2022/10/19 10:42:03 - 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: 1486026974 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 10:42:04 - mmengine - INFO - Config: default_scope = 'mmpose' default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict( type='CheckpointHook', interval=10, max_keep_ckpts=1, save_best='coco/AP', rule='greater'), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='PoseVisualizationHook', enable=False)) custom_hooks = [dict(type='SyncBuffersHook')] env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='PoseLocalVisualizer', vis_backends=[dict(type='LocalVisBackend')], name='visualizer') log_processor = dict( type='LogProcessor', window_size=50, by_epoch=True, num_digits=6) log_level = 'INFO' load_from = None resume = False file_client_args = dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' })) train_cfg = dict(by_epoch=True, max_epochs=210, val_interval=10) val_cfg = dict() test_cfg = dict() optim_wrapper = dict(optimizer=dict(type='Adam', lr=0.0005)) param_scheduler = [ dict( type='LinearLR', begin=0, end=500, start_factor=0.001, by_epoch=False), dict( type='MultiStepLR', begin=0, end=210, milestones=[170, 200], gamma=0.1, by_epoch=True) ] auto_scale_lr = dict(base_batch_size=512) codec = dict( type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) model = dict( type='TopdownPoseEstimator', data_preprocessor=dict( type='PoseDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True), backbone=dict( type='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=(288, 384), heatmap_size=(72, 96), sigma=3)), test_cfg=dict(flip_test=True, flip_mode='heatmap', shift_heatmap=True)) dataset_type = 'CocoDataset' data_mode = 'topdown' data_root = 'data/coco/' train_pipeline = [ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict(type='RandomBBoxTransform'), dict(type='TopdownAffine', input_size=(288, 384)), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3)), dict(type='PackPoseInputs') ] val_pipeline = [ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(288, 384)), dict(type='PackPoseInputs') ] train_dataloader = dict( batch_size=64, num_workers=2, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_train2017.json', data_prefix=dict(img='train2017/'), pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict(type='RandomBBoxTransform'), dict(type='TopdownAffine', input_size=(288, 384)), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3)), dict(type='PackPoseInputs') ])) val_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(288, 384)), dict(type='PackPoseInputs') ])) test_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(288, 384)), dict(type='PackPoseInputs') ])) val_evaluator = dict( type='CocoMetric', ann_file='data/coco/annotations/person_keypoints_val2017.json') test_evaluator = dict( type='CocoMetric', ann_file='data/coco/annotations/person_keypoints_val2017.json') launcher = 'slurm' work_dir = 'work_dirs/20221019/resnetv1d50_384/' 2022/10/19 10:42:57 - 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 10:42:57 - 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 10:42:57 - 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 10:42:57 - 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 10:42:57 - 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 10:42:57 - 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 10:42:57 - 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 10:42:57 - 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 10:43:01 - 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 10:43:03 - 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 10:43:08 - 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 10:43:08 - 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 10:43:08 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_384 by HardDiskBackend. 2022/10/19 10:43:52 - mmengine - INFO - Epoch(train) [1][50/293] lr: 4.954910e-05 eta: 14:57:58 time: 0.876361 data_time: 0.444089 memory: 15356 loss_kpt: 0.002134 acc_pose: 0.167442 loss: 0.002134 2022/10/19 10:44:16 - mmengine - INFO - Epoch(train) [1][100/293] lr: 9.959920e-05 eta: 11:33:35 time: 0.478534 data_time: 0.092290 memory: 15356 loss_kpt: 0.001777 acc_pose: 0.418928 loss: 0.001777 2022/10/19 10:44:38 - mmengine - INFO - Epoch(train) [1][150/293] lr: 1.496493e-04 eta: 10:16:30 time: 0.453055 data_time: 0.092985 memory: 15356 loss_kpt: 0.001506 acc_pose: 0.507808 loss: 0.001506 2022/10/19 10:45:01 - mmengine - INFO - Epoch(train) [1][200/293] lr: 1.996994e-04 eta: 9:40:02 time: 0.461893 data_time: 0.099937 memory: 15356 loss_kpt: 0.001354 acc_pose: 0.552315 loss: 0.001354 2022/10/19 10:45:25 - mmengine - INFO - Epoch(train) [1][250/293] lr: 2.497495e-04 eta: 9:19:53 time: 0.471125 data_time: 0.095396 memory: 15356 loss_kpt: 0.001294 acc_pose: 0.552308 loss: 0.001294 2022/10/19 10:45:45 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 10:46:09 - mmengine - INFO - Epoch(train) [2][50/293] lr: 3.428427e-04 eta: 7:58:57 time: 0.480911 data_time: 0.101098 memory: 15356 loss_kpt: 0.001185 acc_pose: 0.634926 loss: 0.001185 2022/10/19 10:46:31 - mmengine - INFO - Epoch(train) [2][100/293] lr: 3.928928e-04 eta: 7:55:35 time: 0.446746 data_time: 0.083398 memory: 15356 loss_kpt: 0.001159 acc_pose: 0.552563 loss: 0.001159 2022/10/19 10:46:55 - mmengine - INFO - Epoch(train) [2][150/293] lr: 4.429429e-04 eta: 7:55:59 time: 0.473669 data_time: 0.103480 memory: 15356 loss_kpt: 0.001146 acc_pose: 0.671004 loss: 0.001146 2022/10/19 10:47:18 - mmengine - INFO - Epoch(train) [2][200/293] lr: 4.929930e-04 eta: 7:55:01 time: 0.461869 data_time: 0.096534 memory: 15356 loss_kpt: 0.001137 acc_pose: 0.627361 loss: 0.001137 2022/10/19 10:47:41 - mmengine - INFO - Epoch(train) [2][250/293] lr: 5.000000e-04 eta: 7:54:23 time: 0.464311 data_time: 0.098976 memory: 15356 loss_kpt: 0.001121 acc_pose: 0.652727 loss: 0.001121 2022/10/19 10:48:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 10:48:26 - mmengine - INFO - Epoch(train) [3][50/293] lr: 5.000000e-04 eta: 7:23:36 time: 0.491445 data_time: 0.104575 memory: 15356 loss_kpt: 0.001068 acc_pose: 0.659788 loss: 0.001068 2022/10/19 10:48:49 - mmengine - INFO - Epoch(train) [3][100/293] lr: 5.000000e-04 eta: 7:24:47 time: 0.458056 data_time: 0.097001 memory: 15356 loss_kpt: 0.001065 acc_pose: 0.615602 loss: 0.001065 2022/10/19 10:49:12 - mmengine - INFO - Epoch(train) [3][150/293] lr: 5.000000e-04 eta: 7:26:15 time: 0.465255 data_time: 0.089722 memory: 15356 loss_kpt: 0.001048 acc_pose: 0.692149 loss: 0.001048 2022/10/19 10:49:35 - mmengine - INFO - Epoch(train) [3][200/293] lr: 5.000000e-04 eta: 7:27:13 time: 0.460965 data_time: 0.094696 memory: 15356 loss_kpt: 0.001060 acc_pose: 0.635251 loss: 0.001060 2022/10/19 10:49:58 - mmengine - INFO - Epoch(train) [3][250/293] lr: 5.000000e-04 eta: 7:28:41 time: 0.472031 data_time: 0.097262 memory: 15356 loss_kpt: 0.001038 acc_pose: 0.640775 loss: 0.001038 2022/10/19 10:50:17 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 10:50:41 - mmengine - INFO - Epoch(train) [4][50/293] lr: 5.000000e-04 eta: 7:09:16 time: 0.480744 data_time: 0.109130 memory: 15356 loss_kpt: 0.000999 acc_pose: 0.661504 loss: 0.000999 2022/10/19 10:51:05 - mmengine - INFO - Epoch(train) [4][100/293] lr: 5.000000e-04 eta: 7:11:25 time: 0.473352 data_time: 0.103573 memory: 15356 loss_kpt: 0.000991 acc_pose: 0.759680 loss: 0.000991 2022/10/19 10:51:15 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 10:51:29 - mmengine - INFO - Epoch(train) [4][150/293] lr: 5.000000e-04 eta: 7:13:09 time: 0.470166 data_time: 0.102401 memory: 15356 loss_kpt: 0.001006 acc_pose: 0.625763 loss: 0.001006 2022/10/19 10:51:52 - mmengine - INFO - Epoch(train) [4][200/293] lr: 5.000000e-04 eta: 7:14:06 time: 0.457583 data_time: 0.084066 memory: 15356 loss_kpt: 0.000976 acc_pose: 0.688142 loss: 0.000976 2022/10/19 10:52:15 - mmengine - INFO - Epoch(train) [4][250/293] lr: 5.000000e-04 eta: 7:15:36 time: 0.472733 data_time: 0.098951 memory: 15356 loss_kpt: 0.000970 acc_pose: 0.638345 loss: 0.000970 2022/10/19 10:52:35 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 10:53:00 - mmengine - INFO - Epoch(train) [5][50/293] lr: 5.000000e-04 eta: 7:02:32 time: 0.503191 data_time: 0.117239 memory: 15356 loss_kpt: 0.000970 acc_pose: 0.710840 loss: 0.000970 2022/10/19 10:53:24 - mmengine - INFO - Epoch(train) [5][100/293] lr: 5.000000e-04 eta: 7:03:53 time: 0.463673 data_time: 0.096731 memory: 15356 loss_kpt: 0.000940 acc_pose: 0.692871 loss: 0.000940 2022/10/19 10:53:47 - mmengine - INFO - Epoch(train) [5][150/293] lr: 5.000000e-04 eta: 7:05:14 time: 0.466717 data_time: 0.095773 memory: 15356 loss_kpt: 0.000970 acc_pose: 0.685986 loss: 0.000970 2022/10/19 10:54:10 - mmengine - INFO - Epoch(train) [5][200/293] lr: 5.000000e-04 eta: 7:06:38 time: 0.471948 data_time: 0.099715 memory: 15356 loss_kpt: 0.000952 acc_pose: 0.704595 loss: 0.000952 2022/10/19 10:54:33 - mmengine - INFO - Epoch(train) [5][250/293] lr: 5.000000e-04 eta: 7:07:12 time: 0.451707 data_time: 0.089701 memory: 15356 loss_kpt: 0.000936 acc_pose: 0.698474 loss: 0.000936 2022/10/19 10:54:52 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 10:55:16 - mmengine - INFO - Epoch(train) [6][50/293] lr: 5.000000e-04 eta: 6:56:11 time: 0.479445 data_time: 0.106359 memory: 15356 loss_kpt: 0.000934 acc_pose: 0.695112 loss: 0.000934 2022/10/19 10:55:39 - mmengine - INFO - Epoch(train) [6][100/293] lr: 5.000000e-04 eta: 6:57:09 time: 0.456940 data_time: 0.085806 memory: 15356 loss_kpt: 0.000912 acc_pose: 0.715800 loss: 0.000912 2022/10/19 10:56:02 - mmengine - INFO - Epoch(train) [6][150/293] lr: 5.000000e-04 eta: 6:58:08 time: 0.460741 data_time: 0.092839 memory: 15356 loss_kpt: 0.000933 acc_pose: 0.759821 loss: 0.000933 2022/10/19 10:56:26 - mmengine - INFO - Epoch(train) [6][200/293] lr: 5.000000e-04 eta: 6:59:12 time: 0.466149 data_time: 0.095348 memory: 15356 loss_kpt: 0.000894 acc_pose: 0.703579 loss: 0.000894 2022/10/19 10:56:50 - mmengine - INFO - Epoch(train) [6][250/293] lr: 5.000000e-04 eta: 7:00:36 time: 0.480430 data_time: 0.094933 memory: 15356 loss_kpt: 0.000923 acc_pose: 0.721388 loss: 0.000923 2022/10/19 10:57:10 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 10:57:35 - mmengine - INFO - Epoch(train) [7][50/293] lr: 5.000000e-04 eta: 6:51:46 time: 0.487143 data_time: 0.102989 memory: 15356 loss_kpt: 0.000911 acc_pose: 0.770374 loss: 0.000911 2022/10/19 10:57:58 - mmengine - INFO - Epoch(train) [7][100/293] lr: 5.000000e-04 eta: 6:52:54 time: 0.469077 data_time: 0.085669 memory: 15356 loss_kpt: 0.000909 acc_pose: 0.670768 loss: 0.000909 2022/10/19 10:58:21 - mmengine - INFO - Epoch(train) [7][150/293] lr: 5.000000e-04 eta: 6:53:51 time: 0.464685 data_time: 0.097167 memory: 15356 loss_kpt: 0.000892 acc_pose: 0.688792 loss: 0.000892 2022/10/19 10:58:44 - mmengine - INFO - Epoch(train) [7][200/293] lr: 5.000000e-04 eta: 6:54:34 time: 0.458763 data_time: 0.092483 memory: 15356 loss_kpt: 0.000902 acc_pose: 0.669866 loss: 0.000902 2022/10/19 10:59:03 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 10:59:08 - mmengine - INFO - Epoch(train) [7][250/293] lr: 5.000000e-04 eta: 6:55:19 time: 0.462126 data_time: 0.090983 memory: 15356 loss_kpt: 0.000884 acc_pose: 0.732615 loss: 0.000884 2022/10/19 10:59:27 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 10:59:51 - mmengine - INFO - Epoch(train) [8][50/293] lr: 5.000000e-04 eta: 6:47:48 time: 0.487555 data_time: 0.096654 memory: 15356 loss_kpt: 0.000885 acc_pose: 0.720586 loss: 0.000885 2022/10/19 11:00:15 - mmengine - INFO - Epoch(train) [8][100/293] lr: 5.000000e-04 eta: 6:49:02 time: 0.479929 data_time: 0.101462 memory: 15356 loss_kpt: 0.000874 acc_pose: 0.734636 loss: 0.000874 2022/10/19 11:00:38 - mmengine - INFO - Epoch(train) [8][150/293] lr: 5.000000e-04 eta: 6:49:45 time: 0.460710 data_time: 0.093163 memory: 15356 loss_kpt: 0.000875 acc_pose: 0.738204 loss: 0.000875 2022/10/19 11:01:01 - mmengine - INFO - Epoch(train) [8][200/293] lr: 5.000000e-04 eta: 6:50:27 time: 0.461907 data_time: 0.097516 memory: 15356 loss_kpt: 0.000870 acc_pose: 0.703343 loss: 0.000870 2022/10/19 11:01:25 - mmengine - INFO - Epoch(train) [8][250/293] lr: 5.000000e-04 eta: 6:51:24 time: 0.475781 data_time: 0.092630 memory: 15356 loss_kpt: 0.000898 acc_pose: 0.684971 loss: 0.000898 2022/10/19 11:01:45 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:02:09 - mmengine - INFO - Epoch(train) [9][50/293] lr: 5.000000e-04 eta: 6:44:33 time: 0.473651 data_time: 0.108228 memory: 15356 loss_kpt: 0.000866 acc_pose: 0.743648 loss: 0.000866 2022/10/19 11:02:32 - mmengine - INFO - Epoch(train) [9][100/293] lr: 5.000000e-04 eta: 6:45:20 time: 0.466368 data_time: 0.105243 memory: 15356 loss_kpt: 0.000867 acc_pose: 0.730154 loss: 0.000867 2022/10/19 11:02:55 - mmengine - INFO - Epoch(train) [9][150/293] lr: 5.000000e-04 eta: 6:46:08 time: 0.469852 data_time: 0.098471 memory: 15356 loss_kpt: 0.000859 acc_pose: 0.732045 loss: 0.000859 2022/10/19 11:03:18 - mmengine - INFO - Epoch(train) [9][200/293] lr: 5.000000e-04 eta: 6:46:40 time: 0.458314 data_time: 0.088665 memory: 15356 loss_kpt: 0.000847 acc_pose: 0.722874 loss: 0.000847 2022/10/19 11:03:41 - mmengine - INFO - Epoch(train) [9][250/293] lr: 5.000000e-04 eta: 6:47:16 time: 0.463254 data_time: 0.082627 memory: 15356 loss_kpt: 0.000846 acc_pose: 0.764330 loss: 0.000846 2022/10/19 11:04:02 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:04:26 - mmengine - INFO - Epoch(train) [10][50/293] lr: 5.000000e-04 eta: 6:41:23 time: 0.483881 data_time: 0.100993 memory: 15356 loss_kpt: 0.000837 acc_pose: 0.743426 loss: 0.000837 2022/10/19 11:04:50 - mmengine - INFO - Epoch(train) [10][100/293] lr: 5.000000e-04 eta: 6:42:06 time: 0.468573 data_time: 0.085747 memory: 15356 loss_kpt: 0.000858 acc_pose: 0.726097 loss: 0.000858 2022/10/19 11:05:13 - mmengine - INFO - Epoch(train) [10][150/293] lr: 5.000000e-04 eta: 6:42:53 time: 0.474299 data_time: 0.097407 memory: 15356 loss_kpt: 0.000848 acc_pose: 0.753306 loss: 0.000848 2022/10/19 11:05:37 - mmengine - INFO - Epoch(train) [10][200/293] lr: 5.000000e-04 eta: 6:43:32 time: 0.469059 data_time: 0.092059 memory: 15356 loss_kpt: 0.000838 acc_pose: 0.753612 loss: 0.000838 2022/10/19 11:06:00 - mmengine - INFO - Epoch(train) [10][250/293] lr: 5.000000e-04 eta: 6:44:14 time: 0.473992 data_time: 0.089104 memory: 15356 loss_kpt: 0.000858 acc_pose: 0.725412 loss: 0.000858 2022/10/19 11:06:20 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:06:20 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/10/19 11:06:48 - mmengine - INFO - Epoch(val) [10][50/407] eta: 0:02:59 time: 0.502010 data_time: 0.415210 memory: 15356 2022/10/19 11:06:54 - mmengine - INFO - Epoch(val) [10][100/407] eta: 0:00:39 time: 0.129866 data_time: 0.044986 memory: 1465 2022/10/19 11:07:01 - mmengine - INFO - Epoch(val) [10][150/407] eta: 0:00:35 time: 0.137158 data_time: 0.051368 memory: 1465 2022/10/19 11:07:08 - mmengine - INFO - Epoch(val) [10][200/407] eta: 0:00:27 time: 0.132988 data_time: 0.047824 memory: 1465 2022/10/19 11:07:14 - mmengine - INFO - Epoch(val) [10][250/407] eta: 0:00:19 time: 0.124049 data_time: 0.039017 memory: 1465 2022/10/19 11:07:20 - mmengine - INFO - Epoch(val) [10][300/407] eta: 0:00:13 time: 0.128131 data_time: 0.043300 memory: 1465 2022/10/19 11:07:27 - mmengine - INFO - Epoch(val) [10][350/407] eta: 0:00:07 time: 0.133574 data_time: 0.048742 memory: 1465 2022/10/19 11:07:34 - mmengine - INFO - Epoch(val) [10][400/407] eta: 0:00:00 time: 0.136266 data_time: 0.050418 memory: 1465 2022/10/19 11:08:12 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 11:08:27 - mmengine - INFO - Epoch(val) [10][407/407] coco/AP: 0.632505 coco/AP .5: 0.860856 coco/AP .75: 0.691175 coco/AP (M): 0.586602 coco/AP (L): 0.706896 coco/AR: 0.690727 coco/AR .5: 0.901134 coco/AR .75: 0.746851 coco/AR (M): 0.637859 coco/AR (L): 0.765069 2022/10/19 11:08:28 - mmengine - INFO - The best checkpoint with 0.6325 coco/AP at 10 epoch is saved to best_coco/AP_epoch_10.pth. 2022/10/19 11:08:52 - mmengine - INFO - Epoch(train) [11][50/293] lr: 5.000000e-04 eta: 6:38:46 time: 0.474443 data_time: 0.096309 memory: 15356 loss_kpt: 0.000847 acc_pose: 0.692736 loss: 0.000847 2022/10/19 11:09:02 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:09:15 - mmengine - INFO - Epoch(train) [11][100/293] lr: 5.000000e-04 eta: 6:39:18 time: 0.463386 data_time: 0.078006 memory: 15356 loss_kpt: 0.000859 acc_pose: 0.710860 loss: 0.000859 2022/10/19 11:09:39 - mmengine - INFO - Epoch(train) [11][150/293] lr: 5.000000e-04 eta: 6:40:00 time: 0.475756 data_time: 0.095334 memory: 15356 loss_kpt: 0.000824 acc_pose: 0.726651 loss: 0.000824 2022/10/19 11:10:02 - mmengine - INFO - Epoch(train) [11][200/293] lr: 5.000000e-04 eta: 6:40:27 time: 0.461729 data_time: 0.081992 memory: 15356 loss_kpt: 0.000830 acc_pose: 0.737558 loss: 0.000830 2022/10/19 11:10:25 - mmengine - INFO - Epoch(train) [11][250/293] lr: 5.000000e-04 eta: 6:40:56 time: 0.465430 data_time: 0.092270 memory: 15356 loss_kpt: 0.000822 acc_pose: 0.737517 loss: 0.000822 2022/10/19 11:10:45 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:11:10 - mmengine - INFO - Epoch(train) [12][50/293] lr: 5.000000e-04 eta: 6:36:13 time: 0.491891 data_time: 0.107038 memory: 15356 loss_kpt: 0.000837 acc_pose: 0.664792 loss: 0.000837 2022/10/19 11:11:33 - mmengine - INFO - Epoch(train) [12][100/293] lr: 5.000000e-04 eta: 6:36:50 time: 0.472937 data_time: 0.101576 memory: 15356 loss_kpt: 0.000837 acc_pose: 0.769099 loss: 0.000837 2022/10/19 11:11:56 - mmengine - INFO - Epoch(train) [12][150/293] lr: 5.000000e-04 eta: 6:37:12 time: 0.458342 data_time: 0.089949 memory: 15356 loss_kpt: 0.000824 acc_pose: 0.738358 loss: 0.000824 2022/10/19 11:12:19 - mmengine - INFO - Epoch(train) [12][200/293] lr: 5.000000e-04 eta: 6:37:37 time: 0.463942 data_time: 0.091029 memory: 15356 loss_kpt: 0.000830 acc_pose: 0.760860 loss: 0.000830 2022/10/19 11:12:43 - mmengine - INFO - Epoch(train) [12][250/293] lr: 5.000000e-04 eta: 6:38:06 time: 0.469494 data_time: 0.093518 memory: 15356 loss_kpt: 0.000827 acc_pose: 0.751749 loss: 0.000827 2022/10/19 11:13:02 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:13:27 - mmengine - INFO - Epoch(train) [13][50/293] lr: 5.000000e-04 eta: 6:33:48 time: 0.494859 data_time: 0.107895 memory: 15356 loss_kpt: 0.000831 acc_pose: 0.749951 loss: 0.000831 2022/10/19 11:13:50 - mmengine - INFO - Epoch(train) [13][100/293] lr: 5.000000e-04 eta: 6:34:11 time: 0.461696 data_time: 0.090607 memory: 15356 loss_kpt: 0.000819 acc_pose: 0.766265 loss: 0.000819 2022/10/19 11:14:13 - mmengine - INFO - Epoch(train) [13][150/293] lr: 5.000000e-04 eta: 6:34:28 time: 0.455849 data_time: 0.082897 memory: 15356 loss_kpt: 0.000822 acc_pose: 0.760984 loss: 0.000822 2022/10/19 11:14:36 - mmengine - INFO - Epoch(train) [13][200/293] lr: 5.000000e-04 eta: 6:34:51 time: 0.465236 data_time: 0.094003 memory: 15356 loss_kpt: 0.000826 acc_pose: 0.754578 loss: 0.000826 2022/10/19 11:14:59 - mmengine - INFO - Epoch(train) [13][250/293] lr: 5.000000e-04 eta: 6:35:15 time: 0.467031 data_time: 0.096139 memory: 15356 loss_kpt: 0.000816 acc_pose: 0.711843 loss: 0.000816 2022/10/19 11:15:19 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:15:43 - mmengine - INFO - Epoch(train) [14][50/293] lr: 5.000000e-04 eta: 6:31:02 time: 0.476458 data_time: 0.106723 memory: 15356 loss_kpt: 0.000818 acc_pose: 0.752028 loss: 0.000818 2022/10/19 11:16:06 - mmengine - INFO - Epoch(train) [14][100/293] lr: 5.000000e-04 eta: 6:31:20 time: 0.459442 data_time: 0.097726 memory: 15356 loss_kpt: 0.000815 acc_pose: 0.777074 loss: 0.000815 2022/10/19 11:16:29 - mmengine - INFO - Epoch(train) [14][150/293] lr: 5.000000e-04 eta: 6:31:46 time: 0.471193 data_time: 0.095074 memory: 15356 loss_kpt: 0.000812 acc_pose: 0.777017 loss: 0.000812 2022/10/19 11:16:48 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:16:53 - mmengine - INFO - Epoch(train) [14][200/293] lr: 5.000000e-04 eta: 6:32:10 time: 0.469790 data_time: 0.098024 memory: 15356 loss_kpt: 0.000809 acc_pose: 0.773528 loss: 0.000809 2022/10/19 11:17:16 - mmengine - INFO - Epoch(train) [14][250/293] lr: 5.000000e-04 eta: 6:32:28 time: 0.464098 data_time: 0.092092 memory: 15356 loss_kpt: 0.000808 acc_pose: 0.744877 loss: 0.000808 2022/10/19 11:17:35 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:17:59 - mmengine - INFO - Epoch(train) [15][50/293] lr: 5.000000e-04 eta: 6:28:37 time: 0.482944 data_time: 0.105407 memory: 15356 loss_kpt: 0.000788 acc_pose: 0.755156 loss: 0.000788 2022/10/19 11:18:22 - mmengine - INFO - Epoch(train) [15][100/293] lr: 5.000000e-04 eta: 6:28:56 time: 0.463815 data_time: 0.098276 memory: 15356 loss_kpt: 0.000801 acc_pose: 0.723237 loss: 0.000801 2022/10/19 11:18:46 - mmengine - INFO - Epoch(train) [15][150/293] lr: 5.000000e-04 eta: 6:29:22 time: 0.475035 data_time: 0.101835 memory: 15356 loss_kpt: 0.000810 acc_pose: 0.750883 loss: 0.000810 2022/10/19 11:19:10 - mmengine - INFO - Epoch(train) [15][200/293] lr: 5.000000e-04 eta: 6:29:41 time: 0.467211 data_time: 0.092421 memory: 15356 loss_kpt: 0.000804 acc_pose: 0.738015 loss: 0.000804 2022/10/19 11:19:33 - mmengine - INFO - Epoch(train) [15][250/293] lr: 5.000000e-04 eta: 6:30:04 time: 0.474638 data_time: 0.095265 memory: 15356 loss_kpt: 0.000822 acc_pose: 0.766861 loss: 0.000822 2022/10/19 11:19:53 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:20:18 - mmengine - INFO - Epoch(train) [16][50/293] lr: 5.000000e-04 eta: 6:26:32 time: 0.491392 data_time: 0.111357 memory: 15356 loss_kpt: 0.000767 acc_pose: 0.784848 loss: 0.000767 2022/10/19 11:20:41 - mmengine - INFO - Epoch(train) [16][100/293] lr: 5.000000e-04 eta: 6:26:44 time: 0.457400 data_time: 0.098017 memory: 15356 loss_kpt: 0.000808 acc_pose: 0.740536 loss: 0.000808 2022/10/19 11:21:04 - mmengine - INFO - Epoch(train) [16][150/293] lr: 5.000000e-04 eta: 6:26:59 time: 0.462084 data_time: 0.090090 memory: 15356 loss_kpt: 0.000789 acc_pose: 0.781973 loss: 0.000789 2022/10/19 11:21:27 - mmengine - INFO - Epoch(train) [16][200/293] lr: 5.000000e-04 eta: 6:27:13 time: 0.463755 data_time: 0.089295 memory: 15356 loss_kpt: 0.000799 acc_pose: 0.760491 loss: 0.000799 2022/10/19 11:21:51 - mmengine - INFO - Epoch(train) [16][250/293] lr: 5.000000e-04 eta: 6:27:32 time: 0.472334 data_time: 0.099322 memory: 15356 loss_kpt: 0.000807 acc_pose: 0.730226 loss: 0.000807 2022/10/19 11:22:10 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:22:34 - mmengine - INFO - Epoch(train) [17][50/293] lr: 5.000000e-04 eta: 6:24:01 time: 0.471173 data_time: 0.092489 memory: 15356 loss_kpt: 0.000790 acc_pose: 0.727975 loss: 0.000790 2022/10/19 11:22:57 - mmengine - INFO - Epoch(train) [17][100/293] lr: 5.000000e-04 eta: 6:24:21 time: 0.474385 data_time: 0.101230 memory: 15356 loss_kpt: 0.000796 acc_pose: 0.698085 loss: 0.000796 2022/10/19 11:23:21 - mmengine - INFO - Epoch(train) [17][150/293] lr: 5.000000e-04 eta: 6:24:34 time: 0.462445 data_time: 0.086795 memory: 15356 loss_kpt: 0.000777 acc_pose: 0.763748 loss: 0.000777 2022/10/19 11:23:44 - mmengine - INFO - Epoch(train) [17][200/293] lr: 5.000000e-04 eta: 6:24:49 time: 0.468222 data_time: 0.095984 memory: 15356 loss_kpt: 0.000781 acc_pose: 0.757316 loss: 0.000781 2022/10/19 11:24:07 - mmengine - INFO - Epoch(train) [17][250/293] lr: 5.000000e-04 eta: 6:24:59 time: 0.461145 data_time: 0.104064 memory: 15356 loss_kpt: 0.000803 acc_pose: 0.743681 loss: 0.000803 2022/10/19 11:24:27 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:24:36 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:24:51 - mmengine - INFO - Epoch(train) [18][50/293] lr: 5.000000e-04 eta: 6:21:46 time: 0.483234 data_time: 0.104802 memory: 15356 loss_kpt: 0.000772 acc_pose: 0.792036 loss: 0.000772 2022/10/19 11:25:14 - mmengine - INFO - Epoch(train) [18][100/293] lr: 5.000000e-04 eta: 6:22:02 time: 0.470762 data_time: 0.103366 memory: 15356 loss_kpt: 0.000799 acc_pose: 0.750235 loss: 0.000799 2022/10/19 11:25:38 - mmengine - INFO - Epoch(train) [18][150/293] lr: 5.000000e-04 eta: 6:22:14 time: 0.464251 data_time: 0.101830 memory: 15356 loss_kpt: 0.000783 acc_pose: 0.755558 loss: 0.000783 2022/10/19 11:26:00 - mmengine - INFO - Epoch(train) [18][200/293] lr: 5.000000e-04 eta: 6:22:21 time: 0.456612 data_time: 0.094135 memory: 15356 loss_kpt: 0.000784 acc_pose: 0.735139 loss: 0.000784 2022/10/19 11:26:24 - mmengine - INFO - Epoch(train) [18][250/293] lr: 5.000000e-04 eta: 6:22:36 time: 0.472601 data_time: 0.096967 memory: 15356 loss_kpt: 0.000788 acc_pose: 0.761122 loss: 0.000788 2022/10/19 11:26:44 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:27:08 - mmengine - INFO - Epoch(train) [19][50/293] lr: 5.000000e-04 eta: 6:19:30 time: 0.477819 data_time: 0.098238 memory: 15356 loss_kpt: 0.000779 acc_pose: 0.683399 loss: 0.000779 2022/10/19 11:27:32 - mmengine - INFO - Epoch(train) [19][100/293] lr: 5.000000e-04 eta: 6:19:45 time: 0.473806 data_time: 0.099481 memory: 15356 loss_kpt: 0.000780 acc_pose: 0.738864 loss: 0.000780 2022/10/19 11:27:56 - mmengine - INFO - Epoch(train) [19][150/293] lr: 5.000000e-04 eta: 6:20:04 time: 0.481630 data_time: 0.102191 memory: 15356 loss_kpt: 0.000784 acc_pose: 0.783795 loss: 0.000784 2022/10/19 11:28:19 - mmengine - INFO - Epoch(train) [19][200/293] lr: 5.000000e-04 eta: 6:20:15 time: 0.467821 data_time: 0.104703 memory: 15356 loss_kpt: 0.000789 acc_pose: 0.773762 loss: 0.000789 2022/10/19 11:28:43 - mmengine - INFO - Epoch(train) [19][250/293] lr: 5.000000e-04 eta: 6:20:24 time: 0.464030 data_time: 0.097310 memory: 15356 loss_kpt: 0.000783 acc_pose: 0.712211 loss: 0.000783 2022/10/19 11:29:02 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:29:26 - mmengine - INFO - Epoch(train) [20][50/293] lr: 5.000000e-04 eta: 6:17:24 time: 0.472143 data_time: 0.105957 memory: 15356 loss_kpt: 0.000760 acc_pose: 0.793886 loss: 0.000760 2022/10/19 11:29:49 - mmengine - INFO - Epoch(train) [20][100/293] lr: 5.000000e-04 eta: 6:17:32 time: 0.463323 data_time: 0.084960 memory: 15356 loss_kpt: 0.000782 acc_pose: 0.697745 loss: 0.000782 2022/10/19 11:30:12 - mmengine - INFO - Epoch(train) [20][150/293] lr: 5.000000e-04 eta: 6:17:44 time: 0.471705 data_time: 0.093163 memory: 15356 loss_kpt: 0.000775 acc_pose: 0.749508 loss: 0.000775 2022/10/19 11:30:36 - mmengine - INFO - Epoch(train) [20][200/293] lr: 5.000000e-04 eta: 6:17:56 time: 0.472437 data_time: 0.103169 memory: 15356 loss_kpt: 0.000787 acc_pose: 0.789919 loss: 0.000787 2022/10/19 11:31:00 - mmengine - INFO - Epoch(train) [20][250/293] lr: 5.000000e-04 eta: 6:18:07 time: 0.473116 data_time: 0.108700 memory: 15356 loss_kpt: 0.000774 acc_pose: 0.766527 loss: 0.000774 2022/10/19 11:31:19 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:31:19 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/10/19 11:31:29 - mmengine - INFO - Epoch(val) [20][50/407] eta: 0:00:49 time: 0.139595 data_time: 0.048895 memory: 15356 2022/10/19 11:31:36 - mmengine - INFO - Epoch(val) [20][100/407] eta: 0:00:43 time: 0.141812 data_time: 0.054886 memory: 1465 2022/10/19 11:31:43 - mmengine - INFO - Epoch(val) [20][150/407] eta: 0:00:34 time: 0.133733 data_time: 0.048745 memory: 1465 2022/10/19 11:31:49 - mmengine - INFO - Epoch(val) [20][200/407] eta: 0:00:27 time: 0.131796 data_time: 0.046874 memory: 1465 2022/10/19 11:31:56 - mmengine - INFO - Epoch(val) [20][250/407] eta: 0:00:19 time: 0.126095 data_time: 0.039354 memory: 1465 2022/10/19 11:32:02 - mmengine - INFO - Epoch(val) [20][300/407] eta: 0:00:13 time: 0.123618 data_time: 0.036456 memory: 1465 2022/10/19 11:32:09 - mmengine - INFO - Epoch(val) [20][350/407] eta: 0:00:07 time: 0.133589 data_time: 0.046669 memory: 1465 2022/10/19 11:32:14 - mmengine - INFO - Epoch(val) [20][400/407] eta: 0:00:00 time: 0.115319 data_time: 0.028007 memory: 1465 2022/10/19 11:32:52 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 11:33:06 - mmengine - INFO - Epoch(val) [20][407/407] coco/AP: 0.666687 coco/AP .5: 0.874167 coco/AP .75: 0.734806 coco/AP (M): 0.619297 coco/AP (L): 0.746388 coco/AR: 0.722670 coco/AR .5: 0.914515 coco/AR .75: 0.786996 coco/AR (M): 0.669052 coco/AR (L): 0.798625 2022/10/19 11:33:06 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_384/best_coco/AP_epoch_10.pth is removed 2022/10/19 11:33:08 - mmengine - INFO - The best checkpoint with 0.6667 coco/AP at 20 epoch is saved to best_coco/AP_epoch_20.pth. 2022/10/19 11:33:31 - mmengine - INFO - Epoch(train) [21][50/293] lr: 5.000000e-04 eta: 6:15:15 time: 0.472050 data_time: 0.098317 memory: 15356 loss_kpt: 0.000768 acc_pose: 0.735614 loss: 0.000768 2022/10/19 11:33:55 - mmengine - INFO - Epoch(train) [21][100/293] lr: 5.000000e-04 eta: 6:15:28 time: 0.474796 data_time: 0.101490 memory: 15356 loss_kpt: 0.000784 acc_pose: 0.747470 loss: 0.000784 2022/10/19 11:34:14 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:34:19 - mmengine - INFO - Epoch(train) [21][150/293] lr: 5.000000e-04 eta: 6:15:41 time: 0.477885 data_time: 0.096159 memory: 15356 loss_kpt: 0.000771 acc_pose: 0.797847 loss: 0.000771 2022/10/19 11:34:42 - mmengine - INFO - Epoch(train) [21][200/293] lr: 5.000000e-04 eta: 6:15:48 time: 0.465519 data_time: 0.093194 memory: 15356 loss_kpt: 0.000763 acc_pose: 0.746933 loss: 0.000763 2022/10/19 11:35:05 - mmengine - INFO - Epoch(train) [21][250/293] lr: 5.000000e-04 eta: 6:15:53 time: 0.463170 data_time: 0.094348 memory: 15356 loss_kpt: 0.000762 acc_pose: 0.777920 loss: 0.000762 2022/10/19 11:35:25 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:35:48 - mmengine - INFO - Epoch(train) [22][50/293] lr: 5.000000e-04 eta: 6:13:07 time: 0.468171 data_time: 0.096436 memory: 15356 loss_kpt: 0.000754 acc_pose: 0.749796 loss: 0.000754 2022/10/19 11:36:12 - mmengine - INFO - Epoch(train) [22][100/293] lr: 5.000000e-04 eta: 6:13:17 time: 0.474877 data_time: 0.089934 memory: 15356 loss_kpt: 0.000766 acc_pose: 0.788541 loss: 0.000766 2022/10/19 11:36:35 - mmengine - INFO - Epoch(train) [22][150/293] lr: 5.000000e-04 eta: 6:13:22 time: 0.461402 data_time: 0.091014 memory: 15356 loss_kpt: 0.000762 acc_pose: 0.808538 loss: 0.000762 2022/10/19 11:36:59 - mmengine - INFO - Epoch(train) [22][200/293] lr: 5.000000e-04 eta: 6:13:31 time: 0.474072 data_time: 0.098457 memory: 15356 loss_kpt: 0.000780 acc_pose: 0.716653 loss: 0.000780 2022/10/19 11:37:22 - mmengine - INFO - Epoch(train) [22][250/293] lr: 5.000000e-04 eta: 6:13:35 time: 0.462981 data_time: 0.098792 memory: 15356 loss_kpt: 0.000762 acc_pose: 0.724495 loss: 0.000762 2022/10/19 11:37:42 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:38:07 - mmengine - INFO - Epoch(train) [23][50/293] lr: 5.000000e-04 eta: 6:11:04 time: 0.489222 data_time: 0.119298 memory: 15356 loss_kpt: 0.000772 acc_pose: 0.786245 loss: 0.000772 2022/10/19 11:38:30 - mmengine - INFO - Epoch(train) [23][100/293] lr: 5.000000e-04 eta: 6:11:09 time: 0.464184 data_time: 0.094524 memory: 15356 loss_kpt: 0.000755 acc_pose: 0.744583 loss: 0.000755 2022/10/19 11:38:53 - mmengine - INFO - Epoch(train) [23][150/293] lr: 5.000000e-04 eta: 6:11:15 time: 0.467501 data_time: 0.093486 memory: 15356 loss_kpt: 0.000770 acc_pose: 0.794283 loss: 0.000770 2022/10/19 11:39:17 - mmengine - INFO - Epoch(train) [23][200/293] lr: 5.000000e-04 eta: 6:11:23 time: 0.474311 data_time: 0.091157 memory: 15356 loss_kpt: 0.000747 acc_pose: 0.816131 loss: 0.000747 2022/10/19 11:39:41 - mmengine - INFO - Epoch(train) [23][250/293] lr: 5.000000e-04 eta: 6:11:29 time: 0.470594 data_time: 0.105674 memory: 15356 loss_kpt: 0.000753 acc_pose: 0.807277 loss: 0.000753 2022/10/19 11:40:00 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:40:23 - mmengine - INFO - Epoch(train) [24][50/293] lr: 5.000000e-04 eta: 6:09:00 time: 0.478729 data_time: 0.103294 memory: 15356 loss_kpt: 0.000758 acc_pose: 0.772961 loss: 0.000758 2022/10/19 11:40:47 - mmengine - INFO - Epoch(train) [24][100/293] lr: 5.000000e-04 eta: 6:09:02 time: 0.461819 data_time: 0.086858 memory: 15356 loss_kpt: 0.000753 acc_pose: 0.745495 loss: 0.000753 2022/10/19 11:41:10 - mmengine - INFO - Epoch(train) [24][150/293] lr: 5.000000e-04 eta: 6:09:04 time: 0.460058 data_time: 0.093268 memory: 15356 loss_kpt: 0.000745 acc_pose: 0.779438 loss: 0.000745 2022/10/19 11:41:32 - mmengine - INFO - Epoch(train) [24][200/293] lr: 5.000000e-04 eta: 6:09:03 time: 0.455142 data_time: 0.091827 memory: 15356 loss_kpt: 0.000759 acc_pose: 0.747873 loss: 0.000759 2022/10/19 11:41:55 - mmengine - INFO - Epoch(train) [24][250/293] lr: 5.000000e-04 eta: 6:09:02 time: 0.455066 data_time: 0.094041 memory: 15356 loss_kpt: 0.000764 acc_pose: 0.779987 loss: 0.000764 2022/10/19 11:42:00 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:42:16 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:42:40 - mmengine - INFO - Epoch(train) [25][50/293] lr: 5.000000e-04 eta: 6:06:41 time: 0.486465 data_time: 0.109224 memory: 15356 loss_kpt: 0.000747 acc_pose: 0.805485 