2022/10/14 09:45:24 - 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: 936893487 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 ------------------------------------------------------------ 2022/10/14 09:45:25 - mmengine - INFO - Config: default_scope = 'mmpose' default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict( type='CheckpointHook', interval=10, max_keep_ckpts=1, save_best='coco/AP', rule='greater'), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='PoseVisualizationHook', enable=False)) custom_hooks = [dict(type='SyncBuffersHook')] env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='PoseLocalVisualizer', vis_backends=[dict(type='LocalVisBackend')], name='visualizer') log_processor = dict( type='LogProcessor', window_size=50, by_epoch=True, num_digits=6) log_level = 'INFO' load_from = None resume = False file_client_args = dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' })) train_cfg = dict(by_epoch=True, max_epochs=210, val_interval=10) val_cfg = dict() test_cfg = dict() optim_wrapper = dict(optimizer=dict(type='Adam', lr=0.0005)) param_scheduler = [ dict( type='LinearLR', begin=0, end=500, start_factor=0.001, by_epoch=False), dict( type='MultiStepLR', begin=0, end=210, milestones=[170, 200], gamma=0.1, by_epoch=True) ] auto_scale_lr = dict(base_batch_size=512) codec = dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) model = dict( type='TopdownPoseEstimator', data_preprocessor=dict( type='PoseDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True), backbone=dict( type='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=(192, 256), heatmap_size=(48, 64), sigma=2)), test_cfg=dict(flip_test=True, flip_mode='heatmap', shift_heatmap=True)) dataset_type = 'CocoDataset' data_mode = 'topdown' data_root = 'data/coco/' train_pipeline = [ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict(type='RandomBBoxTransform'), dict(type='TopdownAffine', input_size=(192, 256)), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)), dict(type='PackPoseInputs') ] val_pipeline = [ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ] train_dataloader = dict( batch_size=64, num_workers=2, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_train2017.json', data_prefix=dict(img='train2017/'), pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict(type='RandomBBoxTransform'), dict(type='TopdownAffine', input_size=(192, 256)), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)), dict(type='PackPoseInputs') ])) val_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ])) test_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ])) val_evaluator = dict( type='CocoMetric', ann_file='data/coco/annotations/person_keypoints_val2017.json') test_evaluator = dict( type='CocoMetric', ann_file='data/coco/annotations/person_keypoints_val2017.json') launcher = 'slurm' work_dir = 'work_dirs/20221014/mbv2_256/' 2022/10/14 09:46:08 - 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. 2022/10/14 09:46:08 - 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. 2022/10/14 09:46:08 - 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. 2022/10/14 09:46:08 - 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. 2022/10/14 09:46:08 - 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. 2022/10/14 09:46:08 - 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. 2022/10/14 09:46:08 - 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. 2022/10/14 09:46:08 - 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. 2022/10/14 09:46:12 - 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. 2022/10/14 09:46:13 - 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. 2022/10/14 09:46:17 - 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. 2022/10/14 09:46:17 - 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 2022/10/14 09:46:17 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256 by HardDiskBackend. 2022/10/14 09:48:01 - mmengine - INFO - Epoch(train) [1][50/293] lr: 4.954910e-05 eta: 1 day, 11:14:26 time: 2.063533 data_time: 1.042442 memory: 5829 loss_kpt: 0.002191 acc_pose: 0.109279 loss: 0.002191 2022/10/14 09:49:31 - mmengine - INFO - Epoch(train) [1][100/293] lr: 9.959920e-05 eta: 1 day, 8:59:49 time: 1.803950 data_time: 0.200282 memory: 5829 loss_kpt: 0.001968 acc_pose: 0.264831 loss: 0.001968 2022/10/14 09:51:05 - mmengine - INFO - Epoch(train) [1][150/293] lr: 1.496493e-04 eta: 1 day, 8:43:12 time: 1.889739 data_time: 1.062701 memory: 5829 loss_kpt: 0.001812 acc_pose: 0.298938 loss: 0.001812 2022/10/14 09:52:45 - mmengine - INFO - Epoch(train) [1][200/293] lr: 1.996994e-04 eta: 1 day, 9:01:39 time: 1.997515 data_time: 1.098816 memory: 5829 loss_kpt: 0.001698 acc_pose: 0.395482 loss: 0.001698 2022/10/14 09:54:24 - mmengine - INFO - Epoch(train) [1][250/293] lr: 2.497495e-04 eta: 1 day, 9:07:36 time: 1.975725 data_time: 0.058525 memory: 5829 loss_kpt: 0.001588 acc_pose: 0.433585 loss: 0.001588 2022/10/14 09:55:44 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 09:57:20 - mmengine - INFO - Epoch(train) [2][50/293] lr: 3.428427e-04 eta: 1 day, 4:52:56 time: 1.926861 data_time: 0.401925 memory: 5829 loss_kpt: 0.001500 acc_pose: 0.460737 loss: 0.001500 2022/10/14 09:58:47 - mmengine - INFO - Epoch(train) [2][100/293] lr: 3.928928e-04 eta: 1 day, 4:54:31 time: 1.722539 data_time: 0.058950 memory: 5829 loss_kpt: 0.001443 acc_pose: 0.503811 loss: 0.001443 2022/10/14 10:00:12 - mmengine - INFO - Epoch(train) [2][150/293] lr: 4.429429e-04 eta: 1 day, 4:54:38 time: 1.715630 data_time: 0.058152 memory: 5829 loss_kpt: 0.001432 acc_pose: 0.510455 loss: 0.001432 2022/10/14 10:01:47 - mmengine - INFO - Epoch(train) [2][200/293] lr: 4.929930e-04 eta: 1 day, 5:12:56 time: 1.894823 data_time: 0.367663 memory: 5829 loss_kpt: 0.001400 acc_pose: 0.461606 loss: 0.001400 2022/10/14 10:03:23 - mmengine - INFO - Epoch(train) [2][250/293] lr: 5.000000e-04 eta: 1 day, 5:30:09 time: 1.922518 data_time: 0.059789 memory: 5829 loss_kpt: 0.001366 acc_pose: 0.446665 loss: 0.001366 2022/10/14 10:04:41 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 10:06:14 - mmengine - INFO - Epoch(train) [3][50/293] lr: 5.000000e-04 eta: 1 day, 3:37:33 time: 1.861813 data_time: 1.477586 memory: 5829 loss_kpt: 0.001337 acc_pose: 0.497381 loss: 0.001337 2022/10/14 10:07:39 - mmengine - INFO - Epoch(train) [3][100/293] lr: 5.000000e-04 eta: 1 day, 3:40:56 time: 1.697372 data_time: 0.724666 memory: 5829 loss_kpt: 0.001321 acc_pose: 0.568409 loss: 0.001321 2022/10/14 10:09:03 - mmengine - INFO - Epoch(train) [3][150/293] lr: 5.000000e-04 eta: 1 day, 3:42:13 time: 1.676402 data_time: 1.126006 memory: 5829 loss_kpt: 0.001297 acc_pose: 0.540155 loss: 0.001297 2022/10/14 10:10:38 - mmengine - INFO - Epoch(train) [3][200/293] lr: 5.000000e-04 eta: 1 day, 3:58:06 time: 1.908393 data_time: 0.063056 memory: 5829 loss_kpt: 0.001282 acc_pose: 0.567925 loss: 0.001282 2022/10/14 10:12:09 - mmengine - INFO - Epoch(train) [3][250/293] lr: 5.000000e-04 eta: 1 day, 4:05:35 time: 1.803960 data_time: 0.171506 memory: 5829 loss_kpt: 0.001264 acc_pose: 0.538584 loss: 0.001264 2022/10/14 10:13:18 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 10:14:52 - mmengine - INFO - Epoch(train) [4][50/293] lr: 5.000000e-04 eta: 1 day, 2:56:39 time: 1.878723 data_time: 0.418002 memory: 5829 loss_kpt: 0.001239 acc_pose: 0.577531 loss: 0.001239 2022/10/14 10:16:30 - mmengine - INFO - Epoch(train) [4][100/293] lr: 5.000000e-04 eta: 1 day, 3:13:50 time: 1.959923 data_time: 0.127045 memory: 5829 loss_kpt: 0.001237 acc_pose: 0.608906 loss: 0.001237 2022/10/14 10:17:07 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 10:18:02 - mmengine - INFO - Epoch(train) [4][150/293] lr: 5.000000e-04 eta: 1 day, 3:23:16 time: 1.838990 data_time: 0.298109 memory: 5829 loss_kpt: 0.001228 acc_pose: 0.624211 loss: 0.001228 2022/10/14 10:19:27 - mmengine - INFO - Epoch(train) [4][200/293] lr: 5.000000e-04 eta: 1 day, 3:24:56 time: 1.694666 data_time: 0.124769 memory: 5829 loss_kpt: 0.001205 acc_pose: 0.572373 loss: 0.001205 2022/10/14 10:20:47 - mmengine - INFO - Epoch(train) [4][250/293] lr: 5.000000e-04 eta: 1 day, 3:22:39 time: 1.611834 data_time: 0.183176 memory: 5829 loss_kpt: 0.001201 acc_pose: 0.552162 loss: 0.001201 2022/10/14 10:21:53 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 10:23:21 - mmengine - INFO - Epoch(train) [5][50/293] lr: 5.000000e-04 eta: 1 day, 2:27:34 time: 1.757210 data_time: 0.196914 memory: 5829 loss_kpt: 0.001201 acc_pose: 0.624487 loss: 0.001201 2022/10/14 10:24:48 - mmengine - INFO - Epoch(train) [5][100/293] lr: 5.000000e-04 eta: 1 day, 2:32:52 time: 1.747139 data_time: 0.056590 memory: 5829 loss_kpt: 0.001203 acc_pose: 0.578286 loss: 0.001203 2022/10/14 10:26:16 - mmengine - INFO - Epoch(train) [5][150/293] lr: 5.000000e-04 eta: 1 day, 2:37:37 time: 1.746133 data_time: 0.077692 memory: 5829 loss_kpt: 0.001181 acc_pose: 0.597823 loss: 0.001181 2022/10/14 10:27:34 - mmengine - INFO - Epoch(train) [5][200/293] lr: 5.000000e-04 eta: 1 day, 2:35:40 time: 1.575098 data_time: 0.365245 memory: 5829 loss_kpt: 0.001192 acc_pose: 0.632583 loss: 0.001192 2022/10/14 10:29:04 - mmengine - INFO - Epoch(train) [5][250/293] lr: 5.000000e-04 eta: 1 day, 2:41:39 time: 1.798937 data_time: 0.170463 memory: 5829 loss_kpt: 0.001185 acc_pose: 0.563060 loss: 0.001185 2022/10/14 10:30:20 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 10:31:39 - mmengine - INFO - Epoch(train) [6][50/293] lr: 5.000000e-04 eta: 1 day, 1:52:54 time: 1.571956 data_time: 0.222163 memory: 5829 loss_kpt: 0.001148 acc_pose: 0.611966 loss: 0.001148 2022/10/14 10:32:59 - mmengine - INFO - Epoch(train) [6][100/293] lr: 5.000000e-04 eta: 1 day, 1:53:19 time: 1.606324 data_time: 0.077661 memory: 5829 loss_kpt: 0.001172 acc_pose: 0.513889 loss: 0.001172 2022/10/14 10:34:16 - mmengine - INFO - Epoch(train) [6][150/293] lr: 5.000000e-04 eta: 1 day, 1:51:17 time: 1.530328 data_time: 0.664839 memory: 5829 loss_kpt: 0.001158 acc_pose: 0.585680 loss: 0.001158 2022/10/14 10:35:34 - mmengine - INFO - Epoch(train) [6][200/293] lr: 5.000000e-04 eta: 1 day, 1:50:32 time: 1.571355 data_time: 0.203412 memory: 5829 loss_kpt: 0.001157 acc_pose: 0.638702 loss: 0.001157 2022/10/14 10:36:53 - mmengine - INFO - Epoch(train) [6][250/293] lr: 5.000000e-04 eta: 1 day, 1:49:38 time: 1.567589 data_time: 0.430423 memory: 5829 loss_kpt: 0.001158 acc_pose: 0.600080 loss: 0.001158 2022/10/14 10:37:59 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 10:39:29 - mmengine - INFO - Epoch(train) [7][50/293] lr: 5.000000e-04 eta: 1 day, 1:17:30 time: 1.811731 data_time: 1.517658 memory: 5829 loss_kpt: 0.001131 acc_pose: 0.636481 loss: 0.001131 2022/10/14 10:40:59 - mmengine - INFO - Epoch(train) [7][100/293] lr: 5.000000e-04 eta: 1 day, 1:23:20 time: 1.789789 data_time: 0.483987 memory: 5829 loss_kpt: 0.001134 acc_pose: 0.619589 loss: 0.001134 2022/10/14 10:42:16 - mmengine - INFO - Epoch(train) [7][150/293] lr: 5.000000e-04 eta: 1 day, 1:22:34 time: 1.551224 data_time: 0.899141 memory: 5829 loss_kpt: 0.001126 acc_pose: 0.662959 loss: 0.001126 2022/10/14 10:43:40 - mmengine - INFO - Epoch(train) [7][200/293] lr: 5.000000e-04 eta: 1 day, 1:24:59 time: 1.677775 data_time: 1.421510 memory: 5829 loss_kpt: 0.001130 acc_pose: 0.551811 loss: 0.001130 2022/10/14 10:44:48 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 10:44:59 - mmengine - INFO - Epoch(train) [7][250/293] lr: 5.000000e-04 eta: 1 day, 1:24:54 time: 1.584482 data_time: 0.309898 memory: 5829 loss_kpt: 0.001137 acc_pose: 0.650734 loss: 0.001137 2022/10/14 10:46:08 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 10:47:33 - mmengine - INFO - Epoch(train) [8][50/293] lr: 5.000000e-04 eta: 1 day, 0:55:34 time: 1.715652 data_time: 0.383338 memory: 5829 loss_kpt: 0.001132 acc_pose: 0.614719 loss: 0.001132 2022/10/14 10:49:04 - mmengine - INFO - Epoch(train) [8][100/293] lr: 5.000000e-04 eta: 1 day, 1:01:15 time: 1.812074 data_time: 0.154912 memory: 5829 loss_kpt: 0.001115 acc_pose: 0.548521 loss: 0.001115 2022/10/14 10:50:20 - mmengine - INFO - Epoch(train) [8][150/293] lr: 5.000000e-04 eta: 1 day, 0:59:54 time: 1.513018 data_time: 0.096851 memory: 5829 loss_kpt: 0.001132 acc_pose: 0.607804 loss: 0.001132 2022/10/14 10:51:41 - mmengine - INFO - Epoch(train) [8][200/293] lr: 5.000000e-04 eta: 1 day, 1:00:56 time: 1.621359 data_time: 0.060521 memory: 5829 loss_kpt: 0.001105 acc_pose: 0.610452 loss: 0.001105 2022/10/14 10:53:08 - mmengine - INFO - Epoch(train) [8][250/293] lr: 5.000000e-04 eta: 1 day, 1:04:36 time: 1.749669 data_time: 0.551467 memory: 5829 loss_kpt: 0.001114 acc_pose: 0.621297 loss: 0.001114 2022/10/14 10:54:25 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 10:56:07 - mmengine - INFO - Epoch(train) [9][50/293] lr: 5.000000e-04 eta: 1 day, 0:45:59 time: 2.045417 data_time: 0.114894 memory: 5829 loss_kpt: 0.001114 acc_pose: 0.577505 loss: 0.001114 2022/10/14 10:57:37 - mmengine - INFO - Epoch(train) [9][100/293] lr: 5.000000e-04 eta: 1 day, 0:50:26 time: 1.790824 data_time: 0.061125 memory: 5829 loss_kpt: 0.001098 acc_pose: 0.595253 loss: 0.001098 2022/10/14 10:59:04 - mmengine - INFO - Epoch(train) [9][150/293] lr: 5.000000e-04 eta: 1 day, 0:53:39 time: 1.740240 data_time: 0.065756 memory: 5829 loss_kpt: 0.001114 acc_pose: 0.662983 loss: 0.001114 2022/10/14 11:00:25 - mmengine - INFO - Epoch(train) [9][200/293] lr: 5.000000e-04 eta: 1 day, 0:54:17 time: 1.616076 data_time: 0.966127 memory: 5829 loss_kpt: 0.001099 acc_pose: 0.710977 loss: 0.001099 2022/10/14 11:01:58 - mmengine - INFO - Epoch(train) [9][250/293] lr: 5.000000e-04 eta: 1 day, 0:59:23 time: 1.856789 data_time: 0.417187 memory: 5829 loss_kpt: 0.001101 acc_pose: 0.660308 loss: 0.001101 2022/10/14 11:03:16 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 11:04:41 - mmengine - INFO - Epoch(train) [10][50/293] lr: 5.000000e-04 eta: 1 day, 0:36:01 time: 1.688081 data_time: 0.276490 memory: 5829 loss_kpt: 0.001085 acc_pose: 0.638644 loss: 0.001085 2022/10/14 11:06:00 - mmengine - INFO - Epoch(train) [10][100/293] lr: 5.000000e-04 eta: 1 day, 0:36:04 time: 1.577805 data_time: 0.070189 memory: 5829 loss_kpt: 0.001082 acc_pose: 0.630164 loss: 0.001082 2022/10/14 11:07:28 - mmengine - INFO - Epoch(train) [10][150/293] lr: 5.000000e-04 eta: 1 day, 0:39:20 time: 1.763547 data_time: 0.072663 memory: 5829 loss_kpt: 0.001107 acc_pose: 0.606600 loss: 0.001107 2022/10/14 11:08:45 - mmengine - INFO - Epoch(train) [10][200/293] lr: 5.000000e-04 eta: 1 day, 0:38:41 time: 1.547113 data_time: 0.073238 memory: 5829 loss_kpt: 0.001098 acc_pose: 0.613757 loss: 0.001098 2022/10/14 11:10:03 - mmengine - INFO - Epoch(train) [10][250/293] lr: 5.000000e-04 eta: 1 day, 0:38:10 time: 1.555362 data_time: 0.071382 memory: 5829 loss_kpt: 0.001076 acc_pose: 0.610970 loss: 0.001076 2022/10/14 11:11:10 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 11:11:10 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/10/14 11:11:52 - mmengine - INFO - Epoch(val) [10][50/407] eta: 0:04:46 time: 0.803057 data_time: 0.766466 memory: 5829 2022/10/14 11:12:36 - mmengine - INFO - Epoch(val) [10][100/407] eta: 0:04:31 time: 0.884813 data_time: 0.846442 memory: 540 2022/10/14 11:13:19 - mmengine - INFO - Epoch(val) [10][150/407] eta: 0:03:41 time: 0.863373 data_time: 0.827253 memory: 540 2022/10/14 11:13:54 - mmengine - INFO - Epoch(val) [10][200/407] eta: 0:02:23 time: 0.693967 data_time: 0.656331 memory: 540 2022/10/14 11:14:34 - mmengine - INFO - Epoch(val) [10][250/407] eta: 0:02:06 time: 0.805128 data_time: 0.768864 memory: 540 2022/10/14 11:15:10 - mmengine - INFO - Epoch(val) [10][300/407] eta: 0:01:16 time: 0.713670 data_time: 0.677032 memory: 540 2022/10/14 11:15:46 - mmengine - INFO - Epoch(val) [10][350/407] eta: 0:00:40 time: 0.708557 data_time: 0.672959 memory: 540 2022/10/14 11:16:20 - mmengine - INFO - Epoch(val) [10][400/407] eta: 0:00:04 time: 0.683377 data_time: 0.646160 memory: 540 2022/10/14 11:17:44 - mmengine - INFO - Evaluating CocoMetric... 2022/10/14 11:17:58 - mmengine - INFO - Epoch(val) [10][407/407] coco/AP: 0.508000 coco/AP .5: 0.809059 coco/AP .75: 0.545357 coco/AP (M): 0.477188 coco/AP (L): 0.564934 coco/AR: 0.579345 coco/AR .5: 0.860202 coco/AR .75: 0.627519 coco/AR (M): 0.537831 coco/AR (L): 0.637644 2022/10/14 11:18:00 - mmengine - INFO - The best checkpoint with 0.5080 coco/AP at 10 epoch is saved to best_coco/AP_epoch_10.pth. 2022/10/14 11:19:20 - mmengine - INFO - Epoch(train) [11][50/293] lr: 5.000000e-04 eta: 1 day, 0:16:12 time: 1.614181 data_time: 0.142938 memory: 5829 loss_kpt: 0.001058 acc_pose: 0.683676 loss: 0.001058 2022/10/14 11:19:55 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 11:20:39 - mmengine - INFO - Epoch(train) [11][100/293] lr: 5.000000e-04 eta: 1 day, 0:16:22 time: 1.580115 data_time: 0.062851 memory: 5829 loss_kpt: 0.001083 acc_pose: 0.669335 loss: 0.001083 2022/10/14 11:22:06 - mmengine - INFO - Epoch(train) [11][150/293] lr: 5.000000e-04 eta: 1 day, 0:19:04 time: 1.742750 data_time: 0.076074 memory: 5829 loss_kpt: 0.001075 acc_pose: 0.618826 loss: 0.001075 2022/10/14 11:23:25 - mmengine - INFO - Epoch(train) [11][200/293] lr: 5.000000e-04 eta: 1 day, 0:18:54 time: 1.568049 data_time: 0.067872 memory: 5829 loss_kpt: 0.001085 acc_pose: 0.654056 loss: 0.001085 2022/10/14 11:24:56 - mmengine - INFO - Epoch(train) [11][250/293] lr: 5.000000e-04 eta: 1 day, 0:22:35 time: 1.821409 data_time: 0.074894 memory: 5829 loss_kpt: 0.001076 acc_pose: 0.667192 loss: 0.001076 2022/10/14 11:26:06 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 11:27:27 - mmengine - INFO - Epoch(train) [12][50/293] lr: 5.000000e-04 eta: 1 day, 0:02:51 time: 1.624066 data_time: 0.163830 memory: 5829 loss_kpt: 0.001070 acc_pose: 0.686060 loss: 0.001070 2022/10/14 11:28:43 - mmengine - INFO - Epoch(train) [12][100/293] lr: 5.000000e-04 eta: 1 day, 0:02:17 time: 1.531888 data_time: 0.069664 memory: 5829 loss_kpt: 0.001059 acc_pose: 0.606299 loss: 0.001059 2022/10/14 11:30:05 - mmengine - INFO - Epoch(train) [12][150/293] lr: 5.000000e-04 eta: 1 day, 0:03:09 time: 1.632941 data_time: 0.092258 memory: 5829 loss_kpt: 0.001074 acc_pose: 0.642623 loss: 0.001074 2022/10/14 11:31:27 - mmengine - INFO - Epoch(train) [12][200/293] lr: 5.000000e-04 eta: 1 day, 0:04:10 time: 1.648382 data_time: 0.059408 memory: 5829 loss_kpt: 0.001073 acc_pose: 0.609829 loss: 0.001073 2022/10/14 11:32:53 - mmengine - INFO - Epoch(train) [12][250/293] lr: 5.000000e-04 eta: 1 day, 0:05:53 time: 1.704205 data_time: 0.089588 memory: 5829 loss_kpt: 0.001063 acc_pose: 0.611125 loss: 0.001063 2022/10/14 11:34:02 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 11:35:32 - mmengine - INFO - Epoch(train) [13][50/293] lr: 5.000000e-04 eta: 23:50:18 time: 1.800224 data_time: 0.116759 memory: 5829 loss_kpt: 0.001044 acc_pose: 0.704077 loss: 0.001044 2022/10/14 11:37:20 - mmengine - INFO - Epoch(train) [13][100/293] lr: 5.000000e-04 eta: 23:58:21 time: 2.175923 data_time: 0.057737 memory: 5829 loss_kpt: 0.001050 acc_pose: 0.662849 loss: 0.001050 2022/10/14 11:39:01 - mmengine - INFO - Epoch(train) [13][150/293] lr: 5.000000e-04 eta: 1 day, 0:03:58 time: 2.011772 data_time: 0.091471 memory: 5829 loss_kpt: 0.001059 acc_pose: 0.684229 loss: 0.001059 2022/10/14 11:40:29 - mmengine - INFO - Epoch(train) [13][200/293] lr: 5.000000e-04 eta: 1 day, 0:06:10 time: 1.763328 data_time: 0.124191 memory: 5829 loss_kpt: 0.001055 acc_pose: 0.687696 loss: 0.001055 2022/10/14 11:41:42 - mmengine - INFO - Epoch(train) [13][250/293] lr: 5.000000e-04 eta: 1 day, 0:04:30 time: 1.467401 data_time: 0.082573 memory: 5829 loss_kpt: 0.001064 acc_pose: 0.601479 loss: 0.001064 2022/10/14 11:42:59 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 11:44:27 - mmengine - INFO - Epoch(train) [14][50/293] lr: 5.000000e-04 eta: 23:49:29 time: 1.772618 data_time: 0.131659 memory: 5829 loss_kpt: 0.001051 acc_pose: 0.690390 loss: 0.001051 2022/10/14 11:45:44 - mmengine - INFO - Epoch(train) [14][100/293] lr: 5.000000e-04 eta: 23:48:52 time: 1.536732 data_time: 0.062891 memory: 5829 loss_kpt: 0.001034 acc_pose: 0.655823 loss: 0.001034 2022/10/14 11:47:01 - mmengine - INFO - Epoch(train) [14][150/293] lr: 5.000000e-04 eta: 23:48:12 time: 1.535372 data_time: 0.050895 memory: 5829 loss_kpt: 0.001047 acc_pose: 0.630047 loss: 0.001047 2022/10/14 11:48:06 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 11:48:20 - mmengine - INFO - Epoch(train) [14][200/293] lr: 5.000000e-04 eta: 23:48:00 time: 1.576190 data_time: 0.060325 memory: 5829 loss_kpt: 0.001069 acc_pose: 0.668587 loss: 0.001069 2022/10/14 11:49:37 - mmengine - INFO - Epoch(train) [14][250/293] lr: 5.000000e-04 eta: 23:47:22 time: 1.540342 data_time: 0.084919 memory: 5829 loss_kpt: 0.001048 acc_pose: 0.641441 loss: 0.001048 2022/10/14 11:50:49 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 11:52:14 - mmengine - INFO - Epoch(train) [15][50/293] lr: 5.000000e-04 eta: 23:32:52 time: 1.713635 data_time: 0.902028 memory: 5829 loss_kpt: 0.001018 acc_pose: 0.742701 loss: 0.001018 2022/10/14 11:53:35 - mmengine - INFO - Epoch(train) [15][100/293] lr: 5.000000e-04 eta: 23:33:10 time: 1.611797 data_time: 1.281885 memory: 5829 loss_kpt: 0.001038 acc_pose: 0.690619 loss: 0.001038 2022/10/14 11:54:54 - mmengine - INFO - Epoch(train) [15][150/293] lr: 5.000000e-04 eta: 23:33:04 time: 1.580415 data_time: 0.801497 memory: 5829 loss_kpt: 0.001044 acc_pose: 0.674198 loss: 0.001044 2022/10/14 11:56:16 - mmengine - INFO - Epoch(train) [15][200/293] lr: 5.000000e-04 eta: 23:33:42 time: 1.648104 data_time: 1.415498 memory: 5829 loss_kpt: 0.001037 acc_pose: 0.683892 loss: 0.001037 2022/10/14 11:57:29 - mmengine - INFO - Epoch(train) [15][250/293] lr: 5.000000e-04 eta: 23:32:15 time: 1.463361 data_time: 1.210624 memory: 5829 loss_kpt: 0.001045 acc_pose: 0.616317 loss: 0.001045 2022/10/14 11:58:36 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 12:00:02 - mmengine - INFO - Epoch(train) [16][50/293] lr: 5.000000e-04 eta: 23:18:54 time: 1.722487 data_time: 1.534671 memory: 5829 loss_kpt: 0.001032 acc_pose: 0.657953 loss: 0.001032 2022/10/14 12:01:27 - mmengine - INFO - Epoch(train) [16][100/293] lr: 5.000000e-04 eta: 23:20:00 time: 1.691379 data_time: 0.508748 memory: 5829 loss_kpt: 0.001042 acc_pose: 0.621061 loss: 0.001042 2022/10/14 12:03:00 - mmengine - INFO - Epoch(train) [16][150/293] lr: 5.000000e-04 eta: 23:22:57 time: 1.872921 data_time: 0.968078 memory: 5829 loss_kpt: 0.001033 acc_pose: 0.643524 loss: 0.001033 2022/10/14 12:04:29 - mmengine - INFO - Epoch(train) [16][200/293] lr: 5.000000e-04 eta: 23:24:48 time: 1.775149 data_time: 0.063809 memory: 5829 loss_kpt: 0.001041 acc_pose: 0.665582 loss: 0.001041 2022/10/14 12:05:59 - mmengine - INFO - Epoch(train) [16][250/293] lr: 5.000000e-04 eta: 23:26:46 time: 1.793111 data_time: 0.059984 memory: 5829 loss_kpt: 0.001026 acc_pose: 0.688215 loss: 0.001026 2022/10/14 12:07:09 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 12:08:33 - mmengine - INFO - Epoch(train) [17][50/293] lr: 5.000000e-04 eta: 23:13:37 time: 1.674828 data_time: 0.249963 memory: 5829 loss_kpt: 0.001019 acc_pose: 0.659206 loss: 0.001019 2022/10/14 12:10:04 - mmengine - INFO - Epoch(train) [17][100/293] lr: 5.000000e-04 eta: 23:15:47 time: 1.814724 data_time: 0.072029 memory: 5829 loss_kpt: 0.001024 acc_pose: 0.668611 loss: 0.001024 2022/10/14 12:11:35 - mmengine - INFO - Epoch(train) [17][150/293] lr: 5.000000e-04 eta: 23:17:56 time: 1.822045 data_time: 0.064450 memory: 5829 loss_kpt: 0.001044 acc_pose: 0.643047 loss: 0.001044 2022/10/14 12:13:07 - mmengine - INFO - Epoch(train) [17][200/293] lr: 5.000000e-04 eta: 23:20:13 time: 1.844413 data_time: 0.063043 memory: 5829 loss_kpt: 0.001034 acc_pose: 0.693631 loss: 0.001034 2022/10/14 12:14:40 - mmengine - INFO - Epoch(train) [17][250/293] lr: 5.000000e-04 eta: 23:22:34 time: 1.857834 data_time: 0.074437 memory: 5829 loss_kpt: 0.001022 acc_pose: 0.725357 loss: 0.001022 2022/10/14 12:15:49 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 12:16:25 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 12:17:22 - mmengine - INFO - Epoch(train) [18][50/293] lr: 5.000000e-04 eta: 23:11:47 time: 1.860211 data_time: 1.205702 memory: 5829 loss_kpt: 0.001032 acc_pose: 0.618601 loss: 0.001032 2022/10/14 12:19:00 - mmengine - INFO - Epoch(train) [18][100/293] lr: 5.000000e-04 eta: 23:14:54 time: 1.947784 data_time: 1.192273 memory: 5829 loss_kpt: 0.001028 acc_pose: 0.732794 loss: 0.001028 2022/10/14 12:20:40 - mmengine - INFO - Epoch(train) [18][150/293] lr: 5.000000e-04 eta: 23:18:22 time: 1.996605 data_time: 0.562378 memory: 5829 loss_kpt: 0.001017 acc_pose: 0.654659 loss: 0.001017 2022/10/14 12:22:05 - mmengine - INFO - Epoch(train) [18][200/293] lr: 5.000000e-04 eta: 23:19:07 time: 1.707482 data_time: 0.327133 memory: 5829 loss_kpt: 0.001012 acc_pose: 0.672270 loss: 0.001012 2022/10/14 12:23:30 - mmengine - INFO - Epoch(train) [18][250/293] lr: 5.000000e-04 eta: 23:19:45 time: 1.697542 data_time: 0.071936 memory: 5829 loss_kpt: 0.001024 acc_pose: 0.703701 loss: 0.001024 2022/10/14 12:24:40 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 12:26:06 - mmengine - INFO - Epoch(train) [19][50/293] lr: 5.000000e-04 eta: 23:08:05 time: 1.712008 data_time: 0.170854 memory: 5829 loss_kpt: 0.001011 acc_pose: 0.585971 loss: 0.001011 2022/10/14 12:27:31 - mmengine - INFO - Epoch(train) [19][100/293] lr: 5.000000e-04 eta: 23:08:48 time: 1.706296 data_time: 0.096128 memory: 5829 loss_kpt: 0.001009 acc_pose: 0.657574 loss: 0.001009 2022/10/14 12:29:02 - mmengine - INFO - Epoch(train) [19][150/293] lr: 5.000000e-04 eta: 23:10:25 time: 1.815467 data_time: 0.061297 memory: 5829 loss_kpt: 0.001013 acc_pose: 0.695185 loss: 0.001013 2022/10/14 12:30:29 - mmengine - INFO - Epoch(train) [19][200/293] lr: 5.000000e-04 eta: 23:11:23 time: 1.745107 data_time: 0.059995 memory: 5829 loss_kpt: 0.001012 acc_pose: 0.700050 loss: 0.001012 2022/10/14 12:32:07 - mmengine - INFO - Epoch(train) [19][250/293] lr: 5.000000e-04 eta: 23:14:08 time: 1.961456 data_time: 0.091789 memory: 5829 loss_kpt: 0.001015 acc_pose: 0.675003 loss: 0.001015 2022/10/14 12:33:24 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 12:35:10 - mmengine - INFO - Epoch(train) [20][50/293] lr: 5.000000e-04 eta: 23:06:15 time: 2.106150 data_time: 0.110824 memory: 5829 loss_kpt: 0.001021 acc_pose: 0.719103 loss: 0.001021 2022/10/14 12:36:40 - mmengine - INFO - Epoch(train) [20][100/293] lr: 5.000000e-04 eta: 23:07:33 time: 1.796256 data_time: 0.062267 memory: 5829 loss_kpt: 0.001005 acc_pose: 0.743297 loss: 0.001005 2022/10/14 12:38:03 - mmengine - INFO - Epoch(train) [20][150/293] lr: 5.000000e-04 eta: 23:07:43 time: 1.664331 data_time: 0.067314 memory: 5829 loss_kpt: 0.001015 acc_pose: 0.694082 loss: 0.001015 2022/10/14 12:39:32 - mmengine - INFO - Epoch(train) [20][200/293] lr: 5.000000e-04 eta: 23:08:54 time: 1.792342 data_time: 0.068570 memory: 5829 loss_kpt: 0.001003 acc_pose: 0.695571 loss: 0.001003 2022/10/14 12:40:56 - mmengine - INFO - Epoch(train) [20][250/293] lr: 5.000000e-04 eta: 23:09:06 time: 1.676643 data_time: 0.064171 memory: 5829 loss_kpt: 0.001033 acc_pose: 0.642528 loss: 0.001033 2022/10/14 12:42:09 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 12:42:09 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/10/14 12:42:55 - mmengine - INFO - Epoch(val) [20][50/407] eta: 0:05:14 time: 0.882122 data_time: 0.844911 memory: 5829 2022/10/14 12:43:43 - mmengine - INFO - Epoch(val) [20][100/407] eta: 0:04:53 time: 0.955422 data_time: 0.918814 memory: 540 2022/10/14 12:44:28 - mmengine - INFO - Epoch(val) [20][150/407] eta: 0:03:53 time: 0.910061 data_time: 0.873757 memory: 540 2022/10/14 12:45:10 - mmengine - INFO - Epoch(val) [20][200/407] eta: 0:02:51 time: 0.828197 data_time: 0.791624 memory: 540 2022/10/14 12:45:50 - mmengine - INFO - Epoch(val) [20][250/407] eta: 0:02:05 time: 0.798434 data_time: 0.760737 memory: 540 2022/10/14 12:46:39 - mmengine - INFO - Epoch(val) [20][300/407] eta: 0:01:44 time: 0.978803 data_time: 0.942402 memory: 540 2022/10/14 12:47:24 - mmengine - INFO - Epoch(val) [20][350/407] eta: 0:00:52 time: 0.917002 data_time: 0.880676 memory: 540 2022/10/14 12:48:10 - mmengine - INFO - Epoch(val) [20][400/407] eta: 0:00:06 time: 0.910501 data_time: 0.874021 memory: 540 2022/10/14 12:49:26 - mmengine - INFO - Evaluating CocoMetric... 