2022/10/19 10:42:37 - 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: 858477875 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 ------------------------------------------------------------ 2022/10/19 10:42:38 - 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='ResNetV1d', depth=50, init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnet50_v1d')), head=dict( type='HeatmapHead', in_channels=2048, 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/20221019/resnetv1d50_256/' 2022/10/19 10:43:42 - 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. 2022/10/19 10:43:42 - 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. 2022/10/19 10:43:42 - 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. 2022/10/19 10:43:42 - 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. 2022/10/19 10:43:42 - 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. 2022/10/19 10:43:42 - 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. 2022/10/19 10:43:42 - 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. 2022/10/19 10:43:42 - 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. 2022/10/19 10:43:46 - 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. 2022/10/19 10:43:48 - 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. 2022/10/19 10:43:50 - 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. 2022/10/19 10:43:50 - 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.stem.0.conv.weight - torch.Size([32, 3, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.stem.0.bn.weight - torch.Size([32]): PretrainedInit: load from mmcls://resnet50_v1d backbone.stem.0.bn.bias - torch.Size([32]): PretrainedInit: load from mmcls://resnet50_v1d backbone.stem.1.conv.weight - torch.Size([32, 32, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.stem.1.bn.weight - torch.Size([32]): PretrainedInit: load from mmcls://resnet50_v1d backbone.stem.1.bn.bias - torch.Size([32]): PretrainedInit: load from mmcls://resnet50_v1d backbone.stem.2.conv.weight - torch.Size([64, 32, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.stem.2.bn.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet50_v1d backbone.stem.2.bn.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.0.bn1.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.0.bn1.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.0.bn2.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.0.bn2.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.0.bn3.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.0.bn3.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.0.downsample.1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.0.downsample.1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.1.bn1.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.1.bn1.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.1.bn2.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.1.bn2.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.1.bn3.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.1.bn3.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.2.bn1.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.2.bn1.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.2.bn2.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.2.bn2.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.2.bn3.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer1.2.bn3.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.0.bn1.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.0.bn1.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.0.bn2.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.0.bn2.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.0.bn3.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.0.bn3.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.0.downsample.1.weight - torch.Size([512, 256, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.0.downsample.2.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.0.downsample.2.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.1.bn1.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.1.bn1.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.1.bn2.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.1.bn2.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.1.bn3.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.1.bn3.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.2.bn1.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.2.bn1.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.2.bn2.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.2.bn2.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.2.bn3.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.2.bn3.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.3.bn1.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.3.bn1.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.3.bn2.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.3.bn2.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.3.bn3.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer2.3.bn3.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.0.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.0.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.0.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.0.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.0.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.0.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.0.downsample.1.weight - torch.Size([1024, 512, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.0.downsample.2.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.0.downsample.2.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.1.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.1.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.1.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.1.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.1.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.1.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.2.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.2.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.2.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.2.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.2.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.2.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.3.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.3.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.3.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.3.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.3.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.3.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.4.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.4.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.4.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.4.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.4.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.4.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.5.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.5.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.5.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.5.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.5.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer3.5.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.0.bn1.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.0.bn1.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.0.bn2.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.0.bn2.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.0.bn3.weight - torch.Size([2048]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.0.bn3.bias - torch.Size([2048]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.0.downsample.1.weight - torch.Size([2048, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.0.downsample.2.weight - torch.Size([2048]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.0.downsample.2.bias - torch.Size([2048]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.1.bn1.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.1.bn1.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.1.bn2.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.1.bn2.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.1.bn3.weight - torch.Size([2048]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.1.bn3.bias - torch.Size([2048]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.2.bn1.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.2.bn1.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.2.bn2.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.2.bn2.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.2.bn3.weight - torch.Size([2048]): PretrainedInit: load from mmcls://resnet50_v1d backbone.layer4.2.bn3.bias - torch.Size([2048]): PretrainedInit: load from mmcls://resnet50_v1d head.deconv_layers.0.weight - torch.Size([2048, 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 2022/10/19 10:43:50 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_256 by HardDiskBackend. 2022/10/19 10:44:27 - mmengine - INFO - Epoch(train) [1][50/293] lr: 4.954910e-05 eta: 12:46:18 time: 0.747862 data_time: 0.383896 memory: 7187 loss_kpt: 0.002177 acc_pose: 0.129099 loss: 0.002177 2022/10/19 10:44:47 - mmengine - INFO - Epoch(train) [1][100/293] lr: 9.959920e-05 eta: 9:42:44 time: 0.390507 data_time: 0.073627 memory: 7187 loss_kpt: 0.001820 acc_pose: 0.352528 loss: 0.001820 2022/10/19 10:45:06 - mmengine - INFO - Epoch(train) [1][150/293] lr: 1.496493e-04 eta: 8:40:44 time: 0.388754 data_time: 0.063052 memory: 7187 loss_kpt: 0.001587 acc_pose: 0.444791 loss: 0.001587 2022/10/19 10:45:25 - mmengine - INFO - Epoch(train) [1][200/293] lr: 1.996994e-04 eta: 8:06:54 time: 0.378274 data_time: 0.068108 memory: 7187 loss_kpt: 0.001414 acc_pose: 0.496352 loss: 0.001414 2022/10/19 10:45:44 - mmengine - INFO - Epoch(train) [1][250/293] lr: 2.497495e-04 eta: 7:46:57 time: 0.380636 data_time: 0.067048 memory: 7187 loss_kpt: 0.001325 acc_pose: 0.516024 loss: 0.001325 2022/10/19 10:46:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 10:46:21 - mmengine - INFO - Epoch(train) [2][50/293] lr: 3.428427e-04 eta: 6:40:10 time: 0.405871 data_time: 0.079837 memory: 7187 loss_kpt: 0.001239 acc_pose: 0.539485 loss: 0.001239 2022/10/19 10:46:40 - mmengine - INFO - Epoch(train) [2][100/293] lr: 3.928928e-04 eta: 6:39:02 time: 0.386230 data_time: 0.057378 memory: 7187 loss_kpt: 0.001219 acc_pose: 0.601731 loss: 0.001219 2022/10/19 10:47:00 - mmengine - INFO - Epoch(train) [2][150/293] lr: 4.429429e-04 eta: 6:37:42 time: 0.382824 data_time: 0.068703 memory: 7187 loss_kpt: 0.001180 acc_pose: 0.661408 loss: 0.001180 2022/10/19 10:47:18 - mmengine - INFO - Epoch(train) [2][200/293] lr: 4.929930e-04 eta: 6:35:45 time: 0.374908 data_time: 0.063834 memory: 7187 loss_kpt: 0.001177 acc_pose: 0.599732 loss: 0.001177 2022/10/19 10:47:36 - mmengine - INFO - Epoch(train) [2][250/293] lr: 5.000000e-04 eta: 6:32:26 time: 0.357117 data_time: 0.068864 memory: 7187 loss_kpt: 0.001164 acc_pose: 0.613240 loss: 0.001164 2022/10/19 10:47:52 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 10:48:11 - mmengine - INFO - Epoch(train) [3][50/293] lr: 5.000000e-04 eta: 6:05:47 time: 0.391616 data_time: 0.079446 memory: 7187 loss_kpt: 0.001105 acc_pose: 0.621874 loss: 0.001105 2022/10/19 10:48:29 - mmengine - INFO - Epoch(train) [3][100/293] lr: 5.000000e-04 eta: 6:05:25 time: 0.359383 data_time: 0.080113 memory: 7187 loss_kpt: 0.001101 acc_pose: 0.643424 loss: 0.001101 2022/10/19 10:48:48 - mmengine - INFO - Epoch(train) [3][150/293] lr: 5.000000e-04 eta: 6:06:20 time: 0.378056 data_time: 0.113343 memory: 7187 loss_kpt: 0.001093 acc_pose: 0.708886 loss: 0.001093 2022/10/19 10:49:07 - mmengine - INFO - Epoch(train) [3][200/293] lr: 5.000000e-04 eta: 6:06:45 time: 0.372873 data_time: 0.108494 memory: 7187 loss_kpt: 0.001058 acc_pose: 0.647796 loss: 0.001058 2022/10/19 10:49:27 - mmengine - INFO - Epoch(train) [3][250/293] lr: 5.000000e-04 eta: 6:08:27 time: 0.395250 data_time: 0.070319 memory: 7187 loss_kpt: 0.001074 acc_pose: 0.661977 loss: 0.001074 2022/10/19 10:49:43 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 10:50:02 - mmengine - INFO - Epoch(train) [4][50/293] lr: 5.000000e-04 eta: 5:52:10 time: 0.388310 data_time: 0.071750 memory: 7187 loss_kpt: 0.001042 acc_pose: 0.673385 loss: 0.001042 2022/10/19 10:50:22 - mmengine - INFO - Epoch(train) [4][100/293] lr: 5.000000e-04 eta: 5:54:09 time: 0.392985 data_time: 0.062695 memory: 7187 loss_kpt: 0.001041 acc_pose: 0.672204 loss: 0.001041 2022/10/19 10:50:30 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 10:50:40 - mmengine - INFO - Epoch(train) [4][150/293] lr: 5.000000e-04 eta: 5:54:43 time: 0.368217 data_time: 0.065874 memory: 7187 loss_kpt: 0.001031 acc_pose: 0.682805 loss: 0.001031 2022/10/19 10:50:59 - mmengine - INFO - Epoch(train) [4][200/293] lr: 5.000000e-04 eta: 5:55:43 time: 0.379688 data_time: 0.056312 memory: 7187 loss_kpt: 0.001008 acc_pose: 0.630735 loss: 0.001008 2022/10/19 10:51:18 - mmengine - INFO - Epoch(train) [4][250/293] lr: 5.000000e-04 eta: 5:56:36 time: 0.379257 data_time: 0.065592 memory: 7187 loss_kpt: 0.001002 acc_pose: 0.682111 loss: 0.001002 2022/10/19 10:51:34 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 10:51:53 - mmengine - INFO - Epoch(train) [5][50/293] lr: 5.000000e-04 eta: 5:45:12 time: 0.394989 data_time: 0.101135 memory: 7187 loss_kpt: 0.000985 acc_pose: 0.686601 loss: 0.000985 2022/10/19 10:52:12 - mmengine - INFO - Epoch(train) [5][100/293] lr: 5.000000e-04 eta: 5:46:16 time: 0.378045 data_time: 0.129879 memory: 7187 loss_kpt: 0.000996 acc_pose: 0.700070 loss: 0.000996 2022/10/19 10:52:31 - mmengine - INFO - Epoch(train) [5][150/293] lr: 5.000000e-04 eta: 5:47:13 time: 0.377449 data_time: 0.064569 memory: 7187 loss_kpt: 0.000994 acc_pose: 0.710712 loss: 0.000994 2022/10/19 10:52:49 - mmengine - INFO - Epoch(train) [5][200/293] lr: 5.000000e-04 eta: 5:47:42 time: 0.366900 data_time: 0.060831 memory: 7187 loss_kpt: 0.000984 acc_pose: 0.730288 loss: 0.000984 2022/10/19 10:53:08 - mmengine - INFO - Epoch(train) [5][250/293] lr: 5.000000e-04 eta: 5:47:55 time: 0.361290 data_time: 0.057199 memory: 7187 loss_kpt: 0.000968 acc_pose: 0.674422 loss: 0.000968 2022/10/19 10:53:23 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 10:53:43 - mmengine - INFO - Epoch(train) [6][50/293] lr: 5.000000e-04 eta: 5:39:02 time: 0.393251 data_time: 0.091424 memory: 7187 loss_kpt: 0.000970 acc_pose: 0.742736 loss: 0.000970 2022/10/19 10:54:02 - mmengine - INFO - Epoch(train) [6][100/293] lr: 5.000000e-04 eta: 5:39:55 time: 0.375330 data_time: 0.063626 memory: 7187 loss_kpt: 0.000970 acc_pose: 0.667387 loss: 0.000970 2022/10/19 10:54:20 - mmengine - INFO - Epoch(train) [6][150/293] lr: 5.000000e-04 eta: 5:40:32 time: 0.369156 data_time: 0.123955 memory: 7187 loss_kpt: 0.000971 acc_pose: 0.683203 loss: 0.000971 2022/10/19 10:54:39 - mmengine - INFO - Epoch(train) [6][200/293] lr: 5.000000e-04 eta: 5:41:15 time: 0.374770 data_time: 0.065240 memory: 7187 loss_kpt: 0.000957 acc_pose: 0.703139 loss: 0.000957 2022/10/19 10:54:58 - mmengine - INFO - Epoch(train) [6][250/293] lr: 5.000000e-04 eta: 5:42:01 time: 0.378013 data_time: 0.060849 memory: 7187 loss_kpt: 0.000949 acc_pose: 0.693976 loss: 0.000949 2022/10/19 10:55:13 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 10:55:33 - mmengine - INFO - Epoch(train) [7][50/293] lr: 5.000000e-04 eta: 5:34:52 time: 0.397564 data_time: 0.078238 memory: 7187 loss_kpt: 0.000950 acc_pose: 0.638875 loss: 0.000950 2022/10/19 10:55:52 - mmengine - INFO - Epoch(train) [7][100/293] lr: 5.000000e-04 eta: 5:35:44 time: 0.379291 data_time: 0.067366 memory: 7187 loss_kpt: 0.000935 acc_pose: 0.729916 loss: 0.000935 2022/10/19 10:56:10 - mmengine - INFO - Epoch(train) [7][150/293] lr: 5.000000e-04 eta: 5:36:07 time: 0.363244 data_time: 0.065750 memory: 7187 loss_kpt: 0.000938 acc_pose: 0.640286 loss: 0.000938 2022/10/19 10:56:29 - mmengine - INFO - Epoch(train) [7][200/293] lr: 5.000000e-04 eta: 5:36:42 time: 0.372242 data_time: 0.068865 memory: 7187 loss_kpt: 0.000929 acc_pose: 0.743622 loss: 0.000929 2022/10/19 10:56:44 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 10:56:47 - mmengine - INFO - Epoch(train) [7][250/293] lr: 5.000000e-04 eta: 5:37:06 time: 0.366941 data_time: 0.066157 memory: 7187 loss_kpt: 0.000926 acc_pose: 0.692242 loss: 0.000926 2022/10/19 10:57:03 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 10:57:23 - mmengine - INFO - Epoch(train) [8][50/293] lr: 5.000000e-04 eta: 5:31:01 time: 0.396276 data_time: 0.072698 memory: 7187 loss_kpt: 0.000918 acc_pose: 0.702523 loss: 0.000918 2022/10/19 10:57:42 - mmengine - INFO - Epoch(train) [8][100/293] lr: 5.000000e-04 eta: 5:31:43 time: 0.376659 data_time: 0.065809 memory: 7187 loss_kpt: 0.000919 acc_pose: 0.625998 loss: 0.000919 2022/10/19 10:58:02 - mmengine - INFO - Epoch(train) [8][150/293] lr: 5.000000e-04 eta: 5:32:38 time: 0.388135 data_time: 0.064429 memory: 7187 loss_kpt: 0.000929 acc_pose: 0.728049 loss: 0.000929 2022/10/19 10:58:21 - mmengine - INFO - Epoch(train) [8][200/293] lr: 5.000000e-04 eta: 5:33:19 time: 0.380703 data_time: 0.065875 memory: 7187 loss_kpt: 0.000925 acc_pose: 0.695478 loss: 0.000925 2022/10/19 10:58:40 - mmengine - INFO - Epoch(train) [8][250/293] lr: 5.000000e-04 eta: 5:34:02 time: 0.384162 data_time: 0.061320 memory: 7187 loss_kpt: 0.000909 acc_pose: 0.660302 loss: 0.000909 2022/10/19 10:58:56 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 10:59:16 - mmengine - INFO - Epoch(train) [9][50/293] lr: 5.000000e-04 eta: 5:28:48 time: 0.400399 data_time: 0.079303 memory: 7187 loss_kpt: 0.000912 acc_pose: 0.653137 loss: 0.000912 2022/10/19 10:59:35 - mmengine - INFO - Epoch(train) [9][100/293] lr: 5.000000e-04 eta: 5:29:36 time: 0.387386 data_time: 0.107077 memory: 7187 loss_kpt: 0.000900 acc_pose: 0.700405 loss: 0.000900 2022/10/19 10:59:54 - mmengine - INFO - Epoch(train) [9][150/293] lr: 5.000000e-04 eta: 5:30:14 time: 0.380495 data_time: 0.070189 memory: 7187 loss_kpt: 0.000927 acc_pose: 0.755964 loss: 0.000927 2022/10/19 11:00:13 - mmengine - INFO - Epoch(train) [9][200/293] lr: 5.000000e-04 eta: 5:30:37 time: 0.370197 data_time: 0.074183 memory: 7187 loss_kpt: 0.000924 acc_pose: 0.707054 loss: 0.000924 2022/10/19 11:00:31 - mmengine - INFO - Epoch(train) [9][250/293] lr: 5.000000e-04 eta: 5:30:59 time: 0.370450 data_time: 0.060097 memory: 7187 loss_kpt: 0.000885 acc_pose: 0.699249 loss: 0.000885 2022/10/19 11:00:48 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:01:08 - mmengine - INFO - Epoch(train) [10][50/293] lr: 5.000000e-04 eta: 5:26:16 time: 0.397069 data_time: 0.085680 memory: 7187 loss_kpt: 0.000899 acc_pose: 0.754525 loss: 0.000899 2022/10/19 11:01:26 - mmengine - INFO - Epoch(train) [10][100/293] lr: 5.000000e-04 eta: 5:26:47 time: 0.377132 data_time: 0.069989 memory: 7187 loss_kpt: 0.000902 acc_pose: 0.657444 loss: 0.000902 2022/10/19 11:01:46 - mmengine - INFO - Epoch(train) [10][150/293] lr: 5.000000e-04 eta: 5:27:32 time: 0.391910 data_time: 0.071851 memory: 7187 loss_kpt: 0.000883 acc_pose: 0.746874 loss: 0.000883 2022/10/19 11:02:05 - mmengine - INFO - Epoch(train) [10][200/293] lr: 5.000000e-04 eta: 5:27:59 time: 0.376625 data_time: 0.074782 memory: 7187 loss_kpt: 0.000879 acc_pose: 0.795911 loss: 0.000879 2022/10/19 11:02:23 - mmengine - INFO - Epoch(train) [10][250/293] lr: 5.000000e-04 eta: 5:28:13 time: 0.365167 data_time: 0.072372 memory: 7187 loss_kpt: 0.000883 acc_pose: 0.726581 loss: 0.000883 2022/10/19 11:02:39 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:02:39 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/10/19 11:02:58 - mmengine - INFO - Epoch(val) [10][50/407] eta: 0:01:51 time: 0.313152 data_time: 0.257198 memory: 7187 2022/10/19 11:03:04 - mmengine - INFO - Epoch(val) [10][100/407] eta: 0:00:37 time: 0.123386 data_time: 0.065911 memory: 1014 2022/10/19 11:03:10 - mmengine - INFO - Epoch(val) [10][150/407] eta: 0:00:30 time: 0.117371 data_time: 0.061701 memory: 1014 2022/10/19 11:03:16 - mmengine - INFO - Epoch(val) [10][200/407] eta: 0:00:24 time: 0.120484 data_time: 0.060644 memory: 1014 2022/10/19 11:03:22 - mmengine - INFO - Epoch(val) [10][250/407] eta: 0:00:20 time: 0.129137 data_time: 0.074178 memory: 1014 2022/10/19 11:03:29 - mmengine - INFO - Epoch(val) [10][300/407] eta: 0:00:14 time: 0.132475 data_time: 0.077313 memory: 1014 2022/10/19 11:03:36 - mmengine - INFO - Epoch(val) [10][350/407] eta: 0:00:07 time: 0.136598 data_time: 0.080644 memory: 1014 2022/10/19 11:03:42 - mmengine - INFO - Epoch(val) [10][400/407] eta: 0:00:00 time: 0.125001 data_time: 0.069973 memory: 1014 2022/10/19 11:04:18 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 11:04:31 - mmengine - INFO - Epoch(val) [10][407/407] coco/AP: 0.619377 coco/AP .5: 0.861888 coco/AP .75: 0.688940 coco/AP (M): 0.585116 coco/AP (L): 0.682115 coco/AR: 0.682793 coco/AR .5: 0.904754 coco/AR .75: 0.748741 coco/AR (M): 0.639716 coco/AR (L): 0.743776 2022/10/19 11:04:33 - mmengine - INFO - The best checkpoint with 0.6194 coco/AP at 10 epoch is saved to best_coco/AP_epoch_10.pth. 2022/10/19 11:04:53 - mmengine - INFO - Epoch(train) [11][50/293] lr: 5.000000e-04 eta: 5:23:57 time: 0.396430 data_time: 0.086965 memory: 7187 loss_kpt: 0.000879 acc_pose: 0.773802 loss: 0.000879 2022/10/19 11:05:00 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:05:11 - mmengine - INFO - Epoch(train) [11][100/293] lr: 5.000000e-04 eta: 5:23:59 time: 0.350893 data_time: 0.068188 memory: 7187 loss_kpt: 0.000868 acc_pose: 0.725973 loss: 0.000868 2022/10/19 11:05:30 - mmengine - INFO - Epoch(train) [11][150/293] lr: 5.000000e-04 eta: 5:24:30 time: 0.383068 data_time: 0.064569 memory: 7187 loss_kpt: 0.000882 acc_pose: 0.725595 loss: 0.000882 2022/10/19 11:05:49 - mmengine - INFO - Epoch(train) [11][200/293] lr: 5.000000e-04 eta: 5:24:52 time: 0.373572 data_time: 0.098146 memory: 7187 loss_kpt: 0.000860 acc_pose: 0.694661 loss: 0.000860 2022/10/19 11:06:08 - mmengine - INFO - Epoch(train) [11][250/293] lr: 5.000000e-04 eta: 5:25:22 time: 0.385617 data_time: 0.078837 memory: 7187 loss_kpt: 0.000870 acc_pose: 0.691749 loss: 0.000870 2022/10/19 11:06:23 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:06:43 - mmengine - INFO - Epoch(train) [12][50/293] lr: 5.000000e-04 eta: 5:21:27 time: 0.392593 data_time: 0.087283 memory: 7187 loss_kpt: 0.000874 acc_pose: 0.743380 loss: 0.000874 2022/10/19 11:07:01 - mmengine - INFO - Epoch(train) [12][100/293] lr: 5.000000e-04 eta: 5:21:36 time: 0.360846 data_time: 0.066614 memory: 7187 loss_kpt: 0.000876 acc_pose: 0.685361 loss: 0.000876 2022/10/19 11:07:19 - mmengine - INFO - Epoch(train) [12][150/293] lr: 5.000000e-04 eta: 5:21:51 time: 0.367541 data_time: 0.070011 memory: 7187 loss_kpt: 0.000873 acc_pose: 0.743715 loss: 0.000873 2022/10/19 11:07:38 - mmengine - INFO - Epoch(train) [12][200/293] lr: 5.000000e-04 eta: 5:22:08 time: 0.372290 data_time: 0.068395 memory: 7187 loss_kpt: 0.000871 acc_pose: 0.735011 loss: 0.000871 2022/10/19 11:07:58 - mmengine - INFO - Epoch(train) [12][250/293] lr: 5.000000e-04 eta: 5:22:45 time: 0.396317 data_time: 0.069932 memory: 7187 loss_kpt: 0.000866 acc_pose: 0.740580 loss: 0.000866 2022/10/19 11:08:15 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:08:35 - mmengine - INFO - Epoch(train) [13][50/293] lr: 5.000000e-04 eta: 5:19:18 time: 0.404454 data_time: 0.123175 memory: 7187 loss_kpt: 0.000856 acc_pose: 0.735470 loss: 0.000856 2022/10/19 11:08:54 - mmengine - INFO - Epoch(train) [13][100/293] lr: 5.000000e-04 eta: 5:19:45 time: 0.384441 data_time: 0.080180 memory: 7187 loss_kpt: 0.000860 acc_pose: 0.631469 loss: 0.000860 2022/10/19 11:09:13 - mmengine - INFO - Epoch(train) [13][150/293] lr: 5.000000e-04 eta: 5:20:13 time: 0.387622 data_time: 0.088383 memory: 7187 loss_kpt: 0.000866 acc_pose: 0.748092 loss: 0.000866 2022/10/19 11:09:33 - mmengine - INFO - Epoch(train) [13][200/293] lr: 5.000000e-04 eta: 5:20:47 time: 0.396607 data_time: 0.069830 memory: 7187 loss_kpt: 0.000843 acc_pose: 0.764702 loss: 0.000843 2022/10/19 11:09:51 - mmengine - INFO - Epoch(train) [13][250/293] lr: 5.000000e-04 eta: 5:20:53 time: 0.362868 data_time: 0.077762 memory: 7187 loss_kpt: 0.000866 acc_pose: 0.737356 loss: 0.000866 2022/10/19 11:10:08 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:10:28 - mmengine - INFO - Epoch(train) [14][50/293] lr: 5.000000e-04 eta: 5:17:33 time: 0.394205 data_time: 0.082887 memory: 7187 loss_kpt: 0.000851 acc_pose: 0.807262 loss: 0.000851 2022/10/19 11:10:47 - mmengine - INFO - Epoch(train) [14][100/293] lr: 5.000000e-04 eta: 5:17:51 time: 0.376769 data_time: 0.074183 memory: 7187 loss_kpt: 0.000819 acc_pose: 0.736015 loss: 0.000819 2022/10/19 11:11:06 - mmengine - INFO - Epoch(train) [14][150/293] lr: 5.000000e-04 eta: 5:18:14 time: 0.385436 data_time: 0.072234 memory: 7187 loss_kpt: 0.000861 acc_pose: 0.828334 loss: 0.000861 2022/10/19 11:11:21 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:11:24 - mmengine - INFO - Epoch(train) [14][200/293] lr: 5.000000e-04 eta: 5:18:25 time: 0.370640 data_time: 0.071698 memory: 7187 loss_kpt: 0.000875 acc_pose: 0.704361 loss: 0.000875 2022/10/19 11:11:43 - mmengine - INFO - Epoch(train) [14][250/293] lr: 5.000000e-04 eta: 5:18:42 time: 0.378843 data_time: 0.088979 memory: 7187 loss_kpt: 0.000851 acc_pose: 0.718545 loss: 0.000851 2022/10/19 11:12:00 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:12:19 - mmengine - INFO - Epoch(train) [15][50/293] lr: 5.000000e-04 eta: 5:15:30 time: 0.386244 data_time: 0.078521 memory: 7187 loss_kpt: 0.000850 acc_pose: 0.715172 loss: 0.000850 2022/10/19 11:12:39 - mmengine - INFO - Epoch(train) [15][100/293] lr: 5.000000e-04 eta: 5:15:56 time: 0.392146 data_time: 0.064974 memory: 7187 loss_kpt: 0.000840 acc_pose: 0.753066 loss: 0.000840 2022/10/19 11:12:58 - mmengine - INFO - Epoch(train) [15][150/293] lr: 5.000000e-04 eta: 5:16:07 time: 0.371412 data_time: 0.066950 memory: 7187 loss_kpt: 0.000842 acc_pose: 0.735961 loss: 0.000842 2022/10/19 11:13:17 - mmengine - INFO - Epoch(train) [15][200/293] lr: 5.000000e-04 eta: 5:16:26 time: 0.384038 data_time: 0.109429 memory: 7187 loss_kpt: 0.000841 acc_pose: 0.679284 loss: 0.000841 2022/10/19 11:13:36 - mmengine - INFO - Epoch(train) [15][250/293] lr: 5.000000e-04 eta: 5:16:44 time: 0.385574 data_time: 0.084721 memory: 7187 loss_kpt: 0.000840 acc_pose: 0.717829 loss: 0.000840 2022/10/19 11:13:52 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:14:11 - mmengine - INFO - Epoch(train) [16][50/293] lr: 5.000000e-04 eta: 5:13:43 time: 0.383523 data_time: 0.081525 memory: 7187 loss_kpt: 0.000845 acc_pose: 0.777763 loss: 0.000845 2022/10/19 11:14:31 - mmengine - INFO - Epoch(train) [16][100/293] lr: 5.000000e-04 eta: 5:14:06 time: 0.391922 data_time: 0.066636 memory: 7187 loss_kpt: 0.000826 acc_pose: 0.781007 loss: 0.000826 2022/10/19 11:14:49 - mmengine - INFO - Epoch(train) [16][150/293] lr: 5.000000e-04 eta: 5:14:14 time: 0.369822 data_time: 0.070580 memory: 7187 loss_kpt: 0.000839 acc_pose: 0.750782 loss: 0.000839 2022/10/19 11:15:08 - mmengine - INFO - Epoch(train) [16][200/293] lr: 5.000000e-04 eta: 5:14:32 time: 0.387053 data_time: 0.074989 memory: 7187 loss_kpt: 0.000826 acc_pose: 0.753958 loss: 0.000826 2022/10/19 11:15:28 - mmengine - INFO - Epoch(train) [16][250/293] lr: 5.000000e-04 eta: 5:14:53 time: 0.393508 data_time: 0.070725 memory: 7187 loss_kpt: 0.000839 acc_pose: 0.770144 loss: 0.000839 2022/10/19 11:15:44 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:16:04 - mmengine - INFO - Epoch(train) [17][50/293] lr: 5.000000e-04 eta: 5:12:09 time: 0.394103 data_time: 0.076530 memory: 7187 loss_kpt: 0.000826 acc_pose: 0.754980 loss: 0.000826 2022/10/19 11:16:23 - mmengine - INFO - Epoch(train) [17][100/293] lr: 5.000000e-04 eta: 5:12:24 time: 0.384413 data_time: 0.070552 memory: 7187 loss_kpt: 0.000838 acc_pose: 0.752670 loss: 0.000838 2022/10/19 11:16:41 - mmengine - INFO - Epoch(train) [17][150/293] lr: 5.000000e-04 eta: 5:12:32 time: 0.371065 data_time: 0.071369 memory: 7187 loss_kpt: 0.000838 acc_pose: 0.753017 loss: 0.000838 2022/10/19 11:17:01 - mmengine - INFO - Epoch(train) [17][200/293] lr: 5.000000e-04 eta: 5:12:46 time: 0.384708 data_time: 0.065561 memory: 7187 loss_kpt: 0.000829 acc_pose: 0.762252 loss: 0.000829 2022/10/19 11:17:20 - mmengine - INFO - Epoch(train) [17][250/293] lr: 5.000000e-04 eta: 5:12:59 time: 0.381672 data_time: 0.065033 memory: 7187 loss_kpt: 0.000840 acc_pose: 0.765267 loss: 0.000840 2022/10/19 11:17:36 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:17:44 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:17:55 - mmengine - INFO - Epoch(train) [18][50/293] lr: 5.000000e-04 eta: 5:10:22 time: 0.392526 data_time: 0.126116 memory: 7187 loss_kpt: 0.000822 acc_pose: 0.718366 loss: 0.000822 2022/10/19 11:18:13 - mmengine - INFO - Epoch(train) [18][100/293] lr: 5.000000e-04 eta: 5:10:24 time: 0.363734 data_time: 0.103054 memory: 7187 loss_kpt: 0.000818 acc_pose: 0.753332 loss: 0.000818 2022/10/19 11:18:33 - mmengine - INFO - Epoch(train) [18][150/293] lr: 5.000000e-04 eta: 5:10:43 time: 0.393418 data_time: 0.167067 memory: 7187 loss_kpt: 0.000823 acc_pose: 0.723209 loss: 0.000823 2022/10/19 11:18:51 - mmengine - INFO - Epoch(train) [18][200/293] lr: 5.000000e-04 eta: 5:10:40 time: 0.356246 data_time: 0.136537 memory: 7187 loss_kpt: 0.000818 acc_pose: 0.743315 loss: 0.000818 2022/10/19 11:19:10 - mmengine - INFO - Epoch(train) [18][250/293] lr: 5.000000e-04 eta: 5:10:56 time: 0.391150 data_time: 0.083299 memory: 7187 loss_kpt: 0.000834 acc_pose: 0.743646 loss: 0.000834 2022/10/19 11:19:26 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:19:46 - mmengine - INFO - Epoch(train) [19][50/293] lr: 5.000000e-04 eta: 5:08:31 time: 0.399421 data_time: 0.083945 memory: 7187 loss_kpt: 0.000814 acc_pose: 0.731838 loss: 0.000814 2022/10/19 11:20:06 - mmengine - INFO - Epoch(train) [19][100/293] lr: 5.000000e-04 eta: 5:08:48 time: 0.393534 data_time: 0.064361 memory: 7187 loss_kpt: 0.000813 acc_pose: 0.755175 loss: 0.000813 2022/10/19 11:20:25 - mmengine - INFO - Epoch(train) [19][150/293] lr: 5.000000e-04 eta: 5:09:02 time: 0.390399 data_time: 0.071936 memory: 7187 loss_kpt: 0.000831 acc_pose: 0.721120 loss: 0.000831 2022/10/19 11:20:44 - mmengine - INFO - Epoch(train) [19][200/293] lr: 5.000000e-04 eta: 5:09:13 time: 0.383004 data_time: 0.067665 memory: 7187 loss_kpt: 0.000828 acc_pose: 0.730229 loss: 0.000828 2022/10/19 11:21:04 - mmengine - INFO - Epoch(train) [19][250/293] lr: 5.000000e-04 eta: 5:09:29 time: 0.396431 data_time: 0.070860 memory: 7187 loss_kpt: 0.000838 acc_pose: 0.736450 loss: 0.000838 2022/10/19 11:21:20 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:21:40 - mmengine - INFO - Epoch(train) [20][50/293] lr: 5.000000e-04 eta: 5:07:06 time: 0.390049 data_time: 0.088850 memory: 7187 loss_kpt: 0.000816 acc_pose: 0.780411 loss: 0.000816 2022/10/19 11:21:59 - mmengine - INFO - Epoch(train) [20][100/293] lr: 5.000000e-04 eta: 5:07:19 time: 0.389768 data_time: 0.067537 memory: 7187 loss_kpt: 0.000825 acc_pose: 0.744735 loss: 0.000825 2022/10/19 11:22:19 - mmengine - INFO - Epoch(train) [20][150/293] lr: 5.000000e-04 eta: 5:07:34 time: 0.395169 data_time: 0.107815 memory: 7187 loss_kpt: 0.000835 acc_pose: 0.709096 loss: 0.000835 2022/10/19 11:22:39 - mmengine - INFO - Epoch(train) [20][200/293] lr: 5.000000e-04 eta: 5:07:52 time: 0.400997 data_time: 0.075963 memory: 7187 loss_kpt: 0.000818 acc_pose: 0.742972 loss: 0.000818 2022/10/19 11:22:57 - mmengine - INFO - Epoch(train) [20][250/293] lr: 5.000000e-04 eta: 5:07:49 time: 0.359545 data_time: 0.066197 memory: 7187 loss_kpt: 0.000792 acc_pose: 0.729173 loss: 0.000792 2022/10/19 11:23:14 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:23:14 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/10/19 11:23:24 - mmengine - INFO - Epoch(val) [20][50/407] eta: 0:00:46 time: 0.130782 data_time: 0.074116 memory: 7187 2022/10/19 11:23:31 - mmengine - INFO - Epoch(val) [20][100/407] eta: 0:00:41 time: 0.135142 data_time: 0.080178 memory: 1014 2022/10/19 11:23:37 - mmengine - INFO - Epoch(val) [20][150/407] eta: 0:00:30 time: 0.120430 data_time: 0.064428 memory: 1014 2022/10/19 11:23:43 - mmengine - INFO - Epoch(val) [20][200/407] eta: 0:00:26 time: 0.128003 data_time: 0.071932 memory: 1014 2022/10/19 11:23:50 - mmengine - INFO - Epoch(val) [20][250/407] eta: 0:00:21 time: 0.136502 data_time: 0.080980 memory: 1014 2022/10/19 11:23:56 - mmengine - INFO - Epoch(val) [20][300/407] eta: 0:00:13 time: 0.123098 data_time: 0.068574 memory: 1014 2022/10/19 11:24:02 - mmengine - INFO - Epoch(val) [20][350/407] eta: 0:00:06 time: 0.117979 data_time: 0.061977 memory: 1014 2022/10/19 11:24:08 - mmengine - INFO - Epoch(val) [20][400/407] eta: 0:00:00 time: 0.110539 data_time: 0.056866 memory: 1014 2022/10/19 11:24:42 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 11:24:56 - mmengine - INFO - Epoch(val) [20][407/407] coco/AP: 0.650410 coco/AP .5: 0.874257 coco/AP .75: 0.723634 coco/AP (M): 0.612981 coco/AP (L): 0.719453 coco/AR: 0.711477 coco/AR .5: 0.915460 coco/AR .75: 0.779282 coco/AR (M): 0.665884 coco/AR (L): 0.776366 2022/10/19 11:24:56 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_256/best_coco/AP_epoch_10.pth is removed 2022/10/19 11:24:58 - mmengine - INFO - The best checkpoint with 0.6504 coco/AP at 20 epoch is saved to best_coco/AP_epoch_20.pth. 2022/10/19 11:25:17 - mmengine - INFO - Epoch(train) [21][50/293] lr: 5.000000e-04 eta: 5:05:27 time: 0.381056 data_time: 0.176865 memory: 7187 loss_kpt: 0.000796 acc_pose: 0.762805 loss: 0.000796 2022/10/19 11:25:36 - mmengine - INFO - Epoch(train) [21][100/293] lr: 5.000000e-04 eta: 5:05:32 time: 0.374870 data_time: 0.136956 memory: 7187 loss_kpt: 0.000801 acc_pose: 0.788411 loss: 0.000801 2022/10/19 11:25:51 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:25:55 - mmengine - INFO - Epoch(train) [21][150/293] lr: 5.000000e-04 eta: 5:05:39 time: 0.382200 data_time: 0.072967 memory: 7187 loss_kpt: 0.000814 acc_pose: 0.772753 loss: 0.000814 2022/10/19 11:26:14 - mmengine - INFO - Epoch(train) [21][200/293] lr: 5.000000e-04 eta: 5:05:45 time: 0.377636 data_time: 0.103844 memory: 7187 loss_kpt: 0.000811 acc_pose: 0.776915 loss: 0.000811 2022/10/19 11:26:32 - mmengine - INFO - Epoch(train) [21][250/293] lr: 5.000000e-04 eta: 5:05:43 time: 0.364816 data_time: 0.071801 memory: 7187 loss_kpt: 0.000802 acc_pose: 0.831836 loss: 0.000802 2022/10/19 11:26:48 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:27:08 - mmengine - INFO - Epoch(train) [22][50/293] lr: 5.000000e-04 eta: 5:03:34 time: 0.394094 data_time: 0.079884 memory: 7187 loss_kpt: 0.000810 acc_pose: 0.720506 loss: 0.000810 2022/10/19 11:27:26 - mmengine - INFO - Epoch(train) [22][100/293] lr: 5.000000e-04 eta: 5:03:37 time: 0.372591 data_time: 0.068982 memory: 7187 loss_kpt: 0.000800 acc_pose: 0.778391 loss: 0.000800 2022/10/19 11:27:44 - mmengine - INFO - Epoch(train) [22][150/293] lr: 5.000000e-04 eta: 5:03:31 time: 0.353846 data_time: 0.066662 memory: 7187 loss_kpt: 0.000814 acc_pose: 0.795372 loss: 0.000814 2022/10/19 11:28:02 - mmengine - INFO - Epoch(train) [22][200/293] lr: 5.000000e-04 eta: 5:03:26 time: 0.357857 data_time: 0.065658 memory: 7187 loss_kpt: 0.000799 acc_pose: 0.782644 loss: 0.000799 2022/10/19 11:28:21 - mmengine - INFO - Epoch(train) [22][250/293] lr: 5.000000e-04 eta: 5:03:32 time: 0.380303 data_time: 0.065552 memory: 7187 loss_kpt: 0.000786 acc_pose: 0.744481 loss: 0.000786 2022/10/19 11:28:37 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:28:56 - mmengine - INFO - Epoch(train) [23][50/293] lr: 5.000000e-04 eta: 5:01:28 time: 0.394854 data_time: 0.081948 memory: 7187 loss_kpt: 0.000802 acc_pose: 0.745340 loss: 0.000802 2022/10/19 11:29:15 - mmengine - INFO - Epoch(train) [23][100/293] lr: 5.000000e-04 eta: 5:01:28 time: 0.369000 data_time: 0.085767 memory: 7187 loss_kpt: 0.000793 acc_pose: 0.755867 loss: 0.000793 2022/10/19 11:29:34 - mmengine - INFO - Epoch(train) [23][150/293] lr: 5.000000e-04 eta: 5:01:34 time: 0.381852 data_time: 0.075827 memory: 7187 loss_kpt: 0.000806 acc_pose: 0.751896 loss: 0.000806 2022/10/19 11:29:53 - mmengine - INFO - Epoch(train) [23][200/293] lr: 5.000000e-04 eta: 5:01:40 time: 0.382918 data_time: 0.131442 memory: 7187 loss_kpt: 0.000784 acc_pose: 0.816673 loss: 0.000784 2022/10/19 11:30:11 - mmengine - INFO - Epoch(train) [23][250/293] lr: 5.000000e-04 eta: 5:01:38 time: 0.367107 data_time: 0.117529 memory: 7187 loss_kpt: 0.000793 acc_pose: 0.731893 loss: 0.000793 2022/10/19 11:30:27 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:30:49 - mmengine - INFO - Epoch(train) [24][50/293] lr: 5.000000e-04 eta: 4:59:51 time: 0.422577 data_time: 0.084608 memory: 7187 loss_kpt: 0.000791 acc_pose: 0.792014 loss: 0.000791 2022/10/19 11:31:09 - mmengine - INFO - Epoch(train) [24][100/293] lr: 5.000000e-04 eta: 5:00:05 time: 0.407071 data_time: 0.067056 memory: 7187 loss_kpt: 0.000799 acc_pose: 0.708855 loss: 0.000799 2022/10/19 11:31:27 - mmengine - INFO - Epoch(train) [24][150/293] lr: 5.000000e-04 eta: 5:00:05 time: 0.370583 data_time: 0.074438 memory: 7187 loss_kpt: 0.000789 acc_pose: 0.749763 loss: 0.000789 2022/10/19 11:31:47 - mmengine - INFO - Epoch(train) [24][200/293] lr: 5.000000e-04 eta: 5:00:16 time: 0.398449 data_time: 0.067960 memory: 7187 loss_kpt: 0.000790 acc_pose: 0.671993 loss: 0.000790 2022/10/19 11:32:06 - mmengine - INFO - Epoch(train) [24][250/293] lr: 5.000000e-04 eta: 5:00:16 time: 0.371835 data_time: 0.070830 memory: 7187 loss_kpt: 0.000805 acc_pose: 0.783206 loss: 0.000805 2022/10/19 11:32:10 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:32:21 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:32:41 - mmengine - INFO - Epoch(train) [25][50/293] lr: 5.000000e-04 eta: 4:58:24 time: 0.403719 data_time: 0.095709 memory: 7187 loss_kpt: 0.000787 acc_pose: 0.791045 loss: 0.000787 2022/10/19 11:33:01 - mmengine - INFO - Epoch(train) [25][100/293] lr: 5.000000e-04 eta: 4:58:30 time: 0.387363 data_time: 0.065688 memory: 7187 loss_kpt: 0.000780 acc_pose: 0.747847 loss: 0.000780 2022/10/19 11:33:20 - mmengine - INFO - Epoch(train) [25][150/293] lr: 5.000000e-04 eta: 4:58:35 time: 0.386317 data_time: 0.067999 memory: 7187 loss_kpt: 0.000788 acc_pose: 0.770893 loss: 0.000788 2022/10/19 11:33:39 - mmengine - INFO - Epoch(train) [25][200/293] lr: 5.000000e-04 eta: 4:58:37 time: 0.378752 data_time: 0.080943 memory: 7187 loss_kpt: 0.000784 acc_pose: 0.755604 loss: 0.000784 2022/10/19 11:33:58 - mmengine - INFO - Epoch(train) [25][250/293] lr: 5.000000e-04 eta: 4:58:37 time: 0.373380 data_time: 0.069442 memory: 7187 loss_kpt: 0.000795 acc_pose: 0.808263 loss: 0.000795 2022/10/19 11:34:14 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:34:34 - mmengine - INFO - Epoch(train) [26][50/293] lr: 5.000000e-04 eta: 4:56:48 time: 0.402438 data_time: 0.175174 memory: 7187 loss_kpt: 0.000790 acc_pose: 0.758474 loss: 0.000790 2022/10/19 11:34:53 - mmengine - INFO - Epoch(train) [26][100/293] lr: 5.000000e-04 eta: 4:56:50 time: 0.378522 data_time: 0.089125 memory: 7187 loss_kpt: 0.000775 acc_pose: 0.728339 loss: 0.000775 2022/10/19 11:35:12 - mmengine - INFO - Epoch(train) [26][150/293] lr: 5.000000e-04 eta: 4:56:50 time: 0.374219 data_time: 0.067282 memory: 7187 loss_kpt: 0.000786 acc_pose: 0.757954 loss: 0.000786 2022/10/19 11:35:31 - mmengine - INFO - Epoch(train) [26][200/293] lr: 5.000000e-04 eta: 4:56:47 time: 0.368358 data_time: 0.069571 memory: 7187 loss_kpt: 0.000790 acc_pose: 0.790381 loss: 0.000790 2022/10/19 11:35:49 - mmengine - INFO - Epoch(train) [26][250/293] lr: 5.000000e-04 eta: 4:56:47 time: 0.376217 data_time: 0.067431 memory: 7187 loss_kpt: 0.000792 acc_pose: 0.804745 loss: 0.000792 2022/10/19 11:36:06 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:36:26 - mmengine - INFO - Epoch(train) [27][50/293] lr: 5.000000e-04 eta: 4:55:02 time: 0.402918 data_time: 0.084567 memory: 7187 loss_kpt: 0.000778 acc_pose: 0.763821 loss: 0.000778 2022/10/19 11:36:45 - mmengine - INFO - Epoch(train) [27][100/293] lr: 5.000000e-04 eta: 4:55:04 time: 0.379107 data_time: 0.065924 memory: 7187 loss_kpt: 0.000783 acc_pose: 0.760447 loss: 0.000783 2022/10/19 11:37:04 - mmengine - INFO - Epoch(train) [27][150/293] lr: 5.000000e-04 eta: 4:55:03 time: 0.375971 data_time: 0.089701 memory: 7187 loss_kpt: 0.000798 acc_pose: 0.701201 loss: 0.000798 2022/10/19 11:37:24 - mmengine - INFO - Epoch(train) [27][200/293] lr: 5.000000e-04 eta: 4:55:11 time: 0.400683 data_time: 0.071156 memory: 7187 loss_kpt: 0.000788 acc_pose: 0.747072 loss: 0.000788 2022/10/19 11:37:43 - mmengine - INFO - Epoch(train) [27][250/293] lr: 5.000000e-04 eta: 4:55:14 time: 0.386356 data_time: 0.065825 memory: 7187 loss_kpt: 0.000779 acc_pose: 0.732424 loss: 0.000779 2022/10/19 11:37:59 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:38:19 - mmengine - INFO - Epoch(train) [28][50/293] lr: 5.000000e-04 eta: 4:53:32 time: 0.402692 data_time: 0.083450 memory: 7187 loss_kpt: 0.000773 acc_pose: 0.730176 loss: 0.000773 2022/10/19 11:38:33 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:38:37 - mmengine - INFO - Epoch(train) [28][100/293] lr: 5.000000e-04 eta: 4:53:29 time: 0.368416 data_time: 0.063980 memory: 7187 loss_kpt: 0.000787 acc_pose: 0.738955 loss: 0.000787 2022/10/19 11:38:56 - mmengine - INFO - Epoch(train) [28][150/293] lr: 5.000000e-04 eta: 4:53:32 time: 0.385783 data_time: 0.066812 memory: 7187 loss_kpt: 0.000777 acc_pose: 0.765129 loss: 0.000777 2022/10/19 11:39:16 - mmengine - INFO - Epoch(train) [28][200/293] lr: 5.000000e-04 eta: 4:53:38 time: 0.398282 data_time: 0.068664 memory: 7187 loss_kpt: 0.000773 acc_pose: 0.778234 loss: 0.000773 2022/10/19 11:39:35 - mmengine - INFO - Epoch(train) [28][250/293] lr: 5.000000e-04 eta: 4:53:36 time: 0.374201 data_time: 0.068804 memory: 7187 loss_kpt: 0.000788 acc_pose: 0.735988 loss: 0.000788 2022/10/19 11:39:52 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:40:11 - mmengine - INFO - Epoch(train) [29][50/293] lr: 5.000000e-04 eta: 4:51:55 time: 0.396259 data_time: 0.111382 memory: 7187 loss_kpt: 0.000767 acc_pose: 0.773063 loss: 0.000767 2022/10/19 11:40:30 - mmengine - INFO - Epoch(train) [29][100/293] lr: 5.000000e-04 eta: 4:51:53 time: 0.375277 data_time: 0.068063 memory: 7187 loss_kpt: 0.000764 acc_pose: 0.741138 loss: 0.000764 2022/10/19 11:40:51 - mmengine - INFO - Epoch(train) [29][150/293] lr: 5.000000e-04 eta: 4:52:02 time: 0.406374 data_time: 0.069141 memory: 7187 loss_kpt: 0.000777 acc_pose: 0.757151 loss: 0.000777 2022/10/19 11:41:09 - mmengine - INFO - Epoch(train) [29][200/293] lr: 5.000000e-04 eta: 4:51:59 time: 0.373993 data_time: 0.074544 memory: 7187 loss_kpt: 0.000764 acc_pose: 0.818736 loss: 0.000764 2022/10/19 11:41:28 - mmengine - INFO - Epoch(train) [29][250/293] lr: 5.000000e-04 eta: 4:51:58 time: 0.379451 data_time: 0.108334 memory: 7187 loss_kpt: 0.000785 acc_pose: 0.750744 loss: 0.000785 2022/10/19 11:41:44 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:42:03 - mmengine - INFO - Epoch(train) [30][50/293] lr: 5.000000e-04 eta: 4:50:16 time: 0.383652 data_time: 0.082079 memory: 7187 loss_kpt: 0.000770 acc_pose: 0.753169 loss: 0.000770 2022/10/19 11:42:22 - mmengine - INFO - Epoch(train) [30][100/293] lr: 5.000000e-04 eta: 4:50:18 time: 0.387791 data_time: 0.071050 memory: 7187 loss_kpt: 0.000776 acc_pose: 0.841757 loss: 0.000776 2022/10/19 11:42:41 - mmengine - INFO - Epoch(train) [30][150/293] lr: 5.000000e-04 eta: 4:50:12 time: 0.363753 data_time: 0.060337 memory: 7187 loss_kpt: 0.000765 acc_pose: 0.768486 loss: 0.000765 2022/10/19 11:43:00 - mmengine - INFO - Epoch(train) [30][200/293] lr: 5.000000e-04 eta: 4:50:15 time: 0.394208 data_time: 0.061096 memory: 7187 loss_kpt: 0.000772 acc_pose: 0.827954 loss: 0.000772 2022/10/19 11:43:20 - mmengine - INFO - Epoch(train) [30][250/293] lr: 5.000000e-04 eta: 4:50:16 time: 0.386126 data_time: 0.074497 memory: 7187 loss_kpt: 0.000759 acc_pose: 0.746987 loss: 0.000759 2022/10/19 11:43:35 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:43:35 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/10/19 11:43:45 - mmengine - INFO - Epoch(val) [30][50/407] eta: 0:00:44 time: 0.125251 data_time: 0.069572 memory: 7187 2022/10/19 11:43:50 - mmengine - INFO - Epoch(val) [30][100/407] eta: 0:00:36 time: 0.118932 data_time: 0.063542 memory: 1014 2022/10/19 11:43:57 - mmengine - INFO - Epoch(val) [30][150/407] eta: 0:00:33 time: 0.128810 data_time: 0.072842 memory: 1014 2022/10/19 11:44:03 - mmengine - INFO - Epoch(val) [30][200/407] eta: 0:00:24 time: 0.119565 data_time: 0.063602 memory: 1014 2022/10/19 11:44:08 - mmengine - INFO - Epoch(val) [30][250/407] eta: 0:00:17 time: 0.110438 data_time: 0.054706 memory: 1014 2022/10/19 11:44:15 - mmengine - INFO - Epoch(val) [30][300/407] eta: 0:00:14 time: 0.135159 data_time: 0.078595 memory: 1014 2022/10/19 11:44:21 - mmengine - INFO - Epoch(val) [30][350/407] eta: 0:00:07 time: 0.124541 data_time: 0.068158 memory: 1014 2022/10/19 11:44:27 - mmengine - INFO - Epoch(val) [30][400/407] eta: 0:00:00 time: 0.107891 data_time: 0.054340 memory: 1014 2022/10/19 11:45:03 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 11:45:16 - mmengine - INFO - Epoch(val) [30][407/407] coco/AP: 0.667112 coco/AP .5: 0.879313 coco/AP .75: 0.742860 coco/AP (M): 0.630684 coco/AP (L): 0.732731 coco/AR: 0.726071 coco/AR .5: 0.917664 coco/AR .75: 0.796127 coco/AR (M): 0.681426 coco/AR (L): 0.790190 2022/10/19 11:45:16 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_256/best_coco/AP_epoch_20.pth is removed 2022/10/19 11:45:18 - mmengine - INFO - The best checkpoint with 0.6671 coco/AP at 30 epoch is saved to best_coco/AP_epoch_30.pth. 2022/10/19 11:45:37 - mmengine - INFO - Epoch(train) [31][50/293] lr: 5.000000e-04 eta: 4:48:37 time: 0.384978 data_time: 0.118249 memory: 7187 loss_kpt: 0.000762 acc_pose: 0.762745 loss: 0.000762 2022/10/19 11:45:57 - mmengine - INFO - Epoch(train) [31][100/293] lr: 5.000000e-04 eta: 4:48:40 time: 0.394010 data_time: 0.067381 memory: 7187 loss_kpt: 0.000773 acc_pose: 0.789861 loss: 0.000773 2022/10/19 11:46:16 - mmengine - INFO - Epoch(train) [31][150/293] lr: 5.000000e-04 eta: 4:48:38 time: 0.377276 data_time: 0.069782 memory: 7187 loss_kpt: 0.000769 acc_pose: 0.786112 loss: 0.000769 2022/10/19 11:46:35 - mmengine - INFO - Epoch(train) [31][200/293] lr: 5.000000e-04 eta: 4:48:35 time: 0.376483 data_time: 0.063773 memory: 7187 loss_kpt: 0.000761 acc_pose: 0.823041 loss: 0.000761 2022/10/19 11:46:39 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:46:54 - mmengine - INFO - Epoch(train) [31][250/293] lr: 5.000000e-04 eta: 4:48:32 time: 0.377218 data_time: 0.074752 memory: 7187 loss_kpt: 0.000763 acc_pose: 0.773438 loss: 0.000763 2022/10/19 11:47:09 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:47:31 - mmengine - INFO - Epoch(train) [32][50/293] lr: 5.000000e-04 eta: 4:47:07 time: 0.421706 data_time: 0.088048 memory: 7187 loss_kpt: 0.000771 acc_pose: 0.808423 loss: 0.000771 2022/10/19 11:47:50 - mmengine - INFO - Epoch(train) [32][100/293] lr: 5.000000e-04 eta: 4:47:09 time: 0.395667 data_time: 0.062804 memory: 7187 loss_kpt: 0.000759 acc_pose: 0.730662 loss: 0.000759 2022/10/19 11:48:09 - mmengine - INFO - Epoch(train) [32][150/293] lr: 5.000000e-04 eta: 4:47:07 time: 0.377691 data_time: 0.074557 memory: 7187 loss_kpt: 0.000761 acc_pose: 0.767271 loss: 0.000761 2022/10/19 11:48:29 - mmengine - INFO - Epoch(train) [32][200/293] lr: 5.000000e-04 eta: 4:47:08 time: 0.391653 data_time: 0.076860 memory: 7187 loss_kpt: 0.000754 acc_pose: 0.812961 loss: 0.000754 2022/10/19 11:48:47 - mmengine - INFO - Epoch(train) [32][250/293] lr: 5.000000e-04 eta: 4:47:03 time: 0.371244 data_time: 0.067063 memory: 7187 loss_kpt: 0.000762 acc_pose: 0.818804 loss: 0.000762 2022/10/19 11:49:03 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:49:23 - mmengine - INFO - Epoch(train) [33][50/293] lr: 5.000000e-04 eta: 4:45:34 time: 0.402144 data_time: 0.101887 memory: 7187 loss_kpt: 0.000760 acc_pose: 0.793103 loss: 0.000760 2022/10/19 11:49:42 - mmengine - INFO - Epoch(train) [33][100/293] lr: 5.000000e-04 eta: 4:45:31 time: 0.380610 data_time: 0.068626 memory: 7187 loss_kpt: 0.000749 acc_pose: 0.750658 loss: 0.000749 2022/10/19 11:50:02 - mmengine - INFO - Epoch(train) [33][150/293] lr: 5.000000e-04 eta: 4:45:31 time: 0.389500 data_time: 0.070733 memory: 7187 loss_kpt: 0.000760 acc_pose: 0.805664 loss: 0.000760 2022/10/19 11:50:22 - mmengine - INFO - Epoch(train) [33][200/293] lr: 5.000000e-04 eta: 4:45:33 time: 0.394954 data_time: 0.070121 memory: 7187 loss_kpt: 0.000768 acc_pose: 0.773605 loss: 0.000768 2022/10/19 11:50:41 - mmengine - INFO - Epoch(train) [33][250/293] lr: 5.000000e-04 eta: 4:45:32 time: 0.387532 data_time: 0.073992 memory: 7187 loss_kpt: 0.000771 acc_pose: 0.818122 loss: 0.000771 2022/10/19 11:50:58 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:51:18 - mmengine - INFO - Epoch(train) [34][50/293] lr: 5.000000e-04 eta: 4:44:03 time: 0.396762 data_time: 0.081204 memory: 7187 loss_kpt: 0.000745 acc_pose: 0.785305 loss: 0.000745 2022/10/19 11:51:38 - mmengine - INFO - Epoch(train) [34][100/293] lr: 5.000000e-04 eta: 4:44:06 time: 0.401093 data_time: 0.071597 memory: 7187 loss_kpt: 0.000767 acc_pose: 0.722458 loss: 0.000767 2022/10/19 11:51:58 - mmengine - INFO - Epoch(train) [34][150/293] lr: 5.000000e-04 eta: 4:44:06 time: 0.393692 data_time: 0.066605 memory: 7187 loss_kpt: 0.000774 acc_pose: 0.771937 loss: 0.000774 2022/10/19 11:52:17 - mmengine - INFO - Epoch(train) [34][200/293] lr: 5.000000e-04 eta: 4:44:03 time: 0.380828 data_time: 0.068631 memory: 7187 loss_kpt: 0.000767 acc_pose: 0.756615 loss: 0.000767 2022/10/19 11:52:37 - mmengine - INFO - Epoch(train) [34][250/293] lr: 5.000000e-04 eta: 4:44:04 time: 0.395280 data_time: 0.074978 memory: 7187 loss_kpt: 0.000768 acc_pose: 0.819777 loss: 0.000768 2022/10/19 11:52:52 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:53:07 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:53:12 - mmengine - INFO - Epoch(train) [35][50/293] lr: 5.000000e-04 eta: 4:42:37 time: 0.396660 data_time: 0.080090 memory: 7187 loss_kpt: 0.000750 acc_pose: 0.793400 loss: 0.000750 2022/10/19 11:53:31 - mmengine - INFO - Epoch(train) [35][100/293] lr: 5.000000e-04 eta: 4:42:36 time: 0.388779 data_time: 0.078116 memory: 7187 loss_kpt: 0.000747 acc_pose: 0.785814 loss: 0.000747 2022/10/19 11:53:51 - mmengine - INFO - Epoch(train) [35][150/293] lr: 5.000000e-04 eta: 4:42:34 time: 0.388439 data_time: 0.110371 memory: 7187 loss_kpt: 0.000770 acc_pose: 0.827470 loss: 0.000770 2022/10/19 11:54:09 - mmengine - INFO - Epoch(train) [35][200/293] lr: 5.000000e-04 eta: 4:42:28 time: 0.371008 data_time: 0.084717 memory: 7187 loss_kpt: 0.000749 acc_pose: 0.745328 loss: 0.000749 2022/10/19 11:54:29 - mmengine - INFO - Epoch(train) [35][250/293] lr: 5.000000e-04 eta: 4:42:26 time: 0.387389 data_time: 0.076808 memory: 7187 loss_kpt: 0.000767 acc_pose: 0.744594 loss: 0.000767 2022/10/19 11:54:45 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:55:06 - mmengine - INFO - Epoch(train) [36][50/293] lr: 5.000000e-04 eta: 4:41:07 time: 0.417963 data_time: 0.088075 memory: 7187 loss_kpt: 0.000751 acc_pose: 0.804075 loss: 0.000751 2022/10/19 11:55:26 - mmengine - INFO - Epoch(train) [36][100/293] lr: 5.000000e-04 eta: 4:41:07 time: 0.395579 data_time: 0.066885 memory: 7187 loss_kpt: 0.000765 acc_pose: 0.767861 loss: 0.000765 2022/10/19 11:55:45 - mmengine - INFO - Epoch(train) [36][150/293] lr: 5.000000e-04 eta: 4:41:02 time: 0.378000 data_time: 0.071450 memory: 7187 loss_kpt: 0.000761 acc_pose: 0.758151 loss: 0.000761 2022/10/19 11:56:04 - mmengine - INFO - Epoch(train) [36][200/293] lr: 5.000000e-04 eta: 4:40:59 time: 0.386568 data_time: 0.065701 memory: 7187 loss_kpt: 0.000764 acc_pose: 0.761604 loss: 0.000764 2022/10/19 11:56:23 - mmengine - INFO - Epoch(train) [36][250/293] lr: 5.000000e-04 eta: 4:40:53 time: 0.370418 data_time: 0.066582 memory: 7187 loss_kpt: 0.000757 acc_pose: 0.806922 loss: 0.000757 2022/10/19 11:56:39 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:56:59 - mmengine - INFO - Epoch(train) [37][50/293] lr: 5.000000e-04 eta: 4:39:30 time: 0.397646 data_time: 0.089195 memory: 7187 loss_kpt: 0.000748 acc_pose: 0.695358 loss: 0.000748 2022/10/19 11:57:19 - mmengine - INFO - Epoch(train) [37][100/293] lr: 5.000000e-04 eta: 4:39:30 time: 0.397483 data_time: 0.122124 memory: 7187 loss_kpt: 0.000751 acc_pose: 0.748768 loss: 0.000751 2022/10/19 11:57:38 - mmengine - INFO - Epoch(train) [37][150/293] lr: 5.000000e-04 eta: 4:39:27 time: 0.388098 data_time: 0.138901 memory: 7187 loss_kpt: 0.000753 acc_pose: 0.785569 loss: 0.000753 2022/10/19 11:57:58 - mmengine - INFO - Epoch(train) [37][200/293] lr: 5.000000e-04 eta: 4:39:25 time: 0.389839 data_time: 0.066884 memory: 7187 loss_kpt: 0.000753 acc_pose: 0.764876 loss: 0.000753 2022/10/19 11:58:17 - mmengine - INFO - Epoch(train) [37][250/293] lr: 5.000000e-04 eta: 4:39:21 time: 0.383225 data_time: 0.070682 memory: 7187 loss_kpt: 0.000765 acc_pose: 0.828645 loss: 0.000765 2022/10/19 11:58:33 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:58:52 - mmengine - INFO - Epoch(train) [38][50/293] lr: 5.000000e-04 eta: 4:37:56 time: 0.380571 data_time: 0.096339 memory: 7187 loss_kpt: 0.000741 acc_pose: 0.783934 loss: 0.000741 2022/10/19 11:59:12 - mmengine - INFO - Epoch(train) [38][100/293] lr: 5.000000e-04 eta: 4:37:53 time: 0.389035 data_time: 0.070558 memory: 7187 loss_kpt: 0.000757 acc_pose: 0.728318 loss: 0.000757 2022/10/19 11:59:31 - mmengine - INFO - Epoch(train) [38][150/293] lr: 5.000000e-04 eta: 4:37:51 time: 0.392553 data_time: 0.067714 memory: 7187 loss_kpt: 0.000759 acc_pose: 0.789856 loss: 0.000759 2022/10/19 11:59:35 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 11:59:51 - mmengine - INFO - Epoch(train) [38][200/293] lr: 5.000000e-04 eta: 4:37:48 time: 0.389684 data_time: 0.122315 memory: 7187 loss_kpt: 0.000735 acc_pose: 0.809297 loss: 0.000735 2022/10/19 12:00:09 - mmengine - INFO - Epoch(train) [38][250/293] lr: 5.000000e-04 eta: 4:37:41 time: 0.371065 data_time: 0.065587 memory: 7187 loss_kpt: 0.000773 acc_pose: 0.806988 loss: 0.000773 2022/10/19 12:00:26 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:00:47 - mmengine - INFO - Epoch(train) [39][50/293] lr: 5.000000e-04 eta: 4:36:26 time: 0.418117 data_time: 0.172269 memory: 7187 loss_kpt: 0.000752 acc_pose: 0.767925 loss: 0.000752 2022/10/19 12:01:07 - mmengine - INFO - Epoch(train) [39][100/293] lr: 5.000000e-04 eta: 4:36:23 time: 0.389726 data_time: 0.154826 memory: 7187 loss_kpt: 0.000734 acc_pose: 0.816175 loss: 0.000734 2022/10/19 12:01:25 - mmengine - INFO - Epoch(train) [39][150/293] lr: 5.000000e-04 eta: 4:36:15 time: 0.369345 data_time: 0.066434 memory: 7187 loss_kpt: 0.000746 acc_pose: 0.750633 loss: 0.000746 2022/10/19 12:01:44 - mmengine - INFO - Epoch(train) [39][200/293] lr: 5.000000e-04 eta: 4:36:09 time: 0.375698 data_time: 0.069350 memory: 7187 loss_kpt: 0.000742 acc_pose: 0.807953 loss: 0.000742 2022/10/19 12:02:03 - mmengine - INFO - Epoch(train) [39][250/293] lr: 5.000000e-04 eta: 4:36:05 time: 0.386436 data_time: 0.073597 memory: 7187 loss_kpt: 0.000762 acc_pose: 0.745826 loss: 0.000762 2022/10/19 12:02:19 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:02:39 - mmengine - INFO - Epoch(train) [40][50/293] lr: 5.000000e-04 eta: 4:34:47 time: 0.397496 data_time: 0.089863 memory: 7187 loss_kpt: 0.000745 acc_pose: 0.786433 loss: 0.000745 2022/10/19 12:02:58 - mmengine - INFO - Epoch(train) [40][100/293] lr: 5.000000e-04 eta: 4:34:43 time: 0.388603 data_time: 0.117870 memory: 7187 loss_kpt: 0.000748 acc_pose: 0.730161 loss: 0.000748 2022/10/19 12:03:18 - mmengine - INFO - Epoch(train) [40][150/293] lr: 5.000000e-04 eta: 4:34:39 time: 0.384437 data_time: 0.094387 memory: 7187 loss_kpt: 0.000736 acc_pose: 0.733009 loss: 0.000736 2022/10/19 12:03:38 - mmengine - INFO - Epoch(train) [40][200/293] lr: 5.000000e-04 eta: 4:34:37 time: 0.400234 data_time: 0.087844 memory: 7187 loss_kpt: 0.000746 acc_pose: 0.770055 loss: 0.000746 2022/10/19 12:03:56 - mmengine - INFO - Epoch(train) [40][250/293] lr: 5.000000e-04 eta: 4:34:30 time: 0.374187 data_time: 0.067318 memory: 7187 loss_kpt: 0.000738 acc_pose: 0.705845 loss: 0.000738 2022/10/19 12:04:13 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:04:13 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/10/19 12:04:22 - mmengine - INFO - Epoch(val) [40][50/407] eta: 0:00:45 time: 0.127945 data_time: 0.071568 memory: 7187 2022/10/19 12:04:28 - mmengine - INFO - Epoch(val) [40][100/407] eta: 0:00:37 time: 0.122584 data_time: 0.066161 memory: 1014 2022/10/19 12:04:34 - mmengine - INFO - Epoch(val) [40][150/407] eta: 0:00:30 time: 0.120137 data_time: 0.066250 memory: 1014 2022/10/19 12:04:40 - mmengine - INFO - Epoch(val) [40][200/407] eta: 0:00:23 time: 0.112123 data_time: 0.056317 memory: 1014 2022/10/19 12:04:46 - mmengine - INFO - Epoch(val) [40][250/407] eta: 0:00:18 time: 0.116258 data_time: 0.059851 memory: 1014 2022/10/19 12:04:52 - mmengine - INFO - Epoch(val) [40][300/407] eta: 0:00:12 time: 0.120010 data_time: 0.064326 memory: 1014 2022/10/19 12:04:58 - mmengine - INFO - Epoch(val) [40][350/407] eta: 0:00:06 time: 0.120143 data_time: 0.063629 memory: 1014 2022/10/19 12:05:04 - mmengine - INFO - Epoch(val) [40][400/407] eta: 0:00:00 time: 0.122979 data_time: 0.068033 memory: 1014 2022/10/19 12:05:40 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 12:05:54 - mmengine - INFO - Epoch(val) [40][407/407] coco/AP: 0.681014 coco/AP .5: 0.887940 coco/AP .75: 0.757157 coco/AP (M): 0.641890 coco/AP (L): 0.749291 coco/AR: 0.738634 coco/AR .5: 0.927110 coco/AR .75: 0.807777 coco/AR (M): 0.692570 coco/AR (L): 0.804831 2022/10/19 12:05:54 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_256/best_coco/AP_epoch_30.pth is removed 2022/10/19 12:05:56 - mmengine - INFO - The best checkpoint with 0.6810 coco/AP at 40 epoch is saved to best_coco/AP_epoch_40.pth. 2022/10/19 12:06:15 - mmengine - INFO - Epoch(train) [41][50/293] lr: 5.000000e-04 eta: 4:33:13 time: 0.392902 data_time: 0.141808 memory: 7187 loss_kpt: 0.000742 acc_pose: 0.790431 loss: 0.000742 2022/10/19 12:06:34 - mmengine - INFO - Epoch(train) [41][100/293] lr: 5.000000e-04 eta: 4:33:06 time: 0.376210 data_time: 0.078451 memory: 7187 loss_kpt: 0.000738 acc_pose: 0.789429 loss: 0.000738 2022/10/19 12:06:53 - mmengine - INFO - Epoch(train) [41][150/293] lr: 5.000000e-04 eta: 4:32:58 time: 0.370199 data_time: 0.097110 memory: 7187 loss_kpt: 0.000749 acc_pose: 0.828420 loss: 0.000749 2022/10/19 12:07:11 - mmengine - INFO - Epoch(train) [41][200/293] lr: 5.000000e-04 eta: 4:32:50 time: 0.372764 data_time: 0.066601 memory: 7187 loss_kpt: 0.000725 acc_pose: 0.785067 loss: 0.000725 2022/10/19 12:07:31 - mmengine - INFO - Epoch(train) [41][250/293] lr: 5.000000e-04 eta: 4:32:46 time: 0.386576 data_time: 0.132614 memory: 7187 loss_kpt: 0.000740 acc_pose: 0.784078 loss: 0.000740 2022/10/19 12:07:42 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:07:46 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:08:07 - mmengine - INFO - Epoch(train) [42][50/293] lr: 5.000000e-04 eta: 4:31:32 time: 0.405935 data_time: 0.120753 memory: 7187 loss_kpt: 0.000732 acc_pose: 0.755527 loss: 0.000732 2022/10/19 12:08:25 - mmengine - INFO - Epoch(train) [42][100/293] lr: 5.000000e-04 eta: 4:31:25 time: 0.373266 data_time: 0.079447 memory: 7187 loss_kpt: 0.000742 acc_pose: 0.797578 loss: 0.000742 2022/10/19 12:08:45 - mmengine - INFO - Epoch(train) [42][150/293] lr: 5.000000e-04 eta: 4:31:23 time: 0.403150 data_time: 0.074411 memory: 7187 loss_kpt: 0.000741 acc_pose: 0.783204 loss: 0.000741 2022/10/19 12:09:05 - mmengine - INFO - Epoch(train) [42][200/293] lr: 5.000000e-04 eta: 4:31:18 time: 0.387098 data_time: 0.069518 memory: 7187 loss_kpt: 0.000737 acc_pose: 0.755576 loss: 0.000737 2022/10/19 12:09:24 - mmengine - INFO - Epoch(train) [42][250/293] lr: 5.000000e-04 eta: 4:31:11 time: 0.376363 data_time: 0.064301 memory: 7187 loss_kpt: 0.000726 acc_pose: 0.737003 loss: 0.000726 2022/10/19 12:09:39 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:09:59 - mmengine - INFO - Epoch(train) [43][50/293] lr: 5.000000e-04 eta: 4:29:57 time: 0.397210 data_time: 0.085012 memory: 7187 loss_kpt: 0.000734 acc_pose: 0.776358 loss: 0.000734 2022/10/19 12:10:18 - mmengine - INFO - Epoch(train) [43][100/293] lr: 5.000000e-04 eta: 4:29:50 time: 0.378470 data_time: 0.069707 memory: 7187 loss_kpt: 0.000731 acc_pose: 0.757054 loss: 0.000731 2022/10/19 12:10:37 - mmengine - INFO - Epoch(train) [43][150/293] lr: 5.000000e-04 eta: 4:29:44 time: 0.379873 data_time: 0.068957 memory: 7187 loss_kpt: 0.000733 acc_pose: 0.704524 loss: 0.000733 2022/10/19 12:10:57 - mmengine - INFO - Epoch(train) [43][200/293] lr: 5.000000e-04 eta: 4:29:40 time: 0.395586 data_time: 0.109918 memory: 7187 loss_kpt: 0.000739 acc_pose: 0.763074 loss: 0.000739 2022/10/19 12:11:16 - mmengine - INFO - Epoch(train) [43][250/293] lr: 5.000000e-04 eta: 4:29:33 time: 0.379507 data_time: 0.129074 memory: 7187 loss_kpt: 0.000744 acc_pose: 0.785660 loss: 0.000744 2022/10/19 12:11:31 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:11:50 - mmengine - INFO - Epoch(train) [44][50/293] lr: 5.000000e-04 eta: 4:28:18 time: 0.381023 data_time: 0.154452 memory: 7187 loss_kpt: 0.000750 acc_pose: 0.784537 loss: 0.000750 2022/10/19 12:12:10 - mmengine - INFO - Epoch(train) [44][100/293] lr: 5.000000e-04 eta: 4:28:12 time: 0.386146 data_time: 0.091708 memory: 7187 loss_kpt: 0.000748 acc_pose: 0.797170 loss: 0.000748 2022/10/19 12:12:29 - mmengine - INFO - Epoch(train) [44][150/293] lr: 5.000000e-04 eta: 4:28:05 time: 0.378921 data_time: 0.095010 memory: 7187 loss_kpt: 0.000733 acc_pose: 0.767777 loss: 0.000733 2022/10/19 12:12:48 - mmengine - INFO - Epoch(train) [44][200/293] lr: 5.000000e-04 eta: 4:27:58 time: 0.380807 data_time: 0.077775 memory: 7187 loss_kpt: 0.000732 acc_pose: 0.763766 loss: 0.000732 2022/10/19 12:13:07 - mmengine - INFO - Epoch(train) [44][250/293] lr: 5.000000e-04 eta: 4:27:51 time: 0.378491 data_time: 0.066452 memory: 7187 loss_kpt: 0.000723 acc_pose: 0.800303 loss: 0.000723 2022/10/19 12:13:22 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:13:43 - mmengine - INFO - Epoch(train) [45][50/293] lr: 5.000000e-04 eta: 4:26:43 time: 0.413408 data_time: 0.103849 memory: 7187 loss_kpt: 0.000739 acc_pose: 0.754477 loss: 0.000739 2022/10/19 12:14:02 - mmengine - INFO - Epoch(train) [45][100/293] lr: 5.000000e-04 eta: 4:26:34 time: 0.371640 data_time: 0.114577 memory: 7187 loss_kpt: 0.000743 acc_pose: 0.795427 loss: 0.000743 2022/10/19 12:14:05 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:14:21 - mmengine - INFO - Epoch(train) [45][150/293] lr: 5.000000e-04 eta: 4:26:27 time: 0.378537 data_time: 0.065222 memory: 7187 loss_kpt: 0.000747 acc_pose: 0.757748 loss: 0.000747 2022/10/19 12:14:40 - mmengine - INFO - Epoch(train) [45][200/293] lr: 5.000000e-04 eta: 4:26:21 time: 0.387903 data_time: 0.076346 memory: 7187 loss_kpt: 0.000729 acc_pose: 0.813306 loss: 0.000729 2022/10/19 12:15:00 - mmengine - INFO - Epoch(train) [45][250/293] lr: 5.000000e-04 eta: 4:26:16 time: 0.390455 data_time: 0.067371 memory: 7187 loss_kpt: 0.000729 acc_pose: 0.765421 loss: 0.000729 2022/10/19 12:15:15 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:15:36 - mmengine - INFO - Epoch(train) [46][50/293] lr: 5.000000e-04 eta: 4:25:07 time: 0.409231 data_time: 0.111238 memory: 7187 loss_kpt: 0.000736 acc_pose: 0.794076 loss: 0.000736 2022/10/19 12:15:55 - mmengine - INFO - Epoch(train) [46][100/293] lr: 5.000000e-04 eta: 4:25:00 time: 0.380081 data_time: 0.076130 memory: 7187 loss_kpt: 0.000730 acc_pose: 0.784084 loss: 0.000730 2022/10/19 12:16:14 - mmengine - INFO - Epoch(train) [46][150/293] lr: 5.000000e-04 eta: 4:24:53 time: 0.381333 data_time: 0.065637 memory: 7187 loss_kpt: 0.000729 acc_pose: 0.822450 loss: 0.000729 2022/10/19 12:16:33 - mmengine - INFO - Epoch(train) [46][200/293] lr: 5.000000e-04 eta: 4:24:47 time: 0.386110 data_time: 0.070081 memory: 7187 loss_kpt: 0.000730 acc_pose: 0.803191 loss: 0.000730 2022/10/19 12:16:53 - mmengine - INFO - Epoch(train) [46][250/293] lr: 5.000000e-04 eta: 4:24:41 time: 0.389629 data_time: 0.068070 memory: 7187 loss_kpt: 0.000727 acc_pose: 0.729800 loss: 0.000727 2022/10/19 12:17:08 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:17:28 - mmengine - INFO - Epoch(train) [47][50/293] lr: 5.000000e-04 eta: 4:23:31 time: 0.395561 data_time: 0.082232 memory: 7187 loss_kpt: 0.000729 acc_pose: 0.777808 loss: 0.000729 2022/10/19 12:17:48 - mmengine - INFO - Epoch(train) [47][100/293] lr: 5.000000e-04 eta: 4:23:27 time: 0.398753 data_time: 0.071546 memory: 7187 loss_kpt: 0.000733 acc_pose: 0.816381 loss: 0.000733 2022/10/19 12:18:07 - mmengine - INFO - Epoch(train) [47][150/293] lr: 5.000000e-04 eta: 4:23:19 time: 0.375124 data_time: 0.066072 memory: 7187 loss_kpt: 0.000706 acc_pose: 0.806438 loss: 0.000706 2022/10/19 12:18:26 - mmengine - INFO - Epoch(train) [47][200/293] lr: 5.000000e-04 eta: 4:23:10 time: 0.373080 data_time: 0.068950 memory: 7187 loss_kpt: 0.000735 acc_pose: 0.757004 loss: 0.000735 2022/10/19 12:18:45 - mmengine - INFO - Epoch(train) [47][250/293] lr: 5.000000e-04 eta: 4:23:02 time: 0.382225 data_time: 0.102612 memory: 7187 loss_kpt: 0.000731 acc_pose: 0.787964 loss: 0.000731 2022/10/19 12:19:00 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:19:20 - mmengine - INFO - Epoch(train) [48][50/293] lr: 5.000000e-04 eta: 4:21:55 time: 0.399025 data_time: 0.077716 memory: 7187 loss_kpt: 0.000736 acc_pose: 0.786181 loss: 0.000736 2022/10/19 12:19:39 - mmengine - INFO - Epoch(train) [48][100/293] lr: 5.000000e-04 eta: 4:21:46 time: 0.375222 data_time: 0.081948 memory: 7187 loss_kpt: 0.000731 acc_pose: 0.775089 loss: 0.000731 2022/10/19 12:19:58 - mmengine - INFO - Epoch(train) [48][150/293] lr: 5.000000e-04 eta: 4:21:39 time: 0.386688 data_time: 0.066412 memory: 7187 loss_kpt: 0.000723 acc_pose: 0.778742 loss: 0.000723 2022/10/19 12:20:18 - mmengine - INFO - Epoch(train) [48][200/293] lr: 5.000000e-04 eta: 4:21:34 time: 0.397827 data_time: 0.066929 memory: 7187 loss_kpt: 0.000726 acc_pose: 0.782343 loss: 0.000726 2022/10/19 12:20:30 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:20:38 - mmengine - INFO - Epoch(train) [48][250/293] lr: 5.000000e-04 eta: 4:21:27 time: 0.386838 data_time: 0.067202 memory: 7187 loss_kpt: 0.000738 acc_pose: 0.750963 loss: 0.000738 2022/10/19 12:20:55 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:21:15 - mmengine - INFO - Epoch(train) [49][50/293] lr: 5.000000e-04 eta: 4:20:20 time: 0.395979 data_time: 0.127082 memory: 7187 