2022/11/28 14:41:01 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.9.13 (main, Aug 25 2022, 23:26:10) [GCC 11.2.0] CUDA available: True numpy_random_seed: 1801968623 GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/petrelfs/share/cuda-11.3 NVCC: Cuda compilation tools, release 11.3, V11.3.109 GCC: gcc (GCC) 5.4.0 PyTorch: 1.11.0 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.3 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-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;-gencode;arch=compute_37,code=compute_37 - CuDNN 8.2 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, 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 -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.12.0 OpenCV: 4.6.0 MMEngine: 0.3.1 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None diff_rank_seed: False deterministic: False Distributed launcher: pytorch Distributed training: True GPU number: 8 ------------------------------------------------------------ 2022/11/28 14:41:01 - mmengine - INFO - Config: default_scope = 'mmaction' default_hooks = dict( runtime_info=dict(type='RuntimeInfoHook'), timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=100, ignore_last=False), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict(type='CheckpointHook', interval=1, save_best='auto'), sampler_seed=dict(type='DistSamplerSeedHook'), sync_buffers=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')) log_processor = dict(type='LogProcessor', window_size=20, by_epoch=True) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='ActionVisualizer', vis_backends=[dict(type='LocalVisBackend')]) log_level = 'INFO' load_from = None resume = False model = dict( type='RecognizerGCN', backbone=dict( type='STGCN', graph_cfg=dict(layout='coco', mode='stgcn_spatial')), cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) dataset_type = 'PoseDataset' ann_file = 'data/skeleton/ntu60_2d.pkl' train_pipeline = [ dict(type='PreNormalize2D'), dict(type='GenSkeFeat', dataset='coco', feats=['bm']), dict(type='UniformSampleFrames', clip_len=100), dict(type='PoseDecode'), dict(type='FormatGCNInput', num_person=2), dict(type='PackActionInputs') ] val_pipeline = [ dict(type='PreNormalize2D'), dict(type='GenSkeFeat', dataset='coco', feats=['bm']), dict( type='UniformSampleFrames', clip_len=100, num_clips=1, test_mode=True), dict(type='PoseDecode'), dict(type='FormatGCNInput', num_person=2), dict(type='PackActionInputs') ] test_pipeline = [ dict(type='PreNormalize2D'), dict(type='GenSkeFeat', dataset='coco', feats=['bm']), dict( type='UniformSampleFrames', clip_len=100, num_clips=10, test_mode=True), dict(type='PoseDecode'), dict(type='FormatGCNInput', num_person=2), dict(type='PackActionInputs') ] train_dataloader = dict( batch_size=16, num_workers=2, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='RepeatDataset', times=5, dataset=dict( type='PoseDataset', ann_file='data/skeleton/ntu60_2d.pkl', pipeline=[ dict(type='PreNormalize2D'), dict(type='GenSkeFeat', dataset='coco', feats=['bm']), dict(type='UniformSampleFrames', clip_len=100), dict(type='PoseDecode'), dict(type='FormatGCNInput', num_person=2), dict(type='PackActionInputs') ], split='xsub_train'))) val_dataloader = dict( batch_size=16, num_workers=2, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='PoseDataset', ann_file='data/skeleton/ntu60_2d.pkl', pipeline=[ dict(type='PreNormalize2D'), dict(type='GenSkeFeat', dataset='coco', feats=['bm']), dict( type='UniformSampleFrames', clip_len=100, num_clips=1, test_mode=True), dict(type='PoseDecode'), dict(type='FormatGCNInput', num_person=2), dict(type='PackActionInputs') ], split='xsub_val', test_mode=True)) test_dataloader = dict( batch_size=1, num_workers=2, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='PoseDataset', ann_file='data/skeleton/ntu60_2d.pkl', pipeline=[ dict(type='PreNormalize2D'), dict(type='GenSkeFeat', dataset='coco', feats=['bm']), dict( type='UniformSampleFrames', clip_len=100, num_clips=10, test_mode=True), dict(type='PoseDecode'), dict(type='FormatGCNInput', num_person=2), dict(type='PackActionInputs') ], split='xsub_val', test_mode=True)) val_evaluator = [dict(type='AccMetric')] test_evaluator = [dict(type='AccMetric')] train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=16, val_begin=1, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') param_scheduler = [ dict( type='CosineAnnealingLR', eta_min=0, T_max=16, by_epoch=True, convert_to_iter_based=True) ] optim_wrapper = dict( optimizer=dict( type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True)) auto_scale_lr = dict(enable=False, base_batch_size=128) launcher = 'pytorch' work_dir = './work_dirs/stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d' randomness = dict(seed=None, diff_rank_seed=False, deterministic=False) 2022/11/28 14:41:01 - mmengine - INFO - Result has been saved to /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d/modules_statistic_results.json Name of parameter - Initialization information data_bn.weight - torch.Size([51]): The value is the same before and after calling `init_weights` of STGCN data_bn.bias - torch.Size([51]): The value is the same before and after calling `init_weights` of STGCN gcn.0.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.0.gcn.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.0.gcn.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.0.gcn.conv.weight - torch.Size([192, 3, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.0.gcn.conv.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of STGCN gcn.0.tcn.conv.weight - torch.Size([64, 64, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.0.tcn.conv.bias - torch.Size([64]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.0.tcn.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.0.tcn.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.1.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.1.gcn.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.1.gcn.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.1.gcn.conv.weight - torch.Size([192, 64, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.1.gcn.conv.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of STGCN gcn.1.tcn.conv.weight - torch.Size([64, 64, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.1.tcn.conv.bias - torch.Size([64]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.1.tcn.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.1.tcn.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.2.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.2.gcn.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.2.gcn.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.2.gcn.conv.weight - torch.Size([192, 64, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.2.gcn.conv.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of STGCN gcn.2.tcn.conv.weight - torch.Size([64, 64, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.2.tcn.conv.bias - torch.Size([64]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.2.tcn.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.2.tcn.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.3.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.3.gcn.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.3.gcn.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.3.gcn.conv.weight - torch.Size([192, 64, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.3.gcn.conv.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of STGCN gcn.3.tcn.conv.weight - torch.Size([64, 64, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.3.tcn.conv.bias - torch.Size([64]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.3.tcn.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.3.tcn.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.4.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.4.gcn.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.4.gcn.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.4.gcn.conv.weight - torch.Size([384, 64, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.4.gcn.conv.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of STGCN gcn.4.tcn.conv.weight - torch.Size([128, 128, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.4.tcn.conv.bias - torch.Size([128]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.4.tcn.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.4.tcn.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.4.residual.conv.weight - torch.Size([128, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.4.residual.conv.bias - torch.Size([128]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.4.residual.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.4.residual.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.5.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.5.gcn.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.5.gcn.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.5.gcn.conv.weight - torch.Size([384, 128, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.5.gcn.conv.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of STGCN gcn.5.tcn.conv.weight - torch.Size([128, 128, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.5.tcn.conv.bias - torch.Size([128]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.5.tcn.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.5.tcn.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.6.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.6.gcn.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.6.gcn.