr: 5.000000e-05 eta: 0:47:07 time: 0.353324 data_time: 0.074202 memory: 9999 loss_kpt: 0.000527 acc_pose: 0.854336 loss: 0.000527 2022/10/20 15:43:37 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:43:55 - mmengine - INFO - Epoch(train) [180][50/293] lr: 5.000000e-05 eta: 0:46:36 time: 0.358004 data_time: 0.080002 memory: 9999 loss_kpt: 0.000519 acc_pose: 0.916548 loss: 0.000519 2022/10/20 15:44:13 - mmengine - INFO - Epoch(train) [180][100/293] lr: 5.000000e-05 eta: 0:46:21 time: 0.365655 data_time: 0.074989 memory: 9999 loss_kpt: 0.000526 acc_pose: 0.884697 loss: 0.000526 2022/10/20 15:44:30 - mmengine - INFO - Epoch(train) [180][150/293] lr: 5.000000e-05 eta: 0:46:06 time: 0.341841 data_time: 0.065724 memory: 9999 loss_kpt: 0.000504 acc_pose: 0.889369 loss: 0.000504 2022/10/20 15:44:48 - mmengine - INFO - Epoch(train) [180][200/293] lr: 5.000000e-05 eta: 0:45:51 time: 0.357007 data_time: 0.068486 memory: 9999 loss_kpt: 0.000523 acc_pose: 0.879425 loss: 0.000523 2022/10/20 15:45:05 - mmengine - INFO - Epoch(train) [180][250/293] lr: 5.000000e-05 eta: 0:45:36 time: 0.343726 data_time: 0.067313 memory: 9999 loss_kpt: 0.000514 acc_pose: 0.895854 loss: 0.000514 2022/10/20 15:45:20 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:45:20 - mmengine - INFO - Saving checkpoint at 180 epochs 2022/10/20 15:45:30 - mmengine - INFO - Epoch(val) [180][50/407] eta: 0:00:42 time: 0.118539 data_time: 0.051618 memory: 9999 2022/10/20 15:45:36 - mmengine - INFO - Epoch(val) [180][100/407] eta: 0:00:35 time: 0.116378 data_time: 0.050599 memory: 1378 2022/10/20 15:45:42 - mmengine - INFO - Epoch(val) [180][150/407] eta: 0:00:29 time: 0.113167 data_time: 0.048176 memory: 1378 2022/10/20 15:45:47 - mmengine - INFO - Epoch(val) [180][200/407] eta: 0:00:23 time: 0.112976 data_time: 0.045959 memory: 1378 2022/10/20 15:45:53 - mmengine - INFO - Epoch(val) [180][250/407] eta: 0:00:17 time: 0.111832 data_time: 0.047243 memory: 1378 2022/10/20 15:45:59 - mmengine - INFO - Epoch(val) [180][300/407] eta: 0:00:12 time: 0.119292 data_time: 0.053620 memory: 1378 2022/10/20 15:46:05 - mmengine - INFO - Epoch(val) [180][350/407] eta: 0:00:06 time: 0.118037 data_time: 0.053194 memory: 1378 2022/10/20 15:46:10 - mmengine - INFO - Epoch(val) [180][400/407] eta: 0:00:00 time: 0.102884 data_time: 0.038574 memory: 1378 2022/10/20 15:46:45 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 15:46:59 - mmengine - INFO - Epoch(val) [180][407/407] coco/AP: 0.731474 coco/AP .5: 0.900999 coco/AP .75: 0.810622 coco/AP (M): 0.694522 coco/AP (L): 0.798748 coco/AR: 0.785469 coco/AR .5: 0.939389 coco/AR .75: 0.853747 coco/AR (M): 0.741846 coco/AR (L): 0.848458 2022/10/20 15:46:59 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221020/resnetv1d101_256/best_coco/AP_epoch_170.pth is removed 2022/10/20 15:47:01 - mmengine - INFO - The best checkpoint with 0.7315 coco/AP at 180 epoch is saved to best_coco/AP_epoch_180.pth. 2022/10/20 15:47:19 - mmengine - INFO - Epoch(train) [181][50/293] lr: 5.000000e-05 eta: 0:45:05 time: 0.346741 data_time: 0.085669 memory: 9999 loss_kpt: 0.000511 acc_pose: 0.833453 loss: 0.000511 2022/10/20 15:47:37 - mmengine - INFO - Epoch(train) [181][100/293] lr: 5.000000e-05 eta: 0:44:50 time: 0.360248 data_time: 0.070656 memory: 9999 loss_kpt: 0.000521 acc_pose: 0.810931 loss: 0.000521 2022/10/20 15:47:55 - mmengine - INFO - Epoch(train) [181][150/293] lr: 5.000000e-05 eta: 0:44:35 time: 0.354059 data_time: 0.077137 memory: 9999 loss_kpt: 0.000528 acc_pose: 0.899906 loss: 0.000528 2022/10/20 15:48:12 - mmengine - INFO - Epoch(train) [181][200/293] lr: 5.000000e-05 eta: 0:44:20 time: 0.352766 data_time: 0.068330 memory: 9999 loss_kpt: 0.000521 acc_pose: 0.880494 loss: 0.000521 2022/10/20 15:48:30 - mmengine - INFO - Epoch(train) [181][250/293] lr: 5.000000e-05 eta: 0:44:05 time: 0.347155 data_time: 0.077562 memory: 9999 loss_kpt: 0.000510 acc_pose: 0.865381 loss: 0.000510 2022/10/20 15:48:33 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:48:44 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:49:03 - mmengine - INFO - Epoch(train) [182][50/293] lr: 5.000000e-05 eta: 0:43:34 time: 0.368733 data_time: 0.083246 memory: 9999 loss_kpt: 0.000530 acc_pose: 0.871058 loss: 0.000530 2022/10/20 15:49:20 - mmengine - INFO - Epoch(train) [182][100/293] lr: 5.000000e-05 eta: 0:43:19 time: 0.350511 data_time: 0.063760 memory: 9999 loss_kpt: 0.000513 acc_pose: 0.857231 loss: 0.000513 2022/10/20 15:49:38 - mmengine - INFO - Epoch(train) [182][150/293] lr: 5.000000e-05 eta: 0:43:04 time: 0.352153 data_time: 0.089011 memory: 9999 loss_kpt: 0.000518 acc_pose: 0.853710 loss: 0.000518 2022/10/20 15:49:56 - mmengine - INFO - Epoch(train) [182][200/293] lr: 5.000000e-05 eta: 0:42:49 time: 0.349520 data_time: 0.076183 memory: 9999 loss_kpt: 0.000533 acc_pose: 0.839318 loss: 0.000533 2022/10/20 15:50:13 - mmengine - INFO - Epoch(train) [182][250/293] lr: 5.000000e-05 eta: 0:42:34 time: 0.346593 