loss_kpt: 0.000727 acc_pose: 0.776541 loss: 0.000727 2022/10/19 12:21:34 - mmengine - INFO - Epoch(train) [49][100/293] lr: 5.000000e-04 eta: 4:20:12 time: 0.381622 data_time: 0.103124 memory: 7187 loss_kpt: 0.000713 acc_pose: 0.831657 loss: 0.000713 2022/10/19 12:21:52 - mmengine - INFO - Epoch(train) [49][150/293] lr: 5.000000e-04 eta: 4:20:03 time: 0.373066 data_time: 0.074482 memory: 7187 loss_kpt: 0.000734 acc_pose: 0.762928 loss: 0.000734 2022/10/19 12:22:12 - mmengine - INFO - Epoch(train) [49][200/293] lr: 5.000000e-04 eta: 4:19:56 time: 0.387029 data_time: 0.067137 memory: 7187 loss_kpt: 0.000738 acc_pose: 0.747644 loss: 0.000738 2022/10/19 12:22:31 - mmengine - INFO - Epoch(train) [49][250/293] lr: 5.000000e-04 eta: 4:19:50 time: 0.392320 data_time: 0.065568 memory: 7187 loss_kpt: 0.000725 acc_pose: 0.767823 loss: 0.000725 2022/10/19 12:22:47 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:23:08 - mmengine - INFO - Epoch(train) [50][50/293] lr: 5.000000e-04 eta: 4:18:46 time: 0.413107 data_time: 0.080301 memory: 7187 loss_kpt: 0.000718 acc_pose: 0.749812 loss: 0.000718 2022/10/19 12:23:27 - mmengine - INFO - Epoch(train) [50][100/293] lr: 5.000000e-04 eta: 4:18:40 time: 0.389605 data_time: 0.068965 memory: 7187 loss_kpt: 0.000727 acc_pose: 0.812553 loss: 0.000727 2022/10/19 12:23:46 - mmengine - INFO - Epoch(train) [50][150/293] lr: 5.000000e-04 eta: 4:18:31 time: 0.380413 data_time: 0.069007 memory: 7187 loss_kpt: 0.000719 acc_pose: 0.801324 loss: 0.000719 2022/10/19 12:24:06 - mmengine - INFO - Epoch(train) [50][200/293] lr: 5.000000e-04 eta: 4:18:25 time: 0.391002 data_time: 0.066828 memory: 7187 loss_kpt: 0.000728 acc_pose: 0.779418 loss: 0.000728 2022/10/19 12:24:24 - mmengine - INFO - Epoch(train) [50][250/293] lr: 5.000000e-04 eta: 4:18:14 time: 0.366867 data_time: 0.071572 memory: 7187 loss_kpt: 0.000738 acc_pose: 0.790030 loss: 0.000738 2022/10/19 12:24:40 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:24:40 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/10/19 12:24:50 - mmengine - INFO - Epoch(val) [50][50/407] eta: 0:00:52 time: 0.145900 data_time: 0.090265 memory: 7187 2022/10/19 12:24:57 - mmengine - INFO - Epoch(val) [50][100/407] eta: 0:00:40 time: 0.130313 data_time: 0.074109 memory: 1014 2022/10/19 12:25:02 - mmengine - INFO - Epoch(val) [50][150/407] eta: 0:00:29 time: 0.114780 data_time: 0.059069 memory: 1014 2022/10/19 12:25:08 - mmengine - INFO - Epoch(val) [50][200/407] eta: 0:00:24 time: 0.117306 data_time: 0.061227 memory: 1014 2022/10/19 12:25:14 - mmengine - INFO - Epoch(val) [50][250/407] eta: 0:00:19 time: 0.124175 data_time: 0.067733 memory: 1014 2022/10/19 12:25:20 - mmengine - INFO - Epoch(val) [50][300/407] eta: 0:00:12 time: 0.121219 data_time: 0.065067 memory: 1014 2022/10/19 12:25:26 - mmengine - INFO - Epoch(val) [50][350/407] eta: 0:00:06 time: 0.119676 data_time: 0.063500 memory: 1014 2022/10/19 12:25:32 - mmengine - INFO - Epoch(val) [50][400/407] eta: 0:00:00 time: 0.120426 data_time: 0.066196 memory: 1014 2022/10/19 12:26:08 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 12:26:21 - mmengine - INFO - Epoch(val) [50][407/407] coco/AP: 0.683654 coco/AP .5: 0.886589 coco/AP .75: 0.758305 coco/AP (M): 0.645064 coco/AP (L): 0.752545 coco/AR: 0.741782 coco/AR .5: 0.926008 coco/AR .75: 0.810296 coco/AR (M): 0.696312 coco/AR (L): 0.806875 2022/10/19 12:26:21 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_256/best_coco/AP_epoch_40.pth is removed 2022/10/19 12:26:23 - mmengine - INFO - The best checkpoint with 0.6837 coco/AP at 50 epoch is saved to best_coco/AP_epoch_50.pth. 2022/10/19 12:26:44 - mmengine - INFO - Epoch(train) [51][50/293] lr: 5.000000e-04 eta: 4:17:10 time: 0.405254 data_time: 0.127783 memory: 7187 loss_kpt: 0.000721 acc_pose: 0.799959 loss: 0.000721 2022/10/19 12:27:04 - mmengine - INFO - Epoch(train) [51][100/293] lr: 5.000000e-04 eta: 4:17:07 time: 0.410799 data_time: 0.071140 memory: 7187 loss_kpt: 0.000718 acc_pose: 0.836786 loss: 0.000718 2022/10/19 12:27:23 - mmengine - INFO - Epoch(train) [51][150/293] lr: 5.000000e-04 eta: 4:16:58 time: 0.383327 data_time: 0.086265 memory: 7187 loss_kpt: 0.000719 acc_pose: 0.787274 loss: 0.000719 2022/10/19 12:27:42 - mmengine - INFO - Epoch(train) [51][200/293] lr: 5.000000e-04 eta: 4:16:50 time: 0.379305 data_time: 0.068795 memory: 7187 loss_kpt: 0.000724 acc_pose: 0.839737 loss: 0.000724 2022/10/19 12:28:02 - mmengine - INFO - Epoch(train) [51][250/293] lr: 5.000000e-04 eta: 4:16:42 time: 0.389006 data_time: 0.069226 memory: 7187 loss_kpt: 0.000714 acc_pose: 0.801058 loss: 0.000714 2022/10/19 12:28:18 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:28:39 - mmengine - INFO - Epoch(train) [52][50/293] lr: 5.000000e-04 eta: 4:15:42 time: 0.422317 data_time: 0.086798 memory: 7187 loss_kpt: 0.000727 acc_pose: 0.789528 loss: 0.000727 2022/10/19 12:28:42 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:28:59 - mmengine - INFO - Epoch(train) [52][100/293] lr: 5.000000e-04 eta: 4:15:34 time: 0.385959 data_time: 0.068902 memory: 7187 loss_kpt: 0.000723 acc_pose: 0.813481 loss: 0.000723 2022/10/19 12:29:17 - mmengine - INFO - Epoch(train) [52][150/293] lr: 5.000000e-04 eta: 4:15:25 time: 0.376294 data_time: 0.092910 memory: 7187 loss_kpt: 0.000715 acc_pose: 0.778266 loss: 0.000715 2022/10/19 12:29:37 - mmengine - INFO - Epoch(train) [52][200/293] lr: 5.000000e-04 eta: 4:15:16 time: 0.383726 data_time: 0.065892 memory: 7187 loss_kpt: 0.000722 acc_pose: 0.806126 loss: 0.000722 2022/10/19 12:29:56 - mmengine - INFO - Epoch(train) [52][250/293] lr: 5.000000e-04 eta: 4:15:10 time: 0.396931 data_time: 0.068603 memory: 7187 loss_kpt: 0.000724 acc_pose: 0.782725 loss: 0.000724 2022/10/19 12:30:14 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:30:33 - mmengine - INFO - Epoch(train) [53][50/293] lr: 5.000000e-04 eta: 4:14:05 time: 0.386414 data_time: 0.080057 memory: 7187 loss_kpt: 0.000702 acc_pose: 0.773542 loss: 0.000702 2022/10/19 12:30:52 - mmengine - INFO - Epoch(train) [53][100/293] lr: 5.000000e-04 eta: 4:13:55 time: 0.374070 data_time: 0.072896 memory: 7187 loss_kpt: 0.000718 acc_pose: 0.764409 loss: 0.000718 2022/10/19 12:31:10 - mmengine - INFO - Epoch(train) [53][150/293] lr: 5.000000e-04 eta: 4:13:45 time: 0.372231 data_time: 0.068081 memory: 7187 loss_kpt: 0.000724 acc_pose: 0.778769 loss: 0.000724 2022/10/19 12:31:30 - mmengine - INFO - Epoch(train) [53][200/293] lr: 5.000000e-04 eta: 4:13:37 time: 0.389503 data_time: 0.071235 memory: 7187 loss_kpt: 0.000729 acc_pose: 0.817883 loss: 0.000729 2022/10/19 12:31:49 - mmengine - INFO - Epoch(train) [53][250/293] lr: 5.000000e-04 eta: 4:13:30 time: 0.391570 data_time: 0.080888 memory: 7187 loss_kpt: 0.000747 acc_pose: 0.807635 loss: 0.000747 2022/10/19 12:32:05 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:32:26 - mmengine - INFO - Epoch(train) [54][50/293] lr: 5.000000e-04 eta: 4:12:30 time: 0.414566 data_time: 0.177312 memory: 7187 loss_kpt: 0.000706 acc_pose: 0.770561 loss: 0.000706 2022/10/19 12:32:45 - mmengine - INFO - Epoch(train) [54][100/293] lr: 5.000000e-04 eta: 4:12:22 time: 0.391829 data_time: 0.111354 memory: 7187 loss_kpt: 0.000707 acc_pose: 0.839191 loss: 0.000707 2022/10/19 12:33:05 - mmengine - INFO - Epoch(train) [54][150/293] lr: 5.000000e-04 eta: 4:12:16 time: 0.400414 data_time: 0.074025 memory: 7187 loss_kpt: 0.000706 acc_pose: 0.808006 loss: 0.000706 2022/10/19 12:33:24 - mmengine - INFO - Epoch(train) [54][200/293] lr: 5.000000e-04 eta: 4:12:06 time: 0.373237 data_time: 0.069453 memory: 7187 loss_kpt: 0.000707 acc_pose: 0.786046 loss: 0.000707 2022/10/19 12:33:43 - mmengine - INFO - Epoch(train) [54][250/293] lr: 5.000000e-04 eta: 4:11:58 time: 0.386406 data_time: 0.070763 memory: 7187 loss_kpt: 0.000728 acc_pose: 0.814294 loss: 0.000728 2022/10/19 12:33:59 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:34:19 - mmengine - INFO - Epoch(train) [55][50/293] lr: 5.000000e-04 eta: 4:10:55 time: 0.390489 data_time: 0.083368 memory: 7187 loss_kpt: 0.000715 acc_pose: 0.774190 loss: 0.000715 2022/10/19 12:34:38 - mmengine - INFO - Epoch(train) [55][100/293] lr: 5.000000e-04 eta: 4:10:45 time: 0.374125 data_time: 0.067823 memory: 7187 loss_kpt: 0.000731 acc_pose: 0.817888 loss: 0.000731 2022/10/19 12:34:56 - mmengine - INFO - Epoch(train) [55][150/293] lr: 5.000000e-04 eta: 4:10:34 time: 0.368863 data_time: 0.066616 memory: 7187 loss_kpt: 0.000727 acc_pose: 0.801744 loss: 0.000727 2022/10/19 12:35:07 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:35:16 - mmengine - INFO - Epoch(train) [55][200/293] lr: 5.000000e-04 eta: 4:10:26 time: 0.393098 data_time: 0.070764 memory: 7187 loss_kpt: 0.000719 acc_pose: 0.780851 loss: 0.000719 2022/10/19 12:35:35 - mmengine - INFO - Epoch(train) [55][250/293] lr: 5.000000e-04 eta: 4:10:18 time: 0.387907 data_time: 0.069175 memory: 7187 loss_kpt: 0.000710 acc_pose: 0.782383 loss: 0.000710 2022/10/19 12:35:51 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:36:11 - mmengine - INFO - Epoch(train) [56][50/293] lr: 5.000000e-04 eta: 4:09:17 time: 0.401524 data_time: 0.099701 memory: 7187 loss_kpt: 0.000722 acc_pose: 0.765759 loss: 0.000722 2022/10/19 12:36:30 - mmengine - INFO - Epoch(train) [56][100/293] lr: 5.000000e-04 eta: 4:09:08 time: 0.381776 data_time: 0.067475 memory: 7187 loss_kpt: 0.000714 acc_pose: 0.790661 loss: 0.000714 2022/10/19 12:36:50 - mmengine - INFO - Epoch(train) [56][150/293] lr: 5.000000e-04 eta: 4:09:01 time: 0.398306 data_time: 0.125447 memory: 7187 loss_kpt: 0.000708 acc_pose: 0.780283 loss: 0.000708 2022/10/19 12:37:10 - mmengine - INFO - Epoch(train) [56][200/293] lr: 5.000000e-04 eta: 4:08:53 time: 0.393590 data_time: 0.063574 memory: 7187 loss_kpt: 0.000720 acc_pose: 0.804641 loss: 0.000720 2022/10/19 12:37:28 - mmengine - INFO - Epoch(train) [56][250/293] lr: 5.000000e-04 eta: 4:08:42 time: 0.367181 data_time: 0.073470 memory: 7187 loss_kpt: 0.000717 acc_pose: 0.823281 loss: 0.000717 2022/10/19 12:37:44 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:38:04 - mmengine - INFO - Epoch(train) [57][50/293] lr: 5.000000e-04 eta: 4:07:41 time: 0.395176 data_time: 0.085835 memory: 7187 loss_kpt: 0.000709 acc_pose: 0.804106 loss: 0.000709 2022/10/19 12:38:23 - mmengine - INFO - Epoch(train) [57][100/293] lr: 5.000000e-04 eta: 4:07:30 time: 0.372541 data_time: 0.066123 memory: 7187 loss_kpt: 0.000712 acc_pose: 0.851866 loss: 0.000712 2022/10/19 12:38:43 - mmengine - INFO - Epoch(train) [57][150/293] lr: 5.000000e-04 eta: 4:07:24 time: 0.400708 data_time: 0.068648 memory: 7187 loss_kpt: 0.000708 acc_pose: 0.791681 loss: 0.000708 2022/10/19 12:39:02 - mmengine - INFO - Epoch(train) [57][200/293] lr: 5.000000e-04 eta: 4:07:14 time: 0.384278 data_time: 0.066720 memory: 7187 loss_kpt: 0.000716 acc_pose: 0.772046 loss: 0.000716 2022/10/19 12:39:21 - mmengine - INFO - Epoch(train) [57][250/293] lr: 5.000000e-04 eta: 4:07:05 time: 0.384554 data_time: 0.067421 memory: 7187 loss_kpt: 0.000722 acc_pose: 0.839910 loss: 0.000722 2022/10/19 12:39:37 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:39:57 - mmengine - INFO - Epoch(train) [58][50/293] lr: 5.000000e-04 eta: 4:06:05 time: 0.398074 data_time: 0.088962 memory: 7187 loss_kpt: 0.000723 acc_pose: 0.756782 loss: 0.000723 2022/10/19 12:40:16 - mmengine - INFO - Epoch(train) [58][100/293] lr: 5.000000e-04 eta: 4:05:55 time: 0.374198 data_time: 0.068565 memory: 7187 loss_kpt: 0.000709 acc_pose: 0.831137 loss: 0.000709 2022/10/19 12:40:35 - mmengine - INFO - Epoch(train) [58][150/293] lr: 5.000000e-04 eta: 4:05:45 time: 0.381971 data_time: 0.078833 memory: 7187 loss_kpt: 0.000711 acc_pose: 0.798718 loss: 0.000711 2022/10/19 12:40:55 - mmengine - INFO - Epoch(train) [58][200/293] lr: 5.000000e-04 eta: 4:05:38 time: 0.400192 data_time: 0.072559 memory: 7187 loss_kpt: 0.000707 acc_pose: 0.805360 loss: 0.000707 2022/10/19 12:41:14 - mmengine - INFO - Epoch(train) [58][250/293] lr: 5.000000e-04 eta: 4:05:29 time: 0.384598 data_time: 0.073591 memory: 7187 loss_kpt: 0.000727 acc_pose: 0.794831 loss: 0.000727 2022/10/19 12:41:30 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:41:33 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:41:51 - mmengine - INFO - Epoch(train) [59][50/293] lr: 5.000000e-04 eta: 4:04:32 time: 0.418123 data_time: 0.104522 memory: 7187 loss_kpt: 0.000708 acc_pose: 0.810602 loss: 0.000708 2022/10/19 12:42:11 - mmengine - INFO - Epoch(train) [59][100/293] lr: 5.000000e-04 eta: 4:04:24 time: 0.393042 data_time: 0.077952 memory: 7187 loss_kpt: 0.000712 acc_pose: 0.774960 loss: 0.000712 2022/10/19 12:42:31 - mmengine - INFO - Epoch(train) [59][150/293] lr: 5.000000e-04 eta: 4:04:17 time: 0.399150 data_time: 0.116324 memory: 7187 loss_kpt: 0.000698 acc_pose: 0.817444 loss: 0.000698 2022/10/19 12:42:50 - mmengine - INFO - Epoch(train) [59][200/293] lr: 5.000000e-04 eta: 4:04:08 time: 0.388482 data_time: 0.072202 memory: 7187 loss_kpt: 0.000711 acc_pose: 0.802358 loss: 0.000711 2022/10/19 12:43:09 - mmengine - INFO - Epoch(train) [59][250/293] lr: 5.000000e-04 eta: 4:03:58 time: 0.382795 data_time: 0.069598 memory: 7187 loss_kpt: 0.000713 acc_pose: 0.749621 loss: 0.000713 2022/10/19 12:43:26 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:43:46 - mmengine - INFO - Epoch(train) [60][50/293] lr: 5.000000e-04 eta: 4:02:59 time: 0.398102 data_time: 0.080383 memory: 7187 loss_kpt: 0.000706 acc_pose: 0.836709 loss: 0.000706 2022/10/19 12:44:05 - mmengine - INFO - Epoch(train) [60][100/293] lr: 5.000000e-04 eta: 4:02:51 time: 0.391025 data_time: 0.092014 memory: 7187 loss_kpt: 0.000700 acc_pose: 0.818281 loss: 0.000700 2022/10/19 12:44:25 - mmengine - INFO - Epoch(train) [60][150/293] lr: 5.000000e-04 eta: 4:02:41 time: 0.386005 data_time: 0.108179 memory: 7187 loss_kpt: 0.000708 acc_pose: 0.764852 loss: 0.000708 2022/10/19 12:44:44 - mmengine - INFO - Epoch(train) [60][200/293] lr: 5.000000e-04 eta: 4:02:32 time: 0.390513 data_time: 0.065716 memory: 7187 loss_kpt: 0.000695 acc_pose: 0.814705 loss: 0.000695 2022/10/19 12:45:03 - mmengine - INFO - Epoch(train) [60][250/293] lr: 5.000000e-04 eta: 4:02:21 time: 0.373921 data_time: 0.082952 memory: 7187 loss_kpt: 0.000711 acc_pose: 0.825871 loss: 0.000711 2022/10/19 12:45:19 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:45:19 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/10/19 12:45:28 - mmengine - INFO - Epoch(val) [60][50/407] eta: 0:00:43 time: 0.120474 data_time: 0.063943 memory: 7187 2022/10/19 12:45:34 - mmengine - INFO - Epoch(val) [60][100/407] eta: 0:00:36 time: 0.118670 data_time: 0.062846 memory: 1014 2022/10/19 12:45:40 - mmengine - INFO - Epoch(val) [60][150/407] eta: 0:00:31 time: 0.122574 data_time: 0.067211 memory: 1014 2022/10/19 12:45:47 - mmengine - INFO - Epoch(val) [60][200/407] eta: 0:00:26 time: 0.127773 data_time: 0.070904 memory: 1014 2022/10/19 12:45:53 - mmengine - INFO - Epoch(val) [60][250/407] eta: 0:00:19 time: 0.124772 data_time: 0.067748 memory: 1014 2022/10/19 12:45:59 - mmengine - INFO - Epoch(val) [60][300/407] eta: 0:00:12 time: 0.115176 data_time: 0.058954 memory: 1014 2022/10/19 12:46:05 - mmengine - INFO - Epoch(val) [60][350/407] eta: 0:00:06 time: 0.120933 data_time: 0.064765 memory: 1014 2022/10/19 12:46:11 - mmengine - INFO - Epoch(val) [60][400/407] eta: 0:00:00 time: 0.115508 data_time: 0.062371 memory: 1014 2022/10/19 12:46:47 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 12:47:01 - mmengine - INFO - Epoch(val) [60][407/407] coco/AP: 0.689739 coco/AP .5: 0.887635 coco/AP .75: 0.763142 coco/AP (M): 0.648723 coco/AP (L): 0.759295 coco/AR: 0.746757 coco/AR .5: 0.926480 coco/AR .75: 0.814074 coco/AR (M): 0.700519 coco/AR (L): 0.812300 2022/10/19 12:47:01 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_256/best_coco/AP_epoch_50.pth is removed 2022/10/19 12:47:03 - mmengine - INFO - The best checkpoint with 0.6897 coco/AP at 60 epoch is saved to best_coco/AP_epoch_60.pth. 2022/10/19 12:47:23 - mmengine - INFO - Epoch(train) [61][50/293] lr: 5.000000e-04 eta: 4:01:23 time: 0.397884 data_time: 0.107647 memory: 7187 loss_kpt: 0.000699 acc_pose: 0.771223 loss: 0.000699 2022/10/19 12:47:42 - mmengine - INFO - Epoch(train) [61][100/293] lr: 5.000000e-04 eta: 4:01:14 time: 0.385066 data_time: 0.070569 memory: 7187 loss_kpt: 0.000702 acc_pose: 0.806619 loss: 0.000702 2022/10/19 12:48:02 - mmengine - INFO - Epoch(train) [61][150/293] lr: 5.000000e-04 eta: 4:01:05 time: 0.394153 data_time: 0.061776 memory: 7187 loss_kpt: 0.000703 acc_pose: 0.736604 loss: 0.000703 2022/10/19 12:48:22 - mmengine - INFO - Epoch(train) [61][200/293] lr: 5.000000e-04 eta: 4:00:58 time: 0.403519 data_time: 0.068902 memory: 7187 loss_kpt: 0.000706 acc_pose: 0.810047 loss: 0.000706 2022/10/19 12:48:41 - mmengine - INFO - Epoch(train) [61][250/293] lr: 5.000000e-04 eta: 4:00:48 time: 0.388551 data_time: 0.055248 memory: 7187 loss_kpt: 0.000711 acc_pose: 0.783789 loss: 0.000711 2022/10/19 12:48:57 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:49:18 - mmengine - INFO - Epoch(train) [62][50/293] lr: 5.000000e-04 eta: 3:59:52 time: 0.401936 data_time: 0.085615 memory: 7187 loss_kpt: 0.000713 acc_pose: 0.875851 loss: 0.000713 2022/10/19 12:49:36 - mmengine - INFO - Epoch(train) [62][100/293] lr: 5.000000e-04 eta: 3:59:40 time: 0.367984 data_time: 0.065733 memory: 7187 loss_kpt: 0.000699 acc_pose: 0.749472 loss: 0.000699 2022/10/19 12:49:47 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:49:56 - mmengine - INFO - Epoch(train) [62][150/293] lr: 5.000000e-04 eta: 3:59:31 time: 0.393150 data_time: 0.063073 memory: 7187 loss_kpt: 0.000697 acc_pose: 0.786022 loss: 0.000697 2022/10/19 12:50:16 - mmengine - INFO - Epoch(train) [62][200/293] lr: 5.000000e-04 eta: 3:59:22 time: 0.397649 data_time: 0.071616 memory: 7187 loss_kpt: 0.000716 acc_pose: 0.780369 loss: 0.000716 2022/10/19 12:50:35 - mmengine - INFO - Epoch(train) [62][250/293] lr: 5.000000e-04 eta: 3:59:13 time: 0.387111 data_time: 0.063802 memory: 7187 loss_kpt: 0.000717 acc_pose: 0.777823 loss: 0.000717 2022/10/19 12:50:51 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:51:10 - mmengine - INFO - Epoch(train) [63][50/293] lr: 5.000000e-04 eta: 3:58:15 time: 0.387234 data_time: 0.080450 memory: 7187 loss_kpt: 0.000700 acc_pose: 0.792307 loss: 0.000700 2022/10/19 12:51:29 - mmengine - INFO - Epoch(train) [63][100/293] lr: 5.000000e-04 eta: 3:58:04 time: 0.375179 data_time: 0.071756 memory: 7187 loss_kpt: 0.000693 acc_pose: 0.813675 loss: 0.000693 2022/10/19 12:51:49 - mmengine - INFO - Epoch(train) [63][150/293] lr: 5.000000e-04 eta: 3:57:55 time: 0.396098 data_time: 0.077007 memory: 7187 loss_kpt: 0.000702 acc_pose: 0.792886 loss: 0.000702 2022/10/19 12:52:08 - mmengine - INFO - Epoch(train) [63][200/293] lr: 5.000000e-04 eta: 3:57:45 time: 0.385025 data_time: 0.062360 memory: 7187 loss_kpt: 0.000703 acc_pose: 0.819021 loss: 0.000703 2022/10/19 12:52:28 - mmengine - INFO - Epoch(train) [63][250/293] lr: 5.000000e-04 eta: 3:57:37 time: 0.401071 data_time: 0.149681 memory: 7187 loss_kpt: 0.000703 acc_pose: 0.808249 loss: 0.000703 2022/10/19 12:52:44 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:53:03 - mmengine - INFO - Epoch(train) [64][50/293] lr: 5.000000e-04 eta: 3:56:40 time: 0.396271 data_time: 0.091254 memory: 7187 loss_kpt: 0.000710 acc_pose: 0.792280 loss: 0.000710 2022/10/19 12:53:23 - mmengine - INFO - Epoch(train) [64][100/293] lr: 5.000000e-04 eta: 3:56:31 time: 0.388891 data_time: 0.063836 memory: 7187 loss_kpt: 0.000704 acc_pose: 0.779323 loss: 0.000704 2022/10/19 12:53:41 - mmengine - INFO - Epoch(train) [64][150/293] lr: 5.000000e-04 eta: 3:56:19 time: 0.370321 data_time: 0.065027 memory: 7187 loss_kpt: 0.000703 acc_pose: 0.809586 loss: 0.000703 2022/10/19 12:54:01 - mmengine - INFO - Epoch(train) [64][200/293] lr: 5.000000e-04 eta: 3:56:10 time: 0.393808 data_time: 0.079694 memory: 7187 loss_kpt: 0.000702 acc_pose: 0.809146 loss: 0.000702 2022/10/19 12:54:21 - mmengine - INFO - Epoch(train) [64][250/293] lr: 5.000000e-04 eta: 3:56:00 time: 0.392918 data_time: 0.080128 memory: 7187 loss_kpt: 0.000694 acc_pose: 0.805886 loss: 0.000694 2022/10/19 12:54:36 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:54:57 - mmengine - INFO - Epoch(train) [65][50/293] lr: 5.000000e-04 eta: 3:55:06 time: 0.410917 data_time: 0.090418 memory: 7187 loss_kpt: 0.000706 acc_pose: 0.748054 loss: 0.000706 2022/10/19 12:55:17 - mmengine - INFO - Epoch(train) [65][100/293] lr: 5.000000e-04 eta: 3:54:59 time: 0.407620 data_time: 0.057543 memory: 7187 loss_kpt: 0.000704 acc_pose: 0.808497 loss: 0.000704 2022/10/19 12:55:36 - mmengine - INFO - Epoch(train) [65][150/293] lr: 5.000000e-04 eta: 3:54:46 time: 0.362932 data_time: 0.097400 memory: 7187 loss_kpt: 0.000701 acc_pose: 0.805907 loss: 0.000701 2022/10/19 12:55:55 - mmengine - INFO - Epoch(train) [65][200/293] lr: 5.000000e-04 eta: 3:54:35 time: 0.379789 data_time: 0.066730 memory: 7187 loss_kpt: 0.000706 acc_pose: 0.776009 loss: 0.000706 2022/10/19 12:56:13 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:56:14 - mmengine - INFO - Epoch(train) [65][250/293] lr: 5.000000e-04 eta: 3:54:24 time: 0.380983 data_time: 0.077929 memory: 7187 loss_kpt: 0.000699 acc_pose: 0.783177 loss: 0.000699 2022/10/19 12:56:31 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:56:52 - mmengine - INFO - Epoch(train) [66][50/293] lr: 5.000000e-04 eta: 3:53:33 time: 0.430145 data_time: 0.072127 memory: 7187 loss_kpt: 0.000700 acc_pose: 0.771184 loss: 0.000700 2022/10/19 12:57:10 - mmengine - INFO - Epoch(train) [66][100/293] lr: 5.000000e-04 eta: 3:53:20 time: 0.363548 data_time: 0.065848 memory: 7187 loss_kpt: 0.000702 acc_pose: 0.820730 loss: 0.000702 2022/10/19 12:57:29 - mmengine - INFO - Epoch(train) [66][150/293] lr: 5.000000e-04 eta: 3:53:08 time: 0.373848 data_time: 0.060427 memory: 7187 loss_kpt: 0.000712 acc_pose: 0.840058 loss: 0.000712 2022/10/19 12:57:48 - mmengine - INFO - Epoch(train) [66][200/293] lr: 5.000000e-04 eta: 3:52:58 time: 0.387251 data_time: 0.060113 memory: 7187 loss_kpt: 0.000697 acc_pose: 0.786920 loss: 0.000697 2022/10/19 12:58:07 - mmengine - INFO - Epoch(train) [66][250/293] lr: 5.000000e-04 eta: 3:52:45 time: 0.364875 data_time: 0.067394 memory: 7187 loss_kpt: 0.000703 acc_pose: 0.782339 loss: 0.000703 2022/10/19 12:58:22 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 12:58:41 - mmengine - INFO - Epoch(train) [67][50/293] lr: 5.000000e-04 eta: 3:51:48 time: 0.378702 data_time: 0.090258 memory: 7187 loss_kpt: 0.000707 acc_pose: 0.777640 loss: 0.000707 2022/10/19 12:59:01 - mmengine - INFO - Epoch(train) [67][100/293] lr: 5.000000e-04 eta: 3:51:39 time: 0.393343 data_time: 0.080197 memory: 7187 loss_kpt: 0.000705 acc_pose: 0.839480 loss: 0.000705 2022/10/19 12:59:20 - mmengine - INFO - Epoch(train) [67][150/293] lr: 5.000000e-04 eta: 3:51:28 time: 0.381428 data_time: 0.064196 memory: 7187 loss_kpt: 0.000712 acc_pose: 0.760737 loss: 0.000712 2022/10/19 12:59:39 - mmengine - INFO - Epoch(train) [67][200/293] lr: 5.000000e-04 eta: 3:51:17 time: 0.385292 data_time: 0.064743 memory: 7187 loss_kpt: 0.000693 acc_pose: 0.813815 loss: 0.000693 2022/10/19 12:59:59 - mmengine - INFO - Epoch(train) [67][250/293] lr: 5.000000e-04 eta: 3:51:07 time: 0.390316 data_time: 0.092500 memory: 7187 loss_kpt: 0.000693 acc_pose: 0.785035 loss: 0.000693 2022/10/19 13:00:14 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:00:34 - mmengine - INFO - Epoch(train) [68][50/293] lr: 5.000000e-04 eta: 3:50:13 time: 0.393588 data_time: 0.083629 memory: 7187 loss_kpt: 0.000707 acc_pose: 0.817893 loss: 0.000707 2022/10/19 13:00:53 - mmengine - INFO - Epoch(train) [68][100/293] lr: 5.000000e-04 eta: 3:50:02 time: 0.384451 data_time: 0.059502 memory: 7187 loss_kpt: 0.000699 acc_pose: 0.795444 loss: 0.000699 2022/10/19 13:01:12 - mmengine - INFO - Epoch(train) [68][150/293] lr: 5.000000e-04 eta: 3:49:51 time: 0.381908 data_time: 0.060675 memory: 7187 loss_kpt: 0.000693 acc_pose: 0.824615 loss: 0.000693 2022/10/19 13:01:31 - mmengine - INFO - Epoch(train) [68][200/293] lr: 5.000000e-04 eta: 3:49:40 time: 0.387102 data_time: 0.089541 memory: 7187 loss_kpt: 0.000695 acc_pose: 0.839816 loss: 0.000695 2022/10/19 13:01:51 - mmengine - INFO - Epoch(train) [68][250/293] lr: 5.000000e-04 eta: 3:49:29 time: 0.380876 data_time: 0.063707 memory: 7187 loss_kpt: 0.000701 acc_pose: 0.835279 loss: 0.000701 2022/10/19 13:02:07 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:02:27 - mmengine - INFO - Epoch(train) [69][50/293] lr: 5.000000e-04 eta: 3:48:37 time: 0.406555 data_time: 0.072465 memory: 7187 loss_kpt: 0.000704 acc_pose: 0.842899 loss: 0.000704 2022/10/19 13:02:37 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:02:47 - mmengine - INFO - Epoch(train) [69][100/293] lr: 5.000000e-04 eta: 3:48:27 time: 0.393106 data_time: 0.080798 memory: 7187 loss_kpt: 0.000703 acc_pose: 0.807855 loss: 0.000703 2022/10/19 13:03:06 - mmengine - INFO - Epoch(train) [69][150/293] lr: 5.000000e-04 eta: 3:48:16 time: 0.387347 data_time: 0.068036 memory: 7187 loss_kpt: 0.000687 acc_pose: 0.827506 loss: 0.000687 2022/10/19 13:03:25 - mmengine - INFO - Epoch(train) [69][200/293] lr: 5.000000e-04 eta: 3:48:04 time: 0.369040 data_time: 0.067601 memory: 7187 loss_kpt: 0.000712 acc_pose: 0.798983 loss: 0.000712 2022/10/19 13:03:45 - mmengine - INFO - Epoch(train) [69][250/293] lr: 5.000000e-04 eta: 3:47:54 time: 0.402041 data_time: 0.065566 memory: 7187 loss_kpt: 0.000697 acc_pose: 0.799368 loss: 0.000697 2022/10/19 13:04:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:04:21 - mmengine - INFO - Epoch(train) [70][50/293] lr: 5.000000e-04 eta: 3:47:02 time: 0.398047 data_time: 0.091226 memory: 7187 loss_kpt: 0.000692 acc_pose: 0.769415 loss: 0.000692 2022/10/19 13:04:40 - mmengine - INFO - Epoch(train) [70][100/293] lr: 5.000000e-04 eta: 3:46:52 time: 0.393324 data_time: 0.102383 memory: 7187 loss_kpt: 0.000703 acc_pose: 0.802542 loss: 0.000703 2022/10/19 13:04:59 - mmengine - INFO - Epoch(train) [70][150/293] lr: 5.000000e-04 eta: 3:46:39 time: 0.372077 data_time: 0.084068 memory: 7187 loss_kpt: 0.000693 acc_pose: 0.790303 loss: 0.000693 2022/10/19 13:05:18 - mmengine - INFO - Epoch(train) [70][200/293] lr: 5.000000e-04 eta: 3:46:28 time: 0.378329 data_time: 0.109088 memory: 7187 loss_kpt: 0.000705 acc_pose: 0.725240 loss: 0.000705 2022/10/19 13:05:36 - mmengine - INFO - Epoch(train) [70][250/293] lr: 5.000000e-04 eta: 3:46:14 time: 0.360845 data_time: 0.090232 memory: 7187 loss_kpt: 0.000688 acc_pose: 0.810923 loss: 0.000688 2022/10/19 13:05:52 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:05:52 - mmengine - INFO - Saving checkpoint at 70 epochs 2022/10/19 13:06:02 - mmengine - INFO - Epoch(val) [70][50/407] eta: 0:00:46 time: 0.130233 data_time: 0.074683 memory: 7187 2022/10/19 13:06:08 - mmengine - INFO - Epoch(val) [70][100/407] eta: 0:00:38 time: 0.125365 data_time: 0.070601 memory: 1014 2022/10/19 13:06:14 - mmengine - INFO - Epoch(val) [70][150/407] eta: 0:00:31 time: 0.122470 data_time: 0.067352 memory: 1014 2022/10/19 13:06:20 - mmengine - INFO - Epoch(val) [70][200/407] eta: 0:00:24 time: 0.120334 data_time: 0.064529 memory: 1014 2022/10/19 13:06:26 - mmengine - INFO - Epoch(val) [70][250/407] eta: 0:00:18 time: 0.119093 data_time: 0.063630 memory: 1014 2022/10/19 13:06:32 - mmengine - INFO - Epoch(val) [70][300/407] eta: 0:00:12 time: 0.115445 data_time: 0.058780 memory: 1014 2022/10/19 13:06:38 - mmengine - INFO - Epoch(val) [70][350/407] eta: 0:00:06 time: 0.122038 data_time: 0.067260 memory: 1014 2022/10/19 13:06:43 - mmengine - INFO - Epoch(val) [70][400/407] eta: 0:00:00 time: 0.109705 data_time: 0.056385 memory: 1014 2022/10/19 13:07:19 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 13:07:33 - mmengine - INFO - Epoch(val) [70][407/407] coco/AP: 0.694153 coco/AP .5: 0.888367 coco/AP .75: 0.770735 coco/AP (M): 0.655837 coco/AP (L): 0.764128 coco/AR: 0.751354 coco/AR .5: 0.928684 coco/AR .75: 0.820214 coco/AR (M): 0.705682 coco/AR (L): 0.816908 2022/10/19 13:07:33 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_256/best_coco/AP_epoch_60.pth is removed 2022/10/19 13:07:35 - mmengine - INFO - The best checkpoint with 0.6942 coco/AP at 70 epoch is saved to best_coco/AP_epoch_70.pth. 2022/10/19 13:07:55 - mmengine - INFO - Epoch(train) [71][50/293] lr: 5.000000e-04 eta: 3:45:21 time: 0.389330 data_time: 0.150030 memory: 7187 loss_kpt: 0.000686 acc_pose: 0.748137 loss: 0.000686 2022/10/19 13:08:14 - mmengine - INFO - Epoch(train) [71][100/293] lr: 5.000000e-04 eta: 3:45:09 time: 0.379714 data_time: 0.083272 memory: 7187 loss_kpt: 0.000702 acc_pose: 0.828641 loss: 0.000702 2022/10/19 13:08:33 - mmengine - INFO - Epoch(train) [71][150/293] lr: 5.000000e-04 eta: 3:44:59 time: 0.390841 data_time: 0.062354 memory: 7187 loss_kpt: 0.000692 acc_pose: 0.779133 loss: 0.000692 2022/10/19 13:08:53 - mmengine - INFO - Epoch(train) [71][200/293] lr: 5.000000e-04 eta: 3:44:48 time: 0.389799 data_time: 0.067396 memory: 7187 loss_kpt: 0.000705 acc_pose: 0.763238 loss: 0.000705 2022/10/19 13:09:13 - mmengine - INFO - Epoch(train) [71][250/293] lr: 5.000000e-04 eta: 3:44:38 time: 0.397573 data_time: 0.066264 memory: 7187 loss_kpt: 0.000689 acc_pose: 0.797936 loss: 0.000689 2022/10/19 13:09:28 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:09:47 - mmengine - INFO - Epoch(train) [72][50/293] lr: 5.000000e-04 eta: 3:43:44 time: 0.371032 data_time: 0.113101 memory: 7187 loss_kpt: 0.000684 acc_pose: 0.813499 loss: 0.000684 2022/10/19 13:10:07 - mmengine - INFO - Epoch(train) [72][100/293] lr: 5.000000e-04 eta: 3:43:34 time: 0.395361 data_time: 0.092161 memory: 7187 loss_kpt: 0.000678 acc_pose: 0.819776 loss: 0.000678 2022/10/19 13:10:26 - mmengine - INFO - Epoch(train) [72][150/293] lr: 5.000000e-04 eta: 3:43:22 time: 0.379687 data_time: 0.120824 memory: 7187 loss_kpt: 0.000686 acc_pose: 0.790737 loss: 0.000686 2022/10/19 13:10:44 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:10:45 - mmengine - INFO - Epoch(train) [72][200/293] lr: 5.000000e-04 eta: 3:43:11 time: 0.386065 data_time: 0.064525 memory: 7187 loss_kpt: 0.000697 acc_pose: 0.818412 loss: 0.000697 2022/10/19 13:11:04 - mmengine - INFO - Epoch(train) [72][250/293] lr: 5.000000e-04 eta: 3:42:59 time: 0.382300 data_time: 0.091619 memory: 7187 loss_kpt: 0.000693 acc_pose: 0.745515 loss: 0.000693 2022/10/19 13:11:21 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:11:41 - mmengine - INFO - Epoch(train) [73][50/293] lr: 5.000000e-04 eta: 3:42:08 time: 0.398191 data_time: 0.085712 memory: 7187 loss_kpt: 0.000692 acc_pose: 0.768616 loss: 0.000692 2022/10/19 13:11:59 - mmengine - INFO - Epoch(train) [73][100/293] lr: 5.000000e-04 eta: 3:41:56 time: 0.377239 data_time: 0.105559 memory: 7187 loss_kpt: 0.000677 acc_pose: 0.818660 loss: 0.000677 2022/10/19 13:12:18 - mmengine - INFO - Epoch(train) [73][150/293] lr: 5.000000e-04 eta: 3:41:44 time: 0.380956 data_time: 0.069700 memory: 7187 loss_kpt: 0.000702 acc_pose: 0.756465 loss: 0.000702 2022/10/19 13:12:37 - mmengine - INFO - Epoch(train) [73][200/293] lr: 5.000000e-04 eta: 3:41:31 time: 0.365472 data_time: 0.065540 memory: 7187 loss_kpt: 0.000684 acc_pose: 0.806036 loss: 0.000684 2022/10/19 13:12:56 - mmengine - INFO - Epoch(train) [73][250/293] lr: 5.000000e-04 eta: 3:41:20 time: 0.393520 data_time: 0.064763 memory: 7187 loss_kpt: 0.000685 acc_pose: 0.796973 loss: 0.000685 2022/10/19 13:13:12 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:13:32 - mmengine - INFO - Epoch(train) [74][50/293] lr: 5.000000e-04 eta: 3:40:29 time: 0.391440 data_time: 0.137279 memory: 7187 loss_kpt: 0.000689 acc_pose: 0.804353 loss: 0.000689 2022/10/19 13:13:51 - mmengine - INFO - Epoch(train) [74][100/293] lr: 5.000000e-04 eta: 3:40:17 time: 0.382316 data_time: 0.060199 memory: 7187 loss_kpt: 0.000702 acc_pose: 0.803842 loss: 0.000702 2022/10/19 13:14:10 - mmengine - INFO - Epoch(train) [74][150/293] lr: 5.000000e-04 eta: 3:40:06 time: 0.384425 data_time: 0.063158 memory: 7187 loss_kpt: 0.000703 acc_pose: 0.802463 loss: 0.000703 2022/10/19 13:14:29 - mmengine - INFO - Epoch(train) [74][200/293] lr: 5.000000e-04 eta: 3:39:53 time: 0.371061 data_time: 0.070130 memory: 7187 loss_kpt: 0.000701 acc_pose: 0.825803 loss: 0.000701 2022/10/19 13:14:48 - mmengine - INFO - Epoch(train) [74][250/293] lr: 5.000000e-04 eta: 3:39:42 time: 0.390810 data_time: 0.063055 memory: 7187 loss_kpt: 0.000689 acc_pose: 0.829253 loss: 0.000689 2022/10/19 13:15:05 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:15:25 - mmengine - INFO - Epoch(train) [75][50/293] lr: 5.000000e-04 eta: 3:38:52 time: 0.401462 data_time: 0.078825 memory: 7187 loss_kpt: 0.000681 acc_pose: 0.855903 loss: 0.000681 2022/10/19 13:15:43 - mmengine - INFO - Epoch(train) [75][100/293] lr: 5.000000e-04 eta: 3:38:39 time: 0.373944 data_time: 0.067352 memory: 7187 loss_kpt: 0.000685 acc_pose: 0.770984 loss: 0.000685 2022/10/19 13:16:02 - mmengine - INFO - Epoch(train) [75][150/293] lr: 5.000000e-04 eta: 3:38:27 time: 0.379849 data_time: 0.072222 memory: 7187 loss_kpt: 0.000700 acc_pose: 0.825712 loss: 0.000700 2022/10/19 13:16:22 - mmengine - INFO - Epoch(train) [75][200/293] lr: 5.000000e-04 eta: 3:38:16 time: 0.389313 data_time: 0.064041 memory: 7187 loss_kpt: 0.000680 acc_pose: 0.780908 loss: 0.000680 2022/10/19 13:16:41 - mmengine - INFO - Epoch(train) [75][250/293] lr: 5.000000e-04 eta: 3:38:04 time: 0.383182 data_time: 0.073459 memory: 7187 loss_kpt: 