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.6.gcn.conv.weight - torch.Size([384, 128, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.6.gcn.conv.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of STGCN gcn.6.tcn.conv.weight - torch.Size([128, 128, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.6.tcn.conv.bias - torch.Size([128]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.6.tcn.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.6.tcn.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.7.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.7.gcn.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.7.gcn.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.7.gcn.conv.weight - torch.Size([768, 128, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.7.gcn.conv.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of STGCN gcn.7.tcn.conv.weight - torch.Size([256, 256, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.7.tcn.conv.bias - torch.Size([256]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.7.tcn.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.7.tcn.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.7.residual.conv.weight - torch.Size([256, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.7.residual.conv.bias - torch.Size([256]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.7.residual.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.7.residual.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.8.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.8.gcn.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.8.gcn.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.8.gcn.conv.weight - torch.Size([768, 256, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.8.gcn.conv.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of STGCN gcn.8.tcn.conv.weight - torch.Size([256, 256, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.8.tcn.conv.bias - torch.Size([256]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.8.tcn.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.8.tcn.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.9.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.9.gcn.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.9.gcn.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.9.gcn.conv.weight - torch.Size([768, 256, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.9.gcn.conv.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of STGCN gcn.9.tcn.conv.weight - torch.Size([256, 256, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.9.tcn.conv.bias - torch.Size([256]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.9.tcn.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.9.tcn.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN Name of parameter - Initialization information fc.weight - torch.Size([60, 256]): NormalInit: mean=0, std=0.01, bias=0 fc.bias - torch.Size([60]): NormalInit: mean=0, std=0.01, bias=0 2022/11/28 14:41:33 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d. 2022/11/28 14:41:38 - mmengine - INFO - Epoch(train) [1][100/1567] lr: 9.9996e-02 eta: 0:20:40 time: 0.0340 data_time: 0.0063 memory: 1253 loss: 3.1274 top1_acc: 0.0000 top5_acc: 0.5625 loss_cls: 3.1274 2022/11/28 14:41:42 - mmengine - INFO - Epoch(train) [1][200/1567] lr: 9.9984e-02 eta: 0:17:24 time: 0.0346 data_time: 0.0070 memory: 1253 loss: 2.3063 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.3063 2022/11/28 14:41:45 - mmengine - INFO - Epoch(train) [1][300/1567] lr: 9.9965e-02 eta: 0:16:19 time: 0.0349 data_time: 0.0059 memory: 1253 loss: 1.6465 top1_acc: 0.1875 top5_acc: 0.8750 loss_cls: 1.6465 2022/11/28 14:41:49 - mmengine - INFO - Epoch(train) [1][400/1567] lr: 9.9938e-02 eta: 0:15:52 time: 0.0364 data_time: 0.0067 memory: 1253 loss: 1.2476 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2476 2022/11/28 14:41:52 - mmengine - INFO - Epoch(train) [1][500/1567] lr: 9.9902e-02 eta: 0:15:31 time: 0.0350 data_time: 0.0058 memory: 1253 loss: 1.2796 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2796 2022/11/28 14:41:56 - mmengine - INFO - Epoch(train) [1][600/1567] lr: 9.9859e-02 eta: 0:15:14 time: 0.0359 data_time: 0.0067 memory: 1253 loss: 1.0823 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0823 2022/11/28 14:41:59 - mmengine - INFO - Epoch(train) [1][700/1567] lr: 9.9808e-02 eta: 0:14:58 time: 0.0331 data_time: 0.0058 memory: 1253 loss: 1.0195 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0195 2022/11/28 14:42:03 - mmengine - INFO - Epoch(train) [1][800/1567] lr: 9.9750e-02 eta: 0:14:46 time: 0.0348 data_time: 0.0059 memory: 1253 loss: 0.9566 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9566 2022/11/28 14:42:06 - mmengine - INFO - Epoch(train) [1][900/1567] lr: 9.9683e-02 eta: 0:14:38 time: 0.0345 data_time: 0.0058 memory: 1253 loss: 0.8970 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8970 2022/11/28 14:42:10 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:42:10 - mmengine - INFO - Epoch(train) [1][1000/1567] lr: 9.9609e-02 eta: 0:14:31 time: 0.0356 data_time: 0.0058 memory: 1253 loss: 0.8334 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8334 2022/11/28 14:42:13 - mmengine - INFO - Epoch(train) [1][1100/1567] lr: 9.9527e-02 eta: 0:14:26 time: 0.0343 data_time: 0.0058 memory: 1253 loss: 0.7761 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7761 2022/11/28 14:42:17 - mmengine - INFO - Epoch(train) [1][1200/1567] lr: 9.9437e-02 eta: 0:14:21 time: 0.0349 data_time: 0.0061 memory: 1253 loss: 0.7647 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7647 2022/11/28 14:42:20 - mmengine - INFO - Epoch(train) [1][1300/1567] lr: 9.9339e-02 eta: 0:14:13 time: 0.0341 data_time: 0.0062 memory: 1253 loss: 0.6317 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6317 2022/11/28 14:42:23 - mmengine - INFO - Epoch(train) [1][1400/1567] lr: 9.9234e-02 eta: 0:14:06 time: 0.0342 data_time: 0.0061 memory: 1253 loss: 0.6701 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6701 2022/11/28 14:42:27 - mmengine - INFO - Epoch(train) [1][1500/1567] lr: 9.9121e-02 eta: 0:14:00 time: 0.0334 data_time: 0.0058 memory: 1253 loss: 0.6942 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.6942 2022/11/28 14:42:29 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:42:29 - mmengine - INFO - Epoch(train) [1][1567/1567] lr: 9.9040e-02 eta: 0:13:57 time: 0.0349 data_time: 0.0056 memory: 1253 loss: 0.8090 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 0.8090 2022/11/28 14:42:29 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/11/28 14:42:31 - mmengine - INFO - Epoch(val) [1][100/129] eta: 0:00:00 time: 0.0151 data_time: 0.0057 memory: 262 2022/11/28 14:42:32 - mmengine - INFO - Epoch(val) [1][129/129] acc/top1: 0.6013 acc/top5: 0.9285 acc/mean1: 0.6011 2022/11/28 14:42:32 - mmengine - INFO - The best checkpoint with 0.6013 acc/top1 at 1 epoch is saved to best_acc/top1_epoch_1.pth. 2022/11/28 14:42:36 - mmengine - INFO - Epoch(train) [2][100/1567] lr: 9.8914e-02 eta: 0:13:52 time: 0.0338 data_time: 0.0060 memory: 1253 loss: 0.5350 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5350 2022/11/28 14:42:39 - mmengine - INFO - Epoch(train) [2][200/1567] lr: 9.8781e-02 eta: 0:13:47 time: 0.0367 data_time: 0.0066 memory: 1253 loss: 0.6247 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6247 2022/11/28 14:42:43 - mmengine - INFO - Epoch(train) [2][300/1567] lr: 9.8639e-02 eta: 0:13:42 time: 0.0350 data_time: 0.0059 memory: 1253 loss: 0.5937 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5937 2022/11/28 14:42:46 - mmengine - INFO - Epoch(train) [2][400/1567] lr: 9.8491e-02 eta: 0:13:37 time: 0.0338 data_time: 0.0059 memory: 1253 loss: 0.5401 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5401 2022/11/28 14:42:47 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:42:49 - mmengine - INFO - Epoch(train) [2][500/1567] lr: 9.8334e-02 eta: 0:13:33 time: 0.0335 data_time: 0.0059 memory: 1253 loss: 0.6310 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6310 2022/11/28 14:42:53 - mmengine - INFO - Epoch(train) [2][600/1567] lr: 9.8170e-02 eta: 0:13:28 time: 0.0335 data_time: 0.0064 memory: 1253 loss: 0.5881 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.5881 2022/11/28 14:42:56 - mmengine - INFO - Epoch(train) [2][700/1567] lr: 9.7998e-02 eta: 0:13:23 time: 0.0331 data_time: 0.0058 memory: 1253 loss: 0.6205 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6205 2022/11/28 14:43:00 - mmengine - INFO - Epoch(train) [2][800/1567] lr: 9.7819e-02 eta: 0:13:18 time: 0.0334 data_time: 0.0059 memory: 1253 loss: 0.5611 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5611 2022/11/28 14:43:03 - mmengine - INFO - Epoch(train) [2][900/1567] lr: 9.7632e-02 eta: 0:13:14 time: 0.0339 data_time: 0.0059 memory: 1253 loss: 0.6127 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6127 2022/11/28 14:43:06 - mmengine - INFO - Epoch(train) [2][1000/1567] lr: 9.7438e-02 eta: 0:13:09 time: 0.0333 data_time: 0.0058 memory: 1253 loss: 0.5462 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.5462 2022/11/28 14:43:10 - mmengine - INFO - Epoch(train) [2][1100/1567] lr: 9.7236e-02 eta: 0:13:05 time: 0.0354 data_time: 0.0060 memory: 1253 loss: 0.4850 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4850 2022/11/28 14:43:13 - mmengine - INFO - Epoch(train) [2][1200/1567] lr: 9.7027e-02 eta: 0:13:01 time: 0.0334 data_time: 0.0059 memory: 1253 loss: 0.4879 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4879 2022/11/28 14:43:17 - mmengine - INFO - Epoch(train) [2][1300/1567] lr: 9.6810e-02 eta: 0:12:57 time: 0.0341 data_time: 0.0064 memory: 1253 loss: 0.4346 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.4346 2022/11/28 14:43:20 - mmengine - INFO - Epoch(train) [2][1400/1567] lr: 9.6587e-02 eta: 0:12:53 time: 0.0333 data_time: 0.0059 memory: 1253 loss: 0.5365 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5365 2022/11/28 14:43:21 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:43:24 - mmengine - INFO - Epoch(train) [2][1500/1567] lr: 9.6355e-02 eta: 0:12:49 time: 0.0345 data_time: 0.0060 memory: 1253 loss: 0.5402 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5402 2022/11/28 14:43:26 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:43:26 - mmengine - INFO - Epoch(train) [2][1567/1567] lr: 9.6196e-02 eta: 0:12:46 time: 0.0328 data_time: 0.0057 memory: 1253 loss: 0.5755 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 0.5755 2022/11/28 14:43:26 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/11/28 14:43:28 - mmengine - INFO - Epoch(val) [2][100/129] eta: 0:00:00 time: 0.0154 data_time: 0.0060 memory: 262 2022/11/28 14:43:28 - mmengine - INFO - Epoch(val) [2][129/129] acc/top1: 0.6542 acc/top5: 0.9318 acc/mean1: 0.6542 2022/11/28 14:43:28 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d/best_acc/top1_epoch_1.pth is removed 2022/11/28 14:43:29 - mmengine - INFO - The best checkpoint with 0.6542 acc/top1 at 2 epoch is saved to best_acc/top1_epoch_2.pth. 2022/11/28 14:43:32 - mmengine - INFO - Epoch(train) [3][100/1567] lr: 9.5953e-02 eta: 0:12:42 time: 0.0352 data_time: 0.0066 memory: 1253 loss: 0.4126 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4126 2022/11/28 14:43:36 - mmengine - INFO - Epoch(train) [3][200/1567] lr: 9.5703e-02 eta: 0:12:38 time: 0.0344 data_time: 0.0060 memory: 1253 loss: 0.5129 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5129 2022/11/28 14:43:39 - mmengine - INFO - Epoch(train) [3][300/1567] lr: 9.5445e-02 eta: 0:12:34 time: 0.0354 data_time: 0.0065 memory: 1253 loss: 0.4547 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.4547 2022/11/28 14:43:42 - mmengine - INFO - Epoch(train) [3][400/1567] lr: 9.5180e-02 eta: 0:12:30 time: 0.0338 data_time: 0.0059 memory: 1253 loss: 0.5228 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5228 2022/11/28 14:43:46 - mmengine - INFO - Epoch(train) [3][500/1567] lr: 9.4908e-02 eta: 0:12:26 time: 0.0337 data_time: 0.0060 memory: 1253 loss: 0.4872 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4872 2022/11/28 14:43:49 - mmengine - INFO - Epoch(train) [3][600/1567] lr: 9.4629e-02 eta: 0:12:22 time: 0.0330 data_time: 0.0060 memory: 1253 loss: 0.5144 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5144 2022/11/28 14:43:53 - mmengine - INFO - Epoch(train) [3][700/1567] lr: 9.4343e-02 eta: 0:12:18 time: 0.0345 data_time: 0.0058 memory: 1253 loss: 0.4937 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4937 2022/11/28 14:43:56 - mmengine - INFO - Epoch(train) [3][800/1567] lr: 9.4050e-02 eta: 0:12:15 time: 0.0360 data_time: 0.0060 memory: 1253 loss: 0.5151 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5151 2022/11/28 14:43:58 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:44:00 - mmengine - INFO - Epoch(train) [3][900/1567] lr: 9.3750e-02 eta: 0:12:11 time: 0.0335 data_time: 0.0065 memory: 1253 loss: 0.5047 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5047 2022/11/28 14:44:03 - mmengine - INFO - Epoch(train) [3][1000/1567] lr: 9.3444e-02 eta: 0:12:07 time: 0.0337 data_time: 0.0060 memory: 1253 loss: 0.4551 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4551 2022/11/28 14:44:06 - mmengine - INFO - Epoch(train) [3][1100/1567] lr: 9.3130e-02 eta: 0:12:03 time: 0.0349 data_time: 0.0059 memory: 1253 loss: 0.3904 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3904 2022/11/28 14:44:10 - mmengine - INFO - Epoch(train) [3][1200/1567] lr: 9.2810e-02 eta: 0:12:00 time: 0.0346 data_time: 0.0061 memory: 1253 loss: 0.5016 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.5016 2022/11/28 14:44:13 - mmengine - INFO - Epoch(train) [3][1300/1567] lr: 9.2483e-02 eta: 0:11:56 time: 0.0331 data_time: 0.0059 memory: 1253 loss: 0.4150 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4150 2022/11/28 14:44:17 - mmengine - INFO - Epoch(train) [3][1400/1567] lr: 9.2149e-02 eta: 0:11:52 time: 0.0332 data_time: 0.0060 memory: 1253 loss: 0.5902 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5902 2022/11/28 14:44:20 - mmengine - INFO - Epoch(train) [3][1500/1567] lr: 9.1809e-02 eta: 0:11:48 time: 0.0337 data_time: 0.0061 memory: 1253 loss: 0.5181 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5181 2022/11/28 14:44:22 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:44:22 - mmengine - INFO - Epoch(train) [3][1567/1567] lr: 9.1577e-02 eta: 0:11:45 time: 0.0328 data_time: 0.0058 memory: 1253 loss: 0.6379 top1_acc: 0.0000 top5_acc: 1.0000 loss_cls: 0.6379 2022/11/28 14:44:22 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/11/28 14:44:24 - mmengine - INFO - Epoch(val) [3][100/129] eta: 0:00:00 time: 0.0150 data_time: 0.0057 memory: 262 2022/11/28 14:44:25 - mmengine - INFO - Epoch(val) [3][129/129] acc/top1: 0.5627 acc/top5: 0.8496 acc/mean1: 0.5626 2022/11/28 14:44:28 - mmengine - INFO - Epoch(train) [4][100/1567] lr: 9.1226e-02 eta: 0:11:42 time: 0.0338 data_time: 0.0062 memory: 1253 loss: 0.4117 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4117 2022/11/28 14:44:31 - mmengine - INFO - Epoch(train) [4][200/1567] lr: 9.0868e-02 eta: 0:11:38 time: 0.0337 data_time: 0.0061 memory: 1253 loss: 0.3832 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3832 2022/11/28 14:44:35 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:44:35 - mmengine - INFO - Epoch(train) [4][300/1567] lr: 9.0504e-02 eta: 0:11:35 time: 0.0331 data_time: 0.0060 memory: 1253 loss: 0.5534 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5534 2022/11/28 14:44:38 - mmengine - INFO - Epoch(train) [4][400/1567] lr: 9.0133e-02 eta: 0:11:31 time: 0.0349 data_time: 0.0060 memory: 1253 loss: 0.4008 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4008 2022/11/28 14:44:42 - mmengine - INFO - Epoch(train) [4][500/1567] lr: 8.9756e-02 eta: 0:11:27 time: 0.0342 data_time: 0.0064 memory: 1253 loss: 0.4503 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4503 2022/11/28 14:44:45 - mmengine - INFO - Epoch(train) [4][600/1567] lr: 8.9373e-02 eta: 0:11:24 time: 0.0340 data_time: 0.0059 memory: 1253 loss: 0.4314 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4314 2022/11/28 14:44:49 - mmengine - INFO - Epoch(train) [4][700/1567] lr: 8.8984e-02 eta: 0:11:20 time: 0.0337 data_time: 0.0059 memory: 1253 loss: 0.4104 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4104 2022/11/28 14:44:52 - mmengine - INFO - Epoch(train) [4][800/1567] lr: 8.8589e-02 eta: 0:11:16 time: 0.0332 data_time: 0.0060 memory: 1253 loss: 0.4630 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.4630 2022/11/28 14:44:55 - mmengine - INFO - Epoch(train) [4][900/1567] lr: 8.8187e-02 eta: 0:11:13 time: 0.0341 data_time: 0.0066 memory: 1253 loss: 0.3936 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3936 2022/11/28 14:44:59 - mmengine - INFO - Epoch(train) [4][1000/1567] lr: 8.7780e-02 eta: 0:11:09 time: 0.0339 data_time: 0.0059 memory: 1253 loss: 0.4869 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.4869 2022/11/28 14:45:02 - mmengine - INFO - Epoch(train) [4][1100/1567] lr: 8.7367e-02 eta: 0:11:06 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.4146 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4146 2022/11/28 14:45:06 - mmengine - INFO - Epoch(train) [4][1200/1567] lr: 8.6947e-02 eta: 0:11:02 time: 0.0347 data_time: 0.0059 memory: 1253 loss: 0.3927 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3927 2022/11/28 14:45:09 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:45:09 - mmengine - INFO - Epoch(train) [4][1300/1567] lr: 8.6522e-02 eta: 0:10:58 time: 0.0344 data_time: 0.0061 memory: 1253 loss: 0.5591 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5591 2022/11/28 14:45:13 - mmengine - INFO - Epoch(train) [4][1400/1567] lr: 8.6092e-02 eta: 0:10:55 time: 0.0339 data_time: 0.0060 memory: 1253 loss: 0.3903 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3903 2022/11/28 14:45:16 - mmengine - INFO - Epoch(train) [4][1500/1567] lr: 8.5655e-02 eta: 0:10:51 time: 0.0338 data_time: 0.0060 memory: 1253 loss: 0.4025 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4025 2022/11/28 14:45:18 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:45:18 - mmengine - INFO - Epoch(train) [4][1567/1567] lr: 8.5360e-02 eta: 0:10:49 time: 0.0340 data_time: 0.0059 memory: 1253 loss: 0.5499 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 0.5499 2022/11/28 14:45:18 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/11/28 14:45:20 - mmengine - INFO - Epoch(val) [4][100/129] eta: 0:00:00 time: 0.0147 data_time: 0.0056 memory: 262 2022/11/28 14:45:21 - mmengine - INFO - Epoch(val) [4][129/129] acc/top1: 0.6176 acc/top5: 0.9087 acc/mean1: 0.6176 2022/11/28 14:45:24 - mmengine - INFO - Epoch(train) [5][100/1567] lr: 8.4914e-02 eta: 0:10:45 time: 0.0336 data_time: 0.0060 memory: 1253 loss: 0.3104 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.3104 2022/11/28 14:45:28 - mmengine - INFO - Epoch(train) [5][200/1567] lr: 8.4463e-02 eta: 0:10:41 time: 0.0332 data_time: 0.0060 memory: 1253 loss: 0.4656 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4656 2022/11/28 14:45:31 - mmengine - INFO - Epoch(train) [5][300/1567] lr: 8.4006e-02 eta: 0:10:38 time: 0.0332 data_time: 0.0059 memory: 1253 loss: 0.3768 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3768 2022/11/28 14:45:34 - mmengine - INFO - Epoch(train) [5][400/1567] lr: 8.3544e-02 eta: 0:10:34 time: 0.0331 data_time: 0.0059 memory: 1253 loss: 0.4158 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4158 2022/11/28 14:45:38 - mmengine - INFO - Epoch(train) [5][500/1567] lr: 8.3077e-02 eta: 0:10:31 time: 0.0346 data_time: 0.0060 memory: 1253 loss: 0.3024 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3024 2022/11/28 14:45:41 - mmengine - INFO - Epoch(train) [5][600/1567] lr: 8.2605e-02 eta: 0:10:27 time: 0.0355 data_time: 0.0063 memory: 1253 loss: 0.3261 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3261 2022/11/28 14:45:44 - mmengine - INFO - Epoch(train) [5][700/1567] lr: 8.2127e-02 eta: 0:10:23 time: 0.0336 data_time: 0.0066 memory: 1253 loss: 0.3370 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3370 2022/11/28 14:45:46 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:45:48 - mmengine - INFO - Epoch(train) [5][800/1567] lr: 8.1645e-02 eta: 0:10:20 time: 0.0330 data_time: 0.0059 memory: 1253 loss: 0.3200 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3200 2022/11/28 14:45:51 - mmengine - INFO - Epoch(train) [5][900/1567] lr: 8.1157e-02 eta: 0:10:16 time: 0.0330 data_time: 0.0060 memory: 1253 loss: 0.4171 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.4171 2022/11/28 14:45:55 - mmengine - INFO - Epoch(train) [5][1000/1567] lr: 8.0665e-02 eta: 0:10:12 time: 0.0350 data_time: 0.0064 memory: 1253 loss: 0.3917 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3917 2022/11/28 14:45:58 - mmengine - INFO - Epoch(train) [5][1100/1567] lr: 8.0167e-02 eta: 0:10:09 time: 0.0332 data_time: 0.0059 memory: 1253 loss: 0.4138 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4138 2022/11/28 14:46:01 - mmengine - INFO - Epoch(train) [5][1200/1567] lr: 7.9665e-02 eta: 0:10:05 time: 0.0333 data_time: 0.0060 memory: 1253 loss: 0.3439 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3439 2022/11/28 14:46:05 - mmengine - INFO - Epoch(train) [5][1300/1567] lr: 7.9159e-02 eta: 0:10:02 time: 0.0350 data_time: 0.0061 memory: 1253 loss: 0.3304 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3304 2022/11/28 14:46:08 - mmengine - INFO - Epoch(train) [5][1400/1567] lr: 7.8647e-02 eta: 0:09:58 time: 0.0349 data_time: 0.0060 memory: 1253 loss: 0.3380 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3380 2022/11/28 14:46:12 - mmengine - INFO - Epoch(train) [5][1500/1567] lr: 7.8132e-02 eta: 0:09:55 time: 0.0334 data_time: 0.0061 memory: 1253 loss: 0.3815 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.3815 2022/11/28 14:46:14 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:46:14 - mmengine - INFO - Epoch(train) [5][1567/1567] lr: 7.7784e-02 eta: 0:09:53 time: 0.0335 data_time: 0.0057 memory: 1253 loss: 0.5097 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 0.5097 2022/11/28 14:46:14 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/11/28 14:46:16 - mmengine - INFO - Epoch(val) [5][100/129] eta: 0:00:00 time: 0.0148 data_time: 0.0058 memory: 262 2022/11/28 14:46:16 - mmengine - INFO - Epoch(val) [5][129/129] acc/top1: 0.6789 acc/top5: 0.9224 acc/mean1: 0.6789 2022/11/28 14:46:17 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d/best_acc/top1_epoch_2.pth is removed 2022/11/28 14:46:17 - mmengine - INFO - The best checkpoint with 0.6789 acc/top1 at 5 epoch is saved to best_acc/top1_epoch_5.pth. 2022/11/28 14:46:20 - mmengine - INFO - Epoch(train) [6][100/1567] lr: 7.7261e-02 eta: 0:09:50 time: 0.0356 data_time: 0.0065 memory: 1253 loss: 0.2978 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2978 2022/11/28 14:46:23 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:46:24 - mmengine - INFO - Epoch(train) [6][200/1567] lr: 7.6733e-02 eta: 0:09:46 time: 0.0346 data_time: 0.0064 memory: 1253 loss: 0.3877 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3877 2022/11/28 14:46:27 - mmengine - INFO - Epoch(train) [6][300/1567] lr: 7.6202e-02 eta: 0:09:43 time: 0.0350 data_time: 0.0064 memory: 1253 loss: 0.2362 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2362 2022/11/28 14:46:31 - mmengine - INFO - Epoch(train) [6][400/1567] lr: 7.5666e-02 eta: 0:09:39 time: 0.0348 data_time: 0.0064 memory: 1253 loss: 0.3473 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.3473 2022/11/28 14:46:34 - mmengine - INFO - Epoch(train) [6][500/1567] lr: 7.5126e-02 eta: 0:09:36 time: 0.0346 data_time: 0.0063 memory: 1253 loss: 0.3585 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3585 2022/11/28 14:46:38 - mmengine - INFO - Epoch(train) [6][600/1567] lr: 7.4583e-02 eta: 0:09:33 time: 0.0341 data_time: 0.0063 memory: 1253 loss: 0.3434 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.3434 2022/11/28 14:46:41 - mmengine - INFO - Epoch(train) [6][700/1567] lr: 7.4035e-02 eta: 0:09:30 time: 0.0363 data_time: 0.0065 memory: 1253 loss: 0.2604 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2604 2022/11/28 14:46:45 - mmengine - INFO - Epoch(train) [6][800/1567] lr: 7.3484e-02 eta: 0:09:26 time: 0.0346 data_time: 0.0062 memory: 1253 loss: 0.2747 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2747 2022/11/28 14:46:48 - mmengine - INFO - Epoch(train) [6][900/1567] lr: 7.2929e-02 eta: 0:09:23 time: 0.0336 data_time: 0.0062 memory: 1253 loss: 0.3243 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.3243 2022/11/28 14:46:52 - mmengine - INFO - Epoch(train) [6][1000/1567] lr: 7.2371e-02 eta: 0:09:19 time: 0.0341 data_time: 0.0068 memory: 1253 loss: 0.2964 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2964 2022/11/28 14:46:55 - mmengine - INFO - Epoch(train) [6][1100/1567] lr: 7.1809e-02 eta: 0:09:16 time: 0.0340 data_time: 0.0062 memory: 1253 loss: 0.3004 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3004 2022/11/28 14:46:57 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:46:59 - mmengine - INFO - Epoch(train) [6][1200/1567] lr: 7.1243e-02 eta: 0:09:12 time: 0.0335 data_time: 0.0061 memory: 1253 loss: 0.2590 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2590 2022/11/28 14:47:02 - mmengine - INFO - Epoch(train) [6][1300/1567] lr: 7.0674e-02 eta: 0:09:09 time: 0.0354 data_time: 0.0063 memory: 1253 loss: 0.3085 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3085 2022/11/28 14:47:05 - mmengine - INFO - Epoch(train) [6][1400/1567] lr: 7.0102e-02 eta: 0:09:05 time: 0.0339 data_time: 0.0062 memory: 1253 loss: 0.3007 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3007 2022/11/28 14:47:09 - mmengine - INFO - Epoch(train) [6][1500/1567] lr: 6.9527e-02 eta: 0:09:02 time: 0.0345 data_time: 0.0067 memory: 1253 loss: 0.2721 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2721 2022/11/28 14:47:11 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:47:11 - mmengine - INFO - Epoch(train) [6][1567/1567] lr: 6.9140e-02 eta: 0:08:59 time: 0.0331 data_time: 0.0061 memory: 1253 loss: 0.4729 top1_acc: 0.0000 top5_acc: 1.0000 loss_cls: 0.4729 2022/11/28 14:47:11 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/11/28 14:47:13 - mmengine - INFO - Epoch(val) [6][100/129] eta: 0:00:00 time: 0.0151 data_time: 0.0060 memory: 262 2022/11/28 14:47:14 - mmengine - INFO - Epoch(val) [6][129/129] acc/top1: 0.5985 acc/top5: 0.8986 acc/mean1: 0.5985 2022/11/28 14:47:17 - mmengine - INFO - Epoch(train) [7][100/1567] lr: 6.8560e-02 eta: 0:08:56 time: 0.0353 data_time: 0.0063 memory: 1253 loss: 0.3395 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.3395 2022/11/28 14:47:21 - mmengine - INFO - Epoch(train) [7][200/1567] lr: 6.7976e-02 eta: 0:08:53 time: 0.0340 data_time: 0.0062 memory: 1253 loss: 0.2930 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2930 2022/11/28 14:47:24 - mmengine - INFO - Epoch(train) [7][300/1567] lr: 6.7390e-02 eta: 0:08:49 time: 0.0365 data_time: 0.0068 memory: 1253 loss: 0.3331 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3331 2022/11/28 14:47:28 - mmengine - INFO - Epoch(train) [7][400/1567] lr: 6.6802e-02 eta: 0:08:46 time: 0.0350 data_time: 0.0062 memory: 1253 loss: 0.2284 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2284 2022/11/28 14:47:31 - mmengine - INFO - Epoch(train) [7][500/1567] lr: 6.6210e-02 eta: 0:08:42 time: 0.0334 data_time: 0.0061 memory: 1253 loss: 0.3524 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.3524 2022/11/28 14:47:35 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:47:35 - mmengine - INFO - Epoch(train) [7][600/1567] lr: 6.5616e-02 eta: 0:08:39 time: 0.0341 data_time: 0.0065 memory: 1253 loss: 0.3101 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3101 2022/11/28 14:47:38 - mmengine - INFO - Epoch(train) [7][700/1567] lr: 6.5020e-02 eta: 0:08:36 time: 0.0342 data_time: 0.0067 memory: 1253 loss: 0.3667 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3667 2022/11/28 14:47:41 - mmengine - INFO - Epoch(train) [7][800/1567] lr: 6.4421e-02 eta: 0:08:32 time: 0.0346 data_time: 0.0063 memory: 1253 loss: 0.3800 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.3800 2022/11/28 14:47:45 - mmengine - INFO - Epoch(train) [7][900/1567] lr: 6.3820e-02 eta: 0:08:29 time: 0.0336 data_time: 0.0063 memory: 1253 loss: 0.3490 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3490 2022/11/28 14:47:48 - mmengine - INFO - Epoch(train) [7][1000/1567] lr: 6.3217e-02 eta: 0:08:25 time: 0.0342 data_time: 0.0061 memory: 1253 loss: 0.2751 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2751 