2022/10/13 10:13:38 - 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: 1554278561 GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/petrelfs/share/cuda-11.3 NVCC: Cuda compilation tools, release 11.3, V11.3.109 GCC: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44) PyTorch: 1.12.0+cu113 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.3 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86 - CuDNN 8.2.1 - Built with CuDNN 8.3.2 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.13.0+cu113 OpenCV: 4.6.0 MMEngine: 0.1.0 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None Distributed launcher: slurm Distributed training: True GPU number: 8 ------------------------------------------------------------ 2022/10/13 10:13:39 - mmengine - INFO - Config: default_scope = 'mmpose' default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict( type='CheckpointHook', interval=10, max_keep_ckpts=1, save_best='coco/AP', rule='greater'), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='PoseVisualizationHook', enable=False)) custom_hooks = [dict(type='SyncBuffersHook')] env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='PoseLocalVisualizer', vis_backends=[dict(type='LocalVisBackend')], name='visualizer') log_processor = dict( type='LogProcessor', window_size=50, by_epoch=True, num_digits=6) log_level = 'INFO' load_from = None resume = False file_client_args = dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' })) train_cfg = dict(by_epoch=True, max_epochs=210, val_interval=10) val_cfg = dict() test_cfg = dict() optim_wrapper = dict(optimizer=dict(type='Adam', lr=0.0005)) param_scheduler = [ dict( type='LinearLR', begin=0, end=500, start_factor=0.001, by_epoch=False), dict( type='MultiStepLR', begin=0, end=210, milestones=[170, 200], gamma=0.1, by_epoch=True) ] auto_scale_lr = dict(base_batch_size=512) codec = dict( type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) model = dict( type='TopdownPoseEstimator', data_preprocessor=dict( type='PoseDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True), backbone=dict( type='ShuffleNetV1', groups=3, init_cfg=dict(type='Pretrained', checkpoint='mmcls://shufflenet_v1')), head=dict( type='HeatmapHead', in_channels=960, out_channels=17, loss=dict(type='KeypointMSELoss', use_target_weight=True), decoder=dict( type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3)), test_cfg=dict(flip_test=True, flip_mode='heatmap', shift_heatmap=True)) dataset_type = 'CocoDataset' data_mode = 'topdown' data_root = 'data/coco/' train_pipeline = [ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict(type='RandomBBoxTransform'), dict(type='TopdownAffine', input_size=(288, 384)), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3)), dict(type='PackPoseInputs') ] val_pipeline = [ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(288, 384)), dict(type='PackPoseInputs') ] train_dataloader = dict( batch_size=64, num_workers=2, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_train2017.json', data_prefix=dict(img='train2017/'), pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict(type='RandomBBoxTransform'), dict(type='TopdownAffine', input_size=(288, 384)), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3)), dict(type='PackPoseInputs') ])) val_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(288, 384)), dict(type='PackPoseInputs') ])) test_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(288, 384)), dict(type='PackPoseInputs') ])) val_evaluator = dict( type='CocoMetric', ann_file='data/coco/annotations/person_keypoints_val2017.json') test_evaluator = dict( type='CocoMetric', ann_file='data/coco/annotations/person_keypoints_val2017.json') launcher = 'slurm' work_dir = 'work_dirs/20221013/shufflenetv1_384/' 2022/10/13 10:14:14 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "data sampler" registry tree. As a workaround, the current "data sampler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/13 10:14:14 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "optimizer wrapper constructor" registry tree. As a workaround, the current "optimizer wrapper constructor" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/13 10:14:14 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "optimizer" registry tree. As a workaround, the current "optimizer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/13 10:14:14 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "optim_wrapper" registry tree. As a workaround, the current "optim_wrapper" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/13 10:14:14 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/13 10:14:14 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/13 10:14:14 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/13 10:14:14 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/13 10:14:19 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "data sampler" registry tree. As a workaround, the current "data sampler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/13 10:14:20 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/13 10:14:21 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/13 10:14:21 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. Name of parameter - Initialization information backbone.conv1.conv.weight - torch.Size([24, 3, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.conv1.bn.weight - torch.Size([24]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.conv1.bn.bias - torch.Size([24]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.0.g_conv_1x1_compress.conv.weight - torch.Size([60, 24, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.0.g_conv_1x1_compress.bn.weight - torch.Size([60]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.0.g_conv_1x1_compress.bn.bias - torch.Size([60]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.0.depthwise_conv3x3_bn.conv.weight - torch.Size([60, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.0.depthwise_conv3x3_bn.bn.weight - torch.Size([60]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.0.depthwise_conv3x3_bn.bn.bias - torch.Size([60]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.0.g_conv_1x1_expand.conv.weight - torch.Size([216, 20, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.0.g_conv_1x1_expand.bn.weight - torch.Size([216]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.0.g_conv_1x1_expand.bn.bias - torch.Size([216]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.1.g_conv_1x1_compress.conv.weight - torch.Size([60, 80, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.1.g_conv_1x1_compress.bn.weight - torch.Size([60]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.1.g_conv_1x1_compress.bn.bias - torch.Size([60]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.1.depthwise_conv3x3_bn.conv.weight - torch.Size([60, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.1.depthwise_conv3x3_bn.bn.weight - torch.Size([60]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.1.depthwise_conv3x3_bn.bn.bias - torch.Size([60]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.1.g_conv_1x1_expand.conv.weight - torch.Size([240, 20, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.1.g_conv_1x1_expand.bn.weight - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.1.g_conv_1x1_expand.bn.bias - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.2.g_conv_1x1_compress.conv.weight - torch.Size([60, 80, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.2.g_conv_1x1_compress.bn.weight - torch.Size([60]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.2.g_conv_1x1_compress.bn.bias - torch.Size([60]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.2.depthwise_conv3x3_bn.conv.weight - torch.Size([60, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.2.depthwise_conv3x3_bn.bn.weight - torch.Size([60]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.2.depthwise_conv3x3_bn.bn.bias - torch.Size([60]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.2.g_conv_1x1_expand.conv.weight - torch.Size([240, 20, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.2.g_conv_1x1_expand.bn.weight - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.2.g_conv_1x1_expand.bn.bias - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.3.g_conv_1x1_compress.conv.weight - torch.Size([60, 80, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.3.g_conv_1x1_compress.bn.weight - torch.Size([60]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.3.g_conv_1x1_compress.bn.bias - torch.Size([60]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.3.depthwise_conv3x3_bn.conv.weight - torch.Size([60, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.3.depthwise_conv3x3_bn.bn.weight - torch.Size([60]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.3.depthwise_conv3x3_bn.bn.bias - torch.Size([60]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.3.g_conv_1x1_expand.conv.weight - torch.Size([240, 20, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.3.g_conv_1x1_expand.bn.weight - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.0.3.g_conv_1x1_expand.bn.bias - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.0.g_conv_1x1_compress.conv.weight - torch.Size([120, 80, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.0.g_conv_1x1_compress.bn.weight - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.0.g_conv_1x1_compress.bn.bias - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.0.depthwise_conv3x3_bn.conv.weight - torch.Size([120, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.0.depthwise_conv3x3_bn.bn.weight - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.0.depthwise_conv3x3_bn.bn.bias - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.0.g_conv_1x1_expand.conv.weight - torch.Size([240, 40, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.0.g_conv_1x1_expand.bn.weight - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.0.g_conv_1x1_expand.bn.bias - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.1.g_conv_1x1_compress.conv.weight - torch.Size([120, 160, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.1.g_conv_1x1_compress.bn.weight - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.1.g_conv_1x1_compress.bn.bias - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.1.depthwise_conv3x3_bn.conv.weight - torch.Size([120, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.1.depthwise_conv3x3_bn.bn.weight - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.1.depthwise_conv3x3_bn.bn.bias - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.1.g_conv_1x1_expand.conv.weight - torch.Size([480, 40, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.1.g_conv_1x1_expand.bn.weight - torch.Size([480]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.1.g_conv_1x1_expand.bn.bias - torch.Size([480]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.2.g_conv_1x1_compress.conv.weight - torch.Size([120, 160, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.2.g_conv_1x1_compress.bn.weight - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.2.g_conv_1x1_compress.bn.bias - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.2.depthwise_conv3x3_bn.conv.weight - torch.Size([120, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.2.depthwise_conv3x3_bn.bn.weight - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.2.depthwise_conv3x3_bn.bn.bias - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.2.g_conv_1x1_expand.conv.weight - torch.Size([480, 40, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.2.g_conv_1x1_expand.bn.weight - torch.Size([480]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.2.g_conv_1x1_expand.bn.bias - torch.Size([480]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.3.g_conv_1x1_compress.conv.weight - torch.Size([120, 160, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.3.g_conv_1x1_compress.bn.weight - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.3.g_conv_1x1_compress.bn.bias - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.3.depthwise_conv3x3_bn.conv.weight - torch.Size([120, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.3.depthwise_conv3x3_bn.bn.weight - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.3.depthwise_conv3x3_bn.bn.bias - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.3.g_conv_1x1_expand.conv.weight - torch.Size([480, 40, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.3.g_conv_1x1_expand.bn.weight - torch.Size([480]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.3.g_conv_1x1_expand.bn.bias - torch.Size([480]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.4.g_conv_1x1_compress.conv.weight - torch.Size([120, 160, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.4.g_conv_1x1_compress.bn.weight - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.4.g_conv_1x1_compress.bn.bias - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.4.depthwise_conv3x3_bn.conv.weight - torch.Size([120, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.4.depthwise_conv3x3_bn.bn.weight - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.4.depthwise_conv3x3_bn.bn.bias - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.4.g_conv_1x1_expand.conv.weight - torch.Size([480, 40, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.4.g_conv_1x1_expand.bn.weight - torch.Size([480]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.4.g_conv_1x1_expand.bn.bias - torch.Size([480]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.5.g_conv_1x1_compress.conv.weight - torch.Size([120, 160, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.5.g_conv_1x1_compress.bn.weight - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.5.g_conv_1x1_compress.bn.bias - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.5.depthwise_conv3x3_bn.conv.weight - torch.Size([120, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.5.depthwise_conv3x3_bn.bn.weight - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.5.depthwise_conv3x3_bn.bn.bias - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.5.g_conv_1x1_expand.conv.weight - torch.Size([480, 40, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.5.g_conv_1x1_expand.bn.weight - torch.Size([480]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.5.g_conv_1x1_expand.bn.bias - torch.Size([480]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.6.g_conv_1x1_compress.conv.weight - torch.Size([120, 160, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.6.g_conv_1x1_compress.bn.weight - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.6.g_conv_1x1_compress.bn.bias - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.6.depthwise_conv3x3_bn.conv.weight - torch.Size([120, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.6.depthwise_conv3x3_bn.bn.weight - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.6.depthwise_conv3x3_bn.bn.bias - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.6.g_conv_1x1_expand.conv.weight - torch.Size([480, 40, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.6.g_conv_1x1_expand.bn.weight - torch.Size([480]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.6.g_conv_1x1_expand.bn.bias - torch.Size([480]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.7.g_conv_1x1_compress.conv.weight - torch.Size([120, 160, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.7.g_conv_1x1_compress.bn.weight - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.7.g_conv_1x1_compress.bn.bias - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.7.depthwise_conv3x3_bn.conv.weight - torch.Size([120, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.7.depthwise_conv3x3_bn.bn.weight - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.7.depthwise_conv3x3_bn.bn.bias - torch.Size([120]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.7.g_conv_1x1_expand.conv.weight - torch.Size([480, 40, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.7.g_conv_1x1_expand.bn.weight - torch.Size([480]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.1.7.g_conv_1x1_expand.bn.bias - torch.Size([480]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.0.g_conv_1x1_compress.conv.weight - torch.Size([240, 160, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.0.g_conv_1x1_compress.bn.weight - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.0.g_conv_1x1_compress.bn.bias - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.0.depthwise_conv3x3_bn.conv.weight - torch.Size([240, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.0.depthwise_conv3x3_bn.bn.weight - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.0.depthwise_conv3x3_bn.bn.bias - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.0.g_conv_1x1_expand.conv.weight - torch.Size([480, 80, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.0.g_conv_1x1_expand.bn.weight - torch.Size([480]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.0.g_conv_1x1_expand.bn.bias - torch.Size([480]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.1.g_conv_1x1_compress.conv.weight - torch.Size([240, 320, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.1.g_conv_1x1_compress.bn.weight - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.1.g_conv_1x1_compress.bn.bias - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.1.depthwise_conv3x3_bn.conv.weight - torch.Size([240, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.1.depthwise_conv3x3_bn.bn.weight - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.1.depthwise_conv3x3_bn.bn.bias - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.1.g_conv_1x1_expand.conv.weight - torch.Size([960, 80, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.1.g_conv_1x1_expand.bn.weight - torch.Size([960]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.1.g_conv_1x1_expand.bn.bias - torch.Size([960]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.2.g_conv_1x1_compress.conv.weight - torch.Size([240, 320, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.2.g_conv_1x1_compress.bn.weight - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.2.g_conv_1x1_compress.bn.bias - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.2.depthwise_conv3x3_bn.conv.weight - torch.Size([240, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.2.depthwise_conv3x3_bn.bn.weight - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.2.depthwise_conv3x3_bn.bn.bias - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.2.g_conv_1x1_expand.conv.weight - torch.Size([960, 80, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.2.g_conv_1x1_expand.bn.weight - torch.Size([960]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.2.g_conv_1x1_expand.bn.bias - torch.Size([960]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.3.g_conv_1x1_compress.conv.weight - torch.Size([240, 320, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.3.g_conv_1x1_compress.bn.weight - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.3.g_conv_1x1_compress.bn.bias - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.3.depthwise_conv3x3_bn.conv.weight - torch.Size([240, 1, 3, 3]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.3.depthwise_conv3x3_bn.bn.weight - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.3.depthwise_conv3x3_bn.bn.bias - torch.Size([240]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.3.g_conv_1x1_expand.conv.weight - torch.Size([960, 80, 1, 1]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.3.g_conv_1x1_expand.bn.weight - torch.Size([960]): PretrainedInit: load from mmcls://shufflenet_v1 backbone.layers.2.3.g_conv_1x1_expand.bn.bias - torch.Size([960]): PretrainedInit: load from mmcls://shufflenet_v1 head.deconv_layers.0.weight - torch.Size([960, 256, 4, 4]): NormalInit: mean=0, std=0.001, bias=0 head.deconv_layers.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_layers.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_layers.3.weight - torch.Size([256, 256, 4, 4]): NormalInit: mean=0, std=0.001, bias=0 head.deconv_layers.4.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_layers.4.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_layers.6.weight - torch.Size([256, 256, 4, 4]): NormalInit: mean=0, std=0.001, bias=0 head.deconv_layers.7.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_layers.7.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.final_layer.weight - torch.Size([17, 256, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 head.final_layer.bias - torch.Size([17]): NormalInit: mean=0, std=0.001, bias=0 2022/10/13 10:14:21 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384 by HardDiskBackend. 2022/10/13 10:15:15 - mmengine - INFO - Epoch(train) [1][50/293] lr: 4.954910e-05 eta: 18:11:35 time: 1.065316 data_time: 0.220383 memory: 5857 loss_kpt: 0.002155 acc_pose: 0.189931 loss: 0.002155 2022/10/13 10:15:47 - mmengine - INFO - Epoch(train) [1][100/293] lr: 9.959920e-05 eta: 14:40:32 time: 0.654762 data_time: 0.086949 memory: 5857 loss_kpt: 0.001880 acc_pose: 0.298700 loss: 0.001880 2022/10/13 10:16:17 - mmengine - INFO - Epoch(train) [1][150/293] lr: 1.496493e-04 eta: 13:06:34 time: 0.586595 data_time: 0.123865 memory: 5857 loss_kpt: 0.001711 acc_pose: 0.367962 loss: 0.001711 2022/10/13 10:16:44 - mmengine - INFO - Epoch(train) [1][200/293] lr: 1.996994e-04 eta: 12:07:03 time: 0.538521 data_time: 0.075647 memory: 5857 loss_kpt: 0.001666 acc_pose: 0.415839 loss: 0.001666 2022/10/13 10:17:07 - mmengine - INFO - Epoch(train) [1][250/293] lr: 2.497495e-04 eta: 11:17:57 time: 0.473761 data_time: 0.096317 memory: 5857 loss_kpt: 0.001600 acc_pose: 0.452118 loss: 0.001600 2022/10/13 10:17:26 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:17:45 - mmengine - INFO - Epoch(train) [2][50/293] lr: 3.428427e-04 eta: 9:10:37 time: 0.385044 data_time: 0.088416 memory: 5857 loss_kpt: 0.001474 acc_pose: 0.469771 loss: 0.001474 2022/10/13 10:18:03 - mmengine - INFO - Epoch(train) [2][100/293] lr: 3.928928e-04 eta: 8:46:39 time: 0.358504 data_time: 0.097729 memory: 5857 loss_kpt: 0.001460 acc_pose: 0.449817 loss: 0.001460 2022/10/13 10:18:21 - mmengine - INFO - Epoch(train) [2][150/293] lr: 4.429429e-04 eta: 8:28:39 time: 0.364046 data_time: 0.067829 memory: 5857 loss_kpt: 0.001403 acc_pose: 0.453296 loss: 0.001403 2022/10/13 10:18:39 - mmengine - INFO - Epoch(train) [2][200/293] lr: 4.929930e-04 eta: 8:13:51 time: 0.360169 data_time: 0.071411 memory: 5857 loss_kpt: 0.001397 acc_pose: 0.544126 loss: 0.001397 2022/10/13 10:18:57 - mmengine - INFO - Epoch(train) [2][250/293] lr: 5.000000e-04 eta: 8:01:26 time: 0.357078 data_time: 0.072165 memory: 5857 loss_kpt: 0.001374 acc_pose: 0.498372 loss: 0.001374 2022/10/13 10:19:13 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:19:32 - mmengine - INFO - Epoch(train) [3][50/293] lr: 5.000000e-04 eta: 7:20:47 time: 0.380678 data_time: 0.094859 memory: 5857 loss_kpt: 0.001325 acc_pose: 0.552168 loss: 0.001325 2022/10/13 10:19:50 - mmengine - INFO - Epoch(train) [3][100/293] lr: 5.000000e-04 eta: 7:15:07 time: 0.362556 data_time: 0.068141 memory: 5857 loss_kpt: 0.001309 acc_pose: 0.589644 loss: 0.001309 2022/10/13 10:20:08 - mmengine - INFO - Epoch(train) [3][150/293] lr: 5.000000e-04 eta: 7:10:03 time: 0.360685 data_time: 0.064354 memory: 5857 loss_kpt: 0.001307 acc_pose: 0.544097 loss: 0.001307 2022/10/13 10:20:27 - mmengine - INFO - Epoch(train) [3][200/293] lr: 5.000000e-04 eta: 7:06:29 time: 0.374684 data_time: 0.075626 memory: 5857 loss_kpt: 0.001292 acc_pose: 0.540471 loss: 0.001292 2022/10/13 10:20:45 - mmengine - INFO - Epoch(train) [3][250/293] lr: 5.000000e-04 eta: 7:02:41 time: 0.364237 data_time: 0.080969 memory: 5857 loss_kpt: 0.001270 acc_pose: 0.555189 loss: 0.001270 2022/10/13 10:21:00 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:21:20 - mmengine - INFO - Epoch(train) [4][50/293] lr: 5.000000e-04 eta: 6:40:56 time: 0.388942 data_time: 0.098234 memory: 5857 loss_kpt: 0.001255 acc_pose: 0.593145 loss: 0.001255 2022/10/13 10:21:37 - mmengine - INFO - Epoch(train) [4][100/293] lr: 5.000000e-04 eta: 6:38:30 time: 0.356063 data_time: 0.070952 memory: 5857 loss_kpt: 0.001224 acc_pose: 0.573485 loss: 0.001224 2022/10/13 10:21:45 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:21:56 - mmengine - INFO - Epoch(train) [4][150/293] lr: 5.000000e-04 eta: 6:36:50 time: 0.367698 data_time: 0.072478 memory: 5857 loss_kpt: 0.001215 acc_pose: 0.485096 loss: 0.001215 2022/10/13 10:22:14 - mmengine - INFO - Epoch(train) [4][200/293] lr: 5.000000e-04 eta: 6:34:58 time: 0.360799 data_time: 0.068085 memory: 5857 loss_kpt: 0.001235 acc_pose: 0.543060 loss: 0.001235 2022/10/13 10:22:32 - mmengine - INFO - Epoch(train) [4][250/293] lr: 5.000000e-04 eta: 6:33:16 time: 0.361016 data_time: 0.079321 memory: 5857 loss_kpt: 0.001220 acc_pose: 0.592072 loss: 0.001220 2022/10/13 10:22:47 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:23:06 - mmengine - INFO - Epoch(train) [5][50/293] lr: 5.000000e-04 eta: 6:18:02 time: 0.370892 data_time: 0.111854 memory: 5857 loss_kpt: 0.001221 acc_pose: 0.551180 loss: 0.001221 2022/10/13 10:23:24 - mmengine - INFO - Epoch(train) [5][100/293] lr: 5.000000e-04 eta: 6:17:04 time: 0.359773 data_time: 0.078146 memory: 5857 loss_kpt: 0.001198 acc_pose: 0.553726 loss: 0.001198 2022/10/13 10:23:42 - mmengine - INFO - Epoch(train) [5][150/293] lr: 5.000000e-04 eta: 6:16:19 time: 0.363866 data_time: 0.069834 memory: 5857 loss_kpt: 0.001185 acc_pose: 0.559983 loss: 0.001185 2022/10/13 10:24:00 - mmengine - INFO - Epoch(train) [5][200/293] lr: 5.000000e-04 eta: 6:15:37 time: 0.364217 data_time: 0.074264 memory: 5857 loss_kpt: 0.001192 acc_pose: 0.603644 loss: 0.001192 2022/10/13 10:24:19 - mmengine - INFO - Epoch(train) [5][250/293] lr: 5.000000e-04 eta: 6:15:14 time: 0.372790 data_time: 0.081160 memory: 5857 loss_kpt: 0.001192 acc_pose: 0.622614 loss: 0.001192 2022/10/13 10:24:34 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:24:53 - mmengine - INFO - Epoch(train) [6][50/293] lr: 5.000000e-04 eta: 6:03:59 time: 0.373410 data_time: 0.106059 memory: 5857 loss_kpt: 0.001187 acc_pose: 0.599294 loss: 0.001187 2022/10/13 10:25:12 - mmengine - INFO - Epoch(train) [6][100/293] lr: 5.000000e-04 eta: 6:03:54 time: 0.371032 data_time: 0.085150 memory: 5857 loss_kpt: 0.001159 acc_pose: 0.625743 loss: 0.001159 2022/10/13 10:25:30 - mmengine - INFO - Epoch(train) [6][150/293] lr: 5.000000e-04 eta: 6:03:47 time: 0.370246 data_time: 0.105207 memory: 5857 loss_kpt: 0.001162 acc_pose: 0.571189 loss: 0.001162 2022/10/13 10:25:48 - mmengine - INFO - Epoch(train) [6][200/293] lr: 5.000000e-04 eta: 6:03:22 time: 0.360230 data_time: 0.095623 memory: 5857 loss_kpt: 0.001150 acc_pose: 0.696257 loss: 0.001150 2022/10/13 10:26:07 - mmengine - INFO - Epoch(train) [6][250/293] lr: 5.000000e-04 eta: 6:03:16 time: 0.371515 data_time: 0.088145 memory: 5857 loss_kpt: 0.001140 acc_pose: 0.587869 loss: 0.001140 2022/10/13 10:26:22 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:26:42 - mmengine - INFO - Epoch(train) [7][50/293] lr: 5.000000e-04 eta: 5:54:43 time: 0.387406 data_time: 0.092877 memory: 5857 loss_kpt: 0.001133 acc_pose: 0.628814 loss: 0.001133 2022/10/13 10:27:00 - mmengine - INFO - Epoch(train) [7][100/293] lr: 5.000000e-04 eta: 5:54:29 time: 0.358809 data_time: 0.070751 memory: 5857 loss_kpt: 0.001145 acc_pose: 0.566203 loss: 0.001145 2022/10/13 10:27:18 - mmengine - INFO - Epoch(train) [7][150/293] lr: 5.000000e-04 eta: 5:54:27 time: 0.366626 data_time: 0.071134 memory: 5857 loss_kpt: 0.001159 acc_pose: 0.662538 loss: 0.001159 2022/10/13 10:27:36 - mmengine - INFO - Epoch(train) [7][200/293] lr: 5.000000e-04 eta: 5:54:17 time: 0.361827 data_time: 0.072887 memory: 5857 loss_kpt: 0.001142 acc_pose: 0.631904 loss: 0.001142 2022/10/13 10:27:51 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:27:54 - mmengine - INFO - Epoch(train) [7][250/293] lr: 5.000000e-04 eta: 5:54:04 time: 0.360298 data_time: 0.068396 memory: 5857 loss_kpt: 0.001144 acc_pose: 0.601600 loss: 0.001144 2022/10/13 10:28:09 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:28:28 - mmengine - INFO - Epoch(train) [8][50/293] lr: 5.000000e-04 eta: 5:46:44 time: 0.376037 data_time: 0.098522 memory: 5857 loss_kpt: 0.001129 acc_pose: 0.577617 loss: 0.001129 2022/10/13 10:28:46 - mmengine - INFO - Epoch(train) [8][100/293] lr: 5.000000e-04 eta: 5:46:50 time: 0.367260 data_time: 0.074091 memory: 5857 loss_kpt: 0.001126 acc_pose: 0.637937 loss: 0.001126 2022/10/13 10:29:04 - mmengine - INFO - Epoch(train) [8][150/293] lr: 5.000000e-04 eta: 5:46:48 time: 0.361508 data_time: 0.071633 memory: 5857 loss_kpt: 0.001107 acc_pose: 0.589081 loss: 0.001107 2022/10/13 10:29:23 - mmengine - INFO - Epoch(train) [8][200/293] lr: 5.000000e-04 eta: 5:46:53 time: 0.368219 data_time: 0.071461 memory: 5857 loss_kpt: 0.001134 acc_pose: 0.621991 loss: 0.001134 2022/10/13 10:29:41 - mmengine - INFO - Epoch(train) [8][250/293] lr: 5.000000e-04 eta: 5:46:48 time: 0.360563 data_time: 0.073136 memory: 5857 loss_kpt: 0.001119 acc_pose: 0.565975 loss: 0.001119 2022/10/13 10:29:56 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:30:15 - mmengine - INFO - Epoch(train) [9][50/293] lr: 5.000000e-04 eta: 5:40:25 time: 0.370293 data_time: 0.113055 memory: 5857 loss_kpt: 0.001104 acc_pose: 0.604961 loss: 0.001104 2022/10/13 10:30:33 - mmengine - INFO - Epoch(train) [9][100/293] lr: 5.000000e-04 eta: 5:40:38 time: 0.370300 data_time: 0.077820 memory: 5857 loss_kpt: 0.001096 acc_pose: 0.640897 loss: 0.001096 2022/10/13 10:30:52 - mmengine - INFO - Epoch(train) [9][150/293] lr: 5.000000e-04 eta: 5:40:41 time: 0.363240 data_time: 0.070518 memory: 5857 loss_kpt: 0.001103 acc_pose: 0.703734 loss: 0.001103 2022/10/13 10:31:10 - mmengine - INFO - Epoch(train) [9][200/293] lr: 5.000000e-04 eta: 5:40:41 time: 0.360645 data_time: 0.070033 memory: 5857 loss_kpt: 0.001117 acc_pose: 0.622447 loss: 0.001117 2022/10/13 10:31:28 - mmengine - INFO - Epoch(train) [9][250/293] lr: 5.000000e-04 eta: 5:40:47 time: 0.367007 data_time: 0.069010 memory: 5857 loss_kpt: 0.001089 acc_pose: 0.651912 loss: 0.001089 2022/10/13 10:31:43 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:32:02 - mmengine - INFO - Epoch(train) [10][50/293] lr: 5.000000e-04 eta: 5:35:18 time: 0.374436 data_time: 0.097300 memory: 5857 loss_kpt: 0.001108 acc_pose: 0.661441 loss: 0.001108 2022/10/13 10:32:20 - mmengine - INFO - Epoch(train) [10][100/293] lr: 5.000000e-04 eta: 5:35:18 time: 0.358355 data_time: 0.099275 memory: 5857 loss_kpt: 0.001098 acc_pose: 0.699642 loss: 0.001098 2022/10/13 10:32:38 - mmengine - INFO - Epoch(train) [10][150/293] lr: 5.000000e-04 eta: 5:35:26 time: 0.365791 data_time: 0.077150 memory: 5857 loss_kpt: 0.001094 acc_pose: 0.630710 loss: 0.001094 2022/10/13 10:32:56 - mmengine - INFO - Epoch(train) [10][200/293] lr: 5.000000e-04 eta: 5:35:26 time: 0.359009 data_time: 0.080208 memory: 5857 loss_kpt: 0.001098 acc_pose: 0.604713 loss: 0.001098 2022/10/13 10:33:14 - mmengine - INFO - Epoch(train) [10][250/293] lr: 5.000000e-04 eta: 5:35:27 time: 0.361050 data_time: 0.078050 memory: 5857 loss_kpt: 0.001082 acc_pose: 0.660333 loss: 0.001082 2022/10/13 10:33:30 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:33:30 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/10/13 10:33:41 - mmengine - INFO - Epoch(val) [10][50/407] eta: 0:01:08 time: 0.190510 data_time: 0.133907 memory: 5857 2022/10/13 10:33:49 - mmengine - INFO - Epoch(val) [10][100/407] eta: 0:00:46 time: 0.151768 data_time: 0.096276 memory: 760 2022/10/13 10:33:55 - mmengine - INFO - Epoch(val) [10][150/407] eta: 0:00:33 time: 0.129197 data_time: 0.072153 memory: 760 2022/10/13 10:34:01 - mmengine - INFO - Epoch(val) [10][200/407] eta: 0:00:25 time: 0.122715 data_time: 0.066583 memory: 760 2022/10/13 10:34:08 - mmengine - INFO - Epoch(val) [10][250/407] eta: 0:00:21 time: 0.135733 data_time: 0.078755 memory: 760 2022/10/13 10:34:15 - mmengine - INFO - Epoch(val) [10][300/407] eta: 0:00:13 time: 0.130261 data_time: 0.069208 memory: 760 2022/10/13 10:34:22 - mmengine - INFO - Epoch(val) [10][350/407] eta: 0:00:08 time: 0.144551 data_time: 0.088292 memory: 760 2022/10/13 10:34:29 - mmengine - INFO - Epoch(val) [10][400/407] eta: 0:00:00 time: 0.135099 data_time: 0.079793 memory: 760 2022/10/13 10:35:08 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 10:35:23 - mmengine - INFO - Epoch(val) [10][407/407] coco/AP: 0.494505 coco/AP .5: 0.796036 coco/AP .75: 0.518203 coco/AP (M): 0.455738 coco/AP (L): 0.561694 coco/AR: 0.565633 coco/AR .5: 0.847607 coco/AR .75: 0.602645 coco/AR (M): 0.515597 coco/AR (L): 0.635637 2022/10/13 10:35:25 - mmengine - INFO - The best checkpoint with 0.4945 coco/AP at 10 epoch is saved to best_coco/AP_epoch_10.pth. 2022/10/13 10:35:44 - mmengine - INFO - Epoch(train) [11][50/293] lr: 5.000000e-04 eta: 5:30:36 time: 0.373931 data_time: 0.131529 memory: 5857 loss_kpt: 0.001102 acc_pose: 0.635534 loss: 0.001102 2022/10/13 10:35:51 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:36:02 - mmengine - INFO - Epoch(train) [11][100/293] lr: 5.000000e-04 eta: 5:30:41 time: 0.362125 data_time: 0.086588 memory: 5857 loss_kpt: 0.001076 acc_pose: 0.639214 loss: 0.001076 2022/10/13 10:36:20 - mmengine - INFO - Epoch(train) [11][150/293] lr: 5.000000e-04 eta: 5:30:43 time: 0.359066 data_time: 0.074307 memory: 5857 loss_kpt: 0.001078 acc_pose: 0.646751 loss: 0.001078 2022/10/13 10:36:38 - mmengine - INFO - Epoch(train) [11][200/293] lr: 5.000000e-04 eta: 5:30:53 time: 0.368547 data_time: 0.076881 memory: 5857 loss_kpt: 0.001072 acc_pose: 0.681681 loss: 0.001072 2022/10/13 10:36:56 - mmengine - INFO - Epoch(train) [11][250/293] lr: 5.000000e-04 eta: 5:30:54 time: 0.359545 data_time: 0.087334 memory: 5857 loss_kpt: 0.001079 acc_pose: 0.639706 loss: 0.001079 2022/10/13 10:37:12 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:37:31 - mmengine - INFO - Epoch(train) [12][50/293] lr: 5.000000e-04 eta: 5:26:39 time: 0.382341 data_time: 0.098118 memory: 5857 loss_kpt: 0.001069 acc_pose: 0.726814 loss: 0.001069 2022/10/13 10:37:49 - mmengine - INFO - Epoch(train) [12][100/293] lr: 5.000000e-04 eta: 5:26:44 time: 0.361007 data_time: 0.071065 memory: 5857 loss_kpt: 0.001081 acc_pose: 0.641669 loss: 0.001081 2022/10/13 10:38:07 - mmengine - INFO - Epoch(train) [12][150/293] lr: 5.000000e-04 eta: 5:26:52 time: 0.365213 data_time: 0.069897 memory: 5857 loss_kpt: 0.001065 acc_pose: 0.732223 loss: 0.001065 2022/10/13 10:38:25 - mmengine - INFO - Epoch(train) [12][200/293] lr: 5.000000e-04 eta: 5:26:54 time: 0.360076 data_time: 0.066031 memory: 5857 loss_kpt: 0.001083 acc_pose: 0.633910 loss: 0.001083 2022/10/13 10:38:44 - mmengine - INFO - Epoch(train) [12][250/293] lr: 5.000000e-04 eta: 5:27:00 time: 0.364209 data_time: 0.066856 memory: 5857 loss_kpt: 0.001068 acc_pose: 0.647577 loss: 0.001068 2022/10/13 10:38:59 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:39:18 - mmengine - INFO - Epoch(train) [13][50/293] lr: 5.000000e-04 eta: 5:23:16 time: 0.391655 data_time: 0.092352 memory: 5857 loss_kpt: 0.001065 acc_pose: 0.625552 loss: 0.001065 2022/10/13 10:39:36 - mmengine - INFO - Epoch(train) [13][100/293] lr: 5.000000e-04 eta: 5:23:23 time: 0.364108 data_time: 0.074654 memory: 5857 loss_kpt: 0.001058 acc_pose: 0.644246 loss: 0.001058 2022/10/13 10:39:55 - mmengine - INFO - Epoch(train) [13][150/293] lr: 5.000000e-04 eta: 5:23:33 time: 0.369860 data_time: 0.072411 memory: 5857 loss_kpt: 0.001075 acc_pose: 0.661605 loss: 0.001075 2022/10/13 10:40:13 - mmengine - INFO - Epoch(train) [13][200/293] lr: 5.000000e-04 eta: 5:23:39 time: 0.364141 data_time: 0.099943 memory: 5857 loss_kpt: 0.001064 acc_pose: 0.689732 loss: 0.001064 2022/10/13 10:40:31 - mmengine - INFO - Epoch(train) [13][250/293] lr: 5.000000e-04 eta: 5:23:44 time: 0.363956 data_time: 0.113871 memory: 5857 loss_kpt: 0.001062 acc_pose: 0.694835 loss: 0.001062 2022/10/13 10:40:46 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:41:05 - mmengine - INFO - Epoch(train) [14][50/293] lr: 5.000000e-04 eta: 5:20:06 time: 0.376600 data_time: 0.081359 memory: 5857 loss_kpt: 0.001036 acc_pose: 0.671799 loss: 0.001036 2022/10/13 10:41:23 - mmengine - INFO - Epoch(train) [14][100/293] lr: 5.000000e-04 eta: 5:20:13 time: 0.365002 data_time: 0.094141 memory: 5857 loss_kpt: 0.001057 acc_pose: 0.671766 loss: 0.001057 2022/10/13 10:41:42 - mmengine - INFO - Epoch(train) [14][150/293] lr: 5.000000e-04 eta: 5:20:18 time: 0.363463 data_time: 0.069196 memory: 5857 loss_kpt: 0.001059 acc_pose: 0.669451 loss: 0.001059 2022/10/13 10:41:56 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:42:00 - mmengine - INFO - Epoch(train) [14][200/293] lr: 5.000000e-04 eta: 5:20:22 time: 0.361557 data_time: 0.070980 memory: 5857 loss_kpt: 0.001062 acc_pose: 0.656271 loss: 0.001062 2022/10/13 10:42:18 - mmengine - INFO - Epoch(train) [14][250/293] lr: 5.000000e-04 eta: 5:20:28 time: 0.366624 data_time: 0.081333 memory: 5857 loss_kpt: 0.001044 acc_pose: 0.664104 loss: 0.001044 2022/10/13 10:42:33 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:42:52 - mmengine - INFO - Epoch(train) [15][50/293] lr: 5.000000e-04 eta: 5:17:07 time: 0.376609 data_time: 0.130609 memory: 5857 loss_kpt: 0.001044 acc_pose: 0.668470 loss: 0.001044 2022/10/13 10:43:10 - mmengine - INFO - Epoch(train) [15][100/293] lr: 5.000000e-04 eta: 5:17:11 time: 0.361252 data_time: 0.092510 memory: 5857 loss_kpt: 0.001043 acc_pose: 0.660808 loss: 0.001043 2022/10/13 10:43:28 - mmengine - INFO - Epoch(train) [15][150/293] lr: 5.000000e-04 eta: 5:17:17 time: 0.366542 data_time: 0.070758 memory: 5857 loss_kpt: 0.001048 acc_pose: 0.625254 loss: 0.001048 2022/10/13 10:43:46 - mmengine - INFO - Epoch(train) [15][200/293] lr: 5.000000e-04 eta: 5:17:19 time: 0.360560 data_time: 0.083061 memory: 5857 loss_kpt: 0.001071 acc_pose: 0.698523 loss: 0.001071 2022/10/13 10:44:04 - mmengine - INFO - Epoch(train) [15][250/293] lr: 5.000000e-04 eta: 5:17:20 time: 0.358947 data_time: 0.120787 memory: 5857 loss_kpt: 0.001047 acc_pose: 0.676430 loss: 0.001047 2022/10/13 10:44:20 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:44:39 - mmengine - INFO - Epoch(train) [16][50/293] lr: 5.000000e-04 eta: 5:14:15 time: 0.379289 data_time: 0.086455 memory: 5857 loss_kpt: 0.001042 acc_pose: 0.691720 loss: 0.001042 2022/10/13 10:44:57 - mmengine - INFO - Epoch(train) [16][100/293] lr: 5.000000e-04 eta: 5:14:17 time: 0.360334 data_time: 0.077282 memory: 5857 loss_kpt: 0.001064 acc_pose: 0.583703 loss: 0.001064 2022/10/13 10:45:15 - mmengine - INFO - Epoch(train) [16][150/293] lr: 5.000000e-04 eta: 5:14:21 time: 0.362497 data_time: 0.078467 memory: 5857 loss_kpt: 0.001034 acc_pose: 0.723835 loss: 0.001034 2022/10/13 10:45:34 - mmengine - INFO - Epoch(train) [16][200/293] lr: 5.000000e-04 eta: 5:14:29 time: 0.371570 data_time: 0.080700 memory: 5857 loss_kpt: 0.001057 acc_pose: 0.624188 loss: 0.001057 2022/10/13 10:45:52 - mmengine - INFO - Epoch(train) [16][250/293] lr: 5.000000e-04 eta: 5:14:29 time: 0.357594 data_time: 0.074009 memory: 5857 loss_kpt: 0.001060 acc_pose: 0.609757 loss: 0.001060 2022/10/13 10:46:07 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:46:26 - mmengine - INFO - Epoch(train) [17][50/293] lr: 5.000000e-04 eta: 5:11:40 time: 0.386243 data_time: 0.095533 memory: 5857 loss_kpt: 0.001029 acc_pose: 0.629278 loss: 0.001029 2022/10/13 10:46:44 - mmengine - INFO - Epoch(train) [17][100/293] lr: 5.000000e-04 eta: 5:11:43 time: 0.362815 data_time: 0.074721 memory: 5857 loss_kpt: 0.001020 acc_pose: 0.638945 loss: 0.001020 2022/10/13 10:47:03 - mmengine - INFO - Epoch(train) [17][150/293] lr: 5.000000e-04 eta: 5:11:49 time: 0.367296 data_time: 0.075080 memory: 5857 loss_kpt: 0.001051 acc_pose: 0.631101 loss: 0.001051 2022/10/13 10:47:21 - mmengine - INFO - Epoch(train) [17][200/293] lr: 5.000000e-04 eta: 5:11:49 time: 0.359170 data_time: 0.066369 memory: 5857 loss_kpt: 0.001045 acc_pose: 0.723564 loss: 0.001045 2022/10/13 10:47:39 - mmengine - INFO - Epoch(train) [17][250/293] lr: 5.000000e-04 eta: 5:11:57 time: 0.371985 data_time: 0.075122 memory: 5857 loss_kpt: 0.001034 acc_pose: 0.636341 loss: 0.001034 2022/10/13 10:47:54 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:48:02 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:48:14 - mmengine - INFO - Epoch(train) [18][50/293] lr: 5.000000e-04 eta: 5:09:14 time: 0.380822 data_time: 0.088356 memory: 5857 loss_kpt: 0.001041 acc_pose: 0.647000 loss: 0.001041 2022/10/13 10:48:31 - mmengine - INFO - Epoch(train) [18][100/293] lr: 5.000000e-04 eta: 5:09:11 time: 0.352338 data_time: 0.072529 memory: 5857 loss_kpt: 0.001028 acc_pose: 0.652919 loss: 0.001028 2022/10/13 10:48:49 - mmengine - INFO - Epoch(train) [18][150/293] lr: 5.000000e-04 eta: 5:09:12 time: 0.360776 data_time: 0.068859 memory: 5857 loss_kpt: 0.001033 acc_pose: 0.638372 loss: 0.001033 2022/10/13 10:49:07 - mmengine - INFO - Epoch(train) [18][200/293] lr: 5.000000e-04 eta: 5:09:14 time: 0.363129 data_time: 0.072762 memory: 5857 loss_kpt: 0.001025 acc_pose: 0.638750 loss: 0.001025 2022/10/13 10:49:26 - mmengine - INFO - Epoch(train) [18][250/293] lr: 5.000000e-04 eta: 5:09:20 time: 0.370406 data_time: 0.078100 memory: 5857 loss_kpt: 0.001033 acc_pose: 0.604875 loss: 0.001033 2022/10/13 10:49:41 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:50:00 - mmengine - INFO - Epoch(train) [19][50/293] lr: 5.000000e-04 eta: 5:06:45 time: 0.376766 data_time: 0.090256 memory: 5857 loss_kpt: 0.001020 acc_pose: 0.700669 loss: 0.001020 2022/10/13 10:50:18 - mmengine - INFO - Epoch(train) [19][100/293] lr: 5.000000e-04 eta: 5:06:45 time: 0.359991 data_time: 0.083949 memory: 5857 loss_kpt: 0.001019 acc_pose: 0.651855 loss: 0.001019 2022/10/13 10:50:36 - mmengine - INFO - Epoch(train) [19][150/293] lr: 5.000000e-04 eta: 5:06:46 time: 0.360510 data_time: 0.076241 memory: 5857 loss_kpt: 0.001027 acc_pose: 0.697570 loss: 0.001027 2022/10/13 10:50:53 - mmengine - INFO - Epoch(train) [19][200/293] lr: 5.000000e-04 eta: 5:06:42 time: 0.351759 data_time: 0.067348 memory: 5857 loss_kpt: 0.001024 acc_pose: 0.697447 loss: 0.001024 2022/10/13 10:51:12 - mmengine - INFO - Epoch(train) [19][250/293] lr: 5.000000e-04 eta: 5:06:47 time: 0.370549 data_time: 0.067690 memory: 5857 loss_kpt: 0.001022 acc_pose: 0.638058 loss: 0.001022 2022/10/13 10:51:27 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:51:46 - mmengine - INFO - Epoch(train) [20][50/293] lr: 5.000000e-04 eta: 5:04:19 time: 0.376328 data_time: 0.119310 memory: 5857 loss_kpt: 0.001024 acc_pose: 0.626514 loss: 0.001024 2022/10/13 10:52:04 - mmengine - INFO - Epoch(train) [20][100/293] lr: 5.000000e-04 eta: 5:04:20 time: 0.362345 data_time: 0.079394 memory: 5857 loss_kpt: 0.001019 acc_pose: 0.701275 loss: 0.001019 2022/10/13 10:52:22 - mmengine - INFO - Epoch(train) [20][150/293] lr: 5.000000e-04 eta: 5:04:21 time: 0.361152 data_time: 0.078288 memory: 5857 loss_kpt: 0.001009 acc_pose: 0.705934 loss: 0.001009 2022/10/13 10:52:41 - mmengine - INFO - Epoch(train) [20][200/293] lr: 5.000000e-04 eta: 5:04:28 time: 0.375254 data_time: 0.072365 memory: 5857 loss_kpt: 0.001004 acc_pose: 0.668293 loss: 0.001004 2022/10/13 10:52:59 - mmengine - INFO - Epoch(train) [20][250/293] lr: 5.000000e-04 eta: 5:04:29 time: 0.364862 data_time: 0.071336 memory: 5857 loss_kpt: 0.001020 acc_pose: 0.645139 loss: 0.001020 2022/10/13 10:53:15 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:53:15 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/10/13 10:53:24 - mmengine - INFO - Epoch(val) [20][50/407] eta: 0:00:46 time: 0.129823 data_time: 0.072548 memory: 5857 2022/10/13 10:53:30 - mmengine - INFO - Epoch(val) [20][100/407] eta: 0:00:39 time: 0.128778 data_time: 0.071168 memory: 760 2022/10/13 10:53:36 - mmengine - INFO - Epoch(val) [20][150/407] eta: 0:00:32 time: 0.124684 data_time: 0.069978 memory: 760 2022/10/13 10:53:42 - mmengine - INFO - Epoch(val) [20][200/407] eta: 0:00:24 time: 0.120659 data_time: 0.064183 memory: 760 2022/10/13 10:53:49 - mmengine - INFO - Epoch(val) [20][250/407] eta: 0:00:19 time: 0.123477 data_time: 0.064601 memory: 760 2022/10/13 10:53:55 - mmengine - INFO - Epoch(val) [20][300/407] eta: 0:00:13 time: 0.125788 data_time: 0.070690 memory: 760 2022/10/13 10:54:01 - mmengine - INFO - Epoch(val) [20][350/407] eta: 0:00:07 time: 0.123440 data_time: 0.066053 memory: 760 2022/10/13 10:54:07 - mmengine - INFO - Epoch(val) [20][400/407] eta: 0:00:00 time: 0.119758 data_time: 0.065990 memory: 760 2022/10/13 10:54:46 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 10:55:01 - mmengine - INFO - Epoch(val) [20][407/407] coco/AP: 0.539707 coco/AP .5: 0.823587 coco/AP .75: 0.582319 coco/AP (M): 0.498296 coco/AP (L): 0.608594 coco/AR: 0.605337 coco/AR .5: 0.870277 coco/AR .75: 0.656171 coco/AR (M): 0.555285 coco/AR (L): 0.675399 2022/10/13 10:55:01 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384/best_coco/AP_epoch_10.pth is removed 2022/10/13 10:55:02 - mmengine - INFO - The best checkpoint with 0.5397 coco/AP at 20 epoch is saved to best_coco/AP_epoch_20.pth. 2022/10/13 10:55:21 - mmengine - INFO - Epoch(train) [21][50/293] lr: 5.000000e-04 eta: 5:02:09 time: 0.376429 data_time: 0.117486 memory: 5857 loss_kpt: 0.000995 acc_pose: 0.633607 loss: 0.000995 2022/10/13 10:55:39 - mmengine - INFO - Epoch(train) [21][100/293] lr: 5.000000e-04 eta: 5:02:11 time: 0.364877 data_time: 0.072002 memory: 5857 loss_kpt: 0.001015 acc_pose: 0.660764 loss: 0.001015 2022/10/13 10:55:54 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:55:57 - mmengine - INFO - Epoch(train) [21][150/293] lr: 5.000000e-04 eta: 5:02:11 time: 0.362881 data_time: 0.067883 memory: 5857 loss_kpt: 0.001020 acc_pose: 0.685538 loss: 0.001020 2022/10/13 10:56:16 - mmengine - INFO - Epoch(train) [21][200/293] lr: 5.000000e-04 eta: 5:02:14 time: 0.369509 data_time: 0.080281 memory: 5857 loss_kpt: 0.001016 acc_pose: 0.626680 loss: 0.001016 2022/10/13 10:56:34 - mmengine - INFO - Epoch(train) [21][250/293] lr: 5.000000e-04 eta: 5:02:16 time: 0.367092 data_time: 0.079570 memory: 5857 loss_kpt: 0.000998 acc_pose: 0.648656 loss: 0.000998 2022/10/13 10:56:50 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:57:09 - mmengine - INFO - Epoch(train) [22][50/293] lr: 5.000000e-04 eta: 5:00:02 time: 0.375166 data_time: 0.092082 memory: 5857 loss_kpt: 0.001010 acc_pose: 0.718010 loss: 0.001010 2022/10/13 10:57:27 - mmengine - INFO - Epoch(train) [22][100/293] lr: 5.000000e-04 eta: 5:00:02 time: 0.363406 data_time: 0.072481 memory: 5857 loss_kpt: 0.001005 acc_pose: 0.631389 loss: 0.001005 2022/10/13 10:57:45 - mmengine - INFO - Epoch(train) [22][150/293] lr: 5.000000e-04 eta: 5:00:00 time: 0.357258 data_time: 0.071338 memory: 5857 loss_kpt: 0.001003 acc_pose: 0.696791 loss: 0.001003 2022/10/13 10:58:03 - mmengine - INFO - Epoch(train) [22][200/293] lr: 5.000000e-04 eta: 4:59:59 time: 0.361619 data_time: 0.066519 memory: 5857 loss_kpt: 0.001015 acc_pose: 0.632900 loss: 0.001015 2022/10/13 10:58:21 - mmengine - INFO - Epoch(train) [22][250/293] lr: 5.000000e-04 eta: 4:59:58 time: 0.360784 data_time: 0.072930 memory: 5857 loss_kpt: 0.001025 acc_pose: 0.755747 loss: 0.001025 2022/10/13 10:58:37 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 10:58:55 - mmengine - INFO - Epoch(train) [23][50/293] lr: 5.000000e-04 eta: 4:57:49 time: 0.376096 data_time: 0.085798 memory: 5857 loss_kpt: 0.001008 acc_pose: 0.688751 loss: 0.001008 2022/10/13 10:59:14 - mmengine - INFO - Epoch(train) [23][100/293] lr: 5.000000e-04 eta: 4:57:54 time: 0.374284 data_time: 0.071598 memory: 5857 loss_kpt: 0.001012 acc_pose: 0.687490 loss: 0.001012 2022/10/13 10:59:32 - mmengine - INFO - Epoch(train) [23][150/293] lr: 5.000000e-04 eta: 4:57:52 time: 0.359876 data_time: 0.076232 memory: 5857 loss_kpt: 0.001008 acc_pose: 0.649143 loss: 0.001008 2022/10/13 10:59:51 - mmengine - INFO - Epoch(train) [23][200/293] lr: 5.000000e-04 eta: 4:57:54 time: 0.369474 data_time: 0.094776 memory: 5857 loss_kpt: 0.001004 acc_pose: 0.645693 loss: 0.001004 2022/10/13 11:00:09 - mmengine - INFO - Epoch(train) [23][250/293] lr: 5.000000e-04 eta: 4:57:53 time: 0.362522 data_time: 0.115321 memory: 5857 loss_kpt: 0.001005 acc_pose: 0.688209 loss: 0.001005 2022/10/13 11:00:24 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:00:43 - mmengine - INFO - Epoch(train) [24][50/293] lr: 5.000000e-04 eta: 4:55:50 time: 0.377712 data_time: 0.087734 memory: 5857 loss_kpt: 0.001009 acc_pose: 0.614470 loss: 0.001009 2022/10/13 11:01:01 - mmengine - INFO - Epoch(train) [24][100/293] lr: 5.000000e-04 eta: 4:55:47 time: 0.357473 data_time: 0.065927 memory: 5857 loss_kpt: 0.000991 acc_pose: 0.654144 loss: 0.000991 2022/10/13 11:01:19 - mmengine - INFO - Epoch(train) [24][150/293] lr: 5.000000e-04 eta: 4:55:49 time: 0.368211 data_time: 0.080016 memory: 5857 loss_kpt: 0.000986 acc_pose: 0.717163 loss: 0.000986 2022/10/13 11:01:38 - mmengine - INFO - Epoch(train) [24][200/293] lr: 5.000000e-04 eta: 4:55:49 time: 0.366545 data_time: 0.066601 memory: 5857 loss_kpt: 0.000986 acc_pose: 0.666237 loss: 0.000986 2022/10/13 11:01:56 - mmengine - INFO - Epoch(train) [24][250/293] lr: 5.000000e-04 eta: 4:55:51 time: 0.371589 data_time: 0.074095 memory: 5857 loss_kpt: 0.001007 acc_pose: 0.687673 loss: 0.001007 2022/10/13 11:02:00 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:02:12 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:02:31 - mmengine - INFO - Epoch(train) [25][50/293] lr: 5.000000e-04 eta: 4:53:59 time: 0.392513 data_time: 0.093035 memory: 5857 loss_kpt: 0.000990 acc_pose: 0.720843 loss: 0.000990 2022/10/13 11:02:49 - mmengine - INFO - Epoch(train) [25][100/293] lr: 5.000000e-04 eta: 4:53:58 time: 0.364428 data_time: 0.065354 memory: 5857 loss_kpt: 0.000982 acc_pose: 0.713837 loss: 0.000982 2022/10/13 11:03:08 - mmengine - INFO - Epoch(train) [25][150/293] lr: 5.000000e-04 eta: 4:53:58 time: 0.368338 data_time: 0.076698 memory: 5857 loss_kpt: 0.001014 acc_pose: 0.697070 loss: 0.001014 2022/10/13 11:03:27 - mmengine - INFO - Epoch(train) [25][200/293] lr: 5.000000e-04 eta: 4:54:05 time: 0.387110 data_time: 0.078569 memory: 5857 loss_kpt: 0.001002 acc_pose: 0.683451 loss: 0.001002 2022/10/13 11:03:45 - mmengine - INFO - Epoch(train) [25][250/293] lr: 5.000000e-04 eta: 4:54:03 time: 0.362352 data_time: 0.075189 memory: 5857 loss_kpt: 0.000984 acc_pose: 0.666311 loss: 0.000984 2022/10/13 11:04:01 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:04:22 - mmengine - INFO - Epoch(train) [26][50/293] lr: 5.000000e-04 eta: 4:52:19 time: 0.403784 data_time: 0.091220 memory: 5857 loss_kpt: 0.001000 acc_pose: 0.683308 loss: 0.001000 2022/10/13 11:04:40 - mmengine - INFO - Epoch(train) [26][100/293] lr: 5.000000e-04 eta: 4:52:20 time: 0.369957 data_time: 0.084038 memory: 5857 loss_kpt: 0.001001 acc_pose: 0.725982 loss: 0.001001 2022/10/13 11:04:59 - mmengine - INFO - Epoch(train) [26][150/293] lr: 5.000000e-04 eta: 4:52:22 time: 0.376678 data_time: 0.067691 memory: 5857 loss_kpt: 0.001005 acc_pose: 0.643523 loss: 0.001005 2022/10/13 11:05:18 - mmengine - INFO - Epoch(train) [26][200/293] lr: 5.000000e-04 eta: 4:52:25 time: 0.375841 data_time: 0.087413 memory: 5857 loss_kpt: 0.000998 acc_pose: 0.658901 loss: 0.000998 2022/10/13 11:05:37 - mmengine - INFO - Epoch(train) [26][250/293] lr: 5.000000e-04 eta: 4:52:30 time: 0.386329 data_time: 0.070563 memory: 5857 loss_kpt: 0.000977 acc_pose: 0.708937 loss: 0.000977 2022/10/13 11:05:53 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:06:13 - mmengine - INFO - Epoch(train) [27][50/293] lr: 5.000000e-04 eta: 4:50:45 time: 0.391117 data_time: 0.090952 memory: 5857 loss_kpt: 0.000992 acc_pose: 0.692392 loss: 0.000992 2022/10/13 11:06:31 - mmengine - INFO - Epoch(train) [27][100/293] lr: 5.000000e-04 eta: 4:50:45 time: 0.369405 data_time: 0.072844 memory: 5857 loss_kpt: 0.000982 acc_pose: 0.671410 loss: 0.000982 2022/10/13 11:06:50 - mmengine - INFO - Epoch(train) [27][150/293] lr: 5.000000e-04 eta: 4:50:46 time: 0.375982 data_time: 0.071403 memory: 5857 loss_kpt: 0.000996 acc_pose: 0.743347 loss: 0.000996 2022/10/13 11:07:08 - mmengine - INFO - Epoch(train) [27][200/293] lr: 5.000000e-04 eta: 4:50:46 time: 0.370067 data_time: 0.072763 memory: 5857 loss_kpt: 0.000976 acc_pose: 0.727802 loss: 0.000976 2022/10/13 11:07:27 - mmengine - INFO - Epoch(train) [27][250/293] lr: 5.000000e-04 eta: 4:50:47 time: 0.376896 data_time: 0.075779 memory: 5857 loss_kpt: 0.000994 acc_pose: 0.597370 loss: 0.000994 2022/10/13 11:07:43 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:08:02 - mmengine - INFO - Epoch(train) [28][50/293] lr: 5.000000e-04 eta: 4:49:06 time: 0.394058 data_time: 0.089157 memory: 5857 loss_kpt: 0.000991 acc_pose: 0.732569 loss: 0.000991 2022/10/13 11:08:17 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:08:21 - mmengine - INFO - Epoch(train) [28][100/293] lr: 5.000000e-04 eta: 4:49:06 time: 0.373570 data_time: 0.074734 memory: 5857 loss_kpt: 0.000989 acc_pose: 0.695922 loss: 0.000989 2022/10/13 11:08:40 - mmengine - INFO - Epoch(train) [28][150/293] lr: 5.000000e-04 eta: 4:49:10 time: 0.384328 data_time: 0.072138 memory: 5857 loss_kpt: 0.000970 acc_pose: 0.662021 loss: 0.000970 2022/10/13 11:08:59 - mmengine - INFO - Epoch(train) [28][200/293] lr: 5.000000e-04 eta: 4:49:09 time: 0.369795 data_time: 0.063651 memory: 5857 loss_kpt: 0.000996 acc_pose: 0.679658 loss: 0.000996 2022/10/13 11:09:18 - mmengine - INFO - Epoch(train) [28][250/293] lr: 5.000000e-04 eta: 4:49:13 time: 0.386476 data_time: 0.074689 memory: 5857 loss_kpt: 0.000984 acc_pose: 0.692733 loss: 0.000984 2022/10/13 11:09:34 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:09:54 - mmengine - INFO - Epoch(train) [29][50/293] lr: 5.000000e-04 eta: 4:47:39 time: 0.408201 data_time: 0.090713 memory: 5857 loss_kpt: 0.001014 acc_pose: 0.687963 loss: 0.001014 2022/10/13 11:10:13 - mmengine - INFO - Epoch(train) [29][100/293] lr: 5.000000e-04 eta: 4:47:37 time: 0.368281 data_time: 0.074446 memory: 5857 loss_kpt: 0.000981 acc_pose: 0.685888 loss: 0.000981 2022/10/13 11:10:32 - mmengine - INFO - Epoch(train) [29][150/293] lr: 5.000000e-04 eta: 4:47:39 time: 0.381531 data_time: 0.073042 memory: 5857 loss_kpt: 0.000975 acc_pose: 0.673015 loss: 0.000975 2022/10/13 11:10:50 - mmengine - INFO - Epoch(train) [29][200/293] lr: 5.000000e-04 eta: 4:47:36 time: 0.365676 data_time: 0.070858 memory: 5857 loss_kpt: 0.000976 acc_pose: 0.687337 loss: 0.000976 2022/10/13 11:11:09 - mmengine - INFO - Epoch(train) [29][250/293] lr: 5.000000e-04 eta: 4:47:35 time: 0.375014 data_time: 0.068273 memory: 5857 loss_kpt: 0.000997 acc_pose: 0.678965 loss: 0.000997 2022/10/13 11:11:25 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:11:44 - mmengine - INFO - Epoch(train) [30][50/293] lr: 5.000000e-04 eta: 4:45:56 time: 0.381933 data_time: 0.098786 memory: 5857 loss_kpt: 0.000976 acc_pose: 0.719358 loss: 0.000976 2022/10/13 11:12:03 - mmengine - INFO - Epoch(train) [30][100/293] lr: 5.000000e-04 eta: 4:45:57 time: 0.380020 data_time: 0.069262 memory: 5857 loss_kpt: 0.000994 acc_pose: 0.618299 loss: 0.000994 2022/10/13 11:12:22 - mmengine - INFO - Epoch(train) [30][150/293] lr: 5.000000e-04 eta: 4:45:59 time: 0.383667 data_time: 0.078983 memory: 5857 loss_kpt: 0.000984 acc_pose: 0.682630 loss: 0.000984 2022/10/13 11:12:41 - mmengine - INFO - Epoch(train) [30][200/293] lr: 5.000000e-04 eta: 4:46:00 time: 0.380718 data_time: 0.077212 memory: 5857 loss_kpt: 0.000973 acc_pose: 0.694637 loss: 0.000973 2022/10/13 11:13:00 - mmengine - INFO - Epoch(train) [30][250/293] lr: 5.000000e-04 eta: 4:45:59 time: 0.373991 data_time: 0.079502 memory: 5857 loss_kpt: 0.000988 acc_pose: 0.683088 loss: 0.000988 2022/10/13 11:13:16 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:13:16 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/10/13 11:13:24 - mmengine - INFO - Epoch(val) [30][50/407] eta: 0:00:45 time: 0.128408 data_time: 0.071311 memory: 5857 2022/10/13 11:13:31 - mmengine - INFO - Epoch(val) [30][100/407] eta: 0:00:37 time: 0.120988 data_time: 0.065268 memory: 760 2022/10/13 11:13:37 - mmengine - INFO - Epoch(val) [30][150/407] eta: 0:00:32 time: 0.126848 data_time: 0.071422 memory: 760 2022/10/13 11:13:43 - mmengine - INFO - Epoch(val) [30][200/407] eta: 0:00:26 time: 0.128303 data_time: 0.072495 memory: 760 2022/10/13 11:13:50 - mmengine - INFO - Epoch(val) [30][250/407] eta: 0:00:20 time: 0.129666 data_time: 0.074578 memory: 760 2022/10/13 11:13:56 - mmengine - INFO - Epoch(val) [30][300/407] eta: 0:00:13 time: 0.122812 data_time: 0.066208 memory: 760 2022/10/13 11:14:02 - mmengine - INFO - Epoch(val) [30][350/407] eta: 0:00:07 time: 0.124391 data_time: 0.069354 memory: 760 2022/10/13 11:14:08 - mmengine - INFO - Epoch(val) [30][400/407] eta: 0:00:00 time: 0.121774 data_time: 0.065492 memory: 760 2022/10/13 11:14:47 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 11:15:02 - mmengine - INFO - Epoch(val) [30][407/407] coco/AP: 0.560332 coco/AP .5: 0.829613 coco/AP .75: 0.615270 coco/AP (M): 0.518725 coco/AP (L): 0.629583 coco/AR: 0.623520 coco/AR .5: 0.875157 coco/AR .75: 0.680101 coco/AR (M): 0.573860 coco/AR (L): 0.693348 2022/10/13 11:15:02 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384/best_coco/AP_epoch_20.pth is removed 2022/10/13 11:15:04 - mmengine - INFO - The best checkpoint with 0.5603 coco/AP at 30 epoch is saved to best_coco/AP_epoch_30.pth. 2022/10/13 11:15:23 - mmengine - INFO - Epoch(train) [31][50/293] lr: 5.000000e-04 eta: 4:44:21 time: 0.377345 data_time: 0.093991 memory: 5857 loss_kpt: 0.000974 acc_pose: 0.663554 loss: 0.000974 2022/10/13 11:15:42 - mmengine - INFO - Epoch(train) [31][100/293] lr: 5.000000e-04 eta: 4:44:22 time: 0.382099 data_time: 0.077961 memory: 5857 loss_kpt: 0.000961 acc_pose: 0.717003 loss: 0.000961 2022/10/13 11:16:00 - mmengine - INFO - Epoch(train) [31][150/293] lr: 5.000000e-04 eta: 4:44:21 time: 0.375597 data_time: 0.079604 memory: 5857 loss_kpt: 0.000994 acc_pose: 0.640620 loss: 0.000994 2022/10/13 11:16:19 - mmengine - INFO - Epoch(train) [31][200/293] lr: 5.000000e-04 eta: 4:44:21 time: 0.379815 data_time: 0.078857 memory: 5857 loss_kpt: 0.000973 acc_pose: 0.672656 loss: 0.000973 2022/10/13 11:16:23 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:16:39 - mmengine - INFO - Epoch(train) [31][250/293] lr: 5.000000e-04 eta: 4:44:21 time: 0.382270 data_time: 0.080892 memory: 5857 loss_kpt: 0.000983 acc_pose: 0.716459 loss: 0.000983 2022/10/13 11:16:55 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:17:16 - mmengine - INFO - Epoch(train) [32][50/293] lr: 5.000000e-04 eta: 4:42:58 time: 0.420726 data_time: 0.083475 memory: 5857 loss_kpt: 0.000963 acc_pose: 0.605947 loss: 0.000963 2022/10/13 11:17:35 - mmengine - INFO - Epoch(train) [32][100/293] lr: 5.000000e-04 eta: 4:42:58 time: 0.380175 data_time: 0.081266 memory: 5857 loss_kpt: 0.000981 acc_pose: 0.660707 loss: 0.000981 2022/10/13 11:17:56 - mmengine - INFO - Epoch(train) [32][150/293] lr: 5.000000e-04 eta: 4:43:06 time: 0.410116 data_time: 0.080506 memory: 5857 loss_kpt: 0.000977 acc_pose: 0.711764 loss: 0.000977 2022/10/13 11:18:16 - mmengine - INFO - Epoch(train) [32][200/293] lr: 5.000000e-04 eta: 4:43:12 time: 0.401654 data_time: 0.109215 memory: 5857 loss_kpt: 0.000970 acc_pose: 0.677149 loss: 0.000970 2022/10/13 11:18:36 - mmengine - INFO - Epoch(train) [32][250/293] lr: 5.000000e-04 eta: 4:43:14 time: 0.393309 data_time: 0.091998 memory: 5857 loss_kpt: 0.000963 acc_pose: 0.718093 loss: 0.000963 2022/10/13 11:18:52 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:19:11 - mmengine - INFO - Epoch(train) [33][50/293] lr: 5.000000e-04 eta: 4:41:44 time: 0.388781 data_time: 0.098548 memory: 5857 loss_kpt: 0.000989 acc_pose: 0.690244 loss: 0.000989 2022/10/13 11:19:31 - mmengine - INFO - Epoch(train) [33][100/293] lr: 5.000000e-04 eta: 4:41:44 time: 0.383795 data_time: 0.119021 memory: 5857 loss_kpt: 0.000987 acc_pose: 0.632812 loss: 0.000987 2022/10/13 11:19:50 - mmengine - INFO - Epoch(train) [33][150/293] lr: 5.000000e-04 eta: 4:41:45 time: 0.387158 data_time: 0.086796 memory: 5857 loss_kpt: 0.000980 acc_pose: 0.603759 loss: 0.000980 2022/10/13 11:20:09 - mmengine - INFO - Epoch(train) [33][200/293] lr: 5.000000e-04 eta: 4:41:46 time: 0.389811 data_time: 0.080032 memory: 5857 loss_kpt: 0.000984 acc_pose: 0.720270 loss: 0.000984 2022/10/13 11:20:29 - mmengine - INFO - Epoch(train) [33][250/293] lr: 5.000000e-04 eta: 4:41:49 time: 0.395796 data_time: 0.068705 memory: 5857 loss_kpt: 0.000969 acc_pose: 0.722977 loss: 0.000969 2022/10/13 11:20:45 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:21:06 - mmengine - INFO - Epoch(train) [34][50/293] lr: 5.000000e-04 eta: 4:40:27 time: 0.411493 data_time: 0.099689 memory: 5857 loss_kpt: 0.000955 acc_pose: 0.710704 loss: 0.000955 2022/10/13 11:21:25 - mmengine - INFO - Epoch(train) [34][100/293] lr: 5.000000e-04 eta: 4:40:25 time: 0.380653 data_time: 0.100235 memory: 5857 loss_kpt: 0.000968 acc_pose: 0.733492 loss: 0.000968 2022/10/13 11:21:44 - mmengine - INFO - Epoch(train) [34][150/293] lr: 5.000000e-04 eta: 4:40:26 time: 0.387838 data_time: 0.080789 memory: 5857 loss_kpt: 0.000950 acc_pose: 0.720476 loss: 0.000950 2022/10/13 11:22:04 - mmengine - INFO - Epoch(train) [34][200/293] lr: 5.000000e-04 eta: 4:40:28 time: 0.397208 data_time: 0.073059 memory: 5857 loss_kpt: 0.000957 acc_pose: 0.674257 loss: 0.000957 2022/10/13 11:22:23 - mmengine - INFO - Epoch(train) [34][250/293] lr: 5.000000e-04 eta: 4:40:24 time: 0.374107 data_time: 0.072834 memory: 5857 loss_kpt: 0.000975 acc_pose: 0.715043 loss: 0.000975 2022/10/13 11:22:39 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:22:55 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:23:00 - mmengine - INFO - Epoch(train) [35][50/293] lr: 5.000000e-04 eta: 4:39:07 time: 0.421412 data_time: 0.095733 memory: 5857 loss_kpt: 0.000959 acc_pose: 0.752787 loss: 0.000959 2022/10/13 11:23:20 - mmengine - INFO - Epoch(train) [35][100/293] lr: 5.000000e-04 eta: 4:39:08 time: 0.394965 data_time: 0.073858 memory: 5857 loss_kpt: 0.000971 acc_pose: 0.671493 loss: 0.000971 2022/10/13 11:23:38 - mmengine - INFO - Epoch(train) [35][150/293] lr: 5.000000e-04 eta: 4:39:04 time: 0.371640 data_time: 0.075208 memory: 5857 loss_kpt: 0.000970 acc_pose: 0.714296 loss: 0.000970 2022/10/13 11:23:59 - mmengine - INFO - Epoch(train) [35][200/293] lr: 5.000000e-04 eta: 4:39:08 time: 0.408448 data_time: 0.069048 memory: 5857 loss_kpt: 0.000959 acc_pose: 0.727631 loss: 0.000959 2022/10/13 11:24:20 - mmengine - INFO - Epoch(train) [35][250/293] lr: 5.000000e-04 eta: 4:39:15 time: 0.419244 data_time: 0.068518 memory: 5857 loss_kpt: 0.000965 acc_pose: 0.690413 loss: 0.000965 2022/10/13 11:24:37 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:24:59 - mmengine - INFO - Epoch(train) [36][50/293] lr: 5.000000e-04 eta: 4:38:01 time: 0.430661 data_time: 0.094964 memory: 5857 loss_kpt: 0.000976 acc_pose: 0.642239 loss: 0.000976 2022/10/13 11:25:20 - mmengine - INFO - Epoch(train) [36][100/293] lr: 5.000000e-04 eta: 4:38:08 time: 0.417612 data_time: 0.077431 memory: 5857 loss_kpt: 0.000958 acc_pose: 0.677133 loss: 0.000958 2022/10/13 11:25:40 - mmengine - INFO - Epoch(train) [36][150/293] lr: 5.000000e-04 eta: 4:38:10 time: 0.401745 data_time: 0.076997 memory: 5857 loss_kpt: 0.000960 acc_pose: 0.650612 loss: 0.000960 2022/10/13 11:26:01 - mmengine - INFO - Epoch(train) [36][200/293] lr: 5.000000e-04 eta: 4:38:19 time: 0.430273 data_time: 0.156007 memory: 5857 loss_kpt: 0.000965 acc_pose: 0.741228 loss: 0.000965 2022/10/13 11:26:21 - mmengine - INFO - Epoch(train) [36][250/293] lr: 5.000000e-04 eta: 4:38:17 time: 0.388258 data_time: 0.097511 memory: 5857 loss_kpt: 0.000960 acc_pose: 0.676559 loss: 0.000960 2022/10/13 11:26:37 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:26:58 - mmengine - INFO - Epoch(train) [37][50/293] lr: 5.000000e-04 eta: 4:37:03 time: 0.423660 data_time: 0.092771 memory: 5857 loss_kpt: 0.000942 acc_pose: 0.702958 loss: 0.000942 2022/10/13 11:27:18 - mmengine - INFO - Epoch(train) [37][100/293] lr: 5.000000e-04 eta: 4:37:03 time: 0.397569 data_time: 0.075193 memory: 5857 loss_kpt: 0.000949 acc_pose: 0.689823 loss: 0.000949 2022/10/13 11:27:38 - mmengine - INFO - Epoch(train) [37][150/293] lr: 5.000000e-04 eta: 4:37:03 time: 0.396068 data_time: 0.086033 memory: 5857 loss_kpt: 0.000965 acc_pose: 0.709684 loss: 0.000965 2022/10/13 11:27:58 - mmengine - INFO - Epoch(train) [37][200/293] lr: 5.000000e-04 eta: 4:37:03 time: 0.395484 data_time: 0.104251 memory: 5857 loss_kpt: 0.000949 acc_pose: 0.699889 loss: 0.000949 2022/10/13 11:28:17 - mmengine - INFO - Epoch(train) [37][250/293] lr: 5.000000e-04 eta: 4:37:01 time: 0.385433 data_time: 0.071456 memory: 5857 loss_kpt: 0.000950 acc_pose: 0.697874 loss: 0.000950 2022/10/13 11:28:34 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:28:54 - mmengine - INFO - Epoch(train) [38][50/293] lr: 5.000000e-04 eta: 4:35:41 time: 0.398288 data_time: 0.096686 memory: 5857 loss_kpt: 0.000958 acc_pose: 0.703685 loss: 0.000958 2022/10/13 11:29:13 - mmengine - INFO - Epoch(train) [38][100/293] lr: 5.000000e-04 eta: 4:35:37 time: 0.380461 data_time: 0.071958 memory: 5857 loss_kpt: 0.000972 acc_pose: 0.704473 loss: 0.000972 2022/10/13 11:29:32 - mmengine - INFO - Epoch(train) [38][150/293] lr: 5.000000e-04 eta: 4:35:35 time: 0.388391 data_time: 0.080773 memory: 5857 loss_kpt: 0.000977 acc_pose: 0.688036 loss: 0.000977 2022/10/13 11:29:36 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:29:52 - mmengine - INFO - Epoch(train) [38][200/293] lr: 5.000000e-04 eta: 4:35:33 time: 0.391477 data_time: 0.094761 memory: 5857 loss_kpt: 0.000965 acc_pose: 0.660531 loss: 0.000965 2022/10/13 11:30:12 - mmengine - INFO - Epoch(train) [38][250/293] lr: 5.000000e-04 eta: 4:35:33 time: 0.397096 data_time: 0.073906 memory: 5857 loss_kpt: 0.000977 acc_pose: 0.734491 loss: 0.000977 2022/10/13 11:30:30 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:30:51 - mmengine - INFO - Epoch(train) [39][50/293] lr: 5.000000e-04 eta: 4:34:21 time: 0.424879 data_time: 0.110121 memory: 5857 loss_kpt: 0.000952 acc_pose: 0.749670 loss: 0.000952 2022/10/13 11:31:10 - mmengine - INFO - Epoch(train) [39][100/293] lr: 5.000000e-04 eta: 4:34:18 time: 0.389421 data_time: 0.097492 memory: 5857 loss_kpt: 0.000961 acc_pose: 0.682933 loss: 0.000961 2022/10/13 11:31:30 - mmengine - INFO - Epoch(train) [39][150/293] lr: 5.000000e-04 eta: 4:34:17 time: 0.397005 data_time: 0.075781 memory: 5857 loss_kpt: 0.000964 acc_pose: 0.658870 loss: 0.000964 2022/10/13 11:31:50 - mmengine - INFO - Epoch(train) [39][200/293] lr: 5.000000e-04 eta: 4:34:15 time: 0.391191 data_time: 0.081535 memory: 5857 loss_kpt: 0.000982 acc_pose: 0.675286 loss: 0.000982 2022/10/13 11:32:10 - mmengine - INFO - Epoch(train) [39][250/293] lr: 5.000000e-04 eta: 4:34:14 time: 0.399296 data_time: 0.120557 memory: 5857 loss_kpt: 0.000960 acc_pose: 0.646357 loss: 0.000960 2022/10/13 11:32:26 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:32:47 - mmengine - INFO - Epoch(train) [40][50/293] lr: 5.000000e-04 eta: 4:33:01 time: 0.413149 data_time: 0.129435 memory: 5857 loss_kpt: 0.000961 acc_pose: 0.691951 loss: 0.000961 2022/10/13 11:33:07 - mmengine - INFO - Epoch(train) [40][100/293] lr: 5.000000e-04 eta: 4:32:59 time: 0.395202 data_time: 0.121344 memory: 5857 loss_kpt: 0.000978 acc_pose: 0.664519 loss: 0.000978 2022/10/13 11:33:27 - mmengine - INFO - Epoch(train) [40][150/293] lr: 5.000000e-04 eta: 4:33:00 time: 0.405774 data_time: 0.102914 memory: 5857 loss_kpt: 0.000987 acc_pose: 0.647737 loss: 0.000987 2022/10/13 11:33:47 - mmengine - INFO - Epoch(train) [40][200/293] lr: 5.000000e-04 eta: 4:33:00 time: 0.405713 data_time: 0.082716 memory: 5857 loss_kpt: 0.000960 acc_pose: 0.674182 loss: 0.000960 2022/10/13 11:34:07 - mmengine - INFO - Epoch(train) [40][250/293] lr: 5.000000e-04 eta: 4:32:57 time: 0.388851 data_time: 0.083611 memory: 5857 loss_kpt: 0.000961 acc_pose: 0.657266 loss: 0.000961 2022/10/13 11:34:23 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:34:23 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/10/13 11:34:32 - mmengine - INFO - Epoch(val) [40][50/407] eta: 0:00:48 time: 0.136340 data_time: 0.080357 memory: 5857 2022/10/13 11:34:38 - mmengine - INFO - Epoch(val) [40][100/407] eta: 0:00:38 time: 0.124951 data_time: 0.067650 memory: 760 2022/10/13 11:34:45 - mmengine - INFO - Epoch(val) [40][150/407] eta: 0:00:32 time: 0.128338 data_time: 0.071031 memory: 760 2022/10/13 11:34:51 - mmengine - INFO - Epoch(val) [40][200/407] eta: 0:00:25 time: 0.124884 data_time: 0.067373 memory: 760 2022/10/13 11:34:57 - mmengine - INFO - Epoch(val) [40][250/407] eta: 0:00:20 time: 0.127736 data_time: 0.073135 memory: 760 2022/10/13 11:35:03 - mmengine - INFO - Epoch(val) [40][300/407] eta: 0:00:13 time: 0.122260 data_time: 0.064336 memory: 760 2022/10/13 11:35:10 - mmengine - INFO - Epoch(val) [40][350/407] eta: 0:00:07 time: 0.130678 data_time: 0.074470 memory: 760 2022/10/13 11:35:16 - mmengine - INFO - Epoch(val) [40][400/407] eta: 0:00:00 time: 0.119703 data_time: 0.063572 memory: 760 2022/10/13 11:35:55 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 11:36:10 - mmengine - INFO - Epoch(val) [40][407/407] coco/AP: 0.567800 coco/AP .5: 0.835951 coco/AP .75: 0.620918 coco/AP (M): 0.526512 coco/AP (L): 0.638160 coco/AR: 0.631990 coco/AR .5: 0.880825 coco/AR .75: 0.687972 coco/AR (M): 0.581644 coco/AR (L): 0.702713 2022/10/13 11:36:10 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384/best_coco/AP_epoch_30.pth is removed 2022/10/13 11:36:11 - mmengine - INFO - The best checkpoint with 0.5678 coco/AP at 40 epoch is saved to best_coco/AP_epoch_40.pth. 2022/10/13 11:36:32 - mmengine - INFO - Epoch(train) [41][50/293] lr: 5.000000e-04 eta: 4:31:44 time: 0.414371 data_time: 0.112364 memory: 