data_time: 0.062322 memory: 9999 loss_kpt: 0.000519 acc_pose: 0.881915 loss: 0.000519 2022/10/20 15:50:28 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:50:47 - mmengine - INFO - Epoch(train) [183][50/293] lr: 5.000000e-05 eta: 0:42:03 time: 0.380051 data_time: 0.080141 memory: 9999 loss_kpt: 0.000533 acc_pose: 0.875160 loss: 0.000533 2022/10/20 15:51:05 - mmengine - INFO - Epoch(train) [183][100/293] lr: 5.000000e-05 eta: 0:41:48 time: 0.356480 data_time: 0.063506 memory: 9999 loss_kpt: 0.000515 acc_pose: 0.899353 loss: 0.000515 2022/10/20 15:51:22 - mmengine - INFO - Epoch(train) [183][150/293] lr: 5.000000e-05 eta: 0:41:33 time: 0.344056 data_time: 0.080728 memory: 9999 loss_kpt: 0.000517 acc_pose: 0.873965 loss: 0.000517 2022/10/20 15:51:39 - mmengine - INFO - Epoch(train) [183][200/293] lr: 5.000000e-05 eta: 0:41:18 time: 0.335442 data_time: 0.063941 memory: 9999 loss_kpt: 0.000521 acc_pose: 0.901767 loss: 0.000521 2022/10/20 15:51:56 - mmengine - INFO - Epoch(train) [183][250/293] lr: 5.000000e-05 eta: 0:41:02 time: 0.349668 data_time: 0.066718 memory: 9999 loss_kpt: 0.000511 acc_pose: 0.883304 loss: 0.000511 2022/10/20 15:52:11 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:52:29 - mmengine - INFO - Epoch(train) [184][50/293] lr: 5.000000e-05 eta: 0:40:32 time: 0.362740 data_time: 0.093013 memory: 9999 loss_kpt: 0.000526 acc_pose: 0.858630 loss: 0.000526 2022/10/20 15:52:48 - mmengine - INFO - Epoch(train) [184][100/293] lr: 5.000000e-05 eta: 0:40:17 time: 0.366019 data_time: 0.074099 memory: 9999 loss_kpt: 0.000519 acc_pose: 0.867121 loss: 0.000519 2022/10/20 15:53:05 - mmengine - INFO - Epoch(train) [184][150/293] lr: 5.000000e-05 eta: 0:40:02 time: 0.350985 data_time: 0.073806 memory: 9999 loss_kpt: 0.000515 acc_pose: 0.869430 loss: 0.000515 2022/10/20 15:53:22 - mmengine - INFO - Epoch(train) [184][200/293] lr: 5.000000e-05 eta: 0:39:47 time: 0.342477 data_time: 0.070850 memory: 9999 loss_kpt: 0.000511 acc_pose: 0.879772 loss: 0.000511 2022/10/20 15:53:39 - mmengine - INFO - Epoch(train) [184][250/293] lr: 5.000000e-05 eta: 0:39:31 time: 0.337884 data_time: 0.068824 memory: 9999 loss_kpt: 0.000521 acc_pose: 0.888819 loss: 0.000521 2022/10/20 15:53:55 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:54:13 - mmengine - INFO - Epoch(train) [185][50/293] lr: 5.000000e-05 eta: 0:39:01 time: 0.363126 data_time: 0.077951 memory: 9999 loss_kpt: 0.000526 acc_pose: 0.892304 loss: 0.000526 2022/10/20 15:54:27 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:54:31 - mmengine - INFO - Epoch(train) [185][100/293] lr: 5.000000e-05 eta: 0:38:46 time: 0.365968 data_time: 0.078243 memory: 9999 loss_kpt: 0.000512 acc_pose: 0.885039 loss: 0.000512 2022/10/20 15:54:49 - mmengine - INFO - Epoch(train) [185][150/293] lr: 5.000000e-05 eta: 0:38:31 time: 0.358337 data_time: 0.068684 memory: 9999 loss_kpt: 0.000514 acc_pose: 0.878539 loss: 0.000514 2022/10/20 15:55:06 - mmengine - INFO - Epoch(train) [185][200/293] lr: 5.000000e-05 eta: 0:38:16 time: 0.344438 data_time: 0.071232 memory: 9999 loss_kpt: 0.000518 acc_pose: 0.855926 loss: 0.000518 2022/10/20 15:55:24 - mmengine - INFO - Epoch(train) [185][250/293] lr: 5.000000e-05 eta: 0:38:00 time: 0.350617 data_time: 0.072354 memory: 9999 loss_kpt: 0.000516 acc_pose: 0.898821 loss: 0.000516 2022/10/20 15:55:39 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:55:57 - mmengine - INFO - Epoch(train) [186][50/293] lr: 5.000000e-05 eta: 0:37:30 time: 0.369379 data_time: 0.091022 memory: 9999 loss_kpt: 0.000523 acc_pose: 0.876101 loss: 0.000523 2022/10/20 15:56:15 - mmengine - INFO - Epoch(train) [186][100/293] lr: 5.000000e-05 eta: 0:37:15 time: 0.348025 data_time: 0.068264 memory: 9999 loss_kpt: 0.000506 acc_pose: 0.914136 loss: 0.000506 2022/10/20 15:56:32 - mmengine - INFO - Epoch(train) [186][150/293] lr: 5.000000e-05 eta: 0:37:00 time: 0.345232 data_time: 0.064171 memory: 9999 loss_kpt: 0.000511 acc_pose: 0.862029 loss: 0.000511 2022/10/20 15:56:49 - mmengine - INFO - Epoch(train) [186][200/293] lr: 5.000000e-05 eta: 0:36:45 time: 0.339722 data_time: 0.064914 memory: 9999 loss_kpt: 0.000506 acc_pose: 0.848993 loss: 0.000506 2022/10/20 15:57:07 - mmengine - INFO - Epoch(train) [186][250/293] lr: 5.000000e-05 eta: 0:36:29 time: 0.349284 data_time: 0.062161 memory: 9999 loss_kpt: 0.000506 acc_pose: 0.846980 loss: 0.000506 2022/10/20 15:57:21 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:57:39 - mmengine - INFO - Epoch(train) [187][50/293] lr: 5.000000e-05 eta: 0:35:59 time: 0.351338 data_time: 0.098444 memory: 9999 loss_kpt: 0.000502 acc_pose: 0.907106 loss: 0.000502 2022/10/20 15:57:56 - mmengine - INFO - Epoch(train) [187][100/293] lr: 5.000000e-05 eta: 0:35:44 time: 0.346277 data_time: 0.071654 memory: 9999 loss_kpt: 0.000513 acc_pose: 0.871350 loss: 0.000513 2022/10/20 15:58:13 - mmengine - INFO - Epoch(train) [187][150/293] lr: 5.000000e-05 eta: 0:35:29 time: 0.346305 data_time: 0.072193 memory: 9999 