0.000700 acc_pose: 0.797400 loss: 0.000700 2022/10/19 13:16:57 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:17:08 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:17:17 - mmengine - INFO - Epoch(train) [76][50/293] lr: 5.000000e-04 eta: 3:37:15 time: 0.407038 data_time: 0.083565 memory: 7187 loss_kpt: 0.000702 acc_pose: 0.817241 loss: 0.000702 2022/10/19 13:17:36 - mmengine - INFO - Epoch(train) [76][100/293] lr: 5.000000e-04 eta: 3:37:02 time: 0.366947 data_time: 0.077845 memory: 7187 loss_kpt: 0.000687 acc_pose: 0.803251 loss: 0.000687 2022/10/19 13:17:55 - mmengine - INFO - Epoch(train) [76][150/293] lr: 5.000000e-04 eta: 3:36:50 time: 0.381683 data_time: 0.063325 memory: 7187 loss_kpt: 0.000688 acc_pose: 0.793011 loss: 0.000688 2022/10/19 13:18:13 - mmengine - INFO - Epoch(train) [76][200/293] lr: 5.000000e-04 eta: 3:36:37 time: 0.373034 data_time: 0.075626 memory: 7187 loss_kpt: 0.000701 acc_pose: 0.817896 loss: 0.000701 2022/10/19 13:18:32 - mmengine - INFO - Epoch(train) [76][250/293] lr: 5.000000e-04 eta: 3:36:24 time: 0.368362 data_time: 0.064467 memory: 7187 loss_kpt: 0.000699 acc_pose: 0.802275 loss: 0.000699 2022/10/19 13:18:48 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:19:07 - mmengine - INFO - Epoch(train) [77][50/293] lr: 5.000000e-04 eta: 3:35:33 time: 0.381032 data_time: 0.100895 memory: 7187 loss_kpt: 0.000690 acc_pose: 0.796619 loss: 0.000690 2022/10/19 13:19:27 - mmengine - INFO - Epoch(train) [77][100/293] lr: 5.000000e-04 eta: 3:35:22 time: 0.398984 data_time: 0.096313 memory: 7187 loss_kpt: 0.000683 acc_pose: 0.885588 loss: 0.000683 2022/10/19 13:19:45 - mmengine - INFO - Epoch(train) [77][150/293] lr: 5.000000e-04 eta: 3:35:10 time: 0.372710 data_time: 0.061434 memory: 7187 loss_kpt: 0.000685 acc_pose: 0.785653 loss: 0.000685 2022/10/19 13:20:04 - mmengine - INFO - Epoch(train) [77][200/293] lr: 5.000000e-04 eta: 3:34:57 time: 0.370476 data_time: 0.062774 memory: 7187 loss_kpt: 0.000699 acc_pose: 0.821913 loss: 0.000699 2022/10/19 13:20:23 - mmengine - INFO - Epoch(train) [77][250/293] lr: 5.000000e-04 eta: 3:34:45 time: 0.382965 data_time: 0.054850 memory: 7187 loss_kpt: 0.000674 acc_pose: 0.737534 loss: 0.000674 2022/10/19 13:20:39 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:20:58 - mmengine - INFO - Epoch(train) [78][50/293] lr: 5.000000e-04 eta: 3:33:54 time: 0.382720 data_time: 0.084838 memory: 7187 loss_kpt: 0.000678 acc_pose: 0.791757 loss: 0.000678 2022/10/19 13:21:18 - mmengine - INFO - Epoch(train) [78][100/293] lr: 5.000000e-04 eta: 3:33:43 time: 0.390124 data_time: 0.146643 memory: 7187 loss_kpt: 0.000687 acc_pose: 0.829126 loss: 0.000687 2022/10/19 13:21:38 - mmengine - INFO - Epoch(train) [78][150/293] lr: 5.000000e-04 eta: 3:33:33 time: 0.406142 data_time: 0.068052 memory: 7187 loss_kpt: 0.000685 acc_pose: 0.807897 loss: 0.000685 2022/10/19 13:21:58 - mmengine - INFO - Epoch(train) [78][200/293] lr: 5.000000e-04 eta: 3:33:22 time: 0.393043 data_time: 0.076134 memory: 7187 loss_kpt: 0.000681 acc_pose: 0.797297 loss: 0.000681 2022/10/19 13:22:17 - mmengine - INFO - Epoch(train) [78][250/293] lr: 5.000000e-04 eta: 3:33:10 time: 0.389844 data_time: 0.071893 memory: 7187 loss_kpt: 0.000687 acc_pose: 0.839080 loss: 0.000687 2022/10/19 13:22:34 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:22:54 - mmengine - INFO - Epoch(train) [79][50/293] lr: 5.000000e-04 eta: 3:32:21 time: 0.391873 data_time: 0.155961 memory: 7187 loss_kpt: 0.000682 acc_pose: 0.793380 loss: 0.000682 2022/10/19 13:23:14 - mmengine - INFO - Epoch(train) [79][100/293] lr: 5.000000e-04 eta: 3:32:10 time: 0.401333 data_time: 0.071708 memory: 7187 loss_kpt: 0.000686 acc_pose: 0.861513 loss: 0.000686 2022/10/19 13:23:30 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:23:32 - mmengine - INFO - Epoch(train) [79][150/293] lr: 5.000000e-04 eta: 3:31:56 time: 0.363170 data_time: 0.054791 memory: 7187 loss_kpt: 0.000698 acc_pose: 0.797688 loss: 0.000698 2022/10/19 13:23:50 - mmengine - INFO - Epoch(train) [79][200/293] lr: 5.000000e-04 eta: 3:31:43 time: 0.369968 data_time: 0.063337 memory: 7187 loss_kpt: 0.000668 acc_pose: 0.849634 loss: 0.000668 2022/10/19 13:24:10 - mmengine - INFO - Epoch(train) [79][250/293] lr: 5.000000e-04 eta: 3:31:32 time: 0.390734 data_time: 0.066940 memory: 7187 loss_kpt: 0.000672 acc_pose: 0.788556 loss: 0.000672 2022/10/19 13:24:26 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:24:45 - mmengine - INFO - Epoch(train) [80][50/293] lr: 5.000000e-04 eta: 3:30:42 time: 0.381965 data_time: 0.159966 memory: 7187 loss_kpt: 0.000670 acc_pose: 0.801169 loss: 0.000670 2022/10/19 13:25:04 - mmengine - INFO - Epoch(train) [80][100/293] lr: 5.000000e-04 eta: 3:30:29 time: 0.377272 data_time: 0.069902 memory: 7187 loss_kpt: 0.000698 acc_pose: 0.831758 loss: 0.000698 2022/10/19 13:25:22 - mmengine - INFO - Epoch(train) [80][150/293] lr: 5.000000e-04 eta: 3:30:16 time: 0.374506 data_time: 0.062209 memory: 7187 loss_kpt: 0.000684 acc_pose: 0.861721 loss: 0.000684 2022/10/19 13:25:41 - mmengine - INFO - Epoch(train) [80][200/293] lr: 5.000000e-04 eta: 3:30:04 time: 0.375596 data_time: 0.069696 memory: 7187 loss_kpt: 0.000697 acc_pose: 0.824519 loss: 0.000697 2022/10/19 13:26:00 - mmengine - INFO - Epoch(train) [80][250/293] lr: 5.000000e-04 eta: 3:29:51 time: 0.374157 data_time: 0.066360 memory: 7187 loss_kpt: 0.000676 acc_pose: 0.783818 loss: 0.000676 2022/10/19 13:26:15 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:26:15 - mmengine - INFO - Saving checkpoint at 80 epochs 2022/10/19 13:26:24 - mmengine - INFO - Epoch(val) [80][50/407] eta: 0:00:45 time: 0.126376 data_time: 0.069584 memory: 7187 2022/10/19 13:26:30 - mmengine - INFO - Epoch(val) [80][100/407] eta: 0:00:35 time: 0.116467 data_time: 0.059638 memory: 1014 2022/10/19 13:26:36 - mmengine - INFO - Epoch(val) [80][150/407] eta: 0:00:30 time: 0.118904 data_time: 0.063632 memory: 1014 2022/10/19 13:26:42 - mmengine - INFO - Epoch(val) [80][200/407] eta: 0:00:26 time: 0.126348 data_time: 0.069093 memory: 1014 2022/10/19 13:26:49 - mmengine - INFO - Epoch(val) [80][250/407] eta: 0:00:20 time: 0.127804 data_time: 0.071181 memory: 1014 2022/10/19 13:26:55 - mmengine - INFO - Epoch(val) [80][300/407] eta: 0:00:13 time: 0.126445 data_time: 0.070015 memory: 1014 2022/10/19 13:27:02 - mmengine - INFO - Epoch(val) [80][350/407] eta: 0:00:07 time: 0.137474 data_time: 0.080636 memory: 1014 2022/10/19 13:27:07 - mmengine - INFO - Epoch(val) [80][400/407] eta: 0:00:00 time: 0.109697 data_time: 0.057857 memory: 1014 2022/10/19 13:27:44 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 13:27:57 - mmengine - INFO - Epoch(val) [80][407/407] coco/AP: 0.697055 coco/AP .5: 0.891444 coco/AP .75: 0.774441 coco/AP (M): 0.657147 coco/AP (L): 0.766703 coco/AR: 0.754565 coco/AR .5: 0.930573 coco/AR .75: 0.824780 coco/AR (M): 0.708331 coco/AR (L): 0.820996 2022/10/19 13:27:58 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_256/best_coco/AP_epoch_70.pth is removed 2022/10/19 13:27:59 - mmengine - INFO - The best checkpoint with 0.6971 coco/AP at 80 epoch is saved to best_coco/AP_epoch_80.pth. 2022/10/19 13:28:19 - mmengine - INFO - Epoch(train) [81][50/293] lr: 5.000000e-04 eta: 3:29:02 time: 0.388892 data_time: 0.116387 memory: 7187 loss_kpt: 0.000672 acc_pose: 0.824151 loss: 0.000672 2022/10/19 13:28:37 - mmengine - INFO - Epoch(train) [81][100/293] lr: 5.000000e-04 eta: 3:28:49 time: 0.370988 data_time: 0.067312 memory: 7187 loss_kpt: 0.000682 acc_pose: 0.786987 loss: 0.000682 2022/10/19 13:28:57 - mmengine - INFO - Epoch(train) [81][150/293] lr: 5.000000e-04 eta: 3:28:37 time: 0.388064 data_time: 0.071498 memory: 7187 loss_kpt: 0.000691 acc_pose: 0.823177 loss: 0.000691 2022/10/19 13:29:16 - mmengine - INFO - Epoch(train) [81][200/293] lr: 5.000000e-04 eta: 3:28:24 time: 0.380016 data_time: 0.190711 memory: 7187 loss_kpt: 0.000675 acc_pose: 0.813266 loss: 0.000675 2022/10/19 13:29:34 - mmengine - INFO - Epoch(train) [81][250/293] lr: 5.000000e-04 eta: 3:28:11 time: 0.374162 data_time: 0.168128 memory: 7187 loss_kpt: 0.000691 acc_pose: 0.789411 loss: 0.000691 2022/10/19 13:29:50 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:30:10 - mmengine - INFO - Epoch(train) [82][50/293] lr: 5.000000e-04 eta: 3:27:23 time: 0.394485 data_time: 0.080252 memory: 7187 loss_kpt: 0.000693 acc_pose: 0.812548 loss: 0.000693 2022/10/19 13:30:29 - mmengine - INFO - Epoch(train) [82][100/293] lr: 5.000000e-04 eta: 3:27:11 time: 0.384327 data_time: 0.067658 memory: 7187 loss_kpt: 0.000674 acc_pose: 0.842649 loss: 0.000674 2022/10/19 13:30:49 - mmengine - INFO - Epoch(train) [82][150/293] lr: 5.000000e-04 eta: 3:27:01 time: 0.405795 data_time: 0.078057 memory: 7187 loss_kpt: 0.000695 acc_pose: 0.837183 loss: 0.000695 2022/10/19 13:31:10 - mmengine - INFO - Epoch(train) [82][200/293] lr: 5.000000e-04 eta: 3:26:50 time: 0.408135 data_time: 0.073052 memory: 7187 loss_kpt: 0.000689 acc_pose: 0.760994 loss: 0.000689 2022/10/19 13:31:29 - mmengine - INFO - Epoch(train) [82][250/293] lr: 5.000000e-04 eta: 3:26:37 time: 0.373662 data_time: 0.056688 memory: 7187 loss_kpt: 0.000689 acc_pose: 0.808365 loss: 0.000689 2022/10/19 13:31:35 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:31:45 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:32:05 - mmengine - INFO - Epoch(train) [83][50/293] lr: 5.000000e-04 eta: 3:25:49 time: 0.391167 data_time: 0.144578 memory: 7187 loss_kpt: 0.000677 acc_pose: 0.804378 loss: 0.000677 2022/10/19 13:32:23 - mmengine - INFO - Epoch(train) [83][100/293] lr: 5.000000e-04 eta: 3:25:36 time: 0.373672 data_time: 0.094040 memory: 7187 loss_kpt: 0.000688 acc_pose: 0.788665 loss: 0.000688 2022/10/19 13:32:43 - mmengine - INFO - Epoch(train) [83][150/293] lr: 5.000000e-04 eta: 3:25:24 time: 0.391145 data_time: 0.061696 memory: 7187 loss_kpt: 0.000672 acc_pose: 0.803968 loss: 0.000672 2022/10/19 13:33:02 - mmengine - INFO - Epoch(train) [83][200/293] lr: 5.000000e-04 eta: 3:25:12 time: 0.383883 data_time: 0.056777 memory: 7187 loss_kpt: 0.000692 acc_pose: 0.865411 loss: 0.000692 2022/10/19 13:33:22 - mmengine - INFO - Epoch(train) [83][250/293] lr: 5.000000e-04 eta: 3:25:00 time: 0.390224 data_time: 0.074634 memory: 7187 loss_kpt: 0.000684 acc_pose: 0.779872 loss: 0.000684 2022/10/19 13:33:38 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:33:58 - mmengine - INFO - Epoch(train) [84][50/293] lr: 5.000000e-04 eta: 3:24:13 time: 0.395595 data_time: 0.083503 memory: 7187 loss_kpt: 0.000674 acc_pose: 0.791974 loss: 0.000674 2022/10/19 13:34:16 - mmengine - INFO - Epoch(train) [84][100/293] lr: 5.000000e-04 eta: 3:23:59 time: 0.369559 data_time: 0.055805 memory: 7187 loss_kpt: 0.000685 acc_pose: 0.810447 loss: 0.000685 2022/10/19 13:34:36 - mmengine - INFO - Epoch(train) [84][150/293] lr: 5.000000e-04 eta: 3:23:47 time: 0.390764 data_time: 0.114281 memory: 7187 loss_kpt: 0.000680 acc_pose: 0.811919 loss: 0.000680 2022/10/19 13:34:54 - mmengine - INFO - Epoch(train) [84][200/293] lr: 5.000000e-04 eta: 3:23:34 time: 0.372756 data_time: 0.137264 memory: 7187 loss_kpt: 0.000686 acc_pose: 0.803357 loss: 0.000686 2022/10/19 13:35:14 - mmengine - INFO - Epoch(train) [84][250/293] lr: 5.000000e-04 eta: 3:23:22 time: 0.385250 data_time: 0.068062 memory: 7187 loss_kpt: 0.000696 acc_pose: 0.809254 loss: 0.000696 2022/10/19 13:35:30 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:35:50 - mmengine - INFO - Epoch(train) [85][50/293] lr: 5.000000e-04 eta: 3:22:35 time: 0.396855 data_time: 0.082112 memory: 7187 loss_kpt: 0.000672 acc_pose: 0.814587 loss: 0.000672 2022/10/19 13:36:08 - mmengine - INFO - Epoch(train) [85][100/293] lr: 5.000000e-04 eta: 3:22:22 time: 0.374889 data_time: 0.070078 memory: 7187 loss_kpt: 0.000673 acc_pose: 0.842513 loss: 0.000673 2022/10/19 13:36:27 - mmengine - INFO - Epoch(train) [85][150/293] lr: 5.000000e-04 eta: 3:22:08 time: 0.362935 data_time: 0.061758 memory: 7187 loss_kpt: 0.000669 acc_pose: 0.803808 loss: 0.000669 2022/10/19 13:36:47 - mmengine - INFO - Epoch(train) [85][200/293] lr: 5.000000e-04 eta: 3:21:56 time: 0.399430 data_time: 0.060445 memory: 7187 loss_kpt: 0.000672 acc_pose: 0.798858 loss: 0.000672 2022/10/19 13:37:06 - mmengine - INFO - Epoch(train) [85][250/293] lr: 5.000000e-04 eta: 3:21:44 time: 0.382080 data_time: 0.080061 memory: 7187 loss_kpt: 0.000680 acc_pose: 0.802687 loss: 0.000680 2022/10/19 13:37:21 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:37:41 - mmengine - INFO - Epoch(train) [86][50/293] lr: 5.000000e-04 eta: 3:20:57 time: 0.395334 data_time: 0.089646 memory: 7187 loss_kpt: 0.000676 acc_pose: 0.834335 loss: 0.000676 2022/10/19 13:37:57 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:37:59 - mmengine - INFO - Epoch(train) [86][100/293] lr: 5.000000e-04 eta: 3:20:43 time: 0.363596 data_time: 0.057051 memory: 7187 loss_kpt: 0.000682 acc_pose: 0.789727 loss: 0.000682 2022/10/19 13:38:19 - mmengine - INFO - Epoch(train) [86][150/293] lr: 5.000000e-04 eta: 3:20:32 time: 0.403230 data_time: 0.060355 memory: 7187 loss_kpt: 0.000668 acc_pose: 0.819528 loss: 0.000668 2022/10/19 13:38:38 - mmengine - INFO - Epoch(train) [86][200/293] lr: 5.000000e-04 eta: 3:20:18 time: 0.372387 data_time: 0.064950 memory: 7187 loss_kpt: 0.000665 acc_pose: 0.830609 loss: 0.000665 2022/10/19 13:38:57 - mmengine - INFO - Epoch(train) [86][250/293] lr: 5.000000e-04 eta: 3:20:06 time: 0.384923 data_time: 0.107634 memory: 7187 loss_kpt: 0.000690 acc_pose: 0.820358 loss: 0.000690 2022/10/19 13:39:13 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:39:33 - mmengine - INFO - Epoch(train) [87][50/293] lr: 5.000000e-04 eta: 3:19:19 time: 0.386822 data_time: 0.109682 memory: 7187 loss_kpt: 0.000692 acc_pose: 0.732332 loss: 0.000692 2022/10/19 13:39:51 - mmengine - INFO - Epoch(train) [87][100/293] lr: 5.000000e-04 eta: 3:19:04 time: 0.359717 data_time: 0.061144 memory: 7187 loss_kpt: 0.000665 acc_pose: 0.829284 loss: 0.000665 2022/10/19 13:40:09 - mmengine - INFO - Epoch(train) [87][150/293] lr: 5.000000e-04 eta: 3:18:51 time: 0.372913 data_time: 0.060903 memory: 7187 loss_kpt: 0.000682 acc_pose: 0.813892 loss: 0.000682 2022/10/19 13:40:28 - mmengine - INFO - Epoch(train) [87][200/293] lr: 5.000000e-04 eta: 3:18:37 time: 0.365884 data_time: 0.079175 memory: 7187 loss_kpt: 0.000678 acc_pose: 0.744272 loss: 0.000678 2022/10/19 13:40:47 - mmengine - INFO - Epoch(train) [87][250/293] lr: 5.000000e-04 eta: 3:18:24 time: 0.381271 data_time: 0.065506 memory: 7187 loss_kpt: 0.000674 acc_pose: 0.851887 loss: 0.000674 2022/10/19 13:41:03 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:41:22 - mmengine - INFO - Epoch(train) [88][50/293] lr: 5.000000e-04 eta: 3:17:38 time: 0.385926 data_time: 0.073581 memory: 7187 loss_kpt: 0.000673 acc_pose: 0.861109 loss: 0.000673 2022/10/19 13:41:41 - mmengine - INFO - Epoch(train) [88][100/293] lr: 5.000000e-04 eta: 3:17:24 time: 0.372936 data_time: 0.061889 memory: 7187 loss_kpt: 0.000678 acc_pose: 0.798011 loss: 0.000678 2022/10/19 13:42:00 - mmengine - INFO - Epoch(train) [88][150/293] lr: 5.000000e-04 eta: 3:17:11 time: 0.383373 data_time: 0.067270 memory: 7187 loss_kpt: 0.000674 acc_pose: 0.830116 loss: 0.000674 2022/10/19 13:42:19 - mmengine - INFO - Epoch(train) [88][200/293] lr: 5.000000e-04 eta: 3:16:58 time: 0.373733 data_time: 0.080401 memory: 7187 loss_kpt: 0.000669 acc_pose: 0.744590 loss: 0.000669 2022/10/19 13:42:37 - mmengine - INFO - Epoch(train) [88][250/293] lr: 5.000000e-04 eta: 3:16:44 time: 0.371125 data_time: 0.070378 memory: 7187 loss_kpt: 0.000682 acc_pose: 0.815684 loss: 0.000682 2022/10/19 13:42:53 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:43:13 - mmengine - INFO - Epoch(train) [89][50/293] lr: 5.000000e-04 eta: 3:15:58 time: 0.392606 data_time: 0.102366 memory: 7187 loss_kpt: 0.000669 acc_pose: 0.862669 loss: 0.000669 2022/10/19 13:43:33 - mmengine - INFO - Epoch(train) [89][100/293] lr: 5.000000e-04 eta: 3:15:47 time: 0.400099 data_time: 0.062803 memory: 7187 loss_kpt: 0.000671 acc_pose: 0.765995 loss: 0.000671 2022/10/19 13:43:52 - mmengine - INFO - Epoch(train) [89][150/293] lr: 5.000000e-04 eta: 3:15:34 time: 0.376136 data_time: 0.059652 memory: 7187 loss_kpt: 0.000683 acc_pose: 0.797726 loss: 0.000683 2022/10/19 13:44:10 - mmengine - INFO - Epoch(train) [89][200/293] lr: 5.000000e-04 eta: 3:15:20 time: 0.366024 data_time: 0.128289 memory: 7187 loss_kpt: 0.000667 acc_pose: 0.736809 loss: 0.000667 2022/10/19 13:44:16 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:44:29 - mmengine - INFO - Epoch(train) [89][250/293] lr: 5.000000e-04 eta: 3:15:07 time: 0.381517 data_time: 0.064366 memory: 7187 loss_kpt: 0.000678 acc_pose: 0.806358 loss: 0.000678 2022/10/19 13:44:45 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:45:05 - mmengine - INFO - Epoch(train) [90][50/293] lr: 5.000000e-04 eta: 3:14:21 time: 0.400039 data_time: 0.132387 memory: 7187 loss_kpt: 0.000660 acc_pose: 0.828901 loss: 0.000660 2022/10/19 13:45:24 - mmengine - INFO - Epoch(train) [90][100/293] lr: 5.000000e-04 eta: 3:14:08 time: 0.368492 data_time: 0.071767 memory: 7187 loss_kpt: 0.000668 acc_pose: 0.834398 loss: 0.000668 2022/10/19 13:45:42 - mmengine - INFO - Epoch(train) [90][150/293] lr: 5.000000e-04 eta: 3:13:54 time: 0.369876 data_time: 0.066096 memory: 7187 loss_kpt: 0.000679 acc_pose: 0.790950 loss: 0.000679 2022/10/19 13:46:01 - mmengine - INFO - Epoch(train) [90][200/293] lr: 5.000000e-04 eta: 3:13:40 time: 0.374877 data_time: 0.098026 memory: 7187 loss_kpt: 0.000677 acc_pose: 0.869525 loss: 0.000677 2022/10/19 13:46:20 - mmengine - INFO - Epoch(train) [90][250/293] lr: 5.000000e-04 eta: 3:13:28 time: 0.383170 data_time: 0.060919 memory: 7187 loss_kpt: 0.000677 acc_pose: 0.811445 loss: 0.000677 2022/10/19 13:46:36 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:46:36 - mmengine - INFO - Saving checkpoint at 90 epochs 2022/10/19 13:46:46 - mmengine - INFO - Epoch(val) [90][50/407] eta: 0:00:45 time: 0.127395 data_time: 0.070901 memory: 7187 2022/10/19 13:46:52 - mmengine - INFO - Epoch(val) [90][100/407] eta: 0:00:41 time: 0.134362 data_time: 0.074335 memory: 1014 2022/10/19 13:46:58 - mmengine - INFO - Epoch(val) [90][150/407] eta: 0:00:31 time: 0.124391 data_time: 0.067360 memory: 1014 2022/10/19 13:47:05 - mmengine - INFO - Epoch(val) [90][200/407] eta: 0:00:25 time: 0.125359 data_time: 0.069442 memory: 1014 2022/10/19 13:47:11 - mmengine - INFO - Epoch(val) [90][250/407] eta: 0:00:19 time: 0.121837 data_time: 0.066328 memory: 1014 2022/10/19 13:47:17 - mmengine - INFO - Epoch(val) [90][300/407] eta: 0:00:14 time: 0.133213 data_time: 0.077643 memory: 1014 2022/10/19 13:47:24 - mmengine - INFO - Epoch(val) [90][350/407] eta: 0:00:07 time: 0.124775 data_time: 0.069883 memory: 1014 2022/10/19 13:47:29 - mmengine - INFO - Epoch(val) [90][400/407] eta: 0:00:00 time: 0.114935 data_time: 0.062959 memory: 1014 2022/10/19 13:48:06 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 13:48:20 - mmengine - INFO - Epoch(val) [90][407/407] coco/AP: 0.701838 coco/AP .5: 0.894799 coco/AP .75: 0.775744 coco/AP (M): 0.663748 coco/AP (L): 0.769441 coco/AR: 0.758486 coco/AR .5: 0.931990 coco/AR .75: 0.824307 coco/AR (M): 0.713248 coco/AR (L): 0.823040 2022/10/19 13:48:20 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_256/best_coco/AP_epoch_80.pth is removed 2022/10/19 13:48:21 - mmengine - INFO - The best checkpoint with 0.7018 coco/AP at 90 epoch is saved to best_coco/AP_epoch_90.pth. 2022/10/19 13:48:41 - mmengine - INFO - Epoch(train) [91][50/293] lr: 5.000000e-04 eta: 3:12:42 time: 0.381870 data_time: 0.172750 memory: 7187 loss_kpt: 0.000681 acc_pose: 0.790354 loss: 0.000681 2022/10/19 13:49:00 - mmengine - INFO - Epoch(train) [91][100/293] lr: 5.000000e-04 eta: 3:12:29 time: 0.395104 data_time: 0.066890 memory: 7187 loss_kpt: 0.000678 acc_pose: 0.804190 loss: 0.000678 2022/10/19 13:49:19 - mmengine - INFO - Epoch(train) [91][150/293] lr: 5.000000e-04 eta: 3:12:16 time: 0.371560 data_time: 0.062390 memory: 7187 loss_kpt: 0.000689 acc_pose: 0.839668 loss: 0.000689 2022/10/19 13:49:37 - mmengine - INFO - Epoch(train) [91][200/293] lr: 5.000000e-04 eta: 3:12:02 time: 0.364303 data_time: 0.054682 memory: 7187 loss_kpt: 0.000659 acc_pose: 0.834145 loss: 0.000659 2022/10/19 13:49:56 - mmengine - INFO - Epoch(train) [91][250/293] lr: 5.000000e-04 eta: 3:11:48 time: 0.370919 data_time: 0.063408 memory: 7187 loss_kpt: 0.000690 acc_pose: 0.797037 loss: 0.000690 2022/10/19 13:50:11 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:50:30 - mmengine - INFO - Epoch(train) [92][50/293] lr: 5.000000e-04 eta: 3:11:02 time: 0.376603 data_time: 0.126621 memory: 7187 loss_kpt: 0.000666 acc_pose: 0.866800 loss: 0.000666 2022/10/19 13:50:49 - mmengine - INFO - Epoch(train) [92][100/293] lr: 5.000000e-04 eta: 3:10:48 time: 0.373729 data_time: 0.103908 memory: 7187 loss_kpt: 0.000679 acc_pose: 0.790484 loss: 0.000679 2022/10/19 13:51:08 - mmengine - INFO - Epoch(train) [92][150/293] lr: 5.000000e-04 eta: 3:10:35 time: 0.388019 data_time: 0.065279 memory: 7187 loss_kpt: 0.000680 acc_pose: 0.841670 loss: 0.000680 2022/10/19 13:51:26 - mmengine - INFO - Epoch(train) [92][200/293] lr: 5.000000e-04 eta: 3:10:22 time: 0.368722 data_time: 0.092618 memory: 7187 loss_kpt: 0.000682 acc_pose: 0.742823 loss: 0.000682 2022/10/19 13:51:45 - mmengine - INFO - Epoch(train) [92][250/293] lr: 5.000000e-04 eta: 3:10:08 time: 0.376792 data_time: 0.121733 memory: 7187 loss_kpt: 0.000664 acc_pose: 0.830666 loss: 0.000664 2022/10/19 13:52:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:52:18 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:52:20 - mmengine - INFO - Epoch(train) [93][50/293] lr: 5.000000e-04 eta: 3:09:24 time: 0.397392 data_time: 0.099494 memory: 7187 loss_kpt: 0.000663 acc_pose: 0.828240 loss: 0.000663 2022/10/19 13:52:40 - mmengine - INFO - Epoch(train) [93][100/293] lr: 5.000000e-04 eta: 3:09:11 time: 0.382956 data_time: 0.069035 memory: 7187 loss_kpt: 0.000654 acc_pose: 0.843253 loss: 0.000654 2022/10/19 13:52:59 - mmengine - INFO - Epoch(train) [93][150/293] lr: 5.000000e-04 eta: 3:08:57 time: 0.378274 data_time: 0.062223 memory: 7187 loss_kpt: 0.000658 acc_pose: 0.816113 loss: 0.000658 2022/10/19 13:53:18 - mmengine - INFO - Epoch(train) [93][200/293] lr: 5.000000e-04 eta: 3:08:45 time: 0.390290 data_time: 0.066769 memory: 7187 loss_kpt: 0.000674 acc_pose: 0.785831 loss: 0.000674 2022/10/19 13:53:37 - mmengine - INFO - Epoch(train) [93][250/293] lr: 5.000000e-04 eta: 3:08:31 time: 0.371729 data_time: 0.082924 memory: 7187 loss_kpt: 0.000672 acc_pose: 0.796919 loss: 0.000672 2022/10/19 13:53:53 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:54:13 - mmengine - INFO - Epoch(train) [94][50/293] lr: 5.000000e-04 eta: 3:07:46 time: 0.391460 data_time: 0.104425 memory: 7187 loss_kpt: 0.000678 acc_pose: 0.818304 loss: 0.000678 2022/10/19 13:54:31 - mmengine - INFO - Epoch(train) [94][100/293] lr: 5.000000e-04 eta: 3:07:32 time: 0.370635 data_time: 0.145654 memory: 7187 loss_kpt: 0.000670 acc_pose: 0.815936 loss: 0.000670 2022/10/19 13:54:52 - mmengine - INFO - Epoch(train) [94][150/293] lr: 5.000000e-04 eta: 3:07:21 time: 0.409140 data_time: 0.152002 memory: 7187 loss_kpt: 0.000667 acc_pose: 0.799772 loss: 0.000667 2022/10/19 13:55:11 - mmengine - INFO - Epoch(train) [94][200/293] lr: 5.000000e-04 eta: 3:07:08 time: 0.392506 data_time: 0.067733 memory: 7187 loss_kpt: 0.000679 acc_pose: 0.784461 loss: 0.000679 2022/10/19 13:55:31 - mmengine - INFO - Epoch(train) [94][250/293] lr: 5.000000e-04 eta: 3:06:56 time: 0.396129 data_time: 0.065362 memory: 7187 loss_kpt: 0.000676 acc_pose: 0.862910 loss: 0.000676 2022/10/19 13:55:47 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:56:08 - mmengine - INFO - Epoch(train) [95][50/293] lr: 5.000000e-04 eta: 3:06:13 time: 0.404494 data_time: 0.114881 memory: 7187 loss_kpt: 0.000663 acc_pose: 0.793368 loss: 0.000663 2022/10/19 13:56:26 - mmengine - INFO - Epoch(train) [95][100/293] lr: 5.000000e-04 eta: 3:05:59 time: 0.379221 data_time: 0.082000 memory: 7187 loss_kpt: 0.000659 acc_pose: 0.802826 loss: 0.000659 2022/10/19 13:56:45 - mmengine - INFO - Epoch(train) [95][150/293] lr: 5.000000e-04 eta: 3:05:45 time: 0.371688 data_time: 0.101808 memory: 7187 loss_kpt: 0.000667 acc_pose: 0.767948 loss: 0.000667 2022/10/19 13:57:04 - mmengine - INFO - Epoch(train) [95][200/293] lr: 5.000000e-04 eta: 3:05:32 time: 0.384574 data_time: 0.133845 memory: 7187 loss_kpt: 0.000669 acc_pose: 0.796349 loss: 0.000669 2022/10/19 13:57:24 - mmengine - INFO - Epoch(train) [95][250/293] lr: 5.000000e-04 eta: 3:05:20 time: 0.392932 data_time: 0.082725 memory: 7187 loss_kpt: 0.000677 acc_pose: 0.750532 loss: 0.000677 2022/10/19 13:57:39 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:57:59 - mmengine - INFO - Epoch(train) [96][50/293] lr: 5.000000e-04 eta: 3:04:36 time: 0.394759 data_time: 0.137434 memory: 7187 loss_kpt: 0.000665 acc_pose: 0.789577 loss: 0.000665 2022/10/19 13:58:17 - mmengine - INFO - Epoch(train) [96][100/293] lr: 5.000000e-04 eta: 3:04:21 time: 0.361539 data_time: 0.078030 memory: 7187 loss_kpt: 0.000663 acc_pose: 0.775490 loss: 0.000663 2022/10/19 13:58:36 - mmengine - INFO - Epoch(train) [96][150/293] lr: 5.000000e-04 eta: 3:04:08 time: 0.379595 data_time: 0.079981 memory: 7187 loss_kpt: 0.000675 acc_pose: 0.815688 loss: 0.000675 2022/10/19 13:58:41 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:58:55 - mmengine - INFO - Epoch(train) [96][200/293] lr: 5.000000e-04 eta: 3:03:54 time: 0.375492 data_time: 0.067208 memory: 7187 loss_kpt: 0.000678 acc_pose: 0.795683 loss: 0.000678 2022/10/19 13:59:14 - mmengine - INFO - Epoch(train) [96][250/293] lr: 5.000000e-04 eta: 3:03:41 time: 0.389604 data_time: 0.063564 memory: 7187 loss_kpt: 0.000675 acc_pose: 0.751897 loss: 0.000675 2022/10/19 13:59:30 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 13:59:50 - mmengine - INFO - Epoch(train) [97][50/293] lr: 5.000000e-04 eta: 3:02:58 time: 0.396458 data_time: 0.079362 memory: 7187 loss_kpt: 0.000678 acc_pose: 0.815766 loss: 0.000678 2022/10/19 14:00:09 - mmengine - INFO - Epoch(train) [97][100/293] lr: 5.000000e-04 eta: 3:02:45 time: 0.384698 data_time: 0.059030 memory: 7187 loss_kpt: 0.000674 acc_pose: 0.808735 loss: 0.000674 2022/10/19 14:00:28 - mmengine - INFO - Epoch(train) [97][150/293] lr: 5.000000e-04 eta: 3:02:31 time: 0.375411 data_time: 0.064701 memory: 7187 loss_kpt: 0.000666 acc_pose: 0.817063 loss: 0.000666 2022/10/19 14:00:46 - mmengine - INFO - Epoch(train) [97][200/293] lr: 5.000000e-04 eta: 3:02:17 time: 0.372373 data_time: 0.064496 memory: 7187 loss_kpt: 0.000664 acc_pose: 0.827651 loss: 0.000664 2022/10/19 14:01:05 - mmengine - INFO - Epoch(train) [97][250/293] lr: 5.000000e-04 eta: 3:02:03 time: 0.379605 data_time: 0.067980 memory: 7187 loss_kpt: 0.000666 acc_pose: 0.843128 loss: 0.000666 2022/10/19 14:01:20 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:01:41 - mmengine - INFO - Epoch(train) [98][50/293] lr: 5.000000e-04 eta: 3:01:21 time: 0.406609 data_time: 0.077742 memory: 7187 loss_kpt: 0.000680 acc_pose: 0.811161 loss: 0.000680 2022/10/19 14:02:00 - mmengine - INFO - Epoch(train) [98][100/293] lr: 5.000000e-04 eta: 3:01:07 time: 0.376504 data_time: 0.066108 memory: 7187 loss_kpt: 0.000656 acc_pose: 0.823064 loss: 0.000656 2022/10/19 14:02:18 - mmengine - INFO - Epoch(train) [98][150/293] lr: 5.000000e-04 eta: 3:00:52 time: 0.361838 data_time: 0.064839 memory: 7187 loss_kpt: 0.000659 acc_pose: 0.749848 loss: 0.000659 2022/10/19 14:02:37 - mmengine - INFO - Epoch(train) [98][200/293] lr: 5.000000e-04 eta: 3:00:39 time: 0.387957 data_time: 0.066529 memory: 7187 loss_kpt: 0.000672 acc_pose: 0.800706 loss: 0.000672 2022/10/19 14:02:55 - mmengine - INFO - Epoch(train) [98][250/293] lr: 5.000000e-04 eta: 3:00:25 time: 0.363565 data_time: 0.067504 memory: 7187 loss_kpt: 0.000671 acc_pose: 0.824135 loss: 0.000671 2022/10/19 14:03:12 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:03:31 - mmengine - INFO - Epoch(train) [99][50/293] lr: 5.000000e-04 eta: 2:59:42 time: 0.393264 data_time: 0.084735 memory: 7187 loss_kpt: 0.000655 acc_pose: 0.864325 loss: 0.000655 2022/10/19 14:03:51 - mmengine - INFO - Epoch(train) [99][100/293] lr: 5.000000e-04 eta: 2:59:29 time: 0.395409 data_time: 0.067847 memory: 7187 loss_kpt: 0.000665 acc_pose: 0.821675 loss: 0.000665 2022/10/19 14:04:10 - mmengine - INFO - Epoch(train) [99][150/293] lr: 5.000000e-04 eta: 2:59:15 time: 0.368333 data_time: 0.061519 memory: 7187 loss_kpt: 0.000668 acc_pose: 0.831592 loss: 0.000668 2022/10/19 14:04:28 - mmengine - INFO - Epoch(train) [99][200/293] lr: 5.000000e-04 eta: 2:59:01 time: 0.374129 data_time: 0.067950 memory: 7187 loss_kpt: 0.000669 acc_pose: 0.855780 loss: 0.000669 2022/10/19 14:04:48 - mmengine - INFO - Epoch(train) [99][250/293] lr: 5.000000e-04 eta: 2:58:48 time: 0.386273 data_time: 0.064724 memory: 7187 loss_kpt: 0.000668 acc_pose: 0.818707 loss: 0.000668 2022/10/19 14:05:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:05:04 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:05:24 - mmengine - INFO - Epoch(train) [100][50/293] lr: 5.000000e-04 eta: 2:58:05 time: 0.401143 data_time: 0.088310 memory: 7187 loss_kpt: 0.000658 acc_pose: 0.782126 loss: 0.000658 2022/10/19 14:05:43 - mmengine - INFO - Epoch(train) [100][100/293] lr: 5.000000e-04 eta: 2:57:52 time: 0.384477 data_time: 0.083826 memory: 7187 loss_kpt: 0.000671 acc_pose: 0.814409 loss: 0.000671 2022/10/19 14:06:02 - mmengine - INFO - Epoch(train) [100][150/293] lr: 5.000000e-04 eta: 2:57:38 time: 0.380119 data_time: 0.070269 memory: 7187 loss_kpt: 0.000667 acc_pose: 0.824846 loss: 0.000667 2022/10/19 14:06:21 - mmengine - INFO - Epoch(train) [100][200/293] lr: 5.000000e-04 eta: 2:57:24 time: 0.370227 data_time: 0.063239 memory: 7187 loss_kpt: 0.000669 acc_pose: 0.746293 loss: 0.000669 2022/10/19 14:06:40 - mmengine - INFO - Epoch(train) [100][250/293] lr: 5.000000e-04 eta: 2:57:11 time: 0.393273 data_time: 0.087607 memory: 7187 loss_kpt: 0.000663 acc_pose: 0.850593 loss: 0.000663 2022/10/19 14:06:57 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:06:57 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/10/19 14:07:06 - mmengine - INFO - Epoch(val) [100][50/407] eta: 0:00:47 time: 0.132694 data_time: 0.076428 memory: 7187 2022/10/19 14:07:12 - mmengine - INFO - Epoch(val) [100][100/407] eta: 0:00:35 time: 0.114413 data_time: 0.058133 memory: 1014 2022/10/19 14:07:19 - mmengine - INFO - Epoch(val) [100][150/407] eta: 0:00:34 time: 0.134936 data_time: 0.078349 memory: 1014 2022/10/19 14:07:25 - mmengine - INFO - Epoch(val) [100][200/407] eta: 0:00:25 time: 0.122953 data_time: 0.064884 memory: 1014 2022/10/19 14:07:31 - mmengine - INFO - Epoch(val) [100][250/407] eta: 0:00:19 time: 0.125659 data_time: 0.069068 memory: 1014 2022/10/19 14:07:37 - mmengine - INFO - Epoch(val) [100][300/407] eta: 0:00:12 time: 0.113717 data_time: 0.058052 memory: 1014 2022/10/19 14:07:44 - mmengine - INFO - Epoch(val) [100][350/407] eta: 0:00:07 time: 0.129772 data_time: 0.072438 memory: 1014 2022/10/19 14:07:49 - mmengine - INFO - Epoch(val) [100][400/407] eta: 0:00:00 time: 0.102696 data_time: 0.050505 memory: 1014 2022/10/19 14:08:25 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 14:08:39 - mmengine - INFO - Epoch(val) [100][407/407] coco/AP: 0.705755 coco/AP .5: 0.893906 coco/AP .75: 0.784660 coco/AP (M): 0.666925 coco/AP (L): 0.773242 coco/AR: 0.761603 coco/AR .5: 0.931518 coco/AR .75: 0.832494 coco/AR (M): 0.716799 coco/AR (L): 0.825901 2022/10/19 14:08:39 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_256/best_coco/AP_epoch_90.pth is removed 2022/10/19 14:08:41 - mmengine - INFO - The best checkpoint with 0.7058 coco/AP at 100 epoch is saved to best_coco/AP_epoch_100.pth. 2022/10/19 14:09:00 - mmengine - INFO - Epoch(train) [101][50/293] lr: 5.000000e-04 eta: 2:56:28 time: 0.383775 data_time: 0.117940 memory: 7187 loss_kpt: 0.000664 acc_pose: 0.809585 loss: 0.000664 2022/10/19 14:09:20 - mmengine - INFO - Epoch(train) [101][100/293] lr: 5.000000e-04 eta: 2:56:15 time: 0.390365 data_time: 0.060237 memory: 7187 loss_kpt: 0.000662 acc_pose: 0.797866 loss: 0.000662 2022/10/19 14:09:40 - mmengine - INFO - Epoch(train) [101][150/293] lr: 5.000000e-04 eta: 2:56:03 time: 0.409537 data_time: 0.073370 memory: 7187 loss_kpt: 0.000660 acc_pose: 0.767579 loss: 0.000660 2022/10/19 14:09:59 - mmengine - INFO - Epoch(train) [101][200/293] lr: 5.000000e-04 eta: 2:55:48 time: 0.370430 data_time: 0.063322 memory: 7187 loss_kpt: 0.000655 acc_pose: 0.778808 loss: 0.000655 2022/10/19 14:10:18 - mmengine - INFO - Epoch(train) [101][250/293] lr: 5.000000e-04 eta: 2:55:35 time: 0.380708 data_time: 0.095453 memory: 7187 loss_kpt: 0.000661 acc_pose: 0.841703 loss: 0.000661 2022/10/19 14:10:34 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:10:53 - mmengine - INFO - Epoch(train) [102][50/293] lr: 5.000000e-04 eta: 2:54:52 time: 0.389335 data_time: 0.166925 memory: 7187 loss_kpt: 0.000660 acc_pose: 0.864571 loss: 0.000660 2022/10/19 14:11:13 - mmengine - INFO - Epoch(train) [102][100/293] lr: 5.000000e-04 eta: 2:54:39 time: 0.393381 data_time: 0.070793 memory: 7187 loss_kpt: 0.000670 acc_pose: 0.836679 loss: 0.000670 2022/10/19 14:11:32 - mmengine - INFO - Epoch(train) [102][150/293] lr: 5.000000e-04 eta: 2:54:25 time: 0.375946 data_time: 0.068459 memory: 7187 loss_kpt: 0.000658 acc_pose: 0.783489 loss: 0.000658 2022/10/19 14:11:50 - mmengine - INFO - Epoch(train) [102][200/293] lr: 5.000000e-04 eta: 2:54:11 time: 0.370740 data_time: 0.063208 memory: 7187 loss_kpt: 0.000641 acc_pose: 0.821682 loss: 0.000641 2022/10/19 14:12:09 - mmengine - INFO - Epoch(train) [102][250/293] lr: 5.000000e-04 eta: 2:53:57 time: 0.373219 data_time: 0.064135 memory: 7187 loss_kpt: 0.000674 acc_pose: 0.827757 loss: 0.000674 2022/10/19 14:12:25 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:12:45 - mmengine - INFO - Epoch(train) [103][50/293] lr: 5.000000e-04 eta: 2:53:14 time: 0.385321 data_time: 0.096036 memory: 7187 loss_kpt: 0.000663 acc_pose: 0.841433 loss: 0.000663 2022/10/19 14:13:03 - mmengine - INFO - Epoch(train) [103][100/293] lr: 5.000000e-04 eta: 2:53:00 time: 0.372099 data_time: 0.060513 memory: 7187 loss_kpt: 0.000664 acc_pose: 0.765896 loss: 0.000664 2022/10/19 14:13:09 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:13:22 - mmengine - INFO - Epoch(train) [103][150/293] lr: 5.000000e-04 eta: 2:52:46 time: 0.376111 data_time: 0.058726 memory: 7187 loss_kpt: 0.000640 acc_pose: 0.849544 loss: 0.000640 2022/10/19 14:13:41 - mmengine - INFO - Epoch(train) [103][200/293] lr: 5.000000e-04 eta: 2:52:32 time: 0.372709 data_time: 0.066563 memory: 7187 loss_kpt: 0.000666 acc_pose: 0.830248 loss: 0.000666 2022/10/19 14:14:00 - mmengine - INFO - Epoch(train) [103][250/293] lr: 5.000000e-04 eta: 2:52:18 time: 0.379387 data_time: 0.086210 memory: 7187 loss_kpt: 0.000667 acc_pose: 0.836937 loss: 0.000667 2022/10/19 14:14:16 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:14:36 - mmengine - INFO - Epoch(train) [104][50/293] lr: 5.000000e-04 eta: 2:51:36 time: 0.405196 data_time: 0.081147 memory: 7187 loss_kpt: 0.000675 acc_pose: 0.754882 loss: 0.000675 2022/10/19 14:14:55 - mmengine - INFO - Epoch(train) [104][100/293] lr: 5.000000e-04 eta: 2:51:23 time: 0.385430 data_time: 0.068053 memory: 7187 loss_kpt: 0.000648 acc_pose: 0.819138 loss: 0.000648 2022/10/19 14:15:14 - mmengine - INFO - Epoch(train) [104][150/293] lr: 5.000000e-04 eta: 2:51:09 time: 0.381327 data_time: 0.065919 memory: 7187 loss_kpt: 0.000668 acc_pose: 0.826356 loss: 0.000668 2022/10/19 14:15:33 - mmengine - INFO - Epoch(train) [104][200/293] lr: 5.000000e-04 eta: 2:50:55 time: 0.369084 data_time: 0.062106 memory: 7187 loss_kpt: 0.000659 acc_pose: 0.849661 loss: 0.000659 2022/10/19 14:15:53 - mmengine - INFO - Epoch(train) [104][250/293] lr: 5.000000e-04 eta: 2:50:42 time: 0.408233 data_time: 0.064657 memory: 7187 loss_kpt: 0.000638 acc_pose: 0.788637 loss: 0.000638 2022/10/19 14:16:10 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:16:30 - mmengine - INFO - Epoch(train) [105][50/293] lr: 5.000000e-04 eta: 2:50:01 time: 0.400858 data_time: 0.099570 memory: 7187 loss_kpt: 0.000659 acc_pose: 0.755208 loss: 0.000659 2022/10/19 14:16:49 - mmengine - INFO - Epoch(train) [105][100/293] lr: 5.000000e-04 eta: 2:49:47 time: 0.373098 data_time: 0.064928 memory: 7187 loss_kpt: 0.000666 acc_pose: 0.805842 loss: 0.000666 2022/10/19 14:17:09 - mmengine - INFO - Epoch(train) [105][150/293] lr: 5.000000e-04 eta: 2:49:33 time: 0.387972 data_time: 0.060722 memory: 7187 loss_kpt: 0.000661 acc_pose: 0.777607 loss: 0.000661 2022/10/19 14:17:28 - mmengine - INFO - Epoch(train) [105][200/293] lr: 5.000000e-04 eta: 2:49:19 time: 0.378974 data_time: 0.061594 memory: 7187 loss_kpt: 0.000658 acc_pose: 0.851073 loss: 0.000658 2022/10/19 14:17:46 - mmengine - INFO - Epoch(train) [105][250/293] lr: 5.000000e-04 eta: 2:49:05 time: 0.365019 data_time: 0.102063 memory: 7187 loss_kpt: 0.000657 acc_pose: 0.799980 loss: 0.000657 2022/10/19 14:18:02 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:18:22 - mmengine - INFO - Epoch(train) [106][50/293] lr: 5.000000e-04 eta: 2:48:23 time: 0.399144 data_time: 0.084608 memory: 7187 loss_kpt: 0.000654 acc_pose: 0.790560 loss: 0.000654 2022/10/19 14:18:41 - mmengine - INFO - Epoch(train) [106][100/293] lr: 5.000000e-04 eta: 2:48:09 time: 0.369271 data_time: 0.064658 memory: 7187 loss_kpt: 0.000662 acc_pose: 0.789941 loss: 0.000662 2022/10/19 14:19:00 - mmengine - INFO - Epoch(train) [106][150/293] lr: 5.000000e-04 eta: 2:47:55 time: 0.388319 data_time: 0.068120 memory: 7187 loss_kpt: 0.000666 acc_pose: 0.721395 loss: 0.000666 2022/10/19 14:19:20 - mmengine - INFO - Epoch(train) [106][200/293] lr: 5.000000e-04 eta: 2:47:43 time: 0.403683 data_time: 0.067672 memory: 7187 loss_kpt: 0.000669 acc_pose: 0.858021 loss: 0.000669 2022/10/19 14:19:33 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:19:39 - mmengine - INFO - Epoch(train) [106][250/293] lr: 5.000000e-04 eta: 2:47:29 time: 0.382714 data_time: 0.067844 memory: 7187 loss_kpt: 0.000674 acc_pose: 0.830658 loss: 0.000674 2022/10/19 14:19:55 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:20:15 - mmengine - INFO - Epoch(train) [107][50/293] lr: 5.000000e-04 eta: 2:46:48 time: 0.394661 data_time: 0.083129 memory: 7187 loss_kpt: 0.000664 acc_pose: 0.823051 loss: 0.000664 2022/10/19 14:20:35 - mmengine - INFO - Epoch(train) [107][100/293] lr: 5.000000e-04 eta: 2:46:34 time: 0.396373 data_time: 0.082231 memory: 7187 loss_kpt: 0.000661 acc_pose: 0.819614 loss: 0.000661 2022/10/19 14:20:55 - mmengine - INFO - Epoch(train) [107][150/293] lr: 5.000000e-04 eta: 2:46:21 time: 0.400284 data_time: 0.067064 memory: 7187 loss_kpt: 0.000652 acc_pose: 0.852492 loss: 0.000652 2022/10/19 14:21:13 - mmengine - INFO - Epoch(train) [107][200/293] lr: 5.000000e-04 eta: 2:46:07 time: 0.368691 data_time: 0.078774 memory: 7187 loss_kpt: 0.000675 acc_pose: 0.871530 loss: 0.000675 2022/10/19 14:21:32 - mmengine - INFO - Epoch(train) [107][250/293] lr: 5.000000e-04 eta: 2:45:53 time: 0.381158 data_time: 0.071472 memory: 7187 loss_kpt: 0.000662 acc_pose: 0.851973 loss: 0.000662 2022/10/19 14:21:47 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:22:08 - mmengine - INFO - Epoch(train) [108][50/293] lr: 5.000000e-04 eta: 2:45:13 time: 0.414303 data_time: 0.113946 memory: 7187 loss_kpt: 0.000665 acc_pose: 0.859617 loss: 0.000665 2022/10/19 14:22:29 - mmengine - INFO - Epoch(train) [108][100/293] lr: 5.000000e-04 eta: 2:45:00 time: 0.414196 data_time: 0.061872 memory: 7187 loss_kpt: 0.000645 acc_pose: 0.812066 loss: 0.000645 2022/10/19 14:22:48 - mmengine - INFO - Epoch(train) [108][150/293] lr: 5.000000e-04 eta: 2:44:47 time: 0.388501 data_time: 0.063069 memory: 7187 loss_kpt: 0.000660 acc_pose: 0.855281 loss: 0.000660 2022/10/19 14:23:07 - mmengine - INFO - Epoch(train) [108][200/293] lr: 5.000000e-04 eta: 2:44:32 time: 0.370230 data_time: 0.067630 memory: 7187 loss_kpt: 0.000653 acc_pose: 0.830392 loss: 0.000653 2022/10/19 14:23:26 - mmengine - INFO - Epoch(train) [108][250/293] lr: 5.000000e-04 eta: 2:44:18 time: 0.374682 data_time: 0.059352 memory: 7187 loss_kpt: 0.000654 acc_pose: 0.826883 loss: 0.000654 2022/10/19 14:23:43 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:24:04 - mmengine - INFO - Epoch(train) [109][50/293] lr: 5.000000e-04 eta: 2:43:39 time: 0.433992 data_time: 0.080828 memory: 7187 loss_kpt: 0.000647 acc_pose: 0.804170 loss: 0.000647 2022/10/19 14:24:22 - mmengine - INFO - Epoch(train) [109][100/293] lr: 5.000000e-04 eta: 2:43:24 time: 0.360180 data_time: 0.069611 memory: 7187 loss_kpt: 0.000654 acc_pose: 0.800814 loss: 0.000654 2022/10/19 14:24:41 - mmengine - INFO - Epoch(train) [109][150/293] lr: 5.000000e-04 eta: 2:43:09 time: 0.369436 data_time: 0.058782 memory: 7187 loss_kpt: 0.000655 acc_pose: 0.848031 loss: 0.000655 2022/10/19 14:25:00 - mmengine - INFO - Epoch(train) [109][200/293] lr: 5.000000e-04 eta: 2:42:55 time: 0.380286 data_time: 0.108667 memory: 7187 loss_kpt: 0.000657 acc_pose: 0.797656 loss: 0.000657 2022/10/19 14:25:19 - mmengine - INFO - Epoch(train) [109][250/293] lr: 5.000000e-04 eta: 2:42:41 time: 0.380669 data_time: 0.061531 memory: 7187 loss_kpt: 0.000664 acc_pose: 0.781832 loss: 0.000664 2022/10/19 14:25:35 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:25:54 - mmengine - INFO - Epoch(train) [110][50/293] lr: 5.000000e-04 eta: 2:42:00 time: 0.389037 data_time: 0.110557 memory: 7187 loss_kpt: 0.000660 acc_pose: 0.787669 loss: 0.000660 2022/10/19 14:25:59 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:26:13 - mmengine - INFO - Epoch(train) [110][100/293] lr: 5.000000e-04 eta: 2:41:45 time: 0.366257 data_time: 0.072920 memory: 7187 loss_kpt: 0.000655 acc_pose: 0.811685 loss: 0.000655 2022/10/19 14:26:32 - mmengine - INFO - Epoch(train) [110][150/293] lr: 5.000000e-04 eta: 2:41:32 time: 0.384953 data_time: 0.066564 memory: 7187 loss_kpt: 0.000651 acc_pose: 0.804429 loss: 0.000651 2022/10/19 14:26:51 - mmengine - INFO - Epoch(train) [110][200/293] lr: 5.000000e-04 eta: 2:41:18 time: 0.387794 data_time: 0.063646 memory: 7187 loss_kpt: 0.000661 acc_pose: 0.812545 loss: 0.000661 2022/10/19 14:27:10 - mmengine - INFO - Epoch(train) [110][250/293] lr: 5.000000e-04 eta: 2:41:03 time: 0.362436 data_time: 0.064669 memory: 7187 loss_kpt: 0.000657 acc_pose: 0.818426 loss: 0.000657 2022/10/19 14:27:26 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:27:26 - mmengine - INFO - Saving checkpoint at 110 epochs 2022/10/19 14:27:35 - mmengine - INFO - Epoch(val) [110][50/407] eta: 0:00:44 time: 0.123919 data_time: 0.066969 memory: 7187 2022/10/19 14:27:41 - mmengine - INFO - Epoch(val) [110][100/407] eta: 0:00:37 time: 0.121042 data_time: 0.064208 memory: 1014 2022/10/19 14:27:48 - mmengine - INFO - Epoch(val) [110][150/407] eta: 0:00:31 time: 0.123208 data_time: 0.066659 memory: 1014 2022/10/19 14:27:54 - mmengine - INFO - Epoch(val) [110][200/407] eta: 0:00:24 time: 0.120146 data_time: 0.065451 memory: 1014 2022/10/19 14:28:00 - mmengine - INFO - Epoch(val) [110][250/407] eta: 0:00:19 time: 0.121598 data_time: 0.065865 memory: 1014 2022/10/19 14:28:06 - mmengine - INFO - Epoch(val) [110][300/407] eta: 0:00:13 time: 0.122521 data_time: 0.066422 memory: 1014 2022/10/19 14:28:13 - mmengine - INFO - Epoch(val) [110][350/407] eta: 0:00:07 time: 0.139029 data_time: 0.083653 memory: 1014 2022/10/19 14:28:18 - mmengine - INFO - Epoch(val) [110][400/407] eta: 0:00:00 time: 0.104776 data_time: 0.052429 memory: 1014 2022/10/19 14:28:54 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 14:29:08 - mmengine - INFO - Epoch(val) [110][407/407] coco/AP: 0.706629 coco/AP .5: 0.893409 coco/AP .75: 0.785351 coco/AP (M): 0.668315 coco/AP (L): 0.773774 coco/AR: 0.762437 coco/AR .5: 0.931203 coco/AR .75: 0.835013 coco/AR (M): 0.718110 coco/AR (L): 0.826161 2022/10/19 14:29:08 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_256/best_coco/AP_epoch_100.pth is removed 2022/10/19 14:29:10 - mmengine - INFO - The best checkpoint with 0.7066 coco/AP at 110 epoch is saved to best_coco/AP_epoch_110.pth. 2022/10/19 14:29:29 - mmengine - INFO - Epoch(train) [111][50/293] lr: 5.000000e-04 eta: 2:40:22 time: 0.393287 data_time: 0.182464 memory: 7187 loss_kpt: 0.000650 acc_pose: 0.801288 loss: 0.000650 2022/10/19 14:29:49 - mmengine - INFO - Epoch(train) [111][100/293] lr: 5.000000e-04 eta: 2:40:08 time: 0.388354 data_time: 0.113861 memory: 7187 loss_kpt: 0.000660 acc_pose: 0.814773 loss: 0.000660 2022/10/19 14:30:08 - mmengine - INFO - Epoch(train) [111][150/293] lr: 5.000000e-04 eta: 2:39:55 time: 0.387094 data_time: 0.063826 memory: 7187 loss_kpt: 0.000641 acc_pose: 0.800209 loss: 0.000641 2022/10/19 14:30:27 - mmengine - INFO - Epoch(train) [111][200/293] lr: 5.000000e-04 eta: 2:39:40 time: 0.376839 data_time: 0.059463 memory: 7187 loss_kpt: 0.000665 acc_pose: 0.813037 loss: 0.000665 2022/10/19 14:30:45 - mmengine - INFO - Epoch(train) [111][250/293] lr: 5.000000e-04 eta: 2:39:26 time: 0.366616 data_time: 0.065514 memory: 7187 loss_kpt: 0.000653 acc_pose: 0.773039 loss: 0.000653 2022/10/19 14:31:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:31:20 - mmengine - INFO - Epoch(train) [112][50/293] lr: 5.000000e-04 eta: 2:38:45 time: 0.379752 data_time: 0.092253 memory: 7187 loss_kpt: 0.000663 acc_pose: 0.885659 loss: 0.000663 2022/10/19 14:31:38 - mmengine - INFO - Epoch(train) [112][100/293] lr: 5.000000e-04 eta: 2:38:30 time: 0.361309 data_time: 0.125159 memory: 7187 loss_kpt: 0.000654 acc_pose: 0.853962 loss: 0.000654 2022/10/19 14:31:56 - mmengine - INFO - Epoch(train) [112][150/293] lr: 5.000000e-04 eta: 2:38:15 time: 0.374183 data_time: 0.096664 memory: 7187 loss_kpt: 0.000647 acc_pose: 0.859870 loss: 0.000647 2022/10/19 14:32:15 - mmengine - INFO - Epoch(train) [112][200/293] lr: 5.000000e-04 eta: 2:38:01 time: 0.382240 data_time: 0.114730 memory: 7187 loss_kpt: 0.000670 acc_pose: 0.827683 loss: 0.000670 2022/10/19 14:32:35 - mmengine - INFO - Epoch(train) [112][250/293] lr: 5.000000e-04 eta: 2:37:47 time: 0.381300 data_time: 0.106497 memory: 7187 loss_kpt: 0.000655 acc_pose: 0.811775 loss: 0.000655 2022/10/19 14:32:50 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:33:10 - mmengine - INFO - Epoch(train) [113][50/293] lr: 5.000000e-04 eta: 2:37:07 time: 0.393791 data_time: 0.075740 memory: 7187 loss_kpt: 0.000658 acc_pose: 0.819710 loss: 0.000658 2022/10/19 14:33:29 - mmengine - INFO - Epoch(train) [113][100/293] lr: 5.000000e-04 eta: 2:36:52 time: 0.378662 data_time: 0.062835 memory: 7187 loss_kpt: 0.000649 acc_pose: 0.816365 loss: 0.000649 2022/10/19 14:33:48 - mmengine - INFO - Epoch(train) [113][150/293] lr: 5.000000e-04 eta: 2:36:38 time: 0.386760 data_time: 0.065734 memory: 7187 loss_kpt: 0.000653 acc_pose: 0.813749 loss: 0.000653 2022/10/19 14:34:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:34:07 - mmengine - INFO - Epoch(train) [113][200/293] lr: 5.000000e-04 eta: 2:36:24 time: 0.366102 data_time: 0.106234 memory: 7187 loss_kpt: 0.000655 acc_pose: 0.780496 loss: 0.000655 2022/10/19 14:34:25 - mmengine - INFO - Epoch(train) [113][250/293] lr: 5.000000e-04 eta: 2:36:09 time: 0.375484 data_time: 0.062276 memory: 7187 loss_kpt: 0.000639 acc_pose: 0.833898 loss: 0.000639 2022/10/19 14:34:42 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:35:02 - mmengine - INFO - Epoch(train) [114][50/293] lr: 5.000000e-04 eta: 2:35:29 time: 0.396842 data_time: 0.099184 memory: 7187 loss_kpt: 0.000642 acc_pose: 0.846758 loss: 0.000642 2022/10/19 14:35:20 - mmengine - INFO - Epoch(train) [114][100/293] lr: 5.000000e-04 eta: 2:35:15 time: 0.370874 data_time: 0.063131 memory: 7187 loss_kpt: 0.000644 acc_pose: 0.878743 loss: 0.000644 2022/10/19 14:35:40 - mmengine - INFO - Epoch(train) [114][150/293] lr: 5.000000e-04 eta: 2:35:01 time: 0.389466 data_time: 0.065512 memory: 7187 loss_kpt: 0.000670 acc_pose: 0.820423 loss: 0.000670 2022/10/19 14:35:58 - mmengine - INFO - Epoch(train) [114][200/293] lr: 5.000000e-04 eta: 2:34:46 time: 0.358899 data_time: 0.068215 memory: 7187 loss_kpt: 0.000655 acc_pose: 0.851685 loss: 0.000655 2022/10/19 14:36:17 - mmengine - INFO - Epoch(train) [114][250/293] lr: 5.000000e-04 eta: 2:34:31 time: 0.376144 data_time: 0.092198 memory: 7187 loss_kpt: 0.000662 acc_pose: 0.838088 loss: 0.000662 2022/10/19 14:36:33 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:36:53 - mmengine - INFO - Epoch(train) [115][50/293] lr: 5.000000e-04 eta: 2:33:52 time: 0.411842 data_time: 0.142907 memory: 7187 loss_kpt: 0.000654 acc_pose: 0.836569 loss: 0.000654 2022/10/19 14:37:13 - mmengine - INFO - Epoch(train) [115][100/293] lr: 5.000000e-04 eta: 2:33:38 time: 0.388575 data_time: 0.146862 memory: 7187 loss_kpt: 0.000650 acc_pose: 0.848268 loss: 0.000650 2022/10/19 14:37:31 - mmengine - INFO - Epoch(train) [115][150/293] lr: 5.000000e-04 eta: 2:33:23 time: 0.366345 data_time: 0.116842 memory: 7187 loss_kpt: 0.000657 acc_pose: 0.812500 loss: 0.000657 2022/10/19 14:37:50 - mmengine - INFO - Epoch(train) [115][200/293] lr: 5.000000e-04 eta: 2:33:09 time: 0.378673 data_time: 0.141988 memory: 7187 loss_kpt: 0.000646 acc_pose: 0.864442 loss: 0.000646 2022/10/19 14:38:08 - mmengine - INFO - Epoch(train) [115][250/293] lr: 5.000000e-04 eta: 2:32:54 time: 0.365940 data_time: 0.069431 memory: 7187 loss_kpt: 0.000670 acc_pose: 0.755149 loss: 0.000670 2022/10/19 14:38:23 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:38:44 - mmengine - INFO - Epoch(train) [116][50/293] lr: 5.000000e-04 eta: 2:32:15 time: 0.412957 data_time: 0.099172 memory: 7187 loss_kpt: 0.000655 acc_pose: 0.840012 loss: 0.000655 2022/10/19 14:39:03 - mmengine - INFO - Epoch(train) [116][100/293] lr: 5.000000e-04 eta: 2:32:01 time: 0.372251 data_time: 0.066198 memory: 7187 loss_kpt: 0.000646 acc_pose: 0.832924 loss: 0.000646 2022/10/19 14:39:21 - mmengine - INFO - Epoch(train) [116][150/293] lr: 5.000000e-04 eta: 2:31:46 time: 0.368811 data_time: 0.064212 memory: 7187 loss_kpt: 0.000666 acc_pose: 0.872785 loss: 0.000666 2022/10/19 14:39:39 - mmengine - INFO - Epoch(train) [116][200/293] lr: 5.000000e-04 eta: 2:31:30 time: 0.359344 data_time: 0.060408 memory: 7187 loss_kpt: 0.000654 acc_pose: 0.798606 loss: 0.000654 2022/10/19 14:39:59 - mmengine - INFO - Epoch(train) [116][250/293] lr: 5.000000e-04 eta: 2:31:17 time: 0.393874 data_time: 0.068284 memory: 7187 loss_kpt: 0.000665 acc_pose: 0.848094 loss: 0.000665 2022/10/19 14:40:15 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:40:21 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:40:35 - mmengine - INFO - Epoch(train) [117][50/293] lr: 5.000000e-04 eta: 2:30:38 time: 0.404807 data_time: 0.101886 memory: 7187 loss_kpt: 0.000664 acc_pose: 0.805012 loss: 0.000664 2022/10/19 14:40:54 - mmengine - INFO - Epoch(train) [117][100/293] lr: 5.000000e-04 eta: 2:30:23 time: 0.376905 data_time: 0.076436 memory: 7187 loss_kpt: 0.000640 acc_pose: 0.811910 loss: 0.000640 2022/10/19 14:41:12 - mmengine - INFO - Epoch(train) [117][150/293] lr: 5.000000e-04 eta: 2:30:08 time: 0.367171 data_time: 0.056706 memory: 7187 loss_kpt: 0.000654 acc_pose: 0.828380 loss: 0.000654 2022/10/19 14:41:32 - mmengine - INFO - Epoch(train) [117][200/293] lr: 5.000000e-04 eta: 2:29:54 time: 0.392500 data_time: 0.064200 memory: 7187 loss_kpt: 0.000650 acc_pose: 0.796390 loss: 0.000650 2022/10/19 14:41:51 - mmengine - INFO - Epoch(train) [117][250/293] lr: 5.000000e-04 eta: 2:29:40 time: 0.374846 data_time: 0.064696 memory: 7187 loss_kpt: 0.000648 acc_pose: 0.817921 loss: 0.000648 2022/10/19 14:42:06 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:42:26 - mmengine - INFO - Epoch(train) [118][50/293] lr: 5.000000e-04 eta: 2:29:01 time: 0.398505 data_time: 0.079560 memory: 7187 loss_kpt: 0.000653 acc_pose: 0.823257 loss: 0.000653 2022/10/19 14:42:45 - mmengine - INFO - Epoch(train) [118][100/293] lr: 5.000000e-04 eta: 2:28:46 time: 0.375794 data_time: 0.067841 memory: 7187 loss_kpt: 0.000652 acc_pose: 0.846887 loss: 0.000652 2022/10/19 14:43:04 - mmengine - INFO - Epoch(train) [118][150/293] lr: 5.000000e-04 eta: 2:28:32 time: 0.376737 data_time: 0.066460 memory: 7187 loss_kpt: 0.000651 acc_pose: 0.810718 loss: 0.000651 2022/10/19 14:43:23 - mmengine - INFO - Epoch(train) [118][200/293] lr: 5.000000e-04 eta: 2:28:17 time: 0.378767 data_time: 0.057565 memory: 7187 loss_kpt: 0.000659 acc_pose: 0.800783 loss: 0.000659 2022/10/19 14:43:42 - mmengine - INFO - Epoch(train) [118][250/293] lr: 5.000000e-04 eta: 2:28:03 time: 0.392539 data_time: 0.075162 memory: 7187 loss_kpt: 0.000641 acc_pose: 0.837601 loss: 0.000641 2022/10/19 14:43:58 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:44:18 - mmengine - INFO - Epoch(train) [119][50/293] lr: 5.000000e-04 eta: 2:27:24 time: 0.395088 data_time: 0.094761 memory: 7187 loss_kpt: 0.000649 acc_pose: 0.880052 loss: 0.000649 2022/10/19 14:44:37 - mmengine - INFO - Epoch(train) [119][100/293] lr: 5.000000e-04 eta: 2:27:10 time: 0.383017 data_time: 0.062692 memory: 7187 loss_kpt: 0.000652 acc_pose: 0.809310 loss: 0.000652 2022/10/19 14:44:56 - mmengine - INFO - Epoch(train) [119][150/293] lr: 5.000000e-04 eta: 2:26:55 time: 0.383243 data_time: 0.076873 memory: 7187 loss_kpt: 0.000658 acc_pose: 0.845998 loss: 0.000658 2022/10/19 14:45:15 - mmengine - INFO - Epoch(train) [119][200/293] lr: 5.000000e-04 eta: 2:26:41 time: 0.370733 data_time: 0.140021 memory: 7187 loss_kpt: 0.000652 acc_pose: 0.805227 loss: 0.000652 2022/10/19 14:45:33 - mmengine - INFO - Epoch(train) [119][250/293] lr: 5.000000e-04 eta: 2:26:26 time: 0.370148 data_time: 0.136249 memory: 7187 loss_kpt: 0.000658 acc_pose: 0.822181 loss: 0.000658 2022/10/19 14:45:49 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:46:08 - mmengine - INFO - Epoch(train) [120][50/293] lr: 5.000000e-04 eta: 2:25:46 time: 0.384352 data_time: 0.080434 memory: 7187 loss_kpt: 0.000652 acc_pose: 0.791660 loss: 0.000652 2022/10/19 14:46:27 - mmengine - INFO - Epoch(train) [120][100/293] lr: 5.000000e-04 eta: 2:25:32 time: 0.383524 data_time: 0.098019 memory: 7187 loss_kpt: 0.000657 acc_pose: 0.812636 loss: 0.000657 2022/10/19 14:46:39 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:46:46 - mmengine - INFO - Epoch(train) [120][150/293] lr: 5.000000e-04 eta: 2:25:17 time: 0.373623 data_time: 0.123926 memory: 7187 loss_kpt: 0.000643 acc_pose: 0.841867 loss: 0.000643 2022/10/19 14:47:05 - mmengine - INFO - Epoch(train) [120][200/293] lr: 5.000000e-04 eta: 2:25:03 time: 0.384837 data_time: 0.065426 memory: 7187 loss_kpt: 0.000649 acc_pose: 0.834387 loss: 0.000649 2022/10/19 14:47:23 - mmengine - INFO - Epoch(train) [120][250/293] lr: 5.000000e-04 eta: 2:24:48 time: 0.364526 data_time: 0.064939 memory: 7187 loss_kpt: 0.000652 acc_pose: 0.852006 loss: 0.000652 2022/10/19 14:47:39 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:47:39 - mmengine - INFO - Saving checkpoint at 120 epochs 2022/10/19 14:47:48 - mmengine - INFO - Epoch(val) [120][50/407] eta: 0:00:44 time: 0.125169 data_time: 0.069565 memory: 7187 2022/10/19 14:47:54 - mmengine - INFO - Epoch(val) [120][100/407] eta: 0:00:37 time: 0.122074 data_time: 0.066336 memory: 1014 2022/10/19 14:48:01 - mmengine - INFO - Epoch(val) [120][150/407] eta: 0:00:33 time: 0.130718 data_time: 0.073008 memory: 1014 2022/10/19 14:48:07 - mmengine - INFO - Epoch(val) [120][200/407] eta: 0:00:23 time: 0.114880 data_time: 0.060721 memory: 1014 2022/10/19 14:48:13 - mmengine - INFO - Epoch(val) [120][250/407] eta: 0:00:19 time: 0.121158 data_time: 0.065305 memory: 1014 2022/10/19 14:48:18 - mmengine - INFO - Epoch(val) [120][300/407] eta: 0:00:12 time: 0.112457 data_time: 0.056027 memory: 1014 2022/10/19 14:48:24 - mmengine - INFO - Epoch(val) [120][350/407] eta: 0:00:06 time: 0.122776 data_time: 0.068567 memory: 1014 2022/10/19 14:48:30 - mmengine - INFO - Epoch(val) [120][400/407] eta: 0:00:00 time: 0.117714 data_time: 0.063703 memory: 1014 2022/10/19 14:49:06 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 14:49:20 - mmengine - INFO - Epoch(val) [120][407/407] coco/AP: 0.705373 coco/AP .5: 0.892997 coco/AP .75: 0.781447 coco/AP (M): 0.664620 coco/AP (L): 0.775397 coco/AR: 0.761382 coco/AR .5: 0.931203 coco/AR .75: 0.829817 coco/AR (M): 0.715733 coco/AR (L): 0.826719 2022/10/19 14:49:39 - mmengine - INFO - Epoch(train) [121][50/293] lr: 5.000000e-04 eta: 2:24:09 time: 0.388799 data_time: 0.081215 memory: 7187 loss_kpt: 0.000645 acc_pose: 0.800290 loss: 0.000645 2022/10/19 14:49:57 - mmengine - INFO - Epoch(train) [121][100/293] lr: 5.000000e-04 eta: 2:23:53 time: 0.351033 data_time: 0.064767 memory: 7187 loss_kpt: 0.000650 acc_pose: 0.833073 loss: 0.000650 2022/10/19 14:50:15 - mmengine - INFO - Epoch(train) [121][150/293] lr: 5.000000e-04 eta: 2:23:38 time: 0.357159 data_time: 0.074187 memory: 7187 loss_kpt: 0.000638 acc_pose: 0.793899 loss: 0.000638 2022/10/19 14:50:33 - mmengine - INFO - Epoch(train) [121][200/293] lr: 5.000000e-04 eta: 2:23:23 time: 0.365401 data_time: 0.064617 memory: 7187 loss_kpt: 0.000639 acc_pose: 0.830911 loss: 0.000639 2022/10/19 14:50:51 - mmengine - INFO - Epoch(train) [121][250/293] lr: 5.000000e-04 eta: 2:23:08 time: 0.357208 data_time: 0.076770 memory: 7187 loss_kpt: 0.000665 acc_pose: 0.813802 loss: 0.000665 2022/10/19 14:51:06 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:51:26 - mmengine - INFO - Epoch(train) [122][50/293] lr: 5.000000e-04 eta: 2:22:29 time: 0.394727 data_time: 0.125554 memory: 7187 loss_kpt: 0.000649 acc_pose: 0.821735 loss: 0.000649 2022/10/19 14:51:43 - mmengine - INFO - Epoch(train) [122][100/293] lr: 5.000000e-04 eta: 2:22:14 time: 0.354597 data_time: 0.059415 memory: 7187 loss_kpt: 0.000643 acc_pose: 0.870845 loss: 0.000643 2022/10/19 14:52:01 - mmengine - INFO - Epoch(train) [122][150/293] lr: 5.000000e-04 eta: 2:21:58 time: 0.360883 data_time: 0.061901 memory: 7187 loss_kpt: 0.000651 acc_pose: 0.835445 loss: 0.000651 2022/10/19 14:52:20 - mmengine - INFO - Epoch(train) [122][200/293] lr: 5.000000e-04 eta: 2:21:43 time: 0.366023 data_time: 0.066105 memory: 7187 loss_kpt: 0.000646 acc_pose: 0.800140 loss: 0.000646 2022/10/19 14:52:38 - mmengine - INFO - Epoch(train) [122][250/293] lr: 5.000000e-04 eta: 2:21:28 time: 0.367138 data_time: 0.068031 memory: 7187 loss_kpt: 0.000651 acc_pose: 0.807324 loss: 0.000651 2022/10/19 14:52:54 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:53:14 - mmengine - INFO - Epoch(train) [123][50/293] lr: 5.000000e-04 eta: 2:20:50 time: 0.403443 data_time: 0.119459 memory: 7187 loss_kpt: 0.000638 acc_pose: 0.771312 loss: 0.000638 2022/10/19 14:53:33 - mmengine - INFO - Epoch(train) [123][100/293] lr: 5.000000e-04 eta: 2:20:36 time: 0.384001 data_time: 0.153234 memory: 7187 loss_kpt: 0.000651 acc_pose: 0.851886 loss: 0.000651 2022/10/19 14:53:51 - mmengine - INFO - Epoch(train) [123][150/293] lr: 5.000000e-04 eta: 2:20:21 time: 0.363519 data_time: 0.147556 memory: 7187 loss_kpt: 0.000648 acc_pose: 0.791333 loss: 0.000648 2022/10/19 14:54:10 - mmengine - INFO - Epoch(train) [123][200/293] lr: 5.000000e-04 eta: 2:20:06 time: 0.378188 data_time: 0.061663 memory: 7187 loss_kpt: 0.000658 acc_pose: 0.829509 loss: 0.000658 2022/10/19 14:54:30 - mmengine - INFO - Epoch(train) [123][250/293] lr: 5.000000e-04 eta: 2:19:52 time: 0.391039 data_time: 0.079263 memory: 7187 loss_kpt: 0.000648 acc_pose: 0.826574 loss: 0.000648 2022/10/19 14:54:32 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:54:46 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:55:08 - mmengine - INFO - Epoch(train) [124][50/293] lr: 5.000000e-04 eta: 2:19:15 time: 0.425302 data_time: 0.218779 memory: 7187 loss_kpt: 0.000648 acc_pose: 0.844137 loss: 0.000648 2022/10/19 14:55:26 - mmengine - INFO - Epoch(train) [124][100/293] lr: 5.000000e-04 eta: 2:19:00 time: 0.370175 data_time: 0.155597 memory: 7187 loss_kpt: 0.000659 acc_pose: 0.795897 loss: 0.000659 2022/10/19 14:55:45 - mmengine - INFO - Epoch(train) [124][150/293] lr: 5.000000e-04 eta: 2:18:45 time: 0.386397 data_time: 0.065986 memory: 7187 loss_kpt: 0.000635 acc_pose: 0.851515 loss: 0.000635 2022/10/19 14:56:04 - mmengine - INFO - Epoch(train) [124][200/293] lr: 5.000000e-04 eta: 2:18:30 time: 0.373112 data_time: 0.066097 memory: 7187 loss_kpt: 0.000638 acc_pose: 0.816728 loss: 0.000638 2022/10/19 14:56:23 - mmengine - INFO - Epoch(train) [124][250/293] lr: 5.000000e-04 eta: 2:18:16 time: 0.383651 data_time: 0.065109 memory: 7187 loss_kpt: 0.000642 acc_pose: 0.816643 loss: 0.000642 2022/10/19 14:56:39 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:56:58 - mmengine - INFO - Epoch(train) [125][50/293] lr: 5.000000e-04 eta: 2:17:37 time: 0.379935 data_time: 0.093911 memory: 7187 loss_kpt: 0.000640 acc_pose: 0.826008 loss: 0.000640 2022/10/19 14:57:17 - mmengine - INFO - Epoch(train) [125][100/293] lr: 5.000000e-04 eta: 2:17:22 time: 0.369006 data_time: 0.066690 memory: 7187 loss_kpt: 0.000651 acc_pose: 0.843889 loss: 0.000651 2022/10/19 14:57:35 - mmengine - INFO - Epoch(train) [125][150/293] lr: 5.000000e-04 eta: 2:17:07 time: 0.370346 data_time: 0.105589 memory: 7187 loss_kpt: 0.000636 acc_pose: 0.793658 loss: 0.000636 2022/10/19 14:57:55 - mmengine - INFO - Epoch(train) [125][200/293] lr: 5.000000e-04 eta: 2:16:53 time: 0.391390 data_time: 0.082671 memory: 7187 loss_kpt: 0.000636 acc_pose: 0.822664 loss: 0.000636 2022/10/19 14:58:14 - mmengine - INFO - Epoch(train) [125][250/293] lr: 5.000000e-04 eta: 2:16:38 time: 0.384094 data_time: 0.062405 memory: 7187 loss_kpt: 0.000653 acc_pose: 0.828032 loss: 0.000653 2022/10/19 14:58:30 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 14:58:49 - mmengine - INFO - Epoch(train) [126][50/293] lr: 5.000000e-04 eta: 2:16:00 time: 0.384244 data_time: 0.082165 memory: 7187 loss_kpt: 0.000640 acc_pose: 0.824944 loss: 0.000640 2022/10/19 14:59:07 - mmengine - INFO - Epoch(train) [126][100/293] lr: 5.000000e-04 eta: 2:15:45 time: 0.363655 data_time: 0.070566 memory: 7187 loss_kpt: 0.000650 acc_pose: 0.835466 loss: 0.000650 2022/10/19 14:59:26 - mmengine - INFO - Epoch(train) [126][150/293] lr: 5.000000e-04 eta: 2:15:30 time: 0.379034 data_time: 0.066212 memory: 7187 loss_kpt: 0.000652 acc_pose: 0.846150 loss: 0.000652 2022/10/19 14:59:45 - mmengine - INFO - Epoch(train) [126][200/293] lr: 5.000000e-04 eta: 2:15:15 time: 0.362384 data_time: 0.074750 memory: 7187 loss_kpt: 0.000652 acc_pose: 0.798587 loss: 0.000652 2022/10/19 15:00:04 - mmengine - INFO - Epoch(train) [126][250/293] lr: 5.000000e-04 eta: 2:15:01 time: 0.396492 data_time: 0.067382 memory: 7187 loss_kpt: 0.000639 acc_pose: 0.853272 loss: 0.000639 2022/10/19 15:00:20 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:00:40 - mmengine - INFO - Epoch(train) [127][50/293] lr: 5.000000e-04 eta: 2:14:23 time: 0.395949 data_time: 0.087092 memory: 7187 loss_kpt: 0.000658 acc_pose: 0.845693 loss: 0.000658 2022/10/19 15:00:52 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:01:00 - mmengine - INFO - Epoch(train) [127][100/293] lr: 5.000000e-04 eta: 2:14:09 time: 0.385306 data_time: 0.060948 memory: 7187 loss_kpt: 0.000635 acc_pose: 0.782682 loss: 0.000635 2022/10/19 15:01:18 - mmengine - INFO - Epoch(train) [127][150/293] lr: 5.000000e-04 eta: 2:13:54 time: 0.375997 data_time: 0.065199 memory: 7187 loss_kpt: 0.000638 acc_pose: 0.806117 loss: 0.000638 2022/10/19 15:01:38 - mmengine - INFO - Epoch(train) [127][200/293] lr: 5.000000e-04 eta: 2:13:39 time: 0.384043 data_time: 0.070176 memory: 7187 loss_kpt: 0.000647 acc_pose: 0.805682 loss: 0.000647 2022/10/19 15:01:56 - mmengine - INFO - Epoch(train) [127][250/293] lr: 5.000000e-04 eta: 2:13:24 time: 0.372248 data_time: 0.151635 memory: 7187 loss_kpt: 0.000643 acc_pose: 0.857840 loss: 0.000643 2022/10/19 15:02:12 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:02:32 - mmengine - INFO - Epoch(train) [128][50/293] lr: 5.000000e-04 eta: 2:12:46 time: 0.387436 data_time: 0.086600 memory: 7187 loss_kpt: 0.000631 acc_pose: 0.821382 loss: 0.000631 2022/10/19 15:02:51 - mmengine - INFO - Epoch(train) [128][100/293] lr: 5.000000e-04 eta: 2:12:32 time: 0.379376 data_time: 0.066562 memory: 7187 loss_kpt: 0.000651 acc_pose: 0.838348 loss: 0.000651 2022/10/19 15:03:09 - mmengine - INFO - Epoch(train) [128][150/293] lr: 5.000000e-04 eta: 2:12:16 time: 0.367740 data_time: 0.061052 memory: 7187 loss_kpt: 0.000659 acc_pose: 0.776630 loss: 0.000659 2022/10/19 15:03:28 - mmengine - INFO - Epoch(train) [128][200/293] lr: 5.000000e-04 eta: 2:12:02 time: 0.378160 data_time: 0.086697 memory: 7187 loss_kpt: 0.000647 acc_pose: 0.813203 loss: 0.000647 2022/10/19 15:03:47 - mmengine - INFO - Epoch(train) [128][250/293] lr: 5.000000e-04 eta: 2:11:47 time: 0.381235 data_time: 0.062956 memory: 7187 loss_kpt: 0.000645 acc_pose: 0.771043 loss: 0.000645 2022/10/19 15:04:04 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:04:23 - mmengine - INFO - Epoch(train) [129][50/293] lr: 5.000000e-04 eta: 2:11:09 time: 0.389958 data_time: 0.093574 memory: 7187 loss_kpt: 0.000649 acc_pose: 0.832936 loss: 0.000649 2022/10/19 15:04:42 - mmengine - INFO - Epoch(train) [129][100/293] lr: 5.000000e-04 eta: 2:10:55 time: 0.381414 data_time: 0.070761 memory: 7187 loss_kpt: 0.000655 acc_pose: 0.800584 loss: 0.000655 2022/10/19 15:05:02 - mmengine - INFO - Epoch(train) [129][150/293] lr: 5.000000e-04 eta: 2:10:40 time: 0.393163 data_time: 0.067415 memory: 7187 loss_kpt: 0.000640 acc_pose: 0.776323 loss: 0.000640 2022/10/19 15:05:22 - mmengine - INFO - Epoch(train) [129][200/293] lr: 5.000000e-04 eta: 2:10:26 time: 0.395285 data_time: 0.078060 memory: 7187 loss_kpt: 0.000624 acc_pose: 0.820972 loss: 0.000624 2022/10/19 15:05:41 - mmengine - INFO - Epoch(train) [129][250/293] lr: 5.000000e-04 eta: 2:10:11 time: 0.377742 data_time: 0.080337 memory: 7187 loss_kpt: 0.000646 acc_pose: 0.827143 loss: 0.000646 2022/10/19 15:05:56 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:06:16 - mmengine - INFO - Epoch(train) [130][50/293] lr: 5.000000e-04 eta: 2:09:34 time: 0.396588 data_time: 0.154545 memory: 7187 loss_kpt: 0.000647 acc_pose: 0.789107 loss: 0.000647 2022/10/19 15:06:36 - mmengine - INFO - Epoch(train) [130][100/293] lr: 5.000000e-04 eta: 2:09:19 time: 0.387545 data_time: 0.099027 memory: 7187 loss_kpt: 0.000650 acc_pose: 0.785215 loss: 0.000650 2022/10/19 15:06:55 - mmengine - INFO - Epoch(train) [130][150/293] lr: 5.000000e-04 eta: 2:09:05 time: 0.390530 data_time: 0.071099 memory: 7187 loss_kpt: 0.000654 acc_pose: 0.882828 loss: 0.000654 2022/10/19 15:07:14 - mmengine - INFO - Epoch(train) [130][200/293] lr: 5.000000e-04 eta: 2:08:50 time: 0.378625 data_time: 0.063841 memory: 7187 loss_kpt: 0.000644 acc_pose: 0.769337 loss: 0.000644 2022/10/19 15:07:16 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:07:34 - mmengine - INFO - Epoch(train) [130][250/293] lr: 5.000000e-04 eta: 2:08:36 time: 0.399165 data_time: 0.071863 memory: 7187 