2022/10/12 11:02:19 - 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: 549826906 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 ------------------------------------------------------------ 2022/10/12 11:02:20 - 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=0.02)) 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, 190, 200], gamma=0.1, by_epoch=True) ] auto_scale_lr = dict(base_batch_size=256) kernel_sizes = [11, 9, 7, 5] codec = [ dict( type='MegviiHeatmap', input_size=(192, 256), heatmap_size=(48, 64), kernel_size=11), dict( type='MegviiHeatmap', input_size=(192, 256), heatmap_size=(48, 64), kernel_size=9), dict( type='MegviiHeatmap', input_size=(192, 256), heatmap_size=(48, 64), kernel_size=7), dict( type='MegviiHeatmap', input_size=(192, 256), heatmap_size=(48, 64), kernel_size=5) ] 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='RSN', unit_channels=256, num_stages=1, num_units=4, num_blocks=[2, 2, 2, 2], num_steps=4, norm_cfg=dict(type='BN')), head=dict( type='MSPNHead', out_shape=(64, 48), unit_channels=256, out_channels=17, num_stages=1, num_units=4, norm_cfg=dict(type='BN'), level_indices=[0, 1, 2, 3], loss=[ dict( type='KeypointMSELoss', use_target_weight=True, loss_weight=0.25), dict( type='KeypointMSELoss', use_target_weight=True, loss_weight=0.25), dict( type='KeypointMSELoss', use_target_weight=True, loss_weight=0.25), dict( type='KeypointOHKMMSELoss', use_target_weight=True, loss_weight=1.0) ], decoder=dict( type='MegviiHeatmap', input_size=(192, 256), heatmap_size=(48, 64), kernel_size=5)), test_cfg=dict(flip_test=True, flip_mode='heatmap', shift_heatmap=False)) dataset_type = 'CocoDataset' data_mode = 'topdown' data_root = 'data/coco/' train_pipeline = [ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict(type='RandomBBoxTransform'), dict(type='TopdownAffine', input_size=(192, 256)), dict( type='GenerateTarget', target_type='multilevel_heatmap', encoder=[ dict( type='MegviiHeatmap', input_size=(192, 256), heatmap_size=(48, 64), kernel_size=11), dict( type='MegviiHeatmap', input_size=(192, 256), heatmap_size=(48, 64), kernel_size=9), dict( type='MegviiHeatmap', input_size=(192, 256), heatmap_size=(48, 64), kernel_size=7), dict( type='MegviiHeatmap', input_size=(192, 256), heatmap_size=(48, 64), kernel_size=5) ]), dict(type='PackPoseInputs') ] val_pipeline = [ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ] train_dataloader = dict( batch_size=32, num_workers=4, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_train2017.json', data_prefix=dict(img='train2017/'), pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict(type='RandomBBoxTransform'), dict(type='TopdownAffine', input_size=(192, 256)), dict( type='GenerateTarget', target_type='multilevel_heatmap', encoder=[ dict( type='MegviiHeatmap', input_size=(192, 256), heatmap_size=(48, 64), kernel_size=11), dict( type='MegviiHeatmap', input_size=(192, 256), heatmap_size=(48, 64), kernel_size=9), dict( type='MegviiHeatmap', input_size=(192, 256), heatmap_size=(48, 64), kernel_size=7), dict( type='MegviiHeatmap', input_size=(192, 256), heatmap_size=(48, 64), kernel_size=5) ]), dict(type='PackPoseInputs') ])) val_dataloader = dict( batch_size=32, num_workers=4, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ])) test_dataloader = dict( batch_size=32, num_workers=4, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ])) val_evaluator = dict( type='CocoMetric', ann_file='data/coco/annotations/person_keypoints_val2017.json', nms_mode='none') test_evaluator = dict( type='CocoMetric', ann_file='data/coco/annotations/person_keypoints_val2017.json', nms_mode='none') fp16 = dict(loss_scale='dynamic') launcher = 'slurm' work_dir = 'work_dirs/20221012/rsn18/' 2022/10/12 11:03:01 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "data sampler" registry tree. As a workaround, the current "data sampler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/10/12 11:03:01 - 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. 2022/10/12 11:03:01 - 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. 2022/10/12 11:03:01 - 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. 2022/10/12 11:03:01 - 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. 2022/10/12 11:03:01 - 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. 2022/10/12 11:03:01 - 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. 2022/10/12 11:03:01 - 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. 2022/10/12 11:03:04 - 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. 2022/10/12 11:03:06 - 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. 2022/10/12 11:03:06 - 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. 2022/10/12 11:03:06 - 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. 2022/10/12 11:03:06 - 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. 2022/10/12 11:03:06 - 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. 2022/10/12 11:03:06 - 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.top.top.0.conv.weight - torch.Size([64, 3, 7, 7]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.top.top.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.top.top.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu1.conv.weight - torch.Size([104, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu1.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu1.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_1_1.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_1_1.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_1_1.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_2_1.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_2_1.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_2_1.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_2_2.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_2_2.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_2_2.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_3_1.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_3_1.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_3_1.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_3_2.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_3_2.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_3_2.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_3_3.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_3_3.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_3_3.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_4_1.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_4_1.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_4_1.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_4_2.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_4_2.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_4_2.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_4_3.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_4_3.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_4_3.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_4_4.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_4_4.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn_relu2_4_4.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn3.conv.weight - torch.Size([64, 104, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn3.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.0.conv_bn3.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu1.conv.weight - torch.Size([104, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu1.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu1.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_1_1.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_1_1.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_1_1.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_2_1.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_2_1.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_2_1.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_2_2.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_2_2.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_2_2.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_3_1.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_3_1.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_3_1.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_3_2.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_3_2.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_3_2.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_3_3.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_3_3.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_3_3.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_4_1.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_4_1.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_4_1.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_4_2.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_4_2.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_4_2.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_4_3.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_4_3.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_4_3.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_4_4.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_4_4.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn_relu2_4_4.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn3.conv.weight - torch.Size([64, 104, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn3.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer1.1.conv_bn3.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.downsample.conv.weight - torch.Size([128, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.0.downsample.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.downsample.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu1.conv.weight - torch.Size([104, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu1.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu1.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_1_1.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_1_1.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_1_1.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_2_1.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_2_1.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_2_1.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_2_2.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_2_2.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_2_2.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_3_1.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_3_1.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_3_1.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_3_2.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_3_2.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_3_2.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_3_3.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_3_3.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_3_3.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_4_1.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_4_1.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_4_1.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_4_2.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_4_2.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_4_2.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_4_3.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_4_3.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_4_3.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_4_4.conv.weight - torch.Size([26, 26, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_4_4.bn.weight - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn_relu2_4_4.bn.bias - torch.Size([26]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn3.conv.weight - torch.Size([128, 104, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn3.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.0.conv_bn3.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu1.conv.weight - torch.Size([208, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu1.bn.weight - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu1.bn.bias - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_1_1.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_1_1.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_1_1.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_2_1.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_2_1.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_2_1.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_2_2.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_2_2.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_2_2.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_3_1.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_3_1.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_3_1.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_3_2.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_3_2.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_3_2.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_3_3.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_3_3.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_3_3.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_4_1.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_4_1.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_4_1.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_4_2.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_4_2.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_4_2.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_4_3.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_4_3.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_4_3.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_4_4.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_4_4.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn_relu2_4_4.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn3.conv.weight - torch.Size([128, 208, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn3.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer2.1.conv_bn3.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.downsample.conv.weight - torch.Size([256, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.0.downsample.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.downsample.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu1.conv.weight - torch.Size([208, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu1.bn.weight - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu1.bn.bias - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_1_1.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_1_1.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_1_1.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_2_1.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_2_1.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_2_1.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_2_2.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_2_2.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_2_2.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_3_1.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_3_1.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_3_1.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_3_2.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_3_2.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_3_2.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_3_3.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_3_3.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_3_3.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_4_1.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_4_1.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_4_1.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_4_2.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_4_2.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_4_2.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_4_3.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_4_3.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_4_3.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_4_4.conv.weight - torch.Size([52, 52, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_4_4.bn.weight - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn_relu2_4_4.bn.bias - torch.Size([52]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn3.conv.weight - torch.Size([256, 208, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn3.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.0.conv_bn3.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu1.conv.weight - torch.Size([416, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu1.bn.weight - torch.Size([416]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu1.bn.bias - torch.Size([416]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_1_1.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_1_1.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_1_1.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_2_1.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_2_1.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_2_1.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_2_2.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_2_2.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_2_2.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_3_1.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_3_1.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_3_1.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_3_2.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_3_2.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_3_2.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_3_3.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_3_3.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_3_3.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_4_1.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_4_1.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_4_1.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_4_2.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_4_2.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_4_2.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_4_3.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_4_3.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_4_3.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_4_4.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_4_4.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn_relu2_4_4.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn3.conv.weight - torch.Size([256, 416, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn3.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer3.1.conv_bn3.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.downsample.conv.weight - torch.Size([512, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.0.downsample.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.downsample.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu1.conv.weight - torch.Size([416, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu1.bn.weight - torch.Size([416]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu1.bn.bias - torch.Size([416]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_1_1.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_1_1.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_1_1.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_2_1.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_2_1.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_2_1.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_2_2.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_2_2.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_2_2.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_3_1.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_3_1.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_3_1.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_3_2.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_3_2.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_3_2.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_3_3.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_3_3.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_3_3.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_4_1.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_4_1.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_4_1.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_4_2.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_4_2.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_4_2.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_4_3.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_4_3.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_4_3.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_4_4.conv.weight - torch.Size([104, 104, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_4_4.bn.weight - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn_relu2_4_4.bn.bias - torch.Size([104]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn3.conv.weight - torch.Size([512, 416, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn3.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.0.conv_bn3.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu1.conv.weight - torch.Size([832, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu1.bn.weight - torch.Size([832]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu1.bn.bias - torch.Size([832]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_1_1.conv.weight - torch.Size([208, 208, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_1_1.bn.weight - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_1_1.bn.bias - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_2_1.conv.weight - torch.Size([208, 208, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_2_1.bn.weight - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_2_1.bn.bias - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_2_2.conv.weight - torch.Size([208, 208, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_2_2.bn.weight - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_2_2.bn.bias - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_3_1.conv.weight - torch.Size([208, 208, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_3_1.bn.weight - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_3_1.bn.bias - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_3_2.conv.weight - torch.Size([208, 208, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_3_2.bn.weight - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_3_2.bn.bias - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_3_3.conv.weight - torch.Size([208, 208, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_3_3.bn.weight - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_3_3.bn.bias - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_4_1.conv.weight - torch.Size([208, 208, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_4_1.bn.weight - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_4_1.bn.bias - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_4_2.conv.weight - torch.Size([208, 208, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_4_2.bn.weight - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_4_2.bn.bias - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_4_3.conv.weight - torch.Size([208, 208, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_4_3.bn.weight - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_4_3.bn.bias - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_4_4.conv.weight - torch.Size([208, 208, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_4_4.bn.weight - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn_relu2_4_4.bn.bias - torch.Size([208]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn3.conv.weight - torch.Size([512, 832, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn3.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.downsample.layer4.1.conv_bn3.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.upsample.up1.in_skip.conv.weight - torch.Size([256, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.upsample.up1.in_skip.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.upsample.up1.in_skip.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.upsample.up2.in_skip.conv.weight - torch.Size([256, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.upsample.up2.in_skip.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.upsample.up2.in_skip.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.upsample.up2.up_conv.conv.weight - torch.Size([256, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.upsample.up2.up_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.upsample.up2.up_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.upsample.up3.in_skip.conv.weight - torch.Size([256, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.upsample.up3.in_skip.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.upsample.up3.in_skip.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.upsample.up3.up_conv.conv.weight - torch.Size([256, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.upsample.up3.up_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.upsample.up3.up_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.upsample.up4.in_skip.conv.weight - torch.Size([256, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.upsample.up4.in_skip.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.upsample.up4.in_skip.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.upsample.up4.up_conv.conv.weight - torch.Size([256, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.multi_stage_rsn.0.upsample.up4.up_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.multi_stage_rsn.0.upsample.up4.up_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.predict_layers.0.conv_layers.0.conv.weight - torch.Size([256, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 head.predict_layers.0.conv_layers.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.predict_layers.0.conv_layers.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.predict_layers.0.conv_layers.1.conv.weight - torch.Size([17, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 head.predict_layers.0.conv_layers.1.bn.weight - torch.Size([17]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.predict_layers.0.conv_layers.1.bn.bias - torch.Size([17]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.predict_layers.1.conv_layers.0.conv.weight - torch.Size([256, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 head.predict_layers.1.conv_layers.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.predict_layers.1.conv_layers.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.predict_layers.1.conv_layers.1.conv.weight - torch.Size([17, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 head.predict_layers.1.conv_layers.1.bn.weight - torch.Size([17]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.predict_layers.1.conv_layers.1.bn.bias - torch.Size([17]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.predict_layers.2.conv_layers.0.conv.weight - torch.Size([256, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 head.predict_layers.2.conv_layers.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.predict_layers.2.conv_layers.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.predict_layers.2.conv_layers.1.conv.weight - torch.Size([17, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 head.predict_layers.2.conv_layers.1.bn.weight - torch.Size([17]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.predict_layers.2.conv_layers.1.bn.bias - torch.Size([17]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.predict_layers.3.conv_layers.0.conv.weight - torch.Size([256, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 head.predict_layers.3.conv_layers.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.predict_layers.3.conv_layers.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.predict_layers.3.conv_layers.1.conv.weight - torch.Size([17, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 head.predict_layers.3.conv_layers.1.bn.weight - torch.Size([17]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.predict_layers.3.conv_layers.1.bn.bias - torch.Size([17]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator 2022/10/12 11:03:06 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221012/rsn18 by HardDiskBackend. 2022/10/12 11:03:32 - mmengine - INFO - Epoch(train) [1][50/586] lr: 1.981964e-03 eta: 17:51:04 time: 0.522435 data_time: 0.187836 memory: 2937 loss_kpt: 153.866150 acc_pose: 0.101202 loss: 153.866150 2022/10/12 11:03:48 - mmengine - INFO - Epoch(train) [1][100/586] lr: 3.983968e-03 eta: 14:08:48 time: 0.305933 data_time: 0.055157 memory: 2937 loss_kpt: 153.117198 acc_pose: 0.097918 loss: 153.117198 2022/10/12 11:04:01 - mmengine - INFO - Epoch(train) [1][150/586] lr: 5.985972e-03 eta: 12:33:38 time: 0.275328 data_time: 0.051547 memory: 2937 loss_kpt: 146.067873 acc_pose: 0.176263 loss: 146.067873 2022/10/12 11:04:15 - mmengine - INFO - Epoch(train) [1][200/586] lr: 7.987976e-03 eta: 11:43:16 time: 0.270122 data_time: 0.051172 memory: 2937 loss_kpt: 146.742218 acc_pose: 0.195844 loss: 146.742218 2022/10/12 11:04:28 - mmengine - INFO - Epoch(train) [1][250/586] lr: 9.989980e-03 eta: 11:11:32 time: 0.266607 data_time: 0.052513 memory: 2937 loss_kpt: 142.962659 acc_pose: 0.189538 loss: 142.962659 2022/10/12 11:04:42 - mmengine - INFO - Epoch(train) [1][300/586] lr: 1.199198e-02 eta: 10:51:01 time: 0.268760 data_time: 0.051349 memory: 2937 loss_kpt: 144.013573 acc_pose: 0.236461 loss: 144.013573 2022/10/12 11:04:55 - mmengine - INFO - Epoch(train) [1][350/586] lr: 1.399399e-02 eta: 10:36:48 time: 0.270415 data_time: 0.046128 memory: 2937 loss_kpt: 141.538336 acc_pose: 0.327913 loss: 141.538336 2022/10/12 11:05:09 - mmengine - INFO - Epoch(train) [1][400/586] lr: 1.599599e-02 eta: 10:28:51 time: 0.281316 data_time: 0.062225 memory: 2937 loss_kpt: 139.360432 acc_pose: 0.249421 loss: 139.360432 2022/10/12 11:05:23 - mmengine - INFO - Epoch(train) [1][450/586] lr: 1.799800e-02 eta: 10:19:27 time: 0.267332 data_time: 0.046459 memory: 2937 loss_kpt: 135.666342 acc_pose: 0.394059 loss: 135.666342 2022/10/12 11:05:36 - mmengine - INFO - Epoch(train) [1][500/586] lr: 2.000000e-02 eta: 10:12:21 time: 0.269607 data_time: 0.053488 memory: 2937 loss_kpt: 133.722422 acc_pose: 0.362385 loss: 133.722422 2022/10/12 11:05:50 - mmengine - INFO - Epoch(train) [1][550/586] lr: 2.000000e-02 eta: 10:06:04 time: 0.267266 data_time: 0.055200 memory: 2937 loss_kpt: 131.441949 acc_pose: 0.340665 loss: 131.441949 2022/10/12 11:05:58 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:06:12 - mmengine - INFO - Epoch(train) [2][50/586] lr: 2.000000e-02 eta: 9:27:47 time: 0.274554 data_time: 0.059744 memory: 2937 loss_kpt: 130.670342 acc_pose: 0.455377 loss: 130.670342 2022/10/12 11:06:25 - mmengine - INFO - Epoch(train) [2][100/586] lr: 2.000000e-02 eta: 9:24:30 time: 0.257724 data_time: 0.047639 memory: 2937 loss_kpt: 127.209813 acc_pose: 0.445263 loss: 127.209813 2022/10/12 11:06:38 - mmengine - INFO - Epoch(train) [2][150/586] lr: 2.000000e-02 eta: 9:20:59 time: 0.253003 data_time: 0.054924 memory: 2937 loss_kpt: 129.438674 acc_pose: 0.474046 loss: 129.438674 2022/10/12 11:06:51 - mmengine - INFO - Epoch(train) [2][200/586] lr: 2.000000e-02 eta: 9:20:08 time: 0.270437 data_time: 0.052112 memory: 2937 loss_kpt: 126.377744 acc_pose: 0.417398 loss: 126.377744 2022/10/12 11:07:04 - mmengine - INFO - Epoch(train) [2][250/586] lr: 2.000000e-02 eta: 9:18:45 time: 0.265392 data_time: 0.048952 memory: 2937 loss_kpt: 124.278602 acc_pose: 0.458299 loss: 124.278602 2022/10/12 11:07:19 - mmengine - INFO - Epoch(train) [2][300/586] lr: 2.000000e-02 eta: 9:19:56 time: 0.286565 data_time: 0.063175 memory: 2937 loss_kpt: 124.499688 acc_pose: 0.518625 loss: 124.499688 2022/10/12 11:07:32 - mmengine - INFO - Epoch(train) [2][350/586] lr: 2.000000e-02 eta: 9:18:57 time: 0.268110 data_time: 0.050621 memory: 2937 loss_kpt: 124.228251 acc_pose: 0.548029 loss: 124.228251 2022/10/12 11:07:45 - mmengine - INFO - Epoch(train) [2][400/586] lr: 2.000000e-02 eta: 9:17:29 time: 0.262592 data_time: 0.050024 memory: 2937 loss_kpt: 123.274748 acc_pose: 0.489472 loss: 123.274748 2022/10/12 11:07:49 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:07:58 - mmengine - INFO - Epoch(train) [2][450/586] lr: 2.000000e-02 eta: 9:16:03 time: 0.261746 data_time: 0.048907 memory: 2937 loss_kpt: 123.700648 acc_pose: 0.508659 loss: 123.700648 2022/10/12 11:08:11 - mmengine - INFO - Epoch(train) [2][500/586] lr: 2.000000e-02 eta: 9:14:05 time: 0.254835 data_time: 0.050536 memory: 2937 loss_kpt: 125.078120 acc_pose: 0.493537 loss: 125.078120 2022/10/12 11:08:24 - mmengine - INFO - Epoch(train) [2][550/586] lr: 2.000000e-02 eta: 9:12:24 time: 0.256281 data_time: 0.052420 memory: 2937 loss_kpt: 121.068041 acc_pose: 0.561977 loss: 121.068041 2022/10/12 11:08:33 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:08:48 - mmengine - INFO - Epoch(train) [3][50/586] lr: 2.000000e-02 eta: 8:58:05 time: 0.299979 data_time: 0.055557 memory: 2937 loss_kpt: 123.299283 acc_pose: 0.610826 loss: 123.299283 2022/10/12 11:09:02 - mmengine - INFO - Epoch(train) [3][100/586] lr: 2.000000e-02 eta: 8:59:13 time: 0.281817 data_time: 0.052418 memory: 2937 loss_kpt: 120.265260 acc_pose: 0.461330 loss: 120.265260 2022/10/12 11:09:15 - mmengine - INFO - Epoch(train) [3][150/586] lr: 2.000000e-02 eta: 8:58:36 time: 0.260646 data_time: 0.048590 memory: 2937 loss_kpt: 121.046751 acc_pose: 0.640298 loss: 121.046751 2022/10/12 11:09:28 - mmengine - INFO - Epoch(train) [3][200/586] lr: 2.000000e-02 eta: 8:58:03 time: 0.261060 data_time: 0.050703 memory: 2937 loss_kpt: 118.842725 acc_pose: 0.645224 loss: 118.842725 2022/10/12 11:09:41 - mmengine - INFO - Epoch(train) [3][250/586] lr: 2.000000e-02 eta: 8:57:29 time: 0.260270 data_time: 0.048309 memory: 2937 loss_kpt: 119.666890 acc_pose: 0.551618 loss: 119.666890 2022/10/12 11:09:54 - mmengine - INFO - Epoch(train) [3][300/586] lr: 2.000000e-02 eta: 8:56:57 time: 0.260592 data_time: 0.050240 memory: 2937 loss_kpt: 118.122604 acc_pose: 0.598050 loss: 118.122604 2022/10/12 11:10:07 - mmengine - INFO - Epoch(train) [3][350/586] lr: 2.000000e-02 eta: 8:56:02 time: 0.254539 data_time: 0.049512 memory: 2937 loss_kpt: 118.728241 acc_pose: 0.434754 loss: 118.728241 2022/10/12 11:10:20 - mmengine - INFO - Epoch(train) [3][400/586] lr: 2.000000e-02 eta: 8:55:27 time: 0.259130 data_time: 0.047873 memory: 2937 loss_kpt: 119.922354 acc_pose: 0.579506 loss: 119.922354 2022/10/12 11:10:33 - mmengine - INFO - Epoch(train) [3][450/586] lr: 2.000000e-02 eta: 8:54:41 time: 0.255479 data_time: 0.050554 memory: 2937 loss_kpt: 119.406978 acc_pose: 0.558546 loss: 119.406978 2022/10/12 11:10:46 - mmengine - INFO - Epoch(train) [3][500/586] lr: 2.000000e-02 eta: 8:53:49 time: 0.253651 data_time: 0.051223 memory: 2937 loss_kpt: 118.551849 acc_pose: 0.544327 loss: 118.551849 2022/10/12 11:10:58 - mmengine - INFO - Epoch(train) [3][550/586] lr: 2.000000e-02 eta: 8:53:04 time: 0.254840 data_time: 0.046068 memory: 2937 loss_kpt: 117.892210 acc_pose: 0.566014 loss: 117.892210 2022/10/12 11:11:07 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:11:22 - mmengine - INFO - Epoch(train) [4][50/586] lr: 2.000000e-02 eta: 8:43:49 time: 0.294517 data_time: 0.057982 memory: 2937 loss_kpt: 115.977678 acc_pose: 0.590846 loss: 115.977678 2022/10/12 11:11:36 - mmengine - INFO - Epoch(train) [4][100/586] lr: 2.000000e-02 eta: 8:44:32 time: 0.276330 data_time: 0.051842 memory: 2937 loss_kpt: 117.436813 acc_pose: 0.593004 loss: 117.436813 2022/10/12 11:11:50 - mmengine - INFO - Epoch(train) [4][150/586] lr: 2.000000e-02 eta: 8:44:55 time: 0.271176 data_time: 0.056468 memory: 2937 loss_kpt: 114.073903 acc_pose: 0.593856 loss: 114.073903 2022/10/12 11:12:03 - mmengine - INFO - Epoch(train) [4][200/586] lr: 2.000000e-02 eta: 8:45:16 time: 0.270961 data_time: 0.047131 memory: 2937 loss_kpt: 116.402728 acc_pose: 0.373999 loss: 116.402728 2022/10/12 11:12:14 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:12:16 - mmengine - INFO - Epoch(train) [4][250/586] lr: 2.000000e-02 eta: 8:44:40 time: 0.252638 data_time: 0.047371 memory: 2937 loss_kpt: 116.812228 acc_pose: 0.589026 loss: 116.812228 2022/10/12 11:12:29 - mmengine - INFO - Epoch(train) [4][300/586] lr: 2.000000e-02 eta: 8:44:27 time: 0.260032 data_time: 0.050723 memory: 2937 loss_kpt: 115.444139 acc_pose: 0.559162 loss: 115.444139 2022/10/12 11:12:42 - mmengine - INFO - Epoch(train) [4][350/586] lr: 2.000000e-02 eta: 8:44:30 time: 0.265559 data_time: 0.049573 memory: 2937 loss_kpt: 117.064613 acc_pose: 0.623107 loss: 117.064613 2022/10/12 11:12:55 - mmengine - INFO - Epoch(train) [4][400/586] lr: 2.000000e-02 eta: 8:44:23 time: 0.262408 data_time: 0.051275 memory: 2937 loss_kpt: 116.095808 acc_pose: 0.593388 loss: 116.095808 2022/10/12 11:13:08 - mmengine - INFO - Epoch(train) [4][450/586] lr: 2.000000e-02 eta: 8:44:20 time: 0.263746 data_time: 0.047946 memory: 2937 loss_kpt: 113.927054 acc_pose: 0.606120 loss: 113.927054 2022/10/12 11:13:21 - mmengine - INFO - Epoch(train) [4][500/586] lr: 2.000000e-02 eta: 8:43:48 time: 0.253291 data_time: 0.052161 memory: 2937 loss_kpt: 115.738638 acc_pose: 0.595635 loss: 115.738638 2022/10/12 11:13:34 - mmengine - INFO - Epoch(train) [4][550/586] lr: 2.000000e-02 eta: 8:43:15 time: 0.252437 data_time: 0.048275 memory: 2937 loss_kpt: 116.372513 acc_pose: 0.566488 loss: 116.372513 2022/10/12 11:13:43 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:13:57 - mmengine - INFO - Epoch(train) [5][50/586] lr: 2.000000e-02 eta: 8:36:00 time: 0.283787 data_time: 0.063164 memory: 2937 loss_kpt: 114.721725 acc_pose: 0.544322 loss: 114.721725 2022/10/12 11:14:10 - mmengine - INFO - Epoch(train) [5][100/586] lr: 2.000000e-02 eta: 8:36:21 time: 0.269953 data_time: 0.048555 memory: 2937 loss_kpt: 114.657720 acc_pose: 0.576219 loss: 114.657720 2022/10/12 11:14:23 - mmengine - INFO - Epoch(train) [5][150/586] lr: 2.000000e-02 eta: 8:36:12 time: 0.258735 data_time: 0.050728 memory: 2937 loss_kpt: 115.066935 acc_pose: 0.576501 loss: 115.066935 2022/10/12 11:14:36 - mmengine - INFO - Epoch(train) [5][200/586] lr: 2.000000e-02 eta: 8:35:57 time: 0.255928 data_time: 0.050594 memory: 2937 loss_kpt: 114.598349 acc_pose: 0.644847 loss: 114.598349 2022/10/12 11:14:50 - mmengine - INFO - Epoch(train) [5][250/586] lr: 2.000000e-02 eta: 8:36:19 time: 0.271889 data_time: 0.050946 memory: 2937 loss_kpt: 115.713142 acc_pose: 0.639619 loss: 115.713142 2022/10/12 11:15:03 - mmengine - INFO - Epoch(train) [5][300/586] lr: 2.000000e-02 eta: 8:36:23 time: 0.264320 data_time: 0.051069 memory: 2937 loss_kpt: 112.910746 acc_pose: 0.623752 loss: 112.910746 2022/10/12 11:15:16 - mmengine - INFO - Epoch(train) [5][350/586] lr: 2.000000e-02 eta: 8:35:58 time: 0.252073 data_time: 0.057565 memory: 2937 loss_kpt: 112.154115 acc_pose: 0.589936 loss: 112.154115 2022/10/12 11:15:29 - mmengine - INFO - Epoch(train) [5][400/586] lr: 2.000000e-02 eta: 8:36:01 time: 0.264498 data_time: 0.053304 memory: 2937 loss_kpt: 113.961504 acc_pose: 0.578223 loss: 113.961504 2022/10/12 11:15:41 - mmengine - INFO - Epoch(train) [5][450/586] lr: 2.000000e-02 eta: 8:35:35 time: 0.251307 data_time: 0.052362 memory: 2937 loss_kpt: 114.339974 acc_pose: 0.623572 loss: 114.339974 2022/10/12 11:15:55 - mmengine - INFO - Epoch(train) [5][500/586] lr: 2.000000e-02 eta: 8:35:37 time: 0.264111 data_time: 0.053620 memory: 2937 loss_kpt: 112.987956 acc_pose: 0.570176 loss: 112.987956 2022/10/12 11:16:08 - mmengine - INFO - Epoch(train) [5][550/586] lr: 2.000000e-02 eta: 8:35:38 time: 0.264117 data_time: 0.053608 memory: 2937 loss_kpt: 111.932193 acc_pose: 0.605884 loss: 111.932193 2022/10/12 11:16:17 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:16:31 - mmengine - INFO - Epoch(train) [6][50/586] lr: 2.000000e-02 eta: 8:29:50 time: 0.281054 data_time: 0.062626 memory: 2937 loss_kpt: 112.034770 acc_pose: 0.663894 loss: 112.034770 2022/10/12 11:16:37 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:16:45 - mmengine - INFO - Epoch(train) [6][100/586] lr: 2.000000e-02 eta: 8:30:10 time: 0.271456 data_time: 0.052190 memory: 2937 loss_kpt: 111.881175 acc_pose: 0.598262 loss: 111.881175 2022/10/12 11:16:58 - mmengine - INFO - Epoch(train) [6][150/586] lr: 2.000000e-02 eta: 8:30:24 time: 0.268721 data_time: 0.056243 memory: 2937 loss_kpt: 111.890571 acc_pose: 0.635509 loss: 111.890571 2022/10/12 11:17:12 - mmengine - INFO - Epoch(train) [6][200/586] lr: 2.000000e-02 eta: 8:30:54 time: 0.277164 data_time: 0.057147 memory: 2937 loss_kpt: 112.653742 acc_pose: 0.684890 loss: 112.653742 2022/10/12 11:17:25 - mmengine - INFO - Epoch(train) [6][250/586] lr: 2.000000e-02 eta: 8:30:58 time: 0.264620 data_time: 0.052576 memory: 2937 loss_kpt: 110.668672 acc_pose: 0.660022 loss: 110.668672 2022/10/12 11:17:38 - mmengine - INFO - Epoch(train) [6][300/586] lr: 2.000000e-02 eta: 8:30:44 time: 0.255204 data_time: 0.053184 memory: 2937 loss_kpt: 110.967683 acc_pose: 0.625971 loss: 110.967683 2022/10/12 11:17:51 - mmengine - INFO - Epoch(train) [6][350/586] lr: 2.000000e-02 eta: 8:30:54 time: 0.268296 data_time: 0.050068 memory: 2937 loss_kpt: 109.552214 acc_pose: 0.533220 loss: 109.552214 2022/10/12 11:18:05 - mmengine - INFO - Epoch(train) [6][400/586] lr: 2.000000e-02 eta: 8:31:25 time: 0.280436 data_time: 0.054668 memory: 2937 loss_kpt: 110.845465 acc_pose: 0.596676 loss: 110.845465 2022/10/12 11:18:19 - mmengine - INFO - Epoch(train) [6][450/586] lr: 2.000000e-02 eta: 8:31:38 time: 0.270371 data_time: 0.051359 memory: 2937 loss_kpt: 113.230284 acc_pose: 0.618783 loss: 113.230284 2022/10/12 11:18:32 - mmengine - INFO - Epoch(train) [6][500/586] lr: 2.000000e-02 eta: 8:31:48 time: 0.269898 data_time: 0.055280 memory: 2937 loss_kpt: 111.203602 acc_pose: 0.681878 loss: 111.203602 2022/10/12 11:18:46 - mmengine - INFO - Epoch(train) [6][550/586] lr: 2.000000e-02 eta: 8:32:13 time: 0.278390 data_time: 0.057222 memory: 2937 loss_kpt: 110.124144 acc_pose: 0.585499 loss: 110.124144 2022/10/12 11:18:55 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:19:10 - mmengine - INFO - Epoch(train) [7][50/586] lr: 2.000000e-02 eta: 8:27:31 time: 0.287421 data_time: 0.062082 memory: 2937 loss_kpt: 111.348679 acc_pose: 0.669249 loss: 111.348679 2022/10/12 11:19:23 - mmengine - INFO - Epoch(train) [7][100/586] lr: 2.000000e-02 eta: 8:27:32 time: 0.262779 data_time: 0.047777 memory: 2937 loss_kpt: 110.372390 acc_pose: 0.655249 loss: 110.372390 2022/10/12 11:19:37 - mmengine - INFO - Epoch(train) [7][150/586] lr: 2.000000e-02 eta: 8:27:52 time: 0.274967 data_time: 0.051993 memory: 2937 loss_kpt: 110.695891 acc_pose: 0.568307 loss: 110.695891 2022/10/12 11:19:50 - mmengine - INFO - Epoch(train) [7][200/586] lr: 2.000000e-02 eta: 8:27:59 time: 0.267725 data_time: 0.051951 memory: 2937 loss_kpt: 106.514573 acc_pose: 0.637588 loss: 106.514573 2022/10/12 11:20:04 - mmengine - INFO - Epoch(train) [7][250/586] lr: 2.000000e-02 eta: 8:28:10 time: 0.270308 data_time: 0.048057 memory: 2937 loss_kpt: 108.924400 acc_pose: 0.551672 loss: 108.924400 2022/10/12 11:20:17 - mmengine - INFO - Epoch(train) [7][300/586] lr: 2.000000e-02 eta: 8:28:11 time: 0.264370 data_time: 0.051772 memory: 2937 loss_kpt: 110.400876 acc_pose: 0.650717 loss: 110.400876 2022/10/12 11:20:30 - mmengine - INFO - Epoch(train) [7][350/586] lr: 2.000000e-02 eta: 8:28:13 time: 0.265341 data_time: 0.051880 memory: 2937 loss_kpt: 110.092697 acc_pose: 0.704466 loss: 110.092697 2022/10/12 11:20:43 - mmengine - INFO - Epoch(train) [7][400/586] lr: 2.000000e-02 eta: 8:28:12 time: 0.263487 data_time: 0.049780 memory: 2937 loss_kpt: 112.341261 acc_pose: 0.537515 loss: 112.341261 2022/10/12 11:20:56 - mmengine - INFO - Epoch(train) [7][450/586] lr: 2.000000e-02 eta: 8:27:52 time: 0.251207 data_time: 0.046907 memory: 2937 loss_kpt: 111.013293 acc_pose: 0.702938 loss: 111.013293 2022/10/12 11:21:05 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:21:09 - mmengine - INFO - Epoch(train) [7][500/586] lr: 2.000000e-02 eta: 8:27:55 time: 0.266691 data_time: 0.052944 memory: 2937 loss_kpt: 108.501220 acc_pose: 0.600886 loss: 108.501220 2022/10/12 11:21:22 - mmengine - INFO - Epoch(train) [7][550/586] lr: 2.000000e-02 eta: 8:27:52 time: 0.262813 data_time: 0.053766 memory: 2937 loss_kpt: 107.810490 acc_pose: 0.522789 loss: 107.810490 2022/10/12 11:21:31 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:21:45 - mmengine - INFO - Epoch(train) [8][50/586] lr: 2.000000e-02 eta: 8:23:37 time: 0.277700 data_time: 0.061348 memory: 2937 loss_kpt: 109.181636 acc_pose: 0.678695 loss: 109.181636 2022/10/12 11:21:58 - mmengine - INFO - Epoch(train) [8][100/586] lr: 2.000000e-02 eta: 8:23:34 time: 0.260640 data_time: 0.055661 memory: 2937 loss_kpt: 106.150356 acc_pose: 0.664077 loss: 106.150356 2022/10/12 11:22:11 - mmengine - INFO - Epoch(train) [8][150/586] lr: 2.000000e-02 eta: 8:23:17 time: 0.251554 data_time: 0.050742 memory: 2937 loss_kpt: 109.052599 acc_pose: 0.641531 loss: 109.052599 2022/10/12 11:22:25 - mmengine - INFO - Epoch(train) [8][200/586] lr: 2.000000e-02 eta: 8:23:28 time: 0.271570 data_time: 0.053741 memory: 2937 loss_kpt: 108.823962 acc_pose: 0.589422 loss: 108.823962 2022/10/12 11:22:38 - mmengine - INFO - Epoch(train) [8][250/586] lr: 2.000000e-02 eta: 8:23:33 time: 0.266848 data_time: 0.050830 memory: 2937 loss_kpt: 107.943477 acc_pose: 0.518217 loss: 107.943477 2022/10/12 11:22:51 - mmengine - INFO - Epoch(train) [8][300/586] lr: 2.000000e-02 eta: 8:23:21 time: 0.255122 data_time: 0.055603 memory: 2937 loss_kpt: 109.294262 acc_pose: 0.648339 loss: 109.294262 2022/10/12 11:23:03 - mmengine - INFO - Epoch(train) [8][350/586] lr: 2.000000e-02 eta: 8:23:09 time: 0.255337 data_time: 0.048690 memory: 2937 loss_kpt: 108.626359 acc_pose: 0.688721 loss: 108.626359 2022/10/12 11:23:16 - mmengine - INFO - Epoch(train) [8][400/586] lr: 2.000000e-02 eta: 8:22:51 time: 0.250545 data_time: 0.053940 memory: 2937 loss_kpt: 108.667979 acc_pose: 0.608531 loss: 108.667979 2022/10/12 11:23:29 - mmengine - INFO - Epoch(train) [8][450/586] lr: 2.000000e-02 eta: 8:22:39 time: 0.254541 data_time: 0.050623 memory: 2937 loss_kpt: 107.661032 acc_pose: 0.656373 loss: 107.661032 2022/10/12 11:23:42 - mmengine - INFO - Epoch(train) [8][500/586] lr: 2.000000e-02 eta: 8:22:38 time: 0.264200 data_time: 0.051203 memory: 2937 loss_kpt: 108.295858 acc_pose: 0.659551 loss: 108.295858 2022/10/12 11:23:55 - mmengine - INFO - Epoch(train) [8][550/586] lr: 2.000000e-02 eta: 8:22:27 time: 0.255667 data_time: 0.051685 memory: 2937 loss_kpt: 108.313099 acc_pose: 0.610693 loss: 108.313099 2022/10/12 11:24:03 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:24:17 - mmengine - INFO - Epoch(train) [9][50/586] lr: 2.000000e-02 eta: 8:18:45 time: 0.277916 data_time: 0.064934 memory: 2937 loss_kpt: 108.264563 acc_pose: 0.718463 loss: 108.264563 2022/10/12 11:24:31 - mmengine - INFO - Epoch(train) [9][100/586] lr: 2.000000e-02 eta: 8:18:52 time: 0.268685 data_time: 0.056548 memory: 2937 loss_kpt: 105.955965 acc_pose: 0.544065 loss: 105.955965 2022/10/12 11:24:44 - mmengine - INFO - Epoch(train) [9][150/586] lr: 2.000000e-02 eta: 8:19:04 time: 0.273533 data_time: 0.055244 memory: 2937 loss_kpt: 105.878293 acc_pose: 0.707795 loss: 105.878293 2022/10/12 11:24:57 - mmengine - INFO - Epoch(train) [9][200/586] lr: 2.000000e-02 eta: 8:18:59 time: 0.259224 data_time: 0.050996 memory: 2937 loss_kpt: 107.182505 acc_pose: 0.669824 loss: 107.182505 2022/10/12 11:25:11 - mmengine - INFO - Epoch(train) [9][250/586] lr: 2.000000e-02 eta: 8:19:07 time: 0.270358 data_time: 0.050201 memory: 2937 loss_kpt: 108.693881 acc_pose: 0.662872 loss: 108.693881 2022/10/12 11:25:24 - mmengine - INFO - Epoch(train) [9][300/586] lr: 2.000000e-02 eta: 8:19:09 time: 0.266064 data_time: 0.053110 memory: 2937 loss_kpt: 107.180096 acc_pose: 0.648913 loss: 107.180096 2022/10/12 11:25:28 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:25:38 - mmengine - INFO - Epoch(train) [9][350/586] lr: 2.000000e-02 eta: 8:19:21 time: 0.274819 data_time: 0.055059 memory: 2937 loss_kpt: 107.798513 acc_pose: 0.660734 loss: 107.798513 2022/10/12 11:25:51 - mmengine - INFO - Epoch(train) [9][400/586] lr: 2.000000e-02 eta: 8:19:19 time: 0.263487 data_time: 0.049893 memory: 2937 loss_kpt: 108.921470 acc_pose: 0.600094 loss: 108.921470 2022/10/12 11:26:04 - mmengine - INFO - Epoch(train) [9][450/586] lr: 2.000000e-02 eta: 8:19:13 time: 0.259281 data_time: 0.051800 memory: 2937 loss_kpt: 107.781299 acc_pose: 0.591495 loss: 107.781299 2022/10/12 11:26:17 - mmengine - INFO - Epoch(train) [9][500/586] lr: 2.000000e-02 eta: 8:19:13 time: 0.265265 data_time: 0.055507 memory: 2937 loss_kpt: 109.156366 acc_pose: 0.623837 loss: 109.156366 2022/10/12 11:26:31 - mmengine - INFO - Epoch(train) [9][550/586] lr: 2.000000e-02 eta: 8:19:19 time: 0.270796 data_time: 0.053614 memory: 2937 loss_kpt: 110.248340 acc_pose: 0.671344 loss: 110.248340 2022/10/12 11:26:40 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:26:54 - mmengine - INFO - Epoch(train) [10][50/586] lr: 2.000000e-02 eta: 8:16:03 time: 0.279920 data_time: 0.060497 memory: 2937 loss_kpt: 108.783058 acc_pose: 0.747213 loss: 108.783058 2022/10/12 11:27:08 - mmengine - INFO - Epoch(train) [10][100/586] lr: 2.000000e-02 eta: 8:16:15 time: 0.275733 data_time: 0.054695 memory: 2937 loss_kpt: 107.432042 acc_pose: 0.678970 loss: 107.432042 2022/10/12 11:27:21 - mmengine - INFO - Epoch(train) [10][150/586] lr: 2.000000e-02 eta: 8:16:20 time: 0.268654 data_time: 0.054751 memory: 2937 loss_kpt: 104.569032 acc_pose: 0.657707 loss: 104.569032 2022/10/12 11:27:35 - mmengine - INFO - Epoch(train) [10][200/586] lr: 2.000000e-02 eta: 8:16:24 time: 0.268522 data_time: 0.051218 memory: 2937 loss_kpt: 106.871115 acc_pose: 0.678041 loss: 106.871115 2022/10/12 11:27:48 - mmengine - INFO - Epoch(train) [10][250/586] lr: 2.000000e-02 eta: 8:16:15 time: 0.257180 data_time: 0.052720 memory: 2937 loss_kpt: 104.461828 acc_pose: 0.642767 loss: 104.461828 2022/10/12 11:28:01 - mmengine - INFO - Epoch(train) [10][300/586] lr: 2.000000e-02 eta: 8:16:09 time: 0.259551 data_time: 0.053929 memory: 2937 loss_kpt: 107.808633 acc_pose: 0.702998 loss: 107.808633 2022/10/12 11:28:13 - mmengine - INFO - Epoch(train) [10][350/586] lr: 2.000000e-02 eta: 8:15:59 time: 0.256016 data_time: 0.053114 memory: 2937 loss_kpt: 107.565145 acc_pose: 0.737870 loss: 107.565145 2022/10/12 11:28:26 - mmengine - INFO - Epoch(train) [10][400/586] lr: 2.000000e-02 eta: 8:15:53 time: 0.259884 data_time: 0.052341 memory: 2937 loss_kpt: 107.267638 acc_pose: 0.754303 loss: 107.267638 2022/10/12 11:28:39 - mmengine - INFO - Epoch(train) [10][450/586] lr: 2.000000e-02 eta: 8:15:39 time: 0.251869 data_time: 0.052590 memory: 2937 loss_kpt: 107.313762 acc_pose: 0.626370 loss: 107.313762 2022/10/12 11:28:52 - mmengine - INFO - Epoch(train) [10][500/586] lr: 2.000000e-02 eta: 8:15:27 time: 0.254079 data_time: 0.051258 memory: 2937 loss_kpt: 105.675825 acc_pose: 0.631617 loss: 105.675825 2022/10/12 11:29:04 - mmengine - INFO - Epoch(train) [10][550/586] lr: 2.000000e-02 eta: 8:15:13 time: 0.252258 data_time: 0.053250 memory: 2937 loss_kpt: 108.048435 acc_pose: 0.565900 loss: 108.048435 2022/10/12 11:29:13 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:29:13 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/10/12 11:29:26 - mmengine - INFO - Epoch(val) [10][50/407] eta: 0:01:17 time: 0.216962 data_time: 0.114665 memory: 2937 2022/10/12 11:29:32 - mmengine - INFO - Epoch(val) [10][100/407] eta: 0:00:33 time: 0.110067 data_time: 0.009028 memory: 830 2022/10/12 11:29:37 - mmengine - INFO - Epoch(val) [10][150/407] eta: 0:00:27 time: 0.108650 data_time: 0.009419 memory: 830 2022/10/12 11:29:43 - mmengine - INFO - Epoch(val) [10][200/407] eta: 0:00:23 time: 0.112076 data_time: 0.009336 memory: 830 2022/10/12 11:29:49 - mmengine - INFO - Epoch(val) [10][250/407] eta: 0:00:17 time: 0.113957 data_time: 0.009971 memory: 830 2022/10/12 11:29:54 - mmengine - INFO - Epoch(val) [10][300/407] eta: 0:00:11 time: 0.108532 data_time: 0.009250 memory: 830 2022/10/12 11:29:59 - mmengine - INFO - Epoch(val) [10][350/407] eta: 0:00:06 time: 0.108256 data_time: 0.008678 memory: 830 2022/10/12 11:30:05 - mmengine - INFO - Epoch(val) [10][400/407] eta: 0:00:00 time: 0.104836 data_time: 0.008419 memory: 830 2022/10/12 11:30:18 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 11:30:34 - mmengine - INFO - Epoch(val) [10][407/407] coco/AP: 0.527103 coco/AP .5: 0.802211 coco/AP .75: 0.571782 coco/AP (M): 0.501069 coco/AP (L): 0.577908 coco/AR: 0.606927 coco/AR .5: 0.854534 coco/AR .75: 0.653810 coco/AR (M): 0.563125 coco/AR (L): 0.667187 2022/10/12 11:30:35 - mmengine - INFO - The best checkpoint with 0.5271 coco/AP at 10 epoch is saved to best_coco/AP_epoch_10.pth. 2022/10/12 11:30:50 - mmengine - INFO - Epoch(train) [11][50/586] lr: 2.000000e-02 eta: 8:12:20 time: 0.283314 data_time: 0.059974 memory: 2937 loss_kpt: 105.972787 acc_pose: 0.653889 loss: 105.972787 2022/10/12 11:31:02 - mmengine - INFO - Epoch(train) [11][100/586] lr: 2.000000e-02 eta: 8:12:14 time: 0.259207 data_time: 0.054420 memory: 2937 loss_kpt: 107.054707 acc_pose: 0.751718 loss: 107.054707 2022/10/12 11:31:13 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:31:15 - mmengine - INFO - Epoch(train) [11][150/586] lr: 2.000000e-02 eta: 8:12:05 time: 0.255495 data_time: 0.050308 memory: 2937 loss_kpt: 107.486242 acc_pose: 0.670526 loss: 107.486242 2022/10/12 11:31:28 - mmengine - INFO - Epoch(train) [11][200/586] lr: 2.000000e-02 eta: 8:11:57 time: 0.257558 data_time: 0.051642 memory: 2937 loss_kpt: 106.033322 acc_pose: 0.633733 loss: 106.033322 2022/10/12 11:31:41 - mmengine - INFO - Epoch(train) [11][250/586] lr: 2.000000e-02 eta: 8:11:45 time: 0.252887 data_time: 0.051973 memory: 2937 loss_kpt: 106.646634 acc_pose: 0.689815 loss: 106.646634 2022/10/12 11:31:54 - mmengine - INFO - Epoch(train) [11][300/586] lr: 2.000000e-02 eta: 8:11:42 time: 0.262707 data_time: 0.049055 memory: 2937 loss_kpt: 106.255708 acc_pose: 0.686257 loss: 106.255708 2022/10/12 11:32:07 - mmengine - INFO - Epoch(train) [11][350/586] lr: 2.000000e-02 eta: 8:11:36 time: 0.259138 data_time: 0.051807 memory: 2937 loss_kpt: 106.779749 acc_pose: 0.668081 loss: 106.779749 2022/10/12 11:32:20 - mmengine - INFO - Epoch(train) [11][400/586] lr: 2.000000e-02 eta: 8:11:29 time: 0.257793 data_time: 0.049607 memory: 2937 loss_kpt: 105.920203 acc_pose: 0.643784 loss: 105.920203 2022/10/12 11:32:33 - mmengine - INFO - Epoch(train) [11][450/586] lr: 2.000000e-02 eta: 8:11:30 time: 0.267990 data_time: 0.052991 memory: 2937 loss_kpt: 104.898827 acc_pose: 0.707046 loss: 104.898827 2022/10/12 11:32:46 - mmengine - INFO - Epoch(train) [11][500/586] lr: 2.000000e-02 eta: 8:11:18 time: 0.252552 data_time: 0.053871 memory: 2937 loss_kpt: 106.460340 acc_pose: 0.623806 loss: 106.460340 2022/10/12 11:32:58 - mmengine - INFO - Epoch(train) [11][550/586] lr: 2.000000e-02 eta: 8:11:02 time: 0.248833 data_time: 0.051558 memory: 2937 loss_kpt: 107.192990 acc_pose: 0.685713 loss: 107.192990 2022/10/12 11:33:08 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:33:22 - mmengine - INFO - Epoch(train) [12][50/586] lr: 2.000000e-02 eta: 8:08:24 time: 0.283460 data_time: 0.062963 memory: 2937 loss_kpt: 105.981965 acc_pose: 0.603005 loss: 105.981965 2022/10/12 11:33:35 - mmengine - INFO - Epoch(train) [12][100/586] lr: 2.000000e-02 eta: 8:08:26 time: 0.267299 data_time: 0.051140 memory: 2937 loss_kpt: 105.387144 acc_pose: 0.686030 loss: 105.387144 2022/10/12 11:33:48 - mmengine - INFO - Epoch(train) [12][150/586] lr: 2.000000e-02 eta: 8:08:24 time: 0.264065 data_time: 0.052304 memory: 2937 loss_kpt: 108.072402 acc_pose: 0.669191 loss: 108.072402 2022/10/12 11:34:02 - mmengine - INFO - Epoch(train) [12][200/586] lr: 2.000000e-02 eta: 8:08:28 time: 0.269609 data_time: 0.052064 memory: 2937 loss_kpt: 106.328885 acc_pose: 0.652258 loss: 106.328885 2022/10/12 11:34:16 - mmengine - INFO - Epoch(train) [12][250/586] lr: 2.000000e-02 eta: 8:08:37 time: 0.276488 data_time: 0.053309 memory: 2937 loss_kpt: 105.169815 acc_pose: 0.586715 loss: 105.169815 2022/10/12 11:34:29 - mmengine - INFO - Epoch(train) [12][300/586] lr: 2.000000e-02 eta: 8:08:26 time: 0.254359 data_time: 0.057663 memory: 2937 loss_kpt: 104.675264 acc_pose: 0.708756 loss: 104.675264 2022/10/12 11:34:42 - mmengine - INFO - Epoch(train) [12][350/586] lr: 2.000000e-02 eta: 8:08:25 time: 0.265315 data_time: 0.050048 memory: 2937 loss_kpt: 103.953665 acc_pose: 0.628416 loss: 103.953665 2022/10/12 11:34:55 - mmengine - INFO - Epoch(train) [12][400/586] lr: 2.000000e-02 eta: 8:08:24 time: 0.265451 data_time: 0.055390 memory: 2937 loss_kpt: 105.444842 acc_pose: 0.647846 loss: 105.444842 2022/10/12 11:35:08 - mmengine - INFO - Epoch(train) [12][450/586] lr: 2.000000e-02 eta: 8:08:19 time: 0.261969 data_time: 0.050394 memory: 2937 loss_kpt: 104.835400 acc_pose: 0.694928 loss: 104.835400 2022/10/12 11:35:22 - mmengine - INFO - Epoch(train) [12][500/586] lr: 2.000000e-02 eta: 8:08:25 time: 0.274400 data_time: 0.054931 memory: 2937 loss_kpt: 104.569953 acc_pose: 0.669127 loss: 104.569953 2022/10/12 11:35:35 - mmengine - INFO - Epoch(train) [12][550/586] lr: 2.000000e-02 eta: 8:08:28 time: 0.270763 data_time: 0.050159 memory: 2937 loss_kpt: 105.310253 acc_pose: 0.599364 loss: 105.310253 2022/10/12 11:35:37 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:35:45 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:35:59 - mmengine - INFO - Epoch(train) [13][50/586] lr: 2.000000e-02 eta: 8:05:58 time: 0.278194 data_time: 0.063500 memory: 2937 loss_kpt: 106.819090 acc_pose: 0.669959 loss: 106.819090 2022/10/12 11:36:12 - mmengine - INFO - Epoch(train) [13][100/586] lr: 2.000000e-02 eta: 8:05:55 time: 0.262915 data_time: 0.048748 memory: 2937 loss_kpt: 103.984140 acc_pose: 0.738208 loss: 103.984140 2022/10/12 11:36:25 - mmengine - INFO - Epoch(train) [13][150/586] lr: 2.000000e-02 eta: 8:05:52 time: 0.263120 data_time: 0.057147 memory: 2937 loss_kpt: 103.941063 acc_pose: 0.626726 loss: 103.941063 2022/10/12 11:36:39 - mmengine - INFO - Epoch(train) [13][200/586] lr: 2.000000e-02 eta: 8:05:52 time: 0.267275 data_time: 0.051624 memory: 2937 loss_kpt: 104.228382 acc_pose: 0.760931 loss: 104.228382 2022/10/12 11:36:52 - mmengine - INFO - Epoch(train) [13][250/586] lr: 2.000000e-02 eta: 8:05:48 time: 0.262447 data_time: 0.054962 memory: 2937 loss_kpt: 104.891219 acc_pose: 0.835862 loss: 104.891219 2022/10/12 11:37:05 - mmengine - INFO - Epoch(train) [13][300/586] lr: 2.000000e-02 eta: 8:05:49 time: 0.269742 data_time: 0.048939 memory: 2937 loss_kpt: 105.423834 acc_pose: 0.605892 loss: 105.423834 2022/10/12 11:37:19 - mmengine - INFO - Epoch(train) [13][350/586] lr: 2.000000e-02 eta: 8:05:56 time: 0.275831 data_time: 0.054356 memory: 2937 loss_kpt: 105.299243 acc_pose: 0.660085 loss: 105.299243 2022/10/12 11:37:32 - mmengine - INFO - Epoch(train) [13][400/586] lr: 2.000000e-02 eta: 8:05:53 time: 0.264469 data_time: 0.051557 memory: 2937 loss_kpt: 106.619939 acc_pose: 0.734600 loss: 106.619939 2022/10/12 11:37:46 - mmengine - INFO - Epoch(train) [13][450/586] lr: 2.000000e-02 eta: 8:05:50 time: 0.264670 data_time: 0.054164 memory: 2937 loss_kpt: 104.317140 acc_pose: 0.662291 loss: 104.317140 2022/10/12 11:37:59 - mmengine - INFO - Epoch(train) [13][500/586] lr: 2.000000e-02 eta: 8:05:42 time: 0.258573 data_time: 0.053315 memory: 2937 loss_kpt: 105.802707 acc_pose: 0.612334 loss: 105.802707 2022/10/12 11:38:12 - mmengine - INFO - Epoch(train) [13][550/586] lr: 2.000000e-02 eta: 8:05:45 time: 0.272737 data_time: 0.053857 memory: 2937 loss_kpt: 103.328425 acc_pose: 0.639042 loss: 103.328425 2022/10/12 11:38:22 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:38:36 - mmengine - INFO - Epoch(train) [14][50/586] lr: 2.000000e-02 eta: 8:03:31 time: 0.284371 data_time: 0.059145 memory: 2937 loss_kpt: 103.635653 acc_pose: 0.636298 loss: 103.635653 2022/10/12 11:38:50 - mmengine - INFO - Epoch(train) [14][100/586] lr: 2.000000e-02 eta: 8:03:39 time: 0.278984 data_time: 0.060667 memory: 2937 loss_kpt: 104.576249 acc_pose: 0.701145 loss: 104.576249 2022/10/12 11:39:04 - mmengine - INFO - Epoch(train) [14][150/586] lr: 2.000000e-02 eta: 8:03:37 time: 0.266952 data_time: 0.055749 memory: 2937 loss_kpt: 104.072513 acc_pose: 0.638761 loss: 104.072513 2022/10/12 11:39:18 - mmengine - INFO - Epoch(train) [14][200/586] lr: 2.000000e-02 eta: 8:03:47 time: 0.281897 data_time: 0.053340 memory: 2937 loss_kpt: 103.949633 acc_pose: 0.692401 loss: 103.949633 2022/10/12 11:39:32 - mmengine - INFO - Epoch(train) [14][250/586] lr: 2.000000e-02 eta: 8:03:57 time: 0.282652 data_time: 0.058525 memory: 2937 loss_kpt: 103.635524 acc_pose: 0.664264 loss: 103.635524 2022/10/12 11:39:45 - mmengine - INFO - Epoch(train) [14][300/586] lr: 2.000000e-02 eta: 8:03:57 time: 0.269277 data_time: 0.053510 memory: 2937 loss_kpt: 103.803728 acc_pose: 0.652927 loss: 103.803728 2022/10/12 11:39:59 - mmengine - INFO - Epoch(train) [14][350/586] lr: 2.000000e-02 eta: 8:04:02 time: 0.276555 data_time: 0.053888 memory: 2937 loss_kpt: 103.612586 acc_pose: 0.737183 loss: 103.612586 2022/10/12 11:40:07 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:40:12 - mmengine - INFO - Epoch(train) [14][400/586] lr: 2.000000e-02 eta: 8:03:54 time: 0.258934 data_time: 0.054798 memory: 2937 loss_kpt: 105.661340 acc_pose: 0.701058 loss: 105.661340 2022/10/12 11:40:25 - mmengine - INFO - Epoch(train) [14][450/586] lr: 2.000000e-02 eta: 8:03:45 time: 0.257418 data_time: 0.048678 memory: 2937 loss_kpt: 104.496803 acc_pose: 0.669198 loss: 104.496803 2022/10/12 11:40:38 - mmengine - INFO - Epoch(train) [14][500/586] lr: 2.000000e-02 eta: 8:03:42 time: 0.265953 data_time: 0.054736 memory: 2937 loss_kpt: 103.216826 acc_pose: 0.715636 loss: 103.216826 2022/10/12 11:40:51 - mmengine - INFO - Epoch(train) [14][550/586] lr: 2.000000e-02 eta: 8:03:36 time: 0.261544 data_time: 0.055280 memory: 2937 loss_kpt: 103.308220 acc_pose: 0.654360 loss: 103.308220 2022/10/12 11:41:01 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:41:16 - mmengine - INFO - Epoch(train) [15][50/586] lr: 2.000000e-02 eta: 8:01:42 time: 0.302401 data_time: 0.060734 memory: 2937 loss_kpt: 105.045725 acc_pose: 0.493508 loss: 105.045725 2022/10/12 11:41:29 - mmengine - INFO - Epoch(train) [15][100/586] lr: 2.000000e-02 eta: 8:01:41 time: 0.268878 data_time: 0.057784 memory: 2937 loss_kpt: 102.417895 acc_pose: 0.612125 loss: 102.417895 2022/10/12 11:41:43 - mmengine - INFO - Epoch(train) [15][150/586] lr: 2.000000e-02 eta: 8:01:44 time: 0.274062 data_time: 0.050573 memory: 2937 loss_kpt: 104.804378 acc_pose: 0.648020 loss: 104.804378 2022/10/12 11:41:56 - mmengine - INFO - Epoch(train) [15][200/586] lr: 2.000000e-02 eta: 8:01:34 time: 0.255909 data_time: 0.052479 memory: 2937 loss_kpt: 103.119246 acc_pose: 0.661095 loss: 103.119246 2022/10/12 11:42:08 - mmengine - INFO - Epoch(train) [15][250/586] lr: 2.000000e-02 eta: 8:01:24 time: 0.254935 data_time: 0.050931 memory: 2937 loss_kpt: 103.873217 acc_pose: 0.687410 loss: 103.873217 2022/10/12 11:42:22 - mmengine - INFO - Epoch(train) [15][300/586] lr: 2.000000e-02 eta: 8:01:27 time: 0.275495 data_time: 0.057628 memory: 2937 loss_kpt: 103.791296 acc_pose: 0.786263 loss: 103.791296 2022/10/12 11:42:36 - mmengine - INFO - Epoch(train) [15][350/586] lr: 2.000000e-02 eta: 8:01:34 time: 0.281824 data_time: 0.052077 memory: 2937 loss_kpt: 103.048911 acc_pose: 0.643938 loss: 103.048911 2022/10/12 11:42:49 - mmengine - INFO - Epoch(train) [15][400/586] lr: 2.000000e-02 eta: 8:01:29 time: 0.263094 data_time: 0.049501 memory: 2937 loss_kpt: 103.649920 acc_pose: 0.660729 loss: 103.649920 2022/10/12 11:43:03 - mmengine - INFO - Epoch(train) [15][450/586] lr: 2.000000e-02 eta: 8:01:24 time: 0.265371 data_time: 0.056296 memory: 2937 loss_kpt: 103.375016 acc_pose: 0.676907 loss: 103.375016 2022/10/12 11:43:16 - mmengine - INFO - Epoch(train) [15][500/586] lr: 2.000000e-02 eta: 8:01:21 time: 0.266842 data_time: 0.051551 memory: 2937 loss_kpt: 103.292071 acc_pose: 0.717904 loss: 103.292071 2022/10/12 11:43:29 - mmengine - INFO - Epoch(train) [15][550/586] lr: 2.000000e-02 eta: 8:01:16 time: 0.264364 data_time: 0.055395 memory: 2937 loss_kpt: 102.979974 acc_pose: 0.768076 loss: 102.979974 2022/10/12 11:43:39 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:43:54 - mmengine - INFO - Epoch(train) [16][50/586] lr: 2.000000e-02 eta: 7:59:18 time: 0.285291 data_time: 0.062264 memory: 2937 loss_kpt: 103.051006 acc_pose: 0.702408 loss: 103.051006 2022/10/12 11:44:07 - mmengine - INFO - Epoch(train) [16][100/586] lr: 2.000000e-02 eta: 7:59:20 time: 0.273685 data_time: 0.047877 memory: 2937 loss_kpt: 104.043026 acc_pose: 0.687156 loss: 104.043026 2022/10/12 11:44:20 - mmengine - INFO - Epoch(train) [16][150/586] lr: 2.000000e-02 eta: 7:59:10 time: 0.256231 data_time: 0.054321 memory: 2937 loss_kpt: 101.691052 acc_pose: 0.723878 loss: 101.691052 2022/10/12 11:44:33 - mmengine - INFO - Epoch(train) [16][200/586] lr: 2.000000e-02 eta: 7:59:03 time: 0.261572 data_time: 0.055259 memory: 2937 loss_kpt: 104.102050 acc_pose: 0.740511 loss: 104.102050 2022/10/12 11:44:36 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:44:46 - mmengine - INFO - Epoch(train) [16][250/586] lr: 2.000000e-02 eta: 7:58:51 time: 0.252381 data_time: 0.050781 memory: 2937 loss_kpt: 103.950844 acc_pose: 0.702432 loss: 103.950844 2022/10/12 11:44:59 - mmengine - INFO - Epoch(train) [16][300/586] lr: 2.000000e-02 eta: 7:58:44 time: 0.260449 data_time: 0.055796 memory: 2937 loss_kpt: 102.377997 acc_pose: 0.726037 loss: 102.377997 2022/10/12 11:45:12 - mmengine - INFO - Epoch(train) [16][350/586] lr: 2.000000e-02 eta: 7:58:34 time: 0.257011 data_time: 0.050649 memory: 2937 loss_kpt: 102.451230 acc_pose: 0.722463 loss: 102.451230 2022/10/12 11:45:25 - mmengine - INFO - Epoch(train) [16][400/586] lr: 2.000000e-02 eta: 7:58:29 time: 0.263909 data_time: 0.051068 memory: 2937 loss_kpt: 102.385009 acc_pose: 0.787765 loss: 102.385009 2022/10/12 11:45:38 - mmengine - INFO - Epoch(train) [16][450/586] lr: 2.000000e-02 eta: 7:58:23 time: 0.262150 data_time: 0.055992 memory: 2937 loss_kpt: 104.207676 acc_pose: 0.700905 loss: 104.207676 2022/10/12 11:45:51 - mmengine - INFO - Epoch(train) [16][500/586] lr: 2.000000e-02 eta: 7:58:18 time: 0.265394 data_time: 0.055354 memory: 2937 loss_kpt: 104.258538 acc_pose: 0.697901 loss: 104.258538 2022/10/12 11:46:05 - mmengine - INFO - Epoch(train) [16][550/586] lr: 2.000000e-02 eta: 7:58:12 time: 0.263667 data_time: 0.053422 memory: 2937 loss_kpt: 103.475801 acc_pose: 0.720776 loss: 103.475801 2022/10/12 11:46:14 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:46:28 - mmengine - INFO - Epoch(train) [17][50/586] lr: 2.000000e-02 eta: 7:56:15 time: 0.274997 data_time: 0.062657 memory: 2937 loss_kpt: 102.721020 acc_pose: 0.745289 loss: 102.721020 2022/10/12 11:46:41 - mmengine - INFO - Epoch(train) [17][100/586] lr: 2.000000e-02 eta: 7:56:15 time: 0.272075 data_time: 0.052133 memory: 2937 loss_kpt: 101.545331 acc_pose: 0.669556 loss: 101.545331 2022/10/12 11:46:55 - mmengine - INFO - Epoch(train) [17][150/586] lr: 2.000000e-02 eta: 7:56:11 time: 0.265935 data_time: 0.048873 memory: 2937 loss_kpt: 102.518174 acc_pose: 0.646201 loss: 102.518174 2022/10/12 11:47:08 - mmengine - INFO - Epoch(train) [17][200/586] lr: 2.000000e-02 eta: 7:56:03 time: 0.260005 data_time: 0.053410 memory: 2937 loss_kpt: 104.296606 acc_pose: 0.735146 loss: 104.296606 2022/10/12 11:47:21 - mmengine - INFO - Epoch(train) [17][250/586] lr: 2.000000e-02 eta: 7:55:53 time: 0.256429 data_time: 0.052564 memory: 2937 loss_kpt: 101.781941 acc_pose: 0.693537 loss: 101.781941 2022/10/12 11:47:34 - mmengine - INFO - Epoch(train) [17][300/586] lr: 2.000000e-02 eta: 7:55:46 time: 0.261541 data_time: 0.047832 memory: 2937 loss_kpt: 102.223678 acc_pose: 0.663781 loss: 102.223678 2022/10/12 11:47:46 - mmengine - INFO - Epoch(train) [17][350/586] lr: 2.000000e-02 eta: 7:55:36 time: 0.255787 data_time: 0.051312 memory: 2937 loss_kpt: 102.270105 acc_pose: 0.750733 loss: 102.270105 2022/10/12 11:48:00 - mmengine - INFO - Epoch(train) [17][400/586] lr: 2.000000e-02 eta: 7:55:32 time: 0.267151 data_time: 0.050231 memory: 2937 loss_kpt: 100.934637 acc_pose: 0.715553 loss: 100.934637 2022/10/12 11:48:13 - mmengine - INFO - Epoch(train) [17][450/586] lr: 2.000000e-02 eta: 7:55:33 time: 0.274814 data_time: 0.051833 memory: 2937 loss_kpt: 103.941235 acc_pose: 0.729236 loss: 103.941235 2022/10/12 11:48:27 - mmengine - INFO - Epoch(train) [17][500/586] lr: 2.000000e-02 eta: 7:55:26 time: 0.260888 data_time: 0.050445 memory: 2937 loss_kpt: 103.985956 acc_pose: 0.726280 loss: 103.985956 2022/10/12 11:48:40 - mmengine - INFO - Epoch(train) [17][550/586] lr: 2.000000e-02 eta: 7:55:21 time: 0.266401 data_time: 0.050169 memory: 2937 loss_kpt: 101.144951 acc_pose: 0.745178 loss: 101.144951 2022/10/12 11:48:49 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:49:00 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:49:03 - mmengine - INFO - Epoch(train) [18][50/586] lr: 2.000000e-02 eta: 7:53:36 time: 0.286429 data_time: 0.064062 memory: 2937 loss_kpt: 102.006318 acc_pose: 0.623076 loss: 102.006318 2022/10/12 11:49:17 - mmengine - INFO - Epoch(train) [18][100/586] lr: 2.000000e-02 eta: 7:53:34 time: 0.270494 data_time: 0.048045 memory: 2937 loss_kpt: 101.097540 acc_pose: 0.707702 loss: 101.097540 2022/10/12 11:49:30 - mmengine - INFO - Epoch(train) [18][150/586] lr: 2.000000e-02 eta: 7:53:27 time: 0.260634 data_time: 0.056337 memory: 2937 loss_kpt: 103.275315 acc_pose: 0.787830 loss: 103.275315 2022/10/12 11:49:43 - mmengine - INFO - Epoch(train) [18][200/586] lr: 2.000000e-02 eta: 7:53:19 time: 0.258844 data_time: 0.048759 memory: 2937 loss_kpt: 102.221556 acc_pose: 0.710144 loss: 102.221556 2022/10/12 11:49:56 - mmengine - INFO - Epoch(train) [18][250/586] lr: 2.000000e-02 eta: 7:53:18 time: 0.272682 data_time: 0.053830 memory: 2937 loss_kpt: 100.957591 acc_pose: 0.683361 loss: 100.957591 2022/10/12 11:50:10 - mmengine - INFO - Epoch(train) [18][300/586] lr: 2.000000e-02 eta: 7:53:19 time: 0.276130 data_time: 0.048885 memory: 2937 loss_kpt: 102.487597 acc_pose: 0.731216 loss: 102.487597 2022/10/12 11:50:24 - mmengine - INFO - Epoch(train) [18][350/586] lr: 2.000000e-02 eta: 7:53:17 time: 0.271712 data_time: 0.054468 memory: 2937 loss_kpt: 102.260518 acc_pose: 0.747602 loss: 102.260518 2022/10/12 11:50:37 - mmengine - INFO - Epoch(train) [18][400/586] lr: 2.000000e-02 eta: 7:53:11 time: 0.265075 data_time: 0.053491 memory: 2937 loss_kpt: 103.116056 acc_pose: 0.737094 loss: 103.116056 2022/10/12 11:50:50 - mmengine - INFO - Epoch(train) [18][450/586] lr: 2.000000e-02 eta: 7:53:04 time: 0.260661 data_time: 0.051052 memory: 2937 loss_kpt: 101.445121 acc_pose: 0.797025 loss: 101.445121 2022/10/12 11:51:04 - mmengine - INFO - Epoch(train) [18][500/586] lr: 2.000000e-02 eta: 7:53:02 time: 0.272252 data_time: 0.055448 memory: 2937 loss_kpt: 101.903352 acc_pose: 0.810085 loss: 101.903352 2022/10/12 11:51:17 - mmengine - INFO - Epoch(train) [18][550/586] lr: 2.000000e-02 eta: 7:53:04 time: 0.278934 data_time: 0.056150 memory: 2937 loss_kpt: 101.381283 acc_pose: 0.704139 loss: 101.381283 2022/10/12 11:51:27 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:51:43 - mmengine - INFO - Epoch(train) [19][50/586] lr: 2.000000e-02 eta: 7:51:37 time: 0.310918 data_time: 0.063314 memory: 2937 loss_kpt: 104.502389 acc_pose: 0.796952 loss: 104.502389 2022/10/12 11:51:57 - mmengine - INFO - Epoch(train) [19][100/586] lr: 2.000000e-02 eta: 7:51:43 time: 0.288172 data_time: 0.054067 memory: 2937 loss_kpt: 100.895630 acc_pose: 0.655193 loss: 100.895630 2022/10/12 11:52:11 - mmengine - INFO - Epoch(train) [19][150/586] lr: 2.000000e-02 eta: 7:51:46 time: 0.280450 data_time: 0.053286 memory: 2937 loss_kpt: 101.567845 acc_pose: 0.643613 loss: 101.567845 2022/10/12 11:52:25 - mmengine - INFO - Epoch(train) [19][200/586] lr: 2.000000e-02 eta: 7:51:49 time: 0.281722 data_time: 0.055539 memory: 2937 loss_kpt: 102.148767 acc_pose: 0.709447 loss: 102.148767 2022/10/12 11:52:39 - mmengine - INFO - Epoch(train) [19][250/586] lr: 2.000000e-02 eta: 7:51:53 time: 0.284319 data_time: 0.049824 memory: 2937 loss_kpt: 100.905732 acc_pose: 0.647657 loss: 100.905732 2022/10/12 11:52:53 - mmengine - INFO - Epoch(train) [19][300/586] lr: 2.000000e-02 eta: 7:51:57 time: 0.284086 data_time: 0.055033 memory: 2937 loss_kpt: 102.101473 acc_pose: 0.685147 loss: 102.101473 2022/10/12 11:53:07 - mmengine - INFO - Epoch(train) [19][350/586] lr: 2.000000e-02 eta: 7:51:54 time: 0.271146 data_time: 0.055294 memory: 2937 loss_kpt: 102.390512 acc_pose: 0.760216 loss: 102.390512 2022/10/12 11:53:21 - mmengine - INFO - Epoch(train) [19][400/586] lr: 2.000000e-02 eta: 7:51:51 time: 0.271695 data_time: 0.056393 memory: 2937 loss_kpt: 100.764834 acc_pose: 0.726288 loss: 100.764834 2022/10/12 11:53:34 - mmengine - INFO - Epoch(train) [19][450/586] lr: 2.000000e-02 eta: 7:51:45 time: 0.264578 data_time: 0.054206 memory: 2937 loss_kpt: 100.695508 acc_pose: 0.750241 loss: 100.695508 2022/10/12 11:53:34 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:53:48 - mmengine - INFO - Epoch(train) [19][500/586] lr: 2.000000e-02 eta: 7:51:45 time: 0.277043 data_time: 0.059325 memory: 2937 loss_kpt: 101.584247 acc_pose: 0.664461 loss: 101.584247 2022/10/12 11:54:01 - mmengine - INFO - Epoch(train) [19][550/586] lr: 2.000000e-02 eta: 7:51:40 time: 0.268782 data_time: 0.055415 memory: 2937 loss_kpt: 101.001691 acc_pose: 0.713205 loss: 101.001691 2022/10/12 11:54:11 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:54:24 - mmengine - INFO - Epoch(train) [20][50/586] lr: 2.000000e-02 eta: 7:49:59 time: 0.276933 data_time: 0.065320 memory: 2937 loss_kpt: 101.728770 acc_pose: 0.668530 loss: 101.728770 2022/10/12 11:54:37 - mmengine - INFO - Epoch(train) [20][100/586] lr: 2.000000e-02 eta: 7:49:49 time: 0.256727 data_time: 0.053819 memory: 2937 loss_kpt: 102.981913 acc_pose: 0.742189 loss: 102.981913 2022/10/12 11:54:51 - mmengine - INFO - Epoch(train) [20][150/586] lr: 2.000000e-02 eta: 7:49:45 time: 0.270218 data_time: 0.055221 memory: 2937 loss_kpt: 101.041832 acc_pose: 0.794245 loss: 101.041832 2022/10/12 11:55:04 - mmengine - INFO - Epoch(train) [20][200/586] lr: 2.000000e-02 eta: 7:49:43 time: 0.273760 data_time: 0.050628 memory: 2937 loss_kpt: 102.152712 acc_pose: 0.688642 loss: 102.152712 2022/10/12 11:55:18 - mmengine - INFO - Epoch(train) [20][250/586] lr: 2.000000e-02 eta: 7:49:37 time: 0.264811 data_time: 0.055566 memory: 2937 loss_kpt: 101.386145 acc_pose: 0.697071 loss: 101.386145 2022/10/12 11:55:31 - mmengine - INFO - Epoch(train) [20][300/586] lr: 2.000000e-02 eta: 7:49:33 time: 0.269116 data_time: 0.051385 memory: 2937 loss_kpt: 100.815103 acc_pose: 0.721977 loss: 100.815103 2022/10/12 11:55:44 - mmengine - INFO - Epoch(train) [20][350/586] lr: 2.000000e-02 eta: 7:49:27 time: 0.266674 data_time: 0.049960 memory: 2937 loss_kpt: 101.116638 acc_pose: 0.724453 loss: 101.116638 2022/10/12 11:55:58 - mmengine - INFO - Epoch(train) [20][400/586] lr: 2.000000e-02 eta: 7:49:20 time: 0.263298 data_time: 0.051522 memory: 2937 loss_kpt: 100.699960 acc_pose: 0.729308 loss: 100.699960 2022/10/12 11:56:11 - mmengine - INFO - Epoch(train) [20][450/586] lr: 2.000000e-02 eta: 7:49:17 time: 0.272870 data_time: 0.053856 memory: 2937 loss_kpt: 100.894261 acc_pose: 0.687677 loss: 100.894261 2022/10/12 11:56:25 - mmengine - INFO - Epoch(train) [20][500/586] lr: 2.000000e-02 eta: 7:49:10 time: 0.265126 data_time: 0.048693 memory: 2937 loss_kpt: 101.758240 acc_pose: 0.639233 loss: 101.758240 2022/10/12 11:56:39 - mmengine - INFO - Epoch(train) [20][550/586] lr: 2.000000e-02 eta: 7:49:13 time: 0.284695 data_time: 0.050264 memory: 2937 loss_kpt: 100.662549 acc_pose: 0.670389 loss: 100.662549 2022/10/12 11:56:48 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:56:48 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/10/12 11:56:56 - mmengine - INFO - Epoch(val) [20][50/407] eta: 0:00:40 time: 0.114531 data_time: 0.013695 memory: 2937 2022/10/12 11:57:01 - mmengine - INFO - Epoch(val) [20][100/407] eta: 0:00:33 time: 0.109245 data_time: 0.008576 memory: 830 2022/10/12 11:57:07 - mmengine - INFO - Epoch(val) [20][150/407] eta: 0:00:28 time: 0.110364 data_time: 0.009346 memory: 830 2022/10/12 11:57:12 - mmengine - INFO - Epoch(val) [20][200/407] eta: 0:00:22 time: 0.109461 data_time: 0.008880 memory: 830 2022/10/12 11:57:18 - mmengine - INFO - Epoch(val) [20][250/407] eta: 0:00:16 time: 0.107861 data_time: 0.008885 memory: 830 2022/10/12 11:57:23 - mmengine - INFO - Epoch(val) [20][300/407] eta: 0:00:11 time: 0.110238 data_time: 0.009020 memory: 830 2022/10/12 11:57:29 - mmengine - INFO - Epoch(val) [20][350/407] eta: 0:00:06 time: 0.113815 data_time: 0.011125 memory: 830 2022/10/12 11:57:34 - mmengine - INFO - Epoch(val) [20][400/407] eta: 0:00:00 time: 0.105231 data_time: 0.010689 memory: 830 2022/10/12 11:57:47 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 11:58:03 - mmengine - INFO - Epoch(val) [20][407/407] coco/AP: 0.572914 coco/AP .5: 0.828518 coco/AP .75: 0.631095 coco/AP (M): 0.542841 coco/AP (L): 0.629789 coco/AR: 0.651307 coco/AR .5: 0.877991 coco/AR .75: 0.709855 coco/AR (M): 0.605299 coco/AR (L): 0.714530 2022/10/12 11:58:03 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221012/rsn18/best_coco/AP_epoch_10.pth is removed 2022/10/12 11:58:05 - mmengine - INFO - The best checkpoint with 0.5729 coco/AP at 20 epoch is saved to best_coco/AP_epoch_20.pth. 2022/10/12 11:58:19 - mmengine - INFO - Epoch(train) [21][50/586] lr: 2.000000e-02 eta: 7:47:45 time: 0.294430 data_time: 0.060156 memory: 2937 loss_kpt: 101.118072 acc_pose: 0.719783 loss: 101.118072 2022/10/12 11:58:34 - mmengine - INFO - Epoch(train) [21][100/586] lr: 2.000000e-02 eta: 7:47:52 time: 0.295134 data_time: 0.057237 memory: 2937 loss_kpt: 102.422322 acc_pose: 0.657379 loss: 102.422322 2022/10/12 11:58:48 - mmengine - INFO - Epoch(train) [21][150/586] lr: 2.000000e-02 eta: 7:47:50 time: 0.275467 data_time: 0.049825 memory: 2937 loss_kpt: 100.957622 acc_pose: 0.766525 loss: 100.957622 2022/10/12 11:59:02 - mmengine - INFO - Epoch(train) [21][200/586] lr: 2.000000e-02 eta: 7:47:49 time: 0.276841 data_time: 0.054898 memory: 2937 loss_kpt: 100.367037 acc_pose: 0.675363 loss: 100.367037 2022/10/12 11:59:15 - mmengine - INFO - Epoch(train) [21][250/586] lr: 2.000000e-02 eta: 7:47:43 time: 0.266295 data_time: 0.053464 memory: 2937 loss_kpt: 100.601568 acc_pose: 0.659725 loss: 100.601568 2022/10/12 11:59:23 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 11:59:28 - mmengine - INFO - Epoch(train) [21][300/586] lr: 2.000000e-02 eta: 7:47:36 time: 0.265775 data_time: 0.049754 memory: 2937 loss_kpt: 100.548625 acc_pose: 0.763413 loss: 100.548625 2022/10/12 11:59:42 - mmengine - INFO - Epoch(train) [21][350/586] lr: 2.000000e-02 eta: 7:47:29 time: 0.265050 data_time: 0.053661 memory: 2937 loss_kpt: 100.488091 acc_pose: 0.701586 loss: 100.488091 2022/10/12 11:59:55 - mmengine - INFO - Epoch(train) [21][400/586] lr: 2.000000e-02 eta: 7:47:22 time: 0.264515 data_time: 0.054339 memory: 2937 loss_kpt: 101.224590 acc_pose: 0.625281 loss: 101.224590 2022/10/12 12:00:08 - mmengine - INFO - Epoch(train) [21][450/586] lr: 2.000000e-02 eta: 7:47:14 time: 0.263459 data_time: 0.051017 memory: 2937 loss_kpt: 101.915420 acc_pose: 0.731058 loss: 101.915420 2022/10/12 12:00:22 - mmengine - INFO - Epoch(train) [21][500/586] lr: 2.000000e-02 eta: 7:47:09 time: 0.269735 data_time: 0.051850 memory: 2937 loss_kpt: 100.870090 acc_pose: 0.552613 loss: 100.870090 2022/10/12 12:00:35 - mmengine - INFO - Epoch(train) [21][550/586] lr: 2.000000e-02 eta: 7:47:02 time: 0.264762 data_time: 0.056300 memory: 2937 loss_kpt: 100.360892 acc_pose: 0.734109 loss: 100.360892 2022/10/12 12:00:44 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:00:58 - mmengine - INFO - Epoch(train) [22][50/586] lr: 2.000000e-02 eta: 7:45:32 time: 0.282430 data_time: 0.060921 memory: 2937 loss_kpt: 99.337414 acc_pose: 0.761353 loss: 99.337414 2022/10/12 12:01:12 - mmengine - INFO - Epoch(train) [22][100/586] lr: 2.000000e-02 eta: 7:45:29 time: 0.273256 data_time: 0.053062 memory: 2937 loss_kpt: 101.488779 acc_pose: 0.673542 loss: 101.488779 2022/10/12 12:01:25 - mmengine - INFO - Epoch(train) [22][150/586] lr: 2.000000e-02 eta: 7:45:21 time: 0.262603 data_time: 0.052763 memory: 2937 loss_kpt: 99.563116 acc_pose: 0.660339 loss: 99.563116 2022/10/12 12:01:38 - mmengine - INFO - Epoch(train) [22][200/586] lr: 2.000000e-02 eta: 7:45:11 time: 0.259979 data_time: 0.048505 memory: 2937 loss_kpt: 98.631575 acc_pose: 0.760962 loss: 98.631575 2022/10/12 12:01:51 - mmengine - INFO - Epoch(train) [22][250/586] lr: 2.000000e-02 eta: 7:45:04 time: 0.264090 data_time: 0.054598 memory: 2937 loss_kpt: 99.117377 acc_pose: 0.726725 loss: 99.117377 2022/10/12 12:02:04 - mmengine - INFO - Epoch(train) [22][300/586] lr: 2.000000e-02 eta: 7:44:55 time: 0.261187 data_time: 0.047510 memory: 2937 loss_kpt: 102.251829 acc_pose: 0.652396 loss: 102.251829 2022/10/12 12:02:17 - mmengine - INFO - Epoch(train) [22][350/586] lr: 2.000000e-02 eta: 7:44:45 time: 0.258832 data_time: 0.049502 memory: 2937 loss_kpt: 100.254051 acc_pose: 0.809825 loss: 100.254051 2022/10/12 12:02:30 - mmengine - INFO - Epoch(train) [22][400/586] lr: 2.000000e-02 eta: 7:44:37 time: 0.262287 data_time: 0.052340 memory: 2937 loss_kpt: 100.739214 acc_pose: 0.755352 loss: 100.739214 2022/10/12 12:02:44 - mmengine - INFO - Epoch(train) [22][450/586] lr: 2.000000e-02 eta: 7:44:31 time: 0.268061 data_time: 0.048557 memory: 2937 loss_kpt: 100.746208 acc_pose: 0.755525 loss: 100.746208 2022/10/12 12:02:57 - mmengine - INFO - Epoch(train) [22][500/586] lr: 2.000000e-02 eta: 7:44:23 time: 0.264678 data_time: 0.050560 memory: 2937 loss_kpt: 100.379402 acc_pose: 0.638583 loss: 100.379402 2022/10/12 12:03:10 - mmengine - INFO - Epoch(train) [22][550/586] lr: 2.000000e-02 eta: 7:44:17 time: 0.267342 data_time: 0.049453 memory: 2937 loss_kpt: 98.009653 acc_pose: 0.747151 loss: 98.009653 2022/10/12 12:03:20 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:03:35 - mmengine - INFO - Epoch(train) [23][50/586] lr: 2.000000e-02 eta: 7:42:56 time: 0.296602 data_time: 0.065632 memory: 2937 loss_kpt: 102.519497 acc_pose: 0.731797 loss: 102.519497 2022/10/12 12:03:48 - mmengine - INFO - Epoch(train) [23][100/586] lr: 2.000000e-02 eta: 7:42:48 time: 0.262609 data_time: 0.053325 memory: 2937 loss_kpt: 100.871758 acc_pose: 0.731408 loss: 100.871758 2022/10/12 12:03:50 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:04:01 - mmengine - INFO - Epoch(train) [23][150/586] lr: 2.000000e-02 eta: 7:42:42 time: 0.267491 data_time: 0.053539 memory: 2937 loss_kpt: 100.425077 acc_pose: 0.796620 loss: 100.425077 2022/10/12 12:04:14 - mmengine - INFO - Epoch(train) [23][200/586] lr: 2.000000e-02 eta: 7:42:34 time: 0.262892 data_time: 0.048681 memory: 2937 loss_kpt: 100.556273 acc_pose: 0.777306 loss: 100.556273 2022/10/12 12:04:27 - mmengine - INFO - Epoch(train) [23][250/586] lr: 2.000000e-02 eta: 7:42:25 time: 0.261637 data_time: 0.053234 memory: 2937 loss_kpt: 98.542981 acc_pose: 0.676342 loss: 98.542981 2022/10/12 12:04:40 - mmengine - INFO - Epoch(train) [23][300/586] lr: 2.000000e-02 eta: 7:42:16 time: 0.260248 data_time: 0.052146 memory: 2937 loss_kpt: 100.278378 acc_pose: 0.696315 loss: 100.278378 2022/10/12 12:04:53 - mmengine - INFO - Epoch(train) [23][350/586] lr: 2.000000e-02 eta: 7:42:02 time: 0.250273 data_time: 0.049883 memory: 2937 loss_kpt: 99.512020 acc_pose: 0.748856 loss: 99.512020 2022/10/12 12:05:06 - mmengine - INFO - Epoch(train) [23][400/586] lr: 2.000000e-02 eta: 7:41:54 time: 0.263617 data_time: 0.051528 memory: 2937 loss_kpt: 101.055474 acc_pose: 0.661315 loss: 101.055474 2022/10/12 12:05:19 - mmengine - INFO - Epoch(train) [23][450/586] lr: 2.000000e-02 eta: 7:41:42 time: 0.254758 data_time: 0.051931 memory: 2937 loss_kpt: 100.644728 acc_pose: 0.762643 loss: 100.644728 2022/10/12 12:05:32 - mmengine - INFO - Epoch(train) [23][500/586] lr: 2.000000e-02 eta: 7:41:32 time: 0.257157 data_time: 0.049384 memory: 2937 loss_kpt: 101.551352 acc_pose: 0.723863 loss: 101.551352 2022/10/12 12:05:44 - mmengine - INFO - Epoch(train) [23][550/586] lr: 2.000000e-02 eta: 7:41:20 time: 0.253904 data_time: 0.047108 memory: 2937 loss_kpt: 100.626917 acc_pose: 0.699816 loss: 100.626917 2022/10/12 12:05:53 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:06:07 - mmengine - INFO - Epoch(train) [24][50/586] lr: 2.000000e-02 eta: 7:39:53 time: 0.274141 data_time: 0.062167 memory: 2937 loss_kpt: 99.046466 acc_pose: 0.785263 loss: 99.046466 2022/10/12 12:06:20 - mmengine - INFO - Epoch(train) [24][100/586] lr: 2.000000e-02 eta: 7:39:41 time: 0.253695 data_time: 0.049477 memory: 2937 loss_kpt: 100.398998 acc_pose: 0.720271 loss: 100.398998 2022/10/12 12:06:33 - mmengine - INFO - Epoch(train) [24][150/586] lr: 2.000000e-02 eta: 7:39:32 time: 0.261418 data_time: 0.054155 memory: 2937 loss_kpt: 99.511683 acc_pose: 0.723166 loss: 99.511683 2022/10/12 12:06:46 - mmengine - INFO - Epoch(train) [24][200/586] lr: 2.000000e-02 eta: 7:39:25 time: 0.265847 data_time: 0.050219 memory: 2937 loss_kpt: 100.444713 acc_pose: 0.805064 loss: 100.444713 2022/10/12 12:06:58 - mmengine - INFO - Epoch(train) [24][250/586] lr: 2.000000e-02 eta: 7:39:12 time: 0.249690 data_time: 0.051256 memory: 2937 loss_kpt: 100.628482 acc_pose: 0.783185 loss: 100.628482 2022/10/12 12:07:12 - mmengine - INFO - Epoch(train) [24][300/586] lr: 2.000000e-02 eta: 7:39:08 time: 0.273212 data_time: 0.053963 memory: 2937 loss_kpt: 100.608941 acc_pose: 0.680949 loss: 100.608941 2022/10/12 12:07:25 - mmengine - INFO - Epoch(train) [24][350/586] lr: 2.000000e-02 eta: 7:38:57 time: 0.257757 data_time: 0.051987 memory: 2937 loss_kpt: 99.739682 acc_pose: 0.719254 loss: 99.739682 2022/10/12 12:07:38 - mmengine - INFO - Epoch(train) [24][400/586] lr: 2.000000e-02 eta: 7:38:47 time: 0.257959 data_time: 0.051882 memory: 2937 loss_kpt: 100.572948 acc_pose: 0.669174 loss: 100.572948 2022/10/12 12:07:51 - mmengine - INFO - Epoch(train) [24][450/586] lr: 2.000000e-02 eta: 7:38:36 time: 0.257307 data_time: 0.053382 memory: 2937 loss_kpt: 101.937401 acc_pose: 0.681898 loss: 101.937401 2022/10/12 12:08:03 - mmengine - INFO - Epoch(train) [24][500/586] lr: 2.000000e-02 eta: 7:38:23 time: 0.250112 data_time: 0.051981 memory: 2937 loss_kpt: 100.846578 acc_pose: 0.761901 loss: 100.846578 2022/10/12 12:08:09 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:08:16 - mmengine - INFO - Epoch(train) [24][550/586] lr: 2.000000e-02 eta: 7:38:12 time: 0.256105 data_time: 0.055698 memory: 2937 loss_kpt: 99.509255 acc_pose: 0.775212 loss: 99.509255 2022/10/12 12:08:25 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:08:39 - mmengine - INFO - Epoch(train) [25][50/586] lr: 2.000000e-02 eta: 7:36:54 time: 0.286926 data_time: 0.066455 memory: 2937 loss_kpt: 101.697667 acc_pose: 0.675685 loss: 101.697667 2022/10/12 12:08:52 - mmengine - INFO - Epoch(train) [25][100/586] lr: 2.000000e-02 eta: 7:36:45 time: 0.260631 data_time: 0.050816 memory: 2937 loss_kpt: 99.391109 acc_pose: 0.762344 loss: 99.391109 2022/10/12 12:09:07 - mmengine - INFO - Epoch(train) [25][150/586] lr: 2.000000e-02 eta: 7:36:45 time: 0.286755 data_time: 0.057227 memory: 2937 loss_kpt: 100.393347 acc_pose: 0.660998 loss: 100.393347 2022/10/12 12:09:21 - mmengine - INFO - Epoch(train) [25][200/586] lr: 2.000000e-02 eta: 7:36:44 time: 0.280876 data_time: 0.051308 memory: 2937 loss_kpt: 98.592485 acc_pose: 0.675045 loss: 98.592485 2022/10/12 12:09:35 - mmengine - INFO - Epoch(train) [25][250/586] lr: 2.000000e-02 eta: 7:36:45 time: 0.289016 data_time: 0.059701 memory: 2937 loss_kpt: 98.215634 acc_pose: 0.730740 loss: 98.215634 2022/10/12 12:09:49 - mmengine - INFO - Epoch(train) [25][300/586] lr: 2.000000e-02 eta: 7:36:42 time: 0.277304 data_time: 0.051487 memory: 2937 loss_kpt: 100.853637 acc_pose: 0.657336 loss: 100.853637 2022/10/12 12:10:02 - mmengine - INFO - Epoch(train) [25][350/586] lr: 2.000000e-02 eta: 7:36:37 time: 0.271491 data_time: 0.053995 memory: 2937 loss_kpt: 98.195332 acc_pose: 0.752760 loss: 98.195332 2022/10/12 12:10:16 - mmengine - INFO - Epoch(train) [25][400/586] lr: 2.000000e-02 eta: 7:36:32 time: 0.273417 data_time: 0.048478 memory: 2937 loss_kpt: 98.648639 acc_pose: 0.714321 loss: 98.648639 2022/10/12 12:10:30 - mmengine - INFO - Epoch(train) [25][450/586] lr: 2.000000e-02 eta: 7:36:27 time: 0.271570 data_time: 0.053293 memory: 2937 loss_kpt: 98.505554 acc_pose: 0.784590 loss: 98.505554 2022/10/12 12:10:43 - mmengine - INFO - Epoch(train) [25][500/586] lr: 2.000000e-02 eta: 7:36:17 time: 0.259344 data_time: 0.049290 memory: 2937 loss_kpt: 99.604866 acc_pose: 0.748454 loss: 99.604866 2022/10/12 12:10:56 - mmengine - INFO - Epoch(train) [25][550/586] lr: 2.000000e-02 eta: 7:36:13 time: 0.276082 data_time: 0.046832 memory: 2937 loss_kpt: 100.371520 acc_pose: 0.778287 loss: 100.371520 2022/10/12 12:11:06 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:11:20 - mmengine - INFO - Epoch(train) [26][50/586] lr: 2.000000e-02 eta: 7:34:54 time: 0.277522 data_time: 0.060559 memory: 2937 loss_kpt: 99.344084 acc_pose: 0.642035 loss: 99.344084 2022/10/12 12:11:34 - mmengine - INFO - Epoch(train) [26][100/586] lr: 2.000000e-02 eta: 7:34:52 time: 0.280416 data_time: 0.055628 memory: 2937 loss_kpt: 99.968443 acc_pose: 0.725553 loss: 99.968443 2022/10/12 12:11:47 - mmengine - INFO - Epoch(train) [26][150/586] lr: 2.000000e-02 eta: 7:34:42 time: 0.259512 data_time: 0.054442 memory: 2937 loss_kpt: 98.022471 acc_pose: 0.770749 loss: 98.022471 2022/10/12 12:12:00 - mmengine - INFO - Epoch(train) [26][200/586] lr: 2.000000e-02 eta: 7:34:33 time: 0.263113 data_time: 0.052525 memory: 2937 loss_kpt: 99.588006 acc_pose: 0.734471 loss: 99.588006 2022/10/12 12:12:13 - mmengine - INFO - Epoch(train) [26][250/586] lr: 2.000000e-02 eta: 7:34:20 time: 0.251281 data_time: 0.055584 memory: 2937 loss_kpt: 101.641242 acc_pose: 0.761745 loss: 101.641242 2022/10/12 12:12:26 - mmengine - INFO - Epoch(train) [26][300/586] lr: 2.000000e-02 eta: 7:34:12 time: 0.264086 data_time: 0.058624 memory: 2937 loss_kpt: 97.928352 acc_pose: 0.700855 loss: 97.928352 2022/10/12 12:12:39 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:12:39 - mmengine - INFO - Epoch(train) [26][350/586] lr: 2.000000e-02 eta: 7:34:07 time: 0.273990 data_time: 0.052543 memory: 2937 loss_kpt: 98.430382 acc_pose: 0.759531 loss: 98.430382 2022/10/12 12:12:52 - mmengine - INFO - Epoch(train) [26][400/586] lr: 2.000000e-02 eta: 7:33:57 time: 0.257195 data_time: 0.053058 memory: 2937 loss_kpt: 98.823710 acc_pose: 0.742202 loss: 98.823710 2022/10/12 12:13:05 - mmengine - INFO - Epoch(train) [26][450/586] lr: 2.000000e-02 eta: 7:33:45 time: 0.254087 data_time: 0.052687 memory: 2937 loss_kpt: 99.344243 acc_pose: 0.635655 loss: 99.344243 2022/10/12 12:13:18 - mmengine - INFO - Epoch(train) [26][500/586] lr: 2.000000e-02 eta: 7:33:36 time: 0.263700 data_time: 0.053307 memory: 2937 loss_kpt: 99.703735 acc_pose: 0.698448 loss: 99.703735 2022/10/12 12:13:31 - mmengine - INFO - Epoch(train) [26][550/586] lr: 2.000000e-02 eta: 7:33:25 time: 0.255174 data_time: 0.050219 memory: 2937 loss_kpt: 98.878380 acc_pose: 0.636483 loss: 98.878380 2022/10/12 12:13:40 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:13:54 - mmengine - INFO - Epoch(train) [27][50/586] lr: 2.000000e-02 eta: 7:32:07 time: 0.274744 data_time: 0.061273 memory: 2937 loss_kpt: 98.083216 acc_pose: 0.748701 loss: 98.083216 2022/10/12 12:14:06 - mmengine - INFO - Epoch(train) [27][100/586] lr: 2.000000e-02 eta: 7:31:56 time: 0.256057 data_time: 0.049461 memory: 2937 loss_kpt: 100.822536 acc_pose: 0.756922 loss: 100.822536 2022/10/12 12:14:20 - mmengine - INFO - Epoch(train) [27][150/586] lr: 2.000000e-02 eta: 7:31:48 time: 0.266685 data_time: 0.054214 memory: 2937 loss_kpt: 99.081551 acc_pose: 0.771718 loss: 99.081551 2022/10/12 12:14:33 - mmengine - INFO - Epoch(train) [27][200/586] lr: 2.000000e-02 eta: 7:31:40 time: 0.263964 data_time: 0.051173 memory: 2937 loss_kpt: 97.450550 acc_pose: 0.743521 loss: 97.450550 2022/10/12 12:14:46 - mmengine - INFO - Epoch(train) [27][250/586] lr: 2.000000e-02 eta: 7:31:31 time: 0.260871 data_time: 0.049875 memory: 2937 loss_kpt: 97.204897 acc_pose: 0.700789 loss: 97.204897 2022/10/12 12:14:59 - mmengine - INFO - Epoch(train) [27][300/586] lr: 2.000000e-02 eta: 7:31:20 time: 0.257182 data_time: 0.054081 memory: 2937 loss_kpt: 100.454445 acc_pose: 0.595186 loss: 100.454445 2022/10/12 12:15:12 - mmengine - INFO - Epoch(train) [27][350/586] lr: 2.000000e-02 eta: 7:31:14 time: 0.272341 data_time: 0.051689 memory: 2937 loss_kpt: 99.738083 acc_pose: 0.648578 loss: 99.738083 2022/10/12 12:15:26 - mmengine - INFO - Epoch(train) [27][400/586] lr: 2.000000e-02 eta: 7:31:06 time: 0.263762 data_time: 0.047380 memory: 2937 loss_kpt: 97.680077 acc_pose: 0.696792 loss: 97.680077 2022/10/12 12:15:39 - mmengine - INFO - Epoch(train) [27][450/586] lr: 2.000000e-02 eta: 7:30:56 time: 0.259203 data_time: 0.050324 memory: 2937 loss_kpt: 99.297972 acc_pose: 0.758018 loss: 99.297972 2022/10/12 12:15:52 - mmengine - INFO - Epoch(train) [27][500/586] lr: 2.000000e-02 eta: 7:30:46 time: 0.259247 data_time: 0.051765 memory: 2937 loss_kpt: 100.551618 acc_pose: 0.749472 loss: 100.551618 2022/10/12 12:16:05 - mmengine - INFO - Epoch(train) [27][550/586] lr: 2.000000e-02 eta: 7:30:39 time: 0.270703 data_time: 0.050846 memory: 2937 loss_kpt: 99.420924 acc_pose: 0.771462 loss: 99.420924 2022/10/12 12:16:14 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:16:28 - mmengine - INFO - Epoch(train) [28][50/586] lr: 2.000000e-02 eta: 7:29:27 time: 0.283254 data_time: 0.061881 memory: 2937 loss_kpt: 99.236060 acc_pose: 0.668570 loss: 99.236060 2022/10/12 12:16:43 - mmengine - INFO - Epoch(train) [28][100/586] lr: 2.000000e-02 eta: 7:29:25 time: 0.282756 data_time: 0.048055 memory: 2937 loss_kpt: 98.101571 acc_pose: 0.734164 loss: 98.101571 2022/10/12 12:16:56 - mmengine - INFO - Epoch(train) [28][150/586] lr: 2.000000e-02 eta: 7:29:18 time: 0.269857 data_time: 0.052325 memory: 2937 loss_kpt: 99.675508 acc_pose: 0.792720 loss: 99.675508 2022/10/12 12:17:03 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:17:10 - mmengine - INFO - Epoch(train) [28][200/586] lr: 2.000000e-02 eta: 7:29:13 time: 0.272337 data_time: 0.052671 memory: 2937 loss_kpt: 99.073581 acc_pose: 0.702861 loss: 99.073581 2022/10/12 12:17:23 - mmengine - INFO - Epoch(train) [28][250/586] lr: 2.000000e-02 eta: 7:29:07 time: 0.271944 data_time: 0.051375 memory: 2937 loss_kpt: 98.411538 acc_pose: 0.665822 loss: 98.411538 2022/10/12 12:17:36 - mmengine - INFO - Epoch(train) [28][300/586] lr: 2.000000e-02 eta: 7:28:57 time: 0.260359 data_time: 0.052501 memory: 2937 loss_kpt: 98.859999 acc_pose: 0.728833 loss: 98.859999 2022/10/12 12:17:49 - mmengine - INFO - Epoch(train) [28][350/586] lr: 2.000000e-02 eta: 7:28:46 time: 0.257315 data_time: 0.051146 memory: 2937 loss_kpt: 99.999635 acc_pose: 0.738447 loss: 99.999635 2022/10/12 12:18:02 - mmengine - INFO - Epoch(train) [28][400/586] lr: 2.000000e-02 eta: 7:28:37 time: 0.261013 data_time: 0.050101 memory: 2937 loss_kpt: 100.058086 acc_pose: 0.763656 loss: 100.058086 2022/10/12 12:18:15 - mmengine - INFO - Epoch(train) [28][450/586] lr: 2.000000e-02 eta: 7:28:28 time: 0.264291 data_time: 0.053917 memory: 2937 loss_kpt: 97.080540 acc_pose: 0.773196 loss: 97.080540 2022/10/12 12:18:28 - mmengine - INFO - Epoch(train) [28][500/586] lr: 2.000000e-02 eta: 7:28:18 time: 0.260584 data_time: 0.050533 memory: 2937 loss_kpt: 98.156948 acc_pose: 0.692103 loss: 98.156948 2022/10/12 12:18:41 - mmengine - INFO - Epoch(train) [28][550/586] lr: 2.000000e-02 eta: 7:28:06 time: 0.252732 data_time: 0.047674 memory: 2937 loss_kpt: 99.127515 acc_pose: 0.731965 loss: 99.127515 2022/10/12 12:18:50 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:19:05 - mmengine - INFO - Epoch(train) [29][50/586] lr: 2.000000e-02 eta: 7:26:57 time: 0.287765 data_time: 0.059551 memory: 2937 loss_kpt: 100.904908 acc_pose: 0.774203 loss: 100.904908 2022/10/12 12:19:18 - mmengine - INFO - Epoch(train) [29][100/586] lr: 2.000000e-02 eta: 7:26:53 time: 0.277420 data_time: 0.051822 memory: 2937 loss_kpt: 98.950358 acc_pose: 0.776765 loss: 98.950358 2022/10/12 12:19:33 - mmengine - INFO - Epoch(train) [29][150/586] lr: 2.000000e-02 eta: 7:26:50 time: 0.282459 data_time: 0.049652 memory: 2937 loss_kpt: 98.900520 acc_pose: 0.782928 loss: 98.900520 2022/10/12 12:19:47 - mmengine - INFO - Epoch(train) [29][200/586] lr: 2.000000e-02 eta: 7:26:50 time: 0.288921 data_time: 0.057305 memory: 2937 loss_kpt: 98.491260 acc_pose: 0.673938 loss: 98.491260 2022/10/12 12:20:02 - mmengine - INFO - Epoch(train) [29][250/586] lr: 2.000000e-02 eta: 7:26:49 time: 0.289985 data_time: 0.053490 memory: 2937 loss_kpt: 98.054461 acc_pose: 0.781958 loss: 98.054461 2022/10/12 12:20:16 - mmengine - INFO - Epoch(train) [29][300/586] lr: 2.000000e-02 eta: 7:26:46 time: 0.280868 data_time: 0.055715 memory: 2937 loss_kpt: 98.340518 acc_pose: 0.766366 loss: 98.340518 2022/10/12 12:20:29 - mmengine - INFO - Epoch(train) [29][350/586] lr: 2.000000e-02 eta: 7:26:40 time: 0.273006 data_time: 0.052920 memory: 2937 loss_kpt: 100.188460 acc_pose: 0.747379 loss: 100.188460 2022/10/12 12:20:43 - mmengine - INFO - Epoch(train) [29][400/586] lr: 2.000000e-02 eta: 7:26:37 time: 0.282901 data_time: 0.057181 memory: 2937 loss_kpt: 97.498381 acc_pose: 0.730124 loss: 97.498381 2022/10/12 12:20:57 - mmengine - INFO - Epoch(train) [29][450/586] lr: 2.000000e-02 eta: 7:26:33 time: 0.278564 data_time: 0.049725 memory: 2937 loss_kpt: 100.397391 acc_pose: 0.751419 loss: 100.397391 2022/10/12 12:21:11 - mmengine - INFO - Epoch(train) [29][500/586] lr: 2.000000e-02 eta: 7:26:29 time: 0.279981 data_time: 0.052051 memory: 2937 loss_kpt: 99.657821 acc_pose: 0.687861 loss: 99.657821 2022/10/12 12:21:25 - mmengine - INFO - Epoch(train) [29][550/586] lr: 2.000000e-02 eta: 7:26:24 time: 0.277363 data_time: 0.052765 memory: 2937 loss_kpt: 97.828031 acc_pose: 0.784650 loss: 97.828031 2022/10/12 12:21:35 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:21:37 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:21:49 - mmengine - INFO - Epoch(train) [30][50/586] lr: 2.000000e-02 eta: 7:25:20 time: 0.297321 data_time: 0.064364 memory: 2937 loss_kpt: 98.033611 acc_pose: 0.693875 loss: 98.033611 2022/10/12 12:22:04 - mmengine - INFO - Epoch(train) [30][100/586] lr: 2.000000e-02 eta: 7:25:17 time: 0.284720 data_time: 0.052033 memory: 2937 loss_kpt: 97.889274 acc_pose: 0.750979 loss: 97.889274 2022/10/12 12:22:17 - mmengine - INFO - Epoch(train) [30][150/586] lr: 2.000000e-02 eta: 7:25:11 time: 0.274569 data_time: 0.053458 memory: 2937 loss_kpt: 98.259417 acc_pose: 0.757948 loss: 98.259417 2022/10/12 12:22:31 - mmengine - INFO - Epoch(train) [30][200/586] lr: 2.000000e-02 eta: 7:25:05 time: 0.271038 data_time: 0.051029 memory: 2937 loss_kpt: 97.640768 acc_pose: 0.720603 loss: 97.640768 2022/10/12 12:22:44 - mmengine - INFO - Epoch(train) [30][250/586] lr: 2.000000e-02 eta: 7:24:56 time: 0.266859 data_time: 0.051910 memory: 2937 loss_kpt: 99.205869 acc_pose: 0.734257 loss: 99.205869 2022/10/12 12:22:58 - mmengine - INFO - Epoch(train) [30][300/586] lr: 2.000000e-02 eta: 7:24:50 time: 0.273234 data_time: 0.053104 memory: 2937 loss_kpt: 98.446107 acc_pose: 0.685763 loss: 98.446107 2022/10/12 12:23:11 - mmengine - INFO - Epoch(train) [30][350/586] lr: 2.000000e-02 eta: 7:24:43 time: 0.268563 data_time: 0.053745 memory: 2937 loss_kpt: 97.769312 acc_pose: 0.781865 loss: 97.769312 2022/10/12 12:23:24 - mmengine - INFO - Epoch(train) [30][400/586] lr: 2.000000e-02 eta: 7:24:32 time: 0.257786 data_time: 0.052857 memory: 2937 loss_kpt: 98.019605 acc_pose: 0.608164 loss: 98.019605 2022/10/12 12:23:37 - mmengine - INFO - Epoch(train) [30][450/586] lr: 2.000000e-02 eta: 7:24:22 time: 0.261591 data_time: 0.051931 memory: 2937 loss_kpt: 97.841353 acc_pose: 0.713492 loss: 97.841353 2022/10/12 12:23:50 - mmengine - INFO - Epoch(train) [30][500/586] lr: 2.000000e-02 eta: 7:24:12 time: 0.260844 data_time: 0.052543 memory: 2937 loss_kpt: 99.873030 acc_pose: 0.641488 loss: 99.873030 2022/10/12 12:24:04 - mmengine - INFO - Epoch(train) [30][550/586] lr: 2.000000e-02 eta: 7:24:03 time: 0.265771 data_time: 0.053165 memory: 2937 loss_kpt: 96.964225 acc_pose: 0.769461 loss: 96.964225 2022/10/12 12:24:13 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:24:13 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/10/12 12:24:21 - mmengine - INFO - Epoch(val) [30][50/407] eta: 0:00:41 time: 0.115170 data_time: 0.014489 memory: 2937 2022/10/12 12:24:27 - mmengine - INFO - Epoch(val) [30][100/407] eta: 0:00:33 time: 0.108936 data_time: 0.009045 memory: 830 2022/10/12 12:24:32 - mmengine - INFO - Epoch(val) [30][150/407] eta: 0:00:28 time: 0.110986 data_time: 0.009415 memory: 830 2022/10/12 12:24:38 - mmengine - INFO - Epoch(val) [30][200/407] eta: 0:00:24 time: 0.116518 data_time: 0.012698 memory: 830 2022/10/12 12:24:44 - mmengine - INFO - Epoch(val) [30][250/407] eta: 0:00:17 time: 0.108873 data_time: 0.008935 memory: 830 2022/10/12 12:24:49 - mmengine - INFO - Epoch(val) [30][300/407] eta: 0:00:11 time: 0.111589 data_time: 0.008904 memory: 830 2022/10/12 12:24:55 - mmengine - INFO - Epoch(val) [30][350/407] eta: 0:00:06 time: 0.112044 data_time: 0.008894 memory: 830 2022/10/12 12:25:00 - mmengine - INFO - Epoch(val) [30][400/407] eta: 0:00:00 time: 0.099142 data_time: 0.007402 memory: 830 2022/10/12 12:25:13 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 12:25:29 - mmengine - INFO - Epoch(val) [30][407/407] coco/AP: 0.619004 coco/AP .5: 0.852822 coco/AP .75: 0.691167 coco/AP (M): 0.592521 coco/AP (L): 0.671597 coco/AR: 0.694301 coco/AR .5: 0.899874 coco/AR .75: 0.756297 coco/AR (M): 0.649495 coco/AR (L): 0.755890 2022/10/12 12:25:29 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221012/rsn18/best_coco/AP_epoch_20.pth is removed 2022/10/12 12:25:31 - mmengine - INFO - The best checkpoint with 0.6190 coco/AP at 30 epoch is saved to best_coco/AP_epoch_30.pth. 2022/10/12 12:25:44 - mmengine - INFO - Epoch(train) [31][50/586] lr: 2.000000e-02 eta: 7:22:51 time: 0.266914 data_time: 0.058176 memory: 2937 loss_kpt: 99.443207 acc_pose: 0.794984 loss: 99.443207 2022/10/12 12:25:57 - mmengine - INFO - Epoch(train) [31][100/586] lr: 2.000000e-02 eta: 7:22:42 time: 0.262439 data_time: 0.055877 memory: 2937 loss_kpt: 98.515605 acc_pose: 0.804880 loss: 98.515605 2022/10/12 12:26:10 - mmengine - INFO - Epoch(train) [31][150/586] lr: 2.000000e-02 eta: 7:22:33 time: 0.264561 data_time: 0.052204 memory: 2937 loss_kpt: 96.010997 acc_pose: 0.696917 loss: 96.010997 2022/10/12 12:26:23 - mmengine - INFO - Epoch(train) [31][200/586] lr: 2.000000e-02 eta: 7:22:23 time: 0.261482 data_time: 0.051034 memory: 2937 loss_kpt: 99.924117 acc_pose: 0.749882 loss: 99.924117 2022/10/12 12:26:37 - mmengine - INFO - Epoch(train) [31][250/586] lr: 2.000000e-02 eta: 7:22:16 time: 0.270783 data_time: 0.052677 memory: 2937 loss_kpt: 100.072911 acc_pose: 0.636273 loss: 100.072911 2022/10/12 12:26:51 - mmengine - INFO - Epoch(train) [31][300/586] lr: 2.000000e-02 eta: 7:22:09 time: 0.272864 data_time: 0.047983 memory: 2937 loss_kpt: 97.361333 acc_pose: 0.742535 loss: 97.361333 2022/10/12 12:27:04 - mmengine - INFO - Epoch(train) [31][350/586] lr: 2.000000e-02 eta: 7:22:02 time: 0.271507 data_time: 0.051004 memory: 2937 loss_kpt: 97.458760 acc_pose: 0.686870 loss: 97.458760 2022/10/12 12:27:17 - mmengine - INFO - Epoch(train) [31][400/586] lr: 2.000000e-02 eta: 7:21:51 time: 0.256107 data_time: 0.050640 memory: 2937 loss_kpt: 99.363519 acc_pose: 0.723426 loss: 99.363519 2022/10/12 12:27:22 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:27:30 - mmengine - INFO - Epoch(train) [31][450/586] lr: 2.000000e-02 eta: 7:21:40 time: 0.259437 data_time: 0.054282 memory: 2937 loss_kpt: 100.359694 acc_pose: 0.662362 loss: 100.359694 2022/10/12 12:27:43 - mmengine - INFO - Epoch(train) [31][500/586] lr: 2.000000e-02 eta: 7:21:30 time: 0.258342 data_time: 0.053313 memory: 2937 loss_kpt: 98.619674 acc_pose: 0.776115 loss: 98.619674 2022/10/12 12:27:56 - mmengine - INFO - Epoch(train) [31][550/586] lr: 2.000000e-02 eta: 7:21:19 time: 0.258391 data_time: 0.054094 memory: 2937 loss_kpt: 98.386859 acc_pose: 0.674855 loss: 98.386859 2022/10/12 12:28:05 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:28:20 - mmengine - INFO - Epoch(train) [32][50/586] lr: 2.000000e-02 eta: 7:20:19 time: 0.302160 data_time: 0.068337 memory: 2937 loss_kpt: 98.098436 acc_pose: 0.789437 loss: 98.098436 2022/10/12 12:28:34 - mmengine - INFO - Epoch(train) [32][100/586] lr: 2.000000e-02 eta: 7:20:15 time: 0.282875 data_time: 0.052905 memory: 2937 loss_kpt: 99.944725 acc_pose: 0.634749 loss: 99.944725 2022/10/12 12:28:47 - mmengine - INFO - Epoch(train) [32][150/586] lr: 2.000000e-02 eta: 7:20:05 time: 0.259864 data_time: 0.049607 memory: 2937 loss_kpt: 95.697147 acc_pose: 0.701794 loss: 95.697147 2022/10/12 12:29:00 - mmengine - INFO - Epoch(train) [32][200/586] lr: 2.000000e-02 eta: 7:19:54 time: 0.256648 data_time: 0.057430 memory: 2937 loss_kpt: 97.604496 acc_pose: 0.776206 loss: 97.604496 2022/10/12 12:29:13 - mmengine - INFO - Epoch(train) [32][250/586] lr: 2.000000e-02 eta: 7:19:40 time: 0.250099 data_time: 0.051347 memory: 2937 loss_kpt: 97.106902 acc_pose: 0.691207 loss: 97.106902 2022/10/12 12:29:26 - mmengine - INFO - Epoch(train) [32][300/586] lr: 2.000000e-02 eta: 7:19:31 time: 0.261550 data_time: 0.048331 memory: 2937 loss_kpt: 96.925092 acc_pose: 0.726854 loss: 96.925092 2022/10/12 12:29:39 - mmengine - INFO - Epoch(train) [32][350/586] lr: 2.000000e-02 eta: 7:19:19 time: 0.257743 data_time: 0.051643 memory: 2937 loss_kpt: 98.091083 acc_pose: 0.771495 loss: 98.091083 2022/10/12 12:29:52 - mmengine - INFO - Epoch(train) [32][400/586] lr: 2.000000e-02 eta: 7:19:11 time: 0.265466 data_time: 0.053946 memory: 2937 loss_kpt: 97.159055 acc_pose: 0.766592 loss: 97.159055 2022/10/12 12:30:05 - mmengine - INFO - Epoch(train) [32][450/586] lr: 2.000000e-02 eta: 7:19:00 time: 0.260111 data_time: 0.053403 memory: 2937 loss_kpt: 97.120321 acc_pose: 0.625331 loss: 97.120321 2022/10/12 12:30:19 - mmengine - INFO - Epoch(train) [32][500/586] lr: 2.000000e-02 eta: 7:18:53 time: 0.271377 data_time: 0.051831 memory: 2937 loss_kpt: 98.685662 acc_pose: 0.725951 loss: 98.685662 2022/10/12 12:30:32 - mmengine - INFO - Epoch(train) [32][550/586] lr: 2.000000e-02 eta: 7:18:43 time: 0.261963 data_time: 0.054531 memory: 2937 loss_kpt: 98.748685 acc_pose: 0.599839 loss: 98.748685 2022/10/12 12:30:41 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:30:56 - mmengine - INFO - Epoch(train) [33][50/586] lr: 2.000000e-02 eta: 7:17:42 time: 0.291742 data_time: 0.063421 memory: 2937 loss_kpt: 98.860577 acc_pose: 0.734544 loss: 98.860577 2022/10/12 12:31:10 - mmengine - INFO - Epoch(train) [33][100/586] lr: 2.000000e-02 eta: 7:17:36 time: 0.277453 data_time: 0.055401 memory: 2937 loss_kpt: 96.807216 acc_pose: 0.770922 loss: 96.807216 2022/10/12 12:31:23 - mmengine - INFO - Epoch(train) [33][150/586] lr: 2.000000e-02 eta: 7:17:29 time: 0.269565 data_time: 0.055917 memory: 2937 loss_kpt: 97.036396 acc_pose: 0.785411 loss: 97.036396 2022/10/12 12:31:36 - mmengine - INFO - Epoch(train) [33][200/586] lr: 2.000000e-02 eta: 7:17:20 time: 0.265103 data_time: 0.049836 memory: 2937 loss_kpt: 99.771273 acc_pose: 0.738359 loss: 99.771273 2022/10/12 12:31:49 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:31:50 - mmengine - INFO - Epoch(train) [33][250/586] lr: 2.000000e-02 eta: 7:17:12 time: 0.271276 data_time: 0.053114 memory: 2937 loss_kpt: 97.868136 acc_pose: 0.733745 loss: 97.868136 2022/10/12 12:32:04 - mmengine - INFO - Epoch(train) [33][300/586] lr: 2.000000e-02 eta: 7:17:08 time: 0.281946 data_time: 0.047817 memory: 2937 loss_kpt: 96.812937 acc_pose: 0.697828 loss: 96.812937 2022/10/12 12:32:17 - mmengine - INFO - Epoch(train) [33][350/586] lr: 2.000000e-02 eta: 7:16:59 time: 0.264382 data_time: 0.054792 memory: 2937 loss_kpt: 97.487358 acc_pose: 0.701683 loss: 97.487358 2022/10/12 12:32:31 - mmengine - INFO - Epoch(train) [33][400/586] lr: 2.000000e-02 eta: 7:16:52 time: 0.274623 data_time: 0.047250 memory: 2937 loss_kpt: 97.249906 acc_pose: 0.784967 loss: 97.249906 2022/10/12 12:32:44 - mmengine - INFO - Epoch(train) [33][450/586] lr: 2.000000e-02 eta: 7:16:44 time: 0.268235 data_time: 0.051179 memory: 2937 loss_kpt: 95.857729 acc_pose: 0.695414 loss: 95.857729 2022/10/12 12:32:58 - mmengine - INFO - Epoch(train) [33][500/586] lr: 2.000000e-02 eta: 7:16:35 time: 0.267231 data_time: 0.049630 memory: 2937 loss_kpt: 96.974170 acc_pose: 0.688781 loss: 96.974170 2022/10/12 12:33:11 - mmengine - INFO - Epoch(train) [33][550/586] lr: 2.000000e-02 eta: 7:16:25 time: 0.262884 data_time: 0.049557 memory: 2937 loss_kpt: 97.183887 acc_pose: 0.773636 loss: 97.183887 2022/10/12 12:33:20 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:33:34 - mmengine - INFO - Epoch(train) [34][50/586] lr: 2.000000e-02 eta: 7:15:26 time: 0.293966 data_time: 0.068078 memory: 2937 loss_kpt: 96.867533 acc_pose: 0.731402 loss: 96.867533 2022/10/12 12:33:48 - mmengine - INFO - Epoch(train) [34][100/586] lr: 2.000000e-02 eta: 7:15:18 time: 0.266582 data_time: 0.048207 memory: 2937 loss_kpt: 96.810928 acc_pose: 0.646587 loss: 96.810928 2022/10/12 12:34:01 - mmengine - INFO - Epoch(train) [34][150/586] lr: 2.000000e-02 eta: 7:15:11 time: 0.274073 data_time: 0.050896 memory: 2937 loss_kpt: 97.460265 acc_pose: 0.710987 loss: 97.460265 2022/10/12 12:34:15 - mmengine - INFO - Epoch(train) [34][200/586] lr: 2.000000e-02 eta: 7:15:02 time: 0.267403 data_time: 0.048005 memory: 2937 loss_kpt: 97.529303 acc_pose: 0.715999 loss: 97.529303 2022/10/12 12:34:28 - mmengine - INFO - Epoch(train) [34][250/586] lr: 2.000000e-02 eta: 7:14:52 time: 0.259943 data_time: 0.052033 memory: 2937 loss_kpt: 96.639873 acc_pose: 0.657214 loss: 96.639873 2022/10/12 12:34:41 - mmengine - INFO - Epoch(train) [34][300/586] lr: 2.000000e-02 eta: 7:14:41 time: 0.260076 data_time: 0.052251 memory: 2937 loss_kpt: 96.720574 acc_pose: 0.686585 loss: 96.720574 2022/10/12 12:34:54 - mmengine - INFO - Epoch(train) [34][350/586] lr: 2.000000e-02 eta: 7:14:29 time: 0.253592 data_time: 0.046585 memory: 2937 loss_kpt: 96.883270 acc_pose: 0.813392 loss: 96.883270 2022/10/12 12:35:07 - mmengine - INFO - Epoch(train) [34][400/586] lr: 2.000000e-02 eta: 7:14:21 time: 0.270388 data_time: 0.052947 memory: 2937 loss_kpt: 98.682953 acc_pose: 0.752195 loss: 98.682953 2022/10/12 12:35:20 - mmengine - INFO - Epoch(train) [34][450/586] lr: 2.000000e-02 eta: 7:14:12 time: 0.264281 data_time: 0.052179 memory: 2937 loss_kpt: 97.362785 acc_pose: 0.685248 loss: 97.362785 2022/10/12 12:35:33 - mmengine - INFO - Epoch(train) [34][500/586] lr: 2.000000e-02 eta: 7:14:00 time: 0.256781 data_time: 0.048537 memory: 2937 loss_kpt: 96.577085 acc_pose: 0.771221 loss: 96.577085 2022/10/12 12:35:46 - mmengine - INFO - Epoch(train) [34][550/586] lr: 2.000000e-02 eta: 7:13:49 time: 0.259159 data_time: 0.052667 memory: 2937 loss_kpt: 96.940802 acc_pose: 0.777886 loss: 96.940802 2022/10/12 12:35:55 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:36:08 - mmengine - INFO - Epoch(train) [35][50/586] lr: 2.000000e-02 eta: 7:12:44 time: 0.265625 data_time: 0.063284 memory: 2937 loss_kpt: 96.125106 acc_pose: 0.747797 loss: 96.125106 2022/10/12 12:36:15 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:36:21 - mmengine - INFO - Epoch(train) [35][100/586] lr: 2.000000e-02 eta: 7:12:34 time: 0.262776 data_time: 0.054197 memory: 2937 loss_kpt: 97.138882 acc_pose: 0.645855 loss: 97.138882 2022/10/12 12:36:34 - mmengine - INFO - Epoch(train) [35][150/586] lr: 2.000000e-02 eta: 7:12:22 time: 0.252057 data_time: 0.052844 memory: 2937 loss_kpt: 97.530701 acc_pose: 0.737359 loss: 97.530701 2022/10/12 12:36:47 - mmengine - INFO - Epoch(train) [35][200/586] lr: 2.000000e-02 eta: 7:12:12 time: 0.262787 data_time: 0.052468 memory: 2937 loss_kpt: 96.229291 acc_pose: 0.628820 loss: 96.229291 2022/10/12 12:37:00 - mmengine - INFO - Epoch(train) [35][250/586] lr: 2.000000e-02 eta: 7:12:01 time: 0.256387 data_time: 0.054525 memory: 2937 loss_kpt: 98.545932 acc_pose: 0.728373 loss: 98.545932 2022/10/12 12:37:13 - mmengine - INFO - Epoch(train) [35][300/586] lr: 2.000000e-02 eta: 7:11:52 time: 0.266062 data_time: 0.056451 memory: 2937 loss_kpt: 97.247047 acc_pose: 0.811235 loss: 97.247047 2022/10/12 12:37:26 - mmengine - INFO - Epoch(train) [35][350/586] lr: 2.000000e-02 eta: 7:11:40 time: 0.256932 data_time: 0.058602 memory: 2937 loss_kpt: 96.701024 acc_pose: 0.741646 loss: 96.701024 2022/10/12 12:37:39 - mmengine - INFO - Epoch(train) [35][400/586] lr: 2.000000e-02 eta: 7:11:30 time: 0.263156 data_time: 0.056478 memory: 2937 loss_kpt: 97.407106 acc_pose: 0.701971 loss: 97.407106 2022/10/12 12:37:53 - mmengine - INFO - Epoch(train) [35][450/586] lr: 2.000000e-02 eta: 7:11:23 time: 0.274265 data_time: 0.052864 memory: 2937 loss_kpt: 97.275480 acc_pose: 0.702770 loss: 97.275480 2022/10/12 12:38:07 - mmengine - INFO - Epoch(train) [35][500/586] lr: 2.000000e-02 eta: 7:11:16 time: 0.273844 data_time: 0.054591 memory: 2937 loss_kpt: 97.504092 acc_pose: 0.734422 loss: 97.504092 2022/10/12 12:38:20 - mmengine - INFO - Epoch(train) [35][550/586] lr: 2.000000e-02 eta: 7:11:07 time: 0.264777 data_time: 0.053267 memory: 2937 loss_kpt: 97.300672 acc_pose: 0.784475 loss: 97.300672 2022/10/12 12:38:29 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:38:43 - mmengine - INFO - Epoch(train) [36][50/586] lr: 2.000000e-02 eta: 7:10:09 time: 0.287296 data_time: 0.063697 memory: 2937 loss_kpt: 95.729062 acc_pose: 0.661756 loss: 95.729062 2022/10/12 12:38:57 - mmengine - INFO - Epoch(train) [36][100/586] lr: 2.000000e-02 eta: 7:10:01 time: 0.272046 data_time: 0.050292 memory: 2937 loss_kpt: 98.365159 acc_pose: 0.786073 loss: 98.365159 2022/10/12 12:39:10 - mmengine - INFO - Epoch(train) [36][150/586] lr: 2.000000e-02 eta: 7:09:52 time: 0.266899 data_time: 0.053014 memory: 2937 loss_kpt: 96.445867 acc_pose: 0.758413 loss: 96.445867 2022/10/12 12:39:24 - mmengine - INFO - Epoch(train) [36][200/586] lr: 2.000000e-02 eta: 7:09:46 time: 0.277654 data_time: 0.054362 memory: 2937 loss_kpt: 98.213577 acc_pose: 0.761425 loss: 98.213577 2022/10/12 12:39:38 - mmengine - INFO - Epoch(train) [36][250/586] lr: 2.000000e-02 eta: 7:09:41 time: 0.281682 data_time: 0.053282 memory: 2937 loss_kpt: 96.684163 acc_pose: 0.814152 loss: 96.684163 2022/10/12 12:39:52 - mmengine - INFO - Epoch(train) [36][300/586] lr: 2.000000e-02 eta: 7:09:33 time: 0.272293 data_time: 0.057308 memory: 2937 loss_kpt: 97.530894 acc_pose: 0.743909 loss: 97.530894 2022/10/12 12:40:06 - mmengine - INFO - Epoch(train) [36][350/586] lr: 2.000000e-02 eta: 7:09:28 time: 0.281829 data_time: 0.053905 memory: 2937 loss_kpt: 96.843909 acc_pose: 0.740927 loss: 96.843909 2022/10/12 12:40:20 - mmengine - INFO - Epoch(train) [36][400/586] lr: 2.000000e-02 eta: 7:09:21 time: 0.276067 data_time: 0.054323 memory: 2937 loss_kpt: 95.468266 acc_pose: 0.712315 loss: 95.468266 2022/10/12 12:40:34 - mmengine - INFO - Epoch(train) [36][450/586] lr: 2.000000e-02 eta: 7:09:15 time: 0.278395 data_time: 0.050967 memory: 2937 loss_kpt: 97.938483 acc_pose: 0.767824 loss: 97.938483 2022/10/12 12:40:45 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:40:48 - mmengine - INFO - Epoch(train) [36][500/586] lr: 2.000000e-02 eta: 7:09:08 time: 0.277791 data_time: 0.053483 memory: 2937 loss_kpt: 97.638858 acc_pose: 0.772353 loss: 97.638858 2022/10/12 12:41:01 - mmengine - INFO - Epoch(train) [36][550/586] lr: 2.000000e-02 eta: 7:08:59 time: 0.264038 data_time: 0.053805 memory: 2937 loss_kpt: 96.121934 acc_pose: 0.753595 loss: 96.121934 2022/10/12 12:41:11 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:41:26 - mmengine - INFO - Epoch(train) [37][50/586] lr: 2.000000e-02 eta: 7:08:05 time: 0.301823 data_time: 0.066891 memory: 2937 loss_kpt: 96.863891 acc_pose: 0.661946 loss: 96.863891 2022/10/12 12:41:40 - mmengine - INFO - Epoch(train) [37][100/586] lr: 2.000000e-02 eta: 7:08:00 time: 0.282323 data_time: 0.054372 memory: 2937 loss_kpt: 97.017970 acc_pose: 0.736622 loss: 97.017970 2022/10/12 12:41:53 - mmengine - INFO - Epoch(train) [37][150/586] lr: 2.000000e-02 eta: 7:07:50 time: 0.265243 data_time: 0.050258 memory: 2937 loss_kpt: 96.694757 acc_pose: 0.705135 loss: 96.694757 2022/10/12 12:42:07 - mmengine - INFO - Epoch(train) [37][200/586] lr: 2.000000e-02 eta: 7:07:46 time: 0.284674 data_time: 0.047317 memory: 2937 loss_kpt: 96.432635 acc_pose: 0.709873 loss: 96.432635 2022/10/12 12:42:21 - mmengine - INFO - Epoch(train) [37][250/586] lr: 2.000000e-02 eta: 7:07:37 time: 0.269703 data_time: 0.051621 memory: 2937 loss_kpt: 97.511692 acc_pose: 0.712207 loss: 97.511692 2022/10/12 12:42:34 - mmengine - INFO - Epoch(train) [37][300/586] lr: 2.000000e-02 eta: 7:07:28 time: 0.269069 data_time: 0.050909 memory: 2937 loss_kpt: 96.480763 acc_pose: 0.702248 loss: 96.480763 2022/10/12 12:42:48 - mmengine - INFO - Epoch(train) [37][350/586] lr: 2.000000e-02 eta: 7:07:21 time: 0.273776 data_time: 0.052681 memory: 2937 loss_kpt: 95.567414 acc_pose: 0.725441 loss: 95.567414 2022/10/12 12:43:01 - mmengine - INFO - Epoch(train) [37][400/586] lr: 2.000000e-02 eta: 7:07:12 time: 0.268101 data_time: 0.052547 memory: 2937 loss_kpt: 96.412756 acc_pose: 0.685101 loss: 96.412756 2022/10/12 12:43:15 - mmengine - INFO - Epoch(train) [37][450/586] lr: 2.000000e-02 eta: 7:07:03 time: 0.269610 data_time: 0.055365 memory: 2937 loss_kpt: 96.202743 acc_pose: 0.753030 loss: 96.202743 2022/10/12 12:43:28 - mmengine - INFO - Epoch(train) [37][500/586] lr: 2.000000e-02 eta: 7:06:54 time: 0.265016 data_time: 0.050169 memory: 2937 loss_kpt: 98.012716 acc_pose: 0.753055 loss: 98.012716 2022/10/12 12:43:42 - mmengine - INFO - Epoch(train) [37][550/586] lr: 2.000000e-02 eta: 7:06:46 time: 0.270870 data_time: 0.053627 memory: 2937 loss_kpt: 95.915566 acc_pose: 0.729304 loss: 95.915566 2022/10/12 12:43:51 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:44:05 - mmengine - INFO - Epoch(train) [38][50/586] lr: 2.000000e-02 eta: 7:05:49 time: 0.284087 data_time: 0.060963 memory: 2937 loss_kpt: 96.149093 acc_pose: 0.726062 loss: 96.149093 2022/10/12 12:44:19 - mmengine - INFO - Epoch(train) [38][100/586] lr: 2.000000e-02 eta: 7:05:41 time: 0.272184 data_time: 0.059485 memory: 2937 loss_kpt: 97.278016 acc_pose: 0.681811 loss: 97.278016 2022/10/12 12:44:32 - mmengine - INFO - Epoch(train) [38][150/586] lr: 2.000000e-02 eta: 7:05:31 time: 0.264058 data_time: 0.048686 memory: 2937 loss_kpt: 97.068342 acc_pose: 0.735524 loss: 97.068342 2022/10/12 12:44:45 - mmengine - INFO - Epoch(train) [38][200/586] lr: 2.000000e-02 eta: 7:05:21 time: 0.265654 data_time: 0.047632 memory: 2937 loss_kpt: 95.635150 acc_pose: 0.759271 loss: 95.635150 2022/10/12 12:44:59 - mmengine - INFO - Epoch(train) [38][250/586] lr: 2.000000e-02 eta: 7:05:12 time: 0.264470 data_time: 0.051550 memory: 2937 loss_kpt: 96.314405 acc_pose: 0.660267 loss: 96.314405 2022/10/12 12:45:12 - mmengine - INFO - Epoch(train) [38][300/586] lr: 2.000000e-02 eta: 7:05:01 time: 0.262800 data_time: 0.047525 memory: 2937 loss_kpt: 97.801410 acc_pose: 0.710952 loss: 97.801410 2022/10/12 12:45:17 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:45:25 - mmengine - INFO - Epoch(train) [38][350/586] lr: 2.000000e-02 eta: 7:04:50 time: 0.259342 data_time: 0.050135 memory: 2937 loss_kpt: 95.971861 acc_pose: 0.671784 loss: 95.971861 2022/10/12 12:45:38 - mmengine - INFO - Epoch(train) [38][400/586] lr: 2.000000e-02 eta: 7:04:39 time: 0.257142 data_time: 0.050044 memory: 2937 loss_kpt: 96.164691 acc_pose: 0.786915 loss: 96.164691 2022/10/12 12:45:51 - mmengine - INFO - Epoch(train) [38][450/586] lr: 2.000000e-02 eta: 7:04:29 time: 0.263900 data_time: 0.049454 memory: 2937 loss_kpt: 95.994479 acc_pose: 0.714494 loss: 95.994479 2022/10/12 12:46:04 - mmengine - INFO - Epoch(train) [38][500/586] lr: 2.000000e-02 eta: 7:04:20 time: 0.266962 data_time: 0.051684 memory: 2937 loss_kpt: 97.127369 acc_pose: 0.742960 loss: 97.127369 2022/10/12 12:46:18 - mmengine - INFO - Epoch(train) [38][550/586] lr: 2.000000e-02 eta: 7:04:10 time: 0.266339 data_time: 0.054650 memory: 2937 loss_kpt: 96.450063 acc_pose: 0.717338 loss: 96.450063 2022/10/12 12:46:27 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:46:40 - mmengine - INFO - Epoch(train) [39][50/586] lr: 2.000000e-02 eta: 7:03:12 time: 0.273808 data_time: 0.063377 memory: 2937 loss_kpt: 97.872389 acc_pose: 0.765750 loss: 97.872389 2022/10/12 12:46:54 - mmengine - INFO - Epoch(train) [39][100/586] lr: 2.000000e-02 eta: 7:03:03 time: 0.266474 data_time: 0.052965 memory: 2937 loss_kpt: 98.911552 acc_pose: 0.659975 loss: 98.911552 2022/10/12 12:47:07 - mmengine - INFO - Epoch(train) [39][150/586] lr: 2.000000e-02 eta: 7:02:54 time: 0.267819 data_time: 0.052499 memory: 2937 loss_kpt: 96.244675 acc_pose: 0.742073 loss: 96.244675 2022/10/12 12:47:20 - mmengine - INFO - Epoch(train) [39][200/586] lr: 2.000000e-02 eta: 7:02:43 time: 0.259840 data_time: 0.049871 memory: 2937 loss_kpt: 96.323758 acc_pose: 0.719345 loss: 96.323758 2022/10/12 12:47:34 - mmengine - INFO - Epoch(train) [39][250/586] lr: 2.000000e-02 eta: 7:02:34 time: 0.268809 data_time: 0.053490 memory: 2937 loss_kpt: 96.227978 acc_pose: 0.766790 loss: 96.227978 2022/10/12 12:47:47 - mmengine - INFO - Epoch(train) [39][300/586] lr: 2.000000e-02 eta: 7:02:26 time: 0.273905 data_time: 0.051630 memory: 2937 loss_kpt: 96.143956 acc_pose: 0.703629 loss: 96.143956 2022/10/12 12:48:01 - mmengine - INFO - Epoch(train) [39][350/586] lr: 2.000000e-02 eta: 7:02:19 time: 0.275028 data_time: 0.051444 memory: 2937 loss_kpt: 97.022485 acc_pose: 0.765846 loss: 97.022485 2022/10/12 12:48:15 - mmengine - INFO - Epoch(train) [39][400/586] lr: 2.000000e-02 eta: 7:02:11 time: 0.273880 data_time: 0.053705 memory: 2937 loss_kpt: 97.488281 acc_pose: 0.749128 loss: 97.488281 2022/10/12 12:48:28 - mmengine - INFO - Epoch(train) [39][450/586] lr: 2.000000e-02 eta: 7:02:01 time: 0.263364 data_time: 0.054568 memory: 2937 loss_kpt: 96.214372 acc_pose: 0.760815 loss: 96.214372 2022/10/12 12:48:41 - mmengine - INFO - Epoch(train) [39][500/586] lr: 2.000000e-02 eta: 7:01:52 time: 0.267850 data_time: 0.051769 memory: 2937 loss_kpt: 98.933455 acc_pose: 0.767745 loss: 98.933455 2022/10/12 12:48:55 - mmengine - INFO - Epoch(train) [39][550/586] lr: 2.000000e-02 eta: 7:01:44 time: 0.273284 data_time: 0.058235 memory: 2937 loss_kpt: 95.913432 acc_pose: 0.754374 loss: 95.913432 2022/10/12 12:49:05 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:49:19 - mmengine - INFO - Epoch(train) [40][50/586] lr: 2.000000e-02 eta: 7:00:48 time: 0.277564 data_time: 0.060546 memory: 2937 loss_kpt: 97.379569 acc_pose: 0.695162 loss: 97.379569 2022/10/12 12:49:32 - mmengine - INFO - Epoch(train) [40][100/586] lr: 2.000000e-02 eta: 7:00:37 time: 0.260268 data_time: 0.050804 memory: 2937 loss_kpt: 95.391185 acc_pose: 0.723244 loss: 95.391185 2022/10/12 12:49:44 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:49:45 - mmengine - INFO - Epoch(train) [40][150/586] lr: 2.000000e-02 eta: 7:00:27 time: 0.266118 data_time: 0.048977 memory: 2937 loss_kpt: 95.073029 acc_pose: 0.703226 loss: 95.073029 2022/10/12 12:49:58 - mmengine - INFO - Epoch(train) [40][200/586] lr: 2.000000e-02 eta: 7:00:17 time: 0.263141 data_time: 0.050952 memory: 2937 loss_kpt: 95.517652 acc_pose: 0.744170 loss: 95.517652 2022/10/12 12:50:12 - mmengine - INFO - Epoch(train) [40][250/586] lr: 2.000000e-02 eta: 7:00:10 time: 0.278609 data_time: 0.049345 memory: 2937 loss_kpt: 95.117902 acc_pose: 0.707283 loss: 95.117902 2022/10/12 12:50:25 - mmengine - INFO - Epoch(train) [40][300/586] lr: 2.000000e-02 eta: 6:59:59 time: 0.261277 data_time: 0.052300 memory: 2937 loss_kpt: 98.385199 acc_pose: 0.706547 loss: 98.385199 2022/10/12 12:50:39 - mmengine - INFO - Epoch(train) [40][350/586] lr: 2.000000e-02 eta: 6:59:53 time: 0.278901 data_time: 0.055571 memory: 2937 loss_kpt: 95.302185 acc_pose: 0.760119 loss: 95.302185 2022/10/12 12:50:53 - mmengine - INFO - Epoch(train) [40][400/586] lr: 2.000000e-02 eta: 6:59:45 time: 0.275355 data_time: 0.052296 memory: 2937 loss_kpt: 96.548848 acc_pose: 0.713334 loss: 96.548848 2022/10/12 12:51:06 - mmengine - INFO - Epoch(train) [40][450/586] lr: 2.000000e-02 eta: 6:59:35 time: 0.264050 data_time: 0.048440 memory: 2937 loss_kpt: 97.004375 acc_pose: 0.752169 loss: 97.004375 2022/10/12 12:51:19 - mmengine - INFO - Epoch(train) [40][500/586] lr: 2.000000e-02 eta: 6:59:23 time: 0.258275 data_time: 0.052526 memory: 2937 loss_kpt: 96.630498 acc_pose: 0.742193 loss: 96.630498 2022/10/12 12:51:32 - mmengine - INFO - Epoch(train) [40][550/586] lr: 2.000000e-02 eta: 6:59:14 time: 0.268271 data_time: 0.052085 memory: 2937 loss_kpt: 95.021594 acc_pose: 0.752337 loss: 95.021594 2022/10/12 12:51:41 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:51:41 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/10/12 12:51:50 - mmengine - INFO - Epoch(val) [40][50/407] eta: 0:00:41 time: 0.117081 data_time: 0.013885 memory: 2937 2022/10/12 12:51:55 - mmengine - INFO - Epoch(val) [40][100/407] eta: 0:00:33 time: 0.109097 data_time: 0.009193 memory: 830 2022/10/12 12:52:00 - mmengine - INFO - Epoch(val) [40][150/407] eta: 0:00:27 time: 0.108223 data_time: 0.008641 memory: 830 2022/10/12 12:52:06 - mmengine - INFO - Epoch(val) [40][200/407] eta: 0:00:23 time: 0.112696 data_time: 0.012609 memory: 830 2022/10/12 12:52:12 - mmengine - INFO - Epoch(val) [40][250/407] eta: 0:00:17 time: 0.109982 data_time: 0.008862 memory: 830 2022/10/12 12:52:17 - mmengine - INFO - Epoch(val) [40][300/407] eta: 0:00:11 time: 0.108788 data_time: 0.008992 memory: 830 2022/10/12 12:52:22 - mmengine - INFO - Epoch(val) [40][350/407] eta: 0:00:06 time: 0.109271 data_time: 0.008781 memory: 830 2022/10/12 12:52:27 - mmengine - INFO - Epoch(val) [40][400/407] eta: 0:00:00 time: 0.099604 data_time: 0.007942 memory: 830 2022/10/12 12:52:40 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 12:52:56 - mmengine - INFO - Epoch(val) [40][407/407] coco/AP: 0.628773 coco/AP .5: 0.856265 coco/AP .75: 0.698432 coco/AP (M): 0.596538 coco/AP (L): 0.688561 coco/AR: 0.702251 coco/AR .5: 0.901448 coco/AR .75: 0.764641 coco/AR (M): 0.654521 coco/AR (L): 0.767930 2022/10/12 12:52:56 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221012/rsn18/best_coco/AP_epoch_30.pth is removed 2022/10/12 12:52:58 - mmengine - INFO - The best checkpoint with 0.6288 coco/AP at 40 epoch is saved to best_coco/AP_epoch_40.pth. 2022/10/12 12:53:12 - mmengine - INFO - Epoch(train) [41][50/586] lr: 2.000000e-02 eta: 6:58:21 time: 0.285843 data_time: 0.058789 memory: 2937 loss_kpt: 95.964573 acc_pose: 0.701609 loss: 95.964573 2022/10/12 12:53:26 - mmengine - INFO - Epoch(train) [41][100/586] lr: 2.000000e-02 eta: 6:58:12 time: 0.267195 data_time: 0.053210 memory: 2937 loss_kpt: 97.619009 acc_pose: 0.752590 loss: 97.619009 2022/10/12 12:53:38 - mmengine - INFO - Epoch(train) [41][150/586] lr: 2.000000e-02 eta: 6:57:59 time: 0.253857 data_time: 0.051527 memory: 2937 loss_kpt: 94.753907 acc_pose: 0.730835 loss: 94.753907 2022/10/12 12:53:52 - mmengine - INFO - Epoch(train) [41][200/586] lr: 2.000000e-02 eta: 6:57:50 time: 0.267022 data_time: 0.050848 memory: 2937 loss_kpt: 95.604396 acc_pose: 0.760181 loss: 95.604396 2022/10/12 12:54:05 - mmengine - INFO - Epoch(train) [41][250/586] lr: 2.000000e-02 eta: 6:57:41 time: 0.269957 data_time: 0.056842 memory: 2937 loss_kpt: 95.915303 acc_pose: 0.799393 loss: 95.915303 2022/10/12 12:54:18 - mmengine - INFO - Epoch(train) [41][300/586] lr: 2.000000e-02 eta: 6:57:31 time: 0.264461 data_time: 0.050873 memory: 2937 loss_kpt: 96.967888 acc_pose: 0.712402 loss: 96.967888 2022/10/12 12:54:32 - mmengine - INFO - Epoch(train) [41][350/586] lr: 2.000000e-02 eta: 6:57:22 time: 0.267689 data_time: 0.054775 memory: 2937 loss_kpt: 96.481838 acc_pose: 0.708835 loss: 96.481838 2022/10/12 12:54:45 - mmengine - INFO - Epoch(train) [41][400/586] lr: 2.000000e-02 eta: 6:57:13 time: 0.268921 data_time: 0.054356 memory: 2937 loss_kpt: 96.208583 acc_pose: 0.783045 loss: 96.208583 2022/10/12 12:54:58 - mmengine - INFO - Epoch(train) [41][450/586] lr: 2.000000e-02 eta: 6:57:00 time: 0.253259 data_time: 0.055932 memory: 2937 loss_kpt: 94.861633 acc_pose: 0.775214 loss: 94.861633 2022/10/12 12:55:11 - mmengine - INFO - Epoch(train) [41][500/586] lr: 2.000000e-02 eta: 6:56:48 time: 0.255066 data_time: 0.052099 memory: 2937 loss_kpt: 95.322466 acc_pose: 0.740746 loss: 95.322466 2022/10/12 12:55:24 - mmengine - INFO - Epoch(train) [41][550/586] lr: 2.000000e-02 eta: 6:56:37 time: 0.262113 data_time: 0.056265 memory: 2937 loss_kpt: 94.369340 acc_pose: 0.743062 loss: 94.369340 2022/10/12 12:55:26 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:55:33 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:55:47 - mmengine - INFO - Epoch(train) [42][50/586] lr: 2.000000e-02 eta: 6:55:44 time: 0.279630 data_time: 0.062635 memory: 2937 loss_kpt: 97.289204 acc_pose: 0.739762 loss: 97.289204 2022/10/12 12:56:00 - mmengine - INFO - Epoch(train) [42][100/586] lr: 2.000000e-02 eta: 6:55:35 time: 0.267443 data_time: 0.055333 memory: 2937 loss_kpt: 93.950511 acc_pose: 0.723860 loss: 93.950511 2022/10/12 12:56:14 - mmengine - INFO - Epoch(train) [42][150/586] lr: 2.000000e-02 eta: 6:55:26 time: 0.270201 data_time: 0.050516 memory: 2937 loss_kpt: 95.179425 acc_pose: 0.749356 loss: 95.179425 2022/10/12 12:56:28 - mmengine - INFO - Epoch(train) [42][200/586] lr: 2.000000e-02 eta: 6:55:19 time: 0.279890 data_time: 0.059109 memory: 2937 loss_kpt: 94.851660 acc_pose: 0.703672 loss: 94.851660 2022/10/12 12:56:41 - mmengine - INFO - Epoch(train) [42][250/586] lr: 2.000000e-02 eta: 6:55:10 time: 0.269301 data_time: 0.051866 memory: 2937 loss_kpt: 94.898539 acc_pose: 0.749175 loss: 94.898539 2022/10/12 12:56:55 - mmengine - INFO - Epoch(train) [42][300/586] lr: 2.000000e-02 eta: 6:55:02 time: 0.278820 data_time: 0.053248 memory: 2937 loss_kpt: 94.871680 acc_pose: 0.762422 loss: 94.871680 2022/10/12 12:57:09 - mmengine - INFO - Epoch(train) [42][350/586] lr: 2.000000e-02 eta: 6:54:56 time: 0.283690 data_time: 0.049198 memory: 2937 loss_kpt: 95.006874 acc_pose: 0.675362 loss: 95.006874 2022/10/12 12:57:23 - mmengine - INFO - Epoch(train) [42][400/586] lr: 2.000000e-02 eta: 6:54:48 time: 0.274875 data_time: 0.053692 memory: 2937 loss_kpt: 96.786539 acc_pose: 0.720443 loss: 96.786539 2022/10/12 12:57:36 - mmengine - INFO - Epoch(train) [42][450/586] lr: 2.000000e-02 eta: 6:54:38 time: 0.264368 data_time: 0.052545 memory: 2937 loss_kpt: 95.986661 acc_pose: 0.678028 loss: 95.986661 2022/10/12 12:57:50 - mmengine - INFO - Epoch(train) [42][500/586] lr: 2.000000e-02 eta: 6:54:30 time: 0.276033 data_time: 0.055068 memory: 2937 loss_kpt: 95.148841 acc_pose: 0.726338 loss: 95.148841 2022/10/12 12:58:03 - mmengine - INFO - Epoch(train) [42][550/586] lr: 2.000000e-02 eta: 6:54:20 time: 0.265210 data_time: 0.050146 memory: 2937 loss_kpt: 97.089547 acc_pose: 0.743412 loss: 97.089547 2022/10/12 12:58:13 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 12:58:27 - mmengine - INFO - Epoch(train) [43][50/586] lr: 2.000000e-02 eta: 6:53:27 time: 0.274875 data_time: 0.062246 memory: 2937 loss_kpt: 97.854282 acc_pose: 0.799993 loss: 97.854282 2022/10/12 12:58:40 - mmengine - INFO - Epoch(train) [43][100/586] lr: 2.000000e-02 eta: 6:53:17 time: 0.265255 data_time: 0.050876 memory: 2937 loss_kpt: 97.753850 acc_pose: 0.763345 loss: 97.753850 2022/10/12 12:58:53 - mmengine - INFO - Epoch(train) [43][150/586] lr: 2.000000e-02 eta: 6:53:07 time: 0.269051 data_time: 0.054152 memory: 2937 loss_kpt: 95.289747 acc_pose: 0.735448 loss: 95.289747 2022/10/12 12:59:07 - mmengine - INFO - Epoch(train) [43][200/586] lr: 2.000000e-02 eta: 6:52:58 time: 0.268682 data_time: 0.051535 memory: 2937 loss_kpt: 94.772056 acc_pose: 0.739034 loss: 94.772056 2022/10/12 12:59:20 - mmengine - INFO - Epoch(train) [43][250/586] lr: 2.000000e-02 eta: 6:52:50 time: 0.273415 data_time: 0.052445 memory: 2937 loss_kpt: 96.799618 acc_pose: 0.723312 loss: 96.799618 2022/10/12 12:59:34 - mmengine - INFO - Epoch(train) [43][300/586] lr: 2.000000e-02 eta: 6:52:40 time: 0.267089 data_time: 0.056211 memory: 2937 loss_kpt: 95.686508 acc_pose: 0.730900 loss: 95.686508 2022/10/12 12:59:47 - mmengine - INFO - Epoch(train) [43][350/586] lr: 2.000000e-02 eta: 6:52:31 time: 0.271981 data_time: 0.053487 memory: 2937 loss_kpt: 94.646421 acc_pose: 0.615458 loss: 94.646421 2022/10/12 12:59:58 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:00:01 - mmengine - INFO - Epoch(train) [43][400/586] lr: 2.000000e-02 eta: 6:52:21 time: 0.266512 data_time: 0.051680 memory: 2937 loss_kpt: 95.835257 acc_pose: 0.741537 loss: 95.835257 2022/10/12 13:00:14 - mmengine - INFO - Epoch(train) [43][450/586] lr: 2.000000e-02 eta: 6:52:11 time: 0.265443 data_time: 0.052338 memory: 2937 loss_kpt: 95.905566 acc_pose: 0.688227 loss: 95.905566 2022/10/12 13:00:28 - mmengine - INFO - Epoch(train) [43][500/586] lr: 2.000000e-02 eta: 6:52:03 time: 0.273388 data_time: 0.049901 memory: 2937 loss_kpt: 96.089492 acc_pose: 0.745817 loss: 96.089492 2022/10/12 13:00:41 - mmengine - INFO - Epoch(train) [43][550/586] lr: 2.000000e-02 eta: 6:51:52 time: 0.264667 data_time: 0.052184 memory: 2937 loss_kpt: 95.063597 acc_pose: 0.795625 loss: 95.063597 2022/10/12 13:00:50 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:01:04 - mmengine - INFO - Epoch(train) [44][50/586] lr: 2.000000e-02 eta: 6:51:00 time: 0.276366 data_time: 0.061523 memory: 2937 loss_kpt: 95.759867 acc_pose: 0.731406 loss: 95.759867 2022/10/12 13:01:17 - mmengine - INFO - Epoch(train) [44][100/586] lr: 2.000000e-02 eta: 6:50:49 time: 0.260792 data_time: 0.046485 memory: 2937 loss_kpt: 96.353216 acc_pose: 0.718382 loss: 96.353216 2022/10/12 13:01:30 - mmengine - INFO - Epoch(train) [44][150/586] lr: 2.000000e-02 eta: 6:50:41 time: 0.274428 data_time: 0.053433 memory: 2937 loss_kpt: 95.742382 acc_pose: 0.775296 loss: 95.742382 2022/10/12 13:01:44 - mmengine - INFO - Epoch(train) [44][200/586] lr: 2.000000e-02 eta: 6:50:30 time: 0.263067 data_time: 0.049303 memory: 2937 loss_kpt: 95.522857 acc_pose: 0.692100 loss: 95.522857 2022/10/12 13:01:57 - mmengine - INFO - Epoch(train) [44][250/586] lr: 2.000000e-02 eta: 6:50:21 time: 0.267850 data_time: 0.054644 memory: 2937 loss_kpt: 95.862844 acc_pose: 0.751668 loss: 95.862844 2022/10/12 13:02:12 - mmengine - INFO - Epoch(train) [44][300/586] lr: 2.000000e-02 eta: 6:50:16 time: 0.293716 data_time: 0.054162 memory: 2937 loss_kpt: 96.740658 acc_pose: 0.727639 loss: 96.740658 2022/10/12 13:02:26 - mmengine - INFO - Epoch(train) [44][350/586] lr: 2.000000e-02 eta: 6:50:12 time: 0.296800 data_time: 0.053887 memory: 2937 loss_kpt: 94.489501 acc_pose: 0.744381 loss: 94.489501 2022/10/12 13:02:40 - mmengine - INFO - Epoch(train) [44][400/586] lr: 2.000000e-02 eta: 6:50:05 time: 0.279956 data_time: 0.052716 memory: 2937 loss_kpt: 95.959233 acc_pose: 0.739237 loss: 95.959233 2022/10/12 13:02:55 - mmengine - INFO - Epoch(train) [44][450/586] lr: 2.000000e-02 eta: 6:49:58 time: 0.283954 data_time: 0.050535 memory: 2937 loss_kpt: 95.371812 acc_pose: 0.754380 loss: 95.371812 2022/10/12 13:03:09 - mmengine - INFO - Epoch(train) [44][500/586] lr: 2.000000e-02 eta: 6:49:50 time: 0.277512 data_time: 0.051635 memory: 2937 loss_kpt: 97.621210 acc_pose: 0.839031 loss: 97.621210 2022/10/12 13:03:22 - mmengine - INFO - Epoch(train) [44][550/586] lr: 2.000000e-02 eta: 6:49:40 time: 0.267938 data_time: 0.053337 memory: 2937 loss_kpt: 97.540411 acc_pose: 0.747246 loss: 97.540411 2022/10/12 13:03:31 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:03:46 - mmengine - INFO - Epoch(train) [45][50/586] lr: 2.000000e-02 eta: 6:48:52 time: 0.291767 data_time: 0.060117 