loss_kpt: 0.000516 acc_pose: 0.839201 loss: 0.000516 2022/10/20 15:58:32 - mmengine - INFO - Epoch(train) [187][200/293] lr: 5.000000e-05 eta: 0:35:14 time: 0.370384 data_time: 0.084479 memory: 9999 loss_kpt: 0.000514 acc_pose: 0.899593 loss: 0.000514 2022/10/20 15:58:49 - mmengine - INFO - Epoch(train) [187][250/293] lr: 5.000000e-05 eta: 0:34:58 time: 0.340157 data_time: 0.072092 memory: 9999 loss_kpt: 0.000516 acc_pose: 0.911955 loss: 0.000516 2022/10/20 15:59:04 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 15:59:22 - mmengine - INFO - Epoch(train) [188][50/293] lr: 5.000000e-05 eta: 0:34:28 time: 0.364158 data_time: 0.091330 memory: 9999 loss_kpt: 0.000518 acc_pose: 0.884275 loss: 0.000518 2022/10/20 15:59:39 - mmengine - INFO - Epoch(train) [188][100/293] lr: 5.000000e-05 eta: 0:34:13 time: 0.346216 data_time: 0.069225 memory: 9999 loss_kpt: 0.000516 acc_pose: 0.910456 loss: 0.000516 2022/10/20 15:59:56 - mmengine - INFO - Epoch(train) [188][150/293] lr: 5.000000e-05 eta: 0:33:58 time: 0.337040 data_time: 0.069869 memory: 9999 loss_kpt: 0.000518 acc_pose: 0.872914 loss: 0.000518 2022/10/20 16:00:13 - mmengine - INFO - Epoch(train) [188][200/293] lr: 5.000000e-05 eta: 0:33:42 time: 0.345309 data_time: 0.068402 memory: 9999 loss_kpt: 0.000511 acc_pose: 0.894480 loss: 0.000511 2022/10/20 16:00:17 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:00:30 - mmengine - INFO - Epoch(train) [188][250/293] lr: 5.000000e-05 eta: 0:33:27 time: 0.341881 data_time: 0.057846 memory: 9999 loss_kpt: 0.000518 acc_pose: 0.872005 loss: 0.000518 2022/10/20 16:00:45 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:01:03 - mmengine - INFO - Epoch(train) [189][50/293] lr: 5.000000e-05 eta: 0:32:57 time: 0.360022 data_time: 0.084718 memory: 9999 loss_kpt: 0.000513 acc_pose: 0.888621 loss: 0.000513 2022/10/20 16:01:20 - mmengine - INFO - Epoch(train) [189][100/293] lr: 5.000000e-05 eta: 0:32:42 time: 0.343575 data_time: 0.073672 memory: 9999 loss_kpt: 0.000507 acc_pose: 0.891441 loss: 0.000507 2022/10/20 16:01:38 - mmengine - INFO - Epoch(train) [189][150/293] lr: 5.000000e-05 eta: 0:32:27 time: 0.343014 data_time: 0.075581 memory: 9999 loss_kpt: 0.000516 acc_pose: 0.881742 loss: 0.000516 2022/10/20 16:01:55 - mmengine - INFO - Epoch(train) [189][200/293] lr: 5.000000e-05 eta: 0:32:11 time: 0.341245 data_time: 0.059897 memory: 9999 loss_kpt: 0.000508 acc_pose: 0.887984 loss: 0.000508 2022/10/20 16:02:12 - mmengine - INFO - Epoch(train) [189][250/293] lr: 5.000000e-05 eta: 0:31:56 time: 0.349149 data_time: 0.067916 memory: 9999 loss_kpt: 0.000528 acc_pose: 0.865548 loss: 0.000528 2022/10/20 16:02:27 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:02:45 - mmengine - INFO - Epoch(train) [190][50/293] lr: 5.000000e-05 eta: 0:31:26 time: 0.360672 data_time: 0.074947 memory: 9999 loss_kpt: 0.000512 acc_pose: 0.892654 loss: 0.000512 2022/10/20 16:03:02 - mmengine - INFO - Epoch(train) [190][100/293] lr: 5.000000e-05 eta: 0:31:11 time: 0.342467 data_time: 0.078254 memory: 9999 loss_kpt: 0.000507 acc_pose: 0.876520 loss: 0.000507 2022/10/20 16:03:19 - mmengine - INFO - Epoch(train) [190][150/293] lr: 5.000000e-05 eta: 0:30:56 time: 0.350743 data_time: 0.063590 memory: 9999 loss_kpt: 0.000512 acc_pose: 0.879009 loss: 0.000512 2022/10/20 16:03:37 - mmengine - INFO - Epoch(train) [190][200/293] lr: 5.000000e-05 eta: 0:30:40 time: 0.350286 data_time: 0.068612 memory: 9999 loss_kpt: 0.000515 acc_pose: 0.859599 loss: 0.000515 2022/10/20 16:03:54 - mmengine - INFO - Epoch(train) [190][250/293] lr: 5.000000e-05 eta: 0:30:25 time: 0.350602 data_time: 0.068541 memory: 9999 loss_kpt: 0.000516 acc_pose: 0.854694 loss: 0.000516 2022/10/20 16:04:09 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:04:09 - mmengine - INFO - Saving checkpoint at 190 epochs 2022/10/20 16:04:18 - mmengine - INFO - Epoch(val) [190][50/407] eta: 0:00:42 time: 0.120430 data_time: 0.051127 memory: 9999 2022/10/20 16:04:24 - mmengine - INFO - Epoch(val) [190][100/407] eta: 0:00:36 time: 0.120204 data_time: 0.054344 memory: 1378 2022/10/20 16:04:30 - mmengine - INFO - Epoch(val) [190][150/407] eta: 0:00:29 time: 0.113764 data_time: 0.047709 memory: 1378 2022/10/20 16:04:36 - mmengine - INFO - Epoch(val) [190][200/407] eta: 0:00:22 time: 0.110588 data_time: 0.045907 memory: 1378 2022/10/20 16:04:41 - mmengine - INFO - Epoch(val) [190][250/407] eta: 0:00:17 time: 0.112494 data_time: 0.046370 memory: 1378 2022/10/20 16:04:47 - mmengine - INFO - Epoch(val) [190][300/407] eta: 0:00:12 time: 0.117768 data_time: 0.050633 memory: 1378 2022/10/20 16:04:53 - mmengine - INFO - Epoch(val) [190][350/407] eta: 0:00:06 time: 0.113911 data_time: 0.047913 memory: 1378 2022/10/20 16:04:58 - mmengine - INFO - Epoch(val) [190][400/407] eta: 0:00:00 time: 0.107056 data_time: 0.042361 memory: 1378 2022/10/20 16:05:34 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 16:05:48 - mmengine - INFO - Epoch(val) [190][407/407] coco/AP: 