2022/10/13 10:13:32 - 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: 2100449653 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 10:13:33 - mmengine - INFO - Config: default_scope = 'mmpose' default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict( type='CheckpointHook', interval=10, max_keep_ckpts=1, save_best='coco/AP', rule='greater'), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='PoseVisualizationHook', enable=False)) custom_hooks = [dict(type='SyncBuffersHook')] env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='PoseLocalVisualizer', vis_backends=[dict(type='LocalVisBackend')], name='visualizer') log_processor = dict( type='LogProcessor', window_size=50, by_epoch=True, num_digits=6) log_level = 'INFO' load_from = None resume = False file_client_args = dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' })) train_cfg = dict(by_epoch=True, max_epochs=210, val_interval=10) val_cfg = dict() test_cfg = dict() optim_wrapper = dict(optimizer=dict(type='Adam', lr=0.0005)) param_scheduler = [ dict( type='LinearLR', begin=0, end=500, start_factor=0.001, by_epoch=False), dict( type='MultiStepLR', begin=0, end=210, milestones=[170, 200], gamma=0.1, by_epoch=True) ] auto_scale_lr = dict(base_batch_size=512) codec = dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) model = dict( type='TopdownPoseEstimator', data_preprocessor=dict( type='PoseDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True), backbone=dict( type='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=(192, 256), heatmap_size=(48, 64), sigma=2)), test_cfg=dict(flip_test=True, flip_mode='heatmap', shift_heatmap=True)) dataset_type = 'CocoDataset' data_mode = 'topdown' data_root = 'data/coco/' train_pipeline = [ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict(type='RandomBBoxTransform'), dict(type='TopdownAffine', input_size=(192, 256)), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)), dict(type='PackPoseInputs') ] val_pipeline = [ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ] train_dataloader = dict( batch_size=64, num_workers=2, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_train2017.json', data_prefix=dict(img='train2017/'), pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict(type='RandomBBoxTransform'), dict(type='TopdownAffine', input_size=(192, 256)), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)), dict(type='PackPoseInputs') ])) val_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ])) test_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ])) val_evaluator = dict( type='CocoMetric', ann_file='data/coco/annotations/person_keypoints_val2017.json') test_evaluator = dict( type='CocoMetric', ann_file='data/coco/annotations/person_keypoints_val2017.json') launcher = 'slurm' work_dir = 'work_dirs/20221013/shufflenetv1_256/' 2022/10/13 10:14:15 - 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 10:14:15 - 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 10:14:15 - 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 10:14:15 - 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 10:14:15 - 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 10:14:15 - 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 10:14:15 - 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 10:14:15 - 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 10:14:21 - 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 10:14:22 - 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 10:14:22 - 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 10:14:22 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_256 by HardDiskBackend. 2022/10/13 10:15:10 - mmengine - INFO - Epoch(train) [1][50/293] lr: 4.954910e-05 eta: 16:23:15 time: 0.959588 data_time: 0.242317 memory: 2690 loss_kpt: 0.002172 acc_pose: 0.136173 loss: 0.002172 2022/10/13 10:15:40 - mmengine - INFO - Epoch(train) [1][100/293] lr: 9.959920e-05 eta: 13:11:53 time: 0.587317 data_time: 0.169868 memory: 2690 loss_kpt: 0.001948 acc_pose: 0.234758 loss: 0.001948 2022/10/13 10:16:08 - mmengine - INFO - Epoch(train) [1][150/293] lr: 1.496493e-04 eta: 11:58:48 time: 0.561051 data_time: 0.083638 memory: 2690 loss_kpt: 0.001811 acc_pose: 0.298558 loss: 0.001811 2022/10/13 10:16:32 - mmengine - INFO - Epoch(train) [1][200/293] lr: 1.996994e-04 eta: 11:03:31 time: 0.488602 data_time: 0.086480 memory: 2690 loss_kpt: 0.001740 acc_pose: 0.377547 loss: 0.001740 2022/10/13 10:16:55 - mmengine - INFO - Epoch(train) [1][250/293] lr: 2.497495e-04 eta: 10:24:57 time: 0.462931 data_time: 0.083795 memory: 2690 loss_kpt: 0.001679 acc_pose: 0.445303 loss: 0.001679 2022/10/13 10:17:13 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:17:29 - mmengine - INFO - Epoch(train) [2][50/293] lr: 3.428427e-04 eta: 8:23:26 time: 0.327081 data_time: 0.153255 memory: 2690 loss_kpt: 0.001577 acc_pose: 0.462787 loss: 0.001577 2022/10/13 10:17:45 - mmengine - INFO - Epoch(train) [2][100/293] lr: 3.928928e-04 eta: 8:00:34 time: 0.320561 data_time: 0.174648 memory: 2690 loss_kpt: 0.001526 acc_pose: 0.398481 loss: 0.001526 2022/10/13 10:18:01 - mmengine - INFO - Epoch(train) [2][150/293] lr: 4.429429e-04 eta: 7:42:17 time: 0.315892 data_time: 0.180818 memory: 2690 loss_kpt: 0.001500 acc_pose: 0.424072 loss: 0.001500 2022/10/13 10:18:17 - mmengine - INFO - Epoch(train) [2][200/293] lr: 4.929930e-04 eta: 7:27:32 time: 0.314696 data_time: 0.168054 memory: 2690 loss_kpt: 0.001497 acc_pose: 0.444654 loss: 0.001497 2022/10/13 10:18:32 - mmengine - INFO - Epoch(train) [2][250/293] lr: 5.000000e-04 eta: 7:15:05 time: 0.310919 data_time: 0.116869 memory: 2690 loss_kpt: 0.001493 acc_pose: 0.426528 loss: 0.001493 2022/10/13 10:18:45 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:19:02 - mmengine - INFO - Epoch(train) [3][50/293] lr: 5.000000e-04 eta: 6:37:03 time: 0.327703 data_time: 0.111046 memory: 2690 loss_kpt: 0.001417 acc_pose: 0.477583 loss: 0.001417 2022/10/13 10:19:18 - mmengine - INFO - Epoch(train) [3][100/293] lr: 5.000000e-04 eta: 6:31:13 time: 0.316867 data_time: 0.132825 memory: 2690 loss_kpt: 0.001404 acc_pose: 0.540053 loss: 0.001404 2022/10/13 10:19:33 - mmengine - INFO - Epoch(train) [3][150/293] lr: 5.000000e-04 eta: 6:26:03 time: 0.315274 data_time: 0.141934 memory: 2690 loss_kpt: 0.001396 acc_pose: 0.552114 loss: 0.001396 2022/10/13 10:19:49 - mmengine - INFO - Epoch(train) [3][200/293] lr: 5.000000e-04 eta: 6:21:40 time: 0.318036 data_time: 0.158294 memory: 2690 loss_kpt: 0.001384 acc_pose: 0.543860 loss: 0.001384 2022/10/13 10:20:05 - mmengine - INFO - Epoch(train) [3][250/293] lr: 5.000000e-04 eta: 6:17:26 time: 0.312107 data_time: 0.118696 memory: 2690 loss_kpt: 0.001362 acc_pose: 0.555482 loss: 0.001362 2022/10/13 10:20:19 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:20:35 - mmengine - INFO - Epoch(train) [4][50/293] lr: 5.000000e-04 eta: 5:56:48 time: 0.324992 data_time: 0.163401 memory: 2690 loss_kpt: 0.001333 acc_pose: 0.506912 loss: 0.001333 2022/10/13 10:20:51 - mmengine - INFO - Epoch(train) [4][100/293] lr: 5.000000e-04 eta: 5:54:46 time: 0.319776 data_time: 0.176841 memory: 2690 loss_kpt: 0.001338 acc_pose: 0.538737 loss: 0.001338 2022/10/13 10:20:57 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:21:07 - mmengine - INFO - Epoch(train) [4][150/293] lr: 5.000000e-04 eta: 5:52:49 time: 0.317658 data_time: 0.165044 memory: 2690 loss_kpt: 0.001322 acc_pose: 0.568849 loss: 0.001322 2022/10/13 10:21:23 - mmengine - INFO - Epoch(train) [4][200/293] lr: 5.000000e-04 eta: 5:51:21 time: 0.324516 data_time: 0.132503 memory: 2690 loss_kpt: 0.001305 acc_pose: 0.509106 loss: 0.001305 2022/10/13 10:21:39 - mmengine - INFO - Epoch(train) [4][250/293] lr: 5.000000e-04 eta: 5:49:43 time: 0.318771 data_time: 0.167640 memory: 2690 loss_kpt: 0.001284 acc_pose: 0.552471 loss: 0.001284 2022/10/13 10:21:52 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:22:08 - mmengine - INFO - Epoch(train) [5][50/293] lr: 5.000000e-04 eta: 5:35:59 time: 0.325302 data_time: 0.138817 memory: 2690 loss_kpt: 0.001285 acc_pose: 0.561424 loss: 0.001285 2022/10/13 10:22:24 - mmengine - INFO - Epoch(train) [5][100/293] lr: 5.000000e-04 eta: 5:34:58 time: 0.315665 data_time: 0.089900 memory: 2690 loss_kpt: 0.001287 acc_pose: 0.551849 loss: 0.001287 2022/10/13 10:22:40 - mmengine - INFO - Epoch(train) [5][150/293] lr: 5.000000e-04 eta: 5:34:23 time: 0.325323 data_time: 0.169106 memory: 2690 loss_kpt: 0.001295 acc_pose: 0.528661 loss: 0.001295 2022/10/13 10:22:56 - mmengine - INFO - Epoch(train) [5][200/293] lr: 5.000000e-04 eta: 5:33:46 time: 0.324096 data_time: 0.167679 memory: 2690 loss_kpt: 0.001239 acc_pose: 0.530666 loss: 0.001239 2022/10/13 10:23:13 - mmengine - INFO - Epoch(train) [5][250/293] lr: 5.000000e-04 eta: 5:33:08 time: 0.322806 data_time: 0.170820 memory: 2690 loss_kpt: 0.001272 acc_pose: 0.612865 loss: 0.001272 2022/10/13 10:23:26 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:23:42 - mmengine - INFO - Epoch(train) [6][50/293] lr: 5.000000e-04 eta: 5:22:58 time: 0.326285 data_time: 0.142306 memory: 2690 loss_kpt: 0.001265 acc_pose: 0.563748 loss: 0.001265 2022/10/13 10:23:58 - mmengine - INFO - Epoch(train) [6][100/293] lr: 5.000000e-04 eta: 5:22:33 time: 0.318124 data_time: 0.161406 memory: 2690 loss_kpt: 0.001275 acc_pose: 0.561584 loss: 0.001275 2022/10/13 10:24:14 - mmengine - INFO - Epoch(train) [6][150/293] lr: 5.000000e-04 eta: 5:22:05 time: 0.316384 data_time: 0.116032 memory: 2690 loss_kpt: 0.001262 acc_pose: 0.548040 loss: 0.001262 2022/10/13 10:24:30 - mmengine - INFO - Epoch(train) [6][200/293] lr: 5.000000e-04 eta: 5:21:38 time: 0.316512 data_time: 0.166871 memory: 2690 loss_kpt: 0.001245 acc_pose: 0.605350 loss: 0.001245 2022/10/13 10:24:46 - mmengine - INFO - Epoch(train) [6][250/293] lr: 5.000000e-04 eta: 5:21:10 time: 0.315607 data_time: 0.158087 memory: 2690 loss_kpt: 0.001247 acc_pose: 0.621454 loss: 0.001247 2022/10/13 10:24:59 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:25:16 - mmengine - INFO - Epoch(train) [7][50/293] lr: 5.000000e-04 eta: 5:13:20 time: 0.332632 data_time: 0.119084 memory: 2690 loss_kpt: 0.001233 acc_pose: 0.582230 loss: 0.001233 2022/10/13 10:25:32 - mmengine - INFO - Epoch(train) [7][100/293] lr: 5.000000e-04 eta: 5:13:18 time: 0.323367 data_time: 0.061303 memory: 2690 loss_kpt: 0.001242 acc_pose: 0.594107 loss: 0.001242 2022/10/13 10:25:48 - mmengine - INFO - Epoch(train) [7][150/293] lr: 5.000000e-04 eta: 5:13:01 time: 0.314025 data_time: 0.072673 memory: 2690 loss_kpt: 0.001206 acc_pose: 0.566922 loss: 0.001206 2022/10/13 10:26:04 - mmengine - INFO - Epoch(train) [7][200/293] lr: 5.000000e-04 eta: 5:12:47 time: 0.316293 data_time: 0.105192 memory: 2690 loss_kpt: 0.001226 acc_pose: 0.534101 loss: 0.001226 2022/10/13 10:26:17 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:26:19 - mmengine - INFO - Epoch(train) [7][250/293] lr: 5.000000e-04 eta: 5:12:30 time: 0.314113 data_time: 0.116653 memory: 2690 loss_kpt: 0.001223 acc_pose: 0.524224 loss: 0.001223 2022/10/13 10:26:33 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:26:49 - mmengine - INFO - Epoch(train) [8][50/293] lr: 5.000000e-04 eta: 5:05:53 time: 0.325958 data_time: 0.143557 memory: 2690 loss_kpt: 0.001198 acc_pose: 0.604965 loss: 0.001198 2022/10/13 10:27:05 - mmengine - INFO - Epoch(train) [8][100/293] lr: 5.000000e-04 eta: 5:05:53 time: 0.319992 data_time: 0.120111 memory: 2690 loss_kpt: 0.001203 acc_pose: 0.589419 loss: 0.001203 2022/10/13 10:27:21 - mmengine - INFO - Epoch(train) [8][150/293] lr: 5.000000e-04 eta: 5:05:57 time: 0.323850 data_time: 0.083342 memory: 2690 loss_kpt: 0.001201 acc_pose: 0.558082 loss: 0.001201 2022/10/13 10:27:37 - mmengine - INFO - Epoch(train) [8][200/293] lr: 5.000000e-04 eta: 5:05:54 time: 0.318893 data_time: 0.131767 memory: 2690 loss_kpt: 0.001215 acc_pose: 0.583785 loss: 0.001215 2022/10/13 10:27:53 - mmengine - INFO - Epoch(train) [8][250/293] lr: 5.000000e-04 eta: 5:05:46 time: 0.315485 data_time: 0.115946 memory: 2690 loss_kpt: 0.001208 acc_pose: 0.545911 loss: 0.001208 2022/10/13 10:28:06 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:28:23 - mmengine - INFO - Epoch(train) [9][50/293] lr: 5.000000e-04 eta: 5:00:20 time: 0.335722 data_time: 0.098767 memory: 2690 loss_kpt: 0.001199 acc_pose: 0.641748 loss: 0.001199 2022/10/13 10:28:39 - mmengine - INFO - Epoch(train) [9][100/293] lr: 5.000000e-04 eta: 5:00:17 time: 0.314204 data_time: 0.065541 memory: 2690 loss_kpt: 0.001204 acc_pose: 0.562842 loss: 0.001204 2022/10/13 10:28:55 - mmengine - INFO - Epoch(train) [9][150/293] lr: 5.000000e-04 eta: 5:00:20 time: 0.320296 data_time: 0.069597 memory: 2690 loss_kpt: 0.001186 acc_pose: 0.593556 loss: 0.001186 2022/10/13 10:29:11 - mmengine - INFO - Epoch(train) [9][200/293] lr: 5.000000e-04 eta: 5:00:16 time: 0.315683 data_time: 0.097832 memory: 2690 loss_kpt: 0.001189 acc_pose: 0.594051 loss: 0.001189 2022/10/13 10:29:26 - mmengine - INFO - Epoch(train) [9][250/293] lr: 5.000000e-04 eta: 5:00:12 time: 0.314749 data_time: 0.133951 memory: 2690 loss_kpt: 0.001193 acc_pose: 0.582020 loss: 0.001193 2022/10/13 10:29:40 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:29:57 - mmengine - INFO - Epoch(train) [10][50/293] lr: 5.000000e-04 eta: 4:55:19 time: 0.327246 data_time: 0.111070 memory: 2690 loss_kpt: 0.001146 acc_pose: 0.623653 loss: 0.001146 2022/10/13 10:30:13 - mmengine - INFO - Epoch(train) [10][100/293] lr: 5.000000e-04 eta: 4:55:32 time: 0.327074 data_time: 0.174043 memory: 2690 loss_kpt: 0.001194 acc_pose: 0.644472 loss: 0.001194 2022/10/13 10:30:29 - mmengine - INFO - Epoch(train) [10][150/293] lr: 5.000000e-04 eta: 4:55:36 time: 0.319619 data_time: 0.169361 memory: 2690 loss_kpt: 0.001175 acc_pose: 0.631271 loss: 0.001175 2022/10/13 10:30:45 - mmengine - INFO - Epoch(train) [10][200/293] lr: 5.000000e-04 eta: 4:55:47 time: 0.327656 data_time: 0.121614 memory: 2690 loss_kpt: 0.001173 acc_pose: 0.642539 loss: 0.001173 2022/10/13 10:31:01 - mmengine - INFO - Epoch(train) [10][250/293] lr: 5.000000e-04 eta: 4:55:48 time: 0.318385 data_time: 0.152772 memory: 2690 loss_kpt: 0.001178 acc_pose: 0.595200 loss: 0.001178 2022/10/13 10:31:15 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:31:15 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/10/13 10:31:27 - mmengine - INFO - Epoch(val) [10][50/407] eta: 0:01:08 time: 0.192516 data_time: 0.148633 memory: 2690 2022/10/13 10:31:32 - mmengine - INFO - Epoch(val) [10][100/407] eta: 0:00:34 time: 0.112336 data_time: 0.071705 memory: 415 2022/10/13 10:31:38 - mmengine - INFO - Epoch(val) [10][150/407] eta: 0:00:29 time: 0.112941 data_time: 0.072295 memory: 415 2022/10/13 10:31:43 - mmengine - INFO - Epoch(val) [10][200/407] eta: 0:00:21 time: 0.106093 data_time: 0.065844 memory: 415 2022/10/13 10:31:49 - mmengine - INFO - Epoch(val) [10][250/407] eta: 0:00:17 time: 0.111409 data_time: 0.073160 memory: 415 2022/10/13 10:31:54 - mmengine - INFO - Epoch(val) [10][300/407] eta: 0:00:11 time: 0.110507 data_time: 0.066914 memory: 415 2022/10/13 10:32:00 - mmengine - INFO - Epoch(val) [10][350/407] eta: 0:00:06 time: 0.110442 data_time: 0.069153 memory: 415 2022/10/13 10:32:05 - mmengine - INFO - Epoch(val) [10][400/407] eta: 0:00:00 time: 0.110304 data_time: 0.069451 memory: 415 2022/10/13 10:32:44 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 10:32:59 - mmengine - INFO - Epoch(val) [10][407/407] coco/AP: 0.451168 coco/AP .5: 0.775261 coco/AP .75: 0.463581 coco/AP (M): 0.423508 coco/AP (L): 0.504009 coco/AR: 0.526149 coco/AR .5: 0.831707 coco/AR .75: 0.556675 coco/AR (M): 0.484977 coco/AR (L): 0.583798 2022/10/13 10:33:01 - mmengine - INFO - The best checkpoint with 0.4512 coco/AP at 10 epoch is saved to best_coco/AP_epoch_10.pth. 2022/10/13 10:33:17 - mmengine - INFO - Epoch(train) [11][50/293] lr: 5.000000e-04 eta: 4:51:23 time: 0.321379 data_time: 0.184934 memory: 2690 loss_kpt: 0.001173 acc_pose: 0.611225 loss: 0.001173 2022/10/13 10:33:23 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:33:33 - mmengine - INFO - Epoch(train) [11][100/293] lr: 5.000000e-04 eta: 4:51:32 time: 0.323727 data_time: 0.220801 memory: 2690 loss_kpt: 0.001162 acc_pose: 0.674201 loss: 0.001162 2022/10/13 10:33:49 - mmengine - INFO - Epoch(train) [11][150/293] lr: 5.000000e-04 eta: 4:51:39 time: 0.322313 data_time: 0.181741 memory: 2690 loss_kpt: 0.001169 acc_pose: 0.692077 loss: 0.001169 2022/10/13 10:34:05 - mmengine - INFO - Epoch(train) [11][200/293] lr: 5.000000e-04 eta: 4:51:40 time: 0.315808 data_time: 0.189770 memory: 2690 loss_kpt: 0.001175 acc_pose: 0.645707 loss: 0.001175 2022/10/13 10:34:21 - mmengine - INFO - Epoch(train) [11][250/293] lr: 5.000000e-04 eta: 4:51:41 time: 0.317774 data_time: 0.189022 memory: 2690 loss_kpt: 0.001151 acc_pose: 0.602974 loss: 0.001151 2022/10/13 10:34:34 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:34:51 - mmengine - INFO - Epoch(train) [12][50/293] lr: 5.000000e-04 eta: 4:47:52 time: 0.331232 data_time: 0.089867 memory: 2690 loss_kpt: 0.001159 acc_pose: 0.602620 loss: 0.001159 2022/10/13 10:35:07 - mmengine - INFO - Epoch(train) [12][100/293] lr: 5.000000e-04 eta: 4:47:54 time: 0.315479 data_time: 0.150850 memory: 2690 loss_kpt: 0.001164 acc_pose: 0.663278 loss: 0.001164 2022/10/13 10:35:23 - mmengine - INFO - Epoch(train) [12][150/293] lr: 5.000000e-04 eta: 4:48:00 time: 0.321394 data_time: 0.098608 memory: 2690 loss_kpt: 0.001137 acc_pose: 0.586135 loss: 0.001137 2022/10/13 10:35:39 - mmengine - INFO - Epoch(train) [12][200/293] lr: 5.000000e-04 eta: 4:48:01 time: 0.315717 data_time: 0.129336 memory: 2690 loss_kpt: 0.001149 acc_pose: 0.615193 loss: 0.001149 2022/10/13 10:35:54 - mmengine - INFO - Epoch(train) [12][250/293] lr: 5.000000e-04 eta: 4:48:02 time: 0.317031 data_time: 0.146742 memory: 2690 loss_kpt: 0.001168 acc_pose: 0.618567 loss: 0.001168 2022/10/13 10:36:08 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:36:24 - mmengine - INFO - Epoch(train) [13][50/293] lr: 5.000000e-04 eta: 4:44:31 time: 0.327328 data_time: 0.116844 memory: 2690 loss_kpt: 0.001159 acc_pose: 0.637722 loss: 0.001159 2022/10/13 10:36:40 - mmengine - INFO - Epoch(train) [13][100/293] lr: 5.000000e-04 eta: 4:44:30 time: 0.312445 data_time: 0.095063 memory: 2690 loss_kpt: 0.001144 acc_pose: 0.621686 loss: 0.001144 2022/10/13 10:36:56 - mmengine - INFO - Epoch(train) [13][150/293] lr: 5.000000e-04 eta: 4:44:41 time: 0.327267 data_time: 0.102343 memory: 2690 loss_kpt: 0.001159 acc_pose: 0.626498 loss: 0.001159 2022/10/13 10:37:12 - mmengine - INFO - Epoch(train) [13][200/293] lr: 5.000000e-04 eta: 4:44:50 time: 0.325653 data_time: 0.060514 memory: 2690 loss_kpt: 0.001154 acc_pose: 0.604919 loss: 0.001154 2022/10/13 10:37:28 - mmengine - INFO - Epoch(train) [13][250/293] lr: 5.000000e-04 eta: 4:44:51 time: 0.315357 data_time: 0.072177 memory: 2690 loss_kpt: 0.001144 acc_pose: 0.574741 loss: 0.001144 2022/10/13 10:37:41 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:37:58 - mmengine - INFO - Epoch(train) [14][50/293] lr: 5.000000e-04 eta: 4:41:42 time: 0.334589 data_time: 0.114527 memory: 2690 loss_kpt: 0.001159 acc_pose: 0.579220 loss: 0.001159 2022/10/13 10:38:14 - mmengine - INFO - Epoch(train) [14][100/293] lr: 5.000000e-04 eta: 4:41:47 time: 0.320024 data_time: 0.152708 memory: 2690 loss_kpt: 0.001151 acc_pose: 0.616763 loss: 0.001151 2022/10/13 10:38:30 - mmengine - INFO - Epoch(train) [14][150/293] lr: 5.000000e-04 eta: 4:41:52 time: 0.320838 data_time: 0.168832 memory: 2690 loss_kpt: 0.001147 acc_pose: 0.638369 loss: 0.001147 2022/10/13 10:38:43 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:38:46 - mmengine - INFO - Epoch(train) [14][200/293] lr: 5.000000e-04 eta: 4:41:59 time: 0.323337 data_time: 0.172301 memory: 2690 loss_kpt: 0.001128 acc_pose: 0.656267 loss: 0.001128 2022/10/13 10:39:02 - mmengine - INFO - Epoch(train) [14][250/293] lr: 5.000000e-04 eta: 4:42:04 time: 0.321468 data_time: 0.155696 memory: 2690 loss_kpt: 0.001140 acc_pose: 0.555827 loss: 0.001140 2022/10/13 10:39:16 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:39:32 - mmengine - INFO - Epoch(train) [15][50/293] lr: 5.000000e-04 eta: 4:39:04 time: 0.327872 data_time: 0.153327 memory: 2690 loss_kpt: 0.001137 acc_pose: 0.589336 loss: 0.001137 2022/10/13 10:39:48 - mmengine - INFO - Epoch(train) [15][100/293] lr: 5.000000e-04 eta: 4:39:10 time: 0.321115 data_time: 0.073809 memory: 2690 loss_kpt: 0.001123 acc_pose: 0.601621 loss: 0.001123 2022/10/13 10:40:04 - mmengine - INFO - Epoch(train) [15][150/293] lr: 5.000000e-04 eta: 4:39:10 time: 0.314181 data_time: 0.070544 memory: 2690 loss_kpt: 0.001135 acc_pose: 0.669648 loss: 0.001135 2022/10/13 10:40:20 - mmengine - INFO - Epoch(train) [15][200/293] lr: 5.000000e-04 eta: 4:39:13 time: 0.319123 data_time: 0.074530 memory: 2690 loss_kpt: 0.001138 acc_pose: 0.647561 loss: 0.001138 2022/10/13 10:40:36 - mmengine - INFO - Epoch(train) [15][250/293] lr: 5.000000e-04 eta: 4:39:18 time: 0.322656 data_time: 0.066604 memory: 2690 loss_kpt: 0.001132 acc_pose: 0.585628 loss: 0.001132 2022/10/13 10:40:50 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:41:06 - mmengine - INFO - Epoch(train) [16][50/293] lr: 5.000000e-04 eta: 4:36:29 time: 0.324429 data_time: 0.113549 memory: 2690 loss_kpt: 0.001122 acc_pose: 0.616863 loss: 0.001122 2022/10/13 10:41:22 - mmengine - INFO - Epoch(train) [16][100/293] lr: 5.000000e-04 eta: 4:36:30 time: 0.315086 data_time: 0.172629 memory: 2690 loss_kpt: 0.001115 acc_pose: 0.585140 loss: 0.001115 2022/10/13 10:41:38 - mmengine - INFO - Epoch(train) [16][150/293] lr: 5.000000e-04 eta: 4:36:34 time: 0.320863 data_time: 0.172068 memory: 2690 loss_kpt: 0.001137 acc_pose: 0.633721 loss: 0.001137 2022/10/13 10:41:54 - mmengine - INFO - Epoch(train) [16][200/293] lr: 5.000000e-04 eta: 4:36:41 time: 0.325255 data_time: 0.177526 memory: 2690 loss_kpt: 0.001141 acc_pose: 0.606074 loss: 0.001141 2022/10/13 10:42:11 - mmengine - INFO - Epoch(train) [16][250/293] lr: 5.000000e-04 eta: 4:36:52 time: 0.334048 data_time: 0.185783 memory: 2690 loss_kpt: 0.001122 acc_pose: 0.580345 loss: 0.001122 2022/10/13 10:42:25 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:42:41 - mmengine - INFO - Epoch(train) [17][50/293] lr: 5.000000e-04 eta: 4:34:15 time: 0.325917 data_time: 0.167368 memory: 2690 loss_kpt: 0.001130 acc_pose: 0.678313 loss: 0.001130 2022/10/13 10:42:57 - mmengine - INFO - Epoch(train) [17][100/293] lr: 5.000000e-04 eta: 4:34:15 time: 0.314423 data_time: 0.168266 memory: 2690 loss_kpt: 0.001113 acc_pose: 0.634287 loss: 0.001113 2022/10/13 10:43:13 - mmengine - INFO - Epoch(train) [17][150/293] lr: 5.000000e-04 eta: 4:34:25 time: 0.331189 data_time: 0.187426 memory: 2690 loss_kpt: 0.001124 acc_pose: 0.635541 loss: 0.001124 2022/10/13 10:43:29 - mmengine - INFO - Epoch(train) [17][200/293] lr: 5.000000e-04 eta: 4:34:26 time: 0.318112 data_time: 0.156399 memory: 2690 loss_kpt: 0.001110 acc_pose: 0.651781 loss: 0.001110 2022/10/13 10:43:45 - mmengine - INFO - Epoch(train) [17][250/293] lr: 5.000000e-04 eta: 4:34:29 time: 0.320361 data_time: 0.129091 memory: 2690 loss_kpt: 0.001113 acc_pose: 0.643405 loss: 0.001113 2022/10/13 10:43:59 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:44:05 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:44:16 - mmengine - INFO - Epoch(train) [18][50/293] lr: 5.000000e-04 eta: 4:32:06 time: 0.336065 data_time: 0.179640 memory: 2690 loss_kpt: 0.001128 acc_pose: 0.644629 loss: 0.001128 2022/10/13 10:44:31 - mmengine - INFO - Epoch(train) [18][100/293] lr: 5.000000e-04 eta: 4:32:07 time: 0.315945 data_time: 0.148122 memory: 2690 loss_kpt: 0.001108 acc_pose: 0.674380 loss: 0.001108 2022/10/13 10:44:47 - mmengine - INFO - Epoch(train) [18][150/293] lr: 5.000000e-04 eta: 4:32:10 time: 0.321005 data_time: 0.128486 memory: 2690 loss_kpt: 0.001104 acc_pose: 0.579918 loss: 0.001104 2022/10/13 10:45:03 - mmengine - INFO - Epoch(train) [18][200/293] lr: 5.000000e-04 eta: 4:32:09 time: 0.315248 data_time: 0.087373 memory: 2690 loss_kpt: 0.001123 acc_pose: 0.633784 loss: 0.001123 2022/10/13 10:45:19 - mmengine - INFO - Epoch(train) [18][250/293] lr: 5.000000e-04 eta: 4:32:10 time: 0.318695 data_time: 0.121405 memory: 2690 loss_kpt: 0.001094 acc_pose: 0.617462 loss: 0.001094 2022/10/13 10:45:33 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:45:49 - mmengine - INFO - Epoch(train) [19][50/293] lr: 5.000000e-04 eta: 4:29:52 time: 0.328352 data_time: 0.105054 memory: 2690 loss_kpt: 0.001106 acc_pose: 0.635968 loss: 0.001106 2022/10/13 10:46:05 - mmengine - INFO - Epoch(train) [19][100/293] lr: 5.000000e-04 eta: 4:29:55 time: 0.322491 data_time: 0.072199 memory: 2690 loss_kpt: 0.001093 acc_pose: 0.596464 loss: 0.001093 2022/10/13 10:46:21 - mmengine - INFO - Epoch(train) [19][150/293] lr: 5.000000e-04 eta: 4:29:57 time: 0.319080 data_time: 0.104818 memory: 2690 loss_kpt: 0.001094 acc_pose: 0.604291 loss: 0.001094 2022/10/13 10:46:37 - mmengine - INFO - Epoch(train) [19][200/293] lr: 5.000000e-04 eta: 4:29:57 time: 0.316634 data_time: 0.134702 memory: 2690 loss_kpt: 0.001104 acc_pose: 0.597668 loss: 0.001104 2022/10/13 10:46:53 - mmengine - INFO - Epoch(train) [19][250/293] lr: 5.000000e-04 eta: 4:29:54 time: 0.312298 data_time: 0.139258 memory: 2690 loss_kpt: 0.001105 acc_pose: 0.626476 loss: 0.001105 2022/10/13 10:47:06 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:47:22 - mmengine - INFO - Epoch(train) [20][50/293] lr: 5.000000e-04 eta: 4:27:40 time: 0.323109 data_time: 0.131495 memory: 2690 loss_kpt: 0.001113 acc_pose: 0.583341 loss: 0.001113 2022/10/13 10:47:38 - mmengine - INFO - Epoch(train) [20][100/293] lr: 5.000000e-04 eta: 4:27:42 time: 0.320202 data_time: 0.154798 memory: 2690 loss_kpt: 0.001093 acc_pose: 0.626551 loss: 0.001093 2022/10/13 10:47:54 - mmengine - INFO - Epoch(train) [20][150/293] lr: 5.000000e-04 eta: 4:27:41 time: 0.314668 data_time: 0.087367 memory: 2690 loss_kpt: 0.001100 acc_pose: 0.551674 loss: 0.001100 2022/10/13 10:48:10 - mmengine - INFO - Epoch(train) [20][200/293] lr: 5.000000e-04 eta: 4:27:39 time: 0.313060 data_time: 0.099290 memory: 2690 loss_kpt: 0.001104 acc_pose: 0.596159 loss: 0.001104 2022/10/13 10:48:26 - mmengine - INFO - Epoch(train) [20][250/293] lr: 5.000000e-04 eta: 4:27:39 time: 0.318804 data_time: 0.149500 memory: 2690 loss_kpt: 0.001104 acc_pose: 0.623337 loss: 0.001104 2022/10/13 10:48:39 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:48:39 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/10/13 10:48:47 - mmengine - INFO - Epoch(val) [20][50/407] eta: 0:00:41 time: 0.116637 data_time: 0.075219 memory: 2690 2022/10/13 10:48:52 - mmengine - INFO - Epoch(val) [20][100/407] eta: 0:00:32 time: 0.107335 data_time: 0.066651 memory: 415 2022/10/13 10:48:58 - mmengine - INFO - Epoch(val) [20][150/407] eta: 0:00:28 time: 0.110151 data_time: 0.068600 memory: 415 2022/10/13 10:49:03 - mmengine - INFO - Epoch(val) [20][200/407] eta: 0:00:22 time: 0.110068 data_time: 0.068708 memory: 415 2022/10/13 10:49:09 - mmengine - INFO - Epoch(val) [20][250/407] eta: 0:00:17 time: 0.111423 data_time: 0.068266 memory: 415 2022/10/13 10:49:14 - mmengine - INFO - Epoch(val) [20][300/407] eta: 0:00:11 time: 0.110590 data_time: 0.069894 memory: 415 2022/10/13 10:49:20 - mmengine - INFO - Epoch(val) [20][350/407] eta: 0:00:06 time: 0.110911 data_time: 0.070261 memory: 415 2022/10/13 10:49:25 - mmengine - INFO - Epoch(val) [20][400/407] eta: 0:00:00 time: 0.106612 data_time: 0.066927 memory: 415 2022/10/13 10:50:04 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 10:50:19 - mmengine - INFO - Epoch(val) [20][407/407] coco/AP: 0.493606 coco/AP .5: 0.801111 coco/AP .75: 0.524833 coco/AP (M): 0.463746 coco/AP (L): 0.548142 coco/AR: 0.565349 coco/AR .5: 0.855479 coco/AR .75: 0.609887 coco/AR (M): 0.523272 coco/AR (L): 0.623932 2022/10/13 10:50:19 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_256/best_coco/AP_epoch_10.pth is removed 2022/10/13 10:50:21 - mmengine - INFO - The best checkpoint with 0.4936 coco/AP at 20 epoch is saved to best_coco/AP_epoch_20.pth. 2022/10/13 10:50:37 - mmengine - INFO - Epoch(train) [21][50/293] lr: 5.000000e-04 eta: 4:25:34 time: 0.327001 data_time: 0.194324 memory: 2690 loss_kpt: 0.001102 acc_pose: 0.629557 loss: 0.001102 2022/10/13 10:50:53 - mmengine - INFO - Epoch(train) [21][100/293] lr: 5.000000e-04 eta: 4:25:36 time: 0.320917 data_time: 0.174847 memory: 2690 loss_kpt: 0.001097 acc_pose: 0.636870 loss: 0.001097 2022/10/13 10:51:06 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:51:10 - mmengine - INFO - Epoch(train) [21][150/293] lr: 5.000000e-04 eta: 4:25:41 time: 0.328023 data_time: 0.174260 memory: 2690 loss_kpt: 0.001109 acc_pose: 0.648303 loss: 0.001109 2022/10/13 10:51:26 - mmengine - INFO - Epoch(train) [21][200/293] lr: 5.000000e-04 eta: 4:25:47 time: 0.332408 data_time: 0.173086 memory: 2690 loss_kpt: 0.001117 acc_pose: 0.623998 loss: 0.001117 2022/10/13 10:51:42 - mmengine - INFO - Epoch(train) [21][250/293] lr: 5.000000e-04 eta: 4:25:48 time: 0.322583 data_time: 0.171157 memory: 2690 loss_kpt: 0.001114 acc_pose: 0.672855 loss: 0.001114 2022/10/13 10:51:56 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:52:12 - mmengine - INFO - Epoch(train) [22][50/293] lr: 5.000000e-04 eta: 4:23:49 time: 0.327376 data_time: 0.094401 memory: 2690 loss_kpt: 0.001094 acc_pose: 0.673683 loss: 0.001094 2022/10/13 10:52:29 - mmengine - INFO - Epoch(train) [22][100/293] lr: 5.000000e-04 eta: 4:23:55 time: 0.331130 data_time: 0.073653 memory: 2690 loss_kpt: 0.001087 acc_pose: 0.638941 loss: 0.001087 2022/10/13 10:52:46 - mmengine - INFO - Epoch(train) [22][150/293] lr: 5.000000e-04 eta: 4:24:04 time: 0.341444 data_time: 0.068025 memory: 2690 loss_kpt: 0.001093 acc_pose: 0.581534 loss: 0.001093 2022/10/13 10:53:02 - mmengine - INFO - Epoch(train) [22][200/293] lr: 5.000000e-04 eta: 4:24:07 time: 0.326935 data_time: 0.064299 memory: 2690 loss_kpt: 0.001076 acc_pose: 0.671504 loss: 0.001076 2022/10/13 10:53:18 - mmengine - INFO - Epoch(train) [22][250/293] lr: 5.000000e-04 eta: 4:24:09 time: 0.325136 data_time: 0.112887 memory: 2690 loss_kpt: 0.001113 acc_pose: 0.621780 loss: 0.001113 2022/10/13 10:53:32 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:53:49 - mmengine - INFO - Epoch(train) [23][50/293] lr: 5.000000e-04 eta: 4:22:21 time: 0.341752 data_time: 0.161744 memory: 2690 loss_kpt: 0.001100 acc_pose: 0.648931 loss: 0.001100 2022/10/13 10:54:05 - mmengine - INFO - Epoch(train) [23][100/293] lr: 5.000000e-04 eta: 4:22:22 time: 0.323320 data_time: 0.177761 memory: 2690 loss_kpt: 0.001097 acc_pose: 0.695512 loss: 0.001097 2022/10/13 10:54:22 - mmengine - INFO - Epoch(train) [23][150/293] lr: 5.000000e-04 eta: 4:22:27 time: 0.332156 data_time: 0.173272 memory: 2690 loss_kpt: 0.001094 acc_pose: 0.651090 loss: 0.001094 2022/10/13 10:54:38 - mmengine - INFO - Epoch(train) [23][200/293] lr: 5.000000e-04 eta: 4:22:27 time: 0.321111 data_time: 0.154977 memory: 2690 loss_kpt: 0.001103 acc_pose: 0.632012 loss: 0.001103 2022/10/13 10:54:54 - mmengine - INFO - Epoch(train) [23][250/293] lr: 5.000000e-04 eta: 4:22:31 time: 0.332025 data_time: 0.097431 memory: 2690 loss_kpt: 0.001097 acc_pose: 0.621208 loss: 0.001097 2022/10/13 10:55:08 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:55:24 - mmengine - INFO - Epoch(train) [24][50/293] lr: 5.000000e-04 eta: 4:20:42 time: 0.330645 data_time: 0.128234 memory: 2690 loss_kpt: 0.001085 acc_pose: 0.654210 loss: 0.001085 2022/10/13 10:55:40 - mmengine - INFO - Epoch(train) [24][100/293] lr: 5.000000e-04 eta: 4:20:42 time: 0.320969 data_time: 0.098853 memory: 2690 loss_kpt: 0.001088 acc_pose: 0.581187 loss: 0.001088 2022/10/13 10:55:57 - mmengine - INFO - Epoch(train) [24][150/293] lr: 5.000000e-04 eta: 4:20:42 time: 0.321573 data_time: 0.081467 memory: 2690 loss_kpt: 0.001072 acc_pose: 0.669442 loss: 0.001072 2022/10/13 10:56:13 - mmengine - INFO - Epoch(train) [24][200/293] lr: 5.000000e-04 eta: 4:20:44 time: 0.328097 data_time: 0.077093 memory: 2690 loss_kpt: 0.001078 acc_pose: 0.701085 loss: 0.001078 2022/10/13 10:56:29 - mmengine - INFO - Epoch(train) [24][250/293] lr: 5.000000e-04 eta: 4:20:43 time: 0.321792 data_time: 0.118737 memory: 2690 loss_kpt: 0.001088 acc_pose: 0.624445 loss: 0.001088 2022/10/13 10:56:32 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:56:43 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:56:59 - mmengine - INFO - Epoch(train) [25][50/293] lr: 5.000000e-04 eta: 4:18:57 time: 0.327554 data_time: 0.140215 memory: 2690 loss_kpt: 0.001073 acc_pose: 0.615134 loss: 0.001073 2022/10/13 10:57:15 - mmengine - INFO - Epoch(train) [25][100/293] lr: 5.000000e-04 eta: 4:18:57 time: 0.320675 data_time: 0.144184 memory: 2690 loss_kpt: 0.001082 