loss_kpt: 0.000642 acc_pose: 0.771302 loss: 0.000642 2022/10/19 15:07:50 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:07:50 - mmengine - INFO - Saving checkpoint at 130 epochs 2022/10/19 15:07:59 - mmengine - INFO - Epoch(val) [130][50/407] eta: 0:00:46 time: 0.129399 data_time: 0.072466 memory: 7187 2022/10/19 15:08:05 - mmengine - INFO - Epoch(val) [130][100/407] eta: 0:00:36 time: 0.119459 data_time: 0.061756 memory: 1014 2022/10/19 15:08:11 - mmengine - INFO - Epoch(val) [130][150/407] eta: 0:00:28 time: 0.111407 data_time: 0.056084 memory: 1014 2022/10/19 15:08:17 - mmengine - INFO - Epoch(val) [130][200/407] eta: 0:00:26 time: 0.126861 data_time: 0.070657 memory: 1014 2022/10/19 15:08:24 - mmengine - INFO - Epoch(val) [130][250/407] eta: 0:00:20 time: 0.129698 data_time: 0.074014 memory: 1014 2022/10/19 15:08:30 - mmengine - INFO - Epoch(val) [130][300/407] eta: 0:00:12 time: 0.118452 data_time: 0.063885 memory: 1014 2022/10/19 15:08:36 - mmengine - INFO - Epoch(val) [130][350/407] eta: 0:00:06 time: 0.120131 data_time: 0.065324 memory: 1014 2022/10/19 15:08:42 - mmengine - INFO - Epoch(val) [130][400/407] eta: 0:00:00 time: 0.124242 data_time: 0.070849 memory: 1014 2022/10/19 15:09:18 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 15:09:32 - mmengine - INFO - Epoch(val) [130][407/407] coco/AP: 0.705636 coco/AP .5: 0.891292 coco/AP .75: 0.779485 coco/AP (M): 0.666002 coco/AP (L): 0.775859 coco/AR: 0.762594 coco/AR .5: 0.931360 coco/AR .75: 0.829975 coco/AR (M): 0.716744 coco/AR (L): 0.828317 2022/10/19 15:09:51 - mmengine - INFO - Epoch(train) [131][50/293] lr: 5.000000e-04 eta: 2:07:58 time: 0.378096 data_time: 0.074432 memory: 7187 loss_kpt: 0.000647 acc_pose: 0.848520 loss: 0.000647 2022/10/19 15:10:10 - mmengine - INFO - Epoch(train) [131][100/293] lr: 5.000000e-04 eta: 2:07:43 time: 0.376319 data_time: 0.061349 memory: 7187 loss_kpt: 0.000629 acc_pose: 0.799121 loss: 0.000629 2022/10/19 15:10:29 - mmengine - INFO - Epoch(train) [131][150/293] lr: 5.000000e-04 eta: 2:07:28 time: 0.383491 data_time: 0.066928 memory: 7187 loss_kpt: 0.000646 acc_pose: 0.812012 loss: 0.000646 2022/10/19 15:10:48 - mmengine - INFO - Epoch(train) [131][200/293] lr: 5.000000e-04 eta: 2:07:13 time: 0.371404 data_time: 0.120493 memory: 7187 loss_kpt: 0.000636 acc_pose: 0.831865 loss: 0.000636 2022/10/19 15:11:06 - mmengine - INFO - Epoch(train) [131][250/293] lr: 5.000000e-04 eta: 2:06:58 time: 0.376199 data_time: 0.078268 memory: 7187 loss_kpt: 0.000640 acc_pose: 0.783867 loss: 0.000640 2022/10/19 15:11:22 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:11:41 - mmengine - INFO - Epoch(train) [132][50/293] lr: 5.000000e-04 eta: 2:06:21 time: 0.392360 data_time: 0.077039 memory: 7187 loss_kpt: 0.000631 acc_pose: 0.846012 loss: 0.000631 2022/10/19 15:12:00 - mmengine - INFO - Epoch(train) [132][100/293] lr: 5.000000e-04 eta: 2:06:06 time: 0.385066 data_time: 0.115659 memory: 7187 loss_kpt: 0.000629 acc_pose: 0.824131 loss: 0.000629 2022/10/19 15:12:19 - mmengine - INFO - Epoch(train) [132][150/293] lr: 5.000000e-04 eta: 2:05:51 time: 0.372343 data_time: 0.087435 memory: 7187 loss_kpt: 0.000642 acc_pose: 0.848757 loss: 0.000642 2022/10/19 15:12:38 - mmengine - INFO - Epoch(train) [132][200/293] lr: 5.000000e-04 eta: 2:05:36 time: 0.377819 data_time: 0.060524 memory: 7187 loss_kpt: 0.000636 acc_pose: 0.815511 loss: 0.000636 2022/10/19 15:12:57 - mmengine - INFO - Epoch(train) [132][250/293] lr: 5.000000e-04 eta: 2:05:21 time: 0.380275 data_time: 0.062996 memory: 7187 loss_kpt: 0.000651 acc_pose: 0.825536 loss: 0.000651 2022/10/19 15:13:13 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:13:32 - mmengine - INFO - Epoch(train) [133][50/293] lr: 5.000000e-04 eta: 2:04:44 time: 0.389591 data_time: 0.080020 memory: 7187 loss_kpt: 0.000644 acc_pose: 0.799263 loss: 0.000644 2022/10/19 15:13:51 - mmengine - INFO - Epoch(train) [133][100/293] lr: 5.000000e-04 eta: 2:04:29 time: 0.371866 data_time: 0.065793 memory: 7187 loss_kpt: 0.000636 acc_pose: 0.833847 loss: 0.000636 2022/10/19 15:14:08 - mmengine - INFO - Epoch(train) [133][150/293] lr: 5.000000e-04 eta: 2:04:13 time: 0.353797 data_time: 0.067218 memory: 7187 loss_kpt: 0.000633 acc_pose: 0.836927 loss: 0.000633 2022/10/19 15:14:27 - mmengine - INFO - Epoch(train) [133][200/293] lr: 5.000000e-04 eta: 2:03:58 time: 0.372034 data_time: 0.061729 memory: 7187 loss_kpt: 0.000643 acc_pose: 0.841069 loss: 0.000643 2022/10/19 15:14:46 - mmengine - INFO - Epoch(train) [133][250/293] lr: 5.000000e-04 eta: 2:03:43 time: 0.377453 data_time: 0.078655 memory: 7187 loss_kpt: 0.000646 acc_pose: 0.804538 loss: 0.000646 2022/10/19 15:15:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:15:13 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:15:20 - mmengine - INFO - Epoch(train) [134][50/293] lr: 5.000000e-04 eta: 2:03:06 time: 0.383660 data_time: 0.100791 memory: 7187 loss_kpt: 0.000645 acc_pose: 0.839750 loss: 0.000645 2022/10/19 15:15:39 - mmengine - INFO - Epoch(train) [134][100/293] lr: 5.000000e-04 eta: 2:02:51 time: 0.383928 data_time: 0.081563 memory: 7187 loss_kpt: 0.000647 acc_pose: 0.818379 loss: 0.000647 2022/10/19 15:15:58 - mmengine - INFO - Epoch(train) [134][150/293] lr: 5.000000e-04 eta: 2:02:36 time: 0.367063 data_time: 0.072060 memory: 7187 loss_kpt: 0.000637 acc_pose: 0.868686 loss: 0.000637 2022/10/19 15:16:16 - mmengine - INFO - Epoch(train) [134][200/293] lr: 5.000000e-04 eta: 2:02:21 time: 0.365177 data_time: 0.115929 memory: 7187 loss_kpt: 0.000627 acc_pose: 0.831177 loss: 0.000627 2022/10/19 15:16:34 - mmengine - INFO - Epoch(train) [134][250/293] lr: 5.000000e-04 eta: 2:02:05 time: 0.366503 data_time: 0.096441 memory: 7187 loss_kpt: 0.000634 acc_pose: 0.786079 loss: 0.000634 2022/10/19 15:16:49 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:17:09 - mmengine - INFO - Epoch(train) [135][50/293] lr: 5.000000e-04 eta: 2:01:29 time: 0.397099 data_time: 0.074579 memory: 7187 loss_kpt: 0.000649 acc_pose: 0.856943 loss: 0.000649 2022/10/19 15:17:27 - mmengine - INFO - Epoch(train) [135][100/293] lr: 5.000000e-04 eta: 2:01:13 time: 0.360414 data_time: 0.068116 memory: 7187 loss_kpt: 0.000626 acc_pose: 0.827510 loss: 0.000626 2022/10/19 15:17:46 - mmengine - INFO - Epoch(train) [135][150/293] lr: 5.000000e-04 eta: 2:00:58 time: 0.375639 data_time: 0.073887 memory: 7187 loss_kpt: 0.000633 acc_pose: 0.837494 loss: 0.000633 2022/10/19 15:18:04 - mmengine - INFO - Epoch(train) [135][200/293] lr: 5.000000e-04 eta: 2:00:43 time: 0.365568 data_time: 0.076143 memory: 7187 loss_kpt: 0.000631 acc_pose: 0.874470 loss: 0.000631 2022/10/19 15:18:23 - mmengine - INFO - Epoch(train) [135][250/293] lr: 5.000000e-04 eta: 2:00:28 time: 0.378490 data_time: 0.062236 memory: 7187 loss_kpt: 0.000638 acc_pose: 0.807907 loss: 0.000638 2022/10/19 15:18:38 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:18:57 - mmengine - INFO - Epoch(train) [136][50/293] lr: 5.000000e-04 eta: 1:59:51 time: 0.382493 data_time: 0.125824 memory: 7187 loss_kpt: 0.000639 acc_pose: 0.833689 loss: 0.000639 2022/10/19 15:19:17 - mmengine - INFO - Epoch(train) [136][100/293] lr: 5.000000e-04 eta: 1:59:36 time: 0.389495 data_time: 0.070827 memory: 7187 loss_kpt: 0.000645 acc_pose: 0.822720 loss: 0.000645 2022/10/19 15:19:36 - mmengine - INFO - Epoch(train) [136][150/293] lr: 5.000000e-04 eta: 1:59:21 time: 0.377610 data_time: 0.068140 memory: 7187 loss_kpt: 0.000643 acc_pose: 0.827816 loss: 0.000643 2022/10/19 15:19:55 - mmengine - INFO - Epoch(train) [136][200/293] lr: 5.000000e-04 eta: 1:59:06 time: 0.376689 data_time: 0.080018 memory: 7187 loss_kpt: 0.000632 acc_pose: 0.839447 loss: 0.000632 2022/10/19 15:20:13 - mmengine - INFO - Epoch(train) [136][250/293] lr: 5.000000e-04 eta: 1:58:51 time: 0.368168 data_time: 0.073954 memory: 7187 loss_kpt: 0.000648 acc_pose: 0.856870 loss: 0.000648 2022/10/19 15:20:28 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:20:49 - mmengine - INFO - Epoch(train) [137][50/293] lr: 5.000000e-04 eta: 1:58:15 time: 0.406214 data_time: 0.117239 memory: 7187 loss_kpt: 0.000636 acc_pose: 0.847303 loss: 0.000636 2022/10/19 15:21:07 - mmengine - INFO - Epoch(train) [137][100/293] lr: 5.000000e-04 eta: 1:58:00 time: 0.378079 data_time: 0.068121 memory: 7187 loss_kpt: 0.000639 acc_pose: 0.814777 loss: 0.000639 2022/10/19 15:21:27 - mmengine - INFO - Epoch(train) [137][150/293] lr: 5.000000e-04 eta: 1:57:45 time: 0.383838 data_time: 0.072754 memory: 7187 loss_kpt: 0.000632 acc_pose: 0.756925 loss: 0.000632 2022/10/19 15:21:27 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:21:45 - mmengine - INFO - Epoch(train) [137][200/293] lr: 5.000000e-04 eta: 1:57:30 time: 0.366730 data_time: 0.084048 memory: 7187 loss_kpt: 0.000642 acc_pose: 0.826904 loss: 0.000642 2022/10/19 15:22:03 - mmengine - INFO - Epoch(train) [137][250/293] lr: 5.000000e-04 eta: 1:57:14 time: 0.358182 data_time: 0.069796 memory: 7187 loss_kpt: 0.000641 acc_pose: 0.772051 loss: 0.000641 2022/10/19 15:22:19 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:22:40 - mmengine - INFO - Epoch(train) [138][50/293] lr: 5.000000e-04 eta: 1:56:38 time: 0.410962 data_time: 0.081919 memory: 7187 loss_kpt: 0.000621 acc_pose: 0.868996 loss: 0.000621 2022/10/19 15:22:59 - mmengine - INFO - Epoch(train) [138][100/293] lr: 5.000000e-04 eta: 1:56:23 time: 0.378550 data_time: 0.087789 memory: 7187 loss_kpt: 0.000630 acc_pose: 0.869701 loss: 0.000630 2022/10/19 15:23:16 - mmengine - INFO - Epoch(train) [138][150/293] lr: 5.000000e-04 eta: 1:56:07 time: 0.359185 data_time: 0.063914 memory: 7187 loss_kpt: 0.000624 acc_pose: 0.845982 loss: 0.000624 2022/10/19 15:23:35 - mmengine - INFO - Epoch(train) [138][200/293] lr: 5.000000e-04 eta: 1:55:52 time: 0.368955 data_time: 0.072909 memory: 7187 loss_kpt: 0.000636 acc_pose: 0.847783 loss: 0.000636 2022/10/19 15:23:54 - mmengine - INFO - Epoch(train) [138][250/293] lr: 5.000000e-04 eta: 1:55:37 time: 0.382253 data_time: 0.069783 memory: 7187 loss_kpt: 0.000639 acc_pose: 0.827652 loss: 0.000639 2022/10/19 15:24:10 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:24:29 - mmengine - INFO - Epoch(train) [139][50/293] lr: 5.000000e-04 eta: 1:55:01 time: 0.382208 data_time: 0.084617 memory: 7187 loss_kpt: 0.000636 acc_pose: 0.794715 loss: 0.000636 2022/10/19 15:24:47 - mmengine - INFO - Epoch(train) [139][100/293] lr: 5.000000e-04 eta: 1:54:45 time: 0.363823 data_time: 0.065557 memory: 7187 loss_kpt: 0.000652 acc_pose: 0.826116 loss: 0.000652 2022/10/19 15:25:05 - mmengine - INFO - Epoch(train) [139][150/293] lr: 5.000000e-04 eta: 1:54:30 time: 0.356794 data_time: 0.067499 memory: 7187 loss_kpt: 0.000646 acc_pose: 0.838575 loss: 0.000646 2022/10/19 15:25:23 - mmengine - INFO - Epoch(train) [139][200/293] lr: 5.000000e-04 eta: 1:54:14 time: 0.370757 data_time: 0.074823 memory: 7187 loss_kpt: 0.000642 acc_pose: 0.828089 loss: 0.000642 2022/10/19 15:25:42 - mmengine - INFO - Epoch(train) [139][250/293] lr: 5.000000e-04 eta: 1:53:59 time: 0.370205 data_time: 0.139561 memory: 7187 loss_kpt: 0.000635 acc_pose: 0.833157 loss: 0.000635 2022/10/19 15:25:58 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:26:18 - mmengine - INFO - Epoch(train) [140][50/293] lr: 5.000000e-04 eta: 1:53:23 time: 0.409975 data_time: 0.082060 memory: 7187 loss_kpt: 0.000649 acc_pose: 0.788303 loss: 0.000649 2022/10/19 15:26:36 - mmengine - INFO - Epoch(train) [140][100/293] lr: 5.000000e-04 eta: 1:53:08 time: 0.360700 data_time: 0.072966 memory: 7187 loss_kpt: 0.000640 acc_pose: 0.855664 loss: 0.000640 2022/10/19 15:26:54 - mmengine - INFO - Epoch(train) [140][150/293] lr: 5.000000e-04 eta: 1:52:52 time: 0.367577 data_time: 0.068726 memory: 7187 loss_kpt: 0.000635 acc_pose: 0.816701 loss: 0.000635 2022/10/19 15:27:13 - mmengine - INFO - Epoch(train) [140][200/293] lr: 5.000000e-04 eta: 1:52:37 time: 0.363665 data_time: 0.065616 memory: 7187 loss_kpt: 0.000634 acc_pose: 0.844735 loss: 0.000634 2022/10/19 15:27:31 - mmengine - INFO - Epoch(train) [140][250/293] lr: 5.000000e-04 eta: 1:52:22 time: 0.373303 data_time: 0.065589 memory: 7187 loss_kpt: 0.000636 acc_pose: 0.863220 loss: 0.000636 2022/10/19 15:27:40 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:27:46 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:27:46 - mmengine - INFO - Saving checkpoint at 140 epochs 2022/10/19 15:27:56 - mmengine - INFO - Epoch(val) [140][50/407] eta: 0:00:44 time: 0.125498 data_time: 0.068762 memory: 7187 2022/10/19 15:28:02 - mmengine - INFO - Epoch(val) [140][100/407] eta: 0:00:34 time: 0.113840 data_time: 0.057036 memory: 1014 2022/10/19 15:28:07 - mmengine - INFO - Epoch(val) [140][150/407] eta: 0:00:30 time: 0.118477 data_time: 0.061578 memory: 1014 2022/10/19 15:28:14 - mmengine - INFO - Epoch(val) [140][200/407] eta: 0:00:27 time: 0.131887 data_time: 0.076433 memory: 1014 2022/10/19 15:28:20 - mmengine - INFO - Epoch(val) [140][250/407] eta: 0:00:18 time: 0.117987 data_time: 0.061360 memory: 1014 2022/10/19 15:28:26 - mmengine - INFO - Epoch(val) [140][300/407] eta: 0:00:12 time: 0.114907 data_time: 0.058575 memory: 1014 2022/10/19 15:28:32 - mmengine - INFO - Epoch(val) [140][350/407] eta: 0:00:06 time: 0.120028 data_time: 0.062901 memory: 1014 2022/10/19 15:28:38 - mmengine - INFO - Epoch(val) [140][400/407] eta: 0:00:00 time: 0.117434 data_time: 0.062883 memory: 1014 2022/10/19 15:29:13 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 15:29:26 - mmengine - INFO - Epoch(val) [140][407/407] coco/AP: 0.706887 coco/AP .5: 0.894488 coco/AP .75: 0.782649 coco/AP (M): 0.668603 coco/AP (L): 0.775508 coco/AR: 0.763397 coco/AR .5: 0.931990 coco/AR .75: 0.832336 coco/AR (M): 0.718520 coco/AR (L): 0.827945 2022/10/19 15:29:26 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_256/best_coco/AP_epoch_110.pth is removed 2022/10/19 15:29:28 - mmengine - INFO - The best checkpoint with 0.7069 coco/AP at 140 epoch is saved to best_coco/AP_epoch_140.pth. 2022/10/19 15:29:47 - mmengine - INFO - Epoch(train) [141][50/293] lr: 5.000000e-04 eta: 1:51:45 time: 0.368554 data_time: 0.143371 memory: 7187 loss_kpt: 0.000622 acc_pose: 0.831626 loss: 0.000622 2022/10/19 15:30:06 - mmengine - INFO - Epoch(train) [141][100/293] lr: 5.000000e-04 eta: 1:51:30 time: 0.384339 data_time: 0.071563 memory: 7187 loss_kpt: 0.000648 acc_pose: 0.833701 loss: 0.000648 2022/10/19 15:30:25 - mmengine - INFO - Epoch(train) [141][150/293] lr: 5.000000e-04 eta: 1:51:15 time: 0.370869 data_time: 0.065291 memory: 7187 loss_kpt: 0.000629 acc_pose: 0.819081 loss: 0.000629 2022/10/19 15:30:44 - mmengine - INFO - Epoch(train) [141][200/293] lr: 5.000000e-04 eta: 1:51:00 time: 0.382464 data_time: 0.059822 memory: 7187 loss_kpt: 0.000634 acc_pose: 0.788688 loss: 0.000634 2022/10/19 15:31:04 - mmengine - INFO - Epoch(train) [141][250/293] lr: 5.000000e-04 eta: 1:50:45 time: 0.397758 data_time: 0.073869 memory: 7187 loss_kpt: 0.000627 acc_pose: 0.843090 loss: 0.000627 2022/10/19 15:31:19 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:31:40 - mmengine - INFO - Epoch(train) [142][50/293] lr: 5.000000e-04 eta: 1:50:10 time: 0.410129 data_time: 0.084669 memory: 7187 loss_kpt: 0.000654 acc_pose: 0.849055 loss: 0.000654 2022/10/19 15:31:59 - mmengine - INFO - Epoch(train) [142][100/293] lr: 5.000000e-04 eta: 1:49:55 time: 0.384914 data_time: 0.065598 memory: 7187 loss_kpt: 0.000639 acc_pose: 0.828526 loss: 0.000639 2022/10/19 15:32:18 - mmengine - INFO - Epoch(train) [142][150/293] lr: 5.000000e-04 eta: 1:49:39 time: 0.372586 data_time: 0.067071 memory: 7187 loss_kpt: 0.000639 acc_pose: 0.821895 loss: 0.000639 2022/10/19 15:32:36 - mmengine - INFO - Epoch(train) [142][200/293] lr: 5.000000e-04 eta: 1:49:24 time: 0.378110 data_time: 0.062857 memory: 7187 loss_kpt: 0.000635 acc_pose: 0.804017 loss: 0.000635 2022/10/19 15:32:55 - mmengine - INFO - Epoch(train) [142][250/293] lr: 5.000000e-04 eta: 1:49:09 time: 0.365037 data_time: 0.059611 memory: 7187 loss_kpt: 0.000653 acc_pose: 0.801339 loss: 0.000653 2022/10/19 15:33:11 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:33:30 - mmengine - INFO - Epoch(train) [143][50/293] lr: 5.000000e-04 eta: 1:48:33 time: 0.384462 data_time: 0.083303 memory: 7187 loss_kpt: 0.000634 acc_pose: 0.760780 loss: 0.000634 2022/10/19 15:33:49 - mmengine - INFO - Epoch(train) [143][100/293] lr: 5.000000e-04 eta: 1:48:18 time: 0.384602 data_time: 0.076461 memory: 7187 loss_kpt: 0.000630 acc_pose: 0.837506 loss: 0.000630 2022/10/19 15:34:09 - mmengine - INFO - Epoch(train) [143][150/293] lr: 5.000000e-04 eta: 1:48:03 time: 0.391574 data_time: 0.065573 memory: 7187 loss_kpt: 0.000649 acc_pose: 0.798735 loss: 0.000649 2022/10/19 15:34:27 - mmengine - INFO - Epoch(train) [143][200/293] lr: 5.000000e-04 eta: 1:47:47 time: 0.362655 data_time: 0.068066 memory: 7187 loss_kpt: 0.000643 acc_pose: 0.793512 loss: 0.000643 2022/10/19 15:34:45 - mmengine - INFO - Epoch(train) [143][250/293] lr: 5.000000e-04 eta: 1:47:32 time: 0.367883 data_time: 0.066848 memory: 7187 loss_kpt: 0.000636 acc_pose: 0.791304 loss: 0.000636 2022/10/19 15:35:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:35:21 - mmengine - INFO - Epoch(train) [144][50/293] lr: 5.000000e-04 eta: 1:46:56 time: 0.397771 data_time: 0.091647 memory: 7187 loss_kpt: 0.000640 acc_pose: 0.811549 loss: 0.000640 2022/10/19 15:35:40 - mmengine - INFO - Epoch(train) [144][100/293] lr: 5.000000e-04 eta: 1:46:41 time: 0.381320 data_time: 0.067384 memory: 7187 loss_kpt: 0.000644 acc_pose: 0.818812 loss: 0.000644 2022/10/19 15:35:41 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:35:59 - mmengine - INFO - Epoch(train) [144][150/293] lr: 5.000000e-04 eta: 1:46:26 time: 0.370515 data_time: 0.068173 memory: 7187 loss_kpt: 0.000618 acc_pose: 0.863631 loss: 0.000618 2022/10/19 15:36:17 - mmengine - INFO - Epoch(train) [144][200/293] lr: 5.000000e-04 eta: 1:46:10 time: 0.360911 data_time: 0.062657 memory: 7187 loss_kpt: 0.000619 acc_pose: 0.795808 loss: 0.000619 2022/10/19 15:36:36 - mmengine - INFO - Epoch(train) [144][250/293] lr: 5.000000e-04 eta: 1:45:55 time: 0.381766 data_time: 0.071515 memory: 7187 loss_kpt: 0.000633 acc_pose: 0.733870 loss: 0.000633 2022/10/19 15:36:52 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:37:12 - mmengine - INFO - Epoch(train) [145][50/293] lr: 5.000000e-04 eta: 1:45:20 time: 0.387667 data_time: 0.081081 memory: 7187 loss_kpt: 0.000640 acc_pose: 0.807722 loss: 0.000640 2022/10/19 15:37:31 - mmengine - INFO - Epoch(train) [145][100/293] lr: 5.000000e-04 eta: 1:45:04 time: 0.378106 data_time: 0.070612 memory: 7187 loss_kpt: 0.000633 acc_pose: 0.826755 loss: 0.000633 2022/10/19 15:37:50 - mmengine - INFO - Epoch(train) [145][150/293] lr: 5.000000e-04 eta: 1:44:49 time: 0.385059 data_time: 0.070520 memory: 7187 loss_kpt: 0.000631 acc_pose: 0.851853 loss: 0.000631 2022/10/19 15:38:09 - mmengine - INFO - Epoch(train) [145][200/293] lr: 5.000000e-04 eta: 1:44:34 time: 0.376021 data_time: 0.072407 memory: 7187 loss_kpt: 0.000630 acc_pose: 0.850105 loss: 0.000630 2022/10/19 15:38:28 - mmengine - INFO - Epoch(train) [145][250/293] lr: 5.000000e-04 eta: 1:44:19 time: 0.380257 data_time: 0.066232 memory: 7187 loss_kpt: 0.000634 acc_pose: 0.802342 loss: 0.000634 2022/10/19 15:38:44 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:39:03 - mmengine - INFO - Epoch(train) [146][50/293] lr: 5.000000e-04 eta: 1:43:43 time: 0.388279 data_time: 0.088964 memory: 7187 loss_kpt: 0.000621 acc_pose: 0.845995 loss: 0.000621 2022/10/19 15:39:22 - mmengine - INFO - Epoch(train) [146][100/293] lr: 5.000000e-04 eta: 1:43:28 time: 0.379970 data_time: 0.063715 memory: 7187 loss_kpt: 0.000641 acc_pose: 0.804376 loss: 0.000641 2022/10/19 15:39:41 - mmengine - INFO - Epoch(train) [146][150/293] lr: 5.000000e-04 eta: 1:43:13 time: 0.379033 data_time: 0.068655 memory: 7187 loss_kpt: 0.000628 acc_pose: 0.836682 loss: 0.000628 2022/10/19 15:40:01 - mmengine - INFO - Epoch(train) [146][200/293] lr: 5.000000e-04 eta: 1:42:58 time: 0.406209 data_time: 0.070662 memory: 7187 loss_kpt: 0.000628 acc_pose: 0.866845 loss: 0.000628 2022/10/19 15:40:20 - mmengine - INFO - Epoch(train) [146][250/293] lr: 5.000000e-04 eta: 1:42:43 time: 0.368822 data_time: 0.067217 memory: 7187 loss_kpt: 0.000630 acc_pose: 0.812126 loss: 0.000630 2022/10/19 15:40:36 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:40:56 - mmengine - INFO - Epoch(train) [147][50/293] lr: 5.000000e-04 eta: 1:42:07 time: 0.394499 data_time: 0.109439 memory: 7187 loss_kpt: 0.000620 acc_pose: 0.774428 loss: 0.000620 2022/10/19 15:41:15 - mmengine - INFO - Epoch(train) [147][100/293] lr: 5.000000e-04 eta: 1:41:52 time: 0.369806 data_time: 0.084641 memory: 7187 loss_kpt: 0.000622 acc_pose: 0.841741 loss: 0.000622 2022/10/19 15:41:33 - mmengine - INFO - Epoch(train) [147][150/293] lr: 5.000000e-04 eta: 1:41:37 time: 0.370615 data_time: 0.066384 memory: 7187 loss_kpt: 0.000642 acc_pose: 0.820509 loss: 0.000642 2022/10/19 15:41:52 - mmengine - INFO - Epoch(train) [147][200/293] lr: 5.000000e-04 eta: 1:41:21 time: 0.372933 data_time: 0.070395 memory: 7187 loss_kpt: 0.000634 acc_pose: 0.866303 loss: 0.000634 2022/10/19 15:42:00 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:42:11 - mmengine - INFO - Epoch(train) [147][250/293] lr: 5.000000e-04 eta: 1:41:06 time: 0.386736 data_time: 0.067169 memory: 7187 loss_kpt: 0.000641 acc_pose: 0.848264 loss: 0.000641 2022/10/19 15:42:27 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:42:48 - mmengine - INFO - Epoch(train) [148][50/293] lr: 5.000000e-04 eta: 1:40:31 time: 0.411802 data_time: 0.101411 memory: 7187 loss_kpt: 0.000623 acc_pose: 0.818809 loss: 0.000623 2022/10/19 15:43:06 - mmengine - INFO - Epoch(train) [148][100/293] lr: 5.000000e-04 eta: 1:40:16 time: 0.370345 data_time: 0.152745 memory: 7187 loss_kpt: 0.000626 acc_pose: 0.815612 loss: 0.000626 2022/10/19 15:43:25 - mmengine - INFO - Epoch(train) [148][150/293] lr: 5.000000e-04 eta: 1:40:01 time: 0.380964 data_time: 0.086032 memory: 7187 loss_kpt: 0.000626 acc_pose: 0.845883 loss: 0.000626 2022/10/19 15:43:45 - mmengine - INFO - Epoch(train) [148][200/293] lr: 5.000000e-04 eta: 1:39:46 time: 0.394129 data_time: 0.094988 memory: 7187 loss_kpt: 0.000632 acc_pose: 0.867722 loss: 0.000632 2022/10/19 15:44:03 - mmengine - INFO - Epoch(train) [148][250/293] lr: 5.000000e-04 eta: 1:39:30 time: 0.359955 data_time: 0.062665 memory: 7187 loss_kpt: 0.000633 acc_pose: 0.817396 loss: 0.000633 2022/10/19 15:44:20 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:44:40 - mmengine - INFO - Epoch(train) [149][50/293] lr: 5.000000e-04 eta: 1:38:55 time: 0.415968 data_time: 0.086401 memory: 7187 loss_kpt: 0.000645 acc_pose: 0.847068 loss: 0.000645 2022/10/19 15:44:59 - mmengine - INFO - Epoch(train) [149][100/293] lr: 5.000000e-04 eta: 1:38:40 time: 0.376057 data_time: 0.068668 memory: 7187 loss_kpt: 0.000631 acc_pose: 0.859573 loss: 0.000631 2022/10/19 15:45:19 - mmengine - INFO - Epoch(train) [149][150/293] lr: 5.000000e-04 eta: 1:38:25 time: 0.403817 data_time: 0.068221 memory: 7187 loss_kpt: 0.000636 acc_pose: 0.807328 loss: 0.000636 2022/10/19 15:45:38 - mmengine - INFO - Epoch(train) [149][200/293] lr: 5.000000e-04 eta: 1:38:10 time: 0.380258 data_time: 0.070756 memory: 7187 loss_kpt: 0.000644 acc_pose: 0.855107 loss: 0.000644 2022/10/19 15:45:58 - mmengine - INFO - Epoch(train) [149][250/293] lr: 5.000000e-04 eta: 1:37:55 time: 0.386257 data_time: 0.070407 memory: 7187 loss_kpt: 0.000626 acc_pose: 0.850639 loss: 0.000626 2022/10/19 15:46:14 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:46:33 - mmengine - INFO - Epoch(train) [150][50/293] lr: 5.000000e-04 eta: 1:37:20 time: 0.385521 data_time: 0.085496 memory: 7187 loss_kpt: 0.000624 acc_pose: 0.810589 loss: 0.000624 2022/10/19 15:46:53 - mmengine - INFO - Epoch(train) [150][100/293] lr: 5.000000e-04 eta: 1:37:04 time: 0.389952 data_time: 0.069332 memory: 7187 loss_kpt: 0.000636 acc_pose: 0.839301 loss: 0.000636 2022/10/19 15:47:12 - mmengine - INFO - Epoch(train) [150][150/293] lr: 5.000000e-04 eta: 1:36:49 time: 0.373362 data_time: 0.069639 memory: 7187 loss_kpt: 0.000631 acc_pose: 0.779889 loss: 0.000631 2022/10/19 15:47:31 - mmengine - INFO - Epoch(train) [150][200/293] lr: 5.000000e-04 eta: 1:36:34 time: 0.381859 data_time: 0.062859 memory: 7187 loss_kpt: 0.000635 acc_pose: 0.813199 loss: 0.000635 2022/10/19 15:47:49 - mmengine - INFO - Epoch(train) [150][250/293] lr: 5.000000e-04 eta: 1:36:18 time: 0.363240 data_time: 0.070914 memory: 7187 loss_kpt: 0.000632 acc_pose: 0.824713 loss: 0.000632 2022/10/19 15:48:04 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:48:04 - mmengine - INFO - Saving checkpoint at 150 epochs 2022/10/19 15:48:14 - mmengine - INFO - Epoch(val) [150][50/407] eta: 0:00:45 time: 0.128266 data_time: 0.071877 memory: 7187 2022/10/19 15:48:20 - mmengine - INFO - Epoch(val) [150][100/407] eta: 0:00:37 time: 0.122694 data_time: 0.066695 memory: 1014 2022/10/19 15:48:26 - mmengine - INFO - Epoch(val) [150][150/407] eta: 0:00:30 time: 0.119024 data_time: 0.062767 memory: 1014 2022/10/19 15:48:32 - mmengine - INFO - Epoch(val) [150][200/407] eta: 0:00:23 time: 0.115911 data_time: 0.059810 memory: 1014 2022/10/19 15:48:38 - mmengine - INFO - Epoch(val) [150][250/407] eta: 0:00:18 time: 0.116902 data_time: 0.057324 memory: 1014 2022/10/19 15:48:43 - mmengine - INFO - Epoch(val) [150][300/407] eta: 0:00:12 time: 0.115630 data_time: 0.060089 memory: 1014 2022/10/19 15:48:50 - mmengine - INFO - Epoch(val) [150][350/407] eta: 0:00:07 time: 0.122941 data_time: 0.066947 memory: 1014 2022/10/19 15:48:55 - mmengine - INFO - Epoch(val) [150][400/407] eta: 0:00:00 time: 0.113268 data_time: 0.058823 memory: 1014 2022/10/19 15:49:30 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 15:49:44 - mmengine - INFO - Epoch(val) [150][407/407] coco/AP: 0.709255 coco/AP .5: 0.895332 coco/AP .75: 0.783278 coco/AP (M): 0.669933 coco/AP (L): 0.779169 coco/AR: 0.765365 coco/AR .5: 0.933249 coco/AR .75: 0.831077 coco/AR (M): 0.718902 coco/AR (L): 0.832144 2022/10/19 15:49:44 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_256/best_coco/AP_epoch_140.pth is removed 2022/10/19 15:49:46 - mmengine - INFO - The best checkpoint with 0.7093 coco/AP at 150 epoch is saved to best_coco/AP_epoch_150.pth. 2022/10/19 15:50:05 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:50:05 - mmengine - INFO - Epoch(train) [151][50/293] lr: 5.000000e-04 eta: 1:35:43 time: 0.383432 data_time: 0.149042 memory: 7187 loss_kpt: 0.000639 acc_pose: 0.852304 loss: 0.000639 2022/10/19 15:50:24 - mmengine - INFO - Epoch(train) [151][100/293] lr: 5.000000e-04 eta: 1:35:27 time: 0.368497 data_time: 0.069478 memory: 7187 loss_kpt: 0.000628 acc_pose: 0.848602 loss: 0.000628 2022/10/19 15:50:42 - mmengine - INFO - Epoch(train) [151][150/293] lr: 5.000000e-04 eta: 1:35:12 time: 0.374172 data_time: 0.063874 memory: 7187 loss_kpt: 0.000626 acc_pose: 0.817552 loss: 0.000626 2022/10/19 15:51:01 - mmengine - INFO - Epoch(train) [151][200/293] lr: 5.000000e-04 eta: 1:34:57 time: 0.380931 data_time: 0.062501 memory: 7187 loss_kpt: 0.000634 acc_pose: 0.849841 loss: 0.000634 2022/10/19 15:51:21 - mmengine - INFO - Epoch(train) [151][250/293] lr: 5.000000e-04 eta: 1:34:41 time: 0.381984 data_time: 0.089574 memory: 7187 loss_kpt: 0.000626 acc_pose: 0.812742 loss: 0.000626 2022/10/19 15:51:37 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:51:56 - mmengine - INFO - Epoch(train) [152][50/293] lr: 5.000000e-04 eta: 1:34:06 time: 0.386281 data_time: 0.091984 memory: 7187 loss_kpt: 0.000631 acc_pose: 0.829527 loss: 0.000631 2022/10/19 15:52:15 - mmengine - INFO - Epoch(train) [152][100/293] lr: 5.000000e-04 eta: 1:33:51 time: 0.374690 data_time: 0.074913 memory: 7187 loss_kpt: 0.000636 acc_pose: 0.852974 loss: 0.000636 2022/10/19 15:52:35 - mmengine - INFO - Epoch(train) [152][150/293] lr: 5.000000e-04 eta: 1:33:36 time: 0.392573 data_time: 0.177073 memory: 7187 loss_kpt: 0.000617 acc_pose: 0.806881 loss: 0.000617 2022/10/19 15:52:53 - mmengine - INFO - Epoch(train) [152][200/293] lr: 5.000000e-04 eta: 1:33:20 time: 0.368308 data_time: 0.066548 memory: 7187 loss_kpt: 0.000642 acc_pose: 0.879199 loss: 0.000642 2022/10/19 15:53:12 - mmengine - INFO - Epoch(train) [152][250/293] lr: 5.000000e-04 eta: 1:33:05 time: 0.371544 data_time: 0.067894 memory: 7187 loss_kpt: 0.000630 acc_pose: 0.857839 loss: 0.000630 2022/10/19 15:53:28 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:53:47 - mmengine - INFO - Epoch(train) [153][50/293] lr: 5.000000e-04 eta: 1:32:30 time: 0.388726 data_time: 0.084338 memory: 7187 loss_kpt: 0.000628 acc_pose: 0.820476 loss: 0.000628 2022/10/19 15:54:07 - mmengine - INFO - Epoch(train) [153][100/293] lr: 5.000000e-04 eta: 1:32:15 time: 0.382821 data_time: 0.097081 memory: 7187 loss_kpt: 0.000627 acc_pose: 0.822996 loss: 0.000627 2022/10/19 15:54:25 - mmengine - INFO - Epoch(train) [153][150/293] lr: 5.000000e-04 eta: 1:31:59 time: 0.374698 data_time: 0.064706 memory: 7187 loss_kpt: 0.000626 acc_pose: 0.893063 loss: 0.000626 2022/10/19 15:54:44 - mmengine - INFO - Epoch(train) [153][200/293] lr: 5.000000e-04 eta: 1:31:44 time: 0.377391 data_time: 0.070964 memory: 7187 loss_kpt: 0.000633 acc_pose: 0.851397 loss: 0.000633 2022/10/19 15:55:03 - mmengine - INFO - Epoch(train) [153][250/293] lr: 5.000000e-04 eta: 1:31:28 time: 0.375342 data_time: 0.072067 memory: 7187 loss_kpt: 0.000623 acc_pose: 0.840342 loss: 0.000623 2022/10/19 15:55:18 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:55:38 - mmengine - INFO - Epoch(train) [154][50/293] lr: 5.000000e-04 eta: 1:30:54 time: 0.396371 data_time: 0.081132 memory: 7187 loss_kpt: 0.000627 acc_pose: 0.841214 loss: 0.000627 2022/10/19 15:55:57 - mmengine - INFO - Epoch(train) [154][100/293] lr: 5.000000e-04 eta: 1:30:38 time: 0.366543 data_time: 0.063909 memory: 7187 loss_kpt: 0.000625 acc_pose: 0.842101 loss: 0.000625 2022/10/19 15:56:15 - mmengine - INFO - Epoch(train) [154][150/293] lr: 5.000000e-04 eta: 1:30:23 time: 0.377745 data_time: 0.074808 memory: 7187 loss_kpt: 0.000624 acc_pose: 0.834583 loss: 0.000624 2022/10/19 15:56:23 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:56:34 - mmengine - INFO - Epoch(train) [154][200/293] lr: 5.000000e-04 eta: 1:30:07 time: 0.375808 data_time: 0.081309 memory: 7187 loss_kpt: 0.000640 acc_pose: 0.832857 loss: 0.000640 2022/10/19 15:56:54 - mmengine - INFO - Epoch(train) [154][250/293] lr: 5.000000e-04 eta: 1:29:52 time: 0.391104 data_time: 0.080440 memory: 7187 loss_kpt: 0.000620 acc_pose: 0.830413 loss: 0.000620 2022/10/19 15:57:09 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:57:28 - mmengine - INFO - Epoch(train) [155][50/293] lr: 5.000000e-04 eta: 1:29:17 time: 0.386510 data_time: 0.083379 memory: 7187 loss_kpt: 0.000635 acc_pose: 0.831737 loss: 0.000635 2022/10/19 15:57:47 - mmengine - INFO - Epoch(train) [155][100/293] lr: 5.000000e-04 eta: 1:29:02 time: 0.377735 data_time: 0.070126 memory: 7187 loss_kpt: 0.000632 acc_pose: 0.793426 loss: 0.000632 2022/10/19 15:58:06 - mmengine - INFO - Epoch(train) [155][150/293] lr: 5.000000e-04 eta: 1:28:46 time: 0.375566 data_time: 0.066100 memory: 7187 loss_kpt: 0.000634 acc_pose: 0.859213 loss: 0.000634 2022/10/19 15:58:24 - mmengine - INFO - Epoch(train) [155][200/293] lr: 5.000000e-04 eta: 1:28:31 time: 0.371600 data_time: 0.070963 memory: 7187 loss_kpt: 0.000629 acc_pose: 0.829900 loss: 0.000629 2022/10/19 15:58:43 - mmengine - INFO - Epoch(train) [155][250/293] lr: 5.000000e-04 eta: 1:28:15 time: 0.378020 data_time: 0.095394 memory: 7187 loss_kpt: 0.000634 acc_pose: 0.848204 loss: 0.000634 2022/10/19 15:58:59 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 15:59:18 - mmengine - INFO - Epoch(train) [156][50/293] lr: 5.000000e-04 eta: 1:27:41 time: 0.382804 data_time: 0.089327 memory: 7187 loss_kpt: 0.000631 acc_pose: 0.838190 loss: 0.000631 2022/10/19 15:59:36 - mmengine - INFO - Epoch(train) [156][100/293] lr: 5.000000e-04 eta: 1:27:25 time: 0.357989 data_time: 0.073661 memory: 7187 loss_kpt: 0.000633 acc_pose: 0.844245 loss: 0.000633 2022/10/19 15:59:55 - mmengine - INFO - Epoch(train) [156][150/293] lr: 5.000000e-04 eta: 1:27:10 time: 0.382104 data_time: 0.061802 memory: 7187 loss_kpt: 0.000622 acc_pose: 0.795213 loss: 0.000622 2022/10/19 16:00:14 - mmengine - INFO - Epoch(train) [156][200/293] lr: 5.000000e-04 eta: 1:26:54 time: 0.373271 data_time: 0.073457 memory: 7187 loss_kpt: 0.000627 acc_pose: 0.825038 loss: 0.000627 2022/10/19 16:00:32 - mmengine - INFO - Epoch(train) [156][250/293] lr: 5.000000e-04 eta: 1:26:38 time: 0.362385 data_time: 0.073636 memory: 7187 loss_kpt: 0.000629 acc_pose: 0.847684 loss: 0.000629 2022/10/19 16:00:48 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:01:07 - mmengine - INFO - Epoch(train) [157][50/293] lr: 5.000000e-04 eta: 1:26:04 time: 0.387828 data_time: 0.099182 memory: 7187 loss_kpt: 0.000641 acc_pose: 0.856482 loss: 0.000641 2022/10/19 16:01:27 - mmengine - INFO - Epoch(train) [157][100/293] lr: 5.000000e-04 eta: 1:25:49 time: 0.396408 data_time: 0.071434 memory: 7187 loss_kpt: 0.000623 acc_pose: 0.828682 loss: 0.000623 2022/10/19 16:01:46 - mmengine - INFO - Epoch(train) [157][150/293] lr: 5.000000e-04 eta: 1:25:33 time: 0.381062 data_time: 0.072284 memory: 7187 loss_kpt: 0.000630 acc_pose: 0.812455 loss: 0.000630 2022/10/19 16:02:04 - mmengine - INFO - Epoch(train) [157][200/293] lr: 5.000000e-04 eta: 1:25:17 time: 0.355686 data_time: 0.088658 memory: 7187 loss_kpt: 0.000635 