2022/09/21 17:35:49 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0] CUDA available: True numpy_random_seed: 1842354679 GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/cache/share/cuda-11.1 NVCC: Cuda compilation tools, release 11.1, V11.1.74 GCC: gcc (GCC) 5.4.0 PyTorch: 1.9.0+cu111 PyTorch compiling details: PyTorch built with: - GCC 7.3 - 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 v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.1 - 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 8.0.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/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 -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -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 -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.10.0+cu111 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 ------------------------------------------------------------ 2022/09/21 17:35:50 - 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=1, save_best='coco/AP', rule='greater', max_keep_ckpts=1), 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({ '/mnt/lustre/share_data/openmmlab/datasets/detection/coco/': 's3://openmmlab/datasets/detection/coco/' })) train_cfg = dict(by_epoch=True, max_epochs=210, val_interval=10) val_cfg = dict() test_cfg = dict() optim_wrapper = dict(optimizer=dict(type='Adam', lr=0.0005)) param_scheduler = [ dict( type='LinearLR', begin=0, end=500, start_factor=0.001, by_epoch=False), dict( type='MultiStepLR', begin=0, end=210, milestones=[170, 200], gamma=0.1, by_epoch=True) ] auto_scale_lr = dict(base_batch_size=512) codec = dict( type='SimCCLabel', input_size=(192, 256), sigma=6.0, simcc_split_ratio=2.0) 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='ViPNAS_MobileNetV3'), head=dict( type='SimCCHead', in_channels=160, out_channels=17, input_size=(192, 256), in_featuremap_size=(6, 8), simcc_split_ratio=2.0, deconv_type='ViPNAS', deconv_out_channels=(160, 160, 160), deconv_num_groups=(160, 160, 160), loss=dict(type='KLDiscretLoss', use_target_weight=True), decoder=dict( type='SimCCLabel', input_size=(192, 256), sigma=6.0, simcc_split_ratio=2.0)), test_cfg=dict(flip_test=True)) dataset_type = 'CocoDataset' data_mode = 'topdown' data_root = '/mnt/lustre/share_data/openmmlab/datasets/detection/coco/' train_pipeline = [ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ '/mnt/lustre/share_data/openmmlab/datasets/detection/coco/': 's3://openmmlab/datasets/detection/coco/' }))), 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='keypoint_xy_label', encoder=dict( type='SimCCLabel', input_size=(192, 256), sigma=6.0, simcc_split_ratio=2.0)), dict(type='PackPoseInputs') ] val_pipeline = [ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ '/mnt/lustre/share_data/openmmlab/datasets/detection/coco/': 's3://openmmlab/datasets/detection/coco/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ] train_dataloader = dict( batch_size=64, num_workers=4, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='CocoDataset', data_root='/mnt/lustre/share_data/openmmlab/datasets/detection/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({ '/mnt/lustre/share_data/openmmlab/datasets/detection/coco/': 's3://openmmlab/datasets/detection/coco/' }))), 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='keypoint_xy_label', encoder=dict( type='SimCCLabel', input_size=(192, 256), sigma=6.0, simcc_split_ratio=2.0)), dict(type='PackPoseInputs') ])) val_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='/mnt/lustre/share_data/openmmlab/datasets/detection/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= '/mnt/lustre/share_data/openmmlab/datasets/detection/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({ '/mnt/lustre/share_data/openmmlab/datasets/detection/coco/': 's3://openmmlab/datasets/detection/coco/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ])) test_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='/mnt/lustre/share_data/openmmlab/datasets/detection/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= '/mnt/lustre/share_data/openmmlab/datasets/detection/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({ '/mnt/lustre/share_data/openmmlab/datasets/detection/coco/': 's3://openmmlab/datasets/detection/coco/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ])) val_evaluator = dict( type='CocoMetric', ann_file= '/mnt/lustre/share_data/openmmlab/datasets/detection/coco/annotations/person_keypoints_val2017.json' ) test_evaluator = dict( type='CocoMetric', ann_file= '/mnt/lustre/share_data/openmmlab/datasets/detection/coco/annotations/person_keypoints_val2017.json' ) launcher = 'slurm' work_dir = '/mnt/lustre/jiangtao/experiment3/vipnas_256' 2022/09/21 17:36:35 - 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. 2022/09/21 17:36:35 - 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. 2022/09/21 17:36:35 - 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. 2022/09/21 17:36:35 - 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. 2022/09/21 17:36:35 - 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. 2022/09/21 17:36:35 - 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. 2022/09/21 17:36:35 - 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. 2022/09/21 17:36:35 - 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. 2022/09/21 17:36:39 - 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. 2022/09/21 17:36:41 - 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. 2022/09/21 17:36:41 - 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. 2022/09/21 17:36:41 - 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. 2022/09/21 17:36:41 - 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. 2022/09/21 17:36:41 - 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. 2022/09/21 17:36:41 - 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. 2022/09/21 17:36:41 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. Name of parameter - Initialization information backbone.conv1.conv.weight - torch.Size([16, 3, 3, 3]): Initialized by user-defined `init_weights` in ConvModule backbone.conv1.bn.weight - torch.Size([16]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.conv1.bn.bias - torch.Size([16]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer1.depthwise_conv.conv.weight - torch.Size([16, 2, 3, 3]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer1.depthwise_conv.bn.weight - torch.Size([16]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer1.depthwise_conv.bn.bias - torch.Size([16]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer1.se.conv1.conv.weight - torch.Size([4, 16, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer1.se.conv1.conv.bias - torch.Size([4]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer1.se.conv2.conv.weight - torch.Size([16, 4, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer1.se.conv2.conv.bias - torch.Size([16]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer1.linear_conv.conv.weight - torch.Size([16, 16, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer1.linear_conv.bn.weight - torch.Size([16]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer1.linear_conv.bn.bias - torch.Size([16]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer2.expand_conv.conv.weight - torch.Size([120, 16, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer2.expand_conv.bn.weight - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer2.expand_conv.bn.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer2.depthwise_conv.conv.weight - torch.Size([120, 1, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer2.depthwise_conv.bn.weight - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer2.depthwise_conv.bn.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer2.se.conv1.conv.weight - torch.Size([30, 120, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer2.se.conv1.conv.bias - torch.Size([30]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer2.se.conv2.conv.weight - torch.Size([120, 30, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer2.se.conv2.conv.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer2.linear_conv.conv.weight - torch.Size([24, 120, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer2.linear_conv.bn.weight - torch.Size([24]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer2.linear_conv.bn.bias - torch.Size([24]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer3.expand_conv.conv.weight - torch.Size([120, 24, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer3.expand_conv.bn.weight - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer3.expand_conv.bn.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer3.depthwise_conv.conv.weight - torch.Size([120, 1, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer3.depthwise_conv.bn.weight - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer3.depthwise_conv.bn.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer3.se.conv1.conv.weight - torch.Size([30, 120, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer3.se.conv1.conv.bias - torch.Size([30]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer3.se.conv2.conv.weight - torch.Size([120, 30, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer3.se.conv2.conv.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer3.linear_conv.conv.weight - torch.Size([24, 120, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer3.linear_conv.bn.weight - torch.Size([24]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer3.linear_conv.bn.bias - torch.Size([24]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer4.expand_conv.conv.weight - torch.Size([120, 24, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer4.expand_conv.bn.weight - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer4.expand_conv.bn.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer4.depthwise_conv.conv.weight - torch.Size([120, 1, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer4.depthwise_conv.bn.weight - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer4.depthwise_conv.bn.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer4.se.conv1.conv.weight - torch.Size([30, 120, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer4.se.conv1.conv.bias - torch.Size([30]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer4.se.conv2.conv.weight - torch.Size([120, 30, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer4.se.conv2.conv.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer4.linear_conv.conv.weight - torch.Size([24, 120, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer4.linear_conv.bn.weight - torch.Size([24]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer4.linear_conv.bn.bias - torch.Size([24]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer5.expand_conv.conv.weight - torch.Size([120, 24, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer5.expand_conv.bn.weight - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer5.expand_conv.bn.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer5.depthwise_conv.conv.weight - torch.Size([120, 1, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer5.depthwise_conv.bn.weight - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer5.depthwise_conv.bn.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer5.se.conv1.conv.weight - torch.Size([30, 120, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer5.se.conv1.conv.bias - torch.Size([30]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer5.se.conv2.conv.weight - torch.Size([120, 30, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer5.se.conv2.conv.bias - torch.Size([120]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer5.linear_conv.conv.weight - torch.Size([24, 120, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer5.linear_conv.bn.weight - torch.Size([24]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer5.linear_conv.bn.bias - torch.Size([24]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer6.expand_conv.conv.weight - torch.Size([160, 24, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer6.expand_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer6.expand_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer6.depthwise_conv.conv.weight - torch.Size([160, 8, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer6.depthwise_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer6.depthwise_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer6.linear_conv.conv.weight - torch.Size([40, 160, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer6.linear_conv.bn.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer6.linear_conv.bn.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer7.expand_conv.conv.weight - torch.Size([160, 40, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer7.expand_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer7.expand_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer7.depthwise_conv.conv.weight - torch.Size([160, 8, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer7.depthwise_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer7.depthwise_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer7.linear_conv.conv.weight - torch.Size([40, 160, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer7.linear_conv.bn.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer7.linear_conv.bn.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer8.expand_conv.conv.weight - torch.Size([160, 40, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer8.expand_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer8.expand_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer8.depthwise_conv.conv.weight - torch.Size([160, 8, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer8.depthwise_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer8.depthwise_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer8.linear_conv.conv.weight - torch.Size([40, 160, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer8.linear_conv.bn.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer8.linear_conv.bn.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer9.expand_conv.conv.weight - torch.Size([160, 40, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer9.expand_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer9.expand_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer9.depthwise_conv.conv.weight - torch.Size([160, 8, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer9.depthwise_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer9.depthwise_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer9.linear_conv.conv.weight - torch.Size([40, 160, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer9.linear_conv.bn.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer9.linear_conv.bn.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer10.expand_conv.conv.weight - torch.Size([400, 40, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer10.expand_conv.bn.weight - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer10.expand_conv.bn.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer10.depthwise_conv.conv.weight - torch.Size([400, 4, 5, 5]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer10.depthwise_conv.bn.weight - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer10.depthwise_conv.bn.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer10.se.conv1.conv.weight - torch.Size([100, 400, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer10.se.conv1.conv.bias - torch.Size([100]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer10.se.conv2.conv.weight - torch.Size([400, 100, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer10.se.conv2.conv.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer10.linear_conv.conv.weight - torch.Size([80, 400, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer10.linear_conv.bn.weight - torch.Size([80]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer10.linear_conv.bn.bias - torch.Size([80]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer11.expand_conv.conv.weight - torch.Size([400, 80, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer11.expand_conv.bn.weight - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer11.expand_conv.bn.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer11.depthwise_conv.conv.weight - torch.Size([400, 4, 5, 5]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer11.depthwise_conv.bn.weight - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer11.depthwise_conv.bn.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer11.se.conv1.conv.weight - torch.Size([100, 400, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer11.se.conv1.conv.bias - torch.Size([100]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer11.se.conv2.conv.weight - torch.Size([400, 100, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer11.se.conv2.conv.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer11.linear_conv.conv.weight - torch.Size([80, 400, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer11.linear_conv.bn.weight - torch.Size([80]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer11.linear_conv.bn.bias - torch.Size([80]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer12.expand_conv.conv.weight - torch.Size([400, 80, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer12.expand_conv.bn.weight - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer12.expand_conv.bn.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer12.depthwise_conv.conv.weight - torch.Size([400, 4, 5, 5]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer12.depthwise_conv.bn.weight - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer12.depthwise_conv.bn.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer12.se.conv1.conv.weight - torch.Size([100, 400, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer12.se.conv1.conv.bias - torch.Size([100]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer12.se.conv2.conv.weight - torch.Size([400, 100, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer12.se.conv2.conv.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer12.linear_conv.conv.weight - torch.Size([80, 400, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer12.linear_conv.bn.weight - torch.Size([80]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer12.linear_conv.bn.bias - torch.Size([80]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer13.expand_conv.conv.weight - torch.Size([400, 80, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer13.expand_conv.bn.weight - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer13.expand_conv.bn.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer13.depthwise_conv.conv.weight - torch.Size([400, 4, 5, 5]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer13.depthwise_conv.bn.weight - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer13.depthwise_conv.bn.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer13.se.conv1.conv.weight - torch.Size([100, 400, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer13.se.conv1.conv.bias - torch.Size([100]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer13.se.conv2.conv.weight - torch.Size([400, 100, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer13.se.conv2.conv.bias - torch.Size([400]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer13.linear_conv.conv.weight - torch.Size([80, 400, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer13.linear_conv.bn.weight - torch.Size([80]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer13.linear_conv.bn.bias - torch.Size([80]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer14.expand_conv.conv.weight - torch.Size([560, 80, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer14.expand_conv.bn.weight - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer14.expand_conv.bn.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer14.depthwise_conv.conv.weight - torch.Size([560, 2, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer14.depthwise_conv.bn.weight - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer14.depthwise_conv.bn.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer14.se.conv1.conv.weight - torch.Size([140, 560, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer14.se.conv1.conv.bias - torch.Size([140]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer14.se.conv2.conv.weight - torch.Size([560, 140, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer14.se.conv2.conv.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer14.linear_conv.conv.weight - torch.Size([112, 560, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer14.linear_conv.bn.weight - torch.Size([112]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer14.linear_conv.bn.bias - torch.Size([112]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer15.expand_conv.conv.weight - torch.Size([560, 112, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer15.expand_conv.bn.weight - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer15.expand_conv.bn.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer15.depthwise_conv.conv.weight - torch.Size([560, 2, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer15.depthwise_conv.bn.weight - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer15.depthwise_conv.bn.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer15.se.conv1.conv.weight - torch.Size([140, 560, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer15.se.conv1.conv.bias - torch.Size([140]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer15.se.conv2.conv.weight - torch.Size([560, 140, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer15.se.conv2.conv.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer15.linear_conv.conv.weight - torch.Size([112, 560, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer15.linear_conv.bn.weight - torch.Size([112]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer15.linear_conv.bn.bias - torch.Size([112]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer16.expand_conv.conv.weight - torch.Size([560, 112, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer16.expand_conv.bn.weight - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer16.expand_conv.bn.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer16.depthwise_conv.conv.weight - torch.Size([560, 2, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer16.depthwise_conv.bn.weight - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer16.depthwise_conv.bn.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer16.se.conv1.conv.weight - torch.Size([140, 560, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer16.se.conv1.conv.bias - torch.Size([140]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer16.se.conv2.conv.weight - torch.Size([560, 140, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer16.se.conv2.conv.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer16.linear_conv.conv.weight - torch.Size([112, 560, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer16.linear_conv.bn.weight - torch.Size([112]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer16.linear_conv.bn.bias - torch.Size([112]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer17.expand_conv.conv.weight - torch.Size([560, 112, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer17.expand_conv.bn.weight - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer17.expand_conv.bn.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer17.depthwise_conv.conv.weight - torch.Size([560, 2, 7, 7]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer17.depthwise_conv.bn.weight - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer17.depthwise_conv.bn.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer17.se.conv1.conv.weight - torch.Size([140, 560, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer17.se.conv1.conv.bias - torch.Size([140]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer17.se.conv2.conv.weight - torch.Size([560, 140, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer17.se.conv2.conv.bias - torch.Size([560]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer17.linear_conv.conv.weight - torch.Size([112, 560, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer17.linear_conv.bn.weight - torch.Size([112]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer17.linear_conv.bn.bias - torch.Size([112]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer18.expand_conv.conv.weight - torch.Size([960, 112, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer18.expand_conv.bn.weight - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer18.expand_conv.bn.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer18.depthwise_conv.conv.weight - torch.Size([960, 4, 5, 5]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer18.depthwise_conv.bn.weight - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer18.depthwise_conv.bn.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer18.se.conv1.conv.weight - torch.Size([240, 960, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer18.se.conv1.conv.bias - torch.Size([240]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer18.se.conv2.conv.weight - torch.Size([960, 240, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer18.se.conv2.conv.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer18.linear_conv.conv.weight - torch.Size([160, 960, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer18.linear_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer18.linear_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer19.expand_conv.conv.weight - torch.Size([960, 160, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer19.expand_conv.bn.weight - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer19.expand_conv.bn.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer19.depthwise_conv.conv.weight - torch.Size([960, 4, 5, 5]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer19.depthwise_conv.bn.weight - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer19.depthwise_conv.bn.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer19.se.conv1.conv.weight - torch.Size([240, 960, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer19.se.conv1.conv.bias - torch.Size([240]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer19.se.conv2.conv.weight - torch.Size([960, 240, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer19.se.conv2.conv.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer19.linear_conv.conv.weight - torch.Size([160, 960, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer19.linear_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer19.linear_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer20.expand_conv.conv.weight - torch.Size([960, 160, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer20.expand_conv.bn.weight - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer20.expand_conv.bn.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer20.depthwise_conv.conv.weight - torch.Size([960, 4, 5, 5]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer20.depthwise_conv.bn.weight - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer20.depthwise_conv.bn.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer20.se.conv1.conv.weight - torch.Size([240, 960, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer20.se.conv1.conv.bias - torch.Size([240]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer20.se.conv2.conv.weight - torch.Size([960, 240, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer20.se.conv2.conv.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer20.linear_conv.conv.weight - torch.Size([160, 960, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer20.linear_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer20.linear_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer21.expand_conv.conv.weight - torch.Size([960, 160, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer21.expand_conv.bn.weight - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer21.expand_conv.bn.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer21.depthwise_conv.conv.weight - torch.Size([960, 4, 5, 5]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer21.depthwise_conv.bn.weight - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer21.depthwise_conv.bn.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer21.se.conv1.conv.weight - torch.Size([240, 960, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer21.se.conv1.conv.bias - torch.Size([240]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer21.se.conv2.conv.weight - torch.Size([960, 240, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer21.se.conv2.conv.bias - torch.Size([960]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer21.linear_conv.conv.weight - torch.Size([160, 960, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 backbone.layer21.linear_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator backbone.layer21.linear_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_head.deconv_layers.0.weight - torch.Size([160, 1, 4, 4]): NormalInit: mean=0, std=0.001, bias=0 head.deconv_head.deconv_layers.1.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_head.deconv_layers.1.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_head.deconv_layers.3.weight - torch.Size([160, 1, 4, 4]): NormalInit: mean=0, std=0.001, bias=0 head.deconv_head.deconv_layers.4.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_head.deconv_layers.4.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_head.deconv_layers.6.weight - torch.Size([160, 1, 4, 4]): NormalInit: mean=0, std=0.001, bias=0 head.deconv_head.deconv_layers.7.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_head.deconv_layers.7.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of TopdownPoseEstimator head.deconv_head.final_layer.weight - torch.Size([17, 160, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 head.deconv_head.final_layer.bias - torch.Size([17]): NormalInit: mean=0, std=0.001, bias=0 head.mlp_head_x.weight - torch.Size([384, 3072]): NormalInit: mean=0, std=0.01, bias=0 head.mlp_head_x.bias - torch.Size([384]): NormalInit: mean=0, std=0.01, bias=0 head.mlp_head_y.weight - torch.Size([512, 3072]): NormalInit: mean=0, std=0.01, bias=0 head.mlp_head_y.bias - torch.Size([512]): NormalInit: mean=0, std=0.01, bias=0 2022/09/21 17:36:42 - mmengine - INFO - Checkpoints will be saved to /mnt/lustre/jiangtao/experiment3/vipnas_256 by HardDiskBackend. 2022/09/21 17:37:16 - mmengine - INFO - Epoch(train) [1][50/293] lr: 4.954910e-05 eta: 11:43:55 time: 0.686976 data_time: 0.288284 memory: 6338 loss_kpt: 0.475538 acc_pose: 0.037406 loss: 0.475538 2022/09/21 17:37:39 - mmengine - INFO - Epoch(train) [1][100/293] lr: 9.959920e-05 eta: 9:46:53 time: 0.459494 data_time: 0.081594 memory: 6338 loss_kpt: 0.430444 acc_pose: 0.087220 loss: 0.430444 2022/09/21 17:38:02 - mmengine - INFO - Epoch(train) [1][150/293] lr: 1.496493e-04 eta: 9:07:12 time: 0.458242 data_time: 0.087955 memory: 6338 loss_kpt: 0.404975 acc_pose: 0.055314 loss: 0.404975 2022/09/21 17:38:24 - mmengine - INFO - Epoch(train) [1][200/293] lr: 1.996994e-04 eta: 8:45:21 time: 0.451153 data_time: 0.080068 memory: 6338 loss_kpt: 0.390576 acc_pose: 0.100515 loss: 0.390576 2022/09/21 17:38:47 - mmengine - INFO - Epoch(train) [1][250/293] lr: 2.497495e-04 eta: 8:34:11 time: 0.461385 data_time: 0.086399 memory: 6338 loss_kpt: 0.375867 acc_pose: 0.145512 loss: 0.375867 2022/09/21 17:39:06 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 17:39:06 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/09/21 17:39:33 - mmengine - INFO - Epoch(train) [2][50/293] lr: 3.428427e-04 eta: 7:24:26 time: 0.472434 data_time: 0.098551 memory: 6338 loss_kpt: 0.347451 acc_pose: 0.270142 loss: 0.347451 2022/09/21 17:39:56 - mmengine - INFO - Epoch(train) [2][100/293] lr: 3.928928e-04 eta: 7:27:14 time: 0.460277 data_time: 0.082971 memory: 6338 loss_kpt: 0.337908 acc_pose: 0.228555 loss: 0.337908 2022/09/21 17:40:20 - mmengine - INFO - Epoch(train) [2][150/293] lr: 4.429429e-04 eta: 7:31:23 time: 0.478237 data_time: 0.095542 memory: 6338 loss_kpt: 0.328197 acc_pose: 0.231130 loss: 0.328197 2022/09/21 17:40:43 - mmengine - INFO - Epoch(train) [2][200/293] lr: 4.929930e-04 eta: 7:33:32 time: 0.467751 data_time: 0.088171 memory: 6338 loss_kpt: 0.324640 acc_pose: 0.286585 loss: 0.324640 2022/09/21 17:41:06 - mmengine - INFO - Epoch(train) [2][250/293] lr: 5.000000e-04 eta: 7:34:28 time: 0.459767 data_time: 0.089092 memory: 6338 loss_kpt: 0.315084 acc_pose: 0.303081 loss: 0.315084 2022/09/21 17:41:25 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 17:41:25 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/09/21 17:41:53 - mmengine - INFO - Epoch(train) [3][50/293] lr: 5.000000e-04 eta: 7:06:16 time: 0.486953 data_time: 0.092030 memory: 6338 loss_kpt: 0.304586 acc_pose: 0.252156 loss: 0.304586 2022/09/21 17:42:15 - mmengine - INFO - Epoch(train) [3][100/293] lr: 5.000000e-04 eta: 7:08:50 time: 0.459503 data_time: 0.083398 memory: 6338 loss_kpt: 0.291310 acc_pose: 0.318150 loss: 0.291310 2022/09/21 17:42:39 - mmengine - INFO - Epoch(train) [3][150/293] lr: 5.000000e-04 eta: 7:11:27 time: 0.465899 data_time: 0.084476 memory: 6338 loss_kpt: 0.285026 acc_pose: 0.367300 loss: 0.285026 2022/09/21 17:43:02 - mmengine - INFO - Epoch(train) [3][200/293] lr: 5.000000e-04 eta: 7:13:33 time: 0.463987 data_time: 0.082033 memory: 6338 loss_kpt: 0.279729 acc_pose: 0.322993 loss: 0.279729 2022/09/21 17:43:25 - mmengine - INFO - Epoch(train) [3][250/293] lr: 5.000000e-04 eta: 7:15:05 time: 0.459412 data_time: 0.080210 memory: 6338 loss_kpt: 0.275699 acc_pose: 0.332166 loss: 0.275699 2022/09/21 17:43:45 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 17:43:45 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/09/21 17:44:10 - mmengine - INFO - Epoch(train) [4][50/293] lr: 5.000000e-04 eta: 6:55:55 time: 0.459658 data_time: 0.089108 memory: 6338 loss_kpt: 0.273163 acc_pose: 0.358452 loss: 0.273163 2022/09/21 17:44:33 - mmengine - INFO - Epoch(train) [4][100/293] lr: 5.000000e-04 eta: 6:57:44 time: 0.453973 data_time: 0.084874 memory: 6338 loss_kpt: 0.264555 acc_pose: 0.374736 loss: 0.264555 2022/09/21 17:44:43 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 17:44:56 - mmengine - INFO - Epoch(train) [4][150/293] lr: 5.000000e-04 eta: 6:59:34 time: 0.458210 data_time: 0.089360 memory: 6338 loss_kpt: 0.261228 acc_pose: 0.403543 loss: 0.261228 2022/09/21 17:45:19 - mmengine - INFO - Epoch(train) [4][200/293] lr: 5.000000e-04 eta: 7:01:17 time: 0.460335 data_time: 0.085338 memory: 6338 loss_kpt: 0.259146 acc_pose: 0.402195 loss: 0.259146 2022/09/21 17:45:42 - mmengine - INFO - Epoch(train) [4][250/293] lr: 5.000000e-04 eta: 7:02:49 time: 0.460370 data_time: 0.084511 memory: 6338 loss_kpt: 0.253804 acc_pose: 0.485032 loss: 0.253804 2022/09/21 17:46:01 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 17:46:01 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/09/21 17:46:26 - mmengine - INFO - Epoch(train) [5][50/293] lr: 5.000000e-04 eta: 6:48:48 time: 0.456255 data_time: 0.087432 memory: 6338 loss_kpt: 0.252134 acc_pose: 0.398030 loss: 0.252134 2022/09/21 17:46:49 - mmengine - INFO - Epoch(train) [5][100/293] lr: 5.000000e-04 eta: 6:50:04 time: 0.447470 data_time: 0.082307 memory: 6338 loss_kpt: 0.251795 acc_pose: 0.513330 loss: 0.251795 2022/09/21 17:47:11 - mmengine - INFO - Epoch(train) [5][150/293] lr: 5.000000e-04 eta: 6:51:27 time: 0.453440 data_time: 0.081804 memory: 6338 loss_kpt: 0.249042 acc_pose: 0.435299 loss: 0.249042 2022/09/21 17:47:35 - mmengine - INFO - Epoch(train) [5][200/293] lr: 5.000000e-04 eta: 6:53:11 time: 0.467068 data_time: 0.087241 memory: 6338 loss_kpt: 0.242983 acc_pose: 0.456272 loss: 0.242983 2022/09/21 17:47:59 - mmengine - INFO - Epoch(train) [5][250/293] lr: 5.000000e-04 eta: 6:55:07 time: 0.476700 data_time: 0.087337 memory: 6338 loss_kpt: 0.241092 acc_pose: 0.503134 loss: 0.241092 2022/09/21 17:48:18 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 17:48:18 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/09/21 17:48:44 - mmengine - INFO - Epoch(train) [6][50/293] lr: 5.000000e-04 eta: 6:44:06 time: 0.456302 data_time: 0.093358 memory: 6338 loss_kpt: 0.234389 acc_pose: 0.470760 loss: 0.234389 2022/09/21 17:49:07 - mmengine - INFO - Epoch(train) [6][100/293] lr: 5.000000e-04 eta: 6:45:12 time: 0.449049 data_time: 0.084509 memory: 6338 loss_kpt: 0.236601 acc_pose: 0.438184 loss: 0.236601 2022/09/21 17:49:29 - mmengine - INFO - Epoch(train) [6][150/293] lr: 5.000000e-04 eta: 6:46:11 time: 0.448203 data_time: 0.090160 memory: 6338 loss_kpt: 0.233775 acc_pose: 0.408558 loss: 0.233775 2022/09/21 17:49:51 - mmengine - INFO - Epoch(train) [6][200/293] lr: 5.000000e-04 eta: 6:46:55 time: 0.442689 data_time: 0.086202 memory: 6338 loss_kpt: 0.230052 acc_pose: 0.486686 loss: 0.230052 2022/09/21 17:50:14 - mmengine - INFO - Epoch(train) [6][250/293] lr: 5.000000e-04 eta: 6:48:01 time: 0.457566 data_time: 0.091787 memory: 6338 loss_kpt: 0.230619 acc_pose: 0.454467 loss: 0.230619 2022/09/21 17:50:33 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 17:50:33 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/09/21 17:51:00 - mmengine - INFO - Epoch(train) [7][50/293] lr: 5.000000e-04 eta: 6:39:34 time: 0.476837 data_time: 0.094517 memory: 6338 loss_kpt: 0.224217 acc_pose: 0.512408 loss: 0.224217 2022/09/21 17:51:23 - mmengine - INFO - Epoch(train) [7][100/293] lr: 5.000000e-04 eta: 6:41:03 time: 0.469624 data_time: 0.083457 memory: 6338 loss_kpt: 0.225757 acc_pose: 0.548638 loss: 0.225757 2022/09/21 17:51:47 - mmengine - INFO - Epoch(train) [7][150/293] lr: 5.000000e-04 eta: 6:42:40 time: 0.478521 data_time: 0.087305 memory: 6338 loss_kpt: 0.226736 acc_pose: 0.540647 loss: 0.226736 2022/09/21 17:52:11 - mmengine - INFO - Epoch(train) [7][200/293] lr: 5.000000e-04 eta: 6:44:00 time: 0.470568 data_time: 0.086492 memory: 6338 loss_kpt: 0.223566 acc_pose: 0.446934 loss: 0.223566 2022/09/21 17:52:31 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 17:52:34 - mmengine - INFO - Epoch(train) [7][250/293] lr: 5.000000e-04 eta: 6:45:12 time: 0.469331 data_time: 0.085714 memory: 6338 loss_kpt: 0.221618 acc_pose: 0.560187 loss: 0.221618 2022/09/21 17:52:54 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 17:52:54 - mmengine - INFO - Saving checkpoint at 7 epochs 2022/09/21 17:53:21 - mmengine - INFO - Epoch(train) [8][50/293] lr: 5.000000e-04 eta: 6:38:11 time: 0.489414 data_time: 0.101354 memory: 6338 loss_kpt: 0.221807 acc_pose: 0.473596 loss: 0.221807 2022/09/21 17:53:45 - mmengine - INFO - Epoch(train) [8][100/293] lr: 5.000000e-04 eta: 6:39:26 time: 0.470488 data_time: 0.086683 memory: 6338 loss_kpt: 0.214686 acc_pose: 0.520621 loss: 0.214686 2022/09/21 17:54:09 - mmengine - INFO - Epoch(train) [8][150/293] lr: 5.000000e-04 eta: 6:40:59 time: 0.487725 data_time: 0.089626 memory: 6338 loss_kpt: 0.214577 acc_pose: 0.519944 loss: 0.214577 2022/09/21 17:54:32 - mmengine - INFO - Epoch(train) [8][200/293] lr: 5.000000e-04 eta: 6:41:57 time: 0.465068 data_time: 0.080811 memory: 6338 loss_kpt: 0.213822 acc_pose: 0.602983 loss: 0.213822 2022/09/21 17:54:56 - mmengine - INFO - Epoch(train) [8][250/293] lr: 5.000000e-04 eta: 6:43:02 time: 0.472910 data_time: 0.086849 memory: 6338 loss_kpt: 0.214584 acc_pose: 0.570081 loss: 0.214584 2022/09/21 17:55:15 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 17:55:15 - mmengine - INFO - Saving checkpoint at 8 epochs 2022/09/21 17:55:40 - mmengine - INFO - Epoch(train) [9][50/293] lr: 5.000000e-04 eta: 6:35:55 time: 0.444521 data_time: 0.090799 memory: 6338 loss_kpt: 0.212917 acc_pose: 0.573885 loss: 0.212917 2022/09/21 17:56:02 - mmengine - INFO - Epoch(train) [9][100/293] lr: 5.000000e-04 eta: 6:36:16 time: 0.435765 data_time: 0.081912 memory: 6338 loss_kpt: 0.211853 acc_pose: 0.605632 loss: 0.211853 2022/09/21 17:56:24 - mmengine - INFO - Epoch(train) [9][150/293] lr: 5.000000e-04 eta: 6:36:38 time: 0.437806 data_time: 0.083016 memory: 6338 loss_kpt: 0.207197 acc_pose: 0.557204 loss: 0.207197 2022/09/21 17:56:46 - mmengine - INFO - Epoch(train) [9][200/293] lr: 5.000000e-04 eta: 6:37:08 time: 0.446767 data_time: 0.093531 memory: 6338 loss_kpt: 0.207939 acc_pose: 0.542502 loss: 0.207939 2022/09/21 17:57:09 - mmengine - INFO - Epoch(train) [9][250/293] lr: 5.000000e-04 eta: 6:37:35 time: 0.445506 data_time: 0.080857 memory: 6338 loss_kpt: 0.205665 acc_pose: 0.592596 loss: 0.205665 2022/09/21 17:57:27 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 17:57:27 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/09/21 17:57:54 - mmengine - INFO - Epoch(train) [10][50/293] lr: 5.000000e-04 eta: 6:32:07 time: 0.487232 data_time: 0.101805 memory: 6338 loss_kpt: 0.210752 acc_pose: 0.602606 loss: 0.210752 2022/09/21 17:58:18 - mmengine - INFO - Epoch(train) [10][100/293] lr: 5.000000e-04 eta: 6:33:09 time: 0.476497 data_time: 0.083750 memory: 6338 loss_kpt: 0.204783 acc_pose: 0.622301 loss: 0.204783 2022/09/21 17:58:42 - mmengine - INFO - Epoch(train) [10][150/293] lr: 5.000000e-04 eta: 6:34:01 time: 0.469681 data_time: 0.082641 memory: 6338 loss_kpt: 0.205198 acc_pose: 0.592809 loss: 0.205198 2022/09/21 17:59:05 - mmengine - INFO - Epoch(train) [10][200/293] lr: 5.000000e-04 eta: 6:34:54 time: 0.473243 data_time: 0.079150 memory: 6338 loss_kpt: 0.198763 acc_pose: 0.588057 loss: 0.198763 2022/09/21 17:59:30 - mmengine - INFO - Epoch(train) [10][250/293] lr: 5.000000e-04 eta: 6:35:58 time: 0.486060 data_time: 0.084309 memory: 6338 loss_kpt: 0.204629 acc_pose: 0.607420 loss: 0.204629 2022/09/21 17:59:49 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 17:59:49 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/09/21 18:00:04 - mmengine - INFO - Epoch(val) [10][50/407] eta: 0:01:27 time: 0.243852 data_time: 0.147254 memory: 6338 2022/09/21 18:00:10 - mmengine - INFO - Epoch(val) [10][100/407] eta: 0:00:35 time: 0.114864 data_time: 0.050533 memory: 587 2022/09/21 18:00:16 - mmengine - INFO - Epoch(val) [10][150/407] eta: 0:00:30 time: 0.118049 data_time: 0.056407 memory: 587 2022/09/21 18:00:22 - mmengine - INFO - Epoch(val) [10][200/407] eta: 0:00:24 time: 0.119981 data_time: 0.057386 memory: 587 2022/09/21 18:00:27 - mmengine - INFO - Epoch(val) [10][250/407] eta: 0:00:17 time: 0.113386 data_time: 0.052478 memory: 587 2022/09/21 18:00:33 - mmengine - INFO - Epoch(val) [10][300/407] eta: 0:00:12 time: 0.115832 data_time: 0.055035 memory: 587 2022/09/21 18:00:39 - mmengine - INFO - Epoch(val) [10][350/407] eta: 0:00:06 time: 0.120820 data_time: 0.058977 memory: 587 2022/09/21 18:00:45 - mmengine - INFO - Epoch(val) [10][400/407] eta: 0:00:00 time: 0.114530 data_time: 0.054180 memory: 587 2022/09/21 18:01:21 - mmengine - INFO - Evaluating CocoMetric... 