acc_pose: 0.671120 loss: 0.001082 2022/10/13 10:57:31 - mmengine - INFO - Epoch(train) [25][150/293] lr: 5.000000e-04 eta: 4:18:56 time: 0.322489 data_time: 0.201906 memory: 2690 loss_kpt: 0.001096 acc_pose: 0.666105 loss: 0.001096 2022/10/13 10:57:47 - mmengine - INFO - Epoch(train) [25][200/293] lr: 5.000000e-04 eta: 4:18:53 time: 0.314194 data_time: 0.172492 memory: 2690 loss_kpt: 0.001091 acc_pose: 0.657942 loss: 0.001091 2022/10/13 10:58:03 - mmengine - INFO - Epoch(train) [25][250/293] lr: 5.000000e-04 eta: 4:18:50 time: 0.318583 data_time: 0.170437 memory: 2690 loss_kpt: 0.001053 acc_pose: 0.711650 loss: 0.001053 2022/10/13 10:58:17 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 10:58:33 - mmengine - INFO - Epoch(train) [26][50/293] lr: 5.000000e-04 eta: 4:17:12 time: 0.336537 data_time: 0.103959 memory: 2690 loss_kpt: 0.001083 acc_pose: 0.647505 loss: 0.001083 2022/10/13 10:58:49 - mmengine - INFO - Epoch(train) [26][100/293] lr: 5.000000e-04 eta: 4:17:10 time: 0.318998 data_time: 0.099029 memory: 2690 loss_kpt: 0.001093 acc_pose: 0.678799 loss: 0.001093 2022/10/13 10:59:05 - mmengine - INFO - Epoch(train) [26][150/293] lr: 5.000000e-04 eta: 4:17:08 time: 0.317992 data_time: 0.092676 memory: 2690 loss_kpt: 0.001076 acc_pose: 0.661803 loss: 0.001076 2022/10/13 10:59:22 - mmengine - INFO - Epoch(train) [26][200/293] lr: 5.000000e-04 eta: 4:17:09 time: 0.329091 data_time: 0.168150 memory: 2690 loss_kpt: 0.001075 acc_pose: 0.658096 loss: 0.001075 2022/10/13 10:59:38 - mmengine - INFO - Epoch(train) [26][250/293] lr: 5.000000e-04 eta: 4:17:09 time: 0.324615 data_time: 0.180911 memory: 2690 loss_kpt: 0.001081 acc_pose: 0.661969 loss: 0.001081 2022/10/13 10:59:52 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:00:08 - mmengine - INFO - Epoch(train) [27][50/293] lr: 5.000000e-04 eta: 4:15:31 time: 0.330994 data_time: 0.167323 memory: 2690 loss_kpt: 0.001083 acc_pose: 0.657490 loss: 0.001083 2022/10/13 11:00:25 - mmengine - INFO - Epoch(train) [27][100/293] lr: 5.000000e-04 eta: 4:15:32 time: 0.326615 data_time: 0.071890 memory: 2690 loss_kpt: 0.001069 acc_pose: 0.673765 loss: 0.001069 2022/10/13 11:00:41 - mmengine - INFO - Epoch(train) [27][150/293] lr: 5.000000e-04 eta: 4:15:35 time: 0.334142 data_time: 0.068979 memory: 2690 loss_kpt: 0.001079 acc_pose: 0.653733 loss: 0.001079 2022/10/13 11:00:57 - mmengine - INFO - Epoch(train) [27][200/293] lr: 5.000000e-04 eta: 4:15:30 time: 0.313006 data_time: 0.065394 memory: 2690 loss_kpt: 0.001091 acc_pose: 0.658338 loss: 0.001091 2022/10/13 11:01:13 - mmengine - INFO - Epoch(train) [27][250/293] lr: 5.000000e-04 eta: 4:15:30 time: 0.328493 data_time: 0.067571 memory: 2690 loss_kpt: 0.001057 acc_pose: 0.614757 loss: 0.001057 2022/10/13 11:01:27 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:01:44 - mmengine - INFO - Epoch(train) [28][50/293] lr: 5.000000e-04 eta: 4:13:59 time: 0.339294 data_time: 0.091308 memory: 2690 loss_kpt: 0.001070 acc_pose: 0.634226 loss: 0.001070 2022/10/13 11:01:57 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:02:00 - mmengine - INFO - Epoch(train) [28][100/293] lr: 5.000000e-04 eta: 4:13:58 time: 0.325130 data_time: 0.082407 memory: 2690 loss_kpt: 0.001063 acc_pose: 0.690237 loss: 0.001063 2022/10/13 11:02:17 - mmengine - INFO - Epoch(train) [28][150/293] lr: 5.000000e-04 eta: 4:14:02 time: 0.337030 data_time: 0.090276 memory: 2690 loss_kpt: 0.001063 acc_pose: 0.645105 loss: 0.001063 2022/10/13 11:02:34 - mmengine - INFO - Epoch(train) [28][200/293] lr: 5.000000e-04 eta: 4:14:04 time: 0.334877 data_time: 0.090329 memory: 2690 loss_kpt: 0.001049 acc_pose: 0.566612 loss: 0.001049 2022/10/13 11:02:50 - mmengine - INFO - Epoch(train) [28][250/293] lr: 5.000000e-04 eta: 4:14:03 time: 0.326482 data_time: 0.175974 memory: 2690 loss_kpt: 0.001069 acc_pose: 0.604348 loss: 0.001069 2022/10/13 11:03:04 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:03:21 - mmengine - INFO - Epoch(train) [29][50/293] lr: 5.000000e-04 eta: 4:12:38 time: 0.349827 data_time: 0.102824 memory: 2690 loss_kpt: 0.001066 acc_pose: 0.596178 loss: 0.001066 2022/10/13 11:03:38 - mmengine - INFO - Epoch(train) [29][100/293] lr: 5.000000e-04 eta: 4:12:37 time: 0.325267 data_time: 0.070440 memory: 2690 loss_kpt: 0.001072 acc_pose: 0.655235 loss: 0.001072 2022/10/13 11:03:54 - mmengine - INFO - Epoch(train) [29][150/293] lr: 5.000000e-04 eta: 4:12:38 time: 0.333599 data_time: 0.062677 memory: 2690 loss_kpt: 0.001053 acc_pose: 0.703634 loss: 0.001053 2022/10/13 11:04:11 - mmengine - INFO - Epoch(train) [29][200/293] lr: 5.000000e-04 eta: 4:12:40 time: 0.337695 data_time: 0.127137 memory: 2690 loss_kpt: 0.001046 acc_pose: 0.632557 loss: 0.001046 2022/10/13 11:04:28 - mmengine - INFO - Epoch(train) [29][250/293] lr: 5.000000e-04 eta: 4:12:40 time: 0.330832 data_time: 0.088109 memory: 2690 loss_kpt: 0.001068 acc_pose: 0.581200 loss: 0.001068 2022/10/13 11:04:42 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:04:59 - mmengine - INFO - Epoch(train) [30][50/293] lr: 5.000000e-04 eta: 4:11:13 time: 0.335051 data_time: 0.166267 memory: 2690 loss_kpt: 0.001071 acc_pose: 0.646422 loss: 0.001071 2022/10/13 11:05:16 - mmengine - INFO - Epoch(train) [30][100/293] lr: 5.000000e-04 eta: 4:11:15 time: 0.336466 data_time: 0.120677 memory: 2690 loss_kpt: 0.001036 acc_pose: 0.635313 loss: 0.001036 2022/10/13 11:05:32 - mmengine - INFO - Epoch(train) [30][150/293] lr: 5.000000e-04 eta: 4:11:15 time: 0.331916 data_time: 0.103174 memory: 2690 loss_kpt: 0.001053 acc_pose: 0.633880 loss: 0.001053 2022/10/13 11:05:49 - mmengine - INFO - Epoch(train) [30][200/293] lr: 5.000000e-04 eta: 4:11:15 time: 0.331920 data_time: 0.148743 memory: 2690 loss_kpt: 0.001067 acc_pose: 0.636765 loss: 0.001067 2022/10/13 11:06:05 - mmengine - INFO - Epoch(train) [30][250/293] lr: 5.000000e-04 eta: 4:11:11 time: 0.321455 data_time: 0.099495 memory: 2690 loss_kpt: 0.001072 acc_pose: 0.594663 loss: 0.001072 2022/10/13 11:06:19 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:06:19 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/10/13 11:06:27 - mmengine - INFO - Epoch(val) [30][50/407] eta: 0:00:42 time: 0.120329 data_time: 0.080561 memory: 2690 2022/10/13 11:06:32 - mmengine - INFO - Epoch(val) [30][100/407] eta: 0:00:34 time: 0.113943 data_time: 0.073636 memory: 415 2022/10/13 11:06:38 - mmengine - INFO - Epoch(val) [30][150/407] eta: 0:00:29 time: 0.113745 data_time: 0.074410 memory: 415 2022/10/13 11:06:44 - mmengine - INFO - Epoch(val) [30][200/407] eta: 0:00:24 time: 0.116969 data_time: 0.076393 memory: 415 2022/10/13 11:06:50 - mmengine - INFO - Epoch(val) [30][250/407] eta: 0:00:17 time: 0.113229 data_time: 0.071649 memory: 415 2022/10/13 11:06:55 - mmengine - INFO - Epoch(val) [30][300/407] eta: 0:00:12 time: 0.113889 data_time: 0.072463 memory: 415 2022/10/13 11:07:01 - mmengine - INFO - Epoch(val) [30][350/407] eta: 0:00:06 time: 0.114546 data_time: 0.074910 memory: 415 2022/10/13 11:07:07 - mmengine - INFO - Epoch(val) [30][400/407] eta: 0:00:00 time: 0.109421 data_time: 0.068966 memory: 415 2022/10/13 11:07:45 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 11:07:59 - mmengine - INFO - Epoch(val) [30][407/407] coco/AP: 0.513237 coco/AP .5: 0.810665 coco/AP .75: 0.553614 coco/AP (M): 0.483013 coco/AP (L): 0.569221 coco/AR: 0.583816 coco/AR .5: 0.863508 coco/AR .75: 0.631927 coco/AR (M): 0.542884 coco/AR (L): 0.641249 2022/10/13 11:07:59 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_256/best_coco/AP_epoch_20.pth is removed 2022/10/13 11:08:01 - mmengine - INFO - The best checkpoint with 0.5132 coco/AP at 30 epoch is saved to best_coco/AP_epoch_30.pth. 2022/10/13 11:08:17 - mmengine - INFO - Epoch(train) [31][50/293] lr: 5.000000e-04 eta: 4:09:46 time: 0.332588 data_time: 0.181140 memory: 2690 loss_kpt: 0.001063 acc_pose: 0.602376 loss: 0.001063 2022/10/13 11:08:34 - mmengine - INFO - Epoch(train) [31][100/293] lr: 5.000000e-04 eta: 4:09:47 time: 0.336496 data_time: 0.164261 memory: 2690 loss_kpt: 0.001065 acc_pose: 0.676653 loss: 0.001065 2022/10/13 11:08:51 - mmengine - INFO - Epoch(train) [31][150/293] lr: 5.000000e-04 eta: 4:09:46 time: 0.328447 data_time: 0.179294 memory: 2690 loss_kpt: 0.001064 acc_pose: 0.665787 loss: 0.001064 2022/10/13 11:09:07 - mmengine - INFO - Epoch(train) [31][200/293] lr: 5.000000e-04 eta: 4:09:44 time: 0.329788 data_time: 0.126801 memory: 2690 loss_kpt: 0.001069 acc_pose: 0.644602 loss: 0.001069 2022/10/13 11:09:11 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:09:24 - mmengine - INFO - Epoch(train) [31][250/293] lr: 5.000000e-04 eta: 4:09:46 time: 0.339015 data_time: 0.206532 memory: 2690 loss_kpt: 0.001057 acc_pose: 0.602336 loss: 0.001057 2022/10/13 11:09:38 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:09:56 - mmengine - INFO - Epoch(train) [32][50/293] lr: 5.000000e-04 eta: 4:08:28 time: 0.351479 data_time: 0.187091 memory: 2690 loss_kpt: 0.001076 acc_pose: 0.726676 loss: 0.001076 2022/10/13 11:10:13 - mmengine - INFO - Epoch(train) [32][100/293] lr: 5.000000e-04 eta: 4:08:29 time: 0.338467 data_time: 0.132989 memory: 2690 loss_kpt: 0.001039 acc_pose: 0.667602 loss: 0.001039 2022/10/13 11:10:29 - mmengine - INFO - Epoch(train) [32][150/293] lr: 5.000000e-04 eta: 4:08:30 time: 0.338954 data_time: 0.068077 memory: 2690 loss_kpt: 0.001061 acc_pose: 0.622066 loss: 0.001061 2022/10/13 11:10:46 - mmengine - INFO - Epoch(train) [32][200/293] lr: 5.000000e-04 eta: 4:08:30 time: 0.334969 data_time: 0.080666 memory: 2690 loss_kpt: 0.001059 acc_pose: 0.622368 loss: 0.001059 2022/10/13 11:11:03 - mmengine - INFO - Epoch(train) [32][250/293] lr: 5.000000e-04 eta: 4:08:28 time: 0.330150 data_time: 0.065069 memory: 2690 loss_kpt: 0.001048 acc_pose: 0.652066 loss: 0.001048 2022/10/13 11:11:16 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:11:34 - mmengine - INFO - Epoch(train) [33][50/293] lr: 5.000000e-04 eta: 4:07:11 time: 0.349832 data_time: 0.148410 memory: 2690 loss_kpt: 0.001041 acc_pose: 0.667280 loss: 0.001041 2022/10/13 11:11:51 - mmengine - INFO - Epoch(train) [33][100/293] lr: 5.000000e-04 eta: 4:07:10 time: 0.332365 data_time: 0.065813 memory: 2690 loss_kpt: 0.001049 acc_pose: 0.599245 loss: 0.001049 2022/10/13 11:12:07 - mmengine - INFO - Epoch(train) [33][150/293] lr: 5.000000e-04 eta: 4:07:09 time: 0.333852 data_time: 0.069678 memory: 2690 loss_kpt: 0.001069 acc_pose: 0.663427 loss: 0.001069 2022/10/13 11:12:24 - mmengine - INFO - Epoch(train) [33][200/293] lr: 5.000000e-04 eta: 4:07:08 time: 0.333854 data_time: 0.063043 memory: 2690 loss_kpt: 0.001063 acc_pose: 0.639022 loss: 0.001063 2022/10/13 11:12:40 - mmengine - INFO - Epoch(train) [33][250/293] lr: 5.000000e-04 eta: 4:07:06 time: 0.330454 data_time: 0.062599 memory: 2690 loss_kpt: 0.001070 acc_pose: 0.630618 loss: 0.001070 2022/10/13 11:12:54 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:13:12 - mmengine - INFO - Epoch(train) [34][50/293] lr: 5.000000e-04 eta: 4:05:53 time: 0.356748 data_time: 0.087271 memory: 2690 loss_kpt: 0.001072 acc_pose: 0.629120 loss: 0.001072 2022/10/13 11:13:29 - mmengine - INFO - Epoch(train) [34][100/293] lr: 5.000000e-04 eta: 4:05:52 time: 0.333509 data_time: 0.069217 memory: 2690 loss_kpt: 0.001072 acc_pose: 0.608970 loss: 0.001072 2022/10/13 11:13:45 - mmengine - INFO - Epoch(train) [34][150/293] lr: 5.000000e-04 eta: 4:05:47 time: 0.322053 data_time: 0.060346 memory: 2690 loss_kpt: 0.001019 acc_pose: 0.681057 loss: 0.001019 2022/10/13 11:14:02 - mmengine - INFO - Epoch(train) [34][200/293] lr: 5.000000e-04 eta: 4:05:46 time: 0.335637 data_time: 0.067781 memory: 2690 loss_kpt: 0.001033 acc_pose: 0.698418 loss: 0.001033 2022/10/13 11:14:18 - mmengine - INFO - Epoch(train) [34][250/293] lr: 5.000000e-04 eta: 4:05:41 time: 0.321622 data_time: 0.071538 memory: 2690 loss_kpt: 0.001037 acc_pose: 0.648300 loss: 0.001037 2022/10/13 11:14:31 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:14:45 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:14:49 - mmengine - INFO - Epoch(train) [35][50/293] lr: 5.000000e-04 eta: 4:04:30 time: 0.357399 data_time: 0.110824 memory: 2690 loss_kpt: 0.001062 acc_pose: 0.641450 loss: 0.001062 2022/10/13 11:15:05 - mmengine - INFO - Epoch(train) [35][100/293] lr: 5.000000e-04 eta: 4:04:25 time: 0.320217 data_time: 0.091023 memory: 2690 loss_kpt: 0.001051 acc_pose: 0.587871 loss: 0.001051 2022/10/13 11:15:22 - mmengine - INFO - Epoch(train) [35][150/293] lr: 5.000000e-04 eta: 4:04:23 time: 0.334612 data_time: 0.153106 memory: 2690 loss_kpt: 0.001043 acc_pose: 0.647117 loss: 0.001043 2022/10/13 11:15:38 - mmengine - INFO - Epoch(train) [35][200/293] lr: 5.000000e-04 eta: 4:04:20 time: 0.328956 data_time: 0.191217 memory: 2690 loss_kpt: 0.001050 acc_pose: 0.703544 loss: 0.001050 2022/10/13 11:15:55 - mmengine - INFO - Epoch(train) [35][250/293] lr: 5.000000e-04 eta: 4:04:18 time: 0.332715 data_time: 0.181128 memory: 2690 loss_kpt: 0.001026 acc_pose: 0.623871 loss: 0.001026 2022/10/13 11:16:09 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:16:27 - mmengine - INFO - Epoch(train) [36][50/293] lr: 5.000000e-04 eta: 4:03:09 time: 0.362194 data_time: 0.126016 memory: 2690 loss_kpt: 0.001066 acc_pose: 0.696757 loss: 0.001066 2022/10/13 11:16:45 - mmengine - INFO - Epoch(train) [36][100/293] lr: 5.000000e-04 eta: 4:03:11 time: 0.349488 data_time: 0.062460 memory: 2690 loss_kpt: 0.001065 acc_pose: 0.658262 loss: 0.001065 2022/10/13 11:17:03 - mmengine - INFO - Epoch(train) [36][150/293] lr: 5.000000e-04 eta: 4:03:16 time: 0.364013 data_time: 0.069906 memory: 2690 loss_kpt: 0.001039 acc_pose: 0.607258 loss: 0.001039 2022/10/13 11:17:21 - mmengine - INFO - Epoch(train) [36][200/293] lr: 5.000000e-04 eta: 4:03:18 time: 0.353724 data_time: 0.083863 memory: 2690 loss_kpt: 0.001072 acc_pose: 0.709536 loss: 0.001072 2022/10/13 11:17:37 - mmengine - INFO - Epoch(train) [36][250/293] lr: 5.000000e-04 eta: 4:03:14 time: 0.327548 data_time: 0.107437 memory: 2690 loss_kpt: 0.001037 acc_pose: 0.680820 loss: 0.001037 2022/10/13 11:17:52 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:18:11 - mmengine - INFO - Epoch(train) [37][50/293] lr: 5.000000e-04 eta: 4:02:12 time: 0.382957 data_time: 0.102416 memory: 2690 loss_kpt: 0.001033 acc_pose: 0.698667 loss: 0.001033 2022/10/13 11:18:29 - mmengine - INFO - Epoch(train) [37][100/293] lr: 5.000000e-04 eta: 4:02:18 time: 0.370944 data_time: 0.141711 memory: 2690 loss_kpt: 0.001053 acc_pose: 0.654092 loss: 0.001053 2022/10/13 11:18:46 - mmengine - INFO - Epoch(train) [37][150/293] lr: 5.000000e-04 eta: 4:02:13 time: 0.326208 data_time: 0.181499 memory: 2690 loss_kpt: 0.001060 acc_pose: 0.667207 loss: 0.001060 2022/10/13 11:19:03 - mmengine - INFO - Epoch(train) [37][200/293] lr: 5.000000e-04 eta: 4:02:12 time: 0.340986 data_time: 0.223927 memory: 2690 loss_kpt: 0.001041 acc_pose: 0.696255 loss: 0.001041 2022/10/13 11:19:20 - mmengine - INFO - Epoch(train) [37][250/293] lr: 5.000000e-04 eta: 4:02:11 time: 0.341482 data_time: 0.189951 memory: 2690 loss_kpt: 0.001057 acc_pose: 0.700449 loss: 0.001057 2022/10/13 11:19:34 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:19:53 - mmengine - INFO - Epoch(train) [38][50/293] lr: 5.000000e-04 eta: 4:01:07 time: 0.373894 data_time: 0.085322 memory: 2690 loss_kpt: 0.001052 acc_pose: 0.640195 loss: 0.001052 2022/10/13 11:20:11 - mmengine - INFO - Epoch(train) [38][100/293] lr: 5.000000e-04 eta: 4:01:09 time: 0.356277 data_time: 0.078863 memory: 2690 loss_kpt: 0.001016 acc_pose: 0.694259 loss: 0.001016 2022/10/13 11:20:28 - mmengine - INFO - Epoch(train) [38][150/293] lr: 5.000000e-04 eta: 4:01:09 time: 0.349967 data_time: 0.069292 memory: 2690 loss_kpt: 0.001039 acc_pose: 0.627690 loss: 0.001039 2022/10/13 11:20:31 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:20:45 - mmengine - INFO - Epoch(train) [38][200/293] lr: 5.000000e-04 eta: 4:01:05 time: 0.329385 data_time: 0.090953 memory: 2690 loss_kpt: 0.001047 acc_pose: 0.600810 loss: 0.001047 2022/10/13 11:21:02 - mmengine - INFO - Epoch(train) [38][250/293] lr: 5.000000e-04 eta: 4:01:03 time: 0.342218 data_time: 0.120080 memory: 2690 loss_kpt: 0.001042 acc_pose: 0.651082 loss: 0.001042 2022/10/13 11:21:16 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:21:34 - mmengine - INFO - Epoch(train) [39][50/293] lr: 5.000000e-04 eta: 3:59:58 time: 0.360932 data_time: 0.172631 memory: 2690 loss_kpt: 0.001043 acc_pose: 0.640583 loss: 0.001043 2022/10/13 11:21:52 - mmengine - INFO - Epoch(train) [39][100/293] lr: 5.000000e-04 eta: 4:00:02 time: 0.367809 data_time: 0.077392 memory: 2690 loss_kpt: 0.001059 acc_pose: 0.648246 loss: 0.001059 2022/10/13 11:22:10 - mmengine - INFO - Epoch(train) [39][150/293] lr: 5.000000e-04 eta: 4:00:01 time: 0.346468 data_time: 0.061652 memory: 2690 loss_kpt: 0.001028 acc_pose: 0.669545 loss: 0.001028 2022/10/13 11:22:26 - mmengine - INFO - Epoch(train) [39][200/293] lr: 5.000000e-04 eta: 3:59:56 time: 0.329059 data_time: 0.089066 memory: 2690 loss_kpt: 0.001033 acc_pose: 0.632386 loss: 0.001033 2022/10/13 11:22:44 - mmengine - INFO - Epoch(train) [39][250/293] lr: 5.000000e-04 eta: 3:59:59 time: 0.366828 data_time: 0.082189 memory: 2690 loss_kpt: 0.001030 acc_pose: 0.618465 loss: 0.001030 2022/10/13 11:22:59 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:23:16 - mmengine - INFO - Epoch(train) [40][50/293] lr: 5.000000e-04 eta: 3:58:52 time: 0.350212 data_time: 0.096763 memory: 2690 loss_kpt: 0.001043 acc_pose: 0.663346 loss: 0.001043 2022/10/13 11:23:34 - mmengine - INFO - Epoch(train) [40][100/293] lr: 5.000000e-04 eta: 3:58:51 time: 0.345935 data_time: 0.140812 memory: 2690 loss_kpt: 0.001037 acc_pose: 0.643013 loss: 0.001037 2022/10/13 11:23:52 - mmengine - INFO - Epoch(train) [40][150/293] lr: 5.000000e-04 eta: 3:58:55 time: 0.372397 data_time: 0.063417 memory: 2690 loss_kpt: 0.001042 acc_pose: 0.685787 loss: 0.001042 2022/10/13 11:24:11 - mmengine - INFO - Epoch(train) [40][200/293] lr: 5.000000e-04 eta: 3:59:01 time: 0.379601 data_time: 0.070097 memory: 2690 loss_kpt: 0.001052 acc_pose: 0.691528 loss: 0.001052 2022/10/13 11:24:31 - mmengine - INFO - Epoch(train) [40][250/293] lr: 5.000000e-04 eta: 3:59:11 time: 0.400784 data_time: 0.069838 memory: 2690 loss_kpt: 0.001038 acc_pose: 0.722259 loss: 0.001038 2022/10/13 11:24:46 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:24:46 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/10/13 11:24:55 - mmengine - INFO - Epoch(val) [40][50/407] eta: 0:00:44 time: 0.125168 data_time: 0.084541 memory: 2690 2022/10/13 11:25:01 - mmengine - INFO - Epoch(val) [40][100/407] eta: 0:00:38 time: 0.123949 data_time: 0.082740 memory: 415 2022/10/13 11:25:07 - mmengine - INFO - Epoch(val) [40][150/407] eta: 0:00:30 time: 0.119257 data_time: 0.077689 memory: 415 2022/10/13 11:25:13 - mmengine - INFO - Epoch(val) [40][200/407] eta: 0:00:26 time: 0.128039 data_time: 0.086155 memory: 415 2022/10/13 11:25:20 - mmengine - INFO - Epoch(val) [40][250/407] eta: 0:00:20 time: 0.127490 data_time: 0.087759 memory: 415 2022/10/13 11:25:25 - mmengine - INFO - Epoch(val) [40][300/407] eta: 0:00:12 time: 0.112661 data_time: 0.069428 memory: 415 2022/10/13 11:25:31 - mmengine - INFO - Epoch(val) [40][350/407] eta: 0:00:06 time: 0.122022 data_time: 0.082029 memory: 415 2022/10/13 11:25:37 - mmengine - INFO - Epoch(val) [40][400/407] eta: 0:00:00 time: 0.119416 data_time: 0.079931 memory: 415 2022/10/13 11:26:15 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 11:26:30 - mmengine - INFO - Epoch(val) [40][407/407] coco/AP: 0.527076 coco/AP .5: 0.818501 coco/AP .75: 0.576163 coco/AP (M): 0.497813 coco/AP (L): 0.582269 coco/AR: 0.595246 coco/AR .5: 0.869647 coco/AR .75: 0.650504 coco/AR (M): 0.553783 coco/AR (L): 0.652880 2022/10/13 11:26:30 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_256/best_coco/AP_epoch_30.pth is removed 2022/10/13 11:26:32 - mmengine - INFO - The best checkpoint with 0.5271 coco/AP at 40 epoch is saved to best_coco/AP_epoch_40.pth. 2022/10/13 11:26:50 - mmengine - INFO - Epoch(train) [41][50/293] lr: 5.000000e-04 eta: 3:58:06 time: 0.357962 data_time: 0.142490 memory: 2690 loss_kpt: 0.001049 acc_pose: 0.621467 loss: 0.001049 2022/10/13 11:27:09 - mmengine - INFO - Epoch(train) [41][100/293] lr: 5.000000e-04 eta: 3:58:12 time: 0.382131 data_time: 0.064120 memory: 2690 loss_kpt: 0.001055 acc_pose: 0.654808 loss: 0.001055 2022/10/13 11:27:27 - mmengine - INFO - Epoch(train) [41][150/293] lr: 5.000000e-04 eta: 3:58:11 time: 0.353309 data_time: 0.077282 memory: 2690 loss_kpt: 0.001030 acc_pose: 0.666328 loss: 0.001030 2022/10/13 11:27:44 - mmengine - INFO - Epoch(train) [41][200/293] lr: 5.000000e-04 eta: 3:58:09 time: 0.347544 data_time: 0.124049 memory: 2690 loss_kpt: 0.001027 acc_pose: 0.626453 loss: 0.001027 2022/10/13 11:28:01 - mmengine - INFO - Epoch(train) [41][250/293] lr: 5.000000e-04 eta: 3:58:07 time: 0.346269 data_time: 0.081310 memory: 2690 loss_kpt: 0.001039 acc_pose: 0.673251 loss: 0.001039 2022/10/13 11:28:12 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:28:16 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:28:33 - mmengine - INFO - Epoch(train) [42][50/293] lr: 5.000000e-04 eta: 3:57:03 time: 0.356154 data_time: 0.123892 memory: 2690 loss_kpt: 0.001031 acc_pose: 0.627393 loss: 0.001031 2022/10/13 11:28:50 - mmengine - INFO - Epoch(train) [42][100/293] lr: 5.000000e-04 eta: 3:57:00 time: 0.340579 data_time: 0.081377 memory: 2690 loss_kpt: 0.001051 acc_pose: 0.637860 loss: 0.001051 2022/10/13 11:29:07 - mmengine - INFO - Epoch(train) [42][150/293] lr: 5.000000e-04 eta: 3:56:56 time: 0.341040 data_time: 0.069348 memory: 2690 loss_kpt: 0.001033 acc_pose: 0.627431 loss: 0.001033 2022/10/13 11:29:25 - mmengine - INFO - Epoch(train) [42][200/293] lr: 5.000000e-04 eta: 3:56:53 time: 0.344494 data_time: 0.064767 memory: 2690 loss_kpt: 0.001031 acc_pose: 0.619023 loss: 0.001031 2022/10/13 11:29:43 - mmengine - INFO - Epoch(train) [42][250/293] lr: 5.000000e-04 eta: 3:56:53 time: 0.357076 data_time: 0.071569 memory: 2690 loss_kpt: 0.001050 acc_pose: 0.652397 loss: 0.001050 2022/10/13 11:29:57 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:30:15 - mmengine - INFO - Epoch(train) [43][50/293] lr: 5.000000e-04 eta: 3:55:50 time: 0.354421 data_time: 0.202709 memory: 2690 loss_kpt: 0.001032 acc_pose: 0.707471 loss: 0.001032 2022/10/13 11:30:33 - mmengine - INFO - Epoch(train) [43][100/293] lr: 5.000000e-04 eta: 3:55:52 time: 0.375263 data_time: 0.118352 memory: 2690 loss_kpt: 0.001033 acc_pose: 0.686829 loss: 0.001033 2022/10/13 11:30:51 - mmengine - INFO - Epoch(train) [43][150/293] lr: 5.000000e-04 eta: 3:55:52 time: 0.359832 data_time: 0.078681 memory: 2690 loss_kpt: 0.001039 acc_pose: 0.638599 loss: 0.001039 2022/10/13 11:31:10 - mmengine - INFO - Epoch(train) [43][200/293] lr: 5.000000e-04 eta: 3:55:54 time: 0.370991 data_time: 0.065390 memory: 2690 loss_kpt: 0.001031 acc_pose: 0.560961 loss: 0.001031 2022/10/13 11:31:28 - mmengine - INFO - Epoch(train) [43][250/293] lr: 5.000000e-04 eta: 3:55:52 time: 0.354917 data_time: 0.069356 memory: 2690 loss_kpt: 0.001023 acc_pose: 0.689004 loss: 0.001023 2022/10/13 11:31:43 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:32:01 - mmengine - INFO - Epoch(train) [44][50/293] lr: 5.000000e-04 eta: 3:54:51 time: 0.358134 data_time: 0.081081 memory: 2690 loss_kpt: 0.001030 acc_pose: 0.653481 loss: 0.001030 2022/10/13 11:32:18 - mmengine - INFO - Epoch(train) [44][100/293] lr: 5.000000e-04 eta: 3:54:48 time: 0.347567 data_time: 0.064098 memory: 2690 loss_kpt: 0.001035 acc_pose: 0.660227 loss: 0.001035 2022/10/13 11:32:35 - mmengine - INFO - Epoch(train) [44][150/293] lr: 5.000000e-04 eta: 3:54:45 time: 0.350345 data_time: 0.066434 memory: 2690 loss_kpt: 0.001042 acc_pose: 0.644255 loss: 0.001042 2022/10/13 11:32:53 - mmengine - INFO - Epoch(train) [44][200/293] lr: 5.000000e-04 eta: 3:54:41 time: 0.342455 data_time: 0.070684 memory: 2690 loss_kpt: 0.001014 acc_pose: 0.658481 loss: 0.001014 2022/10/13 11:33:10 - mmengine - INFO - Epoch(train) [44][250/293] lr: 5.000000e-04 eta: 3:54:39 time: 0.352493 data_time: 0.076839 memory: 2690 loss_kpt: 0.001022 acc_pose: 0.630963 loss: 0.001022 2022/10/13 11:33:25 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:33:43 - mmengine - INFO - Epoch(train) [45][50/293] lr: 5.000000e-04 eta: 3:53:39 time: 0.362287 data_time: 0.206858 memory: 2690 loss_kpt: 0.001032 acc_pose: 0.712697 loss: 0.001032 2022/10/13 11:34:01 - mmengine - INFO - Epoch(train) [45][100/293] lr: 5.000000e-04 eta: 3:53:36 time: 0.352625 data_time: 0.160538 memory: 2690 loss_kpt: 0.001030 acc_pose: 0.654338 loss: 0.001030 2022/10/13 11:34:03 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:34:17 - mmengine - INFO - Epoch(train) [45][150/293] lr: 5.000000e-04 eta: 3:53:30 time: 0.334170 data_time: 0.162734 memory: 2690 loss_kpt: 0.001022 acc_pose: 0.682914 loss: 0.001022 2022/10/13 11:34:34 - mmengine - INFO - Epoch(train) [45][200/293] lr: 5.000000e-04 eta: 3:53:24 time: 0.333424 data_time: 0.099528 memory: 2690 loss_kpt: 0.001007 acc_pose: 0.665575 loss: 0.001007 2022/10/13 11:34:51 - mmengine - INFO - Epoch(train) [45][250/293] lr: 5.000000e-04 eta: 3:53:19 time: 0.339622 data_time: 0.067103 memory: 2690 loss_kpt: 0.001011 acc_pose: 0.709236 loss: 0.001011 2022/10/13 11:35:05 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:35:24 - mmengine - INFO - Epoch(train) [46][50/293] lr: 5.000000e-04 eta: 3:52:21 time: 0.367302 data_time: 0.088656 memory: 2690 loss_kpt: 0.001032 acc_pose: 0.615620 loss: 0.001032 2022/10/13 11:35:40 - mmengine - INFO - Epoch(train) [46][100/293] lr: 5.000000e-04 eta: 3:52:14 time: 0.332412 data_time: 0.086945 memory: 2690 loss_kpt: 0.001043 acc_pose: 0.665606 loss: 0.001043 2022/10/13 11:35:57 - mmengine - INFO - Epoch(train) [46][150/293] lr: 5.000000e-04 eta: 3:52:09 time: 0.339876 data_time: 0.125639 memory: 2690 loss_kpt: 0.001033 acc_pose: 0.644305 loss: 0.001033 2022/10/13 11:36:15 - mmengine - INFO - Epoch(train) [46][200/293] lr: 5.000000e-04 eta: 3:52:06 time: 0.351804 data_time: 0.178992 memory: 2690 loss_kpt: 0.001037 acc_pose: 0.625616 loss: 0.001037 2022/10/13 11:36:33 - mmengine - INFO - Epoch(train) [46][250/293] lr: 5.000000e-04 eta: 3:52:03 time: 0.354595 data_time: 0.182405 memory: 2690 loss_kpt: 0.001028 acc_pose: 0.631022 loss: 0.001028 2022/10/13 11:36:48 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:37:08 - mmengine - INFO - Epoch(train) [47][50/293] lr: 5.000000e-04 eta: 3:51:08 time: 0.381309 data_time: 0.076630 memory: 2690 loss_kpt: 0.001016 acc_pose: 0.674608 loss: 0.001016 2022/10/13 11:37:26 - mmengine - INFO - Epoch(train) [47][100/293] lr: 5.000000e-04 eta: 3:51:07 time: 0.360263 data_time: 0.063094 memory: 2690 loss_kpt: 0.001021 acc_pose: 0.678600 loss: 0.001021 2022/10/13 11:37:44 - mmengine - INFO - Epoch(train) [47][150/293] lr: 5.000000e-04 eta: 3:51:06 time: 0.369368 data_time: 0.083733 memory: 2690 loss_kpt: 0.001043 acc_pose: 0.637829 loss: 0.001043 2022/10/13 11:38:02 - mmengine - INFO - Epoch(train) [47][200/293] lr: 5.000000e-04 eta: 3:51:03 time: 0.352141 data_time: 0.120659 memory: 2690 loss_kpt: 0.001017 acc_pose: 0.648379 loss: 0.001017 2022/10/13 11:38:19 - mmengine - INFO - Epoch(train) [47][250/293] lr: 5.000000e-04 eta: 3:50:57 time: 0.340771 data_time: 0.067103 memory: 2690 loss_kpt: 0.001026 acc_pose: 0.745639 loss: 0.001026 2022/10/13 11:38:34 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:38:52 - mmengine - INFO - Epoch(train) [48][50/293] lr: 5.000000e-04 eta: 3:49:58 time: 0.354573 data_time: 0.108795 memory: 2690 loss_kpt: 0.001012 acc_pose: 0.637636 loss: 0.001012 2022/10/13 11:39:09 - mmengine - INFO - Epoch(train) [48][100/293] lr: 5.000000e-04 eta: 3:49:53 time: 0.343895 data_time: 0.157323 memory: 2690 loss_kpt: 0.001025 acc_pose: 0.672677 loss: 0.001025 2022/10/13 11:39:27 - mmengine - INFO - Epoch(train) [48][150/293] lr: 5.000000e-04 eta: 3:49:50 time: 0.356107 data_time: 0.065316 memory: 2690 loss_kpt: 0.001012 acc_pose: 0.708934 loss: 0.001012 2022/10/13 11:39:44 - mmengine - INFO - Epoch(train) [48][200/293] lr: 5.000000e-04 eta: 3:49:45 time: 0.342331 data_time: 0.080722 memory: 2690 loss_kpt: 0.001031 acc_pose: 0.661108 loss: 0.001031 2022/10/13 11:39:53 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:40:00 - mmengine - INFO - Epoch(train) [48][250/293] lr: 5.000000e-04 eta: 3:49:35 time: 0.321589 data_time: 0.114544 memory: 2690 loss_kpt: 0.001024 acc_pose: 0.649140 loss: 0.001024 2022/10/13 11:40:14 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:40:32 - mmengine - INFO - Epoch(train) [49][50/293] lr: 5.000000e-04 eta: 3:48:38 time: 0.356250 data_time: 0.090600 memory: 2690 loss_kpt: 0.001034 acc_pose: 0.677122 loss: 0.001034 2022/10/13 11:40:49 - mmengine - INFO - Epoch(train) [49][100/293] lr: 5.000000e-04 eta: 3:48:31 time: 0.333826 data_time: 0.065774 memory: 2690 loss_kpt: 0.001011 acc_pose: 0.620179 loss: 0.001011 2022/10/13 11:41:06 - mmengine - INFO - Epoch(train) [49][150/293] lr: 5.000000e-04 eta: 3:48:25 time: 0.343093 data_time: 0.071525 memory: 2690 loss_kpt: 0.001005 acc_pose: 0.564529 loss: 0.001005 2022/10/13 11:41:24 - mmengine - INFO - Epoch(train) [49][200/293] lr: 5.000000e-04 eta: 3:48:21 time: 0.349681 data_time: 0.064985 memory: 2690 loss_kpt: 0.001012 acc_pose: 0.704243 loss: 0.001012 2022/10/13 11:41:40 - mmengine - INFO - Epoch(train) [49][250/293] lr: 5.000000e-04 eta: 3:48:13 time: 0.328327 data_time: 0.068296 memory: 2690 loss_kpt: 0.001013 acc_pose: 0.679698 loss: 0.001013 2022/10/13 11:41:54 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:42:11 - mmengine - INFO - Epoch(train) [50][50/293] lr: 5.000000e-04 eta: 3:47:14 time: 0.344315 data_time: 0.094602 memory: 2690 loss_kpt: 0.001007 acc_pose: 0.619452 loss: 0.001007 2022/10/13 11:42:28 - mmengine - INFO - Epoch(train) [50][100/293] lr: 5.000000e-04 eta: 3:47:08 time: 0.342631 data_time: 0.065018 memory: 2690 loss_kpt: 0.001017 acc_pose: 0.679719 loss: 0.001017 2022/10/13 11:42:44 - mmengine - INFO - Epoch(train) [50][150/293] lr: 5.000000e-04 eta: 3:46:59 time: 0.324513 data_time: 0.075396 memory: 2690 loss_kpt: 0.001019 acc_pose: 0.667270 loss: 0.001019 2022/10/13 11:43:02 - mmengine - INFO - Epoch(train) [50][200/293] lr: 5.000000e-04 eta: 3:46:53 time: 0.344413 data_time: 0.088121 memory: 2690 loss_kpt: 0.001028 acc_pose: 0.681657 loss: 0.001028 2022/10/13 11:43:18 - mmengine - INFO - Epoch(train) [50][250/293] lr: 5.000000e-04 eta: 3:46:46 time: 0.332691 data_time: 0.062073 memory: 2690 loss_kpt: 0.001036 acc_pose: 0.669675 loss: 0.001036 2022/10/13 11:43:32 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:43:32 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/10/13 11:43:40 - mmengine - INFO - Epoch(val) [50][50/407] eta: 0:00:40 time: 0.112447 data_time: 0.070337 memory: 2690 2022/10/13 11:43:45 - mmengine - INFO - Epoch(val) [50][100/407] eta: 0:00:33 time: 0.109529 data_time: 0.067575 memory: 415 2022/10/13 11:43:51 - mmengine - INFO - Epoch(val) [50][150/407] eta: 0:00:29 time: 0.115595 data_time: 0.074555 memory: 415 2022/10/13 11:43:57 - mmengine - INFO - Epoch(val) [50][200/407] eta: 0:00:22 time: 0.108303 data_time: 0.067595 memory: 415 2022/10/13 11:44:02 - mmengine - INFO - Epoch(val) [50][250/407] eta: 0:00:18 time: 0.115287 data_time: 0.074980 memory: 415 2022/10/13 11:44:08 - mmengine - INFO - Epoch(val) [50][300/407] eta: 0:00:11 time: 0.110989 data_time: 0.070367 memory: 415 2022/10/13 11:44:14 - mmengine - INFO - Epoch(val) [50][350/407] eta: 0:00:06 time: 0.115465 data_time: 0.074060 memory: 415 2022/10/13 11:44:19 - mmengine - INFO - Epoch(val) [50][400/407] eta: 0:00:00 time: 0.106487 data_time: 0.062286 memory: 415 2022/10/13 11:44:57 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 11:45:12 - mmengine - INFO - Epoch(val) [50][407/407] coco/AP: 0.536786 coco/AP .5: 0.822672 coco/AP .75: 0.584586 coco/AP (M): 0.504750 coco/AP (L): 0.596791 coco/AR: 0.606014 coco/AR .5: 0.874055 coco/AR .75: 0.661052 coco/AR (M): 0.562715 coco/AR (L): 0.666555 2022/10/13 11:45:12 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_256/best_coco/AP_epoch_40.pth is removed 2022/10/13 11:45:13 - mmengine - INFO - The best checkpoint with 0.5368 coco/AP at 50 epoch is saved to best_coco/AP_epoch_50.pth. 2022/10/13 11:45:31 - mmengine - INFO - Epoch(train) [51][50/293] lr: 5.000000e-04 eta: 3:45:48 time: 0.346077 data_time: 0.215551 memory: 2690 loss_kpt: 0.000999 acc_pose: 0.697344 loss: 0.000999 2022/10/13 11:45:48 - mmengine - INFO - Epoch(train) [51][100/293] lr: 5.000000e-04 eta: 3:45:42 time: 0.344493 data_time: 0.101138 memory: 2690 loss_kpt: 0.001016 acc_pose: 0.692960 loss: 0.001016 2022/10/13 11:46:04 - mmengine - INFO - Epoch(train) [51][150/293] lr: 5.000000e-04 eta: 3:45:34 time: 0.329275 data_time: 0.065128 memory: 2690 loss_kpt: 0.001015 acc_pose: 0.680760 loss: 0.001015 2022/10/13 11:46:21 - mmengine - INFO - Epoch(train) [51][200/293] lr: 5.000000e-04 eta: 3:45:26 time: 0.331409 data_time: 0.072867 memory: 2690 loss_kpt: 0.001026 acc_pose: 0.591544 loss: 0.001026 2022/10/13 11:46:38 - mmengine - INFO - Epoch(train) [51][250/293] lr: 5.000000e-04 eta: 3:45:21 time: 0.345886 data_time: 0.069032 memory: 2690 loss_kpt: 0.001022 acc_pose: 0.664132 loss: 0.001022 2022/10/13 11:46:52 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:47:10 - mmengine - INFO - Epoch(train) [52][50/293] lr: 5.000000e-04 eta: 3:44:24 time: 0.349035 data_time: 0.087761 memory: 2690 loss_kpt: 0.001016 acc_pose: 0.702382 loss: 0.001016 2022/10/13 11:47:13 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:47:28 - mmengine - INFO - Epoch(train) [52][100/293] lr: 5.000000e-04 eta: 3:44:21 time: 0.360723 data_time: 0.207022 memory: 2690 loss_kpt: 0.001011 acc_pose: 0.736038 loss: 0.001011 2022/10/13 11:47:45 - mmengine - INFO - Epoch(train) [52][150/293] lr: 5.000000e-04 eta: 3:44:15 time: 0.345395 data_time: 0.108272 memory: 2690 loss_kpt: 0.001016 acc_pose: 0.725950 loss: 0.001016 2022/10/13 11:48:01 - mmengine - INFO - Epoch(train) [52][200/293] lr: 5.000000e-04 eta: 3:44:06 time: 0.326088 data_time: 0.061135 memory: 2690 loss_kpt: 0.001017 acc_pose: 0.696086 loss: 0.001017 2022/10/13 11:48:18 - mmengine - INFO - Epoch(train) [52][250/293] lr: 5.000000e-04 eta: 3:43:58 time: 0.336110 data_time: 0.109611 memory: 2690 loss_kpt: 0.001001 acc_pose: 0.646614 loss: 0.001001 2022/10/13 11:48:32 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:48:50 - mmengine - INFO - Epoch(train) [53][50/293] lr: 5.000000e-04 eta: 3:43:04 time: 0.356123 data_time: 0.106898 memory: 2690 loss_kpt: 0.001018 acc_pose: 0.674981 loss: 0.001018 2022/10/13 11:49:07 - mmengine - INFO - Epoch(train) [53][100/293] lr: 5.000000e-04 eta: 3:42:56 time: 0.334707 data_time: 0.132711 memory: 2690 loss_kpt: 0.001009 acc_pose: 0.647548 loss: 0.001009 2022/10/13 11:49:28 - mmengine - INFO - Epoch(train) [53][150/293] lr: 5.000000e-04 eta: 3:43:02 time: 0.424562 data_time: 0.110118 memory: 2690 loss_kpt: 0.001016 acc_pose: 0.663312 loss: 0.001016 2022/10/13 11:49:53 - mmengine - INFO - Epoch(train) [53][200/293] lr: 5.000000e-04 eta: 3:43:20 time: 0.507967 data_time: 0.188241 memory: 2690 loss_kpt: 0.001003 acc_pose: 0.686913 loss: 0.001003 2022/10/13 11:50:10 - mmengine - INFO - Epoch(train) [53][250/293] lr: 5.000000e-04 eta: 3:43:12 time: 0.338563 data_time: 0.093506 memory: 2690 loss_kpt: 0.000996 acc_pose: 0.714596 loss: 0.000996 2022/10/13 11:50:25 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:50:42 - mmengine - INFO - Epoch(train) [54][50/293] lr: 5.000000e-04 eta: 3:42:16 time: 0.343667 data_time: 0.141034 memory: 2690 loss_kpt: 0.001004 acc_pose: 0.680256 loss: 0.001004 2022/10/13 11:50:59 - mmengine - INFO - Epoch(train) [54][100/293] lr: 5.000000e-04 eta: 3:42:08 time: 0.333101 data_time: 0.068692 memory: 2690 loss_kpt: 0.000998 acc_pose: 0.659963 loss: 0.000998 2022/10/13 11:51:15 - mmengine - INFO - Epoch(train) [54][150/293] lr: 5.000000e-04 eta: 3:41:59 time: 0.329170 data_time: 0.106877 memory: 2690 loss_kpt: 0.001005 acc_pose: 0.654035 loss: 0.001005 2022/10/13 11:51:31 - mmengine - INFO - Epoch(train) [54][200/293] lr: 5.000000e-04 eta: 3:41:49 time: 0.323743 data_time: 0.109416 memory: 2690 loss_kpt: 0.001006 acc_pose: 0.699975 loss: 0.001006 2022/10/13 11:51:48 - mmengine - INFO - Epoch(train) [54][250/293] lr: 5.000000e-04 eta: 3:41:42 time: 0.340837 data_time: 0.155015 memory: 2690 loss_kpt: 0.001036 acc_pose: 0.688835 loss: 0.001036 2022/10/13 11:52:02 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:52:19 - mmengine - INFO - Epoch(train) [55][50/293] lr: 5.000000e-04 eta: 3:40:46 time: 0.339496 data_time: 0.137356 memory: 2690 loss_kpt: 0.001021 acc_pose: 0.678486 loss: 0.001021 2022/10/13 11:52:36 - mmengine - INFO - Epoch(train) [55][100/293] lr: 5.000000e-04 eta: 3:40:39 time: 0.342789 data_time: 0.090465 memory: 2690 loss_kpt: 0.001010 acc_pose: 0.686698 loss: 0.001010 2022/10/13 11:52:53 - mmengine - INFO - Epoch(train) [55][150/293] lr: 5.000000e-04 eta: 3:40:32 time: 0.338591 data_time: 0.177970 memory: 2690 loss_kpt: 0.001014 acc_pose: 0.696656 loss: 0.001014 2022/10/13 11:53:02 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:53:10 - mmengine - INFO - Epoch(train) [55][200/293] lr: 5.000000e-04 eta: 3:40:22 time: 0.327028 data_time: 0.134637 memory: 2690 loss_kpt: 0.001021 acc_pose: 0.673626 loss: 0.001021 2022/10/13 11:53:27 - mmengine - INFO - Epoch(train) [55][250/293] lr: 5.000000e-04 eta: 3:40:16 time: 0.350180 data_time: 0.070308 memory: 2690 loss_kpt: 0.001010 acc_pose: 0.651696 loss: 0.001010 2022/10/13 11:53:41 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:54:06 - mmengine - INFO - Epoch(train) [56][50/293] lr: 5.000000e-04 eta: 3:39:44 time: 0.500943 data_time: 0.223996 memory: 2690 loss_kpt: 0.001020 acc_pose: 0.579422 loss: 0.001020 2022/10/13 11:54:27 - mmengine - INFO - Epoch(train) [56][100/293] lr: 5.000000e-04 eta: 3:39:46 time: 0.412005 data_time: 0.080806 memory: 2690 loss_kpt: 0.001038 acc_pose: 0.648439 loss: 0.001038 2022/10/13 11:54:43 - mmengine - INFO - Epoch(train) [56][150/293] lr: 5.000000e-04 eta: 3:39:37 time: 0.333025 data_time: 0.060032 memory: 2690 loss_kpt: 0.001018 acc_pose: 0.627828 loss: 0.001018 2022/10/13 11:55:00 - mmengine - INFO - Epoch(train) [56][200/293] lr: 5.000000e-04 eta: 3:39:29 time: 0.336424 data_time: 0.059808 memory: 2690 loss_kpt: 0.001004 acc_pose: 0.674801 loss: 0.001004 2022/10/13 11:55:17 - mmengine - INFO - Epoch(train) [56][250/293] lr: 5.000000e-04 eta: 3:39:20 time: 0.332679 data_time: 0.097644 memory: 2690 loss_kpt: 0.001012 acc_pose: 0.685693 loss: 0.001012 2022/10/13 11:55:31 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:55:49 - mmengine - INFO - Epoch(train) [57][50/293] lr: 5.000000e-04 eta: 3:38:29 time: 0.366873 data_time: 0.179516 memory: 2690 loss_kpt: 0.001003 acc_pose: 0.651405 loss: 0.001003 2022/10/13 11:56:07 - mmengine - INFO - Epoch(train) [57][100/293] lr: 5.000000e-04 eta: 3:38:22 time: 0.342043 data_time: 0.179411 memory: 2690 loss_kpt: 0.001001 acc_pose: 0.693554 loss: 0.001001 2022/10/13 11:56:24 - mmengine - INFO - Epoch(train) [57][150/293] lr: 5.000000e-04 eta: 3:38:14 time: 0.342629 data_time: 0.189733 memory: 2690 loss_kpt: 0.001015 acc_pose: 0.655324 loss: 0.001015 2022/10/13 11:56:39 - mmengine - INFO - Epoch(train) [57][200/293] lr: 5.000000e-04 eta: 3:38:02 time: 0.311482 data_time: 0.189241 memory: 2690 loss_kpt: 0.001006 acc_pose: 0.742535 loss: 0.001006 2022/10/13 11:56:56 - mmengine - INFO - Epoch(train) [57][250/293] lr: 5.000000e-04 eta: 3:37:53 time: 0.333174 data_time: 0.174890 memory: 2690 loss_kpt: 0.001005 acc_pose: 0.675400 loss: 0.001005 2022/10/13 11:57:11 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:57:28 - mmengine - INFO - Epoch(train) [58][50/293] lr: 5.000000e-04 eta: 3:37:01 time: 0.351040 data_time: 0.120037 memory: 2690 loss_kpt: 0.001021 acc_pose: 0.646539 loss: 0.001021 2022/10/13 11:57:45 - mmengine - INFO - Epoch(train) [58][100/293] lr: 5.000000e-04 eta: 3:36:52 time: 0.333976 data_time: 0.058376 memory: 2690 loss_kpt: 0.000998 acc_pose: 0.657686 loss: 0.000998 2022/10/13 11:58:02 - mmengine - INFO - Epoch(train) [58][150/293] lr: 5.000000e-04 eta: 3:36:43 time: 0.332559 data_time: 0.066132 memory: 2690 loss_kpt: 0.001012 acc_pose: 0.660907 loss: 0.001012 2022/10/13 11:58:18 - mmengine - INFO - Epoch(train) [58][200/293] lr: 5.000000e-04 eta: 3:36:34 time: 0.331753 data_time: 0.069189 memory: 2690 loss_kpt: 0.001015 acc_pose: 0.675946 loss: 0.001015 2022/10/13 11:58:35 - mmengine - INFO - Epoch(train) [58][250/293] lr: 5.000000e-04 eta: 3:36:25 time: 0.334102 data_time: 0.060133 memory: 2690 loss_kpt: 0.000996 acc_pose: 0.663001 loss: 0.000996 2022/10/13 11:58:49 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:58:52 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 11:59:06 - mmengine - INFO - Epoch(train) [59][50/293] lr: 5.000000e-04 eta: 3:35:31 time: 0.336936 data_time: 0.187520 memory: 2690 loss_kpt: 0.001007 acc_pose: 0.708935 loss: 0.001007 2022/10/13 11:59:23 - mmengine - INFO - Epoch(train) [59][100/293] lr: 5.000000e-04 eta: 3:35:22 time: 0.334021 data_time: 0.174873 memory: 2690 loss_kpt: 0.001010 acc_pose: 0.614794 loss: 0.001010 2022/10/13 11:59:40 - mmengine - INFO - Epoch(train) [59][150/293] lr: 5.000000e-04 eta: 3:35:15 time: 0.345715 data_time: 0.089883 memory: 2690 loss_kpt: 0.000993 acc_pose: 0.671409 loss: 0.000993 2022/10/13 11:59:57 - mmengine - INFO - Epoch(train) [59][200/293] lr: 5.000000e-04 eta: 3:35:07 time: 0.341403 data_time: 0.081821 memory: 2690 loss_kpt: 0.001008 acc_pose: 0.649520 loss: 0.001008 2022/10/13 12:00:14 - mmengine - INFO - Epoch(train) [59][250/293] lr: 5.000000e-04 eta: 3:34:58 time: 0.340962 data_time: 0.168321 memory: 2690 loss_kpt: 0.000992 acc_pose: 0.692248 loss: 0.000992 2022/10/13 12:00:29 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:00:46 - mmengine - INFO - Epoch(train) [60][50/293] lr: 5.000000e-04 eta: 3:34:05 time: 0.338937 data_time: 0.109467 memory: 2690 loss_kpt: 0.001009 acc_pose: 0.645572 loss: 0.001009 2022/10/13 12:01:03 - mmengine - INFO - Epoch(train) [60][100/293] lr: 5.000000e-04 eta: 3:33:57 time: 0.340272 data_time: 0.084608 memory: 2690 loss_kpt: 0.001007 acc_pose: 0.681029 loss: 0.001007 2022/10/13 12:01:20 - mmengine - INFO - Epoch(train) [60][150/293] lr: 5.000000e-04 eta: 3:33:49 time: 0.341649 data_time: 0.069561 memory: 2690 loss_kpt: 0.000995 acc_pose: 0.684757 loss: 0.000995 2022/10/13 12:01:37 - mmengine - INFO - Epoch(train) [60][200/293] lr: 5.000000e-04 eta: 3:33:39 time: 0.326791 data_time: 0.085421 memory: 2690 loss_kpt: 0.001001 acc_pose: 0.637306 loss: 0.001001 2022/10/13 12:01:53 - mmengine - INFO - Epoch(train) [60][250/293] lr: 5.000000e-04 eta: 3:33:30 time: 0.336974 data_time: 0.144221 memory: 2690 loss_kpt: 0.000998 acc_pose: 0.769696 loss: 0.000998 2022/10/13 12:02:07 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:02:07 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/10/13 12:02:15 - mmengine - INFO - Epoch(val) [60][50/407] eta: 0:00:42 time: 0.119719 data_time: 0.079641 memory: 2690 2022/10/13 12:02:21 - mmengine - INFO - Epoch(val) [60][100/407] eta: 0:00:33 time: 0.108017 data_time: 0.066110 memory: 415 2022/10/13 12:02:27 - mmengine - INFO - Epoch(val) [60][150/407] eta: 0:00:30 time: 0.118430 data_time: 0.077861 memory: 415 2022/10/13 12:02:32 - mmengine - INFO - Epoch(val) [60][200/407] eta: 0:00:21 time: 0.105079 data_time: 0.059120 memory: 415 2022/10/13 12:02:38 - mmengine - INFO - Epoch(val) [60][250/407] eta: 0:00:18 time: 0.117333 data_time: 0.076017 memory: 415 2022/10/13 12:02:44 - mmengine - INFO - Epoch(val) [60][300/407] eta: 0:00:12 time: 0.120023 data_time: 0.079558 memory: 415 2022/10/13 12:02:49 - mmengine - INFO - Epoch(val) [60][350/407] eta: 0:00:06 time: 0.112521 data_time: 0.071669 memory: 415 2022/10/13 12:02:55 - mmengine - INFO - Epoch(val) [60][400/407] eta: 0:00:00 time: 0.115558 data_time: 0.074896 memory: 415 2022/10/13 12:03:34 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 12:03:49 - mmengine - INFO - Epoch(val) [60][407/407] coco/AP: 0.543164 coco/AP .5: 0.823834 coco/AP .75: 0.595858 coco/AP (M): 0.510164 coco/AP (L): 0.604391 coco/AR: 0.612327 coco/AR .5: 0.875000 coco/AR .75: 0.670497 coco/AR (M): 0.567495 coco/AR (L): 0.675362 2022/10/13 12:03:49 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_256/best_coco/AP_epoch_50.pth is removed 2022/10/13 12:03:50 - mmengine - INFO - The best checkpoint with 0.5432 coco/AP at 60 epoch is saved to best_coco/AP_epoch_60.pth. 2022/10/13 12:04:08 - mmengine - INFO - Epoch(train) [61][50/293] lr: 5.000000e-04 eta: 3:32:40 time: 0.357931 data_time: 0.204932 memory: 2690 loss_kpt: 0.000996 acc_pose: 0.638772 loss: 0.000996 2022/10/13 12:04:25 - mmengine - INFO - Epoch(train) [61][100/293] lr: 5.000000e-04 eta: 3:32:32 time: 0.337522 data_time: 0.132879 memory: 2690 loss_kpt: 0.001001 acc_pose: 0.706980 loss: 0.001001 2022/10/13 12:04:42 - mmengine - INFO - Epoch(train) [61][150/293] lr: 5.000000e-04 eta: 3:32:23 time: 0.343006 data_time: 0.098692 memory: 2690 loss_kpt: 0.001000 acc_pose: 0.702614 loss: 0.001000 2022/10/13 12:04:59 - mmengine - INFO - Epoch(train) [61][200/293] lr: 5.000000e-04 eta: 3:32:15 time: 0.339198 data_time: 0.073613 memory: 2690 loss_kpt: 0.000999 acc_pose: 0.663846 loss: 0.000999 2022/10/13 12:05:16 - mmengine - INFO - Epoch(train) [61][250/293] lr: 5.000000e-04 eta: 3:32:06 time: 0.336579 data_time: 0.069803 memory: 2690 loss_kpt: 0.001003 acc_pose: 0.659131 loss: 0.001003 2022/10/13 12:05:30 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:05:48 - mmengine - INFO - Epoch(train) [62][50/293] lr: 5.000000e-04 eta: 3:31:17 time: 0.358971 data_time: 0.075396 memory: 2690 loss_kpt: 0.000990 acc_pose: 0.636803 loss: 0.000990 2022/10/13 12:06:05 - mmengine - INFO - Epoch(train) [62][100/293] lr: 5.000000e-04 eta: 3:31:06 time: 0.327198 data_time: 0.061180 memory: 2690 loss_kpt: 0.001013 acc_pose: 0.678240 loss: 0.001013 2022/10/13 12:06:13 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:06:22 - mmengine - INFO - Epoch(train) [62][150/293] lr: 5.000000e-04 eta: 3:30:58 time: 0.342095 data_time: 0.063187 memory: 2690 loss_kpt: 0.001013 acc_pose: 0.677180 loss: 0.001013 2022/10/13 12:06:40 - mmengine - INFO - Epoch(train) [62][200/293] lr: 5.000000e-04 eta: 3:30:52 time: 0.363412 data_time: 0.067959 memory: 2690 loss_kpt: 0.001008 acc_pose: 0.614818 loss: 0.001008 2022/10/13 12:06:56 - mmengine - INFO - Epoch(train) [62][250/293] lr: 5.000000e-04 eta: 3:30:43 time: 0.331467 data_time: 0.061808 memory: 2690 loss_kpt: 0.000988 acc_pose: 0.706057 loss: 0.000988 2022/10/13 12:07:11 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:07:29 - mmengine - INFO - Epoch(train) [63][50/293] lr: 5.000000e-04 eta: 3:29:54 time: 0.361678 data_time: 0.214166 memory: 2690 loss_kpt: 0.000985 acc_pose: 0.736292 loss: 0.000985 2022/10/13 12:07:46 - mmengine - INFO - Epoch(train) [63][100/293] lr: 5.000000e-04 eta: 3:29:47 time: 0.352112 data_time: 0.195633 memory: 2690 loss_kpt: 0.001015 acc_pose: 0.697199 loss: 0.001015 2022/10/13 12:08:03 - mmengine - INFO - Epoch(train) [63][150/293] lr: 5.000000e-04 eta: 3:29:38 time: 0.341671 data_time: 0.098092 memory: 2690 loss_kpt: 0.001013 acc_pose: 0.619988 loss: 0.001013 2022/10/13 12:08:20 - mmengine - INFO - Epoch(train) [63][200/293] lr: 5.000000e-04 eta: 3:29:28 time: 0.325205 data_time: 0.068425 memory: 2690 loss_kpt: 0.000992 acc_pose: 0.695454 loss: 0.000992 2022/10/13 12:08:36 - mmengine - INFO - Epoch(train) [63][250/293] lr: 5.000000e-04 eta: 3:29:18 time: 0.332752 data_time: 0.088683 memory: 2690 loss_kpt: 0.000993 acc_pose: 0.702645 loss: 0.000993 2022/10/13 12:08:50 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:09:08 - mmengine - INFO - Epoch(train) [64][50/293] lr: 5.000000e-04 eta: 3:28:28 time: 0.341809 data_time: 0.084442 memory: 2690 loss_kpt: 0.001005 acc_pose: 0.686217 loss: 0.001005 2022/10/13 12:09:24 - mmengine - INFO - Epoch(train) [64][100/293] lr: 5.000000e-04 eta: 3:28:18 time: 0.334741 data_time: 0.067942 memory: 2690 loss_kpt: 0.001003 acc_pose: 0.671145 loss: 0.001003 2022/10/13 12:09:41 - mmengine - INFO - Epoch(train) [64][150/293] lr: 5.000000e-04 eta: 3:28:08 time: 0.326658 data_time: 0.077997 memory: 2690 loss_kpt: 0.000989 acc_pose: 0.651010 loss: 0.000989 2022/10/13 12:09:57 - mmengine - INFO - Epoch(train) [64][200/293] lr: 5.000000e-04 eta: 3:27:58 time: 0.330174 data_time: 0.085802 memory: 2690 loss_kpt: 0.000984 acc_pose: 0.632040 loss: 0.000984 2022/10/13 12:10:14 - mmengine - INFO - Epoch(train) [64][250/293] lr: 5.000000e-04 eta: 3:27:49 time: 0.339012 data_time: 0.064164 memory: 2690 loss_kpt: 0.001001 acc_pose: 0.654173 loss: 0.001001 2022/10/13 12:10:28 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:10:45 - mmengine - INFO - Epoch(train) [65][50/293] lr: 5.000000e-04 eta: 3:26:59 time: 0.341029 data_time: 0.184011 memory: 2690 loss_kpt: 0.000989 acc_pose: 0.763368 loss: 0.000989 2022/10/13 12:11:03 - mmengine - INFO - Epoch(train) [65][100/293] lr: 5.000000e-04 eta: 3:26:51 time: 0.350427 data_time: 0.104966 memory: 2690 loss_kpt: 0.000997 acc_pose: 0.682642 loss: 0.000997 2022/10/13 12:11:20 - mmengine - INFO - Epoch(train) [65][150/293] lr: 5.000000e-04 eta: 3:26:42 time: 0.341830 data_time: 0.072571 memory: 2690 loss_kpt: 0.000996 acc_pose: 0.689998 loss: 0.000996 2022/10/13 12:11:37 - mmengine - INFO - Epoch(train) [65][200/293] lr: 5.000000e-04 eta: 3:26:34 time: 0.345884 data_time: 0.136864 memory: 2690 loss_kpt: 0.000994 acc_pose: 0.723449 loss: 0.000994 2022/10/13 12:11:54 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:11:55 - mmengine - INFO - Epoch(train) [65][250/293] lr: 5.000000e-04 eta: 3:26:27 time: 0.356984 data_time: 0.068128 memory: 2690 loss_kpt: 0.000999 acc_pose: 0.683510 loss: 0.000999 2022/10/13 12:12:09 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:12:26 - mmengine - INFO - Epoch(train) [66][50/293] lr: 5.000000e-04 eta: 3:25:38 time: 0.349374 data_time: 0.105572 memory: 2690 loss_kpt: 0.000988 acc_pose: 0.661942 loss: 0.000988 2022/10/13 12:12:43 - mmengine - INFO - Epoch(train) [66][100/293] lr: 5.000000e-04 eta: 3:25:29 time: 0.341968 data_time: 0.066408 memory: 2690 loss_kpt: 0.000995 acc_pose: 0.604492 loss: 0.000995 2022/10/13 12:13:00 - mmengine - INFO - Epoch(train) [66][150/293] lr: 5.000000e-04 eta: 3:25:19 time: 0.329078 data_time: 0.061515 memory: 2690 loss_kpt: 0.000998 acc_pose: 0.728923 loss: 0.000998 2022/10/13 12:13:17 - mmengine - INFO - Epoch(train) [66][200/293] lr: 5.000000e-04 eta: 3:25:10 time: 0.342612 data_time: 0.068957 memory: 2690 loss_kpt: 0.000999 acc_pose: 0.673586 loss: 0.000999 2022/10/13 12:13:33 - mmengine - INFO - Epoch(train) [66][250/293] lr: 5.000000e-04 eta: 3:25:00 time: 0.331246 data_time: 0.112265 memory: 2690 loss_kpt: 0.000989 acc_pose: 0.691627 loss: 0.000989 2022/10/13 12:13:49 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:14:06 - mmengine - INFO - Epoch(train) [67][50/293] lr: 5.000000e-04 eta: 3:24:12 time: 0.346060 data_time: 0.135023 memory: 2690 loss_kpt: 0.000981 acc_pose: 0.674561 loss: 0.000981 2022/10/13 12:14:23 - mmengine - INFO - Epoch(train) [67][100/293] lr: 5.000000e-04 eta: 3:24:03 time: 0.343892 data_time: 0.072225 memory: 2690 loss_kpt: 0.001007 acc_pose: 0.666289 loss: 0.001007 2022/10/13 12:14:40 - mmengine - INFO - Epoch(train) [67][150/293] lr: 5.000000e-04 eta: 3:23:54 time: 0.339597 data_time: 0.059116 memory: 2690 loss_kpt: 0.000988 acc_pose: 0.699890 loss: 0.000988 2022/10/13 12:14:58 - mmengine - INFO - Epoch(train) [67][200/293] lr: 5.000000e-04 eta: 3:23:46 time: 0.360519 data_time: 0.067130 memory: 2690 loss_kpt: 0.000997 acc_pose: 0.653349 loss: 0.000997 2022/10/13 12:15:15 - mmengine - INFO - Epoch(train) [67][250/293] lr: 5.000000e-04 eta: 3:23:36 time: 0.331493 data_time: 0.065902 memory: 2690 loss_kpt: 0.000988 acc_pose: 0.724657 loss: 0.000988 2022/10/13 12:15:29 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:15:47 - mmengine - INFO - Epoch(train) [68][50/293] lr: 5.000000e-04 eta: 3:22:50 time: 0.357974 data_time: 0.089177 memory: 2690 loss_kpt: 0.001001 acc_pose: 0.699336 loss: 0.001001 2022/10/13 12:16:03 - mmengine - INFO - Epoch(train) [68][100/293] lr: 5.000000e-04 eta: 3:22:39 time: 0.326451 data_time: 0.072856 memory: 2690 loss_kpt: 0.001008 acc_pose: 0.676059 loss: 0.001008 2022/10/13 12:16:20 - mmengine - INFO - Epoch(train) [68][150/293] lr: 5.000000e-04 eta: 3:22:29 time: 0.335559 data_time: 0.107615 memory: 2690 loss_kpt: 0.000992 acc_pose: 0.598733 loss: 0.000992 2022/10/13 12:16:37 - mmengine - INFO - Epoch(train) [68][200/293] lr: 5.000000e-04 eta: 3:22:20 time: 0.342246 data_time: 0.131132 memory: 2690 loss_kpt: 0.000985 acc_pose: 0.670019 loss: 0.000985 2022/10/13 12:16:54 - mmengine - INFO - Epoch(train) [68][250/293] lr: 5.000000e-04 eta: 3:22:10 time: 0.336658 data_time: 0.065737 memory: 2690 loss_kpt: 0.000994 acc_pose: 0.611557 loss: 0.000994 2022/10/13 12:17:09 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:17:26 - mmengine - INFO - Epoch(train) [69][50/293] lr: 5.000000e-04 eta: 3:21:23 time: 0.346714 data_time: 0.120789 memory: 2690 loss_kpt: 0.000977 acc_pose: 0.618928 loss: 0.000977 2022/10/13 12:17:34 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:17:43 - mmengine - INFO - Epoch(train) [69][100/293] lr: 5.000000e-04 eta: 3:21:12 time: 0.328201 data_time: 0.165599 memory: 2690 loss_kpt: 0.000993 acc_pose: 0.669378 loss: 0.000993 2022/10/13 12:17:59 - mmengine - INFO - Epoch(train) [69][150/293] lr: 5.000000e-04 eta: 3:21:02 time: 0.331415 data_time: 0.061026 memory: 2690 loss_kpt: 0.000990 acc_pose: 0.706396 loss: 0.000990 2022/10/13 12:18:16 - mmengine - INFO - Epoch(train) [69][200/293] lr: 5.000000e-04 eta: 3:20:52 time: 0.334744 data_time: 0.062266 memory: 2690 loss_kpt: 0.000998 acc_pose: 0.715023 loss: 0.000998 2022/10/13 12:18:34 - mmengine - INFO - Epoch(train) [69][250/293] lr: 5.000000e-04 eta: 3:20:44 time: 0.354803 data_time: 0.110095 memory: 2690 loss_kpt: 0.000997 acc_pose: 0.625973 loss: 0.000997 2022/10/13 12:18:48 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:19:05 - mmengine - INFO - Epoch(train) [70][50/293] lr: 5.000000e-04 eta: 3:19:57 time: 0.348898 data_time: 0.120411 memory: 2690 loss_kpt: 0.000986 acc_pose: 0.711868 loss: 0.000986 2022/10/13 12:19:22 - mmengine - INFO - Epoch(train) [70][100/293] lr: 5.000000e-04 eta: 3:19:47 time: 0.334212 data_time: 0.067772 memory: 2690 loss_kpt: 0.000976 acc_pose: 0.697064 loss: 0.000976 2022/10/13 12:19:38 - mmengine - INFO - Epoch(train) [70][150/293] lr: 5.000000e-04 eta: 3:19:36 time: 0.327582 data_time: 0.110373 memory: 2690 loss_kpt: 0.000993 acc_pose: 0.622385 loss: 0.000993 2022/10/13 12:19:55 - mmengine - INFO - Epoch(train) [70][200/293] lr: 5.000000e-04 eta: 3:19:27 time: 0.344494 data_time: 0.102225 memory: 2690 loss_kpt: 0.000976 acc_pose: 0.655702 loss: 0.000976 2022/10/13 12:20:13 - mmengine - INFO - Epoch(train) [70][250/293] lr: 5.000000e-04 eta: 3:19:19 time: 0.357175 data_time: 0.184580 memory: 2690 loss_kpt: 0.000989 acc_pose: 0.659209 loss: 0.000989 2022/10/13 12:20:27 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:20:27 - mmengine - INFO - Saving checkpoint at 70 epochs 2022/10/13 12:20:35 - mmengine - INFO - Epoch(val) [70][50/407] eta: 0:00:42 time: 0.117794 data_time: 0.076091 memory: 2690 2022/10/13 12:20:41 - mmengine - INFO - Epoch(val) [70][100/407] eta: 0:00:35 time: 0.114187 data_time: 0.072535 memory: 415 2022/10/13 12:20:47 - mmengine - INFO - Epoch(val) [70][150/407] eta: 0:00:30 time: 0.118850 data_time: 0.075383 memory: 415 2022/10/13 12:20:53 - mmengine - INFO - Epoch(val) [70][200/407] eta: 0:00:24 time: 0.120505 data_time: 0.079461 memory: 415 2022/10/13 12:20:59 - mmengine - INFO - Epoch(val) [70][250/407] eta: 0:00:17 time: 0.109743 data_time: 0.069755 memory: 415 2022/10/13 12:21:04 - mmengine - INFO - Epoch(val) [70][300/407] eta: 0:00:12 time: 0.115921 data_time: 0.074169 memory: 415 2022/10/13 12:21:10 - mmengine - INFO - Epoch(val) [70][350/407] eta: 0:00:06 time: 0.116034 data_time: 0.074598 memory: 415 2022/10/13 12:21:16 - mmengine - INFO - Epoch(val) [70][400/407] eta: 0:00:00 time: 0.107995 data_time: 0.067126 memory: 415 2022/10/13 12:21:53 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 12:22:08 - mmengine - INFO - Epoch(val) [70][407/407] coco/AP: 0.541170 coco/AP .5: 0.822739 coco/AP .75: 0.592708 coco/AP (M): 0.509183 coco/AP (L): 0.602782 coco/AR: 0.612358 coco/AR .5: 0.875157 coco/AR .75: 0.666877 coco/AR (M): 0.567359 coco/AR (L): 0.675251 2022/10/13 12:22:25 - mmengine - INFO - Epoch(train) [71][50/293] lr: 5.000000e-04 eta: 3:18:32 time: 0.341467 data_time: 0.170258 memory: 2690 loss_kpt: 0.000995 acc_pose: 0.659200 loss: 0.000995 2022/10/13 12:22:43 - mmengine - INFO - Epoch(train) [71][100/293] lr: 5.000000e-04 eta: 3:18:23 time: 0.350432 data_time: 0.071972 memory: 2690 loss_kpt: 0.000965 acc_pose: 0.708223 loss: 0.000965 2022/10/13 12:22:59 - mmengine - INFO - Epoch(train) [71][150/293] lr: 5.000000e-04 eta: 3:18:12 time: 0.329212 data_time: 0.084230 memory: 2690 loss_kpt: 0.000989 acc_pose: 0.694849 loss: 0.000989 2022/10/13 12:23:16 - mmengine - INFO - Epoch(train) [71][200/293] lr: 5.000000e-04 eta: 3:18:01 time: 0.326269 data_time: 0.128109 memory: 2690 loss_kpt: 0.000982 acc_pose: 0.722051 loss: 0.000982 2022/10/13 12:23:33 - mmengine - INFO - Epoch(train) [71][250/293] lr: 5.000000e-04 eta: 3:17:53 time: 0.353039 data_time: 0.096525 memory: 2690 loss_kpt: 0.000997 acc_pose: 0.597766 loss: 0.000997 2022/10/13 12:23:48 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:24:06 - mmengine - INFO - Epoch(train) [72][50/293] lr: 5.000000e-04 eta: 3:17:08 time: 0.356006 data_time: 0.087334 memory: 2690 loss_kpt: 0.000978 acc_pose: 0.763647 loss: 0.000978 2022/10/13 12:24:23 - mmengine - INFO - Epoch(train) [72][100/293] lr: 5.000000e-04 eta: 3:16:58 time: 0.342189 data_time: 0.114772 memory: 2690 loss_kpt: 0.000984 acc_pose: 0.708782 loss: 0.000984 2022/10/13 12:24:40 - mmengine - INFO - Epoch(train) [72][150/293] lr: 5.000000e-04 eta: 3:16:48 time: 0.333343 data_time: 0.113332 memory: 2690 loss_kpt: 0.000979 acc_pose: 0.669548 loss: 0.000979 2022/10/13 12:24:56 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:24:57 - mmengine - INFO - Epoch(train) [72][200/293] lr: 5.000000e-04 eta: 3:16:38 time: 0.343002 data_time: 0.070275 memory: 2690 loss_kpt: 0.000982 acc_pose: 0.702971 loss: 0.000982 2022/10/13 12:25:13 - mmengine - INFO - Epoch(train) [72][250/293] lr: 5.000000e-04 eta: 3:16:28 time: 0.331516 data_time: 0.093402 memory: 2690 loss_kpt: 0.000995 acc_pose: 0.623661 loss: 0.000995 2022/10/13 12:25:27 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:25:45 - mmengine - INFO - Epoch(train) [73][50/293] lr: 5.000000e-04 eta: 3:15:42 time: 0.348199 data_time: 0.101760 memory: 2690 loss_kpt: 0.000982 acc_pose: 0.665825 loss: 0.000982 2022/10/13 12:26:02 - mmengine - INFO - Epoch(train) [73][100/293] lr: 5.000000e-04 eta: 3:15:33 time: 0.352706 data_time: 0.148788 memory: 2690 loss_kpt: 0.000994 acc_pose: 0.672356 loss: 0.000994 2022/10/13 12:26:19 - mmengine - INFO - Epoch(train) [73][150/293] lr: 5.000000e-04 eta: 3:15:23 time: 0.339023 data_time: 0.119169 memory: 2690 loss_kpt: 0.000977 acc_pose: 0.698590 loss: 0.000977 2022/10/13 12:26:35 - mmengine - INFO - Epoch(train) [73][200/293] lr: 5.000000e-04 eta: 3:15:11 time: 0.316662 data_time: 0.067813 memory: 2690 loss_kpt: 0.000973 acc_pose: 0.736838 loss: 0.000973 2022/10/13 12:26:52 - mmengine - INFO - Epoch(train) [73][250/293] lr: 5.000000e-04 eta: 3:15:01 time: 0.334484 data_time: 0.106176 memory: 2690 loss_kpt: 0.000987 acc_pose: 0.679294 loss: 0.000987 2022/10/13 12:27:06 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:27:23 - mmengine - INFO - Epoch(train) [74][50/293] lr: 5.000000e-04 eta: 3:14:15 time: 0.347342 data_time: 0.117913 memory: 2690 loss_kpt: 0.000979 acc_pose: 0.710925 loss: 0.000979 2022/10/13 12:27:40 - mmengine - INFO - Epoch(train) [74][100/293] lr: 5.000000e-04 eta: 3:14:05 time: 0.335614 data_time: 0.068939 memory: 2690 loss_kpt: 0.001001 acc_pose: 0.677465 loss: 0.001001 2022/10/13 12:27:57 - mmengine - INFO - Epoch(train) [74][150/293] lr: 5.000000e-04 eta: 3:13:55 time: 0.335372 data_time: 0.138045 memory: 2690 loss_kpt: 0.000966 acc_pose: 0.686020 loss: 0.000966 2022/10/13 12:28:15 - mmengine - INFO - Epoch(train) [74][200/293] lr: 5.000000e-04 eta: 3:13:46 time: 0.356260 data_time: 0.109843 memory: 2690 loss_kpt: 0.000978 acc_pose: 0.736608 loss: 0.000978 2022/10/13 12:28:32 - mmengine - INFO - Epoch(train) [74][250/293] lr: 5.000000e-04 eta: 3:13:36 time: 0.335157 data_time: 0.058661 memory: 2690 loss_kpt: 0.000992 acc_pose: 0.691526 loss: 0.000992 2022/10/13 12:28:45 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:29:03 - mmengine - INFO - Epoch(train) [75][50/293] lr: 5.000000e-04 eta: 3:12:51 time: 0.348131 data_time: 0.122364 memory: 2690 loss_kpt: 0.000979 acc_pose: 0.620658 loss: 0.000979 2022/10/13 12:29:19 - mmengine - INFO - Epoch(train) [75][100/293] lr: 5.000000e-04 eta: 3:12:39 time: 0.321233 data_time: 0.188775 memory: 2690 loss_kpt: 0.000989 acc_pose: 0.722489 loss: 0.000989 2022/10/13 12:29:37 - mmengine - INFO - Epoch(train) [75][150/293] lr: 5.000000e-04 eta: 3:12:31 time: 0.357294 data_time: 0.145713 memory: 2690 loss_kpt: 0.000983 acc_pose: 0.634949 loss: 0.000983 2022/10/13 12:29:55 - mmengine - INFO - Epoch(train) [75][200/293] lr: 5.000000e-04 eta: 3:12:22 time: 0.354090 data_time: 0.065527 memory: 2690 loss_kpt: 0.000990 acc_pose: 0.649468 loss: 0.000990 2022/10/13 12:30:11 - mmengine - INFO - Epoch(train) [75][250/293] lr: 5.000000e-04 eta: 3:12:11 time: 0.331310 data_time: 0.066278 memory: 2690 loss_kpt: 0.000983 acc_pose: 0.735331 loss: 0.000983 2022/10/13 12:30:25 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:30:33 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:30:42 - mmengine - INFO - Epoch(train) [76][50/293] lr: 5.000000e-04 eta: 3:11:26 time: 0.340976 data_time: 0.137808 memory: 2690 loss_kpt: 0.000989 acc_pose: 0.693770 loss: 0.000989 2022/10/13 12:30:59 - mmengine - INFO - Epoch(train) [76][100/293] lr: 5.000000e-04 eta: 3:11:16 time: 0.349012 data_time: 0.085171 memory: 2690 loss_kpt: 0.000991 acc_pose: 0.688971 loss: 0.000991 2022/10/13 12:31:16 - mmengine - INFO - Epoch(train) [76][150/293] lr: 5.000000e-04 eta: 3:11:06 time: 0.334002 data_time: 0.059247 memory: 2690 loss_kpt: 0.000982 acc_pose: 0.651371 loss: 0.000982 2022/10/13 12:31:32 - mmengine - INFO - Epoch(train) [76][200/293] lr: 5.000000e-04 eta: 3:10:55 time: 0.329310 data_time: 0.114961 memory: 2690 loss_kpt: 0.000979 acc_pose: 0.684467 loss: 0.000979 2022/10/13 12:31:49 - mmengine - INFO - Epoch(train) [76][250/293] lr: 5.000000e-04 eta: 3:10:44 time: 0.333896 data_time: 0.075663 memory: 2690 loss_kpt: 0.000999 acc_pose: 0.660694 loss: 0.000999 2022/10/13 12:32:03 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:32:21 - mmengine - INFO - Epoch(train) [77][50/293] lr: 5.000000e-04 eta: 3:10:01 time: 0.363986 data_time: 0.193519 memory: 2690 loss_kpt: 0.000981 acc_pose: 0.670411 loss: 0.000981 2022/10/13 12:32:37 - mmengine - INFO - Epoch(train) [77][100/293] lr: 5.000000e-04 eta: 3:09:50 time: 0.326603 data_time: 0.148236 memory: 2690 loss_kpt: 0.000958 acc_pose: 0.682296 loss: 0.000958 2022/10/13 12:32:55 - mmengine - INFO - Epoch(train) [77][150/293] lr: 5.000000e-04 eta: 3:09:40 time: 0.344266 data_time: 0.186630 memory: 2690 loss_kpt: 0.000990 acc_pose: 0.729596 loss: 0.000990 2022/10/13 12:33:11 - mmengine - INFO - Epoch(train) [77][200/293] lr: 5.000000e-04 eta: 3:09:29 time: 0.330437 data_time: 0.171670 memory: 2690 loss_kpt: 0.000977 acc_pose: 0.718073 loss: 0.000977 2022/10/13 12:33:28 - mmengine - INFO - Epoch(train) [77][250/293] lr: 5.000000e-04 eta: 3:09:19 time: 0.344707 data_time: 0.070920 memory: 2690 loss_kpt: 0.000976 acc_pose: 0.659971 loss: 0.000976 2022/10/13 12:33:43 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:34:00 - mmengine - INFO - Epoch(train) [78][50/293] lr: 5.000000e-04 eta: 3:08:36 time: 0.356398 data_time: 0.197571 memory: 2690 loss_kpt: 0.000978 acc_pose: 0.661059 loss: 0.000978 2022/10/13 12:34:17 - mmengine - INFO - Epoch(train) [78][100/293] lr: 5.000000e-04 eta: 3:08:25 time: 0.333052 data_time: 0.166172 memory: 2690 loss_kpt: 0.000965 acc_pose: 0.735825 loss: 0.000965 2022/10/13 12:34:34 - mmengine - INFO - Epoch(train) [78][150/293] lr: 5.000000e-04 eta: 3:08:15 time: 0.348535 data_time: 0.152431 memory: 2690 loss_kpt: 0.000973 acc_pose: 0.725350 loss: 0.000973 2022/10/13 12:34:51 - mmengine - INFO - Epoch(train) [78][200/293] lr: 5.000000e-04 eta: 3:08:04 time: 0.325542 data_time: 0.166592 memory: 2690 loss_kpt: 0.000987 acc_pose: 0.648640 loss: 0.000987 2022/10/13 12:35:07 - mmengine - INFO - Epoch(train) [78][250/293] lr: 5.000000e-04 eta: 3:07:53 time: 0.333108 data_time: 0.075954 memory: 2690 loss_kpt: 0.000986 acc_pose: 0.694868 loss: 0.000986 2022/10/13 12:35:22 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:35:40 - mmengine - INFO - Epoch(train) [79][50/293] lr: 5.000000e-04 eta: 3:07:11 time: 0.361835 data_time: 0.143179 memory: 2690 loss_kpt: 0.000965 acc_pose: 0.736756 loss: 0.000965 2022/10/13 12:35:56 - mmengine - INFO - Epoch(train) [79][100/293] lr: 5.000000e-04 