2022/10/21 09:58:38 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0] CUDA available: True numpy_random_seed: 1843972684 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 ------------------------------------------------------------ 2022/10/21 09:58:39 - mmengine - INFO - Config: default_scope = 'mmpose' default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict( type='CheckpointHook', interval=10, max_keep_ckpts=1, save_best='coco/AP', rule='greater'), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='PoseVisualizationHook', enable=False)) custom_hooks = [dict(type='SyncBuffersHook')] env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='PoseLocalVisualizer', vis_backends=[dict(type='LocalVisBackend')], name='visualizer') log_processor = dict( type='LogProcessor', window_size=50, by_epoch=True, num_digits=6) log_level = 'INFO' load_from = None resume = False file_client_args = dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' })) train_cfg = dict(by_epoch=True, max_epochs=210, val_interval=10) val_cfg = dict() test_cfg = dict() optim_wrapper = dict(optimizer=dict(type='Adam', lr=0.0005)) param_scheduler = [ dict( type='LinearLR', begin=0, end=500, start_factor=0.001, by_epoch=False), dict( type='MultiStepLR', begin=0, end=210, milestones=[170, 200], gamma=0.1, by_epoch=True) ] auto_scale_lr = dict(base_batch_size=384) 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=152, init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnet152_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=48, 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/20221021/resnetv1d152_384/' 2022/10/21 09:59:20 - 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. 2022/10/21 09:59:20 - 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. 2022/10/21 09:59:20 - 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. 2022/10/21 09:59:20 - 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. 2022/10/21 09:59:20 - 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. 2022/10/21 09:59:20 - 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. 2022/10/21 09:59:20 - 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. 2022/10/21 09:59:20 - 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. 2022/10/21 09:59:24 - 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. 2022/10/21 09:59:26 - 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. 2022/10/21 09:59:28 - 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. 2022/10/21 09:59:28 - 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://resnet152_v1d backbone.stem.0.bn.weight - torch.Size([32]): PretrainedInit: load from mmcls://resnet152_v1d backbone.stem.0.bn.bias - torch.Size([32]): PretrainedInit: load from mmcls://resnet152_v1d backbone.stem.1.conv.weight - torch.Size([32, 32, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.stem.1.bn.weight - torch.Size([32]): PretrainedInit: load from mmcls://resnet152_v1d backbone.stem.1.bn.bias - torch.Size([32]): PretrainedInit: load from mmcls://resnet152_v1d backbone.stem.2.conv.weight - torch.Size([64, 32, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.stem.2.bn.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet152_v1d backbone.stem.2.bn.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.0.bn1.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.0.bn1.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.0.bn2.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.0.bn2.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.0.bn3.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.0.bn3.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.0.downsample.1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.0.downsample.1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.1.bn1.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.1.bn1.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.1.bn2.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.1.bn2.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.1.bn3.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.1.bn3.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.2.bn1.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.2.bn1.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.2.bn2.weight - torch.Size([64]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.2.bn2.bias - torch.Size([64]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.2.bn3.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer1.2.bn3.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.0.bn1.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.0.bn1.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.0.bn2.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.0.bn2.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.0.bn3.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.0.bn3.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.0.downsample.1.weight - torch.Size([512, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.0.downsample.2.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.0.downsample.2.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.1.bn1.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.1.bn1.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.1.bn2.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.1.bn2.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.1.bn3.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.1.bn3.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.2.bn1.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.2.bn1.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.2.bn2.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.2.bn2.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.2.bn3.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.2.bn3.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.3.bn1.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.3.bn1.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.3.bn2.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.3.bn2.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.3.bn3.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.3.bn3.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.4.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.4.bn1.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.4.bn1.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.4.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.4.bn2.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.4.bn2.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.4.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.4.bn3.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.4.bn3.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.5.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.5.bn1.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.5.bn1.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.5.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.5.bn2.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.5.bn2.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.5.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.5.bn3.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.5.bn3.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.6.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.6.bn1.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.6.bn1.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.6.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.6.bn2.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.6.bn2.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.6.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.6.bn3.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.6.bn3.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.7.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.7.bn1.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.7.bn1.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.7.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.7.bn2.weight - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.7.bn2.bias - torch.Size([128]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.7.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.7.bn3.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer2.7.bn3.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.0.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.0.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.0.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.0.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.0.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.0.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.0.downsample.1.weight - torch.Size([1024, 512, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.0.downsample.2.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.0.downsample.2.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.1.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.1.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.1.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.1.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.1.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.1.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.2.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.2.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.2.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.2.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.2.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.2.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.3.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.3.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.3.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.3.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.3.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.3.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.4.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.4.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.4.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.4.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.4.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.4.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.5.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.5.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.5.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.5.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.5.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.5.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.6.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.6.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.6.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.6.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.6.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.6.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.6.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.6.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.6.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.7.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.7.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.7.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.7.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.7.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.7.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.7.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.7.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.7.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.8.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.8.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.8.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.8.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.8.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.8.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.8.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.8.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.8.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.9.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.9.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.9.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.9.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.9.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.9.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.9.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.9.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.9.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.10.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.10.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.10.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.10.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.10.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.10.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.10.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.10.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.10.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.11.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.11.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.11.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.11.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.11.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.11.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.11.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.11.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.11.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.12.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.12.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.12.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.12.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.12.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.12.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.12.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.12.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.12.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.13.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.13.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.13.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.13.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.13.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.13.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.13.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.13.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.13.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.14.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.14.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.14.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.14.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.14.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.14.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.14.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.14.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.14.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.15.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.15.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.15.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.15.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.15.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.15.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.15.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.15.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.15.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.16.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.16.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.16.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.16.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.16.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.16.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.16.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.16.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.16.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.17.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.17.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.17.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.17.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.17.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.17.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.17.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.17.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.17.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.18.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.18.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.18.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.18.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.18.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.18.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.18.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.18.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.18.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.19.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.19.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.19.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.19.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.19.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.19.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.19.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.19.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.19.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.20.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.20.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.20.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.20.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.20.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.20.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.20.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.20.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.20.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.21.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.21.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.21.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.21.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.21.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.21.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.21.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.21.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.21.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.22.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.22.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.22.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.22.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.22.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.22.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.22.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.22.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.22.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.23.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.23.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.23.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.23.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.23.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.23.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.23.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.23.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.23.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.24.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.24.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.24.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.24.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.24.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.24.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.24.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.24.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.24.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.25.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.25.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.25.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.25.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.25.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.25.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.25.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.25.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.25.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.26.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.26.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.26.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.26.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.26.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.26.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.26.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.26.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.26.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.27.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.27.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.27.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.27.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.27.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.27.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.27.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.27.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.27.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.28.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.28.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.28.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.28.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.28.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.28.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.28.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.28.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.28.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.29.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.29.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.29.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.29.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.29.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.29.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.29.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.29.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.29.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.30.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.30.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.30.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.30.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.30.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.30.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.30.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.30.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.30.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.31.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.31.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.31.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.31.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.31.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.31.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.31.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.31.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.31.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.32.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.32.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.32.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.32.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.32.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.32.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.32.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.32.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.32.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.33.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.33.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.33.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.33.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.33.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.33.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.33.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.33.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.33.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.34.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.34.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.34.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.34.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.34.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.34.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.34.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.34.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.34.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.35.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.35.bn1.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.35.bn1.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.35.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.35.bn2.weight - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.35.bn2.bias - torch.Size([256]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.35.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.35.bn3.weight - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer3.35.bn3.bias - torch.Size([1024]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.0.bn1.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.0.bn1.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.0.bn2.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.0.bn2.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.0.bn3.weight - torch.Size([2048]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.0.bn3.bias - torch.Size([2048]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.0.downsample.1.weight - torch.Size([2048, 1024, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.0.downsample.2.weight - torch.Size([2048]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.0.downsample.2.bias - torch.Size([2048]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.1.bn1.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.1.bn1.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.1.bn2.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.1.bn2.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.1.bn3.weight - torch.Size([2048]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.1.bn3.bias - torch.Size([2048]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.2.bn1.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.2.bn1.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.2.bn2.weight - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.2.bn2.bias - torch.Size([512]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.2.bn3.weight - torch.Size([2048]): PretrainedInit: load from mmcls://resnet152_v1d backbone.layer4.2.bn3.bias - torch.Size([2048]): PretrainedInit: load from mmcls://resnet152_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 2022/10/21 09:59:28 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221021/resnetv1d152_384 by HardDiskBackend. 2022/10/21 10:00:00 - mmengine - INFO - Epoch(train) [1][50/391] lr: 4.954910e-05 eta: 14:27:41 time: 0.634426 data_time: 0.119328 memory: 21657 loss_kpt: 0.002098 acc_pose: 0.247381 loss: 0.002098 2022/10/21 10:00:24 - mmengine - INFO - Epoch(train) [1][100/391] lr: 9.959920e-05 eta: 12:38:32 time: 0.475499 data_time: 0.038364 memory: 21657 loss_kpt: 0.001647 acc_pose: 0.467264 loss: 0.001647 2022/10/21 10:00:48 - mmengine - INFO - Epoch(train) [1][150/391] lr: 1.496493e-04 eta: 12:02:41 time: 0.477229 data_time: 0.038491 memory: 21657 loss_kpt: 0.001347 acc_pose: 0.534391 loss: 0.001347 2022/10/21 10:01:11 - mmengine - INFO - Epoch(train) [1][200/391] lr: 1.996994e-04 eta: 11:43:16 time: 0.473486 data_time: 0.038745 memory: 21657 loss_kpt: 0.001235 acc_pose: 0.678582 loss: 0.001235 2022/10/21 10:01:35 - mmengine - INFO - Epoch(train) [1][250/391] lr: 2.497495e-04 eta: 11:33:34 time: 0.481147 data_time: 0.039672 memory: 21657 loss_kpt: 0.001180 acc_pose: 0.555043 loss: 0.001180 2022/10/21 10:01:59 - mmengine - INFO - Epoch(train) [1][300/391] lr: 2.997996e-04 eta: 11:26:15 time: 0.478044 data_time: 0.040379 memory: 21657 loss_kpt: 0.001135 acc_pose: 0.587299 loss: 0.001135 2022/10/21 10:02:23 - mmengine - INFO - Epoch(train) [1][350/391] lr: 3.498497e-04 eta: 11:20:19 time: 0.475013 data_time: 0.039334 memory: 21657 loss_kpt: 0.001141 acc_pose: 0.666823 loss: 0.001141 2022/10/21 10:02:43 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:03:07 - mmengine - INFO - Epoch(train) [2][50/391] lr: 4.409409e-04 eta: 10:15:27 time: 0.493198 data_time: 0.053472 memory: 21657 loss_kpt: 0.001135 acc_pose: 0.699037 loss: 0.001135 2022/10/21 10:03:32 - mmengine - INFO - Epoch(train) [2][100/391] lr: 4.909910e-04 eta: 10:20:38 time: 0.492315 data_time: 0.040670 memory: 21657 loss_kpt: 0.001085 acc_pose: 0.607167 loss: 0.001085 2022/10/21 10:03:56 - mmengine - INFO - Epoch(train) [2][150/391] lr: 5.000000e-04 eta: 10:22:32 time: 0.474355 data_time: 0.041843 memory: 21657 loss_kpt: 0.001068 acc_pose: 0.677756 loss: 0.001068 2022/10/21 10:04:20 - mmengine - INFO - Epoch(train) [2][200/391] lr: 5.000000e-04 eta: 10:25:40 time: 0.488481 data_time: 0.045030 memory: 21657 loss_kpt: 0.001045 acc_pose: 0.740784 loss: 0.001045 2022/10/21 10:04:44 - mmengine - INFO - Epoch(train) [2][250/391] lr: 5.000000e-04 eta: 10:26:41 time: 0.473746 data_time: 0.039916 memory: 21657 loss_kpt: 0.001007 acc_pose: 0.702036 loss: 0.001007 2022/10/21 10:05:08 - mmengine - INFO - Epoch(train) [2][300/391] lr: 5.000000e-04 eta: 10:28:40 time: 0.485777 data_time: 0.039398 memory: 21657 loss_kpt: 0.001021 acc_pose: 0.739565 loss: 0.001021 2022/10/21 10:05:32 - mmengine - INFO - Epoch(train) [2][350/391] lr: 5.000000e-04 eta: 10:29:36 time: 0.477555 data_time: 0.038464 memory: 21657 loss_kpt: 0.001012 acc_pose: 0.715512 loss: 0.001012 2022/10/21 10:05:51 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:06:16 - mmengine - INFO - Epoch(train) [3][50/391] lr: 5.000000e-04 eta: 10:01:27 time: 0.507839 data_time: 0.050222 memory: 21657 loss_kpt: 0.000988 acc_pose: 0.637332 loss: 0.000988 2022/10/21 10:06:40 - mmengine - INFO - Epoch(train) [3][100/391] lr: 5.000000e-04 eta: 10:03:26 time: 0.474726 data_time: 0.037254 memory: 21657 loss_kpt: 0.000989 acc_pose: 0.684618 loss: 0.000989 2022/10/21 10:07:05 - mmengine - INFO - Epoch(train) [3][150/391] lr: 5.000000e-04 eta: 10:06:31 time: 0.493281 data_time: 0.038893 memory: 21657 loss_kpt: 0.000973 acc_pose: 0.710530 loss: 0.000973 2022/10/21 10:07:28 - mmengine - INFO - Epoch(train) [3][200/391] lr: 5.000000e-04 eta: 10:07:51 time: 0.473257 data_time: 0.039784 memory: 21657 loss_kpt: 0.000950 acc_pose: 0.665099 loss: 0.000950 2022/10/21 10:07:37 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:07:53 - mmengine - INFO - Epoch(train) [3][250/391] lr: 5.000000e-04 eta: 10:10:22 time: 0.493661 data_time: 0.037729 memory: 21657 loss_kpt: 0.000954 acc_pose: 0.752803 loss: 0.000954 2022/10/21 10:08:17 - mmengine - INFO - Epoch(train) [3][300/391] lr: 5.000000e-04 eta: 10:11:23 time: 0.474077 data_time: 0.037668 memory: 21657 loss_kpt: 0.000922 acc_pose: 0.660758 loss: 0.000922 2022/10/21 10:08:41 - mmengine - INFO - Epoch(train) [3][350/391] lr: 5.000000e-04 eta: 10:13:05 time: 0.487513 data_time: 0.040422 memory: 21657 loss_kpt: 0.000923 acc_pose: 0.599147 loss: 0.000923 2022/10/21 10:09:00 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:09:25 - mmengine - INFO - Epoch(train) [4][50/391] lr: 5.000000e-04 eta: 9:53:48 time: 0.489342 data_time: 0.052745 memory: 21657 loss_kpt: 0.000920 acc_pose: 0.705699 loss: 0.000920 2022/10/21 10:09:49 - mmengine - INFO - Epoch(train) [4][100/391] lr: 5.000000e-04 eta: 9:56:07 time: 0.491107 data_time: 0.036845 memory: 21657 loss_kpt: 0.000916 acc_pose: 0.577261 loss: 0.000916 2022/10/21 10:10:13 - mmengine - INFO - Epoch(train) [4][150/391] lr: 5.000000e-04 eta: 9:57:24 time: 0.474873 data_time: 0.037305 memory: 21657 loss_kpt: 0.000902 acc_pose: 0.722589 loss: 0.000902 2022/10/21 10:10:38 - mmengine - INFO - Epoch(train) [4][200/391] lr: 5.000000e-04 eta: 9:59:25 time: 0.492605 data_time: 0.040757 memory: 21657 loss_kpt: 0.000893 acc_pose: 0.707198 loss: 0.000893 2022/10/21 10:11:02 - mmengine - INFO - Epoch(train) [4][250/391] lr: 5.000000e-04 eta: 10:00:28 time: 0.475329 data_time: 0.036970 memory: 21657 loss_kpt: 0.000896 acc_pose: 0.758102 loss: 0.000896 2022/10/21 10:11:26 - mmengine - INFO - Epoch(train) [4][300/391] lr: 5.000000e-04 eta: 10:02:05 time: 0.490372 data_time: 0.041619 memory: 21657 loss_kpt: 0.000895 acc_pose: 0.726353 loss: 0.000895 2022/10/21 10:11:50 - mmengine - INFO - Epoch(train) [4][350/391] lr: 5.000000e-04 eta: 10:02:57 time: 0.476169 data_time: 0.036738 memory: 21657 loss_kpt: 0.000885 acc_pose: 0.712371 loss: 0.000885 2022/10/21 10:12:10 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:12:34 - mmengine - INFO - Epoch(train) [5][50/391] lr: 5.000000e-04 eta: 9:48:53 time: 0.494939 data_time: 0.049844 memory: 21657 loss_kpt: 0.000893 acc_pose: 0.748977 loss: 0.000893 2022/10/21 10:12:59 - mmengine - INFO - Epoch(train) [5][100/391] lr: 5.000000e-04 eta: 9:50:32 time: 0.488851 data_time: 0.041258 memory: 21657 loss_kpt: 0.000877 acc_pose: 0.758975 loss: 0.000877 2022/10/21 10:13:23 - mmengine - INFO - Epoch(train) [5][150/391] lr: 5.000000e-04 eta: 9:51:54 time: 0.484723 data_time: 0.036649 memory: 21657 loss_kpt: 0.000851 acc_pose: 0.751531 loss: 0.000851 2022/10/21 10:13:47 - mmengine - INFO - Epoch(train) [5][200/391] lr: 5.000000e-04 eta: 9:52:59 time: 0.479939 data_time: 0.036561 memory: 21657 loss_kpt: 0.000853 acc_pose: 0.669389 loss: 0.000853 2022/10/21 10:14:11 - mmengine - INFO - Epoch(train) [5][250/391] lr: 5.000000e-04 eta: 9:54:10 time: 0.485201 data_time: 0.038003 memory: 21657 loss_kpt: 0.000881 acc_pose: 0.764852 loss: 0.000881 2022/10/21 10:14:35 - mmengine - INFO - Epoch(train) [5][300/391] lr: 5.000000e-04 eta: 9:54:53 time: 0.473982 data_time: 0.034845 memory: 21657 loss_kpt: 0.000886 acc_pose: 0.739114 loss: 0.000886 2022/10/21 10:15:00 - mmengine - INFO - Epoch(train) [5][350/391] lr: 5.000000e-04 eta: 9:56:15 time: 0.494448 data_time: 0.036614 memory: 21657 loss_kpt: 0.000860 acc_pose: 0.712581 loss: 0.000860 2022/10/21 10:15:19 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:15:42 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:15:44 - mmengine - INFO - Epoch(train) [6][50/391] lr: 5.000000e-04 eta: 9:45:26 time: 0.507515 data_time: 0.051444 memory: 21657 loss_kpt: 0.000863 acc_pose: 0.686361 loss: 0.000863 2022/10/21 10:16:08 - mmengine - INFO - Epoch(train) [6][100/391] lr: 5.000000e-04 eta: 9:46:20 time: 0.477595 data_time: 0.038281 memory: 21657 loss_kpt: 0.000867 acc_pose: 0.718131 loss: 0.000867 2022/10/21 10:16:33 - mmengine - INFO - Epoch(train) [6][150/391] lr: 5.000000e-04 eta: 9:47:35 time: 0.490277 data_time: 0.042620 memory: 21657 loss_kpt: 0.000845 acc_pose: 0.704218 loss: 0.000845 2022/10/21 10:16:57 - mmengine - INFO - Epoch(train) [6][200/391] lr: 5.000000e-04 eta: 9:48:24 time: 0.478949 data_time: 0.036711 memory: 21657 loss_kpt: 0.000817 acc_pose: 0.714590 loss: 0.000817 2022/10/21 10:17:21 - mmengine - INFO - Epoch(train) [6][250/391] lr: 5.000000e-04 eta: 9:49:20 time: 0.484715 data_time: 0.037603 memory: 21657 loss_kpt: 0.000831 acc_pose: 0.698079 loss: 0.000831 2022/10/21 10:17:45 - mmengine - INFO - Epoch(train) [6][300/391] lr: 5.000000e-04 eta: 9:50:13 time: 0.485094 data_time: 0.043508 memory: 21657 loss_kpt: 0.000831 acc_pose: 0.689983 loss: 0.000831 2022/10/21 10:18:09 - mmengine - INFO - Epoch(train) [6][350/391] lr: 5.000000e-04 eta: 9:51:00 time: 0.483297 data_time: 0.037417 memory: 21657 loss_kpt: 0.000847 acc_pose: 0.738656 loss: 0.000847 2022/10/21 10:18:29 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:18:54 - mmengine - INFO - Epoch(train) [7][50/391] lr: 5.000000e-04 eta: 9:41:28 time: 0.489112 data_time: 0.048806 memory: 21657 loss_kpt: 0.000812 acc_pose: 0.728352 loss: 0.000812 2022/10/21 10:19:19 - mmengine - INFO - Epoch(train) [7][100/391] lr: 5.000000e-04 eta: 9:42:53 time: 0.503682 data_time: 0.042862 memory: 21657 loss_kpt: 0.000829 acc_pose: 0.678557 loss: 0.000829 2022/10/21 10:19:43 - mmengine - INFO - Epoch(train) [7][150/391] lr: 5.000000e-04 eta: 9:43:28 time: 0.474284 data_time: 0.038404 memory: 21657 loss_kpt: 0.000795 acc_pose: 0.749127 loss: 0.000795 2022/10/21 10:20:07 - mmengine - INFO - Epoch(train) [7][200/391] lr: 5.000000e-04 eta: 9:44:27 time: 0.491858 data_time: 0.036863 memory: 21657 loss_kpt: 0.000822 acc_pose: 0.783781 loss: 0.000822 2022/10/21 10:20:31 - mmengine - INFO - Epoch(train) [7][250/391] lr: 5.000000e-04 eta: 9:45:09 time: 0.482236 data_time: 0.038115 memory: 21657 loss_kpt: 0.000796 acc_pose: 0.778928 loss: 0.000796 2022/10/21 10:20:56 - mmengine - INFO - Epoch(train) [7][300/391] lr: 5.000000e-04 eta: 9:46:15 time: 0.500310 data_time: 0.041346 memory: 21657 loss_kpt: 0.000818 acc_pose: 0.771899 loss: 0.000818 2022/10/21 10:21:21 - mmengine - INFO - Epoch(train) [7][350/391] lr: 5.000000e-04 eta: 9:46:52 time: 0.482712 data_time: 0.040526 memory: 21657 loss_kpt: 0.000820 acc_pose: 0.722175 loss: 0.000820 2022/10/21 10:21:40 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:22:06 - mmengine - INFO - Epoch(train) [8][50/391] lr: 5.000000e-04 eta: 9:39:14 time: 0.513811 data_time: 0.059448 memory: 21657 loss_kpt: 0.000826 acc_pose: 0.762806 loss: 0.000826 2022/10/21 10:22:29 - mmengine - INFO - Epoch(train) [8][100/391] lr: 5.000000e-04 eta: 9:39:43 time: 0.474810 data_time: 0.036947 memory: 21657 loss_kpt: 0.000832 acc_pose: 0.749149 loss: 0.000832 2022/10/21 10:22:54 - mmengine - INFO - Epoch(train) [8][150/391] lr: 5.000000e-04 eta: 9:40:44 time: 0.498596 data_time: 0.041183 memory: 21657 loss_kpt: 0.000825 acc_pose: 0.833221 loss: 0.000825 2022/10/21 10:23:18 - mmengine - INFO - Epoch(train) [8][200/391] lr: 5.000000e-04 eta: 9:41:07 time: 0.473888 data_time: 0.037855 memory: 21657 loss_kpt: 0.000793 acc_pose: 0.756359 loss: 0.000793 2022/10/21 10:23:43 - mmengine - INFO - Epoch(train) [8][250/391] lr: 5.000000e-04 eta: 9:42:07 time: 0.501964 data_time: 0.041741 memory: 21657 loss_kpt: 0.000810 acc_pose: 0.731712 loss: 0.000810 2022/10/21 10:23:49 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:24:07 - mmengine - INFO - Epoch(train) [8][300/391] lr: 5.000000e-04 eta: 9:42:26 time: 0.472634 data_time: 0.038576 memory: 21657 loss_kpt: 0.000811 acc_pose: 0.677175 loss: 0.000811 2022/10/21 10:24:31 - mmengine - INFO - Epoch(train) [8][350/391] lr: 5.000000e-04 eta: 9:43:06 time: 0.491090 data_time: 0.037079 memory: 21657 loss_kpt: 0.000824 acc_pose: 0.747132 loss: 0.000824 2022/10/21 10:24:51 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:25:15 - mmengine - INFO - Epoch(train) [9][50/391] lr: 5.000000e-04 eta: 9:36:00 time: 0.494775 data_time: 0.048600 memory: 21657 loss_kpt: 0.000826 acc_pose: 0.754512 loss: 0.000826 2022/10/21 10:25:40 - mmengine - INFO - Epoch(train) [9][100/391] lr: 5.000000e-04 eta: 9:36:48 time: 0.494935 data_time: 0.036897 memory: 21657 loss_kpt: 0.000785 acc_pose: 0.774628 loss: 0.000785 2022/10/21 10:26:04 - mmengine - INFO - Epoch(train) [9][150/391] lr: 5.000000e-04 eta: 9:37:19 time: 0.483100 data_time: 0.037512 memory: 21657 loss_kpt: 0.000792 acc_pose: 0.705444 loss: 0.000792 2022/10/21 10:26:29 - mmengine - INFO - Epoch(train) [9][200/391] lr: 5.000000e-04 eta: 9:38:01 time: 0.493199 data_time: 0.042200 memory: 21657 loss_kpt: 0.000791 acc_pose: 0.720500 loss: 0.000791 2022/10/21 10:26:53 - mmengine - INFO - Epoch(train) [9][250/391] lr: 5.000000e-04 eta: 9:38:23 time: 0.478316 data_time: 0.035838 memory: 21657 loss_kpt: 0.000811 acc_pose: 0.765447 loss: 0.000811 2022/10/21 10:27:18 - mmengine - INFO - Epoch(train) [9][300/391] lr: 5.000000e-04 eta: 9:39:05 time: 0.496751 data_time: 0.038283 memory: 21657 loss_kpt: 0.000763 acc_pose: 0.815416 loss: 0.000763 2022/10/21 10:27:41 - mmengine - INFO - Epoch(train) [9][350/391] lr: 5.000000e-04 eta: 9:39:19 time: 0.473098 data_time: 0.036909 memory: 21657 loss_kpt: 0.000783 acc_pose: 0.758491 loss: 0.000783 2022/10/21 10:28:01 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:28:26 - mmengine - INFO - Epoch(train) [10][50/391] lr: 5.000000e-04 eta: 9:33:01 time: 0.497720 data_time: 0.051317 memory: 21657 loss_kpt: 0.000791 acc_pose: 0.712028 loss: 0.000791 2022/10/21 10:28:51 - mmengine - INFO - Epoch(train) [10][100/391] lr: 5.000000e-04 eta: 9:33:36 time: 0.490308 data_time: 0.038096 memory: 21657 loss_kpt: 0.000771 acc_pose: 0.743790 loss: 0.000771 2022/10/21 10:29:15 - mmengine - INFO - Epoch(train) [10][150/391] lr: 5.000000e-04 eta: 9:34:03 time: 0.484522 data_time: 0.041031 memory: 21657 loss_kpt: 0.000766 acc_pose: 0.778985 loss: 0.000766 2022/10/21 10:29:39 - mmengine - INFO - Epoch(train) [10][200/391] lr: 5.000000e-04 eta: 9:34:23 time: 0.478822 data_time: 0.038256 memory: 21657 loss_kpt: 0.000755 acc_pose: 0.736765 loss: 0.000755 2022/10/21 10:30:03 - mmengine - INFO - Epoch(train) [10][250/391] lr: 5.000000e-04 eta: 9:34:54 time: 0.489864 data_time: 0.040099 memory: 21657 loss_kpt: 0.000785 acc_pose: 0.764226 loss: 0.000785 2022/10/21 10:30:27 - mmengine - INFO - Epoch(train) [10][300/391] lr: 5.000000e-04 eta: 9:35:17 time: 0.484357 data_time: 0.038053 memory: 21657 loss_kpt: 0.000781 acc_pose: 0.652913 loss: 0.000781 2022/10/21 10:30:52 - mmengine - INFO - Epoch(train) [10][350/391] lr: 5.000000e-04 eta: 9:35:50 time: 0.495603 data_time: 0.039889 memory: 21657 loss_kpt: 0.000785 acc_pose: 0.782540 loss: 0.000785 2022/10/21 10:31:11 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:31:11 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/10/21 10:31:25 - mmengine - INFO - Epoch(val) [10][50/407] eta: 0:01:13 time: 0.204624 data_time: 0.063310 memory: 21657 2022/10/21 10:31:33 - mmengine - INFO - Epoch(val) [10][100/407] eta: 0:00:46 time: 0.151379 data_time: 0.011154 memory: 2142 2022/10/21 10:31:40 - mmengine - INFO - Epoch(val) [10][150/407] eta: 0:00:38 time: 0.149378 data_time: 0.008296 memory: 2142 2022/10/21 10:31:48 - mmengine - INFO - Epoch(val) [10][200/407] eta: 0:00:30 time: 0.148999 data_time: 0.008863 memory: 2142 2022/10/21 10:31:55 - mmengine - INFO - Epoch(val) [10][250/407] eta: 0:00:23 time: 0.150194 data_time: 0.008958 memory: 2142 2022/10/21 10:32:03 - mmengine - INFO - Epoch(val) [10][300/407] eta: 0:00:16 time: 0.149559 data_time: 0.008572 memory: 2142 2022/10/21 10:32:10 - mmengine - INFO - Epoch(val) [10][350/407] eta: 0:00:08 time: 0.148340 data_time: 0.008745 memory: 2142 2022/10/21 10:32:18 - mmengine - INFO - Epoch(val) [10][400/407] eta: 0:00:01 time: 0.152250 data_time: 0.012033 memory: 2142 2022/10/21 10:32:55 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 10:33:09 - mmengine - INFO - Epoch(val) [10][407/407] coco/AP: 0.672110 coco/AP .5: 0.872628 coco/AP .75: 0.740508 coco/AP (M): 0.627241 coco/AP (L): 0.746584 coco/AR: 0.728747 coco/AR .5: 0.912626 coco/AR .75: 0.792349 coco/AR (M): 0.679077 coco/AR (L): 0.799071 2022/10/21 10:33:11 - mmengine - INFO - The best checkpoint with 0.6721 coco/AP at 10 epoch is saved to best_coco/AP_epoch_10.pth. 2022/10/21 10:33:36 - mmengine - INFO - Epoch(train) [11][50/391] lr: 5.000000e-04 eta: 9:30:08 time: 0.497614 data_time: 0.049826 memory: 21657 loss_kpt: 0.000759 acc_pose: 0.835569 loss: 0.000759 2022/10/21 10:33:55 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:34:00 - mmengine - INFO - Epoch(train) [11][100/391] lr: 5.000000e-04 eta: 9:30:29 time: 0.481629 data_time: 0.041353 memory: 21657 loss_kpt: 0.000774 acc_pose: 0.695435 loss: 0.000774 2022/10/21 10:34:25 - mmengine - INFO - Epoch(train) [11][150/391] lr: 5.000000e-04 eta: 9:30:55 time: 0.488726 data_time: 0.042477 memory: 21657 loss_kpt: 0.000772 acc_pose: 0.784022 loss: 0.000772 2022/10/21 10:34:49 - mmengine - INFO - Epoch(train) [11][200/391] lr: 5.000000e-04 eta: 9:31:12 time: 0.479759 data_time: 0.040892 memory: 21657 loss_kpt: 0.000780 acc_pose: 0.722289 loss: 0.000780 2022/10/21 10:35:13 - mmengine - INFO - Epoch(train) [11][250/391] lr: 5.000000e-04 eta: 9:31:34 time: 0.485817 data_time: 0.042686 memory: 21657 loss_kpt: 0.000752 acc_pose: 0.762828 loss: 0.000752 2022/10/21 10:35:37 - mmengine - INFO - Epoch(train) [11][300/391] lr: 5.000000e-04 eta: 9:31:54 time: 0.485239 data_time: 0.041490 memory: 21657 loss_kpt: 0.000774 acc_pose: 0.769902 loss: 0.000774 2022/10/21 10:36:01 - mmengine - INFO - Epoch(train) [11][350/391] lr: 5.000000e-04 eta: 9:32:11 time: 0.483193 data_time: 0.046534 memory: 21657 loss_kpt: 0.000769 acc_pose: 0.702792 loss: 0.000769 2022/10/21 10:36:21 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:36:46 - mmengine - INFO - Epoch(train) [12][50/391] lr: 5.000000e-04 eta: 9:26:55 time: 0.494011 data_time: 0.058374 memory: 21657 loss_kpt: 0.000756 acc_pose: 0.749105 loss: 0.000756 2022/10/21 10:37:10 - mmengine - INFO - Epoch(train) [12][100/391] lr: 5.000000e-04 eta: 9:27:20 time: 0.491238 data_time: 0.042669 memory: 21657 loss_kpt: 0.000772 acc_pose: 0.773256 loss: 0.000772 2022/10/21 10:37:34 - mmengine - INFO - Epoch(train) [12][150/391] lr: 5.000000e-04 eta: 9:27:31 time: 0.475607 data_time: 0.045048 memory: 21657 loss_kpt: 0.000761 acc_pose: 0.724831 loss: 0.000761 2022/10/21 10:37:59 - mmengine - INFO - Epoch(train) [12][200/391] lr: 5.000000e-04 eta: 9:27:56 time: 0.492524 data_time: 0.041295 memory: 21657 loss_kpt: 0.000772 acc_pose: 0.774016 loss: 0.000772 2022/10/21 10:38:23 - mmengine - INFO - Epoch(train) [12][250/391] lr: 5.000000e-04 eta: 9:28:10 time: 0.481063 