2022/09/21 18:01:35 - mmengine - INFO - Epoch(val) [10][407/407] coco/AP: 0.434563 coco/AP .5: 0.752316 coco/AP .75: 0.435411 coco/AP (M): 0.419948 coco/AP (L): 0.471452 coco/AR: 0.499764 coco/AR .5: 0.804943 coco/AR .75: 0.518892 coco/AR (M): 0.471647 coco/AR (L): 0.539539 2022/09/21 18:01:38 - mmengine - INFO - The best checkpoint with 0.4346 coco/AP at 10 epoch is saved to best_coco/AP_epoch_10.pth. 2022/09/21 18:02:02 - mmengine - INFO - Epoch(train) [11][50/293] lr: 5.000000e-04 eta: 6:31:06 time: 0.494976 data_time: 0.103494 memory: 6338 loss_kpt: 0.200360 acc_pose: 0.579164 loss: 0.200360 2022/09/21 18:02:12 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:02:26 - mmengine - INFO - Epoch(train) [11][100/293] lr: 5.000000e-04 eta: 6:31:52 time: 0.468761 data_time: 0.087481 memory: 6338 loss_kpt: 0.196899 acc_pose: 0.632177 loss: 0.196899 2022/09/21 18:02:49 - mmengine - INFO - Epoch(train) [11][150/293] lr: 5.000000e-04 eta: 6:32:40 time: 0.474578 data_time: 0.089819 memory: 6338 loss_kpt: 0.197673 acc_pose: 0.566139 loss: 0.197673 2022/09/21 18:03:13 - mmengine - INFO - Epoch(train) [11][200/293] lr: 5.000000e-04 eta: 6:33:23 time: 0.470663 data_time: 0.095848 memory: 6338 loss_kpt: 0.196338 acc_pose: 0.613760 loss: 0.196338 2022/09/21 18:03:36 - mmengine - INFO - Epoch(train) [11][250/293] lr: 5.000000e-04 eta: 6:34:00 time: 0.466028 data_time: 0.093494 memory: 6338 loss_kpt: 0.201006 acc_pose: 0.568203 loss: 0.201006 2022/09/21 18:03:56 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:03:56 - mmengine - INFO - Saving checkpoint at 11 epochs 2022/09/21 18:04:23 - mmengine - INFO - Epoch(train) [12][50/293] lr: 5.000000e-04 eta: 6:29:25 time: 0.487610 data_time: 0.094975 memory: 6338 loss_kpt: 0.196036 acc_pose: 0.557335 loss: 0.196036 2022/09/21 18:04:46 - mmengine - INFO - Epoch(train) [12][100/293] lr: 5.000000e-04 eta: 6:30:06 time: 0.470190 data_time: 0.083863 memory: 6338 loss_kpt: 0.192553 acc_pose: 0.602142 loss: 0.192553 2022/09/21 18:05:10 - mmengine - INFO - Epoch(train) [12][150/293] lr: 5.000000e-04 eta: 6:30:40 time: 0.464532 data_time: 0.089821 memory: 6338 loss_kpt: 0.194061 acc_pose: 0.595382 loss: 0.194061 2022/09/21 18:05:33 - mmengine - INFO - Epoch(train) [12][200/293] lr: 5.000000e-04 eta: 6:31:17 time: 0.470924 data_time: 0.087316 memory: 6338 loss_kpt: 0.192221 acc_pose: 0.593654 loss: 0.192221 2022/09/21 18:05:57 - mmengine - INFO - Epoch(train) [12][250/293] lr: 5.000000e-04 eta: 6:31:56 time: 0.474874 data_time: 0.090968 memory: 6338 loss_kpt: 0.193134 acc_pose: 0.550170 loss: 0.193134 2022/09/21 18:06:17 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:06:17 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/09/21 18:06:42 - mmengine - INFO - Epoch(train) [13][50/293] lr: 5.000000e-04 eta: 6:27:15 time: 0.454418 data_time: 0.096370 memory: 6338 loss_kpt: 0.187538 acc_pose: 0.575039 loss: 0.187538 2022/09/21 18:07:06 - mmengine - INFO - Epoch(train) [13][100/293] lr: 5.000000e-04 eta: 6:27:48 time: 0.467057 data_time: 0.082678 memory: 6338 loss_kpt: 0.189379 acc_pose: 0.594358 loss: 0.189379 2022/09/21 18:07:29 - mmengine - INFO - Epoch(train) [13][150/293] lr: 5.000000e-04 eta: 6:28:24 time: 0.471985 data_time: 0.093416 memory: 6338 loss_kpt: 0.191551 acc_pose: 0.599089 loss: 0.191551 2022/09/21 18:07:53 - mmengine - INFO - Epoch(train) [13][200/293] lr: 5.000000e-04 eta: 6:28:50 time: 0.462002 data_time: 0.080832 memory: 6338 loss_kpt: 0.188325 acc_pose: 0.647823 loss: 0.188325 2022/09/21 18:08:16 - mmengine - INFO - Epoch(train) [13][250/293] lr: 5.000000e-04 eta: 6:29:14 time: 0.461334 data_time: 0.085332 memory: 6338 loss_kpt: 0.187600 acc_pose: 0.627058 loss: 0.187600 2022/09/21 18:08:35 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:08:35 - mmengine - INFO - Saving checkpoint at 13 epochs 2022/09/21 18:09:00 - mmengine - INFO - Epoch(train) [14][50/293] lr: 5.000000e-04 eta: 6:25:01 time: 0.463662 data_time: 0.103666 memory: 6338 loss_kpt: 0.188261 acc_pose: 0.582493 loss: 0.188261 2022/09/21 18:09:23 - mmengine - INFO - Epoch(train) [14][100/293] lr: 5.000000e-04 eta: 6:25:22 time: 0.456840 data_time: 0.087805 memory: 6338 loss_kpt: 0.189241 acc_pose: 0.634865 loss: 0.189241 2022/09/21 18:09:47 - mmengine - INFO - Epoch(train) [14][150/293] lr: 5.000000e-04 eta: 6:25:48 time: 0.463784 data_time: 0.091655 memory: 6338 loss_kpt: 0.187182 acc_pose: 0.659606 loss: 0.187182 2022/09/21 18:10:05 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:10:10 - mmengine - INFO - Epoch(train) [14][200/293] lr: 5.000000e-04 eta: 6:26:10 time: 0.460736 data_time: 0.080482 memory: 6338 loss_kpt: 0.183064 acc_pose: 0.657120 loss: 0.183064 2022/09/21 18:10:32 - mmengine - INFO - Epoch(train) [14][250/293] lr: 5.000000e-04 eta: 6:26:27 time: 0.455834 data_time: 0.082994 memory: 6338 loss_kpt: 0.185566 acc_pose: 0.610211 loss: 0.185566 2022/09/21 18:10:51 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:10:51 - mmengine - INFO - Saving checkpoint at 14 epochs 2022/09/21 18:11:18 - mmengine - INFO - Epoch(train) [15][50/293] lr: 5.000000e-04 eta: 6:22:46 time: 0.485205 data_time: 0.095447 memory: 6338 loss_kpt: 0.182033 acc_pose: 0.575831 loss: 0.182033 2022/09/21 18:11:42 - mmengine - INFO - Epoch(train) [15][100/293] lr: 5.000000e-04 eta: 6:23:18 time: 0.476892 data_time: 0.086668 memory: 6338 loss_kpt: 0.185522 acc_pose: 0.609933 loss: 0.185522 2022/09/21 18:12:06 - mmengine - INFO - Epoch(train) [15][150/293] lr: 5.000000e-04 eta: 6:23:49 time: 0.477002 data_time: 0.093030 memory: 6338 loss_kpt: 0.183612 acc_pose: 0.604158 loss: 0.183612 2022/09/21 18:12:30 - mmengine - INFO - Epoch(train) [15][200/293] lr: 5.000000e-04 eta: 6:24:20 time: 0.478743 data_time: 0.089260 memory: 6338 loss_kpt: 0.184737 acc_pose: 0.621147 loss: 0.184737 2022/09/21 18:12:53 - mmengine - INFO - Epoch(train) [15][250/293] lr: 5.000000e-04 eta: 6:24:43 time: 0.468655 data_time: 0.095091 memory: 6338 loss_kpt: 0.185504 acc_pose: 0.618144 loss: 0.185504 2022/09/21 18:13:12 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:13:12 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/09/21 18:13:40 - mmengine - INFO - Epoch(train) [16][50/293] lr: 5.000000e-04 eta: 6:21:19 time: 0.490696 data_time: 0.099396 memory: 6338 loss_kpt: 0.182969 acc_pose: 0.647742 loss: 0.182969 2022/09/21 18:14:03 - mmengine - INFO - Epoch(train) [16][100/293] lr: 5.000000e-04 eta: 6:21:46 time: 0.474926 data_time: 0.094163 memory: 6338 loss_kpt: 0.182494 acc_pose: 0.653715 loss: 0.182494 2022/09/21 18:14:28 - mmengine - INFO - Epoch(train) [16][150/293] lr: 5.000000e-04 eta: 6:22:20 time: 0.488240 data_time: 0.096358 memory: 6338 loss_kpt: 0.180822 acc_pose: 0.588898 loss: 0.180822 2022/09/21 18:14:52 - mmengine - INFO - Epoch(train) [16][200/293] lr: 5.000000e-04 eta: 6:22:48 time: 0.481257 data_time: 0.093226 memory: 6338 loss_kpt: 0.183232 acc_pose: 0.651769 loss: 0.183232 2022/09/21 18:15:16 - mmengine - INFO - Epoch(train) [16][250/293] lr: 5.000000e-04 eta: 6:23:18 time: 0.484649 data_time: 0.092766 memory: 6338 loss_kpt: 0.180959 acc_pose: 0.612753 loss: 0.180959 2022/09/21 18:15:37 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:15:37 - mmengine - INFO - Saving checkpoint at 16 epochs 2022/09/21 18:16:04 - mmengine - INFO - Epoch(train) [17][50/293] lr: 5.000000e-04 eta: 6:20:12 time: 0.504054 data_time: 0.109338 memory: 6338 loss_kpt: 0.179472 acc_pose: 0.619115 loss: 0.179472 2022/09/21 18:16:28 - mmengine - INFO - Epoch(train) [17][100/293] lr: 5.000000e-04 eta: 6:20:36 time: 0.476622 data_time: 0.088868 memory: 6338 loss_kpt: 0.177833 acc_pose: 0.597181 loss: 0.177833 2022/09/21 18:16:52 - mmengine - INFO - Epoch(train) [17][150/293] lr: 5.000000e-04 eta: 6:20:59 time: 0.475413 data_time: 0.091988 memory: 6338 loss_kpt: 0.180218 acc_pose: 0.630971 loss: 0.180218 2022/09/21 18:17:16 - mmengine - INFO - Epoch(train) [17][200/293] lr: 5.000000e-04 eta: 6:21:19 time: 0.472259 data_time: 0.086651 memory: 6338 loss_kpt: 0.178695 acc_pose: 0.584838 loss: 0.178695 2022/09/21 18:17:39 - mmengine - INFO - Epoch(train) [17][250/293] lr: 5.000000e-04 eta: 6:21:36 time: 0.470284 data_time: 0.096735 memory: 6338 loss_kpt: 0.179119 acc_pose: 0.633175 loss: 0.179119 2022/09/21 18:17:59 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:17:59 - mmengine - INFO - Saving checkpoint at 17 epochs 2022/09/21 18:18:11 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:18:26 - mmengine - INFO - Epoch(train) [18][50/293] lr: 5.000000e-04 eta: 6:18:31 time: 0.488618 data_time: 0.101429 memory: 6338 loss_kpt: 0.177840 acc_pose: 0.640374 loss: 0.177840 2022/09/21 18:18:49 - mmengine - INFO - Epoch(train) [18][100/293] lr: 5.000000e-04 eta: 6:18:43 time: 0.461087 data_time: 0.085872 memory: 6338 loss_kpt: 0.173606 acc_pose: 0.656846 loss: 0.173606 2022/09/21 18:19:13 - mmengine - INFO - Epoch(train) [18][150/293] lr: 5.000000e-04 eta: 6:19:03 time: 0.475148 data_time: 0.089612 memory: 6338 loss_kpt: 0.177712 acc_pose: 0.627198 loss: 0.177712 2022/09/21 18:19:37 - mmengine - INFO - Epoch(train) [18][200/293] lr: 5.000000e-04 eta: 6:19:23 time: 0.476635 data_time: 0.095509 memory: 6338 loss_kpt: 0.175742 acc_pose: 0.629433 loss: 0.175742 2022/09/21 18:20:01 - mmengine - INFO - Epoch(train) [18][250/293] lr: 5.000000e-04 eta: 6:19:46 time: 0.484177 data_time: 0.090251 memory: 6338 loss_kpt: 0.175106 acc_pose: 0.641078 loss: 0.175106 2022/09/21 18:20:21 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:20:21 - mmengine - INFO - Saving checkpoint at 18 epochs 2022/09/21 18:20:48 - mmengine - INFO - Epoch(train) [19][50/293] lr: 5.000000e-04 eta: 6:16:43 time: 0.478130 data_time: 0.102651 memory: 6338 loss_kpt: 0.175915 acc_pose: 0.640406 loss: 0.175915 2022/09/21 18:21:11 - mmengine - INFO - Epoch(train) [19][100/293] lr: 5.000000e-04 eta: 6:16:54 time: 0.461226 data_time: 0.094191 memory: 6338 loss_kpt: 0.173578 acc_pose: 0.676595 loss: 0.173578 2022/09/21 18:21:34 - mmengine - INFO - Epoch(train) [19][150/293] lr: 5.000000e-04 eta: 6:17:03 time: 0.458640 data_time: 0.094064 memory: 6338 loss_kpt: 0.174284 acc_pose: 0.659517 loss: 0.174284 2022/09/21 18:21:56 - mmengine - INFO - Epoch(train) [19][200/293] lr: 5.000000e-04 eta: 6:17:07 time: 0.450135 data_time: 0.088328 memory: 6338 loss_kpt: 0.173808 acc_pose: 0.659005 loss: 0.173808 2022/09/21 18:22:19 - mmengine - INFO - Epoch(train) [19][250/293] lr: 5.000000e-04 eta: 6:17:15 time: 0.459471 data_time: 0.089650 memory: 6338 loss_kpt: 0.175803 acc_pose: 0.620003 loss: 0.175803 2022/09/21 18:22:38 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:22:38 - mmengine - INFO - Saving checkpoint at 19 epochs 2022/09/21 18:23:04 - mmengine - INFO - Epoch(train) [20][50/293] lr: 5.000000e-04 eta: 6:14:12 time: 0.459970 data_time: 0.090961 memory: 6338 loss_kpt: 0.173724 acc_pose: 0.665792 loss: 0.173724 2022/09/21 18:23:27 - mmengine - INFO - Epoch(train) [20][100/293] lr: 5.000000e-04 eta: 6:14:17 time: 0.452336 data_time: 0.087989 memory: 6338 loss_kpt: 0.172008 acc_pose: 0.601567 loss: 0.172008 2022/09/21 18:23:50 - mmengine - INFO - Epoch(train) [20][150/293] lr: 5.000000e-04 eta: 6:14:25 time: 0.460789 data_time: 0.085527 memory: 6338 loss_kpt: 0.170206 acc_pose: 0.706490 loss: 0.170206 2022/09/21 18:24:12 - mmengine - INFO - Epoch(train) [20][200/293] lr: 5.000000e-04 eta: 6:14:30 time: 0.453875 data_time: 0.085221 memory: 6338 loss_kpt: 0.169705 acc_pose: 0.648793 loss: 0.169705 2022/09/21 18:24:36 - mmengine - INFO - Epoch(train) [20][250/293] lr: 5.000000e-04 eta: 6:14:40 time: 0.465070 data_time: 0.086125 memory: 6338 loss_kpt: 0.170765 acc_pose: 0.666575 loss: 0.170765 2022/09/21 18:24:55 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:24:55 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/09/21 18:25:03 - mmengine - INFO - Epoch(val) [20][50/407] eta: 0:00:42 time: 0.119833 data_time: 0.057349 memory: 6338 2022/09/21 18:25:09 - mmengine - INFO - Epoch(val) [20][100/407] eta: 0:00:34 time: 0.111109 data_time: 0.049366 memory: 587 2022/09/21 18:25:15 - mmengine - INFO - Epoch(val) [20][150/407] eta: 0:00:30 time: 0.117980 data_time: 0.056373 memory: 587 2022/09/21 18:25:21 - mmengine - INFO - Epoch(val) [20][200/407] eta: 0:00:24 time: 0.120058 data_time: 0.058514 memory: 587 2022/09/21 18:25:26 - mmengine - INFO - Epoch(val) [20][250/407] eta: 0:00:17 time: 0.110077 data_time: 0.048700 memory: 587 2022/09/21 18:25:32 - mmengine - INFO - Epoch(val) [20][300/407] eta: 0:00:12 time: 0.112893 data_time: 0.051033 memory: 587 2022/09/21 18:25:38 - mmengine - INFO - Epoch(val) [20][350/407] eta: 0:00:06 time: 0.119095 data_time: 0.054853 memory: 587 2022/09/21 18:25:43 - mmengine - INFO - Epoch(val) [20][400/407] eta: 0:00:00 time: 0.108570 data_time: 0.047725 memory: 587 2022/09/21 18:26:20 - mmengine - INFO - Evaluating CocoMetric... 2022/09/21 18:26:34 - mmengine - INFO - Epoch(val) [20][407/407] coco/AP: 0.538055 coco/AP .5: 0.813593 coco/AP .75: 0.590361 coco/AP (M): 0.514809 coco/AP (L): 0.585028 coco/AR: 0.598772 coco/AR .5: 0.862248 coco/AR .75: 0.655542 coco/AR (M): 0.563862 coco/AR (L): 0.648272 2022/09/21 18:26:34 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_10.pth is removed 2022/09/21 18:26:36 - mmengine - INFO - The best checkpoint with 0.5381 coco/AP at 20 epoch is saved to best_coco/AP_epoch_20.pth. 2022/09/21 18:26:58 - mmengine - INFO - Epoch(train) [21][50/293] lr: 5.000000e-04 eta: 6:11:35 time: 0.436939 data_time: 0.095845 memory: 6338 loss_kpt: 0.170875 acc_pose: 0.712218 loss: 0.170875 2022/09/21 18:27:19 - mmengine - INFO - Epoch(train) [21][100/293] lr: 5.000000e-04 eta: 6:11:23 time: 0.418088 data_time: 0.083051 memory: 6338 loss_kpt: 0.172076 acc_pose: 0.667711 loss: 0.172076 2022/09/21 18:27:36 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:27:40 - mmengine - INFO - Epoch(train) [21][150/293] lr: 5.000000e-04 eta: 6:11:12 time: 0.421544 data_time: 0.087772 memory: 6338 loss_kpt: 0.167602 acc_pose: 0.679856 loss: 0.167602 2022/09/21 18:28:01 - mmengine - INFO - Epoch(train) [21][200/293] lr: 5.000000e-04 eta: 6:11:06 time: 0.430765 data_time: 0.088391 memory: 6338 loss_kpt: 0.168036 acc_pose: 0.682593 loss: 0.168036 2022/09/21 18:28:23 - mmengine - INFO - Epoch(train) [21][250/293] lr: 5.000000e-04 eta: 6:11:00 time: 0.433182 data_time: 0.086912 memory: 6338 loss_kpt: 0.169448 acc_pose: 0.668786 loss: 0.169448 2022/09/21 18:28:41 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:28:41 - mmengine - INFO - Saving checkpoint at 21 epochs 2022/09/21 18:29:07 - mmengine - INFO - Epoch(train) [22][50/293] lr: 5.000000e-04 eta: 6:08:18 time: 0.468740 data_time: 0.098754 memory: 6338 loss_kpt: 0.171479 acc_pose: 0.637318 loss: 0.171479 2022/09/21 18:29:30 - mmengine - INFO - Epoch(train) [22][100/293] lr: 5.000000e-04 eta: 6:08:27 time: 0.462836 data_time: 0.087980 memory: 6338 loss_kpt: 0.169899 acc_pose: 0.630012 loss: 0.169899 2022/09/21 18:29:53 - mmengine - INFO - Epoch(train) [22][150/293] lr: 5.000000e-04 eta: 6:08:32 time: 0.457079 data_time: 0.085062 memory: 6338 loss_kpt: 0.170046 acc_pose: 0.634751 loss: 0.170046 2022/09/21 18:30:16 - mmengine - INFO - Epoch(train) [22][200/293] lr: 5.000000e-04 eta: 6:08:34 time: 0.451803 data_time: 0.084482 memory: 6338 loss_kpt: 0.167951 acc_pose: 0.726498 loss: 0.167951 2022/09/21 18:30:38 - mmengine - INFO - Epoch(train) [22][250/293] lr: 5.000000e-04 eta: 6:08:33 time: 0.445194 data_time: 0.086680 memory: 6338 loss_kpt: 0.169679 acc_pose: 0.665142 loss: 0.169679 2022/09/21 18:30:57 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:30:57 - mmengine - INFO - Saving checkpoint at 22 epochs 2022/09/21 18:31:23 - mmengine - INFO - Epoch(train) [23][50/293] lr: 5.000000e-04 eta: 6:06:01 time: 0.476652 data_time: 0.090952 memory: 6338 loss_kpt: 0.167083 acc_pose: 0.642273 loss: 0.167083 2022/09/21 18:31:47 - mmengine - INFO - Epoch(train) [23][100/293] lr: 5.000000e-04 eta: 6:06:11 time: 0.468569 data_time: 0.088341 memory: 6338 loss_kpt: 0.167223 acc_pose: 0.687824 loss: 0.167223 2022/09/21 18:32:10 - mmengine - INFO - Epoch(train) [23][150/293] lr: 5.000000e-04 eta: 6:06:19 time: 0.466780 data_time: 0.086983 memory: 6338 loss_kpt: 0.166373 acc_pose: 0.716822 loss: 0.166373 2022/09/21 18:32:33 - mmengine - INFO - Epoch(train) [23][200/293] lr: 5.000000e-04 eta: 6:06:27 time: 0.468849 data_time: 0.083604 memory: 6338 loss_kpt: 0.167711 acc_pose: 0.661060 loss: 0.167711 2022/09/21 18:32:57 - mmengine - INFO - Epoch(train) [23][250/293] lr: 5.000000e-04 eta: 6:06:35 time: 0.470055 data_time: 0.090362 memory: 6338 loss_kpt: 0.166180 acc_pose: 0.627726 loss: 0.166180 2022/09/21 18:33:16 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:33:16 - mmengine - INFO - Saving checkpoint at 23 epochs 2022/09/21 18:33:42 - mmengine - INFO - Epoch(train) [24][50/293] lr: 5.000000e-04 eta: 6:04:10 time: 0.477367 data_time: 0.100136 memory: 6338 loss_kpt: 0.169476 acc_pose: 0.620364 loss: 0.169476 2022/09/21 18:34:06 - mmengine - INFO - Epoch(train) [24][100/293] lr: 5.000000e-04 eta: 6:04:15 time: 0.462138 data_time: 0.084422 memory: 6338 loss_kpt: 0.168169 acc_pose: 0.713043 loss: 0.168169 2022/09/21 18:34:29 - mmengine - INFO - Epoch(train) [24][150/293] lr: 5.000000e-04 eta: 6:04:23 time: 0.468982 data_time: 0.090063 memory: 6338 loss_kpt: 0.163869 acc_pose: 0.749694 loss: 0.163869 2022/09/21 18:34:52 - mmengine - INFO - Epoch(train) [24][200/293] lr: 5.000000e-04 eta: 6:04:23 time: 0.453242 data_time: 0.082513 memory: 6338 loss_kpt: 0.165593 acc_pose: 0.718099 loss: 0.165593 2022/09/21 18:35:15 - mmengine - INFO - Epoch(train) [24][250/293] lr: 5.000000e-04 eta: 6:04:26 time: 0.459075 data_time: 0.090872 memory: 6338 loss_kpt: 0.166553 acc_pose: 0.669254 loss: 0.166553 2022/09/21 18:35:20 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:35:34 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:35:34 - mmengine - INFO - Saving checkpoint at 24 epochs 2022/09/21 18:36:01 - mmengine - INFO - Epoch(train) [25][50/293] lr: 5.000000e-04 eta: 6:02:04 time: 0.473589 data_time: 0.093936 memory: 6338 loss_kpt: 0.165363 acc_pose: 0.696000 loss: 0.165363 2022/09/21 18:36:24 - mmengine - INFO - Epoch(train) [25][100/293] lr: 5.000000e-04 eta: 6:02:10 time: 0.465646 data_time: 0.089024 memory: 6338 loss_kpt: 0.163046 acc_pose: 0.721818 loss: 0.163046 2022/09/21 18:36:48 - mmengine - INFO - Epoch(train) [25][150/293] lr: 5.000000e-04 eta: 6:02:19 time: 0.476497 data_time: 0.094688 memory: 6338 loss_kpt: 0.163288 acc_pose: 0.685618 loss: 0.163288 2022/09/21 18:37:11 - mmengine - INFO - Epoch(train) [25][200/293] lr: 5.000000e-04 eta: 6:02:26 time: 0.472097 data_time: 0.090497 memory: 6338 loss_kpt: 0.165336 acc_pose: 0.651985 loss: 0.165336 2022/09/21 18:37:35 - mmengine - INFO - Epoch(train) [25][250/293] lr: 5.000000e-04 eta: 6:02:36 time: 0.479713 data_time: 0.089808 memory: 6338 loss_kpt: 0.161659 acc_pose: 0.719022 loss: 0.161659 2022/09/21 18:37:55 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:37:55 - mmengine - INFO - Saving checkpoint at 25 epochs 2022/09/21 18:38:22 - mmengine - INFO - Epoch(train) [26][50/293] lr: 5.000000e-04 eta: 6:00:20 time: 0.479412 data_time: 0.097464 memory: 6338 loss_kpt: 0.162966 acc_pose: 0.630224 loss: 0.162966 2022/09/21 18:38:45 - mmengine - INFO - Epoch(train) [26][100/293] lr: 5.000000e-04 eta: 6:00:26 time: 0.470615 data_time: 0.089568 memory: 6338 loss_kpt: 0.163620 acc_pose: 0.663196 loss: 0.163620 2022/09/21 18:39:09 - mmengine - INFO - Epoch(train) [26][150/293] lr: 5.000000e-04 eta: 6:00:35 time: 0.478835 data_time: 0.090320 memory: 6338 loss_kpt: 0.162253 acc_pose: 0.687865 loss: 0.162253 2022/09/21 18:39:33 - mmengine - INFO - Epoch(train) [26][200/293] lr: 5.000000e-04 eta: 6:00:41 time: 0.473341 data_time: 0.091071 memory: 6338 loss_kpt: 0.162647 acc_pose: 0.709831 loss: 0.162647 2022/09/21 18:39:57 - mmengine - INFO - Epoch(train) [26][250/293] lr: 5.000000e-04 eta: 6:00:50 time: 0.481852 data_time: 0.087820 memory: 6338 loss_kpt: 0.159570 acc_pose: 0.697500 loss: 0.159570 2022/09/21 18:40:17 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:40:17 - mmengine - INFO - Saving checkpoint at 26 epochs 2022/09/21 18:40:44 - mmengine - INFO - Epoch(train) [27][50/293] lr: 5.000000e-04 eta: 5:58:43 time: 0.490154 data_time: 0.101428 memory: 6338 loss_kpt: 0.161828 acc_pose: 0.680519 loss: 0.161828 2022/09/21 18:41:08 - mmengine - INFO - Epoch(train) [27][100/293] lr: 5.000000e-04 eta: 5:58:47 time: 0.469863 data_time: 0.088217 memory: 6338 loss_kpt: 0.161824 acc_pose: 0.726431 loss: 0.161824 2022/09/21 18:41:28 - mmengine - INFO - Epoch(train) [27][150/293] lr: 5.000000e-04 eta: 5:58:31 time: 0.409558 data_time: 0.084602 memory: 6338 loss_kpt: 0.159258 acc_pose: 0.685290 loss: 0.159258 2022/09/21 18:41:48 - mmengine - INFO - Epoch(train) [27][200/293] lr: 5.000000e-04 eta: 5:58:10 time: 0.398673 data_time: 0.083984 memory: 6338 loss_kpt: 0.160287 acc_pose: 0.716682 loss: 0.160287 2022/09/21 18:42:09 - mmengine - INFO - Epoch(train) [27][250/293] lr: 5.000000e-04 eta: 5:57:56 time: 0.417784 data_time: 0.095152 memory: 6338 loss_kpt: 0.157879 acc_pose: 0.683977 loss: 0.157879 2022/09/21 18:42:26 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:42:26 - mmengine - INFO - Saving checkpoint at 27 epochs 2022/09/21 18:42:53 - mmengine - INFO - Epoch(train) [28][50/293] lr: 5.000000e-04 eta: 5:55:52 time: 0.487269 data_time: 0.099717 memory: 6338 loss_kpt: 0.158185 acc_pose: 0.683106 loss: 0.158185 2022/09/21 18:43:11 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:43:17 - mmengine - INFO - Epoch(train) [28][100/293] lr: 5.000000e-04 eta: 5:55:59 time: 0.478246 data_time: 0.093162 memory: 6338 loss_kpt: 0.162721 acc_pose: 0.694073 loss: 0.162721 2022/09/21 18:43:40 - mmengine - INFO - Epoch(train) [28][150/293] lr: 5.000000e-04 eta: 5:56:04 time: 0.475238 data_time: 0.091535 memory: 6338 loss_kpt: 0.158146 acc_pose: 0.661374 loss: 0.158146 2022/09/21 18:44:05 - mmengine - INFO - Epoch(train) [28][200/293] lr: 5.000000e-04 eta: 5:56:12 time: 0.481999 data_time: 0.097301 memory: 6338 loss_kpt: 0.160898 acc_pose: 0.683510 loss: 0.160898 2022/09/21 18:44:29 - mmengine - INFO - Epoch(train) [28][250/293] lr: 5.000000e-04 eta: 5:56:18 time: 0.482421 data_time: 0.093934 memory: 6338 loss_kpt: 0.161823 acc_pose: 0.715058 loss: 0.161823 2022/09/21 18:44:48 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:44:48 - mmengine - INFO - Saving checkpoint at 28 epochs 2022/09/21 18:45:14 - mmengine - INFO - Epoch(train) [29][50/293] lr: 5.000000e-04 eta: 5:54:11 time: 0.466680 data_time: 0.097668 memory: 6338 loss_kpt: 0.158479 acc_pose: 0.721593 loss: 0.158479 2022/09/21 18:45:37 - mmengine - INFO - Epoch(train) [29][100/293] lr: 5.000000e-04 eta: 5:54:10 time: 0.457430 data_time: 0.090108 memory: 6338 loss_kpt: 0.158566 acc_pose: 0.682114 loss: 0.158566 2022/09/21 18:46:01 - mmengine - INFO - Epoch(train) [29][150/293] lr: 5.000000e-04 eta: 5:54:14 time: 0.474123 data_time: 0.084781 memory: 6338 loss_kpt: 0.163168 acc_pose: 0.675374 loss: 0.163168 2022/09/21 18:46:24 - mmengine - INFO - Epoch(train) [29][200/293] lr: 5.000000e-04 eta: 5:54:12 time: 0.455833 data_time: 0.082546 memory: 6338 loss_kpt: 0.157853 acc_pose: 0.660881 loss: 0.157853 2022/09/21 18:46:47 - mmengine - INFO - Epoch(train) [29][250/293] lr: 5.000000e-04 eta: 5:54:10 time: 0.459187 data_time: 0.088112 memory: 6338 loss_kpt: 0.157781 acc_pose: 0.712825 loss: 0.157781 2022/09/21 18:47:06 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:47:06 - mmengine - INFO - Saving checkpoint at 29 epochs 2022/09/21 18:47:33 - mmengine - INFO - Epoch(train) [30][50/293] lr: 5.000000e-04 eta: 5:52:14 time: 0.489583 data_time: 0.100298 memory: 6338 loss_kpt: 0.153081 acc_pose: 0.728402 loss: 0.153081 2022/09/21 18:47:57 - mmengine - INFO - Epoch(train) [30][100/293] lr: 5.000000e-04 eta: 5:52:18 time: 0.476877 data_time: 0.090446 memory: 6338 loss_kpt: 0.156436 acc_pose: 0.711866 loss: 0.156436 2022/09/21 18:48:20 - mmengine - INFO - Epoch(train) [30][150/293] lr: 5.000000e-04 eta: 5:52:20 time: 0.470910 data_time: 0.084987 memory: 6338 loss_kpt: 0.157514 acc_pose: 0.679027 loss: 0.157514 2022/09/21 18:48:44 - mmengine - INFO - Epoch(train) [30][200/293] lr: 5.000000e-04 eta: 5:52:23 time: 0.474834 data_time: 0.093767 memory: 6338 loss_kpt: 0.159741 acc_pose: 0.697202 loss: 0.159741 2022/09/21 18:49:09 - mmengine - INFO - Epoch(train) [30][250/293] lr: 5.000000e-04 eta: 5:52:29 time: 0.488195 data_time: 0.093941 memory: 6338 loss_kpt: 0.158575 acc_pose: 0.738582 loss: 0.158575 2022/09/21 18:49:29 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:49:29 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/09/21 18:49:38 - mmengine - INFO - Epoch(val) [30][50/407] eta: 0:00:43 time: 0.121167 data_time: 0.058761 memory: 6338 2022/09/21 18:49:44 - mmengine - INFO - Epoch(val) [30][100/407] eta: 0:00:34 time: 0.112948 data_time: 0.051553 memory: 587 2022/09/21 18:49:50 - mmengine - INFO - Epoch(val) [30][150/407] eta: 0:00:32 time: 0.125557 data_time: 0.062847 memory: 587 2022/09/21 18:49:56 - mmengine - INFO - Epoch(val) [30][200/407] eta: 0:00:23 time: 0.114864 data_time: 0.053770 memory: 587 2022/09/21 18:50:01 - mmengine - INFO - Epoch(val) [30][250/407] eta: 0:00:18 time: 0.115079 data_time: 0.054004 memory: 587 2022/09/21 18:50:07 - mmengine - INFO - Epoch(val) [30][300/407] eta: 0:00:12 time: 0.117832 data_time: 0.056999 memory: 587 2022/09/21 18:50:13 - mmengine - INFO - Epoch(val) [30][350/407] eta: 0:00:06 time: 0.119432 data_time: 0.059166 memory: 587 2022/09/21 18:50:18 - mmengine - INFO - Epoch(val) [30][400/407] eta: 0:00:00 time: 0.101842 data_time: 0.043603 memory: 587 2022/09/21 18:50:54 - mmengine - INFO - Evaluating CocoMetric... 2022/09/21 18:51:09 - mmengine - INFO - Epoch(val) [30][407/407] coco/AP: 0.580802 coco/AP .5: 0.836888 coco/AP .75: 0.646663 coco/AP (M): 0.553859 coco/AP (L): 0.633800 coco/AR: 0.642317 coco/AR .5: 0.884446 coco/AR .75: 0.706234 coco/AR (M): 0.603414 coco/AR (L): 0.697696 2022/09/21 18:51:09 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_20.pth is removed 2022/09/21 18:51:11 - mmengine - INFO - The best checkpoint with 0.5808 coco/AP at 30 epoch is saved to best_coco/AP_epoch_30.pth. 2022/09/21 18:51:35 - mmengine - INFO - Epoch(train) [31][50/293] lr: 5.000000e-04 eta: 5:50:33 time: 0.481412 data_time: 0.103972 memory: 6338 loss_kpt: 0.158228 acc_pose: 0.657670 loss: 0.158228 2022/09/21 18:51:59 - mmengine - INFO - Epoch(train) [31][100/293] lr: 5.000000e-04 eta: 5:50:36 time: 0.474518 data_time: 0.090924 memory: 6338 loss_kpt: 0.157083 acc_pose: 0.703753 loss: 0.157083 2022/09/21 18:52:22 - mmengine - INFO - Epoch(train) [31][150/293] lr: 5.000000e-04 eta: 5:50:38 time: 0.474985 data_time: 0.088810 memory: 6338 loss_kpt: 0.156490 acc_pose: 0.712102 loss: 0.156490 2022/09/21 18:52:46 - mmengine - INFO - Epoch(train) [31][200/293] lr: 5.000000e-04 eta: 5:50:41 time: 0.479306 data_time: 0.097911 memory: 6338 loss_kpt: 0.153938 acc_pose: 0.683688 loss: 0.153938 2022/09/21 18:52:51 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:53:10 - mmengine - INFO - Epoch(train) [31][250/293] lr: 5.000000e-04 eta: 5:50:42 time: 0.472730 data_time: 0.087464 memory: 6338 loss_kpt: 0.156308 acc_pose: 0.762512 loss: 0.156308 2022/09/21 18:53:30 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:53:30 - mmengine - INFO - Saving checkpoint at 31 epochs 2022/09/21 18:53:57 - mmengine - INFO - Epoch(train) [32][50/293] lr: 5.000000e-04 eta: 5:48:53 time: 0.495980 data_time: 0.095161 memory: 6338 loss_kpt: 0.155362 acc_pose: 0.759617 loss: 0.155362 2022/09/21 18:54:21 - mmengine - INFO - Epoch(train) [32][100/293] lr: 5.000000e-04 eta: 5:48:53 time: 0.469506 data_time: 0.085191 memory: 6338 loss_kpt: 0.157282 acc_pose: 0.684068 loss: 0.157282 2022/09/21 18:54:44 - mmengine - INFO - Epoch(train) [32][150/293] lr: 5.000000e-04 eta: 5:48:53 time: 0.468483 data_time: 0.092305 memory: 6338 loss_kpt: 0.155063 acc_pose: 0.663439 loss: 0.155063 2022/09/21 18:55:08 - mmengine - INFO - Epoch(train) [32][200/293] lr: 5.000000e-04 eta: 5:48:54 time: 0.475946 data_time: 0.085582 memory: 6338 loss_kpt: 0.157846 acc_pose: 0.677688 loss: 0.157846 2022/09/21 18:55:32 - mmengine - INFO - Epoch(train) [32][250/293] lr: 5.000000e-04 eta: 5:48:55 time: 0.475458 data_time: 0.087725 memory: 6338 loss_kpt: 0.155779 acc_pose: 0.720226 loss: 0.155779 2022/09/21 18:55:52 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:55:52 - mmengine - INFO - Saving checkpoint at 32 epochs 2022/09/21 18:56:18 - mmengine - INFO - Epoch(train) [33][50/293] lr: 5.000000e-04 eta: 5:46:57 time: 0.454839 data_time: 0.100091 memory: 6338 loss_kpt: 0.156974 acc_pose: 0.674986 loss: 0.156974 2022/09/21 18:56:40 - mmengine - INFO - Epoch(train) [33][100/293] lr: 5.000000e-04 eta: 5:46:48 time: 0.441046 data_time: 0.083979 memory: 6338 loss_kpt: 0.154725 acc_pose: 0.684601 loss: 0.154725 2022/09/21 18:57:02 - mmengine - INFO - Epoch(train) [33][150/293] lr: 5.000000e-04 eta: 5:46:43 time: 0.452792 data_time: 0.088452 memory: 6338 loss_kpt: 0.156623 acc_pose: 0.727991 loss: 0.156623 2022/09/21 18:57:24 - mmengine - INFO - Epoch(train) [33][200/293] lr: 5.000000e-04 eta: 5:46:33 time: 0.437337 data_time: 0.080995 memory: 6338 loss_kpt: 0.153356 acc_pose: 0.672473 loss: 0.153356 2022/09/21 18:57:46 - mmengine - INFO - Epoch(train) [33][250/293] lr: 5.000000e-04 eta: 5:46:24 time: 0.440103 data_time: 0.086550 memory: 6338 loss_kpt: 0.155284 acc_pose: 0.747378 loss: 0.155284 2022/09/21 18:58:05 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 18:58:05 - mmengine - INFO - Saving checkpoint at 33 epochs 2022/09/21 18:58:32 - mmengine - INFO - Epoch(train) [34][50/293] lr: 5.000000e-04 eta: 5:44:36 time: 0.478792 data_time: 0.098595 memory: 6338 loss_kpt: 0.154175 acc_pose: 0.707683 loss: 0.154175 2022/09/21 18:58:56 - mmengine - INFO - Epoch(train) [34][100/293] lr: 5.000000e-04 eta: 5:44:39 time: 0.486147 data_time: 0.087172 memory: 6338 loss_kpt: 0.153114 acc_pose: 0.685259 loss: 0.153114 2022/09/21 18:59:20 - mmengine - INFO - Epoch(train) [34][150/293] lr: 5.000000e-04 eta: 5:44:40 time: 0.479686 data_time: 0.092768 memory: 6338 loss_kpt: 0.153417 acc_pose: 0.646362 loss: 0.153417 2022/09/21 18:59:44 - mmengine - INFO - Epoch(train) [34][200/293] lr: 5.000000e-04 eta: 5:44:40 time: 0.476115 data_time: 0.082391 memory: 6338 loss_kpt: 0.153509 acc_pose: 0.798041 loss: 0.153509 2022/09/21 19:00:09 - mmengine - INFO - Epoch(train) [34][250/293] lr: 5.000000e-04 eta: 5:44:44 time: 0.494318 data_time: 0.095473 memory: 6338 loss_kpt: 0.154269 acc_pose: 0.748344 loss: 0.154269 2022/09/21 19:00:29 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:00:29 - mmengine - INFO - Saving checkpoint at 34 epochs 2022/09/21 19:00:49 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:00:54 - mmengine - INFO - Epoch(train) [35][50/293] lr: 5.000000e-04 eta: 5:42:51 time: 0.449686 data_time: 0.088751 memory: 6338 loss_kpt: 0.152062 acc_pose: 0.687795 loss: 0.152062 2022/09/21 19:01:17 - mmengine - INFO - Epoch(train) [35][100/293] lr: 5.000000e-04 eta: 5:42:45 time: 0.453973 data_time: 0.089498 memory: 6338 loss_kpt: 0.156338 acc_pose: 0.674390 loss: 0.156338 2022/09/21 19:01:39 - mmengine - INFO - Epoch(train) [35][150/293] lr: 5.000000e-04 eta: 5:42:35 time: 0.438163 data_time: 0.090607 memory: 6338 loss_kpt: 0.154734 acc_pose: 0.633028 loss: 0.154734 2022/09/21 19:02:00 - mmengine - INFO - Epoch(train) [35][200/293] lr: 5.000000e-04 eta: 5:42:25 time: 0.439404 data_time: 0.091509 memory: 6338 loss_kpt: 0.152555 acc_pose: 0.583816 loss: 0.152555 2022/09/21 19:02:22 - mmengine - INFO - Epoch(train) [35][250/293] lr: 5.000000e-04 eta: 5:42:14 time: 0.438475 data_time: 0.087003 memory: 6338 loss_kpt: 0.152188 acc_pose: 0.746022 loss: 0.152188 2022/09/21 19:02:41 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:02:41 - mmengine - INFO - Saving checkpoint at 35 epochs 2022/09/21 19:03:08 - mmengine - INFO - Epoch(train) [36][50/293] lr: 5.000000e-04 eta: 5:40:29 time: 0.471276 data_time: 0.098947 memory: 6338 loss_kpt: 0.150583 acc_pose: 0.646027 loss: 0.150583 2022/09/21 19:03:30 - mmengine - INFO - Epoch(train) [36][100/293] lr: 5.000000e-04 eta: 5:40:23 time: 0.455049 data_time: 0.085400 memory: 6338 loss_kpt: 0.154377 acc_pose: 0.706696 loss: 0.154377 2022/09/21 19:03:53 - mmengine - INFO - Epoch(train) [36][150/293] lr: 5.000000e-04 eta: 5:40:18 time: 0.459180 data_time: 0.094026 memory: 6338 loss_kpt: 0.152842 acc_pose: 0.773265 loss: 0.152842 2022/09/21 19:04:16 - mmengine - INFO - Epoch(train) [36][200/293] lr: 5.000000e-04 eta: 5:40:11 time: 0.452816 data_time: 0.086386 memory: 6338 loss_kpt: 0.151962 acc_pose: 0.734662 loss: 0.151962 2022/09/21 19:04:39 - mmengine - INFO - Epoch(train) [36][250/293] lr: 