5857 loss_kpt: 0.000960 acc_pose: 0.706541 loss: 0.000960 2022/10/13 11:36:52 - mmengine - INFO - Epoch(train) [41][100/293] lr: 5.000000e-04 eta: 4:31:45 time: 0.410289 data_time: 0.089213 memory: 5857 loss_kpt: 0.000956 acc_pose: 0.711089 loss: 0.000956 2022/10/13 11:37:13 - mmengine - INFO - Epoch(train) [41][150/293] lr: 5.000000e-04 eta: 4:31:46 time: 0.407051 data_time: 0.084852 memory: 5857 loss_kpt: 0.000941 acc_pose: 0.659817 loss: 0.000941 2022/10/13 11:37:33 - mmengine - INFO - Epoch(train) [41][200/293] lr: 5.000000e-04 eta: 4:31:45 time: 0.402657 data_time: 0.081287 memory: 5857 loss_kpt: 0.000951 acc_pose: 0.679576 loss: 0.000951 2022/10/13 11:37:54 - mmengine - INFO - Epoch(train) [41][250/293] lr: 5.000000e-04 eta: 4:31:47 time: 0.418810 data_time: 0.139688 memory: 5857 loss_kpt: 0.000945 acc_pose: 0.699996 loss: 0.000945 2022/10/13 11:38:06 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:38:11 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:38:31 - mmengine - INFO - Epoch(train) [42][50/293] lr: 5.000000e-04 eta: 4:30:35 time: 0.410407 data_time: 0.088508 memory: 5857 loss_kpt: 0.000949 acc_pose: 0.687295 loss: 0.000949 2022/10/13 11:38:50 - mmengine - INFO - Epoch(train) [42][100/293] lr: 5.000000e-04 eta: 4:30:30 time: 0.382830 data_time: 0.078277 memory: 5857 loss_kpt: 0.000957 acc_pose: 0.735286 loss: 0.000957 2022/10/13 11:39:10 - mmengine - INFO - Epoch(train) [42][150/293] lr: 5.000000e-04 eta: 4:30:28 time: 0.398864 data_time: 0.076514 memory: 5857 loss_kpt: 0.000957 acc_pose: 0.666514 loss: 0.000957 2022/10/13 11:39:30 - mmengine - INFO - Epoch(train) [42][200/293] lr: 5.000000e-04 eta: 4:30:25 time: 0.398605 data_time: 0.071501 memory: 5857 loss_kpt: 0.000952 acc_pose: 0.695000 loss: 0.000952 2022/10/13 11:39:49 - mmengine - INFO - Epoch(train) [42][250/293] lr: 5.000000e-04 eta: 4:30:19 time: 0.382113 data_time: 0.076025 memory: 5857 loss_kpt: 0.000958 acc_pose: 0.661361 loss: 0.000958 2022/10/13 11:40:05 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:40:26 - mmengine - INFO - Epoch(train) [43][50/293] lr: 5.000000e-04 eta: 4:29:09 time: 0.413156 data_time: 0.110240 memory: 5857 loss_kpt: 0.000945 acc_pose: 0.649758 loss: 0.000945 2022/10/13 11:40:45 - mmengine - INFO - Epoch(train) [43][100/293] lr: 5.000000e-04 eta: 4:29:03 time: 0.380919 data_time: 0.082692 memory: 5857 loss_kpt: 0.000945 acc_pose: 0.687101 loss: 0.000945 2022/10/13 11:41:04 - mmengine - INFO - Epoch(train) [43][150/293] lr: 5.000000e-04 eta: 4:28:57 time: 0.381347 data_time: 0.078425 memory: 5857 loss_kpt: 0.000952 acc_pose: 0.664217 loss: 0.000952 2022/10/13 11:41:24 - mmengine - INFO - Epoch(train) [43][200/293] lr: 5.000000e-04 eta: 4:28:53 time: 0.392667 data_time: 0.108242 memory: 5857 loss_kpt: 0.000953 acc_pose: 0.684742 loss: 0.000953 2022/10/13 11:41:43 - mmengine - INFO - Epoch(train) [43][250/293] lr: 5.000000e-04 eta: 4:28:47 time: 0.381883 data_time: 0.093662 memory: 5857 loss_kpt: 0.000931 acc_pose: 0.749361 loss: 0.000931 2022/10/13 11:41:59 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:42:19 - mmengine - INFO - Epoch(train) [44][50/293] lr: 5.000000e-04 eta: 4:27:35 time: 0.399354 data_time: 0.090437 memory: 5857 loss_kpt: 0.000939 acc_pose: 0.716038 loss: 0.000939 2022/10/13 11:42:38 - mmengine - INFO - Epoch(train) [44][100/293] lr: 5.000000e-04 eta: 4:27:27 time: 0.373579 data_time: 0.078604 memory: 5857 loss_kpt: 0.000969 acc_pose: 0.674828 loss: 0.000969 2022/10/13 11:42:57 - mmengine - INFO - Epoch(train) [44][150/293] lr: 5.000000e-04 eta: 4:27:24 time: 0.396813 data_time: 0.075426 memory: 5857 loss_kpt: 0.000941 acc_pose: 0.723028 loss: 0.000941 2022/10/13 11:43:17 - mmengine - INFO - Epoch(train) [44][200/293] lr: 5.000000e-04 eta: 4:27:18 time: 0.383713 data_time: 0.087067 memory: 5857 loss_kpt: 0.000938 acc_pose: 0.672339 loss: 0.000938 2022/10/13 11:43:36 - mmengine - INFO - Epoch(train) [44][250/293] lr: 5.000000e-04 eta: 4:27:11 time: 0.377185 data_time: 0.088617 memory: 5857 loss_kpt: 0.000949 acc_pose: 0.673673 loss: 0.000949 2022/10/13 11:43:51 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:44:11 - mmengine - INFO - Epoch(train) [45][50/293] lr: 5.000000e-04 eta: 4:25:59 time: 0.392922 data_time: 0.112925 memory: 5857 loss_kpt: 0.000941 acc_pose: 0.644063 loss: 0.000941 2022/10/13 11:44:29 - mmengine - INFO - Epoch(train) [45][100/293] lr: 5.000000e-04 eta: 4:25:50 time: 0.371429 data_time: 0.098260 memory: 5857 loss_kpt: 0.000938 acc_pose: 0.731591 loss: 0.000938 2022/10/13 11:44:33 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:44:48 - mmengine - INFO - Epoch(train) [45][150/293] lr: 5.000000e-04 eta: 4:25:43 time: 0.376565 data_time: 0.091532 memory: 5857 loss_kpt: 0.000937 acc_pose: 0.743753 loss: 0.000937 2022/10/13 11:45:08 - mmengine - INFO - Epoch(train) [45][200/293] lr: 5.000000e-04 eta: 4:25:38 time: 0.390011 data_time: 0.073150 memory: 5857 loss_kpt: 0.000940 acc_pose: 0.674245 loss: 0.000940 2022/10/13 11:45:27 - mmengine - INFO - Epoch(train) [45][250/293] lr: 5.000000e-04 eta: 4:25:32 time: 0.385495 data_time: 0.079326 memory: 5857 loss_kpt: 0.000947 acc_pose: 0.689551 loss: 0.000947 2022/10/13 11:45:43 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:46:03 - mmengine - INFO - Epoch(train) [46][50/293] lr: 5.000000e-04 eta: 4:24:23 time: 0.400921 data_time: 0.083962 memory: 5857 loss_kpt: 0.000952 acc_pose: 0.735531 loss: 0.000952 2022/10/13 11:46:23 - mmengine - INFO - Epoch(train) [46][100/293] lr: 5.000000e-04 eta: 4:24:16 time: 0.385419 data_time: 0.077650 memory: 5857 loss_kpt: 0.000935 acc_pose: 0.758546 loss: 0.000935 2022/10/13 11:46:42 - mmengine - INFO - Epoch(train) [46][150/293] lr: 5.000000e-04 eta: 4:24:10 time: 0.381089 data_time: 0.071865 memory: 5857 loss_kpt: 0.000938 acc_pose: 0.739789 loss: 0.000938 2022/10/13 11:47:01 - mmengine - INFO - Epoch(train) [46][200/293] lr: 5.000000e-04 eta: 4:24:04 time: 0.387252 data_time: 0.076211 memory: 5857 loss_kpt: 0.000935 acc_pose: 0.725053 loss: 0.000935 2022/10/13 11:47:20 - mmengine - INFO - Epoch(train) [46][250/293] lr: 5.000000e-04 eta: 4:23:56 time: 0.380287 data_time: 0.087083 memory: 5857 loss_kpt: 0.000940 acc_pose: 0.732432 loss: 0.000940 2022/10/13 11:47:36 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:47:55 - mmengine - INFO - Epoch(train) [47][50/293] lr: 5.000000e-04 eta: 4:22:47 time: 0.393431 data_time: 0.103449 memory: 5857 loss_kpt: 0.000954 acc_pose: 0.722661 loss: 0.000954 2022/10/13 11:48:14 - mmengine - INFO - Epoch(train) [47][100/293] lr: 5.000000e-04 eta: 4:22:39 time: 0.376598 data_time: 0.076912 memory: 5857 loss_kpt: 0.000933 acc_pose: 0.721158 loss: 0.000933 2022/10/13 11:48:34 - mmengine - INFO - Epoch(train) [47][150/293] lr: 5.000000e-04 eta: 4:22:34 time: 0.392244 data_time: 0.081386 memory: 5857 loss_kpt: 0.000921 acc_pose: 0.689006 loss: 0.000921 2022/10/13 11:48:53 - mmengine - INFO - Epoch(train) [47][200/293] lr: 5.000000e-04 eta: 4:22:27 time: 0.383996 data_time: 0.084859 memory: 5857 loss_kpt: 0.000939 acc_pose: 0.685950 loss: 0.000939 2022/10/13 11:49:12 - mmengine - INFO - Epoch(train) [47][250/293] lr: 5.000000e-04 eta: 4:22:19 time: 0.376005 data_time: 0.073435 memory: 5857 loss_kpt: 0.000933 acc_pose: 0.724245 loss: 0.000933 2022/10/13 11:49:35 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:50:00 - mmengine - INFO - Epoch(train) [48][50/293] lr: 5.000000e-04 eta: 4:21:29 time: 0.504211 data_time: 0.100072 memory: 5857 loss_kpt: 0.000958 acc_pose: 0.713360 loss: 0.000958 2022/10/13 11:50:19 - mmengine - INFO - Epoch(train) [48][100/293] lr: 5.000000e-04 eta: 4:21:22 time: 0.381154 data_time: 0.073171 memory: 5857 loss_kpt: 0.000938 acc_pose: 0.740340 loss: 0.000938 2022/10/13 11:50:38 - mmengine - INFO - Epoch(train) [48][150/293] lr: 5.000000e-04 eta: 4:21:14 time: 0.380304 data_time: 0.071919 memory: 5857 loss_kpt: 0.000930 acc_pose: 0.661273 loss: 0.000930 2022/10/13 11:50:57 - mmengine - INFO - Epoch(train) [48][200/293] lr: 5.000000e-04 eta: 4:21:06 time: 0.379690 data_time: 0.076528 memory: 5857 loss_kpt: 0.000941 acc_pose: 0.641216 loss: 0.000941 2022/10/13 11:51:08 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:51:16 - mmengine - INFO - Epoch(train) [48][250/293] lr: 5.000000e-04 eta: 4:20:58 time: 0.378912 data_time: 0.076409 memory: 5857 loss_kpt: 0.000941 acc_pose: 0.698218 loss: 0.000941 2022/10/13 11:51:32 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:51:53 - mmengine - INFO - Epoch(train) [49][50/293] lr: 5.000000e-04 eta: 4:19:55 time: 0.417447 data_time: 0.087624 memory: 5857 loss_kpt: 0.000944 acc_pose: 0.670114 loss: 0.000944 2022/10/13 11:52:12 - mmengine - INFO - Epoch(train) [49][100/293] lr: 5.000000e-04 eta: 4:19:46 time: 0.375371 data_time: 0.077224 memory: 5857 loss_kpt: 0.000942 acc_pose: 0.665166 loss: 0.000942 2022/10/13 11:52:31 - mmengine - INFO - Epoch(train) [49][150/293] lr: 5.000000e-04 eta: 4:19:38 time: 0.377664 data_time: 0.086098 memory: 5857 loss_kpt: 0.000941 acc_pose: 0.727225 loss: 0.000941 2022/10/13 11:52:50 - mmengine - INFO - Epoch(train) [49][200/293] lr: 5.000000e-04 eta: 4:19:29 time: 0.376191 data_time: 0.096294 memory: 5857 loss_kpt: 0.000940 acc_pose: 0.686181 loss: 0.000940 2022/10/13 11:53:09 - mmengine - INFO - Epoch(train) [49][250/293] lr: 5.000000e-04 eta: 4:19:21 time: 0.382801 data_time: 0.103389 memory: 5857 loss_kpt: 0.000952 acc_pose: 0.688651 loss: 0.000952 2022/10/13 11:53:25 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:53:45 - mmengine - INFO - Epoch(train) [50][50/293] lr: 5.000000e-04 eta: 4:18:16 time: 0.398678 data_time: 0.109989 memory: 5857 loss_kpt: 0.000941 acc_pose: 0.674661 loss: 0.000941 2022/10/13 11:54:12 - mmengine - INFO - Epoch(train) [50][100/293] lr: 5.000000e-04 eta: 4:18:33 time: 0.536020 data_time: 0.098731 memory: 5857 loss_kpt: 0.000939 acc_pose: 0.711687 loss: 0.000939 2022/10/13 11:54:32 - mmengine - INFO - Epoch(train) [50][150/293] lr: 5.000000e-04 eta: 4:18:31 time: 0.417342 data_time: 0.071718 memory: 5857 loss_kpt: 0.000942 acc_pose: 0.705272 loss: 0.000942 2022/10/13 11:54:52 - mmengine - INFO - Epoch(train) [50][200/293] lr: 5.000000e-04 eta: 4:18:25 time: 0.397361 data_time: 0.076010 memory: 5857 loss_kpt: 0.000935 acc_pose: 0.646304 loss: 0.000935 2022/10/13 11:55:11 - mmengine - INFO - Epoch(train) [50][250/293] lr: 5.000000e-04 eta: 4:18:16 time: 0.374128 data_time: 0.087769 memory: 5857 loss_kpt: 0.000933 acc_pose: 0.666656 loss: 0.000933 2022/10/13 11:55:28 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:55:28 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/10/13 11:55:37 - mmengine - INFO - Epoch(val) [50][50/407] eta: 0:00:47 time: 0.133283 data_time: 0.077961 memory: 5857 2022/10/13 11:55:43 - mmengine - INFO - Epoch(val) [50][100/407] eta: 0:00:38 time: 0.125115 data_time: 0.069422 memory: 760 2022/10/13 11:55:49 - mmengine - INFO - Epoch(val) [50][150/407] eta: 0:00:31 time: 0.121012 data_time: 0.066065 memory: 760 2022/10/13 11:55:55 - mmengine - INFO - Epoch(val) [50][200/407] eta: 0:00:25 time: 0.120946 data_time: 0.065638 memory: 760 2022/10/13 11:56:01 - mmengine - INFO - Epoch(val) [50][250/407] eta: 0:00:19 time: 0.124412 data_time: 0.068067 memory: 760 2022/10/13 11:56:08 - mmengine - INFO - Epoch(val) [50][300/407] eta: 0:00:13 time: 0.125009 data_time: 0.068278 memory: 760 2022/10/13 11:56:14 - mmengine - INFO - Epoch(val) [50][350/407] eta: 0:00:07 time: 0.131997 data_time: 0.076125 memory: 760 2022/10/13 11:56:21 - mmengine - INFO - Epoch(val) [50][400/407] eta: 0:00:00 time: 0.127156 data_time: 0.071392 memory: 760 2022/10/13 11:56:59 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 11:57:14 - mmengine - INFO - Epoch(val) [50][407/407] coco/AP: 0.575513 coco/AP .5: 0.837973 coco/AP .75: 0.631666 coco/AP (M): 0.533313 coco/AP (L): 0.645372 coco/AR: 0.639688 coco/AR .5: 0.882714 coco/AR .75: 0.699465 coco/AR (M): 0.589593 coco/AR (L): 0.709476 2022/10/13 11:57:14 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384/best_coco/AP_epoch_40.pth is removed 2022/10/13 11:57:16 - mmengine - INFO - The best checkpoint with 0.5755 coco/AP at 50 epoch is saved to best_coco/AP_epoch_50.pth. 2022/10/13 11:57:35 - mmengine - INFO - Epoch(train) [51][50/293] lr: 5.000000e-04 eta: 4:17:10 time: 0.392760 data_time: 0.148892 memory: 5857 loss_kpt: 0.000955 acc_pose: 0.704926 loss: 0.000955 2022/10/13 11:57:54 - mmengine - INFO - Epoch(train) [51][100/293] lr: 5.000000e-04 eta: 4:17:01 time: 0.376729 data_time: 0.090438 memory: 5857 loss_kpt: 0.000940 acc_pose: 0.699645 loss: 0.000940 2022/10/13 11:58:14 - mmengine - INFO - Epoch(train) [51][150/293] lr: 5.000000e-04 eta: 4:16:53 time: 0.384780 data_time: 0.069888 memory: 5857 loss_kpt: 0.000944 acc_pose: 0.677889 loss: 0.000944 2022/10/13 11:58:33 - mmengine - INFO - Epoch(train) [51][200/293] lr: 5.000000e-04 eta: 4:16:47 time: 0.395154 data_time: 0.075710 memory: 5857 loss_kpt: 0.000929 acc_pose: 0.720937 loss: 0.000929 2022/10/13 11:58:52 - mmengine - INFO - Epoch(train) [51][250/293] lr: 5.000000e-04 eta: 4:16:36 time: 0.370688 data_time: 0.082930 memory: 5857 loss_kpt: 0.000931 acc_pose: 0.677791 loss: 0.000931 2022/10/13 11:59:08 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:59:27 - mmengine - INFO - Epoch(train) [52][50/293] lr: 5.000000e-04 eta: 4:15:31 time: 0.393863 data_time: 0.081209 memory: 5857 loss_kpt: 0.000931 acc_pose: 0.684457 loss: 0.000931 2022/10/13 11:59:30 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 11:59:46 - mmengine - INFO - Epoch(train) [52][100/293] lr: 5.000000e-04 eta: 4:15:23 time: 0.378700 data_time: 0.079850 memory: 5857 loss_kpt: 0.000942 acc_pose: 0.697130 loss: 0.000942 2022/10/13 12:00:06 - mmengine - INFO - Epoch(train) [52][150/293] lr: 5.000000e-04 eta: 4:15:17 time: 0.401243 data_time: 0.086329 memory: 5857 loss_kpt: 0.000919 acc_pose: 0.749443 loss: 0.000919 2022/10/13 12:00:25 - mmengine - INFO - Epoch(train) [52][200/293] lr: 5.000000e-04 eta: 4:15:08 time: 0.380341 data_time: 0.085003 memory: 5857 loss_kpt: 0.000942 acc_pose: 0.649845 loss: 0.000942 2022/10/13 12:00:44 - mmengine - INFO - Epoch(train) [52][250/293] lr: 5.000000e-04 eta: 4:14:59 time: 0.378147 data_time: 0.074920 memory: 5857 loss_kpt: 0.000943 acc_pose: 0.724851 loss: 0.000943 2022/10/13 12:01:00 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:01:21 - mmengine - INFO - Epoch(train) [53][50/293] lr: 5.000000e-04 eta: 4:13:57 time: 0.406756 data_time: 0.096307 memory: 5857 loss_kpt: 0.000943 acc_pose: 0.667453 loss: 0.000943 2022/10/13 12:01:39 - mmengine - INFO - Epoch(train) [53][100/293] lr: 5.000000e-04 eta: 4:13:47 time: 0.371029 data_time: 0.065723 memory: 5857 loss_kpt: 0.000940 acc_pose: 0.688052 loss: 0.000940 2022/10/13 12:01:58 - mmengine - INFO - Epoch(train) [53][150/293] lr: 5.000000e-04 eta: 4:13:38 time: 0.376747 data_time: 0.077637 memory: 5857 loss_kpt: 0.000930 acc_pose: 0.695626 loss: 0.000930 2022/10/13 12:02:18 - mmengine - INFO - Epoch(train) [53][200/293] lr: 5.000000e-04 eta: 4:13:31 time: 0.393310 data_time: 0.093428 memory: 5857 loss_kpt: 0.000929 acc_pose: 0.751685 loss: 0.000929 2022/10/13 12:02:37 - mmengine - INFO - Epoch(train) [53][250/293] lr: 5.000000e-04 eta: 4:13:22 time: 0.381984 data_time: 0.069678 memory: 5857 loss_kpt: 0.000934 acc_pose: 0.705981 loss: 0.000934 2022/10/13 12:02:53 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:03:14 - mmengine - INFO - Epoch(train) [54][50/293] lr: 5.000000e-04 eta: 4:12:21 time: 0.410010 data_time: 0.102546 memory: 5857 loss_kpt: 0.000919 acc_pose: 0.751394 loss: 0.000919 2022/10/13 12:03:32 - mmengine - INFO - Epoch(train) [54][100/293] lr: 5.000000e-04 eta: 4:12:10 time: 0.366305 data_time: 0.067747 memory: 5857 loss_kpt: 0.000936 acc_pose: 0.676338 loss: 0.000936 2022/10/13 12:03:51 - mmengine - INFO - Epoch(train) [54][150/293] lr: 5.000000e-04 eta: 4:12:01 time: 0.378557 data_time: 0.081889 memory: 5857 loss_kpt: 0.000940 acc_pose: 0.670148 loss: 0.000940 2022/10/13 12:04:10 - mmengine - INFO - Epoch(train) [54][200/293] lr: 5.000000e-04 eta: 4:11:52 time: 0.385723 data_time: 0.074179 memory: 5857 loss_kpt: 0.000938 acc_pose: 0.699220 loss: 0.000938 2022/10/13 12:04:29 - mmengine - INFO - Epoch(train) [54][250/293] lr: 5.000000e-04 eta: 4:11:43 time: 0.381217 data_time: 0.095025 memory: 5857 loss_kpt: 0.000933 acc_pose: 0.706459 loss: 0.000933 2022/10/13 12:04:46 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:05:07 - mmengine - INFO - Epoch(train) [55][50/293] lr: 5.000000e-04 eta: 4:10:44 time: 0.418839 data_time: 0.094269 memory: 5857 loss_kpt: 0.000932 acc_pose: 0.684326 loss: 0.000932 2022/10/13 12:05:26 - mmengine - INFO - Epoch(train) [55][100/293] lr: 5.000000e-04 eta: 4:10:36 time: 0.384776 data_time: 0.084092 memory: 5857 loss_kpt: 0.000920 acc_pose: 0.704288 loss: 0.000920 2022/10/13 12:05:45 - mmengine - INFO - Epoch(train) [55][150/293] lr: 5.000000e-04 eta: 4:10:26 time: 0.378564 data_time: 0.081379 memory: 5857 loss_kpt: 0.000936 acc_pose: 0.689610 loss: 0.000936 2022/10/13 12:05:56 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:06:04 - mmengine - INFO - Epoch(train) [55][200/293] lr: 5.000000e-04 eta: 4:10:17 time: 0.383384 data_time: 0.073336 memory: 5857 loss_kpt: 0.000928 acc_pose: 0.712062 loss: 0.000928 2022/10/13 12:06:23 - mmengine - INFO - Epoch(train) [55][250/293] lr: 5.000000e-04 eta: 4:10:09 time: 0.384184 data_time: 0.069379 memory: 5857 loss_kpt: 0.000918 acc_pose: 0.702758 loss: 0.000918 2022/10/13 12:06:40 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:07:00 - mmengine - INFO - Epoch(train) [56][50/293] lr: 5.000000e-04 eta: 4:09:08 time: 0.401829 data_time: 0.089368 memory: 5857 loss_kpt: 0.000932 acc_pose: 0.714337 loss: 0.000932 2022/10/13 12:07:19 - mmengine - INFO - Epoch(train) [56][100/293] lr: 5.000000e-04 eta: 4:08:59 time: 0.382697 data_time: 0.080107 memory: 5857 loss_kpt: 0.000909 acc_pose: 0.776996 loss: 0.000909 2022/10/13 12:07:38 - mmengine - INFO - Epoch(train) [56][150/293] lr: 5.000000e-04 eta: 4:08:49 time: 0.376178 data_time: 0.070724 memory: 5857 loss_kpt: 0.000923 acc_pose: 0.686374 loss: 0.000923 2022/10/13 12:07:57 - mmengine - INFO - Epoch(train) [56][200/293] lr: 5.000000e-04 eta: 4:08:39 time: 0.379296 data_time: 0.079678 memory: 5857 loss_kpt: 0.000923 acc_pose: 0.727945 loss: 0.000923 2022/10/13 12:08:16 - mmengine - INFO - Epoch(train) [56][250/293] lr: 5.000000e-04 eta: 4:08:32 time: 0.393085 data_time: 0.082571 memory: 5857 loss_kpt: 0.000947 acc_pose: 0.676040 loss: 0.000947 2022/10/13 12:08:33 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:08:52 - mmengine - INFO - Epoch(train) [57][50/293] lr: 5.000000e-04 eta: 4:07:30 time: 0.386675 data_time: 0.090552 memory: 5857 loss_kpt: 0.000928 acc_pose: 0.704486 loss: 0.000928 2022/10/13 12:09:10 - mmengine - INFO - Epoch(train) [57][100/293] lr: 5.000000e-04 eta: 4:07:19 time: 0.370391 data_time: 0.075154 memory: 5857 loss_kpt: 0.000922 acc_pose: 0.685408 loss: 0.000922 2022/10/13 12:09:30 - mmengine - INFO - Epoch(train) [57][150/293] lr: 5.000000e-04 eta: 4:07:10 time: 0.382851 data_time: 0.078019 memory: 5857 loss_kpt: 0.000921 acc_pose: 0.671968 loss: 0.000921 2022/10/13 12:09:49 - mmengine - INFO - Epoch(train) [57][200/293] lr: 5.000000e-04 eta: 4:07:00 time: 0.382199 data_time: 0.067944 memory: 5857 loss_kpt: 0.000943 acc_pose: 0.685832 loss: 0.000943 2022/10/13 12:10:07 - mmengine - INFO - Epoch(train) [57][250/293] lr: 5.000000e-04 eta: 4:06:50 time: 0.373252 data_time: 0.074356 memory: 5857 loss_kpt: 0.000921 acc_pose: 0.697814 loss: 0.000921 2022/10/13 12:10:24 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:10:44 - mmengine - INFO - Epoch(train) [58][50/293] lr: 5.000000e-04 eta: 4:05:50 time: 0.399600 data_time: 0.098652 memory: 5857 loss_kpt: 0.000915 acc_pose: 0.643661 loss: 0.000915 2022/10/13 12:11:04 - mmengine - INFO - Epoch(train) [58][100/293] lr: 5.000000e-04 eta: 4:05:44 time: 0.405816 data_time: 0.078107 memory: 5857 loss_kpt: 0.000924 acc_pose: 0.724190 loss: 0.000924 2022/10/13 12:11:23 - mmengine - INFO - Epoch(train) [58][150/293] lr: 5.000000e-04 eta: 4:05:34 time: 0.378998 data_time: 0.076217 memory: 5857 loss_kpt: 0.000913 acc_pose: 0.729962 loss: 0.000913 2022/10/13 12:11:43 - mmengine - INFO - Epoch(train) [58][200/293] lr: 5.000000e-04 eta: 4:05:26 time: 0.393899 data_time: 0.080060 memory: 5857 loss_kpt: 0.000920 acc_pose: 0.681752 loss: 0.000920 2022/10/13 12:12:02 - mmengine - INFO - Epoch(train) [58][250/293] lr: 5.000000e-04 eta: 4:05:16 time: 0.382655 data_time: 0.082475 memory: 5857 loss_kpt: 0.000915 acc_pose: 0.727511 loss: 0.000915 2022/10/13 12:12:17 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:12:20 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:12:37 - mmengine - INFO - Epoch(train) [59][50/293] lr: 5.000000e-04 eta: 4:04:18 time: 0.399055 data_time: 0.090848 memory: 5857 loss_kpt: 0.000917 acc_pose: 0.709282 loss: 0.000917 2022/10/13 12:12:56 - mmengine - INFO - Epoch(train) [59][100/293] lr: 5.000000e-04 eta: 4:04:08 time: 0.380858 data_time: 0.086759 memory: 5857 loss_kpt: 0.000926 acc_pose: 0.691634 loss: 0.000926 2022/10/13 12:13:15 - mmengine - INFO - Epoch(train) [59][150/293] lr: 5.000000e-04 eta: 4:03:59 time: 0.385858 data_time: 0.085407 memory: 5857 loss_kpt: 0.000918 acc_pose: 0.720263 loss: 0.000918 2022/10/13 12:13:34 - mmengine - INFO - Epoch(train) [59][200/293] lr: 5.000000e-04 eta: 4:03:48 time: 0.373465 data_time: 0.077931 memory: 5857 loss_kpt: 0.000940 acc_pose: 0.727093 loss: 0.000940 2022/10/13 12:13:54 - mmengine - INFO - Epoch(train) [59][250/293] lr: 5.000000e-04 eta: 4:03:39 time: 0.393050 data_time: 0.071788 memory: 5857 loss_kpt: 0.000917 acc_pose: 0.688852 loss: 0.000917 2022/10/13 12:14:11 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:14:31 - mmengine - INFO - Epoch(train) [60][50/293] lr: 5.000000e-04 eta: 4:02:41 time: 0.394809 data_time: 0.119509 memory: 5857 loss_kpt: 0.000928 acc_pose: 0.696085 loss: 0.000928 2022/10/13 12:14:51 - mmengine - INFO - Epoch(train) [60][100/293] lr: 5.000000e-04 eta: 4:02:34 time: 0.402887 data_time: 0.101989 memory: 5857 loss_kpt: 0.000904 acc_pose: 0.708177 loss: 0.000904 2022/10/13 12:15:10 - mmengine - INFO - Epoch(train) [60][150/293] lr: 5.000000e-04 eta: 4:02:25 time: 0.389859 data_time: 0.078096 memory: 5857 loss_kpt: 0.000914 acc_pose: 0.677998 loss: 0.000914 2022/10/13 12:15:30 - mmengine - INFO - Epoch(train) [60][200/293] lr: 5.000000e-04 eta: 4:02:16 time: 0.388533 data_time: 0.089949 memory: 5857 loss_kpt: 0.000938 acc_pose: 0.724135 loss: 0.000938 2022/10/13 12:15:49 - mmengine - INFO - Epoch(train) [60][250/293] lr: 5.000000e-04 eta: 4:02:05 time: 0.377770 data_time: 0.081812 memory: 5857 loss_kpt: 0.000920 acc_pose: 0.734944 loss: 0.000920 2022/10/13 12:16:05 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:16:05 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/10/13 12:16:14 - mmengine - INFO - Epoch(val) [60][50/407] eta: 0:00:50 time: 0.141596 data_time: 0.084913 memory: 5857 2022/10/13 12:16:20 - mmengine - INFO - Epoch(val) [60][100/407] eta: 0:00:38 time: 0.124092 data_time: 0.067345 memory: 760 2022/10/13 12:16:26 - mmengine - INFO - Epoch(val) [60][150/407] eta: 0:00:32 time: 0.126340 data_time: 0.070051 memory: 760 2022/10/13 12:16:33 - mmengine - INFO - Epoch(val) [60][200/407] eta: 0:00:27 time: 0.133323 data_time: 0.077519 memory: 760 2022/10/13 12:16:40 - mmengine - INFO - Epoch(val) [60][250/407] eta: 0:00:21 time: 0.135808 data_time: 0.079858 memory: 760 2022/10/13 12:16:46 - mmengine - INFO - Epoch(val) [60][300/407] eta: 0:00:13 time: 0.122893 data_time: 0.067963 memory: 760 2022/10/13 12:16:53 - mmengine - INFO - Epoch(val) [60][350/407] eta: 0:00:07 time: 0.132988 data_time: 0.077479 memory: 760 2022/10/13 12:16:59 - mmengine - INFO - Epoch(val) [60][400/407] eta: 0:00:00 time: 0.119519 data_time: 0.065420 memory: 760 2022/10/13 12:17:38 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 12:17:52 - mmengine - INFO - Epoch(val) [60][407/407] coco/AP: 0.583237 coco/AP .5: 0.843579 coco/AP .75: 0.642342 coco/AP (M): 0.543003 coco/AP (L): 0.652035 coco/AR: 0.646458 coco/AR .5: 0.888539 coco/AR .75: 0.706864 coco/AR (M): 0.596804 coco/AR (L): 0.715905 2022/10/13 12:17:52 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384/best_coco/AP_epoch_50.pth is removed 2022/10/13 12:17:54 - mmengine - INFO - The best checkpoint with 0.5832 coco/AP at 60 epoch is saved to best_coco/AP_epoch_60.pth. 2022/10/13 12:18:13 - mmengine - INFO - Epoch(train) [61][50/293] lr: 5.000000e-04 eta: 4:01:06 time: 0.387289 data_time: 0.123665 memory: 5857 loss_kpt: 0.000932 acc_pose: 0.725339 loss: 0.000932 2022/10/13 12:18:33 - mmengine - INFO - Epoch(train) [61][100/293] lr: 5.000000e-04 eta: 4:00:57 time: 0.391661 data_time: 0.070090 memory: 5857 loss_kpt: 0.000926 acc_pose: 0.748053 loss: 0.000926 2022/10/13 12:18:52 - mmengine - INFO - Epoch(train) [61][150/293] lr: 5.000000e-04 eta: 4:00:48 time: 0.389163 data_time: 0.078171 memory: 5857 loss_kpt: 0.000922 acc_pose: 0.692754 loss: 0.000922 2022/10/13 12:19:12 - mmengine - INFO - Epoch(train) [61][200/293] lr: 5.000000e-04 eta: 4:00:39 time: 0.387593 data_time: 0.082436 memory: 5857 loss_kpt: 0.000931 acc_pose: 0.678641 loss: 0.000931 2022/10/13 12:19:31 - mmengine - INFO - Epoch(train) [61][250/293] lr: 5.000000e-04 eta: 4:00:28 time: 0.378431 data_time: 0.078206 memory: 5857 loss_kpt: 0.000921 acc_pose: 0.702292 loss: 0.000921 2022/10/13 12:19:47 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:20:07 - mmengine - INFO - Epoch(train) [62][50/293] lr: 5.000000e-04 eta: 3:59:32 time: 0.400546 data_time: 0.091250 memory: 5857 loss_kpt: 0.000913 acc_pose: 0.676986 loss: 0.000913 2022/10/13 12:20:26 - mmengine - INFO - Epoch(train) [62][100/293] lr: 5.000000e-04 eta: 3:59:21 time: 0.376625 data_time: 0.074991 memory: 5857 loss_kpt: 0.000908 acc_pose: 0.714775 loss: 0.000908 2022/10/13 12:20:36 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:20:45 - mmengine - INFO - Epoch(train) [62][150/293] lr: 5.000000e-04 eta: 3:59:12 time: 0.392495 data_time: 0.080058 memory: 5857 loss_kpt: 0.000923 acc_pose: 0.688906 loss: 0.000923 2022/10/13 12:21:05 - mmengine - INFO - Epoch(train) [62][200/293] lr: 5.000000e-04 eta: 3:59:03 time: 0.390529 data_time: 0.081453 memory: 5857 loss_kpt: 0.000925 acc_pose: 0.694028 loss: 0.000925 2022/10/13 12:21:23 - mmengine - INFO - Epoch(train) [62][250/293] lr: 5.000000e-04 eta: 3:58:51 time: 0.369144 data_time: 0.074418 memory: 5857 loss_kpt: 0.000940 acc_pose: 0.600645 loss: 0.000940 2022/10/13 12:21:40 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:22:01 - mmengine - INFO - Epoch(train) [63][50/293] lr: 5.000000e-04 eta: 3:57:57 time: 0.417303 data_time: 0.101601 memory: 5857 loss_kpt: 0.000911 acc_pose: 0.739161 loss: 0.000911 2022/10/13 12:22:19 - mmengine - INFO - Epoch(train) [63][100/293] lr: 5.000000e-04 eta: 3:57:45 time: 0.368158 data_time: 0.076020 memory: 5857 loss_kpt: 0.000922 acc_pose: 0.704715 loss: 0.000922 2022/10/13 12:22:38 - mmengine - INFO - Epoch(train) [63][150/293] lr: 5.000000e-04 eta: 3:57:35 time: 0.384858 data_time: 0.087359 memory: 5857 loss_kpt: 0.000907 acc_pose: 0.674395 loss: 0.000907 2022/10/13 12:22:57 - mmengine - INFO - Epoch(train) [63][200/293] lr: 5.000000e-04 eta: 3:57:24 time: 0.377187 data_time: 0.070281 memory: 5857 loss_kpt: 0.000908 acc_pose: 0.709170 loss: 0.000908 2022/10/13 12:23:16 - mmengine - INFO - Epoch(train) [63][250/293] lr: 5.000000e-04 eta: 3:57:13 time: 0.378281 data_time: 0.072502 memory: 5857 loss_kpt: 0.000908 acc_pose: 0.693426 loss: 0.000908 2022/10/13 12:23:32 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:23:53 - mmengine - INFO - Epoch(train) [64][50/293] lr: 5.000000e-04 eta: 3:56:19 time: 0.413182 data_time: 0.090000 memory: 5857 loss_kpt: 0.000920 acc_pose: 0.667703 loss: 0.000920 2022/10/13 12:24:12 - mmengine - INFO - Epoch(train) [64][100/293] lr: 5.000000e-04 eta: 3:56:08 time: 0.380070 data_time: 0.074279 memory: 5857 loss_kpt: 0.000927 acc_pose: 0.658776 loss: 0.000927 2022/10/13 12:24:31 - mmengine - INFO - Epoch(train) [64][150/293] lr: 5.000000e-04 eta: 3:55:58 time: 0.380895 data_time: 0.076995 memory: 5857 loss_kpt: 0.000909 acc_pose: 0.680161 loss: 0.000909 2022/10/13 12:24:51 - mmengine - INFO - Epoch(train) [64][200/293] lr: 5.000000e-04 eta: 3:55:48 time: 0.392549 data_time: 0.071041 memory: 5857 loss_kpt: 0.000912 acc_pose: 0.713824 loss: 0.000912 2022/10/13 12:25:10 - mmengine - INFO - Epoch(train) [64][250/293] lr: 5.000000e-04 eta: 3:55:38 time: 0.381831 data_time: 0.081908 memory: 5857 loss_kpt: 0.000909 acc_pose: 0.741356 loss: 0.000909 2022/10/13 12:25:26 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:25:46 - mmengine - INFO - Epoch(train) [65][50/293] lr: 5.000000e-04 eta: 3:54:43 time: 0.404392 data_time: 0.084274 memory: 5857 loss_kpt: 0.000912 acc_pose: 0.688024 loss: 0.000912 2022/10/13 12:26:06 - mmengine - INFO - Epoch(train) [65][100/293] lr: 5.000000e-04 eta: 3:54:34 time: 0.393737 data_time: 0.068755 memory: 5857 loss_kpt: 0.000913 acc_pose: 0.660618 loss: 0.000913 2022/10/13 12:26:25 - mmengine - INFO - Epoch(train) [65][150/293] lr: 5.000000e-04 eta: 3:54:25 time: 0.393427 data_time: 0.082374 memory: 5857 loss_kpt: 0.000905 acc_pose: 0.689413 loss: 0.000905 2022/10/13 12:26:44 - mmengine - INFO - Epoch(train) [65][200/293] lr: 5.000000e-04 eta: 3:54:13 time: 0.370870 data_time: 0.076901 memory: 5857 loss_kpt: 0.000910 acc_pose: 0.689311 loss: 0.000910 2022/10/13 12:27:02 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:27:03 - mmengine - INFO - Epoch(train) [65][250/293] lr: 5.000000e-04 eta: 3:54:01 time: 0.374313 data_time: 0.069961 memory: 5857 loss_kpt: 0.000923 acc_pose: 0.711403 loss: 0.000923 2022/10/13 12:27:19 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:27:39 - mmengine - INFO - Epoch(train) [66][50/293] lr: 5.000000e-04 eta: 3:53:07 time: 0.397193 data_time: 0.115825 memory: 5857 loss_kpt: 0.000902 acc_pose: 0.675922 loss: 0.000902 2022/10/13 12:27:58 - mmengine - INFO - Epoch(train) [66][100/293] lr: 5.000000e-04 eta: 3:52:55 time: 0.377166 data_time: 0.112359 memory: 5857 loss_kpt: 0.000915 acc_pose: 0.659796 loss: 0.000915 2022/10/13 12:28:17 - mmengine - INFO - Epoch(train) [66][150/293] lr: 5.000000e-04 eta: 3:52:45 time: 0.389522 data_time: 0.071246 memory: 5857 loss_kpt: 0.000939 acc_pose: 0.657095 loss: 0.000939 2022/10/13 12:28:36 - mmengine - INFO - Epoch(train) [66][200/293] lr: 5.000000e-04 eta: 3:52:34 time: 0.373513 data_time: 0.067289 memory: 5857 loss_kpt: 0.000904 acc_pose: 0.697486 loss: 0.000904 2022/10/13 12:28:56 - mmengine - INFO - Epoch(train) [66][250/293] lr: 5.000000e-04 eta: 3:52:24 time: 0.394136 data_time: 0.073945 memory: 5857 loss_kpt: 0.000938 acc_pose: 0.719176 loss: 0.000938 2022/10/13 12:29:11 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:29:32 - mmengine - INFO - Epoch(train) [67][50/293] lr: 5.000000e-04 eta: 3:51:31 time: 0.405558 data_time: 0.102820 memory: 5857 loss_kpt: 0.000909 acc_pose: 0.779119 loss: 0.000909 2022/10/13 12:29:52 - mmengine - INFO - Epoch(train) [67][100/293] lr: 5.000000e-04 eta: 3:51:23 time: 0.405162 data_time: 0.087354 memory: 5857 loss_kpt: 0.000917 acc_pose: 0.703433 loss: 0.000917 2022/10/13 12:30:11 - mmengine - INFO - Epoch(train) [67][150/293] lr: 5.000000e-04 eta: 3:51:11 time: 0.378872 data_time: 0.077006 memory: 5857 loss_kpt: 0.000928 acc_pose: 0.726218 loss: 0.000928 2022/10/13 12:30:30 - mmengine - INFO - Epoch(train) [67][200/293] lr: 5.000000e-04 eta: 3:51:01 time: 0.386465 data_time: 0.083801 memory: 5857 loss_kpt: 0.000909 acc_pose: 0.676322 loss: 0.000909 2022/10/13 12:30:49 - mmengine - INFO - Epoch(train) [67][250/293] lr: 5.000000e-04 eta: 3:50:50 time: 0.377492 data_time: 0.076089 memory: 5857 loss_kpt: 0.000917 acc_pose: 0.714846 loss: 0.000917 2022/10/13 12:31:05 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:31:25 - mmengine - INFO - Epoch(train) [68][50/293] lr: 5.000000e-04 eta: 3:49:55 time: 0.389843 data_time: 0.095069 memory: 5857 loss_kpt: 0.000912 acc_pose: 0.739737 loss: 0.000912 2022/10/13 12:31:43 - mmengine - INFO - Epoch(train) [68][100/293] lr: 5.000000e-04 eta: 3:49:43 time: 0.373564 data_time: 0.078260 memory: 5857 loss_kpt: 0.000929 acc_pose: 0.701115 loss: 0.000929 2022/10/13 12:32:02 - mmengine - INFO - Epoch(train) [68][150/293] lr: 5.000000e-04 eta: 3:49:31 time: 0.369579 data_time: 0.074456 memory: 5857 loss_kpt: 0.000912 acc_pose: 0.752972 loss: 0.000912 2022/10/13 12:32:21 - mmengine - INFO - Epoch(train) [68][200/293] lr: 5.000000e-04 eta: 3:49:20 time: 0.387480 data_time: 0.078490 memory: 5857 loss_kpt: 0.000928 acc_pose: 0.722605 loss: 0.000928 2022/10/13 12:32:40 - mmengine - INFO - Epoch(train) [68][250/293] lr: 5.000000e-04 eta: 3:49:09 time: 0.379289 data_time: 0.087854 memory: 5857 loss_kpt: 0.000938 acc_pose: 0.641109 loss: 0.000938 2022/10/13 12:32:57 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:33:17 - mmengine - INFO - Epoch(train) [69][50/293] lr: 5.000000e-04 eta: 3:48:17 time: 0.406038 data_time: 0.104618 memory: 5857 loss_kpt: 0.000907 acc_pose: 0.690635 loss: 0.000907 2022/10/13 12:33:27 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:33:36 - mmengine - INFO - Epoch(train) [69][100/293] lr: 5.000000e-04 eta: 3:48:05 time: 0.379582 data_time: 0.072075 memory: 5857 loss_kpt: 0.000917 acc_pose: 0.708094 loss: 0.000917 2022/10/13 12:33:56 - mmengine - INFO - Epoch(train) [69][150/293] lr: 5.000000e-04 eta: 3:47:55 time: 0.392494 data_time: 0.082565 memory: 5857 loss_kpt: 0.000903 acc_pose: 0.721676 loss: 0.000903 2022/10/13 12:34:15 - mmengine - INFO - Epoch(train) [69][200/293] lr: 5.000000e-04 eta: 3:47:44 time: 0.379119 data_time: 0.080749 memory: 5857 loss_kpt: 0.000915 acc_pose: 0.644115 loss: 0.000915 2022/10/13 12:34:34 - mmengine - INFO - Epoch(train) [69][250/293] lr: 5.000000e-04 eta: 3:47:33 time: 0.385037 data_time: 0.081708 memory: 5857 loss_kpt: 0.000923 acc_pose: 0.743173 loss: 0.000923 2022/10/13 12:34:50 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:35:10 - mmengine - INFO - Epoch(train) [70][50/293] lr: 5.000000e-04 eta: 3:46:41 time: 0.403641 data_time: 0.111029 memory: 5857 loss_kpt: 0.000914 acc_pose: 0.721467 loss: 0.000914 2022/10/13 12:35:29 - mmengine - INFO - Epoch(train) [70][100/293] lr: 5.000000e-04 eta: 3:46:29 time: 0.374866 data_time: 0.078507 memory: 5857 loss_kpt: 0.000908 acc_pose: 0.675150 loss: 0.000908 2022/10/13 12:35:48 - mmengine - INFO - Epoch(train) [70][150/293] lr: 5.000000e-04 eta: 3:46:18 time: 0.387156 data_time: 0.076360 memory: 5857 loss_kpt: 0.000909 acc_pose: 0.762641 loss: 0.000909 2022/10/13 12:36:07 - mmengine - INFO - Epoch(train) [70][200/293] lr: 5.000000e-04 eta: 3:46:07 time: 0.382200 data_time: 0.075290 memory: 5857 loss_kpt: 0.000905 acc_pose: 0.725714 loss: 0.000905 2022/10/13 