eta: 3:06:59 time: 0.326193 data_time: 0.061441 memory: 2690 loss_kpt: 0.000981 acc_pose: 0.642484 loss: 0.000981 2022/10/13 12:36:11 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:36:13 - mmengine - INFO - Epoch(train) [79][150/293] lr: 5.000000e-04 eta: 3:06:48 time: 0.335904 data_time: 0.068306 memory: 2690 loss_kpt: 0.000991 acc_pose: 0.690647 loss: 0.000991 2022/10/13 12:36:30 - mmengine - INFO - Epoch(train) [79][200/293] lr: 5.000000e-04 eta: 3:06:38 time: 0.337674 data_time: 0.170810 memory: 2690 loss_kpt: 0.000979 acc_pose: 0.709940 loss: 0.000979 2022/10/13 12:36:47 - mmengine - INFO - Epoch(train) [79][250/293] lr: 5.000000e-04 eta: 3:06:27 time: 0.334519 data_time: 0.179915 memory: 2690 loss_kpt: 0.000983 acc_pose: 0.671889 loss: 0.000983 2022/10/13 12:37:01 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:37:18 - mmengine - INFO - Epoch(train) [80][50/293] lr: 5.000000e-04 eta: 3:05:44 time: 0.356531 data_time: 0.107433 memory: 2690 loss_kpt: 0.000986 acc_pose: 0.685458 loss: 0.000986 2022/10/13 12:37:37 - mmengine - INFO - Epoch(train) [80][100/293] lr: 5.000000e-04 eta: 3:05:36 time: 0.367584 data_time: 0.135670 memory: 2690 loss_kpt: 0.000987 acc_pose: 0.593120 loss: 0.000987 2022/10/13 12:37:54 - mmengine - INFO - Epoch(train) [80][150/293] lr: 5.000000e-04 eta: 3:05:26 time: 0.345758 data_time: 0.065965 memory: 2690 loss_kpt: 0.000963 acc_pose: 0.675189 loss: 0.000963 2022/10/13 12:38:11 - mmengine - INFO - Epoch(train) [80][200/293] lr: 5.000000e-04 eta: 3:05:16 time: 0.345872 data_time: 0.067461 memory: 2690 loss_kpt: 0.000995 acc_pose: 0.700885 loss: 0.000995 2022/10/13 12:38:30 - mmengine - INFO - Epoch(train) [80][250/293] lr: 5.000000e-04 eta: 3:05:08 time: 0.366524 data_time: 0.070180 memory: 2690 loss_kpt: 0.000972 acc_pose: 0.711659 loss: 0.000972 2022/10/13 12:38:44 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:38:44 - mmengine - INFO - Saving checkpoint at 80 epochs 2022/10/13 12:38:52 - mmengine - INFO - Epoch(val) [80][50/407] eta: 0:00:43 time: 0.120989 data_time: 0.079619 memory: 2690 2022/10/13 12:38:58 - mmengine - INFO - Epoch(val) [80][100/407] eta: 0:00:35 time: 0.115994 data_time: 0.074984 memory: 415 2022/10/13 12:39:04 - mmengine - INFO - Epoch(val) [80][150/407] eta: 0:00:29 time: 0.116244 data_time: 0.074221 memory: 415 2022/10/13 12:39:10 - mmengine - INFO - Epoch(val) [80][200/407] eta: 0:00:22 time: 0.110347 data_time: 0.069476 memory: 415 2022/10/13 12:39:16 - mmengine - INFO - Epoch(val) [80][250/407] eta: 0:00:19 time: 0.124942 data_time: 0.082558 memory: 415 2022/10/13 12:39:21 - mmengine - INFO - Epoch(val) [80][300/407] eta: 0:00:11 time: 0.110680 data_time: 0.071148 memory: 415 2022/10/13 12:39:27 - mmengine - INFO - Epoch(val) [80][350/407] eta: 0:00:06 time: 0.116351 data_time: 0.075149 memory: 415 2022/10/13 12:39:33 - mmengine - INFO - Epoch(val) [80][400/407] eta: 0:00:00 time: 0.108381 data_time: 0.067557 memory: 415 2022/10/13 12:40:11 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 12:40:26 - mmengine - INFO - Epoch(val) [80][407/407] coco/AP: 0.552658 coco/AP .5: 0.830137 coco/AP .75: 0.608048 coco/AP (M): 0.520132 coco/AP (L): 0.613373 coco/AR: 0.620151 coco/AR .5: 0.880195 coco/AR .75: 0.678054 coco/AR (M): 0.577110 coco/AR (L): 0.680528 2022/10/13 12:40:26 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_256/best_coco/AP_epoch_60.pth is removed 2022/10/13 12:40:27 - mmengine - INFO - The best checkpoint with 0.5527 coco/AP at 80 epoch is saved to best_coco/AP_epoch_80.pth. 2022/10/13 12:40:45 - mmengine - INFO - Epoch(train) [81][50/293] lr: 5.000000e-04 eta: 3:04:25 time: 0.357309 data_time: 0.207800 memory: 2690 loss_kpt: 0.000978 acc_pose: 0.718904 loss: 0.000978 2022/10/13 12:41:02 - mmengine - INFO - Epoch(train) [81][100/293] lr: 5.000000e-04 eta: 3:04:15 time: 0.343316 data_time: 0.167239 memory: 2690 loss_kpt: 0.000979 acc_pose: 0.710324 loss: 0.000979 2022/10/13 12:41:19 - mmengine - INFO - Epoch(train) [81][150/293] lr: 5.000000e-04 eta: 3:04:04 time: 0.333940 data_time: 0.090076 memory: 2690 loss_kpt: 0.000979 acc_pose: 0.694969 loss: 0.000979 2022/10/13 12:41:36 - mmengine - INFO - Epoch(train) [81][200/293] lr: 5.000000e-04 eta: 3:03:53 time: 0.335664 data_time: 0.073212 memory: 2690 loss_kpt: 0.000983 acc_pose: 0.717761 loss: 0.000983 2022/10/13 12:41:53 - mmengine - INFO - Epoch(train) [81][250/293] lr: 5.000000e-04 eta: 3:03:43 time: 0.344318 data_time: 0.159335 memory: 2690 loss_kpt: 0.000979 acc_pose: 0.686104 loss: 0.000979 2022/10/13 12:42:07 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:42:24 - mmengine - INFO - Epoch(train) [82][50/293] lr: 5.000000e-04 eta: 3:02:59 time: 0.336496 data_time: 0.131310 memory: 2690 loss_kpt: 0.000991 acc_pose: 0.744584 loss: 0.000991 2022/10/13 12:42:41 - mmengine - INFO - Epoch(train) [82][100/293] lr: 5.000000e-04 eta: 3:02:48 time: 0.337067 data_time: 0.073762 memory: 2690 loss_kpt: 0.000966 acc_pose: 0.677006 loss: 0.000966 2022/10/13 12:42:57 - mmengine - INFO - Epoch(train) [82][150/293] lr: 5.000000e-04 eta: 3:02:37 time: 0.331412 data_time: 0.063864 memory: 2690 loss_kpt: 0.000982 acc_pose: 0.669478 loss: 0.000982 2022/10/13 12:43:13 - mmengine - INFO - Epoch(train) [82][200/293] lr: 5.000000e-04 eta: 3:02:25 time: 0.324191 data_time: 0.070408 memory: 2690 loss_kpt: 0.000994 acc_pose: 0.683804 loss: 0.000994 2022/10/13 12:43:30 - mmengine - INFO - Epoch(train) [82][250/293] lr: 5.000000e-04 eta: 3:02:13 time: 0.324903 data_time: 0.128949 memory: 2690 loss_kpt: 0.000976 acc_pose: 0.731381 loss: 0.000976 2022/10/13 12:43:35 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:43:44 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:44:00 - mmengine - INFO - Epoch(train) [83][50/293] lr: 5.000000e-04 eta: 3:01:30 time: 0.337382 data_time: 0.085141 memory: 2690 loss_kpt: 0.000983 acc_pose: 0.676601 loss: 0.000983 2022/10/13 12:44:16 - mmengine - INFO - Epoch(train) [83][100/293] lr: 5.000000e-04 eta: 3:01:18 time: 0.315316 data_time: 0.067043 memory: 2690 loss_kpt: 0.000990 acc_pose: 0.636788 loss: 0.000990 2022/10/13 12:44:32 - mmengine - INFO - Epoch(train) [83][150/293] lr: 5.000000e-04 eta: 3:01:06 time: 0.325056 data_time: 0.068332 memory: 2690 loss_kpt: 0.000984 acc_pose: 0.679369 loss: 0.000984 2022/10/13 12:44:49 - mmengine - INFO - Epoch(train) [83][200/293] lr: 5.000000e-04 eta: 3:00:55 time: 0.336888 data_time: 0.193477 memory: 2690 loss_kpt: 0.000984 acc_pose: 0.717524 loss: 0.000984 2022/10/13 12:45:06 - mmengine - INFO - Epoch(train) [83][250/293] lr: 5.000000e-04 eta: 3:00:44 time: 0.335580 data_time: 0.177946 memory: 2690 loss_kpt: 0.000972 acc_pose: 0.599083 loss: 0.000972 2022/10/13 12:45:20 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:45:38 - mmengine - INFO - Epoch(train) [84][50/293] lr: 5.000000e-04 eta: 3:00:02 time: 0.349364 data_time: 0.083088 memory: 2690 loss_kpt: 0.000998 acc_pose: 0.704972 loss: 0.000998 2022/10/13 12:45:54 - mmengine - INFO - Epoch(train) [84][100/293] lr: 5.000000e-04 eta: 2:59:50 time: 0.322461 data_time: 0.063575 memory: 2690 loss_kpt: 0.000968 acc_pose: 0.676184 loss: 0.000968 2022/10/13 12:46:10 - mmengine - INFO - Epoch(train) [84][150/293] lr: 5.000000e-04 eta: 2:59:38 time: 0.331892 data_time: 0.059604 memory: 2690 loss_kpt: 0.000982 acc_pose: 0.687858 loss: 0.000982 2022/10/13 12:46:27 - mmengine - INFO - Epoch(train) [84][200/293] lr: 5.000000e-04 eta: 2:59:27 time: 0.337987 data_time: 0.065234 memory: 2690 loss_kpt: 0.000977 acc_pose: 0.716935 loss: 0.000977 2022/10/13 12:46:43 - mmengine - INFO - Epoch(train) [84][250/293] lr: 5.000000e-04 eta: 2:59:15 time: 0.324286 data_time: 0.072438 memory: 2690 loss_kpt: 0.000979 acc_pose: 0.731137 loss: 0.000979 2022/10/13 12:46:58 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:47:15 - mmengine - INFO - Epoch(train) [85][50/293] lr: 5.000000e-04 eta: 2:58:33 time: 0.334771 data_time: 0.115364 memory: 2690 loss_kpt: 0.000981 acc_pose: 0.688130 loss: 0.000981 2022/10/13 12:47:31 - mmengine - INFO - Epoch(train) [85][100/293] lr: 5.000000e-04 eta: 2:58:21 time: 0.323270 data_time: 0.062800 memory: 2690 loss_kpt: 0.000983 acc_pose: 0.662558 loss: 0.000983 2022/10/13 12:47:48 - mmengine - INFO - Epoch(train) [85][150/293] lr: 5.000000e-04 eta: 2:58:09 time: 0.331162 data_time: 0.124577 memory: 2690 loss_kpt: 0.000975 acc_pose: 0.681795 loss: 0.000975 2022/10/13 12:48:04 - mmengine - INFO - Epoch(train) [85][200/293] lr: 5.000000e-04 eta: 2:57:58 time: 0.330914 data_time: 0.150330 memory: 2690 loss_kpt: 0.000964 acc_pose: 0.662842 loss: 0.000964 2022/10/13 12:48:21 - mmengine - INFO - Epoch(train) [85][250/293] lr: 5.000000e-04 eta: 2:57:46 time: 0.334361 data_time: 0.060003 memory: 2690 loss_kpt: 0.000970 acc_pose: 0.706414 loss: 0.000970 2022/10/13 12:48:35 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:48:52 - mmengine - INFO - Epoch(train) [86][50/293] lr: 5.000000e-04 eta: 2:57:04 time: 0.335854 data_time: 0.109700 memory: 2690 loss_kpt: 0.000970 acc_pose: 0.685230 loss: 0.000970 2022/10/13 12:49:07 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:49:09 - mmengine - INFO - Epoch(train) [86][100/293] lr: 5.000000e-04 eta: 2:56:53 time: 0.333000 data_time: 0.073298 memory: 2690 loss_kpt: 0.000967 acc_pose: 0.717619 loss: 0.000967 2022/10/13 12:49:25 - mmengine - INFO - Epoch(train) [86][150/293] lr: 5.000000e-04 eta: 2:56:41 time: 0.321280 data_time: 0.089001 memory: 2690 loss_kpt: 0.000983 acc_pose: 0.699194 loss: 0.000983 2022/10/13 12:49:41 - mmengine - INFO - Epoch(train) [86][200/293] lr: 5.000000e-04 eta: 2:56:29 time: 0.331384 data_time: 0.112435 memory: 2690 loss_kpt: 0.000957 acc_pose: 0.714373 loss: 0.000957 2022/10/13 12:49:58 - mmengine - INFO - Epoch(train) [86][250/293] lr: 5.000000e-04 eta: 2:56:17 time: 0.326099 data_time: 0.069282 memory: 2690 loss_kpt: 0.000966 acc_pose: 0.682568 loss: 0.000966 2022/10/13 12:50:11 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:50:28 - mmengine - INFO - Epoch(train) [87][50/293] lr: 5.000000e-04 eta: 2:55:36 time: 0.346294 data_time: 0.089363 memory: 2690 loss_kpt: 0.000994 acc_pose: 0.657109 loss: 0.000994 2022/10/13 12:50:45 - mmengine - INFO - Epoch(train) [87][100/293] lr: 5.000000e-04 eta: 2:55:24 time: 0.325659 data_time: 0.064489 memory: 2690 loss_kpt: 0.000958 acc_pose: 0.740980 loss: 0.000958 2022/10/13 12:51:01 - mmengine - INFO - Epoch(train) [87][150/293] lr: 5.000000e-04 eta: 2:55:12 time: 0.331636 data_time: 0.072003 memory: 2690 loss_kpt: 0.000980 acc_pose: 0.653938 loss: 0.000980 2022/10/13 12:51:18 - mmengine - INFO - Epoch(train) [87][200/293] lr: 5.000000e-04 eta: 2:55:00 time: 0.324274 data_time: 0.065690 memory: 2690 loss_kpt: 0.000965 acc_pose: 0.732937 loss: 0.000965 2022/10/13 12:51:35 - mmengine - INFO - Epoch(train) [87][250/293] lr: 5.000000e-04 eta: 2:54:49 time: 0.341758 data_time: 0.066868 memory: 2690 loss_kpt: 0.000973 acc_pose: 0.697234 loss: 0.000973 2022/10/13 12:51:48 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:52:05 - mmengine - INFO - Epoch(train) [88][50/293] lr: 5.000000e-04 eta: 2:54:08 time: 0.344101 data_time: 0.168985 memory: 2690 loss_kpt: 0.000960 acc_pose: 0.731247 loss: 0.000960 2022/10/13 12:52:21 - mmengine - INFO - Epoch(train) [88][100/293] lr: 5.000000e-04 eta: 2:53:56 time: 0.318492 data_time: 0.088194 memory: 2690 loss_kpt: 0.000973 acc_pose: 0.687033 loss: 0.000973 2022/10/13 12:52:37 - mmengine - INFO - Epoch(train) [88][150/293] lr: 5.000000e-04 eta: 2:53:43 time: 0.316464 data_time: 0.082794 memory: 2690 loss_kpt: 0.000984 acc_pose: 0.688849 loss: 0.000984 2022/10/13 12:52:53 - mmengine - INFO - Epoch(train) [88][200/293] lr: 5.000000e-04 eta: 2:53:31 time: 0.321513 data_time: 0.069942 memory: 2690 loss_kpt: 0.000969 acc_pose: 0.704043 loss: 0.000969 2022/10/13 12:53:09 - mmengine - INFO - Epoch(train) [88][250/293] lr: 5.000000e-04 eta: 2:53:19 time: 0.323539 data_time: 0.065358 memory: 2690 loss_kpt: 0.000969 acc_pose: 0.729441 loss: 0.000969 2022/10/13 12:53:23 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:53:40 - mmengine - INFO - Epoch(train) [89][50/293] lr: 5.000000e-04 eta: 2:52:37 time: 0.332282 data_time: 0.155674 memory: 2690 loss_kpt: 0.000987 acc_pose: 0.699575 loss: 0.000987 2022/10/13 12:53:56 - mmengine - INFO - Epoch(train) [89][100/293] lr: 5.000000e-04 eta: 2:52:25 time: 0.325086 data_time: 0.137802 memory: 2690 loss_kpt: 0.000964 acc_pose: 0.723723 loss: 0.000964 2022/10/13 12:54:13 - mmengine - INFO - Epoch(train) [89][150/293] lr: 5.000000e-04 eta: 2:52:14 time: 0.332542 data_time: 0.078625 memory: 2690 loss_kpt: 0.000963 acc_pose: 0.717060 loss: 0.000963 2022/10/13 12:54:30 - mmengine - INFO - Epoch(train) [89][200/293] lr: 5.000000e-04 eta: 2:52:02 time: 0.332106 data_time: 0.081893 memory: 2690 loss_kpt: 0.000977 acc_pose: 0.648951 loss: 0.000977 2022/10/13 12:54:35 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:54:46 - mmengine - INFO - Epoch(train) [89][250/293] lr: 5.000000e-04 eta: 2:51:49 time: 0.319864 data_time: 0.066933 memory: 2690 loss_kpt: 0.000992 acc_pose: 0.663072 loss: 0.000992 2022/10/13 12:55:00 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:55:17 - mmengine - INFO - Epoch(train) [90][50/293] lr: 5.000000e-04 eta: 2:51:08 time: 0.333063 data_time: 0.112827 memory: 2690 loss_kpt: 0.000972 acc_pose: 0.726209 loss: 0.000972 2022/10/13 12:55:33 - mmengine - INFO - Epoch(train) [90][100/293] lr: 5.000000e-04 eta: 2:50:56 time: 0.327333 data_time: 0.060987 memory: 2690 loss_kpt: 0.000977 acc_pose: 0.726760 loss: 0.000977 2022/10/13 12:55:49 - mmengine - INFO - Epoch(train) [90][150/293] lr: 5.000000e-04 eta: 2:50:44 time: 0.325530 data_time: 0.062986 memory: 2690 loss_kpt: 0.000979 acc_pose: 0.688924 loss: 0.000979 2022/10/13 12:56:05 - mmengine - INFO - Epoch(train) [90][200/293] lr: 5.000000e-04 eta: 2:50:32 time: 0.322973 data_time: 0.062831 memory: 2690 loss_kpt: 0.000983 acc_pose: 0.699322 loss: 0.000983 2022/10/13 12:56:22 - mmengine - INFO - Epoch(train) [90][250/293] lr: 5.000000e-04 eta: 2:50:20 time: 0.324089 data_time: 0.106349 memory: 2690 loss_kpt: 0.000970 acc_pose: 0.685900 loss: 0.000970 2022/10/13 12:56:36 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 12:56:36 - mmengine - INFO - Saving checkpoint at 90 epochs 2022/10/13 12:56:43 - mmengine - INFO - Epoch(val) [90][50/407] eta: 0:00:42 time: 0.118743 data_time: 0.078493 memory: 2690 2022/10/13 12:56:49 - mmengine - INFO - Epoch(val) [90][100/407] eta: 0:00:36 time: 0.118524 data_time: 0.077641 memory: 415 2022/10/13 12:56:55 - mmengine - INFO - Epoch(val) [90][150/407] eta: 0:00:29 time: 0.113747 data_time: 0.073200 memory: 415 2022/10/13 12:57:00 - mmengine - INFO - Epoch(val) [90][200/407] eta: 0:00:22 time: 0.107862 data_time: 0.063368 memory: 415 2022/10/13 12:57:06 - mmengine - INFO - Epoch(val) [90][250/407] eta: 0:00:17 time: 0.112031 data_time: 0.071896 memory: 415 2022/10/13 12:57:11 - mmengine - INFO - Epoch(val) [90][300/407] eta: 0:00:11 time: 0.108354 data_time: 0.067693 memory: 415 2022/10/13 12:57:17 - mmengine - INFO - Epoch(val) [90][350/407] eta: 0:00:06 time: 0.115305 data_time: 0.074083 memory: 415 2022/10/13 12:57:23 - mmengine - INFO - Epoch(val) [90][400/407] eta: 0:00:00 time: 0.119497 data_time: 0.079156 memory: 415 2022/10/13 12:58:01 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 12:58:16 - mmengine - INFO - Epoch(val) [90][407/407] coco/AP: 0.559209 coco/AP .5: 0.834798 coco/AP .75: 0.618239 coco/AP (M): 0.526767 coco/AP (L): 0.620068 coco/AR: 0.627377 coco/AR .5: 0.883659 coco/AR .75: 0.689861 coco/AR (M): 0.583638 coco/AR (L): 0.688554 2022/10/13 12:58:16 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_256/best_coco/AP_epoch_80.pth is removed 2022/10/13 12:58:18 - mmengine - INFO - The best checkpoint with 0.5592 coco/AP at 90 epoch is saved to best_coco/AP_epoch_90.pth. 2022/10/13 12:58:35 - mmengine - INFO - Epoch(train) [91][50/293] lr: 5.000000e-04 eta: 2:49:39 time: 0.337109 data_time: 0.180759 memory: 2690 loss_kpt: 0.000967 acc_pose: 0.750498 loss: 0.000967 2022/10/13 12:58:51 - mmengine - INFO - Epoch(train) [91][100/293] lr: 5.000000e-04 eta: 2:49:27 time: 0.321768 data_time: 0.171616 memory: 2690 loss_kpt: 0.000979 acc_pose: 0.712454 loss: 0.000979 2022/10/13 12:59:07 - mmengine - INFO - Epoch(train) [91][150/293] lr: 5.000000e-04 eta: 2:49:15 time: 0.333481 data_time: 0.182596 memory: 2690 loss_kpt: 0.000987 acc_pose: 0.702391 loss: 0.000987 2022/10/13 12:59:24 - mmengine - INFO - Epoch(train) [91][200/293] lr: 5.000000e-04 eta: 2:49:03 time: 0.333563 data_time: 0.181845 memory: 2690 loss_kpt: 0.000976 acc_pose: 0.718993 loss: 0.000976 2022/10/13 12:59:41 - mmengine - INFO - Epoch(train) [91][250/293] lr: 5.000000e-04 eta: 2:48:52 time: 0.338068 data_time: 0.189345 memory: 2690 loss_kpt: 0.000990 acc_pose: 0.782647 loss: 0.000990 2022/10/13 12:59:55 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:00:12 - mmengine - INFO - Epoch(train) [92][50/293] lr: 5.000000e-04 eta: 2:48:12 time: 0.335642 data_time: 0.118617 memory: 2690 loss_kpt: 0.000960 acc_pose: 0.638541 loss: 0.000960 2022/10/13 13:00:28 - mmengine - INFO - Epoch(train) [92][100/293] lr: 5.000000e-04 eta: 2:48:00 time: 0.331072 data_time: 0.064196 memory: 2690 loss_kpt: 0.000964 acc_pose: 0.641254 loss: 0.000964 2022/10/13 13:00:45 - mmengine - INFO - Epoch(train) [92][150/293] lr: 5.000000e-04 eta: 2:47:48 time: 0.326270 data_time: 0.062520 memory: 2690 loss_kpt: 0.000965 acc_pose: 0.741439 loss: 0.000965 2022/10/13 13:01:01 - mmengine - INFO - Epoch(train) [92][200/293] lr: 5.000000e-04 eta: 2:47:36 time: 0.324546 data_time: 0.064931 memory: 2690 loss_kpt: 0.000995 acc_pose: 0.647582 loss: 0.000995 2022/10/13 13:01:17 - mmengine - INFO - Epoch(train) [92][250/293] lr: 5.000000e-04 eta: 2:47:23 time: 0.326253 data_time: 0.065042 memory: 2690 loss_kpt: 0.000975 acc_pose: 0.740391 loss: 0.000975 2022/10/13 13:01:31 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:01:46 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:01:48 - mmengine - INFO - Epoch(train) [93][50/293] lr: 5.000000e-04 eta: 2:46:43 time: 0.333000 data_time: 0.079009 memory: 2690 loss_kpt: 0.000970 acc_pose: 0.704421 loss: 0.000970 2022/10/13 13:02:04 - mmengine - INFO - Epoch(train) [93][100/293] lr: 5.000000e-04 eta: 2:46:31 time: 0.328572 data_time: 0.124946 memory: 2690 loss_kpt: 0.000961 acc_pose: 0.653422 loss: 0.000961 2022/10/13 13:02:20 - mmengine - INFO - Epoch(train) [93][150/293] lr: 5.000000e-04 eta: 2:46:19 time: 0.327542 data_time: 0.140273 memory: 2690 loss_kpt: 0.000972 acc_pose: 0.682619 loss: 0.000972 2022/10/13 13:02:36 - mmengine - INFO - Epoch(train) [93][200/293] lr: 5.000000e-04 eta: 2:46:06 time: 0.319797 data_time: 0.064609 memory: 2690 loss_kpt: 0.000953 acc_pose: 0.699367 loss: 0.000953 2022/10/13 13:02:53 - mmengine - INFO - Epoch(train) [93][250/293] lr: 5.000000e-04 eta: 2:45:54 time: 0.327790 data_time: 0.067549 memory: 2690 loss_kpt: 0.000965 acc_pose: 0.687854 loss: 0.000965 2022/10/13 13:03:07 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:03:24 - mmengine - INFO - Epoch(train) [94][50/293] lr: 5.000000e-04 eta: 2:45:15 time: 0.346788 data_time: 0.149890 memory: 2690 loss_kpt: 0.000963 acc_pose: 0.676379 loss: 0.000963 2022/10/13 13:03:41 - mmengine - INFO - Epoch(train) [94][100/293] lr: 5.000000e-04 eta: 2:45:04 time: 0.346432 data_time: 0.073784 memory: 2690 loss_kpt: 0.000990 acc_pose: 0.671616 loss: 0.000990 2022/10/13 13:03:58 - mmengine - INFO - Epoch(train) [94][150/293] lr: 5.000000e-04 eta: 2:44:52 time: 0.328915 data_time: 0.057887 memory: 2690 loss_kpt: 0.000956 acc_pose: 0.738436 loss: 0.000956 2022/10/13 13:04:14 - mmengine - INFO - Epoch(train) [94][200/293] lr: 5.000000e-04 eta: 2:44:41 time: 0.334241 data_time: 0.065858 memory: 2690 loss_kpt: 0.000967 acc_pose: 0.708870 loss: 0.000967 2022/10/13 13:04:30 - mmengine - INFO - Epoch(train) [94][250/293] lr: 5.000000e-04 eta: 2:44:28 time: 0.318837 data_time: 0.072514 memory: 2690 loss_kpt: 0.000965 acc_pose: 0.671161 loss: 0.000965 2022/10/13 13:04:45 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:05:02 - mmengine - INFO - Epoch(train) [95][50/293] lr: 5.000000e-04 eta: 2:43:49 time: 0.344425 data_time: 0.093456 memory: 2690 loss_kpt: 0.000967 acc_pose: 0.702390 loss: 0.000967 2022/10/13 13:05:19 - mmengine - INFO - Epoch(train) [95][100/293] lr: 5.000000e-04 eta: 2:43:37 time: 0.335660 data_time: 0.173301 memory: 2690 loss_kpt: 0.000958 acc_pose: 0.682643 loss: 0.000958 2022/10/13 13:05:34 - mmengine - INFO - Epoch(train) [95][150/293] lr: 5.000000e-04 eta: 2:43:24 time: 0.316230 data_time: 0.108373 memory: 2690 loss_kpt: 0.000982 acc_pose: 0.746164 loss: 0.000982 2022/10/13 13:05:51 - mmengine - INFO - Epoch(train) [95][200/293] lr: 5.000000e-04 eta: 2:43:12 time: 0.330408 data_time: 0.144263 memory: 2690 loss_kpt: 0.000973 acc_pose: 0.735927 loss: 0.000973 2022/10/13 13:06:07 - mmengine - INFO - Epoch(train) [95][250/293] lr: 5.000000e-04 eta: 2:43:00 time: 0.324733 data_time: 0.146951 memory: 2690 loss_kpt: 0.000958 acc_pose: 0.647855 loss: 0.000958 2022/10/13 13:06:21 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:06:38 - mmengine - INFO - Epoch(train) [96][50/293] lr: 5.000000e-04 eta: 2:42:21 time: 0.333802 data_time: 0.178022 memory: 2690 loss_kpt: 0.000978 acc_pose: 0.670423 loss: 0.000978 2022/10/13 13:06:54 - mmengine - INFO - Epoch(train) [96][100/293] lr: 5.000000e-04 eta: 2:42:09 time: 0.332357 data_time: 0.169839 memory: 2690 loss_kpt: 0.000973 acc_pose: 0.640316 loss: 0.000973 2022/10/13 13:07:11 - mmengine - INFO - Epoch(train) [96][150/293] lr: 5.000000e-04 eta: 2:41:56 time: 0.324595 data_time: 0.109317 memory: 2690 loss_kpt: 0.000965 acc_pose: 0.672037 loss: 0.000965 2022/10/13 13:07:15 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:07:27 - mmengine - INFO - Epoch(train) [96][200/293] lr: 5.000000e-04 eta: 2:41:44 time: 0.328698 data_time: 0.120828 memory: 2690 loss_kpt: 0.000947 acc_pose: 0.692359 loss: 0.000947 2022/10/13 13:07:44 - mmengine - INFO - Epoch(train) [96][250/293] lr: 5.000000e-04 eta: 2:41:33 time: 0.342979 data_time: 0.085859 memory: 2690 loss_kpt: 0.000985 acc_pose: 0.652523 loss: 0.000985 2022/10/13 13:07:58 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:08:15 - mmengine - INFO - Epoch(train) [97][50/293] lr: 5.000000e-04 eta: 2:40:55 time: 0.346409 data_time: 0.128338 memory: 2690 loss_kpt: 0.000955 acc_pose: 0.757148 loss: 0.000955 2022/10/13 13:08:31 - mmengine - INFO - Epoch(train) [97][100/293] lr: 5.000000e-04 eta: 2:40:42 time: 0.316145 data_time: 0.065306 memory: 2690 loss_kpt: 0.000962 acc_pose: 0.677685 loss: 0.000962 2022/10/13 13:08:47 - mmengine - INFO - Epoch(train) [97][150/293] lr: 5.000000e-04 eta: 2:40:29 time: 0.321044 data_time: 0.070798 memory: 2690 loss_kpt: 0.000972 acc_pose: 0.686646 loss: 0.000972 2022/10/13 13:09:04 - mmengine - INFO - Epoch(train) [97][200/293] lr: 5.000000e-04 eta: 2:40:17 time: 0.336725 data_time: 0.066756 memory: 2690 loss_kpt: 0.000968 acc_pose: 0.682971 loss: 0.000968 2022/10/13 13:09:21 - mmengine - INFO - Epoch(train) [97][250/293] lr: 5.000000e-04 eta: 2:40:05 time: 0.330953 data_time: 0.081040 memory: 2690 loss_kpt: 0.000974 acc_pose: 0.626043 loss: 0.000974 2022/10/13 13:09:35 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:09:52 - mmengine - INFO - Epoch(train) [98][50/293] lr: 5.000000e-04 eta: 2:39:27 time: 0.338920 data_time: 0.076088 memory: 2690 loss_kpt: 0.000972 acc_pose: 0.742779 loss: 0.000972 2022/10/13 13:10:08 - mmengine - INFO - Epoch(train) [98][100/293] lr: 5.000000e-04 eta: 2:39:14 time: 0.321686 data_time: 0.068265 memory: 2690 loss_kpt: 0.000964 acc_pose: 0.696885 loss: 0.000964 2022/10/13 13:10:24 - mmengine - INFO - Epoch(train) [98][150/293] lr: 5.000000e-04 eta: 2:39:01 time: 0.319896 data_time: 0.066181 memory: 2690 loss_kpt: 0.000972 acc_pose: 0.661668 loss: 0.000972 2022/10/13 13:10:40 - mmengine - INFO - Epoch(train) [98][200/293] lr: 5.000000e-04 eta: 2:38:49 time: 0.327433 data_time: 0.060362 memory: 2690 loss_kpt: 0.000976 acc_pose: 0.713500 loss: 0.000976 2022/10/13 13:10:57 - mmengine - INFO - Epoch(train) [98][250/293] lr: 5.000000e-04 eta: 2:38:37 time: 0.324020 data_time: 0.067002 memory: 2690 loss_kpt: 0.000976 acc_pose: 0.676268 loss: 0.000976 2022/10/13 13:11:11 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:11:27 - mmengine - INFO - Epoch(train) [99][50/293] lr: 5.000000e-04 eta: 2:37:58 time: 0.334579 data_time: 0.193565 memory: 2690 loss_kpt: 0.000977 acc_pose: 0.655695 loss: 0.000977 2022/10/13 13:11:44 - mmengine - INFO - Epoch(train) [99][100/293] lr: 5.000000e-04 eta: 2:37:46 time: 0.326577 data_time: 0.149354 memory: 2690 loss_kpt: 0.000969 acc_pose: 0.692036 loss: 0.000969 2022/10/13 13:12:00 - mmengine - INFO - Epoch(train) [99][150/293] lr: 5.000000e-04 eta: 2:37:34 time: 0.330611 data_time: 0.147981 memory: 2690 loss_kpt: 0.000975 acc_pose: 0.689272 loss: 0.000975 2022/10/13 13:12:17 - mmengine - INFO - Epoch(train) [99][200/293] lr: 5.000000e-04 eta: 2:37:21 time: 0.324574 data_time: 0.079815 memory: 2690 loss_kpt: 0.000966 acc_pose: 0.724330 loss: 0.000966 2022/10/13 13:12:33 - mmengine - INFO - Epoch(train) [99][250/293] lr: 5.000000e-04 eta: 2:37:09 time: 0.327823 data_time: 0.065936 memory: 2690 loss_kpt: 0.000974 acc_pose: 0.706470 loss: 0.000974 2022/10/13 13:12:45 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:12:47 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:13:04 - mmengine - INFO - Epoch(train) [100][50/293] lr: 5.000000e-04 eta: 2:36:31 time: 0.340508 data_time: 0.163667 memory: 2690 loss_kpt: 0.000987 acc_pose: 0.705678 loss: 0.000987 2022/10/13 13:13:20 - mmengine - INFO - Epoch(train) [100][100/293] lr: 5.000000e-04 eta: 2:36:18 time: 0.321422 data_time: 0.060920 memory: 2690 loss_kpt: 0.000953 acc_pose: 0.675244 loss: 0.000953 2022/10/13 13:13:36 - mmengine - INFO - Epoch(train) [100][150/293] lr: 5.000000e-04 eta: 2:36:06 time: 0.333188 data_time: 0.080295 memory: 2690 loss_kpt: 0.000970 acc_pose: 0.650632 loss: 0.000970 2022/10/13 13:13:53 - mmengine - INFO - Epoch(train) [100][200/293] lr: 5.000000e-04 eta: 2:35:54 time: 0.335931 data_time: 0.077630 memory: 2690 loss_kpt: 0.000970 acc_pose: 0.666771 loss: 0.000970 2022/10/13 13:14:09 - mmengine - INFO - Epoch(train) [100][250/293] lr: 5.000000e-04 eta: 2:35:42 time: 0.325722 data_time: 0.093593 memory: 2690 loss_kpt: 0.000968 acc_pose: 0.717735 loss: 0.000968 2022/10/13 13:14:23 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:14:23 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/10/13 13:14:31 - mmengine - INFO - Epoch(val) [100][50/407] eta: 0:00:40 time: 0.113462 data_time: 0.073313 memory: 2690 2022/10/13 13:14:36 - mmengine - INFO - Epoch(val) [100][100/407] eta: 0:00:33 time: 0.109380 data_time: 0.069131 memory: 415 2022/10/13 13:14:42 - mmengine - INFO - Epoch(val) [100][150/407] eta: 0:00:28 time: 0.109540 data_time: 0.067556 memory: 415 2022/10/13 13:14:47 - mmengine - INFO - Epoch(val) [100][200/407] eta: 0:00:22 time: 0.107377 data_time: 0.066284 memory: 415 2022/10/13 13:14:53 - mmengine - INFO - Epoch(val) [100][250/407] eta: 0:00:19 time: 0.122636 data_time: 0.081961 memory: 415 2022/10/13 13:14:59 - mmengine - INFO - Epoch(val) [100][300/407] eta: 0:00:11 time: 0.109990 data_time: 0.070317 memory: 415 2022/10/13 13:15:04 - mmengine - INFO - Epoch(val) [100][350/407] eta: 0:00:06 time: 0.112805 data_time: 0.072606 memory: 415 2022/10/13 13:15:10 - mmengine - INFO - Epoch(val) [100][400/407] eta: 0:00:00 time: 0.105168 data_time: 0.065810 memory: 415 2022/10/13 13:15:47 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 13:16:02 - mmengine - INFO - Epoch(val) [100][407/407] coco/AP: 0.557095 coco/AP .5: 0.830665 coco/AP .75: 0.613280 coco/AP (M): 0.525534 coco/AP (L): 0.617954 coco/AR: 0.626700 coco/AR .5: 0.882872 coco/AR .75: 0.685139 coco/AR (M): 0.581945 coco/AR (L): 0.689223 2022/10/13 13:16:19 - mmengine - INFO - Epoch(train) [101][50/293] lr: 5.000000e-04 eta: 2:35:04 time: 0.338198 data_time: 0.087859 memory: 2690 loss_kpt: 0.000974 acc_pose: 0.675994 loss: 0.000974 2022/10/13 13:16:36 - mmengine - INFO - Epoch(train) [101][100/293] lr: 5.000000e-04 eta: 2:34:52 time: 0.335105 data_time: 0.116738 memory: 2690 loss_kpt: 0.000963 acc_pose: 0.746614 loss: 0.000963 2022/10/13 13:16:53 - mmengine - INFO - Epoch(train) [101][150/293] lr: 5.000000e-04 eta: 2:34:40 time: 0.336896 data_time: 0.149300 memory: 2690 loss_kpt: 0.000962 acc_pose: 0.670221 loss: 0.000962 2022/10/13 13:17:09 - mmengine - INFO - Epoch(train) [101][200/293] lr: 5.000000e-04 eta: 2:34:28 time: 0.327461 data_time: 0.094297 memory: 2690 loss_kpt: 0.000960 acc_pose: 0.682285 loss: 0.000960 2022/10/13 13:17:25 - mmengine - INFO - Epoch(train) [101][250/293] lr: 5.000000e-04 eta: 2:34:15 time: 0.326716 data_time: 0.162824 memory: 2690 loss_kpt: 0.000967 acc_pose: 0.684128 loss: 0.000967 2022/10/13 13:17:39 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:17:56 - mmengine - INFO - Epoch(train) [102][50/293] lr: 5.000000e-04 eta: 2:33:38 time: 0.344687 data_time: 0.111605 memory: 2690 loss_kpt: 0.000952 acc_pose: 0.670108 loss: 0.000952 2022/10/13 13:18:12 - mmengine - INFO - Epoch(train) [102][100/293] lr: 5.000000e-04 eta: 2:33:25 time: 0.323804 data_time: 0.157075 memory: 2690 loss_kpt: 0.000967 acc_pose: 0.669911 loss: 0.000967 2022/10/13 13:18:28 - mmengine - INFO - Epoch(train) [102][150/293] lr: 5.000000e-04 eta: 2:33:12 time: 0.318894 data_time: 0.104804 memory: 2690 loss_kpt: 0.000969 acc_pose: 0.700159 loss: 0.000969 2022/10/13 13:18:45 - mmengine - INFO - Epoch(train) [102][200/293] lr: 5.000000e-04 eta: 2:33:00 time: 0.328482 data_time: 0.075533 memory: 2690 loss_kpt: 0.000952 acc_pose: 0.738248 loss: 0.000952 2022/10/13 13:19:01 - mmengine - INFO - Epoch(train) [102][250/293] lr: 5.000000e-04 eta: 2:32:48 time: 0.334184 data_time: 0.151123 memory: 2690 loss_kpt: 0.000956 acc_pose: 0.720305 loss: 0.000956 2022/10/13 13:19:16 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:19:32 - mmengine - INFO - Epoch(train) [103][50/293] lr: 5.000000e-04 eta: 2:32:10 time: 0.332523 data_time: 0.090815 memory: 2690 loss_kpt: 0.000971 acc_pose: 0.696265 loss: 0.000971 2022/10/13 13:19:49 - mmengine - INFO - Epoch(train) [103][100/293] lr: 5.000000e-04 eta: 2:31:58 time: 0.330380 data_time: 0.066080 memory: 2690 loss_kpt: 0.000959 acc_pose: 0.712678 loss: 0.000959 2022/10/13 13:19:53 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:20:05 - mmengine - INFO - Epoch(train) [103][150/293] lr: 5.000000e-04 eta: 2:31:46 time: 0.335235 data_time: 0.065422 memory: 2690 loss_kpt: 0.000974 acc_pose: 0.731483 loss: 0.000974 2022/10/13 13:20:22 - mmengine - INFO - Epoch(train) [103][200/293] lr: 5.000000e-04 eta: 2:31:33 time: 0.328952 data_time: 0.080264 memory: 2690 loss_kpt: 0.000946 acc_pose: 0.670493 loss: 0.000946 2022/10/13 13:20:39 - mmengine - INFO - Epoch(train) [103][250/293] lr: 5.000000e-04 eta: 2:31:21 time: 0.333054 data_time: 0.063806 memory: 2690 loss_kpt: 0.000965 acc_pose: 0.686953 loss: 0.000965 2022/10/13 13:20:53 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:21:09 - mmengine - INFO - Epoch(train) [104][50/293] lr: 5.000000e-04 eta: 2:30:44 time: 0.336255 data_time: 0.141616 memory: 2690 loss_kpt: 0.000959 acc_pose: 0.618984 loss: 0.000959 2022/10/13 13:21:26 - mmengine - INFO - Epoch(train) [104][100/293] lr: 5.000000e-04 eta: 2:30:31 time: 0.327571 data_time: 0.080033 memory: 2690 loss_kpt: 0.000966 acc_pose: 0.683169 loss: 0.000966 2022/10/13 13:21:42 - mmengine - INFO - Epoch(train) [104][150/293] lr: 5.000000e-04 eta: 2:30:19 time: 0.327065 data_time: 0.062138 memory: 2690 loss_kpt: 0.000969 acc_pose: 0.736936 loss: 0.000969 2022/10/13 13:21:59 - mmengine - INFO - Epoch(train) [104][200/293] lr: 5.000000e-04 eta: 2:30:07 time: 0.330942 data_time: 0.082272 memory: 2690 loss_kpt: 0.000960 acc_pose: 0.762208 loss: 0.000960 2022/10/13 13:22:15 - mmengine - INFO - Epoch(train) [104][250/293] lr: 5.000000e-04 eta: 2:29:54 time: 0.326298 data_time: 0.062867 memory: 2690 loss_kpt: 0.000952 acc_pose: 0.744160 loss: 0.000952 2022/10/13 13:22:29 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:22:46 - mmengine - INFO - Epoch(train) [105][50/293] lr: 5.000000e-04 eta: 2:29:17 time: 0.338848 data_time: 0.128510 memory: 2690 loss_kpt: 0.000954 acc_pose: 0.672890 loss: 0.000954 2022/10/13 13:23:03 - mmengine - INFO - Epoch(train) [105][100/293] lr: 5.000000e-04 eta: 2:29:05 time: 0.334322 data_time: 0.063795 memory: 2690 loss_kpt: 0.000949 acc_pose: 0.762102 loss: 0.000949 2022/10/13 13:23:19 - mmengine - INFO - Epoch(train) [105][150/293] lr: 5.000000e-04 eta: 2:28:52 time: 0.323190 data_time: 