0.731343 coco/AP .5: 0.900462 coco/AP .75: 0.809745 coco/AP (M): 0.695747 coco/AP (L): 0.796593 coco/AR: 0.785343 coco/AR .5: 0.938759 coco/AR .75: 0.853275 coco/AR (M): 0.742830 coco/AR (L): 0.847157 2022/10/20 16:06:05 - mmengine - INFO - Epoch(train) [191][50/293] lr: 5.000000e-05 eta: 0:29:55 time: 0.357619 data_time: 0.080500 memory: 9999 loss_kpt: 0.000522 acc_pose: 0.883247 loss: 0.000522 2022/10/20 16:06:22 - mmengine - INFO - Epoch(train) [191][100/293] lr: 5.000000e-05 eta: 0:29:40 time: 0.338054 data_time: 0.070403 memory: 9999 loss_kpt: 0.000518 acc_pose: 0.915009 loss: 0.000518 2022/10/20 16:06:40 - mmengine - INFO - Epoch(train) [191][150/293] lr: 5.000000e-05 eta: 0:29:25 time: 0.343518 data_time: 0.065667 memory: 9999 loss_kpt: 0.000516 acc_pose: 0.869215 loss: 0.000516 2022/10/20 16:06:57 - mmengine - INFO - Epoch(train) [191][200/293] lr: 5.000000e-05 eta: 0:29:09 time: 0.350214 data_time: 0.069215 memory: 9999 loss_kpt: 0.000517 acc_pose: 0.904320 loss: 0.000517 2022/10/20 16:07:14 - mmengine - INFO - Epoch(train) [191][250/293] lr: 5.000000e-05 eta: 0:28:54 time: 0.344120 data_time: 0.066231 memory: 9999 loss_kpt: 0.000514 acc_pose: 0.879060 loss: 0.000514 2022/10/20 16:07:29 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:07:43 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:07:47 - mmengine - INFO - Epoch(train) [192][50/293] lr: 5.000000e-05 eta: 0:28:24 time: 0.367552 data_time: 0.084385 memory: 9999 loss_kpt: 0.000516 acc_pose: 0.898065 loss: 0.000516 2022/10/20 16:08:05 - mmengine - INFO - Epoch(train) [192][100/293] lr: 5.000000e-05 eta: 0:28:09 time: 0.344838 data_time: 0.085452 memory: 9999 loss_kpt: 0.000517 acc_pose: 0.873888 loss: 0.000517 2022/10/20 16:08:22 - mmengine - INFO - Epoch(train) [192][150/293] lr: 5.000000e-05 eta: 0:27:54 time: 0.347925 data_time: 0.077159 memory: 9999 loss_kpt: 0.000504 acc_pose: 0.913170 loss: 0.000504 2022/10/20 16:08:39 - mmengine - INFO - Epoch(train) [192][200/293] lr: 5.000000e-05 eta: 0:27:38 time: 0.344180 data_time: 0.077634 memory: 9999 loss_kpt: 0.000514 acc_pose: 0.894010 loss: 0.000514 2022/10/20 16:08:57 - mmengine - INFO - Epoch(train) [192][250/293] lr: 5.000000e-05 eta: 0:27:23 time: 0.345337 data_time: 0.085167 memory: 9999 loss_kpt: 0.000513 acc_pose: 0.888786 loss: 0.000513 2022/10/20 16:09:11 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:09:30 - mmengine - INFO - Epoch(train) [193][50/293] lr: 5.000000e-05 eta: 0:26:53 time: 0.365814 data_time: 0.083695 memory: 9999 loss_kpt: 0.000521 acc_pose: 0.829060 loss: 0.000521 2022/10/20 16:09:47 - mmengine - INFO - Epoch(train) [193][100/293] lr: 5.000000e-05 eta: 0:26:38 time: 0.344591 data_time: 0.076911 memory: 9999 loss_kpt: 0.000510 acc_pose: 0.893572 loss: 0.000510 2022/10/20 16:10:04 - mmengine - INFO - Epoch(train) [193][150/293] lr: 5.000000e-05 eta: 0:26:23 time: 0.352274 data_time: 0.093037 memory: 9999 loss_kpt: 0.000518 acc_pose: 0.846133 loss: 0.000518 2022/10/20 16:10:21 - mmengine - INFO - Epoch(train) [193][200/293] lr: 5.000000e-05 eta: 0:26:08 time: 0.337033 data_time: 0.068264 memory: 9999 loss_kpt: 0.000515 acc_pose: 0.856984 loss: 0.000515 2022/10/20 16:10:38 - mmengine - INFO - Epoch(train) [193][250/293] lr: 5.000000e-05 eta: 0:25:52 time: 0.339676 data_time: 0.063678 memory: 9999 loss_kpt: 0.000503 acc_pose: 0.898273 loss: 0.000503 2022/10/20 16:10:53 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:11:11 - mmengine - INFO - Epoch(train) [194][50/293] lr: 5.000000e-05 eta: 0:25:23 time: 0.361714 data_time: 0.081770 memory: 9999 loss_kpt: 0.000513 acc_pose: 0.898919 loss: 0.000513 2022/10/20 16:11:28 - mmengine - INFO - Epoch(train) [194][100/293] lr: 5.000000e-05 eta: 0:25:07 time: 0.346755 data_time: 0.064793 memory: 9999 loss_kpt: 0.000507 acc_pose: 0.871995 loss: 0.000507 2022/10/20 16:11:46 - mmengine - INFO - Epoch(train) [194][150/293] lr: 5.000000e-05 eta: 0:24:52 time: 0.349919 data_time: 0.069713 memory: 9999 loss_kpt: 0.000524 acc_pose: 0.882138 loss: 0.000524 2022/10/20 16:12:04 - mmengine - INFO - Epoch(train) [194][200/293] lr: 5.000000e-05 eta: 0:24:37 time: 0.350808 data_time: 0.071995 memory: 9999 loss_kpt: 0.000523 acc_pose: 0.922644 loss: 0.000523 2022/10/20 16:12:21 - mmengine - INFO - Epoch(train) [194][250/293] lr: 5.000000e-05 eta: 0:24:21 time: 0.344045 data_time: 0.073132 memory: 9999 loss_kpt: 0.000517 acc_pose: 0.863508 loss: 0.000517 2022/10/20 16:12:36 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:12:54 - mmengine - INFO - Epoch(train) [195][50/293] lr: 5.000000e-05 eta: 0:23:52 time: 0.374273 data_time: 0.092990 memory: 9999 loss_kpt: 0.000509 acc_pose: 0.900189 loss: 0.000509 2022/10/20 16:13:11 - mmengine - INFO - Epoch(train) [195][100/293] lr: 5.000000e-05 eta: 0:23:37 time: 0.336698 data_time: 0.063876 memory: 9999 loss_kpt: 0.000511 acc_pose: 0.895371 loss: 0.000511 2022/10/20 16:13:28 - mmengine - INFO - Epoch(train) [195][150/293] lr: 5.000000e-05 eta: 