2022/10/13 10:13:05 - 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: 1375567173 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 10:13:06 - 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=(288, 384), heatmap_size=(72, 96), sigma=3) model = dict( type='TopdownPoseEstimator', data_preprocessor=dict( type='PoseDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True), backbone=dict( type='MobileNetV2', widen_factor=1.0, out_indices=(7, ), init_cfg=dict(type='Pretrained', checkpoint='mmcls://mobilenet_v2')), head=dict( type='HeatmapHead', in_channels=1280, out_channels=17, loss=dict(type='KeypointMSELoss', use_target_weight=True), decoder=dict( type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3)), 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=(288, 384)), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3)), 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=(288, 384)), 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=(288, 384)), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3)), dict(type='PackPoseInputs') ])) val_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(288, 384)), dict(type='PackPoseInputs') ])) test_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(288, 384)), 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/mbv2_384/' 2022/10/13 10:13:51 - 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 10:13:51 - 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 10:13:51 - 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 10:13:51 - 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 10:13:51 - 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 10:13:51 - 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 10:13:51 - 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 10:13:51 - 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 10:13:55 - 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 10:13:57 - 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 10:13:59 - 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 10:13:59 - 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([32, 3, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.conv1.bn.weight - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.conv1.bn.bias - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer1.0.conv.0.conv.weight - torch.Size([32, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer1.0.conv.0.bn.weight - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer1.0.conv.0.bn.bias - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer1.0.conv.1.conv.weight - torch.Size([16, 32, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer1.0.conv.1.bn.weight - torch.Size([16]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer1.0.conv.1.bn.bias - torch.Size([16]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.0.conv.0.conv.weight - torch.Size([96, 16, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.0.conv.0.bn.weight - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.0.conv.0.bn.bias - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.0.conv.1.conv.weight - torch.Size([96, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.0.conv.1.bn.weight - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.0.conv.1.bn.bias - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.0.conv.2.conv.weight - torch.Size([24, 96, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.0.conv.2.bn.weight - torch.Size([24]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.0.conv.2.bn.bias - torch.Size([24]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.1.conv.0.conv.weight - torch.Size([144, 24, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.1.conv.0.bn.weight - torch.Size([144]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.1.conv.0.bn.bias - torch.Size([144]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.1.conv.1.conv.weight - torch.Size([144, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.1.conv.1.bn.weight - torch.Size([144]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.1.conv.1.bn.bias - torch.Size([144]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.1.conv.2.conv.weight - torch.Size([24, 144, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.1.conv.2.bn.weight - torch.Size([24]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.1.conv.2.bn.bias - torch.Size([24]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.0.conv.0.conv.weight - torch.Size([144, 24, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.0.conv.0.bn.weight - torch.Size([144]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.0.conv.0.bn.bias - torch.Size([144]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.0.conv.1.conv.weight - torch.Size([144, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.0.conv.1.bn.weight - torch.Size([144]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.0.conv.1.bn.bias - torch.Size([144]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.0.conv.2.conv.weight - torch.Size([32, 144, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.0.conv.2.bn.weight - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.0.conv.2.bn.bias - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.1.conv.0.conv.weight - torch.Size([192, 32, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.1.conv.0.bn.weight - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.1.conv.0.bn.bias - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.1.conv.1.conv.weight - torch.Size([192, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.1.conv.1.bn.weight - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.1.conv.1.bn.bias - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.1.conv.2.conv.weight - torch.Size([32, 192, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.1.conv.2.bn.weight - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.1.conv.2.bn.bias - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.2.conv.0.conv.weight - torch.Size([192, 32, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.2.conv.0.bn.weight - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.2.conv.0.bn.bias - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.2.conv.1.conv.weight - torch.Size([192, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.2.conv.1.bn.weight - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.2.conv.1.bn.bias - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.2.conv.2.conv.weight - torch.Size([32, 192, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.2.conv.2.bn.weight - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.2.conv.2.bn.bias - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.0.conv.0.conv.weight - torch.Size([192, 32, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.0.conv.0.bn.weight - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.0.conv.0.bn.bias - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.0.conv.1.conv.weight - torch.Size([192, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.0.conv.1.bn.weight - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.0.conv.1.bn.bias - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.0.conv.2.conv.weight - torch.Size([64, 192, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.0.conv.2.bn.weight - torch.Size([64]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.0.conv.2.bn.bias - torch.Size([64]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.1.conv.0.conv.weight - torch.Size([384, 64, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.1.conv.0.bn.weight - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.1.conv.0.bn.bias - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.1.conv.1.conv.weight - torch.Size([384, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.1.conv.1.bn.weight - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.1.conv.1.bn.bias - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.1.conv.2.conv.weight - torch.Size([64, 384, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.1.conv.2.bn.weight - torch.Size([64]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.1.conv.2.bn.bias - torch.Size([64]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.2.conv.0.conv.weight - torch.Size([384, 64, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.2.conv.0.bn.weight - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.2.conv.0.bn.bias - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.2.conv.1.conv.weight - torch.Size([384, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.2.conv.1.bn.weight - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.2.conv.1.bn.bias - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.2.conv.2.conv.weight - torch.Size([64, 384, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.2.conv.2.bn.weight - torch.Size([64]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.2.conv.2.bn.bias - torch.Size([64]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.3.conv.0.conv.weight - torch.Size([384, 64, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.3.conv.0.bn.weight - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.3.conv.0.bn.bias - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.3.conv.1.conv.weight - torch.Size([384, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.3.conv.1.bn.weight - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.3.conv.1.bn.bias - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.3.conv.2.conv.weight - torch.Size([64, 384, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.3.conv.2.bn.weight - torch.Size([64]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.3.conv.2.bn.bias - torch.Size([64]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.0.conv.0.conv.weight - torch.Size([384, 64, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.0.conv.0.bn.weight - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.0.conv.0.bn.bias - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.0.conv.1.conv.weight - torch.Size([384, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.0.conv.1.bn.weight - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.0.conv.1.bn.bias - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.0.conv.2.conv.weight - torch.Size([96, 384, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.0.conv.2.bn.weight - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.0.conv.2.bn.bias - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.1.conv.0.conv.weight - torch.Size([576, 96, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.1.conv.0.bn.weight - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.1.conv.0.bn.bias - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.1.conv.1.conv.weight - torch.Size([576, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.1.conv.1.bn.weight - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.1.conv.1.bn.bias - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.1.conv.2.conv.weight - torch.Size([96, 576, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.1.conv.2.bn.weight - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.1.conv.2.bn.bias - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.2.conv.0.conv.weight - torch.Size([576, 96, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.2.conv.0.bn.weight - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.2.conv.0.bn.bias - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.2.conv.1.conv.weight - torch.Size([576, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.2.conv.1.bn.weight - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.2.conv.1.bn.bias - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.2.conv.2.conv.weight - torch.Size([96, 576, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.2.conv.2.bn.weight - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.2.conv.2.bn.bias - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.0.conv.0.conv.weight - torch.Size([576, 96, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.0.conv.0.bn.weight - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.0.conv.0.bn.bias - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.0.conv.1.conv.weight - torch.Size([576, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.0.conv.1.bn.weight - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.0.conv.1.bn.bias - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.0.conv.2.conv.weight - torch.Size([160, 576, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.0.conv.2.bn.weight - torch.Size([160]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.0.conv.2.bn.bias - torch.Size([160]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.1.conv.0.conv.weight - torch.Size([960, 160, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.1.conv.0.bn.weight - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.1.conv.0.bn.bias - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.1.conv.1.conv.weight - torch.Size([960, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.1.conv.1.bn.weight - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.1.conv.1.bn.bias - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.1.conv.2.conv.weight - torch.Size([160, 960, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.1.conv.2.bn.weight - torch.Size([160]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.1.conv.2.bn.bias - torch.Size([160]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.2.conv.0.conv.weight - torch.Size([960, 160, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.2.conv.0.bn.weight - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.2.conv.0.bn.bias - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.2.conv.1.conv.weight - torch.Size([960, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.2.conv.1.bn.weight - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.2.conv.1.bn.bias - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.2.conv.2.conv.weight - torch.Size([160, 960, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.2.conv.2.bn.weight - torch.Size([160]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.2.conv.2.bn.bias - torch.Size([160]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer7.0.conv.0.conv.weight - torch.Size([960, 160, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer7.0.conv.0.bn.weight - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer7.0.conv.0.bn.bias - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer7.0.conv.1.conv.weight - torch.Size([960, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer7.0.conv.1.bn.weight - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer7.0.conv.1.bn.bias - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer7.0.conv.2.conv.weight - torch.Size([320, 960, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer7.0.conv.2.bn.weight - torch.Size([320]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer7.0.conv.2.bn.bias - torch.Size([320]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.conv2.conv.weight - torch.Size([1280, 320, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.conv2.bn.weight - torch.Size([1280]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.conv2.bn.bias - torch.Size([1280]): PretrainedInit: load from mmcls://mobilenet_v2 head.deconv_layers.0.weight - torch.Size([1280, 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 10:13:59 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384 by HardDiskBackend. 2022/10/13 10:14:49 - mmengine - INFO - Epoch(train) [1][50/293] lr: 4.954910e-05 eta: 17:02:35 time: 0.997971 data_time: 0.206228 memory: 12861 loss_kpt: 0.002174 acc_pose: 0.118042 loss: 0.002174 2022/10/13 10:15:22 - mmengine - INFO - Epoch(train) [1][100/293] lr: 9.959920e-05 eta: 14:07:28 time: 0.657528 data_time: 0.139258 memory: 12861 loss_kpt: 0.001923 acc_pose: 0.281815 loss: 0.001923 2022/10/13 10:15:53 - mmengine - INFO - Epoch(train) [1][150/293] lr: 1.496493e-04 eta: 12:54:14 time: 0.615024 data_time: 0.281208 memory: 12861 loss_kpt: 0.001720 acc_pose: 0.335200 loss: 0.001720 2022/10/13 10:16:18 - mmengine - INFO - Epoch(train) [1][200/293] lr: 1.996994e-04 eta: 11:49:19 time: 0.505241 data_time: 0.142897 memory: 12861 loss_kpt: 0.001581 acc_pose: 0.482247 loss: 0.001581 2022/10/13 10:16:43 - mmengine - INFO - Epoch(train) [1][250/293] lr: 2.497495e-04 eta: 11:09:12 time: 0.500377 data_time: 0.082260 memory: 12861 loss_kpt: 0.001495 acc_pose: 0.473385 loss: 0.001495 2022/10/13 10:17:03 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:17:23 - mmengine - INFO - Epoch(train) [2][50/293] lr: 3.428427e-04 eta: 9:06:25 time: 0.399651 data_time: 0.095184 memory: 12861 loss_kpt: 0.001413 acc_pose: 0.479154 loss: 0.001413 2022/10/13 10:17:42 - mmengine - INFO - Epoch(train) [2][100/293] lr: 3.928928e-04 eta: 8:47:01 time: 0.389622 data_time: 0.108799 memory: 12861 loss_kpt: 0.001343 acc_pose: 0.530484 loss: 0.001343 2022/10/13 10:18:01 - mmengine - INFO - Epoch(train) [2][150/293] lr: 4.429429e-04 eta: 8:30:20 time: 0.375755 data_time: 0.083530 memory: 12861 loss_kpt: 0.001338 acc_pose: 0.500663 loss: 0.001338 2022/10/13 10:18:21 - mmengine - INFO - Epoch(train) [2][200/293] lr: 4.929930e-04 eta: 8:18:30 time: 0.390606 data_time: 0.096701 memory: 12861 loss_kpt: 0.001336 acc_pose: 0.538825 loss: 0.001336 2022/10/13 10:18:41 - mmengine - INFO - Epoch(train) [2][250/293] lr: 5.000000e-04 eta: 8:09:45 time: 0.400959 data_time: 0.097066 memory: 12861 loss_kpt: 0.001289 acc_pose: 0.517058 loss: 0.001289 2022/10/13 10:18:57 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:19:17 - mmengine - INFO - Epoch(train) [3][50/293] lr: 5.000000e-04 eta: 7:29:13 time: 0.397477 data_time: 0.107049 memory: 12861 loss_kpt: 0.001242 acc_pose: 0.609320 loss: 0.001242 2022/10/13 10:19:36 - mmengine - INFO - Epoch(train) [3][100/293] lr: 5.000000e-04 eta: 7:24:41 time: 0.386264 data_time: 0.083795 memory: 12861 loss_kpt: 0.001223 acc_pose: 0.553181 loss: 0.001223 2022/10/13 10:19:57 - mmengine - INFO - Epoch(train) [3][150/293] lr: 5.000000e-04 eta: 7:22:47 time: 0.416413 data_time: 0.098365 memory: 12861 loss_kpt: 0.001213 acc_pose: 0.585360 loss: 0.001213 2022/10/13 10:20:16 - mmengine - INFO - Epoch(train) [3][200/293] lr: 5.000000e-04 eta: 7:19:13 time: 0.387246 data_time: 0.097060 memory: 12861 loss_kpt: 0.001196 acc_pose: 0.574523 loss: 0.001196 2022/10/13 10:20:36 - mmengine - INFO - Epoch(train) [3][250/293] lr: 5.000000e-04 eta: 7:16:22 time: 0.392617 data_time: 0.098524 memory: 12861 loss_kpt: 0.001193 acc_pose: 0.638435 loss: 0.001193 2022/10/13 10:20:52 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:21:11 - mmengine - INFO - Epoch(train) [4][50/293] lr: 5.000000e-04 eta: 6:52:56 time: 0.383648 data_time: 0.086535 memory: 12861 loss_kpt: 0.001168 acc_pose: 0.556467 loss: 0.001168 2022/10/13 10:21:29 - mmengine - INFO - Epoch(train) [4][100/293] lr: 5.000000e-04 eta: 6:50:48 time: 0.374043 data_time: 0.072380 memory: 12861 loss_kpt: 0.001163 acc_pose: 0.589757 loss: 0.001163 2022/10/13 10:21:38 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:21:49 - mmengine - INFO - Epoch(train) [4][150/293] lr: 5.000000e-04 eta: 6:49:55 time: 0.395801 data_time: 0.098067 memory: 12861 loss_kpt: 0.001165 acc_pose: 0.565957 loss: 0.001165 2022/10/13 10:22:09 - mmengine - INFO - Epoch(train) [4][200/293] lr: 5.000000e-04 eta: 6:48:40 time: 0.387287 data_time: 0.094417 memory: 12861 loss_kpt: 0.001158 acc_pose: 0.647783 loss: 0.001158 2022/10/13 10:22:29 - mmengine - INFO - Epoch(train) [4][250/293] lr: 5.000000e-04 eta: 6:48:11 time: 0.402214 data_time: 0.088213 memory: 12861 loss_kpt: 0.001145 acc_pose: 0.627400 loss: 0.001145 2022/10/13 10:22:45 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:23:05 - mmengine - INFO - Epoch(train) [5][50/293] lr: 5.000000e-04 eta: 6:32:56 time: 0.398730 data_time: 0.092615 memory: 12861 loss_kpt: 0.001130 acc_pose: 0.617795 loss: 0.001130 2022/10/13 10:23:24 - mmengine - INFO - Epoch(train) [5][100/293] lr: 5.000000e-04 eta: 6:32:09 time: 0.379198 data_time: 0.095264 memory: 12861 loss_kpt: 0.001127 acc_pose: 0.570565 loss: 0.001127 2022/10/13 10:23:44 - mmengine - INFO - Epoch(train) [5][150/293] lr: 5.000000e-04 eta: 6:32:16 time: 0.402378 data_time: 0.103458 memory: 12861 loss_kpt: 0.001113 acc_pose: 0.626374 loss: 0.001113 2022/10/13 10:24:04 - mmengine - INFO - Epoch(train) [5][200/293] lr: 5.000000e-04 eta: 6:32:03 time: 0.393904 data_time: 0.104936 memory: 12861 loss_kpt: 0.001100 acc_pose: 0.652582 loss: 0.001100 2022/10/13 10:24:23 - mmengine - INFO - Epoch(train) [5][250/293] lr: 5.000000e-04 eta: 6:31:59 time: 0.398266 data_time: 0.102122 memory: 12861 loss_kpt: 0.001100 acc_pose: 0.673187 loss: 0.001100 2022/10/13 10:24:40 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:25:00 - mmengine - INFO - Epoch(train) [6][50/293] lr: 5.000000e-04 eta: 6:20:43 time: 0.404744 data_time: 0.099709 memory: 12861 loss_kpt: 0.001077 acc_pose: 0.571370 loss: 0.001077 2022/10/13 10:25:20 - mmengine - INFO - Epoch(train) [6][100/293] lr: 5.000000e-04 eta: 6:20:56 time: 0.397413 data_time: 0.086705 memory: 12861 loss_kpt: 0.001090 acc_pose: 0.647966 loss: 0.001090 2022/10/13 10:25:39 - mmengine - INFO - Epoch(train) [6][150/293] lr: 5.000000e-04 eta: 6:20:29 time: 0.376820 data_time: 0.073633 memory: 12861 loss_kpt: 0.001079 acc_pose: 0.637718 loss: 0.001079 2022/10/13 10:25:59 - mmengine - INFO - Epoch(train) [6][200/293] lr: 5.000000e-04 eta: 6:20:39 time: 0.397334 data_time: 0.086174 memory: 12861 loss_kpt: 0.001076 acc_pose: 0.614963 loss: 0.001076 2022/10/13 10:26:19 - mmengine - INFO - Epoch(train) [6][250/293] lr: 5.000000e-04 eta: 6:20:44 time: 0.395270 data_time: 0.082312 memory: 12861 loss_kpt: 0.001079 acc_pose: 0.646157 loss: 0.001079 2022/10/13 10:26:35 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:26:55 - mmengine - INFO - Epoch(train) [7][50/293] lr: 5.000000e-04 eta: 6:11:37 time: 0.400669 data_time: 0.102619 memory: 12861 loss_kpt: 0.001062 acc_pose: 0.681312 loss: 0.001062 2022/10/13 10:27:14 - mmengine - INFO - Epoch(train) [7][100/293] lr: 5.000000e-04 eta: 6:11:25 time: 0.377409 data_time: 0.087117 memory: 12861 loss_kpt: 0.001073 acc_pose: 0.679912 loss: 0.001073 2022/10/13 10:27:33 - mmengine - INFO - Epoch(train) [7][150/293] lr: 5.000000e-04 eta: 6:11:24 time: 0.384683 data_time: 0.088087 memory: 12861 loss_kpt: 0.001046 acc_pose: 0.650568 loss: 0.001046 2022/10/13 10:27:52 - mmengine - INFO - Epoch(train) [7][200/293] lr: 5.000000e-04 eta: 6:11:18 time: 0.382138 data_time: 0.092447 memory: 12861 loss_kpt: 0.001064 acc_pose: 0.633750 loss: 0.001064 2022/10/13 10:28:09 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:28:12 - mmengine - INFO - Epoch(train) [7][250/293] lr: 5.000000e-04 eta: 6:11:35 time: 0.398345 data_time: 0.104030 memory: 12861 loss_kpt: 0.001063 acc_pose: 0.593096 loss: 0.001063 2022/10/13 10:28:29 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:28:49 - mmengine - INFO - Epoch(train) [8][50/293] lr: 5.000000e-04 eta: 6:04:06 time: 0.403795 data_time: 0.093230 memory: 12861 loss_kpt: 0.001049 acc_pose: 0.524159 loss: 0.001049 2022/10/13 10:29:09 - mmengine - INFO - Epoch(train) [8][100/293] lr: 5.000000e-04 eta: 6:04:34 time: 0.401222 data_time: 0.105069 memory: 12861 loss_kpt: 0.001013 acc_pose: 0.649935 loss: 0.001013 2022/10/13 10:29:29 - mmengine - INFO - Epoch(train) [8][150/293] lr: 5.000000e-04 eta: 6:04:49 time: 0.392974 data_time: 0.093232 memory: 12861 loss_kpt: 0.001033 acc_pose: 0.697021 loss: 0.001033 2022/10/13 10:29:48 - mmengine - INFO - Epoch(train) [8][200/293] lr: 5.000000e-04 eta: 6:04:55 time: 0.387766 data_time: 0.099447 memory: 12861 loss_kpt: 0.001035 acc_pose: 0.624287 loss: 0.001035 2022/10/13 10:30:07 - mmengine - INFO - Epoch(train) [8][250/293] lr: 5.000000e-04 eta: 6:04:42 time: 0.373220 data_time: 0.075494 memory: 12861 loss_kpt: 0.001027 acc_pose: 0.677854 loss: 0.001027 2022/10/13 10:30:23 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:30:43 - mmengine - INFO - Epoch(train) [9][50/293] lr: 5.000000e-04 eta: 5:58:07 time: 0.395742 data_time: 0.095242 memory: 12861 loss_kpt: 0.001029 acc_pose: 0.672027 loss: 0.001029 2022/10/13 10:31:03 - mmengine - INFO - Epoch(train) [9][100/293] lr: 5.000000e-04 eta: 5:58:26 time: 0.393638 data_time: 0.096187 memory: 12861 loss_kpt: 0.001016 acc_pose: 0.683600 loss: 0.001016 2022/10/13 10:31:22 - mmengine - INFO - Epoch(train) [9][150/293] lr: 5.000000e-04 eta: 5:58:40 time: 0.391305 data_time: 0.096556 memory: 12861 loss_kpt: 0.001026 acc_pose: 0.679509 loss: 0.001026 2022/10/13 10:31:42 - mmengine - INFO - Epoch(train) [9][200/293] lr: 5.000000e-04 eta: 5:59:06 time: 0.402992 data_time: 0.099355 memory: 12861 loss_kpt: 0.001018 acc_pose: 0.747248 loss: 0.001018 2022/10/13 10:32:03 - mmengine - INFO - Epoch(train) [9][250/293] lr: 5.000000e-04 eta: 5:59:29 time: 0.401660 data_time: 0.095772 memory: 12861 loss_kpt: 0.001016 acc_pose: 0.693998 loss: 0.001016 2022/10/13 10:32:19 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:32:39 - mmengine - INFO - Epoch(train) [10][50/293] lr: 5.000000e-04 eta: 5:53:38 time: 0.390745 data_time: 0.087159 memory: 12861 loss_kpt: 0.001016 acc_pose: 0.625347 loss: 0.001016 2022/10/13 10:32:58 - mmengine - INFO - Epoch(train) [10][100/293] lr: 5.000000e-04 eta: 5:53:47 time: 0.386038 data_time: 0.101054 memory: 12861 loss_kpt: 0.001019 acc_pose: 0.730217 loss: 0.001019 2022/10/13 10:33:17 - mmengine - INFO - Epoch(train) [10][150/293] lr: 5.000000e-04 eta: 5:53:59 time: 0.389593 data_time: 0.086533 memory: 12861 loss_kpt: 0.001010 acc_pose: 0.664267 loss: 0.001010 2022/10/13 10:33:36 - mmengine - INFO - Epoch(train) [10][200/293] lr: 5.000000e-04 eta: 5:53:58 time: 0.378417 data_time: 0.088622 memory: 12861 loss_kpt: 0.000992 acc_pose: 0.684056 loss: 0.000992 2022/10/13 10:33:56 - mmengine - INFO - Epoch(train) [10][250/293] lr: 5.000000e-04 eta: 5:54:09 time: 0.390387 data_time: 0.095280 memory: 12861 loss_kpt: 0.001022 acc_pose: 0.680094 loss: 0.001022 2022/10/13 10:34:13 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:34:13 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/10/13 10:34:25 - mmengine - INFO - Epoch(val) [10][50/407] eta: 0:01:13 time: 0.205531 data_time: 0.144315 memory: 12861 2022/10/13 10:34:32 - mmengine - INFO - Epoch(val) [10][100/407] eta: 0:00:40 time: 0.132103 data_time: 0.071205 memory: 983 2022/10/13 10:34:38 - mmengine - INFO - Epoch(val) [10][150/407] eta: 0:00:35 time: 0.138422 data_time: 0.076764 memory: 983 2022/10/13 10:34:45 - mmengine - INFO - Epoch(val) [10][200/407] eta: 0:00:27 time: 0.134749 data_time: 0.073991 memory: 983 2022/10/13 10:34:52 - mmengine - INFO - Epoch(val) [10][250/407] eta: 0:00:20 time: 0.131477 data_time: 0.069224 memory: 983 2022/10/13 10:34:58 - mmengine - INFO - Epoch(val) [10][300/407] eta: 0:00:13 time: 0.130237 data_time: 0.068630 memory: 983 2022/10/13 10:35:05 - mmengine - INFO - Epoch(val) [10][350/407] eta: 0:00:08 time: 0.140916 data_time: 0.079133 memory: 983 2022/10/13 10:35:12 - mmengine - INFO - Epoch(val) [10][400/407] eta: 0:00:00 time: 0.128946 data_time: 0.066551 memory: 983 2022/10/13 10:35:49 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 10:36:03 - mmengine - INFO - Epoch(val) [10][407/407] coco/AP: 0.538957 coco/AP .5: 0.823256 coco/AP .75: 0.583396 coco/AP (M): 0.495483 coco/AP (L): 0.612893 coco/AR: 0.608486 coco/AR .5: 0.872639 coco/AR .75: 0.658060 coco/AR (M): 0.555641 coco/AR (L): 0.682572 2022/10/13 10:36:05 - mmengine - INFO - The best checkpoint with 0.5390 coco/AP at 10 epoch is saved to best_coco/AP_epoch_10.pth. 2022/10/13 10:36:25 - mmengine - INFO - Epoch(train) [11][50/293] lr: 5.000000e-04 eta: 5:49:16 time: 0.409327 data_time: 0.105580 memory: 12861 loss_kpt: 0.001001 acc_pose: 0.657504 loss: 0.001001 2022/10/13 10:36:33 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:36:45 - mmengine - INFO - Epoch(train) [11][100/293] lr: 5.000000e-04 eta: 5:49:37 time: 0.399066 data_time: 0.104470 memory: 12861 loss_kpt: 0.001000 acc_pose: 0.742467 loss: 0.001000 2022/10/13 10:37:05 - mmengine - INFO - Epoch(train) [11][150/293] lr: 5.000000e-04 eta: 5:49:53 time: 0.393688 data_time: 0.097933 memory: 12861 loss_kpt: 0.000991 acc_pose: 0.649093 loss: 0.000991 2022/10/13 10:37:24 - mmengine - INFO - Epoch(train) [11][200/293] lr: 5.000000e-04 eta: 5:50:02 time: 0.388320 data_time: 0.094983 memory: 12861 loss_kpt: 0.001008 acc_pose: 0.715279 loss: 0.001008 2022/10/13 10:37:44 - mmengine - INFO - Epoch(train) [11][250/293] lr: 5.000000e-04 eta: 5:50:18 time: 0.396826 data_time: 0.105031 memory: 12861 loss_kpt: 0.000985 acc_pose: 0.675555 loss: 0.000985 2022/10/13 10:38:01 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:38:22 - mmengine - INFO - Epoch(train) [12][50/293] lr: 5.000000e-04 eta: 5:45:57 time: 0.413705 data_time: 0.107524 memory: 12861 loss_kpt: 0.000991 acc_pose: 0.709730 loss: 0.000991 2022/10/13 10:38:41 - mmengine - INFO - Epoch(train) [12][100/293] lr: 5.000000e-04 eta: 5:46:00 time: 0.380799 data_time: 0.096004 memory: 12861 loss_kpt: 0.001002 acc_pose: 0.741573 loss: 0.001002 2022/10/13 10:39:00 - mmengine - INFO - Epoch(train) [12][150/293] lr: 5.000000e-04 eta: 5:46:05 time: 0.383372 data_time: 0.092506 memory: 12861 loss_kpt: 0.001003 acc_pose: 0.650214 loss: 0.001003 2022/10/13 10:39:20 - mmengine - INFO - Epoch(train) [12][200/293] lr: 5.000000e-04 eta: 5:46:19 time: 0.394135 data_time: 0.090662 memory: 12861 loss_kpt: 0.000979 acc_pose: 0.725692 loss: 0.000979 2022/10/13 10:39:39 - mmengine - INFO - Epoch(train) [12][250/293] lr: 5.000000e-04 eta: 5:46:26 time: 0.386987 data_time: 0.099469 memory: 12861 loss_kpt: 0.000995 acc_pose: 0.686412 loss: 0.000995 2022/10/13 10:39:55 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:40:16 - mmengine - INFO - Epoch(train) [13][50/293] lr: 5.000000e-04 eta: 5:42:18 time: 0.402199 data_time: 0.109081 memory: 12861 loss_kpt: 0.000979 acc_pose: 0.648668 loss: 0.000979 2022/10/13 10:40:35 - mmengine - INFO - Epoch(train) [13][100/293] lr: 5.000000e-04 eta: 5:42:31 time: 0.392334 data_time: 0.098859 memory: 12861 loss_kpt: 0.000987 acc_pose: 0.638472 loss: 0.000987 2022/10/13 10:40:55 - mmengine - INFO - Epoch(train) [13][150/293] lr: 5.000000e-04 eta: 5:42:45 time: 0.395112 data_time: 0.097272 memory: 12861 loss_kpt: 0.000974 acc_pose: 0.654067 loss: 0.000974 2022/10/13 10:41:15 - mmengine - INFO - Epoch(train) [13][200/293] lr: 5.000000e-04 eta: 5:43:00 time: 0.397447 data_time: 0.101230 memory: 12861 loss_kpt: 0.000972 acc_pose: 0.741655 loss: 0.000972 2022/10/13 10:41:35 - mmengine - INFO - Epoch(train) [13][250/293] lr: 5.000000e-04 eta: 5:43:10 time: 0.393015 data_time: 0.093107 memory: 12861 loss_kpt: 0.000968 acc_pose: 0.661038 loss: 0.000968 2022/10/13 10:41:50 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:42:10 - mmengine - INFO - Epoch(train) [14][50/293] lr: 5.000000e-04 eta: 5:39:16 time: 0.394288 data_time: 0.084930 memory: 12861 loss_kpt: 0.000972 acc_pose: 0.652598 loss: 0.000972 2022/10/13 10:42:29 - mmengine - INFO - Epoch(train) [14][100/293] lr: 5.000000e-04 eta: 5:39:25 time: 0.388794 data_time: 0.082436 memory: 12861 loss_kpt: 0.000959 acc_pose: 0.656741 loss: 0.000959 2022/10/13 10:42:49 - mmengine - INFO - Epoch(train) [14][150/293] lr: 5.000000e-04 eta: 5:39:33 time: 0.387978 data_time: 0.097059 memory: 12861 loss_kpt: 0.000960 acc_pose: 0.686777 loss: 0.000960 2022/10/13 10:43:05 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:43:09 - mmengine - INFO - Epoch(train) [14][200/293] lr: 5.000000e-04 eta: 5:39:45 time: 0.395770 data_time: 0.097258 memory: 12861 loss_kpt: 0.000963 acc_pose: 0.704989 loss: 0.000963 2022/10/13 10:43:29 - mmengine - INFO - Epoch(train) [14][250/293] lr: 5.000000e-04 eta: 5:40:03 time: 0.405016 data_time: 0.098590 memory: 12861 loss_kpt: 0.000955 acc_pose: 0.730028 loss: 0.000955 2022/10/13 10:43:45 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:44:05 - mmengine - INFO - Epoch(train) [15][50/293] lr: 5.000000e-04 eta: 5:36:29 time: 0.398572 data_time: 0.099266 memory: 12861 loss_kpt: 0.000960 acc_pose: 0.684893 loss: 0.000960 2022/10/13 10:44:24 - mmengine - INFO - Epoch(train) [15][100/293] lr: 5.000000e-04 eta: 5:36:34 time: 0.385505 data_time: 0.091287 memory: 12861 loss_kpt: 0.000966 acc_pose: 0.664795 loss: 0.000966 2022/10/13 10:44:43 - mmengine - INFO - Epoch(train) [15][150/293] lr: 5.000000e-04 eta: 5:36:45 time: 0.393838 data_time: 0.088417 memory: 12861 loss_kpt: 0.000973 acc_pose: 0.641970 loss: 0.000973 2022/10/13 10:45:03 - mmengine - INFO - Epoch(train) [15][200/293] lr: 5.000000e-04 eta: 5:36:56 time: 0.396398 data_time: 0.104739 memory: 12861 loss_kpt: 0.000955 acc_pose: 0.666643 loss: 0.000955 2022/10/13 10:45:23 - mmengine - INFO - Epoch(train) [15][250/293] lr: 5.000000e-04 eta: 5:37:08 time: 0.397979 data_time: 0.098291 memory: 12861 loss_kpt: 0.000949 acc_pose: 0.604160 loss: 0.000949 2022/10/13 10:45:40 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:46:00 - mmengine - INFO - Epoch(train) [16][50/293] lr: 5.000000e-04 eta: 5:33:53 time: 0.405852 data_time: 0.095860 memory: 12861 loss_kpt: 0.000959 acc_pose: 0.701999 loss: 0.000959 2022/10/13 10:46:19 - mmengine - INFO - Epoch(train) [16][100/293] lr: 5.000000e-04 eta: 5:33:58 time: 0.385594 data_time: 0.081281 memory: 12861 loss_kpt: 0.000967 acc_pose: 0.695379 loss: 0.000967 2022/10/13 10:46:39 - mmengine - INFO - Epoch(train) [16][150/293] lr: 5.000000e-04 eta: 5:34:07 time: 0.395182 data_time: 0.092672 memory: 12861 loss_kpt: 0.000951 acc_pose: 0.672144 loss: 0.000951 2022/10/13 10:46:58 - mmengine - INFO - Epoch(train) [16][200/293] lr: 5.000000e-04 eta: 5:34:12 time: 0.387652 data_time: 0.083466 memory: 12861 loss_kpt: 0.000951 acc_pose: 0.655582 loss: 0.000951 2022/10/13 10:47:18 - mmengine - INFO - Epoch(train) [16][250/293] lr: 5.000000e-04 eta: 5:34:14 time: 0.383836 data_time: 0.099259 memory: 12861 loss_kpt: 0.000944 acc_pose: 0.706247 loss: 0.000944 2022/10/13 10:47:33 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:47:53 - mmengine - INFO - Epoch(train) [17][50/293] lr: 5.000000e-04 eta: 5:31:07 time: 0.398216 data_time: 0.103009 memory: 12861 loss_kpt: 0.000945 acc_pose: 0.709582 loss: 0.000945 2022/10/13 10:48:13 - mmengine - INFO - Epoch(train) [17][100/293] lr: 5.000000e-04 eta: 5:31:15 time: 0.393713 data_time: 0.095232 memory: 12861 loss_kpt: 0.000972 acc_pose: 0.693572 loss: 0.000972 2022/10/13 10:48:32 - mmengine - INFO - Epoch(train) [17][150/293] lr: 5.000000e-04 eta: 5:31:22 time: 0.391916 data_time: 0.100193 memory: 12861 loss_kpt: 0.000945 acc_pose: 0.696016 loss: 0.000945 2022/10/13 10:48:52 - mmengine - INFO - Epoch(train) [17][200/293] lr: 5.000000e-04 eta: 5:31:31 time: 0.395503 data_time: 0.101654 memory: 12861 loss_kpt: 0.000960 acc_pose: 0.715931 loss: 0.000960 2022/10/13 10:49:12 - mmengine - INFO - Epoch(train) [17][250/293] lr: 5.000000e-04 eta: 5:31:38 time: 0.395109 data_time: 0.101566 memory: 12861 loss_kpt: 0.000953 acc_pose: 0.693237 loss: 0.000953 2022/10/13 10:49:28 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:49:36 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:49:48 - mmengine - INFO - Epoch(train) [18][50/293] lr: 5.000000e-04 eta: 5:28:44 time: 0.401419 data_time: 0.098077 memory: 12861 loss_kpt: 0.000946 acc_pose: 0.726404 loss: 0.000946 2022/10/13 10:50:08 - mmengine - INFO - Epoch(train) [18][100/293] lr: 5.000000e-04 eta: 5:28:55 time: 0.401337 data_time: 0.093906 memory: 12861 loss_kpt: 0.000950 acc_pose: 0.735700 loss: 0.000950 2022/10/13 10:50:28 - mmengine - INFO - Epoch(train) [18][150/293] lr: 5.000000e-04 eta: 5:29:01 time: 0.392514 data_time: 0.093970 memory: 12861 loss_kpt: 0.000943 acc_pose: 0.658677 loss: 0.000943 2022/10/13 10:50:47 - mmengine - INFO - Epoch(train) [18][200/293] lr: 5.000000e-04 eta: 5:29:04 time: 0.387982 data_time: 0.086594 memory: 12861 loss_kpt: 0.000947 acc_pose: 0.676964 loss: 0.000947 2022/10/13 10:51:07 - mmengine - INFO - Epoch(train) [18][250/293] lr: 5.000000e-04 eta: 5:29:09 time: 0.391646 data_time: 0.085159 memory: 12861 loss_kpt: 0.000943 acc_pose: 0.743844 loss: 0.000943 2022/10/13 10:51:22 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:51:43 - mmengine - INFO - Epoch(train) [19][50/293] lr: 5.000000e-04 eta: 5:26:27 time: 0.406357 data_time: 0.105407 memory: 12861 loss_kpt: 0.000949 acc_pose: 0.665785 loss: 0.000949 2022/10/13 10:52:03 - mmengine - INFO - Epoch(train) [19][100/293] lr: 5.000000e-04 eta: 5:26:34 time: 0.396804 data_time: 0.094046 memory: 12861 loss_kpt: 0.000928 acc_pose: 0.751189 loss: 0.000928 2022/10/13 10:52:22 - mmengine - INFO - Epoch(train) [19][150/293] lr: 5.000000e-04 eta: 5:26:40 time: 0.393099 data_time: 0.098150 memory: 12861 loss_kpt: 0.000933 acc_pose: 0.718446 loss: 0.000933 2022/10/13 10:52:42 - mmengine - INFO - Epoch(train) [19][200/293] lr: 5.000000e-04 eta: 5:26:42 time: 0.387064 data_time: 0.083783 memory: 12861 loss_kpt: 0.000922 acc_pose: 0.705038 loss: 0.000922 2022/10/13 10:53:01 - mmengine - INFO - Epoch(train) [19][250/293] lr: 5.000000e-04 eta: 5:26:47 time: 0.394238 data_time: 0.095539 memory: 12861 loss_kpt: 0.000920 acc_pose: 0.748240 loss: 0.000920 2022/10/13 10:53:18 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:53:38 - mmengine - INFO - Epoch(train) [20][50/293] lr: 5.000000e-04 eta: 5:24:13 time: 0.408464 data_time: 0.098247 memory: 12861 loss_kpt: 0.000945 acc_pose: 0.672391 loss: 0.000945 2022/10/13 10:53:57 - mmengine - INFO - Epoch(train) [20][100/293] lr: 5.000000e-04 eta: 5:24:07 time: 0.371310 data_time: 0.067907 memory: 12861 loss_kpt: 0.000935 acc_pose: 0.755886 loss: 0.000935 2022/10/13 10:54:16 - mmengine - INFO - Epoch(train) [20][150/293] lr: 5.000000e-04 eta: 5:24:10 time: 0.388775 data_time: 0.093318 memory: 12861 loss_kpt: 0.000900 acc_pose: 0.655058 loss: 0.000900 2022/10/13 10:54:36 - mmengine - INFO - Epoch(train) [20][200/293] lr: 5.000000e-04 eta: 5:24:13 time: 0.390139 data_time: 0.092227 memory: 12861 loss_kpt: 0.000915 acc_pose: 0.708960 loss: 0.000915 2022/10/13 10:54:56 - mmengine - INFO - Epoch(train) [20][250/293] lr: 5.000000e-04 eta: 5:24:22 time: 0.404113 data_time: 0.101845 memory: 12861 loss_kpt: 0.000928 acc_pose: 0.654436 loss: 0.000928 2022/10/13 10:55:12 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:55:12 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/10/13 10:55:21 - mmengine - INFO - Epoch(val) [20][50/407] eta: 0:00:47 time: 0.132965 data_time: 0.070668 memory: 12861 2022/10/13 10:55:27 - mmengine - INFO - Epoch(val) [20][100/407] eta: 0:00:37 time: 0.121518 data_time: 0.056878 memory: 983 2022/10/13 10:55:33 - mmengine - INFO - Epoch(val) [20][150/407] eta: 0:00:32 time: 0.127254 data_time: 0.066058 memory: 983 2022/10/13 10:55:39 - mmengine - INFO - Epoch(val) [20][200/407] eta: 0:00:25 time: 0.122287 data_time: 0.062257 memory: 983 2022/10/13 10:55:45 - mmengine - INFO - Epoch(val) [20][250/407] eta: 0:00:19 time: 0.122364 data_time: 0.061713 memory: 983 2022/10/13 10:55:52 - mmengine - INFO - Epoch(val) [20][300/407] eta: 0:00:13 time: 0.123423 data_time: 0.062058 memory: 983 2022/10/13 10:55:58 - mmengine - INFO - Epoch(val) [20][350/407] eta: 0:00:06 time: 0.120625 data_time: 0.059319 memory: 983 2022/10/13 10:56:04 - mmengine - INFO - Epoch(val) [20][400/407] eta: 0:00:00 time: 0.118995 data_time: 0.057937 memory: 983 2022/10/13 10:56:41 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 10:56:55 - mmengine - INFO - Epoch(val) [20][407/407] coco/AP: 0.585844 coco/AP .5: 0.843773 coco/AP .75: 0.645064 coco/AP (M): 0.543320 coco/AP (L): 0.659576 coco/AR: 0.647245 coco/AR .5: 0.887752 coco/AR .75: 0.706392 coco/AR (M): 0.596722 coco/AR (L): 0.718097 2022/10/13 10:56:55 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384/best_coco/AP_epoch_10.pth is removed 2022/10/13 10:56:56 - mmengine - INFO - The best checkpoint with 0.5858 coco/AP at 20 epoch is saved to best_coco/AP_epoch_20.pth. 2022/10/13 10:57:16 - mmengine - INFO - Epoch(train) [21][50/293] lr: 5.000000e-04 eta: 5:21:47 time: 0.390241 data_time: 0.084699 memory: 12861 loss_kpt: 0.000923 acc_pose: 0.703644 loss: 0.000923 2022/10/13 10:57:35 - mmengine - INFO - Epoch(train) [21][100/293] lr: 5.000000e-04 eta: 5:21:48 time: 0.385717 data_time: 0.080068 memory: 12861 loss_kpt: 0.000927 acc_pose: 0.690573 loss: 0.000927 2022/10/13 10:57:51 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:57:55 - mmengine - INFO - Epoch(train) [21][150/293] lr: 5.000000e-04 eta: 5:21:52 time: 0.394145 data_time: 0.086821 memory: 12861 loss_kpt: 0.000908 acc_pose: 0.725388 loss: 0.000908 2022/10/13 10:58:14 - mmengine - INFO - Epoch(train) [21][200/293] lr: 5.000000e-04 eta: 5:21:54 time: 0.389546 data_time: 0.098350 memory: 12861 loss_kpt: 0.000945 acc_pose: 0.705389 loss: 0.000945 2022/10/13 10:58:34 - mmengine - INFO - Epoch(train) [21][250/293] lr: 5.000000e-04 eta: 5:21:52 time: 0.383106 data_time: 0.068667 memory: 12861 loss_kpt: 0.000918 acc_pose: 0.735883 loss: 0.000918 2022/10/13 10:58:50 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 10:59:10 - mmengine - INFO - Epoch(train) [22][50/293] lr: 5.000000e-04 eta: 5:19:25 time: 0.390402 data_time: 0.080139 memory: 12861 loss_kpt: 0.000908 acc_pose: 0.703545 loss: 0.000908 2022/10/13 10:59:28 - mmengine - INFO - Epoch(train) [22][100/293] lr: 5.000000e-04 eta: 5:19:19 time: 0.372697 data_time: 0.076118 memory: 12861 loss_kpt: 0.000924 acc_pose: 0.663047 loss: 0.000924 2022/10/13 10:59:48 - mmengine - INFO - Epoch(train) [22][150/293] lr: 5.000000e-04 eta: 5:19:19 time: 0.385723 data_time: 0.078243 memory: 12861 loss_kpt: 0.000903 acc_pose: 0.693530 loss: 0.000903 2022/10/13 11:00:07 - mmengine - INFO - Epoch(train) [22][200/293] lr: 5.000000e-04 eta: 5:19:18 time: 0.384834 data_time: 0.086122 memory: 12861 loss_kpt: 0.000931 acc_pose: 0.642092 loss: 0.000931 2022/10/13 11:00:26 - mmengine - INFO - Epoch(train) [22][250/293] lr: 5.000000e-04 eta: 5:19:15 time: 0.381268 data_time: 0.077745 memory: 12861 loss_kpt: 0.000956 acc_pose: 0.673680 loss: 0.000956 2022/10/13 11:00:43 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:01:04 - mmengine - INFO - Epoch(train) [23][50/293] lr: 5.000000e-04 eta: 5:17:00 time: 0.404147 data_time: 0.096445 memory: 12861 loss_kpt: 0.000922 acc_pose: 0.694185 loss: 0.000922 2022/10/13 11:01:23 - mmengine - INFO - Epoch(train) [23][100/293] lr: 5.000000e-04 eta: 5:16:58 time: 0.381089 data_time: 0.082453 memory: 12861 loss_kpt: 0.000916 acc_pose: 0.653469 loss: 0.000916 2022/10/13 11:01:42 - mmengine - INFO - Epoch(train) [23][150/293] lr: 5.000000e-04 eta: 5:16:55 time: 0.381380 data_time: 0.092215 memory: 12861 loss_kpt: 0.000914 acc_pose: 0.665836 loss: 0.000914 2022/10/13 11:02:01 - mmengine - INFO - Epoch(train) [23][200/293] lr: 5.000000e-04 eta: 5:16:52 time: 0.380661 data_time: 0.087787 memory: 12861 loss_kpt: 0.000904 acc_pose: 0.719750 loss: 0.000904 2022/10/13 11:02:21 - mmengine - INFO - Epoch(train) [23][250/293] lr: 5.000000e-04 eta: 5:16:59 time: 0.405340 data_time: 0.088786 memory: 12861 loss_kpt: 0.000929 acc_pose: 0.684560 loss: 0.000929 2022/10/13 11:02:37 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:02:57 - mmengine - INFO - Epoch(train) [24][50/293] lr: 5.000000e-04 eta: 5:14:47 time: 0.398518 data_time: 0.108324 memory: 12861 loss_kpt: 0.000891 acc_pose: 0.695343 loss: 0.000891 2022/10/13 11:03:16 - mmengine - INFO - Epoch(train) [24][100/293] lr: 5.000000e-04 eta: 5:14:48 time: 0.390168 data_time: 0.085045 memory: 12861 loss_kpt: 0.000914 acc_pose: 0.696846 loss: 0.000914 2022/10/13 11:03:36 - mmengine - INFO - Epoch(train) [24][150/293] lr: 5.000000e-04 eta: 5:14:49 time: 0.391114 data_time: 0.084841 memory: 12861 loss_kpt: 0.000930 acc_pose: 0.659442 loss: 0.000930 2022/10/13 11:03:55 - mmengine - INFO - Epoch(train) [24][200/293] lr: 5.000000e-04 eta: 5:14:48 time: 0.386344 data_time: 0.081779 memory: 12861 loss_kpt: 0.000921 acc_pose: 0.653578 loss: 0.000921 2022/10/13 11:04:14 - mmengine - INFO - Epoch(train) [24][250/293] lr: 5.000000e-04 eta: 5:14:43 time: 0.378940 data_time: 0.071702 memory: 12861 loss_kpt: 0.000923 acc_pose: 0.661943 loss: 0.000923 2022/10/13 11:04:18 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:04:31 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:04:51 - mmengine - INFO - Epoch(train) [25][50/293] lr: 5.000000e-04 eta: 5:12:39 time: 0.405492 data_time: 0.103107 memory: 12861 loss_kpt: 0.000910 acc_pose: 0.643878 loss: 0.000910 2022/10/13 11:05:11 - mmengine - INFO - Epoch(train) [25][100/293] lr: 5.000000e-04 eta: 5:12:43 time: 0.399951 data_time: 0.085788 memory: 12861 loss_kpt: 0.000907 acc_pose: 0.690055 loss: 0.000907 2022/10/13 11:05:31 - mmengine - INFO - Epoch(train) [25][150/293] lr: 5.000000e-04 eta: 5:12:47 time: 0.399318 data_time: 0.083789 memory: 12861 loss_kpt: 0.000923 acc_pose: 0.659822 loss: 0.000923 2022/10/13 11:05:50 - mmengine - INFO - Epoch(train) [25][200/293] lr: 5.000000e-04 eta: 5:12:45 time: 0.386330 data_time: 0.089149 memory: 12861 loss_kpt: 0.000896 acc_pose: 0.733160 loss: 0.000896 2022/10/13 11:06:09 - mmengine - INFO - Epoch(train) [25][250/293] lr: 5.000000e-04 eta: 5:12:39 time: 0.375861 data_time: 0.076394 memory: 12861 loss_kpt: 0.000905 acc_pose: 0.728328 loss: 0.000905 2022/10/13 11:06:26 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:06:45 - mmengine - INFO - Epoch(train) [26][50/293] lr: 5.000000e-04 eta: 5:10:37 time: 0.398717 data_time: 0.085264 memory: 12861 loss_kpt: 0.000910 acc_pose: 0.725598 loss: 0.000910 2022/10/13 11:07:05 - mmengine - INFO - Epoch(train) [26][100/293] lr: 5.000000e-04 eta: 5:10:37 time: 0.392858 data_time: 0.083634 memory: 12861 loss_kpt: 0.000905 acc_pose: 0.729079 loss: 0.000905 2022/10/13 11:07:24 - mmengine - INFO - Epoch(train) [26][150/293] lr: 5.000000e-04 eta: 5:10:35 time: 0.384701 data_time: 0.077913 memory: 12861 loss_kpt: 0.000900 acc_pose: 0.716484 loss: 0.000900 2022/10/13 11:07:44 - mmengine - INFO - Epoch(train) [26][200/293] lr: 5.000000e-04 eta: 5:10:32 time: 0.384354 data_time: 0.099844 memory: 12861 loss_kpt: 0.000892 acc_pose: 0.740131 loss: 0.000892 2022/10/13 11:08:03 - mmengine - INFO - Epoch(train) [26][250/293] lr: 5.000000e-04 eta: 5:10:31 time: 0.390746 data_time: 0.084435 memory: 12861 loss_kpt: 0.000901 acc_pose: 0.690700 loss: 0.000901 2022/10/13 11:08:20 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:08:40 - mmengine - INFO - Epoch(train) [27][50/293] lr: 5.000000e-04 eta: 5:08:35 time: 0.405067 data_time: 0.089963 memory: 12861 loss_kpt: 0.000894 acc_pose: 0.694784 loss: 0.000894 2022/10/13 11:09:00 - mmengine - INFO - Epoch(train) [27][100/293] lr: 5.000000e-04 eta: 5:08:35 time: 0.392037 data_time: 0.102425 memory: 12861 loss_kpt: 0.000913 acc_pose: 0.693111 loss: 0.000913 2022/10/13 11:09:20 - mmengine - INFO - Epoch(train) [27][150/293] lr: 5.000000e-04 eta: 5:08:38 time: 0.403930 data_time: 0.085809 memory: 12861 loss_kpt: 0.000903 acc_pose: 0.760924 loss: 0.000903 2022/10/13 11:09:39 - mmengine - INFO - Epoch(train) [27][200/293] lr: 5.000000e-04 eta: 5:08:38 time: 0.392290 data_time: 0.085715 memory: 12861 loss_kpt: 0.000929 acc_pose: 0.693743 loss: 0.000929 2022/10/13 11:09:59 - mmengine - INFO - Epoch(train) [27][250/293] lr: 5.000000e-04 eta: 5:08:40 time: 0.401686 data_time: 0.083111 memory: 12861 loss_kpt: 0.000920 acc_pose: 0.639225 loss: 0.000920 2022/10/13 11:10:16 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:10:36 - mmengine - INFO - Epoch(train) [28][50/293] lr: 5.000000e-04 eta: 5:06:48 time: 0.403741 data_time: 0.090081 memory: 12861 loss_kpt: 0.000894 acc_pose: 0.704317 loss: 0.000894 2022/10/13 11:10:51 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:10:56 - mmengine - INFO - Epoch(train) [28][100/293] lr: 5.000000e-04 eta: 5:06:47 time: 0.394106 data_time: 0.085722 memory: 12861 loss_kpt: 0.000906 acc_pose: 0.706128 loss: 0.000906 2022/10/13 11:11:15 - mmengine - INFO - Epoch(train) [28][150/293] lr: 5.000000e-04 eta: 5:06:45 time: 0.389885 data_time: 0.090876 memory: 12861 loss_kpt: 0.000896 acc_pose: 0.636657 loss: 0.000896 2022/10/13 11:11:34 - mmengine - INFO - Epoch(train) [28][200/293] lr: 5.000000e-04 eta: 5:06:42 time: 0.385890 data_time: 0.086053 memory: 12861 loss_kpt: 0.000912 acc_pose: 0.720483 loss: 0.000912 2022/10/13 11:11:54 - mmengine - INFO - Epoch(train) [28][250/293] lr: 5.000000e-04 eta: 5:06:39 time: 0.390048 data_time: 0.082238 memory: 12861 loss_kpt: 0.000909 acc_pose: 0.697862 loss: 0.000909 2022/10/13 11:12:10 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:12:31 - mmengine - INFO - Epoch(train) [29][50/293] lr: 5.000000e-04 eta: 5:04:51 time: 0.405005 data_time: 0.091167 memory: 12861 loss_kpt: 0.000894 acc_pose: 0.782860 loss: 0.000894 2022/10/13 11:12:50 - mmengine - INFO - Epoch(train) [29][100/293] lr: 5.000000e-04 eta: 5:04:49 time: 0.390513 data_time: 0.094423 memory: 12861 loss_kpt: 0.000891 acc_pose: 0.746079 loss: 0.000891 2022/10/13 11:13:09 - mmengine - INFO - Epoch(train) [29][150/293] lr: 5.000000e-04 eta: 5:04:46 time: 0.386260 data_time: 0.082763 memory: 12861 loss_kpt: 0.000888 acc_pose: 0.714995 loss: 0.000888 2022/10/13 11:13:29 - mmengine - INFO - Epoch(train) [29][200/293] lr: 5.000000e-04 eta: 5:04:46 time: 0.398780 data_time: 0.076568 memory: 12861 loss_kpt: 0.000886 acc_pose: 0.699872 loss: 0.000886 2022/10/13 11:13:49 - mmengine - INFO - Epoch(train) [29][250/293] lr: 5.000000e-04 eta: 5:04:44 time: 0.393770 data_time: 0.095589 memory: 12861 loss_kpt: 0.000891 acc_pose: 0.725033 loss: 0.000891 2022/10/13 11:14:05 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:14:25 - mmengine - INFO - Epoch(train) [30][50/293] lr: 5.000000e-04 eta: 5:02:53 time: 0.387522 data_time: 0.090691 memory: 12861 loss_kpt: 0.000894 acc_pose: 0.751454 loss: 0.000894 2022/10/13 11:14:44 - mmengine - INFO - Epoch(train) [30][100/293] lr: 5.000000e-04 eta: 5:02:49 time: 0.382592 data_time: 0.067282 memory: 12861 loss_kpt: 0.000892 acc_pose: 0.659334 loss: 0.000892 2022/10/13 11:15:03 - mmengine - INFO - Epoch(train) [30][150/293] lr: 5.000000e-04 eta: 5:02:43 time: 0.380579 data_time: 0.079900 memory: 12861 loss_kpt: 0.000884 acc_pose: 0.744483 loss: 0.000884 2022/10/13 11:15:23 - mmengine - INFO - Epoch(train) [30][200/293] lr: 5.000000e-04 eta: 5:02:42 time: 0.396957 data_time: 0.091603 memory: 12861 loss_kpt: 0.000890 acc_pose: 0.704745 loss: 0.000890 2022/10/13 11:15:42 - mmengine - INFO - Epoch(train) [30][250/293] lr: 5.000000e-04 eta: 5:02:38 time: 0.387754 data_time: 0.076915 memory: 12861 loss_kpt: 0.000908 acc_pose: 0.694298 loss: 0.000908 2022/10/13 11:15:58 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:15:58 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/10/13 11:16:07 - mmengine - INFO - Epoch(val) [30][50/407] eta: 0:00:45 time: 0.128146 data_time: 0.067669 memory: 12861 2022/10/13 11:16:13 - mmengine - INFO - Epoch(val) [30][100/407] eta: 0:00:38 time: 0.125726 data_time: 0.064118 memory: 983 2022/10/13 11:16:20 - mmengine - INFO - Epoch(val) [30][150/407] eta: 0:00:32 time: 0.127939 data_time: 0.067084 memory: 983 2022/10/13 11:16:26 - mmengine - INFO - Epoch(val) [30][200/407] eta: 0:00:26 time: 0.128187 data_time: 0.066630 memory: 983 2022/10/13 11:16:32 - mmengine - INFO - Epoch(val) [30][250/407] eta: 0:00:19 time: 0.124865 data_time: 0.063423 memory: 983 2022/10/13 11:16:39 - mmengine - INFO - Epoch(val) [30][300/407] eta: 0:00:13 time: 0.129104 data_time: 0.067700 memory: 983 2022/10/13 11:16:45 - mmengine - INFO - Epoch(val) [30][350/407] eta: 0:00:07 time: 0.131643 data_time: 0.069609 memory: 983 2022/10/13 11:16:51 - mmengine - INFO - Epoch(val) [30][400/407] eta: 0:00:00 time: 0.121684 data_time: 0.061029 memory: 983 2022/10/13 11:17:28 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 11:17:42 - mmengine - INFO - Epoch(val) [30][407/407] coco/AP: 0.602596 coco/AP .5: 0.850826 coco/AP .75: 0.661816 coco/AP (M): 0.558539 coco/AP (L): 0.676455 coco/AR: 0.663980 coco/AR .5: 0.894207 coco/AR .75: 0.724181 coco/AR (M): 0.612783 coco/AR (L): 0.735749 2022/10/13 11:17:42 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384/best_coco/AP_epoch_20.pth is removed 2022/10/13 11:17:44 - mmengine - INFO - The best checkpoint with 0.6026 coco/AP at 30 epoch is saved to best_coco/AP_epoch_30.pth. 2022/10/13 11:18:05 - mmengine - INFO - Epoch(train) [31][50/293] lr: 5.000000e-04 eta: 5:01:00 time: 0.417372 data_time: 0.088698 memory: 12861 loss_kpt: 0.000879 acc_pose: 0.761018 loss: 0.000879 2022/10/13 11:18:25 - mmengine - INFO - Epoch(train) [31][100/293] lr: 5.000000e-04 eta: 5:00:59 time: 0.398988 data_time: 0.081520 memory: 12861 loss_kpt: 0.000888 acc_pose: 0.739057 loss: 0.000888 2022/10/13 11:18:44 - mmengine - INFO - Epoch(train) [31][150/293] lr: 5.000000e-04 eta: 5:00:56 time: 0.390687 data_time: 0.075074 memory: 12861 loss_kpt: 0.000879 acc_pose: 0.695239 loss: 0.000879 2022/10/13 11:19:04 - mmengine - INFO - Epoch(train) [31][200/293] lr: 5.000000e-04 eta: 5:00:52 time: 0.388502 data_time: 0.075127 memory: 12861 loss_kpt: 0.000896 acc_pose: 0.758069 loss: 0.000896 2022/10/13 11:19:08 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:19:24 - mmengine - INFO - Epoch(train) [31][250/293] lr: 5.000000e-04 eta: 5:00:51 time: 0.398398 data_time: 0.091748 memory: 12861 loss_kpt: 0.000885 acc_pose: 0.699051 loss: 0.000885 2022/10/13 11:19:40 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:20:02 - mmengine - INFO - Epoch(train) [32][50/293] lr: 5.000000e-04 eta: 4:59:19 time: 0.432199 data_time: 0.098573 memory: 12861 loss_kpt: 0.000879 acc_pose: 0.731509 loss: 0.000879 2022/10/13 11:20:21 - mmengine - INFO - Epoch(train) [32][100/293] lr: 5.000000e-04 eta: 4:59:15 time: 0.389695 data_time: 0.088429 memory: 12861 loss_kpt: 0.000887 acc_pose: 0.760066 loss: 0.000887 2022/10/13 11:20:42 - mmengine - INFO - Epoch(train) [32][150/293] lr: 5.000000e-04 eta: 4:59:17 time: 0.407445 data_time: 0.085136 memory: 12861 loss_kpt: 0.000871 acc_pose: 0.707560 loss: 0.000871 2022/10/13 11:21:01 - mmengine - INFO - Epoch(train) [32][200/293] lr: 5.000000e-04 eta: 4:59:12 time: 0.388870 data_time: 0.081947 memory: 12861 loss_kpt: 0.000910 acc_pose: 0.780686 loss: 0.000910 2022/10/13 11:21:20 - mmengine - INFO - Epoch(train) [32][250/293] lr: 5.000000e-04 eta: 4:59:05 time: 0.379391 data_time: 0.073746 memory: 12861 loss_kpt: 0.000876 acc_pose: 0.692790 loss: 0.000876 2022/10/13 11:21:38 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:21:59 - mmengine - INFO - Epoch(train) [33][50/293] lr: 5.000000e-04 eta: 4:57:33 time: 0.423050 data_time: 0.089674 memory: 12861 loss_kpt: 0.000879 acc_pose: 0.707882 loss: 0.000879 2022/10/13 11:22:18 - mmengine - INFO - Epoch(train) [33][100/293] lr: 5.000000e-04 eta: 4:57:28 time: 0.384610 data_time: 0.077167 memory: 12861 loss_kpt: 0.000871 acc_pose: 0.754815 loss: 0.000871 2022/10/13 11:22:38 - mmengine - INFO - Epoch(train) [33][150/293] lr: 5.000000e-04 eta: 4:57:23 time: 0.388065 data_time: 0.068829 memory: 12861 loss_kpt: 0.000878 acc_pose: 0.704835 loss: 0.000878 2022/10/13 11:22:57 - mmengine - INFO - Epoch(train) [33][200/293] lr: 5.000000e-04 eta: 4:57:20 time: 0.396578 data_time: 0.080895 memory: 12861 loss_kpt: 0.000885 acc_pose: 0.748118 loss: 0.000885 2022/10/13 11:23:18 - mmengine - INFO - Epoch(train) [33][250/293] lr: 5.000000e-04 eta: 4:57:19 time: 0.403192 data_time: 0.082242 memory: 12861 loss_kpt: 0.000875 acc_pose: 0.741412 loss: 0.000875 2022/10/13 11:23:35 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:23:56 - mmengine - INFO - Epoch(train) [34][50/293] lr: 5.000000e-04 eta: 4:55:52 time: 0.431542 data_time: 0.121960 memory: 12861 loss_kpt: 0.000888 acc_pose: 0.731847 loss: 0.000888 2022/10/13 11:24:18 - mmengine - INFO - Epoch(train) [34][100/293] lr: 5.000000e-04 eta: 4:56:01 time: 0.440171 data_time: 0.078631 memory: 12861 loss_kpt: 0.000878 acc_pose: 0.689948 loss: 0.000878 2022/10/13 11:24:40 - mmengine - INFO - Epoch(train) [34][150/293] lr: 5.000000e-04 eta: 4:56:07 time: 0.431556 data_time: 0.082908 memory: 12861 loss_kpt: 0.000869 acc_pose: 0.657666 loss: 0.000869 2022/10/13 11:25:00 - mmengine - INFO - Epoch(train) [34][200/293] lr: 5.000000e-04 eta: 4:56:08 time: 0.413707 data_time: 0.075199 memory: 12861 loss_kpt: 0.000879 acc_pose: 0.712146 loss: 0.000879 2022/10/13 11:25:21 - mmengine - INFO - Epoch(train) [34][250/293] lr: 5.000000e-04 eta: 4:56:07 time: 0.405221 data_time: 0.074821 memory: 12861 loss_kpt: 0.000882 acc_pose: 0.683824 loss: 0.000882 2022/10/13 11:25:38 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:25:55 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:26:00 - mmengine - INFO - Epoch(train) [35][50/293] lr: 5.000000e-04 eta: 4:54:39 time: 0.426306 data_time: 0.101451 memory: 12861 loss_kpt: 0.000879 acc_pose: 0.696062 loss: 0.000879 2022/10/13 11:26:20 - mmengine - INFO - Epoch(train) [35][100/293] lr: 5.000000e-04 eta: 4:54:37 time: 0.401739 data_time: 0.072978 memory: 12861 loss_kpt: 0.000892 acc_pose: 0.729405 loss: 0.000892 2022/10/13 11:26:40 - mmengine - INFO - Epoch(train) [35][150/293] lr: 5.000000e-04 eta: 4:54:37 time: 0.409451 data_time: 0.076615 memory: 12861 loss_kpt: 0.000860 acc_pose: 0.731238 loss: 0.000860 2022/10/13 11:27:01 - mmengine - INFO - Epoch(train) [35][200/293] lr: 5.000000e-04 eta: 4:54:38 time: 0.414846 data_time: 0.090696 memory: 12861 loss_kpt: 0.000885 acc_pose: 0.700081 loss: 0.000885 2022/10/13 11:27:22 - mmengine - INFO - Epoch(train) [35][250/293] lr: 5.000000e-04 eta: 4:54:39 time: 0.416261 data_time: 0.082041 memory: 12861 loss_kpt: 0.000871 acc_pose: 0.734356 loss: 0.000871 2022/10/13 11:27:38 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:27:59 - mmengine - INFO - Epoch(train) [36][50/293] lr: 5.000000e-04 eta: 4:53:10 time: 0.411511 data_time: 0.092099 memory: 12861 loss_kpt: 0.000858 acc_pose: 0.715639 loss: 0.000858 2022/10/13 11:28:18 - mmengine - INFO - Epoch(train) [36][100/293] lr: 5.000000e-04 eta: 4:53:04 time: 0.390702 data_time: 0.084052 memory: 12861 loss_kpt: 0.000862 acc_pose: 0.710297 loss: 0.000862 2022/10/13 11:28:38 - mmengine - INFO - Epoch(train) [36][150/293] lr: 5.000000e-04 eta: 4:52:59 time: 0.392875 data_time: 0.080675 memory: 12861 loss_kpt: 0.000865 acc_pose: 0.740602 loss: 0.000865 2022/10/13 11:28:57 - mmengine - INFO - Epoch(train) [36][200/293] lr: 5.000000e-04 eta: 4:52:51 time: 0.380997 data_time: 0.065915 memory: 12861 loss_kpt: 0.000884 acc_pose: 0.705516 loss: 0.000884 2022/10/13 11:29:17 - mmengine - INFO - Epoch(train) [36][250/293] lr: 5.000000e-04 eta: 4:52:48 time: 0.403435 data_time: 0.101053 memory: 12861 loss_kpt: 0.000878 acc_pose: 0.708049 loss: 0.000878 2022/10/13 11:29:34 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:29:55 - mmengine - INFO - Epoch(train) [37][50/293] lr: 5.000000e-04 eta: 4:51:23 time: 0.419933 data_time: 0.093248 memory: 12861 loss_kpt: 0.000860 acc_pose: 0.714746 loss: 0.000860 2022/10/13 11:30:15 - mmengine - INFO - Epoch(train) [37][100/293] lr: 5.000000e-04 eta: 4:51:20 time: 0.402173 data_time: 0.078635 memory: 12861 loss_kpt: 0.000876 acc_pose: 0.763345 loss: 0.000876 2022/10/13 11:30:36 - mmengine - INFO - Epoch(train) [37][150/293] lr: 5.000000e-04 eta: 4:51:18 time: 0.409441 data_time: 0.078927 memory: 12861 loss_kpt: 0.000874 acc_pose: 0.743673 loss: 0.000874 2022/10/13 11:30:56 - mmengine - INFO - Epoch(train) [37][200/293] lr: 5.000000e-04 eta: 4:51:17 time: 0.409553 data_time: 0.081178 memory: 12861 loss_kpt: 0.000880 acc_pose: 0.717624 loss: 0.000880 2022/10/13 11:31:17 - mmengine - INFO - Epoch(train) [37][250/293] lr: 5.000000e-04 eta: 4:51:18 time: 0.423097 data_time: 0.076828 memory: 12861 loss_kpt: 0.000863 acc_pose: 0.724605 loss: 0.000863 2022/10/13 11:31:34 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:31:55 - mmengine - INFO - Epoch(train) [38][50/293] lr: 5.000000e-04 eta: 4:49:52 time: 0.409808 data_time: 0.085553 memory: 12861 loss_kpt: 0.000872 acc_pose: 0.677249 loss: 0.000872 2022/10/13 11:32:15 - mmengine - INFO - Epoch(train) [38][100/293] lr: 5.000000e-04 eta: 4:49:50 time: 0.407391 data_time: 0.073876 memory: 12861 loss_kpt: 0.000872 acc_pose: 0.707857 loss: 0.000872 2022/10/13 11:32:35 - mmengine - INFO - Epoch(train) [38][150/293] lr: 5.000000e-04 eta: 4:49:45 time: 0.394892 data_time: 0.089311 memory: 12861 loss_kpt: 0.000874 acc_pose: 0.705781 loss: 0.000874 2022/10/13 11:32:38 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:32:54 - mmengine - INFO - Epoch(train) [38][200/293] lr: 5.000000e-04 eta: 4:49:38 time: 0.388337 data_time: 0.076211 memory: 12861 loss_kpt: 0.000866 acc_pose: 0.748551 loss: 0.000866 2022/10/13 11:33:15 - mmengine - INFO - Epoch(train) [38][250/293] lr: 5.000000e-04 eta: 4:49:35 time: 0.409277 data_time: 0.083763 memory: 12861 loss_kpt: 0.000862 acc_pose: 0.746620 loss: 0.000862 2022/10/13 11:33:32 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:33:53 - mmengine - INFO - Epoch(train) [39][50/293] lr: 5.000000e-04 eta: 4:48:13 time: 0.420177 data_time: 0.098373 memory: 12861 loss_kpt: 0.000863 acc_pose: 0.706917 loss: 0.000863 2022/10/13 11:34:14 - mmengine - INFO - Epoch(train) [39][100/293] lr: 5.000000e-04 eta: 4:48:11 time: 0.408323 data_time: 0.083922 memory: 12861 loss_kpt: 0.000887 acc_pose: 0.732933 loss: 0.000887 2022/10/13 11:34:33 - mmengine - INFO - Epoch(train) [39][150/293] lr: 5.000000e-04 eta: 4:48:04 time: 0.389961 data_time: 0.081294 memory: 12861 loss_kpt: 0.000872 acc_pose: 0.749036 loss: 0.000872 2022/10/13 11:34:53 - mmengine - INFO - Epoch(train) [39][200/293] lr: 5.000000e-04 eta: 4:47:56 time: 0.387072 data_time: 0.080792 memory: 12861 loss_kpt: 0.000856 acc_pose: 0.738062 loss: 0.000856 2022/10/13 11:35:12 - mmengine - INFO - Epoch(train) [39][250/293] lr: 5.000000e-04 eta: 4:47:50 time: 0.392148 data_time: 0.078227 memory: 12861 loss_kpt: 0.000875 acc_pose: 0.721356 loss: 0.000875 2022/10/13 11:35:29 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:35:50 - mmengine - INFO - Epoch(train) [40][50/293] lr: 5.000000e-04 eta: 4:46:28 time: 0.412328 data_time: 0.086736 memory: 12861 loss_kpt: 0.000876 acc_pose: 0.726760 loss: 0.000876 2022/10/13 11:36:10 - mmengine - INFO - Epoch(train) [40][100/293] lr: 5.000000e-04 eta: 4:46:25 time: 0.407984 data_time: 0.086464 memory: 12861 loss_kpt: 0.000872 acc_pose: 0.790846 loss: 0.000872 2022/10/13 11:36:30 - mmengine - INFO - Epoch(train) [40][150/293] lr: 5.000000e-04 eta: 4:46:19 time: 0.399135 data_time: 0.076314 memory: 12861 loss_kpt: 0.000854 acc_pose: 0.720188 loss: 0.000854 2022/10/13 11:36:51 - mmengine - INFO - Epoch(train) [40][200/293] lr: 5.000000e-04 eta: 4:46:19 time: 0.423648 data_time: 0.082743 memory: 12861 loss_kpt: 0.000877 acc_pose: 0.724920 loss: 0.000877 2022/10/13 11:37:12 - mmengine - INFO - Epoch(train) [40][250/293] lr: 5.000000e-04 eta: 4:46:16 time: 0.409800 data_time: 0.087026 memory: 12861 loss_kpt: 0.000858 acc_pose: 0.707336 loss: 0.000858 2022/10/13 11:37:29 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:37:29 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/10/13 11:37:38 - mmengine - INFO - Epoch(val) [40][50/407] eta: 0:00:48 time: 0.135563 data_time: 0.073461 memory: 12861 2022/10/13 11:37:44 - mmengine - INFO - Epoch(val) [40][100/407] eta: 0:00:38 time: 0.124574 data_time: 0.064403 memory: 983 2022/10/13 11:37:51 - mmengine - INFO - Epoch(val) [40][150/407] eta: 0:00:33 time: 0.130707 data_time: 0.070290 memory: 983 2022/10/13 11:37:57 - mmengine - INFO - Epoch(val) [40][200/407] eta: 0:00:26 time: 0.128854 data_time: 0.066644 memory: 983 2022/10/13 11:38:04 - mmengine - INFO - Epoch(val) [40][250/407] eta: 0:00:20 time: 0.132446 data_time: 0.072017 memory: 983 2022/10/13 11:38:10 - mmengine - INFO - Epoch(val) [40][300/407] eta: 0:00:13 time: 0.123097 data_time: 0.062314 memory: 983 2022/10/13 11:38:17 - mmengine - INFO - Epoch(val) [40][350/407] eta: 0:00:07 time: 0.136636 data_time: 0.075401 memory: 983 2022/10/13 11:38:23 - mmengine - INFO - Epoch(val) [40][400/407] eta: 0:00:00 time: 0.124687 data_time: 0.063552 memory: 983 2022/10/13 11:39:00 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 11:39:14 - mmengine - INFO - Epoch(val) [40][407/407] coco/AP: 0.615720 coco/AP .5: 0.854670 coco/AP .75: 0.683418 coco/AP (M): 0.573479 coco/AP (L): 0.689078 coco/AR: 0.676291 coco/AR .5: 0.897198 coco/AR .75: 0.741656 coco/AR (M): 0.624146 coco/AR (L): 0.749907 2022/10/13 11:39:14 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384/best_coco/AP_epoch_30.pth is removed 2022/10/13 11:39:15 - mmengine - INFO - The best checkpoint with 0.6157 coco/AP at 40 epoch is saved to best_coco/AP_epoch_40.pth. 2022/10/13 11:39:36 - mmengine - INFO - Epoch(train) [41][50/293] lr: 5.000000e-04 eta: 4:44:55 time: 0.408271 data_time: 0.098410 memory: 12861 loss_kpt: 0.000846 acc_pose: 0.742134 loss: 0.000846 2022/10/13 11:39:55 - mmengine - INFO - Epoch(train) [41][100/293] lr: 5.000000e-04 eta: 4:44:46 time: 0.382297 data_time: 0.076397 memory: 12861 loss_kpt: 0.000855 acc_pose: 0.775775 loss: 0.000855 2022/10/13 11:40:14 - mmengine - INFO - Epoch(train) [41][150/293] lr: 5.000000e-04 eta: 4:44:38 time: 0.387218 data_time: 0.073339 memory: 12861 loss_kpt: 0.000868 acc_pose: 0.741437 loss: 0.000868 2022/10/13 11:40:34 - mmengine - INFO - Epoch(train) [41][200/293] lr: 5.000000e-04 eta: 4:44:32 time: 0.399335 data_time: 0.080611 memory: 12861 loss_kpt: 0.000859 acc_pose: 0.726184 loss: 0.000859 2022/10/13 11:40:54 - mmengine - INFO - Epoch(train) [41][250/293] lr: 5.000000e-04 eta: 4:44:25 time: 0.394374 data_time: 0.078629 memory: 12861 loss_kpt: 0.000859 acc_pose: 0.744268 loss: 0.000859 2022/10/13 11:41:06 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:41:11 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:41:32 - mmengine - INFO - Epoch(train) [42][50/293] lr: 5.000000e-04 eta: 4:43:06 time: 0.409068 data_time: 0.095727 memory: 12861 loss_kpt: 0.000855 acc_pose: 0.811752 loss: 0.000855 2022/10/13 11:41:51 - mmengine - INFO - Epoch(train) [42][100/293] lr: 5.000000e-04 eta: 4:42:57 time: 0.383965 data_time: 0.091619 memory: 12861 loss_kpt: 0.000859 acc_pose: 0.723620 loss: 0.000859 2022/10/13 11:42:10 - mmengine - INFO - Epoch(train) [42][150/293] lr: 5.000000e-04 eta: 4:42:49 time: 0.388319 data_time: 0.074371 memory: 12861 loss_kpt: 0.000860 acc_pose: 0.747063 loss: 0.000860 2022/10/13 11:42:29 - mmengine - INFO - Epoch(train) [42][200/293] lr: 5.000000e-04 eta: 4:42:40 time: 0.384282 data_time: 0.070361 memory: 12861 loss_kpt: 0.000859 acc_pose: 0.714130 loss: 0.000859 2022/10/13 11:42:50 - mmengine - INFO - Epoch(train) [42][250/293] lr: 5.000000e-04 eta: 4:42:35 time: 0.404170 data_time: 0.083828 memory: 12861 loss_kpt: 0.000854 acc_pose: 0.706709 loss: 0.000854 2022/10/13 11:43:06 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:43:26 - mmengine - INFO - Epoch(train) [43][50/293] lr: 5.000000e-04 eta: 4:41:14 time: 0.394724 data_time: 0.092758 memory: 12861 loss_kpt: 0.000854 acc_pose: 0.738916 loss: 0.000854 2022/10/13 11:43:45 - mmengine - INFO - Epoch(train) [43][100/293] lr: 5.000000e-04 eta: 4:41:03 time: 0.376594 data_time: 0.072853 memory: 12861 loss_kpt: 0.000852 acc_pose: 0.746088 loss: 0.000852 2022/10/13 11:44:04 - mmengine - INFO - Epoch(train) [43][150/293] lr: 5.000000e-04 eta: 4:40:54 time: 0.381509 data_time: 0.076885 memory: 12861 loss_kpt: 0.000846 acc_pose: 0.765812 loss: 0.000846 2022/10/13 11:44:23 - mmengine - INFO - Epoch(train) [43][200/293] lr: 5.000000e-04 eta: 4:40:44 time: 0.383027 data_time: 0.077102 memory: 12861 loss_kpt: 0.000852 acc_pose: 0.717417 loss: 0.000852 2022/10/13 11:44:43 - mmengine - INFO - Epoch(train) [43][250/293] lr: 5.000000e-04 eta: 4:40:39 time: 0.402729 data_time: 0.084527 memory: 12861 loss_kpt: 0.000862 acc_pose: 0.744276 loss: 0.000862 2022/10/13 11:45:00 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:45:21 - mmengine - INFO - Epoch(train) [44][50/293] lr: 5.000000e-04 eta: 4:39:24 time: 0.419687 data_time: 0.088283 memory: 12861 loss_kpt: 0.000852 acc_pose: 0.744163 loss: 0.000852 2022/10/13 11:45:41 - mmengine - INFO - Epoch(train) [44][100/293] lr: 5.000000e-04 eta: 4:39:18 time: 0.401678 data_time: 0.079980 memory: 12861 loss_kpt: 0.000842 acc_pose: 0.681386 loss: 0.000842 2022/10/13 11:46:01 - mmengine - INFO - Epoch(train) [44][150/293] lr: 5.000000e-04 eta: 4:39:12 time: 0.399060 data_time: 0.094154 memory: 12861 loss_kpt: 0.000856 acc_pose: 0.683148 loss: 0.000856 2022/10/13 11:46:20 - mmengine - INFO - Epoch(train) [44][200/293] lr: 5.000000e-04 eta: 4:39:04 time: 0.392877 data_time: 0.090280 memory: 12861 loss_kpt: 0.000854 acc_pose: 0.762733 loss: 0.000854 2022/10/13 11:46:40 - mmengine - INFO - Epoch(train) [44][250/293] lr: 5.000000e-04 eta: 4:38:55 time: 0.386223 data_time: 