0.067958 memory: 2690 loss_kpt: 0.000962 acc_pose: 0.721125 loss: 0.000962 2022/10/13 13:23:35 - mmengine - INFO - Epoch(train) [105][200/293] lr: 5.000000e-04 eta: 2:28:39 time: 0.322512 data_time: 0.071863 memory: 2690 loss_kpt: 0.000954 acc_pose: 0.703283 loss: 0.000954 2022/10/13 13:23:51 - mmengine - INFO - Epoch(train) [105][250/293] lr: 5.000000e-04 eta: 2:28:27 time: 0.330532 data_time: 0.064852 memory: 2690 loss_kpt: 0.000954 acc_pose: 0.665182 loss: 0.000954 2022/10/13 13:24:05 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:24:22 - mmengine - INFO - Epoch(train) [106][50/293] lr: 5.000000e-04 eta: 2:27:50 time: 0.342156 data_time: 0.139389 memory: 2690 loss_kpt: 0.000979 acc_pose: 0.697295 loss: 0.000979 2022/10/13 13:24:39 - mmengine - INFO - Epoch(train) [106][100/293] lr: 5.000000e-04 eta: 2:27:38 time: 0.330862 data_time: 0.060221 memory: 2690 loss_kpt: 0.000954 acc_pose: 0.718348 loss: 0.000954 2022/10/13 13:24:55 - mmengine - INFO - Epoch(train) [106][150/293] lr: 5.000000e-04 eta: 2:27:26 time: 0.328044 data_time: 0.065146 memory: 2690 loss_kpt: 0.000976 acc_pose: 0.662640 loss: 0.000976 2022/10/13 13:25:12 - mmengine - INFO - Epoch(train) [106][200/293] lr: 5.000000e-04 eta: 2:27:14 time: 0.338894 data_time: 0.071322 memory: 2690 loss_kpt: 0.000954 acc_pose: 0.705333 loss: 0.000954 2022/10/13 13:25:23 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:25:28 - mmengine - INFO - Epoch(train) [106][250/293] lr: 5.000000e-04 eta: 2:27:01 time: 0.319990 data_time: 0.141179 memory: 2690 loss_kpt: 0.000955 acc_pose: 0.597358 loss: 0.000955 2022/10/13 13:25:42 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:26:00 - mmengine - INFO - Epoch(train) [107][50/293] lr: 5.000000e-04 eta: 2:26:25 time: 0.361488 data_time: 0.088483 memory: 2690 loss_kpt: 0.000955 acc_pose: 0.718612 loss: 0.000955 2022/10/13 13:26:17 - mmengine - INFO - Epoch(train) [107][100/293] lr: 5.000000e-04 eta: 2:26:13 time: 0.338728 data_time: 0.070310 memory: 2690 loss_kpt: 0.000965 acc_pose: 0.634053 loss: 0.000965 2022/10/13 13:26:33 - mmengine - INFO - Epoch(train) [107][150/293] lr: 5.000000e-04 eta: 2:26:00 time: 0.321666 data_time: 0.065509 memory: 2690 loss_kpt: 0.000955 acc_pose: 0.679097 loss: 0.000955 2022/10/13 13:26:49 - mmengine - INFO - Epoch(train) [107][200/293] lr: 5.000000e-04 eta: 2:25:48 time: 0.323345 data_time: 0.066602 memory: 2690 loss_kpt: 0.000956 acc_pose: 0.628145 loss: 0.000956 2022/10/13 13:27:06 - mmengine - INFO - Epoch(train) [107][250/293] lr: 5.000000e-04 eta: 2:25:35 time: 0.328270 data_time: 0.063343 memory: 2690 loss_kpt: 0.000974 acc_pose: 0.665844 loss: 0.000974 2022/10/13 13:27:20 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:27:37 - mmengine - INFO - Epoch(train) [108][50/293] lr: 5.000000e-04 eta: 2:24:59 time: 0.346893 data_time: 0.095203 memory: 2690 loss_kpt: 0.000951 acc_pose: 0.662984 loss: 0.000951 2022/10/13 13:27:53 - mmengine - INFO - Epoch(train) [108][100/293] lr: 5.000000e-04 eta: 2:24:46 time: 0.326432 data_time: 0.068362 memory: 2690 loss_kpt: 0.000972 acc_pose: 0.670206 loss: 0.000972 2022/10/13 13:28:10 - mmengine - INFO - Epoch(train) [108][150/293] lr: 5.000000e-04 eta: 2:24:34 time: 0.335704 data_time: 0.065186 memory: 2690 loss_kpt: 0.000963 acc_pose: 0.689688 loss: 0.000963 2022/10/13 13:28:27 - mmengine - INFO - Epoch(train) [108][200/293] lr: 5.000000e-04 eta: 2:24:22 time: 0.342441 data_time: 0.073961 memory: 2690 loss_kpt: 0.000968 acc_pose: 0.671354 loss: 0.000968 2022/10/13 13:28:44 - mmengine - INFO - Epoch(train) [108][250/293] lr: 5.000000e-04 eta: 2:24:10 time: 0.334816 data_time: 0.065779 memory: 2690 loss_kpt: 0.000951 acc_pose: 0.694497 loss: 0.000951 2022/10/13 13:28:58 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:29:15 - mmengine - INFO - Epoch(train) [109][50/293] lr: 5.000000e-04 eta: 2:23:34 time: 0.345959 data_time: 0.091678 memory: 2690 loss_kpt: 0.000959 acc_pose: 0.684692 loss: 0.000959 2022/10/13 13:29:32 - mmengine - INFO - Epoch(train) [109][100/293] lr: 5.000000e-04 eta: 2:23:22 time: 0.332171 data_time: 0.063247 memory: 2690 loss_kpt: 0.000959 acc_pose: 0.680849 loss: 0.000959 2022/10/13 13:29:48 - mmengine - INFO - Epoch(train) [109][150/293] lr: 5.000000e-04 eta: 2:23:09 time: 0.334346 data_time: 0.066466 memory: 2690 loss_kpt: 0.000932 acc_pose: 0.714327 loss: 0.000932 2022/10/13 13:30:05 - mmengine - INFO - Epoch(train) [109][200/293] lr: 5.000000e-04 eta: 2:22:57 time: 0.324593 data_time: 0.099353 memory: 2690 loss_kpt: 0.000960 acc_pose: 0.703018 loss: 0.000960 2022/10/13 13:30:22 - mmengine - INFO - Epoch(train) [109][250/293] lr: 5.000000e-04 eta: 2:22:45 time: 0.340695 data_time: 0.071833 memory: 2690 loss_kpt: 0.000938 acc_pose: 0.676780 loss: 0.000938 2022/10/13 13:30:35 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:30:52 - mmengine - INFO - Epoch(train) [110][50/293] lr: 5.000000e-04 eta: 2:22:08 time: 0.325973 data_time: 0.156031 memory: 2690 loss_kpt: 0.000955 acc_pose: 0.692508 loss: 0.000955 2022/10/13 13:30:56 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:31:09 - mmengine - INFO - Epoch(train) [110][100/293] lr: 5.000000e-04 eta: 2:21:56 time: 0.342901 data_time: 0.065474 memory: 2690 loss_kpt: 0.000969 acc_pose: 0.731740 loss: 0.000969 2022/10/13 13:31:25 - mmengine - INFO - Epoch(train) [110][150/293] lr: 5.000000e-04 eta: 2:21:43 time: 0.322203 data_time: 0.068499 memory: 2690 loss_kpt: 0.000947 acc_pose: 0.703111 loss: 0.000947 2022/10/13 13:31:41 - mmengine - INFO - Epoch(train) [110][200/293] lr: 5.000000e-04 eta: 2:21:30 time: 0.323763 data_time: 0.105184 memory: 2690 loss_kpt: 0.000979 acc_pose: 0.711578 loss: 0.000979 2022/10/13 13:31:57 - mmengine - INFO - Epoch(train) [110][250/293] lr: 5.000000e-04 eta: 2:21:17 time: 0.320526 data_time: 0.079417 memory: 2690 loss_kpt: 0.000963 acc_pose: 0.751517 loss: 0.000963 2022/10/13 13:32:12 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:32:12 - mmengine - INFO - Saving checkpoint at 110 epochs 2022/10/13 13:32:20 - mmengine - INFO - Epoch(val) [110][50/407] eta: 0:00:43 time: 0.121907 data_time: 0.079574 memory: 2690 2022/10/13 13:32:25 - mmengine - INFO - Epoch(val) [110][100/407] eta: 0:00:34 time: 0.113769 data_time: 0.072806 memory: 415 2022/10/13 13:32:31 - mmengine - INFO - Epoch(val) [110][150/407] eta: 0:00:28 time: 0.110025 data_time: 0.068364 memory: 415 2022/10/13 13:32:36 - mmengine - INFO - Epoch(val) [110][200/407] eta: 0:00:22 time: 0.108247 data_time: 0.066359 memory: 415 2022/10/13 13:32:42 - mmengine - INFO - Epoch(val) [110][250/407] eta: 0:00:17 time: 0.113895 data_time: 0.075701 memory: 415 2022/10/13 13:32:47 - mmengine - INFO - Epoch(val) [110][300/407] eta: 0:00:11 time: 0.109148 data_time: 0.067123 memory: 415 2022/10/13 13:32:53 - mmengine - INFO - Epoch(val) [110][350/407] eta: 0:00:06 time: 0.112895 data_time: 0.071883 memory: 415 2022/10/13 13:32:58 - mmengine - INFO - Epoch(val) [110][400/407] eta: 0:00:00 time: 0.106674 data_time: 0.067728 memory: 415 2022/10/13 13:33:36 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 13:33:51 - mmengine - INFO - Epoch(val) [110][407/407] coco/AP: 0.562241 coco/AP .5: 0.836121 coco/AP .75: 0.621111 coco/AP (M): 0.529975 coco/AP (L): 0.624020 coco/AR: 0.630841 coco/AR .5: 0.884918 coco/AR .75: 0.692380 coco/AR (M): 0.585933 coco/AR (L): 0.694054 2022/10/13 13:33:51 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_256/best_coco/AP_epoch_90.pth is removed 2022/10/13 13:33:53 - mmengine - INFO - The best checkpoint with 0.5622 coco/AP at 110 epoch is saved to best_coco/AP_epoch_110.pth. 2022/10/13 13:34:09 - mmengine - INFO - Epoch(train) [111][50/293] lr: 5.000000e-04 eta: 2:20:41 time: 0.333538 data_time: 0.190640 memory: 2690 loss_kpt: 0.000960 acc_pose: 0.731558 loss: 0.000960 2022/10/13 13:34:26 - mmengine - INFO - Epoch(train) [111][100/293] lr: 5.000000e-04 eta: 2:20:29 time: 0.331896 data_time: 0.203792 memory: 2690 loss_kpt: 0.000960 acc_pose: 0.702688 loss: 0.000960 2022/10/13 13:34:43 - mmengine - INFO - Epoch(train) [111][150/293] lr: 5.000000e-04 eta: 2:20:16 time: 0.333560 data_time: 0.097307 memory: 2690 loss_kpt: 0.000953 acc_pose: 0.650809 loss: 0.000953 2022/10/13 13:34:59 - mmengine - INFO - Epoch(train) [111][200/293] lr: 5.000000e-04 eta: 2:20:03 time: 0.325902 data_time: 0.065303 memory: 2690 loss_kpt: 0.000944 acc_pose: 0.687034 loss: 0.000944 2022/10/13 13:35:15 - mmengine - INFO - Epoch(train) [111][250/293] lr: 5.000000e-04 eta: 2:19:51 time: 0.327287 data_time: 0.069747 memory: 2690 loss_kpt: 0.000970 acc_pose: 0.709158 loss: 0.000970 2022/10/13 13:35:29 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:35:46 - mmengine - INFO - Epoch(train) [112][50/293] lr: 5.000000e-04 eta: 2:19:15 time: 0.332593 data_time: 0.133267 memory: 2690 loss_kpt: 0.000951 acc_pose: 0.736420 loss: 0.000951 2022/10/13 13:36:03 - mmengine - INFO - Epoch(train) [112][100/293] lr: 5.000000e-04 eta: 2:19:03 time: 0.339690 data_time: 0.092084 memory: 2690 loss_kpt: 0.000970 acc_pose: 0.689611 loss: 0.000970 2022/10/13 13:36:20 - mmengine - INFO - Epoch(train) [112][150/293] lr: 5.000000e-04 eta: 2:18:50 time: 0.338528 data_time: 0.153215 memory: 2690 loss_kpt: 0.000954 acc_pose: 0.721555 loss: 0.000954 2022/10/13 13:36:36 - mmengine - INFO - Epoch(train) [112][200/293] lr: 5.000000e-04 eta: 2:18:38 time: 0.326882 data_time: 0.090228 memory: 2690 loss_kpt: 0.000962 acc_pose: 0.698645 loss: 0.000962 2022/10/13 13:36:53 - mmengine - INFO - Epoch(train) [112][250/293] lr: 5.000000e-04 eta: 2:18:25 time: 0.325622 data_time: 0.091642 memory: 2690 loss_kpt: 0.000963 acc_pose: 0.733497 loss: 0.000963 2022/10/13 13:37:07 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:37:24 - mmengine - INFO - Epoch(train) [113][50/293] lr: 5.000000e-04 eta: 2:17:49 time: 0.338129 data_time: 0.095918 memory: 2690 loss_kpt: 0.000967 acc_pose: 0.620173 loss: 0.000967 2022/10/13 13:37:40 - mmengine - INFO - Epoch(train) [113][100/293] lr: 5.000000e-04 eta: 2:17:36 time: 0.321048 data_time: 0.061511 memory: 2690 loss_kpt: 0.000956 acc_pose: 0.686371 loss: 0.000956 2022/10/13 13:37:56 - mmengine - INFO - Epoch(train) [113][150/293] lr: 5.000000e-04 eta: 2:17:23 time: 0.324239 data_time: 0.069499 memory: 2690 loss_kpt: 0.000961 acc_pose: 0.665613 loss: 0.000961 2022/10/13 13:38:07 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:38:13 - mmengine - INFO - Epoch(train) [113][200/293] lr: 5.000000e-04 eta: 2:17:11 time: 0.332151 data_time: 0.079268 memory: 2690 loss_kpt: 0.000950 acc_pose: 0.743745 loss: 0.000950 2022/10/13 13:38:29 - mmengine - INFO - Epoch(train) [113][250/293] lr: 5.000000e-04 eta: 2:16:58 time: 0.329854 data_time: 0.069763 memory: 2690 loss_kpt: 0.000956 acc_pose: 0.775172 loss: 0.000956 2022/10/13 13:38:43 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:39:00 - mmengine - INFO - Epoch(train) [114][50/293] lr: 5.000000e-04 eta: 2:16:23 time: 0.334221 data_time: 0.081579 memory: 2690 loss_kpt: 0.000963 acc_pose: 0.673348 loss: 0.000963 2022/10/13 13:39:16 - mmengine - INFO - Epoch(train) [114][100/293] lr: 5.000000e-04 eta: 2:16:10 time: 0.328808 data_time: 0.065119 memory: 2690 loss_kpt: 0.000963 acc_pose: 0.666650 loss: 0.000963 2022/10/13 13:39:32 - mmengine - INFO - Epoch(train) [114][150/293] lr: 5.000000e-04 eta: 2:15:57 time: 0.320072 data_time: 0.093630 memory: 2690 loss_kpt: 0.000961 acc_pose: 0.698594 loss: 0.000961 2022/10/13 13:39:49 - mmengine - INFO - Epoch(train) [114][200/293] lr: 5.000000e-04 eta: 2:15:44 time: 0.327891 data_time: 0.145912 memory: 2690 loss_kpt: 0.000978 acc_pose: 0.684987 loss: 0.000978 2022/10/13 13:40:05 - mmengine - INFO - Epoch(train) [114][250/293] lr: 5.000000e-04 eta: 2:15:31 time: 0.328472 data_time: 0.091134 memory: 2690 loss_kpt: 0.000949 acc_pose: 0.726260 loss: 0.000949 2022/10/13 13:40:18 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:40:35 - mmengine - INFO - Epoch(train) [115][50/293] lr: 5.000000e-04 eta: 2:14:56 time: 0.339792 data_time: 0.151681 memory: 2690 loss_kpt: 0.000949 acc_pose: 0.665387 loss: 0.000949 2022/10/13 13:40:52 - mmengine - INFO - Epoch(train) [115][100/293] lr: 5.000000e-04 eta: 2:14:43 time: 0.322802 data_time: 0.200464 memory: 2690 loss_kpt: 0.000965 acc_pose: 0.714002 loss: 0.000965 2022/10/13 13:41:08 - mmengine - INFO - Epoch(train) [115][150/293] lr: 5.000000e-04 eta: 2:14:31 time: 0.337040 data_time: 0.150065 memory: 2690 loss_kpt: 0.000967 acc_pose: 0.707280 loss: 0.000967 2022/10/13 13:41:25 - mmengine - INFO - Epoch(train) [115][200/293] lr: 5.000000e-04 eta: 2:14:18 time: 0.322672 data_time: 0.062289 memory: 2690 loss_kpt: 0.000940 acc_pose: 0.720413 loss: 0.000940 2022/10/13 13:41:41 - mmengine - INFO - Epoch(train) [115][250/293] lr: 5.000000e-04 eta: 2:14:05 time: 0.328517 data_time: 0.082193 memory: 2690 loss_kpt: 0.000947 acc_pose: 0.695402 loss: 0.000947 2022/10/13 13:41:56 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:42:12 - mmengine - INFO - Epoch(train) [116][50/293] lr: 5.000000e-04 eta: 2:13:30 time: 0.332635 data_time: 0.124303 memory: 2690 loss_kpt: 0.000947 acc_pose: 0.683614 loss: 0.000947 2022/10/13 13:42:29 - mmengine - INFO - Epoch(train) [116][100/293] lr: 5.000000e-04 eta: 2:13:17 time: 0.333976 data_time: 0.068227 memory: 2690 loss_kpt: 0.000968 acc_pose: 0.728762 loss: 0.000968 2022/10/13 13:42:45 - mmengine - INFO - Epoch(train) [116][150/293] lr: 5.000000e-04 eta: 2:13:04 time: 0.322337 data_time: 0.117523 memory: 2690 loss_kpt: 0.000966 acc_pose: 0.681468 loss: 0.000966 2022/10/13 13:43:02 - mmengine - INFO - Epoch(train) [116][200/293] lr: 5.000000e-04 eta: 2:12:51 time: 0.326958 data_time: 0.068233 memory: 2690 loss_kpt: 0.000966 acc_pose: 0.661394 loss: 0.000966 2022/10/13 13:43:18 - mmengine - INFO - Epoch(train) [116][250/293] lr: 5.000000e-04 eta: 2:12:39 time: 0.330060 data_time: 0.066393 memory: 2690 loss_kpt: 0.000941 acc_pose: 0.699967 loss: 0.000941 2022/10/13 13:43:32 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:43:37 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:43:49 - mmengine - INFO - Epoch(train) [117][50/293] lr: 5.000000e-04 eta: 2:12:04 time: 0.330479 data_time: 0.185859 memory: 2690 loss_kpt: 0.000943 acc_pose: 0.791218 loss: 0.000943 2022/10/13 13:44:05 - mmengine - INFO - Epoch(train) [117][100/293] lr: 5.000000e-04 eta: 2:11:50 time: 0.317223 data_time: 0.187417 memory: 2690 loss_kpt: 0.000957 acc_pose: 0.795818 loss: 0.000957 2022/10/13 13:44:21 - mmengine - INFO - Epoch(train) [117][150/293] lr: 5.000000e-04 eta: 2:11:37 time: 0.319083 data_time: 0.119886 memory: 2690 loss_kpt: 0.000943 acc_pose: 0.639101 loss: 0.000943 2022/10/13 13:44:37 - mmengine - INFO - Epoch(train) [117][200/293] lr: 5.000000e-04 eta: 2:11:24 time: 0.328894 data_time: 0.095482 memory: 2690 loss_kpt: 0.000957 acc_pose: 0.713344 loss: 0.000957 2022/10/13 13:44:54 - mmengine - INFO - Epoch(train) [117][250/293] lr: 5.000000e-04 eta: 2:11:12 time: 0.331252 data_time: 0.065490 memory: 2690 loss_kpt: 0.000953 acc_pose: 0.673208 loss: 0.000953 2022/10/13 13:45:08 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:45:26 - mmengine - INFO - Epoch(train) [118][50/293] lr: 5.000000e-04 eta: 2:10:37 time: 0.346713 data_time: 0.090870 memory: 2690 loss_kpt: 0.000970 acc_pose: 0.685200 loss: 0.000970 2022/10/13 13:45:43 - mmengine - INFO - Epoch(train) [118][100/293] lr: 5.000000e-04 eta: 2:10:25 time: 0.337838 data_time: 0.070315 memory: 2690 loss_kpt: 0.000956 acc_pose: 0.733838 loss: 0.000956 2022/10/13 13:45:59 - mmengine - INFO - Epoch(train) [118][150/293] lr: 5.000000e-04 eta: 2:10:12 time: 0.333623 data_time: 0.068940 memory: 2690 loss_kpt: 0.000955 acc_pose: 0.706595 loss: 0.000955 2022/10/13 13:46:15 - mmengine - INFO - Epoch(train) [118][200/293] lr: 5.000000e-04 eta: 2:09:59 time: 0.324902 data_time: 0.066259 memory: 2690 loss_kpt: 0.000944 acc_pose: 0.738311 loss: 0.000944 2022/10/13 13:46:32 - mmengine - INFO - Epoch(train) [118][250/293] lr: 5.000000e-04 eta: 2:09:46 time: 0.326226 data_time: 0.068292 memory: 2690 loss_kpt: 0.000961 acc_pose: 0.710119 loss: 0.000961 2022/10/13 13:46:45 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:47:02 - mmengine - INFO - Epoch(train) [119][50/293] lr: 5.000000e-04 eta: 2:09:12 time: 0.337038 data_time: 0.160598 memory: 2690 loss_kpt: 0.000955 acc_pose: 0.672109 loss: 0.000955 2022/10/13 13:47:18 - mmengine - INFO - Epoch(train) [119][100/293] lr: 5.000000e-04 eta: 2:08:58 time: 0.314337 data_time: 0.124655 memory: 2690 loss_kpt: 0.000948 acc_pose: 0.689934 loss: 0.000948 2022/10/13 13:47:34 - mmengine - INFO - Epoch(train) [119][150/293] lr: 5.000000e-04 eta: 2:08:45 time: 0.324370 data_time: 0.125282 memory: 2690 loss_kpt: 0.000949 acc_pose: 0.709304 loss: 0.000949 2022/10/13 13:47:51 - mmengine - INFO - Epoch(train) [119][200/293] lr: 5.000000e-04 eta: 2:08:32 time: 0.326756 data_time: 0.099062 memory: 2690 loss_kpt: 0.000956 acc_pose: 0.660966 loss: 0.000956 2022/10/13 13:48:07 - mmengine - INFO - Epoch(train) [119][250/293] lr: 5.000000e-04 eta: 2:08:19 time: 0.325990 data_time: 0.076992 memory: 2690 loss_kpt: 0.000977 acc_pose: 0.691048 loss: 0.000977 2022/10/13 13:48:20 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:48:38 - mmengine - INFO - Epoch(train) [120][50/293] lr: 5.000000e-04 eta: 2:07:45 time: 0.345844 data_time: 0.137718 memory: 2690 loss_kpt: 0.000957 acc_pose: 0.704532 loss: 0.000957 2022/10/13 13:48:54 - mmengine - INFO - Epoch(train) [120][100/293] lr: 5.000000e-04 eta: 2:07:32 time: 0.319032 data_time: 0.067309 memory: 2690 loss_kpt: 0.000943 acc_pose: 0.699954 loss: 0.000943 2022/10/13 13:49:04 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:49:10 - mmengine - INFO - Epoch(train) [120][150/293] lr: 5.000000e-04 eta: 2:07:19 time: 0.320292 data_time: 0.089510 memory: 2690 loss_kpt: 0.000958 acc_pose: 0.702253 loss: 0.000958 2022/10/13 13:49:26 - mmengine - INFO - Epoch(train) [120][200/293] lr: 5.000000e-04 eta: 2:07:06 time: 0.320180 data_time: 0.067128 memory: 2690 loss_kpt: 0.000954 acc_pose: 0.676076 loss: 0.000954 2022/10/13 13:49:42 - mmengine - INFO - Epoch(train) [120][250/293] lr: 5.000000e-04 eta: 2:06:53 time: 0.326232 data_time: 0.067277 memory: 2690 loss_kpt: 0.000956 acc_pose: 0.739776 loss: 0.000956 2022/10/13 13:49:56 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:49:56 - mmengine - INFO - Saving checkpoint at 120 epochs 2022/10/13 13:50:04 - mmengine - INFO - Epoch(val) [120][50/407] eta: 0:00:42 time: 0.119147 data_time: 0.077558 memory: 2690 2022/10/13 13:50:09 - mmengine - INFO - Epoch(val) [120][100/407] eta: 0:00:34 time: 0.111118 data_time: 0.068968 memory: 415 2022/10/13 13:50:15 - mmengine - INFO - Epoch(val) [120][150/407] eta: 0:00:28 time: 0.111334 data_time: 0.065321 memory: 415 2022/10/13 13:50:20 - mmengine - INFO - Epoch(val) [120][200/407] eta: 0:00:23 time: 0.111856 data_time: 0.070160 memory: 415 2022/10/13 13:50:26 - mmengine - INFO - Epoch(val) [120][250/407] eta: 0:00:16 time: 0.105932 data_time: 0.065892 memory: 415 2022/10/13 13:50:32 - mmengine - INFO - Epoch(val) [120][300/407] eta: 0:00:12 time: 0.119672 data_time: 0.079142 memory: 415 2022/10/13 13:50:37 - mmengine - INFO - Epoch(val) [120][350/407] eta: 0:00:06 time: 0.114316 data_time: 0.074372 memory: 415 2022/10/13 13:50:43 - mmengine - INFO - Epoch(val) [120][400/407] eta: 0:00:00 time: 0.108886 data_time: 0.067099 memory: 415 2022/10/13 13:51:21 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 13:51:36 - mmengine - INFO - Epoch(val) [120][407/407] coco/AP: 0.567965 coco/AP .5: 0.836664 coco/AP .75: 0.632995 coco/AP (M): 0.536356 coco/AP (L): 0.628149 coco/AR: 0.634493 coco/AR .5: 0.884288 coco/AR .75: 0.698992 coco/AR (M): 0.591232 coco/AR (L): 0.695169 2022/10/13 13:51:36 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_256/best_coco/AP_epoch_110.pth is removed 2022/10/13 13:51:38 - mmengine - INFO - The best checkpoint with 0.5680 coco/AP at 120 epoch is saved to best_coco/AP_epoch_120.pth. 2022/10/13 13:51:56 - mmengine - INFO - Epoch(train) [121][50/293] lr: 5.000000e-04 eta: 2:06:19 time: 0.358821 data_time: 0.125585 memory: 2690 loss_kpt: 0.000939 acc_pose: 0.726096 loss: 0.000939 2022/10/13 13:52:13 - mmengine - INFO - Epoch(train) [121][100/293] lr: 5.000000e-04 eta: 2:06:07 time: 0.343507 data_time: 0.069491 memory: 2690 loss_kpt: 0.000956 acc_pose: 0.735672 loss: 0.000956 2022/10/13 13:52:30 - mmengine - INFO - Epoch(train) [121][150/293] lr: 5.000000e-04 eta: 2:05:54 time: 0.338735 data_time: 0.081625 memory: 2690 loss_kpt: 0.000950 acc_pose: 0.669144 loss: 0.000950 2022/10/13 13:52:47 - mmengine - INFO - Epoch(train) [121][200/293] lr: 5.000000e-04 eta: 2:05:42 time: 0.354601 data_time: 0.061732 memory: 2690 loss_kpt: 0.000964 acc_pose: 0.777691 loss: 0.000964 2022/10/13 13:53:05 - mmengine - INFO - Epoch(train) [121][250/293] lr: 5.000000e-04 eta: 2:05:31 time: 0.358141 data_time: 0.067454 memory: 2690 loss_kpt: 0.000957 acc_pose: 0.713875 loss: 0.000957 2022/10/13 13:53:20 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:53:38 - mmengine - INFO - Epoch(train) [122][50/293] lr: 5.000000e-04 eta: 2:04:57 time: 0.354833 data_time: 0.076599 memory: 2690 loss_kpt: 0.000947 acc_pose: 0.716737 loss: 0.000947 2022/10/13 13:53:55 - mmengine - INFO - Epoch(train) [122][100/293] lr: 5.000000e-04 eta: 2:04:45 time: 0.339221 data_time: 0.066605 memory: 2690 loss_kpt: 0.000958 acc_pose: 0.675865 loss: 0.000958 2022/10/13 13:54:12 - mmengine - INFO - Epoch(train) [122][150/293] lr: 5.000000e-04 eta: 2:04:32 time: 0.339944 data_time: 0.066476 memory: 2690 loss_kpt: 0.000938 acc_pose: 0.715397 loss: 0.000938 2022/10/13 13:54:29 - mmengine - INFO - Epoch(train) [122][200/293] lr: 5.000000e-04 eta: 2:04:20 time: 0.339395 data_time: 0.070121 memory: 2690 loss_kpt: 0.000950 acc_pose: 0.689153 loss: 0.000950 2022/10/13 13:54:46 - mmengine - INFO - Epoch(train) [122][250/293] lr: 5.000000e-04 eta: 2:04:07 time: 0.335790 data_time: 0.065594 memory: 2690 loss_kpt: 0.000969 acc_pose: 0.695053 loss: 0.000969 2022/10/13 13:55:00 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:55:16 - mmengine - INFO - Epoch(train) [123][50/293] lr: 5.000000e-04 eta: 2:03:33 time: 0.334772 data_time: 0.108452 memory: 2690 loss_kpt: 0.000958 acc_pose: 0.722636 loss: 0.000958 2022/10/13 13:55:33 - mmengine - INFO - Epoch(train) [123][100/293] lr: 5.000000e-04 eta: 2:03:20 time: 0.329968 data_time: 0.075201 memory: 2690 loss_kpt: 0.000944 acc_pose: 0.735527 loss: 0.000944 2022/10/13 13:55:49 - mmengine - INFO - Epoch(train) [123][150/293] lr: 5.000000e-04 eta: 2:03:07 time: 0.328386 data_time: 0.061102 memory: 2690 loss_kpt: 0.000973 acc_pose: 0.690022 loss: 0.000973 2022/10/13 13:56:06 - mmengine - INFO - Epoch(train) [123][200/293] lr: 5.000000e-04 eta: 2:02:54 time: 0.336752 data_time: 0.084935 memory: 2690 loss_kpt: 0.000945 acc_pose: 0.684932 loss: 0.000945 2022/10/13 13:56:23 - mmengine - INFO - Epoch(train) [123][250/293] lr: 5.000000e-04 eta: 2:02:41 time: 0.327949 data_time: 0.096928 memory: 2690 loss_kpt: 0.000951 acc_pose: 0.706010 loss: 0.000951 2022/10/13 13:56:24 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:56:36 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:56:53 - mmengine - INFO - Epoch(train) [124][50/293] lr: 5.000000e-04 eta: 2:02:07 time: 0.336449 data_time: 0.159979 memory: 2690 loss_kpt: 0.000944 acc_pose: 0.666230 loss: 0.000944 2022/10/13 13:57:10 - mmengine - INFO - Epoch(train) [124][100/293] lr: 5.000000e-04 eta: 2:01:55 time: 0.334951 data_time: 0.179543 memory: 2690 loss_kpt: 0.000936 acc_pose: 0.721737 loss: 0.000936 2022/10/13 13:57:26 - mmengine - INFO - Epoch(train) [124][150/293] lr: 5.000000e-04 eta: 2:01:42 time: 0.328517 data_time: 0.091724 memory: 2690 loss_kpt: 0.000965 acc_pose: 0.676127 loss: 0.000965 2022/10/13 13:57:42 - mmengine - INFO - Epoch(train) [124][200/293] lr: 5.000000e-04 eta: 2:01:28 time: 0.321928 data_time: 0.065291 memory: 2690 loss_kpt: 0.000958 acc_pose: 0.719254 loss: 0.000958 2022/10/13 13:57:59 - mmengine - INFO - Epoch(train) [124][250/293] lr: 5.000000e-04 eta: 2:01:16 time: 0.330697 data_time: 0.065376 memory: 2690 loss_kpt: 0.000940 acc_pose: 0.714861 loss: 0.000940 2022/10/13 13:58:12 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 13:58:30 - mmengine - INFO - Epoch(train) [125][50/293] lr: 5.000000e-04 eta: 2:00:42 time: 0.345848 data_time: 0.086429 memory: 2690 loss_kpt: 0.000951 acc_pose: 0.677313 loss: 0.000951 2022/10/13 13:58:46 - mmengine - INFO - Epoch(train) [125][100/293] lr: 5.000000e-04 eta: 2:00:29 time: 0.319564 data_time: 0.069723 memory: 2690 loss_kpt: 0.000956 acc_pose: 0.697370 loss: 0.000956 2022/10/13 13:59:02 - mmengine - INFO - Epoch(train) [125][150/293] lr: 5.000000e-04 eta: 2:00:15 time: 0.317842 data_time: 0.060415 memory: 2690 loss_kpt: 0.000952 acc_pose: 0.707762 loss: 0.000952 2022/10/13 13:59:18 - mmengine - INFO - Epoch(train) [125][200/293] lr: 5.000000e-04 eta: 2:00:03 time: 0.337571 data_time: 0.067193 memory: 2690 loss_kpt: 0.000940 acc_pose: 0.716660 loss: 0.000940 2022/10/13 13:59:35 - mmengine - INFO - Epoch(train) [125][250/293] lr: 5.000000e-04 eta: 1:59:49 time: 0.321316 data_time: 0.060514 memory: 2690 loss_kpt: 0.000953 acc_pose: 0.718223 loss: 0.000953 2022/10/13 13:59:48 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:00:05 - mmengine - INFO - Epoch(train) [126][50/293] lr: 5.000000e-04 eta: 1:59:16 time: 0.336291 data_time: 0.078571 memory: 2690 loss_kpt: 0.000945 acc_pose: 0.651246 loss: 0.000945 2022/10/13 14:00:22 - mmengine - INFO - Epoch(train) [126][100/293] lr: 5.000000e-04 eta: 1:59:03 time: 0.331640 data_time: 0.070547 memory: 2690 loss_kpt: 0.000956 acc_pose: 0.676757 loss: 0.000956 2022/10/13 14:00:38 - mmengine - INFO - Epoch(train) [126][150/293] lr: 5.000000e-04 eta: 1:58:50 time: 0.321826 data_time: 0.070833 memory: 2690 loss_kpt: 0.000952 acc_pose: 0.741083 loss: 0.000952 2022/10/13 14:00:55 - mmengine - INFO - Epoch(train) [126][200/293] lr: 5.000000e-04 eta: 1:58:37 time: 0.340981 data_time: 0.063969 memory: 2690 loss_kpt: 0.000956 acc_pose: 0.711960 loss: 0.000956 2022/10/13 14:01:11 - mmengine - INFO - Epoch(train) [126][250/293] lr: 5.000000e-04 eta: 1:58:24 time: 0.318694 data_time: 0.059789 memory: 2690 loss_kpt: 0.000937 acc_pose: 0.754795 loss: 0.000937 2022/10/13 14:01:25 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:01:41 - mmengine - INFO - Epoch(train) [127][50/293] lr: 5.000000e-04 eta: 1:57:50 time: 0.328598 data_time: 0.142482 memory: 2690 loss_kpt: 0.000951 acc_pose: 0.671711 loss: 0.000951 2022/10/13 14:01:52 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:01:58 - mmengine - INFO - Epoch(train) [127][100/293] lr: 5.000000e-04 eta: 1:57:37 time: 0.329054 data_time: 0.064489 memory: 2690 loss_kpt: 0.000973 acc_pose: 0.721830 loss: 0.000973 2022/10/13 14:02:14 - mmengine - INFO - Epoch(train) [127][150/293] lr: 5.000000e-04 eta: 1:57:23 time: 0.313548 data_time: 0.065494 memory: 2690 loss_kpt: 0.000964 acc_pose: 0.720419 loss: 0.000964 2022/10/13 14:02:30 - mmengine - INFO - Epoch(train) [127][200/293] lr: 5.000000e-04 eta: 1:57:10 time: 0.331940 data_time: 0.087340 memory: 2690 loss_kpt: 0.000957 acc_pose: 0.661222 loss: 0.000957 2022/10/13 14:02:47 - mmengine - INFO - Epoch(train) [127][250/293] lr: 5.000000e-04 eta: 1:56:58 time: 0.331866 data_time: 0.098932 memory: 2690 loss_kpt: 0.000955 acc_pose: 0.704673 loss: 0.000955 2022/10/13 14:03:01 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:03:18 - mmengine - INFO - Epoch(train) [128][50/293] lr: 5.000000e-04 eta: 1:56:25 time: 0.345936 data_time: 0.099422 memory: 2690 loss_kpt: 0.000948 acc_pose: 0.750889 loss: 0.000948 2022/10/13 14:03:35 - mmengine - INFO - Epoch(train) [128][100/293] lr: 5.000000e-04 eta: 1:56:12 time: 0.333204 data_time: 0.068288 memory: 2690 loss_kpt: 0.000945 acc_pose: 0.736662 loss: 0.000945 2022/10/13 14:03:51 - mmengine - INFO - Epoch(train) [128][150/293] lr: 5.000000e-04 eta: 1:55:58 time: 0.323399 data_time: 0.067151 memory: 2690 loss_kpt: 0.000932 acc_pose: 0.714940 loss: 0.000932 2022/10/13 14:04:07 - mmengine - INFO - Epoch(train) [128][200/293] lr: 5.000000e-04 eta: 1:55:45 time: 0.329958 data_time: 0.063528 memory: 2690 loss_kpt: 0.000958 acc_pose: 0.721567 loss: 0.000958 2022/10/13 14:04:23 - mmengine - INFO - Epoch(train) [128][250/293] lr: 5.000000e-04 eta: 1:55:32 time: 0.315821 data_time: 0.090037 memory: 2690 loss_kpt: 0.000941 acc_pose: 0.687123 loss: 0.000941 2022/10/13 14:04:37 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:04:54 - mmengine - INFO - Epoch(train) [129][50/293] lr: 5.000000e-04 eta: 1:54:59 time: 0.334033 data_time: 0.119537 memory: 2690 loss_kpt: 0.000954 acc_pose: 0.676423 loss: 0.000954 2022/10/13 14:05:11 - mmengine - INFO - Epoch(train) [129][100/293] lr: 5.000000e-04 eta: 1:54:46 time: 0.329882 data_time: 0.070650 memory: 2690 loss_kpt: 0.000943 acc_pose: 0.643190 loss: 0.000943 2022/10/13 14:05:27 - mmengine - INFO - Epoch(train) [129][150/293] lr: 5.000000e-04 eta: 1:54:32 time: 0.321962 data_time: 0.067635 memory: 2690 loss_kpt: 0.000955 acc_pose: 0.716967 loss: 0.000955 2022/10/13 14:05:43 - mmengine - INFO - Epoch(train) [129][200/293] lr: 5.000000e-04 eta: 1:54:19 time: 0.329844 data_time: 0.064557 memory: 2690 loss_kpt: 0.000964 acc_pose: 0.713669 loss: 0.000964 2022/10/13 14:06:00 - mmengine - INFO - Epoch(train) [129][250/293] lr: 5.000000e-04 eta: 1:54:06 time: 0.326723 data_time: 0.066117 memory: 2690 loss_kpt: 0.000931 acc_pose: 0.686184 loss: 0.000931 2022/10/13 14:06:13 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:06:30 - mmengine - INFO - Epoch(train) [130][50/293] lr: 5.000000e-04 eta: 1:53:33 time: 0.335829 data_time: 0.093620 memory: 2690 loss_kpt: 0.000949 acc_pose: 0.723876 loss: 0.000949 2022/10/13 14:06:46 - mmengine - INFO - Epoch(train) [130][100/293] lr: 5.000000e-04 eta: 1:53:20 time: 0.317813 data_time: 0.062949 memory: 2690 loss_kpt: 0.000927 acc_pose: 0.687469 loss: 0.000927 2022/10/13 14:07:03 - mmengine - INFO - Epoch(train) [130][150/293] lr: 5.000000e-04 eta: 1:53:07 time: 0.333228 data_time: 0.066661 memory: 2690 loss_kpt: 0.000950 acc_pose: 0.699708 loss: 0.000950 2022/10/13 14:07:19 - mmengine - INFO - Epoch(train) [130][200/293] lr: 5.000000e-04 eta: 1:52:54 time: 0.333787 data_time: 0.066960 memory: 2690 loss_kpt: 0.000959 acc_pose: 0.712353 loss: 0.000959 2022/10/13 14:07:20 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:07:35 - mmengine - INFO - Epoch(train) [130][250/293] lr: 5.000000e-04 eta: 1:52:40 time: 0.319848 data_time: 0.067109 memory: 2690 loss_kpt: 0.000942 acc_pose: 0.684460 loss: 0.000942 2022/10/13 14:07:49 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:07:49 - mmengine - INFO - Saving checkpoint at 130 epochs 2022/10/13 14:07:57 - mmengine - INFO - Epoch(val) [130][50/407] eta: 0:00:41 time: 0.115271 data_time: 0.073094 memory: 2690 2022/10/13 14:08:03 - mmengine - INFO - Epoch(val) [130][100/407] eta: 0:00:34 time: 0.113097 data_time: 0.072210 memory: 415 2022/10/13 14:08:08 - mmengine - INFO - Epoch(val) [130][150/407] eta: 0:00:29 time: 0.113286 data_time: 0.072268 memory: 415 2022/10/13 14:08:14 - mmengine - INFO - Epoch(val) [130][200/407] eta: 0:00:22 time: 0.109687 data_time: 0.067691 memory: 415 2022/10/13 14:08:19 - mmengine - INFO - Epoch(val) [130][250/407] eta: 0:00:17 time: 0.114017 data_time: 0.073480 memory: 415 2022/10/13 14:08:25 - mmengine - INFO - Epoch(val) [130][300/407] eta: 0:00:11 time: 0.109882 data_time: 0.068865 memory: 415 2022/10/13 14:08:31 - mmengine - INFO - Epoch(val) [130][350/407] eta: 0:00:06 time: 0.111547 data_time: 0.067155 memory: 415 2022/10/13 14:08:36 - mmengine - INFO - Epoch(val) [130][400/407] eta: 0:00:00 time: 0.107743 data_time: 0.067522 memory: 415 2022/10/13 14:09:14 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 14:09:29 - mmengine - INFO - Epoch(val) [130][407/407] coco/AP: 0.568890 coco/AP .5: 0.839453 coco/AP .75: 0.633860 coco/AP (M): 0.535309 coco/AP (L): 0.630162 coco/AR: 0.635894 coco/AR .5: 0.885390 coco/AR .75: 0.701984 coco/AR (M): 0.591423 coco/AR (L): 0.698328 2022/10/13 14:09:29 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_256/best_coco/AP_epoch_120.pth is removed 2022/10/13 14:09:31 - mmengine - INFO - The best checkpoint with 0.5689 coco/AP at 130 epoch is saved to best_coco/AP_epoch_130.pth. 2022/10/13 14:09:48 - mmengine - INFO - Epoch(train) [131][50/293] lr: 5.000000e-04 eta: 1:52:07 time: 0.327165 data_time: 0.153729 memory: 2690 loss_kpt: 0.000928 acc_pose: 0.762061 loss: 0.000928 2022/10/13 14:10:04 - mmengine - INFO - Epoch(train) [131][100/293] lr: 5.000000e-04 eta: 1:51:54 time: 0.326886 data_time: 0.131723 memory: 2690 loss_kpt: 0.000959 acc_pose: 