0:23:21 time: 0.343182 data_time: 0.068833 memory: 9999 loss_kpt: 0.000503 acc_pose: 0.896320 loss: 0.000503 2022/10/20 16:13:31 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:13:45 - mmengine - INFO - Epoch(train) [195][200/293] lr: 5.000000e-05 eta: 0:23:06 time: 0.342210 data_time: 0.067571 memory: 9999 loss_kpt: 0.000525 acc_pose: 0.844089 loss: 0.000525 2022/10/20 16:14:03 - mmengine - INFO - Epoch(train) [195][250/293] lr: 5.000000e-05 eta: 0:22:51 time: 0.356554 data_time: 0.073526 memory: 9999 loss_kpt: 0.000514 acc_pose: 0.878680 loss: 0.000514 2022/10/20 16:14:18 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:14:37 - mmengine - INFO - Epoch(train) [196][50/293] lr: 5.000000e-05 eta: 0:22:21 time: 0.363972 data_time: 0.080840 memory: 9999 loss_kpt: 0.000512 acc_pose: 0.867227 loss: 0.000512 2022/10/20 16:14:54 - mmengine - INFO - Epoch(train) [196][100/293] lr: 5.000000e-05 eta: 0:22:06 time: 0.342444 data_time: 0.067860 memory: 9999 loss_kpt: 0.000515 acc_pose: 0.875867 loss: 0.000515 2022/10/20 16:15:11 - mmengine - INFO - Epoch(train) [196][150/293] lr: 5.000000e-05 eta: 0:21:51 time: 0.344410 data_time: 0.071421 memory: 9999 loss_kpt: 0.000513 acc_pose: 0.842243 loss: 0.000513 2022/10/20 16:15:28 - mmengine - INFO - Epoch(train) [196][200/293] lr: 5.000000e-05 eta: 0:21:35 time: 0.344191 data_time: 0.065523 memory: 9999 loss_kpt: 0.000505 acc_pose: 0.854280 loss: 0.000505 2022/10/20 16:15:46 - mmengine - INFO - Epoch(train) [196][250/293] lr: 5.000000e-05 eta: 0:21:20 time: 0.349951 data_time: 0.065426 memory: 9999 loss_kpt: 0.000505 acc_pose: 0.897817 loss: 0.000505 2022/10/20 16:16:00 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:16:19 - mmengine - INFO - Epoch(train) [197][50/293] lr: 5.000000e-05 eta: 0:20:50 time: 0.363277 data_time: 0.084278 memory: 9999 loss_kpt: 0.000512 acc_pose: 0.845456 loss: 0.000512 2022/10/20 16:16:36 - mmengine - INFO - Epoch(train) [197][100/293] lr: 5.000000e-05 eta: 0:20:35 time: 0.344372 data_time: 0.070674 memory: 9999 loss_kpt: 0.000521 acc_pose: 0.870124 loss: 0.000521 2022/10/20 16:16:53 - mmengine - INFO - Epoch(train) [197][150/293] lr: 5.000000e-05 eta: 0:20:20 time: 0.340889 data_time: 0.066370 memory: 9999 loss_kpt: 0.000522 acc_pose: 0.895945 loss: 0.000522 2022/10/20 16:17:11 - mmengine - INFO - Epoch(train) [197][200/293] lr: 5.000000e-05 eta: 0:20:05 time: 0.354600 data_time: 0.076151 memory: 9999 loss_kpt: 0.000503 acc_pose: 0.882574 loss: 0.000503 2022/10/20 16:17:28 - mmengine - INFO - Epoch(train) [197][250/293] lr: 5.000000e-05 eta: 0:19:49 time: 0.345297 data_time: 0.072272 memory: 9999 loss_kpt: 0.000505 acc_pose: 0.894919 loss: 0.000505 2022/10/20 16:17:42 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:18:00 - mmengine - INFO - Epoch(train) [198][50/293] lr: 5.000000e-05 eta: 0:19:20 time: 0.352376 data_time: 0.088306 memory: 9999 loss_kpt: 0.000514 acc_pose: 0.897933 loss: 0.000514 2022/10/20 16:18:17 - mmengine - INFO - Epoch(train) [198][100/293] lr: 5.000000e-05 eta: 0:19:04 time: 0.346711 data_time: 0.074752 memory: 9999 loss_kpt: 0.000516 acc_pose: 0.861859 loss: 0.000516 2022/10/20 16:18:35 - mmengine - INFO - Epoch(train) [198][150/293] lr: 5.000000e-05 eta: 0:18:49 time: 0.346681 data_time: 0.068318 memory: 9999 loss_kpt: 0.000502 acc_pose: 0.921276 loss: 0.000502 2022/10/20 16:18:52 - mmengine - INFO - Epoch(train) [198][200/293] lr: 5.000000e-05 eta: 0:18:34 time: 0.355265 data_time: 0.069335 memory: 9999 loss_kpt: 0.000501 acc_pose: 0.892187 loss: 0.000501 2022/10/20 16:19:09 - mmengine - INFO - Epoch(train) [198][250/293] lr: 5.000000e-05 eta: 0:18:19 time: 0.339567 data_time: 0.067622 memory: 9999 loss_kpt: 0.000502 acc_pose: 0.889876 loss: 0.000502 2022/10/20 16:19:20 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:19:24 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:19:42 - mmengine - INFO - Epoch(train) [199][50/293] lr: 5.000000e-05 eta: 0:17:49 time: 0.357188 data_time: 0.078589 memory: 9999 loss_kpt: 0.000505 acc_pose: 0.864489 loss: 0.000505 2022/10/20 16:19:59 - mmengine - INFO - Epoch(train) [199][100/293] lr: 5.000000e-05 eta: 0:17:34 time: 0.345341 data_time: 0.074463 memory: 9999 loss_kpt: 0.000508 acc_pose: 0.880784 loss: 0.000508 2022/10/20 16:20:17 - mmengine - INFO - Epoch(train) [199][150/293] lr: 5.000000e-05 eta: 0:17:19 time: 0.343092 data_time: 0.069111 memory: 9999 loss_kpt: 0.000507 acc_pose: 0.895641 loss: 0.000507 2022/10/20 16:20:34 - mmengine - INFO - Epoch(train) [199][200/293] lr: 5.000000e-05 eta: 0:17:03 time: 0.342436 data_time: 0.068523 memory: 9999 loss_kpt: 0.000502 acc_pose: 0.866042 loss: 0.000502 2022/10/20 16:20:51 - mmengine - INFO - Epoch(train) [199][250/293] lr: 5.000000e-05 eta: 0:16:48 time: 0.344627 data_time: 0.067611 memory: 9999 loss_kpt: 0.000497 acc_pose: 0.891594 loss: 0.000497 2022/10/20 16:21:06 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:21:24 - mmengine - INFO - Epoch(train) [200][50/293] lr: 