2022/11/28 14:47:52 - mmengine - INFO - Epoch(train) [7][1100/1567] lr: 6.2612e-02 eta: 0:08:22 time: 0.0347 data_time: 0.0068 memory: 1253 loss: 0.3259 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3259 2022/11/28 14:47:55 - mmengine - INFO - Epoch(train) [7][1200/1567] lr: 6.2005e-02 eta: 0:08:18 time: 0.0342 data_time: 0.0062 memory: 1253 loss: 0.3463 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3463 2022/11/28 14:47:59 - mmengine - INFO - Epoch(train) [7][1300/1567] lr: 6.1396e-02 eta: 0:08:15 time: 0.0336 data_time: 0.0061 memory: 1253 loss: 0.2227 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2227 2022/11/28 14:48:02 - mmengine - INFO - Epoch(train) [7][1400/1567] lr: 6.0785e-02 eta: 0:08:11 time: 0.0343 data_time: 0.0063 memory: 1253 loss: 0.2685 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2685 2022/11/28 14:48:05 - mmengine - INFO - Epoch(train) [7][1500/1567] lr: 6.0172e-02 eta: 0:08:08 time: 0.0336 data_time: 0.0061 memory: 1253 loss: 0.2925 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2925 2022/11/28 14:48:08 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:48:08 - mmengine - INFO - Epoch(train) [7][1567/1567] lr: 5.9761e-02 eta: 0:08:05 time: 0.0338 data_time: 0.0060 memory: 1253 loss: 0.4270 top1_acc: 0.0000 top5_acc: 1.0000 loss_cls: 0.4270 2022/11/28 14:48:08 - mmengine - INFO - Saving checkpoint at 7 epochs 2022/11/28 14:48:10 - mmengine - INFO - Epoch(val) [7][100/129] eta: 0:00:00 time: 0.0150 data_time: 0.0058 memory: 262 2022/11/28 14:48:10 - mmengine - INFO - Epoch(val) [7][129/129] acc/top1: 0.7902 acc/top5: 0.9717 acc/mean1: 0.7901 2022/11/28 14:48:10 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d/best_acc/top1_epoch_5.pth is removed 2022/11/28 14:48:11 - mmengine - INFO - The best checkpoint with 0.7902 acc/top1 at 7 epoch is saved to best_acc/top1_epoch_7.pth. 2022/11/28 14:48:12 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:48:14 - mmengine - INFO - Epoch(train) [8][100/1567] lr: 5.9145e-02 eta: 0:08:02 time: 0.0351 data_time: 0.0064 memory: 1253 loss: 0.2946 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.2946 2022/11/28 14:48:18 - mmengine - INFO - Epoch(train) [8][200/1567] lr: 5.8529e-02 eta: 0:07:59 time: 0.0351 data_time: 0.0062 memory: 1253 loss: 0.1941 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1941 2022/11/28 14:48:21 - mmengine - INFO - Epoch(train) [8][300/1567] lr: 5.7911e-02 eta: 0:07:55 time: 0.0342 data_time: 0.0062 memory: 1253 loss: 0.2394 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2394 2022/11/28 14:48:25 - mmengine - INFO - Epoch(train) [8][400/1567] lr: 5.7292e-02 eta: 0:07:52 time: 0.0350 data_time: 0.0070 memory: 1253 loss: 0.2639 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2639 2022/11/28 14:48:28 - mmengine - INFO - Epoch(train) [8][500/1567] lr: 5.6671e-02 eta: 0:07:48 time: 0.0349 data_time: 0.0063 memory: 1253 loss: 0.2268 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2268 2022/11/28 14:48:32 - mmengine - INFO - Epoch(train) [8][600/1567] lr: 5.6050e-02 eta: 0:07:45 time: 0.0343 data_time: 0.0062 memory: 1253 loss: 0.2131 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2131 2022/11/28 14:48:35 - mmengine - INFO - Epoch(train) [8][700/1567] lr: 5.5427e-02 eta: 0:07:42 time: 0.0342 data_time: 0.0063 memory: 1253 loss: 0.1943 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1943 2022/11/28 14:48:38 - mmengine - INFO - Epoch(train) [8][800/1567] lr: 5.4804e-02 eta: 0:07:38 time: 0.0348 data_time: 0.0063 memory: 1253 loss: 0.2114 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2114 2022/11/28 14:48:42 - mmengine - INFO - Epoch(train) [8][900/1567] lr: 5.4180e-02 eta: 0:07:35 time: 0.0347 data_time: 0.0062 memory: 1253 loss: 0.2139 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2139 2022/11/28 14:48:45 - mmengine - INFO - Epoch(train) [8][1000/1567] lr: 5.3556e-02 eta: 0:07:31 time: 0.0341 data_time: 0.0061 memory: 1253 loss: 0.2778 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2778 2022/11/28 14:48:46 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:48:49 - mmengine - INFO - Epoch(train) [8][1100/1567] lr: 5.2930e-02 eta: 0:07:28 time: 0.0345 data_time: 0.0063 memory: 1253 loss: 0.2737 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2737 2022/11/28 14:48:52 - mmengine - INFO - Epoch(train) [8][1200/1567] lr: 5.2305e-02 eta: 0:07:25 time: 0.0360 data_time: 0.0062 memory: 1253 loss: 0.2350 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2350 2022/11/28 14:48:56 - mmengine - INFO - Epoch(train) [8][1300/1567] lr: 5.1679e-02 eta: 0:07:21 time: 0.0374 data_time: 0.0062 memory: 1253 loss: 0.2007 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2007 2022/11/28 14:49:00 - mmengine - INFO - Epoch(train) [8][1400/1567] lr: 5.1052e-02 eta: 0:07:18 time: 0.0353 data_time: 0.0063 memory: 1253 loss: 0.2465 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2465 2022/11/28 14:49:03 - mmengine - INFO - Epoch(train) [8][1500/1567] lr: 5.0426e-02 eta: 0:07:15 time: 0.0358 data_time: 0.0063 memory: 1253 loss: 0.1917 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1917 2022/11/28 14:49:06 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:49:06 - mmengine - INFO - Epoch(train) [8][1567/1567] lr: 5.0006e-02 eta: 0:07:12 time: 0.0347 data_time: 0.0060 memory: 1253 loss: 0.4315 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 0.4315 2022/11/28 14:49:06 - mmengine - INFO - Saving checkpoint at 8 epochs 2022/11/28 14:49:08 - mmengine - INFO - Epoch(val) [8][100/129] eta: 0:00:00 time: 0.0148 data_time: 0.0057 memory: 262 2022/11/28 14:49:08 - mmengine - INFO - Epoch(val) [8][129/129] acc/top1: 0.8151 acc/top5: 0.9782 acc/mean1: 0.8150 2022/11/28 14:49:08 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d/best_acc/top1_epoch_7.pth is removed 2022/11/28 14:49:09 - mmengine - INFO - The best checkpoint with 0.8151 acc/top1 at 8 epoch is saved to best_acc/top1_epoch_8.pth. 2022/11/28 14:49:12 - mmengine - INFO - Epoch(train) [9][100/1567] lr: 4.9380e-02 eta: 0:07:09 time: 0.0345 data_time: 0.0062 memory: 1253 loss: 0.2208 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2208 2022/11/28 14:49:16 - mmengine - INFO - Epoch(train) [9][200/1567] lr: 4.8753e-02 eta: 0:07:06 time: 0.0353 data_time: 0.0063 memory: 1253 loss: 0.2157 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.2157 2022/11/28 14:49:19 - mmengine - INFO - Epoch(train) [9][300/1567] lr: 4.8127e-02 eta: 0:07:02 time: 0.0347 data_time: 0.0063 memory: 1253 loss: 0.2230 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2230 2022/11/28 14:49:23 - mmengine - INFO - Epoch(train) [9][400/1567] lr: 4.7501e-02 eta: 0:06:59 time: 0.0366 data_time: 0.0061 memory: 1253 loss: 0.2471 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2471 2022/11/28 14:49:25 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:49:26 - mmengine - INFO - Epoch(train) [9][500/1567] lr: 4.6876e-02 eta: 0:06:56 time: 0.0350 data_time: 0.0064 memory: 1253 loss: 0.3115 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3115 2022/11/28 14:49:30 - mmengine - INFO - Epoch(train) [9][600/1567] lr: 4.6251e-02 eta: 0:06:52 time: 0.0350 data_time: 0.0065 memory: 1253 loss: 0.1737 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.1737 2022/11/28 14:49:34 - mmengine - INFO - Epoch(train) [9][700/1567] lr: 4.5626e-02 eta: 0:06:49 time: 0.0374 data_time: 0.0063 memory: 1253 loss: 0.2704 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2704 2022/11/28 14:49:37 - mmengine - INFO - Epoch(train) [9][800/1567] lr: 4.5003e-02 eta: 0:06:46 time: 0.0354 data_time: 0.0060 memory: 1253 loss: 0.1478 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1478 2022/11/28 14:49:41 - mmengine - INFO - Epoch(train) [9][900/1567] lr: 4.4380e-02 eta: 0:06:42 time: 0.0344 data_time: 0.0063 memory: 1253 loss: 0.2070 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2070 2022/11/28 14:49:44 - mmengine - INFO - Epoch(train) [9][1000/1567] lr: 4.3757e-02 eta: 0:06:39 time: 0.0349 data_time: 0.0062 memory: 1253 loss: 0.2359 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2359 2022/11/28 14:49:48 - mmengine - INFO - Epoch(train) [9][1100/1567] lr: 4.3136e-02 eta: 0:06:35 time: 0.0344 data_time: 0.0063 memory: 1253 loss: 0.2034 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2034 2022/11/28 14:49:51 - mmengine - INFO - Epoch(train) [9][1200/1567] lr: 4.2516e-02 eta: 0:06:32 time: 0.0347 data_time: 0.0063 memory: 1253 loss: 0.1994 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1994 2022/11/28 14:49:55 - mmengine - INFO - Epoch(train) [9][1300/1567] lr: 4.1897e-02 eta: 0:06:29 time: 0.0345 data_time: 0.0061 memory: 1253 loss: 0.1734 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1734 2022/11/28 14:49:59 - mmengine - INFO - Epoch(train) [9][1400/1567] lr: 4.1280e-02 eta: 0:06:25 time: 0.0354 data_time: 0.0063 memory: 1253 loss: 0.2099 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2099 2022/11/28 14:50:01 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:50:02 - mmengine - INFO - Epoch(train) [9][1500/1567] lr: 4.0664e-02 eta: 0:06:22 time: 0.0348 data_time: 0.0064 memory: 1253 loss: 0.1631 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1631 2022/11/28 14:50:04 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:50:04 - mmengine - INFO - Epoch(train) [9][1567/1567] lr: 4.0252e-02 eta: 0:06:20 time: 0.0348 data_time: 0.0060 memory: 1253 loss: 0.3782 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 0.3782 2022/11/28 14:50:04 