2022/10/14 12:49:40 - mmengine - INFO - Epoch(val) [20][407/407] coco/AP: 0.544868 coco/AP .5: 0.827968 coco/AP .75: 0.600902 coco/AP (M): 0.511817 coco/AP (L): 0.603758 coco/AR: 0.613319 coco/AR .5: 0.876732 coco/AR .75: 0.673804 coco/AR (M): 0.569271 coco/AR (L): 0.675437 2022/10/14 12:49:40 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256/best_coco/AP_epoch_10.pth is removed 2022/10/14 12:49:42 - mmengine - INFO - The best checkpoint with 0.5449 coco/AP at 20 epoch is saved to best_coco/AP_epoch_20.pth. 2022/10/14 12:51:06 - mmengine - INFO - Epoch(train) [21][50/293] lr: 5.000000e-04 eta: 22:58:09 time: 1.682561 data_time: 0.292620 memory: 5829 loss_kpt: 0.000992 acc_pose: 0.688989 loss: 0.000992 2022/10/14 12:52:34 - mmengine - INFO - Epoch(train) [21][100/293] lr: 5.000000e-04 eta: 22:58:58 time: 1.750265 data_time: 0.060202 memory: 5829 loss_kpt: 0.000998 acc_pose: 0.604131 loss: 0.000998 2022/10/14 12:53:51 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 12:54:08 - mmengine - INFO - Epoch(train) [21][150/293] lr: 5.000000e-04 eta: 23:00:48 time: 1.888022 data_time: 0.055803 memory: 5829 loss_kpt: 0.000993 acc_pose: 0.758732 loss: 0.000993 2022/10/14 12:55:39 - mmengine - INFO - Epoch(train) [21][200/293] lr: 5.000000e-04 eta: 23:02:03 time: 1.818251 data_time: 0.110191 memory: 5829 loss_kpt: 0.001005 acc_pose: 0.707964 loss: 0.001005 2022/10/14 12:57:17 - mmengine - INFO - Epoch(train) [21][250/293] lr: 5.000000e-04 eta: 23:04:22 time: 1.966318 data_time: 0.071948 memory: 5829 loss_kpt: 0.000994 acc_pose: 0.623948 loss: 0.000994 2022/10/14 12:58:36 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 13:00:13 - mmengine - INFO - Epoch(train) [22][50/293] lr: 5.000000e-04 eta: 22:55:46 time: 1.943312 data_time: 0.431770 memory: 5829 loss_kpt: 0.000978 acc_pose: 0.719318 loss: 0.000978 2022/10/14 13:01:45 - mmengine - INFO - Epoch(train) [22][100/293] lr: 5.000000e-04 eta: 22:57:04 time: 1.837097 data_time: 0.084982 memory: 5829 loss_kpt: 0.001000 acc_pose: 0.670535 loss: 0.001000 2022/10/14 13:03:22 - mmengine - INFO - Epoch(train) [22][150/293] lr: 5.000000e-04 eta: 22:59:08 time: 1.948291 data_time: 0.159872 memory: 5829 loss_kpt: 0.000999 acc_pose: 0.648374 loss: 0.000999 2022/10/14 13:04:45 - mmengine - INFO - Epoch(train) [22][200/293] lr: 5.000000e-04 eta: 22:58:59 time: 1.651097 data_time: 0.055161 memory: 5829 loss_kpt: 0.000993 acc_pose: 0.695053 loss: 0.000993 2022/10/14 13:06:09 - mmengine - INFO - Epoch(train) [22][250/293] lr: 5.000000e-04 eta: 22:59:00 time: 1.675079 data_time: 0.198024 memory: 5829 loss_kpt: 0.001004 acc_pose: 0.642011 loss: 0.001004 2022/10/14 13:07:21 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 13:08:47 - mmengine - INFO - Epoch(train) [23][50/293] lr: 5.000000e-04 eta: 22:49:09 time: 1.727622 data_time: 0.143516 memory: 5829 loss_kpt: 0.000990 acc_pose: 0.701394 loss: 0.000990 2022/10/14 13:10:20 - mmengine - INFO - Epoch(train) [23][100/293] lr: 5.000000e-04 eta: 22:50:32 time: 1.868391 data_time: 0.200839 memory: 5829 loss_kpt: 0.001002 acc_pose: 0.689050 loss: 0.001002 2022/10/14 13:11:49 - mmengine - INFO - Epoch(train) [23][150/293] lr: 5.000000e-04 eta: 22:51:14 time: 1.774648 data_time: 0.127053 memory: 5829 loss_kpt: 0.001015 acc_pose: 0.687807 loss: 0.001015 2022/10/14 13:13:19 - mmengine - INFO - Epoch(train) [23][200/293] lr: 5.000000e-04 eta: 22:52:05 time: 1.802760 data_time: 0.126960 memory: 5829 loss_kpt: 0.000998 acc_pose: 0.632243 loss: 0.000998 2022/10/14 13:14:48 - mmengine - INFO - Epoch(train) [23][250/293] lr: 5.000000e-04 eta: 22:52:45 time: 1.780421 data_time: 0.062607 memory: 5829 loss_kpt: 0.000992 acc_pose: 0.738013 loss: 0.000992 2022/10/14 13:16:04 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 13:17:40 - mmengine - INFO - Epoch(train) [24][50/293] lr: 5.000000e-04 eta: 22:44:38 time: 1.934143 data_time: 1.257352 memory: 5829 loss_kpt: 0.000981 acc_pose: 0.676239 loss: 0.000981 2022/10/14 13:19:25 - mmengine - INFO - Epoch(train) [24][100/293] lr: 5.000000e-04 eta: 22:47:18 time: 2.082088 data_time: 1.881322 memory: 5829 loss_kpt: 0.000999 acc_pose: 0.664408 loss: 0.000999 2022/10/14 13:20:54 - mmengine - INFO - Epoch(train) [24][150/293] lr: 5.000000e-04 eta: 22:48:01 time: 1.797898 data_time: 1.410833 memory: 5829 loss_kpt: 0.000990 acc_pose: 0.680415 loss: 0.000990 2022/10/14 13:22:28 - mmengine - INFO - Epoch(train) [24][200/293] lr: 5.000000e-04 eta: 22:49:10 time: 1.868696 data_time: 0.420108 memory: 5829 loss_kpt: 0.000991 acc_pose: 0.692805 loss: 0.000991 2022/10/14 13:24:02 - mmengine - INFO - Epoch(train) [24][250/293] lr: 5.000000e-04 eta: 22:50:18 time: 1.871663 data_time: 0.067910 memory: 5829 loss_kpt: 0.000986 acc_pose: 0.684811 loss: 0.000986 2022/10/14 13:24:23 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 13:25:12 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 13:26:42 - mmengine - INFO - Epoch(train) [25][50/293] lr: 5.000000e-04 eta: 22:41:35 time: 1.806532 data_time: 0.320667 memory: 5829 loss_kpt: 0.000972 acc_pose: 0.662812 loss: 0.000972 2022/10/14 13:28:17 - mmengine - INFO - Epoch(train) [25][100/293] lr: 5.000000e-04 eta: 22:42:51 time: 1.898228 data_time: 0.071539 memory: 5829 loss_kpt: 0.000971 acc_pose: 0.729715 loss: 0.000971 2022/10/14 13:29:52 - mmengine - INFO - Epoch(train) [25][150/293] lr: 5.000000e-04 eta: 22:44:07 time: 1.900896 data_time: 0.068633 memory: 5829 loss_kpt: 0.000984 acc_pose: 0.629021 loss: 0.000984 2022/10/14 13:31:31 - mmengine - INFO - Epoch(train) [25][200/293] lr: 5.000000e-04 eta: 22:45:49 time: 1.980566 data_time: 0.075995 memory: 5829 loss_kpt: 0.000981 acc_pose: 0.734536 loss: 0.000981 2022/10/14 13:33:05 - mmengine - INFO - Epoch(train) [25][250/293] lr: 5.000000e-04 eta: 22:46:48 time: 1.868424 data_time: 0.065845 memory: 5829 loss_kpt: 0.000991 acc_pose: 0.727766 loss: 0.000991 2022/10/14 13:34:19 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 13:35:45 - mmengine - INFO - Epoch(train) [26][50/293] lr: 5.000000e-04 eta: 22:37:49 time: 1.726636 data_time: 0.798658 memory: 5829 loss_kpt: 0.000986 acc_pose: 0.609601 loss: 0.000986 2022/10/14 13:37:23 - mmengine - INFO - Epoch(train) [26][100/293] lr: 5.000000e-04 eta: 22:39:16 time: 1.951590 data_time: 0.063530 memory: 5829 loss_kpt: 0.000980 acc_pose: 0.683221 loss: 0.000980 2022/10/14 13:38:55 - mmengine - INFO - Epoch(train) [26][150/293] lr: 5.000000e-04 eta: 22:40:01 time: 1.839381 data_time: 0.069319 memory: 5829 loss_kpt: 0.000979 acc_pose: 0.736297 loss: 0.000979 2022/10/14 13:40:25 - mmengine - INFO - Epoch(train) [26][200/293] lr: 5.000000e-04 eta: 22:40:33 time: 1.809187 data_time: 0.064089 memory: 5829 loss_kpt: 0.000978 acc_pose: 0.656840 loss: 0.000978 2022/10/14 13:41:54 - mmengine - INFO - Epoch(train) [26][250/293] lr: 5.000000e-04 eta: 22:40:50 time: 1.772578 data_time: 0.063434 memory: 5829 loss_kpt: 0.000980 acc_pose: 0.638188 loss: 0.000980 2022/10/14 13:43:07 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 13:44:31 - mmengine - INFO - Epoch(train) [27][50/293] lr: 5.000000e-04 eta: 22:31:57 time: 1.695611 data_time: 0.148292 memory: 5829 loss_kpt: 0.000965 acc_pose: 0.734262 loss: 0.000965 2022/10/14 13:45:54 - mmengine - INFO - Epoch(train) [27][100/293] lr: 5.000000e-04 eta: 22:31:30 time: 1.644677 data_time: 0.855728 memory: 5829 loss_kpt: 0.000981 acc_pose: 0.711402 loss: 0.000981 2022/10/14 13:47:21 - mmengine - INFO - Epoch(train) [27][150/293] lr: 5.000000e-04 eta: 22:31:35 time: 1.740882 data_time: 0.877177 memory: 5829 loss_kpt: 0.000972 acc_pose: 0.644822 loss: 0.000972 2022/10/14 13:48:53 - mmengine - INFO - Epoch(train) [27][200/293] lr: 5.000000e-04 eta: 22:32:19 time: 1.855247 data_time: 1.650833 memory: 5829 loss_kpt: 0.000970 acc_pose: 0.744388 loss: 0.000970 2022/10/14 13:50:21 - mmengine - INFO - Epoch(train) [27][250/293] lr: 5.000000e-04 eta: 22:32:27 time: 1.756383 data_time: 1.380501 memory: 5829 loss_kpt: 0.000979 acc_pose: 0.724131 loss: 0.000979 2022/10/14 13:51:35 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 13:53:07 - mmengine - INFO - Epoch(train) [28][50/293] lr: 5.000000e-04 eta: 22:24:43 time: 1.849800 data_time: 0.188091 memory: 5829 loss_kpt: 0.000967 acc_pose: 0.749615 loss: 0.000967 2022/10/14 13:54:18 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 13:54:37 - mmengine - INFO - Epoch(train) [28][100/293] lr: 5.000000e-04 eta: 22:25:02 time: 1.789124 data_time: 0.075318 memory: 5829 loss_kpt: 0.000969 acc_pose: 0.671914 loss: 0.000969 2022/10/14 13:56:06 - mmengine - INFO - Epoch(train) [28][150/293] lr: 5.000000e-04 eta: 22:25:18 time: 1.783953 data_time: 0.136834 memory: 5829 loss_kpt: 0.000960 acc_pose: 0.704395 loss: 0.000960 2022/10/14 13:57:37 - mmengine - INFO - Epoch(train) [28][200/293] lr: 5.000000e-04 eta: 22:25:46 time: 1.824827 data_time: 1.629990 memory: 5829 loss_kpt: 0.000973 acc_pose: 0.677969 loss: 0.000973 2022/10/14 13:58:55 - mmengine - INFO - Epoch(train) [28][250/293] lr: 5.000000e-04 eta: 22:24:43 time: 1.549180 data_time: 0.562800 memory: 5829 loss_kpt: 0.000961 acc_pose: 0.683396 loss: 0.000961 2022/10/14 14:00:03 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 14:01:24 - mmengine - INFO - Epoch(train) [29][50/293] lr: 5.000000e-04 eta: 22:16:03 time: 1.635952 data_time: 0.269757 memory: 5829 loss_kpt: 0.000980 acc_pose: 0.727831 loss: 0.000980 2022/10/14 14:02:50 - mmengine - INFO - Epoch(train) [29][100/293] lr: 5.000000e-04 eta: 22:15:54 time: 1.710745 data_time: 0.855564 memory: 5829 loss_kpt: 0.000983 acc_pose: 0.705347 loss: 0.000983 2022/10/14 14:04:07 - mmengine - INFO - Epoch(train) [29][150/293] lr: 5.000000e-04 eta: 22:14:51 time: 1.546313 data_time: 0.072974 memory: 5829 loss_kpt: 0.000973 acc_pose: 0.641737 loss: 0.000973 2022/10/14 14:05:38 - mmengine - INFO - Epoch(train) [29][200/293] lr: 5.000000e-04 eta: 22:15:13 time: 1.813375 data_time: 0.113137 memory: 5829 loss_kpt: 0.000967 acc_pose: 0.650814 loss: 0.000967 2022/10/14 14:06:55 - mmengine - INFO - Epoch(train) [29][250/293] lr: 5.000000e-04 eta: 22:14:09 time: 1.542372 data_time: 0.553594 memory: 5829 loss_kpt: 0.000956 acc_pose: 0.736130 loss: 0.000956 2022/10/14 14:08:11 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 14:09:41 - mmengine - INFO - Epoch(train) [30][50/293] lr: 5.000000e-04 eta: 22:06:34 time: 1.791792 data_time: 0.124134 memory: 5829 loss_kpt: 0.000959 acc_pose: 0.698896 loss: 0.000959 2022/10/14 14:11:07 - mmengine - INFO - Epoch(train) [30][100/293] lr: 5.000000e-04 eta: 22:06:26 time: 1.718187 data_time: 0.063162 memory: 5829 loss_kpt: 0.000959 acc_pose: 0.701465 loss: 0.000959 2022/10/14 14:12:28 - mmengine - INFO - Epoch(train) [30][150/293] lr: 5.000000e-04 eta: 22:05:49 time: 1.629471 data_time: 0.072257 memory: 5829 loss_kpt: 0.000951 acc_pose: 0.733633 loss: 0.000951 2022/10/14 14:13:51 - mmengine - INFO - Epoch(train) [30][200/293] lr: 5.000000e-04 eta: 22:05:17 time: 1.648489 data_time: 0.220858 memory: 5829 loss_kpt: 0.000951 acc_pose: 0.717398 loss: 0.000951 2022/10/14 14:15:10 - mmengine - INFO - Epoch(train) [30][250/293] lr: 5.000000e-04 eta: 22:04:29 time: 1.594584 data_time: 0.082674 memory: 5829 loss_kpt: 0.000957 acc_pose: 0.671132 loss: 0.000957 2022/10/14 14:16:24 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 14:16:24 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/10/14 14:17:13 - mmengine - INFO - Epoch(val) [30][50/407] eta: 0:05:38 time: 0.947737 data_time: 0.911226 memory: 5829 2022/10/14 14:17:58 - mmengine - INFO - Epoch(val) [30][100/407] eta: 0:04:35 time: 0.898622 data_time: 0.861471 memory: 540 2022/10/14 14:18:44 - mmengine - INFO - Epoch(val) [30][150/407] eta: 0:03:59 time: 0.929966 data_time: 0.888780 memory: 540 2022/10/14 14:19:25 - mmengine - INFO - Epoch(val) [30][200/407] eta: 0:02:48 time: 0.814369 data_time: 0.778175 memory: 540 2022/10/14 14:20:00 - mmengine - INFO - Epoch(val) [30][250/407] eta: 0:01:48 time: 0.691998 data_time: 0.655306 memory: 540 2022/10/14 14:20:37 - mmengine - INFO - Epoch(val) [30][300/407] eta: 0:01:18 time: 0.737974 data_time: 0.701921 memory: 540 2022/10/14 14:21:18 - mmengine - INFO - Epoch(val) [30][350/407] eta: 0:00:47 time: 0.827471 data_time: 0.791196 memory: 540 2022/10/14 14:21:55 - mmengine - INFO - Epoch(val) [30][400/407] eta: 0:00:05 time: 0.734869 data_time: 0.699184 memory: 540 2022/10/14 14:23:08 - mmengine - INFO - Evaluating CocoMetric... 2022/10/14 14:23:22 - mmengine - INFO - Epoch(val) [30][407/407] coco/AP: 0.567928 coco/AP .5: 0.837136 coco/AP .75: 0.629803 coco/AP (M): 0.534944 coco/AP (L): 0.628966 coco/AR: 0.634635 coco/AR .5: 0.884131 coco/AR .75: 0.698363 coco/AR (M): 0.590358 coco/AR (L): 0.697027 2022/10/14 14:23:22 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256/best_coco/AP_epoch_20.pth is removed 2022/10/14 14:23:23 - mmengine - INFO - The best checkpoint with 0.5679 coco/AP at 30 epoch is saved to best_coco/AP_epoch_30.pth. 2022/10/14 14:24:52 - mmengine - INFO - Epoch(train) [31][50/293] lr: 5.000000e-04 eta: 21:57:02 time: 1.770877 data_time: 0.811786 memory: 5829 loss_kpt: 0.000969 acc_pose: 0.691214 loss: 0.000969 2022/10/14 14:27:13 - mmengine - INFO - Epoch(train) [31][100/293] lr: 5.000000e-04 eta: 22:02:17 time: 2.816796 data_time: 0.851308 memory: 5829 loss_kpt: 0.000966 acc_pose: 0.674766 loss: 0.000966 2022/10/14 14:28:50 - mmengine - INFO - Epoch(train) [31][150/293] lr: 5.000000e-04 eta: 22:03:14 time: 1.955062 data_time: 0.711747 memory: 5829 loss_kpt: 0.000960 acc_pose: 0.729155 loss: 0.000960 2022/10/14 14:30:07 - mmengine - INFO - Epoch(train) [31][200/293] lr: 5.000000e-04 eta: 22:02:05 time: 1.534557 data_time: 0.443553 memory: 5829 loss_kpt: 0.000948 acc_pose: 0.641416 loss: 0.000948 2022/10/14 14:30:23 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 14:31:53 - mmengine - INFO - Epoch(train) [31][250/293] lr: 5.000000e-04 eta: 22:03:45 time: 2.113706 data_time: 0.111755 memory: 5829 loss_kpt: 0.000964 acc_pose: 0.712799 loss: 0.000964 2022/10/14 14:33:21 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 14:34:43 - mmengine - INFO - Epoch(train) [32][50/293] lr: 5.000000e-04 eta: 21:55:42 time: 1.621397 data_time: 0.442331 memory: 5829 loss_kpt: 0.000950 acc_pose: 0.733989 loss: 0.000950 2022/10/14 14:36:07 - mmengine - INFO - Epoch(train) [32][100/293] lr: 5.000000e-04 eta: 21:55:19 time: 1.687979 data_time: 0.731626 memory: 5829 loss_kpt: 0.000962 acc_pose: 0.670050 loss: 0.000962 2022/10/14 14:37:35 - mmengine - INFO - Epoch(train) [32][150/293] lr: 5.000000e-04 eta: 21:55:18 time: 1.770406 data_time: 0.936591 memory: 5829 loss_kpt: 0.000960 acc_pose: 0.689067 loss: 0.000960 2022/10/14 14:38:56 - mmengine - INFO - Epoch(train) [32][200/293] lr: 5.000000e-04 eta: 21:54:33 time: 1.618854 data_time: 0.114892 memory: 5829 loss_kpt: 0.000959 acc_pose: 0.677538 loss: 0.000959 2022/10/14 14:40:12 - mmengine - INFO - Epoch(train) [32][250/293] lr: 5.000000e-04 eta: 21:53:21 time: 1.519924 data_time: 0.094706 memory: 5829 loss_kpt: 0.000960 acc_pose: 0.709065 loss: 0.000960 2022/10/14 14:41:20 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 14:42:43 - mmengine - INFO - Epoch(train) [33][50/293] lr: 5.000000e-04 eta: 21:45:43 time: 1.660887 data_time: 0.717013 memory: 5829 loss_kpt: 0.000957 acc_pose: 0.658590 loss: 0.000957 2022/10/14 14:43:56 - mmengine - INFO - Epoch(train) [33][100/293] lr: 5.000000e-04 eta: 21:44:19 time: 1.471256 data_time: 0.108570 memory: 5829 loss_kpt: 0.000948 acc_pose: 0.679184 loss: 0.000948 2022/10/14 14:45:25 - mmengine - INFO - Epoch(train) [33][150/293] lr: 5.000000e-04 eta: 21:44:18 time: 1.775869 data_time: 0.061554 memory: 5829 loss_kpt: 0.000977 acc_pose: 0.688662 loss: 0.000977 2022/10/14 14:46:48 - mmengine - INFO - Epoch(train) [33][200/293] lr: 5.000000e-04 eta: 21:43:44 time: 1.657659 data_time: 0.063198 memory: 5829 loss_kpt: 0.000936 acc_pose: 0.722355 loss: 0.000936 2022/10/14 14:48:10 - mmengine - INFO - Epoch(train) [33][250/293] lr: 5.000000e-04 eta: 21:43:05 time: 1.639064 data_time: 0.063017 memory: 5829 loss_kpt: 0.000959 acc_pose: 0.735117 loss: 0.000959 2022/10/14 14:49:21 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 14:50:48 - mmengine - INFO - Epoch(train) [34][50/293] lr: 5.000000e-04 eta: 21:36:00 time: 1.733044 data_time: 0.837985 memory: 5829 loss_kpt: 0.000954 acc_pose: 0.707340 loss: 0.000954 2022/10/14 14:52:04 - mmengine - INFO - Epoch(train) [34][100/293] lr: 5.000000e-04 eta: 21:34:51 time: 1.524229 data_time: 0.061161 memory: 5829 loss_kpt: 0.000968 acc_pose: 0.732896 loss: 0.000968 2022/10/14 14:53:21 - mmengine - INFO - Epoch(train) [34][150/293] lr: 5.000000e-04 eta: 21:33:45 time: 1.532414 data_time: 0.338260 memory: 5829 loss_kpt: 0.000941 acc_pose: 0.770664 loss: 0.000941 2022/10/14 14:54:41 - mmengine - INFO - Epoch(train) [34][200/293] lr: 5.000000e-04 eta: 21:32:56 time: 1.602558 data_time: 0.061861 memory: 5829 loss_kpt: 0.000966 acc_pose: 0.719430 loss: 0.000966 2022/10/14 14:56:01 - mmengine - INFO - Epoch(train) [34][250/293] lr: 5.000000e-04 eta: 21:32:06 time: 1.598761 data_time: 0.068013 memory: 5829 loss_kpt: 0.000946 acc_pose: 0.670778 loss: 0.000946 2022/10/14 14:57:10 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 14:58:12 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 14:58:32 - mmengine - INFO - Epoch(train) [35][50/293] lr: 5.000000e-04 eta: 21:24:51 time: 1.644024 data_time: 0.408999 memory: 5829 loss_kpt: 0.000946 acc_pose: 0.717025 loss: 0.000946 2022/10/14 14:59:49 - mmengine - INFO - Epoch(train) [35][100/293] lr: 5.000000e-04 eta: 21:23:44 time: 1.530104 data_time: 0.053442 memory: 5829 loss_kpt: 0.000930 acc_pose: 0.716129 loss: 0.000930 2022/10/14 15:01:09 - mmengine - INFO - Epoch(train) [35][150/293] lr: 5.000000e-04 eta: 21:22:58 time: 1.607411 data_time: 0.071761 memory: 5829 loss_kpt: 0.000950 acc_pose: 0.707244 loss: 0.000950 2022/10/14 15:02:26 - mmengine - INFO - Epoch(train) [35][200/293] lr: 5.000000e-04 eta: 21:21:53 time: 1.534941 data_time: 0.610600 memory: 5829 loss_kpt: 0.000940 acc_pose: 0.648683 loss: 0.000940 2022/10/14 15:03:42 - mmengine - INFO - Epoch(train) [35][250/293] lr: 5.000000e-04 eta: 21:20:45 time: 1.524804 data_time: 1.022200 memory: 5829 loss_kpt: 0.000945 acc_pose: 0.659496 loss: 0.000945 2022/10/14 15:04:47 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 15:06:06 - mmengine - INFO - Epoch(train) [36][50/293] lr: 5.000000e-04 eta: 21:13:24 time: 1.572293 data_time: 0.177472 memory: 5829 loss_kpt: 0.000937 acc_pose: 0.725793 loss: 0.000937 2022/10/14 15:07:25 - mmengine - INFO - Epoch(train) [36][100/293] lr: 5.000000e-04 eta: 21:12:34 time: 1.590449 data_time: 0.278519 memory: 5829 loss_kpt: 0.000950 acc_pose: 0.733296 loss: 0.000950 2022/10/14 15:09:01 - mmengine - INFO - Epoch(train) [36][150/293] lr: 5.000000e-04 eta: 21:13:02 time: 1.911037 data_time: 0.071524 memory: 5829 loss_kpt: 0.000948 acc_pose: 0.721691 loss: 0.000948 2022/10/14 15:10:28 - mmengine - INFO - Epoch(train) [36][200/293] lr: 5.000000e-04 eta: 21:12:45 time: 1.731945 data_time: 0.065262 memory: 5829 loss_kpt: 0.000936 acc_pose: 0.677825 loss: 0.000936 2022/10/14 15:11:58 - mmengine - INFO - Epoch(train) [36][250/293] lr: 5.000000e-04 eta: 21:12:48 time: 1.814152 data_time: 0.063433 memory: 5829 loss_kpt: 0.000941 acc_pose: 0.727031 loss: 0.000941 2022/10/14 15:13:07 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 15:14:30 - mmengine - INFO - Epoch(train) [37][50/293] lr: 5.000000e-04 eta: 21:05:58 time: 1.655880 data_time: 0.897854 memory: 5829 loss_kpt: 0.000949 acc_pose: 0.747469 loss: 0.000949 2022/10/14 15:15:52 - mmengine - INFO - Epoch(train) [37][100/293] lr: 5.000000e-04 eta: 21:05:19 time: 1.641293 data_time: 0.307482 memory: 5829 loss_kpt: 0.000966 acc_pose: 0.739755 loss: 0.000966 2022/10/14 15:17:11 - mmengine - INFO - Epoch(train) [37][150/293] lr: 5.000000e-04 eta: 21:04:25 time: 1.579993 data_time: 0.103743 memory: 5829 loss_kpt: 0.000945 acc_pose: 0.732119 loss: 0.000945 2022/10/14 15:18:32 - mmengine - INFO - Epoch(train) [37][200/293] lr: 5.000000e-04 eta: 21:03:38 time: 1.606941 data_time: 0.704957 memory: 5829 loss_kpt: 0.000938 acc_pose: 0.703398 loss: 0.000938 2022/10/14 15:19:53 - mmengine - INFO - Epoch(train) [37][250/293] lr: 5.000000e-04 eta: 21:02:54 time: 1.623039 data_time: 0.100555 memory: 5829 loss_kpt: 0.000947 acc_pose: 0.720549 loss: 0.000947 2022/10/14 15:21:06 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 15:22:15 - mmengine - INFO - Epoch(train) [38][50/293] lr: 5.000000e-04 eta: 20:55:11 time: 1.385541 data_time: 0.201795 memory: 5829 loss_kpt: 0.000944 acc_pose: 0.682475 loss: 0.000944 2022/10/14 15:23:31 - mmengine - INFO - Epoch(train) [38][100/293] lr: 5.000000e-04 eta: 20:54:04 time: 1.518766 data_time: 0.473647 memory: 5829 loss_kpt: 0.000936 acc_pose: 0.717870 loss: 0.000936 2022/10/14 15:24:53 - mmengine - INFO - Epoch(train) [38][150/293] lr: 5.000000e-04 eta: 20:53:26 time: 1.643354 data_time: 0.083988 memory: 5829 loss_kpt: 0.000938 acc_pose: 0.719114 loss: 0.000938 2022/10/14 15:25:06 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 15:26:13 - mmengine - INFO - Epoch(train) [38][200/293] lr: 5.000000e-04 eta: 20:52:38 time: 1.605015 data_time: 0.096653 memory: 5829 loss_kpt: 0.000935 acc_pose: 0.718201 loss: 0.000935 2022/10/14 15:27:32 - mmengine - INFO - Epoch(train) [38][250/293] lr: 5.000000e-04 eta: 20:51:45 time: 1.582894 data_time: 0.062457 memory: 5829 loss_kpt: 0.000952 acc_pose: 0.648626 loss: 0.000952 2022/10/14 15:28:46 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 15:30:10 - mmengine - INFO - Epoch(train) [39][50/293] lr: 5.000000e-04 eta: 20:45:21 time: 1.678869 data_time: 0.490443 memory: 5829 loss_kpt: 0.000948 acc_pose: 0.750884 loss: 0.000948 2022/10/14 15:31:33 - mmengine - INFO - Epoch(train) [39][100/293] lr: 5.000000e-04 eta: 20:44:45 time: 1.656961 data_time: 0.084365 memory: 5829 loss_kpt: 0.000934 acc_pose: 0.701061 loss: 0.000934 2022/10/14 15:32:50 - mmengine - INFO - Epoch(train) [39][150/293] lr: 5.000000e-04 eta: 20:43:44 time: 1.544938 data_time: 0.062664 memory: 5829 loss_kpt: 0.000950 acc_pose: 0.736421 loss: 0.000950 2022/10/14 15:34:06 - mmengine - INFO - Epoch(train) [39][200/293] lr: 5.000000e-04 eta: 20:42:39 time: 1.525464 data_time: 0.604768 memory: 5829 loss_kpt: 0.000923 acc_pose: 0.738762 loss: 0.000923 2022/10/14 15:35:29 - mmengine - INFO - Epoch(train) [39][250/293] lr: 5.000000e-04 eta: 20:42:01 time: 1.648676 data_time: 0.348738 memory: 5829 loss_kpt: 0.000925 acc_pose: 0.737423 loss: 0.000925 2022/10/14 15:36:36 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 15:37:54 - mmengine - INFO - Epoch(train) [40][50/293] lr: 5.000000e-04 eta: 20:35:17 time: 1.548034 data_time: 0.603585 memory: 5829 loss_kpt: 0.000926 acc_pose: 0.715847 loss: 0.000926 2022/10/14 15:39:09 - mmengine - INFO - Epoch(train) [40][100/293] lr: 5.000000e-04 eta: 20:34:09 time: 1.507638 data_time: 1.304160 memory: 5829 loss_kpt: 0.000934 acc_pose: 0.681294 loss: 0.000934 2022/10/14 15:40:25 - mmengine - INFO - Epoch(train) [40][150/293] lr: 5.000000e-04 eta: 20:33:06 time: 1.529710 data_time: 1.120548 memory: 5829 loss_kpt: 0.000922 acc_pose: 0.712447 loss: 0.000922 2022/10/14 15:41:48 - mmengine - INFO - Epoch(train) [40][200/293] lr: 5.000000e-04 eta: 20:32:30 time: 1.661102 data_time: 1.431049 memory: 5829 loss_kpt: 0.000930 acc_pose: 0.717181 loss: 0.000930 2022/10/14 15:43:12 - mmengine - INFO - Epoch(train) [40][250/293] lr: 5.000000e-04 eta: 20:31:55 time: 1.665865 data_time: 1.061884 memory: 5829 loss_kpt: 0.000946 acc_pose: 0.708919 loss: 0.000946 2022/10/14 15:44:19 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 15:44:19 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/10/14 15:45:03 - mmengine - INFO - Epoch(val) [40][50/407] eta: 0:04:59 time: 0.838115 data_time: 0.801615 memory: 5829 2022/10/14 15:45:51 - mmengine - INFO - Epoch(val) [40][100/407] eta: 0:04:51 time: 0.948920 data_time: 0.912777 memory: 540 2022/10/14 15:46:32 - mmengine - INFO - Epoch(val) [40][150/407] eta: 0:03:31 time: 0.824270 data_time: 0.786164 memory: 540 2022/10/14 15:47:13 - mmengine - INFO - Epoch(val) [40][200/407] eta: 0:02:51 time: 0.826566 data_time: 0.790571 memory: 540 2022/10/14 15:47:53 - mmengine - INFO - Epoch(val) [40][250/407] eta: 0:02:05 time: 0.796736 data_time: 0.760672 memory: 540 2022/10/14 15:48:38 - mmengine - INFO - Epoch(val) [40][300/407] eta: 0:01:35 time: 0.888678 data_time: 0.852251 memory: 540 2022/10/14 15:49:24 - mmengine - INFO - Epoch(val) [40][350/407] eta: 0:00:53 time: 0.935806 data_time: 0.899363 memory: 540 2022/10/14 15:50:11 - mmengine - INFO - Epoch(val) [40][400/407] eta: 0:00:06 time: 0.921729 data_time: 0.885389 memory: 540 2022/10/14 15:51:09 - mmengine - INFO - Evaluating CocoMetric... 2022/10/14 15:51:23 - mmengine - INFO - Epoch(val) [40][407/407] coco/AP: 0.582635 coco/AP .5: 0.842373 coco/AP .75: 0.650356 coco/AP (M): 0.550034 coco/AP (L): 0.642341 coco/AR: 0.647623 coco/AR .5: 0.889484 coco/AR .75: 0.712689 coco/AR (M): 0.604589 coco/AR (L): 0.708138 2022/10/14 15:51:23 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256/best_coco/AP_epoch_30.pth is removed 2022/10/14 15:51:24 - mmengine - INFO - The best checkpoint with 0.5826 coco/AP at 40 epoch is saved to best_coco/AP_epoch_40.pth. 2022/10/14 15:52:57 - mmengine - INFO - Epoch(train) [41][50/293] lr: 5.000000e-04 eta: 20:26:29 time: 1.867665 data_time: 0.574873 memory: 5829 loss_kpt: 0.000932 acc_pose: 0.668456 loss: 0.000932 2022/10/14 15:54:24 - mmengine - INFO - Epoch(train) [41][100/293] lr: 5.000000e-04 eta: 20:26:09 time: 1.732658 data_time: 0.069230 memory: 5829 loss_kpt: 0.000928 acc_pose: 0.660067 loss: 0.000928 2022/10/14 15:55:54 - mmengine - INFO - Epoch(train) [41][150/293] lr: 5.000000e-04 eta: 20:26:00 time: 1.789681 data_time: 0.708963 memory: 5829 loss_kpt: 0.000933 acc_pose: 0.721728 loss: 0.000933 2022/10/14 15:57:17 - mmengine - INFO - Epoch(train) [41][200/293] lr: 5.000000e-04 eta: 20:25:24 time: 1.667725 data_time: 0.229574 memory: 5829 loss_kpt: 0.000932 acc_pose: 0.697062 loss: 0.000932 2022/10/14 15:58:40 - mmengine - INFO - Epoch(train) [41][250/293] lr: 5.000000e-04 eta: 20:24:47 time: 1.660397 data_time: 0.267613 memory: 5829 loss_kpt: 0.000931 acc_pose: 0.761002 loss: 0.000931 2022/10/14 15:59:32 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 15:59:51 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 16:01:08 - mmengine - INFO - Epoch(train) [42][50/293] lr: 5.000000e-04 eta: 20:18:19 time: 1.537410 data_time: 0.339306 memory: 5829 loss_kpt: 0.000919 acc_pose: 0.747042 loss: 0.000919 2022/10/14 16:02:28 - mmengine - INFO - Epoch(train) [42][100/293] lr: 5.000000e-04 eta: 20:17:31 time: 1.603227 data_time: 0.252080 memory: 5829 loss_kpt: 0.000932 acc_pose: 0.729448 loss: 0.000932 2022/10/14 16:03:56 - mmengine - INFO - Epoch(train) [42][150/293] lr: 5.000000e-04 eta: 20:17:13 time: 1.755615 data_time: 0.059298 memory: 5829 loss_kpt: 0.000938 acc_pose: 0.675660 loss: 0.000938 2022/10/14 16:05:27 - mmengine - INFO - Epoch(train) [42][200/293] lr: 5.000000e-04 eta: 20:17:08 time: 1.822536 data_time: 0.069823 memory: 5829 loss_kpt: 0.000939 acc_pose: 0.746750 loss: 0.000939 2022/10/14 16:07:00 - mmengine - INFO - Epoch(train) [42][250/293] lr: 5.000000e-04 eta: 20:17:09 time: 1.854065 data_time: 0.769971 memory: 5829 loss_kpt: 0.000921 acc_pose: 0.631603 loss: 0.000921 2022/10/14 16:08:15 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 16:09:41 - mmengine - INFO - Epoch(train) [43][50/293] lr: 5.000000e-04 eta: 20:11:24 time: 1.714036 data_time: 0.408452 memory: 5829 loss_kpt: 0.000926 acc_pose: 0.705365 loss: 0.000926 2022/10/14 16:11:08 - mmengine - INFO - Epoch(train) [43][100/293] lr: 5.000000e-04 eta: 20:11:04 time: 1.749980 data_time: 0.207605 memory: 5829 loss_kpt: 0.000914 acc_pose: 0.652119 loss: 0.000914 2022/10/14 16:12:35 - mmengine - INFO - Epoch(train) [43][150/293] lr: 5.000000e-04 eta: 20:10:40 time: 1.735570 data_time: 0.061603 memory: 5829 loss_kpt: 0.000933 acc_pose: 0.684879 loss: 0.000933 2022/10/14 16:14:00 - mmengine - INFO - Epoch(train) [43][200/293] lr: 5.000000e-04 eta: 20:10:10 time: 1.700299 data_time: 0.063236 memory: 5829 loss_kpt: 0.000925 acc_pose: 0.736282 loss: 0.000925 2022/10/14 16:15:28 - mmengine - INFO - Epoch(train) [43][250/293] lr: 5.000000e-04 eta: 20:09:48 time: 1.750993 data_time: 0.835896 memory: 5829 loss_kpt: 0.000928 acc_pose: 0.716297 loss: 0.000928 2022/10/14 16:16:37 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 16:18:08 - mmengine - INFO - Epoch(train) [44][50/293] lr: 5.000000e-04 eta: 20:04:29 time: 1.817749 data_time: 0.684671 memory: 5829 loss_kpt: 0.000917 acc_pose: 0.707564 loss: 0.000917 2022/10/14 16:19:41 - mmengine - INFO - Epoch(train) [44][100/293] lr: 5.000000e-04 eta: 20:04:28 time: 1.857677 data_time: 0.559421 memory: 5829 loss_kpt: 0.000947 acc_pose: 0.670764 loss: 0.000947 2022/10/14 16:21:15 - mmengine - INFO - Epoch(train) [44][150/293] lr: 5.000000e-04 eta: 20:04:30 time: 1.879643 data_time: 0.168356 memory: 5829 loss_kpt: 0.000922 acc_pose: 0.714838 loss: 0.000922 2022/10/14 16:22:46 - mmengine - INFO - Epoch(train) [44][200/293] lr: 5.000000e-04 eta: 20:04:21 time: 1.823946 data_time: 0.212154 memory: 5829 loss_kpt: 0.000921 acc_pose: 0.759896 loss: 0.000921 2022/10/14 16:24:22 - mmengine - INFO - Epoch(train) [44][250/293] lr: 5.000000e-04 eta: 20:04:30 time: 1.920918 data_time: 0.059538 memory: 5829 loss_kpt: 0.000912 acc_pose: 0.708494 loss: 0.000912 2022/10/14 16:25:37 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 16:27:08 - mmengine - INFO - Epoch(train) [45][50/293] lr: 5.000000e-04 eta: 19:59:16 time: 1.820387 data_time: 0.285682 memory: 5829 loss_kpt: 0.000921 acc_pose: 0.728697 loss: 0.000921 2022/10/14 16:28:33 - mmengine - INFO - Epoch(train) [45][100/293] lr: 5.000000e-04 eta: 19:58:41 time: 1.692872 data_time: 0.084506 memory: 5829 loss_kpt: 0.000921 acc_pose: 0.759173 loss: 0.000921 2022/10/14 16:28:45 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 16:29:54 - mmengine - INFO - Epoch(train) [45][150/293] lr: 5.000000e-04 eta: 19:57:54 time: 1.627220 data_time: 0.287790 memory: 5829 loss_kpt: 0.000911 acc_pose: 0.684348 loss: 0.000911 2022/10/14 16:31:23 - mmengine - INFO - Epoch(train) [45][200/293] lr: 5.000000e-04 eta: 19:57:37 time: 1.788751 data_time: 0.131197 memory: 5829 loss_kpt: 0.000938 acc_pose: 0.716687 loss: 0.000938 2022/10/14 16:32:45 - mmengine - INFO - Epoch(train) [45][250/293] lr: 5.000000e-04 eta: 19:56:51 time: 1.640444 data_time: 0.658554 memory: 5829 loss_kpt: 0.000916 acc_pose: 0.763092 loss: 0.000916 2022/10/14 16:33:56 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 16:35:20 - mmengine - INFO - Epoch(train) [46][50/293] lr: 5.000000e-04 eta: 19:51:16 time: 1.677053 data_time: 0.437542 memory: 5829 loss_kpt: 0.000915 acc_pose: 0.704772 loss: 0.000915 2022/10/14 16:36:46 - mmengine - INFO - Epoch(train) [46][100/293] lr: 5.000000e-04 eta: 19:50:48 time: 1.737928 data_time: 0.198069 memory: 5829 loss_kpt: 0.000917 acc_pose: 0.678211 loss: 0.000917 2022/10/14 16:38:13 - mmengine - INFO - Epoch(train) [46][150/293] lr: 5.000000e-04 eta: 19:50:19 time: 1.730160 data_time: 0.294341 memory: 5829 loss_kpt: 0.000941 acc_pose: 0.672418 loss: 0.000941 2022/10/14 16:39:36 - mmengine - INFO - Epoch(train) [46][200/293] lr: 5.000000e-04 eta: 19:49:38 time: 1.661552 data_time: 1.147761 memory: 5829 loss_kpt: 0.000933 acc_pose: 0.721456 loss: 0.000933 2022/10/14 16:41:01 - mmengine - INFO - Epoch(train) [46][250/293] lr: 5.000000e-04 eta: 19:49:01 time: 1.693037 data_time: 1.307273 memory: 5829 loss_kpt: 0.000920 acc_pose: 0.635507 loss: 0.000920 2022/10/14 16:42:09 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 16:43:23 - mmengine - INFO - Epoch(train) [47][50/293] lr: 5.000000e-04 eta: 19:42:56 time: 1.480179 data_time: 1.032782 memory: 5829 loss_kpt: 0.000921 acc_pose: 0.635296 loss: 0.000921 2022/10/14 16:44:32 - mmengine - INFO - Epoch(train) [47][100/293] lr: 5.000000e-04 eta: 19:41:26 time: 1.384356 data_time: 0.387538 memory: 5829 loss_kpt: 0.000920 acc_pose: 0.692425 loss: 0.000920 2022/10/14 16:45:45 - mmengine - INFO - Epoch(train) [47][150/293] lr: 5.000000e-04 eta: 19:40:10 time: 1.469395 data_time: 0.068191 memory: 5829 loss_kpt: 0.000932 acc_pose: 0.720456 loss: 0.000932 2022/10/14 16:47:00 - mmengine - INFO - Epoch(train) [47][200/293] lr: 5.000000e-04 eta: 19:39:00 time: 1.496965 data_time: 0.114941 memory: 5829 loss_kpt: 0.000915 acc_pose: 0.710396 loss: 0.000915 2022/10/14 16:48:04 - mmengine - INFO - Epoch(train) [47][250/293] lr: 5.000000e-04 eta: 19:37:11 time: 1.279284 data_time: 0.367994 memory: 5829 loss_kpt: 0.000905 acc_pose: 0.701099 loss: 0.000905 2022/10/14 16:49:03 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 16:50:32 - mmengine - INFO - Epoch(train) [48][50/293] lr: 5.000000e-04 eta: 19:32:06 time: 1.778640 data_time: 1.276766 memory: 5829 loss_kpt: 0.000915 acc_pose: 0.714713 loss: 0.000915 2022/10/14 16:51:51 - mmengine - INFO - Epoch(train) [48][100/293] lr: 5.000000e-04 eta: 19:31:11 time: 1.580207 data_time: 1.349839 memory: 5829 loss_kpt: 0.000926 acc_pose: 0.664308 loss: 0.000926 2022/10/14 16:53:12 - mmengine - INFO - Epoch(train) [48][150/293] lr: 5.000000e-04 eta: 19:30:24 time: 1.630779 data_time: 1.027933 memory: 5829 loss_kpt: 0.000922 acc_pose: 0.695839 loss: 0.000922 2022/10/14 16:54:47 - mmengine - INFO - Epoch(train) [48][200/293] lr: 5.000000e-04 eta: 19:30:21 time: 1.894020 data_time: 0.105302 memory: 5829 loss_kpt: 0.000901 acc_pose: 0.725626 loss: 0.000901 2022/10/14 16:55:36 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 16:56:08 - mmengine - INFO - Epoch(train) [48][250/293] lr: 5.000000e-04 eta: 19:29:32 time: 1.620424 data_time: 0.070690 memory: 5829 loss_kpt: 0.000910 acc_pose: 0.677931 loss: 0.000910 2022/10/14 16:57:13 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 16:58:38 - mmengine - INFO - Epoch(train) [49][50/293] lr: 5.000000e-04 eta: 19:24:21 time: 1.714369 data_time: 0.236078 memory: 5829 loss_kpt: 0.000917 acc_pose: 0.728483 loss: 0.000917 2022/10/14 17:00:08 - mmengine - INFO - Epoch(train) [49][100/293] lr: 5.000000e-04 eta: 19:23:59 time: 1.782316 data_time: 0.066288 memory: 5829 loss_kpt: 0.000914 acc_pose: 0.729275 loss: 0.000914 2022/10/14 17:01:37 - mmengine - INFO - Epoch(train) [49][150/293] lr: 5.000000e-04 eta: 19:23:38 time: 1.790188 data_time: 0.069534 memory: 5829 loss_kpt: 0.000920 acc_pose: 0.700346 loss: 0.000920 2022/10/14 17:02:59 - mmengine - INFO - Epoch(train) [49][200/293] lr: 5.000000e-04 eta: 19:22:52 time: 1.646352 data_time: 0.287965 memory: 5829 loss_kpt: 0.000929 acc_pose: 0.677429 loss: 0.000929 2022/10/14 17:04:23 - mmengine - INFO - Epoch(train) [49][250/293] lr: 5.000000e-04 eta: 19:22:13 time: 1.682487 data_time: 0.625297 memory: 5829 loss_kpt: 0.000915 acc_pose: 0.754176 loss: 0.000915 2022/10/14 17:05:34 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 17:06:58 - mmengine - INFO - Epoch(train) [50][50/293] lr: 5.000000e-04 eta: 19:17:00 time: 1.672981 data_time: 0.111059 memory: 5829 loss_kpt: 0.000919 acc_pose: 0.715302 loss: 0.000919 2022/10/14 17:08:17 - mmengine - INFO - Epoch(train) [50][100/293] lr: 5.000000e-04 eta: 19:16:05 time: 1.588566 data_time: 0.070459 memory: 5829 loss_kpt: 0.000913 acc_pose: 0.740921 loss: 0.000913 2022/10/14 17:09:34 - mmengine - INFO - Epoch(train) [50][150/293] lr: 5.000000e-04 eta: 19:15:01 time: 1.533597 data_time: 0.908923 memory: 5829 loss_kpt: 0.000913 acc_pose: 0.709072 loss: 0.000913 2022/10/14 17:10:49 - mmengine - INFO - Epoch(train) [50][200/293] lr: 5.000000e-04 eta: 19:13:51 time: 1.500168 data_time: 0.087933 memory: 5829 loss_kpt: 0.000919 acc_pose: 0.714033 loss: 0.000919 2022/10/14 17:11:50 - mmengine - INFO - Epoch(train) [50][250/293] lr: 5.000000e-04 eta: 19:11:58 time: 1.226788 data_time: 0.130599 memory: 5829 loss_kpt: 0.000922 acc_pose: 0.727759 loss: 0.000922 2022/10/14 17:12:45 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 17:12:45 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/10/14 17:13:30 - mmengine - INFO - Epoch(val) [50][50/407] eta: 0:05:10 time: 0.868523 data_time: 0.831284 memory: 5829 2022/10/14 17:14:19 - mmengine - INFO - Epoch(val) [50][100/407] eta: 0:04:58 time: 0.971092 data_time: 0.934474 memory: 540 2022/10/14 17:15:05 - mmengine - INFO - Epoch(val) [50][150/407] eta: 0:03:57 time: 0.922869 data_time: 0.886247 memory: 540 2022/10/14 17:16:03 - mmengine - INFO - Epoch(val) [50][200/407] eta: 0:04:00 time: 1.159977 data_time: 1.123936 memory: 540 2022/10/14 17:16:52 - mmengine - INFO - Epoch(val) [50][250/407] eta: 0:02:34 time: 0.985470 data_time: 0.949314 memory: 540 2022/10/14 17:17:42 - mmengine - INFO - Epoch(val) [50][300/407] eta: 0:01:46 time: 0.997828 data_time: 0.956954 memory: 540 2022/10/14 17:18:35 - mmengine - INFO - Epoch(val) [50][350/407] eta: 0:01:00 time: 1.060403 data_time: 1.023922 memory: 540 2022/10/14 17:19:37 - mmengine - INFO - Epoch(val) [50][400/407] eta: 0:00:08 time: 1.238566 data_time: 1.202461 memory: 540 2022/10/14 17:20:29 - mmengine - INFO - Evaluating CocoMetric... 