memory: 2937 loss_kpt: 95.243027 acc_pose: 0.736002 loss: 95.243027 2022/10/12 13:04:00 - mmengine - INFO - Epoch(train) [45][100/586] lr: 2.000000e-02 eta: 6:48:45 time: 0.280926 data_time: 0.051823 memory: 2937 loss_kpt: 96.084866 acc_pose: 0.644556 loss: 96.084866 2022/10/12 13:04:14 - mmengine - INFO - Epoch(train) [45][150/586] lr: 2.000000e-02 eta: 6:48:36 time: 0.273936 data_time: 0.047236 memory: 2937 loss_kpt: 94.501526 acc_pose: 0.709230 loss: 94.501526 2022/10/12 13:04:27 - mmengine - INFO - Epoch(train) [45][200/586] lr: 2.000000e-02 eta: 6:48:27 time: 0.272570 data_time: 0.050981 memory: 2937 loss_kpt: 95.168287 acc_pose: 0.735318 loss: 95.168287 2022/10/12 13:04:32 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:04:41 - mmengine - INFO - Epoch(train) [45][250/586] lr: 2.000000e-02 eta: 6:48:18 time: 0.271542 data_time: 0.061602 memory: 2937 loss_kpt: 95.453397 acc_pose: 0.728797 loss: 95.453397 2022/10/12 13:04:55 - mmengine - INFO - Epoch(train) [45][300/586] lr: 2.000000e-02 eta: 6:48:10 time: 0.278413 data_time: 0.047401 memory: 2937 loss_kpt: 96.117940 acc_pose: 0.774853 loss: 96.117940 2022/10/12 13:05:09 - mmengine - INFO - Epoch(train) [45][350/586] lr: 2.000000e-02 eta: 6:48:02 time: 0.276708 data_time: 0.047280 memory: 2937 loss_kpt: 95.268120 acc_pose: 0.711585 loss: 95.268120 2022/10/12 13:05:22 - mmengine - INFO - Epoch(train) [45][400/586] lr: 2.000000e-02 eta: 6:47:53 time: 0.272011 data_time: 0.052175 memory: 2937 loss_kpt: 94.656629 acc_pose: 0.812481 loss: 94.656629 2022/10/12 13:05:36 - mmengine - INFO - Epoch(train) [45][450/586] lr: 2.000000e-02 eta: 6:47:44 time: 0.271522 data_time: 0.054302 memory: 2937 loss_kpt: 96.073856 acc_pose: 0.754359 loss: 96.073856 2022/10/12 13:05:50 - mmengine - INFO - Epoch(train) [45][500/586] lr: 2.000000e-02 eta: 6:47:37 time: 0.284526 data_time: 0.056027 memory: 2937 loss_kpt: 95.519693 acc_pose: 0.772725 loss: 95.519693 2022/10/12 13:06:04 - mmengine - INFO - Epoch(train) [45][550/586] lr: 2.000000e-02 eta: 6:47:30 time: 0.282961 data_time: 0.051210 memory: 2937 loss_kpt: 96.668447 acc_pose: 0.787522 loss: 96.668447 2022/10/12 13:06:14 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:06:29 - mmengine - INFO - Epoch(train) [46][50/586] lr: 2.000000e-02 eta: 6:46:42 time: 0.289884 data_time: 0.063583 memory: 2937 loss_kpt: 95.755177 acc_pose: 0.674710 loss: 95.755177 2022/10/12 13:06:42 - mmengine - INFO - Epoch(train) [46][100/586] lr: 2.000000e-02 eta: 6:46:31 time: 0.264422 data_time: 0.047768 memory: 2937 loss_kpt: 97.094512 acc_pose: 0.763737 loss: 97.094512 2022/10/12 13:06:56 - mmengine - INFO - Epoch(train) [46][150/586] lr: 2.000000e-02 eta: 6:46:22 time: 0.268565 data_time: 0.051918 memory: 2937 loss_kpt: 95.161698 acc_pose: 0.740320 loss: 95.161698 2022/10/12 13:07:09 - mmengine - INFO - Epoch(train) [46][200/586] lr: 2.000000e-02 eta: 6:46:11 time: 0.264464 data_time: 0.050484 memory: 2937 loss_kpt: 97.023398 acc_pose: 0.752825 loss: 97.023398 2022/10/12 13:07:23 - mmengine - INFO - Epoch(train) [46][250/586] lr: 2.000000e-02 eta: 6:46:04 time: 0.281876 data_time: 0.057048 memory: 2937 loss_kpt: 94.855134 acc_pose: 0.777876 loss: 94.855134 2022/10/12 13:07:37 - mmengine - INFO - Epoch(train) [46][300/586] lr: 2.000000e-02 eta: 6:45:56 time: 0.276085 data_time: 0.048121 memory: 2937 loss_kpt: 94.396875 acc_pose: 0.751814 loss: 94.396875 2022/10/12 13:07:51 - mmengine - INFO - Epoch(train) [46][350/586] lr: 2.000000e-02 eta: 6:45:48 time: 0.278935 data_time: 0.054986 memory: 2937 loss_kpt: 94.292791 acc_pose: 0.811523 loss: 94.292791 2022/10/12 13:08:04 - mmengine - INFO - Epoch(train) [46][400/586] lr: 2.000000e-02 eta: 6:45:38 time: 0.270946 data_time: 0.054581 memory: 2937 loss_kpt: 94.747713 acc_pose: 0.688417 loss: 94.747713 2022/10/12 13:08:17 - mmengine - INFO - Epoch(train) [46][450/586] lr: 2.000000e-02 eta: 6:45:27 time: 0.262866 data_time: 0.051527 memory: 2937 loss_kpt: 94.505014 acc_pose: 0.783643 loss: 94.505014 2022/10/12 13:08:31 - mmengine - INFO - Epoch(train) [46][500/586] lr: 2.000000e-02 eta: 6:45:17 time: 0.265584 data_time: 0.052298 memory: 2937 loss_kpt: 94.663338 acc_pose: 0.728625 loss: 94.663338 2022/10/12 13:08:44 - mmengine - INFO - Epoch(train) [46][550/586] lr: 2.000000e-02 eta: 6:45:07 time: 0.267312 data_time: 0.051120 memory: 2937 loss_kpt: 95.298021 acc_pose: 0.671144 loss: 95.298021 2022/10/12 13:08:54 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:09:05 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:09:07 - mmengine - INFO - Epoch(train) [47][50/586] lr: 2.000000e-02 eta: 6:44:16 time: 0.268784 data_time: 0.065536 memory: 2937 loss_kpt: 94.596290 acc_pose: 0.738137 loss: 94.596290 2022/10/12 13:09:20 - mmengine - INFO - Epoch(train) [47][100/586] lr: 2.000000e-02 eta: 6:44:05 time: 0.265738 data_time: 0.050581 memory: 2937 loss_kpt: 95.279525 acc_pose: 0.743678 loss: 95.279525 2022/10/12 13:09:34 - mmengine - INFO - Epoch(train) [47][150/586] lr: 2.000000e-02 eta: 6:43:58 time: 0.279638 data_time: 0.052421 memory: 2937 loss_kpt: 93.044534 acc_pose: 0.756169 loss: 93.044534 2022/10/12 13:09:48 - mmengine - INFO - Epoch(train) [47][200/586] lr: 2.000000e-02 eta: 6:43:49 time: 0.276297 data_time: 0.051772 memory: 2937 loss_kpt: 94.794676 acc_pose: 0.759359 loss: 94.794676 2022/10/12 13:10:02 - mmengine - INFO - Epoch(train) [47][250/586] lr: 2.000000e-02 eta: 6:43:43 time: 0.287120 data_time: 0.050760 memory: 2937 loss_kpt: 95.739942 acc_pose: 0.709885 loss: 95.739942 2022/10/12 13:10:17 - mmengine - INFO - Epoch(train) [47][300/586] lr: 2.000000e-02 eta: 6:43:36 time: 0.286936 data_time: 0.052990 memory: 2937 loss_kpt: 95.706361 acc_pose: 0.710594 loss: 95.706361 2022/10/12 13:10:31 - mmengine - INFO - Epoch(train) [47][350/586] lr: 2.000000e-02 eta: 6:43:28 time: 0.281972 data_time: 0.052203 memory: 2937 loss_kpt: 94.680634 acc_pose: 0.810889 loss: 94.680634 2022/10/12 13:10:44 - mmengine - INFO - Epoch(train) [47][400/586] lr: 2.000000e-02 eta: 6:43:18 time: 0.266095 data_time: 0.052867 memory: 2937 loss_kpt: 93.654842 acc_pose: 0.760389 loss: 93.654842 2022/10/12 13:10:58 - mmengine - INFO - Epoch(train) [47][450/586] lr: 2.000000e-02 eta: 6:43:11 time: 0.284409 data_time: 0.051154 memory: 2937 loss_kpt: 94.577281 acc_pose: 0.719197 loss: 94.577281 2022/10/12 13:11:13 - mmengine - INFO - Epoch(train) [47][500/586] lr: 2.000000e-02 eta: 6:43:04 time: 0.286128 data_time: 0.053510 memory: 2937 loss_kpt: 95.796544 acc_pose: 0.711710 loss: 95.796544 2022/10/12 13:11:27 - mmengine - INFO - Epoch(train) [47][550/586] lr: 2.000000e-02 eta: 6:42:56 time: 0.277990 data_time: 0.048662 memory: 2937 loss_kpt: 95.201749 acc_pose: 0.719160 loss: 95.201749 2022/10/12 13:11:37 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:11:52 - mmengine - INFO - Epoch(train) [48][50/586] lr: 2.000000e-02 eta: 6:42:10 time: 0.298685 data_time: 0.067708 memory: 2937 loss_kpt: 95.911138 acc_pose: 0.742915 loss: 95.911138 2022/10/12 13:12:05 - mmengine - INFO - Epoch(train) [48][100/586] lr: 2.000000e-02 eta: 6:42:00 time: 0.265553 data_time: 0.049846 memory: 2937 loss_kpt: 94.586478 acc_pose: 0.694486 loss: 94.586478 2022/10/12 13:12:18 - mmengine - INFO - Epoch(train) [48][150/586] lr: 2.000000e-02 eta: 6:41:48 time: 0.258750 data_time: 0.054286 memory: 2937 loss_kpt: 96.591748 acc_pose: 0.777920 loss: 96.591748 2022/10/12 13:12:31 - mmengine - INFO - Epoch(train) [48][200/586] lr: 2.000000e-02 eta: 6:41:38 time: 0.263289 data_time: 0.052671 memory: 2937 loss_kpt: 95.532699 acc_pose: 0.778484 loss: 95.532699 2022/10/12 13:12:45 - mmengine - INFO - Epoch(train) [48][250/586] lr: 2.000000e-02 eta: 6:41:29 time: 0.274506 data_time: 0.050143 memory: 2937 loss_kpt: 93.628820 acc_pose: 0.711451 loss: 93.628820 2022/10/12 13:12:58 - mmengine - INFO - Epoch(train) [48][300/586] lr: 2.000000e-02 eta: 6:41:19 time: 0.269421 data_time: 0.049206 memory: 2937 loss_kpt: 92.915588 acc_pose: 0.660887 loss: 92.915588 2022/10/12 13:13:12 - mmengine - INFO - Epoch(train) [48][350/586] lr: 2.000000e-02 eta: 6:41:09 time: 0.268625 data_time: 0.055161 memory: 2937 loss_kpt: 94.995371 acc_pose: 0.793507 loss: 94.995371 2022/10/12 13:13:25 - mmengine - INFO - Epoch(train) [48][400/586] lr: 2.000000e-02 eta: 6:40:57 time: 0.259845 data_time: 0.052215 memory: 2937 loss_kpt: 97.111742 acc_pose: 0.743991 loss: 97.111742 2022/10/12 13:13:38 - mmengine - INFO - Epoch(train) [48][450/586] lr: 2.000000e-02 eta: 6:40:46 time: 0.262764 data_time: 0.052304 memory: 2937 loss_kpt: 95.475129 acc_pose: 0.752712 loss: 95.475129 2022/10/12 13:13:40 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:13:51 - mmengine - INFO - Epoch(train) [48][500/586] lr: 2.000000e-02 eta: 6:40:36 time: 0.264708 data_time: 0.052910 memory: 2937 loss_kpt: 97.853668 acc_pose: 0.713571 loss: 97.853668 2022/10/12 13:14:04 - mmengine - INFO - Epoch(train) [48][550/586] lr: 2.000000e-02 eta: 6:40:23 time: 0.254806 data_time: 0.050815 memory: 2937 loss_kpt: 96.597197 acc_pose: 0.690787 loss: 96.597197 2022/10/12 13:14:13 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:14:27 - mmengine - INFO - Epoch(train) [49][50/586] lr: 2.000000e-02 eta: 6:39:36 time: 0.284378 data_time: 0.063629 memory: 2937 loss_kpt: 97.470870 acc_pose: 0.644720 loss: 97.470870 2022/10/12 13:14:41 - mmengine - INFO - Epoch(train) [49][100/586] lr: 2.000000e-02 eta: 6:39:27 time: 0.271894 data_time: 0.051550 memory: 2937 loss_kpt: 95.206855 acc_pose: 0.709846 loss: 95.206855 2022/10/12 13:14:55 - mmengine - INFO - Epoch(train) [49][150/586] lr: 2.000000e-02 eta: 6:39:19 time: 0.278665 data_time: 0.053638 memory: 2937 loss_kpt: 95.452997 acc_pose: 0.735154 loss: 95.452997 2022/10/12 13:15:09 - mmengine - INFO - Epoch(train) [49][200/586] lr: 2.000000e-02 eta: 6:39:11 time: 0.283543 data_time: 0.055447 memory: 2937 loss_kpt: 94.736948 acc_pose: 0.762250 loss: 94.736948 2022/10/12 13:15:23 - mmengine - INFO - Epoch(train) [49][250/586] lr: 2.000000e-02 eta: 6:39:04 time: 0.288374 data_time: 0.053745 memory: 2937 loss_kpt: 93.176889 acc_pose: 0.753184 loss: 93.176889 2022/10/12 13:15:37 - mmengine - INFO - Epoch(train) [49][300/586] lr: 2.000000e-02 eta: 6:38:55 time: 0.271340 data_time: 0.050338 memory: 2937 loss_kpt: 95.388020 acc_pose: 0.849438 loss: 95.388020 2022/10/12 13:15:50 - mmengine - INFO - Epoch(train) [49][350/586] lr: 2.000000e-02 eta: 6:38:45 time: 0.268868 data_time: 0.047637 memory: 2937 loss_kpt: 94.252114 acc_pose: 0.665953 loss: 94.252114 2022/10/12 13:16:03 - mmengine - INFO - Epoch(train) [49][400/586] lr: 2.000000e-02 eta: 6:38:34 time: 0.264761 data_time: 0.050115 memory: 2937 loss_kpt: 96.296896 acc_pose: 0.794658 loss: 96.296896 2022/10/12 13:16:17 - mmengine - INFO - Epoch(train) [49][450/586] lr: 2.000000e-02 eta: 6:38:24 time: 0.265839 data_time: 0.052166 memory: 2937 loss_kpt: 94.784399 acc_pose: 0.779651 loss: 94.784399 2022/10/12 13:16:30 - mmengine - INFO - Epoch(train) [49][500/586] lr: 2.000000e-02 eta: 6:38:14 time: 0.268815 data_time: 0.061033 memory: 2937 loss_kpt: 96.057090 acc_pose: 0.748832 loss: 96.057090 2022/10/12 13:16:44 - mmengine - INFO - Epoch(train) [49][550/586] lr: 2.000000e-02 eta: 6:38:05 time: 0.274829 data_time: 0.053024 memory: 2937 loss_kpt: 94.881993 acc_pose: 0.781231 loss: 94.881993 2022/10/12 13:16:53 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:17:07 - mmengine - INFO - Epoch(train) [50][50/586] lr: 2.000000e-02 eta: 6:37:17 time: 0.279511 data_time: 0.062145 memory: 2937 loss_kpt: 95.596900 acc_pose: 0.713771 loss: 95.596900 2022/10/12 13:17:20 - mmengine - INFO - Epoch(train) [50][100/586] lr: 2.000000e-02 eta: 6:37:05 time: 0.257470 data_time: 0.053963 memory: 2937 loss_kpt: 93.819977 acc_pose: 0.827293 loss: 93.819977 2022/10/12 13:17:32 - mmengine - INFO - Epoch(train) [50][150/586] lr: 2.000000e-02 eta: 6:36:52 time: 0.249904 data_time: 0.049567 memory: 2937 loss_kpt: 94.155988 acc_pose: 0.707867 loss: 94.155988 2022/10/12 13:17:45 - mmengine - INFO - Epoch(train) [50][200/586] lr: 2.000000e-02 eta: 6:36:40 time: 0.256707 data_time: 0.053852 memory: 2937 loss_kpt: 93.079102 acc_pose: 0.778774 loss: 93.079102 2022/10/12 13:17:58 - mmengine - INFO - Epoch(train) [50][250/586] lr: 2.000000e-02 eta: 6:36:29 time: 0.263052 data_time: 0.047356 memory: 2937 loss_kpt: 93.730602 acc_pose: 0.766025 loss: 93.730602 2022/10/12 13:18:08 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:18:12 - mmengine - INFO - Epoch(train) [50][300/586] lr: 2.000000e-02 eta: 6:36:19 time: 0.268246 data_time: 0.051460 memory: 2937 loss_kpt: 94.900787 acc_pose: 0.784213 loss: 94.900787 2022/10/12 13:18:24 - mmengine - INFO - Epoch(train) [50][350/586] lr: 2.000000e-02 eta: 6:36:06 time: 0.248897 data_time: 0.046673 memory: 2937 loss_kpt: 95.604407 acc_pose: 0.780827 loss: 95.604407 2022/10/12 13:18:37 - mmengine - INFO - Epoch(train) [50][400/586] lr: 2.000000e-02 eta: 6:35:53 time: 0.251780 data_time: 0.053667 memory: 2937 loss_kpt: 97.479391 acc_pose: 0.743732 loss: 97.479391 2022/10/12 13:18:50 - mmengine - INFO - Epoch(train) [50][450/586] lr: 2.000000e-02 eta: 6:35:41 time: 0.254552 data_time: 0.052197 memory: 2937 loss_kpt: 95.705629 acc_pose: 0.753881 loss: 95.705629 2022/10/12 13:19:02 - mmengine - INFO - Epoch(train) [50][500/586] lr: 2.000000e-02 eta: 6:35:28 time: 0.249876 data_time: 0.047778 memory: 2937 loss_kpt: 95.265023 acc_pose: 0.776150 loss: 95.265023 2022/10/12 13:19:15 - mmengine - INFO - Epoch(train) [50][550/586] lr: 2.000000e-02 eta: 6:35:15 time: 0.253408 data_time: 0.051771 memory: 2937 loss_kpt: 96.772145 acc_pose: 0.664394 loss: 96.772145 2022/10/12 13:19:24 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:19:24 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/10/12 13:19:32 - mmengine - INFO - Epoch(val) [50][50/407] eta: 0:00:42 time: 0.118073 data_time: 0.013796 memory: 2937 2022/10/12 13:19:37 - mmengine - INFO - Epoch(val) [50][100/407] eta: 0:00:33 time: 0.110497 data_time: 0.009043 memory: 830 2022/10/12 13:19:43 - mmengine - INFO - Epoch(val) [50][150/407] eta: 0:00:27 time: 0.108919 data_time: 0.009776 memory: 830 2022/10/12 13:19:48 - mmengine - INFO - Epoch(val) [50][200/407] eta: 0:00:22 time: 0.109704 data_time: 0.008729 memory: 830 2022/10/12 13:19:54 - mmengine - INFO - Epoch(val) [50][250/407] eta: 0:00:17 time: 0.112509 data_time: 0.009170 memory: 830 2022/10/12 13:19:59 - mmengine - INFO - Epoch(val) [50][300/407] eta: 0:00:11 time: 0.110929 data_time: 0.009055 memory: 830 2022/10/12 13:20:05 - mmengine - INFO - Epoch(val) [50][350/407] eta: 0:00:06 time: 0.109067 data_time: 0.009311 memory: 830 2022/10/12 13:20:10 - mmengine - INFO - Epoch(val) [50][400/407] eta: 0:00:00 time: 0.102258 data_time: 0.007546 memory: 830 2022/10/12 13:20:23 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 13:20:39 - mmengine - INFO - Epoch(val) [50][407/407] coco/AP: 0.641004 coco/AP .5: 0.860925 coco/AP .75: 0.714727 coco/AP (M): 0.612115 coco/AP (L): 0.697463 coco/AR: 0.714909 coco/AR .5: 0.905069 coco/AR .75: 0.779754 coco/AR (M): 0.668561 coco/AR (L): 0.778558 2022/10/12 13:20:39 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221012/rsn18/best_coco/AP_epoch_40.pth is removed 2022/10/12 13:20:41 - mmengine - INFO - The best checkpoint with 0.6410 coco/AP at 50 epoch is saved to best_coco/AP_epoch_50.pth. 2022/10/12 13:20:56 - mmengine - INFO - Epoch(train) [51][50/586] lr: 2.000000e-02 eta: 6:34:31 time: 0.295210 data_time: 0.060366 memory: 2937 loss_kpt: 96.806131 acc_pose: 0.725090 loss: 96.806131 2022/10/12 13:21:10 - mmengine - INFO - Epoch(train) [51][100/586] lr: 2.000000e-02 eta: 6:34:23 time: 0.279724 data_time: 0.053894 memory: 2937 loss_kpt: 94.967784 acc_pose: 0.664991 loss: 94.967784 2022/10/12 13:21:24 - mmengine - INFO - Epoch(train) [51][150/586] lr: 2.000000e-02 eta: 6:34:15 time: 0.282315 data_time: 0.052249 memory: 2937 loss_kpt: 94.287641 acc_pose: 0.768527 loss: 94.287641 2022/10/12 13:21:38 - mmengine - INFO - Epoch(train) [51][200/586] lr: 2.000000e-02 eta: 6:34:07 time: 0.286391 data_time: 0.051962 memory: 2937 loss_kpt: 95.210219 acc_pose: 0.687085 loss: 95.210219 2022/10/12 13:21:52 - mmengine - INFO - Epoch(train) [51][250/586] lr: 2.000000e-02 eta: 6:33:58 time: 0.270514 data_time: 0.054669 memory: 2937 loss_kpt: 94.895380 acc_pose: 0.831981 loss: 94.895380 2022/10/12 13:22:05 - mmengine - INFO - Epoch(train) [51][300/586] lr: 2.000000e-02 eta: 6:33:48 time: 0.271897 data_time: 0.053278 memory: 2937 loss_kpt: 95.765414 acc_pose: 0.794214 loss: 95.765414 2022/10/12 13:22:19 - mmengine - INFO - Epoch(train) [51][350/586] lr: 2.000000e-02 eta: 6:33:39 time: 0.279097 data_time: 0.054516 memory: 2937 loss_kpt: 95.781802 acc_pose: 0.805276 loss: 95.781802 2022/10/12 13:22:33 - mmengine - INFO - Epoch(train) [51][400/586] lr: 2.000000e-02 eta: 6:33:31 time: 0.278924 data_time: 0.052638 memory: 2937 loss_kpt: 94.259758 acc_pose: 0.789924 loss: 94.259758 2022/10/12 13:22:47 - mmengine - INFO - Epoch(train) [51][450/586] lr: 2.000000e-02 eta: 6:33:22 time: 0.278659 data_time: 0.057016 memory: 2937 loss_kpt: 95.428644 acc_pose: 0.728886 loss: 95.428644 2022/10/12 13:23:01 - mmengine - INFO - Epoch(train) [51][500/586] lr: 2.000000e-02 eta: 6:33:12 time: 0.270301 data_time: 0.050468 memory: 2937 loss_kpt: 92.772651 acc_pose: 0.685179 loss: 92.772651 2022/10/12 13:23:14 - mmengine - INFO - Epoch(train) [51][550/586] lr: 2.000000e-02 eta: 6:33:01 time: 0.263729 data_time: 0.051999 memory: 2937 loss_kpt: 93.651311 acc_pose: 0.803383 loss: 93.651311 2022/10/12 13:23:24 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:23:38 - mmengine - INFO - Epoch(train) [52][50/586] lr: 2.000000e-02 eta: 6:32:16 time: 0.282119 data_time: 0.061517 memory: 2937 loss_kpt: 96.153530 acc_pose: 0.710430 loss: 96.153530 2022/10/12 13:23:51 - mmengine - INFO - Epoch(train) [52][100/586] lr: 2.000000e-02 eta: 6:32:05 time: 0.261277 data_time: 0.052606 memory: 2937 loss_kpt: 95.435991 acc_pose: 0.791365 loss: 95.435991 2022/10/12 13:23:54 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:24:04 - mmengine - INFO - Epoch(train) [52][150/586] lr: 2.000000e-02 eta: 6:31:54 time: 0.268523 data_time: 0.051072 memory: 2937 loss_kpt: 93.750069 acc_pose: 0.740113 loss: 93.750069 2022/10/12 13:24:17 - mmengine - INFO - Epoch(train) [52][200/586] lr: 2.000000e-02 eta: 6:31:43 time: 0.260934 data_time: 0.053484 memory: 2937 loss_kpt: 94.709150 acc_pose: 0.769412 loss: 94.709150 2022/10/12 13:24:30 - mmengine - INFO - Epoch(train) [52][250/586] lr: 2.000000e-02 eta: 6:31:30 time: 0.247879 data_time: 0.049431 memory: 2937 loss_kpt: 93.865643 acc_pose: 0.710227 loss: 93.865643 2022/10/12 13:24:42 - mmengine - INFO - Epoch(train) [52][300/586] lr: 2.000000e-02 eta: 6:31:16 time: 0.248699 data_time: 0.051813 memory: 2937 loss_kpt: 93.313615 acc_pose: 0.817562 loss: 93.313615 2022/10/12 13:24:55 - mmengine - INFO - Epoch(train) [52][350/586] lr: 2.000000e-02 eta: 6:31:04 time: 0.257591 data_time: 0.051506 memory: 2937 loss_kpt: 95.304325 acc_pose: 0.714469 loss: 95.304325 2022/10/12 13:25:08 - mmengine - INFO - Epoch(train) [52][400/586] lr: 2.000000e-02 eta: 6:30:53 time: 0.258555 data_time: 0.048810 memory: 2937 loss_kpt: 94.139543 acc_pose: 0.731357 loss: 94.139543 2022/10/12 13:25:20 - mmengine - INFO - Epoch(train) [52][450/586] lr: 2.000000e-02 eta: 6:30:39 time: 0.248561 data_time: 0.047746 memory: 2937 loss_kpt: 94.123624 acc_pose: 0.703023 loss: 94.123624 2022/10/12 13:25:33 - mmengine - INFO - Epoch(train) [52][500/586] lr: 2.000000e-02 eta: 6:30:28 time: 0.258677 data_time: 0.052596 memory: 2937 loss_kpt: 93.882844 acc_pose: 0.719733 loss: 93.882844 2022/10/12 13:25:46 - mmengine - INFO - Epoch(train) [52][550/586] lr: 2.000000e-02 eta: 6:30:16 time: 0.257459 data_time: 0.051234 memory: 2937 loss_kpt: 95.253823 acc_pose: 0.781069 loss: 95.253823 2022/10/12 13:25:55 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:26:09 - mmengine - INFO - Epoch(train) [53][50/586] lr: 2.000000e-02 eta: 6:29:30 time: 0.273987 data_time: 0.067090 memory: 2937 loss_kpt: 93.335540 acc_pose: 0.781819 loss: 93.335540 2022/10/12 13:26:22 - mmengine - INFO - Epoch(train) [53][100/586] lr: 2.000000e-02 eta: 6:29:19 time: 0.269224 data_time: 0.047878 memory: 2937 loss_kpt: 95.090092 acc_pose: 0.781184 loss: 95.090092 2022/10/12 13:26:36 - mmengine - INFO - Epoch(train) [53][150/586] lr: 2.000000e-02 eta: 6:29:10 time: 0.270906 data_time: 0.053728 memory: 2937 loss_kpt: 94.873274 acc_pose: 0.737218 loss: 94.873274 2022/10/12 13:26:49 - mmengine - INFO - Epoch(train) [53][200/586] lr: 2.000000e-02 eta: 6:28:59 time: 0.266793 data_time: 0.048830 memory: 2937 loss_kpt: 95.182293 acc_pose: 0.690705 loss: 95.182293 2022/10/12 13:27:02 - mmengine - INFO - Epoch(train) [53][250/586] lr: 2.000000e-02 eta: 6:28:49 time: 0.269166 data_time: 0.054779 memory: 2937 loss_kpt: 94.169054 acc_pose: 0.720821 loss: 94.169054 2022/10/12 13:27:16 - mmengine - INFO - Epoch(train) [53][300/586] lr: 2.000000e-02 eta: 6:28:38 time: 0.263922 data_time: 0.051482 memory: 2937 loss_kpt: 94.554708 acc_pose: 0.747223 loss: 94.554708 2022/10/12 13:27:29 - mmengine - INFO - Epoch(train) [53][350/586] lr: 2.000000e-02 eta: 6:28:27 time: 0.262845 data_time: 0.053219 memory: 2937 loss_kpt: 94.086818 acc_pose: 0.789838 loss: 94.086818 2022/10/12 13:27:42 - mmengine - INFO - Epoch(train) [53][400/586] lr: 2.000000e-02 eta: 6:28:17 time: 0.271622 data_time: 0.055311 memory: 2937 loss_kpt: 94.771213 acc_pose: 0.808097 loss: 94.771213 2022/10/12 13:27:56 - mmengine - INFO - Epoch(train) [53][450/586] lr: 2.000000e-02 eta: 6:28:09 time: 0.282192 data_time: 0.054627 memory: 2937 loss_kpt: 94.841720 acc_pose: 0.747945 loss: 94.841720 2022/10/12 13:28:10 - mmengine - INFO - Epoch(train) [53][500/586] lr: 2.000000e-02 eta: 6:27:59 time: 0.272134 data_time: 0.050553 memory: 2937 loss_kpt: 96.324505 acc_pose: 0.807872 loss: 96.324505 2022/10/12 13:28:18 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:28:24 - mmengine - INFO - Epoch(train) [53][550/586] lr: 2.000000e-02 eta: 6:27:51 time: 0.281422 data_time: 0.053948 memory: 2937 loss_kpt: 95.989218 acc_pose: 0.700897 loss: 95.989218 2022/10/12 13:28:34 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:28:48 - mmengine - INFO - Epoch(train) [54][50/586] lr: 2.000000e-02 eta: 6:27:07 time: 0.287331 data_time: 0.058144 memory: 2937 loss_kpt: 94.933285 acc_pose: 0.757268 loss: 94.933285 2022/10/12 13:29:02 - mmengine - INFO - Epoch(train) [54][100/586] lr: 2.000000e-02 eta: 6:26:58 time: 0.275180 data_time: 0.051081 memory: 2937 loss_kpt: 94.530599 acc_pose: 0.706230 loss: 94.530599 2022/10/12 13:29:15 - mmengine - INFO - Epoch(train) [54][150/586] lr: 2.000000e-02 eta: 6:26:47 time: 0.263985 data_time: 0.049589 memory: 2937 loss_kpt: 95.947857 acc_pose: 0.750816 loss: 95.947857 2022/10/12 13:29:29 - mmengine - INFO - Epoch(train) [54][200/586] lr: 2.000000e-02 eta: 6:26:37 time: 0.272836 data_time: 0.052223 memory: 2937 loss_kpt: 93.036189 acc_pose: 0.786125 loss: 93.036189 2022/10/12 13:29:41 - mmengine - INFO - Epoch(train) [54][250/586] lr: 2.000000e-02 eta: 6:26:25 time: 0.253531 data_time: 0.052722 memory: 2937 loss_kpt: 95.835189 acc_pose: 0.684187 loss: 95.835189 2022/10/12 13:29:55 - mmengine - INFO - Epoch(train) [54][300/586] lr: 2.000000e-02 eta: 6:26:16 time: 0.276692 data_time: 0.047034 memory: 2937 loss_kpt: 95.545568 acc_pose: 0.798687 loss: 95.545568 2022/10/12 13:30:09 - mmengine - INFO - Epoch(train) [54][350/586] lr: 2.000000e-02 eta: 6:26:06 time: 0.272507 data_time: 0.051963 memory: 2937 loss_kpt: 95.577150 acc_pose: 0.774796 loss: 95.577150 2022/10/12 13:30:23 - mmengine - INFO - Epoch(train) [54][400/586] lr: 2.000000e-02 eta: 6:25:57 time: 0.275905 data_time: 0.048433 memory: 2937 loss_kpt: 95.215024 acc_pose: 0.778586 loss: 95.215024 2022/10/12 13:30:37 - mmengine - INFO - Epoch(train) [54][450/586] lr: 2.000000e-02 eta: 6:25:48 time: 0.282887 data_time: 0.050641 memory: 2937 loss_kpt: 93.461904 acc_pose: 0.709785 loss: 93.461904 2022/10/12 13:30:50 - mmengine - INFO - Epoch(train) [54][500/586] lr: 2.000000e-02 eta: 6:25:38 time: 0.267171 data_time: 0.048048 memory: 2937 loss_kpt: 94.821455 acc_pose: 0.767813 loss: 94.821455 2022/10/12 13:31:03 - mmengine - INFO - Epoch(train) [54][550/586] lr: 2.000000e-02 eta: 6:25:26 time: 0.259117 data_time: 0.052082 memory: 2937 loss_kpt: 95.047605 acc_pose: 0.812602 loss: 95.047605 2022/10/12 13:31:12 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:31:27 - mmengine - INFO - Epoch(train) [55][50/586] lr: 2.000000e-02 eta: 6:24:43 time: 0.286779 data_time: 0.058461 memory: 2937 loss_kpt: 96.719900 acc_pose: 0.839897 loss: 96.719900 2022/10/12 13:31:40 - mmengine - INFO - Epoch(train) [55][100/586] lr: 2.000000e-02 eta: 6:24:33 time: 0.269470 data_time: 0.053161 memory: 2937 loss_kpt: 95.987764 acc_pose: 0.770015 loss: 95.987764 2022/10/12 13:31:54 - mmengine - INFO - Epoch(train) [55][150/586] lr: 2.000000e-02 eta: 6:24:23 time: 0.272493 data_time: 0.052282 memory: 2937 loss_kpt: 93.214193 acc_pose: 0.678902 loss: 93.214193 2022/10/12 13:32:07 - mmengine - INFO - Epoch(train) [55][200/586] lr: 2.000000e-02 eta: 6:24:12 time: 0.265042 data_time: 0.048197 memory: 2937 loss_kpt: 93.825941 acc_pose: 0.721644 loss: 93.825941 2022/10/12 13:32:21 - mmengine - INFO - Epoch(train) [55][250/586] lr: 2.000000e-02 eta: 6:24:04 time: 0.282322 data_time: 0.051045 memory: 2937 loss_kpt: 95.073223 acc_pose: 0.707604 loss: 95.073223 2022/10/12 13:32:35 - mmengine - INFO - Epoch(train) [55][300/586] lr: 2.000000e-02 eta: 6:23:55 time: 0.278574 data_time: 0.050613 memory: 2937 loss_kpt: 94.265146 acc_pose: 0.792697 loss: 94.265146 2022/10/12 13:32:48 - mmengine - INFO - Epoch(train) [55][350/586] lr: 2.000000e-02 eta: 6:23:44 time: 0.263750 data_time: 0.051986 memory: 2937 loss_kpt: 93.856866 acc_pose: 0.788601 loss: 93.856866 2022/10/12 13:32:50 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:33:02 - mmengine - INFO - Epoch(train) [55][400/586] lr: 2.000000e-02 eta: 6:23:34 time: 0.272598 data_time: 0.048583 memory: 2937 loss_kpt: 92.771300 acc_pose: 0.836238 loss: 92.771300 2022/10/12 13:33:15 - mmengine - INFO - Epoch(train) [55][450/586] lr: 2.000000e-02 eta: 6:23:22 time: 0.262283 data_time: 0.052358 memory: 2937 loss_kpt: 94.185241 acc_pose: 0.785895 loss: 94.185241 2022/10/12 13:33:28 - mmengine - INFO - Epoch(train) [55][500/586] lr: 2.000000e-02 eta: 6:23:11 time: 0.259264 data_time: 0.052768 memory: 2937 loss_kpt: 94.150936 acc_pose: 0.769085 loss: 94.150936 2022/10/12 13:33:41 - mmengine - INFO - Epoch(train) [55][550/586] lr: 2.000000e-02 eta: 6:22:59 time: 0.258330 data_time: 0.048795 memory: 2937 loss_kpt: 94.288935 acc_pose: 0.722920 loss: 94.288935 2022/10/12 13:33:50 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:34:05 - mmengine - INFO - Epoch(train) [56][50/586] lr: 2.000000e-02 eta: 6:22:17 time: 0.294846 data_time: 0.063393 memory: 2937 loss_kpt: 94.034057 acc_pose: 0.681857 loss: 94.034057 2022/10/12 13:34:19 - mmengine - INFO - Epoch(train) [56][100/586] lr: 2.000000e-02 eta: 6:22:10 time: 0.288643 data_time: 0.054889 memory: 2937 loss_kpt: 94.404683 acc_pose: 0.800417 loss: 94.404683 2022/10/12 13:34:32 - mmengine - INFO - Epoch(train) [56][150/586] lr: 2.000000e-02 eta: 6:21:59 time: 0.263608 data_time: 0.056083 memory: 2937 loss_kpt: 94.038479 acc_pose: 0.787241 loss: 94.038479 2022/10/12 13:34:46 - mmengine - INFO - Epoch(train) [56][200/586] lr: 2.000000e-02 eta: 6:21:48 time: 0.268531 data_time: 0.052644 memory: 2937 loss_kpt: 94.605952 acc_pose: 0.751634 loss: 94.605952 2022/10/12 13:35:00 - mmengine - INFO - Epoch(train) [56][250/586] lr: 2.000000e-02 eta: 6:21:40 time: 0.282046 data_time: 0.055135 memory: 2937 loss_kpt: 93.285752 acc_pose: 0.802391 loss: 93.285752 2022/10/12 13:35:14 - mmengine - INFO - Epoch(train) [56][300/586] lr: 2.000000e-02 eta: 6:21:31 time: 0.279810 data_time: 0.053682 memory: 2937 loss_kpt: 92.607551 acc_pose: 0.720319 loss: 92.607551 2022/10/12 13:35:27 - mmengine - INFO - Epoch(train) [56][350/586] lr: 2.000000e-02 eta: 6:21:20 time: 0.265136 data_time: 0.053468 memory: 2937 loss_kpt: 93.744122 acc_pose: 0.845946 loss: 93.744122 2022/10/12 13:35:40 - mmengine - INFO - Epoch(train) [56][400/586] lr: 2.000000e-02 eta: 6:21:09 time: 0.268295 data_time: 0.053289 memory: 2937 loss_kpt: 93.590988 acc_pose: 0.760169 loss: 93.590988 2022/10/12 13:35:54 - mmengine - INFO - Epoch(train) [56][450/586] lr: 2.000000e-02 eta: 6:20:59 time: 0.268175 data_time: 0.056491 memory: 2937 loss_kpt: 94.638653 acc_pose: 0.776684 loss: 94.638653 2022/10/12 13:36:07 - mmengine - INFO - Epoch(train) [56][500/586] lr: 2.000000e-02 eta: 6:20:48 time: 0.268807 data_time: 0.054876 memory: 2937 loss_kpt: 96.023706 acc_pose: 0.731749 loss: 96.023706 2022/10/12 13:36:21 - mmengine - INFO - Epoch(train) [56][550/586] lr: 2.000000e-02 eta: 6:20:38 time: 0.266455 data_time: 0.054467 memory: 2937 loss_kpt: 94.580011 acc_pose: 0.760832 loss: 94.580011 2022/10/12 13:36:30 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:36:44 - mmengine - INFO - Epoch(train) [57][50/586] lr: 2.000000e-02 eta: 6:19:56 time: 0.287927 data_time: 0.060688 memory: 2937 loss_kpt: 93.567073 acc_pose: 0.777064 loss: 93.567073 2022/10/12 13:36:57 - mmengine - INFO - Epoch(train) [57][100/586] lr: 2.000000e-02 eta: 6:19:45 time: 0.264141 data_time: 0.048444 memory: 2937 loss_kpt: 94.467678 acc_pose: 0.666486 loss: 94.467678 2022/10/12 13:37:11 - mmengine - INFO - Epoch(train) [57][150/586] lr: 2.000000e-02 eta: 6:19:34 time: 0.270840 data_time: 0.052372 memory: 2937 loss_kpt: 94.900711 acc_pose: 0.724611 loss: 94.900711 2022/10/12 13:37:20 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:37:24 - mmengine - INFO - Epoch(train) [57][200/586] lr: 2.000000e-02 eta: 6:19:23 time: 0.261143 data_time: 0.051845 memory: 2937 loss_kpt: 95.014651 acc_pose: 0.702633 loss: 95.014651 2022/10/12 13:37:37 - mmengine - INFO - Epoch(train) [57][250/586] lr: 2.000000e-02 eta: 6:19:11 time: 0.260768 data_time: 0.048959 memory: 2937 loss_kpt: 95.011549 acc_pose: 0.748868 loss: 95.011549 2022/10/12 13:37:50 - mmengine - INFO - Epoch(train) [57][300/586] lr: 2.000000e-02 eta: 6:19:00 time: 0.264666 data_time: 0.054025 memory: 2937 loss_kpt: 91.776478 acc_pose: 0.728943 loss: 91.776478 2022/10/12 13:38:03 - mmengine - INFO - Epoch(train) [57][350/586] lr: 2.000000e-02 eta: 6:18:48 time: 0.253078 data_time: 0.051908 memory: 2937 loss_kpt: 92.698499 acc_pose: 0.772338 loss: 92.698499 2022/10/12 13:38:16 - mmengine - INFO - Epoch(train) [57][400/586] lr: 2.000000e-02 eta: 6:18:35 time: 0.255277 data_time: 0.050037 memory: 2937 loss_kpt: 96.350510 acc_pose: 0.752091 loss: 96.350510 2022/10/12 13:38:28 - mmengine - INFO - Epoch(train) [57][450/586] lr: 2.000000e-02 eta: 6:18:23 time: 0.255689 data_time: 0.055057 memory: 2937 loss_kpt: 92.597599 acc_pose: 0.753281 loss: 92.597599 2022/10/12 13:38:41 - mmengine - INFO - Epoch(train) [57][500/586] lr: 2.000000e-02 eta: 6:18:10 time: 0.246172 data_time: 0.050225 memory: 2937 loss_kpt: 93.387675 acc_pose: 0.874133 loss: 93.387675 2022/10/12 13:38:54 - mmengine - INFO - Epoch(train) [57][550/586] lr: 2.000000e-02 eta: 6:17:58 time: 0.260395 data_time: 0.056246 memory: 2937 loss_kpt: 93.760233 acc_pose: 0.746375 loss: 93.760233 2022/10/12 13:39:03 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:39:17 - mmengine - INFO - Epoch(train) [58][50/586] lr: 2.000000e-02 eta: 6:17:17 time: 0.289602 data_time: 0.068287 memory: 2937 loss_kpt: 94.084280 acc_pose: 0.761034 loss: 94.084280 2022/10/12 13:39:30 - mmengine - INFO - Epoch(train) [58][100/586] lr: 2.000000e-02 eta: 6:17:06 time: 0.265816 data_time: 0.054774 memory: 2937 loss_kpt: 94.742512 acc_pose: 0.755085 loss: 94.742512 2022/10/12 13:39:44 - mmengine - INFO - Epoch(train) [58][150/586] lr: 2.000000e-02 eta: 6:16:54 time: 0.261423 data_time: 0.048959 memory: 2937 loss_kpt: 93.030093 acc_pose: 0.773678 loss: 93.030093 2022/10/12 13:39:57 - mmengine - INFO - Epoch(train) [58][200/586] lr: 2.000000e-02 eta: 6:16:43 time: 0.261941 data_time: 0.053998 memory: 2937 loss_kpt: 96.033667 acc_pose: 0.707122 loss: 96.033667 2022/10/12 13:40:11 - mmengine - INFO - Epoch(train) [58][250/586] lr: 2.000000e-02 eta: 6:16:34 time: 0.278324 data_time: 0.053725 memory: 2937 loss_kpt: 92.895348 acc_pose: 0.748863 loss: 92.895348 2022/10/12 13:40:23 - mmengine - INFO - Epoch(train) [58][300/586] lr: 2.000000e-02 eta: 6:16:22 time: 0.257947 data_time: 0.053408 memory: 2937 loss_kpt: 94.922357 acc_pose: 0.689992 loss: 94.922357 2022/10/12 13:40:36 - mmengine - INFO - Epoch(train) [58][350/586] lr: 2.000000e-02 eta: 6:16:10 time: 0.258249 data_time: 0.054140 memory: 2937 loss_kpt: 93.870904 acc_pose: 0.778929 loss: 93.870904 2022/10/12 13:40:50 - mmengine - INFO - Epoch(train) [58][400/586] lr: 2.000000e-02 eta: 6:15:59 time: 0.262270 data_time: 0.052716 memory: 2937 loss_kpt: 94.622769 acc_pose: 0.730983 loss: 94.622769 2022/10/12 13:41:02 - mmengine - INFO - Epoch(train) [58][450/586] lr: 2.000000e-02 eta: 6:15:46 time: 0.257076 data_time: 0.054727 memory: 2937 loss_kpt: 95.120894 acc_pose: 0.740874 loss: 95.120894 2022/10/12 13:41:15 - mmengine - INFO - Epoch(train) [58][500/586] lr: 2.000000e-02 eta: 6:15:34 time: 0.256438 data_time: 0.054491 memory: 2937 loss_kpt: 93.900946 acc_pose: 0.739764 loss: 93.900946 2022/10/12 13:41:28 - mmengine - INFO - Epoch(train) [58][550/586] lr: 2.000000e-02 eta: 6:15:22 time: 0.257956 data_time: 0.055146 memory: 2937 loss_kpt: 94.877746 acc_pose: 0.787301 loss: 94.877746 2022/10/12 13:41:37 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:41:41 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:41:51 - mmengine - INFO - Epoch(train) [59][50/586] lr: 2.000000e-02 eta: 6:14:40 time: 0.280068 data_time: 0.061424 memory: 2937 loss_kpt: 94.058454 acc_pose: 0.705711 loss: 94.058454 2022/10/12 13:42:05 - mmengine - INFO - Epoch(train) [59][100/586] lr: 2.000000e-02 eta: 6:14:29 time: 0.262686 data_time: 0.049434 memory: 2937 loss_kpt: 93.234625 acc_pose: 0.722087 loss: 93.234625 2022/10/12 13:42:17 - mmengine - INFO - Epoch(train) [59][150/586] lr: 2.000000e-02 eta: 6:14:17 time: 0.257565 data_time: 0.051352 memory: 2937 loss_kpt: 94.264450 acc_pose: 0.809200 loss: 94.264450 2022/10/12 13:42:30 - mmengine - INFO - Epoch(train) [59][200/586] lr: 2.000000e-02 eta: 6:14:05 time: 0.255504 data_time: 0.051562 memory: 2937 loss_kpt: 93.390888 acc_pose: 0.757466 loss: 93.390888 2022/10/12 13:42:42 - mmengine - INFO - Epoch(train) [59][250/586] lr: 2.000000e-02 eta: 6:13:51 time: 0.244673 data_time: 0.048532 memory: 2937 loss_kpt: 94.526873 acc_pose: 0.778774 loss: 94.526873 2022/10/12 13:42:55 - mmengine - INFO - Epoch(train) [59][300/586] lr: 2.000000e-02 eta: 6:13:38 time: 0.249310 data_time: 0.047537 memory: 2937 loss_kpt: 94.086655 acc_pose: 0.794332 loss: 94.086655 2022/10/12 13:43:08 - mmengine - INFO - Epoch(train) [59][350/586] lr: 2.000000e-02 eta: 6:13:25 time: 0.251221 data_time: 0.051646 memory: 2937 loss_kpt: 93.558010 acc_pose: 0.749004 loss: 93.558010 2022/10/12 13:43:20 - mmengine - INFO - Epoch(train) [59][400/586] lr: 2.000000e-02 eta: 6:13:12 time: 0.247257 data_time: 0.052987 memory: 2937 loss_kpt: 95.584419 acc_pose: 0.779726 loss: 95.584419 2022/10/12 13:43:32 - mmengine - INFO - Epoch(train) [59][450/586] lr: 2.000000e-02 eta: 6:12:58 time: 0.243493 data_time: 0.047695 memory: 2937 loss_kpt: 93.550540 acc_pose: 0.745091 loss: 93.550540 2022/10/12 13:43:45 - mmengine - INFO - Epoch(train) [59][500/586] lr: 2.000000e-02 eta: 6:12:45 time: 0.248952 data_time: 0.050453 memory: 2937 loss_kpt: 93.720909 acc_pose: 0.725991 loss: 93.720909 2022/10/12 13:43:57 - mmengine - INFO - Epoch(train) [59][550/586] lr: 2.000000e-02 eta: 6:12:33 time: 0.258030 data_time: 0.052371 memory: 2937 loss_kpt: 93.763186 acc_pose: 0.720091 loss: 93.763186 2022/10/12 13:44:07 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:44:20 - mmengine - INFO - Epoch(train) [60][50/586] lr: 2.000000e-02 eta: 6:11:51 time: 0.275227 data_time: 0.059593 memory: 2937 loss_kpt: 95.183448 acc_pose: 0.713524 loss: 95.183448 2022/10/12 13:44:34 - mmengine - INFO - Epoch(train) [60][100/586] lr: 2.000000e-02 eta: 6:11:41 time: 0.269825 data_time: 0.057393 memory: 2937 loss_kpt: 93.546553 acc_pose: 0.783122 loss: 93.546553 2022/10/12 13:44:47 - mmengine - INFO - Epoch(train) [60][150/586] lr: 2.000000e-02 eta: 6:11:30 time: 0.269706 data_time: 0.051808 memory: 2937 loss_kpt: 94.330346 acc_pose: 0.820718 loss: 94.330346 2022/10/12 13:45:01 - mmengine - INFO - Epoch(train) [60][200/586] lr: 2.000000e-02 eta: 6:11:19 time: 0.263560 data_time: 0.052210 memory: 2937 loss_kpt: 92.928168 acc_pose: 0.695769 loss: 92.928168 2022/10/12 13:45:13 - mmengine - INFO - Epoch(train) [60][250/586] lr: 2.000000e-02 eta: 6:11:07 time: 0.258921 data_time: 0.049665 memory: 2937 loss_kpt: 92.837202 acc_pose: 0.757570 loss: 92.837202 2022/10/12 13:45:27 - mmengine - INFO - Epoch(train) [60][300/586] lr: 2.000000e-02 eta: 6:10:58 time: 0.277898 data_time: 0.056516 memory: 2937 loss_kpt: 94.298065 acc_pose: 0.715081 loss: 94.298065 2022/10/12 13:45:41 - mmengine - INFO - Epoch(train) [60][350/586] lr: 2.000000e-02 eta: 6:10:48 time: 0.272538 data_time: 0.053808 memory: 2937 loss_kpt: 94.243854 acc_pose: 0.655052 loss: 94.243854 2022/10/12 13:45:54 - mmengine - INFO - Epoch(train) [60][400/586] lr: 2.000000e-02 eta: 6:10:37 time: 0.263763 data_time: 0.055736 memory: 2937 loss_kpt: 95.244171 acc_pose: 0.680582 loss: 95.244171 2022/10/12 13:46:01 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:46:07 - mmengine - INFO - Epoch(train) [60][450/586] lr: 2.000000e-02 eta: 6:10:25 time: 0.257646 data_time: 0.053065 memory: 2937 loss_kpt: 95.598227 acc_pose: 0.792114 loss: 95.598227 2022/10/12 13:46:20 - mmengine - INFO - Epoch(train) [60][500/586] lr: 2.000000e-02 eta: 6:10:13 time: 0.258436 data_time: 0.055072 memory: 2937 loss_kpt: 95.060854 acc_pose: 0.724684 loss: 95.060854 2022/10/12 13:46:33 - mmengine - INFO - Epoch(train) [60][550/586] lr: 2.000000e-02 eta: 6:10:02 time: 0.262235 data_time: 0.053936 memory: 2937 loss_kpt: 95.656220 acc_pose: 0.673820 loss: 95.656220 2022/10/12 13:46:43 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:46:43 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/10/12 13:46:51 - mmengine - INFO - Epoch(val) [60][50/407] eta: 0:00:41 time: 0.115907 data_time: 0.013891 memory: 2937 2022/10/12 13:46:56 - mmengine - INFO - Epoch(val) [60][100/407] eta: 0:00:33 time: 0.110375 data_time: 0.009440 memory: 830 2022/10/12 13:47:02 - mmengine - INFO - Epoch(val) [60][150/407] eta: 0:00:28 time: 0.109003 data_time: 0.008922 memory: 830 2022/10/12 13:47:07 - mmengine - INFO - Epoch(val) [60][200/407] eta: 0:00:22 time: 0.109338 data_time: 0.009365 memory: 830 2022/10/12 13:47:13 - mmengine - INFO - Epoch(val) [60][250/407] eta: 0:00:17 time: 0.113095 data_time: 0.008914 memory: 830 2022/10/12 13:47:19 - mmengine - INFO - Epoch(val) [60][300/407] eta: 0:00:12 time: 0.112918 data_time: 0.009488 memory: 830 2022/10/12 13:47:24 - mmengine - INFO - Epoch(val) [60][350/407] eta: 0:00:06 time: 0.109374 data_time: 0.009187 memory: 830 2022/10/12 13:47:29 - mmengine - INFO - Epoch(val) [60][400/407] eta: 0:00:00 time: 0.101501 data_time: 0.008095 memory: 830 2022/10/12 13:47:43 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 13:47:58 - mmengine - INFO - Epoch(val) [60][407/407] coco/AP: 0.653811 coco/AP .5: 0.867588 coco/AP .75: 0.728596 coco/AP (M): 0.622436 coco/AP (L): 0.712593 coco/AR: 0.726559 coco/AR .5: 0.911524 coco/AR .75: 0.790145 coco/AR (M): 0.680197 coco/AR (L): 0.790561 2022/10/12 13:47:58 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221012/rsn18/best_coco/AP_epoch_50.pth is removed 2022/10/12 13:48:00 - mmengine - INFO - The best checkpoint with 0.6538 coco/AP at 60 epoch is saved to best_coco/AP_epoch_60.pth. 2022/10/12 13:48:14 - mmengine - INFO - Epoch(train) [61][50/586] lr: 2.000000e-02 eta: 6:09:21 time: 0.282647 data_time: 0.061578 memory: 2937 loss_kpt: 95.832325 acc_pose: 0.776844 loss: 95.832325 2022/10/12 13:48:27 - mmengine - INFO - Epoch(train) [61][100/586] lr: 2.000000e-02 eta: 6:09:09 time: 0.254562 data_time: 0.051858 memory: 2937 loss_kpt: 95.762673 acc_pose: 0.811079 loss: 95.762673 2022/10/12 13:48:40 - mmengine - INFO - Epoch(train) [61][150/586] lr: 2.000000e-02 eta: 6:08:57 time: 0.264173 data_time: 0.048960 memory: 2937 loss_kpt: 93.778568 acc_pose: 0.744174 loss: 93.778568 2022/10/12 13:48:53 - mmengine - INFO - Epoch(train) [61][200/586] lr: 2.000000e-02 eta: 6:08:46 time: 0.257345 data_time: 0.048940 memory: 2937 loss_kpt: 94.328197 acc_pose: 0.770630 loss: 94.328197 2022/10/12 13:49:07 - mmengine - INFO - Epoch(train) [61][250/586] lr: 2.000000e-02 eta: 6:08:36 time: 0.277846 data_time: 0.053236 memory: 2937 loss_kpt: 94.171065 acc_pose: 0.737656 loss: 94.171065 2022/10/12 13:49:20 - mmengine - INFO - Epoch(train) [61][300/586] lr: 2.000000e-02 eta: 6:08:26 time: 0.272875 data_time: 0.052661 memory: 2937 loss_kpt: 94.695397 acc_pose: 0.707675 loss: 94.695397 2022/10/12 13:49:34 - mmengine - INFO - Epoch(train) [61][350/586] lr: 2.000000e-02 eta: 6:08:15 time: 0.268948 data_time: 0.049388 memory: 2937 loss_kpt: 93.438968 acc_pose: 0.813032 loss: 93.438968 2022/10/12 13:49:47 - mmengine - INFO - Epoch(train) [61][400/586] lr: 2.000000e-02 eta: 6:08:05 time: 0.266421 data_time: 0.054715 memory: 2937 loss_kpt: 93.760620 acc_pose: 0.775897 loss: 93.760620 2022/10/12 13:50:00 - mmengine - INFO - Epoch(train) [61][450/586] lr: 2.000000e-02 eta: 6:07:52 time: 0.254832 data_time: 0.052125 memory: 2937 loss_kpt: 95.214747 acc_pose: 0.753133 loss: 95.214747 2022/10/12 13:50:14 - mmengine - INFO - Epoch(train) [61][500/586] lr: 2.000000e-02 eta: 6:07:42 time: 0.275148 data_time: 0.048186 memory: 2937 loss_kpt: 93.073912 acc_pose: 0.746051 loss: 93.073912 2022/10/12 13:50:27 - mmengine - INFO - Epoch(train) [61][550/586] lr: 2.000000e-02 eta: 6:07:32 time: 0.273478 data_time: 0.053851 memory: 2937 loss_kpt: 93.778483 acc_pose: 0.776277 loss: 93.778483 2022/10/12 13:50:37 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:50:51 - mmengine - INFO - Epoch(train) [62][50/586] lr: 2.000000e-02 eta: 6:06:52 time: 0.282561 data_time: 0.066845 memory: 2937 loss_kpt: 94.302678 acc_pose: 0.770523 loss: 94.302678 2022/10/12 13:51:05 - mmengine - INFO - Epoch(train) [62][100/586] lr: 2.000000e-02 eta: 6:06:42 time: 0.270923 data_time: 0.052172 memory: 2937 loss_kpt: 93.159781 acc_pose: 0.847705 loss: 93.159781 2022/10/12 13:51:17 - mmengine - INFO - Epoch(train) [62][150/586] lr: 2.000000e-02 eta: 6:06:29 time: 0.253319 data_time: 0.056994 memory: 2937 loss_kpt: 96.517531 acc_pose: 0.708565 loss: 96.517531 2022/10/12 13:51:31 - mmengine - INFO - Epoch(train) [62][200/586] lr: 2.000000e-02 eta: 6:06:19 time: 0.273226 data_time: 0.060312 memory: 2937 loss_kpt: 92.496902 acc_pose: 0.778655 loss: 92.496902 2022/10/12 13:51:44 - mmengine - INFO - Epoch(train) [62][250/586] lr: 2.000000e-02 eta: 6:06:07 time: 0.252629 data_time: 0.050394 memory: 2937 loss_kpt: 93.870058 acc_pose: 0.653451 loss: 93.870058 2022/10/12 13:51:45 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:51:56 - mmengine - INFO - Epoch(train) [62][300/586] lr: 2.000000e-02 eta: 6:05:55 time: 0.258405 data_time: 0.051363 memory: 2937 loss_kpt: 93.375857 acc_pose: 0.661209 loss: 93.375857 2022/10/12 13:52:10 - mmengine - INFO - Epoch(train) [62][350/586] lr: 2.000000e-02 eta: 6:05:43 time: 0.261575 data_time: 0.052676 memory: 2937 loss_kpt: 93.591834 acc_pose: 0.681110 loss: 93.591834 2022/10/12 13:52:23 - mmengine - INFO - Epoch(train) [62][400/586] lr: 2.000000e-02 eta: 6:05:33 time: 0.271433 data_time: 0.056164 memory: 2937 loss_kpt: 94.489852 acc_pose: 0.762245 loss: 94.489852 2022/10/12 13:52:36 - mmengine - INFO - Epoch(train) [62][450/586] lr: 2.000000e-02 eta: 6:05:21 time: 0.257235 data_time: 0.055110 memory: 2937 loss_kpt: 91.749077 acc_pose: 0.696215 loss: 91.749077 2022/10/12 13:52:49 - mmengine - INFO - Epoch(train) [62][500/586] lr: 2.000000e-02 eta: 6:05:09 time: 0.260250 data_time: 0.055662 memory: 2937 loss_kpt: 93.709482 acc_pose: 0.794449 loss: 93.709482 2022/10/12 13:53:02 - mmengine - INFO - Epoch(train) [62][550/586] lr: 2.000000e-02 eta: 6:04:57 time: 0.259851 data_time: 0.054339 memory: 2937 loss_kpt: 94.451848 acc_pose: 0.789933 loss: 94.451848 2022/10/12 13:53:11 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:53:26 - mmengine - INFO - Epoch(train) [63][50/586] lr: 2.000000e-02 eta: 6:04:19 time: 0.296710 data_time: 0.066492 memory: 2937 loss_kpt: 95.050296 acc_pose: 0.813296 loss: 95.050296 2022/10/12 13:53:40 - mmengine - INFO - Epoch(train) [63][100/586] lr: 2.000000e-02 eta: 6:04:10 time: 0.277676 data_time: 0.057856 memory: 2937 loss_kpt: 94.304870 acc_pose: 0.740484 loss: 94.304870 2022/10/12 13:53:53 - mmengine - INFO - Epoch(train) [63][150/586] lr: 2.000000e-02 eta: 6:03:59 time: 0.267404 data_time: 0.056067 memory: 2937 loss_kpt: 92.021774 acc_pose: 0.755183 loss: 92.021774 2022/10/12 13:54:07 - mmengine - INFO - Epoch(train) [63][200/586] lr: 2.000000e-02 eta: 6:03:49 time: 0.275226 data_time: 0.054675 memory: 2937 loss_kpt: 93.056556 acc_pose: 0.772513 loss: 93.056556 2022/10/12 13:54:20 - mmengine - INFO - Epoch(train) [63][250/586] lr: 2.000000e-02 eta: 6:03:37 time: 0.258323 data_time: 0.056204 memory: 2937 loss_kpt: 95.268114 acc_pose: 0.690292 loss: 95.268114 2022/10/12 13:54:33 - mmengine - INFO - Epoch(train) [63][300/586] lr: 2.000000e-02 eta: 6:03:26 time: 0.265652 data_time: 0.053198 memory: 2937 loss_kpt: 95.463273 acc_pose: 0.728381 loss: 95.463273 2022/10/12 13:54:46 - mmengine - INFO - Epoch(train) [63][350/586] lr: 2.000000e-02 eta: 6:03:15 time: 0.262065 data_time: 0.055237 memory: 2937 loss_kpt: 95.696360 acc_pose: 0.792654 loss: 95.696360 2022/10/12 13:55:00 - mmengine - INFO - Epoch(train) [63][400/586] lr: 2.000000e-02 eta: 6:03:05 time: 0.272817 data_time: 0.053310 memory: 2937 loss_kpt: 93.890949 acc_pose: 0.726548 loss: 93.890949 2022/10/12 13:55:13 - mmengine - INFO - Epoch(train) [63][450/586] lr: 2.000000e-02 eta: 6:02:54 time: 0.272716 data_time: 0.058934 memory: 2937 loss_kpt: 94.361921 acc_pose: 0.788556 loss: 94.361921 2022/10/12 13:55:26 - mmengine - INFO - Epoch(train) [63][500/586] lr: 2.000000e-02 eta: 6:02:43 time: 0.262893 data_time: 0.051950 memory: 2937 loss_kpt: 92.524659 acc_pose: 0.788807 loss: 92.524659 2022/10/12 13:55:40 - mmengine - INFO - Epoch(train) [63][550/586] lr: 2.000000e-02 eta: 6:02:32 time: 0.268590 data_time: 0.056991 memory: 2937 loss_kpt: 94.111982 acc_pose: 0.811498 loss: 94.111982 2022/10/12 13:55:49 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:56:04 - mmengine - INFO - Epoch(train) [64][50/586] lr: 2.000000e-02 eta: 6:01:54 time: 0.296560 data_time: 0.070047 memory: 2937 loss_kpt: 93.384058 acc_pose: 0.728512 loss: 93.384058 2022/10/12 13:56:13 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:56:18 - mmengine - INFO - Epoch(train) [64][100/586] lr: 2.000000e-02 eta: 6:01:45 time: 0.276333 data_time: 0.053266 memory: 2937 loss_kpt: 93.710020 acc_pose: 0.763038 loss: 93.710020 2022/10/12 13:56:32 - mmengine - INFO - Epoch(train) [64][150/586] lr: 2.000000e-02 eta: 6:01:34 time: 0.270166 data_time: 0.053764 memory: 2937 loss_kpt: 94.468101 acc_pose: 0.707563 loss: 94.468101 2022/10/12 13:56:45 - mmengine - INFO - Epoch(train) [64][200/586] lr: 2.000000e-02 eta: 6:01:23 time: 0.268405 data_time: 0.050493 memory: 2937 loss_kpt: 93.784991 acc_pose: 0.695868 loss: 93.784991 2022/10/12 13:56:58 - mmengine - INFO - Epoch(train) [64][250/586] lr: 2.000000e-02 eta: 6:01:11 time: 0.258438 data_time: 0.051533 memory: 2937 loss_kpt: 92.301400 acc_pose: 0.734176 loss: 92.301400 2022/10/12 13:57:11 - mmengine - INFO - Epoch(train) [64][300/586] lr: 2.000000e-02 eta: 6:01:00 time: 0.262824 data_time: 0.051895 memory: 2937 loss_kpt: 94.417610 acc_pose: 0.671003 loss: 94.417610 2022/10/12 13:57:24 - mmengine - INFO - Epoch(train) [64][350/586] lr: 2.000000e-02 eta: 6:00:48 time: 0.260947 data_time: 0.049036 memory: 2937 loss_kpt: 95.413661 acc_pose: 0.791406 loss: 95.413661 2022/10/12 13:57:37 - mmengine - INFO - Epoch(train) [64][400/586] lr: 2.000000e-02 eta: 6:00:37 time: 0.263007 data_time: 0.051833 memory: 2937 loss_kpt: 92.927492 acc_pose: 0.709903 loss: 92.927492 2022/10/12 13:57:50 - mmengine - INFO - Epoch(train) [64][450/586] lr: 2.000000e-02 eta: 6:00:25 time: 0.255412 data_time: 0.051546 memory: 2937 loss_kpt: 94.782331 acc_pose: 0.777658 loss: 94.782331 2022/10/12 13:58:03 - mmengine - INFO - Epoch(train) [64][500/586] lr: 2.000000e-02 eta: 6:00:13 time: 0.260060 data_time: 0.055793 memory: 2937 loss_kpt: 92.578588 acc_pose: 0.807797 loss: 92.578588 2022/10/12 13:58:17 - mmengine - INFO - Epoch(train) [64][550/586] lr: 2.000000e-02 eta: 6:00:03 time: 0.274729 data_time: 0.059558 memory: 2937 loss_kpt: 93.887117 acc_pose: 0.700510 loss: 93.887117 2022/10/12 13:58:26 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 13:58:41 - mmengine - INFO - Epoch(train) [65][50/586] lr: 2.000000e-02 eta: 5:59:27 time: 0.306962 data_time: 0.059671 memory: 2937 loss_kpt: 95.041077 acc_pose: 0.761581 loss: 95.041077 2022/10/12 13:58:55 - mmengine - INFO - Epoch(train) [65][100/586] lr: 2.000000e-02 eta: 5:59:17 time: 0.276292 data_time: 0.052556 memory: 2937 loss_kpt: 93.703641 acc_pose: 0.748975 loss: 93.703641 2022/10/12 13:59:09 - mmengine - INFO - Epoch(train) [65][150/586] lr: 2.000000e-02 eta: 5:59:07 time: 0.278736 data_time: 0.057017 memory: 2937 loss_kpt: 93.716040 acc_pose: 0.759907 loss: 93.716040 2022/10/12 13:59:22 - mmengine - INFO - Epoch(train) [65][200/586] lr: 2.000000e-02 eta: 5:58:56 time: 0.261226 data_time: 0.053921 memory: 2937 loss_kpt: 93.767667 acc_pose: 0.743935 loss: 93.767667 2022/10/12 13:59:36 - mmengine - INFO - Epoch(train) [65][250/586] lr: 2.000000e-02 eta: 5:58:45 time: 0.265944 data_time: 0.052633 memory: 2937 loss_kpt: 92.553096 acc_pose: 0.751669 loss: 92.553096 2022/10/12 13:59:48 - mmengine - INFO - Epoch(train) [65][300/586] lr: 2.000000e-02 eta: 5:58:32 time: 0.256106 data_time: 0.052153 memory: 2937 loss_kpt: 93.063722 acc_pose: 0.726466 loss: 93.063722 2022/10/12 14:00:02 - mmengine - INFO - Epoch(train) [65][350/586] lr: 2.000000e-02 eta: 5:58:23 time: 0.277304 data_time: 0.052716 memory: 2937 loss_kpt: 94.576219 acc_pose: 0.756123 loss: 94.576219 2022/10/12 14:00:16 - mmengine - INFO - Epoch(train) [65][400/586] lr: 2.000000e-02 eta: 5:58:12 time: 0.273222 data_time: 0.054972 memory: 2937 loss_kpt: 94.428641 acc_pose: 0.769919 loss: 94.428641 2022/10/12 14:00:29 - mmengine - INFO - Epoch(train) [65][450/586] lr: 2.000000e-02 eta: 5:58:01 time: 0.263129 data_time: 0.053802 memory: 2937 loss_kpt: 92.974795 acc_pose: 0.675747 loss: 92.974795 2022/10/12 14:00:41 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:00:42 - mmengine - INFO - Epoch(train) [65][500/586] lr: 2.000000e-02 eta: 5:57:50 time: 0.268613 data_time: 0.053809 memory: 2937 loss_kpt: 92.813717 acc_pose: 0.745897 loss: 92.813717 2022/10/12 14:00:56 - mmengine - INFO - Epoch(train) [65][550/586] lr: 2.000000e-02 eta: 5:57:39 time: 0.264901 data_time: 0.054574 memory: 2937 loss_kpt: 92.162029 acc_pose: 0.801946 loss: 92.162029 2022/10/12 14:01:05 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:01:20 - mmengine - INFO - Epoch(train) [66][50/586] lr: 2.000000e-02 eta: 5:57:01 time: 0.291722 data_time: 0.071864 memory: 2937 loss_kpt: 92.887834 acc_pose: 0.780732 loss: 92.887834 2022/10/12 14:01:33 - mmengine - INFO - Epoch(train) [66][100/586] lr: 2.000000e-02 eta: 5:56:51 time: 0.275579 data_time: 0.056516 memory: 2937 loss_kpt: 94.078334 acc_pose: 0.740728 loss: 94.078334 2022/10/12 14:01:47 - mmengine - INFO - Epoch(train) [66][150/586] lr: 2.000000e-02 eta: 5:56:41 time: 0.276741 data_time: 0.053037 memory: 2937 loss_kpt: 92.977351 acc_pose: 0.790052 loss: 92.977351 2022/10/12 14:02:01 - mmengine - INFO - Epoch(train) [66][200/586] lr: 2.000000e-02 eta: 5:56:31 time: 0.272507 data_time: 0.051834 memory: 2937 loss_kpt: 91.977745 acc_pose: 0.847029 loss: 91.977745 2022/10/12 14:02:15 - mmengine - INFO - Epoch(train) [66][250/586] lr: 2.000000e-02 eta: 5:56:22 time: 0.281529 data_time: 0.048981 memory: 2937 loss_kpt: 94.075683 acc_pose: 0.807826 loss: 94.075683 2022/10/12 14:02:29 - mmengine - INFO - Epoch(train) [66][300/586] lr: 2.000000e-02 eta: 5:56:11 time: 0.272048 data_time: 0.050918 memory: 2937 loss_kpt: 94.727078 acc_pose: 0.776335 loss: 94.727078 2022/10/12 14:02:42 - mmengine - INFO - Epoch(train) [66][350/586] lr: 2.000000e-02 eta: 5:56:01 time: 0.271797 data_time: 0.055667 memory: 2937 loss_kpt: 92.283642 acc_pose: 0.724151 loss: 92.283642 2022/10/12 14:02:56 - mmengine - INFO - Epoch(train) [66][400/586] lr: 2.000000e-02 eta: 5:55:50 time: 0.272317 data_time: 0.055142 memory: 2937 loss_kpt: 93.266722 acc_pose: 0.744453 loss: 93.266722 2022/10/12 14:03:09 - mmengine - INFO - Epoch(train) [66][450/586] lr: 2.000000e-02 eta: 5:55:39 time: 0.267068 data_time: 0.052673 memory: 2937 loss_kpt: 92.810481 acc_pose: 0.830530 loss: 92.810481 2022/10/12 14:03:22 - mmengine - INFO - Epoch(train) [66][500/586] lr: 2.000000e-02 eta: 5:55:28 time: 0.262279 data_time: 0.049140 memory: 2937 loss_kpt: 92.267545 acc_pose: 0.771829 loss: 92.267545 2022/10/12 14:03:35 - mmengine - INFO - Epoch(train) [66][550/586] lr: 2.000000e-02 eta: 5:55:16 time: 0.261007 data_time: 0.053197 memory: 2937 loss_kpt: 93.137892 acc_pose: 0.701639 loss: 93.137892 2022/10/12 14:03:45 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:03:59 - mmengine - INFO - Epoch(train) [67][50/586] lr: 2.000000e-02 eta: 5:54:38 time: 0.281042 data_time: 0.058522 memory: 2937 loss_kpt: 94.004458 acc_pose: 0.788348 loss: 94.004458 2022/10/12 14:04:12 - mmengine - INFO - Epoch(train) [67][100/586] lr: 2.000000e-02 eta: 5:54:26 time: 0.258107 data_time: 0.051403 memory: 2937 loss_kpt: 92.713411 acc_pose: 0.822602 loss: 92.713411 2022/10/12 14:04:25 - mmengine - INFO - Epoch(train) [67][150/586] lr: 2.000000e-02 eta: 5:54:13 time: 0.255051 data_time: 0.049391 memory: 2937 loss_kpt: 92.320997 acc_pose: 0.724775 loss: 92.320997 2022/10/12 14:04:38 - mmengine - INFO - Epoch(train) [67][200/586] lr: 2.000000e-02 eta: 5:54:01 time: 0.259919 data_time: 0.051108 memory: 2937 loss_kpt: 93.363881 acc_pose: 0.812416 loss: 93.363881 2022/10/12 14:04:51 - mmengine - INFO - Epoch(train) [67][250/586] lr: 2.000000e-02 eta: 5:53:51 time: 0.275409 data_time: 0.053888 memory: 2937 loss_kpt: 91.455686 acc_pose: 0.784602 loss: 91.455686 2022/10/12 14:05:04 - mmengine - INFO - Epoch(train) [67][300/586] lr: 2.000000e-02 eta: 5:53:39 time: 0.254165 data_time: 0.048074 memory: 2937 loss_kpt: 93.460848 acc_pose: 0.702064 loss: 93.460848 2022/10/12 14:05:10 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:05:17 - mmengine - INFO - Epoch(train) [67][350/586] lr: 2.000000e-02 eta: 5:53:27 time: 0.261263 data_time: 0.052232 memory: 2937 loss_kpt: 92.655597 acc_pose: 0.688184 loss: 92.655597 2022/10/12 14:05:30 - mmengine - INFO - Epoch(train) [67][400/586] lr: 2.000000e-02 eta: 5:53:15 time: 0.260230 data_time: 0.050793 memory: 2937 loss_kpt: 91.489573 acc_pose: 0.806302 loss: 91.489573 2022/10/12 14:05:44 - mmengine - INFO - Epoch(train) [67][450/586] lr: 2.000000e-02 eta: 5:53:04 time: 0.266696 data_time: 0.047345 memory: 2937 loss_kpt: 95.998892 acc_pose: 0.827465 loss: 95.998892 2022/10/12 14:05:56 - mmengine - INFO - Epoch(train) [67][500/586] lr: 2.000000e-02 eta: 5:52:52 time: 0.252305 data_time: 0.050543 memory: 2937 loss_kpt: 91.345986 acc_pose: 0.792893 loss: 91.345986 2022/10/12 14:06:10 - mmengine - INFO - Epoch(train) [67][550/586] lr: 2.000000e-02 eta: 5:52:41 time: 0.271631 data_time: 0.050504 memory: 2937 loss_kpt: 93.458992 acc_pose: 0.761872 loss: 93.458992 2022/10/12 14:06:18 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:06:32 - mmengine - INFO - Epoch(train) [68][50/586] lr: 2.000000e-02 eta: 5:52:03 time: 0.275824 data_time: 0.059381 memory: 2937 loss_kpt: 91.688802 acc_pose: 0.742207 loss: 91.688802 2022/10/12 14:06:46 - mmengine - INFO - Epoch(train) [68][100/586] lr: 2.000000e-02 eta: 5:51:52 time: 0.269375 data_time: 0.049243 memory: 2937 loss_kpt: 92.836973 acc_pose: 0.782164 loss: 92.836973 2022/10/12 14:06:59 - mmengine - INFO - Epoch(train) [68][150/586] lr: 2.000000e-02 eta: 5:51:41 time: 0.268771 data_time: 0.052436 memory: 2937 loss_kpt: 93.878172 acc_pose: 0.828238 loss: 93.878172 2022/10/12 14:07:13 - mmengine - INFO - Epoch(train) [68][200/586] lr: 2.000000e-02 eta: 5:51:30 time: 0.269531 data_time: 0.048369 memory: 2937 loss_kpt: 93.328032 acc_pose: 0.802989 loss: 93.328032 2022/10/12 14:07:26 - mmengine - INFO - Epoch(train) [68][250/586] lr: 2.000000e-02 eta: 5:51:20 time: 0.270468 data_time: 0.052135 memory: 2937 loss_kpt: 94.908371 acc_pose: 0.794797 loss: 94.908371 2022/10/12 14:07:40 - mmengine - INFO - Epoch(train) [68][300/586] lr: 2.000000e-02 eta: 5:51:09 time: 0.275285 data_time: 0.053657 memory: 2937 loss_kpt: 95.053612 acc_pose: 0.710612 loss: 95.053612 2022/10/12 14:07:54 - mmengine - INFO - Epoch(train) [68][350/586] lr: 2.000000e-02 eta: 5:50:59 time: 0.276223 data_time: 0.059028 memory: 2937 loss_kpt: 93.649482 acc_pose: 0.815896 loss: 93.649482 2022/10/12 14:08:07 - mmengine - INFO - Epoch(train) [68][400/586] lr: 2.000000e-02 eta: 5:50:48 time: 0.265896 data_time: 0.051995 memory: 2937 loss_kpt: 94.197259 acc_pose: 0.819331 loss: 94.197259 2022/10/12 14:08:21 - mmengine - INFO - Epoch(train) [68][450/586] lr: 2.000000e-02 eta: 5:50:38 time: 0.275370 data_time: 0.052410 memory: 2937 loss_kpt: 93.093234 acc_pose: 0.741622 loss: 93.093234 2022/10/12 14:08:34 - mmengine - INFO - Epoch(train) [68][500/586] lr: 2.000000e-02 eta: 5:50:27 time: 0.267159 data_time: 0.052869 memory: 2937 loss_kpt: 92.571773 acc_pose: 0.758186 loss: 92.571773 2022/10/12 14:08:48 - mmengine - INFO - Epoch(train) [68][550/586] lr: 2.000000e-02 eta: 5:50:16 time: 0.268866 data_time: 0.054266 memory: 2937 loss_kpt: 92.535489 acc_pose: 0.795381 loss: 92.535489 2022/10/12 14:08:58 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:09:12 - mmengine - INFO - Epoch(train) [69][50/586] lr: 2.000000e-02 eta: 5:49:38 time: 0.282706 data_time: 0.064633 memory: 2937 loss_kpt: 92.644503 acc_pose: 0.784020 loss: 92.644503 2022/10/12 14:09:24 - mmengine - INFO - Epoch(train) [69][100/586] lr: 2.000000e-02 eta: 5:49:26 time: 0.250962 data_time: 0.051468 memory: 2937 loss_kpt: 92.487679 acc_pose: 0.681119 loss: 92.487679 2022/10/12 14:09:37 - mmengine - INFO - Epoch(train) [69][150/586] lr: 2.000000e-02 eta: 5:49:13 time: 0.253219 data_time: 0.055719 memory: 2937 loss_kpt: 92.153821 acc_pose: 0.764317 loss: 92.153821 2022/10/12 14:09:38 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:09:50 - mmengine - INFO - Epoch(train) [69][200/586] lr: 2.000000e-02 eta: 5:49:01 time: 0.256879 data_time: 0.054507 memory: 2937 loss_kpt: 91.717348 acc_pose: 0.819280 loss: 91.717348 2022/10/12 14:10:03 - mmengine - INFO - Epoch(train) [69][250/586] lr: 2.000000e-02 eta: 5:48:49 time: 0.260624 data_time: 0.052501 memory: 2937 loss_kpt: 94.117543 acc_pose: 0.799895 loss: 94.117543 2022/10/12 14:10:16 - mmengine - INFO - Epoch(train) [69][300/586] lr: 2.000000e-02 eta: 5:48:37 time: 0.256307 data_time: 0.052918 memory: 2937 loss_kpt: 94.260355 acc_pose: 0.709773 loss: 94.260355 2022/10/12 14:10:29 - mmengine - INFO - Epoch(train) [69][350/586] lr: 2.000000e-02 eta: 5:48:25 time: 0.262934 data_time: 0.052434 memory: 2937 loss_kpt: 92.110766 acc_pose: 0.808661 loss: 92.110766 2022/10/12 14:10:42 - mmengine - INFO - Epoch(train) [69][400/586] lr: 2.000000e-02 eta: 5:48:13 time: 0.254542 data_time: 0.048402 memory: 2937 loss_kpt: 95.849170 acc_pose: 0.782471 loss: 95.849170 2022/10/12 14:10:55 - mmengine - INFO - Epoch(train) [69][450/586] lr: 2.000000e-02 eta: 5:48:01 time: 0.258518 data_time: 0.052411 memory: 2937 loss_kpt: 93.045973 acc_pose: 0.798818 loss: 93.045973 2022/10/12 14:11:07 - mmengine - INFO - Epoch(train) [69][500/586] lr: 2.000000e-02 eta: 5:47:49 time: 0.253978 data_time: 0.047821 memory: 2937 loss_kpt: 92.560677 acc_pose: 0.784029 loss: 92.560677 2022/10/12 14:11:20 - mmengine - INFO - Epoch(train) [69][550/586] lr: 2.000000e-02 eta: 5:47:37 time: 0.259374 data_time: 0.053695 memory: 2937 loss_kpt: 90.671858 acc_pose: 0.644921 loss: 90.671858 2022/10/12 14:11:29 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:11:43 - mmengine - INFO - Epoch(train) [70][50/586] lr: 2.000000e-02 eta: 5:46:59 time: 0.280971 data_time: 0.059507 memory: 2937 loss_kpt: 94.337723 acc_pose: 0.802942 loss: 94.337723 2022/10/12 14:11:56 - mmengine - INFO - Epoch(train) [70][100/586] lr: 2.000000e-02 eta: 5:46:48 time: 0.260236 data_time: 0.053390 memory: 2937 loss_kpt: 93.286819 acc_pose: 0.746137 loss: 93.286819 2022/10/12 14:12:09 - mmengine - INFO - Epoch(train) [70][150/586] lr: 2.000000e-02 eta: 5:46:35 time: 0.253140 data_time: 0.050147 memory: 2937 loss_kpt: 91.629704 acc_pose: 0.712707 loss: 91.629704 2022/10/12 14:12:22 - mmengine - INFO - Epoch(train) [70][200/586] lr: 2.000000e-02 eta: 5:46:23 time: 0.258005 data_time: 0.053036 memory: 2937 loss_kpt: 92.579384 acc_pose: 0.809425 loss: 92.579384 2022/10/12 14:12:35 - mmengine - INFO - Epoch(train) [70][250/586] lr: 2.000000e-02 eta: 5:46:12 time: 0.265974 data_time: 0.052023 memory: 2937 loss_kpt: 93.227493 acc_pose: 0.768222 loss: 93.227493 2022/10/12 14:12:49 - mmengine - INFO - Epoch(train) [70][300/586] lr: 2.000000e-02 eta: 5:46:01 time: 0.267472 data_time: 0.052858 memory: 2937 loss_kpt: 92.659533 acc_pose: 0.772964 loss: 92.659533 2022/10/12 14:13:02 - mmengine - INFO - Epoch(train) [70][350/586] lr: 2.000000e-02 eta: 5:45:49 time: 0.260013 data_time: 0.051973 memory: 2937 loss_kpt: 92.757879 acc_pose: 0.787388 loss: 92.757879 2022/10/12 14:13:14 - mmengine - INFO - Epoch(train) [70][400/586] lr: 2.000000e-02 eta: 5:45:37 time: 0.252812 data_time: 0.052987 memory: 2937 loss_kpt: 94.500789 acc_pose: 0.717245 loss: 94.500789 2022/10/12 14:13:27 - mmengine - INFO - Epoch(train) [70][450/586] lr: 2.000000e-02 eta: 5:45:24 time: 0.251601 data_time: 0.048874 memory: 2937 loss_kpt: 91.360628 acc_pose: 0.699827 loss: 91.360628 2022/10/12 14:13:40 - mmengine - INFO - Epoch(train) [70][500/586] lr: 2.000000e-02 eta: 5:45:11 time: 0.251883 data_time: 0.053324 memory: 2937 loss_kpt: 93.842847 acc_pose: 0.754626 loss: 93.842847 2022/10/12 14:13:52 - mmengine - INFO - Epoch(train) [70][550/586] lr: 2.000000e-02 eta: 5:44:59 time: 0.253991 data_time: 0.054862 memory: 2937 loss_kpt: 92.271227 acc_pose: 0.824614 loss: 92.271227 2022/10/12 14:13:56 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:14:01 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:14:01 - mmengine - INFO - Saving checkpoint at 70 epochs 2022/10/12 14:14:09 - mmengine - INFO - Epoch(val) [70][50/407] eta: 0:00:41 time: 0.115259 data_time: 0.014534 memory: 2937 2022/10/12 14:14:15 - mmengine - INFO - Epoch(val) [70][100/407] eta: 0:00:34 time: 0.112311 data_time: 0.009380 memory: 830 2022/10/12 14:14:21 - mmengine - INFO - Epoch(val) [70][150/407] eta: 0:00:28 time: 0.110130 data_time: 0.008822 memory: 830 2022/10/12 14:14:26 - mmengine - INFO - Epoch(val) [70][200/407] eta: 0:00:22 time: 0.110788 data_time: 0.008739 memory: 830 2022/10/12 14:14:32 - mmengine - INFO - Epoch(val) [70][250/407] eta: 0:00:17 time: 0.109994 data_time: 0.009362 memory: 830 2022/10/12 14:14:37 - mmengine - INFO - Epoch(val) [70][300/407] eta: 0:00:12 time: 0.112700 data_time: 0.009290 memory: 830 2022/10/12 14:14:43 - mmengine - INFO - Epoch(val) [70][350/407] eta: 0:00:06 time: 0.114172 data_time: 0.009208 memory: 830 2022/10/12 14:14:48 - mmengine - INFO - Epoch(val) [70][400/407] eta: 0:00:00 time: 0.103526 data_time: 0.007523 memory: 830 2022/10/12 14:15:01 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 14:15:17 - mmengine - INFO - Epoch(val) [70][407/407] coco/AP: 0.661764 coco/AP .5: 0.871968 coco/AP .75: 0.736000 coco/AP (M): 0.630336 coco/AP (L): 0.720738 coco/AR: 0.733848 coco/AR .5: 0.912783 coco/AR .75: 0.796442 coco/AR (M): 0.687654 coco/AR (L): 0.797399 2022/10/12 14:15:17 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221012/rsn18/best_coco/AP_epoch_60.pth is removed 2022/10/12 14:15:19 - mmengine - INFO - The best checkpoint with 0.6618 coco/AP at 70 epoch is saved to best_coco/AP_epoch_70.pth. 2022/10/12 14:15:34 - mmengine - INFO - Epoch(train) [71][50/586] lr: 2.000000e-02 eta: 5:44:23 time: 0.291202 data_time: 0.063273 memory: 2937 loss_kpt: 93.172909 acc_pose: 0.734181 loss: 93.172909 2022/10/12 14:15:47 - mmengine - INFO - Epoch(train) [71][100/586] lr: 2.000000e-02 eta: 5:44:12 time: 0.270579 data_time: 0.055831 memory: 2937 loss_kpt: 94.385772 acc_pose: 0.801416 loss: 94.385772 2022/10/12 14:16:01 - mmengine - INFO - Epoch(train) [71][150/586] lr: 2.000000e-02 eta: 5:44:02 time: 0.281543 data_time: 0.055393 memory: 2937 loss_kpt: 91.213618 acc_pose: 0.801049 loss: 91.213618 2022/10/12 14:16:15 - mmengine - INFO - Epoch(train) [71][200/586] lr: 2.000000e-02 eta: 5:43:52 time: 0.269996 data_time: 0.053030 memory: 2937 loss_kpt: 92.150591 acc_pose: 0.793682 loss: 92.150591 2022/10/12 14:16:28 - mmengine - INFO - Epoch(train) [71][250/586] lr: 2.000000e-02 eta: 5:43:41 time: 0.268203 data_time: 0.055789 memory: 2937 loss_kpt: 93.589410 acc_pose: 0.768895 loss: 93.589410 2022/10/12 14:16:41 - mmengine - INFO - Epoch(train) [71][300/586] lr: 2.000000e-02 eta: 5:43:29 time: 0.264932 data_time: 0.049113 memory: 2937 loss_kpt: 93.509848 acc_pose: 0.809517 loss: 93.509848 2022/10/12 14:16:55 - mmengine - INFO - Epoch(train) [71][350/586] lr: 2.000000e-02 eta: 5:43:18 time: 0.268279 data_time: 0.052568 memory: 2937 loss_kpt: 92.149298 acc_pose: 0.743772 loss: 92.149298 2022/10/12 14:17:08 - mmengine - INFO - Epoch(train) [71][400/586] lr: 2.000000e-02 eta: 5:43:07 time: 0.262701 data_time: 0.047479 memory: 2937 loss_kpt: 92.815025 acc_pose: 0.830509 loss: 92.815025 2022/10/12 14:17:21 - mmengine - INFO - Epoch(train) [71][450/586] lr: 2.000000e-02 eta: 5:42:55 time: 0.261702 data_time: 0.053602 memory: 2937 loss_kpt: 90.960725 acc_pose: 0.773530 loss: 90.960725 2022/10/12 14:17:34 - mmengine - INFO - Epoch(train) [71][500/586] lr: 2.000000e-02 eta: 5:42:43 time: 0.258616 data_time: 0.050686 memory: 2937 loss_kpt: 94.444505 acc_pose: 0.830249 loss: 94.444505 2022/10/12 14:17:48 - mmengine - INFO - Epoch(train) [71][550/586] lr: 2.000000e-02 eta: 5:42:33 time: 0.274803 data_time: 0.057281 memory: 2937 loss_kpt: 93.468235 acc_pose: 0.684001 loss: 93.468235 2022/10/12 14:17:57 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:18:11 - mmengine - INFO - Epoch(train) [72][50/586] lr: 2.000000e-02 eta: 5:41:56 time: 0.284521 data_time: 0.065102 memory: 2937 loss_kpt: 94.510194 acc_pose: 0.771633 loss: 94.510194 2022/10/12 14:18:25 - mmengine - INFO - Epoch(train) [72][100/586] lr: 2.000000e-02 eta: 5:41:45 time: 0.265701 data_time: 0.052190 memory: 2937 loss_kpt: 92.839360 acc_pose: 0.724063 loss: 92.839360 2022/10/12 14:18:38 - mmengine - INFO - Epoch(train) [72][150/586] lr: 2.000000e-02 eta: 5:41:34 time: 0.270921 data_time: 0.059071 memory: 2937 loss_kpt: 92.745034 acc_pose: 0.813434 loss: 92.745034 2022/10/12 14:18:52 - mmengine - INFO - Epoch(train) [72][200/586] lr: 2.000000e-02 eta: 5:41:24 time: 0.278577 data_time: 0.055787 memory: 2937 loss_kpt: 93.653656 acc_pose: 0.777351 loss: 93.653656 2022/10/12 14:19:06 - mmengine - INFO - Epoch(train) [72][250/586] lr: 2.000000e-02 eta: 5:41:13 time: 0.267482 data_time: 0.054708 memory: 2937 loss_kpt: 92.798448 acc_pose: 0.797640 loss: 92.798448 2022/10/12 14:19:18 - mmengine - INFO - Epoch(train) [72][300/586] lr: 2.000000e-02 eta: 5:41:01 time: 0.257914 data_time: 0.053888 memory: 2937 loss_kpt: 93.283381 acc_pose: 0.767692 loss: 93.283381 2022/10/12 14:19:32 - mmengine - INFO - Epoch(train) [72][350/586] lr: 2.000000e-02 eta: 5:40:50 time: 0.269785 data_time: 0.055137 memory: 2937 loss_kpt: 94.327151 acc_pose: 0.754987 loss: 94.327151 2022/10/12 14:19:44 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:19:45 - mmengine - INFO - Epoch(train) [72][400/586] lr: 2.000000e-02 eta: 5:40:39 time: 0.261704 data_time: 0.055977 memory: 2937 loss_kpt: 94.007639 acc_pose: 0.816303 loss: 94.007639 2022/10/12 14:19:58 - mmengine - INFO - Epoch(train) [72][450/586] lr: 2.000000e-02 eta: 5:40:27 time: 0.266489 data_time: 0.054772 memory: 2937 loss_kpt: 91.867846 acc_pose: 0.802925 loss: 91.867846 2022/10/12 14:20:12 - mmengine - INFO - Epoch(train) [72][500/586] lr: 2.000000e-02 eta: 5:40:16 time: 0.268986 data_time: 0.055136 memory: 2937 loss_kpt: 90.721533 acc_pose: 0.773206 loss: 90.721533 2022/10/12 14:20:25 - mmengine - INFO - Epoch(train) [72][550/586] lr: 2.000000e-02 eta: 5:40:04 time: 0.259596 data_time: 0.053053 memory: 2937 loss_kpt: 94.504157 acc_pose: 0.741229 loss: 94.504157 2022/10/12 14:20:34 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:20:49 - mmengine - INFO - Epoch(train) [73][50/586] lr: 2.000000e-02 eta: 5:39:28 time: 0.282948 data_time: 0.064153 memory: 2937 loss_kpt: 93.150207 acc_pose: 0.813853 loss: 93.150207 2022/10/12 14:21:02 - mmengine - INFO - Epoch(train) [73][100/586] lr: 2.000000e-02 eta: 5:39:17 time: 0.266364 data_time: 0.051470 memory: 2937 loss_kpt: 93.062565 acc_pose: 0.799134 loss: 93.062565 2022/10/12 14:21:15 - mmengine - INFO - Epoch(train) [73][150/586] lr: 2.000000e-02 eta: 5:39:05 time: 0.259814 data_time: 0.050451 memory: 2937 loss_kpt: 92.720626 acc_pose: 0.757843 loss: 92.720626 2022/10/12 14:21:28 - mmengine - INFO - Epoch(train) [73][200/586] lr: 2.000000e-02 eta: 5:38:54 time: 0.271707 data_time: 0.052685 memory: 2937 loss_kpt: 94.060849 acc_pose: 0.805451 loss: 94.060849 2022/10/12 14:21:42 - mmengine - INFO - Epoch(train) [73][250/586] lr: 2.000000e-02 eta: 5:38:43 time: 0.267295 data_time: 0.055604 memory: 2937 loss_kpt: 93.240331 acc_pose: 0.726679 loss: 93.240331 2022/10/12 14:21:56 - mmengine - INFO - Epoch(train) [73][300/586] lr: 2.000000e-02 eta: 5:38:33 time: 0.278154 data_time: 0.048551 memory: 2937 loss_kpt: 93.680815 acc_pose: 0.754928 loss: 93.680815 2022/10/12 14:22:10 - mmengine - INFO - Epoch(train) [73][350/586] lr: 2.000000e-02 eta: 5:38:23 time: 0.281135 data_time: 0.051623 memory: 2937 loss_kpt: 92.553028 acc_pose: 0.776808 loss: 92.553028 2022/10/12 14:22:25 - mmengine - INFO - Epoch(train) [73][400/586] lr: 2.000000e-02 eta: 5:38:15 time: 0.296652 data_time: 0.057331 memory: 2937 loss_kpt: 92.848122 acc_pose: 0.762106 loss: 92.848122 2022/10/12 14:22:39 - mmengine - INFO - Epoch(train) [73][450/586] lr: 2.000000e-02 eta: 5:38:05 time: 0.284284 data_time: 0.057161 memory: 2937 loss_kpt: 93.893293 acc_pose: 0.812723 loss: 93.893293 2022/10/12 14:22:54 - mmengine - INFO - Epoch(train) [73][500/586] lr: 2.000000e-02 eta: 5:37:57 time: 0.293361 data_time: 0.051471 memory: 2937 loss_kpt: 91.629031 acc_pose: 0.840783 loss: 91.629031 2022/10/12 14:23:08 - mmengine - INFO - Epoch(train) [73][550/586] lr: 2.000000e-02 eta: 5:37:47 time: 0.283137 data_time: 0.050484 memory: 2937 loss_kpt: 95.069807 acc_pose: 0.796039 loss: 95.069807 2022/10/12 14:23:17 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:23:32 - mmengine - INFO - Epoch(train) [74][50/586] lr: 2.000000e-02 eta: 5:37:12 time: 0.296825 data_time: 0.060790 memory: 2937 loss_kpt: 93.370311 acc_pose: 0.802941 loss: 93.370311 2022/10/12 14:23:46 - mmengine - INFO - Epoch(train) [74][100/586] lr: 2.000000e-02 eta: 5:37:01 time: 0.269813 data_time: 0.051800 memory: 2937 loss_kpt: 93.852470 acc_pose: 0.774644 loss: 93.852470 2022/10/12 14:24:00 - mmengine - INFO - Epoch(train) [74][150/586] lr: 2.000000e-02 eta: 5:36:51 time: 0.275442 data_time: 0.053215 memory: 2937 loss_kpt: 91.786518 acc_pose: 0.844441 loss: 91.786518 2022/10/12 14:24:12 - mmengine - INFO - Epoch(train) [74][200/586] lr: 2.000000e-02 eta: 5:36:39 time: 0.259460 data_time: 0.052216 memory: 2937 loss_kpt: 93.051921 acc_pose: 0.741663 loss: 93.051921 2022/10/12 14:24:18 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:24:25 - mmengine - INFO - Epoch(train) [74][250/586] lr: 2.000000e-02 eta: 5:36:27 time: 0.257575 data_time: 0.048886 memory: 2937 loss_kpt: 94.087430 acc_pose: 0.760423 loss: 94.087430 2022/10/12 14:24:38 - mmengine - INFO - Epoch(train) [74][300/586] lr: 2.000000e-02 eta: 5:36:15 time: 0.257144 data_time: 0.051879 memory: 2937 loss_kpt: 92.212154 acc_pose: 0.780128 loss: 92.212154 2022/10/12 14:24:51 - mmengine - INFO - Epoch(train) [74][350/586] lr: 2.000000e-02 eta: 5:36:03 time: 0.263253 data_time: 0.053043 memory: 2937 loss_kpt: 92.622178 acc_pose: 0.719983 loss: 92.622178 2022/10/12 14:25:05 - mmengine - INFO - Epoch(train) [74][400/586] lr: 2.000000e-02 eta: 5:35:52 time: 0.268533 data_time: 0.050191 memory: 2937 loss_kpt: 93.275203 acc_pose: 0.795939 loss: 93.275203 2022/10/12 14:25:18 - mmengine - INFO - Epoch(train) [74][450/586] lr: 2.000000e-02 eta: 5:35:40 time: 0.263646 data_time: 0.052584 memory: 2937 loss_kpt: 94.344462 acc_pose: 0.766651 loss: 94.344462 2022/10/12 14:25:32 - mmengine - INFO - Epoch(train) [74][500/586] lr: 2.000000e-02 eta: 5:35:29 time: 0.269908 data_time: 0.053414 memory: 2937 loss_kpt: 93.101169 acc_pose: 0.785082 loss: 93.101169 2022/10/12 14:25:45 - mmengine - INFO - Epoch(train) [74][550/586] lr: 2.000000e-02 eta: 5:35:19 time: 0.273764 data_time: 0.050497 memory: 2937 loss_kpt: 91.129426 acc_pose: 0.760682 loss: 91.129426 2022/10/12 14:25:55 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:26:09 - mmengine - INFO - Epoch(train) [75][50/586] lr: 2.000000e-02 eta: 5:34:43 time: 0.283585 data_time: 0.067102 memory: 2937 loss_kpt: 93.430022 acc_pose: 0.775618 loss: 93.430022 2022/10/12 14:26:23 - mmengine - INFO - Epoch(train) [75][100/586] lr: 2.000000e-02 eta: 5:34:33 time: 0.274911 data_time: 0.057247 memory: 2937 loss_kpt: 91.659424 acc_pose: 0.737183 loss: 91.659424 2022/10/12 14:26:36 - mmengine - INFO - Epoch(train) [75][150/586] lr: 2.000000e-02 eta: 5:34:21 time: 0.266081 data_time: 0.050281 memory: 2937 loss_kpt: 93.082396 acc_pose: 0.749391 loss: 93.082396 2022/10/12 14:26:50 - mmengine - INFO - Epoch(train) [75][200/586] lr: 2.000000e-02 eta: 5:34:11 time: 0.275892 data_time: 0.047750 memory: 2937 loss_kpt: 90.962237 acc_pose: 0.737275 loss: 90.962237 2022/10/12 14:27:03 - mmengine - INFO - Epoch(train) [75][250/586] lr: 2.000000e-02 eta: 5:33:59 time: 0.263084 data_time: 0.051932 memory: 2937 loss_kpt: 91.233067 acc_pose: 0.778866 loss: 91.233067 2022/10/12 14:27:17 - mmengine - INFO - Epoch(train) [75][300/586] lr: 2.000000e-02 eta: 5:33:49 time: 0.275322 data_time: 0.049105 memory: 2937 loss_kpt: 92.711873 acc_pose: 0.789334 loss: 92.711873 2022/10/12 14:27:30 - mmengine - INFO - Epoch(train) [75][350/586] lr: 2.000000e-02 eta: 5:33:38 time: 0.267064 data_time: 0.054058 memory: 2937 loss_kpt: 93.750109 acc_pose: 0.749977 loss: 93.750109 2022/10/12 14:27:43 - mmengine - INFO - Epoch(train) [75][400/586] lr: 2.000000e-02 eta: 5:33:26 time: 0.260000 data_time: 0.052988 memory: 2937 loss_kpt: 93.318742 acc_pose: 0.782577 loss: 93.318742 2022/10/12 14:27:57 - mmengine - INFO - Epoch(train) [75][450/586] lr: 2.000000e-02 eta: 5:33:14 time: 0.267475 data_time: 0.051002 memory: 2937 loss_kpt: 92.046613 acc_pose: 0.733719 loss: 92.046613 2022/10/12 14:28:11 - mmengine - INFO - Epoch(train) [75][500/586] lr: 2.000000e-02 eta: 5:33:04 time: 0.275623 data_time: 0.052253 memory: 2937 loss_kpt: 91.484138 acc_pose: 0.739881 loss: 91.484138 2022/10/12 14:28:25 - mmengine - INFO - Epoch(train) [75][550/586] lr: 2.000000e-02 eta: 5:32:54 time: 0.277887 data_time: 0.053788 memory: 2937 loss_kpt: 92.475766 acc_pose: 0.794826 loss: 92.475766 2022/10/12 14:28:34 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:28:48 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:28:48 - mmengine - INFO - Epoch(train) [76][50/586] lr: 2.000000e-02 eta: 5:32:18 time: 0.280684 data_time: 0.066790 memory: 2937 loss_kpt: 93.988794 acc_pose: 0.780443 loss: 93.988794 2022/10/12 14:29:01 - mmengine - INFO - Epoch(train) [76][100/586] lr: 2.000000e-02 eta: 5:32:06 time: 0.262835 data_time: 0.054650 memory: 2937 loss_kpt: 93.170051 acc_pose: 0.793307 loss: 93.170051 2022/10/12 14:29:15 - mmengine - INFO - Epoch(train) [76][150/586] lr: 2.000000e-02 eta: 5:31:56 time: 0.270599 data_time: 0.054103 memory: 2937 loss_kpt: 93.109789 acc_pose: 0.730254 loss: 93.109789 2022/10/12 14:29:28 - mmengine - INFO - Epoch(train) [76][200/586] lr: 2.000000e-02 eta: 5:31:44 time: 0.267521 data_time: 0.053707 memory: 2937 loss_kpt: 92.963515 acc_pose: 0.800672 loss: 92.963515 2022/10/12 14:29:41 - mmengine - INFO - Epoch(train) [76][250/586] lr: 2.000000e-02 eta: 5:31:32 time: 0.256553 data_time: 0.050749 memory: 2937 loss_kpt: 92.790442 acc_pose: 0.761419 loss: 92.790442 2022/10/12 14:29:54 - mmengine - INFO - Epoch(train) [76][300/586] lr: 2.000000e-02 eta: 5:31:20 time: 0.258634 data_time: 0.055170 memory: 2937 loss_kpt: 90.793135 acc_pose: 0.784761 loss: 90.793135 2022/10/12 14:30:07 - mmengine - INFO - Epoch(train) [76][350/586] lr: 2.000000e-02 eta: 5:31:09 time: 0.266630 data_time: 0.056278 memory: 2937 loss_kpt: 92.493052 acc_pose: 0.699385 loss: 92.493052 2022/10/12 14:30:20 - mmengine - INFO - Epoch(train) [76][400/586] lr: 2.000000e-02 eta: 5:30:56 time: 0.256519 data_time: 0.048984 memory: 2937 loss_kpt: 93.052945 acc_pose: 0.773463 loss: 93.052945 2022/10/12 14:30:34 - mmengine - INFO - Epoch(train) [76][450/586] lr: 2.000000e-02 eta: 5:30:46 time: 0.273789 data_time: 0.051965 memory: 2937 loss_kpt: 92.088452 acc_pose: 0.792836 loss: 92.088452 2022/10/12 14:30:47 - mmengine - INFO - Epoch(train) [76][500/586] lr: 2.000000e-02 eta: 5:30:34 time: 0.263841 data_time: 0.054359 memory: 2937 loss_kpt: 92.537989 acc_pose: 0.838181 loss: 92.537989 2022/10/12 14:31:00 - mmengine - INFO - Epoch(train) [76][550/586] lr: 2.000000e-02 eta: 5:30:23 time: 0.264851 data_time: 0.048853 memory: 2937 loss_kpt: 91.827110 acc_pose: 0.756840 loss: 91.827110 2022/10/12 14:31:09 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:31:23 - mmengine - INFO - Epoch(train) [77][50/586] lr: 2.000000e-02 eta: 5:29:47 time: 0.279830 data_time: 0.060227 memory: 2937 loss_kpt: 92.218281 acc_pose: 0.650611 loss: 92.218281 2022/10/12 14:31:36 - mmengine - INFO - Epoch(train) [77][100/586] lr: 2.000000e-02 eta: 5:29:35 time: 0.260229 data_time: 0.055492 memory: 2937 loss_kpt: 93.528253 acc_pose: 0.747400 loss: 93.528253 2022/10/12 14:31:49 - mmengine - INFO - Epoch(train) [77][150/586] lr: 2.000000e-02 eta: 5:29:23 time: 0.256316 data_time: 0.053701 memory: 2937 loss_kpt: 92.515793 acc_pose: 0.794364 loss: 92.515793 2022/10/12 14:32:02 - mmengine - INFO - Epoch(train) [77][200/586] lr: 2.000000e-02 eta: 5:29:12 time: 0.265648 data_time: 0.055358 memory: 2937 loss_kpt: 92.844565 acc_pose: 0.769779 loss: 92.844565 2022/10/12 14:32:15 - mmengine - INFO - Epoch(train) [77][250/586] lr: 2.000000e-02 eta: 5:29:00 time: 0.266518 data_time: 0.053771 memory: 2937 loss_kpt: 93.886831 acc_pose: 0.735810 loss: 93.886831 2022/10/12 14:32:28 - mmengine - INFO - Epoch(train) [77][300/586] lr: 2.000000e-02 eta: 5:28:48 time: 0.259789 data_time: 0.052381 memory: 2937 loss_kpt: 92.624733 acc_pose: 0.791868 loss: 92.624733 2022/10/12 14:32:41 - mmengine - INFO - Epoch(train) [77][350/586] lr: 2.000000e-02 eta: 5:28:37 time: 0.263724 data_time: 0.052407 memory: 2937 loss_kpt: 93.194196 acc_pose: 0.719702 loss: 93.194196 2022/10/12 14:32:55 - mmengine - INFO - Epoch(train) [77][400/586] lr: 2.000000e-02 eta: 5:28:26 time: 0.269140 data_time: 0.052077 memory: 2937 loss_kpt: 92.692651 acc_pose: 0.821261 loss: 92.692651 2022/10/12 14:33:08 - mmengine - INFO - Epoch(train) [77][450/586] lr: 2.000000e-02 eta: 5:28:14 time: 0.263689 data_time: 0.053303 memory: 2937 loss_kpt: 91.875428 acc_pose: 0.801760 loss: 91.875428 2022/10/12 14:33:12 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:33:22 - mmengine - INFO - Epoch(train) [77][500/586] lr: 2.000000e-02 eta: 5:28:03 time: 0.267320 data_time: 0.055214 memory: 2937 loss_kpt: 90.186821 acc_pose: 0.794647 loss: 90.186821 2022/10/12 14:33:35 - mmengine - INFO - Epoch(train) [77][550/586] lr: 2.000000e-02 eta: 5:27:51 time: 0.261375 data_time: 0.055006 memory: 2937 loss_kpt: 93.987219 acc_pose: 0.781070 loss: 93.987219 2022/10/12 14:33:44 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:33:58 - mmengine - INFO - Epoch(train) [78][50/586] lr: 2.000000e-02 eta: 5:27:16 time: 0.283866 data_time: 0.064459 memory: 2937 loss_kpt: 93.276745 acc_pose: 0.834976 loss: 93.276745 2022/10/12 14:34:12 - mmengine - INFO - Epoch(train) [78][100/586] lr: 2.000000e-02 eta: 5:27:05 time: 0.271334 data_time: 0.051427 memory: 2937 loss_kpt: 93.241868 acc_pose: 0.830643 loss: 93.241868 2022/10/12 14:34:26 - mmengine - INFO - Epoch(train) [78][150/586] lr: 2.000000e-02 eta: 5:26:55 time: 0.277983 data_time: 0.053543 memory: 2937 loss_kpt: 93.252140 acc_pose: 0.757116 loss: 93.252140 2022/10/12 14:34:39 - mmengine - INFO - Epoch(train) [78][200/586] lr: 2.000000e-02 eta: 5:26:44 time: 0.270312 data_time: 0.052806 memory: 2937 loss_kpt: 93.057759 acc_pose: 0.764038 loss: 93.057759 2022/10/12 14:34:53 - mmengine - INFO - Epoch(train) [78][250/586] lr: 2.000000e-02 eta: 5:26:33 time: 0.269260 data_time: 0.055938 memory: 2937 loss_kpt: 92.852082 acc_pose: 0.826448 loss: 92.852082 2022/10/12 14:35:06 - mmengine - INFO - Epoch(train) [78][300/586] lr: 2.000000e-02 eta: 5:26:21 time: 0.263790 data_time: 0.050972 memory: 2937 loss_kpt: 92.261859 acc_pose: 0.713281 loss: 92.261859 2022/10/12 14:35:19 - mmengine - INFO - Epoch(train) [78][350/586] lr: 2.000000e-02 eta: 5:26:11 time: 0.275705 data_time: 0.052909 memory: 2937 loss_kpt: 92.366538 acc_pose: 0.745832 loss: 92.366538 2022/10/12 14:35:33 - mmengine - INFO - Epoch(train) [78][400/586] lr: 2.000000e-02 eta: 5:26:00 time: 0.275662 data_time: 0.049937 memory: 2937 loss_kpt: 93.424300 acc_pose: 0.788737 loss: 93.424300 2022/10/12 14:35:47 - mmengine - INFO - Epoch(train) [78][450/586] lr: 2.000000e-02 eta: 5:25:48 time: 0.264596 data_time: 0.051312 memory: 2937 loss_kpt: 92.988893 acc_pose: 0.780824 loss: 92.988893 2022/10/12 14:36:00 - mmengine - INFO - Epoch(train) [78][500/586] lr: 2.000000e-02 eta: 5:25:37 time: 0.262825 data_time: 0.050493 memory: 2937 loss_kpt: 90.995104 acc_pose: 0.783288 loss: 90.995104 2022/10/12 14:36:13 - mmengine - INFO - Epoch(train) [78][550/586] lr: 2.000000e-02 eta: 5:25:25 time: 0.262853 data_time: 0.050719 memory: 2937 loss_kpt: 93.795169 acc_pose: 0.760282 loss: 93.795169 2022/10/12 14:36:22 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:36:37 - mmengine - INFO - Epoch(train) [79][50/586] lr: 2.000000e-02 eta: 5:24:52 time: 0.296326 data_time: 0.062117 memory: 2937 loss_kpt: 93.915887 acc_pose: 0.780458 loss: 93.915887 2022/10/12 14:36:50 - mmengine - INFO - Epoch(train) [79][100/586] lr: 2.000000e-02 eta: 5:24:40 time: 0.267833 data_time: 0.049033 memory: 2937 loss_kpt: 93.750592 acc_pose: 0.769110 loss: 93.750592 2022/10/12 14:37:04 - mmengine - INFO - Epoch(train) [79][150/586] lr: 2.000000e-02 eta: 5:24:30 time: 0.277676 data_time: 0.052248 memory: 2937 loss_kpt: 91.696505 acc_pose: 0.787504 loss: 91.696505 2022/10/12 14:37:18 - mmengine - INFO - Epoch(train) [79][200/586] lr: 2.000000e-02 eta: 5:24:19 time: 0.273341 data_time: 0.050353 memory: 2937 loss_kpt: 94.257061 acc_pose: 0.804767 loss: 94.257061 2022/10/12 14:37:31 - mmengine - INFO - Epoch(train) [79][250/586] lr: 2.000000e-02 eta: 5:24:08 time: 0.268822 data_time: 0.046589 memory: 2937 loss_kpt: 92.874003 acc_pose: 0.810645 loss: 92.874003 2022/10/12 14:37:42 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:37:45 - mmengine - INFO - Epoch(train) [79][300/586] lr: 2.000000e-02 eta: 5:23:57 time: 0.278687 data_time: 0.053766 memory: 2937 loss_kpt: 90.654047 acc_pose: 0.736827 loss: 90.654047 2022/10/12 14:37:59 - mmengine - INFO - Epoch(train) [79][350/586] lr: 2.000000e-02 eta: 5:23:47 time: 0.276892 data_time: 0.051449 memory: 2937 loss_kpt: 91.970301 acc_pose: 0.809874 loss: 91.970301 2022/10/12 14:38:12 - mmengine - INFO - Epoch(train) [79][400/586] lr: 2.000000e-02 eta: 5:23:36 time: 0.272907 data_time: 0.049595 memory: 2937 loss_kpt: 93.673472 acc_pose: 0.774657 loss: 93.673472 2022/10/12 14:38:27 - mmengine - INFO - Epoch(train) [79][450/586] lr: 2.000000e-02 eta: 5:23:26 time: 0.281545 data_time: 0.050318 memory: 2937 loss_kpt: 92.827721 acc_pose: 0.778098 loss: 92.827721 2022/10/12 14:38:40 - mmengine - INFO - Epoch(train) [79][500/586] lr: 2.000000e-02 eta: 5:23:14 time: 0.262253 data_time: 0.048685 memory: 2937 loss_kpt: 92.822450 acc_pose: 0.770752 loss: 92.822450 2022/10/12 14:38:53 - mmengine - INFO - Epoch(train) [79][550/586] lr: 2.000000e-02 eta: 5:23:03 time: 0.267256 data_time: 0.052175 memory: 2937 loss_kpt: 91.873587 acc_pose: 0.824788 loss: 91.873587 2022/10/12 14:39:02 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:39:17 - mmengine - INFO - Epoch(train) [80][50/586] lr: 2.000000e-02 eta: 5:22:30 time: 0.297837 data_time: 0.060450 memory: 2937 loss_kpt: 92.440970 acc_pose: 0.673912 loss: 92.440970 2022/10/12 14:39:31 - mmengine - INFO - Epoch(train) [80][100/586] lr: 2.000000e-02 eta: 5:22:19 time: 0.276031 data_time: 0.053028 memory: 2937 loss_kpt: 92.108700 acc_pose: 0.742187 loss: 92.108700 2022/10/12 14:39:45 - mmengine - INFO - Epoch(train) [80][150/586] lr: 2.000000e-02 eta: 5:22:08 time: 0.275734 data_time: 0.049367 memory: 2937 loss_kpt: 91.391544 acc_pose: 0.791135 loss: 91.391544 2022/10/12 14:39:58 - mmengine - INFO - Epoch(train) [80][200/586] lr: 2.000000e-02 eta: 5:21:57 time: 0.269297 data_time: 0.057000 memory: 2937 loss_kpt: 91.583136 acc_pose: 0.705293 loss: 91.583136 2022/10/12 14:40:12 - mmengine - INFO - Epoch(train) [80][250/586] lr: 2.000000e-02 eta: 5:21:46 time: 0.269019 data_time: 0.052086 memory: 2937 loss_kpt: 91.986582 acc_pose: 0.753381 loss: 91.986582 2022/10/12 14:40:26 - mmengine - INFO - Epoch(train) [80][300/586] lr: 2.000000e-02 eta: 5:21:36 time: 0.281230 data_time: 0.046974 memory: 2937 loss_kpt: 93.924662 acc_pose: 0.701101 loss: 93.924662 2022/10/12 14:40:40 - mmengine - INFO - Epoch(train) [80][350/586] lr: 2.000000e-02 eta: 5:21:25 time: 0.277966 data_time: 0.051178 memory: 2937 loss_kpt: 92.202265 acc_pose: 0.844031 loss: 92.202265 2022/10/12 14:40:53 - mmengine - INFO - Epoch(train) [80][400/586] lr: 2.000000e-02 eta: 5:21:15 time: 0.277324 data_time: 0.053331 memory: 2937 loss_kpt: 93.173208 acc_pose: 0.722944 loss: 93.173208 2022/10/12 14:41:07 - mmengine - INFO - Epoch(train) [80][450/586] lr: 2.000000e-02 eta: 5:21:04 time: 0.274389 data_time: 0.053056 memory: 2937 loss_kpt: 92.138593 acc_pose: 0.760972 loss: 92.138593 2022/10/12 14:41:20 - mmengine - INFO - Epoch(train) [80][500/586] lr: 2.000000e-02 eta: 5:20:52 time: 0.267124 data_time: 0.049440 memory: 2937 loss_kpt: 92.646263 acc_pose: 0.796314 loss: 92.646263 2022/10/12 14:41:34 - mmengine - INFO - Epoch(train) [80][550/586] lr: 2.000000e-02 eta: 5:20:41 time: 0.267802 data_time: 0.056122 memory: 2937 loss_kpt: 91.715866 acc_pose: 0.798782 loss: 91.715866 2022/10/12 14:41:43 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:41:43 - mmengine - INFO - Saving checkpoint at 80 epochs 2022/10/12 14:41:51 - mmengine - INFO - Epoch(val) [80][50/407] eta: 0:00:41 time: 0.114938 data_time: 0.014362 memory: 2937 2022/10/12 14:41:57 - mmengine - INFO - Epoch(val) [80][100/407] eta: 0:00:34 time: 0.111146 data_time: 0.009164 memory: 830 2022/10/12 14:42:02 - mmengine - INFO - Epoch(val) [80][150/407] eta: 0:00:28 time: 0.110742 data_time: 0.009222 memory: 830 2022/10/12 14:42:08 - mmengine - INFO - Epoch(val) [80][200/407] eta: 0:00:22 time: 0.109490 data_time: 0.009237 memory: 830 2022/10/12 14:42:14 - mmengine - INFO - Epoch(val) [80][250/407] eta: 0:00:17 time: 0.111807 data_time: 0.012477 memory: 830 2022/10/12 14:42:19 - mmengine - INFO - Epoch(val) [80][300/407] eta: 0:00:11 time: 0.108649 data_time: 0.008493 memory: 830 2022/10/12 14:42:24 - mmengine - INFO - Epoch(val) [80][350/407] eta: 0:00:06 time: 0.109850 data_time: 0.008955 memory: 830 2022/10/12 14:42:30 - mmengine - INFO - Epoch(val) [80][400/407] eta: 0:00:00 time: 0.103105 data_time: 0.007527 memory: 830 2022/10/12 14:42:43 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 14:42:58 - mmengine - INFO - Epoch(val) [80][407/407] coco/AP: 0.659601 coco/AP .5: 0.869090 coco/AP .75: 0.735800 coco/AP (M): 0.629924 coco/AP (L): 0.717139 coco/AR: 0.730683 coco/AR .5: 0.910422 coco/AR .75: 0.796914 coco/AR (M): 0.685031 coco/AR (L): 0.793683 2022/10/12 14:43:13 - mmengine - INFO - Epoch(train) [81][50/586] lr: 2.000000e-02 eta: 5:20:08 time: 0.292986 data_time: 0.065921 memory: 2937 loss_kpt: 93.551733 acc_pose: 0.657987 loss: 93.551733 2022/10/12 14:43:27 - mmengine - INFO - Epoch(train) [81][100/586] lr: 2.000000e-02 eta: 5:19:58 time: 0.282793 data_time: 0.055367 memory: 2937 loss_kpt: 91.720331 acc_pose: 0.789463 loss: 91.720331 2022/10/12 14:43:33 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:43:41 - mmengine - INFO - Epoch(train) [81][150/586] lr: 2.000000e-02 eta: 5:19:47 time: 0.277105 data_time: 0.050633 memory: 2937 loss_kpt: 92.988178 acc_pose: 0.794084 loss: 92.988178 2022/10/12 14:43:55 - mmengine - INFO - Epoch(train) [81][200/586] lr: 2.000000e-02 eta: 5:19:36 time: 0.272049 data_time: 0.050674 memory: 2937 loss_kpt: 93.804952 acc_pose: 0.749183 loss: 93.804952 2022/10/12 14:44:08 - mmengine - INFO - Epoch(train) [81][250/586] lr: 2.000000e-02 eta: 5:19:24 time: 0.263394 data_time: 0.054249 memory: 2937 loss_kpt: 94.415069 acc_pose: 0.767078 loss: 94.415069 2022/10/12 14:44:21 - mmengine - INFO - Epoch(train) [81][300/586] lr: 2.000000e-02 eta: 5:19:13 time: 0.272412 data_time: 0.048174 memory: 2937 loss_kpt: 93.542635 acc_pose: 0.632009 loss: 93.542635 2022/10/12 14:44:34 - mmengine - INFO - Epoch(train) [81][350/586] lr: 2.000000e-02 eta: 5:19:01 time: 0.263037 data_time: 0.055245 memory: 2937 loss_kpt: 92.882898 acc_pose: 0.784071 loss: 92.882898 2022/10/12 14:44:48 - mmengine - INFO - Epoch(train) [81][400/586] lr: 2.000000e-02 eta: 5:18:50 time: 0.267106 data_time: 0.047014 memory: 2937 loss_kpt: 94.360522 acc_pose: 0.802838 loss: 94.360522 2022/10/12 14:45:01 - mmengine - INFO - Epoch(train) [81][450/586] lr: 2.000000e-02 eta: 5:18:39 time: 0.270486 data_time: 0.051145 memory: 2937 loss_kpt: 93.154167 acc_pose: 0.738190 loss: 93.154167 2022/10/12 14:45:14 - mmengine - INFO - Epoch(train) [81][500/586] lr: 2.000000e-02 eta: 5:18:27 time: 0.257124 data_time: 0.046236 memory: 2937 loss_kpt: 92.302092 acc_pose: 0.734073 loss: 92.302092 2022/10/12 14:45:28 - mmengine - INFO - Epoch(train) [81][550/586] lr: 2.000000e-02 eta: 5:18:15 time: 0.270588 data_time: 0.050031 memory: 2937 loss_kpt: 91.807544 acc_pose: 0.757641 loss: 91.807544 2022/10/12 14:45:37 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:45:53 - mmengine - INFO - Epoch(train) [82][50/586] lr: 2.000000e-02 eta: 5:17:43 time: 0.305267 data_time: 0.066670 memory: 2937 loss_kpt: 93.053497 acc_pose: 0.820771 loss: 93.053497 2022/10/12 14:46:07 - mmengine - INFO - Epoch(train) [82][100/586] lr: 2.000000e-02 eta: 5:17:33 time: 0.283778 data_time: 0.048561 memory: 2937 loss_kpt: 93.495978 acc_pose: 0.844130 loss: 93.495978 2022/10/12 14:46:21 - mmengine - INFO - Epoch(train) [82][150/586] lr: 2.000000e-02 eta: 5:17:23 time: 0.281165 data_time: 0.054525 memory: 2937 loss_kpt: 90.410863 acc_pose: 0.806553 loss: 90.410863 2022/10/12 14:46:35 - mmengine - INFO - Epoch(train) [82][200/586] lr: 2.000000e-02 eta: 5:17:12 time: 0.274631 data_time: 0.048738 memory: 2937 loss_kpt: 92.266959 acc_pose: 0.806133 loss: 92.266959 2022/10/12 14:46:48 - mmengine - INFO - Epoch(train) [82][250/586] lr: 2.000000e-02 eta: 5:17:01 time: 0.266287 data_time: 0.054225 memory: 2937 loss_kpt: 91.495093 acc_pose: 0.794836 loss: 91.495093 2022/10/12 14:47:01 - mmengine - INFO - Epoch(train) [82][300/586] lr: 2.000000e-02 eta: 5:16:48 time: 0.255405 data_time: 0.051676 memory: 2937 loss_kpt: 92.550479 acc_pose: 0.651838 loss: 92.550479 2022/10/12 14:47:13 - mmengine - INFO - Epoch(train) [82][350/586] lr: 2.000000e-02 eta: 5:16:36 time: 0.255208 data_time: 0.055337 memory: 2937 loss_kpt: 92.138237 acc_pose: 0.773372 loss: 92.138237 2022/10/12 14:47:26 - mmengine - INFO - Epoch(train) [82][400/586] lr: 2.000000e-02 eta: 5:16:23 time: 0.250780 data_time: 0.053629 memory: 2937 loss_kpt: 92.680708 acc_pose: 0.802159 loss: 92.680708 2022/10/12 14:47:39 - mmengine - INFO - Epoch(train) [82][450/586] lr: 2.000000e-02 eta: 5:16:11 time: 0.259207 data_time: 0.054290 memory: 2937 loss_kpt: 91.580564 acc_pose: 0.766902 loss: 91.580564 2022/10/12 14:47:52 - mmengine - INFO - Epoch(train) [82][500/586] lr: 2.000000e-02 eta: 5:15:58 time: 0.253187 data_time: 0.052402 memory: 2937 loss_kpt: 92.556347 acc_pose: 0.777624 loss: 92.556347 2022/10/12 14:48:00 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:48:05 - mmengine - INFO - Epoch(train) [82][550/586] lr: 2.000000e-02 eta: 5:15:46 time: 0.257356 data_time: 0.051734 memory: 2937 loss_kpt: 91.350342 acc_pose: 0.793733 loss: 91.350342 2022/10/12 14:48:14 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:48:28 - mmengine - INFO - Epoch(train) [83][50/586] lr: 2.000000e-02 eta: 5:15:13 time: 0.293729 data_time: 0.061863 memory: 2937 loss_kpt: 91.794187 acc_pose: 0.826307 loss: 91.794187 2022/10/12 14:48:42 - mmengine - INFO - Epoch(train) [83][100/586] lr: 2.000000e-02 eta: 5:15:02 time: 0.265045 data_time: 0.048498 memory: 2937 loss_kpt: 94.171721 acc_pose: 0.820741 loss: 94.171721 2022/10/12 14:48:54 - mmengine - INFO - Epoch(train) [83][150/586] lr: 2.000000e-02 eta: 5:14:49 time: 0.254211 data_time: 0.052434 memory: 2937 loss_kpt: 91.749052 acc_pose: 0.686637 loss: 91.749052 2022/10/12 14:49:07 - mmengine - INFO - Epoch(train) [83][200/586] lr: 2.000000e-02 eta: 5:14:37 time: 0.258090 data_time: 0.053751 memory: 2937 loss_kpt: 92.389462 acc_pose: 0.755990 loss: 92.389462 2022/10/12 14:49:21 - mmengine - INFO - Epoch(train) [83][250/586] lr: 2.000000e-02 eta: 5:14:26 time: 0.269712 data_time: 0.054700 memory: 2937 loss_kpt: 92.028953 acc_pose: 0.813883 loss: 92.028953 2022/10/12 14:49:33 - mmengine - INFO - Epoch(train) [83][300/586] lr: 2.000000e-02 eta: 5:14:13 time: 0.253690 data_time: 0.048609 memory: 2937 loss_kpt: 92.557567 acc_pose: 0.793825 loss: 92.557567 2022/10/12 14:49:47 - mmengine - INFO - Epoch(train) [83][350/586] lr: 2.000000e-02 eta: 5:14:02 time: 0.262692 data_time: 0.053170 memory: 2937 loss_kpt: 92.292747 acc_pose: 0.738013 loss: 92.292747 2022/10/12 14:50:00 - mmengine - INFO - Epoch(train) [83][400/586] lr: 2.000000e-02 eta: 5:13:50 time: 0.264109 data_time: 0.056359 memory: 2937 loss_kpt: 93.923795 acc_pose: 0.700811 loss: 93.923795 2022/10/12 14:50:13 - mmengine - INFO - Epoch(train) [83][450/586] lr: 2.000000e-02 eta: 5:13:38 time: 0.260926 data_time: 0.053645 memory: 2937 loss_kpt: 91.965599 acc_pose: 0.781515 loss: 91.965599 2022/10/12 14:50:27 - mmengine - INFO - Epoch(train) [83][500/586] lr: 2.000000e-02 eta: 5:13:27 time: 0.272450 data_time: 0.048124 memory: 2937 loss_kpt: 92.665853 acc_pose: 0.826259 loss: 92.665853 2022/10/12 14:50:40 - mmengine - INFO - Epoch(train) [83][550/586] lr: 2.000000e-02 eta: 5:13:16 time: 0.275547 data_time: 0.053137 memory: 2937 loss_kpt: 91.668404 acc_pose: 0.730062 loss: 91.668404 2022/10/12 14:50:50 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:51:05 - mmengine - INFO - Epoch(train) [84][50/586] lr: 2.000000e-02 eta: 5:12:44 time: 0.303986 data_time: 0.061202 memory: 2937 loss_kpt: 89.891381 acc_pose: 0.750335 loss: 89.891381 2022/10/12 14:51:19 - mmengine - INFO - Epoch(train) [84][100/586] lr: 2.000000e-02 eta: 5:12:33 time: 0.275974 data_time: 0.054594 memory: 2937 loss_kpt: 91.162441 acc_pose: 0.798856 loss: 91.162441 2022/10/12 14:51:33 - mmengine - INFO - Epoch(train) [84][150/586] lr: 2.000000e-02 eta: 5:12:23 time: 0.276720 data_time: 0.048311 memory: 2937 loss_kpt: 92.271624 acc_pose: 0.820152 loss: 92.271624 2022/10/12 14:51:47 - mmengine - INFO - Epoch(train) [84][200/586] lr: 2.000000e-02 eta: 5:12:12 time: 0.279280 data_time: 0.056286 memory: 2937 loss_kpt: 91.474618 acc_pose: 0.735823 loss: 91.474618 2022/10/12 14:52:00 - mmengine - INFO - Epoch(train) [84][250/586] lr: 2.000000e-02 eta: 5:12:00 time: 0.260379 data_time: 0.050070 memory: 2937 loss_kpt: 93.684857 acc_pose: 0.707089 loss: 93.684857 2022/10/12 14:52:14 - mmengine - INFO - Epoch(train) [84][300/586] lr: 2.000000e-02 eta: 5:11:50 time: 0.280115 data_time: 0.051546 memory: 2937 loss_kpt: 91.401960 acc_pose: 0.724121 loss: 91.401960 2022/10/12 14:52:28 - mmengine - INFO - Epoch(train) [84][350/586] lr: 2.000000e-02 eta: 5:11:39 time: 0.278849 data_time: 0.052070 memory: 2937 loss_kpt: 93.461771 acc_pose: 0.762122 loss: 93.461771 2022/10/12 14:52:31 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:52:41 - mmengine - INFO - Epoch(train) [84][400/586] lr: 2.000000e-02 eta: 5:11:27 time: 0.265757 data_time: 0.051594 memory: 2937 loss_kpt: 92.847692 acc_pose: 0.805494 loss: 92.847692 2022/10/12 14:52:54 - mmengine - INFO - Epoch(train) [84][450/586] lr: 2.000000e-02 eta: 5:11:15 time: 0.255335 data_time: 0.055399 memory: 2937 loss_kpt: 92.766149 acc_pose: 0.758616 loss: 92.766149 2022/10/12 14:53:07 - mmengine - INFO - Epoch(train) [84][500/586] lr: 2.000000e-02 eta: 5:11:03 time: 0.266998 data_time: 0.054176 memory: 2937 loss_kpt: 91.772422 acc_pose: 0.806018 loss: 91.772422 2022/10/12 14:53:21 - mmengine - INFO - Epoch(train) [84][550/586] lr: 2.000000e-02 eta: 5:10:52 time: 0.270890 data_time: 0.055421 memory: 2937 loss_kpt: 92.476343 acc_pose: 0.784473 loss: 92.476343 2022/10/12 14:53:30 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:53:45 - mmengine - INFO - Epoch(train) [85][50/586] lr: 2.000000e-02 eta: 5:10:20 time: 0.300067 data_time: 0.063987 memory: 2937 loss_kpt: 91.493905 acc_pose: 0.800000 loss: 91.493905 2022/10/12 14:54:00 - mmengine - INFO - Epoch(train) [85][100/586] lr: 2.000000e-02 eta: 5:10:10 time: 0.285511 data_time: 0.050977 memory: 2937 loss_kpt: 91.715148 acc_pose: 0.739411 loss: 91.715148 2022/10/12 14:54:13 - mmengine - INFO - Epoch(train) [85][150/586] lr: 2.000000e-02 eta: 5:09:59 time: 0.270755 data_time: 0.053574 memory: 2937 loss_kpt: 93.080439 acc_pose: 0.733737 loss: 93.080439 2022/10/12 14:54:26 - mmengine - INFO - Epoch(train) [85][200/586] lr: 2.000000e-02 eta: 5:09:47 time: 0.261975 data_time: 0.050671 memory: 2937 loss_kpt: 91.946617 acc_pose: 0.807958 loss: 91.946617 2022/10/12 14:54:39 - mmengine - INFO - Epoch(train) [85][250/586] lr: 2.000000e-02 eta: 5:09:35 time: 0.262174 data_time: 0.051276 memory: 2937 loss_kpt: 92.710924 acc_pose: 0.759523 loss: 92.710924 2022/10/12 14:54:52 - mmengine - INFO - Epoch(train) [85][300/586] lr: 2.000000e-02 eta: 5:09:23 time: 0.263236 data_time: 0.056154 memory: 2937 loss_kpt: 92.622304 acc_pose: 0.749917 loss: 92.622304 2022/10/12 14:55:06 - mmengine - INFO - Epoch(train) [85][350/586] lr: 2.000000e-02 eta: 5:09:13 time: 0.276760 data_time: 0.055765 memory: 2937 loss_kpt: 90.917283 acc_pose: 0.803612 loss: 90.917283 2022/10/12 14:55:19 - mmengine - INFO - Epoch(train) [85][400/586] lr: 2.000000e-02 eta: 5:09:00 time: 0.253846 data_time: 0.049444 memory: 2937 loss_kpt: 92.550237 acc_pose: 0.771059 loss: 92.550237 2022/10/12 14:55:32 - mmengine - INFO - Epoch(train) [85][450/586] lr: 2.000000e-02 eta: 5:08:48 time: 0.266732 data_time: 0.053666 memory: 2937 loss_kpt: 91.452368 acc_pose: 0.818582 loss: 91.452368 2022/10/12 14:55:45 - mmengine - INFO - Epoch(train) [85][500/586] lr: 2.000000e-02 eta: 5:08:36 time: 0.253862 data_time: 0.049848 memory: 2937 loss_kpt: 93.160497 acc_pose: 0.751686 loss: 93.160497 2022/10/12 14:55:58 - mmengine - INFO - Epoch(train) [85][550/586] lr: 2.000000e-02 eta: 5:08:24 time: 0.262434 data_time: 0.054287 memory: 2937 loss_kpt: 90.987784 acc_pose: 0.810728 loss: 90.987784 2022/10/12 14:56:08 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:56:22 - mmengine - INFO - Epoch(train) [86][50/586] lr: 2.000000e-02 eta: 5:07:52 time: 0.286682 data_time: 0.066748 memory: 2937 loss_kpt: 91.671408 acc_pose: 0.744337 loss: 91.671408 2022/10/12 14:56:35 - mmengine - INFO - Epoch(train) [86][100/586] lr: 2.000000e-02 eta: 5:07:40 time: 0.264209 data_time: 0.051650 memory: 2937 loss_kpt: 91.129709 acc_pose: 0.708630 loss: 91.129709 2022/10/12 14:56:49 - mmengine - INFO - Epoch(train) [86][150/586] lr: 2.000000e-02 eta: 5:07:29 time: 0.278070 data_time: 0.048467 memory: 2937 loss_kpt: 92.399405 acc_pose: 0.802859 loss: 92.399405 2022/10/12 14:57:00 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:57:03 - mmengine - INFO - Epoch(train) [86][200/586] lr: 2.000000e-02 eta: 5:07:18 time: 0.275310 data_time: 0.051897 memory: 2937 loss_kpt: 94.774771 acc_pose: 0.798446 loss: 94.774771 2022/10/12 14:57:16 - mmengine - INFO - Epoch(train) [86][250/586] lr: 2.000000e-02 eta: 5:07:07 time: 0.272008 data_time: 0.050626 memory: 2937 loss_kpt: 90.337942 acc_pose: 0.782327 loss: 90.337942 2022/10/12 14:57:29 - mmengine - INFO - Epoch(train) [86][300/586] lr: 2.000000e-02 eta: 5:06:55 time: 0.257615 data_time: 0.048525 memory: 2937 loss_kpt: 91.767864 acc_pose: 0.770661 loss: 91.767864 2022/10/12 14:57:42 - mmengine - INFO - Epoch(train) [86][350/586] lr: 2.000000e-02 eta: 5:06:42 time: 0.253922 data_time: 0.051993 memory: 2937 loss_kpt: 93.544145 acc_pose: 0.844580 loss: 93.544145 2022/10/12 14:57:55 - mmengine - INFO - Epoch(train) [86][400/586] lr: 2.000000e-02 eta: 5:06:30 time: 0.255162 data_time: 0.054210 memory: 2937 loss_kpt: 94.169528 acc_pose: 0.760944 loss: 94.169528 2022/10/12 14:58:08 - mmengine - INFO - Epoch(train) [86][450/586] lr: 2.000000e-02 eta: 5:06:19 time: 0.272091 data_time: 0.047021 memory: 2937 loss_kpt: 93.177424 acc_pose: 0.677110 loss: 93.177424 2022/10/12 14:58:21 - mmengine - INFO - Epoch(train) [86][500/586] lr: 2.000000e-02 eta: 5:06:06 time: 0.259930 data_time: 0.052506 memory: 2937 loss_kpt: 92.989347 acc_pose: 0.776800 loss: 92.989347 2022/10/12 14:58:34 - mmengine - INFO - Epoch(train) [86][550/586] lr: 2.000000e-02 eta: 5:05:54 time: 0.260032 data_time: 0.047374 memory: 2937 loss_kpt: 93.430750 acc_pose: 0.837189 loss: 93.430750 2022/10/12 14:58:44 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 14:58:58 - mmengine - INFO - Epoch(train) [87][50/586] lr: 2.000000e-02 eta: 5:05:21 time: 0.278188 data_time: 0.058878 memory: 2937 loss_kpt: 94.067412 acc_pose: 0.838624 loss: 94.067412 2022/10/12 14:59:11 - mmengine - INFO - Epoch(train) [87][100/586] lr: 2.000000e-02 eta: 5:05:09 time: 0.255706 data_time: 0.052198 memory: 2937 loss_kpt: 92.580241 acc_pose: 0.747804 loss: 92.580241 2022/10/12 14:59:24 - mmengine - INFO - Epoch(train) [87][150/586] lr: 2.000000e-02 eta: 5:04:57 time: 0.257852 data_time: 0.048633 memory: 2937 loss_kpt: 93.177268 acc_pose: 0.800674 loss: 93.177268 2022/10/12 14:59:37 - mmengine - INFO - Epoch(train) [87][200/586] lr: 2.000000e-02 eta: 5:04:45 time: 0.266399 data_time: 0.050998 memory: 2937 loss_kpt: 92.022429 acc_pose: 0.747157 loss: 92.022429 2022/10/12 14:59:51 - mmengine - INFO - Epoch(train) [87][250/586] lr: 2.000000e-02 eta: 5:04:34 time: 0.271530 data_time: 0.051931 memory: 2937 loss_kpt: 91.571014 acc_pose: 0.777149 loss: 91.571014 2022/10/12 15:00:04 - mmengine - INFO - Epoch(train) [87][300/586] lr: 2.000000e-02 eta: 5:04:23 time: 0.272042 data_time: 0.047086 memory: 2937 loss_kpt: 91.861507 acc_pose: 0.726760 loss: 91.861507 2022/10/12 15:00:18 - mmengine - INFO - Epoch(train) [87][350/586] lr: 2.000000e-02 eta: 5:04:11 time: 0.270483 data_time: 0.051730 memory: 2937 loss_kpt: 90.266740 acc_pose: 0.758945 loss: 90.266740 2022/10/12 15:00:31 - mmengine - INFO - Epoch(train) [87][400/586] lr: 2.000000e-02 eta: 5:03:59 time: 0.258602 data_time: 0.051355 memory: 2937 loss_kpt: 91.338553 acc_pose: 0.744533 loss: 91.338553 2022/10/12 15:00:43 - mmengine - INFO - Epoch(train) [87][450/586] lr: 2.000000e-02 eta: 5:03:47 time: 0.255014 data_time: 0.049259 memory: 2937 loss_kpt: 93.029483 acc_pose: 0.770158 loss: 93.029483 2022/10/12 15:00:57 - mmengine - INFO - Epoch(train) [87][500/586] lr: 2.000000e-02 eta: 5:03:35 time: 0.267345 data_time: 0.050284 memory: 2937 loss_kpt: 92.878676 acc_pose: 0.717991 loss: 92.878676 2022/10/12 15:01:11 - mmengine - INFO - Epoch(train) [87][550/586] lr: 2.000000e-02 eta: 5:03:24 time: 0.277987 data_time: 0.051098 memory: 2937 loss_kpt: 89.993679 acc_pose: 0.784245 loss: 89.993679 2022/10/12 15:01:20 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:01:26 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:01:35 - mmengine - INFO - Epoch(train) [88][50/586] lr: 2.000000e-02 eta: 5:02:53 time: 0.293594 data_time: 0.060632 memory: 2937 loss_kpt: 91.797028 acc_pose: 0.766988 loss: 91.797028 2022/10/12 15:01:49 - mmengine - INFO - Epoch(train) [88][100/586] lr: 2.000000e-02 eta: 5:02:42 time: 0.282745 data_time: 0.048594 memory: 2937 loss_kpt: 92.007732 acc_pose: 0.858793 loss: 92.007732 2022/10/12 15:02:04 - mmengine - INFO - Epoch(train) [88][150/586] lr: 2.000000e-02 eta: 5:02:33 time: 0.293101 data_time: 0.052831 memory: 2937 loss_kpt: 92.474802 acc_pose: 0.813074 loss: 92.474802 2022/10/12 15:02:17 - mmengine - INFO - Epoch(train) [88][200/586] lr: 2.000000e-02 eta: 5:02:21 time: 0.264008 data_time: 0.052041 memory: 2937 loss_kpt: 92.125797 acc_pose: 0.805381 loss: 92.125797 2022/10/12 15:02:31 - mmengine - INFO - Epoch(train) [88][250/586] lr: 2.000000e-02 eta: 5:02:10 time: 0.275653 data_time: 0.055294 memory: 2937 loss_kpt: 90.398669 acc_pose: 0.753013 loss: 90.398669 2022/10/12 15:02:44 - mmengine - INFO - Epoch(train) [88][300/586] lr: 2.000000e-02 eta: 5:01:58 time: 0.265661 data_time: 0.047798 memory: 2937 loss_kpt: 93.083140 acc_pose: 0.804768 loss: 93.083140 2022/10/12 15:02:58 - mmengine - INFO - Epoch(train) [88][350/586] lr: 2.000000e-02 eta: 5:01:47 time: 0.270525 data_time: 0.052866 memory: 2937 loss_kpt: 90.711883 acc_pose: 0.819027 loss: 90.711883 2022/10/12 15:03:11 - mmengine - INFO - Epoch(train) [88][400/586] lr: 2.000000e-02 eta: 5:01:35 time: 0.268679 data_time: 0.053795 memory: 2937 loss_kpt: 92.791880 acc_pose: 0.782732 loss: 92.791880 2022/10/12 15:03:25 - mmengine - INFO - Epoch(train) [88][450/586] lr: 2.000000e-02 eta: 5:01:24 time: 0.270065 data_time: 0.047204 memory: 2937 loss_kpt: 91.521819 acc_pose: 0.697300 loss: 91.521819 2022/10/12 15:03:38 - mmengine - INFO - Epoch(train) [88][500/586] lr: 2.000000e-02 eta: 5:01:12 time: 0.260836 data_time: 0.048010 memory: 2937 loss_kpt: 90.902796 acc_pose: 0.737286 loss: 90.902796 2022/10/12 15:03:51 - mmengine - INFO - Epoch(train) [88][550/586] lr: 2.000000e-02 eta: 5:01:00 time: 0.258237 data_time: 0.056145 memory: 2937 loss_kpt: 91.399730 acc_pose: 0.784316 loss: 91.399730 2022/10/12 15:04:00 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:04:15 - mmengine - INFO - Epoch(train) [89][50/586] lr: 2.000000e-02 eta: 5:00:28 time: 0.288030 data_time: 0.063644 memory: 2937 loss_kpt: 90.282499 acc_pose: 0.749958 loss: 90.282499 2022/10/12 15:04:28 - mmengine - INFO - Epoch(train) [89][100/586] lr: 2.000000e-02 eta: 5:00:17 time: 0.273192 data_time: 0.048415 memory: 2937 loss_kpt: 91.461797 acc_pose: 0.789205 loss: 91.461797 2022/10/12 15:04:42 - mmengine - INFO - Epoch(train) [89][150/586] lr: 2.000000e-02 eta: 5:00:05 time: 0.270128 data_time: 0.050286 memory: 2937 loss_kpt: 93.515619 acc_pose: 0.719685 loss: 93.515619 2022/10/12 15:04:55 - mmengine - INFO - Epoch(train) [89][200/586] lr: 2.000000e-02 eta: 4:59:53 time: 0.263452 data_time: 0.051725 memory: 2937 loss_kpt: 92.544508 acc_pose: 0.801232 loss: 92.544508 2022/10/12 15:05:09 - mmengine - INFO - Epoch(train) [89][250/586] lr: 2.000000e-02 eta: 4:59:42 time: 0.271640 data_time: 0.051738 memory: 2937 loss_kpt: 92.524898 acc_pose: 0.728616 loss: 92.524898 2022/10/12 15:05:23 - mmengine - INFO - Epoch(train) [89][300/586] lr: 2.000000e-02 eta: 4:59:32 time: 0.283648 data_time: 0.051878 memory: 2937 loss_kpt: 92.353681 acc_pose: 0.698336 loss: 92.353681 2022/10/12 15:05:36 - mmengine - INFO - Epoch(train) [89][350/586] lr: 2.000000e-02 eta: 4:59:20 time: 0.272098 data_time: 0.050993 memory: 2937 loss_kpt: 91.006952 acc_pose: 0.695944 loss: 91.006952 2022/10/12 15:05:50 - mmengine - INFO - Epoch(train) [89][400/586] lr: 2.000000e-02 eta: 4:59:09 time: 0.270416 data_time: 0.049605 memory: 2937 loss_kpt: 93.835088 acc_pose: 0.794005 loss: 93.835088 2022/10/12 15:05:58 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:06:03 - mmengine - INFO - Epoch(train) [89][450/586] lr: 2.000000e-02 eta: 4:58:57 time: 0.269298 data_time: 0.051592 memory: 2937 loss_kpt: 92.804266 acc_pose: 0.745744 loss: 92.804266 2022/10/12 15:06:18 - mmengine - INFO - Epoch(train) [89][500/586] lr: 2.000000e-02 eta: 4:58:47 time: 0.285458 data_time: 0.050042 memory: 2937 loss_kpt: 92.327830 acc_pose: 0.797000 loss: 92.327830 2022/10/12 15:06:32 - mmengine - INFO - Epoch(train) [89][550/586] lr: 2.000000e-02 eta: 4:58:36 time: 0.281600 data_time: 0.050979 memory: 2937 loss_kpt: 92.026618 acc_pose: 0.777422 loss: 92.026618 2022/10/12 15:06:41 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:06:56 - mmengine - INFO - Epoch(train) [90][50/586] lr: 2.000000e-02 eta: 4:58:06 time: 0.304609 data_time: 0.067362 memory: 2937 loss_kpt: 92.129510 acc_pose: 0.720714 loss: 92.129510 2022/10/12 15:07:09 - mmengine - INFO - Epoch(train) [90][100/586] lr: 2.000000e-02 eta: 4:57:54 time: 0.261897 data_time: 0.055177 memory: 2937 loss_kpt: 90.430576 acc_pose: 0.736353 loss: 90.430576 2022/10/12 15:07:23 - mmengine - INFO - Epoch(train) [90][150/586] lr: 2.000000e-02 eta: 4:57:42 time: 0.269175 data_time: 0.053140 memory: 2937 loss_kpt: 91.342379 acc_pose: 0.775904 loss: 91.342379 2022/10/12 15:07:36 - mmengine - INFO - Epoch(train) [90][200/586] lr: 2.000000e-02 eta: 4:57:30 time: 0.260098 data_time: 0.048288 memory: 2937 loss_kpt: 91.712079 acc_pose: 0.751808 loss: 91.712079 2022/10/12 15:07:49 - mmengine - INFO - Epoch(train) [90][250/586] lr: 2.000000e-02 eta: 4:57:19 time: 0.273768 data_time: 0.053607 memory: 2937 loss_kpt: 91.675961 acc_pose: 0.704015 loss: 91.675961 2022/10/12 15:08:03 - mmengine - INFO - Epoch(train) [90][300/586] lr: 2.000000e-02 eta: 4:57:08 time: 0.273333 data_time: 0.047933 memory: 2937 loss_kpt: 90.000417 acc_pose: 0.808360 loss: 90.000417 2022/10/12 15:08:17 - mmengine - INFO - Epoch(train) [90][350/586] lr: 2.000000e-02 eta: 4:56:57 time: 0.275096 data_time: 0.052138 memory: 2937 loss_kpt: 91.014268 acc_pose: 0.733495 loss: 91.014268 2022/10/12 15:08:30 - mmengine - INFO - Epoch(train) [90][400/586] lr: 2.000000e-02 eta: 4:56:45 time: 0.267962 data_time: 0.048998 memory: 2937 loss_kpt: 92.985933 acc_pose: 0.750446 loss: 92.985933 2022/10/12 15:08:43 - mmengine - INFO - Epoch(train) [90][450/586] lr: 2.000000e-02 eta: 4:56:33 time: 0.262056 data_time: 0.051665 memory: 2937 loss_kpt: 90.903746 acc_pose: 0.791746 loss: 90.903746 2022/10/12 15:08:56 - mmengine - INFO - Epoch(train) [90][500/586] lr: 2.000000e-02 eta: 4:56:21 time: 0.260399 data_time: 0.050307 memory: 2937 loss_kpt: 92.797729 acc_pose: 0.684346 loss: 92.797729 2022/10/12 15:09:09 - mmengine - INFO - Epoch(train) [90][550/586] lr: 2.000000e-02 eta: 4:56:09 time: 0.261654 data_time: 0.048209 memory: 2937 loss_kpt: 91.052716 acc_pose: 0.823660 loss: 91.052716 2022/10/12 15:09:19 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:09:19 - mmengine - INFO - Saving checkpoint at 90 epochs 2022/10/12 15:09:27 - mmengine - INFO - Epoch(val) [90][50/407] eta: 0:00:42 time: 0.118846 data_time: 0.013509 memory: 2937 2022/10/12 15:09:32 - mmengine - INFO - Epoch(val) [90][100/407] eta: 0:00:33 time: 0.108022 data_time: 0.009049 memory: 830 2022/10/12 15:09:38 - mmengine - INFO - Epoch(val) [90][150/407] eta: 0:00:29 time: 0.113480 data_time: 0.009616 memory: 830 2022/10/12 15:09:44 - mmengine - INFO - Epoch(val) [90][200/407] eta: 0:00:22 time: 0.110499 data_time: 0.008951 memory: 830 2022/10/12 15:09:49 - mmengine - INFO - Epoch(val) [90][250/407] eta: 0:00:17 time: 0.109426 data_time: 0.008892 memory: 830 2022/10/12 15:09:55 - mmengine - INFO - Epoch(val) [90][300/407] eta: 0:00:11 time: 0.111959 data_time: 0.009153 memory: 830 2022/10/12 15:10:00 - mmengine - INFO - Epoch(val) [90][350/407] eta: 0:00:06 time: 0.112430 data_time: 0.009199 memory: 830 2022/10/12 15:10:05 - mmengine - INFO - Epoch(val) [90][400/407] eta: 0:00:00 time: 0.101239 data_time: 0.008487 memory: 830 2022/10/12 15:10:18 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 15:10:34 - mmengine - INFO - Epoch(val) [90][407/407] coco/AP: 0.668682 coco/AP .5: 0.873373 coco/AP .75: 0.743333 coco/AP (M): 0.638671 coco/AP (L): 0.725273 coco/AR: 0.740255 coco/AR .5: 0.917034 coco/AR .75: 0.801795 coco/AR (M): 0.695630 coco/AR (L): 0.801524 2022/10/12 15:10:34 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221012/rsn18/best_coco/AP_epoch_70.pth is removed 2022/10/12 15:10:36 - mmengine - INFO - The best checkpoint with 0.6687 coco/AP at 90 epoch is saved to best_coco/AP_epoch_90.pth. 2022/10/12 15:10:50 - mmengine - INFO - Epoch(train) [91][50/586] lr: 2.000000e-02 eta: 4:55:37 time: 0.282860 data_time: 0.059641 memory: 2937 loss_kpt: 91.753393 acc_pose: 0.750855 loss: 91.753393 2022/10/12 15:11:04 - mmengine - INFO - Epoch(train) [91][100/586] lr: 2.000000e-02 eta: 4:55:26 time: 0.276695 data_time: 0.054402 memory: 2937 loss_kpt: 91.912726 acc_pose: 0.788775 loss: 91.912726 2022/10/12 15:11:18 - mmengine - INFO - Epoch(train) [91][150/586] lr: 2.000000e-02 eta: 4:55:15 time: 0.274907 data_time: 0.054045 memory: 2937 loss_kpt: 91.710899 acc_pose: 0.759014 loss: 91.710899 2022/10/12 15:11:32 - mmengine - INFO - Epoch(train) [91][200/586] lr: 2.000000e-02 eta: 4:55:05 time: 0.283604 data_time: 0.055247 memory: 2937 loss_kpt: 90.561579 acc_pose: 0.833032 loss: 90.561579 2022/10/12 15:11:45 - mmengine - INFO - Epoch(train) [91][250/586] lr: 2.000000e-02 eta: 4:54:53 time: 0.272346 data_time: 0.051915 memory: 2937 loss_kpt: 92.445815 acc_pose: 0.712631 loss: 92.445815 2022/10/12 15:11:48 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:12:00 - mmengine - INFO - Epoch(train) [91][300/586] lr: 2.000000e-02 eta: 4:54:43 time: 0.283207 data_time: 0.062836 memory: 2937 loss_kpt: 89.803490 acc_pose: 0.820217 loss: 89.803490 2022/10/12 15:12:13 - mmengine - INFO - Epoch(train) [91][350/586] lr: 2.000000e-02 eta: 4:54:31 time: 0.267808 data_time: 0.049870 memory: 2937 loss_kpt: 92.541094 acc_pose: 0.640013 loss: 92.541094 2022/10/12 15:12:26 - mmengine - INFO - Epoch(train) [91][400/586] lr: 2.000000e-02 eta: 4:54:19 time: 0.262513 data_time: 0.055358 memory: 2937 loss_kpt: 94.094780 acc_pose: 0.728925 loss: 94.094780 2022/10/12 15:12:39 - mmengine - INFO - Epoch(train) [91][450/586] lr: 2.000000e-02 eta: 4:54:07 time: 0.259011 data_time: 0.050294 memory: 2937 loss_kpt: 92.106354 acc_pose: 0.804431 loss: 92.106354 2022/10/12 15:12:52 - mmengine - INFO - Epoch(train) [91][500/586] lr: 2.000000e-02 eta: 4:53:54 time: 0.256025 data_time: 0.051938 memory: 2937 loss_kpt: 90.924135 acc_pose: 0.767702 loss: 90.924135 2022/10/12 15:13:05 - mmengine - INFO - Epoch(train) [91][550/586] lr: 2.000000e-02 eta: 4:53:43 time: 0.269872 data_time: 0.048462 memory: 2937 loss_kpt: 92.785283 acc_pose: 0.857626 loss: 92.785283 2022/10/12 15:13:15 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:13:30 - mmengine - INFO - Epoch(train) [92][50/586] lr: 2.000000e-02 eta: 4:53:12 time: 0.296557 data_time: 0.063684 memory: 2937 loss_kpt: 90.896600 acc_pose: 0.806442 loss: 90.896600 2022/10/12 15:13:43 - mmengine - INFO - Epoch(train) [92][100/586] lr: 2.000000e-02 eta: 4:53:01 time: 0.267778 data_time: 0.051547 memory: 2937 loss_kpt: 91.991147 acc_pose: 0.831418 loss: 91.991147 2022/10/12 15:13:57 - mmengine - INFO - Epoch(train) [92][150/586] lr: 2.000000e-02 eta: 4:52:50 time: 0.278527 data_time: 0.057924 memory: 2937 loss_kpt: 92.749399 acc_pose: 0.763826 loss: 92.749399 2022/10/12 15:14:11 - mmengine - INFO - Epoch(train) [92][200/586] lr: 2.000000e-02 eta: 4:52:38 time: 0.272307 data_time: 0.053134 memory: 2937 loss_kpt: 93.081404 acc_pose: 0.789996 loss: 93.081404 2022/10/12 15:14:24 - mmengine - INFO - Epoch(train) [92][250/586] lr: 2.000000e-02 eta: 4:52:27 time: 0.275819 data_time: 0.057605 memory: 2937 loss_kpt: 90.973965 acc_pose: 0.806241 loss: 90.973965 2022/10/12 15:14:38 - mmengine - INFO - Epoch(train) [92][300/586] lr: 2.000000e-02 eta: 4:52:16 time: 0.278475 data_time: 0.049911 memory: 2937 loss_kpt: 92.845588 acc_pose: 0.817125 loss: 92.845588 2022/10/12 15:14:52 - mmengine - INFO - Epoch(train) [92][350/586] lr: 2.000000e-02 eta: 4:52:05 time: 0.269673 data_time: 0.053013 memory: 2937 loss_kpt: 91.795598 acc_pose: 0.802325 loss: 91.795598 2022/10/12 15:15:05 - mmengine - INFO - Epoch(train) [92][400/586] lr: 2.000000e-02 eta: 4:51:53 time: 0.262366 data_time: 0.052306 memory: 2937 loss_kpt: 91.829906 acc_pose: 0.829200 loss: 91.829906 2022/10/12 15:15:18 - mmengine - INFO - Epoch(train) [92][450/586] lr: 2.000000e-02 eta: 4:51:40 time: 0.254672 data_time: 0.055079 memory: 2937 loss_kpt: 91.498948 acc_pose: 0.798405 loss: 91.498948 2022/10/12 15:15:30 - mmengine - INFO - Epoch(train) [92][500/586] lr: 2.000000e-02 eta: 4:51:28 time: 0.252829 data_time: 0.047005 memory: 2937 loss_kpt: 91.830546 acc_pose: 0.776421 loss: 91.830546 2022/10/12 15:15:43 - mmengine - INFO - Epoch(train) [92][550/586] lr: 2.000000e-02 eta: 4:51:15 time: 0.260579 data_time: 0.053771 memory: 2937 loss_kpt: 92.691913 acc_pose: 0.827231 loss: 92.691913 2022/10/12 15:15:53 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:16:07 - mmengine - INFO - Epoch(train) [93][50/586] lr: 2.000000e-02 eta: 4:50:44 time: 0.277547 data_time: 0.065474 memory: 2937 loss_kpt: 92.524502 acc_pose: 0.773797 loss: 92.524502 2022/10/12 15:16:17 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:16:20 - mmengine - INFO - Epoch(train) [93][100/586] lr: 2.000000e-02 eta: 4:50:31 time: 0.256664 data_time: 0.047731 memory: 2937 loss_kpt: 90.222225 acc_pose: 0.805531 loss: 90.222225 2022/10/12 15:16:33 - mmengine - INFO - Epoch(train) [93][150/586] lr: 2.000000e-02 eta: 4:50:19 time: 0.261306 data_time: 0.054444 memory: 2937 loss_kpt: 92.016688 acc_pose: 0.740722 loss: 92.016688 2022/10/12 15:16:46 - mmengine - INFO - Epoch(train) [93][200/586] lr: 2.000000e-02 eta: 4:50:07 time: 0.263819 data_time: 0.053034 memory: 2937 loss_kpt: 90.903393 acc_pose: 0.762120 loss: 90.903393 2022/10/12 15:16:59 - mmengine - INFO - Epoch(train) [93][250/586] lr: 2.000000e-02 eta: 4:49:55 time: 0.259181 data_time: 0.051095 memory: 2937 loss_kpt: 90.583261 acc_pose: 0.845912 loss: 90.583261 2022/10/12 15:17:12 - mmengine - INFO - Epoch(train) [93][300/586] lr: 2.000000e-02 eta: 4:49:43 time: 0.261284 data_time: 0.056902 memory: 2937 loss_kpt: 91.801821 acc_pose: 0.745061 loss: 91.801821 2022/10/12 15:17:26 - mmengine - INFO - Epoch(train) [93][350/586] lr: 2.000000e-02 eta: 4:49:32 time: 0.275609 data_time: 0.054430 memory: 2937 loss_kpt: 90.667846 acc_pose: 0.839780 loss: 90.667846 2022/10/12 15:17:39 - mmengine - INFO - Epoch(train) [93][400/586] lr: 2.000000e-02 eta: 4:49:20 time: 0.265836 data_time: 0.055586 memory: 2937 loss_kpt: 92.252549 acc_pose: 0.812231 loss: 92.252549 2022/10/12 15:17:53 - mmengine - INFO - Epoch(train) [93][450/586] lr: 2.000000e-02 eta: 4:49:09 time: 0.269554 data_time: 0.052157 memory: 2937 loss_kpt: 90.871563 acc_pose: 0.797683 loss: 90.871563 2022/10/12 15:18:06 - mmengine - INFO - Epoch(train) [93][500/586] lr: 2.000000e-02 eta: 4:48:57 time: 0.264900 data_time: 0.048325 memory: 2937 loss_kpt: 93.844912 acc_pose: 0.806509 loss: 93.844912 2022/10/12 15:18:19 - mmengine - INFO - Epoch(train) [93][550/586] lr: 2.000000e-02 eta: 4:48:45 time: 0.267607 data_time: 0.053388 memory: 2937 loss_kpt: 90.608935 acc_pose: 0.750883 loss: 90.608935 2022/10/12 15:18:28 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:18:43 - mmengine - INFO - Epoch(train) [94][50/586] lr: 2.000000e-02 eta: 4:48:14 time: 0.284908 data_time: 0.065056 memory: 2937 loss_kpt: 92.074753 acc_pose: 0.778852 loss: 92.074753 2022/10/12 15:18:56 - mmengine - INFO - Epoch(train) [94][100/586] lr: 2.000000e-02 eta: 4:48:02 time: 0.269487 data_time: 0.054665 memory: 2937 loss_kpt: 91.472651 acc_pose: 0.849825 loss: 91.472651 2022/10/12 15:19:10 - mmengine - INFO - Epoch(train) [94][150/586] lr: 2.000000e-02 eta: 4:47:51 time: 0.275542 data_time: 0.054681 memory: 2937 loss_kpt: 92.112703 acc_pose: 0.793479 loss: 92.112703 2022/10/12 15:19:23 - mmengine - INFO - Epoch(train) [94][200/586] lr: 2.000000e-02 eta: 4:47:40 time: 0.269501 data_time: 0.055216 memory: 2937 loss_kpt: 91.821298 acc_pose: 0.787009 loss: 91.821298 2022/10/12 15:19:37 - mmengine - INFO - Epoch(train) [94][250/586] lr: 2.000000e-02 eta: 4:47:28 time: 0.272534 data_time: 0.055085 memory: 2937 loss_kpt: 92.521730 acc_pose: 0.798290 loss: 92.521730 2022/10/12 15:19:51 - mmengine - INFO - Epoch(train) [94][300/586] lr: 2.000000e-02 eta: 4:47:17 time: 0.269403 data_time: 0.052432 memory: 2937 loss_kpt: 90.733507 acc_pose: 0.835762 loss: 90.733507 2022/10/12 15:20:04 - mmengine - INFO - Epoch(train) [94][350/586] lr: 2.000000e-02 eta: 4:47:05 time: 0.260710 data_time: 0.052051 memory: 2937 loss_kpt: 91.854352 acc_pose: 0.787921 loss: 91.854352 2022/10/12 15:20:17 - mmengine - INFO - Epoch(train) [94][400/586] lr: 2.000000e-02 eta: 4:46:52 time: 0.258860 data_time: 0.056057 memory: 2937 loss_kpt: 90.329650 acc_pose: 0.787744 loss: 90.329650 2022/10/12 15:20:29 - mmengine - INFO - Epoch(train) [94][450/586] lr: 2.000000e-02 eta: 4:46:40 time: 0.252128 data_time: 0.052010 memory: 2937 loss_kpt: 91.219159 acc_pose: 0.822198 loss: 91.219159 2022/10/12 15:20:42 - mmengine - INFO - Epoch(train) [94][500/586] lr: 2.000000e-02 eta: 4:46:28 time: 0.265894 data_time: 0.053493 memory: 2937 loss_kpt: 90.700207 acc_pose: 0.684008 loss: 90.700207 2022/10/12 15:20:43 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:20:55 - mmengine - INFO - Epoch(train) [94][550/586] lr: 2.000000e-02 eta: 4:46:15 time: 0.251393 data_time: 0.053091 memory: 2937 loss_kpt: 93.640850 acc_pose: 0.784431 loss: 93.640850 2022/10/12 15:21:04 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:21:20 - mmengine - INFO - Epoch(train) [95][50/586] lr: 2.000000e-02 eta: 4:45:45 time: 0.301230 data_time: 0.068212 memory: 2937 loss_kpt: 93.254402 acc_pose: 0.846416 loss: 93.254402 2022/10/12 15:21:34 - mmengine - INFO - Epoch(train) [95][100/586] lr: 2.000000e-02 eta: 4:45:34 time: 0.279102 data_time: 0.053232 memory: 2937 loss_kpt: 92.804710 acc_pose: 0.809759 loss: 92.804710 2022/10/12 15:21:47 - mmengine - INFO - Epoch(train) [95][150/586] lr: 2.000000e-02 eta: 4:45:23 time: 0.269415 data_time: 0.055446 memory: 2937 loss_kpt: 93.183661 acc_pose: 0.899933 loss: 93.183661 2022/10/12 15:22:01 - mmengine - INFO - Epoch(train) [95][200/586] lr: 2.000000e-02 eta: 4:45:12 time: 0.285985 data_time: 0.056146 memory: 2937 loss_kpt: 91.769701 acc_pose: 0.812772 loss: 91.769701 2022/10/12 15:22:15 - mmengine - INFO - Epoch(train) [95][250/586] lr: 2.000000e-02 eta: 4:45:01 time: 0.267243 data_time: 0.054044 memory: 2937 loss_kpt: 91.793444 acc_pose: 0.721522 loss: 91.793444 2022/10/12 15:22:28 - mmengine - INFO - Epoch(train) [95][300/586] lr: 2.000000e-02 eta: 4:44:49 time: 0.268997 data_time: 0.058170 memory: 2937 loss_kpt: 91.093784 acc_pose: 0.752106 loss: 91.093784 2022/10/12 15:22:42 - mmengine - INFO - Epoch(train) [95][350/586] lr: 2.000000e-02 eta: 4:44:38 time: 0.273976 data_time: 0.056045 memory: 2937 loss_kpt: 90.566085 acc_pose: 0.786083 loss: 90.566085 2022/10/12 15:22:56 - mmengine - INFO - Epoch(train) [95][400/586] lr: 2.000000e-02 eta: 4:44:26 time: 0.276644 data_time: 0.053978 memory: 2937 loss_kpt: 92.520361 acc_pose: 0.769703 loss: 92.520361 2022/10/12 15:23:09 - mmengine - INFO - Epoch(train) [95][450/586] lr: 2.000000e-02 eta: 4:44:15 time: 0.273359 data_time: 0.052770 memory: 2937 loss_kpt: 91.286926 acc_pose: 0.794543 loss: 91.286926 2022/10/12 15:23:23 - mmengine - INFO - Epoch(train) [95][500/586] lr: 2.000000e-02 eta: 4:44:04 time: 0.279186 data_time: 0.054698 memory: 2937 loss_kpt: 90.372105 acc_pose: 0.819355 loss: 90.372105 2022/10/12 15:23:37 - mmengine - INFO - Epoch(train) [95][550/586] lr: 2.000000e-02 eta: 4:43:52 time: 0.264701 data_time: 0.054061 memory: 2937 loss_kpt: 92.166339 acc_pose: 0.803357 loss: 92.166339 2022/10/12 15:23:46 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:24:01 - mmengine - INFO - Epoch(train) [96][50/586] lr: 2.000000e-02 eta: 4:43:23 time: 0.307892 data_time: 0.057361 memory: 2937 loss_kpt: 90.225504 acc_pose: 0.697854 loss: 90.225504 2022/10/12 15:24:15 - mmengine - INFO - Epoch(train) [96][100/586] lr: 2.000000e-02 eta: 4:43:11 time: 0.266079 data_time: 0.052091 memory: 2937 loss_kpt: 91.837246 acc_pose: 0.844525 loss: 91.837246 2022/10/12 15:24:28 - mmengine - INFO - Epoch(train) [96][150/586] lr: 2.000000e-02 eta: 4:42:59 time: 0.265142 data_time: 0.051580 memory: 2937 loss_kpt: 91.345673 acc_pose: 0.763599 loss: 91.345673 2022/10/12 15:24:42 - mmengine - INFO - Epoch(train) [96][200/586] lr: 2.000000e-02 eta: 4:42:48 time: 0.278601 data_time: 0.047829 memory: 2937 loss_kpt: 92.726497 acc_pose: 0.769928 loss: 92.726497 2022/10/12 15:24:55 - mmengine - INFO - Epoch(train) [96][250/586] lr: 2.000000e-02 eta: 4:42:36 time: 0.268398 data_time: 0.051369 memory: 2937 loss_kpt: 91.980607 acc_pose: 0.811355 loss: 91.980607 2022/10/12 15:25:09 - mmengine - INFO - Epoch(train) [96][300/586] lr: 2.000000e-02 eta: 4:42:25 time: 0.273794 data_time: 0.051432 memory: 2937 loss_kpt: 90.311026 acc_pose: 0.829758 loss: 90.311026 2022/10/12 15:25:17 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:25:23 - mmengine - INFO - Epoch(train) [96][350/586] lr: 2.000000e-02 eta: 4:42:13 time: 0.268714 data_time: 0.047235 memory: 2937 loss_kpt: 91.036346 acc_pose: 0.770990 loss: 91.036346 2022/10/12 15:25:36 - mmengine - INFO - Epoch(train) [96][400/586] lr: 2.000000e-02 eta: 4:42:02 time: 0.273257 data_time: 0.051012 memory: 2937 loss_kpt: 90.754712 acc_pose: 0.796327 loss: 90.754712 2022/10/12 15:25:50 - mmengine - INFO - Epoch(train) [96][450/586] lr: 2.000000e-02 eta: 4:41:51 time: 0.274891 data_time: 0.047615 memory: 2937 loss_kpt: 92.902105 acc_pose: 0.697121 loss: 92.902105 2022/10/12 15:26:04 - mmengine - INFO - Epoch(train) [96][500/586] lr: 2.000000e-02 eta: 4:41:39 time: 0.272963 data_time: 0.051044 memory: 2937 loss_kpt: 91.909200 acc_pose: 0.781692 loss: 91.909200 2022/10/12 15:26:17 - mmengine - INFO - Epoch(train) [96][550/586] lr: 2.000000e-02 eta: 4:41:27 time: 0.266179 data_time: 0.048779 memory: 2937 loss_kpt: 91.839210 acc_pose: 0.821928 loss: 91.839210 2022/10/12 15:26:26 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:26:41 - mmengine - INFO - Epoch(train) [97][50/586] lr: 2.000000e-02 eta: 4:40:57 time: 0.291074 data_time: 0.063078 memory: 2937 loss_kpt: 91.394278 acc_pose: 0.757663 loss: 91.394278 2022/10/12 15:26:54 - mmengine - INFO - Epoch(train) [97][100/586] lr: 2.000000e-02 eta: 4:40:45 time: 0.265429 data_time: 0.051431 memory: 2937 loss_kpt: 92.186829 acc_pose: 0.807247 loss: 92.186829 2022/10/12 15:27:08 - mmengine - INFO - Epoch(train) [97][150/586] lr: 2.000000e-02 eta: 4:40:34 time: 0.274079 data_time: 0.054433 memory: 2937 loss_kpt: 92.533055 acc_pose: 0.704254 loss: 92.533055 2022/10/12 15:27:21 - mmengine - INFO - Epoch(train) [97][200/586] lr: 2.000000e-02 eta: 4:40:22 time: 0.261541 data_time: 0.052675 memory: 2937 loss_kpt: 90.635000 acc_pose: 0.809059 loss: 90.635000 2022/10/12 15:27:34 - mmengine - INFO - Epoch(train) [97][250/586] lr: 2.000000e-02 eta: 4:40:10 time: 0.268902 data_time: 0.052060 memory: 2937 loss_kpt: 90.657340 acc_pose: 0.672712 loss: 90.657340 2022/10/12 15:27:47 - mmengine - INFO - Epoch(train) [97][300/586] lr: 2.000000e-02 eta: 4:39:58 time: 0.258546 data_time: 0.051216 memory: 2937 loss_kpt: 93.172446 acc_pose: 0.793745 loss: 93.172446 2022/10/12 15:28:01 - mmengine - INFO - Epoch(train) [97][350/586] lr: 2.000000e-02 eta: 4:39:46 time: 0.271477 data_time: 0.050411 memory: 2937 loss_kpt: 91.594057 acc_pose: 0.666654 loss: 91.594057 2022/10/12 15:28:14 - mmengine - INFO - Epoch(train) [97][400/586] lr: 2.000000e-02 eta: 4:39:34 time: 0.265015 data_time: 0.054487 memory: 2937 loss_kpt: 91.853419 acc_pose: 0.810957 loss: 91.853419 2022/10/12 15:28:27 - mmengine - INFO - Epoch(train) [97][450/586] lr: 2.000000e-02 eta: 4:39:23 time: 0.268248 data_time: 0.051871 memory: 2937 loss_kpt: 91.527244 acc_pose: 0.719744 loss: 91.527244 2022/10/12 15:28:41 - mmengine - INFO - Epoch(train) [97][500/586] lr: 2.000000e-02 eta: 4:39:11 time: 0.269328 data_time: 0.057491 memory: 2937 loss_kpt: 91.199928 acc_pose: 0.730318 loss: 91.199928 2022/10/12 15:28:54 - mmengine - INFO - Epoch(train) [97][550/586] lr: 2.000000e-02 eta: 4:38:59 time: 0.263216 data_time: 0.050110 memory: 2937 loss_kpt: 91.050482 acc_pose: 0.797474 loss: 91.050482 2022/10/12 15:29:03 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:29:18 - mmengine - INFO - Epoch(train) [98][50/586] lr: 2.000000e-02 eta: 4:38:30 time: 0.301993 data_time: 0.062147 memory: 2937 loss_kpt: 92.217528 acc_pose: 0.713605 loss: 92.217528 2022/10/12 15:29:32 - mmengine - INFO - Epoch(train) [98][100/586] lr: 2.000000e-02 eta: 4:38:19 time: 0.277898 data_time: 0.049465 memory: 2937 loss_kpt: 90.421473 acc_pose: 0.781500 loss: 90.421473 2022/10/12 15:29:47 - mmengine - INFO - Epoch(train) [98][150/586] lr: 2.000000e-02 eta: 4:38:08 time: 0.286228 data_time: 0.056367 memory: 2937 loss_kpt: 91.187991 acc_pose: 0.724627 loss: 91.187991 2022/10/12 15:29:49 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:30:00 - mmengine - INFO - Epoch(train) [98][200/586] lr: 2.000000e-02 eta: 4:37:57 time: 0.278153 data_time: 0.051581 memory: 2937 loss_kpt: 91.355684 acc_pose: 0.778469 loss: 91.355684 2022/10/12 15:30:14 - mmengine - INFO - Epoch(train) [98][250/586] lr: 2.000000e-02 eta: 4:37:45 time: 0.273205 data_time: 0.054384 memory: 2937 loss_kpt: 90.873994 acc_pose: 0.767508 loss: 90.873994 2022/10/12 15:30:27 - mmengine - INFO - Epoch(train) [98][300/586] lr: 2.000000e-02 eta: 4:37:33 time: 0.266219 data_time: 0.050506 memory: 2937 loss_kpt: 90.326873 acc_pose: 0.749689 loss: 90.326873 2022/10/12 15:30:40 - mmengine - INFO - Epoch(train) [98][350/586] lr: 2.000000e-02 eta: 4:37:21 time: 0.255893 data_time: 0.049509 memory: 2937 loss_kpt: 91.086122 acc_pose: 0.774368 loss: 91.086122 2022/10/12 15:30:53 - mmengine - INFO - Epoch(train) [98][400/586] lr: 2.000000e-02 eta: 4:37:09 time: 0.259508 data_time: 0.048222 memory: 2937 loss_kpt: 93.278465 acc_pose: 0.683709 loss: 93.278465 2022/10/12 15:31:07 - mmengine - INFO - Epoch(train) [98][450/586] lr: 2.000000e-02 eta: 4:36:57 time: 0.273938 data_time: 0.052995 memory: 2937 loss_kpt: 91.054301 acc_pose: 0.728810 loss: 91.054301 2022/10/12 15:31:21 - mmengine - INFO - Epoch(train) [98][500/586] lr: 2.000000e-02 eta: 4:36:46 time: 0.274442 data_time: 0.049951 memory: 2937 loss_kpt: 91.328229 acc_pose: 0.757466 loss: 91.328229 2022/10/12 15:31:35 - mmengine - INFO - Epoch(train) [98][550/586] lr: 2.000000e-02 eta: 4:36:35 time: 0.281634 data_time: 0.053197 memory: 2937 loss_kpt: 91.872197 acc_pose: 0.826985 loss: 91.872197 2022/10/12 15:31:44 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:31:59 - mmengine - INFO - Epoch(train) [99][50/586] lr: 2.000000e-02 eta: 4:36:05 time: 0.292729 data_time: 0.060434 memory: 2937 loss_kpt: 91.348965 acc_pose: 0.776368 loss: 91.348965 2022/10/12 15:32:13 - mmengine - INFO - Epoch(train) [99][100/586] lr: 2.000000e-02 eta: 4:35:54 time: 0.284764 data_time: 0.053979 memory: 2937 loss_kpt: 90.852159 acc_pose: 0.780645 loss: 90.852159 2022/10/12 15:32:26 - mmengine - INFO - Epoch(train) [99][150/586] lr: 2.000000e-02 eta: 4:35:42 time: 0.267164 data_time: 0.051750 memory: 2937 loss_kpt: 91.848079 acc_pose: 0.816402 loss: 91.848079 2022/10/12 15:32:40 - mmengine - INFO - Epoch(train) [99][200/586] lr: 2.000000e-02 eta: 4:35:31 time: 0.273674 data_time: 0.051623 memory: 2937 loss_kpt: 90.100361 acc_pose: 0.793335 loss: 90.100361 2022/10/12 15:32:54 - mmengine - INFO - Epoch(train) [99][250/586] lr: 2.000000e-02 eta: 4:35:20 time: 0.273940 data_time: 0.048683 memory: 2937 loss_kpt: 90.907228 acc_pose: 0.792259 loss: 90.907228 2022/10/12 15:33:08 - mmengine - INFO - Epoch(train) [99][300/586] lr: 2.000000e-02 eta: 4:35:09 time: 0.279733 data_time: 0.052541 memory: 2937 loss_kpt: 92.808243 acc_pose: 0.814722 loss: 92.808243 2022/10/12 15:33:21 - mmengine - INFO - Epoch(train) [99][350/586] lr: 2.000000e-02 eta: 4:34:57 time: 0.265503 data_time: 0.051349 memory: 2937 loss_kpt: 91.094028 acc_pose: 0.731270 loss: 91.094028 2022/10/12 15:33:34 - mmengine - INFO - Epoch(train) [99][400/586] lr: 2.000000e-02 eta: 4:34:45 time: 0.268268 data_time: 0.055248 memory: 2937 loss_kpt: 91.680865 acc_pose: 0.824099 loss: 91.680865 2022/10/12 15:33:48 - mmengine - INFO - Epoch(train) [99][450/586] lr: 2.000000e-02 eta: 4:34:33 time: 0.273741 data_time: 0.052226 memory: 2937 loss_kpt: 91.971760 acc_pose: 0.816120 loss: 91.971760 2022/10/12 15:34:02 - mmengine - INFO - Epoch(train) [99][500/586] lr: 2.000000e-02 eta: 4:34:22 time: 0.269403 data_time: 0.052855 memory: 2937 loss_kpt: 91.075977 acc_pose: 0.750156 loss: 91.075977 2022/10/12 15:34:15 - mmengine - INFO - Epoch(train) [99][550/586] lr: 2.000000e-02 eta: 4:34:10 time: 0.264320 data_time: 0.054692 memory: 2937 loss_kpt: 91.230277 acc_pose: 0.719144 loss: 91.230277 2022/10/12 15:34:21 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:34:24 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:34:38 - mmengine - INFO - Epoch(train) [100][50/586] lr: 2.000000e-02 eta: 4:33:39 time: 0.281768 data_time: 0.061635 memory: 2937 loss_kpt: 90.566306 acc_pose: 0.823694 loss: 90.566306 2022/10/12 15:34:51 - mmengine - INFO - Epoch(train) [100][100/586] lr: 2.000000e-02 eta: 4:33:27 time: 0.260380 data_time: 0.054927 memory: 2937 loss_kpt: 90.569330 acc_pose: 0.807212 loss: 90.569330 2022/10/12 15:35:05 - mmengine - INFO - Epoch(train) [100][150/586] lr: 2.000000e-02 eta: 4:33:15 time: 0.261490 data_time: 0.048494 memory: 2937 loss_kpt: 89.702626 acc_pose: 0.734066 loss: 89.702626 2022/10/12 15:35:18 - mmengine - INFO - Epoch(train) [100][200/586] lr: 2.000000e-02 eta: 4:33:03 time: 0.262114 data_time: 0.051313 memory: 2937 loss_kpt: 91.271607 acc_pose: 0.773203 loss: 91.271607 2022/10/12 15:35:30 - mmengine - INFO - Epoch(train) [100][250/586] lr: 2.000000e-02 eta: 4:32:50 time: 0.254930 data_time: 0.052024 memory: 2937 loss_kpt: 91.400135 acc_pose: 0.774091 loss: 91.400135 2022/10/12 15:35:43 - mmengine - INFO - Epoch(train) [100][300/586] lr: 2.000000e-02 eta: 4:32:38 time: 0.253339 data_time: 0.047816 memory: 2937 loss_kpt: 91.414528 acc_pose: 0.800915 loss: 91.414528 2022/10/12 15:35:56 - mmengine - INFO - Epoch(train) [100][350/586] lr: 2.000000e-02 eta: 4:32:26 time: 0.260319 data_time: 0.051773 memory: 2937 loss_kpt: 90.882861 acc_pose: 0.655640 loss: 90.882861 2022/10/12 15:36:09 - mmengine - INFO - Epoch(train) [100][400/586] lr: 2.000000e-02 eta: 4:32:13 time: 0.260048 data_time: 0.051513 memory: 2937 loss_kpt: 92.353952 acc_pose: 0.716305 loss: 92.353952 2022/10/12 15:36:22 - mmengine - INFO - Epoch(train) [100][450/586] lr: 2.000000e-02 eta: 4:32:01 time: 0.260929 data_time: 0.048869 memory: 2937 loss_kpt: 91.336092 acc_pose: 0.863706 loss: 91.336092 2022/10/12 15:36:36 - mmengine - INFO - Epoch(train) [100][500/586] lr: 2.000000e-02 eta: 4:31:50 time: 0.270348 data_time: 0.052373 memory: 2937 loss_kpt: 92.355827 acc_pose: 0.744464 loss: 92.355827 2022/10/12 15:36:49 - mmengine - INFO - Epoch(train) [100][550/586] lr: 2.000000e-02 eta: 4:31:37 time: 0.259317 data_time: 0.048678 memory: 2937 loss_kpt: 90.819884 acc_pose: 0.799234 loss: 90.819884 2022/10/12 15:36:58 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:36:58 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/10/12 15:37:07 - mmengine - INFO - Epoch(val) [100][50/407] eta: 0:00:42 time: 