data_time: 0.045011 memory: 21657 loss_kpt: 0.000753 acc_pose: 0.763375 loss: 0.000753 2022/10/21 10:38:47 - mmengine - INFO - Epoch(train) [12][300/391] lr: 5.000000e-04 eta: 9:28:27 time: 0.486577 data_time: 0.041219 memory: 21657 loss_kpt: 0.000765 acc_pose: 0.756452 loss: 0.000765 2022/10/21 10:39:11 - mmengine - INFO - Epoch(train) [12][350/391] lr: 5.000000e-04 eta: 9:28:39 time: 0.480077 data_time: 0.040301 memory: 21657 loss_kpt: 0.000758 acc_pose: 0.800416 loss: 0.000758 2022/10/21 10:39:31 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:39:56 - mmengine - INFO - Epoch(train) [13][50/391] lr: 5.000000e-04 eta: 9:23:56 time: 0.504845 data_time: 0.055804 memory: 21657 loss_kpt: 0.000755 acc_pose: 0.678471 loss: 0.000755 2022/10/21 10:40:20 - mmengine - INFO - Epoch(train) [13][100/391] lr: 5.000000e-04 eta: 9:24:12 time: 0.483836 data_time: 0.045499 memory: 21657 loss_kpt: 0.000754 acc_pose: 0.747351 loss: 0.000754 2022/10/21 10:40:45 - mmengine - INFO - Epoch(train) [13][150/391] lr: 5.000000e-04 eta: 9:24:33 time: 0.491566 data_time: 0.040667 memory: 21657 loss_kpt: 0.000737 acc_pose: 0.767135 loss: 0.000737 2022/10/21 10:41:09 - mmengine - INFO - Epoch(train) [13][200/391] lr: 5.000000e-04 eta: 9:24:45 time: 0.481955 data_time: 0.045054 memory: 21657 loss_kpt: 0.000748 acc_pose: 0.800512 loss: 0.000748 2022/10/21 10:41:33 - mmengine - INFO - Epoch(train) [13][250/391] lr: 5.000000e-04 eta: 9:25:04 time: 0.490239 data_time: 0.041370 memory: 21657 loss_kpt: 0.000752 acc_pose: 0.789545 loss: 0.000752 2022/10/21 10:41:57 - mmengine - INFO - Epoch(train) [13][300/391] lr: 5.000000e-04 eta: 9:25:13 time: 0.480246 data_time: 0.040434 memory: 21657 loss_kpt: 0.000748 acc_pose: 0.768408 loss: 0.000748 2022/10/21 10:42:01 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:42:22 - mmengine - INFO - Epoch(train) [13][350/391] lr: 5.000000e-04 eta: 9:25:31 time: 0.491948 data_time: 0.039552 memory: 21657 loss_kpt: 0.000743 acc_pose: 0.758408 loss: 0.000743 2022/10/21 10:42:41 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:43:06 - mmengine - INFO - Epoch(train) [14][50/391] lr: 5.000000e-04 eta: 9:21:03 time: 0.496784 data_time: 0.054668 memory: 21657 loss_kpt: 0.000743 acc_pose: 0.742720 loss: 0.000743 2022/10/21 10:43:30 - mmengine - INFO - Epoch(train) [14][100/391] lr: 5.000000e-04 eta: 9:21:21 time: 0.490985 data_time: 0.041833 memory: 21657 loss_kpt: 0.000770 acc_pose: 0.769755 loss: 0.000770 2022/10/21 10:43:54 - mmengine - INFO - Epoch(train) [14][150/391] lr: 5.000000e-04 eta: 9:21:30 time: 0.480018 data_time: 0.040777 memory: 21657 loss_kpt: 0.000749 acc_pose: 0.823337 loss: 0.000749 2022/10/21 10:44:19 - mmengine - INFO - Epoch(train) [14][200/391] lr: 5.000000e-04 eta: 9:21:46 time: 0.490048 data_time: 0.040988 memory: 21657 loss_kpt: 0.000734 acc_pose: 0.810014 loss: 0.000734 2022/10/21 10:44:43 - mmengine - INFO - Epoch(train) [14][250/391] lr: 5.000000e-04 eta: 9:21:54 time: 0.481633 data_time: 0.044870 memory: 21657 loss_kpt: 0.000740 acc_pose: 0.781564 loss: 0.000740 2022/10/21 10:45:08 - mmengine - INFO - Epoch(train) [14][300/391] lr: 5.000000e-04 eta: 9:22:10 time: 0.492262 data_time: 0.040186 memory: 21657 loss_kpt: 0.000727 acc_pose: 0.836226 loss: 0.000727 2022/10/21 10:45:32 - mmengine - INFO - Epoch(train) [14][350/391] lr: 5.000000e-04 eta: 9:22:16 time: 0.479346 data_time: 0.044132 memory: 21657 loss_kpt: 0.000734 acc_pose: 0.848942 loss: 0.000734 2022/10/21 10:45:52 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:46:16 - mmengine - INFO - Epoch(train) [15][50/391] lr: 5.000000e-04 eta: 9:18:03 time: 0.493533 data_time: 0.058198 memory: 21657 loss_kpt: 0.000754 acc_pose: 0.770757 loss: 0.000754 2022/10/21 10:46:41 - mmengine - INFO - Epoch(train) [15][100/391] lr: 5.000000e-04 eta: 9:18:14 time: 0.484304 data_time: 0.041692 memory: 21657 loss_kpt: 0.000723 acc_pose: 0.793234 loss: 0.000723 2022/10/21 10:47:05 - mmengine - INFO - Epoch(train) [15][150/391] lr: 5.000000e-04 eta: 9:18:26 time: 0.487627 data_time: 0.041897 memory: 21657 loss_kpt: 0.000737 acc_pose: 0.803737 loss: 0.000737 2022/10/21 10:47:29 - mmengine - INFO - Epoch(train) [15][200/391] lr: 5.000000e-04 eta: 9:18:34 time: 0.482657 data_time: 0.044231 memory: 21657 loss_kpt: 0.000737 acc_pose: 0.760724 loss: 0.000737 2022/10/21 10:47:53 - mmengine - INFO - Epoch(train) [15][250/391] lr: 5.000000e-04 eta: 9:18:43 time: 0.485257 data_time: 0.041348 memory: 21657 loss_kpt: 0.000731 acc_pose: 0.750691 loss: 0.000731 2022/10/21 10:48:18 - mmengine - INFO - Epoch(train) [15][300/391] lr: 5.000000e-04 eta: 9:18:52 time: 0.485060 data_time: 0.046987 memory: 21657 loss_kpt: 0.000732 acc_pose: 0.783396 loss: 0.000732 2022/10/21 10:48:42 - mmengine - INFO - Epoch(train) [15][350/391] lr: 5.000000e-04 eta: 9:19:00 time: 0.485667 data_time: 0.041335 memory: 21657 loss_kpt: 0.000728 acc_pose: 0.805985 loss: 0.000728 2022/10/21 10:49:01 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:49:26 - mmengine - INFO - Epoch(train) [16][50/391] lr: 5.000000e-04 eta: 9:15:08 time: 0.501102 data_time: 0.053912 memory: 21657 loss_kpt: 0.000749 acc_pose: 0.762678 loss: 0.000749 2022/10/21 10:49:50 - mmengine - INFO - Epoch(train) [16][100/391] lr: 5.000000e-04 eta: 9:15:14 time: 0.480834 data_time: 0.042550 memory: 21657 loss_kpt: 0.000715 acc_pose: 0.789535 loss: 0.000715 2022/10/21 10:50:07 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:50:15 - mmengine - INFO - Epoch(train) [16][150/391] lr: 5.000000e-04 eta: 9:15:24 time: 0.488506 data_time: 0.040043 memory: 21657 loss_kpt: 0.000721 acc_pose: 0.786053 loss: 0.000721 2022/10/21 10:50:39 - mmengine - INFO - Epoch(train) [16][200/391] lr: 5.000000e-04 eta: 9:15:30 time: 0.482514 data_time: 0.045078 memory: 21657 loss_kpt: 0.000732 acc_pose: 0.761339 loss: 0.000732 2022/10/21 10:51:03 - mmengine - INFO - Epoch(train) [16][250/391] lr: 5.000000e-04 eta: 9:15:36 time: 0.482208 data_time: 0.040964 memory: 21657 loss_kpt: 0.000726 acc_pose: 0.757779 loss: 0.000726 2022/10/21 10:51:27 - mmengine - INFO - Epoch(train) [16][300/391] lr: 5.000000e-04 eta: 9:15:45 time: 0.488899 data_time: 0.044590 memory: 21657 loss_kpt: 0.000711 acc_pose: 0.783043 loss: 0.000711 2022/10/21 10:51:51 - mmengine - INFO - Epoch(train) [16][350/391] lr: 5.000000e-04 eta: 9:15:47 time: 0.478997 data_time: 0.038634 memory: 21657 loss_kpt: 0.000724 acc_pose: 0.818989 loss: 0.000724 2022/10/21 10:52:11 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:52:36 - mmengine - INFO - Epoch(train) [17][50/391] lr: 5.000000e-04 eta: 9:12:00 time: 0.488669 data_time: 0.051015 memory: 21657 loss_kpt: 0.000720 acc_pose: 0.778247 loss: 0.000720 2022/10/21 10:53:01 - mmengine - INFO - Epoch(train) [17][100/391] lr: 5.000000e-04 eta: 9:12:14 time: 0.496923 data_time: 0.038454 memory: 21657 loss_kpt: 0.000709 acc_pose: 0.812880 loss: 0.000709 2022/10/21 10:53:24 - mmengine - INFO - Epoch(train) [17][150/391] lr: 5.000000e-04 eta: 9:12:15 time: 0.475801 data_time: 0.037299 memory: 21657 loss_kpt: 0.000738 acc_pose: 0.818260 loss: 0.000738 2022/10/21 10:53:49 - mmengine - INFO - Epoch(train) [17][200/391] lr: 5.000000e-04 eta: 9:12:27 time: 0.495550 data_time: 0.039041 memory: 21657 loss_kpt: 0.000709 acc_pose: 0.764701 loss: 0.000709 2022/10/21 10:54:13 - mmengine - INFO - Epoch(train) [17][250/391] lr: 5.000000e-04 eta: 9:12:29 time: 0.479853 data_time: 0.038798 memory: 21657 loss_kpt: 0.000725 acc_pose: 0.787642 loss: 0.000725 2022/10/21 10:54:38 - mmengine - INFO - Epoch(train) [17][300/391] lr: 5.000000e-04 eta: 9:12:40 time: 0.494964 data_time: 0.039478 memory: 21657 loss_kpt: 0.000740 acc_pose: 0.758866 loss: 0.000740 2022/10/21 10:55:02 - mmengine - INFO - Epoch(train) [17][350/391] lr: 5.000000e-04 eta: 9:12:46 time: 0.488441 data_time: 0.038154 memory: 21657 loss_kpt: 0.000715 acc_pose: 0.782541 loss: 0.000715 2022/10/21 10:55:22 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:55:47 - mmengine - INFO - Epoch(train) [18][50/391] lr: 5.000000e-04 eta: 9:09:20 time: 0.504414 data_time: 0.051468 memory: 21657 loss_kpt: 0.000714 acc_pose: 0.795441 loss: 0.000714 2022/10/21 10:56:11 - mmengine - INFO - Epoch(train) [18][100/391] lr: 5.000000e-04 eta: 9:09:26 time: 0.487217 data_time: 0.041064 memory: 21657 loss_kpt: 0.000718 acc_pose: 0.796899 loss: 0.000718 2022/10/21 10:56:36 - mmengine - INFO - Epoch(train) [18][150/391] lr: 5.000000e-04 eta: 9:09:34 time: 0.490587 data_time: 0.039678 memory: 21657 loss_kpt: 0.000720 acc_pose: 0.761578 loss: 0.000720 2022/10/21 10:57:00 - mmengine - INFO - Epoch(train) [18][200/391] lr: 5.000000e-04 eta: 9:09:33 time: 0.475768 data_time: 0.044983 memory: 21657 loss_kpt: 0.000694 acc_pose: 0.808989 loss: 0.000694 2022/10/21 10:57:24 - mmengine - INFO - Epoch(train) [18][250/391] lr: 5.000000e-04 eta: 9:09:40 time: 0.492133 data_time: 0.041693 memory: 21657 loss_kpt: 0.000722 acc_pose: 0.720239 loss: 0.000722 2022/10/21 10:57:48 - mmengine - INFO - Epoch(train) [18][300/391] lr: 5.000000e-04 eta: 9:09:40 time: 0.478651 data_time: 0.042075 memory: 21657 loss_kpt: 0.000729 acc_pose: 0.797223 loss: 0.000729 2022/10/21 10:58:13 - mmengine - INFO - Epoch(train) [18][350/391] lr: 5.000000e-04 eta: 9:09:46 time: 0.491337 data_time: 0.037990 memory: 21657 loss_kpt: 0.000709 acc_pose: 0.768691 loss: 0.000709 2022/10/21 10:58:14 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:58:32 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 10:58:57 - mmengine - INFO - Epoch(train) [19][50/391] lr: 5.000000e-04 eta: 9:06:29 time: 0.501076 data_time: 0.049966 memory: 21657 loss_kpt: 0.000727 acc_pose: 0.816431 loss: 0.000727 2022/10/21 10:59:22 - mmengine - INFO - Epoch(train) [19][100/391] lr: 5.000000e-04 eta: 9:06:32 time: 0.485748 data_time: 0.039428 memory: 21657 loss_kpt: 0.000703 acc_pose: 0.809448 loss: 0.000703 2022/10/21 10:59:46 - mmengine - INFO - Epoch(train) [19][150/391] lr: 5.000000e-04 eta: 9:06:37 time: 0.487780 data_time: 0.043530 memory: 21657 loss_kpt: 0.000701 acc_pose: 0.791458 loss: 0.000701 2022/10/21 11:00:11 - mmengine - INFO - Epoch(train) [19][200/391] lr: 5.000000e-04 eta: 9:06:43 time: 0.491689 data_time: 0.040291 memory: 21657 loss_kpt: 0.000714 acc_pose: 0.774632 loss: 0.000714 2022/10/21 11:00:35 - mmengine - INFO - Epoch(train) [19][250/391] lr: 5.000000e-04 eta: 9:06:45 time: 0.486008 data_time: 0.042307 memory: 21657 loss_kpt: 0.000716 acc_pose: 0.781918 loss: 0.000716 2022/10/21 11:00:59 - mmengine - INFO - Epoch(train) [19][300/391] lr: 5.000000e-04 eta: 9:06:49 time: 0.489167 data_time: 0.040705 memory: 21657 loss_kpt: 0.000708 acc_pose: 0.789483 loss: 0.000708 2022/10/21 11:01:23 - mmengine - INFO - Epoch(train) [19][350/391] lr: 5.000000e-04 eta: 9:06:47 time: 0.477181 data_time: 0.038773 memory: 21657 loss_kpt: 0.000704 acc_pose: 0.814098 loss: 0.000704 2022/10/21 11:01:43 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:02:08 - mmengine - INFO - Epoch(train) [20][50/391] lr: 5.000000e-04 eta: 9:03:35 time: 0.495220 data_time: 0.054418 memory: 21657 loss_kpt: 0.000717 acc_pose: 0.790801 loss: 0.000717 2022/10/21 11:02:33 - mmengine - INFO - Epoch(train) [20][100/391] lr: 5.000000e-04 eta: 9:03:43 time: 0.496213 data_time: 0.038476 memory: 21657 loss_kpt: 0.000707 acc_pose: 0.768000 loss: 0.000707 2022/10/21 11:02:57 - mmengine - INFO - Epoch(train) [20][150/391] lr: 5.000000e-04 eta: 9:03:42 time: 0.480878 data_time: 0.041400 memory: 21657 loss_kpt: 0.000702 acc_pose: 0.768543 loss: 0.000702 2022/10/21 11:03:21 - mmengine - INFO - Epoch(train) [20][200/391] lr: 5.000000e-04 eta: 9:03:42 time: 0.481272 data_time: 0.038499 memory: 21657 loss_kpt: 0.000709 acc_pose: 0.813140 loss: 0.000709 2022/10/21 11:03:46 - mmengine - INFO - Epoch(train) [20][250/391] lr: 5.000000e-04 eta: 9:03:48 time: 0.496393 data_time: 0.039011 memory: 21657 loss_kpt: 0.000702 acc_pose: 0.735809 loss: 0.000702 2022/10/21 11:04:10 - mmengine - INFO - Epoch(train) [20][300/391] lr: 5.000000e-04 eta: 9:03:47 time: 0.481353 data_time: 0.038635 memory: 21657 loss_kpt: 0.000697 acc_pose: 0.738205 loss: 0.000697 2022/10/21 11:04:35 - mmengine - INFO - Epoch(train) [20][350/391] lr: 5.000000e-04 eta: 9:03:51 time: 0.493738 data_time: 0.038250 memory: 21657 loss_kpt: 0.000715 acc_pose: 0.814585 loss: 0.000715 2022/10/21 11:04:54 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:04:54 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/10/21 11:05:06 - mmengine - INFO - Epoch(val) [20][50/407] eta: 0:00:55 time: 0.154425 data_time: 0.014109 memory: 21657 2022/10/21 11:05:13 - mmengine - INFO - Epoch(val) [20][100/407] eta: 0:00:45 time: 0.149640 data_time: 0.009112 memory: 2142 2022/10/21 11:05:21 - mmengine - INFO - Epoch(val) [20][150/407] eta: 0:00:38 time: 0.151513 data_time: 0.010107 memory: 2142 2022/10/21 11:05:28 - mmengine - INFO - Epoch(val) [20][200/407] eta: 0:00:30 time: 0.148796 data_time: 0.009222 memory: 2142 2022/10/21 11:05:36 - mmengine - INFO - Epoch(val) [20][250/407] eta: 0:00:23 time: 0.149851 data_time: 0.009501 memory: 2142 2022/10/21 11:05:43 - mmengine - INFO - Epoch(val) [20][300/407] eta: 0:00:15 time: 0.148925 data_time: 0.009373 memory: 2142 2022/10/21 11:05:51 - mmengine - INFO - Epoch(val) [20][350/407] eta: 0:00:08 time: 0.149279 data_time: 0.009759 memory: 2142 2022/10/21 11:05:58 - mmengine - INFO - Epoch(val) [20][400/407] eta: 0:00:01 time: 0.149059 data_time: 0.008755 memory: 2142 2022/10/21 11:06:36 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 11:06:50 - mmengine - INFO - Epoch(val) [20][407/407] coco/AP: 0.699020 coco/AP .5: 0.885513 coco/AP .75: 0.766452 coco/AP (M): 0.657583 coco/AP (L): 0.770766 coco/AR: 0.754266 coco/AR .5: 0.924591 coco/AR .75: 0.815491 coco/AR (M): 0.708632 coco/AR (L): 0.819770 2022/10/21 11:06:50 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221021/resnetv1d152_384/best_coco/AP_epoch_10.pth is removed 2022/10/21 11:06:52 - mmengine - INFO - The best checkpoint with 0.6990 coco/AP at 20 epoch is saved to best_coco/AP_epoch_20.pth. 2022/10/21 11:07:17 - mmengine - INFO - Epoch(train) [21][50/391] lr: 5.000000e-04 eta: 9:00:50 time: 0.500202 data_time: 0.054742 memory: 21657 loss_kpt: 0.000698 acc_pose: 0.737267 loss: 0.000698 2022/10/21 11:07:41 - mmengine - INFO - Epoch(train) [21][100/391] lr: 5.000000e-04 eta: 9:00:48 time: 0.479817 data_time: 0.043197 memory: 21657 loss_kpt: 0.000695 acc_pose: 0.804930 loss: 0.000695 2022/10/21 11:08:05 - mmengine - INFO - Epoch(train) [21][150/391] lr: 5.000000e-04 eta: 9:00:50 time: 0.487073 data_time: 0.041713 memory: 21657 loss_kpt: 0.000708 acc_pose: 0.827159 loss: 0.000708 2022/10/21 11:08:20 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:08:29 - mmengine - INFO - Epoch(train) [21][200/391] lr: 5.000000e-04 eta: 9:00:49 time: 0.483005 data_time: 0.046096 memory: 21657 loss_kpt: 0.000709 acc_pose: 0.822691 loss: 0.000709 2022/10/21 11:08:54 - mmengine - INFO - Epoch(train) [21][250/391] lr: 5.000000e-04 eta: 9:00:48 time: 0.483958 data_time: 0.040941 memory: 21657 loss_kpt: 0.000714 acc_pose: 0.869804 loss: 0.000714 2022/10/21 11:09:18 - mmengine - INFO - Epoch(train) [21][300/391] lr: 5.000000e-04 eta: 9:00:48 time: 0.487070 data_time: 0.042631 memory: 21657 loss_kpt: 0.000686 acc_pose: 0.854289 loss: 0.000686 2022/10/21 11:09:42 - mmengine - INFO - Epoch(train) [21][350/391] lr: 5.000000e-04 eta: 9:00:46 time: 0.481493 data_time: 0.046683 memory: 21657 loss_kpt: 0.000708 acc_pose: 0.807730 loss: 0.000708 2022/10/21 11:10:02 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:10:27 - mmengine - INFO - Epoch(train) [22][50/391] lr: 5.000000e-04 eta: 8:57:51 time: 0.496464 data_time: 0.062649 memory: 21657 loss_kpt: 0.000699 acc_pose: 0.804890 loss: 0.000699 2022/10/21 11:10:51 - mmengine - INFO - Epoch(train) [22][100/391] lr: 5.000000e-04 eta: 8:57:53 time: 0.491245 data_time: 0.041452 memory: 21657 loss_kpt: 0.000692 acc_pose: 0.796622 loss: 0.000692 2022/10/21 11:11:15 - mmengine - INFO - Epoch(train) [22][150/391] lr: 5.000000e-04 eta: 8:57:50 time: 0.479507 data_time: 0.047511 memory: 21657 loss_kpt: 0.000683 acc_pose: 0.779313 loss: 0.000683 2022/10/21 11:11:40 - mmengine - INFO - Epoch(train) [22][200/391] lr: 5.000000e-04 eta: 8:57:51 time: 0.489818 data_time: 0.042778 memory: 21657 loss_kpt: 0.000691 acc_pose: 0.830367 loss: 0.000691 2022/10/21 11:12:04 - mmengine - INFO - Epoch(train) [22][250/391] lr: 5.000000e-04 eta: 8:57:47 time: 0.480655 data_time: 0.045322 memory: 21657 loss_kpt: 0.000703 acc_pose: 0.761583 loss: 0.000703 2022/10/21 11:12:28 - mmengine - INFO - Epoch(train) [22][300/391] lr: 5.000000e-04 eta: 8:57:47 time: 0.486979 data_time: 0.041357 memory: 21657 loss_kpt: 0.000704 acc_pose: 0.736118 loss: 0.000704 2022/10/21 11:12:52 - mmengine - INFO - Epoch(train) [22][350/391] lr: 5.000000e-04 eta: 8:57:43 time: 0.480762 data_time: 0.044285 memory: 21657 loss_kpt: 0.000708 acc_pose: 0.814245 loss: 0.000708 2022/10/21 11:13:12 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:13:37 - mmengine - INFO - Epoch(train) [23][50/391] lr: 5.000000e-04 eta: 8:54:59 time: 0.505723 data_time: 0.050511 memory: 21657 loss_kpt: 0.000703 acc_pose: 0.787028 loss: 0.000703 2022/10/21 11:14:01 - mmengine - INFO - Epoch(train) [23][100/391] lr: 5.000000e-04 eta: 8:54:55 time: 0.478779 data_time: 0.040571 memory: 21657 loss_kpt: 0.000694 acc_pose: 0.795535 loss: 0.000694 2022/10/21 11:14:26 - mmengine - INFO - Epoch(train) [23][150/391] lr: 5.000000e-04 eta: 8:54:58 time: 0.497679 data_time: 0.041282 memory: 21657 loss_kpt: 0.000672 acc_pose: 0.839517 loss: 0.000672 2022/10/21 11:14:50 - mmengine - INFO - Epoch(train) [23][200/391] lr: 5.000000e-04 eta: 8:54:52 time: 0.475677 data_time: 0.041962 memory: 21657 loss_kpt: 0.000688 acc_pose: 0.786272 loss: 0.000688 2022/10/21 11:15:14 - mmengine - INFO - Epoch(train) [23][250/391] lr: 5.000000e-04 eta: 8:54:51 time: 0.489342 data_time: 0.041297 memory: 21657 loss_kpt: 0.000695 acc_pose: 0.777896 loss: 0.000695 2022/10/21 11:15:38 - mmengine - INFO - Epoch(train) [23][300/391] lr: 5.000000e-04 eta: 8:54:47 time: 0.480008 data_time: 0.045050 memory: 21657 loss_kpt: 0.000693 acc_pose: 0.761680 loss: 0.000693 2022/10/21 11:16:03 - mmengine - INFO - Epoch(train) [23][350/391] lr: 5.000000e-04 eta: 8:54:47 time: 0.491864 data_time: 0.043364 memory: 21657 loss_kpt: 0.000674 acc_pose: 0.790313 loss: 0.000674 2022/10/21 11:16:22 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:16:26 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:16:47 - mmengine - INFO - Epoch(train) [24][50/391] lr: 5.000000e-04 eta: 8:52:03 time: 0.490854 data_time: 0.051565 memory: 21657 loss_kpt: 0.000693 acc_pose: 0.827032 loss: 0.000693 2022/10/21 11:17:11 - mmengine - INFO - Epoch(train) [24][100/391] lr: 5.000000e-04 eta: 8:52:02 time: 0.489775 data_time: 0.042749 memory: 21657 loss_kpt: 0.000684 acc_pose: 0.771754 loss: 0.000684 2022/10/21 11:17:35 - mmengine - INFO - Epoch(train) [24][150/391] lr: 5.000000e-04 eta: 8:51:56 time: 0.476470 data_time: 0.044396 memory: 21657 loss_kpt: 0.000675 acc_pose: 0.805735 loss: 0.000675 2022/10/21 11:18:00 - mmengine - INFO - Epoch(train) [24][200/391] lr: 5.000000e-04 eta: 8:51:57 time: 0.496620 data_time: 0.042231 memory: 21657 loss_kpt: 0.000689 acc_pose: 0.808553 loss: 0.000689 2022/10/21 11:18:24 - mmengine - INFO - Epoch(train) [24][250/391] lr: 5.000000e-04 eta: 8:51:52 time: 0.478498 data_time: 0.046685 memory: 21657 loss_kpt: 0.000690 acc_pose: 0.835668 loss: 0.000690 2022/10/21 11:18:48 - mmengine - INFO - Epoch(train) [24][300/391] lr: 5.000000e-04 eta: 8:51:52 time: 0.495197 data_time: 0.040807 memory: 21657 loss_kpt: 0.000675 acc_pose: 0.830225 loss: 0.000675 2022/10/21 11:19:12 - mmengine - INFO - Epoch(train) [24][350/391] lr: 5.000000e-04 eta: 8:51:46 time: 0.477663 data_time: 0.040936 memory: 21657 loss_kpt: 0.000672 acc_pose: 0.760371 loss: 0.000672 2022/10/21 11:19:32 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:19:57 - mmengine - INFO - Epoch(train) [25][50/391] lr: 5.000000e-04 eta: 8:49:10 time: 0.498679 data_time: 0.057797 memory: 21657 loss_kpt: 0.000672 acc_pose: 0.825734 loss: 0.000672 2022/10/21 11:20:21 - mmengine - INFO - Epoch(train) [25][100/391] lr: 5.000000e-04 eta: 8:49:06 time: 0.481735 data_time: 0.040458 memory: 21657 loss_kpt: 0.000680 acc_pose: 0.813387 loss: 0.000680 2022/10/21 11:20:46 - mmengine - INFO - Epoch(train) [25][150/391] lr: 5.000000e-04 eta: 8:49:06 time: 0.494052 data_time: 0.044733 memory: 21657 loss_kpt: 0.000689 acc_pose: 0.743950 loss: 0.000689 2022/10/21 11:21:09 - mmengine - INFO - Epoch(train) [25][200/391] lr: 5.000000e-04 eta: 8:48:59 time: 0.477067 data_time: 0.040941 memory: 21657 loss_kpt: 0.000687 acc_pose: 0.842283 loss: 0.000687 2022/10/21 11:21:34 - mmengine - INFO - Epoch(train) [25][250/391] lr: 5.000000e-04 eta: 8:48:58 time: 0.495205 data_time: 0.042165 memory: 21657 loss_kpt: 0.000675 acc_pose: 0.797338 loss: 0.000675 2022/10/21 11:21:58 - mmengine - INFO - Epoch(train) [25][300/391] lr: 5.000000e-04 eta: 8:48:51 time: 0.475471 data_time: 0.040236 memory: 21657 loss_kpt: 0.000682 acc_pose: 0.765395 loss: 0.000682 2022/10/21 11:22:22 - mmengine - INFO - Epoch(train) [25][350/391] lr: 5.000000e-04 eta: 8:48:48 time: 0.490489 data_time: 0.040144 memory: 21657 loss_kpt: 0.000691 acc_pose: 0.789732 loss: 0.000691 2022/10/21 11:22:42 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:23:07 - mmengine - INFO - Epoch(train) [26][50/391] lr: 5.000000e-04 eta: 8:46:19 time: 0.499703 data_time: 0.051768 memory: 21657 loss_kpt: 0.000689 acc_pose: 0.831687 loss: 0.000689 2022/10/21 11:23:31 - mmengine - INFO - Epoch(train) [26][100/391] lr: 5.000000e-04 eta: 8:46:15 time: 0.485678 data_time: 0.043342 memory: 21657 loss_kpt: 0.000674 acc_pose: 0.815848 loss: 0.000674 2022/10/21 11:23:55 - mmengine - INFO - Epoch(train) [26][150/391] lr: 5.000000e-04 eta: 8:46:10 time: 0.484013 data_time: 0.039563 memory: 21657 loss_kpt: 0.000683 acc_pose: 0.827737 loss: 0.000683 2022/10/21 11:24:19 - mmengine - INFO - Epoch(train) [26][200/391] lr: 5.000000e-04 eta: 8:46:04 time: 0.482750 data_time: 0.040069 memory: 21657 loss_kpt: 0.000678 acc_pose: 0.841822 loss: 0.000678 2022/10/21 11:24:31 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:24:44 - mmengine - INFO - Epoch(train) [26][250/391] lr: 5.000000e-04 eta: 8:45:59 time: 0.483305 data_time: 0.040377 memory: 21657 loss_kpt: 0.000681 acc_pose: 0.803804 loss: 0.000681 2022/10/21 11:25:08 - mmengine - INFO - Epoch(train) [26][300/391] lr: 5.000000e-04 eta: 8:45:54 time: 0.486264 data_time: 0.043786 memory: 21657 loss_kpt: 0.000673 acc_pose: 0.845472 loss: 0.000673 2022/10/21 11:25:32 - mmengine - INFO - Epoch(train) [26][350/391] lr: 5.000000e-04 eta: 8:45:47 time: 0.479564 data_time: 0.040217 memory: 21657 loss_kpt: 0.000681 acc_pose: 0.786740 loss: 0.000681 2022/10/21 11:25:52 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:26:17 - mmengine - INFO - Epoch(train) [27][50/391] lr: 5.000000e-04 eta: 8:43:21 time: 0.495770 data_time: 0.053425 memory: 21657 loss_kpt: 0.000675 acc_pose: 0.812964 loss: 0.000675 2022/10/21 11:26:41 - mmengine - INFO - Epoch(train) [27][100/391] lr: 5.000000e-04 eta: 8:43:18 time: 0.489073 data_time: 0.040113 memory: 21657 loss_kpt: 0.000684 acc_pose: 0.775852 loss: 0.000684 2022/10/21 11:27:05 - mmengine - INFO - Epoch(train) [27][150/391] lr: 5.000000e-04 eta: 8:43:13 time: 0.485640 data_time: 0.043466 memory: 21657 loss_kpt: 0.000656 acc_pose: 0.750764 loss: 0.000656 2022/10/21 11:27:29 - mmengine - INFO - Epoch(train) [27][200/391] lr: 5.000000e-04 eta: 8:43:05 time: 0.479714 data_time: 0.039824 memory: 21657 loss_kpt: 0.000684 acc_pose: 0.765365 loss: 0.000684 2022/10/21 11:27:54 - mmengine - INFO - Epoch(train) [27][250/391] lr: 5.000000e-04 eta: 8:43:02 time: 0.489883 data_time: 0.046002 memory: 21657 loss_kpt: 0.000702 acc_pose: 0.786535 loss: 0.000702 2022/10/21 11:28:18 - mmengine - INFO - Epoch(train) [27][300/391] lr: 5.000000e-04 eta: 8:42:55 time: 0.483239 data_time: 0.040661 memory: 21657 loss_kpt: 0.000654 acc_pose: 0.778434 loss: 0.000654 2022/10/21 11:28:42 - mmengine - INFO - Epoch(train) [27][350/391] lr: 5.000000e-04 eta: 8:42:50 time: 0.487625 data_time: 0.044123 memory: 21657 loss_kpt: 0.000685 acc_pose: 0.831954 loss: 0.000685 2022/10/21 11:29:02 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:29:27 - mmengine - INFO - Epoch(train) [28][50/391] lr: 5.000000e-04 eta: 8:40:32 time: 0.505553 data_time: 0.052669 memory: 21657 loss_kpt: 0.000661 acc_pose: 0.839588 loss: 0.000661 2022/10/21 11:29:51 - mmengine - INFO - Epoch(train) [28][100/391] lr: 5.000000e-04 eta: 8:40:25 time: 0.479506 data_time: 0.044851 memory: 21657 loss_kpt: 0.000669 acc_pose: 0.778554 loss: 0.000669 2022/10/21 11:30:16 - mmengine - INFO - Epoch(train) [28][150/391] lr: 5.000000e-04 eta: 8:40:21 time: 0.492710 data_time: 0.041620 memory: 21657 loss_kpt: 0.000672 acc_pose: 0.740165 loss: 0.000672 2022/10/21 11:30:40 - mmengine - INFO - Epoch(train) [28][200/391] lr: 5.000000e-04 eta: 8:40:12 time: 0.475592 data_time: 0.044637 memory: 21657 loss_kpt: 0.000670 acc_pose: 0.821566 loss: 0.000670 2022/10/21 11:31:04 - mmengine - INFO - Epoch(train) [28][250/391] lr: 5.000000e-04 eta: 8:40:08 time: 0.491654 data_time: 0.043624 memory: 21657 loss_kpt: 0.000674 acc_pose: 0.814857 loss: 0.000674 2022/10/21 11:31:28 - mmengine - INFO - Epoch(train) [28][300/391] lr: 5.000000e-04 eta: 8:40:00 time: 0.480992 data_time: 0.043843 memory: 21657 loss_kpt: 0.000676 acc_pose: 0.771862 loss: 0.000676 2022/10/21 11:31:52 - mmengine - INFO - Epoch(train) [28][350/391] lr: 5.000000e-04 eta: 8:39:54 time: 0.486118 data_time: 0.039772 memory: 21657 loss_kpt: 0.000690 acc_pose: 0.837436 loss: 0.000690 2022/10/21 11:32:12 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:32:37 - mmengine - INFO - Epoch(train) [29][50/391] lr: 5.000000e-04 eta: 8:37:38 time: 0.498953 data_time: 0.050334 memory: 21657 loss_kpt: 0.000663 acc_pose: 0.780295 loss: 0.000663 2022/10/21 11:32:38 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:33:01 - mmengine - INFO - Epoch(train) [29][100/391] lr: 5.000000e-04 eta: 8:37:32 time: 0.486936 data_time: 0.040938 memory: 21657 loss_kpt: 0.000652 acc_pose: 0.823266 loss: 0.000652 2022/10/21 11:33:25 - mmengine - INFO - Epoch(train) [29][150/391] lr: 5.000000e-04 eta: 8:37:24 time: 0.478646 data_time: 0.040424 memory: 21657 loss_kpt: 0.000682 acc_pose: 0.843591 loss: 0.000682 2022/10/21 11:33:50 - mmengine - INFO - Epoch(train) [29][200/391] lr: 5.000000e-04 eta: 8:37:20 time: 0.494323 data_time: 0.041246 memory: 21657 loss_kpt: 0.000663 acc_pose: 0.778687 loss: 0.000663 2022/10/21 11:34:14 - mmengine - INFO - Epoch(train) [29][250/391] lr: 5.000000e-04 eta: 8:37:11 time: 0.479147 data_time: 0.043718 memory: 21657 loss_kpt: 0.000672 acc_pose: 0.762393 loss: 0.000672 2022/10/21 11:34:39 - mmengine - INFO - Epoch(train) [29][300/391] lr: 5.000000e-04 eta: 8:37:08 time: 0.494891 data_time: 0.040416 memory: 21657 loss_kpt: 0.000678 acc_pose: 0.764955 loss: 0.000678 2022/10/21 11:35:03 - mmengine - INFO - Epoch(train) [29][350/391] lr: 5.000000e-04 eta: 8:36:58 time: 0.476045 data_time: 0.042981 memory: 21657 loss_kpt: 0.000669 acc_pose: 0.793663 loss: 0.000669 2022/10/21 11:35:22 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:35:47 - mmengine - INFO - Epoch(train) [30][50/391] lr: 5.000000e-04 eta: 8:34:47 time: 0.503268 data_time: 0.054294 memory: 21657 loss_kpt: 0.000660 acc_pose: 0.740188 loss: 0.000660 2022/10/21 11:36:12 - mmengine - INFO - Epoch(train) [30][100/391] lr: 5.000000e-04 eta: 8:34:40 time: 0.486759 data_time: 0.040307 memory: 21657 loss_kpt: 0.000675 acc_pose: 0.790146 loss: 0.000675 2022/10/21 11:36:36 - mmengine - INFO - Epoch(train) [30][150/391] lr: 5.000000e-04 eta: 8:34:33 time: 0.484175 data_time: 0.039446 memory: 21657 loss_kpt: 0.000671 acc_pose: 0.779258 loss: 0.000671 2022/10/21 11:37:00 - mmengine - INFO - Epoch(train) [30][200/391] lr: 5.000000e-04 eta: 8:34:26 time: 0.485335 data_time: 0.044212 memory: 21657 loss_kpt: 0.000666 acc_pose: 0.807775 loss: 0.000666 2022/10/21 11:37:24 - mmengine - INFO - Epoch(train) [30][250/391] lr: 5.000000e-04 eta: 8:34:19 time: 0.486794 data_time: 0.041165 memory: 21657 loss_kpt: 0.000673 acc_pose: 0.810017 loss: 0.000673 2022/10/21 11:37:49 - mmengine - INFO - Epoch(train) [30][300/391] lr: 5.000000e-04 eta: 8:34:10 time: 0.481179 data_time: 0.044563 memory: 21657 loss_kpt: 0.000652 acc_pose: 0.778706 loss: 0.000652 2022/10/21 11:38:13 - mmengine - INFO - Epoch(train) [30][350/391] lr: 5.000000e-04 eta: 8:34:04 time: 0.490192 data_time: 0.038809 memory: 21657 loss_kpt: 0.000670 acc_pose: 0.796857 loss: 0.000670 2022/10/21 11:38:32 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:38:32 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/10/21 11:38:45 - mmengine - INFO - Epoch(val) [30][50/407] eta: 0:00:58 time: 0.164334 data_time: 0.020452 memory: 21657 2022/10/21 11:38:52 - mmengine - INFO - Epoch(val) [30][100/407] eta: 0:00:46 time: 0.152029 data_time: 0.009869 memory: 2142 2022/10/21 11:39:00 - mmengine - INFO - Epoch(val) [30][150/407] eta: 0:00:39 time: 0.154611 data_time: 0.012684 memory: 2142 2022/10/21 11:39:07 - mmengine - INFO - Epoch(val) [30][200/407] eta: 0:00:31 time: 0.150267 data_time: 0.009248 memory: 2142 2022/10/21 11:39:15 - mmengine - INFO - Epoch(val) [30][250/407] eta: 0:00:23 time: 0.151078 data_time: 0.009130 memory: 2142 2022/10/21 11:39:23 - mmengine - INFO - Epoch(val) [30][300/407] eta: 0:00:16 time: 0.152876 data_time: 0.009344 memory: 2142 2022/10/21 11:39:30 - mmengine - INFO - Epoch(val) [30][350/407] eta: 0:00:08 time: 0.150882 data_time: 0.009087 memory: 2142 2022/10/21 11:39:38 - mmengine - INFO - Epoch(val) [30][400/407] eta: 0:00:01 time: 0.148256 data_time: 0.008450 memory: 2142 2022/10/21 11:40:12 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 11:40:26 - mmengine - INFO - Epoch(val) [30][407/407] coco/AP: 0.714344 coco/AP .5: 0.888785 coco/AP .75: 0.778666 coco/AP (M): 0.673675 coco/AP (L): 0.787438 coco/AR: 0.767742 coco/AR .5: 0.927897 coco/AR .75: 0.826669 coco/AR (M): 0.720595 coco/AR (L): 0.835563 2022/10/21 11:40:26 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221021/resnetv1d152_384/best_coco/AP_epoch_20.pth is removed 2022/10/21 11:40:28 - mmengine - INFO - The best checkpoint with 0.7143 coco/AP at 30 epoch is saved to best_coco/AP_epoch_30.pth. 2022/10/21 11:40:53 - mmengine - INFO - Epoch(train) [31][50/391] lr: 5.000000e-04 eta: 8:31:55 time: 0.496956 data_time: 0.050551 memory: 21657 loss_kpt: 0.000675 acc_pose: 0.813535 loss: 0.000675 2022/10/21 11:41:17 - mmengine - INFO - Epoch(train) [31][100/391] lr: 5.000000e-04 eta: 8:31:47 time: 0.483320 data_time: 0.043525 memory: 21657 loss_kpt: 0.000655 acc_pose: 0.750186 loss: 0.000655 2022/10/21 11:41:42 - mmengine - INFO - Epoch(train) [31][150/391] lr: 5.000000e-04 eta: 8:31:39 time: 0.483647 data_time: 0.039649 memory: 21657 loss_kpt: 0.000660 acc_pose: 0.792206 loss: 0.000660 2022/10/21 11:42:06 - mmengine - INFO - Epoch(train) [31][200/391] lr: 5.000000e-04 eta: 8:31:31 time: 0.487052 data_time: 0.044233 memory: 21657 loss_kpt: 0.000663 acc_pose: 0.808522 loss: 0.000663 2022/10/21 11:42:30 - mmengine - INFO - Epoch(train) [31][250/391] lr: 5.000000e-04 eta: 8:31:22 time: 0.480785 data_time: 0.042085 memory: 21657 loss_kpt: 0.000661 acc_pose: 0.738853 loss: 0.000661 2022/10/21 11:42:40 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:42:55 - mmengine - INFO - Epoch(train) [31][300/391] lr: 5.000000e-04 eta: 8:31:16 time: 0.491101 data_time: 0.039748 memory: 21657 loss_kpt: 0.000654 acc_pose: 0.913653 loss: 0.000654 2022/10/21 11:43:19 - mmengine - INFO - Epoch(train) [31][350/391] lr: 5.000000e-04 eta: 8:31:07 time: 0.482543 data_time: 0.044061 memory: 21657 loss_kpt: 0.000658 acc_pose: 0.812254 loss: 0.000658 2022/10/21 11:43:39 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:44:03 - mmengine - INFO - Epoch(train) [32][50/391] lr: 5.000000e-04 eta: 8:29:01 time: 0.498161 data_time: 0.052866 memory: 21657 loss_kpt: 0.000675 acc_pose: 0.803015 loss: 0.000675 2022/10/21 11:44:28 - mmengine - INFO - Epoch(train) [32][100/391] lr: 5.000000e-04 eta: 8:28:55 time: 0.489387 data_time: 0.039651 memory: 21657 loss_kpt: 0.000662 acc_pose: 0.753380 loss: 0.000662 2022/10/21 11:44:52 - mmengine - INFO - Epoch(train) [32][150/391] lr: 5.000000e-04 eta: 8:28:45 time: 0.478760 data_time: 0.044759 memory: 21657 loss_kpt: 0.000646 acc_pose: 0.846183 loss: 0.000646 2022/10/21 11:45:16 - mmengine - INFO - Epoch(train) [32][200/391] lr: 5.000000e-04 eta: 8:28:37 time: 0.486160 data_time: 0.040939 memory: 21657 loss_kpt: 0.000659 acc_pose: 0.821443 loss: 0.000659 2022/10/21 11:45:41 - mmengine - INFO - Epoch(train) [32][250/391] lr: 5.000000e-04 eta: 8:28:29 time: 0.487215 data_time: 0.043425 memory: 21657 loss_kpt: 0.000665 acc_pose: 0.848455 loss: 0.000665 2022/10/21 11:46:05 - mmengine - INFO - Epoch(train) [32][300/391] lr: 5.000000e-04 eta: 8:28:19 time: 0.480663 data_time: 0.041258 memory: 21657 loss_kpt: 0.000661 acc_pose: 0.792888 loss: 0.000661 2022/10/21 11:46:29 - mmengine - INFO - Epoch(train) [32][350/391] lr: 5.000000e-04 eta: 8:28:10 time: 0.483942 data_time: 0.040405 memory: 21657 loss_kpt: 0.000669 acc_pose: 0.866684 loss: 0.000669 2022/10/21 11:46:48 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:47:13 - mmengine - INFO - Epoch(train) [33][50/391] lr: 5.000000e-04 eta: 8:26:09 time: 0.504689 data_time: 0.051762 memory: 21657 loss_kpt: 0.000647 acc_pose: 0.798393 loss: 0.000647 2022/10/21 11:47:38 - mmengine - INFO - Epoch(train) [33][100/391] lr: 5.000000e-04 eta: 8:26:00 time: 0.482927 data_time: 0.045316 memory: 21657 loss_kpt: 0.000655 acc_pose: 0.782945 loss: 0.000655 2022/10/21 11:48:02 - mmengine - INFO - Epoch(train) [33][150/391] lr: 5.000000e-04 eta: 8:25:53 time: 0.491310 data_time: 0.039871 memory: 21657 loss_kpt: 0.000661 acc_pose: 0.818227 loss: 0.000661 2022/10/21 11:48:26 - mmengine - INFO - Epoch(train) [33][200/391] lr: 5.000000e-04 eta: 8:25:42 time: 0.475760 data_time: 0.043597 memory: 21657 loss_kpt: 0.000661 acc_pose: 0.758192 loss: 0.000661 2022/10/21 11:48:51 - mmengine - INFO - Epoch(train) [33][250/391] lr: 5.000000e-04 eta: 8:25:35 time: 0.490276 data_time: 0.039821 memory: 21657 loss_kpt: 0.000655 acc_pose: 0.789762 loss: 0.000655 2022/10/21 11:49:15 - mmengine - INFO - Epoch(train) [33][300/391] lr: 5.000000e-04 eta: 8:25:25 time: 0.481681 data_time: 0.045267 memory: 21657 loss_kpt: 0.000634 acc_pose: 0.821354 loss: 0.000634 2022/10/21 11:49:39 - mmengine - INFO - Epoch(train) [33][350/391] lr: 5.000000e-04 eta: 8:25:15 time: 0.483471 data_time: 0.040474 memory: 21657 loss_kpt: 0.000653 acc_pose: 0.806862 loss: 0.000653 2022/10/21 11:49:58 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:50:23 - mmengine - INFO - Epoch(train) [34][50/391] lr: 5.000000e-04 eta: 8:23:15 time: 0.495914 data_time: 0.060641 memory: 21657 loss_kpt: 0.000655 acc_pose: 0.845715 loss: 0.000655 2022/10/21 11:50:46 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:50:48 - mmengine - INFO - Epoch(train) [34][100/391] lr: 5.000000e-04 eta: 8:23:07 time: 0.488243 data_time: 0.040033 memory: 21657 loss_kpt: 0.000632 acc_pose: 0.843188 loss: 0.000632 2022/10/21 11:51:12 - mmengine - INFO - Epoch(train) [34][150/391] lr: 5.000000e-04 eta: 8:22:56 time: 0.478717 data_time: 0.043180 memory: 21657 loss_kpt: 0.000675 acc_pose: 0.782268 loss: 0.000675 2022/10/21 11:51:36 - mmengine - INFO - Epoch(train) [34][200/391] lr: 5.000000e-04 eta: 8:22:48 time: 0.490381 data_time: 0.040051 memory: 21657 loss_kpt: 0.000639 acc_pose: 0.866220 loss: 0.000639 2022/10/21 11:52:00 - mmengine - INFO - Epoch(train) [34][250/391] lr: 5.000000e-04 eta: 8:22:38 time: 0.479797 data_time: 0.043622 memory: 21657 loss_kpt: 0.000661 acc_pose: 0.792559 loss: 0.000661 2022/10/21 11:52:25 - mmengine - INFO - Epoch(train) [34][300/391] lr: 5.000000e-04 eta: 8:22:31 time: 0.494835 data_time: 0.040042 memory: 21657 loss_kpt: 0.000658 acc_pose: 0.839955 loss: 0.000658 2022/10/21 11:52:49 - mmengine - INFO - Epoch(train) [34][350/391] lr: 5.000000e-04 eta: 8:22:21 time: 0.481938 data_time: 0.046644 memory: 21657 loss_kpt: 0.000646 acc_pose: 0.803902 loss: 0.000646 2022/10/21 11:53:08 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:53:34 - mmengine - INFO - Epoch(train) [35][50/391] lr: 5.000000e-04 eta: 8:20:26 time: 0.506781 data_time: 0.051641 memory: 21657 loss_kpt: 0.000663 acc_pose: 0.838408 loss: 0.000663 2022/10/21 11:53:58 - mmengine - INFO - Epoch(train) [35][100/391] lr: 5.000000e-04 eta: 8:20:17 time: 0.485654 data_time: 0.043236 memory: 21657 loss_kpt: 0.000655 acc_pose: 0.830747 loss: 0.000655 2022/10/21 11:54:22 - mmengine - INFO - Epoch(train) [35][150/391] lr: 5.000000e-04 eta: 8:20:07 time: 0.484497 data_time: 0.040280 memory: 21657 loss_kpt: 0.000635 acc_pose: 0.812575 loss: 0.000635 2022/10/21 11:54:46 - mmengine - INFO - Epoch(train) [35][200/391] lr: 5.000000e-04 eta: 8:19:56 time: 0.480807 data_time: 0.043963 memory: 21657 loss_kpt: 0.000635 acc_pose: 0.820027 loss: 0.000635 2022/10/21 11:55:11 - mmengine - INFO - Epoch(train) [35][250/391] lr: 5.000000e-04 eta: 8:19:47 time: 0.487026 data_time: 0.040508 memory: 21657 loss_kpt: 0.000650 acc_pose: 0.834543 loss: 0.000650 2022/10/21 11:55:35 - mmengine - INFO - Epoch(train) [35][300/391] lr: 5.000000e-04 eta: 8:19:37 time: 0.484179 data_time: 0.041257 memory: 21657 loss_kpt: 0.000652 acc_pose: 0.824623 loss: 0.000652 2022/10/21 11:55:59 - mmengine - INFO - Epoch(train) [35][350/391] lr: 5.000000e-04 eta: 8:19:28 time: 0.488265 data_time: 0.041315 memory: 21657 loss_kpt: 0.000655 acc_pose: 0.846984 loss: 0.000655 2022/10/21 11:56:19 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:56:44 - mmengine - INFO - Epoch(train) [36][50/391] lr: 5.000000e-04 eta: 8:17:33 time: 0.493081 data_time: 0.054182 memory: 21657 loss_kpt: 0.000660 acc_pose: 0.777540 loss: 0.000660 2022/10/21 11:57:08 - mmengine - INFO - Epoch(train) [36][100/391] lr: 5.000000e-04 eta: 8:17:23 time: 0.486704 data_time: 0.042868 memory: 21657 loss_kpt: 0.000638 acc_pose: 0.837812 loss: 0.000638 2022/10/21 11:57:32 - mmengine - INFO - Epoch(train) [36][150/391] lr: 5.000000e-04 eta: 8:17:13 time: 0.485329 data_time: 0.041153 memory: 21657 loss_kpt: 0.000642 acc_pose: 0.799562 loss: 0.000642 2022/10/21 11:57:56 - mmengine - INFO - Epoch(train) [36][200/391] lr: 5.000000e-04 eta: 8:17:02 time: 0.480551 data_time: 0.040413 memory: 21657 loss_kpt: 0.000641 acc_pose: 0.822467 loss: 0.000641 2022/10/21 11:58:21 - mmengine - INFO - Epoch(train) [36][250/391] lr: 5.000000e-04 eta: 8:16:53 time: 0.489134 data_time: 0.043735 memory: 21657 loss_kpt: 0.000651 acc_pose: 0.738453 loss: 0.000651 2022/10/21 11:58:45 - mmengine - INFO - Epoch(train) [36][300/391] lr: 5.000000e-04 eta: 8:16:42 time: 0.482473 data_time: 0.040339 memory: 21657 loss_kpt: 0.000634 acc_pose: 0.827921 loss: 0.000634 2022/10/21 11:58:52 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:59:09 - mmengine - INFO - Epoch(train) [36][350/391] lr: 5.000000e-04 eta: 8:16:32 time: 0.485684 data_time: 0.041006 memory: 21657 loss_kpt: 0.000638 acc_pose: 0.815494 loss: 0.000638 2022/10/21 11:59:29 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 11:59:54 - mmengine - INFO - Epoch(train) [37][50/391] lr: 5.000000e-04 eta: 8:14:40 time: 0.497481 data_time: 0.055369 memory: 21657 loss_kpt: 0.000652 acc_pose: 0.795795 loss: 0.000652 2022/10/21 12:00:18 - mmengine - INFO - Epoch(train) [37][100/391] lr: 5.000000e-04 eta: 8:14:31 time: 0.487728 data_time: 0.038653 memory: 21657 loss_kpt: 0.000634 acc_pose: 0.843433 loss: 0.000634 2022/10/21 12:00:42 - mmengine - INFO - Epoch(train) [37][150/391] lr: 5.000000e-04 eta: 8:14:20 time: 0.481862 data_time: 0.041463 memory: 21657 loss_kpt: 0.000644 acc_pose: 0.873154 loss: 0.000644 2022/10/21 12:01:07 - mmengine - INFO - Epoch(train) [37][200/391] lr: 5.000000e-04 eta: 8:14:09 time: 0.483222 data_time: 0.040780 memory: 21657 loss_kpt: 0.000647 acc_pose: 0.820430 loss: 0.000647 2022/10/21 12:01:31 - mmengine - INFO - Epoch(train) [37][250/391] lr: 5.000000e-04 eta: 8:13:58 time: 0.482853 data_time: 0.040110 memory: 21657 loss_kpt: 0.000625 acc_pose: 0.836697 loss: 0.000625 2022/10/21 12:01:55 - mmengine - INFO - Epoch(train) [37][300/391] lr: 5.000000e-04 eta: 8:13:48 time: 0.489423 data_time: 0.041009 memory: 21657 loss_kpt: 0.000639 acc_pose: 0.845293 loss: 0.000639 2022/10/21 12:02:19 - mmengine - INFO - Epoch(train) [37][350/391] lr: 5.000000e-04 eta: 8:13:37 time: 0.482384 data_time: 0.040453 memory: 21657 loss_kpt: 0.000648 acc_pose: 0.809177 loss: 0.000648 2022/10/21 12:02:39 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:03:04 - mmengine - INFO - Epoch(train) [38][50/391] lr: 5.000000e-04 eta: 8:11:49 time: 0.505268 data_time: 0.051547 memory: 21657 loss_kpt: 0.000641 acc_pose: 0.795590 loss: 0.000641 2022/10/21 12:03:28 - mmengine - INFO - Epoch(train) [38][100/391] lr: 5.000000e-04 eta: 8:11:37 time: 0.477738 data_time: 0.044562 memory: 21657 loss_kpt: 0.000640 acc_pose: 0.808199 loss: 0.000640 2022/10/21 12:03:53 - mmengine - INFO - Epoch(train) [38][150/391] lr: 5.000000e-04 eta: 8:11:28 time: 0.492906 data_time: 0.039837 memory: 21657 loss_kpt: 0.000653 acc_pose: 0.826188 loss: 0.000653 2022/10/21 12:04:17 - mmengine - INFO - Epoch(train) [38][200/391] lr: 5.000000e-04 eta: 8:11:16 time: 0.480891 data_time: 0.039796 memory: 21657 loss_kpt: 0.000618 acc_pose: 0.802828 loss: 0.000618 2022/10/21 12:04:41 - mmengine - INFO - Epoch(train) [38][250/391] lr: 5.000000e-04 eta: 8:11:06 time: 0.487170 data_time: 0.040707 memory: 21657 loss_kpt: 0.000647 acc_pose: 0.849911 loss: 0.000647 2022/10/21 12:05:05 - mmengine - INFO - Epoch(train) [38][300/391] lr: 5.000000e-04 eta: 8:10:55 time: 0.484359 data_time: 0.045321 memory: 21657 loss_kpt: 0.000656 acc_pose: 0.780591 loss: 0.000656 2022/10/21 12:05:29 - mmengine - INFO - Epoch(train) [38][350/391] lr: 5.000000e-04 eta: 8:10:44 time: 0.485216 data_time: 0.039760 memory: 21657 loss_kpt: 0.000640 acc_pose: 0.856768 loss: 0.000640 2022/10/21 12:05:49 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:06:14 - mmengine - INFO - Epoch(train) [39][50/391] lr: 5.000000e-04 eta: 8:08:57 time: 0.501034 data_time: 0.057288 memory: 21657 loss_kpt: 0.000660 acc_pose: 0.815635 loss: 0.000660 2022/10/21 12:06:39 - mmengine - INFO - Epoch(train) [39][100/391] lr: 5.000000e-04 eta: 8:08:47 time: 0.486162 data_time: 0.040656 memory: 21657 loss_kpt: 0.000632 acc_pose: 0.809110 loss: 0.000632 2022/10/21 12:06:59 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:07:02 - mmengine - INFO - Epoch(train) [39][150/391] lr: 5.000000e-04 eta: 8:08:34 time: 0.478315 data_time: 0.044349 memory: 21657 loss_kpt: 0.000646 acc_pose: 0.805694 loss: 0.000646 2022/10/21 12:07:27 - mmengine - INFO - Epoch(train) [39][200/391] lr: 5.000000e-04 eta: 8:08:25 time: 0.493754 data_time: 0.039613 memory: 21657 loss_kpt: 0.000650 acc_pose: 0.872199 loss: 0.000650 2022/10/21 12:07:51 - mmengine - INFO - Epoch(train) [39][250/391] lr: 5.000000e-04 eta: 8:08:12 time: 0.477137 data_time: 0.044589 memory: 21657 loss_kpt: 0.000631 acc_pose: 0.775186 loss: 0.000631 2022/10/21 12:08:16 - mmengine - INFO - Epoch(train) [39][300/391] lr: 5.000000e-04 eta: 8:08:02 time: 0.490304 data_time: 0.040278 memory: 21657 loss_kpt: 0.000631 acc_pose: 0.850192 loss: 0.000631 2022/10/21 12:08:40 - mmengine - INFO - Epoch(train) [39][350/391] lr: 5.000000e-04 eta: 8:07:49 time: 0.480490 data_time: 0.044937 memory: 21657 loss_kpt: 0.000668 acc_pose: 0.774034 loss: 0.000668 2022/10/21 12:08:59 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:09:24 - mmengine - INFO - Epoch(train) [40][50/391] lr: 5.000000e-04 eta: 8:06:05 time: 0.498397 data_time: 0.054326 memory: 21657 loss_kpt: 0.000632 acc_pose: 0.787206 loss: 0.000632 2022/10/21 12:09:49 - mmengine - INFO - Epoch(train) [40][100/391] lr: 5.000000e-04 eta: 8:05:54 time: 0.488672 data_time: 0.045719 memory: 21657 loss_kpt: 0.000637 acc_pose: 0.798226 loss: 0.000637 2022/10/21 12:10:13 - mmengine - INFO - Epoch(train) [40][150/391] lr: 5.000000e-04 eta: 8:05:44 time: 0.489055 data_time: 0.041171 memory: 21657 loss_kpt: 0.000638 acc_pose: 0.853240 loss: 0.000638 2022/10/21 12:10:37 - mmengine - INFO - Epoch(train) [40][200/391] lr: 5.000000e-04 eta: 8:05:31 time: 0.479624 data_time: 0.039879 memory: 21657 loss_kpt: 0.000627 acc_pose: 0.858175 loss: 0.000627 2022/10/21 12:11:02 - mmengine - INFO - Epoch(train) [40][250/391] lr: 5.000000e-04 eta: 8:05:22 time: 0.496470 data_time: 0.039651 memory: 21657 loss_kpt: 0.000628 acc_pose: 0.841805 loss: 0.000628 2022/10/21 12:11:26 - mmengine - INFO - Epoch(train) [40][300/391] lr: 5.000000e-04 eta: 8:05:09 time: 0.477298 data_time: 0.043963 memory: 21657 loss_kpt: 0.000637 acc_pose: 0.833173 loss: 0.000637 2022/10/21 12:11:50 - mmengine - INFO - Epoch(train) [40][350/391] lr: 5.000000e-04 eta: 8:04:59 time: 0.492883 data_time: 0.040090 memory: 21657 loss_kpt: 0.000627 acc_pose: 0.797034 loss: 0.000627 2022/10/21 12:12:10 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:12:10 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/10/21 12:12:22 - mmengine - INFO - Epoch(val) [40][50/407] eta: 0:00:56 time: 0.157732 data_time: 0.015069 memory: 21657 2022/10/21 12:12:30 - mmengine - INFO - Epoch(val) [40][100/407] eta: 0:00:46 time: 0.151149 data_time: 0.009198 memory: 2142 2022/10/21 12:12:37 - mmengine - INFO - Epoch(val) [40][150/407] eta: 0:00:38 time: 0.150654 data_time: 0.008920 memory: 2142 2022/10/21 12:12:45 - mmengine - INFO - Epoch(val) [40][200/407] eta: 0:00:32 time: 0.155539 data_time: 0.011684 memory: 2142 2022/10/21 12:12:53 - mmengine - INFO - Epoch(val) [40][250/407] eta: 0:00:24 time: 0.156325 data_time: 0.013170 memory: 2142 2022/10/21 12:13:00 - mmengine - INFO - Epoch(val) [40][300/407] eta: 0:00:16 time: 0.151929 data_time: 0.009866 memory: 2142 2022/10/21 12:13:08 - mmengine - INFO - Epoch(val) [40][350/407] eta: 0:00:08 time: 0.151011 data_time: 0.009198 memory: 2142 2022/10/21 12:13:15 - mmengine - INFO - Epoch(val) [40][400/407] eta: 0:00:01 time: 0.148781 data_time: 0.009619 memory: 2142 2022/10/21 12:13:50 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 12:14:04 - mmengine - INFO - Epoch(val) [40][407/407] coco/AP: 