2022/10/14 17:20:43 - mmengine - INFO - Epoch(val) [50][407/407] coco/AP: 0.592894 coco/AP .5: 0.850329 coco/AP .75: 0.663501 coco/AP (M): 0.558664 coco/AP (L): 0.655604 coco/AR: 0.656738 coco/AR .5: 0.897040 coco/AR .75: 0.726543 coco/AR (M): 0.612346 coco/AR (L): 0.719472 2022/10/14 17:20:43 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256/best_coco/AP_epoch_40.pth is removed 2022/10/14 17:20:45 - mmengine - INFO - The best checkpoint with 0.5929 coco/AP at 50 epoch is saved to best_coco/AP_epoch_50.pth. 2022/10/14 17:22:49 - mmengine - INFO - Epoch(train) [51][50/293] lr: 5.000000e-04 eta: 19:09:00 time: 2.481485 data_time: 0.170872 memory: 5829 loss_kpt: 0.000905 acc_pose: 0.776503 loss: 0.000905 2022/10/14 17:24:35 - mmengine - INFO - Epoch(train) [51][100/293] lr: 5.000000e-04 eta: 19:09:28 time: 2.115167 data_time: 0.238873 memory: 5829 loss_kpt: 0.000910 acc_pose: 0.661148 loss: 0.000910 2022/10/14 17:26:26 - mmengine - INFO - Epoch(train) [51][150/293] lr: 5.000000e-04 eta: 19:10:14 time: 2.233905 data_time: 0.067111 memory: 5829 loss_kpt: 0.000907 acc_pose: 0.776678 loss: 0.000907 2022/10/14 17:28:14 - mmengine - INFO - Epoch(train) [51][200/293] lr: 5.000000e-04 eta: 19:10:46 time: 2.146278 data_time: 0.273324 memory: 5829 loss_kpt: 0.000915 acc_pose: 0.715657 loss: 0.000915 2022/10/14 17:30:04 - mmengine - INFO - Epoch(train) [51][250/293] lr: 5.000000e-04 eta: 19:11:25 time: 2.202167 data_time: 0.261053 memory: 5829 loss_kpt: 0.000925 acc_pose: 0.682288 loss: 0.000925 2022/10/14 17:31:21 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 17:32:40 - mmengine - INFO - Epoch(train) [52][50/293] lr: 5.000000e-04 eta: 19:06:04 time: 1.574780 data_time: 0.252099 memory: 5829 loss_kpt: 0.000919 acc_pose: 0.730638 loss: 0.000919 2022/10/14 17:32:51 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 17:34:16 - mmengine - INFO - Epoch(train) [52][100/293] lr: 5.000000e-04 eta: 19:05:58 time: 1.920061 data_time: 0.446875 memory: 5829 loss_kpt: 0.000908 acc_pose: 0.715499 loss: 0.000908 2022/10/14 17:35:54 - mmengine - INFO - Epoch(train) [52][150/293] lr: 5.000000e-04 eta: 19:06:00 time: 1.967016 data_time: 0.107197 memory: 5829 loss_kpt: 0.000911 acc_pose: 0.692290 loss: 0.000911 2022/10/14 17:37:25 - mmengine - INFO - Epoch(train) [52][200/293] lr: 5.000000e-04 eta: 19:05:38 time: 1.821436 data_time: 0.146977 memory: 5829 loss_kpt: 0.000912 acc_pose: 0.746320 loss: 0.000912 2022/10/14 17:38:57 - mmengine - INFO - Epoch(train) [52][250/293] lr: 5.000000e-04 eta: 19:05:17 time: 1.834900 data_time: 0.055936 memory: 5829 loss_kpt: 0.000921 acc_pose: 0.667434 loss: 0.000921 2022/10/14 17:40:12 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 17:41:46 - mmengine - INFO - Epoch(train) [53][50/293] lr: 5.000000e-04 eta: 19:00:46 time: 1.878679 data_time: 1.480606 memory: 5829 loss_kpt: 0.000910 acc_pose: 0.706285 loss: 0.000910 2022/10/14 17:43:30 - mmengine - INFO - Epoch(train) [53][100/293] lr: 5.000000e-04 eta: 19:01:04 time: 2.086806 data_time: 1.905430 memory: 5829 loss_kpt: 0.000896 acc_pose: 0.685755 loss: 0.000896 2022/10/14 17:44:57 - mmengine - INFO - Epoch(train) [53][150/293] lr: 5.000000e-04 eta: 19:00:27 time: 1.733407 data_time: 1.508209 memory: 5829 loss_kpt: 0.000924 acc_pose: 0.710542 loss: 0.000924 2022/10/14 17:46:23 - mmengine - INFO - Epoch(train) [53][200/293] lr: 5.000000e-04 eta: 18:59:48 time: 1.714864 data_time: 0.471346 memory: 5829 loss_kpt: 0.000891 acc_pose: 0.721872 loss: 0.000891 2022/10/14 17:48:00 - mmengine - INFO - Epoch(train) [53][250/293] lr: 5.000000e-04 eta: 18:59:43 time: 1.950162 data_time: 0.069751 memory: 5829 loss_kpt: 0.000916 acc_pose: 0.664525 loss: 0.000916 2022/10/14 17:49:19 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 17:50:55 - mmengine - INFO - Epoch(train) [54][50/293] lr: 5.000000e-04 eta: 18:55:22 time: 1.928235 data_time: 0.315343 memory: 5829 loss_kpt: 0.000907 acc_pose: 0.697425 loss: 0.000907 2022/10/14 17:52:21 - mmengine - INFO - Epoch(train) [54][100/293] lr: 5.000000e-04 eta: 18:54:43 time: 1.722608 data_time: 0.504668 memory: 5829 loss_kpt: 0.000909 acc_pose: 0.701371 loss: 0.000909 2022/10/14 17:53:47 - mmengine - INFO - Epoch(train) [54][150/293] lr: 5.000000e-04 eta: 18:54:03 time: 1.717740 data_time: 1.252169 memory: 5829 loss_kpt: 0.000899 acc_pose: 0.743651 loss: 0.000899 2022/10/14 17:55:22 - mmengine - INFO - Epoch(train) [54][200/293] lr: 5.000000e-04 eta: 18:53:49 time: 1.898388 data_time: 1.453527 memory: 5829 loss_kpt: 0.000928 acc_pose: 0.660236 loss: 0.000928 2022/10/14 17:56:51 - mmengine - INFO - Epoch(train) [54][250/293] lr: 5.000000e-04 eta: 18:53:16 time: 1.770355 data_time: 0.273177 memory: 5829 loss_kpt: 0.000917 acc_pose: 0.695533 loss: 0.000917 2022/10/14 17:58:15 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 18:00:02 - mmengine - INFO - Epoch(train) [55][50/293] lr: 5.000000e-04 eta: 18:49:28 time: 2.137043 data_time: 0.155382 memory: 5829 loss_kpt: 0.000891 acc_pose: 0.707547 loss: 0.000891 2022/10/14 18:01:42 - mmengine - INFO - Epoch(train) [55][100/293] lr: 5.000000e-04 eta: 18:49:29 time: 2.007468 data_time: 1.335719 memory: 5829 loss_kpt: 0.000893 acc_pose: 0.747069 loss: 0.000893 2022/10/14 18:03:11 - mmengine - INFO - Epoch(train) [55][150/293] lr: 5.000000e-04 eta: 18:48:56 time: 1.777961 data_time: 1.589748 memory: 5829 loss_kpt: 0.000917 acc_pose: 0.698716 loss: 0.000917 2022/10/14 18:04:05 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 18:04:45 - mmengine - INFO - Epoch(train) [55][200/293] lr: 5.000000e-04 eta: 18:48:37 time: 1.875647 data_time: 1.520236 memory: 5829 loss_kpt: 0.000922 acc_pose: 0.718687 loss: 0.000922 2022/10/14 18:06:21 - mmengine - INFO - Epoch(train) [55][250/293] lr: 5.000000e-04 eta: 18:48:25 time: 1.928805 data_time: 0.658921 memory: 5829 loss_kpt: 0.000899 acc_pose: 0.751789 loss: 0.000899 2022/10/14 18:07:44 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 18:09:32 - mmengine - INFO - Epoch(train) [56][50/293] lr: 5.000000e-04 eta: 18:44:40 time: 2.153608 data_time: 0.271815 memory: 5829 loss_kpt: 0.000893 acc_pose: 0.729631 loss: 0.000893 2022/10/14 18:11:14 - mmengine - INFO - Epoch(train) [56][100/293] lr: 5.000000e-04 eta: 18:44:44 time: 2.048327 data_time: 0.134889 memory: 5829 loss_kpt: 0.000907 acc_pose: 0.667026 loss: 0.000907 2022/10/14 18:12:56 - mmengine - INFO - Epoch(train) [56][150/293] lr: 5.000000e-04 eta: 18:44:46 time: 2.039513 data_time: 0.064170 memory: 5829 loss_kpt: 0.000898 acc_pose: 0.766977 loss: 0.000898 2022/10/14 18:14:37 - mmengine - INFO - Epoch(train) [56][200/293] lr: 5.000000e-04 eta: 18:44:44 time: 2.018622 data_time: 0.063399 memory: 5829 loss_kpt: 0.000902 acc_pose: 0.747195 loss: 0.000902 2022/10/14 18:16:18 - mmengine - INFO - Epoch(train) [56][250/293] lr: 5.000000e-04 eta: 18:44:41 time: 2.009958 data_time: 0.059869 memory: 5829 loss_kpt: 0.000919 acc_pose: 0.719599 loss: 0.000919 2022/10/14 18:17:44 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 18:19:23 - mmengine - INFO - Epoch(train) [57][50/293] lr: 5.000000e-04 eta: 18:40:32 time: 1.975859 data_time: 1.620042 memory: 5829 loss_kpt: 0.000914 acc_pose: 0.701849 loss: 0.000914 2022/10/14 18:21:03 - mmengine - INFO - Epoch(train) [57][100/293] lr: 5.000000e-04 eta: 18:40:28 time: 2.005168 data_time: 0.508142 memory: 5829 loss_kpt: 0.000912 acc_pose: 0.689414 loss: 0.000912 2022/10/14 18:22:34 - mmengine - INFO - Epoch(train) [57][150/293] lr: 5.000000e-04 eta: 18:39:58 time: 1.825271 data_time: 0.642096 memory: 5829 loss_kpt: 0.000906 acc_pose: 0.722118 loss: 0.000906 2022/10/14 18:24:06 - mmengine - INFO - Epoch(train) [57][200/293] lr: 5.000000e-04 eta: 18:39:30 time: 1.841921 data_time: 0.067428 memory: 5829 loss_kpt: 0.000923 acc_pose: 0.693043 loss: 0.000923 2022/10/14 18:25:39 - mmengine - INFO - Epoch(train) [57][250/293] lr: 5.000000e-04 eta: 18:39:02 time: 1.841902 data_time: 0.154261 memory: 5829 loss_kpt: 0.000920 acc_pose: 0.737719 loss: 0.000920 2022/10/14 18:27:03 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 18:28:54 - mmengine - INFO - Epoch(train) [58][50/293] lr: 5.000000e-04 eta: 18:35:27 time: 2.216671 data_time: 0.368618 memory: 5829 loss_kpt: 0.000901 acc_pose: 0.730370 loss: 0.000901 2022/10/14 18:30:37 - mmengine - INFO - Epoch(train) [58][100/293] lr: 5.000000e-04 eta: 18:35:27 time: 2.056093 data_time: 0.267636 memory: 5829 loss_kpt: 0.000890 acc_pose: 0.720291 loss: 0.000890 2022/10/14 18:32:09 - mmengine - INFO - Epoch(train) [58][150/293] lr: 5.000000e-04 eta: 18:34:59 time: 1.847002 data_time: 1.457816 memory: 5829 loss_kpt: 0.000906 acc_pose: 0.745481 loss: 0.000906 2022/10/14 18:33:32 - mmengine - INFO - Epoch(train) [58][200/293] lr: 5.000000e-04 eta: 18:34:04 time: 1.643658 data_time: 1.475121 memory: 5829 loss_kpt: 0.000906 acc_pose: 0.692991 loss: 0.000906 2022/10/14 18:34:31 - mmengine - INFO - Epoch(train) [58][250/293] lr: 5.000000e-04 eta: 18:32:09 time: 1.197213 data_time: 0.384984 memory: 5829 loss_kpt: 0.000901 acc_pose: 0.726894 loss: 0.000901 2022/10/14 18:35:27 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 18:35:40 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 18:37:08 - mmengine - INFO - Epoch(train) [59][50/293] lr: 5.000000e-04 eta: 18:28:09 time: 2.007399 data_time: 0.533877 memory: 5829 loss_kpt: 0.000912 acc_pose: 0.675796 loss: 0.000912 2022/10/14 18:38:35 - mmengine - INFO - Epoch(train) [59][100/293] lr: 5.000000e-04 eta: 18:27:27 time: 1.748366 data_time: 0.060568 memory: 5829 loss_kpt: 0.000894 acc_pose: 0.725826 loss: 0.000894 2022/10/14 18:40:09 - mmengine - INFO - Epoch(train) [59][150/293] lr: 5.000000e-04 eta: 18:27:01 time: 1.876790 data_time: 0.639824 memory: 5829 loss_kpt: 0.000922 acc_pose: 0.750321 loss: 0.000922 2022/10/14 18:41:35 - mmengine - INFO - Epoch(train) [59][200/293] lr: 5.000000e-04 eta: 18:26:15 time: 1.714895 data_time: 0.499260 memory: 5829 loss_kpt: 0.000909 acc_pose: 0.685240 loss: 0.000909 2022/10/14 18:43:10 - mmengine - INFO - Epoch(train) [59][250/293] lr: 5.000000e-04 eta: 18:25:53 time: 1.912405 data_time: 0.177958 memory: 5829 loss_kpt: 0.000901 acc_pose: 0.742603 loss: 0.000901 2022/10/14 18:44:33 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 18:46:07 - mmengine - INFO - Epoch(train) [60][50/293] lr: 5.000000e-04 eta: 18:21:38 time: 1.876698 data_time: 0.319001 memory: 5829 loss_kpt: 0.000894 acc_pose: 0.711461 loss: 0.000894 2022/10/14 18:47:45 - mmengine - INFO - Epoch(train) [60][100/293] lr: 5.000000e-04 eta: 18:21:22 time: 1.959073 data_time: 0.064428 memory: 5829 loss_kpt: 0.000909 acc_pose: 0.702239 loss: 0.000909 2022/10/14 18:49:29 - mmengine - INFO - Epoch(train) [60][150/293] lr: 5.000000e-04 eta: 18:21:21 time: 2.079906 data_time: 0.076621 memory: 5829 loss_kpt: 0.000904 acc_pose: 0.739663 loss: 0.000904 2022/10/14 18:51:11 - mmengine - INFO - Epoch(train) [60][200/293] lr: 5.000000e-04 eta: 18:21:14 time: 2.043555 data_time: 0.071215 memory: 5829 loss_kpt: 0.000886 acc_pose: 0.766946 loss: 0.000886 2022/10/14 18:52:41 - mmengine - INFO - Epoch(train) [60][250/293] lr: 5.000000e-04 eta: 18:20:35 time: 1.786443 data_time: 0.071527 memory: 5829 loss_kpt: 0.000905 acc_pose: 0.720834 loss: 0.000905 2022/10/14 18:54:04 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 18:54:04 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/10/14 18:54:57 - mmengine - INFO - Epoch(val) [60][50/407] eta: 0:06:03 time: 1.017430 data_time: 0.980375 memory: 5829 2022/10/14 18:55:44 - mmengine - INFO - Epoch(val) [60][100/407] eta: 0:04:48 time: 0.938877 data_time: 0.901062 memory: 540 2022/10/14 18:56:38 - mmengine - INFO - Epoch(val) [60][150/407] eta: 0:04:35 time: 1.070590 data_time: 1.033985 memory: 540 2022/10/14 18:57:30 - mmengine - INFO - Epoch(val) [60][200/407] eta: 0:03:37 time: 1.051001 data_time: 1.014278 memory: 540 2022/10/14 18:58:16 - mmengine - INFO - Epoch(val) [60][250/407] eta: 0:02:24 time: 0.921009 data_time: 0.884943 memory: 540 2022/10/14 18:59:07 - mmengine - INFO - Epoch(val) [60][300/407] eta: 0:01:49 time: 1.020998 data_time: 0.984878 memory: 540 2022/10/14 18:59:51 - mmengine - INFO - Epoch(val) [60][350/407] eta: 0:00:50 time: 0.880168 data_time: 0.843620 memory: 540 2022/10/14 19:00:41 - mmengine - INFO - Epoch(val) [60][400/407] eta: 0:00:06 time: 0.986858 data_time: 0.951396 memory: 540 2022/10/14 19:01:25 - mmengine - INFO - Evaluating CocoMetric... 2022/10/14 19:01:39 - mmengine - INFO - Epoch(val) [60][407/407] coco/AP: 0.601236 coco/AP .5: 0.852391 coco/AP .75: 0.671491 coco/AP (M): 0.568163 coco/AP (L): 0.662279 coco/AR: 0.664767 coco/AR .5: 0.897198 coco/AR .75: 0.731423 coco/AR (M): 0.621278 coco/AR (L): 0.726421 2022/10/14 19:01:39 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256/best_coco/AP_epoch_50.pth is removed 2022/10/14 19:01:40 - mmengine - INFO - The best checkpoint with 0.6012 coco/AP at 60 epoch is saved to best_coco/AP_epoch_60.pth. 2022/10/14 19:03:30 - mmengine - INFO - Epoch(train) [61][50/293] lr: 5.000000e-04 eta: 18:17:01 time: 2.190299 data_time: 0.150152 memory: 5829 loss_kpt: 0.000879 acc_pose: 0.654197 loss: 0.000879 2022/10/14 19:05:08 - mmengine - INFO - Epoch(train) [61][100/293] lr: 5.000000e-04 eta: 18:16:43 time: 1.963116 data_time: 0.081053 memory: 5829 loss_kpt: 0.000898 acc_pose: 0.690030 loss: 0.000898 2022/10/14 19:06:53 - mmengine - INFO - Epoch(train) [61][150/293] lr: 5.000000e-04 eta: 18:16:44 time: 2.109760 data_time: 0.777157 memory: 5829 loss_kpt: 0.000904 acc_pose: 0.691563 loss: 0.000904 2022/10/14 19:08:32 - mmengine - INFO - Epoch(train) [61][200/293] lr: 5.000000e-04 eta: 18:16:26 time: 1.973177 data_time: 1.766633 memory: 5829 loss_kpt: 0.000887 acc_pose: 0.740971 loss: 0.000887 2022/10/14 19:10:11 - mmengine - INFO - Epoch(train) [61][250/293] lr: 5.000000e-04 eta: 18:16:10 time: 1.983171 data_time: 0.542110 memory: 5829 loss_kpt: 0.000894 acc_pose: 0.727188 loss: 0.000894 2022/10/14 19:11:28 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 19:13:09 - mmengine - INFO - Epoch(train) [62][50/293] lr: 5.000000e-04 eta: 18:12:16 time: 2.022793 data_time: 0.363070 memory: 5829 loss_kpt: 0.000892 acc_pose: 0.717680 loss: 0.000892 2022/10/14 19:14:52 - mmengine - INFO - Epoch(train) [62][100/293] lr: 5.000000e-04 eta: 18:12:07 time: 2.052163 data_time: 0.089302 memory: 5829 loss_kpt: 0.000892 acc_pose: 0.716927 loss: 0.000892 2022/10/14 19:15:43 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 19:16:36 - mmengine - INFO - Epoch(train) [62][150/293] lr: 5.000000e-04 eta: 18:12:00 time: 2.072126 data_time: 0.091233 memory: 5829 loss_kpt: 0.000889 acc_pose: 0.721795 loss: 0.000889 2022/10/14 19:18:19 - mmengine - INFO - Epoch(train) [62][200/293] lr: 5.000000e-04 eta: 18:11:53 time: 2.072904 data_time: 0.063366 memory: 5829 loss_kpt: 0.000906 acc_pose: 0.778426 loss: 0.000906 2022/10/14 19:19:59 - mmengine - INFO - Epoch(train) [62][250/293] lr: 5.000000e-04 eta: 18:11:37 time: 2.004189 data_time: 0.104753 memory: 5829 loss_kpt: 0.000902 acc_pose: 0.703467 loss: 0.000902 2022/10/14 19:21:18 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 19:23:08 - mmengine - INFO - Epoch(train) [63][50/293] lr: 5.000000e-04 eta: 18:08:03 time: 2.186865 data_time: 0.190777 memory: 5829 loss_kpt: 0.000898 acc_pose: 0.746945 loss: 0.000898 2022/10/14 19:24:53 - mmengine - INFO - Epoch(train) [63][100/293] lr: 5.000000e-04 eta: 18:07:58 time: 2.099105 data_time: 0.073606 memory: 5829 loss_kpt: 0.000871 acc_pose: 0.697580 loss: 0.000871 2022/10/14 19:26:20 - mmengine - INFO - Epoch(train) [63][150/293] lr: 5.000000e-04 eta: 18:07:10 time: 1.739924 data_time: 0.106882 memory: 5829 loss_kpt: 0.000916 acc_pose: 0.768688 loss: 0.000916 2022/10/14 19:27:54 - mmengine - INFO - Epoch(train) [63][200/293] lr: 5.000000e-04 eta: 18:06:39 time: 1.891606 data_time: 0.179945 memory: 5829 loss_kpt: 0.000899 acc_pose: 0.713287 loss: 0.000899 2022/10/14 19:29:28 - mmengine - INFO - Epoch(train) [63][250/293] lr: 5.000000e-04 eta: 18:06:06 time: 1.868504 data_time: 0.089622 memory: 5829 loss_kpt: 0.000891 acc_pose: 0.769421 loss: 0.000891 2022/10/14 19:30:48 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 19:32:18 - mmengine - INFO - Epoch(train) [64][50/293] lr: 5.000000e-04 eta: 18:01:49 time: 1.812828 data_time: 1.037133 memory: 5829 loss_kpt: 0.000875 acc_pose: 0.740207 loss: 0.000875 2022/10/14 19:33:52 - mmengine - INFO - Epoch(train) [64][100/293] lr: 5.000000e-04 eta: 18:01:16 time: 1.876402 data_time: 0.114425 memory: 5829 loss_kpt: 0.000879 acc_pose: 0.755403 loss: 0.000879 2022/10/14 19:35:29 - mmengine - INFO - Epoch(train) [64][150/293] lr: 5.000000e-04 eta: 18:00:50 time: 1.942161 data_time: 0.059598 memory: 5829 loss_kpt: 0.000897 acc_pose: 0.691129 loss: 0.000897 2022/10/14 19:37:01 - mmengine - INFO - Epoch(train) [64][200/293] lr: 5.000000e-04 eta: 18:00:13 time: 1.841989 data_time: 0.917667 memory: 5829 loss_kpt: 0.000904 acc_pose: 0.721377 loss: 0.000904 2022/10/14 19:38:31 - mmengine - INFO - Epoch(train) [64][250/293] lr: 5.000000e-04 eta: 17:59:30 time: 1.800217 data_time: 1.620678 memory: 5829 loss_kpt: 0.000919 acc_pose: 0.722769 loss: 0.000919 2022/10/14 19:39:47 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 19:41:23 - mmengine - INFO - Epoch(train) [65][50/293] lr: 5.000000e-04 eta: 17:55:28 time: 1.922067 data_time: 0.153559 memory: 5829 loss_kpt: 0.000884 acc_pose: 0.735539 loss: 0.000884 2022/10/14 19:42:50 - mmengine - INFO - Epoch(train) [65][100/293] lr: 5.000000e-04 eta: 17:54:38 time: 1.735384 data_time: 0.058053 memory: 5829 loss_kpt: 0.000888 acc_pose: 0.735811 loss: 0.000888 2022/10/14 19:44:26 - mmengine - INFO - Epoch(train) [65][150/293] lr: 5.000000e-04 eta: 17:54:09 time: 1.921512 data_time: 1.178007 memory: 5829 loss_kpt: 0.000896 acc_pose: 0.701714 loss: 0.000896 2022/10/14 19:45:56 - mmengine - INFO - Epoch(train) [65][200/293] lr: 5.000000e-04 eta: 17:53:26 time: 1.802924 data_time: 0.740613 memory: 5829 loss_kpt: 0.000883 acc_pose: 0.756119 loss: 0.000883 2022/10/14 19:47:31 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 19:47:34 - mmengine - INFO - Epoch(train) [65][250/293] lr: 5.000000e-04 eta: 17:53:01 time: 1.961895 data_time: 0.726726 memory: 5829 loss_kpt: 0.000900 acc_pose: 0.704100 loss: 0.000900 2022/10/14 19:48:54 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 19:50:34 - mmengine - INFO - Epoch(train) [66][50/293] lr: 5.000000e-04 eta: 17:49:10 time: 2.000774 data_time: 1.666309 memory: 5829 loss_kpt: 0.000904 acc_pose: 0.744347 loss: 0.000904 2022/10/14 19:52:01 - mmengine - INFO - Epoch(train) [66][100/293] lr: 5.000000e-04 eta: 17:48:19 time: 1.736509 data_time: 1.358475 memory: 5829 loss_kpt: 0.000888 acc_pose: 0.660006 loss: 0.000888 2022/10/14 19:53:36 - mmengine - INFO - Epoch(train) [66][150/293] lr: 5.000000e-04 eta: 17:47:46 time: 1.904085 data_time: 0.559727 memory: 5829 loss_kpt: 0.000894 acc_pose: 0.745110 loss: 0.000894 2022/10/14 19:55:14 - mmengine - INFO - Epoch(train) [66][200/293] lr: 5.000000e-04 eta: 17:47:19 time: 1.951538 data_time: 0.112744 memory: 5829 loss_kpt: 0.000889 acc_pose: 0.730363 loss: 0.000889 2022/10/14 19:56:50 - mmengine - INFO - Epoch(train) [66][250/293] lr: 5.000000e-04 eta: 17:46:48 time: 1.927255 data_time: 0.063303 memory: 5829 loss_kpt: 0.000868 acc_pose: 0.711311 loss: 0.000868 2022/10/14 19:58:09 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 19:59:53 - mmengine - INFO - Epoch(train) [67][50/293] lr: 5.000000e-04 eta: 17:43:08 time: 2.088846 data_time: 0.157080 memory: 5829 loss_kpt: 0.000893 acc_pose: 0.713390 loss: 0.000893 2022/10/14 20:01:27 - mmengine - INFO - Epoch(train) [67][100/293] lr: 5.000000e-04 eta: 17:42:31 time: 1.873609 data_time: 0.065413 memory: 5829 loss_kpt: 0.000881 acc_pose: 0.738978 loss: 0.000881 2022/10/14 20:03:00 - mmengine - INFO - Epoch(train) [67][150/293] lr: 5.000000e-04 eta: 17:41:54 time: 1.867215 data_time: 0.271173 memory: 5829 loss_kpt: 0.000889 acc_pose: 0.737757 loss: 0.000889 2022/10/14 20:04:46 - mmengine - INFO - Epoch(train) [67][200/293] lr: 5.000000e-04 eta: 17:41:42 time: 2.107818 data_time: 0.067903 memory: 5829 loss_kpt: 0.000890 acc_pose: 0.711485 loss: 0.000890 2022/10/14 20:06:20 - mmengine - INFO - Epoch(train) [67][250/293] lr: 5.000000e-04 eta: 17:41:04 time: 1.879443 data_time: 0.076660 memory: 5829 loss_kpt: 0.000883 acc_pose: 0.739296 loss: 0.000883 2022/10/14 20:07:36 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 20:09:14 - mmengine - INFO - Epoch(train) [68][50/293] lr: 5.000000e-04 eta: 17:37:10 time: 1.947263 data_time: 0.453279 memory: 5829 loss_kpt: 0.000880 acc_pose: 0.714711 loss: 0.000880 2022/10/14 20:10:39 - mmengine - INFO - Epoch(train) [68][100/293] lr: 5.000000e-04 eta: 17:36:15 time: 1.711309 data_time: 0.085404 memory: 5829 loss_kpt: 0.000906 acc_pose: 0.737802 loss: 0.000906 2022/10/14 20:12:17 - mmengine - INFO - Epoch(train) [68][150/293] lr: 5.000000e-04 eta: 17:35:45 time: 1.951205 data_time: 0.067312 memory: 5829 loss_kpt: 0.000884 acc_pose: 0.705614 loss: 0.000884 2022/10/14 20:14:00 - mmengine - INFO - Epoch(train) [68][200/293] lr: 5.000000e-04 eta: 17:35:26 time: 2.056311 data_time: 0.067892 memory: 5829 loss_kpt: 0.000884 acc_pose: 0.767437 loss: 0.000884 2022/10/14 20:15:37 - mmengine - INFO - Epoch(train) [68][250/293] lr: 5.000000e-04 eta: 17:34:56 time: 1.953974 data_time: 0.060680 memory: 5829 loss_kpt: 0.000878 acc_pose: 0.713693 loss: 0.000878 2022/10/14 20:17:13 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 20:18:52 - mmengine - INFO - Epoch(train) [69][50/293] lr: 5.000000e-04 eta: 17:31:06 time: 1.978044 data_time: 0.148121 memory: 5829 loss_kpt: 0.000873 acc_pose: 0.701436 loss: 0.000873 2022/10/14 20:19:45 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 20:20:30 - mmengine - INFO - Epoch(train) [69][100/293] lr: 5.000000e-04 eta: 17:30:36 time: 1.963876 data_time: 0.073866 memory: 5829 loss_kpt: 0.000889 acc_pose: 0.741270 loss: 0.000889 2022/10/14 20:21:59 - mmengine - INFO - Epoch(train) [69][150/293] lr: 5.000000e-04 eta: 17:29:49 time: 1.793077 data_time: 0.066352 memory: 5829 loss_kpt: 0.000891 acc_pose: 0.791037 loss: 0.000891 2022/10/14 20:23:28 - mmengine - INFO - Epoch(train) [69][200/293] lr: 5.000000e-04 eta: 17:28:58 time: 1.767785 data_time: 0.055338 memory: 5829 loss_kpt: 0.000881 acc_pose: 0.781798 loss: 0.000881 2022/10/14 20:25:02 - mmengine - INFO - Epoch(train) [69][250/293] lr: 5.000000e-04 eta: 17:28:20 time: 1.882443 data_time: 0.072256 memory: 5829 loss_kpt: 0.000882 acc_pose: 0.722950 loss: 0.000882 2022/10/14 20:26:15 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 20:27:48 - mmengine - INFO - Epoch(train) [70][50/293] lr: 5.000000e-04 eta: 17:24:18 time: 1.850714 data_time: 0.611868 memory: 5829 loss_kpt: 0.000875 acc_pose: 0.772102 loss: 0.000875 2022/10/14 20:29:10 - mmengine - INFO - Epoch(train) [70][100/293] lr: 5.000000e-04 eta: 17:23:15 time: 1.639978 data_time: 0.301956 memory: 5829 loss_kpt: 0.000896 acc_pose: 0.753074 loss: 0.000896 2022/10/14 20:30:50 - mmengine - INFO - Epoch(train) [70][150/293] lr: 5.000000e-04 eta: 17:22:47 time: 1.994727 data_time: 0.140014 memory: 5829 loss_kpt: 0.000883 acc_pose: 0.692221 loss: 0.000883 2022/10/14 20:32:19 - mmengine - INFO - Epoch(train) [70][200/293] lr: 5.000000e-04 eta: 17:21:58 time: 1.790696 data_time: 0.382661 memory: 5829 loss_kpt: 0.000896 acc_pose: 0.748099 loss: 0.000896 2022/10/14 20:33:54 - mmengine - INFO - Epoch(train) [70][250/293] lr: 5.000000e-04 eta: 17:21:20 time: 1.892275 data_time: 0.084071 memory: 5829 loss_kpt: 0.000866 acc_pose: 0.706794 loss: 0.000866 2022/10/14 20:35:13 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 20:35:13 - mmengine - INFO - Saving checkpoint at 70 epochs 2022/10/14 20:36:08 - mmengine - INFO - Epoch(val) [70][50/407] eta: 0:06:18 time: 1.060002 data_time: 1.023853 memory: 5829 2022/10/14 20:37:05 - mmengine - INFO - Epoch(val) [70][100/407] eta: 0:05:50 time: 1.142877 data_time: 1.106215 memory: 540 2022/10/14 20:38:01 - mmengine - INFO - Epoch(val) [70][150/407] eta: 0:04:43 time: 1.104056 data_time: 1.067888 memory: 540 2022/10/14 20:39:00 - mmengine - INFO - Epoch(val) [70][200/407] eta: 0:04:07 time: 1.193743 data_time: 1.157357 memory: 540 2022/10/14 20:39:52 - mmengine - INFO - Epoch(val) [70][250/407] eta: 0:02:40 time: 1.024178 data_time: 0.987772 memory: 540 2022/10/14 20:40:48 - mmengine - INFO - Epoch(val) [70][300/407] eta: 0:01:59 time: 1.120779 data_time: 1.084760 memory: 540 2022/10/14 20:41:45 - mmengine - INFO - Epoch(val) [70][350/407] eta: 0:01:05 time: 1.144779 data_time: 1.104952 memory: 540 2022/10/14 20:42:41 - mmengine - INFO - Epoch(val) [70][400/407] eta: 0:00:07 time: 1.129661 data_time: 1.093514 memory: 540 2022/10/14 20:43:24 - mmengine - INFO - Evaluating CocoMetric... 2022/10/14 20:43:38 - mmengine - INFO - Epoch(val) [70][407/407] coco/AP: 0.608419 coco/AP .5: 0.858125 coco/AP .75: 0.679393 coco/AP (M): 0.575969 coco/AP (L): 0.669008 coco/AR: 0.670072 coco/AR .5: 0.901763 coco/AR .75: 0.738035 coco/AR (M): 0.627561 coco/AR (L): 0.730286 2022/10/14 20:43:38 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256/best_coco/AP_epoch_60.pth is removed 2022/10/14 20:43:40 - mmengine - INFO - The best checkpoint with 0.6084 coco/AP at 70 epoch is saved to best_coco/AP_epoch_70.pth. 2022/10/14 20:45:20 - mmengine - INFO - Epoch(train) [71][50/293] lr: 5.000000e-04 eta: 17:17:35 time: 1.998050 data_time: 0.415340 memory: 5829 loss_kpt: 0.000895 acc_pose: 0.762507 loss: 0.000895 2022/10/14 20:47:04 - mmengine - INFO - Epoch(train) [71][100/293] lr: 5.000000e-04 eta: 17:17:15 time: 2.084748 data_time: 0.069149 memory: 5829 loss_kpt: 0.000898 acc_pose: 0.708677 loss: 0.000898 2022/10/14 20:48:40 - mmengine - INFO - Epoch(train) [71][150/293] lr: 5.000000e-04 eta: 17:16:39 time: 1.922329 data_time: 0.122769 memory: 5829 loss_kpt: 0.000888 acc_pose: 0.737360 loss: 0.000888 2022/10/14 20:50:09 - mmengine - INFO - Epoch(train) [71][200/293] lr: 5.000000e-04 eta: 17:15:47 time: 1.770361 data_time: 0.062133 memory: 5829 loss_kpt: 0.000886 acc_pose: 0.744009 loss: 0.000886 2022/10/14 20:51:44 - mmengine - INFO - Epoch(train) [71][250/293] lr: 5.000000e-04 eta: 17:15:08 time: 1.900039 data_time: 1.609426 memory: 5829 loss_kpt: 0.000888 acc_pose: 0.647512 loss: 0.000888 2022/10/14 20:53:12 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 20:54:59 - mmengine - INFO - Epoch(train) [72][50/293] lr: 5.000000e-04 eta: 17:11:40 time: 2.152769 data_time: 0.179712 memory: 5829 loss_kpt: 0.000889 acc_pose: 0.728640 loss: 0.000889 2022/10/14 20:56:52 - mmengine - INFO - Epoch(train) [72][100/293] lr: 5.000000e-04 eta: 17:11:35 time: 2.251785 data_time: 0.068637 memory: 5829 loss_kpt: 0.000880 acc_pose: 0.663824 loss: 0.000880 2022/10/14 20:58:35 - mmengine - INFO - Epoch(train) [72][150/293] lr: 5.000000e-04 eta: 17:11:11 time: 2.064641 data_time: 0.062440 memory: 5829 loss_kpt: 0.000879 acc_pose: 0.724429 loss: 0.000879 2022/10/14 21:00:11 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 21:00:15 - mmengine - INFO - Epoch(train) [72][200/293] lr: 5.000000e-04 eta: 17:10:41 time: 2.005046 data_time: 0.062616 memory: 5829 loss_kpt: 0.000883 acc_pose: 0.716305 loss: 0.000883 2022/10/14 21:02:05 - mmengine - INFO - Epoch(train) [72][250/293] lr: 5.000000e-04 eta: 17:10:28 time: 2.181070 data_time: 0.510871 memory: 5829 loss_kpt: 0.000873 acc_pose: 0.780247 loss: 0.000873 2022/10/14 21:03:34 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 21:05:15 - mmengine - INFO - Epoch(train) [73][50/293] lr: 5.000000e-04 eta: 17:06:48 time: 2.021352 data_time: 0.254153 memory: 5829 loss_kpt: 0.000885 acc_pose: 0.636785 loss: 0.000885 2022/10/14 21:07:00 - mmengine - INFO - Epoch(train) [73][100/293] lr: 5.000000e-04 eta: 17:06:26 time: 2.099615 data_time: 0.268731 memory: 5829 loss_kpt: 0.000863 acc_pose: 0.719001 loss: 0.000863 2022/10/14 21:08:41 - mmengine - INFO - Epoch(train) [73][150/293] lr: 5.000000e-04 eta: 17:05:57 time: 2.026146 data_time: 0.169812 memory: 5829 loss_kpt: 0.000867 acc_pose: 0.746744 loss: 0.000867 2022/10/14 21:10:29 - mmengine - INFO - Epoch(train) [73][200/293] lr: 5.000000e-04 eta: 17:05:41 time: 2.166174 data_time: 0.078233 memory: 5829 loss_kpt: 0.000894 acc_pose: 0.736991 loss: 0.000894 2022/10/14 21:12:07 - mmengine - INFO - Epoch(train) [73][250/293] lr: 5.000000e-04 eta: 17:05:04 time: 1.948497 data_time: 0.057077 memory: 5829 loss_kpt: 0.000888 acc_pose: 0.751766 loss: 0.000888 2022/10/14 21:13:32 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 21:15:20 - mmengine - INFO - Epoch(train) [74][50/293] lr: 5.000000e-04 eta: 17:01:36 time: 2.150080 data_time: 0.259578 memory: 5829 loss_kpt: 0.000899 acc_pose: 0.722002 loss: 0.000899 2022/10/14 21:16:56 - mmengine - INFO - Epoch(train) [74][100/293] lr: 5.000000e-04 eta: 17:00:56 time: 1.920633 data_time: 0.053291 memory: 5829 loss_kpt: 0.000878 acc_pose: 0.687279 loss: 0.000878 2022/10/14 21:18:33 - mmengine - INFO - Epoch(train) [74][150/293] lr: 5.000000e-04 eta: 17:00:18 time: 1.942582 data_time: 0.195969 memory: 5829 loss_kpt: 0.000881 acc_pose: 0.718417 loss: 0.000881 2022/10/14 21:20:19 - mmengine - INFO - Epoch(train) [74][200/293] lr: 5.000000e-04 eta: 16:59:55 time: 2.110305 data_time: 1.150912 memory: 5829 loss_kpt: 0.000866 acc_pose: 0.730999 loss: 0.000866 2022/10/14 21:22:11 - mmengine - INFO - Epoch(train) [74][250/293] lr: 5.000000e-04 eta: 16:59:45 time: 2.251374 data_time: 0.069053 memory: 5829 loss_kpt: 0.000880 acc_pose: 0.693107 loss: 0.000880 2022/10/14 21:23:36 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 21:25:19 - mmengine - INFO - Epoch(train) [75][50/293] lr: 5.000000e-04 eta: 16:56:10 time: 2.063377 data_time: 0.321185 memory: 5829 loss_kpt: 0.000892 acc_pose: 0.718818 loss: 0.000892 2022/10/14 21:27:02 - mmengine - INFO - Epoch(train) [75][100/293] lr: 5.000000e-04 eta: 16:55:42 time: 2.063390 data_time: 0.064009 memory: 5829 loss_kpt: 0.000890 acc_pose: 0.729333 loss: 0.000890 2022/10/14 21:28:40 - mmengine - INFO - Epoch(train) [75][150/293] lr: 5.000000e-04 eta: 16:55:04 time: 1.956486 data_time: 0.059178 memory: 5829 loss_kpt: 0.000884 acc_pose: 0.750032 loss: 0.000884 2022/10/14 21:30:24 - mmengine - INFO - Epoch(train) [75][200/293] lr: 5.000000e-04 eta: 16:54:36 time: 2.077463 data_time: 0.056652 memory: 5829 loss_kpt: 0.000888 acc_pose: 0.753856 loss: 0.000888 2022/10/14 21:32:03 - mmengine - INFO - Epoch(train) [75][250/293] lr: 5.000000e-04 eta: 16:54:00 time: 1.983704 data_time: 0.060391 memory: 5829 loss_kpt: 0.000869 acc_pose: 0.769024 loss: 0.000869 2022/10/14 21:33:32 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 21:34:24 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 21:35:12 - mmengine - INFO - Epoch(train) [76][50/293] lr: 5.000000e-04 eta: 16:50:19 time: 1.992265 data_time: 0.174925 memory: 5829 loss_kpt: 0.000877 acc_pose: 0.767285 loss: 0.000877 2022/10/14 21:36:47 - mmengine - INFO - Epoch(train) [76][100/293] lr: 5.000000e-04 eta: 16:49:36 time: 1.907703 data_time: 0.055441 memory: 5829 loss_kpt: 0.000886 acc_pose: 0.715249 loss: 0.000886 2022/10/14 21:38:21 - mmengine - INFO - Epoch(train) [76][150/293] lr: 5.000000e-04 eta: 16:48:50 time: 1.880288 data_time: 0.192612 memory: 5829 loss_kpt: 0.000897 acc_pose: 0.776662 loss: 0.000897 2022/10/14 21:40:07 - mmengine - INFO - Epoch(train) [76][200/293] lr: 5.000000e-04 eta: 16:48:24 time: 2.107379 data_time: 0.061567 memory: 5829 loss_kpt: 0.000881 acc_pose: 0.737422 loss: 0.000881 2022/10/14 21:41:46 - mmengine - INFO - Epoch(train) [76][250/293] lr: 5.000000e-04 eta: 16:47:46 time: 1.977913 data_time: 0.059662 memory: 5829 loss_kpt: 0.000878 acc_pose: 0.788528 loss: 0.000878 2022/10/14 21:43:14 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 21:45:01 - mmengine - INFO - Epoch(train) [77][50/293] lr: 5.000000e-04 eta: 16:44:19 time: 2.137226 data_time: 0.267839 memory: 5829 loss_kpt: 0.000883 acc_pose: 0.732592 loss: 0.000883 2022/10/14 21:46:50 - mmengine - INFO - Epoch(train) [77][100/293] lr: 5.000000e-04 eta: 16:43:59 time: 2.180096 data_time: 0.056943 memory: 5829 loss_kpt: 0.000892 acc_pose: 0.784905 loss: 0.000892 2022/10/14 21:48:27 - mmengine - INFO - Epoch(train) [77][150/293] lr: 5.000000e-04 eta: 16:43:17 time: 1.939753 data_time: 0.061084 memory: 5829 loss_kpt: 0.000870 acc_pose: 0.759743 loss: 0.000870 2022/10/14 21:50:22 - mmengine - INFO - Epoch(train) [77][200/293] lr: 5.000000e-04 eta: 16:43:07 time: 2.314556 data_time: 0.061608 memory: 5829 loss_kpt: 0.000875 acc_pose: 0.741052 loss: 0.000875 2022/10/14 21:52:03 - mmengine - INFO - Epoch(train) [77][250/293] lr: 5.000000e-04 eta: 16:42:30 time: 2.003746 data_time: 0.079775 memory: 5829 loss_kpt: 0.000894 acc_pose: 0.679620 loss: 0.000894 2022/10/14 21:53:22 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 21:54:57 - mmengine - INFO - Epoch(train) [78][50/293] lr: 5.000000e-04 eta: 16:38:43 time: 1.888249 data_time: 0.169552 memory: 5829 loss_kpt: 0.000863 acc_pose: 0.696747 loss: 0.000863 2022/10/14 21:56:36 - mmengine - INFO - Epoch(train) [78][100/293] lr: 5.000000e-04 eta: 16:38:04 time: 1.986593 data_time: 0.060986 memory: 5829 loss_kpt: 0.000882 acc_pose: 0.768682 loss: 0.000882 2022/10/14 21:58:21 - mmengine - INFO - Epoch(train) [78][150/293] lr: 5.000000e-04 eta: 16:37:35 time: 2.103715 data_time: 0.071668 memory: 5829 loss_kpt: 0.000865 acc_pose: 0.782578 loss: 0.000865 2022/10/14 22:00:03 - mmengine - INFO - Epoch(train) [78][200/293] lr: 5.000000e-04 eta: 16:37:01 time: 2.044248 data_time: 0.065429 memory: 5829 loss_kpt: 0.000875 acc_pose: 0.741945 loss: 0.000875 2022/10/14 22:01:44 - mmengine - INFO - Epoch(train) [78][250/293] lr: 5.000000e-04 eta: 16:36:24 time: 2.018319 data_time: 0.123563 memory: 5829 loss_kpt: 0.000884 acc_pose: 0.728447 loss: 0.000884 2022/10/14 22:03:12 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 22:05:01 - mmengine - INFO - Epoch(train) [79][50/293] lr: 5.000000e-04 eta: 16:33:02 time: 2.173943 data_time: 0.203322 memory: 5829 loss_kpt: 0.000868 acc_pose: 0.740393 loss: 0.000868 2022/10/14 22:06:41 - mmengine - INFO - Epoch(train) [79][100/293] lr: 5.000000e-04 eta: 16:32:23 time: 2.000679 data_time: 0.114654 memory: 5829 loss_kpt: 0.000896 acc_pose: 0.679936 loss: 0.000896 2022/10/14 22:08:12 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 22:08:20 - mmengine - INFO - Epoch(train) [79][150/293] lr: 5.000000e-04 eta: 16:31:42 time: 1.982245 data_time: 1.438185 memory: 5829 loss_kpt: 0.000887 acc_pose: 0.737980 loss: 0.000887 2022/10/14 22:10:01 - mmengine - INFO - Epoch(train) [79][200/293] lr: 5.000000e-04 eta: 16:31:04 time: 2.009544 data_time: 1.821141 memory: 5829 loss_kpt: 0.000878 acc_pose: 0.753467 loss: 0.000878 2022/10/14 22:11:53 - mmengine - INFO - Epoch(train) [79][250/293] lr: 5.000000e-04 eta: 16:30:46 time: 2.253690 data_time: 1.096508 memory: 5829 loss_kpt: 0.000873 acc_pose: 0.767061 loss: 0.000873 2022/10/14 22:13:18 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 22:15:05 - mmengine - INFO - Epoch(train) [80][50/293] lr: 5.000000e-04 eta: 16:27:20 time: 2.130195 data_time: 0.770808 memory: 5829 loss_kpt: 0.000878 acc_pose: 0.738770 loss: 0.000878 2022/10/14 22:16:43 - mmengine - INFO - Epoch(train) [80][100/293] lr: 5.000000e-04 eta: 16:26:37 time: 1.964874 data_time: 0.058676 memory: 5829 loss_kpt: 0.000880 acc_pose: 0.771851 loss: 0.000880 2022/10/14 22:18:27 - mmengine - INFO - Epoch(train) [80][150/293] lr: 5.000000e-04 eta: 16:26:04 time: 2.076878 data_time: 0.910507 memory: 5829 loss_kpt: 0.000871 acc_pose: 0.680154 loss: 0.000871 2022/10/14 22:20:05 - mmengine - INFO - Epoch(train) [80][200/293] lr: 5.000000e-04 eta: 16:25:19 time: 1.950346 data_time: 1.742262 memory: 5829 loss_kpt: 0.000873 acc_pose: 0.710876 loss: 0.000873 2022/10/14 22:21:37 - mmengine - INFO - Epoch(train) [80][250/293] lr: 5.000000e-04 eta: 16:24:26 time: 1.850884 data_time: 1.614986 memory: 5829 loss_kpt: 0.000883 acc_pose: 0.755784 loss: 0.000883 2022/10/14 22:22:53 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 22:22:53 - mmengine - INFO - Saving checkpoint at 80 epochs 2022/10/14 22:23:52 - mmengine - INFO - Epoch(val) [80][50/407] eta: 0:06:44 time: 1.132575 data_time: 1.096588 memory: 5829 2022/10/14 22:24:46 - mmengine - INFO - Epoch(val) [80][100/407] eta: 0:05:28 time: 1.070971 data_time: 1.033955 memory: 540 2022/10/14 22:25:38 - mmengine - INFO - Epoch(val) [80][150/407] eta: 0:04:29 time: 1.049102 data_time: 1.011514 memory: 540 2022/10/14 22:26:32 - mmengine - INFO - Epoch(val) [80][200/407] eta: 0:03:43 time: 1.080405 data_time: 1.044377 memory: 540 2022/10/14 22:27:21 - mmengine - INFO - Epoch(val) [80][250/407] eta: 0:02:34 time: 0.986753 data_time: 0.950332 memory: 540 2022/10/14 22:28:23 - mmengine - INFO - Epoch(val) [80][300/407] eta: 0:02:12 time: 1.238189 data_time: 1.202760 memory: 540 2022/10/14 22:29:25 - mmengine - INFO - Epoch(val) [80][350/407] eta: 0:01:10 time: 1.229278 data_time: 1.193674 memory: 540 2022/10/14 22:30:17 - mmengine - INFO - Epoch(val) [80][400/407] eta: 0:00:07 time: 1.036698 data_time: 1.000967 memory: 540 2022/10/14 22:31:35 - mmengine - INFO - Evaluating CocoMetric... 