0.117901 data_time: 0.014328 memory: 2937 2022/10/12 15:37:12 - mmengine - INFO - Epoch(val) [100][100/407] eta: 0:00:33 time: 0.108111 data_time: 0.009168 memory: 830 2022/10/12 15:37:18 - mmengine - INFO - Epoch(val) [100][150/407] eta: 0:00:28 time: 0.110326 data_time: 0.009212 memory: 830 2022/10/12 15:37:23 - mmengine - INFO - Epoch(val) [100][200/407] eta: 0:00:23 time: 0.111874 data_time: 0.013157 memory: 830 2022/10/12 15:37:28 - mmengine - INFO - Epoch(val) [100][250/407] eta: 0:00:16 time: 0.105895 data_time: 0.008702 memory: 830 2022/10/12 15:37:34 - mmengine - INFO - Epoch(val) [100][300/407] eta: 0:00:11 time: 0.106420 data_time: 0.008436 memory: 830 2022/10/12 15:37:39 - mmengine - INFO - Epoch(val) [100][350/407] eta: 0:00:06 time: 0.107662 data_time: 0.008274 memory: 830 2022/10/12 15:37:44 - mmengine - INFO - Epoch(val) [100][400/407] eta: 0:00:00 time: 0.100938 data_time: 0.008060 memory: 830 2022/10/12 15:37:58 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 15:38:14 - mmengine - INFO - Epoch(val) [100][407/407] coco/AP: 0.668743 coco/AP .5: 0.873525 coco/AP .75: 0.745352 coco/AP (M): 0.638834 coco/AP (L): 0.727098 coco/AR: 0.741452 coco/AR .5: 0.917191 coco/AR .75: 0.806990 coco/AR (M): 0.695575 coco/AR (L): 0.804385 2022/10/12 15:38:14 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221012/rsn18/best_coco/AP_epoch_90.pth is removed 2022/10/12 15:38:16 - mmengine - INFO - The best checkpoint with 0.6687 coco/AP at 100 epoch is saved to best_coco/AP_epoch_100.pth. 2022/10/12 15:38:30 - mmengine - INFO - Epoch(train) [101][50/586] lr: 2.000000e-02 eta: 4:31:08 time: 0.290232 data_time: 0.060964 memory: 2937 loss_kpt: 90.544708 acc_pose: 0.802537 loss: 90.544708 2022/10/12 15:38:45 - mmengine - INFO - Epoch(train) [101][100/586] lr: 2.000000e-02 eta: 4:30:57 time: 0.293296 data_time: 0.058189 memory: 2937 loss_kpt: 91.528678 acc_pose: 0.765578 loss: 91.528678 2022/10/12 15:38:58 - mmengine - INFO - Epoch(train) [101][150/586] lr: 2.000000e-02 eta: 4:30:46 time: 0.272077 data_time: 0.052577 memory: 2937 loss_kpt: 92.045567 acc_pose: 0.822772 loss: 92.045567 2022/10/12 15:39:12 - mmengine - INFO - Epoch(train) [101][200/586] lr: 2.000000e-02 eta: 4:30:34 time: 0.268653 data_time: 0.048399 memory: 2937 loss_kpt: 92.164951 acc_pose: 0.777629 loss: 92.164951 2022/10/12 15:39:25 - mmengine - INFO - Epoch(train) [101][250/586] lr: 2.000000e-02 eta: 4:30:22 time: 0.268236 data_time: 0.054554 memory: 2937 loss_kpt: 91.138574 acc_pose: 0.811477 loss: 91.138574 2022/10/12 15:39:39 - mmengine - INFO - Epoch(train) [101][300/586] lr: 2.000000e-02 eta: 4:30:11 time: 0.274025 data_time: 0.047046 memory: 2937 loss_kpt: 93.054790 acc_pose: 0.820009 loss: 93.054790 2022/10/12 15:39:52 - mmengine - INFO - Epoch(train) [101][350/586] lr: 2.000000e-02 eta: 4:29:59 time: 0.265579 data_time: 0.054138 memory: 2937 loss_kpt: 91.330599 acc_pose: 0.788855 loss: 91.330599 2022/10/12 15:40:06 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:40:06 - mmengine - INFO - Epoch(train) [101][400/586] lr: 2.000000e-02 eta: 4:29:48 time: 0.278781 data_time: 0.048256 memory: 2937 loss_kpt: 90.154676 acc_pose: 0.764399 loss: 90.154676 2022/10/12 15:40:20 - mmengine - INFO - Epoch(train) [101][450/586] lr: 2.000000e-02 eta: 4:29:36 time: 0.270681 data_time: 0.054382 memory: 2937 loss_kpt: 91.575573 acc_pose: 0.768981 loss: 91.575573 2022/10/12 15:40:34 - mmengine - INFO - Epoch(train) [101][500/586] lr: 2.000000e-02 eta: 4:29:24 time: 0.274539 data_time: 0.054134 memory: 2937 loss_kpt: 90.591636 acc_pose: 0.805684 loss: 90.591636 2022/10/12 15:40:47 - mmengine - INFO - Epoch(train) [101][550/586] lr: 2.000000e-02 eta: 4:29:12 time: 0.264088 data_time: 0.057059 memory: 2937 loss_kpt: 90.858401 acc_pose: 0.844225 loss: 90.858401 2022/10/12 15:40:56 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:41:10 - mmengine - INFO - Epoch(train) [102][50/586] lr: 2.000000e-02 eta: 4:28:43 time: 0.283321 data_time: 0.061020 memory: 2937 loss_kpt: 91.219650 acc_pose: 0.803231 loss: 91.219650 2022/10/12 15:41:24 - mmengine - INFO - Epoch(train) [102][100/586] lr: 2.000000e-02 eta: 4:28:31 time: 0.271272 data_time: 0.049921 memory: 2937 loss_kpt: 92.152941 acc_pose: 0.746175 loss: 92.152941 2022/10/12 15:41:37 - mmengine - INFO - Epoch(train) [102][150/586] lr: 2.000000e-02 eta: 4:28:19 time: 0.259342 data_time: 0.051304 memory: 2937 loss_kpt: 91.003904 acc_pose: 0.751438 loss: 91.003904 2022/10/12 15:41:51 - mmengine - INFO - Epoch(train) [102][200/586] lr: 2.000000e-02 eta: 4:28:07 time: 0.274038 data_time: 0.052006 memory: 2937 loss_kpt: 89.416161 acc_pose: 0.802767 loss: 89.416161 2022/10/12 15:42:04 - mmengine - INFO - Epoch(train) [102][250/586] lr: 2.000000e-02 eta: 4:27:55 time: 0.265196 data_time: 0.054985 memory: 2937 loss_kpt: 91.631771 acc_pose: 0.731103 loss: 91.631771 2022/10/12 15:42:17 - mmengine - INFO - Epoch(train) [102][300/586] lr: 2.000000e-02 eta: 4:27:43 time: 0.269435 data_time: 0.052420 memory: 2937 loss_kpt: 91.378860 acc_pose: 0.789226 loss: 91.378860 2022/10/12 15:42:32 - mmengine - INFO - Epoch(train) [102][350/586] lr: 2.000000e-02 eta: 4:27:32 time: 0.283331 data_time: 0.057058 memory: 2937 loss_kpt: 91.443454 acc_pose: 0.777782 loss: 91.443454 2022/10/12 15:42:45 - mmengine - INFO - Epoch(train) [102][400/586] lr: 2.000000e-02 eta: 4:27:21 time: 0.275225 data_time: 0.051113 memory: 2937 loss_kpt: 91.875579 acc_pose: 0.765260 loss: 91.875579 2022/10/12 15:42:59 - mmengine - INFO - Epoch(train) [102][450/586] lr: 2.000000e-02 eta: 4:27:10 time: 0.280444 data_time: 0.056094 memory: 2937 loss_kpt: 92.595830 acc_pose: 0.845552 loss: 92.595830 2022/10/12 15:43:13 - mmengine - INFO - Epoch(train) [102][500/586] lr: 2.000000e-02 eta: 4:26:58 time: 0.274211 data_time: 0.055709 memory: 2937 loss_kpt: 90.516775 acc_pose: 0.776441 loss: 90.516775 2022/10/12 15:43:27 - mmengine - INFO - Epoch(train) [102][550/586] lr: 2.000000e-02 eta: 4:26:47 time: 0.268932 data_time: 0.049831 memory: 2937 loss_kpt: 92.777861 acc_pose: 0.724364 loss: 92.777861 2022/10/12 15:43:36 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:43:50 - mmengine - INFO - Epoch(train) [103][50/586] lr: 2.000000e-02 eta: 4:26:17 time: 0.286784 data_time: 0.061725 memory: 2937 loss_kpt: 91.578318 acc_pose: 0.782013 loss: 91.578318 2022/10/12 15:44:04 - mmengine - INFO - Epoch(train) [103][100/586] lr: 2.000000e-02 eta: 4:26:06 time: 0.276612 data_time: 0.051865 memory: 2937 loss_kpt: 91.424032 acc_pose: 0.759408 loss: 91.424032 2022/10/12 15:44:17 - mmengine - INFO - Epoch(train) [103][150/586] lr: 2.000000e-02 eta: 4:25:53 time: 0.261677 data_time: 0.052928 memory: 2937 loss_kpt: 91.542708 acc_pose: 0.806238 loss: 91.542708 2022/10/12 15:44:30 - mmengine - INFO - Epoch(train) [103][200/586] lr: 2.000000e-02 eta: 4:25:41 time: 0.262838 data_time: 0.056734 memory: 2937 loss_kpt: 91.278332 acc_pose: 0.786557 loss: 91.278332 2022/10/12 15:44:38 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:44:44 - mmengine - INFO - Epoch(train) [103][250/586] lr: 2.000000e-02 eta: 4:25:30 time: 0.285531 data_time: 0.052997 memory: 2937 loss_kpt: 91.970465 acc_pose: 0.800720 loss: 91.970465 2022/10/12 15:44:58 - mmengine - INFO - Epoch(train) [103][300/586] lr: 2.000000e-02 eta: 4:25:19 time: 0.276824 data_time: 0.051848 memory: 2937 loss_kpt: 90.286291 acc_pose: 0.736953 loss: 90.286291 2022/10/12 15:45:11 - mmengine - INFO - Epoch(train) [103][350/586] lr: 2.000000e-02 eta: 4:25:07 time: 0.266886 data_time: 0.052688 memory: 2937 loss_kpt: 89.911053 acc_pose: 0.793564 loss: 89.911053 2022/10/12 15:45:25 - mmengine - INFO - Epoch(train) [103][400/586] lr: 2.000000e-02 eta: 4:24:55 time: 0.261099 data_time: 0.050982 memory: 2937 loss_kpt: 90.375462 acc_pose: 0.691973 loss: 90.375462 2022/10/12 15:45:38 - mmengine - INFO - Epoch(train) [103][450/586] lr: 2.000000e-02 eta: 4:24:43 time: 0.261614 data_time: 0.053583 memory: 2937 loss_kpt: 90.651223 acc_pose: 0.762240 loss: 90.651223 2022/10/12 15:45:51 - mmengine - INFO - Epoch(train) [103][500/586] lr: 2.000000e-02 eta: 4:24:31 time: 0.262582 data_time: 0.051988 memory: 2937 loss_kpt: 91.615089 acc_pose: 0.781443 loss: 91.615089 2022/10/12 15:46:04 - mmengine - INFO - Epoch(train) [103][550/586] lr: 2.000000e-02 eta: 4:24:19 time: 0.263300 data_time: 0.054685 memory: 2937 loss_kpt: 91.586430 acc_pose: 0.741566 loss: 91.586430 2022/10/12 15:46:13 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:46:27 - mmengine - INFO - Epoch(train) [104][50/586] lr: 2.000000e-02 eta: 4:23:49 time: 0.281454 data_time: 0.060909 memory: 2937 loss_kpt: 91.450476 acc_pose: 0.805990 loss: 91.450476 2022/10/12 15:46:41 - mmengine - INFO - Epoch(train) [104][100/586] lr: 2.000000e-02 eta: 4:23:37 time: 0.268622 data_time: 0.053566 memory: 2937 loss_kpt: 91.770204 acc_pose: 0.755093 loss: 91.770204 2022/10/12 15:46:54 - mmengine - INFO - Epoch(train) [104][150/586] lr: 2.000000e-02 eta: 4:23:25 time: 0.260358 data_time: 0.054086 memory: 2937 loss_kpt: 93.404959 acc_pose: 0.748093 loss: 93.404959 2022/10/12 15:47:07 - mmengine - INFO - Epoch(train) [104][200/586] lr: 2.000000e-02 eta: 4:23:13 time: 0.258566 data_time: 0.049303 memory: 2937 loss_kpt: 90.808778 acc_pose: 0.784928 loss: 90.808778 2022/10/12 15:47:20 - mmengine - INFO - Epoch(train) [104][250/586] lr: 2.000000e-02 eta: 4:23:00 time: 0.259384 data_time: 0.054490 memory: 2937 loss_kpt: 92.012011 acc_pose: 0.841613 loss: 92.012011 2022/10/12 15:47:33 - mmengine - INFO - Epoch(train) [104][300/586] lr: 2.000000e-02 eta: 4:22:48 time: 0.264888 data_time: 0.056218 memory: 2937 loss_kpt: 90.074039 acc_pose: 0.703338 loss: 90.074039 2022/10/12 15:47:46 - mmengine - INFO - Epoch(train) [104][350/586] lr: 2.000000e-02 eta: 4:22:36 time: 0.251291 data_time: 0.047798 memory: 2937 loss_kpt: 91.156907 acc_pose: 0.765596 loss: 91.156907 2022/10/12 15:47:58 - mmengine - INFO - Epoch(train) [104][400/586] lr: 2.000000e-02 eta: 4:22:23 time: 0.257499 data_time: 0.052508 memory: 2937 loss_kpt: 90.301783 acc_pose: 0.775410 loss: 90.301783 2022/10/12 15:48:12 - mmengine - INFO - Epoch(train) [104][450/586] lr: 2.000000e-02 eta: 4:22:11 time: 0.270422 data_time: 0.053499 memory: 2937 loss_kpt: 90.762178 acc_pose: 0.773541 loss: 90.762178 2022/10/12 15:48:25 - mmengine - INFO - Epoch(train) [104][500/586] lr: 2.000000e-02 eta: 4:21:59 time: 0.257695 data_time: 0.058258 memory: 2937 loss_kpt: 90.960186 acc_pose: 0.830857 loss: 90.960186 2022/10/12 15:48:37 - mmengine - INFO - Epoch(train) [104][550/586] lr: 2.000000e-02 eta: 4:21:46 time: 0.251711 data_time: 0.049213 memory: 2937 loss_kpt: 91.539035 acc_pose: 0.734660 loss: 91.539035 2022/10/12 15:48:47 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:49:00 - mmengine - INFO - Epoch(train) [105][50/586] lr: 2.000000e-02 eta: 4:21:16 time: 0.274740 data_time: 0.063540 memory: 2937 loss_kpt: 91.052615 acc_pose: 0.817445 loss: 91.052615 2022/10/12 15:49:02 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:49:14 - mmengine - INFO - Epoch(train) [105][100/586] lr: 2.000000e-02 eta: 4:21:04 time: 0.262280 data_time: 0.049654 memory: 2937 loss_kpt: 90.651305 acc_pose: 0.786024 loss: 90.651305 2022/10/12 15:49:26 - mmengine - INFO - Epoch(train) [105][150/586] lr: 2.000000e-02 eta: 4:20:52 time: 0.254711 data_time: 0.056948 memory: 2937 loss_kpt: 91.705085 acc_pose: 0.854140 loss: 91.705085 2022/10/12 15:49:39 - mmengine - INFO - Epoch(train) [105][200/586] lr: 2.000000e-02 eta: 4:20:40 time: 0.262105 data_time: 0.054968 memory: 2937 loss_kpt: 91.035115 acc_pose: 0.779473 loss: 91.035115 2022/10/12 15:49:53 - mmengine - INFO - Epoch(train) [105][250/586] lr: 2.000000e-02 eta: 4:20:28 time: 0.271076 data_time: 0.051812 memory: 2937 loss_kpt: 91.906734 acc_pose: 0.811472 loss: 91.906734 2022/10/12 15:50:06 - mmengine - INFO - Epoch(train) [105][300/586] lr: 2.000000e-02 eta: 4:20:16 time: 0.263602 data_time: 0.051459 memory: 2937 loss_kpt: 91.525244 acc_pose: 0.746607 loss: 91.525244 2022/10/12 15:50:19 - mmengine - INFO - Epoch(train) [105][350/586] lr: 2.000000e-02 eta: 4:20:04 time: 0.261497 data_time: 0.052372 memory: 2937 loss_kpt: 90.712059 acc_pose: 0.782469 loss: 90.712059 2022/10/12 15:50:32 - mmengine - INFO - Epoch(train) [105][400/586] lr: 2.000000e-02 eta: 4:19:51 time: 0.260546 data_time: 0.051435 memory: 2937 loss_kpt: 89.618028 acc_pose: 0.859364 loss: 89.618028 2022/10/12 15:50:45 - mmengine - INFO - Epoch(train) [105][450/586] lr: 2.000000e-02 eta: 4:19:39 time: 0.254535 data_time: 0.052420 memory: 2937 loss_kpt: 91.285224 acc_pose: 0.821649 loss: 91.285224 2022/10/12 15:50:57 - mmengine - INFO - Epoch(train) [105][500/586] lr: 2.000000e-02 eta: 4:19:26 time: 0.246070 data_time: 0.052780 memory: 2937 loss_kpt: 91.379907 acc_pose: 0.696285 loss: 91.379907 2022/10/12 15:51:10 - mmengine - INFO - Epoch(train) [105][550/586] lr: 2.000000e-02 eta: 4:19:13 time: 0.253378 data_time: 0.054412 memory: 2937 loss_kpt: 91.397067 acc_pose: 0.819696 loss: 91.397067 2022/10/12 15:51:19 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:51:34 - mmengine - INFO - Epoch(train) [106][50/586] lr: 2.000000e-02 eta: 4:18:45 time: 0.296788 data_time: 0.064012 memory: 2937 loss_kpt: 90.827549 acc_pose: 0.834272 loss: 90.827549 2022/10/12 15:51:48 - mmengine - INFO - Epoch(train) [106][100/586] lr: 2.000000e-02 eta: 4:18:33 time: 0.275572 data_time: 0.046881 memory: 2937 loss_kpt: 90.838077 acc_pose: 0.763472 loss: 90.838077 2022/10/12 15:52:01 - mmengine - INFO - Epoch(train) [106][150/586] lr: 2.000000e-02 eta: 4:18:22 time: 0.270834 data_time: 0.051181 memory: 2937 loss_kpt: 91.108310 acc_pose: 0.711689 loss: 91.108310 2022/10/12 15:52:15 - mmengine - INFO - Epoch(train) [106][200/586] lr: 2.000000e-02 eta: 4:18:10 time: 0.268045 data_time: 0.052203 memory: 2937 loss_kpt: 90.871231 acc_pose: 0.776996 loss: 90.871231 2022/10/12 15:52:28 - mmengine - INFO - Epoch(train) [106][250/586] lr: 2.000000e-02 eta: 4:17:58 time: 0.266552 data_time: 0.053507 memory: 2937 loss_kpt: 90.262725 acc_pose: 0.815971 loss: 90.262725 2022/10/12 15:52:42 - mmengine - INFO - Epoch(train) [106][300/586] lr: 2.000000e-02 eta: 4:17:46 time: 0.271260 data_time: 0.056564 memory: 2937 loss_kpt: 89.350850 acc_pose: 0.789605 loss: 89.350850 2022/10/12 15:52:55 - mmengine - INFO - Epoch(train) [106][350/586] lr: 2.000000e-02 eta: 4:17:34 time: 0.263451 data_time: 0.053205 memory: 2937 loss_kpt: 93.053189 acc_pose: 0.831804 loss: 93.053189 2022/10/12 15:53:08 - mmengine - INFO - Epoch(train) [106][400/586] lr: 2.000000e-02 eta: 4:17:22 time: 0.257016 data_time: 0.049856 memory: 2937 loss_kpt: 92.494794 acc_pose: 0.766150 loss: 92.494794 2022/10/12 15:53:21 - mmengine - INFO - Epoch(train) [106][450/586] lr: 2.000000e-02 eta: 4:17:09 time: 0.259156 data_time: 0.053199 memory: 2937 loss_kpt: 93.751889 acc_pose: 0.774766 loss: 93.751889 2022/10/12 15:53:26 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:53:34 - mmengine - INFO - Epoch(train) [106][500/586] lr: 2.000000e-02 eta: 4:16:58 time: 0.270765 data_time: 0.053711 memory: 2937 loss_kpt: 91.697380 acc_pose: 0.783455 loss: 91.697380 2022/10/12 15:53:48 - mmengine - INFO - Epoch(train) [106][550/586] lr: 2.000000e-02 eta: 4:16:46 time: 0.271077 data_time: 0.049713 memory: 2937 loss_kpt: 91.762540 acc_pose: 0.722184 loss: 91.762540 2022/10/12 15:53:57 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:54:11 - mmengine - INFO - Epoch(train) [107][50/586] lr: 2.000000e-02 eta: 4:16:16 time: 0.278343 data_time: 0.063006 memory: 2937 loss_kpt: 91.538190 acc_pose: 0.809347 loss: 91.538190 2022/10/12 15:54:24 - mmengine - INFO - Epoch(train) [107][100/586] lr: 2.000000e-02 eta: 4:16:04 time: 0.260821 data_time: 0.050234 memory: 2937 loss_kpt: 90.110787 acc_pose: 0.853454 loss: 90.110787 2022/10/12 15:54:37 - mmengine - INFO - Epoch(train) [107][150/586] lr: 2.000000e-02 eta: 4:15:52 time: 0.264486 data_time: 0.053486 memory: 2937 loss_kpt: 92.140829 acc_pose: 0.777556 loss: 92.140829 2022/10/12 15:54:51 - mmengine - INFO - Epoch(train) [107][200/586] lr: 2.000000e-02 eta: 4:15:40 time: 0.266566 data_time: 0.051473 memory: 2937 loss_kpt: 91.355581 acc_pose: 0.747629 loss: 91.355581 2022/10/12 15:55:04 - mmengine - INFO - Epoch(train) [107][250/586] lr: 2.000000e-02 eta: 4:15:28 time: 0.265387 data_time: 0.049598 memory: 2937 loss_kpt: 90.600066 acc_pose: 0.838373 loss: 90.600066 2022/10/12 15:55:17 - mmengine - INFO - Epoch(train) [107][300/586] lr: 2.000000e-02 eta: 4:15:16 time: 0.262403 data_time: 0.053561 memory: 2937 loss_kpt: 90.914843 acc_pose: 0.758796 loss: 90.914843 2022/10/12 15:55:30 - mmengine - INFO - Epoch(train) [107][350/586] lr: 2.000000e-02 eta: 4:15:04 time: 0.265042 data_time: 0.053300 memory: 2937 loss_kpt: 92.619752 acc_pose: 0.826540 loss: 92.619752 2022/10/12 15:55:43 - mmengine - INFO - Epoch(train) [107][400/586] lr: 2.000000e-02 eta: 4:14:52 time: 0.261000 data_time: 0.048012 memory: 2937 loss_kpt: 90.246227 acc_pose: 0.827576 loss: 90.246227 2022/10/12 15:55:56 - mmengine - INFO - Epoch(train) [107][450/586] lr: 2.000000e-02 eta: 4:14:39 time: 0.256624 data_time: 0.052485 memory: 2937 loss_kpt: 93.961693 acc_pose: 0.788332 loss: 93.961693 2022/10/12 15:56:09 - mmengine - INFO - Epoch(train) [107][500/586] lr: 2.000000e-02 eta: 4:14:27 time: 0.252918 data_time: 0.053080 memory: 2937 loss_kpt: 91.658432 acc_pose: 0.700875 loss: 91.658432 2022/10/12 15:56:22 - mmengine - INFO - Epoch(train) [107][550/586] lr: 2.000000e-02 eta: 4:14:15 time: 0.264262 data_time: 0.048537 memory: 2937 loss_kpt: 91.991513 acc_pose: 0.803444 loss: 91.991513 2022/10/12 15:56:31 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:56:45 - mmengine - INFO - Epoch(train) [108][50/586] lr: 2.000000e-02 eta: 4:13:46 time: 0.289260 data_time: 0.062794 memory: 2937 loss_kpt: 91.925846 acc_pose: 0.838295 loss: 91.925846 2022/10/12 15:56:59 - mmengine - INFO - Epoch(train) [108][100/586] lr: 2.000000e-02 eta: 4:13:34 time: 0.272630 data_time: 0.052651 memory: 2937 loss_kpt: 90.721126 acc_pose: 0.778720 loss: 90.721126 2022/10/12 15:57:12 - mmengine - INFO - Epoch(train) [108][150/586] lr: 2.000000e-02 eta: 4:13:22 time: 0.260974 data_time: 0.051409 memory: 2937 loss_kpt: 91.426038 acc_pose: 0.725306 loss: 91.426038 2022/10/12 15:57:26 - mmengine - INFO - Epoch(train) [108][200/586] lr: 2.000000e-02 eta: 4:13:10 time: 0.272415 data_time: 0.052440 memory: 2937 loss_kpt: 91.219133 acc_pose: 0.705662 loss: 91.219133 2022/10/12 15:57:39 - mmengine - INFO - Epoch(train) [108][250/586] lr: 2.000000e-02 eta: 4:12:59 time: 0.272680 data_time: 0.053213 memory: 2937 loss_kpt: 90.594389 acc_pose: 0.826427 loss: 90.594389 2022/10/12 15:57:53 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:57:53 - mmengine - INFO - Epoch(train) [108][300/586] lr: 2.000000e-02 eta: 4:12:48 time: 0.280527 data_time: 0.053848 memory: 2937 loss_kpt: 90.643471 acc_pose: 0.779972 loss: 90.643471 2022/10/12 15:58:07 - mmengine - INFO - Epoch(train) [108][350/586] lr: 2.000000e-02 eta: 4:12:36 time: 0.281469 data_time: 0.049627 memory: 2937 loss_kpt: 92.085937 acc_pose: 0.705492 loss: 92.085937 2022/10/12 15:58:21 - mmengine - INFO - Epoch(train) [108][400/586] lr: 2.000000e-02 eta: 4:12:24 time: 0.269443 data_time: 0.057481 memory: 2937 loss_kpt: 91.050390 acc_pose: 0.704601 loss: 91.050390 2022/10/12 15:58:35 - mmengine - INFO - Epoch(train) [108][450/586] lr: 2.000000e-02 eta: 4:12:13 time: 0.274030 data_time: 0.050286 memory: 2937 loss_kpt: 90.430133 acc_pose: 0.782255 loss: 90.430133 2022/10/12 15:58:48 - mmengine - INFO - Epoch(train) [108][500/586] lr: 2.000000e-02 eta: 4:12:01 time: 0.272711 data_time: 0.053593 memory: 2937 loss_kpt: 89.614528 acc_pose: 0.680275 loss: 89.614528 2022/10/12 15:59:02 - mmengine - INFO - Epoch(train) [108][550/586] lr: 2.000000e-02 eta: 4:11:50 time: 0.282123 data_time: 0.048089 memory: 2937 loss_kpt: 90.853171 acc_pose: 0.704582 loss: 90.853171 2022/10/12 15:59:12 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 15:59:26 - mmengine - INFO - Epoch(train) [109][50/586] lr: 2.000000e-02 eta: 4:11:21 time: 0.289589 data_time: 0.062500 memory: 2937 loss_kpt: 90.630943 acc_pose: 0.711951 loss: 90.630943 2022/10/12 15:59:40 - mmengine - INFO - Epoch(train) [109][100/586] lr: 2.000000e-02 eta: 4:11:10 time: 0.269277 data_time: 0.049469 memory: 2937 loss_kpt: 91.156927 acc_pose: 0.786527 loss: 91.156927 2022/10/12 15:59:54 - mmengine - INFO - Epoch(train) [109][150/586] lr: 2.000000e-02 eta: 4:10:58 time: 0.279291 data_time: 0.051907 memory: 2937 loss_kpt: 90.744786 acc_pose: 0.788730 loss: 90.744786 2022/10/12 16:00:08 - mmengine - INFO - Epoch(train) [109][200/586] lr: 2.000000e-02 eta: 4:10:47 time: 0.273835 data_time: 0.056795 memory: 2937 loss_kpt: 91.107280 acc_pose: 0.705580 loss: 91.107280 2022/10/12 16:00:21 - mmengine - INFO - Epoch(train) [109][250/586] lr: 2.000000e-02 eta: 4:10:34 time: 0.259969 data_time: 0.051923 memory: 2937 loss_kpt: 90.596243 acc_pose: 0.836707 loss: 90.596243 2022/10/12 16:00:34 - mmengine - INFO - Epoch(train) [109][300/586] lr: 2.000000e-02 eta: 4:10:22 time: 0.263334 data_time: 0.054048 memory: 2937 loss_kpt: 91.465657 acc_pose: 0.768452 loss: 91.465657 2022/10/12 16:00:47 - mmengine - INFO - Epoch(train) [109][350/586] lr: 2.000000e-02 eta: 4:10:10 time: 0.257772 data_time: 0.047914 memory: 2937 loss_kpt: 90.531404 acc_pose: 0.706197 loss: 90.531404 2022/10/12 16:00:59 - mmengine - INFO - Epoch(train) [109][400/586] lr: 2.000000e-02 eta: 4:09:57 time: 0.250770 data_time: 0.051696 memory: 2937 loss_kpt: 91.298561 acc_pose: 0.790185 loss: 91.298561 2022/10/12 16:01:12 - mmengine - INFO - Epoch(train) [109][450/586] lr: 2.000000e-02 eta: 4:09:45 time: 0.258298 data_time: 0.052379 memory: 2937 loss_kpt: 90.940732 acc_pose: 0.772739 loss: 90.940732 2022/10/12 16:01:25 - mmengine - INFO - Epoch(train) [109][500/586] lr: 2.000000e-02 eta: 4:09:32 time: 0.259131 data_time: 0.048112 memory: 2937 loss_kpt: 90.534687 acc_pose: 0.817265 loss: 90.534687 2022/10/12 16:01:38 - mmengine - INFO - Epoch(train) [109][550/586] lr: 2.000000e-02 eta: 4:09:20 time: 0.258482 data_time: 0.058005 memory: 2937 loss_kpt: 90.211290 acc_pose: 0.764092 loss: 90.211290 2022/10/12 16:01:47 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:02:01 - mmengine - INFO - Epoch(train) [110][50/586] lr: 2.000000e-02 eta: 4:08:51 time: 0.275327 data_time: 0.064271 memory: 2937 loss_kpt: 90.761162 acc_pose: 0.724159 loss: 90.761162 2022/10/12 16:02:14 - mmengine - INFO - Epoch(train) [110][100/586] lr: 2.000000e-02 eta: 4:08:39 time: 0.269018 data_time: 0.052550 memory: 2937 loss_kpt: 91.144600 acc_pose: 0.745546 loss: 91.144600 2022/10/12 16:02:22 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:02:29 - mmengine - INFO - Epoch(train) [110][150/586] lr: 2.000000e-02 eta: 4:08:28 time: 0.292902 data_time: 0.051980 memory: 2937 loss_kpt: 91.795974 acc_pose: 0.824750 loss: 91.795974 2022/10/12 16:02:42 - mmengine - INFO - Epoch(train) [110][200/586] lr: 2.000000e-02 eta: 4:08:16 time: 0.267039 data_time: 0.051013 memory: 2937 loss_kpt: 90.486175 acc_pose: 0.724998 loss: 90.486175 2022/10/12 16:02:56 - mmengine - INFO - Epoch(train) [110][250/586] lr: 2.000000e-02 eta: 4:08:05 time: 0.277279 data_time: 0.052318 memory: 2937 loss_kpt: 88.142191 acc_pose: 0.755390 loss: 88.142191 2022/10/12 16:03:10 - mmengine - INFO - Epoch(train) [110][300/586] lr: 2.000000e-02 eta: 4:07:53 time: 0.274682 data_time: 0.052752 memory: 2937 loss_kpt: 90.201554 acc_pose: 0.768215 loss: 90.201554 2022/10/12 16:03:23 - mmengine - INFO - Epoch(train) [110][350/586] lr: 2.000000e-02 eta: 4:07:41 time: 0.269290 data_time: 0.049851 memory: 2937 loss_kpt: 89.792453 acc_pose: 0.763951 loss: 89.792453 2022/10/12 16:03:37 - mmengine - INFO - Epoch(train) [110][400/586] lr: 2.000000e-02 eta: 4:07:29 time: 0.265832 data_time: 0.053816 memory: 2937 loss_kpt: 90.482714 acc_pose: 0.755531 loss: 90.482714 2022/10/12 16:03:50 - mmengine - INFO - Epoch(train) [110][450/586] lr: 2.000000e-02 eta: 4:07:17 time: 0.269070 data_time: 0.052703 memory: 2937 loss_kpt: 90.673387 acc_pose: 0.722187 loss: 90.673387 2022/10/12 16:04:03 - mmengine - INFO - Epoch(train) [110][500/586] lr: 2.000000e-02 eta: 4:07:05 time: 0.267207 data_time: 0.057550 memory: 2937 loss_kpt: 91.351689 acc_pose: 0.748579 loss: 91.351689 2022/10/12 16:04:17 - mmengine - INFO - Epoch(train) [110][550/586] lr: 2.000000e-02 eta: 4:06:53 time: 0.267605 data_time: 0.050235 memory: 2937 loss_kpt: 93.419231 acc_pose: 0.767780 loss: 93.419231 2022/10/12 16:04:26 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:04:26 - mmengine - INFO - Saving checkpoint at 110 epochs 2022/10/12 16:04:34 - mmengine - INFO - Epoch(val) [110][50/407] eta: 0:00:41 time: 0.114859 data_time: 0.014537 memory: 2937 2022/10/12 16:04:40 - mmengine - INFO - Epoch(val) [110][100/407] eta: 0:00:33 time: 0.110015 data_time: 0.008875 memory: 830 2022/10/12 16:04:45 - mmengine - INFO - Epoch(val) [110][150/407] eta: 0:00:27 time: 0.108135 data_time: 0.008901 memory: 830 2022/10/12 16:04:51 - mmengine - INFO - Epoch(val) [110][200/407] eta: 0:00:23 time: 0.112202 data_time: 0.009201 memory: 830 2022/10/12 16:04:56 - mmengine - INFO - Epoch(val) [110][250/407] eta: 0:00:16 time: 0.108064 data_time: 0.008787 memory: 830 2022/10/12 16:05:02 - mmengine - INFO - Epoch(val) [110][300/407] eta: 0:00:11 time: 0.107644 data_time: 0.008814 memory: 830 2022/10/12 16:05:07 - mmengine - INFO - Epoch(val) [110][350/407] eta: 0:00:06 time: 0.109208 data_time: 0.008941 memory: 830 2022/10/12 16:05:12 - mmengine - INFO - Epoch(val) [110][400/407] eta: 0:00:00 time: 0.101185 data_time: 0.007867 memory: 830 2022/10/12 16:05:25 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 16:05:41 - mmengine - INFO - Epoch(val) [110][407/407] coco/AP: 0.674583 coco/AP .5: 0.874209 coco/AP .75: 0.752329 coco/AP (M): 0.643914 coco/AP (L): 0.733468 coco/AR: 0.745482 coco/AR .5: 0.919081 coco/AR .75: 0.808722 coco/AR (M): 0.700246 coco/AR (L): 0.807767 2022/10/12 16:05:41 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221012/rsn18/best_coco/AP_epoch_100.pth is removed 2022/10/12 16:05:43 - mmengine - INFO - The best checkpoint with 0.6746 coco/AP at 110 epoch is saved to best_coco/AP_epoch_110.pth. 2022/10/12 16:05:57 - mmengine - INFO - Epoch(train) [111][50/586] lr: 2.000000e-02 eta: 4:06:25 time: 0.282680 data_time: 0.060189 memory: 2937 loss_kpt: 91.808511 acc_pose: 0.650345 loss: 91.808511 2022/10/12 16:06:11 - mmengine - INFO - Epoch(train) [111][100/586] lr: 2.000000e-02 eta: 4:06:13 time: 0.276413 data_time: 0.053326 memory: 2937 loss_kpt: 92.047941 acc_pose: 0.753017 loss: 92.047941 2022/10/12 16:06:24 - mmengine - INFO - Epoch(train) [111][150/586] lr: 2.000000e-02 eta: 4:06:01 time: 0.267417 data_time: 0.053963 memory: 2937 loss_kpt: 89.768262 acc_pose: 0.830736 loss: 89.768262 2022/10/12 16:06:37 - mmengine - INFO - Epoch(train) [111][200/586] lr: 2.000000e-02 eta: 4:05:49 time: 0.258329 data_time: 0.049395 memory: 2937 loss_kpt: 89.816339 acc_pose: 0.859076 loss: 89.816339 2022/10/12 16:06:51 - mmengine - INFO - Epoch(train) [111][250/586] lr: 2.000000e-02 eta: 4:05:37 time: 0.272745 data_time: 0.052414 memory: 2937 loss_kpt: 90.841310 acc_pose: 0.769136 loss: 90.841310 2022/10/12 16:07:04 - mmengine - INFO - Epoch(train) [111][300/586] lr: 2.000000e-02 eta: 4:05:25 time: 0.257681 data_time: 0.049131 memory: 2937 loss_kpt: 90.122551 acc_pose: 0.724761 loss: 90.122551 2022/10/12 16:07:17 - mmengine - INFO - Epoch(train) [111][350/586] lr: 2.000000e-02 eta: 4:05:12 time: 0.255497 data_time: 0.051864 memory: 2937 loss_kpt: 91.295588 acc_pose: 0.832182 loss: 91.295588 2022/10/12 16:07:30 - mmengine - INFO - Epoch(train) [111][400/586] lr: 2.000000e-02 eta: 4:05:00 time: 0.267627 data_time: 0.053158 memory: 2937 loss_kpt: 91.654765 acc_pose: 0.746979 loss: 91.654765 2022/10/12 16:07:43 - mmengine - INFO - Epoch(train) [111][450/586] lr: 2.000000e-02 eta: 4:04:48 time: 0.249791 data_time: 0.049599 memory: 2937 loss_kpt: 92.288761 acc_pose: 0.656575 loss: 92.288761 2022/10/12 16:07:56 - mmengine - INFO - Epoch(train) [111][500/586] lr: 2.000000e-02 eta: 4:04:36 time: 0.265459 data_time: 0.052381 memory: 2937 loss_kpt: 88.895425 acc_pose: 0.828256 loss: 88.895425 2022/10/12 16:08:06 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:08:09 - mmengine - INFO - Epoch(train) [111][550/586] lr: 2.000000e-02 eta: 4:04:23 time: 0.263319 data_time: 0.054177 memory: 2937 loss_kpt: 91.340764 acc_pose: 0.742324 loss: 91.340764 2022/10/12 16:08:18 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:08:32 - mmengine - INFO - Epoch(train) [112][50/586] lr: 2.000000e-02 eta: 4:03:56 time: 0.296912 data_time: 0.061870 memory: 2937 loss_kpt: 90.462638 acc_pose: 0.833984 loss: 90.462638 2022/10/12 16:08:46 - mmengine - INFO - Epoch(train) [112][100/586] lr: 2.000000e-02 eta: 4:03:44 time: 0.273123 data_time: 0.049424 memory: 2937 loss_kpt: 91.880138 acc_pose: 0.745174 loss: 91.880138 2022/10/12 16:09:00 - mmengine - INFO - Epoch(train) [112][150/586] lr: 2.000000e-02 eta: 4:03:33 time: 0.281725 data_time: 0.054459 memory: 2937 loss_kpt: 90.733473 acc_pose: 0.641482 loss: 90.733473 2022/10/12 16:09:14 - mmengine - INFO - Epoch(train) [112][200/586] lr: 2.000000e-02 eta: 4:03:21 time: 0.273650 data_time: 0.052432 memory: 2937 loss_kpt: 91.084982 acc_pose: 0.752564 loss: 91.084982 2022/10/12 16:09:28 - mmengine - INFO - Epoch(train) [112][250/586] lr: 2.000000e-02 eta: 4:03:09 time: 0.279919 data_time: 0.051087 memory: 2937 loss_kpt: 90.298465 acc_pose: 0.768587 loss: 90.298465 2022/10/12 16:09:41 - mmengine - INFO - Epoch(train) [112][300/586] lr: 2.000000e-02 eta: 4:02:58 time: 0.270821 data_time: 0.051677 memory: 2937 loss_kpt: 89.451533 acc_pose: 0.754318 loss: 89.451533 2022/10/12 16:09:55 - mmengine - INFO - Epoch(train) [112][350/586] lr: 2.000000e-02 eta: 4:02:46 time: 0.269924 data_time: 0.050158 memory: 2937 loss_kpt: 91.903556 acc_pose: 0.757298 loss: 91.903556 2022/10/12 16:10:08 - mmengine - INFO - Epoch(train) [112][400/586] lr: 2.000000e-02 eta: 4:02:33 time: 0.258722 data_time: 0.052590 memory: 2937 loss_kpt: 92.133367 acc_pose: 0.774687 loss: 92.133367 2022/10/12 16:10:21 - mmengine - INFO - Epoch(train) [112][450/586] lr: 2.000000e-02 eta: 4:02:22 time: 0.270961 data_time: 0.054356 memory: 2937 loss_kpt: 90.985723 acc_pose: 0.757532 loss: 90.985723 2022/10/12 16:10:35 - mmengine - INFO - Epoch(train) [112][500/586] lr: 2.000000e-02 eta: 4:02:10 time: 0.266011 data_time: 0.053803 memory: 2937 loss_kpt: 91.287052 acc_pose: 0.798769 loss: 91.287052 2022/10/12 16:10:48 - mmengine - INFO - Epoch(train) [112][550/586] lr: 2.000000e-02 eta: 4:01:58 time: 0.265926 data_time: 0.050077 memory: 2937 loss_kpt: 90.650257 acc_pose: 0.821764 loss: 90.650257 2022/10/12 16:10:57 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:11:12 - mmengine - INFO - Epoch(train) [113][50/586] lr: 2.000000e-02 eta: 4:01:30 time: 0.307637 data_time: 0.064300 memory: 2937 loss_kpt: 89.420101 acc_pose: 0.772502 loss: 89.420101 2022/10/12 16:11:27 - mmengine - INFO - Epoch(train) [113][100/586] lr: 2.000000e-02 eta: 4:01:19 time: 0.284075 data_time: 0.052953 memory: 2937 loss_kpt: 90.731097 acc_pose: 0.777147 loss: 90.731097 2022/10/12 16:11:40 - mmengine - INFO - Epoch(train) [113][150/586] lr: 2.000000e-02 eta: 4:01:07 time: 0.275000 data_time: 0.057014 memory: 2937 loss_kpt: 92.130569 acc_pose: 0.671350 loss: 92.130569 2022/10/12 16:11:54 - mmengine - INFO - Epoch(train) [113][200/586] lr: 2.000000e-02 eta: 4:00:56 time: 0.272512 data_time: 0.053599 memory: 2937 loss_kpt: 91.087003 acc_pose: 0.781559 loss: 91.087003 2022/10/12 16:12:07 - mmengine - INFO - Epoch(train) [113][250/586] lr: 2.000000e-02 eta: 4:00:44 time: 0.266849 data_time: 0.053011 memory: 2937 loss_kpt: 89.296399 acc_pose: 0.723218 loss: 89.296399 2022/10/12 16:12:21 - mmengine - INFO - Epoch(train) [113][300/586] lr: 2.000000e-02 eta: 4:00:32 time: 0.268743 data_time: 0.055084 memory: 2937 loss_kpt: 91.587356 acc_pose: 0.743170 loss: 91.587356 2022/10/12 16:12:34 - mmengine - INFO - Epoch(train) [113][350/586] lr: 2.000000e-02 eta: 4:00:20 time: 0.265413 data_time: 0.055323 memory: 2937 loss_kpt: 89.788200 acc_pose: 0.827625 loss: 89.788200 2022/10/12 16:12:39 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:12:48 - mmengine - INFO - Epoch(train) [113][400/586] lr: 2.000000e-02 eta: 4:00:08 time: 0.282575 data_time: 0.051562 memory: 2937 loss_kpt: 90.849710 acc_pose: 0.770496 loss: 90.849710 2022/10/12 16:13:02 - mmengine - INFO - Epoch(train) [113][450/586] lr: 2.000000e-02 eta: 3:59:57 time: 0.275224 data_time: 0.051049 memory: 2937 loss_kpt: 91.137569 acc_pose: 0.780111 loss: 91.137569 2022/10/12 16:13:16 - mmengine - INFO - Epoch(train) [113][500/586] lr: 2.000000e-02 eta: 3:59:45 time: 0.267639 data_time: 0.049574 memory: 2937 loss_kpt: 90.577959 acc_pose: 0.811323 loss: 90.577959 2022/10/12 16:13:29 - mmengine - INFO - Epoch(train) [113][550/586] lr: 2.000000e-02 eta: 3:59:32 time: 0.262079 data_time: 0.054694 memory: 2937 loss_kpt: 90.769275 acc_pose: 0.775772 loss: 90.769275 2022/10/12 16:13:38 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:13:53 - mmengine - INFO - Epoch(train) [114][50/586] lr: 2.000000e-02 eta: 3:59:05 time: 0.295101 data_time: 0.068712 memory: 2937 loss_kpt: 92.124016 acc_pose: 0.809301 loss: 92.124016 2022/10/12 16:14:06 - mmengine - INFO - Epoch(train) [114][100/586] lr: 2.000000e-02 eta: 3:58:52 time: 0.256582 data_time: 0.052184 memory: 2937 loss_kpt: 89.543937 acc_pose: 0.822723 loss: 89.543937 2022/10/12 16:14:19 - mmengine - INFO - Epoch(train) [114][150/586] lr: 2.000000e-02 eta: 3:58:40 time: 0.260822 data_time: 0.048939 memory: 2937 loss_kpt: 91.527844 acc_pose: 0.770607 loss: 91.527844 2022/10/12 16:14:33 - mmengine - INFO - Epoch(train) [114][200/586] lr: 2.000000e-02 eta: 3:58:28 time: 0.272416 data_time: 0.052120 memory: 2937 loss_kpt: 91.669903 acc_pose: 0.783751 loss: 91.669903 2022/10/12 16:14:46 - mmengine - INFO - Epoch(train) [114][250/586] lr: 2.000000e-02 eta: 3:58:16 time: 0.272082 data_time: 0.053935 memory: 2937 loss_kpt: 89.814323 acc_pose: 0.810716 loss: 89.814323 2022/10/12 16:14:59 - mmengine - INFO - Epoch(train) [114][300/586] lr: 2.000000e-02 eta: 3:58:04 time: 0.254349 data_time: 0.050566 memory: 2937 loss_kpt: 91.799359 acc_pose: 0.771384 loss: 91.799359 2022/10/12 16:15:12 - mmengine - INFO - Epoch(train) [114][350/586] lr: 2.000000e-02 eta: 3:57:51 time: 0.259975 data_time: 0.053828 memory: 2937 loss_kpt: 90.771887 acc_pose: 0.760256 loss: 90.771887 2022/10/12 16:15:25 - mmengine - INFO - Epoch(train) [114][400/586] lr: 2.000000e-02 eta: 3:57:39 time: 0.266476 data_time: 0.054766 memory: 2937 loss_kpt: 90.127013 acc_pose: 0.801698 loss: 90.127013 2022/10/12 16:15:39 - mmengine - INFO - Epoch(train) [114][450/586] lr: 2.000000e-02 eta: 3:57:27 time: 0.270232 data_time: 0.055890 memory: 2937 loss_kpt: 90.629233 acc_pose: 0.761817 loss: 90.629233 2022/10/12 16:15:52 - mmengine - INFO - Epoch(train) [114][500/586] lr: 2.000000e-02 eta: 3:57:15 time: 0.257795 data_time: 0.050593 memory: 2937 loss_kpt: 89.939018 acc_pose: 0.818634 loss: 89.939018 2022/10/12 16:16:05 - mmengine - INFO - Epoch(train) [114][550/586] lr: 2.000000e-02 eta: 3:57:03 time: 0.264372 data_time: 0.055877 memory: 2937 loss_kpt: 90.468990 acc_pose: 0.750967 loss: 90.468990 2022/10/12 16:16:14 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:16:29 - mmengine - INFO - Epoch(train) [115][50/586] lr: 2.000000e-02 eta: 3:56:35 time: 0.287822 data_time: 0.061419 memory: 2937 loss_kpt: 94.748811 acc_pose: 0.693440 loss: 94.748811 2022/10/12 16:16:43 - mmengine - INFO - Epoch(train) [115][100/586] lr: 2.000000e-02 eta: 3:56:24 time: 0.282206 data_time: 0.051191 memory: 2937 loss_kpt: 91.981643 acc_pose: 0.762545 loss: 91.981643 2022/10/12 16:16:56 - mmengine - INFO - Epoch(train) [115][150/586] lr: 2.000000e-02 eta: 3:56:12 time: 0.270924 data_time: 0.051469 memory: 2937 loss_kpt: 90.212580 acc_pose: 0.774997 loss: 90.212580 2022/10/12 16:17:08 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:17:09 - mmengine - INFO - Epoch(train) [115][200/586] lr: 2.000000e-02 eta: 3:56:00 time: 0.261978 data_time: 0.050917 memory: 2937 loss_kpt: 90.883634 acc_pose: 0.791493 loss: 90.883634 2022/10/12 16:17:22 - mmengine - INFO - Epoch(train) [115][250/586] lr: 2.000000e-02 eta: 3:55:47 time: 0.257382 data_time: 0.050810 memory: 2937 loss_kpt: 89.009988 acc_pose: 0.818697 loss: 89.009988 2022/10/12 16:17:35 - mmengine - INFO - Epoch(train) [115][300/586] lr: 2.000000e-02 eta: 3:55:35 time: 0.262764 data_time: 0.050905 memory: 2937 loss_kpt: 91.406794 acc_pose: 0.763739 loss: 91.406794 2022/10/12 16:17:48 - mmengine - INFO - Epoch(train) [115][350/586] lr: 2.000000e-02 eta: 3:55:22 time: 0.253816 data_time: 0.051786 memory: 2937 loss_kpt: 89.336129 acc_pose: 0.773893 loss: 89.336129 2022/10/12 16:18:02 - mmengine - INFO - Epoch(train) [115][400/586] lr: 2.000000e-02 eta: 3:55:10 time: 0.268413 data_time: 0.051076 memory: 2937 loss_kpt: 90.485114 acc_pose: 0.818145 loss: 90.485114 2022/10/12 16:18:15 - mmengine - INFO - Epoch(train) [115][450/586] lr: 2.000000e-02 eta: 3:54:58 time: 0.264024 data_time: 0.046555 memory: 2937 loss_kpt: 90.221886 acc_pose: 0.804301 loss: 90.221886 2022/10/12 16:18:28 - mmengine - INFO - Epoch(train) [115][500/586] lr: 2.000000e-02 eta: 3:54:46 time: 0.272220 data_time: 0.051283 memory: 2937 loss_kpt: 90.188905 acc_pose: 0.809620 loss: 90.188905 2022/10/12 16:18:42 - mmengine - INFO - Epoch(train) [115][550/586] lr: 2.000000e-02 eta: 3:54:35 time: 0.272413 data_time: 0.050388 memory: 2937 loss_kpt: 89.110684 acc_pose: 0.817032 loss: 89.110684 2022/10/12 16:18:52 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:19:06 - mmengine - INFO - Epoch(train) [116][50/586] lr: 2.000000e-02 eta: 3:54:07 time: 0.284510 data_time: 0.063488 memory: 2937 loss_kpt: 91.642941 acc_pose: 0.825578 loss: 91.642941 2022/10/12 16:19:20 - mmengine - INFO - Epoch(train) [116][100/586] lr: 2.000000e-02 eta: 3:53:55 time: 0.287008 data_time: 0.051273 memory: 2937 loss_kpt: 90.035884 acc_pose: 0.759161 loss: 90.035884 2022/10/12 16:19:34 - mmengine - INFO - Epoch(train) [116][150/586] lr: 2.000000e-02 eta: 3:53:44 time: 0.281892 data_time: 0.052575 memory: 2937 loss_kpt: 91.069257 acc_pose: 0.749655 loss: 91.069257 2022/10/12 16:19:48 - mmengine - INFO - Epoch(train) [116][200/586] lr: 2.000000e-02 eta: 3:53:32 time: 0.269723 data_time: 0.054109 memory: 2937 loss_kpt: 89.990897 acc_pose: 0.796066 loss: 89.990897 2022/10/12 16:20:01 - mmengine - INFO - Epoch(train) [116][250/586] lr: 2.000000e-02 eta: 3:53:20 time: 0.274949 data_time: 0.051786 memory: 2937 loss_kpt: 91.142672 acc_pose: 0.747949 loss: 91.142672 2022/10/12 16:20:15 - mmengine - INFO - Epoch(train) [116][300/586] lr: 2.000000e-02 eta: 3:53:08 time: 0.265463 data_time: 0.056188 memory: 2937 loss_kpt: 91.102023 acc_pose: 0.842576 loss: 91.102023 2022/10/12 16:20:28 - mmengine - INFO - Epoch(train) [116][350/586] lr: 2.000000e-02 eta: 3:52:56 time: 0.268587 data_time: 0.051801 memory: 2937 loss_kpt: 91.101194 acc_pose: 0.756226 loss: 91.101194 2022/10/12 16:20:42 - mmengine - INFO - Epoch(train) [116][400/586] lr: 2.000000e-02 eta: 3:52:45 time: 0.278255 data_time: 0.055108 memory: 2937 loss_kpt: 90.725997 acc_pose: 0.804359 loss: 90.725997 2022/10/12 16:20:56 - mmengine - INFO - Epoch(train) [116][450/586] lr: 2.000000e-02 eta: 3:52:33 time: 0.270689 data_time: 0.050113 memory: 2937 loss_kpt: 90.959608 acc_pose: 0.789975 loss: 90.959608 2022/10/12 16:21:09 - mmengine - INFO - Epoch(train) [116][500/586] lr: 2.000000e-02 eta: 3:52:20 time: 0.260847 data_time: 0.053482 memory: 2937 loss_kpt: 91.687977 acc_pose: 0.781677 loss: 91.687977 2022/10/12 16:21:22 - mmengine - INFO - Epoch(train) [116][550/586] lr: 2.000000e-02 eta: 3:52:08 time: 0.267432 data_time: 0.052966 memory: 2937 loss_kpt: 90.895970 acc_pose: 0.719340 loss: 90.895970 2022/10/12 16:21:32 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:21:40 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:21:46 - mmengine - INFO - Epoch(train) [117][50/586] lr: 2.000000e-02 eta: 3:51:40 time: 0.277661 data_time: 0.063806 memory: 2937 loss_kpt: 92.505070 acc_pose: 0.746911 loss: 92.505070 2022/10/12 16:21:59 - mmengine - INFO - Epoch(train) [117][100/586] lr: 2.000000e-02 eta: 3:51:28 time: 0.252741 data_time: 0.049268 memory: 2937 loss_kpt: 89.354494 acc_pose: 0.798376 loss: 89.354494 2022/10/12 16:22:12 - mmengine - INFO - Epoch(train) [117][150/586] lr: 2.000000e-02 eta: 3:51:15 time: 0.258129 data_time: 0.054885 memory: 2937 loss_kpt: 89.396552 acc_pose: 0.736575 loss: 89.396552 2022/10/12 16:22:25 - mmengine - INFO - Epoch(train) [117][200/586] lr: 2.000000e-02 eta: 3:51:03 time: 0.260899 data_time: 0.052544 memory: 2937 loss_kpt: 89.749467 acc_pose: 0.787960 loss: 89.749467 2022/10/12 16:22:38 - mmengine - INFO - Epoch(train) [117][250/586] lr: 2.000000e-02 eta: 3:50:51 time: 0.262529 data_time: 0.053600 memory: 2937 loss_kpt: 89.887531 acc_pose: 0.787581 loss: 89.887531 2022/10/12 16:22:51 - mmengine - INFO - Epoch(train) [117][300/586] lr: 2.000000e-02 eta: 3:50:38 time: 0.256228 data_time: 0.053851 memory: 2937 loss_kpt: 90.025747 acc_pose: 0.806035 loss: 90.025747 2022/10/12 16:23:04 - mmengine - INFO - Epoch(train) [117][350/586] lr: 2.000000e-02 eta: 3:50:26 time: 0.259974 data_time: 0.053835 memory: 2937 loss_kpt: 90.484692 acc_pose: 0.842759 loss: 90.484692 2022/10/12 16:23:18 - mmengine - INFO - Epoch(train) [117][400/586] lr: 2.000000e-02 eta: 3:50:14 time: 0.277253 data_time: 0.054449 memory: 2937 loss_kpt: 89.090314 acc_pose: 0.807768 loss: 89.090314 2022/10/12 16:23:31 - mmengine - INFO - Epoch(train) [117][450/586] lr: 2.000000e-02 eta: 3:50:03 time: 0.274208 data_time: 0.055687 memory: 2937 loss_kpt: 89.306292 acc_pose: 0.734258 loss: 89.306292 2022/10/12 16:23:45 - mmengine - INFO - Epoch(train) [117][500/586] lr: 2.000000e-02 eta: 3:49:51 time: 0.276698 data_time: 0.058413 memory: 2937 loss_kpt: 92.196583 acc_pose: 0.802374 loss: 92.196583 2022/10/12 16:23:59 - mmengine - INFO - Epoch(train) [117][550/586] lr: 2.000000e-02 eta: 3:49:39 time: 0.277508 data_time: 0.054951 memory: 2937 loss_kpt: 89.897788 acc_pose: 0.784859 loss: 89.897788 2022/10/12 16:24:09 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:24:23 - mmengine - INFO - Epoch(train) [118][50/586] lr: 2.000000e-02 eta: 3:49:12 time: 0.292929 data_time: 0.061271 memory: 2937 loss_kpt: 89.861202 acc_pose: 0.814458 loss: 89.861202 2022/10/12 16:24:36 - mmengine - INFO - Epoch(train) [118][100/586] lr: 2.000000e-02 eta: 3:49:00 time: 0.261819 data_time: 0.048317 memory: 2937 loss_kpt: 91.836640 acc_pose: 0.801629 loss: 91.836640 2022/10/12 16:24:50 - mmengine - INFO - Epoch(train) [118][150/586] lr: 2.000000e-02 eta: 3:48:48 time: 0.270726 data_time: 0.055555 memory: 2937 loss_kpt: 92.010059 acc_pose: 0.802855 loss: 92.010059 2022/10/12 16:25:03 - mmengine - INFO - Epoch(train) [118][200/586] lr: 2.000000e-02 eta: 3:48:35 time: 0.262018 data_time: 0.048501 memory: 2937 loss_kpt: 91.241347 acc_pose: 0.816861 loss: 91.241347 2022/10/12 16:25:16 - mmengine - INFO - Epoch(train) [118][250/586] lr: 2.000000e-02 eta: 3:48:23 time: 0.262094 data_time: 0.054203 memory: 2937 loss_kpt: 90.741553 acc_pose: 0.803696 loss: 90.741553 2022/10/12 16:25:29 - mmengine - INFO - Epoch(train) [118][300/586] lr: 2.000000e-02 eta: 3:48:11 time: 0.264497 data_time: 0.057163 memory: 2937 loss_kpt: 89.818177 acc_pose: 0.858606 loss: 89.818177 2022/10/12 16:25:43 - mmengine - INFO - Epoch(train) [118][350/586] lr: 2.000000e-02 eta: 3:47:59 time: 0.260609 data_time: 0.051819 memory: 2937 loss_kpt: 90.799704 acc_pose: 0.827958 loss: 90.799704 2022/10/12 16:25:55 - mmengine - INFO - Epoch(train) [118][400/586] lr: 2.000000e-02 eta: 3:47:46 time: 0.259013 data_time: 0.055140 memory: 2937 loss_kpt: 91.124059 acc_pose: 0.732089 loss: 91.124059 2022/10/12 16:26:05 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:26:09 - mmengine - INFO - Epoch(train) [118][450/586] lr: 2.000000e-02 eta: 3:47:34 time: 0.262103 data_time: 0.055831 memory: 2937 loss_kpt: 90.029392 acc_pose: 0.745220 loss: 90.029392 2022/10/12 16:26:22 - mmengine - INFO - Epoch(train) [118][500/586] lr: 2.000000e-02 eta: 3:47:22 time: 0.266696 data_time: 0.054187 memory: 2937 loss_kpt: 90.508460 acc_pose: 0.859371 loss: 90.508460 2022/10/12 16:26:35 - mmengine - INFO - Epoch(train) [118][550/586] lr: 2.000000e-02 eta: 3:47:10 time: 0.261151 data_time: 0.053924 memory: 2937 loss_kpt: 90.037255 acc_pose: 0.810071 loss: 90.037255 2022/10/12 16:26:44 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:27:00 - mmengine - INFO - Epoch(train) [119][50/586] lr: 2.000000e-02 eta: 3:46:43 time: 0.301784 data_time: 0.064670 memory: 2937 loss_kpt: 92.074944 acc_pose: 0.743003 loss: 92.074944 2022/10/12 16:27:14 - mmengine - INFO - Epoch(train) [119][100/586] lr: 2.000000e-02 eta: 3:46:32 time: 0.286886 data_time: 0.051385 memory: 2937 loss_kpt: 90.163100 acc_pose: 0.766693 loss: 90.163100 2022/10/12 16:27:28 - mmengine - INFO - Epoch(train) [119][150/586] lr: 2.000000e-02 eta: 3:46:20 time: 0.284052 data_time: 0.051441 memory: 2937 loss_kpt: 91.624276 acc_pose: 0.786538 loss: 91.624276 2022/10/12 16:27:42 - mmengine - INFO - Epoch(train) [119][200/586] lr: 2.000000e-02 eta: 3:46:08 time: 0.276895 data_time: 0.048677 memory: 2937 loss_kpt: 90.781006 acc_pose: 0.803045 loss: 90.781006 2022/10/12 16:27:56 - mmengine - INFO - Epoch(train) [119][250/586] lr: 2.000000e-02 eta: 3:45:57 time: 0.284279 data_time: 0.049948 memory: 2937 loss_kpt: 89.080508 acc_pose: 0.735877 loss: 89.080508 2022/10/12 16:28:11 - mmengine - INFO - Epoch(train) [119][300/586] lr: 2.000000e-02 eta: 3:45:46 time: 0.288775 data_time: 0.052478 memory: 2937 loss_kpt: 90.854687 acc_pose: 0.799506 loss: 90.854687 2022/10/12 16:28:25 - mmengine - INFO - Epoch(train) [119][350/586] lr: 2.000000e-02 eta: 3:45:34 time: 0.277514 data_time: 0.052842 memory: 2937 loss_kpt: 90.454525 acc_pose: 0.849456 loss: 90.454525 2022/10/12 16:28:38 - mmengine - INFO - Epoch(train) [119][400/586] lr: 2.000000e-02 eta: 3:45:22 time: 0.269663 data_time: 0.049258 memory: 2937 loss_kpt: 90.564824 acc_pose: 0.790529 loss: 90.564824 2022/10/12 16:28:52 - mmengine - INFO - Epoch(train) [119][450/586] lr: 2.000000e-02 eta: 3:45:10 time: 0.272483 data_time: 0.053388 memory: 2937 loss_kpt: 90.937916 acc_pose: 0.765686 loss: 90.937916 2022/10/12 16:29:05 - mmengine - INFO - Epoch(train) [119][500/586] lr: 2.000000e-02 eta: 3:44:58 time: 0.275308 data_time: 0.050315 memory: 2937 loss_kpt: 92.257962 acc_pose: 0.682794 loss: 92.257962 2022/10/12 16:29:19 - mmengine - INFO - Epoch(train) [119][550/586] lr: 2.000000e-02 eta: 3:44:46 time: 0.265901 data_time: 0.051245 memory: 2937 loss_kpt: 91.442237 acc_pose: 0.799106 loss: 91.442237 2022/10/12 16:29:29 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:29:43 - mmengine - INFO - Epoch(train) [120][50/586] lr: 2.000000e-02 eta: 3:44:19 time: 0.285532 data_time: 0.060128 memory: 2937 loss_kpt: 92.662492 acc_pose: 0.813989 loss: 92.662492 2022/10/12 16:29:56 - mmengine - INFO - Epoch(train) [120][100/586] lr: 2.000000e-02 eta: 3:44:07 time: 0.262731 data_time: 0.050465 memory: 2937 loss_kpt: 91.100478 acc_pose: 0.743883 loss: 91.100478 2022/10/12 16:30:09 - mmengine - INFO - Epoch(train) [120][150/586] lr: 2.000000e-02 eta: 3:43:55 time: 0.268781 data_time: 0.058093 memory: 2937 loss_kpt: 89.380103 acc_pose: 0.809644 loss: 89.380103 2022/10/12 16:30:23 - mmengine - INFO - Epoch(train) [120][200/586] lr: 2.000000e-02 eta: 3:43:43 time: 0.272550 data_time: 0.054185 memory: 2937 loss_kpt: 89.465541 acc_pose: 0.836377 loss: 89.465541 2022/10/12 16:30:36 - mmengine - INFO - Epoch(train) [120][250/586] lr: 2.000000e-02 eta: 3:43:31 time: 0.267127 data_time: 0.052718 memory: 2937 loss_kpt: 89.486711 acc_pose: 0.726475 loss: 89.486711 2022/10/12 16:30:41 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:30:50 - mmengine - INFO - Epoch(train) [120][300/586] lr: 2.000000e-02 eta: 3:43:19 time: 0.265048 data_time: 0.054520 memory: 2937 loss_kpt: 90.879411 acc_pose: 0.857751 loss: 90.879411 2022/10/12 16:31:03 - mmengine - INFO - Epoch(train) [120][350/586] lr: 2.000000e-02 eta: 3:43:07 time: 0.272252 data_time: 0.055398 memory: 2937 loss_kpt: 90.099978 acc_pose: 0.774987 loss: 90.099978 2022/10/12 16:31:17 - mmengine - INFO - Epoch(train) [120][400/586] lr: 2.000000e-02 eta: 3:42:54 time: 0.264983 data_time: 0.051939 memory: 2937 loss_kpt: 91.359890 acc_pose: 0.765847 loss: 91.359890 2022/10/12 16:31:30 - mmengine - INFO - Epoch(train) [120][450/586] lr: 2.000000e-02 eta: 3:42:42 time: 0.262267 data_time: 0.052783 memory: 2937 loss_kpt: 90.727283 acc_pose: 0.721797 loss: 90.727283 2022/10/12 16:31:42 - mmengine - INFO - Epoch(train) [120][500/586] lr: 2.000000e-02 eta: 3:42:30 time: 0.255706 data_time: 0.051359 memory: 2937 loss_kpt: 90.460828 acc_pose: 0.775477 loss: 90.460828 2022/10/12 16:31:56 - mmengine - INFO - Epoch(train) [120][550/586] lr: 2.000000e-02 eta: 3:42:17 time: 0.264932 data_time: 0.053934 memory: 2937 loss_kpt: 89.378928 acc_pose: 0.847570 loss: 89.378928 2022/10/12 16:32:05 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:32:05 - mmengine - INFO - Saving checkpoint at 120 epochs 2022/10/12 16:32:13 - mmengine - INFO - Epoch(val) [120][50/407] eta: 0:00:41 time: 0.115379 data_time: 0.014749 memory: 2937 2022/10/12 16:32:19 - mmengine - INFO - Epoch(val) [120][100/407] eta: 0:00:33 time: 0.110174 data_time: 0.009236 memory: 830 2022/10/12 16:32:24 - mmengine - INFO - Epoch(val) [120][150/407] eta: 0:00:29 time: 0.113078 data_time: 0.012582 memory: 830 2022/10/12 16:32:30 - mmengine - INFO - Epoch(val) [120][200/407] eta: 0:00:22 time: 0.110749 data_time: 0.009208 memory: 830 2022/10/12 16:32:36 - mmengine - INFO - Epoch(val) [120][250/407] eta: 0:00:17 time: 0.109765 data_time: 0.009232 memory: 830 2022/10/12 16:32:41 - mmengine - INFO - Epoch(val) [120][300/407] eta: 0:00:11 time: 0.111106 data_time: 0.009438 memory: 830 2022/10/12 16:32:47 - mmengine - INFO - Epoch(val) [120][350/407] eta: 0:00:06 time: 0.110493 data_time: 0.009730 memory: 830 2022/10/12 16:32:52 - mmengine - INFO - Epoch(val) [120][400/407] eta: 0:00:00 time: 0.102069 data_time: 0.007973 memory: 830 2022/10/12 16:33:05 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 16:33:20 - mmengine - INFO - Epoch(val) [120][407/407] coco/AP: 0.678673 coco/AP .5: 0.876885 coco/AP .75: 0.752464 coco/AP (M): 0.647323 coco/AP (L): 0.739062 coco/AR: 0.748866 coco/AR .5: 0.919081 coco/AR .75: 0.809824 coco/AR (M): 0.702950 coco/AR (L): 0.812226 2022/10/12 16:33:20 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221012/rsn18/best_coco/AP_epoch_110.pth is removed 2022/10/12 16:33:23 - mmengine - INFO - The best checkpoint with 0.6787 coco/AP at 120 epoch is saved to best_coco/AP_epoch_120.pth. 2022/10/12 16:33:37 - mmengine - INFO - Epoch(train) [121][50/586] lr: 2.000000e-02 eta: 3:41:50 time: 0.279221 data_time: 0.058645 memory: 2937 loss_kpt: 91.563823 acc_pose: 0.832949 loss: 91.563823 2022/10/12 16:33:50 - mmengine - INFO - Epoch(train) [121][100/586] lr: 2.000000e-02 eta: 3:41:38 time: 0.276164 data_time: 0.056118 memory: 2937 loss_kpt: 90.791171 acc_pose: 0.706105 loss: 90.791171 2022/10/12 16:34:04 - mmengine - INFO - Epoch(train) [121][150/586] lr: 2.000000e-02 eta: 3:41:26 time: 0.271959 data_time: 0.048841 memory: 2937 loss_kpt: 89.475006 acc_pose: 0.733952 loss: 89.475006 2022/10/12 16:34:18 - mmengine - INFO - Epoch(train) [121][200/586] lr: 2.000000e-02 eta: 3:41:14 time: 0.273613 data_time: 0.052814 memory: 2937 loss_kpt: 91.452725 acc_pose: 0.728801 loss: 91.452725 2022/10/12 16:34:32 - mmengine - INFO - Epoch(train) [121][250/586] lr: 2.000000e-02 eta: 3:41:03 time: 0.281876 data_time: 0.050038 memory: 2937 loss_kpt: 90.533880 acc_pose: 0.754787 loss: 90.533880 2022/10/12 16:34:46 - mmengine - INFO - Epoch(train) [121][300/586] lr: 2.000000e-02 eta: 3:40:51 time: 0.281905 data_time: 0.054727 memory: 2937 loss_kpt: 91.683361 acc_pose: 0.741516 loss: 91.683361 2022/10/12 16:35:00 - mmengine - INFO - Epoch(train) [121][350/586] lr: 2.000000e-02 eta: 3:40:40 time: 0.278530 data_time: 0.053147 memory: 2937 loss_kpt: 89.946979 acc_pose: 0.785023 loss: 89.946979 2022/10/12 16:35:13 - mmengine - INFO - Epoch(train) [121][400/586] lr: 2.000000e-02 eta: 3:40:27 time: 0.263474 data_time: 0.049941 memory: 2937 loss_kpt: 90.821319 acc_pose: 0.802466 loss: 90.821319 2022/10/12 16:35:26 - mmengine - INFO - Epoch(train) [121][450/586] lr: 2.000000e-02 eta: 3:40:15 time: 0.264552 data_time: 0.052500 memory: 2937 loss_kpt: 90.444313 acc_pose: 0.792610 loss: 90.444313 2022/10/12 16:35:40 - mmengine - INFO - Epoch(train) [121][500/586] lr: 2.000000e-02 eta: 3:40:03 time: 0.274827 data_time: 0.055047 memory: 2937 loss_kpt: 90.679427 acc_pose: 0.743906 loss: 90.679427 2022/10/12 16:35:54 - mmengine - INFO - Epoch(train) [121][550/586] lr: 2.000000e-02 eta: 3:39:51 time: 0.271317 data_time: 0.051977 memory: 2937 loss_kpt: 90.966288 acc_pose: 0.769713 loss: 90.966288 2022/10/12 16:36:03 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:36:18 - mmengine - INFO - Epoch(train) [122][50/586] lr: 2.000000e-02 eta: 3:39:24 time: 0.289312 data_time: 0.059326 memory: 2937 loss_kpt: 88.779286 acc_pose: 0.803748 loss: 88.779286 2022/10/12 16:36:30 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:36:32 - mmengine - INFO - Epoch(train) [122][100/586] lr: 2.000000e-02 eta: 3:39:13 time: 0.285388 data_time: 0.051551 memory: 2937 loss_kpt: 90.951415 acc_pose: 0.817624 loss: 90.951415 2022/10/12 16:36:45 - mmengine - INFO - Epoch(train) [122][150/586] lr: 2.000000e-02 eta: 3:39:01 time: 0.264929 data_time: 0.053162 memory: 2937 loss_kpt: 89.640397 acc_pose: 0.745457 loss: 89.640397 2022/10/12 16:36:58 - mmengine - INFO - Epoch(train) [122][200/586] lr: 2.000000e-02 eta: 3:38:48 time: 0.255557 data_time: 0.050034 memory: 2937 loss_kpt: 90.455095 acc_pose: 0.746872 loss: 90.455095 2022/10/12 16:37:11 - mmengine - INFO - Epoch(train) [122][250/586] lr: 2.000000e-02 eta: 3:38:36 time: 0.260755 data_time: 0.053897 memory: 2937 loss_kpt: 89.608502 acc_pose: 0.796284 loss: 89.608502 2022/10/12 16:37:24 - mmengine - INFO - Epoch(train) [122][300/586] lr: 2.000000e-02 eta: 3:38:23 time: 0.259470 data_time: 0.051813 memory: 2937 loss_kpt: 91.040755 acc_pose: 0.700869 loss: 91.040755 2022/10/12 16:37:37 - mmengine - INFO - Epoch(train) [122][350/586] lr: 2.000000e-02 eta: 3:38:11 time: 0.258904 data_time: 0.050389 memory: 2937 loss_kpt: 89.079457 acc_pose: 0.723109 loss: 89.079457 2022/10/12 16:37:51 - mmengine - INFO - Epoch(train) [122][400/586] lr: 2.000000e-02 eta: 3:37:59 time: 0.269556 data_time: 0.055719 memory: 2937 loss_kpt: 89.495385 acc_pose: 0.795022 loss: 89.495385 2022/10/12 16:38:04 - mmengine - INFO - Epoch(train) [122][450/586] lr: 2.000000e-02 eta: 3:37:47 time: 0.265443 data_time: 0.054135 memory: 2937 loss_kpt: 89.471334 acc_pose: 0.723796 loss: 89.471334 2022/10/12 16:38:17 - mmengine - INFO - Epoch(train) [122][500/586] lr: 2.000000e-02 eta: 3:37:35 time: 0.261025 data_time: 0.053485 memory: 2937 loss_kpt: 88.882975 acc_pose: 0.859581 loss: 88.882975 2022/10/12 16:38:30 - mmengine - INFO - Epoch(train) [122][550/586] lr: 2.000000e-02 eta: 3:37:22 time: 0.259266 data_time: 0.053684 memory: 2937 loss_kpt: 91.322245 acc_pose: 0.789328 loss: 91.322245 2022/10/12 16:38:39 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:38:53 - mmengine - INFO - Epoch(train) [123][50/586] lr: 2.000000e-02 eta: 3:36:55 time: 0.275525 data_time: 0.060233 memory: 2937 loss_kpt: 89.917352 acc_pose: 0.735490 loss: 89.917352 2022/10/12 16:39:06 - mmengine - INFO - Epoch(train) [123][100/586] lr: 2.000000e-02 eta: 3:36:42 time: 0.261503 data_time: 0.051650 memory: 2937 loss_kpt: 89.341380 acc_pose: 0.786906 loss: 89.341380 2022/10/12 16:39:20 - mmengine - INFO - Epoch(train) [123][150/586] lr: 2.000000e-02 eta: 3:36:30 time: 0.272754 data_time: 0.050391 memory: 2937 loss_kpt: 90.464578 acc_pose: 0.786437 loss: 90.464578 2022/10/12 16:39:33 - mmengine - INFO - Epoch(train) [123][200/586] lr: 2.000000e-02 eta: 3:36:18 time: 0.269001 data_time: 0.054530 memory: 2937 loss_kpt: 89.447268 acc_pose: 0.751708 loss: 89.447268 2022/10/12 16:39:47 - mmengine - INFO - Epoch(train) [123][250/586] lr: 2.000000e-02 eta: 3:36:06 time: 0.263961 data_time: 0.052993 memory: 2937 loss_kpt: 91.443927 acc_pose: 0.790982 loss: 91.443927 2022/10/12 16:39:59 - mmengine - INFO - Epoch(train) [123][300/586] lr: 2.000000e-02 eta: 3:35:54 time: 0.259286 data_time: 0.050588 memory: 2937 loss_kpt: 91.897122 acc_pose: 0.676524 loss: 91.897122 2022/10/12 16:40:13 - mmengine - INFO - Epoch(train) [123][350/586] lr: 2.000000e-02 eta: 3:35:42 time: 0.264929 data_time: 0.047116 memory: 2937 loss_kpt: 92.685687 acc_pose: 0.799384 loss: 92.685687 2022/10/12 16:40:26 - mmengine - INFO - Epoch(train) [123][400/586] lr: 2.000000e-02 eta: 3:35:30 time: 0.273325 data_time: 0.049907 memory: 2937 loss_kpt: 90.943795 acc_pose: 0.702174 loss: 90.943795 2022/10/12 16:40:40 - mmengine - INFO - Epoch(train) [123][450/586] lr: 2.000000e-02 eta: 3:35:17 time: 0.263010 data_time: 0.048784 memory: 2937 loss_kpt: 89.805650 acc_pose: 0.820992 loss: 89.805650 2022/10/12 16:40:53 - mmengine - INFO - Epoch(train) [123][500/586] lr: 2.000000e-02 eta: 3:35:05 time: 0.260763 data_time: 0.052936 memory: 2937 loss_kpt: 89.725759 acc_pose: 0.753759 loss: 89.725759 2022/10/12 16:40:55 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:41:06 - mmengine - INFO - Epoch(train) [123][550/586] lr: 2.000000e-02 eta: 3:34:53 time: 0.272974 data_time: 0.048760 memory: 2937 loss_kpt: 90.311249 acc_pose: 0.745860 loss: 90.311249 2022/10/12 16:41:16 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:41:31 - mmengine - INFO - Epoch(train) [124][50/586] lr: 2.000000e-02 eta: 3:34:27 time: 0.305308 data_time: 0.065543 memory: 2937 loss_kpt: 89.352580 acc_pose: 0.720305 loss: 89.352580 2022/10/12 16:41:45 - mmengine - INFO - Epoch(train) [124][100/586] lr: 2.000000e-02 eta: 3:34:15 time: 0.283805 data_time: 0.048680 memory: 2937 loss_kpt: 91.592145 acc_pose: 0.792322 loss: 91.592145 2022/10/12 16:42:00 - mmengine - INFO - Epoch(train) [124][150/586] lr: 2.000000e-02 eta: 3:34:04 time: 0.285110 data_time: 0.053987 memory: 2937 loss_kpt: 89.097350 acc_pose: 0.837193 loss: 89.097350 2022/10/12 16:42:14 - mmengine - INFO - Epoch(train) [124][200/586] lr: 2.000000e-02 eta: 3:33:52 time: 0.281677 data_time: 0.047398 memory: 2937 loss_kpt: 91.293195 acc_pose: 0.801606 loss: 91.293195 2022/10/12 16:42:28 - mmengine - INFO - Epoch(train) [124][250/586] lr: 2.000000e-02 eta: 3:33:41 time: 0.295451 data_time: 0.055886 memory: 2937 loss_kpt: 89.925664 acc_pose: 0.755537 loss: 89.925664 2022/10/12 16:42:42 - mmengine - INFO - Epoch(train) [124][300/586] lr: 2.000000e-02 eta: 3:33:29 time: 0.273893 data_time: 0.048367 memory: 2937 loss_kpt: 91.931177 acc_pose: 0.856487 loss: 91.931177 2022/10/12 16:42:56 - mmengine - INFO - Epoch(train) [124][350/586] lr: 2.000000e-02 eta: 3:33:18 time: 0.283287 data_time: 0.047714 memory: 2937 loss_kpt: 90.516074 acc_pose: 0.802466 loss: 90.516074 2022/10/12 16:43:10 - mmengine - INFO - Epoch(train) [124][400/586] lr: 2.000000e-02 eta: 3:33:06 time: 0.274192 data_time: 0.050147 memory: 2937 loss_kpt: 91.695755 acc_pose: 0.808592 loss: 91.695755 2022/10/12 16:43:24 - mmengine - INFO - Epoch(train) [124][450/586] lr: 2.000000e-02 eta: 3:32:54 time: 0.282178 data_time: 0.052150 memory: 2937 loss_kpt: 91.174377 acc_pose: 0.777308 loss: 91.174377 2022/10/12 16:43:38 - mmengine - INFO - Epoch(train) [124][500/586] lr: 2.000000e-02 eta: 3:32:42 time: 0.275219 data_time: 0.053355 memory: 2937 loss_kpt: 90.476522 acc_pose: 0.768018 loss: 90.476522 2022/10/12 16:43:51 - mmengine - INFO - Epoch(train) [124][550/586] lr: 2.000000e-02 eta: 3:32:30 time: 0.265175 data_time: 0.054449 memory: 2937 loss_kpt: 89.221024 acc_pose: 0.783612 loss: 89.221024 2022/10/12 16:44:00 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:44:15 - mmengine - INFO - Epoch(train) [125][50/586] lr: 2.000000e-02 eta: 3:32:03 time: 0.289257 data_time: 0.059950 memory: 2937 loss_kpt: 91.134275 acc_pose: 0.730866 loss: 91.134275 2022/10/12 16:44:29 - mmengine - INFO - Epoch(train) [125][100/586] lr: 2.000000e-02 eta: 3:31:51 time: 0.273534 data_time: 0.054182 memory: 2937 loss_kpt: 91.014694 acc_pose: 0.753245 loss: 91.014694 2022/10/12 16:44:42 - mmengine - INFO - Epoch(train) [125][150/586] lr: 2.000000e-02 eta: 3:31:39 time: 0.267502 data_time: 0.053382 memory: 2937 loss_kpt: 89.169761 acc_pose: 0.731643 loss: 89.169761 2022/10/12 16:44:55 - mmengine - INFO - Epoch(train) [125][200/586] lr: 2.000000e-02 eta: 3:31:27 time: 0.261905 data_time: 0.052921 memory: 2937 loss_kpt: 90.994917 acc_pose: 0.718381 loss: 90.994917 2022/10/12 16:45:09 - mmengine - INFO - Epoch(train) [125][250/586] lr: 2.000000e-02 eta: 3:31:15 time: 0.279643 data_time: 0.052749 memory: 2937 loss_kpt: 91.561749 acc_pose: 0.833134 loss: 91.561749 2022/10/12 16:45:23 - mmengine - INFO - Epoch(train) [125][300/586] lr: 2.000000e-02 eta: 3:31:03 time: 0.274197 data_time: 0.054272 memory: 2937 loss_kpt: 89.251884 acc_pose: 0.839390 loss: 89.251884 2022/10/12 16:45:32 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:45:36 - mmengine - INFO - Epoch(train) [125][350/586] lr: 2.000000e-02 eta: 3:30:51 time: 0.268281 data_time: 0.052777 memory: 2937 loss_kpt: 91.560913 acc_pose: 0.807713 loss: 91.560913 2022/10/12 16:45:50 - mmengine - INFO - Epoch(train) [125][400/586] lr: 2.000000e-02 eta: 3:30:39 time: 0.271240 data_time: 0.060015 memory: 2937 loss_kpt: 89.873839 acc_pose: 0.806971 loss: 89.873839 2022/10/12 16:46:03 - mmengine - INFO - Epoch(train) [125][450/586] lr: 2.000000e-02 eta: 3:30:27 time: 0.267117 data_time: 0.054941 memory: 2937 loss_kpt: 91.601065 acc_pose: 0.770356 loss: 91.601065 2022/10/12 16:46:17 - mmengine - INFO - Epoch(train) [125][500/586] lr: 2.000000e-02 eta: 3:30:15 time: 0.270476 data_time: 0.056900 memory: 2937 loss_kpt: 90.509550 acc_pose: 0.744029 loss: 90.509550 2022/10/12 16:46:30 - mmengine - INFO - Epoch(train) [125][550/586] lr: 2.000000e-02 eta: 3:30:03 time: 0.270421 data_time: 0.057281 memory: 2937 loss_kpt: 91.771882 acc_pose: 0.781588 loss: 91.771882 2022/10/12 16:46:40 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:46:54 - mmengine - INFO - Epoch(train) [126][50/586] lr: 2.000000e-02 eta: 3:29:36 time: 0.285436 data_time: 0.065891 memory: 2937 loss_kpt: 90.327849 acc_pose: 0.806053 loss: 90.327849 2022/10/12 16:47:07 - mmengine - INFO - Epoch(train) [126][100/586] lr: 2.000000e-02 eta: 3:29:24 time: 0.267595 data_time: 0.054317 memory: 2937 loss_kpt: 89.104111 acc_pose: 0.819393 loss: 89.104111 2022/10/12 16:47:21 - mmengine - INFO - Epoch(train) [126][150/586] lr: 2.000000e-02 eta: 3:29:12 time: 0.271699 data_time: 0.054319 memory: 2937 loss_kpt: 88.481809 acc_pose: 0.822177 loss: 88.481809 2022/10/12 16:47:34 - mmengine - INFO - Epoch(train) [126][200/586] lr: 2.000000e-02 eta: 3:29:00 time: 0.266396 data_time: 0.050366 memory: 2937 loss_kpt: 89.682741 acc_pose: 0.778940 loss: 89.682741 2022/10/12 16:47:48 - mmengine - INFO - Epoch(train) [126][250/586] lr: 2.000000e-02 eta: 3:28:48 time: 0.269263 data_time: 0.052118 memory: 2937 loss_kpt: 90.784737 acc_pose: 0.779152 loss: 90.784737 2022/10/12 16:48:01 - mmengine - INFO - Epoch(train) [126][300/586] lr: 2.000000e-02 eta: 3:28:36 time: 0.272298 data_time: 0.052049 memory: 2937 loss_kpt: 90.845109 acc_pose: 0.811739 loss: 90.845109 2022/10/12 16:48:15 - mmengine - INFO - Epoch(train) [126][350/586] lr: 2.000000e-02 eta: 3:28:23 time: 0.264798 data_time: 0.048275 memory: 2937 loss_kpt: 90.070272 acc_pose: 0.775268 loss: 90.070272 2022/10/12 16:48:28 - mmengine - INFO - Epoch(train) [126][400/586] lr: 2.000000e-02 eta: 3:28:12 time: 0.275228 data_time: 0.052934 memory: 2937 loss_kpt: 89.589823 acc_pose: 0.754465 loss: 89.589823 2022/10/12 16:48:42 - mmengine - INFO - Epoch(train) [126][450/586] lr: 2.000000e-02 eta: 3:28:00 time: 0.270723 data_time: 0.052034 memory: 2937 loss_kpt: 89.445871 acc_pose: 0.845737 loss: 89.445871 2022/10/12 16:48:55 - mmengine - INFO - Epoch(train) [126][500/586] lr: 2.000000e-02 eta: 3:27:47 time: 0.267405 data_time: 0.047771 memory: 2937 loss_kpt: 90.227240 acc_pose: 0.760825 loss: 90.227240 2022/10/12 16:49:09 - mmengine - INFO - Epoch(train) [126][550/586] lr: 2.000000e-02 eta: 3:27:35 time: 0.269539 data_time: 0.056209 memory: 2937 loss_kpt: 88.992951 acc_pose: 0.828190 loss: 88.992951 2022/10/12 16:49:19 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:49:33 - mmengine - INFO - Epoch(train) [127][50/586] lr: 2.000000e-02 eta: 3:27:09 time: 0.287568 data_time: 0.064264 memory: 2937 loss_kpt: 91.662523 acc_pose: 0.760282 loss: 91.662523 2022/10/12 16:49:47 - mmengine - INFO - Epoch(train) [127][100/586] lr: 2.000000e-02 eta: 3:26:57 time: 0.277124 data_time: 0.055083 memory: 2937 loss_kpt: 90.124854 acc_pose: 0.763715 loss: 90.124854 2022/10/12 16:50:01 - mmengine - INFO - Epoch(train) [127][150/586] lr: 2.000000e-02 eta: 3:26:45 time: 0.280853 data_time: 0.054336 memory: 2937 loss_kpt: 87.867157 acc_pose: 0.794589 loss: 87.867157 2022/10/12 16:50:05 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:50:15 - mmengine - INFO - Epoch(train) [127][200/586] lr: 2.000000e-02 eta: 3:26:33 time: 0.272646 data_time: 0.051334 memory: 2937 loss_kpt: 89.471972 acc_pose: 0.811020 loss: 89.471972 2022/10/12 16:50:29 - mmengine - INFO - Epoch(train) [127][250/586] lr: 2.000000e-02 eta: 3:26:22 time: 0.284499 data_time: 0.057920 memory: 2937 loss_kpt: 90.271487 acc_pose: 0.844270 loss: 90.271487 2022/10/12 16:50:43 - mmengine - INFO - Epoch(train) [127][300/586] lr: 2.000000e-02 eta: 3:26:10 time: 0.287873 data_time: 0.055248 memory: 2937 loss_kpt: 89.966706 acc_pose: 0.743472 loss: 89.966706 2022/10/12 16:50:57 - mmengine - INFO - Epoch(train) [127][350/586] lr: 2.000000e-02 eta: 3:25:58 time: 0.274925 data_time: 0.057571 memory: 2937 loss_kpt: 91.926751 acc_pose: 0.762586 loss: 91.926751 2022/10/12 16:51:10 - mmengine - INFO - Epoch(train) [127][400/586] lr: 2.000000e-02 eta: 3:25:46 time: 0.266060 data_time: 0.054004 memory: 2937 loss_kpt: 90.981052 acc_pose: 0.816273 loss: 90.981052 2022/10/12 16:51:24 - mmengine - INFO - Epoch(train) [127][450/586] lr: 2.000000e-02 eta: 3:25:34 time: 0.273256 data_time: 0.052197 memory: 2937 loss_kpt: 89.577174 acc_pose: 0.792019 loss: 89.577174 2022/10/12 16:51:37 - mmengine - INFO - Epoch(train) [127][500/586] lr: 2.000000e-02 eta: 3:25:22 time: 0.267939 data_time: 0.056235 memory: 2937 loss_kpt: 91.035566 acc_pose: 0.732389 loss: 91.035566 2022/10/12 16:51:51 - mmengine - INFO - Epoch(train) [127][550/586] lr: 2.000000e-02 eta: 3:25:10 time: 0.271521 data_time: 0.050065 memory: 2937 loss_kpt: 88.378639 acc_pose: 0.806681 loss: 88.378639 2022/10/12 16:52:00 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:52:15 - mmengine - INFO - Epoch(train) [128][50/586] lr: 2.000000e-02 eta: 3:24:43 time: 0.294812 data_time: 0.061310 memory: 2937 loss_kpt: 89.754652 acc_pose: 0.827024 loss: 89.754652 2022/10/12 16:52:29 - mmengine - INFO - Epoch(train) [128][100/586] lr: 2.000000e-02 eta: 3:24:32 time: 0.275270 data_time: 0.052040 memory: 2937 loss_kpt: 91.093661 acc_pose: 0.777526 loss: 91.093661 2022/10/12 16:52:43 - mmengine - INFO - Epoch(train) [128][150/586] lr: 2.000000e-02 eta: 3:24:20 time: 0.284100 data_time: 0.050390 memory: 2937 loss_kpt: 90.651607 acc_pose: 0.789389 loss: 90.651607 2022/10/12 16:52:57 - mmengine - INFO - Epoch(train) [128][200/586] lr: 2.000000e-02 eta: 3:24:08 time: 0.274547 data_time: 0.051657 memory: 2937 loss_kpt: 91.479099 acc_pose: 0.778911 loss: 91.479099 2022/10/12 16:53:10 - mmengine - INFO - Epoch(train) [128][250/586] lr: 2.000000e-02 eta: 3:23:56 time: 0.274136 data_time: 0.050467 memory: 2937 loss_kpt: 91.537751 acc_pose: 0.751536 loss: 91.537751 2022/10/12 16:53:24 - mmengine - INFO - Epoch(train) [128][300/586] lr: 2.000000e-02 eta: 3:23:44 time: 0.263638 data_time: 0.051420 memory: 2937 loss_kpt: 