2022/10/13 19:01:21 - 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: 1972958388 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 ------------------------------------------------------------ 2022/10/13 19:01:22 - 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='ShuffleNetV2', widen_factor=1.0, init_cfg=dict(type='Pretrained', checkpoint='mmcls://shufflenet_v2')), head=dict( type='HeatmapHead', in_channels=1024, 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/20221013/shufflenetv2_256/' 2022/10/13 19:02:05 - 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. 2022/10/13 19:02:05 - 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. 2022/10/13 19:02:05 - 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. 2022/10/13 19:02:05 - 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. 2022/10/13 19:02:05 - 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. 2022/10/13 19:02:05 - 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. 2022/10/13 19:02:05 - 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. 2022/10/13 19:02:05 - 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. 2022/10/13 19:02:09 - 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. 2022/10/13 19:02:11 - 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. 2022/10/13 19:02:12 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/13 19:02:12 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. Name of parameter - Initialization information backbone.conv1.conv.weight - torch.Size([24, 3, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.conv1.bn.weight - torch.Size([24]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.conv1.bn.bias - torch.Size([24]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.0.branch1.0.conv.weight - torch.Size([24, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.0.branch1.0.bn.weight - torch.Size([24]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.0.branch1.0.bn.bias - torch.Size([24]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.0.branch1.1.conv.weight - torch.Size([58, 24, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.0.branch1.1.bn.weight - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.0.branch1.1.bn.bias - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.0.branch2.0.conv.weight - torch.Size([58, 24, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.0.branch2.0.bn.weight - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.0.branch2.0.bn.bias - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.0.branch2.1.conv.weight - torch.Size([58, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.0.branch2.1.bn.weight - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.0.branch2.1.bn.bias - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.0.branch2.2.conv.weight - torch.Size([58, 58, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.0.branch2.2.bn.weight - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.0.branch2.2.bn.bias - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.1.branch2.0.conv.weight - torch.Size([58, 58, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.1.branch2.0.bn.weight - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.1.branch2.0.bn.bias - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.1.branch2.1.conv.weight - torch.Size([58, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.1.branch2.1.bn.weight - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.1.branch2.1.bn.bias - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.1.branch2.2.conv.weight - torch.Size([58, 58, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.1.branch2.2.bn.weight - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.1.branch2.2.bn.bias - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.2.branch2.0.conv.weight - torch.Size([58, 58, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.2.branch2.0.bn.weight - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.2.branch2.0.bn.bias - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.2.branch2.1.conv.weight - torch.Size([58, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.2.branch2.1.bn.weight - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.2.branch2.1.bn.bias - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.2.branch2.2.conv.weight - torch.Size([58, 58, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.2.branch2.2.bn.weight - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.2.branch2.2.bn.bias - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.3.branch2.0.conv.weight - torch.Size([58, 58, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.3.branch2.0.bn.weight - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.3.branch2.0.bn.bias - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.3.branch2.1.conv.weight - torch.Size([58, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.3.branch2.1.bn.weight - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.3.branch2.1.bn.bias - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.3.branch2.2.conv.weight - torch.Size([58, 58, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.3.branch2.2.bn.weight - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.0.3.branch2.2.bn.bias - torch.Size([58]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.0.branch1.0.conv.weight - torch.Size([116, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.0.branch1.0.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.0.branch1.0.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.0.branch1.1.conv.weight - torch.Size([116, 116, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.0.branch1.1.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.0.branch1.1.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.0.branch2.0.conv.weight - torch.Size([116, 116, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.0.branch2.0.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.0.branch2.0.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.0.branch2.1.conv.weight - torch.Size([116, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.0.branch2.1.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.0.branch2.1.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.0.branch2.2.conv.weight - torch.Size([116, 116, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.0.branch2.2.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.0.branch2.2.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.1.branch2.0.conv.weight - torch.Size([116, 116, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.1.branch2.0.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.1.branch2.0.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.1.branch2.1.conv.weight - torch.Size([116, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.1.branch2.1.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.1.branch2.1.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.1.branch2.2.conv.weight - torch.Size([116, 116, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.1.branch2.2.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.1.branch2.2.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.2.branch2.0.conv.weight - torch.Size([116, 116, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.2.branch2.0.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.2.branch2.0.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.2.branch2.1.conv.weight - torch.Size([116, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.2.branch2.1.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.2.branch2.1.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.2.branch2.2.conv.weight - torch.Size([116, 116, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.2.branch2.2.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.2.branch2.2.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.3.branch2.0.conv.weight - torch.Size([116, 116, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.3.branch2.0.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.3.branch2.0.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.3.branch2.1.conv.weight - torch.Size([116, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.3.branch2.1.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.3.branch2.1.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.3.branch2.2.conv.weight - torch.Size([116, 116, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.3.branch2.2.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.3.branch2.2.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.4.branch2.0.conv.weight - torch.Size([116, 116, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.4.branch2.0.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.4.branch2.0.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.4.branch2.1.conv.weight - torch.Size([116, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.4.branch2.1.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.4.branch2.1.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.4.branch2.2.conv.weight - torch.Size([116, 116, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.4.branch2.2.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.4.branch2.2.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.5.branch2.0.conv.weight - torch.Size([116, 116, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.5.branch2.0.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.5.branch2.0.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.5.branch2.1.conv.weight - torch.Size([116, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.5.branch2.1.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.5.branch2.1.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.5.branch2.2.conv.weight - torch.Size([116, 116, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.5.branch2.2.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.5.branch2.2.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.6.branch2.0.conv.weight - torch.Size([116, 116, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.6.branch2.0.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.6.branch2.0.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.6.branch2.1.conv.weight - torch.Size([116, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.6.branch2.1.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.6.branch2.1.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.6.branch2.2.conv.weight - torch.Size([116, 116, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.6.branch2.2.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.6.branch2.2.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.7.branch2.0.conv.weight - torch.Size([116, 116, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.7.branch2.0.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.7.branch2.0.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.7.branch2.1.conv.weight - torch.Size([116, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.7.branch2.1.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.7.branch2.1.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.7.branch2.2.conv.weight - torch.Size([116, 116, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.7.branch2.2.bn.weight - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.1.7.branch2.2.bn.bias - torch.Size([116]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.0.branch1.0.conv.weight - torch.Size([232, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.0.branch1.0.bn.weight - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.0.branch1.0.bn.bias - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.0.branch1.1.conv.weight - torch.Size([232, 232, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.0.branch1.1.bn.weight - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.0.branch1.1.bn.bias - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.0.branch2.0.conv.weight - torch.Size([232, 232, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.0.branch2.0.bn.weight - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.0.branch2.0.bn.bias - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.0.branch2.1.conv.weight - torch.Size([232, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.0.branch2.1.bn.weight - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.0.branch2.1.bn.bias - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.0.branch2.2.conv.weight - torch.Size([232, 232, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.0.branch2.2.bn.weight - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.0.branch2.2.bn.bias - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.1.branch2.0.conv.weight - torch.Size([232, 232, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.1.branch2.0.bn.weight - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.1.branch2.0.bn.bias - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.1.branch2.1.conv.weight - torch.Size([232, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.1.branch2.1.bn.weight - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.1.branch2.1.bn.bias - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.1.branch2.2.conv.weight - torch.Size([232, 232, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.1.branch2.2.bn.weight - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.1.branch2.2.bn.bias - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.2.branch2.0.conv.weight - torch.Size([232, 232, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.2.branch2.0.bn.weight - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.2.branch2.0.bn.bias - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.2.branch2.1.conv.weight - torch.Size([232, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.2.branch2.1.bn.weight - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.2.branch2.1.bn.bias - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.2.branch2.2.conv.weight - torch.Size([232, 232, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.2.branch2.2.bn.weight - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.2.branch2.2.bn.bias - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.3.branch2.0.conv.weight - torch.Size([232, 232, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.3.branch2.0.bn.weight - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.3.branch2.0.bn.bias - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.3.branch2.1.conv.weight - torch.Size([232, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.3.branch2.1.bn.weight - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.3.branch2.1.bn.bias - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.3.branch2.2.conv.weight - torch.Size([232, 232, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.3.branch2.2.bn.weight - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.2.3.branch2.2.bn.bias - torch.Size([232]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.3.conv.weight - torch.Size([1024, 464, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.3.bn.weight - torch.Size([1024]): PretrainedInit: load from mmcls://shufflenet_v2 backbone.layers.3.bn.bias - torch.Size([1024]): PretrainedInit: load from mmcls://shufflenet_v2 head.deconv_layers.0.weight - torch.Size([1024, 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 2022/10/13 19:02:12 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256 by HardDiskBackend. 2022/10/13 19:03:26 - mmengine - INFO - Epoch(train) [1][50/293] lr: 4.954910e-05 eta: 1 day, 1:22:58 time: 1.486311 data_time: 0.801590 memory: 2315 loss_kpt: 0.002152 acc_pose: 0.100346 loss: 0.002152 2022/10/13 19:04:12 - mmengine - INFO - Epoch(train) [1][100/293] lr: 9.959920e-05 eta: 20:33:45 time: 0.923763 data_time: 0.111850 memory: 2315 loss_kpt: 0.001951 acc_pose: 0.250699 loss: 0.001951 2022/10/13 19:04:49 - mmengine - INFO - Epoch(train) [1][150/293] lr: 1.496493e-04 eta: 17:52:37 time: 0.735437 data_time: 0.068377 memory: 2315 loss_kpt: 0.001823 acc_pose: 0.290821 loss: 0.001823 2022/10/13 19:05:24 - mmengine - INFO - Epoch(train) [1][200/293] lr: 1.996994e-04 eta: 16:22:39 time: 0.699895 data_time: 0.115968 memory: 2315 loss_kpt: 0.001712 acc_pose: 0.353591 loss: 0.001712 2022/10/13 19:05:55 - mmengine - INFO - Epoch(train) [1][250/293] lr: 2.497495e-04 eta: 15:10:04 time: 0.609887 data_time: 0.062156 memory: 2315 loss_kpt: 0.001686 acc_pose: 0.357299 loss: 0.001686 2022/10/13 19:06:21 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:06:38 - mmengine - INFO - Epoch(train) [2][50/293] lr: 3.428427e-04 eta: 11:52:14 time: 0.335941 data_time: 0.158653 memory: 2315 loss_kpt: 0.001540 acc_pose: 0.454018 loss: 0.001540 2022/10/13 19:06:55 - mmengine - INFO - Epoch(train) [2][100/293] lr: 3.928928e-04 eta: 11:04:30 time: 0.334677 data_time: 0.108740 memory: 2315 loss_kpt: 0.001513 acc_pose: 0.385701 loss: 0.001513 2022/10/13 19:07:11 - mmengine - INFO - Epoch(train) [2][150/293] lr: 4.429429e-04 eta: 10:27:03 time: 0.331033 data_time: 0.065976 memory: 2315 loss_kpt: 0.001505 acc_pose: 0.480581 loss: 0.001505 2022/10/13 19:07:28 - mmengine - INFO - Epoch(train) [2][200/293] lr: 4.929930e-04 eta: 9:57:45 time: 0.336756 data_time: 0.069410 memory: 2315 loss_kpt: 0.001456 acc_pose: 0.484597 loss: 0.001456 2022/10/13 19:07:45 - mmengine - INFO - Epoch(train) [2][250/293] lr: 5.000000e-04 eta: 9:33:14 time: 0.330885 data_time: 0.060700 memory: 2315 loss_kpt: 0.001418 acc_pose: 0.508818 loss: 0.001418 2022/10/13 19:07:59 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:08:15 - mmengine - INFO - Epoch(train) [3][50/293] lr: 5.000000e-04 eta: 8:35:45 time: 0.339489 data_time: 0.124151 memory: 2315 loss_kpt: 0.001381 acc_pose: 0.445308 loss: 0.001381 2022/10/13 19:08:32 - mmengine - INFO - Epoch(train) [3][100/293] lr: 5.000000e-04 eta: 8:22:14 time: 0.331119 data_time: 0.060033 memory: 2315 loss_kpt: 0.001376 acc_pose: 0.485778 loss: 0.001376 2022/10/13 19:08:49 - mmengine - INFO - Epoch(train) [3][150/293] lr: 5.000000e-04 eta: 8:10:28 time: 0.330363 data_time: 0.069016 memory: 2315 loss_kpt: 0.001319 acc_pose: 0.483511 loss: 0.001319 2022/10/13 19:09:05 - mmengine - INFO - Epoch(train) [3][200/293] lr: 5.000000e-04 eta: 8:00:02 time: 0.328296 data_time: 0.063641 memory: 2315 loss_kpt: 0.001336 acc_pose: 0.560633 loss: 0.001336 2022/10/13 19:09:21 - mmengine - INFO - Epoch(train) [3][250/293] lr: 5.000000e-04 eta: 7:50:39 time: 0.325508 data_time: 0.087030 memory: 2315 loss_kpt: 0.001331 acc_pose: 0.593498 loss: 0.001331 2022/10/13 19:09:35 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:09:52 - mmengine - INFO - Epoch(train) [4][50/293] lr: 5.000000e-04 eta: 7:21:43 time: 0.346528 data_time: 0.168355 memory: 2315 loss_kpt: 0.001304 acc_pose: 0.517375 loss: 0.001304 2022/10/13 19:10:09 - mmengine - INFO - Epoch(train) [4][100/293] lr: 5.000000e-04 eta: 7:15:39 time: 0.326549 data_time: 0.161290 memory: 2315 loss_kpt: 0.001302 acc_pose: 0.540147 loss: 0.001302 2022/10/13 19:10:16 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:10:25 - mmengine - INFO - Epoch(train) [4][150/293] lr: 5.000000e-04 eta: 7:10:11 time: 0.327644 data_time: 0.164733 memory: 2315 loss_kpt: 0.001281 acc_pose: 0.584721 loss: 0.001281 2022/10/13 19:10:42 - mmengine - INFO - Epoch(train) [4][200/293] lr: 5.000000e-04 eta: 7:05:21 time: 0.330724 data_time: 0.142392 memory: 2315 loss_kpt: 0.001289 acc_pose: 0.469660 loss: 0.001289 2022/10/13 19:10:58 - mmengine - INFO - Epoch(train) [4][250/293] lr: 5.000000e-04 eta: 7:00:56 time: 0.330885 data_time: 0.067091 memory: 2315 loss_kpt: 0.001280 acc_pose: 0.583634 loss: 0.001280 2022/10/13 19:11:12 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:11:29 - mmengine - INFO - Epoch(train) [5][50/293] lr: 5.000000e-04 eta: 6:41:55 time: 0.331069 data_time: 0.133569 memory: 2315 loss_kpt: 0.001246 acc_pose: 0.625282 loss: 0.001246 2022/10/13 19:11:45 - mmengine - INFO - Epoch(train) [5][100/293] lr: 5.000000e-04 eta: 6:38:51 time: 0.330845 data_time: 0.087420 memory: 2315 loss_kpt: 0.001256 acc_pose: 0.546114 loss: 0.001256 2022/10/13 19:12:02 - mmengine - INFO - Epoch(train) [5][150/293] lr: 5.000000e-04 eta: 6:35:54 time: 0.328034 data_time: 0.077922 memory: 2315 loss_kpt: 0.001234 acc_pose: 0.583966 loss: 0.001234 2022/10/13 19:12:18 - mmengine - INFO - Epoch(train) [5][200/293] lr: 5.000000e-04 eta: 6:33:26 time: 0.336251 data_time: 0.066482 memory: 2315 loss_kpt: 0.001245 acc_pose: 0.590729 loss: 0.001245 2022/10/13 19:12:35 - mmengine - INFO - Epoch(train) [5][250/293] lr: 5.000000e-04 eta: 6:30:56 time: 0.330433 data_time: 0.066475 memory: 2315 loss_kpt: 0.001251 acc_pose: 0.536915 loss: 0.001251 2022/10/13 19:12:49 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:13:06 - mmengine - INFO - Epoch(train) [6][50/293] lr: 5.000000e-04 eta: 6:18:02 time: 0.353301 data_time: 0.074287 memory: 2315 loss_kpt: 0.001224 acc_pose: 0.556166 loss: 0.001224 2022/10/13 19:13:23 - mmengine - INFO - Epoch(train) [6][100/293] lr: 5.000000e-04 eta: 6:16:04 time: 0.326653 data_time: 0.069776 memory: 2315 loss_kpt: 0.001235 acc_pose: 0.608941 loss: 0.001235 2022/10/13 19:13:39 - mmengine - INFO - Epoch(train) [6][150/293] lr: 5.000000e-04 eta: 6:14:11 time: 0.325308 data_time: 0.069902 memory: 2315 loss_kpt: 0.001232 acc_pose: 0.577322 loss: 0.001232 2022/10/13 19:13:56 - mmengine - INFO - Epoch(train) [6][200/293] lr: 5.000000e-04 eta: 6:12:30 time: 0.328901 data_time: 0.067681 memory: 2315 loss_kpt: 0.001188 acc_pose: 0.556779 loss: 0.001188 2022/10/13 19:14:12 - mmengine - INFO - Epoch(train) [6][250/293] lr: 5.000000e-04 eta: 6:10:55 time: 0.329783 data_time: 0.094929 memory: 2315 loss_kpt: 0.001211 acc_pose: 0.557154 loss: 0.001211 2022/10/13 19:14:26 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:14:43 - mmengine - INFO - Epoch(train) [7][50/293] lr: 5.000000e-04 eta: 6:00:32 time: 0.335613 data_time: 0.158570 memory: 2315 loss_kpt: 0.001211 acc_pose: 0.669427 loss: 0.001211 2022/10/13 19:14:59 - mmengine - INFO - Epoch(train) [7][100/293] lr: 5.000000e-04 eta: 5:59:15 time: 0.325411 data_time: 0.188532 memory: 2315 loss_kpt: 0.001183 acc_pose: 0.586652 loss: 0.001183 2022/10/13 19:15:16 - mmengine - INFO - Epoch(train) [7][150/293] lr: 5.000000e-04 eta: 5:58:22 time: 0.339032 data_time: 0.063852 memory: 2315 loss_kpt: 0.001197 acc_pose: 0.529712 loss: 0.001197 2022/10/13 19:15:32 - mmengine - INFO - Epoch(train) [7][200/293] lr: 5.000000e-04 eta: 5:57:06 time: 0.322588 data_time: 0.075035 memory: 2315 loss_kpt: 0.001209 acc_pose: 0.597940 loss: 0.001209 2022/10/13 19:15:46 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:15:49 - mmengine - INFO - Epoch(train) [7][250/293] lr: 5.000000e-04 eta: 5:56:04 time: 0.330081 data_time: 0.185095 memory: 2315 loss_kpt: 0.001192 acc_pose: 0.568418 loss: 0.001192 2022/10/13 19:16:02 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:16:19 - mmengine - INFO - Epoch(train) [8][50/293] lr: 5.000000e-04 eta: 5:47:41 time: 0.335462 data_time: 0.168342 memory: 2315 loss_kpt: 0.001175 acc_pose: 0.600377 loss: 0.001175 2022/10/13 19:16:35 - mmengine - INFO - Epoch(train) [8][100/293] lr: 5.000000e-04 eta: 5:46:48 time: 0.325288 data_time: 0.097032 memory: 2315 loss_kpt: 0.001190 acc_pose: 0.583076 loss: 0.001190 2022/10/13 19:16:52 - mmengine - INFO - Epoch(train) [8][150/293] lr: 5.000000e-04 eta: 5:46:06 time: 0.332456 data_time: 0.065459 memory: 2315 loss_kpt: 0.001165 acc_pose: 0.510283 loss: 0.001165 2022/10/13 19:17:08 - mmengine - INFO - Epoch(train) [8][200/293] lr: 5.000000e-04 eta: 5:45:12 time: 0.321944 data_time: 0.062683 memory: 2315 loss_kpt: 0.001172 acc_pose: 0.615762 loss: 0.001172 2022/10/13 19:17:24 - mmengine - INFO - Epoch(train) [8][250/293] lr: 5.000000e-04 eta: 5:44:26 time: 0.327672 data_time: 0.139482 memory: 2315 loss_kpt: 0.001189 acc_pose: 0.642996 loss: 0.001189 2022/10/13 19:17:38 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:17:55 - mmengine - INFO - Epoch(train) [9][50/293] lr: 5.000000e-04 eta: 5:37:36 time: 0.343054 data_time: 0.113260 memory: 2315 loss_kpt: 0.001162 acc_pose: 0.625227 loss: 0.001162 2022/10/13 19:18:11 - mmengine - INFO - Epoch(train) [9][100/293] lr: 5.000000e-04 eta: 5:37:01 time: 0.327816 data_time: 0.063144 memory: 2315 loss_kpt: 0.001172 acc_pose: 0.504148 loss: 0.001172 2022/10/13 19:18:28 - mmengine - INFO - Epoch(train) [9][150/293] lr: 5.000000e-04 eta: 5:36:23 time: 0.324593 data_time: 0.136918 memory: 2315 loss_kpt: 0.001154 acc_pose: 0.640364 loss: 0.001154 2022/10/13 19:18:44 - mmengine - INFO - Epoch(train) [9][200/293] lr: 5.000000e-04 eta: 5:35:52 time: 0.330119 data_time: 0.087423 memory: 2315 loss_kpt: 0.001158 acc_pose: 0.586100 loss: 0.001158 2022/10/13 19:19:00 - mmengine - INFO - Epoch(train) [9][250/293] lr: 5.000000e-04 eta: 5:35:15 time: 0.323377 data_time: 0.134228 memory: 2315 loss_kpt: 0.001166 acc_pose: 0.613494 loss: 0.001166 2022/10/13 19:19:14 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:19:32 - mmengine - INFO - Epoch(train) [10][50/293] lr: 5.000000e-04 eta: 5:29:29 time: 0.348377 data_time: 0.087794 memory: 2315 loss_kpt: 0.001156 acc_pose: 0.629310 loss: 0.001156 2022/10/13 19:19:48 - mmengine - INFO - Epoch(train) [10][100/293] lr: 5.000000e-04 eta: 5:29:03 time: 0.327384 data_time: 0.067083 memory: 2315 loss_kpt: 0.001145 acc_pose: 0.607299 loss: 0.001145 2022/10/13 19:20:04 - mmengine - INFO - Epoch(train) [10][150/293] lr: 5.000000e-04 eta: 5:28:37 time: 0.326583 data_time: 0.131239 memory: 2315 loss_kpt: 0.001157 acc_pose: 0.589110 loss: 0.001157 2022/10/13 19:20:21 - mmengine - INFO - Epoch(train) [10][200/293] lr: 5.000000e-04 eta: 5:28:19 time: 0.334798 data_time: 0.070423 memory: 2315 loss_kpt: 0.001156 acc_pose: 0.590527 loss: 0.001156 2022/10/13 19:20:37 - mmengine - INFO - Epoch(train) [10][250/293] lr: 5.000000e-04 eta: 5:27:42 time: 0.315498 data_time: 0.067921 memory: 2315 loss_kpt: 0.001160 acc_pose: 0.603596 loss: 0.001160 2022/10/13 19:20:51 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:20:51 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/10/13 19:21:04 - mmengine - INFO - Epoch(val) [10][50/407] eta: 0:01:14 time: 0.207338 data_time: 0.164926 memory: 2315 2022/10/13 19:21:11 - mmengine - INFO - Epoch(val) [10][100/407] eta: 0:00:43 time: 0.142213 data_time: 0.100211 memory: 426 2022/10/13 19:21:18 - mmengine - INFO - Epoch(val) [10][150/407] eta: 0:00:36 time: 0.140291 data_time: 0.095277 memory: 426 2022/10/13 19:21:24 - mmengine - INFO - Epoch(val) [10][200/407] eta: 0:00:27 time: 0.134247 data_time: 0.090526 memory: 426 2022/10/13 19:21:32 - mmengine - INFO - Epoch(val) [10][250/407] eta: 0:00:22 time: 0.142724 data_time: 0.098316 memory: 426 2022/10/13 19:21:38 - mmengine - INFO - Epoch(val) [10][300/407] eta: 0:00:13 time: 0.129385 data_time: 0.083976 memory: 426 2022/10/13 19:21:45 - mmengine - INFO - Epoch(val) [10][350/407] eta: 0:00:08 time: 0.141412 data_time: 0.097785 memory: 426 2022/10/13 19:21:52 - mmengine - INFO - Epoch(val) [10][400/407] eta: 0:00:00 time: 0.134985 data_time: 0.090579 memory: 426 2022/10/13 19:22:30 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 19:22:45 - mmengine - INFO - Epoch(val) [10][407/407] coco/AP: 0.468001 coco/AP .5: 0.788200 coco/AP .75: 0.489494 coco/AP (M): 0.442229 coco/AP (L): 0.519050 coco/AR: 0.543923 coco/AR .5: 0.844616 coco/AR .75: 0.583123 coco/AR (M): 0.503633 coco/AR (L): 0.600186 2022/10/13 19:22:47 - mmengine - INFO - The best checkpoint with 0.4680 coco/AP at 10 epoch is saved to best_coco/AP_epoch_10.pth. 2022/10/13 19:23:04 - mmengine - INFO - Epoch(train) [11][50/293] lr: 5.000000e-04 eta: 5:22:33 time: 0.340964 data_time: 0.118818 memory: 2315 loss_kpt: 0.001130 acc_pose: 0.619864 loss: 0.001130 2022/10/13 19:23:11 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:23:21 - mmengine - INFO - Epoch(train) [11][100/293] lr: 5.000000e-04 eta: 5:22:17 time: 0.331684 data_time: 0.075933 memory: 2315 loss_kpt: 0.001142 acc_pose: 0.649925 loss: 0.001142 2022/10/13 19:23:37 - mmengine - INFO - Epoch(train) [11][150/293] lr: 5.000000e-04 eta: 5:21:54 time: 0.323520 data_time: 0.083796 memory: 2315 loss_kpt: 0.001139 acc_pose: 0.600075 loss: 0.001139 2022/10/13 19:23:54 - mmengine - INFO - Epoch(train) [11][200/293] lr: 5.000000e-04 eta: 5:21:48 time: 0.341654 data_time: 0.065806 memory: 2315 loss_kpt: 0.001139 acc_pose: 0.626707 loss: 0.001139 2022/10/13 19:24:11 - mmengine - INFO - Epoch(train) [11][250/293] lr: 5.000000e-04 eta: 5:21:45 time: 0.345566 data_time: 0.074197 memory: 2315 loss_kpt: 0.001128 acc_pose: 0.636047 loss: 0.001128 2022/10/13 19:24:25 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:24:42 - mmengine - INFO - Epoch(train) [12][50/293] lr: 5.000000e-04 eta: 5:17:19 time: 0.350415 data_time: 0.109537 memory: 2315 loss_kpt: 0.001156 acc_pose: 0.594308 loss: 0.001156 2022/10/13 19:24:59 - mmengine - INFO - Epoch(train) [12][100/293] lr: 5.000000e-04 eta: 5:17:17 time: 0.343208 data_time: 0.067753 memory: 2315 loss_kpt: 0.001133 acc_pose: 0.607441 loss: 0.001133 2022/10/13 19:25:16 - mmengine - INFO - Epoch(train) [12][150/293] lr: 5.000000e-04 eta: 5:16:59 time: 0.325929 data_time: 0.062507 memory: 2315 loss_kpt: 0.001139 acc_pose: 0.661872 loss: 0.001139 2022/10/13 19:25:32 - mmengine - INFO - Epoch(train) [12][200/293] lr: 5.000000e-04 eta: 5:16:48 time: 0.332894 data_time: 0.067764 memory: 2315 loss_kpt: 0.001129 acc_pose: 0.628183 loss: 0.001129 2022/10/13 19:25:49 - mmengine - INFO - Epoch(train) [12][250/293] lr: 5.000000e-04 eta: 5:16:36 time: 0.331958 data_time: 0.129627 memory: 2315 loss_kpt: 0.001139 acc_pose: 0.581697 loss: 0.001139 2022/10/13 19:26:03 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:26:21 - mmengine - INFO - Epoch(train) [13][50/293] lr: 5.000000e-04 eta: 5:12:42 time: 0.358640 data_time: 0.089734 memory: 2315 loss_kpt: 0.001128 acc_pose: 0.665664 loss: 0.001128 2022/10/13 19:26:38 - mmengine - INFO - Epoch(train) [13][100/293] lr: 5.000000e-04 eta: 5:12:45 time: 0.347198 data_time: 0.080684 memory: 2315 loss_kpt: 0.001122 acc_pose: 0.656099 loss: 0.001122 2022/10/13 19:26:55 - mmengine - INFO - Epoch(train) [13][150/293] lr: 5.000000e-04 eta: 5:12:30 time: 0.326425 data_time: 0.063271 memory: 2315 loss_kpt: 0.001110 acc_pose: 0.647944 loss: 0.001110 2022/10/13 19:27:11 - mmengine - INFO - Epoch(train) [13][200/293] lr: 5.000000e-04 eta: 5:12:17 time: 0.327067 data_time: 0.116467 memory: 2315 loss_kpt: 0.001140 acc_pose: 0.587638 loss: 0.001140 2022/10/13 19:27:28 - mmengine - INFO - Epoch(train) [13][250/293] lr: 5.000000e-04 eta: 5:12:10 time: 0.337160 data_time: 0.069087 memory: 2315 loss_kpt: 0.001131 acc_pose: 0.662686 loss: 0.001131 2022/10/13 19:27:42 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:27:59 - mmengine - INFO - Epoch(train) [14][50/293] lr: 5.000000e-04 eta: 5:08:22 time: 0.337839 data_time: 0.133459 memory: 2315 loss_kpt: 0.001116 acc_pose: 0.643349 loss: 0.001116 2022/10/13 19:28:15 - mmengine - INFO - Epoch(train) [14][100/293] lr: 5.000000e-04 eta: 5:08:06 time: 0.321454 data_time: 0.089177 memory: 2315 loss_kpt: 0.001117 acc_pose: 0.607838 loss: 0.001117 2022/10/13 19:28:31 - mmengine - INFO - Epoch(train) [14][150/293] lr: 5.000000e-04 eta: 5:07:59 time: 0.333064 data_time: 0.104253 memory: 2315 loss_kpt: 0.001105 acc_pose: 0.631559 loss: 0.001105 2022/10/13 19:28:46 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:28:48 - mmengine - INFO - Epoch(train) [14][200/293] lr: 5.000000e-04 eta: 5:07:56 time: 0.338904 data_time: 0.067598 memory: 2315 loss_kpt: 0.001110 acc_pose: 0.620593 loss: 0.001110 2022/10/13 19:29:05 - mmengine - INFO - Epoch(train) [14][250/293] lr: 5.000000e-04 eta: 5:07:40 time: 0.321774 data_time: 0.066822 memory: 2315 loss_kpt: 0.001094 acc_pose: 0.635939 loss: 0.001094 2022/10/13 19:29:19 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:29:36 - mmengine - INFO - Epoch(train) [15][50/293] lr: 5.000000e-04 eta: 5:04:13 time: 0.339940 data_time: 0.155456 memory: 2315 loss_kpt: 0.001107 acc_pose: 0.661299 loss: 0.001107 2022/10/13 19:29:53 - mmengine - INFO - Epoch(train) [15][100/293] lr: 5.000000e-04 eta: 5:04:09 time: 0.336879 data_time: 0.072536 memory: 2315 loss_kpt: 0.001123 acc_pose: 0.639874 loss: 0.001123 2022/10/13 19:30:09 - mmengine - INFO - Epoch(train) [15][150/293] lr: 5.000000e-04 eta: 5:04:00 time: 0.328511 data_time: 0.062758 memory: 2315 loss_kpt: 0.001117 acc_pose: 0.694126 loss: 0.001117 2022/10/13 19:30:26 - mmengine - INFO - Epoch(train) [15][200/293] lr: 5.000000e-04 eta: 5:03:52 time: 0.330110 data_time: 0.063425 memory: 2315 loss_kpt: 0.001101 acc_pose: 0.649656 loss: 0.001101 2022/10/13 19:30:42 - mmengine - INFO - Epoch(train) [15][250/293] lr: 5.000000e-04 eta: 5:03:42 time: 0.326568 data_time: 0.072233 memory: 2315 loss_kpt: 0.001100 acc_pose: 0.586577 loss: 0.001100 2022/10/13 19:30:57 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:31:14 - mmengine - INFO - Epoch(train) [16][50/293] lr: 5.000000e-04 eta: 5:00:29 time: 0.338878 data_time: 0.101851 memory: 2315 loss_kpt: 0.001079 acc_pose: 0.626866 loss: 0.001079 2022/10/13 19:31:30 - mmengine - INFO - Epoch(train) [16][100/293] lr: 5.000000e-04 eta: 5:00:24 time: 0.333211 data_time: 0.077797 memory: 2315 loss_kpt: 0.001111 acc_pose: 0.658093 loss: 0.001111 2022/10/13 19:31:47 - mmengine - INFO - Epoch(train) [16][150/293] lr: 5.000000e-04 eta: 5:00:18 time: 0.331055 data_time: 0.062616 memory: 2315 loss_kpt: 0.001099 acc_pose: 0.604625 loss: 0.001099 2022/10/13 19:32:03 - mmengine - INFO - Epoch(train) [16][200/293] lr: 5.000000e-04 eta: 5:00:04 time: 0.320232 data_time: 0.066067 memory: 2315 loss_kpt: 0.001090 acc_pose: 0.663479 loss: 0.001090 2022/10/13 19:32:20 - mmengine - INFO - Epoch(train) [16][250/293] lr: 5.000000e-04 eta: 5:00:02 time: 0.337557 data_time: 0.065529 memory: 2315 loss_kpt: 0.001089 acc_pose: 0.665850 loss: 0.001089 2022/10/13 19:32:34 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:32:50 - mmengine - INFO - Epoch(train) [17][50/293] lr: 5.000000e-04 eta: 4:57:02 time: 0.338430 data_time: 0.110785 memory: 2315 loss_kpt: 0.001097 acc_pose: 0.640977 loss: 0.001097 2022/10/13 19:33:07 - mmengine - INFO - Epoch(train) [17][100/293] lr: 5.000000e-04 eta: 4:56:58 time: 0.332809 data_time: 0.070347 memory: 2315 loss_kpt: 0.001102 acc_pose: 0.605455 