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/11/28 14:50:06 - mmengine - INFO - Epoch(val) [9][100/129] eta: 0:00:00 time: 0.0150 data_time: 0.0057 memory: 262 2022/11/28 14:50:07 - mmengine - INFO - Epoch(val) [9][129/129] acc/top1: 0.8204 acc/top5: 0.9753 acc/mean1: 0.8204 2022/11/28 14:50:07 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d/best_acc/top1_epoch_8.pth is removed 2022/11/28 14:50:07 - mmengine - INFO - The best checkpoint with 0.8204 acc/top1 at 9 epoch is saved to best_acc/top1_epoch_9.pth. 2022/11/28 14:50:11 - mmengine - INFO - Epoch(train) [10][100/1567] lr: 3.9638e-02 eta: 0:06:16 time: 0.0345 data_time: 0.0062 memory: 1253 loss: 0.1746 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1746 2022/11/28 14:50:14 - mmengine - INFO - Epoch(train) [10][200/1567] lr: 3.9026e-02 eta: 0:06:13 time: 0.0347 data_time: 0.0063 memory: 1253 loss: 0.1810 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1810 2022/11/28 14:50:18 - mmengine - INFO - Epoch(train) [10][300/1567] lr: 3.8415e-02 eta: 0:06:09 time: 0.0355 data_time: 0.0064 memory: 1253 loss: 0.2012 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2012 2022/11/28 14:50:21 - mmengine - INFO - Epoch(train) [10][400/1567] lr: 3.7807e-02 eta: 0:06:06 time: 0.0348 data_time: 0.0069 memory: 1253 loss: 0.1630 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1630 2022/11/28 14:50:25 - mmengine - INFO - Epoch(train) [10][500/1567] lr: 3.7200e-02 eta: 0:06:03 time: 0.0357 data_time: 0.0065 memory: 1253 loss: 0.2200 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.2200 2022/11/28 14:50:28 - mmengine - INFO - Epoch(train) [10][600/1567] lr: 3.6596e-02 eta: 0:05:59 time: 0.0343 data_time: 0.0062 memory: 1253 loss: 0.1916 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1916 2022/11/28 14:50:32 - mmengine - INFO - Epoch(train) [10][700/1567] lr: 3.5993e-02 eta: 0:05:56 time: 0.0362 data_time: 0.0063 memory: 1253 loss: 0.1953 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1953 2022/11/28 14:50:36 - mmengine - INFO - Epoch(train) [10][800/1567] lr: 3.5393e-02 eta: 0:05:52 time: 0.0361 data_time: 0.0063 memory: 1253 loss: 0.1918 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1918 2022/11/28 14:50:39 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:50:39 - mmengine - INFO - Epoch(train) [10][900/1567] lr: 3.4795e-02 eta: 0:05:49 time: 0.0370 data_time: 0.0065 memory: 1253 loss: 0.2351 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2351 2022/11/28 14:50:43 - mmengine - INFO - Epoch(train) [10][1000/1567] lr: 3.4199e-02 eta: 0:05:45 time: 0.0346 data_time: 0.0062 memory: 1253 loss: 0.1621 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1621 2022/11/28 14:50:46 - mmengine - INFO - Epoch(train) [10][1100/1567] lr: 3.3606e-02 eta: 0:05:42 time: 0.0359 data_time: 0.0064 memory: 1253 loss: 0.1553 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1553 2022/11/28 14:50:50 - mmengine - INFO - Epoch(train) [10][1200/1567] lr: 3.3015e-02 eta: 0:05:39 time: 0.0353 data_time: 0.0065 memory: 1253 loss: 0.1753 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1753 2022/11/28 14:50:53 - mmengine - INFO - Epoch(train) [10][1300/1567] lr: 3.2428e-02 eta: 0:05:35 time: 0.0357 data_time: 0.0062 memory: 1253 loss: 0.1942 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1942 2022/11/28 14:50:57 - mmengine - INFO - Epoch(train) [10][1400/1567] lr: 3.1842e-02 eta: 0:05:32 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.1538 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1538 2022/11/28 14:51:00 - mmengine - INFO - Epoch(train) [10][1500/1567] lr: 3.1260e-02 eta: 0:05:28 time: 0.0342 data_time: 0.0061 memory: 1253 loss: 0.1158 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1158 2022/11/28 14:51:03 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:51:03 - mmengine - INFO - Epoch(train) [10][1567/1567] lr: 3.0872e-02 eta: 0:05:26 time: 0.0361 data_time: 0.0058 memory: 1253 loss: 0.2935 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2935 2022/11/28 14:51:03 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/11/28 14:51:05 - mmengine - INFO - Epoch(val) [10][100/129] eta: 0:00:00 time: 0.0150 data_time: 0.0057 memory: 262 2022/11/28 14:51:05 - mmengine - INFO - Epoch(val) [10][129/129] acc/top1: 0.8232 acc/top5: 0.9733 acc/mean1: 0.8231 2022/11/28 14:51:05 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d/best_acc/top1_epoch_9.pth is removed 2022/11/28 14:51:05 - mmengine - INFO - The best checkpoint with 0.8232 acc/top1 at 10 epoch is saved to best_acc/top1_epoch_10.pth. 2022/11/28 14:51:09 - mmengine - INFO - Epoch(train) [11][100/1567] lr: 3.0294e-02 eta: 0:05:23 time: 0.0372 data_time: 0.0064 memory: 1253 loss: 0.1286 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1286 2022/11/28 14:51:13 - mmengine - INFO - Epoch(train) [11][200/1567] lr: 2.9720e-02 eta: 0:05:19 time: 0.0366 data_time: 0.0063 memory: 1253 loss: 0.1118 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1118 2022/11/28 14:51:16 - mmengine - INFO - Epoch(train) [11][300/1567] lr: 2.9149e-02 eta: 0:05:16 time: 0.0355 data_time: 0.0060 memory: 1253 loss: 0.1166 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1166 2022/11/28 14:51:17 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:51:20 - mmengine - INFO - Epoch(train) [11][400/1567] lr: 2.8581e-02 eta: 0:05:12 time: 0.0360 data_time: 0.0064 memory: 1253 loss: 0.1213 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1213 2022/11/28 14:51:23 - mmengine - INFO - Epoch(train) [11][500/1567] lr: 2.8017e-02 eta: 0:05:09 time: 0.0346 data_time: 0.0063 memory: 1253 loss: 0.1434 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1434 2022/11/28 14:51:27 - mmengine - INFO - Epoch(train) [11][600/1567] lr: 2.7456e-02 eta: 0:05:05 time: 0.0351 data_time: 0.0063 memory: 1253 loss: 0.1466 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1466 2022/11/28 14:51:30 - mmengine - INFO - Epoch(train) [11][700/1567] lr: 2.6898e-02 eta: 0:05:02 time: 0.0340 data_time: 0.0062 memory: 1253 loss: 0.1640 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1640 2022/11/28 14:51:34 - mmengine - INFO - Epoch(train) [11][800/1567] lr: 2.6345e-02 eta: 0:04:58 time: 0.0339 data_time: 0.0062 memory: 1253 loss: 0.1440 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1440 2022/11/28 14:51:37 - mmengine - INFO - Epoch(train) [11][900/1567] lr: 2.5794e-02 eta: 0:04:55 time: 0.0358 data_time: 0.0063 memory: 1253 loss: 0.1368 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1368 2022/11/28 14:51:41 - mmengine - INFO - Epoch(train) [11][1000/1567] lr: 2.5248e-02 eta: 0:04:52 time: 0.0351 data_time: 0.0063 memory: 1253 loss: 0.1348 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1348 2022/11/28 14:51:44 - mmengine - INFO - Epoch(train) [11][1100/1567] lr: 2.4706e-02 eta: 0:04:48 time: 0.0339 data_time: 0.0062 memory: 1253 loss: 0.1692 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1692 2022/11/28 14:51:48 - mmengine - INFO - Epoch(train) [11][1200/1567] lr: 2.4167e-02 eta: 0:04:45 time: 0.0352 data_time: 0.0062 memory: 1253 loss: 0.0947 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0947 2022/11/28 14:51:51 - mmengine - INFO - Epoch(train) [11][1300/1567] lr: 2.3633e-02 eta: 0:04:41 time: 0.0340 data_time: 0.0062 memory: 1253 loss: 0.1186 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1186 2022/11/28 14:51:52 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:51:55 - mmengine - INFO - Epoch(train) [11][1400/1567] lr: 2.3103e-02 eta: 0:04:38 time: 0.0340 data_time: 0.0062 memory: 1253 loss: 0.1026 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1026 2022/11/28 14:51:58 - mmengine - INFO - Epoch(train) [11][1500/1567] lr: 2.2577e-02 eta: 0:04:34 time: 0.0340 data_time: 0.0063 memory: 1253 loss: 0.1536 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1536 2022/11/28 14:52:01 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:52:01 - mmengine - INFO - Epoch(train) [11][1567/1567] lr: 2.2227e-02 eta: 0:04:32 time: 0.0349 data_time: 0.0060 memory: 1253 loss: 0.2817 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 0.2817 2022/11/28 14:52:01 - mmengine - INFO - Saving checkpoint at 11 epochs 2022/11/28 14:52:03 - mmengine - INFO - Epoch(val) [11][100/129] eta: 0:00:00 time: 0.0149 data_time: 0.0057 memory: 262 2022/11/28 14:52:03 - mmengine - INFO - Epoch(val) [11][129/129] acc/top1: 0.8357 acc/top5: 0.9788 acc/mean1: 0.8356 2022/11/28 14:52:03 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d/best_acc/top1_epoch_10.pth is removed 2022/11/28 14:52:04 - mmengine - INFO - The best checkpoint with 0.8357 acc/top1 at 11 epoch is saved to best_acc/top1_epoch_11.pth. 2022/11/28 14:52:07 - mmengine - INFO - Epoch(train) [12][100/1567] lr: 2.1708e-02 eta: 0:04:28 time: 0.0338 data_time: 0.0063 memory: 1253 loss: 0.1301 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1301 2022/11/28 14:52:11 - mmengine - INFO - Epoch(train) [12][200/1567] lr: 2.1194e-02 eta: 0:04:25 time: 0.0356 data_time: 0.0063 memory: 1253 loss: 0.0806 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.0806 2022/11/28 14:52:14 - mmengine - INFO - Epoch(train) [12][300/1567] lr: 2.0684e-02 eta: 0:04:21 time: 0.0352 data_time: 0.0063 memory: 1253 loss: 0.0593 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0593 2022/11/28 14:52:18 - mmengine - INFO - Epoch(train) [12][400/1567] lr: 2.0179e-02 eta: 0:04:18 time: 0.0363 data_time: 0.0062 memory: 1253 loss: 0.1026 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1026 2022/11/28 14:52:21 - mmengine - INFO - Epoch(train) [12][500/1567] lr: 1.9678e-02 eta: 0:04:15 time: 0.0355 data_time: 0.0063 memory: 1253 loss: 0.0909 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0909 2022/11/28 14:52:25 - mmengine - INFO - Epoch(train) [12][600/1567] lr: 1.9182e-02 eta: 0:04:11 time: 0.0343 data_time: 0.0065 memory: 1253 loss: 0.0837 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0837 2022/11/28 14:52:29 - mmengine - INFO - Epoch(train) [12][700/1567] lr: 1.8691e-02 eta: 0:04:08 time: 0.0353 data_time: 0.0064 memory: 1253 loss: 0.0823 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0823 2022/11/28 14:52:31 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:52:32 - mmengine - INFO - Epoch(train) [12][800/1567] lr: 1.8205e-02 eta: 0:04:04 time: 0.0342 data_time: 0.0062 memory: 1253 loss: 0.0881 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0881 2022/11/28 14:52:36 - mmengine - INFO - Epoch(train) [12][900/1567] lr: 1.7724e-02 eta: 0:04:01 time: 0.0353 data_time: 0.0063 memory: 1253 loss: 0.1004 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.1004 2022/11/28 14:52:39 - mmengine - INFO - Epoch(train) [12][1000/1567] lr: 1.7248e-02 eta: 0:03:57 time: 0.0339 data_time: 0.0062 memory: 1253 loss: 0.1541 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1541 2022/11/28 14:52:43 - mmengine - INFO - Epoch(train) [12][1100/1567] lr: 1.6778e-02 eta: 0:03:54 time: 0.0357 data_time: 0.0063 memory: 1253 loss: 0.0766 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0766 2022/11/28 14:52:46 - mmengine - INFO - Epoch(train) [12][1200/1567] lr: 1.6312e-02 eta: 0:03:50 time: 0.0342 data_time: 0.0063 memory: 1253 loss: 0.0685 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0685 2022/11/28 14:52:50 - mmengine - INFO - Epoch(train) [12][1300/1567] lr: 1.5852e-02 eta: 0:03:47 time: 0.0347 data_time: 0.0062 memory: 1253 loss: 0.0689 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0689 2022/11/28 14:52:53 - mmengine - INFO - Epoch(train) [12][1400/1567] lr: 1.5397e-02 eta: 0:03:44 time: 0.0344 data_time: 0.0067 memory: 1253 loss: 0.0730 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.0730 2022/11/28 14:52:57 - mmengine - INFO - Epoch(train) [12][1500/1567] lr: 1.4947e-02 eta: 0:03:40 time: 0.0361 data_time: 0.0070 memory: 1253 loss: 0.0662 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0662 2022/11/28 14:52:59 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:52:59 - mmengine - INFO - Epoch(train) [12][1567/1567] lr: 1.4649e-02 eta: 0:03:38 time: 0.0336 data_time: 0.0061 memory: 1253 loss: 0.2494 top1_acc: 0.0000 top5_acc: 1.0000 loss_cls: 0.2494 2022/11/28 14:52:59 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/11/28 14:53:01 - mmengine - INFO - Epoch(val) [12][100/129] eta: 0:00:00 time: 0.0151 data_time: 0.0058 memory: 262 2022/11/28 14:53:02 - mmengine - INFO - Epoch(val) [12][129/129] acc/top1: 0.8486 acc/top5: 0.9818 acc/mean1: 0.8485 2022/11/28 14:53:02 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d/best_acc/top1_epoch_11.pth is removed 2022/11/28 14:53:02 - mmengine - INFO - The best checkpoint with 0.8486 acc/top1 at 12 epoch is saved to best_acc/top1_epoch_12.pth. 2022/11/28 14:53:06 - mmengine - INFO - Epoch(train) [13][100/1567] lr: 1.4209e-02 eta: 0:03:34 time: 0.0349 data_time: 0.0062 memory: 1253 loss: 0.0659 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0659 2022/11/28 14:53:09 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:53:09 - mmengine - INFO - Epoch(train) [13][200/1567] lr: 1.3774e-02 eta: 0:03:31 time: 0.0362 data_time: 0.0065 memory: 1253 loss: 0.0839 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0839 2022/11/28 14:53:13 - mmengine - INFO - Epoch(train) [13][300/1567] lr: 1.3345e-02 eta: 0:03:27 time: 0.0356 data_time: 0.0063 memory: 1253 loss: 0.0639 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0639 2022/11/28 14:53:16 - mmengine - INFO - Epoch(train) [13][400/1567] lr: 1.2922e-02 eta: 0:03:24 time: 0.0338 data_time: 0.0062 memory: 1253 loss: 0.0662 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0662 2022/11/28 14:53:20 - mmengine - INFO - Epoch(train) [13][500/1567] lr: 1.2505e-02 eta: 0:03:20 time: 0.0344 data_time: 0.0063 memory: 1253 loss: 0.0621 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0621 2022/11/28 14:53:23 - mmengine - INFO - Epoch(train) [13][600/1567] lr: 1.2093e-02 eta: 0:03:17 time: 0.0341 data_time: 0.0062 memory: 1253 loss: 0.0541 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0541 2022/11/28 14:53:27 - mmengine - INFO - Epoch(train) [13][700/1567] lr: 1.1687e-02 eta: 0:03:13 time: 0.0336 data_time: 0.0062 memory: 1253 loss: 0.0510 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0510 2022/11/28 14:53:30 - mmengine - INFO - Epoch(train) [13][800/1567] lr: 1.1288e-02 eta: 0:03:10 time: 0.0353 data_time: 0.0061 memory: 1253 loss: 0.0767 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0767 2022/11/28 14:53:34 - mmengine - INFO - Epoch(train) [13][900/1567] lr: 1.0894e-02 eta: 0:03:06 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.0536 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0536 2022/11/28 14:53:37 - mmengine - INFO - Epoch(train) [13][1000/1567] lr: 1.0507e-02 eta: 0:03:03 time: 0.0336 data_time: 0.0061 memory: 1253 loss: 0.0460 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0460 2022/11/28 14:53:41 - mmengine - INFO - Epoch(train) [13][1100/1567] lr: 1.0126e-02 eta: 0:02:59 time: 0.0342 data_time: 0.0062 memory: 1253 loss: 0.0632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0632 2022/11/28 14:53:44 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:53:44 - mmengine - INFO - Epoch(train) [13][1200/1567] lr: 9.7512e-03 eta: 0:02:56 time: 0.0366 data_time: 0.0060 memory: 1253 loss: 0.0621 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0621 2022/11/28 14:53:48 - mmengine - INFO - Epoch(train) [13][1300/1567] lr: 9.3826e-03 eta: 0:02:53 time: 0.0351 data_time: 0.0061 memory: 1253 loss: 0.0282 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0282 2022/11/28 14:53:51 - mmengine - INFO - Epoch(train) [13][1400/1567] lr: 9.0204e-03 eta: 0:02:49 time: 0.0351 data_time: 0.0061 memory: 1253 loss: 0.0620 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0620 2022/11/28 14:53:55 - mmengine - INFO - Epoch(train) [13][1500/1567] lr: 8.6647e-03 eta: 0:02:46 time: 0.0346 data_time: 0.0061 memory: 1253 loss: 0.0523 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0523 2022/11/28 14:53:57 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:53:57 - mmengine - INFO - Epoch(train) [13][1567/1567] lr: 8.4300e-03 eta: 0:02:43 time: 0.0331 data_time: 0.0060 memory: 1253 loss: 0.2328 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 0.2328 2022/11/28 14:53:57 - mmengine - INFO - Saving checkpoint at 13 epochs 2022/11/28 14:53:59 - mmengine - INFO - Epoch(val) [13][100/129] eta: 0:00:00 time: 0.0152 data_time: 0.0059 memory: 262 2022/11/28 14:54:00 - mmengine - INFO - Epoch(val) [13][129/129] acc/top1: 0.8630 acc/top5: 0.9860 acc/mean1: 0.8630 2022/11/28 14:54:00 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d/best_acc/top1_epoch_12.pth is removed 2022/11/28 14:54:00 - mmengine - INFO - The best checkpoint with 0.8630 acc/top1 at 13 epoch is saved to best_acc/top1_epoch_13.pth. 2022/11/28 14:54:04 - mmengine - INFO - Epoch(train) [14][100/1567] lr: 8.0851e-03 eta: 0:02:40 time: 0.0351 data_time: 0.0060 memory: 1253 loss: 0.0504 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0504 2022/11/28 14:54:07 - mmengine - INFO - Epoch(train) [14][200/1567] lr: 7.7469e-03 eta: 0:02:36 time: 0.0337 data_time: 0.0062 memory: 1253 loss: 0.0480 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0480 2022/11/28 14:54:10 - mmengine - INFO - Epoch(train) [14][300/1567] lr: 7.4152e-03 eta: 0:02:33 time: 0.0362 data_time: 0.0060 memory: 1253 loss: 0.0280 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0280 2022/11/28 14:54:14 - mmengine - INFO - Epoch(train) [14][400/1567] lr: 7.0902e-03 eta: 0:02:29 time: 0.0352 data_time: 0.0060 memory: 1253 loss: 0.0388 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0388 2022/11/28 14:54:18 - mmengine - INFO - Epoch(train) [14][500/1567] lr: 6.7720e-03 eta: 0:02:26 time: 0.0353 data_time: 0.0060 memory: 1253 loss: 0.0268 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0268 2022/11/28 14:54:21 - mmengine - INFO - Epoch(train) [14][600/1567] lr: 6.4606e-03 eta: 0:02:22 time: 0.0355 data_time: 0.0063 memory: 1253 loss: 0.0451 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0451 2022/11/28 14:54:22 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:54:25 - mmengine - INFO - Epoch(train) [14][700/1567] lr: 6.1560e-03 eta: 0:02:19 time: 0.0341 data_time: 0.0060 memory: 1253 loss: 0.0466 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0466 2022/11/28 14:54:28 - mmengine - INFO - Epoch(train) [14][800/1567] lr: 5.8582e-03 eta: 0:02:15 time: 0.0348 data_time: 0.0060 memory: 1253 loss: 0.0292 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0292 2022/11/28 14:54:32 - mmengine - INFO - Epoch(train) [14][900/1567] lr: 5.5675e-03 eta: 0:02:12 time: 0.0344 data_time: 0.0062 memory: 1253 loss: 0.0292 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0292 2022/11/28 14:54:35 - mmengine - INFO - Epoch(train) [14][1000/1567] lr: 5.2836e-03 eta: 0:02:08 time: 0.0351 data_time: 0.0060 memory: 1253 loss: 0.0266 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0266 2022/11/28 14:54:39 - mmengine - INFO - Epoch(train) [14][1100/1567] lr: 5.0068e-03 eta: 0:02:05 time: 0.0358 