90.479351 acc_pose: 0.798480 loss: 90.479351 2022/10/12 16:53:36 - mmengine - INFO - Epoch(train) [128][350/586] lr: 2.000000e-02 eta: 3:23:31 time: 0.258105 data_time: 0.048699 memory: 2937 loss_kpt: 91.014148 acc_pose: 0.770574 loss: 91.014148 2022/10/12 16:53:50 - mmengine - INFO - Epoch(train) [128][400/586] lr: 2.000000e-02 eta: 3:23:19 time: 0.272258 data_time: 0.052741 memory: 2937 loss_kpt: 90.090917 acc_pose: 0.801521 loss: 90.090917 2022/10/12 16:54:03 - mmengine - INFO - Epoch(train) [128][450/586] lr: 2.000000e-02 eta: 3:23:07 time: 0.265410 data_time: 0.052239 memory: 2937 loss_kpt: 90.341532 acc_pose: 0.811349 loss: 90.341532 2022/10/12 16:54:16 - mmengine - INFO - Epoch(train) [128][500/586] lr: 2.000000e-02 eta: 3:22:55 time: 0.259210 data_time: 0.051224 memory: 2937 loss_kpt: 91.159969 acc_pose: 0.805070 loss: 91.159969 2022/10/12 16:54:29 - mmengine - INFO - Epoch(train) [128][550/586] lr: 2.000000e-02 eta: 3:22:42 time: 0.260888 data_time: 0.051179 memory: 2937 loss_kpt: 90.293345 acc_pose: 0.765968 loss: 90.293345 2022/10/12 16:54:37 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:54:38 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:54:53 - mmengine - INFO - Epoch(train) [129][50/586] lr: 2.000000e-02 eta: 3:22:16 time: 0.297062 data_time: 0.064893 memory: 2937 loss_kpt: 89.689440 acc_pose: 0.835313 loss: 89.689440 2022/10/12 16:55:07 - mmengine - INFO - Epoch(train) [129][100/586] lr: 2.000000e-02 eta: 3:22:04 time: 0.279943 data_time: 0.053625 memory: 2937 loss_kpt: 89.847974 acc_pose: 0.843355 loss: 89.847974 2022/10/12 16:55:21 - mmengine - INFO - Epoch(train) [129][150/586] lr: 2.000000e-02 eta: 3:21:52 time: 0.272723 data_time: 0.047796 memory: 2937 loss_kpt: 92.104275 acc_pose: 0.756286 loss: 92.104275 2022/10/12 16:55:35 - mmengine - INFO - Epoch(train) [129][200/586] lr: 2.000000e-02 eta: 3:21:40 time: 0.272220 data_time: 0.056393 memory: 2937 loss_kpt: 90.176791 acc_pose: 0.793318 loss: 90.176791 2022/10/12 16:55:48 - mmengine - INFO - Epoch(train) [129][250/586] lr: 2.000000e-02 eta: 3:21:28 time: 0.269477 data_time: 0.052082 memory: 2937 loss_kpt: 90.381470 acc_pose: 0.719194 loss: 90.381470 2022/10/12 16:56:02 - mmengine - INFO - Epoch(train) [129][300/586] lr: 2.000000e-02 eta: 3:21:16 time: 0.280711 data_time: 0.051589 memory: 2937 loss_kpt: 90.719153 acc_pose: 0.880632 loss: 90.719153 2022/10/12 16:56:16 - mmengine - INFO - Epoch(train) [129][350/586] lr: 2.000000e-02 eta: 3:21:05 time: 0.288035 data_time: 0.049607 memory: 2937 loss_kpt: 89.406104 acc_pose: 0.721670 loss: 89.406104 2022/10/12 16:56:30 - mmengine - INFO - Epoch(train) [129][400/586] lr: 2.000000e-02 eta: 3:20:53 time: 0.280536 data_time: 0.052877 memory: 2937 loss_kpt: 90.590850 acc_pose: 0.759721 loss: 90.590850 2022/10/12 16:56:44 - mmengine - INFO - Epoch(train) [129][450/586] lr: 2.000000e-02 eta: 3:20:41 time: 0.266258 data_time: 0.051884 memory: 2937 loss_kpt: 89.699880 acc_pose: 0.788559 loss: 89.699880 2022/10/12 16:56:57 - mmengine - INFO - Epoch(train) [129][500/586] lr: 2.000000e-02 eta: 3:20:29 time: 0.272485 data_time: 0.055094 memory: 2937 loss_kpt: 90.399126 acc_pose: 0.815936 loss: 90.399126 2022/10/12 16:57:11 - mmengine - INFO - Epoch(train) [129][550/586] lr: 2.000000e-02 eta: 3:20:16 time: 0.262166 data_time: 0.053046 memory: 2937 loss_kpt: 89.483319 acc_pose: 0.825339 loss: 89.483319 2022/10/12 16:57:20 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:57:34 - mmengine - INFO - Epoch(train) [130][50/586] lr: 2.000000e-02 eta: 3:19:50 time: 0.281304 data_time: 0.058576 memory: 2937 loss_kpt: 91.343488 acc_pose: 0.818395 loss: 91.343488 2022/10/12 16:57:48 - mmengine - INFO - Epoch(train) [130][100/586] lr: 2.000000e-02 eta: 3:19:37 time: 0.265226 data_time: 0.053153 memory: 2937 loss_kpt: 90.698706 acc_pose: 0.800342 loss: 90.698706 2022/10/12 16:58:01 - mmengine - INFO - Epoch(train) [130][150/586] lr: 2.000000e-02 eta: 3:19:25 time: 0.272504 data_time: 0.052650 memory: 2937 loss_kpt: 89.466514 acc_pose: 0.700655 loss: 89.466514 2022/10/12 16:58:15 - mmengine - INFO - Epoch(train) [130][200/586] lr: 2.000000e-02 eta: 3:19:14 time: 0.281169 data_time: 0.048789 memory: 2937 loss_kpt: 91.150148 acc_pose: 0.800442 loss: 91.150148 2022/10/12 16:58:29 - mmengine - INFO - Epoch(train) [130][250/586] lr: 2.000000e-02 eta: 3:19:02 time: 0.276504 data_time: 0.052363 memory: 2937 loss_kpt: 92.107896 acc_pose: 0.738044 loss: 92.107896 2022/10/12 16:58:43 - mmengine - INFO - Epoch(train) [130][300/586] lr: 2.000000e-02 eta: 3:18:50 time: 0.274421 data_time: 0.053068 memory: 2937 loss_kpt: 89.033242 acc_pose: 0.790402 loss: 89.033242 2022/10/12 16:58:57 - mmengine - INFO - Epoch(train) [130][350/586] lr: 2.000000e-02 eta: 3:18:38 time: 0.270912 data_time: 0.054870 memory: 2937 loss_kpt: 89.358674 acc_pose: 0.798279 loss: 89.358674 2022/10/12 16:59:10 - mmengine - INFO - Epoch(train) [130][400/586] lr: 2.000000e-02 eta: 3:18:26 time: 0.273562 data_time: 0.055800 memory: 2937 loss_kpt: 90.533082 acc_pose: 0.770495 loss: 90.533082 2022/10/12 16:59:12 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 16:59:24 - mmengine - INFO - Epoch(train) [130][450/586] lr: 2.000000e-02 eta: 3:18:14 time: 0.274700 data_time: 0.055534 memory: 2937 loss_kpt: 90.116819 acc_pose: 0.800871 loss: 90.116819 2022/10/12 16:59:37 - mmengine - INFO - Epoch(train) [130][500/586] lr: 2.000000e-02 eta: 3:18:02 time: 0.268387 data_time: 0.053938 memory: 2937 loss_kpt: 90.298686 acc_pose: 0.772566 loss: 90.298686 2022/10/12 16:59:51 - mmengine - INFO - Epoch(train) [130][550/586] lr: 2.000000e-02 eta: 3:17:49 time: 0.266545 data_time: 0.053770 memory: 2937 loss_kpt: 90.522507 acc_pose: 0.776016 loss: 90.522507 2022/10/12 17:00:00 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:00:00 - mmengine - INFO - Saving checkpoint at 130 epochs 2022/10/12 17:00:09 - mmengine - INFO - Epoch(val) [130][50/407] eta: 0:00:41 time: 0.116779 data_time: 0.015494 memory: 2937 2022/10/12 17:00:14 - mmengine - INFO - Epoch(val) [130][100/407] eta: 0:00:34 time: 0.112420 data_time: 0.009439 memory: 830 2022/10/12 17:00:20 - mmengine - INFO - Epoch(val) [130][150/407] eta: 0:00:28 time: 0.109189 data_time: 0.009274 memory: 830 2022/10/12 17:00:25 - mmengine - INFO - Epoch(val) [130][200/407] eta: 0:00:22 time: 0.109317 data_time: 0.009525 memory: 830 2022/10/12 17:00:31 - mmengine - INFO - Epoch(val) [130][250/407] eta: 0:00:17 time: 0.110529 data_time: 0.009276 memory: 830 2022/10/12 17:00:36 - mmengine - INFO - Epoch(val) [130][300/407] eta: 0:00:11 time: 0.110740 data_time: 0.009278 memory: 830 2022/10/12 17:00:42 - mmengine - INFO - Epoch(val) [130][350/407] eta: 0:00:06 time: 0.109621 data_time: 0.008941 memory: 830 2022/10/12 17:00:47 - mmengine - INFO - Epoch(val) [130][400/407] eta: 0:00:00 time: 0.100351 data_time: 0.007704 memory: 830 2022/10/12 17:01:00 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 17:01:16 - mmengine - INFO - Epoch(val) [130][407/407] coco/AP: 0.681087 coco/AP .5: 0.877657 coco/AP .75: 0.757977 coco/AP (M): 0.649973 coco/AP (L): 0.740180 coco/AR: 0.751071 coco/AR .5: 0.919081 coco/AR .75: 0.814704 coco/AR (M): 0.705026 coco/AR (L): 0.814381 2022/10/12 17:01:16 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221012/rsn18/best_coco/AP_epoch_120.pth is removed 2022/10/12 17:01:17 - mmengine - INFO - The best checkpoint with 0.6811 coco/AP at 130 epoch is saved to best_coco/AP_epoch_130.pth. 2022/10/12 17:01:32 - mmengine - INFO - Epoch(train) [131][50/586] lr: 2.000000e-02 eta: 3:17:23 time: 0.287888 data_time: 0.063328 memory: 2937 loss_kpt: 89.845305 acc_pose: 0.771763 loss: 89.845305 2022/10/12 17:01:45 - mmengine - INFO - Epoch(train) [131][100/586] lr: 2.000000e-02 eta: 3:17:11 time: 0.271816 data_time: 0.053805 memory: 2937 loss_kpt: 91.112915 acc_pose: 0.787711 loss: 91.112915 2022/10/12 17:01:59 - mmengine - INFO - Epoch(train) [131][150/586] lr: 2.000000e-02 eta: 3:16:59 time: 0.263591 data_time: 0.050938 memory: 2937 loss_kpt: 91.114999 acc_pose: 0.760661 loss: 91.114999 2022/10/12 17:02:12 - mmengine - INFO - Epoch(train) [131][200/586] lr: 2.000000e-02 eta: 3:16:46 time: 0.268329 data_time: 0.055613 memory: 2937 loss_kpt: 90.044062 acc_pose: 0.745463 loss: 90.044062 2022/10/12 17:02:26 - mmengine - INFO - Epoch(train) [131][250/586] lr: 2.000000e-02 eta: 3:16:34 time: 0.272456 data_time: 0.052785 memory: 2937 loss_kpt: 90.046929 acc_pose: 0.750274 loss: 90.046929 2022/10/12 17:02:38 - mmengine - INFO - Epoch(train) [131][300/586] lr: 2.000000e-02 eta: 3:16:22 time: 0.254090 data_time: 0.053018 memory: 2937 loss_kpt: 89.499952 acc_pose: 0.829004 loss: 89.499952 2022/10/12 17:02:51 - mmengine - INFO - Epoch(train) [131][350/586] lr: 2.000000e-02 eta: 3:16:09 time: 0.256090 data_time: 0.054411 memory: 2937 loss_kpt: 89.430890 acc_pose: 0.822452 loss: 89.430890 2022/10/12 17:03:04 - mmengine - INFO - Epoch(train) [131][400/586] lr: 2.000000e-02 eta: 3:15:57 time: 0.258896 data_time: 0.050944 memory: 2937 loss_kpt: 89.740707 acc_pose: 0.806442 loss: 89.740707 2022/10/12 17:03:18 - mmengine - INFO - Epoch(train) [131][450/586] lr: 2.000000e-02 eta: 3:15:45 time: 0.269389 data_time: 0.051723 memory: 2937 loss_kpt: 89.460560 acc_pose: 0.784509 loss: 89.460560 2022/10/12 17:03:30 - mmengine - INFO - Epoch(train) [131][500/586] lr: 2.000000e-02 eta: 3:15:32 time: 0.257679 data_time: 0.055482 memory: 2937 loss_kpt: 90.877034 acc_pose: 0.751210 loss: 90.877034 2022/10/12 17:03:43 - mmengine - INFO - Epoch(train) [131][550/586] lr: 2.000000e-02 eta: 3:15:20 time: 0.258551 data_time: 0.056581 memory: 2937 loss_kpt: 90.072300 acc_pose: 0.825991 loss: 90.072300 2022/10/12 17:03:53 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:04:07 - mmengine - INFO - Epoch(train) [132][50/586] lr: 2.000000e-02 eta: 3:14:53 time: 0.279022 data_time: 0.061090 memory: 2937 loss_kpt: 91.026262 acc_pose: 0.805734 loss: 91.026262 2022/10/12 17:04:20 - mmengine - INFO - Epoch(train) [132][100/586] lr: 2.000000e-02 eta: 3:14:41 time: 0.267396 data_time: 0.051168 memory: 2937 loss_kpt: 90.336221 acc_pose: 0.802123 loss: 90.336221 2022/10/12 17:04:34 - mmengine - INFO - Epoch(train) [132][150/586] lr: 2.000000e-02 eta: 3:14:29 time: 0.277887 data_time: 0.056400 memory: 2937 loss_kpt: 90.935412 acc_pose: 0.810161 loss: 90.935412 2022/10/12 17:04:48 - mmengine - INFO - Epoch(train) [132][200/586] lr: 2.000000e-02 eta: 3:14:17 time: 0.269747 data_time: 0.049796 memory: 2937 loss_kpt: 90.550627 acc_pose: 0.725205 loss: 90.550627 2022/10/12 17:04:57 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:05:01 - mmengine - INFO - Epoch(train) [132][250/586] lr: 2.000000e-02 eta: 3:14:05 time: 0.261868 data_time: 0.052988 memory: 2937 loss_kpt: 90.079415 acc_pose: 0.765217 loss: 90.079415 2022/10/12 17:05:14 - mmengine - INFO - Epoch(train) [132][300/586] lr: 2.000000e-02 eta: 3:13:52 time: 0.263477 data_time: 0.050138 memory: 2937 loss_kpt: 89.664215 acc_pose: 0.811474 loss: 89.664215 2022/10/12 17:05:27 - mmengine - INFO - Epoch(train) [132][350/586] lr: 2.000000e-02 eta: 3:13:40 time: 0.262151 data_time: 0.052208 memory: 2937 loss_kpt: 89.767334 acc_pose: 0.778363 loss: 89.767334 2022/10/12 17:05:40 - mmengine - INFO - Epoch(train) [132][400/586] lr: 2.000000e-02 eta: 3:13:27 time: 0.254366 data_time: 0.048833 memory: 2937 loss_kpt: 89.941314 acc_pose: 0.769783 loss: 89.941314 2022/10/12 17:05:52 - mmengine - INFO - Epoch(train) [132][450/586] lr: 2.000000e-02 eta: 3:13:15 time: 0.255816 data_time: 0.052486 memory: 2937 loss_kpt: 90.796670 acc_pose: 0.803139 loss: 90.796670 2022/10/12 17:06:06 - mmengine - INFO - Epoch(train) [132][500/586] lr: 2.000000e-02 eta: 3:13:02 time: 0.261154 data_time: 0.052554 memory: 2937 loss_kpt: 91.581994 acc_pose: 0.800849 loss: 91.581994 2022/10/12 17:06:19 - mmengine - INFO - Epoch(train) [132][550/586] lr: 2.000000e-02 eta: 3:12:50 time: 0.258590 data_time: 0.053207 memory: 2937 loss_kpt: 91.179129 acc_pose: 0.822688 loss: 91.179129 2022/10/12 17:06:28 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:06:42 - mmengine - INFO - Epoch(train) [133][50/586] lr: 2.000000e-02 eta: 3:12:24 time: 0.282188 data_time: 0.059792 memory: 2937 loss_kpt: 88.505656 acc_pose: 0.764609 loss: 88.505656 2022/10/12 17:06:55 - mmengine - INFO - Epoch(train) [133][100/586] lr: 2.000000e-02 eta: 3:12:12 time: 0.275781 data_time: 0.055642 memory: 2937 loss_kpt: 90.232731 acc_pose: 0.720375 loss: 90.232731 2022/10/12 17:07:10 - mmengine - INFO - Epoch(train) [133][150/586] lr: 2.000000e-02 eta: 3:12:00 time: 0.293455 data_time: 0.048446 memory: 2937 loss_kpt: 89.016904 acc_pose: 0.773637 loss: 89.016904 2022/10/12 17:07:25 - mmengine - INFO - Epoch(train) [133][200/586] lr: 2.000000e-02 eta: 3:11:49 time: 0.298141 data_time: 0.053744 memory: 2937 loss_kpt: 89.649242 acc_pose: 0.759315 loss: 89.649242 2022/10/12 17:07:39 - mmengine - INFO - Epoch(train) [133][250/586] lr: 2.000000e-02 eta: 3:11:37 time: 0.281349 data_time: 0.054334 memory: 2937 loss_kpt: 88.767644 acc_pose: 0.810110 loss: 88.767644 2022/10/12 17:07:53 - mmengine - INFO - Epoch(train) [133][300/586] lr: 2.000000e-02 eta: 3:11:25 time: 0.266526 data_time: 0.049300 memory: 2937 loss_kpt: 90.074034 acc_pose: 0.778343 loss: 90.074034 2022/10/12 17:08:05 - mmengine - INFO - Epoch(train) [133][350/586] lr: 2.000000e-02 eta: 3:11:12 time: 0.256265 data_time: 0.048187 memory: 2937 loss_kpt: 90.613102 acc_pose: 0.745015 loss: 90.613102 2022/10/12 17:08:19 - mmengine - INFO - Epoch(train) [133][400/586] lr: 2.000000e-02 eta: 3:11:00 time: 0.265368 data_time: 0.052889 memory: 2937 loss_kpt: 89.849348 acc_pose: 0.771623 loss: 89.849348 2022/10/12 17:08:32 - mmengine - INFO - Epoch(train) [133][450/586] lr: 2.000000e-02 eta: 3:10:48 time: 0.272618 data_time: 0.051187 memory: 2937 loss_kpt: 88.934368 acc_pose: 0.762821 loss: 88.934368 2022/10/12 17:08:46 - mmengine - INFO - Epoch(train) [133][500/586] lr: 2.000000e-02 eta: 3:10:36 time: 0.264374 data_time: 0.047574 memory: 2937 loss_kpt: 90.788306 acc_pose: 0.723959 loss: 90.788306 2022/10/12 17:08:59 - mmengine - INFO - Epoch(train) [133][550/586] lr: 2.000000e-02 eta: 3:10:24 time: 0.274801 data_time: 0.050881 memory: 2937 loss_kpt: 89.514657 acc_pose: 0.756471 loss: 89.514657 2022/10/12 17:09:09 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:09:24 - mmengine - INFO - Epoch(train) [134][50/586] lr: 2.000000e-02 eta: 3:09:58 time: 0.300221 data_time: 0.061818 memory: 2937 loss_kpt: 89.277184 acc_pose: 0.772391 loss: 89.277184 2022/10/12 17:09:27 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:09:37 - mmengine - INFO - Epoch(train) [134][100/586] lr: 2.000000e-02 eta: 3:09:46 time: 0.265771 data_time: 0.048581 memory: 2937 loss_kpt: 89.932938 acc_pose: 0.840487 loss: 89.932938 2022/10/12 17:09:51 - mmengine - INFO - Epoch(train) [134][150/586] lr: 2.000000e-02 eta: 3:09:34 time: 0.273655 data_time: 0.053144 memory: 2937 loss_kpt: 90.100233 acc_pose: 0.794874 loss: 90.100233 2022/10/12 17:10:05 - mmengine - INFO - Epoch(train) [134][200/586] lr: 2.000000e-02 eta: 3:09:22 time: 0.278877 data_time: 0.053025 memory: 2937 loss_kpt: 89.289263 acc_pose: 0.789054 loss: 89.289263 2022/10/12 17:10:19 - mmengine - INFO - Epoch(train) [134][250/586] lr: 2.000000e-02 eta: 3:09:10 time: 0.278961 data_time: 0.052874 memory: 2937 loss_kpt: 89.190967 acc_pose: 0.794295 loss: 89.190967 2022/10/12 17:10:33 - mmengine - INFO - Epoch(train) [134][300/586] lr: 2.000000e-02 eta: 3:08:58 time: 0.293108 data_time: 0.053612 memory: 2937 loss_kpt: 90.078634 acc_pose: 0.709904 loss: 90.078634 2022/10/12 17:10:48 - mmengine - INFO - Epoch(train) [134][350/586] lr: 2.000000e-02 eta: 3:08:47 time: 0.287161 data_time: 0.047339 memory: 2937 loss_kpt: 90.377856 acc_pose: 0.822420 loss: 90.377856 2022/10/12 17:11:01 - mmengine - INFO - Epoch(train) [134][400/586] lr: 2.000000e-02 eta: 3:08:35 time: 0.275014 data_time: 0.053474 memory: 2937 loss_kpt: 89.906184 acc_pose: 0.815564 loss: 89.906184 2022/10/12 17:11:15 - mmengine - INFO - Epoch(train) [134][450/586] lr: 2.000000e-02 eta: 3:08:22 time: 0.272295 data_time: 0.053660 memory: 2937 loss_kpt: 88.790720 acc_pose: 0.770844 loss: 88.790720 2022/10/12 17:11:29 - mmengine - INFO - Epoch(train) [134][500/586] lr: 2.000000e-02 eta: 3:08:11 time: 0.276704 data_time: 0.051278 memory: 2937 loss_kpt: 89.366055 acc_pose: 0.845024 loss: 89.366055 2022/10/12 17:11:42 - mmengine - INFO - Epoch(train) [134][550/586] lr: 2.000000e-02 eta: 3:07:58 time: 0.264333 data_time: 0.052760 memory: 2937 loss_kpt: 90.471462 acc_pose: 0.871683 loss: 90.471462 2022/10/12 17:11:52 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:12:07 - mmengine - INFO - Epoch(train) [135][50/586] lr: 2.000000e-02 eta: 3:07:33 time: 0.300845 data_time: 0.065864 memory: 2937 loss_kpt: 90.351962 acc_pose: 0.826992 loss: 90.351962 2022/10/12 17:12:21 - mmengine - INFO - Epoch(train) [135][100/586] lr: 2.000000e-02 eta: 3:07:21 time: 0.287960 data_time: 0.050816 memory: 2937 loss_kpt: 89.632886 acc_pose: 0.776674 loss: 89.632886 2022/10/12 17:12:35 - mmengine - INFO - Epoch(train) [135][150/586] lr: 2.000000e-02 eta: 3:07:09 time: 0.277952 data_time: 0.053826 memory: 2937 loss_kpt: 90.584067 acc_pose: 0.795810 loss: 90.584067 2022/10/12 17:12:49 - mmengine - INFO - Epoch(train) [135][200/586] lr: 2.000000e-02 eta: 3:06:57 time: 0.282804 data_time: 0.048278 memory: 2937 loss_kpt: 89.890310 acc_pose: 0.760666 loss: 89.890310 2022/10/12 17:13:03 - mmengine - INFO - Epoch(train) [135][250/586] lr: 2.000000e-02 eta: 3:06:45 time: 0.281655 data_time: 0.053762 memory: 2937 loss_kpt: 90.204482 acc_pose: 0.803925 loss: 90.204482 2022/10/12 17:13:18 - mmengine - INFO - Epoch(train) [135][300/586] lr: 2.000000e-02 eta: 3:06:34 time: 0.288186 data_time: 0.047632 memory: 2937 loss_kpt: 88.939364 acc_pose: 0.747861 loss: 88.939364 2022/10/12 17:13:32 - mmengine - INFO - Epoch(train) [135][350/586] lr: 2.000000e-02 eta: 3:06:22 time: 0.281624 data_time: 0.052556 memory: 2937 loss_kpt: 88.939545 acc_pose: 0.827098 loss: 88.939545 2022/10/12 17:13:45 - mmengine - INFO - Epoch(train) [135][400/586] lr: 2.000000e-02 eta: 3:06:09 time: 0.261572 data_time: 0.046781 memory: 2937 loss_kpt: 91.767634 acc_pose: 0.746753 loss: 91.767634 2022/10/12 17:13:58 - mmengine - INFO - Epoch(train) [135][450/586] lr: 2.000000e-02 eta: 3:05:57 time: 0.266888 data_time: 0.049179 memory: 2937 loss_kpt: 91.294475 acc_pose: 0.733218 loss: 91.294475 2022/10/12 17:14:05 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:14:11 - mmengine - INFO - Epoch(train) [135][500/586] lr: 2.000000e-02 eta: 3:05:45 time: 0.255707 data_time: 0.052414 memory: 2937 loss_kpt: 89.595511 acc_pose: 0.751898 loss: 89.595511 2022/10/12 17:14:25 - mmengine - INFO - Epoch(train) [135][550/586] lr: 2.000000e-02 eta: 3:05:33 time: 0.275588 data_time: 0.049787 memory: 2937 loss_kpt: 91.718347 acc_pose: 0.784990 loss: 91.718347 2022/10/12 17:14:34 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:14:49 - mmengine - INFO - Epoch(train) [136][50/586] lr: 2.000000e-02 eta: 3:05:07 time: 0.285368 data_time: 0.063040 memory: 2937 loss_kpt: 89.401083 acc_pose: 0.775547 loss: 89.401083 2022/10/12 17:15:03 - mmengine - INFO - Epoch(train) [136][100/586] lr: 2.000000e-02 eta: 3:04:55 time: 0.276241 data_time: 0.050537 memory: 2937 loss_kpt: 90.517948 acc_pose: 0.818446 loss: 90.517948 2022/10/12 17:15:16 - mmengine - INFO - Epoch(train) [136][150/586] lr: 2.000000e-02 eta: 3:04:42 time: 0.271162 data_time: 0.054242 memory: 2937 loss_kpt: 90.071347 acc_pose: 0.813728 loss: 90.071347 2022/10/12 17:15:29 - mmengine - INFO - Epoch(train) [136][200/586] lr: 2.000000e-02 eta: 3:04:30 time: 0.261762 data_time: 0.048322 memory: 2937 loss_kpt: 90.649500 acc_pose: 0.840034 loss: 90.649500 2022/10/12 17:15:42 - mmengine - INFO - Epoch(train) [136][250/586] lr: 2.000000e-02 eta: 3:04:18 time: 0.265308 data_time: 0.050306 memory: 2937 loss_kpt: 90.339865 acc_pose: 0.829166 loss: 90.339865 2022/10/12 17:15:56 - mmengine - INFO - Epoch(train) [136][300/586] lr: 2.000000e-02 eta: 3:04:05 time: 0.266570 data_time: 0.050869 memory: 2937 loss_kpt: 89.846441 acc_pose: 0.753602 loss: 89.846441 2022/10/12 17:16:09 - mmengine - INFO - Epoch(train) [136][350/586] lr: 2.000000e-02 eta: 3:03:53 time: 0.271192 data_time: 0.051518 memory: 2937 loss_kpt: 89.878648 acc_pose: 0.806371 loss: 89.878648 2022/10/12 17:16:23 - mmengine - INFO - Epoch(train) [136][400/586] lr: 2.000000e-02 eta: 3:03:41 time: 0.272498 data_time: 0.050181 memory: 2937 loss_kpt: 90.357429 acc_pose: 0.730275 loss: 90.357429 2022/10/12 17:16:36 - mmengine - INFO - Epoch(train) [136][450/586] lr: 2.000000e-02 eta: 3:03:29 time: 0.258960 data_time: 0.052336 memory: 2937 loss_kpt: 89.031765 acc_pose: 0.784846 loss: 89.031765 2022/10/12 17:16:49 - mmengine - INFO - Epoch(train) [136][500/586] lr: 2.000000e-02 eta: 3:03:16 time: 0.255894 data_time: 0.053763 memory: 2937 loss_kpt: 88.952886 acc_pose: 0.753527 loss: 88.952886 2022/10/12 17:17:02 - mmengine - INFO - Epoch(train) [136][550/586] lr: 2.000000e-02 eta: 3:03:04 time: 0.271982 data_time: 0.051860 memory: 2937 loss_kpt: 90.698232 acc_pose: 0.786056 loss: 90.698232 2022/10/12 17:17:12 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:17:26 - mmengine - INFO - Epoch(train) [137][50/586] lr: 2.000000e-02 eta: 3:02:38 time: 0.285342 data_time: 0.063305 memory: 2937 loss_kpt: 89.299581 acc_pose: 0.797600 loss: 89.299581 2022/10/12 17:17:39 - mmengine - INFO - Epoch(train) [137][100/586] lr: 2.000000e-02 eta: 3:02:26 time: 0.260937 data_time: 0.051376 memory: 2937 loss_kpt: 89.454761 acc_pose: 0.835663 loss: 89.454761 2022/10/12 17:17:52 - mmengine - INFO - Epoch(train) [137][150/586] lr: 2.000000e-02 eta: 3:02:13 time: 0.259630 data_time: 0.055384 memory: 2937 loss_kpt: 90.278654 acc_pose: 0.769767 loss: 90.278654 2022/10/12 17:18:05 - mmengine - INFO - Epoch(train) [137][200/586] lr: 2.000000e-02 eta: 3:02:01 time: 0.260774 data_time: 0.048353 memory: 2937 loss_kpt: 89.980638 acc_pose: 0.793337 loss: 89.980638 2022/10/12 17:18:19 - mmengine - INFO - Epoch(train) [137][250/586] lr: 2.000000e-02 eta: 3:01:48 time: 0.262957 data_time: 0.050101 memory: 2937 loss_kpt: 89.351939 acc_pose: 0.702215 loss: 89.351939 2022/10/12 17:18:32 - mmengine - INFO - Epoch(train) [137][300/586] lr: 2.000000e-02 eta: 3:01:36 time: 0.273734 data_time: 0.053122 memory: 2937 loss_kpt: 89.450158 acc_pose: 0.822188 loss: 89.450158 2022/10/12 17:18:33 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:18:46 - mmengine - INFO - Epoch(train) [137][350/586] lr: 2.000000e-02 eta: 3:01:24 time: 0.270335 data_time: 0.055559 memory: 2937 loss_kpt: 87.908569 acc_pose: 0.850710 loss: 87.908569 2022/10/12 17:18:59 - mmengine - INFO - Epoch(train) [137][400/586] lr: 2.000000e-02 eta: 3:01:12 time: 0.269396 data_time: 0.051578 memory: 2937 loss_kpt: 89.699072 acc_pose: 0.844315 loss: 89.699072 2022/10/12 17:19:13 - mmengine - INFO - Epoch(train) [137][450/586] lr: 2.000000e-02 eta: 3:01:00 time: 0.266140 data_time: 0.054616 memory: 2937 loss_kpt: 89.841850 acc_pose: 0.682946 loss: 89.841850 2022/10/12 17:19:26 - mmengine - INFO - Epoch(train) [137][500/586] lr: 2.000000e-02 eta: 3:00:47 time: 0.260968 data_time: 0.054795 memory: 2937 loss_kpt: 89.981779 acc_pose: 0.780585 loss: 89.981779 2022/10/12 17:19:39 - mmengine - INFO - Epoch(train) [137][550/586] lr: 2.000000e-02 eta: 3:00:35 time: 0.260906 data_time: 0.055335 memory: 2937 loss_kpt: 90.310934 acc_pose: 0.745207 loss: 90.310934 2022/10/12 17:19:48 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:20:03 - mmengine - INFO - Epoch(train) [138][50/586] lr: 2.000000e-02 eta: 3:00:09 time: 0.298990 data_time: 0.061315 memory: 2937 loss_kpt: 91.668054 acc_pose: 0.706869 loss: 91.668054 2022/10/12 17:20:17 - mmengine - INFO - Epoch(train) [138][100/586] lr: 2.000000e-02 eta: 2:59:57 time: 0.281316 data_time: 0.053735 memory: 2937 loss_kpt: 90.712171 acc_pose: 0.762734 loss: 90.712171 2022/10/12 17:20:32 - mmengine - INFO - Epoch(train) [138][150/586] lr: 2.000000e-02 eta: 2:59:46 time: 0.291127 data_time: 0.052622 memory: 2937 loss_kpt: 90.505314 acc_pose: 0.777707 loss: 90.505314 2022/10/12 17:20:47 - mmengine - INFO - Epoch(train) [138][200/586] lr: 2.000000e-02 eta: 2:59:34 time: 0.296820 data_time: 0.052665 memory: 2937 loss_kpt: 89.462988 acc_pose: 0.716943 loss: 89.462988 2022/10/12 17:21:01 - mmengine - INFO - Epoch(train) [138][250/586] lr: 2.000000e-02 eta: 2:59:23 time: 0.286365 data_time: 0.049509 memory: 2937 loss_kpt: 89.917599 acc_pose: 0.746472 loss: 89.917599 2022/10/12 17:21:16 - mmengine - INFO - Epoch(train) [138][300/586] lr: 2.000000e-02 eta: 2:59:11 time: 0.307413 data_time: 0.052586 memory: 2937 loss_kpt: 88.128446 acc_pose: 0.716905 loss: 88.128446 2022/10/12 17:21:31 - mmengine - INFO - Epoch(train) [138][350/586] lr: 2.000000e-02 eta: 2:59:00 time: 0.298674 data_time: 0.051476 memory: 2937 loss_kpt: 90.178961 acc_pose: 0.823374 loss: 90.178961 2022/10/12 17:21:45 - mmengine - INFO - Epoch(train) [138][400/586] lr: 2.000000e-02 eta: 2:58:48 time: 0.282922 data_time: 0.046252 memory: 2937 loss_kpt: 89.542896 acc_pose: 0.769309 loss: 89.542896 2022/10/12 17:21:59 - mmengine - INFO - Epoch(train) [138][450/586] lr: 2.000000e-02 eta: 2:58:36 time: 0.276441 data_time: 0.051212 memory: 2937 loss_kpt: 90.328566 acc_pose: 0.843068 loss: 90.328566 2022/10/12 17:22:13 - mmengine - INFO - Epoch(train) [138][500/586] lr: 2.000000e-02 eta: 2:58:24 time: 0.275951 data_time: 0.051658 memory: 2937 loss_kpt: 89.229878 acc_pose: 0.856468 loss: 89.229878 2022/10/12 17:22:27 - mmengine - INFO - Epoch(train) [138][550/586] lr: 2.000000e-02 eta: 2:58:12 time: 0.280449 data_time: 0.049704 memory: 2937 loss_kpt: 89.510508 acc_pose: 0.758306 loss: 89.510508 2022/10/12 17:22:37 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:22:51 - mmengine - INFO - Epoch(train) [139][50/586] lr: 2.000000e-02 eta: 2:57:46 time: 0.282118 data_time: 0.060583 memory: 2937 loss_kpt: 90.766005 acc_pose: 0.753844 loss: 90.766005 2022/10/12 17:23:05 - mmengine - INFO - Epoch(train) [139][100/586] lr: 2.000000e-02 eta: 2:57:34 time: 0.287068 data_time: 0.052021 memory: 2937 loss_kpt: 91.217530 acc_pose: 0.778109 loss: 91.217530 2022/10/12 17:23:14 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:23:20 - mmengine - INFO - Epoch(train) [139][150/586] lr: 2.000000e-02 eta: 2:57:23 time: 0.285134 data_time: 0.048244 memory: 2937 loss_kpt: 90.272795 acc_pose: 0.810189 loss: 90.272795 2022/10/12 17:23:33 - mmengine - INFO - Epoch(train) [139][200/586] lr: 2.000000e-02 eta: 2:57:10 time: 0.271241 data_time: 0.052131 memory: 2937 loss_kpt: 90.418248 acc_pose: 0.783021 loss: 90.418248 2022/10/12 17:23:47 - mmengine - INFO - Epoch(train) [139][250/586] lr: 2.000000e-02 eta: 2:56:58 time: 0.277825 data_time: 0.051843 memory: 2937 loss_kpt: 89.226526 acc_pose: 0.816104 loss: 89.226526 2022/10/12 17:24:01 - mmengine - INFO - Epoch(train) [139][300/586] lr: 2.000000e-02 eta: 2:56:46 time: 0.279280 data_time: 0.048590 memory: 2937 loss_kpt: 90.101706 acc_pose: 0.787921 loss: 90.101706 2022/10/12 17:24:15 - mmengine - INFO - Epoch(train) [139][350/586] lr: 2.000000e-02 eta: 2:56:34 time: 0.282008 data_time: 0.054121 memory: 2937 loss_kpt: 90.497487 acc_pose: 0.839517 loss: 90.497487 2022/10/12 17:24:29 - mmengine - INFO - Epoch(train) [139][400/586] lr: 2.000000e-02 eta: 2:56:22 time: 0.276104 data_time: 0.050205 memory: 2937 loss_kpt: 90.409761 acc_pose: 0.680270 loss: 90.409761 2022/10/12 17:24:43 - mmengine - INFO - Epoch(train) [139][450/586] lr: 2.000000e-02 eta: 2:56:10 time: 0.275127 data_time: 0.049429 memory: 2937 loss_kpt: 90.123628 acc_pose: 0.824433 loss: 90.123628 2022/10/12 17:24:56 - mmengine - INFO - Epoch(train) [139][500/586] lr: 2.000000e-02 eta: 2:55:58 time: 0.265078 data_time: 0.051402 memory: 2937 loss_kpt: 91.369512 acc_pose: 0.686417 loss: 91.369512 2022/10/12 17:25:10 - mmengine - INFO - Epoch(train) [139][550/586] lr: 2.000000e-02 eta: 2:55:46 time: 0.267822 data_time: 0.048783 memory: 2937 loss_kpt: 89.143444 acc_pose: 0.763799 loss: 89.143444 2022/10/12 17:25:19 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:25:35 - mmengine - INFO - Epoch(train) [140][50/586] lr: 2.000000e-02 eta: 2:55:21 time: 0.309019 data_time: 0.064640 memory: 2937 loss_kpt: 89.860809 acc_pose: 0.736881 loss: 89.860809 2022/10/12 17:25:49 - mmengine - INFO - Epoch(train) [140][100/586] lr: 2.000000e-02 eta: 2:55:09 time: 0.291120 data_time: 0.052207 memory: 2937 loss_kpt: 90.395642 acc_pose: 0.843243 loss: 90.395642 2022/10/12 17:26:04 - mmengine - INFO - Epoch(train) [140][150/586] lr: 2.000000e-02 eta: 2:54:57 time: 0.293370 data_time: 0.055228 memory: 2937 loss_kpt: 91.367000 acc_pose: 0.785980 loss: 91.367000 2022/10/12 17:26:18 - mmengine - INFO - Epoch(train) [140][200/586] lr: 2.000000e-02 eta: 2:54:45 time: 0.288320 data_time: 0.050571 memory: 2937 loss_kpt: 88.807185 acc_pose: 0.742337 loss: 88.807185 2022/10/12 17:26:32 - mmengine - INFO - Epoch(train) [140][250/586] lr: 2.000000e-02 eta: 2:54:34 time: 0.285719 data_time: 0.057347 memory: 2937 loss_kpt: 89.936221 acc_pose: 0.819124 loss: 89.936221 2022/10/12 17:26:47 - mmengine - INFO - Epoch(train) [140][300/586] lr: 2.000000e-02 eta: 2:54:22 time: 0.281175 data_time: 0.052357 memory: 2937 loss_kpt: 90.568592 acc_pose: 0.804364 loss: 90.568592 2022/10/12 17:27:01 - mmengine - INFO - Epoch(train) [140][350/586] lr: 2.000000e-02 eta: 2:54:10 time: 0.284159 data_time: 0.052897 memory: 2937 loss_kpt: 90.076141 acc_pose: 0.780559 loss: 90.076141 2022/10/12 17:27:14 - mmengine - INFO - Epoch(train) [140][400/586] lr: 2.000000e-02 eta: 2:53:58 time: 0.269602 data_time: 0.048861 memory: 2937 loss_kpt: 90.984707 acc_pose: 0.845187 loss: 90.984707 2022/10/12 17:27:28 - mmengine - INFO - Epoch(train) [140][450/586] lr: 2.000000e-02 eta: 2:53:45 time: 0.273960 data_time: 0.054993 memory: 2937 loss_kpt: 89.407356 acc_pose: 0.803854 loss: 89.407356 2022/10/12 17:27:42 - mmengine - INFO - Epoch(train) [140][500/586] lr: 2.000000e-02 eta: 2:53:33 time: 0.272024 data_time: 0.049538 memory: 2937 loss_kpt: 91.032349 acc_pose: 0.799653 loss: 91.032349 2022/10/12 17:27:54 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:27:55 - mmengine - INFO - Epoch(train) [140][550/586] lr: 2.000000e-02 eta: 2:53:21 time: 0.264828 data_time: 0.052954 memory: 2937 loss_kpt: 89.610258 acc_pose: 0.721221 loss: 89.610258 2022/10/12 17:28:05 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:28:05 - mmengine - INFO - Saving checkpoint at 140 epochs 2022/10/12 17:28:13 - mmengine - INFO - Epoch(val) [140][50/407] eta: 0:00:41 time: 0.116148 data_time: 0.014556 memory: 2937 2022/10/12 17:28:18 - mmengine - INFO - Epoch(val) [140][100/407] eta: 0:00:33 time: 0.108595 data_time: 0.009014 memory: 830 2022/10/12 17:28:24 - mmengine - INFO - Epoch(val) [140][150/407] eta: 0:00:28 time: 0.111006 data_time: 0.009526 memory: 830 2022/10/12 17:28:29 - mmengine - INFO - Epoch(val) [140][200/407] eta: 0:00:23 time: 0.111915 data_time: 0.009493 memory: 830 2022/10/12 17:28:35 - mmengine - INFO - Epoch(val) [140][250/407] eta: 0:00:17 time: 0.108959 data_time: 0.008962 memory: 830 2022/10/12 17:28:40 - mmengine - INFO - Epoch(val) [140][300/407] eta: 0:00:11 time: 0.110666 data_time: 0.009754 memory: 830 2022/10/12 17:28:46 - mmengine - INFO - Epoch(val) [140][350/407] eta: 0:00:06 time: 0.114893 data_time: 0.012921 memory: 830 2022/10/12 17:28:51 - mmengine - INFO - Epoch(val) [140][400/407] eta: 0:00:00 time: 0.100461 data_time: 0.007957 memory: 830 2022/10/12 17:29:04 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 17:29:20 - mmengine - INFO - Epoch(val) [140][407/407] coco/AP: 0.685562 coco/AP .5: 0.881598 coco/AP .75: 0.764557 coco/AP (M): 0.654248 coco/AP (L): 0.744080 coco/AR: 0.755054 coco/AR .5: 0.922859 coco/AR .75: 0.821946 coco/AR (M): 0.710352 coco/AR (L): 0.816797 2022/10/12 17:29:20 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221012/rsn18/best_coco/AP_epoch_130.pth is removed 2022/10/12 17:29:21 - mmengine - INFO - The best checkpoint with 0.6856 coco/AP at 140 epoch is saved to best_coco/AP_epoch_140.pth. 2022/10/12 17:29:36 - mmengine - INFO - Epoch(train) [141][50/586] lr: 2.000000e-02 eta: 2:52:55 time: 0.283244 data_time: 0.060689 memory: 2937 loss_kpt: 89.698946 acc_pose: 0.844692 loss: 89.698946 2022/10/12 17:29:49 - mmengine - INFO - Epoch(train) [141][100/586] lr: 2.000000e-02 eta: 2:52:43 time: 0.274911 data_time: 0.048732 memory: 2937 loss_kpt: 91.005166 acc_pose: 0.752522 loss: 91.005166 2022/10/12 17:30:03 - mmengine - INFO - Epoch(train) [141][150/586] lr: 2.000000e-02 eta: 2:52:31 time: 0.278649 data_time: 0.062042 memory: 2937 loss_kpt: 88.526924 acc_pose: 0.794999 loss: 88.526924 2022/10/12 17:30:18 - mmengine - INFO - Epoch(train) [141][200/586] lr: 2.000000e-02 eta: 2:52:19 time: 0.283860 data_time: 0.047965 memory: 2937 loss_kpt: 91.396487 acc_pose: 0.680567 loss: 91.396487 2022/10/12 17:30:32 - mmengine - INFO - Epoch(train) [141][250/586] lr: 2.000000e-02 eta: 2:52:07 time: 0.279433 data_time: 0.053955 memory: 2937 loss_kpt: 90.563168 acc_pose: 0.760025 loss: 90.563168 2022/10/12 17:30:44 - mmengine - INFO - Epoch(train) [141][300/586] lr: 2.000000e-02 eta: 2:51:55 time: 0.254912 data_time: 0.047078 memory: 2937 loss_kpt: 90.918663 acc_pose: 0.807889 loss: 90.918663 2022/10/12 17:30:58 - mmengine - INFO - Epoch(train) [141][350/586] lr: 2.000000e-02 eta: 2:51:42 time: 0.265979 data_time: 0.051363 memory: 2937 loss_kpt: 90.771761 acc_pose: 0.826923 loss: 90.771761 2022/10/12 17:31:11 - mmengine - INFO - Epoch(train) [141][400/586] lr: 2.000000e-02 eta: 2:51:30 time: 0.263963 data_time: 0.046910 memory: 2937 loss_kpt: 91.023280 acc_pose: 0.791310 loss: 91.023280 2022/10/12 17:31:24 - mmengine - INFO - Epoch(train) [141][450/586] lr: 2.000000e-02 eta: 2:51:17 time: 0.263209 data_time: 0.051434 memory: 2937 loss_kpt: 90.084263 acc_pose: 0.851505 loss: 90.084263 2022/10/12 17:31:37 - mmengine - INFO - Epoch(train) [141][500/586] lr: 2.000000e-02 eta: 2:51:05 time: 0.255184 data_time: 0.052572 memory: 2937 loss_kpt: 89.546320 acc_pose: 0.830009 loss: 89.546320 2022/10/12 17:31:50 - mmengine - INFO - Epoch(train) [141][550/586] lr: 2.000000e-02 eta: 2:50:52 time: 0.262319 data_time: 0.048921 memory: 2937 loss_kpt: 90.108448 acc_pose: 0.807871 loss: 90.108448 2022/10/12 17:31:59 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:32:13 - mmengine - INFO - Epoch(train) [142][50/586] lr: 2.000000e-02 eta: 2:50:27 time: 0.287728 data_time: 0.063285 memory: 2937 loss_kpt: 89.952732 acc_pose: 0.752678 loss: 89.952732 2022/10/12 17:32:29 - mmengine - INFO - Epoch(train) [142][100/586] lr: 2.000000e-02 eta: 2:50:15 time: 0.303219 data_time: 0.052228 memory: 2937 loss_kpt: 89.854963 acc_pose: 0.809665 loss: 89.854963 2022/10/12 17:32:43 - mmengine - INFO - Epoch(train) [142][150/586] lr: 2.000000e-02 eta: 2:50:03 time: 0.279591 data_time: 0.048050 memory: 2937 loss_kpt: 90.610509 acc_pose: 0.829420 loss: 90.610509 2022/10/12 17:32:56 - mmengine - INFO - Epoch(train) [142][200/586] lr: 2.000000e-02 eta: 2:49:51 time: 0.262003 data_time: 0.055641 memory: 2937 loss_kpt: 89.140874 acc_pose: 0.804254 loss: 89.140874 2022/10/12 17:33:10 - mmengine - INFO - Epoch(train) [142][250/586] lr: 2.000000e-02 eta: 2:49:39 time: 0.274495 data_time: 0.050820 memory: 2937 loss_kpt: 89.574598 acc_pose: 0.633946 loss: 89.574598 2022/10/12 17:33:23 - mmengine - INFO - Epoch(train) [142][300/586] lr: 2.000000e-02 eta: 2:49:26 time: 0.261306 data_time: 0.050366 memory: 2937 loss_kpt: 90.157384 acc_pose: 0.772008 loss: 90.157384 2022/10/12 17:33:36 - mmengine - INFO - Epoch(train) [142][350/586] lr: 2.000000e-02 eta: 2:49:14 time: 0.268166 data_time: 0.054013 memory: 2937 loss_kpt: 90.372865 acc_pose: 0.802452 loss: 90.372865 2022/10/12 17:33:43 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:33:49 - mmengine - INFO - Epoch(train) [142][400/586] lr: 2.000000e-02 eta: 2:49:02 time: 0.269036 data_time: 0.047750 memory: 2937 loss_kpt: 89.166415 acc_pose: 0.771071 loss: 89.166415 2022/10/12 17:34:03 - mmengine - INFO - Epoch(train) [142][450/586] lr: 2.000000e-02 eta: 2:48:50 time: 0.271157 data_time: 0.052213 memory: 2937 loss_kpt: 88.926736 acc_pose: 0.789764 loss: 88.926736 2022/10/12 17:34:16 - mmengine - INFO - Epoch(train) [142][500/586] lr: 2.000000e-02 eta: 2:48:37 time: 0.264304 data_time: 0.050737 memory: 2937 loss_kpt: 90.253825 acc_pose: 0.837384 loss: 90.253825 2022/10/12 17:34:29 - mmengine - INFO - Epoch(train) [142][550/586] lr: 2.000000e-02 eta: 2:48:25 time: 0.263803 data_time: 0.054083 memory: 2937 loss_kpt: 89.223282 acc_pose: 0.795187 loss: 89.223282 2022/10/12 17:34:39 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:34:54 - mmengine - INFO - Epoch(train) [143][50/586] lr: 2.000000e-02 eta: 2:48:00 time: 0.301096 data_time: 0.062900 memory: 2937 loss_kpt: 89.431547 acc_pose: 0.759414 loss: 89.431547 2022/10/12 17:35:09 - mmengine - INFO - Epoch(train) [143][100/586] lr: 2.000000e-02 eta: 2:47:48 time: 0.288604 data_time: 0.052304 memory: 2937 loss_kpt: 89.759271 acc_pose: 0.714726 loss: 89.759271 2022/10/12 17:35:23 - mmengine - INFO - Epoch(train) [143][150/586] lr: 2.000000e-02 eta: 2:47:36 time: 0.282944 data_time: 0.052400 memory: 2937 loss_kpt: 88.764268 acc_pose: 0.770995 loss: 88.764268 2022/10/12 17:35:36 - mmengine - INFO - Epoch(train) [143][200/586] lr: 2.000000e-02 eta: 2:47:24 time: 0.273949 data_time: 0.053821 memory: 2937 loss_kpt: 88.091008 acc_pose: 0.804630 loss: 88.091008 2022/10/12 17:35:50 - mmengine - INFO - Epoch(train) [143][250/586] lr: 2.000000e-02 eta: 2:47:12 time: 0.278687 data_time: 0.054734 memory: 2937 loss_kpt: 89.426103 acc_pose: 0.751420 loss: 89.426103 2022/10/12 17:36:04 - mmengine - INFO - Epoch(train) [143][300/586] lr: 2.000000e-02 eta: 2:46:59 time: 0.268794 data_time: 0.049999 memory: 2937 loss_kpt: 90.763385 acc_pose: 0.798328 loss: 90.763385 2022/10/12 17:36:18 - mmengine - INFO - Epoch(train) [143][350/586] lr: 2.000000e-02 eta: 2:46:48 time: 0.291530 data_time: 0.053704 memory: 2937 loss_kpt: 90.269770 acc_pose: 0.856127 loss: 90.269770 2022/10/12 17:36:32 - mmengine - INFO - Epoch(train) [143][400/586] lr: 2.000000e-02 eta: 2:46:36 time: 0.279634 data_time: 0.055234 memory: 2937 loss_kpt: 90.455621 acc_pose: 0.761989 loss: 90.455621 2022/10/12 17:36:46 - mmengine - INFO - Epoch(train) [143][450/586] lr: 2.000000e-02 eta: 2:46:23 time: 0.264169 data_time: 0.053449 memory: 2937 loss_kpt: 89.603963 acc_pose: 0.735454 loss: 89.603963 2022/10/12 17:36:58 - mmengine - INFO - Epoch(train) [143][500/586] lr: 2.000000e-02 eta: 2:46:10 time: 0.255113 data_time: 0.050365 memory: 2937 loss_kpt: 89.615652 acc_pose: 0.820270 loss: 89.615652 2022/10/12 17:37:11 - mmengine - INFO - Epoch(train) [143][550/586] lr: 2.000000e-02 eta: 2:45:58 time: 0.259302 data_time: 0.052306 memory: 2937 loss_kpt: 90.643074 acc_pose: 0.783703 loss: 90.643074 2022/10/12 17:37:20 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:37:34 - mmengine - INFO - Epoch(train) [144][50/586] lr: 2.000000e-02 eta: 2:45:33 time: 0.282298 data_time: 0.059985 memory: 2937 loss_kpt: 89.859488 acc_pose: 0.761421 loss: 89.859488 2022/10/12 17:37:47 - mmengine - INFO - Epoch(train) [144][100/586] lr: 2.000000e-02 eta: 2:45:20 time: 0.260298 data_time: 0.050918 memory: 2937 loss_kpt: 89.125053 acc_pose: 0.742832 loss: 89.125053 2022/10/12 17:38:00 - mmengine - INFO - Epoch(train) [144][150/586] lr: 2.000000e-02 eta: 2:45:08 time: 0.259838 data_time: 0.051260 memory: 2937 loss_kpt: 89.764252 acc_pose: 0.792717 loss: 89.764252 2022/10/12 17:38:14 - mmengine - INFO - Epoch(train) [144][200/586] lr: 2.000000e-02 eta: 2:44:55 time: 0.270566 data_time: 0.050256 memory: 2937 loss_kpt: 89.108801 acc_pose: 0.768838 loss: 89.108801 2022/10/12 17:38:15 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:38:27 - mmengine - INFO - Epoch(train) [144][250/586] lr: 2.000000e-02 eta: 2:44:43 time: 0.268828 data_time: 0.053119 memory: 2937 loss_kpt: 92.733818 acc_pose: 0.816884 loss: 92.733818 2022/10/12 17:38:40 - mmengine - INFO - Epoch(train) [144][300/586] lr: 2.000000e-02 eta: 2:44:30 time: 0.258890 data_time: 0.053056 memory: 2937 loss_kpt: 88.008480 acc_pose: 0.757796 loss: 88.008480 2022/10/12 17:38:53 - mmengine - INFO - Epoch(train) [144][350/586] lr: 2.000000e-02 eta: 2:44:18 time: 0.258719 data_time: 0.053795 memory: 2937 loss_kpt: 90.234407 acc_pose: 0.754454 loss: 90.234407 2022/10/12 17:39:07 - mmengine - INFO - Epoch(train) [144][400/586] lr: 2.000000e-02 eta: 2:44:05 time: 0.264118 data_time: 0.052058 memory: 2937 loss_kpt: 89.017212 acc_pose: 0.787311 loss: 89.017212 2022/10/12 17:39:20 - mmengine - INFO - Epoch(train) [144][450/586] lr: 2.000000e-02 eta: 2:43:53 time: 0.257884 data_time: 0.053168 memory: 2937 loss_kpt: 91.019868 acc_pose: 0.760668 loss: 91.019868 2022/10/12 17:39:32 - mmengine - INFO - Epoch(train) [144][500/586] lr: 2.000000e-02 eta: 2:43:40 time: 0.257012 data_time: 0.050970 memory: 2937 loss_kpt: 89.600586 acc_pose: 0.899060 loss: 89.600586 2022/10/12 17:39:45 - mmengine - INFO - Epoch(train) [144][550/586] lr: 2.000000e-02 eta: 2:43:28 time: 0.254188 data_time: 0.051219 memory: 2937 loss_kpt: 88.927308 acc_pose: 0.822350 loss: 88.927308 2022/10/12 17:39:54 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:40:08 - mmengine - INFO - Epoch(train) [145][50/586] lr: 2.000000e-02 eta: 2:43:02 time: 0.273024 data_time: 0.059919 memory: 2937 loss_kpt: 90.484675 acc_pose: 0.774091 loss: 90.484675 2022/10/12 17:40:21 - mmengine - INFO - Epoch(train) [145][100/586] lr: 2.000000e-02 eta: 2:42:50 time: 0.258998 data_time: 0.057301 memory: 2937 loss_kpt: 91.046383 acc_pose: 0.772895 loss: 91.046383 2022/10/12 17:40:34 - mmengine - INFO - Epoch(train) [145][150/586] lr: 2.000000e-02 eta: 2:42:37 time: 0.265819 data_time: 0.053674 memory: 2937 loss_kpt: 91.334197 acc_pose: 0.750447 loss: 91.334197 2022/10/12 17:40:47 - mmengine - INFO - Epoch(train) [145][200/586] lr: 2.000000e-02 eta: 2:42:25 time: 0.258494 data_time: 0.055261 memory: 2937 loss_kpt: 90.669801 acc_pose: 0.789791 loss: 90.669801 2022/10/12 17:41:01 - mmengine - INFO - Epoch(train) [145][250/586] lr: 2.000000e-02 eta: 2:42:12 time: 0.270080 data_time: 0.047977 memory: 2937 loss_kpt: 90.423130 acc_pose: 0.809442 loss: 90.423130 2022/10/12 17:41:14 - mmengine - INFO - Epoch(train) [145][300/586] lr: 2.000000e-02 eta: 2:42:00 time: 0.263193 data_time: 0.053361 memory: 2937 loss_kpt: 89.625284 acc_pose: 0.797916 loss: 89.625284 2022/10/12 17:41:27 - mmengine - INFO - Epoch(train) [145][350/586] lr: 2.000000e-02 eta: 2:41:47 time: 0.257468 data_time: 0.051383 memory: 2937 loss_kpt: 89.945606 acc_pose: 0.778701 loss: 89.945606 2022/10/12 17:41:40 - mmengine - INFO - Epoch(train) [145][400/586] lr: 2.000000e-02 eta: 2:41:35 time: 0.267841 data_time: 0.050493 memory: 2937 loss_kpt: 90.081431 acc_pose: 0.809810 loss: 90.081431 2022/10/12 17:41:53 - mmengine - INFO - Epoch(train) [145][450/586] lr: 2.000000e-02 eta: 2:41:23 time: 0.262249 data_time: 0.054853 memory: 2937 loss_kpt: 90.658828 acc_pose: 0.835910 loss: 90.658828 2022/10/12 17:42:07 - mmengine - INFO - Epoch(train) [145][500/586] lr: 2.000000e-02 eta: 2:41:10 time: 0.269293 data_time: 0.050405 memory: 2937 loss_kpt: 90.825539 acc_pose: 0.798003 loss: 90.825539 2022/10/12 17:42:21 - mmengine - INFO - Epoch(train) [145][550/586] lr: 2.000000e-02 eta: 2:40:58 time: 0.284690 data_time: 0.053858 memory: 2937 loss_kpt: 89.860480 acc_pose: 0.826623 loss: 89.860480 2022/10/12 17:42:31 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:42:40 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:42:46 - mmengine - INFO - Epoch(train) [146][50/586] lr: 2.000000e-02 eta: 2:40:34 time: 0.300363 data_time: 0.059923 memory: 2937 loss_kpt: 87.907762 acc_pose: 0.799743 loss: 87.907762 2022/10/12 17:43:00 - mmengine - INFO - Epoch(train) [146][100/586] lr: 2.000000e-02 eta: 2:40:21 time: 0.276717 data_time: 0.048995 memory: 2937 loss_kpt: 89.692709 acc_pose: 0.845558 loss: 89.692709 2022/10/12 17:43:13 - mmengine - INFO - Epoch(train) [146][150/586] lr: 2.000000e-02 eta: 2:40:09 time: 0.270178 data_time: 0.049367 memory: 2937 loss_kpt: 90.358336 acc_pose: 0.823363 loss: 90.358336 2022/10/12 17:43:26 - mmengine - INFO - Epoch(train) [146][200/586] lr: 2.000000e-02 eta: 2:39:57 time: 0.258391 data_time: 0.051461 memory: 2937 loss_kpt: 89.230670 acc_pose: 0.760542 loss: 89.230670 2022/10/12 17:43:39 - mmengine - INFO - Epoch(train) [146][250/586] lr: 2.000000e-02 eta: 2:39:44 time: 0.268883 data_time: 0.053932 memory: 2937 loss_kpt: 90.781118 acc_pose: 0.599417 loss: 90.781118 2022/10/12 17:43:53 - mmengine - INFO - Epoch(train) [146][300/586] lr: 2.000000e-02 eta: 2:39:32 time: 0.276838 data_time: 0.047048 memory: 2937 loss_kpt: 87.591618 acc_pose: 0.883730 loss: 87.591618 2022/10/12 17:44:07 - mmengine - INFO - Epoch(train) [146][350/586] lr: 2.000000e-02 eta: 2:39:20 time: 0.271551 data_time: 0.052251 memory: 2937 loss_kpt: 89.215878 acc_pose: 0.754103 loss: 89.215878 2022/10/12 17:44:20 - mmengine - INFO - Epoch(train) [146][400/586] lr: 2.000000e-02 eta: 2:39:08 time: 0.270938 data_time: 0.053068 memory: 2937 loss_kpt: 89.797875 acc_pose: 0.774992 loss: 89.797875 2022/10/12 17:44:34 - mmengine - INFO - Epoch(train) [146][450/586] lr: 2.000000e-02 eta: 2:38:55 time: 0.272873 data_time: 0.048360 memory: 2937 loss_kpt: 89.102318 acc_pose: 0.785689 loss: 89.102318 2022/10/12 17:44:48 - mmengine - INFO - Epoch(train) [146][500/586] lr: 2.000000e-02 eta: 2:38:43 time: 0.270413 data_time: 0.052153 memory: 2937 loss_kpt: 89.001135 acc_pose: 0.729169 loss: 89.001135 2022/10/12 17:45:01 - mmengine - INFO - Epoch(train) [146][550/586] lr: 2.000000e-02 eta: 2:38:31 time: 0.267632 data_time: 0.052371 memory: 2937 loss_kpt: 89.780662 acc_pose: 0.731653 loss: 89.780662 2022/10/12 17:45:10 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:45:25 - mmengine - INFO - Epoch(train) [147][50/586] lr: 2.000000e-02 eta: 2:38:06 time: 0.292957 data_time: 0.068383 memory: 2937 loss_kpt: 90.855731 acc_pose: 0.762330 loss: 90.855731 2022/10/12 17:45:38 - mmengine - INFO - Epoch(train) [147][100/586] lr: 2.000000e-02 eta: 2:37:53 time: 0.267254 data_time: 0.048140 memory: 2937 loss_kpt: 89.187154 acc_pose: 0.731029 loss: 89.187154 2022/10/12 17:45:52 - mmengine - INFO - Epoch(train) [147][150/586] lr: 2.000000e-02 eta: 2:37:41 time: 0.279374 data_time: 0.049622 memory: 2937 loss_kpt: 90.873389 acc_pose: 0.749694 loss: 90.873389 2022/10/12 17:46:06 - mmengine - INFO - Epoch(train) [147][200/586] lr: 2.000000e-02 eta: 2:37:29 time: 0.267542 data_time: 0.048884 memory: 2937 loss_kpt: 89.035002 acc_pose: 0.793193 loss: 89.035002 2022/10/12 17:46:19 - mmengine - INFO - Epoch(train) [147][250/586] lr: 2.000000e-02 eta: 2:37:17 time: 0.272111 data_time: 0.051072 memory: 2937 loss_kpt: 88.844334 acc_pose: 0.835609 loss: 88.844334 2022/10/12 17:46:33 - mmengine - INFO - Epoch(train) [147][300/586] lr: 2.000000e-02 eta: 2:37:04 time: 0.271322 data_time: 0.051417 memory: 2937 loss_kpt: 89.833633 acc_pose: 0.766047 loss: 89.833633 2022/10/12 17:46:46 - mmengine - INFO - Epoch(train) [147][350/586] lr: 2.000000e-02 eta: 2:36:52 time: 0.265039 data_time: 0.053343 memory: 2937 loss_kpt: 89.709612 acc_pose: 0.837352 loss: 89.709612 2022/10/12 17:47:00 - mmengine - INFO - Epoch(train) [147][400/586] lr: 2.000000e-02 eta: 2:36:40 time: 0.265842 data_time: 0.049480 memory: 2937 loss_kpt: 89.151062 acc_pose: 0.803564 loss: 89.151062 2022/10/12 17:47:11 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:47:12 - mmengine - INFO - Epoch(train) [147][450/586] lr: 2.000000e-02 eta: 2:36:27 time: 0.251719 data_time: 0.049617 memory: 2937 loss_kpt: 89.559481 acc_pose: 0.786705 loss: 89.559481 2022/10/12 17:47:25 - mmengine - INFO - Epoch(train) [147][500/586] lr: 2.000000e-02 eta: 2:36:14 time: 0.254476 data_time: 0.052561 memory: 2937 loss_kpt: 88.759786 acc_pose: 0.757460 loss: 88.759786 2022/10/12 17:47:37 - mmengine - INFO - Epoch(train) [147][550/586] lr: 2.000000e-02 eta: 2:36:02 time: 0.251168 data_time: 0.053913 memory: 2937 loss_kpt: 90.687616 acc_pose: 0.802820 loss: 90.687616 2022/10/12 17:47:46 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:48:01 - mmengine - INFO - Epoch(train) [148][50/586] lr: 2.000000e-02 eta: 2:35:37 time: 0.293524 data_time: 0.062713 memory: 2937 loss_kpt: 90.168710 acc_pose: 0.783640 loss: 90.168710 2022/10/12 17:48:15 - mmengine - INFO - Epoch(train) [148][100/586] lr: 2.000000e-02 eta: 2:35:25 time: 0.285446 data_time: 0.055036 memory: 2937 loss_kpt: 89.347527 acc_pose: 0.810213 loss: 89.347527 2022/10/12 17:48:29 - mmengine - INFO - Epoch(train) [148][150/586] lr: 2.000000e-02 eta: 2:35:13 time: 0.280387 data_time: 0.055328 memory: 2937 loss_kpt: 90.320870 acc_pose: 0.832018 loss: 90.320870 2022/10/12 17:48:43 - mmengine - INFO - Epoch(train) [148][200/586] lr: 2.000000e-02 eta: 2:35:00 time: 0.274543 data_time: 0.053620 memory: 2937 loss_kpt: 90.347421 acc_pose: 0.735717 loss: 90.347421 2022/10/12 17:48:57 - mmengine - INFO - Epoch(train) [148][250/586] lr: 2.000000e-02 eta: 2:34:48 time: 0.267263 data_time: 0.052357 memory: 2937 loss_kpt: 90.263531 acc_pose: 0.698250 loss: 90.263531 2022/10/12 17:49:10 - mmengine - INFO - Epoch(train) [148][300/586] lr: 2.000000e-02 eta: 2:34:36 time: 0.264942 data_time: 0.054133 memory: 2937 loss_kpt: 89.746269 acc_pose: 0.780766 loss: 89.746269 2022/10/12 17:49:23 - mmengine - INFO - Epoch(train) [148][350/586] lr: 2.000000e-02 eta: 2:34:23 time: 0.264368 data_time: 0.048718 memory: 2937 loss_kpt: 89.620984 acc_pose: 0.791571 loss: 89.620984 2022/10/12 17:49:37 - mmengine - INFO - Epoch(train) [148][400/586] lr: 2.000000e-02 eta: 2:34:11 time: 0.271395 data_time: 0.057533 memory: 2937 loss_kpt: 87.961125 acc_pose: 0.789655 loss: 87.961125 2022/10/12 17:49:50 - mmengine - INFO - Epoch(train) [148][450/586] lr: 2.000000e-02 eta: 2:33:59 time: 0.269564 data_time: 0.052676 memory: 2937 loss_kpt: 89.397208 acc_pose: 0.761805 loss: 89.397208 2022/10/12 17:50:04 - mmengine - INFO - Epoch(train) [148][500/586] lr: 2.000000e-02 eta: 2:33:46 time: 0.277294 data_time: 0.052614 memory: 2937 loss_kpt: 90.423890 acc_pose: 0.824302 loss: 90.423890 2022/10/12 17:50:18 - mmengine - INFO - Epoch(train) [148][550/586] lr: 2.000000e-02 eta: 2:33:34 time: 0.281128 data_time: 0.052236 memory: 2937 loss_kpt: 90.335236 acc_pose: 0.736440 loss: 90.335236 2022/10/12 17:50:28 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:50:42 - mmengine - INFO - Epoch(train) [149][50/586] lr: 2.000000e-02 eta: 2:33:09 time: 0.276904 data_time: 0.066526 memory: 2937 loss_kpt: 88.329628 acc_pose: 0.792760 loss: 88.329628 2022/10/12 17:50:55 - mmengine - INFO - Epoch(train) [149][100/586] lr: 2.000000e-02 eta: 2:32:57 time: 0.263899 data_time: 0.059434 memory: 2937 loss_kpt: 89.816265 acc_pose: 0.759423 loss: 89.816265 2022/10/12 17:51:08 - mmengine - INFO - Epoch(train) [149][150/586] lr: 2.000000e-02 eta: 2:32:44 time: 0.258128 data_time: 0.049949 memory: 2937 loss_kpt: 88.997772 acc_pose: 0.737355 loss: 88.997772 2022/10/12 17:51:21 - mmengine - INFO - Epoch(train) [149][200/586] lr: 2.000000e-02 eta: 2:32:32 time: 0.260591 data_time: 0.054958 memory: 2937 loss_kpt: 90.495607 acc_pose: 0.716831 loss: 90.495607 2022/10/12 17:51:35 - mmengine - INFO - Epoch(train) [149][250/586] lr: 2.000000e-02 eta: 2:32:19 time: 0.267095 data_time: 0.050682 memory: 2937 loss_kpt: 89.565747 acc_pose: 0.715984 loss: 89.565747 2022/10/12 17:51:40 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:51:48 - mmengine - INFO - Epoch(train) [149][300/586] lr: 2.000000e-02 eta: 2:32:07 time: 0.271915 data_time: 0.049101 memory: 2937 loss_kpt: 89.189791 acc_pose: 0.772636 loss: 89.189791 2022/10/12 17:52:02 - mmengine - INFO - Epoch(train) [149][350/586] lr: 2.000000e-02 eta: 2:31:55 time: 0.284498 data_time: 0.054007 memory: 2937 loss_kpt: 89.690429 acc_pose: 0.822422 loss: 89.690429 2022/10/12 17:52:17 - mmengine - INFO - Epoch(train) [149][400/586] lr: 2.000000e-02 eta: 2:31:43 time: 0.282699 data_time: 0.058615 memory: 2937 loss_kpt: 88.851802 acc_pose: 0.852604 loss: 88.851802 2022/10/12 17:52:30 - mmengine - INFO - Epoch(train) [149][450/586] lr: 2.000000e-02 eta: 2:31:31 time: 0.270119 data_time: 0.053670 memory: 2937 loss_kpt: 90.194555 acc_pose: 0.808740 loss: 90.194555 2022/10/12 17:52:43 - mmengine - INFO - Epoch(train) [149][500/586] lr: 2.000000e-02 eta: 2:31:18 time: 0.255054 data_time: 0.049091 memory: 2937 loss_kpt: 90.695838 acc_pose: 0.822042 loss: 90.695838 2022/10/12 17:52:56 - mmengine - INFO - Epoch(train) [149][550/586] lr: 2.000000e-02 eta: 2:31:06 time: 0.261703 data_time: 0.053177 memory: 2937 loss_kpt: 91.054111 acc_pose: 0.793971 loss: 91.054111 2022/10/12 17:53:05 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:53:19 - mmengine - INFO - Epoch(train) [150][50/586] lr: 2.000000e-02 eta: 2:30:41 time: 0.281698 data_time: 0.063875 memory: 2937 loss_kpt: 90.483857 acc_pose: 0.788011 loss: 90.483857 2022/10/12 17:53:32 - mmengine - INFO - Epoch(train) [150][100/586] lr: 2.000000e-02 eta: 2:30:28 time: 0.264097 data_time: 0.052453 memory: 2937 loss_kpt: 88.663210 acc_pose: 0.732569 loss: 88.663210 2022/10/12 17:53:46 - mmengine - INFO - Epoch(train) [150][150/586] lr: 2.000000e-02 eta: 2:30:16 time: 0.268174 data_time: 0.051414 memory: 2937 loss_kpt: 90.005060 acc_pose: 0.754926 loss: 90.005060 2022/10/12 17:53:59 - mmengine - INFO - Epoch(train) [150][200/586] lr: 2.000000e-02 eta: 2:30:03 time: 0.257748 data_time: 0.052381 memory: 2937 loss_kpt: 88.994073 acc_pose: 0.761839 loss: 88.994073 2022/10/12 17:54:12 - mmengine - INFO - Epoch(train) [150][250/586] lr: 2.000000e-02 eta: 2:29:51 time: 0.262464 data_time: 0.049026 memory: 2937 loss_kpt: 89.550360 acc_pose: 0.756095 loss: 89.550360 2022/10/12 17:54:25 - mmengine - INFO - Epoch(train) [150][300/586] lr: 2.000000e-02 eta: 2:29:38 time: 0.264311 data_time: 0.053134 memory: 2937 loss_kpt: 89.486963 acc_pose: 0.799279 loss: 89.486963 2022/10/12 17:54:39 - mmengine - INFO - Epoch(train) [150][350/586] lr: 2.000000e-02 eta: 2:29:26 time: 0.271946 data_time: 0.049171 memory: 2937 loss_kpt: 89.215445 acc_pose: 0.797015 loss: 89.215445 2022/10/12 17:54:52 - mmengine - INFO - Epoch(train) [150][400/586] lr: 2.000000e-02 eta: 2:29:14 time: 0.278284 data_time: 0.052067 memory: 2937 loss_kpt: 88.803185 acc_pose: 0.729362 loss: 88.803185 2022/10/12 17:55:07 - mmengine - INFO - Epoch(train) [150][450/586] lr: 2.000000e-02 eta: 2:29:02 time: 0.281543 data_time: 0.055555 memory: 2937 loss_kpt: 89.157818 acc_pose: 0.765566 loss: 89.157818 2022/10/12 17:55:20 - mmengine - INFO - Epoch(train) [150][500/586] lr: 2.000000e-02 eta: 2:28:49 time: 0.274310 data_time: 0.051321 memory: 2937 loss_kpt: 90.490934 acc_pose: 0.769785 loss: 90.490934 2022/10/12 17:55:34 - mmengine - INFO - Epoch(train) [150][550/586] lr: 2.000000e-02 eta: 2:28:37 time: 0.276656 data_time: 0.049513 memory: 2937 loss_kpt: 89.094478 acc_pose: 0.786993 loss: 89.094478 2022/10/12 17:55:44 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:55:44 - mmengine - INFO - Saving checkpoint at 150 epochs 2022/10/12 17:55:52 - mmengine - INFO - Epoch(val) [150][50/407] eta: 0:00:41 time: 0.115418 data_time: 0.014164 memory: 2937 2022/10/12 17:55:58 - mmengine - INFO - Epoch(val) [150][100/407] eta: 0:00:34 time: 0.110983 data_time: 0.009502 memory: 830 2022/10/12 17:56:03 - mmengine - INFO - Epoch(val) [150][150/407] eta: 0:00:28 time: 0.111769 data_time: 0.009169 memory: 830 2022/10/12 17:56:09 - mmengine - INFO - Epoch(val) [150][200/407] eta: 0:00:22 time: 0.110820 data_time: 0.009520 memory: 830 2022/10/12 17:56:14 - mmengine - INFO - Epoch(val) [150][250/407] eta: 0:00:17 time: 0.113262 data_time: 0.010554 memory: 830 2022/10/12 17:56:20 - mmengine - INFO - Epoch(val) [150][300/407] eta: 0:00:11 time: 0.110359 data_time: 0.009793 memory: 830 2022/10/12 17:56:25 - mmengine - INFO - Epoch(val) [150][350/407] eta: 0:00:06 time: 0.109378 data_time: 0.009101 memory: 830 2022/10/12 17:56:30 - mmengine - INFO - Epoch(val) [150][400/407] eta: 0:00:00 time: 0.100048 data_time: 0.007834 memory: 830 2022/10/12 17:56:44 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 17:56:59 - mmengine - INFO - Epoch(val) [150][407/407] coco/AP: 0.682521 coco/AP .5: 0.875777 coco/AP .75: 0.761567 coco/AP (M): 0.651380 coco/AP (L): 0.741875 coco/AR: 0.752015 coco/AR .5: 0.916877 coco/AR .75: 0.818640 coco/AR (M): 0.706911 coco/AR (L): 0.814344 2022/10/12 17:57:14 - mmengine - INFO - Epoch(train) [151][50/586] lr: 2.000000e-02 eta: 2:28:13 time: 0.287974 data_time: 0.059448 memory: 2937 loss_kpt: 89.549076 acc_pose: 0.753374 loss: 89.549076 2022/10/12 17:57:27 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:57:27 - mmengine - INFO - Epoch(train) [151][100/586] lr: 2.000000e-02 eta: 2:28:00 time: 0.272374 data_time: 0.050039 memory: 2937 loss_kpt: 89.672457 acc_pose: 0.811561 loss: 89.672457 2022/10/12 17:57:41 - mmengine - INFO - Epoch(train) [151][150/586] lr: 2.000000e-02 eta: 2:27:48 time: 0.269649 data_time: 0.051268 memory: 2937 loss_kpt: 90.475560 acc_pose: 0.823879 loss: 90.475560 2022/10/12 17:57:55 - mmengine - INFO - Epoch(train) [151][200/586] lr: 2.000000e-02 eta: 2:27:36 time: 0.277417 data_time: 0.051679 memory: 2937 loss_kpt: 88.821347 acc_pose: 0.729893 loss: 88.821347 2022/10/12 17:58:08 - mmengine - INFO - Epoch(train) [151][250/586] lr: 2.000000e-02 eta: 2:27:23 time: 0.270592 data_time: 0.052565 memory: 2937 loss_kpt: 90.439013 acc_pose: 0.724599 loss: 90.439013 2022/10/12 17:58:22 - mmengine - INFO - Epoch(train) [151][300/586] lr: 2.000000e-02 eta: 2:27:11 time: 0.267905 data_time: 0.055395 memory: 2937 loss_kpt: 90.277110 acc_pose: 0.803519 loss: 90.277110 2022/10/12 17:58:36 - mmengine - INFO - Epoch(train) [151][350/586] lr: 2.000000e-02 eta: 2:26:59 time: 0.290942 data_time: 0.053692 memory: 2937 loss_kpt: 90.490979 acc_pose: 0.864791 loss: 90.490979 2022/10/12 17:58:50 - mmengine - INFO - Epoch(train) [151][400/586] lr: 2.000000e-02 eta: 2:26:47 time: 0.281579 data_time: 0.053733 memory: 2937 loss_kpt: 89.363084 acc_pose: 0.830554 loss: 89.363084 2022/10/12 17:59:04 - mmengine - INFO - Epoch(train) [151][450/586] lr: 2.000000e-02 eta: 2:26:35 time: 0.268932 data_time: 0.049425 memory: 2937 loss_kpt: 89.330639 acc_pose: 0.803562 loss: 89.330639 2022/10/12 17:59:17 - mmengine - INFO - Epoch(train) [151][500/586] lr: 2.000000e-02 eta: 2:26:22 time: 0.270919 data_time: 0.056373 memory: 2937 loss_kpt: 88.653996 acc_pose: 0.778480 loss: 88.653996 2022/10/12 17:59:31 - mmengine - INFO - Epoch(train) [151][550/586] lr: 2.000000e-02 eta: 2:26:10 time: 0.271143 data_time: 0.049098 memory: 2937 loss_kpt: 90.052046 acc_pose: 0.793833 loss: 90.052046 2022/10/12 17:59:40 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 17:59:55 - mmengine - INFO - Epoch(train) [152][50/586] lr: 2.000000e-02 eta: 2:25:45 time: 0.282356 data_time: 0.064400 memory: 2937 loss_kpt: 88.872150 acc_pose: 0.853227 loss: 88.872150 2022/10/12 18:00:09 - mmengine - INFO - Epoch(train) [152][100/586] lr: 2.000000e-02 eta: 2:25:33 time: 0.279907 data_time: 0.049056 memory: 2937 loss_kpt: 88.922767 acc_pose: 0.808199 loss: 88.922767 2022/10/12 18:00:22 - mmengine - INFO - Epoch(train) [152][150/586] lr: 2.000000e-02 eta: 2:25:21 time: 0.270163 data_time: 0.053946 memory: 2937 loss_kpt: 90.163988 acc_pose: 0.776683 loss: 90.163988 2022/10/12 18:00:36 - mmengine - INFO - Epoch(train) [152][200/586] lr: 2.000000e-02 eta: 2:25:08 time: 0.273499 data_time: 0.049180 memory: 2937 loss_kpt: 90.355329 acc_pose: 0.765205 loss: 90.355329 2022/10/12 18:00:50 - mmengine - INFO - Epoch(train) [152][250/586] lr: 2.000000e-02 eta: 2:24:56 time: 0.277383 data_time: 0.052733 memory: 2937 loss_kpt: 90.200332 acc_pose: 0.765440 loss: 90.200332 2022/10/12 18:01:03 - mmengine - INFO - Epoch(train) [152][300/586] lr: 2.000000e-02 eta: 2:24:44 time: 0.262079 data_time: 0.048849 memory: 2937 loss_kpt: 88.440653 acc_pose: 0.720234 loss: 88.440653 2022/10/12 18:01:17 - mmengine - INFO - Epoch(train) [152][350/586] lr: 2.000000e-02 eta: 2:24:32 time: 0.276777 data_time: 0.054447 memory: 2937 loss_kpt: 88.900453 acc_pose: 0.771236 loss: 88.900453 2022/10/12 18:01:30 - mmengine - INFO - Epoch(train) [152][400/586] lr: 2.000000e-02 eta: 2:24:19 time: 0.269324 data_time: 0.051063 memory: 2937 loss_kpt: 90.416002 acc_pose: 0.701917 loss: 90.416002 2022/10/12 18:01:44 - mmengine - INFO - Epoch(train) [152][450/586] lr: 2.000000e-02 eta: 2:24:07 time: 0.269765 data_time: 0.057674 memory: 2937 loss_kpt: 90.002475 acc_pose: 0.764434 loss: 90.002475 2022/10/12 18:01:57 - mmengine - INFO - Epoch(train) [152][500/586] lr: 2.000000e-02 eta: 2:23:54 time: 0.263925 data_time: 0.054654 memory: 2937 loss_kpt: 88.187494 acc_pose: 0.739039 loss: 88.187494 2022/10/12 18:02:01 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:02:10 - mmengine - INFO - Epoch(train) [152][550/586] lr: 2.000000e-02 eta: 2:23:42 time: 0.266888 data_time: 0.054001 memory: 2937 loss_kpt: 91.100327 acc_pose: 0.755704 loss: 91.100327 2022/10/12 18:02:20 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:02:34 - mmengine - INFO - Epoch(train) [153][50/586] lr: 2.000000e-02 eta: 2:23:17 time: 0.271759 data_time: 0.061426 memory: 2937 loss_kpt: 87.792645 acc_pose: 0.769137 loss: 87.792645 2022/10/12 18:02:47 - mmengine - INFO - Epoch(train) [153][100/586] lr: 2.000000e-02 eta: 2:23:05 time: 0.262378 data_time: 0.054431 memory: 2937 loss_kpt: 91.684300 acc_pose: 0.773280 loss: 91.684300 2022/10/12 18:03:00 - mmengine - INFO - Epoch(train) [153][150/586] lr: 2.000000e-02 eta: 2:22:52 time: 0.262926 data_time: 0.054460 memory: 2937 loss_kpt: 89.721368 acc_pose: 0.816826 loss: 89.721368 2022/10/12 18:03:14 - mmengine - INFO - Epoch(train) [153][200/586] lr: 2.000000e-02 eta: 2:22:40 time: 0.274532 data_time: 0.054934 memory: 2937 loss_kpt: 90.102549 acc_pose: 0.803134 loss: 90.102549 2022/10/12 18:03:28 - mmengine - INFO - Epoch(train) [153][250/586] lr: 2.000000e-02 eta: 2:22:28 time: 0.291416 data_time: 0.053398 memory: 2937 loss_kpt: 87.720167 acc_pose: 0.817620 loss: 87.720167 2022/10/12 18:03:42 - mmengine - INFO - Epoch(train) [153][300/586] lr: 2.000000e-02 eta: 2:22:16 time: 0.280934 data_time: 0.053618 memory: 2937 loss_kpt: 88.535752 acc_pose: 0.794560 loss: 88.535752 2022/10/12 18:03:56 - mmengine - INFO - Epoch(train) [153][350/586] lr: 2.000000e-02 eta: 2:22:03 time: 0.270467 data_time: 0.053454 memory: 2937 loss_kpt: 88.696398 acc_pose: 0.810472 loss: 88.696398 2022/10/12 18:04:10 - mmengine - INFO - Epoch(train) [153][400/586] lr: 2.000000e-02 eta: 2:21:51 time: 0.279304 data_time: 0.048657 memory: 2937 loss_kpt: 90.673960 acc_pose: 0.744708 loss: 90.673960 2022/10/12 18:04:23 - mmengine - INFO - Epoch(train) [153][450/586] lr: 2.000000e-02 eta: 2:21:39 time: 0.273340 data_time: 0.054548 memory: 2937 loss_kpt: 88.332479 acc_pose: 0.848857 loss: 88.332479 2022/10/12 18:04:37 - mmengine - INFO - Epoch(train) [153][500/586] lr: 2.000000e-02 eta: 2:21:26 time: 0.266825 data_time: 0.052985 memory: 2937 loss_kpt: 89.360594 acc_pose: 0.774811 loss: 89.360594 2022/10/12 18:04:50 - mmengine - INFO - Epoch(train) [153][550/586] lr: 2.000000e-02 eta: 2:21:14 time: 0.259268 data_time: 0.050663 memory: 2937 loss_kpt: 88.715763 acc_pose: 0.813400 loss: 88.715763 2022/10/12 18:04:59 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:05:14 - mmengine - INFO - Epoch(train) [154][50/586] lr: 2.000000e-02 eta: 2:20:49 time: 0.289569 data_time: 0.063168 memory: 2937 loss_kpt: 91.422383 acc_pose: 0.820961 loss: 91.422383 2022/10/12 18:05:27 - mmengine - INFO - Epoch(train) [154][100/586] lr: 2.000000e-02 eta: 2:20:37 time: 0.268705 data_time: 0.054331 memory: 2937 loss_kpt: 88.948202 acc_pose: 0.811959 loss: 88.948202 2022/10/12 18:05:41 - mmengine - INFO - Epoch(train) [154][150/586] lr: 2.000000e-02 eta: 2:20:25 time: 0.270958 data_time: 0.055070 memory: 2937 loss_kpt: 89.259584 acc_pose: 0.786570 loss: 89.259584 2022/10/12 18:05:54 - mmengine - INFO - Epoch(train) [154][200/586] lr: 2.000000e-02 eta: 2:20:12 time: 0.269230 data_time: 0.052461 memory: 2937 loss_kpt: 89.378574 acc_pose: 0.743363 loss: 89.378574 2022/10/12 18:06:08 - mmengine - INFO - Epoch(train) [154][250/586] lr: 2.000000e-02 eta: 2:20:00 time: 0.276648 data_time: 0.053417 memory: 2937 loss_kpt: 90.442324 acc_pose: 0.830894 loss: 90.442324 2022/10/12 18:06:21 - mmengine - INFO - Epoch(train) [154][300/586] lr: 2.000000e-02 eta: 2:19:48 time: 0.264248 data_time: 0.056559 memory: 2937 loss_kpt: 90.410481 acc_pose: 0.819325 loss: 90.410481 2022/10/12 18:06:32 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:06:34 - mmengine - INFO - Epoch(train) [154][350/586] lr: 2.000000e-02 eta: 2:19:35 time: 0.257344 data_time: 0.055848 memory: 2937 loss_kpt: 87.509848 acc_pose: 0.818904 loss: 87.509848 2022/10/12 18:06:48 - mmengine - INFO - Epoch(train) [154][400/586] lr: 2.000000e-02 eta: 2:19:22 time: 0.265343 data_time: 0.049909 memory: 2937 loss_kpt: 89.802802 acc_pose: 0.761839 loss: 89.802802 2022/10/12 18:07:00 - mmengine - INFO - Epoch(train) [154][450/586] lr: 2.000000e-02 eta: 2:19:10 time: 0.250520 data_time: 0.049167 memory: 2937 loss_kpt: 89.996292 acc_pose: 0.826313 loss: 89.996292 2022/10/12 18:07:13 - mmengine - INFO - Epoch(train) [154][500/586] lr: 2.000000e-02 eta: 2:18:57 time: 0.260573 data_time: 0.052934 memory: 2937 loss_kpt: 89.585081 acc_pose: 0.820645 loss: 89.585081 2022/10/12 18:07:26 - mmengine - INFO - Epoch(train) [154][550/586] lr: 2.000000e-02 eta: 2:18:45 time: 0.253955 data_time: 0.053356 memory: 2937 loss_kpt: 88.823342 acc_pose: 0.717266 loss: 88.823342 2022/10/12 18:07:35 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:07:50 - mmengine - INFO - Epoch(train) [155][50/586] lr: 2.000000e-02 eta: 2:18:20 time: 0.301771 data_time: 0.066185 memory: 2937 loss_kpt: 88.998999 acc_pose: 0.790459 loss: 88.998999 2022/10/12 18:08:03 - mmengine - INFO - Epoch(train) [155][100/586] lr: 2.000000e-02 eta: 2:18:08 time: 0.269843 data_time: 0.048689 memory: 2937 loss_kpt: 88.470736 acc_pose: 0.784017 loss: 88.470736 2022/10/12 18:08:17 - mmengine - INFO - Epoch(train) [155][150/586] lr: 2.000000e-02 eta: 2:17:56 time: 0.266416 data_time: 0.056217 memory: 2937 loss_kpt: 89.779527 acc_pose: 0.795871 loss: 89.779527 2022/10/12 18:08:30 - mmengine - INFO - Epoch(train) [155][200/586] lr: 2.000000e-02 eta: 2:17:43 time: 0.269388 data_time: 0.054680 memory: 2937 loss_kpt: 89.180650 acc_pose: 0.831531 loss: 89.180650 2022/10/12 18:08:44 - mmengine - INFO - Epoch(train) [155][250/586] lr: 2.000000e-02 eta: 2:17:31 time: 0.272573 data_time: 0.054480 memory: 2937 loss_kpt: 90.181248 acc_pose: 0.789539 loss: 90.181248 2022/10/12 18:08:58 - mmengine - INFO - Epoch(train) [155][300/586] lr: 2.000000e-02 eta: 2:17:19 time: 0.274957 data_time: 0.053771 memory: 2937 loss_kpt: 90.305071 acc_pose: 0.831059 loss: 90.305071 2022/10/12 18:09:11 - mmengine - INFO - Epoch(train) [155][350/586] lr: 2.000000e-02 eta: 2:17:06 time: 0.268593 data_time: 0.055886 memory: 2937 loss_kpt: 90.480109 acc_pose: 0.785082 loss: 90.480109 2022/10/12 18:09:25 - mmengine - INFO - Epoch(train) [155][400/586] lr: 2.000000e-02 eta: 2:16:54 time: 0.267908 data_time: 0.054654 memory: 2937 loss_kpt: 89.010734 acc_pose: 0.751229 loss: 89.010734 2022/10/12 18:09:38 - mmengine - INFO - Epoch(train) [155][450/586] lr: 2.000000e-02 eta: 2:16:41 time: 0.267805 data_time: 0.054029 memory: 2937 loss_kpt: 90.338024 acc_pose: 0.799310 loss: 90.338024 2022/10/12 18:09:51 - mmengine - INFO - Epoch(train) [155][500/586] lr: 2.000000e-02 eta: 2:16:29 time: 0.264695 data_time: 0.052431 memory: 2937 loss_kpt: 89.216316 acc_pose: 0.834462 loss: 89.216316 2022/10/12 18:10:05 - mmengine - INFO - Epoch(train) [155][550/586] lr: 2.000000e-02 eta: 2:16:16 time: 0.268443 data_time: 0.053953 memory: 2937 loss_kpt: 90.823231 acc_pose: 0.781361 loss: 90.823231 2022/10/12 18:10:14 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:10:29 - mmengine - INFO - Epoch(train) [156][50/586] lr: 2.000000e-02 eta: 2:15:52 time: 0.291068 data_time: 0.059395 memory: 2937 loss_kpt: 