loss: 0.000747 2022/10/19 11:43:03 - mmengine - INFO - Epoch(train) [25][100/293] lr: 5.000000e-04 eta: 6:06:41 time: 0.457628 data_time: 0.090132 memory: 15356 loss_kpt: 0.000763 acc_pose: 0.774747 loss: 0.000763 2022/10/19 11:43:26 - mmengine - INFO - Epoch(train) [25][150/293] lr: 5.000000e-04 eta: 6:06:45 time: 0.466026 data_time: 0.088805 memory: 15356 loss_kpt: 0.000754 acc_pose: 0.785245 loss: 0.000754 2022/10/19 11:43:50 - mmengine - INFO - Epoch(train) [25][200/293] lr: 5.000000e-04 eta: 6:06:53 time: 0.480727 data_time: 0.101807 memory: 15356 loss_kpt: 0.000741 acc_pose: 0.781165 loss: 0.000741 2022/10/19 11:44:13 - mmengine - INFO - Epoch(train) [25][250/293] lr: 5.000000e-04 eta: 6:06:52 time: 0.457678 data_time: 0.095568 memory: 15356 loss_kpt: 0.000749 acc_pose: 0.745940 loss: 0.000749 2022/10/19 11:44:33 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:44:57 - mmengine - INFO - Epoch(train) [26][50/293] lr: 5.000000e-04 eta: 6:04:33 time: 0.478224 data_time: 0.110968 memory: 15356 loss_kpt: 0.000754 acc_pose: 0.803846 loss: 0.000754 2022/10/19 11:45:21 - mmengine - INFO - Epoch(train) [26][100/293] lr: 5.000000e-04 eta: 6:04:40 time: 0.478524 data_time: 0.097661 memory: 15356 loss_kpt: 0.000752 acc_pose: 0.792807 loss: 0.000752 2022/10/19 11:45:44 - mmengine - INFO - Epoch(train) [26][150/293] lr: 5.000000e-04 eta: 6:04:40 time: 0.461728 data_time: 0.097842 memory: 15356 loss_kpt: 0.000734 acc_pose: 0.759386 loss: 0.000734 2022/10/19 11:46:07 - mmengine - INFO - Epoch(train) [26][200/293] lr: 5.000000e-04 eta: 6:04:41 time: 0.463352 data_time: 0.100747 memory: 15356 loss_kpt: 0.000752 acc_pose: 0.770207 loss: 0.000752 2022/10/19 11:46:31 - mmengine - INFO - Epoch(train) [26][250/293] lr: 5.000000e-04 eta: 6:04:44 time: 0.469350 data_time: 0.092771 memory: 15356 loss_kpt: 0.000756 acc_pose: 0.800453 loss: 0.000756 2022/10/19 11:46:50 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:47:15 - mmengine - INFO - Epoch(train) [27][50/293] lr: 5.000000e-04 eta: 6:02:32 time: 0.488366 data_time: 0.109458 memory: 15356 loss_kpt: 0.000756 acc_pose: 0.755618 loss: 0.000756 2022/10/19 11:47:38 - mmengine - INFO - Epoch(train) [27][100/293] lr: 5.000000e-04 eta: 6:02:36 time: 0.470965 data_time: 0.092539 memory: 15356 loss_kpt: 0.000746 acc_pose: 0.780179 loss: 0.000746 2022/10/19 11:48:01 - mmengine - INFO - Epoch(train) [27][150/293] lr: 5.000000e-04 eta: 6:02:36 time: 0.464312 data_time: 0.087654 memory: 15356 loss_kpt: 0.000755 acc_pose: 0.787108 loss: 0.000755 2022/10/19 11:48:24 - mmengine - INFO - Epoch(train) [27][200/293] lr: 5.000000e-04 eta: 6:02:34 time: 0.457184 data_time: 0.098145 memory: 15356 loss_kpt: 0.000732 acc_pose: 0.784256 loss: 0.000732 2022/10/19 11:48:48 - mmengine - INFO - Epoch(train) [27][250/293] lr: 5.000000e-04 eta: 6:02:35 time: 0.468181 data_time: 0.095979 memory: 15356 loss_kpt: 0.000741 acc_pose: 0.702470 loss: 0.000741 2022/10/19 11:49:08 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:49:32 - mmengine - INFO - Epoch(train) [28][50/293] lr: 5.000000e-04 eta: 6:00:25 time: 0.480228 data_time: 0.094468 memory: 15356 loss_kpt: 0.000731 acc_pose: 0.766361 loss: 0.000731 2022/10/19 11:49:50 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:49:55 - mmengine - INFO - Epoch(train) [28][100/293] lr: 5.000000e-04 eta: 6:00:26 time: 0.466765 data_time: 0.097678 memory: 15356 loss_kpt: 0.000738 acc_pose: 0.769550 loss: 0.000738 2022/10/19 11:50:18 - mmengine - INFO - Epoch(train) [28][150/293] lr: 5.000000e-04 eta: 6:00:26 time: 0.462914 data_time: 0.086729 memory: 15356 loss_kpt: 0.000740 acc_pose: 0.741083 loss: 0.000740 2022/10/19 11:50:42 - mmengine - INFO - Epoch(train) [28][200/293] lr: 5.000000e-04 eta: 6:00:26 time: 0.468052 data_time: 0.090659 memory: 15356 loss_kpt: 0.000742 acc_pose: 0.775332 loss: 0.000742 2022/10/19 11:51:05 - mmengine - INFO - Epoch(train) [28][250/293] lr: 5.000000e-04 eta: 6:00:27 time: 0.468856 data_time: 0.092383 memory: 15356 loss_kpt: 0.000750 acc_pose: 0.744365 loss: 0.000750 2022/10/19 11:51:24 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:51:48 - mmengine - INFO - Epoch(train) [29][50/293] lr: 5.000000e-04 eta: 5:58:20 time: 0.476565 data_time: 0.102128 memory: 15356 loss_kpt: 0.000729 acc_pose: 0.793356 loss: 0.000729 2022/10/19 11:52:11 - mmengine - INFO - Epoch(train) [29][100/293] lr: 5.000000e-04 eta: 5:58:19 time: 0.462926 data_time: 0.091753 memory: 15356 loss_kpt: 0.000743 acc_pose: 0.795187 loss: 0.000743 2022/10/19 11:52:34 - mmengine - INFO - Epoch(train) [29][150/293] lr: 5.000000e-04 eta: 5:58:18 time: 0.463841 data_time: 0.094714 memory: 15356 loss_kpt: 0.000722 acc_pose: 0.730658 loss: 0.000722 2022/10/19 11:52:58 - mmengine - INFO - Epoch(train) [29][200/293] lr: 5.000000e-04 eta: 5:58:21 time: 0.478058 data_time: 0.098487 memory: 15356 loss_kpt: 0.000744 acc_pose: 0.803279 loss: 0.000744 2022/10/19 11:53:21 - mmengine - INFO - Epoch(train) [29][250/293] lr: 5.000000e-04 eta: 5:58:18 time: 0.462115 data_time: 0.093578 memory: 15356 loss_kpt: 0.000728 acc_pose: 0.781317 loss: 0.000728 2022/10/19 11:53:41 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:54:05 - mmengine - INFO - Epoch(train) [30][50/293] lr: 5.000000e-04 eta: 5:56:16 time: 0.481070 data_time: 0.109910 memory: 15356 loss_kpt: 0.000733 acc_pose: 0.835960 loss: 0.000733 2022/10/19 11:54:29 - mmengine - INFO - Epoch(train) [30][100/293] lr: 5.000000e-04 eta: 5:56:16 time: 0.468648 data_time: 0.096876 memory: 15356 loss_kpt: 0.000727 acc_pose: 0.735721 loss: 0.000727 2022/10/19 11:54:52 - mmengine - INFO - Epoch(train) [30][150/293] lr: 5.000000e-04 eta: 5:56:16 time: 0.467435 data_time: 0.090347 memory: 15356 loss_kpt: 0.000744 acc_pose: 0.743319 loss: 0.000744 2022/10/19 11:55:15 - mmengine - INFO - Epoch(train) [30][200/293] lr: 5.000000e-04 eta: 5:56:14 time: 0.465637 data_time: 0.094092 memory: 15356 loss_kpt: 0.000734 acc_pose: 0.803730 loss: 0.000734 2022/10/19 11:55:38 - mmengine - INFO - Epoch(train) [30][250/293] lr: 5.000000e-04 eta: 5:56:11 time: 0.461010 data_time: 0.097552 memory: 15356 loss_kpt: 0.000729 acc_pose: 0.779823 loss: 0.000729 2022/10/19 11:55:58 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:55:58 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/10/19 11:56:07 - mmengine - INFO - Epoch(val) [30][50/407] eta: 0:00:46 time: 0.130213 data_time: 0.040853 memory: 15356 2022/10/19 11:56:13 - mmengine - INFO - Epoch(val) [30][100/407] eta: 0:00:38 time: 0.125921 data_time: 0.039799 memory: 1465 2022/10/19 11:56:20 - mmengine - INFO - Epoch(val) [30][150/407] eta: 0:00:33 time: 0.129962 data_time: 0.040973 memory: 1465 2022/10/19 11:56:26 - mmengine - INFO - Epoch(val) [30][200/407] eta: 0:00:25 time: 0.123314 data_time: 0.035201 memory: 1465 2022/10/19 11:56:33 - mmengine - INFO - Epoch(val) [30][250/407] eta: 0:00:21 time: 0.138271 data_time: 0.050255 memory: 1465 2022/10/19 11:56:39 - mmengine - INFO - Epoch(val) [30][300/407] eta: 0:00:13 time: 0.123918 data_time: 0.036587 memory: 1465 2022/10/19 11:56:46 - mmengine - INFO - Epoch(val) [30][350/407] eta: 0:00:07 time: 0.134409 data_time: 0.046066 memory: 1465 2022/10/19 11:56:53 - mmengine - INFO - Epoch(val) [30][400/407] eta: 0:00:00 time: 0.132608 data_time: 0.044849 memory: 1465 2022/10/19 11:57:29 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 11:57:43 - mmengine - INFO - Epoch(val) [30][407/407] coco/AP: 0.682567 coco/AP .5: 0.881427 coco/AP .75: 0.749344 coco/AP (M): 0.634167 coco/AP (L): 0.760759 coco/AR: 0.737925 coco/AR .5: 0.920655 coco/AR .75: 0.800693 coco/AR (M): 0.685059 coco/AR (L): 0.812077 2022/10/19 11:57:43 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_384/best_coco/AP_epoch_20.pth is removed 2022/10/19 11:57:45 - mmengine - INFO - The best checkpoint with 0.6826 coco/AP at 30 epoch is saved to best_coco/AP_epoch_30.pth. 2022/10/19 11:58:09 - mmengine - INFO - Epoch(train) [31][50/293] lr: 5.000000e-04 eta: 5:54:13 time: 0.484111 data_time: 0.099026 memory: 15356 loss_kpt: 0.000727 acc_pose: 0.824556 loss: 0.000727 2022/10/19 11:58:32 - mmengine - INFO - Epoch(train) [31][100/293] lr: 5.000000e-04 eta: 5:54:09 time: 0.458011 data_time: 0.084906 memory: 15356 loss_kpt: 0.000740 acc_pose: 0.807346 loss: 0.000740 2022/10/19 11:58:56 - mmengine - INFO - Epoch(train) [31][150/293] lr: 5.000000e-04 eta: 5:54:10 time: 0.475209 data_time: 0.101951 memory: 15356 loss_kpt: 0.000729 acc_pose: 0.779201 loss: 0.000729 2022/10/19 11:59:19 - mmengine - INFO - Epoch(train) [31][200/293] lr: 5.000000e-04 eta: 5:54:07 time: 0.465215 data_time: 0.097339 memory: 15356 loss_kpt: 0.000738 acc_pose: 0.793353 loss: 0.000738 2022/10/19 11:59:24 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 11:59:43 - mmengine - INFO - Epoch(train) [31][250/293] lr: 5.000000e-04 eta: 5:54:05 time: 0.464085 data_time: 0.090982 memory: 15356 loss_kpt: 0.000728 acc_pose: 0.808882 loss: 0.000728 2022/10/19 12:00:03 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:00:27 - mmengine - INFO - Epoch(train) [32][50/293] lr: 5.000000e-04 eta: 5:52:08 time: 0.478259 data_time: 0.103189 memory: 15356 loss_kpt: 0.000730 acc_pose: 0.833185 loss: 0.000730 2022/10/19 12:00:50 - mmengine - INFO - Epoch(train) [32][100/293] lr: 5.000000e-04 eta: 5:52:09 time: 0.476474 data_time: 0.098269 memory: 15356 loss_kpt: 0.000728 acc_pose: 0.776280 loss: 0.000728 2022/10/19 12:01:13 - mmengine - INFO - Epoch(train) [32][150/293] lr: 5.000000e-04 eta: 5:52:04 time: 0.459138 data_time: 0.089334 memory: 15356 loss_kpt: 0.000736 acc_pose: 0.782318 loss: 0.000736 2022/10/19 12:01:36 - mmengine - INFO - Epoch(train) [32][200/293] lr: 5.000000e-04 eta: 5:52:00 time: 0.459385 data_time: 0.093136 memory: 15356 loss_kpt: 0.000739 acc_pose: 0.757872 loss: 0.000739 2022/10/19 12:01:59 - mmengine - INFO - Epoch(train) [32][250/293] lr: 5.000000e-04 eta: 5:51:56 time: 0.463603 data_time: 0.097627 memory: 15356 loss_kpt: 0.000715 acc_pose: 0.800005 loss: 0.000715 2022/10/19 12:02:19 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:02:43 - mmengine - INFO - Epoch(train) [33][50/293] lr: 5.000000e-04 eta: 5:50:03 time: 0.480401 data_time: 0.106202 memory: 15356 loss_kpt: 0.000712 acc_pose: 0.787015 loss: 0.000712 2022/10/19 12:03:07 - mmengine - INFO - Epoch(train) [33][100/293] lr: 5.000000e-04 eta: 5:50:00 time: 0.463311 data_time: 0.093235 memory: 15356 loss_kpt: 0.000731 acc_pose: 0.761514 loss: 0.000731 2022/10/19 12:03:30 - mmengine - INFO - Epoch(train) [33][150/293] lr: 5.000000e-04 eta: 5:49:59 time: 0.476242 data_time: 0.102452 memory: 15356 loss_kpt: 0.000713 acc_pose: 0.812678 loss: 0.000713 2022/10/19 12:03:54 - mmengine - INFO - Epoch(train) [33][200/293] lr: 5.000000e-04 eta: 5:49:57 time: 0.469100 data_time: 0.097744 memory: 15356 loss_kpt: 0.000721 acc_pose: 0.806217 loss: 0.000721 2022/10/19 12:04:17 - mmengine - INFO - Epoch(train) [33][250/293] lr: 5.000000e-04 eta: 5:49:52 time: 0.462144 data_time: 0.093745 memory: 15356 loss_kpt: 0.000722 acc_pose: 0.771484 loss: 0.000722 2022/10/19 12:04:37 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:05:00 - mmengine - INFO - Epoch(train) [34][50/293] lr: 5.000000e-04 eta: 5:48:01 time: 0.474787 data_time: 0.108620 memory: 15356 loss_kpt: 0.000714 acc_pose: 0.787007 loss: 0.000714 2022/10/19 12:05:24 - mmengine - INFO - Epoch(train) [34][100/293] lr: 5.000000e-04 eta: 5:47:59 time: 0.474025 data_time: 0.102446 memory: 15356 loss_kpt: 0.000721 acc_pose: 0.788113 loss: 0.000721 2022/10/19 12:05:47 - mmengine - INFO - Epoch(train) [34][150/293] lr: 5.000000e-04 eta: 5:47:54 time: 0.458491 data_time: 0.094351 memory: 15356 loss_kpt: 0.000722 acc_pose: 0.774182 loss: 0.000722 2022/10/19 12:06:11 - mmengine - INFO - Epoch(train) [34][200/293] lr: 5.000000e-04 eta: 5:47:52 time: 0.474206 data_time: 0.094413 memory: 15356 loss_kpt: 0.000725 acc_pose: 0.833324 loss: 0.000725 2022/10/19 12:06:35 - mmengine - INFO - Epoch(train) [34][250/293] lr: 5.000000e-04 eta: 5:47:50 time: 0.474573 data_time: 0.104496 memory: 15356 loss_kpt: 0.000723 acc_pose: 0.797869 loss: 0.000723 2022/10/19 12:06:55 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:07:13 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:07:18 - mmengine - INFO - Epoch(train) [35][50/293] lr: 5.000000e-04 eta: 5:46:01 time: 0.472809 data_time: 0.099992 memory: 15356 loss_kpt: 0.000707 acc_pose: 0.786217 loss: 0.000707 2022/10/19 12:07:42 - mmengine - INFO - Epoch(train) [35][100/293] lr: 5.000000e-04 eta: 5:45:57 time: 0.466252 data_time: 0.100970 memory: 15356 loss_kpt: 0.000732 acc_pose: 0.780157 loss: 0.000732 2022/10/19 12:08:05 - mmengine - INFO - Epoch(train) [35][150/293] lr: 5.000000e-04 eta: 5:45:53 time: 0.466945 data_time: 0.103102 memory: 15356 loss_kpt: 0.000730 acc_pose: 0.786240 loss: 0.000730 2022/10/19 12:08:29 - mmengine - INFO - Epoch(train) [35][200/293] lr: 5.000000e-04 eta: 5:45:50 time: 0.472695 data_time: 0.087593 memory: 15356 loss_kpt: 0.000718 acc_pose: 0.796476 loss: 0.000718 2022/10/19 12:08:52 - mmengine - INFO - Epoch(train) [35][250/293] lr: 5.000000e-04 eta: 5:45:47 time: 0.471705 data_time: 0.104935 memory: 15356 loss_kpt: 0.000713 acc_pose: 0.781971 loss: 0.000713 2022/10/19 12:09:12 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:09:36 - mmengine - INFO - Epoch(train) [36][50/293] lr: 5.000000e-04 eta: 5:44:01 time: 0.477270 data_time: 0.106773 memory: 15356 loss_kpt: 0.000722 acc_pose: 0.796443 loss: 0.000722 2022/10/19 12:09:59 - mmengine - INFO - Epoch(train) [36][100/293] lr: 5.000000e-04 eta: 5:43:57 time: 0.466384 data_time: 0.096745 memory: 15356 loss_kpt: 0.000733 acc_pose: 0.792767 loss: 0.000733 2022/10/19 12:10:23 - mmengine - INFO - Epoch(train) [36][150/293] lr: 5.000000e-04 eta: 5:43:51 time: 0.463929 data_time: 0.094449 memory: 15356 loss_kpt: 0.000727 acc_pose: 0.740471 loss: 0.000727 2022/10/19 12:10:46 - mmengine - INFO - Epoch(train) [36][200/293] lr: 5.000000e-04 eta: 5:43:49 time: 0.476707 data_time: 0.094499 memory: 15356 loss_kpt: 0.000723 acc_pose: 0.738719 loss: 0.000723 2022/10/19 12:11:09 - mmengine - INFO - Epoch(train) [36][250/293] lr: 5.000000e-04 eta: 5:43:41 time: 0.453519 data_time: 0.082611 memory: 15356 loss_kpt: 0.000710 acc_pose: 0.795410 loss: 0.000710 2022/10/19 12:11:29 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:11:54 - mmengine - INFO - Epoch(train) [37][50/293] lr: 5.000000e-04 eta: 5:42:02 time: 0.494643 data_time: 0.111412 memory: 15356 loss_kpt: 0.000701 acc_pose: 0.786486 loss: 0.000701 2022/10/19 12:12:17 - mmengine - INFO - Epoch(train) [37][100/293] lr: 5.000000e-04 eta: 5:41:58 time: 0.472598 data_time: 0.102500 memory: 15356 loss_kpt: 0.000692 acc_pose: 0.796337 loss: 0.000692 2022/10/19 12:12:41 - mmengine - INFO - Epoch(train) [37][150/293] lr: 5.000000e-04 eta: 5:41:56 time: 0.479028 data_time: 0.100239 memory: 15356 loss_kpt: 0.000702 acc_pose: 0.822059 loss: 0.000702 2022/10/19 12:13:05 - mmengine - INFO - Epoch(train) [37][200/293] lr: 5.000000e-04 eta: 5:41:51 time: 0.468416 data_time: 0.101586 memory: 15356 loss_kpt: 0.000717 acc_pose: 0.771864 loss: 0.000717 2022/10/19 12:13:28 - mmengine - INFO - Epoch(train) [37][250/293] lr: 5.000000e-04 eta: 5:41:45 time: 0.465819 data_time: 0.095293 memory: 15356 loss_kpt: 0.000715 acc_pose: 0.800809 loss: 0.000715 2022/10/19 12:13:47 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:14:12 - mmengine - INFO - Epoch(train) [38][50/293] lr: 5.000000e-04 eta: 5:40:08 time: 0.494763 data_time: 0.110177 memory: 15356 loss_kpt: 0.000716 acc_pose: 0.801022 loss: 0.000716 2022/10/19 12:14:36 - mmengine - INFO - Epoch(train) [38][100/293] lr: 5.000000e-04 eta: 5:40:05 time: 0.476887 data_time: 0.095812 memory: 15356 loss_kpt: 0.000696 acc_pose: 0.820083 loss: 0.000696 2022/10/19 12:14:59 - mmengine - INFO - Epoch(train) [38][150/293] lr: 5.000000e-04 eta: 5:39:59 time: 0.466817 data_time: 0.102848 memory: 15356 loss_kpt: 0.000714 acc_pose: 0.789531 loss: 0.000714 2022/10/19 12:15:04 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:15:23 - mmengine - INFO - Epoch(train) [38][200/293] lr: 5.000000e-04 eta: 5:39:55 time: 0.471996 data_time: 0.095150 memory: 15356 loss_kpt: 0.000715 acc_pose: 0.796427 loss: 0.000715 2022/10/19 12:15:46 - mmengine - INFO - Epoch(train) [38][250/293] lr: 5.000000e-04 eta: 5:39:50 time: 0.470324 data_time: 0.094047 memory: 15356 loss_kpt: 0.000719 acc_pose: 0.814710 loss: 0.000719 2022/10/19 12:16:06 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:16:31 - mmengine - INFO - Epoch(train) [39][50/293] lr: 5.000000e-04 eta: 5:38:13 time: 0.488028 data_time: 0.117610 memory: 15356 loss_kpt: 0.000707 acc_pose: 0.821191 loss: 0.000707 2022/10/19 12:16:54 - mmengine - INFO - Epoch(train) [39][100/293] lr: 5.000000e-04 eta: 5:38:06 time: 0.461444 data_time: 0.085886 memory: 15356 loss_kpt: 0.000706 acc_pose: 0.813270 loss: 0.000706 2022/10/19 12:17:17 - mmengine - INFO - Epoch(train) [39][150/293] lr: 5.000000e-04 eta: 5:38:00 time: 0.470270 data_time: 0.099915 memory: 15356 loss_kpt: 0.000713 acc_pose: 0.816540 loss: 0.000713 2022/10/19 12:17:41 - mmengine - INFO - Epoch(train) [39][200/293] lr: 5.000000e-04 eta: 5:37:54 time: 0.465832 data_time: 0.091399 memory: 15356 loss_kpt: 0.000694 acc_pose: 0.797734 loss: 0.000694 2022/10/19 12:18:04 - mmengine - INFO - Epoch(train) [39][250/293] lr: 5.000000e-04 eta: 5:37:46 time: 0.460773 data_time: 0.086845 memory: 15356 loss_kpt: 0.000704 acc_pose: 0.765836 loss: 0.000704 2022/10/19 12:18:24 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:18:48 - mmengine - INFO - Epoch(train) [40][50/293] lr: 5.000000e-04 eta: 5:36:11 time: 0.487485 data_time: 0.107011 memory: 15356 loss_kpt: 0.000683 acc_pose: 0.799529 loss: 0.000683 2022/10/19 12:19:11 - mmengine - INFO - Epoch(train) [40][100/293] lr: 5.000000e-04 eta: 5:36:04 time: 0.463643 data_time: 0.098779 memory: 15356 loss_kpt: 0.000710 acc_pose: 0.781686 loss: 0.000710 2022/10/19 12:19:35 - mmengine - INFO - Epoch(train) [40][150/293] lr: 5.000000e-04 eta: 5:35:57 time: 0.465305 data_time: 0.092042 memory: 15356 loss_kpt: 0.000707 acc_pose: 0.774591 loss: 0.000707 2022/10/19 12:19:58 - mmengine - INFO - Epoch(train) [40][200/293] lr: 5.000000e-04 eta: 5:35:51 time: 0.469704 data_time: 0.089871 memory: 15356 loss_kpt: 0.000699 acc_pose: 0.811825 loss: 0.000699 2022/10/19 12:20:21 - mmengine - INFO - Epoch(train) [40][250/293] lr: 5.000000e-04 eta: 5:35:43 time: 0.459253 data_time: 0.091832 memory: 15356 loss_kpt: 0.000719 acc_pose: 0.698832 loss: 0.000719 2022/10/19 12:20:41 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:20:41 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/10/19 12:20:50 - mmengine - INFO - Epoch(val) [40][50/407] eta: 0:00:48 time: 0.134983 data_time: 0.048613 memory: 15356 2022/10/19 12:20:57 - mmengine - INFO - Epoch(val) [40][100/407] eta: 0:00:40 time: 0.130791 data_time: 0.042447 memory: 1465 2022/10/19 12:21:04 - mmengine - INFO - Epoch(val) [40][150/407] eta: 0:00:35 time: 0.138170 data_time: 0.047154 memory: 1465 2022/10/19 12:21:10 - mmengine - INFO - Epoch(val) [40][200/407] eta: 0:00:26 time: 0.129306 data_time: 0.042042 memory: 1465 2022/10/19 12:21:17 - mmengine - INFO - Epoch(val) [40][250/407] eta: 0:00:21 time: 0.136909 data_time: 0.051716 memory: 1465 2022/10/19 12:21:23 - mmengine - INFO - Epoch(val) [40][300/407] eta: 0:00:13 time: 0.123263 data_time: 0.037782 memory: 1465 2022/10/19 12:21:30 - mmengine - INFO - Epoch(val) [40][350/407] eta: 0:00:07 time: 0.133440 data_time: 0.045922 memory: 1465 2022/10/19 12:21:37 - mmengine - INFO - Epoch(val) [40][400/407] eta: 0:00:00 time: 0.132956 data_time: 0.047466 memory: 1465 2022/10/19 12:22:14 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 12:22:28 - mmengine - INFO - Epoch(val) [40][407/407] coco/AP: 0.692754 coco/AP .5: 0.886579 coco/AP .75: 0.762716 coco/AP (M): 0.646713 coco/AP (L): 0.769092 coco/AR: 0.748410 coco/AR .5: 0.923961 coco/AR .75: 0.814074 coco/AR (M): 0.697569 coco/AR (L): 0.820699 2022/10/19 12:22:28 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_384/best_coco/AP_epoch_30.pth is removed 2022/10/19 12:22:30 - mmengine - INFO - The best checkpoint with 0.6928 coco/AP at 40 epoch is saved to best_coco/AP_epoch_40.pth. 2022/10/19 12:22:54 - mmengine - INFO - Epoch(train) [41][50/293] lr: 5.000000e-04 eta: 5:34:09 time: 0.484455 data_time: 0.101452 memory: 15356 loss_kpt: 0.000705 acc_pose: 0.772575 loss: 0.000705 2022/10/19 12:23:17 - mmengine - INFO - Epoch(train) [41][100/293] lr: 5.000000e-04 eta: 5:34:00 time: 0.455572 data_time: 0.097143 memory: 15356 loss_kpt: 0.000715 acc_pose: 0.758994 loss: 0.000715 2022/10/19 12:23:40 - mmengine - INFO - Epoch(train) [41][150/293] lr: 5.000000e-04 eta: 5:33:52 time: 0.461764 data_time: 0.091929 memory: 15356 loss_kpt: 0.000696 acc_pose: 0.821386 loss: 0.000696 2022/10/19 12:24:04 - mmengine - INFO - Epoch(train) [41][200/293] lr: 5.000000e-04 eta: 5:33:47 time: 0.476454 data_time: 0.094429 memory: 15356 loss_kpt: 0.000706 acc_pose: 0.801969 loss: 0.000706 2022/10/19 12:24:27 - mmengine - INFO - Epoch(train) [41][250/293] lr: 5.000000e-04 eta: 5:33:42 time: 0.476934 data_time: 0.099310 memory: 15356 loss_kpt: 0.000715 acc_pose: 0.760016 loss: 0.000715 2022/10/19 12:24:42 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:24:47 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:25:12 - mmengine - INFO - Epoch(train) [42][50/293] lr: 5.000000e-04 eta: 5:32:10 time: 0.487302 data_time: 0.104789 memory: 15356 loss_kpt: 0.000704 acc_pose: 0.753885 loss: 0.000704 2022/10/19 12:25:35 - mmengine - INFO - Epoch(train) [42][100/293] lr: 5.000000e-04 eta: 5:32:02 time: 0.463513 data_time: 0.097154 memory: 15356 loss_kpt: 0.000702 acc_pose: 0.791384 loss: 0.000702 2022/10/19 12:25:58 - mmengine - INFO - Epoch(train) [42][150/293] lr: 5.000000e-04 eta: 5:31:54 time: 0.462868 data_time: 0.094955 memory: 15356 loss_kpt: 0.000706 acc_pose: 0.793745 loss: 0.000706 2022/10/19 12:26:22 - mmengine - INFO - Epoch(train) [42][200/293] lr: 5.000000e-04 eta: 5:31:47 time: 0.467140 data_time: 0.099918 memory: 15356 loss_kpt: 0.000690 acc_pose: 0.758952 loss: 0.000690 2022/10/19 12:26:45 - mmengine - INFO - Epoch(train) [42][250/293] lr: 5.000000e-04 eta: 5:31:40 time: 0.468617 data_time: 0.086345 memory: 15356 loss_kpt: 0.000705 acc_pose: 0.757656 loss: 0.000705 2022/10/19 12:27:04 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:27:28 - mmengine - INFO - Epoch(train) [43][50/293] lr: 5.000000e-04 eta: 5:30:08 time: 0.477756 data_time: 0.105025 memory: 15356 loss_kpt: 0.000701 acc_pose: 0.798309 loss: 0.000701 2022/10/19 12:27:51 - mmengine - INFO - Epoch(train) [43][100/293] lr: 5.000000e-04 eta: 5:29:59 time: 0.457168 data_time: 0.094131 memory: 15356 loss_kpt: 0.000700 acc_pose: 0.769465 loss: 0.000700 2022/10/19 12:28:15 - mmengine - INFO - Epoch(train) [43][150/293] lr: 5.000000e-04 eta: 5:29:52 time: 0.470802 data_time: 0.089805 memory: 15356 loss_kpt: 0.000694 acc_pose: 0.797995 loss: 0.000694 2022/10/19 12:28:38 - mmengine - INFO - Epoch(train) [43][200/293] lr: 5.000000e-04 eta: 5:29:44 time: 0.468642 data_time: 0.086384 memory: 15356 loss_kpt: 0.000708 acc_pose: 0.771285 loss: 0.000708 2022/10/19 12:29:02 - mmengine - INFO - Epoch(train) [43][250/293] lr: 5.000000e-04 eta: 5:29:38 time: 0.472567 data_time: 0.103791 memory: 15356 loss_kpt: 0.000713 acc_pose: 0.780754 loss: 0.000713 2022/10/19 12:29:22 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:29:46 - mmengine - INFO - Epoch(train) [44][50/293] lr: 5.000000e-04 eta: 5:28:10 time: 0.493515 data_time: 0.116529 memory: 15356 loss_kpt: 0.000700 acc_pose: 0.786467 loss: 0.000700 2022/10/19 12:30:09 - mmengine - INFO - Epoch(train) [44][100/293] lr: 5.000000e-04 eta: 5:28:01 time: 0.458282 data_time: 0.088957 memory: 15356 loss_kpt: 0.000696 acc_pose: 0.762553 loss: 0.000696 2022/10/19 12:30:33 - mmengine - INFO - Epoch(train) [44][150/293] lr: 5.000000e-04 eta: 5:27:52 time: 0.463920 data_time: 0.101329 memory: 15356 loss_kpt: 0.000705 acc_pose: 0.770848 loss: 0.000705 2022/10/19 12:30:56 - mmengine - INFO - Epoch(train) [44][200/293] lr: 5.000000e-04 eta: 5:27:45 time: 0.472079 data_time: 0.097793 memory: 15356 loss_kpt: 0.000692 acc_pose: 0.789041 loss: 0.000692 2022/10/19 12:31:20 - mmengine - INFO - Epoch(train) [44][250/293] lr: 5.000000e-04 eta: 5:27:37 time: 0.467805 data_time: 0.094762 memory: 15356 loss_kpt: 0.000693 acc_pose: 0.760411 loss: 0.000693 2022/10/19 12:31:39 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:32:04 - mmengine - INFO - Epoch(train) [45][50/293] lr: 5.000000e-04 eta: 5:26:11 time: 0.489166 data_time: 0.111282 memory: 15356 loss_kpt: 0.000693 acc_pose: 0.768155 loss: 0.000693 2022/10/19 12:32:27 - mmengine - INFO - Epoch(train) [45][100/293] lr: 5.000000e-04 eta: 5:26:02 time: 0.463800 data_time: 0.085923 memory: 15356 loss_kpt: 0.000693 acc_pose: 0.831398 loss: 0.000693 2022/10/19 12:32:31 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:32:51 - mmengine - INFO - Epoch(train) [45][150/293] lr: 5.000000e-04 eta: 5:25:55 time: 0.474273 data_time: 0.097047 memory: 15356 loss_kpt: 0.000696 acc_pose: 0.784991 loss: 0.000696 2022/10/19 12:33:14 - mmengine - INFO - Epoch(train) [45][200/293] lr: 5.000000e-04 eta: 5:25:47 time: 0.468871 data_time: 0.097400 memory: 15356 loss_kpt: 0.000702 acc_pose: 0.786068 loss: 0.000702 2022/10/19 12:33:37 - mmengine - INFO - Epoch(train) [45][250/293] lr: 5.000000e-04 eta: 5:25:37 time: 0.460493 data_time: 0.096666 memory: 15356 loss_kpt: 0.000695 acc_pose: 0.805321 loss: 0.000695 2022/10/19 12:33:57 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:34:21 - mmengine - INFO - Epoch(train) [46][50/293] lr: 5.000000e-04 eta: 5:24:10 time: 0.478817 data_time: 0.104103 memory: 15356 loss_kpt: 0.000689 acc_pose: 0.761312 loss: 0.000689 2022/10/19 12:34:44 - mmengine - INFO - Epoch(train) [46][100/293] lr: 5.000000e-04 eta: 5:24:02 time: 0.470599 data_time: 0.104718 memory: 15356 loss_kpt: 0.000702 acc_pose: 0.788831 loss: 0.000702 2022/10/19 12:35:08 - mmengine - INFO - Epoch(train) [46][150/293] lr: 5.000000e-04 eta: 5:23:54 time: 0.468252 data_time: 0.098925 memory: 15356 loss_kpt: 0.000682 acc_pose: 0.787448 loss: 0.000682 2022/10/19 12:35:31 - mmengine - INFO - Epoch(train) [46][200/293] lr: 5.000000e-04 eta: 5:23:45 time: 0.466470 data_time: 0.090977 memory: 15356 loss_kpt: 0.000698 acc_pose: 0.841804 loss: 0.000698 2022/10/19 12:35:55 - mmengine - INFO - Epoch(train) [46][250/293] lr: 5.000000e-04 eta: 5:23:38 time: 0.474492 data_time: 0.091138 memory: 15356 loss_kpt: 0.000698 acc_pose: 0.752976 loss: 0.000698 2022/10/19 12:36:15 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:36:39 - mmengine - INFO - Epoch(train) [47][50/293] lr: 5.000000e-04 eta: 5:22:14 time: 0.491026 data_time: 0.110542 memory: 15356 loss_kpt: 0.000699 acc_pose: 0.800130 loss: 0.000699 2022/10/19 12:37:03 - mmengine - INFO - Epoch(train) [47][100/293] lr: 5.000000e-04 eta: 5:22:05 time: 0.466639 data_time: 0.086601 memory: 15356 loss_kpt: 0.000682 acc_pose: 0.785441 loss: 0.000682 2022/10/19 12:37:26 - mmengine - INFO - Epoch(train) [47][150/293] lr: 5.000000e-04 eta: 5:21:56 time: 0.466974 data_time: 0.092446 memory: 15356 loss_kpt: 0.000694 acc_pose: 0.777074 loss: 0.000694 2022/10/19 12:37:49 - mmengine - INFO - Epoch(train) [47][200/293] lr: 5.000000e-04 eta: 5:21:48 time: 0.469562 data_time: 0.094232 memory: 15356 loss_kpt: 0.000697 acc_pose: 0.733110 loss: 0.000697 2022/10/19 12:38:12 - mmengine - INFO - Epoch(train) [47][250/293] lr: 5.000000e-04 eta: 5:21:36 time: 0.454142 data_time: 0.086455 memory: 15356 loss_kpt: 0.000712 acc_pose: 0.768088 loss: 0.000712 2022/10/19 12:38:32 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:38:57 - mmengine - INFO - Epoch(train) [48][50/293] lr: 5.000000e-04 eta: 5:20:13 time: 0.487363 data_time: 0.104298 memory: 15356 loss_kpt: 0.000708 acc_pose: 0.785923 loss: 0.000708 2022/10/19 12:39:20 - mmengine - INFO - Epoch(train) [48][100/293] lr: 5.000000e-04 eta: 5:20:04 time: 0.467773 data_time: 0.097739 memory: 15356 loss_kpt: 0.000696 acc_pose: 0.723864 loss: 0.000696 2022/10/19 12:39:43 - mmengine - INFO - Epoch(train) [48][150/293] lr: 5.000000e-04 eta: 5:19:54 time: 0.462440 data_time: 0.094100 memory: 15356 loss_kpt: 0.000693 acc_pose: 0.799761 loss: 0.000693 2022/10/19 12:40:07 - mmengine - INFO - Epoch(train) [48][200/293] lr: 5.000000e-04 eta: 5:19:45 time: 0.468047 data_time: 0.097363 memory: 15356 loss_kpt: 0.000690 acc_pose: 0.814298 loss: 0.000690 2022/10/19 12:40:20 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:40:31 - mmengine - INFO - Epoch(train) [48][250/293] lr: 5.000000e-04 eta: 5:19:38 time: 0.479454 data_time: 0.093118 memory: 15356 loss_kpt: 0.000688 acc_pose: 0.737362 loss: 0.000688 2022/10/19 12:40:50 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:41:15 - mmengine - INFO - Epoch(train) [49][50/293] lr: 5.000000e-04 eta: 5:18:18 time: 0.500787 data_time: 0.111746 memory: 15356 loss_kpt: 0.000697 acc_pose: 0.785706 loss: 0.000697 2022/10/19 12:41:39 - mmengine - INFO - Epoch(train) [49][100/293] lr: 5.000000e-04 eta: 5:18:09 time: 0.469638 data_time: 0.089303 memory: 15356 loss_kpt: 0.000677 acc_pose: 0.819359 loss: 0.000677 2022/10/19 12:42:02 - mmengine - INFO - Epoch(train) [49][150/293] lr: 5.000000e-04 eta: 5:17:59 time: 0.460463 data_time: 0.087802 memory: 15356 loss_kpt: 0.000695 acc_pose: 0.844346 loss: 0.000695 2022/10/19 12:42:25 - mmengine - INFO - Epoch(train) [49][200/293] lr: 5.000000e-04 eta: 5:17:49 time: 0.465221 data_time: 0.087411 memory: 15356 loss_kpt: 0.000695 acc_pose: 0.845720 loss: 0.000695 2022/10/19 12:42:48 - mmengine - INFO - Epoch(train) [49][250/293] lr: 5.000000e-04 eta: 5:17:39 time: 0.467954 data_time: 0.089841 memory: 15356 loss_kpt: 0.000673 acc_pose: 0.804699 loss: 0.000673 2022/10/19 12:43:08 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:43:33 - mmengine - INFO - Epoch(train) [50][50/293] lr: 5.000000e-04 eta: 5:16:19 time: 0.490155 data_time: 0.102614 memory: 15356 loss_kpt: 0.000684 acc_pose: 0.824454 loss: 0.000684 2022/10/19 12:43:57 - mmengine - INFO - Epoch(train) [50][100/293] lr: 5.000000e-04 eta: 5:16:11 time: 0.474802 data_time: 0.088607 memory: 15356 loss_kpt: 0.000679 acc_pose: 0.806922 loss: 0.000679 2022/10/19 12:44:20 - mmengine - INFO - Epoch(train) [50][150/293] lr: 5.000000e-04 eta: 5:16:00 time: 0.458880 data_time: 0.093620 memory: 15356 loss_kpt: 0.000685 acc_pose: 0.832670 loss: 0.000685 2022/10/19 12:44:42 - mmengine - INFO - Epoch(train) [50][200/293] lr: 5.000000e-04 eta: 5:15:48 time: 0.457224 data_time: 0.087814 memory: 15356 loss_kpt: 0.000691 acc_pose: 0.778344 loss: 0.000691 2022/10/19 12:45:05 - mmengine - INFO - Epoch(train) [50][250/293] lr: 5.000000e-04 eta: 5:15:37 time: 0.458199 data_time: 0.100574 memory: 15356 loss_kpt: 0.000687 acc_pose: 0.748342 loss: 0.000687 2022/10/19 12:45:25 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:45:25 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/10/19 12:45:35 - mmengine - INFO - Epoch(val) [50][50/407] eta: 0:00:48 time: 0.135268 data_time: 0.046703 memory: 15356 2022/10/19 12:45:43 - mmengine - INFO - Epoch(val) [50][100/407] eta: 0:00:44 time: 0.146495 data_time: 0.059956 memory: 1465 2022/10/19 12:45:49 - mmengine - INFO - Epoch(val) [50][150/407] eta: 0:00:33 time: 0.131235 data_time: 0.042210 memory: 1465 2022/10/19 12:45:56 - mmengine - INFO - Epoch(val) [50][200/407] eta: 0:00:26 time: 0.130127 data_time: 0.042974 memory: 1465 2022/10/19 12:46:02 - mmengine - INFO - Epoch(val) [50][250/407] eta: 0:00:20 time: 0.133116 data_time: 0.046496 memory: 1465 2022/10/19 12:46:09 - mmengine - INFO - Epoch(val) [50][300/407] eta: 0:00:14 time: 0.136947 data_time: 0.050832 memory: 1465 2022/10/19 12:46:16 - mmengine - INFO - Epoch(val) [50][350/407] eta: 0:00:07 time: 0.129058 data_time: 0.038694 memory: 1465 2022/10/19 12:46:22 - mmengine - INFO - Epoch(val) [50][400/407] eta: 0:00:00 time: 0.128019 data_time: 0.045172 memory: 1465 2022/10/19 12:46:59 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 12:47:13 - mmengine - INFO - Epoch(val) [50][407/407] coco/AP: 0.697442 coco/AP .5: 0.887403 coco/AP .75: 0.760193 coco/AP (M): 0.648949 coco/AP (L): 0.776510 coco/AR: 0.750850 coco/AR .5: 0.925693 coco/AR .75: 0.809981 coco/AR (M): 0.697924 coco/AR (L): 0.826013 2022/10/19 12:47:13 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_384/best_coco/AP_epoch_40.pth is removed 2022/10/19 12:47:15 - mmengine - INFO - The best checkpoint with 0.6974 coco/AP at 50 epoch is saved to best_coco/AP_epoch_50.pth. 2022/10/19 12:47:39 - mmengine - INFO - Epoch(train) [51][50/293] lr: 5.000000e-04 eta: 5:14:16 time: 0.477107 data_time: 0.108006 memory: 15356 loss_kpt: 0.000681 acc_pose: 0.795765 loss: 0.000681 2022/10/19 12:48:02 - mmengine - INFO - Epoch(train) [51][100/293] lr: 5.000000e-04 eta: 5:14:05 time: 