5.000000e-04 eta: 5:40:06 time: 0.460079 data_time: 0.094518 memory: 6338 loss_kpt: 0.152112 acc_pose: 0.694915 loss: 0.152112 2022/09/21 19:04:58 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:04:58 - mmengine - INFO - Saving checkpoint at 36 epochs 2022/09/21 19:05:25 - mmengine - INFO - Epoch(train) [37][50/293] lr: 5.000000e-04 eta: 5:38:26 time: 0.481618 data_time: 0.095463 memory: 6338 loss_kpt: 0.151315 acc_pose: 0.649123 loss: 0.151315 2022/09/21 19:05:48 - mmengine - INFO - Epoch(train) [37][100/293] lr: 5.000000e-04 eta: 5:38:22 time: 0.466732 data_time: 0.086782 memory: 6338 loss_kpt: 0.153232 acc_pose: 0.654711 loss: 0.153232 2022/09/21 19:06:12 - mmengine - INFO - Epoch(train) [37][150/293] lr: 5.000000e-04 eta: 5:38:22 time: 0.481469 data_time: 0.088263 memory: 6338 loss_kpt: 0.149072 acc_pose: 0.699992 loss: 0.149072 2022/09/21 19:06:36 - mmengine - INFO - Epoch(train) [37][200/293] lr: 5.000000e-04 eta: 5:38:19 time: 0.473785 data_time: 0.092934 memory: 6338 loss_kpt: 0.148187 acc_pose: 0.703000 loss: 0.148187 2022/09/21 19:07:00 - mmengine - INFO - Epoch(train) [37][250/293] lr: 5.000000e-04 eta: 5:38:17 time: 0.473692 data_time: 0.088881 memory: 6338 loss_kpt: 0.149610 acc_pose: 0.748628 loss: 0.149610 2022/09/21 19:07:20 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:07:20 - mmengine - INFO - Saving checkpoint at 37 epochs 2022/09/21 19:07:47 - mmengine - INFO - Epoch(train) [38][50/293] lr: 5.000000e-04 eta: 5:36:41 time: 0.493950 data_time: 0.102776 memory: 6338 loss_kpt: 0.148702 acc_pose: 0.707981 loss: 0.148702 2022/09/21 19:08:11 - mmengine - INFO - Epoch(train) [38][100/293] lr: 5.000000e-04 eta: 5:36:38 time: 0.470805 data_time: 0.095923 memory: 6338 loss_kpt: 0.154246 acc_pose: 0.743955 loss: 0.154246 2022/09/21 19:08:35 - mmengine - INFO - Epoch(train) [38][150/293] lr: 5.000000e-04 eta: 5:36:37 time: 0.483342 data_time: 0.087197 memory: 6338 loss_kpt: 0.152336 acc_pose: 0.745076 loss: 0.152336 2022/09/21 19:08:39 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:08:58 - mmengine - INFO - Epoch(train) [38][200/293] lr: 5.000000e-04 eta: 5:36:32 time: 0.462923 data_time: 0.085774 memory: 6338 loss_kpt: 0.146858 acc_pose: 0.742130 loss: 0.146858 2022/09/21 19:09:22 - mmengine - INFO - Epoch(train) [38][250/293] lr: 5.000000e-04 eta: 5:36:29 time: 0.477518 data_time: 0.087695 memory: 6338 loss_kpt: 0.149661 acc_pose: 0.719895 loss: 0.149661 2022/09/21 19:09:42 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:09:42 - mmengine - INFO - Saving checkpoint at 38 epochs 2022/09/21 19:10:10 - mmengine - INFO - Epoch(train) [39][50/293] lr: 5.000000e-04 eta: 5:34:56 time: 0.496316 data_time: 0.099938 memory: 6338 loss_kpt: 0.148111 acc_pose: 0.749080 loss: 0.148111 2022/09/21 19:10:33 - mmengine - INFO - Epoch(train) [39][100/293] lr: 5.000000e-04 eta: 5:34:53 time: 0.471503 data_time: 0.087212 memory: 6338 loss_kpt: 0.148479 acc_pose: 0.740391 loss: 0.148479 2022/09/21 19:10:58 - mmengine - INFO - Epoch(train) [39][150/293] lr: 5.000000e-04 eta: 5:34:52 time: 0.486989 data_time: 0.092765 memory: 6338 loss_kpt: 0.152898 acc_pose: 0.744659 loss: 0.152898 2022/09/21 19:11:21 - mmengine - INFO - Epoch(train) [39][200/293] lr: 5.000000e-04 eta: 5:34:48 time: 0.472033 data_time: 0.091580 memory: 6338 loss_kpt: 0.150822 acc_pose: 0.705594 loss: 0.150822 2022/09/21 19:11:46 - mmengine - INFO - Epoch(train) [39][250/293] lr: 5.000000e-04 eta: 5:34:48 time: 0.489267 data_time: 0.091437 memory: 6338 loss_kpt: 0.153184 acc_pose: 0.725962 loss: 0.153184 2022/09/21 19:12:06 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:12:06 - mmengine - INFO - Saving checkpoint at 39 epochs 2022/09/21 19:12:32 - mmengine - INFO - Epoch(train) [40][50/293] lr: 5.000000e-04 eta: 5:33:11 time: 0.474877 data_time: 0.103303 memory: 6338 loss_kpt: 0.149237 acc_pose: 0.720376 loss: 0.149237 2022/09/21 19:12:55 - mmengine - INFO - Epoch(train) [40][100/293] lr: 5.000000e-04 eta: 5:33:02 time: 0.449002 data_time: 0.087501 memory: 6338 loss_kpt: 0.150207 acc_pose: 0.705591 loss: 0.150207 2022/09/21 19:13:18 - mmengine - INFO - Epoch(train) [40][150/293] lr: 5.000000e-04 eta: 5:32:57 time: 0.468819 data_time: 0.092221 memory: 6338 loss_kpt: 0.151205 acc_pose: 0.667141 loss: 0.151205 2022/09/21 19:13:41 - mmengine - INFO - Epoch(train) [40][200/293] lr: 5.000000e-04 eta: 5:32:49 time: 0.456939 data_time: 0.088256 memory: 6338 loss_kpt: 0.150625 acc_pose: 0.686189 loss: 0.150625 2022/09/21 19:14:04 - mmengine - INFO - Epoch(train) [40][250/293] lr: 5.000000e-04 eta: 5:32:43 time: 0.464892 data_time: 0.090953 memory: 6338 loss_kpt: 0.150531 acc_pose: 0.681281 loss: 0.150531 2022/09/21 19:14:24 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:14:24 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/09/21 19:14:32 - mmengine - INFO - Epoch(val) [40][50/407] eta: 0:00:41 time: 0.117352 data_time: 0.055052 memory: 6338 2022/09/21 19:14:38 - mmengine - INFO - Epoch(val) [40][100/407] eta: 0:00:36 time: 0.119747 data_time: 0.058589 memory: 587 2022/09/21 19:14:44 - mmengine - INFO - Epoch(val) [40][150/407] eta: 0:00:29 time: 0.113813 data_time: 0.048234 memory: 587 2022/09/21 19:14:50 - mmengine - INFO - Epoch(val) [40][200/407] eta: 0:00:23 time: 0.114172 data_time: 0.053410 memory: 587 2022/09/21 19:14:55 - mmengine - INFO - Epoch(val) [40][250/407] eta: 0:00:18 time: 0.117386 data_time: 0.055867 memory: 587 2022/09/21 19:15:01 - mmengine - INFO - Epoch(val) [40][300/407] eta: 0:00:12 time: 0.115282 data_time: 0.053100 memory: 587 2022/09/21 19:15:07 - mmengine - INFO - Epoch(val) [40][350/407] eta: 0:00:06 time: 0.113166 data_time: 0.051339 memory: 587 2022/09/21 19:15:12 - mmengine - INFO - Epoch(val) [40][400/407] eta: 0:00:00 time: 0.105857 data_time: 0.046204 memory: 587 2022/09/21 19:15:47 - mmengine - INFO - Evaluating CocoMetric... 2022/09/21 19:16:01 - mmengine - INFO - Epoch(val) [40][407/407] coco/AP: 0.603839 coco/AP .5: 0.849428 coco/AP .75: 0.680357 coco/AP (M): 0.575943 coco/AP (L): 0.662265 coco/AR: 0.666247 coco/AR .5: 0.896411 coco/AR .75: 0.736146 coco/AR (M): 0.626687 coco/AR (L): 0.722445 2022/09/21 19:16:01 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_30.pth is removed 2022/09/21 19:16:04 - mmengine - INFO - The best checkpoint with 0.6038 coco/AP at 40 epoch is saved to best_coco/AP_epoch_40.pth. 2022/09/21 19:16:28 - mmengine - INFO - Epoch(train) [41][50/293] lr: 5.000000e-04 eta: 5:31:10 time: 0.477717 data_time: 0.102366 memory: 6338 loss_kpt: 0.150933 acc_pose: 0.690642 loss: 0.150933 2022/09/21 19:16:50 - mmengine - INFO - Epoch(train) [41][100/293] lr: 5.000000e-04 eta: 5:31:01 time: 0.452076 data_time: 0.090509 memory: 6338 loss_kpt: 0.149841 acc_pose: 0.735696 loss: 0.149841 2022/09/21 19:17:14 - mmengine - INFO - Epoch(train) [41][150/293] lr: 5.000000e-04 eta: 5:30:55 time: 0.470146 data_time: 0.088264 memory: 6338 loss_kpt: 0.149998 acc_pose: 0.677406 loss: 0.149998 2022/09/21 19:17:37 - mmengine - INFO - Epoch(train) [41][200/293] lr: 5.000000e-04 eta: 5:30:49 time: 0.463472 data_time: 0.094533 memory: 6338 loss_kpt: 0.150729 acc_pose: 0.698774 loss: 0.150729 2022/09/21 19:18:00 - mmengine - INFO - Epoch(train) [41][250/293] lr: 5.000000e-04 eta: 5:30:43 time: 0.467837 data_time: 0.085336 memory: 6338 loss_kpt: 0.147527 acc_pose: 0.736272 loss: 0.147527 2022/09/21 19:18:14 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:18:20 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:18:20 - mmengine - INFO - Saving checkpoint at 41 epochs 2022/09/21 19:18:45 - mmengine - INFO - Epoch(train) [42][50/293] lr: 5.000000e-04 eta: 5:29:07 time: 0.459098 data_time: 0.095646 memory: 6338 loss_kpt: 0.148676 acc_pose: 0.692558 loss: 0.148676 2022/09/21 19:19:08 - mmengine - INFO - Epoch(train) [42][100/293] lr: 5.000000e-04 eta: 5:28:59 time: 0.459340 data_time: 0.083383 memory: 6338 loss_kpt: 0.149977 acc_pose: 0.737223 loss: 0.149977 2022/09/21 19:19:32 - mmengine - INFO - Epoch(train) [42][150/293] lr: 5.000000e-04 eta: 5:28:54 time: 0.472217 data_time: 0.089717 memory: 6338 loss_kpt: 0.146407 acc_pose: 0.786078 loss: 0.146407 2022/09/21 19:19:55 - mmengine - INFO - Epoch(train) [42][200/293] lr: 5.000000e-04 eta: 5:28:47 time: 0.462757 data_time: 0.085512 memory: 6338 loss_kpt: 0.146372 acc_pose: 0.769813 loss: 0.146372 2022/09/21 19:20:19 - mmengine - INFO - Epoch(train) [42][250/293] lr: 5.000000e-04 eta: 5:28:41 time: 0.469404 data_time: 0.083243 memory: 6338 loss_kpt: 0.148710 acc_pose: 0.731642 loss: 0.148710 2022/09/21 19:20:38 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:20:38 - mmengine - INFO - Saving checkpoint at 42 epochs 2022/09/21 19:21:05 - mmengine - INFO - Epoch(train) [43][50/293] lr: 5.000000e-04 eta: 5:27:14 time: 0.494387 data_time: 0.102059 memory: 6338 loss_kpt: 0.147573 acc_pose: 0.691818 loss: 0.147573 2022/09/21 19:21:29 - mmengine - INFO - Epoch(train) [43][100/293] lr: 5.000000e-04 eta: 5:27:09 time: 0.473924 data_time: 0.085531 memory: 6338 loss_kpt: 0.146135 acc_pose: 0.725004 loss: 0.146135 2022/09/21 19:21:53 - mmengine - INFO - Epoch(train) [43][150/293] lr: 5.000000e-04 eta: 5:27:06 time: 0.489781 data_time: 0.089155 memory: 6338 loss_kpt: 0.149396 acc_pose: 0.744361 loss: 0.149396 2022/09/21 19:22:17 - mmengine - INFO - Epoch(train) [43][200/293] lr: 5.000000e-04 eta: 5:27:01 time: 0.474026 data_time: 0.085685 memory: 6338 loss_kpt: 0.146710 acc_pose: 0.722032 loss: 0.146710 2022/09/21 19:22:42 - mmengine - INFO - Epoch(train) [43][250/293] lr: 5.000000e-04 eta: 5:26:58 time: 0.488694 data_time: 0.089896 memory: 6338 loss_kpt: 0.144985 acc_pose: 0.745948 loss: 0.144985 2022/09/21 19:23:02 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:23:02 - mmengine - INFO - Saving checkpoint at 43 epochs 2022/09/21 19:23:28 - mmengine - INFO - Epoch(train) [44][50/293] lr: 5.000000e-04 eta: 5:25:28 time: 0.473193 data_time: 0.096925 memory: 6338 loss_kpt: 0.144147 acc_pose: 0.734536 loss: 0.144147 2022/09/21 19:23:52 - mmengine - INFO - Epoch(train) [44][100/293] lr: 5.000000e-04 eta: 5:25:25 time: 0.485465 data_time: 0.088208 memory: 6338 loss_kpt: 0.150311 acc_pose: 0.686935 loss: 0.150311 2022/09/21 19:24:16 - mmengine - INFO - Epoch(train) [44][150/293] lr: 5.000000e-04 eta: 5:25:21 time: 0.483631 data_time: 0.090659 memory: 6338 loss_kpt: 0.146811 acc_pose: 0.779441 loss: 0.146811 2022/09/21 19:24:40 - mmengine - INFO - Epoch(train) [44][200/293] lr: 5.000000e-04 eta: 5:25:15 time: 0.474265 data_time: 0.090361 memory: 6338 loss_kpt: 0.147554 acc_pose: 0.708401 loss: 0.147554 2022/09/21 19:25:04 - mmengine - INFO - Epoch(train) [44][250/293] lr: 5.000000e-04 eta: 5:25:09 time: 0.476278 data_time: 0.085185 memory: 6338 loss_kpt: 0.148545 acc_pose: 0.722564 loss: 0.148545 2022/09/21 19:25:24 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:25:24 - mmengine - INFO - Saving checkpoint at 44 epochs 2022/09/21 19:25:50 - mmengine - INFO - Epoch(train) [45][50/293] lr: 5.000000e-04 eta: 5:23:41 time: 0.472066 data_time: 0.095083 memory: 6338 loss_kpt: 0.144614 acc_pose: 0.712199 loss: 0.144614 2022/09/21 19:26:13 - mmengine - INFO - Epoch(train) [45][100/293] lr: 5.000000e-04 eta: 5:23:32 time: 0.458971 data_time: 0.092275 memory: 6338 loss_kpt: 0.147706 acc_pose: 0.661118 loss: 0.147706 2022/09/21 19:26:17 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:26:36 - mmengine - INFO - Epoch(train) [45][150/293] lr: 5.000000e-04 eta: 5:23:24 time: 0.466584 data_time: 0.091744 memory: 6338 loss_kpt: 0.146526 acc_pose: 0.696684 loss: 0.146526 2022/09/21 19:27:00 - mmengine - INFO - Epoch(train) [45][200/293] lr: 5.000000e-04 eta: 5:23:16 time: 0.463549 data_time: 0.093623 memory: 6338 loss_kpt: 0.144808 acc_pose: 0.721854 loss: 0.144808 2022/09/21 19:27:23 - mmengine - INFO - Epoch(train) [45][250/293] lr: 5.000000e-04 eta: 5:23:07 time: 0.462011 data_time: 0.087016 memory: 6338 loss_kpt: 0.145533 acc_pose: 0.665177 loss: 0.145533 2022/09/21 19:27:42 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:27:42 - mmengine - INFO - Saving checkpoint at 45 epochs 2022/09/21 19:28:09 - mmengine - INFO - Epoch(train) [46][50/293] lr: 5.000000e-04 eta: 5:21:41 time: 0.474846 data_time: 0.102777 memory: 6338 loss_kpt: 0.143210 acc_pose: 0.745426 loss: 0.143210 2022/09/21 19:28:33 - mmengine - INFO - Epoch(train) [46][100/293] lr: 5.000000e-04 eta: 5:21:35 time: 0.481187 data_time: 0.088322 memory: 6338 loss_kpt: 0.147591 acc_pose: 0.734746 loss: 0.147591 2022/09/21 19:28:57 - mmengine - INFO - Epoch(train) [46][150/293] lr: 5.000000e-04 eta: 5:21:30 time: 0.481999 data_time: 0.092744 memory: 6338 loss_kpt: 0.144976 acc_pose: 0.736428 loss: 0.144976 2022/09/21 19:29:21 - mmengine - INFO - Epoch(train) [46][200/293] lr: 5.000000e-04 eta: 5:21:23 time: 0.473453 data_time: 0.094425 memory: 6338 loss_kpt: 0.147269 acc_pose: 0.733351 loss: 0.147269 2022/09/21 19:29:45 - mmengine - INFO - Epoch(train) [46][250/293] lr: 5.000000e-04 eta: 5:21:19 time: 0.489475 data_time: 0.097024 memory: 6338 loss_kpt: 0.146202 acc_pose: 0.728974 loss: 0.146202 2022/09/21 19:30:05 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:30:05 - mmengine - INFO - Saving checkpoint at 46 epochs 2022/09/21 19:30:32 - mmengine - INFO - Epoch(train) [47][50/293] lr: 5.000000e-04 eta: 5:19:57 time: 0.491908 data_time: 0.099312 memory: 6338 loss_kpt: 0.145715 acc_pose: 0.671087 loss: 0.145715 2022/09/21 19:30:56 - mmengine - INFO - Epoch(train) [47][100/293] lr: 5.000000e-04 eta: 5:19:50 time: 0.472930 data_time: 0.085176 memory: 6338 loss_kpt: 0.146632 acc_pose: 0.665180 loss: 0.146632 2022/09/21 19:31:20 - mmengine - INFO - Epoch(train) [47][150/293] lr: 5.000000e-04 eta: 5:19:45 time: 0.488759 data_time: 0.090822 memory: 6338 loss_kpt: 0.144549 acc_pose: 0.799910 loss: 0.144549 2022/09/21 19:31:44 - mmengine - INFO - Epoch(train) [47][200/293] lr: 5.000000e-04 eta: 5:19:37 time: 0.467438 data_time: 0.093434 memory: 6338 loss_kpt: 0.144995 acc_pose: 0.777610 loss: 0.144995 2022/09/21 19:32:07 - mmengine - INFO - Epoch(train) [47][250/293] lr: 5.000000e-04 eta: 5:19:27 time: 0.460088 data_time: 0.086456 memory: 6338 loss_kpt: 0.146245 acc_pose: 0.751381 loss: 0.146245 2022/09/21 19:32:26 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:32:26 - mmengine - INFO - Saving checkpoint at 47 epochs 2022/09/21 19:32:53 - mmengine - INFO - Epoch(train) [48][50/293] lr: 5.000000e-04 eta: 5:18:06 time: 0.490197 data_time: 0.102318 memory: 6338 loss_kpt: 0.143825 acc_pose: 0.767286 loss: 0.143825 2022/09/21 19:33:17 - mmengine - INFO - Epoch(train) [48][100/293] lr: 5.000000e-04 eta: 5:17:59 time: 0.478030 data_time: 0.090478 memory: 6338 loss_kpt: 0.144038 acc_pose: 0.742797 loss: 0.144038 2022/09/21 19:33:42 - mmengine - INFO - Epoch(train) [48][150/293] lr: 5.000000e-04 eta: 5:17:54 time: 0.484436 data_time: 0.097491 memory: 6338 loss_kpt: 0.142288 acc_pose: 0.793766 loss: 0.142288 2022/09/21 19:34:06 - mmengine - INFO - Epoch(train) [48][200/293] lr: 5.000000e-04 eta: 5:17:47 time: 0.481052 data_time: 0.093733 memory: 6338 loss_kpt: 0.142565 acc_pose: 0.708050 loss: 0.142565 2022/09/21 19:34:20 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:34:30 - mmengine - INFO - Epoch(train) [48][250/293] lr: 5.000000e-04 eta: 5:17:41 time: 0.483496 data_time: 0.092045 memory: 6338 loss_kpt: 0.145584 acc_pose: 0.785174 loss: 0.145584 2022/09/21 19:34:50 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:34:50 - mmengine - INFO - Saving checkpoint at 48 epochs 2022/09/21 19:35:16 - mmengine - INFO - Epoch(train) [49][50/293] lr: 5.000000e-04 eta: 5:16:18 time: 0.473126 data_time: 0.096096 memory: 6338 loss_kpt: 0.145898 acc_pose: 0.727481 loss: 0.145898 2022/09/21 19:35:39 - mmengine - INFO - Epoch(train) [49][100/293] lr: 5.000000e-04 eta: 5:16:08 time: 0.461745 data_time: 0.086907 memory: 6338 loss_kpt: 0.144887 acc_pose: 0.761645 loss: 0.144887 2022/09/21 19:36:03 - mmengine - INFO - Epoch(train) [49][150/293] lr: 5.000000e-04 eta: 5:16:01 time: 0.473699 data_time: 0.090523 memory: 6338 loss_kpt: 0.146120 acc_pose: 0.728865 loss: 0.146120 2022/09/21 19:36:26 - mmengine - INFO - Epoch(train) [49][200/293] lr: 5.000000e-04 eta: 5:15:51 time: 0.461099 data_time: 0.088359 memory: 6338 loss_kpt: 0.143413 acc_pose: 0.748011 loss: 0.143413 2022/09/21 19:36:50 - mmengine - INFO - Epoch(train) [49][250/293] lr: 5.000000e-04 eta: 5:15:41 time: 0.467257 data_time: 0.093484 memory: 6338 loss_kpt: 0.144045 acc_pose: 0.771378 loss: 0.144045 2022/09/21 19:37:09 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:37:09 - mmengine - INFO - Saving checkpoint at 49 epochs 2022/09/21 19:37:36 - mmengine - INFO - Epoch(train) [50][50/293] lr: 5.000000e-04 eta: 5:14:20 time: 0.474534 data_time: 0.099847 memory: 6338 loss_kpt: 0.144162 acc_pose: 0.721597 loss: 0.144162 2022/09/21 19:37:58 - mmengine - INFO - Epoch(train) [50][100/293] lr: 5.000000e-04 eta: 5:14:09 time: 0.457957 data_time: 0.090801 memory: 6338 loss_kpt: 0.143021 acc_pose: 0.734754 loss: 0.143021 2022/09/21 19:38:22 - mmengine - INFO - Epoch(train) [50][150/293] lr: 5.000000e-04 eta: 5:14:01 time: 0.471219 data_time: 0.091008 memory: 6338 loss_kpt: 0.142349 acc_pose: 0.789156 loss: 0.142349 2022/09/21 19:38:45 - mmengine - INFO - Epoch(train) [50][200/293] lr: 5.000000e-04 eta: 5:13:50 time: 0.457426 data_time: 0.091938 memory: 6338 loss_kpt: 0.142705 acc_pose: 0.765850 loss: 0.142705 2022/09/21 19:39:08 - mmengine - INFO - Epoch(train) [50][250/293] lr: 5.000000e-04 eta: 5:13:39 time: 0.459680 data_time: 0.093660 memory: 6338 loss_kpt: 0.142267 acc_pose: 0.720879 loss: 0.142267 2022/09/21 19:39:27 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:39:27 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/09/21 19:39:36 - mmengine - INFO - Epoch(val) [50][50/407] eta: 0:00:42 time: 0.119642 data_time: 0.058198 memory: 6338 2022/09/21 19:39:42 - mmengine - INFO - Epoch(val) [50][100/407] eta: 0:00:36 time: 0.120431 data_time: 0.058104 memory: 587 2022/09/21 19:39:47 - mmengine - INFO - Epoch(val) [50][150/407] eta: 0:00:29 time: 0.115398 data_time: 0.054356 memory: 587 2022/09/21 19:39:53 - mmengine - INFO - Epoch(val) [50][200/407] eta: 0:00:24 time: 0.117994 data_time: 0.057236 memory: 587 2022/09/21 19:39:59 - mmengine - INFO - Epoch(val) [50][250/407] eta: 0:00:18 time: 0.116185 data_time: 0.055315 memory: 587 2022/09/21 19:40:05 - mmengine - INFO - Epoch(val) [50][300/407] eta: 0:00:12 time: 0.115055 data_time: 0.053472 memory: 587 2022/09/21 19:40:11 - mmengine - INFO - Epoch(val) [50][350/407] eta: 0:00:06 time: 0.114584 data_time: 0.052795 memory: 587 2022/09/21 19:40:16 - mmengine - INFO - Epoch(val) [50][400/407] eta: 0:00:00 time: 0.106289 data_time: 0.045863 memory: 587 2022/09/21 19:40:51 - mmengine - INFO - Evaluating CocoMetric... 2022/09/21 19:41:05 - mmengine - INFO - Epoch(val) [50][407/407] coco/AP: 0.624662 coco/AP .5: 0.857001 coco/AP .75: 0.702690 coco/AP (M): 0.597721 coco/AP (L): 0.680796 coco/AR: 0.685296 coco/AR .5: 0.901606 coco/AR .75: 0.756612 coco/AR (M): 0.646135 coco/AR (L): 0.741434 2022/09/21 19:41:05 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_40.pth is removed 2022/09/21 19:41:07 - mmengine - INFO - The best checkpoint with 0.6247 coco/AP at 50 epoch is saved to best_coco/AP_epoch_50.pth. 2022/09/21 19:41:32 - mmengine - INFO - Epoch(train) [51][50/293] lr: 5.000000e-04 eta: 5:12:21 time: 0.491677 data_time: 0.105597 memory: 6338 loss_kpt: 0.141499 acc_pose: 0.764198 loss: 0.141499 2022/09/21 19:41:56 - mmengine - INFO - Epoch(train) [51][100/293] lr: 5.000000e-04 eta: 5:12:13 time: 0.471956 data_time: 0.091148 memory: 6338 loss_kpt: 0.145832 acc_pose: 0.782609 loss: 0.145832 2022/09/21 19:42:20 - mmengine - INFO - Epoch(train) [51][150/293] lr: 5.000000e-04 eta: 5:12:05 time: 0.481837 data_time: 0.087613 memory: 6338 loss_kpt: 0.146240 acc_pose: 0.784122 loss: 0.146240 2022/09/21 19:42:43 - mmengine - INFO - Epoch(train) [51][200/293] lr: 5.000000e-04 eta: 5:11:55 time: 0.458626 data_time: 0.096433 memory: 6338 loss_kpt: 0.144210 acc_pose: 0.746688 loss: 0.144210 2022/09/21 19:43:06 - mmengine - INFO - Epoch(train) [51][250/293] lr: 5.000000e-04 eta: 5:11:45 time: 0.469544 data_time: 0.089006 memory: 6338 loss_kpt: 0.143630 acc_pose: 0.719702 loss: 0.143630 2022/09/21 19:43:26 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:43:26 - mmengine - INFO - Saving checkpoint at 51 epochs 2022/09/21 19:43:52 - mmengine - INFO - Epoch(train) [52][50/293] lr: 5.000000e-04 eta: 5:10:26 time: 0.476454 data_time: 0.093573 memory: 6338 loss_kpt: 0.143901 acc_pose: 0.708834 loss: 0.143901 2022/09/21 19:43:56 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:44:16 - mmengine - INFO - Epoch(train) [52][100/293] lr: 5.000000e-04 eta: 5:10:16 time: 0.465719 data_time: 0.085524 memory: 6338 loss_kpt: 0.138746 acc_pose: 0.760418 loss: 0.138746 2022/09/21 19:44:39 - mmengine - INFO - Epoch(train) [52][150/293] lr: 5.000000e-04 eta: 5:10:07 time: 0.468405 data_time: 0.085965 memory: 6338 loss_kpt: 0.142888 acc_pose: 0.765771 loss: 0.142888 2022/09/21 19:45:02 - mmengine - INFO - Epoch(train) [52][200/293] lr: 5.000000e-04 eta: 5:09:56 time: 0.463214 data_time: 0.083683 memory: 6338 loss_kpt: 0.143378 acc_pose: 0.766633 loss: 0.143378 2022/09/21 19:45:25 - mmengine - INFO - Epoch(train) [52][250/293] lr: 5.000000e-04 eta: 5:09:46 time: 0.462922 data_time: 0.083449 memory: 6338 loss_kpt: 0.143612 acc_pose: 0.783366 loss: 0.143612 2022/09/21 19:45:45 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:45:45 - mmengine - INFO - Saving checkpoint at 52 epochs 2022/09/21 19:46:11 - mmengine - INFO - Epoch(train) [53][50/293] lr: 5.000000e-04 eta: 5:08:26 time: 0.467585 data_time: 0.097837 memory: 6338 loss_kpt: 0.141611 acc_pose: 0.804699 loss: 0.141611 2022/09/21 19:46:35 - mmengine - INFO - Epoch(train) [53][100/293] lr: 5.000000e-04 eta: 5:08:18 time: 0.478790 data_time: 0.085284 memory: 6338 loss_kpt: 0.144859 acc_pose: 0.750773 loss: 0.144859 2022/09/21 19:46:59 - mmengine - INFO - Epoch(train) [53][150/293] lr: 5.000000e-04 eta: 5:08:10 time: 0.478275 data_time: 0.090717 memory: 6338 loss_kpt: 0.144545 acc_pose: 0.736931 loss: 0.144545 2022/09/21 19:47:22 - mmengine - INFO - Epoch(train) [53][200/293] lr: 5.000000e-04 eta: 5:07:58 time: 0.456292 data_time: 0.081203 memory: 6338 loss_kpt: 0.140136 acc_pose: 0.774848 loss: 0.140136 2022/09/21 19:47:45 - mmengine - INFO - Epoch(train) [53][250/293] lr: 5.000000e-04 eta: 5:07:46 time: 0.457212 data_time: 0.086778 memory: 6338 loss_kpt: 0.143449 acc_pose: 0.765252 loss: 0.143449 2022/09/21 19:48:04 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:48:04 - mmengine - INFO - Saving checkpoint at 53 epochs 2022/09/21 19:48:30 - mmengine - INFO - Epoch(train) [54][50/293] lr: 5.000000e-04 eta: 5:06:25 time: 0.449916 data_time: 0.100017 memory: 6338 loss_kpt: 0.142532 acc_pose: 0.725279 loss: 0.142532 2022/09/21 19:48:52 - mmengine - INFO - Epoch(train) [54][100/293] lr: 5.000000e-04 eta: 5:06:13 time: 0.450770 data_time: 0.088182 memory: 6338 loss_kpt: 0.140532 acc_pose: 0.746302 loss: 0.140532 2022/09/21 19:49:14 - mmengine - INFO - Epoch(train) [54][150/293] lr: 5.000000e-04 eta: 5:05:59 time: 0.445495 data_time: 0.089553 memory: 6338 loss_kpt: 0.145510 acc_pose: 0.753975 loss: 0.145510 2022/09/21 19:49:37 - mmengine - INFO - Epoch(train) [54][200/293] lr: 5.000000e-04 eta: 5:05:45 time: 0.442564 data_time: 0.084279 memory: 6338 loss_kpt: 0.141240 acc_pose: 0.729808 loss: 0.141240 2022/09/21 19:50:00 - mmengine - INFO - Epoch(train) [54][250/293] lr: 5.000000e-04 eta: 5:05:34 time: 0.460641 data_time: 0.092265 memory: 6338 loss_kpt: 0.145003 acc_pose: 0.684801 loss: 0.145003 2022/09/21 19:50:19 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:50:19 - mmengine - INFO - Saving checkpoint at 54 epochs 2022/09/21 19:50:47 - mmengine - INFO - Epoch(train) [55][50/293] lr: 5.000000e-04 eta: 5:04:20 time: 0.492407 data_time: 0.101868 memory: 6338 loss_kpt: 0.143157 acc_pose: 0.727078 loss: 0.143157 2022/09/21 19:51:10 - mmengine - INFO - Epoch(train) [55][100/293] lr: 5.000000e-04 eta: 5:04:09 time: 0.462787 data_time: 0.089569 memory: 6338 loss_kpt: 0.141061 acc_pose: 0.772253 loss: 0.141061 2022/09/21 19:51:34 - mmengine - INFO - Epoch(train) [55][150/293] lr: 5.000000e-04 eta: 5:04:01 time: 0.480024 data_time: 0.091193 memory: 6338 loss_kpt: 0.142222 acc_pose: 0.829517 loss: 0.142222 2022/09/21 19:51:47 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:51:58 - mmengine - INFO - Epoch(train) [55][200/293] lr: 5.000000e-04 eta: 5:03:52 time: 0.481251 data_time: 0.089332 memory: 6338 loss_kpt: 0.142142 acc_pose: 0.761751 loss: 0.142142 2022/09/21 19:52:22 - mmengine - INFO - Epoch(train) [55][250/293] lr: 5.000000e-04 eta: 5:03:44 time: 0.481251 data_time: 0.093517 memory: 6338 loss_kpt: 0.138950 acc_pose: 0.749633 loss: 0.138950 2022/09/21 19:52:42 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:52:42 - mmengine - INFO - Saving checkpoint at 55 epochs 2022/09/21 19:53:07 - mmengine - INFO - Epoch(train) [56][50/293] lr: 5.000000e-04 eta: 5:02:25 time: 0.451514 data_time: 0.104776 memory: 6338 loss_kpt: 0.141988 acc_pose: 0.755637 loss: 0.141988 2022/09/21 19:53:29 - mmengine - INFO - Epoch(train) [56][100/293] lr: 5.000000e-04 eta: 5:02:11 time: 0.441843 data_time: 0.083801 memory: 6338 loss_kpt: 0.139898 acc_pose: 0.660235 loss: 0.139898 2022/09/21 19:53:52 - mmengine - INFO - Epoch(train) [56][150/293] lr: 5.000000e-04 eta: 5:01:59 time: 0.457946 data_time: 0.092211 memory: 6338 loss_kpt: 0.140917 acc_pose: 0.774664 loss: 0.140917 2022/09/21 19:54:15 - mmengine - INFO - Epoch(train) [56][200/293] lr: 5.000000e-04 eta: 5:01:47 time: 0.455330 data_time: 0.089900 memory: 6338 loss_kpt: 0.138382 acc_pose: 0.760408 loss: 0.138382 2022/09/21 19:54:38 - mmengine - INFO - Epoch(train) [56][250/293] lr: 5.000000e-04 eta: 5:01:35 time: 0.460581 data_time: 0.093271 memory: 6338 loss_kpt: 0.139241 acc_pose: 0.759404 loss: 0.139241 2022/09/21 19:54:57 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:54:57 - mmengine - INFO - Saving checkpoint at 56 epochs 2022/09/21 19:55:24 - mmengine - INFO - Epoch(train) [57][50/293] lr: 5.000000e-04 eta: 5:00:20 time: 0.472757 data_time: 0.096354 memory: 6338 loss_kpt: 0.143714 acc_pose: 0.762689 loss: 0.143714 2022/09/21 19:55:47 - mmengine - INFO - Epoch(train) [57][100/293] lr: 5.000000e-04 eta: 5:00:09 time: 0.461889 data_time: 0.090175 memory: 6338 loss_kpt: 0.136557 acc_pose: 0.795005 loss: 0.136557 2022/09/21 19:56:10 - mmengine - INFO - Epoch(train) [57][150/293] lr: 5.000000e-04 eta: 4:59:56 time: 0.455691 data_time: 0.087619 memory: 6338 loss_kpt: 0.139767 acc_pose: 0.786749 loss: 0.139767 2022/09/21 19:56:33 - mmengine - INFO - Epoch(train) [57][200/293] lr: 5.000000e-04 eta: 4:59:45 time: 0.461002 data_time: 0.091871 memory: 6338 loss_kpt: 0.139981 acc_pose: 0.819155 loss: 0.139981 2022/09/21 19:56:56 - mmengine - INFO - Epoch(train) [57][250/293] lr: 5.000000e-04 eta: 4:59:32 time: 0.458352 data_time: 0.088083 memory: 6338 loss_kpt: 0.143571 acc_pose: 0.729021 loss: 0.143571 2022/09/21 19:57:15 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:57:15 - mmengine - INFO - Saving checkpoint at 57 epochs 2022/09/21 19:57:41 - mmengine - INFO - Epoch(train) [58][50/293] lr: 5.000000e-04 eta: 4:58:19 time: 0.474558 data_time: 0.100193 memory: 6338 loss_kpt: 0.136846 acc_pose: 0.785988 loss: 0.136846 2022/09/21 19:58:05 - mmengine - INFO - Epoch(train) [58][100/293] lr: 5.000000e-04 eta: 4:58:08 time: 0.468172 data_time: 0.086542 memory: 6338 loss_kpt: 0.140481 acc_pose: 0.738129 loss: 0.140481 2022/09/21 19:58:28 - mmengine - INFO - Epoch(train) [58][150/293] lr: 5.000000e-04 eta: 4:57:58 time: 0.473909 data_time: 0.093869 memory: 6338 loss_kpt: 0.138596 acc_pose: 0.747022 loss: 0.138596 2022/09/21 19:58:52 - mmengine - INFO - Epoch(train) [58][200/293] lr: 5.000000e-04 eta: 4:57:47 time: 0.473271 data_time: 0.094763 memory: 6338 loss_kpt: 0.139775 acc_pose: 0.783977 loss: 0.139775 2022/09/21 19:59:16 - mmengine - INFO - Epoch(train) [58][250/293] lr: 5.000000e-04 eta: 4:57:37 time: 0.473991 data_time: 0.094230 memory: 6338 loss_kpt: 0.139813 acc_pose: 0.736495 loss: 0.139813 2022/09/21 19:59:36 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 19:59:36 - mmengine - INFO - Saving checkpoint at 58 epochs 2022/09/21 19:59:41 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:00:02 - mmengine - INFO - Epoch(train) [59][50/293] lr: 5.000000e-04 eta: 4:56:25 time: 0.477848 data_time: 0.100180 memory: 6338 loss_kpt: 0.139976 acc_pose: 0.742330 loss: 0.139976 2022/09/21 20:00:25 - mmengine - INFO - Epoch(train) [59][100/293] lr: 5.000000e-04 eta: 4:56:13 time: 0.457449 data_time: 0.092508 memory: 6338 loss_kpt: 0.140879 acc_pose: 0.681216 loss: 0.140879 2022/09/21 20:00:48 - mmengine - INFO - Epoch(train) [59][150/293] lr: 5.000000e-04 eta: 4:56:00 time: 0.454522 data_time: 0.093202 memory: 6338 loss_kpt: 0.139359 acc_pose: 0.749675 loss: 0.139359 2022/09/21 20:01:10 - mmengine - INFO - Epoch(train) [59][200/293] lr: 5.000000e-04 eta: 4:55:45 time: 0.443001 data_time: 0.086608 memory: 6338 loss_kpt: 0.139283 acc_pose: 0.721592 loss: 0.139283 2022/09/21 20:01:33 - mmengine - INFO - Epoch(train) [59][250/293] lr: 5.000000e-04 eta: 4:55:32 time: 0.455031 data_time: 0.092038 memory: 6338 loss_kpt: 0.138964 acc_pose: 0.738515 loss: 0.138964 2022/09/21 20:01:52 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:01:52 - mmengine - INFO - Saving checkpoint at 59 epochs 2022/09/21 20:02:19 - mmengine - INFO - Epoch(train) [60][50/293] lr: 5.000000e-04 eta: 4:54:20 time: 0.474384 data_time: 0.101507 memory: 6338 loss_kpt: 0.139632 acc_pose: 0.783745 loss: 0.139632 2022/09/21 20:02:42 - mmengine - INFO - Epoch(train) [60][100/293] lr: 5.000000e-04 eta: 4:54:09 time: 0.468601 data_time: 0.089422 memory: 6338 loss_kpt: 0.139706 acc_pose: 0.764170 loss: 0.139706 2022/09/21 20:03:06 - mmengine - INFO - Epoch(train) [60][150/293] lr: 5.000000e-04 eta: 4:54:00 time: 0.482343 data_time: 0.095909 memory: 6338 loss_kpt: 0.138268 acc_pose: 0.772840 loss: 0.138268 2022/09/21 20:03:29 - mmengine - INFO - Epoch(train) [60][200/293] lr: 5.000000e-04 eta: 4:53:48 time: 0.464015 data_time: 0.092264 memory: 6338 loss_kpt: 0.139738 acc_pose: 0.744223 loss: 0.139738 2022/09/21 20:03:53 - mmengine - INFO - Epoch(train) [60][250/293] lr: 5.000000e-04 eta: 4:53:37 time: 0.473083 data_time: 0.095128 memory: 6338 loss_kpt: 0.139578 acc_pose: 0.729123 loss: 0.139578 2022/09/21 20:04:13 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:04:13 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/09/21 20:04:22 - mmengine - INFO - Epoch(val) [60][50/407] eta: 0:00:42 time: 0.118070 data_time: 0.054840 memory: 6338 2022/09/21 20:04:27 - mmengine - INFO - Epoch(val) [60][100/407] eta: 0:00:34 time: 0.113559 data_time: 0.052602 memory: 587 2022/09/21 20:04:33 - mmengine - INFO - Epoch(val) [60][150/407] eta: 0:00:30 time: 0.117096 data_time: 0.056310 memory: 587 2022/09/21 20:04:39 - mmengine - INFO - Epoch(val) [60][200/407] eta: 0:00:23 time: 0.111715 data_time: 0.050977 memory: 587 2022/09/21 20:04:45 - mmengine - INFO - Epoch(val) [60][250/407] eta: 0:00:18 time: 0.115550 data_time: 0.053120 memory: 587 2022/09/21 20:04:50 - mmengine - INFO - Epoch(val) [60][300/407] eta: 0:00:11 time: 0.111705 data_time: 0.047814 memory: 587 2022/09/21 20:04:56 - mmengine - INFO - Epoch(val) [60][350/407] eta: 0:00:06 time: 0.118134 data_time: 0.055911 memory: 587 2022/09/21 20:05:01 - mmengine - INFO - Epoch(val) [60][400/407] eta: 0:00:00 time: 0.104240 data_time: 0.043642 memory: 587 2022/09/21 20:05:37 - mmengine - INFO - Evaluating CocoMetric... 2022/09/21 20:05:51 - mmengine - INFO - Epoch(val) [60][407/407] coco/AP: 0.631970 coco/AP .5: 0.857638 coco/AP .75: 0.710513 coco/AP (M): 0.604318 coco/AP (L): 0.689383 coco/AR: 0.692994 coco/AR .5: 0.903652 coco/AR .75: 0.763539 coco/AR (M): 0.652691 coco/AR (L): 0.750427 2022/09/21 20:05:51 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_50.pth is removed 2022/09/21 20:05:53 - mmengine - INFO - The best checkpoint with 0.6320 coco/AP at 60 epoch is saved to best_coco/AP_epoch_60.pth. 2022/09/21 20:06:17 - mmengine - INFO - Epoch(train) [61][50/293] lr: 5.000000e-04 eta: 4:52:27 time: 0.484602 data_time: 0.101788 memory: 6338 loss_kpt: 0.138173 acc_pose: 0.743810 loss: 0.138173 2022/09/21 20:06:41 - mmengine - INFO - Epoch(train) [61][100/293] lr: 5.000000e-04 eta: 4:52:17 time: 0.480126 data_time: 0.093536 memory: 6338 loss_kpt: 0.137509 acc_pose: 0.794943 loss: 0.137509 2022/09/21 20:07:06 - mmengine - INFO - Epoch(train) [61][150/293] lr: 5.000000e-04 eta: 4:52:08 time: 0.491022 data_time: 0.100043 memory: 6338 loss_kpt: 0.138274 acc_pose: 0.736164 loss: 0.138274 2022/09/21 20:07:30 - mmengine - INFO - Epoch(train) [61][200/293] lr: 5.000000e-04 eta: 4:51:58 time: 0.480646 data_time: 0.089348 memory: 6338 loss_kpt: 0.139538 acc_pose: 0.745551 loss: 0.139538 2022/09/21 20:07:54 - mmengine - INFO - Epoch(train) [61][250/293] lr: 5.000000e-04 eta: 4:51:47 time: 0.472137 data_time: 0.088287 memory: 6338 loss_kpt: 0.140590 acc_pose: 0.755183 loss: 0.140590 2022/09/21 20:08:15 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:08:15 - mmengine - INFO - Saving checkpoint at 61 epochs 2022/09/21 20:08:42 - mmengine - INFO - Epoch(train) [62][50/293] lr: 5.000000e-04 eta: 4:50:38 time: 0.486399 data_time: 0.100091 memory: 6338 loss_kpt: 0.139165 acc_pose: 0.741004 loss: 0.139165 2022/09/21 20:09:05 - mmengine - INFO - Epoch(train) [62][100/293] lr: 5.000000e-04 eta: 4:50:27 time: 0.469507 data_time: 0.088326 memory: 6338 loss_kpt: 0.140778 acc_pose: 0.743657 loss: 0.140778 2022/09/21 20:09:18 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:09:29 - mmengine - INFO - Epoch(train) [62][150/293] lr: 5.000000e-04 eta: 4:50:15 time: 0.470209 data_time: 0.089754 memory: 6338 loss_kpt: 0.136955 acc_pose: 0.762493 loss: 0.136955 2022/09/21 20:09:53 - mmengine - INFO - Epoch(train) [62][200/293] lr: 5.000000e-04 eta: 4:50:05 time: 0.478356 data_time: 0.088636 memory: 6338 loss_kpt: 0.138415 acc_pose: 0.751666 loss: 0.138415 2022/09/21 20:10:16 - mmengine - INFO - Epoch(train) [62][250/293] lr: 5.000000e-04 eta: 4:49:53 time: 0.473289 data_time: 0.089584 memory: 6338 loss_kpt: 0.134701 acc_pose: 0.757345 loss: 0.134701 2022/09/21 20:10:36 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:10:36 - mmengine - INFO - Saving checkpoint at 62 epochs 2022/09/21 20:11:03 - mmengine - INFO - Epoch(train) [63][50/293] lr: 5.000000e-04 eta: 4:48:43 time: 0.470882 data_time: 0.096572 memory: 6338 loss_kpt: 0.139760 acc_pose: 0.718532 loss: 0.139760 2022/09/21 20:11:26 - mmengine - INFO - Epoch(train) [63][100/293] lr: 5.000000e-04 eta: 4:48:32 time: 0.471914 data_time: 0.094123 memory: 6338 loss_kpt: 0.137558 acc_pose: 0.757565 loss: 0.137558 2022/09/21 20:11:50 - mmengine - INFO - Epoch(train) [63][150/293] lr: 5.000000e-04 eta: 4:48:21 time: 0.476768 data_time: 0.085782 memory: 6338 loss_kpt: 0.134230 acc_pose: 0.714088 loss: 0.134230 2022/09/21 20:12:13 - mmengine - INFO - Epoch(train) [63][200/293] lr: 5.000000e-04 eta: 4:48:07 time: 0.455297 data_time: 0.090558 memory: 6338 loss_kpt: 0.136748 acc_pose: 0.752046 loss: 0.136748 2022/09/21 20:12:37 - mmengine - INFO - Epoch(train) [63][250/293] lr: 5.000000e-04 eta: 4:47:57 time: 0.483023 data_time: 0.085107 memory: 6338 loss_kpt: 0.136243 acc_pose: 0.766696 loss: 0.136243 2022/09/21 20:12:57 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:12:57 - mmengine - INFO - Saving checkpoint at 63 epochs 2022/09/21 20:13:24 - mmengine - INFO - Epoch(train) [64][50/293] lr: 5.000000e-04 eta: 4:46:49 time: 0.479963 data_time: 0.100883 memory: 6338 loss_kpt: 0.137488 acc_pose: 0.727695 loss: 0.137488 2022/09/21 20:13:47 - mmengine - INFO - Epoch(train) [64][100/293] lr: 5.000000e-04 eta: 4:46:38 time: 0.482387 data_time: 0.094926 memory: 6338 loss_kpt: 0.136826 acc_pose: 0.759973 loss: 0.136826 2022/09/21 20:14:11 - mmengine - INFO - Epoch(train) [64][150/293] lr: 5.000000e-04 eta: 4:46:27 time: 0.474750 data_time: 0.088540 memory: 6338 loss_kpt: 0.137423 acc_pose: 0.723751 loss: 0.137423 2022/09/21 20:14:35 - mmengine - INFO - Epoch(train) [64][200/293] lr: 5.000000e-04 eta: 4:46:16 time: 0.479382 data_time: 0.088758 memory: 6338 loss_kpt: 0.135189 acc_pose: 0.764710 loss: 0.135189 2022/09/21 20:14:59 - mmengine - INFO - Epoch(train) [64][250/293] lr: 5.000000e-04 eta: 4:46:04 time: 0.470796 data_time: 0.094151 memory: 6338 loss_kpt: 0.139789 acc_pose: 0.787638 loss: 0.139789 2022/09/21 20:15:19 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:15:19 - mmengine - INFO - Saving checkpoint at 64 epochs 2022/09/21 20:15:46 - mmengine - INFO - Epoch(train) [65][50/293] lr: 5.000000e-04 eta: 4:44:57 time: 0.485433 data_time: 0.096900 memory: 6338 loss_kpt: 0.138202 acc_pose: 0.814203 loss: 0.138202 2022/09/21 20:16:10 - mmengine - INFO - Epoch(train) [65][100/293] lr: 5.000000e-04 eta: 4:44:47 time: 0.484992 data_time: 0.090923 memory: 6338 loss_kpt: 0.141270 acc_pose: 0.722322 loss: 0.141270 2022/09/21 20:16:33 - mmengine - INFO - Epoch(train) [65][150/293] lr: 5.000000e-04 eta: 4:44:35 time: 0.471116 data_time: 0.088051 memory: 6338 loss_kpt: 0.139234 acc_pose: 0.787650 loss: 0.139234 2022/09/21 20:16:57 - mmengine - INFO - Epoch(train) [65][200/293] lr: 5.000000e-04 eta: 4:44:23 time: 0.474004 data_time: 0.094995 memory: 6338 loss_kpt: 0.139212 acc_pose: 0.777794 loss: 0.139212 2022/09/21 20:17:20 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:17:21 - mmengine - INFO - Epoch(train) [65][250/293] lr: 5.000000e-04 eta: 4:44:11 time: 0.474555 data_time: 0.091044 memory: 6338 loss_kpt: 0.137044 acc_pose: 0.707037 loss: 0.137044 2022/09/21 20:17:41 