88.773356 acc_pose: 0.782873 loss: 88.773356 2022/10/12 18:10:42 - mmengine - INFO - Epoch(train) [156][100/586] lr: 2.000000e-02 eta: 2:15:40 time: 0.265302 data_time: 0.053181 memory: 2937 loss_kpt: 88.833217 acc_pose: 0.808668 loss: 88.833217 2022/10/12 18:10:55 - mmengine - INFO - Epoch(train) [156][150/586] lr: 2.000000e-02 eta: 2:15:27 time: 0.267987 data_time: 0.054065 memory: 2937 loss_kpt: 91.603641 acc_pose: 0.782883 loss: 91.603641 2022/10/12 18:11:01 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:11:10 - mmengine - INFO - Epoch(train) [156][200/586] lr: 2.000000e-02 eta: 2:15:15 time: 0.281716 data_time: 0.050406 memory: 2937 loss_kpt: 88.144673 acc_pose: 0.755684 loss: 88.144673 2022/10/12 18:11:23 - mmengine - INFO - Epoch(train) [156][250/586] lr: 2.000000e-02 eta: 2:15:03 time: 0.270074 data_time: 0.052989 memory: 2937 loss_kpt: 88.567861 acc_pose: 0.818878 loss: 88.567861 2022/10/12 18:11:37 - mmengine - INFO - Epoch(train) [156][300/586] lr: 2.000000e-02 eta: 2:14:50 time: 0.270085 data_time: 0.053737 memory: 2937 loss_kpt: 88.722130 acc_pose: 0.831086 loss: 88.722130 2022/10/12 18:11:50 - mmengine - INFO - Epoch(train) [156][350/586] lr: 2.000000e-02 eta: 2:14:38 time: 0.272770 data_time: 0.050268 memory: 2937 loss_kpt: 89.226257 acc_pose: 0.807969 loss: 89.226257 2022/10/12 18:12:04 - mmengine - INFO - Epoch(train) [156][400/586] lr: 2.000000e-02 eta: 2:14:26 time: 0.273974 data_time: 0.052659 memory: 2937 loss_kpt: 88.388046 acc_pose: 0.810168 loss: 88.388046 2022/10/12 18:12:18 - mmengine - INFO - Epoch(train) [156][450/586] lr: 2.000000e-02 eta: 2:14:13 time: 0.278158 data_time: 0.053268 memory: 2937 loss_kpt: 88.635729 acc_pose: 0.780579 loss: 88.635729 2022/10/12 18:12:31 - mmengine - INFO - Epoch(train) [156][500/586] lr: 2.000000e-02 eta: 2:14:01 time: 0.267489 data_time: 0.051190 memory: 2937 loss_kpt: 90.667166 acc_pose: 0.773154 loss: 90.667166 2022/10/12 18:12:45 - mmengine - INFO - Epoch(train) [156][550/586] lr: 2.000000e-02 eta: 2:13:49 time: 0.272838 data_time: 0.056290 memory: 2937 loss_kpt: 89.159206 acc_pose: 0.813572 loss: 89.159206 2022/10/12 18:12:54 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:13:09 - mmengine - INFO - Epoch(train) [157][50/586] lr: 2.000000e-02 eta: 2:13:24 time: 0.292580 data_time: 0.067542 memory: 2937 loss_kpt: 87.448836 acc_pose: 0.847762 loss: 87.448836 2022/10/12 18:13:22 - mmengine - INFO - Epoch(train) [157][100/586] lr: 2.000000e-02 eta: 2:13:12 time: 0.272050 data_time: 0.050912 memory: 2937 loss_kpt: 89.745195 acc_pose: 0.811990 loss: 89.745195 2022/10/12 18:13:36 - mmengine - INFO - Epoch(train) [157][150/586] lr: 2.000000e-02 eta: 2:13:00 time: 0.274273 data_time: 0.052848 memory: 2937 loss_kpt: 90.767941 acc_pose: 0.729531 loss: 90.767941 2022/10/12 18:13:49 - mmengine - INFO - Epoch(train) [157][200/586] lr: 2.000000e-02 eta: 2:12:47 time: 0.257774 data_time: 0.051785 memory: 2937 loss_kpt: 89.841263 acc_pose: 0.804391 loss: 89.841263 2022/10/12 18:14:03 - mmengine - INFO - Epoch(train) [157][250/586] lr: 2.000000e-02 eta: 2:12:35 time: 0.277995 data_time: 0.051719 memory: 2937 loss_kpt: 88.766128 acc_pose: 0.743647 loss: 88.766128 2022/10/12 18:14:16 - mmengine - INFO - Epoch(train) [157][300/586] lr: 2.000000e-02 eta: 2:12:22 time: 0.268535 data_time: 0.051394 memory: 2937 loss_kpt: 88.786593 acc_pose: 0.853032 loss: 88.786593 2022/10/12 18:14:31 - mmengine - INFO - Epoch(train) [157][350/586] lr: 2.000000e-02 eta: 2:12:10 time: 0.288159 data_time: 0.057673 memory: 2937 loss_kpt: 88.083553 acc_pose: 0.800202 loss: 88.083553 2022/10/12 18:14:44 - mmengine - INFO - Epoch(train) [157][400/586] lr: 2.000000e-02 eta: 2:11:58 time: 0.259456 data_time: 0.062913 memory: 2937 loss_kpt: 90.734192 acc_pose: 0.729082 loss: 90.734192 2022/10/12 18:14:57 - mmengine - INFO - Epoch(train) [157][450/586] lr: 2.000000e-02 eta: 2:11:45 time: 0.259634 data_time: 0.052513 memory: 2937 loss_kpt: 89.458484 acc_pose: 0.862891 loss: 89.458484 2022/10/12 18:15:10 - mmengine - INFO - Epoch(train) [157][500/586] lr: 2.000000e-02 eta: 2:11:33 time: 0.266555 data_time: 0.057550 memory: 2937 loss_kpt: 89.287309 acc_pose: 0.810423 loss: 89.287309 2022/10/12 18:15:23 - mmengine - INFO - Epoch(train) [157][550/586] lr: 2.000000e-02 eta: 2:11:20 time: 0.253571 data_time: 0.052740 memory: 2937 loss_kpt: 90.337016 acc_pose: 0.770641 loss: 90.337016 2022/10/12 18:15:31 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:15:32 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:15:47 - mmengine - INFO - Epoch(train) [158][50/586] lr: 2.000000e-02 eta: 2:10:56 time: 0.300213 data_time: 0.065192 memory: 2937 loss_kpt: 89.312965 acc_pose: 0.801096 loss: 89.312965 2022/10/12 18:16:00 - mmengine - INFO - Epoch(train) [158][100/586] lr: 2.000000e-02 eta: 2:10:44 time: 0.268113 data_time: 0.052679 memory: 2937 loss_kpt: 88.919925 acc_pose: 0.831200 loss: 88.919925 2022/10/12 18:16:14 - mmengine - INFO - Epoch(train) [158][150/586] lr: 2.000000e-02 eta: 2:10:31 time: 0.274847 data_time: 0.055059 memory: 2937 loss_kpt: 89.938073 acc_pose: 0.818987 loss: 89.938073 2022/10/12 18:16:27 - mmengine - INFO - Epoch(train) [158][200/586] lr: 2.000000e-02 eta: 2:10:19 time: 0.263801 data_time: 0.047768 memory: 2937 loss_kpt: 90.488214 acc_pose: 0.798779 loss: 90.488214 2022/10/12 18:16:41 - mmengine - INFO - Epoch(train) [158][250/586] lr: 2.000000e-02 eta: 2:10:07 time: 0.275511 data_time: 0.055499 memory: 2937 loss_kpt: 90.816079 acc_pose: 0.776349 loss: 90.816079 2022/10/12 18:16:54 - mmengine - INFO - Epoch(train) [158][300/586] lr: 2.000000e-02 eta: 2:09:54 time: 0.271662 data_time: 0.051908 memory: 2937 loss_kpt: 89.779679 acc_pose: 0.870174 loss: 89.779679 2022/10/12 18:17:08 - mmengine - INFO - Epoch(train) [158][350/586] lr: 2.000000e-02 eta: 2:09:42 time: 0.265235 data_time: 0.053919 memory: 2937 loss_kpt: 90.296967 acc_pose: 0.832902 loss: 90.296967 2022/10/12 18:17:21 - mmengine - INFO - Epoch(train) [158][400/586] lr: 2.000000e-02 eta: 2:09:29 time: 0.265612 data_time: 0.052402 memory: 2937 loss_kpt: 87.251945 acc_pose: 0.771816 loss: 87.251945 2022/10/12 18:17:34 - mmengine - INFO - Epoch(train) [158][450/586] lr: 2.000000e-02 eta: 2:09:17 time: 0.259962 data_time: 0.056755 memory: 2937 loss_kpt: 90.340266 acc_pose: 0.807552 loss: 90.340266 2022/10/12 18:17:47 - mmengine - INFO - Epoch(train) [158][500/586] lr: 2.000000e-02 eta: 2:09:04 time: 0.257971 data_time: 0.050107 memory: 2937 loss_kpt: 89.112842 acc_pose: 0.776727 loss: 89.112842 2022/10/12 18:18:00 - mmengine - INFO - Epoch(train) [158][550/586] lr: 2.000000e-02 eta: 2:08:52 time: 0.261855 data_time: 0.052066 memory: 2937 loss_kpt: 89.812530 acc_pose: 0.771131 loss: 89.812530 2022/10/12 18:18:09 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:18:24 - mmengine - INFO - Epoch(train) [159][50/586] lr: 2.000000e-02 eta: 2:08:27 time: 0.285779 data_time: 0.063399 memory: 2937 loss_kpt: 88.792551 acc_pose: 0.832684 loss: 88.792551 2022/10/12 18:18:37 - mmengine - INFO - Epoch(train) [159][100/586] lr: 2.000000e-02 eta: 2:08:15 time: 0.270491 data_time: 0.056055 memory: 2937 loss_kpt: 88.765916 acc_pose: 0.796383 loss: 88.765916 2022/10/12 18:18:50 - mmengine - INFO - Epoch(train) [159][150/586] lr: 2.000000e-02 eta: 2:08:02 time: 0.261133 data_time: 0.056900 memory: 2937 loss_kpt: 89.460081 acc_pose: 0.785492 loss: 89.460081 2022/10/12 18:19:03 - mmengine - INFO - Epoch(train) [159][200/586] lr: 2.000000e-02 eta: 2:07:50 time: 0.263386 data_time: 0.055340 memory: 2937 loss_kpt: 88.151071 acc_pose: 0.812117 loss: 88.151071 2022/10/12 18:19:16 - mmengine - INFO - Epoch(train) [159][250/586] lr: 2.000000e-02 eta: 2:07:37 time: 0.258177 data_time: 0.057449 memory: 2937 loss_kpt: 88.935275 acc_pose: 0.786529 loss: 88.935275 2022/10/12 18:19:30 - mmengine - INFO - Epoch(train) [159][300/586] lr: 2.000000e-02 eta: 2:07:25 time: 0.272099 data_time: 0.055940 memory: 2937 loss_kpt: 90.501356 acc_pose: 0.824566 loss: 90.501356 2022/10/12 18:19:43 - mmengine - INFO - Epoch(train) [159][350/586] lr: 2.000000e-02 eta: 2:07:12 time: 0.268391 data_time: 0.058689 memory: 2937 loss_kpt: 88.983430 acc_pose: 0.771695 loss: 88.983430 2022/10/12 18:19:57 - mmengine - INFO - Epoch(train) [159][400/586] lr: 2.000000e-02 eta: 2:07:00 time: 0.276126 data_time: 0.053869 memory: 2937 loss_kpt: 89.859890 acc_pose: 0.825521 loss: 89.859890 2022/10/12 18:20:01 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:20:11 - mmengine - INFO - Epoch(train) [159][450/586] lr: 2.000000e-02 eta: 2:06:48 time: 0.282370 data_time: 0.052921 memory: 2937 loss_kpt: 88.783134 acc_pose: 0.809866 loss: 88.783134 2022/10/12 18:20:25 - mmengine - INFO - Epoch(train) [159][500/586] lr: 2.000000e-02 eta: 2:06:36 time: 0.282784 data_time: 0.057985 memory: 2937 loss_kpt: 89.725473 acc_pose: 0.802702 loss: 89.725473 2022/10/12 18:20:39 - mmengine - INFO - Epoch(train) [159][550/586] lr: 2.000000e-02 eta: 2:06:23 time: 0.278334 data_time: 0.058211 memory: 2937 loss_kpt: 91.089204 acc_pose: 0.681327 loss: 91.089204 2022/10/12 18:20:49 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:21:03 - mmengine - INFO - Epoch(train) [160][50/586] lr: 2.000000e-02 eta: 2:05:59 time: 0.284713 data_time: 0.066026 memory: 2937 loss_kpt: 90.998641 acc_pose: 0.777384 loss: 90.998641 2022/10/12 18:21:17 - mmengine - INFO - Epoch(train) [160][100/586] lr: 2.000000e-02 eta: 2:05:47 time: 0.282999 data_time: 0.062490 memory: 2937 loss_kpt: 90.335314 acc_pose: 0.837107 loss: 90.335314 2022/10/12 18:21:31 - mmengine - INFO - Epoch(train) [160][150/586] lr: 2.000000e-02 eta: 2:05:35 time: 0.268324 data_time: 0.059531 memory: 2937 loss_kpt: 88.846774 acc_pose: 0.774778 loss: 88.846774 2022/10/12 18:21:45 - mmengine - INFO - Epoch(train) [160][200/586] lr: 2.000000e-02 eta: 2:05:22 time: 0.279585 data_time: 0.054034 memory: 2937 loss_kpt: 88.783195 acc_pose: 0.803686 loss: 88.783195 2022/10/12 18:21:59 - mmengine - INFO - Epoch(train) [160][250/586] lr: 2.000000e-02 eta: 2:05:10 time: 0.276943 data_time: 0.056092 memory: 2937 loss_kpt: 90.254609 acc_pose: 0.711340 loss: 90.254609 2022/10/12 18:22:12 - mmengine - INFO - Epoch(train) [160][300/586] lr: 2.000000e-02 eta: 2:04:58 time: 0.267726 data_time: 0.050012 memory: 2937 loss_kpt: 88.113539 acc_pose: 0.729043 loss: 88.113539 2022/10/12 18:22:26 - mmengine - INFO - Epoch(train) [160][350/586] lr: 2.000000e-02 eta: 2:04:45 time: 0.279894 data_time: 0.054920 memory: 2937 loss_kpt: 89.015475 acc_pose: 0.794724 loss: 89.015475 2022/10/12 18:22:40 - mmengine - INFO - Epoch(train) [160][400/586] lr: 2.000000e-02 eta: 2:04:33 time: 0.274498 data_time: 0.052939 memory: 2937 loss_kpt: 89.000989 acc_pose: 0.775583 loss: 89.000989 2022/10/12 18:22:54 - mmengine - INFO - Epoch(train) [160][450/586] lr: 2.000000e-02 eta: 2:04:21 time: 0.274878 data_time: 0.056055 memory: 2937 loss_kpt: 88.175558 acc_pose: 0.755683 loss: 88.175558 2022/10/12 18:23:09 - mmengine - INFO - Epoch(train) [160][500/586] lr: 2.000000e-02 eta: 2:04:09 time: 0.301843 data_time: 0.056379 memory: 2937 loss_kpt: 89.924225 acc_pose: 0.794277 loss: 89.924225 2022/10/12 18:23:23 - mmengine - INFO - Epoch(train) [160][550/586] lr: 2.000000e-02 eta: 2:03:56 time: 0.276999 data_time: 0.058430 memory: 2937 loss_kpt: 88.034508 acc_pose: 0.832866 loss: 88.034508 2022/10/12 18:23:32 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:23:32 - mmengine - INFO - Saving checkpoint at 160 epochs 2022/10/12 18:23:40 - mmengine - INFO - Epoch(val) [160][50/407] eta: 0:00:41 time: 0.116172 data_time: 0.013906 memory: 2937 2022/10/12 18:23:45 - mmengine - INFO - Epoch(val) [160][100/407] eta: 0:00:32 time: 0.105703 data_time: 0.008842 memory: 830 2022/10/12 18:23:51 - mmengine - INFO - Epoch(val) [160][150/407] eta: 0:00:26 time: 0.104545 data_time: 0.008831 memory: 830 2022/10/12 18:23:56 - mmengine - INFO - Epoch(val) [160][200/407] eta: 0:00:21 time: 0.105206 data_time: 0.008921 memory: 830 2022/10/12 18:24:01 - mmengine - INFO - Epoch(val) [160][250/407] eta: 0:00:16 time: 0.105817 data_time: 0.008370 memory: 830 2022/10/12 18:24:07 - mmengine - INFO - Epoch(val) [160][300/407] eta: 0:00:11 time: 0.107127 data_time: 0.008735 memory: 830 2022/10/12 18:24:12 - mmengine - INFO - Epoch(val) [160][350/407] eta: 0:00:05 time: 0.104537 data_time: 0.008499 memory: 830 2022/10/12 18:24:17 - mmengine - INFO - Epoch(val) [160][400/407] eta: 0:00:00 time: 0.103466 data_time: 0.007902 memory: 830 2022/10/12 18:24:30 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 18:24:46 - mmengine - INFO - Epoch(val) [160][407/407] coco/AP: 0.688372 coco/AP .5: 0.880655 coco/AP .75: 0.766055 coco/AP (M): 0.657755 coco/AP (L): 0.746900 coco/AR: 0.758486 coco/AR .5: 0.922544 coco/AR .75: 0.823678 coco/AR (M): 0.714176 coco/AR (L): 0.819658 2022/10/12 18:24:46 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221012/rsn18/best_coco/AP_epoch_140.pth is removed 2022/10/12 18:24:48 - mmengine - INFO - The best checkpoint with 0.6884 coco/AP at 160 epoch is saved to best_coco/AP_epoch_160.pth. 2022/10/12 18:25:02 - mmengine - INFO - Epoch(train) [161][50/586] lr: 2.000000e-02 eta: 2:03:32 time: 0.278968 data_time: 0.058245 memory: 2937 loss_kpt: 89.833656 acc_pose: 0.781775 loss: 89.833656 2022/10/12 18:25:15 - mmengine - INFO - Epoch(train) [161][100/586] lr: 2.000000e-02 eta: 2:03:20 time: 0.260472 data_time: 0.053294 memory: 2937 loss_kpt: 90.365873 acc_pose: 0.816089 loss: 90.365873 2022/10/12 18:25:29 - mmengine - INFO - Epoch(train) [161][150/586] lr: 2.000000e-02 eta: 2:03:07 time: 0.271421 data_time: 0.053753 memory: 2937 loss_kpt: 88.845032 acc_pose: 0.821311 loss: 88.845032 2022/10/12 18:25:42 - mmengine - INFO - Epoch(train) [161][200/586] lr: 2.000000e-02 eta: 2:02:55 time: 0.273243 data_time: 0.059666 memory: 2937 loss_kpt: 89.569842 acc_pose: 0.772179 loss: 89.569842 2022/10/12 18:25:53 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:25:56 - mmengine - INFO - Epoch(train) [161][250/586] lr: 2.000000e-02 eta: 2:02:42 time: 0.262227 data_time: 0.053996 memory: 2937 loss_kpt: 90.277791 acc_pose: 0.761943 loss: 90.277791 2022/10/12 18:26:09 - mmengine - INFO - Epoch(train) [161][300/586] lr: 2.000000e-02 eta: 2:02:30 time: 0.263533 data_time: 0.053515 memory: 2937 loss_kpt: 88.989826 acc_pose: 0.762126 loss: 88.989826 2022/10/12 18:26:22 - mmengine - INFO - Epoch(train) [161][350/586] lr: 2.000000e-02 eta: 2:02:17 time: 0.263413 data_time: 0.052995 memory: 2937 loss_kpt: 89.985102 acc_pose: 0.797723 loss: 89.985102 2022/10/12 18:26:35 - mmengine - INFO - Epoch(train) [161][400/586] lr: 2.000000e-02 eta: 2:02:05 time: 0.265062 data_time: 0.053943 memory: 2937 loss_kpt: 90.304909 acc_pose: 0.801645 loss: 90.304909 2022/10/12 18:26:49 - mmengine - INFO - Epoch(train) [161][450/586] lr: 2.000000e-02 eta: 2:01:52 time: 0.266260 data_time: 0.054545 memory: 2937 loss_kpt: 89.126954 acc_pose: 0.831063 loss: 89.126954 2022/10/12 18:27:02 - mmengine - INFO - Epoch(train) [161][500/586] lr: 2.000000e-02 eta: 2:01:40 time: 0.270734 data_time: 0.060319 memory: 2937 loss_kpt: 89.661192 acc_pose: 0.844182 loss: 89.661192 2022/10/12 18:27:15 - mmengine - INFO - Epoch(train) [161][550/586] lr: 2.000000e-02 eta: 2:01:27 time: 0.251541 data_time: 0.050579 memory: 2937 loss_kpt: 88.951093 acc_pose: 0.733252 loss: 88.951093 2022/10/12 18:27:24 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:27:39 - mmengine - INFO - Epoch(train) [162][50/586] lr: 2.000000e-02 eta: 2:01:03 time: 0.292697 data_time: 0.063129 memory: 2937 loss_kpt: 89.220297 acc_pose: 0.835467 loss: 89.220297 2022/10/12 18:27:52 - mmengine - INFO - Epoch(train) [162][100/586] lr: 2.000000e-02 eta: 2:00:51 time: 0.275188 data_time: 0.055216 memory: 2937 loss_kpt: 88.536232 acc_pose: 0.799592 loss: 88.536232 2022/10/12 18:28:06 - mmengine - INFO - Epoch(train) [162][150/586] lr: 2.000000e-02 eta: 2:00:39 time: 0.272094 data_time: 0.057915 memory: 2937 loss_kpt: 89.470281 acc_pose: 0.832497 loss: 89.470281 2022/10/12 18:28:20 - mmengine - INFO - Epoch(train) [162][200/586] lr: 2.000000e-02 eta: 2:00:26 time: 0.268748 data_time: 0.051503 memory: 2937 loss_kpt: 89.292604 acc_pose: 0.782624 loss: 89.292604 2022/10/12 18:28:33 - mmengine - INFO - Epoch(train) [162][250/586] lr: 2.000000e-02 eta: 2:00:14 time: 0.266799 data_time: 0.050334 memory: 2937 loss_kpt: 88.217504 acc_pose: 0.804178 loss: 88.217504 2022/10/12 18:28:46 - mmengine - INFO - Epoch(train) [162][300/586] lr: 2.000000e-02 eta: 2:00:01 time: 0.267792 data_time: 0.056976 memory: 2937 loss_kpt: 89.393676 acc_pose: 0.841419 loss: 89.393676 2022/10/12 18:29:00 - mmengine - INFO - Epoch(train) [162][350/586] lr: 2.000000e-02 eta: 1:59:49 time: 0.269642 data_time: 0.050357 memory: 2937 loss_kpt: 88.883563 acc_pose: 0.797555 loss: 88.883563 2022/10/12 18:29:13 - mmengine - INFO - Epoch(train) [162][400/586] lr: 2.000000e-02 eta: 1:59:36 time: 0.257190 data_time: 0.053745 memory: 2937 loss_kpt: 89.667537 acc_pose: 0.775868 loss: 89.667537 2022/10/12 18:29:26 - mmengine - INFO - Epoch(train) [162][450/586] lr: 2.000000e-02 eta: 1:59:24 time: 0.261824 data_time: 0.053692 memory: 2937 loss_kpt: 88.016107 acc_pose: 0.902985 loss: 88.016107 2022/10/12 18:29:39 - mmengine - INFO - Epoch(train) [162][500/586] lr: 2.000000e-02 eta: 1:59:11 time: 0.256621 data_time: 0.051665 memory: 2937 loss_kpt: 90.249006 acc_pose: 0.719523 loss: 90.249006 2022/10/12 18:29:51 - mmengine - INFO - Epoch(train) [162][550/586] lr: 2.000000e-02 eta: 1:58:58 time: 0.251821 data_time: 0.048806 memory: 2937 loss_kpt: 89.413727 acc_pose: 0.864850 loss: 89.413727 2022/10/12 18:30:00 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:30:15 - mmengine - INFO - Epoch(train) [163][50/586] lr: 2.000000e-02 eta: 1:58:34 time: 0.299857 data_time: 0.062754 memory: 2937 loss_kpt: 91.262791 acc_pose: 0.676650 loss: 91.262791 2022/10/12 18:30:20 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:30:28 - mmengine - INFO - Epoch(train) [163][100/586] lr: 2.000000e-02 eta: 1:58:22 time: 0.268663 data_time: 0.050012 memory: 2937 loss_kpt: 88.113842 acc_pose: 0.808526 loss: 88.113842 2022/10/12 18:30:42 - mmengine - INFO - Epoch(train) [163][150/586] lr: 2.000000e-02 eta: 1:58:09 time: 0.262810 data_time: 0.054608 memory: 2937 loss_kpt: 87.382342 acc_pose: 0.843640 loss: 87.382342 2022/10/12 18:30:55 - mmengine - INFO - Epoch(train) [163][200/586] lr: 2.000000e-02 eta: 1:57:57 time: 0.271899 data_time: 0.053332 memory: 2937 loss_kpt: 88.806327 acc_pose: 0.804271 loss: 88.806327 2022/10/12 18:31:08 - mmengine - INFO - Epoch(train) [163][250/586] lr: 2.000000e-02 eta: 1:57:45 time: 0.263250 data_time: 0.052580 memory: 2937 loss_kpt: 89.455274 acc_pose: 0.819492 loss: 89.455274 2022/10/12 18:31:22 - mmengine - INFO - Epoch(train) [163][300/586] lr: 2.000000e-02 eta: 1:57:32 time: 0.267034 data_time: 0.052778 memory: 2937 loss_kpt: 89.388532 acc_pose: 0.844704 loss: 89.388532 2022/10/12 18:31:36 - mmengine - INFO - Epoch(train) [163][350/586] lr: 2.000000e-02 eta: 1:57:20 time: 0.275968 data_time: 0.054166 memory: 2937 loss_kpt: 89.498799 acc_pose: 0.833581 loss: 89.498799 2022/10/12 18:31:49 - mmengine - INFO - Epoch(train) [163][400/586] lr: 2.000000e-02 eta: 1:57:07 time: 0.274899 data_time: 0.050341 memory: 2937 loss_kpt: 89.169328 acc_pose: 0.801086 loss: 89.169328 2022/10/12 18:32:03 - mmengine - INFO - Epoch(train) [163][450/586] lr: 2.000000e-02 eta: 1:56:55 time: 0.275356 data_time: 0.052417 memory: 2937 loss_kpt: 88.453350 acc_pose: 0.827263 loss: 88.453350 2022/10/12 18:32:16 - mmengine - INFO - Epoch(train) [163][500/586] lr: 2.000000e-02 eta: 1:56:42 time: 0.262498 data_time: 0.052347 memory: 2937 loss_kpt: 89.500453 acc_pose: 0.759996 loss: 89.500453 2022/10/12 18:32:29 - mmengine - INFO - Epoch(train) [163][550/586] lr: 2.000000e-02 eta: 1:56:30 time: 0.263779 data_time: 0.053884 memory: 2937 loss_kpt: 88.670820 acc_pose: 0.800835 loss: 88.670820 2022/10/12 18:32:39 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:32:54 - mmengine - INFO - Epoch(train) [164][50/586] lr: 2.000000e-02 eta: 1:56:06 time: 0.295573 data_time: 0.063862 memory: 2937 loss_kpt: 90.218652 acc_pose: 0.792237 loss: 90.218652 2022/10/12 18:33:07 - mmengine - INFO - Epoch(train) [164][100/586] lr: 2.000000e-02 eta: 1:55:54 time: 0.267208 data_time: 0.049558 memory: 2937 loss_kpt: 88.114105 acc_pose: 0.794199 loss: 88.114105 2022/10/12 18:33:21 - mmengine - INFO - Epoch(train) [164][150/586] lr: 2.000000e-02 eta: 1:55:41 time: 0.276375 data_time: 0.052845 memory: 2937 loss_kpt: 89.040072 acc_pose: 0.798027 loss: 89.040072 2022/10/12 18:33:34 - mmengine - INFO - Epoch(train) [164][200/586] lr: 2.000000e-02 eta: 1:55:29 time: 0.270353 data_time: 0.048446 memory: 2937 loss_kpt: 88.658329 acc_pose: 0.786266 loss: 88.658329 2022/10/12 18:33:48 - mmengine - INFO - Epoch(train) [164][250/586] lr: 2.000000e-02 eta: 1:55:17 time: 0.275021 data_time: 0.051091 memory: 2937 loss_kpt: 88.592289 acc_pose: 0.800879 loss: 88.592289 2022/10/12 18:34:02 - mmengine - INFO - Epoch(train) [164][300/586] lr: 2.000000e-02 eta: 1:55:04 time: 0.279834 data_time: 0.051269 memory: 2937 loss_kpt: 87.396636 acc_pose: 0.838501 loss: 87.396636 2022/10/12 18:34:16 - mmengine - INFO - Epoch(train) [164][350/586] lr: 2.000000e-02 eta: 1:54:52 time: 0.275821 data_time: 0.050204 memory: 2937 loss_kpt: 88.620652 acc_pose: 0.821429 loss: 88.620652 2022/10/12 18:34:30 - mmengine - INFO - Epoch(train) [164][400/586] lr: 2.000000e-02 eta: 1:54:40 time: 0.281118 data_time: 0.051937 memory: 2937 loss_kpt: 89.083281 acc_pose: 0.761802 loss: 89.083281 2022/10/12 18:34:44 - mmengine - INFO - Epoch(train) [164][450/586] lr: 2.000000e-02 eta: 1:54:27 time: 0.271339 data_time: 0.050338 memory: 2937 loss_kpt: 89.940880 acc_pose: 0.887305 loss: 89.940880 2022/10/12 18:34:53 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:34:57 - mmengine - INFO - Epoch(train) [164][500/586] lr: 2.000000e-02 eta: 1:54:15 time: 0.277562 data_time: 0.053021 memory: 2937 loss_kpt: 90.113954 acc_pose: 0.808556 loss: 90.113954 2022/10/12 18:35:12 - mmengine - INFO - Epoch(train) [164][550/586] lr: 2.000000e-02 eta: 1:54:03 time: 0.299387 data_time: 0.052844 memory: 2937 loss_kpt: 89.062696 acc_pose: 0.744580 loss: 89.062696 2022/10/12 18:35:23 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:35:38 - mmengine - INFO - Epoch(train) [165][50/586] lr: 2.000000e-02 eta: 1:53:39 time: 0.300347 data_time: 0.065850 memory: 2937 loss_kpt: 89.150728 acc_pose: 0.793396 loss: 89.150728 2022/10/12 18:35:51 - mmengine - INFO - Epoch(train) [165][100/586] lr: 2.000000e-02 eta: 1:53:27 time: 0.277746 data_time: 0.050156 memory: 2937 loss_kpt: 89.372659 acc_pose: 0.815788 loss: 89.372659 2022/10/12 18:36:05 - mmengine - INFO - Epoch(train) [165][150/586] lr: 2.000000e-02 eta: 1:53:14 time: 0.267921 data_time: 0.052086 memory: 2937 loss_kpt: 88.858951 acc_pose: 0.815138 loss: 88.858951 2022/10/12 18:36:18 - mmengine - INFO - Epoch(train) [165][200/586] lr: 2.000000e-02 eta: 1:53:02 time: 0.262599 data_time: 0.053008 memory: 2937 loss_kpt: 89.687130 acc_pose: 0.771757 loss: 89.687130 2022/10/12 18:36:32 - mmengine - INFO - Epoch(train) [165][250/586] lr: 2.000000e-02 eta: 1:52:49 time: 0.270315 data_time: 0.048783 memory: 2937 loss_kpt: 87.933478 acc_pose: 0.768549 loss: 87.933478 2022/10/12 18:36:46 - mmengine - INFO - Epoch(train) [165][300/586] lr: 2.000000e-02 eta: 1:52:37 time: 0.286288 data_time: 0.058484 memory: 2937 loss_kpt: 89.114393 acc_pose: 0.734588 loss: 89.114393 2022/10/12 18:37:00 - mmengine - INFO - Epoch(train) [165][350/586] lr: 2.000000e-02 eta: 1:52:25 time: 0.280424 data_time: 0.053856 memory: 2937 loss_kpt: 89.272820 acc_pose: 0.833796 loss: 89.272820 2022/10/12 18:37:14 - mmengine - INFO - Epoch(train) [165][400/586] lr: 2.000000e-02 eta: 1:52:13 time: 0.284069 data_time: 0.056294 memory: 2937 loss_kpt: 88.847890 acc_pose: 0.729613 loss: 88.847890 2022/10/12 18:37:28 - mmengine - INFO - Epoch(train) [165][450/586] lr: 2.000000e-02 eta: 1:52:00 time: 0.279831 data_time: 0.051654 memory: 2937 loss_kpt: 89.748155 acc_pose: 0.757124 loss: 89.748155 2022/10/12 18:37:42 - mmengine - INFO - Epoch(train) [165][500/586] lr: 2.000000e-02 eta: 1:51:48 time: 0.269315 data_time: 0.053256 memory: 2937 loss_kpt: 89.636212 acc_pose: 0.674294 loss: 89.636212 2022/10/12 18:37:55 - mmengine - INFO - Epoch(train) [165][550/586] lr: 2.000000e-02 eta: 1:51:35 time: 0.275010 data_time: 0.048119 memory: 2937 loss_kpt: 89.811947 acc_pose: 0.807769 loss: 89.811947 2022/10/12 18:38:05 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:38:19 - mmengine - INFO - Epoch(train) [166][50/586] lr: 2.000000e-02 eta: 1:51:11 time: 0.276063 data_time: 0.061175 memory: 2937 loss_kpt: 88.648646 acc_pose: 0.814933 loss: 88.648646 2022/10/12 18:38:32 - mmengine - INFO - Epoch(train) [166][100/586] lr: 2.000000e-02 eta: 1:50:59 time: 0.273012 data_time: 0.054253 memory: 2937 loss_kpt: 87.944711 acc_pose: 0.814470 loss: 87.944711 2022/10/12 18:38:45 - mmengine - INFO - Epoch(train) [166][150/586] lr: 2.000000e-02 eta: 1:50:46 time: 0.261553 data_time: 0.052525 memory: 2937 loss_kpt: 91.428329 acc_pose: 0.783092 loss: 91.428329 2022/10/12 18:38:58 - mmengine - INFO - Epoch(train) [166][200/586] lr: 2.000000e-02 eta: 1:50:34 time: 0.258886 data_time: 0.054045 memory: 2937 loss_kpt: 90.111648 acc_pose: 0.829790 loss: 90.111648 2022/10/12 18:39:11 - mmengine - INFO - Epoch(train) [166][250/586] lr: 2.000000e-02 eta: 1:50:21 time: 0.260360 data_time: 0.053418 memory: 2937 loss_kpt: 88.906380 acc_pose: 0.782787 loss: 88.906380 2022/10/12 18:39:24 - mmengine - INFO - Epoch(train) [166][300/586] lr: 2.000000e-02 eta: 1:50:09 time: 0.257128 data_time: 0.053563 memory: 2937 loss_kpt: 89.000826 acc_pose: 0.732887 loss: 89.000826 2022/10/12 18:39:27 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:39:38 - mmengine - INFO - Epoch(train) [166][350/586] lr: 2.000000e-02 eta: 1:49:56 time: 0.268776 data_time: 0.052066 memory: 2937 loss_kpt: 89.373406 acc_pose: 0.806604 loss: 89.373406 2022/10/12 18:39:51 - mmengine - INFO - Epoch(train) [166][400/586] lr: 2.000000e-02 eta: 1:49:44 time: 0.267807 data_time: 0.050300 memory: 2937 loss_kpt: 89.873119 acc_pose: 0.848244 loss: 89.873119 2022/10/12 18:40:05 - mmengine - INFO - Epoch(train) [166][450/586] lr: 2.000000e-02 eta: 1:49:31 time: 0.274728 data_time: 0.052420 memory: 2937 loss_kpt: 88.460140 acc_pose: 0.835183 loss: 88.460140 2022/10/12 18:40:18 - mmengine - INFO - Epoch(train) [166][500/586] lr: 2.000000e-02 eta: 1:49:19 time: 0.266654 data_time: 0.052522 memory: 2937 loss_kpt: 89.376058 acc_pose: 0.739877 loss: 89.376058 2022/10/12 18:40:32 - mmengine - INFO - Epoch(train) [166][550/586] lr: 2.000000e-02 eta: 1:49:06 time: 0.273795 data_time: 0.051718 memory: 2937 loss_kpt: 91.047366 acc_pose: 0.849145 loss: 91.047366 2022/10/12 18:40:41 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:40:56 - mmengine - INFO - Epoch(train) [167][50/586] lr: 2.000000e-02 eta: 1:48:42 time: 0.284353 data_time: 0.068525 memory: 2937 loss_kpt: 88.434851 acc_pose: 0.808585 loss: 88.434851 2022/10/12 18:41:09 - mmengine - INFO - Epoch(train) [167][100/586] lr: 2.000000e-02 eta: 1:48:30 time: 0.273080 data_time: 0.047731 memory: 2937 loss_kpt: 90.078227 acc_pose: 0.804099 loss: 90.078227 2022/10/12 18:41:23 - mmengine - INFO - Epoch(train) [167][150/586] lr: 2.000000e-02 eta: 1:48:18 time: 0.270162 data_time: 0.049348 memory: 2937 loss_kpt: 90.144968 acc_pose: 0.789592 loss: 90.144968 2022/10/12 18:41:37 - mmengine - INFO - Epoch(train) [167][200/586] lr: 2.000000e-02 eta: 1:48:05 time: 0.273916 data_time: 0.052071 memory: 2937 loss_kpt: 88.296820 acc_pose: 0.825430 loss: 88.296820 2022/10/12 18:41:50 - mmengine - INFO - Epoch(train) [167][250/586] lr: 2.000000e-02 eta: 1:47:53 time: 0.262922 data_time: 0.051201 memory: 2937 loss_kpt: 90.079465 acc_pose: 0.789950 loss: 90.079465 2022/10/12 18:42:03 - mmengine - INFO - Epoch(train) [167][300/586] lr: 2.000000e-02 eta: 1:47:40 time: 0.266027 data_time: 0.051247 memory: 2937 loss_kpt: 88.704178 acc_pose: 0.782612 loss: 88.704178 2022/10/12 18:42:17 - mmengine - INFO - Epoch(train) [167][350/586] lr: 2.000000e-02 eta: 1:47:28 time: 0.269622 data_time: 0.052145 memory: 2937 loss_kpt: 89.571961 acc_pose: 0.805791 loss: 89.571961 2022/10/12 18:42:31 - mmengine - INFO - Epoch(train) [167][400/586] lr: 2.000000e-02 eta: 1:47:15 time: 0.280271 data_time: 0.051394 memory: 2937 loss_kpt: 89.758312 acc_pose: 0.840075 loss: 89.758312 2022/10/12 18:42:44 - mmengine - INFO - Epoch(train) [167][450/586] lr: 2.000000e-02 eta: 1:47:03 time: 0.269971 data_time: 0.052037 memory: 2937 loss_kpt: 89.019179 acc_pose: 0.771648 loss: 89.019179 2022/10/12 18:42:57 - mmengine - INFO - Epoch(train) [167][500/586] lr: 2.000000e-02 eta: 1:46:50 time: 0.266229 data_time: 0.053440 memory: 2937 loss_kpt: 90.011522 acc_pose: 0.813917 loss: 90.011522 2022/10/12 18:43:11 - mmengine - INFO - Epoch(train) [167][550/586] lr: 2.000000e-02 eta: 1:46:38 time: 0.272891 data_time: 0.056541 memory: 2937 loss_kpt: 89.037044 acc_pose: 0.816035 loss: 89.037044 2022/10/12 18:43:21 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:43:35 - mmengine - INFO - Epoch(train) [168][50/586] lr: 2.000000e-02 eta: 1:46:14 time: 0.280548 data_time: 0.064581 memory: 2937 loss_kpt: 88.824171 acc_pose: 0.791447 loss: 88.824171 2022/10/12 18:43:49 - mmengine - INFO - Epoch(train) [168][100/586] lr: 2.000000e-02 eta: 1:46:02 time: 0.274930 data_time: 0.047489 memory: 2937 loss_kpt: 90.824069 acc_pose: 0.769154 loss: 90.824069 2022/10/12 18:44:00 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:44:03 - mmengine - INFO - Epoch(train) [168][150/586] lr: 2.000000e-02 eta: 1:45:49 time: 0.283895 data_time: 0.057932 memory: 2937 loss_kpt: 88.860378 acc_pose: 0.842473 loss: 88.860378 2022/10/12 18:44:16 - mmengine - INFO - Epoch(train) [168][200/586] lr: 2.000000e-02 eta: 1:45:37 time: 0.255274 data_time: 0.049132 memory: 2937 loss_kpt: 89.198420 acc_pose: 0.777296 loss: 89.198420 2022/10/12 18:44:29 - mmengine - INFO - Epoch(train) [168][250/586] lr: 2.000000e-02 eta: 1:45:24 time: 0.273523 data_time: 0.053390 memory: 2937 loss_kpt: 87.729981 acc_pose: 0.838700 loss: 87.729981 2022/10/12 18:44:43 - mmengine - INFO - Epoch(train) [168][300/586] lr: 2.000000e-02 eta: 1:45:12 time: 0.271686 data_time: 0.050778 memory: 2937 loss_kpt: 89.605281 acc_pose: 0.755837 loss: 89.605281 2022/10/12 18:44:57 - mmengine - INFO - Epoch(train) [168][350/586] lr: 2.000000e-02 eta: 1:45:00 time: 0.273018 data_time: 0.052003 memory: 2937 loss_kpt: 89.237216 acc_pose: 0.834555 loss: 89.237216 2022/10/12 18:45:09 - mmengine - INFO - Epoch(train) [168][400/586] lr: 2.000000e-02 eta: 1:44:47 time: 0.255620 data_time: 0.047475 memory: 2937 loss_kpt: 88.894899 acc_pose: 0.835603 loss: 88.894899 2022/10/12 18:45:22 - mmengine - INFO - Epoch(train) [168][450/586] lr: 2.000000e-02 eta: 1:44:34 time: 0.257363 data_time: 0.051927 memory: 2937 loss_kpt: 88.798113 acc_pose: 0.710670 loss: 88.798113 2022/10/12 18:45:36 - mmengine - INFO - Epoch(train) [168][500/586] lr: 2.000000e-02 eta: 1:44:22 time: 0.273098 data_time: 0.052341 memory: 2937 loss_kpt: 89.609361 acc_pose: 0.741489 loss: 89.609361 2022/10/12 18:45:49 - mmengine - INFO - Epoch(train) [168][550/586] lr: 2.000000e-02 eta: 1:44:09 time: 0.268659 data_time: 0.052976 memory: 2937 loss_kpt: 90.193816 acc_pose: 0.769694 loss: 90.193816 2022/10/12 18:45:59 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:46:14 - mmengine - INFO - Epoch(train) [169][50/586] lr: 2.000000e-02 eta: 1:43:46 time: 0.297108 data_time: 0.060173 memory: 2937 loss_kpt: 88.563565 acc_pose: 0.841780 loss: 88.563565 2022/10/12 18:46:28 - mmengine - INFO - Epoch(train) [169][100/586] lr: 2.000000e-02 eta: 1:43:33 time: 0.280159 data_time: 0.053089 memory: 2937 loss_kpt: 89.222614 acc_pose: 0.782081 loss: 89.222614 2022/10/12 18:46:41 - mmengine - INFO - Epoch(train) [169][150/586] lr: 2.000000e-02 eta: 1:43:21 time: 0.273031 data_time: 0.048303 memory: 2937 loss_kpt: 88.279892 acc_pose: 0.752994 loss: 88.279892 2022/10/12 18:46:55 - mmengine - INFO - Epoch(train) [169][200/586] lr: 2.000000e-02 eta: 1:43:09 time: 0.271240 data_time: 0.051584 memory: 2937 loss_kpt: 88.726718 acc_pose: 0.802428 loss: 88.726718 2022/10/12 18:47:08 - mmengine - INFO - Epoch(train) [169][250/586] lr: 2.000000e-02 eta: 1:42:56 time: 0.266109 data_time: 0.053134 memory: 2937 loss_kpt: 88.405097 acc_pose: 0.788441 loss: 88.405097 2022/10/12 18:47:21 - mmengine - INFO - Epoch(train) [169][300/586] lr: 2.000000e-02 eta: 1:42:43 time: 0.260948 data_time: 0.049909 memory: 2937 loss_kpt: 89.314872 acc_pose: 0.682329 loss: 89.314872 2022/10/12 18:47:35 - mmengine - INFO - Epoch(train) [169][350/586] lr: 2.000000e-02 eta: 1:42:31 time: 0.273125 data_time: 0.055063 memory: 2937 loss_kpt: 88.901438 acc_pose: 0.742049 loss: 88.901438 2022/10/12 18:47:48 - mmengine - INFO - Epoch(train) [169][400/586] lr: 2.000000e-02 eta: 1:42:18 time: 0.267323 data_time: 0.053774 memory: 2937 loss_kpt: 89.794217 acc_pose: 0.805642 loss: 89.794217 2022/10/12 18:48:02 - mmengine - INFO - Epoch(train) [169][450/586] lr: 2.000000e-02 eta: 1:42:06 time: 0.283260 data_time: 0.056703 memory: 2937 loss_kpt: 89.021464 acc_pose: 0.829060 loss: 89.021464 2022/10/12 18:48:16 - mmengine - INFO - Epoch(train) [169][500/586] lr: 2.000000e-02 eta: 1:41:54 time: 0.272971 data_time: 0.052536 memory: 2937 loss_kpt: 89.674567 acc_pose: 0.788804 loss: 89.674567 2022/10/12 18:48:30 - mmengine - INFO - Epoch(train) [169][550/586] lr: 2.000000e-02 eta: 1:41:41 time: 0.278238 data_time: 0.048537 memory: 2937 loss_kpt: 89.144060 acc_pose: 0.733073 loss: 89.144060 2022/10/12 18:48:31 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:48:39 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:48:54 - mmengine - INFO - Epoch(train) [170][50/586] lr: 2.000000e-02 eta: 1:41:18 time: 0.289490 data_time: 0.069562 memory: 2937 loss_kpt: 89.491658 acc_pose: 0.792719 loss: 89.491658 2022/10/12 18:49:07 - mmengine - INFO - Epoch(train) [170][100/586] lr: 2.000000e-02 eta: 1:41:05 time: 0.263276 data_time: 0.053473 memory: 2937 loss_kpt: 87.514374 acc_pose: 0.865080 loss: 87.514374 2022/10/12 18:49:20 - mmengine - INFO - Epoch(train) [170][150/586] lr: 2.000000e-02 eta: 1:40:53 time: 0.250579 data_time: 0.050298 memory: 2937 loss_kpt: 89.933013 acc_pose: 0.718793 loss: 89.933013 2022/10/12 18:49:33 - mmengine - INFO - Epoch(train) [170][200/586] lr: 2.000000e-02 eta: 1:40:40 time: 0.258085 data_time: 0.057962 memory: 2937 loss_kpt: 90.106594 acc_pose: 0.754577 loss: 90.106594 2022/10/12 18:49:46 - mmengine - INFO - Epoch(train) [170][250/586] lr: 2.000000e-02 eta: 1:40:27 time: 0.264649 data_time: 0.054062 memory: 2937 loss_kpt: 89.870923 acc_pose: 0.748952 loss: 89.870923 2022/10/12 18:49:59 - mmengine - INFO - Epoch(train) [170][300/586] lr: 2.000000e-02 eta: 1:40:15 time: 0.254755 data_time: 0.053009 memory: 2937 loss_kpt: 87.737322 acc_pose: 0.822221 loss: 87.737322 2022/10/12 18:50:12 - mmengine - INFO - Epoch(train) [170][350/586] lr: 2.000000e-02 eta: 1:40:02 time: 0.259855 data_time: 0.053052 memory: 2937 loss_kpt: 89.298484 acc_pose: 0.790780 loss: 89.298484 2022/10/12 18:50:25 - mmengine - INFO - Epoch(train) [170][400/586] lr: 2.000000e-02 eta: 1:39:49 time: 0.259106 data_time: 0.054849 memory: 2937 loss_kpt: 90.095064 acc_pose: 0.707407 loss: 90.095064 2022/10/12 18:50:37 - mmengine - INFO - Epoch(train) [170][450/586] lr: 2.000000e-02 eta: 1:39:37 time: 0.248718 data_time: 0.049976 memory: 2937 loss_kpt: 87.351207 acc_pose: 0.715497 loss: 87.351207 2022/10/12 18:50:50 - mmengine - INFO - Epoch(train) [170][500/586] lr: 2.000000e-02 eta: 1:39:24 time: 0.262793 data_time: 0.054710 memory: 2937 loss_kpt: 89.328044 acc_pose: 0.846546 loss: 89.328044 2022/10/12 18:51:03 - mmengine - INFO - Epoch(train) [170][550/586] lr: 2.000000e-02 eta: 1:39:12 time: 0.261721 data_time: 0.056252 memory: 2937 loss_kpt: 89.271518 acc_pose: 0.789521 loss: 89.271518 2022/10/12 18:51:12 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:51:12 - mmengine - INFO - Saving checkpoint at 170 epochs 2022/10/12 18:51:21 - mmengine - INFO - Epoch(val) [170][50/407] eta: 0:00:41 time: 0.116227 data_time: 0.015048 memory: 2937 2022/10/12 18:51:26 - mmengine - INFO - Epoch(val) [170][100/407] eta: 0:00:33 time: 0.110323 data_time: 0.009218 memory: 830 2022/10/12 18:51:32 - mmengine - INFO - Epoch(val) [170][150/407] eta: 0:00:27 time: 0.107761 data_time: 0.008644 memory: 830 2022/10/12 18:51:37 - mmengine - INFO - Epoch(val) [170][200/407] eta: 0:00:23 time: 0.112289 data_time: 0.009408 memory: 830 2022/10/12 18:51:43 - mmengine - INFO - Epoch(val) [170][250/407] eta: 0:00:17 time: 0.109998 data_time: 0.010781 memory: 830 2022/10/12 18:51:48 - mmengine - INFO - Epoch(val) [170][300/407] eta: 0:00:11 time: 0.106819 data_time: 0.009050 memory: 830 2022/10/12 18:51:53 - mmengine - INFO - Epoch(val) [170][350/407] eta: 0:00:06 time: 0.108970 data_time: 0.009101 memory: 830 2022/10/12 18:51:59 - mmengine - INFO - Epoch(val) [170][400/407] eta: 0:00:00 time: 0.104302 data_time: 0.008549 memory: 830 2022/10/12 18:52:12 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 18:52:28 - mmengine - INFO - Epoch(val) [170][407/407] coco/AP: 0.687096 coco/AP .5: 0.875133 coco/AP .75: 0.768315 coco/AP (M): 0.654372 coco/AP (L): 0.747832 coco/AR: 0.757557 coco/AR .5: 0.918451 coco/AR .75: 0.825724 coco/AR (M): 0.711500 coco/AR (L): 0.821107 2022/10/12 18:52:43 - mmengine - INFO - Epoch(train) [171][50/586] lr: 2.000000e-03 eta: 1:38:48 time: 0.300698 data_time: 0.061117 memory: 2937 loss_kpt: 89.859131 acc_pose: 0.805111 loss: 89.859131 2022/10/12 18:52:57 - mmengine - INFO - Epoch(train) [171][100/586] lr: 2.000000e-03 eta: 1:38:36 time: 0.279574 data_time: 0.057614 memory: 2937 loss_kpt: 86.964016 acc_pose: 0.818041 loss: 86.964016 2022/10/12 18:53:11 - mmengine - INFO - Epoch(train) [171][150/586] lr: 2.000000e-03 eta: 1:38:24 time: 0.284241 data_time: 0.052455 memory: 2937 loss_kpt: 88.684150 acc_pose: 0.815367 loss: 88.684150 2022/10/12 18:53:25 - mmengine - INFO - Epoch(train) [171][200/586] lr: 2.000000e-03 eta: 1:38:11 time: 0.266625 data_time: 0.048974 memory: 2937 loss_kpt: 88.891246 acc_pose: 0.726335 loss: 88.891246 2022/10/12 18:53:39 - mmengine - INFO - Epoch(train) [171][250/586] lr: 2.000000e-03 eta: 1:37:59 time: 0.278049 data_time: 0.053742 memory: 2937 loss_kpt: 87.221675 acc_pose: 0.756246 loss: 87.221675 2022/10/12 18:53:52 - mmengine - INFO - Epoch(train) [171][300/586] lr: 2.000000e-03 eta: 1:37:46 time: 0.267440 data_time: 0.050101 memory: 2937 loss_kpt: 89.138513 acc_pose: 0.807796 loss: 89.138513 2022/10/12 18:54:05 - mmengine - INFO - Epoch(train) [171][350/586] lr: 2.000000e-03 eta: 1:37:34 time: 0.265752 data_time: 0.053354 memory: 2937 loss_kpt: 87.263901 acc_pose: 0.774720 loss: 87.263901 2022/10/12 18:54:14 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:54:19 - mmengine - INFO - Epoch(train) [171][400/586] lr: 2.000000e-03 eta: 1:37:21 time: 0.274540 data_time: 0.052259 memory: 2937 loss_kpt: 88.529029 acc_pose: 0.819581 loss: 88.529029 2022/10/12 18:54:32 - mmengine - INFO - Epoch(train) [171][450/586] lr: 2.000000e-03 eta: 1:37:09 time: 0.264223 data_time: 0.049041 memory: 2937 loss_kpt: 85.942278 acc_pose: 0.856247 loss: 85.942278 2022/10/12 18:54:46 - mmengine - INFO - Epoch(train) [171][500/586] lr: 2.000000e-03 eta: 1:36:56 time: 0.267030 data_time: 0.051729 memory: 2937 loss_kpt: 86.606372 acc_pose: 0.784912 loss: 86.606372 2022/10/12 18:54:59 - mmengine - INFO - Epoch(train) [171][550/586] lr: 2.000000e-03 eta: 1:36:43 time: 0.265419 data_time: 0.052852 memory: 2937 loss_kpt: 87.244186 acc_pose: 0.776649 loss: 87.244186 2022/10/12 18:55:08 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:55:22 - mmengine - INFO - Epoch(train) [172][50/586] lr: 2.000000e-03 eta: 1:36:20 time: 0.287313 data_time: 0.064613 memory: 2937 loss_kpt: 90.077727 acc_pose: 0.805393 loss: 90.077727 2022/10/12 18:55:36 - mmengine - INFO - Epoch(train) [172][100/586] lr: 2.000000e-03 eta: 1:36:07 time: 0.264039 data_time: 0.050975 memory: 2937 loss_kpt: 87.087434 acc_pose: 0.795078 loss: 87.087434 2022/10/12 18:55:50 - mmengine - INFO - Epoch(train) [172][150/586] lr: 2.000000e-03 eta: 1:35:55 time: 0.278707 data_time: 0.051187 memory: 2937 loss_kpt: 86.431785 acc_pose: 0.810360 loss: 86.431785 2022/10/12 18:56:02 - mmengine - INFO - Epoch(train) [172][200/586] lr: 2.000000e-03 eta: 1:35:42 time: 0.255655 data_time: 0.049106 memory: 2937 loss_kpt: 87.755541 acc_pose: 0.847453 loss: 87.755541 2022/10/12 18:56:16 - mmengine - INFO - Epoch(train) [172][250/586] lr: 2.000000e-03 eta: 1:35:30 time: 0.265136 data_time: 0.052813 memory: 2937 loss_kpt: 87.091584 acc_pose: 0.874067 loss: 87.091584 2022/10/12 18:56:28 - mmengine - INFO - Epoch(train) [172][300/586] lr: 2.000000e-03 eta: 1:35:17 time: 0.256256 data_time: 0.047667 memory: 2937 loss_kpt: 88.734919 acc_pose: 0.743776 loss: 88.734919 2022/10/12 18:56:42 - mmengine - INFO - Epoch(train) [172][350/586] lr: 2.000000e-03 eta: 1:35:05 time: 0.272246 data_time: 0.052756 memory: 2937 loss_kpt: 88.154492 acc_pose: 0.695030 loss: 88.154492 2022/10/12 18:56:55 - mmengine - INFO - Epoch(train) [172][400/586] lr: 2.000000e-03 eta: 1:34:52 time: 0.266124 data_time: 0.054565 memory: 2937 loss_kpt: 89.097967 acc_pose: 0.814098 loss: 89.097967 2022/10/12 18:57:09 - mmengine - INFO - Epoch(train) [172][450/586] lr: 2.000000e-03 eta: 1:34:40 time: 0.270636 data_time: 0.053475 memory: 2937 loss_kpt: 88.297791 acc_pose: 0.775329 loss: 88.297791 2022/10/12 18:57:22 - mmengine - INFO - Epoch(train) [172][500/586] lr: 2.000000e-03 eta: 1:34:27 time: 0.261995 data_time: 0.054414 memory: 2937 loss_kpt: 87.662615 acc_pose: 0.853265 loss: 87.662615 2022/10/12 18:57:36 - mmengine - INFO - Epoch(train) [172][550/586] lr: 2.000000e-03 eta: 1:34:15 time: 0.280844 data_time: 0.061383 memory: 2937 loss_kpt: 87.543725 acc_pose: 0.846427 loss: 87.543725 2022/10/12 18:57:46 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:58:00 - mmengine - INFO - Epoch(train) [173][50/586] lr: 2.000000e-03 eta: 1:33:51 time: 0.288070 data_time: 0.060234 memory: 2937 loss_kpt: 88.075859 acc_pose: 0.829682 loss: 88.075859 2022/10/12 18:58:14 - mmengine - INFO - Epoch(train) [173][100/586] lr: 2.000000e-03 eta: 1:33:39 time: 0.271364 data_time: 0.052868 memory: 2937 loss_kpt: 87.860038 acc_pose: 0.840197 loss: 87.860038 2022/10/12 18:58:27 - mmengine - INFO - Epoch(train) [173][150/586] lr: 2.000000e-03 eta: 1:33:26 time: 0.263387 data_time: 0.049480 memory: 2937 loss_kpt: 87.574202 acc_pose: 0.735375 loss: 87.574202 2022/10/12 18:58:41 - mmengine - INFO - Epoch(train) [173][200/586] lr: 2.000000e-03 eta: 1:33:14 time: 0.265282 data_time: 0.053311 memory: 2937 loss_kpt: 88.437988 acc_pose: 0.767397 loss: 88.437988 2022/10/12 18:58:43 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 18:58:53 - mmengine - INFO - Epoch(train) [173][250/586] lr: 2.000000e-03 eta: 1:33:01 time: 0.257041 data_time: 0.048251 memory: 2937 loss_kpt: 88.113552 acc_pose: 0.868886 loss: 88.113552 2022/10/12 18:59:06 - mmengine - INFO - Epoch(train) [173][300/586] lr: 2.000000e-03 eta: 1:32:48 time: 0.257499 data_time: 0.053193 memory: 2937 loss_kpt: 87.447736 acc_pose: 0.879122 loss: 87.447736 2022/10/12 18:59:19 - mmengine - INFO - Epoch(train) [173][350/586] lr: 2.000000e-03 eta: 1:32:36 time: 0.262405 data_time: 0.055511 memory: 2937 loss_kpt: 87.420741 acc_pose: 0.831227 loss: 87.420741 2022/10/12 18:59:33 - mmengine - INFO - Epoch(train) [173][400/586] lr: 2.000000e-03 eta: 1:32:23 time: 0.271601 data_time: 0.049860 memory: 2937 loss_kpt: 88.016608 acc_pose: 0.811394 loss: 88.016608 2022/10/12 18:59:46 - mmengine - INFO - Epoch(train) [173][450/586] lr: 2.000000e-03 eta: 1:32:11 time: 0.266134 data_time: 0.052840 memory: 2937 loss_kpt: 87.016365 acc_pose: 0.873761 loss: 87.016365 2022/10/12 18:59:59 - mmengine - INFO - Epoch(train) [173][500/586] lr: 2.000000e-03 eta: 1:31:58 time: 0.251405 data_time: 0.050399 memory: 2937 loss_kpt: 86.653775 acc_pose: 0.800122 loss: 86.653775 2022/10/12 19:00:12 - mmengine - INFO - Epoch(train) [173][550/586] lr: 2.000000e-03 eta: 1:31:46 time: 0.270733 data_time: 0.049570 memory: 2937 loss_kpt: 86.751413 acc_pose: 0.832238 loss: 86.751413 2022/10/12 19:00:22 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:00:37 - mmengine - INFO - Epoch(train) [174][50/586] lr: 2.000000e-03 eta: 1:31:22 time: 0.299142 data_time: 0.063839 memory: 2937 loss_kpt: 87.357895 acc_pose: 0.863248 loss: 87.357895 2022/10/12 19:00:51 - mmengine - INFO - Epoch(train) [174][100/586] lr: 2.000000e-03 eta: 1:31:10 time: 0.271658 data_time: 0.054291 memory: 2937 loss_kpt: 87.834772 acc_pose: 0.817242 loss: 87.834772 2022/10/12 19:01:04 - mmengine - INFO - Epoch(train) [174][150/586] lr: 2.000000e-03 eta: 1:30:57 time: 0.266708 data_time: 0.055646 memory: 2937 loss_kpt: 86.775120 acc_pose: 0.818750 loss: 86.775120 2022/10/12 19:01:18 - mmengine - INFO - Epoch(train) [174][200/586] lr: 2.000000e-03 eta: 1:30:45 time: 0.268595 data_time: 0.053289 memory: 2937 loss_kpt: 85.942915 acc_pose: 0.797277 loss: 85.942915 2022/10/12 19:01:31 - mmengine - INFO - Epoch(train) [174][250/586] lr: 2.000000e-03 eta: 1:30:32 time: 0.272575 data_time: 0.055402 memory: 2937 loss_kpt: 87.871314 acc_pose: 0.782613 loss: 87.871314 2022/10/12 19:01:45 - mmengine - INFO - Epoch(train) [174][300/586] lr: 2.000000e-03 eta: 1:30:20 time: 0.271663 data_time: 0.051709 memory: 2937 loss_kpt: 87.850853 acc_pose: 0.703096 loss: 87.850853 2022/10/12 19:01:59 - mmengine - INFO - Epoch(train) [174][350/586] lr: 2.000000e-03 eta: 1:30:08 time: 0.285912 data_time: 0.053002 memory: 2937 loss_kpt: 88.967115 acc_pose: 0.750220 loss: 88.967115 2022/10/12 19:02:13 - mmengine - INFO - Epoch(train) [174][400/586] lr: 2.000000e-03 eta: 1:29:55 time: 0.276562 data_time: 0.052817 memory: 2937 loss_kpt: 87.559379 acc_pose: 0.788005 loss: 87.559379 2022/10/12 19:02:26 - mmengine - INFO - Epoch(train) [174][450/586] lr: 2.000000e-03 eta: 1:29:43 time: 0.268397 data_time: 0.054485 memory: 2937 loss_kpt: 87.001317 acc_pose: 0.809955 loss: 87.001317 2022/10/12 19:02:40 - mmengine - INFO - Epoch(train) [174][500/586] lr: 2.000000e-03 eta: 1:29:30 time: 0.261948 data_time: 0.050196 memory: 2937 loss_kpt: 86.225480 acc_pose: 0.822105 loss: 86.225480 2022/10/12 19:02:53 - mmengine - INFO - Epoch(train) [174][550/586] lr: 2.000000e-03 eta: 1:29:18 time: 0.269172 data_time: 0.050723 memory: 2937 loss_kpt: 86.831898 acc_pose: 0.808200 loss: 86.831898 2022/10/12 19:03:03 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:03:13 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:03:17 - mmengine - INFO - Epoch(train) [175][50/586] lr: 2.000000e-03 eta: 1:28:54 time: 0.286921 data_time: 0.063727 memory: 2937 loss_kpt: 87.400562 acc_pose: 0.780685 loss: 87.400562 2022/10/12 19:03:31 - mmengine - INFO - Epoch(train) [175][100/586] lr: 2.000000e-03 eta: 1:28:42 time: 0.268007 data_time: 0.053209 memory: 2937 loss_kpt: 88.286811 acc_pose: 0.759216 loss: 88.286811 2022/10/12 19:03:43 - mmengine - INFO - Epoch(train) [175][150/586] lr: 2.000000e-03 eta: 1:28:29 time: 0.258013 data_time: 0.049101 memory: 2937 loss_kpt: 87.844478 acc_pose: 0.821577 loss: 87.844478 2022/10/12 19:03:57 - mmengine - INFO - Epoch(train) [175][200/586] lr: 2.000000e-03 eta: 1:28:17 time: 0.274906 data_time: 0.054575 memory: 2937 loss_kpt: 87.272724 acc_pose: 0.864013 loss: 87.272724 2022/10/12 19:04:10 - mmengine - INFO - Epoch(train) [175][250/586] lr: 2.000000e-03 eta: 1:28:04 time: 0.261928 data_time: 0.050927 memory: 2937 loss_kpt: 87.796873 acc_pose: 0.861951 loss: 87.796873 2022/10/12 19:04:24 - mmengine - INFO - Epoch(train) [175][300/586] lr: 2.000000e-03 eta: 1:27:52 time: 0.278698 data_time: 0.055191 memory: 2937 loss_kpt: 88.834144 acc_pose: 0.733659 loss: 88.834144 2022/10/12 19:04:38 - mmengine - INFO - Epoch(train) [175][350/586] lr: 2.000000e-03 eta: 1:27:39 time: 0.271123 data_time: 0.056733 memory: 2937 loss_kpt: 87.665402 acc_pose: 0.727131 loss: 87.665402 2022/10/12 19:04:52 - mmengine - INFO - Epoch(train) [175][400/586] lr: 2.000000e-03 eta: 1:27:27 time: 0.283035 data_time: 0.057628 memory: 2937 loss_kpt: 87.540607 acc_pose: 0.799938 loss: 87.540607 2022/10/12 19:05:06 - mmengine - INFO - Epoch(train) [175][450/586] lr: 2.000000e-03 eta: 1:27:14 time: 0.279406 data_time: 0.053664 memory: 2937 loss_kpt: 88.452822 acc_pose: 0.773425 loss: 88.452822 2022/10/12 19:05:20 - mmengine - INFO - Epoch(train) [175][500/586] lr: 2.000000e-03 eta: 1:27:02 time: 0.283477 data_time: 0.054711 memory: 2937 loss_kpt: 87.458484 acc_pose: 0.775858 loss: 87.458484 2022/10/12 19:05:34 - mmengine - INFO - Epoch(train) [175][550/586] lr: 2.000000e-03 eta: 1:26:49 time: 0.270017 data_time: 0.052901 memory: 2937 loss_kpt: 86.339992 acc_pose: 0.852633 loss: 86.339992 2022/10/12 19:05:43 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:05:58 - mmengine - INFO - Epoch(train) [176][50/586] lr: 2.000000e-03 eta: 1:26:26 time: 0.292467 data_time: 0.066190 memory: 2937 loss_kpt: 87.269359 acc_pose: 0.691887 loss: 87.269359 2022/10/12 19:06:12 - mmengine - INFO - Epoch(train) [176][100/586] lr: 2.000000e-03 eta: 1:26:14 time: 0.274539 data_time: 0.053456 memory: 2937 loss_kpt: 88.703729 acc_pose: 0.803681 loss: 88.703729 2022/10/12 19:06:25 - mmengine - INFO - Epoch(train) [176][150/586] lr: 2.000000e-03 eta: 1:26:01 time: 0.265177 data_time: 0.055397 memory: 2937 loss_kpt: 87.195764 acc_pose: 0.841044 loss: 87.195764 2022/10/12 19:06:38 - mmengine - INFO - Epoch(train) [176][200/586] lr: 2.000000e-03 eta: 1:25:49 time: 0.259287 data_time: 0.049324 memory: 2937 loss_kpt: 86.593637 acc_pose: 0.697722 loss: 86.593637 2022/10/12 19:06:52 - mmengine - INFO - Epoch(train) [176][250/586] lr: 2.000000e-03 eta: 1:25:36 time: 0.269677 data_time: 0.059084 memory: 2937 loss_kpt: 86.827677 acc_pose: 0.822876 loss: 86.827677 2022/10/12 19:07:06 - mmengine - INFO - Epoch(train) [176][300/586] lr: 2.000000e-03 eta: 1:25:24 time: 0.280427 data_time: 0.049591 memory: 2937 loss_kpt: 87.553788 acc_pose: 0.864440 loss: 87.553788 2022/10/12 19:07:19 - mmengine - INFO - Epoch(train) [176][350/586] lr: 2.000000e-03 eta: 1:25:11 time: 0.274465 data_time: 0.049846 memory: 2937 loss_kpt: 85.623971 acc_pose: 0.846016 loss: 85.623971 2022/10/12 19:07:33 - mmengine - INFO - Epoch(train) [176][400/586] lr: 2.000000e-03 eta: 1:24:59 time: 0.268977 data_time: 0.050353 memory: 2937 loss_kpt: 87.140113 acc_pose: 0.774568 loss: 87.140113 2022/10/12 19:07:46 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:07:46 - mmengine - INFO - Epoch(train) [176][450/586] lr: 2.000000e-03 eta: 1:24:46 time: 0.261863 data_time: 0.053398 memory: 2937 loss_kpt: 87.685847 acc_pose: 0.809299 loss: 87.685847 2022/10/12 19:07:58 - mmengine - INFO - Epoch(train) [176][500/586] lr: 2.000000e-03 eta: 1:24:33 time: 0.252007 data_time: 0.053333 memory: 2937 loss_kpt: 88.123204 acc_pose: 0.858724 loss: 88.123204 2022/10/12 19:08:11 - mmengine - INFO - Epoch(train) [176][550/586] lr: 2.000000e-03 eta: 1:24:21 time: 0.254949 data_time: 0.054493 memory: 2937 loss_kpt: 86.807097 acc_pose: 0.823108 loss: 86.807097 2022/10/12 19:08:20 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:08:34 - mmengine - INFO - Epoch(train) [177][50/586] lr: 2.000000e-03 eta: 1:23:57 time: 0.285256 data_time: 0.064092 memory: 2937 loss_kpt: 85.389553 acc_pose: 0.850782 loss: 85.389553 2022/10/12 19:08:48 - mmengine - INFO - Epoch(train) [177][100/586] lr: 2.000000e-03 eta: 1:23:45 time: 0.272487 data_time: 0.056396 memory: 2937 loss_kpt: 86.086413 acc_pose: 0.811936 loss: 86.086413 2022/10/12 19:09:01 - mmengine - INFO - Epoch(train) [177][150/586] lr: 2.000000e-03 eta: 1:23:32 time: 0.264417 data_time: 0.050458 memory: 2937 loss_kpt: 85.453041 acc_pose: 0.770891 loss: 85.453041 2022/10/12 19:09:15 - mmengine - INFO - Epoch(train) [177][200/586] lr: 2.000000e-03 eta: 1:23:20 time: 0.264472 data_time: 0.050891 memory: 2937 loss_kpt: 85.120452 acc_pose: 0.863548 loss: 85.120452 2022/10/12 19:09:28 - mmengine - INFO - Epoch(train) [177][250/586] lr: 2.000000e-03 eta: 1:23:07 time: 0.262435 data_time: 0.054984 memory: 2937 loss_kpt: 88.547061 acc_pose: 0.823966 loss: 88.547061 2022/10/12 19:09:41 - mmengine - INFO - Epoch(train) [177][300/586] lr: 2.000000e-03 eta: 1:22:55 time: 0.266027 data_time: 0.055643 memory: 2937 loss_kpt: 87.457439 acc_pose: 0.840765 loss: 87.457439 2022/10/12 19:09:54 - mmengine - INFO - Epoch(train) [177][350/586] lr: 2.000000e-03 eta: 1:22:42 time: 0.260609 data_time: 0.050758 memory: 2937 loss_kpt: 87.369464 acc_pose: 0.710914 loss: 87.369464 2022/10/12 19:10:08 - mmengine - INFO - Epoch(train) [177][400/586] lr: 2.000000e-03 eta: 1:22:29 time: 0.269758 data_time: 0.062229 memory: 2937 loss_kpt: 88.283949 acc_pose: 0.847660 loss: 88.283949 2022/10/12 19:10:22 - mmengine - INFO - Epoch(train) [177][450/586] lr: 2.000000e-03 eta: 1:22:17 time: 0.289526 data_time: 0.057699 memory: 2937 loss_kpt: 88.025575 acc_pose: 0.870516 loss: 88.025575 2022/10/12 19:10:36 - mmengine - INFO - Epoch(train) [177][500/586] lr: 2.000000e-03 eta: 1:22:05 time: 0.277113 data_time: 0.053621 memory: 2937 loss_kpt: 87.215735 acc_pose: 0.825666 loss: 87.215735 2022/10/12 19:10:50 - mmengine - INFO - Epoch(train) [177][550/586] lr: 2.000000e-03 eta: 1:21:52 time: 0.274527 data_time: 0.053608 memory: 2937 loss_kpt: 86.582974 acc_pose: 0.826761 loss: 86.582974 2022/10/12 19:10:59 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:11:14 - mmengine - INFO - Epoch(train) [178][50/586] lr: 2.000000e-03 eta: 1:21:29 time: 0.294384 data_time: 0.060115 memory: 2937 loss_kpt: 87.921343 acc_pose: 0.734465 loss: 87.921343 2022/10/12 19:11:28 - mmengine - INFO - Epoch(train) [178][100/586] lr: 2.000000e-03 eta: 1:21:17 time: 0.284067 data_time: 0.052171 memory: 2937 loss_kpt: 86.993195 acc_pose: 0.741100 loss: 86.993195 2022/10/12 19:11:42 - mmengine - INFO - Epoch(train) [178][150/586] lr: 2.000000e-03 eta: 1:21:04 time: 0.277517 data_time: 0.051874 memory: 2937 loss_kpt: 86.046939 acc_pose: 0.797127 loss: 86.046939 2022/10/12 19:11:55 - mmengine - INFO - Epoch(train) [178][200/586] lr: 2.000000e-03 eta: 1:20:52 time: 0.254586 data_time: 0.051769 memory: 2937 loss_kpt: 88.119954 acc_pose: 0.836385 loss: 88.119954 2022/10/12 19:12:08 - mmengine - INFO - Epoch(train) [178][250/586] lr: 2.000000e-03 eta: 1:20:39 time: 0.267210 data_time: 0.052917 memory: 2937 loss_kpt: 86.208881 acc_pose: 0.768356 loss: 86.208881 2022/10/12 19:12:16 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:12:22 - mmengine - INFO - Epoch(train) [178][300/586] lr: 2.000000e-03 eta: 1:20:26 time: 0.273540 data_time: 0.059304 memory: 2937 loss_kpt: 86.934135 acc_pose: 0.827531 loss: 86.934135 2022/10/12 19:12:36 - mmengine - INFO - Epoch(train) [178][350/586] lr: 2.000000e-03 eta: 1:20:14 time: 0.286039 data_time: 0.055870 memory: 2937 loss_kpt: 85.622905 acc_pose: 0.808990 loss: 85.622905 2022/10/12 19:12:49 - mmengine - INFO - Epoch(train) [178][400/586] lr: 2.000000e-03 eta: 1:20:02 time: 0.261249 data_time: 0.055925 memory: 2937 loss_kpt: 86.263990 acc_pose: 0.816924 loss: 86.263990 2022/10/12 19:13:03 - mmengine - INFO - Epoch(train) [178][450/586] lr: 2.000000e-03 eta: 1:19:49 time: 0.275902 data_time: 0.054807 memory: 2937 loss_kpt: 86.473185 acc_pose: 0.833662 loss: 86.473185 2022/10/12 19:13:16 - mmengine - INFO - Epoch(train) [178][500/586] lr: 2.000000e-03 eta: 1:19:36 time: 0.263003 data_time: 0.056614 memory: 2937 loss_kpt: 86.760450 acc_pose: 0.855311 loss: 86.760450 2022/10/12 19:13:29 - mmengine - INFO - Epoch(train) [178][550/586] lr: 2.000000e-03 eta: 1:19:24 time: 0.263692 data_time: 0.055573 memory: 2937 loss_kpt: 88.598310 acc_pose: 0.745074 loss: 88.598310 2022/10/12 19:13:38 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:13:53 - mmengine - INFO - Epoch(train) [179][50/586] lr: 2.000000e-03 eta: 1:19:01 time: 0.281833 data_time: 0.058326 memory: 2937 loss_kpt: 87.880763 acc_pose: 0.783023 loss: 87.880763 2022/10/12 19:14:06 - mmengine - INFO - Epoch(train) [179][100/586] lr: 2.000000e-03 eta: 1:18:48 time: 0.273193 data_time: 0.052208 memory: 2937 loss_kpt: 88.056359 acc_pose: 0.817408 loss: 88.056359 2022/10/12 19:14:20 - mmengine - INFO - Epoch(train) [179][150/586] lr: 2.000000e-03 eta: 1:18:36 time: 0.274025 data_time: 0.057233 memory: 2937 loss_kpt: 87.039989 acc_pose: 0.834138 loss: 87.039989 2022/10/12 19:14:34 - mmengine - INFO - Epoch(train) [179][200/586] lr: 2.000000e-03 eta: 1:18:23 time: 0.273421 data_time: 0.055023 memory: 2937 loss_kpt: 86.509572 acc_pose: 0.778577 loss: 86.509572 2022/10/12 19:14:48 - mmengine - INFO - Epoch(train) [179][250/586] lr: 2.000000e-03 eta: 1:18:11 time: 0.278799 data_time: 0.053420 memory: 2937 loss_kpt: 86.598673 acc_pose: 0.820391 loss: 86.598673 2022/10/12 19:15:01 - mmengine - INFO - Epoch(train) [179][300/586] lr: 2.000000e-03 eta: 1:17:58 time: 0.267296 data_time: 0.053928 memory: 2937 loss_kpt: 85.348998 acc_pose: 0.761901 loss: 85.348998 2022/10/12 19:15:14 - mmengine - INFO - Epoch(train) [179][350/586] lr: 2.000000e-03 eta: 1:17:46 time: 0.267232 data_time: 0.055064 memory: 2937 loss_kpt: 86.923701 acc_pose: 0.711737 loss: 86.923701 2022/10/12 19:15:28 - mmengine - INFO - Epoch(train) [179][400/586] lr: 2.000000e-03 eta: 1:17:33 time: 0.263004 data_time: 0.051293 memory: 2937 loss_kpt: 85.741912 acc_pose: 0.847303 loss: 85.741912 2022/10/12 19:15:41 - mmengine - INFO - Epoch(train) [179][450/586] lr: 2.000000e-03 eta: 1:17:20 time: 0.267765 data_time: 0.052183 memory: 2937 loss_kpt: 87.744716 acc_pose: 0.827431 loss: 87.744716 2022/10/12 19:15:54 - mmengine - INFO - Epoch(train) [179][500/586] lr: 2.000000e-03 eta: 1:17:08 time: 0.269692 data_time: 0.052617 memory: 2937 loss_kpt: 88.138801 acc_pose: 0.713780 loss: 88.138801 2022/10/12 19:16:08 - mmengine - INFO - Epoch(train) [179][550/586] lr: 2.000000e-03 eta: 1:16:55 time: 0.260329 data_time: 0.051832 memory: 2937 loss_kpt: 85.846618 acc_pose: 0.800482 loss: 85.846618 2022/10/12 19:16:17 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:16:31 - mmengine - INFO - Epoch(train) [180][50/586] lr: 2.000000e-03 eta: 1:16:32 time: 0.277438 data_time: 0.063886 memory: 2937 loss_kpt: 86.384158 acc_pose: 0.815517 loss: 86.384158 2022/10/12 19:16:45 - mmengine - INFO - Epoch(train) [180][100/586] lr: 2.000000e-03 eta: 1:16:20 time: 0.275760 data_time: 0.059322 memory: 2937 loss_kpt: 87.478392 acc_pose: 0.869299 loss: 87.478392 2022/10/12 19:16:46 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:16:58 - mmengine - INFO - Epoch(train) [180][150/586] lr: 2.000000e-03 eta: 1:16:07 time: 0.264264 data_time: 0.052323 memory: 2937 loss_kpt: 87.824747 acc_pose: 0.803703 loss: 87.824747 2022/10/12 19:17:10 - mmengine - INFO - Epoch(train) [180][200/586] lr: 2.000000e-03 eta: 1:15:54 time: 0.250614 data_time: 0.050133 memory: 2937 loss_kpt: 86.543336 acc_pose: 0.857511 loss: 86.543336 2022/10/12 19:17:23 - mmengine - INFO - Epoch(train) [180][250/586] lr: 2.000000e-03 eta: 1:15:42 time: 0.259874 data_time: 0.052179 memory: 2937 loss_kpt: 86.677308 acc_pose: 0.835313 loss: 86.677308 2022/10/12 19:17:37 - mmengine - INFO - Epoch(train) [180][300/586] lr: 2.000000e-03 eta: 1:15:29 time: 0.263704 data_time: 0.052216 memory: 2937 loss_kpt: 85.831385 acc_pose: 0.838840 loss: 85.831385 2022/10/12 19:17:50 - mmengine - INFO - Epoch(train) [180][350/586] lr: 2.000000e-03 eta: 1:15:17 time: 0.262580 data_time: 0.049662 memory: 2937 loss_kpt: 87.343854 acc_pose: 0.801744 loss: 87.343854 2022/10/12 19:18:03 - mmengine - INFO - Epoch(train) [180][400/586] lr: 2.000000e-03 eta: 1:15:04 time: 0.254717 data_time: 0.049482 memory: 2937 loss_kpt: 86.495537 acc_pose: 0.832161 loss: 86.495537 2022/10/12 19:18:16 - mmengine - INFO - Epoch(train) [180][450/586] lr: 2.000000e-03 eta: 1:14:51 time: 0.264179 data_time: 0.051778 memory: 2937 loss_kpt: 87.491268 acc_pose: 0.779639 loss: 87.491268 2022/10/12 19:18:29 - mmengine - INFO - Epoch(train) [180][500/586] lr: 2.000000e-03 eta: 1:14:39 time: 0.261867 data_time: 0.047385 memory: 2937 loss_kpt: 87.999859 acc_pose: 0.860201 loss: 87.999859 2022/10/12 19:18:42 - mmengine - INFO - Epoch(train) [180][550/586] lr: 2.000000e-03 eta: 1:14:26 time: 0.265012 data_time: 0.050964 memory: 2937 loss_kpt: 86.004817 acc_pose: 0.830662 loss: 86.004817 2022/10/12 19:18:52 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:18:52 - mmengine - INFO - Saving checkpoint at 180 epochs 2022/10/12 19:19:00 - mmengine - INFO - Epoch(val) [180][50/407] eta: 0:00:39 time: 0.111881 data_time: 0.013979 memory: 2937 2022/10/12 19:19:05 - mmengine - INFO - Epoch(val) [180][100/407] eta: 0:00:32 time: 0.105174 data_time: 0.008345 memory: 830 2022/10/12 19:19:10 - mmengine - INFO - Epoch(val) [180][150/407] eta: 0:00:28 time: 0.110438 data_time: 0.013335 memory: 830 2022/10/12 19:19:16 - mmengine - INFO - Epoch(val) [180][200/407] eta: 0:00:21 time: 0.104677 data_time: 0.008394 memory: 830 2022/10/12 19:19:21 - mmengine - INFO - Epoch(val) [180][250/407] eta: 0:00:16 time: 0.107239 data_time: 0.008194 memory: 830 2022/10/12 19:19:26 - mmengine - INFO - Epoch(val) [180][300/407] eta: 0:00:11 time: 0.105535 data_time: 0.008165 memory: 830 2022/10/12 19:19:32 - mmengine - INFO - Epoch(val) [180][350/407] eta: 0:00:05 time: 0.103693 data_time: 0.008155 memory: 830 2022/10/12 19:19:37 - mmengine - INFO - Epoch(val) [180][400/407] eta: 0:00:00 time: 0.104242 data_time: 0.008342 memory: 830 2022/10/12 19:19:50 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 19:20:06 - mmengine - INFO - Epoch(val) [180][407/407] coco/AP: 0.701920 coco/AP .5: 0.885287 coco/AP .75: 0.779463 coco/AP (M): 0.669471 coco/AP (L): 0.763550 coco/AR: 0.771127 coco/AR .5: 0.926480 coco/AR .75: 0.835327 coco/AR (M): 0.725676 coco/AR (L): 0.833779 2022/10/12 19:20:06 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221012/rsn18/best_coco/AP_epoch_160.pth is removed 2022/10/12 19:20:08 - mmengine - INFO - The best checkpoint with 0.7019 coco/AP at 180 epoch is saved to best_coco/AP_epoch_180.pth. 2022/10/12 19:20:22 - mmengine - INFO - Epoch(train) [181][50/586] lr: 2.000000e-03 eta: 1:14:03 time: 0.279567 data_time: 0.063504 memory: 2937 loss_kpt: 87.478535 acc_pose: 0.829828 loss: 87.478535 2022/10/12 19:20:35 - mmengine - INFO - Epoch(train) [181][100/586] lr: 2.000000e-03 eta: 1:13:50 time: 0.267584 data_time: 0.049449 memory: 2937 loss_kpt: 86.535955 acc_pose: 0.775768 loss: 86.535955 2022/10/12 19:20:48 - mmengine - INFO - Epoch(train) [181][150/586] lr: 2.000000e-03 eta: 1:13:38 time: 0.260690 data_time: 0.054635 memory: 2937 loss_kpt: 85.833591 acc_pose: 0.813716 loss: 85.833591 2022/10/12 19:21:02 - mmengine - INFO - Epoch(train) [181][200/586] lr: 2.000000e-03 eta: 1:13:25 time: 0.266336 data_time: 0.050666 memory: 2937 loss_kpt: 86.000975 acc_pose: 0.876073 loss: 86.000975 2022/10/12 19:21:15 - mmengine - INFO - Epoch(train) [181][250/586] lr: 2.000000e-03 eta: 1:13:13 time: 0.270789 data_time: 0.054262 memory: 2937 loss_kpt: 87.491613 acc_pose: 0.805350 loss: 87.491613 2022/10/12 19:21:29 - mmengine - INFO - Epoch(train) [181][300/586] lr: 2.000000e-03 eta: 1:13:00 time: 0.268094 data_time: 0.055166 memory: 2937 loss_kpt: 88.334754 acc_pose: 0.811236 loss: 88.334754 2022/10/12 19:21:42 - mmengine - INFO - Epoch(train) [181][350/586] lr: 2.000000e-03 eta: 1:12:48 time: 0.275652 data_time: 0.057442 memory: 2937 loss_kpt: 85.003229 acc_pose: 0.739936 loss: 85.003229 2022/10/12 19:21:56 - mmengine - INFO - Epoch(train) [181][400/586] lr: 2.000000e-03 eta: 1:12:35 time: 0.275189 data_time: 0.053290 memory: 2937 loss_kpt: 87.307947 acc_pose: 0.878496 loss: 87.307947 2022/10/12 19:22:10 - mmengine - INFO - Epoch(train) [181][450/586] lr: 2.000000e-03 eta: 1:12:23 time: 0.273860 data_time: 0.050990 memory: 2937 loss_kpt: 86.696884 acc_pose: 0.862531 loss: 86.696884 2022/10/12 19:22:24 - mmengine - INFO - Epoch(train) [181][500/586] lr: 2.000000e-03 eta: 1:12:10 time: 0.288580 data_time: 0.053182 memory: 2937 loss_kpt: 86.567797 acc_pose: 0.857837 loss: 86.567797 2022/10/12 19:22:30 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:22:38 - mmengine - INFO - Epoch(train) [181][550/586] lr: 2.000000e-03 eta: 1:11:58 time: 0.272302 data_time: 0.051232 memory: 2937 loss_kpt: 87.242741 acc_pose: 0.785860 loss: 87.242741 2022/10/12 19:22:48 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:23:02 - mmengine - INFO - Epoch(train) [182][50/586] lr: 2.000000e-03 eta: 1:11:35 time: 0.287058 data_time: 0.065374 memory: 2937 loss_kpt: 87.523718 acc_pose: 0.833020 loss: 87.523718 2022/10/12 19:23:16 - mmengine - INFO - Epoch(train) [182][100/586] lr: 2.000000e-03 eta: 1:11:22 time: 0.271615 data_time: 0.047732 memory: 2937 loss_kpt: 86.531522 acc_pose: 0.834753 loss: 86.531522 2022/10/12 19:23:29 - mmengine - INFO - Epoch(train) [182][150/586] lr: 2.000000e-03 eta: 1:11:10 time: 0.263716 data_time: 0.056780 memory: 2937 loss_kpt: 87.687648 acc_pose: 0.822404 loss: 87.687648 2022/10/12 19:23:42 - mmengine - INFO - Epoch(train) [182][200/586] lr: 2.000000e-03 eta: 1:10:57 time: 0.266094 data_time: 0.052855 memory: 2937 loss_kpt: 88.788686 acc_pose: 0.822899 loss: 88.788686 2022/10/12 19:23:56 - mmengine - INFO - Epoch(train) [182][250/586] lr: 2.000000e-03 eta: 1:10:44 time: 0.266250 data_time: 0.051362 memory: 2937 loss_kpt: 85.424151 acc_pose: 0.840519 loss: 85.424151 2022/10/12 19:24:09 - mmengine - INFO - Epoch(train) [182][300/586] lr: 2.000000e-03 eta: 1:10:32 time: 0.262341 data_time: 0.054951 memory: 2937 loss_kpt: 86.545475 acc_pose: 0.807727 loss: 86.545475 2022/10/12 19:24:22 - mmengine - INFO - Epoch(train) [182][350/586] lr: 2.000000e-03 eta: 1:10:19 time: 0.273310 data_time: 0.062000 memory: 2937 loss_kpt: 86.581707 acc_pose: 0.780619 loss: 86.581707 2022/10/12 19:24:36 - mmengine - INFO - Epoch(train) [182][400/586] lr: 2.000000e-03 eta: 1:10:07 time: 0.275965 data_time: 0.057225 memory: 2937 loss_kpt: 87.240087 acc_pose: 0.845128 loss: 87.240087 2022/10/12 19:24:50 - mmengine - INFO - Epoch(train) [182][450/586] lr: 2.000000e-03 eta: 1:09:54 time: 0.275221 data_time: 0.053252 memory: 2937 loss_kpt: 85.733377 acc_pose: 0.871233 loss: 85.733377 2022/10/12 19:25:04 - mmengine - INFO - Epoch(train) [182][500/586] lr: 2.000000e-03 eta: 1:09:42 time: 0.274317 data_time: 0.054620 memory: 2937 loss_kpt: 86.527713 acc_pose: 0.832027 loss: 86.527713 2022/10/12 19:25:17 - mmengine - INFO - Epoch(train) [182][550/586] lr: 2.000000e-03 eta: 1:09:29 time: 0.270691 data_time: 0.056517 memory: 2937 loss_kpt: 87.791173 acc_pose: 0.857804 loss: 87.791173 2022/10/12 19:25:27 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:25:42 - mmengine - INFO - Epoch(train) [183][50/586] lr: 2.000000e-03 eta: 1:09:06 time: 0.289828 data_time: 0.064128 memory: 2937 loss_kpt: 86.530594 acc_pose: 0.768745 loss: 86.530594 2022/10/12 19:25:55 - mmengine - INFO - Epoch(train) [183][100/586] lr: 2.000000e-03 eta: 1:08:54 time: 0.268652 data_time: 0.048757 memory: 2937 loss_kpt: 88.054482 acc_pose: 0.850251 loss: 88.054482 2022/10/12 19:26:08 - mmengine - INFO - Epoch(train) [183][150/586] lr: 2.000000e-03 eta: 1:08:41 time: 0.255546 data_time: 0.058516 memory: 2937 loss_kpt: 86.785427 acc_pose: 0.771626 loss: 86.785427 2022/10/12 19:26:21 - mmengine - INFO - Epoch(train) [183][200/586] lr: 2.000000e-03 eta: 1:08:29 time: 0.267227 data_time: 0.051172 memory: 2937 loss_kpt: 85.260180 acc_pose: 0.819530 loss: 85.260180 2022/10/12 19:26:35 - mmengine - INFO - Epoch(train) [183][250/586] lr: 2.000000e-03 eta: 1:08:16 time: 0.262281 data_time: 0.054208 memory: 2937 loss_kpt: 86.698596 acc_pose: 0.819058 loss: 86.698596 2022/10/12 19:26:47 - mmengine - INFO - Epoch(train) [183][300/586] lr: 2.000000e-03 eta: 1:08:03 time: 0.254812 data_time: 0.048873 memory: 2937 loss_kpt: 86.643082 acc_pose: 0.758378 loss: 86.643082 2022/10/12 19:27:00 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:27:00 - mmengine - INFO - Epoch(train) [183][350/586] lr: 2.000000e-03 eta: 1:07:51 time: 0.261900 data_time: 0.055750 memory: 2937 loss_kpt: 