0.671136 loss: 0.000959 2022/10/13 14:10:20 - mmengine - INFO - Epoch(train) [131][150/293] lr: 5.000000e-04 eta: 1:51:41 time: 0.325609 data_time: 0.109254 memory: 2690 loss_kpt: 0.000947 acc_pose: 0.724222 loss: 0.000947 2022/10/13 14:10:37 - mmengine - INFO - Epoch(train) [131][200/293] lr: 5.000000e-04 eta: 1:51:27 time: 0.328446 data_time: 0.068995 memory: 2690 loss_kpt: 0.000952 acc_pose: 0.686713 loss: 0.000952 2022/10/13 14:10:53 - mmengine - INFO - Epoch(train) [131][250/293] lr: 5.000000e-04 eta: 1:51:14 time: 0.322283 data_time: 0.064870 memory: 2690 loss_kpt: 0.000938 acc_pose: 0.721113 loss: 0.000938 2022/10/13 14:11:06 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:11:23 - mmengine - INFO - Epoch(train) [132][50/293] lr: 5.000000e-04 eta: 1:50:41 time: 0.340279 data_time: 0.095474 memory: 2690 loss_kpt: 0.000948 acc_pose: 0.734906 loss: 0.000948 2022/10/13 14:11:40 - mmengine - INFO - Epoch(train) [132][100/293] lr: 5.000000e-04 eta: 1:50:28 time: 0.326604 data_time: 0.147603 memory: 2690 loss_kpt: 0.000948 acc_pose: 0.617546 loss: 0.000948 2022/10/13 14:11:56 - mmengine - INFO - Epoch(train) [132][150/293] lr: 5.000000e-04 eta: 1:50:15 time: 0.331219 data_time: 0.181931 memory: 2690 loss_kpt: 0.000951 acc_pose: 0.657600 loss: 0.000951 2022/10/13 14:12:13 - mmengine - INFO - Epoch(train) [132][200/293] lr: 5.000000e-04 eta: 1:50:02 time: 0.333125 data_time: 0.112605 memory: 2690 loss_kpt: 0.000954 acc_pose: 0.699431 loss: 0.000954 2022/10/13 14:12:29 - mmengine - INFO - Epoch(train) [132][250/293] lr: 5.000000e-04 eta: 1:49:49 time: 0.319014 data_time: 0.109104 memory: 2690 loss_kpt: 0.000943 acc_pose: 0.694724 loss: 0.000943 2022/10/13 14:12:42 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:12:59 - mmengine - INFO - Epoch(train) [133][50/293] lr: 5.000000e-04 eta: 1:49:16 time: 0.337793 data_time: 0.179209 memory: 2690 loss_kpt: 0.000940 acc_pose: 0.669659 loss: 0.000940 2022/10/13 14:13:15 - mmengine - INFO - Epoch(train) [133][100/293] lr: 5.000000e-04 eta: 1:49:03 time: 0.322774 data_time: 0.090856 memory: 2690 loss_kpt: 0.000949 acc_pose: 0.687711 loss: 0.000949 2022/10/13 14:13:31 - mmengine - INFO - Epoch(train) [133][150/293] lr: 5.000000e-04 eta: 1:48:49 time: 0.323021 data_time: 0.065534 memory: 2690 loss_kpt: 0.000941 acc_pose: 0.691390 loss: 0.000941 2022/10/13 14:13:48 - mmengine - INFO - Epoch(train) [133][200/293] lr: 5.000000e-04 eta: 1:48:37 time: 0.338140 data_time: 0.066584 memory: 2690 loss_kpt: 0.000944 acc_pose: 0.711966 loss: 0.000944 2022/10/13 14:14:05 - mmengine - INFO - Epoch(train) [133][250/293] lr: 5.000000e-04 eta: 1:48:23 time: 0.322236 data_time: 0.067483 memory: 2690 loss_kpt: 0.000927 acc_pose: 0.747292 loss: 0.000927 2022/10/13 14:14:18 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:14:29 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:14:36 - mmengine - INFO - Epoch(train) [134][50/293] lr: 5.000000e-04 eta: 1:47:51 time: 0.362461 data_time: 0.151191 memory: 2690 loss_kpt: 0.000949 acc_pose: 0.705814 loss: 0.000949 2022/10/13 14:14:55 - mmengine - INFO - Epoch(train) [134][100/293] lr: 5.000000e-04 eta: 1:47:39 time: 0.363496 data_time: 0.126213 memory: 2690 loss_kpt: 0.000943 acc_pose: 0.763279 loss: 0.000943 2022/10/13 14:15:12 - mmengine - INFO - Epoch(train) [134][150/293] lr: 5.000000e-04 eta: 1:47:27 time: 0.354401 data_time: 0.182895 memory: 2690 loss_kpt: 0.000944 acc_pose: 0.665844 loss: 0.000944 2022/10/13 14:15:30 - mmengine - INFO - Epoch(train) [134][200/293] lr: 5.000000e-04 eta: 1:47:14 time: 0.346557 data_time: 0.065012 memory: 2690 loss_kpt: 0.000972 acc_pose: 0.750747 loss: 0.000972 2022/10/13 14:15:46 - mmengine - INFO - Epoch(train) [134][250/293] lr: 5.000000e-04 eta: 1:47:01 time: 0.333833 data_time: 0.100893 memory: 2690 loss_kpt: 0.000939 acc_pose: 0.671051 loss: 0.000939 2022/10/13 14:16:00 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:16:18 - mmengine - INFO - Epoch(train) [135][50/293] lr: 5.000000e-04 eta: 1:46:29 time: 0.355685 data_time: 0.084413 memory: 2690 loss_kpt: 0.000946 acc_pose: 0.694675 loss: 0.000946 2022/10/13 14:16:35 - mmengine - INFO - Epoch(train) [135][100/293] lr: 5.000000e-04 eta: 1:46:16 time: 0.334721 data_time: 0.117038 memory: 2690 loss_kpt: 0.000949 acc_pose: 0.680255 loss: 0.000949 2022/10/13 14:16:53 - mmengine - INFO - Epoch(train) [135][150/293] lr: 5.000000e-04 eta: 1:46:04 time: 0.357770 data_time: 0.073494 memory: 2690 loss_kpt: 0.000942 acc_pose: 0.723971 loss: 0.000942 2022/10/13 14:17:10 - mmengine - INFO - Epoch(train) [135][200/293] lr: 5.000000e-04 eta: 1:45:51 time: 0.333148 data_time: 0.082199 memory: 2690 loss_kpt: 0.000951 acc_pose: 0.681253 loss: 0.000951 2022/10/13 14:17:27 - mmengine - INFO - Epoch(train) [135][250/293] lr: 5.000000e-04 eta: 1:45:38 time: 0.347981 data_time: 0.070447 memory: 2690 loss_kpt: 0.000939 acc_pose: 0.699866 loss: 0.000939 2022/10/13 14:17:42 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:17:59 - mmengine - INFO - Epoch(train) [136][50/293] lr: 5.000000e-04 eta: 1:45:06 time: 0.348855 data_time: 0.088300 memory: 2690 loss_kpt: 0.000942 acc_pose: 0.694055 loss: 0.000942 2022/10/13 14:18:16 - mmengine - INFO - Epoch(train) [136][100/293] lr: 5.000000e-04 eta: 1:44:53 time: 0.341874 data_time: 0.069956 memory: 2690 loss_kpt: 0.000942 acc_pose: 0.689842 loss: 0.000942 2022/10/13 14:18:32 - mmengine - INFO - Epoch(train) [136][150/293] lr: 5.000000e-04 eta: 1:44:40 time: 0.323516 data_time: 0.065747 memory: 2690 loss_kpt: 0.000933 acc_pose: 0.736246 loss: 0.000933 2022/10/13 14:18:49 - mmengine - INFO - Epoch(train) [136][200/293] lr: 5.000000e-04 eta: 1:44:26 time: 0.333347 data_time: 0.082960 memory: 2690 loss_kpt: 0.000936 acc_pose: 0.657486 loss: 0.000936 2022/10/13 14:19:05 - mmengine - INFO - Epoch(train) [136][250/293] lr: 5.000000e-04 eta: 1:44:13 time: 0.320368 data_time: 0.066136 memory: 2690 loss_kpt: 0.000947 acc_pose: 0.743463 loss: 0.000947 2022/10/13 14:19:19 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:19:35 - mmengine - INFO - Epoch(train) [137][50/293] lr: 5.000000e-04 eta: 1:43:41 time: 0.327425 data_time: 0.083303 memory: 2690 loss_kpt: 0.000956 acc_pose: 0.695908 loss: 0.000956 2022/10/13 14:19:51 - mmengine - INFO - Epoch(train) [137][100/293] lr: 5.000000e-04 eta: 1:43:27 time: 0.326299 data_time: 0.063886 memory: 2690 loss_kpt: 0.000952 acc_pose: 0.720832 loss: 0.000952 2022/10/13 14:20:07 - mmengine - INFO - Epoch(train) [137][150/293] lr: 5.000000e-04 eta: 1:43:14 time: 0.319848 data_time: 0.071576 memory: 2690 loss_kpt: 0.000952 acc_pose: 0.721962 loss: 0.000952 2022/10/13 14:20:08 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:20:23 - mmengine - INFO - Epoch(train) [137][200/293] lr: 5.000000e-04 eta: 1:43:00 time: 0.317654 data_time: 0.081761 memory: 2690 loss_kpt: 0.000962 acc_pose: 0.706796 loss: 0.000962 2022/10/13 14:20:39 - mmengine - INFO - Epoch(train) [137][250/293] lr: 5.000000e-04 eta: 1:42:47 time: 0.320712 data_time: 0.131958 memory: 2690 loss_kpt: 0.000946 acc_pose: 0.703470 loss: 0.000946 2022/10/13 14:20:53 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:21:10 - mmengine - INFO - Epoch(train) [138][50/293] lr: 5.000000e-04 eta: 1:42:15 time: 0.336684 data_time: 0.078929 memory: 2690 loss_kpt: 0.000933 acc_pose: 0.726724 loss: 0.000933 2022/10/13 14:21:26 - mmengine - INFO - Epoch(train) [138][100/293] lr: 5.000000e-04 eta: 1:42:01 time: 0.318412 data_time: 0.072560 memory: 2690 loss_kpt: 0.000944 acc_pose: 0.710544 loss: 0.000944 2022/10/13 14:21:42 - mmengine - INFO - Epoch(train) [138][150/293] lr: 5.000000e-04 eta: 1:41:47 time: 0.313086 data_time: 0.151078 memory: 2690 loss_kpt: 0.000956 acc_pose: 0.724582 loss: 0.000956 2022/10/13 14:21:58 - mmengine - INFO - Epoch(train) [138][200/293] lr: 5.000000e-04 eta: 1:41:34 time: 0.325023 data_time: 0.062636 memory: 2690 loss_kpt: 0.000956 acc_pose: 0.647002 loss: 0.000956 2022/10/13 14:22:15 - mmengine - INFO - Epoch(train) [138][250/293] lr: 5.000000e-04 eta: 1:41:21 time: 0.328397 data_time: 0.067417 memory: 2690 loss_kpt: 0.000957 acc_pose: 0.659108 loss: 0.000957 2022/10/13 14:22:29 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:22:45 - mmengine - INFO - Epoch(train) [139][50/293] lr: 5.000000e-04 eta: 1:40:48 time: 0.328145 data_time: 0.113494 memory: 2690 loss_kpt: 0.000956 acc_pose: 0.729173 loss: 0.000956 2022/10/13 14:23:01 - mmengine - INFO - Epoch(train) [139][100/293] lr: 5.000000e-04 eta: 1:40:35 time: 0.322959 data_time: 0.078648 memory: 2690 loss_kpt: 0.000945 acc_pose: 0.755049 loss: 0.000945 2022/10/13 14:23:17 - mmengine - INFO - Epoch(train) [139][150/293] lr: 5.000000e-04 eta: 1:40:21 time: 0.313327 data_time: 0.132576 memory: 2690 loss_kpt: 0.000965 acc_pose: 0.718056 loss: 0.000965 2022/10/13 14:23:33 - mmengine - INFO - Epoch(train) [139][200/293] lr: 5.000000e-04 eta: 1:40:08 time: 0.317635 data_time: 0.131533 memory: 2690 loss_kpt: 0.000922 acc_pose: 0.703275 loss: 0.000922 2022/10/13 14:23:49 - mmengine - INFO - Epoch(train) [139][250/293] lr: 5.000000e-04 eta: 1:39:54 time: 0.324065 data_time: 0.075374 memory: 2690 loss_kpt: 0.000945 acc_pose: 0.742456 loss: 0.000945 2022/10/13 14:24:03 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:24:20 - mmengine - INFO - Epoch(train) [140][50/293] lr: 5.000000e-04 eta: 1:39:22 time: 0.333394 data_time: 0.168587 memory: 2690 loss_kpt: 0.000941 acc_pose: 0.754485 loss: 0.000941 2022/10/13 14:24:36 - mmengine - INFO - Epoch(train) [140][100/293] lr: 5.000000e-04 eta: 1:39:09 time: 0.321545 data_time: 0.105295 memory: 2690 loss_kpt: 0.000961 acc_pose: 0.700082 loss: 0.000961 2022/10/13 14:24:52 - mmengine - INFO - Epoch(train) [140][150/293] lr: 5.000000e-04 eta: 1:38:55 time: 0.320668 data_time: 0.058880 memory: 2690 loss_kpt: 0.000945 acc_pose: 0.650842 loss: 0.000945 2022/10/13 14:25:08 - mmengine - INFO - Epoch(train) [140][200/293] lr: 5.000000e-04 eta: 1:38:42 time: 0.326054 data_time: 0.095221 memory: 2690 loss_kpt: 0.000934 acc_pose: 0.712490 loss: 0.000934 2022/10/13 14:25:24 - mmengine - INFO - Epoch(train) [140][250/293] lr: 5.000000e-04 eta: 1:38:28 time: 0.318351 data_time: 0.112228 memory: 2690 loss_kpt: 0.000951 acc_pose: 0.749160 loss: 0.000951 2022/10/13 14:25:31 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:25:38 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:25:38 - mmengine - INFO - Saving checkpoint at 140 epochs 2022/10/13 14:25:46 - mmengine - INFO - Epoch(val) [140][50/407] eta: 0:00:44 time: 0.124672 data_time: 0.082544 memory: 2690 2022/10/13 14:25:51 - mmengine - INFO - Epoch(val) [140][100/407] eta: 0:00:33 time: 0.108071 data_time: 0.067031 memory: 415 2022/10/13 14:25:57 - mmengine - INFO - Epoch(val) [140][150/407] eta: 0:00:29 time: 0.113790 data_time: 0.071485 memory: 415 2022/10/13 14:26:03 - mmengine - INFO - Epoch(val) [140][200/407] eta: 0:00:22 time: 0.109704 data_time: 0.069580 memory: 415 2022/10/13 14:26:09 - mmengine - INFO - Epoch(val) [140][250/407] eta: 0:00:18 time: 0.120226 data_time: 0.079300 memory: 415 2022/10/13 14:26:14 - mmengine - INFO - Epoch(val) [140][300/407] eta: 0:00:12 time: 0.114988 data_time: 0.074378 memory: 415 2022/10/13 14:26:20 - mmengine - INFO - Epoch(val) [140][350/407] eta: 0:00:06 time: 0.114720 data_time: 0.075297 memory: 415 2022/10/13 14:26:26 - mmengine - INFO - Epoch(val) [140][400/407] eta: 0:00:00 time: 0.107931 data_time: 0.069681 memory: 415 2022/10/13 14:27:04 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 14:27:19 - mmengine - INFO - Epoch(val) [140][407/407] coco/AP: 0.568362 coco/AP .5: 0.837811 coco/AP .75: 0.629672 coco/AP (M): 0.535002 coco/AP (L): 0.630254 coco/AR: 0.635973 coco/AR .5: 0.886335 coco/AR .75: 0.697103 coco/AR (M): 0.590904 coco/AR (L): 0.699108 2022/10/13 14:27:36 - mmengine - INFO - Epoch(train) [141][50/293] lr: 5.000000e-04 eta: 1:37:57 time: 0.334432 data_time: 0.094433 memory: 2690 loss_kpt: 0.000929 acc_pose: 0.636987 loss: 0.000929 2022/10/13 14:27:52 - mmengine - INFO - Epoch(train) [141][100/293] lr: 5.000000e-04 eta: 1:37:43 time: 0.321956 data_time: 0.067027 memory: 2690 loss_kpt: 0.000927 acc_pose: 0.722015 loss: 0.000927 2022/10/13 14:28:08 - mmengine - INFO - Epoch(train) [141][150/293] lr: 5.000000e-04 eta: 1:37:30 time: 0.330628 data_time: 0.064179 memory: 2690 loss_kpt: 0.000951 acc_pose: 0.712282 loss: 0.000951 2022/10/13 14:28:24 - mmengine - INFO - Epoch(train) [141][200/293] lr: 5.000000e-04 eta: 1:37:16 time: 0.318178 data_time: 0.064473 memory: 2690 loss_kpt: 0.000944 acc_pose: 0.662446 loss: 0.000944 2022/10/13 14:28:41 - mmengine - INFO - Epoch(train) [141][250/293] lr: 5.000000e-04 eta: 1:37:03 time: 0.328352 data_time: 0.069529 memory: 2690 loss_kpt: 0.000952 acc_pose: 0.746208 loss: 0.000952 2022/10/13 14:28:54 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:29:11 - mmengine - INFO - Epoch(train) [142][50/293] lr: 5.000000e-04 eta: 1:36:31 time: 0.334688 data_time: 0.173477 memory: 2690 loss_kpt: 0.000955 acc_pose: 0.725195 loss: 0.000955 2022/10/13 14:29:27 - mmengine - INFO - Epoch(train) [142][100/293] lr: 5.000000e-04 eta: 1:36:18 time: 0.314552 data_time: 0.093781 memory: 2690 loss_kpt: 0.000929 acc_pose: 0.675041 loss: 0.000929 2022/10/13 14:29:43 - mmengine - INFO - Epoch(train) [142][150/293] lr: 5.000000e-04 eta: 1:36:04 time: 0.319052 data_time: 0.067141 memory: 2690 loss_kpt: 0.000949 acc_pose: 0.703122 loss: 0.000949 2022/10/13 14:29:59 - mmengine - INFO - Epoch(train) [142][200/293] lr: 5.000000e-04 eta: 1:35:51 time: 0.325405 data_time: 0.066224 memory: 2690 loss_kpt: 0.000949 acc_pose: 0.714428 loss: 0.000949 2022/10/13 14:30:15 - mmengine - INFO - Epoch(train) [142][250/293] lr: 5.000000e-04 eta: 1:35:37 time: 0.326698 data_time: 0.062873 memory: 2690 loss_kpt: 0.000957 acc_pose: 0.723737 loss: 0.000957 2022/10/13 14:30:29 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:30:46 - mmengine - INFO - Epoch(train) [143][50/293] lr: 5.000000e-04 eta: 1:35:06 time: 0.342753 data_time: 0.103431 memory: 2690 loss_kpt: 0.000942 acc_pose: 0.727630 loss: 0.000942 2022/10/13 14:31:03 - mmengine - INFO - Epoch(train) [143][100/293] lr: 5.000000e-04 eta: 1:34:53 time: 0.330506 data_time: 0.104577 memory: 2690 loss_kpt: 0.000958 acc_pose: 0.710674 loss: 0.000958 2022/10/13 14:31:19 - mmengine - INFO - Epoch(train) [143][150/293] lr: 5.000000e-04 eta: 1:34:39 time: 0.327185 data_time: 0.082627 memory: 2690 loss_kpt: 0.000959 acc_pose: 0.704615 loss: 0.000959 2022/10/13 14:31:35 - mmengine - INFO - Epoch(train) [143][200/293] lr: 5.000000e-04 eta: 1:34:26 time: 0.322619 data_time: 0.060753 memory: 2690 loss_kpt: 0.000947 acc_pose: 0.644969 loss: 0.000947 2022/10/13 14:31:52 - mmengine - INFO - Epoch(train) [143][250/293] lr: 5.000000e-04 eta: 1:34:12 time: 0.324624 data_time: 0.092290 memory: 2690 loss_kpt: 0.000947 acc_pose: 0.744660 loss: 0.000947 2022/10/13 14:32:06 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:32:22 - mmengine - INFO - Epoch(train) [144][50/293] lr: 5.000000e-04 eta: 1:33:41 time: 0.331031 data_time: 0.122874 memory: 2690 loss_kpt: 0.000943 acc_pose: 0.760214 loss: 0.000943 2022/10/13 14:32:39 - mmengine - INFO - Epoch(train) [144][100/293] lr: 5.000000e-04 eta: 1:33:27 time: 0.334965 data_time: 0.091103 memory: 2690 loss_kpt: 0.000950 acc_pose: 0.698467 loss: 0.000950 2022/10/13 14:32:39 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:32:55 - mmengine - INFO - Epoch(train) [144][150/293] lr: 5.000000e-04 eta: 1:33:14 time: 0.318397 data_time: 0.067343 memory: 2690 loss_kpt: 0.000935 acc_pose: 0.633899 loss: 0.000935 2022/10/13 14:33:11 - mmengine - INFO - Epoch(train) [144][200/293] lr: 5.000000e-04 eta: 1:33:00 time: 0.316710 data_time: 0.074854 memory: 2690 loss_kpt: 0.000957 acc_pose: 0.679794 loss: 0.000957 2022/10/13 14:33:27 - mmengine - INFO - Epoch(train) [144][250/293] lr: 5.000000e-04 eta: 1:32:46 time: 0.316790 data_time: 0.168700 memory: 2690 loss_kpt: 0.000939 acc_pose: 0.709301 loss: 0.000939 2022/10/13 14:33:40 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:33:56 - mmengine - INFO - Epoch(train) [145][50/293] lr: 5.000000e-04 eta: 1:32:15 time: 0.324647 data_time: 0.098563 memory: 2690 loss_kpt: 0.000928 acc_pose: 0.669876 loss: 0.000928 2022/10/13 14:34:12 - mmengine - INFO - Epoch(train) [145][100/293] lr: 5.000000e-04 eta: 1:32:01 time: 0.323373 data_time: 0.145537 memory: 2690 loss_kpt: 0.000932 acc_pose: 0.669278 loss: 0.000932 2022/10/13 14:34:29 - mmengine - INFO - Epoch(train) [145][150/293] lr: 5.000000e-04 eta: 1:31:48 time: 0.324274 data_time: 0.115387 memory: 2690 loss_kpt: 0.000927 acc_pose: 0.685159 loss: 0.000927 2022/10/13 14:34:45 - mmengine - INFO - Epoch(train) [145][200/293] lr: 5.000000e-04 eta: 1:31:35 time: 0.331724 data_time: 0.085391 memory: 2690 loss_kpt: 0.000938 acc_pose: 0.711757 loss: 0.000938 2022/10/13 14:35:01 - mmengine - INFO - Epoch(train) [145][250/293] lr: 5.000000e-04 eta: 1:31:21 time: 0.320244 data_time: 0.063045 memory: 2690 loss_kpt: 0.000937 acc_pose: 0.669783 loss: 0.000937 2022/10/13 14:35:14 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:35:31 - mmengine - INFO - Epoch(train) [146][50/293] lr: 5.000000e-04 eta: 1:30:49 time: 0.319519 data_time: 0.197978 memory: 2690 loss_kpt: 0.000935 acc_pose: 0.718096 loss: 0.000935 2022/10/13 14:35:47 - mmengine - INFO - Epoch(train) [146][100/293] lr: 5.000000e-04 eta: 1:30:36 time: 0.330865 data_time: 0.171902 memory: 2690 loss_kpt: 0.000930 acc_pose: 0.739197 loss: 0.000930 2022/10/13 14:36:03 - mmengine - INFO - Epoch(train) [146][150/293] lr: 5.000000e-04 eta: 1:30:23 time: 0.325155 data_time: 0.104405 memory: 2690 loss_kpt: 0.000937 acc_pose: 0.709866 loss: 0.000937 2022/10/13 14:36:19 - mmengine - INFO - Epoch(train) [146][200/293] lr: 5.000000e-04 eta: 1:30:09 time: 0.321080 data_time: 0.062757 memory: 2690 loss_kpt: 0.000940 acc_pose: 0.646682 loss: 0.000940 2022/10/13 14:36:36 - mmengine - INFO - Epoch(train) [146][250/293] lr: 5.000000e-04 eta: 1:29:56 time: 0.331922 data_time: 0.108097 memory: 2690 loss_kpt: 0.000940 acc_pose: 0.674385 loss: 0.000940 2022/10/13 14:36:50 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:37:06 - mmengine - INFO - Epoch(train) [147][50/293] lr: 5.000000e-04 eta: 1:29:25 time: 0.335986 data_time: 0.117111 memory: 2690 loss_kpt: 0.000935 acc_pose: 0.755251 loss: 0.000935 2022/10/13 14:37:23 - mmengine - INFO - Epoch(train) [147][100/293] lr: 5.000000e-04 eta: 1:29:11 time: 0.324747 data_time: 0.134199 memory: 2690 loss_kpt: 0.000950 acc_pose: 0.671180 loss: 0.000950 2022/10/13 14:37:39 - mmengine - INFO - Epoch(train) [147][150/293] lr: 5.000000e-04 eta: 1:28:58 time: 0.329814 data_time: 0.116935 memory: 2690 loss_kpt: 0.000935 acc_pose: 0.726312 loss: 0.000935 2022/10/13 14:37:55 - mmengine - INFO - Epoch(train) [147][200/293] lr: 5.000000e-04 eta: 1:28:44 time: 0.323002 data_time: 0.079019 memory: 2690 loss_kpt: 0.000946 acc_pose: 0.707728 loss: 0.000946 2022/10/13 14:38:02 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:38:11 - mmengine - INFO - Epoch(train) [147][250/293] lr: 5.000000e-04 eta: 1:28:30 time: 0.322697 data_time: 0.066096 memory: 2690 loss_kpt: 0.000957 acc_pose: 0.710978 loss: 0.000957 2022/10/13 14:38:25 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:38:42 - mmengine - INFO - Epoch(train) [148][50/293] lr: 5.000000e-04 eta: 1:27:59 time: 0.332504 data_time: 0.093149 memory: 2690 loss_kpt: 0.000946 acc_pose: 0.625735 loss: 0.000946 2022/10/13 14:38:58 - mmengine - INFO - Epoch(train) [148][100/293] lr: 5.000000e-04 eta: 1:27:46 time: 0.331891 data_time: 0.064572 memory: 2690 loss_kpt: 0.000933 acc_pose: 0.670265 loss: 0.000933 2022/10/13 14:39:14 - mmengine - INFO - Epoch(train) [148][150/293] lr: 5.000000e-04 eta: 1:27:32 time: 0.319254 data_time: 0.066236 memory: 2690 loss_kpt: 0.000940 acc_pose: 0.731858 loss: 0.000940 2022/10/13 14:39:31 - mmengine - INFO - Epoch(train) [148][200/293] lr: 5.000000e-04 eta: 1:27:19 time: 0.322965 data_time: 0.067450 memory: 2690 loss_kpt: 0.000943 acc_pose: 0.662774 loss: 0.000943 2022/10/13 14:39:46 - mmengine - INFO - Epoch(train) [148][250/293] lr: 5.000000e-04 eta: 1:27:05 time: 0.317188 data_time: 0.068172 memory: 2690 loss_kpt: 0.000948 acc_pose: 0.622903 loss: 0.000948 2022/10/13 14:40:00 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:40:17 - mmengine - INFO - Epoch(train) [149][50/293] lr: 5.000000e-04 eta: 1:26:34 time: 0.330990 data_time: 0.109827 memory: 2690 loss_kpt: 0.000947 acc_pose: 0.735301 loss: 0.000947 2022/10/13 14:40:33 - mmengine - INFO - Epoch(train) [149][100/293] lr: 5.000000e-04 eta: 1:26:21 time: 0.324457 data_time: 0.166294 memory: 2690 loss_kpt: 0.000938 acc_pose: 0.708756 loss: 0.000938 2022/10/13 14:40:49 - mmengine - INFO - Epoch(train) [149][150/293] lr: 5.000000e-04 eta: 1:26:07 time: 0.320134 data_time: 0.141167 memory: 2690 loss_kpt: 0.000942 acc_pose: 0.698508 loss: 0.000942 2022/10/13 14:41:05 - mmengine - INFO - Epoch(train) [149][200/293] lr: 5.000000e-04 eta: 1:25:53 time: 0.324029 data_time: 0.114330 memory: 2690 loss_kpt: 0.000914 acc_pose: 0.675904 loss: 0.000914 2022/10/13 14:41:21 - mmengine - INFO - Epoch(train) [149][250/293] lr: 5.000000e-04 eta: 1:25:40 time: 0.323292 data_time: 0.105685 memory: 2690 loss_kpt: 0.000940 acc_pose: 0.683379 loss: 0.000940 2022/10/13 14:41:35 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:41:52 - mmengine - INFO - Epoch(train) [150][50/293] lr: 5.000000e-04 eta: 1:25:09 time: 0.332564 data_time: 0.098617 memory: 2690 loss_kpt: 0.000932 acc_pose: 0.719045 loss: 0.000932 2022/10/13 14:42:08 - mmengine - INFO - Epoch(train) [150][100/293] lr: 5.000000e-04 eta: 1:24:55 time: 0.318115 data_time: 0.063710 memory: 2690 loss_kpt: 0.000935 acc_pose: 0.742159 loss: 0.000935 2022/10/13 14:42:23 - mmengine - INFO - Epoch(train) [150][150/293] lr: 5.000000e-04 eta: 1:24:42 time: 0.317497 data_time: 0.068525 memory: 2690 loss_kpt: 0.000933 acc_pose: 0.696059 loss: 0.000933 2022/10/13 14:42:39 - mmengine - INFO - Epoch(train) [150][200/293] lr: 5.000000e-04 eta: 1:24:28 time: 0.315563 data_time: 0.097595 memory: 2690 loss_kpt: 0.000943 acc_pose: 0.690716 loss: 0.000943 2022/10/13 14:42:55 - mmengine - INFO - Epoch(train) [150][250/293] lr: 5.000000e-04 eta: 1:24:14 time: 0.320214 data_time: 0.094743 memory: 2690 loss_kpt: 0.000938 acc_pose: 0.732756 loss: 0.000938 2022/10/13 14:43:09 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:43:09 - mmengine - INFO - Saving checkpoint at 150 epochs 2022/10/13 14:43:17 - mmengine - INFO - Epoch(val) [150][50/407] eta: 0:00:40 time: 0.114709 data_time: 0.075546 memory: 2690 2022/10/13 14:43:22 - mmengine - INFO - Epoch(val) [150][100/407] eta: 0:00:34 time: 0.112149 data_time: 0.070979 memory: 415 2022/10/13 14:43:28 - mmengine - INFO - Epoch(val) [150][150/407] eta: 0:00:30 time: 0.118586 data_time: 0.074898 memory: 415 2022/10/13 14:43:34 - mmengine - INFO - Epoch(val) [150][200/407] eta: 0:00:22 time: 0.110655 data_time: 0.069100 memory: 415 2022/10/13 14:43:39 - mmengine - INFO - Epoch(val) [150][250/407] eta: 0:00:17 time: 0.110316 data_time: 0.071022 memory: 415 2022/10/13 14:43:45 - mmengine - INFO - Epoch(val) [150][300/407] eta: 0:00:12 time: 0.115990 data_time: 0.074942 memory: 415 2022/10/13 14:43:51 - mmengine - INFO - Epoch(val) [150][350/407] eta: 0:00:06 time: 0.117830 data_time: 0.077567 memory: 415 2022/10/13 14:43:56 - mmengine - INFO - Epoch(val) [150][400/407] eta: 0:00:00 time: 0.104351 data_time: 0.065847 memory: 415 2022/10/13 14:44:35 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 14:44:49 - mmengine - INFO - Epoch(val) [150][407/407] coco/AP: 0.572163 coco/AP .5: 0.837985 coco/AP .75: 0.633767 coco/AP (M): 0.540118 coco/AP (L): 0.632531 coco/AR: 0.639232 coco/AR .5: 0.886650 coco/AR .75: 0.700252 coco/AR (M): 0.595821 coco/AR (L): 0.700149 2022/10/13 14:44:49 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_256/best_coco/AP_epoch_130.pth is removed 2022/10/13 14:44:51 - mmengine - INFO - The best checkpoint with 0.5722 coco/AP at 150 epoch is saved to best_coco/AP_epoch_150.pth. 2022/10/13 14:45:08 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:45:08 - mmengine - INFO - Epoch(train) [151][50/293] lr: 5.000000e-04 eta: 1:23:44 time: 0.345834 data_time: 0.192147 memory: 2690 loss_kpt: 0.000937 acc_pose: 0.727731 loss: 0.000937 2022/10/13 14:45:24 - mmengine - INFO - Epoch(train) [151][100/293] lr: 5.000000e-04 eta: 1:23:30 time: 0.315494 data_time: 0.146896 memory: 2690 loss_kpt: 0.000934 acc_pose: 0.735699 loss: 0.000934 2022/10/13 14:45:40 - mmengine - INFO - Epoch(train) [151][150/293] lr: 5.000000e-04 eta: 1:23:16 time: 0.325184 data_time: 0.143077 memory: 2690 loss_kpt: 0.000927 acc_pose: 0.721767 loss: 0.000927 2022/10/13 14:45:56 - mmengine - INFO - Epoch(train) [151][200/293] lr: 5.000000e-04 eta: 1:23:03 time: 0.315086 data_time: 0.139094 memory: 2690 loss_kpt: 0.000938 acc_pose: 0.629750 loss: 0.000938 2022/10/13 14:46:13 - mmengine - INFO - Epoch(train) [151][250/293] lr: 5.000000e-04 eta: 1:22:49 time: 0.340349 data_time: 0.076353 memory: 2690 loss_kpt: 0.000936 acc_pose: 0.735634 loss: 0.000936 2022/10/13 14:46:26 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:46:43 - mmengine - INFO - Epoch(train) [152][50/293] lr: 5.000000e-04 eta: 1:22:19 time: 0.330153 data_time: 0.105189 memory: 2690 loss_kpt: 0.000947 acc_pose: 0.684559 loss: 0.000947 2022/10/13 14:46:59 - mmengine - INFO - Epoch(train) [152][100/293] lr: 5.000000e-04 eta: 1:22:05 time: 0.325480 data_time: 0.112475 memory: 2690 loss_kpt: 0.000931 acc_pose: 0.753881 loss: 0.000931 2022/10/13 14:47:15 - mmengine - INFO - Epoch(train) [152][150/293] lr: 5.000000e-04 eta: 1:21:52 time: 0.325806 data_time: 0.066508 memory: 2690 loss_kpt: 0.000944 acc_pose: 0.706709 loss: 0.000944 2022/10/13 14:47:31 - mmengine - INFO - Epoch(train) [152][200/293] lr: 5.000000e-04 eta: 1:21:38 time: 0.316466 data_time: 0.062478 memory: 2690 loss_kpt: 0.000917 acc_pose: 0.725036 loss: 0.000917 2022/10/13 14:47:47 - mmengine - INFO - Epoch(train) [152][250/293] lr: 5.000000e-04 eta: 1:21:24 time: 0.320855 data_time: 0.062295 memory: 2690 loss_kpt: 0.000940 acc_pose: 0.694136 loss: 0.000940 2022/10/13 14:48:01 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:48:18 - mmengine - INFO - Epoch(train) [153][50/293] lr: 5.000000e-04 eta: 1:20:54 time: 0.341311 data_time: 0.114612 memory: 2690 loss_kpt: 0.000944 acc_pose: 0.759039 loss: 0.000944 2022/10/13 14:48:35 - mmengine - INFO - Epoch(train) [153][100/293] lr: 5.000000e-04 eta: 1:20:41 time: 0.336909 data_time: 0.064687 memory: 2690 loss_kpt: 0.000943 acc_pose: 0.659207 loss: 0.000943 2022/10/13 14:48:51 - mmengine - INFO - Epoch(train) [153][150/293] lr: 5.000000e-04 eta: 1:20:27 time: 0.329716 data_time: 0.066608 memory: 2690 loss_kpt: 0.000930 acc_pose: 0.645518 loss: 0.000930 2022/10/13 14:49:07 - mmengine - INFO - Epoch(train) [153][200/293] lr: 5.000000e-04 eta: 1:20:13 time: 0.315732 data_time: 0.060865 memory: 2690 loss_kpt: 0.000938 acc_pose: 0.677656 loss: 0.000938 2022/10/13 14:49:23 - mmengine - INFO - Epoch(train) [153][250/293] lr: 5.000000e-04 eta: 1:19:59 time: 0.310808 data_time: 0.062809 memory: 2690 loss_kpt: 0.000940 acc_pose: 0.696813 loss: 0.000940 2022/10/13 14:49:37 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:49:54 - mmengine - INFO - Epoch(train) [154][50/293] lr: 5.000000e-04 eta: 1:19:29 time: 0.339479 data_time: 0.084016 memory: 2690 loss_kpt: 0.000940 acc_pose: 0.709008 loss: 0.000940 2022/10/13 14:50:10 - mmengine - INFO - Epoch(train) [154][100/293] lr: 5.000000e-04 eta: 1:19:16 time: 0.328450 data_time: 0.083059 memory: 2690 loss_kpt: 0.000932 acc_pose: 0.731537 loss: 0.000932 2022/10/13 14:50:27 - mmengine - INFO - Epoch(train) [154][150/293] lr: 5.000000e-04 eta: 1:19:02 time: 0.332079 data_time: 0.185131 memory: 2690 loss_kpt: 0.000945 acc_pose: 0.712875 loss: 0.000945 2022/10/13 14:50:34 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:50:43 - mmengine - INFO - Epoch(train) [154][200/293] lr: 5.000000e-04 eta: 1:18:48 time: 0.323526 data_time: 0.147528 memory: 2690 loss_kpt: 0.000941 acc_pose: 0.718758 loss: 0.000941 2022/10/13 14:50:59 - mmengine - INFO - Epoch(train) [154][250/293] lr: 5.000000e-04 eta: 1:18:35 time: 0.320347 data_time: 0.125030 memory: 2690 loss_kpt: 0.000957 acc_pose: 0.714630 loss: 0.000957 2022/10/13 14:51:12 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:51:29 - mmengine - INFO - Epoch(train) [155][50/293] lr: 5.000000e-04 eta: 1:18:04 time: 0.322840 data_time: 0.117466 memory: 2690 loss_kpt: 0.000942 acc_pose: 0.750942 loss: 0.000942 2022/10/13 14:51:45 - mmengine - INFO - Epoch(train) [155][100/293] lr: 5.000000e-04 eta: 1:17:51 time: 0.329799 data_time: 0.079725 memory: 2690 loss_kpt: 0.000939 acc_pose: 0.601642 loss: 0.000939 2022/10/13 14:52:01 - mmengine - INFO - Epoch(train) [155][150/293] lr: 5.000000e-04 eta: 1:17:37 time: 0.324873 data_time: 0.114175 memory: 2690 loss_kpt: 0.000937 acc_pose: 0.680865 loss: 0.000937 2022/10/13 14:52:17 - mmengine - INFO - Epoch(train) [155][200/293] lr: 5.000000e-04 eta: 1:17:23 time: 0.318969 data_time: 0.140297 memory: 2690 loss_kpt: 0.000930 acc_pose: 0.666333 loss: 0.000930 2022/10/13 14:52:34 - mmengine - INFO - Epoch(train) [155][250/293] lr: 5.000000e-04 eta: 1:17:10 time: 0.324906 data_time: 0.076002 memory: 2690 loss_kpt: 0.000942 acc_pose: 0.766375 loss: 0.000942 2022/10/13 14:52:47 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:53:04 - mmengine - INFO - Epoch(train) [156][50/293] lr: 5.000000e-04 eta: 1:16:40 time: 0.338785 data_time: 0.170698 memory: 2690 loss_kpt: 0.000932 acc_pose: 0.723795 loss: 0.000932 2022/10/13 14:53:20 - mmengine - INFO - Epoch(train) [156][100/293] lr: 5.000000e-04 eta: 1:16:26 time: 0.325418 data_time: 0.169699 memory: 2690 loss_kpt: 0.000937 acc_pose: 0.698960 loss: 0.000937 2022/10/13 14:53:36 - mmengine - INFO - Epoch(train) [156][150/293] lr: 5.000000e-04 eta: 1:16:12 time: 0.320557 data_time: 0.153785 memory: 2690 loss_kpt: 0.000928 acc_pose: 0.798608 loss: 0.000928 2022/10/13 14:53:52 - mmengine - INFO - Epoch(train) [156][200/293] lr: 5.000000e-04 eta: 1:15:59 time: 0.319286 data_time: 0.064634 memory: 2690 loss_kpt: 0.000944 acc_pose: 0.693925 loss: 0.000944 2022/10/13 14:54:08 - mmengine - INFO - Epoch(train) [156][250/293] lr: 5.000000e-04 eta: 1:15:45 time: 0.318193 data_time: 0.064190 memory: 2690 loss_kpt: 0.000938 acc_pose: 0.674818 loss: 0.000938 2022/10/13 14:54:21 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:54:38 - mmengine - INFO - Epoch(train) [157][50/293] lr: 5.000000e-04 eta: 1:15:15 time: 0.343949 data_time: 0.087279 memory: 2690 loss_kpt: 0.000908 acc_pose: 0.655182 loss: 0.000908 2022/10/13 14:54:55 - mmengine - INFO - Epoch(train) [157][100/293] lr: 5.000000e-04 eta: 1:15:01 time: 0.322317 data_time: 0.087972 memory: 2690 loss_kpt: 0.000939 acc_pose: 0.713043 loss: 0.000939 2022/10/13 14:55:11 - mmengine - INFO - Epoch(train) [157][150/293] lr: 5.000000e-04 eta: 1:14:47 time: 0.319796 data_time: 0.065297 memory: 2690 loss_kpt: 0.000931 acc_pose: 0.603893 loss: 0.000931 2022/10/13 14:55:27 - mmengine - INFO - Epoch(train) [157][200/293] lr: 5.000000e-04 eta: 1:14:34 time: 0.319489 data_time: 0.063155 memory: 2690 loss_kpt: 0.000944 acc_pose: 0.669216 loss: 0.000944 2022/10/13 14:55:43 - mmengine - INFO - Epoch(train) [157][250/293] lr: 5.000000e-04 eta: 1:14:20 time: 0.328686 data_time: 0.068463 memory: 2690 loss_kpt: 0.000936 acc_pose: 0.719604 loss: 0.000936 2022/10/13 14:55:56 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:55:56 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:56:13 - mmengine - INFO - Epoch(train) [158][50/293] lr: 5.000000e-04 eta: 1:13:50 time: 0.332144 data_time: 0.091731 memory: 2690 loss_kpt: 0.000920 acc_pose: 0.689997 loss: 0.000920 2022/10/13 14:56:29 - mmengine - INFO - Epoch(train) [158][100/293] lr: 5.000000e-04 eta: 1:13:36 time: 0.323636 data_time: 0.067346 memory: 2690 loss_kpt: 0.000935 acc_pose: 0.676982 loss: 0.000935 2022/10/13 14:56:45 - mmengine - INFO - Epoch(train) [158][150/293] lr: 5.000000e-04 eta: 1:13:23 time: 0.320580 data_time: 0.063609 memory: 2690 loss_kpt: 0.000929 acc_pose: 0.701830 loss: 0.000929 2022/10/13 14:57:01 - mmengine - INFO - Epoch(train) [158][200/293] lr: 5.000000e-04 eta: 1:13:09 time: 0.315662 data_time: 0.067060 memory: 2690 loss_kpt: 0.000938 acc_pose: 0.732859 loss: 0.000938 2022/10/13 14:57:17 - mmengine - INFO - Epoch(train) [158][250/293] lr: 5.000000e-04 eta: 1:12:55 time: 0.325608 data_time: 0.069736 memory: 2690 loss_kpt: 0.000936 acc_pose: 0.696668 loss: 0.000936 2022/10/13 14:57:31 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:57:48 - mmengine - INFO - Epoch(train) [159][50/293] lr: 5.000000e-04 eta: 1:12:25 time: 0.335870 data_time: 0.092888 memory: 2690 loss_kpt: 0.000904 acc_pose: 0.725865 loss: 0.000904 2022/10/13 14:58:04 - mmengine - INFO - Epoch(train) [159][100/293] lr: 5.000000e-04 eta: 1:12:12 time: 0.322241 data_time: 0.059703 memory: 2690 loss_kpt: 0.000929 acc_pose: 0.690218 loss: 0.000929 2022/10/13 14:58:20 - mmengine - INFO - Epoch(train) [159][150/293] lr: 5.000000e-04 eta: 1:11:58 time: 0.321014 data_time: 0.124313 memory: 2690 loss_kpt: 0.000918 acc_pose: 0.731883 loss: 0.000918 2022/10/13 14:58:36 - mmengine - INFO - Epoch(train) [159][200/293] lr: 5.000000e-04 eta: 1:11:44 time: 0.321191 data_time: 0.066752 memory: 2690 loss_kpt: 0.000931 acc_pose: 0.742945 loss: 0.000931 2022/10/13 14:58:53 - mmengine - INFO - Epoch(train) [159][250/293] lr: 5.000000e-04 eta: 1:11:30 time: 0.327197 data_time: 0.064834 memory: 2690 loss_kpt: 0.000932 acc_pose: 0.726537 loss: 0.000932 2022/10/13 14:59:06 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 14:59:23 - mmengine - INFO - Epoch(train) [160][50/293] lr: 5.000000e-04 eta: 1:11:01 time: 0.345958 data_time: 0.176344 memory: 2690 loss_kpt: 0.000918 acc_pose: 0.692497 loss: 0.000918 2022/10/13 14:59:39 - mmengine - INFO - Epoch(train) [160][100/293] lr: 5.000000e-04 eta: 1:10:47 time: 0.317741 data_time: 0.081543 memory: 2690 loss_kpt: 0.000930 acc_pose: 0.727441 loss: 0.000930 2022/10/13 14:59:55 - mmengine - INFO - Epoch(train) [160][150/293] lr: 5.000000e-04 eta: 1:10:33 time: 0.325636 data_time: 0.088286 memory: 2690 loss_kpt: 0.000939 acc_pose: 0.750788 loss: 0.000939 2022/10/13 15:00:12 - mmengine - INFO - Epoch(train) [160][200/293] lr: 5.000000e-04 eta: 1:10:20 time: 0.321795 data_time: 0.081624 memory: 2690 loss_kpt: 0.000917 acc_pose: 0.669645 loss: 0.000917 2022/10/13 15:00:27 - mmengine - INFO - Epoch(train) [160][250/293] lr: 5.000000e-04 eta: 1:10:06 time: 0.316237 data_time: 0.063390 memory: 2690 loss_kpt: 0.000928 acc_pose: 0.679245 loss: 0.000928 2022/10/13 15:00:41 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:00:41 - mmengine - INFO - Saving checkpoint at 160 epochs 2022/10/13 15:00:49 - mmengine - INFO - Epoch(val) [160][50/407] eta: 0:00:40 time: 0.112076 data_time: 0.073164 memory: 2690 2022/10/13 15:00:54 - mmengine - INFO - Epoch(val) [160][100/407] eta: 0:00:32 time: 0.106052 data_time: 0.065777 memory: 415 2022/10/13 15:01:00 - mmengine - INFO - Epoch(val) [160][150/407] eta: 0:00:30 time: 0.117378 data_time: 0.077622 memory: 415 2022/10/13 15:01:05 - mmengine - INFO - Epoch(val) [160][200/407] eta: 0:00:23 time: 0.114404 data_time: 0.074538 memory: 415 2022/10/13 15:01:11 - mmengine - INFO - Epoch(val) [160][250/407] eta: 0:00:17 time: 0.110989 data_time: 0.070514 memory: 415 2022/10/13 15:01:16 - mmengine - INFO - Epoch(val) [160][300/407] eta: 0:00:11 time: 0.109759 data_time: 0.069841 memory: 415 2022/10/13 15:01:22 - mmengine - INFO - Epoch(val) [160][350/407] eta: 0:00:06 time: 0.111622 data_time: 0.071000 memory: 415 2022/10/13 15:01:27 - mmengine - INFO - Epoch(val) [160][400/407] eta: 0:00:00 time: 0.108281 data_time: 0.069766 memory: 415 2022/10/13 15:02:06 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 15:02:20 - mmengine - INFO - Epoch(val) [160][407/407] coco/AP: 0.574540 coco/AP .5: 0.842993 coco/AP .75: 0.638039 coco/AP (M): 0.540735 coco/AP (L): 0.636632 coco/AR: 0.641829 coco/AR .5: 0.892160 coco/AR .75: 0.705919 coco/AR (M): 0.596913 coco/AR (L): 0.704980 2022/10/13 15:02:20 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_256/best_coco/AP_epoch_150.pth is removed 2022/10/13 15:02:22 - mmengine - INFO - The best checkpoint with 0.5745 coco/AP at 160 epoch is saved to best_coco/AP_epoch_160.pth. 2022/10/13 15:02:38 - mmengine - INFO - Epoch(train) [161][50/293] lr: 5.000000e-04 eta: 1:09:36 time: 0.331709 data_time: 0.121491 memory: 2690 loss_kpt: 0.000940 acc_pose: 0.704387 loss: 0.000940 2022/10/13 15:02:54 - mmengine - INFO - Epoch(train) [161][100/293] lr: 5.000000e-04 eta: 1:09:22 time: 0.315141 data_time: 0.068651 memory: 2690 loss_kpt: 0.000930 acc_pose: 0.604761 loss: 0.000930 2022/10/13 15:03:00 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:03:10 - mmengine - INFO - Epoch(train) [161][150/293] lr: 5.000000e-04 eta: 1:09:08 time: 0.314320 data_time: 0.060011 memory: 2690 loss_kpt: 0.000943 acc_pose: 0.695161 loss: 0.000943 2022/10/13 15:03:26 - mmengine - INFO - Epoch(train) [161][200/293] lr: 5.000000e-04 eta: 1:08:54 time: 0.322997 data_time: 0.080585 memory: 2690 loss_kpt: 0.000931 acc_pose: 0.712404 loss: 0.000931 2022/10/13 15:03:42 - mmengine - INFO - Epoch(train) [161][250/293] lr: 5.000000e-04 eta: 1:08:41 time: 0.321184 data_time: 0.065133 memory: 2690 loss_kpt: 0.000953 acc_pose: 0.696234 loss: 0.000953 2022/10/13 15:03:56 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:04:13 - mmengine - INFO - Epoch(train) [162][50/293] lr: 5.000000e-04 eta: 1:08:11 time: 0.329837 data_time: 0.090993 memory: 2690 loss_kpt: 0.000921 acc_pose: 0.724374 loss: 0.000921 2022/10/13 15:04:29 - mmengine - INFO - Epoch(train) [162][100/293] lr: 5.000000e-04 eta: 1:07:57 time: 0.335147 data_time: 0.068338 memory: 2690 loss_kpt: 0.000935 acc_pose: 0.740951 loss: 0.000935 2022/10/13 15:04:46 - mmengine - INFO - Epoch(train) [162][150/293] lr: 5.000000e-04 eta: 1:07:44 time: 0.321932 data_time: 0.065613 memory: 2690 loss_kpt: 0.000945 acc_pose: 0.705454 loss: 0.000945 2022/10/13 15:05:01 - mmengine - INFO - Epoch(train) [162][200/293] lr: 5.000000e-04 eta: 1:07:30 time: 0.315713 data_time: 0.061513 memory: 2690 loss_kpt: 0.000946 acc_pose: 0.702811 loss: 0.000946 2022/10/13 15:05:18 - mmengine - INFO - Epoch(train) [162][250/293] lr: 5.000000e-04 eta: 1:07:16 time: 0.327541 data_time: 0.067125 memory: 2690 loss_kpt: 0.000930 acc_pose: 0.711149 loss: 0.000930 2022/10/13 15:05:32 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:05:48 - mmengine - INFO - Epoch(train) [163][50/293] lr: 5.000000e-04 eta: 1:06:47 time: 0.330354 data_time: 0.079202 memory: 2690 loss_kpt: 0.000926 acc_pose: 0.755666 loss: 0.000926 2022/10/13 15:06:05 - mmengine - INFO - Epoch(train) [163][100/293] lr: 5.000000e-04 eta: 1:06:33 time: 0.328918 data_time: 0.057430 memory: 2690 loss_kpt: 0.000934 acc_pose: 0.712889 loss: 0.000934 2022/10/13 15:06:21 - mmengine - INFO - Epoch(train) [163][150/293] lr: 5.000000e-04 eta: 1:06:19 time: 0.327714 data_time: 0.064930 memory: 2690 loss_kpt: 0.000939 acc_pose: 0.695041 loss: 0.000939 2022/10/13 15:06:37 - mmengine - INFO - Epoch(train) [163][200/293] lr: 5.000000e-04 eta: 1:06:05 time: 0.316929 data_time: 0.064100 memory: 2690 loss_kpt: 0.000926 acc_pose: 0.687885 loss: 0.000926 2022/10/13 15:06:53 - mmengine - INFO - Epoch(train) [163][250/293] lr: 5.000000e-04 eta: 1:05:52 time: 0.325234 data_time: 0.066846 memory: 2690 loss_kpt: 0.000941 acc_pose: 0.686610 loss: 0.000941 2022/10/13 15:07:06 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:07:23 - mmengine - INFO - Epoch(train) [164][50/293] lr: 5.000000e-04 eta: 1:05:22 time: 0.335007 data_time: 0.093912 memory: 2690 loss_kpt: 0.000931 acc_pose: 0.712303 loss: 0.000931 2022/10/13 15:07:39 - mmengine - INFO - Epoch(train) [164][100/293] lr: 5.000000e-04 eta: 1:05:08 time: 0.318097 data_time: 0.059348 memory: 2690 loss_kpt: 0.000939 acc_pose: 0.718591 loss: 0.000939 2022/10/13 15:07:55 - mmengine - INFO - Epoch(train) [164][150/293] lr: 5.000000e-04 eta: 1:04:55 time: 0.326061 data_time: 0.071189 memory: 2690 loss_kpt: 0.000935 acc_pose: 0.734591 loss: 0.000935 2022/10/13 15:08:11 - mmengine - INFO - Epoch(train) [164][200/293] lr: 5.000000e-04 eta: 1:04:41 time: 0.323129 data_time: 0.062329 memory: 2690 loss_kpt: 0.000930 acc_pose: 0.677129 loss: 0.000930 2022/10/13 15:08:25 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:08:28 - mmengine - INFO - Epoch(train) [164][250/293] lr: 5.000000e-04 eta: 1:04:27 time: 0.322580 data_time: 0.063689 memory: 2690 loss_kpt: 0.000925 acc_pose: 0.718163 loss: 0.000925 2022/10/13 15:08:41 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:08:59 - mmengine - INFO - Epoch(train) [165][50/293] lr: 5.000000e-04 eta: 1:03:58 time: 0.339962 data_time: 0.087350 memory: 2690 loss_kpt: 0.000940 acc_pose: 0.741263 loss: 0.000940 2022/10/13 15:09:15 - mmengine - INFO - Epoch(train) [165][100/293] lr: 5.000000e-04 eta: 1:03:44 time: 0.323663 data_time: 0.085713 memory: 2690 loss_kpt: 0.000929 acc_pose: 0.717768 loss: 0.000929 2022/10/13 15:09:30 - mmengine - INFO - Epoch(train) [165][150/293] lr: 5.000000e-04 eta: 1:03:30 time: 0.313673 data_time: 0.103114 memory: 2690 loss_kpt: 0.000934 acc_pose: 0.702408 loss: 0.000934 2022/10/13 15:09:46 - mmengine - INFO - Epoch(train) [165][200/293] lr: 5.000000e-04 eta: 1:03:16 time: 0.321083 data_time: 0.068754 memory: 2690 loss_kpt: 0.000934 acc_pose: 0.762421 loss: 0.000934 2022/10/13 15:10:03 - mmengine - INFO - Epoch(train) [165][250/293] lr: 5.000000e-04 eta: 1:03:03 time: 0.322634 data_time: 0.067656 memory: 2690 loss_kpt: 0.000948 acc_pose: 0.705610 loss: 0.000948 2022/10/13 15:10:16 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:10:33 - mmengine - INFO - Epoch(train) [166][50/293] lr: 5.000000e-04 eta: 1:02:33 time: 0.332997 data_time: 0.128306 memory: 2690 loss_kpt: 0.000919 acc_pose: 0.733097 loss: 0.000919 2022/10/13 15:10:50 - mmengine - INFO - Epoch(train) [166][100/293] lr: 5.000000e-04 eta: 1:02:20 time: 0.331171 data_time: 0.106647 memory: 2690 loss_kpt: 0.000917 acc_pose: 0.689052 loss: 0.000917 2022/10/13 15:11:06 - mmengine - INFO - Epoch(train) [166][150/293] lr: 5.000000e-04 eta: 1:02:06 time: 0.320260 data_time: 0.097131 memory: 2690 loss_kpt: 0.000933 acc_pose: 0.630998 loss: 0.000933 2022/10/13 15:11:21 - mmengine - INFO - Epoch(train) [166][200/293] lr: 5.000000e-04 eta: 1:01:52 time: 0.310688 data_time: 0.113705 memory: 2690 loss_kpt: 0.000927 acc_pose: 0.741703 loss: 0.000927 2022/10/13 15:11:38 - mmengine - INFO - Epoch(train) [166][250/293] lr: 5.000000e-04 eta: 1:01:38 time: 0.328906 data_time: 0.126149 memory: 2690 loss_kpt: 0.000949 acc_pose: 0.709729 loss: 0.000949 2022/10/13 15:11:51 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:12:09 - mmengine - INFO - Epoch(train) [167][50/293] lr: 5.000000e-04 eta: 1:01:09 time: 0.347631 data_time: 0.099054 memory: 2690 loss_kpt: 0.000923 acc_pose: 0.679868 loss: 0.000923 2022/10/13 15:12:25 - mmengine - INFO - Epoch(train) [167][100/293] lr: 5.000000e-04 eta: 1:00:55 time: 0.327269 data_time: 0.062069 memory: 2690 loss_kpt: 0.000915 acc_pose: 0.677260 loss: 0.000915 2022/10/13 15:12:41 - mmengine - INFO - Epoch(train) [167][150/293] lr: 5.000000e-04 eta: 1:00:41 time: 0.313923 data_time: 0.105551 memory: 2690 loss_kpt: 0.000945 acc_pose: 0.724534 loss: 0.000945 2022/10/13 15:12:56 - mmengine - INFO - Epoch(train) [167][200/293] lr: 5.000000e-04 eta: 1:00:27 time: 0.317499 data_time: 0.157901 memory: 2690 loss_kpt: 0.000934 acc_pose: 0.711001 loss: 0.000934 2022/10/13 15:13:12 - mmengine - INFO - Epoch(train) [167][250/293] lr: 5.000000e-04 eta: 1:00:14 time: 0.311650 data_time: 0.149022 memory: 2690 loss_kpt: 0.000947 acc_pose: 0.755834 loss: 0.000947 2022/10/13 15:13:26 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:13:43 - mmengine - INFO - Epoch(train) [168][50/293] lr: 5.000000e-04 eta: 0:59:44 time: 0.340840 data_time: 0.090510 memory: 2690 loss_kpt: 0.000902 acc_pose: 0.704554 loss: 0.000902 2022/10/13 15:13:49 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:13:59 - mmengine - INFO - Epoch(train) [168][100/293] lr: 5.000000e-04 eta: 0:59:31 time: 0.324148 data_time: 0.063884 memory: 2690 loss_kpt: 0.000929 acc_pose: 0.714104 loss: 0.000929 2022/10/13 15:14:15 - mmengine - INFO - Epoch(train) [168][150/293] lr: 5.000000e-04 eta: 0:59:17 time: 0.324957 data_time: 0.066315 memory: 2690 loss_kpt: 0.000927 acc_pose: 0.676440 loss: 0.000927 2022/10/13 15:14:31 - mmengine - INFO - Epoch(train) [168][200/293] lr: 5.000000e-04 eta: 0:59:03 time: 0.321649 data_time: 0.068213 memory: 2690 loss_kpt: 0.000951 acc_pose: 0.678151 loss: 0.000951 2022/10/13 15:14:48 - mmengine - INFO - Epoch(train) [168][250/293] lr: 5.000000e-04 eta: 0:58:49 time: 0.328553 data_time: 0.067618 memory: 2690 loss_kpt: 0.000925 acc_pose: 0.731259 loss: 0.000925 2022/10/13 15:15:01 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:15:18 - mmengine - INFO - Epoch(train) [169][50/293] lr: 5.000000e-04 eta: 0:58:20 time: 0.329882 data_time: 0.096613 memory: 2690 loss_kpt: 0.000937 acc_pose: 0.719998 loss: 0.000937 2022/10/13 15:15:34 - mmengine - INFO - Epoch(train) [169][100/293] lr: 5.000000e-04 eta: 0:58:06 time: 0.326705 data_time: 0.085620 memory: 2690 loss_kpt: 0.000940 acc_pose: 0.726460 loss: 0.000940 2022/10/13 15:15:51 - mmengine - INFO - Epoch(train) [169][150/293] lr: 5.000000e-04 eta: 0:57:53 time: 0.324331 data_time: 0.065758 memory: 2690 loss_kpt: 0.000936 acc_pose: 0.699792 loss: 0.000936 2022/10/13 15:16:07 - mmengine - INFO - Epoch(train) [169][200/293] lr: 5.000000e-04 eta: 0:57:39 time: 0.333137 data_time: 0.068089 memory: 2690 loss_kpt: 0.000927 acc_pose: 0.722773 loss: 0.000927 2022/10/13 15:16:24 - mmengine - INFO - Epoch(train) [169][250/293] lr: 5.000000e-04 eta: 0:57:25 time: 0.334126 data_time: 0.065905 memory: 2690 loss_kpt: 0.000943 acc_pose: 0.677632 loss: 0.000943 2022/10/13 15:16:38 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:16:54 - mmengine - INFO - Epoch(train) [170][50/293] lr: 5.000000e-04 eta: 0:56:56 time: 0.332025 data_time: 0.093490 memory: 2690 loss_kpt: 0.000927 acc_pose: 0.631281 loss: 0.000927 2022/10/13 15:17:11 - mmengine - INFO - Epoch(train) [170][100/293] lr: 5.000000e-04 eta: 0:56:42 time: 0.322949 data_time: 0.062728 memory: 2690 loss_kpt: 0.000908 acc_pose: 0.666468 loss: 0.000908 2022/10/13 15:17:27 - mmengine - INFO - Epoch(train) [170][150/293] lr: 5.000000e-04 eta: 0:56:29 time: 0.327703 data_time: 0.063805 memory: 2690 loss_kpt: 0.000940 acc_pose: 0.693665 loss: 0.000940 2022/10/13 15:17:43 - mmengine - INFO - Epoch(train) [170][200/293] lr: 5.000000e-04 eta: 0:56:15 time: 0.313840 data_time: 0.066763 memory: 2690 loss_kpt: 0.000918 acc_pose: 0.700957 loss: 0.000918 2022/10/13 15:17:58 - mmengine - INFO - Epoch(train) [170][250/293] lr: 5.000000e-04 eta: 0:56:01 time: 0.311069 data_time: 0.070610 memory: 2690 loss_kpt: 0.000917 acc_pose: 0.705084 loss: 0.000917 2022/10/13 15:18:12 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:18:12 - mmengine - INFO - Saving checkpoint at 170 epochs 2022/10/13 15:18:19 - mmengine - INFO - Epoch(val) [170][50/407] eta: 0:00:40 time: 0.114422 data_time: 0.072739 memory: 2690 2022/10/13 15:18:25 - mmengine - INFO - Epoch(val) [170][100/407] eta: 0:00:34 time: 0.111900 data_time: 0.073162 memory: 415 2022/10/13 15:18:31 - mmengine - INFO - Epoch(val) [170][150/407] eta: 0:00:28 time: 0.112243 data_time: 0.068574 memory: 415 2022/10/13 15:18:36 - mmengine - INFO - Epoch(val) [170][200/407] eta: 0:00:22 time: 0.110146 data_time: 0.068747 memory: 415 2022/10/13 15:18:42 - mmengine - INFO - Epoch(val) [170][250/407] eta: 0:00:17 time: 0.108449 data_time: 0.068473 memory: 415 2022/10/13 15:18:47 - mmengine - INFO - Epoch(val) [170][300/407] eta: 0:00:11 time: 0.110390 data_time: 0.068587 memory: 415 2022/10/13 15:18:53 - mmengine - INFO - Epoch(val) [170][350/407] eta: 0:00:06 time: 0.111158 data_time: 0.070581 memory: 415 2022/10/13 15:18:58 - mmengine - INFO - Epoch(val) [170][400/407] eta: 0:00:00 time: 0.109821 data_time: 0.069765 memory: 415 2022/10/13 15:19:36 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 15:19:51 - mmengine - INFO - Epoch(val) [170][407/407] coco/AP: 0.575756 coco/AP .5: 0.844157 coco/AP .75: 0.640169 coco/AP (M): 0.543887 coco/AP (L): 0.635730 coco/AR: 0.642412 coco/AR .5: 0.892632 coco/AR .75: 0.707494 coco/AR (M): 0.599426 coco/AR (L): 0.702787 2022/10/13 15:19:51 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_256/best_coco/AP_epoch_160.pth is removed 2022/10/13 15:19:53 - mmengine - INFO - The best checkpoint with 0.5758 coco/AP at 170 epoch is saved to best_coco/AP_epoch_170.pth. 2022/10/13 15:20:09 - mmengine - INFO - Epoch(train) [171][50/293] lr: 5.000000e-05 eta: 0:55:32 time: 0.328777 data_time: 0.164612 memory: 2690 loss_kpt: 0.000924 acc_pose: 0.734403 loss: 0.000924 2022/10/13 15:20:25 - mmengine - INFO - Epoch(train) [171][100/293] lr: 5.000000e-05 eta: 0:55:18 time: 0.330044 data_time: 0.081117 memory: 2690 loss_kpt: 0.000909 acc_pose: 0.752544 loss: 0.000909 2022/10/13 15:20:41 - mmengine - INFO - Epoch(train) [171][150/293] lr: 5.000000e-05 eta: 0:55:04 time: 0.319419 data_time: 0.068857 memory: 2690 loss_kpt: 0.000924 acc_pose: 0.715674 loss: 0.000924 2022/10/13 15:20:55 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:20:59 - mmengine - INFO - Epoch(train) [171][200/293] lr: 5.000000e-05 eta: 0:54:51 time: 0.351077 data_time: 0.060675 memory: 2690 loss_kpt: 0.000923 acc_pose: 0.696088 loss: 0.000923 2022/10/13 15:21:17 - mmengine - INFO - Epoch(train) [171][250/293] lr: 5.000000e-05 eta: 0:54:37 time: 0.357018 data_time: 0.092111 memory: 2690 loss_kpt: 0.000928 acc_pose: 0.740696 loss: 0.000928 2022/10/13 15:21:31 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:21:49 - mmengine - INFO - Epoch(train) [172][50/293] lr: 5.000000e-05 eta: 0:54:09 time: 0.359088 data_time: 0.081708 memory: 2690 loss_kpt: 0.000903 acc_pose: 0.729066 loss: 0.000903 2022/10/13 15:22:06 - mmengine - INFO - Epoch(train) [172][100/293] lr: 5.000000e-05 eta: 0:53:55 time: 0.336721 data_time: 0.065109 memory: 2690 loss_kpt: 0.000948 acc_pose: 0.666453 loss: 0.000948 2022/10/13 15:22:23 - mmengine - INFO - Epoch(train) [172][150/293] lr: 5.000000e-05 eta: 0:53:41 time: 0.341070 data_time: 0.072571 memory: 2690 loss_kpt: 0.000899 acc_pose: 0.711303 loss: 0.000899 2022/10/13 15:22:41 - mmengine - INFO - Epoch(train) [172][200/293] lr: 5.000000e-05 eta: 0:53:28 time: 0.348220 data_time: 0.065310 memory: 2690 loss_kpt: 0.000937 acc_pose: 0.733060 loss: 0.000937 2022/10/13 15:22:58 - mmengine - INFO - Epoch(train) [172][250/293] lr: 5.000000e-05 eta: 0:53:14 time: 0.350438 data_time: 0.064835 memory: 2690 loss_kpt: 0.000924 acc_pose: 0.708795 loss: 0.000924 2022/10/13 15:23:13 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:23:30 - mmengine - INFO - Epoch(train) [173][50/293] lr: 5.000000e-05 eta: 0:52:46 time: 0.354172 data_time: 0.174887 memory: 2690 loss_kpt: 0.000915 acc_pose: 0.720773 loss: 0.000915 2022/10/13 15:23:47 - mmengine - INFO - Epoch(train) [173][100/293] lr: 5.000000e-05 eta: 0:52:32 time: 0.338771 data_time: 0.156081 memory: 2690 loss_kpt: 0.000916 acc_pose: 0.673974 loss: 0.000916 2022/10/13 15:24:04 - mmengine - INFO - Epoch(train) [173][150/293] lr: 5.000000e-05 eta: 0:52:18 time: 0.327659 data_time: 0.082032 memory: 2690 loss_kpt: 0.000915 acc_pose: 0.724060 loss: 0.000915 2022/10/13 15:24:20 - mmengine - INFO - Epoch(train) [173][200/293] lr: 5.000000e-05 eta: 0:52:04 time: 0.326313 data_time: 0.066959 memory: 2690 loss_kpt: 0.000905 acc_pose: 0.750382 loss: 0.000905 2022/10/13 15:24:37 - mmengine - INFO - Epoch(train) [173][250/293] lr: 5.000000e-05 eta: 0:51:50 time: 0.332062 data_time: 0.069889 memory: 2690 loss_kpt: 0.000902 acc_pose: 0.677237 loss: 0.000902 2022/10/13 15:24:50 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:25:07 - mmengine - INFO - Epoch(train) [174][50/293] lr: 5.000000e-05 eta: 0:51:22 time: 0.338723 data_time: 0.126391 memory: 2690 loss_kpt: 0.000930 acc_pose: 0.719078 loss: 0.000930 2022/10/13 15:25:23 - mmengine - INFO - Epoch(train) [174][100/293] lr: 5.000000e-05 eta: 0:51:08 time: 0.316144 data_time: 0.067359 memory: 2690 loss_kpt: 0.000917 acc_pose: 0.742658 loss: 0.000917 2022/10/13 15:25:40 - mmengine - INFO - Epoch(train) [174][150/293] lr: 5.000000e-05 eta: 0:50:54 time: 0.334803 data_time: 0.059039 memory: 2690 loss_kpt: 0.000909 acc_pose: 0.680873 loss: 0.000909 2022/10/13 15:25:56 - mmengine - INFO - Epoch(train) [174][200/293] lr: 5.000000e-05 eta: 0:50:40 time: 0.324487 data_time: 0.068416 memory: 2690 loss_kpt: 0.000901 acc_pose: 0.741047 loss: 0.000901 2022/10/13 15:26:12 - mmengine - INFO - Epoch(train) [174][250/293] lr: 5.000000e-05 eta: 0:50:26 time: 0.323997 data_time: 0.063855 memory: 2690 loss_kpt: 0.000894 acc_pose: 0.717198 loss: 0.000894 2022/10/13 15:26:26 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:26:32 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:26:43 - mmengine - INFO - Epoch(train) [175][50/293] lr: 5.000000e-05 eta: 0:49:58 time: 0.335391 data_time: 0.124137 memory: 2690 loss_kpt: 0.000930 acc_pose: 0.766911 loss: 0.000930 2022/10/13 15:27:00 - mmengine - INFO - Epoch(train) [175][100/293] lr: 5.000000e-05 eta: 0:49:44 time: 0.333114 data_time: 0.069194 memory: 2690 loss_kpt: 0.000917 acc_pose: 0.709927 loss: 0.000917 2022/10/13 15:27:15 - mmengine - INFO - Epoch(train) [175][150/293] lr: 5.000000e-05 eta: 0:49:30 time: 0.312320 data_time: 0.065869 memory: 2690 loss_kpt: 0.000922 acc_pose: 0.711857 loss: 0.000922 2022/10/13 15:27:31 - mmengine - INFO - Epoch(train) [175][200/293] lr: 5.000000e-05 eta: 0:49:16 time: 0.313060 data_time: 0.063261 memory: 2690 loss_kpt: 0.000898 acc_pose: 0.657934 loss: 0.000898 2022/10/13 15:27:47 - mmengine - INFO - Epoch(train) [175][250/293] lr: 5.000000e-05 eta: 0:49:02 time: 0.319923 data_time: 0.066174 memory: 2690 loss_kpt: 0.000903 acc_pose: 0.751287 loss: 0.000903 2022/10/13 15:28:01 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:28:17 - mmengine - INFO - Epoch(train) [176][50/293] lr: 5.000000e-05 eta: 0:48:33 time: 0.332249 data_time: 0.103073 memory: 2690 loss_kpt: 0.000899 acc_pose: 0.744546 loss: 0.000899 2022/10/13 15:28:34 - mmengine - INFO - Epoch(train) [176][100/293] lr: 5.000000e-05 eta: 0:48:19 time: 0.325813 data_time: 0.069300 memory: 2690 loss_kpt: 0.000909 acc_pose: 0.760570 loss: 0.000909 2022/10/13 15:28:50 - mmengine - INFO - Epoch(train) [176][150/293] lr: 5.000000e-05 eta: 0:48:05 time: 0.316262 data_time: 0.071127 memory: 2690 loss_kpt: 0.000907 acc_pose: 0.665274 loss: 0.000907 2022/10/13 15:29:06 - mmengine - INFO - Epoch(train) [176][200/293] lr: 5.000000e-05 eta: 0:47:52 time: 0.323091 data_time: 0.070325 memory: 2690 loss_kpt: 0.000901 acc_pose: 0.699454 loss: 0.000901 2022/10/13 15:29:21 - mmengine - INFO - Epoch(train) [176][250/293] lr: 5.000000e-05 eta: 0:47:38 time: 0.311921 data_time: 0.065634 memory: 2690 loss_kpt: 0.000905 acc_pose: 0.708953 loss: 0.000905 2022/10/13 15:29:35 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:29:52 - mmengine - INFO - Epoch(train) [177][50/293] lr: 5.000000e-05 eta: 0:47:09 time: 0.335867 data_time: 0.124621 memory: 2690 loss_kpt: 0.000909 acc_pose: 0.735073 loss: 0.000909 2022/10/13 15:30:09 - mmengine - INFO - Epoch(train) [177][100/293] lr: 5.000000e-05 eta: 0:46:55 time: 0.344148 data_time: 0.066570 memory: 2690 loss_kpt: 0.000893 acc_pose: 0.738323 loss: 0.000893 2022/10/13 15:30:25 - mmengine - INFO - Epoch(train) [177][150/293] lr: 5.000000e-05 eta: 0:46:41 time: 0.316746 data_time: 0.069182 memory: 2690 loss_kpt: 0.000920 acc_pose: 0.697276 loss: 0.000920 2022/10/13 15:30:41 - mmengine - INFO - Epoch(train) [177][200/293] lr: 5.000000e-05 eta: 0:46:27 time: 0.314676 data_time: 0.070230 memory: 2690 loss_kpt: 0.000911 acc_pose: 0.755466 loss: 0.000911 2022/10/13 15:30:57 - mmengine - INFO - Epoch(train) [177][250/293] lr: 5.000000e-05 eta: 0:46:14 time: 0.327464 data_time: 0.137721 memory: 2690 loss_kpt: 0.000923 acc_pose: 0.696201 loss: 0.000923 2022/10/13 15:31:10 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:31:27 - mmengine - INFO - Epoch(train) [178][50/293] lr: 5.000000e-05 eta: 0:45:45 time: 0.333537 data_time: 0.094607 memory: 2690 loss_kpt: 0.000898 acc_pose: 0.706902 loss: 0.000898 2022/10/13 15:31:43 - mmengine - INFO - Epoch(train) [178][100/293] lr: 5.000000e-05 eta: 0:45:31 time: 0.315784 data_time: 0.063245 memory: 2690 loss_kpt: 0.000910 acc_pose: 0.672467 loss: 0.000910 2022/10/13 15:31:55 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:31:58 - mmengine - INFO - Epoch(train) [178][150/293] lr: 5.000000e-05 eta: 0:45:17 time: 0.315010 data_time: 0.064747 memory: 2690 loss_kpt: 0.000921 acc_pose: 0.708614 loss: 0.000921 2022/10/13 15:32:14 - mmengine - INFO - Epoch(train) [178][200/293] lr: 5.000000e-05 eta: 0:45:03 time: 0.315210 data_time: 0.066047 memory: 2690 loss_kpt: 0.000915 acc_pose: 0.660949 loss: 0.000915 2022/10/13 15:32:30 - mmengine - INFO - Epoch(train) [178][250/293] lr: 5.000000e-05 eta: 0:44:49 time: 0.321205 data_time: 0.074238 memory: 2690 loss_kpt: 0.000911 acc_pose: 0.706686 loss: 0.000911 2022/10/13 15:32:44 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:33:01 - mmengine - INFO - Epoch(train) [179][50/293] lr: 5.000000e-05 eta: 0:44:21 time: 0.336430 data_time: 0.086069 memory: 2690 loss_kpt: 0.000899 acc_pose: 0.709657 loss: 0.000899 2022/10/13 15:33:16 - mmengine - INFO - Epoch(train) [179][100/293] lr: 5.000000e-05 eta: 0:44:07 time: 0.314493 data_time: 0.105920 memory: 2690 loss_kpt: 0.000907 acc_pose: 0.723561 loss: 0.000907 2022/10/13 15:33:32 - mmengine - INFO - Epoch(train) [179][150/293] lr: 5.000000e-05 eta: 0:43:53 time: 0.320493 data_time: 0.076414 memory: 2690 loss_kpt: 0.000919 acc_pose: 0.695691 loss: 0.000919 2022/10/13 15:33:48 - mmengine - INFO - Epoch(train) [179][200/293] lr: 5.000000e-05 eta: 0:43:39 time: 0.310612 data_time: 0.066106 memory: 2690 loss_kpt: 0.000917 acc_pose: 0.738832 loss: 0.000917 2022/10/13 15:34:04 - mmengine - INFO - Epoch(train) [179][250/293] lr: 5.000000e-05 eta: 0:43:25 time: 0.317010 data_time: 0.064196 memory: 2690 loss_kpt: 0.000915 acc_pose: 0.682956 loss: 0.000915 2022/10/13 15:34:17 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:34:34 - mmengine - INFO - Epoch(train) [180][50/293] lr: 5.000000e-05 eta: 0:42:57 time: 0.327899 data_time: 0.090279 memory: 2690 loss_kpt: 0.000922 acc_pose: 0.695715 loss: 0.000922 2022/10/13 15:34:50 - mmengine - INFO - Epoch(train) [180][100/293] lr: 5.000000e-05 eta: 0:42:43 time: 0.316593 data_time: 0.062863 memory: 2690 loss_kpt: 0.000911 acc_pose: 0.704714 loss: 0.000911 2022/10/13 15:35:05 - mmengine - INFO - Epoch(train) [180][150/293] lr: 5.000000e-05 eta: 0:42:29 time: 0.312165 data_time: 0.096908 memory: 2690 loss_kpt: 0.000898 acc_pose: 0.704663 loss: 0.000898 2022/10/13 15:35:21 - mmengine - INFO - Epoch(train) [180][200/293] lr: 5.000000e-05 eta: 0:42:15 time: 0.319620 data_time: 0.135945 memory: 2690 loss_kpt: 0.000896 acc_pose: 0.723747 loss: 0.000896 2022/10/13 15:35:37 - mmengine - INFO - Epoch(train) [180][250/293] lr: 5.000000e-05 eta: 0:42:01 time: 0.315645 data_time: 0.066125 memory: 2690 loss_kpt: 0.000886 acc_pose: 0.670443 loss: 0.000886 2022/10/13 15:35:51 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:35:51 - mmengine - INFO - Saving checkpoint at 180 epochs 2022/10/13 15:35:59 - mmengine - INFO - Epoch(val) [180][50/407] eta: 0:00:41 time: 0.117574 data_time: 0.075590 memory: 2690 2022/10/13 15:36:04 - mmengine - INFO - Epoch(val) [180][100/407] eta: 0:00:33 time: 0.110576 data_time: 0.068560 memory: 415 2022/10/13 15:36:10 - mmengine - INFO - Epoch(val) [180][150/407] eta: 0:00:28 time: 0.110135 data_time: 0.069436 memory: 415 2022/10/13 15:36:16 - mmengine - INFO - Epoch(val) [180][200/407] eta: 0:00:23 time: 0.114613 data_time: 0.074419 memory: 415 2022/10/13 15:36:21 - mmengine - INFO - Epoch(val) [180][250/407] eta: 0:00:17 time: 0.111024 data_time: 0.070422 memory: 415 2022/10/13 15:36:27 - mmengine - INFO - Epoch(val) [180][300/407] eta: 0:00:11 time: 0.107768 data_time: 0.064032 memory: 415 2022/10/13 15:36:32 - mmengine - INFO - Epoch(val) [180][350/407] eta: 0:00:06 time: 0.109584 data_time: 0.067796 memory: 415 2022/10/13 15:36:37 - mmengine - INFO - Epoch(val) [180][400/407] eta: 0:00:00 time: 0.108271 data_time: 0.068820 memory: 415 2022/10/13 15:37:15 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 15:37:30 - mmengine - INFO - Epoch(val) [180][407/407] coco/AP: 0.584208 coco/AP .5: 0.845708 coco/AP .75: 0.649283 coco/AP (M): 0.551146 coco/AP (L): 0.645408 coco/AR: 0.650346 coco/AR .5: 0.892947 coco/AR .75: 0.713791 coco/AR (M): 0.606173 coco/AR (L): 0.712449 2022/10/13 15:37:30 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_256/best_coco/AP_epoch_170.pth is removed 2022/10/13 15:37:32 - mmengine - INFO - The best checkpoint with 0.5842 coco/AP at 180 epoch is saved to best_coco/AP_epoch_180.pth. 2022/10/13 15:37:48 - mmengine - INFO - Epoch(train) [181][50/293] lr: 5.000000e-05 eta: 0:41:32 time: 0.318580 data_time: 0.166476 memory: 2690 loss_kpt: 0.000903 acc_pose: 0.715986 loss: 0.000903 2022/10/13 15:38:04 - mmengine - INFO - Epoch(train) [181][100/293] lr: 5.000000e-05 eta: 0:41:18 time: 0.327098 data_time: 0.176829 memory: 2690 loss_kpt: 0.000910 acc_pose: 0.752877 loss: 0.000910 2022/10/13 15:38:21 - mmengine - INFO - Epoch(train) [181][150/293] lr: 5.000000e-05 eta: 0:41:04 time: 0.330290 data_time: 0.135048 memory: 2690 loss_kpt: 0.000904 acc_pose: 0.723992 loss: 0.000904 2022/10/13 15:38:37 - mmengine - INFO - Epoch(train) [181][200/293] lr: 5.000000e-05 eta: 0:40:50 time: 0.324627 data_time: 0.069203 memory: 2690 loss_kpt: 0.000908 acc_pose: 0.662839 loss: 0.000908 2022/10/13 15:38:53 - mmengine - INFO - Epoch(train) [181][250/293] lr: 5.000000e-05 eta: 0:40:36 time: 0.317079 data_time: 0.065330 memory: 2690 loss_kpt: 0.000914 acc_pose: 0.701436 loss: 0.000914 2022/10/13 15:38:56 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:39:06 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:39:23 - mmengine - INFO - Epoch(train) [182][50/293] lr: 5.000000e-05 eta: 0:40:08 time: 0.338092 data_time: 0.173300 memory: 2690 loss_kpt: 0.000901 acc_pose: 0.696058 loss: 0.000901 2022/10/13 15:39:39 - mmengine - INFO - Epoch(train) [182][100/293] lr: 5.000000e-05 eta: 0:39:54 time: 0.320571 data_time: 0.159486 memory: 2690 loss_kpt: 0.000887 acc_pose: 0.758470 loss: 0.000887 2022/10/13 15:39:55 - mmengine - INFO - Epoch(train) [182][150/293] lr: 5.000000e-05 eta: 0:39:40 time: 0.316102 data_time: 0.154160 memory: 2690 loss_kpt: 0.000908 acc_pose: 0.730613 loss: 0.000908 2022/10/13 15:40:11 - mmengine - INFO - Epoch(train) [182][200/293] lr: 5.000000e-05 eta: 0:39:26 time: 0.316653 data_time: 0.096900 memory: 2690 loss_kpt: 0.000897 acc_pose: 0.685425 loss: 0.000897 2022/10/13 15:40:27 - mmengine - INFO - Epoch(train) [182][250/293] lr: 5.000000e-05 eta: 0:39:12 time: 0.329323 data_time: 0.072226 memory: 2690 loss_kpt: 0.000905 acc_pose: 0.743409 loss: 0.000905 2022/10/13 15:40:41 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:40:57 - mmengine - INFO - Epoch(train) [183][50/293] lr: 5.000000e-05 eta: 0:38:44 time: 0.326793 data_time: 0.076733 memory: 2690 loss_kpt: 0.000908 acc_pose: 0.709106 loss: 0.000908 2022/10/13 15:41:13 - mmengine - INFO - Epoch(train) [183][100/293] lr: 5.000000e-05 eta: 0:38:30 time: 0.315167 data_time: 0.064578 memory: 2690 loss_kpt: 0.000899 acc_pose: 0.688580 loss: 0.000899 2022/10/13 15:41:29 - mmengine - INFO - Epoch(train) [183][150/293] lr: 5.000000e-05 eta: 0:38:16 time: 0.314439 data_time: 0.067592 memory: 2690 loss_kpt: 0.000912 acc_pose: 0.679490 loss: 0.000912 2022/10/13 15:41:45 - mmengine - INFO - Epoch(train) [183][200/293] lr: 5.000000e-05 eta: 0:38:02 time: 0.319128 data_time: 0.065112 memory: 2690 loss_kpt: 0.000906 acc_pose: 0.742562 loss: 0.000906 2022/10/13 15:42:01 - mmengine - INFO - Epoch(train) [183][250/293] lr: 5.000000e-05 eta: 0:37:48 time: 0.323486 data_time: 0.065866 memory: 2690 loss_kpt: 0.000916 acc_pose: 0.733442 loss: 0.000916 2022/10/13 15:42:15 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:42:31 - mmengine - INFO - Epoch(train) [184][50/293] lr: 5.000000e-05 eta: 0:37:20 time: 0.323963 data_time: 0.094020 memory: 2690 loss_kpt: 0.000914 acc_pose: 0.676768 loss: 0.000914 2022/10/13 15:42:47 - mmengine - INFO - Epoch(train) [184][100/293] lr: 5.000000e-05 eta: 0:37:06 time: 0.319407 data_time: 0.066417 memory: 2690 loss_kpt: 0.000900 acc_pose: 0.719699 loss: 0.000900 2022/10/13 15:43:03 - mmengine - INFO - Epoch(train) [184][150/293] lr: 5.000000e-05 eta: 0:36:52 time: 0.318243 data_time: 0.066984 memory: 2690 loss_kpt: 0.000915 acc_pose: 0.738171 loss: 0.000915 2022/10/13 15:43:19 - mmengine - INFO - Epoch(train) [184][200/293] lr: 5.000000e-05 eta: 0:36:38 time: 0.316823 data_time: 0.063374 memory: 2690 loss_kpt: 0.000925 acc_pose: 0.720762 loss: 0.000925 2022/10/13 15:43:35 - mmengine - INFO - Epoch(train) [184][250/293] lr: 5.000000e-05 eta: 0:36:24 time: 0.322679 data_time: 0.059672 memory: 2690 loss_kpt: 0.000912 acc_pose: 0.717343 loss: 0.000912 2022/10/13 15:43:48 