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:17:41 - mmengine - INFO - Saving checkpoint at 65 epochs 2022/09/21 20:18:08 - mmengine - INFO - Epoch(train) [66][50/293] lr: 5.000000e-04 eta: 4:43:05 time: 0.490055 data_time: 0.099848 memory: 6338 loss_kpt: 0.136079 acc_pose: 0.721019 loss: 0.136079 2022/09/21 20:18:32 - mmengine - INFO - Epoch(train) [66][100/293] lr: 5.000000e-04 eta: 4:42:54 time: 0.476065 data_time: 0.090024 memory: 6338 loss_kpt: 0.135616 acc_pose: 0.699395 loss: 0.135616 2022/09/21 20:18:55 - mmengine - INFO - Epoch(train) [66][150/293] lr: 5.000000e-04 eta: 4:42:42 time: 0.473516 data_time: 0.089264 memory: 6338 loss_kpt: 0.138102 acc_pose: 0.771159 loss: 0.138102 2022/09/21 20:19:19 - mmengine - INFO - Epoch(train) [66][200/293] lr: 5.000000e-04 eta: 4:42:30 time: 0.478707 data_time: 0.086299 memory: 6338 loss_kpt: 0.135458 acc_pose: 0.749281 loss: 0.135458 2022/09/21 20:19:43 - mmengine - INFO - Epoch(train) [66][250/293] lr: 5.000000e-04 eta: 4:42:18 time: 0.471249 data_time: 0.087669 memory: 6338 loss_kpt: 0.136858 acc_pose: 0.750117 loss: 0.136858 2022/09/21 20:20:02 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:20:02 - mmengine - INFO - Saving checkpoint at 66 epochs 2022/09/21 20:20:29 - mmengine - INFO - Epoch(train) [67][50/293] lr: 5.000000e-04 eta: 4:41:12 time: 0.479602 data_time: 0.101973 memory: 6338 loss_kpt: 0.138501 acc_pose: 0.770577 loss: 0.138501 2022/09/21 20:20:52 - mmengine - INFO - Epoch(train) [67][100/293] lr: 5.000000e-04 eta: 4:40:58 time: 0.453441 data_time: 0.085582 memory: 6338 loss_kpt: 0.137217 acc_pose: 0.768481 loss: 0.137217 2022/09/21 20:21:15 - mmengine - INFO - Epoch(train) [67][150/293] lr: 5.000000e-04 eta: 4:40:44 time: 0.460070 data_time: 0.089295 memory: 6338 loss_kpt: 0.136028 acc_pose: 0.780047 loss: 0.136028 2022/09/21 20:21:38 - mmengine - INFO - Epoch(train) [67][200/293] lr: 5.000000e-04 eta: 4:40:30 time: 0.452783 data_time: 0.086792 memory: 6338 loss_kpt: 0.134605 acc_pose: 0.787169 loss: 0.134605 2022/09/21 20:22:00 - mmengine - INFO - Epoch(train) [67][250/293] lr: 5.000000e-04 eta: 4:40:15 time: 0.456545 data_time: 0.095130 memory: 6338 loss_kpt: 0.134180 acc_pose: 0.772299 loss: 0.134180 2022/09/21 20:22:20 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:22:20 - mmengine - INFO - Saving checkpoint at 67 epochs 2022/09/21 20:22:47 - mmengine - INFO - Epoch(train) [68][50/293] lr: 5.000000e-04 eta: 4:39:12 time: 0.503043 data_time: 0.094979 memory: 6338 loss_kpt: 0.135440 acc_pose: 0.756024 loss: 0.135440 2022/09/21 20:23:11 - mmengine - INFO - Epoch(train) [68][100/293] lr: 5.000000e-04 eta: 4:39:00 time: 0.468483 data_time: 0.082566 memory: 6338 loss_kpt: 0.136610 acc_pose: 0.665494 loss: 0.136610 2022/09/21 20:23:34 - mmengine - INFO - Epoch(train) [68][150/293] lr: 5.000000e-04 eta: 4:38:47 time: 0.468552 data_time: 0.087910 memory: 6338 loss_kpt: 0.137740 acc_pose: 0.808041 loss: 0.137740 2022/09/21 20:23:59 - mmengine - INFO - Epoch(train) [68][200/293] lr: 5.000000e-04 eta: 4:38:36 time: 0.486392 data_time: 0.094034 memory: 6338 loss_kpt: 0.136280 acc_pose: 0.800593 loss: 0.136280 2022/09/21 20:24:23 - mmengine - INFO - Epoch(train) [68][250/293] lr: 5.000000e-04 eta: 4:38:24 time: 0.481831 data_time: 0.087440 memory: 6338 loss_kpt: 0.136100 acc_pose: 0.749706 loss: 0.136100 2022/09/21 20:24:43 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:24:43 - mmengine - INFO - Saving checkpoint at 68 epochs 2022/09/21 20:25:09 - mmengine - INFO - Epoch(train) [69][50/293] lr: 5.000000e-04 eta: 4:37:18 time: 0.465110 data_time: 0.094865 memory: 6338 loss_kpt: 0.135944 acc_pose: 0.763033 loss: 0.135944 2022/09/21 20:25:20 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:25:31 - mmengine - INFO - Epoch(train) [69][100/293] lr: 5.000000e-04 eta: 4:37:03 time: 0.450811 data_time: 0.085733 memory: 6338 loss_kpt: 0.138786 acc_pose: 0.711240 loss: 0.138786 2022/09/21 20:25:55 - mmengine - INFO - Epoch(train) [69][150/293] lr: 5.000000e-04 eta: 4:36:50 time: 0.469694 data_time: 0.087886 memory: 6338 loss_kpt: 0.136007 acc_pose: 0.718617 loss: 0.136007 2022/09/21 20:26:17 - mmengine - INFO - Epoch(train) [69][200/293] lr: 5.000000e-04 eta: 4:36:36 time: 0.455914 data_time: 0.084032 memory: 6338 loss_kpt: 0.135801 acc_pose: 0.765673 loss: 0.135801 2022/09/21 20:26:40 - mmengine - INFO - Epoch(train) [69][250/293] lr: 5.000000e-04 eta: 4:36:21 time: 0.455505 data_time: 0.088538 memory: 6338 loss_kpt: 0.136162 acc_pose: 0.718210 loss: 0.136162 2022/09/21 20:26:59 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:26:59 - mmengine - INFO - Saving checkpoint at 69 epochs 2022/09/21 20:27:26 - mmengine - INFO - Epoch(train) [70][50/293] lr: 5.000000e-04 eta: 4:35:17 time: 0.485945 data_time: 0.105835 memory: 6338 loss_kpt: 0.135510 acc_pose: 0.766601 loss: 0.135510 2022/09/21 20:27:50 - mmengine - INFO - Epoch(train) [70][100/293] lr: 5.000000e-04 eta: 4:35:05 time: 0.473814 data_time: 0.087194 memory: 6338 loss_kpt: 0.132555 acc_pose: 0.731681 loss: 0.132555 2022/09/21 20:28:14 - mmengine - INFO - Epoch(train) [70][150/293] lr: 5.000000e-04 eta: 4:34:53 time: 0.483626 data_time: 0.092726 memory: 6338 loss_kpt: 0.134649 acc_pose: 0.777059 loss: 0.134649 2022/09/21 20:28:37 - mmengine - INFO - Epoch(train) [70][200/293] lr: 5.000000e-04 eta: 4:34:40 time: 0.472644 data_time: 0.092500 memory: 6338 loss_kpt: 0.135555 acc_pose: 0.691028 loss: 0.135555 2022/09/21 20:29:01 - mmengine - INFO - Epoch(train) [70][250/293] lr: 5.000000e-04 eta: 4:34:29 time: 0.481453 data_time: 0.096481 memory: 6338 loss_kpt: 0.137089 acc_pose: 0.792849 loss: 0.137089 2022/09/21 20:29:22 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:29:22 - mmengine - INFO - Saving checkpoint at 70 epochs 2022/09/21 20:29:31 - mmengine - INFO - Epoch(val) [70][50/407] eta: 0:00:42 time: 0.118987 data_time: 0.057124 memory: 6338 2022/09/21 20:29:36 - mmengine - INFO - Epoch(val) [70][100/407] eta: 0:00:35 time: 0.115259 data_time: 0.051584 memory: 587 2022/09/21 20:29:42 - mmengine - INFO - Epoch(val) [70][150/407] eta: 0:00:29 time: 0.113191 data_time: 0.052102 memory: 587 2022/09/21 20:29:48 - mmengine - INFO - Epoch(val) [70][200/407] eta: 0:00:25 time: 0.120859 data_time: 0.059270 memory: 587 2022/09/21 20:29:54 - mmengine - INFO - Epoch(val) [70][250/407] eta: 0:00:18 time: 0.114837 data_time: 0.053712 memory: 587 2022/09/21 20:29:59 - mmengine - INFO - Epoch(val) [70][300/407] eta: 0:00:12 time: 0.114169 data_time: 0.053483 memory: 587 2022/09/21 20:30:05 - mmengine - INFO - Epoch(val) [70][350/407] eta: 0:00:06 time: 0.119826 data_time: 0.058117 memory: 587 2022/09/21 20:30:11 - mmengine - INFO - Epoch(val) [70][400/407] eta: 0:00:00 time: 0.105055 data_time: 0.045269 memory: 587 2022/09/21 20:30:46 - mmengine - INFO - Evaluating CocoMetric... 2022/09/21 20:31:00 - mmengine - INFO - Epoch(val) [70][407/407] coco/AP: 0.641640 coco/AP .5: 0.864696 coco/AP .75: 0.722384 coco/AP (M): 0.612240 coco/AP (L): 0.700339 coco/AR: 0.703479 coco/AR .5: 0.910107 coco/AR .75: 0.774874 coco/AR (M): 0.662934 coco/AR (L): 0.761241 2022/09/21 20:31:00 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_60.pth is removed 2022/09/21 20:31:02 - mmengine - INFO - The best checkpoint with 0.6416 coco/AP at 70 epoch is saved to best_coco/AP_epoch_70.pth. 2022/09/21 20:31:26 - mmengine - INFO - Epoch(train) [71][50/293] lr: 5.000000e-04 eta: 4:33:24 time: 0.477574 data_time: 0.098147 memory: 6338 loss_kpt: 0.136121 acc_pose: 0.766644 loss: 0.136121 2022/09/21 20:31:49 - mmengine - INFO - Epoch(train) [71][100/293] lr: 5.000000e-04 eta: 4:33:11 time: 0.461764 data_time: 0.089531 memory: 6338 loss_kpt: 0.133807 acc_pose: 0.730732 loss: 0.133807 2022/09/21 20:32:13 - mmengine - INFO - Epoch(train) [71][150/293] lr: 5.000000e-04 eta: 4:32:58 time: 0.475224 data_time: 0.091571 memory: 6338 loss_kpt: 0.134595 acc_pose: 0.767575 loss: 0.134595 2022/09/21 20:32:37 - mmengine - INFO - Epoch(train) [71][200/293] lr: 5.000000e-04 eta: 4:32:45 time: 0.476058 data_time: 0.088967 memory: 6338 loss_kpt: 0.134133 acc_pose: 0.805821 loss: 0.134133 2022/09/21 20:33:01 - mmengine - INFO - Epoch(train) [71][250/293] lr: 5.000000e-04 eta: 4:32:32 time: 0.471180 data_time: 0.086895 memory: 6338 loss_kpt: 0.132875 acc_pose: 0.788930 loss: 0.132875 2022/09/21 20:33:20 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:33:20 - mmengine - INFO - Saving checkpoint at 71 epochs 2022/09/21 20:33:47 - mmengine - INFO - Epoch(train) [72][50/293] lr: 5.000000e-04 eta: 4:31:29 time: 0.484693 data_time: 0.098054 memory: 6338 loss_kpt: 0.133748 acc_pose: 0.778432 loss: 0.133748 2022/09/21 20:34:11 - mmengine - INFO - Epoch(train) [72][100/293] lr: 5.000000e-04 eta: 4:31:17 time: 0.483369 data_time: 0.089275 memory: 6338 loss_kpt: 0.135775 acc_pose: 0.693221 loss: 0.135775 2022/09/21 20:34:35 - mmengine - INFO - Epoch(train) [72][150/293] lr: 5.000000e-04 eta: 4:31:05 time: 0.482608 data_time: 0.087505 memory: 6338 loss_kpt: 0.135332 acc_pose: 0.706563 loss: 0.135332 2022/09/21 20:34:57 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:34:59 - mmengine - INFO - Epoch(train) [72][200/293] lr: 5.000000e-04 eta: 4:30:52 time: 0.475263 data_time: 0.088235 memory: 6338 loss_kpt: 0.133568 acc_pose: 0.771764 loss: 0.133568 2022/09/21 20:35:23 - mmengine - INFO - Epoch(train) [72][250/293] lr: 5.000000e-04 eta: 4:30:40 time: 0.475881 data_time: 0.082289 memory: 6338 loss_kpt: 0.132047 acc_pose: 0.773613 loss: 0.132047 2022/09/21 20:35:43 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:35:43 - mmengine - INFO - Saving checkpoint at 72 epochs 2022/09/21 20:36:09 - mmengine - INFO - Epoch(train) [73][50/293] lr: 5.000000e-04 eta: 4:29:36 time: 0.474871 data_time: 0.098558 memory: 6338 loss_kpt: 0.133868 acc_pose: 0.807884 loss: 0.133868 2022/09/21 20:36:32 - mmengine - INFO - Epoch(train) [73][100/293] lr: 5.000000e-04 eta: 4:29:22 time: 0.457066 data_time: 0.086846 memory: 6338 loss_kpt: 0.135698 acc_pose: 0.761528 loss: 0.135698 2022/09/21 20:36:55 - mmengine - INFO - Epoch(train) [73][150/293] lr: 5.000000e-04 eta: 4:29:07 time: 0.458865 data_time: 0.087537 memory: 6338 loss_kpt: 0.135125 acc_pose: 0.706113 loss: 0.135125 2022/09/21 20:37:18 - mmengine - INFO - Epoch(train) [73][200/293] lr: 5.000000e-04 eta: 4:28:52 time: 0.457029 data_time: 0.089211 memory: 6338 loss_kpt: 0.132459 acc_pose: 0.782666 loss: 0.132459 2022/09/21 20:37:42 - mmengine - INFO - Epoch(train) [73][250/293] lr: 5.000000e-04 eta: 4:28:39 time: 0.475480 data_time: 0.092033 memory: 6338 loss_kpt: 0.134802 acc_pose: 0.778817 loss: 0.134802 2022/09/21 20:38:01 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:38:01 - mmengine - INFO - Saving checkpoint at 73 epochs 2022/09/21 20:38:28 - mmengine - INFO - Epoch(train) [74][50/293] lr: 5.000000e-04 eta: 4:27:38 time: 0.488922 data_time: 0.095864 memory: 6338 loss_kpt: 0.132278 acc_pose: 0.795212 loss: 0.132278 2022/09/21 20:38:52 - mmengine - INFO - Epoch(train) [74][100/293] lr: 5.000000e-04 eta: 4:27:25 time: 0.478451 data_time: 0.080720 memory: 6338 loss_kpt: 0.134694 acc_pose: 0.787655 loss: 0.134694 2022/09/21 20:39:17 - mmengine - INFO - Epoch(train) [74][150/293] lr: 5.000000e-04 eta: 4:27:14 time: 0.493470 data_time: 0.092835 memory: 6338 loss_kpt: 0.134473 acc_pose: 0.680370 loss: 0.134473 2022/09/21 20:39:41 - mmengine - INFO - Epoch(train) [74][200/293] lr: 5.000000e-04 eta: 4:27:01 time: 0.481759 data_time: 0.083091 memory: 6338 loss_kpt: 0.136516 acc_pose: 0.756816 loss: 0.136516 2022/09/21 20:40:05 - mmengine - INFO - Epoch(train) [74][250/293] lr: 5.000000e-04 eta: 4:26:48 time: 0.476662 data_time: 0.089103 memory: 6338 loss_kpt: 0.134224 acc_pose: 0.753891 loss: 0.134224 2022/09/21 20:40:25 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:40:25 - mmengine - INFO - Saving checkpoint at 74 epochs 2022/09/21 20:40:52 - mmengine - INFO - Epoch(train) [75][50/293] lr: 5.000000e-04 eta: 4:25:47 time: 0.487564 data_time: 0.094390 memory: 6338 loss_kpt: 0.132438 acc_pose: 0.748716 loss: 0.132438 2022/09/21 20:41:15 - mmengine - INFO - Epoch(train) [75][100/293] lr: 5.000000e-04 eta: 4:25:34 time: 0.471615 data_time: 0.090673 memory: 6338 loss_kpt: 0.133423 acc_pose: 0.779024 loss: 0.133423 2022/09/21 20:41:39 - mmengine - INFO - Epoch(train) [75][150/293] lr: 5.000000e-04 eta: 4:25:20 time: 0.472801 data_time: 0.086933 memory: 6338 loss_kpt: 0.133352 acc_pose: 0.766739 loss: 0.133352 2022/09/21 20:42:03 - mmengine - INFO - Epoch(train) [75][200/293] lr: 5.000000e-04 eta: 4:25:06 time: 0.469404 data_time: 0.077707 memory: 6338 loss_kpt: 0.135531 acc_pose: 0.817911 loss: 0.135531 2022/09/21 20:42:26 - mmengine - INFO - Epoch(train) [75][250/293] lr: 5.000000e-04 eta: 4:24:52 time: 0.471442 data_time: 0.084984 memory: 6338 loss_kpt: 0.135420 acc_pose: 0.742520 loss: 0.135420 2022/09/21 20:42:46 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:42:46 - mmengine - INFO - Saving checkpoint at 75 epochs 2022/09/21 20:43:02 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:43:13 - mmengine - INFO - Epoch(train) [76][50/293] lr: 5.000000e-04 eta: 4:23:51 time: 0.482082 data_time: 0.098426 memory: 6338 loss_kpt: 0.132500 acc_pose: 0.787035 loss: 0.132500 2022/09/21 20:43:37 - mmengine - INFO - Epoch(train) [76][100/293] lr: 5.000000e-04 eta: 4:23:38 time: 0.470993 data_time: 0.091121 memory: 6338 loss_kpt: 0.132481 acc_pose: 0.802386 loss: 0.132481 2022/09/21 20:44:01 - mmengine - INFO - Epoch(train) [76][150/293] lr: 5.000000e-04 eta: 4:23:25 time: 0.481421 data_time: 0.092017 memory: 6338 loss_kpt: 0.132978 acc_pose: 0.745440 loss: 0.132978 2022/09/21 20:44:24 - mmengine - INFO - Epoch(train) [76][200/293] lr: 5.000000e-04 eta: 4:23:11 time: 0.471295 data_time: 0.091367 memory: 6338 loss_kpt: 0.133270 acc_pose: 0.710807 loss: 0.133270 2022/09/21 20:44:48 - mmengine - INFO - Epoch(train) [76][250/293] lr: 5.000000e-04 eta: 4:22:57 time: 0.470703 data_time: 0.091044 memory: 6338 loss_kpt: 0.137375 acc_pose: 0.702285 loss: 0.137375 2022/09/21 20:45:07 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:45:07 - mmengine - INFO - Saving checkpoint at 76 epochs 2022/09/21 20:45:32 - mmengine - INFO - Epoch(train) [77][50/293] lr: 5.000000e-04 eta: 4:21:54 time: 0.451032 data_time: 0.097415 memory: 6338 loss_kpt: 0.131713 acc_pose: 0.710108 loss: 0.131713 2022/09/21 20:45:54 - mmengine - INFO - Epoch(train) [77][100/293] lr: 5.000000e-04 eta: 4:21:37 time: 0.434202 data_time: 0.088467 memory: 6338 loss_kpt: 0.131123 acc_pose: 0.706847 loss: 0.131123 2022/09/21 20:46:16 - mmengine - INFO - Epoch(train) [77][150/293] lr: 5.000000e-04 eta: 4:21:20 time: 0.439751 data_time: 0.094545 memory: 6338 loss_kpt: 0.135427 acc_pose: 0.720870 loss: 0.135427 2022/09/21 20:46:38 - mmengine - INFO - Epoch(train) [77][200/293] lr: 5.000000e-04 eta: 4:21:03 time: 0.436156 data_time: 0.083210 memory: 6338 loss_kpt: 0.134298 acc_pose: 0.762791 loss: 0.134298 2022/09/21 20:47:00 - mmengine - INFO - Epoch(train) [77][250/293] lr: 5.000000e-04 eta: 4:20:47 time: 0.446924 data_time: 0.091033 memory: 6338 loss_kpt: 0.134212 acc_pose: 0.781842 loss: 0.134212 2022/09/21 20:47:19 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:47:19 - mmengine - INFO - Saving checkpoint at 77 epochs 2022/09/21 20:47:46 - mmengine - INFO - Epoch(train) [78][50/293] lr: 5.000000e-04 eta: 4:19:47 time: 0.484014 data_time: 0.101109 memory: 6338 loss_kpt: 0.131521 acc_pose: 0.824795 loss: 0.131521 2022/09/21 20:48:10 - mmengine - INFO - Epoch(train) [78][100/293] lr: 5.000000e-04 eta: 4:19:34 time: 0.476258 data_time: 0.086409 memory: 6338 loss_kpt: 0.130995 acc_pose: 0.755888 loss: 0.130995 2022/09/21 20:48:33 - mmengine - INFO - Epoch(train) [78][150/293] lr: 5.000000e-04 eta: 4:19:20 time: 0.471850 data_time: 0.090827 memory: 6338 loss_kpt: 0.131594 acc_pose: 0.743982 loss: 0.131594 2022/09/21 20:48:57 - mmengine - INFO - Epoch(train) [78][200/293] lr: 5.000000e-04 eta: 4:19:06 time: 0.473203 data_time: 0.081520 memory: 6338 loss_kpt: 0.132698 acc_pose: 0.782790 loss: 0.132698 2022/09/21 20:49:21 - mmengine - INFO - Epoch(train) [78][250/293] lr: 5.000000e-04 eta: 4:18:53 time: 0.484599 data_time: 0.095728 memory: 6338 loss_kpt: 0.131595 acc_pose: 0.798449 loss: 0.131595 2022/09/21 20:49:41 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:49:41 - mmengine - INFO - Saving checkpoint at 78 epochs 2022/09/21 20:50:08 - mmengine - INFO - Epoch(train) [79][50/293] lr: 5.000000e-04 eta: 4:17:54 time: 0.488074 data_time: 0.092026 memory: 6338 loss_kpt: 0.129666 acc_pose: 0.783195 loss: 0.129666 2022/09/21 20:50:32 - mmengine - INFO - Epoch(train) [79][100/293] lr: 5.000000e-04 eta: 4:17:40 time: 0.470765 data_time: 0.091524 memory: 6338 loss_kpt: 0.133171 acc_pose: 0.776055 loss: 0.133171 2022/09/21 20:50:53 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:50:55 - mmengine - INFO - Epoch(train) [79][150/293] lr: 5.000000e-04 eta: 4:17:25 time: 0.467406 data_time: 0.087061 memory: 6338 loss_kpt: 0.136725 acc_pose: 0.728932 loss: 0.136725 2022/09/21 20:51:18 - mmengine - INFO - Epoch(train) [79][200/293] lr: 5.000000e-04 eta: 4:17:10 time: 0.458108 data_time: 0.094423 memory: 6338 loss_kpt: 0.134530 acc_pose: 0.690513 loss: 0.134530 2022/09/21 20:51:41 - mmengine - INFO - Epoch(train) [79][250/293] lr: 5.000000e-04 eta: 4:16:55 time: 0.467811 data_time: 0.096996 memory: 6338 loss_kpt: 0.134444 acc_pose: 0.742316 loss: 0.134444 2022/09/21 20:52:01 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:52:01 - mmengine - INFO - Saving checkpoint at 79 epochs 2022/09/21 20:52:29 - mmengine - INFO - Epoch(train) [80][50/293] lr: 5.000000e-04 eta: 4:15:58 time: 0.499879 data_time: 0.097008 memory: 6338 loss_kpt: 0.131316 acc_pose: 0.753374 loss: 0.131316 2022/09/21 20:52:53 - mmengine - INFO - Epoch(train) [80][100/293] lr: 5.000000e-04 eta: 4:15:44 time: 0.477346 data_time: 0.085429 memory: 6338 loss_kpt: 0.132997 acc_pose: 0.738307 loss: 0.132997 2022/09/21 20:53:17 - mmengine - INFO - Epoch(train) [80][150/293] lr: 5.000000e-04 eta: 4:15:30 time: 0.479398 data_time: 0.086618 memory: 6338 loss_kpt: 0.133019 acc_pose: 0.807913 loss: 0.133019 2022/09/21 20:53:41 - mmengine - INFO - Epoch(train) [80][200/293] lr: 5.000000e-04 eta: 4:15:17 time: 0.480611 data_time: 0.086784 memory: 6338 loss_kpt: 0.131422 acc_pose: 0.723079 loss: 0.131422 2022/09/21 20:54:05 - mmengine - INFO - Epoch(train) [80][250/293] lr: 5.000000e-04 eta: 4:15:04 time: 0.483865 data_time: 0.088958 memory: 6338 loss_kpt: 0.134122 acc_pose: 0.760118 loss: 0.134122 2022/09/21 20:54:25 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:54:25 - mmengine - INFO - Saving checkpoint at 80 epochs 2022/09/21 20:54:34 - mmengine - INFO - Epoch(val) [80][50/407] eta: 0:00:44 time: 0.123892 data_time: 0.062037 memory: 6338 2022/09/21 20:54:40 - mmengine - INFO - Epoch(val) [80][100/407] eta: 0:00:36 time: 0.119245 data_time: 0.056577 memory: 587 2022/09/21 20:54:46 - mmengine - INFO - Epoch(val) [80][150/407] eta: 0:00:29 time: 0.115228 data_time: 0.052568 memory: 587 2022/09/21 20:54:51 - mmengine - INFO - Epoch(val) [80][200/407] eta: 0:00:22 time: 0.108201 data_time: 0.045280 memory: 587 2022/09/21 20:54:57 - mmengine - INFO - Epoch(val) [80][250/407] eta: 0:00:18 time: 0.117288 data_time: 0.056991 memory: 587 2022/09/21 20:55:03 - mmengine - INFO - Epoch(val) [80][300/407] eta: 0:00:12 time: 0.116720 data_time: 0.055776 memory: 587 2022/09/21 20:55:09 - mmengine - INFO - Epoch(val) [80][350/407] eta: 0:00:06 time: 0.120433 data_time: 0.055307 memory: 587 2022/09/21 20:55:14 - mmengine - INFO - Epoch(val) [80][400/407] eta: 0:00:00 time: 0.102218 data_time: 0.043396 memory: 587 2022/09/21 20:55:49 - mmengine - INFO - Evaluating CocoMetric... 2022/09/21 20:56:04 - mmengine - INFO - Epoch(val) [80][407/407] coco/AP: 0.649539 coco/AP .5: 0.866043 coco/AP .75: 0.730905 coco/AP (M): 0.619392 coco/AP (L): 0.709466 coco/AR: 0.708911 coco/AR .5: 0.910579 coco/AR .75: 0.781958 coco/AR (M): 0.668096 coco/AR (L): 0.766555 2022/09/21 20:56:04 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_70.pth is removed 2022/09/21 20:56:06 - mmengine - INFO - The best checkpoint with 0.6495 coco/AP at 80 epoch is saved to best_coco/AP_epoch_80.pth. 2022/09/21 20:56:31 - mmengine - INFO - Epoch(train) [81][50/293] lr: 5.000000e-04 eta: 4:14:06 time: 0.494300 data_time: 0.099327 memory: 6338 loss_kpt: 0.131290 acc_pose: 0.741319 loss: 0.131290 2022/09/21 20:56:55 - mmengine - INFO - Epoch(train) [81][100/293] lr: 5.000000e-04 eta: 4:13:52 time: 0.478687 data_time: 0.096560 memory: 6338 loss_kpt: 0.132437 acc_pose: 0.770181 loss: 0.132437 2022/09/21 20:57:19 - mmengine - INFO - Epoch(train) [81][150/293] lr: 5.000000e-04 eta: 4:13:38 time: 0.481451 data_time: 0.089314 memory: 6338 loss_kpt: 0.129280 acc_pose: 0.772020 loss: 0.129280 2022/09/21 20:57:42 - mmengine - INFO - Epoch(train) [81][200/293] lr: 5.000000e-04 eta: 4:13:24 time: 0.475544 data_time: 0.089253 memory: 6338 loss_kpt: 0.133299 acc_pose: 0.762405 loss: 0.133299 2022/09/21 20:58:07 - mmengine - INFO - Epoch(train) [81][250/293] lr: 5.000000e-04 eta: 4:13:11 time: 0.482390 data_time: 0.092233 memory: 6338 loss_kpt: 0.129992 acc_pose: 0.768235 loss: 0.129992 2022/09/21 20:58:27 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 20:58:27 - mmengine - INFO - Saving checkpoint at 81 epochs 2022/09/21 20:58:54 - mmengine - INFO - Epoch(train) [82][50/293] lr: 5.000000e-04 eta: 4:12:12 time: 0.481186 data_time: 0.104324 memory: 6338 loss_kpt: 0.131728 acc_pose: 0.773416 loss: 0.131728 2022/09/21 20:59:17 - mmengine - INFO - Epoch(train) [82][100/293] lr: 5.000000e-04 eta: 4:11:57 time: 0.458879 data_time: 0.092533 memory: 6338 loss_kpt: 0.132703 acc_pose: 0.794279 loss: 0.132703 2022/09/21 20:59:40 - mmengine - INFO - Epoch(train) [82][150/293] lr: 5.000000e-04 eta: 4:11:41 time: 0.459096 data_time: 0.092381 memory: 6338 loss_kpt: 0.131552 acc_pose: 0.775804 loss: 0.131552 2022/09/21 21:00:03 - mmengine - INFO - Epoch(train) [82][200/293] lr: 5.000000e-04 eta: 4:11:26 time: 0.461501 data_time: 0.092470 memory: 6338 loss_kpt: 0.131105 acc_pose: 0.743812 loss: 0.131105 2022/09/21 21:00:26 - mmengine - INFO - Epoch(train) [82][250/293] lr: 5.000000e-04 eta: 4:11:11 time: 0.469393 data_time: 0.098964 memory: 6338 loss_kpt: 0.132566 acc_pose: 0.824468 loss: 0.132566 2022/09/21 21:00:34 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:00:45 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:00:45 - mmengine - INFO - Saving checkpoint at 82 epochs 2022/09/21 21:01:11 - mmengine - INFO - Epoch(train) [83][50/293] lr: 5.000000e-04 eta: 4:10:12 time: 0.466618 data_time: 0.101332 memory: 6338 loss_kpt: 0.129498 acc_pose: 0.748245 loss: 0.129498 2022/09/21 21:01:35 - mmengine - INFO - Epoch(train) [83][100/293] lr: 5.000000e-04 eta: 4:09:57 time: 0.464959 data_time: 0.091423 memory: 6338 loss_kpt: 0.130981 acc_pose: 0.795873 loss: 0.130981 2022/09/21 21:01:59 - mmengine - INFO - Epoch(train) [83][150/293] lr: 5.000000e-04 eta: 4:09:43 time: 0.480614 data_time: 0.103382 memory: 6338 loss_kpt: 0.131427 acc_pose: 0.775818 loss: 0.131427 2022/09/21 21:02:22 - mmengine - INFO - Epoch(train) [83][200/293] lr: 5.000000e-04 eta: 4:09:28 time: 0.466630 data_time: 0.088983 memory: 6338 loss_kpt: 0.130996 acc_pose: 0.752047 loss: 0.130996 2022/09/21 21:02:45 - mmengine - INFO - Epoch(train) [83][250/293] lr: 5.000000e-04 eta: 4:09:13 time: 0.457789 data_time: 0.095439 memory: 6338 loss_kpt: 0.132008 acc_pose: 0.809898 loss: 0.132008 2022/09/21 21:03:04 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:03:04 - mmengine - INFO - Saving checkpoint at 83 epochs 2022/09/21 21:03:30 - mmengine - INFO - Epoch(train) [84][50/293] lr: 5.000000e-04 eta: 4:08:15 time: 0.475052 data_time: 0.107379 memory: 6338 loss_kpt: 0.130485 acc_pose: 0.810550 loss: 0.130485 2022/09/21 21:03:53 - mmengine - INFO - Epoch(train) [84][100/293] lr: 5.000000e-04 eta: 4:07:59 time: 0.460801 data_time: 0.100954 memory: 6338 loss_kpt: 0.127130 acc_pose: 0.853192 loss: 0.127130 2022/09/21 21:04:17 - mmengine - INFO - Epoch(train) [84][150/293] lr: 5.000000e-04 eta: 4:07:44 time: 0.465072 data_time: 0.089089 memory: 6338 loss_kpt: 0.129480 acc_pose: 0.768876 loss: 0.129480 2022/09/21 21:04:39 - mmengine - INFO - Epoch(train) [84][200/293] lr: 5.000000e-04 eta: 4:07:28 time: 0.451205 data_time: 0.089515 memory: 6338 loss_kpt: 0.132693 acc_pose: 0.787467 loss: 0.132693 2022/09/21 21:05:02 - mmengine - INFO - Epoch(train) [84][250/293] lr: 5.000000e-04 eta: 4:07:12 time: 0.455968 data_time: 0.093840 memory: 6338 loss_kpt: 0.129991 acc_pose: 0.762578 loss: 0.129991 2022/09/21 21:05:21 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:05:21 - mmengine - INFO - Saving checkpoint at 84 epochs 2022/09/21 21:05:49 - mmengine - INFO - Epoch(train) [85][50/293] lr: 5.000000e-04 eta: 4:06:16 time: 0.501840 data_time: 0.098591 memory: 6338 loss_kpt: 0.132067 acc_pose: 0.801414 loss: 0.132067 2022/09/21 21:06:13 - mmengine - INFO - Epoch(train) [85][100/293] lr: 5.000000e-04 eta: 4:06:02 time: 0.484129 data_time: 0.085282 memory: 6338 loss_kpt: 0.130308 acc_pose: 0.794931 loss: 0.130308 2022/09/21 21:06:38 - mmengine - INFO - Epoch(train) [85][150/293] lr: 5.000000e-04 eta: 4:05:49 time: 0.488129 data_time: 0.089967 memory: 6338 loss_kpt: 0.128613 acc_pose: 0.707013 loss: 0.128613 2022/09/21 21:07:02 - mmengine - INFO - Epoch(train) [85][200/293] lr: 5.000000e-04 eta: 4:05:35 time: 0.482037 data_time: 0.089356 memory: 6338 loss_kpt: 0.131381 acc_pose: 0.803707 loss: 0.131381 2022/09/21 21:07:26 - mmengine - INFO - Epoch(train) [85][250/293] lr: 5.000000e-04 eta: 4:05:21 time: 0.494728 data_time: 0.087084 memory: 6338 loss_kpt: 0.130356 acc_pose: 0.762385 loss: 0.130356 2022/09/21 21:07:47 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:07:47 - mmengine - INFO - Saving checkpoint at 85 epochs 2022/09/21 21:08:13 - mmengine - INFO - Epoch(train) [86][50/293] lr: 5.000000e-04 eta: 4:04:24 time: 0.474454 data_time: 0.101775 memory: 6338 loss_kpt: 0.129691 acc_pose: 0.785262 loss: 0.129691 2022/09/21 21:08:34 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:08:37 - mmengine - INFO - Epoch(train) [86][100/293] lr: 5.000000e-04 eta: 4:04:09 time: 0.467170 data_time: 0.091438 memory: 6338 loss_kpt: 0.130321 acc_pose: 0.755189 loss: 0.130321 2022/09/21 21:09:00 - mmengine - INFO - Epoch(train) [86][150/293] lr: 5.000000e-04 eta: 4:03:53 time: 0.462981 data_time: 0.090796 memory: 6338 loss_kpt: 0.129987 acc_pose: 0.770305 loss: 0.129987 2022/09/21 21:09:23 - mmengine - INFO - Epoch(train) [86][200/293] lr: 5.000000e-04 eta: 4:03:38 time: 0.463525 data_time: 0.091086 memory: 6338 loss_kpt: 0.129486 acc_pose: 0.774413 loss: 0.129486 2022/09/21 21:09:46 - mmengine - INFO - Epoch(train) [86][250/293] lr: 5.000000e-04 eta: 4:03:22 time: 0.454812 data_time: 0.089570 memory: 6338 loss_kpt: 0.130225 acc_pose: 0.797833 loss: 0.130225 2022/09/21 21:10:05 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:10:05 - mmengine - INFO - Saving checkpoint at 86 epochs 2022/09/21 21:10:32 - mmengine - INFO - Epoch(train) [87][50/293] lr: 5.000000e-04 eta: 4:02:26 time: 0.487495 data_time: 0.102836 memory: 6338 loss_kpt: 0.127280 acc_pose: 0.758362 loss: 0.127280 2022/09/21 21:10:56 - mmengine - INFO - Epoch(train) [87][100/293] lr: 5.000000e-04 eta: 4:02:11 time: 0.479139 data_time: 0.090629 memory: 6338 loss_kpt: 0.130493 acc_pose: 0.816854 loss: 0.130493 2022/09/21 21:11:20 - mmengine - INFO - Epoch(train) [87][150/293] lr: 5.000000e-04 eta: 4:01:57 time: 0.485164 data_time: 0.096763 memory: 6338 loss_kpt: 0.132083 acc_pose: 0.755126 loss: 0.132083 2022/09/21 21:11:44 - mmengine - INFO - Epoch(train) [87][200/293] lr: 5.000000e-04 eta: 4:01:42 time: 0.473375 data_time: 0.085509 memory: 6338 loss_kpt: 0.129618 acc_pose: 0.806469 loss: 0.129618 2022/09/21 21:12:08 - mmengine - INFO - Epoch(train) [87][250/293] lr: 5.000000e-04 eta: 4:01:27 time: 0.470104 data_time: 0.087405 memory: 6338 loss_kpt: 0.129793 acc_pose: 0.744876 loss: 0.129793 2022/09/21 21:12:27 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:12:28 - mmengine - INFO - Saving checkpoint at 87 epochs 2022/09/21 21:12:54 - mmengine - INFO - Epoch(train) [88][50/293] lr: 5.000000e-04 eta: 4:00:31 time: 0.487688 data_time: 0.097605 memory: 6338 loss_kpt: 0.129229 acc_pose: 0.748249 loss: 0.129229 2022/09/21 21:13:19 - mmengine - INFO - Epoch(train) [88][100/293] lr: 5.000000e-04 eta: 4:00:17 time: 0.480545 data_time: 0.094307 memory: 6338 loss_kpt: 0.132570 acc_pose: 0.741746 loss: 0.132570 2022/09/21 21:13:42 - mmengine - INFO - Epoch(train) [88][150/293] lr: 5.000000e-04 eta: 4:00:02 time: 0.477619 data_time: 0.095991 memory: 6338 loss_kpt: 0.132475 acc_pose: 0.797808 loss: 0.132475 2022/09/21 21:14:06 - mmengine - INFO - Epoch(train) [88][200/293] lr: 5.000000e-04 eta: 3:59:47 time: 0.469300 data_time: 0.092833 memory: 6338 loss_kpt: 0.130269 acc_pose: 0.799239 loss: 0.130269 2022/09/21 21:14:29 - mmengine - INFO - Epoch(train) [88][250/293] lr: 5.000000e-04 eta: 3:59:32 time: 0.471250 data_time: 0.095389 memory: 6338 loss_kpt: 0.132247 acc_pose: 0.792855 loss: 0.132247 2022/09/21 21:14:50 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:14:50 - mmengine - INFO - Saving checkpoint at 88 epochs 2022/09/21 21:15:17 - mmengine - INFO - Epoch(train) [89][50/293] lr: 5.000000e-04 eta: 3:58:37 time: 0.495895 data_time: 0.097554 memory: 6338 loss_kpt: 0.131106 acc_pose: 0.776338 loss: 0.131106 2022/09/21 21:15:41 - mmengine - INFO - Epoch(train) [89][100/293] lr: 5.000000e-04 eta: 3:58:22 time: 0.476847 data_time: 0.088548 memory: 6338 loss_kpt: 0.128828 acc_pose: 0.755586 loss: 0.128828 2022/09/21 21:16:05 - mmengine - INFO - Epoch(train) [89][150/293] lr: 5.000000e-04 eta: 3:58:08 time: 0.479990 data_time: 0.090306 memory: 6338 loss_kpt: 0.130851 acc_pose: 0.749888 loss: 0.130851 2022/09/21 21:16:29 - mmengine - INFO - Epoch(train) [89][200/293] lr: 5.000000e-04 eta: 3:57:52 time: 0.474309 data_time: 0.091358 memory: 6338 loss_kpt: 0.129317 acc_pose: 0.736517 loss: 0.129317 2022/09/21 21:16:37 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:16:53 - mmengine - INFO - Epoch(train) [89][250/293] lr: 5.000000e-04 eta: 3:57:38 time: 0.488986 data_time: 0.091443 memory: 6338 loss_kpt: 0.128880 acc_pose: 0.802144 loss: 0.128880 2022/09/21 21:17:13 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:17:13 - mmengine - INFO - Saving checkpoint at 89 epochs 2022/09/21 21:17:40 - mmengine - INFO - Epoch(train) [90][50/293] lr: 5.000000e-04 eta: 3:56:43 time: 0.485576 data_time: 0.097778 memory: 6338 loss_kpt: 0.128485 acc_pose: 0.818025 loss: 0.128485 2022/09/21 21:18:04 - mmengine - INFO - Epoch(train) [90][100/293] lr: 5.000000e-04 eta: 3:56:28 time: 0.477690 data_time: 0.084443 memory: 6338 loss_kpt: 0.130414 acc_pose: 0.801850 loss: 0.130414 2022/09/21 21:18:28 - mmengine - INFO - Epoch(train) [90][150/293] lr: 5.000000e-04 eta: 3:56:13 time: 0.477651 data_time: 0.093976 memory: 6338 loss_kpt: 0.129462 acc_pose: 0.787423 loss: 0.129462 2022/09/21 21:18:51 - mmengine - INFO - Epoch(train) [90][200/293] lr: 5.000000e-04 eta: 3:55:58 time: 0.467794 data_time: 0.080843 memory: 6338 loss_kpt: 0.130001 acc_pose: 0.706847 loss: 0.130001 2022/09/21 21:19:15 - mmengine - INFO - Epoch(train) [90][250/293] lr: 5.000000e-04 eta: 3:55:43 time: 0.479511 data_time: 0.090710 memory: 6338 loss_kpt: 0.131544 acc_pose: 0.815263 loss: 0.131544 2022/09/21 21:19:35 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:19:35 - mmengine - INFO - Saving checkpoint at 90 epochs 2022/09/21 21:19:44 - mmengine - INFO - Epoch(val) [90][50/407] eta: 0:00:42 time: 0.119426 data_time: 0.058508 memory: 6338 2022/09/21 21:19:50 - mmengine - INFO - Epoch(val) [90][100/407] eta: 0:00:34 time: 0.113009 data_time: 0.051028 memory: 587 2022/09/21 21:19:55 - mmengine - INFO - Epoch(val) [90][150/407] eta: 0:00:30 time: 0.116951 data_time: 0.054445 memory: 587 2022/09/21 21:20:01 - mmengine - INFO - Epoch(val) [90][200/407] eta: 0:00:24 time: 0.117229 data_time: 0.054242 memory: 587 2022/09/21 21:20:07 - mmengine - INFO - Epoch(val) [90][250/407] eta: 0:00:18 time: 0.114662 data_time: 0.052687 memory: 587 2022/09/21 21:20:13 - mmengine - INFO - Epoch(val) [90][300/407] eta: 0:00:13 time: 0.122762 data_time: 0.050155 memory: 587 2022/09/21 21:20:19 - mmengine - INFO - Epoch(val) [90][350/407] eta: 0:00:06 time: 0.114660 data_time: 0.050070 memory: 587 2022/09/21 21:20:24 - mmengine - INFO - Epoch(val) [90][400/407] eta: 0:00:00 time: 0.105749 data_time: 0.046100 memory: 587 2022/09/21 21:20:59 - mmengine - INFO - Evaluating CocoMetric... 