0.073695 memory: 12861 loss_kpt: 0.000851 acc_pose: 0.683276 loss: 0.000851 2022/10/13 11:46:55 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:47:17 - mmengine - INFO - Epoch(train) [45][50/293] lr: 5.000000e-04 eta: 4:37:43 time: 0.428308 data_time: 0.090755 memory: 12861 loss_kpt: 0.000852 acc_pose: 0.733860 loss: 0.000852 2022/10/13 11:47:37 - mmengine - INFO - Epoch(train) [45][100/293] lr: 5.000000e-04 eta: 4:37:37 time: 0.399379 data_time: 0.089905 memory: 12861 loss_kpt: 0.000868 acc_pose: 0.710860 loss: 0.000868 2022/10/13 11:47:40 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:47:56 - mmengine - INFO - Epoch(train) [45][150/293] lr: 5.000000e-04 eta: 4:37:28 time: 0.386556 data_time: 0.087008 memory: 12861 loss_kpt: 0.000860 acc_pose: 0.734709 loss: 0.000860 2022/10/13 11:48:16 - mmengine - INFO - Epoch(train) [45][200/293] lr: 5.000000e-04 eta: 4:37:20 time: 0.395774 data_time: 0.090427 memory: 12861 loss_kpt: 0.000847 acc_pose: 0.732847 loss: 0.000847 2022/10/13 11:48:36 - mmengine - INFO - Epoch(train) [45][250/293] lr: 5.000000e-04 eta: 4:37:14 time: 0.401950 data_time: 0.080265 memory: 12861 loss_kpt: 0.000852 acc_pose: 0.782344 loss: 0.000852 2022/10/13 11:48:53 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:49:13 - mmengine - INFO - Epoch(train) [46][50/293] lr: 5.000000e-04 eta: 4:36:01 time: 0.413117 data_time: 0.107546 memory: 12861 loss_kpt: 0.000844 acc_pose: 0.723616 loss: 0.000844 2022/10/13 11:49:42 - mmengine - INFO - Epoch(train) [46][100/293] lr: 5.000000e-04 eta: 4:36:25 time: 0.573752 data_time: 0.077306 memory: 12861 loss_kpt: 0.000869 acc_pose: 0.742382 loss: 0.000869 2022/10/13 11:50:04 - mmengine - INFO - Epoch(train) [46][150/293] lr: 5.000000e-04 eta: 4:36:28 time: 0.451140 data_time: 0.114269 memory: 12861 loss_kpt: 0.000850 acc_pose: 0.739242 loss: 0.000850 2022/10/13 11:50:24 - mmengine - INFO - Epoch(train) [46][200/293] lr: 5.000000e-04 eta: 4:36:18 time: 0.384744 data_time: 0.078484 memory: 12861 loss_kpt: 0.000835 acc_pose: 0.734772 loss: 0.000835 2022/10/13 11:50:43 - mmengine - INFO - Epoch(train) [46][250/293] lr: 5.000000e-04 eta: 4:36:07 time: 0.382422 data_time: 0.077197 memory: 12861 loss_kpt: 0.000853 acc_pose: 0.699294 loss: 0.000853 2022/10/13 11:50:59 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:51:19 - mmengine - INFO - Epoch(train) [47][50/293] lr: 5.000000e-04 eta: 4:34:52 time: 0.395787 data_time: 0.092071 memory: 12861 loss_kpt: 0.000848 acc_pose: 0.746749 loss: 0.000848 2022/10/13 11:51:38 - mmengine - INFO - Epoch(train) [47][100/293] lr: 5.000000e-04 eta: 4:34:43 time: 0.389515 data_time: 0.077488 memory: 12861 loss_kpt: 0.000850 acc_pose: 0.775830 loss: 0.000850 2022/10/13 11:51:58 - mmengine - INFO - Epoch(train) [47][150/293] lr: 5.000000e-04 eta: 4:34:34 time: 0.393364 data_time: 0.072440 memory: 12861 loss_kpt: 0.000836 acc_pose: 0.764053 loss: 0.000836 2022/10/13 11:52:17 - mmengine - INFO - Epoch(train) [47][200/293] lr: 5.000000e-04 eta: 4:34:24 time: 0.382078 data_time: 0.077208 memory: 12861 loss_kpt: 0.000846 acc_pose: 0.703603 loss: 0.000846 2022/10/13 11:52:36 - mmengine - INFO - Epoch(train) [47][250/293] lr: 5.000000e-04 eta: 4:34:13 time: 0.381254 data_time: 0.075692 memory: 12861 loss_kpt: 0.000850 acc_pose: 0.731913 loss: 0.000850 2022/10/13 11:52:53 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:53:13 - mmengine - INFO - Epoch(train) [48][50/293] lr: 5.000000e-04 eta: 4:33:01 time: 0.410136 data_time: 0.088179 memory: 12861 loss_kpt: 0.000842 acc_pose: 0.720616 loss: 0.000842 2022/10/13 11:53:33 - mmengine - INFO - Epoch(train) [48][100/293] lr: 5.000000e-04 eta: 4:32:54 time: 0.400400 data_time: 0.084164 memory: 12861 loss_kpt: 0.000858 acc_pose: 0.744060 loss: 0.000858 2022/10/13 11:53:57 - mmengine - INFO - Epoch(train) [48][150/293] lr: 5.000000e-04 eta: 4:33:00 time: 0.477886 data_time: 0.080197 memory: 12861 loss_kpt: 0.000848 acc_pose: 0.706347 loss: 0.000848 2022/10/13 11:54:23 - mmengine - INFO - Epoch(train) [48][200/293] lr: 5.000000e-04 eta: 4:33:12 time: 0.519572 data_time: 0.082603 memory: 12861 loss_kpt: 0.000852 acc_pose: 0.751965 loss: 0.000852 2022/10/13 11:54:34 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:54:43 - mmengine - INFO - Epoch(train) [48][250/293] lr: 5.000000e-04 eta: 4:33:03 time: 0.393875 data_time: 0.072822 memory: 12861 loss_kpt: 0.000859 acc_pose: 0.756216 loss: 0.000859 2022/10/13 11:55:00 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:55:20 - mmengine - INFO - Epoch(train) [49][50/293] lr: 5.000000e-04 eta: 4:31:53 time: 0.413216 data_time: 0.087810 memory: 12861 loss_kpt: 0.000831 acc_pose: 0.746211 loss: 0.000831 2022/10/13 11:55:40 - mmengine - INFO - Epoch(train) [49][100/293] lr: 5.000000e-04 eta: 4:31:43 time: 0.386601 data_time: 0.071243 memory: 12861 loss_kpt: 0.000835 acc_pose: 0.705463 loss: 0.000835 2022/10/13 11:55:59 - mmengine - INFO - Epoch(train) [49][150/293] lr: 5.000000e-04 eta: 4:31:35 time: 0.397714 data_time: 0.085300 memory: 12861 loss_kpt: 0.000847 acc_pose: 0.709729 loss: 0.000847 2022/10/13 11:56:18 - mmengine - INFO - Epoch(train) [49][200/293] lr: 5.000000e-04 eta: 4:31:23 time: 0.379478 data_time: 0.071859 memory: 12861 loss_kpt: 0.000845 acc_pose: 0.745257 loss: 0.000845 2022/10/13 11:56:38 - mmengine - INFO - Epoch(train) [49][250/293] lr: 5.000000e-04 eta: 4:31:13 time: 0.385721 data_time: 0.083132 memory: 12861 loss_kpt: 0.000841 acc_pose: 0.733748 loss: 0.000841 2022/10/13 11:56:55 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:57:15 - mmengine - INFO - Epoch(train) [50][50/293] lr: 5.000000e-04 eta: 4:30:02 time: 0.404696 data_time: 0.099704 memory: 12861 loss_kpt: 0.000847 acc_pose: 0.731927 loss: 0.000847 2022/10/13 11:57:35 - mmengine - INFO - Epoch(train) [50][100/293] lr: 5.000000e-04 eta: 4:29:52 time: 0.389609 data_time: 0.082829 memory: 12861 loss_kpt: 0.000839 acc_pose: 0.709329 loss: 0.000839 2022/10/13 11:57:54 - mmengine - INFO - Epoch(train) [50][150/293] lr: 5.000000e-04 eta: 4:29:43 time: 0.389685 data_time: 0.077199 memory: 12861 loss_kpt: 0.000830 acc_pose: 0.751386 loss: 0.000830 2022/10/13 11:58:14 - mmengine - INFO - Epoch(train) [50][200/293] lr: 5.000000e-04 eta: 4:29:34 time: 0.398645 data_time: 0.086192 memory: 12861 loss_kpt: 0.000843 acc_pose: 0.728574 loss: 0.000843 2022/10/13 11:58:33 - mmengine - INFO - Epoch(train) [50][250/293] lr: 5.000000e-04 eta: 4:29:24 time: 0.388741 data_time: 0.076701 memory: 12861 loss_kpt: 0.000851 acc_pose: 0.708069 loss: 0.000851 2022/10/13 11:58:50 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 11:58:50 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/10/13 11:58:59 - mmengine - INFO - Epoch(val) [50][50/407] eta: 0:00:47 time: 0.132773 data_time: 0.071237 memory: 12861 2022/10/13 11:59:05 - mmengine - INFO - Epoch(val) [50][100/407] eta: 0:00:39 time: 0.129667 data_time: 0.069179 memory: 983 2022/10/13 11:59:11 - mmengine - INFO - Epoch(val) [50][150/407] eta: 0:00:31 time: 0.124433 data_time: 0.062647 memory: 983 2022/10/13 11:59:18 - mmengine - INFO - Epoch(val) [50][200/407] eta: 0:00:27 time: 0.134468 data_time: 0.073432 memory: 983 2022/10/13 11:59:24 - mmengine - INFO - Epoch(val) [50][250/407] eta: 0:00:19 time: 0.123519 data_time: 0.061934 memory: 983 2022/10/13 11:59:31 - mmengine - INFO - Epoch(val) [50][300/407] eta: 0:00:14 time: 0.133315 data_time: 0.068684 memory: 983 2022/10/13 11:59:37 - mmengine - INFO - Epoch(val) [50][350/407] eta: 0:00:07 time: 0.128300 data_time: 0.068523 memory: 983 2022/10/13 11:59:43 - mmengine - INFO - Epoch(val) [50][400/407] eta: 0:00:00 time: 0.123654 data_time: 0.061954 memory: 983 2022/10/13 12:00:21 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 12:00:35 - mmengine - INFO - Epoch(val) [50][407/407] coco/AP: 0.623986 coco/AP .5: 0.860997 coco/AP .75: 0.689763 coco/AP (M): 0.580937 coco/AP (L): 0.696266 coco/AR: 0.683533 coco/AR .5: 0.901134 coco/AR .75: 0.749843 coco/AR (M): 0.632614 coco/AR (L): 0.755110 2022/10/13 12:00:35 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384/best_coco/AP_epoch_40.pth is removed 2022/10/13 12:00:37 - mmengine - INFO - The best checkpoint with 0.6240 coco/AP at 50 epoch is saved to best_coco/AP_epoch_50.pth. 2022/10/13 12:00:58 - mmengine - INFO - Epoch(train) [51][50/293] lr: 5.000000e-04 eta: 4:28:16 time: 0.415780 data_time: 0.096164 memory: 12861 loss_kpt: 0.000830 acc_pose: 0.768976 loss: 0.000830 2022/10/13 12:01:17 - mmengine - INFO - Epoch(train) [51][100/293] lr: 5.000000e-04 eta: 4:28:05 time: 0.384213 data_time: 0.078182 memory: 12861 loss_kpt: 0.000831 acc_pose: 0.739968 loss: 0.000831 2022/10/13 12:01:37 - mmengine - INFO - Epoch(train) [51][150/293] lr: 5.000000e-04 eta: 4:27:56 time: 0.393270 data_time: 0.075648 memory: 12861 loss_kpt: 0.000835 acc_pose: 0.727229 loss: 0.000835 2022/10/13 12:01:57 - mmengine - INFO - Epoch(train) [51][200/293] lr: 5.000000e-04 eta: 4:27:48 time: 0.402784 data_time: 0.078234 memory: 12861 loss_kpt: 0.000828 acc_pose: 0.761633 loss: 0.000828 2022/10/13 12:02:16 - mmengine - INFO - Epoch(train) [51][250/293] lr: 5.000000e-04 eta: 4:27:38 time: 0.387789 data_time: 0.085996 memory: 12861 loss_kpt: 0.000838 acc_pose: 0.730021 loss: 0.000838 2022/10/13 12:02:33 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:02:54 - mmengine - INFO - Epoch(train) [52][50/293] lr: 5.000000e-04 eta: 4:26:30 time: 0.413500 data_time: 0.093746 memory: 12861 loss_kpt: 0.000860 acc_pose: 0.781530 loss: 0.000860 2022/10/13 12:02:57 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:03:14 - mmengine - INFO - Epoch(train) [52][100/293] lr: 5.000000e-04 eta: 4:26:23 time: 0.404090 data_time: 0.086008 memory: 12861 loss_kpt: 0.000837 acc_pose: 0.731621 loss: 0.000837 2022/10/13 12:03:34 - mmengine - INFO - Epoch(train) [52][150/293] lr: 5.000000e-04 eta: 4:26:13 time: 0.395355 data_time: 0.083604 memory: 12861 loss_kpt: 0.000833 acc_pose: 0.772776 loss: 0.000833 2022/10/13 12:03:52 - mmengine - INFO - Epoch(train) [52][200/293] lr: 5.000000e-04 eta: 4:26:01 time: 0.375866 data_time: 0.077040 memory: 12861 loss_kpt: 0.000840 acc_pose: 0.808572 loss: 0.000840 2022/10/13 12:04:12 - mmengine - INFO - Epoch(train) [52][250/293] lr: 5.000000e-04 eta: 4:25:52 time: 0.398650 data_time: 0.072567 memory: 12861 loss_kpt: 0.000842 acc_pose: 0.764241 loss: 0.000842 2022/10/13 12:04:29 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:04:49 - mmengine - INFO - Epoch(train) [53][50/293] lr: 5.000000e-04 eta: 4:24:45 time: 0.408600 data_time: 0.092608 memory: 12861 loss_kpt: 0.000844 acc_pose: 0.684792 loss: 0.000844 2022/10/13 12:05:10 - mmengine - INFO - Epoch(train) [53][100/293] lr: 5.000000e-04 eta: 4:24:37 time: 0.406891 data_time: 0.080084 memory: 12861 loss_kpt: 0.000844 acc_pose: 0.705422 loss: 0.000844 2022/10/13 12:05:29 - mmengine - INFO - Epoch(train) [53][150/293] lr: 5.000000e-04 eta: 4:24:27 time: 0.389800 data_time: 0.076867 memory: 12861 loss_kpt: 0.000843 acc_pose: 0.729354 loss: 0.000843 2022/10/13 12:05:48 - mmengine - INFO - Epoch(train) [53][200/293] lr: 5.000000e-04 eta: 4:24:16 time: 0.386829 data_time: 0.072656 memory: 12861 loss_kpt: 0.000838 acc_pose: 0.739401 loss: 0.000838 2022/10/13 12:06:08 - mmengine - INFO - Epoch(train) [53][250/293] lr: 5.000000e-04 eta: 4:24:06 time: 0.389293 data_time: 0.075203 memory: 12861 loss_kpt: 0.000831 acc_pose: 0.730147 loss: 0.000831 2022/10/13 12:06:25 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:06:45 - mmengine - INFO - Epoch(train) [54][50/293] lr: 5.000000e-04 eta: 4:23:00 time: 0.409837 data_time: 0.101472 memory: 12861 loss_kpt: 0.000849 acc_pose: 0.727667 loss: 0.000849 2022/10/13 12:07:05 - mmengine - INFO - Epoch(train) [54][100/293] lr: 5.000000e-04 eta: 4:22:49 time: 0.387428 data_time: 0.069981 memory: 12861 loss_kpt: 0.000828 acc_pose: 0.696136 loss: 0.000828 2022/10/13 12:07:24 - mmengine - INFO - Epoch(train) [54][150/293] lr: 5.000000e-04 eta: 4:22:39 time: 0.389037 data_time: 0.076644 memory: 12861 loss_kpt: 0.000840 acc_pose: 0.737393 loss: 0.000840 2022/10/13 12:07:43 - mmengine - INFO - Epoch(train) [54][200/293] lr: 5.000000e-04 eta: 4:22:26 time: 0.376602 data_time: 0.078410 memory: 12861 loss_kpt: 0.000837 acc_pose: 0.701256 loss: 0.000837 2022/10/13 12:08:03 - mmengine - INFO - Epoch(train) [54][250/293] lr: 5.000000e-04 eta: 4:22:18 time: 0.405098 data_time: 0.083069 memory: 12861 loss_kpt: 0.000846 acc_pose: 0.729680 loss: 0.000846 2022/10/13 12:08:20 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:08:42 - mmengine - INFO - Epoch(train) [55][50/293] lr: 5.000000e-04 eta: 4:21:15 time: 0.421723 data_time: 0.105955 memory: 12861 loss_kpt: 0.000841 acc_pose: 0.757965 loss: 0.000841 2022/10/13 12:09:01 - mmengine - INFO - Epoch(train) [55][100/293] lr: 5.000000e-04 eta: 4:21:05 time: 0.394581 data_time: 0.081081 memory: 12861 loss_kpt: 0.000851 acc_pose: 0.735558 loss: 0.000851 2022/10/13 12:09:21 - mmengine - INFO - Epoch(train) [55][150/293] lr: 5.000000e-04 eta: 4:20:55 time: 0.394993 data_time: 0.084248 memory: 12861 loss_kpt: 0.000838 acc_pose: 0.752895 loss: 0.000838 2022/10/13 12:09:32 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:09:41 - mmengine - INFO - Epoch(train) [55][200/293] lr: 5.000000e-04 eta: 4:20:46 time: 0.403615 data_time: 0.091231 memory: 12861 loss_kpt: 0.000833 acc_pose: 0.698281 loss: 0.000833 2022/10/13 12:10:01 - mmengine - INFO - Epoch(train) [55][250/293] lr: 5.000000e-04 eta: 4:20:35 time: 0.386343 data_time: 0.082968 memory: 12861 loss_kpt: 0.000834 acc_pose: 0.736131 loss: 0.000834 2022/10/13 12:10:17 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:10:39 - mmengine - INFO - Epoch(train) [56][50/293] lr: 5.000000e-04 eta: 4:19:33 time: 0.425544 data_time: 0.090022 memory: 12861 loss_kpt: 0.000822 acc_pose: 0.736468 loss: 0.000822 2022/10/13 12:10:59 - mmengine - INFO - Epoch(train) [56][100/293] lr: 5.000000e-04 eta: 4:19:24 time: 0.400813 data_time: 0.075248 memory: 12861 loss_kpt: 0.000837 acc_pose: 0.691923 loss: 0.000837 2022/10/13 12:11:18 - mmengine - INFO - Epoch(train) [56][150/293] lr: 5.000000e-04 eta: 4:19:13 time: 0.389235 data_time: 0.073230 memory: 12861 loss_kpt: 0.000825 acc_pose: 0.758285 loss: 0.000825 2022/10/13 12:11:38 - mmengine - INFO - Epoch(train) [56][200/293] lr: 5.000000e-04 eta: 4:19:03 time: 0.396018 data_time: 0.073134 memory: 12861 loss_kpt: 0.000848 acc_pose: 0.706440 loss: 0.000848 2022/10/13 12:11:58 - mmengine - INFO - Epoch(train) [56][250/293] lr: 5.000000e-04 eta: 4:18:54 time: 0.400745 data_time: 0.090716 memory: 12861 loss_kpt: 0.000848 acc_pose: 0.724304 loss: 0.000848 2022/10/13 12:12:15 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:12:35 - mmengine - INFO - Epoch(train) [57][50/293] lr: 5.000000e-04 eta: 4:17:50 time: 0.405738 data_time: 0.095897 memory: 12861 loss_kpt: 0.000834 acc_pose: 0.727808 loss: 0.000834 2022/10/13 12:12:55 - mmengine - INFO - Epoch(train) [57][100/293] lr: 5.000000e-04 eta: 4:17:39 time: 0.388440 data_time: 0.085770 memory: 12861 loss_kpt: 0.000842 acc_pose: 0.710226 loss: 0.000842 2022/10/13 12:13:15 - mmengine - INFO - Epoch(train) [57][150/293] lr: 5.000000e-04 eta: 4:17:30 time: 0.406267 data_time: 0.067472 memory: 12861 loss_kpt: 0.000833 acc_pose: 0.727420 loss: 0.000833 2022/10/13 12:13:35 - mmengine - INFO - Epoch(train) [57][200/293] lr: 5.000000e-04 eta: 4:17:19 time: 0.389844 data_time: 0.074114 memory: 12861 loss_kpt: 0.000831 acc_pose: 0.750104 loss: 0.000831 2022/10/13 12:13:55 - mmengine - INFO - Epoch(train) [57][250/293] lr: 5.000000e-04 eta: 4:17:10 time: 0.402160 data_time: 0.085775 memory: 12861 loss_kpt: 0.000835 acc_pose: 0.714574 loss: 0.000835 2022/10/13 12:14:11 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:14:32 - mmengine - INFO - Epoch(train) [58][50/293] lr: 5.000000e-04 eta: 4:16:08 time: 0.418364 data_time: 0.102890 memory: 12861 loss_kpt: 0.000822 acc_pose: 0.745424 loss: 0.000822 2022/10/13 12:14:52 - mmengine - INFO - Epoch(train) [58][100/293] lr: 5.000000e-04 eta: 4:15:59 time: 0.401265 data_time: 0.079178 memory: 12861 loss_kpt: 0.000829 acc_pose: 0.750538 loss: 0.000829 2022/10/13 12:15:13 - mmengine - INFO - Epoch(train) [58][150/293] lr: 5.000000e-04 eta: 4:15:50 time: 0.404510 data_time: 0.074720 memory: 12861 loss_kpt: 0.000830 acc_pose: 0.762794 loss: 0.000830 2022/10/13 12:15:32 - mmengine - INFO - Epoch(train) [58][200/293] lr: 5.000000e-04 eta: 4:15:39 time: 0.389988 data_time: 0.078154 memory: 12861 loss_kpt: 0.000852 acc_pose: 0.762001 loss: 0.000852 2022/10/13 12:15:51 - mmengine - INFO - Epoch(train) [58][250/293] lr: 5.000000e-04 eta: 4:15:27 time: 0.381587 data_time: 0.088257 memory: 12861 loss_kpt: 0.000847 acc_pose: 0.750251 loss: 0.000847 2022/10/13 12:16:08 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:16:11 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:16:29 - mmengine - INFO - Epoch(train) [59][50/293] lr: 5.000000e-04 eta: 4:14:26 time: 0.419215 data_time: 0.095383 memory: 12861 loss_kpt: 0.000813 acc_pose: 0.783613 loss: 0.000813 2022/10/13 12:16:48 - mmengine - INFO - Epoch(train) [59][100/293] lr: 5.000000e-04 eta: 4:14:15 time: 0.387440 data_time: 0.065438 memory: 12861 loss_kpt: 0.000820 acc_pose: 0.748044 loss: 0.000820 2022/10/13 12:17:08 - mmengine - INFO - Epoch(train) [59][150/293] lr: 5.000000e-04 eta: 4:14:04 time: 0.395837 data_time: 0.078178 memory: 12861 loss_kpt: 0.000824 acc_pose: 0.713789 loss: 0.000824 2022/10/13 12:17:27 - mmengine - INFO - Epoch(train) [59][200/293] lr: 5.000000e-04 eta: 4:13:54 time: 0.394949 data_time: 0.076196 memory: 12861 loss_kpt: 0.000837 acc_pose: 0.728230 loss: 0.000837 2022/10/13 12:17:47 - mmengine - INFO - Epoch(train) [59][250/293] lr: 5.000000e-04 eta: 4:13:42 time: 0.384816 data_time: 0.079736 memory: 12861 loss_kpt: 0.000825 acc_pose: 0.723259 loss: 0.000825 2022/10/13 12:18:03 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:18:25 - mmengine - INFO - Epoch(train) [60][50/293] lr: 5.000000e-04 eta: 4:12:43 time: 0.428049 data_time: 0.094578 memory: 12861 loss_kpt: 0.000835 acc_pose: 0.798426 loss: 0.000835 2022/10/13 12:18:44 - mmengine - INFO - Epoch(train) [60][100/293] lr: 5.000000e-04 eta: 4:12:31 time: 0.384256 data_time: 0.075137 memory: 12861 loss_kpt: 0.000837 acc_pose: 0.754332 loss: 0.000837 2022/10/13 12:19:04 - mmengine - INFO - Epoch(train) [60][150/293] lr: 5.000000e-04 eta: 4:12:20 time: 0.395864 data_time: 0.080573 memory: 12861 loss_kpt: 0.000827 acc_pose: 0.724903 loss: 0.000827 2022/10/13 12:19:23 - mmengine - INFO - Epoch(train) [60][200/293] lr: 5.000000e-04 eta: 4:12:10 time: 0.397293 data_time: 0.085272 memory: 12861 loss_kpt: 0.000833 acc_pose: 0.749411 loss: 0.000833 2022/10/13 12:19:43 - mmengine - INFO - Epoch(train) [60][250/293] lr: 5.000000e-04 eta: 4:11:59 time: 0.390778 data_time: 0.077628 memory: 12861 loss_kpt: 0.000834 acc_pose: 0.743041 loss: 0.000834 2022/10/13 12:20:00 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:20:00 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/10/13 12:20:08 - mmengine - INFO - Epoch(val) [60][50/407] eta: 0:00:44 time: 0.125712 data_time: 0.063553 memory: 12861 2022/10/13 12:20:14 - mmengine - INFO - Epoch(val) [60][100/407] eta: 0:00:37 time: 0.121441 data_time: 0.061612 memory: 983 2022/10/13 12:20:21 - mmengine - INFO - Epoch(val) [60][150/407] eta: 0:00:32 time: 0.126634 data_time: 0.065657 memory: 983 2022/10/13 12:20:27 - mmengine - INFO - Epoch(val) [60][200/407] eta: 0:00:25 time: 0.124838 data_time: 0.063573 memory: 983 2022/10/13 12:20:33 - mmengine - INFO - Epoch(val) [60][250/407] eta: 0:00:19 time: 0.121326 data_time: 0.061078 memory: 983 2022/10/13 12:20:39 - mmengine - INFO - Epoch(val) [60][300/407] eta: 0:00:13 time: 0.123911 data_time: 0.061656 memory: 983 2022/10/13 12:20:45 - mmengine - INFO - Epoch(val) [60][350/407] eta: 0:00:07 time: 0.122994 data_time: 0.060499 memory: 983 2022/10/13 12:20:51 - mmengine - INFO - Epoch(val) [60][400/407] eta: 0:00:00 time: 0.121236 data_time: 0.060028 memory: 983 2022/10/13 12:21:30 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 12:21:44 - mmengine - INFO - Epoch(val) [60][407/407] coco/AP: 0.632496 coco/AP .5: 0.863631 coco/AP .75: 0.698709 coco/AP (M): 0.588683 coco/AP (L): 0.705253 coco/AR: 0.690995 coco/AR .5: 0.902550 coco/AR .75: 0.753621 coco/AR (M): 0.639852 coco/AR (L): 0.762616 2022/10/13 12:21:44 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384/best_coco/AP_epoch_50.pth is removed 2022/10/13 12:21:46 - mmengine - INFO - The best checkpoint with 0.6325 coco/AP at 60 epoch is saved to best_coco/AP_epoch_60.pth. 2022/10/13 12:22:06 - mmengine - INFO - Epoch(train) [61][50/293] lr: 5.000000e-04 eta: 4:10:57 time: 0.401596 data_time: 0.094438 memory: 12861 loss_kpt: 0.000816 acc_pose: 0.724315 loss: 0.000816 2022/10/13 12:22:25 - mmengine - INFO - Epoch(train) [61][100/293] lr: 5.000000e-04 eta: 4:10:47 time: 0.396826 data_time: 0.086439 memory: 12861 loss_kpt: 0.000837 acc_pose: 0.710605 loss: 0.000837 2022/10/13 12:22:45 - mmengine - INFO - Epoch(train) [61][150/293] lr: 5.000000e-04 eta: 4:10:36 time: 0.397348 data_time: 0.072727 memory: 12861 loss_kpt: 0.000837 acc_pose: 0.788202 loss: 0.000837 2022/10/13 12:23:05 - mmengine - INFO - Epoch(train) [61][200/293] lr: 5.000000e-04 eta: 4:10:25 time: 0.392113 data_time: 0.087894 memory: 12861 loss_kpt: 0.000836 acc_pose: 0.676018 loss: 0.000836 2022/10/13 12:23:24 - mmengine - INFO - Epoch(train) [61][250/293] lr: 5.000000e-04 eta: 4:10:13 time: 0.387970 data_time: 0.083905 memory: 12861 loss_kpt: 0.000836 acc_pose: 0.735588 loss: 0.000836 2022/10/13 12:23:42 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:24:03 - mmengine - INFO - Epoch(train) [62][50/293] lr: 5.000000e-04 eta: 4:09:15 time: 0.423268 data_time: 0.104185 memory: 12861 loss_kpt: 0.000828 acc_pose: 0.753416 loss: 0.000828 2022/10/13 12:24:22 - mmengine - INFO - Epoch(train) [62][100/293] lr: 5.000000e-04 eta: 4:09:04 time: 0.393474 data_time: 0.084090 memory: 12861 loss_kpt: 0.000819 acc_pose: 0.768198 loss: 0.000819 2022/10/13 12:24:33 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:24:42 - mmengine - INFO - Epoch(train) [62][150/293] lr: 5.000000e-04 eta: 4:08:53 time: 0.391089 data_time: 0.085424 memory: 12861 loss_kpt: 0.000831 acc_pose: 0.735118 loss: 0.000831 2022/10/13 12:25:02 - mmengine - INFO - Epoch(train) [62][200/293] lr: 5.000000e-04 eta: 4:08:43 time: 0.400629 data_time: 0.081531 memory: 12861 loss_kpt: 0.000812 acc_pose: 0.736496 loss: 0.000812 2022/10/13 12:25:21 - mmengine - INFO - Epoch(train) [62][250/293] lr: 5.000000e-04 eta: 4:08:30 time: 0.385127 data_time: 0.090726 memory: 12861 loss_kpt: 0.000832 acc_pose: 0.749994 loss: 0.000832 2022/10/13 12:25:38 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:25:59 - mmengine - INFO - Epoch(train) [63][50/293] lr: 5.000000e-04 eta: 4:07:33 time: 0.422669 data_time: 0.099625 memory: 12861 loss_kpt: 0.000831 acc_pose: 0.730875 loss: 0.000831 2022/10/13 12:26:18 - mmengine - INFO - Epoch(train) [63][100/293] lr: 5.000000e-04 eta: 4:07:21 time: 0.392210 data_time: 0.082943 memory: 12861 loss_kpt: 0.000834 acc_pose: 0.707036 loss: 0.000834 2022/10/13 12:26:38 - mmengine - INFO - Epoch(train) [63][150/293] lr: 5.000000e-04 eta: 4:07:09 time: 0.385978 data_time: 0.087500 memory: 12861 loss_kpt: 0.000827 acc_pose: 0.762940 loss: 0.000827 2022/10/13 12:26:57 - mmengine - INFO - Epoch(train) [63][200/293] lr: 5.000000e-04 eta: 4:06:58 time: 0.393599 data_time: 0.084923 memory: 12861 loss_kpt: 0.000843 acc_pose: 0.789743 loss: 0.000843 2022/10/13 12:27:17 - mmengine - INFO - Epoch(train) [63][250/293] lr: 5.000000e-04 eta: 4:06:46 time: 0.384302 data_time: 0.074507 memory: 12861 loss_kpt: 0.000826 acc_pose: 0.709007 loss: 0.000826 2022/10/13 12:27:33 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:27:54 - mmengine - INFO - Epoch(train) [64][50/293] lr: 5.000000e-04 eta: 4:05:48 time: 0.414500 data_time: 0.089700 memory: 12861 loss_kpt: 0.000823 acc_pose: 0.798802 loss: 0.000823 2022/10/13 12:28:13 - mmengine - INFO - Epoch(train) [64][100/293] lr: 5.000000e-04 eta: 4:05:36 time: 0.391851 data_time: 0.075934 memory: 12861 loss_kpt: 0.000835 acc_pose: 0.728828 loss: 0.000835 2022/10/13 12:28:33 - mmengine - INFO - Epoch(train) [64][150/293] lr: 5.000000e-04 eta: 4:05:24 time: 0.386750 data_time: 0.071458 memory: 12861 loss_kpt: 0.000828 acc_pose: 0.705302 loss: 0.000828 2022/10/13 12:28:52 - mmengine - INFO - Epoch(train) [64][200/293] lr: 5.000000e-04 eta: 4:05:12 time: 0.384000 data_time: 0.070333 memory: 12861 loss_kpt: 0.000815 acc_pose: 0.737589 loss: 0.000815 2022/10/13 12:29:12 - mmengine - INFO - Epoch(train) [64][250/293] lr: 5.000000e-04 eta: 4:05:00 time: 0.390190 data_time: 0.080406 memory: 12861 loss_kpt: 0.000830 acc_pose: 0.681373 loss: 0.000830 2022/10/13 12:29:28 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:29:49 - mmengine - INFO - Epoch(train) [65][50/293] lr: 5.000000e-04 eta: 4:04:03 time: 0.415793 data_time: 0.097044 memory: 12861 loss_kpt: 0.000822 acc_pose: 0.748826 loss: 0.000822 2022/10/13 12:30:09 - mmengine - INFO - Epoch(train) [65][100/293] lr: 5.000000e-04 eta: 4:03:51 time: 0.390152 data_time: 0.090390 memory: 12861 loss_kpt: 0.000820 acc_pose: 0.729535 loss: 0.000820 2022/10/13 12:30:28 - mmengine - INFO - Epoch(train) [65][150/293] lr: 5.000000e-04 eta: 4:03:38 time: 0.383406 data_time: 0.077324 memory: 12861 loss_kpt: 0.000812 acc_pose: 0.680197 loss: 0.000812 2022/10/13 12:30:47 - mmengine - INFO - Epoch(train) [65][200/293] lr: 5.000000e-04 eta: 4:03:26 time: 0.385117 data_time: 0.077210 memory: 12861 loss_kpt: 0.000809 acc_pose: 0.728865 loss: 0.000809 2022/10/13 12:31:06 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:31:06 - mmengine - INFO - Epoch(train) [65][250/293] lr: 5.000000e-04 eta: 4:03:14 time: 0.385731 data_time: 0.075193 memory: 12861 loss_kpt: 0.000822 acc_pose: 0.647243 loss: 0.000822 2022/10/13 12:31:23 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:31:43 - mmengine - INFO - Epoch(train) [66][50/293] lr: 5.000000e-04 eta: 4:02:16 time: 0.404127 data_time: 0.083173 memory: 12861 loss_kpt: 0.000805 acc_pose: 0.762428 loss: 0.000805 2022/10/13 12:32:02 - mmengine - INFO - Epoch(train) [66][100/293] lr: 5.000000e-04 eta: 4:02:03 time: 0.386621 data_time: 0.084016 memory: 12861 loss_kpt: 0.000825 acc_pose: 0.780452 loss: 0.000825 2022/10/13 12:32:22 - mmengine - INFO - Epoch(train) [66][150/293] lr: 5.000000e-04 eta: 4:01:51 time: 0.389414 data_time: 0.084008 memory: 12861 loss_kpt: 0.000818 acc_pose: 0.740742 loss: 0.000818 2022/10/13 12:32:42 - mmengine - INFO - Epoch(train) [66][200/293] lr: 5.000000e-04 eta: 4:01:41 time: 0.401597 data_time: 0.085690 memory: 12861 loss_kpt: 0.000836 acc_pose: 0.780569 loss: 0.000836 2022/10/13 12:33:01 - mmengine - INFO - Epoch(train) [66][250/293] lr: 5.000000e-04 eta: 4:01:29 time: 0.390926 data_time: 0.086542 memory: 12861 loss_kpt: 0.000829 acc_pose: 0.785724 loss: 0.000829 2022/10/13 12:33:18 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:33:39 - mmengine - INFO - Epoch(train) [67][50/293] lr: 5.000000e-04 eta: 4:00:32 time: 0.411923 data_time: 0.096044 memory: 12861 loss_kpt: 0.000829 acc_pose: 0.753793 loss: 0.000829 2022/10/13 12:33:58 - mmengine - INFO - Epoch(train) [67][100/293] lr: 5.000000e-04 eta: 4:00:21 time: 0.396165 data_time: 0.081351 memory: 12861 loss_kpt: 0.000832 acc_pose: 0.764284 loss: 0.000832 2022/10/13 12:34:17 - mmengine - INFO - Epoch(train) [67][150/293] lr: 5.000000e-04 eta: 4:00:08 time: 0.378027 data_time: 0.080260 memory: 12861 loss_kpt: 0.000831 acc_pose: 0.727634 loss: 0.000831 2022/10/13 12:34:37 - mmengine - INFO - Epoch(train) [67][200/293] lr: 5.000000e-04 eta: 3:59:56 time: 0.397346 data_time: 0.084515 memory: 12861 loss_kpt: 0.000820 acc_pose: 0.697363 loss: 0.000820 2022/10/13 12:34:56 - mmengine - INFO - Epoch(train) [67][250/293] lr: 5.000000e-04 eta: 3:59:44 time: 0.382442 data_time: 0.095814 memory: 12861 loss_kpt: 0.000820 acc_pose: 0.706536 loss: 0.000820 2022/10/13 12:35:13 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:35:33 - mmengine - INFO - Epoch(train) [68][50/293] lr: 5.000000e-04 eta: 3:58:47 time: 0.405271 data_time: 0.087645 memory: 12861 loss_kpt: 0.000838 acc_pose: 0.780771 loss: 0.000838 2022/10/13 12:35:53 - mmengine - INFO - Epoch(train) [68][100/293] lr: 5.000000e-04 eta: 3:58:35 time: 0.393686 data_time: 0.082382 memory: 12861 loss_kpt: 0.000814 acc_pose: 0.731568 loss: 0.000814 2022/10/13 12:36:13 - mmengine - INFO - Epoch(train) [68][150/293] lr: 5.000000e-04 eta: 3:58:24 time: 0.396046 data_time: 0.095534 memory: 12861 loss_kpt: 0.000808 acc_pose: 0.714303 loss: 0.000808 2022/10/13 12:36:32 - mmengine - INFO - Epoch(train) [68][200/293] lr: 5.000000e-04 eta: 3:58:12 time: 0.389772 data_time: 0.083183 memory: 12861 loss_kpt: 0.000835 acc_pose: 0.773411 loss: 0.000835 2022/10/13 12:36:52 - mmengine - INFO - Epoch(train) [68][250/293] lr: 5.000000e-04 eta: 3:58:00 time: 0.392342 data_time: 0.085134 memory: 12861 loss_kpt: 0.000832 acc_pose: 0.760995 loss: 0.000832 2022/10/13 12:37:08 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:37:30 - mmengine - INFO - Epoch(train) [69][50/293] lr: 5.000000e-04 eta: 3:57:08 time: 0.448512 data_time: 0.100007 memory: 12861 loss_kpt: 0.000819 acc_pose: 0.736062 loss: 0.000819 2022/10/13 12:37:41 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:37:50 - mmengine - INFO - Epoch(train) [69][100/293] lr: 5.000000e-04 eta: 3:56:56 time: 0.389350 data_time: 0.070373 memory: 12861 loss_kpt: 0.000816 acc_pose: 0.749316 loss: 0.000816 2022/10/13 12:38:10 - mmengine - INFO - Epoch(train) [69][150/293] lr: 5.000000e-04 eta: 3:56:45 time: 0.401735 data_time: 0.066825 memory: 12861 loss_kpt: 0.000815 acc_pose: 0.748881 loss: 0.000815 2022/10/13 12:38:30 - mmengine - INFO - Epoch(train) [69][200/293] lr: 5.000000e-04 eta: 3:56:34 time: 0.405698 data_time: 0.074513 memory: 12861 loss_kpt: 0.000811 acc_pose: 0.689092 loss: 0.000811 2022/10/13 12:38:50 - mmengine - INFO - Epoch(train) [69][250/293] lr: 5.000000e-04 eta: 3:56:23 time: 0.396882 data_time: 0.079115 memory: 12861 loss_kpt: 0.000827 acc_pose: 0.830655 loss: 0.000827 2022/10/13 12:39:07 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:39:28 - mmengine - INFO - Epoch(train) [70][50/293] lr: 5.000000e-04 eta: 3:55:29 time: 0.422799 data_time: 0.103946 memory: 12861 loss_kpt: 0.000819 acc_pose: 0.768688 loss: 0.000819 2022/10/13 12:39:48 - mmengine - INFO - Epoch(train) [70][100/293] lr: 5.000000e-04 eta: 3:55:18 time: 0.406106 data_time: 0.077174 memory: 12861 loss_kpt: 0.000819 acc_pose: 0.697911 loss: 0.000819 2022/10/13 12:40:08 - mmengine - INFO - Epoch(train) [70][150/293] lr: 5.000000e-04 eta: 3:55:07 time: 0.397332 data_time: 0.079728 memory: 12861 loss_kpt: 0.000831 acc_pose: 0.724840 loss: 0.000831 2022/10/13 12:40:28 - mmengine - INFO - Epoch(train) [70][200/293] lr: 5.000000e-04 eta: 3:54:54 time: 0.389240 data_time: 0.074272 memory: 12861 loss_kpt: 0.000833 acc_pose: 0.698212 loss: 0.000833 2022/10/13 12:40:48 - mmengine - INFO - Epoch(train) [70][250/293] lr: 5.000000e-04 eta: 3:54:44 time: 0.410054 data_time: 0.088660 memory: 12861 loss_kpt: 0.000821 acc_pose: 0.771025 loss: 0.000821 2022/10/13 12:41:05 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:41:05 - mmengine - INFO - Saving checkpoint at 70 epochs 2022/10/13 12:41:13 - mmengine - INFO - Epoch(val) [70][50/407] eta: 0:00:47 time: 0.132164 data_time: 0.071469 memory: 12861 2022/10/13 12:41:20 - mmengine - INFO - Epoch(val) [70][100/407] eta: 0:00:39 time: 0.127397 data_time: 0.064939 memory: 983 2022/10/13 12:41:26 - mmengine - INFO - Epoch(val) [70][150/407] eta: 0:00:34 time: 0.134236 data_time: 0.071964 memory: 983 2022/10/13 12:41:33 - mmengine - INFO - Epoch(val) [70][200/407] eta: 0:00:26 time: 0.126382 data_time: 0.066377 memory: 983 2022/10/13 12:41:39 - mmengine - INFO - Epoch(val) [70][250/407] eta: 0:00:20 time: 0.132152 data_time: 0.071996 memory: 983 2022/10/13 12:41:46 - mmengine - INFO - Epoch(val) [70][300/407] eta: 0:00:13 time: 0.128475 data_time: 0.067491 memory: 983 2022/10/13 12:41:52 - mmengine - INFO - Epoch(val) [70][350/407] eta: 0:00:07 time: 0.131295 data_time: 0.069321 memory: 983 2022/10/13 12:41:58 - mmengine - INFO - Epoch(val) [70][400/407] eta: 0:00:00 time: 0.116756 data_time: 0.058246 memory: 983 2022/10/13 12:42:36 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 12:42:50 - mmengine - INFO - Epoch(val) [70][407/407] coco/AP: 0.637142 coco/AP .5: 0.865838 coco/AP .75: 0.703783 coco/AP (M): 0.592893 coco/AP (L): 0.710521 coco/AR: 0.696143 coco/AR .5: 0.905069 coco/AR .75: 0.758659 coco/AR (M): 0.645862 coco/AR (L): 0.766964 2022/10/13 12:42:50 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384/best_coco/AP_epoch_60.pth is removed 2022/10/13 12:42:51 - mmengine - INFO - The best checkpoint with 0.6371 coco/AP at 70 epoch is saved to best_coco/AP_epoch_70.pth. 2022/10/13 12:43:11 - mmengine - INFO - Epoch(train) [71][50/293] lr: 5.000000e-04 eta: 3:53:48 time: 0.395964 data_time: 0.086746 memory: 12861 loss_kpt: 0.000830 acc_pose: 0.786249 loss: 0.000830 2022/10/13 12:43:31 - mmengine - INFO - Epoch(train) [71][100/293] lr: 5.000000e-04 eta: 3:53:35 time: 0.387744 data_time: 0.075899 memory: 12861 loss_kpt: 0.000830 acc_pose: 0.746986 loss: 0.000830 2022/10/13 12:43:50 - mmengine - INFO - Epoch(train) [71][150/293] lr: 5.000000e-04 eta: 3:53:22 time: 0.386924 data_time: 0.077265 memory: 12861 loss_kpt: 0.000811 acc_pose: 0.755682 loss: 0.000811 2022/10/13 12:44:10 - mmengine - INFO - Epoch(train) [71][200/293] lr: 5.000000e-04 eta: 3:53:11 time: 0.398387 data_time: 0.088738 memory: 12861 loss_kpt: 0.000822 acc_pose: 0.788588 loss: 0.000822 2022/10/13 12:44:30 - mmengine - INFO - Epoch(train) [71][250/293] lr: 5.000000e-04 eta: 3:52:59 time: 0.394764 data_time: 0.084580 memory: 12861 loss_kpt: 0.000800 acc_pose: 0.754457 loss: 0.000800 2022/10/13 12:44:46 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:45:06 - mmengine - INFO - Epoch(train) [72][50/293] lr: 5.000000e-04 eta: 3:52:04 time: 0.403417 data_time: 0.090320 memory: 12861 loss_kpt: 0.000806 acc_pose: 0.685520 loss: 0.000806 2022/10/13 12:45:26 - mmengine - INFO - Epoch(train) [72][100/293] lr: 5.000000e-04 eta: 3:51:52 time: 0.391567 data_time: 0.081918 memory: 12861 loss_kpt: 0.000820 acc_pose: 