0.464353 data_time: 0.091249 memory: 15356 loss_kpt: 0.000699 acc_pose: 0.760726 loss: 0.000699 2022/10/19 12:48:25 - mmengine - INFO - Epoch(train) [51][150/293] lr: 5.000000e-04 eta: 5:13:54 time: 0.460503 data_time: 0.083414 memory: 15356 loss_kpt: 0.000690 acc_pose: 0.836039 loss: 0.000690 2022/10/19 12:48:49 - mmengine - INFO - Epoch(train) [51][200/293] lr: 5.000000e-04 eta: 5:13:45 time: 0.474802 data_time: 0.091100 memory: 15356 loss_kpt: 0.000688 acc_pose: 0.779243 loss: 0.000688 2022/10/19 12:49:12 - mmengine - INFO - Epoch(train) [51][250/293] lr: 5.000000e-04 eta: 5:13:35 time: 0.466468 data_time: 0.095841 memory: 15356 loss_kpt: 0.000685 acc_pose: 0.808212 loss: 0.000685 2022/10/19 12:49:31 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:49:56 - mmengine - INFO - Epoch(train) [52][50/293] lr: 5.000000e-04 eta: 5:12:17 time: 0.489519 data_time: 0.106288 memory: 15356 loss_kpt: 0.000693 acc_pose: 0.788916 loss: 0.000693 2022/10/19 12:49:59 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:50:19 - mmengine - INFO - Epoch(train) [52][100/293] lr: 5.000000e-04 eta: 5:12:06 time: 0.458582 data_time: 0.090168 memory: 15356 loss_kpt: 0.000677 acc_pose: 0.782824 loss: 0.000677 2022/10/19 12:50:42 - mmengine - INFO - Epoch(train) [52][150/293] lr: 5.000000e-04 eta: 5:11:55 time: 0.466222 data_time: 0.094513 memory: 15356 loss_kpt: 0.000676 acc_pose: 0.698859 loss: 0.000676 2022/10/19 12:51:06 - mmengine - INFO - Epoch(train) [52][200/293] lr: 5.000000e-04 eta: 5:11:45 time: 0.468326 data_time: 0.104533 memory: 15356 loss_kpt: 0.000685 acc_pose: 0.774575 loss: 0.000685 2022/10/19 12:51:29 - mmengine - INFO - Epoch(train) [52][250/293] lr: 5.000000e-04 eta: 5:11:34 time: 0.461606 data_time: 0.088916 memory: 15356 loss_kpt: 0.000678 acc_pose: 0.793402 loss: 0.000678 2022/10/19 12:51:48 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:52:13 - mmengine - INFO - Epoch(train) [53][50/293] lr: 5.000000e-04 eta: 5:10:17 time: 0.489705 data_time: 0.099062 memory: 15356 loss_kpt: 0.000674 acc_pose: 0.784306 loss: 0.000674 2022/10/19 12:52:37 - mmengine - INFO - Epoch(train) [53][100/293] lr: 5.000000e-04 eta: 5:10:08 time: 0.477660 data_time: 0.095941 memory: 15356 loss_kpt: 0.000680 acc_pose: 0.863547 loss: 0.000680 2022/10/19 12:53:00 - mmengine - INFO - Epoch(train) [53][150/293] lr: 5.000000e-04 eta: 5:09:57 time: 0.463907 data_time: 0.101910 memory: 15356 loss_kpt: 0.000680 acc_pose: 0.778421 loss: 0.000680 2022/10/19 12:53:24 - mmengine - INFO - Epoch(train) [53][200/293] lr: 5.000000e-04 eta: 5:09:47 time: 0.470676 data_time: 0.095642 memory: 15356 loss_kpt: 0.000677 acc_pose: 0.779370 loss: 0.000677 2022/10/19 12:53:47 - mmengine - INFO - Epoch(train) [53][250/293] lr: 5.000000e-04 eta: 5:09:36 time: 0.466621 data_time: 0.098286 memory: 15356 loss_kpt: 0.000678 acc_pose: 0.816112 loss: 0.000678 2022/10/19 12:54:07 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:54:31 - mmengine - INFO - Epoch(train) [54][50/293] lr: 5.000000e-04 eta: 5:08:21 time: 0.496351 data_time: 0.105569 memory: 15356 loss_kpt: 0.000679 acc_pose: 0.796853 loss: 0.000679 2022/10/19 12:54:55 - mmengine - INFO - Epoch(train) [54][100/293] lr: 5.000000e-04 eta: 5:08:10 time: 0.463868 data_time: 0.092697 memory: 15356 loss_kpt: 0.000681 acc_pose: 0.803116 loss: 0.000681 2022/10/19 12:55:18 - mmengine - INFO - Epoch(train) [54][150/293] lr: 5.000000e-04 eta: 5:07:59 time: 0.461427 data_time: 0.089470 memory: 15356 loss_kpt: 0.000680 acc_pose: 0.813847 loss: 0.000680 2022/10/19 12:55:42 - mmengine - INFO - Epoch(train) [54][200/293] lr: 5.000000e-04 eta: 5:07:50 time: 0.480656 data_time: 0.096341 memory: 15356 loss_kpt: 0.000694 acc_pose: 0.751879 loss: 0.000694 2022/10/19 12:56:05 - mmengine - INFO - Epoch(train) [54][250/293] lr: 5.000000e-04 eta: 5:07:39 time: 0.469678 data_time: 0.092742 memory: 15356 loss_kpt: 0.000678 acc_pose: 0.845963 loss: 0.000678 2022/10/19 12:56:26 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:56:50 - mmengine - INFO - Epoch(train) [55][50/293] lr: 5.000000e-04 eta: 5:06:23 time: 0.483302 data_time: 0.104569 memory: 15356 loss_kpt: 0.000673 acc_pose: 0.839007 loss: 0.000673 2022/10/19 12:57:13 - mmengine - INFO - Epoch(train) [55][100/293] lr: 5.000000e-04 eta: 5:06:13 time: 0.469184 data_time: 0.102720 memory: 15356 loss_kpt: 0.000679 acc_pose: 0.855873 loss: 0.000679 2022/10/19 12:57:37 - mmengine - INFO - Epoch(train) [55][150/293] lr: 5.000000e-04 eta: 5:06:02 time: 0.466838 data_time: 0.099276 memory: 15356 loss_kpt: 0.000694 acc_pose: 0.793982 loss: 0.000694 2022/10/19 12:57:49 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:57:59 - mmengine - INFO - Epoch(train) [55][200/293] lr: 5.000000e-04 eta: 5:05:49 time: 0.458468 data_time: 0.087840 memory: 15356 loss_kpt: 0.000669 acc_pose: 0.844491 loss: 0.000669 2022/10/19 12:58:23 - mmengine - INFO - Epoch(train) [55][250/293] lr: 5.000000e-04 eta: 5:05:38 time: 0.467836 data_time: 0.096438 memory: 15356 loss_kpt: 0.000678 acc_pose: 0.812970 loss: 0.000678 2022/10/19 12:58:43 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 12:59:07 - mmengine - INFO - Epoch(train) [56][50/293] lr: 5.000000e-04 eta: 5:04:23 time: 0.477906 data_time: 0.109326 memory: 15356 loss_kpt: 0.000674 acc_pose: 0.828373 loss: 0.000674 2022/10/19 12:59:30 - mmengine - INFO - Epoch(train) [56][100/293] lr: 5.000000e-04 eta: 5:04:10 time: 0.458535 data_time: 0.091831 memory: 15356 loss_kpt: 0.000675 acc_pose: 0.813162 loss: 0.000675 2022/10/19 12:59:53 - mmengine - INFO - Epoch(train) [56][150/293] lr: 5.000000e-04 eta: 5:03:58 time: 0.459464 data_time: 0.090507 memory: 15356 loss_kpt: 0.000690 acc_pose: 0.848140 loss: 0.000690 2022/10/19 13:00:17 - mmengine - INFO - Epoch(train) [56][200/293] lr: 5.000000e-04 eta: 5:03:49 time: 0.487074 data_time: 0.103918 memory: 15356 loss_kpt: 0.000682 acc_pose: 0.784350 loss: 0.000682 2022/10/19 13:00:40 - mmengine - INFO - Epoch(train) [56][250/293] lr: 5.000000e-04 eta: 5:03:37 time: 0.455885 data_time: 0.092849 memory: 15356 loss_kpt: 0.000675 acc_pose: 0.811397 loss: 0.000675 2022/10/19 13:01:00 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:01:24 - mmengine - INFO - Epoch(train) [57][50/293] lr: 5.000000e-04 eta: 5:02:23 time: 0.489052 data_time: 0.110865 memory: 15356 loss_kpt: 0.000677 acc_pose: 0.792355 loss: 0.000677 2022/10/19 13:01:47 - mmengine - INFO - Epoch(train) [57][100/293] lr: 5.000000e-04 eta: 5:02:12 time: 0.466331 data_time: 0.090039 memory: 15356 loss_kpt: 0.000683 acc_pose: 0.805673 loss: 0.000683 2022/10/19 13:02:11 - mmengine - INFO - Epoch(train) [57][150/293] lr: 5.000000e-04 eta: 5:02:00 time: 0.464705 data_time: 0.091557 memory: 15356 loss_kpt: 0.000668 acc_pose: 0.794627 loss: 0.000668 2022/10/19 13:02:34 - mmengine - INFO - Epoch(train) [57][200/293] lr: 5.000000e-04 eta: 5:01:47 time: 0.458166 data_time: 0.097041 memory: 15356 loss_kpt: 0.000686 acc_pose: 0.823501 loss: 0.000686 2022/10/19 13:02:58 - mmengine - INFO - Epoch(train) [57][250/293] lr: 5.000000e-04 eta: 5:01:38 time: 0.480237 data_time: 0.097343 memory: 15356 loss_kpt: 0.000673 acc_pose: 0.777831 loss: 0.000673 2022/10/19 13:03:18 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:03:42 - mmengine - INFO - Epoch(train) [58][50/293] lr: 5.000000e-04 eta: 5:00:26 time: 0.495900 data_time: 0.104899 memory: 15356 loss_kpt: 0.000667 acc_pose: 0.791148 loss: 0.000667 2022/10/19 13:04:06 - mmengine - INFO - Epoch(train) [58][100/293] lr: 5.000000e-04 eta: 5:00:14 time: 0.464798 data_time: 0.099527 memory: 15356 loss_kpt: 0.000687 acc_pose: 0.815119 loss: 0.000687 2022/10/19 13:04:29 - mmengine - INFO - Epoch(train) [58][150/293] lr: 5.000000e-04 eta: 5:00:03 time: 0.466574 data_time: 0.096311 memory: 15356 loss_kpt: 0.000685 acc_pose: 0.775004 loss: 0.000685 2022/10/19 13:04:52 - mmengine - INFO - Epoch(train) [58][200/293] lr: 5.000000e-04 eta: 4:59:51 time: 0.468698 data_time: 0.100775 memory: 15356 loss_kpt: 0.000667 acc_pose: 0.825859 loss: 0.000667 2022/10/19 13:05:16 - mmengine - INFO - Epoch(train) [58][250/293] lr: 5.000000e-04 eta: 4:59:40 time: 0.468424 data_time: 0.089473 memory: 15356 loss_kpt: 0.000671 acc_pose: 0.750134 loss: 0.000671 2022/10/19 13:05:36 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:05:40 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:06:00 - mmengine - INFO - Epoch(train) [59][50/293] lr: 5.000000e-04 eta: 4:58:29 time: 0.495296 data_time: 0.102562 memory: 15356 loss_kpt: 0.000675 acc_pose: 0.781209 loss: 0.000675 2022/10/19 13:06:23 - mmengine - INFO - Epoch(train) [59][100/293] lr: 5.000000e-04 eta: 4:58:16 time: 0.460987 data_time: 0.088010 memory: 15356 loss_kpt: 0.000671 acc_pose: 0.817606 loss: 0.000671 2022/10/19 13:06:47 - mmengine - INFO - Epoch(train) [59][150/293] lr: 5.000000e-04 eta: 4:58:06 time: 0.476130 data_time: 0.102295 memory: 15356 loss_kpt: 0.000677 acc_pose: 0.783273 loss: 0.000677 2022/10/19 13:07:10 - mmengine - INFO - Epoch(train) [59][200/293] lr: 5.000000e-04 eta: 4:57:53 time: 0.460216 data_time: 0.088190 memory: 15356 loss_kpt: 0.000671 acc_pose: 0.804441 loss: 0.000671 2022/10/19 13:07:33 - mmengine - INFO - Epoch(train) [59][250/293] lr: 5.000000e-04 eta: 4:57:41 time: 0.465869 data_time: 0.085948 memory: 15356 loss_kpt: 0.000673 acc_pose: 0.789580 loss: 0.000673 2022/10/19 13:07:53 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:08:17 - mmengine - INFO - Epoch(train) [60][50/293] lr: 5.000000e-04 eta: 4:56:29 time: 0.482128 data_time: 0.111418 memory: 15356 loss_kpt: 0.000661 acc_pose: 0.801960 loss: 0.000661 2022/10/19 13:08:41 - mmengine - INFO - Epoch(train) [60][100/293] lr: 5.000000e-04 eta: 4:56:17 time: 0.464091 data_time: 0.085570 memory: 15356 loss_kpt: 0.000662 acc_pose: 0.812905 loss: 0.000662 2022/10/19 13:09:05 - mmengine - INFO - Epoch(train) [60][150/293] lr: 5.000000e-04 eta: 4:56:06 time: 0.477068 data_time: 0.095168 memory: 15356 loss_kpt: 0.000685 acc_pose: 0.787847 loss: 0.000685 2022/10/19 13:09:28 - mmengine - INFO - Epoch(train) [60][200/293] lr: 5.000000e-04 eta: 4:55:54 time: 0.463300 data_time: 0.098454 memory: 15356 loss_kpt: 0.000664 acc_pose: 0.849979 loss: 0.000664 2022/10/19 13:09:51 - mmengine - INFO - Epoch(train) [60][250/293] lr: 5.000000e-04 eta: 4:55:42 time: 0.475194 data_time: 0.100945 memory: 15356 loss_kpt: 0.000686 acc_pose: 0.852914 loss: 0.000686 2022/10/19 13:10:12 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:10:12 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/10/19 13:10:21 - mmengine - INFO - Epoch(val) [60][50/407] eta: 0:00:49 time: 0.139167 data_time: 0.052065 memory: 15356 2022/10/19 13:10:28 - mmengine - INFO - Epoch(val) [60][100/407] eta: 0:00:39 time: 0.128093 data_time: 0.041395 memory: 1465 2022/10/19 13:10:35 - mmengine - INFO - Epoch(val) [60][150/407] eta: 0:00:35 time: 0.139476 data_time: 0.053414 memory: 1465 2022/10/19 13:10:41 - mmengine - INFO - Epoch(val) [60][200/407] eta: 0:00:26 time: 0.127591 data_time: 0.041148 memory: 1465 2022/10/19 13:10:48 - mmengine - INFO - Epoch(val) [60][250/407] eta: 0:00:20 time: 0.132151 data_time: 0.044968 memory: 1465 2022/10/19 13:10:54 - mmengine - INFO - Epoch(val) [60][300/407] eta: 0:00:14 time: 0.134425 data_time: 0.047391 memory: 1465 2022/10/19 13:11:01 - mmengine - INFO - Epoch(val) [60][350/407] eta: 0:00:07 time: 0.130753 data_time: 0.043001 memory: 1465 2022/10/19 13:11:08 - mmengine - INFO - Epoch(val) [60][400/407] eta: 0:00:00 time: 0.132538 data_time: 0.046256 memory: 1465 2022/10/19 13:11:45 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 13:11:59 - mmengine - INFO - Epoch(val) [60][407/407] coco/AP: 0.704961 coco/AP .5: 0.888135 coco/AP .75: 0.775503 coco/AP (M): 0.658107 coco/AP (L): 0.782513 coco/AR: 0.759572 coco/AR .5: 0.925693 coco/AR .75: 0.824622 coco/AR (M): 0.708304 coco/AR (L): 0.832441 2022/10/19 13:11:59 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_384/best_coco/AP_epoch_50.pth is removed 2022/10/19 13:12:01 - mmengine - INFO - The best checkpoint with 0.7050 coco/AP at 60 epoch is saved to best_coco/AP_epoch_60.pth. 2022/10/19 13:12:25 - mmengine - INFO - Epoch(train) [61][50/293] lr: 5.000000e-04 eta: 4:54:32 time: 0.487423 data_time: 0.111544 memory: 15356 loss_kpt: 0.000658 acc_pose: 0.790824 loss: 0.000658 2022/10/19 13:12:49 - mmengine - INFO - Epoch(train) [61][100/293] lr: 5.000000e-04 eta: 4:54:21 time: 0.473271 data_time: 0.094418 memory: 15356 loss_kpt: 0.000665 acc_pose: 0.812437 loss: 0.000665 2022/10/19 13:13:12 - mmengine - INFO - Epoch(train) [61][150/293] lr: 5.000000e-04 eta: 4:54:08 time: 0.464036 data_time: 0.105262 memory: 15356 loss_kpt: 0.000685 acc_pose: 0.843861 loss: 0.000685 2022/10/19 13:13:36 - mmengine - INFO - Epoch(train) [61][200/293] lr: 5.000000e-04 eta: 4:53:58 time: 0.479504 data_time: 0.099413 memory: 15356 loss_kpt: 0.000668 acc_pose: 0.797703 loss: 0.000668 2022/10/19 13:14:00 - mmengine - INFO - Epoch(train) [61][250/293] lr: 5.000000e-04 eta: 4:53:45 time: 0.467412 data_time: 0.098552 memory: 15356 loss_kpt: 0.000667 acc_pose: 0.778442 loss: 0.000667 2022/10/19 13:14:20 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:14:44 - mmengine - INFO - Epoch(train) [62][50/293] lr: 5.000000e-04 eta: 4:52:35 time: 0.479664 data_time: 0.110723 memory: 15356 loss_kpt: 0.000661 acc_pose: 0.823296 loss: 0.000661 2022/10/19 13:15:07 - mmengine - INFO - Epoch(train) [62][100/293] lr: 5.000000e-04 eta: 4:52:22 time: 0.466145 data_time: 0.081832 memory: 15356 loss_kpt: 0.000650 acc_pose: 0.847954 loss: 0.000650 2022/10/19 13:15:19 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:15:31 - mmengine - INFO - Epoch(train) [62][150/293] lr: 5.000000e-04 eta: 4:52:11 time: 0.472201 data_time: 0.093798 memory: 15356 loss_kpt: 0.000655 acc_pose: 0.815126 loss: 0.000655 2022/10/19 13:15:54 - mmengine - INFO - Epoch(train) [62][200/293] lr: 5.000000e-04 eta: 4:51:58 time: 0.463950 data_time: 0.098553 memory: 15356 loss_kpt: 0.000668 acc_pose: 0.824343 loss: 0.000668 2022/10/19 13:16:17 - mmengine - INFO - Epoch(train) [62][250/293] lr: 5.000000e-04 eta: 4:51:44 time: 0.454270 data_time: 0.087940 memory: 15356 loss_kpt: 0.000672 acc_pose: 0.803078 loss: 0.000672 2022/10/19 13:16:37 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:17:02 - mmengine - INFO - Epoch(train) [63][50/293] lr: 5.000000e-04 eta: 4:50:36 time: 0.497666 data_time: 0.113012 memory: 15356 loss_kpt: 0.000657 acc_pose: 0.795046 loss: 0.000657 2022/10/19 13:17:25 - mmengine - INFO - Epoch(train) [63][100/293] lr: 5.000000e-04 eta: 4:50:23 time: 0.461622 data_time: 0.098910 memory: 15356 loss_kpt: 0.000658 acc_pose: 0.799541 loss: 0.000658 2022/10/19 13:17:48 - mmengine - INFO - Epoch(train) [63][150/293] lr: 5.000000e-04 eta: 4:50:11 time: 0.469561 data_time: 0.098068 memory: 15356 loss_kpt: 0.000664 acc_pose: 0.776560 loss: 0.000664 2022/10/19 13:18:12 - mmengine - INFO - Epoch(train) [63][200/293] lr: 5.000000e-04 eta: 4:49:59 time: 0.470181 data_time: 0.092301 memory: 15356 loss_kpt: 0.000661 acc_pose: 0.795141 loss: 0.000661 2022/10/19 13:18:35 - mmengine - INFO - Epoch(train) [63][250/293] lr: 5.000000e-04 eta: 4:49:46 time: 0.464867 data_time: 0.098000 memory: 15356 loss_kpt: 0.000661 acc_pose: 0.776421 loss: 0.000661 2022/10/19 13:18:55 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:19:19 - mmengine - INFO - Epoch(train) [64][50/293] lr: 5.000000e-04 eta: 4:48:37 time: 0.483336 data_time: 0.101159 memory: 15356 loss_kpt: 0.000666 acc_pose: 0.809585 loss: 0.000666 2022/10/19 13:19:42 - mmengine - INFO - Epoch(train) [64][100/293] lr: 5.000000e-04 eta: 4:48:25 time: 0.466075 data_time: 0.092456 memory: 15356 loss_kpt: 0.000660 acc_pose: 0.798751 loss: 0.000660 2022/10/19 13:20:06 - mmengine - INFO - Epoch(train) [64][150/293] lr: 5.000000e-04 eta: 4:48:11 time: 0.463046 data_time: 0.087410 memory: 15356 loss_kpt: 0.000651 acc_pose: 0.733930 loss: 0.000651 2022/10/19 13:20:29 - mmengine - INFO - Epoch(train) [64][200/293] lr: 5.000000e-04 eta: 4:47:59 time: 0.473858 data_time: 0.102906 memory: 15356 loss_kpt: 0.000666 acc_pose: 0.856494 loss: 0.000666 2022/10/19 13:20:53 - mmengine - INFO - Epoch(train) [64][250/293] lr: 5.000000e-04 eta: 4:47:46 time: 0.465709 data_time: 0.088912 memory: 15356 loss_kpt: 0.000662 acc_pose: 0.810371 loss: 0.000662 2022/10/19 13:21:13 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:21:37 - mmengine - INFO - Epoch(train) [65][50/293] lr: 5.000000e-04 eta: 4:46:39 time: 0.485247 data_time: 0.096975 memory: 15356 loss_kpt: 0.000672 acc_pose: 0.788074 loss: 0.000672 2022/10/19 13:22:00 - mmengine - INFO - Epoch(train) [65][100/293] lr: 5.000000e-04 eta: 4:46:25 time: 0.456168 data_time: 0.098978 memory: 15356 loss_kpt: 0.000670 acc_pose: 0.808444 loss: 0.000670 2022/10/19 13:22:23 - mmengine - INFO - Epoch(train) [65][150/293] lr: 5.000000e-04 eta: 4:46:12 time: 0.466681 data_time: 0.089762 memory: 15356 loss_kpt: 0.000662 acc_pose: 0.822102 loss: 0.000662 2022/10/19 13:22:46 - mmengine - INFO - Epoch(train) [65][200/293] lr: 5.000000e-04 eta: 4:45:59 time: 0.464723 data_time: 0.100305 memory: 15356 loss_kpt: 0.000648 acc_pose: 0.857280 loss: 0.000648 2022/10/19 13:23:08 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:23:09 - mmengine - INFO - Epoch(train) [65][250/293] lr: 5.000000e-04 eta: 4:45:44 time: 0.456161 data_time: 0.091496 memory: 15356 loss_kpt: 0.000669 acc_pose: 0.820311 loss: 0.000669 2022/10/19 13:23:29 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:23:54 - mmengine - INFO - Epoch(train) [66][50/293] lr: 5.000000e-04 eta: 4:44:38 time: 0.491011 data_time: 0.110831 memory: 15356 loss_kpt: 0.000659 acc_pose: 0.813870 loss: 0.000659 2022/10/19 13:24:17 - mmengine - INFO - Epoch(train) [66][100/293] lr: 5.000000e-04 eta: 4:44:25 time: 0.462723 data_time: 0.096165 memory: 15356 loss_kpt: 0.000654 acc_pose: 0.840213 loss: 0.000654 2022/10/19 13:24:41 - mmengine - INFO - Epoch(train) [66][150/293] lr: 5.000000e-04 eta: 4:44:12 time: 0.473821 data_time: 0.090476 memory: 15356 loss_kpt: 0.000664 acc_pose: 0.835628 loss: 0.000664 2022/10/19 13:25:04 - mmengine - INFO - Epoch(train) [66][200/293] lr: 5.000000e-04 eta: 4:43:59 time: 0.460870 data_time: 0.095314 memory: 15356 loss_kpt: 0.000656 acc_pose: 0.818760 loss: 0.000656 2022/10/19 13:25:27 - mmengine - INFO - Epoch(train) [66][250/293] lr: 5.000000e-04 eta: 4:43:46 time: 0.470383 data_time: 0.101817 memory: 15356 loss_kpt: 0.000662 acc_pose: 0.825632 loss: 0.000662 2022/10/19 13:25:47 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:26:13 - mmengine - INFO - Epoch(train) [67][50/293] lr: 5.000000e-04 eta: 4:42:42 time: 0.507768 data_time: 0.119363 memory: 15356 loss_kpt: 0.000655 acc_pose: 0.822440 loss: 0.000655 2022/10/19 13:26:37 - mmengine - INFO - Epoch(train) [67][100/293] lr: 5.000000e-04 eta: 4:42:30 time: 0.480438 data_time: 0.098882 memory: 15356 loss_kpt: 0.000655 acc_pose: 0.801621 loss: 0.000655 2022/10/19 13:27:00 - mmengine - INFO - Epoch(train) [67][150/293] lr: 5.000000e-04 eta: 4:42:17 time: 0.465712 data_time: 0.099198 memory: 15356 loss_kpt: 0.000664 acc_pose: 0.852139 loss: 0.000664 2022/10/19 13:27:23 - mmengine - INFO - Epoch(train) [67][200/293] lr: 5.000000e-04 eta: 4:42:04 time: 0.471541 data_time: 0.104984 memory: 15356 loss_kpt: 0.000650 acc_pose: 0.821169 loss: 0.000650 2022/10/19 13:27:47 - mmengine - INFO - Epoch(train) [67][250/293] lr: 5.000000e-04 eta: 4:41:51 time: 0.470129 data_time: 0.091102 memory: 15356 loss_kpt: 0.000665 acc_pose: 0.771766 loss: 0.000665 2022/10/19 13:28:07 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:28:31 - mmengine - INFO - Epoch(train) [68][50/293] lr: 5.000000e-04 eta: 4:40:45 time: 0.480781 data_time: 0.112164 memory: 15356 loss_kpt: 0.000654 acc_pose: 0.814112 loss: 0.000654 2022/10/19 13:28:55 - mmengine - INFO - Epoch(train) [68][100/293] lr: 5.000000e-04 eta: 4:40:33 time: 0.481658 data_time: 0.094945 memory: 15356 loss_kpt: 0.000655 acc_pose: 0.820257 loss: 0.000655 2022/10/19 13:29:18 - mmengine - INFO - Epoch(train) [68][150/293] lr: 5.000000e-04 eta: 4:40:20 time: 0.461514 data_time: 0.099950 memory: 15356 loss_kpt: 0.000647 acc_pose: 0.835472 loss: 0.000647 2022/10/19 13:29:42 - mmengine - INFO - Epoch(train) [68][200/293] lr: 5.000000e-04 eta: 4:40:07 time: 0.472234 data_time: 0.098713 memory: 15356 loss_kpt: 0.000657 acc_pose: 0.846201 loss: 0.000657 2022/10/19 13:30:05 - mmengine - INFO - Epoch(train) [68][250/293] lr: 5.000000e-04 eta: 4:39:53 time: 0.463880 data_time: 0.098487 memory: 15356 loss_kpt: 0.000662 acc_pose: 0.772932 loss: 0.000662 2022/10/19 13:30:25 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:30:49 - mmengine - INFO - Epoch(train) [69][50/293] lr: 5.000000e-04 eta: 4:38:47 time: 0.476358 data_time: 0.102672 memory: 15356 loss_kpt: 0.000659 acc_pose: 0.783512 loss: 0.000659 2022/10/19 13:31:00 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:31:12 - mmengine - INFO - Epoch(train) [69][100/293] lr: 5.000000e-04 eta: 4:38:33 time: 0.459805 data_time: 0.084904 memory: 15356 loss_kpt: 0.000659 acc_pose: 0.830915 loss: 0.000659 2022/10/19 13:31:36 - mmengine - INFO - Epoch(train) [69][150/293] lr: 5.000000e-04 eta: 4:38:20 time: 0.472430 data_time: 0.105895 memory: 15356 loss_kpt: 0.000652 acc_pose: 0.834369 loss: 0.000652 2022/10/19 13:31:58 - mmengine - INFO - Epoch(train) [69][200/293] lr: 5.000000e-04 eta: 4:38:05 time: 0.459240 data_time: 0.088245 memory: 15356 loss_kpt: 0.000663 acc_pose: 0.808657 loss: 0.000663 2022/10/19 13:32:22 - mmengine - INFO - Epoch(train) [69][250/293] lr: 5.000000e-04 eta: 4:37:52 time: 0.464740 data_time: 0.099093 memory: 15356 loss_kpt: 0.000664 acc_pose: 0.800244 loss: 0.000664 2022/10/19 13:32:41 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:33:07 - mmengine - INFO - Epoch(train) [70][50/293] lr: 5.000000e-04 eta: 4:36:50 time: 0.511507 data_time: 0.120930 memory: 15356 loss_kpt: 0.000649 acc_pose: 0.737817 loss: 0.000649 2022/10/19 13:33:30 - mmengine - INFO - Epoch(train) [70][100/293] lr: 5.000000e-04 eta: 4:36:37 time: 0.471507 data_time: 0.100867 memory: 15356 loss_kpt: 0.000662 acc_pose: 0.807834 loss: 0.000662 2022/10/19 13:33:53 - mmengine - INFO - Epoch(train) [70][150/293] lr: 5.000000e-04 eta: 4:36:23 time: 0.462014 data_time: 0.102987 memory: 15356 loss_kpt: 0.000660 acc_pose: 0.808969 loss: 0.000660 2022/10/19 13:34:17 - mmengine - INFO - Epoch(train) [70][200/293] lr: 5.000000e-04 eta: 4:36:10 time: 0.475298 data_time: 0.097171 memory: 15356 loss_kpt: 0.000656 acc_pose: 0.846014 loss: 0.000656 2022/10/19 13:34:40 - mmengine - INFO - Epoch(train) [70][250/293] lr: 5.000000e-04 eta: 4:35:56 time: 0.466114 data_time: 0.090623 memory: 15356 loss_kpt: 0.000638 acc_pose: 0.812053 loss: 0.000638 2022/10/19 13:35:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:35:01 - mmengine - INFO - Saving checkpoint at 70 epochs 2022/10/19 13:35:10 - mmengine - INFO - Epoch(val) [70][50/407] eta: 0:00:49 time: 0.137309 data_time: 0.046985 memory: 15356 2022/10/19 13:35:17 - mmengine - INFO - Epoch(val) [70][100/407] eta: 0:00:40 time: 0.130935 data_time: 0.044731 memory: 1465 2022/10/19 13:35:24 - mmengine - INFO - Epoch(val) [70][150/407] eta: 0:00:36 time: 0.140387 data_time: 0.054095 memory: 1465 2022/10/19 13:35:31 - mmengine - INFO - Epoch(val) [70][200/407] eta: 0:00:27 time: 0.131696 data_time: 0.045127 memory: 1465 2022/10/19 13:35:37 - mmengine - INFO - Epoch(val) [70][250/407] eta: 0:00:19 time: 0.126920 data_time: 0.041634 memory: 1465 2022/10/19 13:35:44 - mmengine - INFO - Epoch(val) [70][300/407] eta: 0:00:14 time: 0.139866 data_time: 0.053040 memory: 1465 2022/10/19 13:35:50 - mmengine - INFO - Epoch(val) [70][350/407] eta: 0:00:07 time: 0.127163 data_time: 0.040351 memory: 1465 2022/10/19 13:35:56 - mmengine - INFO - Epoch(val) [70][400/407] eta: 0:00:00 time: 0.122335 data_time: 0.037066 memory: 1465 2022/10/19 13:36:34 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 13:36:48 - mmengine - INFO - Epoch(val) [70][407/407] coco/AP: 0.707498 coco/AP .5: 0.890261 coco/AP .75: 0.773755 coco/AP (M): 0.661720 coco/AP (L): 0.782346 coco/AR: 0.759367 coco/AR .5: 0.926008 coco/AR .75: 0.821631 coco/AR (M): 0.709123 coco/AR (L): 0.830695 2022/10/19 13:36:48 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_384/best_coco/AP_epoch_60.pth is removed 2022/10/19 13:36:50 - mmengine - INFO - The best checkpoint with 0.7075 coco/AP at 70 epoch is saved to best_coco/AP_epoch_70.pth. 2022/10/19 13:37:13 - mmengine - INFO - Epoch(train) [71][50/293] lr: 5.000000e-04 eta: 4:34:51 time: 0.475324 data_time: 0.101299 memory: 15356 loss_kpt: 0.000640 acc_pose: 0.809255 loss: 0.000640 2022/10/19 13:37:37 - mmengine - INFO - Epoch(train) [71][100/293] lr: 5.000000e-04 eta: 4:34:38 time: 0.471895 data_time: 0.103693 memory: 15356 loss_kpt: 0.000653 acc_pose: 0.807703 loss: 0.000653 2022/10/19 13:38:01 - mmengine - INFO - Epoch(train) [71][150/293] lr: 5.000000e-04 eta: 4:34:25 time: 0.474090 data_time: 0.106037 memory: 15356 loss_kpt: 0.000665 acc_pose: 0.764449 loss: 0.000665 2022/10/19 13:38:24 - mmengine - INFO - Epoch(train) [71][200/293] lr: 5.000000e-04 eta: 4:34:11 time: 0.469204 data_time: 0.090830 memory: 15356 loss_kpt: 0.000656 acc_pose: 0.819714 loss: 0.000656 2022/10/19 13:38:48 - mmengine - INFO - Epoch(train) [71][250/293] lr: 5.000000e-04 eta: 4:33:58 time: 0.471417 data_time: 0.099170 memory: 15356 loss_kpt: 0.000665 acc_pose: 0.821286 loss: 0.000665 2022/10/19 13:39:07 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:39:33 - mmengine - INFO - Epoch(train) [72][50/293] lr: 5.000000e-04 eta: 4:32:57 time: 0.515732 data_time: 0.113414 memory: 15356 loss_kpt: 0.000657 acc_pose: 0.797275 loss: 0.000657 2022/10/19 13:39:57 - mmengine - INFO - Epoch(train) [72][100/293] lr: 5.000000e-04 eta: 4:32:43 time: 0.466578 data_time: 0.089307 memory: 15356 loss_kpt: 0.000647 acc_pose: 0.821022 loss: 0.000647 2022/10/19 13:40:20 - mmengine - INFO - Epoch(train) [72][150/293] lr: 5.000000e-04 eta: 4:32:30 time: 0.467484 data_time: 0.097798 memory: 15356 loss_kpt: 0.000651 acc_pose: 0.813840 loss: 0.000651 2022/10/19 13:40:42 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:40:43 - mmengine - INFO - Epoch(train) [72][200/293] lr: 5.000000e-04 eta: 4:32:16 time: 0.466956 data_time: 0.098691 memory: 15356 loss_kpt: 0.000662 acc_pose: 0.769106 loss: 0.000662 2022/10/19 13:41:07 - mmengine - INFO - Epoch(train) [72][250/293] lr: 5.000000e-04 eta: 4:32:01 time: 0.463894 data_time: 0.086357 memory: 15356 loss_kpt: 0.000660 acc_pose: 0.754745 loss: 0.000660 2022/10/19 13:41:26 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:41:51 - mmengine - INFO - Epoch(train) [73][50/293] lr: 5.000000e-04 eta: 4:30:59 time: 0.489096 data_time: 0.118558 memory: 15356 loss_kpt: 0.000648 acc_pose: 0.781176 loss: 0.000648 2022/10/19 13:42:14 - mmengine - INFO - Epoch(train) [73][100/293] lr: 5.000000e-04 eta: 4:30:45 time: 0.465396 data_time: 0.100912 memory: 15356 loss_kpt: 0.000656 acc_pose: 0.843364 loss: 0.000656 2022/10/19 13:42:38 - mmengine - INFO - Epoch(train) [73][150/293] lr: 5.000000e-04 eta: 4:30:31 time: 0.470382 data_time: 0.103897 memory: 15356 loss_kpt: 0.000660 acc_pose: 0.807463 loss: 0.000660 2022/10/19 13:43:01 - mmengine - INFO - Epoch(train) [73][200/293] lr: 5.000000e-04 eta: 4:30:16 time: 0.459638 data_time: 0.091716 memory: 15356 loss_kpt: 0.000633 acc_pose: 0.848088 loss: 0.000633 2022/10/19 13:43:24 - mmengine - INFO - Epoch(train) [73][250/293] lr: 5.000000e-04 eta: 4:30:03 time: 0.474988 data_time: 0.097823 memory: 15356 loss_kpt: 0.000650 acc_pose: 0.824215 loss: 0.000650 2022/10/19 13:43:44 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:44:08 - mmengine - INFO - Epoch(train) [74][50/293] lr: 5.000000e-04 eta: 4:29:01 time: 0.486084 data_time: 0.109420 memory: 15356 loss_kpt: 0.000652 acc_pose: 0.790074 loss: 0.000652 2022/10/19 13:44:32 - mmengine - INFO - Epoch(train) [74][100/293] lr: 5.000000e-04 eta: 4:28:47 time: 0.470638 data_time: 0.101277 memory: 15356 loss_kpt: 0.000649 acc_pose: 0.838179 loss: 0.000649 2022/10/19 13:44:54 - mmengine - INFO - Epoch(train) [74][150/293] lr: 5.000000e-04 eta: 4:28:32 time: 0.456250 data_time: 0.097379 memory: 15356 loss_kpt: 0.000645 acc_pose: 0.821933 loss: 0.000645 2022/10/19 13:45:18 - mmengine - INFO - Epoch(train) [74][200/293] lr: 5.000000e-04 eta: 4:28:17 time: 0.465715 data_time: 0.095656 memory: 15356 loss_kpt: 0.000673 acc_pose: 0.799197 loss: 0.000673 2022/10/19 13:45:42 - mmengine - INFO - Epoch(train) [74][250/293] lr: 5.000000e-04 eta: 4:28:04 time: 0.476548 data_time: 0.098190 memory: 15356 loss_kpt: 0.000655 acc_pose: 0.787072 loss: 0.000655 2022/10/19 13:46:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:46:25 - mmengine - INFO - Epoch(train) [75][50/293] lr: 5.000000e-04 eta: 4:27:03 time: 0.489494 data_time: 0.106631 memory: 15356 loss_kpt: 0.000640 acc_pose: 0.836921 loss: 0.000640 2022/10/19 13:46:49 - mmengine - INFO - Epoch(train) [75][100/293] lr: 5.000000e-04 eta: 4:26:49 time: 0.468809 data_time: 0.088452 memory: 15356 loss_kpt: 0.000667 acc_pose: 0.834616 loss: 0.000667 2022/10/19 13:47:12 - mmengine - INFO - Epoch(train) [75][150/293] lr: 5.000000e-04 eta: 4:26:35 time: 0.476193 data_time: 0.102994 memory: 15356 loss_kpt: 0.000671 acc_pose: 0.781859 loss: 0.000671 2022/10/19 13:47:36 - mmengine - INFO - Epoch(train) [75][200/293] lr: 5.000000e-04 eta: 4:26:21 time: 0.471297 data_time: 0.096268 memory: 15356 loss_kpt: 0.000657 acc_pose: 0.822972 loss: 0.000657 2022/10/19 13:47:59 - mmengine - INFO - Epoch(train) [75][250/293] lr: 5.000000e-04 eta: 4:26:07 time: 0.464905 data_time: 0.085611 memory: 15356 loss_kpt: 0.000651 acc_pose: 0.850766 loss: 0.000651 2022/10/19 13:48:19 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:48:32 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:48:44 - mmengine - INFO - Epoch(train) [76][50/293] lr: 5.000000e-04 eta: 4:25:05 time: 0.485034 data_time: 0.106625 memory: 15356 loss_kpt: 0.000652 acc_pose: 0.820426 loss: 0.000652 2022/10/19 13:49:07 - mmengine - INFO - Epoch(train) [76][100/293] lr: 5.000000e-04 eta: 4:24:51 time: 0.467639 data_time: 0.101614 memory: 15356 loss_kpt: 0.000661 acc_pose: 0.787252 loss: 0.000661 2022/10/19 13:49:31 - mmengine - INFO - Epoch(train) [76][150/293] lr: 5.000000e-04 eta: 4:24:37 time: 0.474413 data_time: 0.101592 memory: 15356 loss_kpt: 0.000649 acc_pose: 0.823027 loss: 0.000649 2022/10/19 13:49:55 - mmengine - INFO - Epoch(train) [76][200/293] lr: 5.000000e-04 eta: 4:24:24 time: 0.477549 data_time: 0.102660 memory: 15356 loss_kpt: 0.000663 acc_pose: 0.828272 loss: 0.000663 2022/10/19 13:50:18 - mmengine - INFO - Epoch(train) [76][250/293] lr: 5.000000e-04 eta: 4:24:10 time: 0.478111 data_time: 0.103442 memory: 15356 loss_kpt: 0.000642 acc_pose: 0.798931 loss: 0.000642 2022/10/19 13:50:39 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:51:03 - mmengine - INFO - Epoch(train) [77][50/293] lr: 5.000000e-04 eta: 4:23:09 time: 0.481216 data_time: 0.107665 memory: 15356 loss_kpt: 0.000638 acc_pose: 0.768442 loss: 0.000638 2022/10/19 13:51:26 - mmengine - INFO - Epoch(train) [77][100/293] lr: 5.000000e-04 eta: 4:22:54 time: 0.458441 data_time: 0.096101 memory: 15356 loss_kpt: 0.000642 acc_pose: 0.821801 loss: 0.000642 2022/10/19 13:51:49 - mmengine - INFO - Epoch(train) [77][150/293] lr: 5.000000e-04 eta: 4:22:40 time: 0.477665 data_time: 0.102023 memory: 15356 loss_kpt: 0.000654 acc_pose: 0.807883 loss: 0.000654 2022/10/19 13:52:13 - mmengine - INFO - Epoch(train) [77][200/293] lr: 5.000000e-04 eta: 4:22:26 time: 0.468523 data_time: 0.101196 memory: 15356 loss_kpt: 0.000648 acc_pose: 0.843843 loss: 0.000648 2022/10/19 13:52:36 - mmengine - INFO - Epoch(train) [77][250/293] lr: 5.000000e-04 eta: 4:22:11 time: 0.467495 data_time: 0.097721 memory: 15356 loss_kpt: 0.000644 acc_pose: 0.795786 loss: 0.000644 2022/10/19 13:52:56 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:53:21 - mmengine - INFO - Epoch(train) [78][50/293] lr: 5.000000e-04 eta: 4:21:12 time: 0.492667 data_time: 0.104281 memory: 15356 loss_kpt: 0.000645 acc_pose: 0.832542 loss: 0.000645 2022/10/19 13:53:44 - mmengine - INFO - Epoch(train) [78][100/293] lr: 5.000000e-04 eta: 4:20:58 time: 0.478191 data_time: 0.096009 memory: 15356 loss_kpt: 0.000643 acc_pose: 0.758251 loss: 0.000643 2022/10/19 13:54:08 - mmengine - INFO - Epoch(train) [78][150/293] lr: 5.000000e-04 eta: 4:20:43 time: 0.462129 data_time: 0.093206 memory: 15356 loss_kpt: 0.000650 acc_pose: 0.814761 loss: 0.000650 2022/10/19 13:54:31 - mmengine - INFO - Epoch(train) [78][200/293] lr: 5.000000e-04 eta: 4:20:28 time: 0.471356 data_time: 0.096831 memory: 15356 loss_kpt: 0.000648 acc_pose: 0.775153 loss: 0.000648 2022/10/19 13:54:54 - mmengine - INFO - Epoch(train) [78][250/293] lr: 5.000000e-04 eta: 4:20:13 time: 0.456989 data_time: 0.082360 memory: 15356 loss_kpt: 0.000647 acc_pose: 