2022/09/21 21:21:14 - mmengine - INFO - Epoch(val) [90][407/407] coco/AP: 0.653813 coco/AP .5: 0.868203 coco/AP .75: 0.730846 coco/AP (M): 0.623458 coco/AP (L): 0.714704 coco/AR: 0.714074 coco/AR .5: 0.912154 coco/AR .75: 0.783375 coco/AR (M): 0.673231 coco/AR (L): 0.772538 2022/09/21 21:21:14 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_80.pth is removed 2022/09/21 21:21:16 - mmengine - INFO - The best checkpoint with 0.6538 coco/AP at 90 epoch is saved to best_coco/AP_epoch_90.pth. 2022/09/21 21:21:40 - mmengine - INFO - Epoch(train) [91][50/293] lr: 5.000000e-04 eta: 3:54:48 time: 0.481226 data_time: 0.100959 memory: 6338 loss_kpt: 0.129786 acc_pose: 0.749976 loss: 0.129786 2022/09/21 21:22:04 - mmengine - INFO - Epoch(train) [91][100/293] lr: 5.000000e-04 eta: 3:54:33 time: 0.480169 data_time: 0.091737 memory: 6338 loss_kpt: 0.127590 acc_pose: 0.808922 loss: 0.127590 2022/09/21 21:22:27 - mmengine - INFO - Epoch(train) [91][150/293] lr: 5.000000e-04 eta: 3:54:17 time: 0.468609 data_time: 0.087337 memory: 6338 loss_kpt: 0.127635 acc_pose: 0.798445 loss: 0.127635 2022/09/21 21:22:51 - mmengine - INFO - Epoch(train) [91][200/293] lr: 5.000000e-04 eta: 3:54:02 time: 0.474146 data_time: 0.088084 memory: 6338 loss_kpt: 0.127849 acc_pose: 0.775125 loss: 0.127849 2022/09/21 21:23:15 - mmengine - INFO - Epoch(train) [91][250/293] lr: 5.000000e-04 eta: 3:53:47 time: 0.475130 data_time: 0.087916 memory: 6338 loss_kpt: 0.130284 acc_pose: 0.816873 loss: 0.130284 2022/09/21 21:23:35 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:23:35 - mmengine - INFO - Saving checkpoint at 91 epochs 2022/09/21 21:24:02 - mmengine - INFO - Epoch(train) [92][50/293] lr: 5.000000e-04 eta: 3:52:53 time: 0.490067 data_time: 0.103690 memory: 6338 loss_kpt: 0.129743 acc_pose: 0.796914 loss: 0.129743 2022/09/21 21:24:26 - mmengine - INFO - Epoch(train) [92][100/293] lr: 5.000000e-04 eta: 3:52:38 time: 0.480465 data_time: 0.089351 memory: 6338 loss_kpt: 0.128179 acc_pose: 0.697408 loss: 0.128179 2022/09/21 21:24:50 - mmengine - INFO - Epoch(train) [92][150/293] lr: 5.000000e-04 eta: 3:52:23 time: 0.490365 data_time: 0.093471 memory: 6338 loss_kpt: 0.129773 acc_pose: 0.751745 loss: 0.129773 2022/09/21 21:25:15 - mmengine - INFO - Epoch(train) [92][200/293] lr: 5.000000e-04 eta: 3:52:09 time: 0.495567 data_time: 0.091985 memory: 6338 loss_kpt: 0.127910 acc_pose: 0.792813 loss: 0.127910 2022/09/21 21:25:39 - mmengine - INFO - Epoch(train) [92][250/293] lr: 5.000000e-04 eta: 3:51:54 time: 0.479243 data_time: 0.092511 memory: 6338 loss_kpt: 0.127692 acc_pose: 0.785571 loss: 0.127692 2022/09/21 21:25:59 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:25:59 - mmengine - INFO - Saving checkpoint at 92 epochs 2022/09/21 21:26:23 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:26:26 - mmengine - INFO - Epoch(train) [93][50/293] lr: 5.000000e-04 eta: 3:51:00 time: 0.478578 data_time: 0.100601 memory: 6338 loss_kpt: 0.128301 acc_pose: 0.730288 loss: 0.128301 2022/09/21 21:26:50 - mmengine - INFO - Epoch(train) [93][100/293] lr: 5.000000e-04 eta: 3:50:45 time: 0.480588 data_time: 0.089560 memory: 6338 loss_kpt: 0.128574 acc_pose: 0.809569 loss: 0.128574 2022/09/21 21:27:12 - mmengine - INFO - Epoch(train) [93][150/293] lr: 5.000000e-04 eta: 3:50:28 time: 0.457551 data_time: 0.087061 memory: 6338 loss_kpt: 0.127805 acc_pose: 0.849091 loss: 0.127805 2022/09/21 21:27:35 - mmengine - INFO - Epoch(train) [93][200/293] lr: 5.000000e-04 eta: 3:50:12 time: 0.457415 data_time: 0.090169 memory: 6338 loss_kpt: 0.130104 acc_pose: 0.828311 loss: 0.130104 2022/09/21 21:27:58 - mmengine - INFO - Epoch(train) [93][250/293] lr: 5.000000e-04 eta: 3:49:55 time: 0.458034 data_time: 0.082381 memory: 6338 loss_kpt: 0.129000 acc_pose: 0.775315 loss: 0.129000 2022/09/21 21:28:17 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:28:17 - mmengine - INFO - Saving checkpoint at 93 epochs 2022/09/21 21:28:43 - mmengine - INFO - Epoch(train) [94][50/293] lr: 5.000000e-04 eta: 3:49:00 time: 0.468314 data_time: 0.100529 memory: 6338 loss_kpt: 0.130106 acc_pose: 0.778503 loss: 0.130106 2022/09/21 21:29:06 - mmengine - INFO - Epoch(train) [94][100/293] lr: 5.000000e-04 eta: 3:48:43 time: 0.444297 data_time: 0.091906 memory: 6338 loss_kpt: 0.127051 acc_pose: 0.771473 loss: 0.127051 2022/09/21 21:29:29 - mmengine - INFO - Epoch(train) [94][150/293] lr: 5.000000e-04 eta: 3:48:26 time: 0.460243 data_time: 0.093846 memory: 6338 loss_kpt: 0.127671 acc_pose: 0.702398 loss: 0.127671 2022/09/21 21:29:52 - mmengine - INFO - Epoch(train) [94][200/293] lr: 5.000000e-04 eta: 3:48:10 time: 0.465965 data_time: 0.101417 memory: 6338 loss_kpt: 0.130520 acc_pose: 0.791770 loss: 0.130520 2022/09/21 21:30:15 - mmengine - INFO - Epoch(train) [94][250/293] lr: 5.000000e-04 eta: 3:47:54 time: 0.460810 data_time: 0.091627 memory: 6338 loss_kpt: 0.130482 acc_pose: 0.834565 loss: 0.130482 2022/09/21 21:30:34 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:30:34 - mmengine - INFO - Saving checkpoint at 94 epochs 2022/09/21 21:31:01 - mmengine - INFO - Epoch(train) [95][50/293] lr: 5.000000e-04 eta: 3:47:00 time: 0.479590 data_time: 0.099988 memory: 6338 loss_kpt: 0.127722 acc_pose: 0.771670 loss: 0.127722 2022/09/21 21:31:24 - mmengine - INFO - Epoch(train) [95][100/293] lr: 5.000000e-04 eta: 3:46:43 time: 0.458487 data_time: 0.088493 memory: 6338 loss_kpt: 0.128043 acc_pose: 0.753850 loss: 0.128043 2022/09/21 21:31:48 - mmengine - INFO - Epoch(train) [95][150/293] lr: 5.000000e-04 eta: 3:46:28 time: 0.477393 data_time: 0.092313 memory: 6338 loss_kpt: 0.127386 acc_pose: 0.744452 loss: 0.127386 2022/09/21 21:32:10 - mmengine - INFO - Epoch(train) [95][200/293] lr: 5.000000e-04 eta: 3:46:11 time: 0.452285 data_time: 0.093054 memory: 6338 loss_kpt: 0.130562 acc_pose: 0.797614 loss: 0.130562 2022/09/21 21:32:33 - mmengine - INFO - Epoch(train) [95][250/293] lr: 5.000000e-04 eta: 3:45:54 time: 0.456487 data_time: 0.093770 memory: 6338 loss_kpt: 0.128780 acc_pose: 0.808645 loss: 0.128780 2022/09/21 21:32:52 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:32:52 - mmengine - INFO - Saving checkpoint at 95 epochs 2022/09/21 21:33:19 - mmengine - INFO - Epoch(train) [96][50/293] lr: 5.000000e-04 eta: 3:45:00 time: 0.475067 data_time: 0.104450 memory: 6338 loss_kpt: 0.126699 acc_pose: 0.769303 loss: 0.126699 2022/09/21 21:33:42 - mmengine - INFO - Epoch(train) [96][100/293] lr: 5.000000e-04 eta: 3:44:44 time: 0.463271 data_time: 0.088333 memory: 6338 loss_kpt: 0.130529 acc_pose: 0.775248 loss: 0.130529 2022/09/21 21:34:05 - mmengine - INFO - Epoch(train) [96][150/293] lr: 5.000000e-04 eta: 3:44:28 time: 0.462918 data_time: 0.090301 memory: 6338 loss_kpt: 0.128105 acc_pose: 0.807490 loss: 0.128105 2022/09/21 21:34:12 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:34:28 - mmengine - INFO - Epoch(train) [96][200/293] lr: 5.000000e-04 eta: 3:44:11 time: 0.458454 data_time: 0.093479 memory: 6338 loss_kpt: 0.128144 acc_pose: 0.794236 loss: 0.128144 2022/09/21 21:34:52 - mmengine - INFO - Epoch(train) [96][250/293] lr: 5.000000e-04 eta: 3:43:55 time: 0.471715 data_time: 0.098158 memory: 6338 loss_kpt: 0.129832 acc_pose: 0.758467 loss: 0.129832 2022/09/21 21:35:11 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:35:11 - mmengine - INFO - Saving checkpoint at 96 epochs 2022/09/21 21:35:38 - mmengine - INFO - Epoch(train) [97][50/293] lr: 5.000000e-04 eta: 3:43:02 time: 0.476300 data_time: 0.094473 memory: 6338 loss_kpt: 0.127367 acc_pose: 0.777554 loss: 0.127367 2022/09/21 21:36:01 - mmengine - INFO - Epoch(train) [97][100/293] lr: 5.000000e-04 eta: 3:42:46 time: 0.474093 data_time: 0.088959 memory: 6338 loss_kpt: 0.127986 acc_pose: 0.772233 loss: 0.127986 2022/09/21 21:36:25 - mmengine - INFO - Epoch(train) [97][150/293] lr: 5.000000e-04 eta: 3:42:30 time: 0.466235 data_time: 0.091123 memory: 6338 loss_kpt: 0.128775 acc_pose: 0.806895 loss: 0.128775 2022/09/21 21:36:48 - mmengine - INFO - Epoch(train) [97][200/293] lr: 5.000000e-04 eta: 3:42:13 time: 0.464516 data_time: 0.084790 memory: 6338 loss_kpt: 0.131075 acc_pose: 0.802583 loss: 0.131075 2022/09/21 21:37:12 - mmengine - INFO - Epoch(train) [97][250/293] lr: 5.000000e-04 eta: 3:41:58 time: 0.479071 data_time: 0.092281 memory: 6338 loss_kpt: 0.128187 acc_pose: 0.836688 loss: 0.128187 2022/09/21 21:37:31 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:37:31 - mmengine - INFO - Saving checkpoint at 97 epochs 2022/09/21 21:37:59 - mmengine - INFO - Epoch(train) [98][50/293] lr: 5.000000e-04 eta: 3:41:06 time: 0.498035 data_time: 0.107285 memory: 6338 loss_kpt: 0.127179 acc_pose: 0.838733 loss: 0.127179 2022/09/21 21:38:23 - mmengine - INFO - Epoch(train) [98][100/293] lr: 5.000000e-04 eta: 3:40:50 time: 0.472541 data_time: 0.096842 memory: 6338 loss_kpt: 0.127241 acc_pose: 0.806833 loss: 0.127241 2022/09/21 21:38:47 - mmengine - INFO - Epoch(train) [98][150/293] lr: 5.000000e-04 eta: 3:40:35 time: 0.488349 data_time: 0.095312 memory: 6338 loss_kpt: 0.126571 acc_pose: 0.803154 loss: 0.126571 2022/09/21 21:39:11 - mmengine - INFO - Epoch(train) [98][200/293] lr: 5.000000e-04 eta: 3:40:19 time: 0.469588 data_time: 0.096287 memory: 6338 loss_kpt: 0.129047 acc_pose: 0.754845 loss: 0.129047 2022/09/21 21:39:35 - mmengine - INFO - Epoch(train) [98][250/293] lr: 5.000000e-04 eta: 3:40:04 time: 0.487293 data_time: 0.100949 memory: 6338 loss_kpt: 0.127052 acc_pose: 0.744275 loss: 0.127052 2022/09/21 21:39:55 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:39:55 - mmengine - INFO - Saving checkpoint at 98 epochs 2022/09/21 21:40:22 - mmengine - INFO - Epoch(train) [99][50/293] lr: 5.000000e-04 eta: 3:39:10 time: 0.468413 data_time: 0.095666 memory: 6338 loss_kpt: 0.128630 acc_pose: 0.796647 loss: 0.128630 2022/09/21 21:40:45 - mmengine - INFO - Epoch(train) [99][100/293] lr: 5.000000e-04 eta: 3:38:54 time: 0.463474 data_time: 0.087707 memory: 6338 loss_kpt: 0.130642 acc_pose: 0.786399 loss: 0.130642 2022/09/21 21:41:08 - mmengine - INFO - Epoch(train) [99][150/293] lr: 5.000000e-04 eta: 3:38:37 time: 0.465168 data_time: 0.088660 memory: 6338 loss_kpt: 0.127772 acc_pose: 0.811598 loss: 0.127772 2022/09/21 21:41:32 - mmengine - INFO - Epoch(train) [99][200/293] lr: 5.000000e-04 eta: 3:38:21 time: 0.473383 data_time: 0.087648 memory: 6338 loss_kpt: 0.128830 acc_pose: 0.784195 loss: 0.128830 2022/09/21 21:41:55 - mmengine - INFO - Epoch(train) [99][250/293] lr: 5.000000e-04 eta: 3:38:05 time: 0.471871 data_time: 0.092340 memory: 6338 loss_kpt: 0.128721 acc_pose: 0.763911 loss: 0.128721 2022/09/21 21:42:12 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:42:15 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:42:15 - mmengine - INFO - Saving checkpoint at 99 epochs 2022/09/21 21:42:42 - mmengine - INFO - Epoch(train) [100][50/293] lr: 5.000000e-04 eta: 3:37:13 time: 0.481323 data_time: 0.098847 memory: 6338 loss_kpt: 0.128950 acc_pose: 0.772909 loss: 0.128950 2022/09/21 21:43:06 - mmengine - INFO - Epoch(train) [100][100/293] lr: 5.000000e-04 eta: 3:36:57 time: 0.479458 data_time: 0.086556 memory: 6338 loss_kpt: 0.125484 acc_pose: 0.799875 loss: 0.125484 2022/09/21 21:43:30 - mmengine - INFO - Epoch(train) [100][150/293] lr: 5.000000e-04 eta: 3:36:42 time: 0.491592 data_time: 0.093200 memory: 6338 loss_kpt: 0.127775 acc_pose: 0.812018 loss: 0.127775 2022/09/21 21:43:54 - mmengine - INFO - Epoch(train) [100][200/293] lr: 5.000000e-04 eta: 3:36:26 time: 0.464983 data_time: 0.090454 memory: 6338 loss_kpt: 0.127715 acc_pose: 0.770271 loss: 0.127715 2022/09/21 21:44:17 - mmengine - INFO - Epoch(train) [100][250/293] lr: 5.000000e-04 eta: 3:36:10 time: 0.474266 data_time: 0.084927 memory: 6338 loss_kpt: 0.129375 acc_pose: 0.785797 loss: 0.129375 2022/09/21 21:44:37 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:44:37 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/09/21 21:44:46 - mmengine - INFO - Epoch(val) [100][50/407] eta: 0:00:42 time: 0.119605 data_time: 0.057217 memory: 6338 2022/09/21 21:44:52 - mmengine - INFO - Epoch(val) [100][100/407] eta: 0:00:34 time: 0.112662 data_time: 0.050671 memory: 587 2022/09/21 21:44:57 - mmengine - INFO - Epoch(val) [100][150/407] eta: 0:00:29 time: 0.116330 data_time: 0.049310 memory: 587 2022/09/21 21:45:03 - mmengine - INFO - Epoch(val) [100][200/407] eta: 0:00:23 time: 0.113602 data_time: 0.051467 memory: 587 2022/09/21 21:45:09 - mmengine - INFO - Epoch(val) [100][250/407] eta: 0:00:18 time: 0.116918 data_time: 0.055370 memory: 587 2022/09/21 21:45:15 - mmengine - INFO - Epoch(val) [100][300/407] eta: 0:00:12 time: 0.112426 data_time: 0.050951 memory: 587 2022/09/21 21:45:20 - mmengine - INFO - Epoch(val) [100][350/407] eta: 0:00:06 time: 0.117853 data_time: 0.056813 memory: 587 2022/09/21 21:45:26 - mmengine - INFO - Epoch(val) [100][400/407] eta: 0:00:00 time: 0.108801 data_time: 0.048445 memory: 587 2022/09/21 21:46:02 - mmengine - INFO - Evaluating CocoMetric... 2022/09/21 21:46:16 - mmengine - INFO - Epoch(val) [100][407/407] coco/AP: 0.658740 coco/AP .5: 0.871520 coco/AP .75: 0.737914 coco/AP (M): 0.628039 coco/AP (L): 0.721002 coco/AR: 0.719144 coco/AR .5: 0.915302 coco/AR .75: 0.790145 coco/AR (M): 0.678175 coco/AR (L): 0.778298 2022/09/21 21:46:16 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_90.pth is removed 2022/09/21 21:46:19 - mmengine - INFO - The best checkpoint with 0.6587 coco/AP at 100 epoch is saved to best_coco/AP_epoch_100.pth. 2022/09/21 21:46:41 - mmengine - INFO - Epoch(train) [101][50/293] lr: 5.000000e-04 eta: 3:35:16 time: 0.457160 data_time: 0.098403 memory: 6338 loss_kpt: 0.126443 acc_pose: 0.775739 loss: 0.126443 2022/09/21 21:47:04 - mmengine - INFO - Epoch(train) [101][100/293] lr: 5.000000e-04 eta: 3:34:58 time: 0.443022 data_time: 0.085614 memory: 6338 loss_kpt: 0.128572 acc_pose: 0.816500 loss: 0.128572 2022/09/21 21:47:27 - mmengine - INFO - Epoch(train) [101][150/293] lr: 5.000000e-04 eta: 3:34:42 time: 0.460431 data_time: 0.086964 memory: 6338 loss_kpt: 0.132039 acc_pose: 0.802004 loss: 0.132039 2022/09/21 21:47:49 - mmengine - INFO - Epoch(train) [101][200/293] lr: 5.000000e-04 eta: 3:34:24 time: 0.450468 data_time: 0.087543 memory: 6338 loss_kpt: 0.128782 acc_pose: 0.823052 loss: 0.128782 2022/09/21 21:48:12 - mmengine - INFO - Epoch(train) [101][250/293] lr: 5.000000e-04 eta: 3:34:07 time: 0.464515 data_time: 0.092956 memory: 6338 loss_kpt: 0.125463 acc_pose: 0.760210 loss: 0.125463 2022/09/21 21:48:31 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:48:31 - mmengine - INFO - Saving checkpoint at 101 epochs 2022/09/21 21:48:58 - mmengine - INFO - Epoch(train) [102][50/293] lr: 5.000000e-04 eta: 3:33:16 time: 0.488506 data_time: 0.100284 memory: 6338 loss_kpt: 0.128920 acc_pose: 0.756672 loss: 0.128920 2022/09/21 21:49:22 - mmengine - INFO - Epoch(train) [102][100/293] lr: 5.000000e-04 eta: 3:33:00 time: 0.470113 data_time: 0.087498 memory: 6338 loss_kpt: 0.125200 acc_pose: 0.778728 loss: 0.125200 2022/09/21 21:49:46 - mmengine - INFO - Epoch(train) [102][150/293] lr: 5.000000e-04 eta: 3:32:44 time: 0.477402 data_time: 0.088918 memory: 6338 loss_kpt: 0.126742 acc_pose: 0.738962 loss: 0.126742 2022/09/21 21:50:10 - mmengine - INFO - Epoch(train) [102][200/293] lr: 5.000000e-04 eta: 3:32:28 time: 0.486058 data_time: 0.089840 memory: 6338 loss_kpt: 0.124172 acc_pose: 0.800978 loss: 0.124172 2022/09/21 21:50:34 - mmengine - INFO - Epoch(train) [102][250/293] lr: 5.000000e-04 eta: 3:32:12 time: 0.478152 data_time: 0.094935 memory: 6338 loss_kpt: 0.129005 acc_pose: 0.818341 loss: 0.129005 2022/09/21 21:50:54 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:50:54 - mmengine - INFO - Saving checkpoint at 102 epochs 2022/09/21 21:51:20 - mmengine - INFO - Epoch(train) [103][50/293] lr: 5.000000e-04 eta: 3:31:20 time: 0.464109 data_time: 0.097023 memory: 6338 loss_kpt: 0.125403 acc_pose: 0.780228 loss: 0.125403 2022/09/21 21:51:42 - mmengine - INFO - Epoch(train) [103][100/293] lr: 5.000000e-04 eta: 3:31:02 time: 0.446513 data_time: 0.082394 memory: 6338 loss_kpt: 0.129199 acc_pose: 0.800314 loss: 0.129199 2022/09/21 21:51:48 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:52:05 - mmengine - INFO - Epoch(train) [103][150/293] lr: 5.000000e-04 eta: 3:30:45 time: 0.457335 data_time: 0.090951 memory: 6338 loss_kpt: 0.123337 acc_pose: 0.815669 loss: 0.123337 2022/09/21 21:52:28 - mmengine - INFO - Epoch(train) [103][200/293] lr: 5.000000e-04 eta: 3:30:28 time: 0.460161 data_time: 0.082968 memory: 6338 loss_kpt: 0.125544 acc_pose: 0.797385 loss: 0.125544 2022/09/21 21:52:51 - mmengine - INFO - Epoch(train) [103][250/293] lr: 5.000000e-04 eta: 3:30:11 time: 0.458077 data_time: 0.086416 memory: 6338 loss_kpt: 0.126685 acc_pose: 0.817049 loss: 0.126685 2022/09/21 21:53:10 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:53:10 - mmengine - INFO - Saving checkpoint at 103 epochs 2022/09/21 21:53:38 - mmengine - INFO - Epoch(train) [104][50/293] lr: 5.000000e-04 eta: 3:29:21 time: 0.498632 data_time: 0.111094 memory: 6338 loss_kpt: 0.125257 acc_pose: 0.827785 loss: 0.125257 2022/09/21 21:54:01 - mmengine - INFO - Epoch(train) [104][100/293] lr: 5.000000e-04 eta: 3:29:04 time: 0.471382 data_time: 0.092092 memory: 6338 loss_kpt: 0.127285 acc_pose: 0.738247 loss: 0.127285 2022/09/21 21:54:25 - mmengine - INFO - Epoch(train) [104][150/293] lr: 5.000000e-04 eta: 3:28:48 time: 0.481438 data_time: 0.088177 memory: 6338 loss_kpt: 0.126984 acc_pose: 0.780816 loss: 0.126984 2022/09/21 21:54:49 - mmengine - INFO - Epoch(train) [104][200/293] lr: 5.000000e-04 eta: 3:28:32 time: 0.472264 data_time: 0.084991 memory: 6338 loss_kpt: 0.125227 acc_pose: 0.728763 loss: 0.125227 2022/09/21 21:55:13 - mmengine - INFO - Epoch(train) [104][250/293] lr: 5.000000e-04 eta: 3:28:15 time: 0.474050 data_time: 0.088629 memory: 6338 loss_kpt: 0.125041 acc_pose: 0.797264 loss: 0.125041 2022/09/21 21:55:33 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:55:33 - mmengine - INFO - Saving checkpoint at 104 epochs 2022/09/21 21:55:59 - mmengine - INFO - Epoch(train) [105][50/293] lr: 5.000000e-04 eta: 3:27:24 time: 0.472535 data_time: 0.095819 memory: 6338 loss_kpt: 0.123730 acc_pose: 0.779024 loss: 0.123730 2022/09/21 21:56:22 - mmengine - INFO - Epoch(train) [105][100/293] lr: 5.000000e-04 eta: 3:27:07 time: 0.461472 data_time: 0.095638 memory: 6338 loss_kpt: 0.127118 acc_pose: 0.773767 loss: 0.127118 2022/09/21 21:56:44 - mmengine - INFO - Epoch(train) [105][150/293] lr: 5.000000e-04 eta: 3:26:49 time: 0.447394 data_time: 0.090821 memory: 6338 loss_kpt: 0.128485 acc_pose: 0.754152 loss: 0.128485 2022/09/21 21:57:07 - mmengine - INFO - Epoch(train) [105][200/293] lr: 5.000000e-04 eta: 3:26:31 time: 0.444513 data_time: 0.092541 memory: 6338 loss_kpt: 0.127817 acc_pose: 0.785432 loss: 0.127817 2022/09/21 21:57:29 - mmengine - INFO - Epoch(train) [105][250/293] lr: 5.000000e-04 eta: 3:26:14 time: 0.448338 data_time: 0.086800 memory: 6338 loss_kpt: 0.125312 acc_pose: 0.669286 loss: 0.125312 2022/09/21 21:57:48 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:57:48 - mmengine - INFO - Saving checkpoint at 105 epochs 2022/09/21 21:58:15 - mmengine - INFO - Epoch(train) [106][50/293] lr: 5.000000e-04 eta: 3:25:23 time: 0.480753 data_time: 0.096817 memory: 6338 loss_kpt: 0.126578 acc_pose: 0.799546 loss: 0.126578 2022/09/21 21:58:39 - mmengine - INFO - Epoch(train) [106][100/293] lr: 5.000000e-04 eta: 3:25:07 time: 0.475014 data_time: 0.086499 memory: 6338 loss_kpt: 0.125454 acc_pose: 0.819986 loss: 0.125454 2022/09/21 21:59:02 - mmengine - INFO - Epoch(train) [106][150/293] lr: 5.000000e-04 eta: 3:24:50 time: 0.468100 data_time: 0.088085 memory: 6338 loss_kpt: 0.126232 acc_pose: 0.787940 loss: 0.126232 2022/09/21 21:59:26 - mmengine - INFO - Epoch(train) [106][200/293] lr: 5.000000e-04 eta: 3:24:34 time: 0.477307 data_time: 0.091560 memory: 6338 loss_kpt: 0.123844 acc_pose: 0.789625 loss: 0.123844 2022/09/21 21:59:43 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 21:59:50 - mmengine - INFO - Epoch(train) [106][250/293] lr: 5.000000e-04 eta: 3:24:17 time: 0.479678 data_time: 0.092857 memory: 6338 loss_kpt: 0.127929 acc_pose: 0.755613 loss: 0.127929 2022/09/21 22:00:09 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:00:09 - mmengine - INFO - Saving checkpoint at 106 epochs 2022/09/21 22:00:37 - mmengine - INFO - Epoch(train) [107][50/293] lr: 5.000000e-04 eta: 3:23:27 time: 0.490553 data_time: 0.095202 memory: 6338 loss_kpt: 0.125090 acc_pose: 0.838010 loss: 0.125090 2022/09/21 22:01:00 - mmengine - INFO - Epoch(train) [107][100/293] lr: 5.000000e-04 eta: 3:23:11 time: 0.475792 data_time: 0.096089 memory: 6338 loss_kpt: 0.126362 acc_pose: 0.846955 loss: 0.126362 2022/09/21 22:01:25 - mmengine - INFO - Epoch(train) [107][150/293] lr: 5.000000e-04 eta: 3:22:55 time: 0.485666 data_time: 0.091880 memory: 6338 loss_kpt: 0.125499 acc_pose: 0.756800 loss: 0.125499 2022/09/21 22:01:48 - mmengine - INFO - Epoch(train) [107][200/293] lr: 5.000000e-04 eta: 3:22:38 time: 0.474742 data_time: 0.090658 memory: 6338 loss_kpt: 0.124626 acc_pose: 0.738337 loss: 0.124626 2022/09/21 22:02:13 - mmengine - INFO - Epoch(train) [107][250/293] lr: 5.000000e-04 eta: 3:22:23 time: 0.490397 data_time: 0.094233 memory: 6338 loss_kpt: 0.128334 acc_pose: 0.781103 loss: 0.128334 2022/09/21 22:02:33 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:02:33 - mmengine - INFO - Saving checkpoint at 107 epochs 2022/09/21 22:03:00 - mmengine - INFO - Epoch(train) [108][50/293] lr: 5.000000e-04 eta: 3:21:33 time: 0.482595 data_time: 0.100042 memory: 6338 loss_kpt: 0.127124 acc_pose: 0.756608 loss: 0.127124 2022/09/21 22:03:24 - mmengine - INFO - Epoch(train) [108][100/293] lr: 5.000000e-04 eta: 3:21:16 time: 0.466176 data_time: 0.091170 memory: 6338 loss_kpt: 0.124475 acc_pose: 0.814219 loss: 0.124475 2022/09/21 22:03:48 - mmengine - INFO - Epoch(train) [108][150/293] lr: 5.000000e-04 eta: 3:21:00 time: 0.483906 data_time: 0.094939 memory: 6338 loss_kpt: 0.126684 acc_pose: 0.801015 loss: 0.126684 2022/09/21 22:04:12 - mmengine - INFO - Epoch(train) [108][200/293] lr: 5.000000e-04 eta: 3:20:43 time: 0.479384 data_time: 0.090958 memory: 6338 loss_kpt: 0.125563 acc_pose: 0.813597 loss: 0.125563 2022/09/21 22:04:36 - mmengine - INFO - Epoch(train) [108][250/293] lr: 5.000000e-04 eta: 3:20:27 time: 0.479671 data_time: 0.091269 memory: 6338 loss_kpt: 0.125500 acc_pose: 0.834283 loss: 0.125500 2022/09/21 22:04:56 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:04:56 - mmengine - INFO - Saving checkpoint at 108 epochs 2022/09/21 22:05:22 - mmengine - INFO - Epoch(train) [109][50/293] lr: 5.000000e-04 eta: 3:19:36 time: 0.462237 data_time: 0.102590 memory: 6338 loss_kpt: 0.127730 acc_pose: 0.806878 loss: 0.127730 2022/09/21 22:05:45 - mmengine - INFO - Epoch(train) [109][100/293] lr: 5.000000e-04 eta: 3:19:19 time: 0.460572 data_time: 0.087316 memory: 6338 loss_kpt: 0.125655 acc_pose: 0.811928 loss: 0.125655 2022/09/21 22:06:08 - mmengine - INFO - Epoch(train) [109][150/293] lr: 5.000000e-04 eta: 3:19:01 time: 0.461797 data_time: 0.090242 memory: 6338 loss_kpt: 0.125311 acc_pose: 0.770694 loss: 0.125311 2022/09/21 22:06:31 - mmengine - INFO - Epoch(train) [109][200/293] lr: 5.000000e-04 eta: 3:18:44 time: 0.456318 data_time: 0.089832 memory: 6338 loss_kpt: 0.124966 acc_pose: 0.785783 loss: 0.124966 2022/09/21 22:06:54 - mmengine - INFO - Epoch(train) [109][250/293] lr: 5.000000e-04 eta: 3:18:27 time: 0.461186 data_time: 0.087729 memory: 6338 loss_kpt: 0.124665 acc_pose: 0.782672 loss: 0.124665 2022/09/21 22:07:13 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:07:13 - mmengine - INFO - Saving checkpoint at 109 epochs 2022/09/21 22:07:39 - mmengine - INFO - Epoch(train) [110][50/293] lr: 5.000000e-04 eta: 3:17:35 time: 0.449831 data_time: 0.101624 memory: 6338 loss_kpt: 0.125484 acc_pose: 0.771856 loss: 0.125484 2022/09/21 22:07:44 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:08:01 - mmengine - INFO - Epoch(train) [110][100/293] lr: 5.000000e-04 eta: 3:17:18 time: 0.457244 data_time: 0.091478 memory: 6338 loss_kpt: 0.126810 acc_pose: 0.809786 loss: 0.126810 2022/09/21 22:08:24 - mmengine - INFO - Epoch(train) [110][150/293] lr: 5.000000e-04 eta: 3:17:00 time: 0.447066 data_time: 0.093373 memory: 6338 loss_kpt: 0.125620 acc_pose: 0.748441 loss: 0.125620 2022/09/21 22:08:46 - mmengine - INFO - Epoch(train) [110][200/293] lr: 5.000000e-04 eta: 3:16:42 time: 0.451728 data_time: 0.094616 memory: 6338 loss_kpt: 0.127167 acc_pose: 0.773259 loss: 0.127167 2022/09/21 22:09:08 - mmengine - INFO - Epoch(train) [110][250/293] lr: 5.000000e-04 eta: 3:16:24 time: 0.437941 data_time: 0.091612 memory: 6338 loss_kpt: 0.127576 acc_pose: 0.796473 loss: 0.127576 2022/09/21 22:09:27 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:09:27 - mmengine - INFO - Saving checkpoint at 110 epochs 2022/09/21 22:09:36 - mmengine - INFO - Epoch(val) [110][50/407] eta: 0:00:43 time: 0.121043 data_time: 0.054426 memory: 6338 2022/09/21 22:09:42 - mmengine - INFO - Epoch(val) [110][100/407] eta: 0:00:34 time: 0.112837 data_time: 0.051607 memory: 587 2022/09/21 22:09:48 - mmengine - INFO - Epoch(val) [110][150/407] eta: 0:00:29 time: 0.115444 data_time: 0.051888 memory: 587 2022/09/21 22:09:53 - mmengine - INFO - Epoch(val) [110][200/407] eta: 0:00:22 time: 0.110169 data_time: 0.048888 memory: 587 2022/09/21 22:09:59 - mmengine - INFO - Epoch(val) [110][250/407] eta: 0:00:18 time: 0.115921 data_time: 0.054175 memory: 587 2022/09/21 22:10:05 - mmengine - INFO - Epoch(val) [110][300/407] eta: 0:00:12 time: 0.112784 data_time: 0.050003 memory: 587 2022/09/21 22:10:10 - mmengine - INFO - Epoch(val) [110][350/407] eta: 0:00:06 time: 0.115958 data_time: 0.053916 memory: 587 2022/09/21 22:10:16 - mmengine - INFO - Epoch(val) [110][400/407] eta: 0:00:00 time: 0.109129 data_time: 0.044318 memory: 587 2022/09/21 22:10:51 - mmengine - INFO - Evaluating CocoMetric... 2022/09/21 22:11:05 - mmengine - INFO - Epoch(val) [110][407/407] coco/AP: 0.663470 coco/AP .5: 0.869222 coco/AP .75: 0.742036 coco/AP (M): 0.633563 coco/AP (L): 0.725510 coco/AR: 0.724874 coco/AR .5: 0.915617 coco/AR .75: 0.796599 coco/AR (M): 0.683393 coco/AR (L): 0.784281 2022/09/21 22:11:05 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_100.pth is removed 2022/09/21 22:11:07 - mmengine - INFO - The best checkpoint with 0.6635 coco/AP at 110 epoch is saved to best_coco/AP_epoch_110.pth. 2022/09/21 22:11:32 - mmengine - INFO - Epoch(train) [111][50/293] lr: 5.000000e-04 eta: 3:15:35 time: 0.492588 data_time: 0.100625 memory: 6338 loss_kpt: 0.126203 acc_pose: 0.678243 loss: 0.126203 2022/09/21 22:11:56 - mmengine - INFO - Epoch(train) [111][100/293] lr: 5.000000e-04 eta: 3:15:18 time: 0.477798 data_time: 0.088507 memory: 6338 loss_kpt: 0.125544 acc_pose: 0.814300 loss: 0.125544 2022/09/21 22:12:20 - mmengine - INFO - Epoch(train) [111][150/293] lr: 5.000000e-04 eta: 3:15:02 time: 0.479338 data_time: 0.092773 memory: 6338 loss_kpt: 0.125272 acc_pose: 0.770403 loss: 0.125272 2022/09/21 22:12:43 - mmengine - INFO - Epoch(train) [111][200/293] lr: 5.000000e-04 eta: 3:14:44 time: 0.463008 data_time: 0.088900 memory: 6338 loss_kpt: 0.127636 acc_pose: 0.808670 loss: 0.127636 2022/09/21 22:13:07 - mmengine - INFO - Epoch(train) [111][250/293] lr: 5.000000e-04 eta: 3:14:28 time: 0.487917 data_time: 0.089380 memory: 6338 loss_kpt: 0.127591 acc_pose: 0.810908 loss: 0.127591 2022/09/21 22:13:27 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:13:27 - mmengine - INFO - Saving checkpoint at 111 epochs 2022/09/21 22:13:53 - mmengine - INFO - Epoch(train) [112][50/293] lr: 5.000000e-04 eta: 3:13:38 time: 0.458982 data_time: 0.097895 memory: 6338 loss_kpt: 0.122527 acc_pose: 0.781281 loss: 0.122527 2022/09/21 22:14:16 - mmengine - INFO - Epoch(train) [112][100/293] lr: 5.000000e-04 eta: 3:13:21 time: 0.462406 data_time: 0.087320 memory: 6338 loss_kpt: 0.126399 acc_pose: 0.752663 loss: 0.126399 2022/09/21 22:14:39 - mmengine - INFO - Epoch(train) [112][150/293] lr: 5.000000e-04 eta: 3:13:03 time: 0.462974 data_time: 0.087884 memory: 6338 loss_kpt: 0.125055 acc_pose: 0.820700 loss: 0.125055 2022/09/21 22:15:02 - mmengine - INFO - Epoch(train) [112][200/293] lr: 5.000000e-04 eta: 3:12:46 time: 0.461966 data_time: 0.085155 memory: 6338 loss_kpt: 0.126348 acc_pose: 0.859308 loss: 0.126348 2022/09/21 22:15:25 - mmengine - INFO - Epoch(train) [112][250/293] lr: 5.000000e-04 eta: 3:12:28 time: 0.460913 data_time: 0.090434 memory: 6338 loss_kpt: 0.129064 acc_pose: 0.794094 loss: 0.129064 2022/09/21 22:15:45 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:15:45 - mmengine - INFO - Saving checkpoint at 112 epochs 2022/09/21 22:16:12 - mmengine - INFO - Epoch(train) [113][50/293] lr: 5.000000e-04 eta: 3:11:39 time: 0.480356 data_time: 0.101288 memory: 6338 loss_kpt: 0.126300 acc_pose: 0.704429 loss: 0.126300 2022/09/21 22:16:35 - mmengine - INFO - Epoch(train) [113][100/293] lr: 5.000000e-04 eta: 3:11:22 time: 0.460754 data_time: 0.088274 memory: 6338 loss_kpt: 0.123075 acc_pose: 0.804489 loss: 0.123075 2022/09/21 22:16:59 - mmengine - INFO - Epoch(train) [113][150/293] lr: 5.000000e-04 eta: 3:11:05 time: 0.477267 data_time: 0.090788 memory: 6338 loss_kpt: 0.125360 acc_pose: 0.711510 loss: 0.125360 2022/09/21 22:17:14 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:17:21 - mmengine - INFO - Epoch(train) [113][200/293] lr: 5.000000e-04 eta: 3:10:47 time: 0.453870 data_time: 0.086171 memory: 6338 loss_kpt: 0.127774 acc_pose: 0.787657 loss: 0.127774 2022/09/21 22:17:45 - mmengine - INFO - Epoch(train) [113][250/293] lr: 5.000000e-04 eta: 3:10:30 time: 0.463452 data_time: 0.095982 memory: 6338 loss_kpt: 0.126270 acc_pose: 0.769799 loss: 0.126270 2022/09/21 22:18:04 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:18:04 - mmengine - INFO - Saving checkpoint at 113 epochs 2022/09/21 22:18:31 - mmengine - INFO - Epoch(train) [114][50/293] lr: 5.000000e-04 eta: 3:09:41 time: 0.484277 data_time: 0.102234 memory: 6338 loss_kpt: 0.129649 acc_pose: 0.785709 loss: 0.129649 2022/09/21 22:18:55 - mmengine - INFO - Epoch(train) [114][100/293] lr: 5.000000e-04 eta: 3:09:24 time: 0.472967 data_time: 0.090121 memory: 6338 loss_kpt: 0.125620 acc_pose: 0.828026 loss: 0.125620 2022/09/21 22:19:18 - mmengine - INFO - Epoch(train) [114][150/293] lr: 5.000000e-04 eta: 3:09:07 time: 0.467744 data_time: 0.090998 memory: 6338 loss_kpt: 0.125182 acc_pose: 0.806426 loss: 0.125182 2022/09/21 22:19:42 - mmengine - INFO - Epoch(train) [114][200/293] lr: 5.000000e-04 eta: 3:08:50 time: 0.478502 data_time: 0.090422 memory: 6338 loss_kpt: 0.125572 acc_pose: 0.741001 loss: 0.125572 2022/09/21 22:20:06 - mmengine - INFO - Epoch(train) [114][250/293] lr: 5.000000e-04 eta: 3:08:34 time: 0.482735 data_time: 0.102898 memory: 6338 loss_kpt: 0.126457 acc_pose: 0.811181 loss: 0.126457 2022/09/21 22:20:26 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:20:26 - mmengine - INFO - Saving checkpoint at 114 epochs 2022/09/21 22:20:53 - mmengine - INFO - Epoch(train) [115][50/293] lr: 5.000000e-04 eta: 3:07:45 time: 0.473746 data_time: 0.096549 memory: 6338 loss_kpt: 0.123836 acc_pose: 0.833568 loss: 0.123836 2022/09/21 22:21:16 - mmengine - INFO - Epoch(train) [115][100/293] lr: 5.000000e-04 eta: 3:07:28 time: 0.468367 data_time: 0.085411 memory: 6338 loss_kpt: 0.122344 acc_pose: 0.791914 loss: 0.122344 2022/09/21 22:21:40 - mmengine - INFO - Epoch(train) [115][150/293] lr: 5.000000e-04 eta: 3:07:11 time: 0.481143 data_time: 0.105644 memory: 6338 loss_kpt: 0.126020 acc_pose: 0.791184 loss: 0.126020 2022/09/21 22:22:03 - mmengine - INFO - Epoch(train) [115][200/293] lr: 5.000000e-04 eta: 3:06:54 time: 0.466387 data_time: 0.091053 memory: 6338 loss_kpt: 0.126275 acc_pose: 0.841936 loss: 0.126275 2022/09/21 22:22:27 - mmengine - INFO - Epoch(train) [115][250/293] lr: 5.000000e-04 eta: 3:06:36 time: 0.473053 data_time: 0.090630 memory: 6338 loss_kpt: 0.125268 acc_pose: 0.784162 loss: 0.125268 2022/09/21 22:22:47 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:22:47 - mmengine - INFO - Saving checkpoint at 115 epochs 2022/09/21 22:23:14 - mmengine - INFO - Epoch(train) [116][50/293] lr: 5.000000e-04 eta: 3:05:49 time: 0.494224 data_time: 0.100135 memory: 6338 loss_kpt: 0.126085 acc_pose: 0.758153 loss: 0.126085 2022/09/21 22:23:38 - mmengine - INFO - Epoch(train) [116][100/293] lr: 5.000000e-04 eta: 3:05:32 time: 0.473196 data_time: 0.094844 memory: 6338 loss_kpt: 0.127175 acc_pose: 0.777895 loss: 0.127175 2022/09/21 22:24:02 - mmengine - INFO - Epoch(train) [116][150/293] lr: 5.000000e-04 eta: 3:05:15 time: 0.487944 data_time: 0.090173 memory: 6338 loss_kpt: 0.127988 acc_pose: 0.792211 loss: 0.127988 2022/09/21 22:24:26 - mmengine - INFO - Epoch(train) [116][200/293] lr: 5.000000e-04 eta: 3:04:58 time: 0.471593 data_time: 0.089436 memory: 6338 loss_kpt: 0.128633 acc_pose: 0.798199 loss: 0.128633 2022/09/21 22:24:50 - mmengine - INFO - Epoch(train) [116][250/293] lr: 5.000000e-04 eta: 3:04:41 time: 0.487716 data_time: 0.097385 memory: 6338 loss_kpt: 0.125485 acc_pose: 0.775816 loss: 0.125485 2022/09/21 22:25:11 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:25:11 - mmengine - INFO - Saving checkpoint at 116 epochs 2022/09/21 22:25:19 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:25:37 - mmengine - INFO - Epoch(train) [117][50/293] lr: 5.000000e-04 eta: 3:03:53 time: 0.477799 data_time: 0.101158 memory: 6338 loss_kpt: 0.126482 acc_pose: 0.782020 loss: 0.126482 2022/09/21 22:26:00 - mmengine - INFO - Epoch(train) [117][100/293] lr: 5.000000e-04 eta: 3:03:35 time: 0.453245 data_time: 0.088023 memory: 6338 loss_kpt: 0.125219 acc_pose: 0.715462 loss: 0.125219 2022/09/21 22:26:23 - mmengine - INFO - Epoch(train) [117][150/293] lr: 5.000000e-04 eta: 3:03:17 time: 0.452707 data_time: 0.096666 memory: 6338 loss_kpt: 0.126081 acc_pose: 0.813988 loss: 0.126081 2022/09/21 22:26:45 - mmengine - INFO - Epoch(train) [117][200/293] lr: 5.000000e-04 eta: 3:02:59 time: 0.449711 data_time: 0.096252 memory: 6338 loss_kpt: 0.124300 acc_pose: 0.752050 loss: 0.124300 2022/09/21 22:27:09 - mmengine - INFO - Epoch(train) [117][250/293] lr: 5.000000e-04 eta: 3:02:42 time: 0.470873 data_time: 0.092671 memory: 6338 loss_kpt: 0.124713 acc_pose: 0.792424 loss: 0.124713 2022/09/21 22:27:29 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:27:29 - mmengine - INFO - Saving checkpoint at 117 epochs 2022/09/21 22:27:55 - mmengine - INFO - Epoch(train) [118][50/293] lr: 5.000000e-04 eta: 3:01:53 time: 0.467812 data_time: 0.103840 memory: 6338 loss_kpt: 0.125209 acc_pose: 0.796919 loss: 0.125209 2022/09/21 22:28:18 - mmengine - INFO - Epoch(train) [118][100/293] lr: 5.000000e-04 eta: 3:01:36 time: 0.467231 data_time: 0.088809 memory: 6338 loss_kpt: 0.124913 acc_pose: 0.797696 loss: 0.124913 2022/09/21 22:28:42 - mmengine - INFO - Epoch(train) [118][150/293] lr: 5.000000e-04 eta: 3:01:19 time: 0.474581 data_time: 0.091480 memory: 6338 loss_kpt: 0.126380 acc_pose: 0.726239 loss: 0.126380 2022/09/21 22:29:05 - mmengine - INFO - Epoch(train) [118][200/293] lr: 5.000000e-04 eta: 3:01:01 time: 0.470573 data_time: 0.086850 memory: 6338 loss_kpt: 0.125075 acc_pose: 0.823883 loss: 0.125075 2022/09/21 22:29:29 - mmengine - INFO - Epoch(train) [118][250/293] lr: 5.000000e-04 eta: 3:00:44 time: 0.476050 data_time: 0.085383 memory: 6338 loss_kpt: 0.126707 acc_pose: 0.742035 loss: 0.126707 2022/09/21 22:29:49 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:29:49 - mmengine - INFO - Saving checkpoint at 118 epochs 2022/09/21 22:30:15 - mmengine - INFO - Epoch(train) [119][50/293] lr: 5.000000e-04 eta: 2:59:56 time: 0.474062 data_time: 0.100914 memory: 6338 loss_kpt: 0.124755 acc_pose: 0.805065 loss: 0.124755 2022/09/21 22:30:37 - mmengine - INFO - Epoch(train) [119][100/293] lr: 5.000000e-04 eta: 2:59:38 time: 0.443757 data_time: 0.100025 memory: 6338 loss_kpt: 0.129995 acc_pose: 0.792393 loss: 0.129995 2022/09/21 22:31:00 - mmengine - INFO - Epoch(train) [119][150/293] lr: 5.000000e-04 eta: 2:59:19 time: 0.443413 data_time: 0.097213 memory: 6338 loss_kpt: 0.122450 acc_pose: 0.772857 loss: 0.122450 2022/09/21 22:31:22 - mmengine - INFO - Epoch(train) [119][200/293] lr: 5.000000e-04 eta: 2:59:01 time: 0.444514 data_time: 0.101190 memory: 6338 loss_kpt: 0.123794 acc_pose: 0.822670 loss: 0.123794 2022/09/21 22:31:45 - mmengine - INFO - Epoch(train) [119][250/293] lr: 5.000000e-04 eta: 2:58:43 time: 0.453989 data_time: 0.100149 memory: 6338 loss_kpt: 0.122529 acc_pose: 0.767565 loss: 0.122529 2022/09/21 22:32:03 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:32:03 - mmengine - INFO - Saving checkpoint at 119 epochs 2022/09/21 22:32:30 - mmengine - INFO - Epoch(train) [120][50/293] lr: 5.000000e-04 eta: 2:57:55 time: 0.483128 data_time: 0.095417 memory: 6338 loss_kpt: 0.123213 acc_pose: 0.850309 loss: 0.123213 2022/09/21 22:32:54 - mmengine - INFO - Epoch(train) [120][100/293] lr: 5.000000e-04 eta: 2:57:38 time: 0.480916 data_time: 0.094376 memory: 6338 loss_kpt: 0.126414 acc_pose: 0.800408 loss: 0.126414 2022/09/21 22:33:10 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:33:18 - mmengine - INFO - Epoch(train) [120][150/293] lr: 5.000000e-04 eta: 2:57:21 time: 0.473956 data_time: 0.094040 memory: 6338 loss_kpt: 0.123596 acc_pose: 0.814830 loss: 0.123596 2022/09/21 22:33:41 - mmengine - INFO - Epoch(train) [120][200/293] lr: 5.000000e-04 eta: 2:57:04 time: 0.471120 data_time: 0.088931 memory: 6338 loss_kpt: 0.127586 acc_pose: 0.732157 loss: 0.127586 2022/09/21 22:34:05 - mmengine - INFO - Epoch(train) [120][250/293] lr: 5.000000e-04 eta: 2:56:46 time: 0.481799 data_time: 0.091343 memory: 6338 loss_kpt: 0.122841 acc_pose: 0.813962 loss: 0.122841 2022/09/21 22:34:25 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:34:25 - mmengine - INFO - Saving checkpoint at 120 epochs 2022/09/21 22:34:34 - mmengine - INFO - Epoch(val) [120][50/407] eta: 0:00:42 time: 0.118757 data_time: 0.056649 memory: 6338 2022/09/21 22:34:40 - mmengine - INFO - Epoch(val) [120][100/407] eta: 0:00:35 time: 0.114955 data_time: 0.052724 memory: 587 2022/09/21 22:34:46 - mmengine - INFO - Epoch(val) [120][150/407] eta: 0:00:29 time: 0.114735 data_time: 0.053238 memory: 587 2022/09/21 22:34:52 - mmengine - INFO - Epoch(val) [120][200/407] eta: 0:00:23 time: 0.114374 data_time: 0.050839 memory: 587 2022/09/21 22:34:58 - mmengine - INFO - Epoch(val) [120][250/407] eta: 0:00:18 time: 0.119606 data_time: 0.056781 memory: 587 2022/09/21 22:35:03 - mmengine - INFO - Epoch(val) [120][300/407] eta: 0:00:12 time: 0.114467 data_time: 0.052502 memory: 587 2022/09/21 22:35:09 - mmengine - INFO - Epoch(val) [120][350/407] eta: 0:00:06 time: 0.119567 data_time: 0.054986 memory: 587 2022/09/21 22:35:14 - mmengine - INFO - Epoch(val) [120][400/407] eta: 0:00:00 time: 0.102919 data_time: 0.043959 memory: 587 2022/09/21 22:35:50 - mmengine - INFO - Evaluating CocoMetric... 