12:36:26 - mmengine - INFO - Epoch(train) [70][250/293] lr: 5.000000e-04 eta: 3:45:56 time: 0.381373 data_time: 0.074528 memory: 5857 loss_kpt: 0.000944 acc_pose: 0.733088 loss: 0.000944 2022/10/13 12:36:42 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:36:42 - mmengine - INFO - Saving checkpoint at 70 epochs 2022/10/13 12:36:51 - mmengine - INFO - Epoch(val) [70][50/407] eta: 0:00:48 time: 0.135365 data_time: 0.079275 memory: 5857 2022/10/13 12:36:58 - mmengine - INFO - Epoch(val) [70][100/407] eta: 0:00:39 time: 0.128653 data_time: 0.072664 memory: 760 2022/10/13 12:37:04 - mmengine - INFO - Epoch(val) [70][150/407] eta: 0:00:33 time: 0.131606 data_time: 0.075393 memory: 760 2022/10/13 12:37:10 - mmengine - INFO - Epoch(val) [70][200/407] eta: 0:00:25 time: 0.124951 data_time: 0.068233 memory: 760 2022/10/13 12:37:17 - mmengine - INFO - Epoch(val) [70][250/407] eta: 0:00:21 time: 0.135239 data_time: 0.078760 memory: 760 2022/10/13 12:37:23 - mmengine - INFO - Epoch(val) [70][300/407] eta: 0:00:13 time: 0.124745 data_time: 0.067989 memory: 760 2022/10/13 12:37:30 - mmengine - INFO - Epoch(val) [70][350/407] eta: 0:00:07 time: 0.134169 data_time: 0.078710 memory: 760 2022/10/13 12:37:36 - mmengine - INFO - Epoch(val) [70][400/407] eta: 0:00:00 time: 0.125230 data_time: 0.070206 memory: 760 2022/10/13 12:38:15 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 12:38:30 - mmengine - INFO - Epoch(val) [70][407/407] coco/AP: 0.591226 coco/AP .5: 0.846341 coco/AP .75: 0.650348 coco/AP (M): 0.552573 coco/AP (L): 0.658797 coco/AR: 0.653526 coco/AR .5: 0.890113 coco/AR .75: 0.712059 coco/AR (M): 0.604971 coco/AR (L): 0.721256 2022/10/13 12:38:30 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384/best_coco/AP_epoch_60.pth is removed 2022/10/13 12:38:31 - mmengine - INFO - The best checkpoint with 0.5912 coco/AP at 70 epoch is saved to best_coco/AP_epoch_70.pth. 2022/10/13 12:38:51 - mmengine - INFO - Epoch(train) [71][50/293] lr: 5.000000e-04 eta: 3:45:03 time: 0.393130 data_time: 0.114516 memory: 5857 loss_kpt: 0.000919 acc_pose: 0.726166 loss: 0.000919 2022/10/13 12:39:10 - mmengine - INFO - Epoch(train) [71][100/293] lr: 5.000000e-04 eta: 3:44:52 time: 0.379182 data_time: 0.069074 memory: 5857 loss_kpt: 0.000928 acc_pose: 0.731363 loss: 0.000928 2022/10/13 12:39:30 - mmengine - INFO - Epoch(train) [71][150/293] lr: 5.000000e-04 eta: 3:44:42 time: 0.403404 data_time: 0.089529 memory: 5857 loss_kpt: 0.000885 acc_pose: 0.762211 loss: 0.000885 2022/10/13 12:39:50 - mmengine - INFO - Epoch(train) [71][200/293] lr: 5.000000e-04 eta: 3:44:33 time: 0.399604 data_time: 0.088695 memory: 5857 loss_kpt: 0.000907 acc_pose: 0.760100 loss: 0.000907 2022/10/13 12:40:10 - mmengine - INFO - Epoch(train) [71][250/293] lr: 5.000000e-04 eta: 3:44:22 time: 0.392326 data_time: 0.080680 memory: 5857 loss_kpt: 0.000931 acc_pose: 0.714506 loss: 0.000931 2022/10/13 12:40:26 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:40:47 - mmengine - INFO - Epoch(train) [72][50/293] lr: 5.000000e-04 eta: 3:43:32 time: 0.410128 data_time: 0.090551 memory: 5857 loss_kpt: 0.000906 acc_pose: 0.716460 loss: 0.000906 2022/10/13 12:41:06 - mmengine - INFO - Epoch(train) [72][100/293] lr: 5.000000e-04 eta: 3:43:21 time: 0.388913 data_time: 0.090935 memory: 5857 loss_kpt: 0.000907 acc_pose: 0.668663 loss: 0.000907 2022/10/13 12:41:26 - mmengine - INFO - Epoch(train) [72][150/293] lr: 5.000000e-04 eta: 3:43:11 time: 0.393741 data_time: 0.083359 memory: 5857 loss_kpt: 0.000916 acc_pose: 0.753967 loss: 0.000916 2022/10/13 12:41:45 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:41:46 - mmengine - INFO - Epoch(train) [72][200/293] lr: 5.000000e-04 eta: 3:43:01 time: 0.397995 data_time: 0.072754 memory: 5857 loss_kpt: 0.000905 acc_pose: 0.746755 loss: 0.000905 2022/10/13 12:42:05 - mmengine - INFO - Epoch(train) [72][250/293] lr: 5.000000e-04 eta: 3:42:49 time: 0.380559 data_time: 0.075958 memory: 5857 loss_kpt: 0.000909 acc_pose: 0.714355 loss: 0.000909 2022/10/13 12:42:20 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:42:40 - mmengine - INFO - Epoch(train) [73][50/293] lr: 5.000000e-04 eta: 3:41:57 time: 0.390656 data_time: 0.090452 memory: 5857 loss_kpt: 0.000919 acc_pose: 0.724603 loss: 0.000919 2022/10/13 12:42:59 - mmengine - INFO - Epoch(train) [73][100/293] lr: 5.000000e-04 eta: 3:41:45 time: 0.371981 data_time: 0.080613 memory: 5857 loss_kpt: 0.000907 acc_pose: 0.713243 loss: 0.000907 2022/10/13 12:43:18 - mmengine - INFO - Epoch(train) [73][150/293] lr: 5.000000e-04 eta: 3:41:33 time: 0.378713 data_time: 0.074741 memory: 5857 loss_kpt: 0.000903 acc_pose: 0.708048 loss: 0.000903 2022/10/13 12:43:36 - mmengine - INFO - Epoch(train) [73][200/293] lr: 5.000000e-04 eta: 3:41:20 time: 0.373887 data_time: 0.081537 memory: 5857 loss_kpt: 0.000909 acc_pose: 0.727782 loss: 0.000909 2022/10/13 12:43:55 - mmengine - INFO - Epoch(train) [73][250/293] lr: 5.000000e-04 eta: 3:41:08 time: 0.379230 data_time: 0.069362 memory: 5857 loss_kpt: 0.000899 acc_pose: 0.707236 loss: 0.000899 2022/10/13 12:44:10 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:44:30 - mmengine - INFO - Epoch(train) [74][50/293] lr: 5.000000e-04 eta: 3:40:17 time: 0.389660 data_time: 0.082799 memory: 5857 loss_kpt: 0.000912 acc_pose: 0.686226 loss: 0.000912 2022/10/13 12:44:48 - mmengine - INFO - Epoch(train) [74][100/293] lr: 5.000000e-04 eta: 3:40:04 time: 0.375079 data_time: 0.073689 memory: 5857 loss_kpt: 0.000904 acc_pose: 0.711061 loss: 0.000904 2022/10/13 12:45:07 - mmengine - INFO - Epoch(train) [74][150/293] lr: 5.000000e-04 eta: 3:39:52 time: 0.376574 data_time: 0.081846 memory: 5857 loss_kpt: 0.000907 acc_pose: 0.742512 loss: 0.000907 2022/10/13 12:45:27 - mmengine - INFO - Epoch(train) [74][200/293] lr: 5.000000e-04 eta: 3:39:41 time: 0.387998 data_time: 0.081753 memory: 5857 loss_kpt: 0.000914 acc_pose: 0.775640 loss: 0.000914 2022/10/13 12:45:45 - mmengine - INFO - Epoch(train) [74][250/293] lr: 5.000000e-04 eta: 3:39:29 time: 0.371306 data_time: 0.079830 memory: 5857 loss_kpt: 0.000905 acc_pose: 0.729081 loss: 0.000905 2022/10/13 12:46:01 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:46:21 - mmengine - INFO - Epoch(train) [75][50/293] lr: 5.000000e-04 eta: 3:38:38 time: 0.396380 data_time: 0.092866 memory: 5857 loss_kpt: 0.000902 acc_pose: 0.704312 loss: 0.000902 2022/10/13 12:46:39 - mmengine - INFO - Epoch(train) [75][100/293] lr: 5.000000e-04 eta: 3:38:25 time: 0.372569 data_time: 0.077547 memory: 5857 loss_kpt: 0.000902 acc_pose: 0.667416 loss: 0.000902 2022/10/13 12:46:59 - mmengine - INFO - Epoch(train) [75][150/293] lr: 5.000000e-04 eta: 3:38:14 time: 0.391799 data_time: 0.086645 memory: 5857 loss_kpt: 0.000895 acc_pose: 0.679811 loss: 0.000895 2022/10/13 12:47:17 - mmengine - INFO - Epoch(train) [75][200/293] lr: 5.000000e-04 eta: 3:38:02 time: 0.369614 data_time: 0.076395 memory: 5857 loss_kpt: 0.000891 acc_pose: 0.688446 loss: 0.000891 2022/10/13 12:47:36 - mmengine - INFO - Epoch(train) [75][250/293] lr: 5.000000e-04 eta: 3:37:50 time: 0.381948 data_time: 0.077634 memory: 5857 loss_kpt: 0.000890 acc_pose: 0.708573 loss: 0.000890 2022/10/13 12:47:52 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:48:03 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:48:12 - mmengine - INFO - Epoch(train) [76][50/293] lr: 5.000000e-04 eta: 3:36:59 time: 0.389051 data_time: 0.122837 memory: 5857 loss_kpt: 0.000906 acc_pose: 0.666052 loss: 0.000906 2022/10/13 12:48:31 - mmengine - INFO - Epoch(train) [76][100/293] lr: 5.000000e-04 eta: 3:36:47 time: 0.384123 data_time: 0.116203 memory: 5857 loss_kpt: 0.000918 acc_pose: 0.755640 loss: 0.000918 2022/10/13 12:48:50 - mmengine - INFO - Epoch(train) [76][150/293] lr: 5.000000e-04 eta: 3:36:35 time: 0.376246 data_time: 0.074318 memory: 5857 loss_kpt: 0.000893 acc_pose: 0.740659 loss: 0.000893 2022/10/13 12:49:08 - mmengine - INFO - Epoch(train) [76][200/293] lr: 5.000000e-04 eta: 3:36:22 time: 0.372180 data_time: 0.079917 memory: 5857 loss_kpt: 0.000885 acc_pose: 0.655622 loss: 0.000885 2022/10/13 12:49:28 - mmengine - INFO - Epoch(train) [76][250/293] lr: 5.000000e-04 eta: 3:36:10 time: 0.381346 data_time: 0.080548 memory: 5857 loss_kpt: 0.000898 acc_pose: 0.712807 loss: 0.000898 2022/10/13 12:49:43 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:50:03 - mmengine - INFO - Epoch(train) [77][50/293] lr: 5.000000e-04 eta: 3:35:20 time: 0.394380 data_time: 0.089910 memory: 5857 loss_kpt: 0.000913 acc_pose: 0.707307 loss: 0.000913 2022/10/13 12:50:21 - mmengine - INFO - Epoch(train) [77][100/293] lr: 5.000000e-04 eta: 3:35:07 time: 0.366611 data_time: 0.076675 memory: 5857 loss_kpt: 0.000898 acc_pose: 0.732998 loss: 0.000898 2022/10/13 12:50:40 - mmengine - INFO - Epoch(train) [77][150/293] lr: 5.000000e-04 eta: 3:34:54 time: 0.373084 data_time: 0.082930 memory: 5857 loss_kpt: 0.000892 acc_pose: 0.706358 loss: 0.000892 2022/10/13 12:50:59 - mmengine - INFO - Epoch(train) [77][200/293] lr: 5.000000e-04 eta: 3:34:42 time: 0.376820 data_time: 0.073906 memory: 5857 loss_kpt: 0.000908 acc_pose: 0.641655 loss: 0.000908 2022/10/13 12:51:18 - mmengine - INFO - Epoch(train) [77][250/293] lr: 5.000000e-04 eta: 3:34:30 time: 0.377892 data_time: 0.070929 memory: 5857 loss_kpt: 0.000895 acc_pose: 0.720489 loss: 0.000895 2022/10/13 12:51:34 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:51:53 - mmengine - INFO - Epoch(train) [78][50/293] lr: 5.000000e-04 eta: 3:33:39 time: 0.385809 data_time: 0.104829 memory: 5857 loss_kpt: 0.000909 acc_pose: 0.764473 loss: 0.000909 2022/10/13 12:52:12 - mmengine - INFO - Epoch(train) [78][100/293] lr: 5.000000e-04 eta: 3:33:27 time: 0.372490 data_time: 0.071898 memory: 5857 loss_kpt: 0.000903 acc_pose: 0.736938 loss: 0.000903 2022/10/13 12:52:31 - mmengine - INFO - Epoch(train) [78][150/293] lr: 5.000000e-04 eta: 3:33:14 time: 0.369593 data_time: 0.080003 memory: 5857 loss_kpt: 0.000909 acc_pose: 0.675074 loss: 0.000909 2022/10/13 12:52:49 - mmengine - INFO - Epoch(train) [78][200/293] lr: 5.000000e-04 eta: 3:33:01 time: 0.370940 data_time: 0.075443 memory: 5857 loss_kpt: 0.000902 acc_pose: 0.709033 loss: 0.000902 2022/10/13 12:53:08 - mmengine - INFO - Epoch(train) [78][250/293] lr: 5.000000e-04 eta: 3:32:48 time: 0.372743 data_time: 0.085194 memory: 5857 loss_kpt: 0.000918 acc_pose: 0.710120 loss: 0.000918 2022/10/13 12:53:24 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:53:43 - mmengine - INFO - Epoch(train) [79][50/293] lr: 5.000000e-04 eta: 3:31:58 time: 0.389028 data_time: 0.090344 memory: 5857 loss_kpt: 0.000890 acc_pose: 0.737042 loss: 0.000890 2022/10/13 12:54:02 - mmengine - INFO - Epoch(train) [79][100/293] lr: 5.000000e-04 eta: 3:31:46 time: 0.374722 data_time: 0.082611 memory: 5857 loss_kpt: 0.000894 acc_pose: 0.747383 loss: 0.000894 2022/10/13 12:54:19 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:54:20 - mmengine - INFO - Epoch(train) [79][150/293] lr: 5.000000e-04 eta: 3:31:33 time: 0.374079 data_time: 0.086170 memory: 5857 loss_kpt: 0.000898 acc_pose: 0.768295 loss: 0.000898 2022/10/13 12:54:39 - mmengine - INFO - Epoch(train) [79][200/293] lr: 5.000000e-04 eta: 3:31:20 time: 0.377154 data_time: 0.082211 memory: 5857 loss_kpt: 0.000904 acc_pose: 0.714526 loss: 0.000904 2022/10/13 12:54:58 - mmengine - INFO - Epoch(train) [79][250/293] lr: 5.000000e-04 eta: 3:31:07 time: 0.372658 data_time: 0.078671 memory: 5857 loss_kpt: 0.000897 acc_pose: 0.740803 loss: 0.000897 2022/10/13 12:55:14 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:55:33 - mmengine - INFO - Epoch(train) [80][50/293] lr: 5.000000e-04 eta: 3:30:19 time: 0.397129 data_time: 0.085261 memory: 5857 loss_kpt: 0.000911 acc_pose: 0.704617 loss: 0.000911 2022/10/13 12:55:52 - mmengine - INFO - Epoch(train) [80][100/293] lr: 5.000000e-04 eta: 3:30:06 time: 0.368814 data_time: 0.076089 memory: 5857 loss_kpt: 0.000901 acc_pose: 0.693448 loss: 0.000901 2022/10/13 12:56:10 - mmengine - INFO - Epoch(train) [80][150/293] lr: 5.000000e-04 eta: 3:29:53 time: 0.368588 data_time: 0.078528 memory: 5857 loss_kpt: 0.000894 acc_pose: 0.710146 loss: 0.000894 2022/10/13 12:56:30 - mmengine - INFO - Epoch(train) [80][200/293] lr: 5.000000e-04 eta: 3:29:41 time: 0.391540 data_time: 0.092442 memory: 5857 loss_kpt: 0.000900 acc_pose: 0.711129 loss: 0.000900 2022/10/13 12:56:48 - mmengine - INFO - Epoch(train) [80][250/293] lr: 5.000000e-04 eta: 3:29:28 time: 0.366455 data_time: 0.068796 memory: 5857 loss_kpt: 0.000900 acc_pose: 0.721072 loss: 0.000900 2022/10/13 12:57:04 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 12:57:04 - mmengine - INFO - Saving checkpoint at 80 epochs 2022/10/13 12:57:13 - mmengine - INFO - Epoch(val) [80][50/407] eta: 0:00:46 time: 0.131276 data_time: 0.076513 memory: 5857 2022/10/13 12:57:19 - mmengine - INFO - Epoch(val) [80][100/407] eta: 0:00:38 time: 0.125154 data_time: 0.068894 memory: 760 2022/10/13 12:57:25 - mmengine - INFO - Epoch(val) [80][150/407] eta: 0:00:31 time: 0.121211 data_time: 0.065022 memory: 760 2022/10/13 12:57:32 - mmengine - INFO - Epoch(val) [80][200/407] eta: 0:00:27 time: 0.132931 data_time: 0.077503 memory: 760 2022/10/13 12:57:38 - mmengine - INFO - Epoch(val) [80][250/407] eta: 0:00:19 time: 0.123872 data_time: 0.069377 memory: 760 2022/10/13 12:57:45 - mmengine - INFO - Epoch(val) [80][300/407] eta: 0:00:14 time: 0.130898 data_time: 0.075580 memory: 760 2022/10/13 12:57:51 - mmengine - INFO - Epoch(val) [80][350/407] eta: 0:00:07 time: 0.124465 data_time: 0.068945 memory: 760 2022/10/13 12:57:57 - mmengine - INFO - Epoch(val) [80][400/407] eta: 0:00:00 time: 0.120965 data_time: 0.065090 memory: 760 2022/10/13 12:58:35 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 12:58:50 - mmengine - INFO - Epoch(val) [80][407/407] coco/AP: 0.589909 coco/AP .5: 0.841190 coco/AP .75: 0.652709 coco/AP (M): 0.548346 coco/AP (L): 0.661487 coco/AR: 0.653684 coco/AR .5: 0.889484 coco/AR .75: 0.714736 coco/AR (M): 0.603688 coco/AR (L): 0.723709 2022/10/13 12:59:09 - mmengine - INFO - Epoch(train) [81][50/293] lr: 5.000000e-04 eta: 3:28:39 time: 0.388529 data_time: 0.079521 memory: 5857 loss_kpt: 0.000904 acc_pose: 0.705833 loss: 0.000904 2022/10/13 12:59:28 - mmengine - INFO - Epoch(train) [81][100/293] lr: 5.000000e-04 eta: 3:28:26 time: 0.374033 data_time: 0.064826 memory: 5857 loss_kpt: 0.000897 acc_pose: 0.724214 loss: 0.000897 2022/10/13 12:59:47 - mmengine - INFO - Epoch(train) [81][150/293] lr: 5.000000e-04 eta: 3:28:14 time: 0.383663 data_time: 0.080362 memory: 5857 loss_kpt: 0.000888 acc_pose: 0.725600 loss: 0.000888 2022/10/13 13:00:06 - mmengine - INFO - Epoch(train) [81][200/293] lr: 5.000000e-04 eta: 3:28:01 time: 0.368102 data_time: 0.073879 memory: 5857 loss_kpt: 0.000896 acc_pose: 0.756571 loss: 0.000896 2022/10/13 13:00:25 - mmengine - INFO - Epoch(train) [81][250/293] lr: 5.000000e-04 eta: 3:27:48 time: 0.376621 data_time: 0.077754 memory: 5857 loss_kpt: 0.000900 acc_pose: 0.728745 loss: 0.000900 2022/10/13 13:00:40 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:01:00 - mmengine - INFO - Epoch(train) [82][50/293] lr: 5.000000e-04 eta: 3:26:59 time: 0.388674 data_time: 0.098424 memory: 5857 loss_kpt: 0.000887 acc_pose: 0.710875 loss: 0.000887 2022/10/13 13:01:18 - mmengine - INFO - Epoch(train) [82][100/293] lr: 5.000000e-04 eta: 3:26:46 time: 0.371167 data_time: 0.076120 memory: 5857 loss_kpt: 0.000910 acc_pose: 0.748335 loss: 0.000910 2022/10/13 13:01:36 - mmengine - INFO - Epoch(train) [82][150/293] lr: 5.000000e-04 eta: 3:26:33 time: 0.365874 data_time: 0.087108 memory: 5857 loss_kpt: 0.000899 acc_pose: 0.723988 loss: 0.000899 2022/10/13 13:01:55 - mmengine - INFO - Epoch(train) [82][200/293] lr: 5.000000e-04 eta: 3:26:19 time: 0.368681 data_time: 0.077495 memory: 5857 loss_kpt: 0.000887 acc_pose: 0.729131 loss: 0.000887 2022/10/13 13:02:13 - mmengine - INFO - Epoch(train) [82][250/293] lr: 5.000000e-04 eta: 3:26:05 time: 0.359471 data_time: 0.073846 memory: 5857 loss_kpt: 0.000879 acc_pose: 0.719209 loss: 0.000879 2022/10/13 13:02:19 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:02:28 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:02:47 - mmengine - INFO - Epoch(train) [83][50/293] lr: 5.000000e-04 eta: 3:25:17 time: 0.380254 data_time: 0.087087 memory: 5857 loss_kpt: 0.000891 acc_pose: 0.748752 loss: 0.000891 2022/10/13 13:03:06 - mmengine - INFO - Epoch(train) [83][100/293] lr: 5.000000e-04 eta: 3:25:04 time: 0.374724 data_time: 0.068702 memory: 5857 loss_kpt: 0.000900 acc_pose: 0.707504 loss: 0.000900 2022/10/13 13:03:25 - mmengine - INFO - Epoch(train) [83][150/293] lr: 5.000000e-04 eta: 3:24:51 time: 0.383240 data_time: 0.080145 memory: 5857 loss_kpt: 0.000899 acc_pose: 0.687308 loss: 0.000899 2022/10/13 13:03:44 - mmengine - INFO - Epoch(train) [83][200/293] lr: 5.000000e-04 eta: 3:24:39 time: 0.380528 data_time: 0.080813 memory: 5857 loss_kpt: 0.000909 acc_pose: 0.692482 loss: 0.000909 2022/10/13 13:04:04 - mmengine - INFO - Epoch(train) [83][250/293] lr: 5.000000e-04 eta: 3:24:27 time: 0.384386 data_time: 0.085367 memory: 5857 loss_kpt: 0.000895 acc_pose: 0.691402 loss: 0.000895 2022/10/13 13:04:19 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:04:39 - mmengine - INFO - Epoch(train) [84][50/293] lr: 5.000000e-04 eta: 3:23:39 time: 0.388779 data_time: 0.092661 memory: 5857 loss_kpt: 0.000909 acc_pose: 0.749615 loss: 0.000909 2022/10/13 13:04:58 - mmengine - INFO - Epoch(train) [84][100/293] lr: 5.000000e-04 eta: 3:23:26 time: 0.377339 data_time: 0.077940 memory: 5857 loss_kpt: 0.000898 acc_pose: 0.675734 loss: 0.000898 2022/10/13 13:05:17 - mmengine - INFO - Epoch(train) [84][150/293] lr: 5.000000e-04 eta: 3:23:14 time: 0.385414 data_time: 0.079401 memory: 5857 loss_kpt: 0.000902 acc_pose: 0.746866 loss: 0.000902 2022/10/13 13:05:35 - mmengine - INFO - Epoch(train) [84][200/293] lr: 5.000000e-04 eta: 3:23:00 time: 0.365734 data_time: 0.075587 memory: 5857 loss_kpt: 0.000896 acc_pose: 0.704260 loss: 0.000896 2022/10/13 13:05:54 - mmengine - INFO - Epoch(train) [84][250/293] lr: 5.000000e-04 eta: 3:22:47 time: 0.375471 data_time: 0.078235 memory: 5857 loss_kpt: 0.000902 acc_pose: 0.665557 loss: 0.000902 2022/10/13 13:06:10 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:06:29 - mmengine - INFO - Epoch(train) [85][50/293] lr: 5.000000e-04 eta: 3:22:00 time: 0.388330 data_time: 0.092695 memory: 5857 loss_kpt: 0.000890 acc_pose: 0.762945 loss: 0.000890 2022/10/13 13:06:48 - mmengine - INFO - Epoch(train) [85][100/293] lr: 5.000000e-04 eta: 3:21:47 time: 0.373827 data_time: 0.077890 memory: 5857 loss_kpt: 0.000885 acc_pose: 0.712315 loss: 0.000885 2022/10/13 13:07:07 - mmengine - INFO - Epoch(train) [85][150/293] lr: 5.000000e-04 eta: 3:21:34 time: 0.374940 data_time: 0.071849 memory: 5857 loss_kpt: 0.000883 acc_pose: 0.745780 loss: 0.000883 2022/10/13 13:07:25 - mmengine - INFO - Epoch(train) [85][200/293] lr: 5.000000e-04 eta: 3:21:21 time: 0.370516 data_time: 0.074878 memory: 5857 loss_kpt: 0.000892 acc_pose: 0.741310 loss: 0.000892 2022/10/13 13:07:45 - mmengine - INFO - Epoch(train) [85][250/293] lr: 5.000000e-04 eta: 3:21:08 time: 0.384883 data_time: 0.092678 memory: 5857 loss_kpt: 0.000907 acc_pose: 0.644141 loss: 0.000907 2022/10/13 13:08:00 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:08:20 - mmengine - INFO - Epoch(train) [86][50/293] lr: 5.000000e-04 eta: 3:20:21 time: 0.389634 data_time: 0.098006 memory: 5857 loss_kpt: 0.000888 acc_pose: 0.722586 loss: 0.000888 2022/10/13 13:08:36 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:08:38 - mmengine - INFO - Epoch(train) [86][100/293] lr: 5.000000e-04 eta: 3:20:08 time: 0.366621 data_time: 0.070190 memory: 5857 loss_kpt: 0.000897 acc_pose: 0.724296 loss: 0.000897 2022/10/13 13:08:57 - mmengine - INFO - Epoch(train) [86][150/293] lr: 5.000000e-04 eta: 3:19:56 time: 0.386940 data_time: 0.089537 memory: 5857 loss_kpt: 0.000908 acc_pose: 0.677989 loss: 0.000908 2022/10/13 13:09:16 - mmengine - INFO - Epoch(train) [86][200/293] lr: 5.000000e-04 eta: 3:19:43 time: 0.385615 data_time: 0.076339 memory: 5857 loss_kpt: 0.000891 acc_pose: 0.743909 loss: 0.000891 2022/10/13 13:09:36 - mmengine - INFO - Epoch(train) [86][250/293] lr: 5.000000e-04 eta: 3:19:31 time: 0.387525 data_time: 0.103182 memory: 5857 loss_kpt: 0.000888 acc_pose: 0.731241 loss: 0.000888 2022/10/13 13:09:53 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:10:12 - mmengine - INFO - Epoch(train) [87][50/293] lr: 5.000000e-04 eta: 3:18:44 time: 0.385754 data_time: 0.088224 memory: 5857 loss_kpt: 0.000896 acc_pose: 0.744353 loss: 0.000896 2022/10/13 13:10:30 - mmengine - INFO - Epoch(train) [87][100/293] lr: 5.000000e-04 eta: 3:18:30 time: 0.363248 data_time: 0.086457 memory: 5857 loss_kpt: 0.000894 acc_pose: 0.718091 loss: 0.000894 2022/10/13 13:10:48 - mmengine - INFO - Epoch(train) [87][150/293] lr: 5.000000e-04 eta: 3:18:16 time: 0.360001 data_time: 0.072347 memory: 5857 loss_kpt: 0.000890 acc_pose: 0.799997 loss: 0.000890 2022/10/13 13:11:07 - mmengine - INFO - Epoch(train) [87][200/293] lr: 5.000000e-04 eta: 3:18:02 time: 0.368982 data_time: 0.074429 memory: 5857 loss_kpt: 0.000904 acc_pose: 0.730467 loss: 0.000904 2022/10/13 13:11:25 - mmengine - INFO - Epoch(train) [87][250/293] lr: 5.000000e-04 eta: 3:17:49 time: 0.369610 data_time: 0.067415 memory: 5857 loss_kpt: 0.000907 acc_pose: 0.698390 loss: 0.000907 2022/10/13 13:11:41 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:12:00 - mmengine - INFO - Epoch(train) [88][50/293] lr: 5.000000e-04 eta: 3:17:03 time: 0.392545 data_time: 0.081815 memory: 5857 loss_kpt: 0.000880 acc_pose: 0.763107 loss: 0.000880 2022/10/13 13:12:19 - mmengine - INFO - Epoch(train) [88][100/293] lr: 5.000000e-04 eta: 3:16:49 time: 0.369645 data_time: 0.088103 memory: 5857 loss_kpt: 0.000892 acc_pose: 0.678771 loss: 0.000892 2022/10/13 13:12:38 - mmengine - INFO - Epoch(train) [88][150/293] lr: 5.000000e-04 eta: 3:16:36 time: 0.375193 data_time: 0.084305 memory: 5857 loss_kpt: 0.000909 acc_pose: 0.733179 loss: 0.000909 2022/10/13 13:12:56 - mmengine - INFO - Epoch(train) [88][200/293] lr: 5.000000e-04 eta: 3:16:22 time: 0.368003 data_time: 0.072579 memory: 5857 loss_kpt: 0.000900 acc_pose: 0.727623 loss: 0.000900 2022/10/13 13:13:15 - mmengine - INFO - Epoch(train) [88][250/293] lr: 5.000000e-04 eta: 3:16:09 time: 0.369565 data_time: 0.091319 memory: 5857 loss_kpt: 0.000894 acc_pose: 0.681628 loss: 0.000894 2022/10/13 13:13:31 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:13:51 - mmengine - INFO - Epoch(train) [89][50/293] lr: 5.000000e-04 eta: 3:15:24 time: 0.403091 data_time: 0.091433 memory: 5857 loss_kpt: 0.000871 acc_pose: 0.757783 loss: 0.000871 2022/10/13 13:14:10 - mmengine - INFO - Epoch(train) [89][100/293] lr: 5.000000e-04 eta: 3:15:11 time: 0.382199 data_time: 0.072530 memory: 5857 loss_kpt: 0.000909 acc_pose: 0.657676 loss: 0.000909 2022/10/13 13:14:28 - mmengine - INFO - Epoch(train) [89][150/293] lr: 5.000000e-04 eta: 3:14:57 time: 0.369193 data_time: 0.077645 memory: 5857 loss_kpt: 0.000886 acc_pose: 0.684071 loss: 0.000886 2022/10/13 13:14:47 - mmengine - INFO - Epoch(train) [89][200/293] lr: 5.000000e-04 eta: 3:14:44 time: 0.372385 data_time: 0.070611 memory: 5857 loss_kpt: 0.000903 acc_pose: 0.719127 loss: 0.000903 2022/10/13 13:14:53 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:15:06 - mmengine - INFO - Epoch(train) [89][250/293] lr: 5.000000e-04 eta: 3:14:31 time: 0.373790 data_time: 0.074081 memory: 5857 loss_kpt: 0.000911 acc_pose: 0.750403 loss: 0.000911 2022/10/13 13:15:22 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:15:42 - mmengine - INFO - Epoch(train) [90][50/293] lr: 5.000000e-04 eta: 3:13:46 time: 0.398455 data_time: 0.091827 memory: 5857 loss_kpt: 0.000895 acc_pose: 0.707350 loss: 0.000895 2022/10/13 13:16:00 - mmengine - INFO - Epoch(train) [90][100/293] lr: 5.000000e-04 eta: 3:13:32 time: 0.376239 data_time: 0.076598 memory: 5857 loss_kpt: 0.000882 acc_pose: 0.731130 loss: 0.000882 2022/10/13 13:16:19 - mmengine - INFO - Epoch(train) [90][150/293] lr: 5.000000e-04 eta: 3:13:19 time: 0.376868 data_time: 0.076992 memory: 5857 loss_kpt: 0.000887 acc_pose: 0.737680 loss: 0.000887 2022/10/13 13:16:38 - mmengine - INFO - Epoch(train) [90][200/293] lr: 5.000000e-04 eta: 3:13:06 time: 0.376914 data_time: 0.088872 memory: 5857 loss_kpt: 0.000899 acc_pose: 0.621851 loss: 0.000899 2022/10/13 13:16:57 - mmengine - INFO - Epoch(train) [90][250/293] lr: 5.000000e-04 eta: 3:12:53 time: 0.384592 data_time: 0.070608 memory: 5857 loss_kpt: 0.000881 acc_pose: 0.697424 loss: 0.000881 2022/10/13 13:17:13 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:17:13 - mmengine - INFO - Saving checkpoint at 90 epochs 2022/10/13 13:17:22 - mmengine - INFO - Epoch(val) [90][50/407] eta: 0:00:45 time: 0.128514 data_time: 0.073513 memory: 5857 2022/10/13 13:17:28 - mmengine - INFO - Epoch(val) [90][100/407] eta: 0:00:38 time: 0.126755 data_time: 0.070431 memory: 760 2022/10/13 13:17:35 - mmengine - INFO - Epoch(val) [90][150/407] eta: 0:00:33 time: 0.129912 data_time: 0.075457 memory: 760 2022/10/13 13:17:42 - mmengine - INFO - Epoch(val) [90][200/407] eta: 0:00:28 time: 0.137520 data_time: 0.082946 memory: 760 2022/10/13 13:17:48 - mmengine - INFO - Epoch(val) [90][250/407] eta: 0:00:18 time: 0.120514 data_time: 0.066322 memory: 760 2022/10/13 13:17:54 - mmengine - INFO - Epoch(val) [90][300/407] eta: 0:00:13 time: 0.124020 data_time: 0.069537 memory: 760 2022/10/13 13:18:00 - mmengine - INFO - Epoch(val) [90][350/407] eta: 0:00:07 time: 0.129422 data_time: 0.073281 memory: 760 2022/10/13 13:18:06 - mmengine - INFO - Epoch(val) [90][400/407] eta: 0:00:00 time: 0.118672 data_time: 0.064306 memory: 760 2022/10/13 13:18:45 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 13:18:59 - mmengine - INFO - Epoch(val) [90][407/407] coco/AP: 0.596321 coco/AP .5: 0.847205 coco/AP .75: 0.659238 coco/AP (M): 0.557328 coco/AP (L): 0.665782 coco/AR: 0.659304 coco/AR .5: 0.892317 coco/AR .75: 0.721348 coco/AR (M): 0.610052 coco/AR (L): 0.728540 2022/10/13 13:18:59 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384/best_coco/AP_epoch_70.pth is removed 2022/10/13 13:19:01 - mmengine - INFO - The best checkpoint with 0.5963 coco/AP at 90 epoch is saved to best_coco/AP_epoch_90.pth. 2022/10/13 13:19:20 - mmengine - INFO - Epoch(train) [91][50/293] lr: 5.000000e-04 eta: 3:12:07 time: 0.379985 data_time: 0.096858 memory: 5857 loss_kpt: 0.000887 acc_pose: 0.729537 loss: 0.000887 2022/10/13 13:19:39 - mmengine - INFO - Epoch(train) [91][100/293] lr: 5.000000e-04 eta: 3:11:54 time: 0.374924 data_time: 0.075819 memory: 5857 loss_kpt: 0.000885 acc_pose: 0.697523 loss: 0.000885 2022/10/13 13:19:58 - mmengine - INFO - Epoch(train) [91][150/293] lr: 5.000000e-04 eta: 3:11:41 time: 0.378978 data_time: 0.077307 memory: 5857 loss_kpt: 0.000889 acc_pose: 0.700904 loss: 0.000889 2022/10/13 13:20:17 - mmengine - INFO - Epoch(train) [91][200/293] lr: 5.000000e-04 eta: 3:11:28 time: 0.373445 data_time: 0.081512 memory: 5857 loss_kpt: 0.000905 acc_pose: 0.714876 loss: 0.000905 2022/10/13 13:20:35 - mmengine - INFO - Epoch(train) [91][250/293] lr: 5.000000e-04 eta: 3:11:13 time: 0.363779 data_time: 0.072756 memory: 5857 loss_kpt: 0.000901 acc_pose: 0.672600 loss: 0.000901 2022/10/13 13:20:50 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:21:10 - mmengine - INFO - Epoch(train) [92][50/293] lr: 5.000000e-04 eta: 3:10:28 time: 0.383649 data_time: 0.087854 memory: 5857 loss_kpt: 0.000891 acc_pose: 0.755055 loss: 0.000891 2022/10/13 13:21:28 - mmengine - INFO - Epoch(train) [92][100/293] lr: 5.000000e-04 eta: 3:10:14 time: 0.366392 data_time: 0.077342 memory: 5857 loss_kpt: 0.000879 acc_pose: 0.700704 loss: 0.000879 2022/10/13 13:21:46 - mmengine - INFO - Epoch(train) [92][150/293] lr: 5.000000e-04 eta: 3:10:00 time: 0.361396 data_time: 0.076306 memory: 5857 loss_kpt: 0.000902 acc_pose: 0.715953 loss: 0.000902 2022/10/13 13:22:05 - mmengine - INFO - Epoch(train) [92][200/293] lr: 5.000000e-04 eta: 3:09:47 time: 0.380473 data_time: 0.074913 memory: 5857 loss_kpt: 0.000884 acc_pose: 0.697179 loss: 0.000884 2022/10/13 13:22:23 - mmengine - INFO - Epoch(train) [92][250/293] lr: 5.000000e-04 eta: 3:09:32 time: 0.361829 data_time: 0.071261 memory: 5857 loss_kpt: 0.000889 acc_pose: 0.739048 loss: 0.000889 2022/10/13 13:22:39 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:22:57 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:22:59 - mmengine - INFO - Epoch(train) [93][50/293] lr: 5.000000e-04 eta: 3:08:48 time: 0.398468 data_time: 0.129794 memory: 5857 loss_kpt: 0.000895 acc_pose: 0.780149 loss: 0.000895 2022/10/13 13:23:18 - mmengine - INFO - Epoch(train) [93][100/293] lr: 5.000000e-04 eta: 3:08:36 time: 0.387829 data_time: 0.118375 memory: 5857 loss_kpt: 0.000900 acc_pose: 0.786118 loss: 0.000900 2022/10/13 13:23:36 - mmengine - INFO - Epoch(train) [93][150/293] lr: 5.000000e-04 eta: 3:08:21 time: 0.356821 data_time: 0.078480 memory: 5857 loss_kpt: 0.000900 acc_pose: 0.741451 loss: 0.000900 2022/10/13 13:23:55 - mmengine - INFO - Epoch(train) [93][200/293] lr: 5.000000e-04 eta: 3:08:08 time: 0.380798 data_time: 0.077098 memory: 5857 loss_kpt: 0.000889 acc_pose: 0.711309 loss: 0.000889 2022/10/13 13:24:15 - mmengine - INFO - Epoch(train) [93][250/293] lr: 5.000000e-04 eta: 3:07:55 time: 0.387379 data_time: 0.091303 memory: 5857 loss_kpt: 0.000889 acc_pose: 0.744545 loss: 0.000889 2022/10/13 13:24:31 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:24:51 - mmengine - INFO - Epoch(train) [94][50/293] lr: 5.000000e-04 eta: 3:07:11 time: 0.399252 data_time: 0.087096 memory: 5857 loss_kpt: 0.000903 acc_pose: 0.706996 loss: 0.000903 2022/10/13 13:25:10 - mmengine - INFO - Epoch(train) [94][100/293] lr: 5.000000e-04 eta: 3:06:58 time: 0.371747 data_time: 0.070167 memory: 5857 loss_kpt: 0.000886 acc_pose: 0.741463 loss: 0.000886 2022/10/13 13:25:28 - mmengine - INFO - Epoch(train) [94][150/293] lr: 5.000000e-04 eta: 3:06:44 time: 0.368132 data_time: 0.069914 memory: 5857 loss_kpt: 0.000888 acc_pose: 0.727374 loss: 0.000888 2022/10/13 13:25:48 - mmengine - INFO - Epoch(train) [94][200/293] lr: 5.000000e-04 eta: 3:06:31 time: 0.389791 data_time: 0.087936 memory: 5857 loss_kpt: 0.000897 acc_pose: 0.732204 loss: 0.000897 2022/10/13 13:26:06 - mmengine - INFO - Epoch(train) [94][250/293] lr: 5.000000e-04 eta: 3:06:18 time: 0.372681 data_time: 0.065549 memory: 5857 loss_kpt: 0.000902 acc_pose: 0.695010 loss: 0.000902 2022/10/13 13:26:22 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:26:42 - mmengine - INFO - Epoch(train) [95][50/293] lr: 5.000000e-04 eta: 3:05:34 time: 0.399847 data_time: 0.127358 memory: 5857 loss_kpt: 0.000911 acc_pose: 0.686344 loss: 0.000911 2022/10/13 13:27:01 - mmengine - INFO - Epoch(train) [95][100/293] lr: 5.000000e-04 eta: 3:05:20 time: 0.372930 data_time: 0.068465 memory: 5857 loss_kpt: 0.000880 acc_pose: 0.670229 loss: 0.000880 2022/10/13 13:27:20 - mmengine - INFO - Epoch(train) [95][150/293] lr: 5.000000e-04 eta: 3:05:07 time: 0.384106 data_time: 0.074778 memory: 5857 loss_kpt: 0.000880 acc_pose: 0.710375 loss: 0.000880 2022/10/13 13:27:39 - mmengine - INFO - Epoch(train) [95][200/293] lr: 5.000000e-04 eta: 3:04:54 time: 0.377257 data_time: 0.075458 memory: 5857 loss_kpt: 0.000876 acc_pose: 0.685553 loss: 0.000876 2022/10/13 13:27:58 - mmengine - INFO - Epoch(train) [95][250/293] lr: 5.000000e-04 eta: 3:04:41 time: 0.388268 data_time: 0.092849 memory: 5857 loss_kpt: 0.000893 acc_pose: 0.690305 loss: 0.000893 2022/10/13 13:28:15 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:28:34 - mmengine - INFO - Epoch(train) [96][50/293] lr: 5.000000e-04 eta: 3:03:57 time: 0.389195 data_time: 0.089980 memory: 5857 loss_kpt: 0.000891 acc_pose: 0.645651 loss: 0.000891 2022/10/13 13:28:53 - mmengine - INFO - Epoch(train) [96][100/293] lr: 5.000000e-04 eta: 3:03:44 time: 0.373603 data_time: 0.074896 memory: 5857 loss_kpt: 0.000882 acc_pose: 0.677985 loss: 0.000882 2022/10/13 13:29:12 - mmengine - INFO - Epoch(train) [96][150/293] lr: 5.000000e-04 eta: 3:03:30 time: 0.372773 data_time: 0.071228 memory: 5857 loss_kpt: 0.000885 acc_pose: 0.748000 loss: 0.000885 2022/10/13 13:29:18 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:29:30 - mmengine - INFO - Epoch(train) [96][200/293] lr: 5.000000e-04 eta: 3:03:16 time: 0.376241 data_time: 0.065890 memory: 5857 loss_kpt: 0.000895 acc_pose: 0.705462 loss: 0.000895 2022/10/13 13:29:48 - mmengine - INFO - Epoch(train) [96][250/293] lr: 5.000000e-04 eta: 3:03:02 time: 0.362847 data_time: 0.082486 memory: 5857 loss_kpt: 0.000899 acc_pose: 0.706487 loss: 0.000899 2022/10/13 13:30:05 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:30:25 - mmengine - INFO - Epoch(train) [97][50/293] lr: 5.000000e-04 eta: 3:02:19 time: 0.401632 data_time: 0.115335 memory: 5857 loss_kpt: 0.000867 acc_pose: 0.691180 loss: 0.000867 2022/10/13 13:30:43 - mmengine - INFO - Epoch(train) [97][100/293] lr: 5.000000e-04 eta: 3:02:05 time: 0.368954 data_time: 0.075277 memory: 5857 loss_kpt: 0.000885 acc_pose: 0.783499 loss: 0.000885 2022/10/13 13:31:02 - mmengine - INFO - Epoch(train) [97][150/293] lr: 5.000000e-04 eta: 3:01:51 time: 0.372291 data_time: 0.074668 memory: 5857 loss_kpt: 0.000878 acc_pose: 0.712727 loss: 0.000878 2022/10/13 13:31:21 - mmengine - INFO - Epoch(train) [97][200/293] lr: 5.000000e-04 eta: 3:01:38 time: 0.377273 data_time: 0.073575 memory: 5857 loss_kpt: 0.000883 acc_pose: 0.739748 loss: 0.000883 2022/10/13 13:31:39 - mmengine - INFO - Epoch(train) [97][250/293] lr: 5.000000e-04 eta: 3:01:24 time: 0.365206 data_time: 0.076142 memory: 5857 loss_kpt: 0.000892 acc_pose: 0.716986 loss: 0.000892 2022/10/13 13:31:55 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:32:14 - mmengine - INFO - Epoch(train) [98][50/293] lr: 5.000000e-04 eta: 3:00:40 time: 0.388063 data_time: 0.105712 memory: 5857 loss_kpt: 0.000906 acc_pose: 0.706933 loss: 0.000906 2022/10/13 13:32:33 - mmengine - INFO - Epoch(train) [98][100/293] lr: 5.000000e-04 eta: 3:00:26 time: 0.372201 data_time: 0.081162 memory: 5857 loss_kpt: 0.000878 acc_pose: 0.687818 loss: 0.000878 2022/10/13 13:32:51 - mmengine - INFO - Epoch(train) [98][150/293] lr: 5.000000e-04 eta: 3:00:12 time: 0.373574 data_time: 0.076017 memory: 5857 loss_kpt: 0.000880 acc_pose: 0.698333 loss: 0.000880 2022/10/13 13:33:10 - mmengine - INFO - Epoch(train) [98][200/293] lr: 5.000000e-04 eta: 2:59:58 time: 0.364691 data_time: 0.077296 memory: 5857 loss_kpt: 0.000891 acc_pose: 0.728615 loss: 0.000891 2022/10/13 13:33:28 - mmengine - INFO - Epoch(train) [98][250/293] lr: 5.000000e-04 eta: 2:59:44 time: 0.370078 data_time: 0.073444 memory: 5857 loss_kpt: 0.000897 acc_pose: 0.727428 loss: 0.000897 2022/10/13 13:33:44 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:34:03 - mmengine - INFO - Epoch(train) [99][50/293] lr: 5.000000e-04 eta: 2:59:01 time: 0.383201 data_time: 