0.831616 loss: 0.000647 2022/10/19 13:55:14 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:55:38 - mmengine - INFO - Epoch(train) [79][50/293] lr: 5.000000e-04 eta: 4:19:13 time: 0.486607 data_time: 0.109991 memory: 15356 loss_kpt: 0.000637 acc_pose: 0.805855 loss: 0.000637 2022/10/19 13:56:01 - mmengine - INFO - Epoch(train) [79][100/293] lr: 5.000000e-04 eta: 4:18:57 time: 0.456016 data_time: 0.094930 memory: 15356 loss_kpt: 0.000646 acc_pose: 0.779967 loss: 0.000646 2022/10/19 13:56:23 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:56:24 - mmengine - INFO - Epoch(train) [79][150/293] lr: 5.000000e-04 eta: 4:18:42 time: 0.461025 data_time: 0.094416 memory: 15356 loss_kpt: 0.000648 acc_pose: 0.804789 loss: 0.000648 2022/10/19 13:56:48 - mmengine - INFO - Epoch(train) [79][200/293] lr: 5.000000e-04 eta: 4:18:29 time: 0.482175 data_time: 0.098333 memory: 15356 loss_kpt: 0.000644 acc_pose: 0.796593 loss: 0.000644 2022/10/19 13:57:11 - mmengine - INFO - Epoch(train) [79][250/293] lr: 5.000000e-04 eta: 4:18:13 time: 0.460526 data_time: 0.090206 memory: 15356 loss_kpt: 0.000654 acc_pose: 0.788342 loss: 0.000654 2022/10/19 13:57:31 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:57:56 - mmengine - INFO - Epoch(train) [80][50/293] lr: 5.000000e-04 eta: 4:17:14 time: 0.489722 data_time: 0.121383 memory: 15356 loss_kpt: 0.000641 acc_pose: 0.811776 loss: 0.000641 2022/10/19 13:58:19 - mmengine - INFO - Epoch(train) [80][100/293] lr: 5.000000e-04 eta: 4:17:00 time: 0.474793 data_time: 0.098675 memory: 15356 loss_kpt: 0.000644 acc_pose: 0.780029 loss: 0.000644 2022/10/19 13:58:42 - mmengine - INFO - Epoch(train) [80][150/293] lr: 5.000000e-04 eta: 4:16:45 time: 0.459496 data_time: 0.098727 memory: 15356 loss_kpt: 0.000646 acc_pose: 0.795212 loss: 0.000646 2022/10/19 13:59:06 - mmengine - INFO - Epoch(train) [80][200/293] lr: 5.000000e-04 eta: 4:16:30 time: 0.468288 data_time: 0.094923 memory: 15356 loss_kpt: 0.000642 acc_pose: 0.798853 loss: 0.000642 2022/10/19 13:59:30 - mmengine - INFO - Epoch(train) [80][250/293] lr: 5.000000e-04 eta: 4:16:16 time: 0.475176 data_time: 0.100604 memory: 15356 loss_kpt: 0.000663 acc_pose: 0.843355 loss: 0.000663 2022/10/19 13:59:49 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 13:59:49 - mmengine - INFO - Saving checkpoint at 80 epochs 2022/10/19 13:59:59 - mmengine - INFO - Epoch(val) [80][50/407] eta: 0:00:49 time: 0.138146 data_time: 0.049891 memory: 15356 2022/10/19 14:00:06 - mmengine - INFO - Epoch(val) [80][100/407] eta: 0:00:43 time: 0.140320 data_time: 0.052411 memory: 1465 2022/10/19 14:00:13 - mmengine - INFO - Epoch(val) [80][150/407] eta: 0:00:32 time: 0.127759 data_time: 0.040412 memory: 1465 2022/10/19 14:00:19 - mmengine - INFO - Epoch(val) [80][200/407] eta: 0:00:26 time: 0.129426 data_time: 0.041089 memory: 1465 2022/10/19 14:00:26 - mmengine - INFO - Epoch(val) [80][250/407] eta: 0:00:20 time: 0.130688 data_time: 0.043358 memory: 1465 2022/10/19 14:00:33 - mmengine - INFO - Epoch(val) [80][300/407] eta: 0:00:14 time: 0.139428 data_time: 0.052248 memory: 1465 2022/10/19 14:00:39 - mmengine - INFO - Epoch(val) [80][350/407] eta: 0:00:07 time: 0.134174 data_time: 0.046139 memory: 1465 2022/10/19 14:00:46 - mmengine - INFO - Epoch(val) [80][400/407] eta: 0:00:00 time: 0.125486 data_time: 0.040763 memory: 1465 2022/10/19 14:01:23 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 14:01:36 - mmengine - INFO - Epoch(val) [80][407/407] coco/AP: 0.713157 coco/AP .5: 0.890556 coco/AP .75: 0.782520 coco/AP (M): 0.665660 coco/AP (L): 0.791005 coco/AR: 0.766514 coco/AR .5: 0.929471 coco/AR .75: 0.830605 coco/AR (M): 0.716007 coco/AR (L): 0.838276 2022/10/19 14:01:36 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_384/best_coco/AP_epoch_70.pth is removed 2022/10/19 14:01:38 - mmengine - INFO - The best checkpoint with 0.7132 coco/AP at 80 epoch is saved to best_coco/AP_epoch_80.pth. 2022/10/19 14:02:02 - mmengine - INFO - Epoch(train) [81][50/293] lr: 5.000000e-04 eta: 4:15:16 time: 0.481624 data_time: 0.103012 memory: 15356 loss_kpt: 0.000636 acc_pose: 0.801360 loss: 0.000636 2022/10/19 14:02:26 - mmengine - INFO - Epoch(train) [81][100/293] lr: 5.000000e-04 eta: 4:15:01 time: 0.468340 data_time: 0.099052 memory: 15356 loss_kpt: 0.000631 acc_pose: 0.797139 loss: 0.000631 2022/10/19 14:02:49 - mmengine - INFO - Epoch(train) [81][150/293] lr: 5.000000e-04 eta: 4:14:46 time: 0.460169 data_time: 0.097983 memory: 15356 loss_kpt: 0.000656 acc_pose: 0.791111 loss: 0.000656 2022/10/19 14:03:12 - mmengine - INFO - Epoch(train) [81][200/293] lr: 5.000000e-04 eta: 4:14:31 time: 0.467070 data_time: 0.097731 memory: 15356 loss_kpt: 0.000649 acc_pose: 0.813400 loss: 0.000649 2022/10/19 14:03:36 - mmengine - INFO - Epoch(train) [81][250/293] lr: 5.000000e-04 eta: 4:14:16 time: 0.466272 data_time: 0.099613 memory: 15356 loss_kpt: 0.000645 acc_pose: 0.824048 loss: 0.000645 2022/10/19 14:03:55 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:04:19 - mmengine - INFO - Epoch(train) [82][50/293] lr: 5.000000e-04 eta: 4:13:17 time: 0.477957 data_time: 0.110814 memory: 15356 loss_kpt: 0.000649 acc_pose: 0.853450 loss: 0.000649 2022/10/19 14:04:42 - mmengine - INFO - Epoch(train) [82][100/293] lr: 5.000000e-04 eta: 4:13:02 time: 0.463915 data_time: 0.091379 memory: 15356 loss_kpt: 0.000635 acc_pose: 0.851565 loss: 0.000635 2022/10/19 14:05:06 - mmengine - INFO - Epoch(train) [82][150/293] lr: 5.000000e-04 eta: 4:12:46 time: 0.461996 data_time: 0.106278 memory: 15356 loss_kpt: 0.000643 acc_pose: 0.804637 loss: 0.000643 2022/10/19 14:05:29 - mmengine - INFO - Epoch(train) [82][200/293] lr: 5.000000e-04 eta: 4:12:31 time: 0.472589 data_time: 0.098273 memory: 15356 loss_kpt: 0.000632 acc_pose: 0.831708 loss: 0.000632 2022/10/19 14:05:52 - mmengine - INFO - Epoch(train) [82][250/293] lr: 5.000000e-04 eta: 4:12:16 time: 0.459662 data_time: 0.092502 memory: 15356 loss_kpt: 0.000640 acc_pose: 0.801407 loss: 0.000640 2022/10/19 14:06:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:06:12 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:06:36 - mmengine - INFO - Epoch(train) [83][50/293] lr: 5.000000e-04 eta: 4:11:17 time: 0.479786 data_time: 0.109742 memory: 15356 loss_kpt: 0.000639 acc_pose: 0.820606 loss: 0.000639 2022/10/19 14:06:59 - mmengine - INFO - Epoch(train) [83][100/293] lr: 5.000000e-04 eta: 4:11:02 time: 0.465790 data_time: 0.094358 memory: 15356 loss_kpt: 0.000641 acc_pose: 0.810200 loss: 0.000641 2022/10/19 14:07:23 - mmengine - INFO - Epoch(train) [83][150/293] lr: 5.000000e-04 eta: 4:10:47 time: 0.466298 data_time: 0.094195 memory: 15356 loss_kpt: 0.000644 acc_pose: 0.820656 loss: 0.000644 2022/10/19 14:07:46 - mmengine - INFO - Epoch(train) [83][200/293] lr: 5.000000e-04 eta: 4:10:32 time: 0.469762 data_time: 0.096719 memory: 15356 loss_kpt: 0.000648 acc_pose: 0.807448 loss: 0.000648 2022/10/19 14:08:09 - mmengine - INFO - Epoch(train) [83][250/293] lr: 5.000000e-04 eta: 4:10:16 time: 0.462062 data_time: 0.100929 memory: 15356 loss_kpt: 0.000641 acc_pose: 0.802188 loss: 0.000641 2022/10/19 14:08:29 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:08:53 - mmengine - INFO - Epoch(train) [84][50/293] lr: 5.000000e-04 eta: 4:09:18 time: 0.484231 data_time: 0.117685 memory: 15356 loss_kpt: 0.000641 acc_pose: 0.789793 loss: 0.000641 2022/10/19 14:09:16 - mmengine - INFO - Epoch(train) [84][100/293] lr: 5.000000e-04 eta: 4:09:03 time: 0.466494 data_time: 0.102276 memory: 15356 loss_kpt: 0.000643 acc_pose: 0.824049 loss: 0.000643 2022/10/19 14:09:40 - mmengine - INFO - Epoch(train) [84][150/293] lr: 5.000000e-04 eta: 4:08:49 time: 0.479546 data_time: 0.098080 memory: 15356 loss_kpt: 0.000633 acc_pose: 0.841548 loss: 0.000633 2022/10/19 14:10:05 - mmengine - INFO - Epoch(train) [84][200/293] lr: 5.000000e-04 eta: 4:08:35 time: 0.485702 data_time: 0.098023 memory: 15356 loss_kpt: 0.000644 acc_pose: 0.792148 loss: 0.000644 2022/10/19 14:10:28 - mmengine - INFO - Epoch(train) [84][250/293] lr: 5.000000e-04 eta: 4:08:19 time: 0.464709 data_time: 0.092955 memory: 15356 loss_kpt: 0.000651 acc_pose: 0.797545 loss: 0.000651 2022/10/19 14:10:48 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:11:12 - mmengine - INFO - Epoch(train) [85][50/293] lr: 5.000000e-04 eta: 4:07:21 time: 0.475949 data_time: 0.101920 memory: 15356 loss_kpt: 0.000623 acc_pose: 0.791004 loss: 0.000623 2022/10/19 14:11:36 - mmengine - INFO - Epoch(train) [85][100/293] lr: 5.000000e-04 eta: 4:07:07 time: 0.478136 data_time: 0.099705 memory: 15356 loss_kpt: 0.000644 acc_pose: 0.829227 loss: 0.000644 2022/10/19 14:12:00 - mmengine - INFO - Epoch(train) [85][150/293] lr: 5.000000e-04 eta: 4:06:52 time: 0.474680 data_time: 0.094961 memory: 15356 loss_kpt: 0.000633 acc_pose: 0.847782 loss: 0.000633 2022/10/19 14:12:23 - mmengine - INFO - Epoch(train) [85][200/293] lr: 5.000000e-04 eta: 4:06:37 time: 0.471359 data_time: 0.099564 memory: 15356 loss_kpt: 0.000637 acc_pose: 0.789327 loss: 0.000637 2022/10/19 14:12:47 - mmengine - INFO - Epoch(train) [85][250/293] lr: 5.000000e-04 eta: 4:06:22 time: 0.477241 data_time: 0.104605 memory: 15356 loss_kpt: 0.000632 acc_pose: 0.810945 loss: 0.000632 2022/10/19 14:13:07 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:13:31 - mmengine - INFO - Epoch(train) [86][50/293] lr: 5.000000e-04 eta: 4:05:26 time: 0.491602 data_time: 0.111224 memory: 15356 loss_kpt: 0.000638 acc_pose: 0.800657 loss: 0.000638 2022/10/19 14:13:53 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:13:55 - mmengine - INFO - Epoch(train) [86][100/293] lr: 5.000000e-04 eta: 4:05:11 time: 0.468182 data_time: 0.093734 memory: 15356 loss_kpt: 0.000627 acc_pose: 0.801077 loss: 0.000627 2022/10/19 14:14:18 - mmengine - INFO - Epoch(train) [86][150/293] lr: 5.000000e-04 eta: 4:04:55 time: 0.470439 data_time: 0.096997 memory: 15356 loss_kpt: 0.000645 acc_pose: 0.827027 loss: 0.000645 2022/10/19 14:14:42 - mmengine - INFO - Epoch(train) [86][200/293] lr: 5.000000e-04 eta: 4:04:40 time: 0.473413 data_time: 0.089435 memory: 15356 loss_kpt: 0.000638 acc_pose: 0.821794 loss: 0.000638 2022/10/19 14:15:06 - mmengine - INFO - Epoch(train) [86][250/293] lr: 5.000000e-04 eta: 4:04:25 time: 0.473088 data_time: 0.100177 memory: 15356 loss_kpt: 0.000637 acc_pose: 0.757726 loss: 0.000637 2022/10/19 14:15:26 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:15:50 - mmengine - INFO - Epoch(train) [87][50/293] lr: 5.000000e-04 eta: 4:03:28 time: 0.480434 data_time: 0.098026 memory: 15356 loss_kpt: 0.000635 acc_pose: 0.770747 loss: 0.000635 2022/10/19 14:16:13 - mmengine - INFO - Epoch(train) [87][100/293] lr: 5.000000e-04 eta: 4:03:13 time: 0.466970 data_time: 0.082691 memory: 15356 loss_kpt: 0.000638 acc_pose: 0.805214 loss: 0.000638 2022/10/19 14:16:37 - mmengine - INFO - Epoch(train) [87][150/293] lr: 5.000000e-04 eta: 4:02:58 time: 0.470766 data_time: 0.097801 memory: 15356 loss_kpt: 0.000628 acc_pose: 0.842131 loss: 0.000628 2022/10/19 14:17:00 - mmengine - INFO - Epoch(train) [87][200/293] lr: 5.000000e-04 eta: 4:02:42 time: 0.466032 data_time: 0.104598 memory: 15356 loss_kpt: 0.000656 acc_pose: 0.790849 loss: 0.000656 2022/10/19 14:17:23 - mmengine - INFO - Epoch(train) [87][250/293] lr: 5.000000e-04 eta: 4:02:26 time: 0.460800 data_time: 0.087685 memory: 15356 loss_kpt: 0.000650 acc_pose: 0.820758 loss: 0.000650 2022/10/19 14:17:43 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:18:08 - mmengine - INFO - Epoch(train) [88][50/293] lr: 5.000000e-04 eta: 4:01:31 time: 0.502927 data_time: 0.104629 memory: 15356 loss_kpt: 0.000640 acc_pose: 0.801974 loss: 0.000640 2022/10/19 14:18:31 - mmengine - INFO - Epoch(train) [88][100/293] lr: 5.000000e-04 eta: 4:01:16 time: 0.470350 data_time: 0.096563 memory: 15356 loss_kpt: 0.000636 acc_pose: 0.860930 loss: 0.000636 2022/10/19 14:18:55 - mmengine - INFO - Epoch(train) [88][150/293] lr: 5.000000e-04 eta: 4:01:00 time: 0.466377 data_time: 0.096656 memory: 15356 loss_kpt: 0.000637 acc_pose: 0.824784 loss: 0.000637 2022/10/19 14:19:18 - mmengine - INFO - Epoch(train) [88][200/293] lr: 5.000000e-04 eta: 4:00:44 time: 0.458908 data_time: 0.080122 memory: 15356 loss_kpt: 0.000643 acc_pose: 0.821027 loss: 0.000643 2022/10/19 14:19:41 - mmengine - INFO - Epoch(train) [88][250/293] lr: 5.000000e-04 eta: 4:00:27 time: 0.454972 data_time: 0.095814 memory: 15356 loss_kpt: 0.000636 acc_pose: 0.870893 loss: 0.000636 2022/10/19 14:20:00 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:20:25 - mmengine - INFO - Epoch(train) [89][50/293] lr: 5.000000e-04 eta: 3:59:32 time: 0.499905 data_time: 0.110285 memory: 15356 loss_kpt: 0.000629 acc_pose: 0.802598 loss: 0.000629 2022/10/19 14:20:48 - mmengine - INFO - Epoch(train) [89][100/293] lr: 5.000000e-04 eta: 3:59:16 time: 0.459995 data_time: 0.085212 memory: 15356 loss_kpt: 0.000633 acc_pose: 0.836112 loss: 0.000633 2022/10/19 14:21:11 - mmengine - INFO - Epoch(train) [89][150/293] lr: 5.000000e-04 eta: 3:59:00 time: 0.463262 data_time: 0.094974 memory: 15356 loss_kpt: 0.000640 acc_pose: 0.810271 loss: 0.000640 2022/10/19 14:21:35 - mmengine - INFO - Epoch(train) [89][200/293] lr: 5.000000e-04 eta: 3:58:45 time: 0.469121 data_time: 0.089108 memory: 15356 loss_kpt: 0.000638 acc_pose: 0.828092 loss: 0.000638 2022/10/19 14:21:42 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:21:58 - mmengine - INFO - Epoch(train) [89][250/293] lr: 5.000000e-04 eta: 3:58:29 time: 0.463780 data_time: 0.099285 memory: 15356 loss_kpt: 0.000636 acc_pose: 0.807203 loss: 0.000636 2022/10/19 14:22:17 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:22:42 - mmengine - INFO - Epoch(train) [90][50/293] lr: 5.000000e-04 eta: 3:57:33 time: 0.489161 data_time: 0.102662 memory: 15356 loss_kpt: 0.000631 acc_pose: 0.790382 loss: 0.000631 2022/10/19 14:23:05 - mmengine - INFO - Epoch(train) [90][100/293] lr: 5.000000e-04 eta: 3:57:17 time: 0.463388 data_time: 0.095011 memory: 15356 loss_kpt: 0.000630 acc_pose: 0.840139 loss: 0.000630 2022/10/19 14:23:29 - mmengine - INFO - Epoch(train) [90][150/293] lr: 5.000000e-04 eta: 3:57:02 time: 0.475594 data_time: 0.097767 memory: 15356 loss_kpt: 0.000657 acc_pose: 0.790642 loss: 0.000657 2022/10/19 14:23:52 - mmengine - INFO - Epoch(train) [90][200/293] lr: 5.000000e-04 eta: 3:56:47 time: 0.471271 data_time: 0.098735 memory: 15356 loss_kpt: 0.000633 acc_pose: 0.791063 loss: 0.000633 2022/10/19 14:24:15 - mmengine - INFO - Epoch(train) [90][250/293] lr: 5.000000e-04 eta: 3:56:30 time: 0.460510 data_time: 0.099227 memory: 15356 loss_kpt: 0.000615 acc_pose: 0.821098 loss: 0.000615 2022/10/19 14:24:35 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:24:35 - mmengine - INFO - Saving checkpoint at 90 epochs 2022/10/19 14:24:45 - mmengine - INFO - Epoch(val) [90][50/407] eta: 0:00:48 time: 0.135026 data_time: 0.048496 memory: 15356 2022/10/19 14:24:52 - mmengine - INFO - Epoch(val) [90][100/407] eta: 0:00:42 time: 0.137014 data_time: 0.050048 memory: 1465 2022/10/19 14:24:59 - mmengine - INFO - Epoch(val) [90][150/407] eta: 0:00:36 time: 0.140860 data_time: 0.053254 memory: 1465 2022/10/19 14:25:05 - mmengine - INFO - Epoch(val) [90][200/407] eta: 0:00:26 time: 0.129281 data_time: 0.043354 memory: 1465 2022/10/19 14:25:11 - mmengine - INFO - Epoch(val) [90][250/407] eta: 0:00:19 time: 0.125561 data_time: 0.038738 memory: 1465 2022/10/19 14:25:18 - mmengine - INFO - Epoch(val) [90][300/407] eta: 0:00:13 time: 0.126455 data_time: 0.039066 memory: 1465 2022/10/19 14:25:24 - mmengine - INFO - Epoch(val) [90][350/407] eta: 0:00:07 time: 0.132603 data_time: 0.044821 memory: 1465 2022/10/19 14:25:31 - mmengine - INFO - Epoch(val) [90][400/407] eta: 0:00:00 time: 0.130043 data_time: 0.045287 memory: 1465 2022/10/19 14:26:08 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 14:26:22 - mmengine - INFO - Epoch(val) [90][407/407] coco/AP: 0.714923 coco/AP .5: 0.893884 coco/AP .75: 0.780917 coco/AP (M): 0.670830 coco/AP (L): 0.790928 coco/AR: 0.767774 coco/AR .5: 0.930573 coco/AR .75: 0.829188 coco/AR (M): 0.718001 coco/AR (L): 0.838759 2022/10/19 14:26:22 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_384/best_coco/AP_epoch_80.pth is removed 2022/10/19 14:26:24 - mmengine - INFO - The best checkpoint with 0.7149 coco/AP at 90 epoch is saved to best_coco/AP_epoch_90.pth. 2022/10/19 14:26:48 - mmengine - INFO - Epoch(train) [91][50/293] lr: 5.000000e-04 eta: 3:55:35 time: 0.480988 data_time: 0.098579 memory: 15356 loss_kpt: 0.000645 acc_pose: 0.829391 loss: 0.000645 2022/10/19 14:27:12 - mmengine - INFO - Epoch(train) [91][100/293] lr: 5.000000e-04 eta: 3:55:19 time: 0.466565 data_time: 0.095365 memory: 15356 loss_kpt: 0.000630 acc_pose: 0.814552 loss: 0.000630 2022/10/19 14:27:35 - mmengine - INFO - Epoch(train) [91][150/293] lr: 5.000000e-04 eta: 3:55:03 time: 0.463271 data_time: 0.087861 memory: 15356 loss_kpt: 0.000632 acc_pose: 0.789944 loss: 0.000632 2022/10/19 14:27:59 - mmengine - INFO - Epoch(train) [91][200/293] lr: 5.000000e-04 eta: 3:54:48 time: 0.479299 data_time: 0.106370 memory: 15356 loss_kpt: 0.000624 acc_pose: 0.807483 loss: 0.000624 2022/10/19 14:28:23 - mmengine - INFO - Epoch(train) [91][250/293] lr: 5.000000e-04 eta: 3:54:32 time: 0.475147 data_time: 0.105469 memory: 15356 loss_kpt: 0.000630 acc_pose: 0.830657 loss: 0.000630 2022/10/19 14:28:42 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:29:06 - mmengine - INFO - Epoch(train) [92][50/293] lr: 5.000000e-04 eta: 3:53:38 time: 0.485308 data_time: 0.104193 memory: 15356 loss_kpt: 0.000630 acc_pose: 0.860236 loss: 0.000630 2022/10/19 14:29:29 - mmengine - INFO - Epoch(train) [92][100/293] lr: 5.000000e-04 eta: 3:53:22 time: 0.464085 data_time: 0.092049 memory: 15356 loss_kpt: 0.000635 acc_pose: 0.874528 loss: 0.000635 2022/10/19 14:29:53 - mmengine - INFO - Epoch(train) [92][150/293] lr: 5.000000e-04 eta: 3:53:06 time: 0.475897 data_time: 0.095379 memory: 15356 loss_kpt: 0.000608 acc_pose: 0.837566 loss: 0.000608 2022/10/19 14:30:17 - mmengine - INFO - Epoch(train) [92][200/293] lr: 5.000000e-04 eta: 3:52:50 time: 0.470362 data_time: 0.090161 memory: 15356 loss_kpt: 0.000626 acc_pose: 0.851438 loss: 0.000626 2022/10/19 14:30:40 - mmengine - INFO - Epoch(train) [92][250/293] lr: 5.000000e-04 eta: 3:52:34 time: 0.468123 data_time: 0.098599 memory: 15356 loss_kpt: 0.000648 acc_pose: 0.814512 loss: 0.000648 2022/10/19 14:31:00 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:31:21 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:31:24 - mmengine - INFO - Epoch(train) [93][50/293] lr: 5.000000e-04 eta: 3:51:40 time: 0.482926 data_time: 0.106850 memory: 15356 loss_kpt: 0.000637 acc_pose: 0.812979 loss: 0.000637 2022/10/19 14:31:47 - mmengine - INFO - Epoch(train) [93][100/293] lr: 5.000000e-04 eta: 3:51:24 time: 0.461772 data_time: 0.095088 memory: 15356 loss_kpt: 0.000628 acc_pose: 0.836097 loss: 0.000628 2022/10/19 14:32:11 - mmengine - INFO - Epoch(train) [93][150/293] lr: 5.000000e-04 eta: 3:51:08 time: 0.473420 data_time: 0.100455 memory: 15356 loss_kpt: 0.000633 acc_pose: 0.846830 loss: 0.000633 2022/10/19 14:32:34 - mmengine - INFO - Epoch(train) [93][200/293] lr: 5.000000e-04 eta: 3:50:52 time: 0.470312 data_time: 0.099634 memory: 15356 loss_kpt: 0.000635 acc_pose: 0.803685 loss: 0.000635 2022/10/19 14:32:57 - mmengine - INFO - Epoch(train) [93][250/293] lr: 5.000000e-04 eta: 3:50:36 time: 0.463672 data_time: 0.098180 memory: 15356 loss_kpt: 0.000630 acc_pose: 0.843447 loss: 0.000630 2022/10/19 14:33:17 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:33:42 - mmengine - INFO - Epoch(train) [94][50/293] lr: 5.000000e-04 eta: 3:49:42 time: 0.485595 data_time: 0.105105 memory: 15356 loss_kpt: 0.000617 acc_pose: 0.841520 loss: 0.000617 2022/10/19 14:34:05 - mmengine - INFO - Epoch(train) [94][100/293] lr: 5.000000e-04 eta: 3:49:25 time: 0.464103 data_time: 0.091402 memory: 15356 loss_kpt: 0.000614 acc_pose: 0.817172 loss: 0.000614 2022/10/19 14:34:28 - mmengine - INFO - Epoch(train) [94][150/293] lr: 5.000000e-04 eta: 3:49:09 time: 0.470782 data_time: 0.092288 memory: 15356 loss_kpt: 0.000633 acc_pose: 0.851578 loss: 0.000633 2022/10/19 14:34:52 - mmengine - INFO - Epoch(train) [94][200/293] lr: 5.000000e-04 eta: 3:48:54 time: 0.475365 data_time: 0.095541 memory: 15356 loss_kpt: 0.000637 acc_pose: 0.807765 loss: 0.000637 2022/10/19 14:35:16 - mmengine - INFO - Epoch(train) [94][250/293] lr: 5.000000e-04 eta: 3:48:38 time: 0.480508 data_time: 0.097319 memory: 15356 loss_kpt: 0.000635 acc_pose: 0.841936 loss: 0.000635 2022/10/19 14:35:36 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:36:00 - mmengine - INFO - Epoch(train) [95][50/293] lr: 5.000000e-04 eta: 3:47:45 time: 0.493411 data_time: 0.115849 memory: 15356 loss_kpt: 0.000635 acc_pose: 0.765025 loss: 0.000635 2022/10/19 14:36:24 - mmengine - INFO - Epoch(train) [95][100/293] lr: 5.000000e-04 eta: 3:47:29 time: 0.467407 data_time: 0.102624 memory: 15356 loss_kpt: 0.000634 acc_pose: 0.814721 loss: 0.000634 2022/10/19 14:36:47 - mmengine - INFO - Epoch(train) [95][150/293] lr: 5.000000e-04 eta: 3:47:13 time: 0.466145 data_time: 0.094731 memory: 15356 loss_kpt: 0.000629 acc_pose: 0.847736 loss: 0.000629 2022/10/19 14:37:11 - mmengine - INFO - Epoch(train) [95][200/293] lr: 5.000000e-04 eta: 3:46:57 time: 0.469053 data_time: 0.094794 memory: 15356 loss_kpt: 0.000632 acc_pose: 0.817166 loss: 0.000632 2022/10/19 14:37:34 - mmengine - INFO - Epoch(train) [95][250/293] lr: 5.000000e-04 eta: 3:46:41 time: 0.474000 data_time: 0.098832 memory: 15356 loss_kpt: 0.000630 acc_pose: 0.813804 loss: 0.000630 2022/10/19 14:37:54 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:38:19 - mmengine - INFO - Epoch(train) [96][50/293] lr: 5.000000e-04 eta: 3:45:48 time: 0.495363 data_time: 0.108174 memory: 15356 loss_kpt: 0.000630 acc_pose: 0.846148 loss: 0.000630 2022/10/19 14:38:42 - mmengine - INFO - Epoch(train) [96][100/293] lr: 5.000000e-04 eta: 3:45:31 time: 0.463816 data_time: 0.096427 memory: 15356 loss_kpt: 0.000624 acc_pose: 0.770827 loss: 0.000624 2022/10/19 14:39:06 - mmengine - INFO - Epoch(train) [96][150/293] lr: 5.000000e-04 eta: 3:45:16 time: 0.486225 data_time: 0.100485 memory: 15356 loss_kpt: 0.000621 acc_pose: 0.878829 loss: 0.000621 2022/10/19 14:39:14 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:39:30 - mmengine - INFO - Epoch(train) [96][200/293] lr: 5.000000e-04 eta: 3:45:01 time: 0.475609 data_time: 0.100234 memory: 15356 loss_kpt: 0.000631 acc_pose: 0.821321 loss: 0.000631 2022/10/19 14:39:53 - mmengine - INFO - Epoch(train) [96][250/293] lr: 5.000000e-04 eta: 3:44:44 time: 0.461426 data_time: 0.095792 memory: 15356 loss_kpt: 0.000619 acc_pose: 0.825782 loss: 0.000619 2022/10/19 14:40:13 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:40:38 - mmengine - INFO - Epoch(train) [97][50/293] lr: 5.000000e-04 eta: 3:43:52 time: 0.511148 data_time: 0.118795 memory: 15356 loss_kpt: 0.000629 acc_pose: 0.836767 loss: 0.000629 2022/10/19 14:41:02 - mmengine - INFO - Epoch(train) [97][100/293] lr: 5.000000e-04 eta: 3:43:36 time: 0.465418 data_time: 0.096300 memory: 15356 loss_kpt: 0.000631 acc_pose: 0.809245 loss: 0.000631 2022/10/19 14:41:26 - mmengine - INFO - Epoch(train) [97][150/293] lr: 5.000000e-04 eta: 3:43:20 time: 0.479424 data_time: 0.092947 memory: 15356 loss_kpt: 0.000625 acc_pose: 0.817091 loss: 0.000625 2022/10/19 14:41:49 - mmengine - INFO - Epoch(train) [97][200/293] lr: 5.000000e-04 eta: 3:43:04 time: 0.467849 data_time: 0.084772 memory: 15356 loss_kpt: 0.000635 acc_pose: 0.794946 loss: 0.000635 2022/10/19 14:42:12 - mmengine - INFO - Epoch(train) [97][250/293] lr: 5.000000e-04 eta: 3:42:47 time: 0.459112 data_time: 0.096162 memory: 15356 loss_kpt: 0.000631 acc_pose: 0.818025 loss: 0.000631 2022/10/19 14:42:31 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:42:56 - mmengine - INFO - Epoch(train) [98][50/293] lr: 5.000000e-04 eta: 3:41:54 time: 0.484879 data_time: 0.109545 memory: 15356 loss_kpt: 0.000617 acc_pose: 0.828518 loss: 0.000617 2022/10/19 14:43:19 - mmengine - INFO - Epoch(train) [98][100/293] lr: 5.000000e-04 eta: 3:41:38 time: 0.469392 data_time: 0.099143 memory: 15356 loss_kpt: 0.000638 acc_pose: 0.794961 loss: 0.000638 2022/10/19 14:43:42 - mmengine - INFO - Epoch(train) [98][150/293] lr: 5.000000e-04 eta: 3:41:21 time: 0.463099 data_time: 0.091136 memory: 15356 loss_kpt: 0.000633 acc_pose: 0.851302 loss: 0.000633 2022/10/19 14:44:06 - mmengine - INFO - Epoch(train) [98][200/293] lr: 5.000000e-04 eta: 3:41:05 time: 0.473805 data_time: 0.095799 memory: 15356 loss_kpt: 0.000633 acc_pose: 0.823861 loss: 0.000633 2022/10/19 14:44:29 - mmengine - INFO - Epoch(train) [98][250/293] lr: 5.000000e-04 eta: 3:40:48 time: 0.458208 data_time: 0.098299 memory: 15356 loss_kpt: 0.000630 acc_pose: 0.819021 loss: 0.000630 2022/10/19 14:44:49 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:45:13 - mmengine - INFO - Epoch(train) [99][50/293] lr: 5.000000e-04 eta: 3:39:56 time: 0.496386 data_time: 0.111971 memory: 15356 loss_kpt: 0.000622 acc_pose: 0.815178 loss: 0.000622 2022/10/19 14:45:37 - mmengine - INFO - Epoch(train) [99][100/293] lr: 5.000000e-04 eta: 3:39:40 time: 0.475518 data_time: 0.101064 memory: 15356 loss_kpt: 0.000628 acc_pose: 0.857744 loss: 0.000628 2022/10/19 14:46:01 - mmengine - INFO - Epoch(train) [99][150/293] lr: 5.000000e-04 eta: 3:39:24 time: 0.466587 data_time: 0.098265 memory: 15356 loss_kpt: 0.000615 acc_pose: 0.816836 loss: 0.000615 2022/10/19 14:46:24 - mmengine - INFO - Epoch(train) [99][200/293] lr: 5.000000e-04 eta: 3:39:07 time: 0.469285 data_time: 0.094658 memory: 15356 loss_kpt: 0.000626 acc_pose: 0.831902 loss: 0.000626 2022/10/19 14:46:48 - mmengine - INFO - Epoch(train) [99][250/293] lr: 5.000000e-04 eta: 3:38:51 time: 0.474435 data_time: 0.097485 memory: 15356 loss_kpt: 0.000629 acc_pose: 0.811589 loss: 0.000629 2022/10/19 14:47:04 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:47:08 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:47:32 - mmengine - INFO - Epoch(train) [100][50/293] lr: 5.000000e-04 eta: 3:37:58 time: 0.481489 data_time: 0.109459 memory: 15356 loss_kpt: 0.000620 acc_pose: 0.846229 loss: 0.000620 2022/10/19 14:47:55 - mmengine - INFO - Epoch(train) [100][100/293] lr: 5.000000e-04 eta: 3:37:42 time: 0.468045 data_time: 0.099951 memory: 15356 loss_kpt: 0.000622 acc_pose: 0.821053 loss: 0.000622 2022/10/19 14:48:19 - mmengine - INFO - Epoch(train) [100][150/293] lr: 5.000000e-04 eta: 3:37:26 time: 0.476606 data_time: 0.098279 memory: 15356 loss_kpt: 0.000618 acc_pose: 0.833935 loss: 0.000618 2022/10/19 14:48:43 - mmengine - INFO - Epoch(train) [100][200/293] lr: 5.000000e-04 eta: 3:37:10 time: 0.475905 data_time: 0.100658 memory: 15356 loss_kpt: 0.000631 acc_pose: 0.781815 loss: 0.000631 2022/10/19 14:49:06 - mmengine - INFO - Epoch(train) [100][250/293] lr: 5.000000e-04 eta: 3:36:53 time: 0.472404 data_time: 0.097111 memory: 15356 loss_kpt: 0.000620 acc_pose: 0.833371 loss: 0.000620 2022/10/19 14:49:26 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:49:26 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/10/19 14:49:36 - mmengine - INFO - Epoch(val) [100][50/407] eta: 0:00:51 time: 0.143777 data_time: 0.056133 memory: 15356 2022/10/19 14:49:43 - mmengine - INFO - Epoch(val) [100][100/407] eta: 0:00:39 time: 0.128473 data_time: 0.037308 memory: 1465 2022/10/19 14:49:49 - mmengine - INFO - Epoch(val) [100][150/407] eta: 0:00:31 time: 0.122993 data_time: 0.035587 memory: 1465 2022/10/19 14:49:56 - mmengine - INFO - Epoch(val) [100][200/407] eta: 0:00:27 time: 0.132200 data_time: 0.044621 memory: 1465 2022/10/19 14:50:02 - mmengine - INFO - Epoch(val) [100][250/407] eta: 0:00:19 time: 0.126807 data_time: 0.039907 memory: 1465 2022/10/19 14:50:08 - mmengine - INFO - Epoch(val) [100][300/407] eta: 0:00:13 time: 0.127402 data_time: 0.039561 memory: 1465 2022/10/19 14:50:15 - mmengine - INFO - Epoch(val) [100][350/407] eta: 0:00:07 time: 0.134208 data_time: 0.045980 memory: 1465 2022/10/19 14:50:21 - mmengine - INFO - Epoch(val) [100][400/407] eta: 0:00:00 time: 0.118307 data_time: 0.033483 memory: 1465 2022/10/19 14:50:58 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 14:51:11 - mmengine - INFO - Epoch(val) [100][407/407] coco/AP: 0.711284 coco/AP .5: 0.892097 coco/AP .75: 0.780294 coco/AP (M): 0.663249 coco/AP (L): 0.789609 coco/AR: 0.763885 coco/AR .5: 0.929943 coco/AR .75: 0.828243 coco/AR (M): 0.711445 coco/AR (L): 0.838164 2022/10/19 14:51:36 - mmengine - INFO - Epoch(train) [101][50/293] lr: 5.000000e-04 eta: 3:36:02 time: 0.490574 data_time: 0.102993 memory: 15356 loss_kpt: 0.000633 acc_pose: 0.819252 loss: 0.000633 2022/10/19 14:51:59 - mmengine - INFO - Epoch(train) [101][100/293] lr: 5.000000e-04 eta: 3:35:45 time: 0.467132 data_time: 0.102688 memory: 15356 loss_kpt: 0.000630 acc_pose: 0.869165 loss: 0.000630 2022/10/19 14:52:23 - mmengine - INFO - Epoch(train) [101][150/293] lr: 5.000000e-04 eta: 3:35:28 time: 0.466268 data_time: 0.098525 memory: 15356 loss_kpt: 0.000618 acc_pose: 0.803388 loss: 0.000618 2022/10/19 14:52:46 - mmengine - INFO - Epoch(train) [101][200/293] lr: 5.000000e-04 eta: 3:35:12 time: 0.469078 data_time: 0.094287 memory: 15356 loss_kpt: 0.000630 acc_pose: 0.792373 loss: 0.000630 2022/10/19 14:53:09 - mmengine - INFO - Epoch(train) [101][250/293] lr: 5.000000e-04 eta: 3:34:55 time: 0.463860 data_time: 0.096724 memory: 15356 loss_kpt: 0.000619 acc_pose: 0.849558 loss: 0.000619 2022/10/19 14:53:29 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:53:54 - mmengine - INFO - Epoch(train) [102][50/293] lr: 5.000000e-04 eta: 3:34:03 time: 0.489659 data_time: 0.112647 memory: 15356 loss_kpt: 0.000621 acc_pose: 0.843411 loss: 0.000621 2022/10/19 14:54:17 - mmengine - INFO - Epoch(train) [102][100/293] lr: 5.000000e-04 eta: 3:33:47 time: 0.462074 data_time: 0.089267 memory: 15356 loss_kpt: 0.000630 acc_pose: 0.825495 loss: 0.000630 2022/10/19 14:54:40 - mmengine - INFO - Epoch(train) [102][150/293] lr: 5.000000e-04 eta: 3:33:30 time: 0.467180 data_time: 0.083707 memory: 15356 loss_kpt: 0.000619 acc_pose: 0.822574 loss: 0.000619 2022/10/19 14:55:04 - mmengine - INFO - Epoch(train) [102][200/293] lr: 5.000000e-04 eta: 3:33:13 time: 0.462734 data_time: 0.090779 memory: 15356 loss_kpt: 0.000621 acc_pose: 0.848029 loss: 0.000621 2022/10/19 14:55:27 - mmengine - INFO - Epoch(train) [102][250/293] lr: 5.000000e-04 eta: 3:32:56 time: 0.470746 data_time: 0.098062 memory: 15356 loss_kpt: 0.000619 acc_pose: 0.844508 loss: 0.000619 2022/10/19 14:55:47 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:56:12 - mmengine - INFO - Epoch(train) [103][50/293] lr: 5.000000e-04 eta: 3:32:05 time: 0.489069 data_time: 0.101513 memory: 15356 loss_kpt: 0.000630 acc_pose: 0.796453 loss: 0.000630 2022/10/19 14:56:35 - mmengine - INFO - Epoch(train) [103][100/293] lr: 5.000000e-04 eta: 3:31:49 time: 0.473149 data_time: 0.096271 memory: 15356 loss_kpt: 0.000635 acc_pose: 0.774725 loss: 0.000635 2022/10/19 14:56:42 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:56:58 - mmengine - INFO - Epoch(train) [103][150/293] lr: 5.000000e-04 eta: 3:31:32 time: 0.464168 data_time: 0.101971 memory: 15356 loss_kpt: 0.000612 acc_pose: 0.836002 loss: 0.000612 2022/10/19 14:57:22 - mmengine - INFO - Epoch(train) [103][200/293] lr: 5.000000e-04 eta: 3:31:15 time: 0.470789 data_time: 0.098726 memory: 15356 loss_kpt: 0.000628 acc_pose: 0.814408 loss: 0.000628 2022/10/19 14:57:45 - mmengine - INFO - Epoch(train) [103][250/293] lr: 5.000000e-04 eta: 3:30:58 time: 0.468728 data_time: 0.101555 memory: 15356 loss_kpt: 0.000635 acc_pose: 0.770277 loss: 0.000635 2022/10/19 14:58:05 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 14:58:29 - mmengine - INFO - Epoch(train) [104][50/293] lr: 5.000000e-04 eta: 3:30:07 time: 0.489402 data_time: 0.114596 memory: 15356 loss_kpt: 0.000637 acc_pose: 0.781509 loss: 0.000637 2022/10/19 14:58:53 - mmengine - INFO - Epoch(train) [104][100/293] lr: 5.000000e-04 eta: 3:29:50 time: 0.465195 data_time: 0.096830 memory: 15356 loss_kpt: 0.000614 acc_pose: 0.870338 loss: 0.000614 2022/10/19 14:59:16 - mmengine - INFO - Epoch(train) [104][150/293] lr: 5.000000e-04 eta: 3:29:34 time: 0.470430 data_time: 0.098694 memory: 15356 loss_kpt: 0.000627 acc_pose: 0.799052 loss: 0.000627 2022/10/19 14:59:40 - mmengine - INFO - Epoch(train) [104][200/293] lr: 5.000000e-04 eta: 3:29:17 time: 0.478832 data_time: 0.104995 memory: 15356 loss_kpt: 0.000628 acc_pose: 0.846516 loss: 0.000628 2022/10/19 15:00:04 - mmengine - INFO - Epoch(train) [104][250/293] lr: 5.000000e-04 eta: 3:29:01 time: 0.473116 data_time: 0.093489 memory: 15356 loss_kpt: 0.000648 acc_pose: 0.831618 loss: 0.000648 2022/10/19 15:00:23 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:00:48 - mmengine - INFO - Epoch(train) [105][50/293] lr: 5.000000e-04 eta: 3:28:10 time: 0.482928 data_time: 0.110038 memory: 15356 loss_kpt: 0.000626 acc_pose: 0.825570 