0.791052 loss: 0.000820 2022/10/13 12:45:45 - mmengine - INFO - Epoch(train) [72][150/293] lr: 5.000000e-04 eta: 3:51:39 time: 0.386677 data_time: 0.082512 memory: 12861 loss_kpt: 0.000814 acc_pose: 0.723088 loss: 0.000814 2022/10/13 12:46:03 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:46:05 - mmengine - INFO - Epoch(train) [72][200/293] lr: 5.000000e-04 eta: 3:51:26 time: 0.387977 data_time: 0.071939 memory: 12861 loss_kpt: 0.000814 acc_pose: 0.780988 loss: 0.000814 2022/10/13 12:46:24 - mmengine - INFO - Epoch(train) [72][250/293] lr: 5.000000e-04 eta: 3:51:12 time: 0.379461 data_time: 0.075388 memory: 12861 loss_kpt: 0.000818 acc_pose: 0.696862 loss: 0.000818 2022/10/13 12:46:40 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:47:00 - mmengine - INFO - Epoch(train) [73][50/293] lr: 5.000000e-04 eta: 3:50:18 time: 0.400993 data_time: 0.091237 memory: 12861 loss_kpt: 0.000804 acc_pose: 0.715553 loss: 0.000804 2022/10/13 12:47:19 - mmengine - INFO - Epoch(train) [73][100/293] lr: 5.000000e-04 eta: 3:50:04 time: 0.377943 data_time: 0.074557 memory: 12861 loss_kpt: 0.000798 acc_pose: 0.705053 loss: 0.000798 2022/10/13 12:47:39 - mmengine - INFO - Epoch(train) [73][150/293] lr: 5.000000e-04 eta: 3:49:52 time: 0.397353 data_time: 0.099821 memory: 12861 loss_kpt: 0.000822 acc_pose: 0.824039 loss: 0.000822 2022/10/13 12:47:58 - mmengine - INFO - Epoch(train) [73][200/293] lr: 5.000000e-04 eta: 3:49:39 time: 0.386221 data_time: 0.086968 memory: 12861 loss_kpt: 0.000814 acc_pose: 0.762758 loss: 0.000814 2022/10/13 12:48:18 - mmengine - INFO - Epoch(train) [73][250/293] lr: 5.000000e-04 eta: 3:49:27 time: 0.393469 data_time: 0.085640 memory: 12861 loss_kpt: 0.000808 acc_pose: 0.744702 loss: 0.000808 2022/10/13 12:48:34 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:48:54 - mmengine - INFO - Epoch(train) [74][50/293] lr: 5.000000e-04 eta: 3:48:33 time: 0.397694 data_time: 0.080698 memory: 12861 loss_kpt: 0.000809 acc_pose: 0.775406 loss: 0.000809 2022/10/13 12:49:13 - mmengine - INFO - Epoch(train) [74][100/293] lr: 5.000000e-04 eta: 3:48:19 time: 0.375981 data_time: 0.085909 memory: 12861 loss_kpt: 0.000813 acc_pose: 0.807245 loss: 0.000813 2022/10/13 12:49:32 - mmengine - INFO - Epoch(train) [74][150/293] lr: 5.000000e-04 eta: 3:48:06 time: 0.385626 data_time: 0.077245 memory: 12861 loss_kpt: 0.000798 acc_pose: 0.762792 loss: 0.000798 2022/10/13 12:49:52 - mmengine - INFO - Epoch(train) [74][200/293] lr: 5.000000e-04 eta: 3:47:54 time: 0.397165 data_time: 0.094177 memory: 12861 loss_kpt: 0.000820 acc_pose: 0.762484 loss: 0.000820 2022/10/13 12:50:11 - mmengine - INFO - Epoch(train) [74][250/293] lr: 5.000000e-04 eta: 3:47:40 time: 0.377758 data_time: 0.076092 memory: 12861 loss_kpt: 0.000820 acc_pose: 0.747916 loss: 0.000820 2022/10/13 12:50:27 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:50:47 - mmengine - INFO - Epoch(train) [75][50/293] lr: 5.000000e-04 eta: 3:46:48 time: 0.414059 data_time: 0.098695 memory: 12861 loss_kpt: 0.000814 acc_pose: 0.752201 loss: 0.000814 2022/10/13 12:51:07 - mmengine - INFO - Epoch(train) [75][100/293] lr: 5.000000e-04 eta: 3:46:34 time: 0.383625 data_time: 0.076415 memory: 12861 loss_kpt: 0.000806 acc_pose: 0.670977 loss: 0.000806 2022/10/13 12:51:26 - mmengine - INFO - Epoch(train) [75][150/293] lr: 5.000000e-04 eta: 3:46:21 time: 0.387669 data_time: 0.077058 memory: 12861 loss_kpt: 0.000813 acc_pose: 0.809757 loss: 0.000813 2022/10/13 12:51:45 - mmengine - INFO - Epoch(train) [75][200/293] lr: 5.000000e-04 eta: 3:46:08 time: 0.387447 data_time: 0.074816 memory: 12861 loss_kpt: 0.000819 acc_pose: 0.761981 loss: 0.000819 2022/10/13 12:52:05 - mmengine - INFO - Epoch(train) [75][250/293] lr: 5.000000e-04 eta: 3:45:55 time: 0.385982 data_time: 0.093563 memory: 12861 loss_kpt: 0.000807 acc_pose: 0.744207 loss: 0.000807 2022/10/13 12:52:21 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:52:31 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:52:41 - mmengine - INFO - Epoch(train) [76][50/293] lr: 5.000000e-04 eta: 3:45:02 time: 0.400836 data_time: 0.102222 memory: 12861 loss_kpt: 0.000806 acc_pose: 0.698603 loss: 0.000806 2022/10/13 12:53:00 - mmengine - INFO - Epoch(train) [76][100/293] lr: 5.000000e-04 eta: 3:44:49 time: 0.384141 data_time: 0.075250 memory: 12861 loss_kpt: 0.000808 acc_pose: 0.755021 loss: 0.000808 2022/10/13 12:53:19 - mmengine - INFO - Epoch(train) [76][150/293] lr: 5.000000e-04 eta: 3:44:36 time: 0.387226 data_time: 0.079353 memory: 12861 loss_kpt: 0.000803 acc_pose: 0.726917 loss: 0.000803 2022/10/13 12:53:39 - mmengine - INFO - Epoch(train) [76][200/293] lr: 5.000000e-04 eta: 3:44:24 time: 0.395523 data_time: 0.088673 memory: 12861 loss_kpt: 0.000806 acc_pose: 0.770594 loss: 0.000806 2022/10/13 12:53:58 - mmengine - INFO - Epoch(train) [76][250/293] lr: 5.000000e-04 eta: 3:44:09 time: 0.372998 data_time: 0.071831 memory: 12861 loss_kpt: 0.000821 acc_pose: 0.730013 loss: 0.000821 2022/10/13 12:54:14 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:54:34 - mmengine - INFO - Epoch(train) [77][50/293] lr: 5.000000e-04 eta: 3:43:18 time: 0.412676 data_time: 0.102890 memory: 12861 loss_kpt: 0.000792 acc_pose: 0.691591 loss: 0.000792 2022/10/13 12:54:54 - mmengine - INFO - Epoch(train) [77][100/293] lr: 5.000000e-04 eta: 3:43:05 time: 0.388035 data_time: 0.100799 memory: 12861 loss_kpt: 0.000807 acc_pose: 0.719208 loss: 0.000807 2022/10/13 12:55:13 - mmengine - INFO - Epoch(train) [77][150/293] lr: 5.000000e-04 eta: 3:42:51 time: 0.383040 data_time: 0.082143 memory: 12861 loss_kpt: 0.000827 acc_pose: 0.716344 loss: 0.000827 2022/10/13 12:55:32 - mmengine - INFO - Epoch(train) [77][200/293] lr: 5.000000e-04 eta: 3:42:37 time: 0.378912 data_time: 0.072149 memory: 12861 loss_kpt: 0.000809 acc_pose: 0.789068 loss: 0.000809 2022/10/13 12:55:51 - mmengine - INFO - Epoch(train) [77][250/293] lr: 5.000000e-04 eta: 3:42:24 time: 0.383881 data_time: 0.085443 memory: 12861 loss_kpt: 0.000813 acc_pose: 0.749149 loss: 0.000813 2022/10/13 12:56:08 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:56:28 - mmengine - INFO - Epoch(train) [78][50/293] lr: 5.000000e-04 eta: 3:41:32 time: 0.403832 data_time: 0.094949 memory: 12861 loss_kpt: 0.000809 acc_pose: 0.773141 loss: 0.000809 2022/10/13 12:56:47 - mmengine - INFO - Epoch(train) [78][100/293] lr: 5.000000e-04 eta: 3:41:19 time: 0.388564 data_time: 0.086939 memory: 12861 loss_kpt: 0.000807 acc_pose: 0.754833 loss: 0.000807 2022/10/13 12:57:07 - mmengine - INFO - Epoch(train) [78][150/293] lr: 5.000000e-04 eta: 3:41:06 time: 0.387950 data_time: 0.090033 memory: 12861 loss_kpt: 0.000807 acc_pose: 0.704421 loss: 0.000807 2022/10/13 12:57:27 - mmengine - INFO - Epoch(train) [78][200/293] lr: 5.000000e-04 eta: 3:40:55 time: 0.412212 data_time: 0.088476 memory: 12861 loss_kpt: 0.000792 acc_pose: 0.705814 loss: 0.000792 2022/10/13 12:57:47 - mmengine - INFO - Epoch(train) [78][250/293] lr: 5.000000e-04 eta: 3:40:41 time: 0.384971 data_time: 0.079415 memory: 12861 loss_kpt: 0.000815 acc_pose: 0.754485 loss: 0.000815 2022/10/13 12:58:03 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:58:24 - mmengine - INFO - Epoch(train) [79][50/293] lr: 5.000000e-04 eta: 3:39:51 time: 0.414455 data_time: 0.101288 memory: 12861 loss_kpt: 0.000823 acc_pose: 0.772464 loss: 0.000823 2022/10/13 12:58:43 - mmengine - INFO - Epoch(train) [79][100/293] lr: 5.000000e-04 eta: 3:39:37 time: 0.378574 data_time: 0.084523 memory: 12861 loss_kpt: 0.000800 acc_pose: 0.782210 loss: 0.000800 2022/10/13 12:59:01 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 12:59:02 - mmengine - INFO - Epoch(train) [79][150/293] lr: 5.000000e-04 eta: 3:39:24 time: 0.390129 data_time: 0.086169 memory: 12861 loss_kpt: 0.000801 acc_pose: 0.751435 loss: 0.000801 2022/10/13 12:59:22 - mmengine - INFO - Epoch(train) [79][200/293] lr: 5.000000e-04 eta: 3:39:11 time: 0.392175 data_time: 0.086746 memory: 12861 loss_kpt: 0.000815 acc_pose: 0.743950 loss: 0.000815 2022/10/13 12:59:41 - mmengine - INFO - Epoch(train) [79][250/293] lr: 5.000000e-04 eta: 3:38:58 time: 0.390865 data_time: 0.079086 memory: 12861 loss_kpt: 0.000819 acc_pose: 0.743853 loss: 0.000819 2022/10/13 12:59:58 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:00:19 - mmengine - INFO - Epoch(train) [80][50/293] lr: 5.000000e-04 eta: 3:38:07 time: 0.409801 data_time: 0.108839 memory: 12861 loss_kpt: 0.000788 acc_pose: 0.782317 loss: 0.000788 2022/10/13 13:00:38 - mmengine - INFO - Epoch(train) [80][100/293] lr: 5.000000e-04 eta: 3:37:54 time: 0.386797 data_time: 0.078813 memory: 12861 loss_kpt: 0.000803 acc_pose: 0.802632 loss: 0.000803 2022/10/13 13:00:57 - mmengine - INFO - Epoch(train) [80][150/293] lr: 5.000000e-04 eta: 3:37:41 time: 0.383932 data_time: 0.073285 memory: 12861 loss_kpt: 0.000815 acc_pose: 0.769350 loss: 0.000815 2022/10/13 13:01:17 - mmengine - INFO - Epoch(train) [80][200/293] lr: 5.000000e-04 eta: 3:37:27 time: 0.390422 data_time: 0.079142 memory: 12861 loss_kpt: 0.000809 acc_pose: 0.741550 loss: 0.000809 2022/10/13 13:01:36 - mmengine - INFO - Epoch(train) [80][250/293] lr: 5.000000e-04 eta: 3:37:14 time: 0.392453 data_time: 0.087323 memory: 12861 loss_kpt: 0.000803 acc_pose: 0.742730 loss: 0.000803 2022/10/13 13:01:53 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:01:53 - mmengine - INFO - Saving checkpoint at 80 epochs 2022/10/13 13:02:02 - mmengine - INFO - Epoch(val) [80][50/407] eta: 0:00:46 time: 0.131080 data_time: 0.067798 memory: 12861 2022/10/13 13:02:08 - mmengine - INFO - Epoch(val) [80][100/407] eta: 0:00:38 time: 0.124544 data_time: 0.062362 memory: 983 2022/10/13 13:02:14 - mmengine - INFO - Epoch(val) [80][150/407] eta: 0:00:33 time: 0.129713 data_time: 0.066786 memory: 983 2022/10/13 13:02:21 - mmengine - INFO - Epoch(val) [80][200/407] eta: 0:00:28 time: 0.136337 data_time: 0.074308 memory: 983 2022/10/13 13:02:28 - mmengine - INFO - Epoch(val) [80][250/407] eta: 0:00:20 time: 0.127518 data_time: 0.065935 memory: 983 2022/10/13 13:02:34 - mmengine - INFO - Epoch(val) [80][300/407] eta: 0:00:14 time: 0.131073 data_time: 0.068866 memory: 983 2022/10/13 13:02:40 - mmengine - INFO - Epoch(val) [80][350/407] eta: 0:00:06 time: 0.120624 data_time: 0.058714 memory: 983 2022/10/13 13:02:46 - mmengine - INFO - Epoch(val) [80][400/407] eta: 0:00:00 time: 0.119403 data_time: 0.060065 memory: 983 2022/10/13 13:03:23 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 13:03:37 - mmengine - INFO - Epoch(val) [80][407/407] coco/AP: 0.639548 coco/AP .5: 0.865773 coco/AP .75: 0.707772 coco/AP (M): 0.596324 coco/AP (L): 0.713779 coco/AR: 0.698300 coco/AR .5: 0.907116 coco/AR .75: 0.761650 coco/AR (M): 0.646490 coco/AR (L): 0.771200 2022/10/13 13:03:37 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384/best_coco/AP_epoch_70.pth is removed 2022/10/13 13:03:39 - mmengine - INFO - The best checkpoint with 0.6395 coco/AP at 80 epoch is saved to best_coco/AP_epoch_80.pth. 2022/10/13 13:03:59 - mmengine - INFO - Epoch(train) [81][50/293] lr: 5.000000e-04 eta: 3:36:23 time: 0.393384 data_time: 0.087638 memory: 12861 loss_kpt: 0.000793 acc_pose: 0.720876 loss: 0.000793 2022/10/13 13:04:18 - mmengine - INFO - Epoch(train) [81][100/293] lr: 5.000000e-04 eta: 3:36:10 time: 0.389837 data_time: 0.087116 memory: 12861 loss_kpt: 0.000789 acc_pose: 0.706723 loss: 0.000789 2022/10/13 13:04:38 - mmengine - INFO - Epoch(train) [81][150/293] lr: 5.000000e-04 eta: 3:35:56 time: 0.384843 data_time: 0.078963 memory: 12861 loss_kpt: 0.000804 acc_pose: 0.765868 loss: 0.000804 2022/10/13 13:04:57 - mmengine - INFO - Epoch(train) [81][200/293] lr: 5.000000e-04 eta: 3:35:42 time: 0.380337 data_time: 0.077664 memory: 12861 loss_kpt: 0.000807 acc_pose: 0.757671 loss: 0.000807 2022/10/13 13:05:16 - mmengine - INFO - Epoch(train) [81][250/293] lr: 5.000000e-04 eta: 3:35:29 time: 0.388800 data_time: 0.074927 memory: 12861 loss_kpt: 0.000820 acc_pose: 0.785494 loss: 0.000820 2022/10/13 13:05:32 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:05:53 - mmengine - INFO - Epoch(train) [82][50/293] lr: 5.000000e-04 eta: 3:34:39 time: 0.411362 data_time: 0.099490 memory: 12861 loss_kpt: 0.000811 acc_pose: 0.779727 loss: 0.000811 2022/10/13 13:06:13 - mmengine - INFO - Epoch(train) [82][100/293] lr: 5.000000e-04 eta: 3:34:26 time: 0.390247 data_time: 0.092741 memory: 12861 loss_kpt: 0.000779 acc_pose: 0.750836 loss: 0.000779 2022/10/13 13:06:32 - mmengine - INFO - Epoch(train) [82][150/293] lr: 5.000000e-04 eta: 3:34:14 time: 0.395386 data_time: 0.088087 memory: 12861 loss_kpt: 0.000814 acc_pose: 0.691197 loss: 0.000814 2022/10/13 13:06:52 - mmengine - INFO - Epoch(train) [82][200/293] lr: 5.000000e-04 eta: 3:34:00 time: 0.386821 data_time: 0.087348 memory: 12861 loss_kpt: 0.000803 acc_pose: 0.737732 loss: 0.000803 2022/10/13 13:07:11 - mmengine - INFO - Epoch(train) [82][250/293] lr: 5.000000e-04 eta: 3:33:46 time: 0.383591 data_time: 0.083110 memory: 12861 loss_kpt: 0.000796 acc_pose: 0.766315 loss: 0.000796 2022/10/13 13:07:17 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:07:27 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:07:48 - mmengine - INFO - Epoch(train) [83][50/293] lr: 5.000000e-04 eta: 3:32:57 time: 0.417249 data_time: 0.106894 memory: 12861 loss_kpt: 0.000802 acc_pose: 0.780401 loss: 0.000802 2022/10/13 13:08:07 - mmengine - INFO - Epoch(train) [83][100/293] lr: 5.000000e-04 eta: 3:32:44 time: 0.390252 data_time: 0.091690 memory: 12861 loss_kpt: 0.000795 acc_pose: 0.736773 loss: 0.000795 2022/10/13 13:08:27 - mmengine - INFO - Epoch(train) [83][150/293] lr: 5.000000e-04 eta: 3:32:31 time: 0.393980 data_time: 0.081106 memory: 12861 loss_kpt: 0.000812 acc_pose: 0.696410 loss: 0.000812 2022/10/13 13:08:47 - mmengine - INFO - Epoch(train) [83][200/293] lr: 5.000000e-04 eta: 3:32:18 time: 0.392643 data_time: 0.093285 memory: 12861 loss_kpt: 0.000818 acc_pose: 0.804692 loss: 0.000818 2022/10/13 13:09:07 - mmengine - INFO - Epoch(train) [83][250/293] lr: 5.000000e-04 eta: 3:32:05 time: 0.395380 data_time: 0.088168 memory: 12861 loss_kpt: 0.000814 acc_pose: 0.777381 loss: 0.000814 2022/10/13 13:09:23 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:09:43 - mmengine - INFO - Epoch(train) [84][50/293] lr: 5.000000e-04 eta: 3:31:16 time: 0.407598 data_time: 0.091410 memory: 12861 loss_kpt: 0.000797 acc_pose: 0.772176 loss: 0.000797 2022/10/13 13:10:02 - mmengine - INFO - Epoch(train) [84][100/293] lr: 5.000000e-04 eta: 3:31:02 time: 0.382865 data_time: 0.086448 memory: 12861 loss_kpt: 0.000801 acc_pose: 0.761428 loss: 0.000801 2022/10/13 13:10:21 - mmengine - INFO - Epoch(train) [84][150/293] lr: 5.000000e-04 eta: 3:30:48 time: 0.382003 data_time: 0.080922 memory: 12861 loss_kpt: 0.000801 acc_pose: 0.789273 loss: 0.000801 2022/10/13 13:10:41 - mmengine - INFO - Epoch(train) [84][200/293] lr: 5.000000e-04 eta: 3:30:35 time: 0.388711 data_time: 0.099394 memory: 12861 loss_kpt: 0.000800 acc_pose: 0.737887 loss: 0.000800 2022/10/13 13:11:00 - mmengine - INFO - Epoch(train) [84][250/293] lr: 5.000000e-04 eta: 3:30:21 time: 0.383642 data_time: 0.075419 memory: 12861 loss_kpt: 0.000816 acc_pose: 0.763063 loss: 0.000816 2022/10/13 13:11:16 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:11:37 - mmengine - INFO - Epoch(train) [85][50/293] lr: 5.000000e-04 eta: 3:29:32 time: 0.409230 data_time: 0.092940 memory: 12861 loss_kpt: 0.000795 acc_pose: 0.725978 loss: 0.000795 2022/10/13 13:11:56 - mmengine - INFO - Epoch(train) [85][100/293] lr: 5.000000e-04 eta: 3:29:18 time: 0.385850 data_time: 0.085497 memory: 12861 loss_kpt: 0.000809 acc_pose: 0.670636 loss: 0.000809 2022/10/13 13:12:16 - mmengine - INFO - Epoch(train) [85][150/293] lr: 5.000000e-04 eta: 3:29:05 time: 0.386241 data_time: 0.083556 memory: 12861 loss_kpt: 0.000810 acc_pose: 0.786946 loss: 0.000810 2022/10/13 13:12:35 - mmengine - INFO - Epoch(train) [85][200/293] lr: 5.000000e-04 eta: 3:28:51 time: 0.391246 data_time: 0.067739 memory: 12861 loss_kpt: 0.000807 acc_pose: 0.734967 loss: 0.000807 2022/10/13 13:12:54 - mmengine - INFO - Epoch(train) [85][250/293] lr: 5.000000e-04 eta: 3:28:37 time: 0.381793 data_time: 0.070030 memory: 12861 loss_kpt: 0.000806 acc_pose: 0.772973 loss: 0.000806 2022/10/13 13:13:10 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:13:30 - mmengine - INFO - Epoch(train) [86][50/293] lr: 5.000000e-04 eta: 3:27:48 time: 0.397585 data_time: 0.087576 memory: 12861 loss_kpt: 0.000820 acc_pose: 0.723259 loss: 0.000820 2022/10/13 13:13:48 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:13:50 - mmengine - INFO - Epoch(train) [86][100/293] lr: 5.000000e-04 eta: 3:27:35 time: 0.391880 data_time: 0.098499 memory: 12861 loss_kpt: 0.000786 acc_pose: 0.752906 loss: 0.000786 2022/10/13 13:14:09 - mmengine - INFO - Epoch(train) [86][150/293] lr: 5.000000e-04 eta: 3:27:21 time: 0.388591 data_time: 0.087008 memory: 12861 loss_kpt: 0.000791 acc_pose: 0.736029 loss: 0.000791 2022/10/13 13:14:29 - mmengine - INFO - Epoch(train) [86][200/293] lr: 5.000000e-04 eta: 3:27:08 time: 0.387204 data_time: 0.087865 memory: 12861 loss_kpt: 0.000801 acc_pose: 0.799580 loss: 0.000801 2022/10/13 13:14:48 - mmengine - INFO - Epoch(train) [86][250/293] lr: 5.000000e-04 eta: 3:26:54 time: 0.388354 data_time: 0.081865 memory: 12861 loss_kpt: 0.000791 acc_pose: 0.756944 loss: 0.000791 2022/10/13 13:15:04 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:15:24 - mmengine - INFO - Epoch(train) [87][50/293] lr: 5.000000e-04 eta: 3:26:06 time: 0.403200 data_time: 0.092383 memory: 12861 loss_kpt: 0.000807 acc_pose: 0.755625 loss: 0.000807 2022/10/13 13:15:43 - mmengine - INFO - Epoch(train) [87][100/293] lr: 5.000000e-04 eta: 3:25:52 time: 0.384212 data_time: 0.078660 memory: 12861 loss_kpt: 0.000786 acc_pose: 0.776911 loss: 0.000786 2022/10/13 13:16:03 - mmengine - INFO - Epoch(train) [87][150/293] lr: 5.000000e-04 eta: 3:25:38 time: 0.393550 data_time: 0.091524 memory: 12861 loss_kpt: 0.000792 acc_pose: 0.735485 loss: 0.000792 2022/10/13 13:16:22 - mmengine - INFO - Epoch(train) [87][200/293] lr: 5.000000e-04 eta: 3:25:25 time: 0.388045 data_time: 0.091485 memory: 12861 loss_kpt: 0.000800 acc_pose: 0.741850 loss: 0.000800 2022/10/13 13:16:42 - mmengine - INFO - Epoch(train) [87][250/293] lr: 5.000000e-04 eta: 3:25:11 time: 0.388607 data_time: 0.075328 memory: 12861 loss_kpt: 0.000815 acc_pose: 0.682817 loss: 0.000815 2022/10/13 13:16:58 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:17:19 - mmengine - INFO - Epoch(train) [88][50/293] lr: 5.000000e-04 eta: 3:24:24 time: 0.417132 data_time: 0.102760 memory: 12861 loss_kpt: 0.000799 acc_pose: 0.745282 loss: 0.000799 2022/10/13 13:17:39 - mmengine - INFO - Epoch(train) [88][100/293] lr: 5.000000e-04 eta: 3:24:11 time: 0.397129 data_time: 0.104807 memory: 12861 loss_kpt: 0.000811 acc_pose: 0.740598 loss: 0.000811 2022/10/13 13:17:58 - mmengine - INFO - Epoch(train) [88][150/293] lr: 5.000000e-04 eta: 3:23:57 time: 0.380760 data_time: 0.076859 memory: 12861 loss_kpt: 0.000791 acc_pose: 0.747604 loss: 0.000791 2022/10/13 13:18:17 - mmengine - INFO - Epoch(train) [88][200/293] lr: 5.000000e-04 eta: 3:23:43 time: 0.385320 data_time: 0.078973 memory: 12861 loss_kpt: 0.000799 acc_pose: 0.697727 loss: 0.000799 2022/10/13 13:18:36 - mmengine - INFO - Epoch(train) [88][250/293] lr: 5.000000e-04 eta: 3:23:29 time: 0.386690 data_time: 0.094049 memory: 12861 loss_kpt: 0.000824 acc_pose: 0.790319 loss: 0.000824 2022/10/13 13:18:53 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:19:14 - mmengine - INFO - Epoch(train) [89][50/293] lr: 5.000000e-04 eta: 3:22:42 time: 0.413586 data_time: 0.104096 memory: 12861 loss_kpt: 0.000798 acc_pose: 0.707283 loss: 0.000798 2022/10/13 13:19:34 - mmengine - INFO - Epoch(train) [89][100/293] lr: 5.000000e-04 eta: 3:22:29 time: 0.401021 data_time: 0.092805 memory: 12861 loss_kpt: 0.000816 acc_pose: 0.801068 loss: 0.000816 2022/10/13 13:19:53 - mmengine - INFO - Epoch(train) [89][150/293] lr: 5.000000e-04 eta: 3:22:14 time: 0.376104 data_time: 0.070137 memory: 12861 loss_kpt: 0.000809 acc_pose: 0.778715 loss: 0.000809 2022/10/13 13:20:13 - mmengine - INFO - Epoch(train) [89][200/293] lr: 5.000000e-04 eta: 3:22:01 time: 0.396960 data_time: 0.088482 memory: 12861 loss_kpt: 0.000789 acc_pose: 0.714292 loss: 0.000789 2022/10/13 13:20:19 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:20:32 - mmengine - INFO - Epoch(train) [89][250/293] lr: 5.000000e-04 eta: 3:21:48 time: 0.395195 data_time: 0.093727 memory: 12861 loss_kpt: 0.000798 acc_pose: 0.754011 loss: 0.000798 2022/10/13 13:20:49 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:21:10 - mmengine - INFO - Epoch(train) [90][50/293] lr: 5.000000e-04 eta: 3:21:01 time: 0.414796 data_time: 0.103309 memory: 12861 loss_kpt: 0.000792 acc_pose: 0.724036 loss: 0.000792 2022/10/13 13:21:30 - mmengine - INFO - Epoch(train) [90][100/293] lr: 5.000000e-04 eta: 3:20:47 time: 0.391213 data_time: 0.074509 memory: 12861 loss_kpt: 0.000786 acc_pose: 0.800482 loss: 0.000786 2022/10/13 13:21:49 - mmengine - INFO - Epoch(train) [90][150/293] lr: 5.000000e-04 eta: 3:20:33 time: 0.385746 data_time: 0.084594 memory: 12861 loss_kpt: 0.000800 acc_pose: 0.748727 loss: 0.000800 2022/10/13 13:22:08 - mmengine - INFO - Epoch(train) [90][200/293] lr: 5.000000e-04 eta: 3:20:20 time: 0.390426 data_time: 0.084398 memory: 12861 loss_kpt: 0.000805 acc_pose: 0.786619 loss: 0.000805 2022/10/13 13:22:28 - mmengine - INFO - Epoch(train) [90][250/293] lr: 5.000000e-04 eta: 3:20:06 time: 0.397249 data_time: 0.103420 memory: 12861 loss_kpt: 0.000792 acc_pose: 0.782254 loss: 0.000792 2022/10/13 13:22:44 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:22:44 - mmengine - INFO - Saving checkpoint at 90 epochs 2022/10/13 13:22:53 - mmengine - INFO - Epoch(val) [90][50/407] eta: 0:00:47 time: 0.132565 data_time: 0.071833 memory: 12861 2022/10/13 13:22:59 - mmengine - INFO - Epoch(val) [90][100/407] eta: 0:00:37 time: 0.123607 data_time: 0.063726 memory: 983 2022/10/13 13:23:06 - mmengine - INFO - Epoch(val) [90][150/407] eta: 0:00:31 time: 0.124453 data_time: 0.063279 memory: 983 2022/10/13 13:23:12 - mmengine - INFO - Epoch(val) [90][200/407] eta: 0:00:25 time: 0.123907 data_time: 0.056423 memory: 983 2022/10/13 13:23:18 - mmengine - INFO - Epoch(val) [90][250/407] eta: 0:00:20 time: 0.129197 data_time: 0.067892 memory: 983 2022/10/13 13:23:24 - mmengine - INFO - Epoch(val) [90][300/407] eta: 0:00:13 time: 0.124812 data_time: 0.063210 memory: 983 2022/10/13 13:23:31 - mmengine - INFO - Epoch(val) [90][350/407] eta: 0:00:07 time: 0.125538 data_time: 0.064462 memory: 983 2022/10/13 13:23:37 - mmengine - INFO - Epoch(val) [90][400/407] eta: 0:00:00 time: 0.123604 data_time: 0.062924 memory: 983 2022/10/13 13:24:15 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 13:24:29 - mmengine - INFO - Epoch(val) [90][407/407] coco/AP: 0.642314 coco/AP .5: 0.866394 coco/AP .75: 0.710946 coco/AP (M): 0.597679 coco/AP (L): 0.715659 coco/AR: 0.701338 coco/AR .5: 0.907588 coco/AR .75: 0.765271 coco/AR (M): 0.649631 coco/AR (L): 0.774062 2022/10/13 13:24:29 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384/best_coco/AP_epoch_80.pth is removed 2022/10/13 13:24:30 - mmengine - INFO - The best checkpoint with 0.6423 coco/AP at 90 epoch is saved to best_coco/AP_epoch_90.pth. 2022/10/13 13:24:50 - mmengine - INFO - Epoch(train) [91][50/293] lr: 5.000000e-04 eta: 3:19:19 time: 0.399283 data_time: 0.098965 memory: 12861 loss_kpt: 0.000800 acc_pose: 0.787225 loss: 0.000800 2022/10/13 13:25:10 - mmengine - INFO - Epoch(train) [91][100/293] lr: 5.000000e-04 eta: 3:19:05 time: 0.388285 data_time: 0.084841 memory: 12861 loss_kpt: 0.000796 acc_pose: 0.788111 loss: 0.000796 2022/10/13 13:25:29 - mmengine - INFO - Epoch(train) [91][150/293] lr: 5.000000e-04 eta: 3:18:51 time: 0.381790 data_time: 0.085749 memory: 12861 loss_kpt: 0.000798 acc_pose: 0.766754 loss: 0.000798 2022/10/13 13:25:48 - mmengine - INFO - Epoch(train) [91][200/293] lr: 5.000000e-04 eta: 3:18:37 time: 0.383782 data_time: 0.083726 memory: 12861 loss_kpt: 0.000829 acc_pose: 0.780111 loss: 0.000829 2022/10/13 13:26:08 - mmengine - INFO - Epoch(train) [91][250/293] lr: 5.000000e-04 eta: 3:18:24 time: 0.408304 data_time: 0.088217 memory: 12861 loss_kpt: 0.000798 acc_pose: 0.781029 loss: 0.000798 2022/10/13 13:26:25 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:26:45 - mmengine - INFO - Epoch(train) [92][50/293] lr: 5.000000e-04 eta: 3:17:38 time: 0.411132 data_time: 0.101484 memory: 12861 loss_kpt: 0.000803 acc_pose: 0.729833 loss: 0.000803 2022/10/13 13:27:05 - mmengine - INFO - Epoch(train) [92][100/293] lr: 5.000000e-04 eta: 3:17:24 time: 0.392103 data_time: 0.082593 memory: 12861 loss_kpt: 0.000793 acc_pose: 0.749952 loss: 0.000793 2022/10/13 13:27:24 - mmengine - INFO - Epoch(train) [92][150/293] lr: 5.000000e-04 eta: 3:17:10 time: 0.390748 data_time: 0.091405 memory: 12861 loss_kpt: 0.000805 acc_pose: 0.793714 loss: 0.000805 2022/10/13 13:27:44 - mmengine - INFO - Epoch(train) [92][200/293] lr: 5.000000e-04 eta: 3:16:56 time: 0.383921 data_time: 0.084960 memory: 12861 loss_kpt: 0.000787 acc_pose: 0.715728 loss: 0.000787 2022/10/13 13:28:03 - mmengine - INFO - Epoch(train) [92][250/293] lr: 5.000000e-04 eta: 3:16:42 time: 0.394752 data_time: 0.088193 memory: 12861 loss_kpt: 0.000804 acc_pose: 0.791841 loss: 0.000804 2022/10/13 13:28:19 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:28:37 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:28:39 - mmengine - INFO - Epoch(train) [93][50/293] lr: 5.000000e-04 eta: 3:15:55 time: 0.393758 data_time: 0.084776 memory: 12861 loss_kpt: 0.000792 acc_pose: 0.759545 loss: 0.000792 2022/10/13 13:28:58 - mmengine - INFO - Epoch(train) [93][100/293] lr: 5.000000e-04 eta: 3:15:41 time: 0.378982 data_time: 0.076319 memory: 12861 loss_kpt: 0.000787 acc_pose: 0.699781 loss: 0.000787 2022/10/13 13:29:18 - mmengine - INFO - Epoch(train) [93][150/293] lr: 5.000000e-04 eta: 3:15:27 time: 0.396874 data_time: 0.084427 memory: 12861 loss_kpt: 0.000800 acc_pose: 0.754469 loss: 0.000800 2022/10/13 13:29:37 - mmengine - INFO - Epoch(train) [93][200/293] lr: 5.000000e-04 eta: 3:15:13 time: 0.379604 data_time: 0.078430 memory: 12861 loss_kpt: 0.000805 acc_pose: 0.731002 loss: 0.000805 2022/10/13 13:29:56 - mmengine - INFO - Epoch(train) [93][250/293] lr: 5.000000e-04 eta: 3:14:58 time: 0.382219 data_time: 0.071315 memory: 12861 loss_kpt: 0.000809 acc_pose: 0.755010 loss: 0.000809 2022/10/13 13:30:13 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:30:33 - mmengine - INFO - Epoch(train) [94][50/293] lr: 5.000000e-04 eta: 3:14:13 time: 0.411512 data_time: 0.102587 memory: 12861 loss_kpt: 0.000801 acc_pose: 0.748227 loss: 0.000801 2022/10/13 13:30:53 - mmengine - INFO - Epoch(train) [94][100/293] lr: 5.000000e-04 eta: 3:13:59 time: 0.393434 data_time: 0.091495 memory: 12861 loss_kpt: 0.000786 acc_pose: 0.804623 loss: 0.000786 2022/10/13 13:31:12 - mmengine - INFO - Epoch(train) [94][150/293] lr: 5.000000e-04 eta: 3:13:45 time: 0.391046 data_time: 0.084782 memory: 12861 loss_kpt: 0.000788 acc_pose: 0.804793 loss: 0.000788 2022/10/13 13:31:31 - mmengine - INFO - Epoch(train) [94][200/293] lr: 5.000000e-04 eta: 3:13:30 time: 0.374243 data_time: 0.066476 memory: 12861 loss_kpt: 0.000799 acc_pose: 0.717983 loss: 0.000799 2022/10/13 13:31:50 - mmengine - INFO - Epoch(train) [94][250/293] lr: 5.000000e-04 eta: 3:13:16 time: 0.380983 data_time: 0.082721 memory: 12861 loss_kpt: 0.000786 acc_pose: 0.780831 loss: 0.000786 2022/10/13 13:32:07 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:32:27 - mmengine - INFO - Epoch(train) [95][50/293] lr: 5.000000e-04 eta: 3:12:29 time: 0.395015 data_time: 0.094959 memory: 12861 loss_kpt: 0.000795 acc_pose: 0.748070 loss: 0.000795 2022/10/13 13:32:47 - mmengine - INFO - Epoch(train) [95][100/293] lr: 5.000000e-04 eta: 3:12:16 time: 0.396188 data_time: 0.093383 memory: 12861 loss_kpt: 0.000797 acc_pose: 0.788055 loss: 0.000797 2022/10/13 13:33:06 - mmengine - INFO - Epoch(train) [95][150/293] lr: 5.000000e-04 eta: 3:12:02 time: 0.390309 data_time: 0.103704 memory: 12861 loss_kpt: 0.000784 acc_pose: 0.780341 loss: 0.000784 2022/10/13 13:33:26 - mmengine - INFO - Epoch(train) [95][200/293] lr: 5.000000e-04 eta: 3:11:48 time: 0.398907 data_time: 0.093894 memory: 12861 loss_kpt: 0.000803 acc_pose: 0.744406 loss: 0.000803 2022/10/13 13:33:45 - mmengine - INFO - Epoch(train) [95][250/293] lr: 5.000000e-04 eta: 3:11:34 time: 0.386316 data_time: 0.079691 memory: 12861 loss_kpt: 0.000790 acc_pose: 0.742062 loss: 0.000790 2022/10/13 13:34:01 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:34:21 - mmengine - INFO - Epoch(train) [96][50/293] lr: 5.000000e-04 eta: 3:10:48 time: 0.394055 data_time: 0.091342 memory: 12861 loss_kpt: 0.000787 acc_pose: 0.759989 loss: 0.000787 2022/10/13 13:34:40 - mmengine - INFO - Epoch(train) [96][100/293] lr: 5.000000e-04 eta: 3:10:33 time: 0.382781 data_time: 0.066635 memory: 12861 loss_kpt: 0.000796 acc_pose: 0.784108 loss: 0.000796 2022/10/13 13:34:59 - mmengine - INFO - Epoch(train) [96][150/293] lr: 5.000000e-04 eta: 3:10:19 time: 0.385705 data_time: 0.082194 memory: 12861 loss_kpt: 0.000799 acc_pose: 0.749950 loss: 0.000799 2022/10/13 13:35:06 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:35:19 - mmengine - INFO - Epoch(train) [96][200/293] lr: 5.000000e-04 eta: 3:10:05 time: 0.390014 data_time: 0.089538 memory: 12861 loss_kpt: 0.000787 acc_pose: 0.780371 loss: 0.000787 2022/10/13 13:35:38 - mmengine - INFO - Epoch(train) [96][250/293] lr: 5.000000e-04 eta: 3:09:51 time: 0.386291 data_time: 0.086476 memory: 12861 loss_kpt: 0.000798 acc_pose: 0.723478 loss: 0.000798 2022/10/13 13:35:55 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:36:15 - mmengine - INFO - Epoch(train) [97][50/293] lr: 5.000000e-04 eta: 3:09:06 time: 0.410249 data_time: 0.096069 memory: 12861 loss_kpt: 0.000812 acc_pose: 0.770570 loss: 0.000812 2022/10/13 13:36:35 - mmengine - INFO - Epoch(train) [97][100/293] lr: 5.000000e-04 eta: 3:08:52 time: 0.395768 data_time: 0.080030 memory: 12861 loss_kpt: 0.000796 acc_pose: 0.787736 loss: 0.000796 2022/10/13 13:36:55 - mmengine - INFO - Epoch(train) [97][150/293] lr: 5.000000e-04 eta: 3:08:38 time: 0.388976 data_time: 0.085437 memory: 12861 loss_kpt: 0.000793 acc_pose: 0.772527 loss: 0.000793 2022/10/13 13:37:14 - mmengine - INFO - Epoch(train) [97][200/293] lr: 5.000000e-04 eta: 3:08:23 time: 0.382194 data_time: 0.069878 memory: 12861 loss_kpt: 0.000791 acc_pose: 0.697115 loss: 0.000791 2022/10/13 13:37:33 - mmengine - INFO - Epoch(train) [97][250/293] lr: 5.000000e-04 eta: 3:08:09 time: 0.382276 data_time: 0.085986 memory: 12861 loss_kpt: 0.000790 acc_pose: 0.736297 loss: 0.000790 2022/10/13 13:37:50 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:38:10 - mmengine - INFO - Epoch(train) [98][50/293] lr: 5.000000e-04 eta: 3:07:24 time: 0.402238 data_time: 0.090446 memory: 12861 loss_kpt: 0.000787 acc_pose: 0.742409 loss: 0.000787 2022/10/13 13:38:29 - mmengine - INFO - Epoch(train) [98][100/293] lr: 5.000000e-04 eta: 3:07:09 time: 0.380501 data_time: 0.078182 memory: 12861 loss_kpt: 0.000782 acc_pose: 0.750257 loss: 0.000782 2022/10/13 13:38:48 - mmengine - INFO - Epoch(train) [98][150/293] lr: 5.000000e-04 eta: 3:06:55 time: 0.385771 data_time: 0.080521 memory: 12861 loss_kpt: 0.000792 acc_pose: 0.743276 loss: 0.000792 2022/10/13 13:39:08 - mmengine - INFO - Epoch(train) [98][200/293] lr: 5.000000e-04 eta: 3:06:41 time: 0.395098 data_time: 0.091627 memory: 12861 loss_kpt: 0.000787 acc_pose: 0.731096 loss: 0.000787 2022/10/13 13:39:28 - mmengine - INFO - Epoch(train) [98][250/293] lr: 5.000000e-04 eta: 3:06:27 time: 0.397359 data_time: 0.086013 memory: 12861 loss_kpt: 0.000810 acc_pose: 0.798298 loss: 0.000810 2022/10/13 13:39:45 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:40:05 - mmengine - INFO - Epoch(train) [99][50/293] lr: 5.000000e-04 eta: 3:05:42 time: 0.401149 data_time: 0.098997 memory: 12861 loss_kpt: 0.000790 acc_pose: 0.704864 loss: 0.000790 2022/10/13 13:40:24 - mmengine - INFO - Epoch(train) [99][100/293] lr: 5.000000e-04 eta: 3:05:28 time: 0.395387 data_time: 0.089062 memory: 12861 loss_kpt: 0.000792 acc_pose: 0.752713 loss: 0.000792 2022/10/13 13:40:44 - mmengine - INFO - Epoch(train) [99][150/293] lr: 5.000000e-04 eta: 3:05:15 time: 0.400695 data_time: 0.094583 memory: 12861 loss_kpt: 0.000811 acc_pose: 0.783553 loss: 0.000811 2022/10/13 13:41:04 - mmengine - INFO - Epoch(train) [99][200/293] lr: 5.000000e-04 eta: 3:05:00 time: 0.384467 data_time: 0.085160 memory: 12861 loss_kpt: 0.000799 acc_pose: 0.749379 loss: 0.000799 2022/10/13 13:41:23 - mmengine - INFO - Epoch(train) [99][250/293] lr: 5.000000e-04 eta: 3:04:45 time: 0.379509 data_time: 0.079806 memory: 12861 loss_kpt: 0.000797 acc_pose: 0.770053 loss: 0.000797 2022/10/13 13:41:37 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:41:40 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:42:00 - mmengine - INFO - Epoch(train) [100][50/293] lr: 5.000000e-04 eta: 3:04:01 time: 0.413468 data_time: 0.111439 memory: 12861 loss_kpt: 0.000795 acc_pose: 0.780297 loss: 0.000795 2022/10/13 13:42:19 - mmengine - INFO - Epoch(train) [100][100/293] lr: 5.000000e-04 eta: 3:03:47 time: 0.381851 data_time: 0.099238 memory: 12861 loss_kpt: 0.000771 acc_pose: 0.800197 loss: 0.000771 2022/10/13 13:42:39 - mmengine - INFO - Epoch(train) [100][150/293] lr: 5.000000e-04 eta: 3:03:32 time: 0.389310 data_time: 0.090484 memory: 12861 loss_kpt: 0.000793 acc_pose: 0.793927 loss: 0.000793 2022/10/13 13:42:58 - mmengine - INFO - Epoch(train) [100][200/293] lr: 5.000000e-04 eta: 3:03:18 time: 0.379992 data_time: 0.068019 memory: 12861 loss_kpt: 0.000789 acc_pose: 0.773492 loss: 0.000789 2022/10/13 13:43:17 - mmengine - INFO - Epoch(train) [100][250/293] lr: 5.000000e-04 eta: 3:03:02 time: 0.373628 data_time: 0.076569 memory: 12861 loss_kpt: 0.000785 acc_pose: 0.760899 loss: 0.000785 2022/10/13 13:43:33 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:43:33 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/10/13 13:43:41 - mmengine - INFO - Epoch(val) [100][50/407] eta: 0:00:45 time: 0.128075 data_time: 0.066069 memory: 12861 2022/10/13 13:43:48 - mmengine - INFO - Epoch(val) [100][100/407] eta: 0:00:37 time: 0.123145 data_time: 0.062399 memory: 983 2022/10/13 13:43:54 - mmengine - INFO - Epoch(val) [100][150/407] eta: 0:00:33 time: 0.128786 data_time: 0.067592 memory: 983 2022/10/13 13:44:01 - mmengine - INFO - Epoch(val) [100][200/407] eta: 0:00:27 time: 0.131910 data_time: 0.069788 memory: 983 2022/10/13 13:44:07 - mmengine - INFO - Epoch(val) [100][250/407] eta: 0:00:20 time: 0.128541 data_time: 0.065682 memory: 983 2022/10/13 13:44:13 - mmengine - INFO - Epoch(val) [100][300/407] eta: 0:00:13 time: 0.128682 data_time: 0.068365 memory: 983 2022/10/13 13:44:20 - mmengine - INFO - Epoch(val) [100][350/407] eta: 0:00:07 time: 0.122844 data_time: 0.061993 memory: 983 2022/10/13 13:44:26 - mmengine - INFO - Epoch(val) [100][400/407] eta: 0:00:00 time: 0.121500 data_time: 0.061156 memory: 983 2022/10/13 13:45:03 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 13:45:17 - mmengine - INFO - Epoch(val) [100][407/407] coco/AP: 0.648949 coco/AP .5: 0.869297 coco/AP .75: 0.717602 coco/AP (M): 0.604554 coco/AP (L): 0.722086 coco/AR: 0.706628 coco/AR .5: 0.910264 coco/AR .75: 0.771253 coco/AR (M): 0.656050 coco/AR (L): 0.778224 2022/10/13 13:45:17 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384/best_coco/AP_epoch_90.pth is removed 2022/10/13 13:45:19 - mmengine - INFO - The best checkpoint with 0.6489 coco/AP at 100 epoch is saved to best_coco/AP_epoch_100.pth. 2022/10/13 13:45:39 - mmengine - INFO - Epoch(train) [101][50/293] lr: 5.000000e-04 eta: 3:02:18 time: 0.402959 data_time: 0.084472 memory: 12861 loss_kpt: 0.000779 acc_pose: 0.782585 loss: 0.000779 2022/10/13 13:45:58 - mmengine - INFO - Epoch(train) [101][100/293] lr: 5.000000e-04 eta: 3:02:04 time: 0.384800 data_time: 0.080033 memory: 12861 loss_kpt: 0.000795 acc_pose: 0.739183 loss: 0.000795 2022/10/13 13:46:17 - mmengine - INFO - Epoch(train) [101][150/293] lr: 5.000000e-04 eta: 3:01:49 time: 0.388379 data_time: 0.088003 memory: 12861 loss_kpt: 0.000787 acc_pose: 0.829105 loss: 0.000787 2022/10/13 13:46:37 - mmengine - INFO - Epoch(train) [101][200/293] lr: 5.000000e-04 eta: 3:01:35 time: 0.381461 data_time: 0.085826 memory: 12861 loss_kpt: 0.000779 acc_pose: 0.802274 loss: 0.000779 2022/10/13 13:46:56 - mmengine - INFO - Epoch(train) [101][250/293] lr: 5.000000e-04 eta: 3:01:20 time: 0.387885 data_time: 0.083209 memory: 12861 loss_kpt: 0.000783 acc_pose: 0.772606 loss: 0.000783 2022/10/13 13:47:12 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:47:32 - mmengine - INFO - Epoch(train) [102][50/293] lr: 5.000000e-04 eta: 3:00:36 time: 0.405412 data_time: 0.108940 memory: 12861 loss_kpt: 0.000778 acc_pose: 0.746326 loss: 0.000778 2022/10/13 13:47:51 - mmengine - INFO - Epoch(train) [102][100/293] lr: 5.000000e-04 eta: 3:00:21 time: 0.374077 data_time: 0.076833 memory: 12861 loss_kpt: 0.000791 acc_pose: 0.813826 loss: 0.000791 2022/10/13 13:48:10 - mmengine - INFO - Epoch(train) [102][150/293] lr: 5.000000e-04 eta: 3:00:07 time: 0.386064 data_time: 0.073171 memory: 12861 loss_kpt: 0.000791 acc_pose: 0.728385 loss: 0.000791 2022/10/13 13:48:30 - mmengine - INFO - Epoch(train) [102][200/293] lr: 5.000000e-04 eta: 2:59:52 time: 0.381860 data_time: 0.097782 memory: 12861 loss_kpt: 0.000797 acc_pose: 0.740290 loss: 0.000797 2022/10/13 13:48:49 - mmengine - INFO - Epoch(train) [102][250/293] lr: 5.000000e-04 eta: 2:59:37 time: 0.390957 data_time: 0.073889 memory: 12861 loss_kpt: 0.000802 acc_pose: 0.766046 loss: 0.000802 2022/10/13 13:49:06 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:49:25 - mmengine - INFO - Epoch(train) [103][50/293] lr: 5.000000e-04 eta: 2:58:53 time: 0.388683 data_time: 0.090048 memory: 12861 loss_kpt: 0.000772 acc_pose: 0.777860 loss: 0.000772 2022/10/13 13:49:45 - mmengine - INFO - Epoch(train) [103][100/293] lr: 5.000000e-04 eta: 2:58:39 time: 0.392933 data_time: 0.089631 memory: 12861 loss_kpt: 0.000785 acc_pose: 0.762240 loss: 0.000785 2022/10/13 13:49:50 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:50:04 - mmengine - INFO - Epoch(train) [103][150/293] lr: 5.000000e-04 eta: 2:58:24 time: 0.388469 data_time: 0.095826 memory: 12861 loss_kpt: 0.000801 acc_pose: 0.741292 loss: 0.000801 2022/10/13 13:50:23 - mmengine - INFO - Epoch(train) [103][200/293] lr: 5.000000e-04 eta: 2:58:10 time: 0.383570 data_time: 0.090168 memory: 12861 loss_kpt: 0.000789 acc_pose: 0.771023 loss: 0.000789 2022/10/13 13:50:43 - mmengine - INFO - Epoch(train) [103][250/293] lr: 5.000000e-04 eta: 2:57:56 time: 0.396062 data_time: 0.087056 memory: 12861 loss_kpt: 0.000797 acc_pose: 0.768050 loss: 0.000797 2022/10/13 13:51:00 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:51:21 - mmengine - INFO - Epoch(train) [104][50/293] lr: 5.000000e-04 eta: 2:57:13 time: 0.421808 data_time: 0.100708 memory: 12861 loss_kpt: 0.000766 acc_pose: 0.778797 loss: 0.000766 2022/10/13 13:51:40 - mmengine - INFO - Epoch(train) [104][100/293] lr: 5.000000e-04 eta: 2:56:59 time: 0.391732 data_time: 0.095032 memory: 12861 loss_kpt: 0.000779 acc_pose: 0.806289 loss: 0.000779 2022/10/13 13:51:59 - mmengine - INFO - Epoch(train) [104][150/293] lr: 5.000000e-04 eta: 2:56:44 time: 0.380905 data_time: 0.081852 memory: 12861 loss_kpt: 0.000785 acc_pose: 0.730432 loss: 0.000785 2022/10/13 13:52:19 - mmengine - INFO - Epoch(train) [104][200/293] lr: 5.000000e-04 eta: 2:56:30 time: 0.394260 data_time: 0.086245 memory: 12861 loss_kpt: 0.000781 acc_pose: 0.761283 loss: 0.000781 2022/10/13 13:52:39 - mmengine - INFO - Epoch(train) [104][250/293] lr: 5.000000e-04 eta: 2:56:15 time: 0.395016 data_time: 0.095953 memory: 12861 loss_kpt: 0.000802 acc_pose: 0.765340 loss: 0.000802 2022/10/13 13:52:55 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:53:16 - mmengine - INFO - Epoch(train) [105][50/293] lr: 5.000000e-04 eta: 2:55:32 time: 0.405247 data_time: 0.095392 memory: 12861 loss_kpt: 0.000788 acc_pose: 0.782560 loss: 0.000788 2022/10/13 13:53:35 - mmengine - INFO - Epoch(train) [105][100/293] lr: 5.000000e-04 eta: 2:55:18 time: 0.390336 data_time: 0.070177 memory: 12861 loss_kpt: 0.000783 acc_pose: 0.749792 loss: 0.000783 2022/10/13 13:53:54 - mmengine - INFO - Epoch(train) [105][150/293] lr: 5.000000e-04 eta: 2:55:03 time: 0.383853 data_time: 0.078403 memory: 12861 loss_kpt: 0.000783 acc_pose: 0.771405 loss: 0.000783 2022/10/13 13:54:14 - mmengine - INFO - Epoch(train) [105][200/293] lr: 5.000000e-04 eta: 2:54:49 time: 0.394863 data_time: 0.090653 memory: 12861 loss_kpt: 0.000780 acc_pose: 0.804652 loss: 0.000780 2022/10/13 13:54:33 - mmengine - INFO - Epoch(train) [105][250/293] lr: 5.000000e-04 eta: 2:54:34 time: 0.377065 data_time: 0.074223 memory: 12861 loss_kpt: 0.000790 acc_pose: 0.757977 loss: 0.000790 2022/10/13 13:54:49 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:55:10 - mmengine - INFO - Epoch(train) [106][50/293] lr: 5.000000e-04 eta: 2:53:51 time: 0.411474 data_time: 0.102150 memory: 12861 loss_kpt: 0.000761 acc_pose: 0.799862 loss: 0.000761 2022/10/13 13:55:29 - mmengine - INFO - Epoch(train) [106][100/293] lr: 5.000000e-04 eta: 2:53:36 time: 0.384861 data_time: 0.078845 memory: 12861 loss_kpt: 0.000782 acc_pose: 0.766286 loss: 0.000782 2022/10/13 13:55:49 - mmengine - INFO - Epoch(train) [106][150/293] lr: 5.000000e-04 eta: 2:53:22 time: 0.394694 data_time: 0.096720 memory: 12861 loss_kpt: 0.000784 acc_pose: 0.753073 loss: 0.000784 2022/10/13 13:56:08 - mmengine - INFO - Epoch(train) [106][200/293] lr: 5.000000e-04 eta: 2:53:07 time: 0.385793 data_time: 0.081974 memory: 12861 loss_kpt: 0.000786 acc_pose: 0.736475 loss: 0.000786 2022/10/13 13:56:21 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:56:27 - mmengine - INFO - Epoch(train) [106][250/293] lr: 5.000000e-04 eta: 2:52:53 time: 0.382814 data_time: 0.073567 memory: 12861 loss_kpt: 0.000786 acc_pose: 0.763125 loss: 0.000786 2022/10/13 13:56:43 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:57:04 - mmengine - INFO - Epoch(train) [107][50/293] lr: 5.000000e-04 eta: 2:52:10 time: 0.405611 data_time: 0.103699 memory: 12861 loss_kpt: 0.000777 acc_pose: 0.777715 loss: 0.000777 2022/10/13 13:57:22 - mmengine - INFO - Epoch(train) [107][100/293] lr: 5.000000e-04 eta: 2:51:54 time: 0.370107 data_time: 0.079484 memory: 12861 loss_kpt: 0.000797 acc_pose: 0.722611 loss: 0.000797 2022/10/13 13:57:42 - mmengine - INFO - Epoch(train) [107][150/293] lr: 5.000000e-04 eta: 2:51:40 time: 0.392891 data_time: 0.085690 memory: 12861 loss_kpt: 0.000779 acc_pose: 0.716722 loss: 0.000779 2022/10/13 13:58:02 - mmengine - INFO - Epoch(train) [107][200/293] lr: 5.000000e-04 eta: 2:51:26 time: 0.398340 data_time: 0.085160 memory: 12861 loss_kpt: 0.000789 acc_pose: 0.791313 loss: 0.000789 2022/10/13 13:58:21 - mmengine - INFO - Epoch(train) [107][250/293] lr: 5.000000e-04 eta: 2:51:11 time: 0.380684 data_time: 0.081515 memory: 12861 loss_kpt: 0.000779 acc_pose: 0.717977 loss: 0.000779 2022/10/13 13:58:38 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 13:58:58 - mmengine - INFO - Epoch(train) [108][50/293] lr: 5.000000e-04 eta: 2:50:28 time: 0.408889 data_time: 0.113904 memory: 12861 loss_kpt: 0.000783 acc_pose: 0.763349 loss: 0.000783 2022/10/13 13:59:17 - mmengine - INFO - Epoch(train) [108][100/293] lr: 5.000000e-04 eta: 2:50:14 time: 0.386541 data_time: 0.082713 memory: 12861 loss_kpt: 0.000783 acc_pose: 0.751601 loss: 0.000783 2022/10/13 13:59:37 - mmengine - INFO - Epoch(train) [108][150/293] lr: 5.000000e-04 eta: 2:49:59 time: 0.387469 data_time: 0.079441 memory: 12861 loss_kpt: 0.000784 acc_pose: 0.793952 loss: 0.000784 2022/10/13 13:59:56 - mmengine - INFO - Epoch(train) [108][200/293] lr: 5.000000e-04 eta: 2:49:44 time: 0.389851 data_time: 0.079331 memory: 12861 loss_kpt: 0.000807 acc_pose: 0.745957 loss: 0.000807 2022/10/13 14:00:15 - mmengine - INFO - Epoch(train) [108][250/293] lr: 5.000000e-04 eta: 2:49:30 time: 0.385746 data_time: 0.083676 memory: 12861 loss_kpt: 0.000789 acc_pose: 0.721795 loss: 0.000789 2022/10/13 14:00:31 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:00:52 - mmengine - INFO - Epoch(train) [109][50/293] lr: 5.000000e-04 eta: 2:48:47 time: 0.407253 data_time: 0.100446 memory: 12861 loss_kpt: 0.000798 acc_pose: 0.770467 loss: 0.000798 2022/10/13 14:01:11 - mmengine - INFO - Epoch(train) [109][100/293] lr: 5.000000e-04 eta: 2:48:33 time: 0.382903 data_time: 0.080399 memory: 12861 loss_kpt: 0.000794 acc_pose: 0.813677 loss: 0.000794 2022/10/13 14:01:30 - mmengine - INFO - Epoch(train) [109][150/293] lr: 5.000000e-04 eta: 2:48:17 time: 0.375812 data_time: 0.075797 memory: 12861 loss_kpt: 0.000774 acc_pose: 0.775517 loss: 0.000774 2022/10/13 14:01:48 - mmengine - INFO - Epoch(train) [109][200/293] lr: 5.000000e-04 eta: 2:48:02 time: 0.373790 data_time: 0.076619 memory: 12861 loss_kpt: 0.000781 acc_pose: 0.746022 loss: 0.000781 2022/10/13 14:02:08 - mmengine - INFO - Epoch(train) [109][250/293] lr: 5.000000e-04 eta: 2:47:47 time: 0.387613 data_time: 0.089768 memory: 12861 loss_kpt: 0.000778 acc_pose: 0.784363 loss: 0.000778 2022/10/13 14:02:24 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:02:45 - mmengine - INFO - Epoch(train) [110][50/293] lr: 5.000000e-04 eta: 2:47:05 time: 0.403773 data_time: 0.084825 memory: 12861 loss_kpt: 0.000792 acc_pose: 0.819957 loss: 0.000792 2022/10/13 14:02:50 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:03:04 - mmengine - INFO - Epoch(train) [110][100/293] lr: 5.000000e-04 eta: 2:46:50 time: 0.386168 data_time: 0.079372 memory: 12861 loss_kpt: 0.000774 acc_pose: 0.755455 loss: 0.000774 2022/10/13 14:03:24 - mmengine - INFO - Epoch(train) [110][150/293] lr: 5.000000e-04 eta: 2:46:36 time: 0.393656 data_time: 0.094371 memory: 12861 loss_kpt: 0.000786 acc_pose: 0.814694 loss: 0.000786 2022/10/13 14:03:43 - mmengine - INFO - Epoch(train) [110][200/293] lr: 5.000000e-04 eta: 2:46:21 time: 0.386709 data_time: 0.088235 memory: 12861 loss_kpt: 0.000781 acc_pose: 0.771171 loss: 0.000781 2022/10/13 14:04:02 - mmengine - INFO - Epoch(train) [110][250/293] lr: 5.000000e-04 eta: 2:46:06 time: 0.388427 data_time: 0.080146 memory: 12861 loss_kpt: 0.000786 acc_pose: 0.752205 loss: 0.000786 2022/10/13 14:04:19 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:04:19 - mmengine - INFO - Saving checkpoint at 110 epochs 2022/10/13 14:04:28 - mmengine - INFO - Epoch(val) [110][50/407] eta: 0:00:49 time: 0.139693 data_time: 0.078508 memory: 12861 2022/10/13 14:04:34 - mmengine - INFO - Epoch(val) [110][100/407] eta: 0:00:37 time: 0.121047 data_time: 0.060579 memory: 983 2022/10/13 14:04:40 - mmengine - INFO - Epoch(val) [110][150/407] eta: 0:00:32 time: 0.126333 data_time: 0.064784 memory: 983 2022/10/13 14:04:46 - mmengine - INFO - Epoch(val) [110][200/407] eta: 0:00:25 time: 0.122396 data_time: 0.061202 memory: 983 2022/10/13 14:04:53 - mmengine - INFO - Epoch(val) [110][250/407] eta: 0:00:20 time: 0.127491 data_time: 0.066770 memory: 983 2022/10/13 14:04:59 - mmengine - INFO - Epoch(val) [110][300/407] eta: 0:00:13 time: 0.130771 data_time: 0.069826 memory: 983 2022/10/13 14:05:05 - mmengine - INFO - Epoch(val) [110][350/407] eta: 0:00:07 time: 0.125030 data_time: 0.064276 memory: 983 2022/10/13 14:05:11 - mmengine - INFO - Epoch(val) [110][400/407] eta: 0:00:00 time: 0.120104 data_time: 0.059455 memory: 983 2022/10/13 14:05:48 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 14:06:02 - mmengine - INFO - Epoch(val) [110][407/407] coco/AP: 0.648457 coco/AP .5: 0.869644 coco/AP .75: 0.717169 coco/AP (M): 0.606107 coco/AP (L): 0.721633 coco/AR: 0.708013 coco/AR .5: 0.911366 coco/AR .75: 0.772513 coco/AR (M): 0.658618 coco/AR (L): 0.777369 2022/10/13 14:06:22 - mmengine - INFO - Epoch(train) [111][50/293] lr: 5.000000e-04 eta: 2:45:24 time: 0.396923 data_time: 0.093443 memory: 12861 loss_kpt: 0.000796 acc_pose: 0.705199 loss: 0.000796 2022/10/13 14:06:42 - mmengine - INFO - Epoch(train) [111][100/293] lr: 5.000000e-04 eta: 2:45:10 time: 0.399382 data_time: 0.093141 memory: 12861 loss_kpt: 0.000786 acc_pose: 0.800433 loss: 0.000786 2022/10/13 14:07:01 - mmengine - INFO - Epoch(train) [111][150/293] lr: 5.000000e-04 eta: 2:44:55 time: 0.389443 data_time: 0.088667 memory: 12861 loss_kpt: 0.000800 acc_pose: 0.784594 loss: 0.000800 2022/10/13 14:07:21 - mmengine - INFO - Epoch(train) [111][200/293] lr: 5.000000e-04 eta: 2:44:41 time: 0.398787 data_time: 0.081079 memory: 12861 loss_kpt: 0.000793 acc_pose: 0.815635 loss: 0.000793 2022/10/13 14:07:41 - mmengine - INFO - Epoch(train) [111][250/293] lr: 5.000000e-04 eta: 2:44:26 time: 0.393870 data_time: 0.088693 memory: 12861 loss_kpt: 0.000785 acc_pose: 0.746946 loss: 0.000785 2022/10/13 14:07:58 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:08:18 - mmengine - INFO - Epoch(train) [112][50/293] lr: 5.000000e-04 eta: 2:43:44 time: 0.405056 data_time: 0.101932 memory: 12861 loss_kpt: 0.000785 acc_pose: 0.734028 loss: 0.000785 2022/10/13 14:08:38 - mmengine - INFO - Epoch(train) [112][100/293] lr: 5.000000e-04 eta: 2:43:30 time: 0.395265 data_time: 0.099880 memory: 12861 loss_kpt: 0.000785 acc_pose: 0.756706 loss: 0.000785 2022/10/13 14:08:57 - mmengine - INFO - Epoch(train) [112][150/293] lr: 5.000000e-04 eta: 2:43:15 time: 0.380406 data_time: 0.093444 memory: 12861 loss_kpt: 0.000797 acc_pose: 0.755192 loss: 0.000797 2022/10/13 14:09:16 - mmengine - INFO - Epoch(train) [112][200/293] lr: 5.000000e-04 eta: 2:43:00 time: 0.380820 data_time: 0.070241 memory: 12861 loss_kpt: 0.000774 acc_pose: 0.754686 loss: 0.000774 2022/10/13 14:09:35 - mmengine - INFO - Epoch(train) [112][250/293] lr: 5.000000e-04 eta: 2:42:44 time: 0.375792 data_time: 0.079907 memory: 12861 loss_kpt: 0.000777 acc_pose: 0.778083 loss: 0.000777 2022/10/13 14:09:51 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:10:11 - mmengine - INFO - Epoch(train) [113][50/293] lr: 5.000000e-04 eta: 2:42:02 time: 0.395274 data_time: 0.095093 memory: 12861 loss_kpt: 0.000786 acc_pose: 0.758604 loss: 0.000786 2022/10/13 14:10:30 - mmengine - INFO - Epoch(train) [113][100/293] lr: 5.000000e-04 eta: 2:41:47 time: 0.385413 data_time: 0.089706 memory: 12861 loss_kpt: 0.000790 acc_pose: 0.731192 loss: 0.000790 2022/10/13 14:10:50 - mmengine - INFO - Epoch(train) [113][150/293] lr: 5.000000e-04 eta: 2:41:33 time: 0.392356 data_time: 0.084185 memory: 12861 loss_kpt: 0.000783 acc_pose: 0.752514 loss: 0.000783 2022/10/13 14:11:03 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:11:10 - mmengine - INFO - Epoch(train) [113][200/293] lr: 5.000000e-04 eta: 2:41:18 time: 0.397709 data_time: 0.106827 memory: 12861 loss_kpt: 0.000774 acc_pose: 0.718806 loss: 0.000774 2022/10/13 14:11:29 - mmengine - INFO - Epoch(train) [113][250/293] lr: 5.000000e-04 eta: 2:41:03 time: 0.384361 data_time: 0.085233 memory: 12861 loss_kpt: 0.000771 acc_pose: 0.753114 loss: 0.000771 2022/10/13 14:11:45 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:12:04 - mmengine - INFO - Epoch(train) [114][50/293] lr: 5.000000e-04 eta: 2:40:22 time: 0.398537 data_time: 0.101822 memory: 12861 loss_kpt: 0.000781 acc_pose: 0.801919 loss: 0.000781 2022/10/13 14:12:24 - mmengine - INFO - Epoch(train) [114][100/293] lr: 5.000000e-04 eta: 2:40:07 time: 0.398709 data_time: 0.085038 memory: 12861 loss_kpt: 0.000777 acc_pose: 0.797727 loss: 0.000777 2022/10/13 14:12:43 - mmengine - INFO - Epoch(train) [114][150/293] lr: 5.000000e-04 eta: 2:39:52 time: 0.380042 data_time: 0.083892 memory: 12861 loss_kpt: 0.000778 acc_pose: 0.700255 loss: 0.000778 2022/10/13 14:13:03 - mmengine - INFO - Epoch(train) [114][200/293] lr: 5.000000e-04 eta: 2:39:37 time: 0.390949 data_time: 0.089514 memory: 12861 loss_kpt: 0.000780 acc_pose: 0.756522 loss: 0.000780 2022/10/13 14:13:22 - mmengine - INFO - Epoch(train) [114][250/293] lr: 5.000000e-04 eta: 2:39:22 time: 0.381821 data_time: 0.087144 memory: 12861 loss_kpt: 0.000761 acc_pose: 0.760009 loss: 0.000761 2022/10/13 14:13:39 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:13:59 - mmengine - INFO - Epoch(train) [115][50/293] lr: 5.000000e-04 eta: 2:38:41 time: 0.413110 data_time: 0.107706 memory: 12861 loss_kpt: 0.000779 acc_pose: 0.760211 loss: 0.000779 2022/10/13 14:14:18 - mmengine - INFO - Epoch(train) [115][100/293] lr: 5.000000e-04 eta: 2:38:26 time: 0.384433 data_time: 0.094213 memory: 12861 loss_kpt: 0.000771 acc_pose: 0.778046 loss: 0.000771 2022/10/13 14:14:39 - mmengine - INFO - Epoch(train) [115][150/293] lr: 5.000000e-04 eta: 2:38:12 time: 0.401593 data_time: 0.086928 memory: 12861 loss_kpt: 0.000783 acc_pose: 0.823103 loss: 0.000783 2022/10/13 14:15:00 - mmengine - INFO - Epoch(train) [115][200/293] lr: 5.000000e-04 eta: 2:37:58 time: 0.419944 data_time: 0.078823 memory: 12861 loss_kpt: 0.000777 acc_pose: 0.747897 loss: 0.000777 2022/10/13 14:15:19 - mmengine - INFO - Epoch(train) [115][250/293] lr: 5.000000e-04 eta: 2:37:44 time: 0.394404 data_time: 0.085198 memory: 12861 loss_kpt: 0.000787 acc_pose: 0.767537 loss: 0.000787 2022/10/13 14:15:36 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:15:56 - mmengine - INFO - Epoch(train) [116][50/293] lr: 5.000000e-04 eta: 2:37:03 time: 0.405904 data_time: 0.091873 memory: 12861 loss_kpt: 0.000785 acc_pose: 0.687355 loss: 0.000785 2022/10/13 14:16:16 - mmengine - INFO - Epoch(train) [116][100/293] lr: 5.000000e-04 eta: 2:36:48 time: 0.397594 data_time: 0.085134 memory: 12861 loss_kpt: 0.000780 acc_pose: 0.737930 loss: 0.000780 2022/10/13 14:16:36 - mmengine - INFO - Epoch(train) [116][150/293] lr: 5.000000e-04 eta: 2:36:34 time: 0.398752 data_time: 0.082199 memory: 12861 loss_kpt: 0.000791 acc_pose: 0.747126 loss: 0.000791 2022/10/13 14:16:56 - mmengine - INFO - Epoch(train) [116][200/293] lr: 5.000000e-04 eta: 2:36:19 time: 0.386749 data_time: 0.093039 memory: 12861 loss_kpt: 0.000762 acc_pose: 0.804685 loss: 0.000762 2022/10/13 14:17:15 - mmengine - INFO - Epoch(train) [116][250/293] lr: 5.000000e-04 eta: 2:36:03 time: 0.378034 data_time: 0.080935 memory: 12861 loss_kpt: 0.000777 acc_pose: 0.740526 loss: 0.000777 2022/10/13 14:17:31 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:17:37 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:17:51 - mmengine - INFO - Epoch(train) [117][50/293] lr: 5.000000e-04 eta: 2:35:22 time: 0.400738 data_time: 0.095992 memory: 12861 loss_kpt: 0.000765 acc_pose: 0.730738 loss: 0.000765 2022/10/13 14:18:10 - mmengine - INFO - Epoch(train) [117][100/293] lr: 5.000000e-04 eta: 2:35:07 time: 0.384117 data_time: 0.080423 memory: 12861 loss_kpt: 0.000773 acc_pose: 0.761564 loss: 0.000773 2022/10/13 14:18:30 - mmengine - INFO - Epoch(train) [117][150/293] lr: 5.000000e-04 eta: 2:34:52 time: 0.386026 data_time: 0.077024 memory: 12861 loss_kpt: 0.000786 acc_pose: 0.749191 loss: 0.000786 2022/10/13 14:18:49 - mmengine - INFO - Epoch(train) [117][200/293] lr: 5.000000e-04 eta: 2:34:37 time: 0.378325 data_time: 0.095787 memory: 12861 loss_kpt: 0.000787 acc_pose: 0.693491 loss: 0.000787 2022/10/13 14:19:08 - mmengine - INFO - Epoch(train) [117][250/293] lr: 5.000000e-04 eta: 2:34:22 time: 0.383495 data_time: 0.093552 memory: 12861 loss_kpt: 0.000797 acc_pose: 0.747825 loss: 0.000797 2022/10/13 14:19:24 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:19:44 - mmengine - INFO - Epoch(train) [118][50/293] lr: 5.000000e-04 eta: 2:33:41 time: 0.398514 data_time: 0.103722 memory: 12861 loss_kpt: 0.000784 acc_pose: 0.754366 loss: 0.000784 2022/10/13 14:20:03 - mmengine - INFO - Epoch(train) [118][100/293] lr: 5.000000e-04 eta: 2:33:26 time: 0.382263 data_time: 0.075545 memory: 12861 loss_kpt: 0.000776 acc_pose: 0.769040 loss: 0.000776 2022/10/13 14:20:23 - mmengine - INFO - Epoch(train) [118][150/293] lr: 5.000000e-04 eta: 2:33:11 time: 0.397040 data_time: 0.086535 memory: 12861 loss_kpt: 0.000777 acc_pose: 0.739037 loss: 0.000777 2022/10/13 14:20:43 - mmengine - INFO - Epoch(train) [118][200/293] lr: 5.000000e-04 eta: 2:32:56 time: 0.390828 data_time: 0.100118 memory: 12861 loss_kpt: 0.000779 acc_pose: 0.768080 loss: 0.000779 2022/10/13 14:21:03 - mmengine - INFO - Epoch(train) [118][250/293] lr: 5.000000e-04 eta: 2:32:41 time: 0.400932 data_time: 0.079918 memory: 12861 loss_kpt: 0.000797 acc_pose: 0.766041 loss: 0.000797 2022/10/13 14:21:19 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:21:39 - mmengine - INFO - Epoch(train) [119][50/293] lr: 5.000000e-04 eta: 2:32:01 time: 0.399331 data_time: 0.099431 memory: 12861 loss_kpt: 0.000778 acc_pose: 0.693450 loss: 0.000778 2022/10/13 14:21:59 - mmengine - INFO - Epoch(train) [119][100/293] lr: 5.000000e-04 eta: 2:31:46 time: 0.386989 data_time: 0.096213 memory: 12861 loss_kpt: 0.000771 acc_pose: 0.774803 loss: 0.000771 2022/10/13 14:22:18 - mmengine - INFO - Epoch(train) [119][150/293] lr: 5.000000e-04 eta: 2:31:31 time: 0.384212 data_time: 0.084585 memory: 12861 loss_kpt: 0.000774 acc_pose: 0.783073 loss: 0.000774 2022/10/13 14:22:37 - mmengine - INFO - Epoch(train) [119][200/293] lr: 5.000000e-04 eta: 2:31:15 time: 0.386426 data_time: 0.080649 memory: 12861 loss_kpt: 0.000771 acc_pose: 0.767483 loss: 0.000771 2022/10/13 14:22:57 - mmengine - INFO - Epoch(train) [119][250/293] lr: 5.000000e-04 eta: 2:31:01 time: 0.394909 data_time: 0.091723 memory: 12861 loss_kpt: 0.000786 acc_pose: 0.709724 loss: 0.000786 2022/10/13 14:23:13 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:23:33 - mmengine - INFO - Epoch(train) [120][50/293] lr: 5.000000e-04 eta: 2:30:20 time: 0.395375 data_time: 0.091926 memory: 12861 loss_kpt: 0.000773 acc_pose: 0.767816 loss: 0.000773 2022/10/13 14:23:53 - mmengine - INFO - Epoch(train) [120][100/293] lr: 5.000000e-04 eta: 2:30:05 time: 0.394921 data_time: 0.090068 memory: 12861 loss_kpt: 0.000773 acc_pose: 0.734457 loss: 0.000773 2022/10/13 14:24:05 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:24:12 - mmengine - INFO - Epoch(train) [120][150/293] lr: 5.000000e-04 eta: 2:29:50 time: 0.376457 data_time: 0.086054 memory: 12861 loss_kpt: 0.000779 acc_pose: 0.708697 loss: 0.000779 2022/10/13 14:24:31 - mmengine - INFO - Epoch(train) [120][200/293] lr: 5.000000e-04 eta: 2:29:35 time: 0.390335 data_time: 0.093994 memory: 12861 loss_kpt: 0.000785 acc_pose: 0.769857 loss: 0.000785 2022/10/13 14:24:51 - mmengine - INFO - Epoch(train) [120][250/293] lr: 5.000000e-04 eta: 2:29:20 time: 0.388777 data_time: 0.090076 memory: 12861 loss_kpt: 0.000776 acc_pose: 0.652907 loss: 0.000776 2022/10/13 14:25:07 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:25:07 - mmengine - INFO - Saving checkpoint at 120 epochs 2022/10/13 14:25:16 - mmengine - INFO - Epoch(val) [120][50/407] eta: 0:00:46 time: 0.129542 data_time: 0.066725 memory: 12861 2022/10/13 14:25:22 - mmengine - INFO - Epoch(val) [120][100/407] eta: 0:00:38 time: 0.124818 data_time: 0.062687 memory: 983 2022/10/13 14:25:28 - mmengine - INFO - Epoch(val) [120][150/407] eta: 0:00:32 time: 0.125235 data_time: 0.062907 memory: 983 2022/10/13 14:25:35 - mmengine - INFO - Epoch(val) [120][200/407] eta: 0:00:26 time: 0.126744 data_time: 0.066391 memory: 983 2022/10/13 14:25:41 - mmengine - INFO - Epoch(val) [120][250/407] eta: 0:00:19 time: 0.125207 data_time: 0.061965 memory: 983 2022/10/13 14:25:47 - mmengine - INFO - Epoch(val) [120][300/407] eta: 0:00:13 time: 0.124771 data_time: 0.063646 memory: 983 2022/10/13 14:25:53 - mmengine - INFO - Epoch(val) [120][350/407] eta: 0:00:07 time: 0.123158 data_time: 0.062088 memory: 983 2022/10/13 14:26:00 - mmengine - INFO - Epoch(val) [120][400/407] eta: 0:00:00 time: 0.131808 data_time: 0.071492 memory: 983 2022/10/13 14:26:37 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 14:26:51 - mmengine - INFO - Epoch(val) [120][407/407] coco/AP: 0.652702 coco/AP .5: 0.871666 coco/AP .75: 0.721479 coco/AP (M): 0.609567 coco/AP (L): 0.726267 coco/AR: 0.710060 coco/AR .5: 0.911366 coco/AR .75: 0.774874 coco/AR (M): 0.659956 coco/AR (L): 0.780565 2022/10/13 14:26:51 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384/best_coco/AP_epoch_100.pth is removed 2022/10/13 14:26:52 - mmengine - INFO - The best checkpoint with 0.6527 coco/AP at 120 epoch is saved to best_coco/AP_epoch_120.pth. 2022/10/13 14:27:13 - mmengine - INFO - Epoch(train) [121][50/293] lr: 5.000000e-04 eta: 2:28:40 time: 0.405448 data_time: 0.102666 memory: 12861 loss_kpt: 0.000775 acc_pose: 0.781576 loss: 0.000775 2022/10/13 14:27:32 - mmengine - INFO - Epoch(train) [121][100/293] lr: 5.000000e-04 eta: 2:28:24 time: 0.379856 data_time: 0.087169 memory: 12861 loss_kpt: 0.000766 acc_pose: 0.763543 loss: 0.000766 2022/10/13 14:27:51 - mmengine - INFO - Epoch(train) [121][150/293] lr: 5.000000e-04 eta: 2:28:09 time: 0.387754 data_time: 0.098901 memory: 12861 loss_kpt: 0.000761 acc_pose: 0.744334 loss: 0.000761 2022/10/13 14:28:11 - mmengine - INFO - Epoch(train) [121][200/293] lr: 5.000000e-04 eta: 2:27:54 time: 0.396541 data_time: 0.096529 memory: 12861 loss_kpt: 0.000771 acc_pose: 0.771012 loss: 0.000771 2022/10/13 14:28:31 - mmengine - INFO - Epoch(train) [121][250/293] lr: 5.000000e-04 eta: 2:27:39 time: 0.399047 data_time: 0.090322 memory: 12861 loss_kpt: 0.000780 acc_pose: 0.785426 loss: 0.000780 2022/10/13 14:28:47 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:29:07 - mmengine - INFO - Epoch(train) [122][50/293] lr: 5.000000e-04 eta: 2:26:59 time: 0.400669 data_time: 0.102306 memory: 12861 loss_kpt: 0.000789 acc_pose: 0.778697 loss: 0.000789 2022/10/13 14:29:27 - mmengine - INFO - Epoch(train) [122][100/293] lr: 5.000000e-04 eta: 2:26:44 time: 0.392599 data_time: 0.095027 memory: 12861 loss_kpt: 0.000756 acc_pose: 0.805467 loss: 0.000756 2022/10/13 14:29:46 - mmengine - INFO - Epoch(train) [122][150/293] lr: 5.000000e-04 eta: 2:26:29 time: 0.391390 data_time: 0.097215 memory: 12861 loss_kpt: 0.000780 acc_pose: 0.767289 loss: 0.000780 2022/10/13 14:30:05 - mmengine - INFO - Epoch(train) [122][200/293] lr: 5.000000e-04 eta: 2:26:14 time: 0.378996 data_time: 0.090552 memory: 12861 loss_kpt: 0.000787 acc_pose: 0.737841 loss: 0.000787 2022/10/13 14:30:25 - mmengine - INFO - Epoch(train) [122][250/293] lr: 5.000000e-04 eta: 2:25:59 time: 0.390950 data_time: 0.098066 memory: 12861 loss_kpt: 0.000766 acc_pose: 0.761483 loss: 0.000766 2022/10/13 14:30:41 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:31:02 - mmengine - INFO - Epoch(train) [123][50/293] lr: 5.000000e-04 eta: 2:25:20 time: 0.415169 data_time: 0.107240 memory: 12861 loss_kpt: 0.000785 acc_pose: 0.751685 loss: 0.000785 2022/10/13 14:31:22 - mmengine - INFO - Epoch(train) [123][100/293] lr: 5.000000e-04 eta: 2:25:05 time: 0.394317 data_time: 0.091107 memory: 12861 loss_kpt: 0.000759 acc_pose: 0.757356 loss: 0.000759 2022/10/13 14:31:41 - mmengine - INFO - Epoch(train) [123][150/293] lr: 5.000000e-04 eta: 2:24:49 time: 0.385588 data_time: 0.095787 memory: 12861 loss_kpt: 0.000788 acc_pose: 0.758441 loss: 0.000788 2022/10/13 14:32:01 - mmengine - INFO - Epoch(train) [123][200/293] lr: 5.000000e-04 eta: 2:24:34 time: 0.392419 data_time: 0.096968 memory: 12861 loss_kpt: 0.000765 acc_pose: 0.748330 loss: 0.000765 2022/10/13 14:32:20 - mmengine - INFO - Epoch(train) [123][250/293] lr: 5.000000e-04 eta: 2:24:19 time: 0.391164 data_time: 0.099801 memory: 12861 loss_kpt: 0.000797 acc_pose: 0.791438 loss: 0.000797 2022/10/13 14:32:22 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:32:37 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:32:57 - mmengine - INFO - Epoch(train) [124][50/293] lr: 5.000000e-04 eta: 2:23:40 time: 0.400493 data_time: 0.096170 memory: 12861 loss_kpt: 0.000781 acc_pose: 0.782504 loss: 0.000781 2022/10/13 14:33:16 - mmengine - INFO - Epoch(train) [124][100/293] lr: 5.000000e-04 eta: 2:23:25 time: 0.394225 data_time: 0.098370 memory: 12861 loss_kpt: 0.000767 acc_pose: 0.761318 loss: 0.000767 2022/10/13 14:33:36 - mmengine - INFO - Epoch(train) [124][150/293] lr: 5.000000e-04 eta: 2:23:09 time: 0.388591 data_time: 0.092008 memory: 12861 loss_kpt: 0.000778 acc_pose: 0.769429 loss: 0.000778 2022/10/13 14:33:55 - mmengine - INFO - Epoch(train) [124][200/293] lr: 5.000000e-04 eta: 2:22:54 time: 0.388178 data_time: 0.098720 memory: 12861 loss_kpt: 0.000783 acc_pose: 0.752044 loss: 0.000783 2022/10/13 14:34:14 - mmengine - INFO - Epoch(train) [124][250/293] lr: 5.000000e-04 eta: 2:22:39 time: 0.381259 data_time: 0.084275 memory: 12861 loss_kpt: 0.000782 acc_pose: 0.736219 loss: 0.000782 2022/10/13 14:34:31 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:34:50 - mmengine - INFO - Epoch(train) [125][50/293] lr: 5.000000e-04 eta: 2:21:59 time: 0.394509 data_time: 0.080037 memory: 12861 loss_kpt: 0.000781 acc_pose: 0.801985 loss: 0.000781 2022/10/13 14:35:10 - mmengine - INFO - Epoch(train) [125][100/293] lr: 5.000000e-04 eta: 2:21:44 time: 0.390760 data_time: 0.095235 memory: 12861 loss_kpt: 0.000767 acc_pose: 0.781158 loss: 0.000767 2022/10/13 14:35:29 - mmengine - INFO - Epoch(train) [125][150/293] lr: 5.000000e-04 eta: 2:21:29 time: 0.392299 data_time: 0.104426 memory: 12861 loss_kpt: 0.000786 acc_pose: 0.791233 loss: 0.000786 2022/10/13 14:35:49 - mmengine - INFO - Epoch(train) [125][200/293] lr: 5.000000e-04 eta: 2:21:14 time: 0.394837 data_time: 0.087327 memory: 12861 loss_kpt: 0.000774 acc_pose: 0.790183 loss: 0.000774 2022/10/13 14:36:09 - mmengine - INFO - Epoch(train) [125][250/293] lr: 5.000000e-04 eta: 2:20:59 time: 0.396341 data_time: 0.095361 memory: 12861 loss_kpt: 0.000782 acc_pose: 0.739956 loss: 0.000782 2022/10/13 14:36:26 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:36:46 - mmengine - INFO - Epoch(train) [126][50/293] lr: 5.000000e-04 eta: 2:20:20 time: 0.413042 data_time: 0.111247 memory: 12861 loss_kpt: 0.000766 acc_pose: 0.803807 loss: 0.000766 2022/10/13 14:37:06 - mmengine - INFO - Epoch(train) [126][100/293] lr: 5.000000e-04 eta: 2:20:05 time: 0.405863 data_time: 0.089124 memory: 12861 loss_kpt: 0.000774 acc_pose: 0.761640 loss: 0.000774 2022/10/13 14:37:26 - mmengine - INFO - Epoch(train) [126][150/293] lr: 5.000000e-04 eta: 2:19:50 time: 0.391580 data_time: 0.093995 memory: 12861 loss_kpt: 0.000775 acc_pose: 0.793869 loss: 0.000775 2022/10/13 14:37:46 - mmengine - INFO - Epoch(train) [126][200/293] lr: 5.000000e-04 eta: 2:19:35 time: 0.400104 data_time: 0.107189 memory: 12861 loss_kpt: 0.000786 acc_pose: 0.764174 loss: 0.000786 2022/10/13 14:38:05 - mmengine - INFO - Epoch(train) [126][250/293] lr: 5.000000e-04 eta: 2:19:20 time: 0.384780 data_time: 0.084183 memory: 12861 loss_kpt: 0.000781 acc_pose: 0.768234 loss: 0.000781 2022/10/13 14:38:21 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:38:41 - mmengine - INFO - Epoch(train) [127][50/293] lr: 5.000000e-04 eta: 2:18:40 time: 0.396671 data_time: 0.103353 memory: 12861 loss_kpt: 0.000775 acc_pose: 0.751709 loss: 0.000775 2022/10/13 14:38:53 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:39:00 - mmengine - INFO - Epoch(train) [127][100/293] lr: 5.000000e-04 eta: 2:18:25 time: 0.376354 data_time: 0.078906 memory: 12861 loss_kpt: 0.000753 acc_pose: 0.741353 loss: 0.000753 2022/10/13 14:39:20 - mmengine - INFO - Epoch(train) [127][150/293] lr: 5.000000e-04 eta: 2:18:09 time: 0.391691 data_time: 0.082619 memory: 12861 loss_kpt: 0.000760 acc_pose: 0.766161 loss: 0.000760 2022/10/13 14:39:38 - mmengine - INFO - Epoch(train) [127][200/293] lr: 5.000000e-04 eta: 2:17:53 time: 0.367746 data_time: 0.074962 memory: 12861 loss_kpt: 0.000759 acc_pose: 0.766331 loss: 0.000759 2022/10/13 14:39:57 - mmengine - INFO - Epoch(train) [127][250/293] lr: 5.000000e-04 eta: 2:17:38 time: 0.387524 data_time: 0.101865 memory: 12861 loss_kpt: 0.000771 acc_pose: 0.740745 loss: 0.000771 2022/10/13 14:40:14 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:40:35 - mmengine - INFO - Epoch(train) [128][50/293] lr: 5.000000e-04 eta: 2:16:59 time: 0.415168 data_time: 0.107587 memory: 12861 loss_kpt: 0.000784 acc_pose: 0.779422 loss: 0.000784 2022/10/13 14:40:55 - mmengine - INFO - Epoch(train) [128][100/293] lr: 5.000000e-04 eta: 2:16:44 time: 0.397917 data_time: 0.098892 memory: 12861 loss_kpt: 0.000768 acc_pose: 0.813032 loss: 0.000768 2022/10/13 14:41:14 - mmengine - INFO - Epoch(train) [128][150/293] lr: 5.000000e-04 eta: 2:16:29 time: 0.388625 data_time: 0.093703 memory: 12861 loss_kpt: 0.000773 acc_pose: 0.766128 loss: 0.000773 2022/10/13 14:41:33 - mmengine - INFO - Epoch(train) [128][200/293] lr: 5.000000e-04 eta: 2:16:14 time: 0.383470 data_time: 0.084729 memory: 12861 loss_kpt: 0.000772 acc_pose: 0.673266 loss: 0.000772 2022/10/13 14:41:53 - mmengine - INFO - Epoch(train) [128][250/293] lr: 5.000000e-04 eta: 2:15:58 time: 0.387928 data_time: 0.083798 