loss: 0.001102 2022/10/13 19:33:24 - mmengine - INFO - Epoch(train) [17][150/293] lr: 5.000000e-04 eta: 4:56:51 time: 0.329565 data_time: 0.068671 memory: 2315 loss_kpt: 0.001098 acc_pose: 0.621742 loss: 0.001098 2022/10/13 19:33:41 - mmengine - INFO - Epoch(train) [17][200/293] lr: 5.000000e-04 eta: 4:56:54 time: 0.346488 data_time: 0.075991 memory: 2315 loss_kpt: 0.001090 acc_pose: 0.629093 loss: 0.001090 2022/10/13 19:33:57 - mmengine - INFO - Epoch(train) [17][250/293] lr: 5.000000e-04 eta: 4:56:43 time: 0.321685 data_time: 0.090344 memory: 2315 loss_kpt: 0.001080 acc_pose: 0.631451 loss: 0.001080 2022/10/13 19:34:11 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:34:18 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:34:28 - mmengine - INFO - Epoch(train) [18][50/293] lr: 5.000000e-04 eta: 4:53:54 time: 0.337380 data_time: 0.097510 memory: 2315 loss_kpt: 0.001093 acc_pose: 0.679185 loss: 0.001093 2022/10/13 19:34:45 - mmengine - INFO - Epoch(train) [18][100/293] lr: 5.000000e-04 eta: 4:53:50 time: 0.331694 data_time: 0.070842 memory: 2315 loss_kpt: 0.001080 acc_pose: 0.651432 loss: 0.001080 2022/10/13 19:35:01 - mmengine - INFO - Epoch(train) [18][150/293] lr: 5.000000e-04 eta: 4:53:45 time: 0.331968 data_time: 0.068033 memory: 2315 loss_kpt: 0.001087 acc_pose: 0.634030 loss: 0.001087 2022/10/13 19:35:18 - mmengine - INFO - Epoch(train) [18][200/293] lr: 5.000000e-04 eta: 4:53:38 time: 0.329342 data_time: 0.074659 memory: 2315 loss_kpt: 0.001095 acc_pose: 0.642350 loss: 0.001095 2022/10/13 19:35:35 - mmengine - INFO - Epoch(train) [18][250/293] lr: 5.000000e-04 eta: 4:53:38 time: 0.340753 data_time: 0.074056 memory: 2315 loss_kpt: 0.001101 acc_pose: 0.680031 loss: 0.001101 2022/10/13 19:35:49 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:36:06 - mmengine - INFO - Epoch(train) [19][50/293] lr: 5.000000e-04 eta: 4:51:01 time: 0.339684 data_time: 0.107909 memory: 2315 loss_kpt: 0.001085 acc_pose: 0.694934 loss: 0.001085 2022/10/13 19:36:22 - mmengine - INFO - Epoch(train) [19][100/293] lr: 5.000000e-04 eta: 4:50:50 time: 0.320203 data_time: 0.066309 memory: 2315 loss_kpt: 0.001096 acc_pose: 0.610131 loss: 0.001096 2022/10/13 19:36:39 - mmengine - INFO - Epoch(train) [19][150/293] lr: 5.000000e-04 eta: 4:50:51 time: 0.343053 data_time: 0.066664 memory: 2315 loss_kpt: 0.001075 acc_pose: 0.620053 loss: 0.001075 2022/10/13 19:36:55 - mmengine - INFO - Epoch(train) [19][200/293] lr: 5.000000e-04 eta: 4:50:47 time: 0.332719 data_time: 0.083941 memory: 2315 loss_kpt: 0.001075 acc_pose: 0.659191 loss: 0.001075 2022/10/13 19:37:12 - mmengine - INFO - Epoch(train) [19][250/293] lr: 5.000000e-04 eta: 4:50:38 time: 0.324639 data_time: 0.077082 memory: 2315 loss_kpt: 0.001093 acc_pose: 0.654514 loss: 0.001093 2022/10/13 19:37:25 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:37:42 - mmengine - INFO - Epoch(train) [20][50/293] lr: 5.000000e-04 eta: 4:48:10 time: 0.340648 data_time: 0.102772 memory: 2315 loss_kpt: 0.001086 acc_pose: 0.658871 loss: 0.001086 2022/10/13 19:37:59 - mmengine - INFO - Epoch(train) [20][100/293] lr: 5.000000e-04 eta: 4:48:04 time: 0.326726 data_time: 0.095227 memory: 2315 loss_kpt: 0.001070 acc_pose: 0.603537 loss: 0.001070 2022/10/13 19:38:15 - mmengine - INFO - Epoch(train) [20][150/293] lr: 5.000000e-04 eta: 4:47:57 time: 0.327745 data_time: 0.063028 memory: 2315 loss_kpt: 0.001081 acc_pose: 0.632125 loss: 0.001081 2022/10/13 19:38:32 - mmengine - INFO - Epoch(train) [20][200/293] lr: 5.000000e-04 eta: 4:47:53 time: 0.332774 data_time: 0.097811 memory: 2315 loss_kpt: 0.001065 acc_pose: 0.643387 loss: 0.001065 2022/10/13 19:38:49 - mmengine - INFO - Epoch(train) [20][250/293] lr: 5.000000e-04 eta: 4:47:52 time: 0.340894 data_time: 0.073646 memory: 2315 loss_kpt: 0.001077 acc_pose: 0.658524 loss: 0.001077 2022/10/13 19:39:03 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:39:03 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/10/13 19:39:11 - mmengine - INFO - Epoch(val) [20][50/407] eta: 0:00:43 time: 0.122671 data_time: 0.078997 memory: 2315 2022/10/13 19:39:17 - mmengine - INFO - Epoch(val) [20][100/407] eta: 0:00:35 time: 0.114139 data_time: 0.068634 memory: 426 2022/10/13 19:39:22 - mmengine - INFO - Epoch(val) [20][150/407] eta: 0:00:28 time: 0.111958 data_time: 0.070044 memory: 426 2022/10/13 19:39:28 - mmengine - INFO - Epoch(val) [20][200/407] eta: 0:00:23 time: 0.112017 data_time: 0.068705 memory: 426 2022/10/13 19:39:34 - mmengine - INFO - Epoch(val) [20][250/407] eta: 0:00:18 time: 0.118116 data_time: 0.072928 memory: 426 2022/10/13 19:39:40 - mmengine - INFO - Epoch(val) [20][300/407] eta: 0:00:12 time: 0.113896 data_time: 0.071203 memory: 426 2022/10/13 19:39:45 - mmengine - INFO - Epoch(val) [20][350/407] eta: 0:00:06 time: 0.113462 data_time: 0.068710 memory: 426 2022/10/13 19:39:51 - mmengine - INFO - Epoch(val) [20][400/407] eta: 0:00:00 time: 0.110428 data_time: 0.068647 memory: 426 2022/10/13 19:40:29 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 19:40:43 - mmengine - INFO - Epoch(val) [20][407/407] coco/AP: 0.503595 coco/AP .5: 0.804511 coco/AP .75: 0.548266 coco/AP (M): 0.472591 coco/AP (L): 0.563111 coco/AR: 0.577897 coco/AR .5: 0.860044 coco/AR .75: 0.631297 coco/AR (M): 0.533433 coco/AR (L): 0.639762 2022/10/13 19:40:43 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_10.pth is removed 2022/10/13 19:40:45 - mmengine - INFO - The best checkpoint with 0.5036 coco/AP at 20 epoch is saved to best_coco/AP_epoch_20.pth. 2022/10/13 19:41:01 - mmengine - INFO - Epoch(train) [21][50/293] lr: 5.000000e-04 eta: 4:45:29 time: 0.333874 data_time: 0.140494 memory: 2315 loss_kpt: 0.001076 acc_pose: 0.675794 loss: 0.001076 2022/10/13 19:41:18 - mmengine - INFO - Epoch(train) [21][100/293] lr: 5.000000e-04 eta: 4:45:26 time: 0.335188 data_time: 0.073028 memory: 2315 loss_kpt: 0.001063 acc_pose: 0.652483 loss: 0.001063 2022/10/13 19:41:31 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:41:34 - mmengine - INFO - Epoch(train) [21][150/293] lr: 5.000000e-04 eta: 4:45:18 time: 0.323450 data_time: 0.066440 memory: 2315 loss_kpt: 0.001065 acc_pose: 0.604856 loss: 0.001065 2022/10/13 19:41:51 - mmengine - INFO - Epoch(train) [21][200/293] lr: 5.000000e-04 eta: 4:45:13 time: 0.331342 data_time: 0.068111 memory: 2315 loss_kpt: 0.001093 acc_pose: 0.641218 loss: 0.001093 2022/10/13 19:42:07 - mmengine - INFO - Epoch(train) [21][250/293] lr: 5.000000e-04 eta: 4:45:08 time: 0.330277 data_time: 0.065249 memory: 2315 loss_kpt: 0.001052 acc_pose: 0.652593 loss: 0.001052 2022/10/13 19:42:22 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:42:40 - mmengine - INFO - Epoch(train) [22][50/293] lr: 5.000000e-04 eta: 4:43:01 time: 0.353730 data_time: 0.126692 memory: 2315 loss_kpt: 0.001055 acc_pose: 0.665313 loss: 0.001055 2022/10/13 19:42:56 - mmengine - INFO - Epoch(train) [22][100/293] lr: 5.000000e-04 eta: 4:42:54 time: 0.327625 data_time: 0.085860 memory: 2315 loss_kpt: 0.001060 acc_pose: 0.740771 loss: 0.001060 2022/10/13 19:43:12 - mmengine - INFO - Epoch(train) [22][150/293] lr: 5.000000e-04 eta: 4:42:49 time: 0.330600 data_time: 0.129171 memory: 2315 loss_kpt: 0.001042 acc_pose: 0.630825 loss: 0.001042 2022/10/13 19:43:29 - mmengine - INFO - Epoch(train) [22][200/293] lr: 5.000000e-04 eta: 4:42:44 time: 0.329428 data_time: 0.115147 memory: 2315 loss_kpt: 0.001062 acc_pose: 0.664113 loss: 0.001062 2022/10/13 19:43:45 - mmengine - INFO - Epoch(train) [22][250/293] lr: 5.000000e-04 eta: 4:42:32 time: 0.316170 data_time: 0.067778 memory: 2315 loss_kpt: 0.001070 acc_pose: 0.624217 loss: 0.001070 2022/10/13 19:43:59 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:44:17 - mmengine - INFO - Epoch(train) [23][50/293] lr: 5.000000e-04 eta: 4:40:28 time: 0.346773 data_time: 0.086503 memory: 2315 loss_kpt: 0.001087 acc_pose: 0.724101 loss: 0.001087 2022/10/13 19:44:33 - mmengine - INFO - Epoch(train) [23][100/293] lr: 5.000000e-04 eta: 4:40:24 time: 0.333411 data_time: 0.121916 memory: 2315 loss_kpt: 0.001070 acc_pose: 0.648822 loss: 0.001070 2022/10/13 19:44:50 - mmengine - INFO - Epoch(train) [23][150/293] lr: 5.000000e-04 eta: 4:40:20 time: 0.332728 data_time: 0.168164 memory: 2315 loss_kpt: 0.001051 acc_pose: 0.652541 loss: 0.001051 2022/10/13 19:45:06 - mmengine - INFO - Epoch(train) [23][200/293] lr: 5.000000e-04 eta: 4:40:15 time: 0.330048 data_time: 0.147855 memory: 2315 loss_kpt: 0.001057 acc_pose: 0.633373 loss: 0.001057 2022/10/13 19:45:23 - mmengine - INFO - Epoch(train) [23][250/293] lr: 5.000000e-04 eta: 4:40:08 time: 0.328588 data_time: 0.155280 memory: 2315 loss_kpt: 0.001064 acc_pose: 0.639776 loss: 0.001064 2022/10/13 19:45:37 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:45:54 - mmengine - INFO - Epoch(train) [24][50/293] lr: 5.000000e-04 eta: 4:38:08 time: 0.343375 data_time: 0.102813 memory: 2315 loss_kpt: 0.001069 acc_pose: 0.680311 loss: 0.001069 2022/10/13 19:46:11 - mmengine - INFO - Epoch(train) [24][100/293] lr: 5.000000e-04 eta: 4:38:04 time: 0.331460 data_time: 0.072988 memory: 2315 loss_kpt: 0.001079 acc_pose: 0.643213 loss: 0.001079 2022/10/13 19:46:28 - mmengine - INFO - Epoch(train) [24][150/293] lr: 5.000000e-04 eta: 4:38:00 time: 0.333728 data_time: 0.070812 memory: 2315 loss_kpt: 0.001072 acc_pose: 0.706613 loss: 0.001072 2022/10/13 19:46:44 - mmengine - INFO - Epoch(train) [24][200/293] lr: 5.000000e-04 eta: 4:37:58 time: 0.339164 data_time: 0.066271 memory: 2315 loss_kpt: 0.001041 acc_pose: 0.638682 loss: 0.001041 2022/10/13 19:47:01 - mmengine - INFO - Epoch(train) [24][250/293] lr: 5.000000e-04 eta: 4:37:53 time: 0.332150 data_time: 0.069988 memory: 2315 loss_kpt: 0.001070 acc_pose: 0.697778 loss: 0.001070 2022/10/13 19:47:05 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:47:16 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:47:33 - mmengine - INFO - Epoch(train) [25][50/293] lr: 5.000000e-04 eta: 4:35:58 time: 0.343137 data_time: 0.141717 memory: 2315 loss_kpt: 0.001066 acc_pose: 0.664040 loss: 0.001066 2022/10/13 19:47:49 - mmengine - INFO - Epoch(train) [25][100/293] lr: 5.000000e-04 eta: 4:35:52 time: 0.328800 data_time: 0.118298 memory: 2315 loss_kpt: 0.001050 acc_pose: 0.691395 loss: 0.001050 2022/10/13 19:48:06 - mmengine - INFO - Epoch(train) [25][150/293] lr: 5.000000e-04 eta: 4:35:49 time: 0.335391 data_time: 0.144994 memory: 2315 loss_kpt: 0.001055 acc_pose: 0.679262 loss: 0.001055 2022/10/13 19:48:23 - mmengine - INFO - Epoch(train) [25][200/293] lr: 5.000000e-04 eta: 4:35:44 time: 0.332721 data_time: 0.093235 memory: 2315 loss_kpt: 0.001058 acc_pose: 0.619860 loss: 0.001058 2022/10/13 19:48:39 - mmengine - INFO - Epoch(train) [25][250/293] lr: 5.000000e-04 eta: 4:35:39 time: 0.331769 data_time: 0.116605 memory: 2315 loss_kpt: 0.001062 acc_pose: 0.680030 loss: 0.001062 2022/10/13 19:48:53 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:49:10 - mmengine - INFO - Epoch(train) [26][50/293] lr: 5.000000e-04 eta: 4:33:46 time: 0.336508 data_time: 0.081946 memory: 2315 loss_kpt: 0.001049 acc_pose: 0.645302 loss: 0.001049 2022/10/13 19:49:27 - mmengine - INFO - Epoch(train) [26][100/293] lr: 5.000000e-04 eta: 4:33:41 time: 0.332062 data_time: 0.078225 memory: 2315 loss_kpt: 0.001031 acc_pose: 0.644057 loss: 0.001031 2022/10/13 19:49:43 - mmengine - INFO - Epoch(train) [26][150/293] lr: 5.000000e-04 eta: 4:33:36 time: 0.331149 data_time: 0.104192 memory: 2315 loss_kpt: 0.001049 acc_pose: 0.654176 loss: 0.001049 2022/10/13 19:50:00 - mmengine - INFO - Epoch(train) [26][200/293] lr: 5.000000e-04 eta: 4:33:30 time: 0.327991 data_time: 0.153655 memory: 2315 loss_kpt: 0.001072 acc_pose: 0.656856 loss: 0.001072 2022/10/13 19:50:16 - mmengine - INFO - Epoch(train) [26][250/293] lr: 5.000000e-04 eta: 4:33:23 time: 0.328703 data_time: 0.147371 memory: 2315 loss_kpt: 0.001043 acc_pose: 0.645783 loss: 0.001043 2022/10/13 19:50:30 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:50:47 - mmengine - INFO - Epoch(train) [27][50/293] lr: 5.000000e-04 eta: 4:31:37 time: 0.342438 data_time: 0.089149 memory: 2315 loss_kpt: 0.001067 acc_pose: 0.636954 loss: 0.001067 2022/10/13 19:51:04 - mmengine - INFO - Epoch(train) [27][100/293] lr: 5.000000e-04 eta: 4:31:32 time: 0.331629 data_time: 0.184119 memory: 2315 loss_kpt: 0.001057 acc_pose: 0.675315 loss: 0.001057 2022/10/13 19:51:21 - mmengine - INFO - Epoch(train) [27][150/293] lr: 5.000000e-04 eta: 4:31:31 time: 0.345051 data_time: 0.181167 memory: 2315 loss_kpt: 0.001036 acc_pose: 0.647497 loss: 0.001036 2022/10/13 19:51:38 - mmengine - INFO - Epoch(train) [27][200/293] lr: 5.000000e-04 eta: 4:31:26 time: 0.333175 data_time: 0.175930 memory: 2315 loss_kpt: 0.001045 acc_pose: 0.684971 loss: 0.001045 2022/10/13 19:51:55 - mmengine - INFO - Epoch(train) [27][250/293] lr: 5.000000e-04 eta: 4:31:23 time: 0.337725 data_time: 0.186714 memory: 2315 loss_kpt: 0.001040 acc_pose: 0.652392 loss: 0.001040 2022/10/13 19:52:09 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:52:26 - mmengine - INFO - Epoch(train) [28][50/293] lr: 5.000000e-04 eta: 4:29:41 time: 0.344491 data_time: 0.096654 memory: 2315 loss_kpt: 0.001051 acc_pose: 0.665485 loss: 0.001051 2022/10/13 19:52:39 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:52:42 - mmengine - INFO - Epoch(train) [28][100/293] lr: 5.000000e-04 eta: 4:29:35 time: 0.330792 data_time: 0.111835 memory: 2315 loss_kpt: 0.001049 acc_pose: 0.619283 loss: 0.001049 2022/10/13 19:52:59 - mmengine - INFO - Epoch(train) [28][150/293] lr: 5.000000e-04 eta: 4:29:29 time: 0.328173 data_time: 0.172771 memory: 2315 loss_kpt: 0.001060 acc_pose: 0.658713 loss: 0.001060 2022/10/13 19:53:16 - mmengine - INFO - Epoch(train) [28][200/293] lr: 5.000000e-04 eta: 4:29:26 time: 0.340136 data_time: 0.163586 memory: 2315 loss_kpt: 0.001038 acc_pose: 0.650158 loss: 0.001038 2022/10/13 19:53:33 - mmengine - INFO - Epoch(train) [28][250/293] lr: 5.000000e-04 eta: 4:29:22 time: 0.335616 data_time: 0.181946 memory: 2315 loss_kpt: 0.001048 acc_pose: 0.607836 loss: 0.001048 2022/10/13 19:53:47 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:54:04 - mmengine - INFO - Epoch(train) [29][50/293] lr: 5.000000e-04 eta: 4:27:45 time: 0.349488 data_time: 0.095310 memory: 2315 loss_kpt: 0.001032 acc_pose: 0.714486 loss: 0.001032 2022/10/13 19:54:21 - mmengine - INFO - Epoch(train) [29][100/293] lr: 5.000000e-04 eta: 4:27:43 time: 0.343686 data_time: 0.083448 memory: 2315 loss_kpt: 0.001059 acc_pose: 0.621797 loss: 0.001059 2022/10/13 19:54:38 - mmengine - INFO - Epoch(train) [29][150/293] lr: 5.000000e-04 eta: 4:27:36 time: 0.326800 data_time: 0.160546 memory: 2315 loss_kpt: 0.001037 acc_pose: 0.688942 loss: 0.001037 2022/10/13 19:54:54 - mmengine - INFO - Epoch(train) [29][200/293] lr: 5.000000e-04 eta: 4:27:29 time: 0.328858 data_time: 0.152630 memory: 2315 loss_kpt: 0.001042 acc_pose: 0.690390 loss: 0.001042 2022/10/13 19:55:10 - mmengine - INFO - Epoch(train) [29][250/293] lr: 5.000000e-04 eta: 4:27:22 time: 0.327121 data_time: 0.126185 memory: 2315 loss_kpt: 0.001050 acc_pose: 0.655950 loss: 0.001050 2022/10/13 19:55:25 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:55:41 - mmengine - INFO - Epoch(train) [30][50/293] lr: 5.000000e-04 eta: 4:25:45 time: 0.338759 data_time: 0.160264 memory: 2315 loss_kpt: 0.001054 acc_pose: 0.676539 loss: 0.001054 2022/10/13 19:55:58 - mmengine - INFO - Epoch(train) [30][100/293] lr: 5.000000e-04 eta: 4:25:39 time: 0.330578 data_time: 0.115097 memory: 2315 loss_kpt: 0.001039 acc_pose: 0.695820 loss: 0.001039 2022/10/13 19:56:14 - mmengine - INFO - Epoch(train) [30][150/293] lr: 5.000000e-04 eta: 4:25:32 time: 0.328454 data_time: 0.152501 memory: 2315 loss_kpt: 0.001036 acc_pose: 0.649737 loss: 0.001036 2022/10/13 19:56:31 - mmengine - INFO - Epoch(train) [30][200/293] lr: 5.000000e-04 eta: 4:25:28 time: 0.339222 data_time: 0.168195 memory: 2315 loss_kpt: 0.001051 acc_pose: 0.603741 loss: 0.001051 2022/10/13 19:56:48 - mmengine - INFO - Epoch(train) [30][250/293] lr: 5.000000e-04 eta: 4:25:21 time: 0.327808 data_time: 0.166416 memory: 2315 loss_kpt: 0.001040 acc_pose: 0.686072 loss: 0.001040 2022/10/13 19:57:02 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 19:57:02 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/10/13 19:57:10 - mmengine - INFO - Epoch(val) [30][50/407] eta: 0:00:42 time: 0.119186 data_time: 0.073806 memory: 2315 2022/10/13 19:57:15 - mmengine - INFO - Epoch(val) [30][100/407] eta: 0:00:33 time: 0.109646 data_time: 0.066235 memory: 426 2022/10/13 19:57:21 - mmengine - INFO - Epoch(val) [30][150/407] eta: 0:00:29 time: 0.114359 data_time: 0.069668 memory: 426 2022/10/13 19:57:27 - mmengine - INFO - Epoch(val) [30][200/407] eta: 0:00:23 time: 0.112754 data_time: 0.066150 memory: 426 2022/10/13 19:57:33 - mmengine - INFO - Epoch(val) [30][250/407] eta: 0:00:18 time: 0.116186 data_time: 0.070672 memory: 426 2022/10/13 19:57:38 - mmengine - INFO - Epoch(val) [30][300/407] eta: 0:00:12 time: 0.115735 data_time: 0.070977 memory: 426 2022/10/13 19:57:44 - mmengine - INFO - Epoch(val) [30][350/407] eta: 0:00:06 time: 0.117753 data_time: 0.071313 memory: 426 2022/10/13 19:57:50 - mmengine - INFO - Epoch(val) [30][400/407] eta: 0:00:00 time: 0.106061 data_time: 0.065502 memory: 426 2022/10/13 19:58:28 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 19:58:42 - mmengine - INFO - Epoch(val) [30][407/407] coco/AP: 0.528126 coco/AP .5: 0.822691 coco/AP .75: 0.573936 coco/AP (M): 0.499607 coco/AP (L): 0.583876 coco/AR: 0.598835 coco/AR .5: 0.875945 coco/AR .75: 0.651921 coco/AR (M): 0.558072 coco/AR (L): 0.656039 2022/10/13 19:58:42 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_20.pth is removed 2022/10/13 19:58:44 - mmengine - INFO - The best checkpoint with 0.5281 coco/AP at 30 epoch is saved to best_coco/AP_epoch_30.pth. 2022/10/13 19:59:01 - mmengine - INFO - Epoch(train) [31][50/293] lr: 5.000000e-04 eta: 4:23:47 time: 0.338956 data_time: 0.172169 memory: 2315 loss_kpt: 0.001018 acc_pose: 0.651288 loss: 0.001018 2022/10/13 19:59:18 - mmengine - INFO - Epoch(train) [31][100/293] lr: 5.000000e-04 eta: 4:23:45 time: 0.344536 data_time: 0.154667 memory: 2315 loss_kpt: 0.001054 acc_pose: 0.718441 loss: 0.001054 2022/10/13 19:59:34 - mmengine - INFO - Epoch(train) [31][150/293] lr: 5.000000e-04 eta: 4:23:38 time: 0.327201 data_time: 0.075164 memory: 2315 loss_kpt: 0.001043 acc_pose: 0.676747 loss: 0.001043 2022/10/13 19:59:51 - mmengine - INFO - Epoch(train) [31][200/293] lr: 5.000000e-04 eta: 4:23:33 time: 0.334844 data_time: 0.060782 memory: 2315 loss_kpt: 0.001017 acc_pose: 0.732940 loss: 0.001017 2022/10/13 19:59:55 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:00:08 - mmengine - INFO - Epoch(train) [31][250/293] lr: 5.000000e-04 eta: 4:23:27 time: 0.332253 data_time: 0.063923 memory: 2315 loss_kpt: 0.001049 acc_pose: 0.636697 loss: 0.001049 2022/10/13 20:00:22 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:00:39 - mmengine - INFO - Epoch(train) [32][50/293] lr: 5.000000e-04 eta: 4:21:56 time: 0.342116 data_time: 0.079520 memory: 2315 loss_kpt: 0.001031 acc_pose: 0.702367 loss: 0.001031 2022/10/13 20:00:55 - mmengine - INFO - Epoch(train) [32][100/293] lr: 5.000000e-04 eta: 4:21:50 time: 0.329981 data_time: 0.090518 memory: 2315 loss_kpt: 0.001027 acc_pose: 0.603696 loss: 0.001027 2022/10/13 20:01:12 - mmengine - INFO - Epoch(train) [32][150/293] lr: 5.000000e-04 eta: 4:21:42 time: 0.325727 data_time: 0.073601 memory: 2315 loss_kpt: 0.001032 acc_pose: 0.665747 loss: 0.001032 2022/10/13 20:01:28 - mmengine - INFO - Epoch(train) [32][200/293] lr: 5.000000e-04 eta: 4:21:35 time: 0.327516 data_time: 0.113878 memory: 2315 loss_kpt: 0.001030 acc_pose: 0.672023 loss: 0.001030 2022/10/13 20:01:45 - mmengine - INFO - Epoch(train) [32][250/293] lr: 5.000000e-04 eta: 4:21:31 time: 0.339542 data_time: 0.139281 memory: 2315 loss_kpt: 0.001050 acc_pose: 0.650157 loss: 0.001050 2022/10/13 20:01:59 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:02:16 - mmengine - INFO - Epoch(train) [33][50/293] lr: 5.000000e-04 eta: 4:20:04 time: 0.346189 data_time: 0.117513 memory: 2315 loss_kpt: 0.001030 acc_pose: 0.663300 loss: 0.001030 2022/10/13 20:02:33 - mmengine - INFO - Epoch(train) [33][100/293] lr: 5.000000e-04 eta: 4:19:57 time: 0.327267 data_time: 0.068225 memory: 2315 loss_kpt: 0.001027 acc_pose: 0.702962 loss: 0.001027 2022/10/13 20:02:49 - mmengine - INFO - Epoch(train) [33][150/293] lr: 5.000000e-04 eta: 4:19:50 time: 0.332035 data_time: 0.064682 memory: 2315 loss_kpt: 0.001027 acc_pose: 0.690410 loss: 0.001027 2022/10/13 20:03:06 - mmengine - INFO - Epoch(train) [33][200/293] lr: 5.000000e-04 eta: 4:19:44 time: 0.331650 data_time: 0.081758 memory: 2315 loss_kpt: 0.001036 acc_pose: 0.678207 loss: 0.001036 2022/10/13 20:03:22 - mmengine - INFO - Epoch(train) [33][250/293] lr: 5.000000e-04 eta: 4:19:38 time: 0.331309 data_time: 0.126349 memory: 2315 loss_kpt: 0.001012 acc_pose: 0.623810 loss: 0.001012 2022/10/13 20:03:36 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:03:53 - mmengine - INFO - Epoch(train) [34][50/293] lr: 5.000000e-04 eta: 4:18:13 time: 0.344222 data_time: 0.125665 memory: 2315 loss_kpt: 0.001030 acc_pose: 0.673971 loss: 0.001030 2022/10/13 20:04:10 - mmengine - INFO - Epoch(train) [34][100/293] lr: 5.000000e-04 eta: 4:18:06 time: 0.332407 data_time: 0.132668 memory: 2315 loss_kpt: 0.001033 acc_pose: 0.706186 loss: 0.001033 2022/10/13 20:04:26 - mmengine - INFO - Epoch(train) [34][150/293] lr: 5.000000e-04 eta: 4:18:00 time: 0.332716 data_time: 0.182941 memory: 2315 loss_kpt: 0.001013 acc_pose: 0.657420 loss: 0.001013 2022/10/13 20:04:43 - mmengine - INFO - Epoch(train) [34][200/293] lr: 5.000000e-04 eta: 4:17:55 time: 0.335017 data_time: 0.101638 memory: 2315 loss_kpt: 0.001018 acc_pose: 0.680923 loss: 0.001018 2022/10/13 20:04:59 - mmengine - INFO - Epoch(train) [34][250/293] lr: 5.000000e-04 eta: 4:17:46 time: 0.324728 data_time: 0.067748 memory: 2315 loss_kpt: 0.001020 acc_pose: 0.673965 loss: 0.001020 2022/10/13 20:05:13 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:05:27 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:05:31 - mmengine - INFO - Epoch(train) [35][50/293] lr: 5.000000e-04 eta: 4:16:26 time: 0.353770 data_time: 0.125937 memory: 2315 loss_kpt: 0.001034 acc_pose: 0.662264 loss: 0.001034 2022/10/13 20:05:48 - mmengine - INFO - Epoch(train) [35][100/293] lr: 5.000000e-04 eta: 4:16:21 time: 0.337573 data_time: 0.063403 memory: 2315 loss_kpt: 0.001023 acc_pose: 0.612202 loss: 0.001023 2022/10/13 20:06:05 - mmengine - INFO - Epoch(train) [35][150/293] lr: 5.000000e-04 eta: 4:16:16 time: 0.337412 data_time: 0.072898 memory: 2315 loss_kpt: 0.001034 acc_pose: 0.715414 loss: 0.001034 2022/10/13 20:06:21 - mmengine - INFO - Epoch(train) [35][200/293] lr: 5.000000e-04 eta: 4:16:05 time: 0.316709 data_time: 0.066663 memory: 2315 loss_kpt: 0.001015 acc_pose: 0.697110 loss: 0.001015 2022/10/13 20:06:37 - mmengine - INFO - Epoch(train) [35][250/293] lr: 5.000000e-04 eta: 4:15:57 time: 0.325395 data_time: 0.067502 memory: 2315 loss_kpt: 0.001040 acc_pose: 0.682313 loss: 0.001040 2022/10/13 20:06:51 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:07:08 - mmengine - INFO - Epoch(train) [36][50/293] lr: 5.000000e-04 eta: 4:14:36 time: 0.344314 data_time: 0.099646 memory: 2315 loss_kpt: 0.001005 acc_pose: 0.692129 loss: 0.001005 2022/10/13 20:07:25 - mmengine - INFO - Epoch(train) [36][100/293] lr: 5.000000e-04 eta: 4:14:31 time: 0.335739 data_time: 0.071573 memory: 2315 loss_kpt: 0.001018 acc_pose: 0.673590 loss: 0.001018 2022/10/13 20:07:42 - mmengine - INFO - Epoch(train) [36][150/293] lr: 5.000000e-04 eta: 4:14:24 time: 0.330842 data_time: 0.106650 memory: 2315 loss_kpt: 0.000992 acc_pose: 0.662473 loss: 0.000992 2022/10/13 20:07:58 - mmengine - INFO - Epoch(train) [36][200/293] lr: 5.000000e-04 eta: 4:14:16 time: 0.325732 data_time: 0.161616 memory: 2315 loss_kpt: 0.001026 acc_pose: 0.705092 loss: 0.001026 2022/10/13 20:08:15 - mmengine - INFO - Epoch(train) [36][250/293] lr: 5.000000e-04 eta: 4:14:08 time: 0.328206 data_time: 0.169621 memory: 2315 loss_kpt: 0.001015 acc_pose: 0.701948 loss: 0.001015 2022/10/13 20:08:29 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:08:46 - mmengine - INFO - Epoch(train) [37][50/293] lr: 5.000000e-04 eta: 4:12:49 time: 0.343856 data_time: 0.093617 memory: 2315 loss_kpt: 0.001018 acc_pose: 0.687081 loss: 0.001018 2022/10/13 20:09:03 - mmengine - INFO - Epoch(train) [37][100/293] lr: 5.000000e-04 eta: 4:12:42 time: 0.328703 data_time: 0.069235 memory: 2315 loss_kpt: 0.001000 acc_pose: 0.736682 loss: 0.001000 2022/10/13 20:09:19 - mmengine - INFO - Epoch(train) [37][150/293] lr: 5.000000e-04 eta: 4:12:34 time: 0.328848 data_time: 0.146994 memory: 2315 loss_kpt: 0.001025 acc_pose: 0.637193 loss: 0.001025 2022/10/13 20:09:36 - mmengine - INFO - Epoch(train) [37][200/293] lr: 5.000000e-04 eta: 4:12:28 time: 0.334542 data_time: 0.196404 memory: 2315 loss_kpt: 0.001004 acc_pose: 0.721626 loss: 0.001004 2022/10/13 20:09:52 - mmengine - INFO - Epoch(train) [37][250/293] lr: 5.000000e-04 eta: 4:12:19 time: 0.326630 data_time: 0.161634 memory: 2315 loss_kpt: 0.001015 acc_pose: 0.647406 loss: 0.001015 2022/10/13 20:10:06 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:10:23 - mmengine - INFO - Epoch(train) [38][50/293] lr: 5.000000e-04 eta: 4:11:02 time: 0.340093 data_time: 0.169514 memory: 2315 loss_kpt: 0.000999 acc_pose: 0.636241 loss: 0.000999 2022/10/13 20:10:39 - mmengine - INFO - Epoch(train) [38][100/293] lr: 5.000000e-04 eta: 4:10:54 time: 0.329141 data_time: 0.151779 memory: 2315 loss_kpt: 0.001029 acc_pose: 0.712385 loss: 0.001029 2022/10/13 20:10:55 - mmengine - INFO - Epoch(train) [38][150/293] lr: 5.000000e-04 eta: 4:10:45 time: 0.323441 data_time: 0.071307 memory: 2315 loss_kpt: 0.001012 acc_pose: 0.739961 loss: 0.001012 2022/10/13 20:10:58 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:11:12 - mmengine - INFO - Epoch(train) [38][200/293] lr: 5.000000e-04 eta: 4:10:38 time: 0.330933 data_time: 0.066607 memory: 2315 loss_kpt: 0.001006 acc_pose: 0.698691 loss: 0.001006 2022/10/13 20:11:29 - mmengine - INFO - Epoch(train) [38][250/293] lr: 5.000000e-04 eta: 4:10:32 time: 0.335631 data_time: 0.065337 memory: 2315 loss_kpt: 0.001011 acc_pose: 0.655074 loss: 0.001011 2022/10/13 20:11:43 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:12:00 - mmengine - INFO - Epoch(train) [39][50/293] lr: 5.000000e-04 eta: 4:09:15 time: 0.338805 data_time: 0.089531 memory: 2315 loss_kpt: 0.001012 acc_pose: 0.717015 loss: 0.001012 2022/10/13 20:12:16 - mmengine - INFO - Epoch(train) [39][100/293] lr: 5.000000e-04 eta: 4:09:08 time: 0.328824 data_time: 0.091103 memory: 2315 loss_kpt: 0.001005 acc_pose: 0.704748 loss: 0.001005 2022/10/13 20:12:33 - mmengine - INFO - Epoch(train) [39][150/293] lr: 5.000000e-04 eta: 4:08:59 time: 0.324208 data_time: 0.067115 memory: 2315 loss_kpt: 0.001013 acc_pose: 0.669880 loss: 0.001013 2022/10/13 20:12:49 - mmengine - INFO - Epoch(train) [39][200/293] lr: 5.000000e-04 eta: 4:08:50 time: 0.323154 data_time: 0.102703 memory: 2315 loss_kpt: 0.001004 acc_pose: 0.669196 loss: 0.001004 2022/10/13 20:13:05 - mmengine - INFO - Epoch(train) [39][250/293] lr: 5.000000e-04 eta: 4:08:42 time: 0.332334 data_time: 0.109306 memory: 2315 loss_kpt: 0.000997 acc_pose: 0.630535 loss: 0.000997 2022/10/13 20:13:19 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:13:36 - mmengine - INFO - Epoch(train) [40][50/293] lr: 5.000000e-04 eta: 4:07:27 time: 0.335675 data_time: 0.107429 memory: 2315 loss_kpt: 0.001031 acc_pose: 0.698377 loss: 0.001031 2022/10/13 20:13:52 - mmengine - INFO - Epoch(train) [40][100/293] lr: 5.000000e-04 eta: 4:07:20 time: 0.330792 data_time: 0.063117 memory: 2315 loss_kpt: 0.001018 acc_pose: 0.646179 loss: 0.001018 2022/10/13 20:14:09 - mmengine - INFO - Epoch(train) [40][150/293] lr: 5.000000e-04 eta: 4:07:14 time: 0.337579 data_time: 0.103063 memory: 2315 loss_kpt: 0.001016 acc_pose: 0.646074 loss: 0.001016 2022/10/13 20:14:26 - mmengine - INFO - Epoch(train) [40][200/293] lr: 5.000000e-04 eta: 4:07:07 time: 0.334487 data_time: 0.087454 memory: 2315 loss_kpt: 0.001018 acc_pose: 0.701767 loss: 0.001018 2022/10/13 20:14:43 - mmengine - INFO - Epoch(train) [40][250/293] lr: 5.000000e-04 eta: 4:07:00 time: 0.333168 data_time: 0.066243 memory: 2315 loss_kpt: 0.001015 acc_pose: 0.722446 loss: 0.001015 2022/10/13 20:14:57 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:14:57 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/10/13 20:15:05 - mmengine - INFO - Epoch(val) [40][50/407] eta: 0:00:44 time: 0.124442 data_time: 0.081306 memory: 2315 2022/10/13 20:15:11 - mmengine - INFO - Epoch(val) [40][100/407] eta: 0:00:34 time: 0.111735 data_time: 0.068436 memory: 426 2022/10/13 20:15:16 - mmengine - INFO - Epoch(val) [40][150/407] eta: 0:00:28 time: 0.112034 data_time: 0.065097 memory: 426 2022/10/13 20:15:22 - mmengine - INFO - Epoch(val) [40][200/407] eta: 0:00:23 time: 0.114254 data_time: 0.069591 memory: 426 2022/10/13 20:15:28 - mmengine - INFO - Epoch(val) [40][250/407] eta: 0:00:18 time: 0.115820 data_time: 0.073068 memory: 426 2022/10/13 20:15:34 - mmengine - INFO - Epoch(val) [40][300/407] eta: 0:00:12 time: 0.116446 data_time: 0.072779 memory: 426 2022/10/13 20:15:40 - mmengine - INFO - Epoch(val) [40][350/407] eta: 0:00:06 time: 0.114846 data_time: 0.070157 memory: 426 2022/10/13 20:15:45 - mmengine - INFO - Epoch(val) [40][400/407] eta: 0:00:00 time: 0.115726 data_time: 0.073856 memory: 426 2022/10/13 20:16:23 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 20:16:38 - mmengine - INFO - Epoch(val) [40][407/407] coco/AP: 0.541550 coco/AP .5: 0.828308 coco/AP .75: 0.592139 coco/AP (M): 0.511857 coco/AP (L): 0.599575 coco/AR: 0.613208 coco/AR .5: 0.879723 coco/AR .75: 0.669553 coco/AR (M): 0.570992 coco/AR (L): 0.672204 2022/10/13 20:16:38 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_30.pth is removed 2022/10/13 20:16:39 - mmengine - INFO - The best checkpoint with 0.5416 coco/AP at 40 epoch is saved to best_coco/AP_epoch_40.pth. 2022/10/13 20:16:56 - mmengine - INFO - Epoch(train) [41][50/293] lr: 5.000000e-04 eta: 4:05:45 time: 0.328776 data_time: 0.199756 memory: 2315 loss_kpt: 0.001001 acc_pose: 0.724187 loss: 0.001001 2022/10/13 20:17:12 - mmengine - INFO - Epoch(train) [41][100/293] lr: 5.000000e-04 eta: 4:05:36 time: 0.324542 data_time: 0.183768 memory: 2315 loss_kpt: 0.000995 acc_pose: 0.716803 loss: 0.000995 2022/10/13 20:17:29 - mmengine - INFO - Epoch(train) [41][150/293] lr: 5.000000e-04 eta: 4:05:30 time: 0.338501 data_time: 0.171178 memory: 2315 loss_kpt: 0.001014 acc_pose: 0.635680 loss: 0.001014 2022/10/13 20:17:45 - mmengine - INFO - Epoch(train) [41][200/293] lr: 5.000000e-04 eta: 4:05:22 time: 0.330220 data_time: 0.065991 memory: 2315 loss_kpt: 0.001004 acc_pose: 0.641964 loss: 0.001004 2022/10/13 20:18:02 - mmengine - INFO - Epoch(train) [41][250/293] lr: 5.000000e-04 eta: 4:05:14 time: 0.329683 data_time: 0.068142 memory: 2315 loss_kpt: 0.001006 acc_pose: 0.726537 loss: 0.001006 2022/10/13 20:18:12 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:18:16 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:18:33 - mmengine - INFO - Epoch(train) [42][50/293] lr: 5.000000e-04 eta: 4:04:02 time: 0.337871 data_time: 0.127714 memory: 2315 loss_kpt: 0.001025 acc_pose: 0.603581 loss: 0.001025 2022/10/13 20:18:49 - mmengine - INFO - Epoch(train) [42][100/293] lr: 5.000000e-04 eta: 4:03:54 time: 0.327307 data_time: 0.208207 memory: 2315 loss_kpt: 0.000997 acc_pose: 0.733856 loss: 0.000997 2022/10/13 20:19:05 - mmengine - INFO - Epoch(train) [42][150/293] lr: 5.000000e-04 eta: 4:03:46 time: 0.327381 data_time: 0.217147 memory: 2315 loss_kpt: 0.000999 acc_pose: 0.660826 loss: 0.000999 2022/10/13 20:19:22 - mmengine - INFO - Epoch(train) [42][200/293] lr: 5.000000e-04 eta: 4:03:36 time: 0.323808 data_time: 0.174595 memory: 2315 loss_kpt: 0.000990 acc_pose: 0.696342 loss: 0.000990 2022/10/13 20:19:38 - mmengine - INFO - Epoch(train) [42][250/293] lr: 5.000000e-04 eta: 4:03:29 time: 0.335579 data_time: 0.128000 memory: 2315 loss_kpt: 0.000988 acc_pose: 0.660239 loss: 0.000988 2022/10/13 20:19:52 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:20:09 - mmengine - INFO - Epoch(train) [43][50/293] lr: 5.000000e-04 eta: 4:02:20 time: 0.339349 data_time: 0.096328 memory: 2315 loss_kpt: 0.001004 acc_pose: 0.642152 loss: 0.001004 2022/10/13 20:20:28 - mmengine - INFO - Epoch(train) [43][100/293] lr: 5.000000e-04 eta: 4:02:19 time: 0.367652 data_time: 0.073843 memory: 2315 loss_kpt: 0.001007 acc_pose: 0.680138 loss: 0.001007 2022/10/13 20:20:45 - mmengine - INFO - Epoch(train) [43][150/293] lr: 5.000000e-04 eta: 4:02:14 time: 0.344273 data_time: 0.127272 memory: 2315 loss_kpt: 0.001006 acc_pose: 0.693168 loss: 0.001006 2022/10/13 20:21:03 - mmengine - INFO - Epoch(train) [43][200/293] lr: 5.000000e-04 eta: 4:02:10 time: 0.352798 data_time: 0.145057 memory: 2315 loss_kpt: 0.000993 acc_pose: 0.693360 loss: 0.000993 2022/10/13 20:21:20 - mmengine - INFO - Epoch(train) [43][250/293] lr: 5.000000e-04 eta: 4:02:03 time: 0.336824 data_time: 0.065111 memory: 2315 loss_kpt: 0.000999 acc_pose: 0.709200 loss: 0.000999 2022/10/13 20:21:35 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:21:53 - mmengine - INFO - Epoch(train) [44][50/293] lr: 5.000000e-04 eta: 4:00:56 time: 0.348189 data_time: 0.076643 memory: 2315 loss_kpt: 0.001011 acc_pose: 0.617620 loss: 0.001011 2022/10/13 20:22:10 - mmengine - INFO - Epoch(train) [44][100/293] lr: 5.000000e-04 eta: 4:00:53 time: 0.358023 data_time: 0.151789 memory: 2315 loss_kpt: 0.001018 acc_pose: 0.675548 loss: 0.001018 2022/10/13 20:22:28 - mmengine - INFO - Epoch(train) [44][150/293] lr: 5.000000e-04 eta: 4:00:50 time: 0.353432 data_time: 0.191919 memory: 2315 loss_kpt: 0.001002 acc_pose: 0.655697 loss: 0.001002 2022/10/13 20:22:45 - mmengine - INFO - Epoch(train) [44][200/293] lr: 5.000000e-04 eta: 4:00:43 time: 0.337736 data_time: 0.180980 memory: 2315 loss_kpt: 0.001000 acc_pose: 0.696784 loss: 0.001000 2022/10/13 20:23:02 - mmengine - INFO - Epoch(train) [44][250/293] lr: 5.000000e-04 eta: 4:00:37 time: 0.346670 data_time: 0.130973 memory: 2315 loss_kpt: 