0.722604 coco/AP .5: 0.894310 coco/AP .75: 0.790350 coco/AP (M): 0.681374 coco/AP (L): 0.793582 coco/AR: 0.776008 coco/AR .5: 0.931518 coco/AR .75: 0.837846 coco/AR (M): 0.730565 coco/AR (L): 0.841546 2022/10/21 12:14:04 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221021/resnetv1d152_384/best_coco/AP_epoch_30.pth is removed 2022/10/21 12:14:06 - mmengine - INFO - The best checkpoint with 0.7226 coco/AP at 40 epoch is saved to best_coco/AP_epoch_40.pth. 2022/10/21 12:14:31 - mmengine - INFO - Epoch(train) [41][50/391] lr: 5.000000e-04 eta: 8:03:16 time: 0.501420 data_time: 0.050836 memory: 21657 loss_kpt: 0.000642 acc_pose: 0.838124 loss: 0.000642 2022/10/21 12:14:56 - mmengine - INFO - Epoch(train) [41][100/391] lr: 5.000000e-04 eta: 8:03:04 time: 0.482419 data_time: 0.040565 memory: 21657 loss_kpt: 0.000640 acc_pose: 0.758737 loss: 0.000640 2022/10/21 12:15:20 - mmengine - INFO - Epoch(train) [41][150/391] lr: 5.000000e-04 eta: 8:02:53 time: 0.485590 data_time: 0.039995 memory: 21657 loss_kpt: 0.000638 acc_pose: 0.829281 loss: 0.000638 2022/10/21 12:15:44 - mmengine - INFO - Epoch(train) [41][200/391] lr: 5.000000e-04 eta: 8:02:41 time: 0.486868 data_time: 0.039736 memory: 21657 loss_kpt: 0.000636 acc_pose: 0.824098 loss: 0.000636 2022/10/21 12:16:08 - mmengine - INFO - Epoch(train) [41][250/391] lr: 5.000000e-04 eta: 8:02:29 time: 0.483509 data_time: 0.040162 memory: 21657 loss_kpt: 0.000648 acc_pose: 0.848370 loss: 0.000648 2022/10/21 12:16:33 - mmengine - INFO - Epoch(train) [41][300/391] lr: 5.000000e-04 eta: 8:02:18 time: 0.487923 data_time: 0.044810 memory: 21657 loss_kpt: 0.000633 acc_pose: 0.819455 loss: 0.000633 2022/10/21 12:16:57 - mmengine - INFO - Epoch(train) [41][350/391] lr: 5.000000e-04 eta: 8:02:04 time: 0.475046 data_time: 0.040324 memory: 21657 loss_kpt: 0.000631 acc_pose: 0.812349 loss: 0.000631 2022/10/21 12:17:02 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:17:16 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:17:41 - mmengine - INFO - Epoch(train) [42][50/391] lr: 5.000000e-04 eta: 8:00:21 time: 0.490399 data_time: 0.050982 memory: 21657 loss_kpt: 0.000641 acc_pose: 0.773573 loss: 0.000641 2022/10/21 12:18:06 - mmengine - INFO - Epoch(train) [42][100/391] lr: 5.000000e-04 eta: 8:00:11 time: 0.492549 data_time: 0.040796 memory: 21657 loss_kpt: 0.000626 acc_pose: 0.829259 loss: 0.000626 2022/10/21 12:18:30 - mmengine - INFO - Epoch(train) [42][150/391] lr: 5.000000e-04 eta: 7:59:58 time: 0.481422 data_time: 0.040497 memory: 21657 loss_kpt: 0.000641 acc_pose: 0.792900 loss: 0.000641 2022/10/21 12:18:54 - mmengine - INFO - Epoch(train) [42][200/391] lr: 5.000000e-04 eta: 7:59:47 time: 0.487957 data_time: 0.040235 memory: 21657 loss_kpt: 0.000635 acc_pose: 0.861214 loss: 0.000635 2022/10/21 12:19:18 - mmengine - INFO - Epoch(train) [42][250/391] lr: 5.000000e-04 eta: 7:59:35 time: 0.487816 data_time: 0.040347 memory: 21657 loss_kpt: 0.000640 acc_pose: 0.853145 loss: 0.000640 2022/10/21 12:19:43 - mmengine - INFO - Epoch(train) [42][300/391] lr: 5.000000e-04 eta: 7:59:22 time: 0.483140 data_time: 0.039273 memory: 21657 loss_kpt: 0.000638 acc_pose: 0.819417 loss: 0.000638 2022/10/21 12:20:07 - mmengine - INFO - Epoch(train) [42][350/391] lr: 5.000000e-04 eta: 7:59:11 time: 0.491021 data_time: 0.044321 memory: 21657 loss_kpt: 0.000636 acc_pose: 0.841077 loss: 0.000636 2022/10/21 12:20:27 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:20:52 - mmengine - INFO - Epoch(train) [43][50/391] lr: 5.000000e-04 eta: 7:57:34 time: 0.504680 data_time: 0.058052 memory: 21657 loss_kpt: 0.000642 acc_pose: 0.835190 loss: 0.000642 2022/10/21 12:21:16 - mmengine - INFO - Epoch(train) [43][100/391] lr: 5.000000e-04 eta: 7:57:20 time: 0.479647 data_time: 0.040111 memory: 21657 loss_kpt: 0.000631 acc_pose: 0.847564 loss: 0.000631 2022/10/21 12:21:41 - mmengine - INFO - Epoch(train) [43][150/391] lr: 5.000000e-04 eta: 7:57:09 time: 0.491846 data_time: 0.043090 memory: 21657 loss_kpt: 0.000629 acc_pose: 0.837834 loss: 0.000629 2022/10/21 12:22:05 - mmengine - INFO - Epoch(train) [43][200/391] lr: 5.000000e-04 eta: 7:56:56 time: 0.481768 data_time: 0.041003 memory: 21657 loss_kpt: 0.000642 acc_pose: 0.790941 loss: 0.000642 2022/10/21 12:22:29 - mmengine - INFO - Epoch(train) [43][250/391] lr: 5.000000e-04 eta: 7:56:45 time: 0.490502 data_time: 0.045749 memory: 21657 loss_kpt: 0.000639 acc_pose: 0.834902 loss: 0.000639 2022/10/21 12:22:53 - mmengine - INFO - Epoch(train) [43][300/391] lr: 5.000000e-04 eta: 7:56:33 time: 0.486877 data_time: 0.041736 memory: 21657 loss_kpt: 0.000624 acc_pose: 0.801763 loss: 0.000624 2022/10/21 12:23:18 - mmengine - INFO - Epoch(train) [43][350/391] lr: 5.000000e-04 eta: 7:56:21 time: 0.488306 data_time: 0.039793 memory: 21657 loss_kpt: 0.000627 acc_pose: 0.824440 loss: 0.000627 2022/10/21 12:23:37 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:24:02 - mmengine - INFO - Epoch(train) [44][50/391] lr: 5.000000e-04 eta: 7:54:43 time: 0.496265 data_time: 0.053864 memory: 21657 loss_kpt: 0.000636 acc_pose: 0.868921 loss: 0.000636 2022/10/21 12:24:27 - mmengine - INFO - Epoch(train) [44][100/391] lr: 5.000000e-04 eta: 7:54:31 time: 0.488866 data_time: 0.043885 memory: 21657 loss_kpt: 0.000631 acc_pose: 0.785510 loss: 0.000631 2022/10/21 12:24:51 - mmengine - INFO - Epoch(train) [44][150/391] lr: 5.000000e-04 eta: 7:54:20 time: 0.493668 data_time: 0.040019 memory: 21657 loss_kpt: 0.000623 acc_pose: 0.778763 loss: 0.000623 2022/10/21 12:25:10 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:25:16 - mmengine - INFO - Epoch(train) [44][200/391] lr: 5.000000e-04 eta: 7:54:08 time: 0.487674 data_time: 0.044689 memory: 21657 loss_kpt: 0.000651 acc_pose: 0.783232 loss: 0.000651 2022/10/21 12:25:40 - mmengine - INFO - Epoch(train) [44][250/391] lr: 5.000000e-04 eta: 7:53:56 time: 0.487177 data_time: 0.041057 memory: 21657 loss_kpt: 0.000626 acc_pose: 0.848065 loss: 0.000626 2022/10/21 12:26:05 - mmengine - INFO - Epoch(train) [44][300/391] lr: 5.000000e-04 eta: 7:53:44 time: 0.489077 data_time: 0.045877 memory: 21657 loss_kpt: 0.000620 acc_pose: 0.810638 loss: 0.000620 2022/10/21 12:26:29 - mmengine - INFO - Epoch(train) [44][350/391] lr: 5.000000e-04 eta: 7:53:31 time: 0.484475 data_time: 0.043037 memory: 21657 loss_kpt: 0.000623 acc_pose: 0.801176 loss: 0.000623 2022/10/21 12:26:49 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:27:14 - mmengine - INFO - Epoch(train) [45][50/391] lr: 5.000000e-04 eta: 7:51:56 time: 0.501680 data_time: 0.054763 memory: 21657 loss_kpt: 0.000620 acc_pose: 0.777977 loss: 0.000620 2022/10/21 12:27:38 - mmengine - INFO - Epoch(train) [45][100/391] lr: 5.000000e-04 eta: 7:51:43 time: 0.482577 data_time: 0.040841 memory: 21657 loss_kpt: 0.000604 acc_pose: 0.858236 loss: 0.000604 2022/10/21 12:28:02 - mmengine - INFO - Epoch(train) [45][150/391] lr: 5.000000e-04 eta: 7:51:31 time: 0.488399 data_time: 0.042084 memory: 21657 loss_kpt: 0.000622 acc_pose: 0.816373 loss: 0.000622 2022/10/21 12:28:27 - mmengine - INFO - Epoch(train) [45][200/391] lr: 5.000000e-04 eta: 7:51:18 time: 0.488972 data_time: 0.039700 memory: 21657 loss_kpt: 0.000623 acc_pose: 0.842376 loss: 0.000623 2022/10/21 12:28:51 - mmengine - INFO - Epoch(train) [45][250/391] lr: 5.000000e-04 eta: 7:51:06 time: 0.487074 data_time: 0.040461 memory: 21657 loss_kpt: 0.000631 acc_pose: 0.828327 loss: 0.000631 2022/10/21 12:29:15 - mmengine - INFO - Epoch(train) [45][300/391] lr: 5.000000e-04 eta: 7:50:53 time: 0.485502 data_time: 0.040324 memory: 21657 loss_kpt: 0.000619 acc_pose: 0.801556 loss: 0.000619 2022/10/21 12:29:40 - mmengine - INFO - Epoch(train) [45][350/391] lr: 5.000000e-04 eta: 7:50:40 time: 0.484018 data_time: 0.044076 memory: 21657 loss_kpt: 0.000616 acc_pose: 0.885464 loss: 0.000616 2022/10/21 12:29:59 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:30:24 - mmengine - INFO - Epoch(train) [46][50/391] lr: 5.000000e-04 eta: 7:49:05 time: 0.497607 data_time: 0.051262 memory: 21657 loss_kpt: 0.000641 acc_pose: 0.850681 loss: 0.000641 2022/10/21 12:30:48 - mmengine - INFO - Epoch(train) [46][100/391] lr: 5.000000e-04 eta: 7:48:53 time: 0.489910 data_time: 0.043325 memory: 21657 loss_kpt: 0.000610 acc_pose: 0.841143 loss: 0.000610 2022/10/21 12:31:13 - mmengine - INFO - Epoch(train) [46][150/391] lr: 5.000000e-04 eta: 7:48:40 time: 0.486412 data_time: 0.039375 memory: 21657 loss_kpt: 0.000614 acc_pose: 0.864409 loss: 0.000614 2022/10/21 12:31:37 - mmengine - INFO - Epoch(train) [46][200/391] lr: 5.000000e-04 eta: 7:48:28 time: 0.492401 data_time: 0.042857 memory: 21657 loss_kpt: 0.000629 acc_pose: 0.836701 loss: 0.000629 2022/10/21 12:32:02 - mmengine - INFO - Epoch(train) [46][250/391] lr: 5.000000e-04 eta: 7:48:15 time: 0.484174 data_time: 0.041225 memory: 21657 loss_kpt: 0.000623 acc_pose: 0.866728 loss: 0.000623 2022/10/21 12:32:26 - mmengine - INFO - Epoch(train) [46][300/391] lr: 5.000000e-04 eta: 7:48:03 time: 0.489565 data_time: 0.044306 memory: 21657 loss_kpt: 0.000621 acc_pose: 0.882245 loss: 0.000621 2022/10/21 12:32:50 - mmengine - INFO - Epoch(train) [46][350/391] lr: 5.000000e-04 eta: 7:47:49 time: 0.484146 data_time: 0.040881 memory: 21657 loss_kpt: 0.000610 acc_pose: 0.853719 loss: 0.000610 2022/10/21 12:33:10 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:33:18 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:33:35 - mmengine - INFO - Epoch(train) [47][50/391] lr: 5.000000e-04 eta: 7:46:17 time: 0.500278 data_time: 0.054599 memory: 21657 loss_kpt: 0.000625 acc_pose: 0.725478 loss: 0.000625 2022/10/21 12:33:59 - mmengine - INFO - Epoch(train) [47][100/391] lr: 5.000000e-04 eta: 7:46:05 time: 0.491487 data_time: 0.041322 memory: 21657 loss_kpt: 0.000620 acc_pose: 0.882487 loss: 0.000620 2022/10/21 12:34:23 - mmengine - INFO - Epoch(train) [47][150/391] lr: 5.000000e-04 eta: 7:45:51 time: 0.481634 data_time: 0.044297 memory: 21657 loss_kpt: 0.000617 acc_pose: 0.839665 loss: 0.000617 2022/10/21 12:34:48 - mmengine - INFO - Epoch(train) [47][200/391] lr: 5.000000e-04 eta: 7:45:38 time: 0.487460 data_time: 0.039745 memory: 21657 loss_kpt: 0.000618 acc_pose: 0.812640 loss: 0.000618 2022/10/21 12:35:12 - mmengine - INFO - Epoch(train) [47][250/391] lr: 5.000000e-04 eta: 7:45:24 time: 0.485215 data_time: 0.043776 memory: 21657 loss_kpt: 0.000641 acc_pose: 0.771505 loss: 0.000641 2022/10/21 12:35:36 - mmengine - INFO - Epoch(train) [47][300/391] lr: 5.000000e-04 eta: 7:45:10 time: 0.482130 data_time: 0.039945 memory: 21657 loss_kpt: 0.000621 acc_pose: 0.823497 loss: 0.000621 2022/10/21 12:36:01 - mmengine - INFO - Epoch(train) [47][350/391] lr: 5.000000e-04 eta: 7:44:58 time: 0.489727 data_time: 0.042781 memory: 21657 loss_kpt: 0.000615 acc_pose: 0.845111 loss: 0.000615 2022/10/21 12:36:20 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:36:45 - mmengine - INFO - Epoch(train) [48][50/391] lr: 5.000000e-04 eta: 7:43:27 time: 0.500479 data_time: 0.052781 memory: 21657 loss_kpt: 0.000606 acc_pose: 0.780559 loss: 0.000606 2022/10/21 12:37:10 - mmengine - INFO - Epoch(train) [48][100/391] lr: 5.000000e-04 eta: 7:43:13 time: 0.484531 data_time: 0.040593 memory: 21657 loss_kpt: 0.000608 acc_pose: 0.878742 loss: 0.000608 2022/10/21 12:37:34 - mmengine - INFO - Epoch(train) [48][150/391] lr: 5.000000e-04 eta: 7:43:00 time: 0.485529 data_time: 0.041148 memory: 21657 loss_kpt: 0.000600 acc_pose: 0.837674 loss: 0.000600 2022/10/21 12:37:58 - mmengine - INFO - Epoch(train) [48][200/391] lr: 5.000000e-04 eta: 7:42:46 time: 0.485446 data_time: 0.045363 memory: 21657 loss_kpt: 0.000616 acc_pose: 0.820149 loss: 0.000616 2022/10/21 12:38:23 - mmengine - INFO - Epoch(train) [48][250/391] lr: 5.000000e-04 eta: 7:42:33 time: 0.488491 data_time: 0.040814 memory: 21657 loss_kpt: 0.000614 acc_pose: 0.850173 loss: 0.000614 2022/10/21 12:38:47 - mmengine - INFO - Epoch(train) [48][300/391] lr: 5.000000e-04 eta: 7:42:19 time: 0.482723 data_time: 0.043260 memory: 21657 loss_kpt: 0.000621 acc_pose: 0.820284 loss: 0.000621 2022/10/21 12:39:11 - mmengine - INFO - Epoch(train) [48][350/391] lr: 5.000000e-04 eta: 7:42:05 time: 0.487437 data_time: 0.040797 memory: 21657 loss_kpt: 0.000624 acc_pose: 0.785709 loss: 0.000624 2022/10/21 12:39:31 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:39:56 - mmengine - INFO - Epoch(train) [49][50/391] lr: 5.000000e-04 eta: 7:40:37 time: 0.509136 data_time: 0.050143 memory: 21657 loss_kpt: 0.000595 acc_pose: 0.829623 loss: 0.000595 2022/10/21 12:40:21 - mmengine - INFO - Epoch(train) [49][100/391] lr: 5.000000e-04 eta: 7:40:24 time: 0.484270 data_time: 0.043325 memory: 21657 loss_kpt: 0.000642 acc_pose: 0.816210 loss: 0.000642 2022/10/21 12:40:45 - mmengine - INFO - Epoch(train) [49][150/391] lr: 5.000000e-04 eta: 7:40:10 time: 0.488614 data_time: 0.039264 memory: 21657 loss_kpt: 0.000605 acc_pose: 0.799446 loss: 0.000605 2022/10/21 12:41:09 - mmengine - INFO - Epoch(train) [49][200/391] lr: 5.000000e-04 eta: 7:39:56 time: 0.480754 data_time: 0.039584 memory: 21657 loss_kpt: 0.000610 acc_pose: 0.868361 loss: 0.000610 2022/10/21 12:41:25 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:41:33 - mmengine - INFO - Epoch(train) [49][250/391] lr: 5.000000e-04 eta: 7:39:42 time: 0.487319 data_time: 0.044044 memory: 21657 loss_kpt: 0.000618 acc_pose: 0.867780 loss: 0.000618 2022/10/21 12:41:58 - mmengine - INFO - Epoch(train) [49][300/391] lr: 5.000000e-04 eta: 7:39:28 time: 0.486073 data_time: 0.039706 memory: 21657 loss_kpt: 0.000618 acc_pose: 0.829630 loss: 0.000618 2022/10/21 12:42:22 - mmengine - INFO - Epoch(train) [49][350/391] lr: 5.000000e-04 eta: 7:39:14 time: 0.480871 data_time: 0.040448 memory: 21657 loss_kpt: 0.000608 acc_pose: 0.843383 loss: 0.000608 2022/10/21 12:42:41 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:43:06 - mmengine - INFO - Epoch(train) [50][50/391] lr: 5.000000e-04 eta: 7:37:45 time: 0.495406 data_time: 0.055743 memory: 21657 loss_kpt: 0.000616 acc_pose: 0.838031 loss: 0.000616 2022/10/21 12:43:30 - mmengine - INFO - Epoch(train) [50][100/391] lr: 5.000000e-04 eta: 7:37:31 time: 0.483951 data_time: 0.039654 memory: 21657 loss_kpt: 0.000616 acc_pose: 0.810248 loss: 0.000616 2022/10/21 12:43:55 - mmengine - INFO - Epoch(train) [50][150/391] lr: 5.000000e-04 eta: 7:37:17 time: 0.487029 data_time: 0.039517 memory: 21657 loss_kpt: 0.000607 acc_pose: 0.849262 loss: 0.000607 2022/10/21 12:44:19 - mmengine - INFO - Epoch(train) [50][200/391] lr: 5.000000e-04 eta: 7:37:03 time: 0.485908 data_time: 0.040417 memory: 21657 loss_kpt: 0.000598 acc_pose: 0.829414 loss: 0.000598 2022/10/21 12:44:44 - mmengine - INFO - Epoch(train) [50][250/391] lr: 5.000000e-04 eta: 7:36:50 time: 0.489370 data_time: 0.039840 memory: 21657 loss_kpt: 0.000625 acc_pose: 0.884053 loss: 0.000625 2022/10/21 12:45:08 - mmengine - INFO - Epoch(train) [50][300/391] lr: 5.000000e-04 eta: 7:36:35 time: 0.482482 data_time: 0.039205 memory: 21657 loss_kpt: 0.000623 acc_pose: 0.812872 loss: 0.000623 2022/10/21 12:45:32 - mmengine - INFO - Epoch(train) [50][350/391] lr: 5.000000e-04 eta: 7:36:22 time: 0.488953 data_time: 0.040196 memory: 21657 loss_kpt: 0.000606 acc_pose: 0.829183 loss: 0.000606 2022/10/21 12:45:51 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:45:51 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/10/21 12:46:04 - mmengine - INFO - Epoch(val) [50][50/407] eta: 0:00:57 time: 0.160085 data_time: 0.017591 memory: 21657 2022/10/21 12:46:11 - mmengine - INFO - Epoch(val) [50][100/407] eta: 0:00:46 time: 0.150641 data_time: 0.008912 memory: 2142 2022/10/21 12:46:19 - mmengine - INFO - Epoch(val) [50][150/407] eta: 0:00:39 time: 0.152170 data_time: 0.009410 memory: 2142 2022/10/21 12:46:26 - mmengine - INFO - Epoch(val) [50][200/407] eta: 0:00:31 time: 0.149881 data_time: 0.008885 memory: 2142 2022/10/21 12:46:34 - mmengine - INFO - Epoch(val) [50][250/407] eta: 0:00:23 time: 0.150680 data_time: 0.008873 memory: 2142 2022/10/21 12:46:41 - mmengine - INFO - Epoch(val) [50][300/407] eta: 0:00:16 time: 0.150583 data_time: 0.009232 memory: 2142 2022/10/21 12:46:49 - mmengine - INFO - Epoch(val) [50][350/407] eta: 0:00:08 time: 0.151473 data_time: 0.009172 memory: 2142 2022/10/21 12:46:56 - mmengine - INFO - Epoch(val) [50][400/407] eta: 0:00:01 time: 0.149036 data_time: 0.008478 memory: 2142 2022/10/21 12:47:31 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 12:47:45 - mmengine - INFO - Epoch(val) [50][407/407] coco/AP: 0.729465 coco/AP .5: 0.895290 coco/AP .75: 0.797126 coco/AP (M): 0.685953 coco/AP (L): 0.802810 coco/AR: 0.780825 coco/AR .5: 0.933249 coco/AR .75: 0.840523 coco/AR (M): 0.734663 coco/AR (L): 0.847232 2022/10/21 12:47:45 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221021/resnetv1d152_384/best_coco/AP_epoch_40.pth is removed 2022/10/21 12:47:47 - mmengine - INFO - The best checkpoint with 0.7295 coco/AP at 50 epoch is saved to best_coco/AP_epoch_50.pth. 2022/10/21 12:48:12 - mmengine - INFO - Epoch(train) [51][50/391] lr: 5.000000e-04 eta: 7:34:55 time: 0.502444 data_time: 0.056107 memory: 21657 loss_kpt: 0.000615 acc_pose: 0.847337 loss: 0.000615 2022/10/21 12:48:37 - mmengine - INFO - Epoch(train) [51][100/391] lr: 5.000000e-04 eta: 7:34:40 time: 0.482081 data_time: 0.039206 memory: 21657 loss_kpt: 0.000612 acc_pose: 0.855646 loss: 0.000612 2022/10/21 12:49:01 - mmengine - INFO - Epoch(train) [51][150/391] lr: 5.000000e-04 eta: 7:34:26 time: 0.483945 data_time: 0.040358 memory: 21657 loss_kpt: 0.000605 acc_pose: 0.811245 loss: 0.000605 2022/10/21 12:49:25 - mmengine - INFO - Epoch(train) [51][200/391] lr: 5.000000e-04 eta: 7:34:12 time: 0.484560 data_time: 0.043808 memory: 21657 loss_kpt: 0.000623 acc_pose: 0.837411 loss: 0.000623 2022/10/21 12:49:49 - mmengine - INFO - Epoch(train) [51][250/391] lr: 5.000000e-04 eta: 7:33:57 time: 0.483833 data_time: 0.039572 memory: 21657 loss_kpt: 0.000610 acc_pose: 0.809503 loss: 0.000610 2022/10/21 12:50:14 - mmengine - INFO - Epoch(train) [51][300/391] lr: 5.000000e-04 eta: 7:33:44 time: 0.490493 data_time: 0.040287 memory: 21657 loss_kpt: 0.000605 acc_pose: 0.847262 loss: 0.000605 2022/10/21 12:50:38 - mmengine - INFO - Epoch(train) [51][350/391] lr: 5.000000e-04 eta: 7:33:28 time: 0.480002 data_time: 0.039994 memory: 21657 loss_kpt: 0.000629 acc_pose: 0.829201 loss: 0.000629 2022/10/21 12:50:58 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:51:22 - mmengine - INFO - Epoch(train) [52][50/391] lr: 5.000000e-04 eta: 7:32:02 time: 0.495828 data_time: 0.052465 memory: 21657 loss_kpt: 0.000610 acc_pose: 0.892412 loss: 0.000610 2022/10/21 12:51:27 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:51:47 - mmengine - INFO - Epoch(train) [52][100/391] lr: 5.000000e-04 eta: 7:31:49 time: 0.490667 data_time: 0.044088 memory: 21657 loss_kpt: 0.000608 acc_pose: 0.773289 loss: 0.000608 2022/10/21 12:52:11 - mmengine - INFO - Epoch(train) [52][150/391] lr: 5.000000e-04 eta: 7:31:34 time: 0.483516 data_time: 0.041985 memory: 21657 loss_kpt: 0.000602 acc_pose: 0.784495 loss: 0.000602 2022/10/21 12:52:36 - mmengine - INFO - Epoch(train) [52][200/391] lr: 5.000000e-04 eta: 7:31:20 time: 0.490301 data_time: 0.041697 memory: 21657 loss_kpt: 0.000611 acc_pose: 0.853555 loss: 0.000611 2022/10/21 12:53:00 - mmengine - INFO - Epoch(train) [52][250/391] lr: 5.000000e-04 eta: 7:31:06 time: 0.484695 data_time: 0.041021 memory: 21657 loss_kpt: 0.000609 acc_pose: 0.854728 loss: 0.000609 2022/10/21 12:53:24 - mmengine - INFO - Epoch(train) [52][300/391] lr: 5.000000e-04 eta: 7:30:51 time: 0.480394 data_time: 0.041255 memory: 21657 loss_kpt: 0.000604 acc_pose: 0.887399 loss: 0.000604 2022/10/21 12:53:48 - mmengine - INFO - Epoch(train) [52][350/391] lr: 5.000000e-04 eta: 7:30:37 time: 0.489742 data_time: 0.043480 memory: 21657 loss_kpt: 0.000600 acc_pose: 0.830469 loss: 0.000600 2022/10/21 12:54:08 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:54:33 - mmengine - INFO - Epoch(train) [53][50/391] lr: 5.000000e-04 eta: 7:29:13 time: 0.506791 data_time: 0.068064 memory: 21657 loss_kpt: 0.000618 acc_pose: 0.826172 loss: 0.000618 2022/10/21 12:54:57 - mmengine - INFO - Epoch(train) [53][100/391] lr: 5.000000e-04 eta: 7:28:58 time: 0.479319 data_time: 0.040945 memory: 21657 loss_kpt: 0.000600 acc_pose: 0.841180 loss: 0.000600 2022/10/21 12:55:22 - mmengine - INFO - Epoch(train) [53][150/391] lr: 5.000000e-04 eta: 7:28:45 time: 0.496036 data_time: 0.045283 memory: 21657 loss_kpt: 0.000603 acc_pose: 0.865618 loss: 0.000603 2022/10/21 12:55:46 - mmengine - INFO - Epoch(train) [53][200/391] lr: 5.000000e-04 eta: 7:28:29 time: 0.477333 data_time: 0.040593 memory: 21657 loss_kpt: 0.000617 acc_pose: 0.812402 loss: 0.000617 2022/10/21 12:56:10 - mmengine - INFO - Epoch(train) [53][250/391] lr: 5.000000e-04 eta: 7:28:15 time: 0.491019 data_time: 0.042367 memory: 21657 loss_kpt: 0.000607 acc_pose: 0.810833 loss: 0.000607 2022/10/21 12:56:35 - mmengine - INFO - Epoch(train) [53][300/391] lr: 5.000000e-04 eta: 7:28:01 time: 0.486788 data_time: 0.040244 memory: 21657 loss_kpt: 0.000615 acc_pose: 0.851995 loss: 0.000615 2022/10/21 12:56:59 - mmengine - INFO - Epoch(train) [53][350/391] lr: 5.000000e-04 eta: 7:27:47 time: 0.490386 data_time: 0.040891 memory: 21657 loss_kpt: 0.000611 acc_pose: 0.836143 loss: 0.000611 2022/10/21 12:57:19 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:57:44 - mmengine - INFO - Epoch(train) [54][50/391] lr: 5.000000e-04 eta: 7:26:23 time: 0.497560 data_time: 0.051317 memory: 21657 loss_kpt: 0.000610 acc_pose: 0.821297 loss: 0.000610 2022/10/21 12:58:08 - mmengine - INFO - Epoch(train) [54][100/391] lr: 5.000000e-04 eta: 7:26:08 time: 0.486510 data_time: 0.047025 memory: 21657 loss_kpt: 0.000598 acc_pose: 0.833237 loss: 0.000598 2022/10/21 12:58:32 - mmengine - INFO - Epoch(train) [54][150/391] lr: 5.000000e-04 eta: 7:25:53 time: 0.483121 data_time: 0.041357 memory: 21657 loss_kpt: 0.000606 acc_pose: 0.803010 loss: 0.000606 2022/10/21 12:58:57 - mmengine - INFO - Epoch(train) [54][200/391] lr: 5.000000e-04 eta: 7:25:39 time: 0.490467 data_time: 0.040189 memory: 21657 loss_kpt: 0.000605 acc_pose: 0.827390 loss: 0.000605 2022/10/21 12:59:21 - mmengine - INFO - Epoch(train) [54][250/391] lr: 5.000000e-04 eta: 7:25:24 time: 0.478860 data_time: 0.042351 memory: 21657 loss_kpt: 0.000601 acc_pose: 0.877410 loss: 0.000601 2022/10/21 12:59:34 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 12:59:45 - mmengine - INFO - Epoch(train) [54][300/391] lr: 5.000000e-04 eta: 7:25:10 time: 0.494330 data_time: 0.043261 memory: 21657 loss_kpt: 0.000600 acc_pose: 0.822289 loss: 0.000600 2022/10/21 13:00:09 - mmengine - INFO - Epoch(train) [54][350/391] lr: 5.000000e-04 eta: 7:24:55 time: 0.480712 data_time: 0.042761 memory: 21657 loss_kpt: 0.000620 acc_pose: 0.755206 loss: 0.000620 2022/10/21 13:00:29 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:00:54 - mmengine - INFO - Epoch(train) [55][50/391] lr: 5.000000e-04 eta: 7:23:32 time: 0.498237 data_time: 0.051792 memory: 21657 loss_kpt: 0.000613 acc_pose: 0.871350 loss: 0.000613 2022/10/21 13:01:19 - mmengine - INFO - Epoch(train) [55][100/391] lr: 5.000000e-04 eta: 7:23:17 time: 0.487660 data_time: 0.045675 memory: 21657 loss_kpt: 0.000615 acc_pose: 0.798708 loss: 0.000615 2022/10/21 13:01:43 - mmengine - INFO - Epoch(train) [55][150/391] lr: 5.000000e-04 eta: 7:23:03 time: 0.489577 data_time: 0.043171 memory: 21657 loss_kpt: 0.000611 acc_pose: 0.825105 loss: 0.000611 2022/10/21 13:02:07 - mmengine - INFO - Epoch(train) [55][200/391] lr: 5.000000e-04 eta: 7:22:47 time: 0.476079 data_time: 0.040368 memory: 21657 loss_kpt: 0.000613 acc_pose: 0.847460 loss: 0.000613 2022/10/21 13:02:32 - mmengine - INFO - Epoch(train) [55][250/391] lr: 5.000000e-04 eta: 7:22:33 time: 0.493785 data_time: 0.040233 memory: 21657 loss_kpt: 0.000599 acc_pose: 0.827603 loss: 0.000599 2022/10/21 13:02:55 - mmengine - INFO - Epoch(train) [55][300/391] lr: 5.000000e-04 eta: 7:22:17 time: 0.476610 data_time: 0.041792 memory: 21657 loss_kpt: 0.000600 acc_pose: 0.830397 loss: 0.000600 2022/10/21 13:03:20 - mmengine - INFO - Epoch(train) [55][350/391] lr: 5.000000e-04 eta: 7:22:04 time: 0.498237 data_time: 0.040307 memory: 21657 loss_kpt: 0.000605 acc_pose: 0.887511 loss: 0.000605 2022/10/21 13:03:40 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:04:05 - mmengine - INFO - Epoch(train) [56][50/391] lr: 5.000000e-04 eta: 7:20:43 time: 0.505412 data_time: 0.056917 memory: 21657 loss_kpt: 0.000607 acc_pose: 0.829891 loss: 0.000607 2022/10/21 13:04:29 - mmengine - INFO - Epoch(train) [56][100/391] lr: 5.000000e-04 eta: 7:20:29 time: 0.489907 data_time: 0.041181 memory: 21657 loss_kpt: 0.000612 acc_pose: 0.824804 loss: 0.000612 2022/10/21 13:04:54 - mmengine - INFO - Epoch(train) [56][150/391] lr: 5.000000e-04 eta: 7:20:14 time: 0.486675 data_time: 0.044496 memory: 21657 loss_kpt: 0.000615 acc_pose: 0.822506 loss: 0.000615 2022/10/21 13:05:18 - mmengine - INFO - Epoch(train) [56][200/391] lr: 5.000000e-04 eta: 7:19:59 time: 0.489314 data_time: 0.040304 memory: 21657 loss_kpt: 0.000589 acc_pose: 0.871228 loss: 0.000589 2022/10/21 13:05:42 - mmengine - INFO - Epoch(train) [56][250/391] lr: 5.000000e-04 eta: 7:19:44 time: 0.483223 data_time: 0.043530 memory: 21657 loss_kpt: 0.000602 acc_pose: 0.829340 loss: 0.000602 2022/10/21 13:06:07 - mmengine - INFO - Epoch(train) [56][300/391] lr: 5.000000e-04 eta: 7:19:29 time: 0.488606 data_time: 0.040370 memory: 21657 loss_kpt: 0.000600 acc_pose: 0.876419 loss: 0.000600 2022/10/21 13:06:31 - mmengine - INFO - Epoch(train) [56][350/391] lr: 5.000000e-04 eta: 7:19:13 time: 0.479073 data_time: 0.043670 memory: 21657 loss_kpt: 0.000609 acc_pose: 0.840162 loss: 0.000609 2022/10/21 13:06:51 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:07:15 - mmengine - INFO - Epoch(train) [57][50/391] lr: 5.000000e-04 eta: 7:17:51 time: 0.490712 data_time: 0.050799 memory: 21657 loss_kpt: 0.000617 acc_pose: 0.807163 loss: 0.000617 2022/10/21 13:07:40 - mmengine - INFO - Epoch(train) [57][100/391] lr: 5.000000e-04 eta: 7:17:37 time: 0.493605 data_time: 0.045762 memory: 21657 loss_kpt: 0.000593 acc_pose: 0.827311 loss: 0.000593 2022/10/21 13:07:42 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:08:04 - mmengine - INFO - Epoch(train) [57][150/391] lr: 5.000000e-04 eta: 7:17:21 time: 0.482340 data_time: 0.039787 memory: 21657 loss_kpt: 0.000607 acc_pose: 0.829594 loss: 0.000607 2022/10/21 13:08:29 - mmengine - INFO - Epoch(train) [57][200/391] lr: 5.000000e-04 eta: 7:17:07 time: 0.491272 data_time: 0.042969 memory: 21657 loss_kpt: 0.000614 acc_pose: 0.850080 loss: 0.000614 2022/10/21 13:08:53 - mmengine - INFO - Epoch(train) [57][250/391] lr: 5.000000e-04 eta: 7:16:51 time: 0.483086 data_time: 0.040247 memory: 21657 loss_kpt: 0.000610 acc_pose: 0.880311 loss: 0.000610 2022/10/21 13:09:17 - mmengine - INFO - Epoch(train) [57][300/391] lr: 5.000000e-04 eta: 7:16:36 time: 0.486218 data_time: 0.043561 memory: 21657 loss_kpt: 0.000598 acc_pose: 0.840792 loss: 0.000598 2022/10/21 13:09:41 - mmengine - INFO - Epoch(train) [57][350/391] lr: 5.000000e-04 eta: 7:16:21 time: 0.486338 data_time: 0.040377 memory: 21657 loss_kpt: 0.000593 acc_pose: 0.880634 loss: 0.000593 2022/10/21 13:10:01 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:10:26 - mmengine - INFO - Epoch(train) [58][50/391] lr: 5.000000e-04 eta: 7:15:03 time: 0.509929 data_time: 0.055388 memory: 21657 loss_kpt: 0.000587 acc_pose: 0.871499 loss: 0.000587 2022/10/21 13:10:50 - mmengine - INFO - Epoch(train) [58][100/391] lr: 5.000000e-04 eta: 7:14:46 time: 0.477099 data_time: 0.040288 memory: 21657 loss_kpt: 0.000595 acc_pose: 0.845696 loss: 0.000595 2022/10/21 13:11:15 - mmengine - INFO - Epoch(train) [58][150/391] lr: 5.000000e-04 eta: 7:14:32 time: 0.494018 data_time: 0.044535 memory: 21657 loss_kpt: 0.000598 acc_pose: 0.836539 loss: 0.000598 2022/10/21 13:11:39 - mmengine - INFO - Epoch(train) [58][200/391] lr: 5.000000e-04 eta: 7:14:16 time: 0.479368 data_time: 0.040552 memory: 21657 loss_kpt: 0.000598 acc_pose: 0.808797 loss: 0.000598 2022/10/21 13:12:03 - mmengine - INFO - Epoch(train) [58][250/391] lr: 5.000000e-04 eta: 7:14:01 time: 0.490586 data_time: 0.041537 memory: 21657 loss_kpt: 0.000588 acc_pose: 0.807560 loss: 0.000588 2022/10/21 13:12:27 - mmengine - INFO - Epoch(train) [58][300/391] lr: 5.000000e-04 eta: 7:13:45 time: 0.483000 data_time: 0.041615 memory: 21657 loss_kpt: 0.000594 acc_pose: 0.816041 loss: 0.000594 2022/10/21 13:12:52 - mmengine - INFO - Epoch(train) [58][350/391] lr: 5.000000e-04 eta: 7:13:31 time: 0.490918 data_time: 0.044694 memory: 21657 loss_kpt: 0.000597 acc_pose: 0.851412 loss: 0.000597 2022/10/21 13:13:11 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:13:36 - mmengine - INFO - Epoch(train) [59][50/391] lr: 5.000000e-04 eta: 7:12:11 time: 0.493480 data_time: 0.055232 memory: 21657 loss_kpt: 0.000601 acc_pose: 0.870777 loss: 0.000601 2022/10/21 13:14:01 - mmengine - INFO - Epoch(train) [59][100/391] lr: 5.000000e-04 eta: 7:11:57 time: 0.492430 data_time: 0.040785 memory: 21657 loss_kpt: 0.000583 acc_pose: 0.860454 loss: 0.000583 2022/10/21 13:14:25 - mmengine - INFO - Epoch(train) [59][150/391] lr: 5.000000e-04 eta: 7:11:40 time: 0.476921 data_time: 0.041017 memory: 21657 loss_kpt: 0.000609 acc_pose: 0.855834 loss: 0.000609 2022/10/21 13:14:49 - mmengine - INFO - Epoch(train) [59][200/391] lr: 5.000000e-04 eta: 7:11:26 time: 0.494512 data_time: 0.045025 memory: 21657 loss_kpt: 0.000589 acc_pose: 0.860390 loss: 0.000589 2022/10/21 13:15:13 - mmengine - INFO - Epoch(train) [59][250/391] lr: 5.000000e-04 eta: 7:11:09 time: 0.478401 data_time: 0.040146 memory: 21657 loss_kpt: 0.000613 acc_pose: 0.786974 loss: 0.000613 2022/10/21 13:15:38 - mmengine - INFO - Epoch(train) [59][300/391] lr: 5.000000e-04 eta: 7:10:55 time: 0.496695 data_time: 0.043710 memory: 21657 loss_kpt: 0.000606 acc_pose: 0.819459 loss: 0.000606 2022/10/21 13:15:49 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:16:02 - mmengine - INFO - Epoch(train) [59][350/391] lr: 5.000000e-04 eta: 7:10:39 time: 0.479798 data_time: 0.040593 memory: 21657 loss_kpt: 0.000603 acc_pose: 0.899978 loss: 0.000603 2022/10/21 13:16:22 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:16:47 - mmengine - INFO - Epoch(train) [60][50/391] lr: 5.000000e-04 eta: 7:09:21 time: 0.498619 data_time: 0.053623 memory: 21657 loss_kpt: 0.000601 acc_pose: 0.854981 loss: 0.000601 2022/10/21 13:17:11 - mmengine - INFO - Epoch(train) [60][100/391] lr: 5.000000e-04 eta: 7:09:04 time: 0.479578 data_time: 0.043169 memory: 21657 loss_kpt: 0.000587 acc_pose: 0.899737 loss: 0.000587 2022/10/21 13:17:35 - mmengine - INFO - Epoch(train) [60][150/391] lr: 5.000000e-04 eta: 7:08:49 time: 0.487911 data_time: 0.039659 memory: 21657 loss_kpt: 0.000595 acc_pose: 0.843037 loss: 0.000595 2022/10/21 13:17:59 - mmengine - INFO - Epoch(train) [60][200/391] lr: 5.000000e-04 eta: 7:08:32 time: 0.473815 data_time: 0.040624 memory: 21657 loss_kpt: 0.000603 acc_pose: 0.791831 loss: 0.000603 2022/10/21 13:18:24 - mmengine - INFO - Epoch(train) [60][250/391] lr: 5.000000e-04 eta: 7:08:18 time: 0.499840 data_time: 0.042507 memory: 21657 loss_kpt: 0.000602 acc_pose: 0.860662 loss: 0.000602 2022/10/21 13:18:48 - mmengine - INFO - Epoch(train) [60][300/391] lr: 5.000000e-04 eta: 7:08:01 time: 0.476420 data_time: 0.040430 memory: 21657 loss_kpt: 0.000594 acc_pose: 0.855900 loss: 0.000594 2022/10/21 13:19:12 - mmengine - INFO - Epoch(train) [60][350/391] lr: 5.000000e-04 eta: 7:07:46 time: 0.491395 data_time: 0.044745 memory: 21657 loss_kpt: 0.000593 acc_pose: 0.857189 loss: 0.000593 2022/10/21 13:19:32 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:19:32 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/10/21 13:19:44 - mmengine - INFO - Epoch(val) [60][50/407] eta: 0:00:55 time: 0.156454 data_time: 0.015165 memory: 21657 2022/10/21 13:19:51 - mmengine - INFO - Epoch(val) [60][100/407] eta: 0:00:46 time: 0.150960 data_time: 0.009351 memory: 2142 2022/10/21 13:19:59 - mmengine - INFO - Epoch(val) [60][150/407] eta: 0:00:38 time: 0.150387 data_time: 0.009245 memory: 2142 2022/10/21 13:20:06 - mmengine - INFO - Epoch(val) [60][200/407] eta: 0:00:31 time: 0.151637 data_time: 0.009610 memory: 2142 2022/10/21 13:20:14 - mmengine - INFO - Epoch(val) [60][250/407] eta: 0:00:23 time: 0.151133 data_time: 0.009331 memory: 2142 2022/10/21 13:20:22 - mmengine - INFO - Epoch(val) [60][300/407] eta: 0:00:16 time: 0.156310 data_time: 0.009514 memory: 2142 2022/10/21 13:20:29 - mmengine - INFO - Epoch(val) [60][350/407] eta: 0:00:08 time: 0.151195 data_time: 0.009123 memory: 2142 2022/10/21 13:20:37 - mmengine - INFO - Epoch(val) [60][400/407] eta: 0:00:01 time: 0.147581 data_time: 0.008429 memory: 2142 2022/10/21 13:21:12 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 13:21:25 - mmengine - INFO - Epoch(val) [60][407/407] coco/AP: 0.733610 coco/AP .5: 0.896799 coco/AP .75: 0.803555 coco/AP (M): 0.691462 coco/AP (L): 0.805042 coco/AR: 0.783674 coco/AR .5: 0.932147 coco/AR .75: 0.847450 coco/AR (M): 0.738514 coco/AR (L): 0.849387 2022/10/21 13:21:25 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221021/resnetv1d152_384/best_coco/AP_epoch_50.pth is removed 2022/10/21 13:21:28 - mmengine - INFO - The best checkpoint with 0.7336 coco/AP at 60 epoch is saved to best_coco/AP_epoch_60.pth. 2022/10/21 13:21:53 - mmengine - INFO - Epoch(train) [61][50/391] lr: 5.000000e-04 eta: 7:06:29 time: 0.500911 data_time: 0.050048 memory: 21657 loss_kpt: 0.000599 acc_pose: 0.824816 loss: 0.000599 2022/10/21 13:22:17 - mmengine - INFO - Epoch(train) [61][100/391] lr: 5.000000e-04 eta: 7:06:13 time: 0.483361 data_time: 0.040087 memory: 21657 loss_kpt: 0.000582 acc_pose: 0.787181 loss: 0.000582 2022/10/21 13:22:41 - mmengine - INFO - Epoch(train) [61][150/391] lr: 5.000000e-04 eta: 7:05:57 time: 0.481613 data_time: 0.040107 memory: 21657 loss_kpt: 0.000588 acc_pose: 0.806882 loss: 0.000588 2022/10/21 13:23:06 - mmengine - INFO - Epoch(train) [61][200/391] lr: 5.000000e-04 eta: 7:05:42 time: 0.488215 data_time: 0.044283 memory: 21657 loss_kpt: 0.000599 acc_pose: 0.779348 loss: 0.000599 2022/10/21 13:23:29 - mmengine - INFO - Epoch(train) [61][250/391] lr: 5.000000e-04 eta: 7:05:24 time: 0.475237 data_time: 0.040637 memory: 21657 loss_kpt: 0.000595 acc_pose: 0.833100 loss: 0.000595 2022/10/21 13:23:54 - mmengine - INFO - Epoch(train) [61][300/391] lr: 5.000000e-04 eta: 7:05:10 time: 0.496807 data_time: 0.044431 memory: 21657 loss_kpt: 0.000586 acc_pose: 0.871019 loss: 0.000586 2022/10/21 13:24:18 - mmengine - INFO - Epoch(train) [61][350/391] lr: 5.000000e-04 eta: 7:04:53 time: 0.479667 data_time: 0.039741 memory: 21657 loss_kpt: 0.000603 acc_pose: 0.826429 loss: 0.000603 2022/10/21 13:24:38 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:25:03 - mmengine - INFO - Epoch(train) [62][50/391] lr: 5.000000e-04 eta: 7:03:37 time: 0.498650 data_time: 0.054848 memory: 21657 loss_kpt: 0.000585 acc_pose: 0.847762 loss: 0.000585 2022/10/21 13:25:27 - mmengine - INFO - Epoch(train) [62][100/391] lr: 5.000000e-04 eta: 7:03:22 time: 0.486758 data_time: 0.040190 memory: 21657 loss_kpt: 0.000597 acc_pose: 0.859726 loss: 0.000597 2022/10/21 13:25:51 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:25:52 - mmengine - INFO - Epoch(train) [62][150/391] lr: 5.000000e-04 eta: 7:03:06 time: 0.490281 data_time: 0.039864 memory: 21657 loss_kpt: 0.000597 acc_pose: 0.811849 loss: 0.000597 2022/10/21 13:26:16 - mmengine - INFO - Epoch(train) [62][200/391] lr: 5.000000e-04 eta: 7:02:50 time: 0.487872 data_time: 0.040195 memory: 21657 loss_kpt: 0.000591 acc_pose: 0.861560 loss: 0.000591 2022/10/21 13:26:41 - mmengine - INFO - Epoch(train) [62][250/391] lr: 5.000000e-04 eta: 7:02:35 time: 0.486898 data_time: 0.040204 memory: 21657 loss_kpt: 0.000585 acc_pose: 0.899068 loss: 0.000585 2022/10/21 13:27:05 - mmengine - INFO - Epoch(train) [62][300/391] lr: 5.000000e-04 eta: 7:02:18 time: 0.482033 data_time: 0.040396 memory: 21657 loss_kpt: 0.000596 acc_pose: 0.855428 loss: 0.000596 2022/10/21 13:27:29 - mmengine - INFO - Epoch(train) [62][350/391] lr: 5.000000e-04 eta: 7:02:03 time: 0.491004 data_time: 0.043687 memory: 21657 loss_kpt: 0.000603 acc_pose: 0.863328 loss: 0.000603 2022/10/21 13:27:49 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:28:14 - mmengine - INFO - Epoch(train) [63][50/391] lr: 5.000000e-04 eta: 7:00:48 time: 0.506289 data_time: 0.055659 memory: 21657 loss_kpt: 0.000600 acc_pose: 0.827718 loss: 0.000600 2022/10/21 13:28:38 - mmengine - INFO - Epoch(train) [63][100/391] lr: 5.000000e-04 eta: 7:00:33 time: 0.487615 data_time: 0.041329 memory: 21657 loss_kpt: 0.000579 acc_pose: 0.853242 loss: 0.000579 2022/10/21 13:29:03 - mmengine - INFO - Epoch(train) [63][150/391] lr: 5.000000e-04 eta: 7:00:17 time: 0.488881 data_time: 0.044275 memory: 21657 loss_kpt: 0.000583 acc_pose: 0.864393 loss: 0.000583 2022/10/21 13:29:27 - mmengine - INFO - Epoch(train) [63][200/391] lr: 5.000000e-04 eta: 7:00:01 time: 0.484551 data_time: 0.040794 memory: 21657 loss_kpt: 0.000591 acc_pose: 0.866023 loss: 0.000591 2022/10/21 13:29:52 - mmengine - INFO - Epoch(train) [63][250/391] lr: 5.000000e-04 eta: 6:59:45 time: 0.487718 data_time: 0.039912 memory: 21657 loss_kpt: 0.000573 acc_pose: 0.854482 loss: 0.000573 2022/10/21 13:30:16 - mmengine - INFO - Epoch(train) [63][300/391] lr: 5.000000e-04 eta: 6:59:29 time: 0.488688 data_time: 0.040285 memory: 21657 loss_kpt: 0.000595 acc_pose: 0.832016 loss: 0.000595 2022/10/21 13:30:40 - mmengine - INFO - Epoch(train) [63][350/391] lr: 5.000000e-04 eta: 6:59:13 time: 0.484844 data_time: 0.039390 memory: 21657 loss_kpt: 0.000589 acc_pose: 0.790246 loss: 0.000589 2022/10/21 13:31:00 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:31:25 - mmengine - INFO - Epoch(train) [64][50/391] lr: 5.000000e-04 eta: 6:57:58 time: 0.492012 data_time: 0.053664 memory: 21657 loss_kpt: 0.000582 acc_pose: 0.863358 loss: 0.000582 2022/10/21 13:31:49 - mmengine - INFO - Epoch(train) [64][100/391] lr: 5.000000e-04 eta: 6:57:42 time: 0.493315 data_time: 0.041974 memory: 21657 loss_kpt: 0.000593 acc_pose: 0.782439 loss: 0.000593 2022/10/21 13:32:13 - mmengine - INFO - Epoch(train) [64][150/391] lr: 5.000000e-04 eta: 6:57:25 time: 0.478448 data_time: 0.040036 memory: 21657 loss_kpt: 0.000576 acc_pose: 0.869515 loss: 0.000576 2022/10/21 13:32:38 - mmengine - INFO - Epoch(train) [64][200/391] lr: 5.000000e-04 eta: 6:57:10 time: 0.493001 data_time: 0.043633 memory: 21657 loss_kpt: 0.000592 acc_pose: 0.827335 loss: 0.000592 2022/10/21 13:33:02 - mmengine - INFO - Epoch(train) [64][250/391] lr: 5.000000e-04 eta: 6:56:53 time: 0.483343 data_time: 0.040372 memory: 21657 loss_kpt: 0.000589 acc_pose: 0.834298 loss: 0.000589 2022/10/21 13:33:27 - mmengine - INFO - Epoch(train) [64][300/391] lr: 5.000000e-04 eta: 6:56:38 time: 0.494015 data_time: 0.044302 memory: 21657 loss_kpt: 0.000615 acc_pose: 0.817305 loss: 0.000615 2022/10/21 13:33:51 - mmengine - INFO - Epoch(train) [64][350/391] lr: 5.000000e-04 eta: 6:56:22 time: 0.485484 data_time: 0.041186 memory: 21657 loss_kpt: 0.000598 acc_pose: 0.837635 loss: 0.000598 2022/10/21 13:33:59 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:34:11 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:34:36 - mmengine - INFO - Epoch(train) [65][50/391] lr: 5.000000e-04 eta: 6:55:08 time: 0.500667 data_time: 0.057733 memory: 21657 loss_kpt: 0.000607 acc_pose: 0.840319 loss: 0.000607 2022/10/21 13:35:00 - mmengine - INFO - Epoch(train) [65][100/391] lr: 5.000000e-04 eta: 6:54:52 time: 0.482661 data_time: 0.041079 memory: 21657 loss_kpt: 0.000575 acc_pose: 0.853534 loss: 0.000575 2022/10/21 13:35:24 - mmengine - INFO - Epoch(train) [65][150/391] lr: 5.000000e-04 eta: 6:54:35 time: 0.485189 data_time: 0.043065 memory: 21657 loss_kpt: 0.000577 acc_pose: 0.860801 loss: 0.000577 2022/10/21 13:35:48 - mmengine - INFO - Epoch(train) [65][200/391] lr: 5.000000e-04 eta: 6:54:19 time: 0.482463 data_time: 0.041352 memory: 21657 loss_kpt: 0.000575 acc_pose: 0.837626 loss: 0.000575 2022/10/21 13:36:13 - mmengine - INFO - Epoch(train) [65][250/391] lr: 5.000000e-04 eta: 6:54:03 time: 0.488190 data_time: 0.046091 memory: 21657 loss_kpt: 0.000579 acc_pose: 0.811302 loss: 0.000579 2022/10/21 13:36:37 - mmengine - INFO - Epoch(train) [65][300/391] lr: 5.000000e-04 eta: 6:53:46 time: 0.482680 data_time: 0.040368 memory: 21657 loss_kpt: 0.000577 acc_pose: 0.830164 loss: 0.000577 2022/10/21 13:37:01 - mmengine - INFO - Epoch(train) [65][350/391] lr: 5.000000e-04 eta: 6:53:30 time: 0.488689 data_time: 0.042771 memory: 21657 loss_kpt: 0.000594 acc_pose: 0.860878 loss: 0.000594 2022/10/21 13:37:21 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:37:45 - mmengine - INFO - Epoch(train) [66][50/391] lr: 5.000000e-04 eta: 6:52:17 time: 0.496238 data_time: 0.051791 memory: 21657 loss_kpt: 0.000590 acc_pose: 0.809759 loss: 0.000590 2022/10/21 13:38:10 - mmengine - INFO - Epoch(train) [66][100/391] lr: 5.000000e-04 eta: 6:52:01 time: 0.491512 data_time: 0.042978 memory: 21657 loss_kpt: 0.000581 acc_pose: 0.829087 loss: 0.000581 2022/10/21 13:38:34 - mmengine - INFO - Epoch(train) [66][150/391] lr: 5.000000e-04 eta: 6:51:44 time: 0.483882 data_time: 0.041183 memory: 21657 loss_kpt: 0.000583 acc_pose: 0.857106 loss: 0.000583 2022/10/21 13:38:58 - mmengine - INFO - Epoch(train) [66][200/391] lr: 5.000000e-04 eta: 6:51:28 time: 0.485701 data_time: 0.045131 memory: 21657 loss_kpt: 0.000576 acc_pose: 0.797422 loss: 0.000576 2022/10/21 13:39:23 - mmengine - INFO - Epoch(train) [66][250/391] lr: 5.000000e-04 eta: 6:51:11 time: 0.484740 data_time: 0.041784 memory: 21657 loss_kpt: 0.000600 acc_pose: 0.814505 loss: 0.000600 2022/10/21 13:39:47 - mmengine - INFO - Epoch(train) [66][300/391] lr: 5.000000e-04 eta: 6:50:55 time: 0.489663 data_time: 0.043879 memory: 21657 loss_kpt: 0.000592 acc_pose: 0.849223 loss: 0.000592 2022/10/21 13:40:11 - mmengine - INFO - Epoch(train) [66][350/391] lr: 5.000000e-04 eta: 6:50:38 time: 0.478047 data_time: 0.039735 memory: 21657 loss_kpt: 0.000581 acc_pose: 0.849609 loss: 0.000581 2022/10/21 13:40:31 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:40:56 - mmengine - INFO - Epoch(train) [67][50/391] lr: 5.000000e-04 eta: 6:49:26 time: 0.498604 data_time: 0.054464 memory: 21657 loss_kpt: 0.000589 acc_pose: 0.837690 loss: 0.000589 2022/10/21 13:41:21 - mmengine - INFO - Epoch(train) [67][100/391] lr: 5.000000e-04 eta: 6:49:10 time: 0.493103 data_time: 0.043810 memory: 21657 loss_kpt: 0.000580 acc_pose: 0.841913 loss: 0.000580 2022/10/21 13:41:45 - mmengine - INFO - Epoch(train) [67][150/391] lr: 5.000000e-04 eta: 6:48:53 time: 0.479403 data_time: 0.039822 memory: 21657 loss_kpt: 0.000590 acc_pose: 0.810327 loss: 0.000590 2022/10/21 13:42:06 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:42:09 - mmengine - INFO - Epoch(train) [67][200/391] lr: 5.000000e-04 eta: 6:48:36 time: 0.485604 data_time: 0.040955 memory: 21657 loss_kpt: 0.000595 acc_pose: 0.852689 loss: 0.000595 2022/10/21 13:42:33 - mmengine - INFO - Epoch(train) [67][250/391] lr: 5.000000e-04 eta: 6:48:20 time: 0.489240 data_time: 0.044624 memory: 21657 loss_kpt: 0.000573 acc_pose: 0.816729 loss: 0.000573 2022/10/21 13:42:58 - mmengine - INFO - Epoch(train) [67][300/391] lr: 5.000000e-04 eta: 6:48:03 time: 0.481496 data_time: 0.039695 memory: 21657 loss_kpt: 0.000600 acc_pose: 0.865450 loss: 0.000600 2022/10/21 13:43:22 - mmengine - INFO - Epoch(train) [67][350/391] lr: 5.000000e-04 eta: 6:47:46 time: 0.487979 data_time: 0.040726 memory: 21657 loss_kpt: 0.000593 acc_pose: 0.864580 loss: 0.000593 2022/10/21 13:43:41 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:44:07 - mmengine - INFO - Epoch(train) [68][50/391] lr: 5.000000e-04 eta: 6:46:36 time: 0.506899 data_time: 0.056236 memory: 21657 loss_kpt: 0.000588 acc_pose: 0.836685 loss: 0.000588 2022/10/21 13:44:31 - mmengine - INFO - Epoch(train) [68][100/391] lr: 5.000000e-04 eta: 6:46:18 time: 0.477579 data_time: 0.040550 memory: 21657 loss_kpt: 0.000580 acc_pose: 0.856889 loss: 0.000580 2022/10/21 13:44:55 - mmengine - INFO - Epoch(train) [68][150/391] lr: 5.000000e-04 eta: 6:46:02 time: 0.494047 data_time: 0.040526 memory: 21657 loss_kpt: 0.000581 acc_pose: 0.817861 loss: 0.000581 2022/10/21 13:45:19 - mmengine - INFO - Epoch(train) [68][200/391] lr: 5.000000e-04 eta: 6:45:45 time: 0.481701 data_time: 0.040361 memory: 21657 loss_kpt: 0.000586 acc_pose: 0.834864 loss: 0.000586 2022/10/21 13:45:44 - mmengine - INFO - Epoch(train) [68][250/391] lr: 5.000000e-04 eta: 6:45:29 time: 0.487979 data_time: 0.044357 memory: 21657 loss_kpt: 0.000594 acc_pose: 0.825071 loss: 0.000594 2022/10/21 13:46:08 - mmengine - INFO - Epoch(train) [68][300/391] lr: 5.000000e-04 eta: 6:45:12 time: 0.484053 data_time: 0.040292 memory: 21657 loss_kpt: 0.000564 acc_pose: 0.831129 loss: 0.000564 2022/10/21 13:46:32 - mmengine - INFO - Epoch(train) [68][350/391] lr: 5.000000e-04 eta: 6:44:55 time: 0.484167 data_time: 0.041535 memory: 21657 loss_kpt: 0.000576 acc_pose: 0.818239 loss: 0.000576 2022/10/21 13:46:52 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:47:17 - mmengine - INFO - Epoch(train) [69][50/391] lr: 5.000000e-04 eta: 6:43:44 time: 0.497974 data_time: 0.054700 memory: 21657 loss_kpt: 0.000563 acc_pose: 0.833479 loss: 0.000563 2022/10/21 13:47:42 - mmengine - INFO - Epoch(train) [69][100/391] lr: 5.000000e-04 eta: 6:43:28 time: 0.494956 data_time: 0.044346 memory: 21657 loss_kpt: 0.000574 acc_pose: 0.791015 loss: 0.000574 2022/10/21 13:48:06 - mmengine - INFO - Epoch(train) [69][150/391] lr: 5.000000e-04 eta: 6:43:11 time: 0.481829 data_time: 0.040322 memory: 21657 loss_kpt: 0.000595 acc_pose: 0.854975 loss: 0.000595 2022/10/21 13:48:30 - mmengine - INFO - Epoch(train) [69][200/391] lr: 5.000000e-04 eta: 6:42:55 time: 0.493130 data_time: 0.043516 memory: 21657 loss_kpt: 0.000570 acc_pose: 0.824378 loss: 0.000570 2022/10/21 13:48:54 - mmengine - INFO - Epoch(train) [69][250/391] lr: 5.000000e-04 eta: 6:42:38 time: 0.482529 data_time: 0.040114 memory: 21657 loss_kpt: 0.000584 acc_pose: 0.853609 loss: 0.000584 2022/10/21 13:49:19 - mmengine - INFO - Epoch(train) [69][300/391] lr: 5.000000e-04 eta: 6:42:21 time: 0.488408 data_time: 0.039347 memory: 21657 loss_kpt: 0.000596 acc_pose: 0.773949 loss: 0.000596 2022/10/21 13:49:43 - mmengine - INFO - Epoch(train) [69][350/391] lr: 