acc_pose: 0.825794 loss: 0.000635 2022/10/19 16:02:23 - mmengine - INFO - Epoch(train) [157][250/293] lr: 5.000000e-04 eta: 1:25:02 time: 0.370501 data_time: 0.062089 memory: 7187 loss_kpt: 0.000624 acc_pose: 0.836519 loss: 0.000624 2022/10/19 16:02:38 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:02:38 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:02:58 - mmengine - INFO - Epoch(train) [158][50/293] lr: 5.000000e-04 eta: 1:24:28 time: 0.390425 data_time: 0.085058 memory: 7187 loss_kpt: 0.000617 acc_pose: 0.872189 loss: 0.000617 2022/10/19 16:03:16 - mmengine - INFO - Epoch(train) [158][100/293] lr: 5.000000e-04 eta: 1:24:12 time: 0.369985 data_time: 0.064927 memory: 7187 loss_kpt: 0.000649 acc_pose: 0.847828 loss: 0.000649 2022/10/19 16:03:35 - mmengine - INFO - Epoch(train) [158][150/293] lr: 5.000000e-04 eta: 1:23:56 time: 0.376787 data_time: 0.066912 memory: 7187 loss_kpt: 0.000616 acc_pose: 0.835333 loss: 0.000616 2022/10/19 16:03:54 - mmengine - INFO - Epoch(train) [158][200/293] lr: 5.000000e-04 eta: 1:23:41 time: 0.369658 data_time: 0.069264 memory: 7187 loss_kpt: 0.000637 acc_pose: 0.850532 loss: 0.000637 2022/10/19 16:04:13 - mmengine - INFO - Epoch(train) [158][250/293] lr: 5.000000e-04 eta: 1:23:25 time: 0.380431 data_time: 0.065561 memory: 7187 loss_kpt: 0.000622 acc_pose: 0.843986 loss: 0.000622 2022/10/19 16:04:28 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:04:47 - mmengine - INFO - Epoch(train) [159][50/293] lr: 5.000000e-04 eta: 1:22:51 time: 0.380720 data_time: 0.112756 memory: 7187 loss_kpt: 0.000618 acc_pose: 0.865843 loss: 0.000618 2022/10/19 16:05:06 - mmengine - INFO - Epoch(train) [159][100/293] lr: 5.000000e-04 eta: 1:22:35 time: 0.372278 data_time: 0.070271 memory: 7187 loss_kpt: 0.000633 acc_pose: 0.853668 loss: 0.000633 2022/10/19 16:05:24 - mmengine - INFO - Epoch(train) [159][150/293] lr: 5.000000e-04 eta: 1:22:19 time: 0.360398 data_time: 0.080516 memory: 7187 loss_kpt: 0.000617 acc_pose: 0.881105 loss: 0.000617 2022/10/19 16:05:42 - mmengine - INFO - Epoch(train) [159][200/293] lr: 5.000000e-04 eta: 1:22:04 time: 0.369423 data_time: 0.089751 memory: 7187 loss_kpt: 0.000632 acc_pose: 0.865985 loss: 0.000632 2022/10/19 16:06:01 - mmengine - INFO - Epoch(train) [159][250/293] lr: 5.000000e-04 eta: 1:21:48 time: 0.371297 data_time: 0.111119 memory: 7187 loss_kpt: 0.000635 acc_pose: 0.845152 loss: 0.000635 2022/10/19 16:06:17 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:06:36 - mmengine - INFO - Epoch(train) [160][50/293] lr: 5.000000e-04 eta: 1:21:14 time: 0.386681 data_time: 0.096869 memory: 7187 loss_kpt: 0.000623 acc_pose: 0.839676 loss: 0.000623 2022/10/19 16:06:55 - mmengine - INFO - Epoch(train) [160][100/293] lr: 5.000000e-04 eta: 1:20:58 time: 0.372545 data_time: 0.065793 memory: 7187 loss_kpt: 0.000624 acc_pose: 0.821644 loss: 0.000624 2022/10/19 16:07:13 - mmengine - INFO - Epoch(train) [160][150/293] lr: 5.000000e-04 eta: 1:20:43 time: 0.364167 data_time: 0.068832 memory: 7187 loss_kpt: 0.000629 acc_pose: 0.820026 loss: 0.000629 2022/10/19 16:07:32 - mmengine - INFO - Epoch(train) [160][200/293] lr: 5.000000e-04 eta: 1:20:27 time: 0.382111 data_time: 0.064863 memory: 7187 loss_kpt: 0.000613 acc_pose: 0.851961 loss: 0.000613 2022/10/19 16:07:51 - mmengine - INFO - Epoch(train) [160][250/293] lr: 5.000000e-04 eta: 1:20:12 time: 0.376880 data_time: 0.073978 memory: 7187 loss_kpt: 0.000638 acc_pose: 0.831376 loss: 0.000638 2022/10/19 16:08:07 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:08:07 - mmengine - INFO - Saving checkpoint at 160 epochs 2022/10/19 16:08:17 - mmengine - INFO - Epoch(val) [160][50/407] eta: 0:00:43 time: 0.122135 data_time: 0.066057 memory: 7187 2022/10/19 16:08:22 - mmengine - INFO - Epoch(val) [160][100/407] eta: 0:00:34 time: 0.113520 data_time: 0.058710 memory: 1014 2022/10/19 16:08:28 - mmengine - INFO - Epoch(val) [160][150/407] eta: 0:00:31 time: 0.121642 data_time: 0.064971 memory: 1014 2022/10/19 16:08:34 - mmengine - INFO - Epoch(val) [160][200/407] eta: 0:00:24 time: 0.117855 data_time: 0.061217 memory: 1014 2022/10/19 16:08:41 - mmengine - INFO - Epoch(val) [160][250/407] eta: 0:00:19 time: 0.126229 data_time: 0.069626 memory: 1014 2022/10/19 16:08:47 - mmengine - INFO - Epoch(val) [160][300/407] eta: 0:00:12 time: 0.117062 data_time: 0.060354 memory: 1014 2022/10/19 16:08:53 - mmengine - INFO - Epoch(val) [160][350/407] eta: 0:00:07 time: 0.128495 data_time: 0.073258 memory: 1014 2022/10/19 16:08:59 - mmengine - INFO - Epoch(val) [160][400/407] eta: 0:00:00 time: 0.113343 data_time: 0.060432 memory: 1014 2022/10/19 16:09:34 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 16:09:48 - mmengine - INFO - Epoch(val) [160][407/407] coco/AP: 0.710667 coco/AP .5: 0.894508 coco/AP .75: 0.787548 coco/AP (M): 0.669896 coco/AP (L): 0.781266 coco/AR: 0.765586 coco/AR .5: 0.932935 coco/AR .75: 0.833753 coco/AR (M): 0.719039 coco/AR (L): 0.832256 2022/10/19 16:09:48 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_256/best_coco/AP_epoch_150.pth is removed 2022/10/19 16:09:50 - mmengine - INFO - The best checkpoint with 0.7107 coco/AP at 160 epoch is saved to best_coco/AP_epoch_160.pth. 2022/10/19 16:10:09 - mmengine - INFO - Epoch(train) [161][50/293] lr: 5.000000e-04 eta: 1:19:38 time: 0.387440 data_time: 0.170637 memory: 7187 loss_kpt: 0.000613 acc_pose: 0.820496 loss: 0.000613 2022/10/19 16:10:28 - mmengine - INFO - Epoch(train) [161][100/293] lr: 5.000000e-04 eta: 1:19:22 time: 0.376219 data_time: 0.080051 memory: 7187 loss_kpt: 0.000627 acc_pose: 0.855876 loss: 0.000627 2022/10/19 16:10:35 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:10:46 - mmengine - INFO - Epoch(train) [161][150/293] lr: 5.000000e-04 eta: 1:19:06 time: 0.353612 data_time: 0.061304 memory: 7187 loss_kpt: 0.000613 acc_pose: 0.855314 loss: 0.000613 2022/10/19 16:11:04 - mmengine - INFO - Epoch(train) [161][200/293] lr: 5.000000e-04 eta: 1:18:50 time: 0.376449 data_time: 0.066429 memory: 7187 loss_kpt: 0.000633 acc_pose: 0.813212 loss: 0.000633 2022/10/19 16:11:24 - mmengine - INFO - Epoch(train) [161][250/293] lr: 5.000000e-04 eta: 1:18:35 time: 0.382379 data_time: 0.069892 memory: 7187 loss_kpt: 0.000634 acc_pose: 0.804726 loss: 0.000634 2022/10/19 16:11:39 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:11:59 - mmengine - INFO - Epoch(train) [162][50/293] lr: 5.000000e-04 eta: 1:18:01 time: 0.381618 data_time: 0.084853 memory: 7187 loss_kpt: 0.000623 acc_pose: 0.815906 loss: 0.000623 2022/10/19 16:12:17 - mmengine - INFO - Epoch(train) [162][100/293] lr: 5.000000e-04 eta: 1:17:45 time: 0.371241 data_time: 0.068454 memory: 7187 loss_kpt: 0.000630 acc_pose: 0.813883 loss: 0.000630 2022/10/19 16:12:36 - mmengine - INFO - Epoch(train) [162][150/293] lr: 5.000000e-04 eta: 1:17:30 time: 0.386497 data_time: 0.069111 memory: 7187 loss_kpt: 0.000621 acc_pose: 0.819262 loss: 0.000621 2022/10/19 16:12:55 - mmengine - INFO - Epoch(train) [162][200/293] lr: 5.000000e-04 eta: 1:17:14 time: 0.365500 data_time: 0.102668 memory: 7187 loss_kpt: 0.000632 acc_pose: 0.855031 loss: 0.000632 2022/10/19 16:13:13 - mmengine - INFO - Epoch(train) [162][250/293] lr: 5.000000e-04 eta: 1:16:58 time: 0.369433 data_time: 0.073778 memory: 7187 loss_kpt: 0.000627 acc_pose: 0.797631 loss: 0.000627 2022/10/19 16:13:30 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:13:49 - mmengine - INFO - Epoch(train) [163][50/293] lr: 5.000000e-04 eta: 1:16:24 time: 0.372791 data_time: 0.075489 memory: 7187 loss_kpt: 0.000626 acc_pose: 0.824177 loss: 0.000626 2022/10/19 16:14:07 - mmengine - INFO - Epoch(train) [163][100/293] lr: 5.000000e-04 eta: 1:16:09 time: 0.367056 data_time: 0.069768 memory: 7187 loss_kpt: 0.000628 acc_pose: 0.850277 loss: 0.000628 2022/10/19 16:14:25 - mmengine - INFO - Epoch(train) [163][150/293] lr: 5.000000e-04 eta: 1:15:53 time: 0.360193 data_time: 0.072887 memory: 7187 loss_kpt: 0.000611 acc_pose: 0.820879 loss: 0.000611 2022/10/19 16:14:44 - mmengine - INFO - Epoch(train) [163][200/293] lr: 5.000000e-04 eta: 1:15:37 time: 0.382661 data_time: 0.109579 memory: 7187 loss_kpt: 0.000615 acc_pose: 0.863917 loss: 0.000615 2022/10/19 16:15:03 - mmengine - INFO - Epoch(train) [163][250/293] lr: 5.000000e-04 eta: 1:15:22 time: 0.378646 data_time: 0.101289 memory: 7187 loss_kpt: 0.000640 acc_pose: 0.881414 loss: 0.000640 2022/10/19 16:15:19 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:15:38 - mmengine - INFO - Epoch(train) [164][50/293] lr: 5.000000e-04 eta: 1:14:48 time: 0.391042 data_time: 0.122488 memory: 7187 loss_kpt: 0.000617 acc_pose: 0.848572 loss: 0.000617 2022/10/19 16:15:57 - mmengine - INFO - Epoch(train) [164][100/293] lr: 5.000000e-04 eta: 1:14:32 time: 0.376374 data_time: 0.104043 memory: 7187 loss_kpt: 0.000629 acc_pose: 0.832879 loss: 0.000629 2022/10/19 16:16:15 - mmengine - INFO - Epoch(train) [164][150/293] lr: 5.000000e-04 eta: 1:14:16 time: 0.359013 data_time: 0.069914 memory: 7187 loss_kpt: 0.000626 acc_pose: 0.811034 loss: 0.000626 2022/10/19 16:16:34 - mmengine - INFO - Epoch(train) [164][200/293] lr: 5.000000e-04 eta: 1:14:01 time: 0.380080 data_time: 0.069836 memory: 7187 loss_kpt: 0.000628 acc_pose: 0.813362 loss: 0.000628 2022/10/19 16:16:49 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:16:52 - mmengine - INFO - Epoch(train) [164][250/293] lr: 5.000000e-04 eta: 1:13:45 time: 0.355130 data_time: 0.070057 memory: 7187 loss_kpt: 0.000620 acc_pose: 0.839902 loss: 0.000620 2022/10/19 16:17:08 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:17:28 - mmengine - INFO - Epoch(train) [165][50/293] lr: 5.000000e-04 eta: 1:13:11 time: 0.389484 data_time: 0.088289 memory: 7187 loss_kpt: 0.000608 acc_pose: 0.869660 loss: 0.000608 2022/10/19 16:17:45 - mmengine - INFO - Epoch(train) [165][100/293] lr: 5.000000e-04 eta: 1:12:55 time: 0.357777 data_time: 0.096954 memory: 7187 loss_kpt: 0.000626 acc_pose: 0.800949 loss: 0.000626 2022/10/19 16:18:04 - mmengine - INFO - Epoch(train) [165][150/293] lr: 5.000000e-04 eta: 1:12:40 time: 0.375810 data_time: 0.069654 memory: 7187 loss_kpt: 0.000625 acc_pose: 0.804915 loss: 0.000625 2022/10/19 16:18:23 - mmengine - INFO - Epoch(train) [165][200/293] lr: 5.000000e-04 eta: 1:12:24 time: 0.366865 data_time: 0.069120 memory: 7187 loss_kpt: 0.000618 acc_pose: 0.804971 loss: 0.000618 2022/10/19 16:18:41 - mmengine - INFO - Epoch(train) [165][250/293] lr: 5.000000e-04 eta: 1:12:08 time: 0.374625 data_time: 0.075079 memory: 7187 loss_kpt: 0.000625 acc_pose: 0.815513 loss: 0.000625 2022/10/19 16:18:58 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:19:17 - mmengine - INFO - Epoch(train) [166][50/293] lr: 5.000000e-04 eta: 1:11:35 time: 0.379122 data_time: 0.117727 memory: 7187 loss_kpt: 0.000621 acc_pose: 0.841263 loss: 0.000621 2022/10/19 16:19:35 - mmengine - INFO - Epoch(train) [166][100/293] lr: 5.000000e-04 eta: 1:11:19 time: 0.377469 data_time: 0.070308 memory: 7187 loss_kpt: 0.000623 acc_pose: 0.849735 loss: 0.000623 2022/10/19 16:19:54 - mmengine - INFO - Epoch(train) [166][150/293] lr: 5.000000e-04 eta: 1:11:03 time: 0.378565 data_time: 0.067953 memory: 7187 loss_kpt: 0.000610 acc_pose: 0.836905 loss: 0.000610 2022/10/19 16:20:13 - mmengine - INFO - Epoch(train) [166][200/293] lr: 5.000000e-04 eta: 1:10:48 time: 0.380256 data_time: 0.066299 memory: 7187 loss_kpt: 0.000623 acc_pose: 0.847765 loss: 0.000623 2022/10/19 16:20:32 - mmengine - INFO - Epoch(train) [166][250/293] lr: 5.000000e-04 eta: 1:10:32 time: 0.371584 data_time: 0.065299 memory: 7187 loss_kpt: 0.000620 acc_pose: 0.782488 loss: 0.000620 2022/10/19 16:20:48 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:21:07 - mmengine - INFO - Epoch(train) [167][50/293] lr: 5.000000e-04 eta: 1:09:59 time: 0.388253 data_time: 0.097490 memory: 7187 loss_kpt: 0.000619 acc_pose: 0.782765 loss: 0.000619 2022/10/19 16:21:26 - mmengine - INFO - Epoch(train) [167][100/293] lr: 5.000000e-04 eta: 1:09:43 time: 0.367680 data_time: 0.062644 memory: 7187 loss_kpt: 0.000617 acc_pose: 0.774260 loss: 0.000617 2022/10/19 16:21:45 - mmengine - INFO - Epoch(train) [167][150/293] lr: 5.000000e-04 eta: 1:09:27 time: 0.378306 data_time: 0.138574 memory: 7187 loss_kpt: 0.000624 acc_pose: 0.840123 loss: 0.000624 2022/10/19 16:22:03 - mmengine - INFO - Epoch(train) [167][200/293] lr: 5.000000e-04 eta: 1:09:11 time: 0.364994 data_time: 0.151612 memory: 7187 loss_kpt: 0.000627 acc_pose: 0.830097 loss: 0.000627 2022/10/19 16:22:22 - mmengine - INFO - Epoch(train) [167][250/293] lr: 5.000000e-04 eta: 1:08:55 time: 0.374430 data_time: 0.074279 memory: 7187 loss_kpt: 0.000616 acc_pose: 0.849768 loss: 0.000616 2022/10/19 16:22:38 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:22:58 - mmengine - INFO - Epoch(train) [168][50/293] lr: 5.000000e-04 eta: 1:08:22 time: 0.400659 data_time: 0.099508 memory: 7187 loss_kpt: 0.000619 acc_pose: 0.827885 loss: 0.000619 2022/10/19 16:23:05 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:23:17 - mmengine - INFO - Epoch(train) [168][100/293] lr: 5.000000e-04 eta: 1:08:07 time: 0.380216 data_time: 0.074931 memory: 7187 loss_kpt: 0.000616 acc_pose: 0.849255 loss: 0.000616 2022/10/19 16:23:36 - mmengine - INFO - Epoch(train) [168][150/293] lr: 5.000000e-04 eta: 1:07:51 time: 0.376483 data_time: 0.068247 memory: 7187 loss_kpt: 0.000622 acc_pose: 0.860908 loss: 0.000622 2022/10/19 16:23:54 - mmengine - INFO - Epoch(train) [168][200/293] lr: 5.000000e-04 eta: 1:07:35 time: 0.366570 data_time: 0.077718 memory: 7187 loss_kpt: 0.000635 acc_pose: 0.863000 loss: 0.000635 2022/10/19 16:24:13 - mmengine - INFO - Epoch(train) [168][250/293] lr: 5.000000e-04 eta: 1:07:19 time: 0.374494 data_time: 0.112755 memory: 7187 loss_kpt: 0.000624 acc_pose: 0.826201 loss: 0.000624 2022/10/19 16:24:28 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:24:47 - mmengine - INFO - Epoch(train) [169][50/293] lr: 5.000000e-04 eta: 1:06:46 time: 0.379478 data_time: 0.090375 memory: 7187 loss_kpt: 0.000613 acc_pose: 0.799423 loss: 0.000613 2022/10/19 16:25:05 - mmengine - INFO - Epoch(train) [169][100/293] lr: 5.000000e-04 eta: 1:06:30 time: 0.375029 data_time: 0.082519 memory: 7187 loss_kpt: 0.000620 acc_pose: 0.806974 loss: 0.000620 2022/10/19 16:25:23 - mmengine - INFO - Epoch(train) [169][150/293] lr: 5.000000e-04 eta: 1:06:14 time: 0.360077 data_time: 0.065754 memory: 7187 loss_kpt: 0.000618 acc_pose: 0.880891 loss: 0.000618 2022/10/19 16:25:43 - mmengine - INFO - Epoch(train) [169][200/293] lr: 5.000000e-04 eta: 1:05:59 time: 0.382577 data_time: 0.074191 memory: 7187 loss_kpt: 0.000631 acc_pose: 0.827591 loss: 0.000631 2022/10/19 16:26:02 - mmengine - INFO - Epoch(train) [169][250/293] lr: 5.000000e-04 eta: 1:05:43 time: 0.384978 data_time: 0.091570 memory: 7187 loss_kpt: 0.000616 acc_pose: 0.803163 loss: 0.000616 2022/10/19 16:26:17 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:26:36 - mmengine - INFO - Epoch(train) [170][50/293] lr: 5.000000e-04 eta: 1:05:10 time: 0.385625 data_time: 0.118924 memory: 7187 loss_kpt: 0.000615 acc_pose: 0.854645 loss: 0.000615 2022/10/19 16:26:55 - mmengine - INFO - Epoch(train) [170][100/293] lr: 5.000000e-04 eta: 1:04:54 time: 0.375334 data_time: 0.077842 memory: 7187 loss_kpt: 0.000617 acc_pose: 0.808878 loss: 0.000617 2022/10/19 16:27:13 - mmengine - INFO - Epoch(train) [170][150/293] lr: 5.000000e-04 eta: 1:04:38 time: 0.364728 data_time: 0.064698 memory: 7187 loss_kpt: 0.000617 acc_pose: 0.850622 loss: 0.000617 2022/10/19 16:27:32 - mmengine - INFO - Epoch(train) [170][200/293] lr: 5.000000e-04 eta: 1:04:23 time: 0.377083 data_time: 0.077739 memory: 7187 loss_kpt: 0.000648 acc_pose: 0.816616 loss: 0.000648 2022/10/19 16:27:50 - mmengine - INFO - Epoch(train) [170][250/293] lr: 5.000000e-04 eta: 1:04:07 time: 0.351741 data_time: 0.068645 memory: 7187 loss_kpt: 0.000617 acc_pose: 0.821058 loss: 0.000617 2022/10/19 16:28:05 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:28:05 - mmengine - INFO - Saving checkpoint at 170 epochs 2022/10/19 16:28:15 - mmengine - INFO - Epoch(val) [170][50/407] eta: 0:00:43 time: 0.122986 data_time: 0.066476 memory: 7187 2022/10/19 16:28:21 - mmengine - INFO - Epoch(val) [170][100/407] eta: 0:00:39 time: 0.128935 data_time: 0.072114 memory: 1014 2022/10/19 16:28:27 - mmengine - INFO - Epoch(val) [170][150/407] eta: 0:00:32 time: 0.127194 data_time: 0.070000 memory: 1014 2022/10/19 16:28:33 - mmengine - INFO - Epoch(val) [170][200/407] eta: 0:00:24 time: 0.118339 data_time: 0.063300 memory: 1014 2022/10/19 16:28:39 - mmengine - INFO - Epoch(val) [170][250/407] eta: 0:00:18 time: 0.118641 data_time: 0.060510 memory: 1014 2022/10/19 16:28:45 - mmengine - INFO - Epoch(val) [170][300/407] eta: 0:00:13 time: 0.122075 data_time: 0.067149 memory: 1014 2022/10/19 16:28:52 - mmengine - INFO - Epoch(val) [170][350/407] eta: 0:00:07 time: 0.125030 data_time: 0.067471 memory: 1014 2022/10/19 16:28:58 - mmengine - INFO - Epoch(val) [170][400/407] eta: 0:00:00 time: 0.117538 data_time: 0.063383 memory: 1014 2022/10/19 16:29:33 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 16:29:47 - mmengine - INFO - Epoch(val) [170][407/407] coco/AP: 0.712002 coco/AP .5: 0.895486 coco/AP .75: 0.783331 coco/AP (M): 0.673114 coco/AP (L): 0.780486 coco/AR: 0.767554 coco/AR .5: 0.933879 coco/AR .75: 0.831707 coco/AR (M): 0.722726 coco/AR (L): 0.832219 2022/10/19 16:29:47 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_256/best_coco/AP_epoch_160.pth is removed 2022/10/19 16:29:49 - mmengine - INFO - The best checkpoint with 0.7120 coco/AP at 170 epoch is saved to best_coco/AP_epoch_170.pth. 2022/10/19 16:30:09 - mmengine - INFO - Epoch(train) [171][50/293] lr: 5.000000e-05 eta: 1:03:34 time: 0.393742 data_time: 0.148053 memory: 7187 loss_kpt: 0.000604 acc_pose: 0.870597 loss: 0.000604 2022/10/19 16:30:26 - mmengine - INFO - Epoch(train) [171][100/293] lr: 5.000000e-05 eta: 1:03:18 time: 0.352007 data_time: 0.064521 memory: 7187 loss_kpt: 0.000618 acc_pose: 0.827130 loss: 0.000618 2022/10/19 16:30:46 - mmengine - INFO - Epoch(train) [171][150/293] lr: 5.000000e-05 eta: 1:03:02 time: 0.398087 data_time: 0.152812 memory: 7187 loss_kpt: 0.000603 acc_pose: 0.840867 loss: 0.000603 2022/10/19 16:31:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:31:05 - mmengine - INFO - Epoch(train) [171][200/293] lr: 5.000000e-05 eta: 1:02:46 time: 0.369556 data_time: 0.145918 memory: 7187 loss_kpt: 0.000602 acc_pose: 0.850887 loss: 0.000602 2022/10/19 16:31:24 - mmengine - INFO - Epoch(train) [171][250/293] lr: 5.000000e-05 eta: 1:02:30 time: 0.374314 data_time: 0.072510 memory: 7187 loss_kpt: 0.000600 acc_pose: 0.818413 loss: 0.000600 2022/10/19 16:31:41 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:32:00 - mmengine - INFO - Epoch(train) [172][50/293] lr: 5.000000e-05 eta: 1:01:57 time: 0.385153 data_time: 0.086331 memory: 7187 loss_kpt: 0.000590 acc_pose: 0.836267 loss: 0.000590 2022/10/19 16:32:19 - mmengine - INFO - Epoch(train) [172][100/293] lr: 5.000000e-05 eta: 1:01:42 time: 0.375344 data_time: 0.062583 memory: 7187 loss_kpt: 0.000585 acc_pose: 0.822251 loss: 0.000585 2022/10/19 16:32:38 - mmengine - INFO - Epoch(train) [172][150/293] lr: 5.000000e-05 eta: 1:01:26 time: 0.378686 data_time: 0.071452 memory: 7187 loss_kpt: 0.000598 acc_pose: 0.843882 loss: 0.000598 2022/10/19 16:32:56 - mmengine - INFO - Epoch(train) [172][200/293] lr: 5.000000e-05 eta: 1:01:10 time: 0.365452 data_time: 0.071172 memory: 7187 loss_kpt: 0.000610 acc_pose: 0.870125 loss: 0.000610 2022/10/19 16:33:14 - mmengine - INFO - Epoch(train) [172][250/293] lr: 5.000000e-05 eta: 1:00:54 time: 0.368251 data_time: 0.072827 memory: 7187 loss_kpt: 0.000590 acc_pose: 0.844838 loss: 0.000590 2022/10/19 16:33:30 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:33:49 - mmengine - INFO - Epoch(train) [173][50/293] lr: 5.000000e-05 eta: 1:00:21 time: 0.381451 data_time: 0.110017 memory: 7187 loss_kpt: 0.000591 acc_pose: 0.855292 loss: 0.000591 2022/10/19 16:34:07 - mmengine - INFO - Epoch(train) [173][100/293] lr: 5.000000e-05 eta: 1:00:05 time: 0.356278 data_time: 0.072165 memory: 7187 loss_kpt: 0.000592 acc_pose: 0.825015 loss: 0.000592 2022/10/19 16:34:26 - mmengine - INFO - Epoch(train) [173][150/293] lr: 5.000000e-05 eta: 0:59:49 time: 0.376692 data_time: 0.063449 memory: 7187 loss_kpt: 0.000596 acc_pose: 0.831667 loss: 0.000596 2022/10/19 16:34:44 - mmengine - INFO - Epoch(train) [173][200/293] lr: 5.000000e-05 eta: 0:59:33 time: 0.362592 data_time: 0.064774 memory: 7187 loss_kpt: 0.000606 acc_pose: 0.845992 loss: 0.000606 2022/10/19 16:35:03 - mmengine - INFO - Epoch(train) [173][250/293] lr: 5.000000e-05 eta: 0:59:18 time: 0.384417 data_time: 0.070879 memory: 7187 loss_kpt: 0.000591 acc_pose: 0.843502 loss: 0.000591 2022/10/19 16:35:18 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:35:38 - mmengine - INFO - Epoch(train) [174][50/293] lr: 5.000000e-05 eta: 0:58:45 time: 0.392354 data_time: 0.084822 memory: 7187 loss_kpt: 0.000591 acc_pose: 0.849074 loss: 0.000591 2022/10/19 16:35:56 - mmengine - INFO - Epoch(train) [174][100/293] lr: 5.000000e-05 eta: 0:58:29 time: 0.375851 data_time: 0.076195 memory: 7187 loss_kpt: 0.000599 acc_pose: 0.880012 loss: 0.000599 2022/10/19 16:36:15 - mmengine - INFO - Epoch(train) [174][150/293] lr: 5.000000e-05 eta: 0:58:13 time: 0.378425 data_time: 0.080219 memory: 7187 loss_kpt: 0.000584 acc_pose: 0.812150 loss: 0.000584 2022/10/19 16:36:34 - mmengine - INFO - Epoch(train) [174][200/293] lr: 5.000000e-05 eta: 0:57:57 time: 0.367027 data_time: 0.071016 memory: 7187 loss_kpt: 0.000605 acc_pose: 0.855131 loss: 0.000605 2022/10/19 16:36:52 - mmengine - INFO - Epoch(train) [174][250/293] lr: 5.000000e-05 eta: 0:57:42 time: 0.368734 data_time: 0.066991 memory: 7187 loss_kpt: 0.000597 acc_pose: 0.839415 loss: 0.000597 2022/10/19 16:37:08 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:37:15 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:37:28 - mmengine - INFO - Epoch(train) [175][50/293] lr: 5.000000e-05 eta: 0:57:09 time: 0.396215 data_time: 0.089394 memory: 7187 loss_kpt: 0.000588 acc_pose: 0.833066 loss: 0.000588 2022/10/19 16:37:46 - mmengine - INFO - Epoch(train) [175][100/293] lr: 5.000000e-05 eta: 0:56:53 time: 0.359174 data_time: 0.069975 memory: 7187 loss_kpt: 0.000590 acc_pose: 0.836421 loss: 0.000590 2022/10/19 16:38:03 - mmengine - INFO - Epoch(train) [175][150/293] lr: 5.000000e-05 eta: 0:56:37 time: 0.356249 data_time: 0.068533 memory: 7187 loss_kpt: 0.000593 acc_pose: 0.830566 loss: 0.000593 2022/10/19 16:38:22 - mmengine - INFO - Epoch(train) [175][200/293] lr: 5.000000e-05 eta: 0:56:21 time: 0.369672 data_time: 0.069096 memory: 7187 loss_kpt: 0.000595 acc_pose: 0.855729 loss: 0.000595 2022/10/19 16:38:39 - mmengine - INFO - Epoch(train) [175][250/293] lr: 5.000000e-05 eta: 0:56:05 time: 0.345934 data_time: 0.068376 memory: 7187 loss_kpt: 0.000574 acc_pose: 0.871368 loss: 0.000574 2022/10/19 16:38:55 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:39:16 - mmengine - INFO - Epoch(train) [176][50/293] lr: 5.000000e-05 eta: 0:55:33 time: 0.410666 data_time: 0.084084 memory: 7187 loss_kpt: 0.000596 acc_pose: 0.845620 loss: 0.000596 2022/10/19 16:39:36 - mmengine - INFO - Epoch(train) [176][100/293] lr: 5.000000e-05 eta: 0:55:17 time: 0.396735 data_time: 0.065868 memory: 7187 loss_kpt: 0.000587 acc_pose: 0.836620 loss: 0.000587 2022/10/19 16:39:54 - mmengine - INFO - Epoch(train) [176][150/293] lr: 5.000000e-05 eta: 0:55:01 time: 0.373068 data_time: 0.067759 memory: 7187 loss_kpt: 0.000588 acc_pose: 0.884537 loss: 0.000588 2022/10/19 16:40:13 - mmengine - INFO - Epoch(train) [176][200/293] lr: 5.000000e-05 eta: 0:54:45 time: 0.361929 data_time: 0.064031 memory: 7187 loss_kpt: 0.000581 acc_pose: 0.870135 loss: 0.000581 2022/10/19 16:40:32 - mmengine - INFO - Epoch(train) [176][250/293] lr: 5.000000e-05 eta: 0:54:29 time: 0.387122 data_time: 0.073564 memory: 7187 loss_kpt: 0.000592 acc_pose: 0.862511 loss: 0.000592 2022/10/19 16:40:47 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:41:07 - mmengine - INFO - Epoch(train) [177][50/293] lr: 5.000000e-05 eta: 0:53:57 time: 0.387316 data_time: 0.088989 memory: 7187 loss_kpt: 0.000595 acc_pose: 0.829617 loss: 0.000595 2022/10/19 16:41:25 - mmengine - INFO - Epoch(train) [177][100/293] lr: 5.000000e-05 eta: 0:53:41 time: 0.368706 data_time: 0.083962 memory: 7187 loss_kpt: 0.000595 acc_pose: 0.809992 loss: 0.000595 2022/10/19 16:41:43 - mmengine - INFO - Epoch(train) [177][150/293] lr: 5.000000e-05 eta: 0:53:25 time: 0.365494 data_time: 0.072834 memory: 7187 loss_kpt: 0.000587 acc_pose: 0.819800 loss: 0.000587 2022/10/19 16:42:02 - mmengine - INFO - Epoch(train) [177][200/293] lr: 5.000000e-05 eta: 0:53:09 time: 0.368937 data_time: 0.071195 memory: 7187 loss_kpt: 0.000579 acc_pose: 0.840280 loss: 0.000579 2022/10/19 16:42:20 - mmengine - INFO - Epoch(train) [177][250/293] lr: 5.000000e-05 eta: 0:52:53 time: 0.361735 data_time: 0.067789 memory: 7187 loss_kpt: 0.000598 acc_pose: 0.852554 loss: 0.000598 2022/10/19 16:42:36 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:42:56 - mmengine - INFO - Epoch(train) [178][50/293] lr: 5.000000e-05 eta: 0:52:21 time: 0.398827 data_time: 0.131070 memory: 7187 loss_kpt: 0.000580 acc_pose: 0.786818 loss: 0.000580 2022/10/19 16:43:15 - mmengine - INFO - Epoch(train) [178][100/293] lr: 5.000000e-05 eta: 0:52:05 time: 0.366906 data_time: 0.068148 memory: 7187 loss_kpt: 0.000585 acc_pose: 0.806834 loss: 0.000585 2022/10/19 16:43:29 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:43:33 - mmengine - INFO - Epoch(train) [178][150/293] lr: 5.000000e-05 eta: 0:51:49 time: 0.363743 data_time: 0.073057 memory: 7187 loss_kpt: 0.000598 acc_pose: 0.889967 loss: 0.000598 2022/10/19 16:43:51 - mmengine - INFO - Epoch(train) [178][200/293] lr: 5.000000e-05 eta: 0:51:33 time: 0.370157 data_time: 0.066253 memory: 7187 loss_kpt: 0.000589 acc_pose: 0.835098 loss: 0.000589 2022/10/19 16:44:10 - mmengine - INFO - Epoch(train) [178][250/293] lr: 5.000000e-05 eta: 0:51:17 time: 0.378807 data_time: 0.074195 memory: 7187 loss_kpt: 0.000593 acc_pose: 0.823896 loss: 0.000593 2022/10/19 16:44:26 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:44:45 - mmengine - INFO - Epoch(train) [179][50/293] lr: 5.000000e-05 eta: 0:50:44 time: 0.373681 data_time: 0.085004 memory: 7187 loss_kpt: 0.000596 acc_pose: 0.838938 loss: 0.000596 2022/10/19 16:45:03 - mmengine - INFO - Epoch(train) [179][100/293] lr: 5.000000e-05 eta: 0:50:28 time: 0.367538 data_time: 0.061474 memory: 7187 loss_kpt: 0.000605 acc_pose: 0.819545 loss: 0.000605 2022/10/19 16:45:22 - mmengine - INFO - Epoch(train) [179][150/293] lr: 5.000000e-05 eta: 0:50:13 time: 0.382954 data_time: 0.070705 memory: 7187 loss_kpt: 0.000602 acc_pose: 0.856586 loss: 0.000602 2022/10/19 16:45:41 - mmengine - INFO - Epoch(train) [179][200/293] lr: 5.000000e-05 eta: 0:49:57 time: 0.379448 data_time: 0.068484 memory: 7187 loss_kpt: 0.000587 acc_pose: 0.872007 loss: 0.000587 2022/10/19 16:46:00 - mmengine - INFO - Epoch(train) [179][250/293] lr: 5.000000e-05 eta: 0:49:41 time: 0.365164 data_time: 0.069338 memory: 7187 loss_kpt: 0.000587 acc_pose: 0.839548 loss: 0.000587 2022/10/19 16:46:15 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:46:33 - mmengine - INFO - Epoch(train) [180][50/293] lr: 5.000000e-05 eta: 0:49:08 time: 0.374617 data_time: 0.083423 memory: 7187 loss_kpt: 0.000588 acc_pose: 0.811449 loss: 0.000588 2022/10/19 16:46:52 - mmengine - INFO - Epoch(train) [180][100/293] lr: 5.000000e-05 eta: 0:48:52 time: 0.361728 data_time: 0.062591 memory: 7187 loss_kpt: 0.000594 acc_pose: 0.865653 loss: 0.000594 2022/10/19 16:47:11 - mmengine - INFO - Epoch(train) [180][150/293] lr: 5.000000e-05 eta: 0:48:37 time: 0.390458 data_time: 0.101299 memory: 7187 loss_kpt: 0.000586 acc_pose: 0.826800 loss: 0.000586 2022/10/19 16:47:29 - mmengine - INFO - Epoch(train) [180][200/293] lr: 5.000000e-05 eta: 0:48:21 time: 0.358983 data_time: 0.110380 memory: 7187 loss_kpt: 0.000579 acc_pose: 0.850443 loss: 0.000579 2022/10/19 16:47:48 - mmengine - INFO - Epoch(train) [180][250/293] lr: 5.000000e-05 eta: 0:48:05 time: 0.381670 data_time: 0.073499 memory: 7187 loss_kpt: 0.000603 acc_pose: 0.883587 loss: 0.000603 2022/10/19 16:48:04 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:48:04 - mmengine - INFO - Saving checkpoint at 180 epochs 2022/10/19 16:48:13 - mmengine - INFO - Epoch(val) [180][50/407] eta: 0:00:43 time: 0.122435 data_time: 0.065800 memory: 7187 2022/10/19 16:48:19 - mmengine - INFO - Epoch(val) [180][100/407] eta: 0:00:36 time: 0.120084 data_time: 0.064261 memory: 1014 2022/10/19 16:48:26 - mmengine - INFO - Epoch(val) [180][150/407] eta: 0:00:32 time: 0.126431 data_time: 0.070734 memory: 1014 2022/10/19 16:48:31 - mmengine - INFO - Epoch(val) [180][200/407] eta: 0:00:24 time: 0.119249 data_time: 0.063459 memory: 1014 2022/10/19 16:48:38 - mmengine - INFO - Epoch(val) [180][250/407] eta: 0:00:19 time: 0.125620 data_time: 0.069351 memory: 1014 2022/10/19 16:48:44 - mmengine - INFO - Epoch(val) [180][300/407] eta: 0:00:14 time: 0.131196 data_time: 0.075108 memory: 1014 2022/10/19 16:48:50 - mmengine - INFO - Epoch(val) [180][350/407] eta: 0:00:06 time: 0.116227 data_time: 0.061238 memory: 1014 2022/10/19 16:48:56 - mmengine - INFO - Epoch(val) [180][400/407] eta: 0:00:00 time: 0.109886 data_time: 0.057124 memory: 1014 2022/10/19 16:49:31 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 16:49:45 - mmengine - INFO - Epoch(val) [180][407/407] coco/AP: 0.720215 coco/AP .5: 0.896261 coco/AP .75: 0.795571 coco/AP (M): 0.681937 coco/AP (L): 0.788509 coco/AR: 0.774937 coco/AR .5: 0.934509 coco/AR .75: 0.843199 coco/AR (M): 0.730757 coco/AR (L): 0.838685 2022/10/19 16:49:45 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_256/best_coco/AP_epoch_170.pth is removed 2022/10/19 16:49:47 - mmengine - INFO - The best checkpoint with 0.7202 coco/AP at 180 epoch is saved to best_coco/AP_epoch_180.pth. 2022/10/19 16:50:06 - mmengine - INFO - Epoch(train) [181][50/293] lr: 5.000000e-05 eta: 0:47:32 time: 0.392714 data_time: 0.192399 memory: 7187 loss_kpt: 0.000578 acc_pose: 0.829564 loss: 0.000578 2022/10/19 16:50:25 - mmengine - INFO - Epoch(train) [181][100/293] lr: 5.000000e-05 eta: 0:47:17 time: 0.377333 data_time: 0.120236 memory: 7187 loss_kpt: 0.000583 acc_pose: 0.888727 loss: 0.000583 2022/10/19 16:50:43 - mmengine - INFO - Epoch(train) [181][150/293] lr: 5.000000e-05 eta: 0:47:01 time: 0.360058 data_time: 0.069679 memory: 7187 loss_kpt: 0.000588 acc_pose: 0.843840 loss: 0.000588 2022/10/19 16:51:01 - mmengine - INFO - Epoch(train) [181][200/293] lr: 5.000000e-05 eta: 0:46:45 time: 0.367068 data_time: 0.075883 memory: 7187 loss_kpt: 0.000582 acc_pose: 0.901384 loss: 0.000582 2022/10/19 16:51:20 - mmengine - INFO - Epoch(train) [181][250/293] lr: 5.000000e-05 eta: 0:46:29 time: 0.369410 data_time: 0.071336 memory: 7187 loss_kpt: 0.000580 acc_pose: 0.834855 loss: 0.000580 2022/10/19 16:51:23 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:51:36 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:51:56 - mmengine - INFO - Epoch(train) [182][50/293] lr: 5.000000e-05 eta: 0:45:57 time: 0.411193 data_time: 0.085832 memory: 7187 loss_kpt: 0.000591 acc_pose: 0.868372 loss: 0.000591 2022/10/19 16:52:14 - mmengine - INFO - Epoch(train) [182][100/293] lr: 5.000000e-05 eta: 0:45:41 time: 0.359391 data_time: 0.068826 memory: 7187 loss_kpt: 0.000578 acc_pose: 0.834477 loss: 0.000578 2022/10/19 16:52:33 - mmengine - INFO - Epoch(train) [182][150/293] lr: 5.000000e-05 eta: 0:45:25 time: 0.371908 data_time: 0.070790 memory: 7187 loss_kpt: 0.000597 acc_pose: 0.866823 loss: 0.000597 2022/10/19 16:52:52 - mmengine - INFO - Epoch(train) [182][200/293] lr: 5.000000e-05 eta: 0:45:09 time: 0.376054 data_time: 0.066151 memory: 7187 loss_kpt: 0.000608 acc_pose: 0.852970 loss: 0.000608 2022/10/19 16:53:11 - mmengine - INFO - Epoch(train) [182][250/293] lr: 5.000000e-05 eta: 0:44:53 time: 0.379754 data_time: 0.062558 memory: 7187 loss_kpt: 0.000588 acc_pose: 0.851957 loss: 0.000588 2022/10/19 16:53:26 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:53:46 - mmengine - INFO - Epoch(train) [183][50/293] lr: 5.000000e-05 eta: 0:44:21 time: 0.387056 data_time: 0.092088 memory: 7187 loss_kpt: 0.000587 acc_pose: 0.832076 loss: 0.000587 2022/10/19 16:54:04 - mmengine - INFO - Epoch(train) [183][100/293] lr: 5.000000e-05 eta: 0:44:05 time: 0.371560 data_time: 0.074137 memory: 7187 loss_kpt: 0.000590 acc_pose: 0.827770 loss: 0.000590 2022/10/19 16:54:23 - mmengine - INFO - Epoch(train) [183][150/293] lr: 5.000000e-05 eta: 0:43:49 time: 0.365464 data_time: 0.105342 memory: 7187 loss_kpt: 0.000592 acc_pose: 0.819977 loss: 0.000592 2022/10/19 16:54:41 - mmengine - INFO - Epoch(train) [183][200/293] lr: 5.000000e-05 eta: 0:43:33 time: 0.365177 data_time: 0.074430 memory: 7187 loss_kpt: 0.000571 acc_pose: 0.874629 loss: 0.000571 2022/10/19 16:54:59 - mmengine - INFO - Epoch(train) [183][250/293] lr: 5.000000e-05 eta: 0:43:17 time: 0.363412 data_time: 0.065612 memory: 7187 loss_kpt: 0.000584 acc_pose: 0.791612 loss: 0.000584 2022/10/19 16:55:15 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:55:34 - mmengine - INFO - Epoch(train) [184][50/293] lr: 5.000000e-05 eta: 0:42:45 time: 0.396727 data_time: 0.140949 memory: 7187 loss_kpt: 0.000581 acc_pose: 0.829951 loss: 0.000581 2022/10/19 16:55:54 - mmengine - INFO - Epoch(train) [184][100/293] lr: 5.000000e-05 