2022/09/21 22:36:04 - mmengine - INFO - Epoch(val) [120][407/407] coco/AP: 0.667265 coco/AP .5: 0.873256 coco/AP .75: 0.743209 coco/AP (M): 0.637841 coco/AP (L): 0.728114 coco/AR: 0.728243 coco/AR .5: 0.917349 coco/AR .75: 0.799591 coco/AR (M): 0.688145 coco/AR (L): 0.786139 2022/09/21 22:36:04 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_110.pth is removed 2022/09/21 22:36:06 - mmengine - INFO - The best checkpoint with 0.6673 coco/AP at 120 epoch is saved to best_coco/AP_epoch_120.pth. 2022/09/21 22:36:30 - mmengine - INFO - Epoch(train) [121][50/293] lr: 5.000000e-04 eta: 2:55:59 time: 0.485763 data_time: 0.104421 memory: 6338 loss_kpt: 0.124538 acc_pose: 0.802983 loss: 0.124538 2022/09/21 22:36:54 - mmengine - INFO - Epoch(train) [121][100/293] lr: 5.000000e-04 eta: 2:55:42 time: 0.463606 data_time: 0.089297 memory: 6338 loss_kpt: 0.122346 acc_pose: 0.819318 loss: 0.122346 2022/09/21 22:37:18 - mmengine - INFO - Epoch(train) [121][150/293] lr: 5.000000e-04 eta: 2:55:24 time: 0.478812 data_time: 0.094595 memory: 6338 loss_kpt: 0.123918 acc_pose: 0.822961 loss: 0.123918 2022/09/21 22:37:41 - mmengine - INFO - Epoch(train) [121][200/293] lr: 5.000000e-04 eta: 2:55:07 time: 0.471241 data_time: 0.097096 memory: 6338 loss_kpt: 0.124442 acc_pose: 0.801908 loss: 0.124442 2022/09/21 22:38:05 - mmengine - INFO - Epoch(train) [121][250/293] lr: 5.000000e-04 eta: 2:54:50 time: 0.476549 data_time: 0.096283 memory: 6338 loss_kpt: 0.123212 acc_pose: 0.804931 loss: 0.123212 2022/09/21 22:38:25 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:38:25 - mmengine - INFO - Saving checkpoint at 121 epochs 2022/09/21 22:38:51 - mmengine - INFO - Epoch(train) [122][50/293] lr: 5.000000e-04 eta: 2:54:03 time: 0.482055 data_time: 0.092784 memory: 6338 loss_kpt: 0.124252 acc_pose: 0.755359 loss: 0.124252 2022/09/21 22:39:15 - mmengine - INFO - Epoch(train) [122][100/293] lr: 5.000000e-04 eta: 2:53:45 time: 0.477727 data_time: 0.088776 memory: 6338 loss_kpt: 0.124479 acc_pose: 0.815870 loss: 0.124479 2022/09/21 22:39:39 - mmengine - INFO - Epoch(train) [122][150/293] lr: 5.000000e-04 eta: 2:53:28 time: 0.471872 data_time: 0.089317 memory: 6338 loss_kpt: 0.123271 acc_pose: 0.799123 loss: 0.123271 2022/09/21 22:40:02 - mmengine - INFO - Epoch(train) [122][200/293] lr: 5.000000e-04 eta: 2:53:10 time: 0.467889 data_time: 0.088331 memory: 6338 loss_kpt: 0.123731 acc_pose: 0.754438 loss: 0.123731 2022/09/21 22:40:26 - mmengine - INFO - Epoch(train) [122][250/293] lr: 5.000000e-04 eta: 2:52:53 time: 0.469021 data_time: 0.087268 memory: 6338 loss_kpt: 0.123020 acc_pose: 0.835030 loss: 0.123020 2022/09/21 22:40:45 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:40:45 - mmengine - INFO - Saving checkpoint at 122 epochs 2022/09/21 22:41:12 - mmengine - INFO - Epoch(train) [123][50/293] lr: 5.000000e-04 eta: 2:52:05 time: 0.475770 data_time: 0.096838 memory: 6338 loss_kpt: 0.123551 acc_pose: 0.771418 loss: 0.123551 2022/09/21 22:41:35 - mmengine - INFO - Epoch(train) [123][100/293] lr: 5.000000e-04 eta: 2:51:48 time: 0.463772 data_time: 0.094440 memory: 6338 loss_kpt: 0.123755 acc_pose: 0.797499 loss: 0.123755 2022/09/21 22:41:58 - mmengine - INFO - Epoch(train) [123][150/293] lr: 5.000000e-04 eta: 2:51:30 time: 0.467651 data_time: 0.086903 memory: 6338 loss_kpt: 0.125794 acc_pose: 0.796649 loss: 0.125794 2022/09/21 22:42:21 - mmengine - INFO - Epoch(train) [123][200/293] lr: 5.000000e-04 eta: 2:51:12 time: 0.458815 data_time: 0.086969 memory: 6338 loss_kpt: 0.123998 acc_pose: 0.759242 loss: 0.123998 2022/09/21 22:42:45 - mmengine - INFO - Epoch(train) [123][250/293] lr: 5.000000e-04 eta: 2:50:54 time: 0.467992 data_time: 0.089020 memory: 6338 loss_kpt: 0.125061 acc_pose: 0.811913 loss: 0.125061 2022/09/21 22:42:47 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:43:04 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:43:04 - mmengine - INFO - Saving checkpoint at 123 epochs 2022/09/21 22:43:30 - mmengine - INFO - Epoch(train) [124][50/293] lr: 5.000000e-04 eta: 2:50:07 time: 0.469064 data_time: 0.093068 memory: 6338 loss_kpt: 0.123237 acc_pose: 0.756897 loss: 0.123237 2022/09/21 22:43:53 - mmengine - INFO - Epoch(train) [124][100/293] lr: 5.000000e-04 eta: 2:49:49 time: 0.462596 data_time: 0.083205 memory: 6338 loss_kpt: 0.121991 acc_pose: 0.820556 loss: 0.121991 2022/09/21 22:44:17 - mmengine - INFO - Epoch(train) [124][150/293] lr: 5.000000e-04 eta: 2:49:32 time: 0.473777 data_time: 0.093651 memory: 6338 loss_kpt: 0.120881 acc_pose: 0.769108 loss: 0.120881 2022/09/21 22:44:40 - mmengine - INFO - Epoch(train) [124][200/293] lr: 5.000000e-04 eta: 2:49:14 time: 0.459900 data_time: 0.086618 memory: 6338 loss_kpt: 0.125472 acc_pose: 0.832655 loss: 0.125472 2022/09/21 22:45:03 - mmengine - INFO - Epoch(train) [124][250/293] lr: 5.000000e-04 eta: 2:48:55 time: 0.458758 data_time: 0.090718 memory: 6338 loss_kpt: 0.126345 acc_pose: 0.796200 loss: 0.126345 2022/09/21 22:45:22 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:45:22 - mmengine - INFO - Saving checkpoint at 124 epochs 2022/09/21 22:45:47 - mmengine - INFO - Epoch(train) [125][50/293] lr: 5.000000e-04 eta: 2:48:08 time: 0.455816 data_time: 0.095219 memory: 6338 loss_kpt: 0.123391 acc_pose: 0.799035 loss: 0.123391 2022/09/21 22:46:09 - mmengine - INFO - Epoch(train) [125][100/293] lr: 5.000000e-04 eta: 2:47:49 time: 0.440018 data_time: 0.095898 memory: 6338 loss_kpt: 0.123351 acc_pose: 0.800234 loss: 0.123351 2022/09/21 22:46:32 - mmengine - INFO - Epoch(train) [125][150/293] lr: 5.000000e-04 eta: 2:47:31 time: 0.447546 data_time: 0.091171 memory: 6338 loss_kpt: 0.123171 acc_pose: 0.779833 loss: 0.123171 2022/09/21 22:46:55 - mmengine - INFO - Epoch(train) [125][200/293] lr: 5.000000e-04 eta: 2:47:13 time: 0.461529 data_time: 0.092656 memory: 6338 loss_kpt: 0.123603 acc_pose: 0.763004 loss: 0.123603 2022/09/21 22:47:18 - mmengine - INFO - Epoch(train) [125][250/293] lr: 5.000000e-04 eta: 2:46:55 time: 0.470287 data_time: 0.092346 memory: 6338 loss_kpt: 0.125348 acc_pose: 0.835768 loss: 0.125348 2022/09/21 22:47:38 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:47:38 - mmengine - INFO - Saving checkpoint at 125 epochs 2022/09/21 22:48:04 - mmengine - INFO - Epoch(train) [126][50/293] lr: 5.000000e-04 eta: 2:46:09 time: 0.473663 data_time: 0.098263 memory: 6338 loss_kpt: 0.123640 acc_pose: 0.787269 loss: 0.123640 2022/09/21 22:48:27 - mmengine - INFO - Epoch(train) [126][100/293] lr: 5.000000e-04 eta: 2:45:51 time: 0.463241 data_time: 0.091058 memory: 6338 loss_kpt: 0.121872 acc_pose: 0.819927 loss: 0.121872 2022/09/21 22:48:50 - mmengine - INFO - Epoch(train) [126][150/293] lr: 5.000000e-04 eta: 2:45:33 time: 0.461175 data_time: 0.090066 memory: 6338 loss_kpt: 0.124305 acc_pose: 0.810735 loss: 0.124305 2022/09/21 22:49:14 - mmengine - INFO - Epoch(train) [126][200/293] lr: 5.000000e-04 eta: 2:45:15 time: 0.465634 data_time: 0.091372 memory: 6338 loss_kpt: 0.123419 acc_pose: 0.806336 loss: 0.123419 2022/09/21 22:49:37 - mmengine - INFO - Epoch(train) [126][250/293] lr: 5.000000e-04 eta: 2:44:57 time: 0.462990 data_time: 0.089905 memory: 6338 loss_kpt: 0.121558 acc_pose: 0.780916 loss: 0.121558 2022/09/21 22:49:56 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:49:56 - mmengine - INFO - Saving checkpoint at 126 epochs 2022/09/21 22:50:24 - mmengine - INFO - Epoch(train) [127][50/293] lr: 5.000000e-04 eta: 2:44:11 time: 0.499762 data_time: 0.106107 memory: 6338 loss_kpt: 0.124321 acc_pose: 0.775431 loss: 0.124321 2022/09/21 22:50:40 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:50:48 - mmengine - INFO - Epoch(train) [127][100/293] lr: 5.000000e-04 eta: 2:43:54 time: 0.478075 data_time: 0.089194 memory: 6338 loss_kpt: 0.125214 acc_pose: 0.778209 loss: 0.125214 2022/09/21 22:51:12 - mmengine - INFO - Epoch(train) [127][150/293] lr: 5.000000e-04 eta: 2:43:36 time: 0.484590 data_time: 0.093947 memory: 6338 loss_kpt: 0.119211 acc_pose: 0.813600 loss: 0.119211 2022/09/21 22:51:36 - mmengine - INFO - Epoch(train) [127][200/293] lr: 5.000000e-04 eta: 2:43:19 time: 0.470220 data_time: 0.088713 memory: 6338 loss_kpt: 0.123506 acc_pose: 0.774288 loss: 0.123506 2022/09/21 22:52:00 - mmengine - INFO - Epoch(train) [127][250/293] lr: 5.000000e-04 eta: 2:43:01 time: 0.477643 data_time: 0.093864 memory: 6338 loss_kpt: 0.122850 acc_pose: 0.787139 loss: 0.122850 2022/09/21 22:52:19 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:52:19 - mmengine - INFO - Saving checkpoint at 127 epochs 2022/09/21 22:52:46 - mmengine - INFO - Epoch(train) [128][50/293] lr: 5.000000e-04 eta: 2:42:15 time: 0.474820 data_time: 0.100311 memory: 6338 loss_kpt: 0.123864 acc_pose: 0.814611 loss: 0.123864 2022/09/21 22:53:10 - mmengine - INFO - Epoch(train) [128][100/293] lr: 5.000000e-04 eta: 2:41:57 time: 0.468061 data_time: 0.087631 memory: 6338 loss_kpt: 0.121633 acc_pose: 0.797533 loss: 0.121633 2022/09/21 22:53:33 - mmengine - INFO - Epoch(train) [128][150/293] lr: 5.000000e-04 eta: 2:41:39 time: 0.472505 data_time: 0.097517 memory: 6338 loss_kpt: 0.122162 acc_pose: 0.821125 loss: 0.122162 2022/09/21 22:53:56 - mmengine - INFO - Epoch(train) [128][200/293] lr: 5.000000e-04 eta: 2:41:21 time: 0.461166 data_time: 0.086336 memory: 6338 loss_kpt: 0.120148 acc_pose: 0.768951 loss: 0.120148 2022/09/21 22:54:20 - mmengine - INFO - Epoch(train) [128][250/293] lr: 5.000000e-04 eta: 2:41:03 time: 0.468310 data_time: 0.086143 memory: 6338 loss_kpt: 0.123120 acc_pose: 0.799866 loss: 0.123120 2022/09/21 22:54:40 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:54:40 - mmengine - INFO - Saving checkpoint at 128 epochs 2022/09/21 22:55:06 - mmengine - INFO - Epoch(train) [129][50/293] lr: 5.000000e-04 eta: 2:40:17 time: 0.470501 data_time: 0.094730 memory: 6338 loss_kpt: 0.121506 acc_pose: 0.779602 loss: 0.121506 2022/09/21 22:55:28 - mmengine - INFO - Epoch(train) [129][100/293] lr: 5.000000e-04 eta: 2:39:58 time: 0.448156 data_time: 0.083976 memory: 6338 loss_kpt: 0.122818 acc_pose: 0.784211 loss: 0.122818 2022/09/21 22:55:52 - mmengine - INFO - Epoch(train) [129][150/293] lr: 5.000000e-04 eta: 2:39:40 time: 0.469373 data_time: 0.089090 memory: 6338 loss_kpt: 0.122847 acc_pose: 0.814545 loss: 0.122847 2022/09/21 22:56:15 - mmengine - INFO - Epoch(train) [129][200/293] lr: 5.000000e-04 eta: 2:39:22 time: 0.455288 data_time: 0.080215 memory: 6338 loss_kpt: 0.121065 acc_pose: 0.746726 loss: 0.121065 2022/09/21 22:56:38 - mmengine - INFO - Epoch(train) [129][250/293] lr: 5.000000e-04 eta: 2:39:04 time: 0.460574 data_time: 0.090100 memory: 6338 loss_kpt: 0.125222 acc_pose: 0.825530 loss: 0.125222 2022/09/21 22:56:57 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:56:57 - mmengine - INFO - Saving checkpoint at 129 epochs 2022/09/21 22:57:24 - mmengine - INFO - Epoch(train) [130][50/293] lr: 5.000000e-04 eta: 2:38:18 time: 0.483716 data_time: 0.100802 memory: 6338 loss_kpt: 0.122910 acc_pose: 0.837976 loss: 0.122910 2022/09/21 22:57:48 - mmengine - INFO - Epoch(train) [130][100/293] lr: 5.000000e-04 eta: 2:38:00 time: 0.472449 data_time: 0.094599 memory: 6338 loss_kpt: 0.122263 acc_pose: 0.771057 loss: 0.122263 2022/09/21 22:58:12 - mmengine - INFO - Epoch(train) [130][150/293] lr: 5.000000e-04 eta: 2:37:43 time: 0.488402 data_time: 0.090653 memory: 6338 loss_kpt: 0.119055 acc_pose: 0.825087 loss: 0.119055 2022/09/21 22:58:36 - mmengine - INFO - Epoch(train) [130][200/293] lr: 5.000000e-04 eta: 2:37:25 time: 0.466867 data_time: 0.091218 memory: 6338 loss_kpt: 0.123125 acc_pose: 0.829496 loss: 0.123125 2022/09/21 22:58:37 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:59:00 - mmengine - INFO - Epoch(train) [130][250/293] lr: 5.000000e-04 eta: 2:37:08 time: 0.491948 data_time: 0.099108 memory: 6338 loss_kpt: 0.122164 acc_pose: 0.724097 loss: 0.122164 2022/09/21 22:59:20 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 22:59:20 - mmengine - INFO - Saving checkpoint at 130 epochs 2022/09/21 22:59:29 - mmengine - INFO - Epoch(val) [130][50/407] eta: 0:00:41 time: 0.116898 data_time: 0.054399 memory: 6338 2022/09/21 22:59:35 - mmengine - INFO - Epoch(val) [130][100/407] eta: 0:00:35 time: 0.116712 data_time: 0.054180 memory: 587 2022/09/21 22:59:40 - mmengine - INFO - Epoch(val) [130][150/407] eta: 0:00:29 time: 0.115106 data_time: 0.051146 memory: 587 2022/09/21 22:59:46 - mmengine - INFO - Epoch(val) [130][200/407] eta: 0:00:23 time: 0.112128 data_time: 0.050349 memory: 587 2022/09/21 22:59:52 - mmengine - INFO - Epoch(val) [130][250/407] eta: 0:00:18 time: 0.116147 data_time: 0.050052 memory: 587 2022/09/21 22:59:58 - mmengine - INFO - Epoch(val) [130][300/407] eta: 0:00:12 time: 0.113565 data_time: 0.052860 memory: 587 2022/09/21 23:00:03 - mmengine - INFO - Epoch(val) [130][350/407] eta: 0:00:06 time: 0.117233 data_time: 0.054894 memory: 587 2022/09/21 23:00:09 - mmengine - INFO - Epoch(val) [130][400/407] eta: 0:00:00 time: 0.105664 data_time: 0.045836 memory: 587 2022/09/21 23:00:44 - mmengine - INFO - Evaluating CocoMetric... 2022/09/21 23:00:58 - mmengine - INFO - Epoch(val) [130][407/407] coco/AP: 0.670494 coco/AP .5: 0.874302 coco/AP .75: 0.749788 coco/AP (M): 0.640295 coco/AP (L): 0.732702 coco/AR: 0.730368 coco/AR .5: 0.918923 coco/AR .75: 0.800535 coco/AR (M): 0.690003 coco/AR (L): 0.788517 2022/09/21 23:00:58 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_120.pth is removed 2022/09/21 23:01:01 - mmengine - INFO - The best checkpoint with 0.6705 coco/AP at 130 epoch is saved to best_coco/AP_epoch_130.pth. 2022/09/21 23:01:25 - mmengine - INFO - Epoch(train) [131][50/293] lr: 5.000000e-04 eta: 2:36:23 time: 0.496749 data_time: 0.100032 memory: 6338 loss_kpt: 0.123167 acc_pose: 0.768924 loss: 0.123167 2022/09/21 23:01:49 - mmengine - INFO - Epoch(train) [131][100/293] lr: 5.000000e-04 eta: 2:36:05 time: 0.474146 data_time: 0.090082 memory: 6338 loss_kpt: 0.126553 acc_pose: 0.784493 loss: 0.126553 2022/09/21 23:02:13 - mmengine - INFO - Epoch(train) [131][150/293] lr: 5.000000e-04 eta: 2:35:47 time: 0.476357 data_time: 0.089108 memory: 6338 loss_kpt: 0.123663 acc_pose: 0.805599 loss: 0.123663 2022/09/21 23:02:37 - mmengine - INFO - Epoch(train) [131][200/293] lr: 5.000000e-04 eta: 2:35:30 time: 0.482961 data_time: 0.087780 memory: 6338 loss_kpt: 0.124387 acc_pose: 0.784318 loss: 0.124387 2022/09/21 23:03:01 - mmengine - INFO - Epoch(train) [131][250/293] lr: 5.000000e-04 eta: 2:35:12 time: 0.485100 data_time: 0.087379 memory: 6338 loss_kpt: 0.125543 acc_pose: 0.736432 loss: 0.125543 2022/09/21 23:03:22 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:03:22 - mmengine - INFO - Saving checkpoint at 131 epochs 2022/09/21 23:03:48 - mmengine - INFO - Epoch(train) [132][50/293] lr: 5.000000e-04 eta: 2:34:26 time: 0.462178 data_time: 0.097924 memory: 6338 loss_kpt: 0.122805 acc_pose: 0.801040 loss: 0.122805 2022/09/21 23:04:11 - mmengine - INFO - Epoch(train) [132][100/293] lr: 5.000000e-04 eta: 2:34:08 time: 0.465757 data_time: 0.090359 memory: 6338 loss_kpt: 0.122155 acc_pose: 0.775327 loss: 0.122155 2022/09/21 23:04:35 - mmengine - INFO - Epoch(train) [132][150/293] lr: 5.000000e-04 eta: 2:33:50 time: 0.479800 data_time: 0.086306 memory: 6338 loss_kpt: 0.120994 acc_pose: 0.847077 loss: 0.120994 2022/09/21 23:04:59 - mmengine - INFO - Epoch(train) [132][200/293] lr: 5.000000e-04 eta: 2:33:32 time: 0.468468 data_time: 0.086962 memory: 6338 loss_kpt: 0.121685 acc_pose: 0.752548 loss: 0.121685 2022/09/21 23:05:22 - mmengine - INFO - Epoch(train) [132][250/293] lr: 5.000000e-04 eta: 2:33:14 time: 0.460346 data_time: 0.089792 memory: 6338 loss_kpt: 0.121698 acc_pose: 0.776835 loss: 0.121698 2022/09/21 23:05:41 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:05:41 - mmengine - INFO - Saving checkpoint at 132 epochs 2022/09/21 23:06:08 - mmengine - INFO - Epoch(train) [133][50/293] lr: 5.000000e-04 eta: 2:32:29 time: 0.497023 data_time: 0.096552 memory: 6338 loss_kpt: 0.122736 acc_pose: 0.829016 loss: 0.122736 2022/09/21 23:06:32 - mmengine - INFO - Epoch(train) [133][100/293] lr: 5.000000e-04 eta: 2:32:11 time: 0.468717 data_time: 0.090950 memory: 6338 loss_kpt: 0.122978 acc_pose: 0.818097 loss: 0.122978 2022/09/21 23:06:56 - mmengine - INFO - Epoch(train) [133][150/293] lr: 5.000000e-04 eta: 2:31:54 time: 0.489141 data_time: 0.101605 memory: 6338 loss_kpt: 0.121498 acc_pose: 0.822416 loss: 0.121498 2022/09/21 23:07:21 - mmengine - INFO - Epoch(train) [133][200/293] lr: 5.000000e-04 eta: 2:31:36 time: 0.490236 data_time: 0.091415 memory: 6338 loss_kpt: 0.124704 acc_pose: 0.788382 loss: 0.124704 2022/09/21 23:07:45 - mmengine - INFO - Epoch(train) [133][250/293] lr: 5.000000e-04 eta: 2:31:18 time: 0.479957 data_time: 0.090261 memory: 6338 loss_kpt: 0.123216 acc_pose: 0.742648 loss: 0.123216 2022/09/21 23:08:04 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:08:04 - mmengine - INFO - Saving checkpoint at 133 epochs 2022/09/21 23:08:22 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:08:30 - mmengine - INFO - Epoch(train) [134][50/293] lr: 5.000000e-04 eta: 2:30:33 time: 0.463956 data_time: 0.099062 memory: 6338 loss_kpt: 0.121446 acc_pose: 0.801702 loss: 0.121446 2022/09/21 23:08:54 - mmengine - INFO - Epoch(train) [134][100/293] lr: 5.000000e-04 eta: 2:30:15 time: 0.471271 data_time: 0.093920 memory: 6338 loss_kpt: 0.121488 acc_pose: 0.794555 loss: 0.121488 2022/09/21 23:09:17 - mmengine - INFO - Epoch(train) [134][150/293] lr: 5.000000e-04 eta: 2:29:56 time: 0.459791 data_time: 0.091867 memory: 6338 loss_kpt: 0.125051 acc_pose: 0.828346 loss: 0.125051 2022/09/21 23:09:40 - mmengine - INFO - Epoch(train) [134][200/293] lr: 5.000000e-04 eta: 2:29:38 time: 0.464010 data_time: 0.090223 memory: 6338 loss_kpt: 0.123012 acc_pose: 0.806900 loss: 0.123012 2022/09/21 23:10:04 - mmengine - INFO - Epoch(train) [134][250/293] lr: 5.000000e-04 eta: 2:29:20 time: 0.469321 data_time: 0.090027 memory: 6338 loss_kpt: 0.121956 acc_pose: 0.773233 loss: 0.121956 2022/09/21 23:10:23 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:10:23 - mmengine - INFO - Saving checkpoint at 134 epochs 2022/09/21 23:10:50 - mmengine - INFO - Epoch(train) [135][50/293] lr: 5.000000e-04 eta: 2:28:35 time: 0.485392 data_time: 0.096211 memory: 6338 loss_kpt: 0.124890 acc_pose: 0.808015 loss: 0.124890 2022/09/21 23:11:14 - mmengine - INFO - Epoch(train) [135][100/293] lr: 5.000000e-04 eta: 2:28:17 time: 0.469242 data_time: 0.089217 memory: 6338 loss_kpt: 0.123902 acc_pose: 0.810117 loss: 0.123902 2022/09/21 23:11:37 - mmengine - INFO - Epoch(train) [135][150/293] lr: 5.000000e-04 eta: 2:27:59 time: 0.461950 data_time: 0.092274 memory: 6338 loss_kpt: 0.122128 acc_pose: 0.741328 loss: 0.122128 2022/09/21 23:12:00 - mmengine - INFO - Epoch(train) [135][200/293] lr: 5.000000e-04 eta: 2:27:40 time: 0.462380 data_time: 0.091350 memory: 6338 loss_kpt: 0.120565 acc_pose: 0.842535 loss: 0.120565 2022/09/21 23:12:23 - mmengine - INFO - Epoch(train) [135][250/293] lr: 5.000000e-04 eta: 2:27:22 time: 0.470176 data_time: 0.092566 memory: 6338 loss_kpt: 0.119342 acc_pose: 0.756238 loss: 0.119342 2022/09/21 23:12:43 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:12:43 - mmengine - INFO - Saving checkpoint at 135 epochs 2022/09/21 23:13:10 - mmengine - INFO - Epoch(train) [136][50/293] lr: 5.000000e-04 eta: 2:26:37 time: 0.476706 data_time: 0.099011 memory: 6338 loss_kpt: 0.119766 acc_pose: 0.802550 loss: 0.119766 2022/09/21 23:13:33 - mmengine - INFO - Epoch(train) [136][100/293] lr: 5.000000e-04 eta: 2:26:19 time: 0.460707 data_time: 0.088465 memory: 6338 loss_kpt: 0.119070 acc_pose: 0.817346 loss: 0.119070 2022/09/21 23:13:56 - mmengine - INFO - Epoch(train) [136][150/293] lr: 5.000000e-04 eta: 2:26:01 time: 0.467847 data_time: 0.092957 memory: 6338 loss_kpt: 0.122583 acc_pose: 0.805386 loss: 0.122583 2022/09/21 23:14:20 - mmengine - INFO - Epoch(train) [136][200/293] lr: 5.000000e-04 eta: 2:25:42 time: 0.467885 data_time: 0.086706 memory: 6338 loss_kpt: 0.121642 acc_pose: 0.830048 loss: 0.121642 2022/09/21 23:14:43 - mmengine - INFO - Epoch(train) [136][250/293] lr: 5.000000e-04 eta: 2:25:24 time: 0.469652 data_time: 0.087243 memory: 6338 loss_kpt: 0.122466 acc_pose: 0.863551 loss: 0.122466 2022/09/21 23:15:03 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:15:03 - mmengine - INFO - Saving checkpoint at 136 epochs 2022/09/21 23:15:30 - mmengine - INFO - Epoch(train) [137][50/293] lr: 5.000000e-04 eta: 2:24:40 time: 0.489657 data_time: 0.094102 memory: 6338 loss_kpt: 0.119839 acc_pose: 0.813262 loss: 0.119839 2022/09/21 23:15:53 - mmengine - INFO - Epoch(train) [137][100/293] lr: 5.000000e-04 eta: 2:24:22 time: 0.471459 data_time: 0.091771 memory: 6338 loss_kpt: 0.120539 acc_pose: 0.838307 loss: 0.120539 2022/09/21 23:16:17 - mmengine - INFO - Epoch(train) [137][150/293] lr: 5.000000e-04 eta: 2:24:04 time: 0.477352 data_time: 0.089836 memory: 6338 loss_kpt: 0.121466 acc_pose: 0.826449 loss: 0.121466 2022/09/21 23:16:18 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:16:41 - mmengine - INFO - Epoch(train) [137][200/293] lr: 5.000000e-04 eta: 2:23:46 time: 0.478246 data_time: 0.088046 memory: 6338 loss_kpt: 0.121113 acc_pose: 0.779545 loss: 0.121113 2022/09/21 23:17:05 - mmengine - INFO - Epoch(train) [137][250/293] lr: 5.000000e-04 eta: 2:23:28 time: 0.480148 data_time: 0.097389 memory: 6338 loss_kpt: 0.120879 acc_pose: 0.818219 loss: 0.120879 2022/09/21 23:17:26 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:17:26 - mmengine - INFO - Saving checkpoint at 137 epochs 2022/09/21 23:17:53 - mmengine - INFO - Epoch(train) [138][50/293] lr: 5.000000e-04 eta: 2:22:43 time: 0.484638 data_time: 0.096958 memory: 6338 loss_kpt: 0.119456 acc_pose: 0.803771 loss: 0.119456 2022/09/21 23:18:16 - mmengine - INFO - Epoch(train) [138][100/293] lr: 5.000000e-04 eta: 2:22:25 time: 0.460747 data_time: 0.085515 memory: 6338 loss_kpt: 0.119134 acc_pose: 0.810245 loss: 0.119134 2022/09/21 23:18:38 - mmengine - INFO - Epoch(train) [138][150/293] lr: 5.000000e-04 eta: 2:22:06 time: 0.456459 data_time: 0.089606 memory: 6338 loss_kpt: 0.123642 acc_pose: 0.765237 loss: 0.123642 2022/09/21 23:19:01 - mmengine - INFO - Epoch(train) [138][200/293] lr: 5.000000e-04 eta: 2:21:48 time: 0.452292 data_time: 0.081837 memory: 6338 loss_kpt: 0.123606 acc_pose: 0.672306 loss: 0.123606 2022/09/21 23:19:24 - mmengine - INFO - Epoch(train) [138][250/293] lr: 5.000000e-04 eta: 2:21:29 time: 0.459612 data_time: 0.082707 memory: 6338 loss_kpt: 0.121854 acc_pose: 0.778046 loss: 0.121854 2022/09/21 23:19:43 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:19:43 - mmengine - INFO - Saving checkpoint at 138 epochs 2022/09/21 23:20:10 - mmengine - INFO - Epoch(train) [139][50/293] lr: 5.000000e-04 eta: 2:20:45 time: 0.496098 data_time: 0.100815 memory: 6338 loss_kpt: 0.119586 acc_pose: 0.763426 loss: 0.119586 2022/09/21 23:20:34 - mmengine - INFO - Epoch(train) [139][100/293] lr: 5.000000e-04 eta: 2:20:27 time: 0.482884 data_time: 0.082453 memory: 6338 loss_kpt: 0.121506 acc_pose: 0.785287 loss: 0.121506 2022/09/21 23:20:59 - mmengine - INFO - Epoch(train) [139][150/293] lr: 5.000000e-04 eta: 2:20:09 time: 0.489369 data_time: 0.095583 memory: 6338 loss_kpt: 0.120213 acc_pose: 0.805892 loss: 0.120213 2022/09/21 23:21:23 - mmengine - INFO - Epoch(train) [139][200/293] lr: 5.000000e-04 eta: 2:19:51 time: 0.479425 data_time: 0.082519 memory: 6338 loss_kpt: 0.120144 acc_pose: 0.796937 loss: 0.120144 2022/09/21 23:21:47 - mmengine - INFO - Epoch(train) [139][250/293] lr: 5.000000e-04 eta: 2:19:34 time: 0.492758 data_time: 0.092494 memory: 6338 loss_kpt: 0.121590 acc_pose: 0.718656 loss: 0.121590 2022/09/21 23:22:08 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:22:08 - mmengine - INFO - Saving checkpoint at 139 epochs 2022/09/21 23:22:36 - mmengine - INFO - Epoch(train) [140][50/293] lr: 5.000000e-04 eta: 2:18:50 time: 0.503993 data_time: 0.100411 memory: 6338 loss_kpt: 0.122332 acc_pose: 0.800891 loss: 0.122332 2022/09/21 23:23:00 - mmengine - INFO - Epoch(train) [140][100/293] lr: 5.000000e-04 eta: 2:18:32 time: 0.484555 data_time: 0.084272 memory: 6338 loss_kpt: 0.120691 acc_pose: 0.833077 loss: 0.120691 2022/09/21 23:23:24 - mmengine - INFO - Epoch(train) [140][150/293] lr: 5.000000e-04 eta: 2:18:14 time: 0.488222 data_time: 0.084057 memory: 6338 loss_kpt: 0.122162 acc_pose: 0.731303 loss: 0.122162 2022/09/21 23:23:48 - mmengine - INFO - Epoch(train) [140][200/293] lr: 5.000000e-04 eta: 2:17:56 time: 0.483440 data_time: 0.086120 memory: 6338 loss_kpt: 0.120471 acc_pose: 0.783483 loss: 0.120471 2022/09/21 23:24:13 - mmengine - INFO - Epoch(train) [140][250/293] lr: 5.000000e-04 eta: 2:17:38 time: 0.489715 data_time: 0.086449 memory: 6338 loss_kpt: 0.120719 acc_pose: 0.771594 loss: 0.120719 2022/09/21 23:24:24 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:24:33 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:24:33 - mmengine - INFO - Saving checkpoint at 140 epochs 2022/09/21 23:24:42 - mmengine - INFO - Epoch(val) [140][50/407] eta: 0:00:41 time: 0.117208 data_time: 0.054925 memory: 6338 2022/09/21 23:24:48 - mmengine - INFO - Epoch(val) [140][100/407] eta: 0:00:36 time: 0.119536 data_time: 0.054477 memory: 587 2022/09/21 23:24:54 - mmengine - INFO - Epoch(val) [140][150/407] eta: 0:00:29 time: 0.114767 data_time: 0.053423 memory: 587 2022/09/21 23:24:59 - mmengine - INFO - Epoch(val) [140][200/407] eta: 0:00:23 time: 0.113797 data_time: 0.052585 memory: 587 2022/09/21 23:25:05 - mmengine - INFO - Epoch(val) [140][250/407] eta: 0:00:17 time: 0.110885 data_time: 0.049206 memory: 587 2022/09/21 23:25:11 - mmengine - INFO - Epoch(val) [140][300/407] eta: 0:00:12 time: 0.115743 data_time: 0.055104 memory: 587 2022/09/21 23:25:16 - mmengine - INFO - Epoch(val) [140][350/407] eta: 0:00:06 time: 0.113990 data_time: 0.052478 memory: 587 2022/09/21 23:25:22 - mmengine - INFO - Epoch(val) [140][400/407] eta: 0:00:00 time: 0.107905 data_time: 0.047638 memory: 587 2022/09/21 23:25:57 - mmengine - INFO - Evaluating CocoMetric... 2022/09/21 23:26:11 - mmengine - INFO - Epoch(val) [140][407/407] coco/AP: 0.672941 coco/AP .5: 0.876897 coco/AP .75: 0.751112 coco/AP (M): 0.642241 coco/AP (L): 0.733680 coco/AR: 0.732604 coco/AR .5: 0.921442 coco/AR .75: 0.802424 coco/AR (M): 0.692543 coco/AR (L): 0.789929 2022/09/21 23:26:11 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_130.pth is removed 2022/09/21 23:26:14 - mmengine - INFO - The best checkpoint with 0.6729 coco/AP at 140 epoch is saved to best_coco/AP_epoch_140.pth. 2022/09/21 23:26:37 - mmengine - INFO - Epoch(train) [141][50/293] lr: 5.000000e-04 eta: 2:16:54 time: 0.472508 data_time: 0.096271 memory: 6338 loss_kpt: 0.122381 acc_pose: 0.778992 loss: 0.122381 2022/09/21 23:27:01 - mmengine - INFO - Epoch(train) [141][100/293] lr: 5.000000e-04 eta: 2:16:36 time: 0.480855 data_time: 0.084518 memory: 6338 loss_kpt: 0.120722 acc_pose: 0.854697 loss: 0.120722 2022/09/21 23:27:25 - mmengine - INFO - Epoch(train) [141][150/293] lr: 5.000000e-04 eta: 2:16:17 time: 0.468597 data_time: 0.092136 memory: 6338 loss_kpt: 0.119661 acc_pose: 0.755747 loss: 0.119661 2022/09/21 23:27:49 - mmengine - INFO - Epoch(train) [141][200/293] lr: 5.000000e-04 eta: 2:15:59 time: 0.475818 data_time: 0.086182 memory: 6338 loss_kpt: 0.120829 acc_pose: 0.755156 loss: 0.120829 2022/09/21 23:28:13 - mmengine - INFO - Epoch(train) [141][250/293] lr: 5.000000e-04 eta: 2:15:41 time: 0.489298 data_time: 0.089625 memory: 6338 loss_kpt: 0.121368 acc_pose: 0.829038 loss: 0.121368 2022/09/21 23:28:33 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:28:33 - mmengine - INFO - Saving checkpoint at 141 epochs 2022/09/21 23:29:00 - mmengine - INFO - Epoch(train) [142][50/293] lr: 5.000000e-04 eta: 2:14:57 time: 0.482782 data_time: 0.099998 memory: 6338 loss_kpt: 0.123893 acc_pose: 0.740203 loss: 0.123893 2022/09/21 23:29:23 - mmengine - INFO - Epoch(train) [142][100/293] lr: 5.000000e-04 eta: 2:14:39 time: 0.469885 data_time: 0.088576 memory: 6338 loss_kpt: 0.120529 acc_pose: 0.817650 loss: 0.120529 2022/09/21 23:29:47 - mmengine - INFO - Epoch(train) [142][150/293] lr: 5.000000e-04 eta: 2:14:21 time: 0.474975 data_time: 0.087308 memory: 6338 loss_kpt: 0.122732 acc_pose: 0.761493 loss: 0.122732 2022/09/21 23:30:11 - mmengine - INFO - Epoch(train) [142][200/293] lr: 5.000000e-04 eta: 2:14:03 time: 0.484837 data_time: 0.090118 memory: 6338 loss_kpt: 0.122403 acc_pose: 0.812537 loss: 0.122403 2022/09/21 23:30:35 - mmengine - INFO - Epoch(train) [142][250/293] lr: 5.000000e-04 eta: 2:13:44 time: 0.470901 data_time: 0.098731 memory: 6338 loss_kpt: 0.120368 acc_pose: 0.731395 loss: 0.120368 2022/09/21 23:30:54 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:30:54 - mmengine - INFO - Saving checkpoint at 142 epochs 2022/09/21 23:31:21 - mmengine - INFO - Epoch(train) [143][50/293] lr: 5.000000e-04 eta: 2:13:00 time: 0.481679 data_time: 0.099854 memory: 6338 loss_kpt: 0.119137 acc_pose: 0.835491 loss: 0.119137 2022/09/21 23:31:45 - mmengine - INFO - Epoch(train) [143][100/293] lr: 5.000000e-04 eta: 2:12:42 time: 0.481989 data_time: 0.087256 memory: 6338 loss_kpt: 0.122970 acc_pose: 0.818707 loss: 0.122970 2022/09/21 23:32:09 - mmengine - INFO - Epoch(train) [143][150/293] lr: 5.000000e-04 eta: 2:12:24 time: 0.473585 data_time: 0.088725 memory: 6338 loss_kpt: 0.121465 acc_pose: 0.774326 loss: 0.121465 2022/09/21 23:32:32 - mmengine - INFO - Epoch(train) [143][200/293] lr: 5.000000e-04 eta: 2:12:05 time: 0.466541 data_time: 0.081165 memory: 6338 loss_kpt: 0.118496 acc_pose: 0.806433 loss: 0.118496 2022/09/21 23:32:57 - mmengine - INFO - Epoch(train) [143][250/293] lr: 5.000000e-04 eta: 2:11:47 time: 0.489733 data_time: 0.087091 memory: 6338 loss_kpt: 0.121589 acc_pose: 0.773761 loss: 0.121589 2022/09/21 23:33:17 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:33:17 - mmengine - INFO - Saving checkpoint at 143 epochs 2022/09/21 23:33:44 - mmengine - INFO - Epoch(train) [144][50/293] lr: 5.000000e-04 eta: 2:11:04 time: 0.493512 data_time: 0.101286 memory: 6338 loss_kpt: 0.120559 acc_pose: 0.808169 loss: 0.120559 2022/09/21 23:34:08 - mmengine - INFO - Epoch(train) [144][100/293] lr: 5.000000e-04 eta: 2:10:46 time: 0.477969 data_time: 0.091492 memory: 6338 loss_kpt: 0.120251 acc_pose: 0.743769 loss: 0.120251 2022/09/21 23:34:09 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:34:32 - mmengine - INFO - Epoch(train) [144][150/293] lr: 5.000000e-04 eta: 2:10:28 time: 0.482625 data_time: 0.091832 memory: 6338 loss_kpt: 0.118573 acc_pose: 0.796992 loss: 0.118573 2022/09/21 23:34:57 - mmengine - INFO - Epoch(train) [144][200/293] lr: 5.000000e-04 eta: 2:10:09 time: 0.487323 data_time: 0.091114 memory: 6338 loss_kpt: 0.121706 acc_pose: 0.802902 loss: 0.121706 2022/09/21 23:35:21 - mmengine - INFO - Epoch(train) [144][250/293] lr: 5.000000e-04 eta: 2:09:51 time: 0.486264 data_time: 0.088782 memory: 6338 loss_kpt: 0.123515 acc_pose: 0.814103 loss: 0.123515 2022/09/21 23:35:41 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:35:41 - mmengine - INFO - Saving checkpoint at 144 epochs 2022/09/21 23:36:08 - mmengine - INFO - Epoch(train) [145][50/293] lr: 5.000000e-04 eta: 2:09:08 time: 0.494385 data_time: 0.095807 memory: 6338 loss_kpt: 0.122033 acc_pose: 0.762933 loss: 0.122033 2022/09/21 23:36:32 - mmengine - INFO - Epoch(train) [145][100/293] lr: 5.000000e-04 eta: 2:08:50 time: 0.470162 data_time: 0.090916 memory: 6338 loss_kpt: 0.118801 acc_pose: 0.798240 loss: 0.118801 2022/09/21 23:36:55 - mmengine - INFO - Epoch(train) [145][150/293] lr: 5.000000e-04 eta: 2:08:31 time: 0.458879 data_time: 0.086522 memory: 6338 loss_kpt: 0.120911 acc_pose: 0.789642 loss: 0.120911 2022/09/21 23:37:18 - mmengine - INFO - Epoch(train) [145][200/293] lr: 5.000000e-04 eta: 2:08:12 time: 0.454250 data_time: 0.084735 memory: 6338 loss_kpt: 0.120556 acc_pose: 0.747366 loss: 0.120556 2022/09/21 23:37:40 - mmengine - INFO - Epoch(train) [145][250/293] lr: 5.000000e-04 eta: 2:07:53 time: 0.456889 data_time: 0.089823 memory: 6338 loss_kpt: 0.121317 acc_pose: 0.796319 loss: 0.121317 2022/09/21 23:38:00 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:38:00 - mmengine - INFO - Saving checkpoint at 145 epochs 2022/09/21 23:38:26 - mmengine - INFO - Epoch(train) [146][50/293] lr: 5.000000e-04 eta: 2:07:10 time: 0.480297 data_time: 0.101266 memory: 6338 loss_kpt: 0.122314 acc_pose: 0.756693 loss: 0.122314 2022/09/21 23:38:50 - mmengine - INFO - Epoch(train) [146][100/293] lr: 5.000000e-04 eta: 2:06:51 time: 0.474802 data_time: 0.090695 memory: 6338 loss_kpt: 0.119522 acc_pose: 0.798724 loss: 0.119522 2022/09/21 23:39:13 - mmengine - INFO - Epoch(train) [146][150/293] lr: 5.000000e-04 eta: 2:06:33 time: 0.467478 data_time: 0.094352 memory: 6338 loss_kpt: 0.120735 acc_pose: 0.789143 loss: 0.120735 2022/09/21 23:39:37 - mmengine - INFO - Epoch(train) [146][200/293] lr: 5.000000e-04 eta: 2:06:14 time: 0.472616 data_time: 0.095853 memory: 6338 loss_kpt: 0.123035 acc_pose: 0.756367 loss: 0.123035 2022/09/21 23:40:01 - mmengine - INFO - Epoch(train) [146][250/293] lr: 5.000000e-04 eta: 2:05:56 time: 0.477398 data_time: 0.093224 memory: 6338 loss_kpt: 0.120091 acc_pose: 0.820768 loss: 0.120091 2022/09/21 23:40:21 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:40:21 - mmengine - INFO - Saving checkpoint at 146 epochs 2022/09/21 23:40:48 - mmengine - INFO - Epoch(train) [147][50/293] lr: 5.000000e-04 eta: 2:05:13 time: 0.488420 data_time: 0.103429 memory: 6338 loss_kpt: 0.120588 acc_pose: 0.811535 loss: 0.120588 2022/09/21 23:41:12 - mmengine - INFO - Epoch(train) [147][100/293] lr: 5.000000e-04 eta: 2:04:54 time: 0.475810 data_time: 0.084851 memory: 6338 loss_kpt: 0.120901 acc_pose: 0.840997 loss: 0.120901 2022/09/21 23:41:36 - mmengine - INFO - Epoch(train) [147][150/293] lr: 5.000000e-04 eta: 2:04:36 time: 0.483099 data_time: 0.089035 memory: 6338 loss_kpt: 0.120103 acc_pose: 0.807338 loss: 0.120103 2022/09/21 23:41:59 - mmengine - INFO - Epoch(train) [147][200/293] lr: 5.000000e-04 eta: 2:04:17 time: 0.465135 data_time: 0.085567 memory: 6338 loss_kpt: 0.118022 acc_pose: 0.773631 loss: 0.118022 2022/09/21 23:42:09 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:42:22 - mmengine - INFO - Epoch(train) [147][250/293] lr: 5.000000e-04 eta: 2:03:58 time: 0.460323 data_time: 0.092971 memory: 6338 loss_kpt: 0.121721 acc_pose: 0.802650 loss: 0.121721 2022/09/21 23:42:41 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:42:41 - mmengine - INFO - Saving checkpoint at 147 epochs 2022/09/21 23:43:07 - mmengine - INFO - Epoch(train) [148][50/293] lr: 5.000000e-04 eta: 2:03:15 time: 0.468078 data_time: 0.106201 memory: 6338 loss_kpt: 0.121761 acc_pose: 0.798233 loss: 0.121761 2022/09/21 23:43:31 - mmengine - INFO - Epoch(train) [148][100/293] lr: 5.000000e-04 eta: 2:02:56 time: 0.462327 data_time: 0.085363 memory: 6338 loss_kpt: 0.119497 acc_pose: 0.838541 loss: 0.119497 2022/09/21 23:43:54 - mmengine - INFO - Epoch(train) [148][150/293] lr: 5.000000e-04 eta: 2:02:37 time: 0.470240 data_time: 0.092497 memory: 6338 loss_kpt: 0.121876 acc_pose: 0.837219 loss: 0.121876 2022/09/21 23:44:17 - mmengine - INFO - Epoch(train) [148][200/293] lr: 5.000000e-04 eta: 2:02:19 time: 0.464224 data_time: 0.087026 memory: 6338 loss_kpt: 0.122260 acc_pose: 0.803326 loss: 0.122260 2022/09/21 23:44:41 - mmengine - INFO - Epoch(train) [148][250/293] lr: 5.000000e-04 eta: 2:02:00 time: 0.477468 data_time: 0.095219 memory: 6338 loss_kpt: 0.121037 acc_pose: 0.733265 loss: 0.121037 2022/09/21 23:45:01 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:45:01 - mmengine - INFO - Saving checkpoint at 148 epochs 2022/09/21 23:45:27 - mmengine - INFO - Epoch(train) [149][50/293] lr: 5.000000e-04 eta: 2:01:17 time: 0.469959 data_time: 0.106027 memory: 6338 loss_kpt: 0.121183 acc_pose: 0.768639 loss: 0.121183 2022/09/21 23:45:51 - mmengine - INFO - Epoch(train) [149][100/293] lr: 5.000000e-04 eta: 2:00:58 time: 0.472825 data_time: 0.085598 memory: 6338 loss_kpt: 0.117723 acc_pose: 0.788003 loss: 0.117723 2022/09/21 23:46:15 - mmengine - INFO - Epoch(train) [149][150/293] lr: 5.000000e-04 eta: 2:00:40 time: 0.474839 data_time: 0.094728 memory: 6338 loss_kpt: 0.119758 acc_pose: 0.852760 loss: 0.119758 2022/09/21 23:46:38 - mmengine - INFO - Epoch(train) [149][200/293] lr: 5.000000e-04 eta: 2:00:21 time: 0.464309 data_time: 0.085091 memory: 6338 loss_kpt: 0.119948 acc_pose: 0.790026 loss: 0.119948 2022/09/21 23:47:02 - mmengine - INFO - Epoch(train) [149][250/293] lr: 5.000000e-04 eta: 2:00:02 time: 0.468919 data_time: 0.089523 memory: 6338 loss_kpt: 0.122212 acc_pose: 0.805888 loss: 0.122212 2022/09/21 23:47:21 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:47:21 - mmengine - INFO - Saving checkpoint at 149 epochs 2022/09/21 23:47:49 - mmengine - INFO - Epoch(train) [150][50/293] lr: 5.000000e-04 eta: 1:59:20 time: 0.500004 data_time: 0.106351 memory: 6338 loss_kpt: 0.119750 acc_pose: 0.779322 loss: 0.119750 2022/09/21 23:48:12 - mmengine - INFO - Epoch(train) [150][100/293] lr: 5.000000e-04 eta: 1:59:01 time: 0.467865 data_time: 0.089233 memory: 6338 loss_kpt: 0.120671 acc_pose: 0.799217 loss: 0.120671 2022/09/21 23:48:37 - mmengine - INFO - Epoch(train) [150][150/293] lr: 5.000000e-04 eta: 1:58:43 time: 0.483718 data_time: 0.087258 memory: 6338 loss_kpt: 0.119650 acc_pose: 0.808905 loss: 0.119650 2022/09/21 23:49:01 - mmengine - INFO - Epoch(train) [150][200/293] lr: 5.000000e-04 eta: 1:58:24 time: 0.487995 data_time: 0.083860 memory: 6338 loss_kpt: 0.119062 acc_pose: 0.781520 loss: 0.119062 2022/09/21 23:49:26 - mmengine - INFO - Epoch(train) [150][250/293] lr: 5.000000e-04 eta: 1:58:06 time: 0.493070 data_time: 0.086999 memory: 6338 loss_kpt: 0.121673 acc_pose: 0.864914 loss: 0.121673 2022/09/21 23:49:46 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:49:46 - mmengine - INFO - Saving checkpoint at 150 epochs 2022/09/21 23:49:55 - mmengine - INFO - Epoch(val) [150][50/407] eta: 0:00:42 time: 0.118097 data_time: 0.054229 memory: 6338 2022/09/21 23:50:00 - mmengine - INFO - Epoch(val) [150][100/407] eta: 0:00:34 time: 0.111585 data_time: 0.049929 memory: 587 2022/09/21 23:50:06 - mmengine - INFO - Epoch(val) [150][150/407] eta: 0:00:30 time: 0.117658 data_time: 0.054723 memory: 587 2022/09/21 23:50:12 - mmengine - INFO - Epoch(val) [150][200/407] eta: 0:00:23 time: 0.112492 data_time: 0.051446 memory: 587 2022/09/21 23:50:18 - mmengine - INFO - Epoch(val) [150][250/407] eta: 0:00:18 time: 0.116212 data_time: 0.053701 memory: 587 2022/09/21 23:50:23 - mmengine - INFO - Epoch(val) [150][300/407] eta: 0:00:12 time: 0.116020 data_time: 0.054529 memory: 587 2022/09/21 23:50:29 - mmengine - INFO - Epoch(val) [150][350/407] eta: 0:00:06 time: 0.117606 data_time: 0.054736 memory: 587 2022/09/21 23:50:35 - mmengine - INFO - Epoch(val) [150][400/407] eta: 0:00:00 time: 0.106219 data_time: 0.046644 memory: 587 2022/09/21 23:51:09 - mmengine - INFO - Evaluating CocoMetric... 