5.000000e-04 eta: 6:42:04 time: 0.484762 data_time: 0.040082 memory: 21657 loss_kpt: 0.000594 acc_pose: 0.864858 loss: 0.000594 2022/10/21 13:50:03 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:50:14 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:50:28 - mmengine - INFO - Epoch(train) [70][50/391] lr: 5.000000e-04 eta: 6:40:55 time: 0.507432 data_time: 0.052368 memory: 21657 loss_kpt: 0.000570 acc_pose: 0.847649 loss: 0.000570 2022/10/21 13:50:52 - mmengine - INFO - Epoch(train) [70][100/391] lr: 5.000000e-04 eta: 6:40:38 time: 0.481287 data_time: 0.040331 memory: 21657 loss_kpt: 0.000588 acc_pose: 0.797464 loss: 0.000588 2022/10/21 13:51:17 - mmengine - INFO - Epoch(train) [70][150/391] lr: 5.000000e-04 eta: 6:40:22 time: 0.491378 data_time: 0.043998 memory: 21657 loss_kpt: 0.000585 acc_pose: 0.842645 loss: 0.000585 2022/10/21 13:51:41 - mmengine - INFO - Epoch(train) [70][200/391] lr: 5.000000e-04 eta: 6:40:04 time: 0.478835 data_time: 0.039817 memory: 21657 loss_kpt: 0.000575 acc_pose: 0.834872 loss: 0.000575 2022/10/21 13:52:05 - mmengine - INFO - Epoch(train) [70][250/391] lr: 5.000000e-04 eta: 6:39:48 time: 0.492041 data_time: 0.044319 memory: 21657 loss_kpt: 0.000565 acc_pose: 0.864685 loss: 0.000565 2022/10/21 13:52:29 - mmengine - INFO - Epoch(train) [70][300/391] lr: 5.000000e-04 eta: 6:39:30 time: 0.478750 data_time: 0.039807 memory: 21657 loss_kpt: 0.000600 acc_pose: 0.862514 loss: 0.000600 2022/10/21 13:52:54 - mmengine - INFO - Epoch(train) [70][350/391] lr: 5.000000e-04 eta: 6:39:14 time: 0.495879 data_time: 0.039397 memory: 21657 loss_kpt: 0.000569 acc_pose: 0.854199 loss: 0.000569 2022/10/21 13:53:14 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:53:14 - mmengine - INFO - Saving checkpoint at 70 epochs 2022/10/21 13:53:26 - mmengine - INFO - Epoch(val) [70][50/407] eta: 0:00:56 time: 0.156936 data_time: 0.014681 memory: 21657 2022/10/21 13:53:33 - mmengine - INFO - Epoch(val) [70][100/407] eta: 0:00:46 time: 0.151005 data_time: 0.009042 memory: 2142 2022/10/21 13:53:41 - mmengine - INFO - Epoch(val) [70][150/407] eta: 0:00:38 time: 0.151694 data_time: 0.009226 memory: 2142 2022/10/21 13:53:48 - mmengine - INFO - Epoch(val) [70][200/407] eta: 0:00:31 time: 0.150608 data_time: 0.009141 memory: 2142 2022/10/21 13:53:56 - mmengine - INFO - Epoch(val) [70][250/407] eta: 0:00:23 time: 0.151310 data_time: 0.009429 memory: 2142 2022/10/21 13:54:04 - mmengine - INFO - Epoch(val) [70][300/407] eta: 0:00:16 time: 0.152896 data_time: 0.009474 memory: 2142 2022/10/21 13:54:11 - mmengine - INFO - Epoch(val) [70][350/407] eta: 0:00:08 time: 0.153874 data_time: 0.009259 memory: 2142 2022/10/21 13:54:19 - mmengine - INFO - Epoch(val) [70][400/407] eta: 0:00:01 time: 0.148331 data_time: 0.008335 memory: 2142 2022/10/21 13:54:54 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 13:55:08 - mmengine - INFO - Epoch(val) [70][407/407] coco/AP: 0.735200 coco/AP .5: 0.897442 coco/AP .75: 0.803511 coco/AP (M): 0.690239 coco/AP (L): 0.809769 coco/AR: 0.787059 coco/AR .5: 0.933564 coco/AR .75: 0.849339 coco/AR (M): 0.739825 coco/AR (L): 0.855110 2022/10/21 13:55:08 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221021/resnetv1d152_384/best_coco/AP_epoch_60.pth is removed 2022/10/21 13:55:10 - mmengine - INFO - The best checkpoint with 0.7352 coco/AP at 70 epoch is saved to best_coco/AP_epoch_70.pth. 2022/10/21 13:55:35 - mmengine - INFO - Epoch(train) [71][50/391] lr: 5.000000e-04 eta: 6:38:04 time: 0.492091 data_time: 0.052059 memory: 21657 loss_kpt: 0.000580 acc_pose: 0.822531 loss: 0.000580 2022/10/21 13:55:59 - mmengine - INFO - Epoch(train) [71][100/391] lr: 5.000000e-04 eta: 6:37:47 time: 0.485451 data_time: 0.040535 memory: 21657 loss_kpt: 0.000576 acc_pose: 0.882762 loss: 0.000576 2022/10/21 13:56:23 - mmengine - INFO - Epoch(train) [71][150/391] lr: 5.000000e-04 eta: 6:37:30 time: 0.480719 data_time: 0.045360 memory: 21657 loss_kpt: 0.000578 acc_pose: 0.860058 loss: 0.000578 2022/10/21 13:56:48 - mmengine - INFO - Epoch(train) [71][200/391] lr: 5.000000e-04 eta: 6:37:14 time: 0.496331 data_time: 0.042027 memory: 21657 loss_kpt: 0.000596 acc_pose: 0.850762 loss: 0.000596 2022/10/21 13:57:12 - mmengine - INFO - Epoch(train) [71][250/391] lr: 5.000000e-04 eta: 6:36:56 time: 0.479511 data_time: 0.043531 memory: 21657 loss_kpt: 0.000573 acc_pose: 0.845120 loss: 0.000573 2022/10/21 13:57:37 - mmengine - INFO - Epoch(train) [71][300/391] lr: 5.000000e-04 eta: 6:36:39 time: 0.489640 data_time: 0.039808 memory: 21657 loss_kpt: 0.000585 acc_pose: 0.875673 loss: 0.000585 2022/10/21 13:58:01 - mmengine - INFO - Epoch(train) [71][350/391] lr: 5.000000e-04 eta: 6:36:22 time: 0.485075 data_time: 0.044343 memory: 21657 loss_kpt: 0.000582 acc_pose: 0.786674 loss: 0.000582 2022/10/21 13:58:20 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 13:58:45 - mmengine - INFO - Epoch(train) [72][50/391] lr: 5.000000e-04 eta: 6:35:14 time: 0.502074 data_time: 0.050973 memory: 21657 loss_kpt: 0.000578 acc_pose: 0.868297 loss: 0.000578 2022/10/21 13:59:09 - mmengine - INFO - Epoch(train) [72][100/391] lr: 5.000000e-04 eta: 6:34:56 time: 0.481445 data_time: 0.040639 memory: 21657 loss_kpt: 0.000577 acc_pose: 0.837508 loss: 0.000577 2022/10/21 13:59:34 - mmengine - INFO - Epoch(train) [72][150/391] lr: 5.000000e-04 eta: 6:34:39 time: 0.489156 data_time: 0.039516 memory: 21657 loss_kpt: 0.000576 acc_pose: 0.840572 loss: 0.000576 2022/10/21 13:59:58 - mmengine - INFO - Epoch(train) [72][200/391] lr: 5.000000e-04 eta: 6:34:22 time: 0.480356 data_time: 0.040634 memory: 21657 loss_kpt: 0.000572 acc_pose: 0.892277 loss: 0.000572 2022/10/21 14:00:17 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:00:23 - mmengine - INFO - Epoch(train) [72][250/391] lr: 5.000000e-04 eta: 6:34:05 time: 0.494669 data_time: 0.039386 memory: 21657 loss_kpt: 0.000578 acc_pose: 0.868723 loss: 0.000578 2022/10/21 14:00:47 - mmengine - INFO - Epoch(train) [72][300/391] lr: 5.000000e-04 eta: 6:33:48 time: 0.482132 data_time: 0.044799 memory: 21657 loss_kpt: 0.000571 acc_pose: 0.900788 loss: 0.000571 2022/10/21 14:01:11 - mmengine - INFO - Epoch(train) [72][350/391] lr: 5.000000e-04 eta: 6:33:31 time: 0.492767 data_time: 0.039206 memory: 21657 loss_kpt: 0.000583 acc_pose: 0.858976 loss: 0.000583 2022/10/21 14:01:31 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:01:56 - mmengine - INFO - Epoch(train) [73][50/391] lr: 5.000000e-04 eta: 6:32:23 time: 0.498557 data_time: 0.057863 memory: 21657 loss_kpt: 0.000568 acc_pose: 0.863774 loss: 0.000568 2022/10/21 14:02:20 - mmengine - INFO - Epoch(train) [73][100/391] lr: 5.000000e-04 eta: 6:32:07 time: 0.492659 data_time: 0.042952 memory: 21657 loss_kpt: 0.000580 acc_pose: 0.856930 loss: 0.000580 2022/10/21 14:02:44 - mmengine - INFO - Epoch(train) [73][150/391] lr: 5.000000e-04 eta: 6:31:49 time: 0.477786 data_time: 0.039347 memory: 21657 loss_kpt: 0.000565 acc_pose: 0.810518 loss: 0.000565 2022/10/21 14:03:09 - mmengine - INFO - Epoch(train) [73][200/391] lr: 5.000000e-04 eta: 6:31:33 time: 0.495613 data_time: 0.044050 memory: 21657 loss_kpt: 0.000570 acc_pose: 0.805083 loss: 0.000570 2022/10/21 14:03:33 - mmengine - INFO - Epoch(train) [73][250/391] lr: 5.000000e-04 eta: 6:31:15 time: 0.483592 data_time: 0.040033 memory: 21657 loss_kpt: 0.000591 acc_pose: 0.816251 loss: 0.000591 2022/10/21 14:03:58 - mmengine - INFO - Epoch(train) [73][300/391] lr: 5.000000e-04 eta: 6:30:58 time: 0.491347 data_time: 0.041584 memory: 21657 loss_kpt: 0.000578 acc_pose: 0.821228 loss: 0.000578 2022/10/21 14:04:22 - mmengine - INFO - Epoch(train) [73][350/391] lr: 5.000000e-04 eta: 6:30:41 time: 0.481631 data_time: 0.040711 memory: 21657 loss_kpt: 0.000580 acc_pose: 0.800073 loss: 0.000580 2022/10/21 14:04:42 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:05:07 - mmengine - INFO - Epoch(train) [74][50/391] lr: 5.000000e-04 eta: 6:29:33 time: 0.494413 data_time: 0.052632 memory: 21657 loss_kpt: 0.000597 acc_pose: 0.815768 loss: 0.000597 2022/10/21 14:05:31 - mmengine - INFO - Epoch(train) [74][100/391] lr: 5.000000e-04 eta: 6:29:16 time: 0.487975 data_time: 0.040687 memory: 21657 loss_kpt: 0.000593 acc_pose: 0.864927 loss: 0.000593 2022/10/21 14:05:56 - mmengine - INFO - Epoch(train) [74][150/391] lr: 5.000000e-04 eta: 6:28:59 time: 0.490327 data_time: 0.043876 memory: 21657 loss_kpt: 0.000580 acc_pose: 0.851506 loss: 0.000580 2022/10/21 14:06:20 - mmengine - INFO - Epoch(train) [74][200/391] lr: 5.000000e-04 eta: 6:28:41 time: 0.481048 data_time: 0.040718 memory: 21657 loss_kpt: 0.000572 acc_pose: 0.856845 loss: 0.000572 2022/10/21 14:06:44 - mmengine - INFO - Epoch(train) [74][250/391] lr: 5.000000e-04 eta: 6:28:24 time: 0.489741 data_time: 0.041151 memory: 21657 loss_kpt: 0.000573 acc_pose: 0.864928 loss: 0.000573 2022/10/21 14:07:08 - mmengine - INFO - Epoch(train) [74][300/391] lr: 5.000000e-04 eta: 6:28:06 time: 0.482112 data_time: 0.040704 memory: 21657 loss_kpt: 0.000586 acc_pose: 0.797023 loss: 0.000586 2022/10/21 14:07:33 - mmengine - INFO - Epoch(train) [74][350/391] lr: 5.000000e-04 eta: 6:27:49 time: 0.489570 data_time: 0.043685 memory: 21657 loss_kpt: 0.000576 acc_pose: 0.826241 loss: 0.000576 2022/10/21 14:07:52 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:08:18 - mmengine - INFO - Epoch(train) [75][50/391] lr: 5.000000e-04 eta: 6:26:44 time: 0.515570 data_time: 0.052459 memory: 21657 loss_kpt: 0.000581 acc_pose: 0.807467 loss: 0.000581 2022/10/21 14:08:26 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:08:42 - mmengine - INFO - Epoch(train) [75][100/391] lr: 5.000000e-04 eta: 6:26:26 time: 0.480391 data_time: 0.042870 memory: 21657 loss_kpt: 0.000576 acc_pose: 0.843429 loss: 0.000576 2022/10/21 14:09:06 - mmengine - INFO - Epoch(train) [75][150/391] lr: 5.000000e-04 eta: 6:26:09 time: 0.487094 data_time: 0.041020 memory: 21657 loss_kpt: 0.000570 acc_pose: 0.833444 loss: 0.000570 2022/10/21 14:09:31 - mmengine - INFO - Epoch(train) [75][200/391] lr: 5.000000e-04 eta: 6:25:52 time: 0.488709 data_time: 0.045717 memory: 21657 loss_kpt: 0.000586 acc_pose: 0.835535 loss: 0.000586 2022/10/21 14:09:55 - mmengine - INFO - Epoch(train) [75][250/391] lr: 5.000000e-04 eta: 6:25:34 time: 0.483976 data_time: 0.041416 memory: 21657 loss_kpt: 0.000572 acc_pose: 0.867320 loss: 0.000572 2022/10/21 14:10:20 - mmengine - INFO - Epoch(train) [75][300/391] lr: 5.000000e-04 eta: 6:25:17 time: 0.490091 data_time: 0.040788 memory: 21657 loss_kpt: 0.000582 acc_pose: 0.891264 loss: 0.000582 2022/10/21 14:10:44 - mmengine - INFO - Epoch(train) [75][350/391] lr: 5.000000e-04 eta: 6:24:59 time: 0.485941 data_time: 0.039703 memory: 21657 loss_kpt: 0.000570 acc_pose: 0.830306 loss: 0.000570 2022/10/21 14:11:04 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:11:29 - mmengine - INFO - Epoch(train) [76][50/391] lr: 5.000000e-04 eta: 6:23:53 time: 0.497767 data_time: 0.058006 memory: 21657 loss_kpt: 0.000589 acc_pose: 0.859333 loss: 0.000589 2022/10/21 14:11:53 - mmengine - INFO - Epoch(train) [76][100/391] lr: 5.000000e-04 eta: 6:23:36 time: 0.494542 data_time: 0.041146 memory: 21657 loss_kpt: 0.000578 acc_pose: 0.803854 loss: 0.000578 2022/10/21 14:12:17 - mmengine - INFO - Epoch(train) [76][150/391] lr: 5.000000e-04 eta: 6:23:18 time: 0.482121 data_time: 0.045232 memory: 21657 loss_kpt: 0.000586 acc_pose: 0.812571 loss: 0.000586 2022/10/21 14:12:42 - mmengine - INFO - Epoch(train) [76][200/391] lr: 5.000000e-04 eta: 6:23:01 time: 0.491951 data_time: 0.041466 memory: 21657 loss_kpt: 0.000556 acc_pose: 0.840538 loss: 0.000556 2022/10/21 14:13:06 - mmengine - INFO - Epoch(train) [76][250/391] lr: 5.000000e-04 eta: 6:22:44 time: 0.484352 data_time: 0.046880 memory: 21657 loss_kpt: 0.000588 acc_pose: 0.829086 loss: 0.000588 2022/10/21 14:13:30 - mmengine - INFO - Epoch(train) [76][300/391] lr: 5.000000e-04 eta: 6:22:26 time: 0.486171 data_time: 0.044170 memory: 21657 loss_kpt: 0.000592 acc_pose: 0.858465 loss: 0.000592 2022/10/21 14:13:55 - mmengine - INFO - Epoch(train) [76][350/391] lr: 5.000000e-04 eta: 6:22:09 time: 0.487169 data_time: 0.042952 memory: 21657 loss_kpt: 0.000572 acc_pose: 0.834203 loss: 0.000572 2022/10/21 14:14:15 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:14:40 - mmengine - INFO - Epoch(train) [77][50/391] lr: 5.000000e-04 eta: 6:21:04 time: 0.512433 data_time: 0.062539 memory: 21657 loss_kpt: 0.000570 acc_pose: 0.843167 loss: 0.000570 2022/10/21 14:15:04 - mmengine - INFO - Epoch(train) [77][100/391] lr: 5.000000e-04 eta: 6:20:46 time: 0.480309 data_time: 0.041293 memory: 21657 loss_kpt: 0.000574 acc_pose: 0.848472 loss: 0.000574 2022/10/21 14:15:29 - mmengine - INFO - Epoch(train) [77][150/391] lr: 5.000000e-04 eta: 6:20:29 time: 0.493794 data_time: 0.041849 memory: 21657 loss_kpt: 0.000570 acc_pose: 0.827863 loss: 0.000570 2022/10/21 14:15:53 - mmengine - INFO - Epoch(train) [77][200/391] lr: 5.000000e-04 eta: 6:20:11 time: 0.482576 data_time: 0.044794 memory: 21657 loss_kpt: 0.000570 acc_pose: 0.907252 loss: 0.000570 2022/10/21 14:16:18 - mmengine - INFO - Epoch(train) [77][250/391] lr: 5.000000e-04 eta: 6:19:54 time: 0.493639 data_time: 0.039151 memory: 21657 loss_kpt: 0.000585 acc_pose: 0.856686 loss: 0.000585 2022/10/21 14:16:34 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:16:42 - mmengine - INFO - Epoch(train) [77][300/391] lr: 5.000000e-04 eta: 6:19:37 time: 0.482710 data_time: 0.040266 memory: 21657 loss_kpt: 0.000574 acc_pose: 0.860001 loss: 0.000574 2022/10/21 14:17:06 - mmengine - INFO - Epoch(train) [77][350/391] lr: 5.000000e-04 eta: 6:19:19 time: 0.492464 data_time: 0.039099 memory: 21657 loss_kpt: 0.000575 acc_pose: 0.885635 loss: 0.000575 2022/10/21 14:17:26 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:17:51 - mmengine - INFO - Epoch(train) [78][50/391] lr: 5.000000e-04 eta: 6:18:14 time: 0.497969 data_time: 0.052706 memory: 21657 loss_kpt: 0.000579 acc_pose: 0.865900 loss: 0.000579 2022/10/21 14:18:15 - mmengine - INFO - Epoch(train) [78][100/391] lr: 5.000000e-04 eta: 6:17:57 time: 0.491634 data_time: 0.039477 memory: 21657 loss_kpt: 0.000583 acc_pose: 0.806007 loss: 0.000583 2022/10/21 14:18:40 - mmengine - INFO - Epoch(train) [78][150/391] lr: 5.000000e-04 eta: 6:17:39 time: 0.483158 data_time: 0.040752 memory: 21657 loss_kpt: 0.000570 acc_pose: 0.840448 loss: 0.000570 2022/10/21 14:19:04 - mmengine - INFO - Epoch(train) [78][200/391] lr: 5.000000e-04 eta: 6:17:22 time: 0.497203 data_time: 0.044917 memory: 21657 loss_kpt: 0.000562 acc_pose: 0.876168 loss: 0.000562 2022/10/21 14:19:29 - mmengine - INFO - Epoch(train) [78][250/391] lr: 5.000000e-04 eta: 6:17:04 time: 0.482726 data_time: 0.040543 memory: 21657 loss_kpt: 0.000555 acc_pose: 0.792802 loss: 0.000555 2022/10/21 14:19:53 - mmengine - INFO - Epoch(train) [78][300/391] lr: 5.000000e-04 eta: 6:16:47 time: 0.489997 data_time: 0.043402 memory: 21657 loss_kpt: 0.000559 acc_pose: 0.868571 loss: 0.000559 2022/10/21 14:20:17 - mmengine - INFO - Epoch(train) [78][350/391] lr: 5.000000e-04 eta: 6:16:29 time: 0.481495 data_time: 0.041002 memory: 21657 loss_kpt: 0.000578 acc_pose: 0.809960 loss: 0.000578 2022/10/21 14:20:37 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:21:02 - mmengine - INFO - Epoch(train) [79][50/391] lr: 5.000000e-04 eta: 6:15:24 time: 0.504457 data_time: 0.053417 memory: 21657 loss_kpt: 0.000572 acc_pose: 0.884542 loss: 0.000572 2022/10/21 14:21:26 - mmengine - INFO - Epoch(train) [79][100/391] lr: 5.000000e-04 eta: 6:15:07 time: 0.485218 data_time: 0.039979 memory: 21657 loss_kpt: 0.000572 acc_pose: 0.886771 loss: 0.000572 2022/10/21 14:21:51 - mmengine - INFO - Epoch(train) [79][150/391] lr: 5.000000e-04 eta: 6:14:49 time: 0.486487 data_time: 0.038991 memory: 21657 loss_kpt: 0.000567 acc_pose: 0.824646 loss: 0.000567 2022/10/21 14:22:15 - mmengine - INFO - Epoch(train) [79][200/391] lr: 5.000000e-04 eta: 6:14:31 time: 0.484390 data_time: 0.041177 memory: 21657 loss_kpt: 0.000546 acc_pose: 0.802700 loss: 0.000546 2022/10/21 14:22:39 - mmengine - INFO - Epoch(train) [79][250/391] lr: 5.000000e-04 eta: 6:14:14 time: 0.489861 data_time: 0.040278 memory: 21657 loss_kpt: 0.000557 acc_pose: 0.807585 loss: 0.000557 2022/10/21 14:23:04 - mmengine - INFO - Epoch(train) [79][300/391] lr: 5.000000e-04 eta: 6:13:55 time: 0.481686 data_time: 0.039892 memory: 21657 loss_kpt: 0.000568 acc_pose: 0.874618 loss: 0.000568 2022/10/21 14:23:28 - mmengine - INFO - Epoch(train) [79][350/391] lr: 5.000000e-04 eta: 6:13:38 time: 0.492790 data_time: 0.042991 memory: 21657 loss_kpt: 0.000583 acc_pose: 0.875437 loss: 0.000583 2022/10/21 14:23:48 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:24:12 - mmengine - INFO - Epoch(train) [80][50/391] lr: 5.000000e-04 eta: 6:12:34 time: 0.497412 data_time: 0.052881 memory: 21657 loss_kpt: 0.000557 acc_pose: 0.786382 loss: 0.000557 2022/10/21 14:24:37 - mmengine - INFO - Epoch(train) [80][100/391] lr: 5.000000e-04 eta: 6:12:16 time: 0.490307 data_time: 0.038308 memory: 21657 loss_kpt: 0.000573 acc_pose: 0.831981 loss: 0.000573 2022/10/21 14:24:42 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:25:01 - mmengine - INFO - Epoch(train) [80][150/391] lr: 5.000000e-04 eta: 6:11:58 time: 0.483385 data_time: 0.043438 memory: 21657 loss_kpt: 0.000555 acc_pose: 0.829514 loss: 0.000555 2022/10/21 14:25:26 - mmengine - INFO - Epoch(train) [80][200/391] lr: 5.000000e-04 eta: 6:11:41 time: 0.488445 data_time: 0.044112 memory: 21657 loss_kpt: 0.000563 acc_pose: 0.833692 loss: 0.000563 2022/10/21 14:25:50 - mmengine - INFO - Epoch(train) [80][250/391] lr: 5.000000e-04 eta: 6:11:23 time: 0.488258 data_time: 0.041428 memory: 21657 loss_kpt: 0.000560 acc_pose: 0.849410 loss: 0.000560 2022/10/21 14:26:15 - mmengine - INFO - Epoch(train) [80][300/391] lr: 5.000000e-04 eta: 6:11:06 time: 0.491751 data_time: 0.039304 memory: 21657 loss_kpt: 0.000568 acc_pose: 0.813710 loss: 0.000568 2022/10/21 14:26:39 - mmengine - INFO - Epoch(train) [80][350/391] lr: 5.000000e-04 eta: 6:10:47 time: 0.481804 data_time: 0.038681 memory: 21657 loss_kpt: 0.000560 acc_pose: 0.803867 loss: 0.000560 2022/10/21 14:26:59 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:26:59 - mmengine - INFO - Saving checkpoint at 80 epochs 2022/10/21 14:27:11 - mmengine - INFO - Epoch(val) [80][50/407] eta: 0:00:55 time: 0.156466 data_time: 0.016313 memory: 21657 2022/10/21 14:27:18 - mmengine - INFO - Epoch(val) [80][100/407] eta: 0:00:46 time: 0.151833 data_time: 0.011792 memory: 2142 2022/10/21 14:27:26 - mmengine - INFO - Epoch(val) [80][150/407] eta: 0:00:38 time: 0.149817 data_time: 0.009251 memory: 2142 2022/10/21 14:27:33 - mmengine - INFO - Epoch(val) [80][200/407] eta: 0:00:32 time: 0.154652 data_time: 0.013797 memory: 2142 2022/10/21 14:27:41 - mmengine - INFO - Epoch(val) [80][250/407] eta: 0:00:24 time: 0.154633 data_time: 0.014173 memory: 2142 2022/10/21 14:27:49 - mmengine - INFO - Epoch(val) [80][300/407] eta: 0:00:16 time: 0.156160 data_time: 0.015436 memory: 2142 2022/10/21 14:27:56 - mmengine - INFO - Epoch(val) [80][350/407] eta: 0:00:08 time: 0.148848 data_time: 0.008845 memory: 2142 2022/10/21 14:28:04 - mmengine - INFO - Epoch(val) [80][400/407] eta: 0:00:01 time: 0.149091 data_time: 0.009411 memory: 2142 2022/10/21 14:28:39 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 14:28:53 - mmengine - INFO - Epoch(val) [80][407/407] coco/AP: 0.741791 coco/AP .5: 0.900589 coco/AP .75: 0.812924 coco/AP (M): 0.699724 coco/AP (L): 0.814002 coco/AR: 0.791751 coco/AR .5: 0.937500 coco/AR .75: 0.853747 coco/AR (M): 0.746736 coco/AR (L): 0.856819 2022/10/21 14:28:53 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221021/resnetv1d152_384/best_coco/AP_epoch_70.pth is removed 2022/10/21 14:28:56 - mmengine - INFO - The best checkpoint with 0.7418 coco/AP at 80 epoch is saved to best_coco/AP_epoch_80.pth. 2022/10/21 14:29:20 - mmengine - INFO - Epoch(train) [81][50/391] lr: 5.000000e-04 eta: 6:09:42 time: 0.486392 data_time: 0.052124 memory: 21657 loss_kpt: 0.000569 acc_pose: 0.790647 loss: 0.000569 2022/10/21 14:29:44 - mmengine - INFO - Epoch(train) [81][100/391] lr: 5.000000e-04 eta: 6:09:25 time: 0.488301 data_time: 0.041597 memory: 21657 loss_kpt: 0.000562 acc_pose: 0.881099 loss: 0.000562 2022/10/21 14:30:09 - mmengine - INFO - Epoch(train) [81][150/391] lr: 5.000000e-04 eta: 6:09:07 time: 0.487632 data_time: 0.047363 memory: 21657 loss_kpt: 0.000565 acc_pose: 0.853998 loss: 0.000565 2022/10/21 14:30:33 - mmengine - INFO - Epoch(train) [81][200/391] lr: 5.000000e-04 eta: 6:08:49 time: 0.481015 data_time: 0.041313 memory: 21657 loss_kpt: 0.000569 acc_pose: 0.852146 loss: 0.000569 2022/10/21 14:30:57 - mmengine - INFO - Epoch(train) [81][250/391] lr: 5.000000e-04 eta: 6:08:31 time: 0.492398 data_time: 0.046845 memory: 21657 loss_kpt: 0.000555 acc_pose: 0.839355 loss: 0.000555 2022/10/21 14:31:22 - mmengine - INFO - Epoch(train) [81][300/391] lr: 5.000000e-04 eta: 6:08:13 time: 0.487094 data_time: 0.041412 memory: 21657 loss_kpt: 0.000567 acc_pose: 0.773797 loss: 0.000567 2022/10/21 14:31:46 - mmengine - INFO - Epoch(train) [81][350/391] lr: 5.000000e-04 eta: 6:07:56 time: 0.492932 data_time: 0.040518 memory: 21657 loss_kpt: 0.000563 acc_pose: 0.817208 loss: 0.000563 2022/10/21 14:32:06 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:32:32 - mmengine - INFO - Epoch(train) [82][50/391] lr: 5.000000e-04 eta: 6:06:54 time: 0.513402 data_time: 0.052593 memory: 21657 loss_kpt: 0.000558 acc_pose: 0.850154 loss: 0.000558 2022/10/21 14:32:56 - mmengine - INFO - Epoch(train) [82][100/391] lr: 5.000000e-04 eta: 6:06:36 time: 0.486587 data_time: 0.041619 memory: 21657 loss_kpt: 0.000562 acc_pose: 0.884106 loss: 0.000562 2022/10/21 14:33:20 - mmengine - INFO - Epoch(train) [82][150/391] lr: 5.000000e-04 eta: 6:06:18 time: 0.490745 data_time: 0.045124 memory: 21657 loss_kpt: 0.000566 acc_pose: 0.866618 loss: 0.000566 2022/10/21 14:33:45 - mmengine - INFO - Epoch(train) [82][200/391] lr: 5.000000e-04 eta: 6:06:00 time: 0.484881 data_time: 0.042056 memory: 21657 loss_kpt: 0.000555 acc_pose: 0.851028 loss: 0.000555 2022/10/21 14:34:09 - mmengine - INFO - Epoch(train) [82][250/391] lr: 5.000000e-04 eta: 6:05:42 time: 0.489228 data_time: 0.040200 memory: 21657 loss_kpt: 0.000574 acc_pose: 0.853852 loss: 0.000574 2022/10/21 14:34:33 - mmengine - INFO - Epoch(train) [82][300/391] lr: 5.000000e-04 eta: 6:05:24 time: 0.486454 data_time: 0.041133 memory: 21657 loss_kpt: 0.000563 acc_pose: 0.820948 loss: 0.000563 2022/10/21 14:34:48 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:34:58 - mmengine - INFO - Epoch(train) [82][350/391] lr: 5.000000e-04 eta: 6:05:06 time: 0.483667 data_time: 0.041330 memory: 21657 loss_kpt: 0.000576 acc_pose: 0.867721 loss: 0.000576 2022/10/21 14:35:18 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:35:43 - mmengine - INFO - Epoch(train) [83][50/391] lr: 5.000000e-04 eta: 6:04:03 time: 0.497709 data_time: 0.056456 memory: 21657 loss_kpt: 0.000564 acc_pose: 0.884802 loss: 0.000564 2022/10/21 14:36:07 - mmengine - INFO - Epoch(train) [83][100/391] lr: 5.000000e-04 eta: 6:03:46 time: 0.490883 data_time: 0.039974 memory: 21657 loss_kpt: 0.000559 acc_pose: 0.824963 loss: 0.000559 2022/10/21 14:36:31 - mmengine - INFO - Epoch(train) [83][150/391] lr: 5.000000e-04 eta: 6:03:27 time: 0.482972 data_time: 0.039413 memory: 21657 loss_kpt: 0.000562 acc_pose: 0.828664 loss: 0.000562 2022/10/21 14:36:56 - mmengine - INFO - Epoch(train) [83][200/391] lr: 5.000000e-04 eta: 6:03:09 time: 0.486019 data_time: 0.043446 memory: 21657 loss_kpt: 0.000557 acc_pose: 0.849243 loss: 0.000557 2022/10/21 14:37:20 - mmengine - INFO - Epoch(train) [83][250/391] lr: 5.000000e-04 eta: 6:02:51 time: 0.487037 data_time: 0.040515 memory: 21657 loss_kpt: 0.000563 acc_pose: 0.891918 loss: 0.000563 2022/10/21 14:37:44 - mmengine - INFO - Epoch(train) [83][300/391] lr: 5.000000e-04 eta: 6:02:33 time: 0.485866 data_time: 0.041927 memory: 21657 loss_kpt: 0.000572 acc_pose: 0.852773 loss: 0.000572 2022/10/21 14:38:08 - mmengine - INFO - Epoch(train) [83][350/391] lr: 5.000000e-04 eta: 6:02:15 time: 0.483892 data_time: 0.039434 memory: 21657 loss_kpt: 0.000559 acc_pose: 0.848470 loss: 0.000559 2022/10/21 14:38:28 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:38:53 - mmengine - INFO - Epoch(train) [84][50/391] lr: 5.000000e-04 eta: 6:01:12 time: 0.500577 data_time: 0.054892 memory: 21657 loss_kpt: 0.000569 acc_pose: 0.846468 loss: 0.000569 2022/10/21 14:39:17 - mmengine - INFO - Epoch(train) [84][100/391] lr: 5.000000e-04 eta: 6:00:54 time: 0.488226 data_time: 0.041182 memory: 21657 loss_kpt: 0.000562 acc_pose: 0.869217 loss: 0.000562 2022/10/21 14:39:42 - mmengine - INFO - Epoch(train) [84][150/391] lr: 5.000000e-04 eta: 6:00:36 time: 0.486499 data_time: 0.040383 memory: 21657 loss_kpt: 0.000568 acc_pose: 0.862257 loss: 0.000568 2022/10/21 14:40:06 - mmengine - INFO - Epoch(train) [84][200/391] lr: 5.000000e-04 eta: 6:00:19 time: 0.491103 data_time: 0.039946 memory: 21657 loss_kpt: 0.000554 acc_pose: 0.859430 loss: 0.000554 2022/10/21 14:40:31 - mmengine - INFO - Epoch(train) [84][250/391] lr: 5.000000e-04 eta: 6:00:01 time: 0.490555 data_time: 0.040562 memory: 21657 loss_kpt: 0.000559 acc_pose: 0.893484 loss: 0.000559 2022/10/21 14:40:55 - mmengine - INFO - Epoch(train) [84][300/391] lr: 5.000000e-04 eta: 5:59:42 time: 0.483380 data_time: 0.039943 memory: 21657 loss_kpt: 0.000564 acc_pose: 0.842443 loss: 0.000564 2022/10/21 14:41:20 - mmengine - INFO - Epoch(train) [84][350/391] lr: 5.000000e-04 eta: 5:59:25 time: 0.492632 data_time: 0.047182 memory: 21657 loss_kpt: 0.000561 acc_pose: 0.876791 loss: 0.000561 2022/10/21 14:41:39 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:42:05 - mmengine - INFO - Epoch(train) [85][50/391] lr: 5.000000e-04 eta: 5:58:23 time: 0.509485 data_time: 0.052362 memory: 21657 loss_kpt: 0.000553 acc_pose: 0.818048 loss: 0.000553 2022/10/21 14:42:29 - mmengine - INFO - Epoch(train) [85][100/391] lr: 5.000000e-04 eta: 5:58:05 time: 0.481459 data_time: 0.044024 memory: 21657 loss_kpt: 0.000563 acc_pose: 0.794352 loss: 0.000563 2022/10/21 14:42:53 - mmengine - INFO - Epoch(train) [85][150/391] lr: 5.000000e-04 eta: 5:57:47 time: 0.487485 data_time: 0.040265 memory: 21657 loss_kpt: 0.000548 acc_pose: 0.798859 loss: 0.000548 2022/10/21 14:42:56 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:43:17 - mmengine - INFO - Epoch(train) [85][200/391] lr: 5.000000e-04 eta: 5:57:29 time: 0.486880 data_time: 0.045115 memory: 21657 loss_kpt: 0.000554 acc_pose: 0.860582 loss: 0.000554 2022/10/21 14:43:42 - mmengine - INFO - Epoch(train) [85][250/391] lr: 5.000000e-04 eta: 5:57:11 time: 0.489672 data_time: 0.041910 memory: 21657 loss_kpt: 0.000562 acc_pose: 0.819699 loss: 0.000562 2022/10/21 14:44:06 - mmengine - INFO - Epoch(train) [85][300/391] lr: 5.000000e-04 eta: 5:56:53 time: 0.487397 data_time: 0.043840 memory: 21657 loss_kpt: 0.000559 acc_pose: 0.834096 loss: 0.000559 2022/10/21 14:44:31 - mmengine - INFO - Epoch(train) [85][350/391] lr: 5.000000e-04 eta: 5:56:34 time: 0.487287 data_time: 0.039712 memory: 21657 loss_kpt: 0.000543 acc_pose: 0.849443 loss: 0.000543 2022/10/21 14:44:50 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:45:15 - mmengine - INFO - Epoch(train) [86][50/391] lr: 5.000000e-04 eta: 5:55:33 time: 0.498671 data_time: 0.054293 memory: 21657 loss_kpt: 0.000555 acc_pose: 0.844967 loss: 0.000555 2022/10/21 14:45:40 - mmengine - INFO - Epoch(train) [86][100/391] lr: 5.000000e-04 eta: 5:55:15 time: 0.493858 data_time: 0.039759 memory: 21657 loss_kpt: 0.000549 acc_pose: 0.847395 loss: 0.000549 2022/10/21 14:46:04 - mmengine - INFO - Epoch(train) [86][150/391] lr: 5.000000e-04 eta: 5:54:57 time: 0.485397 data_time: 0.040695 memory: 21657 loss_kpt: 0.000542 acc_pose: 0.873977 loss: 0.000542 2022/10/21 14:46:29 - mmengine - INFO - Epoch(train) [86][200/391] lr: 5.000000e-04 eta: 5:54:39 time: 0.488387 data_time: 0.040938 memory: 21657 loss_kpt: 0.000552 acc_pose: 0.868166 loss: 0.000552 2022/10/21 14:46:53 - mmengine - INFO - Epoch(train) [86][250/391] lr: 5.000000e-04 eta: 5:54:20 time: 0.482838 data_time: 0.039867 memory: 21657 loss_kpt: 0.000557 acc_pose: 0.883792 loss: 0.000557 2022/10/21 14:47:17 - mmengine - INFO - Epoch(train) [86][300/391] lr: 5.000000e-04 eta: 5:54:02 time: 0.486781 data_time: 0.039141 memory: 21657 loss_kpt: 0.000565 acc_pose: 0.870566 loss: 0.000565 2022/10/21 14:47:42 - mmengine - INFO - Epoch(train) [86][350/391] lr: 5.000000e-04 eta: 5:53:44 time: 0.490747 data_time: 0.043140 memory: 21657 loss_kpt: 0.000572 acc_pose: 0.886936 loss: 0.000572 2022/10/21 14:48:01 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:48:27 - mmengine - INFO - Epoch(train) [87][50/391] lr: 5.000000e-04 eta: 5:52:43 time: 0.505678 data_time: 0.055118 memory: 21657 loss_kpt: 0.000576 acc_pose: 0.797850 loss: 0.000576 2022/10/21 14:48:51 - mmengine - INFO - Epoch(train) [87][100/391] lr: 5.000000e-04 eta: 5:52:25 time: 0.488739 data_time: 0.041062 memory: 21657 loss_kpt: 0.000566 acc_pose: 0.840095 loss: 0.000566 2022/10/21 14:49:16 - mmengine - INFO - Epoch(train) [87][150/391] lr: 5.000000e-04 eta: 5:52:07 time: 0.488345 data_time: 0.045052 memory: 21657 loss_kpt: 0.000574 acc_pose: 0.836804 loss: 0.000574 2022/10/21 14:49:40 - mmengine - INFO - Epoch(train) [87][200/391] lr: 5.000000e-04 eta: 5:51:48 time: 0.484205 data_time: 0.040797 memory: 21657 loss_kpt: 0.000557 acc_pose: 0.881454 loss: 0.000557 2022/10/21 14:50:04 - mmengine - INFO - Epoch(train) [87][250/391] lr: 5.000000e-04 eta: 5:51:30 time: 0.483814 data_time: 0.040206 memory: 21657 loss_kpt: 0.000565 acc_pose: 0.853502 loss: 0.000565 2022/10/21 14:50:28 - mmengine - INFO - Epoch(train) [87][300/391] lr: 5.000000e-04 eta: 5:51:11 time: 0.482416 data_time: 0.045453 memory: 21657 loss_kpt: 0.000561 acc_pose: 0.825775 loss: 0.000561 2022/10/21 14:50:53 - mmengine - INFO - Epoch(train) [87][350/391] lr: 5.000000e-04 eta: 5:50:53 time: 0.486921 data_time: 0.040944 memory: 21657 loss_kpt: 0.000566 acc_pose: 0.908866 loss: 0.000566 2022/10/21 14:51:04 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:51:12 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:51:37 - mmengine - INFO - Epoch(train) [88][50/391] lr: 5.000000e-04 eta: 5:49:52 time: 0.501039 data_time: 0.051157 memory: 21657 loss_kpt: 0.000560 acc_pose: 0.836423 loss: 0.000560 2022/10/21 14:52:01 - mmengine - INFO - Epoch(train) [88][100/391] lr: 5.000000e-04 eta: 5:49:34 time: 0.484950 data_time: 0.044414 memory: 21657 loss_kpt: 0.000569 acc_pose: 0.867631 loss: 0.000569 2022/10/21 14:52:26 - mmengine - INFO - Epoch(train) [88][150/391] lr: 5.000000e-04 eta: 5:49:15 time: 0.486733 data_time: 0.037997 memory: 21657 loss_kpt: 0.000564 acc_pose: 0.867872 loss: 0.000564 2022/10/21 14:52:50 - mmengine - INFO - Epoch(train) [88][200/391] lr: 5.000000e-04 eta: 5:48:57 time: 0.490612 data_time: 0.042598 memory: 21657 loss_kpt: 0.000558 acc_pose: 0.913426 loss: 0.000558 2022/10/21 14:53:14 - mmengine - INFO - Epoch(train) [88][250/391] lr: 5.000000e-04 eta: 5:48:39 time: 0.482312 data_time: 0.039171 memory: 21657 loss_kpt: 0.000560 acc_pose: 0.825872 loss: 0.000560 2022/10/21 14:53:38 - mmengine - INFO - Epoch(train) [88][300/391] lr: 5.000000e-04 eta: 5:48:20 time: 0.483335 data_time: 0.040643 memory: 21657 loss_kpt: 0.000574 acc_pose: 0.841033 loss: 0.000574 2022/10/21 14:54:03 - mmengine - INFO - Epoch(train) [88][350/391] lr: 5.000000e-04 eta: 5:48:02 time: 0.498034 data_time: 0.041116 memory: 21657 loss_kpt: 0.000571 acc_pose: 0.794231 loss: 0.000571 2022/10/21 14:54:23 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:54:48 - mmengine - INFO - Epoch(train) [89][50/391] lr: 5.000000e-04 eta: 5:47:02 time: 0.499107 data_time: 0.053830 memory: 21657 loss_kpt: 0.000569 acc_pose: 0.857165 loss: 0.000569 2022/10/21 14:55:12 - mmengine - INFO - Epoch(train) [89][100/391] lr: 5.000000e-04 eta: 5:46:44 time: 0.488034 data_time: 0.041259 memory: 21657 loss_kpt: 0.000562 acc_pose: 0.862530 loss: 0.000562 2022/10/21 14:55:37 - mmengine - INFO - Epoch(train) [89][150/391] lr: 5.000000e-04 eta: 5:46:25 time: 0.486427 data_time: 0.045493 memory: 21657 loss_kpt: 0.000568 acc_pose: 0.884075 loss: 0.000568 2022/10/21 14:56:01 - mmengine - INFO - Epoch(train) [89][200/391] lr: 5.000000e-04 eta: 5:46:07 time: 0.486255 data_time: 0.041071 memory: 21657 loss_kpt: 0.000558 acc_pose: 0.874711 loss: 0.000558 2022/10/21 14:56:25 - mmengine - INFO - Epoch(train) [89][250/391] lr: 5.000000e-04 eta: 5:45:48 time: 0.488799 data_time: 0.044327 memory: 21657 loss_kpt: 0.000559 acc_pose: 0.864226 loss: 0.000559 2022/10/21 14:56:50 - mmengine - INFO - Epoch(train) [89][300/391] lr: 5.000000e-04 eta: 5:45:30 time: 0.483621 data_time: 0.039676 memory: 21657 loss_kpt: 0.000560 acc_pose: 0.886630 loss: 0.000560 2022/10/21 14:57:14 - mmengine - INFO - Epoch(train) [89][350/391] lr: 5.000000e-04 eta: 5:45:11 time: 0.491759 data_time: 0.041581 memory: 21657 loss_kpt: 0.000548 acc_pose: 0.854598 loss: 0.000548 2022/10/21 14:57:34 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:57:59 - mmengine - INFO - Epoch(train) [90][50/391] lr: 5.000000e-04 eta: 5:44:12 time: 0.502416 data_time: 0.051014 memory: 21657 loss_kpt: 0.000562 acc_pose: 0.895684 loss: 0.000562 2022/10/21 14:58:23 - mmengine - INFO - Epoch(train) [90][100/391] lr: 5.000000e-04 eta: 5:43:54 time: 0.490736 data_time: 0.046653 memory: 21657 loss_kpt: 0.000558 acc_pose: 0.832317 loss: 0.000558 2022/10/21 14:58:48 - mmengine - INFO - Epoch(train) [90][150/391] lr: 5.000000e-04 eta: 5:43:35 time: 0.485800 data_time: 0.039479 memory: 21657 loss_kpt: 0.000563 acc_pose: 0.829520 loss: 0.000563 2022/10/21 14:59:12 - mmengine - INFO - Epoch(train) [90][200/391] lr: 5.000000e-04 eta: 5:43:16 time: 0.484542 data_time: 0.040135 memory: 21657 loss_kpt: 0.000567 acc_pose: 0.836574 loss: 0.000567 2022/10/21 14:59:12 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 14:59:36 - mmengine - INFO - Epoch(train) [90][250/391] lr: 5.000000e-04 eta: 5:42:58 time: 0.488855 data_time: 0.041569 memory: 21657 loss_kpt: 0.000565 acc_pose: 0.786094 loss: 0.000565 2022/10/21 15:00:01 - mmengine - INFO - Epoch(train) [90][300/391] lr: 5.000000e-04 eta: 5:42:39 time: 0.487831 data_time: 0.042076 memory: 21657 loss_kpt: 0.000558 acc_pose: 0.856880 loss: 0.000558 2022/10/21 15:00:25 - mmengine - INFO - Epoch(train) [90][350/391] lr: 5.000000e-04 eta: 5:42:21 time: 0.488837 data_time: 0.041472 memory: 21657 loss_kpt: 0.000569 acc_pose: 0.848039 loss: 0.000569 2022/10/21 15:00:45 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:00:45 - mmengine - INFO - Saving checkpoint at 90 epochs 2022/10/21 15:00:57 - mmengine - INFO - Epoch(val) [90][50/407] eta: 0:00:57 time: 0.160661 data_time: 0.018990 memory: 21657 2022/10/21 15:01:05 - mmengine - INFO - Epoch(val) [90][100/407] eta: 0:00:47 time: 0.154831 data_time: 0.013668 memory: 2142 2022/10/21 15:01:12 - mmengine - INFO - Epoch(val) [90][150/407] eta: 0:00:38 time: 0.150447 data_time: 0.009051 memory: 2142 2022/10/21 15:01:20 - mmengine - INFO - Epoch(val) [90][200/407] eta: 0:00:31 time: 0.150276 data_time: 0.009751 memory: 2142 2022/10/21 15:01:27 - mmengine - INFO - Epoch(val) [90][250/407] eta: 0:00:23 time: 0.149976 data_time: 0.008729 memory: 2142 2022/10/21 15:01:35 - mmengine - INFO - Epoch(val) [90][300/407] eta: 0:00:16 time: 0.150510 data_time: 0.008959 memory: 2142 2022/10/21 15:01:42 - mmengine - INFO - Epoch(val) [90][350/407] eta: 0:00:08 time: 0.151849 data_time: 0.010695 memory: 2142 2022/10/21 15:01:50 - mmengine - INFO - Epoch(val) [90][400/407] eta: 0:00:01 time: 0.148715 data_time: 0.009021 memory: 2142 2022/10/21 15:02:26 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 15:02:40 - mmengine - INFO - Epoch(val) [90][407/407] coco/AP: 0.739384 coco/AP .5: 0.898419 coco/AP .75: 0.807868 coco/AP (M): 0.697441 coco/AP (L): 0.811318 coco/AR: 0.790176 coco/AR .5: 0.935139 coco/AR .75: 0.850913 coco/AR (M): 0.743813 coco/AR (L): 0.857042 2022/10/21 15:03:04 - mmengine - INFO - Epoch(train) [91][50/391] lr: 5.000000e-04 eta: 5:41:21 time: 0.495408 data_time: 0.051119 memory: 21657 loss_kpt: 0.000557 acc_pose: 0.859291 loss: 0.000557 2022/10/21 15:03:29 - mmengine - INFO - Epoch(train) [91][100/391] lr: 5.000000e-04 eta: 5:41:03 time: 0.486040 data_time: 0.040327 memory: 21657 loss_kpt: 0.000553 acc_pose: 0.826181 loss: 0.000553 2022/10/21 15:03:53 - mmengine - INFO - Epoch(train) [91][150/391] lr: 5.000000e-04 eta: 5:40:44 time: 0.480675 data_time: 0.041147 memory: 21657 loss_kpt: 0.000555 acc_pose: 0.829879 loss: 0.000555 2022/10/21 15:04:17 - mmengine - INFO - Epoch(train) [91][200/391] lr: 5.000000e-04 eta: 5:40:25 time: 0.487948 data_time: 0.040411 memory: 21657 loss_kpt: 0.000572 acc_pose: 0.825660 loss: 0.000572 2022/10/21 15:04:41 - mmengine - INFO - Epoch(train) [91][250/391] lr: 5.000000e-04 eta: 5:40:06 time: 0.481127 data_time: 0.041187 memory: 21657 loss_kpt: 0.000550 acc_pose: 0.890848 loss: 0.000550 2022/10/21 15:05:06 - mmengine - INFO - Epoch(train) [91][300/391] lr: 5.000000e-04 eta: 5:39:48 time: 0.493606 data_time: 0.039738 memory: 21657 loss_kpt: 0.000550 acc_pose: 0.889757 loss: 0.000550 2022/10/21 15:05:30 - mmengine - INFO - Epoch(train) [91][350/391] lr: 5.000000e-04 eta: 5:39:29 time: 0.481398 data_time: 0.039811 memory: 21657 loss_kpt: 0.000541 acc_pose: 0.859574 loss: 0.000541 2022/10/21 15:05:50 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:06:15 - mmengine - INFO - Epoch(train) [92][50/391] lr: 5.000000e-04 eta: 5:38:30 time: 0.493682 data_time: 0.052069 memory: 21657 loss_kpt: 0.000583 acc_pose: 0.814913 loss: 0.000583 2022/10/21 15:06:39 - mmengine - INFO - Epoch(train) [92][100/391] lr: 5.000000e-04 eta: 5:38:11 time: 0.490300 data_time: 0.045287 memory: 21657 loss_kpt: 0.000551 acc_pose: 0.831032 loss: 0.000551 2022/10/21 15:07:03 - mmengine - INFO - Epoch(train) [92][150/391] lr: 5.000000e-04 eta: 5:37:52 time: 0.482681 data_time: 0.040862 memory: 21657 loss_kpt: 0.000565 acc_pose: 0.780077 loss: 0.000565 2022/10/21 15:07:27 - mmengine - INFO - Epoch(train) [92][200/391] lr: 5.000000e-04 eta: 5:37:33 time: 0.481316 data_time: 0.039689 memory: 21657 loss_kpt: 0.000558 acc_pose: 0.847275 loss: 0.000558 2022/10/21 15:07:52 - mmengine - INFO - Epoch(train) [92][250/391] lr: 5.000000e-04 eta: 5:37:15 time: 0.486760 data_time: 0.040423 memory: 21657 loss_kpt: 0.000556 acc_pose: 0.822055 loss: 0.000556 2022/10/21 15:08:16 - mmengine - INFO - Epoch(train) [92][300/391] lr: 5.000000e-04 eta: 5:36:56 time: 0.488982 data_time: 0.041327 memory: 21657 loss_kpt: 0.000551 acc_pose: 0.851378 loss: 0.000551 2022/10/21 15:08:40 - mmengine - INFO - Epoch(train) [92][350/391] lr: 5.000000e-04 eta: 5:36:37 time: 0.488337 data_time: 0.040595 memory: 21657 loss_kpt: 0.000575 acc_pose: 0.846293 loss: 0.000575 2022/10/21 15:09:00 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:09:15 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:09:25 - mmengine - INFO - Epoch(train) [93][50/391] lr: 5.000000e-04 eta: 5:35:39 time: 0.509539 data_time: 0.050825 memory: 21657 loss_kpt: 0.000557 acc_pose: 0.876843 loss: 0.000557 2022/10/21 15:09:49 - mmengine - INFO - Epoch(train) [93][100/391] lr: 5.000000e-04 eta: 5:35:20 time: 0.480518 data_time: 0.040922 memory: 21657 loss_kpt: 0.000560 acc_pose: 0.866643 loss: 0.000560 2022/10/21 15:10:14 - mmengine - INFO - Epoch(train) [93][150/391] lr: 5.000000e-04 eta: 5:35:02 time: 0.492329 data_time: 0.045019 memory: 21657 loss_kpt: 0.000534 acc_pose: 0.896486 loss: 0.000534 2022/10/21 15:10:38 - mmengine - INFO - Epoch(train) [93][200/391] lr: 5.000000e-04 eta: 5:34:43 time: 0.478780 data_time: 0.040054 memory: 21657 loss_kpt: 0.000555 acc_pose: 0.854674 loss: 0.000555 2022/10/21 15:11:02 - mmengine - INFO - Epoch(train) [93][250/391] lr: 5.000000e-04 eta: 5:34:24 time: 0.488644 data_time: 0.045078 memory: 21657 loss_kpt: 0.000543 acc_pose: 0.881623 loss: 0.000543 2022/10/21 15:11:26 - mmengine - INFO - Epoch(train) [93][300/391] lr: 5.000000e-04 eta: 5:34:05 time: 0.480723 data_time: 0.040292 memory: 21657 loss_kpt: 0.000570 acc_pose: 0.861679 loss: 0.000570 2022/10/21 15:11:51 - mmengine - INFO - Epoch(train) [93][350/391] lr: 5.000000e-04 eta: 5:33:46 time: 0.487829 data_time: 0.044246 memory: 21657 loss_kpt: 0.000531 acc_pose: 0.868452 loss: 0.000531 2022/10/21 15:12:10 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:12:35 - mmengine - INFO - Epoch(train) [94][50/391] lr: 5.000000e-04 eta: 5:32:48 time: 0.499283 data_time: 0.052338 memory: 21657 loss_kpt: 0.000561 acc_pose: 0.844382 loss: 0.000561 2022/10/21 15:13:00 - mmengine - INFO - Epoch(train) [94][100/391] lr: 5.000000e-04 eta: 5:32:30 time: 0.492519 data_time: 0.045087 memory: 21657 loss_kpt: 0.000548 acc_pose: 0.878885 loss: 0.000548 2022/10/21 15:13:24 - mmengine - INFO - Epoch(train) [94][150/391] lr: 5.000000e-04 eta: 5:32:11 time: 0.482740 data_time: 0.040667 memory: 21657 loss_kpt: 0.000547 acc_pose: 0.860075 loss: 0.000547 2022/10/21 15:13:48 - mmengine - INFO - Epoch(train) [94][200/391] lr: 5.000000e-04 eta: 5:31:52 time: 0.488332 data_time: 0.041755 memory: 21657 loss_kpt: 0.000542 acc_pose: 0.847242 loss: 0.000542 2022/10/21 15:14:12 - mmengine - INFO - Epoch(train) [94][250/391] lr: 5.000000e-04 eta: 5:31:33 time: 0.480995 data_time: 0.039796 memory: 21657 loss_kpt: 0.000546 acc_pose: 0.869203 loss: 0.000546 2022/10/21 15:14:37 - mmengine - INFO - Epoch(train) [94][300/391] lr: 5.000000e-04 eta: 5:31:15 time: 0.496482 data_time: 0.044620 memory: 21657 loss_kpt: 0.000544 acc_pose: 0.895773 loss: 0.000544 2022/10/21 15:15:01 - mmengine - INFO - Epoch(train) [94][350/391] lr: 5.000000e-04 eta: 5:30:55 time: 0.481798 data_time: 0.039836 memory: 21657 loss_kpt: 0.000554 acc_pose: 0.876818 loss: 0.000554 2022/10/21 15:15:21 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:15:46 - mmengine - INFO - Epoch(train) [95][50/391] lr: 5.000000e-04 eta: 5:29:57 time: 0.500332 data_time: 0.050883 memory: 21657 loss_kpt: 0.000553 acc_pose: 0.867703 loss: 0.000553 2022/10/21 15:16:10 - mmengine - INFO - Epoch(train) [95][100/391] lr: 5.000000e-04 eta: 5:29:38 time: 0.483613 data_time: 0.040374 memory: 21657 loss_kpt: 0.000544 acc_pose: 0.883227 loss: 0.000544 2022/10/21 15:16:35 - mmengine - INFO - Epoch(train) [95][150/391] lr: 5.000000e-04 eta: 5:29:20 time: 0.490754 data_time: 0.040466 memory: 21657 loss_kpt: 0.000534 acc_pose: 0.842346 loss: 0.000534 2022/10/21 15:16:59 - mmengine - INFO - Epoch(train) [95][200/391] lr: 5.000000e-04 eta: 5:29:01 time: 0.481176 data_time: 0.040249 memory: 21657 loss_kpt: 0.000559 acc_pose: 0.856481 loss: 0.000559 2022/10/21 15:17:21 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:17:23 - mmengine - INFO - Epoch(train) [95][250/391] lr: 5.000000e-04 eta: 5:28:42 time: 0.489006 data_time: 0.043685 memory: 21657 loss_kpt: 0.000542 acc_pose: 0.821143 loss: 0.000542 2022/10/21 15:17:47 - mmengine - INFO - Epoch(train) [95][300/391] lr: 5.000000e-04 eta: 5:28:23 time: 0.482884 data_time: 0.040152 memory: 21657 loss_kpt: 0.000548 acc_pose: 0.856811 loss: 0.000548 2022/10/21 15:18:12 - mmengine - INFO - Epoch(train) [95][350/391] lr: 5.000000e-04 eta: 5:28:04 time: 0.488566 data_time: 0.040421 memory: 21657 loss_kpt: 0.000550 acc_pose: 0.852894 loss: 0.000550 2022/10/21 15:18:31 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:18:57 - mmengine - INFO - Epoch(train) [96][50/391] lr: 5.000000e-04 eta: 5:27:07 time: 0.506600 data_time: 0.057977 memory: 21657 loss_kpt: 0.000559 acc_pose: 0.895073 loss: 0.000559 2022/10/21 15:19:21 - mmengine - INFO - Epoch(train) [96][100/391] lr: 5.000000e-04 eta: 5:26:48 time: 0.484899 data_time: 0.042468 memory: 21657 loss_kpt: 0.000544 acc_pose: 0.846510 loss: 0.000544 2022/10/21 15:19:45 - mmengine - INFO - Epoch(train) [96][150/391] lr: 5.000000e-04 eta: 5:26:29 time: 0.488308 data_time: 0.047410 memory: 21657 loss_kpt: 0.000551 acc_pose: 0.830001 loss: 0.000551 2022/10/21 15:20:10 - mmengine - INFO - Epoch(train) [96][200/391] lr: 5.000000e-04 eta: 5:26:10 time: 0.487531 data_time: 0.040878 memory: 21657 loss_kpt: 0.000544 acc_pose: 0.857103 loss: 0.000544 2022/10/21 15:20:34 - mmengine - INFO - Epoch(train) [96][250/391] lr: 5.000000e-04 eta: 5:25:51 time: 0.482111 data_time: 0.043205 memory: 21657 loss_kpt: 0.000560 acc_pose: 0.870374 loss: 0.000560 2022/10/21 15:20:58 - mmengine - INFO - Epoch(train) [96][300/391] lr: 5.000000e-04 eta: 5:25:32 time: 0.487568 data_time: 0.045022 memory: 21657 loss_kpt: 0.000536 acc_pose: 0.892544 loss: 0.000536 2022/10/21 15:21:23 - mmengine - INFO - Epoch(train) [96][350/391] lr: 5.000000e-04 eta: 5:25:13 time: 0.484558 data_time: 0.040870 memory: 21657 loss_kpt: 0.000547 acc_pose: 0.881070 loss: 0.000547 2022/10/21 15:21:42 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:22:08 - mmengine - INFO - Epoch(train) [97][50/391] lr: 5.000000e-04 eta: 5:24:16 time: 0.503628 data_time: 0.053073 memory: 21657 loss_kpt: 0.000531 acc_pose: 0.879386 loss: 0.000531 2022/10/21 15:22:32 - mmengine - INFO - Epoch(train) [97][100/391] lr: 5.000000e-04 eta: 5:23:58 time: 0.494750 data_time: 0.045669 memory: 21657 loss_kpt: 0.000553 acc_pose: 0.867580 loss: 0.000553 2022/10/21 15:22:57 - mmengine - INFO - Epoch(train) [97][150/391] lr: 5.000000e-04 eta: 5:23:38 time: 0.481412 data_time: 0.043305 memory: 21657 loss_kpt: 0.000550 acc_pose: 0.861853 loss: 0.000550 2022/10/21 15:23:21 - mmengine - INFO - Epoch(train) [97][200/391] lr: 5.000000e-04 eta: 5:23:19 time: 0.482036 data_time: 0.041872 memory: 21657 loss_kpt: 0.000550 acc_pose: 0.899972 loss: 0.000550 2022/10/21 15:23:45 - mmengine - INFO - Epoch(train) [97][250/391] lr: 5.000000e-04 eta: 5:23:00 time: 0.485796 data_time: 0.040855 memory: 21657 loss_kpt: 0.000532 acc_pose: 0.833926 loss: 0.000532 2022/10/21 15:24:09 - mmengine - INFO - Epoch(train) [97][300/391] lr: 5.000000e-04 eta: 5:22:41 time: 0.489943 data_time: 0.042166 memory: 21657 loss_kpt: 0.000555 acc_pose: 0.859821 loss: 0.000555 2022/10/21 15:24:34 - mmengine - INFO - Epoch(train) [97][350/391] lr: 5.000000e-04 eta: 5:22:22 time: 0.486224 data_time: 0.040804 memory: 21657 loss_kpt: 0.000537 acc_pose: 0.889040 loss: 0.000537 2022/10/21 15:24:53 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:25:18 - mmengine - INFO - Epoch(train) [98][50/391] lr: 5.000000e-04 eta: 5:21:26 time: 0.502479 data_time: 