86.665895 acc_pose: 0.828830 loss: 86.665895 2022/10/12 19:27:13 - mmengine - INFO - Epoch(train) [183][400/586] lr: 2.000000e-03 eta: 1:07:38 time: 0.261380 data_time: 0.049863 memory: 2937 loss_kpt: 87.063949 acc_pose: 0.739310 loss: 87.063949 2022/10/12 19:27:27 - mmengine - INFO - Epoch(train) [183][450/586] lr: 2.000000e-03 eta: 1:07:25 time: 0.270829 data_time: 0.053663 memory: 2937 loss_kpt: 85.986692 acc_pose: 0.825791 loss: 85.986692 2022/10/12 19:27:40 - mmengine - INFO - Epoch(train) [183][500/586] lr: 2.000000e-03 eta: 1:07:13 time: 0.258008 data_time: 0.049037 memory: 2937 loss_kpt: 88.373118 acc_pose: 0.816991 loss: 88.373118 2022/10/12 19:27:54 - mmengine - INFO - Epoch(train) [183][550/586] lr: 2.000000e-03 eta: 1:07:00 time: 0.273617 data_time: 0.060755 memory: 2937 loss_kpt: 86.797520 acc_pose: 0.802208 loss: 86.797520 2022/10/12 19:28:03 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:28:18 - mmengine - INFO - Epoch(train) [184][50/586] lr: 2.000000e-03 eta: 1:06:37 time: 0.287423 data_time: 0.061303 memory: 2937 loss_kpt: 87.049499 acc_pose: 0.772379 loss: 87.049499 2022/10/12 19:28:31 - mmengine - INFO - Epoch(train) [184][100/586] lr: 2.000000e-03 eta: 1:06:25 time: 0.266377 data_time: 0.055218 memory: 2937 loss_kpt: 88.662658 acc_pose: 0.769168 loss: 88.662658 2022/10/12 19:28:45 - mmengine - INFO - Epoch(train) [184][150/586] lr: 2.000000e-03 eta: 1:06:12 time: 0.267486 data_time: 0.053445 memory: 2937 loss_kpt: 86.040829 acc_pose: 0.826743 loss: 86.040829 2022/10/12 19:28:58 - mmengine - INFO - Epoch(train) [184][200/586] lr: 2.000000e-03 eta: 1:06:00 time: 0.260261 data_time: 0.057442 memory: 2937 loss_kpt: 87.061250 acc_pose: 0.775973 loss: 87.061250 2022/10/12 19:29:11 - mmengine - INFO - Epoch(train) [184][250/586] lr: 2.000000e-03 eta: 1:05:47 time: 0.261722 data_time: 0.054851 memory: 2937 loss_kpt: 86.964404 acc_pose: 0.802531 loss: 86.964404 2022/10/12 19:29:24 - mmengine - INFO - Epoch(train) [184][300/586] lr: 2.000000e-03 eta: 1:05:34 time: 0.257473 data_time: 0.052700 memory: 2937 loss_kpt: 87.667880 acc_pose: 0.811145 loss: 87.667880 2022/10/12 19:29:37 - mmengine - INFO - Epoch(train) [184][350/586] lr: 2.000000e-03 eta: 1:05:22 time: 0.263891 data_time: 0.050326 memory: 2937 loss_kpt: 87.593646 acc_pose: 0.887492 loss: 87.593646 2022/10/12 19:29:50 - mmengine - INFO - Epoch(train) [184][400/586] lr: 2.000000e-03 eta: 1:05:09 time: 0.263327 data_time: 0.054603 memory: 2937 loss_kpt: 86.402641 acc_pose: 0.846489 loss: 86.402641 2022/10/12 19:30:03 - mmengine - INFO - Epoch(train) [184][450/586] lr: 2.000000e-03 eta: 1:04:57 time: 0.259081 data_time: 0.050057 memory: 2937 loss_kpt: 86.461950 acc_pose: 0.857063 loss: 86.461950 2022/10/12 19:30:16 - mmengine - INFO - Epoch(train) [184][500/586] lr: 2.000000e-03 eta: 1:04:44 time: 0.266779 data_time: 0.056601 memory: 2937 loss_kpt: 86.002084 acc_pose: 0.799620 loss: 86.002084 2022/10/12 19:30:30 - mmengine - INFO - Epoch(train) [184][550/586] lr: 2.000000e-03 eta: 1:04:31 time: 0.279355 data_time: 0.053921 memory: 2937 loss_kpt: 87.234665 acc_pose: 0.800845 loss: 87.234665 2022/10/12 19:30:40 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:30:54 - mmengine - INFO - Epoch(train) [185][50/586] lr: 2.000000e-03 eta: 1:04:09 time: 0.285722 data_time: 0.065110 memory: 2937 loss_kpt: 86.200582 acc_pose: 0.707225 loss: 86.200582 2022/10/12 19:31:08 - mmengine - INFO - Epoch(train) [185][100/586] lr: 2.000000e-03 eta: 1:03:56 time: 0.269341 data_time: 0.051802 memory: 2937 loss_kpt: 86.164551 acc_pose: 0.793232 loss: 86.164551 2022/10/12 19:31:22 - mmengine - INFO - Epoch(train) [185][150/586] lr: 2.000000e-03 eta: 1:03:44 time: 0.275972 data_time: 0.050060 memory: 2937 loss_kpt: 86.530111 acc_pose: 0.841428 loss: 86.530111 2022/10/12 19:31:29 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:31:35 - mmengine - INFO - Epoch(train) [185][200/586] lr: 2.000000e-03 eta: 1:03:31 time: 0.272963 data_time: 0.050255 memory: 2937 loss_kpt: 87.042013 acc_pose: 0.737976 loss: 87.042013 2022/10/12 19:31:49 - mmengine - INFO - Epoch(train) [185][250/586] lr: 2.000000e-03 eta: 1:03:18 time: 0.267605 data_time: 0.059448 memory: 2937 loss_kpt: 86.160676 acc_pose: 0.778328 loss: 86.160676 2022/10/12 19:32:02 - mmengine - INFO - Epoch(train) [185][300/586] lr: 2.000000e-03 eta: 1:03:06 time: 0.259966 data_time: 0.051056 memory: 2937 loss_kpt: 85.974930 acc_pose: 0.777202 loss: 85.974930 2022/10/12 19:32:15 - mmengine - INFO - Epoch(train) [185][350/586] lr: 2.000000e-03 eta: 1:02:53 time: 0.265538 data_time: 0.055163 memory: 2937 loss_kpt: 86.804872 acc_pose: 0.835284 loss: 86.804872 2022/10/12 19:32:29 - mmengine - INFO - Epoch(train) [185][400/586] lr: 2.000000e-03 eta: 1:02:41 time: 0.279520 data_time: 0.054756 memory: 2937 loss_kpt: 87.744324 acc_pose: 0.848666 loss: 87.744324 2022/10/12 19:32:43 - mmengine - INFO - Epoch(train) [185][450/586] lr: 2.000000e-03 eta: 1:02:28 time: 0.270084 data_time: 0.051968 memory: 2937 loss_kpt: 86.757141 acc_pose: 0.810857 loss: 86.757141 2022/10/12 19:32:57 - mmengine - INFO - Epoch(train) [185][500/586] lr: 2.000000e-03 eta: 1:02:16 time: 0.278473 data_time: 0.054720 memory: 2937 loss_kpt: 87.445289 acc_pose: 0.809751 loss: 87.445289 2022/10/12 19:33:10 - mmengine - INFO - Epoch(train) [185][550/586] lr: 2.000000e-03 eta: 1:02:03 time: 0.264791 data_time: 0.052151 memory: 2937 loss_kpt: 85.189800 acc_pose: 0.777340 loss: 85.189800 2022/10/12 19:33:19 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:33:33 - mmengine - INFO - Epoch(train) [186][50/586] lr: 2.000000e-03 eta: 1:01:40 time: 0.281649 data_time: 0.064080 memory: 2937 loss_kpt: 87.017683 acc_pose: 0.795783 loss: 87.017683 2022/10/12 19:33:47 - mmengine - INFO - Epoch(train) [186][100/586] lr: 2.000000e-03 eta: 1:01:28 time: 0.277203 data_time: 0.059817 memory: 2937 loss_kpt: 85.213513 acc_pose: 0.799661 loss: 85.213513 2022/10/12 19:34:01 - mmengine - INFO - Epoch(train) [186][150/586] lr: 2.000000e-03 eta: 1:01:15 time: 0.275336 data_time: 0.054827 memory: 2937 loss_kpt: 87.764649 acc_pose: 0.752045 loss: 87.764649 2022/10/12 19:34:14 - mmengine - INFO - Epoch(train) [186][200/586] lr: 2.000000e-03 eta: 1:01:03 time: 0.268487 data_time: 0.056416 memory: 2937 loss_kpt: 86.776060 acc_pose: 0.781191 loss: 86.776060 2022/10/12 19:34:27 - mmengine - INFO - Epoch(train) [186][250/586] lr: 2.000000e-03 eta: 1:00:50 time: 0.264963 data_time: 0.050636 memory: 2937 loss_kpt: 86.335141 acc_pose: 0.884002 loss: 86.335141 2022/10/12 19:34:40 - mmengine - INFO - Epoch(train) [186][300/586] lr: 2.000000e-03 eta: 1:00:37 time: 0.259449 data_time: 0.053258 memory: 2937 loss_kpt: 86.225152 acc_pose: 0.864517 loss: 86.225152 2022/10/12 19:34:53 - mmengine - INFO - Epoch(train) [186][350/586] lr: 2.000000e-03 eta: 1:00:25 time: 0.252088 data_time: 0.051702 memory: 2937 loss_kpt: 86.226557 acc_pose: 0.860959 loss: 86.226557 2022/10/12 19:35:06 - mmengine - INFO - Epoch(train) [186][400/586] lr: 2.000000e-03 eta: 1:00:12 time: 0.259365 data_time: 0.051641 memory: 2937 loss_kpt: 87.906856 acc_pose: 0.847915 loss: 87.906856 2022/10/12 19:35:19 - mmengine - INFO - Epoch(train) [186][450/586] lr: 2.000000e-03 eta: 0:59:59 time: 0.256598 data_time: 0.053471 memory: 2937 loss_kpt: 87.764287 acc_pose: 0.802582 loss: 87.764287 2022/10/12 19:35:32 - mmengine - INFO - Epoch(train) [186][500/586] lr: 2.000000e-03 eta: 0:59:47 time: 0.258387 data_time: 0.053463 memory: 2937 loss_kpt: 86.672097 acc_pose: 0.748729 loss: 86.672097 2022/10/12 19:35:45 - mmengine - INFO - Epoch(train) [186][550/586] lr: 2.000000e-03 eta: 0:59:34 time: 0.272157 data_time: 0.053382 memory: 2937 loss_kpt: 86.495090 acc_pose: 0.856423 loss: 86.495090 2022/10/12 19:35:54 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:35:56 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:36:09 - mmengine - INFO - Epoch(train) [187][50/586] lr: 2.000000e-03 eta: 0:59:11 time: 0.296582 data_time: 0.063937 memory: 2937 loss_kpt: 87.262815 acc_pose: 0.773563 loss: 87.262815 2022/10/12 19:36:23 - mmengine - INFO - Epoch(train) [187][100/586] lr: 2.000000e-03 eta: 0:58:59 time: 0.273209 data_time: 0.054318 memory: 2937 loss_kpt: 86.806858 acc_pose: 0.840209 loss: 86.806858 2022/10/12 19:36:36 - mmengine - INFO - Epoch(train) [187][150/586] lr: 2.000000e-03 eta: 0:58:46 time: 0.278266 data_time: 0.049651 memory: 2937 loss_kpt: 86.664233 acc_pose: 0.791992 loss: 86.664233 2022/10/12 19:36:50 - mmengine - INFO - Epoch(train) [187][200/586] lr: 2.000000e-03 eta: 0:58:34 time: 0.274582 data_time: 0.048802 memory: 2937 loss_kpt: 86.884109 acc_pose: 0.828753 loss: 86.884109 2022/10/12 19:37:05 - mmengine - INFO - Epoch(train) [187][250/586] lr: 2.000000e-03 eta: 0:58:21 time: 0.291749 data_time: 0.059766 memory: 2937 loss_kpt: 87.849711 acc_pose: 0.851034 loss: 87.849711 2022/10/12 19:37:18 - mmengine - INFO - Epoch(train) [187][300/586] lr: 2.000000e-03 eta: 0:58:09 time: 0.273180 data_time: 0.052234 memory: 2937 loss_kpt: 88.171610 acc_pose: 0.877528 loss: 88.171610 2022/10/12 19:37:32 - mmengine - INFO - Epoch(train) [187][350/586] lr: 2.000000e-03 eta: 0:57:56 time: 0.271501 data_time: 0.052715 memory: 2937 loss_kpt: 84.804676 acc_pose: 0.789096 loss: 84.804676 2022/10/12 19:37:45 - mmengine - INFO - Epoch(train) [187][400/586] lr: 2.000000e-03 eta: 0:57:44 time: 0.255884 data_time: 0.051596 memory: 2937 loss_kpt: 87.145147 acc_pose: 0.855227 loss: 87.145147 2022/10/12 19:37:58 - mmengine - INFO - Epoch(train) [187][450/586] lr: 2.000000e-03 eta: 0:57:31 time: 0.256332 data_time: 0.053215 memory: 2937 loss_kpt: 85.186772 acc_pose: 0.858196 loss: 85.186772 2022/10/12 19:38:11 - mmengine - INFO - Epoch(train) [187][500/586] lr: 2.000000e-03 eta: 0:57:18 time: 0.259296 data_time: 0.047971 memory: 2937 loss_kpt: 86.370498 acc_pose: 0.792175 loss: 86.370498 2022/10/12 19:38:24 - mmengine - INFO - Epoch(train) [187][550/586] lr: 2.000000e-03 eta: 0:57:06 time: 0.269388 data_time: 0.051234 memory: 2937 loss_kpt: 86.478319 acc_pose: 0.822338 loss: 86.478319 2022/10/12 19:38:33 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:38:48 - mmengine - INFO - Epoch(train) [188][50/586] lr: 2.000000e-03 eta: 0:56:43 time: 0.295789 data_time: 0.065239 memory: 2937 loss_kpt: 85.996482 acc_pose: 0.777972 loss: 85.996482 2022/10/12 19:39:02 - mmengine - INFO - Epoch(train) [188][100/586] lr: 2.000000e-03 eta: 0:56:31 time: 0.295062 data_time: 0.054228 memory: 2937 loss_kpt: 86.608693 acc_pose: 0.823348 loss: 86.608693 2022/10/12 19:39:17 - mmengine - INFO - Epoch(train) [188][150/586] lr: 2.000000e-03 eta: 0:56:18 time: 0.289415 data_time: 0.053963 memory: 2937 loss_kpt: 87.241275 acc_pose: 0.770123 loss: 87.241275 2022/10/12 19:39:31 - mmengine - INFO - Epoch(train) [188][200/586] lr: 2.000000e-03 eta: 0:56:06 time: 0.277432 data_time: 0.051952 memory: 2937 loss_kpt: 85.902214 acc_pose: 0.843893 loss: 85.902214 2022/10/12 19:39:45 - mmengine - INFO - Epoch(train) [188][250/586] lr: 2.000000e-03 eta: 0:55:53 time: 0.293527 data_time: 0.056218 memory: 2937 loss_kpt: 85.935720 acc_pose: 0.821707 loss: 85.935720 2022/10/12 19:40:00 - mmengine - INFO - Epoch(train) [188][300/586] lr: 2.000000e-03 eta: 0:55:41 time: 0.285614 data_time: 0.051886 memory: 2937 loss_kpt: 86.980722 acc_pose: 0.811672 loss: 86.980722 2022/10/12 19:40:13 - mmengine - INFO - Epoch(train) [188][350/586] lr: 2.000000e-03 eta: 0:55:28 time: 0.261116 data_time: 0.050394 memory: 2937 loss_kpt: 85.823490 acc_pose: 0.874723 loss: 85.823490 2022/10/12 19:40:26 - mmengine - INFO - Epoch(train) [188][400/586] lr: 2.000000e-03 eta: 0:55:15 time: 0.260304 data_time: 0.049892 memory: 2937 loss_kpt: 86.674659 acc_pose: 0.762212 loss: 86.674659 2022/10/12 19:40:31 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:40:39 - mmengine - INFO - Epoch(train) [188][450/586] lr: 2.000000e-03 eta: 0:55:03 time: 0.263887 data_time: 0.051906 memory: 2937 loss_kpt: 87.211092 acc_pose: 0.831530 loss: 87.211092 2022/10/12 19:40:52 - mmengine - INFO - Epoch(train) [188][500/586] lr: 2.000000e-03 eta: 0:54:50 time: 0.260092 data_time: 0.052161 memory: 2937 loss_kpt: 86.125863 acc_pose: 0.840221 loss: 86.125863 2022/10/12 19:41:05 - mmengine - INFO - Epoch(train) [188][550/586] lr: 2.000000e-03 eta: 0:54:38 time: 0.258402 data_time: 0.047807 memory: 2937 loss_kpt: 86.952792 acc_pose: 0.851631 loss: 86.952792 2022/10/12 19:41:14 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:41:29 - mmengine - INFO - Epoch(train) [189][50/586] lr: 2.000000e-03 eta: 0:54:15 time: 0.287099 data_time: 0.065443 memory: 2937 loss_kpt: 84.668071 acc_pose: 0.895378 loss: 84.668071 2022/10/12 19:41:42 - mmengine - INFO - Epoch(train) [189][100/586] lr: 2.000000e-03 eta: 0:54:02 time: 0.258193 data_time: 0.047016 memory: 2937 loss_kpt: 86.685784 acc_pose: 0.825861 loss: 86.685784 2022/10/12 19:41:54 - mmengine - INFO - Epoch(train) [189][150/586] lr: 2.000000e-03 eta: 0:53:50 time: 0.253813 data_time: 0.049274 memory: 2937 loss_kpt: 87.375874 acc_pose: 0.832221 loss: 87.375874 2022/10/12 19:42:07 - mmengine - INFO - Epoch(train) [189][200/586] lr: 2.000000e-03 eta: 0:53:37 time: 0.251651 data_time: 0.048653 memory: 2937 loss_kpt: 86.738064 acc_pose: 0.800041 loss: 86.738064 2022/10/12 19:42:20 - mmengine - INFO - Epoch(train) [189][250/586] lr: 2.000000e-03 eta: 0:53:24 time: 0.259253 data_time: 0.050177 memory: 2937 loss_kpt: 86.791795 acc_pose: 0.785949 loss: 86.791795 2022/10/12 19:42:33 - mmengine - INFO - Epoch(train) [189][300/586] lr: 2.000000e-03 eta: 0:53:12 time: 0.257509 data_time: 0.049263 memory: 2937 loss_kpt: 87.701231 acc_pose: 0.836349 loss: 87.701231 2022/10/12 19:42:46 - mmengine - INFO - Epoch(train) [189][350/586] lr: 2.000000e-03 eta: 0:52:59 time: 0.253654 data_time: 0.049668 memory: 2937 loss_kpt: 87.071732 acc_pose: 0.772886 loss: 87.071732 2022/10/12 19:42:59 - mmengine - INFO - Epoch(train) [189][400/586] lr: 2.000000e-03 eta: 0:52:46 time: 0.258493 data_time: 0.053975 memory: 2937 loss_kpt: 84.823036 acc_pose: 0.838891 loss: 84.823036 2022/10/12 19:43:11 - mmengine - INFO - Epoch(train) [189][450/586] lr: 2.000000e-03 eta: 0:52:34 time: 0.254933 data_time: 0.051425 memory: 2937 loss_kpt: 85.273581 acc_pose: 0.812942 loss: 85.273581 2022/10/12 19:43:24 - mmengine - INFO - Epoch(train) [189][500/586] lr: 2.000000e-03 eta: 0:52:21 time: 0.250709 data_time: 0.048091 memory: 2937 loss_kpt: 86.406927 acc_pose: 0.808393 loss: 86.406927 2022/10/12 19:43:37 - mmengine - INFO - Epoch(train) [189][550/586] lr: 2.000000e-03 eta: 0:52:08 time: 0.253628 data_time: 0.052557 memory: 2937 loss_kpt: 87.333913 acc_pose: 0.775098 loss: 87.333913 2022/10/12 19:43:45 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:44:00 - mmengine - INFO - Epoch(train) [190][50/586] lr: 2.000000e-03 eta: 0:51:46 time: 0.295878 data_time: 0.065057 memory: 2937 loss_kpt: 87.725475 acc_pose: 0.810404 loss: 87.725475 2022/10/12 19:44:13 - mmengine - INFO - Epoch(train) [190][100/586] lr: 2.000000e-03 eta: 0:51:33 time: 0.265781 data_time: 0.052996 memory: 2937 loss_kpt: 88.194027 acc_pose: 0.809220 loss: 88.194027 2022/10/12 19:44:28 - mmengine - INFO - Epoch(train) [190][150/586] lr: 2.000000e-03 eta: 0:51:21 time: 0.282646 data_time: 0.050828 memory: 2937 loss_kpt: 86.983661 acc_pose: 0.797480 loss: 86.983661 2022/10/12 19:44:42 - mmengine - INFO - Epoch(train) [190][200/586] lr: 2.000000e-03 eta: 0:51:08 time: 0.281571 data_time: 0.053656 memory: 2937 loss_kpt: 86.763564 acc_pose: 0.781013 loss: 86.763564 2022/10/12 19:44:54 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:44:56 - mmengine - INFO - Epoch(train) [190][250/586] lr: 2.000000e-03 eta: 0:50:55 time: 0.277485 data_time: 0.056873 memory: 2937 loss_kpt: 86.713431 acc_pose: 0.811386 loss: 86.713431 2022/10/12 19:45:09 - mmengine - INFO - Epoch(train) [190][300/586] lr: 2.000000e-03 eta: 0:50:43 time: 0.266580 data_time: 0.049307 memory: 2937 loss_kpt: 86.495839 acc_pose: 0.792374 loss: 86.495839 2022/10/12 19:45:22 - mmengine - INFO - Epoch(train) [190][350/586] lr: 2.000000e-03 eta: 0:50:30 time: 0.266392 data_time: 0.051358 memory: 2937 loss_kpt: 85.294463 acc_pose: 0.859116 loss: 85.294463 2022/10/12 19:45:36 - mmengine - INFO - Epoch(train) [190][400/586] lr: 2.000000e-03 eta: 0:50:18 time: 0.270948 data_time: 0.054836 memory: 2937 loss_kpt: 86.143218 acc_pose: 0.867950 loss: 86.143218 2022/10/12 19:45:50 - mmengine - INFO - Epoch(train) [190][450/586] lr: 2.000000e-03 eta: 0:50:05 time: 0.275418 data_time: 0.056620 memory: 2937 loss_kpt: 86.342018 acc_pose: 0.854468 loss: 86.342018 2022/10/12 19:46:03 - mmengine - INFO - Epoch(train) [190][500/586] lr: 2.000000e-03 eta: 0:49:53 time: 0.264567 data_time: 0.055383 memory: 2937 loss_kpt: 87.526873 acc_pose: 0.797126 loss: 87.526873 2022/10/12 19:46:16 - mmengine - INFO - Epoch(train) [190][550/586] lr: 2.000000e-03 eta: 0:49:40 time: 0.272086 data_time: 0.052609 memory: 2937 loss_kpt: 86.111600 acc_pose: 0.798515 loss: 86.111600 2022/10/12 19:46:26 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:46:26 - mmengine - INFO - Saving checkpoint at 190 epochs 2022/10/12 19:46:34 - mmengine - INFO - Epoch(val) [190][50/407] eta: 0:00:41 time: 0.116361 data_time: 0.013988 memory: 2937 2022/10/12 19:46:39 - mmengine - INFO - Epoch(val) [190][100/407] eta: 0:00:32 time: 0.105880 data_time: 0.008612 memory: 830 2022/10/12 19:46:45 - mmengine - INFO - Epoch(val) [190][150/407] eta: 0:00:27 time: 0.107503 data_time: 0.008812 memory: 830 2022/10/12 19:46:50 - mmengine - INFO - Epoch(val) [190][200/407] eta: 0:00:22 time: 0.107369 data_time: 0.008686 memory: 830 2022/10/12 19:46:55 - mmengine - INFO - Epoch(val) [190][250/407] eta: 0:00:16 time: 0.105994 data_time: 0.008615 memory: 830 2022/10/12 19:47:01 - mmengine - INFO - Epoch(val) [190][300/407] eta: 0:00:11 time: 0.106780 data_time: 0.008697 memory: 830 2022/10/12 19:47:06 - mmengine - INFO - Epoch(val) [190][350/407] eta: 0:00:06 time: 0.106436 data_time: 0.008288 memory: 830 2022/10/12 19:47:11 - mmengine - INFO - Epoch(val) [190][400/407] eta: 0:00:00 time: 0.102220 data_time: 0.008048 memory: 830 2022/10/12 19:47:25 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 19:47:41 - mmengine - INFO - Epoch(val) [190][407/407] coco/AP: 0.703475 coco/AP .5: 0.887227 coco/AP .75: 0.780433 coco/AP (M): 0.670110 coco/AP (L): 0.766450 coco/AR: 0.771757 coco/AR .5: 0.926952 coco/AR .75: 0.836272 coco/AR (M): 0.725977 coco/AR (L): 0.834783 2022/10/12 19:47:41 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221012/rsn18/best_coco/AP_epoch_180.pth is removed 2022/10/12 19:47:43 - mmengine - INFO - The best checkpoint with 0.7035 coco/AP at 190 epoch is saved to best_coco/AP_epoch_190.pth. 2022/10/12 19:47:57 - mmengine - INFO - Epoch(train) [191][50/586] lr: 2.000000e-04 eta: 0:49:17 time: 0.290818 data_time: 0.061085 memory: 2937 loss_kpt: 87.517563 acc_pose: 0.844729 loss: 87.517563 2022/10/12 19:48:11 - mmengine - INFO - Epoch(train) [191][100/586] lr: 2.000000e-04 eta: 0:49:05 time: 0.271399 data_time: 0.051362 memory: 2937 loss_kpt: 87.231394 acc_pose: 0.870551 loss: 87.231394 2022/10/12 19:48:24 - mmengine - INFO - Epoch(train) [191][150/586] lr: 2.000000e-04 eta: 0:48:52 time: 0.272174 data_time: 0.054954 memory: 2937 loss_kpt: 86.854579 acc_pose: 0.838330 loss: 86.854579 2022/10/12 19:48:38 - mmengine - INFO - Epoch(train) [191][200/586] lr: 2.000000e-04 eta: 0:48:40 time: 0.273766 data_time: 0.054902 memory: 2937 loss_kpt: 85.241695 acc_pose: 0.802450 loss: 85.241695 2022/10/12 19:48:51 - mmengine - INFO - Epoch(train) [191][250/586] lr: 2.000000e-04 eta: 0:48:27 time: 0.262293 data_time: 0.050106 memory: 2937 loss_kpt: 85.200974 acc_pose: 0.860265 loss: 85.200974 2022/10/12 19:49:04 - mmengine - INFO - Epoch(train) [191][300/586] lr: 2.000000e-04 eta: 0:48:14 time: 0.262310 data_time: 0.052360 memory: 2937 loss_kpt: 85.041189 acc_pose: 0.882826 loss: 85.041189 2022/10/12 19:49:18 - mmengine - INFO - Epoch(train) [191][350/586] lr: 2.000000e-04 eta: 0:48:02 time: 0.273627 data_time: 0.054694 memory: 2937 loss_kpt: 85.202148 acc_pose: 0.857682 loss: 85.202148 2022/10/12 19:49:31 - mmengine - INFO - Epoch(train) [191][400/586] lr: 2.000000e-04 eta: 0:47:49 time: 0.264895 data_time: 0.057029 memory: 2937 loss_kpt: 87.500547 acc_pose: 0.733917 loss: 87.500547 2022/10/12 19:49:44 - mmengine - INFO - Epoch(train) [191][450/586] lr: 2.000000e-04 eta: 0:47:37 time: 0.260765 data_time: 0.053471 memory: 2937 loss_kpt: 85.632853 acc_pose: 0.806076 loss: 85.632853 2022/10/12 19:49:58 - mmengine - INFO - Epoch(train) [191][500/586] lr: 2.000000e-04 eta: 0:47:24 time: 0.263072 data_time: 0.057327 memory: 2937 loss_kpt: 85.560564 acc_pose: 0.817379 loss: 85.560564 2022/10/12 19:50:11 - mmengine - INFO - Epoch(train) [191][550/586] lr: 2.000000e-04 eta: 0:47:11 time: 0.266049 data_time: 0.053876 memory: 2937 loss_kpt: 86.755604 acc_pose: 0.801589 loss: 86.755604 2022/10/12 19:50:20 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:50:34 - mmengine - INFO - Epoch(train) [192][50/586] lr: 2.000000e-04 eta: 0:46:49 time: 0.279906 data_time: 0.062155 memory: 2937 loss_kpt: 87.819463 acc_pose: 0.806299 loss: 87.819463 2022/10/12 19:50:41 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:50:47 - mmengine - INFO - Epoch(train) [192][100/586] lr: 2.000000e-04 eta: 0:46:36 time: 0.260865 data_time: 0.054648 memory: 2937 loss_kpt: 86.338704 acc_pose: 0.839343 loss: 86.338704 2022/10/12 19:51:01 - mmengine - INFO - Epoch(train) [192][150/586] lr: 2.000000e-04 eta: 0:46:24 time: 0.277470 data_time: 0.054828 memory: 2937 loss_kpt: 87.249733 acc_pose: 0.769459 loss: 87.249733 2022/10/12 19:51:15 - mmengine - INFO - Epoch(train) [192][200/586] lr: 2.000000e-04 eta: 0:46:11 time: 0.280530 data_time: 0.054603 memory: 2937 loss_kpt: 86.585712 acc_pose: 0.785971 loss: 86.585712 2022/10/12 19:51:29 - mmengine - INFO - Epoch(train) [192][250/586] lr: 2.000000e-04 eta: 0:45:58 time: 0.279204 data_time: 0.052146 memory: 2937 loss_kpt: 86.383719 acc_pose: 0.797378 loss: 86.383719 2022/10/12 19:51:43 - mmengine - INFO - Epoch(train) [192][300/586] lr: 2.000000e-04 eta: 0:45:46 time: 0.273480 data_time: 0.049446 memory: 2937 loss_kpt: 85.643382 acc_pose: 0.788823 loss: 85.643382 2022/10/12 19:51:57 - mmengine - INFO - Epoch(train) [192][350/586] lr: 2.000000e-04 eta: 0:45:33 time: 0.275894 data_time: 0.052112 memory: 2937 loss_kpt: 85.200999 acc_pose: 0.729869 loss: 85.200999 2022/10/12 19:52:11 - mmengine - INFO - Epoch(train) [192][400/586] lr: 2.000000e-04 eta: 0:45:21 time: 0.282505 data_time: 0.050315 memory: 2937 loss_kpt: 85.987358 acc_pose: 0.790759 loss: 85.987358 2022/10/12 19:52:25 - mmengine - INFO - Epoch(train) [192][450/586] lr: 2.000000e-04 eta: 0:45:08 time: 0.278123 data_time: 0.051768 memory: 2937 loss_kpt: 85.937135 acc_pose: 0.871240 loss: 85.937135 2022/10/12 19:52:38 - mmengine - INFO - Epoch(train) [192][500/586] lr: 2.000000e-04 eta: 0:44:56 time: 0.268780 data_time: 0.056002 memory: 2937 loss_kpt: 86.158548 acc_pose: 0.780258 loss: 86.158548 2022/10/12 19:52:52 - mmengine - INFO - Epoch(train) [192][550/586] lr: 2.000000e-04 eta: 0:44:43 time: 0.276676 data_time: 0.049944 memory: 2937 loss_kpt: 86.986939 acc_pose: 0.821396 loss: 86.986939 2022/10/12 19:53:02 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:53:17 - mmengine - INFO - Epoch(train) [193][50/586] lr: 2.000000e-04 eta: 0:44:21 time: 0.303880 data_time: 0.064544 memory: 2937 loss_kpt: 84.983857 acc_pose: 0.750153 loss: 84.983857 2022/10/12 19:53:32 - mmengine - INFO - Epoch(train) [193][100/586] lr: 2.000000e-04 eta: 0:44:08 time: 0.288934 data_time: 0.061074 memory: 2937 loss_kpt: 86.352398 acc_pose: 0.839872 loss: 86.352398 2022/10/12 19:53:46 - mmengine - INFO - Epoch(train) [193][150/586] lr: 2.000000e-04 eta: 0:43:56 time: 0.282578 data_time: 0.057511 memory: 2937 loss_kpt: 85.986437 acc_pose: 0.810928 loss: 85.986437 2022/10/12 19:54:00 - mmengine - INFO - Epoch(train) [193][200/586] lr: 2.000000e-04 eta: 0:43:43 time: 0.277898 data_time: 0.058315 memory: 2937 loss_kpt: 85.923979 acc_pose: 0.740841 loss: 85.923979 2022/10/12 19:54:14 - mmengine - INFO - Epoch(train) [193][250/586] lr: 2.000000e-04 eta: 0:43:31 time: 0.294731 data_time: 0.057373 memory: 2937 loss_kpt: 86.129316 acc_pose: 0.809222 loss: 86.129316 2022/10/12 19:54:28 - mmengine - INFO - Epoch(train) [193][300/586] lr: 2.000000e-04 eta: 0:43:18 time: 0.272475 data_time: 0.056789 memory: 2937 loss_kpt: 86.741286 acc_pose: 0.793520 loss: 86.741286 2022/10/12 19:54:41 - mmengine - INFO - Epoch(train) [193][350/586] lr: 2.000000e-04 eta: 0:43:05 time: 0.267869 data_time: 0.051708 memory: 2937 loss_kpt: 85.466583 acc_pose: 0.862676 loss: 85.466583 2022/10/12 19:54:55 - mmengine - INFO - Epoch(train) [193][400/586] lr: 2.000000e-04 eta: 0:42:53 time: 0.264345 data_time: 0.050219 memory: 2937 loss_kpt: 85.505911 acc_pose: 0.747951 loss: 85.505911 2022/10/12 19:55:08 - mmengine - INFO - Epoch(train) [193][450/586] lr: 2.000000e-04 eta: 0:42:40 time: 0.268700 data_time: 0.051445 memory: 2937 loss_kpt: 85.359127 acc_pose: 0.721083 loss: 85.359127 2022/10/12 19:55:18 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:55:22 - mmengine - INFO - Epoch(train) [193][500/586] lr: 2.000000e-04 eta: 0:42:27 time: 0.268898 data_time: 0.055065 memory: 2937 loss_kpt: 86.582295 acc_pose: 0.796079 loss: 86.582295 2022/10/12 19:55:35 - mmengine - INFO - Epoch(train) [193][550/586] lr: 2.000000e-04 eta: 0:42:15 time: 0.267972 data_time: 0.054354 memory: 2937 loss_kpt: 86.879844 acc_pose: 0.826924 loss: 86.879844 2022/10/12 19:55:44 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:55:59 - mmengine - INFO - Epoch(train) [194][50/586] lr: 2.000000e-04 eta: 0:41:52 time: 0.296385 data_time: 0.069152 memory: 2937 loss_kpt: 87.210808 acc_pose: 0.840678 loss: 87.210808 2022/10/12 19:56:13 - mmengine - INFO - Epoch(train) [194][100/586] lr: 2.000000e-04 eta: 0:41:40 time: 0.281579 data_time: 0.052170 memory: 2937 loss_kpt: 88.014115 acc_pose: 0.735324 loss: 88.014115 2022/10/12 19:56:27 - mmengine - INFO - Epoch(train) [194][150/586] lr: 2.000000e-04 eta: 0:41:27 time: 0.269916 data_time: 0.049807 memory: 2937 loss_kpt: 86.660813 acc_pose: 0.776747 loss: 86.660813 2022/10/12 19:56:40 - mmengine - INFO - Epoch(train) [194][200/586] lr: 2.000000e-04 eta: 0:41:15 time: 0.268952 data_time: 0.051628 memory: 2937 loss_kpt: 86.212475 acc_pose: 0.788728 loss: 86.212475 2022/10/12 19:56:53 - mmengine - INFO - Epoch(train) [194][250/586] lr: 2.000000e-04 eta: 0:41:02 time: 0.267050 data_time: 0.048340 memory: 2937 loss_kpt: 86.813987 acc_pose: 0.821047 loss: 86.813987 2022/10/12 19:57:07 - mmengine - INFO - Epoch(train) [194][300/586] lr: 2.000000e-04 eta: 0:40:49 time: 0.268309 data_time: 0.051481 memory: 2937 loss_kpt: 85.934800 acc_pose: 0.693861 loss: 85.934800 2022/10/12 19:57:20 - mmengine - INFO - Epoch(train) [194][350/586] lr: 2.000000e-04 eta: 0:40:37 time: 0.265193 data_time: 0.053035 memory: 2937 loss_kpt: 87.522465 acc_pose: 0.834152 loss: 87.522465 2022/10/12 19:57:33 - mmengine - INFO - Epoch(train) [194][400/586] lr: 2.000000e-04 eta: 0:40:24 time: 0.266027 data_time: 0.053122 memory: 2937 loss_kpt: 88.777263 acc_pose: 0.731085 loss: 88.777263 2022/10/12 19:57:47 - mmengine - INFO - Epoch(train) [194][450/586] lr: 2.000000e-04 eta: 0:40:12 time: 0.264343 data_time: 0.053256 memory: 2937 loss_kpt: 86.513027 acc_pose: 0.835677 loss: 86.513027 2022/10/12 19:58:00 - mmengine - INFO - Epoch(train) [194][500/586] lr: 2.000000e-04 eta: 0:39:59 time: 0.263875 data_time: 0.050708 memory: 2937 loss_kpt: 86.614667 acc_pose: 0.849202 loss: 86.614667 2022/10/12 19:58:14 - mmengine - INFO - Epoch(train) [194][550/586] lr: 2.000000e-04 eta: 0:39:46 time: 0.286397 data_time: 0.054969 memory: 2937 loss_kpt: 87.285912 acc_pose: 0.809594 loss: 87.285912 2022/10/12 19:58:24 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 19:58:39 - mmengine - INFO - Epoch(train) [195][50/586] lr: 2.000000e-04 eta: 0:39:24 time: 0.301543 data_time: 0.067795 memory: 2937 loss_kpt: 86.921613 acc_pose: 0.817155 loss: 86.921613 2022/10/12 19:58:54 - mmengine - INFO - Epoch(train) [195][100/586] lr: 2.000000e-04 eta: 0:39:11 time: 0.285530 data_time: 0.056260 memory: 2937 loss_kpt: 86.234422 acc_pose: 0.792585 loss: 86.234422 2022/10/12 19:59:08 - mmengine - INFO - Epoch(train) [195][150/586] lr: 2.000000e-04 eta: 0:38:59 time: 0.286591 data_time: 0.055518 memory: 2937 loss_kpt: 86.391080 acc_pose: 0.800299 loss: 86.391080 2022/10/12 19:59:22 - mmengine - INFO - Epoch(train) [195][200/586] lr: 2.000000e-04 eta: 0:38:46 time: 0.277193 data_time: 0.057110 memory: 2937 loss_kpt: 87.183356 acc_pose: 0.847285 loss: 87.183356 2022/10/12 19:59:36 - mmengine - INFO - Epoch(train) [195][250/586] lr: 2.000000e-04 eta: 0:38:34 time: 0.275010 data_time: 0.054780 memory: 2937 loss_kpt: 85.976103 acc_pose: 0.832936 loss: 85.976103 2022/10/12 19:59:49 - mmengine - INFO - Epoch(train) [195][300/586] lr: 2.000000e-04 eta: 0:38:21 time: 0.266903 data_time: 0.060186 memory: 2937 loss_kpt: 86.131802 acc_pose: 0.801219 loss: 86.131802 2022/10/12 19:59:53 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:00:03 - mmengine - INFO - Epoch(train) [195][350/586] lr: 2.000000e-04 eta: 0:38:09 time: 0.280050 data_time: 0.057940 memory: 2937 loss_kpt: 86.200801 acc_pose: 0.835817 loss: 86.200801 2022/10/12 20:00:16 - mmengine - INFO - Epoch(train) [195][400/586] lr: 2.000000e-04 eta: 0:37:56 time: 0.263531 data_time: 0.051810 memory: 2937 loss_kpt: 85.874797 acc_pose: 0.793572 loss: 85.874797 2022/10/12 20:00:29 - mmengine - INFO - Epoch(train) [195][450/586] lr: 2.000000e-04 eta: 0:37:43 time: 0.267694 data_time: 0.056340 memory: 2937 loss_kpt: 86.551968 acc_pose: 0.771188 loss: 86.551968 2022/10/12 20:00:43 - mmengine - INFO - Epoch(train) [195][500/586] lr: 2.000000e-04 eta: 0:37:31 time: 0.274479 data_time: 0.050084 memory: 2937 loss_kpt: 86.636346 acc_pose: 0.816111 loss: 86.636346 2022/10/12 20:00:57 - mmengine - INFO - Epoch(train) [195][550/586] lr: 2.000000e-04 eta: 0:37:18 time: 0.274450 data_time: 0.055426 memory: 2937 loss_kpt: 86.708349 acc_pose: 0.753929 loss: 86.708349 2022/10/12 20:01:06 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:01:20 - mmengine - INFO - Epoch(train) [196][50/586] lr: 2.000000e-04 eta: 0:36:56 time: 0.279138 data_time: 0.065868 memory: 2937 loss_kpt: 86.134088 acc_pose: 0.797590 loss: 86.134088 2022/10/12 20:01:34 - mmengine - INFO - Epoch(train) [196][100/586] lr: 2.000000e-04 eta: 0:36:43 time: 0.275597 data_time: 0.054525 memory: 2937 loss_kpt: 85.980867 acc_pose: 0.799582 loss: 85.980867 2022/10/12 20:01:47 - mmengine - INFO - Epoch(train) [196][150/586] lr: 2.000000e-04 eta: 0:36:30 time: 0.267905 data_time: 0.052860 memory: 2937 loss_kpt: 84.783796 acc_pose: 0.829839 loss: 84.783796 2022/10/12 20:02:01 - mmengine - INFO - Epoch(train) [196][200/586] lr: 2.000000e-04 eta: 0:36:18 time: 0.267191 data_time: 0.055076 memory: 2937 loss_kpt: 86.370359 acc_pose: 0.822981 loss: 86.370359 2022/10/12 20:02:15 - mmengine - INFO - Epoch(train) [196][250/586] lr: 2.000000e-04 eta: 0:36:05 time: 0.276772 data_time: 0.052318 memory: 2937 loss_kpt: 87.379223 acc_pose: 0.805997 loss: 87.379223 2022/10/12 20:02:29 - mmengine - INFO - Epoch(train) [196][300/586] lr: 2.000000e-04 eta: 0:35:53 time: 0.280240 data_time: 0.050164 memory: 2937 loss_kpt: 86.353826 acc_pose: 0.790041 loss: 86.353826 2022/10/12 20:02:42 - mmengine - INFO - Epoch(train) [196][350/586] lr: 2.000000e-04 eta: 0:35:40 time: 0.276587 data_time: 0.059050 memory: 2937 loss_kpt: 86.570615 acc_pose: 0.783384 loss: 86.570615 2022/10/12 20:02:56 - mmengine - INFO - Epoch(train) [196][400/586] lr: 2.000000e-04 eta: 0:35:27 time: 0.268227 data_time: 0.054122 memory: 2937 loss_kpt: 86.884218 acc_pose: 0.854047 loss: 86.884218 2022/10/12 20:03:09 - mmengine - INFO - Epoch(train) [196][450/586] lr: 2.000000e-04 eta: 0:35:15 time: 0.262796 data_time: 0.053787 memory: 2937 loss_kpt: 86.566527 acc_pose: 0.829891 loss: 86.566527 2022/10/12 20:03:22 - mmengine - INFO - Epoch(train) [196][500/586] lr: 2.000000e-04 eta: 0:35:02 time: 0.264018 data_time: 0.053307 memory: 2937 loss_kpt: 86.669470 acc_pose: 0.851219 loss: 86.669470 2022/10/12 20:03:36 - mmengine - INFO - Epoch(train) [196][550/586] lr: 2.000000e-04 eta: 0:34:49 time: 0.265001 data_time: 0.053714 memory: 2937 loss_kpt: 84.392434 acc_pose: 0.704979 loss: 84.392434 2022/10/12 20:03:45 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:03:59 - mmengine - INFO - Epoch(train) [197][50/586] lr: 2.000000e-04 eta: 0:34:27 time: 0.292864 data_time: 0.061756 memory: 2937 loss_kpt: 87.217953 acc_pose: 0.749204 loss: 87.217953 2022/10/12 20:04:13 - mmengine - INFO - Epoch(train) [197][100/586] lr: 2.000000e-04 eta: 0:34:15 time: 0.272273 data_time: 0.052262 memory: 2937 loss_kpt: 86.603328 acc_pose: 0.808440 loss: 86.603328 2022/10/12 20:04:25 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:04:27 - mmengine - INFO - Epoch(train) [197][150/586] lr: 2.000000e-04 eta: 0:34:02 time: 0.284376 data_time: 0.054927 memory: 2937 loss_kpt: 86.079390 acc_pose: 0.762778 loss: 86.079390 2022/10/12 20:04:41 - mmengine - INFO - Epoch(train) [197][200/586] lr: 2.000000e-04 eta: 0:33:49 time: 0.270887 data_time: 0.053539 memory: 2937 loss_kpt: 85.800490 acc_pose: 0.841419 loss: 85.800490 2022/10/12 20:04:55 - mmengine - INFO - Epoch(train) [197][250/586] lr: 2.000000e-04 eta: 0:33:37 time: 0.279622 data_time: 0.055479 memory: 2937 loss_kpt: 87.218583 acc_pose: 0.776759 loss: 87.218583 2022/10/12 20:05:08 - mmengine - INFO - Epoch(train) [197][300/586] lr: 2.000000e-04 eta: 0:33:24 time: 0.267426 data_time: 0.053505 memory: 2937 loss_kpt: 86.122285 acc_pose: 0.733443 loss: 86.122285 2022/10/12 20:05:21 - mmengine - INFO - Epoch(train) [197][350/586] lr: 2.000000e-04 eta: 0:33:11 time: 0.262083 data_time: 0.051011 memory: 2937 loss_kpt: 84.598360 acc_pose: 0.787511 loss: 84.598360 2022/10/12 20:05:34 - mmengine - INFO - Epoch(train) [197][400/586] lr: 2.000000e-04 eta: 0:32:59 time: 0.262107 data_time: 0.052449 memory: 2937 loss_kpt: 85.957261 acc_pose: 0.847089 loss: 85.957261 2022/10/12 20:05:48 - mmengine - INFO - Epoch(train) [197][450/586] lr: 2.000000e-04 eta: 0:32:46 time: 0.263024 data_time: 0.052369 memory: 2937 loss_kpt: 86.122166 acc_pose: 0.815268 loss: 86.122166 2022/10/12 20:06:01 - mmengine - INFO - Epoch(train) [197][500/586] lr: 2.000000e-04 eta: 0:32:34 time: 0.261631 data_time: 0.053010 memory: 2937 loss_kpt: 87.382734 acc_pose: 0.769521 loss: 87.382734 2022/10/12 20:06:14 - mmengine - INFO - Epoch(train) [197][550/586] lr: 2.000000e-04 eta: 0:32:21 time: 0.259785 data_time: 0.051414 memory: 2937 loss_kpt: 87.101115 acc_pose: 0.746427 loss: 87.101115 2022/10/12 20:06:22 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:06:38 - mmengine - INFO - Epoch(train) [198][50/586] lr: 2.000000e-04 eta: 0:31:59 time: 0.302691 data_time: 0.064673 memory: 2937 loss_kpt: 86.491286 acc_pose: 0.845188 loss: 86.491286 2022/10/12 20:06:52 - mmengine - INFO - Epoch(train) [198][100/586] lr: 2.000000e-04 eta: 0:31:46 time: 0.282194 data_time: 0.055901 memory: 2937 loss_kpt: 85.351526 acc_pose: 0.865500 loss: 85.351526 2022/10/12 20:07:05 - mmengine - INFO - Epoch(train) [198][150/586] lr: 2.000000e-04 eta: 0:31:33 time: 0.274543 data_time: 0.055655 memory: 2937 loss_kpt: 87.179371 acc_pose: 0.826541 loss: 87.179371 2022/10/12 20:07:19 - mmengine - INFO - Epoch(train) [198][200/586] lr: 2.000000e-04 eta: 0:31:21 time: 0.279137 data_time: 0.055682 memory: 2937 loss_kpt: 86.317042 acc_pose: 0.832394 loss: 86.317042 2022/10/12 20:07:33 - mmengine - INFO - Epoch(train) [198][250/586] lr: 2.000000e-04 eta: 0:31:08 time: 0.278557 data_time: 0.056225 memory: 2937 loss_kpt: 85.729400 acc_pose: 0.862656 loss: 85.729400 2022/10/12 20:07:47 - mmengine - INFO - Epoch(train) [198][300/586] lr: 2.000000e-04 eta: 0:30:56 time: 0.278064 data_time: 0.051407 memory: 2937 loss_kpt: 86.503290 acc_pose: 0.811913 loss: 86.503290 2022/10/12 20:08:02 - mmengine - INFO - Epoch(train) [198][350/586] lr: 2.000000e-04 eta: 0:30:43 time: 0.287479 data_time: 0.053243 memory: 2937 loss_kpt: 85.879158 acc_pose: 0.792901 loss: 85.879158 2022/10/12 20:08:16 - mmengine - INFO - Epoch(train) [198][400/586] lr: 2.000000e-04 eta: 0:30:30 time: 0.279572 data_time: 0.053603 memory: 2937 loss_kpt: 84.621438 acc_pose: 0.870175 loss: 84.621438 2022/10/12 20:08:30 - mmengine - INFO - Epoch(train) [198][450/586] lr: 2.000000e-04 eta: 0:30:18 time: 0.290128 data_time: 0.055147 memory: 2937 loss_kpt: 86.973271 acc_pose: 0.787146 loss: 86.973271 2022/10/12 20:08:44 - mmengine - INFO - Epoch(train) [198][500/586] lr: 2.000000e-04 eta: 0:30:05 time: 0.277056 data_time: 0.055651 memory: 2937 loss_kpt: 86.283217 acc_pose: 0.827635 loss: 86.283217 2022/10/12 20:08:58 - mmengine - INFO - Epoch(train) [198][550/586] lr: 2.000000e-04 eta: 0:29:53 time: 0.279770 data_time: 0.053058 memory: 2937 loss_kpt: 87.005952 acc_pose: 0.856811 loss: 87.005952 2022/10/12 20:09:00 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:09:07 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:09:22 - mmengine - INFO - Epoch(train) [199][50/586] lr: 2.000000e-04 eta: 0:29:30 time: 0.294109 data_time: 0.062724 memory: 2937 loss_kpt: 86.184583 acc_pose: 0.877149 loss: 86.184583 2022/10/12 20:09:36 - mmengine - INFO - Epoch(train) [199][100/586] lr: 2.000000e-04 eta: 0:29:18 time: 0.271763 data_time: 0.051988 memory: 2937 loss_kpt: 86.000978 acc_pose: 0.783109 loss: 86.000978 2022/10/12 20:09:50 - mmengine - INFO - Epoch(train) [199][150/586] lr: 2.000000e-04 eta: 0:29:05 time: 0.276250 data_time: 0.051521 memory: 2937 loss_kpt: 87.571175 acc_pose: 0.804982 loss: 87.571175 2022/10/12 20:10:03 - mmengine - INFO - Epoch(train) [199][200/586] lr: 2.000000e-04 eta: 0:28:53 time: 0.275469 data_time: 0.053337 memory: 2937 loss_kpt: 85.920264 acc_pose: 0.822137 loss: 85.920264 2022/10/12 20:10:18 - mmengine - INFO - Epoch(train) [199][250/586] lr: 2.000000e-04 eta: 0:28:40 time: 0.295528 data_time: 0.048867 memory: 2937 loss_kpt: 86.617580 acc_pose: 0.852193 loss: 86.617580 2022/10/12 20:10:33 - mmengine - INFO - Epoch(train) [199][300/586] lr: 2.000000e-04 eta: 0:28:27 time: 0.293107 data_time: 0.051937 memory: 2937 loss_kpt: 85.931109 acc_pose: 0.838871 loss: 85.931109 2022/10/12 20:10:47 - mmengine - INFO - Epoch(train) [199][350/586] lr: 2.000000e-04 eta: 0:28:15 time: 0.278625 data_time: 0.052924 memory: 2937 loss_kpt: 86.507945 acc_pose: 0.817972 loss: 86.507945 2022/10/12 20:11:01 - mmengine - INFO - Epoch(train) [199][400/586] lr: 2.000000e-04 eta: 0:28:02 time: 0.284424 data_time: 0.052486 memory: 2937 loss_kpt: 86.198353 acc_pose: 0.837456 loss: 86.198353 2022/10/12 20:11:16 - mmengine - INFO - Epoch(train) [199][450/586] lr: 2.000000e-04 eta: 0:27:50 time: 0.291846 data_time: 0.053103 memory: 2937 loss_kpt: 86.021236 acc_pose: 0.828097 loss: 86.021236 2022/10/12 20:11:30 - mmengine - INFO - Epoch(train) [199][500/586] lr: 2.000000e-04 eta: 0:27:37 time: 0.288006 data_time: 0.053027 memory: 2937 loss_kpt: 85.901378 acc_pose: 0.764517 loss: 85.901378 2022/10/12 20:11:45 - mmengine - INFO - Epoch(train) [199][550/586] lr: 2.000000e-04 eta: 0:27:24 time: 0.289173 data_time: 0.047903 memory: 2937 loss_kpt: 86.090159 acc_pose: 0.845235 loss: 86.090159 2022/10/12 20:11:54 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:12:09 - mmengine - INFO - Epoch(train) [200][50/586] lr: 2.000000e-04 eta: 0:27:02 time: 0.288204 data_time: 0.060249 memory: 2937 loss_kpt: 87.107300 acc_pose: 0.815214 loss: 87.107300 2022/10/12 20:12:23 - mmengine - INFO - Epoch(train) [200][100/586] lr: 2.000000e-04 eta: 0:26:50 time: 0.272813 data_time: 0.053763 memory: 2937 loss_kpt: 84.514162 acc_pose: 0.860206 loss: 84.514162 2022/10/12 20:12:37 - mmengine - INFO - Epoch(train) [200][150/586] lr: 2.000000e-04 eta: 0:26:37 time: 0.278644 data_time: 0.053970 memory: 2937 loss_kpt: 86.162370 acc_pose: 0.763138 loss: 86.162370 2022/10/12 20:12:50 - mmengine - INFO - Epoch(train) [200][200/586] lr: 2.000000e-04 eta: 0:26:24 time: 0.272841 data_time: 0.049909 memory: 2937 loss_kpt: 84.606973 acc_pose: 0.845027 loss: 84.606973 2022/10/12 20:13:04 - mmengine - INFO - Epoch(train) [200][250/586] lr: 2.000000e-04 eta: 0:26:12 time: 0.266434 data_time: 0.050406 memory: 2937 loss_kpt: 86.734467 acc_pose: 0.863703 loss: 86.734467 2022/10/12 20:13:17 - mmengine - INFO - Epoch(train) [200][300/586] lr: 2.000000e-04 eta: 0:25:59 time: 0.269121 data_time: 0.051002 memory: 2937 loss_kpt: 85.760762 acc_pose: 0.823168 loss: 85.760762 2022/10/12 20:13:30 - mmengine - INFO - Epoch(train) [200][350/586] lr: 2.000000e-04 eta: 0:25:46 time: 0.268181 data_time: 0.053353 memory: 2937 loss_kpt: 86.566281 acc_pose: 0.841232 loss: 86.566281 2022/10/12 20:13:40 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:13:44 - mmengine - INFO - Epoch(train) [200][400/586] lr: 2.000000e-04 eta: 0:25:34 time: 0.268390 data_time: 0.050357 memory: 2937 loss_kpt: 87.078519 acc_pose: 0.826381 loss: 87.078519 2022/10/12 20:13:57 - mmengine - INFO - Epoch(train) [200][450/586] lr: 2.000000e-04 eta: 0:25:21 time: 0.255280 data_time: 0.054232 memory: 2937 loss_kpt: 87.229092 acc_pose: 0.778143 loss: 87.229092 2022/10/12 20:14:10 - mmengine - INFO - Epoch(train) [200][500/586] lr: 2.000000e-04 eta: 0:25:08 time: 0.264022 data_time: 0.049319 memory: 2937 loss_kpt: 87.594584 acc_pose: 0.842808 loss: 87.594584 2022/10/12 20:14:24 - mmengine - INFO - Epoch(train) [200][550/586] lr: 2.000000e-04 eta: 0:24:56 time: 0.284602 data_time: 0.054506 memory: 2937 loss_kpt: 86.522771 acc_pose: 0.781289 loss: 86.522771 2022/10/12 20:14:34 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:14:34 - mmengine - INFO - Saving checkpoint at 200 epochs 2022/10/12 20:14:42 - mmengine - INFO - Epoch(val) [200][50/407] eta: 0:00:41 time: 0.116279 data_time: 0.017229 memory: 2937 2022/10/12 20:14:47 - mmengine - INFO - Epoch(val) [200][100/407] eta: 0:00:33 time: 0.108114 data_time: 0.008714 memory: 830 2022/10/12 20:14:53 - mmengine - INFO - Epoch(val) [200][150/407] eta: 0:00:27 time: 0.106177 data_time: 0.008642 memory: 830 2022/10/12 20:14:58 - mmengine - INFO - Epoch(val) [200][200/407] eta: 0:00:23 time: 0.111441 data_time: 0.012084 memory: 830 2022/10/12 20:15:04 - mmengine - INFO - Epoch(val) [200][250/407] eta: 0:00:16 time: 0.105901 data_time: 0.008651 memory: 830 2022/10/12 20:15:09 - mmengine - INFO - Epoch(val) [200][300/407] eta: 0:00:11 time: 0.104981 data_time: 0.008628 memory: 830 2022/10/12 20:15:14 - mmengine - INFO - Epoch(val) [200][350/407] eta: 0:00:06 time: 0.105343 data_time: 0.008798 memory: 830 2022/10/12 20:15:19 - mmengine - INFO - Epoch(val) [200][400/407] eta: 0:00:00 time: 0.103602 data_time: 0.007977 memory: 830 2022/10/12 20:15:33 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 20:15:49 - mmengine - INFO - Epoch(val) [200][407/407] coco/AP: 0.703699 coco/AP .5: 0.887368 coco/AP .75: 0.780296 coco/AP (M): 0.670657 coco/AP (L): 0.766204 coco/AR: 0.772528 coco/AR .5: 0.926165 coco/AR .75: 0.835485 coco/AR (M): 0.726659 coco/AR (L): 0.835749 2022/10/12 20:15:49 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221012/rsn18/best_coco/AP_epoch_190.pth is removed 2022/10/12 20:15:51 - mmengine - INFO - The best checkpoint with 0.7037 coco/AP at 200 epoch is saved to best_coco/AP_epoch_200.pth. 2022/10/12 20:16:05 - mmengine - INFO - Epoch(train) [201][50/586] lr: 2.000000e-05 eta: 0:24:34 time: 0.284874 data_time: 0.062952 memory: 2937 loss_kpt: 86.796989 acc_pose: 0.751129 loss: 86.796989 2022/10/12 20:16:19 - mmengine - INFO - Epoch(train) [201][100/586] lr: 2.000000e-05 eta: 0:24:21 time: 0.272121 data_time: 0.055883 memory: 2937 loss_kpt: 85.042867 acc_pose: 0.852668 loss: 85.042867 2022/10/12 20:16:32 - mmengine - INFO - Epoch(train) [201][150/586] lr: 2.000000e-05 eta: 0:24:08 time: 0.274569 data_time: 0.048942 memory: 2937 loss_kpt: 86.328312 acc_pose: 0.773362 loss: 86.328312 2022/10/12 20:16:47 - mmengine - INFO - Epoch(train) [201][200/586] lr: 2.000000e-05 eta: 0:23:56 time: 0.292014 data_time: 0.052373 memory: 2937 loss_kpt: 86.863822 acc_pose: 0.828470 loss: 86.863822 2022/10/12 20:17:01 - mmengine - INFO - Epoch(train) [201][250/586] lr: 2.000000e-05 eta: 0:23:43 time: 0.289947 data_time: 0.049600 memory: 2937 loss_kpt: 85.608874 acc_pose: 0.705237 loss: 85.608874 2022/10/12 20:17:16 - mmengine - INFO - Epoch(train) [201][300/586] lr: 2.000000e-05 eta: 0:23:30 time: 0.290725 data_time: 0.050669 memory: 2937 loss_kpt: 85.809611 acc_pose: 0.828076 loss: 85.809611 2022/10/12 20:17:30 - mmengine - INFO - Epoch(train) [201][350/586] lr: 2.000000e-05 eta: 0:23:18 time: 0.284258 data_time: 0.048938 memory: 2937 loss_kpt: 87.220459 acc_pose: 0.844495 loss: 87.220459 2022/10/12 20:17:44 - mmengine - INFO - Epoch(train) [201][400/586] lr: 2.000000e-05 eta: 0:23:05 time: 0.274174 data_time: 0.052241 memory: 2937 loss_kpt: 87.956729 acc_pose: 0.759773 loss: 87.956729 2022/10/12 20:17:58 - mmengine - INFO - Epoch(train) [201][450/586] lr: 2.000000e-05 eta: 0:22:53 time: 0.274994 data_time: 0.051612 memory: 2937 loss_kpt: 86.009174 acc_pose: 0.835123 loss: 86.009174 2022/10/12 20:18:11 - mmengine - INFO - Epoch(train) [201][500/586] lr: 2.000000e-05 eta: 0:22:40 time: 0.271627 data_time: 0.047950 memory: 2937 loss_kpt: 87.318252 acc_pose: 0.790899 loss: 87.318252 2022/10/12 20:18:25 - mmengine - INFO - Epoch(train) [201][550/586] lr: 2.000000e-05 eta: 0:22:27 time: 0.277350 data_time: 0.052287 memory: 2937 loss_kpt: 86.120677 acc_pose: 0.809778 loss: 86.120677 2022/10/12 20:18:35 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:18:50 - mmengine - INFO - Epoch(train) [202][50/586] lr: 2.000000e-05 eta: 0:22:05 time: 0.310933 data_time: 0.064613 memory: 2937 loss_kpt: 85.903109 acc_pose: 0.843846 loss: 85.903109 2022/10/12 20:19:06 - mmengine - INFO - Epoch(train) [202][100/586] lr: 2.000000e-05 eta: 0:21:53 time: 0.309671 data_time: 0.046732 memory: 2937 loss_kpt: 87.450760 acc_pose: 0.756484 loss: 87.450760 2022/10/12 20:19:19 - mmengine - INFO - Epoch(train) [202][150/586] lr: 2.000000e-05 eta: 0:21:40 time: 0.276235 data_time: 0.051858 memory: 2937 loss_kpt: 85.390831 acc_pose: 0.781181 loss: 85.390831 2022/10/12 20:19:34 - mmengine - INFO - Epoch(train) [202][200/586] lr: 2.000000e-05 eta: 0:21:27 time: 0.286685 data_time: 0.052001 memory: 2937 loss_kpt: 86.076317 acc_pose: 0.782818 loss: 86.076317 2022/10/12 20:19:38 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:19:48 - mmengine - INFO - Epoch(train) [202][250/586] lr: 2.000000e-05 eta: 0:21:15 time: 0.281149 data_time: 0.058139 memory: 2937 loss_kpt: 86.690014 acc_pose: 0.824975 loss: 86.690014 2022/10/12 20:20:01 - mmengine - INFO - Epoch(train) [202][300/586] lr: 2.000000e-05 eta: 0:21:02 time: 0.266562 data_time: 0.054679 memory: 2937 loss_kpt: 87.581451 acc_pose: 0.820470 loss: 87.581451 2022/10/12 20:20:15 - mmengine - INFO - Epoch(train) [202][350/586] lr: 2.000000e-05 eta: 0:20:49 time: 0.264994 data_time: 0.050834 memory: 2937 loss_kpt: 87.309475 acc_pose: 0.820111 loss: 87.309475 2022/10/12 20:20:28 - mmengine - INFO - Epoch(train) [202][400/586] lr: 2.000000e-05 eta: 0:20:37 time: 0.276526 data_time: 0.054618 memory: 2937 loss_kpt: 85.844257 acc_pose: 0.845252 loss: 85.844257 2022/10/12 20:20:42 - mmengine - INFO - Epoch(train) [202][450/586] lr: 2.000000e-05 eta: 0:20:24 time: 0.264521 data_time: 0.054232 memory: 2937 loss_kpt: 85.649109 acc_pose: 0.789940 loss: 85.649109 2022/10/12 20:20:55 - mmengine - INFO - Epoch(train) [202][500/586] lr: 2.000000e-05 eta: 0:20:11 time: 0.268886 data_time: 0.052049 memory: 2937 loss_kpt: 86.777906 acc_pose: 0.830203 loss: 86.777906 2022/10/12 20:21:09 - mmengine - INFO - Epoch(train) [202][550/586] lr: 2.000000e-05 eta: 0:19:59 time: 0.283255 data_time: 0.057710 memory: 2937 loss_kpt: 86.897819 acc_pose: 0.771038 loss: 86.897819 2022/10/12 20:21:18 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:21:34 - mmengine - INFO - Epoch(train) [203][50/586] lr: 2.000000e-05 eta: 0:19:37 time: 0.309359 data_time: 0.068421 memory: 2937 loss_kpt: 86.291522 acc_pose: 0.814235 loss: 86.291522 2022/10/12 20:21:48 - mmengine - INFO - Epoch(train) [203][100/586] lr: 2.000000e-05 eta: 0:19:24 time: 0.282356 data_time: 0.049640 memory: 2937 loss_kpt: 86.930684 acc_pose: 0.814761 loss: 86.930684 2022/10/12 20:22:03 - mmengine - INFO - Epoch(train) [203][150/586] lr: 2.000000e-05 eta: 0:19:11 time: 0.292932 data_time: 0.061766 memory: 2937 loss_kpt: 86.678042 acc_pose: 0.696723 loss: 86.678042 2022/10/12 20:22:17 - mmengine - INFO - Epoch(train) [203][200/586] lr: 2.000000e-05 eta: 0:18:59 time: 0.279382 data_time: 0.056363 memory: 2937 loss_kpt: 88.748792 acc_pose: 0.771909 loss: 88.748792 2022/10/12 20:22:30 - mmengine - INFO - Epoch(train) [203][250/586] lr: 2.000000e-05 eta: 0:18:46 time: 0.259051 data_time: 0.053392 memory: 2937 loss_kpt: 87.728657 acc_pose: 0.791050 loss: 87.728657 2022/10/12 20:22:43 - mmengine - INFO - Epoch(train) [203][300/586] lr: 2.000000e-05 eta: 0:18:33 time: 0.263896 data_time: 0.047878 memory: 2937 loss_kpt: 87.017600 acc_pose: 0.804100 loss: 87.017600 2022/10/12 20:22:57 - mmengine - INFO - Epoch(train) [203][350/586] lr: 2.000000e-05 eta: 0:18:21 time: 0.274740 data_time: 0.052234 memory: 2937 loss_kpt: 86.706691 acc_pose: 0.836677 loss: 86.706691 2022/10/12 20:23:10 - mmengine - INFO - Epoch(train) [203][400/586] lr: 2.000000e-05 eta: 0:18:08 time: 0.266401 data_time: 0.049820 memory: 2937 loss_kpt: 85.018316 acc_pose: 0.834620 loss: 85.018316 2022/10/12 20:23:23 - mmengine - INFO - Epoch(train) [203][450/586] lr: 2.000000e-05 eta: 0:17:55 time: 0.268051 data_time: 0.055883 memory: 2937 loss_kpt: 85.559732 acc_pose: 0.855174 loss: 85.559732 2022/10/12 20:23:36 - mmengine - INFO - Epoch(train) [203][500/586] lr: 2.000000e-05 eta: 0:17:43 time: 0.262272 data_time: 0.048290 memory: 2937 loss_kpt: 87.532696 acc_pose: 0.841869 loss: 87.532696 2022/10/12 20:23:50 - mmengine - INFO - Epoch(train) [203][550/586] lr: 2.000000e-05 eta: 0:17:30 time: 0.264881 data_time: 0.055242 memory: 2937 loss_kpt: 85.091894 acc_pose: 0.822112 loss: 85.091894 2022/10/12 20:24:00 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:24:12 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:24:14 - mmengine - INFO - Epoch(train) [204][50/586] lr: 2.000000e-05 eta: 0:17:08 time: 0.290883 data_time: 0.063427 memory: 2937 loss_kpt: 85.907259 acc_pose: 0.835077 loss: 85.907259 2022/10/12 20:24:28 - mmengine - INFO - Epoch(train) [204][100/586] lr: 2.000000e-05 eta: 0:16:55 time: 0.274122 data_time: 0.053016 memory: 2937 loss_kpt: 86.195266 acc_pose: 0.777658 loss: 86.195266 2022/10/12 20:24:41 - mmengine - INFO - Epoch(train) [204][150/586] lr: 2.000000e-05 eta: 0:16:43 time: 0.265947 data_time: 0.052646 memory: 2937 loss_kpt: 86.384323 acc_pose: 0.831611 loss: 86.384323 2022/10/12 20:24:54 - mmengine - INFO - Epoch(train) [204][200/586] lr: 2.000000e-05 eta: 0:16:30 time: 0.264065 data_time: 0.046848 memory: 2937 loss_kpt: 85.686361 acc_pose: 0.813042 loss: 85.686361 2022/10/12 20:25:08 - mmengine - INFO - Epoch(train) [204][250/586] lr: 2.000000e-05 eta: 0:16:17 time: 0.271849 data_time: 0.051822 memory: 2937 loss_kpt: 88.077083 acc_pose: 0.858323 loss: 88.077083 2022/10/12 20:25:21 - mmengine - INFO - Epoch(train) [204][300/586] lr: 2.000000e-05 eta: 0:16:05 time: 0.266526 data_time: 0.054150 memory: 2937 loss_kpt: 85.676765 acc_pose: 0.820713 loss: 85.676765 2022/10/12 20:25:35 - mmengine - INFO - Epoch(train) [204][350/586] lr: 2.000000e-05 eta: 0:15:52 time: 0.270853 data_time: 0.049874 memory: 2937 loss_kpt: 86.533911 acc_pose: 0.758222 loss: 86.533911 2022/10/12 20:25:48 - mmengine - INFO - Epoch(train) [204][400/586] lr: 2.000000e-05 eta: 0:15:39 time: 0.262878 data_time: 0.050241 memory: 2937 loss_kpt: 86.563915 acc_pose: 0.779486 loss: 86.563915 2022/10/12 20:26:01 - mmengine - INFO - Epoch(train) [204][450/586] lr: 2.000000e-05 eta: 0:15:27 time: 0.263931 data_time: 0.050822 memory: 2937 loss_kpt: 87.693485 acc_pose: 0.782462 loss: 87.693485 2022/10/12 20:26:14 - mmengine - INFO - Epoch(train) [204][500/586] lr: 2.000000e-05 eta: 0:15:14 time: 0.260210 data_time: 0.049843 memory: 2937 loss_kpt: 85.513799 acc_pose: 0.832722 loss: 85.513799 2022/10/12 20:26:28 - mmengine - INFO - Epoch(train) [204][550/586] lr: 2.000000e-05 eta: 0:15:01 time: 0.263128 data_time: 0.054561 memory: 2937 loss_kpt: 86.273042 acc_pose: 0.866424 loss: 86.273042 2022/10/12 20:26:37 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:26:51 - mmengine - INFO - Epoch(train) [205][50/586] lr: 2.000000e-05 eta: 0:14:39 time: 0.278046 data_time: 0.061789 memory: 2937 loss_kpt: 86.544689 acc_pose: 0.784850 loss: 86.544689 2022/10/12 20:27:04 - mmengine - INFO - Epoch(train) [205][100/586] lr: 2.000000e-05 eta: 0:14:27 time: 0.263907 data_time: 0.055515 memory: 2937 loss_kpt: 86.849905 acc_pose: 0.782658 loss: 86.849905 2022/10/12 20:27:18 - mmengine - INFO - Epoch(train) [205][150/586] lr: 2.000000e-05 eta: 0:14:14 time: 0.275422 data_time: 0.053592 memory: 2937 loss_kpt: 88.297251 acc_pose: 0.822590 loss: 88.297251 2022/10/12 20:27:32 - mmengine - INFO - Epoch(train) [205][200/586] lr: 2.000000e-05 eta: 0:14:01 time: 0.279769 data_time: 0.049368 memory: 2937 loss_kpt: 86.690693 acc_pose: 0.806765 loss: 86.690693 2022/10/12 20:27:46 - mmengine - INFO - Epoch(train) [205][250/586] lr: 2.000000e-05 eta: 0:13:49 time: 0.279545 data_time: 0.055500 memory: 2937 loss_kpt: 85.812256 acc_pose: 0.790036 loss: 85.812256 2022/10/12 20:28:00 - mmengine - INFO - Epoch(train) [205][300/586] lr: 2.000000e-05 eta: 0:13:36 time: 0.271573 data_time: 0.051468 memory: 2937 loss_kpt: 86.187229 acc_pose: 0.832763 loss: 86.187229 2022/10/12 20:28:13 - mmengine - INFO - Epoch(train) [205][350/586] lr: 2.000000e-05 eta: 0:13:23 time: 0.263562 data_time: 0.051474 memory: 2937 loss_kpt: 86.569721 acc_pose: 0.781482 loss: 86.569721 2022/10/12 20:28:26 - mmengine - INFO - Epoch(train) [205][400/586] lr: 2.000000e-05 eta: 0:13:11 time: 0.256792 data_time: 0.054431 memory: 2937 loss_kpt: 86.733521 acc_pose: 0.789312 loss: 86.733521 2022/10/12 20:28:39 - mmengine - INFO - Epoch(train) [205][450/586] lr: 2.000000e-05 eta: 0:12:58 time: 0.261043 data_time: 0.052173 memory: 2937 loss_kpt: 86.183899 acc_pose: 0.760541 loss: 86.183899 2022/10/12 20:28:40 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:28:52 - mmengine - INFO - Epoch(train) [205][500/586] lr: 2.000000e-05 eta: 0:12:45 time: 0.263339 data_time: 0.051609 memory: 2937 loss_kpt: 86.570342 acc_pose: 0.758960 loss: 86.570342 2022/10/12 20:29:05 - mmengine - INFO - Epoch(train) [205][550/586] lr: 2.000000e-05 eta: 0:12:33 time: 0.267121 data_time: 0.053851 memory: 2937 loss_kpt: 85.758560 acc_pose: 0.758670 loss: 85.758560 2022/10/12 20:29:15 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:29:29 - mmengine - INFO - Epoch(train) [206][50/586] lr: 2.000000e-05 eta: 0:12:10 time: 0.286188 data_time: 0.066069 memory: 2937 loss_kpt: 85.312737 acc_pose: 0.791209 loss: 85.312737 2022/10/12 20:29:42 - mmengine - INFO - Epoch(train) [206][100/586] lr: 2.000000e-05 eta: 0:11:58 time: 0.262082 data_time: 0.055954 memory: 2937 loss_kpt: 85.389481 acc_pose: 0.816142 loss: 85.389481 2022/10/12 20:29:56 - mmengine - INFO - Epoch(train) [206][150/586] lr: 2.000000e-05 eta: 0:11:45 time: 0.267504 data_time: 0.058020 memory: 2937 loss_kpt: 86.320465 acc_pose: 0.820073 loss: 86.320465 2022/10/12 20:30:09 - mmengine - INFO - Epoch(train) [206][200/586] lr: 2.000000e-05 eta: 0:11:32 time: 0.266311 data_time: 0.053405 memory: 2937 loss_kpt: 84.514996 acc_pose: 0.816730 loss: 84.514996 2022/10/12 20:30:23 - mmengine - INFO - Epoch(train) [206][250/586] lr: 2.000000e-05 eta: 0:11:20 time: 0.270429 data_time: 0.048245 memory: 2937 loss_kpt: 86.547244 acc_pose: 0.802711 loss: 86.547244 2022/10/12 20:30:36 - mmengine - INFO - Epoch(train) [206][300/586] lr: 2.000000e-05 eta: 0:11:07 time: 0.269132 data_time: 0.050933 memory: 2937 loss_kpt: 86.523894 acc_pose: 0.835968 loss: 86.523894 2022/10/12 20:30:49 - mmengine - INFO - Epoch(train) [206][350/586] lr: 2.000000e-05 eta: 0:10:54 time: 0.264261 data_time: 0.049795 memory: 2937 loss_kpt: 85.788068 acc_pose: 0.800076 loss: 85.788068 2022/10/12 20:31:03 - mmengine - INFO - Epoch(train) [206][400/586] lr: 2.000000e-05 eta: 0:10:42 time: 0.262553 data_time: 0.052133 memory: 2937 loss_kpt: 86.410604 acc_pose: 0.806416 loss: 86.410604 2022/10/12 20:31:15 - mmengine - INFO - Epoch(train) [206][450/586] lr: 2.000000e-05 eta: 0:10:29 time: 0.255478 data_time: 0.048012 memory: 2937 loss_kpt: 86.327258 acc_pose: 0.740352 loss: 86.327258 2022/10/12 20:31:28 - mmengine - INFO - Epoch(train) [206][500/586] lr: 2.000000e-05 eta: 0:10:16 time: 0.257184 data_time: 0.050797 memory: 2937 loss_kpt: 87.312177 acc_pose: 0.871689 loss: 87.312177 2022/10/12 20:31:41 - mmengine - INFO - Epoch(train) [206][550/586] lr: 2.000000e-05 eta: 0:10:04 time: 0.252554 data_time: 0.051322 memory: 2937 loss_kpt: 87.742094 acc_pose: 0.745913 loss: 87.742094 2022/10/12 20:31:50 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:32:05 - mmengine - INFO - Epoch(train) [207][50/586] lr: 2.000000e-05 eta: 0:09:42 time: 0.296729 data_time: 0.072573 memory: 2937 loss_kpt: 85.019494 acc_pose: 0.836511 loss: 85.019494 2022/10/12 20:32:18 - mmengine - INFO - Epoch(train) [207][100/586] lr: 2.000000e-05 eta: 0:09:29 time: 0.265566 data_time: 0.050050 memory: 2937 loss_kpt: 86.105923 acc_pose: 0.891617 loss: 86.105923 2022/10/12 20:32:31 - mmengine - INFO - Epoch(train) [207][150/586] lr: 2.000000e-05 eta: 0:09:16 time: 0.269372 data_time: 0.055224 memory: 2937 loss_kpt: 86.377268 acc_pose: 0.821641 loss: 86.377268 2022/10/12 20:32:45 - mmengine - INFO - Epoch(train) [207][200/586] lr: 2.000000e-05 eta: 0:09:04 time: 0.271697 data_time: 0.055341 memory: 2937 loss_kpt: 87.008808 acc_pose: 0.887344 loss: 87.008808 2022/10/12 20:32:59 - mmengine - INFO - Epoch(train) [207][250/586] lr: 2.000000e-05 eta: 0:08:51 time: 0.283190 data_time: 0.053135 memory: 2937 loss_kpt: 86.484298 acc_pose: 0.807222 loss: 86.484298 2022/10/12 20:33:09 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:33:14 - mmengine - INFO - Epoch(train) [207][300/586] lr: 2.000000e-05 eta: 0:08:38 time: 0.292450 data_time: 0.052148 memory: 2937 loss_kpt: 87.065361 acc_pose: 0.804127 loss: 87.065361 2022/10/12 20:33:28 - mmengine - INFO - Epoch(train) [207][350/586] lr: 2.000000e-05 eta: 0:08:26 time: 0.288276 data_time: 0.054619 memory: 2937 loss_kpt: 85.774183 acc_pose: 0.754983 loss: 85.774183 2022/10/12 20:33:42 - mmengine - INFO - Epoch(train) [207][400/586] lr: 2.000000e-05 eta: 0:08:13 time: 0.275027 data_time: 0.055058 memory: 2937 loss_kpt: 86.812462 acc_pose: 0.868642 loss: 86.812462 2022/10/12 20:33:56 - mmengine - INFO - Epoch(train) [207][450/586] lr: 2.000000e-05 eta: 0:08:00 time: 0.273531 data_time: 0.057303 memory: 2937 loss_kpt: 85.905307 acc_pose: 0.798233 loss: 85.905307 2022/10/12 20:34:09 - mmengine - INFO - Epoch(train) [207][500/586] lr: 2.000000e-05 eta: 0:07:48 time: 0.271021 data_time: 0.048478 memory: 2937 loss_kpt: 86.973553 acc_pose: 0.773689 loss: 86.973553 2022/10/12 20:34:23 - mmengine - INFO - Epoch(train) [207][550/586] lr: 2.000000e-05 eta: 0:07:35 time: 0.263796 data_time: 0.052333 memory: 2937 loss_kpt: 86.564906 acc_pose: 0.841448 loss: 86.564906 2022/10/12 20:34:32 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:34:46 - mmengine - INFO - Epoch(train) [208][50/586] lr: 2.000000e-05 eta: 0:07:13 time: 0.286862 data_time: 0.063125 memory: 2937 loss_kpt: 86.868730 acc_pose: 0.796184 loss: 86.868730 2022/10/12 20:34:59 - mmengine - INFO - Epoch(train) [208][100/586] lr: 2.000000e-05 eta: 0:07:00 time: 0.262546 data_time: 0.047537 memory: 2937 loss_kpt: 86.539321 acc_pose: 0.802480 loss: 86.539321 2022/10/12 20:35:13 - mmengine - INFO - Epoch(train) [208][150/586] lr: 2.000000e-05 eta: 0:06:48 time: 0.269808 data_time: 0.047418 memory: 2937 loss_kpt: 86.256129 acc_pose: 0.833957 loss: 86.256129 2022/10/12 20:35:26 - mmengine - INFO - Epoch(train) [208][200/586] lr: 2.000000e-05 eta: 0:06:35 time: 0.267470 data_time: 0.053291 memory: 2937 loss_kpt: 87.895558 acc_pose: 0.813561 loss: 87.895558 2022/10/12 20:35:39 - mmengine - INFO - Epoch(train) [208][250/586] lr: 2.000000e-05 eta: 0:06:22 time: 0.259910 data_time: 0.048679 memory: 2937 loss_kpt: 86.630663 acc_pose: 0.837476 loss: 86.630663 2022/10/12 20:35:53 - mmengine - INFO - Epoch(train) [208][300/586] lr: 2.000000e-05 eta: 0:06:10 time: 0.268817 data_time: 0.051432 memory: 2937 loss_kpt: 85.274051 acc_pose: 0.903795 loss: 85.274051 2022/10/12 20:36:06 - mmengine - INFO - Epoch(train) [208][350/586] lr: 2.000000e-05 eta: 0:05:57 time: 0.271169 data_time: 0.051842 memory: 2937 loss_kpt: 85.757695 acc_pose: 0.830756 loss: 85.757695 2022/10/12 20:36:20 - mmengine - INFO - Epoch(train) [208][400/586] lr: 2.000000e-05 eta: 0:05:44 time: 0.277217 data_time: 0.051623 memory: 2937 loss_kpt: 85.754368 acc_pose: 0.814412 loss: 85.754368 2022/10/12 20:36:34 - mmengine - INFO - Epoch(train) [208][450/586] lr: 2.000000e-05 eta: 0:05:32 time: 0.277762 data_time: 0.052193 memory: 2937 loss_kpt: 85.635569 acc_pose: 0.793721 loss: 85.635569 2022/10/12 20:36:47 - mmengine - INFO - Epoch(train) [208][500/586] lr: 2.000000e-05 eta: 0:05:19 time: 0.265027 data_time: 0.051866 memory: 2937 loss_kpt: 84.654720 acc_pose: 0.843043 loss: 84.654720 2022/10/12 20:37:01 - mmengine - INFO - Epoch(train) [208][550/586] lr: 2.000000e-05 eta: 0:05:06 time: 0.275438 data_time: 0.056305 memory: 2937 loss_kpt: 88.346605 acc_pose: 0.765325 loss: 88.346605 2022/10/12 20:37:10 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:37:24 - mmengine - INFO - Epoch(train) [209][50/586] lr: 2.000000e-05 eta: 0:04:44 time: 0.282651 data_time: 0.061354 memory: 2937 loss_kpt: 87.673510 acc_pose: 0.780494 loss: 87.673510 2022/10/12 20:37:38 - mmengine - INFO - Epoch(train) [209][100/586] lr: 2.000000e-05 eta: 0:04:32 time: 0.266606 data_time: 0.055097 memory: 2937 loss_kpt: 85.773977 acc_pose: 0.752030 loss: 85.773977 2022/10/12 20:37:41 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:37:51 - mmengine - INFO - Epoch(train) [209][150/586] lr: 2.000000e-05 eta: 0:04:19 time: 0.265457 data_time: 0.058295 memory: 2937 loss_kpt: 85.860134 acc_pose: 0.804163 loss: 85.860134 2022/10/12 20:38:04 - mmengine - INFO - Epoch(train) [209][200/586] lr: 2.000000e-05 eta: 0:04:06 time: 0.262877 data_time: 0.055759 memory: 2937 loss_kpt: 86.337194 acc_pose: 0.820473 loss: 86.337194 2022/10/12 20:38:18 - mmengine - INFO - Epoch(train) [209][250/586] lr: 2.000000e-05 eta: 0:03:54 time: 0.272719 data_time: 0.059447 memory: 2937 loss_kpt: 86.883107 acc_pose: 0.752127 loss: 86.883107 2022/10/12 20:38:31 - mmengine - INFO - Epoch(train) [209][300/586] lr: 2.000000e-05 eta: 0:03:41 time: 0.270471 data_time: 0.051350 memory: 2937 loss_kpt: 87.135908 acc_pose: 0.826124 loss: 87.135908 2022/10/12 20:38:45 - mmengine - INFO - Epoch(train) [209][350/586] lr: 2.000000e-05 eta: 0:03:28 time: 0.275796 data_time: 0.054756 memory: 2937 loss_kpt: 86.468190 acc_pose: 0.809475 loss: 86.468190 2022/10/12 20:39:00 - mmengine - INFO - Epoch(train) [209][400/586] lr: 2.000000e-05 eta: 0:03:15 time: 0.286195 data_time: 0.054796 memory: 2937 loss_kpt: 85.998980 acc_pose: 0.826961 loss: 85.998980 2022/10/12 20:39:13 - mmengine - INFO - Epoch(train) [209][450/586] lr: 2.000000e-05 eta: 0:03:03 time: 0.272852 data_time: 0.054359 memory: 2937 loss_kpt: 86.256358 acc_pose: 0.746906 loss: 86.256358 2022/10/12 20:39:27 - mmengine - INFO - Epoch(train) [209][500/586] lr: 2.000000e-05 eta: 0:02:50 time: 0.280204 data_time: 0.053214 memory: 2937 loss_kpt: 86.183475 acc_pose: 0.829615 loss: 86.183475 2022/10/12 20:39:40 - mmengine - INFO - Epoch(train) [209][550/586] lr: 2.000000e-05 eta: 0:02:37 time: 0.263134 data_time: 0.055260 memory: 2937 loss_kpt: 87.398425 acc_pose: 0.800561 loss: 87.398425 2022/10/12 20:39:50 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:40:05 - mmengine - INFO - Epoch(train) [210][50/586] lr: 2.000000e-05 eta: 0:02:16 time: 0.297194 data_time: 0.068273 memory: 2937 loss_kpt: 86.653238 acc_pose: 0.822084 loss: 86.653238 2022/10/12 20:40:18 - mmengine - INFO - Epoch(train) [210][100/586] lr: 2.000000e-05 eta: 0:02:03 time: 0.271846 data_time: 0.057433 memory: 2937 loss_kpt: 86.550396 acc_pose: 0.872078 loss: 86.550396 2022/10/12 20:40:32 - mmengine - INFO - Epoch(train) [210][150/586] lr: 2.000000e-05 eta: 0:01:50 time: 0.284028 data_time: 0.055385 memory: 2937 loss_kpt: 86.321871 acc_pose: 0.814126 loss: 86.321871 2022/10/12 20:40:46 - mmengine - INFO - Epoch(train) [210][200/586] lr: 2.000000e-05 eta: 0:01:37 time: 0.281977 data_time: 0.058269 memory: 2937 loss_kpt: 86.230160 acc_pose: 0.810547 loss: 86.230160 2022/10/12 20:41:01 - mmengine - INFO - Epoch(train) [210][250/586] lr: 2.000000e-05 eta: 0:01:25 time: 0.282573 data_time: 0.056424 memory: 2937 loss_kpt: 85.653862 acc_pose: 0.816489 loss: 85.653862 2022/10/12 20:41:15 - mmengine - INFO - Epoch(train) [210][300/586] lr: 2.000000e-05 eta: 0:01:12 time: 0.284330 data_time: 0.057211 memory: 2937 loss_kpt: 87.184699 acc_pose: 0.808031 loss: 87.184699 2022/10/12 20:41:29 - mmengine - INFO - Epoch(train) [210][350/586] lr: 2.000000e-05 eta: 0:00:59 time: 0.280336 data_time: 0.053910 memory: 2937 loss_kpt: 86.096271 acc_pose: 0.765342 loss: 86.096271 2022/10/12 20:41:43 - mmengine - INFO - Epoch(train) [210][400/586] lr: 2.000000e-05 eta: 0:00:47 time: 0.286036 data_time: 0.060159 memory: 2937 loss_kpt: 87.837130 acc_pose: 0.808721 loss: 87.837130 2022/10/12 20:41:58 - mmengine - INFO - Epoch(train) [210][450/586] lr: 2.000000e-05 eta: 0:00:34 time: 0.300768 data_time: 0.053459 memory: 2937 loss_kpt: 86.644189 acc_pose: 0.796621 loss: 86.644189 2022/10/12 20:42:12 - mmengine - INFO - Epoch(train) [210][500/586] lr: 2.000000e-05 eta: 0:00:21 time: 0.272925 data_time: 0.051462 memory: 2937 loss_kpt: 86.612830 acc_pose: 0.830573 loss: 86.612830 2022/10/12 20:42:19 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:42:26 - mmengine - INFO - Epoch(train) [210][550/586] lr: 2.000000e-05 eta: 0:00:09 time: 0.280191 data_time: 0.048226 memory: 2937 loss_kpt: 87.168876 acc_pose: 0.775184 loss: 87.168876 2022/10/12 20:42:36 - mmengine - INFO - Exp name: td-hm_rsn18_8xb32-210e_coco-256x192_20221012_110213 2022/10/12 20:42:36 - mmengine - INFO - Saving checkpoint at 210 epochs 2022/10/12 20:42:44 - mmengine - INFO - Epoch(val) [210][50/407] eta: 0:00:40 time: 0.112660 data_time: 0.013776 memory: 2937 2022/10/12 20:42:49 - mmengine - INFO - Epoch(val) [210][100/407] eta: 0:00:32 time: 0.105426 data_time: 0.008964 memory: 830 2022/10/12 20:42:54 - mmengine - INFO - Epoch(val) [210][150/407] eta: 0:00:27 time: 0.108001 data_time: 0.008414 memory: 830 2022/10/12 20:42:59 - mmengine - INFO - Epoch(val) [210][200/407] eta: 0:00:21 time: 0.104040 data_time: 0.008367 memory: 830 2022/10/12 20:43:05 - mmengine - INFO - Epoch(val) [210][250/407] eta: 0:00:16 time: 0.106730 data_time: 0.008783 memory: 830 2022/10/12 20:43:10 - mmengine - INFO - Epoch(val) [210][300/407] eta: 0:00:11 time: 0.107818 data_time: 0.009092 memory: 830 2022/10/12 20:43:15 - mmengine - INFO - Epoch(val) [210][350/407] eta: 0:00:05 time: 0.104794 data_time: 0.008506 memory: 830 2022/10/12 20:43:21 - mmengine - INFO - Epoch(val) [210][400/407] eta: 0:00:00 time: 0.105915 data_time: 0.007879 memory: 830 2022/10/12 20:43:34 - mmengine - INFO - Evaluating CocoMetric... 2022/10/12 20:43:50 - mmengine - INFO - Epoch(val) [210][407/407] coco/AP: 0.703901 coco/AP .5: 0.886967 coco/AP .75: 0.780580 coco/AP (M): 0.670991 coco/AP (L): 0.766270 coco/AR: 0.772780 coco/AR .5: 0.926637 coco/AR .75: 0.835642 coco/AR (M): 0.727233 coco/AR (L): 0.835600 2022/10/12 20:43:50 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/liqikai/openmmlab/pt112cu113py38/mmpose/work_dirs/20221012/rsn18/best_coco/AP_epoch_200.pth is removed 2022/10/12 20:43:52 - mmengine - INFO - The best checkpoint with 0.7039 coco/AP at 210 epoch is saved to best_coco/AP_epoch_210.pth.