0.001015 acc_pose: 0.725905 loss: 0.001015 2022/10/13 20:23:16 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:23:33 - mmengine - INFO - Epoch(train) [45][50/293] lr: 5.000000e-04 eta: 3:59:30 time: 0.340399 data_time: 0.108492 memory: 2315 loss_kpt: 0.000995 acc_pose: 0.722021 loss: 0.000995 2022/10/13 20:23:51 - mmengine - INFO - Epoch(train) [45][100/293] lr: 5.000000e-04 eta: 3:59:26 time: 0.353943 data_time: 0.076329 memory: 2315 loss_kpt: 0.001007 acc_pose: 0.696214 loss: 0.001007 2022/10/13 20:23:53 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:24:08 - mmengine - INFO - Epoch(train) [45][150/293] lr: 5.000000e-04 eta: 3:59:19 time: 0.338310 data_time: 0.070493 memory: 2315 loss_kpt: 0.001017 acc_pose: 0.728279 loss: 0.001017 2022/10/13 20:24:24 - mmengine - INFO - Epoch(train) [45][200/293] lr: 5.000000e-04 eta: 3:59:10 time: 0.326644 data_time: 0.062823 memory: 2315 loss_kpt: 0.001011 acc_pose: 0.696555 loss: 0.001011 2022/10/13 20:24:41 - mmengine - INFO - Epoch(train) [45][250/293] lr: 5.000000e-04 eta: 3:59:03 time: 0.335259 data_time: 0.073061 memory: 2315 loss_kpt: 0.001001 acc_pose: 0.657839 loss: 0.001001 2022/10/13 20:24:55 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:25:12 - mmengine - INFO - Epoch(train) [46][50/293] lr: 5.000000e-04 eta: 3:57:56 time: 0.338773 data_time: 0.085387 memory: 2315 loss_kpt: 0.000992 acc_pose: 0.670522 loss: 0.000992 2022/10/13 20:25:28 - mmengine - INFO - Epoch(train) [46][100/293] lr: 5.000000e-04 eta: 3:57:47 time: 0.324658 data_time: 0.064850 memory: 2315 loss_kpt: 0.000989 acc_pose: 0.668995 loss: 0.000989 2022/10/13 20:25:45 - mmengine - INFO - Epoch(train) [46][150/293] lr: 5.000000e-04 eta: 3:57:39 time: 0.337304 data_time: 0.068920 memory: 2315 loss_kpt: 0.001006 acc_pose: 0.703591 loss: 0.001006 2022/10/13 20:26:02 - mmengine - INFO - Epoch(train) [46][200/293] lr: 5.000000e-04 eta: 3:57:31 time: 0.331695 data_time: 0.066399 memory: 2315 loss_kpt: 0.000994 acc_pose: 0.673419 loss: 0.000994 2022/10/13 20:26:19 - mmengine - INFO - Epoch(train) [46][250/293] lr: 5.000000e-04 eta: 3:57:24 time: 0.336886 data_time: 0.073638 memory: 2315 loss_kpt: 0.000986 acc_pose: 0.669636 loss: 0.000986 2022/10/13 20:26:32 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:26:50 - mmengine - INFO - Epoch(train) [47][50/293] lr: 5.000000e-04 eta: 3:56:20 time: 0.348799 data_time: 0.083747 memory: 2315 loss_kpt: 0.001007 acc_pose: 0.624550 loss: 0.001007 2022/10/13 20:27:06 - mmengine - INFO - Epoch(train) [47][100/293] lr: 5.000000e-04 eta: 3:56:11 time: 0.329683 data_time: 0.145881 memory: 2315 loss_kpt: 0.000985 acc_pose: 0.602223 loss: 0.000985 2022/10/13 20:27:23 - mmengine - INFO - Epoch(train) [47][150/293] lr: 5.000000e-04 eta: 3:56:03 time: 0.330400 data_time: 0.132188 memory: 2315 loss_kpt: 0.000991 acc_pose: 0.647220 loss: 0.000991 2022/10/13 20:27:40 - mmengine - INFO - Epoch(train) [47][200/293] lr: 5.000000e-04 eta: 3:55:57 time: 0.344546 data_time: 0.184647 memory: 2315 loss_kpt: 0.001001 acc_pose: 0.703323 loss: 0.001001 2022/10/13 20:27:56 - mmengine - INFO - Epoch(train) [47][250/293] lr: 5.000000e-04 eta: 3:55:46 time: 0.318336 data_time: 0.145954 memory: 2315 loss_kpt: 0.000990 acc_pose: 0.680919 loss: 0.000990 2022/10/13 20:28:09 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:28:27 - mmengine - INFO - Epoch(train) [48][50/293] lr: 5.000000e-04 eta: 3:54:42 time: 0.342344 data_time: 0.086798 memory: 2315 loss_kpt: 0.000998 acc_pose: 0.725218 loss: 0.000998 2022/10/13 20:28:43 - mmengine - INFO - Epoch(train) [48][100/293] lr: 5.000000e-04 eta: 3:54:32 time: 0.324288 data_time: 0.061543 memory: 2315 loss_kpt: 0.000994 acc_pose: 0.665764 loss: 0.000994 2022/10/13 20:28:59 - mmengine - INFO - Epoch(train) [48][150/293] lr: 5.000000e-04 eta: 3:54:24 time: 0.331809 data_time: 0.068772 memory: 2315 loss_kpt: 0.000991 acc_pose: 0.695351 loss: 0.000991 2022/10/13 20:29:16 - mmengine - INFO - Epoch(train) [48][200/293] lr: 5.000000e-04 eta: 3:54:15 time: 0.328875 data_time: 0.069073 memory: 2315 loss_kpt: 0.000982 acc_pose: 0.729584 loss: 0.000982 2022/10/13 20:29:25 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:29:32 - mmengine - INFO - Epoch(train) [48][250/293] lr: 5.000000e-04 eta: 3:54:06 time: 0.331301 data_time: 0.065203 memory: 2315 loss_kpt: 0.000974 acc_pose: 0.596956 loss: 0.000974 2022/10/13 20:29:46 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:30:04 - mmengine - INFO - Epoch(train) [49][50/293] lr: 5.000000e-04 eta: 3:53:05 time: 0.347114 data_time: 0.080414 memory: 2315 loss_kpt: 0.000967 acc_pose: 0.670497 loss: 0.000967 2022/10/13 20:30:20 - mmengine - INFO - Epoch(train) [49][100/293] lr: 5.000000e-04 eta: 3:52:56 time: 0.334553 data_time: 0.177681 memory: 2315 loss_kpt: 0.000993 acc_pose: 0.647237 loss: 0.000993 2022/10/13 20:30:36 - mmengine - INFO - Epoch(train) [49][150/293] lr: 5.000000e-04 eta: 3:52:46 time: 0.319625 data_time: 0.174671 memory: 2315 loss_kpt: 0.000987 acc_pose: 0.678910 loss: 0.000987 2022/10/13 20:30:53 - mmengine - INFO - Epoch(train) [49][200/293] lr: 5.000000e-04 eta: 3:52:37 time: 0.330206 data_time: 0.112690 memory: 2315 loss_kpt: 0.000990 acc_pose: 0.679446 loss: 0.000990 2022/10/13 20:31:09 - mmengine - INFO - Epoch(train) [49][250/293] lr: 5.000000e-04 eta: 3:52:27 time: 0.327434 data_time: 0.070711 memory: 2315 loss_kpt: 0.000987 acc_pose: 0.697835 loss: 0.000987 2022/10/13 20:31:23 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:31:40 - mmengine - INFO - Epoch(train) [50][50/293] lr: 5.000000e-04 eta: 3:51:26 time: 0.343552 data_time: 0.083735 memory: 2315 loss_kpt: 0.001006 acc_pose: 0.667840 loss: 0.001006 2022/10/13 20:31:57 - mmengine - INFO - Epoch(train) [50][100/293] lr: 5.000000e-04 eta: 3:51:18 time: 0.336359 data_time: 0.102561 memory: 2315 loss_kpt: 0.000996 acc_pose: 0.706128 loss: 0.000996 2022/10/13 20:32:14 - mmengine - INFO - Epoch(train) [50][150/293] lr: 5.000000e-04 eta: 3:51:09 time: 0.330839 data_time: 0.166669 memory: 2315 loss_kpt: 0.000999 acc_pose: 0.694968 loss: 0.000999 2022/10/13 20:32:30 - mmengine - INFO - Epoch(train) [50][200/293] lr: 5.000000e-04 eta: 3:51:00 time: 0.325678 data_time: 0.147885 memory: 2315 loss_kpt: 0.000998 acc_pose: 0.718645 loss: 0.000998 2022/10/13 20:32:47 - mmengine - INFO - Epoch(train) [50][250/293] lr: 5.000000e-04 eta: 3:50:51 time: 0.330540 data_time: 0.074547 memory: 2315 loss_kpt: 0.000973 acc_pose: 0.684688 loss: 0.000973 2022/10/13 20:33:01 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:33:01 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/10/13 20:33:08 - mmengine - INFO - Epoch(val) [50][50/407] eta: 0:00:42 time: 0.117909 data_time: 0.071411 memory: 2315 2022/10/13 20:33:14 - mmengine - INFO - Epoch(val) [50][100/407] eta: 0:00:34 time: 0.113552 data_time: 0.070331 memory: 426 2022/10/13 20:33:20 - mmengine - INFO - Epoch(val) [50][150/407] eta: 0:00:28 time: 0.111916 data_time: 0.067858 memory: 426 2022/10/13 20:33:25 - mmengine - INFO - Epoch(val) [50][200/407] eta: 0:00:23 time: 0.113559 data_time: 0.070086 memory: 426 2022/10/13 20:33:31 - mmengine - INFO - Epoch(val) [50][250/407] eta: 0:00:17 time: 0.112990 data_time: 0.070221 memory: 426 2022/10/13 20:33:37 - mmengine - INFO - Epoch(val) [50][300/407] eta: 0:00:11 time: 0.110719 data_time: 0.066454 memory: 426 2022/10/13 20:33:42 - mmengine - INFO - Epoch(val) [50][350/407] eta: 0:00:06 time: 0.115441 data_time: 0.072274 memory: 426 2022/10/13 20:33:48 - mmengine - INFO - Epoch(val) [50][400/407] eta: 0:00:00 time: 0.111205 data_time: 0.067902 memory: 426 2022/10/13 20:34:26 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 20:34:40 - mmengine - INFO - Epoch(val) [50][407/407] coco/AP: 0.548992 coco/AP .5: 0.832261 coco/AP .75: 0.602403 coco/AP (M): 0.518585 coco/AP (L): 0.607323 coco/AR: 0.618734 coco/AR .5: 0.882399 coco/AR .75: 0.676952 coco/AR (M): 0.576345 coco/AR (L): 0.678187 2022/10/13 20:34:40 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_40.pth is removed 2022/10/13 20:34:42 - mmengine - INFO - The best checkpoint with 0.5490 coco/AP at 50 epoch is saved to best_coco/AP_epoch_50.pth. 2022/10/13 20:34:59 - mmengine - INFO - Epoch(train) [51][50/293] lr: 5.000000e-04 eta: 3:49:50 time: 0.342248 data_time: 0.189515 memory: 2315 loss_kpt: 0.000999 acc_pose: 0.717568 loss: 0.000999 2022/10/13 20:35:15 - mmengine - INFO - Epoch(train) [51][100/293] lr: 5.000000e-04 eta: 3:49:40 time: 0.326045 data_time: 0.167984 memory: 2315 loss_kpt: 0.001004 acc_pose: 0.687163 loss: 0.001004 2022/10/13 20:35:31 - mmengine - INFO - Epoch(train) [51][150/293] lr: 5.000000e-04 eta: 3:49:31 time: 0.325631 data_time: 0.184958 memory: 2315 loss_kpt: 0.000985 acc_pose: 0.715541 loss: 0.000985 2022/10/13 20:35:48 - mmengine - INFO - Epoch(train) [51][200/293] lr: 5.000000e-04 eta: 3:49:22 time: 0.331384 data_time: 0.140649 memory: 2315 loss_kpt: 0.000993 acc_pose: 0.658987 loss: 0.000993 2022/10/13 20:36:04 - mmengine - INFO - Epoch(train) [51][250/293] lr: 5.000000e-04 eta: 3:49:13 time: 0.330822 data_time: 0.181222 memory: 2315 loss_kpt: 0.000988 acc_pose: 0.743296 loss: 0.000988 2022/10/13 20:36:18 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:36:35 - mmengine - INFO - Epoch(train) [52][50/293] lr: 5.000000e-04 eta: 3:48:13 time: 0.339768 data_time: 0.102751 memory: 2315 loss_kpt: 0.000990 acc_pose: 0.613962 loss: 0.000990 2022/10/13 20:36:37 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:36:51 - mmengine - INFO - Epoch(train) [52][100/293] lr: 5.000000e-04 eta: 3:48:02 time: 0.319420 data_time: 0.123458 memory: 2315 loss_kpt: 0.001000 acc_pose: 0.671291 loss: 0.001000 2022/10/13 20:37:07 - mmengine - INFO - Epoch(train) [52][150/293] lr: 5.000000e-04 eta: 3:47:52 time: 0.326142 data_time: 0.068429 memory: 2315 loss_kpt: 0.000998 acc_pose: 0.591259 loss: 0.000998 2022/10/13 20:37:24 - mmengine - INFO - Epoch(train) [52][200/293] lr: 5.000000e-04 eta: 3:47:43 time: 0.331385 data_time: 0.095437 memory: 2315 loss_kpt: 0.000992 acc_pose: 0.625598 loss: 0.000992 2022/10/13 20:37:40 - mmengine - INFO - Epoch(train) [52][250/293] lr: 5.000000e-04 eta: 3:47:33 time: 0.327380 data_time: 0.118119 memory: 2315 loss_kpt: 0.000984 acc_pose: 0.698325 loss: 0.000984 2022/10/13 20:37:54 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:38:11 - mmengine - INFO - Epoch(train) [53][50/293] lr: 5.000000e-04 eta: 3:46:34 time: 0.338567 data_time: 0.156615 memory: 2315 loss_kpt: 0.000981 acc_pose: 0.670402 loss: 0.000981 2022/10/13 20:38:28 - mmengine - INFO - Epoch(train) [53][100/293] lr: 5.000000e-04 eta: 3:46:26 time: 0.337371 data_time: 0.067523 memory: 2315 loss_kpt: 0.000990 acc_pose: 0.676882 loss: 0.000990 2022/10/13 20:38:45 - mmengine - INFO - Epoch(train) [53][150/293] lr: 5.000000e-04 eta: 3:46:18 time: 0.340604 data_time: 0.065498 memory: 2315 loss_kpt: 0.000988 acc_pose: 0.641118 loss: 0.000988 2022/10/13 20:39:01 - mmengine - INFO - Epoch(train) [53][200/293] lr: 5.000000e-04 eta: 3:46:08 time: 0.327287 data_time: 0.081626 memory: 2315 loss_kpt: 0.000976 acc_pose: 0.668766 loss: 0.000976 2022/10/13 20:39:17 - mmengine - INFO - Epoch(train) [53][250/293] lr: 5.000000e-04 eta: 3:45:59 time: 0.327505 data_time: 0.113319 memory: 2315 loss_kpt: 0.000976 acc_pose: 0.684100 loss: 0.000976 2022/10/13 20:39:31 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:39:48 - mmengine - INFO - Epoch(train) [54][50/293] lr: 5.000000e-04 eta: 3:45:00 time: 0.337461 data_time: 0.089281 memory: 2315 loss_kpt: 0.000983 acc_pose: 0.764704 loss: 0.000983 2022/10/13 20:40:05 - mmengine - INFO - Epoch(train) [54][100/293] lr: 5.000000e-04 eta: 3:44:52 time: 0.338222 data_time: 0.071953 memory: 2315 loss_kpt: 0.000991 acc_pose: 0.698151 loss: 0.000991 2022/10/13 20:40:22 - mmengine - INFO - Epoch(train) [54][150/293] lr: 5.000000e-04 eta: 3:44:43 time: 0.331440 data_time: 0.069740 memory: 2315 loss_kpt: 0.000975 acc_pose: 0.713205 loss: 0.000975 2022/10/13 20:40:38 - mmengine - INFO - Epoch(train) [54][200/293] lr: 5.000000e-04 eta: 3:44:33 time: 0.326318 data_time: 0.085382 memory: 2315 loss_kpt: 0.000992 acc_pose: 0.631811 loss: 0.000992 2022/10/13 20:40:55 - mmengine - INFO - Epoch(train) [54][250/293] lr: 5.000000e-04 eta: 3:44:25 time: 0.340877 data_time: 0.170814 memory: 2315 loss_kpt: 0.000993 acc_pose: 0.681526 loss: 0.000993 2022/10/13 20:41:09 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:41:26 - mmengine - INFO - Epoch(train) [55][50/293] lr: 5.000000e-04 eta: 3:43:27 time: 0.336743 data_time: 0.109117 memory: 2315 loss_kpt: 0.000988 acc_pose: 0.718981 loss: 0.000988 2022/10/13 20:41:42 - mmengine - INFO - Epoch(train) [55][100/293] lr: 5.000000e-04 eta: 3:43:17 time: 0.325443 data_time: 0.065352 memory: 2315 loss_kpt: 0.000988 acc_pose: 0.728113 loss: 0.000988 2022/10/13 20:41:58 - mmengine - INFO - Epoch(train) [55][150/293] lr: 5.000000e-04 eta: 3:43:07 time: 0.326756 data_time: 0.077279 memory: 2315 loss_kpt: 0.000993 acc_pose: 0.664084 loss: 0.000993 2022/10/13 20:42:07 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:42:15 - mmengine - INFO - Epoch(train) [55][200/293] lr: 5.000000e-04 eta: 3:42:57 time: 0.326039 data_time: 0.072270 memory: 2315 loss_kpt: 0.000981 acc_pose: 0.739142 loss: 0.000981 2022/10/13 20:42:31 - mmengine - INFO - Epoch(train) [55][250/293] lr: 5.000000e-04 eta: 3:42:48 time: 0.332219 data_time: 0.065531 memory: 2315 loss_kpt: 0.000982 acc_pose: 0.712554 loss: 0.000982 2022/10/13 20:42:45 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:43:02 - mmengine - INFO - Epoch(train) [56][50/293] lr: 5.000000e-04 eta: 3:41:51 time: 0.338075 data_time: 0.108833 memory: 2315 loss_kpt: 0.000991 acc_pose: 0.717177 loss: 0.000991 2022/10/13 20:43:19 - mmengine - INFO - Epoch(train) [56][100/293] lr: 5.000000e-04 eta: 3:41:42 time: 0.336042 data_time: 0.072503 memory: 2315 loss_kpt: 0.000988 acc_pose: 0.684703 loss: 0.000988 2022/10/13 20:43:35 - mmengine - INFO - Epoch(train) [56][150/293] lr: 5.000000e-04 eta: 3:41:32 time: 0.327760 data_time: 0.066309 memory: 2315 loss_kpt: 0.000983 acc_pose: 0.720497 loss: 0.000983 2022/10/13 20:43:52 - mmengine - INFO - Epoch(train) [56][200/293] lr: 5.000000e-04 eta: 3:41:22 time: 0.323676 data_time: 0.064144 memory: 2315 loss_kpt: 0.000986 acc_pose: 0.724221 loss: 0.000986 2022/10/13 20:44:08 - mmengine - INFO - Epoch(train) [56][250/293] lr: 5.000000e-04 eta: 3:41:12 time: 0.325383 data_time: 0.068446 memory: 2315 loss_kpt: 0.000993 acc_pose: 0.695331 loss: 0.000993 2022/10/13 20:44:22 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:44:39 - mmengine - INFO - Epoch(train) [57][50/293] lr: 5.000000e-04 eta: 3:40:17 time: 0.344360 data_time: 0.087223 memory: 2315 loss_kpt: 0.000996 acc_pose: 0.654319 loss: 0.000996 2022/10/13 20:44:55 - mmengine - INFO - Epoch(train) [57][100/293] lr: 5.000000e-04 eta: 3:40:06 time: 0.324463 data_time: 0.147410 memory: 2315 loss_kpt: 0.000973 acc_pose: 0.684639 loss: 0.000973 2022/10/13 20:45:12 - mmengine - INFO - Epoch(train) [57][150/293] lr: 5.000000e-04 eta: 3:39:56 time: 0.324106 data_time: 0.103105 memory: 2315 loss_kpt: 0.000992 acc_pose: 0.644850 loss: 0.000992 2022/10/13 20:45:28 - mmengine - INFO - Epoch(train) [57][200/293] lr: 5.000000e-04 eta: 3:39:45 time: 0.324891 data_time: 0.144276 memory: 2315 loss_kpt: 0.000987 acc_pose: 0.678452 loss: 0.000987 2022/10/13 20:45:44 - mmengine - INFO - Epoch(train) [57][250/293] lr: 5.000000e-04 eta: 3:39:35 time: 0.329704 data_time: 0.074026 memory: 2315 loss_kpt: 0.000980 acc_pose: 0.626913 loss: 0.000980 2022/10/13 20:45:58 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:46:16 - mmengine - INFO - Epoch(train) [58][50/293] lr: 5.000000e-04 eta: 3:38:42 time: 0.349300 data_time: 0.091235 memory: 2315 loss_kpt: 0.000986 acc_pose: 0.632975 loss: 0.000986 2022/10/13 20:46:32 - mmengine - INFO - Epoch(train) [58][100/293] lr: 5.000000e-04 eta: 3:38:32 time: 0.330046 data_time: 0.064547 memory: 2315 loss_kpt: 0.000992 acc_pose: 0.620748 loss: 0.000992 2022/10/13 20:46:48 - mmengine - INFO - Epoch(train) [58][150/293] lr: 5.000000e-04 eta: 3:38:21 time: 0.322272 data_time: 0.069334 memory: 2315 loss_kpt: 0.000972 acc_pose: 0.691025 loss: 0.000972 2022/10/13 20:47:05 - mmengine - INFO - Epoch(train) [58][200/293] lr: 5.000000e-04 eta: 3:38:12 time: 0.332412 data_time: 0.066101 memory: 2315 loss_kpt: 0.000993 acc_pose: 0.679410 loss: 0.000993 2022/10/13 20:47:21 - mmengine - INFO - Epoch(train) [58][250/293] lr: 5.000000e-04 eta: 3:38:02 time: 0.332065 data_time: 0.064224 memory: 2315 loss_kpt: 0.000986 acc_pose: 0.679726 loss: 0.000986 2022/10/13 20:47:35 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:47:38 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:47:52 - mmengine - INFO - Epoch(train) [59][50/293] lr: 5.000000e-04 eta: 3:37:08 time: 0.336225 data_time: 0.125387 memory: 2315 loss_kpt: 0.000982 acc_pose: 0.704859 loss: 0.000982 2022/10/13 20:48:09 - mmengine - INFO - Epoch(train) [59][100/293] lr: 5.000000e-04 eta: 3:36:58 time: 0.333792 data_time: 0.063619 memory: 2315 loss_kpt: 0.000988 acc_pose: 0.706829 loss: 0.000988 2022/10/13 20:48:26 - mmengine - INFO - Epoch(train) [59][150/293] lr: 5.000000e-04 eta: 3:36:50 time: 0.341242 data_time: 0.072991 memory: 2315 loss_kpt: 0.000989 acc_pose: 0.714714 loss: 0.000989 2022/10/13 20:48:42 - mmengine - INFO - Epoch(train) [59][200/293] lr: 5.000000e-04 eta: 3:36:40 time: 0.332255 data_time: 0.067312 memory: 2315 loss_kpt: 0.000984 acc_pose: 0.716141 loss: 0.000984 2022/10/13 20:48:59 - mmengine - INFO - Epoch(train) [59][250/293] lr: 5.000000e-04 eta: 3:36:31 time: 0.333680 data_time: 0.061853 memory: 2315 loss_kpt: 0.000994 acc_pose: 0.750941 loss: 0.000994 2022/10/13 20:49:13 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:49:30 - mmengine - INFO - Epoch(train) [60][50/293] lr: 5.000000e-04 eta: 3:35:37 time: 0.340510 data_time: 0.159868 memory: 2315 loss_kpt: 0.000967 acc_pose: 0.700466 loss: 0.000967 2022/10/13 20:49:46 - mmengine - INFO - Epoch(train) [60][100/293] lr: 5.000000e-04 eta: 3:35:28 time: 0.330446 data_time: 0.179514 memory: 2315 loss_kpt: 0.000985 acc_pose: 0.641462 loss: 0.000985 2022/10/13 20:50:02 - mmengine - INFO - Epoch(train) [60][150/293] lr: 5.000000e-04 eta: 3:35:16 time: 0.319655 data_time: 0.136948 memory: 2315 loss_kpt: 0.000982 acc_pose: 0.669213 loss: 0.000982 2022/10/13 20:50:19 - mmengine - INFO - Epoch(train) [60][200/293] lr: 5.000000e-04 eta: 3:35:07 time: 0.333576 data_time: 0.103670 memory: 2315 loss_kpt: 0.000980 acc_pose: 0.632212 loss: 0.000980 2022/10/13 20:50:36 - mmengine - INFO - Epoch(train) [60][250/293] lr: 5.000000e-04 eta: 3:34:57 time: 0.332900 data_time: 0.173602 memory: 2315 loss_kpt: 0.000995 acc_pose: 0.674207 loss: 0.000995 2022/10/13 20:50:50 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:50:50 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/10/13 20:50:58 - mmengine - INFO - Epoch(val) [60][50/407] eta: 0:00:41 time: 0.116466 data_time: 0.072414 memory: 2315 2022/10/13 20:51:04 - mmengine - INFO - Epoch(val) [60][100/407] eta: 0:00:35 time: 0.115989 data_time: 0.071520 memory: 426 2022/10/13 20:51:09 - mmengine - INFO - Epoch(val) [60][150/407] eta: 0:00:29 time: 0.113441 data_time: 0.069543 memory: 426 2022/10/13 20:51:15 - mmengine - INFO - Epoch(val) [60][200/407] eta: 0:00:22 time: 0.109125 data_time: 0.065410 memory: 426 2022/10/13 20:51:20 - mmengine - INFO - Epoch(val) [60][250/407] eta: 0:00:17 time: 0.112714 data_time: 0.069888 memory: 426 2022/10/13 20:51:26 - mmengine - INFO - Epoch(val) [60][300/407] eta: 0:00:12 time: 0.116990 data_time: 0.072772 memory: 426 2022/10/13 20:51:32 - mmengine - INFO - Epoch(val) [60][350/407] eta: 0:00:06 time: 0.117097 data_time: 0.073113 memory: 426 2022/10/13 20:51:37 - mmengine - INFO - Epoch(val) [60][400/407] eta: 0:00:00 time: 0.105616 data_time: 0.065246 memory: 426 2022/10/13 20:52:16 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 20:52:30 - mmengine - INFO - Epoch(val) [60][407/407] coco/AP: 0.556806 coco/AP .5: 0.838077 coco/AP .75: 0.609545 coco/AP (M): 0.523732 coco/AP (L): 0.616683 coco/AR: 0.624480 coco/AR .5: 0.886492 coco/AR .75: 0.680888 coco/AR (M): 0.581043 coco/AR (L): 0.685173 2022/10/13 20:52:30 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_50.pth is removed 2022/10/13 20:52:32 - mmengine - INFO - The best checkpoint with 0.5568 coco/AP at 60 epoch is saved to best_coco/AP_epoch_60.pth. 2022/10/13 20:52:48 - mmengine - INFO - Epoch(train) [61][50/293] lr: 5.000000e-04 eta: 3:34:03 time: 0.330813 data_time: 0.201820 memory: 2315 loss_kpt: 0.000983 acc_pose: 0.660142 loss: 0.000983 2022/10/13 20:53:05 - mmengine - INFO - Epoch(train) [61][100/293] lr: 5.000000e-04 eta: 3:33:53 time: 0.325622 data_time: 0.118852 memory: 2315 loss_kpt: 0.000964 acc_pose: 0.742707 loss: 0.000964 2022/10/13 20:53:21 - mmengine - INFO - Epoch(train) [61][150/293] lr: 5.000000e-04 eta: 3:33:42 time: 0.325390 data_time: 0.110656 memory: 2315 loss_kpt: 0.000987 acc_pose: 0.700101 loss: 0.000987 2022/10/13 20:53:37 - mmengine - INFO - Epoch(train) [61][200/293] lr: 5.000000e-04 eta: 3:33:32 time: 0.332323 data_time: 0.138778 memory: 2315 loss_kpt: 0.000977 acc_pose: 0.691530 loss: 0.000977 2022/10/13 20:53:54 - mmengine - INFO - Epoch(train) [61][250/293] lr: 5.000000e-04 eta: 3:33:22 time: 0.331557 data_time: 0.171756 memory: 2315 loss_kpt: 0.000968 acc_pose: 0.674209 loss: 0.000968 2022/10/13 20:54:08 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:54:25 - mmengine - INFO - Epoch(train) [62][50/293] lr: 5.000000e-04 eta: 3:32:31 time: 0.346678 data_time: 0.092029 memory: 2315 loss_kpt: 0.000967 acc_pose: 0.700554 loss: 0.000967 2022/10/13 20:54:42 - mmengine - INFO - Epoch(train) [62][100/293] lr: 5.000000e-04 eta: 3:32:22 time: 0.335785 data_time: 0.066893 memory: 2315 loss_kpt: 0.000985 acc_pose: 0.640358 loss: 0.000985 2022/10/13 20:54:51 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:54:58 - mmengine - INFO - Epoch(train) [62][150/293] lr: 5.000000e-04 eta: 3:32:12 time: 0.331676 data_time: 0.062733 memory: 2315 loss_kpt: 0.000978 acc_pose: 0.653144 loss: 0.000978 2022/10/13 20:55:15 - mmengine - INFO - Epoch(train) [62][200/293] lr: 5.000000e-04 eta: 3:32:01 time: 0.327741 data_time: 0.063655 memory: 2315 loss_kpt: 0.000975 acc_pose: 0.748531 loss: 0.000975 2022/10/13 20:55:32 - mmengine - INFO - Epoch(train) [62][250/293] lr: 5.000000e-04 eta: 3:31:53 time: 0.344750 data_time: 0.067556 memory: 2315 loss_kpt: 0.000974 acc_pose: 0.669590 loss: 0.000974 2022/10/13 20:55:46 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:56:03 - mmengine - INFO - Epoch(train) [63][50/293] lr: 5.000000e-04 eta: 3:31:01 time: 0.336459 data_time: 0.119309 memory: 2315 loss_kpt: 0.000968 acc_pose: 0.681627 loss: 0.000968 2022/10/13 20:56:20 - mmengine - INFO - Epoch(train) [63][100/293] lr: 5.000000e-04 eta: 3:30:52 time: 0.338738 data_time: 0.072760 memory: 2315 loss_kpt: 0.000973 acc_pose: 0.668236 loss: 0.000973 2022/10/13 20:56:38 - mmengine - INFO - Epoch(train) [63][150/293] lr: 5.000000e-04 eta: 3:30:44 time: 0.354508 data_time: 0.072839 memory: 2315 loss_kpt: 0.000987 acc_pose: 0.687975 loss: 0.000987 2022/10/13 20:56:54 - mmengine - INFO - Epoch(train) [63][200/293] lr: 5.000000e-04 eta: 3:30:34 time: 0.325877 data_time: 0.071162 memory: 2315 loss_kpt: 0.000965 acc_pose: 0.747153 loss: 0.000965 2022/10/13 20:57:10 - mmengine - INFO - Epoch(train) [63][250/293] lr: 5.000000e-04 eta: 3:30:23 time: 0.328068 data_time: 0.067788 memory: 2315 loss_kpt: 0.000960 acc_pose: 0.694122 loss: 0.000960 2022/10/13 20:57:24 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:57:41 - mmengine - INFO - Epoch(train) [64][50/293] lr: 5.000000e-04 eta: 3:29:31 time: 0.333295 data_time: 0.155548 memory: 2315 loss_kpt: 0.000961 acc_pose: 0.651557 loss: 0.000961 2022/10/13 20:57:58 - mmengine - INFO - Epoch(train) [64][100/293] lr: 5.000000e-04 eta: 3:29:21 time: 0.330457 data_time: 0.060012 memory: 2315 loss_kpt: 0.000971 acc_pose: 0.712220 loss: 0.000971 2022/10/13 20:58:15 - mmengine - INFO - Epoch(train) [64][150/293] lr: 5.000000e-04 eta: 3:29:12 time: 0.338179 data_time: 0.093285 memory: 2315 loss_kpt: 0.000980 acc_pose: 0.678283 loss: 0.000980 2022/10/13 20:58:32 - mmengine - INFO - Epoch(train) [64][200/293] lr: 5.000000e-04 eta: 3:29:03 time: 0.339954 data_time: 0.171373 memory: 2315 loss_kpt: 0.000966 acc_pose: 0.711154 loss: 0.000966 2022/10/13 20:58:48 - mmengine - INFO - Epoch(train) [64][250/293] lr: 5.000000e-04 eta: 3:28:52 time: 0.326817 data_time: 0.164478 memory: 2315 loss_kpt: 0.000964 acc_pose: 0.706147 loss: 0.000964 2022/10/13 20:59:02 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 20:59:19 - mmengine - INFO - Epoch(train) [65][50/293] lr: 5.000000e-04 eta: 3:28:02 time: 0.347152 data_time: 0.142016 memory: 2315 loss_kpt: 0.000975 acc_pose: 0.694327 loss: 0.000975 2022/10/13 20:59:36 - mmengine - INFO - Epoch(train) [65][100/293] lr: 5.000000e-04 eta: 3:27:52 time: 0.328848 data_time: 0.142225 memory: 2315 loss_kpt: 0.000962 acc_pose: 0.688676 loss: 0.000962 2022/10/13 20:59:53 - mmengine - INFO - Epoch(train) [65][150/293] lr: 5.000000e-04 eta: 3:27:43 time: 0.339102 data_time: 0.178619 memory: 2315 loss_kpt: 0.000956 acc_pose: 0.708963 loss: 0.000956 2022/10/13 21:00:10 - mmengine - INFO - Epoch(train) [65][200/293] lr: 5.000000e-04 eta: 3:27:34 time: 0.346942 data_time: 0.190842 memory: 2315 loss_kpt: 0.000961 acc_pose: 0.677881 loss: 0.000961 2022/10/13 21:00:26 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:00:26 - mmengine - INFO - Epoch(train) [65][250/293] lr: 5.000000e-04 eta: 3:27:23 time: 0.327420 data_time: 0.168998 memory: 2315 loss_kpt: 0.000982 acc_pose: 0.699863 loss: 0.000982 2022/10/13 21:00:40 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:00:57 - mmengine - INFO - Epoch(train) [66][50/293] lr: 5.000000e-04 eta: 3:26:33 time: 0.334211 data_time: 0.093162 memory: 2315 loss_kpt: 0.000952 acc_pose: 0.668998 loss: 0.000952 2022/10/13 21:01:14 - mmengine - INFO - Epoch(train) [66][100/293] lr: 5.000000e-04 eta: 3:26:23 time: 0.336608 data_time: 0.099486 memory: 2315 loss_kpt: 0.000965 acc_pose: 0.711380 loss: 0.000965 2022/10/13 21:01:30 - mmengine - INFO - Epoch(train) [66][150/293] lr: 5.000000e-04 eta: 3:26:12 time: 0.325671 data_time: 0.150750 memory: 2315 loss_kpt: 0.000976 acc_pose: 0.716087 loss: 0.000976 2022/10/13 21:01:47 - mmengine - INFO - Epoch(train) [66][200/293] lr: 5.000000e-04 eta: 3:26:02 time: 0.332842 data_time: 0.120417 memory: 2315 loss_kpt: 0.000972 acc_pose: 0.640085 loss: 0.000972 2022/10/13 21:02:03 - mmengine - INFO - Epoch(train) [66][250/293] lr: 5.000000e-04 eta: 3:25:52 time: 0.333946 data_time: 0.076236 memory: 2315 loss_kpt: 0.000980 acc_pose: 0.751249 loss: 0.000980 2022/10/13 21:02:17 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:02:34 - mmengine - INFO - Epoch(train) [67][50/293] lr: 5.000000e-04 eta: 3:25:02 time: 0.336650 data_time: 0.155667 memory: 2315 loss_kpt: 0.000973 acc_pose: 0.677445 loss: 0.000973 2022/10/13 21:02:50 - mmengine - INFO - Epoch(train) [67][100/293] lr: 5.000000e-04 eta: 3:24:52 time: 0.335135 data_time: 0.071048 memory: 2315 loss_kpt: 0.000972 acc_pose: 0.628556 loss: 0.000972 2022/10/13 21:03:07 - mmengine - INFO - Epoch(train) [67][150/293] lr: 5.000000e-04 eta: 3:24:42 time: 0.328882 data_time: 0.097928 memory: 2315 loss_kpt: 0.000982 acc_pose: 0.712858 loss: 0.000982 2022/10/13 21:03:24 - mmengine - INFO - Epoch(train) [67][200/293] lr: 5.000000e-04 eta: 3:24:31 time: 0.331411 data_time: 0.087481 memory: 2315 loss_kpt: 0.000972 acc_pose: 0.647026 loss: 0.000972 2022/10/13 21:03:40 - mmengine - INFO - Epoch(train) [67][250/293] lr: 5.000000e-04 eta: 3:24:20 time: 0.326501 data_time: 0.079503 memory: 2315 loss_kpt: 0.000964 acc_pose: 0.669514 loss: 0.000964 2022/10/13 21:03:54 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:04:11 - mmengine - INFO - Epoch(train) [68][50/293] lr: 5.000000e-04 eta: 3:23:31 time: 0.335612 data_time: 0.113276 memory: 2315 loss_kpt: 0.000968 acc_pose: 0.695924 loss: 0.000968 2022/10/13 21:04:27 - mmengine - INFO - Epoch(train) [68][100/293] lr: 5.000000e-04 eta: 3:23:21 time: 0.337090 data_time: 0.182665 memory: 2315 loss_kpt: 0.000975 acc_pose: 0.720412 loss: 0.000975 2022/10/13 21:04:44 - mmengine - INFO - Epoch(train) [68][150/293] lr: 5.000000e-04 eta: 3:23:10 time: 0.327404 data_time: 0.116705 memory: 2315 loss_kpt: 0.000969 acc_pose: 0.688940 loss: 0.000969 2022/10/13 21:05:00 - mmengine - INFO - Epoch(train) [68][200/293] lr: 5.000000e-04 eta: 3:22:59 time: 0.325998 data_time: 0.123096 memory: 2315 loss_kpt: 0.000964 acc_pose: 0.686330 loss: 0.000964 2022/10/13 21:05:17 - mmengine - INFO - Epoch(train) [68][250/293] lr: 5.000000e-04 eta: 3:22:49 time: 0.328377 data_time: 0.127407 memory: 2315 loss_kpt: 0.000954 acc_pose: 0.642002 loss: 0.000954 2022/10/13 21:05:31 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:05:48 - mmengine - INFO - Epoch(train) [69][50/293] lr: 5.000000e-04 eta: 3:22:01 time: 0.350146 data_time: 0.090560 memory: 2315 loss_kpt: 0.000959 acc_pose: 0.637740 loss: 0.000959 2022/10/13 21:05:57 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:06:04 - mmengine - INFO - Epoch(train) [69][100/293] lr: 5.000000e-04 eta: 3:21:50 time: 0.323902 data_time: 0.070389 memory: 2315 loss_kpt: 0.000973 acc_pose: 0.638153 loss: 0.000973 2022/10/13 21:06:21 - mmengine - INFO - Epoch(train) [69][150/293] lr: 5.000000e-04 eta: 3:21:39 time: 0.327617 data_time: 0.061875 memory: 2315 loss_kpt: 0.000977 acc_pose: 0.680352 loss: 0.000977 2022/10/13 21:06:37 - mmengine - INFO - Epoch(train) [69][200/293] lr: 5.000000e-04 eta: 3:21:29 time: 0.331653 data_time: 0.110225 memory: 2315 loss_kpt: 0.000963 acc_pose: 0.717186 loss: 0.000963 2022/10/13 21:06:53 - mmengine - INFO - Epoch(train) [69][250/293] lr: 5.000000e-04 eta: 3:21:17 time: 0.322351 data_time: 0.129414 memory: 2315 loss_kpt: 0.000974 acc_pose: 0.719943 loss: 0.000974 2022/10/13 21:07:07 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:07:25 - mmengine - INFO - Epoch(train) [70][50/293] lr: 5.000000e-04 eta: 3:20:30 time: 0.344923 data_time: 0.087209 memory: 2315 loss_kpt: 0.000962 acc_pose: 0.696336 loss: 0.000962 2022/10/13 21:07:41 - mmengine - INFO - Epoch(train) [70][100/293] lr: 5.000000e-04 eta: 3:20:19 time: 0.332071 data_time: 0.072331 memory: 2315 loss_kpt: 0.000951 acc_pose: 0.685887 loss: 0.000951 2022/10/13 21:07:58 - mmengine - INFO - Epoch(train) [70][150/293] lr: 5.000000e-04 eta: 3:20:09 time: 0.327737 data_time: 0.066949 memory: 2315 loss_kpt: 0.000974 acc_pose: 0.670479 loss: 0.000974 2022/10/13 21:08:14 - mmengine - INFO - Epoch(train) [70][200/293] lr: 5.000000e-04 eta: 3:19:58 time: 0.329153 data_time: 0.092400 memory: 2315 loss_kpt: 0.000970 acc_pose: 0.686743 loss: 0.000970 2022/10/13 21:08:30 - mmengine - INFO - Epoch(train) [70][250/293] lr: 5.000000e-04 eta: 3:19:47 time: 0.327459 data_time: 0.140449 memory: 2315 loss_kpt: 0.000990 acc_pose: 0.714784 loss: 0.000990 2022/10/13 21:08:44 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:08:44 - mmengine - INFO - Saving checkpoint at 70 epochs 2022/10/13 21:08:52 - mmengine - INFO - Epoch(val) [70][50/407] eta: 0:00:43 time: 0.122695 data_time: 0.079209 memory: 2315 2022/10/13 21:08:58 - mmengine - INFO - Epoch(val) [70][100/407] eta: 0:00:34 time: 0.112093 data_time: 0.069132 memory: 426 2022/10/13 21:09:04 - mmengine - INFO - Epoch(val) [70][150/407] eta: 0:00:29 time: 0.113267 data_time: 0.069427 memory: 426 2022/10/13 21:09:09 - mmengine - INFO - Epoch(val) [70][200/407] eta: 0:00:24 time: 0.115982 data_time: 0.071826 memory: 426 2022/10/13 21:09:15 - mmengine - INFO - Epoch(val) [70][250/407] eta: 0:00:16 time: 0.107142 data_time: 0.064521 memory: 426 2022/10/13 21:09:20 - mmengine - INFO - Epoch(val) [70][300/407] eta: 0:00:11 time: 0.107911 data_time: 0.064742 memory: 426 2022/10/13 21:09:26 - mmengine - INFO - Epoch(val) [70][350/407] eta: 0:00:06 time: 0.116943 data_time: 0.072619 memory: 426 2022/10/13 21:09:32 - mmengine - INFO - Epoch(val) [70][400/407] eta: 0:00:00 time: 0.112617 data_time: 0.071112 memory: 426 2022/10/13 21:10:09 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 21:10:24 - mmengine - INFO - Epoch(val) [70][407/407] coco/AP: 0.559580 coco/AP .5: 0.838226 coco/AP .75: 0.619726 coco/AP (M): 0.529312 coco/AP (L): 0.618048 coco/AR: 0.628715 coco/AR .5: 0.887437 coco/AR .75: 0.691278 coco/AR (M): 0.586534 coco/AR (L): 0.687700 2022/10/13 21:10:24 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_60.pth is removed 2022/10/13 21:10:25 - mmengine - INFO - The best checkpoint with 0.5596 coco/AP at 70 epoch is saved to best_coco/AP_epoch_70.pth. 2022/10/13 21:10:43 - mmengine - INFO - Epoch(train) [71][50/293] lr: 5.000000e-04 eta: 3:19:01 time: 0.353787 data_time: 0.206095 memory: 2315 loss_kpt: 0.000968 acc_pose: 0.728615 loss: 0.000968 2022/10/13 21:11:00 - mmengine - INFO - Epoch(train) [71][100/293] lr: 5.000000e-04 eta: 3:18:50 time: 0.330627 data_time: 0.169409 memory: 2315 loss_kpt: 0.000960 acc_pose: 0.643281 loss: 0.000960 2022/10/13 21:11:17 - mmengine - INFO - Epoch(train) [71][150/293] lr: 5.000000e-04 eta: 3:18:40 time: 0.342902 data_time: 0.173986 memory: 2315 loss_kpt: 0.000974 acc_pose: 0.682813 loss: 0.000974 2022/10/13 21:11:33 - mmengine - INFO - Epoch(train) [71][200/293] lr: 5.000000e-04 eta: 3:18:29 time: 0.325756 data_time: 0.089130 memory: 2315 loss_kpt: 0.000950 acc_pose: 0.695271 loss: 0.000950 2022/10/13 21:11:50 - mmengine - INFO - Epoch(train) [71][250/293] lr: 5.000000e-04 eta: 3:18:20 time: 0.343194 data_time: 0.118828 memory: 2315 loss_kpt: 0.000959 acc_pose: 0.696654 loss: 0.000959 2022/10/13 21:12:05 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:12:22 - mmengine - INFO - Epoch(train) [72][50/293] lr: 5.000000e-04 eta: 3:17:33 time: 0.343602 data_time: 0.089267 memory: 2315 loss_kpt: 0.000955 acc_pose: 0.621860 loss: 0.000955 2022/10/13 21:12:38 - mmengine - INFO - Epoch(train) [72][100/293] lr: 5.000000e-04 eta: 3:17:22 time: 0.329553 data_time: 0.066314 memory: 2315 loss_kpt: 0.000979 acc_pose: 0.662864 loss: 0.000979 2022/10/13 21:12:55 - mmengine - INFO - Epoch(train) [72][150/293] lr: 