0.058403 memory: 21657 loss_kpt: 0.000549 acc_pose: 0.854803 loss: 0.000549 2022/10/21 15:25:29 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:25:42 - mmengine - INFO - Epoch(train) [98][100/391] lr: 5.000000e-04 eta: 5:21:06 time: 0.480216 data_time: 0.041079 memory: 21657 loss_kpt: 0.000545 acc_pose: 0.863132 loss: 0.000545 2022/10/21 15:26:07 - mmengine - INFO - Epoch(train) [98][150/391] lr: 5.000000e-04 eta: 5:20:48 time: 0.496812 data_time: 0.045458 memory: 21657 loss_kpt: 0.000554 acc_pose: 0.868475 loss: 0.000554 2022/10/21 15:26:31 - mmengine - INFO - Epoch(train) [98][200/391] lr: 5.000000e-04 eta: 5:20:29 time: 0.482437 data_time: 0.042637 memory: 21657 loss_kpt: 0.000551 acc_pose: 0.859533 loss: 0.000551 2022/10/21 15:26:56 - mmengine - INFO - Epoch(train) [98][250/391] lr: 5.000000e-04 eta: 5:20:10 time: 0.493015 data_time: 0.039935 memory: 21657 loss_kpt: 0.000549 acc_pose: 0.871489 loss: 0.000549 2022/10/21 15:27:20 - mmengine - INFO - Epoch(train) [98][300/391] lr: 5.000000e-04 eta: 5:19:51 time: 0.480433 data_time: 0.040816 memory: 21657 loss_kpt: 0.000542 acc_pose: 0.814861 loss: 0.000542 2022/10/21 15:27:45 - mmengine - INFO - Epoch(train) [98][350/391] lr: 5.000000e-04 eta: 5:19:32 time: 0.490225 data_time: 0.046492 memory: 21657 loss_kpt: 0.000558 acc_pose: 0.866398 loss: 0.000558 2022/10/21 15:28:04 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:28:29 - mmengine - INFO - Epoch(train) [99][50/391] lr: 5.000000e-04 eta: 5:18:35 time: 0.495315 data_time: 0.056165 memory: 21657 loss_kpt: 0.000552 acc_pose: 0.869626 loss: 0.000552 2022/10/21 15:28:54 - mmengine - INFO - Epoch(train) [99][100/391] lr: 5.000000e-04 eta: 5:18:16 time: 0.495047 data_time: 0.042595 memory: 21657 loss_kpt: 0.000543 acc_pose: 0.812708 loss: 0.000543 2022/10/21 15:29:18 - mmengine - INFO - Epoch(train) [99][150/391] lr: 5.000000e-04 eta: 5:17:57 time: 0.487902 data_time: 0.044364 memory: 21657 loss_kpt: 0.000559 acc_pose: 0.857462 loss: 0.000559 2022/10/21 15:29:43 - mmengine - INFO - Epoch(train) [99][200/391] lr: 5.000000e-04 eta: 5:17:38 time: 0.490055 data_time: 0.040967 memory: 21657 loss_kpt: 0.000547 acc_pose: 0.894842 loss: 0.000547 2022/10/21 15:30:07 - mmengine - INFO - Epoch(train) [99][250/391] lr: 5.000000e-04 eta: 5:17:19 time: 0.484118 data_time: 0.041174 memory: 21657 loss_kpt: 0.000551 acc_pose: 0.858260 loss: 0.000551 2022/10/21 15:30:31 - mmengine - INFO - Epoch(train) [99][300/391] lr: 5.000000e-04 eta: 5:17:00 time: 0.490215 data_time: 0.043924 memory: 21657 loss_kpt: 0.000540 acc_pose: 0.897174 loss: 0.000540 2022/10/21 15:30:55 - mmengine - INFO - Epoch(train) [99][350/391] lr: 5.000000e-04 eta: 5:16:41 time: 0.480272 data_time: 0.039516 memory: 21657 loss_kpt: 0.000545 acc_pose: 0.857649 loss: 0.000545 2022/10/21 15:31:15 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:31:40 - mmengine - INFO - Epoch(train) [100][50/391] lr: 5.000000e-04 eta: 5:15:45 time: 0.503966 data_time: 0.054684 memory: 21657 loss_kpt: 0.000551 acc_pose: 0.883927 loss: 0.000551 2022/10/21 15:32:05 - mmengine - INFO - Epoch(train) [100][100/391] lr: 5.000000e-04 eta: 5:15:26 time: 0.491602 data_time: 0.041706 memory: 21657 loss_kpt: 0.000552 acc_pose: 0.858082 loss: 0.000552 2022/10/21 15:32:29 - mmengine - INFO - Epoch(train) [100][150/391] lr: 5.000000e-04 eta: 5:15:07 time: 0.484666 data_time: 0.042789 memory: 21657 loss_kpt: 0.000548 acc_pose: 0.872973 loss: 0.000548 2022/10/21 15:32:54 - mmengine - INFO - Epoch(train) [100][200/391] lr: 5.000000e-04 eta: 5:14:48 time: 0.487503 data_time: 0.039021 memory: 21657 loss_kpt: 0.000553 acc_pose: 0.857330 loss: 0.000553 2022/10/21 15:33:18 - mmengine - INFO - Epoch(train) [100][250/391] lr: 5.000000e-04 eta: 5:14:29 time: 0.487112 data_time: 0.040377 memory: 21657 loss_kpt: 0.000546 acc_pose: 0.850626 loss: 0.000546 2022/10/21 15:33:38 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:33:42 - mmengine - INFO - Epoch(train) [100][300/391] lr: 5.000000e-04 eta: 5:14:09 time: 0.482954 data_time: 0.043824 memory: 21657 loss_kpt: 0.000547 acc_pose: 0.874690 loss: 0.000547 2022/10/21 15:34:06 - mmengine - INFO - Epoch(train) [100][350/391] lr: 5.000000e-04 eta: 5:13:50 time: 0.487337 data_time: 0.039431 memory: 21657 loss_kpt: 0.000540 acc_pose: 0.887167 loss: 0.000540 2022/10/21 15:34:26 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:34:26 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/10/21 15:34:38 - mmengine - INFO - Epoch(val) [100][50/407] eta: 0:00:55 time: 0.156186 data_time: 0.015400 memory: 21657 2022/10/21 15:34:46 - mmengine - INFO - Epoch(val) [100][100/407] eta: 0:00:45 time: 0.149578 data_time: 0.009645 memory: 2142 2022/10/21 15:34:53 - mmengine - INFO - Epoch(val) [100][150/407] eta: 0:00:38 time: 0.149327 data_time: 0.008764 memory: 2142 2022/10/21 15:35:01 - mmengine - INFO - Epoch(val) [100][200/407] eta: 0:00:31 time: 0.149915 data_time: 0.008891 memory: 2142 2022/10/21 15:35:08 - mmengine - INFO - Epoch(val) [100][250/407] eta: 0:00:23 time: 0.152742 data_time: 0.011922 memory: 2142 2022/10/21 15:35:16 - mmengine - INFO - Epoch(val) [100][300/407] eta: 0:00:16 time: 0.149819 data_time: 0.009349 memory: 2142 2022/10/21 15:35:23 - mmengine - INFO - Epoch(val) [100][350/407] eta: 0:00:08 time: 0.149898 data_time: 0.008816 memory: 2142 2022/10/21 15:35:31 - mmengine - INFO - Epoch(val) [100][400/407] eta: 0:00:01 time: 0.147955 data_time: 0.009102 memory: 2142 2022/10/21 15:36:06 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 15:36:20 - mmengine - INFO - Epoch(val) [100][407/407] coco/AP: 0.740496 coco/AP .5: 0.899859 coco/AP .75: 0.809592 coco/AP (M): 0.698516 coco/AP (L): 0.811612 coco/AR: 0.789499 coco/AR .5: 0.934351 coco/AR .75: 0.852173 coco/AR (M): 0.744168 coco/AR (L): 0.854849 2022/10/21 15:36:45 - mmengine - INFO - Epoch(train) [101][50/391] lr: 5.000000e-04 eta: 5:12:54 time: 0.498163 data_time: 0.051570 memory: 21657 loss_kpt: 0.000529 acc_pose: 0.883991 loss: 0.000529 2022/10/21 15:37:09 - mmengine - INFO - Epoch(train) [101][100/391] lr: 5.000000e-04 eta: 5:12:35 time: 0.487734 data_time: 0.042084 memory: 21657 loss_kpt: 0.000538 acc_pose: 0.911314 loss: 0.000538 2022/10/21 15:37:34 - mmengine - INFO - Epoch(train) [101][150/391] lr: 5.000000e-04 eta: 5:12:16 time: 0.484336 data_time: 0.041808 memory: 21657 loss_kpt: 0.000551 acc_pose: 0.785655 loss: 0.000551 2022/10/21 15:37:58 - mmengine - INFO - Epoch(train) [101][200/391] lr: 5.000000e-04 eta: 5:11:57 time: 0.489320 data_time: 0.044714 memory: 21657 loss_kpt: 0.000536 acc_pose: 0.859698 loss: 0.000536 2022/10/21 15:38:22 - mmengine - INFO - Epoch(train) [101][250/391] lr: 5.000000e-04 eta: 5:11:37 time: 0.485275 data_time: 0.041800 memory: 21657 loss_kpt: 0.000557 acc_pose: 0.782652 loss: 0.000557 2022/10/21 15:38:47 - mmengine - INFO - Epoch(train) [101][300/391] lr: 5.000000e-04 eta: 5:11:18 time: 0.485273 data_time: 0.044366 memory: 21657 loss_kpt: 0.000555 acc_pose: 0.851273 loss: 0.000555 2022/10/21 15:39:11 - mmengine - INFO - Epoch(train) [101][350/391] lr: 5.000000e-04 eta: 5:10:59 time: 0.486619 data_time: 0.040033 memory: 21657 loss_kpt: 0.000550 acc_pose: 0.885915 loss: 0.000550 2022/10/21 15:39:30 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:39:55 - mmengine - INFO - Epoch(train) [102][50/391] lr: 5.000000e-04 eta: 5:10:03 time: 0.500714 data_time: 0.054234 memory: 21657 loss_kpt: 0.000544 acc_pose: 0.899565 loss: 0.000544 2022/10/21 15:40:20 - mmengine - INFO - Epoch(train) [102][100/391] lr: 5.000000e-04 eta: 5:09:44 time: 0.486805 data_time: 0.040450 memory: 21657 loss_kpt: 0.000538 acc_pose: 0.834800 loss: 0.000538 2022/10/21 15:40:44 - mmengine - INFO - Epoch(train) [102][150/391] lr: 5.000000e-04 eta: 5:09:24 time: 0.481348 data_time: 0.044742 memory: 21657 loss_kpt: 0.000545 acc_pose: 0.854732 loss: 0.000545 2022/10/21 15:41:08 - mmengine - INFO - Epoch(train) [102][200/391] lr: 5.000000e-04 eta: 5:09:05 time: 0.482255 data_time: 0.040222 memory: 21657 loss_kpt: 0.000532 acc_pose: 0.842839 loss: 0.000532 2022/10/21 15:41:32 - mmengine - INFO - Epoch(train) [102][250/391] lr: 5.000000e-04 eta: 5:08:46 time: 0.485700 data_time: 0.039950 memory: 21657 loss_kpt: 0.000534 acc_pose: 0.787846 loss: 0.000534 2022/10/21 15:41:56 - mmengine - INFO - Epoch(train) [102][300/391] lr: 5.000000e-04 eta: 5:08:26 time: 0.483080 data_time: 0.042097 memory: 21657 loss_kpt: 0.000547 acc_pose: 0.867818 loss: 0.000547 2022/10/21 15:42:21 - mmengine - INFO - Epoch(train) [102][350/391] lr: 5.000000e-04 eta: 5:08:07 time: 0.484021 data_time: 0.043212 memory: 21657 loss_kpt: 0.000548 acc_pose: 0.887074 loss: 0.000548 2022/10/21 15:42:40 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:43:05 - mmengine - INFO - Epoch(train) [103][50/391] lr: 5.000000e-04 eta: 5:07:12 time: 0.504470 data_time: 0.051388 memory: 21657 loss_kpt: 0.000543 acc_pose: 0.826456 loss: 0.000543 2022/10/21 15:43:29 - mmengine - INFO - Epoch(train) [103][100/391] lr: 5.000000e-04 eta: 5:06:52 time: 0.480176 data_time: 0.039406 memory: 21657 loss_kpt: 0.000530 acc_pose: 0.878002 loss: 0.000530 2022/10/21 15:43:38 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:43:54 - mmengine - INFO - Epoch(train) [103][150/391] lr: 5.000000e-04 eta: 5:06:33 time: 0.483042 data_time: 0.040229 memory: 21657 loss_kpt: 0.000530 acc_pose: 0.836741 loss: 0.000530 2022/10/21 15:44:18 - mmengine - INFO - Epoch(train) [103][200/391] lr: 5.000000e-04 eta: 5:06:13 time: 0.486816 data_time: 0.043996 memory: 21657 loss_kpt: 0.000543 acc_pose: 0.857381 loss: 0.000543 2022/10/21 15:44:43 - mmengine - INFO - Epoch(train) [103][250/391] lr: 5.000000e-04 eta: 5:05:54 time: 0.491993 data_time: 0.040277 memory: 21657 loss_kpt: 0.000560 acc_pose: 0.826679 loss: 0.000560 2022/10/21 15:45:07 - mmengine - INFO - Epoch(train) [103][300/391] lr: 5.000000e-04 eta: 5:05:35 time: 0.486801 data_time: 0.043421 memory: 21657 loss_kpt: 0.000550 acc_pose: 0.823593 loss: 0.000550 2022/10/21 15:45:31 - mmengine - INFO - Epoch(train) [103][350/391] lr: 5.000000e-04 eta: 5:05:16 time: 0.486547 data_time: 0.040942 memory: 21657 loss_kpt: 0.000552 acc_pose: 0.896512 loss: 0.000552 2022/10/21 15:45:51 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:46:16 - mmengine - INFO - Epoch(train) [104][50/391] lr: 5.000000e-04 eta: 5:04:21 time: 0.498512 data_time: 0.054652 memory: 21657 loss_kpt: 0.000546 acc_pose: 0.832535 loss: 0.000546 2022/10/21 15:46:41 - mmengine - INFO - Epoch(train) [104][100/391] lr: 5.000000e-04 eta: 5:04:02 time: 0.497557 data_time: 0.044241 memory: 21657 loss_kpt: 0.000556 acc_pose: 0.877180 loss: 0.000556 2022/10/21 15:47:05 - mmengine - INFO - Epoch(train) [104][150/391] lr: 5.000000e-04 eta: 5:03:43 time: 0.483976 data_time: 0.043129 memory: 21657 loss_kpt: 0.000528 acc_pose: 0.879115 loss: 0.000528 2022/10/21 15:47:30 - mmengine - INFO - Epoch(train) [104][200/391] lr: 5.000000e-04 eta: 5:03:23 time: 0.490149 data_time: 0.040648 memory: 21657 loss_kpt: 0.000540 acc_pose: 0.850089 loss: 0.000540 2022/10/21 15:47:54 - mmengine - INFO - Epoch(train) [104][250/391] lr: 5.000000e-04 eta: 5:03:04 time: 0.484099 data_time: 0.039522 memory: 21657 loss_kpt: 0.000530 acc_pose: 0.860336 loss: 0.000530 2022/10/21 15:48:18 - mmengine - INFO - Epoch(train) [104][300/391] lr: 5.000000e-04 eta: 5:02:45 time: 0.489879 data_time: 0.043083 memory: 21657 loss_kpt: 0.000540 acc_pose: 0.895304 loss: 0.000540 2022/10/21 15:48:43 - mmengine - INFO - Epoch(train) [104][350/391] lr: 5.000000e-04 eta: 5:02:25 time: 0.482845 data_time: 0.041148 memory: 21657 loss_kpt: 0.000545 acc_pose: 0.894651 loss: 0.000545 2022/10/21 15:49:02 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:49:28 - mmengine - INFO - Epoch(train) [105][50/391] lr: 5.000000e-04 eta: 5:01:31 time: 0.510621 data_time: 0.052530 memory: 21657 loss_kpt: 0.000541 acc_pose: 0.892737 loss: 0.000541 2022/10/21 15:49:52 - mmengine - INFO - Epoch(train) [105][100/391] lr: 5.000000e-04 eta: 5:01:11 time: 0.484841 data_time: 0.041266 memory: 21657 loss_kpt: 0.000547 acc_pose: 0.862494 loss: 0.000547 2022/10/21 15:50:16 - mmengine - INFO - Epoch(train) [105][150/391] lr: 5.000000e-04 eta: 5:00:52 time: 0.489496 data_time: 0.044760 memory: 21657 loss_kpt: 0.000537 acc_pose: 0.884810 loss: 0.000537 2022/10/21 15:50:41 - mmengine - INFO - Epoch(train) [105][200/391] lr: 5.000000e-04 eta: 5:00:33 time: 0.485361 data_time: 0.039799 memory: 21657 loss_kpt: 0.000554 acc_pose: 0.852236 loss: 0.000554 2022/10/21 15:51:05 - mmengine - INFO - Epoch(train) [105][250/391] lr: 5.000000e-04 eta: 5:00:13 time: 0.485644 data_time: 0.042722 memory: 21657 loss_kpt: 0.000534 acc_pose: 0.845457 loss: 0.000534 2022/10/21 15:51:29 - mmengine - INFO - Epoch(train) [105][300/391] lr: 5.000000e-04 eta: 4:59:54 time: 0.487287 data_time: 0.040893 memory: 21657 loss_kpt: 0.000536 acc_pose: 0.868774 loss: 0.000536 2022/10/21 15:51:47 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:51:54 - mmengine - INFO - Epoch(train) [105][350/391] lr: 5.000000e-04 eta: 4:59:35 time: 0.484540 data_time: 0.045661 memory: 21657 loss_kpt: 0.000548 acc_pose: 0.873996 loss: 0.000548 2022/10/21 15:52:13 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:52:38 - mmengine - INFO - Epoch(train) [106][50/391] lr: 5.000000e-04 eta: 4:58:40 time: 0.502331 data_time: 0.054142 memory: 21657 loss_kpt: 0.000566 acc_pose: 0.861178 loss: 0.000566 2022/10/21 15:53:03 - mmengine - INFO - Epoch(train) [106][100/391] lr: 5.000000e-04 eta: 4:58:21 time: 0.487966 data_time: 0.045013 memory: 21657 loss_kpt: 0.000548 acc_pose: 0.894379 loss: 0.000548 2022/10/21 15:53:27 - mmengine - INFO - Epoch(train) [106][150/391] lr: 5.000000e-04 eta: 4:58:01 time: 0.484487 data_time: 0.043360 memory: 21657 loss_kpt: 0.000528 acc_pose: 0.857250 loss: 0.000528 2022/10/21 15:53:51 - mmengine - INFO - Epoch(train) [106][200/391] lr: 5.000000e-04 eta: 4:57:42 time: 0.491620 data_time: 0.042347 memory: 21657 loss_kpt: 0.000531 acc_pose: 0.779375 loss: 0.000531 2022/10/21 15:54:16 - mmengine - INFO - Epoch(train) [106][250/391] lr: 5.000000e-04 eta: 4:57:23 time: 0.483195 data_time: 0.041398 memory: 21657 loss_kpt: 0.000533 acc_pose: 0.813062 loss: 0.000533 2022/10/21 15:54:40 - mmengine - INFO - Epoch(train) [106][300/391] lr: 5.000000e-04 eta: 4:57:03 time: 0.488098 data_time: 0.045729 memory: 21657 loss_kpt: 0.000546 acc_pose: 0.829969 loss: 0.000546 2022/10/21 15:55:05 - mmengine - INFO - Epoch(train) [106][350/391] lr: 5.000000e-04 eta: 4:56:44 time: 0.488554 data_time: 0.040598 memory: 21657 loss_kpt: 0.000541 acc_pose: 0.863199 loss: 0.000541 2022/10/21 15:55:24 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:55:49 - mmengine - INFO - Epoch(train) [107][50/391] lr: 5.000000e-04 eta: 4:55:50 time: 0.503358 data_time: 0.050956 memory: 21657 loss_kpt: 0.000530 acc_pose: 0.854325 loss: 0.000530 2022/10/21 15:56:14 - mmengine - INFO - Epoch(train) [107][100/391] lr: 5.000000e-04 eta: 4:55:30 time: 0.489685 data_time: 0.044113 memory: 21657 loss_kpt: 0.000529 acc_pose: 0.854887 loss: 0.000529 2022/10/21 15:56:38 - mmengine - INFO - Epoch(train) [107][150/391] lr: 5.000000e-04 eta: 4:55:11 time: 0.483272 data_time: 0.045912 memory: 21657 loss_kpt: 0.000526 acc_pose: 0.842947 loss: 0.000526 2022/10/21 15:57:02 - mmengine - INFO - Epoch(train) [107][200/391] lr: 5.000000e-04 eta: 4:54:52 time: 0.489171 data_time: 0.039554 memory: 21657 loss_kpt: 0.000528 acc_pose: 0.896252 loss: 0.000528 2022/10/21 15:57:27 - mmengine - INFO - Epoch(train) [107][250/391] lr: 5.000000e-04 eta: 4:54:32 time: 0.487311 data_time: 0.040948 memory: 21657 loss_kpt: 0.000536 acc_pose: 0.861970 loss: 0.000536 2022/10/21 15:57:51 - mmengine - INFO - Epoch(train) [107][300/391] lr: 5.000000e-04 eta: 4:54:12 time: 0.484356 data_time: 0.042519 memory: 21657 loss_kpt: 0.000544 acc_pose: 0.790782 loss: 0.000544 2022/10/21 15:58:15 - mmengine - INFO - Epoch(train) [107][350/391] lr: 5.000000e-04 eta: 4:53:53 time: 0.485785 data_time: 0.044376 memory: 21657 loss_kpt: 0.000535 acc_pose: 0.862883 loss: 0.000535 2022/10/21 15:58:35 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 15:59:00 - mmengine - INFO - Epoch(train) [108][50/391] lr: 5.000000e-04 eta: 4:52:59 time: 0.503748 data_time: 0.056891 memory: 21657 loss_kpt: 0.000530 acc_pose: 0.847320 loss: 0.000530 2022/10/21 15:59:25 - mmengine - INFO - Epoch(train) [108][100/391] lr: 5.000000e-04 eta: 4:52:40 time: 0.485130 data_time: 0.041097 memory: 21657 loss_kpt: 0.000550 acc_pose: 0.861521 loss: 0.000550 2022/10/21 15:59:49 - mmengine - INFO - Epoch(train) [108][150/391] lr: 5.000000e-04 eta: 4:52:20 time: 0.482013 data_time: 0.039873 memory: 21657 loss_kpt: 0.000533 acc_pose: 0.894448 loss: 0.000533 2022/10/21 15:59:55 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:00:13 - mmengine - INFO - Epoch(train) [108][200/391] lr: 5.000000e-04 eta: 4:52:00 time: 0.484733 data_time: 0.044630 memory: 21657 loss_kpt: 0.000535 acc_pose: 0.916791 loss: 0.000535 2022/10/21 16:00:37 - mmengine - INFO - Epoch(train) [108][250/391] lr: 5.000000e-04 eta: 4:51:41 time: 0.487273 data_time: 0.040062 memory: 21657 loss_kpt: 0.000547 acc_pose: 0.850682 loss: 0.000547 2022/10/21 16:01:02 - mmengine - INFO - Epoch(train) [108][300/391] lr: 5.000000e-04 eta: 4:51:21 time: 0.486396 data_time: 0.043938 memory: 21657 loss_kpt: 0.000542 acc_pose: 0.888646 loss: 0.000542 2022/10/21 16:01:26 - mmengine - INFO - Epoch(train) [108][350/391] lr: 5.000000e-04 eta: 4:51:02 time: 0.488134 data_time: 0.042903 memory: 21657 loss_kpt: 0.000530 acc_pose: 0.880286 loss: 0.000530 2022/10/21 16:01:46 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:02:11 - mmengine - INFO - Epoch(train) [109][50/391] lr: 5.000000e-04 eta: 4:50:08 time: 0.504225 data_time: 0.063575 memory: 21657 loss_kpt: 0.000543 acc_pose: 0.870543 loss: 0.000543 2022/10/21 16:02:35 - mmengine - INFO - Epoch(train) [109][100/391] lr: 5.000000e-04 eta: 4:49:49 time: 0.487824 data_time: 0.048004 memory: 21657 loss_kpt: 0.000542 acc_pose: 0.835521 loss: 0.000542 2022/10/21 16:03:00 - mmengine - INFO - Epoch(train) [109][150/391] lr: 5.000000e-04 eta: 4:49:29 time: 0.487175 data_time: 0.040034 memory: 21657 loss_kpt: 0.000531 acc_pose: 0.866210 loss: 0.000531 2022/10/21 16:03:24 - mmengine - INFO - Epoch(train) [109][200/391] lr: 5.000000e-04 eta: 4:49:09 time: 0.482070 data_time: 0.039797 memory: 21657 loss_kpt: 0.000536 acc_pose: 0.828034 loss: 0.000536 2022/10/21 16:03:48 - mmengine - INFO - Epoch(train) [109][250/391] lr: 5.000000e-04 eta: 4:48:50 time: 0.484418 data_time: 0.043796 memory: 21657 loss_kpt: 0.000551 acc_pose: 0.822420 loss: 0.000551 2022/10/21 16:04:13 - mmengine - INFO - Epoch(train) [109][300/391] lr: 5.000000e-04 eta: 4:48:30 time: 0.488670 data_time: 0.041514 memory: 21657 loss_kpt: 0.000530 acc_pose: 0.885886 loss: 0.000530 2022/10/21 16:04:37 - mmengine - INFO - Epoch(train) [109][350/391] lr: 5.000000e-04 eta: 4:48:11 time: 0.489850 data_time: 0.041015 memory: 21657 loss_kpt: 0.000534 acc_pose: 0.867814 loss: 0.000534 2022/10/21 16:04:57 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:05:22 - mmengine - INFO - Epoch(train) [110][50/391] lr: 5.000000e-04 eta: 4:47:17 time: 0.496971 data_time: 0.053230 memory: 21657 loss_kpt: 0.000539 acc_pose: 0.868052 loss: 0.000539 2022/10/21 16:05:46 - mmengine - INFO - Epoch(train) [110][100/391] lr: 5.000000e-04 eta: 4:46:58 time: 0.485163 data_time: 0.040745 memory: 21657 loss_kpt: 0.000551 acc_pose: 0.859097 loss: 0.000551 2022/10/21 16:06:10 - mmengine - INFO - Epoch(train) [110][150/391] lr: 5.000000e-04 eta: 4:46:38 time: 0.486526 data_time: 0.044229 memory: 21657 loss_kpt: 0.000537 acc_pose: 0.845977 loss: 0.000537 2022/10/21 16:06:35 - mmengine - INFO - Epoch(train) [110][200/391] lr: 5.000000e-04 eta: 4:46:18 time: 0.482515 data_time: 0.039364 memory: 21657 loss_kpt: 0.000530 acc_pose: 0.803107 loss: 0.000530 2022/10/21 16:06:59 - mmengine - INFO - Epoch(train) [110][250/391] lr: 5.000000e-04 eta: 4:45:58 time: 0.481161 data_time: 0.039987 memory: 21657 loss_kpt: 0.000531 acc_pose: 0.873467 loss: 0.000531 2022/10/21 16:07:23 - mmengine - INFO - Epoch(train) [110][300/391] lr: 5.000000e-04 eta: 4:45:39 time: 0.495395 data_time: 0.044679 memory: 21657 loss_kpt: 0.000534 acc_pose: 0.789824 loss: 0.000534 2022/10/21 16:07:47 - mmengine - INFO - Epoch(train) [110][350/391] lr: 5.000000e-04 eta: 4:45:19 time: 0.480596 data_time: 0.039536 memory: 21657 loss_kpt: 0.000535 acc_pose: 0.838135 loss: 0.000535 2022/10/21 16:08:03 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:08:07 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:08:07 - mmengine - INFO - Saving checkpoint at 110 epochs 2022/10/21 16:08:19 - mmengine - INFO - Epoch(val) [110][50/407] eta: 0:00:56 time: 0.159579 data_time: 0.017679 memory: 21657 2022/10/21 16:08:27 - mmengine - INFO - Epoch(val) [110][100/407] eta: 0:00:46 time: 0.150433 data_time: 0.009783 memory: 2142 2022/10/21 16:08:34 - mmengine - INFO - Epoch(val) [110][150/407] eta: 0:00:38 time: 0.150306 data_time: 0.009733 memory: 2142 2022/10/21 16:08:42 - mmengine - INFO - Epoch(val) [110][200/407] eta: 0:00:31 time: 0.149766 data_time: 0.009237 memory: 2142 2022/10/21 16:08:49 - mmengine - INFO - Epoch(val) [110][250/407] eta: 0:00:23 time: 0.152092 data_time: 0.009704 memory: 2142 2022/10/21 16:08:57 - mmengine - INFO - Epoch(val) [110][300/407] eta: 0:00:16 time: 0.150812 data_time: 0.010851 memory: 2142 2022/10/21 16:09:04 - mmengine - INFO - Epoch(val) [110][350/407] eta: 0:00:08 time: 0.149868 data_time: 0.009206 memory: 2142 2022/10/21 16:09:12 - mmengine - INFO - Epoch(val) [110][400/407] eta: 0:00:01 time: 0.148482 data_time: 0.008808 memory: 2142 2022/10/21 16:09:47 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 16:10:01 - mmengine - INFO - Epoch(val) [110][407/407] coco/AP: 0.743004 coco/AP .5: 0.900763 coco/AP .75: 0.813327 coco/AP (M): 0.699966 coco/AP (L): 0.817826 coco/AR: 0.793829 coco/AR .5: 0.937500 coco/AR .75: 0.854691 coco/AR (M): 0.746272 coco/AR (L): 0.862245 2022/10/21 16:10:01 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221021/resnetv1d152_384/best_coco/AP_epoch_80.pth is removed 2022/10/21 16:10:04 - mmengine - INFO - The best checkpoint with 0.7430 coco/AP at 110 epoch is saved to best_coco/AP_epoch_110.pth. 2022/10/21 16:10:28 - mmengine - INFO - Epoch(train) [111][50/391] lr: 5.000000e-04 eta: 4:44:26 time: 0.490326 data_time: 0.053617 memory: 21657 loss_kpt: 0.000554 acc_pose: 0.852346 loss: 0.000554 2022/10/21 16:10:52 - mmengine - INFO - Epoch(train) [111][100/391] lr: 5.000000e-04 eta: 4:44:06 time: 0.487616 data_time: 0.045081 memory: 21657 loss_kpt: 0.000547 acc_pose: 0.827739 loss: 0.000547 2022/10/21 16:11:17 - mmengine - INFO - Epoch(train) [111][150/391] lr: 5.000000e-04 eta: 4:43:47 time: 0.486660 data_time: 0.041193 memory: 21657 loss_kpt: 0.000534 acc_pose: 0.818152 loss: 0.000534 2022/10/21 16:11:41 - mmengine - INFO - Epoch(train) [111][200/391] lr: 5.000000e-04 eta: 4:43:27 time: 0.486361 data_time: 0.043610 memory: 21657 loss_kpt: 0.000523 acc_pose: 0.817439 loss: 0.000523 2022/10/21 16:12:05 - mmengine - INFO - Epoch(train) [111][250/391] lr: 5.000000e-04 eta: 4:43:07 time: 0.486040 data_time: 0.041547 memory: 21657 loss_kpt: 0.000537 acc_pose: 0.885701 loss: 0.000537 2022/10/21 16:12:30 - mmengine - INFO - Epoch(train) [111][300/391] lr: 5.000000e-04 eta: 4:42:48 time: 0.487896 data_time: 0.043703 memory: 21657 loss_kpt: 0.000519 acc_pose: 0.847192 loss: 0.000519 2022/10/21 16:12:54 - mmengine - INFO - Epoch(train) [111][350/391] lr: 5.000000e-04 eta: 4:42:28 time: 0.489228 data_time: 0.040925 memory: 21657 loss_kpt: 0.000536 acc_pose: 0.842841 loss: 0.000536 2022/10/21 16:13:14 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:13:39 - mmengine - INFO - Epoch(train) [112][50/391] lr: 5.000000e-04 eta: 4:41:35 time: 0.494945 data_time: 0.051445 memory: 21657 loss_kpt: 0.000545 acc_pose: 0.866705 loss: 0.000545 2022/10/21 16:14:03 - mmengine - INFO - Epoch(train) [112][100/391] lr: 5.000000e-04 eta: 4:41:15 time: 0.489000 data_time: 0.040989 memory: 21657 loss_kpt: 0.000523 acc_pose: 0.887815 loss: 0.000523 2022/10/21 16:14:27 - mmengine - INFO - Epoch(train) [112][150/391] lr: 5.000000e-04 eta: 4:40:56 time: 0.489704 data_time: 0.043502 memory: 21657 loss_kpt: 0.000532 acc_pose: 0.815905 loss: 0.000532 2022/10/21 16:14:52 - mmengine - INFO - Epoch(train) [112][200/391] lr: 5.000000e-04 eta: 4:40:36 time: 0.482724 data_time: 0.041409 memory: 21657 loss_kpt: 0.000525 acc_pose: 0.882568 loss: 0.000525 2022/10/21 16:15:16 - mmengine - INFO - Epoch(train) [112][250/391] lr: 5.000000e-04 eta: 4:40:16 time: 0.488248 data_time: 0.043658 memory: 21657 loss_kpt: 0.000530 acc_pose: 0.863221 loss: 0.000530 2022/10/21 16:15:40 - mmengine - INFO - Epoch(train) [112][300/391] lr: 5.000000e-04 eta: 4:39:56 time: 0.483391 data_time: 0.040483 memory: 21657 loss_kpt: 0.000523 acc_pose: 0.870879 loss: 0.000523 2022/10/21 16:16:05 - mmengine - INFO - Epoch(train) [112][350/391] lr: 5.000000e-04 eta: 4:39:37 time: 0.486480 data_time: 0.044209 memory: 21657 loss_kpt: 0.000517 acc_pose: 0.892056 loss: 0.000517 2022/10/21 16:16:24 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:16:49 - mmengine - INFO - Epoch(train) [113][50/391] lr: 5.000000e-04 eta: 4:38:44 time: 0.499679 data_time: 0.053572 memory: 21657 loss_kpt: 0.000538 acc_pose: 0.867642 loss: 0.000538 2022/10/21 16:17:13 - mmengine - INFO - Epoch(train) [113][100/391] lr: 5.000000e-04 eta: 4:38:24 time: 0.484605 data_time: 0.040865 memory: 21657 loss_kpt: 0.000537 acc_pose: 0.868126 loss: 0.000537 2022/10/21 16:17:38 - mmengine - INFO - Epoch(train) [113][150/391] lr: 5.000000e-04 eta: 4:38:04 time: 0.482489 data_time: 0.040855 memory: 21657 loss_kpt: 0.000535 acc_pose: 0.885916 loss: 0.000535 2022/10/21 16:18:02 - mmengine - INFO - Epoch(train) [113][200/391] lr: 5.000000e-04 eta: 4:37:45 time: 0.491844 data_time: 0.040217 memory: 21657 loss_kpt: 0.000537 acc_pose: 0.810168 loss: 0.000537 2022/10/21 16:18:06 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:18:26 - mmengine - INFO - Epoch(train) [113][250/391] lr: 5.000000e-04 eta: 4:37:25 time: 0.484285 data_time: 0.039408 memory: 21657 loss_kpt: 0.000528 acc_pose: 0.845762 loss: 0.000528 2022/10/21 16:18:51 - mmengine - INFO - Epoch(train) [113][300/391] lr: 5.000000e-04 eta: 4:37:05 time: 0.486596 data_time: 0.044735 memory: 21657 loss_kpt: 0.000527 acc_pose: 0.905347 loss: 0.000527 2022/10/21 16:19:15 - mmengine - INFO - Epoch(train) [113][350/391] lr: 5.000000e-04 eta: 4:36:45 time: 0.482498 data_time: 0.040288 memory: 21657 loss_kpt: 0.000542 acc_pose: 0.854508 loss: 0.000542 2022/10/21 16:19:34 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:20:00 - mmengine - INFO - Epoch(train) [114][50/391] lr: 5.000000e-04 eta: 4:35:53 time: 0.506311 data_time: 0.060329 memory: 21657 loss_kpt: 0.000532 acc_pose: 0.850322 loss: 0.000532 2022/10/21 16:20:24 - mmengine - INFO - Epoch(train) [114][100/391] lr: 5.000000e-04 eta: 4:35:33 time: 0.485294 data_time: 0.044490 memory: 21657 loss_kpt: 0.000531 acc_pose: 0.861621 loss: 0.000531 2022/10/21 16:20:49 - mmengine - INFO - Epoch(train) [114][150/391] lr: 5.000000e-04 eta: 4:35:14 time: 0.498011 data_time: 0.039467 memory: 21657 loss_kpt: 0.000520 acc_pose: 0.901582 loss: 0.000520 2022/10/21 16:21:13 - mmengine - INFO - Epoch(train) [114][200/391] lr: 5.000000e-04 eta: 4:34:54 time: 0.480427 data_time: 0.039387 memory: 21657 loss_kpt: 0.000541 acc_pose: 0.815000 loss: 0.000541 2022/10/21 16:21:38 - mmengine - INFO - Epoch(train) [114][250/391] lr: 5.000000e-04 eta: 4:34:35 time: 0.494256 data_time: 0.040700 memory: 21657 loss_kpt: 0.000539 acc_pose: 0.872581 loss: 0.000539 2022/10/21 16:22:02 - mmengine - INFO - Epoch(train) [114][300/391] lr: 5.000000e-04 eta: 4:34:15 time: 0.477949 data_time: 0.040342 memory: 21657 loss_kpt: 0.000526 acc_pose: 0.863131 loss: 0.000526 2022/10/21 16:22:26 - mmengine - INFO - Epoch(train) [114][350/391] lr: 5.000000e-04 eta: 4:33:55 time: 0.488674 data_time: 0.042673 memory: 21657 loss_kpt: 0.000528 acc_pose: 0.860147 loss: 0.000528 2022/10/21 16:22:46 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:23:11 - mmengine - INFO - Epoch(train) [115][50/391] lr: 5.000000e-04 eta: 4:33:03 time: 0.503911 data_time: 0.057698 memory: 21657 loss_kpt: 0.000524 acc_pose: 0.877351 loss: 0.000524 2022/10/21 16:23:35 - mmengine - INFO - Epoch(train) [115][100/391] lr: 5.000000e-04 eta: 4:32:43 time: 0.483489 data_time: 0.040361 memory: 21657 loss_kpt: 0.000539 acc_pose: 0.855830 loss: 0.000539 2022/10/21 16:24:00 - mmengine - INFO - Epoch(train) [115][150/391] lr: 5.000000e-04 eta: 4:32:23 time: 0.487446 data_time: 0.044026 memory: 21657 loss_kpt: 0.000536 acc_pose: 0.866298 loss: 0.000536 2022/10/21 16:24:24 - mmengine - INFO - Epoch(train) [115][200/391] lr: 5.000000e-04 eta: 4:32:03 time: 0.486959 data_time: 0.039481 memory: 21657 loss_kpt: 0.000530 acc_pose: 0.892217 loss: 0.000530 2022/10/21 16:24:48 - mmengine - INFO - Epoch(train) [115][250/391] lr: 5.000000e-04 eta: 4:31:44 time: 0.487284 data_time: 0.044645 memory: 21657 loss_kpt: 0.000526 acc_pose: 0.832523 loss: 0.000526 2022/10/21 16:25:13 - mmengine - INFO - Epoch(train) [115][300/391] lr: 5.000000e-04 eta: 4:31:24 time: 0.490179 data_time: 0.040757 memory: 21657 loss_kpt: 0.000520 acc_pose: 0.813198 loss: 0.000520 2022/10/21 16:25:37 - mmengine - INFO - Epoch(train) [115][350/391] lr: 5.000000e-04 eta: 4:31:04 time: 0.480244 data_time: 0.040563 memory: 21657 loss_kpt: 0.000534 acc_pose: 0.873339 loss: 0.000534 2022/10/21 16:25:57 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:26:15 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:26:22 - mmengine - INFO - Epoch(train) [116][50/391] lr: 5.000000e-04 eta: 4:30:12 time: 0.499859 data_time: 0.053796 memory: 21657 loss_kpt: 0.000524 acc_pose: 0.865170 loss: 0.000524 2022/10/21 16:26:46 - mmengine - INFO - Epoch(train) [116][100/391] lr: 5.000000e-04 eta: 4:29:52 time: 0.482224 data_time: 0.040337 memory: 21657 loss_kpt: 0.000525 acc_pose: 0.855329 loss: 0.000525 2022/10/21 16:27:10 - mmengine - INFO - Epoch(train) [116][150/391] lr: 5.000000e-04 eta: 4:29:32 time: 0.487347 data_time: 0.039668 memory: 21657 loss_kpt: 0.000514 acc_pose: 0.894487 loss: 0.000514 2022/10/21 16:27:35 - mmengine - INFO - Epoch(train) [116][200/391] lr: 5.000000e-04 eta: 4:29:12 time: 0.484720 data_time: 0.040052 memory: 21657 loss_kpt: 0.000545 acc_pose: 0.875387 loss: 0.000545 2022/10/21 16:27:59 - mmengine - INFO - Epoch(train) [116][250/391] lr: 5.000000e-04 eta: 4:28:52 time: 0.490668 data_time: 0.040750 memory: 21657 loss_kpt: 0.000520 acc_pose: 0.881440 loss: 0.000520 2022/10/21 16:28:23 - mmengine - INFO - Epoch(train) [116][300/391] lr: 5.000000e-04 eta: 4:28:32 time: 0.482591 data_time: 0.043938 memory: 21657 loss_kpt: 0.000517 acc_pose: 0.865670 loss: 0.000517 2022/10/21 16:28:48 - mmengine - INFO - Epoch(train) [116][350/391] lr: 5.000000e-04 eta: 4:28:13 time: 0.490914 data_time: 0.040179 memory: 21657 loss_kpt: 0.000538 acc_pose: 0.879572 loss: 0.000538 2022/10/21 16:29:07 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:29:33 - mmengine - INFO - Epoch(train) [117][50/391] lr: 5.000000e-04 eta: 4:27:21 time: 0.510800 data_time: 0.058083 memory: 21657 loss_kpt: 0.000514 acc_pose: 0.851194 loss: 0.000514 2022/10/21 16:29:57 - mmengine - INFO - Epoch(train) [117][100/391] lr: 5.000000e-04 eta: 4:27:01 time: 0.480279 data_time: 0.039895 memory: 21657 loss_kpt: 0.000522 acc_pose: 0.858497 loss: 0.000522 2022/10/21 16:30:21 - mmengine - INFO - Epoch(train) [117][150/391] lr: 5.000000e-04 eta: 4:26:42 time: 0.488951 data_time: 0.045009 memory: 21657 loss_kpt: 0.000525 acc_pose: 0.846877 loss: 0.000525 2022/10/21 16:30:46 - mmengine - INFO - Epoch(train) [117][200/391] lr: 5.000000e-04 eta: 4:26:22 time: 0.488038 data_time: 0.040221 memory: 21657 loss_kpt: 0.000521 acc_pose: 0.842725 loss: 0.000521 2022/10/21 16:31:10 - mmengine - INFO - Epoch(train) [117][250/391] lr: 5.000000e-04 eta: 4:26:02 time: 0.485858 data_time: 0.040722 memory: 21657 loss_kpt: 0.000521 acc_pose: 0.846887 loss: 0.000521 2022/10/21 16:31:34 - mmengine - INFO - Epoch(train) [117][300/391] lr: 5.000000e-04 eta: 4:25:42 time: 0.486850 data_time: 0.040906 memory: 21657 loss_kpt: 0.000548 acc_pose: 0.834277 loss: 0.000548 2022/10/21 16:31:59 - mmengine - INFO - Epoch(train) [117][350/391] lr: 5.000000e-04 eta: 4:25:22 time: 0.486269 data_time: 0.043786 memory: 21657 loss_kpt: 0.000525 acc_pose: 0.841116 loss: 0.000525 2022/10/21 16:32:18 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:32:43 - mmengine - INFO - Epoch(train) [118][50/391] lr: 5.000000e-04 eta: 4:24:30 time: 0.495733 data_time: 0.058489 memory: 21657 loss_kpt: 0.000529 acc_pose: 0.909289 loss: 0.000529 2022/10/21 16:33:08 - mmengine - INFO - Epoch(train) [118][100/391] lr: 5.000000e-04 eta: 4:24:11 time: 0.490306 data_time: 0.040670 memory: 21657 loss_kpt: 0.000542 acc_pose: 0.876325 loss: 0.000542 2022/10/21 16:33:32 - mmengine - INFO - Epoch(train) [118][150/391] lr: 5.000000e-04 eta: 4:23:50 time: 0.483325 data_time: 0.039601 memory: 21657 loss_kpt: 0.000521 acc_pose: 0.911225 loss: 0.000521 2022/10/21 16:33:57 - mmengine - INFO - Epoch(train) [118][200/391] lr: 5.000000e-04 eta: 4:23:31 time: 0.496769 data_time: 0.040792 memory: 21657 loss_kpt: 0.000529 acc_pose: 0.859302 loss: 0.000529 2022/10/21 16:34:21 - mmengine - INFO - Epoch(train) [118][250/391] lr: 5.000000e-04 eta: 4:23:11 time: 0.481465 data_time: 0.041089 memory: 21657 loss_kpt: 0.000541 acc_pose: 0.871718 loss: 0.000541 2022/10/21 16:34:22 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:34:45 - mmengine - INFO - Epoch(train) [118][300/391] lr: 5.000000e-04 eta: 4:22:51 time: 0.487595 data_time: 0.044973 memory: 21657 loss_kpt: 0.000531 acc_pose: 0.848351 loss: 0.000531 2022/10/21 16:35:09 - mmengine - INFO - Epoch(train) [118][350/391] lr: 5.000000e-04 eta: 4:22:31 time: 0.482499 data_time: 0.040762 memory: 21657 loss_kpt: 0.000528 acc_pose: 0.871824 loss: 0.000528 2022/10/21 16:35:29 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:35:54 - mmengine - INFO - Epoch(train) [119][50/391] lr: 5.000000e-04 eta: 4:21:40 time: 0.506206 data_time: 0.050796 memory: 21657 loss_kpt: 0.000535 acc_pose: 0.882380 loss: 0.000535 2022/10/21 16:36:18 - mmengine - INFO - Epoch(train) [119][100/391] lr: 5.000000e-04 eta: 4:21:20 time: 0.483969 data_time: 0.043838 memory: 21657 loss_kpt: 0.000523 acc_pose: 0.880498 loss: 0.000523 2022/10/21 16:36:43 - mmengine - INFO - Epoch(train) [119][150/391] lr: 5.000000e-04 eta: 4:21:00 time: 0.493278 data_time: 0.041478 memory: 21657 loss_kpt: 0.000521 acc_pose: 0.894868 loss: 0.000521 2022/10/21 16:37:07 - mmengine - INFO - Epoch(train) [119][200/391] lr: 5.000000e-04 eta: 4:20:40 time: 0.486102 data_time: 0.041365 memory: 21657 loss_kpt: 0.000520 acc_pose: 0.866683 loss: 0.000520 2022/10/21 16:37:32 - mmengine - INFO - Epoch(train) [119][250/391] lr: 5.000000e-04 eta: 4:20:20 time: 0.490813 data_time: 0.040622 memory: 21657 loss_kpt: 0.000528 acc_pose: 0.862525 loss: 0.000528 2022/10/21 16:37:56 - mmengine - INFO - Epoch(train) [119][300/391] lr: 5.000000e-04 eta: 4:20:00 time: 0.483677 data_time: 0.042854 memory: 21657 loss_kpt: 0.000518 acc_pose: 0.876133 loss: 0.000518 2022/10/21 16:38:21 - mmengine - INFO - Epoch(train) [119][350/391] lr: 5.000000e-04 eta: 4:19:40 time: 0.493636 data_time: 0.043795 memory: 21657 loss_kpt: 0.000524 acc_pose: 0.882490 loss: 0.000524 2022/10/21 16:38:40 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:39:06 - mmengine - INFO - Epoch(train) [120][50/391] lr: 5.000000e-04 eta: 4:18:50 time: 0.511479 data_time: 0.055049 memory: 21657 loss_kpt: 0.000522 acc_pose: 0.877579 loss: 0.000522 2022/10/21 16:39:30 - mmengine - INFO - Epoch(train) [120][100/391] lr: 5.000000e-04 eta: 4:18:30 time: 0.483647 data_time: 0.040118 memory: 21657 loss_kpt: 0.000512 acc_pose: 0.883718 loss: 0.000512 2022/10/21 16:39:54 - mmengine - INFO - Epoch(train) [120][150/391] lr: 5.000000e-04 eta: 4:18:10 time: 0.481283 data_time: 0.043097 memory: 21657 loss_kpt: 0.000519 acc_pose: 0.889519 loss: 0.000519 2022/10/21 16:40:19 - mmengine - INFO - Epoch(train) [120][200/391] lr: 5.000000e-04 eta: 4:17:50 time: 0.490022 data_time: 0.039304 memory: 21657 loss_kpt: 0.000517 acc_pose: 0.936403 loss: 0.000517 2022/10/21 16:40:43 - mmengine - INFO - Epoch(train) [120][250/391] lr: 5.000000e-04 eta: 4:17:30 time: 0.481312 data_time: 0.041632 memory: 21657 loss_kpt: 0.000531 acc_pose: 0.870905 loss: 0.000531 2022/10/21 16:41:08 - mmengine - INFO - Epoch(train) [120][300/391] lr: 5.000000e-04 eta: 4:17:10 time: 0.493941 data_time: 0.043945 memory: 21657 loss_kpt: 0.000523 acc_pose: 0.891735 loss: 0.000523 2022/10/21 16:41:31 - mmengine - INFO - Epoch(train) [120][350/391] lr: 5.000000e-04 eta: 4:16:49 time: 0.477749 data_time: 0.039830 memory: 21657 loss_kpt: 0.000531 acc_pose: 0.849899 loss: 0.000531 2022/10/21 16:41:51 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:41:51 - mmengine - INFO - Saving checkpoint at 120 epochs 2022/10/21 16:42:04 - mmengine - INFO - Epoch(val) [120][50/407] eta: 0:00:56 time: 0.158574 data_time: 0.014791 memory: 21657 2022/10/21 16:42:11 - mmengine - INFO - Epoch(val) [120][100/407] eta: 0:00:46 time: 0.151844 data_time: 0.009342 memory: 2142 2022/10/21 16:42:19 - mmengine - INFO - Epoch(val) [120][150/407] eta: 0:00:38 time: 0.150906 data_time: 0.009033 memory: 2142 2022/10/21 16:42:26 - mmengine - INFO - Epoch(val) [120][200/407] eta: 0:00:31 time: 0.149850 data_time: 0.008930 memory: 2142 2022/10/21 16:42:34 - mmengine - INFO - Epoch(val) [120][250/407] eta: 0:00:23 time: 0.151203 data_time: 0.009420 memory: 2142 2022/10/21 16:42:41 - mmengine - INFO - Epoch(val) [120][300/407] eta: 0:00:16 time: 0.151811 data_time: 0.009379 memory: 2142 2022/10/21 16:42:49 - mmengine - INFO - Epoch(val) [120][350/407] eta: 0:00:08 time: 0.152985 data_time: 0.009669 memory: 2142 2022/10/21 16:42:56 - mmengine - INFO - Epoch(val) [120][400/407] eta: 0:00:01 time: 0.148107 data_time: 0.008860 memory: 2142 2022/10/21 16:43:33 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 16:43:46 - mmengine - INFO - Epoch(val) [120][407/407] coco/AP: 0.741930 coco/AP .5: 0.900672 coco/AP .75: 0.813215 coco/AP (M): 0.699438 coco/AP (L): 0.814200 coco/AR: 0.792742 coco/AR .5: 0.936870 coco/AR .75: 0.857053 coco/AR (M): 0.746599 coco/AR (L): 0.858714 2022/10/21 16:44:12 - mmengine - INFO - Epoch(train) [121][50/391] lr: 5.000000e-04 eta: 4:15:59 time: 0.502654 data_time: 0.052870 memory: 21657 loss_kpt: 0.000525 acc_pose: 0.847818 loss: 0.000525 2022/10/21 16:44:26 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:44:36 - mmengine - INFO - Epoch(train) [121][100/391] lr: 5.000000e-04 eta: 4:15:39 time: 0.482707 data_time: 0.039464 memory: 21657 loss_kpt: 0.000513 acc_pose: 0.853569 loss: 0.000513 2022/10/21 16:45:00 - mmengine - INFO - Epoch(train) [121][150/391] lr: 5.000000e-04 eta: 4:15:19 time: 0.495214 data_time: 0.045072 memory: 21657 loss_kpt: 0.000514 acc_pose: 0.879564 loss: 0.000514 2022/10/21 16:45:24 - mmengine - INFO - Epoch(train) [121][200/391] lr: 5.000000e-04 eta: 4:14:58 time: 0.476328 data_time: 0.041362 memory: 21657 loss_kpt: 0.000527 acc_pose: 0.880099 loss: 0.000527 2022/10/21 16:45:49 - mmengine - INFO - Epoch(train) [121][250/391] lr: 5.000000e-04 eta: 4:14:39 time: 0.491743 data_time: 0.039760 memory: 21657 loss_kpt: 0.000539 acc_pose: 0.883049 loss: 0.000539 2022/10/21 16:46:13 - mmengine - INFO - Epoch(train) [121][300/391] lr: 5.000000e-04 eta: 4:14:18 time: 0.484602 data_time: 0.043983 memory: 21657 loss_kpt: 0.000522 acc_pose: 0.866466 loss: 0.000522 2022/10/21 16:46:38 - mmengine - INFO - Epoch(train) [121][350/391] lr: 5.000000e-04 eta: 4:13:59 time: 0.491290 data_time: 0.040413 memory: 21657 loss_kpt: 0.000517 acc_pose: 0.891297 loss: 0.000517 2022/10/21 16:46:57 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:47:23 - mmengine - INFO - Epoch(train) [122][50/391] lr: 5.000000e-04 eta: 4:13:08 time: 0.509756 data_time: 0.053461 memory: 21657 loss_kpt: 0.000523 acc_pose: 0.841008 loss: 0.000523 2022/10/21 16:47:47 - mmengine - INFO - Epoch(train) [122][100/391] lr: 5.000000e-04 eta: 4:12:48 time: 0.483065 data_time: 0.043740 memory: 21657 loss_kpt: 0.000521 acc_pose: 0.832162 loss: 0.000521 2022/10/21 16:48:11 - mmengine - INFO - Epoch(train) [122][150/391] lr: 5.000000e-04 eta: 4:12:28 time: 0.486329 data_time: 0.040314 memory: 21657 loss_kpt: 0.000543 acc_pose: 0.869176 loss: 0.000543 2022/10/21 16:48:35 - mmengine - INFO - Epoch(train) [122][200/391] lr: 5.000000e-04 eta: 4:12:08 time: 0.488061 data_time: 0.039918 memory: 21657 loss_kpt: 0.000531 acc_pose: 0.881745 loss: 0.000531 2022/10/21 16:49:00 - mmengine - INFO - Epoch(train) [122][250/391] lr: 5.000000e-04 eta: 4:11:48 time: 0.480454 data_time: 0.040606 memory: 21657 loss_kpt: 0.000536 acc_pose: 0.861978 loss: 0.000536 2022/10/21 16:49:24 - mmengine - INFO - Epoch(train) [122][300/391] lr: 5.000000e-04 eta: 4:11:28 time: 0.490934 data_time: 0.041506 memory: 21657 loss_kpt: 0.000526 acc_pose: 0.794617 loss: 0.000526 2022/10/21 16:49:48 - mmengine - INFO - Epoch(train) [122][350/391] lr: 5.000000e-04 eta: 4:11:08 time: 0.484836 data_time: 0.044121 memory: 21657 loss_kpt: 0.000527 acc_pose: 0.863098 loss: 0.000527 2022/10/21 16:50:08 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:50:34 - mmengine - INFO - Epoch(train) [123][50/391] lr: 5.000000e-04 eta: 4:10:17 time: 0.501944 data_time: 0.054069 memory: 21657 loss_kpt: 0.000514 acc_pose: 0.871621 loss: 0.000514 2022/10/21 16:50:58 - mmengine - INFO - Epoch(train) [123][100/391] lr: 5.000000e-04 eta: 4:09:57 time: 0.493775 data_time: 0.039756 memory: 21657 loss_kpt: 0.000514 acc_pose: 0.847624 loss: 0.000514 2022/10/21 16:51:22 - mmengine - INFO - Epoch(train) [123][150/391] lr: 5.000000e-04 eta: 4:09:37 time: 0.483860 data_time: 0.041899 memory: 21657 loss_kpt: 0.000516 acc_pose: 0.881929 loss: 0.000516 2022/10/21 16:51:47 - mmengine - INFO - Epoch(train) [123][200/391] lr: 5.000000e-04 eta: 4:09:18 time: 0.496389 data_time: 0.040560 memory: 21657 loss_kpt: 0.000521 acc_pose: 0.867774 loss: 0.000521 2022/10/21 16:52:11 - mmengine - INFO - Epoch(train) [123][250/391] lr: 5.000000e-04 eta: 4:08:57 time: 0.480326 data_time: 0.043996 memory: 21657 loss_kpt: 0.000517 acc_pose: 0.867079 loss: 0.000517 2022/10/21 16:52:35 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:52:36 - mmengine - INFO - Epoch(train) [123][300/391] lr: 5.000000e-04 eta: 4:08:37 time: 0.487493 data_time: 0.039494 memory: 21657 loss_kpt: 0.000545 acc_pose: 0.830861 loss: 0.000545 2022/10/21 16:53:00 - mmengine - INFO - Epoch(train) [123][350/391] lr: 5.000000e-04 eta: 4:08:17 time: 0.486204 data_time: 0.043847 memory: 21657 loss_kpt: 0.000524 acc_pose: 0.854706 loss: 0.000524 2022/10/21 16:53:19 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:53:45 - mmengine - INFO - Epoch(train) [124][50/391] lr: 5.000000e-04 eta: 4:07:27 time: 0.505631 data_time: 0.050673 memory: 21657 loss_kpt: 0.000523 acc_pose: 0.907333 loss: 0.000523 2022/10/21 16:54:09 - mmengine - INFO - Epoch(train) [124][100/391] lr: 5.000000e-04 eta: 4:07:07 time: 0.483414 data_time: 0.039598 memory: 21657 loss_kpt: 0.000523 acc_pose: 0.788066 loss: 0.000523 2022/10/21 16:54:33 - mmengine - INFO - Epoch(train) [124][150/391] lr: 5.000000e-04 eta: 4:06:47 time: 0.489746 data_time: 0.038652 memory: 21657 loss_kpt: 0.000521 acc_pose: 0.875612 loss: 0.000521 2022/10/21 16:54:57 - mmengine - INFO - Epoch(train) [124][200/391] lr: 5.000000e-04 eta: 4:06:26 time: 0.480283 data_time: 0.041158 memory: 21657 loss_kpt: 0.000538 acc_pose: 0.867386 loss: 0.000538 2022/10/21 16:55:22 - mmengine - INFO - Epoch(train) [124][250/391] lr: 5.000000e-04 eta: 4:06:06 time: 0.491118 data_time: 0.040750 memory: 21657 loss_kpt: 0.000533 acc_pose: 0.874538 loss: 0.000533 2022/10/21 16:55:46 - mmengine - INFO - Epoch(train) [124][300/391] lr: 5.000000e-04 eta: 4:05:46 time: 0.483074 data_time: 0.044637 memory: 21657 loss_kpt: 0.000525 acc_pose: 0.855420 loss: 0.000525 2022/10/21 16:56:11 - mmengine - INFO - Epoch(train) [124][350/391] lr: 5.000000e-04 eta: 4:05:26 time: 0.489802 data_time: 0.039736 memory: 21657 loss_kpt: 0.000521 acc_pose: 0.859587 loss: 0.000521 2022/10/21 16:56:30 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 16:56:55 - mmengine - INFO - Epoch(train) [125][50/391] lr: 5.000000e-04 eta: 4:04:36 time: 0.492582 data_time: 0.051169 memory: 21657 loss_kpt: 0.000521 acc_pose: 0.879403 loss: 0.000521 2022/10/21 16:57:19 - mmengine - INFO - Epoch(train) [125][100/391] lr: 5.000000e-04 eta: 4:04:16 time: 0.491916 data_time: 0.040238 memory: 21657 loss_kpt: 0.000507 acc_pose: 0.857064 loss: 0.000507 2022/10/21 16:57:43 - mmengine - INFO - Epoch(train) [125][150/391] lr: 5.000000e-04 eta: 4:03:55 time: 0.480195 data_time: 0.045348 memory: 21657 loss_kpt: 0.000527 acc_pose: 0.869060 loss: 0.000527 2022/10/21 16:58:08 - mmengine - INFO - Epoch(train) [125][200/391] lr: 5.000000e-04 eta: 4:03:35 time: 0.491170 data_time: 0.040504 memory: 21657 loss_kpt: 0.000515 acc_pose: 0.864762 loss: 0.000515 2022/10/21 16:58:32 - mmengine - INFO - Epoch(train) [125][250/391] lr: 5.000000e-04 eta: 4:03:15 time: 0.483660 data_time: 0.044166 memory: 21657 loss_kpt: 0.000509 acc_pose: 0.872940 loss: 0.000509 2022/10/21 16:58:57 - mmengine - INFO - Epoch(train) [125][300/391] lr: 5.000000e-04 eta: 4:02:55 time: 0.500350 data_time: 0.040291 memory: 21657 loss_kpt: 0.000511 acc_pose: 0.917151 loss: 0.000511 2022/10/21 16:59:21 - mmengine - INFO - Epoch(train) [125][350/391] lr: 5.000000e-04 eta: 4:02:35 time: 0.482548 data_time: 0.045123 memory: 21657 loss_kpt: 0.000517 acc_pose: 0.850735 loss: 0.000517 2022/10/21 16:59:41 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:00:06 - mmengine - INFO - Epoch(train) [126][50/391] lr: 5.000000e-04 eta: 4:01:45 time: 0.503889 data_time: 0.052713 memory: 21657 loss_kpt: 0.000518 acc_pose: 0.879566 loss: 0.000518 2022/10/21 17:00:30 - mmengine - INFO - Epoch(train) [126][100/391] lr: 5.000000e-04 eta: 4:01:25 time: 0.485077 data_time: 0.040492 memory: 21657 loss_kpt: 0.000516 acc_pose: 0.900067 loss: 0.000516 2022/10/21 17:00:43 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:00:55 - mmengine - INFO - Epoch(train) [126][150/391] lr: 5.000000e-04 eta: 4:01:05 time: 0.493604 data_time: 0.040914 memory: 21657 loss_kpt: 0.000521 acc_pose: 0.879343 loss: 0.000521 2022/10/21 17:01:19 - mmengine - INFO - Epoch(train) [126][200/391] lr: 5.000000e-04 eta: 4:00:45 time: 0.485035 data_time: 0.040214 memory: 21657 loss_kpt: 0.000508 acc_pose: 0.862342 loss: 0.000508 2022/10/21 17:01:44 - mmengine - INFO - Epoch(train) [126][250/391] lr: 5.000000e-04 eta: 4:00:25 time: 0.498642 data_time: 0.043613 memory: 21657 loss_kpt: 0.000517 acc_pose: 0.828581 loss: 0.000517 2022/10/21 17:02:08 - mmengine - INFO - Epoch(train) [126][300/391] lr: 5.000000e-04 eta: 4:00:05 time: 0.481008 data_time: 0.041436 memory: 21657 loss_kpt: 0.000523 acc_pose: 0.868062 loss: 0.000523 2022/10/21 17:02:33 - mmengine - INFO - Epoch(train) [126][350/391] lr: 5.000000e-04 eta: 3:59:45 time: 0.494802 data_time: 0.040749 memory: 21657 loss_kpt: 0.000519 acc_pose: 0.889583 loss: 0.000519 2022/10/21 17:02:53 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:03:18 - mmengine - INFO - Epoch(train) [127][50/391] lr: 5.000000e-04 eta: 3:58:55 time: 0.500950 data_time: 0.050833 memory: 21657 loss_kpt: 0.000513 acc_pose: 0.873167 loss: 0.000513 2022/10/21 17:03:42 - mmengine - INFO - Epoch(train) [127][100/391] lr: 5.000000e-04 eta: 3:58:35 time: 0.486287 data_time: 0.043965 memory: 21657 loss_kpt: 0.000528 acc_pose: 0.903636 loss: 0.000528 2022/10/21 17:04:06 - mmengine - INFO - Epoch(train) [127][150/391] lr: 5.000000e-04 eta: 3:58:15 time: 0.484300 data_time: 0.042084 memory: 21657 loss_kpt: 0.000518 acc_pose: 0.896933 loss: 0.000518 2022/10/21 17:04:31 - mmengine - INFO - Epoch(train) [127][200/391] lr: 5.000000e-04 eta: 3:57:55 time: 0.493240 data_time: 0.043694 memory: 21657 loss_kpt: 0.000517 acc_pose: 0.864320 loss: 0.000517 2022/10/21 17:04:55 - mmengine - INFO - Epoch(train) [127][250/391] lr: 5.000000e-04 eta: 3:57:34 time: 0.485086 data_time: 0.039898 memory: 21657 loss_kpt: 0.000529 acc_pose: 0.886172 loss: 0.000529 2022/10/21 17:05:20 - mmengine - INFO - Epoch(train) [127][300/391] lr: 5.000000e-04 eta: 3:57:14 time: 0.489060 data_time: 0.040118 memory: 21657 loss_kpt: 0.000521 acc_pose: 0.883528 loss: 0.000521 2022/10/21 17:05:43 - mmengine - INFO - Epoch(train) [127][350/391] lr: 5.000000e-04 eta: 3:56:54 time: 0.479143 data_time: 0.040595 memory: 21657 loss_kpt: 0.000512 acc_pose: 0.886494 loss: 0.000512 2022/10/21 17:06:03 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:06:28 - mmengine - INFO - Epoch(train) [128][50/391] lr: 5.000000e-04 eta: 3:56:04 time: 0.497319 data_time: 0.056789 memory: 21657 loss_kpt: 0.000513 acc_pose: 0.901813 loss: 0.000513 2022/10/21 17:06:53 - mmengine - INFO - Epoch(train) [128][100/391] lr: 5.000000e-04 eta: 3:55:44 time: 0.489281 data_time: 0.040105 memory: 21657 loss_kpt: 0.000511 acc_pose: 0.890483 loss: 0.000511 2022/10/21 17:07:17 - mmengine - INFO - Epoch(train) [128][150/391] lr: 5.000000e-04 eta: 3:55:24 time: 0.487519 data_time: 0.044210 memory: 21657 loss_kpt: 0.000503 acc_pose: 0.852247 loss: 0.000503 2022/10/21 17:07:41 - mmengine - INFO - Epoch(train) [128][200/391] lr: 5.000000e-04 eta: 3:55:03 time: 0.485693 data_time: 0.039626 memory: 21657 loss_kpt: 0.000512 acc_pose: 0.859426 loss: 0.000512 2022/10/21 17:08:06 - mmengine - INFO - Epoch(train) [128][250/391] lr: 5.000000e-04 eta: 3:54:43 time: 0.489838 data_time: 0.043612 memory: 21657 loss_kpt: 0.000518 acc_pose: 0.860725 loss: 0.000518 2022/10/21 17:08:30 - mmengine - INFO - Epoch(train) [128][300/391] lr: 5.000000e-04 eta: 3:54:23 time: 0.482565 data_time: 0.040170 memory: 21657 loss_kpt: 0.000505 acc_pose: 0.873839 loss: 0.000505 2022/10/21 17:08:51 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:08:55 - mmengine - INFO - Epoch(train) [128][350/391] lr: 5.000000e-04 eta: 3:54:03 time: 0.488926 data_time: 0.043367 memory: 21657 loss_kpt: 0.000501 acc_pose: 0.869287 loss: 0.000501 2022/10/21 17:09:14 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:09:39 - mmengine - INFO - Epoch(train) [129][50/391] lr: 5.000000e-04 eta: 3:53:13 time: 0.504303 data_time: 0.053013 memory: 21657 loss_kpt: 0.000518 acc_pose: 0.843435 loss: 0.000518 2022/10/21 17:10:03 - mmengine - INFO - Epoch(train) [129][100/391] lr: 5.000000e-04 eta: 3:52:53 time: 0.485314 data_time: 0.043430 memory: 21657 loss_kpt: 0.000511 acc_pose: 0.862046 loss: 0.000511 2022/10/21 17:10:28 - mmengine - INFO - Epoch(train) [129][150/391] lr: 5.000000e-04 eta: 3:52:33 time: 0.491409 data_time: 0.040066 memory: 21657 loss_kpt: 0.000506 acc_pose: 0.886325 loss: 0.000506 2022/10/21 17:10:52 - mmengine - INFO - Epoch(train) [129][200/391] lr: 5.000000e-04 eta: 3:52:13 time: 0.481859 data_time: 0.044407 memory: 21657 loss_kpt: 0.000509 acc_pose: 0.869506 loss: 0.000509 2022/10/21 17:11:17 - mmengine - INFO - Epoch(train) [129][250/391] lr: 5.000000e-04 eta: 3:51:52 time: 0.491569 data_time: 0.040933 memory: 21657 loss_kpt: 0.000521 acc_pose: 0.871137 loss: 0.000521 2022/10/21 17:11:41 - mmengine - INFO - Epoch(train) [129][300/391] lr: 5.000000e-04 eta: 3:51:32 time: 0.485617 data_time: 0.044456 memory: 21657 loss_kpt: 0.000525 acc_pose: 0.839423 loss: 0.000525 2022/10/21 17:12:05 - mmengine - INFO - Epoch(train) [129][350/391] lr: 5.000000e-04 eta: 3:51:12 time: 0.490026 data_time: 0.039422 memory: 21657 loss_kpt: 0.000519 acc_pose: 0.904697 loss: 0.000519 2022/10/21 17:12:25 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:12:50 - mmengine - INFO - Epoch(train) [130][50/391] lr: 5.000000e-04 eta: 3:50:23 time: 0.500047 data_time: 0.055577 memory: 21657 loss_kpt: 0.000517 acc_pose: 0.912263 loss: 0.000517 2022/10/21 17:13:15 - mmengine - INFO - Epoch(train) [130][100/391] lr: 5.000000e-04 eta: 3:50:03 time: 0.497450 data_time: 0.043839 memory: 21657 loss_kpt: 0.000507 acc_pose: 0.866450 loss: 0.000507 2022/10/21 17:13:39 - mmengine - INFO - Epoch(train) [130][150/391] lr: 5.000000e-04 eta: 3:49:42 time: 0.479660 data_time: 0.041441 memory: 21657 loss_kpt: 0.000519 acc_pose: 0.851242 loss: 0.000519 2022/10/21 17:14:04 - mmengine - INFO - Epoch(train) [130][200/391] lr: 5.000000e-04 eta: 3:49:22 time: 0.493173 data_time: 0.040133 memory: 21657 loss_kpt: 0.000521 acc_pose: 0.865765 loss: 0.000521 2022/10/21 17:14:28 - mmengine - INFO - Epoch(train) [130][250/391] lr: 5.000000e-04 eta: 3:49:02 time: 0.482387 data_time: 0.044090 memory: 21657 loss_kpt: 0.000510 acc_pose: 0.857140 loss: 0.000510 2022/10/21 17:14:53 - mmengine - INFO - Epoch(train) [130][300/391] lr: 5.000000e-04 eta: 3:48:42 time: 0.495042 data_time: 0.040340 memory: 21657 loss_kpt: 0.000503 acc_pose: 0.826179 loss: 0.000503 2022/10/21 17:15:17 - mmengine - INFO - Epoch(train) [130][350/391] lr: 5.000000e-04 eta: 3:48:21 time: 0.481247 data_time: 0.040107 memory: 21657 loss_kpt: 0.000534 acc_pose: 0.899882 loss: 0.000534 2022/10/21 17:15:36 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:15:36 - mmengine - INFO - Saving checkpoint at 130 epochs 2022/10/21 17:15:48 - mmengine - INFO - Epoch(val) [130][50/407] eta: 0:00:55 time: 0.156852 data_time: 0.014383 memory: 21657 2022/10/21 17:15:56 - mmengine - INFO - Epoch(val) [130][100/407] eta: 0:00:46 time: 0.151612 data_time: 0.009406 memory: 2142 2022/10/21 17:16:04 - mmengine - INFO - Epoch(val) [130][150/407] eta: 0:00:39 time: 0.152749 data_time: 0.009924 memory: 2142 2022/10/21 17:16:11 - mmengine - INFO - Epoch(val) [130][200/407] eta: 0:00:31 time: 0.152768 data_time: 0.009691 memory: 2142 2022/10/21 17:16:19 - mmengine - INFO - Epoch(val) [130][250/407] eta: 0:00:24 time: 0.153250 data_time: 0.011885 memory: 2142 2022/10/21 17:16:27 - mmengine - INFO - Epoch(val) [130][300/407] eta: 0:00:16 time: 0.153108 data_time: 0.010512 memory: 2142 2022/10/21 17:16:34 - mmengine - INFO - Epoch(val) [130][350/407] eta: 0:00:08 time: 0.155750 data_time: 0.012900 memory: 2142 2022/10/21 17:16:42 - mmengine - INFO - Epoch(val) [130][400/407] eta: 0:00:01 time: 0.147610 data_time: 0.008396 memory: 2142 2022/10/21 17:17:17 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 17:17:31 - mmengine - INFO - Epoch(val) [130][407/407] coco/AP: 0.741509 coco/AP .5: 0.899657 coco/AP .75: 0.811126 coco/AP (M): 0.699358 coco/AP (L): 0.814927 coco/AR: 0.793183 coco/AR .5: 0.936713 coco/AR .75: 0.855951 coco/AR (M): 0.746490 coco/AR (L): 0.860609 2022/10/21 17:17:56 - mmengine - INFO - Epoch(train) [131][50/391] lr: 5.000000e-04 eta: 3:47:32 time: 0.502824 data_time: 0.051328 memory: 21657 loss_kpt: 0.000514 acc_pose: 0.890697 loss: 0.000514 2022/10/21 17:18:20 - mmengine - INFO - Epoch(train) [131][100/391] lr: 5.000000e-04 eta: 3:47:12 time: 0.486136 data_time: 0.044567 memory: 21657 loss_kpt: 0.000518 acc_pose: 0.838725 loss: 0.000518 2022/10/21 17:18:45 - mmengine - INFO - Epoch(train) [131][150/391] lr: 5.000000e-04 eta: 3:46:52 time: 0.489489 data_time: 0.040236 memory: 21657 loss_kpt: 0.000518 acc_pose: 0.904238 loss: 0.000518 2022/10/21 17:18:54 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:19:09 - mmengine - INFO - Epoch(train) [131][200/391] lr: 5.000000e-04 eta: 3:46:31 time: 0.481975 data_time: 0.043148 memory: 21657 loss_kpt: 0.000514 acc_pose: 0.908132 loss: 0.000514 2022/10/21 17:19:34 - mmengine - INFO - Epoch(train) [131][250/391] lr: 5.000000e-04 eta: 3:46:11 time: 0.491028 data_time: 0.039823 memory: 21657 loss_kpt: 0.000509 acc_pose: 0.873142 loss: 0.000509 2022/10/21 17:19:57 - mmengine - INFO - Epoch(train) [131][300/391] lr: 5.000000e-04 eta: 3:45:50 time: 0.478402 data_time: 0.040107 memory: 21657 loss_kpt: 0.000504 acc_pose: 0.883485 loss: 0.000504 2022/10/21 17:20:22 - mmengine - INFO - Epoch(train) [131][350/391] lr: 5.000000e-04 eta: 3:45:30 time: 0.490632 data_time: 0.039479 memory: 21657 loss_kpt: 0.000510 acc_pose: 0.866638 loss: 0.000510 2022/10/21 17:20:42 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:21:07 - mmengine - INFO - Epoch(train) [132][50/391] lr: 5.000000e-04 eta: 3:44:42 time: 0.509885 data_time: 0.055546 memory: 21657 loss_kpt: 0.000510 acc_pose: 0.904524 loss: 0.000510 2022/10/21 17:21:31 - mmengine - INFO - Epoch(train) [132][100/391] lr: 5.000000e-04 eta: 3:44:21 time: 0.482726 data_time: 0.044500 memory: 21657 loss_kpt: 0.000514 acc_pose: 0.887317 loss: 0.000514 2022/10/21 17:21:55 - mmengine - INFO - Epoch(train) [132][150/391] lr: 5.000000e-04 eta: 3:44:01 time: 0.483675 data_time: 0.042723 memory: 21657 loss_kpt: 0.000513 acc_pose: 0.891608 loss: 0.000513 2022/10/21 17:22:20 - mmengine - INFO - Epoch(train) [132][200/391] lr: 5.000000e-04 eta: 3:43:40 time: 0.492312 data_time: 0.040426 memory: 21657 loss_kpt: 0.000527 acc_pose: 0.837812 loss: 0.000527 2022/10/21 17:22:44 - mmengine - INFO - Epoch(train) [132][250/391] lr: 5.000000e-04 eta: 3:43:20 time: 0.485163 data_time: 0.042942 memory: 21657 loss_kpt: 0.000524 acc_pose: 0.905051 loss: 0.000524 2022/10/21 17:23:09 - mmengine - INFO - Epoch(train) [132][300/391] lr: 5.000000e-04 eta: 3:43:00 time: 0.490246 data_time: 0.039892 memory: 21657 loss_kpt: 0.000503 acc_pose: 0.870309 loss: 0.000503 2022/10/21 17:23:33 - mmengine - INFO - Epoch(train) [132][350/391] lr: 5.000000e-04 eta: 3:42:39 time: 0.486023 data_time: 0.045098 memory: 21657 loss_kpt: 0.000518 acc_pose: 0.881458 loss: 0.000518 2022/10/21 17:23:53 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:24:18 - mmengine - INFO - Epoch(train) [133][50/391] lr: 5.000000e-04 eta: 3:41:51 time: 0.494711 data_time: 0.054736 memory: 21657 loss_kpt: 0.000523 acc_pose: 0.867196 loss: 0.000523 2022/10/21 17:24:43 - mmengine - INFO - Epoch(train) [133][100/391] lr: 5.000000e-04 eta: 3:41:30 time: 0.498341 data_time: 0.040173 memory: 21657 loss_kpt: 0.000516 acc_pose: 0.887671 loss: 0.000516 2022/10/21 17:25:06 - mmengine - INFO - Epoch(train) [133][150/391] lr: 5.000000e-04 eta: 3:41:10 time: 0.477670 data_time: 0.041238 memory: 21657 loss_kpt: 0.000519 acc_pose: 0.840445 loss: 0.000519 2022/10/21 17:25:31 - mmengine - INFO - Epoch(train) [133][200/391] lr: 5.000000e-04 eta: 3:40:50 time: 0.491906 data_time: 0.043993 memory: 21657 loss_kpt: 0.000520 acc_pose: 0.919937 loss: 0.000520 2022/10/21 17:25:55 - mmengine - INFO - Epoch(train) [133][250/391] lr: 5.000000e-04 eta: 3:40:29 time: 0.485430 data_time: 0.039468 memory: 21657 loss_kpt: 0.000516 acc_pose: 0.797776 loss: 0.000516 2022/10/21 17:26:20 - mmengine - INFO - Epoch(train) [133][300/391] lr: 5.000000e-04 eta: 3:40:09 time: 0.484937 data_time: 0.043619 memory: 21657 loss_kpt: 0.000511 acc_pose: 0.836280 loss: 0.000511 2022/10/21 17:26:44 - mmengine - INFO - Epoch(train) [133][350/391] lr: 5.000000e-04 eta: 3:39:48 time: 0.494172 data_time: 0.040558 memory: 21657 loss_kpt: 0.000510 acc_pose: 0.856543 loss: 0.000510 2022/10/21 17:27:03 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:27:04 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:27:29 - mmengine - INFO - Epoch(train) [134][50/391] lr: 5.000000e-04 eta: 3:39:00 time: 0.509987 data_time: 0.050952 memory: 21657 loss_kpt: 0.000508 acc_pose: 0.842840 loss: 0.000508 2022/10/21 17:27:53 - mmengine - INFO - Epoch(train) [134][100/391] lr: 5.000000e-04 eta: 3:38:40 time: 0.485319 data_time: 0.046283 memory: 21657 loss_kpt: 0.000523 acc_pose: 0.930014 loss: 0.000523 2022/10/21 17:28:18 - mmengine - INFO - Epoch(train) [134][150/391] lr: 5.000000e-04 eta: 3:38:20 time: 0.493232 data_time: 0.040714 memory: 21657 loss_kpt: 0.000513 acc_pose: 0.914484 loss: 0.000513 2022/10/21 17:28:42 - mmengine - INFO - Epoch(train) [134][200/391] lr: 5.000000e-04 eta: 3:37:59 time: 0.483504 data_time: 0.039522 memory: 21657 loss_kpt: 0.000514 acc_pose: 0.838582 loss: 0.000514 2022/10/21 17:29:07 - mmengine - INFO - Epoch(train) [134][250/391] lr: 5.000000e-04 eta: 3:37:39 time: 0.491846 data_time: 0.040510 memory: 21657 loss_kpt: 0.000519 acc_pose: 0.887920 loss: 0.000519 2022/10/21 17:29:31 - mmengine - INFO - Epoch(train) [134][300/391] lr: 5.000000e-04 eta: 3:37:18 time: 0.483069 data_time: 0.039544 memory: 21657 loss_kpt: 0.000524 acc_pose: 0.834047 loss: 0.000524 2022/10/21 17:29:56 - mmengine - INFO - Epoch(train) [134][350/391] lr: 5.000000e-04 eta: 3:36:58 time: 0.491038 data_time: 0.043099 memory: 21657 loss_kpt: 0.000493 acc_pose: 0.887757 loss: 0.000493 2022/10/21 17:30:15 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:30:40 - mmengine - INFO - Epoch(train) [135][50/391] lr: 5.000000e-04 eta: 3:36:09 time: 0.493358 data_time: 0.057613 memory: 21657 loss_kpt: 0.000514 acc_pose: 0.911126 loss: 0.000514 2022/10/21 17:31:05 - mmengine - INFO - Epoch(train) [135][100/391] lr: 5.000000e-04 eta: 3:35:49 time: 0.492911 data_time: 0.040082 memory: 21657 loss_kpt: 0.000502 acc_pose: 0.913051 loss: 0.000502 2022/10/21 17:31:29 - mmengine - INFO - Epoch(train) [135][150/391] lr: 5.000000e-04 eta: 3:35:28 time: 0.479344 data_time: 0.043496 memory: 21657 loss_kpt: 0.000507 acc_pose: 0.837187 loss: 0.000507 2022/10/21 17:31:53 - mmengine - INFO - Epoch(train) [135][200/391] lr: 5.000000e-04 eta: 3:35:08 time: 0.493976 data_time: 0.040468 memory: 21657 loss_kpt: 0.000517 acc_pose: 0.886085 loss: 0.000517 2022/10/21 17:32:17 - mmengine - INFO - Epoch(train) [135][250/391] lr: 5.000000e-04 eta: 3:34:48 time: 0.480755 data_time: 0.043738 memory: 21657 loss_kpt: 0.000526 acc_pose: 0.792460 loss: 0.000526 2022/10/21 17:32:42 - mmengine - INFO - Epoch(train) [135][300/391] lr: 5.000000e-04 eta: 3:34:27 time: 0.487617 data_time: 0.039773 memory: 21657 loss_kpt: 0.000512 acc_pose: 0.876615 loss: 0.000512 2022/10/21 17:33:06 - mmengine - INFO - Epoch(train) [135][350/391] lr: 5.000000e-04 eta: 3:34:06 time: 0.483946 data_time: 0.043457 memory: 21657 loss_kpt: 0.000513 acc_pose: 0.896844 loss: 0.000513 2022/10/21 17:33:25 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:33:51 - mmengine - INFO - Epoch(train) [136][50/391] lr: 5.000000e-04 eta: 3:33:19 time: 0.501964 data_time: 0.050904 memory: 21657 loss_kpt: 0.000507 acc_pose: 0.892246 loss: 0.000507 2022/10/21 17:34:15 - mmengine - INFO - Epoch(train) [136][100/391] lr: 5.000000e-04 eta: 3:32:58 time: 0.485456 data_time: 0.043619 memory: 21657 loss_kpt: 0.000508 acc_pose: 0.829500 loss: 0.000508 2022/10/21 17:34:39 - mmengine - INFO - Epoch(train) [136][150/391] lr: 5.000000e-04 eta: 3:32:38 time: 0.490705 data_time: 0.041002 memory: 21657 loss_kpt: 0.000502 acc_pose: 0.884897 loss: 0.000502 2022/10/21 17:35:03 - mmengine - INFO - Epoch(train) [136][200/391] lr: 5.000000e-04 eta: 3:32:17 time: 0.479689 data_time: 0.043038 memory: 21657 loss_kpt: 0.000505 acc_pose: 0.867886 loss: 0.000505 2022/10/21 17:35:11 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:35:28 - mmengine - INFO - Epoch(train) [136][250/391] lr: 5.000000e-04 eta: 3:31:57 time: 0.497684 data_time: 0.039659 memory: 21657 loss_kpt: 0.000507 acc_pose: 0.893575 loss: 0.000507 2022/10/21 17:35:52 - mmengine - INFO - Epoch(train) [136][300/391] lr: 5.000000e-04 eta: 3:31:36 time: 0.480596 data_time: 0.042850 memory: 21657 loss_kpt: 0.000504 acc_pose: 0.916501 loss: 0.000504 2022/10/21 17:36:17 - mmengine - INFO - Epoch(train) [136][350/391] lr: 5.000000e-04 eta: 3:31:16 time: 0.489359 data_time: 0.039730 memory: 21657 loss_kpt: 0.000510 acc_pose: 0.853525 loss: 0.000510 2022/10/21 17:36:42 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:37:11 - mmengine - INFO - Epoch(train) [137][50/391] lr: 5.000000e-04 eta: 3:30:30 time: 0.574527 data_time: 0.067845 memory: 21657 loss_kpt: 0.000505 acc_pose: 0.868678 loss: 0.000505 2022/10/21 17:37:35 - mmengine - INFO - Epoch(train) [137][100/391] lr: 5.000000e-04 eta: 3:30:09 time: 0.483721 data_time: 0.040284 memory: 21657 loss_kpt: 0.000507 acc_pose: 0.875687 loss: 0.000507 2022/10/21 17:37:59 - mmengine - INFO - Epoch(train) [137][150/391] lr: 5.000000e-04 eta: 3:29:49 time: 0.493108 data_time: 0.042919 memory: 21657 loss_kpt: 0.000519 acc_pose: 0.872869 loss: 0.000519 2022/10/21 17:38:24 - mmengine - INFO - Epoch(train) [137][200/391] lr: 5.000000e-04 eta: 3:29:28 time: 0.484775 data_time: 0.039071 memory: 21657 loss_kpt: 0.000504 acc_pose: 0.891734 loss: 0.000504 2022/10/21 17:38:48 - mmengine - INFO - Epoch(train) [137][250/391] lr: 5.000000e-04 eta: 3:29:08 time: 0.489652 data_time: 0.039948 memory: 21657 loss_kpt: 0.000506 acc_pose: 0.853655 loss: 0.000506 2022/10/21 17:39:13 - mmengine - INFO - Epoch(train) [137][300/391] lr: 5.000000e-04 eta: 3:28:47 time: 0.492000 data_time: 0.040150 memory: 21657 loss_kpt: 0.000509 acc_pose: 0.859184 loss: 0.000509 2022/10/21 17:39:37 - mmengine - INFO - Epoch(train) [137][350/391] lr: 5.000000e-04 eta: 3:28:27 time: 0.485744 data_time: 0.041082 memory: 21657 loss_kpt: 0.000507 acc_pose: 0.916554 loss: 0.000507 2022/10/21 17:39:57 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:40:22 - mmengine - INFO - Epoch(train) [138][50/391] lr: 5.000000e-04 eta: 3:27:39 time: 0.495333 data_time: 0.049485 memory: 21657 loss_kpt: 0.000495 acc_pose: 0.862221 loss: 0.000495 2022/10/21 17:40:46 - mmengine - INFO - Epoch(train) [138][100/391] lr: 5.000000e-04 eta: 3:27:18 time: 0.490379 data_time: 0.039318 memory: 21657 loss_kpt: 0.000516 acc_pose: 0.831516 loss: 0.000516 2022/10/21 17:41:11 - mmengine - INFO - Epoch(train) [138][150/391] lr: 5.000000e-04 eta: 3:26:58 time: 0.484436 data_time: 0.043387 memory: 21657 loss_kpt: 0.000509 acc_pose: 0.852843 loss: 0.000509 2022/10/21 17:41:35 - mmengine - INFO - Epoch(train) [138][200/391] lr: 5.000000e-04 eta: 3:26:37 time: 0.485768 data_time: 0.039348 memory: 21657 loss_kpt: 0.000501 acc_pose: 0.862681 loss: 0.000501 2022/10/21 17:41:59 - mmengine - INFO - Epoch(train) [138][250/391] lr: 5.000000e-04 eta: 3:26:17 time: 0.484528 data_time: 0.040393 memory: 21657 loss_kpt: 0.000515 acc_pose: 0.900731 loss: 0.000515 2022/10/21 17:42:24 - mmengine - INFO - Epoch(train) [138][300/391] lr: 5.000000e-04 eta: 3:25:56 time: 0.489327 data_time: 0.043368 memory: 21657 loss_kpt: 0.000507 acc_pose: 0.854312 loss: 0.000507 2022/10/21 17:42:48 - mmengine - INFO - Epoch(train) [138][350/391] lr: 5.000000e-04 eta: 3:25:36 time: 0.489872 data_time: 0.039159 memory: 21657 loss_kpt: 0.000518 acc_pose: 0.878242 loss: 0.000518 2022/10/21 17:43:08 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:43:29 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:43:33 - mmengine - INFO - Epoch(train) [139][50/391] lr: 5.000000e-04 eta: 3:24:48 time: 0.506867 data_time: 0.051542 memory: 21657 loss_kpt: 0.000508 acc_pose: 0.883741 loss: 0.000508 2022/10/21 17:43:57 - mmengine - INFO - Epoch(train) [139][100/391] lr: 5.000000e-04 eta: 3:24:28 time: 0.487281 data_time: 0.040203 memory: 21657 loss_kpt: 0.000514 acc_pose: 0.903616 loss: 0.000514 2022/10/21 17:44:22 - mmengine - INFO - Epoch(train) [139][150/391] lr: 5.000000e-04 eta: 3:24:07 time: 0.487970 data_time: 0.042294 memory: 21657 loss_kpt: 0.000503 acc_pose: 0.896853 loss: 0.000503 2022/10/21 17:44:46 - mmengine - INFO - Epoch(train) [139][200/391] lr: 5.000000e-04 eta: 3:23:46 time: 0.486191 data_time: 0.044812 memory: 21657 loss_kpt: 0.000511 acc_pose: 0.837788 loss: 0.000511 2022/10/21 17:45:11 - mmengine - INFO - Epoch(train) [139][250/391] lr: 5.000000e-04 eta: 3:23:26 time: 0.488586 data_time: 0.040527 memory: 21657 loss_kpt: 0.000502 acc_pose: 0.913543 loss: 0.000502 2022/10/21 17:45:35 - mmengine - INFO - Epoch(train) [139][300/391] lr: 5.000000e-04 eta: 3:23:05 time: 0.486200 data_time: 0.039718 memory: 21657 loss_kpt: 0.000507 acc_pose: 0.886614 loss: 0.000507 2022/10/21 17:45:59 - mmengine - INFO - Epoch(train) [139][350/391] lr: 5.000000e-04 eta: 3:22:45 time: 0.489481 data_time: 0.043909 memory: 21657 loss_kpt: 0.000498 acc_pose: 0.898016 loss: 0.000498 2022/10/21 17:46:19 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:46:45 - mmengine - INFO - Epoch(train) [140][50/391] lr: 5.000000e-04 eta: 3:21:58 time: 0.511071 data_time: 0.053714 memory: 21657 loss_kpt: 0.000492 acc_pose: 0.863414 loss: 0.000492 2022/10/21 17:47:09 - mmengine - INFO - Epoch(train) [140][100/391] lr: 5.000000e-04 eta: 3:21:37 time: 0.488493 data_time: 0.040413 memory: 21657 loss_kpt: 0.000507 acc_pose: 0.882000 loss: 0.000507 2022/10/21 17:47:33 - mmengine - INFO - Epoch(train) [140][150/391] lr: 5.000000e-04 eta: 3:21:16 time: 0.483735 data_time: 0.044133 memory: 21657 loss_kpt: 0.000504 acc_pose: 0.826297 loss: 0.000504 2022/10/21 17:48:03 - mmengine - INFO - Epoch(train) [140][200/391] lr: 5.000000e-04 eta: 3:20:58 time: 0.581591 data_time: 0.055300 memory: 21657 loss_kpt: 0.000502 acc_pose: 0.906882 loss: 0.000502 2022/10/21 17:48:27 - mmengine - INFO - Epoch(train) [140][250/391] lr: 5.000000e-04 eta: 3:20:38 time: 0.491275 data_time: 0.045262 memory: 21657 loss_kpt: 0.000516 acc_pose: 0.881443 loss: 0.000516 2022/10/21 17:48:52 - mmengine - INFO - Epoch(train) [140][300/391] lr: 5.000000e-04 eta: 3:20:17 time: 0.491366 data_time: 0.042718 memory: 21657 loss_kpt: 0.000500 acc_pose: 0.889695 loss: 0.000500 2022/10/21 17:49:16 - mmengine - INFO - Epoch(train) [140][350/391] lr: 5.000000e-04 eta: 3:19:56 time: 0.483796 data_time: 0.040318 memory: 21657 loss_kpt: 0.000505 acc_pose: 0.866605 loss: 0.000505 2022/10/21 17:49:36 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:49:36 - mmengine - INFO - Saving checkpoint at 140 epochs 2022/10/21 17:49:48 - mmengine - INFO - Epoch(val) [140][50/407] eta: 0:00:55 time: 0.156550 data_time: 0.014767 memory: 21657 2022/10/21 17:49:55 - mmengine - INFO - Epoch(val) [140][100/407] eta: 0:00:46 time: 0.150859 data_time: 0.008860 memory: 2142 2022/10/21 17:50:03 - mmengine - INFO - Epoch(val) [140][150/407] eta: 0:00:39 time: 0.153268 data_time: 0.010555 memory: 2142 2022/10/21 17:50:10 - mmengine - INFO - Epoch(val) [140][200/407] eta: 0:00:31 time: 0.151286 data_time: 0.009685 memory: 2142 2022/10/21 17:50:18 - mmengine - INFO - Epoch(val) [140][250/407] eta: 0:00:23 time: 0.150927 data_time: 0.009027 memory: 2142 2022/10/21 17:50:26 - mmengine - INFO - Epoch(val) [140][300/407] eta: 0:00:16 time: 0.152089 data_time: 0.010596 memory: 2142 2022/10/21 17:50:33 - mmengine - INFO - Epoch(val) [140][350/407] eta: 0:00:08 time: 0.152092 data_time: 0.010116 memory: 2142 2022/10/21 17:50:41 - mmengine - INFO - Epoch(val) [140][400/407] eta: 0:00:01 time: 0.151295 data_time: 0.010514 memory: 2142 2022/10/21 17:51:17 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 17:51:30 - mmengine - INFO - Epoch(val) [140][407/407] coco/AP: 0.742712 coco/AP .5: 0.902008 coco/AP .75: 0.812714 coco/AP (M): 0.701023 coco/AP (L): 0.814684 coco/AR: 0.793734 coco/AR .5: 0.937815 coco/AR .75: 0.855479 coco/AR (M): 0.747501 coco/AR (L): 0.860795 2022/10/21 17:51:56 - mmengine - INFO - Epoch(train) [141][50/391] lr: 5.000000e-04 eta: 3:19:09 time: 0.504345 data_time: 0.050120 memory: 21657 loss_kpt: 0.000505 acc_pose: 0.852907 loss: 0.000505 2022/10/21 17:52:20 - mmengine - INFO - Epoch(train) [141][100/391] lr: 5.000000e-04 eta: 3:18:49 time: 0.485732 data_time: 0.040766 memory: 21657 loss_kpt: 0.000500 acc_pose: 0.910808 loss: 0.000500 2022/10/21 17:52:44 - mmengine - INFO - Epoch(train) [141][150/391] lr: 5.000000e-04 eta: 3:18:28 time: 0.488133 data_time: 0.040370 memory: 21657 loss_kpt: 0.000503 acc_pose: 0.896424 loss: 0.000503 2022/10/21 17:53:08 - mmengine - INFO - Epoch(train) [141][200/391] lr: 5.000000e-04 eta: 3:18:07 time: 0.483571 data_time: 0.043991 memory: 21657 loss_kpt: 0.000515 acc_pose: 0.857342 loss: 0.000515 2022/10/21 17:53:33 - mmengine - INFO - Epoch(train) [141][250/391] lr: 5.000000e-04 eta: 3:17:47 time: 0.491391 data_time: 0.040613 memory: 21657 loss_kpt: 0.000508 acc_pose: 0.876612 loss: 0.000508 2022/10/21 17:53:38 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:53:57 - mmengine - INFO - Epoch(train) [141][300/391] lr: 5.000000e-04 eta: 3:17:26 time: 0.480469 data_time: 0.041268 memory: 21657 loss_kpt: 0.000498 acc_pose: 0.887285 loss: 0.000498 2022/10/21 17:54:22 - mmengine - INFO - Epoch(train) [141][350/391] lr: 5.000000e-04 eta: 3:17:05 time: 0.491542 data_time: 0.043994 memory: 21657 loss_kpt: 0.000512 acc_pose: 0.878213 loss: 0.000512 2022/10/21 17:54:41 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:55:06 - mmengine - INFO - Epoch(train) [142][50/391] lr: 5.000000e-04 eta: 3:16:18 time: 0.500917 data_time: 0.057274 memory: 21657 loss_kpt: 0.000511 acc_pose: 0.853276 loss: 0.000511 2022/10/21 17:55:31 - mmengine - INFO - Epoch(train) [142][100/391] lr: 5.000000e-04 eta: 3:15:58 time: 0.490291 data_time: 0.041236 memory: 21657 loss_kpt: 0.000513 acc_pose: 0.829854 loss: 0.000513 2022/10/21 17:55:55 - mmengine - INFO - Epoch(train) [142][150/391] lr: 5.000000e-04 eta: 3:15:37 time: 0.484830 data_time: 0.045446 memory: 21657 loss_kpt: 0.000485 acc_pose: 0.897092 loss: 0.000485 2022/10/21 17:56:19 - mmengine - INFO - Epoch(train) [142][200/391] lr: 5.000000e-04 eta: 3:15:16 time: 0.484597 data_time: 0.041791 memory: 21657 loss_kpt: 0.000489 acc_pose: 0.854306 loss: 0.000489 2022/10/21 17:56:44 - mmengine - INFO - Epoch(train) [142][250/391] lr: 5.000000e-04 eta: 3:14:56 time: 0.489785 data_time: 0.045603 memory: 21657 loss_kpt: 0.000497 acc_pose: 0.864183 loss: 0.000497 2022/10/21 17:57:08 - mmengine - INFO - Epoch(train) [142][300/391] lr: 5.000000e-04 eta: 3:14:35 time: 0.486436 data_time: 0.040973 memory: 21657 loss_kpt: 0.000505 acc_pose: 0.868495 loss: 0.000505 2022/10/21 17:57:32 - mmengine - INFO - Epoch(train) [142][350/391] lr: 5.000000e-04 eta: 3:14:14 time: 0.489093 data_time: 0.044625 memory: 21657 loss_kpt: 0.000499 acc_pose: 0.873755 loss: 0.000499 2022/10/21 17:57:52 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 17:58:18 - mmengine - INFO - Epoch(train) [143][50/391] lr: 5.000000e-04 eta: 3:13:28 time: 0.506989 data_time: 0.052628 memory: 21657 loss_kpt: 0.000495 acc_pose: 0.862527 loss: 0.000495 2022/10/21 17:58:42 - mmengine - INFO - Epoch(train) [143][100/391] lr: 5.000000e-04 eta: 3:13:07 time: 0.486145 data_time: 0.042050 memory: 21657 loss_kpt: 0.000495 acc_pose: 0.886484 loss: 0.000495 2022/10/21 17:59:06 - mmengine - INFO - Epoch(train) [143][150/391] lr: 5.000000e-04 eta: 3:12:46 time: 0.484695 data_time: 0.039804 memory: 21657 loss_kpt: 0.000494 acc_pose: 0.905495 loss: 0.000494 2022/10/21 17:59:31 - mmengine - INFO - Epoch(train) [143][200/391] lr: 5.000000e-04 eta: 3:12:25 time: 0.491780 data_time: 0.041581 memory: 21657 loss_kpt: 0.000510 acc_pose: 0.852998 loss: 0.000510 2022/10/21 17:59:55 - mmengine - INFO - Epoch(train) [143][250/391] lr: 5.000000e-04 eta: 3:12:05 time: 0.487260 data_time: 0.041460 memory: 21657 loss_kpt: 0.000511 acc_pose: 0.812336 loss: 0.000511 2022/10/21 18:00:20 - mmengine - INFO - Epoch(train) [143][300/391] lr: 5.000000e-04 eta: 3:11:44 time: 0.494314 data_time: 0.045510 memory: 21657 loss_kpt: 0.000515 acc_pose: 0.881329 loss: 0.000515 2022/10/21 18:00:44 - mmengine - INFO - Epoch(train) [143][350/391] lr: 5.000000e-04 eta: 3:11:23 time: 0.486291 data_time: 0.041011 memory: 21657 loss_kpt: 0.000497 acc_pose: 0.922974 loss: 0.000497 2022/10/21 18:01:04 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:01:29 - mmengine - INFO - Epoch(train) [144][50/391] lr: 5.000000e-04 eta: 3:10:37 time: 0.505527 data_time: 0.051961 memory: 21657 loss_kpt: 0.000487 acc_pose: 0.887612 loss: 0.000487 2022/10/21 18:01:47 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:01:53 - mmengine - INFO - Epoch(train) [144][100/391] lr: 5.000000e-04 eta: 3:10:16 time: 0.481292 data_time: 0.041138 memory: 21657 loss_kpt: 0.000487 acc_pose: 0.915204 loss: 0.000487 2022/10/21 18:02:18 - mmengine - INFO - Epoch(train) [144][150/391] lr: 5.000000e-04 eta: 3:09:55 time: 0.492213 data_time: 0.042432 memory: 21657 loss_kpt: 0.000498 acc_pose: 0.899933 loss: 0.000498 2022/10/21 18:02:42 - mmengine - INFO - Epoch(train) [144][200/391] lr: 5.000000e-04 eta: 3:09:35 time: 0.483112 data_time: 0.041590 memory: 21657 loss_kpt: 0.000502 acc_pose: 0.882046 loss: 0.000502 2022/10/21 18:03:07 - mmengine - INFO - Epoch(train) [144][250/391] lr: 5.000000e-04 eta: 3:09:14 time: 0.490827 data_time: 0.044266 memory: 21657 loss_kpt: 0.000489 acc_pose: 0.888422 loss: 0.000489 2022/10/21 18:03:31 - mmengine - INFO - Epoch(train) [144][300/391] lr: 5.000000e-04 eta: 3:08:53 time: 0.486728 data_time: 0.041847 memory: 21657 loss_kpt: 0.000503 acc_pose: 0.890911 loss: 0.000503 2022/10/21 18:03:55 - mmengine - INFO - Epoch(train) [144][350/391] lr: 5.000000e-04 eta: 3:08:32 time: 0.485453 data_time: 0.041265 memory: 21657 loss_kpt: 0.000510 acc_pose: 0.829719 loss: 0.000510 2022/10/21 18:04:15 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:04:40 - mmengine - INFO - Epoch(train) [145][50/391] lr: 5.000000e-04 eta: 3:07:46 time: 0.499870 data_time: 0.052093 memory: 21657 loss_kpt: 0.000509 acc_pose: 0.885471 loss: 0.000509 2022/10/21 18:05:05 - mmengine - INFO - Epoch(train) [145][100/391] lr: 5.000000e-04 eta: 3:07:25 time: 0.494668 data_time: 0.040670 memory: 21657 loss_kpt: 0.000512 acc_pose: 0.883842 loss: 0.000512 2022/10/21 18:05:29 - mmengine - INFO - Epoch(train) [145][150/391] lr: 5.000000e-04 eta: 3:07:04 time: 0.482268 data_time: 0.040396 memory: 21657 loss_kpt: 0.000510 acc_pose: 0.873816 loss: 0.000510 2022/10/21 18:05:54 - mmengine - INFO - Epoch(train) [145][200/391] lr: 5.000000e-04 eta: 3:06:44 time: 0.493408 data_time: 0.043748 memory: 21657 loss_kpt: 0.000502 acc_pose: 0.841264 loss: 0.000502 2022/10/21 18:06:18 - mmengine - INFO - Epoch(train) [145][250/391] lr: 5.000000e-04 eta: 3:06:23 time: 0.480705 data_time: 0.042465 memory: 21657 loss_kpt: 0.000506 acc_pose: 0.824324 loss: 0.000506 2022/10/21 18:06:42 - mmengine - INFO - Epoch(train) [145][300/391] lr: 5.000000e-04 eta: 3:06:02 time: 0.491854 data_time: 0.040321 memory: 21657 loss_kpt: 0.000488 acc_pose: 0.905569 loss: 0.000488 2022/10/21 18:07:07 - mmengine - INFO - Epoch(train) [145][350/391] lr: 5.000000e-04 eta: 3:05:41 time: 0.485306 data_time: 0.039414 memory: 21657 loss_kpt: 0.000502 acc_pose: 0.908066 loss: 0.000502 2022/10/21 18:07:26 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:07:52 - mmengine - INFO - Epoch(train) [146][50/391] lr: 5.000000e-04 eta: 3:04:55 time: 0.503905 data_time: 0.050596 memory: 21657 loss_kpt: 0.000513 acc_pose: 0.881948 loss: 0.000513 2022/10/21 18:08:16 - mmengine - INFO - Epoch(train) [146][100/391] lr: 5.000000e-04 eta: 3:04:34 time: 0.483048 data_time: 0.044181 memory: 21657 loss_kpt: 0.000502 acc_pose: 0.868851 loss: 0.000502 2022/10/21 18:08:40 - mmengine - INFO - Epoch(train) [146][150/391] lr: 5.000000e-04 eta: 3:04:14 time: 0.493653 data_time: 0.040506 memory: 21657 loss_kpt: 0.000498 acc_pose: 0.847548 loss: 0.000498 2022/10/21 18:09:05 - mmengine - INFO - Epoch(train) [146][200/391] lr: 5.000000e-04 eta: 3:03:53 time: 0.488128 data_time: 0.044691 memory: 21657 loss_kpt: 0.000503 acc_pose: 0.849293 loss: 0.000503 2022/10/21 18:09:29 - mmengine - INFO - Epoch(train) [146][250/391] lr: 5.000000e-04 eta: 3:03:32 time: 0.489006 data_time: 0.040016 memory: 21657 loss_kpt: 0.000503 acc_pose: 0.862797 loss: 0.000503 2022/10/21 18:09:53 - mmengine - INFO - Epoch(train) [146][300/391] lr: 5.000000e-04 eta: 3:03:11 time: 0.480906 data_time: 0.040041 memory: 21657 loss_kpt: 0.000491 acc_pose: 0.897750 loss: 0.000491 2022/10/21 18:09:56 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:10:18 - mmengine - INFO - Epoch(train) [146][350/391] lr: 5.000000e-04 eta: 3:02:50 time: 0.488680 data_time: 0.043904 memory: 21657 loss_kpt: 0.000520 acc_pose: 0.888148 loss: 0.000520 2022/10/21 18:10:37 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:11:03 - mmengine - INFO - Epoch(train) [147][50/391] lr: 5.000000e-04 eta: 3:02:04 time: 0.510181 data_time: 0.054037 memory: 21657 loss_kpt: 0.000496 acc_pose: 0.822995 loss: 0.000496 2022/10/21 18:11:27 - mmengine - INFO - Epoch(train) [147][100/391] lr: 5.000000e-04 eta: 3:01:43 time: 0.486599 data_time: 0.040785 memory: 21657 loss_kpt: 0.000498 acc_pose: 0.889112 loss: 0.000498 2022/10/21 18:11:52 - mmengine - INFO - Epoch(train) [147][150/391] lr: 5.000000e-04 eta: 3:01:23 time: 0.488305 data_time: 0.039349 memory: 21657 loss_kpt: 0.000496 acc_pose: 0.890723 loss: 0.000496 2022/10/21 18:12:16 - mmengine - INFO - Epoch(train) [147][200/391] lr: 5.000000e-04 eta: 3:01:02 time: 0.490344 data_time: 0.041447 memory: 21657 loss_kpt: 0.000484 acc_pose: 0.872604 loss: 0.000484 2022/10/21 18:12:40 - mmengine - INFO - Epoch(train) [147][250/391] lr: 5.000000e-04 eta: 3:00:41 time: 0.486207 data_time: 0.044465 memory: 21657 loss_kpt: 0.000498 acc_pose: 0.910064 loss: 0.000498 2022/10/21 18:13:05 - mmengine - INFO - Epoch(train) [147][300/391] lr: 5.000000e-04 eta: 3:00:20 time: 0.490181 data_time: 0.041594 memory: 21657 loss_kpt: 0.000508 acc_pose: 0.883246 loss: 0.000508 2022/10/21 18:13:29 - mmengine - INFO - Epoch(train) [147][350/391] lr: 5.000000e-04 eta: 3:00:00 time: 0.483937 data_time: 0.041555 memory: 21657 loss_kpt: 0.000498 acc_pose: 0.867041 loss: 0.000498 2022/10/21 18:13:49 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:14:14 - mmengine - INFO - Epoch(train) [148][50/391] lr: 5.000000e-04 eta: 2:59:13 time: 0.496784 data_time: 0.052452 memory: 21657 loss_kpt: 0.000494 acc_pose: 0.878202 loss: 0.000494 2022/10/21 18:14:39 - mmengine - INFO - Epoch(train) [148][100/391] lr: 5.000000e-04 eta: 2:58:53 time: 0.491662 data_time: 0.044778 memory: 21657 loss_kpt: 0.000493 acc_pose: 0.835781 loss: 0.000493 2022/10/21 18:15:03 - mmengine - INFO - Epoch(train) [148][150/391] lr: 5.000000e-04 eta: 2:58:32 time: 0.482064 data_time: 0.039506 memory: 21657 loss_kpt: 0.000495 acc_pose: 0.827408 loss: 0.000495 2022/10/21 18:15:27 - mmengine - INFO - Epoch(train) [148][200/391] lr: 5.000000e-04 eta: 2:58:11 time: 0.489179 data_time: 0.042093 memory: 21657 loss_kpt: 0.000493 acc_pose: 0.874730 loss: 0.000493 2022/10/21 18:15:52 - mmengine - INFO - Epoch(train) [148][250/391] lr: 5.000000e-04 eta: 2:57:50 time: 0.491211 data_time: 0.041024 memory: 21657 loss_kpt: 0.000493 acc_pose: 0.899423 loss: 0.000493 2022/10/21 18:16:16 - mmengine - INFO - Epoch(train) [148][300/391] lr: 5.000000e-04 eta: 2:57:29 time: 0.487938 data_time: 0.045756 memory: 21657 loss_kpt: 0.000506 acc_pose: 0.913035 loss: 0.000506 2022/10/21 18:16:40 - mmengine - INFO - Epoch(train) [148][350/391] lr: 5.000000e-04 eta: 2:57:08 time: 0.484296 data_time: 0.039660 memory: 21657 loss_kpt: 0.000512 acc_pose: 0.883172 loss: 0.000512 2022/10/21 18:17:00 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:17:26 - mmengine - INFO - Epoch(train) [149][50/391] lr: 5.000000e-04 eta: 2:56:23 time: 0.511556 data_time: 0.053185 memory: 21657 loss_kpt: 0.000482 acc_pose: 0.919415 loss: 0.000482 2022/10/21 18:17:50 - mmengine - INFO - Epoch(train) [149][100/391] lr: 5.000000e-04 eta: 2:56:02 time: 0.482072 data_time: 0.039167 memory: 21657 loss_kpt: 0.000493 acc_pose: 0.921122 loss: 0.000493 2022/10/21 18:18:05 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:18:14 - mmengine - INFO - Epoch(train) [149][150/391] lr: 5.000000e-04 eta: 2:55:41 time: 0.489756 data_time: 0.039827 memory: 21657 loss_kpt: 0.000501 acc_pose: 0.902339 loss: 0.000501 2022/10/21 18:18:39 - mmengine - INFO - Epoch(train) [149][200/391] lr: 5.000000e-04 eta: 2:55:20 time: 0.486238 data_time: 0.040775 memory: 21657 loss_kpt: 0.000483 acc_pose: 0.891921 loss: 0.000483 2022/10/21 18:19:03 - mmengine - INFO - Epoch(train) [149][250/391] lr: 5.000000e-04 eta: 2:54:59 time: 0.486377 data_time: 0.040387 memory: 21657 loss_kpt: 0.000492 acc_pose: 0.899918 loss: 0.000492 2022/10/21 18:19:27 - mmengine - INFO - Epoch(train) [149][300/391] lr: 5.000000e-04 eta: 2:54:38 time: 0.485227 data_time: 0.040552 memory: 21657 loss_kpt: 0.000499 acc_pose: 0.885884 loss: 0.000499 2022/10/21 18:19:52 - mmengine - INFO - Epoch(train) [149][350/391] lr: 5.000000e-04 eta: 2:54:17 time: 0.485666 data_time: 0.044129 memory: 21657 loss_kpt: 0.000499 acc_pose: 0.875304 loss: 0.000499 2022/10/21 18:20:11 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:20:36 - mmengine - INFO - Epoch(train) [150][50/391] lr: 5.000000e-04 eta: 2:53:31 time: 0.500695 data_time: 0.054561 memory: 21657 loss_kpt: 0.000511 acc_pose: 0.831004 loss: 0.000511 2022/10/21 18:21:01 - mmengine - INFO - Epoch(train) [150][100/391] lr: 5.000000e-04 eta: 2:53:11 time: 0.488184 data_time: 0.040177 memory: 21657 loss_kpt: 0.000489 acc_pose: 0.890298 loss: 0.000489 2022/10/21 18:21:25 - mmengine - INFO - Epoch(train) [150][150/391] lr: 5.000000e-04 eta: 2:52:50 time: 0.486473 data_time: 0.046250 memory: 21657 loss_kpt: 0.000507 acc_pose: 0.904373 loss: 0.000507 2022/10/21 18:21:50 - mmengine - INFO - Epoch(train) [150][200/391] lr: 5.000000e-04 eta: 2:52:29 time: 0.489407 data_time: 0.040433 memory: 21657 loss_kpt: 0.000491 acc_pose: 0.892752 loss: 0.000491 2022/10/21 18:22:14 - mmengine - INFO - Epoch(train) [150][250/391] lr: 5.000000e-04 eta: 2:52:08 time: 0.485004 data_time: 0.040228 memory: 21657 loss_kpt: 0.000491 acc_pose: 0.923213 loss: 0.000491 2022/10/21 18:22:38 - mmengine - INFO - Epoch(train) [150][300/391] lr: 5.000000e-04 eta: 2:51:47 time: 0.491684 data_time: 0.040093 memory: 21657 loss_kpt: 0.000493 acc_pose: 0.855065 loss: 0.000493 2022/10/21 18:23:03 - mmengine - INFO - Epoch(train) [150][350/391] lr: 5.000000e-04 eta: 2:51:26 time: 0.484222 data_time: 0.040815 memory: 21657 loss_kpt: 0.000503 acc_pose: 0.877729 loss: 0.000503 2022/10/21 18:23:22 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:23:22 - mmengine - INFO - Saving checkpoint at 150 epochs 2022/10/21 18:23:34 - mmengine - INFO - Epoch(val) [150][50/407] eta: 0:00:57 time: 0.160298 data_time: 0.018248 memory: 21657 2022/10/21 18:23:42 - mmengine - INFO - Epoch(val) [150][100/407] eta: 0:00:46 time: 0.150293 data_time: 0.009078 memory: 2142 2022/10/21 18:23:49 - mmengine - INFO - Epoch(val) [150][150/407] eta: 0:00:38 time: 0.150329 data_time: 0.008892 memory: 2142 2022/10/21 18:23:57 - mmengine - INFO - Epoch(val) [150][200/407] eta: 0:00:31 time: 0.154025 data_time: 0.013128 memory: 2142 2022/10/21 18:24:04 - mmengine - INFO - Epoch(val) [150][250/407] eta: 0:00:23 time: 0.149226 data_time: 0.008865 memory: 2142 2022/10/21 18:24:12 - mmengine - INFO - Epoch(val) [150][300/407] eta: 0:00:16 time: 0.150260 data_time: 0.009291 memory: 2142 2022/10/21 18:24:20 - mmengine - INFO - Epoch(val) [150][350/407] eta: 0:00:08 time: 0.154749 data_time: 0.012335 memory: 2142 2022/10/21 18:24:27 - mmengine - INFO - Epoch(val) [150][400/407] eta: 0:00:01 time: 0.148794 data_time: 0.008973 memory: 2142 2022/10/21 18:25:03 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 18:25:17 - mmengine - INFO - Epoch(val) [150][407/407] coco/AP: 0.740797 coco/AP .5: 0.899478 coco/AP .75: 0.808146 coco/AP (M): 0.697733 coco/AP (L): 0.814840 coco/AR: 0.791278 coco/AR .5: 0.935611 coco/AR .75: 0.851543 coco/AR (M): 0.743158 coco/AR (L): 0.860535 2022/10/21 18:25:42 - mmengine - INFO - Epoch(train) [151][50/391] lr: 5.000000e-04 eta: 2:50:41 time: 0.504436 data_time: 0.055006 memory: 21657 loss_kpt: 0.000483 acc_pose: 0.914526 loss: 0.000483 2022/10/21 18:26:06 - mmengine - INFO - Epoch(train) [151][100/391] lr: 5.000000e-04 eta: 2:50:20 time: 0.483231 data_time: 0.041107 memory: 21657 loss_kpt: 0.000501 acc_pose: 0.896039 loss: 0.000501 2022/10/21 18:26:31 - mmengine - INFO - Epoch(train) [151][150/391] lr: 5.000000e-04 eta: 2:49:59 time: 0.492135 data_time: 0.046903 memory: 21657 loss_kpt: 0.000497 acc_pose: 0.885753 loss: 0.000497 2022/10/21 18:26:55 - mmengine - INFO - Epoch(train) [151][200/391] lr: 5.000000e-04 eta: 2:49:38 time: 0.483418 data_time: 0.042092 memory: 21657 loss_kpt: 0.000498 acc_pose: 0.895451 loss: 0.000498 2022/10/21 18:27:19 - mmengine - INFO - Epoch(train) [151][250/391] lr: 5.000000e-04 eta: 2:49:17 time: 0.486447 data_time: 0.045491 memory: 21657 loss_kpt: 0.000511 acc_pose: 0.844960 loss: 0.000511 2022/10/21 18:27:43 - mmengine - INFO - Epoch(train) [151][300/391] lr: 5.000000e-04 eta: 2:48:56 time: 0.482780 data_time: 0.042580 memory: 21657 loss_kpt: 0.000490 acc_pose: 0.798128 loss: 0.000490 2022/10/21 18:28:08 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:28:08 - mmengine - INFO - Epoch(train) [151][350/391] lr: 5.000000e-04 eta: 2:48:35 time: 0.489253 data_time: 0.043966 memory: 21657 loss_kpt: 0.000489 acc_pose: 0.869202 loss: 0.000489 2022/10/21 18:28:27 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:28:53 - mmengine - INFO - Epoch(train) [152][50/391] lr: 5.000000e-04 eta: 2:47:50 time: 0.505424 data_time: 0.053897 memory: 21657 loss_kpt: 0.000503 acc_pose: 0.876978 loss: 0.000503 2022/10/21 18:29:17 - mmengine - INFO - Epoch(train) [152][100/391] lr: 5.000000e-04 eta: 2:47:29 time: 0.486469 data_time: 0.041659 memory: 21657 loss_kpt: 0.000497 acc_pose: 0.897354 loss: 0.000497 2022/10/21 18:29:41 - mmengine - INFO - Epoch(train) [152][150/391] lr: 5.000000e-04 eta: 2:47:08 time: 0.490197 data_time: 0.045938 memory: 21657 loss_kpt: 0.000498 acc_pose: 0.896483 loss: 0.000498 2022/10/21 18:30:06 - mmengine - INFO - Epoch(train) [152][200/391] lr: 5.000000e-04 eta: 2:46:47 time: 0.490276 data_time: 0.042473 memory: 21657 loss_kpt: 0.000486 acc_pose: 0.862520 loss: 0.000486 2022/10/21 18:30:30 - mmengine - INFO - Epoch(train) [152][250/391] lr: 5.000000e-04 eta: 2:46:26 time: 0.489037 data_time: 0.039665 memory: 21657 loss_kpt: 0.000511 acc_pose: 0.877667 loss: 0.000511 2022/10/21 18:30:55 - mmengine - INFO - Epoch(train) [152][300/391] lr: 5.000000e-04 eta: 2:46:05 time: 0.491365 data_time: 0.040346 memory: 21657 loss_kpt: 0.000499 acc_pose: 0.892070 loss: 0.000499 2022/10/21 18:31:19 - mmengine - INFO - Epoch(train) [152][350/391] lr: 5.000000e-04 eta: 2:45:44 time: 0.483972 data_time: 0.040496 memory: 21657 loss_kpt: 0.000506 acc_pose: 0.855306 loss: 0.000506 2022/10/21 18:31:39 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:32:04 - mmengine - INFO - Epoch(train) [153][50/391] lr: 5.000000e-04 eta: 2:44:59 time: 0.503091 data_time: 0.053185 memory: 21657 loss_kpt: 0.000494 acc_pose: 0.925350 loss: 0.000494 2022/10/21 18:32:29 - mmengine - INFO - Epoch(train) [153][100/391] lr: 5.000000e-04 eta: 2:44:38 time: 0.494454 data_time: 0.040796 memory: 21657 loss_kpt: 0.000489 acc_pose: 0.889501 loss: 0.000489 2022/10/21 18:32:53 - mmengine - INFO - Epoch(train) [153][150/391] lr: 5.000000e-04 eta: 2:44:17 time: 0.479791 data_time: 0.040017 memory: 21657 loss_kpt: 0.000504 acc_pose: 0.892063 loss: 0.000504 2022/10/21 18:33:18 - mmengine - INFO - Epoch(train) [153][200/391] lr: 5.000000e-04 eta: 2:43:56 time: 0.493731 data_time: 0.041179 memory: 21657 loss_kpt: 0.000512 acc_pose: 0.900468 loss: 0.000512 2022/10/21 18:33:42 - mmengine - INFO - Epoch(train) [153][250/391] lr: 5.000000e-04 eta: 2:43:35 time: 0.483199 data_time: 0.041176 memory: 21657 loss_kpt: 0.000487 acc_pose: 0.868837 loss: 0.000487 2022/10/21 18:34:07 - mmengine - INFO - Epoch(train) [153][300/391] lr: 5.000000e-04 eta: 2:43:14 time: 0.492667 data_time: 0.043619 memory: 21657 loss_kpt: 0.000494 acc_pose: 0.861919 loss: 0.000494 2022/10/21 18:34:31 - mmengine - INFO - Epoch(train) [153][350/391] lr: 5.000000e-04 eta: 2:42:53 time: 0.486400 data_time: 0.039342 memory: 21657 loss_kpt: 0.000492 acc_pose: 0.920083 loss: 0.000492 2022/10/21 18:34:50 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:35:16 - mmengine - INFO - Epoch(train) [154][50/391] lr: 5.000000e-04 eta: 2:42:08 time: 0.509755 data_time: 0.055720 memory: 21657 loss_kpt: 0.000491 acc_pose: 0.931132 loss: 0.000491 2022/10/21 18:35:40 - mmengine - INFO - Epoch(train) [154][100/391] lr: 5.000000e-04 eta: 2:41:47 time: 0.486956 data_time: 0.040024 memory: 21657 loss_kpt: 0.000492 acc_pose: 0.896571 loss: 0.000492 2022/10/21 18:36:05 - mmengine - INFO - Epoch(train) [154][150/391] lr: 5.000000e-04 eta: 2:41:26 time: 0.493431 data_time: 0.045084 memory: 21657 loss_kpt: 0.000494 acc_pose: 0.831776 loss: 0.000494 2022/10/21 18:36:18 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:36:29 - mmengine - INFO - Epoch(train) [154][200/391] lr: 5.000000e-04 eta: 2:41:05 time: 0.486774 data_time: 0.040365 memory: 21657 loss_kpt: 0.000492 acc_pose: 0.922880 loss: 0.000492 2022/10/21 18:36:54 - mmengine - INFO - Epoch(train) [154][250/391] lr: 5.000000e-04 eta: 2:40:44 time: 0.487809 data_time: 0.039726 memory: 21657 loss_kpt: 0.000495 acc_pose: 0.869712 loss: 0.000495 2022/10/21 18:37:18 - mmengine - INFO - Epoch(train) [154][300/391] lr: 