loss: 0.000626 2022/10/19 15:01:11 - mmengine - INFO - Epoch(train) [105][100/293] lr: 5.000000e-04 eta: 3:27:53 time: 0.465390 data_time: 0.095006 memory: 15356 loss_kpt: 0.000614 acc_pose: 0.771367 loss: 0.000614 2022/10/19 15:01:34 - mmengine - INFO - Epoch(train) [105][150/293] lr: 5.000000e-04 eta: 3:27:36 time: 0.461499 data_time: 0.090363 memory: 15356 loss_kpt: 0.000618 acc_pose: 0.858758 loss: 0.000618 2022/10/19 15:01:57 - mmengine - INFO - Epoch(train) [105][200/293] lr: 5.000000e-04 eta: 3:27:19 time: 0.469109 data_time: 0.100852 memory: 15356 loss_kpt: 0.000610 acc_pose: 0.840805 loss: 0.000610 2022/10/19 15:02:21 - mmengine - INFO - Epoch(train) [105][250/293] lr: 5.000000e-04 eta: 3:27:02 time: 0.466369 data_time: 0.095577 memory: 15356 loss_kpt: 0.000622 acc_pose: 0.792418 loss: 0.000622 2022/10/19 15:02:40 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:03:05 - mmengine - INFO - Epoch(train) [106][50/293] lr: 5.000000e-04 eta: 3:26:11 time: 0.486083 data_time: 0.108563 memory: 15356 loss_kpt: 0.000629 acc_pose: 0.803574 loss: 0.000629 2022/10/19 15:03:29 - mmengine - INFO - Epoch(train) [106][100/293] lr: 5.000000e-04 eta: 3:25:55 time: 0.480598 data_time: 0.094265 memory: 15356 loss_kpt: 0.000617 acc_pose: 0.831853 loss: 0.000617 2022/10/19 15:03:52 - mmengine - INFO - Epoch(train) [106][150/293] lr: 5.000000e-04 eta: 3:25:38 time: 0.465081 data_time: 0.094214 memory: 15356 loss_kpt: 0.000633 acc_pose: 0.843459 loss: 0.000633 2022/10/19 15:04:15 - mmengine - INFO - Epoch(train) [106][200/293] lr: 5.000000e-04 eta: 3:25:21 time: 0.465436 data_time: 0.088244 memory: 15356 loss_kpt: 0.000623 acc_pose: 0.858152 loss: 0.000623 2022/10/19 15:04:31 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:04:38 - mmengine - INFO - Epoch(train) [106][250/293] lr: 5.000000e-04 eta: 3:25:04 time: 0.463018 data_time: 0.103447 memory: 15356 loss_kpt: 0.000632 acc_pose: 0.740394 loss: 0.000632 2022/10/19 15:04:58 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:05:23 - mmengine - INFO - Epoch(train) [107][50/293] lr: 5.000000e-04 eta: 3:24:13 time: 0.488179 data_time: 0.104878 memory: 15356 loss_kpt: 0.000618 acc_pose: 0.852573 loss: 0.000618 2022/10/19 15:05:46 - mmengine - INFO - Epoch(train) [107][100/293] lr: 5.000000e-04 eta: 3:23:56 time: 0.466014 data_time: 0.095294 memory: 15356 loss_kpt: 0.000616 acc_pose: 0.831859 loss: 0.000616 2022/10/19 15:06:09 - mmengine - INFO - Epoch(train) [107][150/293] lr: 5.000000e-04 eta: 3:23:39 time: 0.458522 data_time: 0.095544 memory: 15356 loss_kpt: 0.000610 acc_pose: 0.763206 loss: 0.000610 2022/10/19 15:06:32 - mmengine - INFO - Epoch(train) [107][200/293] lr: 5.000000e-04 eta: 3:23:22 time: 0.471067 data_time: 0.106166 memory: 15356 loss_kpt: 0.000623 acc_pose: 0.836473 loss: 0.000623 2022/10/19 15:06:56 - mmengine - INFO - Epoch(train) [107][250/293] lr: 5.000000e-04 eta: 3:23:05 time: 0.469494 data_time: 0.091274 memory: 15356 loss_kpt: 0.000632 acc_pose: 0.866338 loss: 0.000632 2022/10/19 15:07:16 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:07:40 - mmengine - INFO - Epoch(train) [108][50/293] lr: 5.000000e-04 eta: 3:22:15 time: 0.481697 data_time: 0.089519 memory: 15356 loss_kpt: 0.000612 acc_pose: 0.793570 loss: 0.000612 2022/10/19 15:08:03 - mmengine - INFO - Epoch(train) [108][100/293] lr: 5.000000e-04 eta: 3:21:57 time: 0.466206 data_time: 0.089434 memory: 15356 loss_kpt: 0.000622 acc_pose: 0.826056 loss: 0.000622 2022/10/19 15:08:27 - mmengine - INFO - Epoch(train) [108][150/293] lr: 5.000000e-04 eta: 3:21:40 time: 0.466492 data_time: 0.090069 memory: 15356 loss_kpt: 0.000624 acc_pose: 0.834962 loss: 0.000624 2022/10/19 15:08:50 - mmengine - INFO - Epoch(train) [108][200/293] lr: 5.000000e-04 eta: 3:21:23 time: 0.465179 data_time: 0.097763 memory: 15356 loss_kpt: 0.000625 acc_pose: 0.832834 loss: 0.000625 2022/10/19 15:09:13 - mmengine - INFO - Epoch(train) [108][250/293] lr: 5.000000e-04 eta: 3:21:06 time: 0.468360 data_time: 0.075568 memory: 15356 loss_kpt: 0.000617 acc_pose: 0.821683 loss: 0.000617 2022/10/19 15:09:33 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:09:58 - mmengine - INFO - Epoch(train) [109][50/293] lr: 5.000000e-04 eta: 3:20:16 time: 0.485544 data_time: 0.113044 memory: 15356 loss_kpt: 0.000619 acc_pose: 0.859322 loss: 0.000619 2022/10/19 15:10:21 - mmengine - INFO - Epoch(train) [109][100/293] lr: 5.000000e-04 eta: 3:19:59 time: 0.464696 data_time: 0.094390 memory: 15356 loss_kpt: 0.000609 acc_pose: 0.802654 loss: 0.000609 2022/10/19 15:10:44 - mmengine - INFO - Epoch(train) [109][150/293] lr: 5.000000e-04 eta: 3:19:42 time: 0.467535 data_time: 0.099724 memory: 15356 loss_kpt: 0.000606 acc_pose: 0.824083 loss: 0.000606 2022/10/19 15:11:07 - mmengine - INFO - Epoch(train) [109][200/293] lr: 5.000000e-04 eta: 3:19:24 time: 0.459674 data_time: 0.086950 memory: 15356 loss_kpt: 0.000611 acc_pose: 0.831428 loss: 0.000611 2022/10/19 15:11:30 - mmengine - INFO - Epoch(train) [109][250/293] lr: 5.000000e-04 eta: 3:19:07 time: 0.461669 data_time: 0.100642 memory: 15356 loss_kpt: 0.000607 acc_pose: 0.828059 loss: 0.000607 2022/10/19 15:11:50 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:12:14 - mmengine - INFO - Epoch(train) [110][50/293] lr: 5.000000e-04 eta: 3:18:17 time: 0.489669 data_time: 0.104183 memory: 15356 loss_kpt: 0.000604 acc_pose: 0.841881 loss: 0.000604 2022/10/19 15:12:21 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:12:38 - mmengine - INFO - Epoch(train) [110][100/293] lr: 5.000000e-04 eta: 3:18:01 time: 0.473715 data_time: 0.097214 memory: 15356 loss_kpt: 0.000624 acc_pose: 0.809830 loss: 0.000624 2022/10/19 15:13:01 - mmengine - INFO - Epoch(train) [110][150/293] lr: 5.000000e-04 eta: 3:17:43 time: 0.459430 data_time: 0.085795 memory: 15356 loss_kpt: 0.000609 acc_pose: 0.840084 loss: 0.000609 2022/10/19 15:13:25 - mmengine - INFO - Epoch(train) [110][200/293] lr: 5.000000e-04 eta: 3:17:26 time: 0.481985 data_time: 0.098816 memory: 15356 loss_kpt: 0.000612 acc_pose: 0.834359 loss: 0.000612 2022/10/19 15:13:49 - mmengine - INFO - Epoch(train) [110][250/293] lr: 5.000000e-04 eta: 3:17:10 time: 0.476747 data_time: 0.094883 memory: 15356 loss_kpt: 0.000605 acc_pose: 0.869200 loss: 0.000605 2022/10/19 15:14:09 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:14:09 - mmengine - INFO - Saving checkpoint at 110 epochs 2022/10/19 15:14:19 - mmengine - INFO - Epoch(val) [110][50/407] eta: 0:00:48 time: 0.136174 data_time: 0.047895 memory: 15356 2022/10/19 15:14:26 - mmengine - INFO - Epoch(val) [110][100/407] eta: 0:00:45 time: 0.148022 data_time: 0.060227 memory: 1465 2022/10/19 15:14:32 - mmengine - INFO - Epoch(val) [110][150/407] eta: 0:00:31 time: 0.123730 data_time: 0.034724 memory: 1465 2022/10/19 15:14:38 - mmengine - INFO - Epoch(val) [110][200/407] eta: 0:00:25 time: 0.121150 data_time: 0.034516 memory: 1465 2022/10/19 15:14:45 - mmengine - INFO - Epoch(val) [110][250/407] eta: 0:00:21 time: 0.135636 data_time: 0.047782 memory: 1465 2022/10/19 15:14:52 - mmengine - INFO - Epoch(val) [110][300/407] eta: 0:00:14 time: 0.131391 data_time: 0.043794 memory: 1465 2022/10/19 15:14:58 - mmengine - INFO - Epoch(val) [110][350/407] eta: 0:00:07 time: 0.130695 data_time: 0.043573 memory: 1465 2022/10/19 15:15:05 - mmengine - INFO - Epoch(val) [110][400/407] eta: 0:00:00 time: 0.124383 data_time: 0.040115 memory: 1465 2022/10/19 15:15:42 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 15:15:56 - mmengine - INFO - Epoch(val) [110][407/407] coco/AP: 0.717637 coco/AP .5: 0.896080 coco/AP .75: 0.786285 coco/AP (M): 0.670480 coco/AP (L): 0.794637 coco/AR: 0.770120 coco/AR .5: 0.931203 coco/AR .75: 0.834383 coco/AR (M): 0.720268 coco/AR (L): 0.841583 2022/10/19 15:15:56 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_384/best_coco/AP_epoch_90.pth is removed 2022/10/19 15:15:58 - mmengine - INFO - The best checkpoint with 0.7176 coco/AP at 110 epoch is saved to best_coco/AP_epoch_110.pth. 2022/10/19 15:16:22 - mmengine - INFO - Epoch(train) [111][50/293] lr: 5.000000e-04 eta: 3:16:20 time: 0.489375 data_time: 0.108308 memory: 15356 loss_kpt: 0.000634 acc_pose: 0.836882 loss: 0.000634 2022/10/19 15:16:46 - mmengine - INFO - Epoch(train) [111][100/293] lr: 5.000000e-04 eta: 3:16:03 time: 0.466858 data_time: 0.098785 memory: 15356 loss_kpt: 0.000614 acc_pose: 0.843094 loss: 0.000614 2022/10/19 15:17:09 - mmengine - INFO - Epoch(train) [111][150/293] lr: 5.000000e-04 eta: 3:15:46 time: 0.471285 data_time: 0.092449 memory: 15356 loss_kpt: 0.000607 acc_pose: 0.854042 loss: 0.000607 2022/10/19 15:17:33 - mmengine - INFO - Epoch(train) [111][200/293] lr: 5.000000e-04 eta: 3:15:29 time: 0.474127 data_time: 0.102011 memory: 15356 loss_kpt: 0.000631 acc_pose: 0.766587 loss: 0.000631 2022/10/19 15:17:56 - mmengine - INFO - Epoch(train) [111][250/293] lr: 5.000000e-04 eta: 3:15:12 time: 0.465772 data_time: 0.094525 memory: 15356 loss_kpt: 0.000629 acc_pose: 0.827897 loss: 0.000629 2022/10/19 15:18:16 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:18:41 - mmengine - INFO - Epoch(train) [112][50/293] lr: 5.000000e-04 eta: 3:14:23 time: 0.489310 data_time: 0.111535 memory: 15356 loss_kpt: 0.000612 acc_pose: 0.820921 loss: 0.000612 2022/10/19 15:19:04 - mmengine - INFO - Epoch(train) [112][100/293] lr: 5.000000e-04 eta: 3:14:05 time: 0.463462 data_time: 0.104525 memory: 15356 loss_kpt: 0.000624 acc_pose: 0.824101 loss: 0.000624 2022/10/19 15:19:27 - mmengine - INFO - Epoch(train) [112][150/293] lr: 5.000000e-04 eta: 3:13:48 time: 0.468124 data_time: 0.092017 memory: 15356 loss_kpt: 0.000613 acc_pose: 0.848256 loss: 0.000613 2022/10/19 15:19:51 - mmengine - INFO - Epoch(train) [112][200/293] lr: 5.000000e-04 eta: 3:13:31 time: 0.473376 data_time: 0.098192 memory: 15356 loss_kpt: 0.000607 acc_pose: 0.838985 loss: 0.000607 2022/10/19 15:20:15 - mmengine - INFO - Epoch(train) [112][250/293] lr: 5.000000e-04 eta: 3:13:14 time: 0.474598 data_time: 0.099080 memory: 15356 loss_kpt: 0.000614 acc_pose: 0.870864 loss: 0.000614 2022/10/19 15:20:35 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:21:00 - mmengine - INFO - Epoch(train) [113][50/293] lr: 5.000000e-04 eta: 3:12:25 time: 0.497769 data_time: 0.111919 memory: 15356 loss_kpt: 0.000609 acc_pose: 0.841805 loss: 0.000609 2022/10/19 15:21:23 - mmengine - INFO - Epoch(train) [113][100/293] lr: 5.000000e-04 eta: 3:12:08 time: 0.465959 data_time: 0.094618 memory: 15356 loss_kpt: 0.000622 acc_pose: 0.768190 loss: 0.000622 2022/10/19 15:21:47 - mmengine - INFO - Epoch(train) [113][150/293] lr: 5.000000e-04 eta: 3:11:51 time: 0.471198 data_time: 0.105154 memory: 15356 loss_kpt: 0.000622 acc_pose: 0.816430 loss: 0.000622 2022/10/19 15:22:03 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:22:10 - mmengine - INFO - Epoch(train) [113][200/293] lr: 5.000000e-04 eta: 3:11:34 time: 0.469582 data_time: 0.103099 memory: 15356 loss_kpt: 0.000607 acc_pose: 0.823918 loss: 0.000607 2022/10/19 15:22:33 - mmengine - INFO - Epoch(train) [113][250/293] lr: 5.000000e-04 eta: 3:11:16 time: 0.462344 data_time: 0.096615 memory: 15356 loss_kpt: 0.000624 acc_pose: 0.815598 loss: 0.000624 2022/10/19 15:22:54 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:23:19 - mmengine - INFO - Epoch(train) [114][50/293] lr: 5.000000e-04 eta: 3:10:28 time: 0.499958 data_time: 0.119282 memory: 15356 loss_kpt: 0.000606 acc_pose: 0.798492 loss: 0.000606 2022/10/19 15:23:42 - mmengine - INFO - Epoch(train) [114][100/293] lr: 5.000000e-04 eta: 3:10:11 time: 0.473918 data_time: 0.101015 memory: 15356 loss_kpt: 0.000620 acc_pose: 0.816052 loss: 0.000620 2022/10/19 15:24:06 - mmengine - INFO - Epoch(train) [114][150/293] lr: 5.000000e-04 eta: 3:09:53 time: 0.466606 data_time: 0.099518 memory: 15356 loss_kpt: 0.000618 acc_pose: 0.828151 loss: 0.000618 2022/10/19 15:24:29 - mmengine - INFO - Epoch(train) [114][200/293] lr: 5.000000e-04 eta: 3:09:36 time: 0.475680 data_time: 0.105784 memory: 15356 loss_kpt: 0.000612 acc_pose: 0.867931 loss: 0.000612 2022/10/19 15:24:53 - mmengine - INFO - Epoch(train) [114][250/293] lr: 5.000000e-04 eta: 3:09:19 time: 0.479419 data_time: 0.108248 memory: 15356 loss_kpt: 0.000610 acc_pose: 0.802637 loss: 0.000610 2022/10/19 15:25:13 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:25:37 - mmengine - INFO - Epoch(train) [115][50/293] lr: 5.000000e-04 eta: 3:08:30 time: 0.480703 data_time: 0.110537 memory: 15356 loss_kpt: 0.000612 acc_pose: 0.830398 loss: 0.000612 2022/10/19 15:26:01 - mmengine - INFO - Epoch(train) [115][100/293] lr: 5.000000e-04 eta: 3:08:13 time: 0.468506 data_time: 0.096393 memory: 15356 loss_kpt: 0.000628 acc_pose: 0.852755 loss: 0.000628 2022/10/19 15:26:24 - mmengine - INFO - Epoch(train) [115][150/293] lr: 5.000000e-04 eta: 3:07:56 time: 0.470931 data_time: 0.094240 memory: 15356 loss_kpt: 0.000609 acc_pose: 0.837146 loss: 0.000609 2022/10/19 15:26:48 - mmengine - INFO - Epoch(train) [115][200/293] lr: 5.000000e-04 eta: 3:07:38 time: 0.466909 data_time: 0.097560 memory: 15356 loss_kpt: 0.000616 acc_pose: 0.808838 loss: 0.000616 2022/10/19 15:27:11 - mmengine - INFO - Epoch(train) [115][250/293] lr: 5.000000e-04 eta: 3:07:21 time: 0.473183 data_time: 0.103612 memory: 15356 loss_kpt: 0.000610 acc_pose: 0.841379 loss: 0.000610 2022/10/19 15:27:31 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:27:56 - mmengine - INFO - Epoch(train) [116][50/293] lr: 5.000000e-04 eta: 3:06:33 time: 0.489139 data_time: 0.110280 memory: 15356 loss_kpt: 0.000606 acc_pose: 0.829744 loss: 0.000606 2022/10/19 15:28:19 - mmengine - INFO - Epoch(train) [116][100/293] lr: 5.000000e-04 eta: 3:06:15 time: 0.459274 data_time: 0.099393 memory: 15356 loss_kpt: 0.000603 acc_pose: 0.856643 loss: 0.000603 2022/10/19 15:28:42 - mmengine - INFO - Epoch(train) [116][150/293] lr: 5.000000e-04 eta: 3:05:57 time: 0.463580 data_time: 0.092944 memory: 15356 loss_kpt: 0.000612 acc_pose: 0.826713 loss: 0.000612 2022/10/19 15:29:06 - mmengine - INFO - Epoch(train) [116][200/293] lr: 5.000000e-04 eta: 3:05:40 time: 0.470160 data_time: 0.101476 memory: 15356 loss_kpt: 0.000617 acc_pose: 0.804180 loss: 0.000617 2022/10/19 15:29:29 - mmengine - INFO - Epoch(train) [116][250/293] lr: 5.000000e-04 eta: 3:05:22 time: 0.461837 data_time: 0.099574 memory: 15356 loss_kpt: 0.000623 acc_pose: 0.851124 loss: 0.000623 2022/10/19 15:29:49 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:29:56 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:30:13 - mmengine - INFO - Epoch(train) [117][50/293] lr: 5.000000e-04 eta: 3:04:34 time: 0.478790 data_time: 0.109165 memory: 15356 loss_kpt: 0.000605 acc_pose: 0.857739 loss: 0.000605 2022/10/19 15:30:36 - mmengine - INFO - Epoch(train) [117][100/293] lr: 5.000000e-04 eta: 3:04:16 time: 0.459329 data_time: 0.095690 memory: 15356 loss_kpt: 0.000608 acc_pose: 0.874603 loss: 0.000608 2022/10/19 15:30:59 - mmengine - INFO - Epoch(train) [117][150/293] lr: 5.000000e-04 eta: 3:03:58 time: 0.458207 data_time: 0.100571 memory: 15356 loss_kpt: 0.000612 acc_pose: 0.822874 loss: 0.000612 2022/10/19 15:31:22 - mmengine - INFO - Epoch(train) [117][200/293] lr: 5.000000e-04 eta: 3:03:40 time: 0.471178 data_time: 0.096618 memory: 15356 loss_kpt: 0.000610 acc_pose: 0.851369 loss: 0.000610 2022/10/19 15:31:46 - mmengine - INFO - Epoch(train) [117][250/293] lr: 5.000000e-04 eta: 3:03:23 time: 0.471694 data_time: 0.093274 memory: 15356 loss_kpt: 0.000614 acc_pose: 0.832882 loss: 0.000614 2022/10/19 15:32:06 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:32:29 - mmengine - INFO - Epoch(train) [118][50/293] lr: 5.000000e-04 eta: 3:02:34 time: 0.470975 data_time: 0.099706 memory: 15356 loss_kpt: 0.000608 acc_pose: 0.820938 loss: 0.000608 2022/10/19 15:32:53 - mmengine - INFO - Epoch(train) [118][100/293] lr: 5.000000e-04 eta: 3:02:17 time: 0.475934 data_time: 0.101408 memory: 15356 loss_kpt: 0.000612 acc_pose: 0.787059 loss: 0.000612 2022/10/19 15:33:16 - mmengine - INFO - Epoch(train) [118][150/293] lr: 5.000000e-04 eta: 3:02:00 time: 0.468720 data_time: 0.097282 memory: 15356 loss_kpt: 0.000619 acc_pose: 0.818757 loss: 0.000619 2022/10/19 15:33:40 - mmengine - INFO - Epoch(train) [118][200/293] lr: 5.000000e-04 eta: 3:01:42 time: 0.465224 data_time: 0.103976 memory: 15356 loss_kpt: 0.000614 acc_pose: 0.834349 loss: 0.000614 2022/10/19 15:34:03 - mmengine - INFO - Epoch(train) [118][250/293] lr: 5.000000e-04 eta: 3:01:24 time: 0.467483 data_time: 0.100050 memory: 15356 loss_kpt: 0.000627 acc_pose: 0.820499 loss: 0.000627 2022/10/19 15:34:23 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:34:48 - mmengine - INFO - Epoch(train) [119][50/293] lr: 5.000000e-04 eta: 3:00:37 time: 0.495242 data_time: 0.114802 memory: 15356 loss_kpt: 0.000609 acc_pose: 0.829238 loss: 0.000609 2022/10/19 15:35:11 - mmengine - INFO - Epoch(train) [119][100/293] lr: 5.000000e-04 eta: 3:00:19 time: 0.470668 data_time: 0.099167 memory: 15356 loss_kpt: 0.000608 acc_pose: 0.867725 loss: 0.000608 2022/10/19 15:35:34 - mmengine - INFO - Epoch(train) [119][150/293] lr: 5.000000e-04 eta: 3:00:02 time: 0.463277 data_time: 0.088009 memory: 15356 loss_kpt: 0.000609 acc_pose: 0.760022 loss: 0.000609 2022/10/19 15:35:58 - mmengine - INFO - Epoch(train) [119][200/293] lr: 5.000000e-04 eta: 2:59:44 time: 0.469487 data_time: 0.089369 memory: 15356 loss_kpt: 0.000615 acc_pose: 0.811844 loss: 0.000615 2022/10/19 15:36:21 - mmengine - INFO - Epoch(train) [119][250/293] lr: 5.000000e-04 eta: 2:59:26 time: 0.460586 data_time: 0.092414 memory: 15356 loss_kpt: 0.000609 acc_pose: 0.849121 loss: 0.000609 2022/10/19 15:36:41 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:37:04 - mmengine - INFO - Epoch(train) [120][50/293] lr: 5.000000e-04 eta: 2:58:38 time: 0.469334 data_time: 0.093275 memory: 15356 loss_kpt: 0.000615 acc_pose: 0.856071 loss: 0.000615 2022/10/19 15:37:28 - mmengine - INFO - Epoch(train) [120][100/293] lr: 5.000000e-04 eta: 2:58:20 time: 0.464637 data_time: 0.081066 memory: 15356 loss_kpt: 0.000601 acc_pose: 0.819891 loss: 0.000601 2022/10/19 15:37:43 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:37:51 - mmengine - INFO - Epoch(train) [120][150/293] lr: 5.000000e-04 eta: 2:58:02 time: 0.464850 data_time: 0.091217 memory: 15356 loss_kpt: 0.000614 acc_pose: 0.816605 loss: 0.000614 2022/10/19 15:38:15 - mmengine - INFO - Epoch(train) [120][200/293] lr: 5.000000e-04 eta: 2:57:45 time: 0.482261 data_time: 0.100002 memory: 15356 loss_kpt: 0.000616 acc_pose: 0.852447 loss: 0.000616 2022/10/19 15:38:38 - mmengine - INFO - Epoch(train) [120][250/293] lr: 5.000000e-04 eta: 2:57:27 time: 0.458294 data_time: 0.091814 memory: 15356 loss_kpt: 0.000606 acc_pose: 0.864830 loss: 0.000606 2022/10/19 15:38:58 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:38:58 - mmengine - INFO - Saving checkpoint at 120 epochs 2022/10/19 15:39:08 - mmengine - INFO - Epoch(val) [120][50/407] eta: 0:00:49 time: 0.137797 data_time: 0.052310 memory: 15356 2022/10/19 15:39:15 - mmengine - INFO - Epoch(val) [120][100/407] eta: 0:00:41 time: 0.135880 data_time: 0.049363 memory: 1465 2022/10/19 15:39:21 - mmengine - INFO - Epoch(val) [120][150/407] eta: 0:00:33 time: 0.131253 data_time: 0.044986 memory: 1465 2022/10/19 15:39:27 - mmengine - INFO - Epoch(val) [120][200/407] eta: 0:00:25 time: 0.123079 data_time: 0.037150 memory: 1465 2022/10/19 15:39:35 - mmengine - INFO - Epoch(val) [120][250/407] eta: 0:00:23 time: 0.147403 data_time: 0.060539 memory: 1465 2022/10/19 15:39:41 - mmengine - INFO - Epoch(val) [120][300/407] eta: 0:00:13 time: 0.126473 data_time: 0.039772 memory: 1465 2022/10/19 15:39:48 - mmengine - INFO - Epoch(val) [120][350/407] eta: 0:00:07 time: 0.139058 data_time: 0.052004 memory: 1465 2022/10/19 15:39:55 - mmengine - INFO - Epoch(val) [120][400/407] eta: 0:00:00 time: 0.132953 data_time: 0.047474 memory: 1465 2022/10/19 15:40:32 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 15:40:46 - mmengine - INFO - Epoch(val) [120][407/407] coco/AP: 0.718518 coco/AP .5: 0.891305 coco/AP .75: 0.784141 coco/AP (M): 0.671900 coco/AP (L): 0.795856 coco/AR: 0.771174 coco/AR .5: 0.928999 coco/AR .75: 0.831864 coco/AR (M): 0.720732 coco/AR (L): 0.843181 2022/10/19 15:40:46 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_384/best_coco/AP_epoch_110.pth is removed 2022/10/19 15:40:48 - mmengine - INFO - The best checkpoint with 0.7185 coco/AP at 120 epoch is saved to best_coco/AP_epoch_120.pth. 2022/10/19 15:41:12 - mmengine - INFO - Epoch(train) [121][50/293] lr: 5.000000e-04 eta: 2:56:40 time: 0.484945 data_time: 0.108977 memory: 15356 loss_kpt: 0.000587 acc_pose: 0.823789 loss: 0.000587 2022/10/19 15:41:36 - mmengine - INFO - Epoch(train) [121][100/293] lr: 5.000000e-04 eta: 2:56:22 time: 0.473846 data_time: 0.101654 memory: 15356 loss_kpt: 0.000604 acc_pose: 0.857406 loss: 0.000604 2022/10/19 15:42:00 - mmengine - INFO - Epoch(train) [121][150/293] lr: 5.000000e-04 eta: 2:56:05 time: 0.484173 data_time: 0.104833 memory: 15356 loss_kpt: 0.000604 acc_pose: 0.833022 loss: 0.000604 2022/10/19 15:42:24 - mmengine - INFO - Epoch(train) [121][200/293] lr: 5.000000e-04 eta: 2:55:47 time: 0.463630 data_time: 0.093293 memory: 15356 loss_kpt: 0.000613 acc_pose: 0.843589 loss: 0.000613 2022/10/19 15:42:47 - mmengine - INFO - Epoch(train) [121][250/293] lr: 5.000000e-04 eta: 2:55:29 time: 0.461189 data_time: 0.092693 memory: 15356 loss_kpt: 0.000611 acc_pose: 0.836933 loss: 0.000611 2022/10/19 15:43:07 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:43:32 - mmengine - INFO - Epoch(train) [122][50/293] lr: 5.000000e-04 eta: 2:54:42 time: 0.496764 data_time: 0.111913 memory: 15356 loss_kpt: 0.000601 acc_pose: 0.801069 loss: 0.000601 2022/10/19 15:43:55 - mmengine - INFO - Epoch(train) [122][100/293] lr: 5.000000e-04 eta: 2:54:25 time: 0.467061 data_time: 0.095357 memory: 15356 loss_kpt: 0.000606 acc_pose: 0.798749 loss: 0.000606 2022/10/19 15:44:19 - mmengine - INFO - Epoch(train) [122][150/293] lr: 5.000000e-04 eta: 2:54:07 time: 0.473405 data_time: 0.103751 memory: 15356 loss_kpt: 0.000606 acc_pose: 0.889903 loss: 0.000606 2022/10/19 15:44:42 - mmengine - INFO - Epoch(train) [122][200/293] lr: 5.000000e-04 eta: 2:53:49 time: 0.472937 data_time: 0.092516 memory: 15356 loss_kpt: 0.000606 acc_pose: 0.820965 loss: 0.000606 2022/10/19 15:45:06 - mmengine - INFO - Epoch(train) [122][250/293] lr: 5.000000e-04 eta: 2:53:32 time: 0.473591 data_time: 0.093055 memory: 15356 loss_kpt: 0.000618 acc_pose: 0.816993 loss: 0.000618 2022/10/19 15:45:26 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:45:50 - mmengine - INFO - Epoch(train) [123][50/293] lr: 5.000000e-04 eta: 2:52:45 time: 0.485527 data_time: 0.110844 memory: 15356 loss_kpt: 0.000608 acc_pose: 0.845014 loss: 0.000608 2022/10/19 15:46:14 - mmengine - INFO - Epoch(train) [123][100/293] lr: 5.000000e-04 eta: 2:52:27 time: 0.467162 data_time: 0.094371 memory: 15356 loss_kpt: 0.000621 acc_pose: 0.802141 loss: 0.000621 2022/10/19 15:46:37 - mmengine - INFO - Epoch(train) [123][150/293] lr: 5.000000e-04 eta: 2:52:09 time: 0.475354 data_time: 0.106667 memory: 15356 loss_kpt: 0.000608 acc_pose: 0.839991 loss: 0.000608 2022/10/19 15:47:01 - mmengine - INFO - Epoch(train) [123][200/293] lr: 5.000000e-04 eta: 2:51:51 time: 0.465143 data_time: 0.096805 memory: 15356 loss_kpt: 0.000609 acc_pose: 0.810981 loss: 0.000609 2022/10/19 15:47:24 - mmengine - INFO - Epoch(train) [123][250/293] lr: 5.000000e-04 eta: 2:51:34 time: 0.468296 data_time: 0.092841 memory: 15356 loss_kpt: 0.000598 acc_pose: 0.817851 loss: 0.000598 2022/10/19 15:47:26 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:47:43 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:48:07 - mmengine - INFO - Epoch(train) [124][50/293] lr: 5.000000e-04 eta: 2:50:46 time: 0.476176 data_time: 0.105149 memory: 15356 loss_kpt: 0.000611 acc_pose: 0.794820 loss: 0.000611 2022/10/19 15:48:31 - mmengine - INFO - Epoch(train) [124][100/293] lr: 5.000000e-04 eta: 2:50:29 time: 0.475542 data_time: 0.098573 memory: 15356 loss_kpt: 0.000608 acc_pose: 0.835428 loss: 0.000608 2022/10/19 15:48:54 - mmengine - INFO - Epoch(train) [124][150/293] lr: 5.000000e-04 eta: 2:50:11 time: 0.467305 data_time: 0.093734 memory: 15356 loss_kpt: 0.000607 acc_pose: 0.845284 loss: 0.000607 2022/10/19 15:49:18 - mmengine - INFO - Epoch(train) [124][200/293] lr: 5.000000e-04 eta: 2:49:53 time: 0.475644 data_time: 0.096249 memory: 15356 loss_kpt: 0.000615 acc_pose: 0.838363 loss: 0.000615 2022/10/19 15:49:42 - mmengine - INFO - Epoch(train) [124][250/293] lr: 5.000000e-04 eta: 2:49:36 time: 0.470896 data_time: 0.101380 memory: 15356 loss_kpt: 0.000610 acc_pose: 0.874259 loss: 0.000610 2022/10/19 15:50:02 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:50:27 - mmengine - INFO - Epoch(train) [125][50/293] lr: 5.000000e-04 eta: 2:48:49 time: 0.488322 data_time: 0.114467 memory: 15356 loss_kpt: 0.000600 acc_pose: 0.860249 loss: 0.000600 2022/10/19 15:50:50 - mmengine - INFO - Epoch(train) [125][100/293] lr: 5.000000e-04 eta: 2:48:31 time: 0.470797 data_time: 0.098016 memory: 15356 loss_kpt: 0.000606 acc_pose: 0.793352 loss: 0.000606 2022/10/19 15:51:14 - mmengine - INFO - Epoch(train) [125][150/293] lr: 5.000000e-04 eta: 2:48:13 time: 0.468169 data_time: 0.101857 memory: 15356 loss_kpt: 0.000612 acc_pose: 0.821690 loss: 0.000612 2022/10/19 15:51:37 - mmengine - INFO - Epoch(train) [125][200/293] lr: 5.000000e-04 eta: 2:47:56 time: 0.473551 data_time: 0.094018 memory: 15356 loss_kpt: 0.000612 acc_pose: 0.835935 loss: 0.000612 2022/10/19 15:52:01 - mmengine - INFO - Epoch(train) [125][250/293] lr: 5.000000e-04 eta: 2:47:38 time: 0.467070 data_time: 0.095243 memory: 15356 loss_kpt: 0.000614 acc_pose: 0.828789 loss: 0.000614 2022/10/19 15:52:21 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:52:44 - mmengine - INFO - Epoch(train) [126][50/293] lr: 5.000000e-04 eta: 2:46:51 time: 0.474165 data_time: 0.113351 memory: 15356 loss_kpt: 0.000610 acc_pose: 0.846676 loss: 0.000610 2022/10/19 15:53:08 - mmengine - INFO - Epoch(train) [126][100/293] lr: 5.000000e-04 eta: 2:46:33 time: 0.477830 data_time: 0.100752 memory: 15356 loss_kpt: 0.000606 acc_pose: 0.831910 loss: 0.000606 2022/10/19 15:53:32 - mmengine - INFO - Epoch(train) [126][150/293] lr: 5.000000e-04 eta: 2:46:16 time: 0.479705 data_time: 0.102759 memory: 15356 loss_kpt: 0.000609 acc_pose: 0.865633 loss: 0.000609 2022/10/19 15:53:56 - mmengine - INFO - Epoch(train) [126][200/293] lr: 5.000000e-04 eta: 2:45:58 time: 0.472167 data_time: 0.094062 memory: 15356 loss_kpt: 0.000605 acc_pose: 0.863706 loss: 0.000605 2022/10/19 15:54:20 - mmengine - INFO - Epoch(train) [126][250/293] lr: 5.000000e-04 eta: 2:45:40 time: 0.476569 data_time: 0.097938 memory: 15356 loss_kpt: 0.000601 acc_pose: 0.800398 loss: 0.000601 2022/10/19 15:54:40 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:55:05 - mmengine - INFO - Epoch(train) [127][50/293] lr: 5.000000e-04 eta: 2:44:54 time: 0.498287 data_time: 0.114158 memory: 15356 loss_kpt: 0.000613 acc_pose: 0.854828 loss: 0.000613 2022/10/19 15:55:19 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:55:28 - mmengine - INFO - Epoch(train) [127][100/293] lr: 5.000000e-04 eta: 2:44:36 time: 0.468180 data_time: 0.099304 memory: 15356 loss_kpt: 0.000603 acc_pose: 0.758732 loss: 0.000603 2022/10/19 15:55:52 - mmengine - INFO - Epoch(train) [127][150/293] lr: 5.000000e-04 eta: 2:44:18 time: 0.471974 data_time: 0.103408 memory: 15356 loss_kpt: 0.000591 acc_pose: 0.820614 loss: 0.000591 2022/10/19 15:56:15 - mmengine - INFO - Epoch(train) [127][200/293] lr: 5.000000e-04 eta: 2:44:01 time: 0.473858 data_time: 0.092275 memory: 15356 loss_kpt: 0.000613 acc_pose: 0.792825 loss: 0.000613 2022/10/19 15:56:39 - mmengine - INFO - Epoch(train) [127][250/293] lr: 5.000000e-04 eta: 2:43:43 time: 0.467593 data_time: 0.098474 memory: 15356 loss_kpt: 0.000601 acc_pose: 0.856174 loss: 0.000601 2022/10/19 15:56:58 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:57:23 - mmengine - INFO - Epoch(train) [128][50/293] lr: 5.000000e-04 eta: 2:42:57 time: 0.500577 data_time: 0.113656 memory: 15356 loss_kpt: 0.000602 acc_pose: 0.819271 loss: 0.000602 2022/10/19 15:57:47 - mmengine - INFO - Epoch(train) [128][100/293] lr: 5.000000e-04 eta: 2:42:39 time: 0.463426 data_time: 0.089481 memory: 15356 loss_kpt: 0.000599 acc_pose: 0.813462 loss: 0.000599 2022/10/19 15:58:10 - mmengine - INFO - Epoch(train) [128][150/293] lr: 5.000000e-04 eta: 2:42:21 time: 0.470020 data_time: 0.106062 memory: 15356 loss_kpt: 0.000595 acc_pose: 0.826483 loss: 0.000595 2022/10/19 15:58:33 - mmengine - INFO - Epoch(train) [128][200/293] lr: 5.000000e-04 eta: 2:42:02 time: 0.461926 data_time: 0.098845 memory: 15356 loss_kpt: 0.000606 acc_pose: 0.831518 loss: 0.000606 2022/10/19 15:58:57 - mmengine - INFO - Epoch(train) [128][250/293] lr: 5.000000e-04 eta: 2:41:44 time: 0.469275 data_time: 0.097477 memory: 15356 loss_kpt: 0.000602 acc_pose: 0.830480 loss: 0.000602 2022/10/19 15:59:16 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 15:59:40 - mmengine - INFO - Epoch(train) [129][50/293] lr: 5.000000e-04 eta: 2:40:59 time: 0.488758 data_time: 0.113078 memory: 15356 loss_kpt: 0.000615 acc_pose: 0.809774 loss: 0.000615 2022/10/19 16:00:04 - mmengine - INFO - Epoch(train) [129][100/293] lr: 5.000000e-04 eta: 2:40:41 time: 0.473370 data_time: 0.094223 memory: 15356 loss_kpt: 0.000595 acc_pose: 0.825281 loss: 0.000595 2022/10/19 16:00:28 - mmengine - INFO - Epoch(train) [129][150/293] lr: 5.000000e-04 eta: 2:40:23 time: 0.472264 data_time: 0.095466 memory: 15356 loss_kpt: 0.000599 acc_pose: 0.800041 loss: 0.000599 2022/10/19 16:00:52 - mmengine - INFO - Epoch(train) [129][200/293] lr: 5.000000e-04 eta: 2:40:05 time: 0.477682 data_time: 0.095030 memory: 15356 loss_kpt: 0.000611 acc_pose: 0.840932 loss: 0.000611 2022/10/19 16:01:15 - mmengine - INFO - Epoch(train) [129][250/293] lr: 5.000000e-04 eta: 2:39:47 time: 0.470153 data_time: 0.102313 memory: 15356 loss_kpt: 0.000610 acc_pose: 0.822590 loss: 0.000610 2022/10/19 16:01:35 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:02:00 - mmengine - INFO - Epoch(train) [130][50/293] lr: 5.000000e-04 eta: 2:39:02 time: 0.505759 data_time: 0.110232 memory: 15356 loss_kpt: 0.000609 acc_pose: 0.830371 loss: 0.000609 2022/10/19 16:02:24 - mmengine - INFO - Epoch(train) [130][100/293] lr: 5.000000e-04 eta: 2:38:44 time: 0.478860 data_time: 0.094998 memory: 15356 loss_kpt: 0.000606 acc_pose: 0.827409 loss: 0.000606 2022/10/19 16:02:47 - mmengine - INFO - Epoch(train) [130][150/293] lr: 5.000000e-04 eta: 2:38:26 time: 0.462897 data_time: 0.095116 memory: 15356 loss_kpt: 0.000599 acc_pose: 0.802742 loss: 0.000599 2022/10/19 16:03:11 - mmengine - INFO - Epoch(train) [130][200/293] lr: 5.000000e-04 eta: 2:38:08 time: 0.472265 data_time: 0.097469 memory: 15356 loss_kpt: 0.000602 acc_pose: 0.795500 loss: 0.000602 2022/10/19 16:03:12 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:03:34 - mmengine - INFO - Epoch(train) [130][250/293] lr: 5.000000e-04 eta: 2:37:49 time: 0.466119 data_time: 0.101810 memory: 15356 loss_kpt: 0.000598 acc_pose: 0.859598 loss: 0.000598 2022/10/19 16:03:54 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:03:54 - mmengine - INFO - Saving checkpoint at 130 epochs 2022/10/19 16:04:04 - mmengine - INFO - Epoch(val) [130][50/407] eta: 0:00:49 time: 0.138125 data_time: 0.049030 memory: 15356 2022/10/19 16:04:10 - mmengine - INFO - Epoch(val) [130][100/407] eta: 0:00:40 time: 0.133396 data_time: 0.045257 memory: 1465 2022/10/19 16:04:17 - mmengine - INFO - Epoch(val) [130][150/407] eta: 0:00:34 time: 0.135773 data_time: 0.042846 memory: 1465 2022/10/19 16:04:24 - mmengine - INFO - Epoch(val) [130][200/407] eta: 0:00:27 time: 0.131121 data_time: 0.042258 memory: 1465 2022/10/19 16:04:30 - mmengine - INFO - Epoch(val) [130][250/407] eta: 0:00:20 time: 0.130618 data_time: 0.042974 memory: 1465 2022/10/19 16:04:37 - mmengine - INFO - Epoch(val) [130][300/407] eta: 0:00:14 time: 0.133641 data_time: 0.045045 memory: 1465 2022/10/19 16:04:44 - mmengine - INFO - Epoch(val) [130][350/407] eta: 0:00:07 time: 0.138848 data_time: 0.052433 memory: 1465 2022/10/19 16:04:50 - mmengine - INFO - Epoch(val) [130][400/407] eta: 0:00:00 time: 0.118556 data_time: 0.034188 memory: 1465 2022/10/19 16:05:26 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 16:05:40 - mmengine - INFO - Epoch(val) [130][407/407] coco/AP: 0.718851 coco/AP .5: 0.894686 coco/AP .75: 0.783968 coco/AP (M): 0.673700 coco/AP (L): 0.794595 coco/AR: 0.772497 coco/AR .5: 0.932462 coco/AR .75: 0.832021 coco/AR (M): 0.722262 coco/AR (L): 0.843813 2022/10/19 16:05:40 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_384/best_coco/AP_epoch_120.pth is removed 2022/10/19 16:05:42 - mmengine - INFO - The best checkpoint with 0.7189 coco/AP at 130 epoch is saved to best_coco/AP_epoch_130.pth. 2022/10/19 16:06:07 - mmengine - INFO - Epoch(train) [131][50/293] lr: 