2022/10/14 22:31:48 - mmengine - INFO - Epoch(val) [80][407/407] coco/AP: 0.608640 coco/AP .5: 0.857834 coco/AP .75: 0.680753 coco/AP (M): 0.575849 coco/AP (L): 0.671343 coco/AR: 0.673567 coco/AR .5: 0.903967 coco/AR .75: 0.743703 coco/AR (M): 0.629910 coco/AR (L): 0.735377 2022/10/14 22:31:48 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256/best_coco/AP_epoch_70.pth is removed 2022/10/14 22:31:50 - mmengine - INFO - The best checkpoint with 0.6086 coco/AP at 80 epoch is saved to best_coco/AP_epoch_80.pth. 2022/10/14 22:33:57 - mmengine - INFO - Epoch(train) [81][50/293] lr: 5.000000e-04 eta: 16:21:36 time: 2.550307 data_time: 2.364095 memory: 5829 loss_kpt: 0.000857 acc_pose: 0.706542 loss: 0.000857 2022/10/14 22:35:50 - mmengine - INFO - Epoch(train) [81][100/293] lr: 5.000000e-04 eta: 16:21:15 time: 2.252823 data_time: 1.109577 memory: 5829 loss_kpt: 0.000858 acc_pose: 0.712252 loss: 0.000858 2022/10/14 22:37:44 - mmengine - INFO - Epoch(train) [81][150/293] lr: 5.000000e-04 eta: 16:20:56 time: 2.279108 data_time: 0.889660 memory: 5829 loss_kpt: 0.000867 acc_pose: 0.745510 loss: 0.000867 2022/10/14 22:39:30 - mmengine - INFO - Epoch(train) [81][200/293] lr: 5.000000e-04 eta: 16:20:25 time: 2.127467 data_time: 1.326054 memory: 5829 loss_kpt: 0.000867 acc_pose: 0.774195 loss: 0.000867 2022/10/14 22:41:17 - mmengine - INFO - Epoch(train) [81][250/293] lr: 5.000000e-04 eta: 16:19:53 time: 2.128819 data_time: 1.774938 memory: 5829 loss_kpt: 0.000869 acc_pose: 0.743073 loss: 0.000869 2022/10/14 22:42:42 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 22:44:28 - mmengine - INFO - Epoch(train) [82][50/293] lr: 5.000000e-04 eta: 16:16:28 time: 2.124976 data_time: 1.399547 memory: 5829 loss_kpt: 0.000872 acc_pose: 0.715893 loss: 0.000872 2022/10/14 22:46:13 - mmengine - INFO - Epoch(train) [82][100/293] lr: 5.000000e-04 eta: 16:15:52 time: 2.084418 data_time: 1.614423 memory: 5829 loss_kpt: 0.000886 acc_pose: 0.689348 loss: 0.000886 2022/10/14 22:48:02 - mmengine - INFO - Epoch(train) [82][150/293] lr: 5.000000e-04 eta: 16:15:25 time: 2.189478 data_time: 0.434781 memory: 5829 loss_kpt: 0.000858 acc_pose: 0.721449 loss: 0.000858 2022/10/14 22:49:38 - mmengine - INFO - Epoch(train) [82][200/293] lr: 5.000000e-04 eta: 16:14:35 time: 1.912315 data_time: 0.093425 memory: 5829 loss_kpt: 0.000886 acc_pose: 0.700016 loss: 0.000886 2022/10/14 22:51:20 - mmengine - INFO - Epoch(train) [82][250/293] lr: 5.000000e-04 eta: 16:13:56 time: 2.048888 data_time: 1.341254 memory: 5829 loss_kpt: 0.000852 acc_pose: 0.773565 loss: 0.000852 2022/10/14 22:51:58 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 22:52:51 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 22:54:32 - mmengine - INFO - Epoch(train) [83][50/293] lr: 5.000000e-04 eta: 16:10:23 time: 2.011792 data_time: 1.229615 memory: 5829 loss_kpt: 0.000873 acc_pose: 0.710560 loss: 0.000873 2022/10/14 22:56:14 - mmengine - INFO - Epoch(train) [83][100/293] lr: 5.000000e-04 eta: 16:09:43 time: 2.051007 data_time: 1.812204 memory: 5829 loss_kpt: 0.000875 acc_pose: 0.738114 loss: 0.000875 2022/10/14 22:58:10 - mmengine - INFO - Epoch(train) [83][150/293] lr: 5.000000e-04 eta: 16:09:23 time: 2.303428 data_time: 0.637277 memory: 5829 loss_kpt: 0.000876 acc_pose: 0.693536 loss: 0.000876 2022/10/14 23:00:00 - mmengine - INFO - Epoch(train) [83][200/293] lr: 5.000000e-04 eta: 16:08:56 time: 2.206793 data_time: 0.124264 memory: 5829 loss_kpt: 0.000873 acc_pose: 0.727861 loss: 0.000873 2022/10/14 23:01:43 - mmengine - INFO - Epoch(train) [83][250/293] lr: 5.000000e-04 eta: 16:08:17 time: 2.064735 data_time: 0.157427 memory: 5829 loss_kpt: 0.000865 acc_pose: 0.679783 loss: 0.000865 2022/10/14 23:03:09 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 23:04:52 - mmengine - INFO - Epoch(train) [84][50/293] lr: 5.000000e-04 eta: 16:04:46 time: 2.046623 data_time: 1.258767 memory: 5829 loss_kpt: 0.000880 acc_pose: 0.696934 loss: 0.000880 2022/10/14 23:06:39 - mmengine - INFO - Epoch(train) [84][100/293] lr: 5.000000e-04 eta: 16:04:12 time: 2.136187 data_time: 0.771548 memory: 5829 loss_kpt: 0.000874 acc_pose: 0.639855 loss: 0.000874 2022/10/14 23:08:20 - mmengine - INFO - Epoch(train) [84][150/293] lr: 5.000000e-04 eta: 16:03:30 time: 2.021860 data_time: 1.840965 memory: 5829 loss_kpt: 0.000887 acc_pose: 0.716218 loss: 0.000887 2022/10/14 23:10:11 - mmengine - INFO - Epoch(train) [84][200/293] lr: 5.000000e-04 eta: 16:03:02 time: 2.222561 data_time: 0.569607 memory: 5829 loss_kpt: 0.000886 acc_pose: 0.743645 loss: 0.000886 2022/10/14 23:11:54 - mmengine - INFO - Epoch(train) [84][250/293] lr: 5.000000e-04 eta: 16:02:21 time: 2.061319 data_time: 1.207556 memory: 5829 loss_kpt: 0.000878 acc_pose: 0.678606 loss: 0.000878 2022/10/14 23:13:21 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 23:15:07 - mmengine - INFO - Epoch(train) [85][50/293] lr: 5.000000e-04 eta: 15:58:57 time: 2.112563 data_time: 1.836444 memory: 5829 loss_kpt: 0.000869 acc_pose: 0.719026 loss: 0.000869 2022/10/14 23:16:53 - mmengine - INFO - Epoch(train) [85][100/293] lr: 5.000000e-04 eta: 15:58:21 time: 2.127582 data_time: 1.656598 memory: 5829 loss_kpt: 0.000881 acc_pose: 0.775104 loss: 0.000881 2022/10/14 23:18:39 - mmengine - INFO - Epoch(train) [85][150/293] lr: 5.000000e-04 eta: 15:57:44 time: 2.116711 data_time: 0.061430 memory: 5829 loss_kpt: 0.000856 acc_pose: 0.683521 loss: 0.000856 2022/10/14 23:20:34 - mmengine - INFO - Epoch(train) [85][200/293] lr: 5.000000e-04 eta: 15:57:21 time: 2.304114 data_time: 0.075078 memory: 5829 loss_kpt: 0.000864 acc_pose: 0.695236 loss: 0.000864 2022/10/14 23:22:24 - mmengine - INFO - Epoch(train) [85][250/293] lr: 5.000000e-04 eta: 15:56:49 time: 2.194556 data_time: 0.185314 memory: 5829 loss_kpt: 0.000873 acc_pose: 0.746724 loss: 0.000873 2022/10/14 23:23:56 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 23:25:50 - mmengine - INFO - Epoch(train) [86][50/293] lr: 5.000000e-04 eta: 15:53:36 time: 2.270568 data_time: 0.510065 memory: 5829 loss_kpt: 0.000879 acc_pose: 0.765050 loss: 0.000879 2022/10/14 23:27:22 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 23:27:30 - mmengine - INFO - Epoch(train) [86][100/293] lr: 5.000000e-04 eta: 15:52:51 time: 2.010545 data_time: 0.057822 memory: 5829 loss_kpt: 0.000878 acc_pose: 0.769331 loss: 0.000878 2022/10/14 23:29:14 - mmengine - INFO - Epoch(train) [86][150/293] lr: 5.000000e-04 eta: 15:52:09 time: 2.065566 data_time: 0.438931 memory: 5829 loss_kpt: 0.000874 acc_pose: 0.687188 loss: 0.000874 2022/10/14 23:31:03 - mmengine - INFO - Epoch(train) [86][200/293] lr: 5.000000e-04 eta: 15:51:35 time: 2.186967 data_time: 0.445888 memory: 5829 loss_kpt: 0.000890 acc_pose: 0.778791 loss: 0.000890 2022/10/14 23:32:51 - mmengine - INFO - Epoch(train) [86][250/293] lr: 5.000000e-04 eta: 15:50:59 time: 2.152762 data_time: 0.141493 memory: 5829 loss_kpt: 0.000873 acc_pose: 0.716750 loss: 0.000873 2022/10/14 23:34:08 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 23:35:49 - mmengine - INFO - Epoch(train) [87][50/293] lr: 5.000000e-04 eta: 15:47:29 time: 2.014918 data_time: 0.342435 memory: 5829 loss_kpt: 0.000859 acc_pose: 0.801619 loss: 0.000859 2022/10/14 23:37:30 - mmengine - INFO - Epoch(train) [87][100/293] lr: 5.000000e-04 eta: 15:46:43 time: 2.016258 data_time: 0.679416 memory: 5829 loss_kpt: 0.000886 acc_pose: 0.745874 loss: 0.000886 2022/10/14 23:39:11 - mmengine - INFO - Epoch(train) [87][150/293] lr: 5.000000e-04 eta: 15:45:56 time: 2.019150 data_time: 1.108816 memory: 5829 loss_kpt: 0.000869 acc_pose: 0.743268 loss: 0.000869 2022/10/14 23:40:49 - mmengine - INFO - Epoch(train) [87][200/293] lr: 5.000000e-04 eta: 15:45:06 time: 1.958307 data_time: 0.334997 memory: 5829 loss_kpt: 0.000861 acc_pose: 0.709343 loss: 0.000861 2022/10/14 23:42:27 - mmengine - INFO - Epoch(train) [87][250/293] lr: 5.000000e-04 eta: 15:44:16 time: 1.971289 data_time: 0.231864 memory: 5829 loss_kpt: 0.000863 acc_pose: 0.772827 loss: 0.000863 2022/10/14 23:43:48 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 23:45:38 - mmengine - INFO - Epoch(train) [88][50/293] lr: 5.000000e-04 eta: 15:40:58 time: 2.190847 data_time: 0.234646 memory: 5829 loss_kpt: 0.000875 acc_pose: 0.710850 loss: 0.000875 2022/10/14 23:47:14 - mmengine - INFO - Epoch(train) [88][100/293] lr: 5.000000e-04 eta: 15:40:05 time: 1.923175 data_time: 0.064912 memory: 5829 loss_kpt: 0.000860 acc_pose: 0.754368 loss: 0.000860 2022/10/14 23:49:04 - mmengine - INFO - Epoch(train) [88][150/293] lr: 5.000000e-04 eta: 15:39:30 time: 2.195936 data_time: 0.213868 memory: 5829 loss_kpt: 0.000873 acc_pose: 0.734825 loss: 0.000873 2022/10/14 23:50:48 - mmengine - INFO - Epoch(train) [88][200/293] lr: 5.000000e-04 eta: 15:38:47 time: 2.085937 data_time: 1.181820 memory: 5829 loss_kpt: 0.000862 acc_pose: 0.730841 loss: 0.000862 2022/10/14 23:52:45 - mmengine - INFO - Epoch(train) [88][250/293] lr: 5.000000e-04 eta: 15:38:22 time: 2.332372 data_time: 0.068850 memory: 5829 loss_kpt: 0.000869 acc_pose: 0.800942 loss: 0.000869 2022/10/14 23:54:10 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/14 23:55:56 - mmengine - INFO - Epoch(train) [89][50/293] lr: 5.000000e-04 eta: 15:35:00 time: 2.117152 data_time: 0.173743 memory: 5829 loss_kpt: 0.000863 acc_pose: 0.744930 loss: 0.000863 2022/10/14 23:57:38 - mmengine - INFO - Epoch(train) [89][100/293] lr: 5.000000e-04 eta: 15:34:14 time: 2.052471 data_time: 0.062727 memory: 5829 loss_kpt: 0.000853 acc_pose: 0.699433 loss: 0.000853 2022/10/14 23:59:37 - mmengine - INFO - Epoch(train) [89][150/293] lr: 5.000000e-04 eta: 15:33:50 time: 2.371580 data_time: 0.939708 memory: 5829 loss_kpt: 0.000866 acc_pose: 0.732423 loss: 0.000866 2022/10/15 00:01:15 - mmengine - INFO - Epoch(train) [89][200/293] lr: 5.000000e-04 eta: 15:32:58 time: 1.958571 data_time: 0.492685 memory: 5829 loss_kpt: 0.000868 acc_pose: 0.705129 loss: 0.000868 2022/10/15 00:01:51 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 00:02:58 - mmengine - INFO - Epoch(train) [89][250/293] lr: 5.000000e-04 eta: 15:32:13 time: 2.064840 data_time: 0.066774 memory: 5829 loss_kpt: 0.000860 acc_pose: 0.678699 loss: 0.000860 2022/10/15 00:04:37 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 00:06:25 - mmengine - INFO - Epoch(train) [90][50/293] lr: 5.000000e-04 eta: 15:28:54 time: 2.164448 data_time: 0.211659 memory: 5829 loss_kpt: 0.000869 acc_pose: 0.752645 loss: 0.000869 2022/10/15 00:08:12 - mmengine - INFO - Epoch(train) [90][100/293] lr: 5.000000e-04 eta: 15:28:13 time: 2.139225 data_time: 0.069037 memory: 5829 loss_kpt: 0.000875 acc_pose: 0.710428 loss: 0.000875 2022/10/15 00:10:05 - mmengine - INFO - Epoch(train) [90][150/293] lr: 5.000000e-04 eta: 15:27:41 time: 2.256842 data_time: 0.066522 memory: 5829 loss_kpt: 0.000863 acc_pose: 0.759310 loss: 0.000863 2022/10/15 00:11:53 - mmengine - INFO - Epoch(train) [90][200/293] lr: 5.000000e-04 eta: 15:27:02 time: 2.174485 data_time: 0.058807 memory: 5829 loss_kpt: 0.000883 acc_pose: 0.759022 loss: 0.000883 2022/10/15 00:13:46 - mmengine - INFO - Epoch(train) [90][250/293] lr: 5.000000e-04 eta: 15:26:28 time: 2.245013 data_time: 0.063542 memory: 5829 loss_kpt: 0.000871 acc_pose: 0.762707 loss: 0.000871 2022/10/15 00:15:14 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 00:15:14 - mmengine - INFO - Saving checkpoint at 90 epochs 2022/10/15 00:16:05 - mmengine - INFO - Epoch(val) [90][50/407] eta: 0:05:50 time: 0.982542 data_time: 0.945307 memory: 5829 2022/10/15 00:16:55 - mmengine - INFO - Epoch(val) [90][100/407] eta: 0:05:08 time: 1.004287 data_time: 0.968209 memory: 540 2022/10/15 00:17:45 - mmengine - INFO - Epoch(val) [90][150/407] eta: 0:04:16 time: 0.997874 data_time: 0.961470 memory: 540 2022/10/15 00:18:42 - mmengine - INFO - Epoch(val) [90][200/407] eta: 0:03:53 time: 1.128121 data_time: 1.091946 memory: 540 2022/10/15 00:19:33 - mmengine - INFO - Epoch(val) [90][250/407] eta: 0:02:41 time: 1.029745 data_time: 0.992549 memory: 540 2022/10/15 00:20:31 - mmengine - INFO - Epoch(val) [90][300/407] eta: 0:02:03 time: 1.155594 data_time: 1.119118 memory: 540 2022/10/15 00:21:23 - mmengine - INFO - Epoch(val) [90][350/407] eta: 0:00:59 time: 1.049665 data_time: 1.013363 memory: 540 2022/10/15 00:22:17 - mmengine - INFO - Epoch(val) [90][400/407] eta: 0:00:07 time: 1.077617 data_time: 1.042112 memory: 540 2022/10/15 00:23:48 - mmengine - INFO - Evaluating CocoMetric... 2022/10/15 00:24:02 - mmengine - INFO - Epoch(val) [90][407/407] coco/AP: 0.616532 coco/AP .5: 0.860692 coco/AP .75: 0.688602 coco/AP (M): 0.582370 coco/AP (L): 0.679599 coco/AR: 0.678589 coco/AR .5: 0.905384 coco/AR .75: 0.744175 coco/AR (M): 0.635564 coco/AR (L): 0.739614 2022/10/15 00:24:02 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256/best_coco/AP_epoch_80.pth is removed 2022/10/15 00:24:04 - mmengine - INFO - The best checkpoint with 0.6165 coco/AP at 90 epoch is saved to best_coco/AP_epoch_90.pth. 2022/10/15 00:25:56 - mmengine - INFO - Epoch(train) [91][50/293] lr: 5.000000e-04 eta: 15:23:15 time: 2.244227 data_time: 0.523521 memory: 5829 loss_kpt: 0.000865 acc_pose: 0.749953 loss: 0.000865 2022/10/15 00:27:52 - mmengine - INFO - Epoch(train) [91][100/293] lr: 5.000000e-04 eta: 15:22:45 time: 2.316864 data_time: 0.423875 memory: 5829 loss_kpt: 0.000874 acc_pose: 0.724716 loss: 0.000874 2022/10/15 00:29:53 - mmengine - INFO - Epoch(train) [91][150/293] lr: 5.000000e-04 eta: 15:22:21 time: 2.408612 data_time: 0.056353 memory: 5829 loss_kpt: 0.000891 acc_pose: 0.704842 loss: 0.000891 2022/10/15 00:31:43 - mmengine - INFO - Epoch(train) [91][200/293] lr: 5.000000e-04 eta: 15:21:43 time: 2.207846 data_time: 0.060735 memory: 5829 loss_kpt: 0.000859 acc_pose: 0.716009 loss: 0.000859 2022/10/15 00:33:39 - mmengine - INFO - Epoch(train) [91][250/293] lr: 5.000000e-04 eta: 15:21:12 time: 2.324815 data_time: 0.069986 memory: 5829 loss_kpt: 0.000867 acc_pose: 0.710519 loss: 0.000867 2022/10/15 00:35:20 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 00:37:07 - mmengine - INFO - Epoch(train) [92][50/293] lr: 5.000000e-04 eta: 15:17:53 time: 2.140473 data_time: 0.577935 memory: 5829 loss_kpt: 0.000861 acc_pose: 0.723657 loss: 0.000861 2022/10/15 00:38:51 - mmengine - INFO - Epoch(train) [92][100/293] lr: 5.000000e-04 eta: 15:17:06 time: 2.086703 data_time: 0.217735 memory: 5829 loss_kpt: 0.000865 acc_pose: 0.696753 loss: 0.000865 2022/10/15 00:40:33 - mmengine - INFO - Epoch(train) [92][150/293] lr: 5.000000e-04 eta: 15:16:17 time: 2.040680 data_time: 0.063327 memory: 5829 loss_kpt: 0.000861 acc_pose: 0.765099 loss: 0.000861 2022/10/15 00:42:22 - mmengine - INFO - Epoch(train) [92][200/293] lr: 5.000000e-04 eta: 15:15:35 time: 2.168660 data_time: 0.133035 memory: 5829 loss_kpt: 0.000857 acc_pose: 0.744527 loss: 0.000857 2022/10/15 00:44:17 - mmengine - INFO - Epoch(train) [92][250/293] lr: 5.000000e-04 eta: 15:15:03 time: 2.309344 data_time: 0.911276 memory: 5829 loss_kpt: 0.000855 acc_pose: 0.731333 loss: 0.000855 2022/10/15 00:45:45 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 00:47:22 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 00:47:36 - mmengine - INFO - Epoch(train) [93][50/293] lr: 5.000000e-04 eta: 15:11:48 time: 2.202453 data_time: 0.149997 memory: 5829 loss_kpt: 0.000855 acc_pose: 0.802156 loss: 0.000855 2022/10/15 00:49:24 - mmengine - INFO - Epoch(train) [93][100/293] lr: 5.000000e-04 eta: 15:11:06 time: 2.171541 data_time: 0.221048 memory: 5829 loss_kpt: 0.000857 acc_pose: 0.706424 loss: 0.000857 2022/10/15 00:51:25 - mmengine - INFO - Epoch(train) [93][150/293] lr: 5.000000e-04 eta: 15:10:40 time: 2.425507 data_time: 0.068688 memory: 5829 loss_kpt: 0.000869 acc_pose: 0.733833 loss: 0.000869 2022/10/15 00:53:01 - mmengine - INFO - Epoch(train) [93][200/293] lr: 5.000000e-04 eta: 15:09:42 time: 1.921721 data_time: 0.058165 memory: 5829 loss_kpt: 0.000867 acc_pose: 0.711913 loss: 0.000867 2022/10/15 00:54:54 - mmengine - INFO - Epoch(train) [93][250/293] lr: 5.000000e-04 eta: 15:09:04 time: 2.245533 data_time: 0.065261 memory: 5829 loss_kpt: 0.000863 acc_pose: 0.712341 loss: 0.000863 2022/10/15 00:56:29 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 00:58:36 - mmengine - INFO - Epoch(train) [94][50/293] lr: 5.000000e-04 eta: 15:06:09 time: 2.523576 data_time: 0.170937 memory: 5829 loss_kpt: 0.000862 acc_pose: 0.699301 loss: 0.000862 2022/10/15 01:00:31 - mmengine - INFO - Epoch(train) [94][100/293] lr: 5.000000e-04 eta: 15:05:34 time: 2.310292 data_time: 0.064421 memory: 5829 loss_kpt: 0.000866 acc_pose: 0.737855 loss: 0.000866 2022/10/15 01:02:33 - mmengine - INFO - Epoch(train) [94][150/293] lr: 5.000000e-04 eta: 15:05:08 time: 2.444998 data_time: 0.063601 memory: 5829 loss_kpt: 0.000860 acc_pose: 0.747390 loss: 0.000860 2022/10/15 01:04:39 - mmengine - INFO - Epoch(train) [94][200/293] lr: 5.000000e-04 eta: 15:04:45 time: 2.505481 data_time: 0.067520 memory: 5829 loss_kpt: 0.000857 acc_pose: 0.763406 loss: 0.000857 2022/10/15 01:06:40 - mmengine - INFO - Epoch(train) [94][250/293] lr: 5.000000e-04 eta: 15:04:18 time: 2.431408 data_time: 0.065874 memory: 5829 loss_kpt: 0.000847 acc_pose: 0.737012 loss: 0.000847 2022/10/15 01:08:16 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 01:10:11 - mmengine - INFO - Epoch(train) [95][50/293] lr: 5.000000e-04 eta: 15:01:08 time: 2.297602 data_time: 0.192672 memory: 5829 loss_kpt: 0.000845 acc_pose: 0.674443 loss: 0.000845 2022/10/15 01:12:04 - mmengine - INFO - Epoch(train) [95][100/293] lr: 5.000000e-04 eta: 15:00:30 time: 2.263331 data_time: 0.063046 memory: 5829 loss_kpt: 0.000859 acc_pose: 0.725410 loss: 0.000859 2022/10/15 01:13:51 - mmengine - INFO - Epoch(train) [95][150/293] lr: 5.000000e-04 eta: 14:59:43 time: 2.137263 data_time: 0.073764 memory: 5829 loss_kpt: 0.000870 acc_pose: 0.701109 loss: 0.000870 2022/10/15 01:15:40 - mmengine - INFO - Epoch(train) [95][200/293] lr: 5.000000e-04 eta: 14:59:00 time: 2.187712 data_time: 0.059491 memory: 5829 loss_kpt: 0.000867 acc_pose: 0.712661 loss: 0.000867 2022/10/15 01:17:39 - mmengine - INFO - Epoch(train) [95][250/293] lr: 5.000000e-04 eta: 14:58:27 time: 2.370668 data_time: 0.059921 memory: 5829 loss_kpt: 0.000863 acc_pose: 0.730378 loss: 0.000863 2022/10/15 01:19:17 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 01:21:14 - mmengine - INFO - Epoch(train) [96][50/293] lr: 5.000000e-04 eta: 14:55:20 time: 2.345798 data_time: 0.160508 memory: 5829 loss_kpt: 0.000863 acc_pose: 0.715462 loss: 0.000863 2022/10/15 01:23:06 - mmengine - INFO - Epoch(train) [96][100/293] lr: 5.000000e-04 eta: 14:54:39 time: 2.235223 data_time: 0.080230 memory: 5829 loss_kpt: 0.000860 acc_pose: 0.732835 loss: 0.000860 2022/10/15 01:25:01 - mmengine - INFO - Epoch(train) [96][150/293] lr: 5.000000e-04 eta: 14:54:02 time: 2.313029 data_time: 0.113320 memory: 5829 loss_kpt: 0.000872 acc_pose: 0.721159 loss: 0.000872 2022/10/15 01:25:37 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 01:26:49 - mmengine - INFO - Epoch(train) [96][200/293] lr: 5.000000e-04 eta: 14:53:15 time: 2.157413 data_time: 0.097974 memory: 5829 loss_kpt: 0.000875 acc_pose: 0.723269 loss: 0.000875 2022/10/15 01:28:39 - mmengine - INFO - Epoch(train) [96][250/293] lr: 5.000000e-04 eta: 14:52:31 time: 2.200970 data_time: 0.066863 memory: 5829 loss_kpt: 0.000854 acc_pose: 0.761223 loss: 0.000854 2022/10/15 01:30:07 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 01:31:54 - mmengine - INFO - Epoch(train) [97][50/293] lr: 5.000000e-04 eta: 14:49:13 time: 2.140555 data_time: 0.872789 memory: 5829 loss_kpt: 0.000866 acc_pose: 0.765935 loss: 0.000866 2022/10/15 01:33:47 - mmengine - INFO - Epoch(train) [97][100/293] lr: 5.000000e-04 eta: 14:48:32 time: 2.265734 data_time: 0.068640 memory: 5829 loss_kpt: 0.000847 acc_pose: 0.737455 loss: 0.000847 2022/10/15 01:35:39 - mmengine - INFO - Epoch(train) [97][150/293] lr: 5.000000e-04 eta: 14:47:50 time: 2.248210 data_time: 0.067193 memory: 5829 loss_kpt: 0.000848 acc_pose: 0.752280 loss: 0.000848 2022/10/15 01:37:29 - mmengine - INFO - Epoch(train) [97][200/293] lr: 5.000000e-04 eta: 14:47:04 time: 2.185934 data_time: 0.065901 memory: 5829 loss_kpt: 0.000859 acc_pose: 0.752969 loss: 0.000859 2022/10/15 01:39:23 - mmengine - INFO - Epoch(train) [97][250/293] lr: 5.000000e-04 eta: 14:46:24 time: 2.293508 data_time: 0.072631 memory: 5829 loss_kpt: 0.000858 acc_pose: 0.730715 loss: 0.000858 2022/10/15 01:40:55 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 01:42:55 - mmengine - INFO - Epoch(train) [98][50/293] lr: 5.000000e-04 eta: 14:43:21 time: 2.402391 data_time: 0.338468 memory: 5829 loss_kpt: 0.000868 acc_pose: 0.752735 loss: 0.000868 2022/10/15 01:44:40 - mmengine - INFO - Epoch(train) [98][100/293] lr: 5.000000e-04 eta: 14:42:30 time: 2.096606 data_time: 0.061009 memory: 5829 loss_kpt: 0.000864 acc_pose: 0.760546 loss: 0.000864 2022/10/15 01:46:37 - mmengine - INFO - Epoch(train) [98][150/293] lr: 5.000000e-04 eta: 14:41:52 time: 2.336019 data_time: 0.070788 memory: 5829 loss_kpt: 0.000861 acc_pose: 0.727482 loss: 0.000861 2022/10/15 01:48:32 - mmengine - INFO - Epoch(train) [98][200/293] lr: 5.000000e-04 eta: 14:41:12 time: 2.308693 data_time: 0.069981 memory: 5829 loss_kpt: 0.000848 acc_pose: 0.757459 loss: 0.000848 2022/10/15 01:50:33 - mmengine - INFO - Epoch(train) [98][250/293] lr: 5.000000e-04 eta: 14:40:38 time: 2.421390 data_time: 0.092640 memory: 5829 loss_kpt: 0.000849 acc_pose: 0.758079 loss: 0.000849 2022/10/15 01:52:06 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 01:54:00 - mmengine - INFO - Epoch(train) [99][50/293] lr: 5.000000e-04 eta: 14:37:28 time: 2.272131 data_time: 0.154426 memory: 5829 loss_kpt: 0.000852 acc_pose: 0.736343 loss: 0.000852 2022/10/15 01:55:48 - mmengine - INFO - Epoch(train) [99][100/293] lr: 5.000000e-04 eta: 14:36:39 time: 2.159117 data_time: 0.092213 memory: 5829 loss_kpt: 0.000857 acc_pose: 0.723443 loss: 0.000857 2022/10/15 01:57:39 - mmengine - INFO - Epoch(train) [99][150/293] lr: 5.000000e-04 eta: 14:35:53 time: 2.220439 data_time: 0.082058 memory: 5829 loss_kpt: 0.000863 acc_pose: 0.744803 loss: 0.000863 2022/10/15 01:59:33 - mmengine - INFO - Epoch(train) [99][200/293] lr: 5.000000e-04 eta: 14:35:10 time: 2.276043 data_time: 0.064746 memory: 5829 loss_kpt: 0.000864 acc_pose: 0.717339 loss: 0.000864 2022/10/15 02:01:17 - mmengine - INFO - Epoch(train) [99][250/293] lr: 5.000000e-04 eta: 14:34:16 time: 2.075703 data_time: 0.063770 memory: 5829 loss_kpt: 0.000858 acc_pose: 0.785704 loss: 0.000858 2022/10/15 02:02:32 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 02:02:46 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 02:04:32 - mmengine - INFO - Epoch(train) [100][50/293] lr: 5.000000e-04 eta: 14:30:57 time: 2.122918 data_time: 0.181963 memory: 5829 loss_kpt: 0.000868 acc_pose: 0.742715 loss: 0.000868 2022/10/15 02:06:24 - mmengine - INFO - Epoch(train) [100][100/293] lr: 5.000000e-04 eta: 14:30:12 time: 2.241711 data_time: 0.067190 memory: 5829 loss_kpt: 0.000873 acc_pose: 0.783775 loss: 0.000873 2022/10/15 02:08:14 - mmengine - INFO - Epoch(train) [100][150/293] lr: 5.000000e-04 eta: 14:29:23 time: 2.184924 data_time: 0.063780 memory: 5829 loss_kpt: 0.000874 acc_pose: 0.768371 loss: 0.000874 2022/10/15 02:09:53 - mmengine - INFO - Epoch(train) [100][200/293] lr: 5.000000e-04 eta: 14:28:24 time: 1.991667 data_time: 0.056878 memory: 5829 loss_kpt: 0.000841 acc_pose: 0.677282 loss: 0.000841 2022/10/15 02:11:37 - mmengine - INFO - Epoch(train) [100][250/293] lr: 5.000000e-04 eta: 14:27:29 time: 2.071476 data_time: 0.062388 memory: 5829 loss_kpt: 0.000842 acc_pose: 0.755078 loss: 0.000842 2022/10/15 02:12:59 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 02:12:59 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/10/15 02:13:55 - mmengine - INFO - Epoch(val) [100][50/407] eta: 0:06:27 time: 1.084607 data_time: 1.047817 memory: 5829 2022/10/15 02:15:00 - mmengine - INFO - Epoch(val) [100][100/407] eta: 0:06:36 time: 1.291445 data_time: 1.254651 memory: 540 2022/10/15 02:15:57 - mmengine - INFO - Epoch(val) [100][150/407] eta: 0:04:52 time: 1.136846 data_time: 1.095524 memory: 540 2022/10/15 02:16:58 - mmengine - INFO - Epoch(val) [100][200/407] eta: 0:04:12 time: 1.222002 data_time: 1.185133 memory: 540 2022/10/15 02:17:58 - mmengine - INFO - Epoch(val) [100][250/407] eta: 0:03:09 time: 1.205168 data_time: 1.168746 memory: 540 2022/10/15 02:19:00 - mmengine - INFO - Epoch(val) [100][300/407] eta: 0:02:11 time: 1.229658 data_time: 1.193307 memory: 540 2022/10/15 02:20:03 - mmengine - INFO - Epoch(val) [100][350/407] eta: 0:01:12 time: 1.270085 data_time: 1.234226 memory: 540 2022/10/15 02:21:05 - mmengine - INFO - Epoch(val) [100][400/407] eta: 0:00:08 time: 1.230501 data_time: 1.194839 memory: 540 2022/10/15 02:21:48 - mmengine - INFO - Evaluating CocoMetric... 