5.000000e-04 eta: 3:17:11 time: 0.330140 data_time: 0.061204 memory: 2315 loss_kpt: 0.000948 acc_pose: 0.769864 loss: 0.000948 2022/10/13 21:13:10 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:13:11 - mmengine - INFO - Epoch(train) [72][200/293] lr: 5.000000e-04 eta: 3:17:01 time: 0.334262 data_time: 0.066424 memory: 2315 loss_kpt: 0.000962 acc_pose: 0.759020 loss: 0.000962 2022/10/13 21:13:28 - mmengine - INFO - Epoch(train) [72][250/293] lr: 5.000000e-04 eta: 3:16:50 time: 0.330698 data_time: 0.062148 memory: 2315 loss_kpt: 0.000967 acc_pose: 0.692101 loss: 0.000967 2022/10/13 21:13:43 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:14:00 - mmengine - INFO - Epoch(train) [73][50/293] lr: 5.000000e-04 eta: 3:16:04 time: 0.338686 data_time: 0.113283 memory: 2315 loss_kpt: 0.000961 acc_pose: 0.701556 loss: 0.000961 2022/10/13 21:14:17 - mmengine - INFO - Epoch(train) [73][100/293] lr: 5.000000e-04 eta: 3:15:54 time: 0.343707 data_time: 0.070598 memory: 2315 loss_kpt: 0.000954 acc_pose: 0.691268 loss: 0.000954 2022/10/13 21:14:33 - mmengine - INFO - Epoch(train) [73][150/293] lr: 5.000000e-04 eta: 3:15:43 time: 0.330265 data_time: 0.068503 memory: 2315 loss_kpt: 0.000965 acc_pose: 0.619225 loss: 0.000965 2022/10/13 21:14:50 - mmengine - INFO - Epoch(train) [73][200/293] lr: 5.000000e-04 eta: 3:15:32 time: 0.332539 data_time: 0.094245 memory: 2315 loss_kpt: 0.000958 acc_pose: 0.718790 loss: 0.000958 2022/10/13 21:15:06 - mmengine - INFO - Epoch(train) [73][250/293] lr: 5.000000e-04 eta: 3:15:21 time: 0.328910 data_time: 0.077205 memory: 2315 loss_kpt: 0.000963 acc_pose: 0.667491 loss: 0.000963 2022/10/13 21:15:20 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:15:37 - mmengine - INFO - Epoch(train) [74][50/293] lr: 5.000000e-04 eta: 3:14:35 time: 0.341484 data_time: 0.109239 memory: 2315 loss_kpt: 0.000962 acc_pose: 0.699550 loss: 0.000962 2022/10/13 21:15:54 - mmengine - INFO - Epoch(train) [74][100/293] lr: 5.000000e-04 eta: 3:14:24 time: 0.328114 data_time: 0.067654 memory: 2315 loss_kpt: 0.000958 acc_pose: 0.705918 loss: 0.000958 2022/10/13 21:16:11 - mmengine - INFO - Epoch(train) [74][150/293] lr: 5.000000e-04 eta: 3:14:14 time: 0.338438 data_time: 0.150145 memory: 2315 loss_kpt: 0.000955 acc_pose: 0.707432 loss: 0.000955 2022/10/13 21:16:27 - mmengine - INFO - Epoch(train) [74][200/293] lr: 5.000000e-04 eta: 3:14:03 time: 0.329587 data_time: 0.104523 memory: 2315 loss_kpt: 0.000962 acc_pose: 0.732778 loss: 0.000962 2022/10/13 21:16:43 - mmengine - INFO - Epoch(train) [74][250/293] lr: 5.000000e-04 eta: 3:13:51 time: 0.319610 data_time: 0.086486 memory: 2315 loss_kpt: 0.000953 acc_pose: 0.736601 loss: 0.000953 2022/10/13 21:16:57 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:17:14 - mmengine - INFO - Epoch(train) [75][50/293] lr: 5.000000e-04 eta: 3:13:06 time: 0.344454 data_time: 0.090830 memory: 2315 loss_kpt: 0.000964 acc_pose: 0.662319 loss: 0.000964 2022/10/13 21:17:31 - mmengine - INFO - Epoch(train) [75][100/293] lr: 5.000000e-04 eta: 3:12:56 time: 0.339084 data_time: 0.068201 memory: 2315 loss_kpt: 0.000968 acc_pose: 0.693828 loss: 0.000968 2022/10/13 21:17:48 - mmengine - INFO - Epoch(train) [75][150/293] lr: 5.000000e-04 eta: 3:12:46 time: 0.340847 data_time: 0.065350 memory: 2315 loss_kpt: 0.000974 acc_pose: 0.683363 loss: 0.000974 2022/10/13 21:18:05 - mmengine - INFO - Epoch(train) [75][200/293] lr: 5.000000e-04 eta: 3:12:35 time: 0.333032 data_time: 0.102411 memory: 2315 loss_kpt: 0.000946 acc_pose: 0.653877 loss: 0.000946 2022/10/13 21:18:21 - mmengine - INFO - Epoch(train) [75][250/293] lr: 5.000000e-04 eta: 3:12:24 time: 0.334637 data_time: 0.168504 memory: 2315 loss_kpt: 0.000956 acc_pose: 0.656655 loss: 0.000956 2022/10/13 21:18:36 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:18:45 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:18:53 - mmengine - INFO - Epoch(train) [76][50/293] lr: 5.000000e-04 eta: 3:11:40 time: 0.353338 data_time: 0.111387 memory: 2315 loss_kpt: 0.000960 acc_pose: 0.692069 loss: 0.000960 2022/10/13 21:19:10 - mmengine - INFO - Epoch(train) [76][100/293] lr: 5.000000e-04 eta: 3:11:30 time: 0.337161 data_time: 0.090858 memory: 2315 loss_kpt: 0.000956 acc_pose: 0.683318 loss: 0.000956 2022/10/13 21:19:26 - mmengine - INFO - Epoch(train) [76][150/293] lr: 5.000000e-04 eta: 3:11:18 time: 0.328280 data_time: 0.108920 memory: 2315 loss_kpt: 0.000964 acc_pose: 0.732316 loss: 0.000964 2022/10/13 21:19:43 - mmengine - INFO - Epoch(train) [76][200/293] lr: 5.000000e-04 eta: 3:11:07 time: 0.331086 data_time: 0.169160 memory: 2315 loss_kpt: 0.000967 acc_pose: 0.679037 loss: 0.000967 2022/10/13 21:20:00 - mmengine - INFO - Epoch(train) [76][250/293] lr: 5.000000e-04 eta: 3:10:57 time: 0.337165 data_time: 0.120664 memory: 2315 loss_kpt: 0.000949 acc_pose: 0.676565 loss: 0.000949 2022/10/13 21:20:14 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:20:31 - mmengine - INFO - Epoch(train) [77][50/293] lr: 5.000000e-04 eta: 3:10:12 time: 0.346117 data_time: 0.099507 memory: 2315 loss_kpt: 0.000957 acc_pose: 0.696936 loss: 0.000957 2022/10/13 21:20:48 - mmengine - INFO - Epoch(train) [77][100/293] lr: 5.000000e-04 eta: 3:10:01 time: 0.331248 data_time: 0.065219 memory: 2315 loss_kpt: 0.000960 acc_pose: 0.704237 loss: 0.000960 2022/10/13 21:21:04 - mmengine - INFO - Epoch(train) [77][150/293] lr: 5.000000e-04 eta: 3:09:50 time: 0.330093 data_time: 0.069810 memory: 2315 loss_kpt: 0.000969 acc_pose: 0.708143 loss: 0.000969 2022/10/13 21:21:21 - mmengine - INFO - Epoch(train) [77][200/293] lr: 5.000000e-04 eta: 3:09:40 time: 0.337855 data_time: 0.071072 memory: 2315 loss_kpt: 0.000960 acc_pose: 0.708851 loss: 0.000960 2022/10/13 21:21:38 - mmengine - INFO - Epoch(train) [77][250/293] lr: 5.000000e-04 eta: 3:09:29 time: 0.335275 data_time: 0.076459 memory: 2315 loss_kpt: 0.000965 acc_pose: 0.681051 loss: 0.000965 2022/10/13 21:21:52 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:22:09 - mmengine - INFO - Epoch(train) [78][50/293] lr: 5.000000e-04 eta: 3:08:44 time: 0.340098 data_time: 0.135913 memory: 2315 loss_kpt: 0.000957 acc_pose: 0.719737 loss: 0.000957 2022/10/13 21:22:26 - mmengine - INFO - Epoch(train) [78][100/293] lr: 5.000000e-04 eta: 3:08:34 time: 0.343544 data_time: 0.189130 memory: 2315 loss_kpt: 0.000973 acc_pose: 0.629393 loss: 0.000973 2022/10/13 21:22:43 - mmengine - INFO - Epoch(train) [78][150/293] lr: 5.000000e-04 eta: 3:08:24 time: 0.335066 data_time: 0.161059 memory: 2315 loss_kpt: 0.000947 acc_pose: 0.692467 loss: 0.000947 2022/10/13 21:22:59 - mmengine - INFO - Epoch(train) [78][200/293] lr: 5.000000e-04 eta: 3:08:12 time: 0.328462 data_time: 0.078959 memory: 2315 loss_kpt: 0.000970 acc_pose: 0.734137 loss: 0.000970 2022/10/13 21:23:16 - mmengine - INFO - Epoch(train) [78][250/293] lr: 5.000000e-04 eta: 3:08:02 time: 0.342285 data_time: 0.066762 memory: 2315 loss_kpt: 0.000936 acc_pose: 0.691678 loss: 0.000936 2022/10/13 21:23:30 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:23:47 - mmengine - INFO - Epoch(train) [79][50/293] lr: 5.000000e-04 eta: 3:07:19 time: 0.350591 data_time: 0.104028 memory: 2315 loss_kpt: 0.000964 acc_pose: 0.673952 loss: 0.000964 2022/10/13 21:24:04 - mmengine - INFO - Epoch(train) [79][100/293] lr: 5.000000e-04 eta: 3:07:08 time: 0.333458 data_time: 0.073498 memory: 2315 loss_kpt: 0.000951 acc_pose: 0.737713 loss: 0.000951 2022/10/13 21:24:19 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:24:21 - mmengine - INFO - Epoch(train) [79][150/293] lr: 5.000000e-04 eta: 3:06:57 time: 0.330102 data_time: 0.068881 memory: 2315 loss_kpt: 0.000951 acc_pose: 0.701716 loss: 0.000951 2022/10/13 21:24:37 - mmengine - INFO - Epoch(train) [79][200/293] lr: 5.000000e-04 eta: 3:06:45 time: 0.330294 data_time: 0.064582 memory: 2315 loss_kpt: 0.000959 acc_pose: 0.727010 loss: 0.000959 2022/10/13 21:24:54 - mmengine - INFO - Epoch(train) [79][250/293] lr: 5.000000e-04 eta: 3:06:34 time: 0.328972 data_time: 0.088079 memory: 2315 loss_kpt: 0.000969 acc_pose: 0.732370 loss: 0.000969 2022/10/13 21:25:08 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:25:25 - mmengine - INFO - Epoch(train) [80][50/293] lr: 5.000000e-04 eta: 3:05:51 time: 0.348680 data_time: 0.109767 memory: 2315 loss_kpt: 0.000966 acc_pose: 0.698041 loss: 0.000966 2022/10/13 21:25:42 - mmengine - INFO - Epoch(train) [80][100/293] lr: 5.000000e-04 eta: 3:05:39 time: 0.330686 data_time: 0.090884 memory: 2315 loss_kpt: 0.000958 acc_pose: 0.702961 loss: 0.000958 2022/10/13 21:25:58 - mmengine - INFO - Epoch(train) [80][150/293] lr: 5.000000e-04 eta: 3:05:29 time: 0.334936 data_time: 0.125680 memory: 2315 loss_kpt: 0.000968 acc_pose: 0.679137 loss: 0.000968 2022/10/13 21:26:15 - mmengine - INFO - Epoch(train) [80][200/293] lr: 5.000000e-04 eta: 3:05:17 time: 0.333118 data_time: 0.140639 memory: 2315 loss_kpt: 0.000946 acc_pose: 0.708276 loss: 0.000946 2022/10/13 21:26:32 - mmengine - INFO - Epoch(train) [80][250/293] lr: 5.000000e-04 eta: 3:05:07 time: 0.339987 data_time: 0.161662 memory: 2315 loss_kpt: 0.000945 acc_pose: 0.655978 loss: 0.000945 2022/10/13 21:26:46 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:26:46 - mmengine - INFO - Saving checkpoint at 80 epochs 2022/10/13 21:26:54 - mmengine - INFO - Epoch(val) [80][50/407] eta: 0:00:41 time: 0.116111 data_time: 0.071200 memory: 2315 2022/10/13 21:26:59 - mmengine - INFO - Epoch(val) [80][100/407] eta: 0:00:33 time: 0.109342 data_time: 0.061470 memory: 426 2022/10/13 21:27:05 - mmengine - INFO - Epoch(val) [80][150/407] eta: 0:00:28 time: 0.111290 data_time: 0.067496 memory: 426 2022/10/13 21:27:11 - mmengine - INFO - Epoch(val) [80][200/407] eta: 0:00:23 time: 0.115839 data_time: 0.071550 memory: 426 2022/10/13 21:27:16 - mmengine - INFO - Epoch(val) [80][250/407] eta: 0:00:17 time: 0.113618 data_time: 0.070195 memory: 426 2022/10/13 21:27:22 - mmengine - INFO - Epoch(val) [80][300/407] eta: 0:00:12 time: 0.116127 data_time: 0.071823 memory: 426 2022/10/13 21:27:28 - mmengine - INFO - Epoch(val) [80][350/407] eta: 0:00:06 time: 0.109791 data_time: 0.066402 memory: 426 2022/10/13 21:27:33 - mmengine - INFO - Epoch(val) [80][400/407] eta: 0:00:00 time: 0.111738 data_time: 0.067827 memory: 426 2022/10/13 21:28:11 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 21:28:26 - mmengine - INFO - Epoch(val) [80][407/407] coco/AP: 0.569063 coco/AP .5: 0.841802 coco/AP .75: 0.631723 coco/AP (M): 0.539657 coco/AP (L): 0.626528 coco/AR: 0.636792 coco/AR .5: 0.890428 coco/AR .75: 0.703401 coco/AR (M): 0.595275 coco/AR (L): 0.695243 2022/10/13 21:28:26 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_70.pth is removed 2022/10/13 21:28:27 - mmengine - INFO - The best checkpoint with 0.5691 coco/AP at 80 epoch is saved to best_coco/AP_epoch_80.pth. 2022/10/13 21:28:44 - mmengine - INFO - Epoch(train) [81][50/293] lr: 5.000000e-04 eta: 3:04:23 time: 0.333311 data_time: 0.218911 memory: 2315 loss_kpt: 0.000958 acc_pose: 0.696739 loss: 0.000958 2022/10/13 21:29:01 - mmengine - INFO - Epoch(train) [81][100/293] lr: 5.000000e-04 eta: 3:04:13 time: 0.345336 data_time: 0.170912 memory: 2315 loss_kpt: 0.000963 acc_pose: 0.727342 loss: 0.000963 2022/10/13 21:29:18 - mmengine - INFO - Epoch(train) [81][150/293] lr: 5.000000e-04 eta: 3:04:01 time: 0.327944 data_time: 0.185074 memory: 2315 loss_kpt: 0.000964 acc_pose: 0.714050 loss: 0.000964 2022/10/13 21:29:35 - mmengine - INFO - Epoch(train) [81][200/293] lr: 5.000000e-04 eta: 3:03:51 time: 0.342152 data_time: 0.187486 memory: 2315 loss_kpt: 0.000960 acc_pose: 0.667642 loss: 0.000960 2022/10/13 21:29:52 - mmengine - INFO - Epoch(train) [81][250/293] lr: 5.000000e-04 eta: 3:03:40 time: 0.334383 data_time: 0.170730 memory: 2315 loss_kpt: 0.000961 acc_pose: 0.743387 loss: 0.000961 2022/10/13 21:30:06 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:30:23 - mmengine - INFO - Epoch(train) [82][50/293] lr: 5.000000e-04 eta: 3:02:57 time: 0.340976 data_time: 0.131368 memory: 2315 loss_kpt: 0.000949 acc_pose: 0.663579 loss: 0.000949 2022/10/13 21:30:39 - mmengine - INFO - Epoch(train) [82][100/293] lr: 5.000000e-04 eta: 3:02:46 time: 0.334735 data_time: 0.165523 memory: 2315 loss_kpt: 0.000953 acc_pose: 0.750486 loss: 0.000953 2022/10/13 21:30:56 - mmengine - INFO - Epoch(train) [82][150/293] lr: 5.000000e-04 eta: 3:02:34 time: 0.329861 data_time: 0.163502 memory: 2315 loss_kpt: 0.000962 acc_pose: 0.759178 loss: 0.000962 2022/10/13 21:31:13 - mmengine - INFO - Epoch(train) [82][200/293] lr: 5.000000e-04 eta: 3:02:23 time: 0.338208 data_time: 0.148771 memory: 2315 loss_kpt: 0.000963 acc_pose: 0.701120 loss: 0.000963 2022/10/13 21:31:30 - mmengine - INFO - Epoch(train) [82][250/293] lr: 5.000000e-04 eta: 3:02:12 time: 0.338890 data_time: 0.184225 memory: 2315 loss_kpt: 0.000957 acc_pose: 0.693160 loss: 0.000957 2022/10/13 21:31:35 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:31:44 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:32:01 - mmengine - INFO - Epoch(train) [83][50/293] lr: 5.000000e-04 eta: 3:01:30 time: 0.339827 data_time: 0.118078 memory: 2315 loss_kpt: 0.000957 acc_pose: 0.767444 loss: 0.000957 2022/10/13 21:32:18 - mmengine - INFO - Epoch(train) [83][100/293] lr: 5.000000e-04 eta: 3:01:19 time: 0.339683 data_time: 0.145059 memory: 2315 loss_kpt: 0.000959 acc_pose: 0.666755 loss: 0.000959 2022/10/13 21:32:35 - mmengine - INFO - Epoch(train) [83][150/293] lr: 5.000000e-04 eta: 3:01:08 time: 0.342829 data_time: 0.184760 memory: 2315 loss_kpt: 0.000958 acc_pose: 0.630586 loss: 0.000958 2022/10/13 21:32:52 - mmengine - INFO - Epoch(train) [83][200/293] lr: 5.000000e-04 eta: 3:00:57 time: 0.334743 data_time: 0.175546 memory: 2315 loss_kpt: 0.000952 acc_pose: 0.669297 loss: 0.000952 2022/10/13 21:33:08 - mmengine - INFO - Epoch(train) [83][250/293] lr: 5.000000e-04 eta: 3:00:46 time: 0.332291 data_time: 0.164480 memory: 2315 loss_kpt: 0.000968 acc_pose: 0.626249 loss: 0.000968 2022/10/13 21:33:22 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:33:40 - mmengine - INFO - Epoch(train) [84][50/293] lr: 5.000000e-04 eta: 3:00:04 time: 0.349079 data_time: 0.114414 memory: 2315 loss_kpt: 0.000950 acc_pose: 0.686946 loss: 0.000950 2022/10/13 21:33:56 - mmengine - INFO - Epoch(train) [84][100/293] lr: 5.000000e-04 eta: 2:59:52 time: 0.329071 data_time: 0.063980 memory: 2315 loss_kpt: 0.000953 acc_pose: 0.706744 loss: 0.000953 2022/10/13 21:34:13 - mmengine - INFO - Epoch(train) [84][150/293] lr: 5.000000e-04 eta: 2:59:41 time: 0.327686 data_time: 0.068343 memory: 2315 loss_kpt: 0.000945 acc_pose: 0.611457 loss: 0.000945 2022/10/13 21:34:29 - mmengine - INFO - Epoch(train) [84][200/293] lr: 5.000000e-04 eta: 2:59:30 time: 0.338103 data_time: 0.064994 memory: 2315 loss_kpt: 0.000960 acc_pose: 0.686064 loss: 0.000960 2022/10/13 21:34:45 - mmengine - INFO - Epoch(train) [84][250/293] lr: 5.000000e-04 eta: 2:59:17 time: 0.319629 data_time: 0.067467 memory: 2315 loss_kpt: 0.000962 acc_pose: 0.738302 loss: 0.000962 2022/10/13 21:34:59 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:35:16 - mmengine - INFO - Epoch(train) [85][50/293] lr: 5.000000e-04 eta: 2:58:35 time: 0.333224 data_time: 0.084341 memory: 2315 loss_kpt: 0.000947 acc_pose: 0.709453 loss: 0.000947 2022/10/13 21:35:32 - mmengine - INFO - Epoch(train) [85][100/293] lr: 5.000000e-04 eta: 2:58:23 time: 0.332911 data_time: 0.066260 memory: 2315 loss_kpt: 0.000957 acc_pose: 0.730684 loss: 0.000957 2022/10/13 21:35:49 - mmengine - INFO - Epoch(train) [85][150/293] lr: 5.000000e-04 eta: 2:58:12 time: 0.337942 data_time: 0.065762 memory: 2315 loss_kpt: 0.000943 acc_pose: 0.730408 loss: 0.000943 2022/10/13 21:36:06 - mmengine - INFO - Epoch(train) [85][200/293] lr: 5.000000e-04 eta: 2:58:01 time: 0.329794 data_time: 0.097131 memory: 2315 loss_kpt: 0.000947 acc_pose: 0.715397 loss: 0.000947 2022/10/13 21:36:23 - mmengine - INFO - Epoch(train) [85][250/293] lr: 5.000000e-04 eta: 2:57:49 time: 0.337292 data_time: 0.173955 memory: 2315 loss_kpt: 0.000950 acc_pose: 0.692147 loss: 0.000950 2022/10/13 21:36:36 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:36:54 - mmengine - INFO - Epoch(train) [86][50/293] lr: 5.000000e-04 eta: 2:57:08 time: 0.347953 data_time: 0.087710 memory: 2315 loss_kpt: 0.000950 acc_pose: 0.703070 loss: 0.000950 2022/10/13 21:37:08 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:37:10 - mmengine - INFO - Epoch(train) [86][100/293] lr: 5.000000e-04 eta: 2:56:56 time: 0.326226 data_time: 0.069199 memory: 2315 loss_kpt: 0.000946 acc_pose: 0.712124 loss: 0.000946 2022/10/13 21:37:26 - mmengine - INFO - Epoch(train) [86][150/293] lr: 5.000000e-04 eta: 2:56:44 time: 0.327169 data_time: 0.129772 memory: 2315 loss_kpt: 0.000944 acc_pose: 0.750553 loss: 0.000944 2022/10/13 21:37:43 - mmengine - INFO - Epoch(train) [86][200/293] lr: 5.000000e-04 eta: 2:56:33 time: 0.336412 data_time: 0.092439 memory: 2315 loss_kpt: 0.000960 acc_pose: 0.678915 loss: 0.000960 2022/10/13 21:38:00 - mmengine - INFO - Epoch(train) [86][250/293] lr: 5.000000e-04 eta: 2:56:21 time: 0.326262 data_time: 0.165043 memory: 2315 loss_kpt: 0.000948 acc_pose: 0.720872 loss: 0.000948 2022/10/13 21:38:13 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:38:30 - mmengine - INFO - Epoch(train) [87][50/293] lr: 5.000000e-04 eta: 2:55:39 time: 0.333817 data_time: 0.117850 memory: 2315 loss_kpt: 0.000957 acc_pose: 0.671877 loss: 0.000957 2022/10/13 21:38:47 - mmengine - INFO - Epoch(train) [87][100/293] lr: 5.000000e-04 eta: 2:55:28 time: 0.336075 data_time: 0.143864 memory: 2315 loss_kpt: 0.000968 acc_pose: 0.635808 loss: 0.000968 2022/10/13 21:39:03 - mmengine - INFO - Epoch(train) [87][150/293] lr: 5.000000e-04 eta: 2:55:16 time: 0.326068 data_time: 0.173035 memory: 2315 loss_kpt: 0.000938 acc_pose: 0.774698 loss: 0.000938 2022/10/13 21:39:19 - mmengine - INFO - Epoch(train) [87][200/293] lr: 5.000000e-04 eta: 2:55:04 time: 0.324919 data_time: 0.148380 memory: 2315 loss_kpt: 0.000959 acc_pose: 0.715237 loss: 0.000959 2022/10/13 21:39:36 - mmengine - INFO - Epoch(train) [87][250/293] lr: 5.000000e-04 eta: 2:54:52 time: 0.325447 data_time: 0.144749 memory: 2315 loss_kpt: 0.000970 acc_pose: 0.714604 loss: 0.000970 2022/10/13 21:39:50 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:40:07 - mmengine - INFO - Epoch(train) [88][50/293] lr: 5.000000e-04 eta: 2:54:11 time: 0.345250 data_time: 0.083361 memory: 2315 loss_kpt: 0.000975 acc_pose: 0.649151 loss: 0.000975 2022/10/13 21:40:24 - mmengine - INFO - Epoch(train) [88][100/293] lr: 5.000000e-04 eta: 2:54:00 time: 0.336619 data_time: 0.070124 memory: 2315 loss_kpt: 0.000939 acc_pose: 0.683388 loss: 0.000939 2022/10/13 21:40:40 - mmengine - INFO - Epoch(train) [88][150/293] lr: 5.000000e-04 eta: 2:53:48 time: 0.325963 data_time: 0.069759 memory: 2315 loss_kpt: 0.000946 acc_pose: 0.678761 loss: 0.000946 2022/10/13 21:40:57 - mmengine - INFO - Epoch(train) [88][200/293] lr: 5.000000e-04 eta: 2:53:36 time: 0.333824 data_time: 0.153636 memory: 2315 loss_kpt: 0.000940 acc_pose: 0.705410 loss: 0.000940 2022/10/13 21:41:13 - mmengine - INFO - Epoch(train) [88][250/293] lr: 5.000000e-04 eta: 2:53:25 time: 0.334124 data_time: 0.103464 memory: 2315 loss_kpt: 0.000965 acc_pose: 0.714705 loss: 0.000965 2022/10/13 21:41:28 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:41:45 - mmengine - INFO - Epoch(train) [89][50/293] lr: 5.000000e-04 eta: 2:52:44 time: 0.339600 data_time: 0.112763 memory: 2315 loss_kpt: 0.000962 acc_pose: 0.698380 loss: 0.000962 2022/10/13 21:42:01 - mmengine - INFO - Epoch(train) [89][100/293] lr: 5.000000e-04 eta: 2:52:32 time: 0.332317 data_time: 0.065655 memory: 2315 loss_kpt: 0.000933 acc_pose: 0.644347 loss: 0.000933 2022/10/13 21:42:18 - mmengine - INFO - Epoch(train) [89][150/293] lr: 5.000000e-04 eta: 2:52:20 time: 0.332756 data_time: 0.070904 memory: 2315 loss_kpt: 0.000957 acc_pose: 0.702855 loss: 0.000957 2022/10/13 21:42:34 - mmengine - INFO - Epoch(train) [89][200/293] lr: 5.000000e-04 eta: 2:52:09 time: 0.328383 data_time: 0.137404 memory: 2315 loss_kpt: 0.000952 acc_pose: 0.722824 loss: 0.000952 2022/10/13 21:42:40 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:42:51 - mmengine - INFO - Epoch(train) [89][250/293] lr: 5.000000e-04 eta: 2:51:57 time: 0.329716 data_time: 0.161793 memory: 2315 loss_kpt: 0.000956 acc_pose: 0.712755 loss: 0.000956 2022/10/13 21:43:05 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:43:22 - mmengine - INFO - Epoch(train) [90][50/293] lr: 5.000000e-04 eta: 2:51:16 time: 0.336984 data_time: 0.117737 memory: 2315 loss_kpt: 0.000959 acc_pose: 0.720187 loss: 0.000959 2022/10/13 21:43:39 - mmengine - INFO - Epoch(train) [90][100/293] lr: 5.000000e-04 eta: 2:51:04 time: 0.333145 data_time: 0.071588 memory: 2315 loss_kpt: 0.000950 acc_pose: 0.638301 loss: 0.000950 2022/10/13 21:43:55 - mmengine - INFO - Epoch(train) [90][150/293] lr: 5.000000e-04 eta: 2:50:53 time: 0.334671 data_time: 0.101266 memory: 2315 loss_kpt: 0.000946 acc_pose: 0.718327 loss: 0.000946 2022/10/13 21:44:12 - mmengine - INFO - Epoch(train) [90][200/293] lr: 5.000000e-04 eta: 2:50:41 time: 0.339095 data_time: 0.071639 memory: 2315 loss_kpt: 0.000947 acc_pose: 0.748825 loss: 0.000947 2022/10/13 21:44:29 - mmengine - INFO - Epoch(train) [90][250/293] lr: 5.000000e-04 eta: 2:50:30 time: 0.337255 data_time: 0.079469 memory: 2315 loss_kpt: 0.000931 acc_pose: 0.704583 loss: 0.000931 2022/10/13 21:44:43 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:44:43 - mmengine - INFO - Saving checkpoint at 90 epochs 2022/10/13 21:44:51 - mmengine - INFO - Epoch(val) [90][50/407] eta: 0:00:42 time: 0.119582 data_time: 0.076825 memory: 2315 2022/10/13 21:44:57 - mmengine - INFO - Epoch(val) [90][100/407] eta: 0:00:34 time: 0.112027 data_time: 0.068560 memory: 426 2022/10/13 21:45:03 - mmengine - INFO - Epoch(val) [90][150/407] eta: 0:00:31 time: 0.123096 data_time: 0.080162 memory: 426 2022/10/13 21:45:09 - mmengine - INFO - Epoch(val) [90][200/407] eta: 0:00:23 time: 0.112132 data_time: 0.067965 memory: 426 2022/10/13 21:45:14 - mmengine - INFO - Epoch(val) [90][250/407] eta: 0:00:17 time: 0.111977 data_time: 0.068794 memory: 426 2022/10/13 21:45:20 - mmengine - INFO - Epoch(val) [90][300/407] eta: 0:00:12 time: 0.114395 data_time: 0.069650 memory: 426 2022/10/13 21:45:26 - mmengine - INFO - Epoch(val) [90][350/407] eta: 0:00:06 time: 0.118620 data_time: 0.073623 memory: 426 2022/10/13 21:45:31 - mmengine - INFO - Epoch(val) [90][400/407] eta: 0:00:00 time: 0.105839 data_time: 0.064952 memory: 426 2022/10/13 21:46:10 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 21:46:24 - mmengine - INFO - Epoch(val) [90][407/407] coco/AP: 0.572007 coco/AP .5: 0.845546 coco/AP .75: 0.633841 coco/AP (M): 0.540622 coco/AP (L): 0.629865 coco/AR: 0.639358 coco/AR .5: 0.891373 coco/AR .75: 0.702928 coco/AR (M): 0.597378 coco/AR (L): 0.698068 2022/10/13 21:46:24 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_80.pth is removed 2022/10/13 21:46:26 - mmengine - INFO - The best checkpoint with 0.5720 coco/AP at 90 epoch is saved to best_coco/AP_epoch_90.pth. 2022/10/13 21:46:43 - mmengine - INFO - Epoch(train) [91][50/293] lr: 5.000000e-04 eta: 2:49:50 time: 0.345199 data_time: 0.198253 memory: 2315 loss_kpt: 0.000934 acc_pose: 0.676111 loss: 0.000934 2022/10/13 21:46:59 - mmengine - INFO - Epoch(train) [91][100/293] lr: 5.000000e-04 eta: 2:49:38 time: 0.327972 data_time: 0.197319 memory: 2315 loss_kpt: 0.000941 acc_pose: 0.676933 loss: 0.000941 2022/10/13 21:47:16 - mmengine - INFO - Epoch(train) [91][150/293] lr: 5.000000e-04 eta: 2:49:26 time: 0.331511 data_time: 0.203523 memory: 2315 loss_kpt: 0.000951 acc_pose: 0.657428 loss: 0.000951 2022/10/13 21:47:33 - mmengine - INFO - Epoch(train) [91][200/293] lr: 5.000000e-04 eta: 2:49:15 time: 0.336894 data_time: 0.116370 memory: 2315 loss_kpt: 0.000966 acc_pose: 0.674493 loss: 0.000966 2022/10/13 21:47:49 - mmengine - INFO - Epoch(train) [91][250/293] lr: 5.000000e-04 eta: 2:49:03 time: 0.330079 data_time: 0.079274 memory: 2315 loss_kpt: 0.000935 acc_pose: 0.733870 loss: 0.000935 2022/10/13 21:48:04 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:48:22 - mmengine - INFO - Epoch(train) [92][50/293] lr: 5.000000e-04 eta: 2:48:23 time: 0.346396 data_time: 0.081922 memory: 2315 loss_kpt: 0.000941 acc_pose: 0.725206 loss: 0.000941 2022/10/13 21:48:39 - mmengine - INFO - Epoch(train) [92][100/293] lr: 5.000000e-04 eta: 2:48:12 time: 0.342682 data_time: 0.066882 memory: 2315 loss_kpt: 0.000955 acc_pose: 0.707822 loss: 0.000955 2022/10/13 21:48:56 - mmengine - INFO - Epoch(train) [92][150/293] lr: 5.000000e-04 eta: 2:48:01 time: 0.338078 data_time: 0.062659 memory: 2315 loss_kpt: 0.000940 acc_pose: 0.724764 loss: 0.000940 2022/10/13 21:49:13 - mmengine - INFO - Epoch(train) [92][200/293] lr: 5.000000e-04 eta: 2:47:50 time: 0.344982 data_time: 0.077490 memory: 2315 loss_kpt: 0.000942 acc_pose: 0.660044 loss: 0.000942 2022/10/13 21:49:29 - mmengine - INFO - Epoch(train) [92][250/293] lr: 5.000000e-04 eta: 2:47:37 time: 0.322665 data_time: 0.097622 memory: 2315 loss_kpt: 0.000949 acc_pose: 0.688984 loss: 0.000949 2022/10/13 21:49:43 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:49:58 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:50:00 - mmengine - INFO - Epoch(train) [93][50/293] lr: 5.000000e-04 eta: 2:46:58 time: 0.347563 data_time: 0.113190 memory: 2315 loss_kpt: 0.000943 acc_pose: 0.617202 loss: 0.000943 2022/10/13 21:50:18 - mmengine - INFO - Epoch(train) [93][100/293] lr: 5.000000e-04 eta: 2:46:47 time: 0.343415 data_time: 0.075605 memory: 2315 loss_kpt: 0.000933 acc_pose: 0.715254 loss: 0.000933 2022/10/13 21:50:34 - mmengine - INFO - Epoch(train) [93][150/293] lr: 5.000000e-04 eta: 2:46:35 time: 0.328795 data_time: 0.069484 memory: 2315 loss_kpt: 0.000931 acc_pose: 0.729657 loss: 0.000931 2022/10/13 21:50:50 - mmengine - INFO - Epoch(train) [93][200/293] lr: 5.000000e-04 eta: 2:46:23 time: 0.329653 data_time: 0.105264 memory: 2315 loss_kpt: 0.000958 acc_pose: 0.720859 loss: 0.000958 2022/10/13 21:51:08 - mmengine - INFO - Epoch(train) [93][250/293] lr: 5.000000e-04 eta: 2:46:11 time: 0.340761 data_time: 0.100193 memory: 2315 loss_kpt: 0.000955 acc_pose: 0.740340 loss: 0.000955 2022/10/13 21:51:21 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:51:39 - mmengine - INFO - Epoch(train) [94][50/293] lr: 5.000000e-04 eta: 2:45:32 time: 0.348454 data_time: 0.090296 memory: 2315 loss_kpt: 0.000966 acc_pose: 0.707358 loss: 0.000966 2022/10/13 21:51:55 - mmengine - INFO - Epoch(train) [94][100/293] lr: 5.000000e-04 eta: 2:45:20 time: 0.331347 data_time: 0.090263 memory: 2315 loss_kpt: 0.000945 acc_pose: 0.738136 loss: 0.000945 2022/10/13 21:52:12 - mmengine - INFO - Epoch(train) [94][150/293] lr: 5.000000e-04 eta: 2:45:08 time: 0.332463 data_time: 0.173195 memory: 2315 loss_kpt: 0.000953 acc_pose: 0.667271 loss: 0.000953 2022/10/13 21:52:29 - mmengine - INFO - Epoch(train) [94][200/293] lr: 5.000000e-04 eta: 2:44:56 time: 0.328828 data_time: 0.153178 memory: 2315 loss_kpt: 0.000945 acc_pose: 0.675109 loss: 0.000945 2022/10/13 21:52:45 - mmengine - INFO - Epoch(train) [94][250/293] lr: 5.000000e-04 eta: 2:44:44 time: 0.325685 data_time: 0.126224 memory: 2315 loss_kpt: 0.000949 acc_pose: 0.695978 loss: 0.000949 2022/10/13 21:52:59 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:53:16 - mmengine - INFO - Epoch(train) [95][50/293] lr: 5.000000e-04 eta: 2:44:05 time: 0.349067 data_time: 0.084270 memory: 2315 loss_kpt: 0.000943 acc_pose: 0.655617 loss: 0.000943 2022/10/13 21:53:34 - mmengine - INFO - Epoch(train) [95][100/293] lr: 5.000000e-04 eta: 2:43:54 time: 0.344656 data_time: 0.157034 memory: 2315 loss_kpt: 0.000937 acc_pose: 0.711137 loss: 0.000937 2022/10/13 21:53:51 - mmengine - INFO - Epoch(train) [95][150/293] lr: 5.000000e-04 eta: 2:43:43 time: 0.342640 data_time: 0.071473 memory: 2315 loss_kpt: 0.000945 acc_pose: 0.649372 loss: 0.000945 2022/10/13 21:54:07 - mmengine - INFO - Epoch(train) [95][200/293] lr: 5.000000e-04 eta: 2:43:31 time: 0.332127 data_time: 0.114682 memory: 2315 loss_kpt: 0.000962 acc_pose: 0.687957 loss: 0.000962 2022/10/13 21:54:24 - mmengine - INFO - Epoch(train) [95][250/293] lr: 5.000000e-04 eta: 2:43:19 time: 0.336489 data_time: 0.180717 memory: 2315 loss_kpt: 0.000929 acc_pose: 0.765986 loss: 0.000929 2022/10/13 21:54:38 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:54:55 - mmengine - INFO - Epoch(train) [96][50/293] lr: 5.000000e-04 eta: 2:42:40 time: 0.342264 data_time: 0.083420 memory: 2315 loss_kpt: 0.000934 acc_pose: 0.699002 loss: 0.000934 2022/10/13 21:55:12 - mmengine - INFO - Epoch(train) [96][100/293] lr: 5.000000e-04 eta: 2:42:28 time: 0.336448 data_time: 0.121958 memory: 2315 loss_kpt: 0.000956 acc_pose: 0.699494 loss: 0.000956 2022/10/13 21:55:29 - mmengine - INFO - Epoch(train) [96][150/293] lr: 5.000000e-04 eta: 2:42:16 time: 0.325474 data_time: 0.168507 memory: 2315 loss_kpt: 0.000935 acc_pose: 0.698473 loss: 0.000935 2022/10/13 21:55:33 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:55:45 - mmengine - INFO - Epoch(train) [96][200/293] lr: 5.000000e-04 eta: 2:42:04 time: 0.328311 data_time: 0.184512 memory: 2315 loss_kpt: 0.000940 acc_pose: 0.723086 loss: 0.000940 2022/10/13 21:56:01 - mmengine - INFO - Epoch(train) [96][250/293] lr: 5.000000e-04 eta: 2:41:51 time: 0.325649 data_time: 0.166380 memory: 2315 loss_kpt: 0.000933 acc_pose: 0.753143 loss: 0.000933 2022/10/13 21:56:15 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:56:33 - mmengine - INFO - Epoch(train) [97][50/293] lr: 5.000000e-04 eta: 2:41:13 time: 0.343841 data_time: 0.082406 memory: 2315 loss_kpt: 0.000953 acc_pose: 0.717539 loss: 0.000953 2022/10/13 21:56:49 - mmengine - INFO - Epoch(train) [97][100/293] lr: 5.000000e-04 eta: 2:41:01 time: 0.331213 data_time: 0.066791 memory: 2315 loss_kpt: 0.000935 acc_pose: 0.676375 loss: 0.000935 2022/10/13 21:57:07 - mmengine - INFO - Epoch(train) [97][150/293] lr: 5.000000e-04 eta: 2:40:50 time: 0.350824 data_time: 0.167791 memory: 2315 loss_kpt: 0.000960 acc_pose: 0.653646 loss: 0.000960 2022/10/13 21:57:24 - mmengine - INFO - Epoch(train) [97][200/293] lr: 5.000000e-04 eta: 2:40:38 time: 0.335405 data_time: 0.172954 memory: 2315 loss_kpt: 0.000953 acc_pose: 0.781784 loss: 0.000953 2022/10/13 21:57:40 - mmengine - INFO - Epoch(train) [97][250/293] lr: 5.000000e-04 eta: 2:40:26 time: 0.331736 data_time: 0.185880 memory: 2315 loss_kpt: 0.000937 acc_pose: 0.741545 loss: 0.000937 2022/10/13 21:57:54 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:58:11 - mmengine - INFO - Epoch(train) [98][50/293] lr: 5.000000e-04 eta: 2:39:47 time: 0.340158 data_time: 0.089717 memory: 2315 loss_kpt: 0.000917 acc_pose: 0.659408 loss: 0.000917 2022/10/13 21:58:27 - mmengine - INFO - Epoch(train) [98][100/293] lr: 5.000000e-04 eta: 2:39:35 time: 0.326573 data_time: 0.067572 memory: 2315 loss_kpt: 0.000945 acc_pose: 0.777563 loss: 0.000945 2022/10/13 21:58:45 - mmengine - INFO - Epoch(train) [98][150/293] lr: 5.000000e-04 eta: 2:39:24 time: 0.351681 data_time: 0.066419 memory: 2315 loss_kpt: 0.000947 acc_pose: 0.674740 loss: 0.000947 2022/10/13 21:59:02 - mmengine - INFO - Epoch(train) [98][200/293] lr: 5.000000e-04 eta: 2:39:12 time: 0.340788 data_time: 0.122839 memory: 2315 loss_kpt: 0.000952 acc_pose: 0.696541 loss: 0.000952 2022/10/13 21:59:19 - mmengine - INFO - Epoch(train) [98][250/293] lr: 5.000000e-04 eta: 2:39:01 time: 0.346850 data_time: 0.190894 memory: 2315 loss_kpt: 0.000944 acc_pose: 0.748174 loss: 0.000944 2022/10/13 21:59:34 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 21:59:51 - mmengine - INFO - Epoch(train) [99][50/293] lr: 5.000000e-04 eta: 2:38:23 time: 0.343859 data_time: 0.083965 memory: 2315 loss_kpt: 0.000934 acc_pose: 0.703492 loss: 0.000934 2022/10/13 22:00:08 - mmengine - INFO - Epoch(train) [99][100/293] lr: 5.000000e-04 eta: 2:38:11 time: 0.337136 data_time: 0.077009 memory: 2315 loss_kpt: 0.000932 acc_pose: 0.752369 loss: 0.000932 2022/10/13 22:00:24 - mmengine - INFO - Epoch(train) [99][150/293] lr: 5.000000e-04 eta: 2:37:59 time: 0.328730 data_time: 0.110223 memory: 2315 loss_kpt: 0.000962 acc_pose: 0.742041 loss: 0.000962 2022/10/13 22:00:41 - mmengine - INFO - Epoch(train) [99][200/293] lr: 5.000000e-04 eta: 2:37:46 time: 0.323910 data_time: 0.079497 memory: 2315 loss_kpt: 0.000939 acc_pose: 0.735865 loss: 0.000939 2022/10/13 22:00:58 - mmengine - INFO - Epoch(train) [99][250/293] lr: 5.000000e-04 eta: 2:37:34 time: 0.342502 data_time: 0.184789 memory: 2315 loss_kpt: 0.000934 acc_pose: 0.673035 loss: 0.000934 2022/10/13 22:01:10 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:01:12 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:01:29 - mmengine - INFO - Epoch(train) [100][50/293] lr: 5.000000e-04 eta: 2:36:57 time: 0.347844 data_time: 0.103926 memory: 2315 loss_kpt: 0.000949 acc_pose: 0.700597 loss: 0.000949 2022/10/13 22:01:46 - mmengine - INFO - Epoch(train) [100][100/293] lr: 5.000000e-04 eta: 2:36:45 time: 0.345355 data_time: 0.068362 memory: 2315 loss_kpt: 0.000942 acc_pose: 0.724514 loss: 0.000942 2022/10/13 22:02:03 - mmengine - INFO - Epoch(train) [100][150/293] lr: 5.000000e-04 eta: 2:36:33 time: 0.323752 data_time: 0.065411 memory: 2315 loss_kpt: 0.000959 acc_pose: 0.704306 loss: 0.000959 2022/10/13 22:02:20 - mmengine - INFO - Epoch(train) [100][200/293] lr: 5.000000e-04 eta: 2:36:21 time: 0.337264 data_time: 0.091763 memory: 2315 loss_kpt: 0.000935 acc_pose: 0.745910 loss: 0.000935 2022/10/13 22:02:36 - mmengine - INFO - Epoch(train) [100][250/293] lr: 5.000000e-04 eta: 2:36:08 time: 0.331309 data_time: 0.066773 memory: 2315 loss_kpt: 0.000941 acc_pose: 0.647312 loss: 0.000941 2022/10/13 22:02:50 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:02:50 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/10/13 22:02:58 - mmengine - INFO - Epoch(val) [100][50/407] eta: 0:00:45 time: 0.127727 data_time: 0.084031 memory: 2315 2022/10/13 22:03:04 - mmengine - INFO - Epoch(val) [100][100/407] eta: 0:00:35 time: 0.115949 data_time: 0.071354 memory: 426 2022/10/13 22:03:10 - mmengine - INFO - Epoch(val) [100][150/407] eta: 0:00:29 time: 0.113397 data_time: 0.070562 memory: 426 2022/10/13 22:03:16 - mmengine - INFO - Epoch(val) [100][200/407] eta: 0:00:24 time: 0.119441 data_time: 0.076311 memory: 426 2022/10/13 22:03:22 - mmengine - INFO - Epoch(val) [100][250/407] eta: 0:00:17 time: 0.114438 data_time: 0.073137 memory: 426 2022/10/13 22:03:27 - mmengine - INFO - Epoch(val) [100][300/407] eta: 0:00:11 time: 0.110113 data_time: 0.066594 memory: 426 2022/10/13 22:03:33 - mmengine - INFO - Epoch(val) [100][350/407] eta: 0:00:06 time: 0.120375 data_time: 0.078349 memory: 426 2022/10/13 22:03:39 - mmengine - INFO - Epoch(val) [100][400/407] eta: 0:00:00 time: 0.107027 data_time: 0.065961 memory: 426 2022/10/13 22:04:16 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 22:04:30 - mmengine - INFO - Epoch(val) [100][407/407] coco/AP: 0.572650 coco/AP .5: 0.845633 coco/AP .75: 0.631994 coco/AP (M): 0.541030 coco/AP (L): 0.632585 coco/AR: 0.640570 coco/AR .5: 0.894049 coco/AR .75: 0.700567 coco/AR (M): 0.597706 coco/AR (L): 0.700223 2022/10/13 22:04:30 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_90.pth is removed 2022/10/13 22:04:32 - mmengine - INFO - The best checkpoint with 0.5727 coco/AP at 100 epoch is saved to best_coco/AP_epoch_100.pth. 2022/10/13 22:04:49 - mmengine - INFO - Epoch(train) [101][50/293] lr: 5.000000e-04 eta: 2:35:31 time: 0.340298 data_time: 0.185002 memory: 2315 loss_kpt: 0.000937 acc_pose: 0.717051 loss: 0.000937 2022/10/13 22:05:06 - mmengine - INFO - Epoch(train) [101][100/293] lr: 5.000000e-04 eta: 2:35:19 time: 0.337554 data_time: 0.181456 memory: 2315 loss_kpt: 0.000933 acc_pose: 0.616664 loss: 0.000933 2022/10/13 22:05:22 - mmengine - INFO - Epoch(train) [101][150/293] lr: 5.000000e-04 eta: 2:35:06 time: 0.331215 data_time: 0.081458 memory: 2315 loss_kpt: 0.000948 acc_pose: 0.671261 loss: 0.000948 2022/10/13 22:05:39 - mmengine - INFO - Epoch(train) [101][200/293] lr: 5.000000e-04 eta: 2:34:54 time: 0.326247 data_time: 0.066585 memory: 2315 loss_kpt: 0.000938 acc_pose: 0.752813 loss: 0.000938 2022/10/13 22:05:55 - mmengine - INFO - Epoch(train) [101][250/293] lr: 5.000000e-04 eta: 2:34:42 time: 0.334560 data_time: 0.070740 memory: 2315 loss_kpt: 0.000946 acc_pose: 0.714775 loss: 0.000946 2022/10/13 22:06:09 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:06:27 - mmengine - INFO - Epoch(train) [102][50/293] lr: 5.000000e-04 eta: 2:34:04 time: 0.343599 data_time: 0.098373 memory: 2315 loss_kpt: 0.000960 acc_pose: 0.763339 loss: 0.000960 2022/10/13 22:06:44 - mmengine - INFO - Epoch(train) [102][100/293] lr: 5.000000e-04 eta: 2:33:52 time: 0.342464 data_time: 0.068633 memory: 2315 loss_kpt: 0.000940 acc_pose: 0.711806 loss: 0.000940 2022/10/13 22:07:00 - mmengine - INFO - Epoch(train) [102][150/293] lr: 5.000000e-04 eta: 2:33:40 time: 0.331109 data_time: 0.113314 memory: 2315 loss_kpt: 0.000933 acc_pose: 0.617850 loss: 0.000933 2022/10/13 22:07:17 - mmengine - INFO - Epoch(train) [102][200/293] lr: 5.000000e-04 eta: 2:33:28 time: 0.337222 data_time: 0.128016 memory: 2315 loss_kpt: 0.000937 acc_pose: 0.725595 loss: 0.000937 2022/10/13 22:07:34 - mmengine - INFO - Epoch(train) [102][250/293] lr: 5.000000e-04 eta: 2:33:16 time: 0.340046 data_time: 0.173338 memory: 2315 loss_kpt: 0.000935 acc_pose: 0.694846 loss: 0.000935 2022/10/13 22:07:48 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:08:05 - mmengine - INFO - Epoch(train) [103][50/293] lr: 5.000000e-04 eta: 2:32:39 time: 0.339936 data_time: 0.120663 memory: 2315 loss_kpt: 0.000952 acc_pose: 0.618052 loss: 0.000952 2022/10/13 22:08:22 - mmengine - INFO - Epoch(train) [103][100/293] lr: 5.000000e-04 eta: 2:32:26 time: 0.328788 data_time: 0.072034 memory: 2315 loss_kpt: 0.000941 acc_pose: 0.672840 loss: 0.000941 2022/10/13 22:08:26 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:08:38 - mmengine - INFO - Epoch(train) [103][150/293] lr: 5.000000e-04 eta: 2:32:14 time: 0.331074 data_time: 0.086150 memory: 2315 loss_kpt: 0.000934 acc_pose: 0.729908 loss: 0.000934 2022/10/13 22:08:55 - mmengine - INFO - Epoch(train) [103][200/293] lr: 5.000000e-04 eta: 2:32:01 time: 0.327004 data_time: 0.109352 memory: 2315 loss_kpt: 0.000927 acc_pose: 0.702599 loss: 0.000927 2022/10/13 22:09:11 - mmengine - INFO - Epoch(train) [103][250/293] lr: 5.000000e-04 eta: 2:31:49 time: 0.336229 data_time: 0.091842 memory: 2315 loss_kpt: 0.000947 acc_pose: 0.724180 loss: 0.000947 2022/10/13 22:09:26 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:09:44 - mmengine - INFO - Epoch(train) [104][50/293] lr: 5.000000e-04 eta: 2:31:13 time: 0.369007 data_time: 0.088024 memory: 2315 loss_kpt: 0.000924 acc_pose: 0.704606 loss: 0.000924 2022/10/13 22:10:01 - mmengine - INFO - Epoch(train) [104][100/293] lr: 5.000000e-04 eta: 2:31:02 time: 0.340527 data_time: 0.073398 memory: 2315 loss_kpt: 0.000938 acc_pose: 0.746944 loss: 0.000938 2022/10/13 22:10:18 - mmengine - INFO - Epoch(train) [104][150/293] lr: 5.000000e-04 eta: 2:30:49 time: 0.327935 data_time: 0.090903 memory: 2315 loss_kpt: 0.000945 acc_pose: 0.724059 loss: 0.000945 2022/10/13 22:10:34 - mmengine - INFO - Epoch(train) [104][200/293] lr: 5.000000e-04 eta: 2:30:36 time: 0.325087 data_time: 0.140993 memory: 2315 loss_kpt: 0.000940 acc_pose: 0.729952 loss: 0.000940 2022/10/13 22:10:51 - mmengine - INFO - Epoch(train) [104][250/293] lr: 5.000000e-04 eta: 2:30:24 time: 0.333494 data_time: 0.168156 memory: 2315 loss_kpt: 0.000937 acc_pose: 0.718521 loss: 0.000937 2022/10/13 22:11:05 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:11:23 - mmengine - INFO - Epoch(train) [105][50/293] lr: 5.000000e-04 eta: 2:29:48 time: 0.354828 data_time: 0.116783 memory: 2315 loss_kpt: 0.000919 acc_pose: 0.663122 loss: 0.000919 2022/10/13 22:11:39 - mmengine - INFO - Epoch(train) [105][100/293] lr: 5.000000e-04 eta: 2:29:35 time: 0.333772 data_time: 0.110217 memory: 2315 loss_kpt: 0.000950 acc_pose: 0.697921 loss: 0.000950 2022/10/13 22:11:56 - mmengine - INFO - Epoch(train) [105][150/293] lr: 5.000000e-04 eta: 2:29:23 time: 0.331564 data_time: 0.076912 memory: 2315 loss_kpt: 0.000940 acc_pose: 0.686400 loss: 0.000940 2022/10/13 22:12:12 - mmengine - INFO - Epoch(train) [105][200/293] lr: 5.000000e-04 eta: 2:29:10 time: 0.328393 data_time: 0.063279 memory: 2315 loss_kpt: 0.000945 acc_pose: 0.665229 loss: 0.000945 2022/10/13 22:12:29 - mmengine - INFO - Epoch(train) [105][250/293] lr: 5.000000e-04 eta: 2:28:58 time: 0.330551 data_time: 0.100382 memory: 2315 loss_kpt: 0.000935 acc_pose: 0.763857 loss: 0.000935 2022/10/13 22:12:43 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:13:00 - mmengine - INFO - Epoch(train) [106][50/293] lr: 5.000000e-04 eta: 2:28:21 time: 0.341693 data_time: 0.090401 memory: 2315 loss_kpt: 0.000930 acc_pose: 0.687109 loss: 0.000930 2022/10/13 22:13:17 - mmengine - INFO - Epoch(train) [106][100/293] lr: 5.000000e-04 eta: 2:28:09 time: 0.338587 data_time: 0.096117 memory: 2315 loss_kpt: 0.000940 acc_pose: 0.638479 loss: 0.000940 2022/10/13 22:13:33 - mmengine - INFO - Epoch(train) [106][150/293] lr: 5.000000e-04 eta: 2:27:56 time: 0.323005 data_time: 0.067602 memory: 2315 loss_kpt: 0.000917 acc_pose: 0.711856 loss: 0.000917 2022/10/13 22:13:49 - mmengine - INFO - Epoch(train) [106][200/293] lr: 5.000000e-04 eta: 2:27:43 time: 0.325843 data_time: 0.066278 memory: 2315 loss_kpt: 0.000943 acc_pose: 0.710165 loss: 0.000943 2022/10/13 22:14:01 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:14:06 - mmengine - INFO - Epoch(train) [106][250/293] lr: 5.000000e-04 eta: 2:27:31 time: 0.325673 data_time: 0.072292 memory: 2315 loss_kpt: 0.000953 acc_pose: 0.660755 loss: 0.000953 2022/10/13 22:14:20 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:14:38 - mmengine - INFO - Epoch(train) [107][50/293] lr: 5.000000e-04 eta: 2:26:55 time: 0.356069 data_time: 0.181853 memory: 2315 loss_kpt: 0.000935 acc_pose: 0.718189 loss: 0.000935 2022/10/13 22:14:54 - mmengine - INFO - Epoch(train) [107][100/293] lr: 5.000000e-04 eta: 2:26:42 time: 0.322969 data_time: 0.142271 memory: 2315 loss_kpt: 0.000933 acc_pose: 0.702822 loss: 0.000933 2022/10/13 22:15:11 - mmengine - INFO - Epoch(train) [107][150/293] lr: 5.000000e-04 eta: 2:26:29 time: 0.329958 data_time: 0.066585 memory: 2315 loss_kpt: 0.000940 acc_pose: 0.752474 loss: 0.000940 2022/10/13 22:15:28 - mmengine - INFO - Epoch(train) [107][200/293] lr: 5.000000e-04 eta: 2:26:17 time: 0.340240 data_time: 0.104637 memory: 2315 loss_kpt: 0.000939 acc_pose: 0.732843 loss: 0.000939 2022/10/13 22:15:45 - mmengine - INFO - Epoch(train) [107][250/293] lr: 5.000000e-04 eta: 2:26:05 time: 0.344568 data_time: 0.182983 memory: 2315 loss_kpt: 0.000941 acc_pose: 0.704333 loss: 0.000941 2022/10/13 22:15:59 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:16:16 - mmengine - INFO - Epoch(train) [108][50/293] lr: 5.000000e-04 eta: 2:25:29 time: 0.347690 data_time: 0.117480 memory: 2315 loss_kpt: 0.000936 acc_pose: 0.717523 loss: 0.000936 2022/10/13 22:16:33 - mmengine - INFO - Epoch(train) [108][100/293] lr: 5.000000e-04 eta: 2:25:17 time: 0.332559 data_time: 0.069287 memory: 2315 loss_kpt: 0.000944 acc_pose: 0.642760 loss: 0.000944 2022/10/13 22:16:49 - mmengine - INFO - Epoch(train) [108][150/293] lr: 5.000000e-04 eta: 2:25:04 time: 0.329018 data_time: 0.128956 memory: 2315 loss_kpt: 0.000944 acc_pose: 0.742142 loss: 0.000944 2022/10/13 22:17:06 - mmengine - INFO - Epoch(train) [108][200/293] lr: 5.000000e-04 eta: 2:24:52 time: 0.337219 data_time: 0.168838 memory: 2315 loss_kpt: 0.000948 acc_pose: 0.697275 loss: 0.000948 2022/10/13 22:17:23 - mmengine - INFO - Epoch(train) [108][250/293] lr: 5.000000e-04 eta: 2:24:40 time: 0.334984 data_time: 0.177111 memory: 2315 loss_kpt: 0.000947 acc_pose: 0.711563 loss: 0.000947 2022/10/13 22:17:38 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:17:55 - mmengine - INFO - Epoch(train) [109][50/293] lr: 5.000000e-04 eta: 2:24:04 time: 0.351323 data_time: 0.202674 memory: 2315 loss_kpt: 0.000951 acc_pose: 0.679711 loss: 0.000951 2022/10/13 22:18:12 - mmengine - INFO - Epoch(train) [109][100/293] lr: 5.000000e-04 eta: 2:23:51 time: 0.333613 data_time: 0.151858 memory: 2315 loss_kpt: 0.000931 acc_pose: 0.687578 loss: 0.000931 2022/10/13 22:18:29 - mmengine - INFO - Epoch(train) [109][150/293] lr: 5.000000e-04 eta: 2:23:39 time: 0.335827 data_time: 0.077599 memory: 2315 loss_kpt: 0.000944 acc_pose: 0.697933 loss: 0.000944 2022/10/13 22:18:46 - mmengine - INFO - Epoch(train) [109][200/293] lr: 5.000000e-04 eta: 2:23:27 time: 0.348157 data_time: 0.072358 memory: 2315 loss_kpt: 0.000937 acc_pose: 0.736084 loss: 0.000937 2022/10/13 22:19:03 - mmengine - INFO - Epoch(train) [109][250/293] lr: 5.000000e-04 eta: 2:23:15 time: 0.331113 data_time: 0.064026 memory: 2315 loss_kpt: 0.000942 acc_pose: 0.730643 loss: 0.000942 2022/10/13 22:19:17 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:19:33 - mmengine - INFO - Epoch(train) [110][50/293] lr: 5.000000e-04 eta: 2:22:38 time: 0.332797 data_time: 0.116853 memory: 2315 loss_kpt: 0.000941 acc_pose: 0.732119 loss: 0.000941 2022/10/13 22:19:38 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:19:51 - mmengine - INFO - Epoch(train) [110][100/293] lr: 5.000000e-04 eta: 2:22:26 time: 0.346201 data_time: 0.067515 memory: 2315 loss_kpt: 0.000933 acc_pose: 0.685614 loss: 0.000933 2022/10/13 22:20:07 - mmengine - INFO - Epoch(train) [110][150/293] lr: 5.000000e-04 eta: 2:22:14 time: 0.329119 data_time: 0.094228 memory: 2315 loss_kpt: 0.000930 acc_pose: 0.771836 loss: 0.000930 2022/10/13 22:20:24 - mmengine - INFO - Epoch(train) [110][200/293] lr: 5.000000e-04 eta: 2:22:02 time: 0.345035 data_time: 0.093699 memory: 2315 loss_kpt: 0.000937 acc_pose: 0.716902 loss: 0.000937 2022/10/13 22:20:41 - mmengine - INFO - Epoch(train) [110][250/293] lr: 5.000000e-04 eta: 2:21:49 time: 0.340928 data_time: 0.069478 memory: 2315 loss_kpt: 0.000950 acc_pose: 0.733201 loss: 0.000950 2022/10/13 22:20:56 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:20:56 - mmengine - INFO - Saving checkpoint at 110 epochs 2022/10/13 22:21:04 - mmengine - INFO - Epoch(val) [110][50/407] eta: 0:00:42 time: 0.120408 data_time: 0.076189 memory: 2315 2022/10/13 22:21:09 - mmengine - INFO - Epoch(val) [110][100/407] eta: 0:00:35 time: 0.115350 data_time: 0.069906 memory: 426 2022/10/13 22:21:16 - mmengine - INFO - Epoch(val) [110][150/407] eta: 0:00:32 time: 0.125597 data_time: 0.081540 memory: 426 2022/10/13 22:21:21 - mmengine - INFO - Epoch(val) [110][200/407] eta: 0:00:22 time: 0.108532 data_time: 0.063901 memory: 426 2022/10/13 22:21:28 - mmengine - INFO - Epoch(val) [110][250/407] eta: 0:00:19 time: 0.126769 data_time: 0.081388 memory: 426 2022/10/13 22:21:33 - mmengine - INFO - Epoch(val) [110][300/407] eta: 0:00:12 time: 0.112711 data_time: 0.066888 memory: 426 2022/10/13 22:21:39 - mmengine - INFO - Epoch(val) [110][350/407] eta: 0:00:06 time: 0.118401 data_time: 0.074267 memory: 426 2022/10/13 22:21:44 - mmengine - INFO - Epoch(val) [110][400/407] eta: 0:00:00 time: 0.105754 data_time: 0.064436 memory: 426 2022/10/13 22:22:22 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 22:22:37 - mmengine - INFO - Epoch(val) [110][407/407] coco/AP: 0.575568 coco/AP .5: 0.844793 coco/AP .75: 0.634562 coco/AP (M): 0.544749 coco/AP (L): 0.634371 coco/AR: 0.644663 coco/AR .5: 0.894207 coco/AR .75: 0.707494 coco/AR (M): 0.601803 coco/AR (L): 0.704868 2022/10/13 22:22:37 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_100.pth is removed 2022/10/13 22:22:38 - mmengine - INFO - The best checkpoint with 0.5756 coco/AP at 110 epoch is saved to best_coco/AP_epoch_110.pth. 2022/10/13 22:22:56 - mmengine - INFO - Epoch(train) [111][50/293] lr: 5.000000e-04 eta: 2:21:14 time: 0.348497 data_time: 0.236297 memory: 2315 loss_kpt: 0.000921 acc_pose: 0.748363 loss: 0.000921 2022/10/13 22:23:12 - mmengine - INFO - Epoch(train) [111][100/293] lr: 5.000000e-04 eta: 2:21:01 time: 0.328953 data_time: 0.180502 memory: 2315 loss_kpt: 0.000937 acc_pose: 0.682060 loss: 0.000937 2022/10/13 22:23:29 - mmengine - INFO - Epoch(train) [111][150/293] lr: 5.000000e-04 eta: 2:20:49 time: 0.337389 data_time: 0.177188 memory: 2315 loss_kpt: 0.000930 acc_pose: 0.728995 loss: 0.000930 2022/10/13 22:23:47 - mmengine - INFO - Epoch(train) [111][200/293] lr: 5.000000e-04 eta: 2:20:37 time: 0.354744 data_time: 0.207926 memory: 2315 loss_kpt: 0.000945 acc_pose: 0.761691 loss: 0.000945 2022/10/13 22:24:04 - mmengine - INFO - Epoch(train) [111][250/293] lr: 5.000000e-04 eta: 2:20:25 time: 0.334178 data_time: 0.148746 memory: 2315 loss_kpt: 0.000945 acc_pose: 0.628925 loss: 0.000945 2022/10/13 22:24:18 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:24:35 - mmengine - INFO - Epoch(train) [112][50/293] lr: 5.000000e-04 eta: 2:19:49 time: 0.351177 data_time: 0.094745 memory: 2315 loss_kpt: 0.000942 acc_pose: 0.699539 loss: 0.000942 2022/10/13 22:24:52 - mmengine - INFO - Epoch(train) [112][100/293] lr: 5.000000e-04 eta: 2:19:37 time: 0.336016 data_time: 0.069555 memory: 2315 loss_kpt: 0.000932 acc_pose: 0.703857 loss: 0.000932 2022/10/13 22:25:09 - mmengine - INFO - Epoch(train) [112][150/293] lr: 5.000000e-04 eta: 2:19:24 time: 0.331449 data_time: 0.105507 memory: 2315 loss_kpt: 0.000931 acc_pose: 0.736498 loss: 0.000931 2022/10/13 22:25:25 - mmengine - INFO - Epoch(train) [112][200/293] lr: 5.000000e-04 eta: 2:19:12 time: 0.332632 data_time: 0.196636 memory: 2315 loss_kpt: 0.000933 acc_pose: 0.751740 loss: 0.000933 2022/10/13 22:25:42 - mmengine - INFO - Epoch(train) [112][250/293] lr: 5.000000e-04 eta: 2:18:59 time: 0.333036 data_time: 0.169307 memory: 2315 loss_kpt: 0.000935 acc_pose: 0.713438 loss: 0.000935 2022/10/13 22:25:56 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:26:13 - mmengine - INFO - Epoch(train) [113][50/293] lr: 5.000000e-04 eta: 2:18:23 time: 0.342983 data_time: 0.166748 memory: 2315 loss_kpt: 0.000937 acc_pose: 0.712638 loss: 0.000937 2022/10/13 22:26:30 - mmengine - INFO - Epoch(train) [113][100/293] lr: 5.000000e-04 eta: 2:18:11 time: 0.334305 data_time: 0.099973 memory: 2315 loss_kpt: 0.000926 acc_pose: 0.715122 loss: 0.000926 2022/10/13 22:26:47 - mmengine - INFO - Epoch(train) [113][150/293] lr: 5.000000e-04 eta: 2:17:58 time: 0.331200 data_time: 0.074055 memory: 2315 loss_kpt: 0.000925 acc_pose: 0.706544 loss: 0.000925 2022/10/13 22:26:58 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:27:04 - mmengine - INFO - Epoch(train) [113][200/293] lr: 5.000000e-04 eta: 2:17:46 time: 0.339577 data_time: 0.160801 memory: 2315 loss_kpt: 0.000957 acc_pose: 0.633661 loss: 0.000957 2022/10/13 22:27:21 - mmengine - INFO - Epoch(train) [113][250/293] lr: 5.000000e-04 eta: 2:17:34 time: 0.338832 data_time: 0.191393 memory: 2315 loss_kpt: 0.000938 acc_pose: 0.742889 loss: 0.000938 2022/10/13 22:27:34 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:27:52 - mmengine - INFO - Epoch(train) [114][50/293] lr: 5.000000e-04 eta: 2:16:58 time: 0.347505 data_time: 0.114678 memory: 2315 loss_kpt: 0.000921 acc_pose: 0.755219 loss: 0.000921 2022/10/13 22:28:09 - mmengine - INFO - Epoch(train) [114][100/293] lr: 5.000000e-04 eta: 2:16:46 time: 0.343057 data_time: 0.091670 memory: 2315 loss_kpt: 0.000931 acc_pose: 0.743470 loss: 0.000931 2022/10/13 22:28:27 - mmengine - INFO - Epoch(train) [114][150/293] lr: 5.000000e-04 eta: 2:16:34 time: 0.352693 data_time: 0.184221 memory: 2315 loss_kpt: 0.000921 acc_pose: 0.640703 loss: 0.000921 2022/10/13 22:28:44 - mmengine - INFO - Epoch(train) [114][200/293] lr: 5.000000e-04 eta: 2:16:22 time: 0.340263 data_time: 0.187019 memory: 2315 loss_kpt: 0.000941 acc_pose: 0.691029 loss: 0.000941 2022/10/13 22:29:00 - mmengine - INFO - Epoch(train) [114][250/293] lr: 5.000000e-04 eta: 2:16:09 time: 0.330413 data_time: 0.172818 memory: 2315 loss_kpt: 0.000937 acc_pose: 0.734729 loss: 0.000937 2022/10/13 22:29:14 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:29:32 - mmengine - INFO - Epoch(train) [115][50/293] lr: 5.000000e-04 eta: 2:15:34 time: 0.348710 data_time: 0.091174 memory: 2315 loss_kpt: 0.000942 acc_pose: 0.678723 loss: 0.000942 2022/10/13 22:29:48 - mmengine - INFO - Epoch(train) [115][100/293] lr: 5.000000e-04 eta: 2:15:21 time: 0.329246 data_time: 0.064528 memory: 2315 loss_kpt: 0.000922 acc_pose: 0.702806 loss: 0.000922 2022/10/13 22:30:06 - mmengine - INFO - Epoch(train) [115][150/293] lr: 5.000000e-04 eta: 2:15:09 time: 0.346280 data_time: 0.118599 memory: 2315 loss_kpt: 0.000932 acc_pose: 0.676365 loss: 0.000932 2022/10/13 22:30:22 - mmengine - INFO - Epoch(train) [115][200/293] lr: 5.000000e-04 eta: 2:14:57 time: 0.333682 data_time: 0.178193 memory: 2315 loss_kpt: 0.000921 acc_pose: 0.686046 loss: 0.000921 2022/10/13 22:30:39 - mmengine - INFO - Epoch(train) [115][250/293] lr: 5.000000e-04 eta: 2:14:44 time: 0.328182 data_time: 0.149219 memory: 2315 loss_kpt: 0.000931 acc_pose: 0.697965 loss: 0.000931 2022/10/13 22:30:53 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:31:10 - mmengine - INFO - Epoch(train) [116][50/293] lr: 5.000000e-04 eta: 2:14:09 time: 0.341618 data_time: 0.082408 memory: 2315 loss_kpt: 0.000924 acc_pose: 0.719368 loss: 0.000924 2022/10/13 22:31:27 - mmengine - INFO - Epoch(train) [116][100/293] lr: 5.000000e-04 eta: 2:13:57 time: 0.352302 data_time: 0.069570 memory: 2315 loss_kpt: 0.000922 acc_pose: 0.744073 loss: 0.000922 2022/10/13 22:31:44 - mmengine - INFO - Epoch(train) [116][150/293] lr: 5.000000e-04 eta: 2:13:44 time: 0.339237 data_time: 0.069636 memory: 2315 loss_kpt: 0.000944 acc_pose: 0.705177 loss: 0.000944 2022/10/13 22:32:01 - mmengine - INFO - Epoch(train) [116][200/293] lr: 5.000000e-04 eta: 2:13:32 time: 0.338495 data_time: 0.068324 memory: 2315 loss_kpt: 0.000931 acc_pose: 0.699736 loss: 0.000931 2022/10/13 22:32:18 - mmengine - INFO - Epoch(train) [116][250/293] lr: 5.000000e-04 eta: 2:13:19 time: 0.330677 data_time: 0.062636 memory: 2315 loss_kpt: 0.000920 acc_pose: 0.737714 loss: 0.000920 2022/10/13 22:32:32 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:32:37 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:32:49 - mmengine - INFO - Epoch(train) [117][50/293] lr: 5.000000e-04 eta: 2:12:44 time: 0.342179 data_time: 0.110525 memory: 2315 loss_kpt: 0.000935 acc_pose: 0.684953 loss: 0.000935 2022/10/13 22:33:07 - mmengine - INFO - Epoch(train) [117][100/293] lr: 5.000000e-04 eta: 2:12:32 time: 0.355445 data_time: 0.069444 memory: 2315 loss_kpt: 0.000922 acc_pose: 0.646299 loss: 0.000922 2022/10/13 22:33:24 - mmengine - INFO - Epoch(train) [117][150/293] lr: 5.000000e-04 eta: 2:12:19 time: 0.332619 data_time: 0.150201 memory: 2315 loss_kpt: 0.000930 acc_pose: 0.683423 loss: 0.000930 2022/10/13 22:33:41 - mmengine - INFO - Epoch(train) [117][200/293] lr: 5.000000e-04 eta: 2:12:07 time: 0.341811 data_time: 0.169351 memory: 2315 loss_kpt: 0.000925 acc_pose: 0.739610 loss: 0.000925 2022/10/13 22:33:58 - mmengine - INFO - Epoch(train) [117][250/293] lr: 5.000000e-04 eta: 2:11:54 time: 0.340658 data_time: 0.073146 memory: 2315 loss_kpt: 0.000930 acc_pose: 0.694282 loss: 0.000930 2022/10/13 22:34:12 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:34:29 - mmengine - INFO - Epoch(train) [118][50/293] lr: 5.000000e-04 eta: 2:11:20 time: 0.350381 data_time: 0.093400 memory: 2315 loss_kpt: 0.000917 acc_pose: 0.679016 loss: 0.000917 2022/10/13 22:34:46 - mmengine - INFO - Epoch(train) [118][100/293] lr: 5.000000e-04 eta: 2:11:07 time: 0.333374 data_time: 0.124198 memory: 2315 loss_kpt: 0.000959 acc_pose: 0.745188 loss: 0.000959 2022/10/13 22:35:03 - mmengine - INFO - Epoch(train) [118][150/293] lr: 5.000000e-04 eta: 2:10:55 time: 0.344420 data_time: 0.119040 memory: 2315 loss_kpt: 0.000924 acc_pose: 0.733358 loss: 0.000924 2022/10/13 22:35:20 - mmengine - INFO - Epoch(train) [118][200/293] lr: 5.000000e-04 eta: 2:10:42 time: 0.328794 data_time: 0.141840 memory: 2315 loss_kpt: 0.000942 acc_pose: 0.722668 loss: 0.000942 2022/10/13 22:35:36 - mmengine - INFO - Epoch(train) [118][250/293] lr: 5.000000e-04 eta: 2:10:29 time: 0.327089 data_time: 0.115226 memory: 2315 loss_kpt: 0.000926 acc_pose: 0.751500 loss: 0.000926 2022/10/13 22:35:51 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:36:08 - mmengine - INFO - Epoch(train) [119][50/293] lr: 5.000000e-04 eta: 2:09:54 time: 0.343374 data_time: 0.133278 memory: 2315 loss_kpt: 0.000921 acc_pose: 0.702895 loss: 0.000921 2022/10/13 22:36:24 - mmengine - INFO - Epoch(train) [119][100/293] lr: 5.000000e-04 eta: 2:09:41 time: 0.329117 data_time: 0.081534 memory: 2315 loss_kpt: 0.000927 acc_pose: 0.726877 loss: 0.000927 2022/10/13 22:36:41 - mmengine - INFO - Epoch(train) [119][150/293] lr: 5.000000e-04 eta: 2:09:29 time: 0.336623 data_time: 0.158482 memory: 2315 loss_kpt: 0.000948 acc_pose: 0.753052 loss: 0.000948 2022/10/13 22:36:58 - mmengine - INFO - Epoch(train) [119][200/293] lr: 5.000000e-04 eta: 2:09:16 time: 0.331972 data_time: 0.073470 memory: 2315 loss_kpt: 0.000926 acc_pose: 0.697190 loss: 0.000926 2022/10/13 22:37:15 - mmengine - INFO - Epoch(train) [119][250/293] lr: 5.000000e-04 eta: 2:09:03 time: 0.336702 data_time: 0.076276 memory: 2315 loss_kpt: 0.000925 acc_pose: 0.738248 loss: 0.000925 2022/10/13 22:37:28 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:37:45 - mmengine - INFO - Epoch(train) [120][50/293] lr: 5.000000e-04 eta: 2:08:28 time: 0.335723 data_time: 0.138517 memory: 2315 loss_kpt: 0.000928 acc_pose: 0.702949 loss: 0.000928 2022/10/13 22:38:02 - mmengine - INFO - Epoch(train) [120][100/293] lr: 5.000000e-04 eta: 2:08:16 time: 0.340167 data_time: 0.127281 memory: 2315 loss_kpt: 0.000946 acc_pose: 0.737019 loss: 0.000946 2022/10/13 22:38:13 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:38:19 - mmengine - INFO - Epoch(train) [120][150/293] lr: 5.000000e-04 eta: 2:08:03 time: 0.340609 data_time: 0.129770 memory: 2315 loss_kpt: 0.000943 acc_pose: 0.645081 loss: 0.000943 2022/10/13 22:38:36 - mmengine - INFO - Epoch(train) [120][200/293] lr: 5.000000e-04 eta: 2:07:50 time: 0.334093 data_time: 0.154778 memory: 2315 loss_kpt: 0.000917 acc_pose: 0.743177 loss: 0.000917 2022/10/13 22:38:53 - mmengine - INFO - Epoch(train) [120][250/293] lr: 5.000000e-04 eta: 2:07:38 time: 0.334969 data_time: 0.158274 memory: 2315 loss_kpt: 0.000931 acc_pose: 0.736100 loss: 0.000931 2022/10/13 22:39:07 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:39:07 - mmengine - INFO - Saving checkpoint at 120 epochs 2022/10/13 22:39:14 - mmengine - INFO - Epoch(val) [120][50/407] eta: 0:00:40 time: 0.113236 data_time: 0.068195 memory: 2315 2022/10/13 22:39:20 - mmengine - INFO - Epoch(val) [120][100/407] eta: 0:00:34 time: 0.112461 data_time: 0.069763 memory: 426 2022/10/13 22:39:26 - mmengine - INFO - Epoch(val) [120][150/407] eta: 0:00:30 time: 0.118308 data_time: 0.073722 memory: 426 2022/10/13 22:39:32 - mmengine - INFO - Epoch(val) [120][200/407] eta: 0:00:23 time: 0.114148 data_time: 0.069623 memory: 426 2022/10/13 22:39:37 - mmengine - INFO - Epoch(val) [120][250/407] eta: 0:00:17 time: 0.109324 data_time: 0.066209 memory: 426 2022/10/13 22:39:43 - mmengine - INFO - Epoch(val) [120][300/407] eta: 0:00:12 time: 0.114720 data_time: 0.071537 memory: 426 2022/10/13 22:39:49 - mmengine - INFO - Epoch(val) [120][350/407] eta: 0:00:06 time: 0.114593 data_time: 0.070029 memory: 426 2022/10/13 22:39:54 - mmengine - INFO - Epoch(val) [120][400/407] eta: 0:00:00 time: 0.111428 data_time: 0.068107 memory: 426 2022/10/13 22:40:32 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 22:40:46 - mmengine - INFO - Epoch(val) [120][407/407] coco/AP: 0.581734 coco/AP .5: 0.848542 coco/AP .75: 0.643726 coco/AP (M): 0.549776 coco/AP (L): 0.642905 coco/AR: 0.648552 coco/AR .5: 0.897513 coco/AR .75: 0.712531 coco/AR (M): 0.605955 coco/AR (L): 0.708510 2022/10/13 22:40:46 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_110.pth is removed 2022/10/13 22:40:48 - mmengine - INFO - The best checkpoint with 0.5817 coco/AP at 120 epoch is saved to best_coco/AP_epoch_120.pth. 2022/10/13 22:41:05 - mmengine - INFO - Epoch(train) [121][50/293] lr: 5.000000e-04 eta: 2:07:03 time: 0.337352 data_time: 0.202825 memory: 2315 loss_kpt: 0.000925 acc_pose: 0.721534 loss: 0.000925 2022/10/13 22:41:22 - mmengine - INFO - Epoch(train) [121][100/293] lr: 5.000000e-04 eta: 2:06:50 time: 0.333075 data_time: 0.120507 memory: 2315 loss_kpt: 0.000922 acc_pose: 0.685387 loss: 0.000922 2022/10/13 22:41:39 - mmengine - INFO - Epoch(train) [121][150/293] lr: 5.000000e-04 eta: 2:06:38 time: 0.340008 data_time: 0.136473 memory: 2315 loss_kpt: 0.000913 acc_pose: 0.685324 loss: 0.000913 2022/10/13 22:41:55 - mmengine - INFO - Epoch(train) [121][200/293] lr: 5.000000e-04 eta: 2:06:24 time: 0.324195 data_time: 0.102682 memory: 2315 loss_kpt: 0.000921 acc_pose: 0.734188 loss: 0.000921 2022/10/13 22:42:11 - mmengine - INFO - Epoch(train) [121][250/293] lr: 5.000000e-04 eta: 2:06:11 time: 0.328777 data_time: 0.068042 memory: 2315 loss_kpt: 0.000932 acc_pose: 0.721278 loss: 0.000932 2022/10/13 22:42:25 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:42:42 - mmengine - INFO - Epoch(train) [122][50/293] lr: 5.000000e-04 eta: 2:05:37 time: 0.348128 data_time: 0.092067 memory: 2315 loss_kpt: 0.000928 acc_pose: 0.698732 loss: 0.000928 2022/10/13 22:42:59 - mmengine - INFO - Epoch(train) [122][100/293] lr: 5.000000e-04 eta: 2:05:25 time: 0.336299 data_time: 0.097075 memory: 2315 loss_kpt: 0.000925 acc_pose: 0.733362 loss: 0.000925 2022/10/13 22:43:16 - mmengine - INFO - Epoch(train) [122][150/293] lr: 5.000000e-04 eta: 2:05:12 time: 0.341265 data_time: 0.175926 memory: 2315 loss_kpt: 0.000945 acc_pose: 0.678025 loss: 0.000945 2022/10/13 22:43:33 - mmengine - INFO - Epoch(train) [122][200/293] lr: 5.000000e-04 eta: 2:04:59 time: 0.329989 data_time: 0.185228 memory: 2315 loss_kpt: 0.000915 acc_pose: 0.700042 loss: 0.000915 2022/10/13 22:43:49 - mmengine - INFO - Epoch(train) [122][250/293] lr: 5.000000e-04 eta: 2:04:46 time: 0.328155 data_time: 0.138636 memory: 2315 loss_kpt: 0.000920 acc_pose: 0.706279 loss: 0.000920 2022/10/13 22:44:03 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:44:20 - mmengine - INFO - Epoch(train) [123][50/293] lr: 5.000000e-04 eta: 2:04:12 time: 0.346601 data_time: 0.084748 memory: 2315 loss_kpt: 0.000919 acc_pose: 0.697091 loss: 0.000919 2022/10/13 22:44:37 - mmengine - INFO - Epoch(train) [123][100/293] lr: 5.000000e-04 eta: 2:03:59 time: 0.336654 data_time: 0.062091 memory: 2315 loss_kpt: 0.000939 acc_pose: 0.629043 loss: 0.000939 2022/10/13 22:44:54 - mmengine - INFO - Epoch(train) [123][150/293] lr: 5.000000e-04 eta: 2:03:46 time: 0.326916 data_time: 0.094692 memory: 2315 loss_kpt: 0.000925 acc_pose: 0.703207 loss: 0.000925 2022/10/13 22:45:10 - mmengine - INFO - Epoch(train) [123][200/293] lr: 5.000000e-04 eta: 2:03:33 time: 0.328861 data_time: 0.117893 memory: 2315 loss_kpt: 0.000922 acc_pose: 0.745606 loss: 0.000922 2022/10/13 22:45:27 - mmengine - INFO - Epoch(train) [123][250/293] lr: 5.000000e-04 eta: 2:03:20 time: 0.338110 data_time: 0.064031 memory: 2315 loss_kpt: 0.000932 acc_pose: 0.738773 loss: 0.000932 2022/10/13 22:45:28 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:45:41 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:45:59 - mmengine - INFO - Epoch(train) [124][50/293] lr: 5.000000e-04 eta: 2:02:46 time: 0.345142 data_time: 0.114448 memory: 2315 loss_kpt: 0.000938 acc_pose: 0.761591 loss: 0.000938 2022/10/13 22:46:15 - mmengine - INFO - Epoch(train) [124][100/293] lr: 5.000000e-04 eta: 2:02:33 time: 0.329989 data_time: 0.129642 memory: 2315 loss_kpt: 0.000925 acc_pose: 0.733123 loss: 0.000925 2022/10/13 22:46:32 - mmengine - INFO - Epoch(train) [124][150/293] lr: 5.000000e-04 eta: 2:02:20 time: 0.335271 data_time: 0.097083 memory: 2315 loss_kpt: 0.000924 acc_pose: 0.705680 loss: 0.000924 2022/10/13 22:46:49 - mmengine - INFO - Epoch(train) [124][200/293] lr: 5.000000e-04 eta: 2:02:08 time: 0.339030 data_time: 0.151849 memory: 2315 loss_kpt: 0.000935 acc_pose: 0.716156 loss: 0.000935 2022/10/13 22:47:06 - mmengine - INFO - Epoch(train) [124][250/293] lr: 5.000000e-04 eta: 2:01:55 time: 0.338635 data_time: 0.162428 memory: 2315 loss_kpt: 0.000918 acc_pose: 0.662035 loss: 0.000918 2022/10/13 22:47:19 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:47:37 - mmengine - INFO - Epoch(train) [125][50/293] lr: 5.000000e-04 eta: 2:01:21 time: 0.349739 data_time: 0.096216 memory: 2315 loss_kpt: 0.000941 acc_pose: 0.768031 loss: 0.000941 2022/10/13 22:47:53 - mmengine - INFO - Epoch(train) [125][100/293] lr: 5.000000e-04 eta: 2:01:08 time: 0.329860 data_time: 0.066349 memory: 2315 loss_kpt: 0.000945 acc_pose: 0.665763 loss: 0.000945 2022/10/13 22:48:10 - mmengine - INFO - Epoch(train) [125][150/293] lr: 5.000000e-04 eta: 2:00:55 time: 0.329736 data_time: 0.146741 memory: 2315 loss_kpt: 0.000931 acc_pose: 0.733264 loss: 0.000931 2022/10/13 22:48:27 - mmengine - INFO - Epoch(train) [125][200/293] lr: 5.000000e-04 eta: 2:00:42 time: 0.337525 data_time: 0.117135 memory: 2315 loss_kpt: 0.000937 acc_pose: 0.721879 loss: 0.000937 2022/10/13 22:48:43 - mmengine - INFO - Epoch(train) [125][250/293] lr: 5.000000e-04 eta: 2:00:29 time: 0.333252 data_time: 0.098844 memory: 2315 loss_kpt: 0.000921 acc_pose: 0.757185 loss: 0.000921 2022/10/13 22:48:57 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:49:14 - mmengine - INFO - Epoch(train) [126][50/293] lr: 5.000000e-04 eta: 1:59:56 time: 0.338599 data_time: 0.086511 memory: 2315 loss_kpt: 0.000915 acc_pose: 0.719447 loss: 0.000915 2022/10/13 22:49:30 - mmengine - INFO - Epoch(train) [126][100/293] lr: 5.000000e-04 eta: 1:59:42 time: 0.325701 data_time: 0.063511 memory: 2315 loss_kpt: 0.000914 acc_pose: 0.769879 loss: 0.000914 2022/10/13 22:49:47 - mmengine - INFO - Epoch(train) [126][150/293] lr: 5.000000e-04 eta: 1:59:29 time: 0.335554 data_time: 0.110038 memory: 2315 loss_kpt: 0.000924 acc_pose: 0.719261 loss: 0.000924 2022/10/13 22:50:04 - mmengine - INFO - Epoch(train) [126][200/293] lr: 5.000000e-04 eta: 1:59:16 time: 0.331935 data_time: 0.095223 memory: 2315 loss_kpt: 0.000923 acc_pose: 0.736542 loss: 0.000923 2022/10/13 22:50:20 - mmengine - INFO - Epoch(train) [126][250/293] lr: 5.000000e-04 eta: 1:59:03 time: 0.328011 data_time: 0.164211 memory: 2315 loss_kpt: 0.000923 acc_pose: 0.714081 loss: 0.000923 2022/10/13 22:50:35 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:50:51 - mmengine - INFO - Epoch(train) [127][50/293] lr: 5.000000e-04 eta: 1:58:29 time: 0.330715 data_time: 0.081549 memory: 2315 loss_kpt: 0.000925 acc_pose: 0.694250 loss: 0.000925 2022/10/13 22:51:02 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:51:08 - mmengine - INFO - Epoch(train) [127][100/293] lr: 5.000000e-04 eta: 1:58:16 time: 0.327151 data_time: 0.068392 memory: 2315 loss_kpt: 0.000912 acc_pose: 0.723653 loss: 0.000912 2022/10/13 22:51:24 - mmengine - INFO - Epoch(train) [127][150/293] lr: 5.000000e-04 eta: 1:58:03 time: 0.329911 data_time: 0.089873 memory: 2315 loss_kpt: 0.000941 acc_pose: 0.744874 loss: 0.000941 2022/10/13 22:51:41 - mmengine - INFO - Epoch(train) [127][200/293] lr: 5.000000e-04 eta: 1:57:50 time: 0.328463 data_time: 0.066913 memory: 2315 loss_kpt: 0.000919 acc_pose: 0.742287 loss: 0.000919 2022/10/13 22:51:57 - mmengine - INFO - Epoch(train) [127][250/293] lr: 5.000000e-04 eta: 1:57:36 time: 0.327950 data_time: 0.078954 memory: 2315 loss_kpt: 0.000928 acc_pose: 0.733948 loss: 0.000928 2022/10/13 22:52:11 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:52:28 - mmengine - INFO - Epoch(train) [128][50/293] lr: 5.000000e-04 eta: 1:57:03 time: 0.337899 data_time: 0.082541 memory: 2315 loss_kpt: 0.000927 acc_pose: 0.677587 loss: 0.000927 2022/10/13 22:52:44 - mmengine - INFO - Epoch(train) [128][100/293] lr: 5.000000e-04 eta: 1:56:50 time: 0.325796 data_time: 0.067005 memory: 2315 loss_kpt: 0.000927 acc_pose: 0.638052 loss: 0.000927 2022/10/13 22:53:01 - mmengine - INFO - Epoch(train) [128][150/293] lr: 5.000000e-04 eta: 1:56:37 time: 0.335329 data_time: 0.070314 memory: 2315 loss_kpt: 0.000922 acc_pose: 0.716369 loss: 0.000922 2022/10/13 22:53:18 - mmengine - INFO - Epoch(train) [128][200/293] lr: 5.000000e-04 eta: 1:56:23 time: 0.328452 data_time: 0.111063 memory: 2315 loss_kpt: 0.000931 acc_pose: 0.690161 loss: 0.000931 2022/10/13 22:53:34 - mmengine - INFO - Epoch(train) [128][250/293] lr: 