2022/09/21 23:51:23 - mmengine - INFO - Epoch(val) [150][407/407] coco/AP: 0.674703 coco/AP .5: 0.873289 coco/AP .75: 0.752764 coco/AP (M): 0.644557 coco/AP (L): 0.737457 coco/AR: 0.735170 coco/AR .5: 0.919238 coco/AR .75: 0.803841 coco/AR (M): 0.693936 coco/AR (L): 0.794946 2022/09/21 23:51:23 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_140.pth is removed 2022/09/21 23:51:26 - mmengine - INFO - The best checkpoint with 0.6747 coco/AP at 150 epoch is saved to best_coco/AP_epoch_150.pth. 2022/09/21 23:51:50 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:51:50 - mmengine - INFO - Epoch(train) [151][50/293] lr: 5.000000e-04 eta: 1:57:23 time: 0.482878 data_time: 0.102684 memory: 6338 loss_kpt: 0.118290 acc_pose: 0.812593 loss: 0.118290 2022/09/21 23:52:14 - mmengine - INFO - Epoch(train) [151][100/293] lr: 5.000000e-04 eta: 1:57:04 time: 0.468494 data_time: 0.083041 memory: 6338 loss_kpt: 0.119268 acc_pose: 0.756986 loss: 0.119268 2022/09/21 23:52:38 - mmengine - INFO - Epoch(train) [151][150/293] lr: 5.000000e-04 eta: 1:56:46 time: 0.479391 data_time: 0.089517 memory: 6338 loss_kpt: 0.120105 acc_pose: 0.827751 loss: 0.120105 2022/09/21 23:53:02 - mmengine - INFO - Epoch(train) [151][200/293] lr: 5.000000e-04 eta: 1:56:27 time: 0.479327 data_time: 0.086928 memory: 6338 loss_kpt: 0.122058 acc_pose: 0.752658 loss: 0.122058 2022/09/21 23:53:26 - mmengine - INFO - Epoch(train) [151][250/293] lr: 5.000000e-04 eta: 1:56:09 time: 0.481966 data_time: 0.091764 memory: 6338 loss_kpt: 0.120818 acc_pose: 0.785920 loss: 0.120818 2022/09/21 23:53:45 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:53:45 - mmengine - INFO - Saving checkpoint at 151 epochs 2022/09/21 23:54:12 - mmengine - INFO - Epoch(train) [152][50/293] lr: 5.000000e-04 eta: 1:55:26 time: 0.473356 data_time: 0.099153 memory: 6338 loss_kpt: 0.120906 acc_pose: 0.783935 loss: 0.120906 2022/09/21 23:54:35 - mmengine - INFO - Epoch(train) [152][100/293] lr: 5.000000e-04 eta: 1:55:07 time: 0.459832 data_time: 0.092974 memory: 6338 loss_kpt: 0.121558 acc_pose: 0.837890 loss: 0.121558 2022/09/21 23:54:58 - mmengine - INFO - Epoch(train) [152][150/293] lr: 5.000000e-04 eta: 1:54:48 time: 0.461593 data_time: 0.088749 memory: 6338 loss_kpt: 0.119554 acc_pose: 0.783228 loss: 0.119554 2022/09/21 23:55:21 - mmengine - INFO - Epoch(train) [152][200/293] lr: 5.000000e-04 eta: 1:54:29 time: 0.451975 data_time: 0.086083 memory: 6338 loss_kpt: 0.118877 acc_pose: 0.793430 loss: 0.118877 2022/09/21 23:55:44 - mmengine - INFO - Epoch(train) [152][250/293] lr: 5.000000e-04 eta: 1:54:10 time: 0.472784 data_time: 0.093925 memory: 6338 loss_kpt: 0.119232 acc_pose: 0.768162 loss: 0.119232 2022/09/21 23:56:04 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:56:04 - mmengine - INFO - Saving checkpoint at 152 epochs 2022/09/21 23:56:30 - mmengine - INFO - Epoch(train) [153][50/293] lr: 5.000000e-04 eta: 1:53:28 time: 0.482274 data_time: 0.098556 memory: 6338 loss_kpt: 0.119669 acc_pose: 0.770251 loss: 0.119669 2022/09/21 23:56:54 - mmengine - INFO - Epoch(train) [153][100/293] lr: 5.000000e-04 eta: 1:53:09 time: 0.469768 data_time: 0.089139 memory: 6338 loss_kpt: 0.120913 acc_pose: 0.804047 loss: 0.120913 2022/09/21 23:57:18 - mmengine - INFO - Epoch(train) [153][150/293] lr: 5.000000e-04 eta: 1:52:50 time: 0.478878 data_time: 0.093302 memory: 6338 loss_kpt: 0.118589 acc_pose: 0.805857 loss: 0.118589 2022/09/21 23:57:42 - mmengine - INFO - Epoch(train) [153][200/293] lr: 5.000000e-04 eta: 1:52:32 time: 0.471710 data_time: 0.093575 memory: 6338 loss_kpt: 0.119289 acc_pose: 0.791538 loss: 0.119289 2022/09/21 23:58:06 - mmengine - INFO - Epoch(train) [153][250/293] lr: 5.000000e-04 eta: 1:52:13 time: 0.481896 data_time: 0.091978 memory: 6338 loss_kpt: 0.119962 acc_pose: 0.820239 loss: 0.119962 2022/09/21 23:58:25 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/21 23:58:25 - mmengine - INFO - Saving checkpoint at 153 epochs 2022/09/21 23:58:52 - mmengine - INFO - Epoch(train) [154][50/293] lr: 5.000000e-04 eta: 1:51:30 time: 0.472565 data_time: 0.101003 memory: 6338 loss_kpt: 0.119960 acc_pose: 0.737953 loss: 0.119960 2022/09/21 23:59:15 - mmengine - INFO - Epoch(train) [154][100/293] lr: 5.000000e-04 eta: 1:51:11 time: 0.461813 data_time: 0.093158 memory: 6338 loss_kpt: 0.120150 acc_pose: 0.781621 loss: 0.120150 2022/09/21 23:59:38 - mmengine - INFO - Epoch(train) [154][150/293] lr: 5.000000e-04 eta: 1:50:53 time: 0.464190 data_time: 0.094732 memory: 6338 loss_kpt: 0.118534 acc_pose: 0.793566 loss: 0.118534 2022/09/21 23:59:48 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:00:01 - mmengine - INFO - Epoch(train) [154][200/293] lr: 5.000000e-04 eta: 1:50:33 time: 0.455939 data_time: 0.085991 memory: 6338 loss_kpt: 0.120452 acc_pose: 0.792434 loss: 0.120452 2022/09/22 00:00:27 - mmengine - INFO - Epoch(train) [154][250/293] lr: 5.000000e-04 eta: 1:50:15 time: 0.512382 data_time: 0.096884 memory: 6338 loss_kpt: 0.119496 acc_pose: 0.836169 loss: 0.119496 2022/09/22 00:00:46 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:00:46 - mmengine - INFO - Saving checkpoint at 154 epochs 2022/09/22 00:01:13 - mmengine - INFO - Epoch(train) [155][50/293] lr: 5.000000e-04 eta: 1:49:33 time: 0.478669 data_time: 0.095442 memory: 6338 loss_kpt: 0.120129 acc_pose: 0.835720 loss: 0.120129 2022/09/22 00:01:37 - mmengine - INFO - Epoch(train) [155][100/293] lr: 5.000000e-04 eta: 1:49:14 time: 0.483682 data_time: 0.097429 memory: 6338 loss_kpt: 0.121547 acc_pose: 0.808675 loss: 0.121547 2022/09/22 00:02:01 - mmengine - INFO - Epoch(train) [155][150/293] lr: 5.000000e-04 eta: 1:48:56 time: 0.484292 data_time: 0.089714 memory: 6338 loss_kpt: 0.119480 acc_pose: 0.736520 loss: 0.119480 2022/09/22 00:02:25 - mmengine - INFO - Epoch(train) [155][200/293] lr: 5.000000e-04 eta: 1:48:37 time: 0.473075 data_time: 0.089434 memory: 6338 loss_kpt: 0.119416 acc_pose: 0.845692 loss: 0.119416 2022/09/22 00:02:49 - mmengine - INFO - Epoch(train) [155][250/293] lr: 5.000000e-04 eta: 1:48:18 time: 0.475350 data_time: 0.091543 memory: 6338 loss_kpt: 0.118238 acc_pose: 0.810857 loss: 0.118238 2022/09/22 00:03:08 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:03:08 - mmengine - INFO - Saving checkpoint at 155 epochs 2022/09/22 00:03:35 - mmengine - INFO - Epoch(train) [156][50/293] lr: 5.000000e-04 eta: 1:47:36 time: 0.465023 data_time: 0.098730 memory: 6338 loss_kpt: 0.120360 acc_pose: 0.807835 loss: 0.120360 2022/09/22 00:03:58 - mmengine - INFO - Epoch(train) [156][100/293] lr: 5.000000e-04 eta: 1:47:17 time: 0.462361 data_time: 0.090583 memory: 6338 loss_kpt: 0.119319 acc_pose: 0.840176 loss: 0.119319 2022/09/22 00:04:21 - mmengine - INFO - Epoch(train) [156][150/293] lr: 5.000000e-04 eta: 1:46:58 time: 0.458757 data_time: 0.087264 memory: 6338 loss_kpt: 0.119810 acc_pose: 0.767925 loss: 0.119810 2022/09/22 00:04:44 - mmengine - INFO - Epoch(train) [156][200/293] lr: 5.000000e-04 eta: 1:46:39 time: 0.463857 data_time: 0.088601 memory: 6338 loss_kpt: 0.119509 acc_pose: 0.776888 loss: 0.119509 2022/09/22 00:05:07 - mmengine - INFO - Epoch(train) [156][250/293] lr: 5.000000e-04 eta: 1:46:19 time: 0.458215 data_time: 0.091336 memory: 6338 loss_kpt: 0.121087 acc_pose: 0.823874 loss: 0.121087 2022/09/22 00:05:26 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:05:26 - mmengine - INFO - Saving checkpoint at 156 epochs 2022/09/22 00:05:53 - mmengine - INFO - Epoch(train) [157][50/293] lr: 5.000000e-04 eta: 1:45:37 time: 0.475850 data_time: 0.100372 memory: 6338 loss_kpt: 0.120452 acc_pose: 0.802083 loss: 0.120452 2022/09/22 00:06:15 - mmengine - INFO - Epoch(train) [157][100/293] lr: 5.000000e-04 eta: 1:45:18 time: 0.456383 data_time: 0.090555 memory: 6338 loss_kpt: 0.119311 acc_pose: 0.797227 loss: 0.119311 2022/09/22 00:06:40 - mmengine - INFO - Epoch(train) [157][150/293] lr: 5.000000e-04 eta: 1:44:59 time: 0.480985 data_time: 0.097602 memory: 6338 loss_kpt: 0.120060 acc_pose: 0.803856 loss: 0.120060 2022/09/22 00:07:03 - mmengine - INFO - Epoch(train) [157][200/293] lr: 5.000000e-04 eta: 1:44:41 time: 0.472941 data_time: 0.091012 memory: 6338 loss_kpt: 0.120303 acc_pose: 0.814416 loss: 0.120303 2022/09/22 00:07:27 - mmengine - INFO - Epoch(train) [157][250/293] lr: 5.000000e-04 eta: 1:44:22 time: 0.475856 data_time: 0.092062 memory: 6338 loss_kpt: 0.117625 acc_pose: 0.801602 loss: 0.117625 2022/09/22 00:07:47 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:07:47 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:07:47 - mmengine - INFO - Saving checkpoint at 157 epochs 2022/09/22 00:08:16 - mmengine - INFO - Epoch(train) [158][50/293] lr: 5.000000e-04 eta: 1:43:40 time: 0.515446 data_time: 0.101021 memory: 6338 loss_kpt: 0.120786 acc_pose: 0.786733 loss: 0.120786 2022/09/22 00:08:40 - mmengine - INFO - Epoch(train) [158][100/293] lr: 5.000000e-04 eta: 1:43:22 time: 0.485207 data_time: 0.087273 memory: 6338 loss_kpt: 0.120018 acc_pose: 0.810318 loss: 0.120018 2022/09/22 00:09:04 - mmengine - INFO - Epoch(train) [158][150/293] lr: 5.000000e-04 eta: 1:43:03 time: 0.482041 data_time: 0.085115 memory: 6338 loss_kpt: 0.118313 acc_pose: 0.847785 loss: 0.118313 2022/09/22 00:09:28 - mmengine - INFO - Epoch(train) [158][200/293] lr: 5.000000e-04 eta: 1:42:44 time: 0.475059 data_time: 0.086622 memory: 6338 loss_kpt: 0.119562 acc_pose: 0.797605 loss: 0.119562 2022/09/22 00:09:53 - mmengine - INFO - Epoch(train) [158][250/293] lr: 5.000000e-04 eta: 1:42:25 time: 0.489256 data_time: 0.099151 memory: 6338 loss_kpt: 0.117961 acc_pose: 0.826578 loss: 0.117961 2022/09/22 00:10:12 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:10:12 - mmengine - INFO - Saving checkpoint at 158 epochs 2022/09/22 00:10:38 - mmengine - INFO - Epoch(train) [159][50/293] lr: 5.000000e-04 eta: 1:41:43 time: 0.463516 data_time: 0.101532 memory: 6338 loss_kpt: 0.120352 acc_pose: 0.842419 loss: 0.120352 2022/09/22 00:11:01 - mmengine - INFO - Epoch(train) [159][100/293] lr: 5.000000e-04 eta: 1:41:24 time: 0.455277 data_time: 0.089424 memory: 6338 loss_kpt: 0.116050 acc_pose: 0.732701 loss: 0.116050 2022/09/22 00:11:25 - mmengine - INFO - Epoch(train) [159][150/293] lr: 5.000000e-04 eta: 1:41:05 time: 0.469312 data_time: 0.091702 memory: 6338 loss_kpt: 0.116152 acc_pose: 0.841929 loss: 0.116152 2022/09/22 00:11:47 - mmengine - INFO - Epoch(train) [159][200/293] lr: 5.000000e-04 eta: 1:40:46 time: 0.453838 data_time: 0.090428 memory: 6338 loss_kpt: 0.120096 acc_pose: 0.847554 loss: 0.120096 2022/09/22 00:12:11 - mmengine - INFO - Epoch(train) [159][250/293] lr: 5.000000e-04 eta: 1:40:27 time: 0.466608 data_time: 0.092639 memory: 6338 loss_kpt: 0.120652 acc_pose: 0.800817 loss: 0.120652 2022/09/22 00:12:30 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:12:30 - mmengine - INFO - Saving checkpoint at 159 epochs 2022/09/22 00:12:58 - mmengine - INFO - Epoch(train) [160][50/293] lr: 5.000000e-04 eta: 1:39:45 time: 0.494461 data_time: 0.104191 memory: 6338 loss_kpt: 0.119816 acc_pose: 0.780006 loss: 0.119816 2022/09/22 00:13:21 - mmengine - INFO - Epoch(train) [160][100/293] lr: 5.000000e-04 eta: 1:39:26 time: 0.475094 data_time: 0.087079 memory: 6338 loss_kpt: 0.116359 acc_pose: 0.782811 loss: 0.116359 2022/09/22 00:13:46 - mmengine - INFO - Epoch(train) [160][150/293] lr: 5.000000e-04 eta: 1:39:08 time: 0.486319 data_time: 0.087520 memory: 6338 loss_kpt: 0.119159 acc_pose: 0.820862 loss: 0.119159 2022/09/22 00:14:09 - mmengine - INFO - Epoch(train) [160][200/293] lr: 5.000000e-04 eta: 1:38:49 time: 0.476781 data_time: 0.089531 memory: 6338 loss_kpt: 0.121260 acc_pose: 0.796763 loss: 0.121260 2022/09/22 00:14:33 - mmengine - INFO - Epoch(train) [160][250/293] lr: 5.000000e-04 eta: 1:38:30 time: 0.479571 data_time: 0.090536 memory: 6338 loss_kpt: 0.117893 acc_pose: 0.849153 loss: 0.117893 2022/09/22 00:14:54 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:14:54 - mmengine - INFO - Saving checkpoint at 160 epochs 2022/09/22 00:15:03 - mmengine - INFO - Epoch(val) [160][50/407] eta: 0:00:41 time: 0.116416 data_time: 0.055407 memory: 6338 2022/09/22 00:15:08 - mmengine - INFO - Epoch(val) [160][100/407] eta: 0:00:34 time: 0.113964 data_time: 0.051499 memory: 587 2022/09/22 00:15:14 - mmengine - INFO - Epoch(val) [160][150/407] eta: 0:00:30 time: 0.117260 data_time: 0.056844 memory: 587 2022/09/22 00:15:20 - mmengine - INFO - Epoch(val) [160][200/407] eta: 0:00:24 time: 0.116515 data_time: 0.055692 memory: 587 2022/09/22 00:15:26 - mmengine - INFO - Epoch(val) [160][250/407] eta: 0:00:17 time: 0.111079 data_time: 0.050255 memory: 587 2022/09/22 00:15:31 - mmengine - INFO - Epoch(val) [160][300/407] eta: 0:00:11 time: 0.111213 data_time: 0.048429 memory: 587 2022/09/22 00:15:37 - mmengine - INFO - Epoch(val) [160][350/407] eta: 0:00:06 time: 0.118630 data_time: 0.057452 memory: 587 2022/09/22 00:15:42 - mmengine - INFO - Epoch(val) [160][400/407] eta: 0:00:00 time: 0.105770 data_time: 0.045901 memory: 587 2022/09/22 00:16:18 - mmengine - INFO - Evaluating CocoMetric... 2022/09/22 00:16:32 - mmengine - INFO - Epoch(val) [160][407/407] coco/AP: 0.678631 coco/AP .5: 0.878033 coco/AP .75: 0.756279 coco/AP (M): 0.646872 coco/AP (L): 0.740206 coco/AR: 0.738177 coco/AR .5: 0.921442 coco/AR .75: 0.807147 coco/AR (M): 0.697815 coco/AR (L): 0.796730 2022/09/22 00:16:32 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_150.pth is removed 2022/09/22 00:16:34 - mmengine - INFO - The best checkpoint with 0.6786 coco/AP at 160 epoch is saved to best_coco/AP_epoch_160.pth. 2022/09/22 00:16:58 - mmengine - INFO - Epoch(train) [161][50/293] lr: 5.000000e-04 eta: 1:37:48 time: 0.480761 data_time: 0.100719 memory: 6338 loss_kpt: 0.119735 acc_pose: 0.807726 loss: 0.119735 2022/09/22 00:17:21 - mmengine - INFO - Epoch(train) [161][100/293] lr: 5.000000e-04 eta: 1:37:29 time: 0.456903 data_time: 0.095561 memory: 6338 loss_kpt: 0.119383 acc_pose: 0.783074 loss: 0.119383 2022/09/22 00:17:30 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:17:44 - mmengine - INFO - Epoch(train) [161][150/293] lr: 5.000000e-04 eta: 1:37:10 time: 0.459546 data_time: 0.094962 memory: 6338 loss_kpt: 0.120322 acc_pose: 0.827143 loss: 0.120322 2022/09/22 00:18:07 - mmengine - INFO - Epoch(train) [161][200/293] lr: 5.000000e-04 eta: 1:36:51 time: 0.457344 data_time: 0.088220 memory: 6338 loss_kpt: 0.118054 acc_pose: 0.834313 loss: 0.118054 2022/09/22 00:18:30 - mmengine - INFO - Epoch(train) [161][250/293] lr: 5.000000e-04 eta: 1:36:31 time: 0.451838 data_time: 0.090012 memory: 6338 loss_kpt: 0.119009 acc_pose: 0.810923 loss: 0.119009 2022/09/22 00:18:49 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:18:49 - mmengine - INFO - Saving checkpoint at 161 epochs 2022/09/22 00:19:15 - mmengine - INFO - Epoch(train) [162][50/293] lr: 5.000000e-04 eta: 1:35:49 time: 0.465892 data_time: 0.096990 memory: 6338 loss_kpt: 0.120208 acc_pose: 0.834041 loss: 0.120208 2022/09/22 00:19:38 - mmengine - INFO - Epoch(train) [162][100/293] lr: 5.000000e-04 eta: 1:35:30 time: 0.459523 data_time: 0.086565 memory: 6338 loss_kpt: 0.119105 acc_pose: 0.820450 loss: 0.119105 2022/09/22 00:20:01 - mmengine - INFO - Epoch(train) [162][150/293] lr: 5.000000e-04 eta: 1:35:11 time: 0.456378 data_time: 0.090991 memory: 6338 loss_kpt: 0.118884 acc_pose: 0.769952 loss: 0.118884 2022/09/22 00:20:24 - mmengine - INFO - Epoch(train) [162][200/293] lr: 5.000000e-04 eta: 1:34:52 time: 0.458711 data_time: 0.097814 memory: 6338 loss_kpt: 0.120070 acc_pose: 0.820397 loss: 0.120070 2022/09/22 00:20:47 - mmengine - INFO - Epoch(train) [162][250/293] lr: 5.000000e-04 eta: 1:34:32 time: 0.458784 data_time: 0.092450 memory: 6338 loss_kpt: 0.117563 acc_pose: 0.800572 loss: 0.117563 2022/09/22 00:21:07 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:21:07 - mmengine - INFO - Saving checkpoint at 162 epochs 2022/09/22 00:21:34 - mmengine - INFO - Epoch(train) [163][50/293] lr: 5.000000e-04 eta: 1:33:51 time: 0.474328 data_time: 0.097505 memory: 6338 loss_kpt: 0.118497 acc_pose: 0.821727 loss: 0.118497 2022/09/22 00:21:57 - mmengine - INFO - Epoch(train) [163][100/293] lr: 5.000000e-04 eta: 1:33:32 time: 0.456693 data_time: 0.086715 memory: 6338 loss_kpt: 0.119121 acc_pose: 0.785318 loss: 0.119121 2022/09/22 00:22:20 - mmengine - INFO - Epoch(train) [163][150/293] lr: 5.000000e-04 eta: 1:33:13 time: 0.472217 data_time: 0.092375 memory: 6338 loss_kpt: 0.121417 acc_pose: 0.812928 loss: 0.121417 2022/09/22 00:22:44 - mmengine - INFO - Epoch(train) [163][200/293] lr: 5.000000e-04 eta: 1:32:53 time: 0.465914 data_time: 0.088681 memory: 6338 loss_kpt: 0.118738 acc_pose: 0.806626 loss: 0.118738 2022/09/22 00:23:07 - mmengine - INFO - Epoch(train) [163][250/293] lr: 5.000000e-04 eta: 1:32:34 time: 0.469073 data_time: 0.091174 memory: 6338 loss_kpt: 0.118931 acc_pose: 0.814741 loss: 0.118931 2022/09/22 00:23:26 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:23:26 - mmengine - INFO - Saving checkpoint at 163 epochs 2022/09/22 00:23:53 - mmengine - INFO - Epoch(train) [164][50/293] lr: 5.000000e-04 eta: 1:31:53 time: 0.478018 data_time: 0.100737 memory: 6338 loss_kpt: 0.119407 acc_pose: 0.810526 loss: 0.119407 2022/09/22 00:24:16 - mmengine - INFO - Epoch(train) [164][100/293] lr: 5.000000e-04 eta: 1:31:34 time: 0.455552 data_time: 0.088177 memory: 6338 loss_kpt: 0.118658 acc_pose: 0.784958 loss: 0.118658 2022/09/22 00:24:39 - mmengine - INFO - Epoch(train) [164][150/293] lr: 5.000000e-04 eta: 1:31:15 time: 0.465643 data_time: 0.086402 memory: 6338 loss_kpt: 0.119644 acc_pose: 0.839207 loss: 0.119644 2022/09/22 00:25:02 - mmengine - INFO - Epoch(train) [164][200/293] lr: 5.000000e-04 eta: 1:30:55 time: 0.455039 data_time: 0.086089 memory: 6338 loss_kpt: 0.120769 acc_pose: 0.849187 loss: 0.120769 2022/09/22 00:25:21 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:25:25 - mmengine - INFO - Epoch(train) [164][250/293] lr: 5.000000e-04 eta: 1:30:36 time: 0.466228 data_time: 0.088088 memory: 6338 loss_kpt: 0.119135 acc_pose: 0.767673 loss: 0.119135 2022/09/22 00:25:45 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:25:45 - mmengine - INFO - Saving checkpoint at 164 epochs 2022/09/22 00:26:12 - mmengine - INFO - Epoch(train) [165][50/293] lr: 5.000000e-04 eta: 1:29:55 time: 0.482200 data_time: 0.108907 memory: 6338 loss_kpt: 0.116923 acc_pose: 0.783174 loss: 0.116923 2022/09/22 00:26:34 - mmengine - INFO - Epoch(train) [165][100/293] lr: 5.000000e-04 eta: 1:29:36 time: 0.452591 data_time: 0.091004 memory: 6338 loss_kpt: 0.117874 acc_pose: 0.828487 loss: 0.117874 2022/09/22 00:26:57 - mmengine - INFO - Epoch(train) [165][150/293] lr: 5.000000e-04 eta: 1:29:16 time: 0.460555 data_time: 0.094542 memory: 6338 loss_kpt: 0.119799 acc_pose: 0.740519 loss: 0.119799 2022/09/22 00:27:20 - mmengine - INFO - Epoch(train) [165][200/293] lr: 5.000000e-04 eta: 1:28:57 time: 0.456880 data_time: 0.087595 memory: 6338 loss_kpt: 0.121630 acc_pose: 0.800549 loss: 0.121630 2022/09/22 00:27:44 - mmengine - INFO - Epoch(train) [165][250/293] lr: 5.000000e-04 eta: 1:28:38 time: 0.465738 data_time: 0.094506 memory: 6338 loss_kpt: 0.118831 acc_pose: 0.846178 loss: 0.118831 2022/09/22 00:28:03 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:28:03 - mmengine - INFO - Saving checkpoint at 165 epochs 2022/09/22 00:28:29 - mmengine - INFO - Epoch(train) [166][50/293] lr: 5.000000e-04 eta: 1:27:57 time: 0.469228 data_time: 0.094836 memory: 6338 loss_kpt: 0.120249 acc_pose: 0.780900 loss: 0.120249 2022/09/22 00:28:52 - mmengine - INFO - Epoch(train) [166][100/293] lr: 5.000000e-04 eta: 1:27:37 time: 0.453971 data_time: 0.085699 memory: 6338 loss_kpt: 0.118912 acc_pose: 0.793030 loss: 0.118912 2022/09/22 00:29:15 - mmengine - INFO - Epoch(train) [166][150/293] lr: 5.000000e-04 eta: 1:27:18 time: 0.464021 data_time: 0.093997 memory: 6338 loss_kpt: 0.119931 acc_pose: 0.803371 loss: 0.119931 2022/09/22 00:29:38 - mmengine - INFO - Epoch(train) [166][200/293] lr: 5.000000e-04 eta: 1:26:59 time: 0.458996 data_time: 0.081547 memory: 6338 loss_kpt: 0.120852 acc_pose: 0.806250 loss: 0.120852 2022/09/22 00:30:01 - mmengine - INFO - Epoch(train) [166][250/293] lr: 5.000000e-04 eta: 1:26:39 time: 0.460108 data_time: 0.092596 memory: 6338 loss_kpt: 0.119973 acc_pose: 0.808373 loss: 0.119973 2022/09/22 00:30:21 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:30:21 - mmengine - INFO - Saving checkpoint at 166 epochs 2022/09/22 00:30:48 - mmengine - INFO - Epoch(train) [167][50/293] lr: 5.000000e-04 eta: 1:25:58 time: 0.481067 data_time: 0.097145 memory: 6338 loss_kpt: 0.117134 acc_pose: 0.820339 loss: 0.117134 2022/09/22 00:31:12 - mmengine - INFO - Epoch(train) [167][100/293] lr: 5.000000e-04 eta: 1:25:39 time: 0.482993 data_time: 0.090085 memory: 6338 loss_kpt: 0.117731 acc_pose: 0.771260 loss: 0.117731 2022/09/22 00:31:35 - mmengine - INFO - Epoch(train) [167][150/293] lr: 5.000000e-04 eta: 1:25:20 time: 0.474363 data_time: 0.091318 memory: 6338 loss_kpt: 0.120373 acc_pose: 0.791191 loss: 0.120373 2022/09/22 00:31:59 - mmengine - INFO - Epoch(train) [167][200/293] lr: 5.000000e-04 eta: 1:25:01 time: 0.479770 data_time: 0.089646 memory: 6338 loss_kpt: 0.118607 acc_pose: 0.809270 loss: 0.118607 2022/09/22 00:32:23 - mmengine - INFO - Epoch(train) [167][250/293] lr: 5.000000e-04 eta: 1:24:42 time: 0.477450 data_time: 0.088562 memory: 6338 loss_kpt: 0.120535 acc_pose: 0.810010 loss: 0.120535 2022/09/22 00:32:43 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:32:43 - mmengine - INFO - Saving checkpoint at 167 epochs 2022/09/22 00:33:10 - mmengine - INFO - Epoch(train) [168][50/293] lr: 5.000000e-04 eta: 1:24:01 time: 0.479470 data_time: 0.100677 memory: 6338 loss_kpt: 0.116425 acc_pose: 0.788998 loss: 0.116425 2022/09/22 00:33:19 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:33:34 - mmengine - INFO - Epoch(train) [168][100/293] lr: 5.000000e-04 eta: 1:23:42 time: 0.479025 data_time: 0.095209 memory: 6338 loss_kpt: 0.119468 acc_pose: 0.798351 loss: 0.119468 2022/09/22 00:33:58 - mmengine - INFO - Epoch(train) [168][150/293] lr: 5.000000e-04 eta: 1:23:23 time: 0.479132 data_time: 0.093396 memory: 6338 loss_kpt: 0.119695 acc_pose: 0.816318 loss: 0.119695 2022/09/22 00:34:22 - mmengine - INFO - Epoch(train) [168][200/293] lr: 5.000000e-04 eta: 1:23:04 time: 0.476796 data_time: 0.092549 memory: 6338 loss_kpt: 0.116648 acc_pose: 0.792247 loss: 0.116648 2022/09/22 00:34:46 - mmengine - INFO - Epoch(train) [168][250/293] lr: 5.000000e-04 eta: 1:22:45 time: 0.488693 data_time: 0.091920 memory: 6338 loss_kpt: 0.118557 acc_pose: 0.831400 loss: 0.118557 2022/09/22 00:35:06 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:35:06 - mmengine - INFO - Saving checkpoint at 168 epochs 2022/09/22 00:35:32 - mmengine - INFO - Epoch(train) [169][50/293] lr: 5.000000e-04 eta: 1:22:04 time: 0.467125 data_time: 0.096815 memory: 6338 loss_kpt: 0.118295 acc_pose: 0.786349 loss: 0.118295 2022/09/22 00:35:55 - mmengine - INFO - Epoch(train) [169][100/293] lr: 5.000000e-04 eta: 1:21:45 time: 0.458987 data_time: 0.087456 memory: 6338 loss_kpt: 0.116859 acc_pose: 0.796712 loss: 0.116859 2022/09/22 00:36:19 - mmengine - INFO - Epoch(train) [169][150/293] lr: 5.000000e-04 eta: 1:21:25 time: 0.466723 data_time: 0.094840 memory: 6338 loss_kpt: 0.118561 acc_pose: 0.812643 loss: 0.118561 2022/09/22 00:36:42 - mmengine - INFO - Epoch(train) [169][200/293] lr: 5.000000e-04 eta: 1:21:06 time: 0.469383 data_time: 0.095090 memory: 6338 loss_kpt: 0.118673 acc_pose: 0.796237 loss: 0.118673 2022/09/22 00:37:06 - mmengine - INFO - Epoch(train) [169][250/293] lr: 5.000000e-04 eta: 1:20:47 time: 0.472462 data_time: 0.087133 memory: 6338 loss_kpt: 0.119413 acc_pose: 0.812526 loss: 0.119413 2022/09/22 00:37:25 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:37:25 - mmengine - INFO - Saving checkpoint at 169 epochs 2022/09/22 00:37:53 - mmengine - INFO - Epoch(train) [170][50/293] lr: 5.000000e-04 eta: 1:20:06 time: 0.488703 data_time: 0.100316 memory: 6338 loss_kpt: 0.117185 acc_pose: 0.835309 loss: 0.117185 2022/09/22 00:38:16 - mmengine - INFO - Epoch(train) [170][100/293] lr: 5.000000e-04 eta: 1:19:47 time: 0.474900 data_time: 0.089922 memory: 6338 loss_kpt: 0.119483 acc_pose: 0.803838 loss: 0.119483 2022/09/22 00:38:40 - mmengine - INFO - Epoch(train) [170][150/293] lr: 5.000000e-04 eta: 1:19:28 time: 0.469540 data_time: 0.090697 memory: 6338 loss_kpt: 0.118997 acc_pose: 0.838237 loss: 0.118997 2022/09/22 00:39:03 - mmengine - INFO - Epoch(train) [170][200/293] lr: 5.000000e-04 eta: 1:19:08 time: 0.454656 data_time: 0.082417 memory: 6338 loss_kpt: 0.119290 acc_pose: 0.813210 loss: 0.119290 2022/09/22 00:39:26 - mmengine - INFO - Epoch(train) [170][250/293] lr: 5.000000e-04 eta: 1:18:49 time: 0.465531 data_time: 0.096075 memory: 6338 loss_kpt: 0.118460 acc_pose: 0.867080 loss: 0.118460 2022/09/22 00:39:46 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:39:46 - mmengine - INFO - Saving checkpoint at 170 epochs 2022/09/22 00:39:55 - mmengine - INFO - Epoch(val) [170][50/407] eta: 0:00:43 time: 0.121429 data_time: 0.058530 memory: 6338 2022/09/22 00:40:01 - mmengine - INFO - Epoch(val) [170][100/407] eta: 0:00:36 time: 0.118194 data_time: 0.057395 memory: 587 2022/09/22 00:40:06 - mmengine - INFO - Epoch(val) [170][150/407] eta: 0:00:29 time: 0.115402 data_time: 0.053693 memory: 587 2022/09/22 00:40:12 - mmengine - INFO - Epoch(val) [170][200/407] eta: 0:00:23 time: 0.111290 data_time: 0.048336 memory: 587 2022/09/22 00:40:18 - mmengine - INFO - Epoch(val) [170][250/407] eta: 0:00:18 time: 0.118651 data_time: 0.057243 memory: 587 2022/09/22 00:40:24 - mmengine - INFO - Epoch(val) [170][300/407] eta: 0:00:12 time: 0.117521 data_time: 0.054785 memory: 587 2022/09/22 00:40:30 - mmengine - INFO - Epoch(val) [170][350/407] eta: 0:00:06 time: 0.117279 data_time: 0.056531 memory: 587 2022/09/22 00:40:35 - mmengine - INFO - Epoch(val) [170][400/407] eta: 0:00:00 time: 0.104817 data_time: 0.045699 memory: 587 2022/09/22 00:41:10 - mmengine - INFO - Evaluating CocoMetric... 2022/09/22 00:41:24 - mmengine - INFO - Epoch(val) [170][407/407] coco/AP: 0.680913 coco/AP .5: 0.879101 coco/AP .75: 0.758006 coco/AP (M): 0.651153 coco/AP (L): 0.743278 coco/AR: 0.741373 coco/AR .5: 0.924118 coco/AR .75: 0.811398 coco/AR (M): 0.700410 coco/AR (L): 0.800372 2022/09/22 00:41:24 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_160.pth is removed 2022/09/22 00:41:26 - mmengine - INFO - The best checkpoint with 0.6809 coco/AP at 170 epoch is saved to best_coco/AP_epoch_170.pth. 2022/09/22 00:41:49 - mmengine - INFO - Epoch(train) [171][50/293] lr: 5.000000e-05 eta: 1:18:08 time: 0.454979 data_time: 0.097786 memory: 6338 loss_kpt: 0.117528 acc_pose: 0.809486 loss: 0.117528 2022/09/22 00:42:11 - mmengine - INFO - Epoch(train) [171][100/293] lr: 5.000000e-05 eta: 1:17:48 time: 0.440370 data_time: 0.081140 memory: 6338 loss_kpt: 0.117226 acc_pose: 0.832480 loss: 0.117226 2022/09/22 00:42:33 - mmengine - INFO - Epoch(train) [171][150/293] lr: 5.000000e-05 eta: 1:17:29 time: 0.446879 data_time: 0.089303 memory: 6338 loss_kpt: 0.115170 acc_pose: 0.774327 loss: 0.115170 2022/09/22 00:42:51 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:42:56 - mmengine - INFO - Epoch(train) [171][200/293] lr: 5.000000e-05 eta: 1:17:09 time: 0.444044 data_time: 0.081182 memory: 6338 loss_kpt: 0.115475 acc_pose: 0.810276 loss: 0.115475 2022/09/22 00:43:18 - mmengine - INFO - Epoch(train) [171][250/293] lr: 5.000000e-05 eta: 1:16:50 time: 0.448460 data_time: 0.093437 memory: 6338 loss_kpt: 0.114086 acc_pose: 0.858035 loss: 0.114086 2022/09/22 00:43:37 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:43:37 - mmengine - INFO - Saving checkpoint at 171 epochs 2022/09/22 00:44:04 - mmengine - INFO - Epoch(train) [172][50/293] lr: 5.000000e-05 eta: 1:16:09 time: 0.490021 data_time: 0.100925 memory: 6338 loss_kpt: 0.115645 acc_pose: 0.814262 loss: 0.115645 2022/09/22 00:44:27 - mmengine - INFO - Epoch(train) [172][100/293] lr: 5.000000e-05 eta: 1:15:50 time: 0.469872 data_time: 0.088109 memory: 6338 loss_kpt: 0.112932 acc_pose: 0.782582 loss: 0.112932 2022/09/22 00:44:52 - mmengine - INFO - Epoch(train) [172][150/293] lr: 5.000000e-05 eta: 1:15:31 time: 0.482514 data_time: 0.090484 memory: 6338 loss_kpt: 0.114467 acc_pose: 0.818379 loss: 0.114467 2022/09/22 00:45:15 - mmengine - INFO - Epoch(train) [172][200/293] lr: 5.000000e-05 eta: 1:15:12 time: 0.471993 data_time: 0.092698 memory: 6338 loss_kpt: 0.114845 acc_pose: 0.822256 loss: 0.114845 2022/09/22 00:45:39 - mmengine - INFO - Epoch(train) [172][250/293] lr: 5.000000e-05 eta: 1:14:52 time: 0.484278 data_time: 0.098308 memory: 6338 loss_kpt: 0.115411 acc_pose: 0.797988 loss: 0.115411 2022/09/22 00:45:59 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:45:59 - mmengine - INFO - Saving checkpoint at 172 epochs 2022/09/22 00:46:26 - mmengine - INFO - Epoch(train) [173][50/293] lr: 5.000000e-05 eta: 1:14:12 time: 0.483632 data_time: 0.099633 memory: 6338 loss_kpt: 0.111313 acc_pose: 0.779871 loss: 0.111313 2022/09/22 00:46:49 - mmengine - INFO - Epoch(train) [173][100/293] lr: 5.000000e-05 eta: 1:13:53 time: 0.465476 data_time: 0.087089 memory: 6338 loss_kpt: 0.111875 acc_pose: 0.790153 loss: 0.111875 2022/09/22 00:47:13 - mmengine - INFO - Epoch(train) [173][150/293] lr: 5.000000e-05 eta: 1:13:33 time: 0.468930 data_time: 0.091672 memory: 6338 loss_kpt: 0.115909 acc_pose: 0.800162 loss: 0.115909 2022/09/22 00:47:36 - mmengine - INFO - Epoch(train) [173][200/293] lr: 5.000000e-05 eta: 1:13:14 time: 0.463881 data_time: 0.095300 memory: 6338 loss_kpt: 0.113210 acc_pose: 0.839016 loss: 0.113210 2022/09/22 00:48:00 - mmengine - INFO - Epoch(train) [173][250/293] lr: 5.000000e-05 eta: 1:12:55 time: 0.477785 data_time: 0.090953 memory: 6338 loss_kpt: 0.111671 acc_pose: 0.795080 loss: 0.111671 2022/09/22 00:48:19 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:48:19 - mmengine - INFO - Saving checkpoint at 173 epochs 2022/09/22 00:48:46 - mmengine - INFO - Epoch(train) [174][50/293] lr: 5.000000e-05 eta: 1:12:14 time: 0.479840 data_time: 0.101326 memory: 6338 loss_kpt: 0.113716 acc_pose: 0.800480 loss: 0.113716 2022/09/22 00:49:09 - mmengine - INFO - Epoch(train) [174][100/293] lr: 5.000000e-05 eta: 1:11:55 time: 0.467562 data_time: 0.093551 memory: 6338 loss_kpt: 0.113082 acc_pose: 0.846427 loss: 0.113082 2022/09/22 00:49:33 - mmengine - INFO - Epoch(train) [174][150/293] lr: 5.000000e-05 eta: 1:11:36 time: 0.477595 data_time: 0.101029 memory: 6338 loss_kpt: 0.111777 acc_pose: 0.830635 loss: 0.111777 2022/09/22 00:49:57 - mmengine - INFO - Epoch(train) [174][200/293] lr: 5.000000e-05 eta: 1:11:16 time: 0.464412 data_time: 0.090430 memory: 6338 loss_kpt: 0.112494 acc_pose: 0.830562 loss: 0.112494 2022/09/22 00:50:21 - mmengine - INFO - Epoch(train) [174][250/293] lr: 5.000000e-05 eta: 1:10:57 time: 0.483400 data_time: 0.094463 memory: 6338 loss_kpt: 0.115503 acc_pose: 0.806873 loss: 0.115503 2022/09/22 00:50:40 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:50:40 - mmengine - INFO - Saving checkpoint at 174 epochs 2022/09/22 00:50:52 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:51:07 - mmengine - INFO - Epoch(train) [175][50/293] lr: 5.000000e-05 eta: 1:10:17 time: 0.481032 data_time: 0.112326 memory: 6338 loss_kpt: 0.112323 acc_pose: 0.805630 loss: 0.112323 2022/09/22 00:51:30 - mmengine - INFO - Epoch(train) [175][100/293] lr: 5.000000e-05 eta: 1:09:57 time: 0.450966 data_time: 0.091213 memory: 6338 loss_kpt: 0.110772 acc_pose: 0.814024 loss: 0.110772 2022/09/22 00:51:53 - mmengine - INFO - Epoch(train) [175][150/293] lr: 5.000000e-05 eta: 1:09:38 time: 0.462003 data_time: 0.093059 memory: 6338 loss_kpt: 0.115979 acc_pose: 0.845099 loss: 0.115979 2022/09/22 00:52:15 - mmengine - INFO - Epoch(train) [175][200/293] lr: 5.000000e-05 eta: 1:09:18 time: 0.451465 data_time: 0.089893 memory: 6338 loss_kpt: 0.113813 acc_pose: 0.825408 loss: 0.113813 2022/09/22 00:52:39 - mmengine - INFO - Epoch(train) [175][250/293] lr: 5.000000e-05 eta: 1:08:59 time: 0.471055 data_time: 0.099666 memory: 6338 loss_kpt: 0.112903 acc_pose: 0.809521 loss: 0.112903 2022/09/22 00:52:58 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:52:58 - mmengine - INFO - Saving checkpoint at 175 epochs 2022/09/22 00:53:26 - mmengine - INFO - Epoch(train) [176][50/293] lr: 5.000000e-05 eta: 1:08:19 time: 0.496092 data_time: 0.101363 memory: 6338 loss_kpt: 0.114710 acc_pose: 0.815628 loss: 0.114710 2022/09/22 00:53:49 - mmengine - INFO - Epoch(train) [176][100/293] lr: 5.000000e-05 eta: 1:08:00 time: 0.474251 data_time: 0.087248 memory: 6338 loss_kpt: 0.113145 acc_pose: 0.828297 loss: 0.113145 2022/09/22 00:54:14 - mmengine - INFO - Epoch(train) [176][150/293] lr: 5.000000e-05 eta: 1:07:40 time: 0.483616 data_time: 0.089022 memory: 6338 loss_kpt: 0.114243 acc_pose: 0.781518 loss: 0.114243 2022/09/22 00:54:37 - mmengine - INFO - Epoch(train) [176][200/293] lr: 5.000000e-05 eta: 1:07:21 time: 0.468845 data_time: 0.089557 memory: 6338 loss_kpt: 0.113959 acc_pose: 0.815228 loss: 0.113959 2022/09/22 00:55:01 - mmengine - INFO - Epoch(train) [176][250/293] lr: 5.000000e-05 eta: 1:07:02 time: 0.482869 data_time: 0.090512 memory: 6338 loss_kpt: 0.111483 acc_pose: 0.846333 loss: 0.111483 2022/09/22 00:55:21 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:55:21 - mmengine - INFO - Saving checkpoint at 176 epochs 2022/09/22 00:55:48 - mmengine - INFO - Epoch(train) [177][50/293] lr: 5.000000e-05 eta: 1:06:22 time: 0.479433 data_time: 0.102396 memory: 6338 loss_kpt: 0.111797 acc_pose: 0.836197 loss: 0.111797 2022/09/22 00:56:11 - mmengine - INFO - Epoch(train) [177][100/293] lr: 5.000000e-05 eta: 1:06:02 time: 0.475145 data_time: 0.085683 memory: 6338 loss_kpt: 0.112140 acc_pose: 0.860294 loss: 0.112140 2022/09/22 00:56:35 - mmengine - INFO - Epoch(train) [177][150/293] lr: 5.000000e-05 eta: 1:05:43 time: 0.476823 data_time: 0.091131 memory: 6338 loss_kpt: 0.113110 acc_pose: 0.836849 loss: 0.113110 2022/09/22 00:56:59 - mmengine - INFO - Epoch(train) [177][200/293] lr: 5.000000e-05 eta: 1:05:23 time: 0.466969 data_time: 0.085983 memory: 6338 loss_kpt: 0.112807 acc_pose: 0.815390 loss: 0.112807 2022/09/22 00:57:23 - mmengine - INFO - Epoch(train) [177][250/293] lr: 5.000000e-05 eta: 1:05:04 time: 0.486767 data_time: 0.086787 memory: 6338 loss_kpt: 0.111323 acc_pose: 0.817298 loss: 0.111323 2022/09/22 00:57:43 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:57:43 - mmengine - INFO - Saving checkpoint at 177 epochs 2022/09/22 00:58:10 - mmengine - INFO - Epoch(train) [178][50/293] lr: 5.000000e-05 eta: 1:04:24 time: 0.483390 data_time: 0.099444 memory: 6338 loss_kpt: 0.111117 acc_pose: 0.821517 loss: 0.111117 2022/09/22 00:58:34 - mmengine - INFO - Epoch(train) [178][100/293] lr: 5.000000e-05 eta: 1:04:05 time: 0.477542 data_time: 0.093406 memory: 6338 loss_kpt: 0.113754 acc_pose: 0.800849 loss: 0.113754 2022/09/22 00:58:53 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 00:58:58 - mmengine - INFO - Epoch(train) [178][150/293] lr: 5.000000e-05 eta: 1:03:45 time: 0.473222 data_time: 0.089422 memory: 6338 loss_kpt: 0.113481 acc_pose: 0.788004 loss: 0.113481 2022/09/22 00:59:21 - mmengine - INFO - Epoch(train) [178][200/293] lr: 5.000000e-05 eta: 1:03:26 time: 0.469369 data_time: 0.087953 memory: 6338 loss_kpt: 0.113881 acc_pose: 0.852970 loss: 0.113881 2022/09/22 00:59:45 - mmengine - INFO - Epoch(train) [178][250/293] lr: 5.000000e-05 eta: 1:03:07 time: 0.488155 data_time: 0.090955 memory: 6338 loss_kpt: 0.111435 acc_pose: 0.810193 loss: 0.111435 2022/09/22 01:00:05 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:00:05 - mmengine - INFO - Saving checkpoint at 178 epochs 2022/09/22 01:00:33 - mmengine - INFO - Epoch(train) [179][50/293] lr: 5.000000e-05 eta: 1:02:27 time: 0.495539 data_time: 0.097449 memory: 6338 loss_kpt: 0.110769 acc_pose: 0.815747 loss: 0.110769 2022/09/22 01:00:57 - mmengine - INFO - Epoch(train) [179][100/293] lr: 5.000000e-05 eta: 1:02:08 time: 0.477558 data_time: 0.088318 memory: 6338 loss_kpt: 0.111342 acc_pose: 0.827985 loss: 0.111342 2022/09/22 01:01:22 - mmengine - INFO - Epoch(train) [179][150/293] lr: 5.000000e-05 eta: 1:01:48 time: 0.491974 data_time: 0.094952 memory: 6338 loss_kpt: 0.114102 acc_pose: 0.819294 loss: 0.114102 2022/09/22 01:01:46 - mmengine - INFO - Epoch(train) [179][200/293] lr: 5.000000e-05 eta: 1:01:29 time: 0.480059 data_time: 0.091736 memory: 6338 loss_kpt: 0.111808 acc_pose: 0.821797 loss: 0.111808 2022/09/22 01:02:10 - mmengine - INFO - Epoch(train) [179][250/293] lr: 5.000000e-05 eta: 1:01:09 time: 0.482141 data_time: 0.091285 memory: 6338 loss_kpt: 0.112361 acc_pose: 0.810394 loss: 0.112361 2022/09/22 01:02:30 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:02:30 - mmengine - INFO - Saving checkpoint at 179 epochs 2022/09/22 01:02:57 - mmengine - INFO - Epoch(train) [180][50/293] lr: 5.000000e-05 eta: 1:00:30 time: 0.478727 data_time: 0.099180 memory: 6338 loss_kpt: 0.110675 acc_pose: 0.836866 loss: 0.110675 2022/09/22 01:03:20 - mmengine - INFO - Epoch(train) [180][100/293] lr: 5.000000e-05 eta: 1:00:10 time: 0.460892 data_time: 0.086548 memory: 6338 loss_kpt: 0.112559 acc_pose: 0.863137 loss: 0.112559 2022/09/22 01:03:44 - mmengine - INFO - Epoch(train) [180][150/293] lr: 5.000000e-05 eta: 0:59:51 time: 0.483940 data_time: 0.094073 memory: 6338 loss_kpt: 0.111453 acc_pose: 0.838781 loss: 0.111453 2022/09/22 01:04:07 - mmengine - INFO - Epoch(train) [180][200/293] lr: 5.000000e-05 eta: 0:59:31 time: 0.466410 data_time: 0.082440 memory: 6338 loss_kpt: 0.111528 acc_pose: 0.816188 loss: 0.111528 2022/09/22 01:04:31 - mmengine - INFO - Epoch(train) [180][250/293] lr: 5.000000e-05 eta: 0:59:12 time: 0.475973 data_time: 0.087754 memory: 6338 loss_kpt: 0.112563 acc_pose: 0.769457 loss: 0.112563 2022/09/22 01:04:51 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:04:51 - mmengine - INFO - Saving checkpoint at 180 epochs 2022/09/22 01:05:00 - mmengine - INFO - Epoch(val) [180][50/407] eta: 0:00:42 time: 0.117904 data_time: 0.055566 memory: 6338 2022/09/22 01:05:06 - mmengine - INFO - Epoch(val) [180][100/407] eta: 0:00:35 time: 0.115110 data_time: 0.052810 memory: 587 2022/09/22 01:05:11 - mmengine - INFO - Epoch(val) [180][150/407] eta: 0:00:28 time: 0.111810 data_time: 0.050263 memory: 587 2022/09/22 01:05:17 - mmengine - INFO - Epoch(val) [180][200/407] eta: 0:00:23 time: 0.113745 data_time: 0.048805 memory: 587 2022/09/22 01:05:23 - mmengine - INFO - Epoch(val) [180][250/407] eta: 0:00:17 time: 0.114237 data_time: 0.052418 memory: 587 2022/09/22 01:05:28 - mmengine - INFO - Epoch(val) [180][300/407] eta: 0:00:12 time: 0.113687 data_time: 0.051405 memory: 587 2022/09/22 01:05:34 - mmengine - INFO - Epoch(val) [180][350/407] eta: 0:00:07 time: 0.125231 data_time: 0.064633 memory: 587 2022/09/22 01:05:40 - mmengine - INFO - Epoch(val) [180][400/407] eta: 0:00:00 time: 0.108356 data_time: 0.048639 memory: 587 2022/09/22 01:06:15 - mmengine - INFO - Evaluating CocoMetric... 