5.000000e-04 eta: 2:37:04 time: 0.487501 data_time: 0.112443 memory: 15356 loss_kpt: 0.000601 acc_pose: 0.859682 loss: 0.000601 2022/10/19 16:06:30 - mmengine - INFO - Epoch(train) [131][100/293] lr: 5.000000e-04 eta: 2:36:46 time: 0.464405 data_time: 0.097598 memory: 15356 loss_kpt: 0.000588 acc_pose: 0.865939 loss: 0.000588 2022/10/19 16:06:54 - mmengine - INFO - Epoch(train) [131][150/293] lr: 5.000000e-04 eta: 2:36:28 time: 0.476839 data_time: 0.098129 memory: 15356 loss_kpt: 0.000601 acc_pose: 0.801885 loss: 0.000601 2022/10/19 16:07:18 - mmengine - INFO - Epoch(train) [131][200/293] lr: 5.000000e-04 eta: 2:36:10 time: 0.481872 data_time: 0.104258 memory: 15356 loss_kpt: 0.000603 acc_pose: 0.842809 loss: 0.000603 2022/10/19 16:07:41 - mmengine - INFO - Epoch(train) [131][250/293] lr: 5.000000e-04 eta: 2:35:52 time: 0.459764 data_time: 0.089461 memory: 15356 loss_kpt: 0.000610 acc_pose: 0.844456 loss: 0.000610 2022/10/19 16:08:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:08:25 - mmengine - INFO - Epoch(train) [132][50/293] lr: 5.000000e-04 eta: 2:35:06 time: 0.494322 data_time: 0.107850 memory: 15356 loss_kpt: 0.000609 acc_pose: 0.809864 loss: 0.000609 2022/10/19 16:08:49 - mmengine - INFO - Epoch(train) [132][100/293] lr: 5.000000e-04 eta: 2:34:48 time: 0.473177 data_time: 0.102476 memory: 15356 loss_kpt: 0.000609 acc_pose: 0.861406 loss: 0.000609 2022/10/19 16:09:13 - mmengine - INFO - Epoch(train) [132][150/293] lr: 5.000000e-04 eta: 2:34:30 time: 0.467765 data_time: 0.102124 memory: 15356 loss_kpt: 0.000598 acc_pose: 0.853190 loss: 0.000598 2022/10/19 16:09:36 - mmengine - INFO - Epoch(train) [132][200/293] lr: 5.000000e-04 eta: 2:34:12 time: 0.467804 data_time: 0.102752 memory: 15356 loss_kpt: 0.000597 acc_pose: 0.804032 loss: 0.000597 2022/10/19 16:09:59 - mmengine - INFO - Epoch(train) [132][250/293] lr: 5.000000e-04 eta: 2:33:53 time: 0.455260 data_time: 0.104424 memory: 15356 loss_kpt: 0.000608 acc_pose: 0.844559 loss: 0.000608 2022/10/19 16:10:18 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:10:43 - mmengine - INFO - Epoch(train) [133][50/293] lr: 5.000000e-04 eta: 2:33:08 time: 0.487970 data_time: 0.113222 memory: 15356 loss_kpt: 0.000607 acc_pose: 0.820606 loss: 0.000607 2022/10/19 16:11:07 - mmengine - INFO - Epoch(train) [133][100/293] lr: 5.000000e-04 eta: 2:32:50 time: 0.478911 data_time: 0.092033 memory: 15356 loss_kpt: 0.000595 acc_pose: 0.839715 loss: 0.000595 2022/10/19 16:11:30 - mmengine - INFO - Epoch(train) [133][150/293] lr: 5.000000e-04 eta: 2:32:32 time: 0.467352 data_time: 0.097593 memory: 15356 loss_kpt: 0.000599 acc_pose: 0.809353 loss: 0.000599 2022/10/19 16:11:54 - mmengine - INFO - Epoch(train) [133][200/293] lr: 5.000000e-04 eta: 2:32:14 time: 0.469878 data_time: 0.096208 memory: 15356 loss_kpt: 0.000600 acc_pose: 0.830082 loss: 0.000600 2022/10/19 16:12:18 - mmengine - INFO - Epoch(train) [133][250/293] lr: 5.000000e-04 eta: 2:31:56 time: 0.480385 data_time: 0.097979 memory: 15356 loss_kpt: 0.000601 acc_pose: 0.827329 loss: 0.000601 2022/10/19 16:12:38 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:12:53 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:13:02 - mmengine - INFO - Epoch(train) [134][50/293] lr: 5.000000e-04 eta: 2:31:10 time: 0.480372 data_time: 0.112504 memory: 15356 loss_kpt: 0.000581 acc_pose: 0.855713 loss: 0.000581 2022/10/19 16:13:25 - mmengine - INFO - Epoch(train) [134][100/293] lr: 5.000000e-04 eta: 2:30:52 time: 0.464866 data_time: 0.090371 memory: 15356 loss_kpt: 0.000593 acc_pose: 0.795366 loss: 0.000593 2022/10/19 16:13:48 - mmengine - INFO - Epoch(train) [134][150/293] lr: 5.000000e-04 eta: 2:30:34 time: 0.469221 data_time: 0.095779 memory: 15356 loss_kpt: 0.000597 acc_pose: 0.777307 loss: 0.000597 2022/10/19 16:14:12 - mmengine - INFO - Epoch(train) [134][200/293] lr: 5.000000e-04 eta: 2:30:16 time: 0.470851 data_time: 0.099264 memory: 15356 loss_kpt: 0.000598 acc_pose: 0.851635 loss: 0.000598 2022/10/19 16:14:35 - mmengine - INFO - Epoch(train) [134][250/293] lr: 5.000000e-04 eta: 2:29:57 time: 0.464826 data_time: 0.097426 memory: 15356 loss_kpt: 0.000596 acc_pose: 0.848205 loss: 0.000596 2022/10/19 16:14:55 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:15:19 - mmengine - INFO - Epoch(train) [135][50/293] lr: 5.000000e-04 eta: 2:29:12 time: 0.477679 data_time: 0.104501 memory: 15356 loss_kpt: 0.000601 acc_pose: 0.839415 loss: 0.000601 2022/10/19 16:15:43 - mmengine - INFO - Epoch(train) [135][100/293] lr: 5.000000e-04 eta: 2:28:54 time: 0.470343 data_time: 0.099379 memory: 15356 loss_kpt: 0.000590 acc_pose: 0.791336 loss: 0.000590 2022/10/19 16:16:06 - mmengine - INFO - Epoch(train) [135][150/293] lr: 5.000000e-04 eta: 2:28:35 time: 0.460603 data_time: 0.101585 memory: 15356 loss_kpt: 0.000594 acc_pose: 0.829278 loss: 0.000594 2022/10/19 16:16:30 - mmengine - INFO - Epoch(train) [135][200/293] lr: 5.000000e-04 eta: 2:28:17 time: 0.474624 data_time: 0.093330 memory: 15356 loss_kpt: 0.000605 acc_pose: 0.845151 loss: 0.000605 2022/10/19 16:16:53 - mmengine - INFO - Epoch(train) [135][250/293] lr: 5.000000e-04 eta: 2:27:59 time: 0.471368 data_time: 0.104282 memory: 15356 loss_kpt: 0.000605 acc_pose: 0.833334 loss: 0.000605 2022/10/19 16:17:13 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:17:36 - mmengine - INFO - Epoch(train) [136][50/293] lr: 5.000000e-04 eta: 2:27:14 time: 0.474484 data_time: 0.106248 memory: 15356 loss_kpt: 0.000600 acc_pose: 0.849023 loss: 0.000600 2022/10/19 16:18:00 - mmengine - INFO - Epoch(train) [136][100/293] lr: 5.000000e-04 eta: 2:26:55 time: 0.464893 data_time: 0.089915 memory: 15356 loss_kpt: 0.000609 acc_pose: 0.840941 loss: 0.000609 2022/10/19 16:18:23 - mmengine - INFO - Epoch(train) [136][150/293] lr: 5.000000e-04 eta: 2:26:37 time: 0.466985 data_time: 0.096797 memory: 15356 loss_kpt: 0.000600 acc_pose: 0.791748 loss: 0.000600 2022/10/19 16:18:46 - mmengine - INFO - Epoch(train) [136][200/293] lr: 5.000000e-04 eta: 2:26:18 time: 0.458717 data_time: 0.097915 memory: 15356 loss_kpt: 0.000598 acc_pose: 0.805978 loss: 0.000598 2022/10/19 16:19:09 - mmengine - INFO - Epoch(train) [136][250/293] lr: 5.000000e-04 eta: 2:26:00 time: 0.466627 data_time: 0.094350 memory: 15356 loss_kpt: 0.000596 acc_pose: 0.856868 loss: 0.000596 2022/10/19 16:19:30 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:19:55 - mmengine - INFO - Epoch(train) [137][50/293] lr: 5.000000e-04 eta: 2:25:15 time: 0.495095 data_time: 0.116258 memory: 15356 loss_kpt: 0.000611 acc_pose: 0.852841 loss: 0.000611 2022/10/19 16:20:18 - mmengine - INFO - Epoch(train) [137][100/293] lr: 5.000000e-04 eta: 2:24:57 time: 0.469536 data_time: 0.094951 memory: 15356 loss_kpt: 0.000612 acc_pose: 0.838781 loss: 0.000612 2022/10/19 16:20:42 - mmengine - INFO - Epoch(train) [137][150/293] lr: 5.000000e-04 eta: 2:24:39 time: 0.474050 data_time: 0.096898 memory: 15356 loss_kpt: 0.000598 acc_pose: 0.834916 loss: 0.000598 2022/10/19 16:20:43 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:21:05 - mmengine - INFO - Epoch(train) [137][200/293] lr: 5.000000e-04 eta: 2:24:20 time: 0.469383 data_time: 0.098542 memory: 15356 loss_kpt: 0.000591 acc_pose: 0.808315 loss: 0.000591 2022/10/19 16:21:29 - mmengine - INFO - Epoch(train) [137][250/293] lr: 5.000000e-04 eta: 2:24:02 time: 0.466746 data_time: 0.104266 memory: 15356 loss_kpt: 0.000593 acc_pose: 0.866992 loss: 0.000593 2022/10/19 16:21:48 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:22:13 - mmengine - INFO - Epoch(train) [138][50/293] lr: 5.000000e-04 eta: 2:23:18 time: 0.491885 data_time: 0.109032 memory: 15356 loss_kpt: 0.000596 acc_pose: 0.818356 loss: 0.000596 2022/10/19 16:22:36 - mmengine - INFO - Epoch(train) [138][100/293] lr: 5.000000e-04 eta: 2:22:59 time: 0.465823 data_time: 0.101590 memory: 15356 loss_kpt: 0.000600 acc_pose: 0.851149 loss: 0.000600 2022/10/19 16:22:59 - mmengine - INFO - Epoch(train) [138][150/293] lr: 5.000000e-04 eta: 2:22:40 time: 0.457895 data_time: 0.095554 memory: 15356 loss_kpt: 0.000607 acc_pose: 0.841404 loss: 0.000607 2022/10/19 16:23:23 - mmengine - INFO - Epoch(train) [138][200/293] lr: 5.000000e-04 eta: 2:22:22 time: 0.470610 data_time: 0.098959 memory: 15356 loss_kpt: 0.000597 acc_pose: 0.863851 loss: 0.000597 2022/10/19 16:23:46 - mmengine - INFO - Epoch(train) [138][250/293] lr: 5.000000e-04 eta: 2:22:03 time: 0.466262 data_time: 0.098619 memory: 15356 loss_kpt: 0.000591 acc_pose: 0.855405 loss: 0.000591 2022/10/19 16:24:06 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:24:30 - mmengine - INFO - Epoch(train) [139][50/293] lr: 5.000000e-04 eta: 2:21:19 time: 0.488042 data_time: 0.114356 memory: 15356 loss_kpt: 0.000581 acc_pose: 0.860849 loss: 0.000581 2022/10/19 16:24:54 - mmengine - INFO - Epoch(train) [139][100/293] lr: 5.000000e-04 eta: 2:21:01 time: 0.470946 data_time: 0.108063 memory: 15356 loss_kpt: 0.000587 acc_pose: 0.845432 loss: 0.000587 2022/10/19 16:25:18 - mmengine - INFO - Epoch(train) [139][150/293] lr: 5.000000e-04 eta: 2:20:42 time: 0.470257 data_time: 0.097417 memory: 15356 loss_kpt: 0.000605 acc_pose: 0.789907 loss: 0.000605 2022/10/19 16:25:41 - mmengine - INFO - Epoch(train) [139][200/293] lr: 5.000000e-04 eta: 2:20:24 time: 0.464382 data_time: 0.099227 memory: 15356 loss_kpt: 0.000592 acc_pose: 0.855013 loss: 0.000592 2022/10/19 16:26:04 - mmengine - INFO - Epoch(train) [139][250/293] lr: 5.000000e-04 eta: 2:20:05 time: 0.469539 data_time: 0.099138 memory: 15356 loss_kpt: 0.000596 acc_pose: 0.863026 loss: 0.000596 2022/10/19 16:26:24 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:26:48 - mmengine - INFO - Epoch(train) [140][50/293] lr: 5.000000e-04 eta: 2:19:21 time: 0.476888 data_time: 0.105632 memory: 15356 loss_kpt: 0.000587 acc_pose: 0.823276 loss: 0.000587 2022/10/19 16:27:12 - mmengine - INFO - Epoch(train) [140][100/293] lr: 5.000000e-04 eta: 2:19:02 time: 0.469322 data_time: 0.100037 memory: 15356 loss_kpt: 0.000590 acc_pose: 0.835833 loss: 0.000590 2022/10/19 16:27:35 - mmengine - INFO - Epoch(train) [140][150/293] lr: 5.000000e-04 eta: 2:18:44 time: 0.468047 data_time: 0.093968 memory: 15356 loss_kpt: 0.000604 acc_pose: 0.801724 loss: 0.000604 2022/10/19 16:27:58 - mmengine - INFO - Epoch(train) [140][200/293] lr: 5.000000e-04 eta: 2:18:25 time: 0.470618 data_time: 0.101376 memory: 15356 loss_kpt: 0.000607 acc_pose: 0.845930 loss: 0.000607 2022/10/19 16:28:22 - mmengine - INFO - Epoch(train) [140][250/293] lr: 5.000000e-04 eta: 2:18:07 time: 0.474168 data_time: 0.103974 memory: 15356 loss_kpt: 0.000603 acc_pose: 0.842527 loss: 0.000603 2022/10/19 16:28:34 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:28:43 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:28:43 - mmengine - INFO - Saving checkpoint at 140 epochs 2022/10/19 16:28:53 - mmengine - INFO - Epoch(val) [140][50/407] eta: 0:00:48 time: 0.134577 data_time: 0.042064 memory: 15356 2022/10/19 16:29:00 - mmengine - INFO - Epoch(val) [140][100/407] eta: 0:00:43 time: 0.140739 data_time: 0.055001 memory: 1465 2022/10/19 16:29:06 - mmengine - INFO - Epoch(val) [140][150/407] eta: 0:00:34 time: 0.133740 data_time: 0.045549 memory: 1465 2022/10/19 16:29:12 - mmengine - INFO - Epoch(val) [140][200/407] eta: 0:00:25 time: 0.121799 data_time: 0.034301 memory: 1465 2022/10/19 16:29:19 - mmengine - INFO - Epoch(val) [140][250/407] eta: 0:00:20 time: 0.131153 data_time: 0.043393 memory: 1465 2022/10/19 16:29:25 - mmengine - INFO - Epoch(val) [140][300/407] eta: 0:00:13 time: 0.126537 data_time: 0.039656 memory: 1465 2022/10/19 16:29:32 - mmengine - INFO - Epoch(val) [140][350/407] eta: 0:00:07 time: 0.130064 data_time: 0.042702 memory: 1465 2022/10/19 16:29:38 - mmengine - INFO - Epoch(val) [140][400/407] eta: 0:00:00 time: 0.123275 data_time: 0.036145 memory: 1465 2022/10/19 16:30:15 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 16:30:29 - mmengine - INFO - Epoch(val) [140][407/407] coco/AP: 0.714147 coco/AP .5: 0.891547 coco/AP .75: 0.779721 coco/AP (M): 0.666634 coco/AP (L): 0.792369 coco/AR: 0.766074 coco/AR .5: 0.929314 coco/AR .75: 0.825567 coco/AR (M): 0.714422 coco/AR (L): 0.840171 2022/10/19 16:30:53 - mmengine - INFO - Epoch(train) [141][50/293] lr: 5.000000e-04 eta: 2:17:23 time: 0.479412 data_time: 0.113430 memory: 15356 loss_kpt: 0.000596 acc_pose: 0.885519 loss: 0.000596 2022/10/19 16:31:16 - mmengine - INFO - Epoch(train) [141][100/293] lr: 5.000000e-04 eta: 2:17:04 time: 0.459001 data_time: 0.093097 memory: 15356 loss_kpt: 0.000599 acc_pose: 0.853916 loss: 0.000599 2022/10/19 16:31:40 - mmengine - INFO - Epoch(train) [141][150/293] lr: 5.000000e-04 eta: 2:16:46 time: 0.469719 data_time: 0.095057 memory: 15356 loss_kpt: 0.000596 acc_pose: 0.832775 loss: 0.000596 2022/10/19 16:32:04 - mmengine - INFO - Epoch(train) [141][200/293] lr: 5.000000e-04 eta: 2:16:27 time: 0.477942 data_time: 0.104484 memory: 15356 loss_kpt: 0.000600 acc_pose: 0.850935 loss: 0.000600 2022/10/19 16:32:26 - mmengine - INFO - Epoch(train) [141][250/293] lr: 5.000000e-04 eta: 2:16:08 time: 0.452097 data_time: 0.091551 memory: 15356 loss_kpt: 0.000594 acc_pose: 0.813314 loss: 0.000594 2022/10/19 16:32:46 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:33:10 - mmengine - INFO - Epoch(train) [142][50/293] lr: 5.000000e-04 eta: 2:15:25 time: 0.493133 data_time: 0.116916 memory: 15356 loss_kpt: 0.000591 acc_pose: 0.823336 loss: 0.000591 2022/10/19 16:33:34 - mmengine - INFO - Epoch(train) [142][100/293] lr: 5.000000e-04 eta: 2:15:06 time: 0.479816 data_time: 0.098308 memory: 15356 loss_kpt: 0.000589 acc_pose: 0.826707 loss: 0.000589 2022/10/19 16:33:58 - mmengine - INFO - Epoch(train) [142][150/293] lr: 5.000000e-04 eta: 2:14:48 time: 0.463382 data_time: 0.099240 memory: 15356 loss_kpt: 0.000594 acc_pose: 0.843517 loss: 0.000594 2022/10/19 16:34:21 - mmengine - INFO - Epoch(train) [142][200/293] lr: 5.000000e-04 eta: 2:14:29 time: 0.470501 data_time: 0.094686 memory: 15356 loss_kpt: 0.000596 acc_pose: 0.837527 loss: 0.000596 2022/10/19 16:34:45 - mmengine - INFO - Epoch(train) [142][250/293] lr: 5.000000e-04 eta: 2:14:11 time: 0.470316 data_time: 0.094486 memory: 15356 loss_kpt: 0.000599 acc_pose: 0.834593 loss: 0.000599 2022/10/19 16:35:04 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:35:29 - mmengine - INFO - Epoch(train) [143][50/293] lr: 5.000000e-04 eta: 2:13:27 time: 0.492772 data_time: 0.111450 memory: 15356 loss_kpt: 0.000592 acc_pose: 0.793382 loss: 0.000592 2022/10/19 16:35:52 - mmengine - INFO - Epoch(train) [143][100/293] lr: 5.000000e-04 eta: 2:13:08 time: 0.470879 data_time: 0.102776 memory: 15356 loss_kpt: 0.000605 acc_pose: 0.844618 loss: 0.000605 2022/10/19 16:36:16 - mmengine - INFO - Epoch(train) [143][150/293] lr: 5.000000e-04 eta: 2:12:50 time: 0.473528 data_time: 0.097771 memory: 15356 loss_kpt: 0.000600 acc_pose: 0.807131 loss: 0.000600 2022/10/19 16:36:39 - mmengine - INFO - Epoch(train) [143][200/293] lr: 5.000000e-04 eta: 2:12:31 time: 0.468773 data_time: 0.103917 memory: 15356 loss_kpt: 0.000589 acc_pose: 0.866856 loss: 0.000589 2022/10/19 16:37:02 - mmengine - INFO - Epoch(train) [143][250/293] lr: 5.000000e-04 eta: 2:12:13 time: 0.463842 data_time: 0.094258 memory: 15356 loss_kpt: 0.000602 acc_pose: 0.814372 loss: 0.000602 2022/10/19 16:37:22 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:37:47 - mmengine - INFO - Epoch(train) [144][50/293] lr: 5.000000e-04 eta: 2:11:29 time: 0.492639 data_time: 0.108854 memory: 15356 loss_kpt: 0.000598 acc_pose: 0.822228 loss: 0.000598 2022/10/19 16:38:10 - mmengine - INFO - Epoch(train) [144][100/293] lr: 5.000000e-04 eta: 2:11:10 time: 0.460517 data_time: 0.095027 memory: 15356 loss_kpt: 0.000586 acc_pose: 0.898988 loss: 0.000586 2022/10/19 16:38:10 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:38:33 - mmengine - INFO - Epoch(train) [144][150/293] lr: 5.000000e-04 eta: 2:10:52 time: 0.470188 data_time: 0.097451 memory: 15356 loss_kpt: 0.000586 acc_pose: 0.883231 loss: 0.000586 2022/10/19 16:38:57 - mmengine - INFO - Epoch(train) [144][200/293] lr: 5.000000e-04 eta: 2:10:33 time: 0.472083 data_time: 0.092532 memory: 15356 loss_kpt: 0.000609 acc_pose: 0.826045 loss: 0.000609 2022/10/19 16:39:20 - mmengine - INFO - Epoch(train) [144][250/293] lr: 5.000000e-04 eta: 2:10:14 time: 0.458929 data_time: 0.094103 memory: 15356 loss_kpt: 0.000602 acc_pose: 0.853077 loss: 0.000602 2022/10/19 16:39:39 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:40:03 - mmengine - INFO - Epoch(train) [145][50/293] lr: 5.000000e-04 eta: 2:09:30 time: 0.476468 data_time: 0.103414 memory: 15356 loss_kpt: 0.000599 acc_pose: 0.790783 loss: 0.000599 2022/10/19 16:40:27 - mmengine - INFO - Epoch(train) [145][100/293] lr: 5.000000e-04 eta: 2:09:12 time: 0.480015 data_time: 0.098138 memory: 15356 loss_kpt: 0.000581 acc_pose: 0.868975 loss: 0.000581 2022/10/19 16:40:51 - mmengine - INFO - Epoch(train) [145][150/293] lr: 5.000000e-04 eta: 2:08:53 time: 0.467539 data_time: 0.098052 memory: 15356 loss_kpt: 0.000598 acc_pose: 0.827069 loss: 0.000598 2022/10/19 16:41:14 - mmengine - INFO - Epoch(train) [145][200/293] lr: 5.000000e-04 eta: 2:08:35 time: 0.472926 data_time: 0.103134 memory: 15356 loss_kpt: 0.000597 acc_pose: 0.834609 loss: 0.000597 2022/10/19 16:41:39 - mmengine - INFO - Epoch(train) [145][250/293] lr: 5.000000e-04 eta: 2:08:17 time: 0.486130 data_time: 0.102664 memory: 15356 loss_kpt: 0.000586 acc_pose: 0.809505 loss: 0.000586 2022/10/19 16:41:58 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:42:23 - mmengine - INFO - Epoch(train) [146][50/293] lr: 5.000000e-04 eta: 2:07:33 time: 0.485475 data_time: 0.109767 memory: 15356 loss_kpt: 0.000590 acc_pose: 0.860996 loss: 0.000590 2022/10/19 16:42:47 - mmengine - INFO - Epoch(train) [146][100/293] lr: 5.000000e-04 eta: 2:07:15 time: 0.480152 data_time: 0.103695 memory: 15356 loss_kpt: 0.000596 acc_pose: 0.851564 loss: 0.000596 2022/10/19 16:43:11 - mmengine - INFO - Epoch(train) [146][150/293] lr: 5.000000e-04 eta: 2:06:56 time: 0.477594 data_time: 0.105388 memory: 15356 loss_kpt: 0.000591 acc_pose: 0.830042 loss: 0.000591 2022/10/19 16:43:34 - mmengine - INFO - Epoch(train) [146][200/293] lr: 5.000000e-04 eta: 2:06:37 time: 0.467657 data_time: 0.097384 memory: 15356 loss_kpt: 0.000585 acc_pose: 0.812576 loss: 0.000585 2022/10/19 16:43:57 - mmengine - INFO - Epoch(train) [146][250/293] lr: 5.000000e-04 eta: 2:06:19 time: 0.460838 data_time: 0.098461 memory: 15356 loss_kpt: 0.000598 acc_pose: 0.815038 loss: 0.000598 2022/10/19 16:44:16 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:44:41 - mmengine - INFO - Epoch(train) [147][50/293] lr: 5.000000e-04 eta: 2:05:35 time: 0.487246 data_time: 0.105917 memory: 15356 loss_kpt: 0.000573 acc_pose: 0.845981 loss: 0.000573 2022/10/19 16:45:04 - mmengine - INFO - Epoch(train) [147][100/293] lr: 5.000000e-04 eta: 2:05:17 time: 0.466122 data_time: 0.092086 memory: 15356 loss_kpt: 0.000595 acc_pose: 0.890750 loss: 0.000595 2022/10/19 16:45:27 - mmengine - INFO - Epoch(train) [147][150/293] lr: 5.000000e-04 eta: 2:04:58 time: 0.461410 data_time: 0.094712 memory: 15356 loss_kpt: 0.000593 acc_pose: 0.848288 loss: 0.000593 2022/10/19 16:45:51 - mmengine - INFO - Epoch(train) [147][200/293] lr: 5.000000e-04 eta: 2:04:39 time: 0.466648 data_time: 0.091968 memory: 15356 loss_kpt: 0.000595 acc_pose: 0.866683 loss: 0.000595 2022/10/19 16:46:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:46:14 - mmengine - INFO - Epoch(train) [147][250/293] lr: 5.000000e-04 eta: 2:04:20 time: 0.471779 data_time: 0.096421 memory: 15356 loss_kpt: 0.000591 acc_pose: 0.843552 loss: 0.000591 2022/10/19 16:46:34 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:46:58 - mmengine - INFO - Epoch(train) [148][50/293] lr: 5.000000e-04 eta: 2:03:37 time: 0.483860 data_time: 0.115914 memory: 15356 loss_kpt: 0.000594 acc_pose: 0.831051 loss: 0.000594 2022/10/19 16:47:21 - mmengine - INFO - Epoch(train) [148][100/293] lr: 5.000000e-04 eta: 2:03:18 time: 0.466795 data_time: 0.094976 memory: 15356 loss_kpt: 0.000600 acc_pose: 0.837148 loss: 0.000600 2022/10/19 16:47:45 - mmengine - INFO - Epoch(train) [148][150/293] lr: 5.000000e-04 eta: 2:03:00 time: 0.472305 data_time: 0.103159 memory: 15356 loss_kpt: 0.000597 acc_pose: 0.823519 loss: 0.000597 2022/10/19 16:48:09 - mmengine - INFO - Epoch(train) [148][200/293] lr: 5.000000e-04 eta: 2:02:41 time: 0.470184 data_time: 0.091029 memory: 15356 loss_kpt: 0.000590 acc_pose: 0.813098 loss: 0.000590 2022/10/19 16:48:32 - mmengine - INFO - Epoch(train) [148][250/293] lr: 5.000000e-04 eta: 2:02:22 time: 0.466244 data_time: 0.088938 memory: 15356 loss_kpt: 0.000589 acc_pose: 0.863981 loss: 0.000589 2022/10/19 16:48:52 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:49:16 - mmengine - INFO - Epoch(train) [149][50/293] lr: 5.000000e-04 eta: 2:01:39 time: 0.493795 data_time: 0.111082 memory: 15356 loss_kpt: 0.000584 acc_pose: 0.858517 loss: 0.000584 2022/10/19 16:49:41 - mmengine - INFO - Epoch(train) [149][100/293] lr: 5.000000e-04 eta: 2:01:21 time: 0.484034 data_time: 0.102052 memory: 15356 loss_kpt: 0.000588 acc_pose: 0.849587 loss: 0.000588 2022/10/19 16:50:04 - mmengine - INFO - Epoch(train) [149][150/293] lr: 5.000000e-04 eta: 2:01:02 time: 0.464940 data_time: 0.096932 memory: 15356 loss_kpt: 0.000585 acc_pose: 0.842341 loss: 0.000585 2022/10/19 16:50:27 - mmengine - INFO - Epoch(train) [149][200/293] lr: 5.000000e-04 eta: 2:00:43 time: 0.468785 data_time: 0.092681 memory: 15356 loss_kpt: 0.000607 acc_pose: 0.850840 loss: 0.000607 2022/10/19 16:50:51 - mmengine - INFO - Epoch(train) [149][250/293] lr: 5.000000e-04 eta: 2:00:24 time: 0.470634 data_time: 0.100194 memory: 15356 loss_kpt: 0.000578 acc_pose: 0.846702 loss: 0.000578 2022/10/19 16:51:11 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:51:35 - mmengine - INFO - Epoch(train) [150][50/293] lr: 5.000000e-04 eta: 1:59:41 time: 0.486673 data_time: 0.110437 memory: 15356 loss_kpt: 0.000602 acc_pose: 0.819659 loss: 0.000602 2022/10/19 16:51:58 - mmengine - INFO - Epoch(train) [150][100/293] lr: 5.000000e-04 eta: 1:59:22 time: 0.459537 data_time: 0.088943 memory: 15356 loss_kpt: 0.000586 acc_pose: 0.836508 loss: 0.000586 2022/10/19 16:52:22 - mmengine - INFO - Epoch(train) [150][150/293] lr: 5.000000e-04 eta: 1:59:04 time: 0.478916 data_time: 0.097953 memory: 15356 loss_kpt: 0.000596 acc_pose: 0.856673 loss: 0.000596 2022/10/19 16:52:46 - mmengine - INFO - Epoch(train) [150][200/293] lr: 5.000000e-04 eta: 1:58:45 time: 0.474083 data_time: 0.103898 memory: 15356 loss_kpt: 0.000583 acc_pose: 0.858452 loss: 0.000583 2022/10/19 16:53:09 - mmengine - INFO - Epoch(train) [150][250/293] lr: 5.000000e-04 eta: 1:58:26 time: 0.470998 data_time: 0.092947 memory: 15356 loss_kpt: 0.000579 acc_pose: 0.844206 loss: 0.000579 2022/10/19 16:53:29 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:53:29 - mmengine - INFO - Saving checkpoint at 150 epochs 2022/10/19 16:53:40 - mmengine - INFO - Epoch(val) [150][50/407] eta: 0:00:50 time: 0.142709 data_time: 0.054443 memory: 15356 2022/10/19 16:53:46 - mmengine - INFO - Epoch(val) [150][100/407] eta: 0:00:39 time: 0.128931 data_time: 0.041166 memory: 1465 2022/10/19 16:53:53 - mmengine - INFO - Epoch(val) [150][150/407] eta: 0:00:36 time: 0.143149 data_time: 0.056149 memory: 1465 2022/10/19 16:54:00 - mmengine - INFO - Epoch(val) [150][200/407] eta: 0:00:28 time: 0.137620 data_time: 0.049446 memory: 1465 2022/10/19 16:54:06 - mmengine - INFO - Epoch(val) [150][250/407] eta: 0:00:20 time: 0.129195 data_time: 0.041468 memory: 1465 2022/10/19 16:54:13 - mmengine - INFO - Epoch(val) [150][300/407] eta: 0:00:14 time: 0.134013 data_time: 0.045272 memory: 1465 2022/10/19 16:54:20 - mmengine - INFO - Epoch(val) [150][350/407] eta: 0:00:07 time: 0.135629 data_time: 0.043622 memory: 1465 2022/10/19 16:54:26 - mmengine - INFO - Epoch(val) [150][400/407] eta: 0:00:00 time: 0.116688 data_time: 0.032590 memory: 1465 2022/10/19 16:55:03 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 16:55:17 - mmengine - INFO - Epoch(val) [150][407/407] coco/AP: 0.720565 coco/AP .5: 0.892064 coco/AP .75: 0.787476 coco/AP (M): 0.672943 coco/AP (L): 0.798763 coco/AR: 0.772528 coco/AR .5: 0.929314 coco/AR .75: 0.832809 coco/AR (M): 0.721497 coco/AR (L): 0.844853 2022/10/19 16:55:17 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_384/best_coco/AP_epoch_130.pth is removed 2022/10/19 16:55:19 - mmengine - INFO - The best checkpoint with 0.7206 coco/AP at 150 epoch is saved to best_coco/AP_epoch_150.pth. 2022/10/19 16:55:43 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:55:43 - mmengine - INFO - Epoch(train) [151][50/293] lr: 5.000000e-04 eta: 1:57:44 time: 0.491157 data_time: 0.105668 memory: 15356 loss_kpt: 0.000586 acc_pose: 0.834070 loss: 0.000586 2022/10/19 16:56:06 - mmengine - INFO - Epoch(train) [151][100/293] lr: 5.000000e-04 eta: 1:57:25 time: 0.461451 data_time: 0.094133 memory: 15356 loss_kpt: 0.000578 acc_pose: 0.828089 loss: 0.000578 2022/10/19 16:56:30 - mmengine - INFO - Epoch(train) [151][150/293] lr: 5.000000e-04 eta: 1:57:06 time: 0.468757 data_time: 0.099699 memory: 15356 loss_kpt: 0.000574 acc_pose: 0.866075 loss: 0.000574 2022/10/19 16:56:54 - mmengine - INFO - Epoch(train) [151][200/293] lr: 5.000000e-04 eta: 1:56:47 time: 0.483110 data_time: 0.098386 memory: 15356 loss_kpt: 0.000579 acc_pose: 0.828131 loss: 0.000579 2022/10/19 16:57:18 - mmengine - INFO - Epoch(train) [151][250/293] lr: 5.000000e-04 eta: 1:56:28 time: 0.476856 data_time: 0.102568 memory: 15356 loss_kpt: 0.000585 acc_pose: 0.795285 loss: 0.000585 2022/10/19 16:57:38 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 16:58:02 - mmengine - INFO - Epoch(train) [152][50/293] lr: 5.000000e-04 eta: 1:55:46 time: 0.489253 data_time: 0.107811 memory: 15356 loss_kpt: 0.000595 acc_pose: 0.864189 loss: 0.000595 2022/10/19 16:58:26 - mmengine - INFO - Epoch(train) [152][100/293] lr: 5.000000e-04 eta: 1:55:27 time: 0.486189 data_time: 0.105988 memory: 15356 loss_kpt: 0.000592 acc_pose: 0.825927 loss: 0.000592 2022/10/19 16:58:50 - mmengine - INFO - Epoch(train) [152][150/293] lr: 5.000000e-04 eta: 1:55:08 time: 0.471529 data_time: 0.101062 memory: 15356 loss_kpt: 0.000578 acc_pose: 0.804058 loss: 0.000578 2022/10/19 16:59:14 - mmengine - INFO - Epoch(train) [152][200/293] lr: 5.000000e-04 eta: 1:54:50 time: 0.481009 data_time: 0.101917 memory: 15356 loss_kpt: 0.000585 acc_pose: 0.849399 loss: 0.000585 2022/10/19 16:59:38 - mmengine - INFO - Epoch(train) [152][250/293] lr: 5.000000e-04 eta: 1:54:31 time: 0.477527 data_time: 0.098270 memory: 15356 loss_kpt: 0.000585 acc_pose: 0.851365 loss: 0.000585 2022/10/19 16:59:58 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:00:22 - mmengine - INFO - Epoch(train) [153][50/293] lr: 5.000000e-04 eta: 1:53:49 time: 0.483792 data_time: 0.114980 memory: 15356 loss_kpt: 0.000574 acc_pose: 0.822479 loss: 0.000574 2022/10/19 17:00:46 - mmengine - INFO - Epoch(train) [153][100/293] lr: 5.000000e-04 eta: 1:53:30 time: 0.476651 data_time: 0.095643 memory: 15356 loss_kpt: 0.000585 acc_pose: 0.846019 loss: 0.000585 2022/10/19 17:01:09 - mmengine - INFO - Epoch(train) [153][150/293] lr: 5.000000e-04 eta: 1:53:11 time: 0.466758 data_time: 0.099361 memory: 15356 loss_kpt: 0.000599 acc_pose: 0.851428 loss: 0.000599 2022/10/19 17:01:33 - mmengine - INFO - Epoch(train) [153][200/293] lr: 5.000000e-04 eta: 1:52:52 time: 0.473467 data_time: 0.095789 memory: 15356 loss_kpt: 0.000601 acc_pose: 0.834699 loss: 0.000601 2022/10/19 17:01:57 - mmengine - INFO - Epoch(train) [153][250/293] lr: 5.000000e-04 eta: 1:52:33 time: 0.483152 data_time: 0.101754 memory: 15356 loss_kpt: 0.000581 acc_pose: 0.791498 loss: 0.000581 2022/10/19 17:02:16 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:02:41 - mmengine - INFO - Epoch(train) [154][50/293] lr: 5.000000e-04 eta: 1:51:51 time: 0.483201 data_time: 0.107738 memory: 15356 loss_kpt: 0.000579 acc_pose: 0.836060 loss: 0.000579 2022/10/19 17:03:04 - mmengine - INFO - Epoch(train) [154][100/293] lr: 5.000000e-04 eta: 1:51:32 time: 0.461004 data_time: 0.091927 memory: 15356 loss_kpt: 0.000573 acc_pose: 0.842998 loss: 0.000573 2022/10/19 17:03:27 - mmengine - INFO - Epoch(train) [154][150/293] lr: 5.000000e-04 eta: 1:51:13 time: 0.469596 data_time: 0.093623 memory: 15356 loss_kpt: 0.000596 acc_pose: 0.833010 loss: 0.000596 2022/10/19 17:03:37 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:03:51 - mmengine - INFO - Epoch(train) [154][200/293] lr: 5.000000e-04 eta: 1:50:54 time: 0.473413 data_time: 0.101703 memory: 15356 loss_kpt: 0.000587 acc_pose: 0.858946 loss: 0.000587 2022/10/19 17:04:14 - mmengine - INFO - Epoch(train) [154][250/293] lr: 5.000000e-04 eta: 1:50:35 time: 0.467444 data_time: 0.093538 memory: 15356 loss_kpt: 0.000590 acc_pose: 0.827472 loss: 0.000590 2022/10/19 17:04:34 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:04:58 - mmengine - INFO - Epoch(train) [155][50/293] lr: 5.000000e-04 eta: 1:49:53 time: 0.478720 data_time: 0.111368 memory: 15356 loss_kpt: 0.000581 acc_pose: 0.842117 loss: 0.000581 2022/10/19 17:05:21 - mmengine - INFO - Epoch(train) [155][100/293] lr: 5.000000e-04 eta: 1:49:33 time: 0.456605 data_time: 0.096966 memory: 15356 loss_kpt: 0.000585 acc_pose: 0.830099 loss: 0.000585 2022/10/19 17:05:44 - mmengine - INFO - Epoch(train) [155][150/293] lr: 5.000000e-04 eta: 1:49:14 time: 0.474363 data_time: 0.101104 memory: 15356 loss_kpt: 0.000585 acc_pose: 0.772375 loss: 0.000585 2022/10/19 17:06:08 - mmengine - INFO - Epoch(train) [155][200/293] lr: 5.000000e-04 eta: 1:48:55 time: 0.471367 data_time: 0.097848 memory: 15356 loss_kpt: 0.000587 acc_pose: 0.829657 loss: 0.000587 2022/10/19 17:06:32 - mmengine - INFO - Epoch(train) [155][250/293] lr: 5.000000e-04 eta: 1:48:37 time: 0.473440 data_time: 0.108754 memory: 15356 loss_kpt: 0.000569 acc_pose: 0.862466 loss: 0.000569 2022/10/19 17:06:51 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:07:16 - mmengine - INFO - Epoch(train) [156][50/293] lr: 5.000000e-04 eta: 1:47:54 time: 0.489311 data_time: 0.107183 memory: 15356 loss_kpt: 0.000583 acc_pose: 0.815220 loss: 0.000583 2022/10/19 17:07:39 - mmengine - INFO - Epoch(train) [156][100/293] lr: 5.000000e-04 eta: 1:47:35 time: 0.467312 data_time: 0.096297 memory: 15356 loss_kpt: 0.000595 acc_pose: 0.857109 loss: 0.000595 2022/10/19 17:08:02 - mmengine - INFO - Epoch(train) [156][150/293] lr: 5.000000e-04 eta: 1:47:16 time: 0.462625 data_time: 0.095810 memory: 15356 loss_kpt: 0.000588 acc_pose: 0.849445 loss: 0.000588 2022/10/19 17:08:25 - mmengine - INFO - Epoch(train) [156][200/293] lr: 5.000000e-04 eta: 1:46:57 time: 0.464202 data_time: 0.091349 memory: 15356 loss_kpt: 0.000573 acc_pose: 0.860302 loss: 0.000573 2022/10/19 17:08:49 - mmengine - INFO - Epoch(train) [156][250/293] lr: 5.000000e-04 eta: 1:46:38 time: 0.477562 data_time: 0.100272 memory: 15356 loss_kpt: 0.000592 acc_pose: 0.796620 loss: 0.000592 2022/10/19 17:09:09 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:09:33 - mmengine - INFO - Epoch(train) [157][50/293] lr: 5.000000e-04 eta: 1:45:56 time: 0.476342 data_time: 0.109560 memory: 15356 loss_kpt: 0.000594 acc_pose: 0.831626 loss: 0.000594 2022/10/19 17:09:57 - mmengine - INFO - Epoch(train) [157][100/293] lr: 5.000000e-04 eta: 1:45:37 time: 0.477219 data_time: 0.094694 memory: 15356 loss_kpt: 0.000575 acc_pose: 0.851432 loss: 0.000575 2022/10/19 17:10:20 - mmengine - INFO - Epoch(train) [157][150/293] lr: 5.000000e-04 eta: 1:45:18 time: 0.471557 data_time: 0.091360 memory: 15356 loss_kpt: 0.000588 acc_pose: 0.831995 loss: 0.000588 2022/10/19 17:10:44 - mmengine - INFO - Epoch(train) [157][200/293] lr: 5.000000e-04 eta: 1:44:59 time: 0.471074 data_time: 0.105801 memory: 15356 loss_kpt: 0.000585 acc_pose: 0.827838 loss: 0.000585 2022/10/19 17:11:08 - mmengine - INFO - Epoch(train) [157][250/293] lr: 5.000000e-04 eta: 1:44:40 time: 0.476526 data_time: 0.097219 memory: 15356 loss_kpt: 0.000580 acc_pose: 0.866326 loss: 0.000580 2022/10/19 17:11:28 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:11:28 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:11:52 - mmengine - INFO - Epoch(train) [158][50/293] lr: 5.000000e-04 eta: 1:43:58 time: 0.479186 data_time: 0.105036 memory: 15356 loss_kpt: 0.000579 acc_pose: 0.800219 loss: 0.000579 2022/10/19 17:12:15 - mmengine - INFO - Epoch(train) [158][100/293] lr: 5.000000e-04 eta: 1:43:39 time: 0.465528 data_time: 0.100364 memory: 15356 loss_kpt: 0.000588 acc_pose: 0.859555 loss: 0.000588 2022/10/19 17:12:39 - mmengine - INFO - Epoch(train) [158][150/293] lr: 5.000000e-04 eta: 1:43:20 time: 0.483223 data_time: 0.108373 memory: 15356 loss_kpt: 0.000584 acc_pose: 0.813106 loss: 0.000584 2022/10/19 17:13:03 - mmengine - INFO - Epoch(train) [158][200/293] lr: 5.000000e-04 eta: 1:43:01 time: 0.465148 data_time: 0.087008 memory: 15356 loss_kpt: 0.000582 acc_pose: 0.842632 loss: 0.000582 2022/10/19 17:13:26 - mmengine - INFO - Epoch(train) [158][250/293] lr: 5.000000e-04 eta: 1:42:42 time: 0.465006 data_time: 0.092838 memory: 15356 loss_kpt: 0.000583 acc_pose: 0.868012 loss: 0.000583 2022/10/19 17:13:45 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:14:10 - mmengine - INFO - Epoch(train) [159][50/293] lr: 5.000000e-04 eta: 1:42:00 time: 0.482358 data_time: 0.108610 memory: 15356 loss_kpt: 0.000575 acc_pose: 0.869667 loss: 0.000575 2022/10/19 17:14:33 - mmengine - INFO - Epoch(train) [159][100/293] lr: 5.000000e-04 eta: 1:41:41 time: 0.466184 data_time: 0.093478 memory: 15356 loss_kpt: 0.000583 acc_pose: 0.861027 loss: 0.000583 2022/10/19 17:14:56 - mmengine - INFO - Epoch(train) [159][150/293] lr: 5.000000e-04 eta: 1:41:22 time: 0.465927 data_time: 0.101163 memory: 15356 loss_kpt: 0.000578 acc_pose: 0.815395 loss: 0.000578 2022/10/19 17:15:19 - mmengine - INFO - Epoch(train) [159][200/293] lr: 5.000000e-04 eta: 1:41:03 time: 0.464963 data_time: 0.095370 memory: 15356 loss_kpt: 0.000587 acc_pose: 0.857297 loss: 0.000587 2022/10/19 17:15:43 - mmengine - INFO - Epoch(train) [159][250/293] lr: 5.000000e-04 eta: 1:40:44 time: 0.474072 data_time: 0.097770 memory: 15356 loss_kpt: 0.000594 acc_pose: 0.811824 loss: 0.000594 2022/10/19 17:16:03 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:16:28 - mmengine - INFO - Epoch(train) [160][50/293] lr: 5.000000e-04 eta: 1:40:02 time: 0.492292 data_time: 0.108349 memory: 15356 loss_kpt: 0.000584 acc_pose: 0.845542 loss: 0.000584 2022/10/19 17:16:51 - mmengine - INFO - Epoch(train) [160][100/293] lr: 5.000000e-04 eta: 1:39:43 time: 0.475343 data_time: 0.102510 memory: 15356 loss_kpt: 0.000599 acc_pose: 0.808606 loss: 0.000599 2022/10/19 17:17:15 - mmengine - INFO - Epoch(train) [160][150/293] lr: 5.000000e-04 eta: 1:39:24 time: 0.467278 data_time: 0.102720 memory: 15356 loss_kpt: 0.000584 acc_pose: 0.869856 loss: 0.000584 2022/10/19 17:17:38 - mmengine - INFO - Epoch(train) [160][200/293] lr: 5.000000e-04 eta: 1:39:05 time: 0.468487 data_time: 0.101245 memory: 15356 loss_kpt: 0.000583 acc_pose: 0.840144 loss: 0.000583 2022/10/19 17:18:01 - mmengine - INFO - Epoch(train) [160][250/293] lr: 5.000000e-04 eta: 1:38:45 time: 0.454869 data_time: 0.082131 memory: 15356 loss_kpt: 0.000587 acc_pose: 0.844866 loss: 0.000587 2022/10/19 17:18:21 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:18:21 - mmengine - INFO - Saving checkpoint at 160 epochs 2022/10/19 17:18:31 - mmengine - INFO - Epoch(val) [160][50/407] eta: 0:00:47 time: 0.133024 data_time: 0.041682 memory: 15356 2022/10/19 17:18:37 - mmengine - INFO - Epoch(val) [160][100/407] eta: 0:00:42 time: 0.138918 data_time: 0.051677 memory: 1465 2022/10/19 17:18:44 - mmengine - INFO - Epoch(val) [160][150/407] eta: 0:00:35 time: 0.139703 data_time: 0.051820 memory: 1465 2022/10/19 17:18:51 - mmengine - INFO - Epoch(val) [160][200/407] eta: 0:00:26 time: 0.126731 data_time: 0.040637 memory: 1465 2022/10/19 17:18:58 - mmengine - INFO - Epoch(val) [160][250/407] eta: 0:00:21 time: 0.134036 data_time: 0.047621 memory: 1465 2022/10/19 17:19:04 - mmengine - INFO - Epoch(val) [160][300/407] eta: 0:00:12 time: 0.120763 data_time: 0.033222 memory: 1465 2022/10/19 17:19:11 - mmengine - INFO - Epoch(val) [160][350/407] eta: 0:00:08 time: 0.143364 data_time: 0.056717 memory: 1465 2022/10/19 17:19:17 - mmengine - INFO - Epoch(val) [160][400/407] eta: 0:00:00 time: 0.132602 data_time: 0.046470 memory: 1465 2022/10/19 17:19:54 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 17:20:08 - mmengine - INFO - Epoch(val) [160][407/407] coco/AP: 0.718701 coco/AP .5: 0.894142 coco/AP .75: 0.784177 coco/AP (M): 0.671149 coco/AP (L): 0.796547 coco/AR: 0.771568 coco/AR .5: 0.931675 coco/AR .75: 0.830762 coco/AR (M): 0.719394 coco/AR (L): 0.845819 2022/10/19 17:20:33 - mmengine - INFO - Epoch(train) [161][50/293] lr: 5.000000e-04 eta: 1:38:04 time: 0.492056 data_time: 0.108182 memory: 15356 loss_kpt: 0.000581 acc_pose: 0.824871 loss: 0.000581 2022/10/19 17:20:57 - mmengine - INFO - Epoch(train) [161][100/293] lr: 5.000000e-04 eta: 1:37:45 time: 0.475201 data_time: 0.098977 memory: 15356 loss_kpt: 0.000591 acc_pose: 0.861089 loss: 0.000591 2022/10/19 17:21:06 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:21:20 - mmengine - INFO - Epoch(train) [161][150/293] lr: 5.000000e-04 eta: 1:37:26 time: 0.465958 data_time: 0.102092 memory: 15356 loss_kpt: 0.000582 acc_pose: 0.816044 loss: 0.000582 2022/10/19 17:21:44 - mmengine - INFO - Epoch(train) [161][200/293] lr: 5.000000e-04 eta: 1:37:07 time: 0.478370 data_time: 0.101951 memory: 15356 loss_kpt: 0.000585 acc_pose: 0.852443 loss: 0.000585 2022/10/19 17:22:08 - mmengine - INFO - Epoch(train) [161][250/293] lr: 5.000000e-04 eta: 1:36:48 time: 0.477107 data_time: 0.097795 memory: 15356 loss_kpt: 0.000589 acc_pose: 0.851229 loss: 0.000589 2022/10/19 17:22:28 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:22:52 - mmengine - INFO - Epoch(train) [162][50/293] lr: 5.000000e-04 eta: 1:36:06 time: 0.495427 data_time: 0.110287 memory: 15356 loss_kpt: 0.000589 acc_pose: 0.814439 loss: 0.000589 2022/10/19 17:23:16 - mmengine - INFO - Epoch(train) [162][100/293] lr: 5.000000e-04 eta: 1:35:47 time: 0.469021 data_time: 0.099945 memory: 15356 loss_kpt: 0.000585 acc_pose: 0.799409 loss: 0.000585 2022/10/19 17:23:40 - mmengine - INFO - Epoch(train) [162][150/293] lr: 5.000000e-04 eta: 1:35:28 time: 0.479774 data_time: 0.095619 memory: 15356 loss_kpt: 0.000587 acc_pose: 0.852549 loss: 0.000587 2022/10/19 17:24:03 - mmengine - INFO - Epoch(train) [162][200/293] lr: 5.000000e-04 eta: 1:35:09 time: 0.471993 data_time: 0.099987 memory: 15356 loss_kpt: 0.000581 acc_pose: 0.849631 loss: 0.000581 2022/10/19 17:24:27 - mmengine - INFO - Epoch(train) [162][250/293] lr: 5.000000e-04 eta: 1:34:50 time: 0.465637 data_time: 0.097317 memory: 15356 loss_kpt: 0.000592 acc_pose: 0.868591 loss: 0.000592 2022/10/19 17:24:47 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:25:11 - mmengine - INFO - Epoch(train) [163][50/293] lr: 5.000000e-04 eta: 1:34:08 time: 0.487349 data_time: 0.111182 memory: 15356 loss_kpt: 0.000586 acc_pose: 0.848621 loss: 0.000586 2022/10/19 17:25:35 - mmengine - INFO - Epoch(train) [163][100/293] lr: 5.000000e-04 eta: 1:33:49 time: 0.472895 data_time: 0.094047 memory: 15356 loss_kpt: 0.000579 acc_pose: 0.888272 loss: 0.000579 2022/10/19 17:25:59 - mmengine - INFO - Epoch(train) [163][150/293] lr: 5.000000e-04 eta: 1:33:30 time: 0.477299 data_time: 0.101817 memory: 15356 loss_kpt: 0.000582 acc_pose: 0.797324 loss: 0.000582 2022/10/19 17:26:22 - mmengine - INFO - Epoch(train) [163][200/293] lr: 5.000000e-04 eta: 1:33:11 time: 0.469951 data_time: 0.095170 memory: 15356 loss_kpt: 0.000579 acc_pose: 0.829881 loss: 0.000579 2022/10/19 17:26:46 - mmengine - INFO - Epoch(train) [163][250/293] lr: 5.000000e-04 eta: 1:32:52 time: 0.474685 data_time: 0.097538 memory: 15356 loss_kpt: 0.000589 acc_pose: 0.848252 loss: 0.000589 2022/10/19 17:27:06 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:27:31 - mmengine - INFO - Epoch(train) [164][50/293] lr: 5.000000e-04 eta: 1:32:11 time: 0.493595 data_time: 0.118430 memory: 15356 loss_kpt: 0.000580 acc_pose: 0.858154 loss: 0.000580 2022/10/19 17:27:54 - mmengine - INFO - Epoch(train) [164][100/293] lr: 5.000000e-04 eta: 1:31:51 time: 0.467716 data_time: 0.098648 memory: 15356 loss_kpt: 0.000589 acc_pose: 0.841107 loss: 0.000589 2022/10/19 17:28:18 - mmengine - INFO - Epoch(train) [164][150/293] lr: 5.000000e-04 eta: 1:31:32 time: 0.474401 data_time: 0.094112 memory: 15356 loss_kpt: 0.000583 acc_pose: 0.898730 loss: 0.000583 2022/10/19 17:28:41 - mmengine - INFO - Epoch(train) [164][200/293] lr: 5.000000e-04 eta: 1:31:13 time: 0.470882 data_time: 0.095335 memory: 15356 loss_kpt: 0.000591 acc_pose: 0.801634 loss: 0.000591 2022/10/19 17:29:00 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:29:05 - mmengine - INFO - Epoch(train) [164][250/293] lr: 5.000000e-04 eta: 1:30:54 time: 0.468858 data_time: 0.098981 memory: 15356 loss_kpt: 0.000586 acc_pose: 0.855360 loss: 0.000586 2022/10/19 17:29:24 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:29:48 - mmengine - INFO - Epoch(train) [165][50/293] lr: 5.000000e-04 eta: 1:30:12 time: 0.474396 data_time: 0.110529 memory: 15356 loss_kpt: 0.000580 acc_pose: 0.824881 loss: 0.000580 2022/10/19 17:30:12 - mmengine - INFO - Epoch(train) [165][100/293] lr: 5.000000e-04 eta: 1:29:53 time: 0.475992 data_time: 0.098420 memory: 15356 loss_kpt: 0.000588 acc_pose: 0.825945 loss: 0.000588 2022/10/19 17:30:35 - mmengine - INFO - Epoch(train) [165][150/293] lr: 5.000000e-04 eta: 1:29:34 time: 0.471975 data_time: 0.097258 memory: 15356 loss_kpt: 0.000580 acc_pose: 0.824791 loss: 0.000580 2022/10/19 17:30:59 - mmengine - INFO - Epoch(train) [165][200/293] lr: 5.000000e-04 eta: 1:29:15 time: 0.477447 data_time: 0.093991 memory: 15356 loss_kpt: 0.000565 acc_pose: 0.842417 loss: 0.000565 2022/10/19 17:31:23 - mmengine - INFO - Epoch(train) [165][250/293] lr: 5.000000e-04 eta: 1:28:56 time: 0.468593 data_time: 0.093403 memory: 15356 loss_kpt: 0.000584 acc_pose: 0.852156 loss: 0.000584 2022/10/19 17:31:42 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:32:07 - mmengine - INFO - Epoch(train) [166][50/293] lr: 5.000000e-04 eta: 1:28:15 time: 0.487722 data_time: 0.112108 memory: 15356 loss_kpt: 0.000594 acc_pose: 0.894257 loss: 0.000594 2022/10/19 17:32:30 - mmengine - INFO - Epoch(train) [166][100/293] lr: 5.000000e-04 eta: 1:27:55 time: 0.467327 data_time: 0.097974 memory: 15356 loss_kpt: 0.000589 acc_pose: 0.851735 loss: 0.000589 2022/10/19 17:32:53 - mmengine - INFO - Epoch(train) [166][150/293] lr: 5.000000e-04 eta: 1:27:36 time: 0.468315 data_time: 0.100906 memory: 15356 loss_kpt: 0.000579 acc_pose: 0.854281 loss: 0.000579 2022/10/19 17:33:17 - mmengine - INFO - Epoch(train) [166][200/293] lr: 5.000000e-04 eta: 1:27:17 time: 0.471561 data_time: 0.102642 memory: 15356 loss_kpt: 0.000576 acc_pose: 0.830826 loss: 0.000576 2022/10/19 17:33:41 - mmengine - INFO - Epoch(train) [166][250/293] lr: 5.000000e-04 eta: 1:26:58 time: 0.472176 data_time: 0.097032 memory: 15356 loss_kpt: 0.000570 acc_pose: 0.837185 loss: 0.000570 2022/10/19 17:34:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:34:25 - mmengine - INFO - Epoch(train) [167][50/293] lr: 5.000000e-04 eta: 1:26:17 time: 0.489423 data_time: 0.112911 memory: 15356 loss_kpt: 0.000579 acc_pose: 0.869480 loss: 0.000579 2022/10/19 17:34:49 - mmengine - INFO - Epoch(train) [167][100/293] lr: 5.000000e-04 eta: 1:25:57 time: 0.474125 data_time: 0.102575 memory: 15356 loss_kpt: 0.000588 acc_pose: 0.885155 loss: 0.000588 2022/10/19 17:35:13 - mmengine - INFO - Epoch(train) [167][150/293] lr: 5.000000e-04 eta: 1:25:38 time: 0.474881 data_time: 0.100948 memory: 15356 loss_kpt: 0.000578 acc_pose: 0.827342 loss: 0.000578 2022/10/19 17:35:36 - mmengine - INFO - Epoch(train) [167][200/293] lr: 5.000000e-04 eta: 1:25:19 time: 0.469035 data_time: 0.099810 memory: 15356 loss_kpt: 0.000586 acc_pose: 0.830230 loss: 0.000586 2022/10/19 17:35:59 - mmengine - INFO - Epoch(train) [167][250/293] lr: 5.000000e-04 eta: 1:24:59 time: 0.465181 data_time: 0.109798 memory: 15356 loss_kpt: 0.000586 acc_pose: 0.834058 loss: 0.000586 2022/10/19 17:36:19 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:36:43 - mmengine - INFO - Epoch(train) [168][50/293] lr: 5.000000e-04 eta: 1:24:19 time: 0.486498 data_time: 0.109849 memory: 15356 loss_kpt: 0.000569 acc_pose: 0.834161 loss: 0.000569 2022/10/19 17:36:53 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:37:07 - mmengine - INFO - Epoch(train) [168][100/293] lr: 5.000000e-04 eta: 1:23:59 time: 0.466738 data_time: 0.100515 memory: 15356 loss_kpt: 0.000576 acc_pose: 0.871809 loss: 0.000576 2022/10/19 17:37:31 - mmengine - INFO - Epoch(train) [168][150/293] lr: 5.000000e-04 eta: 1:23:40 time: 0.478135 data_time: 0.105674 memory: 15356 loss_kpt: 0.000588 acc_pose: 0.863093 loss: 0.000588 2022/10/19 17:37:55 - mmengine - INFO - Epoch(train) [168][200/293] lr: 5.000000e-04 eta: 1:23:21 time: 0.476168 data_time: 0.100086 memory: 15356 loss_kpt: 0.000584 acc_pose: 0.849376 loss: 0.000584 2022/10/19 17:38:19 - mmengine - INFO - Epoch(train) [168][250/293] lr: 5.000000e-04 eta: 1:23:02 time: 0.478926 data_time: 0.094413 memory: 15356 loss_kpt: 0.000576 acc_pose: 0.845786 loss: 0.000576 2022/10/19 17:38:38 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:39:03 - mmengine - INFO - Epoch(train) [169][50/293] lr: 5.000000e-04 eta: 1:22:21 time: 0.487632 data_time: 0.116046 memory: 15356 loss_kpt: 0.000561 acc_pose: 0.877127 loss: 0.000561 2022/10/19 17:39:26 - mmengine - INFO - Epoch(train) [169][100/293] lr: 5.000000e-04 eta: 1:22:01 time: 0.469148 data_time: 0.092016 memory: 15356 loss_kpt: 0.000568 acc_pose: 0.809479 loss: 0.000568 2022/10/19 17:39:50 - mmengine - INFO - Epoch(train) [169][150/293] lr: 5.000000e-04 eta: 1:21:42 time: 0.469389 data_time: 0.095096 memory: 15356 loss_kpt: 0.000591 acc_pose: 0.826026 loss: 0.000591 2022/10/19 17:40:13 - mmengine - INFO - Epoch(train) [169][200/293] lr: 5.000000e-04 eta: 1:21:23 time: 0.463234 data_time: 0.099991 memory: 15356 loss_kpt: 0.000586 acc_pose: 0.853244 loss: 0.000586 2022/10/19 17:40:37 - mmengine - INFO - Epoch(train) [169][250/293] lr: 5.000000e-04 eta: 1:21:03 time: 0.474714 data_time: 0.095809 memory: 15356 loss_kpt: 0.000579 acc_pose: 0.834374 loss: 0.000579 2022/10/19 17:40:56 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:41:21 - mmengine - INFO - Epoch(train) [170][50/293] lr: 5.000000e-04 eta: 1:20:23 time: 0.489354 data_time: 0.104687 memory: 15356 loss_kpt: 0.000580 acc_pose: 0.822887 loss: 0.000580 2022/10/19 17:41:44 - mmengine - INFO - Epoch(train) [170][100/293] lr: 5.000000e-04 eta: 1:20:03 time: 0.466291 data_time: 0.101393 memory: 15356 loss_kpt: 0.000580 acc_pose: 0.850847 loss: 0.000580 2022/10/19 17:42:08 - mmengine - INFO - Epoch(train) [170][150/293] lr: 5.000000e-04 eta: 1:19:44 time: 0.479932 data_time: 0.100416 memory: 15356 loss_kpt: 0.000571 acc_pose: 0.832112 loss: 0.000571 2022/10/19 17:42:32 - mmengine - INFO - Epoch(train) [170][200/293] lr: 5.000000e-04 eta: 1:19:25 time: 0.473283 data_time: 0.096086 memory: 15356 loss_kpt: 0.000581 acc_pose: 0.792496 loss: 0.000581 2022/10/19 17:42:55 - mmengine - INFO - Epoch(train) [170][250/293] lr: 5.000000e-04 eta: 1:19:05 time: 0.471395 data_time: 0.098130 memory: 15356 loss_kpt: 0.000585 acc_pose: 0.837956 loss: 0.000585 2022/10/19 17:43:15 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:43:15 - mmengine - INFO - Saving checkpoint at 170 epochs 2022/10/19 17:43:25 - mmengine - INFO - Epoch(val) [170][50/407] eta: 0:00:50 time: 0.141226 data_time: 0.054644 memory: 15356 2022/10/19 17:43:32 - mmengine - INFO - Epoch(val) [170][100/407] eta: 0:00:39 time: 0.127516 data_time: 0.041822 memory: 1465 2022/10/19 17:43:38 - mmengine - INFO - Epoch(val) [170][150/407] eta: 0:00:33 time: 0.129214 data_time: 0.042273 memory: 1465 2022/10/19 17:43:45 - mmengine - INFO - Epoch(val) [170][200/407] eta: 0:00:25 time: 0.125448 data_time: 0.038255 memory: 1465 2022/10/19 17:43:51 - mmengine - INFO - Epoch(val) [170][250/407] eta: 0:00:20 time: 0.129363 data_time: 0.041889 memory: 1465 2022/10/19 17:43:58 - mmengine - INFO - Epoch(val) [170][300/407] eta: 0:00:13 time: 0.130372 data_time: 0.044489 memory: 1465 2022/10/19 17:44:05 - mmengine - INFO - Epoch(val) [170][350/407] eta: 0:00:08 time: 0.151871 data_time: 0.064985 memory: 1465 2022/10/19 17:44:11 - mmengine - INFO - Epoch(val) [170][400/407] eta: 0:00:00 time: 0.120234 data_time: 0.034831 memory: 1465 2022/10/19 17:44:48 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 17:45:02 - mmengine - INFO - Epoch(val) [170][407/407] coco/AP: 0.721753 coco/AP .5: 0.896733 coco/AP .75: 0.789071 coco/AP (M): 0.673448 coco/AP (L): 0.798924 coco/AR: 0.772497 coco/AR .5: 0.931360 coco/AR .75: 0.835327 coco/AR (M): 0.720951 coco/AR (L): 0.845522 2022/10/19 17:45:02 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_384/best_coco/AP_epoch_150.pth is removed 2022/10/19 17:45:05 - mmengine - INFO - The best checkpoint with 0.7218 coco/AP at 170 epoch is saved to best_coco/AP_epoch_170.pth. 2022/10/19 17:45:29 - mmengine - INFO - Epoch(train) [171][50/293] lr: 5.000000e-05 eta: 1:18:25 time: 0.492764 data_time: 0.107168 memory: 15356 loss_kpt: 0.000573 acc_pose: 0.843014 loss: 0.000573 2022/10/19 17:45:53 - mmengine - INFO - Epoch(train) [171][100/293] lr: 5.000000e-05 eta: 1:18:05 time: 0.471648 data_time: 0.101025 memory: 15356 loss_kpt: 0.000574 acc_pose: 0.838742 loss: 0.000574 2022/10/19 17:46:16 - mmengine - INFO - Epoch(train) [171][150/293] lr: 5.000000e-05 eta: 1:17:46 time: 0.462145 data_time: 0.100111 memory: 15356 loss_kpt: 0.000548 acc_pose: 0.829316 loss: 0.000548 2022/10/19 17:46:35 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:46:40 - mmengine - INFO - Epoch(train) [171][200/293] lr: 5.000000e-05 eta: 1:17:27 time: 0.477910 data_time: 0.096384 memory: 15356 loss_kpt: 0.000569 acc_pose: 0.843325 loss: 0.000569 2022/10/19 17:47:03 - mmengine - INFO - Epoch(train) [171][250/293] lr: 5.000000e-05 eta: 1:17:07 time: 0.472988 data_time: 0.111796 memory: 15356 loss_kpt: 0.000561 acc_pose: 0.862339 loss: 0.000561 2022/10/19 17:47:24 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:47:47 - mmengine - INFO - Epoch(train) [172][50/293] lr: 5.000000e-05 eta: 1:16:27 time: 0.474562 data_time: 0.098323 memory: 15356 loss_kpt: 0.000550 acc_pose: 0.855625 loss: 0.000550 2022/10/19 17:48:11 - mmengine - INFO - Epoch(train) [172][100/293] lr: 5.000000e-05 eta: 1:16:07 time: 0.479517 data_time: 0.103189 memory: 15356 loss_kpt: 0.000557 acc_pose: 0.850750 loss: 0.000557 2022/10/19 17:48:35 - mmengine - INFO - Epoch(train) [172][150/293] lr: 5.000000e-05 eta: 1:15:48 time: 0.476206 data_time: 0.108245 memory: 15356 loss_kpt: 0.000550 acc_pose: 0.838749 loss: 0.000550 2022/10/19 17:48:58 - mmengine - INFO - Epoch(train) [172][200/293] lr: 5.000000e-05 eta: 1:15:28 time: 0.465612 data_time: 0.091051 memory: 15356 loss_kpt: 0.000559 acc_pose: 0.848807 loss: 0.000559 2022/10/19 17:49:22 - mmengine - INFO - Epoch(train) [172][250/293] lr: 5.000000e-05 eta: 1:15:09 time: 0.471573 data_time: 0.086321 memory: 15356 loss_kpt: 0.000551 acc_pose: 0.837998 loss: 0.000551 2022/10/19 17:49:42 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:50:06 - mmengine - INFO - Epoch(train) [173][50/293] lr: 5.000000e-05 eta: 1:14:29 time: 0.483546 data_time: 0.108692 memory: 15356 loss_kpt: 0.000556 acc_pose: 0.859630 loss: 0.000556 2022/10/19 17:50:30 - mmengine - INFO - Epoch(train) [173][100/293] lr: 5.000000e-05 eta: 1:14:09 time: 0.471628 data_time: 0.102496 memory: 15356 loss_kpt: 0.000554 acc_pose: 0.834703 loss: 0.000554 2022/10/19 17:50:54 - mmengine - INFO - Epoch(train) [173][150/293] lr: 5.000000e-05 eta: 1:13:50 time: 0.474386 data_time: 0.098251 memory: 15356 loss_kpt: 0.000563 acc_pose: 0.866848 loss: 0.000563 2022/10/19 17:51:17 - mmengine - INFO - Epoch(train) [173][200/293] lr: 5.000000e-05 eta: 1:13:30 time: 0.466186 data_time: 0.102940 memory: 15356 loss_kpt: 0.000555 acc_pose: 0.853135 loss: 0.000555 2022/10/19 17:51:40 - mmengine - INFO - Epoch(train) [173][250/293] lr: 5.000000e-05 eta: 1:13:11 time: 0.465826 data_time: 0.087684 memory: 15356 loss_kpt: 0.000550 acc_pose: 0.832759 loss: 0.000550 2022/10/19 17:52:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:52:25 - mmengine - INFO - Epoch(train) [174][50/293] lr: 5.000000e-05 eta: 1:12:30 time: 0.481323 data_time: 0.114018 memory: 15356 loss_kpt: 0.000546 acc_pose: 0.873075 loss: 0.000546 2022/10/19 17:52:48 - mmengine - INFO - Epoch(train) [174][100/293] lr: 5.000000e-05 eta: 1:12:11 time: 0.465605 data_time: 0.096345 memory: 15356 loss_kpt: 0.000556 acc_pose: 0.875977 loss: 0.000556 2022/10/19 17:53:12 - mmengine - INFO - Epoch(train) [174][150/293] lr: 5.000000e-05 eta: 1:11:52 time: 0.471675 data_time: 0.096867 memory: 15356 loss_kpt: 0.000553 acc_pose: 0.832655 loss: 0.000553 2022/10/19 17:53:35 - mmengine - INFO - Epoch(train) [174][200/293] lr: 5.000000e-05 eta: 1:11:32 time: 0.476830 data_time: 0.099181 memory: 15356 loss_kpt: 0.000555 acc_pose: 0.868517 loss: 0.000555 2022/10/19 17:53:59 - mmengine - INFO - Epoch(train) [174][250/293] lr: 5.000000e-05 eta: 1:11:13 time: 0.467862 data_time: 0.101190 memory: 15356 loss_kpt: 0.000536 acc_pose: 0.890538 loss: 0.000536 2022/10/19 17:54:18 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:54:28 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:54:43 - mmengine - INFO - Epoch(train) [175][50/293] lr: 5.000000e-05 eta: 1:10:32 time: 0.487576 data_time: 0.114480 memory: 15356 loss_kpt: 0.000536 acc_pose: 0.884968 loss: 0.000536 2022/10/19 17:55:06 - mmengine - INFO - Epoch(train) [175][100/293] lr: 5.000000e-05 eta: 1:10:13 time: 0.465266 data_time: 0.092682 memory: 15356 loss_kpt: 0.000555 acc_pose: 0.853452 loss: 0.000555 2022/10/19 17:55:29 - mmengine - INFO - Epoch(train) [175][150/293] lr: 5.000000e-05 eta: 1:09:53 time: 0.463361 data_time: 0.096902 memory: 15356 loss_kpt: 0.000547 acc_pose: 0.868077 loss: 0.000547 2022/10/19 17:55:53 - mmengine - INFO - Epoch(train) [175][200/293] lr: 5.000000e-05 eta: 1:09:34 time: 0.465597 data_time: 0.088874 memory: 15356 loss_kpt: 0.000545 acc_pose: 0.821621 loss: 0.000545 2022/10/19 17:56:16 - mmengine - INFO - Epoch(train) [175][250/293] lr: 5.000000e-05 eta: 1:09:14 time: 0.476446 data_time: 0.096463 memory: 15356 loss_kpt: 0.000542 acc_pose: 0.879409 loss: 0.000542 2022/10/19 17:56:37 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:57:01 - mmengine - INFO - Epoch(train) [176][50/293] lr: 5.000000e-05 eta: 1:08:34 time: 0.480964 data_time: 0.115775 memory: 15356 loss_kpt: 0.000559 acc_pose: 0.852996 loss: 0.000559 2022/10/19 17:57:24 - mmengine - INFO - Epoch(train) [176][100/293] lr: 5.000000e-05 eta: 1:08:15 time: 0.463852 data_time: 0.101281 memory: 15356 loss_kpt: 0.000539 acc_pose: 0.832155 loss: 0.000539 2022/10/19 17:57:48 - mmengine - INFO - Epoch(train) [176][150/293] lr: 5.000000e-05 eta: 1:07:55 time: 0.484366 data_time: 0.100528 memory: 15356 loss_kpt: 0.000551 acc_pose: 0.874960 loss: 0.000551 2022/10/19 17:58:11 - mmengine - INFO - Epoch(train) [176][200/293] lr: 5.000000e-05 eta: 1:07:36 time: 0.461749 data_time: 0.095459 memory: 15356 loss_kpt: 0.000537 acc_pose: 0.863304 loss: 0.000537 2022/10/19 17:58:35 - mmengine - INFO - Epoch(train) [176][250/293] lr: 5.000000e-05 eta: 1:07:16 time: 0.470262 data_time: 0.099346 memory: 15356 loss_kpt: 0.000551 acc_pose: 0.869291 loss: 0.000551 2022/10/19 17:58:55 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 17:59:19 - mmengine - INFO - Epoch(train) [177][50/293] lr: 5.000000e-05 eta: 1:06:36 time: 0.485311 data_time: 0.110272 memory: 15356 loss_kpt: 0.000546 acc_pose: 0.890064 loss: 0.000546 2022/10/19 17:59:43 - mmengine - INFO - Epoch(train) [177][100/293] lr: 5.000000e-05 eta: 1:06:16 time: 0.468033 data_time: 0.100702 memory: 15356 loss_kpt: 0.000555 acc_pose: 0.810103 loss: 0.000555 2022/10/19 18:00:06 - mmengine - INFO - Epoch(train) [177][150/293] lr: 5.000000e-05 eta: 1:05:57 time: 0.472284 data_time: 0.100465 memory: 15356 loss_kpt: 0.000549 acc_pose: 0.872677 loss: 0.000549 2022/10/19 18:00:30 - mmengine - INFO - Epoch(train) [177][200/293] lr: 5.000000e-05 eta: 1:05:37 time: 0.469404 data_time: 0.088230 memory: 15356 loss_kpt: 0.000538 acc_pose: 0.868219 loss: 0.000538 2022/10/19 18:00:53 - mmengine - INFO - Epoch(train) [177][250/293] lr: 5.000000e-05 eta: 1:05:18 time: 0.471390 data_time: 0.107122 memory: 15356 loss_kpt: 0.000553 acc_pose: 0.891205 loss: 0.000553 2022/10/19 18:01:14 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:01:38 - mmengine - INFO - Epoch(train) [178][50/293] lr: 5.000000e-05 eta: 1:04:38 time: 0.487961 data_time: 0.115496 memory: 15356 loss_kpt: 0.000540 acc_pose: 0.877891 loss: 0.000540 2022/10/19 18:02:01 - mmengine - INFO - Epoch(train) [178][100/293] lr: 5.000000e-05 eta: 1:04:18 time: 0.468624 data_time: 0.094959 memory: 15356 loss_kpt: 0.000549 acc_pose: 0.873625 loss: 0.000549 2022/10/19 18:02:20 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:02:25 - mmengine - INFO - Epoch(train) [178][150/293] lr: 5.000000e-05 eta: 1:03:59 time: 0.467942 data_time: 0.093952 memory: 15356 loss_kpt: 0.000528 acc_pose: 0.813836 loss: 0.000528 2022/10/19 18:02:49 - mmengine - INFO - Epoch(train) [178][200/293] lr: 5.000000e-05 eta: 1:03:39 time: 0.474877 data_time: 0.087507 memory: 15356 loss_kpt: 0.000542 acc_pose: 0.838995 loss: 0.000542 2022/10/19 18:03:12 - mmengine - INFO - Epoch(train) [178][250/293] lr: 5.000000e-05 eta: 1:03:20 time: 0.473978 data_time: 0.101942 memory: 15356 loss_kpt: 0.000543 acc_pose: 0.849091 loss: 0.000543 2022/10/19 18:03:32 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:03:57 - mmengine - INFO - Epoch(train) [179][50/293] lr: 5.000000e-05 eta: 1:02:40 time: 0.488645 data_time: 0.109974 memory: 15356 loss_kpt: 0.000542 acc_pose: 0.854132 loss: 0.000542 2022/10/19 18:04:21 - mmengine - INFO - Epoch(train) [179][100/293] lr: 5.000000e-05 eta: 1:02:20 time: 0.472860 data_time: 0.095414 memory: 15356 loss_kpt: 0.000537 acc_pose: 0.845784 loss: 0.000537 2022/10/19 18:04:44 - mmengine - INFO - Epoch(train) [179][150/293] lr: 5.000000e-05 eta: 1:02:01 time: 0.475986 data_time: 0.101981 memory: 15356 loss_kpt: 0.000545 acc_pose: 0.836790 loss: 0.000545 2022/10/19 18:05:08 - mmengine - INFO - Epoch(train) [179][200/293] lr: 5.000000e-05 eta: 1:01:41 time: 0.475979 data_time: 0.099270 memory: 15356 loss_kpt: 0.000549 acc_pose: 0.857639 loss: 0.000549 2022/10/19 18:05:31 - mmengine - INFO - Epoch(train) [179][250/293] lr: 5.000000e-05 eta: 1:01:22 time: 0.455729 data_time: 0.098168 memory: 15356 loss_kpt: 0.000549 acc_pose: 0.835948 loss: 0.000549 2022/10/19 18:05:50 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:06:16 - mmengine - INFO - Epoch(train) [180][50/293] lr: 5.000000e-05 eta: 1:00:42 time: 0.504794 data_time: 0.113107 memory: 15356 loss_kpt: 0.000556 acc_pose: 0.867893 loss: 0.000556 2022/10/19 18:06:39 - mmengine - INFO - Epoch(train) [180][100/293] lr: 5.000000e-05 eta: 1:00:22 time: 0.467058 data_time: 0.100128 memory: 15356 loss_kpt: 0.000546 acc_pose: 0.871300 loss: 0.000546 2022/10/19 18:07:02 - mmengine - INFO - Epoch(train) [180][150/293] lr: 5.000000e-05 eta: 1:00:03 time: 0.470921 data_time: 0.102034 memory: 15356 loss_kpt: 0.000534 acc_pose: 0.884504 loss: 0.000534 2022/10/19 18:07:26 - mmengine - INFO - Epoch(train) [180][200/293] lr: 5.000000e-05 eta: 0:59:43 time: 0.472521 data_time: 0.101402 memory: 15356 loss_kpt: 0.000546 acc_pose: 0.866463 loss: 0.000546 2022/10/19 18:07:51 - mmengine - INFO - Epoch(train) [180][250/293] lr: 5.000000e-05 eta: 0:59:24 time: 0.488816 data_time: 0.101733 memory: 15356 loss_kpt: 0.000544 acc_pose: 0.845822 loss: 0.000544 2022/10/19 18:08:11 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:08:11 - mmengine - INFO - Saving checkpoint at 180 epochs 2022/10/19 18:08:20 - mmengine - INFO - Epoch(val) [180][50/407] eta: 0:00:47 time: 0.132587 data_time: 0.044018 memory: 15356 2022/10/19 18:08:27 - mmengine - INFO - Epoch(val) [180][100/407] eta: 0:00:38 time: 0.123792 data_time: 0.035869 memory: 1465 2022/10/19 18:08:33 - mmengine - INFO - Epoch(val) [180][150/407] eta: 0:00:32 time: 0.125538 data_time: 0.037842 memory: 1465 2022/10/19 18:08:39 - mmengine - INFO - Epoch(val) [180][200/407] eta: 0:00:26 time: 0.130075 data_time: 0.040964 memory: 1465 2022/10/19 18:08:46 - mmengine - INFO - Epoch(val) [180][250/407] eta: 0:00:20 time: 0.132156 data_time: 0.040829 memory: 1465 2022/10/19 18:08:53 - mmengine - INFO - Epoch(val) [180][300/407] eta: 0:00:14 time: 0.137671 data_time: 0.048906 memory: 1465 2022/10/19 18:09:00 - mmengine - INFO - Epoch(val) [180][350/407] eta: 0:00:07 time: 0.133558 data_time: 0.046471 memory: 1465 2022/10/19 18:09:06 - mmengine - INFO - Epoch(val) [180][400/407] eta: 0:00:00 time: 0.122425 data_time: 0.038292 memory: 1465 2022/10/19 18:09:42 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 18:09:56 - mmengine - INFO - Epoch(val) [180][407/407] coco/AP: 0.729835 coco/AP .5: 0.898417 coco/AP .75: 0.800073 coco/AP (M): 0.683770 coco/AP (L): 0.806340 coco/AR: 0.781077 coco/AR .5: 0.935926 coco/AR .75: 0.846190 coco/AR (M): 0.730811 coco/AR (L): 0.852917 2022/10/19 18:09:56 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_384/best_coco/AP_epoch_170.pth is removed 2022/10/19 18:09:58 - mmengine - INFO - The best checkpoint with 0.7298 coco/AP at 180 epoch is saved to best_coco/AP_epoch_180.pth. 2022/10/19 18:10:22 - mmengine - INFO - Epoch(train) [181][50/293] lr: 5.000000e-05 eta: 0:58:44 time: 0.474714 data_time: 0.104719 memory: 15356 loss_kpt: 0.000540 acc_pose: 0.867818 loss: 0.000540 2022/10/19 18:10:46 - mmengine - INFO - Epoch(train) [181][100/293] lr: 5.000000e-05 eta: 0:58:24 time: 0.481132 data_time: 0.101743 memory: 15356 loss_kpt: 0.000542 acc_pose: 0.878009 loss: 0.000542 2022/10/19 18:11:09 - mmengine - INFO - Epoch(train) [181][150/293] lr: 5.000000e-05 eta: 0:58:05 time: 0.465405 data_time: 0.093873 memory: 15356 loss_kpt: 0.000529 acc_pose: 0.787942 loss: 0.000529 2022/10/19 18:11:33 - mmengine - INFO - Epoch(train) [181][200/293] lr: 5.000000e-05 eta: 0:57:45 time: 0.479273 data_time: 0.102683 memory: 15356 loss_kpt: 0.000546 acc_pose: 0.867003 loss: 0.000546 2022/10/19 18:11:57 - mmengine - INFO - Epoch(train) [181][250/293] lr: 5.000000e-05 eta: 0:57:26 time: 0.474102 data_time: 0.094689 memory: 15356 loss_kpt: 0.000535 acc_pose: 0.895794 loss: 0.000535 2022/10/19 18:12:02 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:12:17 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:12:41 - mmengine - INFO - Epoch(train) [182][50/293] lr: 5.000000e-05 eta: 0:56:46 time: 0.478349 data_time: 0.110841 memory: 15356 loss_kpt: 0.000537 acc_pose: 0.825989 loss: 0.000537 2022/10/19 18:13:05 - mmengine - INFO - Epoch(train) [182][100/293] lr: 5.000000e-05 eta: 0:56:26 time: 0.472999 data_time: 0.100271 memory: 15356 loss_kpt: 0.000542 acc_pose: 0.895044 loss: 0.000542 2022/10/19 18:13:29 - mmengine - INFO - Epoch(train) [182][150/293] lr: 5.000000e-05 eta: 0:56:07 time: 0.475773 data_time: 0.101820 memory: 15356 loss_kpt: 0.000550 acc_pose: 0.813947 loss: 0.000550 2022/10/19 18:13:53 - mmengine - INFO - Epoch(train) [182][200/293] lr: 5.000000e-05 eta: 0:55:47 time: 0.475654 data_time: 0.102636 memory: 15356 loss_kpt: 0.000538 acc_pose: 0.822054 loss: 0.000538 2022/10/19 18:14:16 - mmengine - INFO - Epoch(train) [182][250/293] lr: 5.000000e-05 eta: 0:55:27 time: 0.463979 data_time: 0.100964 memory: 15356 loss_kpt: 0.000538 acc_pose: 0.849637 loss: 0.000538 2022/10/19 18:14:36 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:15:00 - mmengine - INFO - Epoch(train) [183][50/293] lr: 5.000000e-05 eta: 0:54:48 time: 0.493026 data_time: 0.116542 memory: 15356 loss_kpt: 0.000538 acc_pose: 0.854688 loss: 0.000538 2022/10/19 18:15:24 - mmengine - INFO - Epoch(train) [183][100/293] lr: 5.000000e-05 eta: 0:54:28 time: 0.470170 data_time: 0.095993 memory: 15356 loss_kpt: 0.000546 acc_pose: 0.916097 loss: 0.000546 2022/10/19 18:15:47 - mmengine - INFO - Epoch(train) [183][150/293] lr: 5.000000e-05 eta: 0:54:08 time: 0.463979 data_time: 0.102017 memory: 15356 loss_kpt: 0.000532 acc_pose: 0.853423 loss: 0.000532 2022/10/19 18:16:11 - mmengine - INFO - Epoch(train) [183][200/293] lr: 5.000000e-05 eta: 0:53:49 time: 0.480555 data_time: 0.099201 memory: 15356 loss_kpt: 0.000539 acc_pose: 0.893796 loss: 0.000539 2022/10/19 18:16:34 - mmengine - INFO - Epoch(train) [183][250/293] lr: 5.000000e-05 eta: 0:53:29 time: 0.465614 data_time: 0.103386 memory: 15356 loss_kpt: 0.000528 acc_pose: 0.878117 loss: 0.000528 2022/10/19 18:16:54 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:17:19 - mmengine - INFO - Epoch(train) [184][50/293] lr: 5.000000e-05 eta: 0:52:50 time: 0.499170 data_time: 0.113997 memory: 15356 loss_kpt: 0.000549 acc_pose: 0.846168 loss: 0.000549 2022/10/19 18:17:43 - mmengine - INFO - Epoch(train) [184][100/293] lr: 5.000000e-05 eta: 0:52:30 time: 0.480813 data_time: 0.098024 memory: 15356 loss_kpt: 0.000550 acc_pose: 0.832129 loss: 0.000550 2022/10/19 18:18:07 - mmengine - INFO - Epoch(train) [184][150/293] lr: 5.000000e-05 eta: 0:52:11 time: 0.474404 data_time: 0.098899 memory: 15356 loss_kpt: 0.000545 acc_pose: 0.875949 loss: 0.000545 2022/10/19 18:18:31 - mmengine - INFO - Epoch(train) [184][200/293] lr: 5.000000e-05 eta: 0:51:51 time: 0.469166 data_time: 0.103034 memory: 15356 loss_kpt: 0.000535 acc_pose: 0.866678 loss: 0.000535 2022/10/19 18:18:54 - mmengine - INFO - Epoch(train) [184][250/293] lr: 5.000000e-05 eta: 0:51:31 time: 0.470965 data_time: 0.101111 memory: 15356 loss_kpt: 0.000536 acc_pose: 0.875082 loss: 0.000536 2022/10/19 18:19:14 