5.000000e-04 eta: 1:56:10 time: 0.328307 data_time: 0.165835 memory: 2315 loss_kpt: 0.000927 acc_pose: 0.683990 loss: 0.000927 2022/10/13 22:53:48 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:54:05 - mmengine - INFO - Epoch(train) [129][50/293] lr: 5.000000e-04 eta: 1:55:37 time: 0.334585 data_time: 0.189323 memory: 2315 loss_kpt: 0.000920 acc_pose: 0.727290 loss: 0.000920 2022/10/13 22:54:21 - mmengine - INFO - Epoch(train) [129][100/293] lr: 5.000000e-04 eta: 1:55:23 time: 0.325468 data_time: 0.122292 memory: 2315 loss_kpt: 0.000918 acc_pose: 0.661642 loss: 0.000918 2022/10/13 22:54:37 - mmengine - INFO - Epoch(train) [129][150/293] lr: 5.000000e-04 eta: 1:55:10 time: 0.330670 data_time: 0.108523 memory: 2315 loss_kpt: 0.000912 acc_pose: 0.693222 loss: 0.000912 2022/10/13 22:54:54 - mmengine - INFO - Epoch(train) [129][200/293] lr: 5.000000e-04 eta: 1:54:57 time: 0.330791 data_time: 0.128660 memory: 2315 loss_kpt: 0.000933 acc_pose: 0.734407 loss: 0.000933 2022/10/13 22:55:10 - mmengine - INFO - Epoch(train) [129][250/293] lr: 5.000000e-04 eta: 1:54:44 time: 0.328793 data_time: 0.080236 memory: 2315 loss_kpt: 0.000927 acc_pose: 0.711441 loss: 0.000927 2022/10/13 22:55:25 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:55:42 - mmengine - INFO - Epoch(train) [130][50/293] lr: 5.000000e-04 eta: 1:54:11 time: 0.338902 data_time: 0.094455 memory: 2315 loss_kpt: 0.000929 acc_pose: 0.668553 loss: 0.000929 2022/10/13 22:55:59 - mmengine - INFO - Epoch(train) [130][100/293] lr: 5.000000e-04 eta: 1:53:58 time: 0.342243 data_time: 0.071025 memory: 2315 loss_kpt: 0.000902 acc_pose: 0.698163 loss: 0.000902 2022/10/13 22:56:15 - mmengine - INFO - Epoch(train) [130][150/293] lr: 5.000000e-04 eta: 1:53:45 time: 0.329608 data_time: 0.065904 memory: 2315 loss_kpt: 0.000926 acc_pose: 0.705591 loss: 0.000926 2022/10/13 22:56:32 - mmengine - INFO - Epoch(train) [130][200/293] lr: 5.000000e-04 eta: 1:53:32 time: 0.333408 data_time: 0.116722 memory: 2315 loss_kpt: 0.000934 acc_pose: 0.747334 loss: 0.000934 2022/10/13 22:56:33 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:56:49 - mmengine - INFO - Epoch(train) [130][250/293] lr: 5.000000e-04 eta: 1:53:19 time: 0.335632 data_time: 0.117917 memory: 2315 loss_kpt: 0.000927 acc_pose: 0.672979 loss: 0.000927 2022/10/13 22:57:03 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 22:57:03 - mmengine - INFO - Saving checkpoint at 130 epochs 2022/10/13 22:57:11 - mmengine - INFO - Epoch(val) [130][50/407] eta: 0:00:42 time: 0.117673 data_time: 0.073686 memory: 2315 2022/10/13 22:57:16 - mmengine - INFO - Epoch(val) [130][100/407] eta: 0:00:34 time: 0.113898 data_time: 0.066828 memory: 426 2022/10/13 22:57:22 - mmengine - INFO - Epoch(val) [130][150/407] eta: 0:00:30 time: 0.118278 data_time: 0.073741 memory: 426 2022/10/13 22:57:28 - mmengine - INFO - Epoch(val) [130][200/407] eta: 0:00:22 time: 0.110274 data_time: 0.067144 memory: 426 2022/10/13 22:57:34 - mmengine - INFO - Epoch(val) [130][250/407] eta: 0:00:17 time: 0.114289 data_time: 0.069296 memory: 426 2022/10/13 22:57:40 - mmengine - INFO - Epoch(val) [130][300/407] eta: 0:00:12 time: 0.118320 data_time: 0.074713 memory: 426 2022/10/13 22:57:45 - mmengine - INFO - Epoch(val) [130][350/407] eta: 0:00:06 time: 0.112520 data_time: 0.069150 memory: 426 2022/10/13 22:57:50 - mmengine - INFO - Epoch(val) [130][400/407] eta: 0:00:00 time: 0.103760 data_time: 0.063089 memory: 426 2022/10/13 22:58:28 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 22:58:42 - mmengine - INFO - Epoch(val) [130][407/407] coco/AP: 0.584466 coco/AP .5: 0.849728 coco/AP .75: 0.649806 coco/AP (M): 0.554045 coco/AP (L): 0.643841 coco/AR: 0.651732 coco/AR .5: 0.897040 coco/AR .75: 0.719931 coco/AR (M): 0.610270 coco/AR (L): 0.709959 2022/10/13 22:58:42 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_120.pth is removed 2022/10/13 22:58:44 - mmengine - INFO - The best checkpoint with 0.5845 coco/AP at 130 epoch is saved to best_coco/AP_epoch_130.pth. 2022/10/13 22:59:01 - mmengine - INFO - Epoch(train) [131][50/293] lr: 5.000000e-04 eta: 1:52:45 time: 0.337603 data_time: 0.162914 memory: 2315 loss_kpt: 0.000917 acc_pose: 0.727475 loss: 0.000917 2022/10/13 22:59:17 - mmengine - INFO - Epoch(train) [131][100/293] lr: 5.000000e-04 eta: 1:52:32 time: 0.326147 data_time: 0.078315 memory: 2315 loss_kpt: 0.000931 acc_pose: 0.664000 loss: 0.000931 2022/10/13 22:59:34 - mmengine - INFO - Epoch(train) [131][150/293] lr: 5.000000e-04 eta: 1:52:19 time: 0.331566 data_time: 0.156017 memory: 2315 loss_kpt: 0.000945 acc_pose: 0.710558 loss: 0.000945 2022/10/13 22:59:50 - mmengine - INFO - Epoch(train) [131][200/293] lr: 5.000000e-04 eta: 1:52:06 time: 0.329887 data_time: 0.172660 memory: 2315 loss_kpt: 0.000942 acc_pose: 0.736970 loss: 0.000942 2022/10/13 23:00:07 - mmengine - INFO - Epoch(train) [131][250/293] lr: 5.000000e-04 eta: 1:51:52 time: 0.329973 data_time: 0.151735 memory: 2315 loss_kpt: 0.000926 acc_pose: 0.740325 loss: 0.000926 2022/10/13 23:00:20 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:00:37 - mmengine - INFO - Epoch(train) [132][50/293] lr: 5.000000e-04 eta: 1:51:20 time: 0.339992 data_time: 0.096191 memory: 2315 loss_kpt: 0.000937 acc_pose: 0.685646 loss: 0.000937 2022/10/13 23:00:54 - mmengine - INFO - Epoch(train) [132][100/293] lr: 5.000000e-04 eta: 1:51:06 time: 0.326707 data_time: 0.069480 memory: 2315 loss_kpt: 0.000930 acc_pose: 0.649241 loss: 0.000930 2022/10/13 23:01:10 - mmengine - INFO - Epoch(train) [132][150/293] lr: 5.000000e-04 eta: 1:50:53 time: 0.337502 data_time: 0.122069 memory: 2315 loss_kpt: 0.000923 acc_pose: 0.690751 loss: 0.000923 2022/10/13 23:01:27 - mmengine - INFO - Epoch(train) [132][200/293] lr: 5.000000e-04 eta: 1:50:40 time: 0.332257 data_time: 0.163659 memory: 2315 loss_kpt: 0.000925 acc_pose: 0.705042 loss: 0.000925 2022/10/13 23:01:44 - mmengine - INFO - Epoch(train) [132][250/293] lr: 5.000000e-04 eta: 1:50:27 time: 0.328784 data_time: 0.159887 memory: 2315 loss_kpt: 0.000915 acc_pose: 0.667163 loss: 0.000915 2022/10/13 23:01:58 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:02:15 - mmengine - INFO - Epoch(train) [133][50/293] lr: 5.000000e-04 eta: 1:49:54 time: 0.341332 data_time: 0.092196 memory: 2315 loss_kpt: 0.000920 acc_pose: 0.735986 loss: 0.000920 2022/10/13 23:02:31 - mmengine - INFO - Epoch(train) [133][100/293] lr: 5.000000e-04 eta: 1:49:41 time: 0.333990 data_time: 0.065536 memory: 2315 loss_kpt: 0.000916 acc_pose: 0.738910 loss: 0.000916 2022/10/13 23:02:48 - mmengine - INFO - Epoch(train) [133][150/293] lr: 5.000000e-04 eta: 1:49:28 time: 0.330991 data_time: 0.072731 memory: 2315 loss_kpt: 0.000915 acc_pose: 0.697123 loss: 0.000915 2022/10/13 23:03:04 - mmengine - INFO - Epoch(train) [133][200/293] lr: 5.000000e-04 eta: 1:49:14 time: 0.325290 data_time: 0.083576 memory: 2315 loss_kpt: 0.000916 acc_pose: 0.675467 loss: 0.000916 2022/10/13 23:03:21 - mmengine - INFO - Epoch(train) [133][250/293] lr: 5.000000e-04 eta: 1:49:01 time: 0.335411 data_time: 0.081157 memory: 2315 loss_kpt: 0.000930 acc_pose: 0.760218 loss: 0.000930 2022/10/13 23:03:35 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:03:46 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:03:52 - mmengine - INFO - Epoch(train) [134][50/293] lr: 5.000000e-04 eta: 1:48:28 time: 0.337287 data_time: 0.130181 memory: 2315 loss_kpt: 0.000925 acc_pose: 0.780073 loss: 0.000925 2022/10/13 23:04:08 - mmengine - INFO - Epoch(train) [134][100/293] lr: 5.000000e-04 eta: 1:48:15 time: 0.335462 data_time: 0.101103 memory: 2315 loss_kpt: 0.000901 acc_pose: 0.663178 loss: 0.000901 2022/10/13 23:04:25 - mmengine - INFO - Epoch(train) [134][150/293] lr: 5.000000e-04 eta: 1:48:02 time: 0.328477 data_time: 0.114510 memory: 2315 loss_kpt: 0.000939 acc_pose: 0.678663 loss: 0.000939 2022/10/13 23:04:42 - mmengine - INFO - Epoch(train) [134][200/293] lr: 5.000000e-04 eta: 1:47:49 time: 0.337760 data_time: 0.120263 memory: 2315 loss_kpt: 0.000919 acc_pose: 0.725727 loss: 0.000919 2022/10/13 23:04:58 - mmengine - INFO - Epoch(train) [134][250/293] lr: 5.000000e-04 eta: 1:47:36 time: 0.334200 data_time: 0.168944 memory: 2315 loss_kpt: 0.000913 acc_pose: 0.725291 loss: 0.000913 2022/10/13 23:05:12 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:05:29 - mmengine - INFO - Epoch(train) [135][50/293] lr: 5.000000e-04 eta: 1:47:03 time: 0.334721 data_time: 0.090801 memory: 2315 loss_kpt: 0.000916 acc_pose: 0.706459 loss: 0.000916 2022/10/13 23:05:45 - mmengine - INFO - Epoch(train) [135][100/293] lr: 5.000000e-04 eta: 1:46:50 time: 0.331283 data_time: 0.109651 memory: 2315 loss_kpt: 0.000911 acc_pose: 0.624385 loss: 0.000911 2022/10/13 23:06:02 - mmengine - INFO - Epoch(train) [135][150/293] lr: 5.000000e-04 eta: 1:46:37 time: 0.334953 data_time: 0.111670 memory: 2315 loss_kpt: 0.000913 acc_pose: 0.782452 loss: 0.000913 2022/10/13 23:06:19 - mmengine - INFO - Epoch(train) [135][200/293] lr: 5.000000e-04 eta: 1:46:23 time: 0.334617 data_time: 0.171360 memory: 2315 loss_kpt: 0.000933 acc_pose: 0.723698 loss: 0.000933 2022/10/13 23:06:35 - mmengine - INFO - Epoch(train) [135][250/293] lr: 5.000000e-04 eta: 1:46:10 time: 0.333083 data_time: 0.173269 memory: 2315 loss_kpt: 0.000934 acc_pose: 0.742372 loss: 0.000934 2022/10/13 23:06:50 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:07:07 - mmengine - INFO - Epoch(train) [136][50/293] lr: 5.000000e-04 eta: 1:45:38 time: 0.335726 data_time: 0.116855 memory: 2315 loss_kpt: 0.000915 acc_pose: 0.725918 loss: 0.000915 2022/10/13 23:07:23 - mmengine - INFO - Epoch(train) [136][100/293] lr: 5.000000e-04 eta: 1:45:24 time: 0.330276 data_time: 0.097081 memory: 2315 loss_kpt: 0.000927 acc_pose: 0.687878 loss: 0.000927 2022/10/13 23:07:40 - mmengine - INFO - Epoch(train) [136][150/293] lr: 5.000000e-04 eta: 1:45:11 time: 0.336052 data_time: 0.073155 memory: 2315 loss_kpt: 0.000924 acc_pose: 0.719094 loss: 0.000924 2022/10/13 23:07:56 - mmengine - INFO - Epoch(train) [136][200/293] lr: 5.000000e-04 eta: 1:44:58 time: 0.323823 data_time: 0.125259 memory: 2315 loss_kpt: 0.000917 acc_pose: 0.763363 loss: 0.000917 2022/10/13 23:08:13 - mmengine - INFO - Epoch(train) [136][250/293] lr: 5.000000e-04 eta: 1:44:44 time: 0.338034 data_time: 0.173509 memory: 2315 loss_kpt: 0.000928 acc_pose: 0.721931 loss: 0.000928 2022/10/13 23:08:28 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:08:45 - mmengine - INFO - Epoch(train) [137][50/293] lr: 5.000000e-04 eta: 1:44:12 time: 0.348756 data_time: 0.140158 memory: 2315 loss_kpt: 0.000919 acc_pose: 0.731272 loss: 0.000919 2022/10/13 23:09:02 - mmengine - INFO - Epoch(train) [137][100/293] lr: 5.000000e-04 eta: 1:43:59 time: 0.333082 data_time: 0.075009 memory: 2315 loss_kpt: 0.000921 acc_pose: 0.663034 loss: 0.000921 2022/10/13 23:09:18 - mmengine - INFO - Epoch(train) [137][150/293] lr: 5.000000e-04 eta: 1:43:46 time: 0.335139 data_time: 0.093472 memory: 2315 loss_kpt: 0.000919 acc_pose: 0.719814 loss: 0.000919 2022/10/13 23:09:19 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:09:35 - mmengine - INFO - Epoch(train) [137][200/293] lr: 5.000000e-04 eta: 1:43:33 time: 0.330320 data_time: 0.107269 memory: 2315 loss_kpt: 0.000923 acc_pose: 0.717688 loss: 0.000923 2022/10/13 23:09:52 - mmengine - INFO - Epoch(train) [137][250/293] lr: 5.000000e-04 eta: 1:43:19 time: 0.338642 data_time: 0.102969 memory: 2315 loss_kpt: 0.000916 acc_pose: 0.732064 loss: 0.000916 2022/10/13 23:10:06 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:10:23 - mmengine - INFO - Epoch(train) [138][50/293] lr: 5.000000e-04 eta: 1:42:47 time: 0.345632 data_time: 0.087119 memory: 2315 loss_kpt: 0.000928 acc_pose: 0.687659 loss: 0.000928 2022/10/13 23:10:40 - mmengine - INFO - Epoch(train) [138][100/293] lr: 5.000000e-04 eta: 1:42:34 time: 0.332260 data_time: 0.069142 memory: 2315 loss_kpt: 0.000902 acc_pose: 0.745619 loss: 0.000902 2022/10/13 23:10:56 - mmengine - INFO - Epoch(train) [138][150/293] lr: 5.000000e-04 eta: 1:42:21 time: 0.330783 data_time: 0.136259 memory: 2315 loss_kpt: 0.000913 acc_pose: 0.767745 loss: 0.000913 2022/10/13 23:11:13 - mmengine - INFO - Epoch(train) [138][200/293] lr: 5.000000e-04 eta: 1:42:07 time: 0.330599 data_time: 0.162594 memory: 2315 loss_kpt: 0.000891 acc_pose: 0.733525 loss: 0.000891 2022/10/13 23:11:30 - mmengine - INFO - Epoch(train) [138][250/293] lr: 5.000000e-04 eta: 1:41:54 time: 0.333482 data_time: 0.169553 memory: 2315 loss_kpt: 0.000918 acc_pose: 0.658907 loss: 0.000918 2022/10/13 23:11:44 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:12:01 - mmengine - INFO - Epoch(train) [139][50/293] lr: 5.000000e-04 eta: 1:41:22 time: 0.342411 data_time: 0.101444 memory: 2315 loss_kpt: 0.000918 acc_pose: 0.660451 loss: 0.000918 2022/10/13 23:12:18 - mmengine - INFO - Epoch(train) [139][100/293] lr: 5.000000e-04 eta: 1:41:09 time: 0.333114 data_time: 0.080316 memory: 2315 loss_kpt: 0.000928 acc_pose: 0.679628 loss: 0.000928 2022/10/13 23:12:34 - mmengine - INFO - Epoch(train) [139][150/293] lr: 5.000000e-04 eta: 1:40:55 time: 0.330093 data_time: 0.105405 memory: 2315 loss_kpt: 0.000925 acc_pose: 0.713242 loss: 0.000925 2022/10/13 23:12:51 - mmengine - INFO - Epoch(train) [139][200/293] lr: 5.000000e-04 eta: 1:40:42 time: 0.332925 data_time: 0.064536 memory: 2315 loss_kpt: 0.000925 acc_pose: 0.745129 loss: 0.000925 2022/10/13 23:13:08 - mmengine - INFO - Epoch(train) [139][250/293] lr: 5.000000e-04 eta: 1:40:29 time: 0.334150 data_time: 0.067211 memory: 2315 loss_kpt: 0.000932 acc_pose: 0.666628 loss: 0.000932 2022/10/13 23:13:21 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:13:38 - mmengine - INFO - Epoch(train) [140][50/293] lr: 5.000000e-04 eta: 1:39:57 time: 0.330810 data_time: 0.163319 memory: 2315 loss_kpt: 0.000919 acc_pose: 0.723975 loss: 0.000919 2022/10/13 23:13:55 - mmengine - INFO - Epoch(train) [140][100/293] lr: 5.000000e-04 eta: 1:39:43 time: 0.329861 data_time: 0.108927 memory: 2315 loss_kpt: 0.000926 acc_pose: 0.695363 loss: 0.000926 2022/10/13 23:14:11 - mmengine - INFO - Epoch(train) [140][150/293] lr: 5.000000e-04 eta: 1:39:30 time: 0.324669 data_time: 0.159657 memory: 2315 loss_kpt: 0.000917 acc_pose: 0.747242 loss: 0.000917 2022/10/13 23:14:28 - mmengine - INFO - Epoch(train) [140][200/293] lr: 5.000000e-04 eta: 1:39:17 time: 0.344158 data_time: 0.165924 memory: 2315 loss_kpt: 0.000917 acc_pose: 0.711241 loss: 0.000917 2022/10/13 23:14:44 - mmengine - INFO - Epoch(train) [140][250/293] lr: 5.000000e-04 eta: 1:39:03 time: 0.329099 data_time: 0.180105 memory: 2315 loss_kpt: 0.000918 acc_pose: 0.695565 loss: 0.000918 2022/10/13 23:14:52 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:14:59 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:14:59 - mmengine - INFO - Saving checkpoint at 140 epochs 2022/10/13 23:15:07 - mmengine - INFO - Epoch(val) [140][50/407] eta: 0:00:43 time: 0.121023 data_time: 0.074577 memory: 2315 2022/10/13 23:15:12 - mmengine - INFO - Epoch(val) [140][100/407] eta: 0:00:34 time: 0.110886 data_time: 0.065706 memory: 426 2022/10/13 23:15:18 - mmengine - INFO - Epoch(val) [140][150/407] eta: 0:00:29 time: 0.113398 data_time: 0.069359 memory: 426 2022/10/13 23:15:24 - mmengine - INFO - Epoch(val) [140][200/407] eta: 0:00:23 time: 0.115791 data_time: 0.073219 memory: 426 2022/10/13 23:15:29 - mmengine - INFO - Epoch(val) [140][250/407] eta: 0:00:17 time: 0.113177 data_time: 0.070343 memory: 426 2022/10/13 23:15:35 - mmengine - INFO - Epoch(val) [140][300/407] eta: 0:00:12 time: 0.115080 data_time: 0.071127 memory: 426 2022/10/13 23:15:41 - mmengine - INFO - Epoch(val) [140][350/407] eta: 0:00:06 time: 0.112705 data_time: 0.070919 memory: 426 2022/10/13 23:15:46 - mmengine - INFO - Epoch(val) [140][400/407] eta: 0:00:00 time: 0.110080 data_time: 0.069599 memory: 426 2022/10/13 23:16:24 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 23:16:39 - mmengine - INFO - Epoch(val) [140][407/407] coco/AP: 0.583383 coco/AP .5: 0.848285 coco/AP .75: 0.647776 coco/AP (M): 0.551381 coco/AP (L): 0.644813 coco/AR: 0.651999 coco/AR .5: 0.896096 coco/AR .75: 0.719144 coco/AR (M): 0.608440 coco/AR (L): 0.713266 2022/10/13 23:16:55 - mmengine - INFO - Epoch(train) [141][50/293] lr: 5.000000e-04 eta: 1:38:31 time: 0.336754 data_time: 0.092466 memory: 2315 loss_kpt: 0.000920 acc_pose: 0.719253 loss: 0.000920 2022/10/13 23:17:12 - mmengine - INFO - Epoch(train) [141][100/293] lr: 5.000000e-04 eta: 1:38:18 time: 0.339845 data_time: 0.087968 memory: 2315 loss_kpt: 0.000923 acc_pose: 0.692367 loss: 0.000923 2022/10/13 23:17:29 - mmengine - INFO - Epoch(train) [141][150/293] lr: 5.000000e-04 eta: 1:38:05 time: 0.330163 data_time: 0.069164 memory: 2315 loss_kpt: 0.000920 acc_pose: 0.694389 loss: 0.000920 2022/10/13 23:17:45 - mmengine - INFO - Epoch(train) [141][200/293] lr: 5.000000e-04 eta: 1:37:51 time: 0.322016 data_time: 0.069716 memory: 2315 loss_kpt: 0.000908 acc_pose: 0.767604 loss: 0.000908 2022/10/13 23:18:02 - mmengine - INFO - Epoch(train) [141][250/293] lr: 5.000000e-04 eta: 1:37:38 time: 0.340569 data_time: 0.073274 memory: 2315 loss_kpt: 0.000905 acc_pose: 0.701540 loss: 0.000905 2022/10/13 23:18:17 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:18:34 - mmengine - INFO - Epoch(train) [142][50/293] lr: 5.000000e-04 eta: 1:37:06 time: 0.343805 data_time: 0.104661 memory: 2315 loss_kpt: 0.000924 acc_pose: 0.714734 loss: 0.000924 2022/10/13 23:18:50 - mmengine - INFO - Epoch(train) [142][100/293] lr: 5.000000e-04 eta: 1:36:53 time: 0.329873 data_time: 0.069356 memory: 2315 loss_kpt: 0.000910 acc_pose: 0.719418 loss: 0.000910 2022/10/13 23:19:07 - mmengine - INFO - Epoch(train) [142][150/293] lr: 5.000000e-04 eta: 1:36:39 time: 0.334782 data_time: 0.071373 memory: 2315 loss_kpt: 0.000918 acc_pose: 0.708875 loss: 0.000918 2022/10/13 23:19:23 - mmengine - INFO - Epoch(train) [142][200/293] lr: 5.000000e-04 eta: 1:36:26 time: 0.326623 data_time: 0.109945 memory: 2315 loss_kpt: 0.000921 acc_pose: 0.705425 loss: 0.000921 2022/10/13 23:19:40 - mmengine - INFO - Epoch(train) [142][250/293] lr: 5.000000e-04 eta: 1:36:12 time: 0.323490 data_time: 0.162224 memory: 2315 loss_kpt: 0.000918 acc_pose: 0.686932 loss: 0.000918 2022/10/13 23:19:53 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:20:11 - mmengine - INFO - Epoch(train) [143][50/293] lr: 5.000000e-04 eta: 1:35:41 time: 0.341196 data_time: 0.091190 memory: 2315 loss_kpt: 0.000899 acc_pose: 0.723066 loss: 0.000899 2022/10/13 23:20:27 - mmengine - INFO - Epoch(train) [143][100/293] lr: 5.000000e-04 eta: 1:35:27 time: 0.334693 data_time: 0.066848 memory: 2315 loss_kpt: 0.000927 acc_pose: 0.733109 loss: 0.000927 2022/10/13 23:20:44 - mmengine - INFO - Epoch(train) [143][150/293] lr: 5.000000e-04 eta: 1:35:14 time: 0.328455 data_time: 0.076467 memory: 2315 loss_kpt: 0.000902 acc_pose: 0.754683 loss: 0.000902 2022/10/13 23:21:00 - mmengine - INFO - Epoch(train) [143][200/293] lr: 5.000000e-04 eta: 1:35:00 time: 0.324673 data_time: 0.068894 memory: 2315 loss_kpt: 0.000923 acc_pose: 0.691950 loss: 0.000923 2022/10/13 23:21:16 - mmengine - INFO - Epoch(train) [143][250/293] lr: 5.000000e-04 eta: 1:34:47 time: 0.325678 data_time: 0.079432 memory: 2315 loss_kpt: 0.000917 acc_pose: 0.699229 loss: 0.000917 2022/10/13 23:21:30 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:21:47 - mmengine - INFO - Epoch(train) [144][50/293] lr: 5.000000e-04 eta: 1:34:15 time: 0.347045 data_time: 0.087983 memory: 2315 loss_kpt: 0.000919 acc_pose: 0.654164 loss: 0.000919 2022/10/13 23:22:04 - mmengine - INFO - Epoch(train) [144][100/293] lr: 5.000000e-04 eta: 1:34:02 time: 0.342211 data_time: 0.068151 memory: 2315 loss_kpt: 0.000914 acc_pose: 0.731058 loss: 0.000914 2022/10/13 23:22:05 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:22:21 - mmengine - INFO - Epoch(train) [144][150/293] lr: 5.000000e-04 eta: 1:33:48 time: 0.327272 data_time: 0.132549 memory: 2315 loss_kpt: 0.000932 acc_pose: 0.741067 loss: 0.000932 2022/10/13 23:22:37 - mmengine - INFO - Epoch(train) [144][200/293] lr: 5.000000e-04 eta: 1:33:35 time: 0.328841 data_time: 0.158834 memory: 2315 loss_kpt: 0.000917 acc_pose: 0.684138 loss: 0.000917 2022/10/13 23:22:53 - mmengine - INFO - Epoch(train) [144][250/293] lr: 5.000000e-04 eta: 1:33:21 time: 0.325441 data_time: 0.145365 memory: 2315 loss_kpt: 0.000910 acc_pose: 0.676383 loss: 0.000910 2022/10/13 23:23:07 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:23:24 - mmengine - INFO - Epoch(train) [145][50/293] lr: 5.000000e-04 eta: 1:32:50 time: 0.334159 data_time: 0.114776 memory: 2315 loss_kpt: 0.000899 acc_pose: 0.770212 loss: 0.000899 2022/10/13 23:23:40 - mmengine - INFO - Epoch(train) [145][100/293] lr: 5.000000e-04 eta: 1:32:36 time: 0.323248 data_time: 0.137243 memory: 2315 loss_kpt: 0.000921 acc_pose: 0.706045 loss: 0.000921 2022/10/13 23:23:57 - mmengine - INFO - Epoch(train) [145][150/293] lr: 5.000000e-04 eta: 1:32:23 time: 0.331349 data_time: 0.078998 memory: 2315 loss_kpt: 0.000909 acc_pose: 0.759927 loss: 0.000909 2022/10/13 23:24:13 - mmengine - INFO - Epoch(train) [145][200/293] lr: 5.000000e-04 eta: 1:32:09 time: 0.333392 data_time: 0.070136 memory: 2315 loss_kpt: 0.000913 acc_pose: 0.705366 loss: 0.000913 2022/10/13 23:24:30 - mmengine - INFO - Epoch(train) [145][250/293] lr: 5.000000e-04 eta: 1:31:56 time: 0.328602 data_time: 0.070585 memory: 2315 loss_kpt: 0.000920 acc_pose: 0.710407 loss: 0.000920 2022/10/13 23:24:44 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:25:01 - mmengine - INFO - Epoch(train) [146][50/293] lr: 5.000000e-04 eta: 1:31:24 time: 0.346062 data_time: 0.114533 memory: 2315 loss_kpt: 0.000908 acc_pose: 0.722316 loss: 0.000908 2022/10/13 23:25:18 - mmengine - INFO - Epoch(train) [146][100/293] lr: 5.000000e-04 eta: 1:31:11 time: 0.336138 data_time: 0.070874 memory: 2315 loss_kpt: 0.000909 acc_pose: 0.719273 loss: 0.000909 2022/10/13 23:25:35 - mmengine - INFO - Epoch(train) [146][150/293] lr: 5.000000e-04 eta: 1:30:58 time: 0.330514 data_time: 0.124763 memory: 2315 loss_kpt: 0.000934 acc_pose: 0.715768 loss: 0.000934 2022/10/13 23:25:51 - mmengine - INFO - Epoch(train) [146][200/293] lr: 5.000000e-04 eta: 1:30:44 time: 0.321875 data_time: 0.174942 memory: 2315 loss_kpt: 0.000904 acc_pose: 0.728398 loss: 0.000904 2022/10/13 23:26:08 - mmengine - INFO - Epoch(train) [146][250/293] lr: 5.000000e-04 eta: 1:30:31 time: 0.342699 data_time: 0.178038 memory: 2315 loss_kpt: 0.000909 acc_pose: 0.725714 loss: 0.000909 2022/10/13 23:26:22 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:26:39 - mmengine - INFO - Epoch(train) [147][50/293] lr: 5.000000e-04 eta: 1:29:59 time: 0.334465 data_time: 0.137599 memory: 2315 loss_kpt: 0.000905 acc_pose: 0.648869 loss: 0.000905 2022/10/13 23:26:55 - mmengine - INFO - Epoch(train) [147][100/293] lr: 5.000000e-04 eta: 1:29:46 time: 0.334050 data_time: 0.111460 memory: 2315 loss_kpt: 0.000906 acc_pose: 0.730558 loss: 0.000906 2022/10/13 23:27:12 - mmengine - INFO - Epoch(train) [147][150/293] lr: 5.000000e-04 eta: 1:29:32 time: 0.327948 data_time: 0.082304 memory: 2315 loss_kpt: 0.000925 acc_pose: 0.663689 loss: 0.000925 2022/10/13 23:27:28 - mmengine - INFO - Epoch(train) [147][200/293] lr: 5.000000e-04 eta: 1:29:19 time: 0.331179 data_time: 0.111854 memory: 2315 loss_kpt: 0.000911 acc_pose: 0.704623 loss: 0.000911 2022/10/13 23:27:36 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:27:45 - mmengine - INFO - Epoch(train) [147][250/293] lr: 5.000000e-04 eta: 1:29:05 time: 0.329411 data_time: 0.077817 memory: 2315 loss_kpt: 0.000931 acc_pose: 0.673561 loss: 0.000931 2022/10/13 23:27:59 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:28:16 - mmengine - INFO - Epoch(train) [148][50/293] lr: 5.000000e-04 eta: 1:28:34 time: 0.342026 data_time: 0.119803 memory: 2315 loss_kpt: 0.000895 acc_pose: 0.713731 loss: 0.000895 2022/10/13 23:28:32 - mmengine - INFO - Epoch(train) [148][100/293] lr: 5.000000e-04 eta: 1:28:20 time: 0.322884 data_time: 0.119612 memory: 2315 loss_kpt: 0.000912 acc_pose: 0.679845 loss: 0.000912 2022/10/13 23:28:48 - mmengine - INFO - Epoch(train) [148][150/293] lr: 5.000000e-04 eta: 1:28:07 time: 0.324714 data_time: 0.073282 memory: 2315 loss_kpt: 0.000907 acc_pose: 0.775251 loss: 0.000907 2022/10/13 23:29:05 - mmengine - INFO - Epoch(train) [148][200/293] lr: 5.000000e-04 eta: 1:27:53 time: 0.335003 data_time: 0.125439 memory: 2315 loss_kpt: 0.000920 acc_pose: 0.751866 loss: 0.000920 2022/10/13 23:29:21 - mmengine - INFO - Epoch(train) [148][250/293] lr: 5.000000e-04 eta: 1:27:40 time: 0.325654 data_time: 0.068080 memory: 2315 loss_kpt: 0.000911 acc_pose: 0.694608 loss: 0.000911 2022/10/13 23:29:36 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:29:53 - mmengine - INFO - Epoch(train) [149][50/293] lr: 5.000000e-04 eta: 1:27:09 time: 0.345241 data_time: 0.124134 memory: 2315 loss_kpt: 0.000923 acc_pose: 0.722745 loss: 0.000923 2022/10/13 23:30:10 - mmengine - INFO - Epoch(train) [149][100/293] lr: 5.000000e-04 eta: 1:26:55 time: 0.328547 data_time: 0.070116 memory: 2315 loss_kpt: 0.000907 acc_pose: 0.745061 loss: 0.000907 2022/10/13 23:30:27 - mmengine - INFO - Epoch(train) [149][150/293] lr: 5.000000e-04 eta: 1:26:42 time: 0.349762 data_time: 0.067781 memory: 2315 loss_kpt: 0.000906 acc_pose: 0.696515 loss: 0.000906 2022/10/13 23:30:44 - mmengine - INFO - Epoch(train) [149][200/293] lr: 5.000000e-04 eta: 1:26:28 time: 0.329552 data_time: 0.068934 memory: 2315 loss_kpt: 0.000928 acc_pose: 0.759447 loss: 0.000928 2022/10/13 23:31:01 - mmengine - INFO - Epoch(train) [149][250/293] lr: 5.000000e-04 eta: 1:26:15 time: 0.339487 data_time: 0.068822 memory: 2315 loss_kpt: 0.000912 acc_pose: 0.702309 loss: 0.000912 2022/10/13 23:31:15 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:31:32 - mmengine - INFO - Epoch(train) [150][50/293] lr: 5.000000e-04 eta: 1:25:44 time: 0.345981 data_time: 0.119548 memory: 2315 loss_kpt: 0.000923 acc_pose: 0.741843 loss: 0.000923 2022/10/13 23:31:48 - mmengine - INFO - Epoch(train) [150][100/293] lr: 5.000000e-04 eta: 1:25:31 time: 0.328488 data_time: 0.089127 memory: 2315 loss_kpt: 0.000910 acc_pose: 0.736679 loss: 0.000910 2022/10/13 23:32:05 - mmengine - INFO - Epoch(train) [150][150/293] lr: 5.000000e-04 eta: 1:25:17 time: 0.334937 data_time: 0.069159 memory: 2315 loss_kpt: 0.000911 acc_pose: 0.709872 loss: 0.000911 2022/10/13 23:32:22 - mmengine - INFO - Epoch(train) [150][200/293] lr: 5.000000e-04 eta: 1:25:04 time: 0.335482 data_time: 0.067422 memory: 2315 loss_kpt: 0.000919 acc_pose: 0.767590 loss: 0.000919 2022/10/13 23:32:38 - mmengine - INFO - Epoch(train) [150][250/293] lr: 5.000000e-04 eta: 1:24:50 time: 0.325113 data_time: 0.066111 memory: 2315 loss_kpt: 0.000912 acc_pose: 0.758111 loss: 0.000912 2022/10/13 23:32:52 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:32:52 - mmengine - INFO - Saving checkpoint at 150 epochs 2022/10/13 23:33:00 - mmengine - INFO - Epoch(val) [150][50/407] eta: 0:00:44 time: 0.123838 data_time: 0.080194 memory: 2315 2022/10/13 23:33:06 - mmengine - INFO - Epoch(val) [150][100/407] eta: 0:00:33 time: 0.108517 data_time: 0.064345 memory: 426 2022/10/13 23:33:11 - mmengine - INFO - Epoch(val) [150][150/407] eta: 0:00:29 time: 0.113524 data_time: 0.070410 memory: 426 2022/10/13 23:33:17 - mmengine - INFO - Epoch(val) [150][200/407] eta: 0:00:22 time: 0.109607 data_time: 0.063903 memory: 426 2022/10/13 23:33:23 - mmengine - INFO - Epoch(val) [150][250/407] eta: 0:00:18 time: 0.115553 data_time: 0.073317 memory: 426 2022/10/13 23:33:28 - mmengine - INFO - Epoch(val) [150][300/407] eta: 0:00:11 time: 0.111996 data_time: 0.069426 memory: 426 2022/10/13 23:33:34 - mmengine - INFO - Epoch(val) [150][350/407] eta: 0:00:06 time: 0.113554 data_time: 0.069105 memory: 426 2022/10/13 23:33:39 - mmengine - INFO - Epoch(val) [150][400/407] eta: 0:00:00 time: 0.107625 data_time: 0.063349 memory: 426 2022/10/13 23:34:17 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 23:34:32 - mmengine - INFO - Epoch(val) [150][407/407] coco/AP: 0.585545 coco/AP .5: 0.850596 coco/AP .75: 0.655031 coco/AP (M): 0.553471 coco/AP (L): 0.646117 coco/AR: 0.652393 coco/AR .5: 0.897355 coco/AR .75: 0.722292 coco/AR (M): 0.608905 coco/AR (L): 0.713192 2022/10/13 23:34:32 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_130.pth is removed 2022/10/13 23:34:33 - mmengine - INFO - The best checkpoint with 0.5855 coco/AP at 150 epoch is saved to best_coco/AP_epoch_150.pth. 2022/10/13 23:34:50 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:34:50 - mmengine - INFO - Epoch(train) [151][50/293] lr: 5.000000e-04 eta: 1:24:19 time: 0.336887 data_time: 0.215011 memory: 2315 loss_kpt: 0.000909 acc_pose: 0.704943 loss: 0.000909 2022/10/13 23:35:07 - mmengine - INFO - Epoch(train) [151][100/293] lr: 5.000000e-04 eta: 1:24:05 time: 0.334497 data_time: 0.178803 memory: 2315 loss_kpt: 0.000923 acc_pose: 0.756045 loss: 0.000923 2022/10/13 23:35:24 - mmengine - INFO - Epoch(train) [151][150/293] lr: 5.000000e-04 eta: 1:23:52 time: 0.333404 data_time: 0.182642 memory: 2315 loss_kpt: 0.000908 acc_pose: 0.744319 loss: 0.000908 2022/10/13 23:35:40 - mmengine - INFO - Epoch(train) [151][200/293] lr: 5.000000e-04 eta: 1:23:38 time: 0.328407 data_time: 0.167937 memory: 2315 loss_kpt: 0.000901 acc_pose: 0.690652 loss: 0.000901 2022/10/13 23:35:57 - mmengine - INFO - Epoch(train) [151][250/293] lr: 5.000000e-04 eta: 1:23:25 time: 0.330791 data_time: 0.176404 memory: 2315 loss_kpt: 0.000912 acc_pose: 0.717254 loss: 0.000912 2022/10/13 23:36:11 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:36:28 - mmengine - INFO - Epoch(train) [152][50/293] lr: 5.000000e-04 eta: 1:22:54 time: 0.348655 data_time: 0.114419 memory: 2315 loss_kpt: 0.000912 acc_pose: 0.693992 loss: 0.000912 2022/10/13 23:36:45 - mmengine - INFO - Epoch(train) [152][100/293] lr: 5.000000e-04 eta: 1:22:41 time: 0.333029 data_time: 0.067190 memory: 2315 loss_kpt: 0.000906 acc_pose: 0.743225 loss: 0.000906 2022/10/13 23:37:01 - mmengine - INFO - Epoch(train) [152][150/293] lr: 5.000000e-04 eta: 1:22:27 time: 0.325939 data_time: 0.068646 memory: 2315 loss_kpt: 0.000923 acc_pose: 0.691761 loss: 0.000923 2022/10/13 23:37:18 - mmengine - INFO - Epoch(train) [152][200/293] lr: 5.000000e-04 eta: 1:22:13 time: 0.329784 data_time: 0.164195 memory: 2315 loss_kpt: 0.000916 acc_pose: 0.737476 loss: 0.000916 2022/10/13 23:37:34 - mmengine - INFO - Epoch(train) [152][250/293] lr: 5.000000e-04 eta: 1:22:00 time: 0.333090 data_time: 0.178398 memory: 2315 loss_kpt: 0.000909 acc_pose: 0.668200 loss: 0.000909 2022/10/13 23:37:48 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:38:05 - mmengine - INFO - Epoch(train) [153][50/293] lr: 5.000000e-04 eta: 1:21:29 time: 0.337187 data_time: 0.087457 memory: 2315 loss_kpt: 0.000917 acc_pose: 0.689028 loss: 0.000917 2022/10/13 23:38:21 - mmengine - INFO - Epoch(train) [153][100/293] lr: 5.000000e-04 eta: 1:21:15 time: 0.317027 data_time: 0.067869 memory: 2315 loss_kpt: 0.000921 acc_pose: 0.645311 loss: 0.000921 2022/10/13 23:38:38 - mmengine - INFO - Epoch(train) [153][150/293] lr: 5.000000e-04 eta: 1:21:02 time: 0.335027 data_time: 0.082550 memory: 2315 loss_kpt: 0.000906 acc_pose: 0.664629 loss: 0.000906 2022/10/13 23:38:54 - mmengine - INFO - Epoch(train) [153][200/293] lr: 5.000000e-04 eta: 1:20:48 time: 0.334764 data_time: 0.102934 memory: 2315 loss_kpt: 0.000900 acc_pose: 0.744209 loss: 0.000900 2022/10/13 23:39:11 - mmengine - INFO - Epoch(train) [153][250/293] lr: 5.000000e-04 eta: 1:20:34 time: 0.325055 data_time: 0.143557 memory: 2315 loss_kpt: 0.000903 acc_pose: 0.701963 loss: 0.000903 2022/10/13 23:39:25 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:39:42 - mmengine - INFO - Epoch(train) [154][50/293] lr: 5.000000e-04 eta: 1:20:04 time: 0.337711 data_time: 0.083111 memory: 2315 loss_kpt: 0.000896 acc_pose: 0.677238 loss: 0.000896 2022/10/13 23:39:58 - mmengine - INFO - Epoch(train) [154][100/293] lr: 5.000000e-04 eta: 1:19:50 time: 0.332730 data_time: 0.072352 memory: 2315 loss_kpt: 0.000892 acc_pose: 0.744337 loss: 0.000892 2022/10/13 23:40:15 - mmengine - INFO - Epoch(train) [154][150/293] lr: 5.000000e-04 eta: 1:19:36 time: 0.326273 data_time: 0.114286 memory: 2315 loss_kpt: 0.000910 acc_pose: 0.716009 loss: 0.000910 2022/10/13 23:40:21 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:40:31 - mmengine - INFO - Epoch(train) [154][200/293] lr: 5.000000e-04 eta: 1:19:23 time: 0.333547 data_time: 0.067119 memory: 2315 loss_kpt: 0.000913 acc_pose: 0.710552 loss: 0.000913 2022/10/13 23:40:48 - mmengine - INFO - Epoch(train) [154][250/293] lr: 5.000000e-04 eta: 1:19:09 time: 0.334591 data_time: 0.069838 memory: 2315 loss_kpt: 0.000928 acc_pose: 0.689703 loss: 0.000928 2022/10/13 23:41:02 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:41:19 - mmengine - INFO - Epoch(train) [155][50/293] lr: 5.000000e-04 eta: 1:18:39 time: 0.341456 data_time: 0.095609 memory: 2315 loss_kpt: 0.000922 acc_pose: 0.748760 loss: 0.000922 2022/10/13 23:41:35 - mmengine - INFO - Epoch(train) [155][100/293] lr: 5.000000e-04 eta: 1:18:25 time: 0.327346 data_time: 0.139109 memory: 2315 loss_kpt: 0.000908 acc_pose: 0.673160 loss: 0.000908 2022/10/13 23:41:52 - mmengine - INFO - Epoch(train) [155][150/293] lr: 5.000000e-04 eta: 1:18:11 time: 0.326047 data_time: 0.096680 memory: 2315 loss_kpt: 0.000923 acc_pose: 0.725560 loss: 0.000923 2022/10/13 23:42:08 - mmengine - INFO - Epoch(train) [155][200/293] lr: 5.000000e-04 eta: 1:17:58 time: 0.328770 data_time: 0.081279 memory: 2315 loss_kpt: 0.000916 acc_pose: 0.718530 loss: 0.000916 2022/10/13 23:42:25 - mmengine - INFO - Epoch(train) [155][250/293] lr: 5.000000e-04 eta: 1:17:44 time: 0.340384 data_time: 0.067943 memory: 2315 loss_kpt: 0.000900 acc_pose: 0.676639 loss: 0.000900 2022/10/13 23:42:39 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:42:56 - mmengine - INFO - Epoch(train) [156][50/293] lr: 5.000000e-04 eta: 1:17:14 time: 0.338258 data_time: 0.095667 memory: 2315 loss_kpt: 0.000909 acc_pose: 0.738523 loss: 0.000909 2022/10/13 23:43:13 - mmengine - INFO - Epoch(train) [156][100/293] lr: 5.000000e-04 eta: 1:17:00 time: 0.335314 data_time: 0.152948 memory: 2315 loss_kpt: 0.000910 acc_pose: 0.663917 loss: 0.000910 2022/10/13 23:43:29 - mmengine - INFO - Epoch(train) [156][150/293] lr: 5.000000e-04 eta: 1:16:46 time: 0.327519 data_time: 0.135770 memory: 2315 loss_kpt: 0.000902 acc_pose: 0.748520 loss: 0.000902 2022/10/13 23:43:46 - mmengine - INFO - Epoch(train) [156][200/293] lr: 5.000000e-04 eta: 1:16:33 time: 0.330919 data_time: 0.154996 memory: 2315 loss_kpt: 0.000908 acc_pose: 0.688418 loss: 0.000908 2022/10/13 23:44:03 - mmengine - INFO - Epoch(train) [156][250/293] lr: 5.000000e-04 eta: 1:16:19 time: 0.342351 data_time: 0.150009 memory: 2315 loss_kpt: 0.000916 acc_pose: 0.695010 loss: 0.000916 2022/10/13 23:44:17 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:44:34 - mmengine - INFO - Epoch(train) [157][50/293] lr: 5.000000e-04 eta: 1:15:49 time: 0.358688 data_time: 0.090118 memory: 2315 loss_kpt: 0.000913 acc_pose: 0.709564 loss: 0.000913 2022/10/13 23:44:51 - mmengine - INFO - Epoch(train) [157][100/293] lr: 5.000000e-04 eta: 1:15:35 time: 0.325021 data_time: 0.073484 memory: 2315 loss_kpt: 0.000914 acc_pose: 0.737976 loss: 0.000914 2022/10/13 23:45:08 - mmengine - INFO - Epoch(train) [157][150/293] lr: 5.000000e-04 eta: 1:15:22 time: 0.345237 data_time: 0.169544 memory: 2315 loss_kpt: 0.000902 acc_pose: 0.700469 loss: 0.000902 2022/10/13 23:45:25 - mmengine - INFO - Epoch(train) [157][200/293] lr: 5.000000e-04 eta: 1:15:08 time: 0.334805 data_time: 0.172537 memory: 2315 loss_kpt: 0.000910 acc_pose: 0.725257 loss: 0.000910 2022/10/13 23:45:41 - mmengine - INFO - Epoch(train) [157][250/293] lr: 5.000000e-04 eta: 1:14:55 time: 0.327751 data_time: 0.155102 memory: 2315 loss_kpt: 0.000905 acc_pose: 0.722208 loss: 0.000905 2022/10/13 23:45:55 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:45:55 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:46:13 - mmengine - INFO - Epoch(train) [158][50/293] lr: 5.000000e-04 eta: 1:14:25 time: 0.349726 data_time: 0.171118 memory: 2315 loss_kpt: 0.000918 acc_pose: 0.700083 loss: 0.000918 2022/10/13 23:46:29 - mmengine - INFO - Epoch(train) [158][100/293] lr: 5.000000e-04 eta: 1:14:11 time: 0.327862 data_time: 0.067066 memory: 2315 loss_kpt: 0.000920 acc_pose: 0.762868 loss: 0.000920 2022/10/13 23:46:46 - mmengine - INFO - Epoch(train) [158][150/293] lr: 5.000000e-04 eta: 1:13:57 time: 0.328917 data_time: 0.066147 memory: 2315 loss_kpt: 0.000909 acc_pose: 0.739830 loss: 0.000909 2022/10/13 23:47:02 - mmengine - INFO - Epoch(train) [158][200/293] lr: 5.000000e-04 eta: 1:13:43 time: 0.332376 data_time: 0.065163 memory: 2315 loss_kpt: 0.000915 acc_pose: 0.707827 loss: 0.000915 2022/10/13 23:47:19 - mmengine - INFO - Epoch(train) [158][250/293] lr: 5.000000e-04 eta: 1:13:30 time: 0.332974 data_time: 0.067197 memory: 2315 loss_kpt: 0.000909 acc_pose: 0.655505 loss: 0.000909 2022/10/13 23:47:33 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:47:50 - mmengine - INFO - Epoch(train) [159][50/293] lr: 5.000000e-04 eta: 1:12:59 time: 0.334448 data_time: 0.098405 memory: 2315 loss_kpt: 0.000923 acc_pose: 0.706296 loss: 0.000923 2022/10/13 23:48:06 - mmengine - INFO - Epoch(train) [159][100/293] lr: 5.000000e-04 eta: 1:12:46 time: 0.328877 data_time: 0.068197 memory: 2315 loss_kpt: 0.000910 acc_pose: 0.738390 loss: 0.000910 2022/10/13 23:48:23 - mmengine - INFO - Epoch(train) [159][150/293] lr: 5.000000e-04 eta: 1:12:32 time: 0.328897 data_time: 0.067386 memory: 2315 loss_kpt: 0.000904 acc_pose: 0.749901 loss: 0.000904 2022/10/13 23:48:39 - mmengine - INFO - Epoch(train) [159][200/293] lr: 5.000000e-04 eta: 1:12:18 time: 0.323590 data_time: 0.072563 memory: 2315 loss_kpt: 0.000919 acc_pose: 0.698754 loss: 0.000919 2022/10/13 23:48:55 - mmengine - INFO - Epoch(train) [159][250/293] lr: 5.000000e-04 eta: 1:12:04 time: 0.327243 data_time: 0.104760 memory: 2315 loss_kpt: 0.000907 acc_pose: 0.738581 loss: 0.000907 2022/10/13 23:49:09 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:49:26 - mmengine - INFO - Epoch(train) [160][50/293] lr: 5.000000e-04 eta: 1:11:34 time: 0.334398 data_time: 0.126573 memory: 2315 loss_kpt: 0.000914 acc_pose: 0.719197 loss: 0.000914 2022/10/13 23:49:42 - mmengine - INFO - Epoch(train) [160][100/293] lr: 5.000000e-04 eta: 1:11:20 time: 0.329180 data_time: 0.107827 memory: 2315 loss_kpt: 0.000900 acc_pose: 0.720220 loss: 0.000900 2022/10/13 23:49:58 - mmengine - INFO - Epoch(train) [160][150/293] lr: 5.000000e-04 eta: 1:11:07 time: 0.328730 data_time: 0.201604 memory: 2315 loss_kpt: 0.000910 acc_pose: 0.730532 loss: 0.000910 2022/10/13 23:50:15 - mmengine - INFO - Epoch(train) [160][200/293] lr: 5.000000e-04 eta: 1:10:53 time: 0.332561 data_time: 0.102901 memory: 2315 loss_kpt: 0.000924 acc_pose: 0.711196 loss: 0.000924 2022/10/13 23:50:32 - mmengine - INFO - Epoch(train) [160][250/293] lr: 5.000000e-04 eta: 1:10:39 time: 0.331746 data_time: 0.072175 memory: 2315 loss_kpt: 0.000931 acc_pose: 0.725733 loss: 0.000931 2022/10/13 23:50:46 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:50:46 - mmengine - INFO - Saving checkpoint at 160 epochs 2022/10/13 23:50:54 - mmengine - INFO - Epoch(val) [160][50/407] eta: 0:00:42 time: 0.118072 data_time: 0.074472 memory: 2315 2022/10/13 23:51:00 - mmengine - INFO - Epoch(val) [160][100/407] eta: 0:00:34 time: 0.111264 data_time: 0.065913 memory: 426 2022/10/13 23:51:05 - mmengine - INFO - Epoch(val) [160][150/407] eta: 0:00:29 time: 0.113075 data_time: 0.065550 memory: 426 2022/10/13 23:51:11 - mmengine - INFO - Epoch(val) [160][200/407] eta: 0:00:22 time: 0.106831 data_time: 0.061209 memory: 426 2022/10/13 23:51:16 - mmengine - INFO - Epoch(val) [160][250/407] eta: 0:00:17 time: 0.113924 data_time: 0.071343 memory: 426 2022/10/13 23:51:22 - mmengine - INFO - Epoch(val) [160][300/407] eta: 0:00:12 time: 0.112168 data_time: 0.067754 memory: 426 2022/10/13 23:51:28 - mmengine - INFO - Epoch(val) [160][350/407] eta: 0:00:06 time: 0.116912 data_time: 0.073465 memory: 426 2022/10/13 23:51:33 - mmengine - INFO - Epoch(val) [160][400/407] eta: 0:00:00 time: 0.103672 data_time: 0.062115 memory: 426 2022/10/13 23:52:10 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 23:52:25 - mmengine - INFO - Epoch(val) [160][407/407] coco/AP: 0.588204 coco/AP .5: 0.850964 coco/AP .75: 0.656884 coco/AP (M): 0.556961 coco/AP (L): 0.648226 coco/AR: 0.655510 coco/AR .5: 0.896096 coco/AR .75: 0.723866 coco/AR (M): 0.612701 coco/AR (L): 0.715347 2022/10/13 23:52:25 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_150.pth is removed 2022/10/13 23:52:27 - mmengine - INFO - The best checkpoint with 0.5882 coco/AP at 160 epoch is saved to best_coco/AP_epoch_160.pth. 2022/10/13 23:52:44 - mmengine - INFO - Epoch(train) [161][50/293] lr: 5.000000e-04 eta: 1:10:09 time: 0.349748 data_time: 0.161171 memory: 2315 loss_kpt: 0.000925 acc_pose: 0.704878 loss: 0.000925 2022/10/13 23:53:01 - mmengine - INFO - Epoch(train) [161][100/293] lr: 5.000000e-04 eta: 1:09:56 time: 0.330706 data_time: 0.060906 memory: 2315 loss_kpt: 0.000913 acc_pose: 0.741412 loss: 0.000913 2022/10/13 23:53:07 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:53:18 - mmengine - INFO - Epoch(train) [161][150/293] lr: 5.000000e-04 eta: 1:09:42 time: 0.338298 data_time: 0.111193 memory: 2315 loss_kpt: 0.000910 acc_pose: 0.697874 loss: 0.000910 2022/10/13 23:53:34 - mmengine - INFO - Epoch(train) [161][200/293] lr: 5.000000e-04 eta: 1:09:28 time: 0.332836 data_time: 0.090655 memory: 2315 loss_kpt: 0.000899 acc_pose: 0.754237 loss: 0.000899 2022/10/13 23:53:50 - mmengine - INFO - Epoch(train) [161][250/293] lr: 5.000000e-04 eta: 1:09:14 time: 0.323870 data_time: 0.150870 memory: 2315 loss_kpt: 0.000898 acc_pose: 0.732206 loss: 0.000898 2022/10/13 23:54:04 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:54:21 - mmengine - INFO - Epoch(train) [162][50/293] lr: 5.000000e-04 eta: 1:08:45 time: 0.346260 data_time: 0.118144 memory: 2315 loss_kpt: 0.000911 acc_pose: 0.687380 loss: 0.000911 2022/10/13 23:54:38 - mmengine - INFO - Epoch(train) [162][100/293] lr: 5.000000e-04 eta: 1:08:31 time: 0.325055 data_time: 0.077783 memory: 2315 loss_kpt: 0.000907 acc_pose: 0.742917 loss: 0.000907 2022/10/13 23:54:55 - mmengine - INFO - Epoch(train) [162][150/293] lr: 5.000000e-04 eta: 1:08:17 time: 0.341835 data_time: 0.079699 memory: 2315 loss_kpt: 0.000916 acc_pose: 0.728819 loss: 0.000916 2022/10/13 23:55:11 - mmengine - INFO - Epoch(train) [162][200/293] lr: 5.000000e-04 eta: 1:08:04 time: 0.335326 data_time: 0.096583 memory: 2315 loss_kpt: 0.000918 acc_pose: 0.742687 loss: 0.000918 2022/10/13 23:55:28 - mmengine - INFO - Epoch(train) [162][250/293] lr: 5.000000e-04 eta: 1:07:50 time: 0.339024 data_time: 0.082565 memory: 2315 loss_kpt: 0.000923 acc_pose: 0.694019 loss: 0.000923 2022/10/13 23:55:42 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:56:00 - mmengine - INFO - Epoch(train) [163][50/293] lr: 5.000000e-04 eta: 1:07:20 time: 0.345375 data_time: 0.117470 memory: 2315 loss_kpt: 0.000899 acc_pose: 0.713106 loss: 0.000899 2022/10/13 23:56:16 - mmengine - INFO - Epoch(train) [163][100/293] lr: 5.000000e-04 eta: 1:07:06 time: 0.328643 data_time: 0.149990 memory: 2315 loss_kpt: 0.000918 acc_pose: 0.658329 loss: 0.000918 2022/10/13 23:56:33 - mmengine - INFO - Epoch(train) [163][150/293] lr: 5.000000e-04 eta: 1:06:53 time: 0.337901 data_time: 0.174996 memory: 2315 loss_kpt: 0.000901 acc_pose: 0.723645 loss: 0.000901 2022/10/13 23:56:49 - mmengine - INFO - Epoch(train) [163][200/293] lr: 5.000000e-04 eta: 1:06:39 time: 0.328369 data_time: 0.176680 memory: 2315 loss_kpt: 0.000910 acc_pose: 0.724272 loss: 0.000910 2022/10/13 23:57:06 - mmengine - INFO - Epoch(train) [163][250/293] lr: 5.000000e-04 eta: 1:06:25 time: 0.333279 data_time: 0.182045 memory: 2315 loss_kpt: 0.000897 acc_pose: 0.734485 loss: 0.000897 2022/10/13 23:57:20 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:57:37 - mmengine - INFO - Epoch(train) [164][50/293] lr: 5.000000e-04 eta: 1:05:55 time: 0.334179 data_time: 0.130132 memory: 2315 loss_kpt: 0.000923 acc_pose: 0.751832 loss: 0.000923 2022/10/13 23:57:54 - mmengine - INFO - Epoch(train) [164][100/293] lr: 5.000000e-04 eta: 1:05:42 time: 0.331110 data_time: 0.078071 memory: 2315 loss_kpt: 0.000912 acc_pose: 0.739937 loss: 0.000912 2022/10/13 23:58:11 - mmengine - INFO - Epoch(train) [164][150/293] lr: 5.000000e-04 eta: 1:05:28 time: 0.339149 data_time: 0.067951 memory: 2315 loss_kpt: 0.000911 acc_pose: 0.685464 loss: 0.000911 2022/10/13 23:58:27 - mmengine - INFO - Epoch(train) [164][200/293] lr: 5.000000e-04 eta: 1:05:14 time: 0.327908 data_time: 0.093617 memory: 2315 loss_kpt: 0.000922 acc_pose: 0.721452 loss: 0.000922 2022/10/13 23:58:41 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:58:44 - mmengine - INFO - Epoch(train) [164][250/293] lr: 5.000000e-04 eta: 1:05:00 time: 0.333639 data_time: 0.090085 memory: 2315 loss_kpt: 0.000912 acc_pose: 0.712769 loss: 0.000912 2022/10/13 23:58:58 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/13 23:59:15 - mmengine - INFO - Epoch(train) [165][50/293] lr: 5.000000e-04 eta: 1:04:31 time: 0.339056 data_time: 0.123617 memory: 2315 loss_kpt: 0.000907 acc_pose: 0.740126 loss: 0.000907 2022/10/13 23:59:32 - mmengine - INFO - Epoch(train) [165][100/293] lr: 5.000000e-04 eta: 1:04:17 time: 0.337385 data_time: 0.069922 memory: 2315 loss_kpt: 0.000918 acc_pose: 0.711617 loss: 0.000918 2022/10/13 23:59:49 - mmengine - INFO - Epoch(train) [165][150/293] lr: 5.000000e-04 eta: 1:04:03 time: 0.334799 data_time: 0.067471 memory: 2315 loss_kpt: 0.000908 acc_pose: 0.751267 loss: 0.000908 2022/10/14 00:00:05 - mmengine - INFO - Epoch(train) [165][200/293] lr: 5.000000e-04 eta: 1:03:49 time: 0.331575 data_time: 0.069132 memory: 2315 loss_kpt: 0.000904 acc_pose: 0.699828 loss: 0.000904 2022/10/14 00:00:22 - mmengine - INFO - Epoch(train) [165][250/293] lr: 5.000000e-04 eta: 1:03:35 time: 0.331401 data_time: 0.122610 memory: 2315 loss_kpt: 0.000908 acc_pose: 0.653849 loss: 0.000908 2022/10/14 00:00:35 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:00:52 - mmengine - INFO - Epoch(train) [166][50/293] lr: 5.000000e-04 eta: 1:03:06 time: 0.337091 data_time: 0.160861 memory: 2315 loss_kpt: 0.000905 acc_pose: 0.728553 loss: 0.000905 2022/10/14 00:01:09 - mmengine - INFO - Epoch(train) [166][100/293] lr: 5.000000e-04 eta: 1:02:52 time: 0.333801 data_time: 0.217982 memory: 2315 loss_kpt: 0.000917 acc_pose: 0.716793 loss: 0.000917 2022/10/14 00:01:25 - mmengine - INFO - Epoch(train) [166][150/293] lr: 5.000000e-04 eta: 1:02:38 time: 0.325317 data_time: 0.209480 memory: 2315 loss_kpt: 0.000899 acc_pose: 0.705908 loss: 0.000899 2022/10/14 00:01:41 - mmengine - INFO - Epoch(train) [166][200/293] lr: 5.000000e-04 eta: 1:02:24 time: 0.326648 data_time: 0.110215 memory: 2315 loss_kpt: 0.000904 acc_pose: 0.749598 loss: 0.000904 2022/10/14 00:01:58 - mmengine - INFO - Epoch(train) [166][250/293] lr: 5.000000e-04 eta: 1:02:10 time: 0.329601 data_time: 0.115496 memory: 2315 loss_kpt: 0.000901 acc_pose: 0.720549 loss: 0.000901 2022/10/14 00:02:12 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:02:29 - mmengine - INFO - Epoch(train) [167][50/293] lr: 5.000000e-04 eta: 1:01:41 time: 0.339767 data_time: 0.095963 memory: 2315 loss_kpt: 0.000911 acc_pose: 0.638956 loss: 0.000911 2022/10/14 00:02:45 - mmengine - INFO - Epoch(train) [167][100/293] lr: 5.000000e-04 eta: 1:01:27 time: 0.323089 data_time: 0.093920 memory: 2315 loss_kpt: 0.000908 acc_pose: 0.657741 loss: 0.000908 2022/10/14 00:03:02 - mmengine - INFO - Epoch(train) [167][150/293] lr: 5.000000e-04 eta: 1:01:13 time: 0.330914 data_time: 0.147729 memory: 2315 loss_kpt: 0.000911 acc_pose: 0.699358 loss: 0.000911 2022/10/14 00:03:18 - mmengine - INFO - Epoch(train) [167][200/293] lr: 5.000000e-04 eta: 1:00:59 time: 0.332153 data_time: 0.184081 memory: 2315 loss_kpt: 0.000905 acc_pose: 0.745674 loss: 0.000905 2022/10/14 00:03:35 - mmengine - INFO - Epoch(train) [167][250/293] lr: 5.000000e-04 eta: 1:00:45 time: 0.333539 data_time: 0.157763 memory: 2315 loss_kpt: 0.000911 acc_pose: 0.728642 loss: 0.000911 2022/10/14 00:03:49 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:04:06 - mmengine - INFO - Epoch(train) [168][50/293] lr: 5.000000e-04 eta: 1:00:16 time: 0.335874 data_time: 0.086611 memory: 2315 loss_kpt: 0.000925 acc_pose: 0.636964 loss: 0.000925 2022/10/14 00:04:12 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:04:21 - mmengine - INFO - Epoch(train) [168][100/293] lr: 5.000000e-04 eta: 1:00:02 time: 0.316955 data_time: 0.100828 memory: 2315 loss_kpt: 0.000894 acc_pose: 0.762085 loss: 0.000894 2022/10/14 00:04:38 - mmengine - INFO - Epoch(train) [168][150/293] lr: 5.000000e-04 eta: 0:59:48 time: 0.324247 data_time: 0.075401 memory: 2315 loss_kpt: 0.000924 acc_pose: 0.747123 loss: 0.000924 2022/10/14 00:04:54 - mmengine - INFO - Epoch(train) [168][200/293] lr: 5.000000e-04 eta: 0:59:34 time: 0.327079 data_time: 0.068873 memory: 2315 loss_kpt: 0.000897 acc_pose: 0.704345 loss: 0.000897 2022/10/14 00:05:10 - mmengine - INFO - Epoch(train) [168][250/293] lr: 5.000000e-04 eta: 0:59:20 time: 0.327600 data_time: 0.068108 memory: 2315 loss_kpt: 0.000902 acc_pose: 0.730705 loss: 0.000902 2022/10/14 00:05:24 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:05:41 - mmengine - INFO - Epoch(train) [169][50/293] lr: 5.000000e-04 eta: 0:58:51 time: 0.344018 data_time: 0.100278 memory: 2315 loss_kpt: 0.000912 acc_pose: 0.742269 loss: 0.000912 2022/10/14 00:05:58 - mmengine - INFO - Epoch(train) [169][100/293] lr: 5.000000e-04 eta: 0:58:37 time: 0.330465 data_time: 0.159844 memory: 2315 loss_kpt: 0.000907 acc_pose: 0.696653 loss: 0.000907 2022/10/14 00:06:14 - mmengine - INFO - Epoch(train) [169][150/293] lr: 5.000000e-04 eta: 0:58:23 time: 0.323360 data_time: 0.166859 memory: 2315 loss_kpt: 0.000903 acc_pose: 0.743898 loss: 0.000903 2022/10/14 00:06:30 - mmengine - INFO - Epoch(train) [169][200/293] lr: 5.000000e-04 eta: 0:58:09 time: 0.329923 data_time: 0.133550 memory: 2315 loss_kpt: 0.000909 acc_pose: 0.715451 loss: 0.000909 2022/10/14 00:06:47 - mmengine - INFO - Epoch(train) [169][250/293] lr: 5.000000e-04 eta: 0:57:55 time: 0.332989 data_time: 0.091028 memory: 2315 loss_kpt: 0.000898 acc_pose: 0.685610 loss: 0.000898 2022/10/14 00:07:01 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:07:18 - mmengine - INFO - Epoch(train) [170][50/293] lr: 5.000000e-04 eta: 0:57:26 time: 0.341952 data_time: 0.111532 memory: 2315 loss_kpt: 0.000914 acc_pose: 0.768653 loss: 0.000914 2022/10/14 00:07:34 - mmengine - INFO - Epoch(train) [170][100/293] lr: 5.000000e-04 eta: 0:57:12 time: 0.325014 data_time: 0.070398 memory: 2315 loss_kpt: 0.000914 acc_pose: 0.741393 loss: 0.000914 2022/10/14 00:07:51 - mmengine - INFO - Epoch(train) [170][150/293] lr: 5.000000e-04 eta: 0:56:58 time: 0.325539 data_time: 0.064537 memory: 2315 loss_kpt: 0.000905 acc_pose: 0.683340 loss: 0.000905 2022/10/14 00:08:07 - mmengine - INFO - Epoch(train) [170][200/293] lr: 5.000000e-04 eta: 0:56:44 time: 0.327782 data_time: 0.066146 memory: 2315 loss_kpt: 0.000916 acc_pose: 0.724852 loss: 0.000916 2022/10/14 00:08:23 - mmengine - INFO - Epoch(train) [170][250/293] lr: 5.000000e-04 eta: 0:56:30 time: 0.316976 data_time: 0.071482 memory: 2315 loss_kpt: 0.000923 acc_pose: 0.773052 loss: 0.000923 2022/10/14 00:08:37 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:08:37 - mmengine - INFO - Saving checkpoint at 170 epochs 2022/10/14 00:08:44 - mmengine - INFO - Epoch(val) [170][50/407] eta: 0:00:39 time: 0.111559 data_time: 0.068896 memory: 2315 2022/10/14 00:08:50 - mmengine - INFO - Epoch(val) [170][100/407] eta: 0:00:35 time: 0.116630 data_time: 0.069037 memory: 426 2022/10/14 00:08:56 - mmengine - INFO - Epoch(val) [170][150/407] eta: 0:00:28 time: 0.111039 data_time: 0.068252 memory: 426 2022/10/14 00:09:01 - mmengine - INFO - Epoch(val) [170][200/407] eta: 0:00:23 time: 0.112385 data_time: 0.068219 memory: 426 2022/10/14 00:09:07 - mmengine - INFO - Epoch(val) [170][250/407] eta: 0:00:17 time: 0.111677 data_time: 0.069402 memory: 426 2022/10/14 00:09:13 - mmengine - INFO - Epoch(val) [170][300/407] eta: 0:00:12 time: 0.114424 data_time: 0.071304 memory: 426 2022/10/14 00:09:18 - mmengine - INFO - Epoch(val) [170][350/407] eta: 0:00:06 time: 0.110556 data_time: 0.067000 memory: 426 2022/10/14 00:09:24 - mmengine - INFO - Epoch(val) [170][400/407] eta: 0:00:00 time: 0.110119 data_time: 0.069269 memory: 426 2022/10/14 00:10:02 - mmengine - INFO - Evaluating CocoMetric... 2022/10/14 00:10:17 - mmengine - INFO - Epoch(val) [170][407/407] coco/AP: 0.590716 coco/AP .5: 0.851498 coco/AP .75: 0.660045 coco/AP (M): 0.557875 coco/AP (L): 0.652245 coco/AR: 0.657336 coco/AR .5: 0.898615 coco/AR .75: 0.726543 coco/AR (M): 0.613330 coco/AR (L): 0.718952 2022/10/14 00:10:17 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_160.pth is removed 2022/10/14 00:10:18 - mmengine - INFO - The best checkpoint with 0.5907 coco/AP at 170 epoch is saved to best_coco/AP_epoch_170.pth. 2022/10/14 00:10:35 - mmengine - INFO - Epoch(train) [171][50/293] lr: 5.000000e-05 eta: 0:56:01 time: 0.331764 data_time: 0.189213 memory: 2315 loss_kpt: 0.000898 acc_pose: 0.706860 loss: 0.000898 2022/10/14 00:10:51 - mmengine - INFO - Epoch(train) [171][100/293] lr: 5.000000e-05 eta: 0:55:47 time: 0.327811 data_time: 0.189498 memory: 2315 loss_kpt: 0.000917 acc_pose: 0.671661 loss: 0.000917 2022/10/14 00:11:08 - mmengine - INFO - Epoch(train) [171][150/293] lr: 5.000000e-05 eta: 0:55:33 time: 0.329794 data_time: 0.115910 memory: 2315 loss_kpt: 0.000895 acc_pose: 0.729506 loss: 0.000895 2022/10/14 00:11:21 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:11:24 - mmengine - INFO - Epoch(train) [171][200/293] lr: 5.000000e-05 eta: 0:55:19 time: 0.322375 data_time: 0.096611 memory: 2315 loss_kpt: 0.000905 acc_pose: 0.740063 loss: 0.000905 2022/10/14 00:11:40 - mmengine - INFO - Epoch(train) [171][250/293] lr: 5.000000e-05 eta: 0:55:05 time: 0.325563 data_time: 0.123364 memory: 2315 loss_kpt: 0.000891 acc_pose: 0.755130 loss: 0.000891 2022/10/14 00:11:54 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:12:11 - mmengine - INFO - Epoch(train) [172][50/293] lr: 5.000000e-05 eta: 0:54:36 time: 0.330999 data_time: 0.132414 memory: 2315 loss_kpt: 0.000890 acc_pose: 0.761298 loss: 0.000890 2022/10/14 00:12:27 - mmengine - INFO - Epoch(train) [172][100/293] lr: 5.000000e-05 eta: 0:54:22 time: 0.334506 data_time: 0.076311 memory: 2315 loss_kpt: 0.000907 acc_pose: 0.710149 loss: 0.000907 2022/10/14 00:12:44 - mmengine - INFO - Epoch(train) [172][150/293] lr: 5.000000e-05 eta: 0:54:08 time: 0.324054 data_time: 0.074076 memory: 2315 loss_kpt: 0.000907 acc_pose: 0.747793 loss: 0.000907 2022/10/14 00:13:00 - mmengine - INFO - Epoch(train) [172][200/293] lr: 5.000000e-05 eta: 0:53:54 time: 0.325041 data_time: 0.078700 memory: 2315 loss_kpt: 0.000896 acc_pose: 0.639916 loss: 0.000896 2022/10/14 00:13:17 - mmengine - INFO - Epoch(train) [172][250/293] lr: 5.000000e-05 eta: 0:53:40 time: 0.336777 data_time: 0.066279 memory: 2315 loss_kpt: 0.000892 acc_pose: 0.701211 loss: 0.000892 2022/10/14 00:13:30 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:13:48 - mmengine - INFO - Epoch(train) [173][50/293] lr: 5.000000e-05 eta: 0:53:12 time: 0.350609 data_time: 0.092005 memory: 2315 loss_kpt: 0.000878 acc_pose: 0.717059 loss: 0.000878 2022/10/14 00:14:04 - mmengine - INFO - Epoch(train) [173][100/293] lr: 5.000000e-05 eta: 0:52:58 time: 0.324702 data_time: 0.071328 memory: 2315 loss_kpt: 0.000891 acc_pose: 0.737105 loss: 0.000891 2022/10/14 00:14:21 - mmengine - INFO - Epoch(train) [173][150/293] lr: 5.000000e-05 eta: 0:52:44 time: 0.341185 data_time: 0.071029 memory: 2315 loss_kpt: 0.000881 acc_pose: 0.694389 loss: 0.000881 2022/10/14 00:14:37 - mmengine - INFO - Epoch(train) [173][200/293] lr: 5.000000e-05 eta: 0:52:30 time: 0.322490 data_time: 0.114402 memory: 2315 loss_kpt: 0.000889 acc_pose: 0.733942 loss: 0.000889 2022/10/14 00:14:54 - mmengine - INFO - Epoch(train) [173][250/293] lr: 5.000000e-05 eta: 0:52:16 time: 0.325815 data_time: 0.148680 memory: 2315 loss_kpt: 0.000903 acc_pose: 0.747040 loss: 0.000903 2022/10/14 00:15:08 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:15:25 - mmengine - INFO - Epoch(train) [174][50/293] lr: 5.000000e-05 eta: 0:51:47 time: 0.341779 data_time: 0.134010 memory: 2315 loss_kpt: 0.000892 acc_pose: 0.741511 loss: 0.000892 2022/10/14 00:15:42 - mmengine - INFO - Epoch(train) [174][100/293] lr: 5.000000e-05 eta: 0:51:33 time: 0.338882 data_time: 0.162863 memory: 2315 loss_kpt: 0.000881 acc_pose: 0.691718 loss: 0.000881 2022/10/14 00:15:59 - mmengine - INFO - Epoch(train) [174][150/293] lr: 5.000000e-05 eta: 0:51:19 time: 0.334401 data_time: 0.140529 memory: 2315 loss_kpt: 0.000887 acc_pose: 0.770357 loss: 0.000887 2022/10/14 00:16:15 - mmengine - INFO - Epoch(train) [174][200/293] lr: 5.000000e-05 eta: 0:51:05 time: 0.320366 data_time: 0.066269 memory: 2315 loss_kpt: 0.000887 acc_pose: 0.743117 loss: 0.000887 2022/10/14 00:16:31 - mmengine - INFO - Epoch(train) [174][250/293] lr: 5.000000e-05 eta: 0:50:51 time: 0.325817 data_time: 0.061857 memory: 2315 loss_kpt: 0.000886 acc_pose: 0.735533 loss: 0.000886 2022/10/14 00:16:45 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:16:51 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:17:02 - mmengine - INFO - Epoch(train) [175][50/293] lr: 5.000000e-05 eta: 0:50:22 time: 0.335151 data_time: 0.087451 memory: 2315 loss_kpt: 0.000889 acc_pose: 0.751999 loss: 0.000889 2022/10/14 00:17:18 - mmengine - INFO - Epoch(train) [175][100/293] lr: 5.000000e-05 eta: 0:50:08 time: 0.333318 data_time: 0.072133 memory: 2315 loss_kpt: 0.000885 acc_pose: 0.720986 loss: 0.000885 2022/10/14 00:17:35 - mmengine - INFO - Epoch(train) [175][150/293] lr: 5.000000e-05 eta: 0:49:54 time: 0.334973 data_time: 0.164012 memory: 2315 loss_kpt: 0.000885 acc_pose: 0.659795 loss: 0.000885 2022/10/14 00:17:51 - mmengine - INFO - Epoch(train) [175][200/293] lr: 5.000000e-05 eta: 0:49:40 time: 0.324448 data_time: 0.167717 memory: 2315 loss_kpt: 0.000884 acc_pose: 0.772597 loss: 0.000884 2022/10/14 00:18:08 - mmengine - INFO - Epoch(train) [175][250/293] lr: 5.000000e-05 eta: 0:49:26 time: 0.331661 data_time: 0.116145 memory: 2315 loss_kpt: 0.000874 acc_pose: 0.746333 loss: 0.000874 2022/10/14 00:18:22 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:18:39 - mmengine - INFO - Epoch(train) [176][50/293] lr: 5.000000e-05 eta: 0:48:58 time: 0.341602 data_time: 0.161467 memory: 2315 loss_kpt: 0.000878 acc_pose: 0.730105 loss: 0.000878 2022/10/14 00:18:55 - mmengine - INFO - Epoch(train) [176][100/293] lr: 5.000000e-05 eta: 0:48:44 time: 0.326267 data_time: 0.111037 memory: 2315 loss_kpt: 0.000880 acc_pose: 0.743635 loss: 0.000880 2022/10/14 00:19:12 - mmengine - INFO - Epoch(train) [176][150/293] lr: 5.000000e-05 eta: 0:48:30 time: 0.332136 data_time: 0.079440 memory: 2315 loss_kpt: 0.000888 acc_pose: 0.737720 loss: 0.000888 2022/10/14 00:19:28 - mmengine - INFO - Epoch(train) [176][200/293] lr: 5.000000e-05 eta: 0:48:16 time: 0.328424 data_time: 0.069034 memory: 2315 loss_kpt: 0.000900 acc_pose: 0.726723 loss: 0.000900 2022/10/14 00:19:44 - mmengine - INFO - Epoch(train) [176][250/293] lr: 5.000000e-05 eta: 0:48:01 time: 0.318247 data_time: 0.122803 memory: 2315 loss_kpt: 0.000872 acc_pose: 0.760202 loss: 0.000872 2022/10/14 00:19:58 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:20:16 - mmengine - INFO - Epoch(train) [177][50/293] lr: 5.000000e-05 eta: 0:47:33 time: 0.343385 data_time: 0.171232 memory: 2315 loss_kpt: 0.000896 acc_pose: 0.722629 loss: 0.000896 2022/10/14 00:20:32 - mmengine - INFO - Epoch(train) [177][100/293] lr: 5.000000e-05 eta: 0:47:19 time: 0.336381 data_time: 0.161485 memory: 2315 loss_kpt: 0.000880 acc_pose: 0.715621 loss: 0.000880 2022/10/14 00:20:48 - mmengine - INFO - Epoch(train) [177][150/293] lr: 5.000000e-05 eta: 0:47:05 time: 0.319666 data_time: 0.163869 memory: 2315 loss_kpt: 0.000888 acc_pose: 0.704765 loss: 0.000888 2022/10/14 00:21:05 - mmengine - INFO - Epoch(train) [177][200/293] lr: 5.000000e-05 eta: 0:46:51 time: 0.328620 data_time: 0.126793 memory: 2315 loss_kpt: 0.000885 acc_pose: 0.765970 loss: 0.000885 2022/10/14 00:21:22 - mmengine - INFO - Epoch(train) [177][250/293] lr: 5.000000e-05 eta: 0:46:37 time: 0.340526 data_time: 0.066667 memory: 2315 loss_kpt: 0.000875 acc_pose: 0.762570 loss: 0.000875 2022/10/14 00:21:36 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:21:53 - mmengine - INFO - Epoch(train) [178][50/293] lr: 5.000000e-05 eta: 0:46:08 time: 0.339518 data_time: 0.165538 memory: 2315 loss_kpt: 0.000890 acc_pose: 0.792358 loss: 0.000890 2022/10/14 00:22:09 - mmengine - INFO - Epoch(train) [178][100/293] lr: 5.000000e-05 eta: 0:45:54 time: 0.324690 data_time: 0.102497 memory: 2315 loss_kpt: 0.000889 acc_pose: 0.737001 loss: 0.000889 2022/10/14 00:22:22 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:22:25 - mmengine - INFO - Epoch(train) [178][150/293] lr: 5.000000e-05 eta: 0:45:40 time: 0.318792 data_time: 0.147179 memory: 2315 loss_kpt: 0.000883 acc_pose: 0.762776 loss: 0.000883 2022/10/14 00:22:41 - mmengine - INFO - Epoch(train) [178][200/293] lr: 5.000000e-05 eta: 0:45:26 time: 0.323942 data_time: 0.128644 memory: 2315 loss_kpt: 0.000888 acc_pose: 0.736119 loss: 0.000888 2022/10/14 00:22:58 - mmengine - INFO - Epoch(train) [178][250/293] lr: 5.000000e-05 eta: 0:45:12 time: 0.332702 data_time: 0.173732 memory: 2315 loss_kpt: 0.000889 acc_pose: 0.751958 loss: 0.000889 2022/10/14 00:23:12 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:23:29 - mmengine - INFO - Epoch(train) [179][50/293] lr: 5.000000e-05 eta: 0:44:44 time: 0.348288 data_time: 0.189581 memory: 2315 loss_kpt: 0.000888 acc_pose: 0.728672 loss: 0.000888 2022/10/14 00:23:46 - mmengine - INFO - Epoch(train) [179][100/293] lr: 5.000000e-05 eta: 0:44:30 time: 0.326089 data_time: 0.136777 memory: 2315 loss_kpt: 0.000872 acc_pose: 0.735041 loss: 0.000872 2022/10/14 00:24:02 - mmengine - INFO - Epoch(train) [179][150/293] lr: 5.000000e-05 eta: 0:44:15 time: 0.326209 data_time: 0.071295 memory: 2315 loss_kpt: 0.000876 acc_pose: 0.730375 loss: 0.000876 2022/10/14 00:24:18 - mmengine - INFO - Epoch(train) [179][200/293] lr: 5.000000e-05 eta: 0:44:01 time: 0.331049 data_time: 0.074694 memory: 2315 loss_kpt: 0.000915 acc_pose: 0.757778 loss: 0.000915 2022/10/14 00:24:35 - mmengine - INFO - Epoch(train) [179][250/293] lr: 5.000000e-05 eta: 0:43:47 time: 0.323934 data_time: 0.066443 memory: 2315 loss_kpt: 0.000892 acc_pose: 0.705247 loss: 0.000892 2022/10/14 00:24:49 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:25:06 - mmengine - INFO - Epoch(train) [180][50/293] lr: 5.000000e-05 eta: 0:43:19 time: 0.338152 data_time: 0.141509 memory: 2315 loss_kpt: 0.000890 acc_pose: 0.762384 loss: 0.000890 2022/10/14 00:25:22 - mmengine - INFO - Epoch(train) [180][100/293] lr: 5.000000e-05 eta: 0:43:05 time: 0.326002 data_time: 0.137987 memory: 2315 loss_kpt: 0.000885 acc_pose: 0.765554 loss: 0.000885 2022/10/14 00:25:39 - mmengine - INFO - Epoch(train) [180][150/293] lr: 5.000000e-05 eta: 0:42:51 time: 0.329732 data_time: 0.163027 memory: 2315 loss_kpt: 0.000906 acc_pose: 0.727044 loss: 0.000906 2022/10/14 00:25:55 - mmengine - INFO - Epoch(train) [180][200/293] lr: 5.000000e-05 eta: 0:42:37 time: 0.332804 data_time: 0.172778 memory: 2315 loss_kpt: 0.000875 acc_pose: 0.770837 loss: 0.000875 2022/10/14 00:26:12 - mmengine - INFO - Epoch(train) [180][250/293] lr: 5.000000e-05 eta: 0:42:23 time: 0.330744 data_time: 0.164735 memory: 2315 loss_kpt: 0.000892 acc_pose: 0.707962 loss: 0.000892 2022/10/14 00:26:26 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:26:26 - mmengine - INFO - Saving checkpoint at 180 epochs 2022/10/14 00:26:34 - mmengine - INFO - Epoch(val) [180][50/407] eta: 0:00:42 time: 0.119326 data_time: 0.075382 memory: 2315 2022/10/14 00:26:39 - mmengine - INFO - Epoch(val) [180][100/407] eta: 0:00:35 time: 0.115196 data_time: 0.071827 memory: 426 2022/10/14 00:26:45 - mmengine - INFO - Epoch(val) [180][150/407] eta: 0:00:28 time: 0.109493 data_time: 0.063685 memory: 426 2022/10/14 00:26:51 - mmengine - INFO - Epoch(val) [180][200/407] eta: 0:00:24 time: 0.117732 data_time: 0.073293 memory: 426 2022/10/14 00:26:56 - mmengine - INFO - Epoch(val) [180][250/407] eta: 0:00:17 time: 0.114455 data_time: 0.069548 memory: 426 2022/10/14 00:27:02 - mmengine - INFO - Epoch(val) [180][300/407] eta: 0:00:11 time: 0.109506 data_time: 0.065298 memory: 426 2022/10/14 00:27:08 - mmengine - INFO - Epoch(val) [180][350/407] eta: 0:00:06 time: 0.112294 data_time: 0.069048 memory: 426 2022/10/14 00:27:13 - mmengine - INFO - Epoch(val) [180][400/407] eta: 0:00:00 time: 0.107606 data_time: 0.066373 memory: 426 2022/10/14 00:27:50 - mmengine - INFO - Evaluating CocoMetric... 