data_time: 0.0063 memory: 1253 loss: 0.0403 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0403 2022/11/28 14:54:42 - mmengine - INFO - Epoch(train) [14][1200/1567] lr: 4.7371e-03 eta: 0:02:02 time: 0.0351 data_time: 0.0061 memory: 1253 loss: 0.0387 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0387 2022/11/28 14:54:46 - mmengine - INFO - Epoch(train) [14][1300/1567] lr: 4.4745e-03 eta: 0:01:58 time: 0.0347 data_time: 0.0073 memory: 1253 loss: 0.0253 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0253 2022/11/28 14:54:49 - mmengine - INFO - Epoch(train) [14][1400/1567] lr: 4.2190e-03 eta: 0:01:55 time: 0.0342 data_time: 0.0062 memory: 1253 loss: 0.0380 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0380 2022/11/28 14:54:53 - mmengine - INFO - Epoch(train) [14][1500/1567] lr: 3.9707e-03 eta: 0:01:51 time: 0.0342 data_time: 0.0061 memory: 1253 loss: 0.0369 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0369 2022/11/28 14:54:55 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:54:55 - mmengine - INFO - Epoch(train) [14][1567/1567] lr: 3.8084e-03 eta: 0:01:49 time: 0.0358 data_time: 0.0058 memory: 1253 loss: 0.1742 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1742 2022/11/28 14:54:55 - mmengine - INFO - Saving checkpoint at 14 epochs 2022/11/28 14:54:57 - mmengine - INFO - Epoch(val) [14][100/129] eta: 0:00:00 time: 0.0152 data_time: 0.0058 memory: 262 2022/11/28 14:54:58 - mmengine - INFO - Epoch(val) [14][129/129] acc/top1: 0.8706 acc/top5: 0.9853 acc/mean1: 0.8705 2022/11/28 14:54:58 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d/best_acc/top1_epoch_13.pth is removed 2022/11/28 14:54:58 - mmengine - INFO - The best checkpoint with 0.8706 acc/top1 at 14 epoch is saved to best_acc/top1_epoch_14.pth. 2022/11/28 14:55:00 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:55:02 - mmengine - INFO - Epoch(train) [15][100/1567] lr: 3.5722e-03 eta: 0:01:45 time: 0.0358 data_time: 0.0065 memory: 1253 loss: 0.0315 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0315 2022/11/28 14:55:05 - mmengine - INFO - Epoch(train) [15][200/1567] lr: 3.3433e-03 eta: 0:01:42 time: 0.0337 data_time: 0.0062 memory: 1253 loss: 0.0267 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0267 2022/11/28 14:55:08 - mmengine - INFO - Epoch(train) [15][300/1567] lr: 3.1217e-03 eta: 0:01:38 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.0263 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0263 2022/11/28 14:55:12 - mmengine - INFO - Epoch(train) [15][400/1567] lr: 2.9075e-03 eta: 0:01:35 time: 0.0350 data_time: 0.0060 memory: 1253 loss: 0.0643 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0643 2022/11/28 14:55:15 - mmengine - INFO - Epoch(train) [15][500/1567] lr: 2.7007e-03 eta: 0:01:31 time: 0.0339 data_time: 0.0066 memory: 1253 loss: 0.0266 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0266 2022/11/28 14:55:19 - mmengine - INFO - Epoch(train) [15][600/1567] lr: 2.5013e-03 eta: 0:01:28 time: 0.0342 data_time: 0.0061 memory: 1253 loss: 0.0132 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0132 2022/11/28 14:55:22 - mmengine - INFO - Epoch(train) [15][700/1567] lr: 2.3093e-03 eta: 0:01:24 time: 0.0338 data_time: 0.0060 memory: 1253 loss: 0.0311 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0311 2022/11/28 14:55:26 - mmengine - INFO - Epoch(train) [15][800/1567] lr: 2.1249e-03 eta: 0:01:21 time: 0.0345 data_time: 0.0062 memory: 1253 loss: 0.0122 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0122 2022/11/28 14:55:29 - mmengine - INFO - Epoch(train) [15][900/1567] lr: 1.9479e-03 eta: 0:01:17 time: 0.0354 data_time: 0.0061 memory: 1253 loss: 0.0349 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0349 2022/11/28 14:55:33 - mmengine - INFO - Epoch(train) [15][1000/1567] lr: 1.7785e-03 eta: 0:01:14 time: 0.0375 data_time: 0.0062 memory: 1253 loss: 0.0279 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0279 2022/11/28 14:55:35 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:55:37 - mmengine - INFO - Epoch(train) [15][1100/1567] lr: 1.6167e-03 eta: 0:01:10 time: 0.0374 data_time: 0.0062 memory: 1253 loss: 0.0486 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0486 2022/11/28 14:55:40 - mmengine - INFO - Epoch(train) [15][1200/1567] lr: 1.4625e-03 eta: 0:01:07 time: 0.0346 data_time: 0.0061 memory: 1253 loss: 0.0294 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0294 2022/11/28 14:55:44 - mmengine - INFO - Epoch(train) [15][1300/1567] lr: 1.3159e-03 eta: 0:01:03 time: 0.0337 data_time: 0.0061 memory: 1253 loss: 0.0153 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0153 2022/11/28 14:55:47 - mmengine - INFO - Epoch(train) [15][1400/1567] lr: 1.1769e-03 eta: 0:01:00 time: 0.0369 data_time: 0.0061 memory: 1253 loss: 0.0180 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0180 2022/11/28 14:55:51 - mmengine - INFO - Epoch(train) [15][1500/1567] lr: 1.0456e-03 eta: 0:00:56 time: 0.0342 data_time: 0.0061 memory: 1253 loss: 0.0483 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0483 2022/11/28 14:55:53 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:55:53 - mmengine - INFO - Epoch(train) [15][1567/1567] lr: 9.6196e-04 eta: 0:00:54 time: 0.0330 data_time: 0.0060 memory: 1253 loss: 0.2283 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 0.2283 2022/11/28 14:55:53 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/11/28 14:55:55 - mmengine - INFO - Epoch(val) [15][100/129] eta: 0:00:00 time: 0.0151 data_time: 0.0057 memory: 262 2022/11/28 14:55:56 - mmengine - INFO - Epoch(val) [15][129/129] acc/top1: 0.8690 acc/top5: 0.9858 acc/mean1: 0.8690 2022/11/28 14:55:59 - mmengine - INFO - Epoch(train) [16][100/1567] lr: 8.4351e-04 eta: 0:00:51 time: 0.0349 data_time: 0.0064 memory: 1253 loss: 0.0142 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0142 2022/11/28 14:56:03 - mmengine - INFO - Epoch(train) [16][200/1567] lr: 7.3277e-04 eta: 0:00:47 time: 0.0352 data_time: 0.0062 memory: 1253 loss: 0.0130 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0130 2022/11/28 14:56:06 - mmengine - INFO - Epoch(train) [16][300/1567] lr: 6.2978e-04 eta: 0:00:44 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.0309 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0309 2022/11/28 14:56:10 - mmengine - INFO - Epoch(train) [16][400/1567] lr: 5.3453e-04 eta: 0:00:40 time: 0.0357 data_time: 0.0060 memory: 1253 loss: 0.0304 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0304 2022/11/28 14:56:13 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:56:13 - mmengine - INFO - Epoch(train) [16][500/1567] lr: 4.4705e-04 eta: 0:00:37 time: 0.0345 data_time: 0.0061 memory: 1253 loss: 0.0139 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0139 2022/11/28 14:56:17 - mmengine - INFO - Epoch(train) [16][600/1567] lr: 3.6735e-04 eta: 0:00:33 time: 0.0347 data_time: 0.0062 memory: 1253 loss: 0.0527 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0527 2022/11/28 14:56:20 - mmengine - INFO - Epoch(train) [16][700/1567] lr: 2.9544e-04 eta: 0:00:30 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.0124 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0124 2022/11/28 14:56:23 - mmengine - INFO - Epoch(train) [16][800/1567] lr: 2.3134e-04 eta: 0:00:26 time: 0.0336 data_time: 0.0060 memory: 1253 loss: 0.0131 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0131 2022/11/28 14:56:27 - mmengine - INFO - Epoch(train) [16][900/1567] lr: 1.7505e-04 eta: 0:00:23 time: 0.0341 data_time: 0.0060 memory: 1253 loss: 0.0215 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0215 2022/11/28 14:56:30 - mmengine - INFO - Epoch(train) [16][1000/1567] lr: 1.2658e-04 eta: 0:00:19 time: 0.0336 data_time: 0.0060 memory: 1253 loss: 0.0264 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0264 2022/11/28 14:56:34 - mmengine - INFO - Epoch(train) [16][1100/1567] lr: 8.5947e-05 eta: 0:00:16 time: 0.0338 data_time: 0.0060 memory: 1253 loss: 0.0318 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0318 2022/11/28 14:56:37 - mmengine - INFO - Epoch(train) [16][1200/1567] lr: 5.3147e-05 eta: 0:00:12 time: 0.0338 data_time: 0.0061 memory: 1253 loss: 0.0158 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0158 2022/11/28 14:56:41 - mmengine - INFO - Epoch(train) [16][1300/1567] lr: 2.8190e-05 eta: 0:00:09 time: 0.0358 data_time: 0.0061 memory: 1253 loss: 0.0217 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0217 2022/11/28 14:56:44 - mmengine - INFO - Epoch(train) [16][1400/1567] lr: 1.1078e-05 eta: 0:00:05 time: 0.0340 data_time: 0.0060 memory: 1253 loss: 0.0300 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0300 2022/11/28 14:56:47 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:56:48 - mmengine - INFO - Epoch(train) [16][1500/1567] lr: 1.8150e-06 eta: 0:00:02 time: 0.0336 data_time: 0.0061 memory: 1253 loss: 0.0234 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0234 2022/11/28 14:56:50 - mmengine - INFO - Exp name: stgcn_8xb16-bone-motion-u100-80e_ntu60-xsub-keypoint-2d_20221128_144054 2022/11/28 14:56:50 - mmengine - INFO - Epoch(train) [16][1567/1567] lr: 3.9252e-10 eta: 0:00:00 time: 0.0332 data_time: 0.0059 memory: 1253 loss: 0.1769 top1_acc: 0.0000 top5_acc: 1.0000 loss_cls: 0.1769 2022/11/28 14:56:50 - mmengine - INFO - Saving checkpoint at 16 epochs 2022/11/28 14:56:52 - mmengine - INFO - Epoch(val) [16][100/129] eta: 0:00:00 time: 0.0153 data_time: 0.0058 memory: 262 2022/11/28 14:56:53 - mmengine - INFO - Epoch(val) [16][129/129] acc/top1: 0.8703 acc/top5: 0.9862 acc/mean1: 0.8703