2022/10/15 02:22:02 - mmengine - INFO - Epoch(val) [100][407/407] coco/AP: 0.616610 coco/AP .5: 0.860208 coco/AP .75: 0.689396 coco/AP (M): 0.582129 coco/AP (L): 0.679372 coco/AR: 0.678936 coco/AR .5: 0.902865 coco/AR .75: 0.747639 coco/AR (M): 0.634854 coco/AR (L): 0.741434 2022/10/15 02:22:02 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256/best_coco/AP_epoch_90.pth is removed 2022/10/15 02:22:03 - mmengine - INFO - The best checkpoint with 0.6166 coco/AP at 100 epoch is saved to best_coco/AP_epoch_100.pth. 2022/10/15 02:24:07 - mmengine - INFO - Epoch(train) [101][50/293] lr: 5.000000e-04 eta: 14:24:29 time: 2.466353 data_time: 0.414862 memory: 5829 loss_kpt: 0.000861 acc_pose: 0.724432 loss: 0.000861 2022/10/15 02:26:02 - mmengine - INFO - Epoch(train) [101][100/293] lr: 5.000000e-04 eta: 14:23:46 time: 2.301673 data_time: 0.063529 memory: 5829 loss_kpt: 0.000884 acc_pose: 0.729628 loss: 0.000884 2022/10/15 02:28:00 - mmengine - INFO - Epoch(train) [101][150/293] lr: 5.000000e-04 eta: 14:23:07 time: 2.375727 data_time: 0.103043 memory: 5829 loss_kpt: 0.000851 acc_pose: 0.705972 loss: 0.000851 2022/10/15 02:29:53 - mmengine - INFO - Epoch(train) [101][200/293] lr: 5.000000e-04 eta: 14:22:21 time: 2.251069 data_time: 0.151633 memory: 5829 loss_kpt: 0.000869 acc_pose: 0.776977 loss: 0.000869 2022/10/15 02:31:48 - mmengine - INFO - Epoch(train) [101][250/293] lr: 5.000000e-04 eta: 14:21:37 time: 2.294510 data_time: 0.062851 memory: 5829 loss_kpt: 0.000868 acc_pose: 0.764105 loss: 0.000868 2022/10/15 02:33:34 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 02:35:28 - mmengine - INFO - Epoch(train) [102][50/293] lr: 5.000000e-04 eta: 14:18:28 time: 2.276531 data_time: 0.182774 memory: 5829 loss_kpt: 0.000850 acc_pose: 0.785004 loss: 0.000850 2022/10/15 02:37:31 - mmengine - INFO - Epoch(train) [102][100/293] lr: 5.000000e-04 eta: 14:17:53 time: 2.472527 data_time: 0.064905 memory: 5829 loss_kpt: 0.000850 acc_pose: 0.707887 loss: 0.000850 2022/10/15 02:39:33 - mmengine - INFO - Epoch(train) [102][150/293] lr: 5.000000e-04 eta: 14:17:16 time: 2.432391 data_time: 0.067324 memory: 5829 loss_kpt: 0.000861 acc_pose: 0.743931 loss: 0.000861 2022/10/15 02:41:25 - mmengine - INFO - Epoch(train) [102][200/293] lr: 5.000000e-04 eta: 14:16:28 time: 2.245143 data_time: 0.068607 memory: 5829 loss_kpt: 0.000862 acc_pose: 0.670267 loss: 0.000862 2022/10/15 02:43:17 - mmengine - INFO - Epoch(train) [102][250/293] lr: 5.000000e-04 eta: 14:15:39 time: 2.227900 data_time: 0.062054 memory: 5829 loss_kpt: 0.000868 acc_pose: 0.750232 loss: 0.000868 2022/10/15 02:44:53 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 02:46:44 - mmengine - INFO - Epoch(train) [103][50/293] lr: 5.000000e-04 eta: 14:12:27 time: 2.220259 data_time: 0.646759 memory: 5829 loss_kpt: 0.000839 acc_pose: 0.748595 loss: 0.000839 2022/10/15 02:48:40 - mmengine - INFO - Epoch(train) [103][100/293] lr: 5.000000e-04 eta: 14:11:43 time: 2.324927 data_time: 0.062664 memory: 5829 loss_kpt: 0.000854 acc_pose: 0.687987 loss: 0.000854 2022/10/15 02:49:10 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 02:50:25 - mmengine - INFO - Epoch(train) [103][150/293] lr: 5.000000e-04 eta: 14:10:47 time: 2.092413 data_time: 0.065498 memory: 5829 loss_kpt: 0.000862 acc_pose: 0.715869 loss: 0.000862 2022/10/15 02:52:16 - mmengine - INFO - Epoch(train) [103][200/293] lr: 5.000000e-04 eta: 14:09:57 time: 2.220229 data_time: 0.096188 memory: 5829 loss_kpt: 0.000867 acc_pose: 0.765265 loss: 0.000867 2022/10/15 02:54:04 - mmengine - INFO - Epoch(train) [103][250/293] lr: 5.000000e-04 eta: 14:09:04 time: 2.159055 data_time: 1.027198 memory: 5829 loss_kpt: 0.000859 acc_pose: 0.756972 loss: 0.000859 2022/10/15 02:55:25 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 02:57:22 - mmengine - INFO - Epoch(train) [104][50/293] lr: 5.000000e-04 eta: 14:05:58 time: 2.334177 data_time: 1.523542 memory: 5829 loss_kpt: 0.000870 acc_pose: 0.678363 loss: 0.000870 2022/10/15 02:59:15 - mmengine - INFO - Epoch(train) [104][100/293] lr: 5.000000e-04 eta: 14:05:10 time: 2.262277 data_time: 0.268480 memory: 5829 loss_kpt: 0.000859 acc_pose: 0.686928 loss: 0.000859 2022/10/15 03:01:11 - mmengine - INFO - Epoch(train) [104][150/293] lr: 5.000000e-04 eta: 14:04:24 time: 2.308006 data_time: 0.074780 memory: 5829 loss_kpt: 0.000855 acc_pose: 0.746219 loss: 0.000855 2022/10/15 03:02:56 - mmengine - INFO - Epoch(train) [104][200/293] lr: 5.000000e-04 eta: 14:03:27 time: 2.111372 data_time: 0.965913 memory: 5829 loss_kpt: 0.000847 acc_pose: 0.724517 loss: 0.000847 2022/10/15 03:04:49 - mmengine - INFO - Epoch(train) [104][250/293] lr: 5.000000e-04 eta: 14:02:38 time: 2.244368 data_time: 0.422007 memory: 5829 loss_kpt: 0.000854 acc_pose: 0.753946 loss: 0.000854 2022/10/15 03:06:20 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 03:08:15 - mmengine - INFO - Epoch(train) [105][50/293] lr: 5.000000e-04 eta: 13:59:30 time: 2.298943 data_time: 0.211869 memory: 5829 loss_kpt: 0.000849 acc_pose: 0.746223 loss: 0.000849 2022/10/15 03:10:07 - mmengine - INFO - Epoch(train) [105][100/293] lr: 5.000000e-04 eta: 13:58:39 time: 2.221251 data_time: 0.065119 memory: 5829 loss_kpt: 0.000870 acc_pose: 0.720555 loss: 0.000870 2022/10/15 03:11:55 - mmengine - INFO - Epoch(train) [105][150/293] lr: 5.000000e-04 eta: 13:57:45 time: 2.167545 data_time: 0.058885 memory: 5829 loss_kpt: 0.000866 acc_pose: 0.661500 loss: 0.000866 2022/10/15 03:13:43 - mmengine - INFO - Epoch(train) [105][200/293] lr: 5.000000e-04 eta: 13:56:50 time: 2.152395 data_time: 0.080673 memory: 5829 loss_kpt: 0.000869 acc_pose: 0.724588 loss: 0.000869 2022/10/15 03:15:29 - mmengine - INFO - Epoch(train) [105][250/293] lr: 5.000000e-04 eta: 13:55:54 time: 2.129326 data_time: 0.138902 memory: 5829 loss_kpt: 0.000855 acc_pose: 0.714492 loss: 0.000855 2022/10/15 03:17:00 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 03:18:53 - mmengine - INFO - Epoch(train) [106][50/293] lr: 5.000000e-04 eta: 13:52:44 time: 2.261125 data_time: 0.137808 memory: 5829 loss_kpt: 0.000840 acc_pose: 0.721749 loss: 0.000840 2022/10/15 03:20:39 - mmengine - INFO - Epoch(train) [106][100/293] lr: 5.000000e-04 eta: 13:51:48 time: 2.128288 data_time: 0.075808 memory: 5829 loss_kpt: 0.000867 acc_pose: 0.763279 loss: 0.000867 2022/10/15 03:22:28 - mmengine - INFO - Epoch(train) [106][150/293] lr: 5.000000e-04 eta: 13:50:54 time: 2.176885 data_time: 0.105048 memory: 5829 loss_kpt: 0.000858 acc_pose: 0.737964 loss: 0.000858 2022/10/15 03:24:17 - mmengine - INFO - Epoch(train) [106][200/293] lr: 5.000000e-04 eta: 13:50:00 time: 2.181606 data_time: 0.354102 memory: 5829 loss_kpt: 0.000846 acc_pose: 0.770623 loss: 0.000846 2022/10/15 03:25:29 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 03:26:04 - mmengine - INFO - Epoch(train) [106][250/293] lr: 5.000000e-04 eta: 13:49:03 time: 2.132234 data_time: 0.951874 memory: 5829 loss_kpt: 0.000850 acc_pose: 0.732944 loss: 0.000850 2022/10/15 03:27:34 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 03:29:27 - mmengine - INFO - Epoch(train) [107][50/293] lr: 5.000000e-04 eta: 13:45:54 time: 2.264019 data_time: 0.178447 memory: 5829 loss_kpt: 0.000866 acc_pose: 0.735387 loss: 0.000866 2022/10/15 03:31:03 - mmengine - INFO - Epoch(train) [107][100/293] lr: 5.000000e-04 eta: 13:44:47 time: 1.923926 data_time: 0.844450 memory: 5829 loss_kpt: 0.000851 acc_pose: 0.733875 loss: 0.000851 2022/10/15 03:32:49 - mmengine - INFO - Epoch(train) [107][150/293] lr: 5.000000e-04 eta: 13:43:49 time: 2.124442 data_time: 1.788205 memory: 5829 loss_kpt: 0.000852 acc_pose: 0.779412 loss: 0.000852 2022/10/15 03:34:42 - mmengine - INFO - Epoch(train) [107][200/293] lr: 5.000000e-04 eta: 13:42:58 time: 2.259137 data_time: 1.409498 memory: 5829 loss_kpt: 0.000856 acc_pose: 0.741360 loss: 0.000856 2022/10/15 03:36:25 - mmengine - INFO - Epoch(train) [107][250/293] lr: 5.000000e-04 eta: 13:41:57 time: 2.052842 data_time: 0.060981 memory: 5829 loss_kpt: 0.000865 acc_pose: 0.755803 loss: 0.000865 2022/10/15 03:37:53 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 03:39:42 - mmengine - INFO - Epoch(train) [108][50/293] lr: 5.000000e-04 eta: 13:38:44 time: 2.167481 data_time: 0.718055 memory: 5829 loss_kpt: 0.000860 acc_pose: 0.731255 loss: 0.000860 2022/10/15 03:41:25 - mmengine - INFO - Epoch(train) [108][100/293] lr: 5.000000e-04 eta: 13:37:43 time: 2.071976 data_time: 0.790430 memory: 5829 loss_kpt: 0.000860 acc_pose: 0.749131 loss: 0.000860 2022/10/15 03:43:09 - mmengine - INFO - Epoch(train) [108][150/293] lr: 5.000000e-04 eta: 13:36:43 time: 2.077178 data_time: 0.242731 memory: 5829 loss_kpt: 0.000853 acc_pose: 0.743458 loss: 0.000853 2022/10/15 03:44:59 - mmengine - INFO - Epoch(train) [108][200/293] lr: 5.000000e-04 eta: 13:35:48 time: 2.193927 data_time: 1.287278 memory: 5829 loss_kpt: 0.000830 acc_pose: 0.754648 loss: 0.000830 2022/10/15 03:46:43 - mmengine - INFO - Epoch(train) [108][250/293] lr: 5.000000e-04 eta: 13:34:48 time: 2.078246 data_time: 1.253530 memory: 5829 loss_kpt: 0.000852 acc_pose: 0.773438 loss: 0.000852 2022/10/15 03:48:19 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 03:50:22 - mmengine - INFO - Epoch(train) [109][50/293] lr: 5.000000e-04 eta: 13:31:48 time: 2.462617 data_time: 0.160039 memory: 5829 loss_kpt: 0.000860 acc_pose: 0.716475 loss: 0.000860 2022/10/15 03:51:33 - mmengine - INFO - Epoch(train) [109][100/293] lr: 5.000000e-04 eta: 13:30:17 time: 1.427757 data_time: 0.098496 memory: 5829 loss_kpt: 0.000857 acc_pose: 0.760708 loss: 0.000857 2022/10/15 03:52:27 - mmengine - INFO - Epoch(train) [109][150/293] lr: 5.000000e-04 eta: 13:28:30 time: 1.074440 data_time: 0.086259 memory: 5829 loss_kpt: 0.000847 acc_pose: 0.720019 loss: 0.000847 2022/10/15 03:53:26 - mmengine - INFO - Epoch(train) [109][200/293] lr: 5.000000e-04 eta: 13:26:47 time: 1.187785 data_time: 0.375949 memory: 5829 loss_kpt: 0.000850 acc_pose: 0.722011 loss: 0.000850 2022/10/15 03:55:02 - mmengine - INFO - Epoch(train) [109][250/293] lr: 5.000000e-04 eta: 13:25:39 time: 1.907920 data_time: 0.363426 memory: 5829 loss_kpt: 0.000872 acc_pose: 0.718043 loss: 0.000872 2022/10/15 03:56:32 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 03:58:24 - mmengine - INFO - Epoch(train) [110][50/293] lr: 5.000000e-04 eta: 13:22:30 time: 2.232422 data_time: 0.564006 memory: 5829 loss_kpt: 0.000862 acc_pose: 0.749324 loss: 0.000862 2022/10/15 03:58:58 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 04:00:10 - mmengine - INFO - Epoch(train) [110][100/293] lr: 5.000000e-04 eta: 13:21:31 time: 2.122497 data_time: 0.610478 memory: 5829 loss_kpt: 0.000869 acc_pose: 0.680149 loss: 0.000869 2022/10/15 04:01:53 - mmengine - INFO - Epoch(train) [110][150/293] lr: 5.000000e-04 eta: 13:20:30 time: 2.076552 data_time: 1.888368 memory: 5829 loss_kpt: 0.000862 acc_pose: 0.718857 loss: 0.000862 2022/10/15 04:03:41 - mmengine - INFO - Epoch(train) [110][200/293] lr: 5.000000e-04 eta: 13:19:33 time: 2.157483 data_time: 1.281718 memory: 5829 loss_kpt: 0.000866 acc_pose: 0.730764 loss: 0.000866 2022/10/15 04:05:31 - mmengine - INFO - Epoch(train) [110][250/293] lr: 5.000000e-04 eta: 13:18:37 time: 2.193442 data_time: 0.060327 memory: 5829 loss_kpt: 0.000854 acc_pose: 0.744380 loss: 0.000854 2022/10/15 04:07:00 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 04:07:00 - mmengine - INFO - Saving checkpoint at 110 epochs 2022/10/15 04:07:54 - mmengine - INFO - Epoch(val) [110][50/407] eta: 0:06:09 time: 1.034081 data_time: 0.996315 memory: 5829 2022/10/15 04:08:49 - mmengine - INFO - Epoch(val) [110][100/407] eta: 0:05:36 time: 1.097340 data_time: 1.060088 memory: 540 2022/10/15 04:09:55 - mmengine - INFO - Epoch(val) [110][150/407] eta: 0:05:42 time: 1.331662 data_time: 1.295259 memory: 540 2022/10/15 04:10:53 - mmengine - INFO - Epoch(val) [110][200/407] eta: 0:03:59 time: 1.158293 data_time: 1.121076 memory: 540 2022/10/15 04:11:40 - mmengine - INFO - Epoch(val) [110][250/407] eta: 0:02:26 time: 0.933609 data_time: 0.896760 memory: 540 2022/10/15 04:12:39 - mmengine - INFO - Epoch(val) [110][300/407] eta: 0:02:07 time: 1.187499 data_time: 1.149187 memory: 540 2022/10/15 04:13:36 - mmengine - INFO - Epoch(val) [110][350/407] eta: 0:01:04 time: 1.127915 data_time: 1.091070 memory: 540 2022/10/15 04:14:31 - mmengine - INFO - Epoch(val) [110][400/407] eta: 0:00:07 time: 1.111997 data_time: 1.071873 memory: 540 2022/10/15 04:15:45 - mmengine - INFO - Evaluating CocoMetric... 2022/10/15 04:15:59 - mmengine - INFO - Epoch(val) [110][407/407] coco/AP: 0.620561 coco/AP .5: 0.860987 coco/AP .75: 0.690824 coco/AP (M): 0.585773 coco/AP (L): 0.684524 coco/AR: 0.682572 coco/AR .5: 0.906329 coco/AR .75: 0.751102 coco/AR (M): 0.639033 coco/AR (L): 0.744705 2022/10/15 04:15:59 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256/best_coco/AP_epoch_100.pth is removed 2022/10/15 04:16:00 - mmengine - INFO - The best checkpoint with 0.6206 coco/AP at 110 epoch is saved to best_coco/AP_epoch_110.pth. 2022/10/15 04:18:04 - mmengine - INFO - Epoch(train) [111][50/293] lr: 5.000000e-04 eta: 13:15:39 time: 2.473570 data_time: 1.209510 memory: 5829 loss_kpt: 0.000844 acc_pose: 0.772014 loss: 0.000844 2022/10/15 04:20:13 - mmengine - INFO - Epoch(train) [111][100/293] lr: 5.000000e-04 eta: 13:15:01 time: 2.586502 data_time: 0.356917 memory: 5829 loss_kpt: 0.000845 acc_pose: 0.720175 loss: 0.000845 2022/10/15 04:22:15 - mmengine - INFO - Epoch(train) [111][150/293] lr: 5.000000e-04 eta: 13:14:15 time: 2.429862 data_time: 0.201068 memory: 5829 loss_kpt: 0.000853 acc_pose: 0.690099 loss: 0.000853 2022/10/15 04:24:27 - mmengine - INFO - Epoch(train) [111][200/293] lr: 5.000000e-04 eta: 13:13:39 time: 2.652365 data_time: 0.070316 memory: 5829 loss_kpt: 0.000848 acc_pose: 0.706173 loss: 0.000848 2022/10/15 04:26:32 - mmengine - INFO - Epoch(train) [111][250/293] lr: 5.000000e-04 eta: 13:12:56 time: 2.501573 data_time: 1.586792 memory: 5829 loss_kpt: 0.000855 acc_pose: 0.712882 loss: 0.000855 2022/10/15 04:28:20 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 04:30:18 - mmengine - INFO - Epoch(train) [112][50/293] lr: 5.000000e-04 eta: 13:09:52 time: 2.348549 data_time: 0.577119 memory: 5829 loss_kpt: 0.000844 acc_pose: 0.759452 loss: 0.000844 2022/10/15 04:32:10 - mmengine - INFO - Epoch(train) [112][100/293] lr: 5.000000e-04 eta: 13:08:57 time: 2.246089 data_time: 0.228632 memory: 5829 loss_kpt: 0.000838 acc_pose: 0.740204 loss: 0.000838 2022/10/15 04:34:09 - mmengine - INFO - Epoch(train) [112][150/293] lr: 5.000000e-04 eta: 13:08:08 time: 2.369343 data_time: 0.064235 memory: 5829 loss_kpt: 0.000851 acc_pose: 0.781360 loss: 0.000851 2022/10/15 04:36:00 - mmengine - INFO - Epoch(train) [112][200/293] lr: 5.000000e-04 eta: 13:07:12 time: 2.222986 data_time: 0.090655 memory: 5829 loss_kpt: 0.000826 acc_pose: 0.738387 loss: 0.000826 2022/10/15 04:37:53 - mmengine - INFO - Epoch(train) [112][250/293] lr: 5.000000e-04 eta: 13:06:17 time: 2.267638 data_time: 0.132214 memory: 5829 loss_kpt: 0.000859 acc_pose: 0.797239 loss: 0.000859 2022/10/15 04:39:39 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 04:41:33 - mmengine - INFO - Epoch(train) [113][50/293] lr: 5.000000e-04 eta: 13:03:11 time: 2.289186 data_time: 0.298829 memory: 5829 loss_kpt: 0.000855 acc_pose: 0.767047 loss: 0.000855 2022/10/15 04:43:26 - mmengine - INFO - Epoch(train) [113][100/293] lr: 5.000000e-04 eta: 13:02:16 time: 2.257629 data_time: 0.063595 memory: 5829 loss_kpt: 0.000842 acc_pose: 0.750236 loss: 0.000842 2022/10/15 04:45:14 - mmengine - INFO - Epoch(train) [113][150/293] lr: 5.000000e-04 eta: 13:01:17 time: 2.163938 data_time: 0.276801 memory: 5829 loss_kpt: 0.000851 acc_pose: 0.729097 loss: 0.000851 2022/10/15 04:46:27 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 04:47:00 - mmengine - INFO - Epoch(train) [113][200/293] lr: 5.000000e-04 eta: 13:00:16 time: 2.125760 data_time: 0.069590 memory: 5829 loss_kpt: 0.000852 acc_pose: 0.706065 loss: 0.000852 2022/10/15 04:48:55 - mmengine - INFO - Epoch(train) [113][250/293] lr: 5.000000e-04 eta: 12:59:22 time: 2.291688 data_time: 0.071616 memory: 5829 loss_kpt: 0.000840 acc_pose: 0.666068 loss: 0.000840 2022/10/15 04:50:28 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 04:52:14 - mmengine - INFO - Epoch(train) [114][50/293] lr: 5.000000e-04 eta: 12:56:09 time: 2.119285 data_time: 0.575270 memory: 5829 loss_kpt: 0.000864 acc_pose: 0.735697 loss: 0.000864 2022/10/15 04:54:01 - mmengine - INFO - Epoch(train) [114][100/293] lr: 5.000000e-04 eta: 12:55:08 time: 2.139202 data_time: 0.066313 memory: 5829 loss_kpt: 0.000841 acc_pose: 0.702282 loss: 0.000841 2022/10/15 04:55:47 - mmengine - INFO - Epoch(train) [114][150/293] lr: 5.000000e-04 eta: 12:54:06 time: 2.123567 data_time: 0.281209 memory: 5829 loss_kpt: 0.000858 acc_pose: 0.729341 loss: 0.000858 2022/10/15 04:57:44 - mmengine - INFO - Epoch(train) [114][200/293] lr: 5.000000e-04 eta: 12:53:13 time: 2.327236 data_time: 0.071170 memory: 5829 loss_kpt: 0.000852 acc_pose: 0.745762 loss: 0.000852 2022/10/15 04:59:38 - mmengine - INFO - Epoch(train) [114][250/293] lr: 5.000000e-04 eta: 12:52:18 time: 2.284341 data_time: 0.065848 memory: 5829 loss_kpt: 0.000859 acc_pose: 0.796975 loss: 0.000859 2022/10/15 05:01:21 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 05:03:21 - mmengine - INFO - Epoch(train) [115][50/293] lr: 5.000000e-04 eta: 12:49:18 time: 2.404580 data_time: 0.230244 memory: 5829 loss_kpt: 0.000843 acc_pose: 0.698656 loss: 0.000843 2022/10/15 05:05:18 - mmengine - INFO - Epoch(train) [115][100/293] lr: 5.000000e-04 eta: 12:48:25 time: 2.341957 data_time: 0.068893 memory: 5829 loss_kpt: 0.000842 acc_pose: 0.723867 loss: 0.000842 2022/10/15 05:07:08 - mmengine - INFO - Epoch(train) [115][150/293] lr: 5.000000e-04 eta: 12:47:26 time: 2.200316 data_time: 0.060209 memory: 5829 loss_kpt: 0.000874 acc_pose: 0.747168 loss: 0.000874 2022/10/15 05:09:01 - mmengine - INFO - Epoch(train) [115][200/293] lr: 5.000000e-04 eta: 12:46:28 time: 2.246272 data_time: 0.065780 memory: 5829 loss_kpt: 0.000839 acc_pose: 0.715474 loss: 0.000839 2022/10/15 05:10:49 - mmengine - INFO - Epoch(train) [115][250/293] lr: 5.000000e-04 eta: 12:45:28 time: 2.171179 data_time: 0.059130 memory: 5829 loss_kpt: 0.000851 acc_pose: 0.740502 loss: 0.000851 2022/10/15 05:12:21 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 05:14:16 - mmengine - INFO - Epoch(train) [116][50/293] lr: 5.000000e-04 eta: 12:42:23 time: 2.312181 data_time: 0.120386 memory: 5829 loss_kpt: 0.000835 acc_pose: 0.773066 loss: 0.000835 2022/10/15 05:16:06 - mmengine - INFO - Epoch(train) [116][100/293] lr: 5.000000e-04 eta: 12:41:23 time: 2.183831 data_time: 0.063557 memory: 5829 loss_kpt: 0.000851 acc_pose: 0.763494 loss: 0.000851 2022/10/15 05:17:48 - mmengine - INFO - Epoch(train) [116][150/293] lr: 5.000000e-04 eta: 12:40:17 time: 2.048336 data_time: 0.088105 memory: 5829 loss_kpt: 0.000846 acc_pose: 0.743436 loss: 0.000846 2022/10/15 05:19:41 - mmengine - INFO - Epoch(train) [116][200/293] lr: 5.000000e-04 eta: 12:39:20 time: 2.256202 data_time: 0.060003 memory: 5829 loss_kpt: 0.000856 acc_pose: 0.744061 loss: 0.000856 2022/10/15 05:21:37 - mmengine - INFO - Epoch(train) [116][250/293] lr: 5.000000e-04 eta: 12:38:25 time: 2.322314 data_time: 0.709524 memory: 5829 loss_kpt: 0.000854 acc_pose: 0.737031 loss: 0.000854 2022/10/15 05:23:01 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 05:23:28 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 05:24:58 - mmengine - INFO - Epoch(train) [117][50/293] lr: 5.000000e-04 eta: 12:35:21 time: 2.327465 data_time: 0.295711 memory: 5829 loss_kpt: 0.000852 acc_pose: 0.734474 loss: 0.000852 2022/10/15 05:26:44 - mmengine - INFO - Epoch(train) [117][100/293] lr: 5.000000e-04 eta: 12:34:18 time: 2.118101 data_time: 0.055424 memory: 5829 loss_kpt: 0.000835 acc_pose: 0.686010 loss: 0.000835 2022/10/15 05:28:32 - mmengine - INFO - Epoch(train) [117][150/293] lr: 5.000000e-04 eta: 12:33:16 time: 2.165458 data_time: 0.072128 memory: 5829 loss_kpt: 0.000835 acc_pose: 0.791341 loss: 0.000835 2022/10/15 05:30:16 - mmengine - INFO - Epoch(train) [117][200/293] lr: 5.000000e-04 eta: 12:32:11 time: 2.082550 data_time: 0.060045 memory: 5829 loss_kpt: 0.000839 acc_pose: 0.757345 loss: 0.000839 2022/10/15 05:32:05 - mmengine - INFO - Epoch(train) [117][250/293] lr: 5.000000e-04 eta: 12:31:09 time: 2.183766 data_time: 0.061098 memory: 5829 loss_kpt: 0.000857 acc_pose: 0.778827 loss: 0.000857 2022/10/15 05:33:36 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 05:35:25 - mmengine - INFO - Epoch(train) [118][50/293] lr: 5.000000e-04 eta: 12:28:01 time: 2.181184 data_time: 0.197705 memory: 5829 loss_kpt: 0.000846 acc_pose: 0.740378 loss: 0.000846 2022/10/15 05:37:12 - mmengine - INFO - Epoch(train) [118][100/293] lr: 5.000000e-04 eta: 12:26:58 time: 2.149992 data_time: 0.385871 memory: 5829 loss_kpt: 0.000849 acc_pose: 0.785048 loss: 0.000849 2022/10/15 05:38:57 - mmengine - INFO - Epoch(train) [118][150/293] lr: 5.000000e-04 eta: 12:25:53 time: 2.099675 data_time: 0.078191 memory: 5829 loss_kpt: 0.000850 acc_pose: 0.739520 loss: 0.000850 2022/10/15 05:40:43 - mmengine - INFO - Epoch(train) [118][200/293] lr: 5.000000e-04 eta: 12:24:48 time: 2.107370 data_time: 0.055747 memory: 5829 loss_kpt: 0.000854 acc_pose: 0.727020 loss: 0.000854 2022/10/15 05:42:26 - mmengine - INFO - Epoch(train) [118][250/293] lr: 5.000000e-04 eta: 12:23:42 time: 2.075903 data_time: 0.068297 memory: 5829 loss_kpt: 0.000850 acc_pose: 0.774665 loss: 0.000850 2022/10/15 05:44:02 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 05:46:01 - mmengine - INFO - Epoch(train) [119][50/293] lr: 5.000000e-04 eta: 12:20:42 time: 2.387246 data_time: 0.148493 memory: 5829 loss_kpt: 0.000834 acc_pose: 0.751726 loss: 0.000834 2022/10/15 05:47:52 - mmengine - INFO - Epoch(train) [119][100/293] lr: 5.000000e-04 eta: 12:19:41 time: 2.225280 data_time: 0.343750 memory: 5829 loss_kpt: 0.000841 acc_pose: 0.723256 loss: 0.000841 2022/10/15 05:50:04 - mmengine - INFO - Epoch(train) [119][150/293] lr: 5.000000e-04 eta: 12:18:57 time: 2.637053 data_time: 0.350257 memory: 5829 loss_kpt: 0.000843 acc_pose: 0.771956 loss: 0.000843 2022/10/15 05:51:47 - mmengine - INFO - Epoch(train) [119][200/293] lr: 5.000000e-04 eta: 12:17:50 time: 2.056233 data_time: 0.071722 memory: 5829 loss_kpt: 0.000842 acc_pose: 0.787490 loss: 0.000842 2022/10/15 05:53:37 - mmengine - INFO - Epoch(train) [119][250/293] lr: 5.000000e-04 eta: 12:16:47 time: 2.189763 data_time: 0.067708 memory: 5829 loss_kpt: 0.000838 acc_pose: 0.777353 loss: 0.000838 2022/10/15 05:55:06 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 05:57:01 - mmengine - INFO - Epoch(train) [120][50/293] lr: 5.000000e-04 eta: 12:13:44 time: 2.294504 data_time: 0.156339 memory: 5829 loss_kpt: 0.000856 acc_pose: 0.755129 loss: 0.000856 2022/10/15 05:58:48 - mmengine - INFO - Epoch(train) [120][100/293] lr: 5.000000e-04 eta: 12:12:39 time: 2.144206 data_time: 0.119281 memory: 5829 loss_kpt: 0.000829 acc_pose: 0.769636 loss: 0.000829 2022/10/15 05:59:56 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 06:00:31 - mmengine - INFO - Epoch(train) [120][150/293] lr: 5.000000e-04 eta: 12:11:32 time: 2.058598 data_time: 0.319520 memory: 5829 loss_kpt: 0.000838 acc_pose: 0.686800 loss: 0.000838 2022/10/15 06:02:23 - mmengine - INFO - Epoch(train) [120][200/293] lr: 5.000000e-04 eta: 12:10:31 time: 2.228403 data_time: 0.172222 memory: 5829 loss_kpt: 0.000845 acc_pose: 0.705381 loss: 0.000845 2022/10/15 06:04:18 - mmengine - INFO - Epoch(train) [120][250/293] lr: 5.000000e-04 eta: 12:09:32 time: 2.299521 data_time: 0.069758 memory: 5829 loss_kpt: 0.000846 acc_pose: 0.735033 loss: 0.000846 2022/10/15 06:05:51 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 06:05:51 - mmengine - INFO - Saving checkpoint at 120 epochs 2022/10/15 06:06:44 - mmengine - INFO - Epoch(val) [120][50/407] eta: 0:06:01 time: 1.013579 data_time: 0.976745 memory: 5829 2022/10/15 06:07:34 - mmengine - INFO - Epoch(val) [120][100/407] eta: 0:05:07 time: 1.002012 data_time: 0.964978 memory: 540 2022/10/15 06:08:26 - mmengine - INFO - Epoch(val) [120][150/407] eta: 0:04:26 time: 1.035563 data_time: 0.998866 memory: 540 2022/10/15 06:09:14 - mmengine - INFO - Epoch(val) [120][200/407] eta: 0:03:21 time: 0.974746 data_time: 0.938325 memory: 540 2022/10/15 06:10:00 - mmengine - INFO - Epoch(val) [120][250/407] eta: 0:02:22 time: 0.908043 data_time: 0.871432 memory: 540 2022/10/15 06:10:57 - mmengine - INFO - Epoch(val) [120][300/407] eta: 0:02:02 time: 1.142619 data_time: 1.106688 memory: 540 2022/10/15 06:11:55 - mmengine - INFO - Epoch(val) [120][350/407] eta: 0:01:05 time: 1.157202 data_time: 1.121109 memory: 540 2022/10/15 06:12:52 - mmengine - INFO - Epoch(val) [120][400/407] eta: 0:00:08 time: 1.151374 data_time: 1.115504 memory: 540 2022/10/15 06:14:41 - mmengine - INFO - Evaluating CocoMetric... 2022/10/15 06:14:55 - mmengine - INFO - Epoch(val) [120][407/407] coco/AP: 0.618837 coco/AP .5: 0.860775 coco/AP .75: 0.694021 coco/AP (M): 0.582600 coco/AP (L): 0.684598 coco/AR: 0.682352 coco/AR .5: 0.905699 coco/AR .75: 0.751732 coco/AR (M): 0.636766 coco/AR (L): 0.746340 2022/10/15 06:17:02 - mmengine - INFO - Epoch(train) [121][50/293] lr: 5.000000e-04 eta: 12:06:38 time: 2.542494 data_time: 0.285168 memory: 5829 loss_kpt: 0.000838 acc_pose: 0.732189 loss: 0.000838 2022/10/15 06:19:02 - mmengine - INFO - Epoch(train) [121][100/293] lr: 5.000000e-04 eta: 12:05:43 time: 2.400611 data_time: 0.070321 memory: 5829 loss_kpt: 0.000859 acc_pose: 0.748092 loss: 0.000859 2022/10/15 06:20:55 - mmengine - INFO - Epoch(train) [121][150/293] lr: 5.000000e-04 eta: 12:04:42 time: 2.254216 data_time: 0.060250 memory: 5829 loss_kpt: 0.000862 acc_pose: 0.788259 loss: 0.000862 2022/10/15 06:22:50 - mmengine - INFO - Epoch(train) [121][200/293] lr: 5.000000e-04 eta: 12:03:43 time: 2.290524 data_time: 0.058157 memory: 5829 loss_kpt: 0.000847 acc_pose: 0.753665 loss: 0.000847 2022/10/15 06:24:47 - mmengine - INFO - Epoch(train) [121][250/293] lr: 5.000000e-04 eta: 12:02:45 time: 2.353698 data_time: 0.214111 memory: 5829 loss_kpt: 0.000854 acc_pose: 0.704213 loss: 0.000854 2022/10/15 06:26:20 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 06:28:13 - mmengine - INFO - Epoch(train) [122][50/293] lr: 5.000000e-04 eta: 11:59:40 time: 2.262033 data_time: 0.170355 memory: 5829 loss_kpt: 0.000831 acc_pose: 0.748351 loss: 0.000831 2022/10/15 06:29:56 - mmengine - INFO - Epoch(train) [122][100/293] lr: 5.000000e-04 eta: 11:58:32 time: 2.066787 data_time: 0.060749 memory: 5829 loss_kpt: 0.000847 acc_pose: 0.763678 loss: 0.000847 2022/10/15 06:31:48 - mmengine - INFO - Epoch(train) [122][150/293] lr: 5.000000e-04 eta: 11:57:30 time: 2.237530 data_time: 0.114021 memory: 5829 loss_kpt: 0.000845 acc_pose: 0.734322 loss: 0.000845 2022/10/15 06:33:37 - mmengine - INFO - Epoch(train) [122][200/293] lr: 5.000000e-04 eta: 11:56:26 time: 2.174865 data_time: 0.072537 memory: 5829 loss_kpt: 0.000834 acc_pose: 0.813136 loss: 0.000834 2022/10/15 06:35:26 - mmengine - INFO - Epoch(train) [122][250/293] lr: 5.000000e-04 eta: 11:55:22 time: 2.180865 data_time: 0.061285 memory: 5829 loss_kpt: 0.000843 acc_pose: 0.737313 loss: 0.000843 2022/10/15 06:36:59 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 06:38:54 - mmengine - INFO - Epoch(train) [123][50/293] lr: 5.000000e-04 eta: 11:52:20 time: 2.318813 data_time: 1.227158 memory: 5829 loss_kpt: 0.000854 acc_pose: 0.735771 loss: 0.000854 2022/10/15 06:40:37 - mmengine - INFO - Epoch(train) [123][100/293] lr: 5.000000e-04 eta: 11:51:10 time: 2.041261 data_time: 0.260493 memory: 5829 loss_kpt: 0.000843 acc_pose: 0.715500 loss: 0.000843 2022/10/15 06:42:29 - mmengine - INFO - Epoch(train) [123][150/293] lr: 5.000000e-04 eta: 11:50:08 time: 2.240650 data_time: 0.056984 memory: 5829 loss_kpt: 0.000847 acc_pose: 0.738033 loss: 0.000847 2022/10/15 06:44:17 - mmengine - INFO - Epoch(train) [123][200/293] lr: 5.000000e-04 eta: 11:49:03 time: 2.176218 data_time: 1.480882 memory: 5829 loss_kpt: 0.000847 acc_pose: 0.726205 loss: 0.000847 2022/10/15 06:46:02 - mmengine - INFO - Epoch(train) [123][250/293] lr: 5.000000e-04 eta: 11:47:55 time: 2.092031 data_time: 1.860391 memory: 5829 loss_kpt: 0.000857 acc_pose: 0.731217 loss: 0.000857 2022/10/15 06:46:10 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 06:47:33 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 06:49:13 - mmengine - INFO - Epoch(train) [124][50/293] lr: 5.000000e-04 eta: 11:44:42 time: 1.997481 data_time: 0.317693 memory: 5829 loss_kpt: 0.000841 acc_pose: 0.738542 loss: 0.000841 2022/10/15 06:50:59 - mmengine - INFO - Epoch(train) [124][100/293] lr: 5.000000e-04 eta: 11:43:35 time: 2.128943 data_time: 0.810808 memory: 5829 loss_kpt: 0.000850 acc_pose: 0.713739 loss: 0.000850 2022/10/15 06:52:46 - mmengine - INFO - Epoch(train) [124][150/293] lr: 5.000000e-04 eta: 11:42:28 time: 2.133047 data_time: 1.938190 memory: 5829 loss_kpt: 0.000855 acc_pose: 0.739831 loss: 0.000855 2022/10/15 06:54:35 - mmengine - INFO - Epoch(train) [124][200/293] lr: 5.000000e-04 eta: 11:41:23 time: 2.177047 data_time: 0.390638 memory: 5829 loss_kpt: 0.000862 acc_pose: 0.756048 loss: 0.000862 2022/10/15 06:56:24 - mmengine - INFO - Epoch(train) [124][250/293] lr: 5.000000e-04 eta: 11:40:18 time: 2.195695 data_time: 0.066942 memory: 5829 loss_kpt: 0.000853 acc_pose: 0.784683 loss: 0.000853 2022/10/15 06:57:54 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 06:59:44 - mmengine - INFO - Epoch(train) [125][50/293] lr: 5.000000e-04 eta: 11:37:13 time: 2.207467 data_time: 1.028304 memory: 5829 loss_kpt: 0.000853 acc_pose: 0.735904 loss: 0.000853 2022/10/15 07:01:36 - mmengine - INFO - Epoch(train) [125][100/293] lr: 5.000000e-04 eta: 11:36:09 time: 2.238837 data_time: 0.105028 memory: 5829 loss_kpt: 0.000858 acc_pose: 0.717378 loss: 0.000858 2022/10/15 07:03:41 - mmengine - INFO - Epoch(train) [125][150/293] lr: 5.000000e-04 eta: 11:35:15 time: 2.498806 data_time: 0.072890 memory: 5829 loss_kpt: 0.000849 acc_pose: 0.752630 loss: 0.000849 2022/10/15 07:05:33 - mmengine - INFO - Epoch(train) [125][200/293] lr: 5.000000e-04 eta: 11:34:11 time: 2.237986 data_time: 0.067483 memory: 5829 loss_kpt: 0.000843 acc_pose: 0.799249 loss: 0.000843 2022/10/15 07:07:20 - mmengine - INFO - Epoch(train) [125][250/293] lr: 5.000000e-04 eta: 11:33:04 time: 2.146914 data_time: 0.409001 memory: 5829 loss_kpt: 0.000850 acc_pose: 0.760850 loss: 0.000850 2022/10/15 07:08:57 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 07:10:47 - mmengine - INFO - Epoch(train) [126][50/293] lr: 5.000000e-04 eta: 11:29:59 time: 2.193926 data_time: 0.948340 memory: 5829 loss_kpt: 0.000840 acc_pose: 0.743872 loss: 0.000840 2022/10/15 07:12:40 - mmengine - INFO - Epoch(train) [126][100/293] lr: 5.000000e-04 eta: 11:28:55 time: 2.261151 data_time: 2.054982 memory: 5829 loss_kpt: 0.000845 acc_pose: 0.751705 loss: 0.000845 2022/10/15 07:14:33 - mmengine - INFO - Epoch(train) [126][150/293] lr: 5.000000e-04 eta: 11:27:52 time: 2.263744 data_time: 2.077493 memory: 5829 loss_kpt: 0.000841 acc_pose: 0.796790 loss: 0.000841 2022/10/15 07:16:33 - mmengine - INFO - Epoch(train) [126][200/293] lr: 5.000000e-04 eta: 11:26:53 time: 2.393176 data_time: 1.170004 memory: 5829 loss_kpt: 0.000837 acc_pose: 0.717728 loss: 0.000837 2022/10/15 07:18:26 - mmengine - INFO - Epoch(train) [126][250/293] lr: 5.000000e-04 eta: 11:25:49 time: 2.250504 data_time: 0.110247 memory: 5829 loss_kpt: 0.000846 acc_pose: 0.729117 loss: 0.000846 2022/10/15 07:19:55 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 07:21:38 - mmengine - INFO - Epoch(train) [127][50/293] lr: 5.000000e-04 eta: 11:22:39 time: 2.063299 data_time: 0.348197 memory: 5829 loss_kpt: 0.000844 acc_pose: 0.724891 loss: 0.000844 2022/10/15 07:22:41 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 07:23:18 - mmengine - INFO - Epoch(train) [127][100/293] lr: 5.000000e-04 eta: 11:21:27 time: 2.005550 data_time: 0.074455 memory: 5829 loss_kpt: 0.000843 acc_pose: 0.738974 loss: 0.000843 2022/10/15 07:25:00 - mmengine - INFO - Epoch(train) [127][150/293] lr: 5.000000e-04 eta: 11:20:16 time: 2.037446 data_time: 0.070453 memory: 5829 loss_kpt: 0.000842 acc_pose: 0.777205 loss: 0.000842 2022/10/15 07:26:49 - mmengine - INFO - Epoch(train) [127][200/293] lr: 5.000000e-04 eta: 11:19:09 time: 2.169876 data_time: 0.113885 memory: 5829 loss_kpt: 0.000839 acc_pose: 0.713122 loss: 0.000839 2022/10/15 07:28:35 - mmengine - INFO - Epoch(train) [127][250/293] lr: 5.000000e-04 eta: 11:18:01 time: 2.126211 data_time: 0.071217 memory: 5829 loss_kpt: 0.000849 acc_pose: 0.686783 loss: 0.000849 2022/10/15 07:30:08 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 07:31:55 - mmengine - INFO - Epoch(train) [128][50/293] lr: 5.000000e-04 eta: 11:14:54 time: 2.134381 data_time: 0.121756 memory: 5829 loss_kpt: 0.000828 acc_pose: 0.723856 loss: 0.000828 2022/10/15 07:33:47 - mmengine - INFO - Epoch(train) [128][100/293] lr: 5.000000e-04 eta: 11:13:49 time: 2.244345 data_time: 0.075792 memory: 5829 loss_kpt: 0.000839 acc_pose: 0.755863 loss: 0.000839 2022/10/15 07:35:32 - mmengine - INFO - Epoch(train) [128][150/293] lr: 5.000000e-04 eta: 11:12:39 time: 2.099960 data_time: 0.056293 memory: 5829 loss_kpt: 0.000845 acc_pose: 0.755930 loss: 0.000845 2022/10/15 07:37:17 - mmengine - INFO - Epoch(train) [128][200/293] lr: 5.000000e-04 eta: 11:11:30 time: 2.102160 data_time: 0.937270 memory: 5829 loss_kpt: 0.000831 acc_pose: 0.754550 loss: 0.000831 2022/10/15 07:39:09 - mmengine - INFO - Epoch(train) [128][250/293] lr: 5.000000e-04 eta: 11:10:24 time: 2.231133 data_time: 1.744107 memory: 5829 loss_kpt: 0.000842 acc_pose: 0.672599 loss: 0.000842 2022/10/15 07:40:42 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 07:42:35 - mmengine - INFO - Epoch(train) [129][50/293] lr: 5.000000e-04 eta: 11:07:22 time: 2.254582 data_time: 0.353403 memory: 5829 loss_kpt: 0.000852 acc_pose: 0.728840 loss: 0.000852 2022/10/15 07:44:28 - mmengine - INFO - Epoch(train) [129][100/293] lr: 5.000000e-04 eta: 11:06:17 time: 2.268782 data_time: 2.075714 memory: 5829 loss_kpt: 0.000841 acc_pose: 0.733165 loss: 0.000841 2022/10/15 07:46:24 - mmengine - INFO - Epoch(train) [129][150/293] lr: 5.000000e-04 eta: 11:05:14 time: 2.314533 data_time: 2.098407 memory: 5829 loss_kpt: 0.000838 acc_pose: 0.742222 loss: 0.000838 2022/10/15 07:48:11 - mmengine - INFO - Epoch(train) [129][200/293] lr: 5.000000e-04 eta: 11:04:05 time: 2.132069 data_time: 0.472390 memory: 5829 loss_kpt: 0.000850 acc_pose: 0.764043 loss: 0.000850 2022/10/15 07:50:05 - mmengine - INFO - Epoch(train) [129][250/293] lr: 5.000000e-04 eta: 11:03:00 time: 2.279629 data_time: 0.063091 memory: 5829 loss_kpt: 0.000827 acc_pose: 0.782600 loss: 0.000827 2022/10/15 07:51:38 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 07:53:35 - mmengine - INFO - Epoch(train) [130][50/293] lr: 5.000000e-04 eta: 11:00:01 time: 2.344419 data_time: 0.196517 memory: 5829 loss_kpt: 0.000822 acc_pose: 0.793480 loss: 0.000822 2022/10/15 07:55:17 - mmengine - INFO - Epoch(train) [130][100/293] lr: 5.000000e-04 eta: 10:58:48 time: 2.022477 data_time: 0.089188 memory: 5829 loss_kpt: 0.000820 acc_pose: 0.769140 loss: 0.000820 2022/10/15 07:57:01 - mmengine - INFO - Epoch(train) [130][150/293] lr: 5.000000e-04 eta: 10:57:37 time: 2.079362 data_time: 0.064551 memory: 5829 loss_kpt: 0.000851 acc_pose: 0.634551 loss: 0.000851 2022/10/15 07:58:44 - mmengine - INFO - Epoch(train) [130][200/293] lr: 5.000000e-04 eta: 10:56:26 time: 2.076228 data_time: 0.296841 memory: 5829 loss_kpt: 0.000842 acc_pose: 0.766459 loss: 0.000842 2022/10/15 07:58:55 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 08:00:27 - mmengine - INFO - Epoch(train) [130][250/293] lr: 5.000000e-04 eta: 10:55:14 time: 2.047055 data_time: 0.103587 memory: 5829 loss_kpt: 0.000859 acc_pose: 0.736034 loss: 0.000859 2022/10/15 08:01:52 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 08:01:52 - mmengine - INFO - Saving checkpoint at 130 epochs 2022/10/15 08:02:54 - mmengine - INFO - Epoch(val) [130][50/407] eta: 0:07:08 time: 1.200217 data_time: 1.162580 memory: 5829 2022/10/15 08:03:51 - mmengine - INFO - Epoch(val) [130][100/407] eta: 0:05:48 time: 1.135277 data_time: 1.098909 memory: 540 2022/10/15 08:04:39 - mmengine - INFO - Epoch(val) [130][150/407] eta: 0:04:10 time: 0.973560 data_time: 0.937332 memory: 540 2022/10/15 08:05:38 - mmengine - INFO - Epoch(val) [130][200/407] eta: 0:04:02 time: 1.170690 data_time: 1.134211 memory: 540 2022/10/15 08:06:29 - mmengine - INFO - Epoch(val) [130][250/407] eta: 0:02:41 time: 1.028853 data_time: 0.992444 memory: 540 2022/10/15 08:07:22 - mmengine - INFO - Epoch(val) [130][300/407] eta: 0:01:53 time: 1.059419 data_time: 1.022149 memory: 540 2022/10/15 08:08:23 - mmengine - INFO - Epoch(val) [130][350/407] eta: 0:01:09 time: 1.218272 data_time: 1.176843 memory: 540 2022/10/15 08:09:23 - mmengine - INFO - Epoch(val) [130][400/407] eta: 0:00:08 time: 1.191581 data_time: 1.155137 memory: 540 2022/10/15 08:10:41 - mmengine - INFO - Evaluating CocoMetric... 