2022/10/14 00:28:05 - mmengine - INFO - Epoch(val) [180][407/407] coco/AP: 0.600532 coco/AP .5: 0.856303 coco/AP .75: 0.670822 coco/AP (M): 0.567743 coco/AP (L): 0.661925 coco/AR: 0.666546 coco/AR .5: 0.902236 coco/AR .75: 0.736933 coco/AR (M): 0.623136 coco/AR (L): 0.727462 2022/10/14 00:28:05 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_170.pth is removed 2022/10/14 00:28:06 - mmengine - INFO - The best checkpoint with 0.6005 coco/AP at 180 epoch is saved to best_coco/AP_epoch_180.pth. 2022/10/14 00:28:24 - mmengine - INFO - Epoch(train) [181][50/293] lr: 5.000000e-05 eta: 0:41:54 time: 0.345494 data_time: 0.234778 memory: 2315 loss_kpt: 0.000901 acc_pose: 0.744961 loss: 0.000901 2022/10/14 00:28:40 - mmengine - INFO - Epoch(train) [181][100/293] lr: 5.000000e-05 eta: 0:41:40 time: 0.329581 data_time: 0.162493 memory: 2315 loss_kpt: 0.000889 acc_pose: 0.738961 loss: 0.000889 2022/10/14 00:28:57 - mmengine - INFO - Epoch(train) [181][150/293] lr: 5.000000e-05 eta: 0:41:26 time: 0.331012 data_time: 0.160317 memory: 2315 loss_kpt: 0.000878 acc_pose: 0.760334 loss: 0.000878 2022/10/14 00:29:13 - mmengine - INFO - Epoch(train) [181][200/293] lr: 5.000000e-05 eta: 0:41:12 time: 0.319384 data_time: 0.086109 memory: 2315 loss_kpt: 0.000903 acc_pose: 0.777120 loss: 0.000903 2022/10/14 00:29:29 - mmengine - INFO - Epoch(train) [181][250/293] lr: 5.000000e-05 eta: 0:40:58 time: 0.331077 data_time: 0.072637 memory: 2315 loss_kpt: 0.000869 acc_pose: 0.658464 loss: 0.000869 2022/10/14 00:29:33 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:29:43 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:30:01 - mmengine - INFO - Epoch(train) [182][50/293] lr: 5.000000e-05 eta: 0:40:30 time: 0.343073 data_time: 0.160669 memory: 2315 loss_kpt: 0.000864 acc_pose: 0.717034 loss: 0.000864 2022/10/14 00:30:17 - mmengine - INFO - Epoch(train) [182][100/293] lr: 5.000000e-05 eta: 0:40:16 time: 0.334995 data_time: 0.218868 memory: 2315 loss_kpt: 0.000878 acc_pose: 0.762199 loss: 0.000878 2022/10/14 00:30:34 - mmengine - INFO - Epoch(train) [182][150/293] lr: 5.000000e-05 eta: 0:40:02 time: 0.324724 data_time: 0.182658 memory: 2315 loss_kpt: 0.000872 acc_pose: 0.746046 loss: 0.000872 2022/10/14 00:30:50 - mmengine - INFO - Epoch(train) [182][200/293] lr: 5.000000e-05 eta: 0:39:48 time: 0.334291 data_time: 0.167387 memory: 2315 loss_kpt: 0.000892 acc_pose: 0.696072 loss: 0.000892 2022/10/14 00:31:07 - mmengine - INFO - Epoch(train) [182][250/293] lr: 5.000000e-05 eta: 0:39:34 time: 0.337445 data_time: 0.162835 memory: 2315 loss_kpt: 0.000893 acc_pose: 0.740990 loss: 0.000893 2022/10/14 00:31:21 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:31:38 - mmengine - INFO - Epoch(train) [183][50/293] lr: 5.000000e-05 eta: 0:39:05 time: 0.342871 data_time: 0.103538 memory: 2315 loss_kpt: 0.000891 acc_pose: 0.748857 loss: 0.000891 2022/10/14 00:31:55 - mmengine - INFO - Epoch(train) [183][100/293] lr: 5.000000e-05 eta: 0:38:51 time: 0.339481 data_time: 0.089791 memory: 2315 loss_kpt: 0.000884 acc_pose: 0.713361 loss: 0.000884 2022/10/14 00:32:12 - mmengine - INFO - Epoch(train) [183][150/293] lr: 5.000000e-05 eta: 0:38:37 time: 0.323673 data_time: 0.062424 memory: 2315 loss_kpt: 0.000897 acc_pose: 0.688719 loss: 0.000897 2022/10/14 00:32:28 - mmengine - INFO - Epoch(train) [183][200/293] lr: 5.000000e-05 eta: 0:38:23 time: 0.334030 data_time: 0.062890 memory: 2315 loss_kpt: 0.000893 acc_pose: 0.790163 loss: 0.000893 2022/10/14 00:32:45 - mmengine - INFO - Epoch(train) [183][250/293] lr: 5.000000e-05 eta: 0:38:09 time: 0.331081 data_time: 0.066122 memory: 2315 loss_kpt: 0.000890 acc_pose: 0.695795 loss: 0.000890 2022/10/14 00:32:59 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:33:16 - mmengine - INFO - Epoch(train) [184][50/293] lr: 5.000000e-05 eta: 0:37:41 time: 0.344097 data_time: 0.092164 memory: 2315 loss_kpt: 0.000888 acc_pose: 0.763180 loss: 0.000888 2022/10/14 00:33:32 - mmengine - INFO - Epoch(train) [184][100/293] lr: 5.000000e-05 eta: 0:37:27 time: 0.321621 data_time: 0.067473 memory: 2315 loss_kpt: 0.000892 acc_pose: 0.720293 loss: 0.000892 2022/10/14 00:33:48 - mmengine - INFO - Epoch(train) [184][150/293] lr: 5.000000e-05 eta: 0:37:13 time: 0.324169 data_time: 0.071392 memory: 2315 loss_kpt: 0.000894 acc_pose: 0.736401 loss: 0.000894 2022/10/14 00:34:05 - mmengine - INFO - Epoch(train) [184][200/293] lr: 5.000000e-05 eta: 0:36:59 time: 0.332214 data_time: 0.064915 memory: 2315 loss_kpt: 0.000884 acc_pose: 0.681260 loss: 0.000884 2022/10/14 00:34:21 - mmengine - INFO - Epoch(train) [184][250/293] lr: 5.000000e-05 eta: 0:36:45 time: 0.331409 data_time: 0.065266 memory: 2315 loss_kpt: 0.000878 acc_pose: 0.685026 loss: 0.000878 2022/10/14 00:34:35 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:34:52 - mmengine - INFO - Epoch(train) [185][50/293] lr: 5.000000e-05 eta: 0:36:17 time: 0.343506 data_time: 0.123545 memory: 2315 loss_kpt: 0.000892 acc_pose: 0.710074 loss: 0.000892 2022/10/14 00:35:05 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:35:09 - mmengine - INFO - Epoch(train) [185][100/293] lr: 5.000000e-05 eta: 0:36:02 time: 0.332596 data_time: 0.071863 memory: 2315 loss_kpt: 0.000882 acc_pose: 0.727445 loss: 0.000882 2022/10/14 00:35:26 - mmengine - INFO - Epoch(train) [185][150/293] lr: 5.000000e-05 eta: 0:35:48 time: 0.336477 data_time: 0.069335 memory: 2315 loss_kpt: 0.000889 acc_pose: 0.721388 loss: 0.000889 2022/10/14 00:35:42 - mmengine - INFO - Epoch(train) [185][200/293] lr: 5.000000e-05 eta: 0:35:34 time: 0.328088 data_time: 0.069586 memory: 2315 loss_kpt: 0.000885 acc_pose: 0.627168 loss: 0.000885 2022/10/14 00:35:59 - mmengine - INFO - Epoch(train) [185][250/293] lr: 5.000000e-05 eta: 0:35:20 time: 0.327774 data_time: 0.070604 memory: 2315 loss_kpt: 0.000887 acc_pose: 0.669623 loss: 0.000887 2022/10/14 00:36:13 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:36:30 - mmengine - INFO - Epoch(train) [186][50/293] lr: 5.000000e-05 eta: 0:34:52 time: 0.340717 data_time: 0.097706 memory: 2315 loss_kpt: 0.000887 acc_pose: 0.734693 loss: 0.000887 2022/10/14 00:36:46 - mmengine - INFO - Epoch(train) [186][100/293] lr: 5.000000e-05 eta: 0:34:38 time: 0.323217 data_time: 0.132832 memory: 2315 loss_kpt: 0.000879 acc_pose: 0.732623 loss: 0.000879 2022/10/14 00:37:02 - mmengine - INFO - Epoch(train) [186][150/293] lr: 5.000000e-05 eta: 0:34:24 time: 0.328194 data_time: 0.073758 memory: 2315 loss_kpt: 0.000897 acc_pose: 0.693729 loss: 0.000897 2022/10/14 00:37:19 - mmengine - INFO - Epoch(train) [186][200/293] lr: 5.000000e-05 eta: 0:34:10 time: 0.328696 data_time: 0.065792 memory: 2315 loss_kpt: 0.000885 acc_pose: 0.750472 loss: 0.000885 2022/10/14 00:37:35 - mmengine - INFO - Epoch(train) [186][250/293] lr: 5.000000e-05 eta: 0:33:56 time: 0.333540 data_time: 0.070502 memory: 2315 loss_kpt: 0.000862 acc_pose: 0.761625 loss: 0.000862 2022/10/14 00:37:49 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:38:06 - mmengine - INFO - Epoch(train) [187][50/293] lr: 5.000000e-05 eta: 0:33:28 time: 0.339853 data_time: 0.103650 memory: 2315 loss_kpt: 0.000889 acc_pose: 0.698202 loss: 0.000889 2022/10/14 00:38:22 - mmengine - INFO - Epoch(train) [187][100/293] lr: 5.000000e-05 eta: 0:33:14 time: 0.329492 data_time: 0.080819 memory: 2315 loss_kpt: 0.000896 acc_pose: 0.725725 loss: 0.000896 2022/10/14 00:38:38 - mmengine - INFO - Epoch(train) [187][150/293] lr: 5.000000e-05 eta: 0:32:59 time: 0.320346 data_time: 0.069213 memory: 2315 loss_kpt: 0.000883 acc_pose: 0.705576 loss: 0.000883 2022/10/14 00:38:55 - mmengine - INFO - Epoch(train) [187][200/293] lr: 5.000000e-05 eta: 0:32:45 time: 0.325993 data_time: 0.063947 memory: 2315 loss_kpt: 0.000888 acc_pose: 0.733979 loss: 0.000888 2022/10/14 00:39:12 - mmengine - INFO - Epoch(train) [187][250/293] lr: 5.000000e-05 eta: 0:32:31 time: 0.339032 data_time: 0.080059 memory: 2315 loss_kpt: 0.000871 acc_pose: 0.682472 loss: 0.000871 2022/10/14 00:39:25 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:39:43 - mmengine - INFO - Epoch(train) [188][50/293] lr: 5.000000e-05 eta: 0:32:03 time: 0.343538 data_time: 0.084103 memory: 2315 loss_kpt: 0.000870 acc_pose: 0.656270 loss: 0.000870 2022/10/14 00:39:59 - mmengine - INFO - Epoch(train) [188][100/293] lr: 5.000000e-05 eta: 0:31:49 time: 0.335463 data_time: 0.065488 memory: 2315 loss_kpt: 0.000877 acc_pose: 0.739958 loss: 0.000877 2022/10/14 00:40:16 - mmengine - INFO - Epoch(train) [188][150/293] lr: 5.000000e-05 eta: 0:31:35 time: 0.331959 data_time: 0.069881 memory: 2315 loss_kpt: 0.000894 acc_pose: 0.734351 loss: 0.000894 2022/10/14 00:40:33 - mmengine - INFO - Epoch(train) [188][200/293] lr: 5.000000e-05 eta: 0:31:21 time: 0.329445 data_time: 0.072003 memory: 2315 loss_kpt: 0.000883 acc_pose: 0.749899 loss: 0.000883 2022/10/14 00:40:35 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:40:49 - mmengine - INFO - Epoch(train) [188][250/293] lr: 5.000000e-05 eta: 0:31:07 time: 0.327822 data_time: 0.067564 memory: 2315 loss_kpt: 0.000888 acc_pose: 0.748491 loss: 0.000888 2022/10/14 00:41:03 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:41:20 - mmengine - INFO - Epoch(train) [189][50/293] lr: 5.000000e-05 eta: 0:30:39 time: 0.345144 data_time: 0.155951 memory: 2315 loss_kpt: 0.000901 acc_pose: 0.718827 loss: 0.000901 2022/10/14 00:41:37 - mmengine - INFO - Epoch(train) [189][100/293] lr: 5.000000e-05 eta: 0:30:25 time: 0.330178 data_time: 0.161036 memory: 2315 loss_kpt: 0.000893 acc_pose: 0.751753 loss: 0.000893 2022/10/14 00:41:53 - mmengine - INFO - Epoch(train) [189][150/293] lr: 5.000000e-05 eta: 0:30:11 time: 0.331833 data_time: 0.183998 memory: 2315 loss_kpt: 0.000880 acc_pose: 0.758812 loss: 0.000880 2022/10/14 00:42:10 - mmengine - INFO - Epoch(train) [189][200/293] lr: 5.000000e-05 eta: 0:29:56 time: 0.331030 data_time: 0.159238 memory: 2315 loss_kpt: 0.000883 acc_pose: 0.642591 loss: 0.000883 2022/10/14 00:42:26 - mmengine - INFO - Epoch(train) [189][250/293] lr: 5.000000e-05 eta: 0:29:42 time: 0.316783 data_time: 0.139114 memory: 2315 loss_kpt: 0.000881 acc_pose: 0.746692 loss: 0.000881 2022/10/14 00:42:39 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:42:56 - mmengine - INFO - Epoch(train) [190][50/293] lr: 5.000000e-05 eta: 0:29:14 time: 0.340287 data_time: 0.102089 memory: 2315 loss_kpt: 0.000889 acc_pose: 0.740170 loss: 0.000889 2022/10/14 00:43:13 - mmengine - INFO - Epoch(train) [190][100/293] lr: 5.000000e-05 eta: 0:29:00 time: 0.329057 data_time: 0.068791 memory: 2315 loss_kpt: 0.000850 acc_pose: 0.698297 loss: 0.000850 2022/10/14 00:43:29 - mmengine - INFO - Epoch(train) [190][150/293] lr: 5.000000e-05 eta: 0:28:46 time: 0.333306 data_time: 0.067518 memory: 2315 loss_kpt: 0.000885 acc_pose: 0.702093 loss: 0.000885 2022/10/14 00:43:46 - mmengine - INFO - Epoch(train) [190][200/293] lr: 5.000000e-05 eta: 0:28:32 time: 0.335267 data_time: 0.069757 memory: 2315 loss_kpt: 0.000872 acc_pose: 0.746048 loss: 0.000872 2022/10/14 00:44:03 - mmengine - INFO - Epoch(train) [190][250/293] lr: 5.000000e-05 eta: 0:28:18 time: 0.332486 data_time: 0.169379 memory: 2315 loss_kpt: 0.000884 acc_pose: 0.752884 loss: 0.000884 2022/10/14 00:44:17 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:44:17 - mmengine - INFO - Saving checkpoint at 190 epochs 2022/10/14 00:44:25 - mmengine - INFO - Epoch(val) [190][50/407] eta: 0:00:41 time: 0.115662 data_time: 0.071180 memory: 2315 2022/10/14 00:44:30 - mmengine - INFO - Epoch(val) [190][100/407] eta: 0:00:34 time: 0.111274 data_time: 0.066101 memory: 426 2022/10/14 00:44:36 - mmengine - INFO - Epoch(val) [190][150/407] eta: 0:00:28 time: 0.110977 data_time: 0.067738 memory: 426 2022/10/14 00:44:41 - mmengine - INFO - Epoch(val) [190][200/407] eta: 0:00:22 time: 0.110742 data_time: 0.068608 memory: 426 2022/10/14 00:44:48 - mmengine - INFO - Epoch(val) [190][250/407] eta: 0:00:19 time: 0.124221 data_time: 0.079354 memory: 426 2022/10/14 00:44:53 - mmengine - INFO - Epoch(val) [190][300/407] eta: 0:00:11 time: 0.109997 data_time: 0.066632 memory: 426 2022/10/14 00:44:59 - mmengine - INFO - Epoch(val) [190][350/407] eta: 0:00:06 time: 0.113713 data_time: 0.069624 memory: 426 2022/10/14 00:45:04 - mmengine - INFO - Epoch(val) [190][400/407] eta: 0:00:00 time: 0.109289 data_time: 0.068679 memory: 426 2022/10/14 00:45:42 - mmengine - INFO - Evaluating CocoMetric... 2022/10/14 00:45:56 - mmengine - INFO - Epoch(val) [190][407/407] coco/AP: 0.602214 coco/AP .5: 0.859092 coco/AP .75: 0.672697 coco/AP (M): 0.568763 coco/AP (L): 0.664205 coco/AR: 0.667711 coco/AR .5: 0.903810 coco/AR .75: 0.737720 coco/AR (M): 0.624092 coco/AR (L): 0.729060 2022/10/14 00:45:56 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_180.pth is removed 2022/10/14 00:45:58 - mmengine - INFO - The best checkpoint with 0.6022 coco/AP at 190 epoch is saved to best_coco/AP_epoch_190.pth. 2022/10/14 00:46:14 - mmengine - INFO - Epoch(train) [191][50/293] lr: 5.000000e-05 eta: 0:27:50 time: 0.333877 data_time: 0.182983 memory: 2315 loss_kpt: 0.000879 acc_pose: 0.741046 loss: 0.000879 2022/10/14 00:46:31 - mmengine - INFO - Epoch(train) [191][100/293] lr: 5.000000e-05 eta: 0:27:36 time: 0.326576 data_time: 0.146325 memory: 2315 loss_kpt: 0.000895 acc_pose: 0.733160 loss: 0.000895 2022/10/14 00:46:47 - mmengine - INFO - Epoch(train) [191][150/293] lr: 5.000000e-05 eta: 0:27:22 time: 0.335319 data_time: 0.174548 memory: 2315 loss_kpt: 0.000863 acc_pose: 0.749674 loss: 0.000863 2022/10/14 00:47:04 - mmengine - INFO - Epoch(train) [191][200/293] lr: 5.000000e-05 eta: 0:27:08 time: 0.339884 data_time: 0.186416 memory: 2315 loss_kpt: 0.000873 acc_pose: 0.731987 loss: 0.000873 2022/10/14 00:47:20 - mmengine - INFO - Epoch(train) [191][250/293] lr: 5.000000e-05 eta: 0:26:53 time: 0.318297 data_time: 0.133676 memory: 2315 loss_kpt: 0.000869 acc_pose: 0.686737 loss: 0.000869 2022/10/14 00:47:34 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:47:47 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:47:52 - mmengine - INFO - Epoch(train) [192][50/293] lr: 5.000000e-05 eta: 0:26:26 time: 0.356926 data_time: 0.105595 memory: 2315 loss_kpt: 0.000876 acc_pose: 0.718136 loss: 0.000876 2022/10/14 00:48:08 - mmengine - INFO - Epoch(train) [192][100/293] lr: 5.000000e-05 eta: 0:26:12 time: 0.321283 data_time: 0.070424 memory: 2315 loss_kpt: 0.000877 acc_pose: 0.782853 loss: 0.000877 2022/10/14 00:48:25 - mmengine - INFO - Epoch(train) [192][150/293] lr: 5.000000e-05 eta: 0:25:57 time: 0.327039 data_time: 0.120035 memory: 2315 loss_kpt: 0.000873 acc_pose: 0.708921 loss: 0.000873 2022/10/14 00:48:41 - mmengine - INFO - Epoch(train) [192][200/293] lr: 5.000000e-05 eta: 0:25:43 time: 0.328065 data_time: 0.118853 memory: 2315 loss_kpt: 0.000890 acc_pose: 0.770593 loss: 0.000890 2022/10/14 00:48:58 - mmengine - INFO - Epoch(train) [192][250/293] lr: 5.000000e-05 eta: 0:25:29 time: 0.342235 data_time: 0.067232 memory: 2315 loss_kpt: 0.000874 acc_pose: 0.722724 loss: 0.000874 2022/10/14 00:49:12 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:49:28 - mmengine - INFO - Epoch(train) [193][50/293] lr: 5.000000e-05 eta: 0:25:01 time: 0.330018 data_time: 0.150239 memory: 2315 loss_kpt: 0.000878 acc_pose: 0.682424 loss: 0.000878 2022/10/14 00:49:45 - mmengine - INFO - Epoch(train) [193][100/293] lr: 5.000000e-05 eta: 0:24:47 time: 0.331222 data_time: 0.085875 memory: 2315 loss_kpt: 0.000870 acc_pose: 0.705819 loss: 0.000870 2022/10/14 00:50:01 - mmengine - INFO - Epoch(train) [193][150/293] lr: 5.000000e-05 eta: 0:24:33 time: 0.326297 data_time: 0.070098 memory: 2315 loss_kpt: 0.000884 acc_pose: 0.767196 loss: 0.000884 2022/10/14 00:50:18 - mmengine - INFO - Epoch(train) [193][200/293] lr: 5.000000e-05 eta: 0:24:19 time: 0.331157 data_time: 0.066511 memory: 2315 loss_kpt: 0.000880 acc_pose: 0.763221 loss: 0.000880 2022/10/14 00:50:34 - mmengine - INFO - Epoch(train) [193][250/293] lr: 5.000000e-05 eta: 0:24:05 time: 0.328126 data_time: 0.083126 memory: 2315 loss_kpt: 0.000892 acc_pose: 0.768480 loss: 0.000892 2022/10/14 00:50:48 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:51:06 - mmengine - INFO - Epoch(train) [194][50/293] lr: 5.000000e-05 eta: 0:23:37 time: 0.352789 data_time: 0.089752 memory: 2315 loss_kpt: 0.000876 acc_pose: 0.756547 loss: 0.000876 2022/10/14 00:51:22 - mmengine - INFO - Epoch(train) [194][100/293] lr: 5.000000e-05 eta: 0:23:23 time: 0.329715 data_time: 0.068939 memory: 2315 loss_kpt: 0.000880 acc_pose: 0.764620 loss: 0.000880 2022/10/14 00:51:39 - mmengine - INFO - Epoch(train) [194][150/293] lr: 5.000000e-05 eta: 0:23:09 time: 0.323353 data_time: 0.065426 memory: 2315 loss_kpt: 0.000898 acc_pose: 0.764243 loss: 0.000898 2022/10/14 00:51:55 - mmengine - INFO - Epoch(train) [194][200/293] lr: 5.000000e-05 eta: 0:22:54 time: 0.317604 data_time: 0.066000 memory: 2315 loss_kpt: 0.000881 acc_pose: 0.732709 loss: 0.000881 2022/10/14 00:52:11 - mmengine - INFO - Epoch(train) [194][250/293] lr: 5.000000e-05 eta: 0:22:40 time: 0.330484 data_time: 0.070666 memory: 2315 loss_kpt: 0.000872 acc_pose: 0.798410 loss: 0.000872 2022/10/14 00:52:25 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:52:42 - mmengine - INFO - Epoch(train) [195][50/293] lr: 5.000000e-05 eta: 0:22:13 time: 0.338598 data_time: 0.106078 memory: 2315 loss_kpt: 0.000865 acc_pose: 0.703322 loss: 0.000865 2022/10/14 00:52:59 - mmengine - INFO - Epoch(train) [195][100/293] lr: 5.000000e-05 eta: 0:21:58 time: 0.333084 data_time: 0.086759 memory: 2315 loss_kpt: 0.000891 acc_pose: 0.704222 loss: 0.000891 2022/10/14 00:53:15 - mmengine - INFO - Epoch(train) [195][150/293] lr: 5.000000e-05 eta: 0:21:44 time: 0.326918 data_time: 0.142319 memory: 2315 loss_kpt: 0.000862 acc_pose: 0.710566 loss: 0.000862 2022/10/14 00:53:18 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:53:32 - mmengine - INFO - Epoch(train) [195][200/293] lr: 5.000000e-05 eta: 0:21:30 time: 0.331321 data_time: 0.069917 memory: 2315 loss_kpt: 0.000873 acc_pose: 0.745207 loss: 0.000873 2022/10/14 00:53:48 - mmengine - INFO - Epoch(train) [195][250/293] lr: 5.000000e-05 eta: 0:21:16 time: 0.324404 data_time: 0.066789 memory: 2315 loss_kpt: 0.000863 acc_pose: 0.681236 loss: 0.000863 2022/10/14 00:54:02 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:54:19 - mmengine - INFO - Epoch(train) [196][50/293] lr: 5.000000e-05 eta: 0:20:48 time: 0.336831 data_time: 0.080830 memory: 2315 loss_kpt: 0.000885 acc_pose: 0.717749 loss: 0.000885 2022/10/14 00:54:35 - mmengine - INFO - Epoch(train) [196][100/293] lr: 5.000000e-05 eta: 0:20:34 time: 0.332122 data_time: 0.075817 memory: 2315 loss_kpt: 0.000880 acc_pose: 0.728264 loss: 0.000880 2022/10/14 00:54:53 - mmengine - INFO - Epoch(train) [196][150/293] lr: 5.000000e-05 eta: 0:20:20 time: 0.344229 data_time: 0.064552 memory: 2315 loss_kpt: 0.000866 acc_pose: 0.690415 loss: 0.000866 2022/10/14 00:55:09 - mmengine - INFO - Epoch(train) [196][200/293] lr: 5.000000e-05 eta: 0:20:06 time: 0.324155 data_time: 0.101119 memory: 2315 loss_kpt: 0.000874 acc_pose: 0.736335 loss: 0.000874 2022/10/14 00:55:26 - mmengine - INFO - Epoch(train) [196][250/293] lr: 5.000000e-05 eta: 0:19:51 time: 0.340332 data_time: 0.091130 memory: 2315 loss_kpt: 0.000870 acc_pose: 0.688603 loss: 0.000870 2022/10/14 00:55:40 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:55:57 - mmengine - INFO - Epoch(train) [197][50/293] lr: 5.000000e-05 eta: 0:19:24 time: 0.339652 data_time: 0.100442 memory: 2315 loss_kpt: 0.000891 acc_pose: 0.779514 loss: 0.000891 2022/10/14 00:56:14 - mmengine - INFO - Epoch(train) [197][100/293] lr: 5.000000e-05 eta: 0:19:10 time: 0.333400 data_time: 0.116419 memory: 2315 loss_kpt: 0.000876 acc_pose: 0.703112 loss: 0.000876 2022/10/14 00:56:30 - mmengine - INFO - Epoch(train) [197][150/293] lr: 5.000000e-05 eta: 0:18:56 time: 0.319783 data_time: 0.079000 memory: 2315 loss_kpt: 0.000889 acc_pose: 0.705708 loss: 0.000889 2022/10/14 00:56:46 - mmengine - INFO - Epoch(train) [197][200/293] lr: 5.000000e-05 eta: 0:18:41 time: 0.323463 data_time: 0.064992 memory: 2315 loss_kpt: 0.000884 acc_pose: 0.694402 loss: 0.000884 2022/10/14 00:57:03 - mmengine - INFO - Epoch(train) [197][250/293] lr: 5.000000e-05 eta: 0:18:27 time: 0.342783 data_time: 0.069440 memory: 2315 loss_kpt: 0.000873 acc_pose: 0.734741 loss: 0.000873 2022/10/14 00:57:17 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:57:35 - mmengine - INFO - Epoch(train) [198][50/293] lr: 5.000000e-05 eta: 0:18:00 time: 0.350643 data_time: 0.080349 memory: 2315 loss_kpt: 0.000876 acc_pose: 0.742572 loss: 0.000876 2022/10/14 00:57:51 - mmengine - INFO - Epoch(train) [198][100/293] lr: 5.000000e-05 eta: 0:17:46 time: 0.322727 data_time: 0.073650 memory: 2315 loss_kpt: 0.000868 acc_pose: 0.625087 loss: 0.000868 2022/10/14 00:58:07 - mmengine - INFO - Epoch(train) [198][150/293] lr: 5.000000e-05 eta: 0:17:31 time: 0.326875 data_time: 0.072692 memory: 2315 loss_kpt: 0.000879 acc_pose: 0.738910 loss: 0.000879 2022/10/14 00:58:24 - mmengine - INFO - Epoch(train) [198][200/293] lr: 5.000000e-05 eta: 0:17:17 time: 0.337243 data_time: 0.076084 memory: 2315 loss_kpt: 0.000875 acc_pose: 0.682659 loss: 0.000875 2022/10/14 00:58:41 - mmengine - INFO - Epoch(train) [198][250/293] lr: 5.000000e-05 eta: 0:17:03 time: 0.329041 data_time: 0.067940 memory: 2315 loss_kpt: 0.000876 acc_pose: 0.726356 loss: 0.000876 2022/10/14 00:58:51 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:58:55 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 00:59:11 - mmengine - INFO - Epoch(train) [199][50/293] lr: 5.000000e-05 eta: 0:16:35 time: 0.333922 data_time: 0.094608 memory: 2315 loss_kpt: 0.000874 acc_pose: 0.717631 loss: 0.000874 2022/10/14 00:59:28 - mmengine - INFO - Epoch(train) [199][100/293] lr: 5.000000e-05 eta: 0:16:21 time: 0.335100 data_time: 0.072885 memory: 2315 loss_kpt: 0.000866 acc_pose: 0.749510 loss: 0.000866 2022/10/14 00:59:45 - mmengine - INFO - Epoch(train) [199][150/293] lr: 5.000000e-05 eta: 0:16:07 time: 0.338232 data_time: 0.067536 memory: 2315 loss_kpt: 0.000864 acc_pose: 0.744999 loss: 0.000864 2022/10/14 01:00:01 - mmengine - INFO - Epoch(train) [199][200/293] lr: 5.000000e-05 eta: 0:15:53 time: 0.329950 data_time: 0.078212 memory: 2315 loss_kpt: 0.000874 acc_pose: 0.706049 loss: 0.000874 2022/10/14 01:00:18 - mmengine - INFO - Epoch(train) [199][250/293] lr: 5.000000e-05 eta: 0:15:39 time: 0.328421 data_time: 0.082755 memory: 2315 loss_kpt: 0.000877 acc_pose: 0.714897 loss: 0.000877 2022/10/14 01:00:32 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 01:00:49 - mmengine - INFO - Epoch(train) [200][50/293] lr: 5.000000e-05 eta: 0:15:11 time: 0.337859 data_time: 0.093184 memory: 2315 loss_kpt: 0.000866 acc_pose: 0.682263 loss: 0.000866 2022/10/14 01:01:05 - mmengine - INFO - Epoch(train) [200][100/293] lr: 5.000000e-05 eta: 0:14:57 time: 0.319477 data_time: 0.092455 memory: 2315 loss_kpt: 0.000867 acc_pose: 0.722372 loss: 0.000867 2022/10/14 01:01:21 - mmengine - INFO - Epoch(train) [200][150/293] lr: 5.000000e-05 eta: 0:14:43 time: 0.335050 data_time: 0.062495 memory: 2315 loss_kpt: 0.000871 acc_pose: 0.714739 loss: 0.000871 2022/10/14 01:01:38 - mmengine - INFO - Epoch(train) [200][200/293] lr: 5.000000e-05 eta: 0:14:28 time: 0.334777 data_time: 0.068582 memory: 2315 loss_kpt: 0.000894 acc_pose: 0.692045 loss: 0.000894 2022/10/14 01:01:55 - mmengine - INFO - Epoch(train) [200][250/293] lr: 5.000000e-05 eta: 0:14:14 time: 0.332628 data_time: 0.071303 memory: 2315 loss_kpt: 0.000884 acc_pose: 0.722476 loss: 0.000884 2022/10/14 01:02:09 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 01:02:09 - mmengine - INFO - Saving checkpoint at 200 epochs 2022/10/14 01:02:17 - mmengine - INFO - Epoch(val) [200][50/407] eta: 0:00:42 time: 0.118607 data_time: 0.074063 memory: 2315 2022/10/14 01:02:23 - mmengine - INFO - Epoch(val) [200][100/407] eta: 0:00:34 time: 0.113403 data_time: 0.070225 memory: 426 2022/10/14 01:02:28 - mmengine - INFO - Epoch(val) [200][150/407] eta: 0:00:30 time: 0.117349 data_time: 0.073157 memory: 426 2022/10/14 01:02:34 - mmengine - INFO - Epoch(val) [200][200/407] eta: 0:00:24 time: 0.116903 data_time: 0.070640 memory: 426 2022/10/14 01:02:40 - mmengine - INFO - Epoch(val) [200][250/407] eta: 0:00:17 time: 0.113124 data_time: 0.068583 memory: 426 2022/10/14 01:02:46 - mmengine - INFO - Epoch(val) [200][300/407] eta: 0:00:12 time: 0.114969 data_time: 0.071464 memory: 426 2022/10/14 01:02:52 - mmengine - INFO - Epoch(val) [200][350/407] eta: 0:00:06 time: 0.117149 data_time: 0.072252 memory: 426 2022/10/14 01:02:57 - mmengine - INFO - Epoch(val) [200][400/407] eta: 0:00:00 time: 0.100211 data_time: 0.060020 memory: 426 2022/10/14 01:03:34 - mmengine - INFO - Evaluating CocoMetric... 2022/10/14 01:03:48 - mmengine - INFO - Epoch(val) [200][407/407] coco/AP: 0.602375 coco/AP .5: 0.857624 coco/AP .75: 0.671192 coco/AP (M): 0.567847 coco/AP (L): 0.665331 coco/AR: 0.667774 coco/AR .5: 0.902865 coco/AR .75: 0.736618 coco/AR (M): 0.623709 coco/AR (L): 0.729654 2022/10/14 01:03:48 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_190.pth is removed 2022/10/14 01:03:50 - mmengine - INFO - The best checkpoint with 0.6024 coco/AP at 200 epoch is saved to best_coco/AP_epoch_200.pth. 2022/10/14 01:04:06 - mmengine - INFO - Epoch(train) [201][50/293] lr: 5.000000e-06 eta: 0:13:47 time: 0.323510 data_time: 0.181746 memory: 2315 loss_kpt: 0.000889 acc_pose: 0.719690 loss: 0.000889 2022/10/14 01:04:23 - mmengine - INFO - Epoch(train) [201][100/293] lr: 5.000000e-06 eta: 0:13:33 time: 0.335565 data_time: 0.154380 memory: 2315 loss_kpt: 0.000888 acc_pose: 0.690101 loss: 0.000888 2022/10/14 01:04:39 - mmengine - INFO - Epoch(train) [201][150/293] lr: 5.000000e-06 eta: 0:13:18 time: 0.328504 data_time: 0.073394 memory: 2315 loss_kpt: 0.000880 acc_pose: 0.753389 loss: 0.000880 2022/10/14 01:04:56 - mmengine - INFO - Epoch(train) [201][200/293] lr: 5.000000e-06 eta: 0:13:04 time: 0.336894 data_time: 0.070085 memory: 2315 loss_kpt: 0.000871 acc_pose: 0.694370 loss: 0.000871 2022/10/14 01:05:12 - mmengine - INFO - Epoch(train) [201][250/293] lr: 5.000000e-06 eta: 0:12:50 time: 0.326466 data_time: 0.066110 memory: 2315 loss_kpt: 0.000879 acc_pose: 0.737440 loss: 0.000879 2022/10/14 01:05:26 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 01:05:43 - mmengine - INFO - Epoch(train) [202][50/293] lr: 5.000000e-06 eta: 0:12:23 time: 0.334122 data_time: 0.190175 memory: 2315 loss_kpt: 0.000887 acc_pose: 0.755180 loss: 0.000887 2022/10/14 01:06:00 - mmengine - INFO - Epoch(train) [202][100/293] lr: 5.000000e-06 eta: 0:12:08 time: 0.338462 data_time: 0.130081 memory: 2315 loss_kpt: 0.000888 acc_pose: 0.783716 loss: 0.000888 2022/10/14 01:06:02 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 01:06:16 - mmengine - INFO - Epoch(train) [202][150/293] lr: 5.000000e-06 eta: 0:11:54 time: 0.330871 data_time: 0.067843 memory: 2315 loss_kpt: 0.000871 acc_pose: 0.776734 loss: 0.000871 2022/10/14 01:06:32 - mmengine - INFO - Epoch(train) [202][200/293] lr: 5.000000e-06 eta: 0:11:40 time: 0.319775 data_time: 0.066118 memory: 2315 loss_kpt: 0.000871 acc_pose: 0.686024 loss: 0.000871 2022/10/14 01:06:49 - mmengine - INFO - Epoch(train) [202][250/293] lr: 5.000000e-06 eta: 0:11:26 time: 0.325569 data_time: 0.073907 memory: 2315 loss_kpt: 0.000884 acc_pose: 0.726774 loss: 0.000884 2022/10/14 01:07:03 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 01:07:20 - mmengine - INFO - Epoch(train) [203][50/293] lr: 5.000000e-06 eta: 0:10:59 time: 0.337092 data_time: 0.092649 memory: 2315 loss_kpt: 0.000873 acc_pose: 0.745969 loss: 0.000873 2022/10/14 01:07:36 - mmengine - INFO - Epoch(train) [203][100/293] lr: 5.000000e-06 eta: 0:10:44 time: 0.330210 data_time: 0.066910 memory: 2315 loss_kpt: 0.000858 acc_pose: 0.801129 loss: 0.000858 2022/10/14 01:07:53 - mmengine - INFO - Epoch(train) [203][150/293] lr: 5.000000e-06 eta: 0:10:30 time: 0.330071 data_time: 0.080703 memory: 2315 loss_kpt: 0.000875 acc_pose: 0.723299 loss: 0.000875 2022/10/14 01:08:09 - mmengine - INFO - Epoch(train) [203][200/293] lr: 5.000000e-06 eta: 0:10:16 time: 0.322542 data_time: 0.071423 memory: 2315 loss_kpt: 0.000874 acc_pose: 0.712323 loss: 0.000874 2022/10/14 01:08:25 - mmengine - INFO - Epoch(train) [203][250/293] lr: 5.000000e-06 eta: 0:10:01 time: 0.330670 data_time: 0.066829 memory: 2315 loss_kpt: 0.000868 acc_pose: 0.800900 loss: 0.000868 2022/10/14 01:08:39 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 01:08:57 - mmengine - INFO - Epoch(train) [204][50/293] lr: 5.000000e-06 eta: 0:09:34 time: 0.349042 data_time: 0.095703 memory: 2315 loss_kpt: 0.000869 acc_pose: 0.646750 loss: 0.000869 2022/10/14 01:09:13 - mmengine - INFO - Epoch(train) [204][100/293] lr: 5.000000e-06 eta: 0:09:20 time: 0.331816 data_time: 0.075405 memory: 2315 loss_kpt: 0.000887 acc_pose: 0.779616 loss: 0.000887 2022/10/14 01:09:30 - mmengine - INFO - Epoch(train) [204][150/293] lr: 5.000000e-06 eta: 0:09:06 time: 0.327666 data_time: 0.079964 memory: 2315 loss_kpt: 0.000870 acc_pose: 0.775374 loss: 0.000870 2022/10/14 01:09:46 - mmengine - INFO - Epoch(train) [204][200/293] lr: 5.000000e-06 eta: 0:08:51 time: 0.333383 data_time: 0.063916 memory: 2315 loss_kpt: 0.000898 acc_pose: 0.756016 loss: 0.000898 2022/10/14 01:10:03 - mmengine - INFO - Epoch(train) [204][250/293] lr: 5.000000e-06 eta: 0:08:37 time: 0.335263 data_time: 0.072605 memory: 2315 loss_kpt: 0.000882 acc_pose: 0.699663 loss: 0.000882 2022/10/14 01:10:17 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 01:10:34 - mmengine - INFO - Epoch(train) [205][50/293] lr: 5.000000e-06 eta: 0:08:10 time: 0.337459 data_time: 0.086203 memory: 2315 loss_kpt: 0.000878 acc_pose: 0.713007 loss: 0.000878 2022/10/14 01:10:51 - mmengine - INFO - Epoch(train) [205][100/293] lr: 5.000000e-06 eta: 0:07:56 time: 0.333498 data_time: 0.067920 memory: 2315 loss_kpt: 0.000868 acc_pose: 0.742089 loss: 0.000868 2022/10/14 01:11:07 - mmengine - INFO - Epoch(train) [205][150/293] lr: 5.000000e-06 eta: 0:07:41 time: 0.329078 data_time: 0.069926 memory: 2315 loss_kpt: 0.000863 acc_pose: 0.725710 loss: 0.000863 2022/10/14 01:11:23 - mmengine - INFO - Epoch(train) [205][200/293] lr: 5.000000e-06 eta: 0:07:27 time: 0.328215 data_time: 0.079252 memory: 2315 loss_kpt: 0.000867 acc_pose: 0.705305 loss: 0.000867 2022/10/14 01:11:33 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 01:11:40 - mmengine - INFO - Epoch(train) [205][250/293] lr: 5.000000e-06 eta: 0:07:13 time: 0.324242 data_time: 0.069188 memory: 2315 loss_kpt: 0.000873 acc_pose: 0.771045 loss: 0.000873 2022/10/14 01:11:53 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 01:12:11 - mmengine - INFO - Epoch(train) [206][50/293] lr: 5.000000e-06 eta: 0:06:46 time: 0.343473 data_time: 0.139628 memory: 2315 loss_kpt: 0.000868 acc_pose: 0.724771 loss: 0.000868 2022/10/14 01:12:27 - mmengine - INFO - Epoch(train) [206][100/293] lr: 5.000000e-06 eta: 0:06:32 time: 0.332214 data_time: 0.072271 memory: 2315 loss_kpt: 0.000866 acc_pose: 0.705878 loss: 0.000866 2022/10/14 01:12:44 - mmengine - INFO - Epoch(train) [206][150/293] lr: 5.000000e-06 eta: 0:06:17 time: 0.336204 data_time: 0.070336 memory: 2315 loss_kpt: 0.000867 acc_pose: 0.742325 loss: 0.000867 2022/10/14 01:13:01 - mmengine - INFO - Epoch(train) [206][200/293] lr: 5.000000e-06 eta: 0:06:03 time: 0.331658 data_time: 0.076274 memory: 2315 loss_kpt: 0.000889 acc_pose: 0.726827 loss: 0.000889 2022/10/14 01:13:17 - mmengine - INFO - Epoch(train) [206][250/293] lr: 5.000000e-06 eta: 0:05:49 time: 0.322039 data_time: 0.080968 memory: 2315 loss_kpt: 0.000867 acc_pose: 0.703656 loss: 0.000867 2022/10/14 01:13:31 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 01:13:48 - mmengine - INFO - Epoch(train) [207][50/293] lr: 5.000000e-06 eta: 0:05:22 time: 0.343569 data_time: 0.149199 memory: 2315 loss_kpt: 0.000881 acc_pose: 0.736322 loss: 0.000881 2022/10/14 01:14:05 - mmengine - INFO - Epoch(train) [207][100/293] lr: 5.000000e-06 eta: 0:05:07 time: 0.331574 data_time: 0.064007 memory: 2315 loss_kpt: 0.000889 acc_pose: 0.757947 loss: 0.000889 2022/10/14 01:14:21 - mmengine - INFO - Epoch(train) [207][150/293] lr: 5.000000e-06 eta: 0:04:53 time: 0.322830 data_time: 0.072180 memory: 2315 loss_kpt: 0.000874 acc_pose: 0.726980 loss: 0.000874 2022/10/14 01:14:38 - mmengine - INFO - Epoch(train) [207][200/293] lr: 5.000000e-06 eta: 0:04:39 time: 0.337068 data_time: 0.134835 memory: 2315 loss_kpt: 0.000874 acc_pose: 0.731259 loss: 0.000874 2022/10/14 01:14:54 - mmengine - INFO - Epoch(train) [207][250/293] lr: 5.000000e-06 eta: 0:04:24 time: 0.330035 data_time: 0.117761 memory: 2315 loss_kpt: 0.000870 acc_pose: 0.722581 loss: 0.000870 2022/10/14 01:15:08 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 01:15:25 - mmengine - INFO - Epoch(train) [208][50/293] lr: 5.000000e-06 eta: 0:03:58 time: 0.336939 data_time: 0.106871 memory: 2315 loss_kpt: 0.000877 acc_pose: 0.756002 loss: 0.000877 2022/10/14 01:15:41 - mmengine - INFO - Epoch(train) [208][100/293] lr: 5.000000e-06 eta: 0:03:43 time: 0.334768 data_time: 0.066833 memory: 2315 loss_kpt: 0.000896 acc_pose: 0.703011 loss: 0.000896 2022/10/14 01:15:58 - mmengine - INFO - Epoch(train) [208][150/293] lr: 5.000000e-06 eta: 0:03:29 time: 0.331153 data_time: 0.125815 memory: 2315 loss_kpt: 0.000864 acc_pose: 0.762586 loss: 0.000864 2022/10/14 01:16:15 - mmengine - INFO - Epoch(train) [208][200/293] lr: 5.000000e-06 eta: 0:03:15 time: 0.333138 data_time: 0.095014 memory: 2315 loss_kpt: 0.000877 acc_pose: 0.734501 loss: 0.000877 2022/10/14 01:16:31 - mmengine - INFO - Epoch(train) [208][250/293] lr: 5.000000e-06 eta: 0:03:00 time: 0.335153 data_time: 0.086294 memory: 2315 loss_kpt: 0.000877 acc_pose: 0.727206 loss: 0.000877 2022/10/14 01:16:46 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 01:17:03 - mmengine - INFO - Epoch(train) [209][50/293] lr: 5.000000e-06 eta: 0:02:33 time: 0.336426 data_time: 0.088911 memory: 2315 loss_kpt: 0.000873 acc_pose: 0.714149 loss: 0.000873 2022/10/14 01:17:05 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 01:17:19 - mmengine - INFO - Epoch(train) [209][100/293] lr: 5.000000e-06 eta: 0:02:19 time: 0.331026 data_time: 0.071799 memory: 2315 loss_kpt: 0.000863 acc_pose: 0.716064 loss: 0.000863 2022/10/14 01:17:35 - mmengine - INFO - Epoch(train) [209][150/293] lr: 5.000000e-06 eta: 0:02:05 time: 0.326413 data_time: 0.066816 memory: 2315 loss_kpt: 0.000885 acc_pose: 0.722372 loss: 0.000885 2022/10/14 01:17:52 - mmengine - INFO - Epoch(train) [209][200/293] lr: 5.000000e-06 eta: 0:01:50 time: 0.335554 data_time: 0.064869 memory: 2315 loss_kpt: 0.000891 acc_pose: 0.771848 loss: 0.000891 2022/10/14 01:18:09 - mmengine - INFO - Epoch(train) [209][250/293] lr: 5.000000e-06 eta: 0:01:36 time: 0.333496 data_time: 0.065950 memory: 2315 loss_kpt: 0.000890 acc_pose: 0.705502 loss: 0.000890 2022/10/14 01:18:23 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 01:18:41 - mmengine - INFO - Epoch(train) [210][50/293] lr: 5.000000e-06 eta: 0:01:09 time: 0.348408 data_time: 0.081389 memory: 2315 loss_kpt: 0.000869 acc_pose: 0.764837 loss: 0.000869 2022/10/14 01:18:57 - mmengine - INFO - Epoch(train) [210][100/293] lr: 5.000000e-06 eta: 0:00:55 time: 0.319419 data_time: 0.066869 memory: 2315 loss_kpt: 0.000860 acc_pose: 0.821967 loss: 0.000860 2022/10/14 01:19:13 - mmengine - INFO - Epoch(train) [210][150/293] lr: 5.000000e-06 eta: 0:00:41 time: 0.333687 data_time: 0.070604 memory: 2315 loss_kpt: 0.000872 acc_pose: 0.792222 loss: 0.000872 2022/10/14 01:19:30 - mmengine - INFO - Epoch(train) [210][200/293] lr: 5.000000e-06 eta: 0:00:26 time: 0.328630 data_time: 0.076347 memory: 2315 loss_kpt: 0.000879 acc_pose: 0.720014 loss: 0.000879 2022/10/14 01:19:46 - mmengine - INFO - Epoch(train) [210][250/293] lr: 5.000000e-06 eta: 0:00:12 time: 0.330077 data_time: 0.079126 memory: 2315 loss_kpt: 0.000888 acc_pose: 0.747446 loss: 0.000888 2022/10/14 01:20:00 - mmengine - INFO - Exp name: td-hm_shufflenetv2_8xb64-210e_coco-256x192_20221013_190116 2022/10/14 01:20:00 - mmengine - INFO - Saving checkpoint at 210 epochs 2022/10/14 01:20:08 - mmengine - INFO - Epoch(val) [210][50/407] eta: 0:00:41 time: 0.115334 data_time: 0.071510 memory: 2315 2022/10/14 01:20:14 - mmengine - INFO - Epoch(val) [210][100/407] eta: 0:00:35 time: 0.115083 data_time: 0.072076 memory: 426 2022/10/14 01:20:20 - mmengine - INFO - Epoch(val) [210][150/407] eta: 0:00:30 time: 0.117684 data_time: 0.075066 memory: 426 2022/10/14 01:20:25 - mmengine - INFO - Epoch(val) [210][200/407] eta: 0:00:23 time: 0.111233 data_time: 0.068396 memory: 426 2022/10/14 01:20:31 - mmengine - INFO - Epoch(val) [210][250/407] eta: 0:00:18 time: 0.114803 data_time: 0.067628 memory: 426 2022/10/14 01:20:37 - mmengine - INFO - Epoch(val) [210][300/407] eta: 0:00:12 time: 0.115956 data_time: 0.070077 memory: 426 2022/10/14 01:20:43 - mmengine - INFO - Epoch(val) [210][350/407] eta: 0:00:06 time: 0.115376 data_time: 0.071545 memory: 426 2022/10/14 01:20:48 - mmengine - INFO - Epoch(val) [210][400/407] eta: 0:00:00 time: 0.102026 data_time: 0.061809 memory: 426 2022/10/14 01:21:25 - mmengine - INFO - Evaluating CocoMetric... 2022/10/14 01:21:39 - mmengine - INFO - Epoch(val) [210][407/407] coco/AP: 0.602438 coco/AP .5: 0.857443 coco/AP .75: 0.671471 coco/AP (M): 0.568650 coco/AP (L): 0.665040 coco/AR: 0.667994 coco/AR .5: 0.902550 coco/AR .75: 0.735989 coco/AR (M): 0.623682 coco/AR (L): 0.730175 2022/10/14 01:21:40 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv2_256/best_coco/AP_epoch_200.pth is removed 2022/10/14 01:21:41 - mmengine - INFO - The best checkpoint with 0.6024 coco/AP at 210 epoch is saved to best_coco/AP_epoch_210.pth.