eta: 0:42:29 time: 0.383381 data_time: 0.061351 memory: 7187 loss_kpt: 0.000574 acc_pose: 0.832595 loss: 0.000574 2022/10/19 16:56:12 - mmengine - INFO - Epoch(train) [184][150/293] lr: 5.000000e-05 eta: 0:42:13 time: 0.375194 data_time: 0.067301 memory: 7187 loss_kpt: 0.000590 acc_pose: 0.827604 loss: 0.000590 2022/10/19 16:56:30 - mmengine - INFO - Epoch(train) [184][200/293] lr: 5.000000e-05 eta: 0:41:57 time: 0.358643 data_time: 0.074119 memory: 7187 loss_kpt: 0.000583 acc_pose: 0.820348 loss: 0.000583 2022/10/19 16:56:48 - mmengine - INFO - Epoch(train) [184][250/293] lr: 5.000000e-05 eta: 0:41:41 time: 0.361602 data_time: 0.104233 memory: 7187 loss_kpt: 0.000587 acc_pose: 0.865266 loss: 0.000587 2022/10/19 16:57:04 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:57:23 - mmengine - INFO - Epoch(train) [185][50/293] lr: 5.000000e-05 eta: 0:41:09 time: 0.394953 data_time: 0.087842 memory: 7187 loss_kpt: 0.000595 acc_pose: 0.874985 loss: 0.000595 2022/10/19 16:57:38 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:57:42 - mmengine - INFO - Epoch(train) [185][100/293] lr: 5.000000e-05 eta: 0:40:53 time: 0.373031 data_time: 0.069796 memory: 7187 loss_kpt: 0.000588 acc_pose: 0.845717 loss: 0.000588 2022/10/19 16:58:01 - mmengine - INFO - Epoch(train) [185][150/293] lr: 5.000000e-05 eta: 0:40:37 time: 0.383739 data_time: 0.078189 memory: 7187 loss_kpt: 0.000584 acc_pose: 0.858188 loss: 0.000584 2022/10/19 16:58:19 - mmengine - INFO - Epoch(train) [185][200/293] lr: 5.000000e-05 eta: 0:40:21 time: 0.353749 data_time: 0.068387 memory: 7187 loss_kpt: 0.000587 acc_pose: 0.861658 loss: 0.000587 2022/10/19 16:58:37 - mmengine - INFO - Epoch(train) [185][250/293] lr: 5.000000e-05 eta: 0:40:05 time: 0.368323 data_time: 0.070333 memory: 7187 loss_kpt: 0.000584 acc_pose: 0.837411 loss: 0.000584 2022/10/19 16:58:53 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 16:59:12 - mmengine - INFO - Epoch(train) [186][50/293] lr: 5.000000e-05 eta: 0:39:33 time: 0.385505 data_time: 0.085245 memory: 7187 loss_kpt: 0.000589 acc_pose: 0.854924 loss: 0.000589 2022/10/19 16:59:30 - mmengine - INFO - Epoch(train) [186][100/293] lr: 5.000000e-05 eta: 0:39:17 time: 0.359505 data_time: 0.065482 memory: 7187 loss_kpt: 0.000577 acc_pose: 0.844495 loss: 0.000577 2022/10/19 16:59:48 - mmengine - INFO - Epoch(train) [186][150/293] lr: 5.000000e-05 eta: 0:39:01 time: 0.369654 data_time: 0.090480 memory: 7187 loss_kpt: 0.000579 acc_pose: 0.852916 loss: 0.000579 2022/10/19 17:00:07 - mmengine - INFO - Epoch(train) [186][200/293] lr: 5.000000e-05 eta: 0:38:45 time: 0.370545 data_time: 0.065356 memory: 7187 loss_kpt: 0.000585 acc_pose: 0.854075 loss: 0.000585 2022/10/19 17:00:25 - mmengine - INFO - Epoch(train) [186][250/293] lr: 5.000000e-05 eta: 0:38:29 time: 0.366659 data_time: 0.081339 memory: 7187 loss_kpt: 0.000586 acc_pose: 0.847318 loss: 0.000586 2022/10/19 17:00:40 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:01:00 - mmengine - INFO - Epoch(train) [187][50/293] lr: 5.000000e-05 eta: 0:37:57 time: 0.398419 data_time: 0.101227 memory: 7187 loss_kpt: 0.000585 acc_pose: 0.852498 loss: 0.000585 2022/10/19 17:01:19 - mmengine - INFO - Epoch(train) [187][100/293] lr: 5.000000e-05 eta: 0:37:41 time: 0.375895 data_time: 0.084048 memory: 7187 loss_kpt: 0.000574 acc_pose: 0.838836 loss: 0.000574 2022/10/19 17:01:38 - mmengine - INFO - Epoch(train) [187][150/293] lr: 5.000000e-05 eta: 0:37:25 time: 0.379529 data_time: 0.074931 memory: 7187 loss_kpt: 0.000585 acc_pose: 0.863162 loss: 0.000585 2022/10/19 17:01:57 - mmengine - INFO - Epoch(train) [187][200/293] lr: 5.000000e-05 eta: 0:37:09 time: 0.375358 data_time: 0.075408 memory: 7187 loss_kpt: 0.000577 acc_pose: 0.840854 loss: 0.000577 2022/10/19 17:02:15 - mmengine - INFO - Epoch(train) [187][250/293] lr: 5.000000e-05 eta: 0:36:53 time: 0.361922 data_time: 0.070132 memory: 7187 loss_kpt: 0.000580 acc_pose: 0.844453 loss: 0.000580 2022/10/19 17:02:31 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:02:50 - mmengine - INFO - Epoch(train) [188][50/293] lr: 5.000000e-05 eta: 0:36:21 time: 0.393394 data_time: 0.085295 memory: 7187 loss_kpt: 0.000576 acc_pose: 0.852537 loss: 0.000576 2022/10/19 17:03:10 - mmengine - INFO - Epoch(train) [188][100/293] lr: 5.000000e-05 eta: 0:36:05 time: 0.387870 data_time: 0.070845 memory: 7187 loss_kpt: 0.000591 acc_pose: 0.873957 loss: 0.000591 2022/10/19 17:03:28 - mmengine - INFO - Epoch(train) [188][150/293] lr: 5.000000e-05 eta: 0:35:49 time: 0.368430 data_time: 0.072960 memory: 7187 loss_kpt: 0.000584 acc_pose: 0.812916 loss: 0.000584 2022/10/19 17:03:46 - mmengine - INFO - Epoch(train) [188][200/293] lr: 5.000000e-05 eta: 0:35:33 time: 0.362415 data_time: 0.113892 memory: 7187 loss_kpt: 0.000590 acc_pose: 0.831272 loss: 0.000590 2022/10/19 17:03:50 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:04:05 - mmengine - INFO - Epoch(train) [188][250/293] lr: 5.000000e-05 eta: 0:35:17 time: 0.370954 data_time: 0.066578 memory: 7187 loss_kpt: 0.000587 acc_pose: 0.883637 loss: 0.000587 2022/10/19 17:04:21 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:04:40 - mmengine - INFO - Epoch(train) [189][50/293] lr: 5.000000e-05 eta: 0:34:45 time: 0.382214 data_time: 0.159075 memory: 7187 loss_kpt: 0.000574 acc_pose: 0.883403 loss: 0.000574 2022/10/19 17:04:58 - mmengine - INFO - Epoch(train) [189][100/293] lr: 5.000000e-05 eta: 0:34:29 time: 0.358506 data_time: 0.139009 memory: 7187 loss_kpt: 0.000589 acc_pose: 0.845393 loss: 0.000589 2022/10/19 17:05:17 - mmengine - INFO - Epoch(train) [189][150/293] lr: 5.000000e-05 eta: 0:34:13 time: 0.371308 data_time: 0.098370 memory: 7187 loss_kpt: 0.000582 acc_pose: 0.849832 loss: 0.000582 2022/10/19 17:05:35 - mmengine - INFO - Epoch(train) [189][200/293] lr: 5.000000e-05 eta: 0:33:57 time: 0.357835 data_time: 0.098288 memory: 7187 loss_kpt: 0.000588 acc_pose: 0.880179 loss: 0.000588 2022/10/19 17:05:54 - mmengine - INFO - Epoch(train) [189][250/293] lr: 5.000000e-05 eta: 0:33:41 time: 0.385124 data_time: 0.065881 memory: 7187 loss_kpt: 0.000581 acc_pose: 0.837904 loss: 0.000581 2022/10/19 17:06:10 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:06:30 - mmengine - INFO - Epoch(train) [190][50/293] lr: 5.000000e-05 eta: 0:33:10 time: 0.401920 data_time: 0.085661 memory: 7187 loss_kpt: 0.000575 acc_pose: 0.839497 loss: 0.000575 2022/10/19 17:06:49 - mmengine - INFO - Epoch(train) [190][100/293] lr: 5.000000e-05 eta: 0:32:54 time: 0.377883 data_time: 0.065280 memory: 7187 loss_kpt: 0.000567 acc_pose: 0.869320 loss: 0.000567 2022/10/19 17:07:07 - mmengine - INFO - Epoch(train) [190][150/293] lr: 5.000000e-05 eta: 0:32:38 time: 0.375741 data_time: 0.062694 memory: 7187 loss_kpt: 0.000592 acc_pose: 0.873532 loss: 0.000592 2022/10/19 17:07:26 - mmengine - INFO - Epoch(train) [190][200/293] lr: 5.000000e-05 eta: 0:32:22 time: 0.369609 data_time: 0.066857 memory: 7187 loss_kpt: 0.000592 acc_pose: 0.846683 loss: 0.000592 2022/10/19 17:07:45 - mmengine - INFO - Epoch(train) [190][250/293] lr: 5.000000e-05 eta: 0:32:06 time: 0.389921 data_time: 0.074147 memory: 7187 loss_kpt: 0.000584 acc_pose: 0.880392 loss: 0.000584 2022/10/19 17:08:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:08:01 - mmengine - INFO - Saving checkpoint at 190 epochs 2022/10/19 17:08:10 - mmengine - INFO - Epoch(val) [190][50/407] eta: 0:00:43 time: 0.121189 data_time: 0.059528 memory: 7187 2022/10/19 17:08:16 - mmengine - INFO - Epoch(val) [190][100/407] eta: 0:00:38 time: 0.125234 data_time: 0.068263 memory: 1014 2022/10/19 17:08:22 - mmengine - INFO - Epoch(val) [190][150/407] eta: 0:00:31 time: 0.120863 data_time: 0.064601 memory: 1014 2022/10/19 17:08:28 - mmengine - INFO - Epoch(val) [190][200/407] eta: 0:00:23 time: 0.113542 data_time: 0.056970 memory: 1014 2022/10/19 17:08:34 - mmengine - INFO - Epoch(val) [190][250/407] eta: 0:00:19 time: 0.125186 data_time: 0.070289 memory: 1014 2022/10/19 17:08:41 - mmengine - INFO - Epoch(val) [190][300/407] eta: 0:00:13 time: 0.129626 data_time: 0.073420 memory: 1014 2022/10/19 17:08:47 - mmengine - INFO - Epoch(val) [190][350/407] eta: 0:00:07 time: 0.128869 data_time: 0.072245 memory: 1014 2022/10/19 17:08:53 - mmengine - INFO - Epoch(val) [190][400/407] eta: 0:00:00 time: 0.122387 data_time: 0.069591 memory: 1014 2022/10/19 17:09:28 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 17:09:42 - mmengine - INFO - Epoch(val) [190][407/407] coco/AP: 0.720822 coco/AP .5: 0.895869 coco/AP .75: 0.797089 coco/AP (M): 0.682717 coco/AP (L): 0.789050 coco/AR: 0.775551 coco/AR .5: 0.933722 coco/AR .75: 0.843514 coco/AR (M): 0.731467 coco/AR (L): 0.839168 2022/10/19 17:09:42 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_256/best_coco/AP_epoch_180.pth is removed 2022/10/19 17:09:44 - mmengine - INFO - The best checkpoint with 0.7208 coco/AP at 190 epoch is saved to best_coco/AP_epoch_190.pth. 2022/10/19 17:10:03 - mmengine - INFO - Epoch(train) [191][50/293] lr: 5.000000e-05 eta: 0:31:34 time: 0.379334 data_time: 0.130730 memory: 7187 loss_kpt: 0.000583 acc_pose: 0.867788 loss: 0.000583 2022/10/19 17:10:22 - mmengine - INFO - Epoch(train) [191][100/293] lr: 5.000000e-05 eta: 0:31:18 time: 0.393288 data_time: 0.065156 memory: 7187 loss_kpt: 0.000579 acc_pose: 0.846297 loss: 0.000579 2022/10/19 17:10:41 - mmengine - INFO - Epoch(train) [191][150/293] lr: 5.000000e-05 eta: 0:31:02 time: 0.372152 data_time: 0.063543 memory: 7187 loss_kpt: 0.000569 acc_pose: 0.839119 loss: 0.000569 2022/10/19 17:10:59 - mmengine - INFO - Epoch(train) [191][200/293] lr: 5.000000e-05 eta: 0:30:46 time: 0.362380 data_time: 0.069823 memory: 7187 loss_kpt: 0.000567 acc_pose: 0.863947 loss: 0.000567 2022/10/19 17:11:18 - mmengine - INFO - Epoch(train) [191][250/293] lr: 5.000000e-05 eta: 0:30:30 time: 0.379014 data_time: 0.064196 memory: 7187 loss_kpt: 0.000574 acc_pose: 0.820058 loss: 0.000574 2022/10/19 17:11:35 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:11:50 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:11:54 - mmengine - INFO - Epoch(train) [192][50/293] lr: 5.000000e-05 eta: 0:29:58 time: 0.386352 data_time: 0.089093 memory: 7187 loss_kpt: 0.000579 acc_pose: 0.854015 loss: 0.000579 2022/10/19 17:12:12 - mmengine - INFO - Epoch(train) [192][100/293] lr: 5.000000e-05 eta: 0:29:42 time: 0.365407 data_time: 0.067626 memory: 7187 loss_kpt: 0.000575 acc_pose: 0.863510 loss: 0.000575 2022/10/19 17:12:31 - mmengine - INFO - Epoch(train) [192][150/293] lr: 5.000000e-05 eta: 0:29:26 time: 0.361741 data_time: 0.070774 memory: 7187 loss_kpt: 0.000585 acc_pose: 0.858142 loss: 0.000585 2022/10/19 17:12:49 - mmengine - INFO - Epoch(train) [192][200/293] lr: 5.000000e-05 eta: 0:29:10 time: 0.371795 data_time: 0.067837 memory: 7187 loss_kpt: 0.000571 acc_pose: 0.884136 loss: 0.000571 2022/10/19 17:13:08 - mmengine - INFO - Epoch(train) [192][250/293] lr: 5.000000e-05 eta: 0:28:54 time: 0.380967 data_time: 0.085731 memory: 7187 loss_kpt: 0.000574 acc_pose: 0.846405 loss: 0.000574 2022/10/19 17:13:24 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:13:45 - mmengine - INFO - Epoch(train) [193][50/293] lr: 5.000000e-05 eta: 0:28:23 time: 0.409628 data_time: 0.092902 memory: 7187 loss_kpt: 0.000582 acc_pose: 0.850337 loss: 0.000582 2022/10/19 17:14:03 - mmengine - INFO - Epoch(train) [193][100/293] lr: 5.000000e-05 eta: 0:28:07 time: 0.366300 data_time: 0.079223 memory: 7187 loss_kpt: 0.000599 acc_pose: 0.843910 loss: 0.000599 2022/10/19 17:14:21 - mmengine - INFO - Epoch(train) [193][150/293] lr: 5.000000e-05 eta: 0:27:51 time: 0.353777 data_time: 0.116277 memory: 7187 loss_kpt: 0.000576 acc_pose: 0.819961 loss: 0.000576 2022/10/19 17:14:40 - mmengine - INFO - Epoch(train) [193][200/293] lr: 5.000000e-05 eta: 0:27:34 time: 0.373884 data_time: 0.069967 memory: 7187 loss_kpt: 0.000584 acc_pose: 0.875507 loss: 0.000584 2022/10/19 17:14:58 - mmengine - INFO - Epoch(train) [193][250/293] lr: 5.000000e-05 eta: 0:27:18 time: 0.373480 data_time: 0.075644 memory: 7187 loss_kpt: 0.000580 acc_pose: 0.852443 loss: 0.000580 2022/10/19 17:15:14 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:15:34 - mmengine - INFO - Epoch(train) [194][50/293] lr: 5.000000e-05 eta: 0:26:47 time: 0.395052 data_time: 0.080987 memory: 7187 loss_kpt: 0.000585 acc_pose: 0.862801 loss: 0.000585 2022/10/19 17:15:51 - mmengine - INFO - Epoch(train) [194][100/293] lr: 5.000000e-05 eta: 0:26:31 time: 0.353501 data_time: 0.062183 memory: 7187 loss_kpt: 0.000583 acc_pose: 0.876247 loss: 0.000583 2022/10/19 17:16:10 - mmengine - INFO - Epoch(train) [194][150/293] lr: 5.000000e-05 eta: 0:26:15 time: 0.381293 data_time: 0.067469 memory: 7187 loss_kpt: 0.000574 acc_pose: 0.839001 loss: 0.000574 2022/10/19 17:16:29 - mmengine - INFO - Epoch(train) [194][200/293] lr: 5.000000e-05 eta: 0:25:59 time: 0.363938 data_time: 0.061747 memory: 7187 loss_kpt: 0.000582 acc_pose: 0.840109 loss: 0.000582 2022/10/19 17:16:47 - mmengine - INFO - Epoch(train) [194][250/293] lr: 5.000000e-05 eta: 0:25:43 time: 0.370937 data_time: 0.075109 memory: 7187 loss_kpt: 0.000584 acc_pose: 0.855454 loss: 0.000584 2022/10/19 17:17:03 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:17:22 - mmengine - INFO - Epoch(train) [195][50/293] lr: 5.000000e-05 eta: 0:25:11 time: 0.371319 data_time: 0.087328 memory: 7187 loss_kpt: 0.000582 acc_pose: 0.859244 loss: 0.000582 2022/10/19 17:17:41 - mmengine - INFO - Epoch(train) [195][100/293] lr: 5.000000e-05 eta: 0:24:55 time: 0.378514 data_time: 0.073790 memory: 7187 loss_kpt: 0.000577 acc_pose: 0.854558 loss: 0.000577 2022/10/19 17:18:00 - mmengine - INFO - Epoch(train) [195][150/293] lr: 5.000000e-05 eta: 0:24:39 time: 0.383589 data_time: 0.068818 memory: 7187 loss_kpt: 0.000577 acc_pose: 0.884850 loss: 0.000577 2022/10/19 17:18:03 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:18:18 - mmengine - INFO - Epoch(train) [195][200/293] lr: 5.000000e-05 eta: 0:24:23 time: 0.366730 data_time: 0.078239 memory: 7187 loss_kpt: 0.000571 acc_pose: 0.846261 loss: 0.000571 2022/10/19 17:18:37 - mmengine - INFO - Epoch(train) [195][250/293] lr: 5.000000e-05 eta: 0:24:07 time: 0.370403 data_time: 0.064925 memory: 7187 loss_kpt: 0.000579 acc_pose: 0.832429 loss: 0.000579 2022/10/19 17:18:53 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:19:13 - mmengine - INFO - Epoch(train) [196][50/293] lr: 5.000000e-05 eta: 0:23:36 time: 0.401486 data_time: 0.074644 memory: 7187 loss_kpt: 0.000568 acc_pose: 0.828672 loss: 0.000568 2022/10/19 17:19:32 - mmengine - INFO - Epoch(train) [196][100/293] lr: 5.000000e-05 eta: 0:23:20 time: 0.376942 data_time: 0.063603 memory: 7187 loss_kpt: 0.000577 acc_pose: 0.847370 loss: 0.000577 2022/10/19 17:19:51 - mmengine - INFO - Epoch(train) [196][150/293] lr: 5.000000e-05 eta: 0:23:03 time: 0.372832 data_time: 0.066216 memory: 7187 loss_kpt: 0.000571 acc_pose: 0.805694 loss: 0.000571 2022/10/19 17:20:10 - mmengine - INFO - Epoch(train) [196][200/293] lr: 5.000000e-05 eta: 0:22:47 time: 0.385402 data_time: 0.066698 memory: 7187 loss_kpt: 0.000569 acc_pose: 0.866513 loss: 0.000569 2022/10/19 17:20:28 - mmengine - INFO - Epoch(train) [196][250/293] lr: 5.000000e-05 eta: 0:22:31 time: 0.369723 data_time: 0.070876 memory: 7187 loss_kpt: 0.000584 acc_pose: 0.847878 loss: 0.000584 2022/10/19 17:20:44 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:21:03 - mmengine - INFO - Epoch(train) [197][50/293] lr: 5.000000e-05 eta: 0:22:00 time: 0.382839 data_time: 0.083405 memory: 7187 loss_kpt: 0.000578 acc_pose: 0.822498 loss: 0.000578 2022/10/19 17:21:22 - mmengine - INFO - Epoch(train) [197][100/293] lr: 5.000000e-05 eta: 0:21:44 time: 0.373012 data_time: 0.071349 memory: 7187 loss_kpt: 0.000578 acc_pose: 0.878132 loss: 0.000578 2022/10/19 17:21:41 - mmengine - INFO - Epoch(train) [197][150/293] lr: 5.000000e-05 eta: 0:21:28 time: 0.373541 data_time: 0.070972 memory: 7187 loss_kpt: 0.000576 acc_pose: 0.853291 loss: 0.000576 2022/10/19 17:21:59 - mmengine - INFO - Epoch(train) [197][200/293] lr: 5.000000e-05 eta: 0:21:12 time: 0.362797 data_time: 0.065468 memory: 7187 loss_kpt: 0.000564 acc_pose: 0.880280 loss: 0.000564 2022/10/19 17:22:16 - mmengine - INFO - Epoch(train) [197][250/293] lr: 5.000000e-05 eta: 0:20:55 time: 0.352112 data_time: 0.068457 memory: 7187 loss_kpt: 0.000587 acc_pose: 0.884004 loss: 0.000587 2022/10/19 17:22:32 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:22:51 - mmengine - INFO - Epoch(train) [198][50/293] lr: 5.000000e-05 eta: 0:20:24 time: 0.390797 data_time: 0.090930 memory: 7187 loss_kpt: 0.000575 acc_pose: 0.804594 loss: 0.000575 2022/10/19 17:23:10 - mmengine - INFO - Epoch(train) [198][100/293] lr: 5.000000e-05 eta: 0:20:08 time: 0.365820 data_time: 0.093443 memory: 7187 loss_kpt: 0.000581 acc_pose: 0.855495 loss: 0.000581 2022/10/19 17:23:29 - mmengine - INFO - Epoch(train) [198][150/293] lr: 5.000000e-05 eta: 0:19:52 time: 0.383157 data_time: 0.114923 memory: 7187 loss_kpt: 0.000576 acc_pose: 0.883487 loss: 0.000576 2022/10/19 17:23:47 - mmengine - INFO - Epoch(train) [198][200/293] lr: 5.000000e-05 eta: 0:19:36 time: 0.369565 data_time: 0.066754 memory: 7187 loss_kpt: 0.000575 acc_pose: 0.854061 loss: 0.000575 2022/10/19 17:24:06 - mmengine - INFO - Epoch(train) [198][250/293] lr: 5.000000e-05 eta: 0:19:20 time: 0.367776 data_time: 0.063773 memory: 7187 loss_kpt: 0.000589 acc_pose: 0.863545 loss: 0.000589 2022/10/19 17:24:16 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:24:22 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:24:41 - mmengine - INFO - Epoch(train) [199][50/293] lr: 5.000000e-05 eta: 0:18:49 time: 0.389971 data_time: 0.115123 memory: 7187 loss_kpt: 0.000579 acc_pose: 0.895190 loss: 0.000579 2022/10/19 17:24:59 - mmengine - INFO - Epoch(train) [199][100/293] lr: 5.000000e-05 eta: 0:18:33 time: 0.364453 data_time: 0.064690 memory: 7187 loss_kpt: 0.000569 acc_pose: 0.844069 loss: 0.000569 2022/10/19 17:25:18 - mmengine - INFO - Epoch(train) [199][150/293] lr: 5.000000e-05 eta: 0:18:17 time: 0.364718 data_time: 0.068167 memory: 7187 loss_kpt: 0.000581 acc_pose: 0.832573 loss: 0.000581 2022/10/19 17:25:36 - mmengine - INFO - Epoch(train) [199][200/293] lr: 5.000000e-05 eta: 0:18:00 time: 0.373936 data_time: 0.060727 memory: 7187 loss_kpt: 0.000570 acc_pose: 0.859948 loss: 0.000570 2022/10/19 17:25:54 - mmengine - INFO - Epoch(train) [199][250/293] lr: 5.000000e-05 eta: 0:17:44 time: 0.359455 data_time: 0.067025 memory: 7187 loss_kpt: 0.000567 acc_pose: 0.860020 loss: 0.000567 2022/10/19 17:26:10 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:26:28 - mmengine - INFO - Epoch(train) [200][50/293] lr: 5.000000e-05 eta: 0:17:13 time: 0.366580 data_time: 0.085442 memory: 7187 loss_kpt: 0.000585 acc_pose: 0.877059 loss: 0.000585 2022/10/19 17:26:47 - mmengine - INFO - Epoch(train) [200][100/293] lr: 5.000000e-05 eta: 0:16:57 time: 0.382334 data_time: 0.064571 memory: 7187 loss_kpt: 0.000567 acc_pose: 0.822378 loss: 0.000567 2022/10/19 17:27:06 - mmengine - INFO - Epoch(train) [200][150/293] lr: 5.000000e-05 eta: 0:16:41 time: 0.370512 data_time: 0.071559 memory: 7187 loss_kpt: 0.000568 acc_pose: 0.839246 loss: 0.000568 2022/10/19 17:27:24 - mmengine - INFO - Epoch(train) [200][200/293] lr: 5.000000e-05 eta: 0:16:25 time: 0.356988 data_time: 0.071731 memory: 7187 loss_kpt: 0.000569 acc_pose: 0.826099 loss: 0.000569 2022/10/19 17:27:42 - mmengine - INFO - Epoch(train) [200][250/293] lr: 5.000000e-05 eta: 0:16:08 time: 0.374926 data_time: 0.067724 memory: 7187 loss_kpt: 0.000572 acc_pose: 0.853401 loss: 0.000572 2022/10/19 17:27:58 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:27:58 - mmengine - INFO - Saving checkpoint at 200 epochs 2022/10/19 17:28:08 - mmengine - INFO - Epoch(val) [200][50/407] eta: 0:00:48 time: 0.135331 data_time: 0.079566 memory: 7187 2022/10/19 17:28:14 - mmengine - INFO - Epoch(val) [200][100/407] eta: 0:00:38 time: 0.125177 data_time: 0.068168 memory: 1014 2022/10/19 17:28:20 - mmengine - INFO - Epoch(val) [200][150/407] eta: 0:00:30 time: 0.118144 data_time: 0.062626 memory: 1014 2022/10/19 17:28:26 - mmengine - INFO - Epoch(val) [200][200/407] eta: 0:00:24 time: 0.119582 data_time: 0.059694 memory: 1014 2022/10/19 17:28:32 - mmengine - INFO - Epoch(val) [200][250/407] eta: 0:00:18 time: 0.119259 data_time: 0.063344 memory: 1014 2022/10/19 17:28:38 - mmengine - INFO - Epoch(val) [200][300/407] eta: 0:00:12 time: 0.121055 data_time: 0.065717 memory: 1014 2022/10/19 17:28:44 - mmengine - INFO - Epoch(val) [200][350/407] eta: 0:00:06 time: 0.116848 data_time: 0.059624 memory: 1014 2022/10/19 17:28:49 - mmengine - INFO - Epoch(val) [200][400/407] eta: 0:00:00 time: 0.106251 data_time: 0.052746 memory: 1014 2022/10/19 17:29:25 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 17:29:39 - mmengine - INFO - Epoch(val) [200][407/407] coco/AP: 0.721833 coco/AP .5: 0.896851 coco/AP .75: 0.795744 coco/AP (M): 0.683548 coco/AP (L): 0.789941 coco/AR: 0.777298 coco/AR .5: 0.935926 coco/AR .75: 0.843829 coco/AR (M): 0.733160 coco/AR (L): 0.840654 2022/10/19 17:29:39 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_256/best_coco/AP_epoch_190.pth is removed 2022/10/19 17:29:41 - mmengine - INFO - The best checkpoint with 0.7218 coco/AP at 200 epoch is saved to best_coco/AP_epoch_200.pth. 2022/10/19 17:30:00 - mmengine - INFO - Epoch(train) [201][50/293] lr: 5.000000e-06 eta: 0:15:38 time: 0.373386 data_time: 0.121885 memory: 7187 loss_kpt: 0.000568 acc_pose: 0.838200 loss: 0.000568 2022/10/19 17:30:19 - mmengine - INFO - Epoch(train) [201][100/293] lr: 5.000000e-06 eta: 0:15:21 time: 0.376388 data_time: 0.094270 memory: 7187 loss_kpt: 0.000574 acc_pose: 0.802487 loss: 0.000574 2022/10/19 17:30:37 - mmengine - INFO - Epoch(train) [201][150/293] lr: 5.000000e-06 eta: 0:15:05 time: 0.365130 data_time: 0.103537 memory: 7187 loss_kpt: 0.000572 acc_pose: 0.817848 loss: 0.000572 2022/10/19 17:30:55 - mmengine - INFO - Epoch(train) [201][200/293] lr: 5.000000e-06 eta: 0:14:49 time: 0.370854 data_time: 0.141341 memory: 7187 loss_kpt: 0.000573 acc_pose: 0.811289 loss: 0.000573 2022/10/19 17:31:14 - mmengine - INFO - Epoch(train) [201][250/293] lr: 5.000000e-06 eta: 0:14:33 time: 0.377008 data_time: 0.073840 memory: 7187 loss_kpt: 0.000582 acc_pose: 0.843852 loss: 0.000582 2022/10/19 17:31:30 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:31:48 - mmengine - INFO - Epoch(train) [202][50/293] lr: 5.000000e-06 eta: 0:14:02 time: 0.371568 data_time: 0.110914 memory: 7187 loss_kpt: 0.000574 acc_pose: 0.876599 loss: 0.000574 2022/10/19 17:32:07 - mmengine - INFO - Epoch(train) [202][100/293] lr: 5.000000e-06 eta: 0:13:46 time: 0.365753 data_time: 0.124975 memory: 7187 loss_kpt: 0.000572 acc_pose: 0.836025 loss: 0.000572 2022/10/19 17:32:09 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:32:25 - mmengine - INFO - Epoch(train) [202][150/293] lr: 5.000000e-06 eta: 0:13:30 time: 0.370397 data_time: 0.127647 memory: 7187 loss_kpt: 0.000575 acc_pose: 0.862968 loss: 0.000575 2022/10/19 17:32:43 - mmengine - INFO - Epoch(train) [202][200/293] lr: 5.000000e-06 eta: 0:13:13 time: 0.365811 data_time: 0.068408 memory: 7187 loss_kpt: 0.000579 acc_pose: 0.866068 loss: 0.000579 2022/10/19 17:33:02 - mmengine - INFO - Epoch(train) [202][250/293] lr: 5.000000e-06 eta: 0:12:57 time: 0.368519 data_time: 0.061222 memory: 7187 loss_kpt: 0.000579 acc_pose: 0.885447 loss: 0.000579 2022/10/19 17:33:17 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:33:37 - mmengine - INFO - Epoch(train) [203][50/293] lr: 5.000000e-06 eta: 0:12:27 time: 0.387923 data_time: 0.129495 memory: 7187 loss_kpt: 0.000569 acc_pose: 0.874250 loss: 0.000569 2022/10/19 17:33:54 - mmengine - INFO - Epoch(train) [203][100/293] lr: 5.000000e-06 eta: 0:12:10 time: 0.350401 data_time: 0.061580 memory: 7187 loss_kpt: 0.000572 acc_pose: 0.867050 loss: 0.000572 2022/10/19 17:34:13 - mmengine - INFO - Epoch(train) [203][150/293] lr: 5.000000e-06 eta: 0:11:54 time: 0.382698 data_time: 0.064073 memory: 7187 loss_kpt: 0.000569 acc_pose: 0.857664 loss: 0.000569 2022/10/19 17:34:32 - mmengine - INFO - Epoch(train) [203][200/293] lr: 5.000000e-06 eta: 0:11:38 time: 0.366775 data_time: 0.066223 memory: 7187 loss_kpt: 0.000588 acc_pose: 0.814627 loss: 0.000588 2022/10/19 17:34:50 - mmengine - INFO - Epoch(train) [203][250/293] lr: 5.000000e-06 eta: 0:11:22 time: 0.360294 data_time: 0.065717 memory: 7187 loss_kpt: 0.000581 acc_pose: 0.859319 loss: 0.000581 2022/10/19 17:35:05 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:35:25 - mmengine - INFO - Epoch(train) [204][50/293] lr: 5.000000e-06 eta: 0:10:51 time: 0.381192 data_time: 0.082946 memory: 7187 loss_kpt: 0.000574 acc_pose: 0.899457 loss: 0.000574 2022/10/19 17:35:44 - mmengine - INFO - Epoch(train) [204][100/293] lr: 5.000000e-06 eta: 0:10:35 time: 0.379738 data_time: 0.102036 memory: 7187 loss_kpt: 0.000573 acc_pose: 0.836597 loss: 0.000573 2022/10/19 17:36:02 - mmengine - INFO - Epoch(train) [204][150/293] lr: 5.000000e-06 eta: 0:10:19 time: 0.374048 data_time: 0.070831 memory: 7187 loss_kpt: 0.000572 acc_pose: 0.800156 loss: 0.000572 2022/10/19 17:36:21 - mmengine - INFO - Epoch(train) [204][200/293] lr: 5.000000e-06 eta: 0:10:02 time: 0.372938 data_time: 0.096261 memory: 7187 loss_kpt: 0.000585 acc_pose: 0.834398 loss: 0.000585 2022/10/19 17:36:40 - mmengine - INFO - Epoch(train) [204][250/293] lr: 5.000000e-06 eta: 0:09:46 time: 0.379589 data_time: 0.064812 memory: 7187 loss_kpt: 0.000571 acc_pose: 0.838546 loss: 0.000571 2022/10/19 17:36:55 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:37:15 - mmengine - INFO - Epoch(train) [205][50/293] lr: 5.000000e-06 eta: 0:09:16 time: 0.389351 data_time: 0.079995 memory: 7187 loss_kpt: 0.000581 acc_pose: 0.878390 loss: 0.000581 2022/10/19 17:37:34 - mmengine - INFO - Epoch(train) [205][100/293] lr: 5.000000e-06 eta: 0:08:59 time: 0.373387 data_time: 0.059824 memory: 7187 loss_kpt: 0.000584 acc_pose: 0.841748 loss: 0.000584 2022/10/19 17:37:52 - mmengine - INFO - Epoch(train) [205][150/293] lr: 5.000000e-06 eta: 0:08:43 time: 0.369409 data_time: 0.062558 memory: 7187 loss_kpt: 0.000583 acc_pose: 0.823931 loss: 0.000583 2022/10/19 17:38:10 - mmengine - INFO - Epoch(train) [205][200/293] lr: 5.000000e-06 eta: 0:08:27 time: 0.366054 data_time: 0.065525 memory: 7187 loss_kpt: 0.000586 acc_pose: 0.870579 loss: 0.000586 2022/10/19 17:38:21 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:38:29 - mmengine - INFO - Epoch(train) [205][250/293] lr: 5.000000e-06 eta: 0:08:11 time: 0.371051 data_time: 0.085060 memory: 7187 loss_kpt: 0.000565 acc_pose: 0.862770 loss: 0.000565 2022/10/19 17:38:44 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:39:03 - mmengine - INFO - Epoch(train) [206][50/293] lr: 5.000000e-06 eta: 0:07:40 time: 0.377247 data_time: 0.091054 memory: 7187 loss_kpt: 0.000578 acc_pose: 0.811523 loss: 0.000578 2022/10/19 17:39:22 - mmengine - INFO - Epoch(train) [206][100/293] lr: 5.000000e-06 eta: 0:07:24 time: 0.391843 data_time: 0.064558 memory: 7187 loss_kpt: 0.000578 acc_pose: 0.802500 loss: 0.000578 2022/10/19 17:39:41 - mmengine - INFO - Epoch(train) [206][150/293] lr: 5.000000e-06 eta: 0:07:08 time: 0.375267 data_time: 0.061526 memory: 7187 loss_kpt: 0.000582 acc_pose: 0.865803 loss: 0.000582 2022/10/19 17:39:59 - mmengine - INFO - Epoch(train) [206][200/293] lr: 5.000000e-06 eta: 0:06:51 time: 0.363390 data_time: 0.058344 memory: 7187 loss_kpt: 0.000567 acc_pose: 0.830021 loss: 0.000567 2022/10/19 17:40:18 - mmengine - INFO - Epoch(train) [206][250/293] lr: 5.000000e-06 eta: 0:06:35 time: 0.375254 data_time: 0.061923 memory: 7187 loss_kpt: 0.000566 acc_pose: 0.846020 loss: 0.000566 2022/10/19 17:40:33 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:40:53 - mmengine - INFO - Epoch(train) [207][50/293] lr: 5.000000e-06 eta: 0:06:05 time: 0.387524 data_time: 0.147914 memory: 7187 loss_kpt: 0.000580 acc_pose: 0.873413 loss: 0.000580 2022/10/19 17:41:11 - mmengine - INFO - Epoch(train) [207][100/293] lr: 5.000000e-06 eta: 0:05:49 time: 0.369678 data_time: 0.092851 memory: 7187 loss_kpt: 0.000589 acc_pose: 0.867149 loss: 0.000589 2022/10/19 17:41:30 - mmengine - INFO - Epoch(train) [207][150/293] lr: 5.000000e-06 eta: 0:05:32 time: 0.371452 data_time: 0.147352 memory: 7187 loss_kpt: 0.000575 acc_pose: 0.842853 loss: 0.000575 2022/10/19 17:41:48 - mmengine - INFO - Epoch(train) [207][200/293] lr: 5.000000e-06 eta: 0:05:16 time: 0.374245 data_time: 0.126154 memory: 7187 loss_kpt: 0.000563 acc_pose: 0.828127 loss: 0.000563 2022/10/19 17:42:08 - mmengine - INFO - Epoch(train) [207][250/293] lr: 5.000000e-06 eta: 0:05:00 time: 0.381430 data_time: 0.072160 memory: 7187 loss_kpt: 0.000578 acc_pose: 0.807102 loss: 0.000578 2022/10/19 17:42:24 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:42:43 - mmengine - INFO - Epoch(train) [208][50/293] lr: 5.000000e-06 eta: 0:04:29 time: 0.383122 data_time: 0.074741 memory: 7187 loss_kpt: 0.000572 acc_pose: 0.810570 loss: 0.000572 2022/10/19 17:43:01 - mmengine - INFO - Epoch(train) [208][100/293] lr: 5.000000e-06 eta: 0:04:13 time: 0.371975 data_time: 0.071059 memory: 7187 loss_kpt: 0.000585 acc_pose: 0.869268 loss: 0.000585 2022/10/19 17:43:19 - mmengine - INFO - Epoch(train) [208][150/293] lr: 5.000000e-06 eta: 0:03:57 time: 0.361535 data_time: 0.054082 memory: 7187 loss_kpt: 0.000584 acc_pose: 0.824860 loss: 0.000584 2022/10/19 17:43:38 - mmengine - INFO - Epoch(train) [208][200/293] lr: 5.000000e-06 eta: 0:03:41 time: 0.373818 data_time: 0.090991 memory: 7187 loss_kpt: 0.000569 acc_pose: 0.869726 loss: 0.000569 2022/10/19 17:43:56 - mmengine - INFO - Epoch(train) [208][250/293] lr: 5.000000e-06 eta: 0:03:24 time: 0.366773 data_time: 0.092417 memory: 7187 loss_kpt: 0.000579 acc_pose: 0.857560 loss: 0.000579 2022/10/19 17:44:13 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:44:32 - mmengine - INFO - Epoch(train) [209][50/293] lr: 5.000000e-06 eta: 0:02:54 time: 0.387421 data_time: 0.115049 memory: 7187 loss_kpt: 0.000575 acc_pose: 0.843699 loss: 0.000575 2022/10/19 17:44:34 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:44:50 - mmengine - INFO - Epoch(train) [209][100/293] lr: 5.000000e-06 eta: 0:02:38 time: 0.368237 data_time: 0.087927 memory: 7187 loss_kpt: 0.000573 acc_pose: 0.847229 loss: 0.000573 2022/10/19 17:45:08 - mmengine - INFO - Epoch(train) [209][150/293] lr: 5.000000e-06 eta: 0:02:21 time: 0.347676 data_time: 0.060605 memory: 7187 loss_kpt: 0.000581 acc_pose: 0.842107 loss: 0.000581 2022/10/19 17:45:26 - mmengine - INFO - Epoch(train) [209][200/293] lr: 5.000000e-06 eta: 0:02:05 time: 0.367119 data_time: 0.066152 memory: 7187 loss_kpt: 0.000571 acc_pose: 0.843566 loss: 0.000571 2022/10/19 17:45:44 - mmengine - INFO - Epoch(train) [209][250/293] lr: 5.000000e-06 eta: 0:01:49 time: 0.365578 data_time: 0.122349 memory: 7187 loss_kpt: 0.000576 acc_pose: 0.837371 loss: 0.000576 2022/10/19 17:46:00 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:46:20 - mmengine - INFO - Epoch(train) [210][50/293] lr: 5.000000e-06 eta: 0:01:19 time: 0.388131 data_time: 0.084347 memory: 7187 loss_kpt: 0.000577 acc_pose: 0.805893 loss: 0.000577 2022/10/19 17:46:38 - mmengine - INFO - Epoch(train) [210][100/293] lr: 5.000000e-06 eta: 0:01:02 time: 0.371011 data_time: 0.076497 memory: 7187 loss_kpt: 0.000580 acc_pose: 0.840553 loss: 0.000580 2022/10/19 17:46:56 - mmengine - INFO - Epoch(train) [210][150/293] lr: 5.000000e-06 eta: 0:00:46 time: 0.367162 data_time: 0.092135 memory: 7187 loss_kpt: 0.000568 acc_pose: 0.865369 loss: 0.000568 2022/10/19 17:47:15 - mmengine - INFO - Epoch(train) [210][200/293] lr: 5.000000e-06 eta: 0:00:30 time: 0.371132 data_time: 0.091727 memory: 7187 loss_kpt: 0.000568 acc_pose: 0.867687 loss: 0.000568 2022/10/19 17:47:34 - mmengine - INFO - Epoch(train) [210][250/293] lr: 5.000000e-06 eta: 0:00:13 time: 0.380935 data_time: 0.078081 memory: 7187 loss_kpt: 0.000571 acc_pose: 0.880091 loss: 0.000571 2022/10/19 17:47:50 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-256x192_20221019_104231 2022/10/19 17:47:50 - mmengine - INFO - Saving checkpoint at 210 epochs 2022/10/19 17:47:59 - mmengine - INFO - Epoch(val) [210][50/407] eta: 0:00:43 time: 0.121067 data_time: 0.064374 memory: 7187 2022/10/19 17:48:05 - mmengine - INFO - Epoch(val) [210][100/407] eta: 0:00:39 time: 0.127190 data_time: 0.070603 memory: 1014 2022/10/19 17:48:11 - mmengine - INFO - Epoch(val) [210][150/407] eta: 0:00:31 time: 0.121918 data_time: 0.066634 memory: 1014 2022/10/19 17:48:18 - mmengine - INFO - Epoch(val) [210][200/407] eta: 0:00:25 time: 0.122110 data_time: 0.066582 memory: 1014 2022/10/19 17:48:23 - mmengine - INFO - Epoch(val) [210][250/407] eta: 0:00:17 time: 0.110919 data_time: 0.055202 memory: 1014 2022/10/19 17:48:29 - mmengine - INFO - Epoch(val) [210][300/407] eta: 0:00:13 time: 0.123844 data_time: 0.068728 memory: 1014 2022/10/19 17:48:35 - mmengine - INFO - Epoch(val) [210][350/407] eta: 0:00:06 time: 0.122586 data_time: 0.065908 memory: 1014 2022/10/19 17:48:41 - mmengine - INFO - Epoch(val) [210][400/407] eta: 0:00:00 time: 0.116075 data_time: 0.062287 memory: 1014 2022/10/19 17:49:17 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 17:49:31 - mmengine - INFO - Epoch(val) [210][407/407] coco/AP: 0.721137 coco/AP .5: 0.896671 coco/AP .75: 0.795568 coco/AP (M): 0.683141 coco/AP (L): 0.789575 coco/AR: 0.776181 coco/AR .5: 0.934824 coco/AR .75: 0.842569 coco/AR (M): 0.731494 coco/AR (L): 0.840320