5.000000e-04 eta: 2:40:23 time: 0.488632 data_time: 0.038687 memory: 21657 loss_kpt: 0.000501 acc_pose: 0.871529 loss: 0.000501 2022/10/21 18:37:43 - mmengine - INFO - Epoch(train) [154][350/391] lr: 5.000000e-04 eta: 2:40:03 time: 0.495879 data_time: 0.040615 memory: 21657 loss_kpt: 0.000488 acc_pose: 0.871632 loss: 0.000488 2022/10/21 18:38:03 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:38:28 - mmengine - INFO - Epoch(train) [155][50/391] lr: 5.000000e-04 eta: 2:39:17 time: 0.500712 data_time: 0.051139 memory: 21657 loss_kpt: 0.000490 acc_pose: 0.858693 loss: 0.000490 2022/10/21 18:38:52 - mmengine - INFO - Epoch(train) [155][100/391] lr: 5.000000e-04 eta: 2:38:56 time: 0.489661 data_time: 0.043515 memory: 21657 loss_kpt: 0.000489 acc_pose: 0.873999 loss: 0.000489 2022/10/21 18:39:16 - mmengine - INFO - Epoch(train) [155][150/391] lr: 5.000000e-04 eta: 2:38:35 time: 0.479071 data_time: 0.040007 memory: 21657 loss_kpt: 0.000498 acc_pose: 0.834325 loss: 0.000498 2022/10/21 18:39:41 - mmengine - INFO - Epoch(train) [155][200/391] lr: 5.000000e-04 eta: 2:38:14 time: 0.492081 data_time: 0.045084 memory: 21657 loss_kpt: 0.000494 acc_pose: 0.864459 loss: 0.000494 2022/10/21 18:40:05 - mmengine - INFO - Epoch(train) [155][250/391] lr: 5.000000e-04 eta: 2:37:53 time: 0.485658 data_time: 0.039709 memory: 21657 loss_kpt: 0.000495 acc_pose: 0.873486 loss: 0.000495 2022/10/21 18:40:30 - mmengine - INFO - Epoch(train) [155][300/391] lr: 5.000000e-04 eta: 2:37:33 time: 0.497431 data_time: 0.039818 memory: 21657 loss_kpt: 0.000507 acc_pose: 0.920369 loss: 0.000507 2022/10/21 18:40:54 - mmengine - INFO - Epoch(train) [155][350/391] lr: 5.000000e-04 eta: 2:37:11 time: 0.485343 data_time: 0.039845 memory: 21657 loss_kpt: 0.000484 acc_pose: 0.914266 loss: 0.000484 2022/10/21 18:41:14 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:41:39 - mmengine - INFO - Epoch(train) [156][50/391] lr: 5.000000e-04 eta: 2:36:26 time: 0.496022 data_time: 0.051371 memory: 21657 loss_kpt: 0.000495 acc_pose: 0.889925 loss: 0.000495 2022/10/21 18:42:02 - mmengine - INFO - Epoch(train) [156][100/391] lr: 5.000000e-04 eta: 2:36:05 time: 0.477717 data_time: 0.040276 memory: 21657 loss_kpt: 0.000488 acc_pose: 0.889615 loss: 0.000488 2022/10/21 18:42:28 - mmengine - INFO - Epoch(train) [156][150/391] lr: 5.000000e-04 eta: 2:35:44 time: 0.501218 data_time: 0.044893 memory: 21657 loss_kpt: 0.000496 acc_pose: 0.858945 loss: 0.000496 2022/10/21 18:42:51 - mmengine - INFO - Epoch(train) [156][200/391] lr: 5.000000e-04 eta: 2:35:23 time: 0.477382 data_time: 0.040768 memory: 21657 loss_kpt: 0.000496 acc_pose: 0.874095 loss: 0.000496 2022/10/21 18:43:16 - mmengine - INFO - Epoch(train) [156][250/391] lr: 5.000000e-04 eta: 2:35:02 time: 0.492400 data_time: 0.044888 memory: 21657 loss_kpt: 0.000485 acc_pose: 0.845774 loss: 0.000485 2022/10/21 18:43:40 - mmengine - INFO - Epoch(train) [156][300/391] lr: 5.000000e-04 eta: 2:34:41 time: 0.478608 data_time: 0.040444 memory: 21657 loss_kpt: 0.000486 acc_pose: 0.853145 loss: 0.000486 2022/10/21 18:44:05 - mmengine - INFO - Epoch(train) [156][350/391] lr: 5.000000e-04 eta: 2:34:20 time: 0.494570 data_time: 0.040432 memory: 21657 loss_kpt: 0.000477 acc_pose: 0.903344 loss: 0.000477 2022/10/21 18:44:24 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:44:27 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:44:49 - mmengine - INFO - Epoch(train) [157][50/391] lr: 5.000000e-04 eta: 2:33:35 time: 0.500425 data_time: 0.057230 memory: 21657 loss_kpt: 0.000485 acc_pose: 0.881704 loss: 0.000485 2022/10/21 18:45:14 - mmengine - INFO - Epoch(train) [157][100/391] lr: 5.000000e-04 eta: 2:33:14 time: 0.488420 data_time: 0.039934 memory: 21657 loss_kpt: 0.000470 acc_pose: 0.925668 loss: 0.000470 2022/10/21 18:45:38 - mmengine - INFO - Epoch(train) [157][150/391] lr: 5.000000e-04 eta: 2:32:53 time: 0.486740 data_time: 0.040773 memory: 21657 loss_kpt: 0.000498 acc_pose: 0.896658 loss: 0.000498 2022/10/21 18:46:03 - mmengine - INFO - Epoch(train) [157][200/391] lr: 5.000000e-04 eta: 2:32:32 time: 0.488436 data_time: 0.040033 memory: 21657 loss_kpt: 0.000490 acc_pose: 0.875167 loss: 0.000490 2022/10/21 18:46:27 - mmengine - INFO - Epoch(train) [157][250/391] lr: 5.000000e-04 eta: 2:32:11 time: 0.484978 data_time: 0.040496 memory: 21657 loss_kpt: 0.000503 acc_pose: 0.898301 loss: 0.000503 2022/10/21 18:46:51 - mmengine - INFO - Epoch(train) [157][300/391] lr: 5.000000e-04 eta: 2:31:50 time: 0.490979 data_time: 0.043859 memory: 21657 loss_kpt: 0.000485 acc_pose: 0.882361 loss: 0.000485 2022/10/21 18:47:15 - mmengine - INFO - Epoch(train) [157][350/391] lr: 5.000000e-04 eta: 2:31:29 time: 0.480853 data_time: 0.040931 memory: 21657 loss_kpt: 0.000484 acc_pose: 0.888732 loss: 0.000484 2022/10/21 18:47:36 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:48:01 - mmengine - INFO - Epoch(train) [158][50/391] lr: 5.000000e-04 eta: 2:30:44 time: 0.501582 data_time: 0.049665 memory: 21657 loss_kpt: 0.000490 acc_pose: 0.893095 loss: 0.000490 2022/10/21 18:48:25 - mmengine - INFO - Epoch(train) [158][100/391] lr: 5.000000e-04 eta: 2:30:23 time: 0.487300 data_time: 0.043102 memory: 21657 loss_kpt: 0.000483 acc_pose: 0.883408 loss: 0.000483 2022/10/21 18:48:49 - mmengine - INFO - Epoch(train) [158][150/391] lr: 5.000000e-04 eta: 2:30:02 time: 0.487112 data_time: 0.040006 memory: 21657 loss_kpt: 0.000479 acc_pose: 0.887040 loss: 0.000479 2022/10/21 18:49:14 - mmengine - INFO - Epoch(train) [158][200/391] lr: 5.000000e-04 eta: 2:29:41 time: 0.487559 data_time: 0.039150 memory: 21657 loss_kpt: 0.000485 acc_pose: 0.896338 loss: 0.000485 2022/10/21 18:49:38 - mmengine - INFO - Epoch(train) [158][250/391] lr: 5.000000e-04 eta: 2:29:20 time: 0.483352 data_time: 0.039476 memory: 21657 loss_kpt: 0.000489 acc_pose: 0.876567 loss: 0.000489 2022/10/21 18:50:02 - mmengine - INFO - Epoch(train) [158][300/391] lr: 5.000000e-04 eta: 2:28:59 time: 0.489196 data_time: 0.040735 memory: 21657 loss_kpt: 0.000507 acc_pose: 0.883599 loss: 0.000507 2022/10/21 18:50:27 - mmengine - INFO - Epoch(train) [158][350/391] lr: 5.000000e-04 eta: 2:28:38 time: 0.483434 data_time: 0.043340 memory: 21657 loss_kpt: 0.000489 acc_pose: 0.874161 loss: 0.000489 2022/10/21 18:50:46 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:51:12 - mmengine - INFO - Epoch(train) [159][50/391] lr: 5.000000e-04 eta: 2:27:53 time: 0.512933 data_time: 0.059685 memory: 21657 loss_kpt: 0.000495 acc_pose: 0.886116 loss: 0.000495 2022/10/21 18:51:36 - mmengine - INFO - Epoch(train) [159][100/391] lr: 5.000000e-04 eta: 2:27:32 time: 0.482647 data_time: 0.040294 memory: 21657 loss_kpt: 0.000486 acc_pose: 0.861289 loss: 0.000486 2022/10/21 18:52:00 - mmengine - INFO - Epoch(train) [159][150/391] lr: 5.000000e-04 eta: 2:27:11 time: 0.490096 data_time: 0.044465 memory: 21657 loss_kpt: 0.000495 acc_pose: 0.873133 loss: 0.000495 2022/10/21 18:52:25 - mmengine - INFO - Epoch(train) [159][200/391] lr: 5.000000e-04 eta: 2:26:50 time: 0.485299 data_time: 0.039781 memory: 21657 loss_kpt: 0.000486 acc_pose: 0.881801 loss: 0.000486 2022/10/21 18:52:35 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:52:49 - mmengine - INFO - Epoch(train) [159][250/391] lr: 5.000000e-04 eta: 2:26:29 time: 0.486049 data_time: 0.044622 memory: 21657 loss_kpt: 0.000491 acc_pose: 0.869591 loss: 0.000491 2022/10/21 18:53:13 - mmengine - INFO - Epoch(train) [159][300/391] lr: 5.000000e-04 eta: 2:26:08 time: 0.487279 data_time: 0.040481 memory: 21657 loss_kpt: 0.000490 acc_pose: 0.859070 loss: 0.000490 2022/10/21 18:53:38 - mmengine - INFO - Epoch(train) [159][350/391] lr: 5.000000e-04 eta: 2:25:47 time: 0.490046 data_time: 0.042386 memory: 21657 loss_kpt: 0.000489 acc_pose: 0.853375 loss: 0.000489 2022/10/21 18:53:57 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:54:22 - mmengine - INFO - Epoch(train) [160][50/391] lr: 5.000000e-04 eta: 2:25:02 time: 0.497507 data_time: 0.056957 memory: 21657 loss_kpt: 0.000488 acc_pose: 0.901136 loss: 0.000488 2022/10/21 18:54:47 - mmengine - INFO - Epoch(train) [160][100/391] lr: 5.000000e-04 eta: 2:24:41 time: 0.492372 data_time: 0.043604 memory: 21657 loss_kpt: 0.000471 acc_pose: 0.857407 loss: 0.000471 2022/10/21 18:55:11 - mmengine - INFO - Epoch(train) [160][150/391] lr: 5.000000e-04 eta: 2:24:20 time: 0.481790 data_time: 0.040666 memory: 21657 loss_kpt: 0.000487 acc_pose: 0.879023 loss: 0.000487 2022/10/21 18:55:35 - mmengine - INFO - Epoch(train) [160][200/391] lr: 5.000000e-04 eta: 2:23:59 time: 0.491529 data_time: 0.043288 memory: 21657 loss_kpt: 0.000486 acc_pose: 0.915522 loss: 0.000486 2022/10/21 18:56:00 - mmengine - INFO - Epoch(train) [160][250/391] lr: 5.000000e-04 eta: 2:23:38 time: 0.484163 data_time: 0.040908 memory: 21657 loss_kpt: 0.000487 acc_pose: 0.893600 loss: 0.000487 2022/10/21 18:56:24 - mmengine - INFO - Epoch(train) [160][300/391] lr: 5.000000e-04 eta: 2:23:17 time: 0.487083 data_time: 0.044084 memory: 21657 loss_kpt: 0.000493 acc_pose: 0.895107 loss: 0.000493 2022/10/21 18:56:48 - mmengine - INFO - Epoch(train) [160][350/391] lr: 5.000000e-04 eta: 2:22:56 time: 0.481459 data_time: 0.039122 memory: 21657 loss_kpt: 0.000490 acc_pose: 0.907138 loss: 0.000490 2022/10/21 18:57:08 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 18:57:08 - mmengine - INFO - Saving checkpoint at 160 epochs 2022/10/21 18:57:20 - mmengine - INFO - Epoch(val) [160][50/407] eta: 0:00:55 time: 0.156839 data_time: 0.014204 memory: 21657 2022/10/21 18:57:27 - mmengine - INFO - Epoch(val) [160][100/407] eta: 0:00:46 time: 0.152273 data_time: 0.009287 memory: 2142 2022/10/21 18:57:35 - mmengine - INFO - Epoch(val) [160][150/407] eta: 0:00:39 time: 0.152749 data_time: 0.009851 memory: 2142 2022/10/21 18:57:42 - mmengine - INFO - Epoch(val) [160][200/407] eta: 0:00:31 time: 0.151587 data_time: 0.009296 memory: 2142 2022/10/21 18:57:50 - mmengine - INFO - Epoch(val) [160][250/407] eta: 0:00:24 time: 0.156383 data_time: 0.010651 memory: 2142 2022/10/21 18:57:58 - mmengine - INFO - Epoch(val) [160][300/407] eta: 0:00:16 time: 0.150027 data_time: 0.009047 memory: 2142 2022/10/21 18:58:05 - mmengine - INFO - Epoch(val) [160][350/407] eta: 0:00:08 time: 0.150557 data_time: 0.009540 memory: 2142 2022/10/21 18:58:13 - mmengine - INFO - Epoch(val) [160][400/407] eta: 0:00:01 time: 0.149126 data_time: 0.008590 memory: 2142 2022/10/21 18:58:48 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 18:59:02 - mmengine - INFO - Epoch(val) [160][407/407] coco/AP: 0.742305 coco/AP .5: 0.900733 coco/AP .75: 0.812202 coco/AP (M): 0.699674 coco/AP (L): 0.815280 coco/AR: 0.793215 coco/AR .5: 0.937343 coco/AR .75: 0.855006 coco/AR (M): 0.747228 coco/AR (L): 0.859978 2022/10/21 18:59:27 - mmengine - INFO - Epoch(train) [161][50/391] lr: 5.000000e-04 eta: 2:22:11 time: 0.499931 data_time: 0.052241 memory: 21657 loss_kpt: 0.000497 acc_pose: 0.875038 loss: 0.000497 2022/10/21 18:59:51 - mmengine - INFO - Epoch(train) [161][100/391] lr: 5.000000e-04 eta: 2:21:50 time: 0.486210 data_time: 0.040853 memory: 21657 loss_kpt: 0.000484 acc_pose: 0.927684 loss: 0.000484 2022/10/21 19:00:16 - mmengine - INFO - Epoch(train) [161][150/391] lr: 5.000000e-04 eta: 2:21:29 time: 0.484971 data_time: 0.040542 memory: 21657 loss_kpt: 0.000478 acc_pose: 0.927350 loss: 0.000478 2022/10/21 19:00:40 - mmengine - INFO - Epoch(train) [161][200/391] lr: 5.000000e-04 eta: 2:21:08 time: 0.487578 data_time: 0.043574 memory: 21657 loss_kpt: 0.000486 acc_pose: 0.909263 loss: 0.000486 2022/10/21 19:01:04 - mmengine - INFO - Epoch(train) [161][250/391] lr: 5.000000e-04 eta: 2:20:47 time: 0.489032 data_time: 0.040661 memory: 21657 loss_kpt: 0.000486 acc_pose: 0.849541 loss: 0.000486 2022/10/21 19:01:28 - mmengine - INFO - Epoch(train) [161][300/391] lr: 5.000000e-04 eta: 2:20:25 time: 0.480319 data_time: 0.044417 memory: 21657 loss_kpt: 0.000484 acc_pose: 0.907173 loss: 0.000484 2022/10/21 19:01:53 - mmengine - INFO - Epoch(train) [161][350/391] lr: 5.000000e-04 eta: 2:20:04 time: 0.497728 data_time: 0.039583 memory: 21657 loss_kpt: 0.000484 acc_pose: 0.889592 loss: 0.000484 2022/10/21 19:02:13 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:02:38 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:02:38 - mmengine - INFO - Epoch(train) [162][50/391] lr: 5.000000e-04 eta: 2:19:20 time: 0.507776 data_time: 0.054527 memory: 21657 loss_kpt: 0.000490 acc_pose: 0.914242 loss: 0.000490 2022/10/21 19:03:02 - mmengine - INFO - Epoch(train) [162][100/391] lr: 5.000000e-04 eta: 2:18:59 time: 0.484479 data_time: 0.041705 memory: 21657 loss_kpt: 0.000482 acc_pose: 0.879596 loss: 0.000482 2022/10/21 19:03:27 - mmengine - INFO - Epoch(train) [162][150/391] lr: 5.000000e-04 eta: 2:18:38 time: 0.490705 data_time: 0.043955 memory: 21657 loss_kpt: 0.000489 acc_pose: 0.872455 loss: 0.000489 2022/10/21 19:03:51 - mmengine - INFO - Epoch(train) [162][200/391] lr: 5.000000e-04 eta: 2:18:17 time: 0.488360 data_time: 0.040899 memory: 21657 loss_kpt: 0.000485 acc_pose: 0.893566 loss: 0.000485 2022/10/21 19:04:16 - mmengine - INFO - Epoch(train) [162][250/391] lr: 5.000000e-04 eta: 2:17:56 time: 0.486663 data_time: 0.045212 memory: 21657 loss_kpt: 0.000474 acc_pose: 0.926362 loss: 0.000474 2022/10/21 19:04:40 - mmengine - INFO - Epoch(train) [162][300/391] lr: 5.000000e-04 eta: 2:17:35 time: 0.489712 data_time: 0.041287 memory: 21657 loss_kpt: 0.000481 acc_pose: 0.819200 loss: 0.000481 2022/10/21 19:05:05 - mmengine - INFO - Epoch(train) [162][350/391] lr: 5.000000e-04 eta: 2:17:13 time: 0.484708 data_time: 0.041146 memory: 21657 loss_kpt: 0.000487 acc_pose: 0.910544 loss: 0.000487 2022/10/21 19:05:24 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:05:49 - mmengine - INFO - Epoch(train) [163][50/391] lr: 5.000000e-04 eta: 2:16:29 time: 0.497243 data_time: 0.055861 memory: 21657 loss_kpt: 0.000479 acc_pose: 0.879232 loss: 0.000479 2022/10/21 19:06:14 - mmengine - INFO - Epoch(train) [163][100/391] lr: 5.000000e-04 eta: 2:16:08 time: 0.492873 data_time: 0.041222 memory: 21657 loss_kpt: 0.000484 acc_pose: 0.872859 loss: 0.000484 2022/10/21 19:06:38 - mmengine - INFO - Epoch(train) [163][150/391] lr: 5.000000e-04 eta: 2:15:47 time: 0.487922 data_time: 0.040427 memory: 21657 loss_kpt: 0.000482 acc_pose: 0.841681 loss: 0.000482 2022/10/21 19:07:03 - mmengine - INFO - Epoch(train) [163][200/391] lr: 5.000000e-04 eta: 2:15:26 time: 0.489871 data_time: 0.045215 memory: 21657 loss_kpt: 0.000466 acc_pose: 0.898410 loss: 0.000466 2022/10/21 19:07:27 - mmengine - INFO - Epoch(train) [163][250/391] lr: 5.000000e-04 eta: 2:15:05 time: 0.484291 data_time: 0.040274 memory: 21657 loss_kpt: 0.000475 acc_pose: 0.908104 loss: 0.000475 2022/10/21 19:07:51 - mmengine - INFO - Epoch(train) [163][300/391] lr: 5.000000e-04 eta: 2:14:43 time: 0.485433 data_time: 0.043280 memory: 21657 loss_kpt: 0.000482 acc_pose: 0.925539 loss: 0.000482 2022/10/21 19:08:16 - mmengine - INFO - Epoch(train) [163][350/391] lr: 5.000000e-04 eta: 2:14:22 time: 0.492330 data_time: 0.040759 memory: 21657 loss_kpt: 0.000490 acc_pose: 0.817774 loss: 0.000490 2022/10/21 19:08:35 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:09:01 - mmengine - INFO - Epoch(train) [164][50/391] lr: 5.000000e-04 eta: 2:13:38 time: 0.504135 data_time: 0.053113 memory: 21657 loss_kpt: 0.000484 acc_pose: 0.890472 loss: 0.000484 2022/10/21 19:09:25 - mmengine - INFO - Epoch(train) [164][100/391] lr: 5.000000e-04 eta: 2:13:17 time: 0.481089 data_time: 0.040392 memory: 21657 loss_kpt: 0.000481 acc_pose: 0.886607 loss: 0.000481 2022/10/21 19:09:50 - mmengine - INFO - Epoch(train) [164][150/391] lr: 5.000000e-04 eta: 2:12:56 time: 0.496883 data_time: 0.041653 memory: 21657 loss_kpt: 0.000483 acc_pose: 0.859429 loss: 0.000483 2022/10/21 19:10:14 - mmengine - INFO - Epoch(train) [164][200/391] lr: 5.000000e-04 eta: 2:12:35 time: 0.482373 data_time: 0.041194 memory: 21657 loss_kpt: 0.000480 acc_pose: 0.871867 loss: 0.000480 2022/10/21 19:10:38 - mmengine - INFO - Epoch(train) [164][250/391] lr: 5.000000e-04 eta: 2:12:14 time: 0.487683 data_time: 0.043838 memory: 21657 loss_kpt: 0.000487 acc_pose: 0.819590 loss: 0.000487 2022/10/21 19:10:46 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:11:03 - mmengine - INFO - Epoch(train) [164][300/391] lr: 5.000000e-04 eta: 2:11:52 time: 0.488808 data_time: 0.041400 memory: 21657 loss_kpt: 0.000488 acc_pose: 0.892716 loss: 0.000488 2022/10/21 19:11:27 - mmengine - INFO - Epoch(train) [164][350/391] lr: 5.000000e-04 eta: 2:11:31 time: 0.492631 data_time: 0.046632 memory: 21657 loss_kpt: 0.000494 acc_pose: 0.856234 loss: 0.000494 2022/10/21 19:11:47 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:12:12 - mmengine - INFO - Epoch(train) [165][50/391] lr: 5.000000e-04 eta: 2:10:47 time: 0.499936 data_time: 0.060525 memory: 21657 loss_kpt: 0.000493 acc_pose: 0.877651 loss: 0.000493 2022/10/21 19:12:36 - mmengine - INFO - Epoch(train) [165][100/391] lr: 5.000000e-04 eta: 2:10:26 time: 0.489713 data_time: 0.044152 memory: 21657 loss_kpt: 0.000481 acc_pose: 0.909227 loss: 0.000481 2022/10/21 19:13:01 - mmengine - INFO - Epoch(train) [165][150/391] lr: 5.000000e-04 eta: 2:10:05 time: 0.485806 data_time: 0.047148 memory: 21657 loss_kpt: 0.000481 acc_pose: 0.830526 loss: 0.000481 2022/10/21 19:13:25 - mmengine - INFO - Epoch(train) [165][200/391] lr: 5.000000e-04 eta: 2:09:44 time: 0.490359 data_time: 0.041293 memory: 21657 loss_kpt: 0.000496 acc_pose: 0.866627 loss: 0.000496 2022/10/21 19:13:50 - mmengine - INFO - Epoch(train) [165][250/391] lr: 5.000000e-04 eta: 2:09:23 time: 0.488431 data_time: 0.044598 memory: 21657 loss_kpt: 0.000482 acc_pose: 0.890761 loss: 0.000482 2022/10/21 19:14:14 - mmengine - INFO - Epoch(train) [165][300/391] lr: 5.000000e-04 eta: 2:09:01 time: 0.488713 data_time: 0.039367 memory: 21657 loss_kpt: 0.000477 acc_pose: 0.903016 loss: 0.000477 2022/10/21 19:14:38 - mmengine - INFO - Epoch(train) [165][350/391] lr: 5.000000e-04 eta: 2:08:40 time: 0.485185 data_time: 0.040426 memory: 21657 loss_kpt: 0.000476 acc_pose: 0.866993 loss: 0.000476 2022/10/21 19:14:58 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:15:23 - mmengine - INFO - Epoch(train) [166][50/391] lr: 5.000000e-04 eta: 2:07:56 time: 0.503096 data_time: 0.052138 memory: 21657 loss_kpt: 0.000484 acc_pose: 0.892639 loss: 0.000484 2022/10/21 19:15:47 - mmengine - INFO - Epoch(train) [166][100/391] lr: 5.000000e-04 eta: 2:07:35 time: 0.486509 data_time: 0.046015 memory: 21657 loss_kpt: 0.000480 acc_pose: 0.848799 loss: 0.000480 2022/10/21 19:16:12 - mmengine - INFO - Epoch(train) [166][150/391] lr: 5.000000e-04 eta: 2:07:14 time: 0.485565 data_time: 0.040016 memory: 21657 loss_kpt: 0.000481 acc_pose: 0.886119 loss: 0.000481 2022/10/21 19:16:36 - mmengine - INFO - Epoch(train) [166][200/391] lr: 5.000000e-04 eta: 2:06:53 time: 0.490394 data_time: 0.044190 memory: 21657 loss_kpt: 0.000470 acc_pose: 0.886127 loss: 0.000470 2022/10/21 19:17:00 - mmengine - INFO - Epoch(train) [166][250/391] lr: 5.000000e-04 eta: 2:06:32 time: 0.485700 data_time: 0.039116 memory: 21657 loss_kpt: 0.000493 acc_pose: 0.886440 loss: 0.000493 2022/10/21 19:17:25 - mmengine - INFO - Epoch(train) [166][300/391] lr: 5.000000e-04 eta: 2:06:10 time: 0.492196 data_time: 0.042455 memory: 21657 loss_kpt: 0.000490 acc_pose: 0.853926 loss: 0.000490 2022/10/21 19:17:49 - mmengine - INFO - Epoch(train) [166][350/391] lr: 5.000000e-04 eta: 2:05:49 time: 0.484161 data_time: 0.039981 memory: 21657 loss_kpt: 0.000484 acc_pose: 0.865228 loss: 0.000484 2022/10/21 19:18:09 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:18:34 - mmengine - INFO - Epoch(train) [167][50/391] lr: 5.000000e-04 eta: 2:05:05 time: 0.499499 data_time: 0.055095 memory: 21657 loss_kpt: 0.000480 acc_pose: 0.891497 loss: 0.000480 2022/10/21 19:18:55 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:18:58 - mmengine - INFO - Epoch(train) [167][100/391] lr: 5.000000e-04 eta: 2:04:44 time: 0.489885 data_time: 0.040279 memory: 21657 loss_kpt: 0.000473 acc_pose: 0.878029 loss: 0.000473 2022/10/21 19:19:23 - mmengine - INFO - Epoch(train) [167][150/391] lr: 5.000000e-04 eta: 2:04:23 time: 0.485996 data_time: 0.042022 memory: 21657 loss_kpt: 0.000483 acc_pose: 0.913165 loss: 0.000483 2022/10/21 19:19:47 - mmengine - INFO - Epoch(train) [167][200/391] lr: 5.000000e-04 eta: 2:04:02 time: 0.486888 data_time: 0.040891 memory: 21657 loss_kpt: 0.000498 acc_pose: 0.827163 loss: 0.000498 2022/10/21 19:20:11 - mmengine - INFO - Epoch(train) [167][250/391] lr: 5.000000e-04 eta: 2:03:40 time: 0.486112 data_time: 0.043290 memory: 21657 loss_kpt: 0.000492 acc_pose: 0.856849 loss: 0.000492 2022/10/21 19:20:36 - mmengine - INFO - Epoch(train) [167][300/391] lr: 5.000000e-04 eta: 2:03:19 time: 0.487766 data_time: 0.040304 memory: 21657 loss_kpt: 0.000477 acc_pose: 0.899533 loss: 0.000477 2022/10/21 19:21:00 - mmengine - INFO - Epoch(train) [167][350/391] lr: 5.000000e-04 eta: 2:02:58 time: 0.480746 data_time: 0.040885 memory: 21657 loss_kpt: 0.000495 acc_pose: 0.898567 loss: 0.000495 2022/10/21 19:21:20 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:21:45 - mmengine - INFO - Epoch(train) [168][50/391] lr: 5.000000e-04 eta: 2:02:14 time: 0.504905 data_time: 0.054702 memory: 21657 loss_kpt: 0.000482 acc_pose: 0.901636 loss: 0.000482 2022/10/21 19:22:09 - mmengine - INFO - Epoch(train) [168][100/391] lr: 5.000000e-04 eta: 2:01:53 time: 0.484781 data_time: 0.040489 memory: 21657 loss_kpt: 0.000472 acc_pose: 0.906368 loss: 0.000472 2022/10/21 19:22:33 - mmengine - INFO - Epoch(train) [168][150/391] lr: 5.000000e-04 eta: 2:01:32 time: 0.482753 data_time: 0.040562 memory: 21657 loss_kpt: 0.000474 acc_pose: 0.888721 loss: 0.000474 2022/10/21 19:22:58 - mmengine - INFO - Epoch(train) [168][200/391] lr: 5.000000e-04 eta: 2:01:10 time: 0.487806 data_time: 0.044661 memory: 21657 loss_kpt: 0.000471 acc_pose: 0.897386 loss: 0.000471 2022/10/21 19:23:22 - mmengine - INFO - Epoch(train) [168][250/391] lr: 5.000000e-04 eta: 2:00:49 time: 0.482537 data_time: 0.040705 memory: 21657 loss_kpt: 0.000478 acc_pose: 0.873888 loss: 0.000478 2022/10/21 19:23:46 - mmengine - INFO - Epoch(train) [168][300/391] lr: 5.000000e-04 eta: 2:00:28 time: 0.486580 data_time: 0.044329 memory: 21657 loss_kpt: 0.000486 acc_pose: 0.911758 loss: 0.000486 2022/10/21 19:24:10 - mmengine - INFO - Epoch(train) [168][350/391] lr: 5.000000e-04 eta: 2:00:07 time: 0.483484 data_time: 0.039563 memory: 21657 loss_kpt: 0.000490 acc_pose: 0.856155 loss: 0.000490 2022/10/21 19:24:30 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:24:55 - mmengine - INFO - Epoch(train) [169][50/391] lr: 5.000000e-04 eta: 1:59:23 time: 0.505152 data_time: 0.058704 memory: 21657 loss_kpt: 0.000491 acc_pose: 0.897681 loss: 0.000491 2022/10/21 19:25:20 - mmengine - INFO - Epoch(train) [169][100/391] lr: 5.000000e-04 eta: 1:59:02 time: 0.489049 data_time: 0.040962 memory: 21657 loss_kpt: 0.000486 acc_pose: 0.881902 loss: 0.000486 2022/10/21 19:25:44 - mmengine - INFO - Epoch(train) [169][150/391] lr: 5.000000e-04 eta: 1:58:41 time: 0.489836 data_time: 0.040733 memory: 21657 loss_kpt: 0.000490 acc_pose: 0.896024 loss: 0.000490 2022/10/21 19:26:09 - mmengine - INFO - Epoch(train) [169][200/391] lr: 5.000000e-04 eta: 1:58:19 time: 0.483397 data_time: 0.042782 memory: 21657 loss_kpt: 0.000484 acc_pose: 0.899517 loss: 0.000484 2022/10/21 19:26:33 - mmengine - INFO - Epoch(train) [169][250/391] lr: 5.000000e-04 eta: 1:57:58 time: 0.481861 data_time: 0.039871 memory: 21657 loss_kpt: 0.000488 acc_pose: 0.894980 loss: 0.000488 2022/10/21 19:26:57 - mmengine - INFO - Epoch(train) [169][300/391] lr: 5.000000e-04 eta: 1:57:37 time: 0.490186 data_time: 0.041465 memory: 21657 loss_kpt: 0.000479 acc_pose: 0.830682 loss: 0.000479 2022/10/21 19:27:03 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:27:21 - mmengine - INFO - Epoch(train) [169][350/391] lr: 5.000000e-04 eta: 1:57:15 time: 0.483403 data_time: 0.043351 memory: 21657 loss_kpt: 0.000475 acc_pose: 0.870984 loss: 0.000475 2022/10/21 19:27:41 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:28:06 - mmengine - INFO - Epoch(train) [170][50/391] lr: 5.000000e-04 eta: 1:56:32 time: 0.501862 data_time: 0.055380 memory: 21657 loss_kpt: 0.000473 acc_pose: 0.836765 loss: 0.000473 2022/10/21 19:28:31 - mmengine - INFO - Epoch(train) [170][100/391] lr: 5.000000e-04 eta: 1:56:11 time: 0.491812 data_time: 0.041640 memory: 21657 loss_kpt: 0.000479 acc_pose: 0.852863 loss: 0.000479 2022/10/21 19:28:55 - mmengine - INFO - Epoch(train) [170][150/391] lr: 5.000000e-04 eta: 1:55:49 time: 0.489856 data_time: 0.044392 memory: 21657 loss_kpt: 0.000486 acc_pose: 0.865488 loss: 0.000486 2022/10/21 19:29:20 - mmengine - INFO - Epoch(train) [170][200/391] lr: 5.000000e-04 eta: 1:55:28 time: 0.484766 data_time: 0.040844 memory: 21657 loss_kpt: 0.000469 acc_pose: 0.899223 loss: 0.000469 2022/10/21 19:29:44 - mmengine - INFO - Epoch(train) [170][250/391] lr: 5.000000e-04 eta: 1:55:07 time: 0.490945 data_time: 0.040947 memory: 21657 loss_kpt: 0.000477 acc_pose: 0.889263 loss: 0.000477 2022/10/21 19:30:09 - mmengine - INFO - Epoch(train) [170][300/391] lr: 5.000000e-04 eta: 1:54:46 time: 0.492741 data_time: 0.039724 memory: 21657 loss_kpt: 0.000466 acc_pose: 0.886940 loss: 0.000466 2022/10/21 19:30:33 - mmengine - INFO - Epoch(train) [170][350/391] lr: 5.000000e-04 eta: 1:54:24 time: 0.486970 data_time: 0.044544 memory: 21657 loss_kpt: 0.000478 acc_pose: 0.874474 loss: 0.000478 2022/10/21 19:30:53 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:30:53 - mmengine - INFO - Saving checkpoint at 170 epochs 2022/10/21 19:31:05 - mmengine - INFO - Epoch(val) [170][50/407] eta: 0:00:56 time: 0.159259 data_time: 0.016346 memory: 21657 2022/10/21 19:31:12 - mmengine - INFO - Epoch(val) [170][100/407] eta: 0:00:46 time: 0.151742 data_time: 0.009194 memory: 2142 2022/10/21 19:31:20 - mmengine - INFO - Epoch(val) [170][150/407] eta: 0:00:38 time: 0.151379 data_time: 0.009232 memory: 2142 2022/10/21 19:31:27 - mmengine - INFO - Epoch(val) [170][200/407] eta: 0:00:31 time: 0.150500 data_time: 0.009374 memory: 2142 2022/10/21 19:31:35 - mmengine - INFO - Epoch(val) [170][250/407] eta: 0:00:23 time: 0.151190 data_time: 0.009428 memory: 2142 2022/10/21 19:31:43 - mmengine - INFO - Epoch(val) [170][300/407] eta: 0:00:16 time: 0.151587 data_time: 0.009098 memory: 2142 2022/10/21 19:31:50 - mmengine - INFO - Epoch(val) [170][350/407] eta: 0:00:08 time: 0.151190 data_time: 0.009332 memory: 2142 2022/10/21 19:31:58 - mmengine - INFO - Epoch(val) [170][400/407] eta: 0:00:01 time: 0.151465 data_time: 0.011561 memory: 2142 2022/10/21 19:32:33 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 19:32:47 - mmengine - INFO - Epoch(val) [170][407/407] coco/AP: 0.743564 coco/AP .5: 0.901760 coco/AP .75: 0.813220 coco/AP (M): 0.701032 coco/AP (L): 0.815239 coco/AR: 0.794238 coco/AR .5: 0.938602 coco/AR .75: 0.855479 coco/AR (M): 0.748156 coco/AR (L): 0.860126 2022/10/21 19:32:47 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221021/resnetv1d152_384/best_coco/AP_epoch_110.pth is removed 2022/10/21 19:32:49 - mmengine - INFO - The best checkpoint with 0.7436 coco/AP at 170 epoch is saved to best_coco/AP_epoch_170.pth. 2022/10/21 19:33:14 - mmengine - INFO - Epoch(train) [171][50/391] lr: 5.000000e-05 eta: 1:53:41 time: 0.505310 data_time: 0.049969 memory: 21657 loss_kpt: 0.000470 acc_pose: 0.880464 loss: 0.000470 2022/10/21 19:33:39 - mmengine - INFO - Epoch(train) [171][100/391] lr: 5.000000e-05 eta: 1:53:20 time: 0.487450 data_time: 0.040100 memory: 21657 loss_kpt: 0.000473 acc_pose: 0.903229 loss: 0.000473 2022/10/21 19:34:03 - mmengine - INFO - Epoch(train) [171][150/391] lr: 5.000000e-05 eta: 1:52:58 time: 0.483052 data_time: 0.040715 memory: 21657 loss_kpt: 0.000466 acc_pose: 0.880734 loss: 0.000466 2022/10/21 19:34:28 - mmengine - INFO - Epoch(train) [171][200/391] lr: 5.000000e-05 eta: 1:52:37 time: 0.489167 data_time: 0.038793 memory: 21657 loss_kpt: 0.000459 acc_pose: 0.902334 loss: 0.000459 2022/10/21 19:34:52 - mmengine - INFO - Epoch(train) [171][250/391] lr: 5.000000e-05 eta: 1:52:16 time: 0.488119 data_time: 0.044274 memory: 21657 loss_kpt: 0.000460 acc_pose: 0.894808 loss: 0.000460 2022/10/21 19:35:16 - mmengine - INFO - Epoch(train) [171][300/391] lr: 5.000000e-05 eta: 1:51:55 time: 0.489083 data_time: 0.041563 memory: 21657 loss_kpt: 0.000455 acc_pose: 0.862931 loss: 0.000455 2022/10/21 19:35:41 - mmengine - INFO - Epoch(train) [171][350/391] lr: 5.000000e-05 eta: 1:51:33 time: 0.484938 data_time: 0.043579 memory: 21657 loss_kpt: 0.000447 acc_pose: 0.880528 loss: 0.000447 2022/10/21 19:36:00 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:36:25 - mmengine - INFO - Epoch(train) [172][50/391] lr: 5.000000e-05 eta: 1:50:50 time: 0.500568 data_time: 0.052820 memory: 21657 loss_kpt: 0.000458 acc_pose: 0.872623 loss: 0.000458 2022/10/21 19:36:49 - mmengine - INFO - Epoch(train) [172][100/391] lr: 5.000000e-05 eta: 1:50:29 time: 0.483005 data_time: 0.041462 memory: 21657 loss_kpt: 0.000465 acc_pose: 0.883332 loss: 0.000465 2022/10/21 19:37:08 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:37:14 - mmengine - INFO - Epoch(train) [172][150/391] lr: 5.000000e-05 eta: 1:50:07 time: 0.489750 data_time: 0.042304 memory: 21657 loss_kpt: 0.000460 acc_pose: 0.873852 loss: 0.000460 2022/10/21 19:37:38 - mmengine - INFO - Epoch(train) [172][200/391] lr: 5.000000e-05 eta: 1:49:46 time: 0.485924 data_time: 0.041753 memory: 21657 loss_kpt: 0.000439 acc_pose: 0.860110 loss: 0.000439 2022/10/21 19:38:02 - mmengine - INFO - Epoch(train) [172][250/391] lr: 5.000000e-05 eta: 1:49:25 time: 0.486858 data_time: 0.039617 memory: 21657 loss_kpt: 0.000459 acc_pose: 0.886196 loss: 0.000459 2022/10/21 19:38:27 - mmengine - INFO - Epoch(train) [172][300/391] lr: 5.000000e-05 eta: 1:49:03 time: 0.488366 data_time: 0.043945 memory: 21657 loss_kpt: 0.000453 acc_pose: 0.922678 loss: 0.000453 2022/10/21 19:38:51 - mmengine - INFO - Epoch(train) [172][350/391] lr: 5.000000e-05 eta: 1:48:42 time: 0.485020 data_time: 0.039873 memory: 21657 loss_kpt: 0.000459 acc_pose: 0.874852 loss: 0.000459 2022/10/21 19:39:11 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:39:36 - mmengine - INFO - Epoch(train) [173][50/391] lr: 5.000000e-05 eta: 1:47:59 time: 0.493125 data_time: 0.050601 memory: 21657 loss_kpt: 0.000455 acc_pose: 0.876174 loss: 0.000455 2022/10/21 19:40:00 - mmengine - INFO - Epoch(train) [173][100/391] lr: 5.000000e-05 eta: 1:47:37 time: 0.486628 data_time: 0.039781 memory: 21657 loss_kpt: 0.000452 acc_pose: 0.871120 loss: 0.000452 2022/10/21 19:40:24 - mmengine - INFO - Epoch(train) [173][150/391] lr: 5.000000e-05 eta: 1:47:16 time: 0.482603 data_time: 0.039554 memory: 21657 loss_kpt: 0.000453 acc_pose: 0.886780 loss: 0.000453 2022/10/21 19:40:49 - mmengine - INFO - Epoch(train) [173][200/391] lr: 5.000000e-05 eta: 1:46:55 time: 0.486792 data_time: 0.039429 memory: 21657 loss_kpt: 0.000463 acc_pose: 0.882485 loss: 0.000463 2022/10/21 19:41:13 - mmengine - INFO - Epoch(train) [173][250/391] lr: 5.000000e-05 eta: 1:46:33 time: 0.484621 data_time: 0.041543 memory: 21657 loss_kpt: 0.000431 acc_pose: 0.938899 loss: 0.000431 2022/10/21 19:41:37 - mmengine - INFO - Epoch(train) [173][300/391] lr: 5.000000e-05 eta: 1:46:12 time: 0.485705 data_time: 0.042168 memory: 21657 loss_kpt: 0.000446 acc_pose: 0.897673 loss: 0.000446 2022/10/21 19:42:02 - mmengine - INFO - Epoch(train) [173][350/391] lr: 5.000000e-05 eta: 1:45:51 time: 0.489000 data_time: 0.043711 memory: 21657 loss_kpt: 0.000444 acc_pose: 0.901862 loss: 0.000444 2022/10/21 19:42:21 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:42:47 - mmengine - INFO - Epoch(train) [174][50/391] lr: 5.000000e-05 eta: 1:45:08 time: 0.506153 data_time: 0.056420 memory: 21657 loss_kpt: 0.000441 acc_pose: 0.890394 loss: 0.000441 2022/10/21 19:43:11 - mmengine - INFO - Epoch(train) [174][100/391] lr: 5.000000e-05 eta: 1:44:46 time: 0.485911 data_time: 0.039946 memory: 21657 loss_kpt: 0.000442 acc_pose: 0.923873 loss: 0.000442 2022/10/21 19:43:35 - mmengine - INFO - Epoch(train) [174][150/391] lr: 5.000000e-05 eta: 1:44:25 time: 0.488182 data_time: 0.044219 memory: 21657 loss_kpt: 0.000446 acc_pose: 0.867096 loss: 0.000446 2022/10/21 19:43:59 - mmengine - INFO - Epoch(train) [174][200/391] lr: 5.000000e-05 eta: 1:44:04 time: 0.483043 data_time: 0.041413 memory: 21657 loss_kpt: 0.000446 acc_pose: 0.864380 loss: 0.000446 2022/10/21 19:44:24 - mmengine - INFO - Epoch(train) [174][250/391] lr: 5.000000e-05 eta: 1:43:42 time: 0.489217 data_time: 0.044069 memory: 21657 loss_kpt: 0.000447 acc_pose: 0.889862 loss: 0.000447 2022/10/21 19:44:48 - mmengine - INFO - Epoch(train) [174][300/391] lr: 5.000000e-05 eta: 1:43:21 time: 0.483395 data_time: 0.040841 memory: 21657 loss_kpt: 0.000453 acc_pose: 0.899048 loss: 0.000453 2022/10/21 19:45:12 - mmengine - INFO - Epoch(train) [174][350/391] lr: 5.000000e-05 eta: 1:42:59 time: 0.483110 data_time: 0.040179 memory: 21657 loss_kpt: 0.000444 acc_pose: 0.896535 loss: 0.000444 2022/10/21 19:45:16 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:45:32 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:45:57 - mmengine - INFO - Epoch(train) [175][50/391] lr: 5.000000e-05 eta: 1:42:17 time: 0.499278 data_time: 0.052460 memory: 21657 loss_kpt: 0.000443 acc_pose: 0.927912 loss: 0.000443 2022/10/21 19:46:22 - mmengine - INFO - Epoch(train) [175][100/391] lr: 5.000000e-05 eta: 1:41:55 time: 0.488495 data_time: 0.044906 memory: 21657 loss_kpt: 0.000431 acc_pose: 0.938378 loss: 0.000431 2022/10/21 19:46:46 - mmengine - INFO - Epoch(train) [175][150/391] lr: 5.000000e-05 eta: 1:41:34 time: 0.483864 data_time: 0.041055 memory: 21657 loss_kpt: 0.000435 acc_pose: 0.930920 loss: 0.000435 2022/10/21 19:47:11 - mmengine - INFO - Epoch(train) [175][200/391] lr: 5.000000e-05 eta: 1:41:12 time: 0.493040 data_time: 0.044428 memory: 21657 loss_kpt: 0.000440 acc_pose: 0.880014 loss: 0.000440 2022/10/21 19:47:35 - mmengine - INFO - Epoch(train) [175][250/391] lr: 5.000000e-05 eta: 1:40:51 time: 0.485424 data_time: 0.040659 memory: 21657 loss_kpt: 0.000450 acc_pose: 0.890063 loss: 0.000450 2022/10/21 19:47:59 - mmengine - INFO - Epoch(train) [175][300/391] lr: 5.000000e-05 eta: 1:40:30 time: 0.481282 data_time: 0.041399 memory: 21657 loss_kpt: 0.000432 acc_pose: 0.894446 loss: 0.000432 2022/10/21 19:48:23 - mmengine - INFO - Epoch(train) [175][350/391] lr: 5.000000e-05 eta: 1:40:08 time: 0.486629 data_time: 0.039377 memory: 21657 loss_kpt: 0.000445 acc_pose: 0.900017 loss: 0.000445 2022/10/21 19:48:43 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:49:08 - mmengine - INFO - Epoch(train) [176][50/391] lr: 5.000000e-05 eta: 1:39:25 time: 0.504531 data_time: 0.055036 memory: 21657 loss_kpt: 0.000440 acc_pose: 0.919580 loss: 0.000440 2022/10/21 19:49:32 - mmengine - INFO - Epoch(train) [176][100/391] lr: 5.000000e-05 eta: 1:39:04 time: 0.486121 data_time: 0.039618 memory: 21657 loss_kpt: 0.000441 acc_pose: 0.876406 loss: 0.000441 2022/10/21 19:49:57 - mmengine - INFO - Epoch(train) [176][150/391] lr: 5.000000e-05 eta: 1:38:43 time: 0.492322 data_time: 0.044177 memory: 21657 loss_kpt: 0.000451 acc_pose: 0.899357 loss: 0.000451 2022/10/21 19:50:21 - mmengine - INFO - Epoch(train) [176][200/391] lr: 5.000000e-05 eta: 1:38:21 time: 0.488712 data_time: 0.041568 memory: 21657 loss_kpt: 0.000440 acc_pose: 0.932124 loss: 0.000440 2022/10/21 19:50:46 - mmengine - INFO - Epoch(train) [176][250/391] lr: 5.000000e-05 eta: 1:38:00 time: 0.485835 data_time: 0.043816 memory: 21657 loss_kpt: 0.000435 acc_pose: 0.899307 loss: 0.000435 2022/10/21 19:51:10 - mmengine - INFO - Epoch(train) [176][300/391] lr: 5.000000e-05 eta: 1:37:39 time: 0.486895 data_time: 0.040367 memory: 21657 loss_kpt: 0.000447 acc_pose: 0.920321 loss: 0.000447 2022/10/21 19:51:34 - mmengine - INFO - Epoch(train) [176][350/391] lr: 5.000000e-05 eta: 1:37:17 time: 0.487535 data_time: 0.044909 memory: 21657 loss_kpt: 0.000455 acc_pose: 0.896776 loss: 0.000455 2022/10/21 19:51:54 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:52:19 - mmengine - INFO - Epoch(train) [177][50/391] lr: 5.000000e-05 eta: 1:36:34 time: 0.497533 data_time: 0.052721 memory: 21657 loss_kpt: 0.000437 acc_pose: 0.885260 loss: 0.000437 2022/10/21 19:52:44 - mmengine - INFO - Epoch(train) [177][100/391] lr: 5.000000e-05 eta: 1:36:13 time: 0.489810 data_time: 0.043024 memory: 21657 loss_kpt: 0.000439 acc_pose: 0.921717 loss: 0.000439 2022/10/21 19:53:08 - mmengine - INFO - Epoch(train) [177][150/391] lr: 5.000000e-05 eta: 1:35:52 time: 0.495513 data_time: 0.041216 memory: 21657 loss_kpt: 0.000433 acc_pose: 0.914745 loss: 0.000433 2022/10/21 19:53:25 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:53:33 - mmengine - INFO - Epoch(train) [177][200/391] lr: 5.000000e-05 eta: 1:35:30 time: 0.489877 data_time: 0.042716 memory: 21657 loss_kpt: 0.000440 acc_pose: 0.925360 loss: 0.000440 2022/10/21 19:53:57 - mmengine - INFO - Epoch(train) [177][250/391] lr: 5.000000e-05 eta: 1:35:09 time: 0.488420 data_time: 0.041323 memory: 21657 loss_kpt: 0.000432 acc_pose: 0.861627 loss: 0.000432 2022/10/21 19:54:22 - mmengine - INFO - Epoch(train) [177][300/391] lr: 5.000000e-05 eta: 1:34:48 time: 0.490496 data_time: 0.046436 memory: 21657 loss_kpt: 0.000448 acc_pose: 0.876884 loss: 0.000448 2022/10/21 19:54:46 - mmengine - INFO - Epoch(train) [177][350/391] lr: 5.000000e-05 eta: 1:34:26 time: 0.486265 data_time: 0.041432 memory: 21657 loss_kpt: 0.000439 acc_pose: 0.945923 loss: 0.000439 2022/10/21 19:55:06 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:55:32 - mmengine - INFO - Epoch(train) [178][50/391] lr: 5.000000e-05 eta: 1:33:44 time: 0.509935 data_time: 0.056346 memory: 21657 loss_kpt: 0.000442 acc_pose: 0.909164 loss: 0.000442 2022/10/21 19:55:56 - mmengine - INFO - Epoch(train) [178][100/391] lr: 5.000000e-05 eta: 1:33:22 time: 0.489447 data_time: 0.046834 memory: 21657 loss_kpt: 0.000437 acc_pose: 0.897404 loss: 0.000437 2022/10/21 19:56:20 - mmengine - INFO - Epoch(train) [178][150/391] lr: 5.000000e-05 eta: 1:33:01 time: 0.487191 data_time: 0.041142 memory: 21657 loss_kpt: 0.000426 acc_pose: 0.911190 loss: 0.000426 2022/10/21 19:56:45 - mmengine - INFO - Epoch(train) [178][200/391] lr: 5.000000e-05 eta: 1:32:39 time: 0.490595 data_time: 0.042668 memory: 21657 loss_kpt: 0.000434 acc_pose: 0.935235 loss: 0.000434 2022/10/21 19:57:10 - mmengine - INFO - Epoch(train) [178][250/391] lr: 5.000000e-05 eta: 1:32:18 time: 0.489830 data_time: 0.040874 memory: 21657 loss_kpt: 0.000446 acc_pose: 0.886273 loss: 0.000446 2022/10/21 19:57:34 - mmengine - INFO - Epoch(train) [178][300/391] lr: 5.000000e-05 eta: 1:31:57 time: 0.484724 data_time: 0.040878 memory: 21657 loss_kpt: 0.000444 acc_pose: 0.878252 loss: 0.000444 2022/10/21 19:57:58 - mmengine - INFO - Epoch(train) [178][350/391] lr: 5.000000e-05 eta: 1:31:35 time: 0.490804 data_time: 0.044706 memory: 21657 loss_kpt: 0.000440 acc_pose: 0.909840 loss: 0.000440 2022/10/21 19:58:18 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 19:58:43 - mmengine - INFO - Epoch(train) [179][50/391] lr: 5.000000e-05 eta: 1:30:53 time: 0.502995 data_time: 0.054968 memory: 21657 loss_kpt: 0.000436 acc_pose: 0.904356 loss: 0.000436 2022/10/21 19:59:07 - mmengine - INFO - Epoch(train) [179][100/391] lr: 5.000000e-05 eta: 1:30:31 time: 0.484793 data_time: 0.042404 memory: 21657 loss_kpt: 0.000439 acc_pose: 0.924716 loss: 0.000439 2022/10/21 19:59:32 - mmengine - INFO - Epoch(train) [179][150/391] lr: 5.000000e-05 eta: 1:30:10 time: 0.487296 data_time: 0.044695 memory: 21657 loss_kpt: 0.000450 acc_pose: 0.916431 loss: 0.000450 2022/10/21 19:59:56 - mmengine - INFO - Epoch(train) [179][200/391] lr: 5.000000e-05 eta: 1:29:48 time: 0.482510 data_time: 0.041514 memory: 21657 loss_kpt: 0.000431 acc_pose: 0.925178 loss: 0.000431 2022/10/21 20:00:20 - mmengine - INFO - Epoch(train) [179][250/391] lr: 5.000000e-05 eta: 1:29:27 time: 0.483910 data_time: 0.040940 memory: 21657 loss_kpt: 0.000434 acc_pose: 0.912248 loss: 0.000434 2022/10/21 20:00:45 - mmengine - INFO - Epoch(train) [179][300/391] lr: 5.000000e-05 eta: 1:29:05 time: 0.487911 data_time: 0.040373 memory: 21657 loss_kpt: 0.000433 acc_pose: 0.880672 loss: 0.000433 2022/10/21 20:01:09 - mmengine - INFO - Epoch(train) [179][350/391] lr: 5.000000e-05 eta: 1:28:44 time: 0.486420 data_time: 0.039975 memory: 21657 loss_kpt: 0.000439 acc_pose: 0.904549 loss: 0.000439 2022/10/21 20:01:29 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:01:35 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:01:54 - mmengine - INFO - Epoch(train) [180][50/391] lr: 5.000000e-05 eta: 1:28:02 time: 0.503907 data_time: 0.050997 memory: 21657 loss_kpt: 0.000431 acc_pose: 0.949153 loss: 0.000431 2022/10/21 20:02:18 - mmengine - INFO - Epoch(train) [180][100/391] lr: 5.000000e-05 eta: 1:27:40 time: 0.488383 data_time: 0.043554 memory: 21657 loss_kpt: 0.000442 acc_pose: 0.893872 loss: 0.000442 2022/10/21 20:02:43 - mmengine - INFO - Epoch(train) [180][150/391] lr: 5.000000e-05 eta: 1:27:19 time: 0.486103 data_time: 0.041001 memory: 21657 loss_kpt: 0.000446 acc_pose: 0.913837 loss: 0.000446 2022/10/21 20:03:07 - mmengine - INFO - Epoch(train) [180][200/391] lr: 5.000000e-05 eta: 1:26:57 time: 0.486736 data_time: 0.040456 memory: 21657 loss_kpt: 0.000429 acc_pose: 0.895308 loss: 0.000429 2022/10/21 20:03:31 - mmengine - INFO - Epoch(train) [180][250/391] lr: 5.000000e-05 eta: 1:26:36 time: 0.486790 data_time: 0.041867 memory: 21657 loss_kpt: 0.000427 acc_pose: 0.927899 loss: 0.000427 2022/10/21 20:03:56 - mmengine - INFO - Epoch(train) [180][300/391] lr: 5.000000e-05 eta: 1:26:14 time: 0.490246 data_time: 0.046172 memory: 21657 loss_kpt: 0.000434 acc_pose: 0.868707 loss: 0.000434 2022/10/21 20:04:20 - mmengine - INFO - Epoch(train) [180][350/391] lr: 5.000000e-05 eta: 1:25:53 time: 0.483069 data_time: 0.042492 memory: 21657 loss_kpt: 0.000439 acc_pose: 0.894769 loss: 0.000439 2022/10/21 20:04:39 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:04:39 - mmengine - INFO - Saving checkpoint at 180 epochs 2022/10/21 20:04:52 - mmengine - INFO - Epoch(val) [180][50/407] eta: 0:00:56 time: 0.157161 data_time: 0.014259 memory: 21657 2022/10/21 20:04:59 - mmengine - INFO - Epoch(val) [180][100/407] eta: 0:00:47 time: 0.154937 data_time: 0.011900 memory: 2142 2022/10/21 20:05:07 - mmengine - INFO - Epoch(val) [180][150/407] eta: 0:00:39 time: 0.151963 data_time: 0.010006 memory: 2142 2022/10/21 20:05:15 - mmengine - INFO - Epoch(val) [180][200/407] eta: 0:00:32 time: 0.155528 data_time: 0.012704 memory: 2142 2022/10/21 20:05:22 - mmengine - INFO - Epoch(val) [180][250/407] eta: 0:00:23 time: 0.151451 data_time: 0.009462 memory: 2142 2022/10/21 20:05:30 - mmengine - INFO - Epoch(val) [180][300/407] eta: 0:00:16 time: 0.151400 data_time: 0.008982 memory: 2142 2022/10/21 20:05:37 - mmengine - INFO - Epoch(val) [180][350/407] eta: 0:00:08 time: 0.150483 data_time: 0.009136 memory: 2142 2022/10/21 20:05:45 - mmengine - INFO - Epoch(val) [180][400/407] eta: 0:00:01 time: 0.148106 data_time: 0.008240 memory: 2142 2022/10/21 20:06:20 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 20:06:34 - mmengine - INFO - Epoch(val) [180][407/407] coco/AP: 0.750735 coco/AP .5: 0.908025 coco/AP .75: 0.820549 coco/AP (M): 0.708050 coco/AP (L): 0.822599 coco/AR: 0.799969 coco/AR .5: 0.941908 coco/AR .75: 0.861933 coco/AR (M): 0.754548 coco/AR (L): 0.865663 2022/10/21 20:06:34 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221021/resnetv1d152_384/best_coco/AP_epoch_170.pth is removed 2022/10/21 20:06:37 - mmengine - INFO - The best checkpoint with 0.7507 coco/AP at 180 epoch is saved to best_coco/AP_epoch_180.pth. 2022/10/21 20:07:02 - mmengine - INFO - Epoch(train) [181][50/391] lr: 5.000000e-05 eta: 1:25:10 time: 0.499053 data_time: 0.050587 memory: 21657 loss_kpt: 0.000422 acc_pose: 0.891101 loss: 0.000422 2022/10/21 20:07:26 - mmengine - INFO - Epoch(train) [181][100/391] lr: 5.000000e-05 eta: 1:24:49 time: 0.490044 data_time: 0.046255 memory: 21657 loss_kpt: 0.000429 acc_pose: 0.894598 loss: 0.000429 2022/10/21 20:07:50 - mmengine - INFO - Epoch(train) [181][150/391] lr: 5.000000e-05 eta: 1:24:27 time: 0.485005 data_time: 0.043150 memory: 21657 loss_kpt: 0.000440 acc_pose: 0.916520 loss: 0.000440 2022/10/21 20:08:15 - mmengine - INFO - Epoch(train) [181][200/391] lr: 5.000000e-05 eta: 1:24:06 time: 0.486875 data_time: 0.044931 memory: 21657 loss_kpt: 0.000434 acc_pose: 0.884902 loss: 0.000434 2022/10/21 20:08:39 - mmengine - INFO - Epoch(train) [181][250/391] lr: 5.000000e-05 eta: 1:23:44 time: 0.484048 data_time: 0.042421 memory: 21657 loss_kpt: 0.000436 acc_pose: 0.915557 loss: 0.000436 2022/10/21 20:09:03 - mmengine - INFO - Epoch(train) [181][300/391] lr: 5.000000e-05 eta: 1:23:23 time: 0.483174 data_time: 0.041294 memory: 21657 loss_kpt: 0.000432 acc_pose: 0.897583 loss: 0.000432 2022/10/21 20:09:28 - mmengine - INFO - Epoch(train) [181][350/391] lr: 5.000000e-05 eta: 1:23:01 time: 0.489077 data_time: 0.044360 memory: 21657 loss_kpt: 0.000421 acc_pose: 0.907856 loss: 0.000421 2022/10/21 20:09:47 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:10:12 - mmengine - INFO - Epoch(train) [182][50/391] lr: 5.000000e-05 eta: 1:22:19 time: 0.503149 data_time: 0.058957 memory: 21657 loss_kpt: 0.000432 acc_pose: 0.914067 loss: 0.000432 2022/10/21 20:10:37 - mmengine - INFO - Epoch(train) [182][100/391] lr: 5.000000e-05 eta: 1:21:58 time: 0.493910 data_time: 0.039688 memory: 21657 loss_kpt: 0.000434 acc_pose: 0.891987 loss: 0.000434 2022/10/21 20:11:01 - mmengine - INFO - Epoch(train) [182][150/391] lr: 5.000000e-05 eta: 1:21:36 time: 0.485413 data_time: 0.041066 memory: 21657 loss_kpt: 0.000421 acc_pose: 0.923330 loss: 0.000421 2022/10/21 20:11:26 - mmengine - INFO - Epoch(train) [182][200/391] lr: 5.000000e-05 eta: 1:21:15 time: 0.488743 data_time: 0.042386 memory: 21657 loss_kpt: 0.000421 acc_pose: 0.905869 loss: 0.000421 2022/10/21 20:11:40 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:11:50 - mmengine - INFO - Epoch(train) [182][250/391] lr: 5.000000e-05 eta: 1:20:53 time: 0.488023 data_time: 0.040294 memory: 21657 loss_kpt: 0.000436 acc_pose: 0.941761 loss: 0.000436 2022/10/21 20:12:15 - mmengine - INFO - Epoch(train) [182][300/391] lr: 5.000000e-05 eta: 1:20:32 time: 0.484198 data_time: 0.042568 memory: 21657 loss_kpt: 