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:44:05 - mmengine - INFO - Epoch(train) [185][50/293] lr: 5.000000e-05 eta: 0:35:56 time: 0.324575 data_time: 0.152182 memory: 2690 loss_kpt: 0.000917 acc_pose: 0.700043 loss: 0.000917 2022/10/13 15:44:17 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:44:21 - mmengine - INFO - Epoch(train) [185][100/293] lr: 5.000000e-05 eta: 0:35:42 time: 0.323355 data_time: 0.099527 memory: 2690 loss_kpt: 0.000911 acc_pose: 0.718445 loss: 0.000911 2022/10/13 15:44:37 - mmengine - INFO - Epoch(train) [185][150/293] lr: 5.000000e-05 eta: 0:35:28 time: 0.316430 data_time: 0.067502 memory: 2690 loss_kpt: 0.000899 acc_pose: 0.708682 loss: 0.000899 2022/10/13 15:44:53 - mmengine - INFO - Epoch(train) [185][200/293] lr: 5.000000e-05 eta: 0:35:14 time: 0.325879 data_time: 0.068114 memory: 2690 loss_kpt: 0.000901 acc_pose: 0.721923 loss: 0.000901 2022/10/13 15:45:09 - mmengine - INFO - Epoch(train) [185][250/293] lr: 5.000000e-05 eta: 0:35:00 time: 0.327024 data_time: 0.067248 memory: 2690 loss_kpt: 0.000911 acc_pose: 0.736675 loss: 0.000911 2022/10/13 15:45:23 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:45:40 - mmengine - INFO - Epoch(train) [186][50/293] lr: 5.000000e-05 eta: 0:34:32 time: 0.327403 data_time: 0.141885 memory: 2690 loss_kpt: 0.000910 acc_pose: 0.719382 loss: 0.000910 2022/10/13 15:45:56 - mmengine - INFO - Epoch(train) [186][100/293] lr: 5.000000e-05 eta: 0:34:18 time: 0.317843 data_time: 0.097845 memory: 2690 loss_kpt: 0.000904 acc_pose: 0.742856 loss: 0.000904 2022/10/13 15:46:12 - mmengine - INFO - Epoch(train) [186][150/293] lr: 5.000000e-05 eta: 0:34:04 time: 0.315555 data_time: 0.131964 memory: 2690 loss_kpt: 0.000891 acc_pose: 0.749724 loss: 0.000891 2022/10/13 15:46:28 - mmengine - INFO - Epoch(train) [186][200/293] lr: 5.000000e-05 eta: 0:33:50 time: 0.325542 data_time: 0.096750 memory: 2690 loss_kpt: 0.000901 acc_pose: 0.747809 loss: 0.000901 2022/10/13 15:46:43 - mmengine - INFO - Epoch(train) [186][250/293] lr: 5.000000e-05 eta: 0:33:36 time: 0.311781 data_time: 0.142427 memory: 2690 loss_kpt: 0.000909 acc_pose: 0.701680 loss: 0.000909 2022/10/13 15:46:57 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:47:13 - mmengine - INFO - Epoch(train) [187][50/293] lr: 5.000000e-05 eta: 0:33:08 time: 0.322570 data_time: 0.097515 memory: 2690 loss_kpt: 0.000908 acc_pose: 0.710425 loss: 0.000908 2022/10/13 15:47:29 - mmengine - INFO - Epoch(train) [187][100/293] lr: 5.000000e-05 eta: 0:32:54 time: 0.323721 data_time: 0.073578 memory: 2690 loss_kpt: 0.000909 acc_pose: 0.741082 loss: 0.000909 2022/10/13 15:47:45 - mmengine - INFO - Epoch(train) [187][150/293] lr: 5.000000e-05 eta: 0:32:40 time: 0.315627 data_time: 0.063460 memory: 2690 loss_kpt: 0.000902 acc_pose: 0.751227 loss: 0.000902 2022/10/13 15:48:01 - mmengine - INFO - Epoch(train) [187][200/293] lr: 5.000000e-05 eta: 0:32:26 time: 0.322548 data_time: 0.065659 memory: 2690 loss_kpt: 0.000895 acc_pose: 0.732859 loss: 0.000895 2022/10/13 15:48:17 - mmengine - INFO - Epoch(train) [187][250/293] lr: 5.000000e-05 eta: 0:32:12 time: 0.321069 data_time: 0.069654 memory: 2690 loss_kpt: 0.000909 acc_pose: 0.718774 loss: 0.000909 2022/10/13 15:48:31 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:48:47 - mmengine - INFO - Epoch(train) [188][50/293] lr: 5.000000e-05 eta: 0:31:45 time: 0.323743 data_time: 0.167028 memory: 2690 loss_kpt: 0.000921 acc_pose: 0.735826 loss: 0.000921 2022/10/13 15:49:03 - mmengine - INFO - Epoch(train) [188][100/293] lr: 5.000000e-05 eta: 0:31:30 time: 0.304333 data_time: 0.103692 memory: 2690 loss_kpt: 0.000891 acc_pose: 0.727618 loss: 0.000891 2022/10/13 15:49:18 - mmengine - INFO - Epoch(train) [188][150/293] lr: 5.000000e-05 eta: 0:31:16 time: 0.307797 data_time: 0.091668 memory: 2690 loss_kpt: 0.000901 acc_pose: 0.704149 loss: 0.000901 2022/10/13 15:49:33 - mmengine - INFO - Epoch(train) [188][200/293] lr: 5.000000e-05 eta: 0:31:02 time: 0.307394 data_time: 0.065530 memory: 2690 loss_kpt: 0.000893 acc_pose: 0.726299 loss: 0.000893 2022/10/13 15:49:36 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:49:49 - mmengine - INFO - Epoch(train) [188][250/293] lr: 5.000000e-05 eta: 0:30:48 time: 0.312362 data_time: 0.065640 memory: 2690 loss_kpt: 0.000889 acc_pose: 0.678232 loss: 0.000889 2022/10/13 15:50:03 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:50:19 - mmengine - INFO - Epoch(train) [189][50/293] lr: 5.000000e-05 eta: 0:30:21 time: 0.328842 data_time: 0.098531 memory: 2690 loss_kpt: 0.000911 acc_pose: 0.722164 loss: 0.000911 2022/10/13 15:50:35 - mmengine - INFO - Epoch(train) [189][100/293] lr: 5.000000e-05 eta: 0:30:07 time: 0.322618 data_time: 0.066326 memory: 2690 loss_kpt: 0.000888 acc_pose: 0.712601 loss: 0.000888 2022/10/13 15:50:51 - mmengine - INFO - Epoch(train) [189][150/293] lr: 5.000000e-05 eta: 0:29:52 time: 0.320152 data_time: 0.070435 memory: 2690 loss_kpt: 0.000899 acc_pose: 0.729667 loss: 0.000899 2022/10/13 15:51:07 - mmengine - INFO - Epoch(train) [189][200/293] lr: 5.000000e-05 eta: 0:29:38 time: 0.317465 data_time: 0.064008 memory: 2690 loss_kpt: 0.000902 acc_pose: 0.737734 loss: 0.000902 2022/10/13 15:51:23 - mmengine - INFO - Epoch(train) [189][250/293] lr: 5.000000e-05 eta: 0:29:24 time: 0.316814 data_time: 0.058253 memory: 2690 loss_kpt: 0.000909 acc_pose: 0.721500 loss: 0.000909 2022/10/13 15:51:37 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:51:53 - mmengine - INFO - Epoch(train) [190][50/293] lr: 5.000000e-05 eta: 0:28:57 time: 0.330452 data_time: 0.104660 memory: 2690 loss_kpt: 0.000911 acc_pose: 0.658788 loss: 0.000911 2022/10/13 15:52:09 - mmengine - INFO - Epoch(train) [190][100/293] lr: 5.000000e-05 eta: 0:28:43 time: 0.317078 data_time: 0.065602 memory: 2690 loss_kpt: 0.000902 acc_pose: 0.728694 loss: 0.000902 2022/10/13 15:52:25 - mmengine - INFO - Epoch(train) [190][150/293] lr: 5.000000e-05 eta: 0:28:29 time: 0.321847 data_time: 0.076342 memory: 2690 loss_kpt: 0.000888 acc_pose: 0.754660 loss: 0.000888 2022/10/13 15:52:41 - mmengine - INFO - Epoch(train) [190][200/293] lr: 5.000000e-05 eta: 0:28:15 time: 0.322980 data_time: 0.075639 memory: 2690 loss_kpt: 0.000899 acc_pose: 0.734945 loss: 0.000899 2022/10/13 15:52:57 - mmengine - INFO - Epoch(train) [190][250/293] lr: 5.000000e-05 eta: 0:28:01 time: 0.319034 data_time: 0.137983 memory: 2690 loss_kpt: 0.000895 acc_pose: 0.653896 loss: 0.000895 2022/10/13 15:53:10 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:53:10 - mmengine - INFO - Saving checkpoint at 190 epochs 2022/10/13 15:53:18 - mmengine - INFO - Epoch(val) [190][50/407] eta: 0:00:40 time: 0.114395 data_time: 0.072427 memory: 2690 2022/10/13 15:53:24 - mmengine - INFO - Epoch(val) [190][100/407] eta: 0:00:33 time: 0.108066 data_time: 0.064988 memory: 415 2022/10/13 15:53:29 - mmengine - INFO - Epoch(val) [190][150/407] eta: 0:00:28 time: 0.112726 data_time: 0.071178 memory: 415 2022/10/13 15:53:35 - mmengine - INFO - Epoch(val) [190][200/407] eta: 0:00:23 time: 0.111809 data_time: 0.069880 memory: 415 2022/10/13 15:53:40 - mmengine - INFO - Epoch(val) [190][250/407] eta: 0:00:17 time: 0.111820 data_time: 0.071525 memory: 415 2022/10/13 15:53:46 - mmengine - INFO - Epoch(val) [190][300/407] eta: 0:00:12 time: 0.120096 data_time: 0.080253 memory: 415 2022/10/13 15:53:52 - mmengine - INFO - Epoch(val) [190][350/407] eta: 0:00:06 time: 0.114771 data_time: 0.074638 memory: 415 2022/10/13 15:53:57 - mmengine - INFO - Epoch(val) [190][400/407] eta: 0:00:00 time: 0.105743 data_time: 0.068058 memory: 415 2022/10/13 15:54:35 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 15:54:50 - mmengine - INFO - Epoch(val) [190][407/407] coco/AP: 0.585601 coco/AP .5: 0.846859 coco/AP .75: 0.650679 coco/AP (M): 0.553442 coco/AP (L): 0.645960 coco/AR: 0.651842 coco/AR .5: 0.894521 coco/AR .75: 0.715208 coco/AR (M): 0.609014 coco/AR (L): 0.712152 2022/10/13 15:54:50 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_256/best_coco/AP_epoch_180.pth is removed 2022/10/13 15:54:52 - mmengine - INFO - The best checkpoint with 0.5856 coco/AP at 190 epoch is saved to best_coco/AP_epoch_190.pth. 2022/10/13 15:55:08 - mmengine - INFO - Epoch(train) [191][50/293] lr: 5.000000e-05 eta: 0:27:33 time: 0.337274 data_time: 0.221867 memory: 2690 loss_kpt: 0.000903 acc_pose: 0.730898 loss: 0.000903 2022/10/13 15:55:24 - mmengine - INFO - Epoch(train) [191][100/293] lr: 5.000000e-05 eta: 0:27:19 time: 0.316446 data_time: 0.202229 memory: 2690 loss_kpt: 0.000893 acc_pose: 0.696863 loss: 0.000893 2022/10/13 15:55:41 - mmengine - INFO - Epoch(train) [191][150/293] lr: 5.000000e-05 eta: 0:27:05 time: 0.326058 data_time: 0.116516 memory: 2690 loss_kpt: 0.000904 acc_pose: 0.731661 loss: 0.000904 2022/10/13 15:55:56 - mmengine - INFO - Epoch(train) [191][200/293] lr: 5.000000e-05 eta: 0:26:51 time: 0.315193 data_time: 0.132981 memory: 2690 loss_kpt: 0.000882 acc_pose: 0.683389 loss: 0.000882 2022/10/13 15:56:13 - mmengine - INFO - Epoch(train) [191][250/293] lr: 5.000000e-05 eta: 0:26:37 time: 0.324296 data_time: 0.123490 memory: 2690 loss_kpt: 0.000898 acc_pose: 0.740764 loss: 0.000898 2022/10/13 15:56:26 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:56:38 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:56:42 - mmengine - INFO - Epoch(train) [192][50/293] lr: 5.000000e-05 eta: 0:26:09 time: 0.326993 data_time: 0.121300 memory: 2690 loss_kpt: 0.000909 acc_pose: 0.737038 loss: 0.000909 2022/10/13 15:56:58 - mmengine - INFO - Epoch(train) [192][100/293] lr: 5.000000e-05 eta: 0:25:55 time: 0.317028 data_time: 0.082704 memory: 2690 loss_kpt: 0.000894 acc_pose: 0.725580 loss: 0.000894 2022/10/13 15:57:15 - mmengine - INFO - Epoch(train) [192][150/293] lr: 5.000000e-05 eta: 0:25:41 time: 0.334800 data_time: 0.071800 memory: 2690 loss_kpt: 0.000916 acc_pose: 0.746192 loss: 0.000916 2022/10/13 15:57:31 - mmengine - INFO - Epoch(train) [192][200/293] lr: 5.000000e-05 eta: 0:25:27 time: 0.315878 data_time: 0.094780 memory: 2690 loss_kpt: 0.000888 acc_pose: 0.733385 loss: 0.000888 2022/10/13 15:57:47 - mmengine - INFO - Epoch(train) [192][250/293] lr: 5.000000e-05 eta: 0:25:13 time: 0.318669 data_time: 0.077804 memory: 2690 loss_kpt: 0.000902 acc_pose: 0.718466 loss: 0.000902 2022/10/13 15:58:00 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:58:18 - mmengine - INFO - Epoch(train) [193][50/293] lr: 5.000000e-05 eta: 0:24:46 time: 0.346139 data_time: 0.080568 memory: 2690 loss_kpt: 0.000906 acc_pose: 0.707017 loss: 0.000906 2022/10/13 15:58:33 - mmengine - INFO - Epoch(train) [193][100/293] lr: 5.000000e-05 eta: 0:24:32 time: 0.314677 data_time: 0.111722 memory: 2690 loss_kpt: 0.000895 acc_pose: 0.666644 loss: 0.000895 2022/10/13 15:58:50 - mmengine - INFO - Epoch(train) [193][150/293] lr: 5.000000e-05 eta: 0:24:18 time: 0.323323 data_time: 0.060268 memory: 2690 loss_kpt: 0.000893 acc_pose: 0.736241 loss: 0.000893 2022/10/13 15:59:06 - mmengine - INFO - Epoch(train) [193][200/293] lr: 5.000000e-05 eta: 0:24:04 time: 0.317453 data_time: 0.070159 memory: 2690 loss_kpt: 0.000907 acc_pose: 0.656807 loss: 0.000907 2022/10/13 15:59:21 - mmengine - INFO - Epoch(train) [193][250/293] lr: 5.000000e-05 eta: 0:23:50 time: 0.313489 data_time: 0.059847 memory: 2690 loss_kpt: 0.000900 acc_pose: 0.749971 loss: 0.000900 2022/10/13 15:59:35 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 15:59:52 - mmengine - INFO - Epoch(train) [194][50/293] lr: 5.000000e-05 eta: 0:23:22 time: 0.335794 data_time: 0.112027 memory: 2690 loss_kpt: 0.000901 acc_pose: 0.771861 loss: 0.000901 2022/10/13 16:00:07 - mmengine - INFO - Epoch(train) [194][100/293] lr: 5.000000e-05 eta: 0:23:08 time: 0.317482 data_time: 0.068515 memory: 2690 loss_kpt: 0.000894 acc_pose: 0.746256 loss: 0.000894 2022/10/13 16:00:24 - mmengine - INFO - Epoch(train) [194][150/293] lr: 5.000000e-05 eta: 0:22:54 time: 0.320547 data_time: 0.070036 memory: 2690 loss_kpt: 0.000912 acc_pose: 0.699532 loss: 0.000912 2022/10/13 16:00:40 - mmengine - INFO - Epoch(train) [194][200/293] lr: 5.000000e-05 eta: 0:22:40 time: 0.325519 data_time: 0.061809 memory: 2690 loss_kpt: 0.000913 acc_pose: 0.742944 loss: 0.000913 2022/10/13 16:00:56 - mmengine - INFO - Epoch(train) [194][250/293] lr: 5.000000e-05 eta: 0:22:26 time: 0.318234 data_time: 0.121199 memory: 2690 loss_kpt: 0.000910 acc_pose: 0.713341 loss: 0.000910 2022/10/13 16:01:09 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:01:26 - mmengine - INFO - Epoch(train) [195][50/293] lr: 5.000000e-05 eta: 0:21:59 time: 0.332524 data_time: 0.098739 memory: 2690 loss_kpt: 0.000902 acc_pose: 0.722723 loss: 0.000902 2022/10/13 16:01:42 - mmengine - INFO - Epoch(train) [195][100/293] lr: 5.000000e-05 eta: 0:21:45 time: 0.316648 data_time: 0.079405 memory: 2690 loss_kpt: 0.000889 acc_pose: 0.722625 loss: 0.000889 2022/10/13 16:01:58 - mmengine - INFO - Epoch(train) [195][150/293] lr: 5.000000e-05 eta: 0:21:31 time: 0.321761 data_time: 0.088915 memory: 2690 loss_kpt: 0.000918 acc_pose: 0.698824 loss: 0.000918 2022/10/13 16:02:01 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:02:14 - mmengine - INFO - Epoch(train) [195][200/293] lr: 5.000000e-05 eta: 0:21:16 time: 0.317505 data_time: 0.071871 memory: 2690 loss_kpt: 0.000899 acc_pose: 0.749600 loss: 0.000899 2022/10/13 16:02:30 - mmengine - INFO - Epoch(train) [195][250/293] lr: 5.000000e-05 eta: 0:21:02 time: 0.312963 data_time: 0.106216 memory: 2690 loss_kpt: 0.000902 acc_pose: 0.680130 loss: 0.000902 2022/10/13 16:02:43 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:03:00 - mmengine - INFO - Epoch(train) [196][50/293] lr: 5.000000e-05 eta: 0:20:35 time: 0.334458 data_time: 0.106420 memory: 2690 loss_kpt: 0.000897 acc_pose: 0.702394 loss: 0.000897 2022/10/13 16:03:16 - mmengine - INFO - Epoch(train) [196][100/293] lr: 5.000000e-05 eta: 0:20:21 time: 0.328775 data_time: 0.079702 memory: 2690 loss_kpt: 0.000902 acc_pose: 0.693179 loss: 0.000902 2022/10/13 16:03:32 - mmengine - INFO - Epoch(train) [196][150/293] lr: 5.000000e-05 eta: 0:20:07 time: 0.316264 data_time: 0.161278 memory: 2690 loss_kpt: 0.000893 acc_pose: 0.698663 loss: 0.000893 2022/10/13 16:03:48 - mmengine - INFO - Epoch(train) [196][200/293] lr: 5.000000e-05 eta: 0:19:53 time: 0.315822 data_time: 0.148378 memory: 2690 loss_kpt: 0.000908 acc_pose: 0.694782 loss: 0.000908 2022/10/13 16:04:04 - mmengine - INFO - Epoch(train) [196][250/293] lr: 5.000000e-05 eta: 0:19:39 time: 0.316672 data_time: 0.071067 memory: 2690 loss_kpt: 0.000912 acc_pose: 0.755517 loss: 0.000912 2022/10/13 16:04:17 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:04:33 - mmengine - INFO - Epoch(train) [197][50/293] lr: 5.000000e-05 eta: 0:19:12 time: 0.325324 data_time: 0.156270 memory: 2690 loss_kpt: 0.000893 acc_pose: 0.749299 loss: 0.000893 2022/10/13 16:04:50 - mmengine - INFO - Epoch(train) [197][100/293] lr: 5.000000e-05 eta: 0:18:57 time: 0.321497 data_time: 0.118080 memory: 2690 loss_kpt: 0.000904 acc_pose: 0.762461 loss: 0.000904 2022/10/13 16:05:06 - mmengine - INFO - Epoch(train) [197][150/293] lr: 5.000000e-05 eta: 0:18:43 time: 0.319799 data_time: 0.169746 memory: 2690 loss_kpt: 0.000910 acc_pose: 0.701361 loss: 0.000910 2022/10/13 16:05:22 - mmengine - INFO - Epoch(train) [197][200/293] lr: 5.000000e-05 eta: 0:18:29 time: 0.325272 data_time: 0.092863 memory: 2690 loss_kpt: 0.000895 acc_pose: 0.715233 loss: 0.000895 2022/10/13 16:05:38 - mmengine - INFO - Epoch(train) [197][250/293] lr: 5.000000e-05 eta: 0:18:15 time: 0.318479 data_time: 0.065496 memory: 2690 loss_kpt: 0.000890 acc_pose: 0.727567 loss: 0.000890 2022/10/13 16:05:51 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:06:08 - mmengine - INFO - Epoch(train) [198][50/293] lr: 5.000000e-05 eta: 0:17:48 time: 0.336787 data_time: 0.088747 memory: 2690 loss_kpt: 0.000896 acc_pose: 0.690026 loss: 0.000896 2022/10/13 16:06:23 - mmengine - INFO - Epoch(train) [198][100/293] lr: 5.000000e-05 eta: 0:17:34 time: 0.311601 data_time: 0.075402 memory: 2690 loss_kpt: 0.000913 acc_pose: 0.720074 loss: 0.000913 2022/10/13 16:06:40 - mmengine - INFO - Epoch(train) [198][150/293] lr: 5.000000e-05 eta: 0:17:20 time: 0.324462 data_time: 0.107970 memory: 2690 loss_kpt: 0.000907 acc_pose: 0.731255 loss: 0.000907 2022/10/13 16:06:56 - mmengine - INFO - Epoch(train) [198][200/293] lr: 5.000000e-05 eta: 0:17:06 time: 0.321865 data_time: 0.169396 memory: 2690 loss_kpt: 0.000912 acc_pose: 0.763649 loss: 0.000912 2022/10/13 16:07:12 - mmengine - INFO - Epoch(train) [198][250/293] lr: 5.000000e-05 eta: 0:16:52 time: 0.327841 data_time: 0.120553 memory: 2690 loss_kpt: 0.000921 acc_pose: 0.764558 loss: 0.000921 2022/10/13 16:07:22 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:07:26 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:07:42 - mmengine - INFO - Epoch(train) [199][50/293] lr: 5.000000e-05 eta: 0:16:25 time: 0.333174 data_time: 0.137977 memory: 2690 loss_kpt: 0.000900 acc_pose: 0.719982 loss: 0.000900 2022/10/13 16:07:58 - mmengine - INFO - Epoch(train) [199][100/293] lr: 5.000000e-05 eta: 0:16:11 time: 0.320922 data_time: 0.144358 memory: 2690 loss_kpt: 0.000897 acc_pose: 0.761350 loss: 0.000897 2022/10/13 16:08:14 - mmengine - INFO - Epoch(train) [199][150/293] lr: 5.000000e-05 eta: 0:15:56 time: 0.320492 data_time: 0.083011 memory: 2690 loss_kpt: 0.000902 acc_pose: 0.728350 loss: 0.000902 2022/10/13 16:08:30 - mmengine - INFO - Epoch(train) [199][200/293] lr: 5.000000e-05 eta: 0:15:42 time: 0.314281 data_time: 0.068084 memory: 2690 loss_kpt: 0.000906 acc_pose: 0.708863 loss: 0.000906 2022/10/13 16:08:46 - mmengine - INFO - Epoch(train) [199][250/293] lr: 5.000000e-05 eta: 0:15:28 time: 0.317999 data_time: 0.130960 memory: 2690 loss_kpt: 0.000917 acc_pose: 0.729097 loss: 0.000917 2022/10/13 16:09:00 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:09:17 - mmengine - INFO - Epoch(train) [200][50/293] lr: 5.000000e-05 eta: 0:15:01 time: 0.345574 data_time: 0.168035 memory: 2690 loss_kpt: 0.000892 acc_pose: 0.730787 loss: 0.000892 2022/10/13 16:09:33 - mmengine - INFO - Epoch(train) [200][100/293] lr: 5.000000e-05 eta: 0:14:47 time: 0.316907 data_time: 0.077681 memory: 2690 loss_kpt: 0.000913 acc_pose: 0.735667 loss: 0.000913 2022/10/13 16:09:49 - mmengine - INFO - Epoch(train) [200][150/293] lr: 5.000000e-05 eta: 0:14:33 time: 0.316114 data_time: 0.079098 memory: 2690 loss_kpt: 0.000903 acc_pose: 0.730283 loss: 0.000903 2022/10/13 16:10:05 - mmengine - INFO - Epoch(train) [200][200/293] lr: 5.000000e-05 eta: 0:14:19 time: 0.320085 data_time: 0.067987 memory: 2690 loss_kpt: 0.000890 acc_pose: 0.746854 loss: 0.000890 2022/10/13 16:10:21 - mmengine - INFO - Epoch(train) [200][250/293] lr: 5.000000e-05 eta: 0:14:05 time: 0.318898 data_time: 0.069012 memory: 2690 loss_kpt: 0.000911 acc_pose: 0.689502 loss: 0.000911 2022/10/13 16:10:34 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:10:34 - mmengine - INFO - Saving checkpoint at 200 epochs 2022/10/13 16:10:42 - mmengine - INFO - Epoch(val) [200][50/407] eta: 0:00:42 time: 0.118074 data_time: 0.076063 memory: 2690 2022/10/13 16:10:48 - mmengine - INFO - Epoch(val) [200][100/407] eta: 0:00:35 time: 0.116801 data_time: 0.073457 memory: 415 2022/10/13 16:10:54 - mmengine - INFO - Epoch(val) [200][150/407] eta: 0:00:30 time: 0.117966 data_time: 0.074679 memory: 415 2022/10/13 16:10:59 - mmengine - INFO - Epoch(val) [200][200/407] eta: 0:00:23 time: 0.111685 data_time: 0.070412 memory: 415 2022/10/13 16:11:05 - mmengine - INFO - Epoch(val) [200][250/407] eta: 0:00:17 time: 0.111609 data_time: 0.070756 memory: 415 2022/10/13 16:11:11 - mmengine - INFO - Epoch(val) [200][300/407] eta: 0:00:12 time: 0.114045 data_time: 0.073269 memory: 415 2022/10/13 16:11:16 - mmengine - INFO - Epoch(val) [200][350/407] eta: 0:00:06 time: 0.112386 data_time: 0.071684 memory: 415 2022/10/13 16:11:22 - mmengine - INFO - Epoch(val) [200][400/407] eta: 0:00:00 time: 0.117309 data_time: 0.079181 memory: 415 2022/10/13 16:12:00 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 16:12:15 - mmengine - INFO - Epoch(val) [200][407/407] coco/AP: 0.587490 coco/AP .5: 0.849002 coco/AP .75: 0.653617 coco/AP (M): 0.555032 coco/AP (L): 0.648653 coco/AR: 0.653542 coco/AR .5: 0.896096 coco/AR .75: 0.719616 coco/AR (M): 0.610352 coco/AR (L): 0.714381 2022/10/13 16:12:15 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221013/shufflenetv1_256/best_coco/AP_epoch_190.pth is removed 2022/10/13 16:12:17 - mmengine - INFO - The best checkpoint with 0.5875 coco/AP at 200 epoch is saved to best_coco/AP_epoch_200.pth. 2022/10/13 16:12:41 - mmengine - INFO - Epoch(train) [201][50/293] lr: 5.000000e-06 eta: 0:13:38 time: 0.478512 data_time: 0.210301 memory: 2690 loss_kpt: 0.000907 acc_pose: 0.760717 loss: 0.000907 2022/10/13 16:12:58 - mmengine - INFO - Epoch(train) [201][100/293] lr: 5.000000e-06 eta: 0:13:24 time: 0.345230 data_time: 0.063118 memory: 2690 loss_kpt: 0.000883 acc_pose: 0.735874 loss: 0.000883 2022/10/13 16:13:14 - mmengine - INFO - Epoch(train) [201][150/293] lr: 5.000000e-06 eta: 0:13:10 time: 0.316716 data_time: 0.061098 memory: 2690 loss_kpt: 0.000890 acc_pose: 0.734031 loss: 0.000890 2022/10/13 16:13:30 - mmengine - INFO - Epoch(train) [201][200/293] lr: 5.000000e-06 eta: 0:12:56 time: 0.329890 data_time: 0.061773 memory: 2690 loss_kpt: 0.000922 acc_pose: 0.733270 loss: 0.000922 2022/10/13 16:13:46 - mmengine - INFO - Epoch(train) [201][250/293] lr: 5.000000e-06 eta: 0:12:42 time: 0.318530 data_time: 0.065938 memory: 2690 loss_kpt: 0.000903 acc_pose: 0.760478 loss: 0.000903 2022/10/13 16:14:00 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:14:16 - mmengine - INFO - Epoch(train) [202][50/293] lr: 5.000000e-06 eta: 0:12:15 time: 0.330459 data_time: 0.102458 memory: 2690 loss_kpt: 0.000887 acc_pose: 0.698678 loss: 0.000887 2022/10/13 16:14:32 - mmengine - INFO - Epoch(train) [202][100/293] lr: 5.000000e-06 eta: 0:12:01 time: 0.323976 data_time: 0.091129 memory: 2690 loss_kpt: 0.000896 acc_pose: 0.699085 loss: 0.000896 2022/10/13 16:14:35 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:14:48 - mmengine - INFO - Epoch(train) [202][150/293] lr: 5.000000e-06 eta: 0:11:47 time: 0.316437 data_time: 0.069369 memory: 2690 loss_kpt: 0.000896 acc_pose: 0.755592 loss: 0.000896 2022/10/13 16:15:04 - mmengine - INFO - Epoch(train) [202][200/293] lr: 5.000000e-06 eta: 0:11:32 time: 0.322295 data_time: 0.058633 memory: 2690 loss_kpt: 0.000916 acc_pose: 0.702550 loss: 0.000916 2022/10/13 16:15:20 - mmengine - INFO - Epoch(train) [202][250/293] lr: 5.000000e-06 eta: 0:11:18 time: 0.312799 data_time: 0.068060 memory: 2690 loss_kpt: 0.000901 acc_pose: 0.721708 loss: 0.000901 2022/10/13 16:15:34 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:15:50 - mmengine - INFO - Epoch(train) [203][50/293] lr: 5.000000e-06 eta: 0:10:51 time: 0.326010 data_time: 0.085302 memory: 2690 loss_kpt: 0.000888 acc_pose: 0.679703 loss: 0.000888 2022/10/13 16:16:07 - mmengine - INFO - Epoch(train) [203][100/293] lr: 5.000000e-06 eta: 0:10:37 time: 0.326528 data_time: 0.067861 memory: 2690 loss_kpt: 0.000902 acc_pose: 0.678296 loss: 0.000902 2022/10/13 16:16:23 - mmengine - INFO - Epoch(train) [203][150/293] lr: 5.000000e-06 eta: 0:10:23 time: 0.316927 data_time: 0.066392 memory: 2690 loss_kpt: 0.000895 acc_pose: 0.708924 loss: 0.000895 2022/10/13 16:16:39 - mmengine - INFO - Epoch(train) [203][200/293] lr: 5.000000e-06 eta: 0:10:09 time: 0.321203 data_time: 0.064554 memory: 2690 loss_kpt: 0.000901 acc_pose: 0.690014 loss: 0.000901 2022/10/13 16:16:54 - mmengine - INFO - Epoch(train) [203][250/293] lr: 5.000000e-06 eta: 0:09:55 time: 0.312621 data_time: 0.072318 memory: 2690 loss_kpt: 0.000900 acc_pose: 0.693305 loss: 0.000900 2022/10/13 16:17:08 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:17:24 - mmengine - INFO - Epoch(train) [204][50/293] lr: 5.000000e-06 eta: 0:09:28 time: 0.323107 data_time: 0.088217 memory: 2690 loss_kpt: 0.000896 acc_pose: 0.687216 loss: 0.000896 2022/10/13 16:17:40 - mmengine - INFO - Epoch(train) [204][100/293] lr: 5.000000e-06 eta: 0:09:14 time: 0.313212 data_time: 0.067560 memory: 2690 loss_kpt: 0.000889 acc_pose: 0.726399 loss: 0.000889 2022/10/13 16:17:56 - mmengine - INFO - Epoch(train) [204][150/293] lr: 5.000000e-06 eta: 0:09:00 time: 0.320866 data_time: 0.065649 memory: 2690 loss_kpt: 0.000900 acc_pose: 0.764570 loss: 0.000900 2022/10/13 16:18:12 - mmengine - INFO - Epoch(train) [204][200/293] lr: 5.000000e-06 eta: 0:08:46 time: 0.318332 data_time: 0.064700 memory: 2690 loss_kpt: 0.000888 acc_pose: 0.781564 loss: 0.000888 2022/10/13 16:18:27 - mmengine - INFO - Epoch(train) [204][250/293] lr: 5.000000e-06 eta: 0:08:31 time: 0.313918 data_time: 0.064643 memory: 2690 loss_kpt: 0.000910 acc_pose: 0.690303 loss: 0.000910 2022/10/13 16:18:41 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:18:57 - mmengine - INFO - Epoch(train) [205][50/293] lr: 5.000000e-06 eta: 0:08:05 time: 0.320099 data_time: 0.125649 memory: 2690 loss_kpt: 0.000902 acc_pose: 0.655584 loss: 0.000902 2022/10/13 16:19:13 - mmengine - INFO - Epoch(train) [205][100/293] lr: 5.000000e-06 eta: 0:07:51 time: 0.316760 data_time: 0.066480 memory: 2690 loss_kpt: 0.000899 acc_pose: 0.737704 loss: 0.000899 2022/10/13 16:19:29 - mmengine - INFO - Epoch(train) [205][150/293] lr: 5.000000e-06 eta: 0:07:36 time: 0.333108 data_time: 0.069939 memory: 2690 loss_kpt: 0.000886 acc_pose: 0.775697 loss: 0.000886 2022/10/13 16:19:45 - mmengine - INFO - Epoch(train) [205][200/293] lr: 5.000000e-06 eta: 0:07:22 time: 0.317134 data_time: 0.076898 memory: 2690 loss_kpt: 0.000886 acc_pose: 0.748921 loss: 0.000886 2022/10/13 16:19:54 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:20:01 - mmengine - INFO - Epoch(train) [205][250/293] lr: 5.000000e-06 eta: 0:07:08 time: 0.319003 data_time: 0.080755 memory: 2690 loss_kpt: 0.000884 acc_pose: 0.758323 loss: 0.000884 2022/10/13 16:20:15 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:20:32 - mmengine - INFO - Epoch(train) [206][50/293] lr: 5.000000e-06 eta: 0:06:41 time: 0.332285 data_time: 0.137804 memory: 2690 loss_kpt: 0.000893 acc_pose: 0.730374 loss: 0.000893 2022/10/13 16:20:47 - mmengine - INFO - Epoch(train) [206][100/293] lr: 5.000000e-06 eta: 0:06:27 time: 0.314234 data_time: 0.123172 memory: 2690 loss_kpt: 0.000901 acc_pose: 0.729485 loss: 0.000901 2022/10/13 16:21:04 - mmengine - INFO - Epoch(train) [206][150/293] lr: 5.000000e-06 eta: 0:06:13 time: 0.323234 data_time: 0.067725 memory: 2690 loss_kpt: 0.000908 acc_pose: 0.701053 loss: 0.000908 2022/10/13 16:21:19 - mmengine - INFO - Epoch(train) [206][200/293] lr: 5.000000e-06 eta: 0:05:59 time: 0.315730 data_time: 0.070496 memory: 2690 loss_kpt: 0.000887 acc_pose: 0.776285 loss: 0.000887 2022/10/13 16:21:35 - mmengine - INFO - Epoch(train) [206][250/293] lr: 5.000000e-06 eta: 0:05:45 time: 0.321057 data_time: 0.066697 memory: 2690 loss_kpt: 0.000903 acc_pose: 0.747023 loss: 0.000903 2022/10/13 16:21:49 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:22:05 - mmengine - INFO - Epoch(train) [207][50/293] lr: 5.000000e-06 eta: 0:05:18 time: 0.324870 data_time: 0.079702 memory: 2690 loss_kpt: 0.000885 acc_pose: 0.727879 loss: 0.000885 2022/10/13 16:22:22 - mmengine - INFO - Epoch(train) [207][100/293] lr: 5.000000e-06 eta: 0:05:04 time: 0.330737 data_time: 0.166214 memory: 2690 loss_kpt: 0.000905 acc_pose: 0.723366 loss: 0.000905 2022/10/13 16:22:37 - mmengine - INFO - Epoch(train) [207][150/293] lr: 5.000000e-06 eta: 0:04:50 time: 0.316017 data_time: 0.181304 memory: 2690 loss_kpt: 0.000921 acc_pose: 0.719983 loss: 0.000921 2022/10/13 16:22:53 - mmengine - INFO - Epoch(train) [207][200/293] lr: 5.000000e-06 eta: 0:04:36 time: 0.312299 data_time: 0.132402 memory: 2690 loss_kpt: 0.000896 acc_pose: 0.745328 loss: 0.000896 2022/10/13 16:23:09 - mmengine - INFO - Epoch(train) [207][250/293] lr: 5.000000e-06 eta: 0:04:21 time: 0.312305 data_time: 0.075334 memory: 2690 loss_kpt: 0.000895 acc_pose: 0.713521 loss: 0.000895 2022/10/13 16:23:23 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:23:39 - mmengine - INFO - Epoch(train) [208][50/293] lr: 5.000000e-06 eta: 0:03:55 time: 0.332270 data_time: 0.117992 memory: 2690 loss_kpt: 0.000902 acc_pose: 0.750880 loss: 0.000902 2022/10/13 16:23:56 - mmengine - INFO - Epoch(train) [208][100/293] lr: 5.000000e-06 eta: 0:03:41 time: 0.324137 data_time: 0.066380 memory: 2690 loss_kpt: 0.000905 acc_pose: 0.692174 loss: 0.000905 2022/10/13 16:24:12 - mmengine - INFO - Epoch(train) [208][150/293] lr: 5.000000e-06 eta: 0:03:27 time: 0.321094 data_time: 0.065330 memory: 2690 loss_kpt: 0.000890 acc_pose: 0.731430 loss: 0.000890 2022/10/13 16:24:28 - mmengine - INFO - Epoch(train) [208][200/293] lr: 5.000000e-06 eta: 0:03:12 time: 0.320217 data_time: 0.068886 memory: 2690 loss_kpt: 0.000912 acc_pose: 0.731373 loss: 0.000912 2022/10/13 16:24:44 - mmengine - INFO - Epoch(train) [208][250/293] lr: 5.000000e-06 eta: 0:02:58 time: 0.316412 data_time: 0.065652 memory: 2690 loss_kpt: 0.000901 acc_pose: 0.704700 loss: 0.000901 2022/10/13 16:24:58 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:25:15 - mmengine - INFO - Epoch(train) [209][50/293] lr: 5.000000e-06 eta: 0:02:32 time: 0.334971 data_time: 0.098538 memory: 2690 loss_kpt: 0.000914 acc_pose: 0.703291 loss: 0.000914 2022/10/13 16:25:17 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:25:31 - mmengine - INFO - Epoch(train) [209][100/293] lr: 5.000000e-06 eta: 0:02:17 time: 0.316185 data_time: 0.122457 memory: 2690 loss_kpt: 0.000915 acc_pose: 0.711631 loss: 0.000915 2022/10/13 16:25:46 - mmengine - INFO - Epoch(train) [209][150/293] lr: 5.000000e-06 eta: 0:02:03 time: 0.316498 data_time: 0.155450 memory: 2690 loss_kpt: 0.000880 acc_pose: 0.761319 loss: 0.000880 2022/10/13 16:26:02 - mmengine - INFO - Epoch(train) [209][200/293] lr: 5.000000e-06 eta: 0:01:49 time: 0.313095 data_time: 0.197309 memory: 2690 loss_kpt: 0.000897 acc_pose: 0.648067 loss: 0.000897 2022/10/13 16:26:18 - mmengine - INFO - Epoch(train) [209][250/293] lr: 5.000000e-06 eta: 0:01:35 time: 0.315069 data_time: 0.154225 memory: 2690 loss_kpt: 0.000899 acc_pose: 0.667463 loss: 0.000899 2022/10/13 16:26:31 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:26:48 - mmengine - INFO - Epoch(train) [210][50/293] lr: 5.000000e-06 eta: 0:01:08 time: 0.336042 data_time: 0.087773 memory: 2690 loss_kpt: 0.000895 acc_pose: 0.713447 loss: 0.000895 2022/10/13 16:27:04 - mmengine - INFO - Epoch(train) [210][100/293] lr: 5.000000e-06 eta: 0:00:54 time: 0.322053 data_time: 0.080676 memory: 2690 loss_kpt: 0.000881 acc_pose: 0.760972 loss: 0.000881 2022/10/13 16:27:20 - mmengine - INFO - Epoch(train) [210][150/293] lr: 5.000000e-06 eta: 0:00:40 time: 0.317035 data_time: 0.161043 memory: 2690 loss_kpt: 0.000901 acc_pose: 0.738578 loss: 0.000901 2022/10/13 16:27:36 - mmengine - INFO - Epoch(train) [210][200/293] lr: 5.000000e-06 eta: 0:00:26 time: 0.318303 data_time: 0.136719 memory: 2690 loss_kpt: 0.000892 acc_pose: 0.709422 loss: 0.000892 2022/10/13 16:27:52 - mmengine - INFO - Epoch(train) [210][250/293] lr: 5.000000e-06 eta: 0:00:12 time: 0.318622 data_time: 0.167178 memory: 2690 loss_kpt: 0.000907 acc_pose: 0.748413 loss: 0.000907 2022/10/13 16:28:06 - mmengine - INFO - Exp name: td-hm_shufflenetv1_8xb64-210e_coco-256x192_20221013_101327 2022/10/13 16:28:06 - mmengine - INFO - Saving checkpoint at 210 epochs 2022/10/13 16:28:14 - mmengine - INFO - Epoch(val) [210][50/407] eta: 0:00:41 time: 0.115395 data_time: 0.074351 memory: 2690 2022/10/13 16:28:20 - mmengine - INFO - Epoch(val) [210][100/407] eta: 0:00:33 time: 0.109566 data_time: 0.067724 memory: 415 2022/10/13 16:28:25 - mmengine - INFO - Epoch(val) [210][150/407] eta: 0:00:28 time: 0.109849 data_time: 0.069361 memory: 415 2022/10/13 16:28:31 - mmengine - INFO - Epoch(val) [210][200/407] eta: 0:00:23 time: 0.112156 data_time: 0.070955 memory: 415 2022/10/13 16:28:36 - mmengine - INFO - Epoch(val) [210][250/407] eta: 0:00:17 time: 0.110288 data_time: 0.070306 memory: 415 2022/10/13 16:28:42 - mmengine - INFO - Epoch(val) [210][300/407] eta: 0:00:11 time: 0.108846 data_time: 0.066844 memory: 415 2022/10/13 16:28:48 - mmengine - INFO - Epoch(val) [210][350/407] eta: 0:00:06 time: 0.118941 data_time: 0.079208 memory: 415 2022/10/13 16:28:53 - mmengine - INFO - Epoch(val) [210][400/407] eta: 0:00:00 time: 0.106693 data_time: 0.068080 memory: 415 2022/10/13 16:29:30 - mmengine - INFO - Evaluating CocoMetric... 2022/10/13 16:29:45 - mmengine - INFO - Epoch(val) [210][407/407] coco/AP: 0.587261 coco/AP .5: 0.848534 coco/AP .75: 0.653307 coco/AP (M): 0.555567 coco/AP (L): 0.647651 coco/AR: 0.653700 coco/AR .5: 0.896725 coco/AR .75: 0.718671 coco/AR (M): 0.610789 coco/AR (L): 0.714121