memory: 12861 loss_kpt: 0.000780 acc_pose: 0.782720 loss: 0.000780 2022/10/13 14:42:10 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:42:30 - mmengine - INFO - Epoch(train) [129][50/293] lr: 5.000000e-04 eta: 2:15:19 time: 0.398533 data_time: 0.099875 memory: 12861 loss_kpt: 0.000776 acc_pose: 0.763038 loss: 0.000776 2022/10/13 14:42:49 - mmengine - INFO - Epoch(train) [129][100/293] lr: 5.000000e-04 eta: 2:15:04 time: 0.397490 data_time: 0.098050 memory: 12861 loss_kpt: 0.000759 acc_pose: 0.740532 loss: 0.000759 2022/10/13 14:43:09 - mmengine - INFO - Epoch(train) [129][150/293] lr: 5.000000e-04 eta: 2:14:49 time: 0.392254 data_time: 0.090940 memory: 12861 loss_kpt: 0.000766 acc_pose: 0.756091 loss: 0.000766 2022/10/13 14:43:29 - mmengine - INFO - Epoch(train) [129][200/293] lr: 5.000000e-04 eta: 2:14:34 time: 0.390508 data_time: 0.084998 memory: 12861 loss_kpt: 0.000756 acc_pose: 0.791505 loss: 0.000756 2022/10/13 14:43:48 - mmengine - INFO - Epoch(train) [129][250/293] lr: 5.000000e-04 eta: 2:14:18 time: 0.391793 data_time: 0.100620 memory: 12861 loss_kpt: 0.000787 acc_pose: 0.777341 loss: 0.000787 2022/10/13 14:44:05 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:44:26 - mmengine - INFO - Epoch(train) [130][50/293] lr: 5.000000e-04 eta: 2:13:40 time: 0.419523 data_time: 0.110127 memory: 12861 loss_kpt: 0.000773 acc_pose: 0.798697 loss: 0.000773 2022/10/13 14:44:45 - mmengine - INFO - Epoch(train) [130][100/293] lr: 5.000000e-04 eta: 2:13:25 time: 0.385490 data_time: 0.101500 memory: 12861 loss_kpt: 0.000764 acc_pose: 0.711284 loss: 0.000764 2022/10/13 14:45:05 - mmengine - INFO - Epoch(train) [130][150/293] lr: 5.000000e-04 eta: 2:13:09 time: 0.387421 data_time: 0.089012 memory: 12861 loss_kpt: 0.000778 acc_pose: 0.746654 loss: 0.000778 2022/10/13 14:45:24 - mmengine - INFO - Epoch(train) [130][200/293] lr: 5.000000e-04 eta: 2:12:54 time: 0.393935 data_time: 0.095335 memory: 12861 loss_kpt: 0.000785 acc_pose: 0.737315 loss: 0.000785 2022/10/13 14:45:26 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:45:43 - mmengine - INFO - Epoch(train) [130][250/293] lr: 5.000000e-04 eta: 2:12:38 time: 0.379055 data_time: 0.086631 memory: 12861 loss_kpt: 0.000789 acc_pose: 0.760653 loss: 0.000789 2022/10/13 14:46:00 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:46:00 - mmengine - INFO - Saving checkpoint at 130 epochs 2022/10/13 14:46:08 - mmengine - INFO - Epoch(val) [130][50/407] eta: 0:00:46 time: 0.129492 data_time: 0.069281 memory: 12861 2022/10/13 14:46:14 - mmengine - INFO - Epoch(val) [130][100/407] eta: 0:00:37 time: 0.123073 data_time: 0.060953 memory: 983 2022/10/13 14:46:21 - mmengine - INFO - Epoch(val) [130][150/407] eta: 0:00:32 time: 0.125485 data_time: 0.064812 memory: 983 2022/10/13 14:46:27 - mmengine - INFO - Epoch(val) [130][200/407] eta: 0:00:24 time: 0.119752 data_time: 0.059689 memory: 983 2022/10/13 14:46:33 - mmengine - INFO - Epoch(val) [130][250/407] eta: 0:00:19 time: 0.123327 data_time: 0.062410 memory: 983 2022/10/13 14:46:39 - mmengine - INFO - Epoch(val) [130][300/407] eta: 0:00:13 time: 0.125507 data_time: 0.064978 memory: 983 2022/10/13 14:46:45 - mmengine - INFO - Epoch(val) [130][350/407] eta: 0:00:06 time: 0.122774 data_time: 0.062861 memory: 983 2022/10/13 14:46:51 - mmengine - INFO - Epoch(val) [130][400/407] eta: 0:00:00 time: 0.121856 data_time: 0.060322 memory: 983 2022/10/13 14:47:29 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 14:47:43 - mmengine - INFO - Epoch(val) [130][407/407] coco/AP: 0.653460 coco/AP .5: 0.872417 coco/AP .75: 0.723295 coco/AP (M): 0.611467 coco/AP (L): 0.725877 coco/AR: 0.711933 coco/AR .5: 0.911052 coco/AR .75: 0.777393 coco/AR (M): 0.662223 coco/AR (L): 0.782386 2022/10/13 14:47:43 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384/best_coco/AP_epoch_120.pth is removed 2022/10/13 14:47:44 - mmengine - INFO - The best checkpoint with 0.6535 coco/AP at 130 epoch is saved to best_coco/AP_epoch_130.pth. 2022/10/13 14:48:04 - mmengine - INFO - Epoch(train) [131][50/293] lr: 5.000000e-04 eta: 2:12:00 time: 0.399110 data_time: 0.098886 memory: 12861 loss_kpt: 0.000764 acc_pose: 0.826377 loss: 0.000764 2022/10/13 14:48:23 - mmengine - INFO - Epoch(train) [131][100/293] lr: 5.000000e-04 eta: 2:11:44 time: 0.378840 data_time: 0.090520 memory: 12861 loss_kpt: 0.000775 acc_pose: 0.791806 loss: 0.000775 2022/10/13 14:48:43 - mmengine - INFO - Epoch(train) [131][150/293] lr: 5.000000e-04 eta: 2:11:29 time: 0.391740 data_time: 0.087313 memory: 12861 loss_kpt: 0.000764 acc_pose: 0.804228 loss: 0.000764 2022/10/13 14:49:03 - mmengine - INFO - Epoch(train) [131][200/293] lr: 5.000000e-04 eta: 2:11:14 time: 0.402358 data_time: 0.085007 memory: 12861 loss_kpt: 0.000777 acc_pose: 0.737245 loss: 0.000777 2022/10/13 14:49:23 - mmengine - INFO - Epoch(train) [131][250/293] lr: 5.000000e-04 eta: 2:10:59 time: 0.403173 data_time: 0.098675 memory: 12861 loss_kpt: 0.000758 acc_pose: 0.725875 loss: 0.000758 2022/10/13 14:49:40 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:50:00 - mmengine - INFO - Epoch(train) [132][50/293] lr: 5.000000e-04 eta: 2:10:20 time: 0.400466 data_time: 0.085996 memory: 12861 loss_kpt: 0.000769 acc_pose: 0.771580 loss: 0.000769 2022/10/13 14:50:19 - mmengine - INFO - Epoch(train) [132][100/293] lr: 5.000000e-04 eta: 2:10:05 time: 0.388688 data_time: 0.078627 memory: 12861 loss_kpt: 0.000782 acc_pose: 0.796131 loss: 0.000782 2022/10/13 14:50:38 - mmengine - INFO - Epoch(train) [132][150/293] lr: 5.000000e-04 eta: 2:09:49 time: 0.383754 data_time: 0.089905 memory: 12861 loss_kpt: 0.000775 acc_pose: 0.793343 loss: 0.000775 2022/10/13 14:50:58 - mmengine - INFO - Epoch(train) [132][200/293] lr: 5.000000e-04 eta: 2:09:34 time: 0.388537 data_time: 0.081410 memory: 12861 loss_kpt: 0.000775 acc_pose: 0.787804 loss: 0.000775 2022/10/13 14:51:17 - mmengine - INFO - Epoch(train) [132][250/293] lr: 5.000000e-04 eta: 2:09:18 time: 0.386992 data_time: 0.094710 memory: 12861 loss_kpt: 0.000783 acc_pose: 0.783883 loss: 0.000783 2022/10/13 14:51:34 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:51:55 - mmengine - INFO - Epoch(train) [133][50/293] lr: 5.000000e-04 eta: 2:08:40 time: 0.408755 data_time: 0.102764 memory: 12861 loss_kpt: 0.000772 acc_pose: 0.762156 loss: 0.000772 2022/10/13 14:52:14 - mmengine - INFO - Epoch(train) [133][100/293] lr: 5.000000e-04 eta: 2:08:25 time: 0.385831 data_time: 0.083629 memory: 12861 loss_kpt: 0.000783 acc_pose: 0.779055 loss: 0.000783 2022/10/13 14:52:33 - mmengine - INFO - Epoch(train) [133][150/293] lr: 5.000000e-04 eta: 2:08:09 time: 0.381577 data_time: 0.085982 memory: 12861 loss_kpt: 0.000767 acc_pose: 0.782571 loss: 0.000767 2022/10/13 14:52:52 - mmengine - INFO - Epoch(train) [133][200/293] lr: 5.000000e-04 eta: 2:07:54 time: 0.387272 data_time: 0.092818 memory: 12861 loss_kpt: 0.000778 acc_pose: 0.729137 loss: 0.000778 2022/10/13 14:53:12 - mmengine - INFO - Epoch(train) [133][250/293] lr: 5.000000e-04 eta: 2:07:38 time: 0.384189 data_time: 0.098038 memory: 12861 loss_kpt: 0.000767 acc_pose: 0.781210 loss: 0.000767 2022/10/13 14:53:28 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:53:41 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:53:48 - mmengine - INFO - Epoch(train) [134][50/293] lr: 5.000000e-04 eta: 2:07:00 time: 0.401258 data_time: 0.102587 memory: 12861 loss_kpt: 0.000760 acc_pose: 0.838107 loss: 0.000760 2022/10/13 14:54:07 - mmengine - INFO - Epoch(train) [134][100/293] lr: 5.000000e-04 eta: 2:06:44 time: 0.383584 data_time: 0.096142 memory: 12861 loss_kpt: 0.000772 acc_pose: 0.816919 loss: 0.000772 2022/10/13 14:54:27 - mmengine - INFO - Epoch(train) [134][150/293] lr: 5.000000e-04 eta: 2:06:29 time: 0.389389 data_time: 0.094763 memory: 12861 loss_kpt: 0.000767 acc_pose: 0.729320 loss: 0.000767 2022/10/13 14:54:46 - mmengine - INFO - Epoch(train) [134][200/293] lr: 5.000000e-04 eta: 2:06:13 time: 0.383863 data_time: 0.080399 memory: 12861 loss_kpt: 0.000765 acc_pose: 0.763674 loss: 0.000765 2022/10/13 14:55:05 - mmengine - INFO - Epoch(train) [134][250/293] lr: 5.000000e-04 eta: 2:05:57 time: 0.379124 data_time: 0.088121 memory: 12861 loss_kpt: 0.000775 acc_pose: 0.729234 loss: 0.000775 2022/10/13 14:55:21 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:55:42 - mmengine - INFO - Epoch(train) [135][50/293] lr: 5.000000e-04 eta: 2:05:19 time: 0.403756 data_time: 0.094794 memory: 12861 loss_kpt: 0.000771 acc_pose: 0.802076 loss: 0.000771 2022/10/13 14:56:01 - mmengine - INFO - Epoch(train) [135][100/293] lr: 5.000000e-04 eta: 2:05:04 time: 0.389269 data_time: 0.090073 memory: 12861 loss_kpt: 0.000766 acc_pose: 0.794221 loss: 0.000766 2022/10/13 14:56:21 - mmengine - INFO - Epoch(train) [135][150/293] lr: 5.000000e-04 eta: 2:04:49 time: 0.391198 data_time: 0.076902 memory: 12861 loss_kpt: 0.000766 acc_pose: 0.757410 loss: 0.000766 2022/10/13 14:56:40 - mmengine - INFO - Epoch(train) [135][200/293] lr: 5.000000e-04 eta: 2:04:33 time: 0.384017 data_time: 0.085175 memory: 12861 loss_kpt: 0.000759 acc_pose: 0.775628 loss: 0.000759 2022/10/13 14:56:59 - mmengine - INFO - Epoch(train) [135][250/293] lr: 5.000000e-04 eta: 2:04:17 time: 0.383858 data_time: 0.086392 memory: 12861 loss_kpt: 0.000772 acc_pose: 0.828226 loss: 0.000772 2022/10/13 14:57:16 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:57:36 - mmengine - INFO - Epoch(train) [136][50/293] lr: 5.000000e-04 eta: 2:03:40 time: 0.412136 data_time: 0.100115 memory: 12861 loss_kpt: 0.000772 acc_pose: 0.753733 loss: 0.000772 2022/10/13 14:57:56 - mmengine - INFO - Epoch(train) [136][100/293] lr: 5.000000e-04 eta: 2:03:24 time: 0.395286 data_time: 0.088794 memory: 12861 loss_kpt: 0.000757 acc_pose: 0.802208 loss: 0.000757 2022/10/13 14:58:15 - mmengine - INFO - Epoch(train) [136][150/293] lr: 5.000000e-04 eta: 2:03:08 time: 0.376129 data_time: 0.082491 memory: 12861 loss_kpt: 0.000779 acc_pose: 0.821544 loss: 0.000779 2022/10/13 14:58:34 - mmengine - INFO - Epoch(train) [136][200/293] lr: 5.000000e-04 eta: 2:02:53 time: 0.393114 data_time: 0.087774 memory: 12861 loss_kpt: 0.000771 acc_pose: 0.736860 loss: 0.000771 2022/10/13 14:58:54 - mmengine - INFO - Epoch(train) [136][250/293] lr: 5.000000e-04 eta: 2:02:37 time: 0.384437 data_time: 0.088364 memory: 12861 loss_kpt: 0.000765 acc_pose: 0.818290 loss: 0.000765 2022/10/13 14:59:10 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 14:59:30 - mmengine - INFO - Epoch(train) [137][50/293] lr: 5.000000e-04 eta: 2:01:59 time: 0.389554 data_time: 0.082571 memory: 12861 loss_kpt: 0.000775 acc_pose: 0.757754 loss: 0.000775 2022/10/13 14:59:49 - mmengine - INFO - Epoch(train) [137][100/293] lr: 5.000000e-04 eta: 2:01:44 time: 0.394123 data_time: 0.091036 memory: 12861 loss_kpt: 0.000769 acc_pose: 0.768091 loss: 0.000769 2022/10/13 15:00:08 - mmengine - INFO - Epoch(train) [137][150/293] lr: 5.000000e-04 eta: 2:01:28 time: 0.379657 data_time: 0.088030 memory: 12861 loss_kpt: 0.000752 acc_pose: 0.740580 loss: 0.000752 2022/10/13 15:00:09 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:00:28 - mmengine - INFO - Epoch(train) [137][200/293] lr: 5.000000e-04 eta: 2:01:13 time: 0.389684 data_time: 0.086084 memory: 12861 loss_kpt: 0.000776 acc_pose: 0.776425 loss: 0.000776 2022/10/13 15:00:47 - mmengine - INFO - Epoch(train) [137][250/293] lr: 5.000000e-04 eta: 2:00:57 time: 0.391100 data_time: 0.084341 memory: 12861 loss_kpt: 0.000768 acc_pose: 0.772651 loss: 0.000768 2022/10/13 15:01:04 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:01:25 - mmengine - INFO - Epoch(train) [138][50/293] lr: 5.000000e-04 eta: 2:00:20 time: 0.403949 data_time: 0.097168 memory: 12861 loss_kpt: 0.000768 acc_pose: 0.762640 loss: 0.000768 2022/10/13 15:01:44 - mmengine - INFO - Epoch(train) [138][100/293] lr: 5.000000e-04 eta: 2:00:04 time: 0.383291 data_time: 0.084836 memory: 12861 loss_kpt: 0.000761 acc_pose: 0.706960 loss: 0.000761 2022/10/13 15:02:03 - mmengine - INFO - Epoch(train) [138][150/293] lr: 5.000000e-04 eta: 1:59:48 time: 0.374516 data_time: 0.071369 memory: 12861 loss_kpt: 0.000775 acc_pose: 0.784558 loss: 0.000775 2022/10/13 15:02:22 - mmengine - INFO - Epoch(train) [138][200/293] lr: 5.000000e-04 eta: 1:59:32 time: 0.386949 data_time: 0.084733 memory: 12861 loss_kpt: 0.000768 acc_pose: 0.761783 loss: 0.000768 2022/10/13 15:02:41 - mmengine - INFO - Epoch(train) [138][250/293] lr: 5.000000e-04 eta: 1:59:16 time: 0.381861 data_time: 0.105306 memory: 12861 loss_kpt: 0.000781 acc_pose: 0.734393 loss: 0.000781 2022/10/13 15:02:58 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:03:18 - mmengine - INFO - Epoch(train) [139][50/293] lr: 5.000000e-04 eta: 1:58:39 time: 0.411129 data_time: 0.097180 memory: 12861 loss_kpt: 0.000752 acc_pose: 0.800655 loss: 0.000752 2022/10/13 15:03:37 - mmengine - INFO - Epoch(train) [139][100/293] lr: 5.000000e-04 eta: 1:58:23 time: 0.374618 data_time: 0.071659 memory: 12861 loss_kpt: 0.000750 acc_pose: 0.784499 loss: 0.000750 2022/10/13 15:03:56 - mmengine - INFO - Epoch(train) [139][150/293] lr: 5.000000e-04 eta: 1:58:07 time: 0.381687 data_time: 0.062408 memory: 12861 loss_kpt: 0.000754 acc_pose: 0.779092 loss: 0.000754 2022/10/13 15:04:16 - mmengine - INFO - Epoch(train) [139][200/293] lr: 5.000000e-04 eta: 1:57:52 time: 0.406540 data_time: 0.095872 memory: 12861 loss_kpt: 0.000760 acc_pose: 0.804367 loss: 0.000760 2022/10/13 15:04:36 - mmengine - INFO - Epoch(train) [139][250/293] lr: 5.000000e-04 eta: 1:57:37 time: 0.388678 data_time: 0.087976 memory: 12861 loss_kpt: 0.000748 acc_pose: 0.772420 loss: 0.000748 2022/10/13 15:04:53 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:05:13 - mmengine - INFO - Epoch(train) [140][50/293] lr: 5.000000e-04 eta: 1:56:59 time: 0.404183 data_time: 0.106754 memory: 12861 loss_kpt: 0.000770 acc_pose: 0.764830 loss: 0.000770 2022/10/13 15:05:32 - mmengine - INFO - Epoch(train) [140][100/293] lr: 5.000000e-04 eta: 1:56:44 time: 0.391169 data_time: 0.084829 memory: 12861 loss_kpt: 0.000785 acc_pose: 0.755757 loss: 0.000785 2022/10/13 15:05:51 - mmengine - INFO - Epoch(train) [140][150/293] lr: 5.000000e-04 eta: 1:56:28 time: 0.379404 data_time: 0.082471 memory: 12861 loss_kpt: 0.000762 acc_pose: 0.777161 loss: 0.000762 2022/10/13 15:06:12 - mmengine - INFO - Epoch(train) [140][200/293] lr: 5.000000e-04 eta: 1:56:13 time: 0.406183 data_time: 0.098961 memory: 12861 loss_kpt: 0.000767 acc_pose: 0.771859 loss: 0.000767 2022/10/13 15:06:31 - mmengine - INFO - Epoch(train) [140][250/293] lr: 5.000000e-04 eta: 1:55:57 time: 0.394163 data_time: 0.101222 memory: 12861 loss_kpt: 0.000754 acc_pose: 0.823310 loss: 0.000754 2022/10/13 15:06:41 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:06:48 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:06:48 - mmengine - INFO - Saving checkpoint at 140 epochs 2022/10/13 15:06:56 - mmengine - INFO - Epoch(val) [140][50/407] eta: 0:00:45 time: 0.127287 data_time: 0.063662 memory: 12861 2022/10/13 15:07:02 - mmengine - INFO - Epoch(val) [140][100/407] eta: 0:00:37 time: 0.121834 data_time: 0.059672 memory: 983 2022/10/13 15:07:09 - mmengine - INFO - Epoch(val) [140][150/407] eta: 0:00:33 time: 0.130210 data_time: 0.068308 memory: 983 2022/10/13 15:07:15 - mmengine - INFO - Epoch(val) [140][200/407] eta: 0:00:26 time: 0.126752 data_time: 0.065587 memory: 983 2022/10/13 15:07:21 - mmengine - INFO - Epoch(val) [140][250/407] eta: 0:00:19 time: 0.124726 data_time: 0.063267 memory: 983 2022/10/13 15:07:28 - mmengine - INFO - Epoch(val) [140][300/407] eta: 0:00:13 time: 0.125856 data_time: 0.065623 memory: 983 2022/10/13 15:07:34 - mmengine - INFO - Epoch(val) [140][350/407] eta: 0:00:06 time: 0.121143 data_time: 0.060440 memory: 983 2022/10/13 15:07:40 - mmengine - INFO - Epoch(val) [140][400/407] eta: 0:00:00 time: 0.121415 data_time: 0.060015 memory: 983 2022/10/13 15:08:16 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 15:08:31 - mmengine - INFO - Epoch(val) [140][407/407] coco/AP: 0.657345 coco/AP .5: 0.875109 coco/AP .75: 0.726999 coco/AP (M): 0.616078 coco/AP (L): 0.727580 coco/AR: 0.715349 coco/AR .5: 0.915460 coco/AR .75: 0.780856 coco/AR (M): 0.667604 coco/AR (L): 0.782869 2022/10/13 15:08:31 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384/best_coco/AP_epoch_130.pth is removed 2022/10/13 15:08:32 - mmengine - INFO - The best checkpoint with 0.6573 coco/AP at 140 epoch is saved to best_coco/AP_epoch_140.pth. 2022/10/13 15:08:52 - mmengine - INFO - Epoch(train) [141][50/293] lr: 5.000000e-04 eta: 1:55:20 time: 0.388943 data_time: 0.083336 memory: 12861 loss_kpt: 0.000764 acc_pose: 0.758081 loss: 0.000764 2022/10/13 15:09:11 - mmengine - INFO - Epoch(train) [141][100/293] lr: 5.000000e-04 eta: 1:55:04 time: 0.382823 data_time: 0.081789 memory: 12861 loss_kpt: 0.000768 acc_pose: 0.801185 loss: 0.000768 2022/10/13 15:09:30 - mmengine - INFO - Epoch(train) [141][150/293] lr: 5.000000e-04 eta: 1:54:48 time: 0.387020 data_time: 0.081688 memory: 12861 loss_kpt: 0.000763 acc_pose: 0.731661 loss: 0.000763 2022/10/13 15:09:50 - mmengine - INFO - Epoch(train) [141][200/293] lr: 5.000000e-04 eta: 1:54:33 time: 0.390811 data_time: 0.090014 memory: 12861 loss_kpt: 0.000771 acc_pose: 0.786157 loss: 0.000771 2022/10/13 15:10:10 - mmengine - INFO - Epoch(train) [141][250/293] lr: 5.000000e-04 eta: 1:54:17 time: 0.395715 data_time: 0.091763 memory: 12861 loss_kpt: 0.000759 acc_pose: 0.732648 loss: 0.000759 2022/10/13 15:10:26 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:10:46 - mmengine - INFO - Epoch(train) [142][50/293] lr: 5.000000e-04 eta: 1:53:40 time: 0.399763 data_time: 0.093196 memory: 12861 loss_kpt: 0.000760 acc_pose: 0.788293 loss: 0.000760 2022/10/13 15:11:06 - mmengine - INFO - Epoch(train) [142][100/293] lr: 5.000000e-04 eta: 1:53:24 time: 0.393432 data_time: 0.090504 memory: 12861 loss_kpt: 0.000781 acc_pose: 0.761437 loss: 0.000781 2022/10/13 15:11:25 - mmengine - INFO - Epoch(train) [142][150/293] lr: 5.000000e-04 eta: 1:53:09 time: 0.383236 data_time: 0.073615 memory: 12861 loss_kpt: 0.000770 acc_pose: 0.781851 loss: 0.000770 2022/10/13 15:11:45 - mmengine - INFO - Epoch(train) [142][200/293] lr: 5.000000e-04 eta: 1:52:53 time: 0.405067 data_time: 0.093026 memory: 12861 loss_kpt: 0.000774 acc_pose: 0.751659 loss: 0.000774 2022/10/13 15:12:05 - mmengine - INFO - Epoch(train) [142][250/293] lr: 5.000000e-04 eta: 1:52:38 time: 0.395907 data_time: 0.096387 memory: 12861 loss_kpt: 0.000758 acc_pose: 0.769679 loss: 0.000758 2022/10/13 15:12:22 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:12:43 - mmengine - INFO - Epoch(train) [143][50/293] lr: 5.000000e-04 eta: 1:52:01 time: 0.417328 data_time: 0.113127 memory: 12861 loss_kpt: 0.000762 acc_pose: 0.719060 loss: 0.000762 2022/10/13 15:13:02 - mmengine - INFO - Epoch(train) [143][100/293] lr: 5.000000e-04 eta: 1:51:46 time: 0.389299 data_time: 0.092031 memory: 12861 loss_kpt: 0.000762 acc_pose: 0.749597 loss: 0.000762 2022/10/13 15:13:21 - mmengine - INFO - Epoch(train) [143][150/293] lr: 5.000000e-04 eta: 1:51:30 time: 0.384678 data_time: 0.095484 memory: 12861 loss_kpt: 0.000767 acc_pose: 0.772576 loss: 0.000767 2022/10/13 15:13:41 - mmengine - INFO - Epoch(train) [143][200/293] lr: 5.000000e-04 eta: 1:51:14 time: 0.385719 data_time: 0.087704 memory: 12861 loss_kpt: 0.000764 acc_pose: 0.796074 loss: 0.000764 2022/10/13 15:14:00 - mmengine - INFO - Epoch(train) [143][250/293] lr: 5.000000e-04 eta: 1:50:58 time: 0.393381 data_time: 0.104826 memory: 12861 loss_kpt: 0.000775 acc_pose: 0.698746 loss: 0.000775 2022/10/13 15:14:17 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:14:37 - mmengine - INFO - Epoch(train) [144][50/293] lr: 5.000000e-04 eta: 1:50:22 time: 0.405399 data_time: 0.098995 memory: 12861 loss_kpt: 0.000776 acc_pose: 0.711714 loss: 0.000776 2022/10/13 15:14:57 - mmengine - INFO - Epoch(train) [144][100/293] lr: 5.000000e-04 eta: 1:50:06 time: 0.394255 data_time: 0.092874 memory: 12861 loss_kpt: 0.000770 acc_pose: 0.725378 loss: 0.000770 2022/10/13 15:14:57 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:15:17 - mmengine - INFO - Epoch(train) [144][150/293] lr: 5.000000e-04 eta: 1:49:50 time: 0.393904 data_time: 0.086462 memory: 12861 loss_kpt: 0.000775 acc_pose: 0.748195 loss: 0.000775 2022/10/13 15:15:36 - mmengine - INFO - Epoch(train) [144][200/293] lr: 5.000000e-04 eta: 1:49:34 time: 0.381855 data_time: 0.095190 memory: 12861 loss_kpt: 0.000761 acc_pose: 0.778983 loss: 0.000761 2022/10/13 15:15:55 - mmengine - INFO - Epoch(train) [144][250/293] lr: 5.000000e-04 eta: 1:49:19 time: 0.385600 data_time: 0.088505 memory: 12861 loss_kpt: 0.000764 acc_pose: 0.784948 loss: 0.000764 2022/10/13 15:16:12 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:16:33 - mmengine - INFO - Epoch(train) [145][50/293] lr: 5.000000e-04 eta: 1:48:42 time: 0.419647 data_time: 0.101064 memory: 12861 loss_kpt: 0.000780 acc_pose: 0.773474 loss: 0.000780 2022/10/13 15:16:53 - mmengine - INFO - Epoch(train) [145][100/293] lr: 5.000000e-04 eta: 1:48:27 time: 0.403766 data_time: 0.105473 memory: 12861 loss_kpt: 0.000752 acc_pose: 0.700257 loss: 0.000752 2022/10/13 15:17:13 - mmengine - INFO - Epoch(train) [145][150/293] lr: 5.000000e-04 eta: 1:48:11 time: 0.396000 data_time: 0.093208 memory: 12861 loss_kpt: 0.000769 acc_pose: 0.760801 loss: 0.000769 2022/10/13 15:17:31 - mmengine - INFO - Epoch(train) [145][200/293] lr: 5.000000e-04 eta: 1:47:55 time: 0.373682 data_time: 0.080644 memory: 12861 loss_kpt: 0.000765 acc_pose: 0.811247 loss: 0.000765 2022/10/13 15:17:50 - mmengine - INFO - Epoch(train) [145][250/293] lr: 5.000000e-04 eta: 1:47:39 time: 0.372822 data_time: 0.085267 memory: 12861 loss_kpt: 0.000771 acc_pose: 0.793957 loss: 0.000771 2022/10/13 15:18:07 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:18:27 - mmengine - INFO - Epoch(train) [146][50/293] lr: 5.000000e-04 eta: 1:47:03 time: 0.408492 data_time: 0.100076 memory: 12861 loss_kpt: 0.000761 acc_pose: 0.759170 loss: 0.000761 2022/10/13 15:18:46 - mmengine - INFO - Epoch(train) [146][100/293] lr: 5.000000e-04 eta: 1:46:47 time: 0.379875 data_time: 0.087796 memory: 12861 loss_kpt: 0.000758 acc_pose: 0.777068 loss: 0.000758 2022/10/13 15:19:06 - mmengine - INFO - Epoch(train) [146][150/293] lr: 5.000000e-04 eta: 1:46:31 time: 0.391698 data_time: 0.085635 memory: 12861 loss_kpt: 0.000776 acc_pose: 0.749030 loss: 0.000776 2022/10/13 15:19:25 - mmengine - INFO - Epoch(train) [146][200/293] lr: 5.000000e-04 eta: 1:46:15 time: 0.388519 data_time: 0.092281 memory: 12861 loss_kpt: 0.000762 acc_pose: 0.761442 loss: 0.000762 2022/10/13 15:19:44 - mmengine - INFO - Epoch(train) [146][250/293] lr: 5.000000e-04 eta: 1:45:59 time: 0.384420 data_time: 0.091861 memory: 12861 loss_kpt: 0.000759 acc_pose: 0.768864 loss: 0.000759 2022/10/13 15:20:01 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:20:22 - mmengine - INFO - Epoch(train) [147][50/293] lr: 5.000000e-04 eta: 1:45:23 time: 0.421175 data_time: 0.102884 memory: 12861 loss_kpt: 0.000766 acc_pose: 0.759462 loss: 0.000766 2022/10/13 15:20:42 - mmengine - INFO - Epoch(train) [147][100/293] lr: 5.000000e-04 eta: 1:45:08 time: 0.395932 data_time: 0.082437 memory: 12861 loss_kpt: 0.000760 acc_pose: 0.736516 loss: 0.000760 2022/10/13 15:21:01 - mmengine - INFO - Epoch(train) [147][150/293] lr: 5.000000e-04 eta: 1:44:52 time: 0.392956 data_time: 0.069475 memory: 12861 loss_kpt: 0.000776 acc_pose: 0.711816 loss: 0.000776 2022/10/13 15:21:21 - mmengine - INFO - Epoch(train) [147][200/293] lr: 5.000000e-04 eta: 1:44:36 time: 0.389894 data_time: 0.076087 memory: 12861 loss_kpt: 0.000778 acc_pose: 0.833189 loss: 0.000778 2022/10/13 15:21:29 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:21:40 - mmengine - INFO - Epoch(train) [147][250/293] lr: 5.000000e-04 eta: 1:44:20 time: 0.390801 data_time: 0.067041 memory: 12861 loss_kpt: 0.000773 acc_pose: 0.740252 loss: 0.000773 2022/10/13 15:21:57 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:22:18 - mmengine - INFO - Epoch(train) [148][50/293] lr: 5.000000e-04 eta: 1:43:44 time: 0.418375 data_time: 0.100084 memory: 12861 loss_kpt: 0.000742 acc_pose: 0.772710 loss: 0.000742 2022/10/13 15:22:37 - mmengine - INFO - Epoch(train) [148][100/293] lr: 5.000000e-04 eta: 1:43:28 time: 0.388420 data_time: 0.085090 memory: 12861 loss_kpt: 0.000763 acc_pose: 0.745927 loss: 0.000763 2022/10/13 15:22:56 - mmengine - INFO - Epoch(train) [148][150/293] lr: 5.000000e-04 eta: 1:43:13 time: 0.384966 data_time: 0.069219 memory: 12861 loss_kpt: 0.000776 acc_pose: 0.758236 loss: 0.000776 2022/10/13 15:23:16 - mmengine - INFO - Epoch(train) [148][200/293] lr: 5.000000e-04 eta: 1:42:57 time: 0.398431 data_time: 0.085230 memory: 12861 loss_kpt: 0.000765 acc_pose: 0.790069 loss: 0.000765 2022/10/13 15:23:36 - mmengine - INFO - Epoch(train) [148][250/293] lr: 5.000000e-04 eta: 1:42:41 time: 0.389669 data_time: 0.085038 memory: 12861 loss_kpt: 0.000780 acc_pose: 0.782803 loss: 0.000780 2022/10/13 15:23:52 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:24:13 - mmengine - INFO - Epoch(train) [149][50/293] lr: 5.000000e-04 eta: 1:42:05 time: 0.409526 data_time: 0.080784 memory: 12861 loss_kpt: 0.000763 acc_pose: 0.822436 loss: 0.000763 2022/10/13 15:24:32 - mmengine - INFO - Epoch(train) [149][100/293] lr: 5.000000e-04 eta: 1:41:49 time: 0.382460 data_time: 0.078805 memory: 12861 loss_kpt: 0.000756 acc_pose: 0.782037 loss: 0.000756 2022/10/13 15:24:51 - mmengine - INFO - Epoch(train) [149][150/293] lr: 5.000000e-04 eta: 1:41:33 time: 0.381903 data_time: 0.082303 memory: 12861 loss_kpt: 0.000753 acc_pose: 0.760558 loss: 0.000753 2022/10/13 15:25:10 - mmengine - INFO - Epoch(train) [149][200/293] lr: 5.000000e-04 eta: 1:41:17 time: 0.384635 data_time: 0.089131 memory: 12861 loss_kpt: 0.000770 acc_pose: 0.772363 loss: 0.000770 2022/10/13 15:25:30 - mmengine - INFO - Epoch(train) [149][250/293] lr: 5.000000e-04 eta: 1:41:01 time: 0.388471 data_time: 0.090628 memory: 12861 loss_kpt: 0.000774 acc_pose: 0.735873 loss: 0.000774 2022/10/13 15:25:46 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:26:07 - mmengine - INFO - Epoch(train) [150][50/293] lr: 5.000000e-04 eta: 1:40:25 time: 0.412781 data_time: 0.096796 memory: 12861 loss_kpt: 0.000766 acc_pose: 0.764257 loss: 0.000766 2022/10/13 15:26:26 - mmengine - INFO - Epoch(train) [150][100/293] lr: 5.000000e-04 eta: 1:40:09 time: 0.390980 data_time: 0.102604 memory: 12861 loss_kpt: 0.000752 acc_pose: 0.740908 loss: 0.000752 2022/10/13 15:26:45 - mmengine - INFO - Epoch(train) [150][150/293] lr: 5.000000e-04 eta: 1:39:54 time: 0.387283 data_time: 0.092741 memory: 12861 loss_kpt: 0.000764 acc_pose: 0.772963 loss: 0.000764 2022/10/13 15:27:05 - mmengine - INFO - Epoch(train) [150][200/293] lr: 5.000000e-04 eta: 1:39:38 time: 0.391825 data_time: 0.080874 memory: 12861 loss_kpt: 0.000765 acc_pose: 0.752788 loss: 0.000765 2022/10/13 15:27:24 - mmengine - INFO - Epoch(train) [150][250/293] lr: 5.000000e-04 eta: 1:39:22 time: 0.386125 data_time: 0.101283 memory: 12861 loss_kpt: 0.000757 acc_pose: 0.798888 loss: 0.000757 2022/10/13 15:27:41 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:27:41 - mmengine - INFO - Saving checkpoint at 150 epochs 2022/10/13 15:27:50 - mmengine - INFO - Epoch(val) [150][50/407] eta: 0:00:47 time: 0.133577 data_time: 0.071744 memory: 12861 2022/10/13 15:27:56 - mmengine - INFO - Epoch(val) [150][100/407] eta: 0:00:36 time: 0.120207 data_time: 0.059611 memory: 983 2022/10/13 15:28:02 - mmengine - INFO - Epoch(val) [150][150/407] eta: 0:00:31 time: 0.121878 data_time: 0.060233 memory: 983 2022/10/13 15:28:09 - mmengine - INFO - Epoch(val) [150][200/407] eta: 0:00:26 time: 0.127711 data_time: 0.066313 memory: 983 2022/10/13 15:28:15 - mmengine - INFO - Epoch(val) [150][250/407] eta: 0:00:19 time: 0.123488 data_time: 0.062457 memory: 983 2022/10/13 15:28:21 - mmengine - INFO - Epoch(val) [150][300/407] eta: 0:00:13 time: 0.122930 data_time: 0.062067 memory: 983 2022/10/13 15:28:27 - mmengine - INFO - Epoch(val) [150][350/407] eta: 0:00:07 time: 0.126391 data_time: 0.064638 memory: 983 2022/10/13 15:28:33 - mmengine - INFO - Epoch(val) [150][400/407] eta: 0:00:00 time: 0.116760 data_time: 0.057577 memory: 983 2022/10/13 15:29:10 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 15:29:24 - mmengine - INFO - Epoch(val) [150][407/407] coco/AP: 0.661476 coco/AP .5: 0.875362 coco/AP .75: 0.732343 coco/AP (M): 0.618075 coco/AP (L): 0.735213 coco/AR: 0.719994 coco/AR .5: 0.915302 coco/AR .75: 0.785894 coco/AR (M): 0.669462 coco/AR (L): 0.791787 2022/10/13 15:29:24 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384/best_coco/AP_epoch_140.pth is removed 2022/10/13 15:29:26 - mmengine - INFO - The best checkpoint with 0.6615 coco/AP at 150 epoch is saved to best_coco/AP_epoch_150.pth. 2022/10/13 15:29:46 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:29:46 - mmengine - INFO - Epoch(train) [151][50/293] lr: 5.000000e-04 eta: 1:38:46 time: 0.400116 data_time: 0.097339 memory: 12861 loss_kpt: 0.000754 acc_pose: 0.777662 loss: 0.000754 2022/10/13 15:30:05 - mmengine - INFO - Epoch(train) [151][100/293] lr: 5.000000e-04 eta: 1:38:30 time: 0.386628 data_time: 0.087790 memory: 12861 loss_kpt: 0.000765 acc_pose: 0.752727 loss: 0.000765 2022/10/13 15:30:25 - mmengine - INFO - Epoch(train) [151][150/293] lr: 5.000000e-04 eta: 1:38:14 time: 0.390071 data_time: 0.088292 memory: 12861 loss_kpt: 0.000768 acc_pose: 0.789004 loss: 0.000768 2022/10/13 15:30:43 - mmengine - INFO - Epoch(train) [151][200/293] lr: 5.000000e-04 eta: 1:37:58 time: 0.376811 data_time: 0.074545 memory: 12861 loss_kpt: 0.000760 acc_pose: 0.743986 loss: 0.000760 2022/10/13 15:31:02 - mmengine - INFO - Epoch(train) [151][250/293] lr: 5.000000e-04 eta: 1:37:42 time: 0.381623 data_time: 0.087317 memory: 12861 loss_kpt: 0.000754 acc_pose: 0.807604 loss: 0.000754 2022/10/13 15:31:19 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:31:39 - mmengine - INFO - Epoch(train) [152][50/293] lr: 5.000000e-04 eta: 1:37:06 time: 0.407436 data_time: 0.098040 memory: 12861 loss_kpt: 0.000767 acc_pose: 0.785373 loss: 0.000767 2022/10/13 15:31:59 - mmengine - INFO - Epoch(train) [152][100/293] lr: 5.000000e-04 eta: 1:36:50 time: 0.388242 data_time: 0.094113 memory: 12861 loss_kpt: 0.000761 acc_pose: 0.790228 loss: 0.000761 2022/10/13 15:32:19 - mmengine - INFO - Epoch(train) [152][150/293] lr: 5.000000e-04 eta: 1:36:34 time: 0.397091 data_time: 0.092497 memory: 12861 loss_kpt: 0.000753 acc_pose: 0.772562 loss: 0.000753 2022/10/13 15:32:38 - mmengine - INFO - Epoch(train) [152][200/293] lr: 5.000000e-04 eta: 1:36:18 time: 0.389030 data_time: 0.098108 memory: 12861 loss_kpt: 0.000770 acc_pose: 0.722909 loss: 0.000770 2022/10/13 15:32:58 - mmengine - INFO - Epoch(train) [152][250/293] lr: 5.000000e-04 eta: 1:36:02 time: 0.390490 data_time: 0.096355 memory: 12861 loss_kpt: 0.000758 acc_pose: 0.768146 loss: 0.000758 2022/10/13 15:33:14 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:33:34 - mmengine - INFO - Epoch(train) [153][50/293] lr: 5.000000e-04 eta: 1:35:27 time: 0.403423 data_time: 0.099027 memory: 12861 loss_kpt: 0.000765 acc_pose: 0.756049 loss: 0.000765 2022/10/13 15:33:54 - mmengine - INFO - Epoch(train) [153][100/293] lr: 5.000000e-04 eta: 1:35:11 time: 0.397734 data_time: 0.095501 memory: 12861 loss_kpt: 0.000758 acc_pose: 0.786380 loss: 0.000758 2022/10/13 15:34:13 - mmengine - INFO - Epoch(train) [153][150/293] lr: 5.000000e-04 eta: 1:34:55 time: 0.386729 data_time: 0.085042 memory: 12861 loss_kpt: 0.000771 acc_pose: 0.775806 loss: 0.000771 2022/10/13 15:34:32 - mmengine - INFO - Epoch(train) [153][200/293] lr: 5.000000e-04 eta: 1:34:39 time: 0.381119 data_time: 0.084487 memory: 12861 loss_kpt: 0.000766 acc_pose: 0.740251 loss: 0.000766 2022/10/13 15:34:52 - mmengine - INFO - Epoch(train) [153][250/293] lr: 5.000000e-04 eta: 1:34:23 time: 0.395744 data_time: 0.091587 memory: 12861 loss_kpt: 0.000758 acc_pose: 0.804789 loss: 0.000758 2022/10/13 15:35:09 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:35:29 - mmengine - INFO - Epoch(train) [154][50/293] lr: 5.000000e-04 eta: 1:33:47 time: 0.408013 data_time: 0.104751 memory: 12861 loss_kpt: 0.000747 acc_pose: 0.795698 loss: 0.000747 2022/10/13 15:35:49 - mmengine - INFO - Epoch(train) [154][100/293] lr: 5.000000e-04 eta: 1:33:32 time: 0.395154 data_time: 0.099438 memory: 12861 loss_kpt: 0.000753 acc_pose: 0.733273 loss: 0.000753 2022/10/13 15:36:08 - mmengine - INFO - Epoch(train) [154][150/293] lr: 5.000000e-04 eta: 1:33:16 time: 0.394556 data_time: 0.087144 memory: 12861 loss_kpt: 0.000749 acc_pose: 0.804174 loss: 0.000749 2022/10/13 15:36:16 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:36:28 - mmengine - INFO - Epoch(train) [154][200/293] lr: 5.000000e-04 eta: 1:33:00 time: 0.388461 data_time: 0.098866 memory: 12861 loss_kpt: 0.000772 acc_pose: 0.767314 loss: 0.000772 2022/10/13 15:36:47 - mmengine - INFO - Epoch(train) [154][250/293] lr: 5.000000e-04 eta: 1:32:44 time: 0.386795 data_time: 0.087086 memory: 12861 loss_kpt: 0.000761 acc_pose: 0.764062 loss: 0.000761 2022/10/13 15:37:04 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:37:24 - mmengine - INFO - Epoch(train) [155][50/293] lr: 5.000000e-04 eta: 1:32:08 time: 0.400481 data_time: 0.098076 memory: 12861 loss_kpt: 0.000766 acc_pose: 0.756143 loss: 0.000766 2022/10/13 15:37:43 - mmengine - INFO - Epoch(train) [155][100/293] lr: 5.000000e-04 eta: 1:31:52 time: 0.378959 data_time: 0.085178 memory: 12861 loss_kpt: 0.000779 acc_pose: 0.745513 loss: 0.000779 2022/10/13 15:38:02 - mmengine - INFO - Epoch(train) [155][150/293] lr: 5.000000e-04 eta: 1:31:36 time: 0.377916 data_time: 0.082968 memory: 12861 loss_kpt: 0.000751 acc_pose: 0.818389 loss: 0.000751 2022/10/13 15:38:21 - mmengine - INFO - Epoch(train) [155][200/293] lr: 5.000000e-04 eta: 1:31:20 time: 0.389961 data_time: 0.082817 memory: 12861 loss_kpt: 0.000765 acc_pose: 0.842037 loss: 0.000765 2022/10/13 15:38:41 - mmengine - INFO - Epoch(train) [155][250/293] lr: 5.000000e-04 eta: 1:31:04 time: 0.387873 data_time: 0.088123 memory: 12861 loss_kpt: 0.000763 acc_pose: 0.763767 loss: 0.000763 2022/10/13 15:38:57 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:39:17 - mmengine - INFO - Epoch(train) [156][50/293] lr: 5.000000e-04 eta: 1:30:28 time: 0.407117 data_time: 0.099921 memory: 12861 loss_kpt: 0.000757 acc_pose: 0.760754 loss: 0.000757 