2022/09/22 01:06:29 - mmengine - INFO - Epoch(val) [180][407/407] coco/AP: 0.691202 coco/AP .5: 0.882580 coco/AP .75: 0.765100 coco/AP (M): 0.660767 coco/AP (L): 0.752836 coco/AR: 0.751606 coco/AR .5: 0.925693 coco/AR .75: 0.818640 coco/AR (M): 0.710953 coco/AR (L): 0.809625 2022/09/22 01:06:29 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_170.pth is removed 2022/09/22 01:06:31 - mmengine - INFO - The best checkpoint with 0.6912 coco/AP at 180 epoch is saved to best_coco/AP_epoch_180.pth. 2022/09/22 01:06:55 - mmengine - INFO - Epoch(train) [181][50/293] lr: 5.000000e-05 eta: 0:58:32 time: 0.488808 data_time: 0.099024 memory: 6338 loss_kpt: 0.113576 acc_pose: 0.819001 loss: 0.113576 2022/09/22 01:07:20 - mmengine - INFO - Epoch(train) [181][100/293] lr: 5.000000e-05 eta: 0:58:13 time: 0.482320 data_time: 0.086001 memory: 6338 loss_kpt: 0.108839 acc_pose: 0.819403 loss: 0.108839 2022/09/22 01:07:44 - mmengine - INFO - Epoch(train) [181][150/293] lr: 5.000000e-05 eta: 0:57:53 time: 0.482680 data_time: 0.088090 memory: 6338 loss_kpt: 0.110964 acc_pose: 0.856093 loss: 0.110964 2022/09/22 01:08:08 - mmengine - INFO - Epoch(train) [181][200/293] lr: 5.000000e-05 eta: 0:57:34 time: 0.481741 data_time: 0.085592 memory: 6338 loss_kpt: 0.112261 acc_pose: 0.813101 loss: 0.112261 2022/09/22 01:08:32 - mmengine - INFO - Epoch(train) [181][250/293] lr: 5.000000e-05 eta: 0:57:14 time: 0.480418 data_time: 0.091544 memory: 6338 loss_kpt: 0.112337 acc_pose: 0.804486 loss: 0.112337 2022/09/22 01:08:37 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:08:52 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:08:52 - mmengine - INFO - Saving checkpoint at 181 epochs 2022/09/22 01:09:19 - mmengine - INFO - Epoch(train) [182][50/293] lr: 5.000000e-05 eta: 0:56:35 time: 0.491612 data_time: 0.097500 memory: 6338 loss_kpt: 0.113963 acc_pose: 0.790225 loss: 0.113963 2022/09/22 01:09:44 - mmengine - INFO - Epoch(train) [182][100/293] lr: 5.000000e-05 eta: 0:56:15 time: 0.483038 data_time: 0.089769 memory: 6338 loss_kpt: 0.112101 acc_pose: 0.806843 loss: 0.112101 2022/09/22 01:10:07 - mmengine - INFO - Epoch(train) [182][150/293] lr: 5.000000e-05 eta: 0:55:56 time: 0.474999 data_time: 0.088903 memory: 6338 loss_kpt: 0.113154 acc_pose: 0.843512 loss: 0.113154 2022/09/22 01:10:31 - mmengine - INFO - Epoch(train) [182][200/293] lr: 5.000000e-05 eta: 0:55:36 time: 0.481545 data_time: 0.095791 memory: 6338 loss_kpt: 0.110469 acc_pose: 0.878683 loss: 0.110469 2022/09/22 01:10:56 - mmengine - INFO - Epoch(train) [182][250/293] lr: 5.000000e-05 eta: 0:55:17 time: 0.483677 data_time: 0.090230 memory: 6338 loss_kpt: 0.114129 acc_pose: 0.798282 loss: 0.114129 2022/09/22 01:11:16 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:11:16 - mmengine - INFO - Saving checkpoint at 182 epochs 2022/09/22 01:11:42 - mmengine - INFO - Epoch(train) [183][50/293] lr: 5.000000e-05 eta: 0:54:37 time: 0.469504 data_time: 0.093578 memory: 6338 loss_kpt: 0.111229 acc_pose: 0.889269 loss: 0.111229 2022/09/22 01:12:05 - mmengine - INFO - Epoch(train) [183][100/293] lr: 5.000000e-05 eta: 0:54:18 time: 0.451988 data_time: 0.087216 memory: 6338 loss_kpt: 0.112458 acc_pose: 0.867364 loss: 0.112458 2022/09/22 01:12:28 - mmengine - INFO - Epoch(train) [183][150/293] lr: 5.000000e-05 eta: 0:53:58 time: 0.453810 data_time: 0.084466 memory: 6338 loss_kpt: 0.111882 acc_pose: 0.835442 loss: 0.111882 2022/09/22 01:12:51 - mmengine - INFO - Epoch(train) [183][200/293] lr: 5.000000e-05 eta: 0:53:38 time: 0.467525 data_time: 0.082573 memory: 6338 loss_kpt: 0.112718 acc_pose: 0.791795 loss: 0.112718 2022/09/22 01:13:14 - mmengine - INFO - Epoch(train) [183][250/293] lr: 5.000000e-05 eta: 0:53:19 time: 0.461992 data_time: 0.085799 memory: 6338 loss_kpt: 0.111245 acc_pose: 0.838049 loss: 0.111245 2022/09/22 01:13:34 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:13:34 - mmengine - INFO - Saving checkpoint at 183 epochs 2022/09/22 01:14:01 - mmengine - INFO - Epoch(train) [184][50/293] lr: 5.000000e-05 eta: 0:52:39 time: 0.483891 data_time: 0.097171 memory: 6338 loss_kpt: 0.114004 acc_pose: 0.768640 loss: 0.114004 2022/09/22 01:14:24 - mmengine - INFO - Epoch(train) [184][100/293] lr: 5.000000e-05 eta: 0:52:20 time: 0.460688 data_time: 0.091150 memory: 6338 loss_kpt: 0.112513 acc_pose: 0.816547 loss: 0.112513 2022/09/22 01:14:48 - mmengine - INFO - Epoch(train) [184][150/293] lr: 5.000000e-05 eta: 0:52:00 time: 0.469160 data_time: 0.087648 memory: 6338 loss_kpt: 0.111994 acc_pose: 0.872773 loss: 0.111994 2022/09/22 01:15:10 - mmengine - INFO - Epoch(train) [184][200/293] lr: 5.000000e-05 eta: 0:51:40 time: 0.450278 data_time: 0.087201 memory: 6338 loss_kpt: 0.111873 acc_pose: 0.833662 loss: 0.111873 2022/09/22 01:15:34 - mmengine - INFO - Epoch(train) [184][250/293] lr: 5.000000e-05 eta: 0:51:21 time: 0.468309 data_time: 0.091154 memory: 6338 loss_kpt: 0.112263 acc_pose: 0.845154 loss: 0.112263 2022/09/22 01:15:53 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:15:53 - mmengine - INFO - Saving checkpoint at 184 epochs 2022/09/22 01:16:21 - mmengine - INFO - Epoch(train) [185][50/293] lr: 5.000000e-05 eta: 0:50:41 time: 0.497899 data_time: 0.100918 memory: 6338 loss_kpt: 0.111344 acc_pose: 0.825621 loss: 0.111344 2022/09/22 01:16:39 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:16:45 - mmengine - INFO - Epoch(train) [185][100/293] lr: 5.000000e-05 eta: 0:50:22 time: 0.473586 data_time: 0.088940 memory: 6338 loss_kpt: 0.110918 acc_pose: 0.847246 loss: 0.110918 2022/09/22 01:17:09 - mmengine - INFO - Epoch(train) [185][150/293] lr: 5.000000e-05 eta: 0:50:02 time: 0.487304 data_time: 0.090712 memory: 6338 loss_kpt: 0.111943 acc_pose: 0.875343 loss: 0.111943 2022/09/22 01:17:33 - mmengine - INFO - Epoch(train) [185][200/293] lr: 5.000000e-05 eta: 0:49:43 time: 0.481689 data_time: 0.084992 memory: 6338 loss_kpt: 0.112616 acc_pose: 0.833271 loss: 0.112616 2022/09/22 01:17:57 - mmengine - INFO - Epoch(train) [185][250/293] lr: 5.000000e-05 eta: 0:49:23 time: 0.485160 data_time: 0.085043 memory: 6338 loss_kpt: 0.113394 acc_pose: 0.801831 loss: 0.113394 2022/09/22 01:18:18 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:18:18 - mmengine - INFO - Saving checkpoint at 185 epochs 2022/09/22 01:18:44 - mmengine - INFO - Epoch(train) [186][50/293] lr: 5.000000e-05 eta: 0:48:44 time: 0.480746 data_time: 0.091675 memory: 6338 loss_kpt: 0.110657 acc_pose: 0.871954 loss: 0.110657 2022/09/22 01:19:07 - mmengine - INFO - Epoch(train) [186][100/293] lr: 5.000000e-05 eta: 0:48:24 time: 0.453318 data_time: 0.088261 memory: 6338 loss_kpt: 0.110870 acc_pose: 0.877530 loss: 0.110870 2022/09/22 01:19:31 - mmengine - INFO - Epoch(train) [186][150/293] lr: 5.000000e-05 eta: 0:48:05 time: 0.475706 data_time: 0.094872 memory: 6338 loss_kpt: 0.111594 acc_pose: 0.807289 loss: 0.111594 2022/09/22 01:19:54 - mmengine - INFO - Epoch(train) [186][200/293] lr: 5.000000e-05 eta: 0:47:45 time: 0.459089 data_time: 0.082632 memory: 6338 loss_kpt: 0.112416 acc_pose: 0.838123 loss: 0.112416 2022/09/22 01:20:18 - mmengine - INFO - Epoch(train) [186][250/293] lr: 5.000000e-05 eta: 0:47:25 time: 0.473933 data_time: 0.088186 memory: 6338 loss_kpt: 0.110133 acc_pose: 0.855127 loss: 0.110133 2022/09/22 01:20:37 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:20:37 - mmengine - INFO - Saving checkpoint at 186 epochs 2022/09/22 01:21:03 - mmengine - INFO - Epoch(train) [187][50/293] lr: 5.000000e-05 eta: 0:46:46 time: 0.466129 data_time: 0.093233 memory: 6338 loss_kpt: 0.113036 acc_pose: 0.845072 loss: 0.113036 2022/09/22 01:21:26 - mmengine - INFO - Epoch(train) [187][100/293] lr: 5.000000e-05 eta: 0:46:26 time: 0.454088 data_time: 0.085510 memory: 6338 loss_kpt: 0.111459 acc_pose: 0.835140 loss: 0.111459 2022/09/22 01:21:48 - mmengine - INFO - Epoch(train) [187][150/293] lr: 5.000000e-05 eta: 0:46:07 time: 0.453425 data_time: 0.084497 memory: 6338 loss_kpt: 0.108147 acc_pose: 0.860979 loss: 0.108147 2022/09/22 01:22:11 - mmengine - INFO - Epoch(train) [187][200/293] lr: 5.000000e-05 eta: 0:45:47 time: 0.453653 data_time: 0.091177 memory: 6338 loss_kpt: 0.109919 acc_pose: 0.846126 loss: 0.109919 2022/09/22 01:22:34 - mmengine - INFO - Epoch(train) [187][250/293] lr: 5.000000e-05 eta: 0:45:27 time: 0.465405 data_time: 0.086662 memory: 6338 loss_kpt: 0.112242 acc_pose: 0.784767 loss: 0.112242 2022/09/22 01:22:53 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:22:53 - mmengine - INFO - Saving checkpoint at 187 epochs 2022/09/22 01:23:21 - mmengine - INFO - Epoch(train) [188][50/293] lr: 5.000000e-05 eta: 0:44:48 time: 0.492393 data_time: 0.099112 memory: 6338 loss_kpt: 0.110994 acc_pose: 0.773003 loss: 0.110994 2022/09/22 01:23:45 - mmengine - INFO - Epoch(train) [188][100/293] lr: 5.000000e-05 eta: 0:44:29 time: 0.488726 data_time: 0.085350 memory: 6338 loss_kpt: 0.112273 acc_pose: 0.812565 loss: 0.112273 2022/09/22 01:24:10 - mmengine - INFO - Epoch(train) [188][150/293] lr: 5.000000e-05 eta: 0:44:09 time: 0.488949 data_time: 0.091201 memory: 6338 loss_kpt: 0.112183 acc_pose: 0.815653 loss: 0.112183 2022/09/22 01:24:34 - mmengine - INFO - Epoch(train) [188][200/293] lr: 5.000000e-05 eta: 0:43:49 time: 0.489921 data_time: 0.091012 memory: 6338 loss_kpt: 0.111538 acc_pose: 0.798545 loss: 0.111538 2022/09/22 01:24:38 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:24:59 - mmengine - INFO - Epoch(train) [188][250/293] lr: 5.000000e-05 eta: 0:43:30 time: 0.483521 data_time: 0.090585 memory: 6338 loss_kpt: 0.112886 acc_pose: 0.764987 loss: 0.112886 2022/09/22 01:25:19 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:25:19 - mmengine - INFO - Saving checkpoint at 188 epochs 2022/09/22 01:25:45 - mmengine - INFO - Epoch(train) [189][50/293] lr: 5.000000e-05 eta: 0:42:51 time: 0.453736 data_time: 0.097665 memory: 6338 loss_kpt: 0.111730 acc_pose: 0.829739 loss: 0.111730 2022/09/22 01:26:07 - mmengine - INFO - Epoch(train) [189][100/293] lr: 5.000000e-05 eta: 0:42:31 time: 0.453682 data_time: 0.090088 memory: 6338 loss_kpt: 0.111629 acc_pose: 0.816461 loss: 0.111629 2022/09/22 01:26:30 - mmengine - INFO - Epoch(train) [189][150/293] lr: 5.000000e-05 eta: 0:42:11 time: 0.458878 data_time: 0.094474 memory: 6338 loss_kpt: 0.110514 acc_pose: 0.842506 loss: 0.110514 2022/09/22 01:26:53 - mmengine - INFO - Epoch(train) [189][200/293] lr: 5.000000e-05 eta: 0:41:51 time: 0.447072 data_time: 0.085308 memory: 6338 loss_kpt: 0.114444 acc_pose: 0.838225 loss: 0.114444 2022/09/22 01:27:15 - mmengine - INFO - Epoch(train) [189][250/293] lr: 5.000000e-05 eta: 0:41:31 time: 0.455690 data_time: 0.092982 memory: 6338 loss_kpt: 0.112159 acc_pose: 0.818641 loss: 0.112159 2022/09/22 01:27:34 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:27:34 - mmengine - INFO - Saving checkpoint at 189 epochs 2022/09/22 01:28:02 - mmengine - INFO - Epoch(train) [190][50/293] lr: 5.000000e-05 eta: 0:40:53 time: 0.484285 data_time: 0.101453 memory: 6338 loss_kpt: 0.110812 acc_pose: 0.826009 loss: 0.110812 2022/09/22 01:28:26 - mmengine - INFO - Epoch(train) [190][100/293] lr: 5.000000e-05 eta: 0:40:33 time: 0.479088 data_time: 0.086669 memory: 6338 loss_kpt: 0.109755 acc_pose: 0.835836 loss: 0.109755 2022/09/22 01:28:49 - mmengine - INFO - Epoch(train) [190][150/293] lr: 5.000000e-05 eta: 0:40:13 time: 0.477303 data_time: 0.094491 memory: 6338 loss_kpt: 0.114212 acc_pose: 0.802616 loss: 0.114212 2022/09/22 01:29:13 - mmengine - INFO - Epoch(train) [190][200/293] lr: 5.000000e-05 eta: 0:39:53 time: 0.479554 data_time: 0.090900 memory: 6338 loss_kpt: 0.112023 acc_pose: 0.874595 loss: 0.112023 2022/09/22 01:29:38 - mmengine - INFO - Epoch(train) [190][250/293] lr: 5.000000e-05 eta: 0:39:34 time: 0.481733 data_time: 0.093896 memory: 6338 loss_kpt: 0.111267 acc_pose: 0.793892 loss: 0.111267 2022/09/22 01:29:58 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:29:58 - mmengine - INFO - Saving checkpoint at 190 epochs 2022/09/22 01:30:06 - mmengine - INFO - Epoch(val) [190][50/407] eta: 0:00:42 time: 0.118222 data_time: 0.052076 memory: 6338 2022/09/22 01:30:12 - mmengine - INFO - Epoch(val) [190][100/407] eta: 0:00:35 time: 0.115665 data_time: 0.054526 memory: 587 2022/09/22 01:30:18 - mmengine - INFO - Epoch(val) [190][150/407] eta: 0:00:30 time: 0.118327 data_time: 0.054647 memory: 587 2022/09/22 01:30:24 - mmengine - INFO - Epoch(val) [190][200/407] eta: 0:00:23 time: 0.113537 data_time: 0.053693 memory: 587 2022/09/22 01:30:30 - mmengine - INFO - Epoch(val) [190][250/407] eta: 0:00:18 time: 0.117206 data_time: 0.055808 memory: 587 2022/09/22 01:30:35 - mmengine - INFO - Epoch(val) [190][300/407] eta: 0:00:12 time: 0.117066 data_time: 0.055893 memory: 587 2022/09/22 01:30:42 - mmengine - INFO - Epoch(val) [190][350/407] eta: 0:00:07 time: 0.122984 data_time: 0.060649 memory: 587 2022/09/22 01:30:47 - mmengine - INFO - Epoch(val) [190][400/407] eta: 0:00:00 time: 0.102188 data_time: 0.042737 memory: 587 2022/09/22 01:31:21 - mmengine - INFO - Evaluating CocoMetric... 2022/09/22 01:31:35 - mmengine - INFO - Epoch(val) [190][407/407] coco/AP: 0.692766 coco/AP .5: 0.882007 coco/AP .75: 0.771495 coco/AP (M): 0.662838 coco/AP (L): 0.754972 coco/AR: 0.754046 coco/AR .5: 0.925535 coco/AR .75: 0.824937 coco/AR (M): 0.713138 coco/AR (L): 0.812635 2022/09/22 01:31:35 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_180.pth is removed 2022/09/22 01:31:38 - mmengine - INFO - The best checkpoint with 0.6928 coco/AP at 190 epoch is saved to best_coco/AP_epoch_190.pth. 2022/09/22 01:32:01 - mmengine - INFO - Epoch(train) [191][50/293] lr: 5.000000e-05 eta: 0:38:55 time: 0.463574 data_time: 0.099866 memory: 6338 loss_kpt: 0.112877 acc_pose: 0.831759 loss: 0.112877 2022/09/22 01:32:23 - mmengine - INFO - Epoch(train) [191][100/293] lr: 5.000000e-05 eta: 0:38:35 time: 0.449692 data_time: 0.086301 memory: 6338 loss_kpt: 0.111651 acc_pose: 0.844473 loss: 0.111651 2022/09/22 01:32:46 - mmengine - INFO - Epoch(train) [191][150/293] lr: 5.000000e-05 eta: 0:38:15 time: 0.459089 data_time: 0.090222 memory: 6338 loss_kpt: 0.112322 acc_pose: 0.812358 loss: 0.112322 2022/09/22 01:33:09 - mmengine - INFO - Epoch(train) [191][200/293] lr: 5.000000e-05 eta: 0:37:55 time: 0.448547 data_time: 0.088414 memory: 6338 loss_kpt: 0.107881 acc_pose: 0.805460 loss: 0.107881 2022/09/22 01:33:31 - mmengine - INFO - Epoch(train) [191][250/293] lr: 5.000000e-05 eta: 0:37:35 time: 0.447362 data_time: 0.087181 memory: 6338 loss_kpt: 0.111409 acc_pose: 0.830241 loss: 0.111409 2022/09/22 01:33:50 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:33:50 - mmengine - INFO - Saving checkpoint at 191 epochs 2022/09/22 01:34:10 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:34:17 - mmengine - INFO - Epoch(train) [192][50/293] lr: 5.000000e-05 eta: 0:36:57 time: 0.473603 data_time: 0.101337 memory: 6338 loss_kpt: 0.110582 acc_pose: 0.857064 loss: 0.110582 2022/09/22 01:34:41 - mmengine - INFO - Epoch(train) [192][100/293] lr: 5.000000e-05 eta: 0:36:37 time: 0.481797 data_time: 0.087666 memory: 6338 loss_kpt: 0.111265 acc_pose: 0.808071 loss: 0.111265 2022/09/22 01:35:05 - mmengine - INFO - Epoch(train) [192][150/293] lr: 5.000000e-05 eta: 0:36:17 time: 0.493740 data_time: 0.098235 memory: 6338 loss_kpt: 0.110731 acc_pose: 0.802514 loss: 0.110731 2022/09/22 01:35:29 - mmengine - INFO - Epoch(train) [192][200/293] lr: 5.000000e-05 eta: 0:35:58 time: 0.476238 data_time: 0.085633 memory: 6338 loss_kpt: 0.113737 acc_pose: 0.815521 loss: 0.113737 2022/09/22 01:35:54 - mmengine - INFO - Epoch(train) [192][250/293] lr: 5.000000e-05 eta: 0:35:38 time: 0.486017 data_time: 0.090404 memory: 6338 loss_kpt: 0.110360 acc_pose: 0.865535 loss: 0.110360 2022/09/22 01:36:14 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:36:14 - mmengine - INFO - Saving checkpoint at 192 epochs 2022/09/22 01:36:41 - mmengine - INFO - Epoch(train) [193][50/293] lr: 5.000000e-05 eta: 0:34:59 time: 0.491414 data_time: 0.111031 memory: 6338 loss_kpt: 0.108506 acc_pose: 0.857395 loss: 0.108506 2022/09/22 01:37:04 - mmengine - INFO - Epoch(train) [193][100/293] lr: 5.000000e-05 eta: 0:34:40 time: 0.465655 data_time: 0.087463 memory: 6338 loss_kpt: 0.109538 acc_pose: 0.826639 loss: 0.109538 2022/09/22 01:37:28 - mmengine - INFO - Epoch(train) [193][150/293] lr: 5.000000e-05 eta: 0:34:20 time: 0.476787 data_time: 0.087524 memory: 6338 loss_kpt: 0.110991 acc_pose: 0.821768 loss: 0.110991 2022/09/22 01:37:51 - mmengine - INFO - Epoch(train) [193][200/293] lr: 5.000000e-05 eta: 0:34:00 time: 0.460589 data_time: 0.088262 memory: 6338 loss_kpt: 0.111183 acc_pose: 0.850871 loss: 0.111183 2022/09/22 01:38:15 - mmengine - INFO - Epoch(train) [193][250/293] lr: 5.000000e-05 eta: 0:33:40 time: 0.481425 data_time: 0.093378 memory: 6338 loss_kpt: 0.110404 acc_pose: 0.828623 loss: 0.110404 2022/09/22 01:38:35 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:38:35 - mmengine - INFO - Saving checkpoint at 193 epochs 2022/09/22 01:39:01 - mmengine - INFO - Epoch(train) [194][50/293] lr: 5.000000e-05 eta: 0:33:02 time: 0.468085 data_time: 0.099373 memory: 6338 loss_kpt: 0.112573 acc_pose: 0.846425 loss: 0.112573 2022/09/22 01:39:24 - mmengine - INFO - Epoch(train) [194][100/293] lr: 5.000000e-05 eta: 0:32:42 time: 0.457374 data_time: 0.083416 memory: 6338 loss_kpt: 0.110516 acc_pose: 0.840507 loss: 0.110516 2022/09/22 01:39:47 - mmengine - INFO - Epoch(train) [194][150/293] lr: 5.000000e-05 eta: 0:32:22 time: 0.468976 data_time: 0.097604 memory: 6338 loss_kpt: 0.112052 acc_pose: 0.790745 loss: 0.112052 2022/09/22 01:40:10 - mmengine - INFO - Epoch(train) [194][200/293] lr: 5.000000e-05 eta: 0:32:02 time: 0.450157 data_time: 0.083422 memory: 6338 loss_kpt: 0.109607 acc_pose: 0.843764 loss: 0.109607 2022/09/22 01:40:33 - mmengine - INFO - Epoch(train) [194][250/293] lr: 5.000000e-05 eta: 0:31:42 time: 0.465260 data_time: 0.091566 memory: 6338 loss_kpt: 0.110737 acc_pose: 0.845590 loss: 0.110737 2022/09/22 01:40:53 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:40:53 - mmengine - INFO - Saving checkpoint at 194 epochs 2022/09/22 01:41:20 - mmengine - INFO - Epoch(train) [195][50/293] lr: 5.000000e-05 eta: 0:31:04 time: 0.482627 data_time: 0.104052 memory: 6338 loss_kpt: 0.111585 acc_pose: 0.828379 loss: 0.111585 2022/09/22 01:41:43 - mmengine - INFO - Epoch(train) [195][100/293] lr: 5.000000e-05 eta: 0:30:44 time: 0.459055 data_time: 0.081962 memory: 6338 loss_kpt: 0.113342 acc_pose: 0.840984 loss: 0.113342 2022/09/22 01:42:06 - mmengine - INFO - Epoch(train) [195][150/293] lr: 5.000000e-05 eta: 0:30:24 time: 0.469391 data_time: 0.093125 memory: 6338 loss_kpt: 0.110785 acc_pose: 0.838685 loss: 0.110785 2022/09/22 01:42:10 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:42:29 - mmengine - INFO - Epoch(train) [195][200/293] lr: 5.000000e-05 eta: 0:30:04 time: 0.453983 data_time: 0.082219 memory: 6338 loss_kpt: 0.109609 acc_pose: 0.779146 loss: 0.109609 2022/09/22 01:42:53 - mmengine - INFO - Epoch(train) [195][250/293] lr: 5.000000e-05 eta: 0:29:44 time: 0.478265 data_time: 0.088812 memory: 6338 loss_kpt: 0.111191 acc_pose: 0.831016 loss: 0.111191 2022/09/22 01:43:12 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:43:12 - mmengine - INFO - Saving checkpoint at 195 epochs 2022/09/22 01:43:40 - mmengine - INFO - Epoch(train) [196][50/293] lr: 5.000000e-05 eta: 0:29:06 time: 0.499533 data_time: 0.104778 memory: 6338 loss_kpt: 0.109467 acc_pose: 0.826843 loss: 0.109467 2022/09/22 01:44:04 - mmengine - INFO - Epoch(train) [196][100/293] lr: 5.000000e-05 eta: 0:28:46 time: 0.479253 data_time: 0.092478 memory: 6338 loss_kpt: 0.108330 acc_pose: 0.836175 loss: 0.108330 2022/09/22 01:44:28 - mmengine - INFO - Epoch(train) [196][150/293] lr: 5.000000e-05 eta: 0:28:26 time: 0.491361 data_time: 0.092388 memory: 6338 loss_kpt: 0.110111 acc_pose: 0.804831 loss: 0.110111 2022/09/22 01:44:52 - mmengine - INFO - Epoch(train) [196][200/293] lr: 5.000000e-05 eta: 0:28:06 time: 0.478283 data_time: 0.089688 memory: 6338 loss_kpt: 0.112043 acc_pose: 0.824270 loss: 0.112043 2022/09/22 01:45:17 - mmengine - INFO - Epoch(train) [196][250/293] lr: 5.000000e-05 eta: 0:27:47 time: 0.490903 data_time: 0.095537 memory: 6338 loss_kpt: 0.108725 acc_pose: 0.780859 loss: 0.108725 2022/09/22 01:45:36 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:45:36 - mmengine - INFO - Saving checkpoint at 196 epochs 2022/09/22 01:46:05 - mmengine - INFO - Epoch(train) [197][50/293] lr: 5.000000e-05 eta: 0:27:08 time: 0.506905 data_time: 0.098751 memory: 6338 loss_kpt: 0.111512 acc_pose: 0.811762 loss: 0.111512 2022/09/22 01:46:29 - mmengine - INFO - Epoch(train) [197][100/293] lr: 5.000000e-05 eta: 0:26:49 time: 0.484098 data_time: 0.087472 memory: 6338 loss_kpt: 0.108973 acc_pose: 0.826191 loss: 0.108973 2022/09/22 01:46:53 - mmengine - INFO - Epoch(train) [197][150/293] lr: 5.000000e-05 eta: 0:26:29 time: 0.480112 data_time: 0.091592 memory: 6338 loss_kpt: 0.111162 acc_pose: 0.837532 loss: 0.111162 2022/09/22 01:47:18 - mmengine - INFO - Epoch(train) [197][200/293] lr: 5.000000e-05 eta: 0:26:09 time: 0.498121 data_time: 0.095562 memory: 6338 loss_kpt: 0.110822 acc_pose: 0.801122 loss: 0.110822 2022/09/22 01:47:43 - mmengine - INFO - Epoch(train) [197][250/293] lr: 5.000000e-05 eta: 0:25:49 time: 0.500534 data_time: 0.098486 memory: 6338 loss_kpt: 0.112398 acc_pose: 0.831280 loss: 0.112398 2022/09/22 01:48:03 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:48:03 - mmengine - INFO - Saving checkpoint at 197 epochs 2022/09/22 01:48:30 - mmengine - INFO - Epoch(train) [198][50/293] lr: 5.000000e-05 eta: 0:25:11 time: 0.482127 data_time: 0.098695 memory: 6338 loss_kpt: 0.113191 acc_pose: 0.771607 loss: 0.113191 2022/09/22 01:48:54 - mmengine - INFO - Epoch(train) [198][100/293] lr: 5.000000e-05 eta: 0:24:51 time: 0.476273 data_time: 0.093005 memory: 6338 loss_kpt: 0.109598 acc_pose: 0.858975 loss: 0.109598 2022/09/22 01:49:19 - mmengine - INFO - Epoch(train) [198][150/293] lr: 5.000000e-05 eta: 0:24:31 time: 0.491363 data_time: 0.096629 memory: 6338 loss_kpt: 0.111789 acc_pose: 0.875415 loss: 0.111789 2022/09/22 01:49:42 - mmengine - INFO - Epoch(train) [198][200/293] lr: 5.000000e-05 eta: 0:24:11 time: 0.466384 data_time: 0.089042 memory: 6338 loss_kpt: 0.109962 acc_pose: 0.811274 loss: 0.109962 2022/09/22 01:50:06 - mmengine - INFO - Epoch(train) [198][250/293] lr: 5.000000e-05 eta: 0:23:51 time: 0.484955 data_time: 0.092897 memory: 6338 loss_kpt: 0.111426 acc_pose: 0.854846 loss: 0.111426 2022/09/22 01:50:20 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:50:26 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:50:26 - mmengine - INFO - Saving checkpoint at 198 epochs 2022/09/22 01:50:53 - mmengine - INFO - Epoch(train) [199][50/293] lr: 5.000000e-05 eta: 0:23:13 time: 0.479435 data_time: 0.098927 memory: 6338 loss_kpt: 0.113240 acc_pose: 0.804623 loss: 0.113240 2022/09/22 01:51:17 - mmengine - INFO - Epoch(train) [199][100/293] lr: 5.000000e-05 eta: 0:22:53 time: 0.466447 data_time: 0.087752 memory: 6338 loss_kpt: 0.112628 acc_pose: 0.797747 loss: 0.112628 2022/09/22 01:51:40 - mmengine - INFO - Epoch(train) [199][150/293] lr: 5.000000e-05 eta: 0:22:33 time: 0.465129 data_time: 0.087604 memory: 6338 loss_kpt: 0.109776 acc_pose: 0.848628 loss: 0.109776 2022/09/22 01:52:03 - mmengine - INFO - Epoch(train) [199][200/293] lr: 5.000000e-05 eta: 0:22:13 time: 0.456848 data_time: 0.089150 memory: 6338 loss_kpt: 0.112488 acc_pose: 0.826554 loss: 0.112488 2022/09/22 01:52:26 - mmengine - INFO - Epoch(train) [199][250/293] lr: 5.000000e-05 eta: 0:21:54 time: 0.473146 data_time: 0.089971 memory: 6338 loss_kpt: 0.110248 acc_pose: 0.812310 loss: 0.110248 2022/09/22 01:52:46 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:52:46 - mmengine - INFO - Saving checkpoint at 199 epochs 2022/09/22 01:53:12 - mmengine - INFO - Epoch(train) [200][50/293] lr: 5.000000e-05 eta: 0:21:15 time: 0.474270 data_time: 0.097086 memory: 6338 loss_kpt: 0.109420 acc_pose: 0.790632 loss: 0.109420 2022/09/22 01:53:35 - mmengine - INFO - Epoch(train) [200][100/293] lr: 5.000000e-05 eta: 0:20:55 time: 0.452510 data_time: 0.087541 memory: 6338 loss_kpt: 0.110957 acc_pose: 0.819008 loss: 0.110957 2022/09/22 01:53:58 - mmengine - INFO - Epoch(train) [200][150/293] lr: 5.000000e-05 eta: 0:20:35 time: 0.464535 data_time: 0.094358 memory: 6338 loss_kpt: 0.110607 acc_pose: 0.781106 loss: 0.110607 2022/09/22 01:54:22 - mmengine - INFO - Epoch(train) [200][200/293] lr: 5.000000e-05 eta: 0:20:16 time: 0.467746 data_time: 0.092209 memory: 6338 loss_kpt: 0.108547 acc_pose: 0.786497 loss: 0.108547 2022/09/22 01:54:45 - mmengine - INFO - Epoch(train) [200][250/293] lr: 5.000000e-05 eta: 0:19:56 time: 0.463542 data_time: 0.091473 memory: 6338 loss_kpt: 0.110643 acc_pose: 0.843936 loss: 0.110643 2022/09/22 01:55:04 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:55:04 - mmengine - INFO - Saving checkpoint at 200 epochs 2022/09/22 01:55:13 - mmengine - INFO - Epoch(val) [200][50/407] eta: 0:00:42 time: 0.118401 data_time: 0.055076 memory: 6338 2022/09/22 01:55:18 - mmengine - INFO - Epoch(val) [200][100/407] eta: 0:00:35 time: 0.115000 data_time: 0.052593 memory: 587 2022/09/22 01:55:24 - mmengine - INFO - Epoch(val) [200][150/407] eta: 0:00:30 time: 0.116804 data_time: 0.053134 memory: 587 2022/09/22 01:55:30 - mmengine - INFO - Epoch(val) [200][200/407] eta: 0:00:24 time: 0.120098 data_time: 0.058881 memory: 587 2022/09/22 01:55:36 - mmengine - INFO - Epoch(val) [200][250/407] eta: 0:00:18 time: 0.120445 data_time: 0.056172 memory: 587 2022/09/22 01:55:42 - mmengine - INFO - Epoch(val) [200][300/407] eta: 0:00:12 time: 0.112603 data_time: 0.050658 memory: 587 2022/09/22 01:55:48 - mmengine - INFO - Epoch(val) [200][350/407] eta: 0:00:06 time: 0.117755 data_time: 0.056448 memory: 587 2022/09/22 01:55:53 - mmengine - INFO - Epoch(val) [200][400/407] eta: 0:00:00 time: 0.109047 data_time: 0.049904 memory: 587 2022/09/22 01:56:29 - mmengine - INFO - Evaluating CocoMetric... 2022/09/22 01:56:43 - mmengine - INFO - Epoch(val) [200][407/407] coco/AP: 0.693498 coco/AP .5: 0.883810 coco/AP .75: 0.770502 coco/AP (M): 0.663654 coco/AP (L): 0.755258 coco/AR: 0.754597 coco/AR .5: 0.928369 coco/AR .75: 0.824307 coco/AR (M): 0.714668 coco/AR (L): 0.812003 2022/09/22 01:56:43 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_190.pth is removed 2022/09/22 01:56:45 - mmengine - INFO - The best checkpoint with 0.6935 coco/AP at 200 epoch is saved to best_coco/AP_epoch_200.pth. 2022/09/22 01:57:09 - mmengine - INFO - Epoch(train) [201][50/293] lr: 5.000000e-06 eta: 0:19:18 time: 0.486287 data_time: 0.106365 memory: 6338 loss_kpt: 0.109560 acc_pose: 0.831645 loss: 0.109560 2022/09/22 01:57:33 - mmengine - INFO - Epoch(train) [201][100/293] lr: 5.000000e-06 eta: 0:18:58 time: 0.468873 data_time: 0.093729 memory: 6338 loss_kpt: 0.110843 acc_pose: 0.763219 loss: 0.110843 2022/09/22 01:57:57 - mmengine - INFO - Epoch(train) [201][150/293] lr: 5.000000e-06 eta: 0:18:38 time: 0.489153 data_time: 0.101803 memory: 6338 loss_kpt: 0.109144 acc_pose: 0.823926 loss: 0.109144 2022/09/22 01:58:21 - mmengine - INFO - Epoch(train) [201][200/293] lr: 5.000000e-06 eta: 0:18:18 time: 0.466550 data_time: 0.091326 memory: 6338 loss_kpt: 0.108481 acc_pose: 0.837987 loss: 0.108481 2022/09/22 01:58:44 - mmengine - INFO - Epoch(train) [201][250/293] lr: 5.000000e-06 eta: 0:17:58 time: 0.475105 data_time: 0.098512 memory: 6338 loss_kpt: 0.110220 acc_pose: 0.862097 loss: 0.110220 2022/09/22 01:59:04 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 01:59:04 - mmengine - INFO - Saving checkpoint at 201 epochs 2022/09/22 01:59:31 - mmengine - INFO - Epoch(train) [202][50/293] lr: 5.000000e-06 eta: 0:17:20 time: 0.491290 data_time: 0.103329 memory: 6338 loss_kpt: 0.111091 acc_pose: 0.830237 loss: 0.111091 2022/09/22 01:59:55 - mmengine - INFO - Epoch(train) [202][100/293] lr: 5.000000e-06 eta: 0:17:00 time: 0.475510 data_time: 0.094144 memory: 6338 loss_kpt: 0.108601 acc_pose: 0.857332 loss: 0.108601 2022/09/22 01:59:58 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 02:00:20 - mmengine - INFO - Epoch(train) [202][150/293] lr: 5.000000e-06 eta: 0:16:40 time: 0.504281 data_time: 0.101807 memory: 6338 loss_kpt: 0.111595 acc_pose: 0.841998 loss: 0.111595 2022/09/22 02:00:44 - mmengine - INFO - Epoch(train) [202][200/293] lr: 5.000000e-06 eta: 0:16:20 time: 0.473274 data_time: 0.092330 memory: 6338 loss_kpt: 0.108165 acc_pose: 0.852138 loss: 0.108165 2022/09/22 02:01:08 - mmengine - INFO - Epoch(train) [202][250/293] lr: 5.000000e-06 eta: 0:16:00 time: 0.487722 data_time: 0.095464 memory: 6338 loss_kpt: 0.109255 acc_pose: 0.777201 loss: 0.109255 2022/09/22 02:01:28 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 02:01:28 - mmengine - INFO - Saving checkpoint at 202 epochs 2022/09/22 02:01:55 - mmengine - INFO - Epoch(train) [203][50/293] lr: 5.000000e-06 eta: 0:15:22 time: 0.484869 data_time: 0.100298 memory: 6338 loss_kpt: 0.111675 acc_pose: 0.835959 loss: 0.111675 2022/09/22 02:02:19 - mmengine - INFO - Epoch(train) [203][100/293] lr: 5.000000e-06 eta: 0:15:02 time: 0.483890 data_time: 0.091699 memory: 6338 loss_kpt: 0.110980 acc_pose: 0.825554 loss: 0.110980 2022/09/22 02:02:43 - mmengine - INFO - Epoch(train) [203][150/293] lr: 5.000000e-06 eta: 0:14:42 time: 0.472969 data_time: 0.089116 memory: 6338 loss_kpt: 0.111358 acc_pose: 0.838510 loss: 0.111358 2022/09/22 02:03:06 - mmengine - INFO - Epoch(train) [203][200/293] lr: 5.000000e-06 eta: 0:14:22 time: 0.466954 data_time: 0.092108 memory: 6338 loss_kpt: 0.109750 acc_pose: 0.844877 loss: 0.109750 2022/09/22 02:03:30 - mmengine - INFO - Epoch(train) [203][250/293] lr: 5.000000e-06 eta: 0:14:02 time: 0.481277 data_time: 0.093703 memory: 6338 loss_kpt: 0.113159 acc_pose: 0.811022 loss: 0.113159 2022/09/22 02:03:51 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 02:03:51 - mmengine - INFO - Saving checkpoint at 203 epochs 2022/09/22 02:04:18 - mmengine - INFO - Epoch(train) [204][50/293] lr: 5.000000e-06 eta: 0:13:24 time: 0.485878 data_time: 0.093581 memory: 6338 loss_kpt: 0.109797 acc_pose: 0.823790 loss: 0.109797 2022/09/22 02:04:41 - mmengine - INFO - Epoch(train) [204][100/293] lr: 5.000000e-06 eta: 0:13:04 time: 0.472149 data_time: 0.089423 memory: 6338 loss_kpt: 0.110074 acc_pose: 0.855893 loss: 0.110074 2022/09/22 02:05:04 - mmengine - INFO - Epoch(train) [204][150/293] lr: 5.000000e-06 eta: 0:12:44 time: 0.464081 data_time: 0.092118 memory: 6338 loss_kpt: 0.109430 acc_pose: 0.876798 loss: 0.109430 2022/09/22 02:05:29 - mmengine - INFO - Epoch(train) [204][200/293] lr: 5.000000e-06 eta: 0:12:24 time: 0.484127 data_time: 0.089483 memory: 6338 loss_kpt: 0.111979 acc_pose: 0.817165 loss: 0.111979 2022/09/22 02:05:53 - mmengine - INFO - Epoch(train) [204][250/293] lr: 5.000000e-06 eta: 0:12:04 time: 0.477031 data_time: 0.088491 memory: 6338 loss_kpt: 0.112140 acc_pose: 0.850183 loss: 0.112140 2022/09/22 02:06:12 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 02:06:12 - mmengine - INFO - Saving checkpoint at 204 epochs 2022/09/22 02:06:38 - mmengine - INFO - Epoch(train) [205][50/293] lr: 5.000000e-06 eta: 0:11:26 time: 0.468613 data_time: 0.101189 memory: 6338 loss_kpt: 0.111622 acc_pose: 0.849029 loss: 0.111622 2022/09/22 02:07:02 - mmengine - INFO - Epoch(train) [205][100/293] lr: 5.000000e-06 eta: 0:11:06 time: 0.465464 data_time: 0.098346 memory: 6338 loss_kpt: 0.109172 acc_pose: 0.818670 loss: 0.109172 2022/09/22 02:07:25 - mmengine - INFO - Epoch(train) [205][150/293] lr: 5.000000e-06 eta: 0:10:46 time: 0.477484 data_time: 0.098436 memory: 6338 loss_kpt: 0.109892 acc_pose: 0.856435 loss: 0.109892 2022/09/22 02:07:49 - mmengine - INFO - Epoch(train) [205][200/293] lr: 5.000000e-06 eta: 0:10:26 time: 0.464691 data_time: 0.092212 memory: 6338 loss_kpt: 0.110793 acc_pose: 0.835243 loss: 0.110793 2022/09/22 02:08:01 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 02:08:11 - mmengine - INFO - Epoch(train) [205][250/293] lr: 5.000000e-06 eta: 0:10:06 time: 0.455994 data_time: 0.093470 memory: 6338 loss_kpt: 0.111600 acc_pose: 0.825247 loss: 0.111600 2022/09/22 02:08:31 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 02:08:31 - mmengine - INFO - Saving checkpoint at 205 epochs 2022/09/22 02:08:59 - mmengine - INFO - Epoch(train) [206][50/293] lr: 5.000000e-06 eta: 0:09:29 time: 0.502919 data_time: 0.099589 memory: 6338 loss_kpt: 0.110469 acc_pose: 0.818237 loss: 0.110469 2022/09/22 02:09:23 - mmengine - INFO - Epoch(train) [206][100/293] lr: 5.000000e-06 eta: 0:09:09 time: 0.477116 data_time: 0.087793 memory: 6338 loss_kpt: 0.108256 acc_pose: 0.846833 loss: 0.108256 2022/09/22 02:09:47 - mmengine - INFO - Epoch(train) [206][150/293] lr: 5.000000e-06 eta: 0:08:49 time: 0.487296 data_time: 0.088688 memory: 6338 loss_kpt: 0.111095 acc_pose: 0.862024 loss: 0.111095 2022/09/22 02:10:12 - mmengine - INFO - Epoch(train) [206][200/293] lr: 5.000000e-06 eta: 0:08:29 time: 0.490698 data_time: 0.092098 memory: 6338 loss_kpt: 0.110719 acc_pose: 0.846138 loss: 0.110719 2022/09/22 02:10:36 - mmengine - INFO - Epoch(train) [206][250/293] lr: 5.000000e-06 eta: 0:08:09 time: 0.487933 data_time: 0.094703 memory: 6338 loss_kpt: 0.107498 acc_pose: 0.835585 loss: 0.107498 2022/09/22 02:10:56 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 02:10:56 - mmengine - INFO - Saving checkpoint at 206 epochs 2022/09/22 02:11:24 - mmengine - INFO - Epoch(train) [207][50/293] lr: 5.000000e-06 eta: 0:07:31 time: 0.490522 data_time: 0.101658 memory: 6338 loss_kpt: 0.110463 acc_pose: 0.818365 loss: 0.110463 2022/09/22 02:11:48 - mmengine - INFO - Epoch(train) [207][100/293] lr: 5.000000e-06 eta: 0:07:11 time: 0.485184 data_time: 0.092471 memory: 6338 loss_kpt: 0.110179 acc_pose: 0.841895 loss: 0.110179 2022/09/22 02:12:13 - mmengine - INFO - Epoch(train) [207][150/293] lr: 5.000000e-06 eta: 0:06:51 time: 0.506795 data_time: 0.094992 memory: 6338 loss_kpt: 0.111870 acc_pose: 0.865998 loss: 0.111870 2022/09/22 02:12:38 - mmengine - INFO - Epoch(train) [207][200/293] lr: 5.000000e-06 eta: 0:06:31 time: 0.485665 data_time: 0.090553 memory: 6338 loss_kpt: 0.109500 acc_pose: 0.811776 loss: 0.109500 2022/09/22 02:13:02 - mmengine - INFO - Epoch(train) [207][250/293] lr: 5.000000e-06 eta: 0:06:11 time: 0.487815 data_time: 0.096468 memory: 6338 loss_kpt: 0.111227 acc_pose: 0.830254 loss: 0.111227 2022/09/22 02:13:22 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 02:13:22 - mmengine - INFO - Saving checkpoint at 207 epochs 2022/09/22 02:13:49 - mmengine - INFO - Epoch(train) [208][50/293] lr: 5.000000e-06 eta: 0:05:33 time: 0.484047 data_time: 0.099220 memory: 6338 loss_kpt: 0.110095 acc_pose: 0.819780 loss: 0.110095 2022/09/22 02:14:12 - mmengine - INFO - Epoch(train) [208][100/293] lr: 5.000000e-06 eta: 0:05:13 time: 0.460358 data_time: 0.088821 memory: 6338 loss_kpt: 0.109159 acc_pose: 0.781408 loss: 0.109159 2022/09/22 02:14:35 - mmengine - INFO - Epoch(train) [208][150/293] lr: 5.000000e-06 eta: 0:04:53 time: 0.452775 data_time: 0.086481 memory: 6338 loss_kpt: 0.111652 acc_pose: 0.785080 loss: 0.111652 2022/09/22 02:14:58 - mmengine - INFO - Epoch(train) [208][200/293] lr: 5.000000e-06 eta: 0:04:33 time: 0.464628 data_time: 0.090076 memory: 6338 loss_kpt: 0.107837 acc_pose: 0.804097 loss: 0.107837 2022/09/22 02:15:22 - mmengine - INFO - Epoch(train) [208][250/293] lr: 5.000000e-06 eta: 0:04:13 time: 0.474263 data_time: 0.090764 memory: 6338 loss_kpt: 0.109568 acc_pose: 0.853056 loss: 0.109568 2022/09/22 02:15:41 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 02:15:41 - mmengine - INFO - Saving checkpoint at 208 epochs 2022/09/22 02:16:08 - mmengine - INFO - Epoch(train) [209][50/293] lr: 5.000000e-06 eta: 0:03:35 time: 0.476657 data_time: 0.093980 memory: 6338 loss_kpt: 0.108960 acc_pose: 0.862621 loss: 0.108960 2022/09/22 02:16:10 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 02:16:30 - mmengine - INFO - Epoch(train) [209][100/293] lr: 5.000000e-06 eta: 0:03:15 time: 0.453241 data_time: 0.087999 memory: 6338 loss_kpt: 0.109614 acc_pose: 0.818461 loss: 0.109614 2022/09/22 02:16:53 - mmengine - INFO - Epoch(train) [209][150/293] lr: 5.000000e-06 eta: 0:02:55 time: 0.459785 data_time: 0.094862 memory: 6338 loss_kpt: 0.109669 acc_pose: 0.799008 loss: 0.109669 2022/09/22 02:17:17 - mmengine - INFO - Epoch(train) [209][200/293] lr: 5.000000e-06 eta: 0:02:35 time: 0.462247 data_time: 0.091264 memory: 6338 loss_kpt: 0.111137 acc_pose: 0.844456 loss: 0.111137 2022/09/22 02:17:40 - mmengine - INFO - Epoch(train) [209][250/293] lr: 5.000000e-06 eta: 0:02:15 time: 0.460085 data_time: 0.089949 memory: 6338 loss_kpt: 0.111655 acc_pose: 0.863550 loss: 0.111655 2022/09/22 02:17:59 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 02:17:59 - mmengine - INFO - Saving checkpoint at 209 epochs 2022/09/22 02:18:26 - mmengine - INFO - Epoch(train) [210][50/293] lr: 5.000000e-06 eta: 0:01:37 time: 0.491212 data_time: 0.097698 memory: 6338 loss_kpt: 0.109434 acc_pose: 0.825825 loss: 0.109434 2022/09/22 02:18:50 - mmengine - INFO - Epoch(train) [210][100/293] lr: 5.000000e-06 eta: 0:01:17 time: 0.474251 data_time: 0.089274 memory: 6338 loss_kpt: 0.112078 acc_pose: 0.835303 loss: 0.112078 2022/09/22 02:19:13 - mmengine - INFO - Epoch(train) [210][150/293] lr: 5.000000e-06 eta: 0:00:57 time: 0.471852 data_time: 0.093043 memory: 6338 loss_kpt: 0.110849 acc_pose: 0.862965 loss: 0.110849 2022/09/22 02:19:37 - mmengine - INFO - Epoch(train) [210][200/293] lr: 5.000000e-06 eta: 0:00:37 time: 0.471355 data_time: 0.086180 memory: 6338 loss_kpt: 0.109926 acc_pose: 0.831454 loss: 0.109926 2022/09/22 02:20:00 - mmengine - INFO - Epoch(train) [210][250/293] lr: 5.000000e-06 eta: 0:00:17 time: 0.467891 data_time: 0.086469 memory: 6338 loss_kpt: 0.112293 acc_pose: 0.824376 loss: 0.112293 2022/09/22 02:20:20 - mmengine - INFO - Exp name: simcc_vipnas-mbv3_8xb64-210e_coco-256x192_20220921_173540 2022/09/22 02:20:20 - mmengine - INFO - Saving checkpoint at 210 epochs 2022/09/22 02:20:29 - mmengine - INFO - Epoch(val) [210][50/407] eta: 0:00:41 time: 0.116948 data_time: 0.054163 memory: 6338 2022/09/22 02:20:35 - mmengine - INFO - Epoch(val) [210][100/407] eta: 0:00:35 time: 0.115821 data_time: 0.053758 memory: 587 2022/09/22 02:20:40 - mmengine - INFO - Epoch(val) [210][150/407] eta: 0:00:29 time: 0.115616 data_time: 0.053286 memory: 587 2022/09/22 02:20:46 - mmengine - INFO - Epoch(val) [210][200/407] eta: 0:00:23 time: 0.113204 data_time: 0.051525 memory: 587 2022/09/22 02:20:52 - mmengine - INFO - Epoch(val) [210][250/407] eta: 0:00:18 time: 0.117849 data_time: 0.056600 memory: 587 2022/09/22 02:20:58 - mmengine - INFO - Epoch(val) [210][300/407] eta: 0:00:12 time: 0.115068 data_time: 0.053187 memory: 587 2022/09/22 02:21:04 - mmengine - INFO - Epoch(val) [210][350/407] eta: 0:00:06 time: 0.118510 data_time: 0.056714 memory: 587 2022/09/22 02:21:09 - mmengine - INFO - Epoch(val) [210][400/407] eta: 0:00:00 time: 0.109334 data_time: 0.049586 memory: 587 2022/09/22 02:21:44 - mmengine - INFO - Evaluating CocoMetric... 2022/09/22 02:21:58 - mmengine - INFO - Epoch(val) [210][407/407] coco/AP: 0.694570 coco/AP .5: 0.883231 coco/AP .75: 0.772033 coco/AP (M): 0.665106 coco/AP (L): 0.756461 coco/AR: 0.755116 coco/AR .5: 0.926795 coco/AR .75: 0.823992 coco/AR (M): 0.714859 coco/AR (L): 0.813155 2022/09/22 02:21:58 - mmengine - INFO - The previous best checkpoint /mnt/lustre/jiangtao/experiment3/vipnas_256/best_coco/AP_epoch_200.pth is removed 2022/09/22 02:22:00 - mmengine - INFO - The best checkpoint with 0.6946 coco/AP at 210 epoch is saved to best_coco/AP_epoch_210.pth.