0.090223 memory: 5857 loss_kpt: 0.000894 acc_pose: 0.736164 loss: 0.000894 2022/10/13 13:34:22 - mmengine - INFO - Epoch(train) [99][100/293] lr: 5.000000e-04 eta: 2:58:47 time: 0.382962 data_time: 0.086291 memory: 5857 loss_kpt: 0.000889 acc_pose: 0.668134 loss: 0.000889 2022/10/13 13:34:42 - mmengine - INFO - Epoch(train) [99][150/293] lr: 5.000000e-04 eta: 2:58:35 time: 0.392452 data_time: 0.083237 memory: 5857 loss_kpt: 0.000890 acc_pose: 0.735108 loss: 0.000890 2022/10/13 13:35:01 - mmengine - INFO - Epoch(train) [99][200/293] lr: 5.000000e-04 eta: 2:58:21 time: 0.371677 data_time: 0.072863 memory: 5857 loss_kpt: 0.000905 acc_pose: 0.725267 loss: 0.000905 2022/10/13 13:35:19 - mmengine - INFO - Epoch(train) [99][250/293] lr: 5.000000e-04 eta: 2:58:07 time: 0.374773 data_time: 0.082085 memory: 5857 loss_kpt: 0.000890 acc_pose: 0.755166 loss: 0.000890 2022/10/13 13:35:33 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:35:35 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:35:55 - mmengine - INFO - Epoch(train) [100][50/293] lr: 5.000000e-04 eta: 2:57:24 time: 0.388909 data_time: 0.096474 memory: 5857 loss_kpt: 0.000866 acc_pose: 0.603259 loss: 0.000866 2022/10/13 13:36:13 - mmengine - INFO - Epoch(train) [100][100/293] lr: 5.000000e-04 eta: 2:57:10 time: 0.377646 data_time: 0.076414 memory: 5857 loss_kpt: 0.000896 acc_pose: 0.746544 loss: 0.000896 2022/10/13 13:36:33 - mmengine - INFO - Epoch(train) [100][150/293] lr: 5.000000e-04 eta: 2:56:57 time: 0.386793 data_time: 0.073111 memory: 5857 loss_kpt: 0.000901 acc_pose: 0.752062 loss: 0.000901 2022/10/13 13:36:52 - mmengine - INFO - Epoch(train) [100][200/293] lr: 5.000000e-04 eta: 2:56:44 time: 0.375616 data_time: 0.070200 memory: 5857 loss_kpt: 0.000880 acc_pose: 0.752262 loss: 0.000880 2022/10/13 13:37:10 - mmengine - INFO - Epoch(train) [100][250/293] lr: 5.000000e-04 eta: 2:56:29 time: 0.366265 data_time: 0.075223 memory: 5857 loss_kpt: 0.000881 acc_pose: 0.692930 loss: 0.000881 2022/10/13 13:37:26 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:37:26 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/10/13 13:37:34 - mmengine - INFO - Epoch(val) [100][50/407] eta: 0:00:44 time: 0.126029 data_time: 0.070001 memory: 5857 2022/10/13 13:37:41 - mmengine - INFO - Epoch(val) [100][100/407] eta: 0:00:38 time: 0.125693 data_time: 0.068585 memory: 760 2022/10/13 13:37:47 - mmengine - INFO - Epoch(val) [100][150/407] eta: 0:00:32 time: 0.124942 data_time: 0.068747 memory: 760 2022/10/13 13:37:53 - mmengine - INFO - Epoch(val) [100][200/407] eta: 0:00:26 time: 0.125881 data_time: 0.070095 memory: 760 2022/10/13 13:38:00 - mmengine - INFO - Epoch(val) [100][250/407] eta: 0:00:19 time: 0.125523 data_time: 0.068732 memory: 760 2022/10/13 13:38:06 - mmengine - INFO - Epoch(val) [100][300/407] eta: 0:00:13 time: 0.129338 data_time: 0.070879 memory: 760 2022/10/13 13:38:12 - mmengine - INFO - Epoch(val) [100][350/407] eta: 0:00:07 time: 0.125519 data_time: 0.068525 memory: 760 2022/10/13 13:38:19 - mmengine - INFO - Epoch(val) [100][400/407] eta: 0:00:00 time: 0.131207 data_time: 0.075629 memory: 760 2022/10/13 13:38:57 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 13:39:12 - mmengine - INFO - Epoch(val) [100][407/407] coco/AP: 0.599644 coco/AP .5: 0.849945 coco/AP .75: 0.663217 coco/AP (M): 0.559311 coco/AP (L): 0.668973 coco/AR: 0.659619 coco/AR .5: 0.891215 coco/AR .75: 0.721190 coco/AR (M): 0.611636 coco/AR (L): 0.727722 2022/10/13 13:39:12 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384/best_coco/AP_epoch_90.pth is removed 2022/10/13 13:39:14 - mmengine - INFO - The best checkpoint with 0.5996 coco/AP at 100 epoch is saved to best_coco/AP_epoch_100.pth. 2022/10/13 13:39:32 - mmengine - INFO - Epoch(train) [101][50/293] lr: 5.000000e-04 eta: 2:55:45 time: 0.367243 data_time: 0.089797 memory: 5857 loss_kpt: 0.000906 acc_pose: 0.670794 loss: 0.000906 2022/10/13 13:39:51 - mmengine - INFO - Epoch(train) [101][100/293] lr: 5.000000e-04 eta: 2:55:32 time: 0.375183 data_time: 0.080286 memory: 5857 loss_kpt: 0.000889 acc_pose: 0.780512 loss: 0.000889 2022/10/13 13:40:09 - mmengine - INFO - Epoch(train) [101][150/293] lr: 5.000000e-04 eta: 2:55:17 time: 0.369329 data_time: 0.075975 memory: 5857 loss_kpt: 0.000876 acc_pose: 0.765210 loss: 0.000876 2022/10/13 13:40:28 - mmengine - INFO - Epoch(train) [101][200/293] lr: 5.000000e-04 eta: 2:55:04 time: 0.374224 data_time: 0.076637 memory: 5857 loss_kpt: 0.000867 acc_pose: 0.791023 loss: 0.000867 2022/10/13 13:40:47 - mmengine - INFO - Epoch(train) [101][250/293] lr: 5.000000e-04 eta: 2:54:50 time: 0.373733 data_time: 0.071621 memory: 5857 loss_kpt: 0.000895 acc_pose: 0.702568 loss: 0.000895 2022/10/13 13:41:02 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:41:22 - mmengine - INFO - Epoch(train) [102][50/293] lr: 5.000000e-04 eta: 2:54:07 time: 0.384490 data_time: 0.130193 memory: 5857 loss_kpt: 0.000875 acc_pose: 0.683705 loss: 0.000875 2022/10/13 13:41:41 - mmengine - INFO - Epoch(train) [102][100/293] lr: 5.000000e-04 eta: 2:53:53 time: 0.378443 data_time: 0.126695 memory: 5857 loss_kpt: 0.000882 acc_pose: 0.713843 loss: 0.000882 2022/10/13 13:41:59 - mmengine - INFO - Epoch(train) [102][150/293] lr: 5.000000e-04 eta: 2:53:39 time: 0.370495 data_time: 0.099392 memory: 5857 loss_kpt: 0.000887 acc_pose: 0.676932 loss: 0.000887 2022/10/13 13:42:18 - mmengine - INFO - Epoch(train) [102][200/293] lr: 5.000000e-04 eta: 2:53:25 time: 0.372724 data_time: 0.073254 memory: 5857 loss_kpt: 0.000882 acc_pose: 0.665839 loss: 0.000882 2022/10/13 13:42:36 - mmengine - INFO - Epoch(train) [102][250/293] lr: 5.000000e-04 eta: 2:53:11 time: 0.369282 data_time: 0.071626 memory: 5857 loss_kpt: 0.000887 acc_pose: 0.721166 loss: 0.000887 2022/10/13 13:42:52 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:43:11 - mmengine - INFO - Epoch(train) [103][50/293] lr: 5.000000e-04 eta: 2:52:28 time: 0.379355 data_time: 0.086912 memory: 5857 loss_kpt: 0.000889 acc_pose: 0.694216 loss: 0.000889 2022/10/13 13:43:29 - mmengine - INFO - Epoch(train) [103][100/293] lr: 5.000000e-04 eta: 2:52:14 time: 0.370392 data_time: 0.065203 memory: 5857 loss_kpt: 0.000889 acc_pose: 0.725740 loss: 0.000889 2022/10/13 13:43:34 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:43:48 - mmengine - INFO - Epoch(train) [103][150/293] lr: 5.000000e-04 eta: 2:52:00 time: 0.373803 data_time: 0.078867 memory: 5857 loss_kpt: 0.000878 acc_pose: 0.702960 loss: 0.000878 2022/10/13 13:44:07 - mmengine - INFO - Epoch(train) [103][200/293] lr: 5.000000e-04 eta: 2:51:46 time: 0.368401 data_time: 0.082875 memory: 5857 loss_kpt: 0.000885 acc_pose: 0.730000 loss: 0.000885 2022/10/13 13:44:25 - mmengine - INFO - Epoch(train) [103][250/293] lr: 5.000000e-04 eta: 2:51:32 time: 0.373579 data_time: 0.073736 memory: 5857 loss_kpt: 0.000885 acc_pose: 0.657384 loss: 0.000885 2022/10/13 13:44:41 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:45:01 - mmengine - INFO - Epoch(train) [104][50/293] lr: 5.000000e-04 eta: 2:50:50 time: 0.395135 data_time: 0.124518 memory: 5857 loss_kpt: 0.000896 acc_pose: 0.781062 loss: 0.000896 2022/10/13 13:45:20 - mmengine - INFO - Epoch(train) [104][100/293] lr: 5.000000e-04 eta: 2:50:37 time: 0.384631 data_time: 0.097188 memory: 5857 loss_kpt: 0.000891 acc_pose: 0.717278 loss: 0.000891 2022/10/13 13:45:39 - mmengine - INFO - Epoch(train) [104][150/293] lr: 5.000000e-04 eta: 2:50:23 time: 0.376804 data_time: 0.069607 memory: 5857 loss_kpt: 0.000889 acc_pose: 0.726387 loss: 0.000889 2022/10/13 13:45:58 - mmengine - INFO - Epoch(train) [104][200/293] lr: 5.000000e-04 eta: 2:50:10 time: 0.390565 data_time: 0.084160 memory: 5857 loss_kpt: 0.000887 acc_pose: 0.811441 loss: 0.000887 2022/10/13 13:46:17 - mmengine - INFO - Epoch(train) [104][250/293] lr: 5.000000e-04 eta: 2:49:56 time: 0.372468 data_time: 0.069074 memory: 5857 loss_kpt: 0.000879 acc_pose: 0.690761 loss: 0.000879 2022/10/13 13:46:32 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:46:51 - mmengine - INFO - Epoch(train) [105][50/293] lr: 5.000000e-04 eta: 2:49:14 time: 0.383513 data_time: 0.092006 memory: 5857 loss_kpt: 0.000888 acc_pose: 0.745911 loss: 0.000888 2022/10/13 13:47:10 - mmengine - INFO - Epoch(train) [105][100/293] lr: 5.000000e-04 eta: 2:49:00 time: 0.365443 data_time: 0.075821 memory: 5857 loss_kpt: 0.000875 acc_pose: 0.749614 loss: 0.000875 2022/10/13 13:47:28 - mmengine - INFO - Epoch(train) [105][150/293] lr: 5.000000e-04 eta: 2:48:45 time: 0.374387 data_time: 0.080908 memory: 5857 loss_kpt: 0.000880 acc_pose: 0.729276 loss: 0.000880 2022/10/13 13:47:47 - mmengine - INFO - Epoch(train) [105][200/293] lr: 5.000000e-04 eta: 2:48:31 time: 0.361538 data_time: 0.085034 memory: 5857 loss_kpt: 0.000879 acc_pose: 0.737243 loss: 0.000879 2022/10/13 13:48:05 - mmengine - INFO - Epoch(train) [105][250/293] lr: 5.000000e-04 eta: 2:48:17 time: 0.375790 data_time: 0.076712 memory: 5857 loss_kpt: 0.000874 acc_pose: 0.728201 loss: 0.000874 2022/10/13 13:48:21 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:48:41 - mmengine - INFO - Epoch(train) [106][50/293] lr: 5.000000e-04 eta: 2:47:35 time: 0.388547 data_time: 0.088560 memory: 5857 loss_kpt: 0.000884 acc_pose: 0.750395 loss: 0.000884 2022/10/13 13:48:59 - mmengine - INFO - Epoch(train) [106][100/293] lr: 5.000000e-04 eta: 2:47:21 time: 0.363060 data_time: 0.067864 memory: 5857 loss_kpt: 0.000889 acc_pose: 0.710807 loss: 0.000889 2022/10/13 13:49:17 - mmengine - INFO - Epoch(train) [106][150/293] lr: 5.000000e-04 eta: 2:47:07 time: 0.370293 data_time: 0.079085 memory: 5857 loss_kpt: 0.000877 acc_pose: 0.762945 loss: 0.000877 2022/10/13 13:49:36 - mmengine - INFO - Epoch(train) [106][200/293] lr: 5.000000e-04 eta: 2:46:52 time: 0.369673 data_time: 0.073207 memory: 5857 loss_kpt: 0.000882 acc_pose: 0.768678 loss: 0.000882 2022/10/13 13:49:49 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:49:54 - mmengine - INFO - Epoch(train) [106][250/293] lr: 5.000000e-04 eta: 2:46:38 time: 0.370098 data_time: 0.078611 memory: 5857 loss_kpt: 0.000870 acc_pose: 0.718258 loss: 0.000870 2022/10/13 13:50:10 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:50:29 - mmengine - INFO - Epoch(train) [107][50/293] lr: 5.000000e-04 eta: 2:45:57 time: 0.387595 data_time: 0.090544 memory: 5857 loss_kpt: 0.000868 acc_pose: 0.719569 loss: 0.000868 2022/10/13 13:50:48 - mmengine - INFO - Epoch(train) [107][100/293] lr: 5.000000e-04 eta: 2:45:43 time: 0.379281 data_time: 0.075126 memory: 5857 loss_kpt: 0.000886 acc_pose: 0.725411 loss: 0.000886 2022/10/13 13:51:07 - mmengine - INFO - Epoch(train) [107][150/293] lr: 5.000000e-04 eta: 2:45:29 time: 0.377206 data_time: 0.093830 memory: 5857 loss_kpt: 0.000881 acc_pose: 0.762444 loss: 0.000881 2022/10/13 13:51:26 - mmengine - INFO - Epoch(train) [107][200/293] lr: 5.000000e-04 eta: 2:45:15 time: 0.384078 data_time: 0.084967 memory: 5857 loss_kpt: 0.000878 acc_pose: 0.703403 loss: 0.000878 2022/10/13 13:51:46 - mmengine - INFO - Epoch(train) [107][250/293] lr: 5.000000e-04 eta: 2:45:02 time: 0.390348 data_time: 0.104087 memory: 5857 loss_kpt: 0.000879 acc_pose: 0.709673 loss: 0.000879 2022/10/13 13:52:03 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:52:24 - mmengine - INFO - Epoch(train) [108][50/293] lr: 5.000000e-04 eta: 2:44:22 time: 0.417938 data_time: 0.083768 memory: 5857 loss_kpt: 0.000872 acc_pose: 0.713589 loss: 0.000872 2022/10/13 13:52:44 - mmengine - INFO - Epoch(train) [108][100/293] lr: 5.000000e-04 eta: 2:44:09 time: 0.400461 data_time: 0.079914 memory: 5857 loss_kpt: 0.000877 acc_pose: 0.678274 loss: 0.000877 2022/10/13 13:53:02 - mmengine - INFO - Epoch(train) [108][150/293] lr: 5.000000e-04 eta: 2:43:55 time: 0.372873 data_time: 0.072693 memory: 5857 loss_kpt: 0.000882 acc_pose: 0.718345 loss: 0.000882 2022/10/13 13:53:21 - mmengine - INFO - Epoch(train) [108][200/293] lr: 5.000000e-04 eta: 2:43:41 time: 0.379010 data_time: 0.079273 memory: 5857 loss_kpt: 0.000886 acc_pose: 0.744197 loss: 0.000886 2022/10/13 13:53:41 - mmengine - INFO - Epoch(train) [108][250/293] lr: 5.000000e-04 eta: 2:43:28 time: 0.388426 data_time: 0.078610 memory: 5857 loss_kpt: 0.000884 acc_pose: 0.715854 loss: 0.000884 2022/10/13 13:53:57 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:54:17 - mmengine - INFO - Epoch(train) [109][50/293] lr: 5.000000e-04 eta: 2:42:48 time: 0.403759 data_time: 0.122339 memory: 5857 loss_kpt: 0.000890 acc_pose: 0.673009 loss: 0.000890 2022/10/13 13:54:36 - mmengine - INFO - Epoch(train) [109][100/293] lr: 5.000000e-04 eta: 2:42:33 time: 0.376743 data_time: 0.073086 memory: 5857 loss_kpt: 0.000895 acc_pose: 0.703478 loss: 0.000895 2022/10/13 13:54:55 - mmengine - INFO - Epoch(train) [109][150/293] lr: 5.000000e-04 eta: 2:42:20 time: 0.382323 data_time: 0.068159 memory: 5857 loss_kpt: 0.000885 acc_pose: 0.716793 loss: 0.000885 2022/10/13 13:55:13 - mmengine - INFO - Epoch(train) [109][200/293] lr: 5.000000e-04 eta: 2:42:05 time: 0.369656 data_time: 0.071550 memory: 5857 loss_kpt: 0.000888 acc_pose: 0.708420 loss: 0.000888 2022/10/13 13:55:32 - mmengine - INFO - Epoch(train) [109][250/293] lr: 5.000000e-04 eta: 2:41:51 time: 0.372736 data_time: 0.094686 memory: 5857 loss_kpt: 0.000875 acc_pose: 0.669524 loss: 0.000875 2022/10/13 13:55:48 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:56:08 - mmengine - INFO - Epoch(train) [110][50/293] lr: 5.000000e-04 eta: 2:41:11 time: 0.402373 data_time: 0.096141 memory: 5857 loss_kpt: 0.000890 acc_pose: 0.734179 loss: 0.000890 2022/10/13 13:56:12 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:56:26 - mmengine - INFO - Epoch(train) [110][100/293] lr: 5.000000e-04 eta: 2:40:56 time: 0.365622 data_time: 0.076345 memory: 5857 loss_kpt: 0.000869 acc_pose: 0.708144 loss: 0.000869 2022/10/13 13:56:44 - mmengine - INFO - Epoch(train) [110][150/293] lr: 5.000000e-04 eta: 2:40:42 time: 0.364546 data_time: 0.109539 memory: 5857 loss_kpt: 0.000863 acc_pose: 0.700470 loss: 0.000863 2022/10/13 13:57:03 - mmengine - INFO - Epoch(train) [110][200/293] lr: 5.000000e-04 eta: 2:40:27 time: 0.371036 data_time: 0.089391 memory: 5857 loss_kpt: 0.000886 acc_pose: 0.722627 loss: 0.000886 2022/10/13 13:57:21 - mmengine - INFO - Epoch(train) [110][250/293] lr: 5.000000e-04 eta: 2:40:13 time: 0.370998 data_time: 0.077267 memory: 5857 loss_kpt: 0.000889 acc_pose: 0.752061 loss: 0.000889 2022/10/13 13:57:37 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 13:57:37 - mmengine - INFO - Saving checkpoint at 110 epochs 2022/10/13 13:57:46 - mmengine - INFO - Epoch(val) [110][50/407] eta: 0:00:45 time: 0.127976 data_time: 0.070949 memory: 5857 2022/10/13 13:57:52 - mmengine - INFO - Epoch(val) [110][100/407] eta: 0:00:36 time: 0.119787 data_time: 0.064480 memory: 760 2022/10/13 13:57:58 - mmengine - INFO - Epoch(val) [110][150/407] eta: 0:00:33 time: 0.128649 data_time: 0.071339 memory: 760 2022/10/13 13:58:04 - mmengine - INFO - Epoch(val) [110][200/407] eta: 0:00:25 time: 0.121797 data_time: 0.066117 memory: 760 2022/10/13 13:58:11 - mmengine - INFO - Epoch(val) [110][250/407] eta: 0:00:20 time: 0.128065 data_time: 0.071996 memory: 760 2022/10/13 13:58:17 - mmengine - INFO - Epoch(val) [110][300/407] eta: 0:00:13 time: 0.127168 data_time: 0.071646 memory: 760 2022/10/13 13:58:23 - mmengine - INFO - Epoch(val) [110][350/407] eta: 0:00:07 time: 0.126844 data_time: 0.070716 memory: 760 2022/10/13 13:58:29 - mmengine - INFO - Epoch(val) [110][400/407] eta: 0:00:00 time: 0.123684 data_time: 0.066410 memory: 760 2022/10/13 13:59:08 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 13:59:23 - mmengine - INFO - Epoch(val) [110][407/407] coco/AP: 0.602488 coco/AP .5: 0.849783 coco/AP .75: 0.663503 coco/AP (M): 0.560435 coco/AP (L): 0.674577 coco/AR: 0.664326 coco/AR .5: 0.893419 coco/AR .75: 0.724181 coco/AR (M): 0.613985 coco/AR (L): 0.734857 2022/10/13 13:59:23 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384/best_coco/AP_epoch_100.pth is removed 2022/10/13 13:59:25 - mmengine - INFO - The best checkpoint with 0.6025 coco/AP at 110 epoch is saved to best_coco/AP_epoch_110.pth. 2022/10/13 13:59:43 - mmengine - INFO - Epoch(train) [111][50/293] lr: 5.000000e-04 eta: 2:39:32 time: 0.375013 data_time: 0.108826 memory: 5857 loss_kpt: 0.000889 acc_pose: 0.744531 loss: 0.000889 2022/10/13 14:00:02 - mmengine - INFO - Epoch(train) [111][100/293] lr: 5.000000e-04 eta: 2:39:17 time: 0.367410 data_time: 0.083115 memory: 5857 loss_kpt: 0.000886 acc_pose: 0.732492 loss: 0.000886 2022/10/13 14:00:21 - mmengine - INFO - Epoch(train) [111][150/293] lr: 5.000000e-04 eta: 2:39:03 time: 0.375554 data_time: 0.070628 memory: 5857 loss_kpt: 0.000885 acc_pose: 0.743452 loss: 0.000885 2022/10/13 14:00:39 - mmengine - INFO - Epoch(train) [111][200/293] lr: 5.000000e-04 eta: 2:38:48 time: 0.367815 data_time: 0.074372 memory: 5857 loss_kpt: 0.000882 acc_pose: 0.718388 loss: 0.000882 2022/10/13 14:00:58 - mmengine - INFO - Epoch(train) [111][250/293] lr: 5.000000e-04 eta: 2:38:34 time: 0.374519 data_time: 0.077968 memory: 5857 loss_kpt: 0.000894 acc_pose: 0.718631 loss: 0.000894 2022/10/13 14:01:13 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:01:32 - mmengine - INFO - Epoch(train) [112][50/293] lr: 5.000000e-04 eta: 2:37:53 time: 0.376519 data_time: 0.080595 memory: 5857 loss_kpt: 0.000893 acc_pose: 0.755418 loss: 0.000893 2022/10/13 14:01:51 - mmengine - INFO - Epoch(train) [112][100/293] lr: 5.000000e-04 eta: 2:37:39 time: 0.371425 data_time: 0.076870 memory: 5857 loss_kpt: 0.000868 acc_pose: 0.774593 loss: 0.000868 2022/10/13 14:02:09 - mmengine - INFO - Epoch(train) [112][150/293] lr: 5.000000e-04 eta: 2:37:24 time: 0.361355 data_time: 0.070992 memory: 5857 loss_kpt: 0.000884 acc_pose: 0.743257 loss: 0.000884 2022/10/13 14:02:27 - mmengine - INFO - Epoch(train) [112][200/293] lr: 5.000000e-04 eta: 2:37:10 time: 0.374227 data_time: 0.075320 memory: 5857 loss_kpt: 0.000870 acc_pose: 0.703198 loss: 0.000870 2022/10/13 14:02:46 - mmengine - INFO - Epoch(train) [112][250/293] lr: 5.000000e-04 eta: 2:36:55 time: 0.367489 data_time: 0.076291 memory: 5857 loss_kpt: 0.000870 acc_pose: 0.680698 loss: 0.000870 2022/10/13 14:03:02 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:03:21 - mmengine - INFO - Epoch(train) [113][50/293] lr: 5.000000e-04 eta: 2:36:15 time: 0.385206 data_time: 0.092318 memory: 5857 loss_kpt: 0.000872 acc_pose: 0.802379 loss: 0.000872 2022/10/13 14:03:40 - mmengine - INFO - Epoch(train) [113][100/293] lr: 5.000000e-04 eta: 2:36:01 time: 0.371085 data_time: 0.073688 memory: 5857 loss_kpt: 0.000899 acc_pose: 0.707046 loss: 0.000899 2022/10/13 14:03:58 - mmengine - INFO - Epoch(train) [113][150/293] lr: 5.000000e-04 eta: 2:35:46 time: 0.365684 data_time: 0.073894 memory: 5857 loss_kpt: 0.000870 acc_pose: 0.714721 loss: 0.000870 2022/10/13 14:04:11 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:04:17 - mmengine - INFO - Epoch(train) [113][200/293] lr: 5.000000e-04 eta: 2:35:31 time: 0.368737 data_time: 0.069150 memory: 5857 loss_kpt: 0.000888 acc_pose: 0.728296 loss: 0.000888 2022/10/13 14:04:36 - mmengine - INFO - Epoch(train) [113][250/293] lr: 5.000000e-04 eta: 2:35:17 time: 0.380716 data_time: 0.077912 memory: 5857 loss_kpt: 0.000882 acc_pose: 0.737496 loss: 0.000882 2022/10/13 14:04:51 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:05:11 - mmengine - INFO - Epoch(train) [114][50/293] lr: 5.000000e-04 eta: 2:34:38 time: 0.396267 data_time: 0.097839 memory: 5857 loss_kpt: 0.000889 acc_pose: 0.700522 loss: 0.000889 2022/10/13 14:05:29 - mmengine - INFO - Epoch(train) [114][100/293] lr: 5.000000e-04 eta: 2:34:23 time: 0.370361 data_time: 0.075869 memory: 5857 loss_kpt: 0.000857 acc_pose: 0.775187 loss: 0.000857 2022/10/13 14:05:48 - mmengine - INFO - Epoch(train) [114][150/293] lr: 5.000000e-04 eta: 2:34:09 time: 0.375462 data_time: 0.083303 memory: 5857 loss_kpt: 0.000885 acc_pose: 0.726157 loss: 0.000885 2022/10/13 14:06:07 - mmengine - INFO - Epoch(train) [114][200/293] lr: 5.000000e-04 eta: 2:33:54 time: 0.371819 data_time: 0.073071 memory: 5857 loss_kpt: 0.000877 acc_pose: 0.633701 loss: 0.000877 2022/10/13 14:06:25 - mmengine - INFO - Epoch(train) [114][250/293] lr: 5.000000e-04 eta: 2:33:40 time: 0.370489 data_time: 0.070049 memory: 5857 loss_kpt: 0.000875 acc_pose: 0.672991 loss: 0.000875 2022/10/13 14:06:41 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:07:00 - mmengine - INFO - Epoch(train) [115][50/293] lr: 5.000000e-04 eta: 2:33:00 time: 0.386994 data_time: 0.084698 memory: 5857 loss_kpt: 0.000888 acc_pose: 0.725263 loss: 0.000888 2022/10/13 14:07:19 - mmengine - INFO - Epoch(train) [115][100/293] lr: 5.000000e-04 eta: 2:32:46 time: 0.368415 data_time: 0.092038 memory: 5857 loss_kpt: 0.000871 acc_pose: 0.728483 loss: 0.000871 2022/10/13 14:07:37 - mmengine - INFO - Epoch(train) [115][150/293] lr: 5.000000e-04 eta: 2:32:31 time: 0.365820 data_time: 0.079133 memory: 5857 loss_kpt: 0.000890 acc_pose: 0.749099 loss: 0.000890 2022/10/13 14:07:56 - mmengine - INFO - Epoch(train) [115][200/293] lr: 5.000000e-04 eta: 2:32:16 time: 0.374245 data_time: 0.082777 memory: 5857 loss_kpt: 0.000872 acc_pose: 0.713516 loss: 0.000872 2022/10/13 14:08:14 - mmengine - INFO - Epoch(train) [115][250/293] lr: 5.000000e-04 eta: 2:32:02 time: 0.370513 data_time: 0.069116 memory: 5857 loss_kpt: 0.000875 acc_pose: 0.768035 loss: 0.000875 2022/10/13 14:08:29 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:08:49 - mmengine - INFO - Epoch(train) [116][50/293] lr: 5.000000e-04 eta: 2:31:22 time: 0.386127 data_time: 0.116867 memory: 5857 loss_kpt: 0.000866 acc_pose: 0.663692 loss: 0.000866 2022/10/13 14:09:08 - mmengine - INFO - Epoch(train) [116][100/293] lr: 5.000000e-04 eta: 2:31:08 time: 0.379355 data_time: 0.069858 memory: 5857 loss_kpt: 0.000892 acc_pose: 0.732932 loss: 0.000892 2022/10/13 14:09:26 - mmengine - INFO - Epoch(train) [116][150/293] lr: 5.000000e-04 eta: 2:30:53 time: 0.363164 data_time: 0.075280 memory: 5857 loss_kpt: 0.000874 acc_pose: 0.746438 loss: 0.000874 2022/10/13 14:09:44 - mmengine - INFO - Epoch(train) [116][200/293] lr: 5.000000e-04 eta: 2:30:39 time: 0.369713 data_time: 0.075595 memory: 5857 loss_kpt: 0.000871 acc_pose: 0.698984 loss: 0.000871 2022/10/13 14:10:03 - mmengine - INFO - Epoch(train) [116][250/293] lr: 5.000000e-04 eta: 2:30:24 time: 0.364718 data_time: 0.073480 memory: 5857 loss_kpt: 0.000874 acc_pose: 0.751151 loss: 0.000874 2022/10/13 14:10:18 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:10:23 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:10:38 - mmengine - INFO - Epoch(train) [117][50/293] lr: 5.000000e-04 eta: 2:29:45 time: 0.393886 data_time: 0.098572 memory: 5857 loss_kpt: 0.000879 acc_pose: 0.756634 loss: 0.000879 2022/10/13 14:10:56 - mmengine - INFO - Epoch(train) [117][100/293] lr: 5.000000e-04 eta: 2:29:30 time: 0.365262 data_time: 0.091833 memory: 5857 loss_kpt: 0.000877 acc_pose: 0.725740 loss: 0.000877 2022/10/13 14:11:14 - mmengine - INFO - Epoch(train) [117][150/293] lr: 5.000000e-04 eta: 2:29:15 time: 0.362427 data_time: 0.084360 memory: 5857 loss_kpt: 0.000887 acc_pose: 0.727981 loss: 0.000887 2022/10/13 14:11:32 - mmengine - INFO - Epoch(train) [117][200/293] lr: 5.000000e-04 eta: 2:29:00 time: 0.365157 data_time: 0.073721 memory: 5857 loss_kpt: 0.000879 acc_pose: 0.724265 loss: 0.000879 2022/10/13 14:11:51 - mmengine - INFO - Epoch(train) [117][250/293] lr: 5.000000e-04 eta: 2:28:45 time: 0.365522 data_time: 0.083254 memory: 5857 loss_kpt: 0.000889 acc_pose: 0.684024 loss: 0.000889 2022/10/13 14:12:07 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:12:26 - mmengine - INFO - Epoch(train) [118][50/293] lr: 5.000000e-04 eta: 2:28:06 time: 0.378381 data_time: 0.087634 memory: 5857 loss_kpt: 0.000880 acc_pose: 0.723688 loss: 0.000880 2022/10/13 14:12:44 - mmengine - INFO - Epoch(train) [118][100/293] lr: 5.000000e-04 eta: 2:27:51 time: 0.373123 data_time: 0.068385 memory: 5857 loss_kpt: 0.000896 acc_pose: 0.667970 loss: 0.000896 2022/10/13 14:13:03 - mmengine - INFO - Epoch(train) [118][150/293] lr: 5.000000e-04 eta: 2:27:37 time: 0.370260 data_time: 0.077829 memory: 5857 loss_kpt: 0.000880 acc_pose: 0.698492 loss: 0.000880 2022/10/13 14:13:21 - mmengine - INFO - Epoch(train) [118][200/293] lr: 5.000000e-04 eta: 2:27:22 time: 0.367964 data_time: 0.070981 memory: 5857 loss_kpt: 0.000875 acc_pose: 0.739434 loss: 0.000875 2022/10/13 14:13:40 - mmengine - INFO - Epoch(train) [118][250/293] lr: 5.000000e-04 eta: 2:27:07 time: 0.372227 data_time: 0.075245 memory: 5857 loss_kpt: 0.000870 acc_pose: 0.715474 loss: 0.000870 2022/10/13 14:13:56 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:14:15 - mmengine - INFO - Epoch(train) [119][50/293] lr: 5.000000e-04 eta: 2:26:29 time: 0.393306 data_time: 0.088738 memory: 5857 loss_kpt: 0.000865 acc_pose: 0.757428 loss: 0.000865 2022/10/13 14:14:34 - mmengine - INFO - Epoch(train) [119][100/293] lr: 5.000000e-04 eta: 2:26:14 time: 0.380973 data_time: 0.077068 memory: 5857 loss_kpt: 0.000870 acc_pose: 0.751087 loss: 0.000870 2022/10/13 14:14:55 - mmengine - INFO - Epoch(train) [119][150/293] lr: 5.000000e-04 eta: 2:26:02 time: 0.418881 data_time: 0.083041 memory: 5857 loss_kpt: 0.000886 acc_pose: 0.666775 loss: 0.000886 2022/10/13 14:15:16 - mmengine - INFO - Epoch(train) [119][200/293] lr: 5.000000e-04 eta: 2:25:49 time: 0.418879 data_time: 0.103945 memory: 5857 loss_kpt: 0.000870 acc_pose: 0.756579 loss: 0.000870 2022/10/13 14:15:35 - mmengine - INFO - Epoch(train) [119][250/293] lr: 5.000000e-04 eta: 2:25:34 time: 0.377031 data_time: 0.085165 memory: 5857 loss_kpt: 0.000894 acc_pose: 0.738495 loss: 0.000894 2022/10/13 14:15:51 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:16:11 - mmengine - INFO - Epoch(train) [120][50/293] lr: 5.000000e-04 eta: 2:24:56 time: 0.398575 data_time: 0.085036 memory: 5857 loss_kpt: 0.000868 acc_pose: 0.716328 loss: 0.000868 2022/10/13 14:16:29 - mmengine - INFO - Epoch(train) [120][100/293] lr: 5.000000e-04 eta: 2:24:41 time: 0.368670 data_time: 0.065526 memory: 5857 loss_kpt: 0.000863 acc_pose: 0.719463 loss: 0.000863 2022/10/13 14:16:42 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:16:49 - mmengine - INFO - Epoch(train) [120][150/293] lr: 5.000000e-04 eta: 2:24:27 time: 0.383350 data_time: 0.075965 memory: 5857 loss_kpt: 0.000887 acc_pose: 0.743752 loss: 0.000887 2022/10/13 14:17:08 - mmengine - INFO - Epoch(train) [120][200/293] lr: 5.000000e-04 eta: 2:24:13 time: 0.383396 data_time: 0.088729 memory: 5857 loss_kpt: 0.000876 acc_pose: 0.671970 loss: 0.000876 2022/10/13 14:17:27 - mmengine - INFO - Epoch(train) [120][250/293] lr: 5.000000e-04 eta: 2:23:58 time: 0.373777 data_time: 0.082980 memory: 5857 loss_kpt: 0.000885 acc_pose: 0.738376 loss: 0.000885 2022/10/13 14:17:44 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:17:44 - mmengine - INFO - Saving checkpoint at 120 epochs 2022/10/13 14:17:52 - mmengine - INFO - Epoch(val) [120][50/407] eta: 0:00:45 time: 0.128022 data_time: 0.071570 memory: 5857 2022/10/13 14:17:58 - mmengine - INFO - Epoch(val) [120][100/407] eta: 0:00:38 time: 0.123890 data_time: 0.066810 memory: 760 2022/10/13 14:18:05 - mmengine - INFO - Epoch(val) [120][150/407] eta: 0:00:31 time: 0.123087 data_time: 0.066842 memory: 760 2022/10/13 14:18:11 - mmengine - INFO - Epoch(val) [120][200/407] eta: 0:00:25 time: 0.123398 data_time: 0.067248 memory: 760 2022/10/13 14:18:17 - mmengine - INFO - Epoch(val) [120][250/407] eta: 0:00:20 time: 0.130648 data_time: 0.072813 memory: 760 2022/10/13 14:18:24 - mmengine - INFO - Epoch(val) [120][300/407] eta: 0:00:13 time: 0.128498 data_time: 0.072544 memory: 760 2022/10/13 14:18:30 - mmengine - INFO - Epoch(val) [120][350/407] eta: 0:00:07 time: 0.124150 data_time: 0.067728 memory: 760 2022/10/13 14:18:36 - mmengine - INFO - Epoch(val) [120][400/407] eta: 0:00:00 time: 0.127319 data_time: 0.069468 memory: 760 2022/10/13 14:19:15 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 14:19:30 - mmengine - INFO - Epoch(val) [120][407/407] coco/AP: 0.601701 coco/AP .5: 0.848683 coco/AP .75: 0.663234 coco/AP (M): 0.559136 coco/AP (L): 0.674090 coco/AR: 0.665775 coco/AR .5: 0.893262 coco/AR .75: 0.726228 coco/AR (M): 0.615105 coco/AR (L): 0.737161 2022/10/13 14:19:49 - mmengine - INFO - Epoch(train) [121][50/293] lr: 5.000000e-04 eta: 2:23:20 time: 0.390550 data_time: 0.115216 memory: 5857 loss_kpt: 0.000887 acc_pose: 0.709830 loss: 0.000887 2022/10/13 14:20:07 - mmengine - INFO - Epoch(train) [121][100/293] lr: 5.000000e-04 eta: 2:23:05 time: 0.366234 data_time: 0.090405 memory: 5857 loss_kpt: 0.000873 acc_pose: 0.700704 loss: 0.000873 2022/10/13 14:20:26 - mmengine - INFO - Epoch(train) [121][150/293] lr: 5.000000e-04 eta: 2:22:50 time: 0.370990 data_time: 0.072952 memory: 5857 loss_kpt: 0.000882 acc_pose: 0.709989 loss: 0.000882 2022/10/13 14:20:45 - mmengine - INFO - Epoch(train) [121][200/293] lr: 5.000000e-04 eta: 2:22:35 time: 0.373224 data_time: 0.071784 memory: 5857 loss_kpt: 0.000866 acc_pose: 0.739584 loss: 0.000866 2022/10/13 14:21:03 - mmengine - INFO - Epoch(train) [121][250/293] lr: 5.000000e-04 eta: 2:22:21 time: 0.373937 data_time: 0.083008 memory: 5857 loss_kpt: 0.000862 acc_pose: 0.732303 loss: 0.000862 2022/10/13 14:21:19 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:21:38 - mmengine - INFO - Epoch(train) [122][50/293] lr: 5.000000e-04 eta: 2:21:42 time: 0.378901 data_time: 0.093957 memory: 5857 loss_kpt: 0.000879 acc_pose: 0.687741 loss: 0.000879 2022/10/13 14:21:57 - mmengine - INFO - Epoch(train) [122][100/293] lr: 5.000000e-04 eta: 2:21:27 time: 0.369910 data_time: 0.072047 memory: 5857 loss_kpt: 0.000867 acc_pose: 0.743148 loss: 0.000867 2022/10/13 14:22:15 - mmengine - INFO - Epoch(train) [122][150/293] lr: 5.000000e-04 eta: 2:21:12 time: 0.363245 data_time: 0.065541 memory: 5857 loss_kpt: 0.000883 acc_pose: 0.707332 loss: 0.000883 2022/10/13 14:22:33 - mmengine - INFO - Epoch(train) [122][200/293] lr: 5.000000e-04 eta: 2:20:57 time: 0.367874 data_time: 0.068858 memory: 5857 loss_kpt: 0.000894 acc_pose: 0.691677 loss: 0.000894 2022/10/13 14:22:52 - mmengine - INFO - Epoch(train) [122][250/293] lr: 5.000000e-04 eta: 2:20:43 time: 0.368762 data_time: 0.077809 memory: 5857 loss_kpt: 0.000867 acc_pose: 0.750649 loss: 0.000867 2022/10/13 14:23:07 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:23:26 - mmengine - INFO - Epoch(train) [123][50/293] lr: 5.000000e-04 eta: 2:20:04 time: 0.374343 data_time: 0.088869 memory: 5857 loss_kpt: 0.000862 acc_pose: 0.736365 loss: 0.000862 2022/10/13 14:23:44 - mmengine - INFO - Epoch(train) [123][100/293] lr: 5.000000e-04 eta: 2:19:49 time: 0.369789 data_time: 0.071154 memory: 5857 loss_kpt: 0.000887 acc_pose: 0.699126 loss: 0.000887 2022/10/13 14:24:03 - mmengine - INFO - Epoch(train) [123][150/293] lr: 5.000000e-04 eta: 2:19:34 time: 0.370452 data_time: 0.081223 memory: 5857 loss_kpt: 0.000880 acc_pose: 0.736966 loss: 0.000880 2022/10/13 14:24:21 - mmengine - INFO - Epoch(train) [123][200/293] lr: 5.000000e-04 eta: 2:19:19 time: 0.367534 data_time: 0.069030 memory: 5857 loss_kpt: 0.000878 acc_pose: 0.749459 loss: 0.000878 2022/10/13 14:24:39 - mmengine - INFO - Epoch(train) [123][250/293] lr: 5.000000e-04 eta: 2:19:04 time: 0.365727 data_time: 0.070354 memory: 5857 loss_kpt: 0.000856 acc_pose: 0.749537 loss: 0.000856 2022/10/13 14:24:41 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:24:55 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:25:14 - mmengine - INFO - Epoch(train) [124][50/293] lr: 5.000000e-04 eta: 2:18:26 time: 0.390832 data_time: 0.093917 memory: 5857 loss_kpt: 0.000875 acc_pose: 0.727813 loss: 0.000875 2022/10/13 14:25:32 - mmengine - INFO - Epoch(train) [124][100/293] lr: 5.000000e-04 eta: 2:18:11 time: 0.353731 data_time: 0.075033 memory: 5857 loss_kpt: 0.000873 acc_pose: 0.657920 loss: 0.000873 2022/10/13 14:25:51 - mmengine - INFO - Epoch(train) [124][150/293] lr: 5.000000e-04 eta: 2:17:57 time: 0.380061 data_time: 0.101243 memory: 5857 loss_kpt: 0.000873 acc_pose: 0.756414 loss: 0.000873 2022/10/13 14:26:09 - mmengine - INFO - Epoch(train) [124][200/293] lr: 5.000000e-04 eta: 2:17:42 time: 0.368460 data_time: 0.108190 memory: 5857 loss_kpt: 0.000867 acc_pose: 0.728072 loss: 0.000867 2022/10/13 14:26:28 - mmengine - INFO - Epoch(train) [124][250/293] lr: 5.000000e-04 eta: 2:17:27 time: 0.376122 data_time: 0.085548 memory: 5857 loss_kpt: 0.000882 acc_pose: 0.728035 loss: 0.000882 2022/10/13 14:26:44 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:27:03 - mmengine - INFO - Epoch(train) [125][50/293] lr: 5.000000e-04 eta: 2:16:49 time: 0.387693 data_time: 0.091187 memory: 5857 loss_kpt: 0.000874 acc_pose: 0.702858 loss: 0.000874 2022/10/13 14:27:21 - mmengine - INFO - Epoch(train) [125][100/293] lr: 5.000000e-04 eta: 2:16:34 time: 0.368961 data_time: 0.074331 memory: 5857 loss_kpt: 0.000871 acc_pose: 0.673063 loss: 0.000871 2022/10/13 14:27:40 - mmengine - INFO - Epoch(train) [125][150/293] lr: 5.000000e-04 eta: 2:16:19 time: 0.366844 data_time: 0.069068 memory: 5857 loss_kpt: 0.000857 acc_pose: 0.726438 loss: 0.000857 2022/10/13 14:27:58 - mmengine - INFO - Epoch(train) [125][200/293] lr: 5.000000e-04 eta: 2:16:04 time: 0.360454 data_time: 0.071786 memory: 5857 loss_kpt: 0.000870 acc_pose: 0.709948 loss: 0.000870 2022/10/13 14:28:16 - mmengine - INFO - Epoch(train) [125][250/293] lr: 5.000000e-04 eta: 2:15:49 time: 0.369979 data_time: 0.077720 memory: 5857 loss_kpt: 0.000865 acc_pose: 0.790284 loss: 0.000865 2022/10/13 14:28:32 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:28:51 - mmengine - INFO - Epoch(train) [126][50/293] lr: 5.000000e-04 eta: 2:15:11 time: 0.384076 data_time: 0.104036 memory: 5857 loss_kpt: 0.000874 acc_pose: 0.739179 loss: 0.000874 2022/10/13 14:29:09 - mmengine - INFO - Epoch(train) [126][100/293] lr: 5.000000e-04 eta: 2:14:56 time: 0.365516 data_time: 0.082249 memory: 5857 loss_kpt: 0.000869 acc_pose: 0.738891 loss: 0.000869 2022/10/13 14:29:28 - mmengine - INFO - Epoch(train) [126][150/293] lr: 5.000000e-04 eta: 2:14:41 time: 0.371672 data_time: 0.101661 memory: 5857 loss_kpt: 0.000892 acc_pose: 0.680011 loss: 0.000892 2022/10/13 14:29:46 - mmengine - INFO - Epoch(train) [126][200/293] lr: 5.000000e-04 eta: 2:14:26 time: 0.362546 data_time: 0.083570 memory: 5857 loss_kpt: 0.000884 acc_pose: 0.709454 loss: 0.000884 2022/10/13 14:30:04 - mmengine - INFO - Epoch(train) [126][250/293] lr: 5.000000e-04 eta: 2:14:11 time: 0.365353 data_time: 0.077558 memory: 5857 loss_kpt: 0.000894 acc_pose: 0.731231 loss: 0.000894 2022/10/13 14:30:20 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:30:40 - mmengine - INFO - Epoch(train) [127][50/293] lr: 5.000000e-04 eta: 2:13:34 time: 0.398756 data_time: 0.100176 memory: 5857 loss_kpt: 0.000873 acc_pose: 0.729523 loss: 0.000873 2022/10/13 14:30:51 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:30:58 - mmengine - INFO - Epoch(train) [127][100/293] lr: 