5.000000e-05 eta: 0:16:19 time: 0.358649 data_time: 0.083070 memory: 9999 loss_kpt: 0.000508 acc_pose: 0.847628 loss: 0.000508 2022/10/20 16:21:41 - mmengine - INFO - Epoch(train) [200][100/293] lr: 5.000000e-05 eta: 0:16:03 time: 0.351107 data_time: 0.074213 memory: 9999 loss_kpt: 0.000498 acc_pose: 0.900265 loss: 0.000498 2022/10/20 16:21:58 - mmengine - INFO - Epoch(train) [200][150/293] lr: 5.000000e-05 eta: 0:15:48 time: 0.338851 data_time: 0.062849 memory: 9999 loss_kpt: 0.000497 acc_pose: 0.896583 loss: 0.000497 2022/10/20 16:22:16 - mmengine - INFO - Epoch(train) [200][200/293] lr: 5.000000e-05 eta: 0:15:33 time: 0.357768 data_time: 0.075111 memory: 9999 loss_kpt: 0.000507 acc_pose: 0.844411 loss: 0.000507 2022/10/20 16:22:34 - mmengine - INFO - Epoch(train) [200][250/293] lr: 5.000000e-05 eta: 0:15:17 time: 0.351251 data_time: 0.088712 memory: 9999 loss_kpt: 0.000512 acc_pose: 0.867849 loss: 0.000512 2022/10/20 16:22:48 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:22:48 - mmengine - INFO - Saving checkpoint at 200 epochs 2022/10/20 16:22:58 - mmengine - INFO - Epoch(val) [200][50/407] eta: 0:00:41 time: 0.116807 data_time: 0.049218 memory: 9999 2022/10/20 16:23:04 - mmengine - INFO - Epoch(val) [200][100/407] eta: 0:00:34 time: 0.112171 data_time: 0.041412 memory: 1378 2022/10/20 16:23:10 - mmengine - INFO - Epoch(val) [200][150/407] eta: 0:00:30 time: 0.117591 data_time: 0.050501 memory: 1378 2022/10/20 16:23:15 - mmengine - INFO - Epoch(val) [200][200/407] eta: 0:00:23 time: 0.113128 data_time: 0.045588 memory: 1378 2022/10/20 16:23:21 - mmengine - INFO - Epoch(val) [200][250/407] eta: 0:00:18 time: 0.117716 data_time: 0.050549 memory: 1378 2022/10/20 16:23:27 - mmengine - INFO - Epoch(val) [200][300/407] eta: 0:00:12 time: 0.117919 data_time: 0.050398 memory: 1378 2022/10/20 16:23:33 - mmengine - INFO - Epoch(val) [200][350/407] eta: 0:00:06 time: 0.115460 data_time: 0.047083 memory: 1378 2022/10/20 16:23:38 - mmengine - INFO - Epoch(val) [200][400/407] eta: 0:00:00 time: 0.102408 data_time: 0.038739 memory: 1378 2022/10/20 16:24:13 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 16:24:27 - mmengine - INFO - Epoch(val) [200][407/407] coco/AP: 0.731253 coco/AP .5: 0.903572 coco/AP .75: 0.809785 coco/AP (M): 0.696033 coco/AP (L): 0.796961 coco/AR: 0.785076 coco/AR .5: 0.940806 coco/AR .75: 0.853432 coco/AR (M): 0.742666 coco/AR (L): 0.846748 2022/10/20 16:24:45 - mmengine - INFO - Epoch(train) [201][50/293] lr: 5.000000e-06 eta: 0:14:48 time: 0.365065 data_time: 0.078299 memory: 9999 loss_kpt: 0.000504 acc_pose: 0.852982 loss: 0.000504 2022/10/20 16:25:02 - mmengine - INFO - Epoch(train) [201][100/293] lr: 5.000000e-06 eta: 0:14:33 time: 0.340337 data_time: 0.072536 memory: 9999 loss_kpt: 0.000508 acc_pose: 0.892803 loss: 0.000508 2022/10/20 16:25:20 - mmengine - INFO - Epoch(train) [201][150/293] lr: 5.000000e-06 eta: 0:14:17 time: 0.355317 data_time: 0.065485 memory: 9999 loss_kpt: 0.000502 acc_pose: 0.882865 loss: 0.000502 2022/10/20 16:25:38 - mmengine - INFO - Epoch(train) [201][200/293] lr: 5.000000e-06 eta: 0:14:02 time: 0.361149 data_time: 0.070683 memory: 9999 loss_kpt: 0.000496 acc_pose: 0.863393 loss: 0.000496 2022/10/20 16:25:55 - mmengine - INFO - Epoch(train) [201][250/293] lr: 5.000000e-06 eta: 0:13:47 time: 0.354756 data_time: 0.072015 memory: 9999 loss_kpt: 0.000513 acc_pose: 0.890506 loss: 0.000513 2022/10/20 16:26:10 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:26:29 - mmengine - INFO - Epoch(train) [202][50/293] lr: 5.000000e-06 eta: 0:13:18 time: 0.372738 data_time: 0.088036 memory: 9999 loss_kpt: 0.000508 acc_pose: 0.884981 loss: 0.000508 2022/10/20 16:26:46 - mmengine - INFO - Epoch(train) [202][100/293] lr: 5.000000e-06 eta: 0:13:02 time: 0.340805 data_time: 0.069809 memory: 9999 loss_kpt: 0.000507 acc_pose: 0.870727 loss: 0.000507 2022/10/20 16:26:49 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:27:04 - mmengine - INFO - Epoch(train) [202][150/293] lr: 5.000000e-06 eta: 0:12:47 time: 0.353724 data_time: 0.082108 memory: 9999 loss_kpt: 0.000507 acc_pose: 0.870886 loss: 0.000507 2022/10/20 16:27:21 - mmengine - INFO - Epoch(train) [202][200/293] lr: 5.000000e-06 eta: 0:12:32 time: 0.349938 data_time: 0.067441 memory: 9999 loss_kpt: 0.000510 acc_pose: 0.900449 loss: 0.000510 2022/10/20 16:27:39 - mmengine - INFO - Epoch(train) [202][250/293] lr: 5.000000e-06 eta: 0:12:16 time: 0.351692 data_time: 0.070538 memory: 9999 loss_kpt: 0.000509 acc_pose: 0.851003 loss: 0.000509 2022/10/20 16:27:53 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:28:12 - mmengine - INFO - Epoch(train) [203][50/293] lr: 5.000000e-06 eta: 0:11:47 time: 0.364864 data_time: 0.088526 memory: 9999 loss_kpt: 0.000501 acc_pose: 0.900994 loss: 0.000501 2022/10/20 16:28:29 - mmengine - INFO - Epoch(train) [203][100/293] lr: 5.000000e-06 eta: 0:11:32 time: 0.351694 data_time: 0.079541 memory: 9999 loss_kpt: 0.000500 acc_pose: 