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:19:38 - mmengine - INFO - Epoch(train) [185][50/293] lr: 5.000000e-05 eta: 0:50:52 time: 0.486183 data_time: 0.113899 memory: 15356 loss_kpt: 0.000537 acc_pose: 0.822873 loss: 0.000537 2022/10/19 18:19:56 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:20:02 - mmengine - INFO - Epoch(train) [185][100/293] lr: 5.000000e-05 eta: 0:50:32 time: 0.469905 data_time: 0.099212 memory: 15356 loss_kpt: 0.000538 acc_pose: 0.874253 loss: 0.000538 2022/10/19 18:20:25 - mmengine - INFO - Epoch(train) [185][150/293] lr: 5.000000e-05 eta: 0:50:12 time: 0.468792 data_time: 0.094897 memory: 15356 loss_kpt: 0.000540 acc_pose: 0.864735 loss: 0.000540 2022/10/19 18:20:49 - mmengine - INFO - Epoch(train) [185][200/293] lr: 5.000000e-05 eta: 0:49:53 time: 0.465406 data_time: 0.097878 memory: 15356 loss_kpt: 0.000536 acc_pose: 0.829328 loss: 0.000536 2022/10/19 18:21:12 - mmengine - INFO - Epoch(train) [185][250/293] lr: 5.000000e-05 eta: 0:49:33 time: 0.474041 data_time: 0.099979 memory: 15356 loss_kpt: 0.000528 acc_pose: 0.831624 loss: 0.000528 2022/10/19 18:21:33 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:21:58 - mmengine - INFO - Epoch(train) [186][50/293] lr: 5.000000e-05 eta: 0:48:54 time: 0.488112 data_time: 0.113270 memory: 15356 loss_kpt: 0.000543 acc_pose: 0.846236 loss: 0.000543 2022/10/19 18:22:21 - mmengine - INFO - Epoch(train) [186][100/293] lr: 5.000000e-05 eta: 0:48:34 time: 0.476689 data_time: 0.107124 memory: 15356 loss_kpt: 0.000531 acc_pose: 0.807776 loss: 0.000531 2022/10/19 18:22:45 - mmengine - INFO - Epoch(train) [186][150/293] lr: 5.000000e-05 eta: 0:48:14 time: 0.469612 data_time: 0.099060 memory: 15356 loss_kpt: 0.000544 acc_pose: 0.899015 loss: 0.000544 2022/10/19 18:23:09 - mmengine - INFO - Epoch(train) [186][200/293] lr: 5.000000e-05 eta: 0:47:55 time: 0.476099 data_time: 0.095597 memory: 15356 loss_kpt: 0.000549 acc_pose: 0.846795 loss: 0.000549 2022/10/19 18:23:32 - mmengine - INFO - Epoch(train) [186][250/293] lr: 5.000000e-05 eta: 0:47:35 time: 0.463044 data_time: 0.102363 memory: 15356 loss_kpt: 0.000536 acc_pose: 0.882106 loss: 0.000536 2022/10/19 18:23:52 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:24:16 - mmengine - INFO - Epoch(train) [187][50/293] lr: 5.000000e-05 eta: 0:46:55 time: 0.479471 data_time: 0.105478 memory: 15356 loss_kpt: 0.000527 acc_pose: 0.859379 loss: 0.000527 2022/10/19 18:24:39 - mmengine - INFO - Epoch(train) [187][100/293] lr: 5.000000e-05 eta: 0:46:36 time: 0.473763 data_time: 0.102924 memory: 15356 loss_kpt: 0.000531 acc_pose: 0.879699 loss: 0.000531 2022/10/19 18:25:03 - mmengine - INFO - Epoch(train) [187][150/293] lr: 5.000000e-05 eta: 0:46:16 time: 0.468402 data_time: 0.099606 memory: 15356 loss_kpt: 0.000531 acc_pose: 0.874791 loss: 0.000531 2022/10/19 18:25:26 - mmengine - INFO - Epoch(train) [187][200/293] lr: 5.000000e-05 eta: 0:45:56 time: 0.468109 data_time: 0.097071 memory: 15356 loss_kpt: 0.000542 acc_pose: 0.894166 loss: 0.000542 2022/10/19 18:25:50 - mmengine - INFO - Epoch(train) [187][250/293] lr: 5.000000e-05 eta: 0:45:36 time: 0.474862 data_time: 0.104509 memory: 15356 loss_kpt: 0.000533 acc_pose: 0.887146 loss: 0.000533 2022/10/19 18:26:10 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:26:35 - mmengine - INFO - Epoch(train) [188][50/293] lr: 5.000000e-05 eta: 0:44:57 time: 0.498592 data_time: 0.114367 memory: 15356 loss_kpt: 0.000539 acc_pose: 0.819803 loss: 0.000539 2022/10/19 18:26:58 - mmengine - INFO - Epoch(train) [188][100/293] lr: 5.000000e-05 eta: 0:44:38 time: 0.459705 data_time: 0.103093 memory: 15356 loss_kpt: 0.000541 acc_pose: 0.870700 loss: 0.000541 2022/10/19 18:27:21 - mmengine - INFO - Epoch(train) [188][150/293] lr: 5.000000e-05 eta: 0:44:18 time: 0.472161 data_time: 0.098913 memory: 15356 loss_kpt: 0.000535 acc_pose: 0.861341 loss: 0.000535 2022/10/19 18:27:45 - mmengine - INFO - Epoch(train) [188][200/293] lr: 5.000000e-05 eta: 0:43:58 time: 0.467671 data_time: 0.097529 memory: 15356 loss_kpt: 0.000539 acc_pose: 0.852878 loss: 0.000539 2022/10/19 18:27:49 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:28:08 - mmengine - INFO - Epoch(train) [188][250/293] lr: 5.000000e-05 eta: 0:43:38 time: 0.466085 data_time: 0.091812 memory: 15356 loss_kpt: 0.000535 acc_pose: 0.885513 loss: 0.000535 2022/10/19 18:28:28 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:28:52 - mmengine - INFO - Epoch(train) [189][50/293] lr: 5.000000e-05 eta: 0:42:59 time: 0.481113 data_time: 0.105864 memory: 15356 loss_kpt: 0.000535 acc_pose: 0.864217 loss: 0.000535 2022/10/19 18:29:16 - mmengine - INFO - Epoch(train) [189][100/293] lr: 5.000000e-05 eta: 0:42:39 time: 0.474501 data_time: 0.099891 memory: 15356 loss_kpt: 0.000545 acc_pose: 0.882505 loss: 0.000545 2022/10/19 18:29:40 - mmengine - INFO - Epoch(train) [189][150/293] lr: 5.000000e-05 eta: 0:42:20 time: 0.474417 data_time: 0.107702 memory: 15356 loss_kpt: 0.000528 acc_pose: 0.898483 loss: 0.000528 2022/10/19 18:30:03 - mmengine - INFO - Epoch(train) [189][200/293] lr: 5.000000e-05 eta: 0:42:00 time: 0.467322 data_time: 0.101240 memory: 15356 loss_kpt: 0.000540 acc_pose: 0.812659 loss: 0.000540 2022/10/19 18:30:27 - mmengine - INFO - Epoch(train) [189][250/293] lr: 5.000000e-05 eta: 0:41:40 time: 0.473486 data_time: 0.095844 memory: 15356 loss_kpt: 0.000535 acc_pose: 0.869729 loss: 0.000535 2022/10/19 18:30:47 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:31:11 - mmengine - INFO - Epoch(train) [190][50/293] lr: 5.000000e-05 eta: 0:41:01 time: 0.481018 data_time: 0.114498 memory: 15356 loss_kpt: 0.000535 acc_pose: 0.902187 loss: 0.000535 2022/10/19 18:31:33 - mmengine - INFO - Epoch(train) [190][100/293] lr: 5.000000e-05 eta: 0:40:41 time: 0.453120 data_time: 0.084271 memory: 15356 loss_kpt: 0.000541 acc_pose: 0.841429 loss: 0.000541 2022/10/19 18:31:57 - mmengine - INFO - Epoch(train) [190][150/293] lr: 5.000000e-05 eta: 0:40:21 time: 0.477287 data_time: 0.098062 memory: 15356 loss_kpt: 0.000529 acc_pose: 0.879914 loss: 0.000529 2022/10/19 18:32:21 - mmengine - INFO - Epoch(train) [190][200/293] lr: 5.000000e-05 eta: 0:40:02 time: 0.473696 data_time: 0.096217 memory: 15356 loss_kpt: 0.000540 acc_pose: 0.837821 loss: 0.000540 2022/10/19 18:32:44 - mmengine - INFO - Epoch(train) [190][250/293] lr: 5.000000e-05 eta: 0:39:42 time: 0.458788 data_time: 0.095737 memory: 15356 loss_kpt: 0.000541 acc_pose: 0.882052 loss: 0.000541 2022/10/19 18:33:05 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:33:05 - mmengine - INFO - Saving checkpoint at 190 epochs 2022/10/19 18:33:14 - mmengine - INFO - Epoch(val) [190][50/407] eta: 0:00:48 time: 0.134841 data_time: 0.047218 memory: 15356 2022/10/19 18:33:21 - mmengine - INFO - Epoch(val) [190][100/407] eta: 0:00:40 time: 0.130410 data_time: 0.042845 memory: 1465 2022/10/19 18:33:27 - mmengine - INFO - Epoch(val) [190][150/407] eta: 0:00:31 time: 0.124130 data_time: 0.036531 memory: 1465 2022/10/19 18:33:34 - mmengine - INFO - Epoch(val) [190][200/407] eta: 0:00:26 time: 0.130033 data_time: 0.043054 memory: 1465 2022/10/19 18:33:40 - mmengine - INFO - Epoch(val) [190][250/407] eta: 0:00:20 time: 0.131479 data_time: 0.044344 memory: 1465 2022/10/19 18:33:46 - mmengine - INFO - Epoch(val) [190][300/407] eta: 0:00:13 time: 0.123188 data_time: 0.036569 memory: 1465 2022/10/19 18:33:53 - mmengine - INFO - Epoch(val) [190][350/407] eta: 0:00:07 time: 0.126323 data_time: 0.039667 memory: 1465 2022/10/19 18:33:59 - mmengine - INFO - Epoch(val) [190][400/407] eta: 0:00:00 time: 0.129897 data_time: 0.043536 memory: 1465 2022/10/19 18:34:36 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 18:34:50 - mmengine - INFO - Epoch(val) [190][407/407] coco/AP: 0.729344 coco/AP .5: 0.897374 coco/AP .75: 0.799227 coco/AP (M): 0.683473 coco/AP (L): 0.805570 coco/AR: 0.781344 coco/AR .5: 0.935768 coco/AR .75: 0.845246 coco/AR (M): 0.731194 coco/AR (L): 0.852768 2022/10/19 18:35:14 - mmengine - INFO - Epoch(train) [191][50/293] lr: 5.000000e-05 eta: 0:39:03 time: 0.488241 data_time: 0.101262 memory: 15356 loss_kpt: 0.000542 acc_pose: 0.839933 loss: 0.000542 2022/10/19 18:35:38 - mmengine - INFO - Epoch(train) [191][100/293] lr: 5.000000e-05 eta: 0:38:43 time: 0.472038 data_time: 0.096001 memory: 15356 loss_kpt: 0.000529 acc_pose: 0.895903 loss: 0.000529 2022/10/19 18:36:02 - mmengine - INFO - Epoch(train) [191][150/293] lr: 5.000000e-05 eta: 0:38:23 time: 0.480488 data_time: 0.107163 memory: 15356 loss_kpt: 0.000533 acc_pose: 0.875723 loss: 0.000533 2022/10/19 18:36:25 - mmengine - INFO - Epoch(train) [191][200/293] lr: 5.000000e-05 eta: 0:38:03 time: 0.469028 data_time: 0.102626 memory: 15356 loss_kpt: 0.000542 acc_pose: 0.848168 loss: 0.000542 2022/10/19 18:36:49 - mmengine - INFO - Epoch(train) [191][250/293] lr: 5.000000e-05 eta: 0:37:43 time: 0.468965 data_time: 0.100592 memory: 15356 loss_kpt: 0.000547 acc_pose: 0.890508 loss: 0.000547 2022/10/19 18:37:08 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:37:27 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:37:33 - mmengine - INFO - Epoch(train) [192][50/293] lr: 5.000000e-05 eta: 0:37:05 time: 0.497379 data_time: 0.111652 memory: 15356 loss_kpt: 0.000543 acc_pose: 0.853147 loss: 0.000543 2022/10/19 18:37:57 - mmengine - INFO - Epoch(train) [192][100/293] lr: 5.000000e-05 eta: 0:36:45 time: 0.478154 data_time: 0.097812 memory: 15356 loss_kpt: 0.000538 acc_pose: 0.899200 loss: 0.000538 2022/10/19 18:38:21 - mmengine - INFO - Epoch(train) [192][150/293] lr: 5.000000e-05 eta: 0:36:25 time: 0.471940 data_time: 0.101715 memory: 15356 loss_kpt: 0.000536 acc_pose: 0.860310 loss: 0.000536 2022/10/19 18:38:45 - mmengine - INFO - Epoch(train) [192][200/293] lr: 5.000000e-05 eta: 0:36:05 time: 0.476568 data_time: 0.110652 memory: 15356 loss_kpt: 0.000534 acc_pose: 0.844281 loss: 0.000534 2022/10/19 18:39:08 - mmengine - INFO - Epoch(train) [192][250/293] lr: 5.000000e-05 eta: 0:35:45 time: 0.466888 data_time: 0.098013 memory: 15356 loss_kpt: 0.000541 acc_pose: 0.868438 loss: 0.000541 2022/10/19 18:39:28 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:39:52 - mmengine - INFO - Epoch(train) [193][50/293] lr: 5.000000e-05 eta: 0:35:07 time: 0.481815 data_time: 0.113795 memory: 15356 loss_kpt: 0.000525 acc_pose: 0.870912 loss: 0.000525 2022/10/19 18:40:16 - mmengine - INFO - Epoch(train) [193][100/293] lr: 5.000000e-05 eta: 0:34:47 time: 0.465606 data_time: 0.105368 memory: 15356 loss_kpt: 0.000538 acc_pose: 0.821375 loss: 0.000538 2022/10/19 18:40:39 - mmengine - INFO - Epoch(train) [193][150/293] lr: 5.000000e-05 eta: 0:34:27 time: 0.467366 data_time: 0.094552 memory: 15356 loss_kpt: 0.000529 acc_pose: 0.883812 loss: 0.000529 2022/10/19 18:41:03 - mmengine - INFO - Epoch(train) [193][200/293] lr: 5.000000e-05 eta: 0:34:07 time: 0.469603 data_time: 0.100152 memory: 15356 loss_kpt: 0.000537 acc_pose: 0.840544 loss: 0.000537 2022/10/19 18:41:26 - mmengine - INFO - Epoch(train) [193][250/293] lr: 5.000000e-05 eta: 0:33:47 time: 0.466834 data_time: 0.092637 memory: 15356 loss_kpt: 0.000534 acc_pose: 0.877704 loss: 0.000534 2022/10/19 18:41:46 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:42:10 - mmengine - INFO - Epoch(train) [194][50/293] lr: 5.000000e-05 eta: 0:33:08 time: 0.479059 data_time: 0.101438 memory: 15356 loss_kpt: 0.000528 acc_pose: 0.872482 loss: 0.000528 2022/10/19 18:42:33 - mmengine - INFO - Epoch(train) [194][100/293] lr: 5.000000e-05 eta: 0:32:48 time: 0.462614 data_time: 0.093239 memory: 15356 loss_kpt: 0.000530 acc_pose: 0.835377 loss: 0.000530 2022/10/19 18:42:57 - mmengine - INFO - Epoch(train) [194][150/293] lr: 5.000000e-05 eta: 0:32:29 time: 0.473760 data_time: 0.101818 memory: 15356 loss_kpt: 0.000538 acc_pose: 0.885895 loss: 0.000538 2022/10/19 18:43:20 - mmengine - INFO - Epoch(train) [194][200/293] lr: 5.000000e-05 eta: 0:32:09 time: 0.474990 data_time: 0.097889 memory: 15356 loss_kpt: 0.000533 acc_pose: 0.887073 loss: 0.000533 2022/10/19 18:43:44 - mmengine - INFO - Epoch(train) [194][250/293] lr: 5.000000e-05 eta: 0:31:49 time: 0.472102 data_time: 0.097954 memory: 15356 loss_kpt: 0.000536 acc_pose: 0.843275 loss: 0.000536 2022/10/19 18:44:04 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:44:29 - mmengine - INFO - Epoch(train) [195][50/293] lr: 5.000000e-05 eta: 0:31:10 time: 0.496555 data_time: 0.102210 memory: 15356 loss_kpt: 0.000542 acc_pose: 0.841863 loss: 0.000542 2022/10/19 18:44:53 - mmengine - INFO - Epoch(train) [195][100/293] lr: 5.000000e-05 eta: 0:30:50 time: 0.471966 data_time: 0.097471 memory: 15356 loss_kpt: 0.000528 acc_pose: 0.850243 loss: 0.000528 2022/10/19 18:45:16 - mmengine - INFO - Epoch(train) [195][150/293] lr: 5.000000e-05 eta: 0:30:30 time: 0.468734 data_time: 0.103771 memory: 15356 loss_kpt: 0.000541 acc_pose: 0.861757 loss: 0.000541 2022/10/19 18:45:20 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:45:39 - mmengine - INFO - Epoch(train) [195][200/293] lr: 5.000000e-05 eta: 0:30:10 time: 0.457639 data_time: 0.102699 memory: 15356 loss_kpt: 0.000529 acc_pose: 0.818285 loss: 0.000529 2022/10/19 18:46:03 - mmengine - INFO - Epoch(train) [195][250/293] lr: 5.000000e-05 eta: 0:29:51 time: 0.477588 data_time: 0.099532 memory: 15356 loss_kpt: 0.000535 acc_pose: 0.857047 loss: 0.000535 2022/10/19 18:46:23 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:46:47 - mmengine - INFO - Epoch(train) [196][50/293] lr: 5.000000e-05 eta: 0:29:12 time: 0.485573 data_time: 0.115048 memory: 15356 loss_kpt: 0.000547 acc_pose: 0.848512 loss: 0.000547 2022/10/19 18:47:10 - mmengine - INFO - Epoch(train) [196][100/293] lr: 5.000000e-05 eta: 0:28:52 time: 0.468118 data_time: 0.095187 memory: 15356 loss_kpt: 0.000530 acc_pose: 0.850179 loss: 0.000530 2022/10/19 18:47:34 - mmengine - INFO - Epoch(train) [196][150/293] lr: 5.000000e-05 eta: 0:28:32 time: 0.477402 data_time: 0.101041 memory: 15356 loss_kpt: 0.000526 acc_pose: 0.886757 loss: 0.000526 2022/10/19 18:47:58 - mmengine - INFO - Epoch(train) [196][200/293] lr: 5.000000e-05 eta: 0:28:12 time: 0.467521 data_time: 0.107484 memory: 15356 loss_kpt: 0.000548 acc_pose: 0.837422 loss: 0.000548 2022/10/19 18:48:21 - mmengine - INFO - Epoch(train) [196][250/293] lr: 5.000000e-05 eta: 0:27:52 time: 0.475697 data_time: 0.092497 memory: 15356 loss_kpt: 0.000519 acc_pose: 0.902464 loss: 0.000519 2022/10/19 18:48:41 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:49:05 - mmengine - INFO - Epoch(train) [197][50/293] lr: 5.000000e-05 eta: 0:27:14 time: 0.477607 data_time: 0.104453 memory: 15356 loss_kpt: 0.000526 acc_pose: 0.833725 loss: 0.000526 2022/10/19 18:49:29 - mmengine - INFO - Epoch(train) [197][100/293] lr: 5.000000e-05 eta: 0:26:54 time: 0.474381 data_time: 0.104758 memory: 15356 loss_kpt: 0.000528 acc_pose: 0.844852 loss: 0.000528 2022/10/19 18:49:52 - mmengine - INFO - Epoch(train) [197][150/293] lr: 5.000000e-05 eta: 0:26:34 time: 0.473181 data_time: 0.096541 memory: 15356 loss_kpt: 0.000538 acc_pose: 0.835934 loss: 0.000538 2022/10/19 18:50:16 - mmengine - INFO - Epoch(train) [197][200/293] lr: 5.000000e-05 eta: 0:26:14 time: 0.466962 data_time: 0.098922 memory: 15356 loss_kpt: 0.000546 acc_pose: 0.839051 loss: 0.000546 2022/10/19 18:50:39 - mmengine - INFO - Epoch(train) [197][250/293] lr: 5.000000e-05 eta: 0:25:54 time: 0.457431 data_time: 0.096347 memory: 15356 loss_kpt: 0.000536 acc_pose: 0.873090 loss: 0.000536 2022/10/19 18:50:58 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:51:23 - mmengine - INFO - Epoch(train) [198][50/293] lr: 5.000000e-05 eta: 0:25:16 time: 0.492173 data_time: 0.111296 memory: 15356 loss_kpt: 0.000532 acc_pose: 0.869557 loss: 0.000532 2022/10/19 18:51:46 - mmengine - INFO - Epoch(train) [198][100/293] lr: 5.000000e-05 eta: 0:24:56 time: 0.466507 data_time: 0.098033 memory: 15356 loss_kpt: 0.000540 acc_pose: 0.889075 loss: 0.000540 2022/10/19 18:52:10 - mmengine - INFO - Epoch(train) [198][150/293] lr: 5.000000e-05 eta: 0:24:36 time: 0.473529 data_time: 0.093063 memory: 15356 loss_kpt: 0.000528 acc_pose: 0.903168 loss: 0.000528 2022/10/19 18:52:34 - mmengine - INFO - Epoch(train) [198][200/293] lr: 5.000000e-05 eta: 0:24:16 time: 0.467308 data_time: 0.095933 memory: 15356 loss_kpt: 0.000529 acc_pose: 0.876396 loss: 0.000529 2022/10/19 18:52:57 - mmengine - INFO - Epoch(train) [198][250/293] lr: 5.000000e-05 eta: 0:23:56 time: 0.463834 data_time: 0.095353 memory: 15356 loss_kpt: 0.000530 acc_pose: 0.894406 loss: 0.000530 2022/10/19 18:53:10 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:53:16 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:53:41 - mmengine - INFO - Epoch(train) [199][50/293] lr: 5.000000e-05 eta: 0:23:17 time: 0.482601 data_time: 0.113706 memory: 15356 loss_kpt: 0.000523 acc_pose: 0.854808 loss: 0.000523 2022/10/19 18:54:04 - mmengine - INFO - Epoch(train) [199][100/293] lr: 5.000000e-05 eta: 0:22:58 time: 0.468444 data_time: 0.106535 memory: 15356 loss_kpt: 0.000519 acc_pose: 0.891072 loss: 0.000519 2022/10/19 18:54:28 - mmengine - INFO - Epoch(train) [199][150/293] lr: 5.000000e-05 eta: 0:22:38 time: 0.470857 data_time: 0.097732 memory: 15356 loss_kpt: 0.000532 acc_pose: 0.826883 loss: 0.000532 2022/10/19 18:54:51 - mmengine - INFO - Epoch(train) [199][200/293] lr: 5.000000e-05 eta: 0:22:18 time: 0.477625 data_time: 0.099057 memory: 15356 loss_kpt: 0.000520 acc_pose: 0.881112 loss: 0.000520 2022/10/19 18:55:15 - mmengine - INFO - Epoch(train) [199][250/293] lr: 5.000000e-05 eta: 0:21:58 time: 0.468386 data_time: 0.092779 memory: 15356 loss_kpt: 0.000532 acc_pose: 0.883665 loss: 0.000532 2022/10/19 18:55:34 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:55:59 - mmengine - INFO - Epoch(train) [200][50/293] lr: 5.000000e-05 eta: 0:21:19 time: 0.488130 data_time: 0.114378 memory: 15356 loss_kpt: 0.000522 acc_pose: 0.817581 loss: 0.000522 2022/10/19 18:56:23 - mmengine - INFO - Epoch(train) [200][100/293] lr: 5.000000e-05 eta: 0:20:59 time: 0.480433 data_time: 0.104470 memory: 15356 loss_kpt: 0.000533 acc_pose: 0.864490 loss: 0.000533 2022/10/19 18:56:47 - mmengine - INFO - Epoch(train) [200][150/293] lr: 5.000000e-05 eta: 0:20:39 time: 0.474188 data_time: 0.100530 memory: 15356 loss_kpt: 0.000542 acc_pose: 0.833295 loss: 0.000542 2022/10/19 18:57:10 - mmengine - INFO - Epoch(train) [200][200/293] lr: 5.000000e-05 eta: 0:20:19 time: 0.469514 data_time: 0.098363 memory: 15356 loss_kpt: 0.000524 acc_pose: 0.873765 loss: 0.000524 2022/10/19 18:57:33 - mmengine - INFO - Epoch(train) [200][250/293] lr: 5.000000e-05 eta: 0:19:59 time: 0.468883 data_time: 0.102822 memory: 15356 loss_kpt: 0.000535 acc_pose: 0.878140 loss: 0.000535 2022/10/19 18:57:54 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 18:57:54 - mmengine - INFO - Saving checkpoint at 200 epochs 2022/10/19 18:58:04 - mmengine - INFO - Epoch(val) [200][50/407] eta: 0:00:48 time: 0.136041 data_time: 0.048313 memory: 15356 2022/10/19 18:58:10 - mmengine - INFO - Epoch(val) [200][100/407] eta: 0:00:40 time: 0.131948 data_time: 0.043695 memory: 1465 2022/10/19 18:58:17 - mmengine - INFO - Epoch(val) [200][150/407] eta: 0:00:35 time: 0.137416 data_time: 0.045814 memory: 1465 2022/10/19 18:58:23 - mmengine - INFO - Epoch(val) [200][200/407] eta: 0:00:25 time: 0.121679 data_time: 0.034929 memory: 1465 2022/10/19 18:58:30 - mmengine - INFO - Epoch(val) [200][250/407] eta: 0:00:21 time: 0.134387 data_time: 0.047082 memory: 1465 2022/10/19 18:58:37 - mmengine - INFO - Epoch(val) [200][300/407] eta: 0:00:14 time: 0.138191 data_time: 0.051940 memory: 1465 2022/10/19 18:58:43 - mmengine - INFO - Epoch(val) [200][350/407] eta: 0:00:07 time: 0.125111 data_time: 0.038028 memory: 1465 2022/10/19 18:58:49 - mmengine - INFO - Epoch(val) [200][400/407] eta: 0:00:00 time: 0.127446 data_time: 0.043684 memory: 1465 2022/10/19 18:59:26 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 18:59:40 - mmengine - INFO - Epoch(val) [200][407/407] coco/AP: 0.729889 coco/AP .5: 0.898607 coco/AP .75: 0.798652 coco/AP (M): 0.684366 coco/AP (L): 0.806134 coco/AR: 0.781124 coco/AR .5: 0.934824 coco/AR .75: 0.844144 coco/AR (M): 0.730948 coco/AR (L): 0.852397 2022/10/19 18:59:40 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_384/best_coco/AP_epoch_180.pth is removed 2022/10/19 18:59:42 - mmengine - INFO - The best checkpoint with 0.7299 coco/AP at 200 epoch is saved to best_coco/AP_epoch_200.pth. 2022/10/19 19:00:06 - mmengine - INFO - Epoch(train) [201][50/293] lr: 5.000000e-06 eta: 0:19:21 time: 0.479906 data_time: 0.106729 memory: 15356 loss_kpt: 0.000538 acc_pose: 0.874529 loss: 0.000538 2022/10/19 19:00:30 - mmengine - INFO - Epoch(train) [201][100/293] lr: 5.000000e-06 eta: 0:19:01 time: 0.466218 data_time: 0.099693 memory: 15356 loss_kpt: 0.000520 acc_pose: 0.832787 loss: 0.000520 2022/10/19 19:00:54 - mmengine - INFO - Epoch(train) [201][150/293] lr: 5.000000e-06 eta: 0:18:41 time: 0.484005 data_time: 0.105464 memory: 15356 loss_kpt: 0.000527 acc_pose: 0.826806 loss: 0.000527 2022/10/19 19:01:17 - mmengine - INFO - Epoch(train) [201][200/293] lr: 5.000000e-06 eta: 0:18:21 time: 0.468699 data_time: 0.092331 memory: 15356 loss_kpt: 0.000545 acc_pose: 0.826614 loss: 0.000545 2022/10/19 19:01:41 - mmengine - INFO - Epoch(train) [201][250/293] lr: 5.000000e-06 eta: 0:18:01 time: 0.471435 data_time: 0.095860 memory: 15356 loss_kpt: 0.000543 acc_pose: 0.830569 loss: 0.000543 2022/10/19 19:02:01 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 19:02:26 - mmengine - INFO - Epoch(train) [202][50/293] lr: 5.000000e-06 eta: 0:17:23 time: 0.488790 data_time: 0.114137 memory: 15356 loss_kpt: 0.000525 acc_pose: 0.859853 loss: 0.000525 2022/10/19 19:02:49 - mmengine - INFO - Epoch(train) [202][100/293] lr: 5.000000e-06 eta: 0:17:03 time: 0.465926 data_time: 0.100994 memory: 15356 loss_kpt: 0.000533 acc_pose: 0.832310 loss: 0.000533 2022/10/19 19:02:52 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 19:03:12 - mmengine - INFO - Epoch(train) [202][150/293] lr: 5.000000e-06 eta: 0:16:43 time: 0.467771 data_time: 0.100153 memory: 15356 loss_kpt: 0.000522 acc_pose: 0.857213 loss: 0.000522 2022/10/19 19:03:36 - mmengine - INFO - Epoch(train) [202][200/293] lr: 5.000000e-06 eta: 0:16:23 time: 0.469799 data_time: 0.099102 memory: 15356 loss_kpt: 0.000527 acc_pose: 0.825404 loss: 0.000527 2022/10/19 19:03:59 - mmengine - INFO - Epoch(train) [202][250/293] lr: 5.000000e-06 eta: 0:16:03 time: 0.472958 data_time: 0.098244 memory: 15356 loss_kpt: 0.000545 acc_pose: 0.833547 loss: 0.000545 2022/10/19 19:04:19 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 19:04:44 - mmengine - INFO - Epoch(train) [203][50/293] lr: 5.000000e-06 eta: 0:15:25 time: 0.495906 data_time: 0.114024 memory: 15356 loss_kpt: 0.000541 acc_pose: 0.876796 loss: 0.000541 2022/10/19 19:05:08 - mmengine - INFO - Epoch(train) [203][100/293] lr: 5.000000e-06 eta: 0:15:05 time: 0.474297 data_time: 0.106720 memory: 15356 loss_kpt: 0.000529 acc_pose: 0.875940 loss: 0.000529 2022/10/19 19:05:31 - mmengine - INFO - Epoch(train) [203][150/293] lr: 5.000000e-06 eta: 0:14:45 time: 0.469225 data_time: 0.101854 memory: 15356 loss_kpt: 0.000533 acc_pose: 0.905697 loss: 0.000533 2022/10/19 19:05:54 - mmengine - INFO - Epoch(train) [203][200/293] lr: 5.000000e-06 eta: 0:14:25 time: 0.467226 data_time: 0.094067 memory: 15356 loss_kpt: 0.000538 acc_pose: 0.861439 loss: 0.000538 2022/10/19 19:06:18 - mmengine - INFO - Epoch(train) [203][250/293] lr: 5.000000e-06 eta: 0:14:05 time: 0.473035 data_time: 0.107356 memory: 15356 loss_kpt: 0.000523 acc_pose: 0.824520 loss: 0.000523 2022/10/19 19:06:38 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 19:07:02 - mmengine - INFO - Epoch(train) [204][50/293] lr: 5.000000e-06 eta: 0:13:27 time: 0.479026 data_time: 0.109158 memory: 15356 loss_kpt: 0.000530 acc_pose: 0.856387 loss: 0.000530 2022/10/19 19:07:26 - mmengine - INFO - Epoch(train) [204][100/293] lr: 5.000000e-06 eta: 0:13:07 time: 0.470535 data_time: 0.101469 memory: 15356 loss_kpt: 0.000522 acc_pose: 0.852148 loss: 0.000522 2022/10/19 19:07:50 - mmengine - INFO - Epoch(train) [204][150/293] lr: 5.000000e-06 eta: 0:12:47 time: 0.492226 data_time: 0.110800 memory: 15356 loss_kpt: 0.000534 acc_pose: 0.862294 loss: 0.000534 2022/10/19 19:08:14 - mmengine - INFO - Epoch(train) [204][200/293] lr: 5.000000e-06 eta: 0:12:26 time: 0.467746 data_time: 0.097005 memory: 15356 loss_kpt: 0.000519 acc_pose: 0.864460 loss: 0.000519 2022/10/19 19:08:38 - mmengine - INFO - Epoch(train) [204][250/293] lr: 5.000000e-06 eta: 0:12:06 time: 0.477456 data_time: 0.103312 memory: 15356 loss_kpt: 0.000518 acc_pose: 0.885490 loss: 0.000518 2022/10/19 19:08:57 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 19:09:22 - mmengine - INFO - Epoch(train) [205][50/293] lr: 5.000000e-06 eta: 0:11:29 time: 0.487643 data_time: 0.101542 memory: 15356 loss_kpt: 0.000525 acc_pose: 0.879135 loss: 0.000525 2022/10/19 19:09:45 - mmengine - INFO - Epoch(train) [205][100/293] lr: 5.000000e-06 eta: 0:11:08 time: 0.466792 data_time: 0.092186 memory: 15356 loss_kpt: 0.000528 acc_pose: 0.890507 loss: 0.000528 2022/10/19 19:10:09 - mmengine - INFO - Epoch(train) [205][150/293] lr: 5.000000e-06 eta: 0:10:48 time: 0.483057 data_time: 0.103768 memory: 15356 loss_kpt: 0.000526 acc_pose: 0.857979 loss: 0.000526 2022/10/19 19:10:33 - mmengine - INFO - Epoch(train) [205][200/293] lr: 5.000000e-06 eta: 0:10:28 time: 0.471106 data_time: 0.097704 memory: 15356 loss_kpt: 0.000536 acc_pose: 0.880908 loss: 0.000536 2022/10/19 19:10:46 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 19:10:56 - mmengine - INFO - Epoch(train) [205][250/293] lr: 5.000000e-06 eta: 0:10:08 time: 0.468267 data_time: 0.093304 memory: 15356 loss_kpt: 0.000522 acc_pose: 0.865337 loss: 0.000522 2022/10/19 19:11:16 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 19:11:40 - mmengine - INFO - Epoch(train) [206][50/293] lr: 5.000000e-06 eta: 0:09:30 time: 0.488225 data_time: 0.110661 memory: 15356 loss_kpt: 0.000537 acc_pose: 0.851615 loss: 0.000537 2022/10/19 19:12:04 - mmengine - INFO - Epoch(train) [206][100/293] lr: 5.000000e-06 eta: 0:09:10 time: 0.481300 data_time: 0.098489 memory: 15356 loss_kpt: 0.000537 acc_pose: 0.866748 loss: 0.000537 2022/10/19 19:12:28 - mmengine - INFO - Epoch(train) [206][150/293] lr: 5.000000e-06 eta: 0:08:50 time: 0.465515 data_time: 0.097751 memory: 15356 loss_kpt: 0.000543 acc_pose: 0.819502 loss: 0.000543 2022/10/19 19:12:51 - mmengine - INFO - Epoch(train) [206][200/293] lr: 5.000000e-06 eta: 0:08:30 time: 0.468435 data_time: 0.104298 memory: 15356 loss_kpt: 0.000528 acc_pose: 0.877952 loss: 0.000528 2022/10/19 19:13:15 - mmengine - INFO - Epoch(train) [206][250/293] lr: 5.000000e-06 eta: 0:08:10 time: 0.471500 data_time: 0.099080 memory: 15356 loss_kpt: 0.000531 acc_pose: 0.877388 loss: 0.000531 2022/10/19 19:13:35 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 19:14:00 - mmengine - INFO - Epoch(train) [207][50/293] lr: 5.000000e-06 eta: 0:07:32 time: 0.487778 data_time: 0.113127 memory: 15356 loss_kpt: 0.000533 acc_pose: 0.857597 loss: 0.000533 2022/10/19 19:14:24 - mmengine - INFO - Epoch(train) [207][100/293] lr: 5.000000e-06 eta: 0:07:12 time: 0.482603 data_time: 0.108478 memory: 15356 loss_kpt: 0.000524 acc_pose: 0.875590 loss: 0.000524 2022/10/19 19:14:47 - mmengine - INFO - Epoch(train) [207][150/293] lr: 5.000000e-06 eta: 0:06:52 time: 0.466334 data_time: 0.092192 memory: 15356 loss_kpt: 0.000533 acc_pose: 0.866882 loss: 0.000533 2022/10/19 19:15:11 - mmengine - INFO - Epoch(train) [207][200/293] lr: 5.000000e-06 eta: 0:06:32 time: 0.468719 data_time: 0.088346 memory: 15356 loss_kpt: 0.000523 acc_pose: 0.868429 loss: 0.000523 2022/10/19 19:15:34 - mmengine - INFO - Epoch(train) [207][250/293] lr: 5.000000e-06 eta: 0:06:12 time: 0.465095 data_time: 0.097035 memory: 15356 loss_kpt: 0.000532 acc_pose: 0.882088 loss: 0.000532 2022/10/19 19:15:54 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 19:16:19 - mmengine - INFO - Epoch(train) [208][50/293] lr: 5.000000e-06 eta: 0:05:34 time: 0.498925 data_time: 0.107439 memory: 15356 loss_kpt: 0.000526 acc_pose: 0.852262 loss: 0.000526 2022/10/19 19:16:42 - mmengine - INFO - Epoch(train) [208][100/293] lr: 5.000000e-06 eta: 0:05:14 time: 0.462960 data_time: 0.097302 memory: 15356 loss_kpt: 0.000522 acc_pose: 0.895792 loss: 0.000522 2022/10/19 19:17:05 - mmengine - INFO - Epoch(train) [208][150/293] lr: 5.000000e-06 eta: 0:04:54 time: 0.471081 data_time: 0.096759 memory: 15356 loss_kpt: 0.000530 acc_pose: 0.890780 loss: 0.000530 2022/10/19 19:17:29 - mmengine - INFO - Epoch(train) [208][200/293] lr: 5.000000e-06 eta: 0:04:34 time: 0.476458 data_time: 0.100857 memory: 15356 loss_kpt: 0.000532 acc_pose: 0.834108 loss: 0.000532 2022/10/19 19:17:53 - mmengine - INFO - Epoch(train) [208][250/293] lr: 5.000000e-06 eta: 0:04:13 time: 0.473989 data_time: 0.098952 memory: 15356 loss_kpt: 0.000522 acc_pose: 0.889570 loss: 0.000522 2022/10/19 19:18:13 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 19:18:37 - mmengine - INFO - Epoch(train) [209][50/293] lr: 5.000000e-06 eta: 0:03:36 time: 0.479306 data_time: 0.113156 memory: 15356 loss_kpt: 0.000526 acc_pose: 0.866423 loss: 0.000526 2022/10/19 19:18:40 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 19:19:00 - mmengine - INFO - Epoch(train) [209][100/293] lr: 5.000000e-06 eta: 0:03:16 time: 0.469225 data_time: 0.097584 memory: 15356 loss_kpt: 0.000530 acc_pose: 0.880612 loss: 0.000530 2022/10/19 19:19:24 - mmengine - INFO - Epoch(train) [209][150/293] lr: 5.000000e-06 eta: 0:02:55 time: 0.468846 data_time: 0.104090 memory: 15356 loss_kpt: 0.000526 acc_pose: 0.894969 loss: 0.000526 2022/10/19 19:19:48 - mmengine - INFO - Epoch(train) [209][200/293] lr: 5.000000e-06 eta: 0:02:35 time: 0.477451 data_time: 0.099618 memory: 15356 loss_kpt: 0.000528 acc_pose: 0.847587 loss: 0.000528 2022/10/19 19:20:11 - mmengine - INFO - Epoch(train) [209][250/293] lr: 5.000000e-06 eta: 0:02:15 time: 0.469342 data_time: 0.100895 memory: 15356 loss_kpt: 0.000526 acc_pose: 0.866042 loss: 0.000526 2022/10/19 19:20:31 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 19:20:56 - mmengine - INFO - Epoch(train) [210][50/293] lr: 5.000000e-06 eta: 0:01:38 time: 0.487888 data_time: 0.107626 memory: 15356 loss_kpt: 0.000528 acc_pose: 0.885112 loss: 0.000528 2022/10/19 19:21:20 - mmengine - INFO - Epoch(train) [210][100/293] lr: 5.000000e-06 eta: 0:01:17 time: 0.476028 data_time: 0.103147 memory: 15356 loss_kpt: 0.000523 acc_pose: 0.802083 loss: 0.000523 2022/10/19 19:21:43 - mmengine - INFO - Epoch(train) [210][150/293] lr: 5.000000e-06 eta: 0:00:57 time: 0.471016 data_time: 0.091108 memory: 15356 loss_kpt: 0.000534 acc_pose: 0.877355 loss: 0.000534 2022/10/19 19:22:07 - mmengine - INFO - Epoch(train) [210][200/293] lr: 5.000000e-06 eta: 0:00:37 time: 0.466711 data_time: 0.095309 memory: 15356 loss_kpt: 0.000528 acc_pose: 0.873941 loss: 0.000528 2022/10/19 19:22:31 - mmengine - INFO - Epoch(train) [210][250/293] lr: 5.000000e-06 eta: 0:00:17 time: 0.479649 data_time: 0.110132 memory: 15356 loss_kpt: 0.000523 acc_pose: 0.871536 loss: 0.000523 2022/10/19 19:22:51 - mmengine - INFO - Exp name: td-hm_resnetv1d50_8xb64-210e_coco-384x288_20221019_104158 2022/10/19 19:22:51 - mmengine - INFO - Saving checkpoint at 210 epochs 2022/10/19 19:23:00 - mmengine - INFO - Epoch(val) [210][50/407] eta: 0:00:47 time: 0.133090 data_time: 0.043577 memory: 15356 2022/10/19 19:23:07 - mmengine - INFO - Epoch(val) [210][100/407] eta: 0:00:42 time: 0.137849 data_time: 0.049702 memory: 1465 2022/10/19 19:23:14 - mmengine - INFO - Epoch(val) [210][150/407] eta: 0:00:33 time: 0.130587 data_time: 0.043326 memory: 1465 2022/10/19 19:23:20 - mmengine - INFO - Epoch(val) [210][200/407] eta: 0:00:26 time: 0.130408 data_time: 0.041856 memory: 1465 2022/10/19 19:23:27 - mmengine - INFO - Epoch(val) [210][250/407] eta: 0:00:20 time: 0.129362 data_time: 0.042114 memory: 1465 2022/10/19 19:23:33 - mmengine - INFO - Epoch(val) [210][300/407] eta: 0:00:13 time: 0.125353 data_time: 0.038917 memory: 1465 2022/10/19 19:23:39 - mmengine - INFO - Epoch(val) [210][350/407] eta: 0:00:07 time: 0.128306 data_time: 0.041712 memory: 1465 2022/10/19 19:23:45 - mmengine - INFO - Epoch(val) [210][400/407] eta: 0:00:00 time: 0.117767 data_time: 0.032034 memory: 1465 2022/10/19 19:24:23 - mmengine - INFO - Evaluating CocoMetric... 2022/10/19 19:24:37 - mmengine - INFO - Epoch(val) [210][407/407] coco/AP: 0.730545 coco/AP .5: 0.898703 coco/AP .75: 0.799675 coco/AP (M): 0.684404 coco/AP (L): 0.806209 coco/AR: 0.781958 coco/AR .5: 0.934981 coco/AR .75: 0.844773 coco/AR (M): 0.731849 coco/AR (L): 0.853400 2022/10/19 19:24:37 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221019/resnetv1d50_384/best_coco/AP_epoch_200.pth is removed 2022/10/19 19:24:38 - mmengine - INFO - The best checkpoint with 0.7305 coco/AP at 210 epoch is saved to best_coco/AP_epoch_210.pth.