5.000000e-04 eta: 2:13:19 time: 0.373191 data_time: 0.075505 memory: 5857 loss_kpt: 0.000863 acc_pose: 0.735169 loss: 0.000863 2022/10/13 14:31:17 - mmengine - INFO - Epoch(train) [127][150/293] lr: 5.000000e-04 eta: 2:13:05 time: 0.373665 data_time: 0.074267 memory: 5857 loss_kpt: 0.000885 acc_pose: 0.728115 loss: 0.000885 2022/10/13 14:31:35 - mmengine - INFO - Epoch(train) [127][200/293] lr: 5.000000e-04 eta: 2:12:49 time: 0.361048 data_time: 0.079470 memory: 5857 loss_kpt: 0.000859 acc_pose: 0.806821 loss: 0.000859 2022/10/13 14:31:54 - mmengine - INFO - Epoch(train) [127][250/293] lr: 5.000000e-04 eta: 2:12:35 time: 0.374009 data_time: 0.088207 memory: 5857 loss_kpt: 0.000881 acc_pose: 0.727011 loss: 0.000881 2022/10/13 14:32:10 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:32:29 - mmengine - INFO - Epoch(train) [128][50/293] lr: 5.000000e-04 eta: 2:11:57 time: 0.381900 data_time: 0.117223 memory: 5857 loss_kpt: 0.000873 acc_pose: 0.741580 loss: 0.000873 2022/10/13 14:32:47 - mmengine - INFO - Epoch(train) [128][100/293] lr: 5.000000e-04 eta: 2:11:42 time: 0.366855 data_time: 0.075012 memory: 5857 loss_kpt: 0.000872 acc_pose: 0.721666 loss: 0.000872 2022/10/13 14:33:05 - mmengine - INFO - Epoch(train) [128][150/293] lr: 5.000000e-04 eta: 2:11:27 time: 0.364067 data_time: 0.076157 memory: 5857 loss_kpt: 0.000879 acc_pose: 0.733001 loss: 0.000879 2022/10/13 14:33:24 - mmengine - INFO - Epoch(train) [128][200/293] lr: 5.000000e-04 eta: 2:11:12 time: 0.370868 data_time: 0.066593 memory: 5857 loss_kpt: 0.000867 acc_pose: 0.744940 loss: 0.000867 2022/10/13 14:33:42 - mmengine - INFO - Epoch(train) [128][250/293] lr: 5.000000e-04 eta: 2:10:57 time: 0.356825 data_time: 0.068661 memory: 5857 loss_kpt: 0.000878 acc_pose: 0.698500 loss: 0.000878 2022/10/13 14:33:57 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:34:16 - mmengine - INFO - Epoch(train) [129][50/293] lr: 5.000000e-04 eta: 2:10:19 time: 0.381718 data_time: 0.083652 memory: 5857 loss_kpt: 0.000870 acc_pose: 0.741742 loss: 0.000870 2022/10/13 14:34:34 - mmengine - INFO - Epoch(train) [129][100/293] lr: 5.000000e-04 eta: 2:10:04 time: 0.365252 data_time: 0.081763 memory: 5857 loss_kpt: 0.000864 acc_pose: 0.738645 loss: 0.000864 2022/10/13 14:34:53 - mmengine - INFO - Epoch(train) [129][150/293] lr: 5.000000e-04 eta: 2:09:49 time: 0.372490 data_time: 0.078312 memory: 5857 loss_kpt: 0.000884 acc_pose: 0.699670 loss: 0.000884 2022/10/13 14:35:12 - mmengine - INFO - Epoch(train) [129][200/293] lr: 5.000000e-04 eta: 2:09:34 time: 0.376934 data_time: 0.073921 memory: 5857 loss_kpt: 0.000870 acc_pose: 0.744645 loss: 0.000870 2022/10/13 14:35:30 - mmengine - INFO - Epoch(train) [129][250/293] lr: 5.000000e-04 eta: 2:09:19 time: 0.368206 data_time: 0.116774 memory: 5857 loss_kpt: 0.000861 acc_pose: 0.736755 loss: 0.000861 2022/10/13 14:35:46 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:36:04 - mmengine - INFO - Epoch(train) [130][50/293] lr: 5.000000e-04 eta: 2:08:42 time: 0.377901 data_time: 0.094043 memory: 5857 loss_kpt: 0.000869 acc_pose: 0.746211 loss: 0.000869 2022/10/13 14:36:23 - mmengine - INFO - Epoch(train) [130][100/293] lr: 5.000000e-04 eta: 2:08:27 time: 0.367276 data_time: 0.071273 memory: 5857 loss_kpt: 0.000861 acc_pose: 0.697268 loss: 0.000861 2022/10/13 14:36:42 - mmengine - INFO - Epoch(train) [130][150/293] lr: 5.000000e-04 eta: 2:08:12 time: 0.378264 data_time: 0.076086 memory: 5857 loss_kpt: 0.000864 acc_pose: 0.755910 loss: 0.000864 2022/10/13 14:37:01 - mmengine - INFO - Epoch(train) [130][200/293] lr: 5.000000e-04 eta: 2:07:58 time: 0.380386 data_time: 0.081412 memory: 5857 loss_kpt: 0.000864 acc_pose: 0.741546 loss: 0.000864 2022/10/13 14:37:02 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:37:19 - mmengine - INFO - Epoch(train) [130][250/293] lr: 5.000000e-04 eta: 2:07:43 time: 0.368947 data_time: 0.074447 memory: 5857 loss_kpt: 0.000869 acc_pose: 0.673221 loss: 0.000869 2022/10/13 14:37:35 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:37:35 - mmengine - INFO - Saving checkpoint at 130 epochs 2022/10/13 14:37:43 - mmengine - INFO - Epoch(val) [130][50/407] eta: 0:00:45 time: 0.128062 data_time: 0.072013 memory: 5857 2022/10/13 14:37:50 - mmengine - INFO - Epoch(val) [130][100/407] eta: 0:00:38 time: 0.126339 data_time: 0.070242 memory: 760 2022/10/13 14:37:56 - mmengine - INFO - Epoch(val) [130][150/407] eta: 0:00:31 time: 0.124439 data_time: 0.067743 memory: 760 2022/10/13 14:38:02 - mmengine - INFO - Epoch(val) [130][200/407] eta: 0:00:25 time: 0.125145 data_time: 0.068704 memory: 760 2022/10/13 14:38:09 - mmengine - INFO - Epoch(val) [130][250/407] eta: 0:00:19 time: 0.126647 data_time: 0.071670 memory: 760 2022/10/13 14:38:15 - mmengine - INFO - Epoch(val) [130][300/407] eta: 0:00:12 time: 0.120925 data_time: 0.064580 memory: 760 2022/10/13 14:38:21 - mmengine - INFO - Epoch(val) [130][350/407] eta: 0:00:07 time: 0.127696 data_time: 0.071586 memory: 760 2022/10/13 14:38:27 - mmengine - INFO - Epoch(val) [130][400/407] eta: 0:00:00 time: 0.123380 data_time: 0.067819 memory: 760 2022/10/13 14:39:06 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 14:39:20 - mmengine - INFO - Epoch(val) [130][407/407] coco/AP: 0.607046 coco/AP .5: 0.851704 coco/AP .75: 0.670409 coco/AP (M): 0.566055 coco/AP (L): 0.678259 coco/AR: 0.670214 coco/AR .5: 0.897670 coco/AR .75: 0.732210 coco/AR (M): 0.620022 coco/AR (L): 0.740320 2022/10/13 14:39:20 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384/best_coco/AP_epoch_110.pth is removed 2022/10/13 14:39:22 - mmengine - INFO - The best checkpoint with 0.6070 coco/AP at 130 epoch is saved to best_coco/AP_epoch_130.pth. 2022/10/13 14:39:40 - mmengine - INFO - Epoch(train) [131][50/293] lr: 5.000000e-04 eta: 2:07:05 time: 0.370464 data_time: 0.090384 memory: 5857 loss_kpt: 0.000890 acc_pose: 0.735603 loss: 0.000890 2022/10/13 14:39:59 - mmengine - INFO - Epoch(train) [131][100/293] lr: 5.000000e-04 eta: 2:06:50 time: 0.360555 data_time: 0.075408 memory: 5857 loss_kpt: 0.000869 acc_pose: 0.741951 loss: 0.000869 2022/10/13 14:40:17 - mmengine - INFO - Epoch(train) [131][150/293] lr: 5.000000e-04 eta: 2:06:35 time: 0.370982 data_time: 0.090068 memory: 5857 loss_kpt: 0.000885 acc_pose: 0.729633 loss: 0.000885 2022/10/13 14:40:36 - mmengine - INFO - Epoch(train) [131][200/293] lr: 5.000000e-04 eta: 2:06:20 time: 0.371730 data_time: 0.101569 memory: 5857 loss_kpt: 0.000869 acc_pose: 0.712569 loss: 0.000869 2022/10/13 14:40:54 - mmengine - INFO - Epoch(train) [131][250/293] lr: 5.000000e-04 eta: 2:06:05 time: 0.369241 data_time: 0.096261 memory: 5857 loss_kpt: 0.000865 acc_pose: 0.731803 loss: 0.000865 2022/10/13 14:41:10 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:41:29 - mmengine - INFO - Epoch(train) [132][50/293] lr: 5.000000e-04 eta: 2:05:28 time: 0.379771 data_time: 0.104183 memory: 5857 loss_kpt: 0.000861 acc_pose: 0.729789 loss: 0.000861 2022/10/13 14:41:47 - mmengine - INFO - Epoch(train) [132][100/293] lr: 5.000000e-04 eta: 2:05:12 time: 0.356737 data_time: 0.079059 memory: 5857 loss_kpt: 0.000853 acc_pose: 0.740400 loss: 0.000853 2022/10/13 14:42:05 - mmengine - INFO - Epoch(train) [132][150/293] lr: 5.000000e-04 eta: 2:04:57 time: 0.360926 data_time: 0.072751 memory: 5857 loss_kpt: 0.000879 acc_pose: 0.765658 loss: 0.000879 2022/10/13 14:42:24 - mmengine - INFO - Epoch(train) [132][200/293] lr: 5.000000e-04 eta: 2:04:42 time: 0.377562 data_time: 0.086001 memory: 5857 loss_kpt: 0.000856 acc_pose: 0.711684 loss: 0.000856 2022/10/13 14:42:42 - mmengine - INFO - Epoch(train) [132][250/293] lr: 5.000000e-04 eta: 2:04:27 time: 0.358695 data_time: 0.067609 memory: 5857 loss_kpt: 0.000877 acc_pose: 0.767711 loss: 0.000877 2022/10/13 14:42:57 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:43:16 - mmengine - INFO - Epoch(train) [133][50/293] lr: 5.000000e-04 eta: 2:03:50 time: 0.385076 data_time: 0.100366 memory: 5857 loss_kpt: 0.000870 acc_pose: 0.713986 loss: 0.000870 2022/10/13 14:43:35 - mmengine - INFO - Epoch(train) [133][100/293] lr: 5.000000e-04 eta: 2:03:35 time: 0.378208 data_time: 0.104539 memory: 5857 loss_kpt: 0.000867 acc_pose: 0.709324 loss: 0.000867 2022/10/13 14:43:53 - mmengine - INFO - Epoch(train) [133][150/293] lr: 5.000000e-04 eta: 2:03:20 time: 0.365454 data_time: 0.104403 memory: 5857 loss_kpt: 0.000878 acc_pose: 0.727376 loss: 0.000878 2022/10/13 14:44:11 - mmengine - INFO - Epoch(train) [133][200/293] lr: 5.000000e-04 eta: 2:03:05 time: 0.363778 data_time: 0.089450 memory: 5857 loss_kpt: 0.000880 acc_pose: 0.741257 loss: 0.000880 2022/10/13 14:44:29 - mmengine - INFO - Epoch(train) [133][250/293] lr: 5.000000e-04 eta: 2:02:50 time: 0.364858 data_time: 0.084726 memory: 5857 loss_kpt: 0.000866 acc_pose: 0.712775 loss: 0.000866 2022/10/13 14:44:45 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:44:58 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:45:04 - mmengine - INFO - Epoch(train) [134][50/293] lr: 5.000000e-04 eta: 2:02:13 time: 0.383037 data_time: 0.099205 memory: 5857 loss_kpt: 0.000859 acc_pose: 0.738820 loss: 0.000859 2022/10/13 14:45:23 - mmengine - INFO - Epoch(train) [134][100/293] lr: 5.000000e-04 eta: 2:01:58 time: 0.370165 data_time: 0.070889 memory: 5857 loss_kpt: 0.000878 acc_pose: 0.633536 loss: 0.000878 2022/10/13 14:45:42 - mmengine - INFO - Epoch(train) [134][150/293] lr: 5.000000e-04 eta: 2:01:43 time: 0.375950 data_time: 0.068542 memory: 5857 loss_kpt: 0.000856 acc_pose: 0.739555 loss: 0.000856 2022/10/13 14:46:00 - mmengine - INFO - Epoch(train) [134][200/293] lr: 5.000000e-04 eta: 2:01:28 time: 0.365682 data_time: 0.074307 memory: 5857 loss_kpt: 0.000868 acc_pose: 0.783769 loss: 0.000868 2022/10/13 14:46:19 - mmengine - INFO - Epoch(train) [134][250/293] lr: 5.000000e-04 eta: 2:01:13 time: 0.371525 data_time: 0.077330 memory: 5857 loss_kpt: 0.000864 acc_pose: 0.705482 loss: 0.000864 2022/10/13 14:46:34 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:46:54 - mmengine - INFO - Epoch(train) [135][50/293] lr: 5.000000e-04 eta: 2:00:37 time: 0.391019 data_time: 0.094821 memory: 5857 loss_kpt: 0.000852 acc_pose: 0.757812 loss: 0.000852 2022/10/13 14:47:12 - mmengine - INFO - Epoch(train) [135][100/293] lr: 5.000000e-04 eta: 2:00:21 time: 0.364878 data_time: 0.087075 memory: 5857 loss_kpt: 0.000866 acc_pose: 0.687974 loss: 0.000866 2022/10/13 14:47:30 - mmengine - INFO - Epoch(train) [135][150/293] lr: 5.000000e-04 eta: 2:00:06 time: 0.360217 data_time: 0.076405 memory: 5857 loss_kpt: 0.000858 acc_pose: 0.690179 loss: 0.000858 2022/10/13 14:47:48 - mmengine - INFO - Epoch(train) [135][200/293] lr: 5.000000e-04 eta: 1:59:51 time: 0.367395 data_time: 0.072909 memory: 5857 loss_kpt: 0.000857 acc_pose: 0.758221 loss: 0.000857 2022/10/13 14:48:06 - mmengine - INFO - Epoch(train) [135][250/293] lr: 5.000000e-04 eta: 1:59:36 time: 0.358098 data_time: 0.065419 memory: 5857 loss_kpt: 0.000874 acc_pose: 0.672069 loss: 0.000874 2022/10/13 14:48:22 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:48:41 - mmengine - INFO - Epoch(train) [136][50/293] lr: 5.000000e-04 eta: 1:58:59 time: 0.386079 data_time: 0.101411 memory: 5857 loss_kpt: 0.000866 acc_pose: 0.704627 loss: 0.000866 2022/10/13 14:49:00 - mmengine - INFO - Epoch(train) [136][100/293] lr: 5.000000e-04 eta: 1:58:44 time: 0.376227 data_time: 0.091593 memory: 5857 loss_kpt: 0.000854 acc_pose: 0.785505 loss: 0.000854 2022/10/13 14:49:18 - mmengine - INFO - Epoch(train) [136][150/293] lr: 5.000000e-04 eta: 1:58:29 time: 0.354953 data_time: 0.071405 memory: 5857 loss_kpt: 0.000872 acc_pose: 0.714080 loss: 0.000872 2022/10/13 14:49:37 - mmengine - INFO - Epoch(train) [136][200/293] lr: 5.000000e-04 eta: 1:58:14 time: 0.375623 data_time: 0.114948 memory: 5857 loss_kpt: 0.000874 acc_pose: 0.745282 loss: 0.000874 2022/10/13 14:49:55 - mmengine - INFO - Epoch(train) [136][250/293] lr: 5.000000e-04 eta: 1:57:59 time: 0.366024 data_time: 0.091279 memory: 5857 loss_kpt: 0.000865 acc_pose: 0.727984 loss: 0.000865 2022/10/13 14:50:10 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:50:29 - mmengine - INFO - Epoch(train) [137][50/293] lr: 5.000000e-04 eta: 1:57:22 time: 0.379813 data_time: 0.091376 memory: 5857 loss_kpt: 0.000866 acc_pose: 0.779908 loss: 0.000866 2022/10/13 14:50:48 - mmengine - INFO - Epoch(train) [137][100/293] lr: 5.000000e-04 eta: 1:57:07 time: 0.378097 data_time: 0.070870 memory: 5857 loss_kpt: 0.000877 acc_pose: 0.751338 loss: 0.000877 2022/10/13 14:51:06 - mmengine - INFO - Epoch(train) [137][150/293] lr: 5.000000e-04 eta: 1:56:52 time: 0.360094 data_time: 0.082202 memory: 5857 loss_kpt: 0.000878 acc_pose: 0.737092 loss: 0.000878 2022/10/13 14:51:07 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:51:24 - mmengine - INFO - Epoch(train) [137][200/293] lr: 5.000000e-04 eta: 1:56:37 time: 0.362370 data_time: 0.071528 memory: 5857 loss_kpt: 0.000877 acc_pose: 0.783300 loss: 0.000877 2022/10/13 14:51:43 - mmengine - INFO - Epoch(train) [137][250/293] lr: 5.000000e-04 eta: 1:56:22 time: 0.370340 data_time: 0.075439 memory: 5857 loss_kpt: 0.000869 acc_pose: 0.803386 loss: 0.000869 2022/10/13 14:51:59 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:52:18 - mmengine - INFO - Epoch(train) [138][50/293] lr: 5.000000e-04 eta: 1:55:45 time: 0.379927 data_time: 0.082156 memory: 5857 loss_kpt: 0.000876 acc_pose: 0.715529 loss: 0.000876 2022/10/13 14:52:36 - mmengine - INFO - Epoch(train) [138][100/293] lr: 5.000000e-04 eta: 1:55:30 time: 0.361686 data_time: 0.077211 memory: 5857 loss_kpt: 0.000870 acc_pose: 0.730035 loss: 0.000870 2022/10/13 14:52:54 - mmengine - INFO - Epoch(train) [138][150/293] lr: 5.000000e-04 eta: 1:55:15 time: 0.360669 data_time: 0.071628 memory: 5857 loss_kpt: 0.000863 acc_pose: 0.694821 loss: 0.000863 2022/10/13 14:53:12 - mmengine - INFO - Epoch(train) [138][200/293] lr: 5.000000e-04 eta: 1:55:00 time: 0.373582 data_time: 0.075542 memory: 5857 loss_kpt: 0.000870 acc_pose: 0.727286 loss: 0.000870 2022/10/13 14:53:31 - mmengine - INFO - Epoch(train) [138][250/293] lr: 5.000000e-04 eta: 1:54:44 time: 0.366161 data_time: 0.072832 memory: 5857 loss_kpt: 0.000876 acc_pose: 0.707574 loss: 0.000876 2022/10/13 14:53:46 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:54:05 - mmengine - INFO - Epoch(train) [139][50/293] lr: 5.000000e-04 eta: 1:54:08 time: 0.378649 data_time: 0.087970 memory: 5857 loss_kpt: 0.000879 acc_pose: 0.723443 loss: 0.000879 2022/10/13 14:54:24 - mmengine - INFO - Epoch(train) [139][100/293] lr: 5.000000e-04 eta: 1:53:53 time: 0.368299 data_time: 0.077836 memory: 5857 loss_kpt: 0.000871 acc_pose: 0.665185 loss: 0.000871 2022/10/13 14:54:43 - mmengine - INFO - Epoch(train) [139][150/293] lr: 5.000000e-04 eta: 1:53:38 time: 0.381575 data_time: 0.081640 memory: 5857 loss_kpt: 0.000862 acc_pose: 0.713699 loss: 0.000862 2022/10/13 14:55:01 - mmengine - INFO - Epoch(train) [139][200/293] lr: 5.000000e-04 eta: 1:53:23 time: 0.369999 data_time: 0.073234 memory: 5857 loss_kpt: 0.000856 acc_pose: 0.773599 loss: 0.000856 2022/10/13 14:55:19 - mmengine - INFO - Epoch(train) [139][250/293] lr: 5.000000e-04 eta: 1:53:08 time: 0.359430 data_time: 0.093389 memory: 5857 loss_kpt: 0.000859 acc_pose: 0.750019 loss: 0.000859 2022/10/13 14:55:35 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:55:54 - mmengine - INFO - Epoch(train) [140][50/293] lr: 5.000000e-04 eta: 1:52:32 time: 0.390036 data_time: 0.091920 memory: 5857 loss_kpt: 0.000864 acc_pose: 0.717153 loss: 0.000864 2022/10/13 14:56:12 - mmengine - INFO - Epoch(train) [140][100/293] lr: 5.000000e-04 eta: 1:52:17 time: 0.361338 data_time: 0.074362 memory: 5857 loss_kpt: 0.000873 acc_pose: 0.782887 loss: 0.000873 2022/10/13 14:56:31 - mmengine - INFO - Epoch(train) [140][150/293] lr: 5.000000e-04 eta: 1:52:01 time: 0.372189 data_time: 0.065381 memory: 5857 loss_kpt: 0.000853 acc_pose: 0.718717 loss: 0.000853 2022/10/13 14:56:49 - mmengine - INFO - Epoch(train) [140][200/293] lr: 5.000000e-04 eta: 1:51:46 time: 0.362711 data_time: 0.070341 memory: 5857 loss_kpt: 0.000874 acc_pose: 0.744306 loss: 0.000874 2022/10/13 14:57:07 - mmengine - INFO - Epoch(train) [140][250/293] lr: 5.000000e-04 eta: 1:51:31 time: 0.366631 data_time: 0.074696 memory: 5857 loss_kpt: 0.000861 acc_pose: 0.800091 loss: 0.000861 2022/10/13 14:57:16 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:57:23 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 14:57:23 - mmengine - INFO - Saving checkpoint at 140 epochs 2022/10/13 14:57:32 - mmengine - INFO - Epoch(val) [140][50/407] eta: 0:00:46 time: 0.130151 data_time: 0.073678 memory: 5857 2022/10/13 14:57:38 - mmengine - INFO - Epoch(val) [140][100/407] eta: 0:00:37 time: 0.121536 data_time: 0.064506 memory: 760 2022/10/13 14:57:44 - mmengine - INFO - Epoch(val) [140][150/407] eta: 0:00:33 time: 0.130667 data_time: 0.073063 memory: 760 2022/10/13 14:57:51 - mmengine - INFO - Epoch(val) [140][200/407] eta: 0:00:27 time: 0.130521 data_time: 0.074948 memory: 760 2022/10/13 14:57:57 - mmengine - INFO - Epoch(val) [140][250/407] eta: 0:00:19 time: 0.122525 data_time: 0.066742 memory: 760 2022/10/13 14:58:03 - mmengine - INFO - Epoch(val) [140][300/407] eta: 0:00:13 time: 0.123480 data_time: 0.068942 memory: 760 2022/10/13 14:58:09 - mmengine - INFO - Epoch(val) [140][350/407] eta: 0:00:07 time: 0.126945 data_time: 0.069597 memory: 760 2022/10/13 14:58:16 - mmengine - INFO - Epoch(val) [140][400/407] eta: 0:00:00 time: 0.124239 data_time: 0.069970 memory: 760 2022/10/13 14:58:54 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 14:59:09 - mmengine - INFO - Epoch(val) [140][407/407] coco/AP: 0.610098 coco/AP .5: 0.853095 coco/AP .75: 0.673076 coco/AP (M): 0.568223 coco/AP (L): 0.681257 coco/AR: 0.671080 coco/AR .5: 0.894836 coco/AR .75: 0.733312 coco/AR (M): 0.621278 coco/AR (L): 0.741620 2022/10/13 14:59:09 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384/best_coco/AP_epoch_130.pth is removed 2022/10/13 14:59:11 - mmengine - INFO - The best checkpoint with 0.6101 coco/AP at 140 epoch is saved to best_coco/AP_epoch_140.pth. 2022/10/13 14:59:30 - mmengine - INFO - Epoch(train) [141][50/293] lr: 5.000000e-04 eta: 1:50:55 time: 0.380047 data_time: 0.083406 memory: 5857 loss_kpt: 0.000861 acc_pose: 0.718291 loss: 0.000861 2022/10/13 14:59:48 - mmengine - INFO - Epoch(train) [141][100/293] lr: 5.000000e-04 eta: 1:50:40 time: 0.365014 data_time: 0.089839 memory: 5857 loss_kpt: 0.000879 acc_pose: 0.773464 loss: 0.000879 2022/10/13 15:00:06 - mmengine - INFO - Epoch(train) [141][150/293] lr: 5.000000e-04 eta: 1:50:25 time: 0.369261 data_time: 0.116413 memory: 5857 loss_kpt: 0.000872 acc_pose: 0.744573 loss: 0.000872 2022/10/13 15:00:25 - mmengine - INFO - Epoch(train) [141][200/293] lr: 5.000000e-04 eta: 1:50:09 time: 0.366474 data_time: 0.072632 memory: 5857 loss_kpt: 0.000871 acc_pose: 0.704713 loss: 0.000871 2022/10/13 15:00:43 - mmengine - INFO - Epoch(train) [141][250/293] lr: 5.000000e-04 eta: 1:49:54 time: 0.367397 data_time: 0.067528 memory: 5857 loss_kpt: 0.000862 acc_pose: 0.700540 loss: 0.000862 2022/10/13 15:00:58 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:01:17 - mmengine - INFO - Epoch(train) [142][50/293] lr: 5.000000e-04 eta: 1:49:18 time: 0.378663 data_time: 0.111001 memory: 5857 loss_kpt: 0.000847 acc_pose: 0.706819 loss: 0.000847 2022/10/13 15:01:36 - mmengine - INFO - Epoch(train) [142][100/293] lr: 5.000000e-04 eta: 1:49:03 time: 0.375345 data_time: 0.109526 memory: 5857 loss_kpt: 0.000868 acc_pose: 0.726196 loss: 0.000868 2022/10/13 15:01:54 - mmengine - INFO - Epoch(train) [142][150/293] lr: 5.000000e-04 eta: 1:48:48 time: 0.371855 data_time: 0.094312 memory: 5857 loss_kpt: 0.000864 acc_pose: 0.771745 loss: 0.000864 2022/10/13 15:02:13 - mmengine - INFO - Epoch(train) [142][200/293] lr: 5.000000e-04 eta: 1:48:33 time: 0.369602 data_time: 0.083360 memory: 5857 loss_kpt: 0.000871 acc_pose: 0.735260 loss: 0.000871 2022/10/13 15:02:31 - mmengine - INFO - Epoch(train) [142][250/293] lr: 5.000000e-04 eta: 1:48:17 time: 0.361837 data_time: 0.080358 memory: 5857 loss_kpt: 0.000850 acc_pose: 0.771330 loss: 0.000850 2022/10/13 15:02:46 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:03:06 - mmengine - INFO - Epoch(train) [143][50/293] lr: 5.000000e-04 eta: 1:47:42 time: 0.381752 data_time: 0.105341 memory: 5857 loss_kpt: 0.000860 acc_pose: 0.702033 loss: 0.000860 2022/10/13 15:03:24 - mmengine - INFO - Epoch(train) [143][100/293] lr: 5.000000e-04 eta: 1:47:27 time: 0.368243 data_time: 0.075715 memory: 5857 loss_kpt: 0.000873 acc_pose: 0.716640 loss: 0.000873 2022/10/13 15:03:43 - mmengine - INFO - Epoch(train) [143][150/293] lr: 5.000000e-04 eta: 1:47:11 time: 0.372298 data_time: 0.072345 memory: 5857 loss_kpt: 0.000873 acc_pose: 0.749904 loss: 0.000873 2022/10/13 15:04:01 - mmengine - INFO - Epoch(train) [143][200/293] lr: 5.000000e-04 eta: 1:46:56 time: 0.365824 data_time: 0.079507 memory: 5857 loss_kpt: 0.000849 acc_pose: 0.728906 loss: 0.000849 2022/10/13 15:04:19 - mmengine - INFO - Epoch(train) [143][250/293] lr: 5.000000e-04 eta: 1:46:41 time: 0.362118 data_time: 0.076167 memory: 5857 loss_kpt: 0.000877 acc_pose: 0.690556 loss: 0.000877 2022/10/13 15:04:35 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:04:54 - mmengine - INFO - Epoch(train) [144][50/293] lr: 5.000000e-04 eta: 1:46:05 time: 0.378684 data_time: 0.087420 memory: 5857 loss_kpt: 0.000881 acc_pose: 0.712749 loss: 0.000881 2022/10/13 15:05:12 - mmengine - INFO - Epoch(train) [144][100/293] lr: 5.000000e-04 eta: 1:45:50 time: 0.360473 data_time: 0.081125 memory: 5857 loss_kpt: 0.000865 acc_pose: 0.727837 loss: 0.000865 2022/10/13 15:05:12 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:05:30 - mmengine - INFO - Epoch(train) [144][150/293] lr: 5.000000e-04 eta: 1:45:35 time: 0.367630 data_time: 0.063294 memory: 5857 loss_kpt: 0.000861 acc_pose: 0.731232 loss: 0.000861 2022/10/13 15:05:48 - mmengine - INFO - Epoch(train) [144][200/293] lr: 5.000000e-04 eta: 1:45:19 time: 0.365401 data_time: 0.075432 memory: 5857 loss_kpt: 0.000870 acc_pose: 0.737328 loss: 0.000870 2022/10/13 15:06:07 - mmengine - INFO - Epoch(train) [144][250/293] lr: 5.000000e-04 eta: 1:45:04 time: 0.368839 data_time: 0.068817 memory: 5857 loss_kpt: 0.000868 acc_pose: 0.681768 loss: 0.000868 2022/10/13 15:06:22 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:06:42 - mmengine - INFO - Epoch(train) [145][50/293] lr: 5.000000e-04 eta: 1:44:29 time: 0.381428 data_time: 0.085746 memory: 5857 loss_kpt: 0.000858 acc_pose: 0.732720 loss: 0.000858 2022/10/13 15:07:00 - mmengine - INFO - Epoch(train) [145][100/293] lr: 5.000000e-04 eta: 1:44:13 time: 0.370899 data_time: 0.078617 memory: 5857 loss_kpt: 0.000871 acc_pose: 0.718692 loss: 0.000871 2022/10/13 15:07:18 - mmengine - INFO - Epoch(train) [145][150/293] lr: 5.000000e-04 eta: 1:43:58 time: 0.365251 data_time: 0.089339 memory: 5857 loss_kpt: 0.000868 acc_pose: 0.698606 loss: 0.000868 2022/10/13 15:07:37 - mmengine - INFO - Epoch(train) [145][200/293] lr: 5.000000e-04 eta: 1:43:43 time: 0.369935 data_time: 0.092413 memory: 5857 loss_kpt: 0.000868 acc_pose: 0.720854 loss: 0.000868 2022/10/13 15:07:55 - mmengine - INFO - Epoch(train) [145][250/293] lr: 5.000000e-04 eta: 1:43:27 time: 0.367199 data_time: 0.076260 memory: 5857 loss_kpt: 0.000849 acc_pose: 0.767200 loss: 0.000849 2022/10/13 15:08:11 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:08:30 - mmengine - INFO - Epoch(train) [146][50/293] lr: 5.000000e-04 eta: 1:42:52 time: 0.375284 data_time: 0.080846 memory: 5857 loss_kpt: 0.000860 acc_pose: 0.701304 loss: 0.000860 2022/10/13 15:08:49 - mmengine - INFO - Epoch(train) [146][100/293] lr: 5.000000e-04 eta: 1:42:37 time: 0.380184 data_time: 0.073978 memory: 5857 loss_kpt: 0.000859 acc_pose: 0.670420 loss: 0.000859 2022/10/13 15:09:07 - mmengine - INFO - Epoch(train) [146][150/293] lr: 5.000000e-04 eta: 1:42:22 time: 0.369498 data_time: 0.077725 memory: 5857 loss_kpt: 0.000859 acc_pose: 0.704638 loss: 0.000859 2022/10/13 15:09:26 - mmengine - INFO - Epoch(train) [146][200/293] lr: 5.000000e-04 eta: 1:42:07 time: 0.371501 data_time: 0.083518 memory: 5857 loss_kpt: 0.000857 acc_pose: 0.709106 loss: 0.000857 2022/10/13 15:09:45 - mmengine - INFO - Epoch(train) [146][250/293] lr: 5.000000e-04 eta: 1:41:51 time: 0.372315 data_time: 0.066583 memory: 5857 loss_kpt: 0.000862 acc_pose: 0.736365 loss: 0.000862 2022/10/13 15:10:00 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:10:19 - mmengine - INFO - Epoch(train) [147][50/293] lr: 5.000000e-04 eta: 1:41:16 time: 0.385101 data_time: 0.097057 memory: 5857 loss_kpt: 0.000856 acc_pose: 0.731368 loss: 0.000856 2022/10/13 15:10:37 - mmengine - INFO - Epoch(train) [147][100/293] lr: 5.000000e-04 eta: 1:41:01 time: 0.364700 data_time: 0.072351 memory: 5857 loss_kpt: 0.000870 acc_pose: 0.701886 loss: 0.000870 2022/10/13 15:10:56 - mmengine - INFO - Epoch(train) [147][150/293] lr: 5.000000e-04 eta: 1:40:46 time: 0.373685 data_time: 0.080866 memory: 5857 loss_kpt: 0.000874 acc_pose: 0.742427 loss: 0.000874 2022/10/13 15:11:14 - mmengine - INFO - Epoch(train) [147][200/293] lr: 5.000000e-04 eta: 1:40:30 time: 0.359699 data_time: 0.066854 memory: 5857 loss_kpt: 0.000867 acc_pose: 0.714382 loss: 0.000867 2022/10/13 15:11:22 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:11:32 - mmengine - INFO - Epoch(train) [147][250/293] lr: 5.000000e-04 eta: 1:40:15 time: 0.365116 data_time: 0.075838 memory: 5857 loss_kpt: 0.000863 acc_pose: 0.678924 loss: 0.000863 2022/10/13 15:11:48 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:12:08 - mmengine - INFO - Epoch(train) [148][50/293] lr: 5.000000e-04 eta: 1:39:40 time: 0.393395 data_time: 0.096718 memory: 5857 loss_kpt: 0.000881 acc_pose: 0.776157 loss: 0.000881 2022/10/13 15:12:27 - mmengine - INFO - Epoch(train) [148][100/293] lr: 5.000000e-04 eta: 1:39:25 time: 0.373392 data_time: 0.083512 memory: 5857 loss_kpt: 0.000871 acc_pose: 0.722275 loss: 0.000871 2022/10/13 15:12:45 - mmengine - INFO - Epoch(train) [148][150/293] lr: 5.000000e-04 eta: 1:39:10 time: 0.367291 data_time: 0.068821 memory: 5857 loss_kpt: 0.000870 acc_pose: 0.698938 loss: 0.000870 2022/10/13 15:13:03 - mmengine - INFO - Epoch(train) [148][200/293] lr: 5.000000e-04 eta: 1:38:54 time: 0.355486 data_time: 0.079937 memory: 5857 loss_kpt: 0.000847 acc_pose: 0.778014 loss: 0.000847 2022/10/13 15:13:21 - mmengine - INFO - Epoch(train) [148][250/293] lr: 5.000000e-04 eta: 1:38:39 time: 0.369762 data_time: 0.107840 memory: 5857 loss_kpt: 0.000847 acc_pose: 0.685627 loss: 0.000847 2022/10/13 15:13:37 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:13:56 - mmengine - INFO - Epoch(train) [149][50/293] lr: 5.000000e-04 eta: 1:38:04 time: 0.374714 data_time: 0.089932 memory: 5857 loss_kpt: 0.000865 acc_pose: 0.759985 loss: 0.000865 2022/10/13 15:14:14 - mmengine - INFO - Epoch(train) [149][100/293] lr: 5.000000e-04 eta: 1:37:48 time: 0.368031 data_time: 0.078527 memory: 5857 loss_kpt: 0.000861 acc_pose: 0.709134 loss: 0.000861 2022/10/13 15:14:33 - mmengine - INFO - Epoch(train) [149][150/293] lr: 5.000000e-04 eta: 1:37:33 time: 0.368172 data_time: 0.107003 memory: 5857 loss_kpt: 0.000852 acc_pose: 0.701111 loss: 0.000852 2022/10/13 15:14:52 - mmengine - INFO - Epoch(train) [149][200/293] lr: 5.000000e-04 eta: 1:37:18 time: 0.374090 data_time: 0.068695 memory: 5857 loss_kpt: 0.000861 acc_pose: 0.729974 loss: 0.000861 2022/10/13 15:15:10 - mmengine - INFO - Epoch(train) [149][250/293] lr: 5.000000e-04 eta: 1:37:02 time: 0.368492 data_time: 0.104564 memory: 5857 loss_kpt: 0.000858 acc_pose: 0.718808 loss: 0.000858 2022/10/13 15:15:25 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:15:44 - mmengine - INFO - Epoch(train) [150][50/293] lr: 5.000000e-04 eta: 1:36:27 time: 0.378317 data_time: 0.092596 memory: 5857 loss_kpt: 0.000880 acc_pose: 0.740523 loss: 0.000880 2022/10/13 15:16:04 - mmengine - INFO - Epoch(train) [150][100/293] lr: 5.000000e-04 eta: 1:36:13 time: 0.392556 data_time: 0.079572 memory: 5857 loss_kpt: 0.000877 acc_pose: 0.748535 loss: 0.000877 2022/10/13 15:16:22 - mmengine - INFO - Epoch(train) [150][150/293] lr: 5.000000e-04 eta: 1:35:57 time: 0.369224 data_time: 0.071135 memory: 5857 loss_kpt: 0.000863 acc_pose: 0.728312 loss: 0.000863 2022/10/13 15:16:41 - mmengine - INFO - Epoch(train) [150][200/293] lr: 5.000000e-04 eta: 1:35:42 time: 0.363355 data_time: 0.071436 memory: 5857 loss_kpt: 0.000864 acc_pose: 0.774706 loss: 0.000864 2022/10/13 15:16:59 - mmengine - INFO - Epoch(train) [150][250/293] lr: 5.000000e-04 eta: 1:35:26 time: 0.364605 data_time: 0.083523 memory: 5857 loss_kpt: 0.000869 acc_pose: 0.707741 loss: 0.000869 2022/10/13 15:17:15 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:17:15 - mmengine - INFO - Saving checkpoint at 150 epochs 2022/10/13 15:17:24 - mmengine - INFO - Epoch(val) [150][50/407] eta: 0:00:46 time: 0.131048 data_time: 0.074166 memory: 5857 2022/10/13 15:17:30 - mmengine - INFO - Epoch(val) [150][100/407] eta: 0:00:37 time: 0.123126 data_time: 0.066721 memory: 760 2022/10/13 15:17:36 - mmengine - INFO - Epoch(val) [150][150/407] eta: 0:00:31 time: 0.123683 data_time: 0.061181 memory: 760 2022/10/13 15:17:42 - mmengine - INFO - Epoch(val) [150][200/407] eta: 0:00:26 time: 0.129871 data_time: 0.071965 memory: 760 2022/10/13 15:17:49 - mmengine - INFO - Epoch(val) [150][250/407] eta: 0:00:20 time: 0.129601 data_time: 0.074208 memory: 760 2022/10/13 15:17:55 - mmengine - INFO - Epoch(val) [150][300/407] eta: 0:00:13 time: 0.124340 data_time: 0.068965 memory: 760 2022/10/13 15:18:02 - mmengine - INFO - Epoch(val) [150][350/407] eta: 0:00:07 time: 0.128667 data_time: 0.073438 memory: 760 2022/10/13 15:18:07 - mmengine - INFO - Epoch(val) [150][400/407] eta: 0:00:00 time: 0.117260 data_time: 0.062518 memory: 760 2022/10/13 15:18:46 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 15:19:01 - mmengine - INFO - Epoch(val) [150][407/407] coco/AP: 0.610978 coco/AP .5: 0.853551 coco/AP .75: 0.677502 coco/AP (M): 0.570632 coco/AP (L): 0.679681 coco/AR: 0.672544 coco/AR .5: 0.895623 coco/AR .75: 0.735359 coco/AR (M): 0.624037 coco/AR (L): 0.740951 2022/10/13 15:19:01 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384/best_coco/AP_epoch_140.pth is removed 2022/10/13 15:19:03 - mmengine - INFO - The best checkpoint with 0.6110 coco/AP at 150 epoch is saved to best_coco/AP_epoch_150.pth. 2022/10/13 15:19:21 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:19:21 - mmengine - INFO - Epoch(train) [151][50/293] lr: 5.000000e-04 eta: 1:34:52 time: 0.372168 data_time: 0.102331 memory: 5857 loss_kpt: 0.000846 acc_pose: 0.680651 loss: 0.000846 2022/10/13 15:19:39 - mmengine - INFO - Epoch(train) [151][100/293] lr: 5.000000e-04 eta: 1:34:36 time: 0.356959 data_time: 0.069698 memory: 5857 loss_kpt: 0.000873 acc_pose: 0.752019 loss: 0.000873 2022/10/13 15:19:57 - mmengine - INFO - Epoch(train) [151][150/293] lr: 5.000000e-04 eta: 1:34:20 time: 0.357535 data_time: 0.075494 memory: 5857 loss_kpt: 0.000858 acc_pose: 0.763617 loss: 0.000858 2022/10/13 15:20:16 - mmengine - INFO - Epoch(train) [151][200/293] lr: 5.000000e-04 eta: 1:34:05 time: 0.376766 data_time: 0.077274 memory: 5857 loss_kpt: 0.000860 acc_pose: 0.688988 loss: 0.000860 2022/10/13 15:20:34 - mmengine - INFO - Epoch(train) [151][250/293] lr: 5.000000e-04 eta: 1:33:50 time: 0.370757 data_time: 0.078686 memory: 5857 loss_kpt: 0.000878 acc_pose: 0.773841 loss: 0.000878 2022/10/13 15:20:50 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:21:12 - mmengine - INFO - Epoch(train) [152][50/293] lr: 5.000000e-04 eta: 1:33:16 time: 0.442673 data_time: 0.132681 memory: 5857 loss_kpt: 0.000874 acc_pose: 0.761163 loss: 0.000874 2022/10/13 15:21:32 - mmengine - INFO - Epoch(train) [152][100/293] lr: 5.000000e-04 eta: 1:33:02 time: 0.403898 data_time: 0.111102 memory: 5857 loss_kpt: 0.000856 acc_pose: 0.713833 loss: 0.000856 2022/10/13 15:21:51 - mmengine - INFO - Epoch(train) [152][150/293] lr: 5.000000e-04 eta: 1:32:47 time: 0.378761 data_time: 0.100100 memory: 5857 loss_kpt: 0.000857 acc_pose: 0.800922 loss: 0.000857 2022/10/13 15:22:11 - mmengine - INFO - Epoch(train) [152][200/293] lr: 5.000000e-04 eta: 1:32:32 time: 0.389706 data_time: 0.074516 memory: 5857 loss_kpt: 0.000848 acc_pose: 0.697417 loss: 0.000848 2022/10/13 15:22:30 - mmengine - INFO - Epoch(train) [152][250/293] lr: 5.000000e-04 eta: 1:32:16 time: 0.388267 data_time: 0.069178 memory: 5857 loss_kpt: 0.000859 acc_pose: 0.743819 loss: 0.000859 2022/10/13 15:22:47 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:23:08 - mmengine - INFO - Epoch(train) [153][50/293] lr: 5.000000e-04 eta: 1:31:43 time: 0.413088 data_time: 0.119623 memory: 5857 loss_kpt: 0.000860 acc_pose: 0.781420 loss: 0.000860 2022/10/13 15:23:27 - mmengine - INFO - Epoch(train) [153][100/293] lr: 5.000000e-04 eta: 1:31:28 time: 0.387029 data_time: 0.079322 memory: 5857 loss_kpt: 0.000852 acc_pose: 0.789558 loss: 0.000852 2022/10/13 15:23:46 - mmengine - INFO - Epoch(train) [153][150/293] lr: 5.000000e-04 eta: 1:31:12 time: 0.369660 data_time: 0.075384 memory: 5857 loss_kpt: 0.000848 acc_pose: 0.726286 loss: 0.000848 2022/10/13 15:24:05 - mmengine - INFO - Epoch(train) [153][200/293] lr: 5.000000e-04 eta: 1:30:57 time: 0.391045 data_time: 0.087440 memory: 5857 loss_kpt: 0.000863 acc_pose: 0.723650 loss: 0.000863 2022/10/13 15:24:24 - mmengine - INFO - Epoch(train) [153][250/293] lr: 5.000000e-04 eta: 1:30:42 time: 0.379567 data_time: 0.081475 memory: 5857 loss_kpt: 0.000871 acc_pose: 0.744112 loss: 0.000871 2022/10/13 15:24:40 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:24:59 - mmengine - INFO - Epoch(train) [154][50/293] lr: 5.000000e-04 eta: 1:30:08 time: 0.387096 data_time: 0.090688 memory: 5857 loss_kpt: 0.000859 acc_pose: 0.728645 loss: 0.000859 2022/10/13 15:25:17 - mmengine - INFO - Epoch(train) [154][100/293] lr: 5.000000e-04 eta: 1:29:52 time: 0.363427 data_time: 0.079651 memory: 5857 loss_kpt: 0.000870 acc_pose: 0.731424 loss: 0.000870 2022/10/13 15:25:36 - mmengine - INFO - Epoch(train) [154][150/293] lr: 5.000000e-04 eta: 1:29:37 time: 0.371828 data_time: 0.090109 memory: 5857 loss_kpt: 0.000868 acc_pose: 0.726087 loss: 0.000868 2022/10/13 15:25:44 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:25:55 - mmengine - INFO - Epoch(train) [154][200/293] lr: 5.000000e-04 eta: 1:29:21 time: 0.371054 data_time: 0.071131 memory: 5857 loss_kpt: 0.000883 acc_pose: 0.683657 loss: 0.000883 2022/10/13 15:26:13 - mmengine - INFO - Epoch(train) [154][250/293] lr: 5.000000e-04 eta: 1:29:06 time: 0.365276 data_time: 0.070005 memory: 5857 loss_kpt: 0.000863 acc_pose: 0.703100 loss: 0.000863 2022/10/13 15:26:29 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:26:48 - mmengine - INFO - Epoch(train) [155][50/293] lr: 5.000000e-04 eta: 1:28:31 time: 0.374839 data_time: 0.092875 memory: 5857 loss_kpt: 0.000871 acc_pose: 0.647799 loss: 0.000871 