0.883350 loss: 0.000500 2022/10/20 16:28:46 - mmengine - INFO - Epoch(train) [203][150/293] lr: 5.000000e-06 eta: 0:11:16 time: 0.342072 data_time: 0.071961 memory: 9999 loss_kpt: 0.000509 acc_pose: 0.902615 loss: 0.000509 2022/10/20 16:29:04 - mmengine - INFO - Epoch(train) [203][200/293] lr: 5.000000e-06 eta: 0:11:01 time: 0.350140 data_time: 0.070357 memory: 9999 loss_kpt: 0.000508 acc_pose: 0.897098 loss: 0.000508 2022/10/20 16:29:21 - mmengine - INFO - Epoch(train) [203][250/293] lr: 5.000000e-06 eta: 0:10:46 time: 0.346005 data_time: 0.069441 memory: 9999 loss_kpt: 0.000503 acc_pose: 0.892985 loss: 0.000503 2022/10/20 16:29:37 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:29:56 - mmengine - INFO - Epoch(train) [204][50/293] lr: 5.000000e-06 eta: 0:10:17 time: 0.383285 data_time: 0.104186 memory: 9999 loss_kpt: 0.000504 acc_pose: 0.904657 loss: 0.000504 2022/10/20 16:30:13 - mmengine - INFO - Epoch(train) [204][100/293] lr: 5.000000e-06 eta: 0:10:01 time: 0.337989 data_time: 0.069094 memory: 9999 loss_kpt: 0.000514 acc_pose: 0.814701 loss: 0.000514 2022/10/20 16:30:31 - mmengine - INFO - Epoch(train) [204][150/293] lr: 5.000000e-06 eta: 0:09:46 time: 0.356886 data_time: 0.073039 memory: 9999 loss_kpt: 0.000500 acc_pose: 0.874908 loss: 0.000500 2022/10/20 16:30:49 - mmengine - INFO - Epoch(train) [204][200/293] lr: 5.000000e-06 eta: 0:09:31 time: 0.356299 data_time: 0.073538 memory: 9999 loss_kpt: 0.000503 acc_pose: 0.888959 loss: 0.000503 2022/10/20 16:31:06 - mmengine - INFO - Epoch(train) [204][250/293] lr: 5.000000e-06 eta: 0:09:15 time: 0.343355 data_time: 0.070504 memory: 9999 loss_kpt: 0.000513 acc_pose: 0.889500 loss: 0.000513 2022/10/20 16:31:21 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:31:39 - mmengine - INFO - Epoch(train) [205][50/293] lr: 5.000000e-06 eta: 0:08:46 time: 0.370285 data_time: 0.089377 memory: 9999 loss_kpt: 0.000508 acc_pose: 0.909137 loss: 0.000508 2022/10/20 16:31:57 - mmengine - INFO - Epoch(train) [205][100/293] lr: 5.000000e-06 eta: 0:08:31 time: 0.348160 data_time: 0.066393 memory: 9999 loss_kpt: 0.000513 acc_pose: 0.891882 loss: 0.000513 2022/10/20 16:32:14 - mmengine - INFO - Epoch(train) [205][150/293] lr: 5.000000e-06 eta: 0:08:16 time: 0.352416 data_time: 0.072993 memory: 9999 loss_kpt: 0.000493 acc_pose: 0.870010 loss: 0.000493 2022/10/20 16:32:32 - mmengine - INFO - Epoch(train) [205][200/293] lr: 5.000000e-06 eta: 0:08:00 time: 0.352802 data_time: 0.071547 memory: 9999 loss_kpt: 0.000508 acc_pose: 0.866913 loss: 0.000508 2022/10/20 16:32:42 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:32:50 - mmengine - INFO - Epoch(train) [205][250/293] lr: 5.000000e-06 eta: 0:07:45 time: 0.352050 data_time: 0.081675 memory: 9999 loss_kpt: 0.000500 acc_pose: 0.893031 loss: 0.000500 2022/10/20 16:33:04 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:33:22 - mmengine - INFO - Epoch(train) [206][50/293] lr: 5.000000e-06 eta: 0:07:16 time: 0.362656 data_time: 0.078831 memory: 9999 loss_kpt: 0.000501 acc_pose: 0.904524 loss: 0.000501 2022/10/20 16:33:39 - mmengine - INFO - Epoch(train) [206][100/293] lr: 5.000000e-06 eta: 0:07:00 time: 0.343695 data_time: 0.067505 memory: 9999 loss_kpt: 0.000498 acc_pose: 0.897221 loss: 0.000498 2022/10/20 16:33:57 - mmengine - INFO - Epoch(train) [206][150/293] lr: 5.000000e-06 eta: 0:06:45 time: 0.344612 data_time: 0.066630 memory: 9999 loss_kpt: 0.000493 acc_pose: 0.862817 loss: 0.000493 2022/10/20 16:34:15 - mmengine - INFO - Epoch(train) [206][200/293] lr: 5.000000e-06 eta: 0:06:30 time: 0.365140 data_time: 0.075238 memory: 9999 loss_kpt: 0.000505 acc_pose: 0.891357 loss: 0.000505 2022/10/20 16:34:33 - mmengine - INFO - Epoch(train) [206][250/293] lr: 5.000000e-06 eta: 0:06:14 time: 0.362084 data_time: 0.076860 memory: 9999 loss_kpt: 0.000498 acc_pose: 0.886259 loss: 0.000498 2022/10/20 16:34:47 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:35:06 - mmengine - INFO - Epoch(train) [207][50/293] lr: 5.000000e-06 eta: 0:05:45 time: 0.373814 data_time: 0.087818 memory: 9999 loss_kpt: 0.000504 acc_pose: 0.866302 loss: 0.000504 2022/10/20 16:35:24 - mmengine - INFO - Epoch(train) [207][100/293] lr: 5.000000e-06 eta: 0:05:30 time: 0.355850 data_time: 0.067951 memory: 9999 loss_kpt: 0.000514 acc_pose: 0.889588 loss: 0.000514 2022/10/20 16:35:42 - mmengine - INFO - Epoch(train) [207][150/293] lr: 5.000000e-06 eta: 0:05:15 time: 0.355971 data_time: 0.064756 memory: 9999 loss_kpt: 0.000502 acc_pose: 0.873809 loss: 0.000502 2022/10/20 16:35:59 - mmengine - INFO - Epoch(train) [207][200/293] lr: 5.000000e-06 eta: 0:04:59 time: 0.341630 data_time: 0.072912 memory: 9999 loss_kpt: 0.000507 acc_pose: 0.853632 loss: 0.000507 2022/10/20 16:36:17 - mmengine - INFO - Epoch(train) [207][250/293] lr: 5.000000e-06 eta: 0:04:44 time: 0.361040 data_time: 0.067869 memory: 9999 loss_kpt: 0.000503 acc_pose: 0.873423 loss: 0.000503 2022/10/20 16:36:31 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:36:49 - mmengine - INFO - Epoch(train) [208][50/293] lr: 5.000000e-06 eta: 0:04:15 time: 0.362043 data_time: 0.090361 memory: 9999 loss_kpt: 0.000497 acc_pose: 0.819366 loss: 0.000497 2022/10/20 16:37:07 - mmengine - INFO - Epoch(train) [208][100/293] lr: 5.000000e-06 eta: 0:04:00 time: 0.353698 data_time: 0.071442 memory: 9999 loss_kpt: 0.000512 acc_pose: 0.912193 loss: 0.000512 2022/10/20 16:37:24 - mmengine - INFO - Epoch(train) [208][150/293] lr: 5.000000e-06 eta: 0:03:44 time: 0.341065 data_time: 0.069575 memory: 9999 loss_kpt: 0.000504 acc_pose: 0.911154 loss: 0.000504 2022/10/20 16:37:42 - mmengine - INFO - Epoch(train) [208][200/293] lr: 5.000000e-06 eta: 0:03:29 time: 0.352073 data_time: 0.068859 memory: 9999 loss_kpt: 0.000500 acc_pose: 0.880565 loss: 0.000500 2022/10/20 16:37:59 - mmengine - INFO - Epoch(train) [208][250/293] lr: 5.000000e-06 eta: 0:03:14 time: 0.349070 data_time: 0.070094 memory: 9999 loss_kpt: 0.000504 acc_pose: 0.909959 loss: 0.000504 2022/10/20 16:38:14 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:38:33 - mmengine - INFO - Epoch(train) [209][50/293] lr: 5.000000e-06 eta: 0:02:45 time: 0.370678 data_time: 0.091145 memory: 9999 loss_kpt: 0.000499 acc_pose: 0.881523 loss: 0.000499 2022/10/20 16:38:35 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:38:50 - mmengine - INFO - Epoch(train) [209][100/293] lr: 5.000000e-06 eta: 0:02:29 time: 0.346070 data_time: 0.072627 memory: 9999 loss_kpt: 0.000515 acc_pose: 0.834679 loss: 0.000515 2022/10/20 16:39:07 - mmengine - INFO - Epoch(train) [209][150/293] lr: 5.000000e-06 eta: 0:02:14 time: 0.344069 data_time: 0.070630 memory: 9999 loss_kpt: 0.000500 acc_pose: 0.878351 loss: 0.000500 2022/10/20 16:39:24 - mmengine - INFO - Epoch(train) [209][200/293] lr: 5.000000e-06 eta: 0:01:59 time: 0.344429 data_time: 0.068578 memory: 9999 loss_kpt: 0.000496 acc_pose: 0.887536 loss: 0.000496 2022/10/20 16:39:42 - mmengine - INFO - Epoch(train) [209][250/293] lr: 5.000000e-06 eta: 0:01:43 time: 0.346291 data_time: 0.069753 memory: 9999 loss_kpt: 0.000501 acc_pose: 0.899744 loss: 0.000501 2022/10/20 16:39:56 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:40:15 - mmengine - INFO - Epoch(train) [210][50/293] lr: 5.000000e-06 eta: 0:01:14 time: 0.368131 data_time: 0.088032 memory: 9999 loss_kpt: 0.000504 acc_pose: 0.896245 loss: 0.000504 2022/10/20 16:40:32 - mmengine - INFO - Epoch(train) [210][100/293] lr: 5.000000e-06 eta: 0:00:59 time: 0.344941 data_time: 0.072253 memory: 9999 loss_kpt: 0.000513 acc_pose: 0.886444 loss: 0.000513 2022/10/20 16:40:50 - mmengine - INFO - Epoch(train) [210][150/293] lr: 5.000000e-06 eta: 0:00:44 time: 0.350782 data_time: 0.063565 memory: 9999 loss_kpt: 0.000508 acc_pose: 0.867083 loss: 0.000508 2022/10/20 16:41:07 - mmengine - INFO - Epoch(train) [210][200/293] lr: 5.000000e-06 eta: 0:00:28 time: 0.351625 data_time: 0.074842 memory: 9999 loss_kpt: 0.000509 acc_pose: 0.870280 loss: 0.000509 2022/10/20 16:41:24 - mmengine - INFO - Epoch(train) [210][250/293] lr: 5.000000e-06 eta: 0:00:13 time: 0.345390 data_time: 0.068193 memory: 9999 loss_kpt: 0.000510 acc_pose: 0.830460 loss: 0.000510 2022/10/20 16:41:39 - mmengine - INFO - Exp name: td-hm_resnetv1d101_8xb64-210e_coco-256x192_20221020_095657 2022/10/20 16:41:39 - mmengine - INFO - Saving checkpoint at 210 epochs 2022/10/20 16:41:49 - mmengine - INFO - Epoch(val) [210][50/407] eta: 0:00:44 time: 0.125555 data_time: 0.058388 memory: 9999 2022/10/20 16:41:55 - mmengine - INFO - Epoch(val) [210][100/407] eta: 0:00:35 time: 0.116193 data_time: 0.046825 memory: 1378 2022/10/20 16:42:00 - mmengine - INFO - Epoch(val) [210][150/407] eta: 0:00:28 time: 0.112261 data_time: 0.044823 memory: 1378 2022/10/20 16:42:06 - mmengine - INFO - Epoch(val) [210][200/407] eta: 0:00:23 time: 0.111189 data_time: 0.044441 memory: 1378 2022/10/20 16:42:12 - mmengine - INFO - Epoch(val) [210][250/407] eta: 0:00:18 time: 0.116306 data_time: 0.049104 memory: 1378 2022/10/20 16:42:17 - mmengine - INFO - Epoch(val) [210][300/407] eta: 0:00:12 time: 0.113222 data_time: 0.042154 memory: 1378 2022/10/20 16:42:23 - mmengine - INFO - Epoch(val) [210][350/407] eta: 0:00:06 time: 0.116373 data_time: 0.050219 memory: 1378 2022/10/20 16:42:29 - mmengine - INFO - Epoch(val) [210][400/407] eta: 0:00:00 time: 0.107243 data_time: 0.043201 memory: 1378 2022/10/20 16:43:04 - mmengine - INFO - Evaluating CocoMetric... 2022/10/20 16:43:18 - mmengine - INFO - Epoch(val) [210][407/407] coco/AP: 0.731520 coco/AP .5: 0.900775 coco/AP .75: 0.807893 coco/AP (M): 0.695231 coco/AP (L): 0.797284 coco/AR: 0.785296 coco/AR .5: 0.939861 coco/AR .75: 0.851385 coco/AR (M): 0.742229 coco/AR (L): 0.847715 2022/10/20 16:43:18 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221020/resnetv1d101_256/best_coco/AP_epoch_180.pth is removed 2022/10/20 16:43:20 - mmengine - INFO - The best checkpoint with 0.7315 coco/AP at 210 epoch is saved to best_coco/AP_epoch_210.pth.