2022/10/15 08:10:55 - mmengine - INFO - Epoch(val) [130][407/407] coco/AP: 0.628338 coco/AP .5: 0.866727 coco/AP .75: 0.703050 coco/AP (M): 0.592231 coco/AP (L): 0.694440 coco/AR: 0.690806 coco/AR .5: 0.910264 coco/AR .75: 0.759288 coco/AR (M): 0.645315 coco/AR (L): 0.754961 2022/10/15 08:10:55 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256/best_coco/AP_epoch_110.pth is removed 2022/10/15 08:10:57 - mmengine - INFO - The best checkpoint with 0.6283 coco/AP at 130 epoch is saved to best_coco/AP_epoch_130.pth. 2022/10/15 08:13:00 - mmengine - INFO - Epoch(train) [131][50/293] lr: 5.000000e-04 eta: 10:52:18 time: 2.454503 data_time: 1.039928 memory: 5829 loss_kpt: 0.000833 acc_pose: 0.698408 loss: 0.000833 2022/10/15 08:14:59 - mmengine - INFO - Epoch(train) [131][100/293] lr: 5.000000e-04 eta: 10:51:16 time: 2.381191 data_time: 0.069631 memory: 5829 loss_kpt: 0.000834 acc_pose: 0.746747 loss: 0.000834 2022/10/15 08:17:01 - mmengine - INFO - Epoch(train) [131][150/293] lr: 5.000000e-04 eta: 10:50:15 time: 2.438907 data_time: 0.065987 memory: 5829 loss_kpt: 0.000836 acc_pose: 0.739144 loss: 0.000836 2022/10/15 08:18:53 - mmengine - INFO - Epoch(train) [131][200/293] lr: 5.000000e-04 eta: 10:49:09 time: 2.248853 data_time: 0.451735 memory: 5829 loss_kpt: 0.000840 acc_pose: 0.771578 loss: 0.000840 2022/10/15 08:20:51 - mmengine - INFO - Epoch(train) [131][250/293] lr: 5.000000e-04 eta: 10:48:06 time: 2.355009 data_time: 0.226369 memory: 5829 loss_kpt: 0.000834 acc_pose: 0.798418 loss: 0.000834 2022/10/15 08:22:24 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 08:24:22 - mmengine - INFO - Epoch(train) [132][50/293] lr: 5.000000e-04 eta: 10:45:07 time: 2.358456 data_time: 0.956974 memory: 5829 loss_kpt: 0.000829 acc_pose: 0.792705 loss: 0.000829 2022/10/15 08:26:15 - mmengine - INFO - Epoch(train) [132][100/293] lr: 5.000000e-04 eta: 10:44:01 time: 2.259559 data_time: 0.969105 memory: 5829 loss_kpt: 0.000833 acc_pose: 0.798109 loss: 0.000833 2022/10/15 08:28:06 - mmengine - INFO - Epoch(train) [132][150/293] lr: 5.000000e-04 eta: 10:42:54 time: 2.235331 data_time: 0.057256 memory: 5829 loss_kpt: 0.000825 acc_pose: 0.734837 loss: 0.000825 2022/10/15 08:29:51 - mmengine - INFO - Epoch(train) [132][200/293] lr: 5.000000e-04 eta: 10:41:42 time: 2.099304 data_time: 0.113434 memory: 5829 loss_kpt: 0.000841 acc_pose: 0.717765 loss: 0.000841 2022/10/15 08:31:45 - mmengine - INFO - Epoch(train) [132][250/293] lr: 5.000000e-04 eta: 10:40:36 time: 2.267383 data_time: 0.140905 memory: 5829 loss_kpt: 0.000845 acc_pose: 0.743055 loss: 0.000845 2022/10/15 08:33:15 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 08:35:12 - mmengine - INFO - Epoch(train) [133][50/293] lr: 5.000000e-04 eta: 10:37:36 time: 2.332145 data_time: 0.207260 memory: 5829 loss_kpt: 0.000827 acc_pose: 0.812717 loss: 0.000827 2022/10/15 08:37:00 - mmengine - INFO - Epoch(train) [133][100/293] lr: 5.000000e-04 eta: 10:36:27 time: 2.163200 data_time: 0.060014 memory: 5829 loss_kpt: 0.000822 acc_pose: 0.764765 loss: 0.000822 2022/10/15 08:39:02 - mmengine - INFO - Epoch(train) [133][150/293] lr: 5.000000e-04 eta: 10:35:25 time: 2.426519 data_time: 0.069421 memory: 5829 loss_kpt: 0.000830 acc_pose: 0.762120 loss: 0.000830 2022/10/15 08:40:54 - mmengine - INFO - Epoch(train) [133][200/293] lr: 5.000000e-04 eta: 10:34:18 time: 2.253425 data_time: 0.072047 memory: 5829 loss_kpt: 0.000841 acc_pose: 0.669349 loss: 0.000841 2022/10/15 08:42:53 - mmengine - INFO - Epoch(train) [133][250/293] lr: 5.000000e-04 eta: 10:33:14 time: 2.379279 data_time: 0.096474 memory: 5829 loss_kpt: 0.000820 acc_pose: 0.755821 loss: 0.000820 2022/10/15 08:44:31 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 08:45:47 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 08:46:43 - mmengine - INFO - Epoch(train) [134][50/293] lr: 5.000000e-04 eta: 10:30:23 time: 2.626177 data_time: 0.667459 memory: 5829 loss_kpt: 0.000851 acc_pose: 0.780498 loss: 0.000851 2022/10/15 08:48:40 - mmengine - INFO - Epoch(train) [134][100/293] lr: 5.000000e-04 eta: 10:29:18 time: 2.342251 data_time: 1.358301 memory: 5829 loss_kpt: 0.000832 acc_pose: 0.751537 loss: 0.000832 2022/10/15 08:50:47 - mmengine - INFO - Epoch(train) [134][150/293] lr: 5.000000e-04 eta: 10:28:19 time: 2.548636 data_time: 1.186823 memory: 5829 loss_kpt: 0.000837 acc_pose: 0.757299 loss: 0.000837 2022/10/15 08:52:53 - mmengine - INFO - Epoch(train) [134][200/293] lr: 5.000000e-04 eta: 10:27:19 time: 2.520008 data_time: 0.063179 memory: 5829 loss_kpt: 0.000837 acc_pose: 0.753386 loss: 0.000837 2022/10/15 08:55:09 - mmengine - INFO - Epoch(train) [134][250/293] lr: 5.000000e-04 eta: 10:26:24 time: 2.722781 data_time: 0.073643 memory: 5829 loss_kpt: 0.000835 acc_pose: 0.743858 loss: 0.000835 2022/10/15 08:57:07 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 08:59:21 - mmengine - INFO - Epoch(train) [135][50/293] lr: 5.000000e-04 eta: 10:23:34 time: 2.672436 data_time: 0.257970 memory: 5829 loss_kpt: 0.000817 acc_pose: 0.774584 loss: 0.000817 2022/10/15 09:01:27 - mmengine - INFO - Epoch(train) [135][100/293] lr: 5.000000e-04 eta: 10:22:34 time: 2.520046 data_time: 0.998909 memory: 5829 loss_kpt: 0.000847 acc_pose: 0.744054 loss: 0.000847 2022/10/15 09:03:29 - mmengine - INFO - Epoch(train) [135][150/293] lr: 5.000000e-04 eta: 10:21:31 time: 2.445193 data_time: 0.063477 memory: 5829 loss_kpt: 0.000845 acc_pose: 0.800165 loss: 0.000845 2022/10/15 09:05:40 - mmengine - INFO - Epoch(train) [135][200/293] lr: 5.000000e-04 eta: 10:20:33 time: 2.622292 data_time: 0.066232 memory: 5829 loss_kpt: 0.000858 acc_pose: 0.731962 loss: 0.000858 2022/10/15 09:07:53 - mmengine - INFO - Epoch(train) [135][250/293] lr: 5.000000e-04 eta: 10:19:35 time: 2.653610 data_time: 0.069279 memory: 5829 loss_kpt: 0.000831 acc_pose: 0.777191 loss: 0.000831 2022/10/15 09:09:32 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 09:11:39 - mmengine - INFO - Epoch(train) [136][50/293] lr: 5.000000e-04 eta: 10:16:42 time: 2.538507 data_time: 0.369055 memory: 5829 loss_kpt: 0.000834 acc_pose: 0.690027 loss: 0.000834 2022/10/15 09:13:46 - mmengine - INFO - Epoch(train) [136][100/293] lr: 5.000000e-04 eta: 10:15:41 time: 2.537895 data_time: 0.075995 memory: 5829 loss_kpt: 0.000850 acc_pose: 0.786221 loss: 0.000850 2022/10/15 09:15:58 - mmengine - INFO - Epoch(train) [136][150/293] lr: 5.000000e-04 eta: 10:14:42 time: 2.637617 data_time: 1.101995 memory: 5829 loss_kpt: 0.000848 acc_pose: 0.795764 loss: 0.000848 2022/10/15 09:17:55 - mmengine - INFO - Epoch(train) [136][200/293] lr: 5.000000e-04 eta: 10:13:36 time: 2.347389 data_time: 0.130570 memory: 5829 loss_kpt: 0.000858 acc_pose: 0.764228 loss: 0.000858 2022/10/15 09:19:49 - mmengine - INFO - Epoch(train) [136][250/293] lr: 5.000000e-04 eta: 10:12:27 time: 2.262286 data_time: 0.556992 memory: 5829 loss_kpt: 0.000822 acc_pose: 0.761234 loss: 0.000822 2022/10/15 09:21:25 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 09:23:29 - mmengine - INFO - Epoch(train) [137][50/293] lr: 5.000000e-04 eta: 10:09:31 time: 2.464952 data_time: 1.017347 memory: 5829 loss_kpt: 0.000824 acc_pose: 0.735545 loss: 0.000824 2022/10/15 09:25:26 - mmengine - INFO - Epoch(train) [137][100/293] lr: 5.000000e-04 eta: 10:08:24 time: 2.337370 data_time: 1.986202 memory: 5829 loss_kpt: 0.000816 acc_pose: 0.740017 loss: 0.000816 2022/10/15 09:27:23 - mmengine - INFO - Epoch(train) [137][150/293] lr: 5.000000e-04 eta: 10:07:17 time: 2.351240 data_time: 0.787806 memory: 5829 loss_kpt: 0.000835 acc_pose: 0.744928 loss: 0.000835 2022/10/15 09:27:28 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 09:29:26 - mmengine - INFO - Epoch(train) [137][200/293] lr: 5.000000e-04 eta: 10:06:13 time: 2.452466 data_time: 0.813613 memory: 5829 loss_kpt: 0.000848 acc_pose: 0.765049 loss: 0.000848 2022/10/15 09:31:28 - mmengine - INFO - Epoch(train) [137][250/293] lr: 5.000000e-04 eta: 10:05:08 time: 2.437786 data_time: 1.556312 memory: 5829 loss_kpt: 0.000825 acc_pose: 0.698519 loss: 0.000825 2022/10/15 09:33:12 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 09:35:10 - mmengine - INFO - Epoch(train) [138][50/293] lr: 5.000000e-04 eta: 10:02:10 time: 2.372347 data_time: 0.295792 memory: 5829 loss_kpt: 0.000833 acc_pose: 0.683260 loss: 0.000833 2022/10/15 09:37:03 - mmengine - INFO - Epoch(train) [138][100/293] lr: 5.000000e-04 eta: 10:01:00 time: 2.259941 data_time: 1.188973 memory: 5829 loss_kpt: 0.000824 acc_pose: 0.752182 loss: 0.000824 2022/10/15 09:38:55 - mmengine - INFO - Epoch(train) [138][150/293] lr: 5.000000e-04 eta: 9:59:50 time: 2.235354 data_time: 0.648398 memory: 5829 loss_kpt: 0.000837 acc_pose: 0.737788 loss: 0.000837 2022/10/15 09:40:51 - mmengine - INFO - Epoch(train) [138][200/293] lr: 5.000000e-04 eta: 9:58:42 time: 2.319980 data_time: 0.060490 memory: 5829 loss_kpt: 0.000841 acc_pose: 0.802272 loss: 0.000841 2022/10/15 09:43:09 - mmengine - INFO - Epoch(train) [138][250/293] lr: 5.000000e-04 eta: 9:57:45 time: 2.768473 data_time: 0.124958 memory: 5829 loss_kpt: 0.000819 acc_pose: 0.742690 loss: 0.000819 2022/10/15 09:44:54 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 09:46:58 - mmengine - INFO - Epoch(train) [139][50/293] lr: 5.000000e-04 eta: 9:54:50 time: 2.482106 data_time: 0.567189 memory: 5829 loss_kpt: 0.000838 acc_pose: 0.717468 loss: 0.000838 2022/10/15 09:49:10 - mmengine - INFO - Epoch(train) [139][100/293] lr: 5.000000e-04 eta: 9:53:49 time: 2.639394 data_time: 0.636207 memory: 5829 loss_kpt: 0.000845 acc_pose: 0.748728 loss: 0.000845 2022/10/15 09:51:09 - mmengine - INFO - Epoch(train) [139][150/293] lr: 5.000000e-04 eta: 9:52:42 time: 2.377069 data_time: 0.321175 memory: 5829 loss_kpt: 0.000837 acc_pose: 0.746359 loss: 0.000837 2022/10/15 09:53:20 - mmengine - INFO - Epoch(train) [139][200/293] lr: 5.000000e-04 eta: 9:51:41 time: 2.614024 data_time: 0.141042 memory: 5829 loss_kpt: 0.000836 acc_pose: 0.730202 loss: 0.000836 2022/10/15 09:55:23 - mmengine - INFO - Epoch(train) [139][250/293] lr: 5.000000e-04 eta: 9:50:35 time: 2.465863 data_time: 0.071184 memory: 5829 loss_kpt: 0.000839 acc_pose: 0.730628 loss: 0.000839 2022/10/15 09:56:49 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 09:58:46 - mmengine - INFO - Epoch(train) [140][50/293] lr: 5.000000e-04 eta: 9:47:36 time: 2.343956 data_time: 0.134912 memory: 5829 loss_kpt: 0.000825 acc_pose: 0.771989 loss: 0.000825 2022/10/15 10:00:43 - mmengine - INFO - Epoch(train) [140][100/293] lr: 5.000000e-04 eta: 9:46:27 time: 2.327595 data_time: 0.139617 memory: 5829 loss_kpt: 0.000838 acc_pose: 0.712596 loss: 0.000838 2022/10/15 10:02:37 - mmengine - INFO - Epoch(train) [140][150/293] lr: 5.000000e-04 eta: 9:45:17 time: 2.284589 data_time: 0.077430 memory: 5829 loss_kpt: 0.000854 acc_pose: 0.776593 loss: 0.000854 2022/10/15 10:04:30 - mmengine - INFO - Epoch(train) [140][200/293] lr: 5.000000e-04 eta: 9:44:06 time: 2.263535 data_time: 0.061041 memory: 5829 loss_kpt: 0.000838 acc_pose: 0.769503 loss: 0.000838 2022/10/15 10:06:30 - mmengine - INFO - Epoch(train) [140][250/293] lr: 5.000000e-04 eta: 9:42:59 time: 2.393462 data_time: 0.054306 memory: 5829 loss_kpt: 0.000837 acc_pose: 0.771588 loss: 0.000837 2022/10/15 10:07:19 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 10:08:05 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 10:08:05 - mmengine - INFO - Saving checkpoint at 140 epochs 2022/10/15 10:09:25 - mmengine - INFO - Epoch(val) [140][50/407] eta: 0:09:17 time: 1.560738 data_time: 1.523742 memory: 5829 2022/10/15 10:10:44 - mmengine - INFO - Epoch(val) [140][100/407] eta: 0:08:04 time: 1.577165 data_time: 1.540270 memory: 540 2022/10/15 10:11:51 - mmengine - INFO - Epoch(val) [140][150/407] eta: 0:05:46 time: 1.348888 data_time: 1.308484 memory: 540 2022/10/15 10:12:56 - mmengine - INFO - Epoch(val) [140][200/407] eta: 0:04:28 time: 1.298370 data_time: 1.262480 memory: 540 2022/10/15 10:14:09 - mmengine - INFO - Epoch(val) [140][250/407] eta: 0:03:50 time: 1.467426 data_time: 1.431345 memory: 540 2022/10/15 10:15:14 - mmengine - INFO - Epoch(val) [140][300/407] eta: 0:02:18 time: 1.293253 data_time: 1.257113 memory: 540 2022/10/15 10:16:20 - mmengine - INFO - Epoch(val) [140][350/407] eta: 0:01:15 time: 1.322176 data_time: 1.284945 memory: 540 2022/10/15 10:17:35 - mmengine - INFO - Epoch(val) [140][400/407] eta: 0:00:10 time: 1.500544 data_time: 1.464998 memory: 540 2022/10/15 10:18:19 - mmengine - INFO - Evaluating CocoMetric... 2022/10/15 10:18:33 - mmengine - INFO - Epoch(val) [140][407/407] coco/AP: 0.628409 coco/AP .5: 0.864411 coco/AP .75: 0.700283 coco/AP (M): 0.592874 coco/AP (L): 0.692862 coco/AR: 0.691577 coco/AR .5: 0.908533 coco/AR .75: 0.759446 coco/AR (M): 0.647856 coco/AR (L): 0.753698 2022/10/15 10:18:33 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256/best_coco/AP_epoch_130.pth is removed 2022/10/15 10:18:34 - mmengine - INFO - The best checkpoint with 0.6284 coco/AP at 140 epoch is saved to best_coco/AP_epoch_140.pth. 2022/10/15 10:21:05 - mmengine - INFO - Epoch(train) [141][50/293] lr: 5.000000e-04 eta: 9:40:17 time: 3.018905 data_time: 0.443706 memory: 5829 loss_kpt: 0.000862 acc_pose: 0.715058 loss: 0.000862 2022/10/15 10:23:23 - mmengine - INFO - Epoch(train) [141][100/293] lr: 5.000000e-04 eta: 9:39:18 time: 2.749649 data_time: 0.102603 memory: 5829 loss_kpt: 0.000838 acc_pose: 0.821582 loss: 0.000838 2022/10/15 10:25:33 - mmengine - INFO - Epoch(train) [141][150/293] lr: 5.000000e-04 eta: 9:38:15 time: 2.609516 data_time: 0.065659 memory: 5829 loss_kpt: 0.000843 acc_pose: 0.723478 loss: 0.000843 2022/10/15 10:27:34 - mmengine - INFO - Epoch(train) [141][200/293] lr: 5.000000e-04 eta: 9:37:07 time: 2.422314 data_time: 0.061179 memory: 5829 loss_kpt: 0.000840 acc_pose: 0.733574 loss: 0.000840 2022/10/15 10:29:45 - mmengine - INFO - Epoch(train) [141][250/293] lr: 5.000000e-04 eta: 9:36:04 time: 2.611147 data_time: 0.061524 memory: 5829 loss_kpt: 0.000838 acc_pose: 0.674445 loss: 0.000838 2022/10/15 10:31:36 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 10:33:45 - mmengine - INFO - Epoch(train) [142][50/293] lr: 5.000000e-04 eta: 9:33:11 time: 2.584120 data_time: 1.010870 memory: 5829 loss_kpt: 0.000842 acc_pose: 0.738067 loss: 0.000842 2022/10/15 10:36:02 - mmengine - INFO - Epoch(train) [142][100/293] lr: 5.000000e-04 eta: 9:32:11 time: 2.724841 data_time: 0.087174 memory: 5829 loss_kpt: 0.000823 acc_pose: 0.805808 loss: 0.000823 2022/10/15 10:38:29 - mmengine - INFO - Epoch(train) [142][150/293] lr: 5.000000e-04 eta: 9:31:16 time: 2.954277 data_time: 0.076333 memory: 5829 loss_kpt: 0.000854 acc_pose: 0.772007 loss: 0.000854 2022/10/15 10:41:06 - mmengine - INFO - Epoch(train) [142][200/293] lr: 5.000000e-04 eta: 9:30:25 time: 3.130939 data_time: 0.243209 memory: 5829 loss_kpt: 0.000829 acc_pose: 0.755824 loss: 0.000829 2022/10/15 10:43:42 - mmengine - INFO - Epoch(train) [142][250/293] lr: 5.000000e-04 eta: 9:29:33 time: 3.116802 data_time: 0.347305 memory: 5829 loss_kpt: 0.000843 acc_pose: 0.736432 loss: 0.000843 2022/10/15 10:45:54 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 10:48:12 - mmengine - INFO - Epoch(train) [143][50/293] lr: 5.000000e-04 eta: 9:26:44 time: 2.765574 data_time: 0.126374 memory: 5829 loss_kpt: 0.000821 acc_pose: 0.680692 loss: 0.000821 2022/10/15 10:50:36 - mmengine - INFO - Epoch(train) [143][100/293] lr: 5.000000e-04 eta: 9:25:46 time: 2.877597 data_time: 0.059115 memory: 5829 loss_kpt: 0.000820 acc_pose: 0.786920 loss: 0.000820 2022/10/15 10:53:07 - mmengine - INFO - Epoch(train) [143][150/293] lr: 5.000000e-04 eta: 9:24:52 time: 3.029677 data_time: 0.560270 memory: 5829 loss_kpt: 0.000845 acc_pose: 0.788208 loss: 0.000845 2022/10/15 10:55:43 - mmengine - INFO - Epoch(train) [143][200/293] lr: 5.000000e-04 eta: 9:23:59 time: 3.109598 data_time: 0.877984 memory: 5829 loss_kpt: 0.000833 acc_pose: 0.739399 loss: 0.000833 2022/10/15 10:57:41 - mmengine - INFO - Epoch(train) [143][250/293] lr: 5.000000e-04 eta: 9:22:48 time: 2.369251 data_time: 2.042452 memory: 5829 loss_kpt: 0.000817 acc_pose: 0.735949 loss: 0.000817 2022/10/15 10:59:28 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 11:01:36 - mmengine - INFO - Epoch(train) [144][50/293] lr: 5.000000e-04 eta: 9:19:54 time: 2.545814 data_time: 1.368667 memory: 5829 loss_kpt: 0.000829 acc_pose: 0.725335 loss: 0.000829 2022/10/15 11:03:38 - mmengine - INFO - Epoch(train) [144][100/293] lr: 5.000000e-04 eta: 9:18:45 time: 2.457927 data_time: 0.369171 memory: 5829 loss_kpt: 0.000825 acc_pose: 0.691441 loss: 0.000825 2022/10/15 11:03:39 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 11:05:40 - mmengine - INFO - Epoch(train) [144][150/293] lr: 5.000000e-04 eta: 9:17:36 time: 2.425211 data_time: 1.226762 memory: 5829 loss_kpt: 0.000825 acc_pose: 0.734565 loss: 0.000825 2022/10/15 11:07:37 - mmengine - INFO - Epoch(train) [144][200/293] lr: 5.000000e-04 eta: 9:16:24 time: 2.342742 data_time: 1.906602 memory: 5829 loss_kpt: 0.000823 acc_pose: 0.758836 loss: 0.000823 2022/10/15 11:09:43 - mmengine - INFO - Epoch(train) [144][250/293] lr: 5.000000e-04 eta: 9:15:17 time: 2.524769 data_time: 0.697508 memory: 5829 loss_kpt: 0.000829 acc_pose: 0.751432 loss: 0.000829 2022/10/15 11:11:21 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 11:13:27 - mmengine - INFO - Epoch(train) [145][50/293] lr: 5.000000e-04 eta: 9:12:22 time: 2.524194 data_time: 0.280761 memory: 5829 loss_kpt: 0.000834 acc_pose: 0.735695 loss: 0.000834 2022/10/15 11:15:17 - mmengine - INFO - Epoch(train) [145][100/293] lr: 5.000000e-04 eta: 9:11:07 time: 2.205091 data_time: 0.058473 memory: 5829 loss_kpt: 0.000830 acc_pose: 0.729554 loss: 0.000830 2022/10/15 11:17:04 - mmengine - INFO - Epoch(train) [145][150/293] lr: 5.000000e-04 eta: 9:09:50 time: 2.138283 data_time: 0.063029 memory: 5829 loss_kpt: 0.000827 acc_pose: 0.698844 loss: 0.000827 2022/10/15 11:18:47 - mmengine - INFO - Epoch(train) [145][200/293] lr: 5.000000e-04 eta: 9:08:32 time: 2.055603 data_time: 0.110923 memory: 5829 loss_kpt: 0.000845 acc_pose: 0.809147 loss: 0.000845 2022/10/15 11:20:16 - mmengine - INFO - Epoch(train) [145][250/293] lr: 5.000000e-04 eta: 9:07:07 time: 1.780143 data_time: 0.091731 memory: 5829 loss_kpt: 0.000831 acc_pose: 0.770430 loss: 0.000831 2022/10/15 11:21:41 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 11:23:52 - mmengine - INFO - Epoch(train) [146][50/293] lr: 5.000000e-04 eta: 9:04:14 time: 2.609133 data_time: 0.528565 memory: 5829 loss_kpt: 0.000827 acc_pose: 0.753063 loss: 0.000827 2022/10/15 11:25:39 - mmengine - INFO - Epoch(train) [146][100/293] lr: 5.000000e-04 eta: 9:02:58 time: 2.152646 data_time: 0.055759 memory: 5829 loss_kpt: 0.000820 acc_pose: 0.786925 loss: 0.000820 2022/10/15 11:27:32 - mmengine - INFO - Epoch(train) [146][150/293] lr: 5.000000e-04 eta: 9:01:44 time: 2.248444 data_time: 0.059109 memory: 5829 loss_kpt: 0.000829 acc_pose: 0.753232 loss: 0.000829 2022/10/15 11:29:06 - mmengine - INFO - Epoch(train) [146][200/293] lr: 5.000000e-04 eta: 9:00:21 time: 1.885535 data_time: 0.071791 memory: 5829 loss_kpt: 0.000832 acc_pose: 0.731699 loss: 0.000832 2022/10/15 11:30:39 - mmengine - INFO - Epoch(train) [146][250/293] lr: 5.000000e-04 eta: 8:58:59 time: 1.866572 data_time: 0.195314 memory: 5829 loss_kpt: 0.000829 acc_pose: 0.776658 loss: 0.000829 2022/10/15 11:32:18 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 11:34:28 - mmengine - INFO - Epoch(train) [147][50/293] lr: 5.000000e-04 eta: 8:56:05 time: 2.596519 data_time: 0.497443 memory: 5829 loss_kpt: 0.000820 acc_pose: 0.718995 loss: 0.000820 2022/10/15 11:36:48 - mmengine - INFO - Epoch(train) [147][100/293] lr: 5.000000e-04 eta: 8:55:03 time: 2.807422 data_time: 0.059021 memory: 5829 loss_kpt: 0.000836 acc_pose: 0.730177 loss: 0.000836 2022/10/15 11:38:57 - mmengine - INFO - Epoch(train) [147][150/293] lr: 5.000000e-04 eta: 8:53:55 time: 2.567978 data_time: 0.056151 memory: 5829 loss_kpt: 0.000817 acc_pose: 0.736836 loss: 0.000817 2022/10/15 11:41:03 - mmengine - INFO - Epoch(train) [147][200/293] lr: 5.000000e-04 eta: 8:52:47 time: 2.535148 data_time: 1.657509 memory: 5829 loss_kpt: 0.000824 acc_pose: 0.775918 loss: 0.000824 2022/10/15 11:42:01 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 11:43:17 - mmengine - INFO - Epoch(train) [147][250/293] lr: 5.000000e-04 eta: 8:51:41 time: 2.673778 data_time: 2.078255 memory: 5829 loss_kpt: 0.000815 acc_pose: 0.764045 loss: 0.000815 2022/10/15 11:45:09 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 11:47:28 - mmengine - INFO - Epoch(train) [148][50/293] lr: 5.000000e-04 eta: 8:48:51 time: 2.774716 data_time: 0.246942 memory: 5829 loss_kpt: 0.000838 acc_pose: 0.746062 loss: 0.000838 2022/10/15 11:49:33 - mmengine - INFO - Epoch(train) [148][100/293] lr: 5.000000e-04 eta: 8:47:42 time: 2.507364 data_time: 0.287815 memory: 5829 loss_kpt: 0.000829 acc_pose: 0.705865 loss: 0.000829 2022/10/15 11:51:43 - mmengine - INFO - Epoch(train) [148][150/293] lr: 5.000000e-04 eta: 8:46:34 time: 2.594540 data_time: 0.061836 memory: 5829 loss_kpt: 0.000833 acc_pose: 0.739546 loss: 0.000833 2022/10/15 11:53:59 - mmengine - INFO - Epoch(train) [148][200/293] lr: 5.000000e-04 eta: 8:45:29 time: 2.724796 data_time: 0.066224 memory: 5829 loss_kpt: 0.000822 acc_pose: 0.724401 loss: 0.000822 2022/10/15 11:56:19 - mmengine - INFO - Epoch(train) [148][250/293] lr: 5.000000e-04 eta: 8:44:25 time: 2.805638 data_time: 0.064002 memory: 5829 loss_kpt: 0.000835 acc_pose: 0.709946 loss: 0.000835 2022/10/15 11:58:18 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 12:00:27 - mmengine - INFO - Epoch(train) [149][50/293] lr: 5.000000e-04 eta: 8:41:31 time: 2.581556 data_time: 0.150046 memory: 5829 loss_kpt: 0.000820 acc_pose: 0.827377 loss: 0.000820 2022/10/15 12:02:52 - mmengine - INFO - Epoch(train) [149][100/293] lr: 5.000000e-04 eta: 8:40:29 time: 2.892416 data_time: 0.072743 memory: 5829 loss_kpt: 0.000820 acc_pose: 0.739280 loss: 0.000820 2022/10/15 12:05:01 - mmengine - INFO - Epoch(train) [149][150/293] lr: 5.000000e-04 eta: 8:39:21 time: 2.586124 data_time: 0.193614 memory: 5829 loss_kpt: 0.000838 acc_pose: 0.766637 loss: 0.000838 2022/10/15 12:07:16 - mmengine - INFO - Epoch(train) [149][200/293] lr: 5.000000e-04 eta: 8:38:14 time: 2.691739 data_time: 0.070787 memory: 5829 loss_kpt: 0.000829 acc_pose: 0.709808 loss: 0.000829 2022/10/15 12:09:22 - mmengine - INFO - Epoch(train) [149][250/293] lr: 5.000000e-04 eta: 8:37:04 time: 2.523685 data_time: 0.343670 memory: 5829 loss_kpt: 0.000832 acc_pose: 0.796818 loss: 0.000832 2022/10/15 12:11:11 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 12:13:20 - mmengine - INFO - Epoch(train) [150][50/293] lr: 5.000000e-04 eta: 8:34:10 time: 2.591057 data_time: 0.201430 memory: 5829 loss_kpt: 0.000836 acc_pose: 0.780172 loss: 0.000836 2022/10/15 12:15:41 - mmengine - INFO - Epoch(train) [150][100/293] lr: 5.000000e-04 eta: 8:33:05 time: 2.805451 data_time: 0.067647 memory: 5829 loss_kpt: 0.000817 acc_pose: 0.745451 loss: 0.000817 2022/10/15 12:18:08 - mmengine - INFO - Epoch(train) [150][150/293] lr: 5.000000e-04 eta: 8:32:03 time: 2.951046 data_time: 0.064885 memory: 5829 loss_kpt: 0.000836 acc_pose: 0.782018 loss: 0.000836 2022/10/15 12:20:40 - mmengine - INFO - Epoch(train) [150][200/293] lr: 5.000000e-04 eta: 8:31:03 time: 3.040731 data_time: 0.066698 memory: 5829 loss_kpt: 0.000839 acc_pose: 0.710850 loss: 0.000839 2022/10/15 12:23:21 - mmengine - INFO - Epoch(train) [150][250/293] lr: 5.000000e-04 eta: 8:30:06 time: 3.207322 data_time: 0.067784 memory: 5829 loss_kpt: 0.000835 acc_pose: 0.765682 loss: 0.000835 2022/10/15 12:25:24 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 12:25:24 - mmengine - INFO - Saving checkpoint at 150 epochs 2022/10/15 12:26:36 - mmengine - INFO - Epoch(val) [150][50/407] eta: 0:08:17 time: 1.394392 data_time: 1.358040 memory: 5829 2022/10/15 12:27:48 - mmengine - INFO - Epoch(val) [150][100/407] eta: 0:07:20 time: 1.434787 data_time: 1.395740 memory: 540 2022/10/15 12:29:09 - mmengine - INFO - Epoch(val) [150][150/407] eta: 0:07:00 time: 1.636290 data_time: 1.600247 memory: 540 2022/10/15 12:30:18 - mmengine - INFO - Epoch(val) [150][200/407] eta: 0:04:44 time: 1.372581 data_time: 1.335749 memory: 540 2022/10/15 12:31:36 - mmengine - INFO - Epoch(val) [150][250/407] eta: 0:04:04 time: 1.554561 data_time: 1.518899 memory: 540 2022/10/15 12:32:52 - mmengine - INFO - Epoch(val) [150][300/407] eta: 0:02:42 time: 1.519989 data_time: 1.483344 memory: 540 2022/10/15 12:34:00 - mmengine - INFO - Epoch(val) [150][350/407] eta: 0:01:17 time: 1.362235 data_time: 1.326516 memory: 540 2022/10/15 12:35:09 - mmengine - INFO - Epoch(val) [150][400/407] eta: 0:00:09 time: 1.390867 data_time: 1.354854 memory: 540 2022/10/15 12:36:00 - mmengine - INFO - Evaluating CocoMetric... 2022/10/15 12:36:13 - mmengine - INFO - Epoch(val) [150][407/407] coco/AP: 0.625041 coco/AP .5: 0.862933 coco/AP .75: 0.695614 coco/AP (M): 0.589840 coco/AP (L): 0.690821 coco/AR: 0.688759 coco/AR .5: 0.907588 coco/AR .75: 0.754880 coco/AR (M): 0.642830 coco/AR (L): 0.753735 2022/10/15 12:39:12 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 12:39:12 - mmengine - INFO - Epoch(train) [151][50/293] lr: 5.000000e-04 eta: 8:27:31 time: 3.579225 data_time: 1.887441 memory: 5829 loss_kpt: 0.000818 acc_pose: 0.776020 loss: 0.000818 2022/10/15 12:41:57 - mmengine - INFO - Epoch(train) [151][100/293] lr: 5.000000e-04 eta: 8:26:35 time: 3.291480 data_time: 2.895984 memory: 5829 loss_kpt: 0.000823 acc_pose: 0.755463 loss: 0.000823 2022/10/15 12:44:24 - mmengine - INFO - Epoch(train) [151][150/293] lr: 5.000000e-04 eta: 8:25:32 time: 2.952504 data_time: 1.009966 memory: 5829 loss_kpt: 0.000831 acc_pose: 0.692579 loss: 0.000831 2022/10/15 12:46:56 - mmengine - INFO - Epoch(train) [151][200/293] lr: 5.000000e-04 eta: 8:24:31 time: 3.038636 data_time: 0.096784 memory: 5829 loss_kpt: 0.000830 acc_pose: 0.714263 loss: 0.000830 2022/10/15 12:49:32 - mmengine - INFO - Epoch(train) [151][250/293] lr: 5.000000e-04 eta: 8:23:30 time: 3.107602 data_time: 0.058799 memory: 5829 loss_kpt: 0.000831 acc_pose: 0.710368 loss: 0.000831 2022/10/15 12:51:37 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 12:53:58 - mmengine - INFO - Epoch(train) [152][50/293] lr: 5.000000e-04 eta: 8:20:40 time: 2.817623 data_time: 0.280866 memory: 5829 loss_kpt: 0.000836 acc_pose: 0.719774 loss: 0.000836 2022/10/15 12:56:22 - mmengine - INFO - Epoch(train) [152][100/293] lr: 5.000000e-04 eta: 8:19:35 time: 2.887794 data_time: 0.056180 memory: 5829 loss_kpt: 0.000842 acc_pose: 0.716859 loss: 0.000842 2022/10/15 12:58:41 - mmengine - INFO - Epoch(train) [152][150/293] lr: 5.000000e-04 eta: 8:18:27 time: 2.770116 data_time: 0.057841 memory: 5829 loss_kpt: 0.000834 acc_pose: 0.719792 loss: 0.000834 2022/10/15 13:00:58 - mmengine - INFO - Epoch(train) [152][200/293] lr: 5.000000e-04 eta: 8:17:20 time: 2.751873 data_time: 0.140843 memory: 5829 loss_kpt: 0.000846 acc_pose: 0.763150 loss: 0.000846 2022/10/15 13:03:10 - mmengine - INFO - Epoch(train) [152][250/293] lr: 5.000000e-04 eta: 8:16:09 time: 2.625125 data_time: 0.088246 memory: 5829 loss_kpt: 0.000836 acc_pose: 0.750643 loss: 0.000836 2022/10/15 13:05:16 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 13:07:43 - mmengine - INFO - Epoch(train) [153][50/293] lr: 5.000000e-04 eta: 8:13:21 time: 2.946286 data_time: 0.275122 memory: 5829 loss_kpt: 0.000835 acc_pose: 0.752552 loss: 0.000835 2022/10/15 13:10:18 - mmengine - INFO - Epoch(train) [153][100/293] lr: 5.000000e-04 eta: 8:12:19 time: 3.100012 data_time: 0.072093 memory: 5829 loss_kpt: 0.000831 acc_pose: 0.736757 loss: 0.000831 2022/10/15 13:12:42 - mmengine - INFO - Epoch(train) [153][150/293] lr: 5.000000e-04 eta: 8:11:13 time: 2.874926 data_time: 0.065505 memory: 5829 loss_kpt: 0.000834 acc_pose: 0.674706 loss: 0.000834 2022/10/15 13:15:12 - mmengine - INFO - Epoch(train) [153][200/293] lr: 5.000000e-04 eta: 8:10:09 time: 2.995321 data_time: 0.069371 memory: 5829 loss_kpt: 0.000822 acc_pose: 0.744802 loss: 0.000822 2022/10/15 13:17:04 - mmengine - INFO - Epoch(train) [153][250/293] lr: 5.000000e-04 eta: 8:08:50 time: 2.233648 data_time: 0.064521 memory: 5829 loss_kpt: 0.000817 acc_pose: 0.773088 loss: 0.000817 2022/10/15 13:18:46 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 13:21:07 - mmengine - INFO - Epoch(train) [154][50/293] lr: 5.000000e-04 eta: 8:05:59 time: 2.825929 data_time: 0.244513 memory: 5829 loss_kpt: 0.000837 acc_pose: 0.711496 loss: 0.000837 2022/10/15 13:23:22 - mmengine - INFO - Epoch(train) [154][100/293] lr: 5.000000e-04 eta: 8:04:49 time: 2.705664 data_time: 0.111778 memory: 5829 loss_kpt: 0.000823 acc_pose: 0.718870 loss: 0.000823 2022/10/15 13:25:27 - mmengine - INFO - Epoch(train) [154][150/293] lr: 5.000000e-04 eta: 8:03:35 time: 2.496145 data_time: 0.594584 memory: 5829 loss_kpt: 0.000834 acc_pose: 0.790813 loss: 0.000834 2022/10/15 13:26:21 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 13:27:49 - mmengine - INFO - Epoch(train) [154][200/293] lr: 5.000000e-04 eta: 8:02:28 time: 2.831852 data_time: 1.379172 memory: 5829 loss_kpt: 0.000843 acc_pose: 0.734567 loss: 0.000843 2022/10/15 13:29:39 - mmengine - INFO - Epoch(train) [154][250/293] lr: 5.000000e-04 eta: 8:01:08 time: 2.209192 data_time: 0.063205 memory: 5829 loss_kpt: 0.000832 acc_pose: 0.777636 loss: 0.000832 2022/10/15 13:31:12 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 13:33:06 - mmengine - INFO - Epoch(train) [155][50/293] lr: 5.000000e-04 eta: 7:58:07 time: 2.272280 data_time: 0.308842 memory: 5829 loss_kpt: 0.000832 acc_pose: 0.765196 loss: 0.000832 2022/10/15 13:35:45 - mmengine - INFO - Epoch(train) [155][100/293] lr: 5.000000e-04 eta: 7:57:05 time: 3.187731 data_time: 0.107401 memory: 5829 loss_kpt: 0.000826 acc_pose: 0.716099 loss: 0.000826 2022/10/15 13:38:23 - mmengine - INFO - Epoch(train) [155][150/293] lr: 5.000000e-04 eta: 7:56:02 time: 3.148876 data_time: 0.065917 memory: 5829 loss_kpt: 0.000829 acc_pose: 0.777091 loss: 0.000829 2022/10/15 13:40:40 - mmengine - INFO - Epoch(train) [155][200/293] lr: 5.000000e-04 eta: 7:54:52 time: 2.753966 data_time: 0.062842 memory: 5829 loss_kpt: 0.000826 acc_pose: 0.673880 loss: 0.000826 2022/10/15 13:42:52 - mmengine - INFO - Epoch(train) [155][250/293] lr: 5.000000e-04 eta: 7:53:40 time: 2.634016 data_time: 0.411568 memory: 5829 loss_kpt: 0.000832 acc_pose: 0.788201 loss: 0.000832 2022/10/15 13:44:45 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 13:47:13 - mmengine - INFO - Epoch(train) [156][50/293] lr: 5.000000e-04 eta: 7:50:51 time: 2.952698 data_time: 0.311576 memory: 5829 loss_kpt: 0.000827 acc_pose: 0.748120 loss: 0.000827 2022/10/15 13:49:34 - mmengine - INFO - Epoch(train) [156][100/293] lr: 5.000000e-04 eta: 7:49:42 time: 2.832807 data_time: 0.294878 memory: 5829 loss_kpt: 0.000844 acc_pose: 0.758729 loss: 0.000844 2022/10/15 13:51:52 - mmengine - INFO - Epoch(train) [156][150/293] lr: 5.000000e-04 eta: 7:48:31 time: 2.762947 data_time: 0.064404 memory: 5829 loss_kpt: 0.000843 acc_pose: 0.741111 loss: 0.000843 2022/10/15 13:54:31 - mmengine - INFO - Epoch(train) [156][200/293] lr: 5.000000e-04 eta: 7:47:28 time: 3.168917 data_time: 0.071582 memory: 5829 loss_kpt: 0.000830 acc_pose: 0.748492 loss: 0.000830 2022/10/15 13:56:54 - mmengine - INFO - Epoch(train) [156][250/293] lr: 5.000000e-04 eta: 7:46:19 time: 2.865216 data_time: 0.066428 memory: 5829 loss_kpt: 0.000818 acc_pose: 0.744417 loss: 0.000818 2022/10/15 13:58:46 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 14:01:03 - mmengine - INFO - Epoch(train) [157][50/293] lr: 5.000000e-04 eta: 7:43:26 time: 2.731776 data_time: 0.191110 memory: 5829 loss_kpt: 0.000806 acc_pose: 0.751323 loss: 0.000806 2022/10/15 14:03:08 - mmengine - INFO - Epoch(train) [157][100/293] lr: 5.000000e-04 eta: 7:42:10 time: 2.496230 data_time: 0.059976 memory: 5829 loss_kpt: 0.000833 acc_pose: 0.730449 loss: 0.000833 2022/10/15 14:05:14 - mmengine - INFO - Epoch(train) [157][150/293] lr: 5.000000e-04 eta: 7:40:55 time: 2.537606 data_time: 0.119483 memory: 5829 loss_kpt: 0.000842 acc_pose: 0.735870 loss: 0.000842 2022/10/15 14:07:21 - mmengine - INFO - Epoch(train) [157][200/293] lr: 5.000000e-04 eta: 7:39:40 time: 2.528995 data_time: 0.065661 memory: 5829 loss_kpt: 0.000811 acc_pose: 0.746001 loss: 0.000811 2022/10/15 14:09:41 - mmengine - INFO - Epoch(train) [157][250/293] lr: 5.000000e-04 eta: 7:38:29 time: 2.795038 data_time: 0.064045 memory: 5829 loss_kpt: 0.000821 acc_pose: 0.684076 loss: 0.000821 2022/10/15 14:11:36 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 14:11:36 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 14:13:57 - mmengine - INFO - Epoch(train) [158][50/293] lr: 5.000000e-04 eta: 7:35:37 time: 2.808312 data_time: 0.212304 memory: 5829 loss_kpt: 0.000820 acc_pose: 0.731638 loss: 0.000820 2022/10/15 14:16:09 - mmengine - INFO - Epoch(train) [158][100/293] lr: 5.000000e-04 eta: 7:34:23 time: 2.649305 data_time: 0.078462 memory: 5829 loss_kpt: 0.000818 acc_pose: 0.763938 loss: 0.000818 2022/10/15 14:18:27 - mmengine - INFO - Epoch(train) [158][150/293] lr: 5.000000e-04 eta: 7:33:11 time: 2.759951 data_time: 0.070404 memory: 5829 loss_kpt: 0.000814 acc_pose: 0.783066 loss: 0.000814 2022/10/15 14:20:38 - mmengine - INFO - Epoch(train) [158][200/293] lr: 5.000000e-04 eta: 7:31:57 time: 2.602512 data_time: 0.077706 memory: 5829 loss_kpt: 0.000827 acc_pose: 0.741872 loss: 0.000827 2022/10/15 14:22:40 - mmengine - INFO - Epoch(train) [158][250/293] lr: 5.000000e-04 eta: 7:30:40 time: 2.455953 data_time: 0.060627 memory: 5829 loss_kpt: 0.000824 acc_pose: 0.777087 loss: 0.000824 2022/10/15 14:24:32 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 14:26:56 - mmengine - INFO - Epoch(train) [159][50/293] lr: 5.000000e-04 eta: 7:27:48 time: 2.876686 data_time: 0.241587 memory: 5829 loss_kpt: 0.000814 acc_pose: 0.789833 loss: 0.000814 2022/10/15 14:28:57 - mmengine - INFO - Epoch(train) [159][100/293] lr: 5.000000e-04 eta: 7:26:30 time: 2.425886 data_time: 0.070416 memory: 5829 loss_kpt: 0.000820 acc_pose: 0.708037 loss: 0.000820 2022/10/15 14:31:08 - mmengine - INFO - Epoch(train) [159][150/293] lr: 5.000000e-04 eta: 7:25:16 time: 2.610842 data_time: 0.065976 memory: 5829 loss_kpt: 0.000825 acc_pose: 0.766926 loss: 0.000825 2022/10/15 14:33:28 - mmengine - INFO - Epoch(train) [159][200/293] lr: 5.000000e-04 eta: 7:24:04 time: 2.795005 data_time: 0.189821 memory: 5829 loss_kpt: 0.000833 acc_pose: 0.768562 loss: 0.000833 2022/10/15 14:35:38 - mmengine - INFO - Epoch(train) [159][250/293] lr: 5.000000e-04 eta: 7:22:48 time: 2.605985 data_time: 0.136785 memory: 5829 loss_kpt: 0.000833 acc_pose: 0.747119 loss: 0.000833 2022/10/15 14:37:12 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 14:39:19 - mmengine - INFO - Epoch(train) [160][50/293] lr: 5.000000e-04 eta: 7:19:51 time: 2.535312 data_time: 0.338551 memory: 5829 loss_kpt: 0.000814 acc_pose: 0.741522 loss: 0.000814 2022/10/15 14:41:22 - mmengine - INFO - Epoch(train) [160][100/293] lr: 5.000000e-04 eta: 7:18:34 time: 2.467476 data_time: 0.132494 memory: 5829 loss_kpt: 0.000818 acc_pose: 0.776375 loss: 0.000818 2022/10/15 14:43:19 - mmengine - INFO - Epoch(train) [160][150/293] lr: 5.000000e-04 eta: 7:17:14 time: 2.331809 data_time: 0.074917 memory: 5829 loss_kpt: 0.000831 acc_pose: 0.744014 loss: 0.000831 2022/10/15 14:45:18 - mmengine - INFO - Epoch(train) [160][200/293] lr: 5.000000e-04 eta: 7:15:55 time: 2.378789 data_time: 0.076021 memory: 5829 loss_kpt: 0.000826 acc_pose: 0.721909 loss: 0.000826 2022/10/15 14:47:34 - mmengine - INFO - Epoch(train) [160][250/293] lr: 5.000000e-04 eta: 7:14:41 time: 2.727020 data_time: 0.078302 memory: 5829 loss_kpt: 0.000828 acc_pose: 0.749566 loss: 0.000828 2022/10/15 14:49:30 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 14:49:30 - mmengine - INFO - Saving checkpoint at 160 epochs 2022/10/15 14:50:46 - mmengine - INFO - Epoch(val) [160][50/407] eta: 0:08:48 time: 1.481323 data_time: 1.444799 memory: 5829 2022/10/15 14:52:01 - mmengine - INFO - Epoch(val) [160][100/407] eta: 0:07:41 time: 1.503497 data_time: 1.462534 memory: 540 2022/10/15 14:53:19 - mmengine - INFO - Epoch(val) [160][150/407] eta: 0:06:38 time: 1.552426 data_time: 1.516326 memory: 540 2022/10/15 14:54:35 - mmengine - INFO - Epoch(val) [160][200/407] eta: 0:05:14 time: 1.520487 data_time: 1.484038 memory: 540 2022/10/15 14:55:35 - mmengine - INFO - Epoch(val) [160][250/407] eta: 0:03:10 time: 1.210250 data_time: 1.173987 memory: 540 2022/10/15 14:56:39 - mmengine - INFO - Epoch(val) [160][300/407] eta: 0:02:17 time: 1.282253 data_time: 1.246252 memory: 540 2022/10/15 14:57:49 - mmengine - INFO - Epoch(val) [160][350/407] eta: 0:01:19 time: 1.392460 data_time: 1.356301 memory: 540 2022/10/15 14:58:53 - mmengine - INFO - Epoch(val) [160][400/407] eta: 0:00:09 time: 1.288247 data_time: 1.251874 memory: 540 2022/10/15 14:59:38 - mmengine - INFO - Evaluating CocoMetric... 