2022/10/13 15:27:07 - mmengine - INFO - Epoch(train) [155][100/293] lr: 5.000000e-04 eta: 1:28:16 time: 0.381512 data_time: 0.074943 memory: 5857 loss_kpt: 0.000854 acc_pose: 0.744103 loss: 0.000854 2022/10/13 15:27:26 - mmengine - INFO - Epoch(train) [155][150/293] lr: 5.000000e-04 eta: 1:28:01 time: 0.373209 data_time: 0.090757 memory: 5857 loss_kpt: 0.000857 acc_pose: 0.740618 loss: 0.000857 2022/10/13 15:27:43 - mmengine - INFO - Epoch(train) [155][200/293] lr: 5.000000e-04 eta: 1:27:45 time: 0.356796 data_time: 0.072432 memory: 5857 loss_kpt: 0.000849 acc_pose: 0.701676 loss: 0.000849 2022/10/13 15:28:02 - mmengine - INFO - Epoch(train) [155][250/293] lr: 5.000000e-04 eta: 1:27:30 time: 0.361173 data_time: 0.068110 memory: 5857 loss_kpt: 0.000848 acc_pose: 0.737638 loss: 0.000848 2022/10/13 15:28:17 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:28:36 - mmengine - INFO - Epoch(train) [156][50/293] lr: 5.000000e-04 eta: 1:26:55 time: 0.374191 data_time: 0.087434 memory: 5857 loss_kpt: 0.000851 acc_pose: 0.756474 loss: 0.000851 2022/10/13 15:28:54 - mmengine - INFO - Epoch(train) [156][100/293] lr: 5.000000e-04 eta: 1:26:40 time: 0.365726 data_time: 0.070367 memory: 5857 loss_kpt: 0.000876 acc_pose: 0.716312 loss: 0.000876 2022/10/13 15:29:12 - mmengine - INFO - Epoch(train) [156][150/293] lr: 5.000000e-04 eta: 1:26:24 time: 0.363801 data_time: 0.071596 memory: 5857 loss_kpt: 0.000867 acc_pose: 0.727601 loss: 0.000867 2022/10/13 15:29:31 - mmengine - INFO - Epoch(train) [156][200/293] lr: 5.000000e-04 eta: 1:26:09 time: 0.363323 data_time: 0.080843 memory: 5857 loss_kpt: 0.000883 acc_pose: 0.680882 loss: 0.000883 2022/10/13 15:29:49 - mmengine - INFO - Epoch(train) [156][250/293] lr: 5.000000e-04 eta: 1:25:53 time: 0.368173 data_time: 0.076653 memory: 5857 loss_kpt: 0.000859 acc_pose: 0.790285 loss: 0.000859 2022/10/13 15:30:05 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:30:24 - mmengine - INFO - Epoch(train) [157][50/293] lr: 5.000000e-04 eta: 1:25:19 time: 0.387277 data_time: 0.090515 memory: 5857 loss_kpt: 0.000868 acc_pose: 0.701560 loss: 0.000868 2022/10/13 15:30:42 - mmengine - INFO - Epoch(train) [157][100/293] lr: 5.000000e-04 eta: 1:25:04 time: 0.356808 data_time: 0.072353 memory: 5857 loss_kpt: 0.000861 acc_pose: 0.752674 loss: 0.000861 2022/10/13 15:31:01 - mmengine - INFO - Epoch(train) [157][150/293] lr: 5.000000e-04 eta: 1:24:48 time: 0.367584 data_time: 0.068152 memory: 5857 loss_kpt: 0.000855 acc_pose: 0.749312 loss: 0.000855 2022/10/13 15:31:19 - mmengine - INFO - Epoch(train) [157][200/293] lr: 5.000000e-04 eta: 1:24:32 time: 0.358931 data_time: 0.070358 memory: 5857 loss_kpt: 0.000860 acc_pose: 0.731650 loss: 0.000860 2022/10/13 15:31:37 - mmengine - INFO - Epoch(train) [157][250/293] lr: 5.000000e-04 eta: 1:24:17 time: 0.364277 data_time: 0.079204 memory: 5857 loss_kpt: 0.000850 acc_pose: 0.762520 loss: 0.000850 2022/10/13 15:31:52 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:31:52 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:32:11 - mmengine - INFO - Epoch(train) [158][50/293] lr: 5.000000e-04 eta: 1:23:43 time: 0.380034 data_time: 0.090519 memory: 5857 loss_kpt: 0.000853 acc_pose: 0.705253 loss: 0.000853 2022/10/13 15:32:29 - mmengine - INFO - Epoch(train) [158][100/293] lr: 5.000000e-04 eta: 1:23:27 time: 0.368887 data_time: 0.069353 memory: 5857 loss_kpt: 0.000854 acc_pose: 0.665274 loss: 0.000854 2022/10/13 15:32:48 - mmengine - INFO - Epoch(train) [158][150/293] lr: 5.000000e-04 eta: 1:23:12 time: 0.363582 data_time: 0.071207 memory: 5857 loss_kpt: 0.000873 acc_pose: 0.732458 loss: 0.000873 2022/10/13 15:33:07 - mmengine - INFO - Epoch(train) [158][200/293] lr: 5.000000e-04 eta: 1:22:57 time: 0.381473 data_time: 0.086424 memory: 5857 loss_kpt: 0.000848 acc_pose: 0.700453 loss: 0.000848 2022/10/13 15:33:26 - mmengine - INFO - Epoch(train) [158][250/293] lr: 5.000000e-04 eta: 1:22:41 time: 0.378570 data_time: 0.075414 memory: 5857 loss_kpt: 0.000867 acc_pose: 0.717267 loss: 0.000867 2022/10/13 15:33:41 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:34:00 - mmengine - INFO - Epoch(train) [159][50/293] lr: 5.000000e-04 eta: 1:22:07 time: 0.375134 data_time: 0.087254 memory: 5857 loss_kpt: 0.000843 acc_pose: 0.781491 loss: 0.000843 2022/10/13 15:34:18 - mmengine - INFO - Epoch(train) [159][100/293] lr: 5.000000e-04 eta: 1:21:52 time: 0.373922 data_time: 0.082712 memory: 5857 loss_kpt: 0.000875 acc_pose: 0.710386 loss: 0.000875 2022/10/13 15:34:37 - mmengine - INFO - Epoch(train) [159][150/293] lr: 5.000000e-04 eta: 1:21:36 time: 0.377503 data_time: 0.080248 memory: 5857 loss_kpt: 0.000852 acc_pose: 0.734899 loss: 0.000852 2022/10/13 15:34:55 - mmengine - INFO - Epoch(train) [159][200/293] lr: 5.000000e-04 eta: 1:21:21 time: 0.358974 data_time: 0.074040 memory: 5857 loss_kpt: 0.000842 acc_pose: 0.699707 loss: 0.000842 2022/10/13 15:35:14 - mmengine - INFO - Epoch(train) [159][250/293] lr: 5.000000e-04 eta: 1:21:05 time: 0.377842 data_time: 0.072385 memory: 5857 loss_kpt: 0.000862 acc_pose: 0.720518 loss: 0.000862 2022/10/13 15:35:29 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:35:48 - mmengine - INFO - Epoch(train) [160][50/293] lr: 5.000000e-04 eta: 1:20:32 time: 0.376843 data_time: 0.109683 memory: 5857 loss_kpt: 0.000856 acc_pose: 0.734735 loss: 0.000856 2022/10/13 15:36:06 - mmengine - INFO - Epoch(train) [160][100/293] lr: 5.000000e-04 eta: 1:20:16 time: 0.367285 data_time: 0.096523 memory: 5857 loss_kpt: 0.000851 acc_pose: 0.767204 loss: 0.000851 2022/10/13 15:36:24 - mmengine - INFO - Epoch(train) [160][150/293] lr: 5.000000e-04 eta: 1:20:00 time: 0.357885 data_time: 0.081394 memory: 5857 loss_kpt: 0.000872 acc_pose: 0.696421 loss: 0.000872 2022/10/13 15:36:43 - mmengine - INFO - Epoch(train) [160][200/293] lr: 5.000000e-04 eta: 1:19:45 time: 0.372494 data_time: 0.078687 memory: 5857 loss_kpt: 0.000865 acc_pose: 0.779747 loss: 0.000865 2022/10/13 15:37:01 - mmengine - INFO - Epoch(train) [160][250/293] lr: 5.000000e-04 eta: 1:19:29 time: 0.356509 data_time: 0.080400 memory: 5857 loss_kpt: 0.000851 acc_pose: 0.756931 loss: 0.000851 2022/10/13 15:37:16 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:37:16 - mmengine - INFO - Saving checkpoint at 160 epochs 2022/10/13 15:37:24 - mmengine - INFO - Epoch(val) [160][50/407] eta: 0:00:44 time: 0.125225 data_time: 0.070463 memory: 5857 2022/10/13 15:37:30 - mmengine - INFO - Epoch(val) [160][100/407] eta: 0:00:37 time: 0.122749 data_time: 0.067624 memory: 760 2022/10/13 15:37:36 - mmengine - INFO - Epoch(val) [160][150/407] eta: 0:00:31 time: 0.123761 data_time: 0.064659 memory: 760 2022/10/13 15:37:42 - mmengine - INFO - Epoch(val) [160][200/407] eta: 0:00:25 time: 0.121648 data_time: 0.065509 memory: 760 2022/10/13 15:37:48 - mmengine - INFO - Epoch(val) [160][250/407] eta: 0:00:18 time: 0.119267 data_time: 0.065020 memory: 760 2022/10/13 15:37:55 - mmengine - INFO - Epoch(val) [160][300/407] eta: 0:00:13 time: 0.124797 data_time: 0.070013 memory: 760 2022/10/13 15:38:01 - mmengine - INFO - Epoch(val) [160][350/407] eta: 0:00:07 time: 0.126037 data_time: 0.070776 memory: 760 2022/10/13 15:38:07 - mmengine - INFO - Epoch(val) [160][400/407] eta: 0:00:00 time: 0.118391 data_time: 0.062156 memory: 760 2022/10/13 15:38:46 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 15:39:00 - mmengine - INFO - Epoch(val) [160][407/407] coco/AP: 0.611740 coco/AP .5: 0.852104 coco/AP .75: 0.677008 coco/AP (M): 0.569865 coco/AP (L): 0.682685 coco/AR: 0.671647 coco/AR .5: 0.894994 coco/AR .75: 0.733785 coco/AR (M): 0.622043 coco/AR (L): 0.740951 2022/10/13 15:39:00 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384/best_coco/AP_epoch_150.pth is removed 2022/10/13 15:39:02 - mmengine - INFO - The best checkpoint with 0.6117 coco/AP at 160 epoch is saved to best_coco/AP_epoch_160.pth. 2022/10/13 15:39:21 - mmengine - INFO - Epoch(train) [161][50/293] lr: 5.000000e-04 eta: 1:18:55 time: 0.374794 data_time: 0.110499 memory: 5857 loss_kpt: 0.000849 acc_pose: 0.722385 loss: 0.000849 2022/10/13 15:39:39 - mmengine - INFO - Epoch(train) [161][100/293] lr: 5.000000e-04 eta: 1:18:40 time: 0.360870 data_time: 0.115325 memory: 5857 loss_kpt: 0.000857 acc_pose: 0.757173 loss: 0.000857 2022/10/13 15:39:46 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:39:57 - mmengine - INFO - Epoch(train) [161][150/293] lr: 5.000000e-04 eta: 1:18:24 time: 0.361358 data_time: 0.075150 memory: 5857 loss_kpt: 0.000859 acc_pose: 0.728703 loss: 0.000859 2022/10/13 15:40:16 - mmengine - INFO - Epoch(train) [161][200/293] lr: 5.000000e-04 eta: 1:18:09 time: 0.372721 data_time: 0.075174 memory: 5857 loss_kpt: 0.000851 acc_pose: 0.759989 loss: 0.000851 2022/10/13 15:40:34 - mmengine - INFO - Epoch(train) [161][250/293] lr: 5.000000e-04 eta: 1:17:53 time: 0.364274 data_time: 0.070947 memory: 5857 loss_kpt: 0.000850 acc_pose: 0.705988 loss: 0.000850 2022/10/13 15:40:49 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:41:08 - mmengine - INFO - Epoch(train) [162][50/293] lr: 5.000000e-04 eta: 1:17:19 time: 0.374178 data_time: 0.092465 memory: 5857 loss_kpt: 0.000859 acc_pose: 0.725927 loss: 0.000859 2022/10/13 15:41:26 - mmengine - INFO - Epoch(train) [162][100/293] lr: 5.000000e-04 eta: 1:17:04 time: 0.366492 data_time: 0.084692 memory: 5857 loss_kpt: 0.000866 acc_pose: 0.737319 loss: 0.000866 2022/10/13 15:41:44 - mmengine - INFO - Epoch(train) [162][150/293] lr: 5.000000e-04 eta: 1:16:48 time: 0.366280 data_time: 0.071822 memory: 5857 loss_kpt: 0.000839 acc_pose: 0.720279 loss: 0.000839 2022/10/13 15:42:03 - mmengine - INFO - Epoch(train) [162][200/293] lr: 5.000000e-04 eta: 1:16:33 time: 0.369154 data_time: 0.080336 memory: 5857 loss_kpt: 0.000860 acc_pose: 0.768942 loss: 0.000860 2022/10/13 15:42:21 - mmengine - INFO - Epoch(train) [162][250/293] lr: 5.000000e-04 eta: 1:16:17 time: 0.357196 data_time: 0.075065 memory: 5857 loss_kpt: 0.000858 acc_pose: 0.791845 loss: 0.000858 2022/10/13 15:42:36 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:42:55 - mmengine - INFO - Epoch(train) [163][50/293] lr: 5.000000e-04 eta: 1:15:44 time: 0.388946 data_time: 0.090498 memory: 5857 loss_kpt: 0.000859 acc_pose: 0.754928 loss: 0.000859 2022/10/13 15:43:13 - mmengine - INFO - Epoch(train) [163][100/293] lr: 5.000000e-04 eta: 1:15:28 time: 0.355533 data_time: 0.077441 memory: 5857 loss_kpt: 0.000848 acc_pose: 0.734606 loss: 0.000848 2022/10/13 15:43:31 - mmengine - INFO - Epoch(train) [163][150/293] lr: 5.000000e-04 eta: 1:15:12 time: 0.359494 data_time: 0.073326 memory: 5857 loss_kpt: 0.000842 acc_pose: 0.691563 loss: 0.000842 2022/10/13 15:43:50 - mmengine - INFO - Epoch(train) [163][200/293] lr: 5.000000e-04 eta: 1:14:57 time: 0.369822 data_time: 0.075431 memory: 5857 loss_kpt: 0.000871 acc_pose: 0.707232 loss: 0.000871 2022/10/13 15:44:08 - mmengine - INFO - Epoch(train) [163][250/293] lr: 5.000000e-04 eta: 1:14:41 time: 0.360489 data_time: 0.074395 memory: 5857 loss_kpt: 0.000869 acc_pose: 0.752570 loss: 0.000869 2022/10/13 15:44:23 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:44:42 - mmengine - INFO - Epoch(train) [164][50/293] lr: 5.000000e-04 eta: 1:14:07 time: 0.376559 data_time: 0.090751 memory: 5857 loss_kpt: 0.000841 acc_pose: 0.685536 loss: 0.000841 2022/10/13 15:45:00 - mmengine - INFO - Epoch(train) [164][100/293] lr: 5.000000e-04 eta: 1:13:52 time: 0.364361 data_time: 0.078159 memory: 5857 loss_kpt: 0.000866 acc_pose: 0.758919 loss: 0.000866 2022/10/13 15:45:19 - mmengine - INFO - Epoch(train) [164][150/293] lr: 5.000000e-04 eta: 1:13:36 time: 0.372816 data_time: 0.077942 memory: 5857 loss_kpt: 0.000867 acc_pose: 0.629346 loss: 0.000867 2022/10/13 15:45:37 - mmengine - INFO - Epoch(train) [164][200/293] lr: 5.000000e-04 eta: 1:13:21 time: 0.361197 data_time: 0.077999 memory: 5857 loss_kpt: 0.000872 acc_pose: 0.753675 loss: 0.000872 2022/10/13 15:45:52 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:45:55 - mmengine - INFO - Epoch(train) [164][250/293] lr: 5.000000e-04 eta: 1:13:05 time: 0.364093 data_time: 0.089431 memory: 5857 loss_kpt: 0.000855 acc_pose: 0.769846 loss: 0.000855 2022/10/13 15:46:10 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:46:29 - mmengine - INFO - Epoch(train) [165][50/293] lr: 5.000000e-04 eta: 1:12:32 time: 0.387862 data_time: 0.092090 memory: 5857 loss_kpt: 0.000860 acc_pose: 0.759544 loss: 0.000860 2022/10/13 15:46:48 - mmengine - INFO - Epoch(train) [165][100/293] lr: 5.000000e-04 eta: 1:12:16 time: 0.375856 data_time: 0.081481 memory: 5857 loss_kpt: 0.000853 acc_pose: 0.810283 loss: 0.000853 2022/10/13 15:47:06 - mmengine - INFO - Epoch(train) [165][150/293] lr: 5.000000e-04 eta: 1:12:01 time: 0.360156 data_time: 0.075390 memory: 5857 loss_kpt: 0.000845 acc_pose: 0.721462 loss: 0.000845 2022/10/13 15:47:24 - mmengine - INFO - Epoch(train) [165][200/293] lr: 5.000000e-04 eta: 1:11:45 time: 0.359210 data_time: 0.070668 memory: 5857 loss_kpt: 0.000864 acc_pose: 0.699985 loss: 0.000864 2022/10/13 15:47:43 - mmengine - INFO - Epoch(train) [165][250/293] lr: 5.000000e-04 eta: 1:11:29 time: 0.365610 data_time: 0.098969 memory: 5857 loss_kpt: 0.000871 acc_pose: 0.710674 loss: 0.000871 2022/10/13 15:47:58 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:48:18 - mmengine - INFO - Epoch(train) [166][50/293] lr: 5.000000e-04 eta: 1:10:56 time: 0.397495 data_time: 0.099512 memory: 5857 loss_kpt: 0.000867 acc_pose: 0.716614 loss: 0.000867 2022/10/13 15:48:36 - mmengine - INFO - Epoch(train) [166][100/293] lr: 5.000000e-04 eta: 1:10:41 time: 0.361526 data_time: 0.077186 memory: 5857 loss_kpt: 0.000853 acc_pose: 0.743582 loss: 0.000853 2022/10/13 15:48:54 - mmengine - INFO - Epoch(train) [166][150/293] lr: 5.000000e-04 eta: 1:10:25 time: 0.357097 data_time: 0.068878 memory: 5857 loss_kpt: 0.000841 acc_pose: 0.770429 loss: 0.000841 2022/10/13 15:49:11 - mmengine - INFO - Epoch(train) [166][200/293] lr: 5.000000e-04 eta: 1:10:09 time: 0.350264 data_time: 0.065210 memory: 5857 loss_kpt: 0.000845 acc_pose: 0.703015 loss: 0.000845 2022/10/13 15:49:29 - mmengine - INFO - Epoch(train) [166][250/293] lr: 5.000000e-04 eta: 1:09:53 time: 0.351975 data_time: 0.080407 memory: 5857 loss_kpt: 0.000859 acc_pose: 0.668016 loss: 0.000859 2022/10/13 15:49:44 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:50:03 - mmengine - INFO - Epoch(train) [167][50/293] lr: 5.000000e-04 eta: 1:09:20 time: 0.383398 data_time: 0.091432 memory: 5857 loss_kpt: 0.000865 acc_pose: 0.727476 loss: 0.000865 2022/10/13 15:50:21 - mmengine - INFO - Epoch(train) [167][100/293] lr: 5.000000e-04 eta: 1:09:04 time: 0.363514 data_time: 0.075001 memory: 5857 loss_kpt: 0.000856 acc_pose: 0.636879 loss: 0.000856 2022/10/13 15:50:40 - mmengine - INFO - Epoch(train) [167][150/293] lr: 5.000000e-04 eta: 1:08:49 time: 0.363946 data_time: 0.081764 memory: 5857 loss_kpt: 0.000851 acc_pose: 0.725431 loss: 0.000851 2022/10/13 15:50:58 - mmengine - INFO - Epoch(train) [167][200/293] lr: 5.000000e-04 eta: 1:08:33 time: 0.358869 data_time: 0.075144 memory: 5857 loss_kpt: 0.000851 acc_pose: 0.750915 loss: 0.000851 2022/10/13 15:51:16 - mmengine - INFO - Epoch(train) [167][250/293] lr: 5.000000e-04 eta: 1:08:17 time: 0.360213 data_time: 0.086773 memory: 5857 loss_kpt: 0.000853 acc_pose: 0.719265 loss: 0.000853 2022/10/13 15:51:31 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:51:50 - mmengine - INFO - Epoch(train) [168][50/293] lr: 5.000000e-04 eta: 1:07:44 time: 0.382041 data_time: 0.087393 memory: 5857 loss_kpt: 0.000858 acc_pose: 0.683203 loss: 0.000858 2022/10/13 15:51:57 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:52:09 - mmengine - INFO - Epoch(train) [168][100/293] lr: 5.000000e-04 eta: 1:07:29 time: 0.365894 data_time: 0.073477 memory: 5857 loss_kpt: 0.000859 acc_pose: 0.777236 loss: 0.000859 2022/10/13 15:52:27 - mmengine - INFO - Epoch(train) [168][150/293] lr: 5.000000e-04 eta: 1:07:13 time: 0.367786 data_time: 0.073356 memory: 5857 loss_kpt: 0.000851 acc_pose: 0.655413 loss: 0.000851 2022/10/13 15:52:45 - mmengine - INFO - Epoch(train) [168][200/293] lr: 5.000000e-04 eta: 1:06:57 time: 0.363344 data_time: 0.067073 memory: 5857 loss_kpt: 0.000854 acc_pose: 0.742816 loss: 0.000854 2022/10/13 15:53:03 - mmengine - INFO - Epoch(train) [168][250/293] lr: 5.000000e-04 eta: 1:06:42 time: 0.363545 data_time: 0.071212 memory: 5857 loss_kpt: 0.000840 acc_pose: 0.728744 loss: 0.000840 2022/10/13 15:53:19 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:53:38 - mmengine - INFO - Epoch(train) [169][50/293] lr: 5.000000e-04 eta: 1:06:09 time: 0.384941 data_time: 0.091006 memory: 5857 loss_kpt: 0.000866 acc_pose: 0.750742 loss: 0.000866 2022/10/13 15:53:56 - mmengine - INFO - Epoch(train) [169][100/293] lr: 5.000000e-04 eta: 1:05:53 time: 0.366806 data_time: 0.081991 memory: 5857 loss_kpt: 0.000850 acc_pose: 0.708682 loss: 0.000850 2022/10/13 15:54:14 - mmengine - INFO - Epoch(train) [169][150/293] lr: 5.000000e-04 eta: 1:05:37 time: 0.353740 data_time: 0.085915 memory: 5857 loss_kpt: 0.000852 acc_pose: 0.764445 loss: 0.000852 2022/10/13 15:54:33 - mmengine - INFO - Epoch(train) [169][200/293] lr: 5.000000e-04 eta: 1:05:22 time: 0.374440 data_time: 0.090683 memory: 5857 loss_kpt: 0.000845 acc_pose: 0.723648 loss: 0.000845 2022/10/13 15:54:51 - mmengine - INFO - Epoch(train) [169][250/293] lr: 5.000000e-04 eta: 1:05:06 time: 0.365954 data_time: 0.079675 memory: 5857 loss_kpt: 0.000851 acc_pose: 0.763019 loss: 0.000851 2022/10/13 15:55:07 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:55:26 - mmengine - INFO - Epoch(train) [170][50/293] lr: 5.000000e-04 eta: 1:04:33 time: 0.386611 data_time: 0.101509 memory: 5857 loss_kpt: 0.000846 acc_pose: 0.724865 loss: 0.000846 2022/10/13 15:55:45 - mmengine - INFO - Epoch(train) [170][100/293] lr: 5.000000e-04 eta: 1:04:17 time: 0.365770 data_time: 0.072592 memory: 5857 loss_kpt: 0.000850 acc_pose: 0.715187 loss: 0.000850 2022/10/13 15:56:03 - mmengine - INFO - Epoch(train) [170][150/293] lr: 5.000000e-04 eta: 1:04:02 time: 0.356543 data_time: 0.070779 memory: 5857 loss_kpt: 0.000860 acc_pose: 0.674850 loss: 0.000860 2022/10/13 15:56:21 - mmengine - INFO - Epoch(train) [170][200/293] lr: 5.000000e-04 eta: 1:03:46 time: 0.367133 data_time: 0.075441 memory: 5857 loss_kpt: 0.000852 acc_pose: 0.759604 loss: 0.000852 2022/10/13 15:56:39 - mmengine - INFO - Epoch(train) [170][250/293] lr: 5.000000e-04 eta: 1:03:30 time: 0.357526 data_time: 0.087447 memory: 5857 loss_kpt: 0.000869 acc_pose: 0.756607 loss: 0.000869 2022/10/13 15:56:54 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:56:54 - mmengine - INFO - Saving checkpoint at 170 epochs 2022/10/13 15:57:03 - mmengine - INFO - Epoch(val) [170][50/407] eta: 0:00:45 time: 0.127890 data_time: 0.072072 memory: 5857 2022/10/13 15:57:09 - mmengine - INFO - Epoch(val) [170][100/407] eta: 0:00:39 time: 0.128499 data_time: 0.073296 memory: 760 2022/10/13 15:57:15 - mmengine - INFO - Epoch(val) [170][150/407] eta: 0:00:31 time: 0.121724 data_time: 0.064560 memory: 760 2022/10/13 15:57:21 - mmengine - INFO - Epoch(val) [170][200/407] eta: 0:00:26 time: 0.125612 data_time: 0.069848 memory: 760 2022/10/13 15:57:28 - mmengine - INFO - Epoch(val) [170][250/407] eta: 0:00:20 time: 0.127521 data_time: 0.071555 memory: 760 2022/10/13 15:57:34 - mmengine - INFO - Epoch(val) [170][300/407] eta: 0:00:13 time: 0.123629 data_time: 0.067495 memory: 760 2022/10/13 15:57:40 - mmengine - INFO - Epoch(val) [170][350/407] eta: 0:00:07 time: 0.126543 data_time: 0.071530 memory: 760 2022/10/13 15:57:46 - mmengine - INFO - Epoch(val) [170][400/407] eta: 0:00:00 time: 0.118861 data_time: 0.065319 memory: 760 2022/10/13 15:58:24 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 15:58:39 - mmengine - INFO - Epoch(val) [170][407/407] coco/AP: 0.614340 coco/AP .5: 0.854493 coco/AP .75: 0.680159 coco/AP (M): 0.574201 coco/AP (L): 0.685381 coco/AR: 0.675850 coco/AR .5: 0.896568 coco/AR .75: 0.740082 coco/AR (M): 0.625977 coco/AR (L): 0.746525 2022/10/13 15:58:39 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384/best_coco/AP_epoch_160.pth is removed 2022/10/13 15:58:41 - mmengine - INFO - The best checkpoint with 0.6143 coco/AP at 170 epoch is saved to best_coco/AP_epoch_170.pth. 2022/10/13 15:59:00 - mmengine - INFO - Epoch(train) [171][50/293] lr: 5.000000e-05 eta: 1:02:58 time: 0.391947 data_time: 0.097259 memory: 5857 loss_kpt: 0.000837 acc_pose: 0.756735 loss: 0.000837 2022/10/13 15:59:18 - mmengine - INFO - Epoch(train) [171][100/293] lr: 5.000000e-05 eta: 1:02:42 time: 0.356520 data_time: 0.079497 memory: 5857 loss_kpt: 0.000843 acc_pose: 0.726527 loss: 0.000843 2022/10/13 15:59:37 - mmengine - INFO - Epoch(train) [171][150/293] lr: 5.000000e-05 eta: 1:02:26 time: 0.368967 data_time: 0.071314 memory: 5857 loss_kpt: 0.000845 acc_pose: 0.703507 loss: 0.000845 2022/10/13 15:59:51 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 15:59:55 - mmengine - INFO - Epoch(train) [171][200/293] lr: 5.000000e-05 eta: 1:02:10 time: 0.360868 data_time: 0.069504 memory: 5857 loss_kpt: 0.000848 acc_pose: 0.685170 loss: 0.000848 2022/10/13 16:00:13 - mmengine - INFO - Epoch(train) [171][250/293] lr: 5.000000e-05 eta: 1:01:55 time: 0.364428 data_time: 0.067110 memory: 5857 loss_kpt: 0.000837 acc_pose: 0.670370 loss: 0.000837 2022/10/13 16:00:28 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:00:48 - mmengine - INFO - Epoch(train) [172][50/293] lr: 5.000000e-05 eta: 1:01:22 time: 0.392397 data_time: 0.104235 memory: 5857 loss_kpt: 0.000838 acc_pose: 0.777448 loss: 0.000838 2022/10/13 16:01:06 - mmengine - INFO - Epoch(train) [172][100/293] lr: 5.000000e-05 eta: 1:01:06 time: 0.367787 data_time: 0.075033 memory: 5857 loss_kpt: 0.000850 acc_pose: 0.634167 loss: 0.000850 2022/10/13 16:01:25 - mmengine - INFO - Epoch(train) [172][150/293] lr: 5.000000e-05 eta: 1:00:51 time: 0.360987 data_time: 0.076031 memory: 5857 loss_kpt: 0.000835 acc_pose: 0.736065 loss: 0.000835 2022/10/13 16:01:43 - mmengine - INFO - Epoch(train) [172][200/293] lr: 5.000000e-05 eta: 1:00:35 time: 0.369534 data_time: 0.085875 memory: 5857 loss_kpt: 0.000836 acc_pose: 0.765594 loss: 0.000836 2022/10/13 16:02:02 - mmengine - INFO - Epoch(train) [172][250/293] lr: 5.000000e-05 eta: 1:00:19 time: 0.371654 data_time: 0.074396 memory: 5857 loss_kpt: 0.000834 acc_pose: 0.737325 loss: 0.000834 2022/10/13 16:02:17 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:02:36 - mmengine - INFO - Epoch(train) [173][50/293] lr: 5.000000e-05 eta: 0:59:47 time: 0.373144 data_time: 0.091186 memory: 5857 loss_kpt: 0.000850 acc_pose: 0.730094 loss: 0.000850 2022/10/13 16:02:54 - mmengine - INFO - Epoch(train) [173][100/293] lr: 5.000000e-05 eta: 0:59:31 time: 0.367002 data_time: 0.070492 memory: 5857 loss_kpt: 0.000838 acc_pose: 0.677629 loss: 0.000838 2022/10/13 16:03:13 - mmengine - INFO - Epoch(train) [173][150/293] lr: 5.000000e-05 eta: 0:59:15 time: 0.377899 data_time: 0.078930 memory: 5857 loss_kpt: 0.000843 acc_pose: 0.694743 loss: 0.000843 2022/10/13 16:03:31 - mmengine - INFO - Epoch(train) [173][200/293] lr: 5.000000e-05 eta: 0:59:00 time: 0.362345 data_time: 0.076238 memory: 5857 loss_kpt: 0.000841 acc_pose: 0.753036 loss: 0.000841 2022/10/13 16:03:49 - mmengine - INFO - Epoch(train) [173][250/293] lr: 5.000000e-05 eta: 0:58:44 time: 0.362886 data_time: 0.076665 memory: 5857 loss_kpt: 0.000832 acc_pose: 0.771591 loss: 0.000832 2022/10/13 16:04:04 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:04:23 - mmengine - INFO - Epoch(train) [174][50/293] lr: 5.000000e-05 eta: 0:58:11 time: 0.389605 data_time: 0.093333 memory: 5857 loss_kpt: 0.000831 acc_pose: 0.726589 loss: 0.000831 2022/10/13 16:04:42 - mmengine - INFO - Epoch(train) [174][100/293] lr: 5.000000e-05 eta: 0:57:56 time: 0.367140 data_time: 0.080515 memory: 5857 loss_kpt: 0.000830 acc_pose: 0.771258 loss: 0.000830 2022/10/13 16:05:00 - mmengine - INFO - Epoch(train) [174][150/293] lr: 5.000000e-05 eta: 0:57:40 time: 0.366637 data_time: 0.073817 memory: 5857 loss_kpt: 0.000825 acc_pose: 0.720235 loss: 0.000825 2022/10/13 16:05:19 - mmengine - INFO - Epoch(train) [174][200/293] lr: 5.000000e-05 eta: 0:57:24 time: 0.371429 data_time: 0.079070 memory: 5857 loss_kpt: 0.000834 acc_pose: 0.724174 loss: 0.000834 2022/10/13 16:05:37 - mmengine - INFO - Epoch(train) [174][250/293] lr: 5.000000e-05 eta: 0:57:09 time: 0.360068 data_time: 0.080787 memory: 5857 loss_kpt: 0.000827 acc_pose: 0.738667 loss: 0.000827 2022/10/13 16:05:52 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:05:59 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:06:11 - mmengine - INFO - Epoch(train) [175][50/293] lr: 5.000000e-05 eta: 0:56:36 time: 0.381469 data_time: 0.087431 memory: 5857 loss_kpt: 0.000841 acc_pose: 0.779449 loss: 0.000841 2022/10/13 16:06:29 - mmengine - INFO - Epoch(train) [175][100/293] lr: 5.000000e-05 eta: 0:56:20 time: 0.366370 data_time: 0.067608 memory: 5857 loss_kpt: 0.000830 acc_pose: 0.758893 loss: 0.000830 2022/10/13 16:06:47 - mmengine - INFO - Epoch(train) [175][150/293] lr: 5.000000e-05 eta: 0:56:05 time: 0.361146 data_time: 0.071613 memory: 5857 loss_kpt: 0.000837 acc_pose: 0.700530 loss: 0.000837 2022/10/13 16:07:05 - mmengine - INFO - Epoch(train) [175][200/293] lr: 5.000000e-05 eta: 0:55:49 time: 0.361031 data_time: 0.074251 memory: 5857 loss_kpt: 0.000844 acc_pose: 0.763746 loss: 0.000844 2022/10/13 16:07:24 - mmengine - INFO - Epoch(train) [175][250/293] lr: 5.000000e-05 eta: 0:55:33 time: 0.366181 data_time: 0.074903 memory: 5857 loss_kpt: 0.000834 acc_pose: 0.767830 loss: 0.000834 2022/10/13 16:07:39 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:07:59 - mmengine - INFO - Epoch(train) [176][50/293] lr: 5.000000e-05 eta: 0:55:01 time: 0.392766 data_time: 0.090494 memory: 5857 loss_kpt: 0.000839 acc_pose: 0.774640 loss: 0.000839 2022/10/13 16:08:16 - mmengine - INFO - Epoch(train) [176][100/293] lr: 5.000000e-05 eta: 0:54:45 time: 0.356517 data_time: 0.072022 memory: 5857 loss_kpt: 0.000827 acc_pose: 0.721546 loss: 0.000827 2022/10/13 16:08:35 - mmengine - INFO - Epoch(train) [176][150/293] lr: 5.000000e-05 eta: 0:54:29 time: 0.376794 data_time: 0.077925 memory: 5857 loss_kpt: 0.000831 acc_pose: 0.758177 loss: 0.000831 2022/10/13 16:08:54 - mmengine - INFO - Epoch(train) [176][200/293] lr: 5.000000e-05 eta: 0:54:14 time: 0.365029 data_time: 0.085140 memory: 5857 loss_kpt: 0.000845 acc_pose: 0.784541 loss: 0.000845 2022/10/13 16:09:13 - mmengine - INFO - Epoch(train) [176][250/293] lr: 5.000000e-05 eta: 0:53:58 time: 0.395246 data_time: 0.073144 memory: 5857 loss_kpt: 0.000835 acc_pose: 0.693191 loss: 0.000835 2022/10/13 16:09:29 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:09:48 - mmengine - INFO - Epoch(train) [177][50/293] lr: 5.000000e-05 eta: 0:53:26 time: 0.380925 data_time: 0.088044 memory: 5857 loss_kpt: 0.000834 acc_pose: 0.741390 loss: 0.000834 2022/10/13 16:10:06 - mmengine - INFO - Epoch(train) [177][100/293] lr: 5.000000e-05 eta: 0:53:10 time: 0.361613 data_time: 0.075667 memory: 5857 loss_kpt: 0.000828 acc_pose: 0.743528 loss: 0.000828 2022/10/13 16:10:24 - mmengine - INFO - Epoch(train) [177][150/293] lr: 5.000000e-05 eta: 0:52:54 time: 0.360483 data_time: 0.082497 memory: 5857 loss_kpt: 0.000837 acc_pose: 0.746271 loss: 0.000837 2022/10/13 16:10:42 - mmengine - INFO - Epoch(train) [177][200/293] lr: 5.000000e-05 eta: 0:52:38 time: 0.366862 data_time: 0.082329 memory: 5857 loss_kpt: 0.000836 acc_pose: 0.708317 loss: 0.000836 2022/10/13 16:11:01 - mmengine - INFO - Epoch(train) [177][250/293] lr: 5.000000e-05 eta: 0:52:23 time: 0.366148 data_time: 0.087386 memory: 5857 loss_kpt: 0.000828 acc_pose: 0.730384 loss: 0.000828 2022/10/13 16:11:16 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:11:35 - mmengine - INFO - Epoch(train) [178][50/293] lr: 5.000000e-05 eta: 0:51:51 time: 0.382622 data_time: 0.096437 memory: 5857 loss_kpt: 0.000835 acc_pose: 0.717348 loss: 0.000835 2022/10/13 16:12:00 - mmengine - INFO - Epoch(train) [178][100/293] lr: 5.000000e-05 eta: 0:51:36 time: 0.494287 data_time: 0.086629 memory: 5857 loss_kpt: 0.000820 acc_pose: 0.704384 loss: 0.000820 2022/10/13 16:12:19 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:12:24 - mmengine - INFO - Epoch(train) [178][150/293] lr: 5.000000e-05 eta: 0:51:21 time: 0.484279 data_time: 0.085323 memory: 5857 loss_kpt: 0.000835 acc_pose: 0.769911 loss: 0.000835 2022/10/13 16:12:48 - mmengine - INFO - Epoch(train) [178][200/293] lr: 5.000000e-05 eta: 0:51:06 time: 0.473111 data_time: 0.082620 memory: 5857 loss_kpt: 0.000837 acc_pose: 0.702799 loss: 0.000837 2022/10/13 16:13:06 - mmengine - INFO - Epoch(train) [178][250/293] lr: 5.000000e-05 eta: 0:50:51 time: 0.359129 data_time: 0.076817 memory: 5857 loss_kpt: 0.000837 acc_pose: 0.693839 loss: 0.000837 2022/10/13 16:13:21 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:13:41 - mmengine - INFO - Epoch(train) [179][50/293] lr: 5.000000e-05 eta: 0:50:19 time: 0.395065 data_time: 0.095334 memory: 5857 loss_kpt: 0.000833 acc_pose: 0.759967 loss: 0.000833 2022/10/13 16:13:59 - mmengine - INFO - Epoch(train) [179][100/293] lr: 5.000000e-05 eta: 0:50:03 time: 0.352612 data_time: 0.070202 memory: 5857 loss_kpt: 0.000827 acc_pose: 0.669035 loss: 0.000827 2022/10/13 16:14:17 - mmengine - INFO - Epoch(train) [179][150/293] lr: 5.000000e-05 eta: 0:49:47 time: 0.378830 data_time: 0.086151 memory: 5857 loss_kpt: 0.000835 acc_pose: 0.801652 loss: 0.000835 2022/10/13 16:14:36 - mmengine - INFO - Epoch(train) [179][200/293] lr: 5.000000e-05 eta: 0:49:31 time: 0.361126 data_time: 0.092412 memory: 5857 loss_kpt: 0.000824 acc_pose: 0.735430 loss: 0.000824 2022/10/13 16:14:55 - mmengine - INFO - Epoch(train) [179][250/293] lr: 5.000000e-05 eta: 0:49:15 time: 0.379629 data_time: 0.074278 memory: 5857 loss_kpt: 0.000833 acc_pose: 0.773088 loss: 0.000833 2022/10/13 16:15:10 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:15:29 - mmengine - INFO - Epoch(train) [180][50/293] lr: 5.000000e-05 eta: 0:48:43 time: 0.389008 data_time: 0.090829 memory: 5857 loss_kpt: 0.000835 acc_pose: 0.706187 loss: 0.000835 2022/10/13 16:15:48 - mmengine - INFO - Epoch(train) [180][100/293] lr: 5.000000e-05 eta: 0:48:28 time: 0.367399 data_time: 0.071566 memory: 5857 loss_kpt: 0.000816 acc_pose: 0.724251 loss: 0.000816 2022/10/13 16:16:06 - mmengine - INFO - Epoch(train) [180][150/293] lr: 5.000000e-05 eta: 0:48:12 time: 0.359670 data_time: 0.071473 memory: 5857 loss_kpt: 0.000828 acc_pose: 0.746072 loss: 0.000828 2022/10/13 16:16:24 - mmengine - INFO - Epoch(train) [180][200/293] lr: 5.000000e-05 eta: 0:47:56 time: 0.368608 data_time: 0.071045 memory: 5857 loss_kpt: 0.000828 acc_pose: 0.724834 loss: 0.000828 2022/10/13 16:16:43 - mmengine - INFO - Epoch(train) [180][250/293] lr: 5.000000e-05 eta: 0:47:40 time: 0.373810 data_time: 0.082466 memory: 5857 loss_kpt: 0.000833 acc_pose: 0.717555 loss: 0.000833 2022/10/13 16:16:58 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:16:58 - mmengine - INFO - Saving checkpoint at 180 epochs 2022/10/13 16:17:07 - mmengine - INFO - Epoch(val) [180][50/407] eta: 0:00:45 time: 0.128403 data_time: 0.072401 memory: 5857 2022/10/13 16:17:13 - mmengine - INFO - Epoch(val) [180][100/407] eta: 0:00:38 time: 0.126179 data_time: 0.070792 memory: 760 2022/10/13 16:17:19 - mmengine - INFO - Epoch(val) [180][150/407] eta: 0:00:32 time: 0.125569 data_time: 0.069909 memory: 760 2022/10/13 16:17:26 - mmengine - INFO - Epoch(val) [180][200/407] eta: 0:00:25 time: 0.122148 data_time: 0.066957 memory: 760 2022/10/13 16:17:32 - mmengine - INFO - Epoch(val) [180][250/407] eta: 0:00:19 time: 0.123467 data_time: 0.068107 memory: 760 2022/10/13 16:17:38 - mmengine - INFO - Epoch(val) [180][300/407] eta: 0:00:13 time: 0.122627 data_time: 0.064588 memory: 760 2022/10/13 16:17:44 - mmengine - INFO - Epoch(val) [180][350/407] eta: 0:00:07 time: 0.126721 data_time: 0.070001 memory: 760 2022/10/13 16:17:50 - mmengine - INFO - Epoch(val) [180][400/407] eta: 0:00:00 time: 0.117148 data_time: 0.063747 memory: 760 2022/10/13 16:18:28 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 16:18:43 - mmengine - INFO - Epoch(val) [180][407/407] coco/AP: 0.623085 coco/AP .5: 0.859413 coco/AP .75: 0.688130 coco/AP (M): 0.580758 coco/AP (L): 0.695154 coco/AR: 0.683423 coco/AR .5: 0.901448 coco/AR .75: 0.747481 coco/AR (M): 0.632969 coco/AR (L): 0.754366 2022/10/13 16:18:43 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384/best_coco/AP_epoch_170.pth is removed 2022/10/13 16:18:45 - mmengine - INFO - The best checkpoint with 0.6231 coco/AP at 180 epoch is saved to best_coco/AP_epoch_180.pth. 2022/10/13 16:19:03 - mmengine - INFO - Epoch(train) [181][50/293] lr: 5.000000e-05 eta: 0:47:08 time: 0.361994 data_time: 0.104454 memory: 5857 loss_kpt: 0.000835 acc_pose: 0.725271 loss: 0.000835 2022/10/13 16:19:21 - mmengine - INFO - Epoch(train) [181][100/293] lr: 5.000000e-05 eta: 0:46:52 time: 0.359602 data_time: 0.089891 memory: 5857 loss_kpt: 0.000838 acc_pose: 0.746287 loss: 0.000838 2022/10/13 16:19:40 - mmengine - INFO - Epoch(train) [181][150/293] lr: 5.000000e-05 eta: 0:46:36 time: 0.376681 data_time: 0.108253 memory: 5857 loss_kpt: 0.000826 acc_pose: 0.752286 loss: 0.000826 2022/10/13 16:19:58 - mmengine - INFO - Epoch(train) [181][200/293] lr: 5.000000e-05 eta: 0:46:21 time: 0.369036 data_time: 0.103877 memory: 5857 loss_kpt: 0.000826 acc_pose: 0.741900 loss: 0.000826 2022/10/13 16:20:17 - mmengine - INFO - Epoch(train) [181][250/293] lr: 5.000000e-05 eta: 0:46:05 time: 0.368560 data_time: 0.099194 memory: 5857 loss_kpt: 0.000836 acc_pose: 0.754658 loss: 0.000836 2022/10/13 16:20:20 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:20:32 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:20:50 - mmengine - INFO - Epoch(train) [182][50/293] lr: 5.000000e-05 eta: 0:45:33 time: 0.372870 data_time: 0.083496 memory: 5857 loss_kpt: 0.000822 acc_pose: 0.774874 loss: 0.000822 2022/10/13 16:21:08 - mmengine - INFO - Epoch(train) [182][100/293] lr: 5.000000e-05 eta: 0:45:17 time: 0.362254 data_time: 0.071081 memory: 5857 loss_kpt: 0.000828 acc_pose: 0.672636 loss: 0.000828 2022/10/13 16:21:26 - mmengine - INFO - Epoch(train) [182][150/293] lr: 5.000000e-05 eta: 0:45:01 time: 0.360264 data_time: 0.077644 memory: 5857 loss_kpt: 0.000820 acc_pose: 0.786493 loss: 0.000820 2022/10/13 16:21:45 - mmengine - INFO - Epoch(train) [182][200/293] lr: 5.000000e-05 eta: 0:44:45 time: 0.367840 data_time: 0.086976 memory: 5857 loss_kpt: 0.000827 acc_pose: 0.716416 loss: 0.000827 2022/10/13 16:22:03 - mmengine - INFO - Epoch(train) [182][250/293] lr: 5.000000e-05 eta: 0:44:29 time: 0.353011 data_time: 0.079173 memory: 5857 loss_kpt: 0.000827 acc_pose: 0.746556 loss: 0.000827 2022/10/13 16:22:19 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:22:39 - mmengine - INFO - Epoch(train) [183][50/293] lr: 5.000000e-05 eta: 0:43:58 time: 0.399607 data_time: 0.095036 