2022/10/13 15:39:37 - mmengine - INFO - Epoch(train) [156][100/293] lr: 5.000000e-04 eta: 1:30:12 time: 0.386391 data_time: 0.080391 memory: 12861 loss_kpt: 0.000766 acc_pose: 0.737830 loss: 0.000766 2022/10/13 15:39:56 - mmengine - INFO - Epoch(train) [156][150/293] lr: 5.000000e-04 eta: 1:29:56 time: 0.388567 data_time: 0.086284 memory: 12861 loss_kpt: 0.000756 acc_pose: 0.783354 loss: 0.000756 2022/10/13 15:40:15 - mmengine - INFO - Epoch(train) [156][200/293] lr: 5.000000e-04 eta: 1:29:40 time: 0.382172 data_time: 0.097536 memory: 12861 loss_kpt: 0.000751 acc_pose: 0.710393 loss: 0.000751 2022/10/13 15:40:34 - mmengine - INFO - Epoch(train) [156][250/293] lr: 5.000000e-04 eta: 1:29:24 time: 0.373596 data_time: 0.072660 memory: 12861 loss_kpt: 0.000770 acc_pose: 0.793509 loss: 0.000770 2022/10/13 15:40:50 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:41:11 - mmengine - INFO - Epoch(train) [157][50/293] lr: 5.000000e-04 eta: 1:28:49 time: 0.409303 data_time: 0.101896 memory: 12861 loss_kpt: 0.000760 acc_pose: 0.758231 loss: 0.000760 2022/10/13 15:41:30 - mmengine - INFO - Epoch(train) [157][100/293] lr: 5.000000e-04 eta: 1:28:33 time: 0.380886 data_time: 0.085739 memory: 12861 loss_kpt: 0.000758 acc_pose: 0.713649 loss: 0.000758 2022/10/13 15:41:49 - mmengine - INFO - Epoch(train) [157][150/293] lr: 5.000000e-04 eta: 1:28:16 time: 0.381348 data_time: 0.089422 memory: 12861 loss_kpt: 0.000766 acc_pose: 0.820505 loss: 0.000766 2022/10/13 15:42:08 - mmengine - INFO - Epoch(train) [157][200/293] lr: 5.000000e-04 eta: 1:28:00 time: 0.384133 data_time: 0.082355 memory: 12861 loss_kpt: 0.000774 acc_pose: 0.798770 loss: 0.000774 2022/10/13 15:42:27 - mmengine - INFO - Epoch(train) [157][250/293] lr: 5.000000e-04 eta: 1:27:44 time: 0.389457 data_time: 0.094904 memory: 12861 loss_kpt: 0.000767 acc_pose: 0.766927 loss: 0.000767 2022/10/13 15:42:43 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:42:43 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:43:04 - mmengine - INFO - Epoch(train) [158][50/293] lr: 5.000000e-04 eta: 1:27:09 time: 0.413759 data_time: 0.094742 memory: 12861 loss_kpt: 0.000765 acc_pose: 0.787443 loss: 0.000765 2022/10/13 15:43:23 - mmengine - INFO - Epoch(train) [158][100/293] lr: 5.000000e-04 eta: 1:26:53 time: 0.383126 data_time: 0.085593 memory: 12861 loss_kpt: 0.000772 acc_pose: 0.747273 loss: 0.000772 2022/10/13 15:43:42 - mmengine - INFO - Epoch(train) [158][150/293] lr: 5.000000e-04 eta: 1:26:37 time: 0.380384 data_time: 0.078254 memory: 12861 loss_kpt: 0.000767 acc_pose: 0.816934 loss: 0.000767 2022/10/13 15:44:02 - mmengine - INFO - Epoch(train) [158][200/293] lr: 5.000000e-04 eta: 1:26:21 time: 0.401042 data_time: 0.092895 memory: 12861 loss_kpt: 0.000758 acc_pose: 0.729951 loss: 0.000758 2022/10/13 15:44:22 - mmengine - INFO - Epoch(train) [158][250/293] lr: 5.000000e-04 eta: 1:26:05 time: 0.387189 data_time: 0.091042 memory: 12861 loss_kpt: 0.000776 acc_pose: 0.716715 loss: 0.000776 2022/10/13 15:44:38 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:44:59 - mmengine - INFO - Epoch(train) [159][50/293] lr: 5.000000e-04 eta: 1:25:30 time: 0.411590 data_time: 0.100140 memory: 12861 loss_kpt: 0.000754 acc_pose: 0.815415 loss: 0.000754 2022/10/13 15:45:18 - mmengine - INFO - Epoch(train) [159][100/293] lr: 5.000000e-04 eta: 1:25:14 time: 0.383401 data_time: 0.087074 memory: 12861 loss_kpt: 0.000761 acc_pose: 0.733875 loss: 0.000761 2022/10/13 15:45:38 - mmengine - INFO - Epoch(train) [159][150/293] lr: 5.000000e-04 eta: 1:24:58 time: 0.393400 data_time: 0.091601 memory: 12861 loss_kpt: 0.000759 acc_pose: 0.757755 loss: 0.000759 2022/10/13 15:45:57 - mmengine - INFO - Epoch(train) [159][200/293] lr: 5.000000e-04 eta: 1:24:42 time: 0.379815 data_time: 0.083291 memory: 12861 loss_kpt: 0.000766 acc_pose: 0.765242 loss: 0.000766 2022/10/13 15:46:16 - mmengine - INFO - Epoch(train) [159][250/293] lr: 5.000000e-04 eta: 1:24:25 time: 0.388172 data_time: 0.091739 memory: 12861 loss_kpt: 0.000766 acc_pose: 0.708758 loss: 0.000766 2022/10/13 15:46:32 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:46:53 - mmengine - INFO - Epoch(train) [160][50/293] lr: 5.000000e-04 eta: 1:23:51 time: 0.411135 data_time: 0.101939 memory: 12861 loss_kpt: 0.000760 acc_pose: 0.789706 loss: 0.000760 2022/10/13 15:47:12 - mmengine - INFO - Epoch(train) [160][100/293] lr: 5.000000e-04 eta: 1:23:34 time: 0.388429 data_time: 0.088917 memory: 12861 loss_kpt: 0.000765 acc_pose: 0.766985 loss: 0.000765 2022/10/13 15:47:31 - mmengine - INFO - Epoch(train) [160][150/293] lr: 5.000000e-04 eta: 1:23:18 time: 0.382771 data_time: 0.082848 memory: 12861 loss_kpt: 0.000773 acc_pose: 0.740911 loss: 0.000773 2022/10/13 15:47:51 - mmengine - INFO - Epoch(train) [160][200/293] lr: 5.000000e-04 eta: 1:23:02 time: 0.388213 data_time: 0.079053 memory: 12861 loss_kpt: 0.000763 acc_pose: 0.780328 loss: 0.000763 2022/10/13 15:48:10 - mmengine - INFO - Epoch(train) [160][250/293] lr: 5.000000e-04 eta: 1:22:46 time: 0.392483 data_time: 0.091483 memory: 12861 loss_kpt: 0.000765 acc_pose: 0.756634 loss: 0.000765 2022/10/13 15:48:27 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:48:27 - mmengine - INFO - Saving checkpoint at 160 epochs 2022/10/13 15:48:35 - mmengine - INFO - Epoch(val) [160][50/407] eta: 0:00:45 time: 0.126544 data_time: 0.063729 memory: 12861 2022/10/13 15:48:42 - mmengine - INFO - Epoch(val) [160][100/407] eta: 0:00:37 time: 0.123126 data_time: 0.062264 memory: 983 2022/10/13 15:48:48 - mmengine - INFO - Epoch(val) [160][150/407] eta: 0:00:32 time: 0.126635 data_time: 0.064939 memory: 983 2022/10/13 15:48:54 - mmengine - INFO - Epoch(val) [160][200/407] eta: 0:00:25 time: 0.121833 data_time: 0.060730 memory: 983 2022/10/13 15:49:00 - mmengine - INFO - Epoch(val) [160][250/407] eta: 0:00:19 time: 0.124157 data_time: 0.063302 memory: 983 2022/10/13 15:49:06 - mmengine - INFO - Epoch(val) [160][300/407] eta: 0:00:13 time: 0.122850 data_time: 0.060470 memory: 983 2022/10/13 15:49:13 - mmengine - INFO - Epoch(val) [160][350/407] eta: 0:00:07 time: 0.129067 data_time: 0.067018 memory: 983 2022/10/13 15:49:19 - mmengine - INFO - Epoch(val) [160][400/407] eta: 0:00:00 time: 0.116035 data_time: 0.058052 memory: 983 2022/10/13 15:49:56 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 15:50:10 - mmengine - INFO - Epoch(val) [160][407/407] coco/AP: 0.660577 coco/AP .5: 0.877039 coco/AP .75: 0.731683 coco/AP (M): 0.617730 coco/AP (L): 0.731933 coco/AR: 0.718435 coco/AR .5: 0.916719 coco/AR .75: 0.784320 coco/AR (M): 0.669598 coco/AR (L): 0.787254 2022/10/13 15:50:30 - mmengine - INFO - Epoch(train) [161][50/293] lr: 5.000000e-04 eta: 1:22:11 time: 0.401668 data_time: 0.091543 memory: 12861 loss_kpt: 0.000752 acc_pose: 0.745620 loss: 0.000752 2022/10/13 15:50:49 - mmengine - INFO - Epoch(train) [161][100/293] lr: 5.000000e-04 eta: 1:21:55 time: 0.382149 data_time: 0.088669 memory: 12861 loss_kpt: 0.000754 acc_pose: 0.766129 loss: 0.000754 2022/10/13 15:50:57 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:51:09 - mmengine - INFO - Epoch(train) [161][150/293] lr: 5.000000e-04 eta: 1:21:39 time: 0.394309 data_time: 0.095460 memory: 12861 loss_kpt: 0.000753 acc_pose: 0.779111 loss: 0.000753 2022/10/13 15:51:28 - mmengine - INFO - Epoch(train) [161][200/293] lr: 5.000000e-04 eta: 1:21:23 time: 0.393832 data_time: 0.102994 memory: 12861 loss_kpt: 0.000774 acc_pose: 0.703335 loss: 0.000774 2022/10/13 15:51:48 - mmengine - INFO - Epoch(train) [161][250/293] lr: 5.000000e-04 eta: 1:21:07 time: 0.392642 data_time: 0.093946 memory: 12861 loss_kpt: 0.000769 acc_pose: 0.682008 loss: 0.000769 2022/10/13 15:52:04 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:52:24 - mmengine - INFO - Epoch(train) [162][50/293] lr: 5.000000e-04 eta: 1:20:32 time: 0.393555 data_time: 0.101798 memory: 12861 loss_kpt: 0.000763 acc_pose: 0.725217 loss: 0.000763 2022/10/13 15:52:44 - mmengine - INFO - Epoch(train) [162][100/293] lr: 5.000000e-04 eta: 1:20:16 time: 0.400810 data_time: 0.100318 memory: 12861 loss_kpt: 0.000767 acc_pose: 0.797092 loss: 0.000767 2022/10/13 15:53:04 - mmengine - INFO - Epoch(train) [162][150/293] lr: 5.000000e-04 eta: 1:20:00 time: 0.391490 data_time: 0.090446 memory: 12861 loss_kpt: 0.000780 acc_pose: 0.801969 loss: 0.000780 2022/10/13 15:53:23 - mmengine - INFO - Epoch(train) [162][200/293] lr: 5.000000e-04 eta: 1:19:44 time: 0.393766 data_time: 0.090675 memory: 12861 loss_kpt: 0.000750 acc_pose: 0.769009 loss: 0.000750 2022/10/13 15:53:43 - mmengine - INFO - Epoch(train) [162][250/293] lr: 5.000000e-04 eta: 1:19:28 time: 0.391703 data_time: 0.082465 memory: 12861 loss_kpt: 0.000749 acc_pose: 0.792424 loss: 0.000749 2022/10/13 15:53:59 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:54:19 - mmengine - INFO - Epoch(train) [163][50/293] lr: 5.000000e-04 eta: 1:18:53 time: 0.404120 data_time: 0.088893 memory: 12861 loss_kpt: 0.000752 acc_pose: 0.859943 loss: 0.000752 2022/10/13 15:54:39 - mmengine - INFO - Epoch(train) [163][100/293] lr: 5.000000e-04 eta: 1:18:37 time: 0.387796 data_time: 0.087481 memory: 12861 loss_kpt: 0.000762 acc_pose: 0.768799 loss: 0.000762 2022/10/13 15:54:58 - mmengine - INFO - Epoch(train) [163][150/293] lr: 5.000000e-04 eta: 1:18:21 time: 0.388492 data_time: 0.091127 memory: 12861 loss_kpt: 0.000758 acc_pose: 0.773037 loss: 0.000758 2022/10/13 15:55:17 - mmengine - INFO - Epoch(train) [163][200/293] lr: 5.000000e-04 eta: 1:18:04 time: 0.381050 data_time: 0.079182 memory: 12861 loss_kpt: 0.000763 acc_pose: 0.751132 loss: 0.000763 2022/10/13 15:55:36 - mmengine - INFO - Epoch(train) [163][250/293] lr: 5.000000e-04 eta: 1:17:48 time: 0.382190 data_time: 0.086258 memory: 12861 loss_kpt: 0.000756 acc_pose: 0.766685 loss: 0.000756 2022/10/13 15:55:53 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:56:13 - mmengine - INFO - Epoch(train) [164][50/293] lr: 5.000000e-04 eta: 1:17:13 time: 0.399482 data_time: 0.101377 memory: 12861 loss_kpt: 0.000750 acc_pose: 0.795949 loss: 0.000750 2022/10/13 15:56:33 - mmengine - INFO - Epoch(train) [164][100/293] lr: 5.000000e-04 eta: 1:16:57 time: 0.384026 data_time: 0.092952 memory: 12861 loss_kpt: 0.000754 acc_pose: 0.764563 loss: 0.000754 2022/10/13 15:56:52 - mmengine - INFO - Epoch(train) [164][150/293] lr: 5.000000e-04 eta: 1:16:41 time: 0.387473 data_time: 0.097304 memory: 12861 loss_kpt: 0.000756 acc_pose: 0.787778 loss: 0.000756 2022/10/13 15:57:12 - mmengine - INFO - Epoch(train) [164][200/293] lr: 5.000000e-04 eta: 1:16:25 time: 0.394946 data_time: 0.089274 memory: 12861 loss_kpt: 0.000763 acc_pose: 0.742207 loss: 0.000763 2022/10/13 15:57:28 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:57:31 - mmengine - INFO - Epoch(train) [164][250/293] lr: 5.000000e-04 eta: 1:16:09 time: 0.384399 data_time: 0.086900 memory: 12861 loss_kpt: 0.000763 acc_pose: 0.775300 loss: 0.000763 2022/10/13 15:57:48 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 15:58:08 - mmengine - INFO - Epoch(train) [165][50/293] lr: 5.000000e-04 eta: 1:15:34 time: 0.406596 data_time: 0.101111 memory: 12861 loss_kpt: 0.000757 acc_pose: 0.755336 loss: 0.000757 2022/10/13 15:58:27 - mmengine - INFO - Epoch(train) [165][100/293] lr: 5.000000e-04 eta: 1:15:18 time: 0.390064 data_time: 0.085709 memory: 12861 loss_kpt: 0.000761 acc_pose: 0.739440 loss: 0.000761 2022/10/13 15:58:47 - mmengine - INFO - Epoch(train) [165][150/293] lr: 5.000000e-04 eta: 1:15:02 time: 0.391592 data_time: 0.104333 memory: 12861 loss_kpt: 0.000751 acc_pose: 0.709236 loss: 0.000751 2022/10/13 15:59:06 - mmengine - INFO - Epoch(train) [165][200/293] lr: 5.000000e-04 eta: 1:14:46 time: 0.385427 data_time: 0.097366 memory: 12861 loss_kpt: 0.000751 acc_pose: 0.699264 loss: 0.000751 2022/10/13 15:59:25 - mmengine - INFO - Epoch(train) [165][250/293] lr: 5.000000e-04 eta: 1:14:29 time: 0.381187 data_time: 0.091353 memory: 12861 loss_kpt: 0.000752 acc_pose: 0.769740 loss: 0.000752 2022/10/13 15:59:42 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:00:03 - mmengine - INFO - Epoch(train) [166][50/293] lr: 5.000000e-04 eta: 1:13:55 time: 0.409385 data_time: 0.097986 memory: 12861 loss_kpt: 0.000762 acc_pose: 0.810759 loss: 0.000762 2022/10/13 16:00:22 - mmengine - INFO - Epoch(train) [166][100/293] lr: 5.000000e-04 eta: 1:13:39 time: 0.384064 data_time: 0.098557 memory: 12861 loss_kpt: 0.000749 acc_pose: 0.718589 loss: 0.000749 2022/10/13 16:00:41 - mmengine - INFO - Epoch(train) [166][150/293] lr: 5.000000e-04 eta: 1:13:22 time: 0.379985 data_time: 0.090679 memory: 12861 loss_kpt: 0.000748 acc_pose: 0.774792 loss: 0.000748 2022/10/13 16:01:01 - mmengine - INFO - Epoch(train) [166][200/293] lr: 5.000000e-04 eta: 1:13:06 time: 0.399948 data_time: 0.087630 memory: 12861 loss_kpt: 0.000757 acc_pose: 0.782087 loss: 0.000757 2022/10/13 16:01:21 - mmengine - INFO - Epoch(train) [166][250/293] lr: 5.000000e-04 eta: 1:12:50 time: 0.391791 data_time: 0.085001 memory: 12861 loss_kpt: 0.000760 acc_pose: 0.778384 loss: 0.000760 2022/10/13 16:01:37 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:01:58 - mmengine - INFO - Epoch(train) [167][50/293] lr: 5.000000e-04 eta: 1:12:16 time: 0.401537 data_time: 0.096737 memory: 12861 loss_kpt: 0.000766 acc_pose: 0.776916 loss: 0.000766 2022/10/13 16:02:17 - mmengine - INFO - Epoch(train) [167][100/293] lr: 5.000000e-04 eta: 1:11:59 time: 0.380377 data_time: 0.087648 memory: 12861 loss_kpt: 0.000753 acc_pose: 0.833161 loss: 0.000753 2022/10/13 16:02:36 - mmengine - INFO - Epoch(train) [167][150/293] lr: 5.000000e-04 eta: 1:11:43 time: 0.389012 data_time: 0.092287 memory: 12861 loss_kpt: 0.000753 acc_pose: 0.755498 loss: 0.000753 2022/10/13 16:02:56 - mmengine - INFO - Epoch(train) [167][200/293] lr: 5.000000e-04 eta: 1:11:27 time: 0.394980 data_time: 0.092304 memory: 12861 loss_kpt: 0.000748 acc_pose: 0.746214 loss: 0.000748 2022/10/13 16:03:15 - mmengine - INFO - Epoch(train) [167][250/293] lr: 5.000000e-04 eta: 1:11:11 time: 0.385196 data_time: 0.082780 memory: 12861 loss_kpt: 0.000751 acc_pose: 0.733334 loss: 0.000751 2022/10/13 16:03:31 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:03:51 - mmengine - INFO - Epoch(train) [168][50/293] lr: 5.000000e-04 eta: 1:10:36 time: 0.403329 data_time: 0.103680 memory: 12861 loss_kpt: 0.000768 acc_pose: 0.745222 loss: 0.000768 2022/10/13 16:03:58 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:04:11 - mmengine - INFO - Epoch(train) [168][100/293] lr: 5.000000e-04 eta: 1:10:20 time: 0.389958 data_time: 0.089973 memory: 12861 loss_kpt: 0.000751 acc_pose: 0.775879 loss: 0.000751 2022/10/13 16:04:30 - mmengine - INFO - Epoch(train) [168][150/293] lr: 5.000000e-04 eta: 1:10:04 time: 0.393764 data_time: 0.093671 memory: 12861 loss_kpt: 0.000749 acc_pose: 0.849566 loss: 0.000749 2022/10/13 16:04:49 - mmengine - INFO - Epoch(train) [168][200/293] lr: 5.000000e-04 eta: 1:09:48 time: 0.381286 data_time: 0.089207 memory: 12861 loss_kpt: 0.000756 acc_pose: 0.754634 loss: 0.000756 2022/10/13 16:05:09 - mmengine - INFO - Epoch(train) [168][250/293] lr: 5.000000e-04 eta: 1:09:32 time: 0.396147 data_time: 0.102560 memory: 12861 loss_kpt: 0.000765 acc_pose: 0.811871 loss: 0.000765 2022/10/13 16:05:26 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:05:46 - mmengine - INFO - Epoch(train) [169][50/293] lr: 5.000000e-04 eta: 1:08:57 time: 0.403851 data_time: 0.110469 memory: 12861 loss_kpt: 0.000757 acc_pose: 0.805134 loss: 0.000757 2022/10/13 16:06:05 - mmengine - INFO - Epoch(train) [169][100/293] lr: 5.000000e-04 eta: 1:08:41 time: 0.388912 data_time: 0.091975 memory: 12861 loss_kpt: 0.000747 acc_pose: 0.764886 loss: 0.000747 2022/10/13 16:06:25 - mmengine - INFO - Epoch(train) [169][150/293] lr: 5.000000e-04 eta: 1:08:25 time: 0.395427 data_time: 0.102884 memory: 12861 loss_kpt: 0.000768 acc_pose: 0.786280 loss: 0.000768 2022/10/13 16:06:44 - mmengine - INFO - Epoch(train) [169][200/293] lr: 5.000000e-04 eta: 1:08:08 time: 0.374057 data_time: 0.081923 memory: 12861 loss_kpt: 0.000753 acc_pose: 0.673420 loss: 0.000753 2022/10/13 16:07:03 - mmengine - INFO - Epoch(train) [169][250/293] lr: 5.000000e-04 eta: 1:07:52 time: 0.381874 data_time: 0.087025 memory: 12861 loss_kpt: 0.000743 acc_pose: 0.804794 loss: 0.000743 2022/10/13 16:07:19 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:07:39 - mmengine - INFO - Epoch(train) [170][50/293] lr: 5.000000e-04 eta: 1:07:18 time: 0.401183 data_time: 0.099579 memory: 12861 loss_kpt: 0.000774 acc_pose: 0.731822 loss: 0.000774 2022/10/13 16:07:59 - mmengine - INFO - Epoch(train) [170][100/293] lr: 5.000000e-04 eta: 1:07:02 time: 0.391708 data_time: 0.093801 memory: 12861 loss_kpt: 0.000755 acc_pose: 0.796071 loss: 0.000755 2022/10/13 16:08:19 - mmengine - INFO - Epoch(train) [170][150/293] lr: 5.000000e-04 eta: 1:06:46 time: 0.399917 data_time: 0.101255 memory: 12861 loss_kpt: 0.000747 acc_pose: 0.777331 loss: 0.000747 2022/10/13 16:08:38 - mmengine - INFO - Epoch(train) [170][200/293] lr: 5.000000e-04 eta: 1:06:29 time: 0.385304 data_time: 0.093482 memory: 12861 loss_kpt: 0.000747 acc_pose: 0.805476 loss: 0.000747 2022/10/13 16:08:57 - mmengine - INFO - Epoch(train) [170][250/293] lr: 5.000000e-04 eta: 1:06:13 time: 0.379597 data_time: 0.094814 memory: 12861 loss_kpt: 0.000772 acc_pose: 0.775848 loss: 0.000772 2022/10/13 16:09:14 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:09:14 - mmengine - INFO - Saving checkpoint at 170 epochs 2022/10/13 16:09:22 - mmengine - INFO - Epoch(val) [170][50/407] eta: 0:00:46 time: 0.128963 data_time: 0.066280 memory: 12861 2022/10/13 16:09:28 - mmengine - INFO - Epoch(val) [170][100/407] eta: 0:00:37 time: 0.122158 data_time: 0.062072 memory: 983 2022/10/13 16:09:35 - mmengine - INFO - Epoch(val) [170][150/407] eta: 0:00:34 time: 0.133405 data_time: 0.072665 memory: 983 2022/10/13 16:09:41 - mmengine - INFO - Epoch(val) [170][200/407] eta: 0:00:24 time: 0.118037 data_time: 0.053211 memory: 983 2022/10/13 16:09:47 - mmengine - INFO - Epoch(val) [170][250/407] eta: 0:00:19 time: 0.123860 data_time: 0.063617 memory: 983 2022/10/13 16:09:53 - mmengine - INFO - Epoch(val) [170][300/407] eta: 0:00:13 time: 0.124283 data_time: 0.063863 memory: 983 2022/10/13 16:10:00 - mmengine - INFO - Epoch(val) [170][350/407] eta: 0:00:07 time: 0.129506 data_time: 0.068611 memory: 983 2022/10/13 16:10:05 - mmengine - INFO - Epoch(val) [170][400/407] eta: 0:00:00 time: 0.116486 data_time: 0.057667 memory: 983 2022/10/13 16:10:43 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 16:10:57 - mmengine - INFO - Epoch(val) [170][407/407] coco/AP: 0.664547 coco/AP .5: 0.877825 coco/AP .75: 0.735285 coco/AP (M): 0.619364 coco/AP (L): 0.738090 coco/AR: 0.720891 coco/AR .5: 0.915932 coco/AR .75: 0.785422 coco/AR (M): 0.669680 coco/AR (L): 0.793274 2022/10/13 16:10:57 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384/best_coco/AP_epoch_150.pth is removed 2022/10/13 16:10:59 - mmengine - INFO - The best checkpoint with 0.6645 coco/AP at 170 epoch is saved to best_coco/AP_epoch_170.pth. 2022/10/13 16:11:20 - mmengine - INFO - Epoch(train) [171][50/293] lr: 5.000000e-05 eta: 1:05:39 time: 0.413392 data_time: 0.096507 memory: 12861 loss_kpt: 0.000747 acc_pose: 0.809900 loss: 0.000747 2022/10/13 16:11:39 - mmengine - INFO - Epoch(train) [171][100/293] lr: 5.000000e-05 eta: 1:05:23 time: 0.385832 data_time: 0.086625 memory: 12861 loss_kpt: 0.000737 acc_pose: 0.788150 loss: 0.000737 2022/10/13 16:12:03 - mmengine - INFO - Epoch(train) [171][150/293] lr: 5.000000e-05 eta: 1:05:07 time: 0.477280 data_time: 0.102026 memory: 12861 loss_kpt: 0.000736 acc_pose: 0.778191 loss: 0.000736 2022/10/13 16:12:23 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:12:28 - mmengine - INFO - Epoch(train) [171][200/293] lr: 5.000000e-05 eta: 1:04:52 time: 0.498612 data_time: 0.072759 memory: 12861 loss_kpt: 0.000737 acc_pose: 0.785423 loss: 0.000737 2022/10/13 16:12:51 - mmengine - INFO - Epoch(train) [171][250/293] lr: 5.000000e-05 eta: 1:04:37 time: 0.454270 data_time: 0.094369 memory: 12861 loss_kpt: 0.000721 acc_pose: 0.761253 loss: 0.000721 2022/10/13 16:13:07 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:13:27 - mmengine - INFO - Epoch(train) [172][50/293] lr: 5.000000e-05 eta: 1:04:03 time: 0.391884 data_time: 0.094937 memory: 12861 loss_kpt: 0.000719 acc_pose: 0.836577 loss: 0.000719 2022/10/13 16:13:46 - mmengine - INFO - Epoch(train) [172][100/293] lr: 5.000000e-05 eta: 1:03:46 time: 0.382405 data_time: 0.090356 memory: 12861 loss_kpt: 0.000721 acc_pose: 0.730854 loss: 0.000721 2022/10/13 16:14:05 - mmengine - INFO - Epoch(train) [172][150/293] lr: 5.000000e-05 eta: 1:03:30 time: 0.393219 data_time: 0.093577 memory: 12861 loss_kpt: 0.000725 acc_pose: 0.827195 loss: 0.000725 2022/10/13 16:14:24 - mmengine - INFO - Epoch(train) [172][200/293] lr: 5.000000e-05 eta: 1:03:14 time: 0.381087 data_time: 0.080939 memory: 12861 loss_kpt: 0.000734 acc_pose: 0.774783 loss: 0.000734 2022/10/13 16:14:44 - mmengine - INFO - Epoch(train) [172][250/293] lr: 5.000000e-05 eta: 1:02:57 time: 0.384717 data_time: 0.082372 memory: 12861 loss_kpt: 0.000724 acc_pose: 0.773419 loss: 0.000724 2022/10/13 16:14:59 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:15:19 - mmengine - INFO - Epoch(train) [173][50/293] lr: 5.000000e-05 eta: 1:02:23 time: 0.390541 data_time: 0.086228 memory: 12861 loss_kpt: 0.000739 acc_pose: 0.833885 loss: 0.000739 2022/10/13 16:15:39 - mmengine - INFO - Epoch(train) [173][100/293] lr: 5.000000e-05 eta: 1:02:07 time: 0.396402 data_time: 0.084248 memory: 12861 loss_kpt: 0.000736 acc_pose: 0.732762 loss: 0.000736 2022/10/13 16:15:58 - mmengine - INFO - Epoch(train) [173][150/293] lr: 5.000000e-05 eta: 1:01:51 time: 0.386029 data_time: 0.091031 memory: 12861 loss_kpt: 0.000729 acc_pose: 0.792748 loss: 0.000729 2022/10/13 16:16:17 - mmengine - INFO - Epoch(train) [173][200/293] lr: 5.000000e-05 eta: 1:01:34 time: 0.381321 data_time: 0.079338 memory: 12861 loss_kpt: 0.000738 acc_pose: 0.767528 loss: 0.000738 2022/10/13 16:16:36 - mmengine - INFO - Epoch(train) [173][250/293] lr: 5.000000e-05 eta: 1:01:18 time: 0.388374 data_time: 0.084175 memory: 12861 loss_kpt: 0.000731 acc_pose: 0.807743 loss: 0.000731 2022/10/13 16:16:53 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:17:13 - mmengine - INFO - Epoch(train) [174][50/293] lr: 5.000000e-05 eta: 1:00:44 time: 0.402032 data_time: 0.093921 memory: 12861 loss_kpt: 0.000713 acc_pose: 0.788292 loss: 0.000713 2022/10/13 16:17:32 - mmengine - INFO - Epoch(train) [174][100/293] lr: 5.000000e-05 eta: 1:00:27 time: 0.381569 data_time: 0.096736 memory: 12861 loss_kpt: 0.000718 acc_pose: 0.809936 loss: 0.000718 2022/10/13 16:17:51 - mmengine - INFO - Epoch(train) [174][150/293] lr: 5.000000e-05 eta: 1:00:11 time: 0.372858 data_time: 0.088076 memory: 12861 loss_kpt: 0.000721 acc_pose: 0.830179 loss: 0.000721 2022/10/13 16:18:10 - mmengine - INFO - Epoch(train) [174][200/293] lr: 5.000000e-05 eta: 0:59:55 time: 0.388208 data_time: 0.094963 memory: 12861 loss_kpt: 0.000725 acc_pose: 0.772878 loss: 0.000725 2022/10/13 16:18:30 - mmengine - INFO - Epoch(train) [174][250/293] lr: 5.000000e-05 eta: 0:59:38 time: 0.397313 data_time: 0.099311 memory: 12861 loss_kpt: 0.000726 acc_pose: 0.771898 loss: 0.000726 2022/10/13 16:18:46 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:18:54 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:19:07 - mmengine - INFO - Epoch(train) [175][50/293] lr: 5.000000e-05 eta: 0:59:05 time: 0.405263 data_time: 0.097541 memory: 12861 loss_kpt: 0.000740 acc_pose: 0.766342 loss: 0.000740 2022/10/13 16:19:26 - mmengine - INFO - Epoch(train) [175][100/293] lr: 5.000000e-05 eta: 0:58:48 time: 0.379858 data_time: 0.085033 memory: 12861 loss_kpt: 0.000719 acc_pose: 0.802874 loss: 0.000719 2022/10/13 16:19:45 - mmengine - INFO - Epoch(train) [175][150/293] lr: 5.000000e-05 eta: 0:58:32 time: 0.390479 data_time: 0.093151 memory: 12861 loss_kpt: 0.000717 acc_pose: 0.795184 loss: 0.000717 2022/10/13 16:20:05 - mmengine - INFO - Epoch(train) [175][200/293] lr: 5.000000e-05 eta: 0:58:15 time: 0.388586 data_time: 0.086127 memory: 12861 loss_kpt: 0.000735 acc_pose: 0.808216 loss: 0.000735 2022/10/13 16:20:24 - mmengine - INFO - Epoch(train) [175][250/293] lr: 5.000000e-05 eta: 0:57:59 time: 0.387456 data_time: 0.094859 memory: 12861 loss_kpt: 0.000740 acc_pose: 0.750180 loss: 0.000740 2022/10/13 16:20:41 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:21:01 - mmengine - INFO - Epoch(train) [176][50/293] lr: 5.000000e-05 eta: 0:57:25 time: 0.401104 data_time: 0.101619 memory: 12861 loss_kpt: 0.000729 acc_pose: 0.754203 loss: 0.000729 2022/10/13 16:21:20 - mmengine - INFO - Epoch(train) [176][100/293] lr: 5.000000e-05 eta: 0:57:09 time: 0.375553 data_time: 0.079926 memory: 12861 loss_kpt: 0.000725 acc_pose: 0.739964 loss: 0.000725 2022/10/13 16:21:39 - mmengine - INFO - Epoch(train) [176][150/293] lr: 5.000000e-05 eta: 0:56:52 time: 0.387578 data_time: 0.083214 memory: 12861 loss_kpt: 0.000717 acc_pose: 0.796288 loss: 0.000717 2022/10/13 16:21:59 - mmengine - INFO - Epoch(train) [176][200/293] lr: 5.000000e-05 eta: 0:56:36 time: 0.400347 data_time: 0.094347 memory: 12861 loss_kpt: 0.000711 acc_pose: 0.764155 loss: 0.000711 2022/10/13 16:22:19 - mmengine - INFO - Epoch(train) [176][250/293] lr: 5.000000e-05 eta: 0:56:20 time: 0.389762 data_time: 0.095813 memory: 12861 loss_kpt: 0.000728 acc_pose: 0.784700 loss: 0.000728 2022/10/13 16:22:35 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:22:56 - mmengine - INFO - Epoch(train) [177][50/293] lr: 5.000000e-05 eta: 0:55:46 time: 0.410015 data_time: 0.106460 memory: 12861 loss_kpt: 0.000717 acc_pose: 0.762039 loss: 0.000717 2022/10/13 16:23:16 - mmengine - INFO - Epoch(train) [177][100/293] lr: 5.000000e-05 eta: 0:55:30 time: 0.400177 data_time: 0.094891 memory: 12861 loss_kpt: 0.000733 acc_pose: 0.802330 loss: 0.000733 2022/10/13 16:23:35 - mmengine - INFO - Epoch(train) [177][150/293] lr: 5.000000e-05 eta: 0:55:14 time: 0.384232 data_time: 0.091355 memory: 12861 loss_kpt: 0.000722 acc_pose: 0.796694 loss: 0.000722 2022/10/13 16:23:55 - mmengine - INFO - Epoch(train) [177][200/293] lr: 5.000000e-05 eta: 0:54:57 time: 0.388867 data_time: 0.093241 memory: 12861 loss_kpt: 0.000733 acc_pose: 0.818167 loss: 0.000733 2022/10/13 16:24:14 - mmengine - INFO - Epoch(train) [177][250/293] lr: 5.000000e-05 eta: 0:54:41 time: 0.388904 data_time: 0.084554 memory: 12861 loss_kpt: 0.000735 acc_pose: 0.744077 loss: 0.000735 2022/10/13 16:24:31 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:24:51 - mmengine - INFO - Epoch(train) [178][50/293] lr: 5.000000e-05 eta: 0:54:07 time: 0.407972 data_time: 0.107183 memory: 12861 loss_kpt: 0.000729 acc_pose: 0.737161 loss: 0.000729 2022/10/13 16:25:10 - mmengine - INFO - Epoch(train) [178][100/293] lr: 5.000000e-05 eta: 0:53:51 time: 0.385236 data_time: 0.086388 memory: 12861 loss_kpt: 0.000712 acc_pose: 0.783382 loss: 0.000712 2022/10/13 16:25:26 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:25:30 - mmengine - INFO - Epoch(train) [178][150/293] lr: 5.000000e-05 eta: 0:53:34 time: 0.386530 data_time: 0.090053 memory: 12861 loss_kpt: 0.000724 acc_pose: 0.771903 loss: 0.000724 2022/10/13 16:25:50 - mmengine - INFO - Epoch(train) [178][200/293] lr: 5.000000e-05 eta: 0:53:18 time: 0.396942 data_time: 0.095183 memory: 12861 loss_kpt: 0.000713 acc_pose: 0.813299 loss: 0.000713 2022/10/13 16:26:10 - mmengine - INFO - Epoch(train) [178][250/293] lr: 5.000000e-05 eta: 0:53:02 time: 0.400740 data_time: 0.098218 memory: 12861 loss_kpt: 0.000724 acc_pose: 0.768212 loss: 0.000724 2022/10/13 16:26:26 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:26:46 - mmengine - INFO - Epoch(train) [179][50/293] lr: 5.000000e-05 eta: 0:52:28 time: 0.407906 data_time: 0.102708 memory: 12861 loss_kpt: 0.000728 acc_pose: 0.796104 loss: 0.000728 2022/10/13 16:27:06 - mmengine - INFO - Epoch(train) [179][100/293] lr: 5.000000e-05 eta: 0:52:12 time: 0.389447 data_time: 0.089861 memory: 12861 loss_kpt: 0.000728 acc_pose: 0.765887 loss: 0.000728 2022/10/13 16:27:25 - mmengine - INFO - Epoch(train) [179][150/293] lr: 5.000000e-05 eta: 0:51:55 time: 0.389321 data_time: 0.082611 memory: 12861 loss_kpt: 0.000728 acc_pose: 0.765803 loss: 0.000728 2022/10/13 16:27:45 - mmengine - INFO - Epoch(train) [179][200/293] lr: 5.000000e-05 eta: 0:51:39 time: 0.397236 data_time: 0.090993 memory: 12861 loss_kpt: 0.000718 acc_pose: 0.778720 loss: 0.000718 2022/10/13 16:28:05 - mmengine - INFO - Epoch(train) [179][250/293] lr: 5.000000e-05 eta: 0:51:23 time: 0.394361 data_time: 0.091182 memory: 12861 loss_kpt: 0.000740 acc_pose: 0.743199 loss: 0.000740 2022/10/13 16:28:22 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:28:41 - mmengine - INFO - Epoch(train) [180][50/293] lr: 5.000000e-05 eta: 0:50:49 time: 0.386952 data_time: 0.087569 memory: 12861 loss_kpt: 0.000720 acc_pose: 0.771758 loss: 0.000720 2022/10/13 16:29:00 - mmengine - INFO - Epoch(train) [180][100/293] lr: 5.000000e-05 eta: 0:50:33 time: 0.387207 data_time: 0.084894 memory: 12861 loss_kpt: 0.000720 acc_pose: 0.795501 loss: 0.000720 2022/10/13 16:29:20 - mmengine - INFO - Epoch(train) [180][150/293] lr: 5.000000e-05 eta: 0:50:16 time: 0.385638 data_time: 0.092173 memory: 12861 loss_kpt: 0.000734 acc_pose: 0.759373 loss: 0.000734 2022/10/13 16:29:38 - mmengine - INFO - Epoch(train) [180][200/293] lr: 5.000000e-05 eta: 0:50:00 time: 0.378282 data_time: 0.071657 memory: 12861 loss_kpt: 0.000712 acc_pose: 0.805901 loss: 0.000712 2022/10/13 16:29:58 - mmengine - INFO - Epoch(train) [180][250/293] lr: 5.000000e-05 eta: 0:49:43 time: 0.391922 data_time: 0.089284 memory: 12861 loss_kpt: 0.000716 acc_pose: 0.815499 loss: 0.000716 2022/10/13 16:30:15 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:30:15 - mmengine - INFO - Saving checkpoint at 180 epochs 2022/10/13 16:30:24 - mmengine - INFO - Epoch(val) [180][50/407] eta: 0:00:46 time: 0.130063 data_time: 0.067477 memory: 12861 2022/10/13 16:30:30 - mmengine - INFO - Epoch(val) [180][100/407] eta: 0:00:38 time: 0.126949 data_time: 0.065543 memory: 983 2022/10/13 16:30:36 - mmengine - INFO - Epoch(val) [180][150/407] eta: 0:00:31 time: 0.120875 data_time: 0.059726 memory: 983 2022/10/13 16:30:42 - mmengine - INFO - Epoch(val) [180][200/407] eta: 0:00:27 time: 0.130569 data_time: 0.069075 memory: 983 2022/10/13 16:30:49 - mmengine - INFO - Epoch(val) [180][250/407] eta: 0:00:19 time: 0.125787 data_time: 0.065688 memory: 983 2022/10/13 16:30:55 - mmengine - INFO - Epoch(val) [180][300/407] eta: 0:00:12 time: 0.120581 data_time: 0.058752 memory: 983 2022/10/13 16:31:01 - mmengine - INFO - Epoch(val) [180][350/407] eta: 0:00:07 time: 0.128250 data_time: 0.066826 memory: 983 2022/10/13 16:31:07 - mmengine - INFO - Epoch(val) [180][400/407] eta: 0:00:00 time: 0.117624 data_time: 0.056993 memory: 983 2022/10/13 16:31:44 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 16:31:58 - mmengine - INFO - Epoch(val) [180][407/407] coco/AP: 0.673142 coco/AP .5: 0.879271 coco/AP .75: 0.741217 coco/AP (M): 0.628461 coco/AP (L): 0.747130 coco/AR: 0.730479 coco/AR .5: 0.919395 coco/AR .75: 0.792349 coco/AR (M): 0.680279 coco/AR (L): 0.801412 2022/10/13 16:31:58 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384/best_coco/AP_epoch_170.pth is removed 2022/10/13 16:32:00 - mmengine - INFO - The best checkpoint with 0.6731 coco/AP at 180 epoch is saved to best_coco/AP_epoch_180.pth. 2022/10/13 16:32:19 - mmengine - INFO - Epoch(train) [181][50/293] lr: 5.000000e-05 eta: 0:49:10 time: 0.397353 data_time: 0.089740 memory: 12861 loss_kpt: 0.000735 acc_pose: 0.818246 loss: 0.000735 2022/10/13 16:32:39 - mmengine - INFO - Epoch(train) [181][100/293] lr: 5.000000e-05 eta: 0:48:54 time: 0.389297 data_time: 0.098732 memory: 12861 loss_kpt: 0.000727 acc_pose: 0.789568 loss: 0.000727 2022/10/13 16:32:59 - mmengine - INFO - Epoch(train) [181][150/293] lr: 5.000000e-05 eta: 0:48:37 time: 0.399414 data_time: 0.100459 memory: 12861 loss_kpt: 0.000713 acc_pose: 0.791346 loss: 0.000713 2022/10/13 16:33:18 - mmengine - INFO - Epoch(train) [181][200/293] lr: 5.000000e-05 eta: 0:48:21 time: 0.391289 data_time: 0.095100 memory: 12861 loss_kpt: 0.000714 acc_pose: 0.763338 loss: 0.000714 2022/10/13 16:33:38 - mmengine - INFO - Epoch(train) [181][250/293] lr: 5.000000e-05 eta: 0:48:04 time: 0.394071 data_time: 0.103839 memory: 12861 loss_kpt: 0.000709 acc_pose: 0.769005 loss: 0.000709 2022/10/13 16:33:42 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:33:54 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:34:15 - mmengine - INFO - Epoch(train) [182][50/293] lr: 5.000000e-05 eta: 0:47:31 time: 0.410665 data_time: 0.096653 memory: 12861 loss_kpt: 0.000731 acc_pose: 0.786002 loss: 0.000731 2022/10/13 16:34:34 - mmengine - INFO - Epoch(train) [182][100/293] lr: 5.000000e-05 eta: 0:47:15 time: 0.382427 data_time: 0.089295 memory: 12861 loss_kpt: 0.000714 acc_pose: 0.796371 loss: 0.000714 2022/10/13 16:34:53 - mmengine - INFO - Epoch(train) [182][150/293] lr: 5.000000e-05 eta: 0:46:58 time: 0.382126 data_time: 0.090318 memory: 12861 loss_kpt: 0.000726 acc_pose: 0.796777 loss: 0.000726 2022/10/13 16:35:13 - mmengine - INFO - Epoch(train) [182][200/293] lr: 5.000000e-05 eta: 0:46:42 time: 0.390918 data_time: 0.099997 memory: 12861 loss_kpt: 0.000727 acc_pose: 0.791895 loss: 0.000727 2022/10/13 16:35:32 - mmengine - INFO - Epoch(train) [182][250/293] lr: 5.000000e-05 eta: 0:46:25 time: 0.395932 data_time: 0.082928 memory: 12861 loss_kpt: 0.000718 acc_pose: 0.751738 loss: 0.000718 2022/10/13 16:35:49 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:36:09 - mmengine - INFO - Epoch(train) [183][50/293] lr: 5.000000e-05 eta: 0:45:52 time: 0.404534 data_time: 0.097535 memory: 12861 loss_kpt: 0.000721 acc_pose: 0.788427 loss: 0.000721 2022/10/13 16:36:29 - mmengine - INFO - Epoch(train) [183][100/293] lr: 5.000000e-05 eta: 0:45:36 time: 0.390061 data_time: 0.091602 memory: 12861 loss_kpt: 0.000714 acc_pose: 0.753015 loss: 0.000714 2022/10/13 16:36:48 - mmengine - INFO - Epoch(train) [183][150/293] lr: 5.000000e-05 eta: 0:45:19 time: 0.386992 data_time: 0.091382 memory: 12861 loss_kpt: 0.000721 acc_pose: 0.802022 loss: 0.000721 2022/10/13 16:37:08 - mmengine - INFO - Epoch(train) [183][200/293] lr: 5.000000e-05 eta: 0:45:03 time: 0.390158 data_time: 0.093787 memory: 12861 loss_kpt: 0.000717 acc_pose: 