0.000430 acc_pose: 0.931426 loss: 0.000430 2022/10/21 20:12:39 - mmengine - INFO - Epoch(train) [182][350/391] lr: 5.000000e-05 eta: 1:20:10 time: 0.489657 data_time: 0.040344 memory: 21657 loss_kpt: 0.000435 acc_pose: 0.906359 loss: 0.000435 2022/10/21 20:12:59 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:13:24 - mmengine - INFO - Epoch(train) [183][50/391] lr: 5.000000e-05 eta: 1:19:28 time: 0.501383 data_time: 0.050830 memory: 21657 loss_kpt: 0.000435 acc_pose: 0.881607 loss: 0.000435 2022/10/21 20:13:49 - mmengine - INFO - Epoch(train) [183][100/391] lr: 5.000000e-05 eta: 1:19:07 time: 0.492372 data_time: 0.041460 memory: 21657 loss_kpt: 0.000440 acc_pose: 0.904062 loss: 0.000440 2022/10/21 20:14:13 - mmengine - INFO - Epoch(train) [183][150/391] lr: 5.000000e-05 eta: 1:18:45 time: 0.487816 data_time: 0.043000 memory: 21657 loss_kpt: 0.000437 acc_pose: 0.856775 loss: 0.000437 2022/10/21 20:14:37 - mmengine - INFO - Epoch(train) [183][200/391] lr: 5.000000e-05 eta: 1:18:24 time: 0.485831 data_time: 0.042670 memory: 21657 loss_kpt: 0.000432 acc_pose: 0.913233 loss: 0.000432 2022/10/21 20:15:02 - mmengine - INFO - Epoch(train) [183][250/391] lr: 5.000000e-05 eta: 1:18:02 time: 0.491530 data_time: 0.042518 memory: 21657 loss_kpt: 0.000434 acc_pose: 0.893919 loss: 0.000434 2022/10/21 20:15:26 - mmengine - INFO - Epoch(train) [183][300/391] lr: 5.000000e-05 eta: 1:17:41 time: 0.485985 data_time: 0.047858 memory: 21657 loss_kpt: 0.000423 acc_pose: 0.935645 loss: 0.000423 2022/10/21 20:15:51 - mmengine - INFO - Epoch(train) [183][350/391] lr: 5.000000e-05 eta: 1:17:19 time: 0.490308 data_time: 0.040526 memory: 21657 loss_kpt: 0.000434 acc_pose: 0.917090 loss: 0.000434 2022/10/21 20:16:10 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:16:35 - mmengine - INFO - Epoch(train) [184][50/391] lr: 5.000000e-05 eta: 1:16:37 time: 0.499985 data_time: 0.053814 memory: 21657 loss_kpt: 0.000429 acc_pose: 0.928048 loss: 0.000429 2022/10/21 20:17:00 - mmengine - INFO - Epoch(train) [184][100/391] lr: 5.000000e-05 eta: 1:16:16 time: 0.488531 data_time: 0.039094 memory: 21657 loss_kpt: 0.000432 acc_pose: 0.926385 loss: 0.000432 2022/10/21 20:17:24 - mmengine - INFO - Epoch(train) [184][150/391] lr: 5.000000e-05 eta: 1:15:54 time: 0.481541 data_time: 0.043598 memory: 21657 loss_kpt: 0.000432 acc_pose: 0.888453 loss: 0.000432 2022/10/21 20:17:49 - mmengine - INFO - Epoch(train) [184][200/391] lr: 5.000000e-05 eta: 1:15:33 time: 0.495206 data_time: 0.041493 memory: 21657 loss_kpt: 0.000423 acc_pose: 0.915184 loss: 0.000423 2022/10/21 20:18:13 - mmengine - INFO - Epoch(train) [184][250/391] lr: 5.000000e-05 eta: 1:15:11 time: 0.483397 data_time: 0.044466 memory: 21657 loss_kpt: 0.000442 acc_pose: 0.881939 loss: 0.000442 2022/10/21 20:18:37 - mmengine - INFO - Epoch(train) [184][300/391] lr: 5.000000e-05 eta: 1:14:50 time: 0.491604 data_time: 0.041267 memory: 21657 loss_kpt: 0.000435 acc_pose: 0.928091 loss: 0.000435 2022/10/21 20:19:02 - mmengine - INFO - Epoch(train) [184][350/391] lr: 5.000000e-05 eta: 1:14:28 time: 0.484322 data_time: 0.039739 memory: 21657 loss_kpt: 0.000434 acc_pose: 0.918301 loss: 0.000434 2022/10/21 20:19:21 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:19:46 - mmengine - INFO - Epoch(train) [185][50/391] lr: 5.000000e-05 eta: 1:13:46 time: 0.507334 data_time: 0.054439 memory: 21657 loss_kpt: 0.000428 acc_pose: 0.918432 loss: 0.000428 2022/10/21 20:19:49 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:20:11 - mmengine - INFO - Epoch(train) [185][100/391] lr: 5.000000e-05 eta: 1:13:25 time: 0.484286 data_time: 0.041202 memory: 21657 loss_kpt: 0.000437 acc_pose: 0.903981 loss: 0.000437 2022/10/21 20:20:35 - mmengine - INFO - Epoch(train) [185][150/391] lr: 5.000000e-05 eta: 1:13:03 time: 0.488345 data_time: 0.040716 memory: 21657 loss_kpt: 0.000436 acc_pose: 0.904431 loss: 0.000436 2022/10/21 20:20:59 - mmengine - INFO - Epoch(train) [185][200/391] lr: 5.000000e-05 eta: 1:12:42 time: 0.479226 data_time: 0.040640 memory: 21657 loss_kpt: 0.000449 acc_pose: 0.800468 loss: 0.000449 2022/10/21 20:21:24 - mmengine - INFO - Epoch(train) [185][250/391] lr: 5.000000e-05 eta: 1:12:20 time: 0.492163 data_time: 0.040074 memory: 21657 loss_kpt: 0.000425 acc_pose: 0.888976 loss: 0.000425 2022/10/21 20:21:48 - mmengine - INFO - Epoch(train) [185][300/391] lr: 5.000000e-05 eta: 1:11:58 time: 0.482170 data_time: 0.045657 memory: 21657 loss_kpt: 0.000425 acc_pose: 0.895584 loss: 0.000425 2022/10/21 20:22:13 - mmengine - INFO - Epoch(train) [185][350/391] lr: 5.000000e-05 eta: 1:11:37 time: 0.495297 data_time: 0.041497 memory: 21657 loss_kpt: 0.000435 acc_pose: 0.908442 loss: 0.000435 2022/10/21 20:22:32 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:22:57 - mmengine - INFO - Epoch(train) [186][50/391] lr: 5.000000e-05 eta: 1:10:55 time: 0.502717 data_time: 0.050183 memory: 21657 loss_kpt: 0.000433 acc_pose: 0.917864 loss: 0.000433 2022/10/21 20:23:22 - mmengine - INFO - Epoch(train) [186][100/391] lr: 5.000000e-05 eta: 1:10:34 time: 0.489367 data_time: 0.043545 memory: 21657 loss_kpt: 0.000424 acc_pose: 0.933281 loss: 0.000424 2022/10/21 20:23:46 - mmengine - INFO - Epoch(train) [186][150/391] lr: 5.000000e-05 eta: 1:10:12 time: 0.483851 data_time: 0.040410 memory: 21657 loss_kpt: 0.000437 acc_pose: 0.883388 loss: 0.000437 2022/10/21 20:24:11 - mmengine - INFO - Epoch(train) [186][200/391] lr: 5.000000e-05 eta: 1:09:51 time: 0.490210 data_time: 0.040061 memory: 21657 loss_kpt: 0.000429 acc_pose: 0.946942 loss: 0.000429 2022/10/21 20:24:35 - mmengine - INFO - Epoch(train) [186][250/391] lr: 5.000000e-05 eta: 1:09:29 time: 0.484571 data_time: 0.040541 memory: 21657 loss_kpt: 0.000440 acc_pose: 0.908210 loss: 0.000440 2022/10/21 20:24:59 - mmengine - INFO - Epoch(train) [186][300/391] lr: 5.000000e-05 eta: 1:09:07 time: 0.482554 data_time: 0.043504 memory: 21657 loss_kpt: 0.000432 acc_pose: 0.904236 loss: 0.000432 2022/10/21 20:25:23 - mmengine - INFO - Epoch(train) [186][350/391] lr: 5.000000e-05 eta: 1:08:46 time: 0.483133 data_time: 0.038154 memory: 21657 loss_kpt: 0.000434 acc_pose: 0.898257 loss: 0.000434 2022/10/21 20:25:43 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:26:08 - mmengine - INFO - Epoch(train) [187][50/391] lr: 5.000000e-05 eta: 1:08:04 time: 0.499961 data_time: 0.052611 memory: 21657 loss_kpt: 0.000445 acc_pose: 0.866424 loss: 0.000445 2022/10/21 20:26:32 - mmengine - INFO - Epoch(train) [187][100/391] lr: 5.000000e-05 eta: 1:07:42 time: 0.485302 data_time: 0.040574 memory: 21657 loss_kpt: 0.000423 acc_pose: 0.922936 loss: 0.000423 2022/10/21 20:26:57 - mmengine - INFO - Epoch(train) [187][150/391] lr: 5.000000e-05 eta: 1:07:21 time: 0.492558 data_time: 0.042025 memory: 21657 loss_kpt: 0.000427 acc_pose: 0.904341 loss: 0.000427 2022/10/21 20:27:21 - mmengine - INFO - Epoch(train) [187][200/391] lr: 5.000000e-05 eta: 1:06:59 time: 0.489903 data_time: 0.041580 memory: 21657 loss_kpt: 0.000426 acc_pose: 0.906999 loss: 0.000426 2022/10/21 20:27:46 - mmengine - INFO - Epoch(train) [187][250/391] lr: 5.000000e-05 eta: 1:06:38 time: 0.486631 data_time: 0.045057 memory: 21657 loss_kpt: 0.000418 acc_pose: 0.887371 loss: 0.000418 2022/10/21 20:27:57 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:28:10 - mmengine - INFO - Epoch(train) [187][300/391] lr: 5.000000e-05 eta: 1:06:16 time: 0.486586 data_time: 0.042679 memory: 21657 loss_kpt: 0.000435 acc_pose: 0.933217 loss: 0.000435 2022/10/21 20:28:34 - mmengine - INFO - Epoch(train) [187][350/391] lr: 5.000000e-05 eta: 1:05:55 time: 0.487614 data_time: 0.044359 memory: 21657 loss_kpt: 0.000432 acc_pose: 0.910521 loss: 0.000432 2022/10/21 20:28:54 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:29:19 - mmengine - INFO - Epoch(train) [188][50/391] lr: 5.000000e-05 eta: 1:05:13 time: 0.503655 data_time: 0.052758 memory: 21657 loss_kpt: 0.000428 acc_pose: 0.909230 loss: 0.000428 2022/10/21 20:29:44 - mmengine - INFO - Epoch(train) [188][100/391] lr: 5.000000e-05 eta: 1:04:51 time: 0.487105 data_time: 0.046343 memory: 21657 loss_kpt: 0.000419 acc_pose: 0.923480 loss: 0.000419 2022/10/21 20:30:08 - mmengine - INFO - Epoch(train) [188][150/391] lr: 5.000000e-05 eta: 1:04:30 time: 0.489847 data_time: 0.041035 memory: 21657 loss_kpt: 0.000434 acc_pose: 0.905015 loss: 0.000434 2022/10/21 20:30:33 - mmengine - INFO - Epoch(train) [188][200/391] lr: 5.000000e-05 eta: 1:04:08 time: 0.489756 data_time: 0.040255 memory: 21657 loss_kpt: 0.000439 acc_pose: 0.889322 loss: 0.000439 2022/10/21 20:30:57 - mmengine - INFO - Epoch(train) [188][250/391] lr: 5.000000e-05 eta: 1:03:47 time: 0.491353 data_time: 0.041011 memory: 21657 loss_kpt: 0.000437 acc_pose: 0.902354 loss: 0.000437 2022/10/21 20:31:22 - mmengine - INFO - Epoch(train) [188][300/391] lr: 5.000000e-05 eta: 1:03:25 time: 0.485693 data_time: 0.045846 memory: 21657 loss_kpt: 0.000425 acc_pose: 0.928045 loss: 0.000425 2022/10/21 20:31:46 - mmengine - INFO - Epoch(train) [188][350/391] lr: 5.000000e-05 eta: 1:03:04 time: 0.495451 data_time: 0.041801 memory: 21657 loss_kpt: 0.000432 acc_pose: 0.892343 loss: 0.000432 2022/10/21 20:32:06 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:32:31 - mmengine - INFO - Epoch(train) [189][50/391] lr: 5.000000e-05 eta: 1:02:22 time: 0.505487 data_time: 0.052189 memory: 21657 loss_kpt: 0.000434 acc_pose: 0.893094 loss: 0.000434 2022/10/21 20:32:55 - mmengine - INFO - Epoch(train) [189][100/391] lr: 5.000000e-05 eta: 1:02:00 time: 0.483143 data_time: 0.040589 memory: 21657 loss_kpt: 0.000434 acc_pose: 0.863276 loss: 0.000434 2022/10/21 20:33:20 - mmengine - INFO - Epoch(train) [189][150/391] lr: 5.000000e-05 eta: 1:01:39 time: 0.492302 data_time: 0.039774 memory: 21657 loss_kpt: 0.000432 acc_pose: 0.954535 loss: 0.000432 2022/10/21 20:33:44 - mmengine - INFO - Epoch(train) [189][200/391] lr: 5.000000e-05 eta: 1:01:17 time: 0.484905 data_time: 0.040313 memory: 21657 loss_kpt: 0.000424 acc_pose: 0.859665 loss: 0.000424 2022/10/21 20:34:09 - mmengine - INFO - Epoch(train) [189][250/391] lr: 5.000000e-05 eta: 1:00:56 time: 0.485636 data_time: 0.043493 memory: 21657 loss_kpt: 0.000443 acc_pose: 0.908278 loss: 0.000443 2022/10/21 20:34:33 - mmengine - INFO - Epoch(train) [189][300/391] lr: 5.000000e-05 eta: 1:00:34 time: 0.492745 data_time: 0.041755 memory: 21657 loss_kpt: 0.000422 acc_pose: 0.875438 loss: 0.000422 2022/10/21 20:34:57 - mmengine - INFO - Epoch(train) [189][350/391] lr: 5.000000e-05 eta: 1:00:12 time: 0.482702 data_time: 0.041819 memory: 21657 loss_kpt: 0.000429 acc_pose: 0.869702 loss: 0.000429 2022/10/21 20:35:18 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:35:43 - mmengine - INFO - Epoch(train) [190][50/391] lr: 5.000000e-05 eta: 0:59:31 time: 0.501939 data_time: 0.055214 memory: 21657 loss_kpt: 0.000422 acc_pose: 0.923384 loss: 0.000422 2022/10/21 20:36:07 - mmengine - INFO - Epoch(train) [190][100/391] lr: 5.000000e-05 eta: 0:59:09 time: 0.487482 data_time: 0.038607 memory: 21657 loss_kpt: 0.000424 acc_pose: 0.905372 loss: 0.000424 2022/10/21 20:36:08 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:36:31 - mmengine - INFO - Epoch(train) [190][150/391] lr: 5.000000e-05 eta: 0:58:48 time: 0.485346 data_time: 0.041636 memory: 21657 loss_kpt: 0.000415 acc_pose: 0.901182 loss: 0.000415 2022/10/21 20:36:56 - mmengine - INFO - Epoch(train) [190][200/391] lr: 5.000000e-05 eta: 0:58:26 time: 0.489546 data_time: 0.044156 memory: 21657 loss_kpt: 0.000428 acc_pose: 0.878840 loss: 0.000428 2022/10/21 20:37:20 - mmengine - INFO - Epoch(train) [190][250/391] lr: 5.000000e-05 eta: 0:58:04 time: 0.482761 data_time: 0.040845 memory: 21657 loss_kpt: 0.000429 acc_pose: 0.916699 loss: 0.000429 2022/10/21 20:37:44 - mmengine - INFO - Epoch(train) [190][300/391] lr: 5.000000e-05 eta: 0:57:43 time: 0.488015 data_time: 0.043597 memory: 21657 loss_kpt: 0.000430 acc_pose: 0.900740 loss: 0.000430 2022/10/21 20:38:09 - mmengine - INFO - Epoch(train) [190][350/391] lr: 5.000000e-05 eta: 0:57:21 time: 0.489623 data_time: 0.040127 memory: 21657 loss_kpt: 0.000434 acc_pose: 0.877878 loss: 0.000434 2022/10/21 20:38:28 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:38:28 - mmengine - INFO - Saving checkpoint at 190 epochs 2022/10/21 20:38:40 - mmengine - INFO - Epoch(val) [190][50/407] eta: 0:00:58 time: 0.163014 data_time: 0.021693 memory: 21657 2022/10/21 20:38:48 - mmengine - INFO - Epoch(val) [190][100/407] eta: 0:00:45 time: 0.149466 data_time: 0.009121 memory: 2142 2022/10/21 20:38:56 - mmengine - INFO - Epoch(val) [190][150/407] eta: 0:00:38 time: 0.150863 data_time: 0.009579 memory: 2142 2022/10/21 20:39:03 - mmengine - INFO - Epoch(val) [190][200/407] eta: 0:00:31 time: 0.150150 data_time: 0.009501 memory: 2142 2022/10/21 20:39:11 - mmengine - INFO - Epoch(val) [190][250/407] eta: 0:00:23 time: 0.150587 data_time: 0.009630 memory: 2142 2022/10/21 20:39:18 - mmengine - INFO - Epoch(val) [190][300/407] eta: 0:00:16 time: 0.150394 data_time: 0.010053 memory: 2142 2022/10/21 20:39:26 - mmengine - INFO - Epoch(val) [190][350/407] eta: 0:00:08 time: 0.149788 data_time: 0.009593 memory: 2142 2022/10/21 20:39:33 - mmengine - INFO - Epoch(val) [190][400/407] eta: 0:00:01 time: 0.148156 data_time: 0.009295 memory: 2142 2022/10/21 20:40:09 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 20:40:23 - mmengine - INFO - Epoch(val) [190][407/407] coco/AP: 0.751420 coco/AP .5: 0.907262 coco/AP .75: 0.821193 coco/AP (M): 0.709811 coco/AP (L): 0.822542 coco/AR: 0.801039 coco/AR .5: 0.941751 coco/AR .75: 0.863665 coco/AR (M): 0.755859 coco/AR (L): 0.866221 2022/10/21 20:40:23 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221021/resnetv1d152_384/best_coco/AP_epoch_180.pth is removed 2022/10/21 20:40:25 - mmengine - INFO - The best checkpoint with 0.7514 coco/AP at 190 epoch is saved to best_coco/AP_epoch_190.pth. 2022/10/21 20:40:51 - mmengine - INFO - Epoch(train) [191][50/391] lr: 5.000000e-05 eta: 0:56:40 time: 0.504747 data_time: 0.051538 memory: 21657 loss_kpt: 0.000426 acc_pose: 0.872520 loss: 0.000426 2022/10/21 20:41:15 - mmengine - INFO - Epoch(train) [191][100/391] lr: 5.000000e-05 eta: 0:56:18 time: 0.485979 data_time: 0.047936 memory: 21657 loss_kpt: 0.000411 acc_pose: 0.935841 loss: 0.000411 2022/10/21 20:41:39 - mmengine - INFO - Epoch(train) [191][150/391] lr: 5.000000e-05 eta: 0:55:57 time: 0.489877 data_time: 0.040997 memory: 21657 loss_kpt: 0.000422 acc_pose: 0.923075 loss: 0.000422 2022/10/21 20:42:04 - mmengine - INFO - Epoch(train) [191][200/391] lr: 5.000000e-05 eta: 0:55:35 time: 0.486712 data_time: 0.047459 memory: 21657 loss_kpt: 0.000428 acc_pose: 0.915543 loss: 0.000428 2022/10/21 20:42:28 - mmengine - INFO - Epoch(train) [191][250/391] lr: 5.000000e-05 eta: 0:55:13 time: 0.490876 data_time: 0.043340 memory: 21657 loss_kpt: 0.000420 acc_pose: 0.906334 loss: 0.000420 2022/10/21 20:42:52 - mmengine - INFO - Epoch(train) [191][300/391] lr: 5.000000e-05 eta: 0:54:52 time: 0.482956 data_time: 0.042699 memory: 21657 loss_kpt: 0.000438 acc_pose: 0.934767 loss: 0.000438 2022/10/21 20:43:17 - mmengine - INFO - Epoch(train) [191][350/391] lr: 5.000000e-05 eta: 0:54:30 time: 0.492665 data_time: 0.045126 memory: 21657 loss_kpt: 0.000431 acc_pose: 0.907376 loss: 0.000431 2022/10/21 20:43:37 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:44:02 - mmengine - INFO - Epoch(train) [192][50/391] lr: 5.000000e-05 eta: 0:53:49 time: 0.497809 data_time: 0.052097 memory: 21657 loss_kpt: 0.000422 acc_pose: 0.877305 loss: 0.000422 2022/10/21 20:44:26 - mmengine - INFO - Epoch(train) [192][100/391] lr: 5.000000e-05 eta: 0:53:27 time: 0.493901 data_time: 0.041263 memory: 21657 loss_kpt: 0.000428 acc_pose: 0.906121 loss: 0.000428 2022/10/21 20:44:50 - mmengine - INFO - Epoch(train) [192][150/391] lr: 5.000000e-05 eta: 0:53:05 time: 0.482050 data_time: 0.044058 memory: 21657 loss_kpt: 0.000439 acc_pose: 0.906276 loss: 0.000439 2022/10/21 20:45:15 - mmengine - INFO - Epoch(train) [192][200/391] lr: 5.000000e-05 eta: 0:52:44 time: 0.492930 data_time: 0.039811 memory: 21657 loss_kpt: 0.000437 acc_pose: 0.902996 loss: 0.000437 2022/10/21 20:45:39 - mmengine - INFO - Epoch(train) [192][250/391] lr: 5.000000e-05 eta: 0:52:22 time: 0.486914 data_time: 0.042542 memory: 21657 loss_kpt: 0.000424 acc_pose: 0.904920 loss: 0.000424 2022/10/21 20:46:04 - mmengine - INFO - Epoch(train) [192][300/391] lr: 5.000000e-05 eta: 0:52:01 time: 0.493352 data_time: 0.041228 memory: 21657 loss_kpt: 0.000422 acc_pose: 0.922894 loss: 0.000422 2022/10/21 20:46:13 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:46:28 - mmengine - INFO - Epoch(train) [192][350/391] lr: 5.000000e-05 eta: 0:51:39 time: 0.485312 data_time: 0.042890 memory: 21657 loss_kpt: 0.000437 acc_pose: 0.920243 loss: 0.000437 2022/10/21 20:46:48 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:47:13 - mmengine - INFO - Epoch(train) [193][50/391] lr: 5.000000e-05 eta: 0:50:58 time: 0.495106 data_time: 0.053787 memory: 21657 loss_kpt: 0.000416 acc_pose: 0.908668 loss: 0.000416 2022/10/21 20:47:37 - mmengine - INFO - Epoch(train) [193][100/391] lr: 5.000000e-05 eta: 0:50:36 time: 0.489958 data_time: 0.043284 memory: 21657 loss_kpt: 0.000422 acc_pose: 0.916824 loss: 0.000422 2022/10/21 20:48:02 - mmengine - INFO - Epoch(train) [193][150/391] lr: 5.000000e-05 eta: 0:50:14 time: 0.487220 data_time: 0.040679 memory: 21657 loss_kpt: 0.000426 acc_pose: 0.890009 loss: 0.000426 2022/10/21 20:48:26 - mmengine - INFO - Epoch(train) [193][200/391] lr: 5.000000e-05 eta: 0:49:53 time: 0.484663 data_time: 0.045713 memory: 21657 loss_kpt: 0.000423 acc_pose: 0.867923 loss: 0.000423 2022/10/21 20:48:51 - mmengine - INFO - Epoch(train) [193][250/391] lr: 5.000000e-05 eta: 0:49:31 time: 0.495535 data_time: 0.041381 memory: 21657 loss_kpt: 0.000428 acc_pose: 0.907897 loss: 0.000428 2022/10/21 20:49:15 - mmengine - INFO - Epoch(train) [193][300/391] lr: 5.000000e-05 eta: 0:49:09 time: 0.481453 data_time: 0.041672 memory: 21657 loss_kpt: 0.000418 acc_pose: 0.916461 loss: 0.000418 2022/10/21 20:49:40 - mmengine - INFO - Epoch(train) [193][350/391] lr: 5.000000e-05 eta: 0:48:48 time: 0.491340 data_time: 0.040973 memory: 21657 loss_kpt: 0.000422 acc_pose: 0.889050 loss: 0.000422 2022/10/21 20:49:59 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:50:25 - mmengine - INFO - Epoch(train) [194][50/391] lr: 5.000000e-05 eta: 0:48:07 time: 0.511955 data_time: 0.057588 memory: 21657 loss_kpt: 0.000435 acc_pose: 0.924979 loss: 0.000435 2022/10/21 20:50:49 - mmengine - INFO - Epoch(train) [194][100/391] lr: 5.000000e-05 eta: 0:47:45 time: 0.482734 data_time: 0.040902 memory: 21657 loss_kpt: 0.000420 acc_pose: 0.878765 loss: 0.000420 2022/10/21 20:51:14 - mmengine - INFO - Epoch(train) [194][150/391] lr: 5.000000e-05 eta: 0:47:23 time: 0.493476 data_time: 0.047596 memory: 21657 loss_kpt: 0.000426 acc_pose: 0.938409 loss: 0.000426 2022/10/21 20:51:38 - mmengine - INFO - Epoch(train) [194][200/391] lr: 5.000000e-05 eta: 0:47:02 time: 0.481856 data_time: 0.039148 memory: 21657 loss_kpt: 0.000429 acc_pose: 0.895712 loss: 0.000429 2022/10/21 20:52:02 - mmengine - INFO - Epoch(train) [194][250/391] lr: 5.000000e-05 eta: 0:46:40 time: 0.489221 data_time: 0.039058 memory: 21657 loss_kpt: 0.000431 acc_pose: 0.912219 loss: 0.000431 2022/10/21 20:52:26 - mmengine - INFO - Epoch(train) [194][300/391] lr: 5.000000e-05 eta: 0:46:18 time: 0.487060 data_time: 0.041501 memory: 21657 loss_kpt: 0.000421 acc_pose: 0.924472 loss: 0.000421 2022/10/21 20:52:51 - mmengine - INFO - Epoch(train) [194][350/391] lr: 5.000000e-05 eta: 0:45:57 time: 0.484827 data_time: 0.044727 memory: 21657 loss_kpt: 0.000420 acc_pose: 0.921282 loss: 0.000420 2022/10/21 20:53:11 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:53:36 - mmengine - INFO - Epoch(train) [195][50/391] lr: 5.000000e-05 eta: 0:45:16 time: 0.499959 data_time: 0.052418 memory: 21657 loss_kpt: 0.000409 acc_pose: 0.905894 loss: 0.000409 2022/10/21 20:54:00 - mmengine - INFO - Epoch(train) [195][100/391] lr: 5.000000e-05 eta: 0:44:54 time: 0.493737 data_time: 0.046144 memory: 21657 loss_kpt: 0.000420 acc_pose: 0.919195 loss: 0.000420 2022/10/21 20:54:22 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:54:25 - mmengine - INFO - Epoch(train) [195][150/391] lr: 5.000000e-05 eta: 0:44:32 time: 0.485294 data_time: 0.042423 memory: 21657 loss_kpt: 0.000417 acc_pose: 0.917536 loss: 0.000417 2022/10/21 20:54:49 - mmengine - INFO - Epoch(train) [195][200/391] lr: 5.000000e-05 eta: 0:44:10 time: 0.488114 data_time: 0.044430 memory: 21657 loss_kpt: 0.000425 acc_pose: 0.910472 loss: 0.000425 2022/10/21 20:55:13 - mmengine - INFO - Epoch(train) [195][250/391] lr: 5.000000e-05 eta: 0:43:49 time: 0.481266 data_time: 0.041717 memory: 21657 loss_kpt: 0.000429 acc_pose: 0.881033 loss: 0.000429 2022/10/21 20:55:38 - mmengine - INFO - Epoch(train) [195][300/391] lr: 5.000000e-05 eta: 0:43:27 time: 0.494491 data_time: 0.045458 memory: 21657 loss_kpt: 0.000412 acc_pose: 0.911469 loss: 0.000412 2022/10/21 20:56:02 - mmengine - INFO - Epoch(train) [195][350/391] lr: 5.000000e-05 eta: 0:43:05 time: 0.483808 data_time: 0.040758 memory: 21657 loss_kpt: 0.000418 acc_pose: 0.931624 loss: 0.000418 2022/10/21 20:56:22 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:56:47 - mmengine - INFO - Epoch(train) [196][50/391] lr: 5.000000e-05 eta: 0:42:24 time: 0.508782 data_time: 0.054639 memory: 21657 loss_kpt: 0.000414 acc_pose: 0.939419 loss: 0.000414 2022/10/21 20:57:11 - mmengine - INFO - Epoch(train) [196][100/391] lr: 5.000000e-05 eta: 0:42:03 time: 0.483464 data_time: 0.042987 memory: 21657 loss_kpt: 0.000418 acc_pose: 0.882167 loss: 0.000418 2022/10/21 20:57:36 - mmengine - INFO - Epoch(train) [196][150/391] lr: 5.000000e-05 eta: 0:41:41 time: 0.493248 data_time: 0.045521 memory: 21657 loss_kpt: 0.000424 acc_pose: 0.916153 loss: 0.000424 2022/10/21 20:58:00 - mmengine - INFO - Epoch(train) [196][200/391] lr: 5.000000e-05 eta: 0:41:19 time: 0.488552 data_time: 0.039516 memory: 21657 loss_kpt: 0.000420 acc_pose: 0.883831 loss: 0.000420 2022/10/21 20:58:25 - mmengine - INFO - Epoch(train) [196][250/391] lr: 5.000000e-05 eta: 0:40:58 time: 0.492550 data_time: 0.044313 memory: 21657 loss_kpt: 0.000427 acc_pose: 0.922103 loss: 0.000427 2022/10/21 20:58:50 - mmengine - INFO - Epoch(train) [196][300/391] lr: 5.000000e-05 eta: 0:40:36 time: 0.488840 data_time: 0.041047 memory: 21657 loss_kpt: 0.000414 acc_pose: 0.904393 loss: 0.000414 2022/10/21 20:59:14 - mmengine - INFO - Epoch(train) [196][350/391] lr: 5.000000e-05 eta: 0:40:14 time: 0.492859 data_time: 0.042686 memory: 21657 loss_kpt: 0.000415 acc_pose: 0.869231 loss: 0.000415 2022/10/21 20:59:34 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 20:59:59 - mmengine - INFO - Epoch(train) [197][50/391] lr: 5.000000e-05 eta: 0:39:33 time: 0.505341 data_time: 0.056742 memory: 21657 loss_kpt: 0.000415 acc_pose: 0.911711 loss: 0.000415 2022/10/21 21:00:24 - mmengine - INFO - Epoch(train) [197][100/391] lr: 5.000000e-05 eta: 0:39:12 time: 0.486357 data_time: 0.040450 memory: 21657 loss_kpt: 0.000429 acc_pose: 0.919458 loss: 0.000429 2022/10/21 21:00:48 - mmengine - INFO - Epoch(train) [197][150/391] lr: 5.000000e-05 eta: 0:38:50 time: 0.483447 data_time: 0.046291 memory: 21657 loss_kpt: 0.000424 acc_pose: 0.889823 loss: 0.000424 2022/10/21 21:01:12 - mmengine - INFO - Epoch(train) [197][200/391] lr: 5.000000e-05 eta: 0:38:28 time: 0.487841 data_time: 0.041429 memory: 21657 loss_kpt: 0.000419 acc_pose: 0.938538 loss: 0.000419 2022/10/21 21:01:36 - mmengine - INFO - Epoch(train) [197][250/391] lr: 5.000000e-05 eta: 0:38:07 time: 0.485923 data_time: 0.040564 memory: 21657 loss_kpt: 0.000441 acc_pose: 0.882905 loss: 0.000441 2022/10/21 21:02:01 - mmengine - INFO - Epoch(train) [197][300/391] lr: 5.000000e-05 eta: 0:37:45 time: 0.490268 data_time: 0.044465 memory: 21657 loss_kpt: 0.000411 acc_pose: 0.939460 loss: 0.000411 2022/10/21 21:02:25 - mmengine - INFO - Epoch(train) [197][350/391] lr: 5.000000e-05 eta: 0:37:23 time: 0.484957 data_time: 0.039552 memory: 21657 loss_kpt: 0.000427 acc_pose: 0.899080 loss: 0.000427 2022/10/21 21:02:32 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:02:45 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:03:10 - mmengine - INFO - Epoch(train) [198][50/391] lr: 5.000000e-05 eta: 0:36:42 time: 0.499612 data_time: 0.052381 memory: 21657 loss_kpt: 0.000429 acc_pose: 0.894578 loss: 0.000429 2022/10/21 21:03:35 - mmengine - INFO - Epoch(train) [198][100/391] lr: 5.000000e-05 eta: 0:36:21 time: 0.495364 data_time: 0.041317 memory: 21657 loss_kpt: 0.000429 acc_pose: 0.863635 loss: 0.000429 2022/10/21 21:03:59 - mmengine - INFO - Epoch(train) [198][150/391] lr: 5.000000e-05 eta: 0:35:59 time: 0.485158 data_time: 0.040540 memory: 21657 loss_kpt: 0.000415 acc_pose: 0.893809 loss: 0.000415 2022/10/21 21:04:23 - mmengine - INFO - Epoch(train) [198][200/391] lr: 5.000000e-05 eta: 0:35:37 time: 0.487393 data_time: 0.047942 memory: 21657 loss_kpt: 0.000425 acc_pose: 0.911362 loss: 0.000425 2022/10/21 21:04:48 - mmengine - INFO - Epoch(train) [198][250/391] lr: 5.000000e-05 eta: 0:35:15 time: 0.491084 data_time: 0.041501 memory: 21657 loss_kpt: 0.000419 acc_pose: 0.925290 loss: 0.000419 2022/10/21 21:05:12 - mmengine - INFO - Epoch(train) [198][300/391] lr: 5.000000e-05 eta: 0:34:54 time: 0.483525 data_time: 0.039965 memory: 21657 loss_kpt: 0.000427 acc_pose: 0.886555 loss: 0.000427 2022/10/21 21:05:37 - mmengine - INFO - Epoch(train) [198][350/391] lr: 5.000000e-05 eta: 0:34:32 time: 0.492493 data_time: 0.045406 memory: 21657 loss_kpt: 0.000423 acc_pose: 0.877791 loss: 0.000423 2022/10/21 21:05:56 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:06:22 - mmengine - INFO - Epoch(train) [199][50/391] lr: 5.000000e-05 eta: 0:33:51 time: 0.508111 data_time: 0.052468 memory: 21657 loss_kpt: 0.000424 acc_pose: 0.897916 loss: 0.000424 2022/10/21 21:06:46 - mmengine - INFO - Epoch(train) [199][100/391] lr: 5.000000e-05 eta: 0:33:29 time: 0.486627 data_time: 0.041288 memory: 21657 loss_kpt: 0.000427 acc_pose: 0.900128 loss: 0.000427 2022/10/21 21:07:11 - mmengine - INFO - Epoch(train) [199][150/391] lr: 5.000000e-05 eta: 0:33:08 time: 0.491610 data_time: 0.044852 memory: 21657 loss_kpt: 0.000417 acc_pose: 0.916266 loss: 0.000417 2022/10/21 21:07:35 - mmengine - INFO - Epoch(train) [199][200/391] lr: 5.000000e-05 eta: 0:32:46 time: 0.489280 data_time: 0.040699 memory: 21657 loss_kpt: 0.000423 acc_pose: 0.897881 loss: 0.000423 2022/10/21 21:08:00 - mmengine - INFO - Epoch(train) [199][250/391] lr: 5.000000e-05 eta: 0:32:24 time: 0.488497 data_time: 0.046243 memory: 21657 loss_kpt: 0.000413 acc_pose: 0.902007 loss: 0.000413 2022/10/21 21:08:24 - mmengine - INFO - Epoch(train) [199][300/391] lr: 5.000000e-05 eta: 0:32:02 time: 0.484906 data_time: 0.040538 memory: 21657 loss_kpt: 0.000427 acc_pose: 0.940011 loss: 0.000427 2022/10/21 21:08:48 - mmengine - INFO - Epoch(train) [199][350/391] lr: 5.000000e-05 eta: 0:31:41 time: 0.487268 data_time: 0.039823 memory: 21657 loss_kpt: 0.000416 acc_pose: 0.917013 loss: 0.000416 2022/10/21 21:09:08 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:09:33 - mmengine - INFO - Epoch(train) [200][50/391] lr: 5.000000e-05 eta: 0:31:00 time: 0.500953 data_time: 0.055410 memory: 21657 loss_kpt: 0.000435 acc_pose: 0.881991 loss: 0.000435 2022/10/21 21:09:58 - mmengine - INFO - Epoch(train) [200][100/391] lr: 5.000000e-05 eta: 0:30:38 time: 0.490503 data_time: 0.044929 memory: 21657 loss_kpt: 0.000414 acc_pose: 0.928423 loss: 0.000414 2022/10/21 21:10:22 - mmengine - INFO - Epoch(train) [200][150/391] lr: 5.000000e-05 eta: 0:30:17 time: 0.484314 data_time: 0.041476 memory: 21657 loss_kpt: 0.000405 acc_pose: 0.894063 loss: 0.000405 2022/10/21 21:10:42 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:10:46 - mmengine - INFO - Epoch(train) [200][200/391] lr: 5.000000e-05 eta: 0:29:55 time: 0.485697 data_time: 0.046865 memory: 21657 loss_kpt: 0.000429 acc_pose: 0.902591 loss: 0.000429 2022/10/21 21:11:11 - mmengine - INFO - Epoch(train) [200][250/391] lr: 5.000000e-05 eta: 0:29:33 time: 0.488483 data_time: 0.041290 memory: 21657 loss_kpt: 0.000427 acc_pose: 0.931449 loss: 0.000427 2022/10/21 21:11:35 - mmengine - INFO - Epoch(train) [200][300/391] lr: 5.000000e-05 eta: 0:29:11 time: 0.485290 data_time: 0.042544 memory: 21657 loss_kpt: 0.000417 acc_pose: 0.886886 loss: 0.000417 2022/10/21 21:12:00 - mmengine - INFO - Epoch(train) [200][350/391] lr: 5.000000e-05 eta: 0:28:50 time: 0.489383 data_time: 0.043224 memory: 21657 loss_kpt: 0.000424 acc_pose: 0.907071 loss: 0.000424 2022/10/21 21:12:19 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:12:19 - mmengine - INFO - Saving checkpoint at 200 epochs 2022/10/21 21:12:31 - mmengine - INFO - Epoch(val) [200][50/407] eta: 0:00:57 time: 0.160755 data_time: 0.014491 memory: 21657 2022/10/21 21:12:39 - mmengine - INFO - Epoch(val) [200][100/407] eta: 0:00:46 time: 0.150875 data_time: 0.009126 memory: 2142 2022/10/21 21:12:46 - mmengine - INFO - Epoch(val) [200][150/407] eta: 0:00:38 time: 0.148751 data_time: 0.009188 memory: 2142 2022/10/21 21:12:54 - mmengine - INFO - Epoch(val) [200][200/407] eta: 0:00:31 time: 0.151002 data_time: 0.009575 memory: 2142 2022/10/21 21:13:01 - mmengine - INFO - Epoch(val) [200][250/407] eta: 0:00:23 time: 0.149747 data_time: 0.008826 memory: 2142 2022/10/21 21:13:09 - mmengine - INFO - Epoch(val) [200][300/407] eta: 0:00:15 time: 0.149339 data_time: 0.008665 memory: 2142 2022/10/21 21:13:16 - mmengine - INFO - Epoch(val) [200][350/407] eta: 0:00:08 time: 0.150061 data_time: 0.009995 memory: 2142 2022/10/21 21:13:24 - mmengine - INFO - Epoch(val) [200][400/407] eta: 0:00:01 time: 0.148262 data_time: 0.008818 memory: 2142 2022/10/21 21:14:00 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 21:14:14 - mmengine - INFO - Epoch(val) [200][407/407] coco/AP: 0.750155 coco/AP .5: 0.906372 coco/AP .75: 0.820753 coco/AP (M): 0.708062 coco/AP (L): 0.821396 coco/AR: 0.799055 coco/AR .5: 0.940176 coco/AR .75: 0.861776 coco/AR (M): 0.753510 coco/AR (L): 0.864734 2022/10/21 21:14:39 - mmengine - INFO - Epoch(train) [201][50/391] lr: 5.000000e-06 eta: 0:28:09 time: 0.504174 data_time: 0.056181 memory: 21657 loss_kpt: 0.000414 acc_pose: 0.929115 loss: 0.000414 2022/10/21 21:15:03 - mmengine - INFO - Epoch(train) [201][100/391] lr: 5.000000e-06 eta: 0:27:47 time: 0.488070 data_time: 0.044013 memory: 21657 loss_kpt: 0.000426 acc_pose: 0.925496 loss: 0.000426 2022/10/21 21:15:28 - mmengine - INFO - Epoch(train) [201][150/391] lr: 5.000000e-06 eta: 0:27:25 time: 0.484915 data_time: 0.041544 memory: 21657 loss_kpt: 0.000413 acc_pose: 0.922607 loss: 0.000413 2022/10/21 21:15:52 - mmengine - INFO - Epoch(train) [201][200/391] lr: 5.000000e-06 eta: 0:27:04 time: 0.486151 data_time: 0.040582 memory: 21657 loss_kpt: 0.000420 acc_pose: 0.914181 loss: 0.000420 2022/10/21 21:16:16 - mmengine - INFO - Epoch(train) [201][250/391] lr: 5.000000e-06 eta: 0:26:42 time: 0.490253 data_time: 0.040944 memory: 21657 loss_kpt: 0.000419 acc_pose: 0.883952 loss: 0.000419 2022/10/21 21:16:41 - mmengine - INFO - Epoch(train) [201][300/391] lr: 5.000000e-06 eta: 0:26:20 time: 0.486147 data_time: 0.041334 memory: 21657 loss_kpt: 0.000416 acc_pose: 0.870302 loss: 0.000416 2022/10/21 21:17:05 - mmengine - INFO - Epoch(train) [201][350/391] lr: 5.000000e-06 eta: 0:25:58 time: 0.482878 data_time: 0.045827 memory: 21657 loss_kpt: 0.000410 acc_pose: 0.946086 loss: 0.000410 2022/10/21 21:17:25 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:17:49 - mmengine - INFO - Epoch(train) [202][50/391] lr: 5.000000e-06 eta: 0:25:18 time: 0.491320 data_time: 0.060788 memory: 21657 loss_kpt: 0.000414 acc_pose: 0.857563 loss: 0.000414 2022/10/21 21:18:14 - mmengine - INFO - Epoch(train) [202][100/391] lr: 5.000000e-06 eta: 0:24:56 time: 0.495555 data_time: 0.040508 memory: 21657 loss_kpt: 0.000428 acc_pose: 0.933911 loss: 0.000428 2022/10/21 21:18:38 - mmengine - INFO - Epoch(train) [202][150/391] lr: 5.000000e-06 eta: 0:24:34 time: 0.482168 data_time: 0.043457 memory: 21657 loss_kpt: 0.000413 acc_pose: 0.888724 loss: 0.000413 2022/10/21 21:19:03 - mmengine - INFO - Epoch(train) [202][200/391] lr: 5.000000e-06 eta: 0:24:12 time: 0.488291 data_time: 0.041399 memory: 21657 loss_kpt: 0.000412 acc_pose: 0.907495 loss: 0.000412 2022/10/21 21:19:27 - mmengine - INFO - Epoch(train) [202][250/391] lr: 5.000000e-06 eta: 0:23:51 time: 0.490227 data_time: 0.040566 memory: 21657 loss_kpt: 0.000423 acc_pose: 0.922641 loss: 0.000423 2022/10/21 21:19:52 - mmengine - INFO - Epoch(train) [202][300/391] lr: 5.000000e-06 eta: 0:23:29 time: 0.488064 data_time: 0.041123 memory: 21657 loss_kpt: 0.000409 acc_pose: 0.946764 loss: 0.000409 2022/10/21 21:20:16 - mmengine - INFO - Epoch(train) [202][350/391] lr: 5.000000e-06 eta: 0:23:07 time: 0.488403 data_time: 0.040585 memory: 21657 loss_kpt: 0.000420 acc_pose: 0.943949 loss: 0.000420 2022/10/21 21:20:36 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:20:45 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:21:01 - mmengine - INFO - Epoch(train) [203][50/391] lr: 5.000000e-06 eta: 0:22:27 time: 0.502229 data_time: 0.051407 memory: 21657 loss_kpt: 0.000425 acc_pose: 0.900952 loss: 0.000425 2022/10/21 21:21:25 - mmengine - INFO - Epoch(train) [203][100/391] lr: 5.000000e-06 eta: 0:22:05 time: 0.481827 data_time: 0.041154 memory: 21657 loss_kpt: 0.000424 acc_pose: 0.904515 loss: 0.000424 2022/10/21 21:21:49 - mmengine - INFO - Epoch(train) [203][150/391] lr: 5.000000e-06 eta: 0:21:43 time: 0.484687 data_time: 0.039655 memory: 21657 loss_kpt: 0.000411 acc_pose: 0.900209 loss: 0.000411 2022/10/21 21:22:13 - mmengine - INFO - Epoch(train) [203][200/391] lr: 5.000000e-06 eta: 0:21:21 time: 0.482378 data_time: 0.040879 memory: 21657 loss_kpt: 0.000422 acc_pose: 0.903838 loss: 0.000422 2022/10/21 21:22:38 - mmengine - INFO - Epoch(train) [203][250/391] lr: 5.000000e-06 eta: 0:20:59 time: 0.493756 data_time: 0.040775 memory: 21657 loss_kpt: 0.000419 acc_pose: 0.927036 loss: 0.000419 2022/10/21 21:23:02 - mmengine - INFO - Epoch(train) [203][300/391] lr: 5.000000e-06 eta: 0:20:38 time: 0.483997 data_time: 0.044037 memory: 21657 loss_kpt: 0.000417 acc_pose: 0.900520 loss: 0.000417 2022/10/21 21:23:27 - mmengine - INFO - Epoch(train) [203][350/391] lr: 5.000000e-06 eta: 0:20:16 time: 0.485940 data_time: 0.041449 memory: 21657 loss_kpt: 0.000409 acc_pose: 0.915713 loss: 0.000409 2022/10/21 21:23:46 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:24:11 - mmengine - INFO - Epoch(train) [204][50/391] lr: 5.000000e-06 eta: 0:19:36 time: 0.500240 data_time: 0.055691 memory: 21657 loss_kpt: 0.000418 acc_pose: 0.905974 loss: 0.000418 2022/10/21 21:24:36 - mmengine - INFO - Epoch(train) [204][100/391] lr: 5.000000e-06 eta: 0:19:14 time: 0.488145 data_time: 0.040947 memory: 21657 loss_kpt: 0.000414 acc_pose: 0.944556 loss: 0.000414 2022/10/21 21:25:00 - mmengine - INFO - Epoch(train) [204][150/391] lr: 5.000000e-06 eta: 0:18:52 time: 0.485384 data_time: 0.045660 memory: 21657 loss_kpt: 0.000418 acc_pose: 0.912633 loss: 0.000418 2022/10/21 21:25:24 - mmengine - INFO - Epoch(train) [204][200/391] lr: 5.000000e-06 eta: 0:18:30 time: 0.483902 data_time: 0.039898 memory: 21657 loss_kpt: 0.000416 acc_pose: 0.879882 loss: 0.000416 2022/10/21 21:25:49 - mmengine - INFO - Epoch(train) [204][250/391] lr: 5.000000e-06 eta: 0:18:08 time: 0.485252 data_time: 0.043414 memory: 21657 loss_kpt: 0.000422 acc_pose: 0.901446 loss: 0.000422 2022/10/21 21:26:13 - mmengine - INFO - Epoch(train) [204][300/391] lr: 5.000000e-06 eta: 0:17:46 time: 0.489921 data_time: 0.040171 memory: 21657 loss_kpt: 0.000414 acc_pose: 0.909183 loss: 0.000414 2022/10/21 21:26:38 - mmengine - INFO - Epoch(train) [204][350/391] lr: 5.000000e-06 eta: 0:17:25 time: 0.489103 data_time: 0.044489 memory: 21657 loss_kpt: 0.000431 acc_pose: 0.847137 loss: 0.000431 2022/10/21 21:26:57 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:27:22 - mmengine - INFO - Epoch(train) [205][50/391] lr: 5.000000e-06 eta: 0:16:44 time: 0.502996 data_time: 0.052379 memory: 21657 loss_kpt: 0.000413 acc_pose: 0.903355 loss: 0.000413 2022/10/21 21:27:47 - mmengine - INFO - Epoch(train) [205][100/391] lr: 5.000000e-06 eta: 0:16:23 time: 0.491811 data_time: 0.045537 memory: 21657 loss_kpt: 0.000424 acc_pose: 0.920902 loss: 0.000424 2022/10/21 21:28:11 - mmengine - INFO - Epoch(train) [205][150/391] lr: 5.000000e-06 eta: 0:16:01 time: 0.485938 data_time: 0.040341 memory: 21657 loss_kpt: 0.000436 acc_pose: 0.896898 loss: 0.000436 2022/10/21 21:28:36 - mmengine - INFO - Epoch(train) [205][200/391] lr: 5.000000e-06 eta: 0:15:39 time: 0.490320 data_time: 0.039928 memory: 21657 loss_kpt: 0.000424 acc_pose: 0.898987 loss: 0.000424 2022/10/21 21:28:53 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:29:00 - mmengine - INFO - Epoch(train) [205][250/391] lr: 5.000000e-06 eta: 0:15:17 time: 0.484762 data_time: 0.039900 memory: 21657 loss_kpt: 0.000420 acc_pose: 0.915556 loss: 0.000420 2022/10/21 21:29:24 - mmengine - INFO - Epoch(train) [205][300/391] lr: 5.000000e-06 eta: 0:14:55 time: 0.485619 data_time: 0.043754 memory: 21657 loss_kpt: 0.000417 acc_pose: 0.870236 loss: 0.000417 2022/10/21 21:29:49 - mmengine - INFO - Epoch(train) [205][350/391] lr: 5.000000e-06 eta: 0:14:33 time: 0.488853 data_time: 0.040396 memory: 21657 loss_kpt: 0.000418 acc_pose: 0.909445 loss: 0.000418 2022/10/21 21:30:08 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:30:34 - mmengine - INFO - Epoch(train) [206][50/391] lr: 5.000000e-06 eta: 0:13:53 time: 0.507070 data_time: 0.051787 memory: 21657 loss_kpt: 0.000421 acc_pose: 0.884001 loss: 0.000421 2022/10/21 21:30:58 - mmengine - INFO - Epoch(train) [206][100/391] lr: 5.000000e-06 eta: 0:13:31 time: 0.487576 data_time: 0.040208 memory: 21657 loss_kpt: 0.000417 acc_pose: 0.944120 loss: 0.000417 2022/10/21 21:31:23 - mmengine - INFO - Epoch(train) [206][150/391] lr: 5.000000e-06 eta: 0:13:10 time: 0.491641 data_time: 0.043764 memory: 21657 loss_kpt: 0.000419 acc_pose: 0.872363 loss: 0.000419 2022/10/21 21:31:47 - mmengine - INFO - Epoch(train) [206][200/391] lr: 5.000000e-06 eta: 0:12:48 time: 0.484267 data_time: 0.040333 memory: 21657 loss_kpt: 0.000426 acc_pose: 0.910353 loss: 0.000426 2022/10/21 21:32:11 - mmengine - INFO - Epoch(train) [206][250/391] lr: 5.000000e-06 eta: 0:12:26 time: 0.489246 data_time: 0.043613 memory: 21657 loss_kpt: 0.000419 acc_pose: 0.932212 loss: 0.000419 2022/10/21 21:32:36 - mmengine - INFO - Epoch(train) [206][300/391] lr: 5.000000e-06 eta: 0:12:04 time: 0.493407 data_time: 0.040154 memory: 21657 loss_kpt: 0.000408 acc_pose: 0.897381 loss: 0.000408 2022/10/21 21:33:01 - mmengine - INFO - Epoch(train) [206][350/391] lr: 5.000000e-06 eta: 0:11:42 time: 0.492297 data_time: 0.043959 memory: 21657 loss_kpt: 0.000421 acc_pose: 0.916306 loss: 0.000421 2022/10/21 21:33:20 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:33:45 - mmengine - INFO - Epoch(train) [207][50/391] lr: 5.000000e-06 eta: 0:11:02 time: 0.507763 data_time: 0.059120 memory: 21657 loss_kpt: 0.000418 acc_pose: 0.927963 loss: 0.000418 2022/10/21 21:34:10 - mmengine - INFO - Epoch(train) [207][100/391] lr: 5.000000e-06 eta: 0:10:40 time: 0.489573 data_time: 0.042108 memory: 21657 loss_kpt: 0.000421 acc_pose: 0.890414 loss: 0.000421 2022/10/21 21:34:34 - mmengine - INFO - Epoch(train) [207][150/391] lr: 5.000000e-06 eta: 0:10:18 time: 0.482768 data_time: 0.040750 memory: 21657 loss_kpt: 0.000424 acc_pose: 0.914160 loss: 0.000424 2022/10/21 21:34:59 - mmengine - INFO - Epoch(train) [207][200/391] lr: 5.000000e-06 eta: 0:09:57 time: 0.487896 data_time: 0.045149 memory: 21657 loss_kpt: 0.000428 acc_pose: 0.873369 loss: 0.000428 2022/10/21 21:35:23 - mmengine - INFO - Epoch(train) [207][250/391] lr: 5.000000e-06 eta: 0:09:35 time: 0.484318 data_time: 0.039406 memory: 21657 loss_kpt: 0.000416 acc_pose: 0.916054 loss: 0.000416 2022/10/21 21:35:47 - mmengine - INFO - Epoch(train) [207][300/391] lr: 5.000000e-06 eta: 0:09:13 time: 0.489165 data_time: 0.041143 memory: 21657 loss_kpt: 0.000413 acc_pose: 0.924989 loss: 0.000413 2022/10/21 21:36:12 - mmengine - INFO - Epoch(train) [207][350/391] lr: 5.000000e-06 eta: 0:08:51 time: 0.487507 data_time: 0.041785 memory: 21657 loss_kpt: 0.000419 acc_pose: 0.924412 loss: 0.000419 2022/10/21 21:36:32 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:36:57 - mmengine - INFO - Epoch(train) [208][50/391] lr: 5.000000e-06 eta: 0:08:11 time: 0.504208 data_time: 0.054731 memory: 21657 loss_kpt: 0.000422 acc_pose: 0.913257 loss: 0.000422 2022/10/21 21:37:03 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:37:21 - mmengine - INFO - Epoch(train) [208][100/391] lr: 5.000000e-06 eta: 0:07:49 time: 0.486518 data_time: 0.043528 memory: 21657 loss_kpt: 0.000418 acc_pose: 0.906400 loss: 0.000418 2022/10/21 21:37:46 - mmengine - INFO - Epoch(train) [208][150/391] lr: 5.000000e-06 eta: 0:07:27 time: 0.486434 data_time: 0.040341 memory: 21657 loss_kpt: 0.000417 acc_pose: 0.933093 loss: 0.000417 2022/10/21 21:38:10 - mmengine - INFO - Epoch(train) [208][200/391] lr: 5.000000e-06 eta: 0:07:05 time: 0.485273 data_time: 0.040954 memory: 21657 loss_kpt: 0.000432 acc_pose: 0.904554 loss: 0.000432 2022/10/21 21:38:34 - mmengine - INFO - Epoch(train) [208][250/391] lr: 5.000000e-06 eta: 0:06:44 time: 0.486839 data_time: 0.040495 memory: 21657 loss_kpt: 0.000413 acc_pose: 0.912814 loss: 0.000413 2022/10/21 21:38:59 - mmengine - INFO - Epoch(train) [208][300/391] lr: 5.000000e-06 eta: 0:06:22 time: 0.488751 data_time: 0.044901 memory: 21657 loss_kpt: 0.000419 acc_pose: 0.909848 loss: 0.000419 2022/10/21 21:39:23 - mmengine - INFO - Epoch(train) [208][350/391] lr: 5.000000e-06 eta: 0:06:00 time: 0.489106 data_time: 0.039523 memory: 21657 loss_kpt: 0.000422 acc_pose: 0.856802 loss: 0.000422 2022/10/21 21:39:43 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:40:08 - mmengine - INFO - Epoch(train) [209][50/391] lr: 5.000000e-06 eta: 0:05:20 time: 0.506184 data_time: 0.056125 memory: 21657 loss_kpt: 0.000410 acc_pose: 0.883085 loss: 0.000410 2022/10/21 21:40:33 - mmengine - INFO - Epoch(train) [209][100/391] lr: 5.000000e-06 eta: 0:04:58 time: 0.486680 data_time: 0.040958 memory: 21657 loss_kpt: 0.000429 acc_pose: 0.938784 loss: 0.000429 2022/10/21 21:40:57 - mmengine - INFO - Epoch(train) [209][150/391] lr: 5.000000e-06 eta: 0:04:36 time: 0.484951 data_time: 0.043718 memory: 21657 loss_kpt: 0.000408 acc_pose: 0.922367 loss: 0.000408 2022/10/21 21:41:21 - mmengine - INFO - Epoch(train) [209][200/391] lr: 5.000000e-06 eta: 0:04:14 time: 0.484850 data_time: 0.039756 memory: 21657 loss_kpt: 0.000421 acc_pose: 0.895524 loss: 0.000421 2022/10/21 21:41:45 - mmengine - INFO - Epoch(train) [209][250/391] lr: 5.000000e-06 eta: 0:03:52 time: 0.488085 data_time: 0.043692 memory: 21657 loss_kpt: 0.000411 acc_pose: 0.896400 loss: 0.000411 2022/10/21 21:42:10 - mmengine - INFO - Epoch(train) [209][300/391] lr: 5.000000e-06 eta: 0:03:31 time: 0.483860 data_time: 0.040421 memory: 21657 loss_kpt: 0.000424 acc_pose: 0.890353 loss: 0.000424 2022/10/21 21:42:34 - mmengine - INFO - Epoch(train) [209][350/391] lr: 5.000000e-06 eta: 0:03:09 time: 0.487688 data_time: 0.040987 memory: 21657 loss_kpt: 0.000431 acc_pose: 0.911622 loss: 0.000431 2022/10/21 21:42:54 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:43:19 - mmengine - INFO - Epoch(train) [210][50/391] lr: 5.000000e-06 eta: 0:02:29 time: 0.501078 data_time: 0.052303 memory: 21657 loss_kpt: 0.000418 acc_pose: 0.894856 loss: 0.000418 2022/10/21 21:43:43 - mmengine - INFO - Epoch(train) [210][100/391] lr: 5.000000e-06 eta: 0:02:07 time: 0.490838 data_time: 0.044113 memory: 21657 loss_kpt: 0.000422 acc_pose: 0.939992 loss: 0.000422 2022/10/21 21:44:07 - mmengine - INFO - Epoch(train) [210][150/391] lr: 5.000000e-06 eta: 0:01:45 time: 0.485054 data_time: 0.040885 memory: 21657 loss_kpt: 0.000426 acc_pose: 0.925335 loss: 0.000426 2022/10/21 21:44:32 - mmengine - INFO - Epoch(train) [210][200/391] lr: 5.000000e-06 eta: 0:01:23 time: 0.490702 data_time: 0.044758 memory: 21657 loss_kpt: 0.000415 acc_pose: 0.922561 loss: 0.000415 2022/10/21 21:44:56 - mmengine - INFO - Epoch(train) [210][250/391] lr: 5.000000e-06 eta: 0:01:01 time: 0.482228 data_time: 0.040638 memory: 21657 loss_kpt: 0.000421 acc_pose: 0.897359 loss: 0.000421 2022/10/21 21:45:11 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:45:21 - mmengine - INFO - Epoch(train) [210][300/391] lr: 5.000000e-06 eta: 0:00:39 time: 0.491470 data_time: 0.041750 memory: 21657 loss_kpt: 0.000408 acc_pose: 0.914556 loss: 0.000408 2022/10/21 21:45:45 - mmengine - INFO - Epoch(train) [210][350/391] lr: 5.000000e-06 eta: 0:00:17 time: 0.483570 data_time: 0.042980 memory: 21657 loss_kpt: 0.000424 acc_pose: 0.917305 loss: 0.000424 2022/10/21 21:46:05 - mmengine - INFO - Exp name: td-hm_resnetv1d152_8xb48-210e_coco-384x288_20221021_095833 2022/10/21 21:46:05 - mmengine - INFO - Saving checkpoint at 210 epochs 2022/10/21 21:46:17 - mmengine - INFO - Epoch(val) [210][50/407] eta: 0:00:56 time: 0.157570 data_time: 0.014625 memory: 21657 2022/10/21 21:46:24 - mmengine - INFO - Epoch(val) [210][100/407] eta: 0:00:46 time: 0.150300 data_time: 0.008788 memory: 2142 2022/10/21 21:46:32 - mmengine - INFO - Epoch(val) [210][150/407] eta: 0:00:38 time: 0.151055 data_time: 0.008948 memory: 2142 2022/10/21 21:46:39 - mmengine - INFO - Epoch(val) [210][200/407] eta: 0:00:31 time: 0.150826 data_time: 0.009370 memory: 2142 2022/10/21 21:46:47 - mmengine - INFO - Epoch(val) [210][250/407] eta: 0:00:23 time: 0.151386 data_time: 0.009286 memory: 2142 2022/10/21 21:46:55 - mmengine - INFO - Epoch(val) [210][300/407] eta: 0:00:16 time: 0.154679 data_time: 0.008983 memory: 2142 2022/10/21 21:47:02 - mmengine - INFO - Epoch(val) [210][350/407] eta: 0:00:08 time: 0.150730 data_time: 0.008962 memory: 2142 2022/10/21 21:47:10 - mmengine - INFO - Epoch(val) [210][400/407] eta: 0:00:01 time: 0.147611 data_time: 0.008219 memory: 2142 2022/10/21 21:47:45 - mmengine - INFO - Evaluating CocoMetric... 2022/10/21 21:47:59 - mmengine - INFO - Epoch(val) [210][407/407] coco/AP: 0.750821 coco/AP .5: 0.908137 coco/AP .75: 0.822047 coco/AP (M): 0.709253 coco/AP (L): 0.821722 coco/AR: 0.799906 coco/AR .5: 0.941593 coco/AR .75: 0.863665 coco/AR (M): 0.754357 coco/AR (L): 0.865700