memory: 5857 loss_kpt: 0.000836 acc_pose: 0.719230 loss: 0.000836 2022/10/13 16:22:57 - mmengine - INFO - Epoch(train) [183][100/293] lr: 5.000000e-05 eta: 0:43:42 time: 0.355413 data_time: 0.070989 memory: 5857 loss_kpt: 0.000833 acc_pose: 0.723129 loss: 0.000833 2022/10/13 16:23:15 - mmengine - INFO - Epoch(train) [183][150/293] lr: 5.000000e-05 eta: 0:43:26 time: 0.372224 data_time: 0.083094 memory: 5857 loss_kpt: 0.000845 acc_pose: 0.760243 loss: 0.000845 2022/10/13 16:23:34 - mmengine - INFO - Epoch(train) [183][200/293] lr: 5.000000e-05 eta: 0:43:10 time: 0.368074 data_time: 0.081246 memory: 5857 loss_kpt: 0.000825 acc_pose: 0.761427 loss: 0.000825 2022/10/13 16:23:52 - mmengine - INFO - Epoch(train) [183][250/293] lr: 5.000000e-05 eta: 0:42:54 time: 0.363957 data_time: 0.075620 memory: 5857 loss_kpt: 0.000832 acc_pose: 0.689438 loss: 0.000832 2022/10/13 16:24:08 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:24:27 - mmengine - INFO - Epoch(train) [184][50/293] lr: 5.000000e-05 eta: 0:42:22 time: 0.379923 data_time: 0.087422 memory: 5857 loss_kpt: 0.000834 acc_pose: 0.757058 loss: 0.000834 2022/10/13 16:24:45 - mmengine - INFO - Epoch(train) [184][100/293] lr: 5.000000e-05 eta: 0:42:07 time: 0.373140 data_time: 0.073158 memory: 5857 loss_kpt: 0.000820 acc_pose: 0.758151 loss: 0.000820 2022/10/13 16:25:04 - mmengine - INFO - Epoch(train) [184][150/293] lr: 5.000000e-05 eta: 0:41:51 time: 0.367489 data_time: 0.071147 memory: 5857 loss_kpt: 0.000836 acc_pose: 0.718796 loss: 0.000836 2022/10/13 16:25:22 - mmengine - INFO - Epoch(train) [184][200/293] lr: 5.000000e-05 eta: 0:41:35 time: 0.370549 data_time: 0.083488 memory: 5857 loss_kpt: 0.000825 acc_pose: 0.727259 loss: 0.000825 2022/10/13 16:25:40 - mmengine - INFO - Epoch(train) [184][250/293] lr: 5.000000e-05 eta: 0:41:19 time: 0.365086 data_time: 0.075742 memory: 5857 loss_kpt: 0.000836 acc_pose: 0.713183 loss: 0.000836 2022/10/13 16:25:56 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:26:14 - mmengine - INFO - Epoch(train) [185][50/293] lr: 5.000000e-05 eta: 0:40:47 time: 0.372084 data_time: 0.091441 memory: 5857 loss_kpt: 0.000815 acc_pose: 0.738955 loss: 0.000815 2022/10/13 16:26:29 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:26:34 - mmengine - INFO - Epoch(train) [185][100/293] lr: 5.000000e-05 eta: 0:40:32 time: 0.384858 data_time: 0.077635 memory: 5857 loss_kpt: 0.000809 acc_pose: 0.707456 loss: 0.000809 2022/10/13 16:26:52 - mmengine - INFO - Epoch(train) [185][150/293] lr: 5.000000e-05 eta: 0:40:16 time: 0.364968 data_time: 0.080734 memory: 5857 loss_kpt: 0.000820 acc_pose: 0.713760 loss: 0.000820 2022/10/13 16:27:10 - mmengine - INFO - Epoch(train) [185][200/293] lr: 5.000000e-05 eta: 0:40:00 time: 0.367718 data_time: 0.078248 memory: 5857 loss_kpt: 0.000816 acc_pose: 0.729603 loss: 0.000816 2022/10/13 16:27:29 - mmengine - INFO - Epoch(train) [185][250/293] lr: 5.000000e-05 eta: 0:39:44 time: 0.376309 data_time: 0.076747 memory: 5857 loss_kpt: 0.000832 acc_pose: 0.719746 loss: 0.000832 2022/10/13 16:27:45 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:28:04 - mmengine - INFO - Epoch(train) [186][50/293] lr: 5.000000e-05 eta: 0:39:12 time: 0.388729 data_time: 0.114040 memory: 5857 loss_kpt: 0.000836 acc_pose: 0.763639 loss: 0.000836 2022/10/13 16:28:23 - mmengine - INFO - Epoch(train) [186][100/293] lr: 5.000000e-05 eta: 0:38:57 time: 0.383466 data_time: 0.075774 memory: 5857 loss_kpt: 0.000838 acc_pose: 0.794092 loss: 0.000838 2022/10/13 16:28:41 - mmengine - INFO - Epoch(train) [186][150/293] lr: 5.000000e-05 eta: 0:38:41 time: 0.366538 data_time: 0.072025 memory: 5857 loss_kpt: 0.000830 acc_pose: 0.745982 loss: 0.000830 2022/10/13 16:29:00 - mmengine - INFO - Epoch(train) [186][200/293] lr: 5.000000e-05 eta: 0:38:25 time: 0.367216 data_time: 0.083897 memory: 5857 loss_kpt: 0.000834 acc_pose: 0.778912 loss: 0.000834 2022/10/13 16:29:19 - mmengine - INFO - Epoch(train) [186][250/293] lr: 5.000000e-05 eta: 0:38:09 time: 0.372734 data_time: 0.079224 memory: 5857 loss_kpt: 0.000837 acc_pose: 0.705471 loss: 0.000837 2022/10/13 16:29:34 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:29:53 - mmengine - INFO - Epoch(train) [187][50/293] lr: 5.000000e-05 eta: 0:37:38 time: 0.380763 data_time: 0.089252 memory: 5857 loss_kpt: 0.000810 acc_pose: 0.749637 loss: 0.000810 2022/10/13 16:30:12 - mmengine - INFO - Epoch(train) [187][100/293] lr: 5.000000e-05 eta: 0:37:22 time: 0.371837 data_time: 0.070943 memory: 5857 loss_kpt: 0.000839 acc_pose: 0.727248 loss: 0.000839 2022/10/13 16:30:31 - mmengine - INFO - Epoch(train) [187][150/293] lr: 5.000000e-05 eta: 0:37:06 time: 0.384135 data_time: 0.077428 memory: 5857 loss_kpt: 0.000815 acc_pose: 0.705081 loss: 0.000815 2022/10/13 16:30:50 - mmengine - INFO - Epoch(train) [187][200/293] lr: 5.000000e-05 eta: 0:36:50 time: 0.368320 data_time: 0.069979 memory: 5857 loss_kpt: 0.000811 acc_pose: 0.725184 loss: 0.000811 2022/10/13 16:31:08 - mmengine - INFO - Epoch(train) [187][250/293] lr: 5.000000e-05 eta: 0:36:34 time: 0.366418 data_time: 0.077196 memory: 5857 loss_kpt: 0.000837 acc_pose: 0.749015 loss: 0.000837 2022/10/13 16:31:24 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:31:43 - mmengine - INFO - Epoch(train) [188][50/293] lr: 5.000000e-05 eta: 0:36:03 time: 0.378085 data_time: 0.087431 memory: 5857 loss_kpt: 0.000826 acc_pose: 0.709344 loss: 0.000826 2022/10/13 16:32:02 - mmengine - INFO - Epoch(train) [188][100/293] lr: 5.000000e-05 eta: 0:35:47 time: 0.374080 data_time: 0.102933 memory: 5857 loss_kpt: 0.000832 acc_pose: 0.712574 loss: 0.000832 2022/10/13 16:32:20 - mmengine - INFO - Epoch(train) [188][150/293] lr: 5.000000e-05 eta: 0:35:31 time: 0.369084 data_time: 0.074851 memory: 5857 loss_kpt: 0.000826 acc_pose: 0.713253 loss: 0.000826 2022/10/13 16:32:38 - mmengine - INFO - Epoch(train) [188][200/293] lr: 5.000000e-05 eta: 0:35:15 time: 0.359348 data_time: 0.078664 memory: 5857 loss_kpt: 0.000827 acc_pose: 0.767525 loss: 0.000827 2022/10/13 16:32:41 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:32:56 - mmengine - INFO - Epoch(train) [188][250/293] lr: 5.000000e-05 eta: 0:34:59 time: 0.359457 data_time: 0.077986 memory: 5857 loss_kpt: 0.000818 acc_pose: 0.721886 loss: 0.000818 2022/10/13 16:33:12 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:33:30 - mmengine - INFO - Epoch(train) [189][50/293] lr: 5.000000e-05 eta: 0:34:28 time: 0.377123 data_time: 0.086150 memory: 5857 loss_kpt: 0.000828 acc_pose: 0.777465 loss: 0.000828 2022/10/13 16:33:48 - mmengine - INFO - Epoch(train) [189][100/293] lr: 5.000000e-05 eta: 0:34:12 time: 0.359042 data_time: 0.082922 memory: 5857 loss_kpt: 0.000821 acc_pose: 0.731086 loss: 0.000821 2022/10/13 16:34:07 - mmengine - INFO - Epoch(train) [189][150/293] lr: 5.000000e-05 eta: 0:33:56 time: 0.361616 data_time: 0.075106 memory: 5857 loss_kpt: 0.000827 acc_pose: 0.785923 loss: 0.000827 2022/10/13 16:34:25 - mmengine - INFO - Epoch(train) [189][200/293] lr: 5.000000e-05 eta: 0:33:40 time: 0.367962 data_time: 0.072531 memory: 5857 loss_kpt: 0.000842 acc_pose: 0.721000 loss: 0.000842 2022/10/13 16:34:43 - mmengine - INFO - Epoch(train) [189][250/293] lr: 5.000000e-05 eta: 0:33:24 time: 0.370433 data_time: 0.087007 memory: 5857 loss_kpt: 0.000831 acc_pose: 0.754376 loss: 0.000831 2022/10/13 16:34:59 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:35:18 - mmengine - INFO - Epoch(train) [190][50/293] lr: 5.000000e-05 eta: 0:32:52 time: 0.373489 data_time: 0.109436 memory: 5857 loss_kpt: 0.000824 acc_pose: 0.764332 loss: 0.000824 2022/10/13 16:35:36 - mmengine - INFO - Epoch(train) [190][100/293] lr: 5.000000e-05 eta: 0:32:37 time: 0.370936 data_time: 0.073690 memory: 5857 loss_kpt: 0.000833 acc_pose: 0.743651 loss: 0.000833 2022/10/13 16:35:54 - mmengine - INFO - Epoch(train) [190][150/293] lr: 5.000000e-05 eta: 0:32:21 time: 0.362705 data_time: 0.073934 memory: 5857 loss_kpt: 0.000838 acc_pose: 0.695867 loss: 0.000838 2022/10/13 16:36:12 - mmengine - INFO - Epoch(train) [190][200/293] lr: 5.000000e-05 eta: 0:32:05 time: 0.364230 data_time: 0.078452 memory: 5857 loss_kpt: 0.000839 acc_pose: 0.711026 loss: 0.000839 2022/10/13 16:36:31 - mmengine - INFO - Epoch(train) [190][250/293] lr: 5.000000e-05 eta: 0:31:49 time: 0.361848 data_time: 0.074089 memory: 5857 loss_kpt: 0.000830 acc_pose: 0.788266 loss: 0.000830 2022/10/13 16:36:46 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:36:46 - mmengine - INFO - Saving checkpoint at 190 epochs 2022/10/13 16:36:55 - mmengine - INFO - Epoch(val) [190][50/407] eta: 0:00:49 time: 0.138216 data_time: 0.080358 memory: 5857 2022/10/13 16:37:01 - mmengine - INFO - Epoch(val) [190][100/407] eta: 0:00:38 time: 0.125910 data_time: 0.069301 memory: 760 2022/10/13 16:37:08 - mmengine - INFO - Epoch(val) [190][150/407] eta: 0:00:31 time: 0.123389 data_time: 0.067055 memory: 760 2022/10/13 16:37:14 - mmengine - INFO - Epoch(val) [190][200/407] eta: 0:00:25 time: 0.125109 data_time: 0.068475 memory: 760 2022/10/13 16:37:20 - mmengine - INFO - Epoch(val) [190][250/407] eta: 0:00:19 time: 0.126581 data_time: 0.071407 memory: 760 2022/10/13 16:37:26 - mmengine - INFO - Epoch(val) [190][300/407] eta: 0:00:13 time: 0.123745 data_time: 0.067418 memory: 760 2022/10/13 16:37:33 - mmengine - INFO - Epoch(val) [190][350/407] eta: 0:00:07 time: 0.124903 data_time: 0.069989 memory: 760 2022/10/13 16:37:39 - mmengine - INFO - Epoch(val) [190][400/407] eta: 0:00:00 time: 0.125299 data_time: 0.071687 memory: 760 2022/10/13 16:38:17 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 16:38:32 - mmengine - INFO - Epoch(val) [190][407/407] coco/AP: 0.624472 coco/AP .5: 0.860920 coco/AP .75: 0.690370 coco/AP (M): 0.583305 coco/AP (L): 0.695766 coco/AR: 0.685312 coco/AR .5: 0.903180 coco/AR .75: 0.748741 coco/AR (M): 0.635783 coco/AR (L): 0.754998 2022/10/13 16:38:32 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384/best_coco/AP_epoch_180.pth is removed 2022/10/13 16:38:34 - mmengine - INFO - The best checkpoint with 0.6245 coco/AP at 190 epoch is saved to best_coco/AP_epoch_190.pth. 2022/10/13 16:38:53 - mmengine - INFO - Epoch(train) [191][50/293] lr: 5.000000e-05 eta: 0:31:17 time: 0.377277 data_time: 0.109185 memory: 5857 loss_kpt: 0.000822 acc_pose: 0.709133 loss: 0.000822 2022/10/13 16:39:11 - mmengine - INFO - Epoch(train) [191][100/293] lr: 5.000000e-05 eta: 0:31:02 time: 0.375463 data_time: 0.074957 memory: 5857 loss_kpt: 0.000820 acc_pose: 0.758167 loss: 0.000820 2022/10/13 16:39:29 - mmengine - INFO - Epoch(train) [191][150/293] lr: 5.000000e-05 eta: 0:30:46 time: 0.358220 data_time: 0.076169 memory: 5857 loss_kpt: 0.000839 acc_pose: 0.757162 loss: 0.000839 2022/10/13 16:39:48 - mmengine - INFO - Epoch(train) [191][200/293] lr: 5.000000e-05 eta: 0:30:30 time: 0.372169 data_time: 0.073715 memory: 5857 loss_kpt: 0.000830 acc_pose: 0.746826 loss: 0.000830 2022/10/13 16:40:05 - mmengine - INFO - Epoch(train) [191][250/293] lr: 5.000000e-05 eta: 0:30:14 time: 0.348208 data_time: 0.067886 memory: 5857 loss_kpt: 0.000834 acc_pose: 0.789019 loss: 0.000834 2022/10/13 16:40:21 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:40:36 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:40:40 - mmengine - INFO - Epoch(train) [192][50/293] lr: 5.000000e-05 eta: 0:29:42 time: 0.385134 data_time: 0.095103 memory: 5857 loss_kpt: 0.000833 acc_pose: 0.728515 loss: 0.000833 2022/10/13 16:40:59 - mmengine - INFO - Epoch(train) [192][100/293] lr: 5.000000e-05 eta: 0:29:27 time: 0.373654 data_time: 0.076151 memory: 5857 loss_kpt: 0.000822 acc_pose: 0.708644 loss: 0.000822 2022/10/13 16:41:18 - mmengine - INFO - Epoch(train) [192][150/293] lr: 5.000000e-05 eta: 0:29:11 time: 0.374406 data_time: 0.073252 memory: 5857 loss_kpt: 0.000826 acc_pose: 0.756633 loss: 0.000826 2022/10/13 16:41:36 - mmengine - INFO - Epoch(train) [192][200/293] lr: 5.000000e-05 eta: 0:28:55 time: 0.356751 data_time: 0.068189 memory: 5857 loss_kpt: 0.000833 acc_pose: 0.732128 loss: 0.000833 2022/10/13 16:41:54 - mmengine - INFO - Epoch(train) [192][250/293] lr: 5.000000e-05 eta: 0:28:39 time: 0.359693 data_time: 0.070099 memory: 5857 loss_kpt: 0.000809 acc_pose: 0.739036 loss: 0.000809 2022/10/13 16:42:09 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:42:28 - mmengine - INFO - Epoch(train) [193][50/293] lr: 5.000000e-05 eta: 0:28:08 time: 0.383157 data_time: 0.091726 memory: 5857 loss_kpt: 0.000821 acc_pose: 0.779291 loss: 0.000821 2022/10/13 16:42:46 - mmengine - INFO - Epoch(train) [193][100/293] lr: 5.000000e-05 eta: 0:27:52 time: 0.364545 data_time: 0.077739 memory: 5857 loss_kpt: 0.000845 acc_pose: 0.737886 loss: 0.000845 2022/10/13 16:43:05 - mmengine - INFO - Epoch(train) [193][150/293] lr: 5.000000e-05 eta: 0:27:36 time: 0.368123 data_time: 0.085821 memory: 5857 loss_kpt: 0.000837 acc_pose: 0.761249 loss: 0.000837 2022/10/13 16:43:24 - mmengine - INFO - Epoch(train) [193][200/293] lr: 5.000000e-05 eta: 0:27:20 time: 0.373460 data_time: 0.077350 memory: 5857 loss_kpt: 0.000830 acc_pose: 0.728779 loss: 0.000830 2022/10/13 16:43:41 - mmengine - INFO - Epoch(train) [193][250/293] lr: 5.000000e-05 eta: 0:27:04 time: 0.356776 data_time: 0.079393 memory: 5857 loss_kpt: 0.000818 acc_pose: 0.773075 loss: 0.000818 2022/10/13 16:43:57 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:44:16 - mmengine - INFO - Epoch(train) [194][50/293] lr: 5.000000e-05 eta: 0:26:33 time: 0.373407 data_time: 0.095595 memory: 5857 loss_kpt: 0.000818 acc_pose: 0.753746 loss: 0.000818 2022/10/13 16:44:34 - mmengine - INFO - Epoch(train) [194][100/293] lr: 5.000000e-05 eta: 0:26:17 time: 0.369446 data_time: 0.090982 memory: 5857 loss_kpt: 0.000817 acc_pose: 0.674384 loss: 0.000817 2022/10/13 16:44:53 - mmengine - INFO - Epoch(train) [194][150/293] lr: 5.000000e-05 eta: 0:26:01 time: 0.376902 data_time: 0.094368 memory: 5857 loss_kpt: 0.000829 acc_pose: 0.756295 loss: 0.000829 2022/10/13 16:45:12 - mmengine - INFO - Epoch(train) [194][200/293] lr: 5.000000e-05 eta: 0:25:45 time: 0.375642 data_time: 0.072280 memory: 5857 loss_kpt: 0.000831 acc_pose: 0.712204 loss: 0.000831 2022/10/13 16:45:30 - mmengine - INFO - Epoch(train) [194][250/293] lr: 5.000000e-05 eta: 0:25:29 time: 0.358304 data_time: 0.080979 memory: 5857 loss_kpt: 0.000822 acc_pose: 0.690490 loss: 0.000822 2022/10/13 16:45:46 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:46:06 - mmengine - INFO - Epoch(train) [195][50/293] lr: 5.000000e-05 eta: 0:24:58 time: 0.396252 data_time: 0.088507 memory: 5857 loss_kpt: 0.000819 acc_pose: 0.746369 loss: 0.000819 2022/10/13 16:46:25 - mmengine - INFO - Epoch(train) [195][100/293] lr: 5.000000e-05 eta: 0:24:42 time: 0.369556 data_time: 0.069298 memory: 5857 loss_kpt: 0.000821 acc_pose: 0.690957 loss: 0.000821 2022/10/13 16:46:43 - mmengine - INFO - Epoch(train) [195][150/293] lr: 5.000000e-05 eta: 0:24:26 time: 0.363534 data_time: 0.072672 memory: 5857 loss_kpt: 0.000833 acc_pose: 0.718017 loss: 0.000833 2022/10/13 16:46:46 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:47:01 - mmengine - INFO - Epoch(train) [195][200/293] lr: 5.000000e-05 eta: 0:24:10 time: 0.364980 data_time: 0.069844 memory: 5857 loss_kpt: 0.000825 acc_pose: 0.748172 loss: 0.000825 2022/10/13 16:47:19 - mmengine - INFO - Epoch(train) [195][250/293] lr: 5.000000e-05 eta: 0:23:54 time: 0.364453 data_time: 0.079650 memory: 5857 loss_kpt: 0.000818 acc_pose: 0.724825 loss: 0.000818 2022/10/13 16:47:35 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:47:54 - mmengine - INFO - Epoch(train) [196][50/293] lr: 5.000000e-05 eta: 0:23:23 time: 0.375899 data_time: 0.087940 memory: 5857 loss_kpt: 0.000824 acc_pose: 0.799172 loss: 0.000824 2022/10/13 16:48:12 - mmengine - INFO - Epoch(train) [196][100/293] lr: 5.000000e-05 eta: 0:23:07 time: 0.363632 data_time: 0.092316 memory: 5857 loss_kpt: 0.000813 acc_pose: 0.766066 loss: 0.000813 2022/10/13 16:48:31 - mmengine - INFO - Epoch(train) [196][150/293] lr: 5.000000e-05 eta: 0:22:51 time: 0.373478 data_time: 0.090831 memory: 5857 loss_kpt: 0.000832 acc_pose: 0.699298 loss: 0.000832 2022/10/13 16:48:50 - mmengine - INFO - Epoch(train) [196][200/293] lr: 5.000000e-05 eta: 0:22:35 time: 0.378391 data_time: 0.072936 memory: 5857 loss_kpt: 0.000837 acc_pose: 0.736542 loss: 0.000837 2022/10/13 16:49:08 - mmengine - INFO - Epoch(train) [196][250/293] lr: 5.000000e-05 eta: 0:22:19 time: 0.363845 data_time: 0.078884 memory: 5857 loss_kpt: 0.000829 acc_pose: 0.790052 loss: 0.000829 2022/10/13 16:49:23 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:49:43 - mmengine - INFO - Epoch(train) [197][50/293] lr: 5.000000e-05 eta: 0:21:48 time: 0.390656 data_time: 0.098742 memory: 5857 loss_kpt: 0.000823 acc_pose: 0.703149 loss: 0.000823 2022/10/13 16:50:01 - mmengine - INFO - Epoch(train) [197][100/293] lr: 5.000000e-05 eta: 0:21:32 time: 0.367208 data_time: 0.079197 memory: 5857 loss_kpt: 0.000823 acc_pose: 0.754914 loss: 0.000823 2022/10/13 16:50:20 - mmengine - INFO - Epoch(train) [197][150/293] lr: 5.000000e-05 eta: 0:21:16 time: 0.376814 data_time: 0.082293 memory: 5857 loss_kpt: 0.000836 acc_pose: 0.736234 loss: 0.000836 2022/10/13 16:50:39 - mmengine - INFO - Epoch(train) [197][200/293] lr: 5.000000e-05 eta: 0:21:00 time: 0.374797 data_time: 0.067846 memory: 5857 loss_kpt: 0.000819 acc_pose: 0.771936 loss: 0.000819 2022/10/13 16:50:57 - mmengine - INFO - Epoch(train) [197][250/293] lr: 5.000000e-05 eta: 0:20:44 time: 0.361735 data_time: 0.066646 memory: 5857 loss_kpt: 0.000835 acc_pose: 0.737484 loss: 0.000835 2022/10/13 16:51:12 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:51:31 - mmengine - INFO - Epoch(train) [198][50/293] lr: 5.000000e-05 eta: 0:20:14 time: 0.380939 data_time: 0.089399 memory: 5857 loss_kpt: 0.000819 acc_pose: 0.774440 loss: 0.000819 2022/10/13 16:51:50 - mmengine - INFO - Epoch(train) [198][100/293] lr: 5.000000e-05 eta: 0:19:58 time: 0.372486 data_time: 0.080734 memory: 5857 loss_kpt: 0.000824 acc_pose: 0.773086 loss: 0.000824 2022/10/13 16:52:08 - mmengine - INFO - Epoch(train) [198][150/293] lr: 5.000000e-05 eta: 0:19:42 time: 0.360513 data_time: 0.075738 memory: 5857 loss_kpt: 0.000818 acc_pose: 0.720986 loss: 0.000818 2022/10/13 16:52:26 - mmengine - INFO - Epoch(train) [198][200/293] lr: 5.000000e-05 eta: 0:19:26 time: 0.372213 data_time: 0.068214 memory: 5857 loss_kpt: 0.000827 acc_pose: 0.774395 loss: 0.000827 2022/10/13 16:52:45 - mmengine - INFO - Epoch(train) [198][250/293] lr: 5.000000e-05 eta: 0:19:09 time: 0.363929 data_time: 0.083962 memory: 5857 loss_kpt: 0.000837 acc_pose: 0.732817 loss: 0.000837 2022/10/13 16:52:55 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:53:00 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:53:19 - mmengine - INFO - Epoch(train) [199][50/293] lr: 5.000000e-05 eta: 0:18:39 time: 0.385682 data_time: 0.095918 memory: 5857 loss_kpt: 0.000832 acc_pose: 0.773260 loss: 0.000832 2022/10/13 16:53:38 - mmengine - INFO - Epoch(train) [199][100/293] lr: 5.000000e-05 eta: 0:18:23 time: 0.365311 data_time: 0.071298 memory: 5857 loss_kpt: 0.000829 acc_pose: 0.730629 loss: 0.000829 2022/10/13 16:53:56 - mmengine - INFO - Epoch(train) [199][150/293] lr: 5.000000e-05 eta: 0:18:07 time: 0.360485 data_time: 0.077665 memory: 5857 loss_kpt: 0.000837 acc_pose: 0.701521 loss: 0.000837 2022/10/13 16:54:14 - mmengine - INFO - Epoch(train) [199][200/293] lr: 5.000000e-05 eta: 0:17:51 time: 0.361196 data_time: 0.070817 memory: 5857 loss_kpt: 0.000824 acc_pose: 0.737037 loss: 0.000824 2022/10/13 16:54:32 - mmengine - INFO - Epoch(train) [199][250/293] lr: 5.000000e-05 eta: 0:17:35 time: 0.377261 data_time: 0.072487 memory: 5857 loss_kpt: 0.000815 acc_pose: 0.773737 loss: 0.000815 2022/10/13 16:54:48 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:55:07 - mmengine - INFO - Epoch(train) [200][50/293] lr: 5.000000e-05 eta: 0:17:04 time: 0.381524 data_time: 0.088047 memory: 5857 loss_kpt: 0.000821 acc_pose: 0.747121 loss: 0.000821 2022/10/13 16:55:25 - mmengine - INFO - Epoch(train) [200][100/293] lr: 5.000000e-05 eta: 0:16:48 time: 0.368896 data_time: 0.069921 memory: 5857 loss_kpt: 0.000826 acc_pose: 0.700456 loss: 0.000826 2022/10/13 16:55:43 - mmengine - INFO - Epoch(train) [200][150/293] lr: 5.000000e-05 eta: 0:16:32 time: 0.359739 data_time: 0.094810 memory: 5857 loss_kpt: 0.000846 acc_pose: 0.786278 loss: 0.000846 2022/10/13 16:56:02 - mmengine - INFO - Epoch(train) [200][200/293] lr: 5.000000e-05 eta: 0:16:16 time: 0.369862 data_time: 0.078645 memory: 5857 loss_kpt: 0.000832 acc_pose: 0.677793 loss: 0.000832 2022/10/13 16:56:20 - mmengine - INFO - Epoch(train) [200][250/293] lr: 5.000000e-05 eta: 0:16:00 time: 0.369370 data_time: 0.078694 memory: 5857 loss_kpt: 0.000832 acc_pose: 0.760761 loss: 0.000832 2022/10/13 16:56:36 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 16:56:36 - mmengine - INFO - Saving checkpoint at 200 epochs 2022/10/13 16:56:45 - mmengine - INFO - Epoch(val) [200][50/407] eta: 0:00:49 time: 0.137999 data_time: 0.080984 memory: 5857 2022/10/13 16:56:51 - mmengine - INFO - Epoch(val) [200][100/407] eta: 0:00:37 time: 0.121001 data_time: 0.064604 memory: 760 2022/10/13 16:56:57 - mmengine - INFO - Epoch(val) [200][150/407] eta: 0:00:31 time: 0.121991 data_time: 0.065619 memory: 760 2022/10/13 16:57:03 - mmengine - INFO - Epoch(val) [200][200/407] eta: 0:00:25 time: 0.124657 data_time: 0.069291 memory: 760 2022/10/13 16:57:09 - mmengine - INFO - Epoch(val) [200][250/407] eta: 0:00:18 time: 0.118598 data_time: 0.062231 memory: 760 2022/10/13 16:57:15 - mmengine - INFO - Epoch(val) [200][300/407] eta: 0:00:12 time: 0.120567 data_time: 0.064699 memory: 760 2022/10/13 16:57:21 - mmengine - INFO - Epoch(val) [200][350/407] eta: 0:00:07 time: 0.127785 data_time: 0.070907 memory: 760 2022/10/13 16:57:28 - mmengine - INFO - Epoch(val) [200][400/407] eta: 0:00:00 time: 0.121996 data_time: 0.063148 memory: 760 2022/10/13 16:58:06 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 16:58:20 - mmengine - INFO - Epoch(val) [200][407/407] coco/AP: 0.626161 coco/AP .5: 0.861808 coco/AP .75: 0.693186 coco/AP (M): 0.583443 coco/AP (L): 0.698468 coco/AR: 0.686319 coco/AR .5: 0.903495 coco/AR .75: 0.750787 coco/AR (M): 0.636384 coco/AR (L): 0.756745 2022/10/13 16:58:20 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384/best_coco/AP_epoch_190.pth is removed 2022/10/13 16:58:22 - mmengine - INFO - The best checkpoint with 0.6262 coco/AP at 200 epoch is saved to best_coco/AP_epoch_200.pth. 2022/10/13 16:58:41 - mmengine - INFO - Epoch(train) [201][50/293] lr: 5.000000e-06 eta: 0:15:29 time: 0.373563 data_time: 0.088119 memory: 5857 loss_kpt: 0.000826 acc_pose: 0.753692 loss: 0.000826 2022/10/13 16:58:59 - mmengine - INFO - Epoch(train) [201][100/293] lr: 5.000000e-06 eta: 0:15:13 time: 0.356835 data_time: 0.084443 memory: 5857 loss_kpt: 0.000839 acc_pose: 0.706629 loss: 0.000839 2022/10/13 16:59:17 - mmengine - INFO - Epoch(train) [201][150/293] lr: 5.000000e-06 eta: 0:14:57 time: 0.371500 data_time: 0.077576 memory: 5857 loss_kpt: 0.000820 acc_pose: 0.776705 loss: 0.000820 2022/10/13 16:59:36 - mmengine - INFO - Epoch(train) [201][200/293] lr: 5.000000e-06 eta: 0:14:41 time: 0.370278 data_time: 0.076454 memory: 5857 loss_kpt: 0.000813 acc_pose: 0.736347 loss: 0.000813 2022/10/13 16:59:53 - mmengine - INFO - Epoch(train) [201][250/293] lr: 5.000000e-06 eta: 0:14:25 time: 0.351222 data_time: 0.070957 memory: 5857 loss_kpt: 0.000822 acc_pose: 0.750939 loss: 0.000822 2022/10/13 17:00:09 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 17:00:29 - mmengine - INFO - Epoch(train) [202][50/293] lr: 5.000000e-06 eta: 0:13:55 time: 0.398582 data_time: 0.087948 memory: 5857 loss_kpt: 0.000814 acc_pose: 0.733115 loss: 0.000814 2022/10/13 17:00:47 - mmengine - INFO - Epoch(train) [202][100/293] lr: 5.000000e-06 eta: 0:13:39 time: 0.359152 data_time: 0.072795 memory: 5857 loss_kpt: 0.000820 acc_pose: 0.736465 loss: 0.000820 2022/10/13 17:00:49 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 17:01:06 - mmengine - INFO - Epoch(train) [202][150/293] lr: 5.000000e-06 eta: 0:13:23 time: 0.378226 data_time: 0.071412 memory: 5857 loss_kpt: 0.000826 acc_pose: 0.752307 loss: 0.000826 2022/10/13 17:01:24 - mmengine - INFO - Epoch(train) [202][200/293] lr: 5.000000e-06 eta: 0:13:06 time: 0.354675 data_time: 0.081330 memory: 5857 loss_kpt: 0.000818 acc_pose: 0.769424 loss: 0.000818 2022/10/13 17:01:42 - mmengine - INFO - Epoch(train) [202][250/293] lr: 5.000000e-06 eta: 0:12:50 time: 0.359020 data_time: 0.088166 memory: 5857 loss_kpt: 0.000842 acc_pose: 0.741945 loss: 0.000842 2022/10/13 17:01:57 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 17:02:16 - mmengine - INFO - Epoch(train) [203][50/293] lr: 5.000000e-06 eta: 0:12:20 time: 0.378552 data_time: 0.081160 memory: 5857 loss_kpt: 0.000812 acc_pose: 0.769415 loss: 0.000812 2022/10/13 17:02:34 - mmengine - INFO - Epoch(train) [203][100/293] lr: 5.000000e-06 eta: 0:12:04 time: 0.367529 data_time: 0.080167 memory: 5857 loss_kpt: 0.000841 acc_pose: 0.738790 loss: 0.000841 2022/10/13 17:02:52 - mmengine - INFO - Epoch(train) [203][150/293] lr: 5.000000e-06 eta: 0:11:48 time: 0.354997 data_time: 0.073270 memory: 5857 loss_kpt: 0.000821 acc_pose: 0.779599 loss: 0.000821 2022/10/13 17:03:10 - mmengine - INFO - Epoch(train) [203][200/293] lr: 5.000000e-06 eta: 0:11:32 time: 0.361703 data_time: 0.073289 memory: 5857 loss_kpt: 0.000820 acc_pose: 0.718295 loss: 0.000820 2022/10/13 17:03:28 - mmengine - INFO - Epoch(train) [203][250/293] lr: 5.000000e-06 eta: 0:11:16 time: 0.362300 data_time: 0.077328 memory: 5857 loss_kpt: 0.000828 acc_pose: 0.734994 loss: 0.000828 2022/10/13 17:03:44 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 17:04:03 - mmengine - INFO - Epoch(train) [204][50/293] lr: 5.000000e-06 eta: 0:10:45 time: 0.382961 data_time: 0.093413 memory: 5857 loss_kpt: 0.000828 acc_pose: 0.712081 loss: 0.000828 2022/10/13 17:04:22 - mmengine - INFO - Epoch(train) [204][100/293] lr: 5.000000e-06 eta: 0:10:29 time: 0.367113 data_time: 0.074318 memory: 5857 loss_kpt: 0.000831 acc_pose: 0.733608 loss: 0.000831 2022/10/13 17:04:41 - mmengine - INFO - Epoch(train) [204][150/293] lr: 5.000000e-06 eta: 0:10:13 time: 0.382388 data_time: 0.085679 memory: 5857 loss_kpt: 0.000823 acc_pose: 0.778544 loss: 0.000823 2022/10/13 17:04:59 - mmengine - INFO - Epoch(train) [204][200/293] lr: 5.000000e-06 eta: 0:09:57 time: 0.359464 data_time: 0.077595 memory: 5857 loss_kpt: 0.000816 acc_pose: 0.764988 loss: 0.000816 2022/10/13 17:05:17 - mmengine - INFO - Epoch(train) [204][250/293] lr: 5.000000e-06 eta: 0:09:41 time: 0.359350 data_time: 0.078930 memory: 5857 loss_kpt: 0.000828 acc_pose: 0.772614 loss: 0.000828 2022/10/13 17:05:32 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 17:05:51 - mmengine - INFO - Epoch(train) [205][50/293] lr: 5.000000e-06 eta: 0:09:11 time: 0.386550 data_time: 0.091828 memory: 5857 loss_kpt: 0.000820 acc_pose: 0.730949 loss: 0.000820 2022/10/13 17:06:09 - mmengine - INFO - Epoch(train) [205][100/293] lr: 5.000000e-06 eta: 0:08:55 time: 0.365204 data_time: 0.081602 memory: 5857 loss_kpt: 0.000819 acc_pose: 0.715254 loss: 0.000819 2022/10/13 17:06:27 - mmengine - INFO - Epoch(train) [205][150/293] lr: 5.000000e-06 eta: 0:08:38 time: 0.357541 data_time: 0.075275 memory: 5857 loss_kpt: 0.000828 acc_pose: 0.717956 loss: 0.000828 2022/10/13 17:06:46 - mmengine - INFO - Epoch(train) [205][200/293] lr: 5.000000e-06 eta: 0:08:22 time: 0.368662 data_time: 0.073003 memory: 5857 loss_kpt: 0.000819 acc_pose: 0.762140 loss: 0.000819 2022/10/13 17:06:56 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 17:07:05 - mmengine - INFO - Epoch(train) [205][250/293] lr: 5.000000e-06 eta: 0:08:06 time: 0.379997 data_time: 0.080049 memory: 5857 loss_kpt: 0.000830 acc_pose: 0.740181 loss: 0.000830 2022/10/13 17:07:20 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 17:07:39 - mmengine - INFO - Epoch(train) [206][50/293] lr: 5.000000e-06 eta: 0:07:36 time: 0.391708 data_time: 0.094218 memory: 5857 loss_kpt: 0.000831 acc_pose: 0.697116 loss: 0.000831 2022/10/13 17:07:58 - mmengine - INFO - Epoch(train) [206][100/293] lr: 5.000000e-06 eta: 0:07:20 time: 0.368927 data_time: 0.078257 memory: 5857 loss_kpt: 0.000825 acc_pose: 0.741302 loss: 0.000825 2022/10/13 17:08:16 - mmengine - INFO - Epoch(train) [206][150/293] lr: 5.000000e-06 eta: 0:07:04 time: 0.360806 data_time: 0.076669 memory: 5857 loss_kpt: 0.000833 acc_pose: 0.697182 loss: 0.000833 2022/10/13 17:08:35 - mmengine - INFO - Epoch(train) [206][200/293] lr: 5.000000e-06 eta: 0:06:48 time: 0.384159 data_time: 0.082387 memory: 5857 loss_kpt: 0.000830 acc_pose: 0.759147 loss: 0.000830 2022/10/13 17:08:54 - mmengine - INFO - Epoch(train) [206][250/293] lr: 5.000000e-06 eta: 0:06:32 time: 0.367925 data_time: 0.079156 memory: 5857 loss_kpt: 0.000828 acc_pose: 0.736898 loss: 0.000828 2022/10/13 17:09:09 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 17:09:28 - mmengine - INFO - Epoch(train) [207][50/293] lr: 5.000000e-06 eta: 0:06:02 time: 0.382440 data_time: 0.105999 memory: 5857 loss_kpt: 0.000833 acc_pose: 0.732960 loss: 0.000833 2022/10/13 17:09:47 - mmengine - INFO - Epoch(train) [207][100/293] lr: 5.000000e-06 eta: 0:05:45 time: 0.364824 data_time: 0.095972 memory: 5857 loss_kpt: 0.000838 acc_pose: 0.714480 loss: 0.000838 2022/10/13 17:10:05 - mmengine - INFO - Epoch(train) [207][150/293] lr: 5.000000e-06 eta: 0:05:29 time: 0.372579 data_time: 0.083812 memory: 5857 loss_kpt: 0.000827 acc_pose: 0.744757 loss: 0.000827 2022/10/13 17:10:23 - mmengine - INFO - Epoch(train) [207][200/293] lr: 5.000000e-06 eta: 0:05:13 time: 0.363714 data_time: 0.083757 memory: 5857 loss_kpt: 0.000812 acc_pose: 0.742575 loss: 0.000812 2022/10/13 17:10:41 - mmengine - INFO - Epoch(train) [207][250/293] lr: 5.000000e-06 eta: 0:04:57 time: 0.360533 data_time: 0.077373 memory: 5857 loss_kpt: 0.000822 acc_pose: 0.695269 loss: 0.000822 2022/10/13 17:10:57 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 17:11:15 - mmengine - INFO - Epoch(train) [208][50/293] lr: 5.000000e-06 eta: 0:04:27 time: 0.371846 data_time: 0.093199 memory: 5857 loss_kpt: 0.000807 acc_pose: 0.772106 loss: 0.000807 2022/10/13 17:11:33 - mmengine - INFO - Epoch(train) [208][100/293] lr: 5.000000e-06 eta: 0:04:11 time: 0.357529 data_time: 0.074130 memory: 5857 loss_kpt: 0.000806 acc_pose: 0.676812 loss: 0.000806 2022/10/13 17:11:51 - mmengine - INFO - Epoch(train) [208][150/293] lr: 5.000000e-06 eta: 0:03:55 time: 0.357589 data_time: 0.081290 memory: 5857 loss_kpt: 0.000828 acc_pose: 0.666442 loss: 0.000828 2022/10/13 17:12:09 - mmengine - INFO - Epoch(train) [208][200/293] lr: 5.000000e-06 eta: 0:03:39 time: 0.364938 data_time: 0.078470 memory: 5857 loss_kpt: 0.000833 acc_pose: 0.743082 loss: 0.000833 2022/10/13 17:12:27 - mmengine - INFO - Epoch(train) [208][250/293] lr: 5.000000e-06 eta: 0:03:22 time: 0.361221 data_time: 0.069743 memory: 5857 loss_kpt: 0.000824 acc_pose: 0.751948 loss: 0.000824 2022/10/13 17:12:43 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 17:13:02 - mmengine - INFO - Epoch(train) [209][50/293] lr: 5.000000e-06 eta: 0:02:52 time: 0.383097 data_time: 0.091017 memory: 5857 loss_kpt: 0.000811 acc_pose: 0.814686 loss: 0.000811 2022/10/13 17:13:04 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 17:13:20 - mmengine - INFO - Epoch(train) [209][100/293] lr: 5.000000e-06 eta: 0:02:36 time: 0.359846 data_time: 0.069985 memory: 5857 loss_kpt: 0.000823 acc_pose: 0.813052 loss: 0.000823 2022/10/13 17:13:38 - mmengine - INFO - Epoch(train) [209][150/293] lr: 5.000000e-06 eta: 0:02:20 time: 0.358351 data_time: 0.077785 memory: 5857 loss_kpt: 0.000834 acc_pose: 0.748181 loss: 0.000834 2022/10/13 17:13:56 - mmengine - INFO - Epoch(train) [209][200/293] lr: 5.000000e-06 eta: 0:02:04 time: 0.361055 data_time: 0.076971 memory: 5857 loss_kpt: 0.000823 acc_pose: 0.786684 loss: 0.000823 2022/10/13 17:14:14 - mmengine - INFO - Epoch(train) [209][250/293] lr: 5.000000e-06 eta: 0:01:48 time: 0.369637 data_time: 0.069206 memory: 5857 loss_kpt: 0.000834 acc_pose: 0.768085 loss: 0.000834 2022/10/13 17:14:30 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 17:14:49 - mmengine - INFO - Epoch(train) [210][50/293] lr: 5.000000e-06 eta: 0:01:18 time: 0.394457 data_time: 0.090275 memory: 5857 loss_kpt: 0.000837 acc_pose: 0.729282 loss: 0.000837 2022/10/13 17:15:08 - mmengine - INFO - Epoch(train) [210][100/293] lr: 5.000000e-06 eta: 0:01:02 time: 0.364658 data_time: 0.089866 memory: 5857 loss_kpt: 0.000828 acc_pose: 0.747741 loss: 0.000828 2022/10/13 17:15:26 - mmengine - INFO - Epoch(train) [210][150/293] lr: 5.000000e-06 eta: 0:00:46 time: 0.357854 data_time: 0.076868 memory: 5857 loss_kpt: 0.000837 acc_pose: 0.694338 loss: 0.000837 2022/10/13 17:15:44 - mmengine - INFO - Epoch(train) [210][200/293] lr: 5.000000e-06 eta: 0:00:30 time: 0.367288 data_time: 0.076847 memory: 5857 loss_kpt: 0.000819 acc_pose: 0.706581 loss: 0.000819 2022/10/13 17:16:02 - mmengine - INFO - Epoch(train) [210][250/293] lr: 5.000000e-06 eta: 0:00:13 time: 0.351354 data_time: 0.074306 memory: 5857 loss_kpt: 0.000827 acc_pose: 0.785429 loss: 0.000827 2022/10/13 17:16:17 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-384x288_20221013_101333 2022/10/13 17:16:17 - mmengine - INFO - Saving checkpoint at 210 epochs 2022/10/13 17:16:25 - mmengine - INFO - Epoch(val) [210][50/407] eta: 0:00:45 time: 0.127365 data_time: 0.070184 memory: 5857 2022/10/13 17:16:32 - mmengine - INFO - Epoch(val) [210][100/407] eta: 0:00:37 time: 0.122904 data_time: 0.067213 memory: 760 2022/10/13 17:16:38 - mmengine - INFO - Epoch(val) [210][150/407] eta: 0:00:32 time: 0.126845 data_time: 0.070587 memory: 760 2022/10/13 17:16:44 - mmengine - INFO - Epoch(val) [210][200/407] eta: 0:00:25 time: 0.123896 data_time: 0.067839 memory: 760 2022/10/13 17:16:50 - mmengine - INFO - Epoch(val) [210][250/407] eta: 0:00:19 time: 0.127063 data_time: 0.069342 memory: 760 2022/10/13 17:16:57 - mmengine - INFO - Epoch(val) [210][300/407] eta: 0:00:13 time: 0.124210 data_time: 0.068453 memory: 760 2022/10/13 17:17:03 - mmengine - INFO - Epoch(val) [210][350/407] eta: 0:00:06 time: 0.122756 data_time: 0.067879 memory: 760 2022/10/13 17:17:09 - mmengine - INFO - Epoch(val) [210][400/407] eta: 0:00:00 time: 0.119396 data_time: 0.065492 memory: 760 2022/10/13 17:17:47 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 17:18:02 - mmengine - INFO - Epoch(val) [210][407/407] coco/AP: 0.626186 coco/AP .5: 0.861515 coco/AP .75: 0.695822 coco/AP (M): 0.583027 coco/AP (L): 0.699290 coco/AR: 0.686760 coco/AR .5: 0.903180 coco/AR .75: 0.753463 coco/AR (M): 0.635974 coco/AR (L): 0.758305 2022/10/13 17:18:02 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_384/best_coco/AP_epoch_200.pth is removed 2022/10/13 17:18:04 - mmengine - INFO - The best checkpoint with 0.6262 coco/AP at 210 epoch is saved to best_coco/AP_epoch_210.pth.