0.746939 loss: 0.000717 2022/10/13 16:37:27 - mmengine - INFO - Epoch(train) [183][250/293] lr: 5.000000e-05 eta: 0:44:46 time: 0.389614 data_time: 0.100465 memory: 12861 loss_kpt: 0.000715 acc_pose: 0.772173 loss: 0.000715 2022/10/13 16:37:44 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:38:04 - mmengine - INFO - Epoch(train) [184][50/293] lr: 5.000000e-05 eta: 0:44:13 time: 0.406991 data_time: 0.101767 memory: 12861 loss_kpt: 0.000729 acc_pose: 0.797335 loss: 0.000729 2022/10/13 16:38:24 - mmengine - INFO - Epoch(train) [184][100/293] lr: 5.000000e-05 eta: 0:43:56 time: 0.385928 data_time: 0.093230 memory: 12861 loss_kpt: 0.000721 acc_pose: 0.773376 loss: 0.000721 2022/10/13 16:38:43 - mmengine - INFO - Epoch(train) [184][150/293] lr: 5.000000e-05 eta: 0:43:40 time: 0.388124 data_time: 0.094410 memory: 12861 loss_kpt: 0.000719 acc_pose: 0.783109 loss: 0.000719 2022/10/13 16:39:02 - mmengine - INFO - Epoch(train) [184][200/293] lr: 5.000000e-05 eta: 0:43:23 time: 0.381782 data_time: 0.090500 memory: 12861 loss_kpt: 0.000711 acc_pose: 0.802286 loss: 0.000711 2022/10/13 16:39:22 - mmengine - INFO - Epoch(train) [184][250/293] lr: 5.000000e-05 eta: 0:43:07 time: 0.401780 data_time: 0.096629 memory: 12861 loss_kpt: 0.000732 acc_pose: 0.825627 loss: 0.000732 2022/10/13 16:39:39 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:39:58 - mmengine - INFO - Epoch(train) [185][50/293] lr: 5.000000e-05 eta: 0:42:34 time: 0.396153 data_time: 0.091384 memory: 12861 loss_kpt: 0.000705 acc_pose: 0.810593 loss: 0.000705 2022/10/13 16:40:13 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:40:18 - mmengine - INFO - Epoch(train) [185][100/293] lr: 5.000000e-05 eta: 0:42:17 time: 0.392513 data_time: 0.092856 memory: 12861 loss_kpt: 0.000716 acc_pose: 0.782139 loss: 0.000716 2022/10/13 16:40:37 - mmengine - INFO - Epoch(train) [185][150/293] lr: 5.000000e-05 eta: 0:42:01 time: 0.386960 data_time: 0.075875 memory: 12861 loss_kpt: 0.000718 acc_pose: 0.796136 loss: 0.000718 2022/10/13 16:40:56 - mmengine - INFO - Epoch(train) [185][200/293] lr: 5.000000e-05 eta: 0:41:44 time: 0.381030 data_time: 0.087919 memory: 12861 loss_kpt: 0.000717 acc_pose: 0.790133 loss: 0.000717 2022/10/13 16:41:16 - mmengine - INFO - Epoch(train) [185][250/293] lr: 5.000000e-05 eta: 0:41:28 time: 0.389035 data_time: 0.088794 memory: 12861 loss_kpt: 0.000708 acc_pose: 0.794280 loss: 0.000708 2022/10/13 16:41:33 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:41:53 - mmengine - INFO - Epoch(train) [186][50/293] lr: 5.000000e-05 eta: 0:40:55 time: 0.397672 data_time: 0.093705 memory: 12861 loss_kpt: 0.000706 acc_pose: 0.769031 loss: 0.000706 2022/10/13 16:42:12 - mmengine - INFO - Epoch(train) [186][100/293] lr: 5.000000e-05 eta: 0:40:38 time: 0.385133 data_time: 0.070160 memory: 12861 loss_kpt: 0.000716 acc_pose: 0.752436 loss: 0.000716 2022/10/13 16:42:32 - mmengine - INFO - Epoch(train) [186][150/293] lr: 5.000000e-05 eta: 0:40:22 time: 0.394933 data_time: 0.088310 memory: 12861 loss_kpt: 0.000713 acc_pose: 0.802221 loss: 0.000713 2022/10/13 16:42:51 - mmengine - INFO - Epoch(train) [186][200/293] lr: 5.000000e-05 eta: 0:40:05 time: 0.385739 data_time: 0.089532 memory: 12861 loss_kpt: 0.000717 acc_pose: 0.775629 loss: 0.000717 2022/10/13 16:43:11 - mmengine - INFO - Epoch(train) [186][250/293] lr: 5.000000e-05 eta: 0:39:49 time: 0.387139 data_time: 0.095546 memory: 12861 loss_kpt: 0.000716 acc_pose: 0.792887 loss: 0.000716 2022/10/13 16:43:27 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:43:48 - mmengine - INFO - Epoch(train) [187][50/293] lr: 5.000000e-05 eta: 0:39:16 time: 0.410588 data_time: 0.104027 memory: 12861 loss_kpt: 0.000717 acc_pose: 0.802671 loss: 0.000717 2022/10/13 16:44:07 - mmengine - INFO - Epoch(train) [187][100/293] lr: 5.000000e-05 eta: 0:38:59 time: 0.384097 data_time: 0.083741 memory: 12861 loss_kpt: 0.000711 acc_pose: 0.769441 loss: 0.000711 2022/10/13 16:44:26 - mmengine - INFO - Epoch(train) [187][150/293] lr: 5.000000e-05 eta: 0:38:43 time: 0.392631 data_time: 0.097394 memory: 12861 loss_kpt: 0.000726 acc_pose: 0.797749 loss: 0.000726 2022/10/13 16:44:46 - mmengine - INFO - Epoch(train) [187][200/293] lr: 5.000000e-05 eta: 0:38:26 time: 0.394346 data_time: 0.098900 memory: 12861 loss_kpt: 0.000708 acc_pose: 0.818876 loss: 0.000708 2022/10/13 16:45:05 - mmengine - INFO - Epoch(train) [187][250/293] lr: 5.000000e-05 eta: 0:38:10 time: 0.381973 data_time: 0.074388 memory: 12861 loss_kpt: 0.000715 acc_pose: 0.759950 loss: 0.000715 2022/10/13 16:45:22 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:45:42 - mmengine - INFO - Epoch(train) [188][50/293] lr: 5.000000e-05 eta: 0:37:37 time: 0.391860 data_time: 0.081829 memory: 12861 loss_kpt: 0.000704 acc_pose: 0.784035 loss: 0.000704 2022/10/13 16:46:02 - mmengine - INFO - Epoch(train) [188][100/293] lr: 5.000000e-05 eta: 0:37:20 time: 0.401091 data_time: 0.093448 memory: 12861 loss_kpt: 0.000712 acc_pose: 0.791160 loss: 0.000712 2022/10/13 16:46:21 - mmengine - INFO - Epoch(train) [188][150/293] lr: 5.000000e-05 eta: 0:37:04 time: 0.384351 data_time: 0.083380 memory: 12861 loss_kpt: 0.000720 acc_pose: 0.796287 loss: 0.000720 2022/10/13 16:46:41 - mmengine - INFO - Epoch(train) [188][200/293] lr: 5.000000e-05 eta: 0:36:47 time: 0.400091 data_time: 0.095445 memory: 12861 loss_kpt: 0.000711 acc_pose: 0.772837 loss: 0.000711 2022/10/13 16:46:45 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:47:00 - mmengine - INFO - Epoch(train) [188][250/293] lr: 5.000000e-05 eta: 0:36:31 time: 0.378002 data_time: 0.078889 memory: 12861 loss_kpt: 0.000711 acc_pose: 0.802678 loss: 0.000711 2022/10/13 16:47:17 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:47:38 - mmengine - INFO - Epoch(train) [189][50/293] lr: 5.000000e-05 eta: 0:35:58 time: 0.412793 data_time: 0.108173 memory: 12861 loss_kpt: 0.000728 acc_pose: 0.816552 loss: 0.000728 2022/10/13 16:47:57 - mmengine - INFO - Epoch(train) [189][100/293] lr: 5.000000e-05 eta: 0:35:41 time: 0.384945 data_time: 0.094115 memory: 12861 loss_kpt: 0.000706 acc_pose: 0.755576 loss: 0.000706 2022/10/13 16:48:16 - mmengine - INFO - Epoch(train) [189][150/293] lr: 5.000000e-05 eta: 0:35:25 time: 0.375274 data_time: 0.079045 memory: 12861 loss_kpt: 0.000719 acc_pose: 0.800726 loss: 0.000719 2022/10/13 16:48:35 - mmengine - INFO - Epoch(train) [189][200/293] lr: 5.000000e-05 eta: 0:35:08 time: 0.384755 data_time: 0.088727 memory: 12861 loss_kpt: 0.000713 acc_pose: 0.796330 loss: 0.000713 2022/10/13 16:48:54 - mmengine - INFO - Epoch(train) [189][250/293] lr: 5.000000e-05 eta: 0:34:51 time: 0.387964 data_time: 0.084119 memory: 12861 loss_kpt: 0.000714 acc_pose: 0.836068 loss: 0.000714 2022/10/13 16:49:11 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:49:30 - mmengine - INFO - Epoch(train) [190][50/293] lr: 5.000000e-05 eta: 0:34:19 time: 0.397375 data_time: 0.097014 memory: 12861 loss_kpt: 0.000703 acc_pose: 0.852884 loss: 0.000703 2022/10/13 16:49:50 - mmengine - INFO - Epoch(train) [190][100/293] lr: 5.000000e-05 eta: 0:34:02 time: 0.395667 data_time: 0.082380 memory: 12861 loss_kpt: 0.000711 acc_pose: 0.727065 loss: 0.000711 2022/10/13 16:50:09 - mmengine - INFO - Epoch(train) [190][150/293] lr: 5.000000e-05 eta: 0:33:46 time: 0.383268 data_time: 0.080878 memory: 12861 loss_kpt: 0.000734 acc_pose: 0.762735 loss: 0.000734 2022/10/13 16:50:29 - mmengine - INFO - Epoch(train) [190][200/293] lr: 5.000000e-05 eta: 0:33:29 time: 0.396769 data_time: 0.081446 memory: 12861 loss_kpt: 0.000718 acc_pose: 0.794861 loss: 0.000718 2022/10/13 16:50:49 - mmengine - INFO - Epoch(train) [190][250/293] lr: 5.000000e-05 eta: 0:33:12 time: 0.385834 data_time: 0.091269 memory: 12861 loss_kpt: 0.000724 acc_pose: 0.816238 loss: 0.000724 2022/10/13 16:51:05 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:51:05 - mmengine - INFO - Saving checkpoint at 190 epochs 2022/10/13 16:51:14 - mmengine - INFO - Epoch(val) [190][50/407] eta: 0:00:46 time: 0.130281 data_time: 0.069082 memory: 12861 2022/10/13 16:51:20 - mmengine - INFO - Epoch(val) [190][100/407] eta: 0:00:37 time: 0.120647 data_time: 0.060299 memory: 983 2022/10/13 16:51:26 - mmengine - INFO - Epoch(val) [190][150/407] eta: 0:00:31 time: 0.121270 data_time: 0.058556 memory: 983 2022/10/13 16:51:32 - mmengine - INFO - Epoch(val) [190][200/407] eta: 0:00:24 time: 0.119188 data_time: 0.057885 memory: 983 2022/10/13 16:51:39 - mmengine - INFO - Epoch(val) [190][250/407] eta: 0:00:20 time: 0.128995 data_time: 0.066863 memory: 983 2022/10/13 16:51:45 - mmengine - INFO - Epoch(val) [190][300/407] eta: 0:00:13 time: 0.122345 data_time: 0.061153 memory: 983 2022/10/13 16:51:51 - mmengine - INFO - Epoch(val) [190][350/407] eta: 0:00:07 time: 0.126831 data_time: 0.064645 memory: 983 2022/10/13 16:51:57 - mmengine - INFO - Epoch(val) [190][400/407] eta: 0:00:00 time: 0.120091 data_time: 0.059527 memory: 983 2022/10/13 16:52:34 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 16:52:48 - mmengine - INFO - Epoch(val) [190][407/407] coco/AP: 0.675199 coco/AP .5: 0.882186 coco/AP .75: 0.742355 coco/AP (M): 0.631288 coco/AP (L): 0.748082 coco/AR: 0.731943 coco/AR .5: 0.920183 coco/AR .75: 0.793766 coco/AR (M): 0.681590 coco/AR (L): 0.803084 2022/10/13 16:52:48 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384/best_coco/AP_epoch_180.pth is removed 2022/10/13 16:52:50 - mmengine - INFO - The best checkpoint with 0.6752 coco/AP at 190 epoch is saved to best_coco/AP_epoch_190.pth. 2022/10/13 16:53:09 - mmengine - INFO - Epoch(train) [191][50/293] lr: 5.000000e-05 eta: 0:32:40 time: 0.395517 data_time: 0.085816 memory: 12861 loss_kpt: 0.000699 acc_pose: 0.785971 loss: 0.000699 2022/10/13 16:53:29 - mmengine - INFO - Epoch(train) [191][100/293] lr: 5.000000e-05 eta: 0:32:23 time: 0.386724 data_time: 0.077583 memory: 12861 loss_kpt: 0.000727 acc_pose: 0.735053 loss: 0.000727 2022/10/13 16:53:48 - mmengine - INFO - Epoch(train) [191][150/293] lr: 5.000000e-05 eta: 0:32:07 time: 0.388493 data_time: 0.094511 memory: 12861 loss_kpt: 0.000726 acc_pose: 0.796008 loss: 0.000726 2022/10/13 16:54:08 - mmengine - INFO - Epoch(train) [191][200/293] lr: 5.000000e-05 eta: 0:31:50 time: 0.397118 data_time: 0.085410 memory: 12861 loss_kpt: 0.000727 acc_pose: 0.750349 loss: 0.000727 2022/10/13 16:54:27 - mmengine - INFO - Epoch(train) [191][250/293] lr: 5.000000e-05 eta: 0:31:33 time: 0.385500 data_time: 0.091359 memory: 12861 loss_kpt: 0.000714 acc_pose: 0.835231 loss: 0.000714 2022/10/13 16:54:44 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:55:00 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:55:05 - mmengine - INFO - Epoch(train) [192][50/293] lr: 5.000000e-05 eta: 0:31:01 time: 0.417486 data_time: 0.107200 memory: 12861 loss_kpt: 0.000725 acc_pose: 0.765087 loss: 0.000725 2022/10/13 16:55:24 - mmengine - INFO - Epoch(train) [192][100/293] lr: 5.000000e-05 eta: 0:30:44 time: 0.383341 data_time: 0.094824 memory: 12861 loss_kpt: 0.000731 acc_pose: 0.753955 loss: 0.000731 2022/10/13 16:55:44 - mmengine - INFO - Epoch(train) [192][150/293] lr: 5.000000e-05 eta: 0:30:28 time: 0.390436 data_time: 0.087421 memory: 12861 loss_kpt: 0.000721 acc_pose: 0.768178 loss: 0.000721 2022/10/13 16:56:03 - mmengine - INFO - Epoch(train) [192][200/293] lr: 5.000000e-05 eta: 0:30:11 time: 0.393973 data_time: 0.096345 memory: 12861 loss_kpt: 0.000721 acc_pose: 0.752560 loss: 0.000721 2022/10/13 16:56:23 - mmengine - INFO - Epoch(train) [192][250/293] lr: 5.000000e-05 eta: 0:29:54 time: 0.387910 data_time: 0.093397 memory: 12861 loss_kpt: 0.000697 acc_pose: 0.795447 loss: 0.000697 2022/10/13 16:56:39 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:56:59 - mmengine - INFO - Epoch(train) [193][50/293] lr: 5.000000e-05 eta: 0:29:22 time: 0.397059 data_time: 0.095155 memory: 12861 loss_kpt: 0.000712 acc_pose: 0.779049 loss: 0.000712 2022/10/13 16:57:19 - mmengine - INFO - Epoch(train) [193][100/293] lr: 5.000000e-05 eta: 0:29:05 time: 0.396117 data_time: 0.097447 memory: 12861 loss_kpt: 0.000715 acc_pose: 0.816956 loss: 0.000715 2022/10/13 16:57:39 - mmengine - INFO - Epoch(train) [193][150/293] lr: 5.000000e-05 eta: 0:28:49 time: 0.398892 data_time: 0.106883 memory: 12861 loss_kpt: 0.000700 acc_pose: 0.778960 loss: 0.000700 2022/10/13 16:57:58 - mmengine - INFO - Epoch(train) [193][200/293] lr: 5.000000e-05 eta: 0:28:32 time: 0.379863 data_time: 0.084333 memory: 12861 loss_kpt: 0.000711 acc_pose: 0.749985 loss: 0.000711 2022/10/13 16:58:17 - mmengine - INFO - Epoch(train) [193][250/293] lr: 5.000000e-05 eta: 0:28:15 time: 0.391813 data_time: 0.085130 memory: 12861 loss_kpt: 0.000724 acc_pose: 0.841887 loss: 0.000724 2022/10/13 16:58:34 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 16:58:54 - mmengine - INFO - Epoch(train) [194][50/293] lr: 5.000000e-05 eta: 0:27:43 time: 0.406947 data_time: 0.096662 memory: 12861 loss_kpt: 0.000713 acc_pose: 0.799348 loss: 0.000713 2022/10/13 16:59:14 - mmengine - INFO - Epoch(train) [194][100/293] lr: 5.000000e-05 eta: 0:27:26 time: 0.389815 data_time: 0.094822 memory: 12861 loss_kpt: 0.000710 acc_pose: 0.806900 loss: 0.000710 2022/10/13 16:59:33 - mmengine - INFO - Epoch(train) [194][150/293] lr: 5.000000e-05 eta: 0:27:10 time: 0.386585 data_time: 0.086808 memory: 12861 loss_kpt: 0.000731 acc_pose: 0.779417 loss: 0.000731 2022/10/13 16:59:52 - mmengine - INFO - Epoch(train) [194][200/293] lr: 5.000000e-05 eta: 0:26:53 time: 0.388278 data_time: 0.099832 memory: 12861 loss_kpt: 0.000721 acc_pose: 0.812576 loss: 0.000721 2022/10/13 17:00:12 - mmengine - INFO - Epoch(train) [194][250/293] lr: 5.000000e-05 eta: 0:26:36 time: 0.391981 data_time: 0.104004 memory: 12861 loss_kpt: 0.000715 acc_pose: 0.807097 loss: 0.000715 2022/10/13 17:00:28 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:00:49 - mmengine - INFO - Epoch(train) [195][50/293] lr: 5.000000e-05 eta: 0:26:04 time: 0.407960 data_time: 0.101518 memory: 12861 loss_kpt: 0.000705 acc_pose: 0.777254 loss: 0.000705 2022/10/13 17:01:08 - mmengine - INFO - Epoch(train) [195][100/293] lr: 5.000000e-05 eta: 0:25:47 time: 0.376746 data_time: 0.087279 memory: 12861 loss_kpt: 0.000708 acc_pose: 0.731481 loss: 0.000708 2022/10/13 17:01:28 - mmengine - INFO - Epoch(train) [195][150/293] lr: 5.000000e-05 eta: 0:25:31 time: 0.412629 data_time: 0.096096 memory: 12861 loss_kpt: 0.000729 acc_pose: 0.749145 loss: 0.000729 2022/10/13 17:01:32 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:01:48 - mmengine - INFO - Epoch(train) [195][200/293] lr: 5.000000e-05 eta: 0:25:14 time: 0.396130 data_time: 0.098198 memory: 12861 loss_kpt: 0.000736 acc_pose: 0.814071 loss: 0.000736 2022/10/13 17:02:07 - mmengine - INFO - Epoch(train) [195][250/293] lr: 5.000000e-05 eta: 0:24:58 time: 0.386651 data_time: 0.085374 memory: 12861 loss_kpt: 0.000711 acc_pose: 0.820003 loss: 0.000711 2022/10/13 17:02:24 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:02:45 - mmengine - INFO - Epoch(train) [196][50/293] lr: 5.000000e-05 eta: 0:24:25 time: 0.418234 data_time: 0.098088 memory: 12861 loss_kpt: 0.000718 acc_pose: 0.766090 loss: 0.000718 2022/10/13 17:03:04 - mmengine - INFO - Epoch(train) [196][100/293] lr: 5.000000e-05 eta: 0:24:09 time: 0.388436 data_time: 0.095398 memory: 12861 loss_kpt: 0.000721 acc_pose: 0.781602 loss: 0.000721 2022/10/13 17:03:23 - mmengine - INFO - Epoch(train) [196][150/293] lr: 5.000000e-05 eta: 0:23:52 time: 0.380879 data_time: 0.087161 memory: 12861 loss_kpt: 0.000710 acc_pose: 0.793803 loss: 0.000710 2022/10/13 17:03:43 - mmengine - INFO - Epoch(train) [196][200/293] lr: 5.000000e-05 eta: 0:23:35 time: 0.384496 data_time: 0.082523 memory: 12861 loss_kpt: 0.000723 acc_pose: 0.790052 loss: 0.000723 2022/10/13 17:04:02 - mmengine - INFO - Epoch(train) [196][250/293] lr: 5.000000e-05 eta: 0:23:19 time: 0.387947 data_time: 0.094035 memory: 12861 loss_kpt: 0.000714 acc_pose: 0.800172 loss: 0.000714 2022/10/13 17:04:18 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:04:38 - mmengine - INFO - Epoch(train) [197][50/293] lr: 5.000000e-05 eta: 0:22:46 time: 0.398841 data_time: 0.095771 memory: 12861 loss_kpt: 0.000717 acc_pose: 0.812639 loss: 0.000717 2022/10/13 17:04:58 - mmengine - INFO - Epoch(train) [197][100/293] lr: 5.000000e-05 eta: 0:22:30 time: 0.397616 data_time: 0.091352 memory: 12861 loss_kpt: 0.000731 acc_pose: 0.808608 loss: 0.000731 2022/10/13 17:05:19 - mmengine - INFO - Epoch(train) [197][150/293] lr: 5.000000e-05 eta: 0:22:13 time: 0.407949 data_time: 0.106890 memory: 12861 loss_kpt: 0.000726 acc_pose: 0.795181 loss: 0.000726 2022/10/13 17:05:39 - mmengine - INFO - Epoch(train) [197][200/293] lr: 5.000000e-05 eta: 0:21:56 time: 0.399739 data_time: 0.098667 memory: 12861 loss_kpt: 0.000725 acc_pose: 0.810016 loss: 0.000725 2022/10/13 17:05:58 - mmengine - INFO - Epoch(train) [197][250/293] lr: 5.000000e-05 eta: 0:21:40 time: 0.383271 data_time: 0.081136 memory: 12861 loss_kpt: 0.000729 acc_pose: 0.756541 loss: 0.000729 2022/10/13 17:06:14 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:06:34 - mmengine - INFO - Epoch(train) [198][50/293] lr: 5.000000e-05 eta: 0:21:08 time: 0.404790 data_time: 0.102299 memory: 12861 loss_kpt: 0.000741 acc_pose: 0.874399 loss: 0.000741 2022/10/13 17:06:54 - mmengine - INFO - Epoch(train) [198][100/293] lr: 5.000000e-05 eta: 0:20:51 time: 0.388669 data_time: 0.090648 memory: 12861 loss_kpt: 0.000720 acc_pose: 0.790364 loss: 0.000720 2022/10/13 17:07:13 - mmengine - INFO - Epoch(train) [198][150/293] lr: 5.000000e-05 eta: 0:20:34 time: 0.382825 data_time: 0.083483 memory: 12861 loss_kpt: 0.000704 acc_pose: 0.801312 loss: 0.000704 2022/10/13 17:07:32 - mmengine - INFO - Epoch(train) [198][200/293] lr: 5.000000e-05 eta: 0:20:17 time: 0.389294 data_time: 0.095825 memory: 12861 loss_kpt: 0.000718 acc_pose: 0.788708 loss: 0.000718 2022/10/13 17:07:52 - mmengine - INFO - Epoch(train) [198][250/293] lr: 5.000000e-05 eta: 0:20:01 time: 0.392678 data_time: 0.094440 memory: 12861 loss_kpt: 0.000709 acc_pose: 0.748666 loss: 0.000709 2022/10/13 17:08:04 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:08:09 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:08:29 - mmengine - INFO - Epoch(train) [199][50/293] lr: 5.000000e-05 eta: 0:19:29 time: 0.402573 data_time: 0.095645 memory: 12861 loss_kpt: 0.000714 acc_pose: 0.760395 loss: 0.000714 2022/10/13 17:08:49 - mmengine - INFO - Epoch(train) [199][100/293] lr: 5.000000e-05 eta: 0:19:12 time: 0.383919 data_time: 0.088109 memory: 12861 loss_kpt: 0.000730 acc_pose: 0.784998 loss: 0.000730 2022/10/13 17:09:08 - mmengine - INFO - Epoch(train) [199][150/293] lr: 5.000000e-05 eta: 0:18:55 time: 0.395264 data_time: 0.091410 memory: 12861 loss_kpt: 0.000720 acc_pose: 0.795598 loss: 0.000720 2022/10/13 17:09:27 - mmengine - INFO - Epoch(train) [199][200/293] lr: 5.000000e-05 eta: 0:18:39 time: 0.382454 data_time: 0.073152 memory: 12861 loss_kpt: 0.000727 acc_pose: 0.800321 loss: 0.000727 2022/10/13 17:09:46 - mmengine - INFO - Epoch(train) [199][250/293] lr: 5.000000e-05 eta: 0:18:22 time: 0.377324 data_time: 0.082074 memory: 12861 loss_kpt: 0.000713 acc_pose: 0.817055 loss: 0.000713 2022/10/13 17:10:03 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:10:23 - mmengine - INFO - Epoch(train) [200][50/293] lr: 5.000000e-05 eta: 0:17:50 time: 0.400505 data_time: 0.097130 memory: 12861 loss_kpt: 0.000722 acc_pose: 0.789036 loss: 0.000722 2022/10/13 17:10:43 - mmengine - INFO - Epoch(train) [200][100/293] lr: 5.000000e-05 eta: 0:17:33 time: 0.388815 data_time: 0.096820 memory: 12861 loss_kpt: 0.000728 acc_pose: 0.742606 loss: 0.000728 2022/10/13 17:11:02 - mmengine - INFO - Epoch(train) [200][150/293] lr: 5.000000e-05 eta: 0:17:16 time: 0.396701 data_time: 0.083814 memory: 12861 loss_kpt: 0.000714 acc_pose: 0.772557 loss: 0.000714 2022/10/13 17:11:22 - mmengine - INFO - Epoch(train) [200][200/293] lr: 5.000000e-05 eta: 0:17:00 time: 0.386221 data_time: 0.090833 memory: 12861 loss_kpt: 0.000720 acc_pose: 0.847727 loss: 0.000720 2022/10/13 17:11:41 - mmengine - INFO - Epoch(train) [200][250/293] lr: 5.000000e-05 eta: 0:16:43 time: 0.392778 data_time: 0.098515 memory: 12861 loss_kpt: 0.000708 acc_pose: 0.813584 loss: 0.000708 2022/10/13 17:11:58 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:11:58 - mmengine - INFO - Saving checkpoint at 200 epochs 2022/10/13 17:12:07 - mmengine - INFO - Epoch(val) [200][50/407] eta: 0:00:48 time: 0.134481 data_time: 0.071118 memory: 12861 2022/10/13 17:12:13 - mmengine - INFO - Epoch(val) [200][100/407] eta: 0:00:38 time: 0.126568 data_time: 0.062335 memory: 983 2022/10/13 17:12:20 - mmengine - INFO - Epoch(val) [200][150/407] eta: 0:00:32 time: 0.124716 data_time: 0.063407 memory: 983 2022/10/13 17:12:26 - mmengine - INFO - Epoch(val) [200][200/407] eta: 0:00:25 time: 0.122080 data_time: 0.060596 memory: 983 2022/10/13 17:12:32 - mmengine - INFO - Epoch(val) [200][250/407] eta: 0:00:18 time: 0.118172 data_time: 0.057044 memory: 983 2022/10/13 17:12:38 - mmengine - INFO - Epoch(val) [200][300/407] eta: 0:00:13 time: 0.124021 data_time: 0.061843 memory: 983 2022/10/13 17:12:44 - mmengine - INFO - Epoch(val) [200][350/407] eta: 0:00:07 time: 0.124300 data_time: 0.063897 memory: 983 2022/10/13 17:12:50 - mmengine - INFO - Epoch(val) [200][400/407] eta: 0:00:00 time: 0.116305 data_time: 0.055415 memory: 983 2022/10/13 17:13:26 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 17:13:41 - mmengine - INFO - Epoch(val) [200][407/407] coco/AP: 0.676504 coco/AP .5: 0.879921 coco/AP .75: 0.744891 coco/AP (M): 0.633563 coco/AP (L): 0.749067 coco/AR: 0.733265 coco/AR .5: 0.919868 coco/AR .75: 0.796757 coco/AR (M): 0.684349 coco/AR (L): 0.802490 2022/10/13 17:13:41 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384/best_coco/AP_epoch_190.pth is removed 2022/10/13 17:13:42 - mmengine - INFO - The best checkpoint with 0.6765 coco/AP at 200 epoch is saved to best_coco/AP_epoch_200.pth. 2022/10/13 17:14:03 - mmengine - INFO - Epoch(train) [201][50/293] lr: 5.000000e-06 eta: 0:16:11 time: 0.403983 data_time: 0.094965 memory: 12861 loss_kpt: 0.000718 acc_pose: 0.768532 loss: 0.000718 2022/10/13 17:14:22 - mmengine - INFO - Epoch(train) [201][100/293] lr: 5.000000e-06 eta: 0:15:54 time: 0.390661 data_time: 0.088810 memory: 12861 loss_kpt: 0.000708 acc_pose: 0.756172 loss: 0.000708 2022/10/13 17:14:41 - mmengine - INFO - Epoch(train) [201][150/293] lr: 5.000000e-06 eta: 0:15:37 time: 0.380300 data_time: 0.090148 memory: 12861 loss_kpt: 0.000706 acc_pose: 0.798716 loss: 0.000706 2022/10/13 17:15:01 - mmengine - INFO - Epoch(train) [201][200/293] lr: 5.000000e-06 eta: 0:15:21 time: 0.396028 data_time: 0.080870 memory: 12861 loss_kpt: 0.000720 acc_pose: 0.790552 loss: 0.000720 2022/10/13 17:15:20 - mmengine - INFO - Epoch(train) [201][250/293] lr: 5.000000e-06 eta: 0:15:04 time: 0.381508 data_time: 0.083236 memory: 12861 loss_kpt: 0.000701 acc_pose: 0.749636 loss: 0.000701 2022/10/13 17:15:36 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:15:56 - mmengine - INFO - Epoch(train) [202][50/293] lr: 5.000000e-06 eta: 0:14:32 time: 0.397944 data_time: 0.096047 memory: 12861 loss_kpt: 0.000709 acc_pose: 0.799561 loss: 0.000709 2022/10/13 17:16:16 - mmengine - INFO - Epoch(train) [202][100/293] lr: 5.000000e-06 eta: 0:14:15 time: 0.401190 data_time: 0.098583 memory: 12861 loss_kpt: 0.000706 acc_pose: 0.779670 loss: 0.000706 2022/10/13 17:16:19 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:16:36 - mmengine - INFO - Epoch(train) [202][150/293] lr: 5.000000e-06 eta: 0:13:59 time: 0.403526 data_time: 0.104207 memory: 12861 loss_kpt: 0.000703 acc_pose: 0.767345 loss: 0.000703 2022/10/13 17:16:56 - mmengine - INFO - Epoch(train) [202][200/293] lr: 5.000000e-06 eta: 0:13:42 time: 0.391854 data_time: 0.086970 memory: 12861 loss_kpt: 0.000699 acc_pose: 0.801128 loss: 0.000699 2022/10/13 17:17:15 - mmengine - INFO - Epoch(train) [202][250/293] lr: 5.000000e-06 eta: 0:13:25 time: 0.385766 data_time: 0.087414 memory: 12861 loss_kpt: 0.000720 acc_pose: 0.793626 loss: 0.000720 2022/10/13 17:17:32 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:17:53 - mmengine - INFO - Epoch(train) [203][50/293] lr: 5.000000e-06 eta: 0:12:53 time: 0.410639 data_time: 0.110907 memory: 12861 loss_kpt: 0.000698 acc_pose: 0.827394 loss: 0.000698 2022/10/13 17:18:12 - mmengine - INFO - Epoch(train) [203][100/293] lr: 5.000000e-06 eta: 0:12:36 time: 0.390427 data_time: 0.098063 memory: 12861 loss_kpt: 0.000711 acc_pose: 0.767980 loss: 0.000711 2022/10/13 17:18:32 - mmengine - INFO - Epoch(train) [203][150/293] lr: 5.000000e-06 eta: 0:12:20 time: 0.393276 data_time: 0.095327 memory: 12861 loss_kpt: 0.000702 acc_pose: 0.802244 loss: 0.000702 2022/10/13 17:18:51 - mmengine - INFO - Epoch(train) [203][200/293] lr: 5.000000e-06 eta: 0:12:03 time: 0.378856 data_time: 0.082697 memory: 12861 loss_kpt: 0.000712 acc_pose: 0.787810 loss: 0.000712 2022/10/13 17:19:10 - mmengine - INFO - Epoch(train) [203][250/293] lr: 5.000000e-06 eta: 0:11:46 time: 0.390056 data_time: 0.093797 memory: 12861 loss_kpt: 0.000721 acc_pose: 0.844626 loss: 0.000721 2022/10/13 17:19:27 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:19:48 - mmengine - INFO - Epoch(train) [204][50/293] lr: 5.000000e-06 eta: 0:11:14 time: 0.414182 data_time: 0.096651 memory: 12861 loss_kpt: 0.000719 acc_pose: 0.788342 loss: 0.000719 2022/10/13 17:20:07 - mmengine - INFO - Epoch(train) [204][100/293] lr: 5.000000e-06 eta: 0:10:58 time: 0.385641 data_time: 0.088352 memory: 12861 loss_kpt: 0.000721 acc_pose: 0.804298 loss: 0.000721 2022/10/13 17:20:27 - mmengine - INFO - Epoch(train) [204][150/293] lr: 5.000000e-06 eta: 0:10:41 time: 0.392738 data_time: 0.088054 memory: 12861 loss_kpt: 0.000729 acc_pose: 0.750378 loss: 0.000729 2022/10/13 17:20:46 - mmengine - INFO - Epoch(train) [204][200/293] lr: 5.000000e-06 eta: 0:10:24 time: 0.387338 data_time: 0.084151 memory: 12861 loss_kpt: 0.000713 acc_pose: 0.754170 loss: 0.000713 2022/10/13 17:21:05 - mmengine - INFO - Epoch(train) [204][250/293] lr: 5.000000e-06 eta: 0:10:07 time: 0.374984 data_time: 0.069578 memory: 12861 loss_kpt: 0.000719 acc_pose: 0.751946 loss: 0.000719 2022/10/13 17:21:21 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:21:41 - mmengine - INFO - Epoch(train) [205][50/293] lr: 5.000000e-06 eta: 0:09:36 time: 0.400397 data_time: 0.100105 memory: 12861 loss_kpt: 0.000703 acc_pose: 0.801346 loss: 0.000703 2022/10/13 17:22:01 - mmengine - INFO - Epoch(train) [205][100/293] lr: 5.000000e-06 eta: 0:09:19 time: 0.394832 data_time: 0.101368 memory: 12861 loss_kpt: 0.000714 acc_pose: 0.804283 loss: 0.000714 2022/10/13 17:22:21 - mmengine - INFO - Epoch(train) [205][150/293] lr: 5.000000e-06 eta: 0:09:02 time: 0.387081 data_time: 0.093493 memory: 12861 loss_kpt: 0.000720 acc_pose: 0.806572 loss: 0.000720 2022/10/13 17:22:41 - mmengine - INFO - Epoch(train) [205][200/293] lr: 5.000000e-06 eta: 0:08:45 time: 0.408920 data_time: 0.104431 memory: 12861 loss_kpt: 0.000721 acc_pose: 0.755601 loss: 0.000721 2022/10/13 17:22:52 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:23:01 - mmengine - INFO - Epoch(train) [205][250/293] lr: 5.000000e-06 eta: 0:08:28 time: 0.391784 data_time: 0.096830 memory: 12861 loss_kpt: 0.000707 acc_pose: 0.757705 loss: 0.000707 2022/10/13 17:23:17 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:23:37 - mmengine - INFO - Epoch(train) [206][50/293] lr: 5.000000e-06 eta: 0:07:57 time: 0.399262 data_time: 0.086887 memory: 12861 loss_kpt: 0.000714 acc_pose: 0.799893 loss: 0.000714 2022/10/13 17:23:57 - mmengine - INFO - Epoch(train) [206][100/293] lr: 5.000000e-06 eta: 0:07:40 time: 0.390782 data_time: 0.079467 memory: 12861 loss_kpt: 0.000713 acc_pose: 0.781485 loss: 0.000713 2022/10/13 17:24:16 - mmengine - INFO - Epoch(train) [206][150/293] lr: 5.000000e-06 eta: 0:07:23 time: 0.389148 data_time: 0.097119 memory: 12861 loss_kpt: 0.000721 acc_pose: 0.782491 loss: 0.000721 2022/10/13 17:24:35 - mmengine - INFO - Epoch(train) [206][200/293] lr: 5.000000e-06 eta: 0:07:06 time: 0.372226 data_time: 0.079985 memory: 12861 loss_kpt: 0.000700 acc_pose: 0.759885 loss: 0.000700 2022/10/13 17:24:53 - mmengine - INFO - Epoch(train) [206][250/293] lr: 5.000000e-06 eta: 0:06:49 time: 0.373404 data_time: 0.075844 memory: 12861 loss_kpt: 0.000714 acc_pose: 0.771855 loss: 0.000714 2022/10/13 17:25:10 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:25:31 - mmengine - INFO - Epoch(train) [207][50/293] lr: 5.000000e-06 eta: 0:06:18 time: 0.400663 data_time: 0.098682 memory: 12861 loss_kpt: 0.000699 acc_pose: 0.782112 loss: 0.000699 2022/10/13 17:25:50 - mmengine - INFO - Epoch(train) [207][100/293] lr: 5.000000e-06 eta: 0:06:01 time: 0.389701 data_time: 0.086634 memory: 12861 loss_kpt: 0.000705 acc_pose: 0.794265 loss: 0.000705 2022/10/13 17:26:10 - mmengine - INFO - Epoch(train) [207][150/293] lr: 5.000000e-06 eta: 0:05:44 time: 0.394734 data_time: 0.087883 memory: 12861 loss_kpt: 0.000712 acc_pose: 0.783308 loss: 0.000712 2022/10/13 17:26:29 - mmengine - INFO - Epoch(train) [207][200/293] lr: 5.000000e-06 eta: 0:05:27 time: 0.394054 data_time: 0.096102 memory: 12861 loss_kpt: 0.000702 acc_pose: 0.758436 loss: 0.000702 2022/10/13 17:26:49 - mmengine - INFO - Epoch(train) [207][250/293] lr: 5.000000e-06 eta: 0:05:11 time: 0.394913 data_time: 0.088037 memory: 12861 loss_kpt: 0.000701 acc_pose: 0.810566 loss: 0.000701 2022/10/13 17:27:06 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:27:25 - mmengine - INFO - Epoch(train) [208][50/293] lr: 5.000000e-06 eta: 0:04:39 time: 0.394626 data_time: 0.091146 memory: 12861 loss_kpt: 0.000714 acc_pose: 0.796993 loss: 0.000714 2022/10/13 17:27:45 - mmengine - INFO - Epoch(train) [208][100/293] lr: 5.000000e-06 eta: 0:04:22 time: 0.388558 data_time: 0.097909 memory: 12861 loss_kpt: 0.000710 acc_pose: 0.805987 loss: 0.000710 2022/10/13 17:28:04 - mmengine - INFO - Epoch(train) [208][150/293] lr: 5.000000e-06 eta: 0:04:05 time: 0.386136 data_time: 0.086271 memory: 12861 loss_kpt: 0.000725 acc_pose: 0.725461 loss: 0.000725 2022/10/13 17:28:24 - mmengine - INFO - Epoch(train) [208][200/293] lr: 5.000000e-06 eta: 0:03:49 time: 0.393003 data_time: 0.084520 memory: 12861 loss_kpt: 0.000707 acc_pose: 0.772214 loss: 0.000707 2022/10/13 17:28:43 - mmengine - INFO - Epoch(train) [208][250/293] lr: 5.000000e-06 eta: 0:03:32 time: 0.386918 data_time: 0.084905 memory: 12861 loss_kpt: 0.000716 acc_pose: 0.822855 loss: 0.000716 2022/10/13 17:28:59 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:29:19 - mmengine - INFO - Epoch(train) [209][50/293] lr: 5.000000e-06 eta: 0:03:00 time: 0.393784 data_time: 0.100614 memory: 12861 loss_kpt: 0.000719 acc_pose: 0.772011 loss: 0.000719 2022/10/13 17:29:21 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:29:38 - mmengine - INFO - Epoch(train) [209][100/293] lr: 5.000000e-06 eta: 0:02:43 time: 0.381703 data_time: 0.083669 memory: 12861 loss_kpt: 0.000712 acc_pose: 0.813847 loss: 0.000712 2022/10/13 17:29:58 - mmengine - INFO - Epoch(train) [209][150/293] lr: 5.000000e-06 eta: 0:02:27 time: 0.389708 data_time: 0.086224 memory: 12861 loss_kpt: 0.000709 acc_pose: 0.764620 loss: 0.000709 2022/10/13 17:30:18 - mmengine - INFO - Epoch(train) [209][200/293] lr: 5.000000e-06 eta: 0:02:10 time: 0.402548 data_time: 0.100718 memory: 12861 loss_kpt: 0.000712 acc_pose: 0.812772 loss: 0.000712 2022/10/13 17:30:37 - mmengine - INFO - Epoch(train) [209][250/293] lr: 5.000000e-06 eta: 0:01:53 time: 0.389287 data_time: 0.094991 memory: 12861 loss_kpt: 0.000708 acc_pose: 0.793831 loss: 0.000708 2022/10/13 17:30:54 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:31:13 - mmengine - INFO - Epoch(train) [210][50/293] lr: 5.000000e-06 eta: 0:01:21 time: 0.385742 data_time: 0.093397 memory: 12861 loss_kpt: 0.000714 acc_pose: 0.786910 loss: 0.000714 2022/10/13 17:31:33 - mmengine - INFO - Epoch(train) [210][100/293] lr: 5.000000e-06 eta: 0:01:05 time: 0.388669 data_time: 0.089442 memory: 12861 loss_kpt: 0.000728 acc_pose: 0.748325 loss: 0.000728 2022/10/13 17:31:52 - mmengine - INFO - Epoch(train) [210][150/293] lr: 5.000000e-06 eta: 0:00:48 time: 0.386350 data_time: 0.091090 memory: 12861 loss_kpt: 0.000702 acc_pose: 0.805319 loss: 0.000702 2022/10/13 17:32:11 - mmengine - INFO - Epoch(train) [210][200/293] lr: 5.000000e-06 eta: 0:00:31 time: 0.377541 data_time: 0.086180 memory: 12861 loss_kpt: 0.000707 acc_pose: 0.793874 loss: 0.000707 2022/10/13 17:32:30 - mmengine - INFO - Epoch(train) [210][250/293] lr: 5.000000e-06 eta: 0:00:14 time: 0.374418 data_time: 0.079502 memory: 12861 loss_kpt: 0.000711 acc_pose: 0.786850 loss: 0.000711 2022/10/13 17:32:46 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-384x288_20221013_101300 2022/10/13 17:32:46 - mmengine - INFO - Saving checkpoint at 210 epochs 2022/10/13 17:32:54 - mmengine - INFO - Epoch(val) [210][50/407] eta: 0:00:44 time: 0.126041 data_time: 0.064611 memory: 12861 2022/10/13 17:33:01 - mmengine - INFO - Epoch(val) [210][100/407] eta: 0:00:40 time: 0.130847 data_time: 0.068573 memory: 983 2022/10/13 17:33:07 - mmengine - INFO - Epoch(val) [210][150/407] eta: 0:00:31 time: 0.120939 data_time: 0.060110 memory: 983 2022/10/13 17:33:13 - mmengine - INFO - Epoch(val) [210][200/407] eta: 0:00:24 time: 0.119143 data_time: 0.059205 memory: 983 2022/10/13 17:33:19 - mmengine - INFO - Epoch(val) [210][250/407] eta: 0:00:20 time: 0.130792 data_time: 0.069937 memory: 983 2022/10/13 17:33:26 - mmengine - INFO - Epoch(val) [210][300/407] eta: 0:00:14 time: 0.132043 data_time: 0.071382 memory: 983 2022/10/13 17:33:32 - mmengine - INFO - Epoch(val) [210][350/407] eta: 0:00:07 time: 0.122893 data_time: 0.062596 memory: 983 2022/10/13 17:33:38 - mmengine - INFO - Epoch(val) [210][400/407] eta: 0:00:00 time: 0.120367 data_time: 0.060375 memory: 983 2022/10/13 17:34:15 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 17:34:29 - mmengine - INFO - Epoch(val) [210][407/407] coco/AP: 0.677408 coco/AP .5: 0.882179 coco/AP .75: 0.745842 coco/AP (M): 0.633797 coco/AP (L): 0.750071 coco/AR: 0.734052 coco/AR .5: 0.920025 coco/AR .75: 0.797229 coco/AR (M): 0.684731 coco/AR (L): 0.803753 2022/10/13 17:34:29 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/mbv2_384/best_coco/AP_epoch_200.pth is removed 2022/10/13 17:34:31 - mmengine - INFO - The best checkpoint with 0.6774 coco/AP at 210 epoch is saved to best_coco/AP_epoch_210.pth.