2022/10/15 14:59:52 - mmengine - INFO - Epoch(val) [160][407/407] coco/AP: 0.629738 coco/AP .5: 0.863905 coco/AP .75: 0.706929 coco/AP (M): 0.596005 coco/AP (L): 0.693330 coco/AR: 0.691971 coco/AR .5: 0.908060 coco/AR .75: 0.764169 coco/AR (M): 0.648293 coco/AR (L): 0.754589 2022/10/15 14:59:52 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256/best_coco/AP_epoch_140.pth is removed 2022/10/15 14:59:54 - mmengine - INFO - The best checkpoint with 0.6297 coco/AP at 160 epoch is saved to best_coco/AP_epoch_160.pth. 2022/10/15 15:02:26 - mmengine - INFO - Epoch(train) [161][50/293] lr: 5.000000e-04 eta: 7:11:52 time: 3.048183 data_time: 1.331932 memory: 5829 loss_kpt: 0.000828 acc_pose: 0.749125 loss: 0.000828 2022/10/15 15:04:45 - mmengine - INFO - Epoch(train) [161][100/293] lr: 5.000000e-04 eta: 7:10:39 time: 2.769989 data_time: 0.062606 memory: 5829 loss_kpt: 0.000821 acc_pose: 0.727392 loss: 0.000821 2022/10/15 15:05:45 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 15:07:09 - mmengine - INFO - Epoch(train) [161][150/293] lr: 5.000000e-04 eta: 7:09:27 time: 2.884507 data_time: 0.058625 memory: 5829 loss_kpt: 0.000820 acc_pose: 0.749435 loss: 0.000820 2022/10/15 15:09:36 - mmengine - INFO - Epoch(train) [161][200/293] lr: 5.000000e-04 eta: 7:08:16 time: 2.948253 data_time: 0.073474 memory: 5829 loss_kpt: 0.000811 acc_pose: 0.767351 loss: 0.000811 2022/10/15 15:11:53 - mmengine - INFO - Epoch(train) [161][250/293] lr: 5.000000e-04 eta: 7:07:02 time: 2.737571 data_time: 0.072369 memory: 5829 loss_kpt: 0.000840 acc_pose: 0.751318 loss: 0.000840 2022/10/15 15:13:42 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 15:15:47 - mmengine - INFO - Epoch(train) [162][50/293] lr: 5.000000e-04 eta: 7:04:04 time: 2.490366 data_time: 0.213695 memory: 5829 loss_kpt: 0.000817 acc_pose: 0.821726 loss: 0.000817 2022/10/15 15:17:48 - mmengine - INFO - Epoch(train) [162][100/293] lr: 5.000000e-04 eta: 7:02:45 time: 2.418946 data_time: 0.146840 memory: 5829 loss_kpt: 0.000828 acc_pose: 0.707162 loss: 0.000828 2022/10/15 15:19:36 - mmengine - INFO - Epoch(train) [162][150/293] lr: 5.000000e-04 eta: 7:01:21 time: 2.154445 data_time: 0.078077 memory: 5829 loss_kpt: 0.000829 acc_pose: 0.789339 loss: 0.000829 2022/10/15 15:21:15 - mmengine - INFO - Epoch(train) [162][200/293] lr: 5.000000e-04 eta: 6:59:56 time: 1.987986 data_time: 0.059562 memory: 5829 loss_kpt: 0.000838 acc_pose: 0.745883 loss: 0.000838 2022/10/15 15:22:45 - mmengine - INFO - Epoch(train) [162][250/293] lr: 5.000000e-04 eta: 6:58:27 time: 1.792174 data_time: 0.351335 memory: 5829 loss_kpt: 0.000822 acc_pose: 0.720486 loss: 0.000822 2022/10/15 15:23:53 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 15:25:54 - mmengine - INFO - Epoch(train) [163][50/293] lr: 5.000000e-04 eta: 6:55:28 time: 2.424777 data_time: 0.375996 memory: 5829 loss_kpt: 0.000821 acc_pose: 0.771937 loss: 0.000821 2022/10/15 15:28:07 - mmengine - INFO - Epoch(train) [163][100/293] lr: 5.000000e-04 eta: 6:54:12 time: 2.648120 data_time: 0.056474 memory: 5829 loss_kpt: 0.000818 acc_pose: 0.752205 loss: 0.000818 2022/10/15 15:29:48 - mmengine - INFO - Epoch(train) [163][150/293] lr: 5.000000e-04 eta: 6:52:47 time: 2.030484 data_time: 0.064491 memory: 5829 loss_kpt: 0.000833 acc_pose: 0.696001 loss: 0.000833 2022/10/15 15:31:30 - mmengine - INFO - Epoch(train) [163][200/293] lr: 5.000000e-04 eta: 6:51:22 time: 2.039770 data_time: 0.156635 memory: 5829 loss_kpt: 0.000826 acc_pose: 0.736917 loss: 0.000826 2022/10/15 15:33:03 - mmengine - INFO - Epoch(train) [163][250/293] lr: 5.000000e-04 eta: 6:49:54 time: 1.859665 data_time: 0.398144 memory: 5829 loss_kpt: 0.000837 acc_pose: 0.737539 loss: 0.000837 2022/10/15 15:34:14 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 15:35:52 - mmengine - INFO - Epoch(train) [164][50/293] lr: 5.000000e-04 eta: 6:46:48 time: 1.947552 data_time: 0.143465 memory: 5829 loss_kpt: 0.000818 acc_pose: 0.765874 loss: 0.000818 2022/10/15 15:37:14 - mmengine - INFO - Epoch(train) [164][100/293] lr: 5.000000e-04 eta: 6:45:18 time: 1.649683 data_time: 0.796876 memory: 5829 loss_kpt: 0.000831 acc_pose: 0.790876 loss: 0.000831 2022/10/15 15:39:19 - mmengine - INFO - Epoch(train) [164][150/293] lr: 5.000000e-04 eta: 6:43:59 time: 2.492258 data_time: 0.796954 memory: 5829 loss_kpt: 0.000830 acc_pose: 0.712934 loss: 0.000830 2022/10/15 15:41:06 - mmengine - INFO - Epoch(train) [164][200/293] lr: 5.000000e-04 eta: 6:42:35 time: 2.148494 data_time: 0.065882 memory: 5829 loss_kpt: 0.000834 acc_pose: 0.765938 loss: 0.000834 2022/10/15 15:42:27 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 15:42:44 - mmengine - INFO - Epoch(train) [164][250/293] lr: 5.000000e-04 eta: 6:41:09 time: 1.949960 data_time: 0.057086 memory: 5829 loss_kpt: 0.000810 acc_pose: 0.782086 loss: 0.000810 2022/10/15 15:44:15 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 15:46:04 - mmengine - INFO - Epoch(train) [165][50/293] lr: 5.000000e-04 eta: 6:38:07 time: 2.172582 data_time: 1.590028 memory: 5829 loss_kpt: 0.000835 acc_pose: 0.784647 loss: 0.000835 2022/10/15 15:48:00 - mmengine - INFO - Epoch(train) [165][100/293] lr: 5.000000e-04 eta: 6:36:46 time: 2.319389 data_time: 2.127084 memory: 5829 loss_kpt: 0.000833 acc_pose: 0.749551 loss: 0.000833 2022/10/15 15:49:50 - mmengine - INFO - Epoch(train) [165][150/293] lr: 5.000000e-04 eta: 6:35:23 time: 2.205114 data_time: 1.834613 memory: 5829 loss_kpt: 0.000831 acc_pose: 0.771905 loss: 0.000831 2022/10/15 15:51:42 - mmengine - INFO - Epoch(train) [165][200/293] lr: 5.000000e-04 eta: 6:34:00 time: 2.241174 data_time: 2.073101 memory: 5829 loss_kpt: 0.000835 acc_pose: 0.746218 loss: 0.000835 2022/10/15 15:53:00 - mmengine - INFO - Epoch(train) [165][250/293] lr: 5.000000e-04 eta: 6:32:28 time: 1.547619 data_time: 1.224991 memory: 5829 loss_kpt: 0.000825 acc_pose: 0.720333 loss: 0.000825 2022/10/15 15:54:09 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 15:55:28 - mmengine - INFO - Epoch(train) [166][50/293] lr: 5.000000e-04 eta: 6:29:19 time: 1.572390 data_time: 0.540226 memory: 5829 loss_kpt: 0.000828 acc_pose: 0.761574 loss: 0.000828 2022/10/15 15:56:55 - mmengine - INFO - Epoch(train) [166][100/293] lr: 5.000000e-04 eta: 6:27:49 time: 1.731576 data_time: 0.131629 memory: 5829 loss_kpt: 0.000813 acc_pose: 0.728436 loss: 0.000813 2022/10/15 15:58:56 - mmengine - INFO - Epoch(train) [166][150/293] lr: 5.000000e-04 eta: 6:26:29 time: 2.422391 data_time: 0.389453 memory: 5829 loss_kpt: 0.000809 acc_pose: 0.743427 loss: 0.000809 2022/10/15 16:00:39 - mmengine - INFO - Epoch(train) [166][200/293] lr: 5.000000e-04 eta: 6:25:04 time: 2.063198 data_time: 0.061246 memory: 5829 loss_kpt: 0.000833 acc_pose: 0.805570 loss: 0.000833 2022/10/15 16:02:21 - mmengine - INFO - Epoch(train) [166][250/293] lr: 5.000000e-04 eta: 6:23:38 time: 2.033096 data_time: 0.952143 memory: 5829 loss_kpt: 0.000822 acc_pose: 0.796905 loss: 0.000822 2022/10/15 16:03:23 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 16:04:52 - mmengine - INFO - Epoch(train) [167][50/293] lr: 5.000000e-04 eta: 6:20:32 time: 1.773943 data_time: 1.051345 memory: 5829 loss_kpt: 0.000828 acc_pose: 0.779419 loss: 0.000828 2022/10/15 16:06:37 - mmengine - INFO - Epoch(train) [167][100/293] lr: 5.000000e-04 eta: 6:19:08 time: 2.099190 data_time: 1.920952 memory: 5829 loss_kpt: 0.000822 acc_pose: 0.751928 loss: 0.000822 2022/10/15 16:08:18 - mmengine - INFO - Epoch(train) [167][150/293] lr: 5.000000e-04 eta: 6:17:42 time: 2.021746 data_time: 1.681407 memory: 5829 loss_kpt: 0.000830 acc_pose: 0.740462 loss: 0.000830 2022/10/15 16:09:33 - mmengine - INFO - Epoch(train) [167][200/293] lr: 5.000000e-04 eta: 6:16:09 time: 1.487116 data_time: 1.233861 memory: 5829 loss_kpt: 0.000832 acc_pose: 0.722055 loss: 0.000832 2022/10/15 16:10:55 - mmengine - INFO - Epoch(train) [167][250/293] lr: 5.000000e-04 eta: 6:14:39 time: 1.650621 data_time: 0.864164 memory: 5829 loss_kpt: 0.000811 acc_pose: 0.781370 loss: 0.000811 2022/10/15 16:12:00 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 16:14:07 - mmengine - INFO - Epoch(train) [168][50/293] lr: 5.000000e-04 eta: 6:11:43 time: 2.543365 data_time: 0.302995 memory: 5829 loss_kpt: 0.000827 acc_pose: 0.750949 loss: 0.000827 2022/10/15 16:14:47 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 16:16:27 - mmengine - INFO - Epoch(train) [168][100/293] lr: 5.000000e-04 eta: 6:10:27 time: 2.794218 data_time: 0.063504 memory: 5829 loss_kpt: 0.000822 acc_pose: 0.720134 loss: 0.000822 2022/10/15 16:19:21 - mmengine - INFO - Epoch(train) [168][150/293] lr: 5.000000e-04 eta: 6:09:20 time: 3.478469 data_time: 1.697854 memory: 5829 loss_kpt: 0.000825 acc_pose: 0.785715 loss: 0.000825 2022/10/15 16:22:03 - mmengine - INFO - Epoch(train) [168][200/293] lr: 5.000000e-04 eta: 6:08:10 time: 3.241330 data_time: 3.054723 memory: 5829 loss_kpt: 0.000831 acc_pose: 0.773004 loss: 0.000831 2022/10/15 16:24:45 - mmengine - INFO - Epoch(train) [168][250/293] lr: 5.000000e-04 eta: 6:06:59 time: 3.252285 data_time: 1.737929 memory: 5829 loss_kpt: 0.000815 acc_pose: 0.750182 loss: 0.000815 2022/10/15 16:26:46 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 16:29:23 - mmengine - INFO - Epoch(train) [169][50/293] lr: 5.000000e-04 eta: 6:04:11 time: 3.148421 data_time: 0.340959 memory: 5829 loss_kpt: 0.000810 acc_pose: 0.791077 loss: 0.000810 2022/10/15 16:31:39 - mmengine - INFO - Epoch(train) [169][100/293] lr: 5.000000e-04 eta: 6:02:53 time: 2.708427 data_time: 0.120268 memory: 5829 loss_kpt: 0.000816 acc_pose: 0.787870 loss: 0.000816 2022/10/15 16:34:15 - mmengine - INFO - Epoch(train) [169][150/293] lr: 5.000000e-04 eta: 6:01:41 time: 3.118203 data_time: 0.340347 memory: 5829 loss_kpt: 0.000823 acc_pose: 0.759655 loss: 0.000823 2022/10/15 16:36:45 - mmengine - INFO - Epoch(train) [169][200/293] lr: 5.000000e-04 eta: 6:00:26 time: 3.002541 data_time: 0.230766 memory: 5829 loss_kpt: 0.000825 acc_pose: 0.686121 loss: 0.000825 2022/10/15 16:39:20 - mmengine - INFO - Epoch(train) [169][250/293] lr: 5.000000e-04 eta: 5:59:13 time: 3.096768 data_time: 0.061070 memory: 5829 loss_kpt: 0.000814 acc_pose: 0.798317 loss: 0.000814 2022/10/15 16:41:33 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 16:44:30 - mmengine - INFO - Epoch(train) [170][50/293] lr: 5.000000e-04 eta: 5:56:29 time: 3.522620 data_time: 0.244098 memory: 5829 loss_kpt: 0.000822 acc_pose: 0.775341 loss: 0.000822 2022/10/15 16:47:01 - mmengine - INFO - Epoch(train) [170][100/293] lr: 5.000000e-04 eta: 5:55:14 time: 3.028298 data_time: 0.058274 memory: 5829 loss_kpt: 0.000842 acc_pose: 0.743645 loss: 0.000842 2022/10/15 16:49:30 - mmengine - INFO - Epoch(train) [170][150/293] lr: 5.000000e-04 eta: 5:53:59 time: 2.984039 data_time: 0.858728 memory: 5829 loss_kpt: 0.000818 acc_pose: 0.788868 loss: 0.000818 2022/10/15 16:51:31 - mmengine - INFO - Epoch(train) [170][200/293] lr: 5.000000e-04 eta: 5:52:37 time: 2.408997 data_time: 2.219743 memory: 5829 loss_kpt: 0.000819 acc_pose: 0.743741 loss: 0.000819 2022/10/15 16:53:46 - mmengine - INFO - Epoch(train) [170][250/293] lr: 5.000000e-04 eta: 5:51:18 time: 2.700406 data_time: 1.192314 memory: 5829 loss_kpt: 0.000805 acc_pose: 0.679137 loss: 0.000805 2022/10/15 16:55:27 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 16:55:27 - mmengine - INFO - Saving checkpoint at 170 epochs 2022/10/15 16:56:22 - mmengine - INFO - Epoch(val) [170][50/407] eta: 0:06:16 time: 1.054563 data_time: 1.018708 memory: 5829 2022/10/15 16:57:29 - mmengine - INFO - Epoch(val) [170][100/407] eta: 0:06:48 time: 1.329167 data_time: 1.292547 memory: 540 2022/10/15 16:58:51 - mmengine - INFO - Epoch(val) [170][150/407] eta: 0:07:04 time: 1.651034 data_time: 1.615002 memory: 540 2022/10/15 17:00:10 - mmengine - INFO - Epoch(val) [170][200/407] eta: 0:05:25 time: 1.571060 data_time: 1.535608 memory: 540 2022/10/15 17:01:12 - mmengine - INFO - Epoch(val) [170][250/407] eta: 0:03:16 time: 1.253466 data_time: 1.218204 memory: 540 2022/10/15 17:02:10 - mmengine - INFO - Epoch(val) [170][300/407] eta: 0:02:03 time: 1.155162 data_time: 1.118886 memory: 540 2022/10/15 17:03:14 - mmengine - INFO - Epoch(val) [170][350/407] eta: 0:01:13 time: 1.282862 data_time: 1.247056 memory: 540 2022/10/15 17:04:22 - mmengine - INFO - Epoch(val) [170][400/407] eta: 0:00:09 time: 1.348384 data_time: 1.312284 memory: 540 2022/10/15 17:05:39 - mmengine - INFO - Evaluating CocoMetric... 2022/10/15 17:05:53 - mmengine - INFO - Epoch(val) [170][407/407] coco/AP: 0.629797 coco/AP .5: 0.866347 coco/AP .75: 0.705131 coco/AP (M): 0.594245 coco/AP (L): 0.694477 coco/AR: 0.691798 coco/AR .5: 0.909635 coco/AR .75: 0.761335 coco/AR (M): 0.647583 coco/AR (L): 0.754143 2022/10/15 17:05:53 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256/best_coco/AP_epoch_160.pth is removed 2022/10/15 17:05:54 - mmengine - INFO - The best checkpoint with 0.6298 coco/AP at 170 epoch is saved to best_coco/AP_epoch_170.pth. 2022/10/15 17:08:38 - mmengine - INFO - Epoch(train) [171][50/293] lr: 5.000000e-05 eta: 5:48:31 time: 3.275754 data_time: 2.224044 memory: 5829 loss_kpt: 0.000808 acc_pose: 0.770777 loss: 0.000808 2022/10/15 17:10:52 - mmengine - INFO - Epoch(train) [171][100/293] lr: 5.000000e-05 eta: 5:47:12 time: 2.674482 data_time: 0.197056 memory: 5829 loss_kpt: 0.000809 acc_pose: 0.691462 loss: 0.000809 2022/10/15 17:13:20 - mmengine - INFO - Epoch(train) [171][150/293] lr: 5.000000e-05 eta: 5:45:56 time: 2.960565 data_time: 0.051642 memory: 5829 loss_kpt: 0.000800 acc_pose: 0.729013 loss: 0.000800 2022/10/15 17:15:22 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 17:15:50 - mmengine - INFO - Epoch(train) [171][200/293] lr: 5.000000e-05 eta: 5:44:40 time: 2.993390 data_time: 0.058011 memory: 5829 loss_kpt: 0.000803 acc_pose: 0.749305 loss: 0.000803 2022/10/15 17:18:06 - mmengine - INFO - Epoch(train) [171][250/293] lr: 5.000000e-05 eta: 5:43:21 time: 2.735548 data_time: 0.109683 memory: 5829 loss_kpt: 0.000810 acc_pose: 0.704411 loss: 0.000810 2022/10/15 17:20:37 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 17:23:33 - mmengine - INFO - Epoch(train) [172][50/293] lr: 5.000000e-05 eta: 5:40:36 time: 3.520689 data_time: 1.347224 memory: 5829 loss_kpt: 0.000804 acc_pose: 0.783772 loss: 0.000804 2022/10/15 17:26:09 - mmengine - INFO - Epoch(train) [172][100/293] lr: 5.000000e-05 eta: 5:39:21 time: 3.125651 data_time: 1.384651 memory: 5829 loss_kpt: 0.000810 acc_pose: 0.815338 loss: 0.000810 2022/10/15 17:28:56 - mmengine - INFO - Epoch(train) [172][150/293] lr: 5.000000e-05 eta: 5:38:08 time: 3.337610 data_time: 0.067709 memory: 5829 loss_kpt: 0.000800 acc_pose: 0.744939 loss: 0.000800 2022/10/15 17:31:20 - mmengine - INFO - Epoch(train) [172][200/293] lr: 5.000000e-05 eta: 5:36:50 time: 2.869197 data_time: 0.065459 memory: 5829 loss_kpt: 0.000804 acc_pose: 0.743417 loss: 0.000804 2022/10/15 17:33:56 - mmengine - INFO - Epoch(train) [172][250/293] lr: 5.000000e-05 eta: 5:35:35 time: 3.117159 data_time: 1.278527 memory: 5829 loss_kpt: 0.000797 acc_pose: 0.748423 loss: 0.000797 2022/10/15 17:36:06 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 17:38:20 - mmengine - INFO - Epoch(train) [173][50/293] lr: 5.000000e-05 eta: 5:32:40 time: 2.691130 data_time: 0.955145 memory: 5829 loss_kpt: 0.000790 acc_pose: 0.794107 loss: 0.000790 2022/10/15 17:40:34 - mmengine - INFO - Epoch(train) [173][100/293] lr: 5.000000e-05 eta: 5:31:20 time: 2.681887 data_time: 0.109979 memory: 5829 loss_kpt: 0.000799 acc_pose: 0.724342 loss: 0.000799 2022/10/15 17:42:50 - mmengine - INFO - Epoch(train) [173][150/293] lr: 5.000000e-05 eta: 5:30:00 time: 2.717763 data_time: 0.052592 memory: 5829 loss_kpt: 0.000789 acc_pose: 0.790618 loss: 0.000789 2022/10/15 17:45:13 - mmengine - INFO - Epoch(train) [173][200/293] lr: 5.000000e-05 eta: 5:28:41 time: 2.855948 data_time: 0.061440 memory: 5829 loss_kpt: 0.000797 acc_pose: 0.737283 loss: 0.000797 2022/10/15 17:47:16 - mmengine - INFO - Epoch(train) [173][250/293] lr: 5.000000e-05 eta: 5:27:18 time: 2.454973 data_time: 0.505645 memory: 5829 loss_kpt: 0.000785 acc_pose: 0.725321 loss: 0.000785 2022/10/15 17:48:57 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 17:50:36 - mmengine - INFO - Epoch(train) [174][50/293] lr: 5.000000e-05 eta: 5:24:15 time: 1.993777 data_time: 0.660987 memory: 5829 loss_kpt: 0.000810 acc_pose: 0.734664 loss: 0.000810 2022/10/15 17:53:01 - mmengine - INFO - Epoch(train) [174][100/293] lr: 5.000000e-05 eta: 5:22:57 time: 2.888928 data_time: 0.156441 memory: 5829 loss_kpt: 0.000801 acc_pose: 0.781513 loss: 0.000801 2022/10/15 17:55:19 - mmengine - INFO - Epoch(train) [174][150/293] lr: 5.000000e-05 eta: 5:21:37 time: 2.770025 data_time: 1.378245 memory: 5829 loss_kpt: 0.000771 acc_pose: 0.790555 loss: 0.000771 2022/10/15 17:57:38 - mmengine - INFO - Epoch(train) [174][200/293] lr: 5.000000e-05 eta: 5:20:16 time: 2.764657 data_time: 0.486071 memory: 5829 loss_kpt: 0.000796 acc_pose: 0.727922 loss: 0.000796 2022/10/15 17:59:41 - mmengine - INFO - Epoch(train) [174][250/293] lr: 5.000000e-05 eta: 5:18:53 time: 2.476723 data_time: 0.350230 memory: 5829 loss_kpt: 0.000797 acc_pose: 0.721367 loss: 0.000797 2022/10/15 18:01:29 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 18:02:15 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 18:03:19 - mmengine - INFO - Epoch(train) [175][50/293] lr: 5.000000e-05 eta: 5:15:53 time: 2.203033 data_time: 0.331567 memory: 5829 loss_kpt: 0.000790 acc_pose: 0.753224 loss: 0.000790 2022/10/15 18:05:08 - mmengine - INFO - Epoch(train) [175][100/293] lr: 5.000000e-05 eta: 5:14:27 time: 2.183453 data_time: 0.065282 memory: 5829 loss_kpt: 0.000794 acc_pose: 0.742745 loss: 0.000794 2022/10/15 18:06:54 - mmengine - INFO - Epoch(train) [175][150/293] lr: 5.000000e-05 eta: 5:13:00 time: 2.107839 data_time: 0.281718 memory: 5829 loss_kpt: 0.000794 acc_pose: 0.770639 loss: 0.000794 2022/10/15 18:08:31 - mmengine - INFO - Epoch(train) [175][200/293] lr: 5.000000e-05 eta: 5:11:31 time: 1.948528 data_time: 0.469102 memory: 5829 loss_kpt: 0.000790 acc_pose: 0.726810 loss: 0.000790 2022/10/15 18:10:16 - mmengine - INFO - Epoch(train) [175][250/293] lr: 5.000000e-05 eta: 5:10:03 time: 2.089821 data_time: 1.383062 memory: 5829 loss_kpt: 0.000780 acc_pose: 0.750958 loss: 0.000780 2022/10/15 18:11:48 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 18:13:39 - mmengine - INFO - Epoch(train) [176][50/293] lr: 5.000000e-05 eta: 5:07:04 time: 2.222529 data_time: 0.169179 memory: 5829 loss_kpt: 0.000796 acc_pose: 0.801810 loss: 0.000796 2022/10/15 18:15:42 - mmengine - INFO - Epoch(train) [176][100/293] lr: 5.000000e-05 eta: 5:05:40 time: 2.448185 data_time: 0.077872 memory: 5829 loss_kpt: 0.000786 acc_pose: 0.780018 loss: 0.000786 2022/10/15 18:17:51 - mmengine - INFO - Epoch(train) [176][150/293] lr: 5.000000e-05 eta: 5:04:17 time: 2.582062 data_time: 0.108676 memory: 5829 loss_kpt: 0.000805 acc_pose: 0.796466 loss: 0.000805 2022/10/15 18:19:45 - mmengine - INFO - Epoch(train) [176][200/293] lr: 5.000000e-05 eta: 5:02:52 time: 2.295702 data_time: 1.829279 memory: 5829 loss_kpt: 0.000776 acc_pose: 0.779532 loss: 0.000776 2022/10/15 18:21:53 - mmengine - INFO - Epoch(train) [176][250/293] lr: 5.000000e-05 eta: 5:01:29 time: 2.555051 data_time: 1.689038 memory: 5829 loss_kpt: 0.000791 acc_pose: 0.739342 loss: 0.000791 2022/10/15 18:23:28 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 18:25:24 - mmengine - INFO - Epoch(train) [177][50/293] lr: 5.000000e-05 eta: 4:58:31 time: 2.329036 data_time: 0.126449 memory: 5829 loss_kpt: 0.000799 acc_pose: 0.754430 loss: 0.000799 2022/10/15 18:27:17 - mmengine - INFO - Epoch(train) [177][100/293] lr: 5.000000e-05 eta: 4:57:04 time: 2.259676 data_time: 0.063619 memory: 5829 loss_kpt: 0.000800 acc_pose: 0.826040 loss: 0.000800 2022/10/15 18:29:04 - mmengine - INFO - Epoch(train) [177][150/293] lr: 5.000000e-05 eta: 4:55:37 time: 2.147295 data_time: 0.064407 memory: 5829 loss_kpt: 0.000795 acc_pose: 0.760318 loss: 0.000795 2022/10/15 18:30:58 - mmengine - INFO - Epoch(train) [177][200/293] lr: 5.000000e-05 eta: 4:54:11 time: 2.276299 data_time: 0.074181 memory: 5829 loss_kpt: 0.000772 acc_pose: 0.764488 loss: 0.000772 2022/10/15 18:32:51 - mmengine - INFO - Epoch(train) [177][250/293] lr: 5.000000e-05 eta: 4:52:45 time: 2.245967 data_time: 0.312142 memory: 5829 loss_kpt: 0.000776 acc_pose: 0.778258 loss: 0.000776 2022/10/15 18:34:24 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 18:36:28 - mmengine - INFO - Epoch(train) [178][50/293] lr: 5.000000e-05 eta: 4:49:49 time: 2.492745 data_time: 0.213890 memory: 5829 loss_kpt: 0.000773 acc_pose: 0.764314 loss: 0.000773 2022/10/15 18:38:41 - mmengine - INFO - Epoch(train) [178][100/293] lr: 5.000000e-05 eta: 4:48:26 time: 2.653256 data_time: 0.064200 memory: 5829 loss_kpt: 0.000803 acc_pose: 0.756684 loss: 0.000803 2022/10/15 18:40:33 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 18:41:02 - mmengine - INFO - Epoch(train) [178][150/293] lr: 5.000000e-05 eta: 4:47:05 time: 2.818516 data_time: 0.092139 memory: 5829 loss_kpt: 0.000797 acc_pose: 0.755920 loss: 0.000797 2022/10/15 18:43:26 - mmengine - INFO - Epoch(train) [178][200/293] lr: 5.000000e-05 eta: 4:45:44 time: 2.872631 data_time: 0.128678 memory: 5829 loss_kpt: 0.000789 acc_pose: 0.761031 loss: 0.000789 2022/10/15 18:45:29 - mmengine - INFO - Epoch(train) [178][250/293] lr: 5.000000e-05 eta: 4:44:20 time: 2.462754 data_time: 0.054183 memory: 5829 loss_kpt: 0.000795 acc_pose: 0.738884 loss: 0.000795 2022/10/15 18:47:22 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 18:49:28 - mmengine - INFO - Epoch(train) [179][50/293] lr: 5.000000e-05 eta: 4:41:23 time: 2.513882 data_time: 1.250172 memory: 5829 loss_kpt: 0.000779 acc_pose: 0.783146 loss: 0.000779 2022/10/15 18:51:50 - mmengine - INFO - Epoch(train) [179][100/293] lr: 5.000000e-05 eta: 4:40:02 time: 2.834007 data_time: 0.078309 memory: 5829 loss_kpt: 0.000802 acc_pose: 0.763468 loss: 0.000802 2022/10/15 18:53:55 - mmengine - INFO - Epoch(train) [179][150/293] lr: 5.000000e-05 eta: 4:38:38 time: 2.509254 data_time: 0.058466 memory: 5829 loss_kpt: 0.000778 acc_pose: 0.769632 loss: 0.000778 2022/10/15 18:56:10 - mmengine - INFO - Epoch(train) [179][200/293] lr: 5.000000e-05 eta: 4:37:15 time: 2.702716 data_time: 0.231800 memory: 5829 loss_kpt: 0.000777 acc_pose: 0.791370 loss: 0.000777 2022/10/15 18:58:15 - mmengine - INFO - Epoch(train) [179][250/293] lr: 5.000000e-05 eta: 4:35:50 time: 2.495310 data_time: 0.224843 memory: 5829 loss_kpt: 0.000787 acc_pose: 0.795604 loss: 0.000787 2022/10/15 19:00:03 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 19:02:22 - mmengine - INFO - Epoch(train) [180][50/293] lr: 5.000000e-05 eta: 4:32:56 time: 2.777292 data_time: 0.184913 memory: 5829 loss_kpt: 0.000791 acc_pose: 0.738612 loss: 0.000791 2022/10/15 19:04:32 - mmengine - INFO - Epoch(train) [180][100/293] lr: 5.000000e-05 eta: 4:31:32 time: 2.601812 data_time: 0.065396 memory: 5829 loss_kpt: 0.000793 acc_pose: 0.795833 loss: 0.000793 2022/10/15 19:06:38 - mmengine - INFO - Epoch(train) [180][150/293] lr: 5.000000e-05 eta: 4:30:08 time: 2.516771 data_time: 0.097011 memory: 5829 loss_kpt: 0.000771 acc_pose: 0.746515 loss: 0.000771 2022/10/15 19:08:41 - mmengine - INFO - Epoch(train) [180][200/293] lr: 5.000000e-05 eta: 4:28:42 time: 2.461286 data_time: 0.355276 memory: 5829 loss_kpt: 0.000768 acc_pose: 0.782279 loss: 0.000768 2022/10/15 19:10:40 - mmengine - INFO - Epoch(train) [180][250/293] lr: 5.000000e-05 eta: 4:27:16 time: 2.368017 data_time: 0.095784 memory: 5829 loss_kpt: 0.000777 acc_pose: 0.762010 loss: 0.000777 2022/10/15 19:12:33 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 19:12:33 - mmengine - INFO - Saving checkpoint at 180 epochs 2022/10/15 19:13:36 - mmengine - INFO - Epoch(val) [180][50/407] eta: 0:07:13 time: 1.213166 data_time: 1.176025 memory: 5829 2022/10/15 19:14:46 - mmengine - INFO - Epoch(val) [180][100/407] eta: 0:07:12 time: 1.410284 data_time: 1.373837 memory: 540 2022/10/15 19:15:48 - mmengine - INFO - Epoch(val) [180][150/407] eta: 0:05:17 time: 1.234238 data_time: 1.198182 memory: 540 2022/10/15 19:16:56 - mmengine - INFO - Epoch(val) [180][200/407] eta: 0:04:42 time: 1.362910 data_time: 1.326882 memory: 540 2022/10/15 19:18:04 - mmengine - INFO - Epoch(val) [180][250/407] eta: 0:03:31 time: 1.348808 data_time: 1.313121 memory: 540 2022/10/15 19:19:06 - mmengine - INFO - Epoch(val) [180][300/407] eta: 0:02:13 time: 1.246791 data_time: 1.210799 memory: 540 2022/10/15 19:20:07 - mmengine - INFO - Epoch(val) [180][350/407] eta: 0:01:09 time: 1.227928 data_time: 1.192025 memory: 540 2022/10/15 19:21:17 - mmengine - INFO - Epoch(val) [180][400/407] eta: 0:00:09 time: 1.391466 data_time: 1.355525 memory: 540 2022/10/15 19:23:08 - mmengine - INFO - Evaluating CocoMetric... 2022/10/15 19:23:22 - mmengine - INFO - Epoch(val) [180][407/407] coco/AP: 0.644216 coco/AP .5: 0.872378 coco/AP .75: 0.722421 coco/AP (M): 0.608125 coco/AP (L): 0.710370 coco/AR: 0.705101 coco/AR .5: 0.913571 coco/AR .75: 0.778180 coco/AR (M): 0.660284 coco/AR (L): 0.768859 2022/10/15 19:23:22 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256/best_coco/AP_epoch_170.pth is removed 2022/10/15 19:23:23 - mmengine - INFO - The best checkpoint with 0.6442 coco/AP at 180 epoch is saved to best_coco/AP_epoch_180.pth. 2022/10/15 19:25:52 - mmengine - INFO - Epoch(train) [181][50/293] lr: 5.000000e-05 eta: 4:24:24 time: 2.975605 data_time: 2.041860 memory: 5829 loss_kpt: 0.000787 acc_pose: 0.753429 loss: 0.000787 2022/10/15 19:27:53 - mmengine - INFO - Epoch(train) [181][100/293] lr: 5.000000e-05 eta: 4:22:58 time: 2.415073 data_time: 0.061992 memory: 5829 loss_kpt: 0.000782 acc_pose: 0.717980 loss: 0.000782 2022/10/15 19:29:43 - mmengine - INFO - Epoch(train) [181][150/293] lr: 5.000000e-05 eta: 4:21:31 time: 2.201726 data_time: 0.056281 memory: 5829 loss_kpt: 0.000790 acc_pose: 0.715573 loss: 0.000790 2022/10/15 19:31:53 - mmengine - INFO - Epoch(train) [181][200/293] lr: 5.000000e-05 eta: 4:20:06 time: 2.602163 data_time: 0.059013 memory: 5829 loss_kpt: 0.000786 acc_pose: 0.753969 loss: 0.000786 2022/10/15 19:34:00 - mmengine - INFO - Epoch(train) [181][250/293] lr: 5.000000e-05 eta: 4:18:41 time: 2.545187 data_time: 0.564462 memory: 5829 loss_kpt: 0.000787 acc_pose: 0.769765 loss: 0.000787 2022/10/15 19:34:27 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 19:36:03 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 19:38:14 - mmengine - INFO - Epoch(train) [182][50/293] lr: 5.000000e-05 eta: 4:15:46 time: 2.625160 data_time: 0.395173 memory: 5829 loss_kpt: 0.000801 acc_pose: 0.749625 loss: 0.000801 2022/10/15 19:40:26 - mmengine - INFO - Epoch(train) [182][100/293] lr: 5.000000e-05 eta: 4:14:22 time: 2.637792 data_time: 0.072406 memory: 5829 loss_kpt: 0.000802 acc_pose: 0.739250 loss: 0.000802 2022/10/15 19:42:53 - mmengine - INFO - Epoch(train) [182][150/293] lr: 5.000000e-05 eta: 4:13:00 time: 2.936178 data_time: 0.068095 memory: 5829 loss_kpt: 0.000768 acc_pose: 0.747200 loss: 0.000768 2022/10/15 19:45:12 - mmengine - INFO - Epoch(train) [182][200/293] lr: 5.000000e-05 eta: 4:11:36 time: 2.769476 data_time: 0.062912 memory: 5829 loss_kpt: 0.000779 acc_pose: 0.797134 loss: 0.000779 2022/10/15 19:47:23 - mmengine - INFO - Epoch(train) [182][250/293] lr: 5.000000e-05 eta: 4:10:12 time: 2.631518 data_time: 0.627913 memory: 5829 loss_kpt: 0.000782 acc_pose: 0.768267 loss: 0.000782 2022/10/15 19:49:08 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 19:51:21 - mmengine - INFO - Epoch(train) [183][50/293] lr: 5.000000e-05 eta: 4:07:17 time: 2.652729 data_time: 0.326483 memory: 5829 loss_kpt: 0.000803 acc_pose: 0.750933 loss: 0.000803 2022/10/15 19:53:37 - mmengine - INFO - Epoch(train) [183][100/293] lr: 5.000000e-05 eta: 4:05:53 time: 2.726403 data_time: 2.018042 memory: 5829 loss_kpt: 0.000789 acc_pose: 0.756742 loss: 0.000789 2022/10/15 19:55:52 - mmengine - INFO - Epoch(train) [183][150/293] lr: 5.000000e-05 eta: 4:04:28 time: 2.688493 data_time: 2.118926 memory: 5829 loss_kpt: 0.000783 acc_pose: 0.787872 loss: 0.000783 2022/10/15 19:58:09 - mmengine - INFO - Epoch(train) [183][200/293] lr: 5.000000e-05 eta: 4:03:04 time: 2.756080 data_time: 1.712062 memory: 5829 loss_kpt: 0.000777 acc_pose: 0.751945 loss: 0.000777 2022/10/15 20:00:23 - mmengine - INFO - Epoch(train) [183][250/293] lr: 5.000000e-05 eta: 4:01:39 time: 2.678203 data_time: 0.059085 memory: 5829 loss_kpt: 0.000783 acc_pose: 0.760402 loss: 0.000783 2022/10/15 20:02:24 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 20:04:28 - mmengine - INFO - Epoch(train) [184][50/293] lr: 5.000000e-05 eta: 3:58:43 time: 2.488265 data_time: 0.599812 memory: 5829 loss_kpt: 0.000788 acc_pose: 0.753229 loss: 0.000788 2022/10/15 20:06:32 - mmengine - INFO - Epoch(train) [184][100/293] lr: 5.000000e-05 eta: 3:57:17 time: 2.461007 data_time: 0.101802 memory: 5829 loss_kpt: 0.000767 acc_pose: 0.719692 loss: 0.000767 2022/10/15 20:08:34 - mmengine - INFO - Epoch(train) [184][150/293] lr: 5.000000e-05 eta: 3:55:50 time: 2.439953 data_time: 0.117196 memory: 5829 loss_kpt: 0.000790 acc_pose: 0.773570 loss: 0.000790 2022/10/15 20:10:28 - mmengine - INFO - Epoch(train) [184][200/293] lr: 5.000000e-05 eta: 3:54:22 time: 2.288480 data_time: 0.076849 memory: 5829 loss_kpt: 0.000766 acc_pose: 0.773861 loss: 0.000766 2022/10/15 20:12:31 - mmengine - INFO - Epoch(train) [184][250/293] lr: 5.000000e-05 eta: 3:52:55 time: 2.452197 data_time: 0.117269 memory: 5829 loss_kpt: 0.000791 acc_pose: 0.732986 loss: 0.000791 2022/10/15 20:14:17 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 20:16:25 - mmengine - INFO - Epoch(train) [185][50/293] lr: 5.000000e-05 eta: 3:50:00 time: 2.553812 data_time: 0.238583 memory: 5829 loss_kpt: 0.000780 acc_pose: 0.778645 loss: 0.000780 2022/10/15 20:17:59 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 20:18:28 - mmengine - INFO - Epoch(train) [185][100/293] lr: 5.000000e-05 eta: 3:48:33 time: 2.462840 data_time: 0.160337 memory: 5829 loss_kpt: 0.000792 acc_pose: 0.746321 loss: 0.000792 2022/10/15 20:20:37 - mmengine - INFO - Epoch(train) [185][150/293] lr: 5.000000e-05 eta: 3:47:07 time: 2.572226 data_time: 0.061999 memory: 5829 loss_kpt: 0.000779 acc_pose: 0.780698 loss: 0.000779 2022/10/15 20:22:29 - mmengine - INFO - Epoch(train) [185][200/293] lr: 5.000000e-05 eta: 3:45:39 time: 2.249591 data_time: 0.579297 memory: 5829 loss_kpt: 0.000791 acc_pose: 0.798028 loss: 0.000791 2022/10/15 20:24:35 - mmengine - INFO - Epoch(train) [185][250/293] lr: 5.000000e-05 eta: 3:44:12 time: 2.528126 data_time: 0.640579 memory: 5829 loss_kpt: 0.000791 acc_pose: 0.767098 loss: 0.000791 2022/10/15 20:26:11 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 20:28:29 - mmengine - INFO - Epoch(train) [186][50/293] lr: 5.000000e-05 eta: 3:41:18 time: 2.750114 data_time: 0.175420 memory: 5829 loss_kpt: 0.000784 acc_pose: 0.787352 loss: 0.000784 2022/10/15 20:30:33 - mmengine - INFO - Epoch(train) [186][100/293] lr: 5.000000e-05 eta: 3:39:51 time: 2.488344 data_time: 0.060583 memory: 5829 loss_kpt: 0.000783 acc_pose: 0.781384 loss: 0.000783 2022/10/15 20:32:35 - mmengine - INFO - Epoch(train) [186][150/293] lr: 5.000000e-05 eta: 3:38:24 time: 2.443012 data_time: 0.171684 memory: 5829 loss_kpt: 0.000777 acc_pose: 0.766426 loss: 0.000777 2022/10/15 20:34:40 - mmengine - INFO - Epoch(train) [186][200/293] lr: 5.000000e-05 eta: 3:36:57 time: 2.499001 data_time: 0.148771 memory: 5829 loss_kpt: 0.000794 acc_pose: 0.733735 loss: 0.000794 2022/10/15 20:36:43 - mmengine - INFO - Epoch(train) [186][250/293] lr: 5.000000e-05 eta: 3:35:30 time: 2.450561 data_time: 0.520829 memory: 5829 loss_kpt: 0.000786 acc_pose: 0.753291 loss: 0.000786 2022/10/15 20:38:21 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 20:40:25 - mmengine - INFO - Epoch(train) [187][50/293] lr: 5.000000e-05 eta: 3:32:34 time: 2.469780 data_time: 0.466653 memory: 5829 loss_kpt: 0.000789 acc_pose: 0.773075 loss: 0.000789 2022/10/15 20:42:25 - mmengine - INFO - Epoch(train) [187][100/293] lr: 5.000000e-05 eta: 3:31:06 time: 2.401940 data_time: 0.688590 memory: 5829 loss_kpt: 0.000771 acc_pose: 0.780693 loss: 0.000771 2022/10/15 20:44:25 - mmengine - INFO - Epoch(train) [187][150/293] lr: 5.000000e-05 eta: 3:29:39 time: 2.400436 data_time: 0.791842 memory: 5829 loss_kpt: 0.000783 acc_pose: 0.804330 loss: 0.000783 2022/10/15 20:46:16 - mmengine - INFO - Epoch(train) [187][200/293] lr: 5.000000e-05 eta: 3:28:10 time: 2.225238 data_time: 0.205440 memory: 5829 loss_kpt: 0.000779 acc_pose: 0.769074 loss: 0.000779 2022/10/15 20:48:19 - mmengine - INFO - Epoch(train) [187][250/293] lr: 5.000000e-05 eta: 3:26:42 time: 2.460141 data_time: 0.060929 memory: 5829 loss_kpt: 0.000789 acc_pose: 0.830841 loss: 0.000789 2022/10/15 20:49:53 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 20:51:46 - mmengine - INFO - Epoch(train) [188][50/293] lr: 5.000000e-05 eta: 3:23:45 time: 2.276215 data_time: 1.965283 memory: 5829 loss_kpt: 0.000779 acc_pose: 0.809804 loss: 0.000779 2022/10/15 20:53:38 - mmengine - INFO - Epoch(train) [188][100/293] lr: 5.000000e-05 eta: 3:22:16 time: 2.241152 data_time: 2.072305 memory: 5829 loss_kpt: 0.000788 acc_pose: 0.737950 loss: 0.000788 2022/10/15 20:55:41 - mmengine - INFO - Epoch(train) [188][150/293] lr: 5.000000e-05 eta: 3:20:49 time: 2.447820 data_time: 0.586042 memory: 5829 loss_kpt: 0.000791 acc_pose: 0.727388 loss: 0.000791 2022/10/15 20:57:40 - mmengine - INFO - Epoch(train) [188][200/293] lr: 5.000000e-05 eta: 3:19:21 time: 2.390922 data_time: 0.832212 memory: 5829 loss_kpt: 0.000781 acc_pose: 0.797826 loss: 0.000781 2022/10/15 20:58:00 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 20:59:42 - mmengine - INFO - Epoch(train) [188][250/293] lr: 5.000000e-05 eta: 3:17:53 time: 2.432603 data_time: 1.240605 memory: 5829 loss_kpt: 0.000769 acc_pose: 0.773573 loss: 0.000769 2022/10/15 21:01:26 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 21:03:28 - mmengine - INFO - Epoch(train) [189][50/293] lr: 5.000000e-05 eta: 3:14:57 time: 2.441698 data_time: 0.228513 memory: 5829 loss_kpt: 0.000775 acc_pose: 0.770344 loss: 0.000775 2022/10/15 21:05:30 - mmengine - INFO - Epoch(train) [189][100/293] lr: 5.000000e-05 eta: 3:13:29 time: 2.439484 data_time: 0.071731 memory: 5829 loss_kpt: 0.000772 acc_pose: 0.781327 loss: 0.000772 2022/10/15 21:07:28 - mmengine - INFO - Epoch(train) [189][150/293] lr: 5.000000e-05 eta: 3:12:00 time: 2.347491 data_time: 0.835580 memory: 5829 loss_kpt: 0.000789 acc_pose: 0.725739 loss: 0.000789 2022/10/15 21:08:52 - mmengine - INFO - Epoch(train) [189][200/293] lr: 5.000000e-05 eta: 3:10:28 time: 1.673329 data_time: 0.059924 memory: 5829 loss_kpt: 0.000789 acc_pose: 0.775984 loss: 0.000789 2022/10/15 21:10:53 - mmengine - INFO - Epoch(train) [189][250/293] lr: 5.000000e-05 eta: 3:09:00 time: 2.427724 data_time: 0.060241 memory: 5829 loss_kpt: 0.000772 acc_pose: 0.744672 loss: 0.000772 2022/10/15 21:12:35 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 21:14:38 - mmengine - INFO - Epoch(train) [190][50/293] lr: 5.000000e-05 eta: 3:06:05 time: 2.463032 data_time: 0.215808 memory: 5829 loss_kpt: 0.000775 acc_pose: 0.733390 loss: 0.000775 2022/10/15 21:16:37 - mmengine - INFO - Epoch(train) [190][100/293] lr: 5.000000e-05 eta: 3:04:36 time: 2.386903 data_time: 0.976294 memory: 5829 loss_kpt: 0.000785 acc_pose: 0.808022 loss: 0.000785 2022/10/15 21:18:34 - mmengine - INFO - Epoch(train) [190][150/293] lr: 5.000000e-05 eta: 3:03:07 time: 2.346617 data_time: 0.558835 memory: 5829 loss_kpt: 0.000773 acc_pose: 0.785012 loss: 0.000773 2022/10/15 21:20:38 - mmengine - INFO - Epoch(train) [190][200/293] lr: 5.000000e-05 eta: 3:01:39 time: 2.473259 data_time: 0.059614 memory: 5829 loss_kpt: 0.000782 acc_pose: 0.738614 loss: 0.000782 2022/10/15 21:22:39 - mmengine - INFO - Epoch(train) [190][250/293] lr: 5.000000e-05 eta: 3:00:11 time: 2.419058 data_time: 0.316923 memory: 5829 loss_kpt: 0.000787 acc_pose: 0.759534 loss: 0.000787 2022/10/15 21:24:16 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 21:24:17 - mmengine - INFO - Saving checkpoint at 190 epochs 2022/10/15 21:25:21 - mmengine - INFO - Epoch(val) [190][50/407] eta: 0:07:29 time: 1.258314 data_time: 1.220242 memory: 5829 2022/10/15 21:26:16 - mmengine - INFO - Epoch(val) [190][100/407] eta: 0:05:36 time: 1.096772 data_time: 1.060208 memory: 540 2022/10/15 21:27:14 - mmengine - INFO - Epoch(val) [190][150/407] eta: 0:04:57 time: 1.157996 data_time: 1.116768 memory: 540 2022/10/15 21:28:16 - mmengine - INFO - Epoch(val) [190][200/407] eta: 0:04:18 time: 1.246786 data_time: 1.211028 memory: 540 2022/10/15 21:29:20 - mmengine - INFO - Epoch(val) [190][250/407] eta: 0:03:19 time: 1.271992 data_time: 1.235972 memory: 540 2022/10/15 21:30:22 - mmengine - INFO - Epoch(val) [190][300/407] eta: 0:02:12 time: 1.235717 data_time: 1.199588 memory: 540 2022/10/15 21:31:24 - mmengine - INFO - Epoch(val) [190][350/407] eta: 0:01:10 time: 1.239955 data_time: 1.203031 memory: 540 2022/10/15 21:32:20 - mmengine - INFO - Epoch(val) [190][400/407] eta: 0:00:07 time: 1.117507 data_time: 1.081078 memory: 540 2022/10/15 21:33:47 - mmengine - INFO - Evaluating CocoMetric... 2022/10/15 21:34:00 - mmengine - INFO - Epoch(val) [190][407/407] coco/AP: 0.645547 coco/AP .5: 0.872215 coco/AP .75: 0.722588 coco/AP (M): 0.608434 coco/AP (L): 0.712909 coco/AR: 0.706659 coco/AR .5: 0.916247 coco/AR .75: 0.778338 coco/AR (M): 0.661158 coco/AR (L): 0.771275 2022/10/15 21:34:00 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256/best_coco/AP_epoch_180.pth is removed 2022/10/15 21:34:02 - mmengine - INFO - The best checkpoint with 0.6455 coco/AP at 190 epoch is saved to best_coco/AP_epoch_190.pth. 2022/10/15 21:36:13 - mmengine - INFO - Epoch(train) [191][50/293] lr: 5.000000e-05 eta: 2:57:16 time: 2.617031 data_time: 1.787449 memory: 5829 loss_kpt: 0.000783 acc_pose: 0.796718 loss: 0.000783 2022/10/15 21:38:15 - mmengine - INFO - Epoch(train) [191][100/293] lr: 5.000000e-05 eta: 2:55:48 time: 2.443760 data_time: 0.792296 memory: 5829 loss_kpt: 0.000777 acc_pose: 0.758758 loss: 0.000777 2022/10/15 21:40:19 - mmengine - INFO - Epoch(train) [191][150/293] lr: 5.000000e-05 eta: 2:54:20 time: 2.484547 data_time: 0.055303 memory: 5829 loss_kpt: 0.000778 acc_pose: 0.735038 loss: 0.000778 2022/10/15 21:42:21 - mmengine - INFO - Epoch(train) [191][200/293] lr: 5.000000e-05 eta: 2:52:51 time: 2.431648 data_time: 0.072534 memory: 5829 loss_kpt: 0.000772 acc_pose: 0.778595 loss: 0.000772 2022/10/15 21:44:26 - mmengine - INFO - Epoch(train) [191][250/293] lr: 5.000000e-05 eta: 2:51:23 time: 2.509043 data_time: 0.889386 memory: 5829 loss_kpt: 0.000778 acc_pose: 0.769464 loss: 0.000778 2022/10/15 21:46:02 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 21:47:39 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 21:48:07 - mmengine - INFO - Epoch(train) [192][50/293] lr: 5.000000e-05 eta: 2:48:28 time: 2.490939 data_time: 0.250963 memory: 5829 loss_kpt: 0.000774 acc_pose: 0.786425 loss: 0.000774 2022/10/15 21:50:06 - mmengine - INFO - Epoch(train) [192][100/293] lr: 5.000000e-05 eta: 2:46:59 time: 2.373628 data_time: 1.407542 memory: 5829 loss_kpt: 0.000783 acc_pose: 0.780905 loss: 0.000783 2022/10/15 21:52:05 - mmengine - INFO - Epoch(train) [192][150/293] lr: 5.000000e-05 eta: 2:45:30 time: 2.385812 data_time: 0.665283 memory: 5829 loss_kpt: 0.000790 acc_pose: 0.783096 loss: 0.000790 2022/10/15 21:54:08 - mmengine - INFO - Epoch(train) [192][200/293] lr: 5.000000e-05 eta: 2:44:01 time: 2.465207 data_time: 0.086337 memory: 5829 loss_kpt: 0.000774 acc_pose: 0.810854 loss: 0.000774 2022/10/15 21:56:04 - mmengine - INFO - Epoch(train) [192][250/293] lr: 5.000000e-05 eta: 2:42:32 time: 2.316030 data_time: 0.099387 memory: 5829 loss_kpt: 0.000780 acc_pose: 0.774599 loss: 0.000780 2022/10/15 21:57:42 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 21:59:47 - mmengine - INFO - Epoch(train) [193][50/293] lr: 5.000000e-05 eta: 2:39:37 time: 2.500345 data_time: 0.414479 memory: 5829 loss_kpt: 0.000768 acc_pose: 0.781402 loss: 0.000768 2022/10/15 22:01:53 - mmengine - INFO - Epoch(train) [193][100/293] lr: 5.000000e-05 eta: 2:38:09 time: 2.530845 data_time: 0.067626 memory: 5829 loss_kpt: 0.000767 acc_pose: 0.729499 loss: 0.000767 2022/10/15 22:03:59 - mmengine - INFO - Epoch(train) [193][150/293] lr: 5.000000e-05 eta: 2:36:40 time: 2.515151 data_time: 0.828978 memory: 5829 loss_kpt: 0.000771 acc_pose: 0.842332 loss: 0.000771 2022/10/15 22:05:47 - mmengine - INFO - Epoch(train) [193][200/293] lr: 5.000000e-05 eta: 2:35:10 time: 2.162655 data_time: 1.461984 memory: 5829 loss_kpt: 0.000770 acc_pose: 0.814338 loss: 0.000770 2022/10/15 22:07:47 - mmengine - INFO - Epoch(train) [193][250/293] lr: 5.000000e-05 eta: 2:33:41 time: 2.390806 data_time: 0.110042 memory: 5829 loss_kpt: 0.000783 acc_pose: 0.772359 loss: 0.000783 2022/10/15 22:09:18 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 22:11:20 - mmengine - INFO - Epoch(train) [194][50/293] lr: 5.000000e-05 eta: 2:30:46 time: 2.437593 data_time: 1.034037 memory: 5829 loss_kpt: 0.000775 acc_pose: 0.752272 loss: 0.000775 2022/10/15 22:13:24 - mmengine - INFO - Epoch(train) [194][100/293] lr: 5.000000e-05 eta: 2:29:17 time: 2.473395 data_time: 0.059573 memory: 5829 loss_kpt: 0.000781 acc_pose: 0.775790 loss: 0.000781 2022/10/15 22:15:16 - mmengine - INFO - Epoch(train) [194][150/293] lr: 5.000000e-05 eta: 2:27:47 time: 2.236685 data_time: 0.970730 memory: 5829 loss_kpt: 0.000779 acc_pose: 0.759649 loss: 0.000779 2022/10/15 22:17:01 - mmengine - INFO - Epoch(train) [194][200/293] lr: 5.000000e-05 eta: 2:26:16 time: 2.116360 data_time: 1.251917 memory: 5829 loss_kpt: 0.000795 acc_pose: 0.737608 loss: 0.000795 2022/10/15 22:18:52 - mmengine - INFO - Epoch(train) [194][250/293] lr: 5.000000e-05 eta: 2:24:46 time: 2.213217 data_time: 1.381477 memory: 5829 loss_kpt: 0.000786 acc_pose: 0.747072 loss: 0.000786 2022/10/15 22:20:35 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 22:22:35 - mmengine - INFO - Epoch(train) [195][50/293] lr: 5.000000e-05 eta: 2:21:51 time: 2.395134 data_time: 1.442597 memory: 5829 loss_kpt: 0.000778 acc_pose: 0.743878 loss: 0.000778 2022/10/15 22:24:38 - mmengine - INFO - Epoch(train) [195][100/293] lr: 5.000000e-05 eta: 2:20:22 time: 2.463913 data_time: 0.074586 memory: 5829 loss_kpt: 0.000778 acc_pose: 0.800945 loss: 0.000778 2022/10/15 22:26:31 - mmengine - INFO - Epoch(train) [195][150/293] lr: 5.000000e-05 eta: 2:18:52 time: 2.262886 data_time: 0.399545 memory: 5829 loss_kpt: 0.000783 acc_pose: 0.734513 loss: 0.000783 2022/10/15 22:26:50 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 22:28:28 - mmengine - INFO - Epoch(train) [195][200/293] lr: 5.000000e-05 eta: 2:17:22 time: 2.334557 data_time: 0.176780 memory: 5829 loss_kpt: 0.000767 acc_pose: 0.762264 loss: 0.000767 2022/10/15 22:30:35 - mmengine - INFO - Epoch(train) [195][250/293] lr: 5.000000e-05 eta: 2:15:53 time: 2.545126 data_time: 0.065151 memory: 5829 loss_kpt: 0.000774 acc_pose: 0.775576 loss: 0.000774 2022/10/15 22:32:17 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 22:34:31 - mmengine - INFO - Epoch(train) [196][50/293] lr: 5.000000e-05 eta: 2:12:59 time: 2.661466 data_time: 0.616138 memory: 5829 loss_kpt: 0.000771 acc_pose: 0.819653 loss: 0.000771 2022/10/15 22:36:23 - mmengine - INFO - Epoch(train) [196][100/293] lr: 5.000000e-05 eta: 2:11:29 time: 2.253414 data_time: 0.056608 memory: 5829 loss_kpt: 0.000782 acc_pose: 0.806916 loss: 0.000782 2022/10/15 22:38:20 - mmengine - INFO - Epoch(train) [196][150/293] lr: 5.000000e-05 eta: 2:09:59 time: 2.335528 data_time: 0.139772 memory: 5829 loss_kpt: 0.000788 acc_pose: 0.718920 loss: 0.000788 2022/10/15 22:40:09 - mmengine - INFO - Epoch(train) [196][200/293] lr: 5.000000e-05 eta: 2:08:28 time: 2.172936 data_time: 0.223204 memory: 5829 loss_kpt: 0.000787 acc_pose: 0.709925 loss: 0.000787 2022/10/15 22:42:10 - mmengine - INFO - Epoch(train) [196][250/293] lr: 5.000000e-05 eta: 2:06:58 time: 2.430704 data_time: 0.057859 memory: 5829 loss_kpt: 0.000777 acc_pose: 0.785179 loss: 0.000777 2022/10/15 22:43:49 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 22:45:57 - mmengine - INFO - Epoch(train) [197][50/293] lr: 5.000000e-05 eta: 2:04:04 time: 2.568192 data_time: 0.811563 memory: 5829 loss_kpt: 0.000781 acc_pose: 0.790173 loss: 0.000781 2022/10/15 22:47:45 - mmengine - INFO - Epoch(train) [197][100/293] lr: 5.000000e-05 eta: 2:02:34 time: 2.145351 data_time: 0.062846 memory: 5829 loss_kpt: 0.000793 acc_pose: 0.762121 loss: 0.000793 2022/10/15 22:49:12 - mmengine - INFO - Epoch(train) [197][150/293] lr: 5.000000e-05 eta: 2:01:01 time: 1.741688 data_time: 0.061385 memory: 5829 loss_kpt: 0.000783 acc_pose: 0.749218 loss: 0.000783 2022/10/15 22:50:35 - mmengine - INFO - Epoch(train) [197][200/293] lr: 5.000000e-05 eta: 1:59:29 time: 1.667556 data_time: 0.065659 memory: 5829 loss_kpt: 0.000761 acc_pose: 0.760637 loss: 0.000761 2022/10/15 22:52:01 - mmengine - INFO - Epoch(train) [197][250/293] lr: 5.000000e-05 eta: 1:57:57 time: 1.723096 data_time: 0.062973 memory: 5829 loss_kpt: 0.000791 acc_pose: 0.701995 loss: 0.000791 2022/10/15 22:53:11 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 22:54:40 - mmengine - INFO - Epoch(train) [198][50/293] lr: 5.000000e-05 eta: 1:55:00 time: 1.768063 data_time: 0.134989 memory: 5829 loss_kpt: 0.000774 acc_pose: 0.757993 loss: 0.000774 2022/10/15 22:56:14 - mmengine - INFO - Epoch(train) [198][100/293] lr: 5.000000e-05 eta: 1:53:29 time: 1.876348 data_time: 0.066322 memory: 5829 loss_kpt: 0.000774 acc_pose: 0.734058 loss: 0.000774 2022/10/15 22:57:48 - mmengine - INFO - Epoch(train) [198][150/293] lr: 5.000000e-05 eta: 1:51:57 time: 1.890732 data_time: 0.065912 memory: 5829 loss_kpt: 0.000780 acc_pose: 0.790830 loss: 0.000780 2022/10/15 22:59:23 - mmengine - INFO - Epoch(train) [198][200/293] lr: 5.000000e-05 eta: 1:50:26 time: 1.901898 data_time: 0.064148 memory: 5829 loss_kpt: 0.000782 acc_pose: 0.756501 loss: 0.000782 2022/10/15 23:00:56 - mmengine - INFO - Epoch(train) [198][250/293] lr: 5.000000e-05 eta: 1:48:54 time: 1.848874 data_time: 0.071022 memory: 5829 loss_kpt: 0.000780 acc_pose: 0.758395 loss: 0.000780 2022/10/15 23:01:40 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 23:01:57 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 23:03:01 - mmengine - INFO - Epoch(train) [199][50/293] lr: 5.000000e-05 eta: 1:45:57 time: 1.287533 data_time: 0.126615 memory: 5829 loss_kpt: 0.000771 acc_pose: 0.712038 loss: 0.000771 2022/10/15 23:04:07 - mmengine - INFO - Epoch(train) [199][100/293] lr: 5.000000e-05 eta: 1:44:24 time: 1.325904 data_time: 0.065938 memory: 5829 loss_kpt: 0.000772 acc_pose: 0.758687 loss: 0.000772 2022/10/15 23:05:13 - mmengine - INFO - Epoch(train) [199][150/293] lr: 5.000000e-05 eta: 1:42:50 time: 1.318493 data_time: 0.058296 memory: 5829 loss_kpt: 0.000776 acc_pose: 0.746267 loss: 0.000776 2022/10/15 23:07:08 - mmengine - INFO - Epoch(train) [199][200/293] lr: 5.000000e-05 eta: 1:41:20 time: 2.288411 data_time: 0.068545 memory: 5829 loss_kpt: 0.000763 acc_pose: 0.830226 loss: 0.000763 2022/10/15 23:08:49 - mmengine - INFO - Epoch(train) [199][250/293] lr: 5.000000e-05 eta: 1:39:49 time: 2.029103 data_time: 0.060985 memory: 5829 loss_kpt: 0.000781 acc_pose: 0.790089 loss: 0.000781 2022/10/15 23:10:23 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 23:12:05 - mmengine - INFO - Epoch(train) [200][50/293] lr: 5.000000e-05 eta: 1:36:55 time: 2.036088 data_time: 0.516312 memory: 5829 loss_kpt: 0.000786 acc_pose: 0.745303 loss: 0.000786 2022/10/15 23:13:51 - mmengine - INFO - Epoch(train) [200][100/293] lr: 5.000000e-05 eta: 1:35:24 time: 2.117951 data_time: 0.060569 memory: 5829 loss_kpt: 0.000796 acc_pose: 0.805065 loss: 0.000796 2022/10/15 23:15:43 - mmengine - INFO - Epoch(train) [200][150/293] lr: 5.000000e-05 eta: 1:33:53 time: 2.237333 data_time: 0.065096 memory: 5829 loss_kpt: 0.000778 acc_pose: 0.820351 loss: 0.000778 2022/10/15 23:17:24 - mmengine - INFO - Epoch(train) [200][200/293] lr: 5.000000e-05 eta: 1:32:22 time: 2.030846 data_time: 0.054368 memory: 5829 loss_kpt: 0.000774 acc_pose: 0.744482 loss: 0.000774 2022/10/15 23:19:08 - mmengine - INFO - Epoch(train) [200][250/293] lr: 5.000000e-05 eta: 1:30:51 time: 2.076572 data_time: 0.061333 memory: 5829 loss_kpt: 0.000790 acc_pose: 0.742403 loss: 0.000790 2022/10/15 23:20:42 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 23:20:42 - mmengine - INFO - Saving checkpoint at 200 epochs 2022/10/15 23:21:44 - mmengine - INFO - Epoch(val) [200][50/407] eta: 0:07:11 time: 1.209832 data_time: 1.172891 memory: 5829 2022/10/15 23:22:44 - mmengine - INFO - Epoch(val) [200][100/407] eta: 0:06:09 time: 1.204822 data_time: 1.168441 memory: 540 2022/10/15 23:23:39 - mmengine - INFO - Epoch(val) [200][150/407] eta: 0:04:39 time: 1.088288 data_time: 1.051680 memory: 540 2022/10/15 23:24:28 - mmengine - INFO - Epoch(val) [200][200/407] eta: 0:03:25 time: 0.991023 data_time: 0.955042 memory: 540 2022/10/15 23:25:23 - mmengine - INFO - Epoch(val) [200][250/407] eta: 0:02:51 time: 1.093377 data_time: 1.052235 memory: 540 2022/10/15 23:26:20 - mmengine - INFO - Epoch(val) [200][300/407] eta: 0:02:02 time: 1.140982 data_time: 1.105127 memory: 540 2022/10/15 23:27:17 - mmengine - INFO - Epoch(val) [200][350/407] eta: 0:01:05 time: 1.145765 data_time: 1.109652 memory: 540 2022/10/15 23:28:09 - mmengine - INFO - Epoch(val) [200][400/407] eta: 0:00:07 time: 1.025144 data_time: 0.989133 memory: 540 2022/10/15 23:29:12 - mmengine - INFO - Evaluating CocoMetric... 2022/10/15 23:29:26 - mmengine - INFO - Epoch(val) [200][407/407] coco/AP: 0.646879 coco/AP .5: 0.873293 coco/AP .75: 0.722777 coco/AP (M): 0.609668 coco/AP (L): 0.714398 coco/AR: 0.707604 coco/AR .5: 0.916719 coco/AR .75: 0.779125 coco/AR (M): 0.661541 coco/AR (L): 0.772761 2022/10/15 23:29:26 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256/best_coco/AP_epoch_190.pth is removed 2022/10/15 23:29:28 - mmengine - INFO - The best checkpoint with 0.6469 coco/AP at 200 epoch is saved to best_coco/AP_epoch_200.pth. 2022/10/15 23:31:14 - mmengine - INFO - Epoch(train) [201][50/293] lr: 5.000000e-06 eta: 1:27:57 time: 2.118633 data_time: 0.355782 memory: 5829 loss_kpt: 0.000787 acc_pose: 0.733424 loss: 0.000787 2022/10/15 23:32:46 - mmengine - INFO - Epoch(train) [201][100/293] lr: 5.000000e-06 eta: 1:26:26 time: 1.839928 data_time: 0.064306 memory: 5829 loss_kpt: 0.000772 acc_pose: 0.755457 loss: 0.000772 2022/10/15 23:33:58 - mmengine - INFO - Epoch(train) [201][150/293] lr: 5.000000e-06 eta: 1:24:53 time: 1.439724 data_time: 0.307165 memory: 5829 loss_kpt: 0.000788 acc_pose: 0.799641 loss: 0.000788 2022/10/15 23:35:10 - mmengine - INFO - Epoch(train) [201][200/293] lr: 5.000000e-06 eta: 1:23:21 time: 1.442789 data_time: 1.120408 memory: 5829 loss_kpt: 0.000786 acc_pose: 0.766428 loss: 0.000786 2022/10/15 23:36:20 - mmengine - INFO - Epoch(train) [201][250/293] lr: 5.000000e-06 eta: 1:21:48 time: 1.410977 data_time: 0.190921 memory: 5829 loss_kpt: 0.000774 acc_pose: 0.745945 loss: 0.000774 2022/10/15 23:37:47 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 23:39:27 - mmengine - INFO - Epoch(train) [202][50/293] lr: 5.000000e-06 eta: 1:18:55 time: 2.004730 data_time: 0.396004 memory: 5829 loss_kpt: 0.000776 acc_pose: 0.793551 loss: 0.000776 2022/10/15 23:41:13 - mmengine - INFO - Epoch(train) [202][100/293] lr: 5.000000e-06 eta: 1:17:24 time: 2.126998 data_time: 0.091788 memory: 5829 loss_kpt: 0.000778 acc_pose: 0.768579 loss: 0.000778 2022/10/15 23:41:31 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 23:43:12 - mmengine - INFO - Epoch(train) [202][150/293] lr: 5.000000e-06 eta: 1:15:53 time: 2.365368 data_time: 0.064384 memory: 5829 loss_kpt: 0.000783 acc_pose: 0.773014 loss: 0.000783 2022/10/15 23:45:10 - mmengine - INFO - Epoch(train) [202][200/293] lr: 5.000000e-06 eta: 1:14:23 time: 2.362665 data_time: 0.060125 memory: 5829 loss_kpt: 0.000762 acc_pose: 0.797412 loss: 0.000762 2022/10/15 23:47:21 - mmengine - INFO - Epoch(train) [202][250/293] lr: 5.000000e-06 eta: 1:12:53 time: 2.631713 data_time: 0.066749 memory: 5829 loss_kpt: 0.000783 acc_pose: 0.778455 loss: 0.000783 2022/10/15 23:49:10 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/15 23:51:08 - mmengine - INFO - Epoch(train) [203][50/293] lr: 5.000000e-06 eta: 1:10:00 time: 2.360275 data_time: 0.163183 memory: 5829 loss_kpt: 0.000774 acc_pose: 0.790650 loss: 0.000774 2022/10/15 23:53:11 - mmengine - INFO - Epoch(train) [203][100/293] lr: 5.000000e-06 eta: 1:08:30 time: 2.453193 data_time: 0.074133 memory: 5829 loss_kpt: 0.000784 acc_pose: 0.786886 loss: 0.000784 2022/10/15 23:55:15 - mmengine - INFO - Epoch(train) [203][150/293] lr: 5.000000e-06 eta: 1:07:00 time: 2.483876 data_time: 0.085703 memory: 5829 loss_kpt: 0.000787 acc_pose: 0.761035 loss: 0.000787 2022/10/15 23:57:32 - mmengine - INFO - Epoch(train) [203][200/293] lr: 5.000000e-06 eta: 1:05:30 time: 2.735878 data_time: 2.225967 memory: 5829 loss_kpt: 0.000788 acc_pose: 0.773760 loss: 0.000788 2022/10/15 23:59:39 - mmengine - INFO - Epoch(train) [203][250/293] lr: 5.000000e-06 eta: 1:03:59 time: 2.542808 data_time: 2.195619 memory: 5829 loss_kpt: 0.000785 acc_pose: 0.780487 loss: 0.000785 2022/10/16 00:01:29 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/16 00:03:25 - mmengine - INFO - Epoch(train) [204][50/293] lr: 5.000000e-06 eta: 1:01:07 time: 2.325973 data_time: 1.097965 memory: 5829 loss_kpt: 0.000785 acc_pose: 0.750668 loss: 0.000785 2022/10/16 00:05:31 - mmengine - INFO - Epoch(train) [204][100/293] lr: 5.000000e-06 eta: 0:59:36 time: 2.504248 data_time: 0.677495 memory: 5829 loss_kpt: 0.000762 acc_pose: 0.794241 loss: 0.000762 2022/10/16 00:07:26 - mmengine - INFO - Epoch(train) [204][150/293] lr: 5.000000e-06 eta: 0:58:05 time: 2.305503 data_time: 0.059398 memory: 5829 loss_kpt: 0.000781 acc_pose: 0.792370 loss: 0.000781 2022/10/16 00:09:26 - mmengine - INFO - Epoch(train) [204][200/293] lr: 5.000000e-06 eta: 0:56:35 time: 2.406641 data_time: 0.067774 memory: 5829 loss_kpt: 0.000777 acc_pose: 0.814751 loss: 0.000777 2022/10/16 00:11:22 - mmengine - INFO - Epoch(train) [204][250/293] lr: 5.000000e-06 eta: 0:55:04 time: 2.316280 data_time: 0.096299 memory: 5829 loss_kpt: 0.000782 acc_pose: 0.794140 loss: 0.000782 2022/10/16 00:13:11 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/16 00:15:17 - mmengine - INFO - Epoch(train) [205][50/293] lr: 5.000000e-06 eta: 0:52:12 time: 2.525921 data_time: 0.126635 memory: 5829 loss_kpt: 0.000780 acc_pose: 0.780885 loss: 0.000780 2022/10/16 00:17:31 - mmengine - INFO - Epoch(train) [205][100/293] lr: 5.000000e-06 eta: 0:50:41 time: 2.662845 data_time: 0.068303 memory: 5829 loss_kpt: 0.000791 acc_pose: 0.772481 loss: 0.000791 2022/10/16 00:19:24 - mmengine - INFO - Epoch(train) [205][150/293] lr: 5.000000e-06 eta: 0:49:10 time: 2.274678 data_time: 0.066317 memory: 5829 loss_kpt: 0.000766 acc_pose: 0.735457 loss: 0.000766 2022/10/16 00:21:24 - mmengine - INFO - Epoch(train) [205][200/293] lr: 5.000000e-06 eta: 0:47:39 time: 2.395280 data_time: 0.212095 memory: 5829 loss_kpt: 0.000777 acc_pose: 0.771118 loss: 0.000777 2022/10/16 00:22:36 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/16 00:23:30 - mmengine - INFO - Epoch(train) [205][250/293] lr: 5.000000e-06 eta: 0:46:08 time: 2.508627 data_time: 0.062470 memory: 5829 loss_kpt: 0.000775 acc_pose: 0.768779 loss: 0.000775 2022/10/16 00:25:12 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/16 00:27:26 - mmengine - INFO - Epoch(train) [206][50/293] lr: 5.000000e-06 eta: 0:43:17 time: 2.689657 data_time: 0.187083 memory: 5829 loss_kpt: 0.000769 acc_pose: 0.793229 loss: 0.000769 2022/10/16 00:29:32 - mmengine - INFO - Epoch(train) [206][100/293] lr: 5.000000e-06 eta: 0:41:46 time: 2.519682 data_time: 0.077250 memory: 5829 loss_kpt: 0.000779 acc_pose: 0.800993 loss: 0.000779 2022/10/16 00:31:10 - mmengine - INFO - Epoch(train) [206][150/293] lr: 5.000000e-06 eta: 0:40:14 time: 1.957702 data_time: 0.071015 memory: 5829 loss_kpt: 0.000768 acc_pose: 0.735660 loss: 0.000768 2022/10/16 00:32:43 - mmengine - INFO - Epoch(train) [206][200/293] lr: 5.000000e-06 eta: 0:38:42 time: 1.856051 data_time: 0.071498 memory: 5829 loss_kpt: 0.000769 acc_pose: 0.767938 loss: 0.000769 2022/10/16 00:34:14 - mmengine - INFO - Epoch(train) [206][250/293] lr: 5.000000e-06 eta: 0:37:10 time: 1.822538 data_time: 0.066385 memory: 5829 loss_kpt: 0.000760 acc_pose: 0.779794 loss: 0.000760 2022/10/16 00:35:13 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/16 00:36:26 - mmengine - INFO - Epoch(train) [207][50/293] lr: 5.000000e-06 eta: 0:34:18 time: 1.455376 data_time: 0.169248 memory: 5829 loss_kpt: 0.000778 acc_pose: 0.756493 loss: 0.000778 2022/10/16 00:37:28 - mmengine - INFO - Epoch(train) [207][100/293] lr: 5.000000e-06 eta: 0:32:46 time: 1.250075 data_time: 0.068930 memory: 5829 loss_kpt: 0.000779 acc_pose: 0.740916 loss: 0.000779 2022/10/16 00:38:44 - mmengine - INFO - Epoch(train) [207][150/293] lr: 5.000000e-06 eta: 0:31:14 time: 1.512367 data_time: 0.075429 memory: 5829 loss_kpt: 0.000795 acc_pose: 0.726422 loss: 0.000795 2022/10/16 00:40:49 - mmengine - INFO - Epoch(train) [207][200/293] lr: 5.000000e-06 eta: 0:29:42 time: 2.514265 data_time: 0.070000 memory: 5829 loss_kpt: 0.000772 acc_pose: 0.731489 loss: 0.000772 2022/10/16 00:42:38 - mmengine - INFO - Epoch(train) [207][250/293] lr: 5.000000e-06 eta: 0:28:11 time: 2.165211 data_time: 0.841587 memory: 5829 loss_kpt: 0.000777 acc_pose: 0.807362 loss: 0.000777 2022/10/16 00:44:06 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/16 00:46:04 - mmengine - INFO - Epoch(train) [208][50/293] lr: 5.000000e-06 eta: 0:25:20 time: 2.347625 data_time: 0.249473 memory: 5829 loss_kpt: 0.000773 acc_pose: 0.777021 loss: 0.000773 2022/10/16 00:47:56 - mmengine - INFO - Epoch(train) [208][100/293] lr: 5.000000e-06 eta: 0:23:48 time: 2.249364 data_time: 0.645736 memory: 5829 loss_kpt: 0.000769 acc_pose: 0.743716 loss: 0.000769 2022/10/16 00:50:04 - mmengine - INFO - Epoch(train) [208][150/293] lr: 5.000000e-06 eta: 0:22:17 time: 2.564176 data_time: 2.366247 memory: 5829 loss_kpt: 0.000778 acc_pose: 0.736150 loss: 0.000778 2022/10/16 00:51:54 - mmengine - INFO - Epoch(train) [208][200/293] lr: 5.000000e-06 eta: 0:20:45 time: 2.185203 data_time: 1.917824 memory: 5829 loss_kpt: 0.000774 acc_pose: 0.738094 loss: 0.000774 2022/10/16 00:53:52 - mmengine - INFO - Epoch(train) [208][250/293] lr: 5.000000e-06 eta: 0:19:14 time: 2.358572 data_time: 2.180926 memory: 5829 loss_kpt: 0.000769 acc_pose: 0.766547 loss: 0.000769 2022/10/16 00:55:30 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/16 00:57:09 - mmengine - INFO - Epoch(train) [209][50/293] lr: 5.000000e-06 eta: 0:16:23 time: 1.988633 data_time: 1.718009 memory: 5829 loss_kpt: 0.000755 acc_pose: 0.782786 loss: 0.000755 2022/10/16 00:57:23 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/16 00:58:55 - mmengine - INFO - Epoch(train) [209][100/293] lr: 5.000000e-06 eta: 0:14:51 time: 2.113416 data_time: 1.261281 memory: 5829 loss_kpt: 0.000791 acc_pose: 0.786044 loss: 0.000791 2022/10/16 01:00:54 - mmengine - INFO - Epoch(train) [209][150/293] lr: 5.000000e-06 eta: 0:13:19 time: 2.367944 data_time: 0.067352 memory: 5829 loss_kpt: 0.000780 acc_pose: 0.809996 loss: 0.000780 2022/10/16 01:03:05 - mmengine - INFO - Epoch(train) [209][200/293] lr: 5.000000e-06 eta: 0:11:48 time: 2.621752 data_time: 0.064265 memory: 5829 loss_kpt: 0.000771 acc_pose: 0.745172 loss: 0.000771 2022/10/16 01:05:12 - mmengine - INFO - Epoch(train) [209][250/293] lr: 5.000000e-06 eta: 0:10:16 time: 2.554810 data_time: 0.209074 memory: 5829 loss_kpt: 0.000783 acc_pose: 0.726730 loss: 0.000783 2022/10/16 01:07:02 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/16 01:09:07 - mmengine - INFO - Epoch(train) [210][50/293] lr: 5.000000e-06 eta: 0:07:25 time: 2.498055 data_time: 0.298606 memory: 5829 loss_kpt: 0.000773 acc_pose: 0.747085 loss: 0.000773 2022/10/16 01:10:43 - mmengine - INFO - Epoch(train) [210][100/293] lr: 5.000000e-06 eta: 0:05:54 time: 1.930013 data_time: 0.056949 memory: 5829 loss_kpt: 0.000770 acc_pose: 0.840063 loss: 0.000770 2022/10/16 01:12:26 - mmengine - INFO - Epoch(train) [210][150/293] lr: 5.000000e-06 eta: 0:04:22 time: 2.052188 data_time: 0.062097 memory: 5829 loss_kpt: 0.000777 acc_pose: 0.801752 loss: 0.000777 2022/10/16 01:14:20 - mmengine - INFO - Epoch(train) [210][200/293] lr: 5.000000e-06 eta: 0:02:50 time: 2.281276 data_time: 0.059161 memory: 5829 loss_kpt: 0.000793 acc_pose: 0.801297 loss: 0.000793 2022/10/16 01:15:56 - mmengine - INFO - Epoch(train) [210][250/293] lr: 5.000000e-06 eta: 0:01:18 time: 1.915005 data_time: 0.069805 memory: 5829 loss_kpt: 0.000776 acc_pose: 0.758958 loss: 0.000776 2022/10/16 01:17:00 - mmengine - INFO - Exp name: td-hm_mobilenetv2_8xb64-210e_coco-256x192_20221014_094519 2022/10/16 01:17:00 - mmengine - INFO - Saving checkpoint at 210 epochs 2022/10/16 01:17:56 - mmengine - INFO - Epoch(val) [210][50/407] eta: 0:06:26 time: 1.083659 data_time: 1.045908 memory: 5829 2022/10/16 01:18:41 - mmengine - INFO - Epoch(val) [210][100/407] eta: 0:04:32 time: 0.888810 data_time: 0.852301 memory: 540 2022/10/16 01:19:28 - mmengine - INFO - Epoch(val) [210][150/407] eta: 0:04:02 time: 0.943617 data_time: 0.902736 memory: 540 2022/10/16 01:20:45 - mmengine - INFO - Epoch(val) [210][200/407] eta: 0:05:21 time: 1.552217 data_time: 1.515650 memory: 540 2022/10/16 01:21:56 - mmengine - INFO - Epoch(val) [210][250/407] eta: 0:03:40 time: 1.405419 data_time: 1.369327 memory: 540 2022/10/16 01:23:07 - mmengine - INFO - Epoch(val) [210][300/407] eta: 0:02:31 time: 1.417503 data_time: 1.380310 memory: 540 2022/10/16 01:24:00 - mmengine - INFO - Epoch(val) [210][350/407] eta: 0:01:00 time: 1.065825 data_time: 1.028977 memory: 540 2022/10/16 01:25:02 - mmengine - INFO - Epoch(val) [210][400/407] eta: 0:00:08 time: 1.235735 data_time: 1.200318 memory: 540 2022/10/16 01:25:43 - mmengine - INFO - Evaluating CocoMetric... 2022/10/16 01:25:57 - mmengine - INFO - Epoch(val) [210][407/407] coco/AP: 0.647808 coco/AP .5: 0.873653 coco/AP .75: 0.725106 coco/AP (M): 0.611236 coco/AP (L): 0.714987 coco/AR: 0.709084 coco/AR .5: 0.917979 coco/AR .75: 0.780227 coco/AR (M): 0.663480 coco/AR (L): 0.773616 2022/10/16 01:25:57 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221014/mbv2_256/best_coco/AP_epoch_200.pth is removed 2022/10/16 01:25:58 - mmengine - INFO - The best checkpoint with 0.6478 coco/AP at 210 epoch is saved to best_coco/AP_epoch_210.pth.