2022/11/28 15:32:24 - 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: 413560960 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 15:32:24 - 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='nturgb+d', mode='stgcn_spatial')), cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) dataset_type = 'PoseDataset' ann_file = 'data/skeleton/ntu120_3d.pkl' train_pipeline = [ dict(type='PreNormalize3D'), dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), dict(type='UniformSampleFrames', clip_len=100), dict(type='PoseDecode'), dict(type='FormatGCNInput', num_person=2), dict(type='PackActionInputs') ] val_pipeline = [ dict(type='PreNormalize3D'), dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), 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='PreNormalize3D'), dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), 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/ntu120_3d.pkl', pipeline=[ dict(type='PreNormalize3D'), dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), 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/ntu120_3d.pkl', pipeline=[ dict(type='PreNormalize3D'), dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), 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/ntu120_3d.pkl', pipeline=[ dict(type='PreNormalize3D'), dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), 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-joint-u100-80e_ntu120-xsub-keypoint-3d' randomness = dict(seed=None, diff_rank_seed=False, deterministic=False) 2022/11/28 15:32:24 - mmengine - INFO - Result has been saved to /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d/modules_statistic_results.json Name of parameter - Initialization information data_bn.weight - torch.Size([75]): The value is the same before and after calling `init_weights` of STGCN data_bn.bias - torch.Size([75]): The value is the same before and after calling `init_weights` of STGCN gcn.0.gcn.PA - torch.Size([3, 25, 25]): 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, 25, 25]): 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, 25, 25]): 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, 25, 25]): 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, 25, 25]): 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, 25, 25]): 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, 25, 25]): 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, 25, 25]): 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, 25, 25]): 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, 25, 25]): 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([120, 256]): NormalInit: mean=0, std=0.01, bias=0 fc.bias - torch.Size([120]): NormalInit: mean=0, std=0.01, bias=0 2022/11/28 15:34:18 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d. 2022/11/28 15:34:27 - mmengine - INFO - Epoch(train) [1][100/2462] lr: 9.9998e-02 eta: 0:55:45 time: 0.0449 data_time: 0.0060 memory: 1794 loss: 3.7071 top1_acc: 0.1250 top5_acc: 0.1875 loss_cls: 3.7071 2022/11/28 15:34:31 - mmengine - INFO - Epoch(train) [1][200/2462] lr: 9.9994e-02 eta: 0:42:45 time: 0.0491 data_time: 0.0061 memory: 1794 loss: 2.8590 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 2.8590 2022/11/28 15:34:36 - mmengine - INFO - Epoch(train) [1][300/2462] lr: 9.9986e-02 eta: 0:38:00 time: 0.0435 data_time: 0.0063 memory: 1794 loss: 2.4605 top1_acc: 0.3125 top5_acc: 0.8750 loss_cls: 2.4605 2022/11/28 15:34:40 - mmengine - INFO - Epoch(train) [1][400/2462] lr: 9.9975e-02 eta: 0:35:30 time: 0.0431 data_time: 0.0060 memory: 1794 loss: 2.3378 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3378 2022/11/28 15:34:44 - mmengine - INFO - Epoch(train) [1][500/2462] lr: 9.9960e-02 eta: 0:33:54 time: 0.0428 data_time: 0.0060 memory: 1794 loss: 2.1953 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.1953 2022/11/28 15:34:49 - mmengine - INFO - Epoch(train) [1][600/2462] lr: 9.9943e-02 eta: 0:32:50 time: 0.0428 data_time: 0.0060 memory: 1794 loss: 2.1430 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1430 2022/11/28 15:34:53 - mmengine - INFO - Epoch(train) [1][700/2462] lr: 9.9922e-02 eta: 0:32:05 time: 0.0436 data_time: 0.0059 memory: 1794 loss: 2.0193 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.0193 2022/11/28 15:34:57 - mmengine - INFO - Epoch(train) [1][800/2462] lr: 9.9899e-02 eta: 0:31:28 time: 0.0429 data_time: 0.0060 memory: 1794 loss: 1.9311 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9311 2022/11/28 15:35:02 - mmengine - INFO - Epoch(train) [1][900/2462] lr: 9.9872e-02 eta: 0:31:00 time: 0.0430 data_time: 0.0061 memory: 1794 loss: 1.7083 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7083 2022/11/28 15:35:06 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:35:06 - mmengine - INFO - Epoch(train) [1][1000/2462] lr: 9.9841e-02 eta: 0:30:35 time: 0.0428 data_time: 0.0060 memory: 1794 loss: 1.7340 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7340 2022/11/28 15:35:10 - mmengine - INFO - Epoch(train) [1][1100/2462] lr: 9.9808e-02 eta: 0:30:16 time: 0.0436 data_time: 0.0071 memory: 1794 loss: 1.4522 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4522 2022/11/28 15:35:15 - mmengine - INFO - Epoch(train) [1][1200/2462] lr: 9.9772e-02 eta: 0:29:59 time: 0.0432 data_time: 0.0060 memory: 1794 loss: 1.4440 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4440 2022/11/28 15:35:19 - mmengine - INFO - Epoch(train) [1][1300/2462] lr: 9.9732e-02 eta: 0:29:43 time: 0.0434 data_time: 0.0059 memory: 1794 loss: 1.3457 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3457 2022/11/28 15:35:24 - mmengine - INFO - Epoch(train) [1][1400/2462] lr: 9.9689e-02 eta: 0:29:30 time: 0.0428 data_time: 0.0060 memory: 1794 loss: 1.2951 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2951 2022/11/28 15:35:28 - mmengine - INFO - Epoch(train) [1][1500/2462] lr: 9.9643e-02 eta: 0:29:17 time: 0.0433 data_time: 0.0059 memory: 1794 loss: 1.4163 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4163 2022/11/28 15:35:32 - mmengine - INFO - Epoch(train) [1][1600/2462] lr: 9.9594e-02 eta: 0:29:05 time: 0.0431 data_time: 0.0060 memory: 1794 loss: 1.2752 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2752 2022/11/28 15:35:37 - mmengine - INFO - Epoch(train) [1][1700/2462] lr: 9.9542e-02 eta: 0:28:55 time: 0.0432 data_time: 0.0059 memory: 1794 loss: 1.3389 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3389 2022/11/28 15:35:41 - mmengine - INFO - Epoch(train) [1][1800/2462] lr: 9.9486e-02 eta: 0:28:45 time: 0.0435 data_time: 0.0061 memory: 1794 loss: 1.2996 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.2996 2022/11/28 15:35:45 - mmengine - INFO - Epoch(train) [1][1900/2462] lr: 9.9428e-02 eta: 0:28:36 time: 0.0432 data_time: 0.0060 memory: 1794 loss: 1.2241 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2241 2022/11/28 15:35:50 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:35:50 - mmengine - INFO - Epoch(train) [1][2000/2462] lr: 9.9366e-02 eta: 0:28:27 time: 0.0431 data_time: 0.0059 memory: 1794 loss: 1.2160 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2160 2022/11/28 15:35:54 - mmengine - INFO - Epoch(train) [1][2100/2462] lr: 9.9301e-02 eta: 0:28:18 time: 0.0429 data_time: 0.0060 memory: 1794 loss: 1.1727 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1727 2022/11/28 15:35:58 - mmengine - INFO - Epoch(train) [1][2200/2462] lr: 9.9233e-02 eta: 0:28:10 time: 0.0432 data_time: 0.0059 memory: 1794 loss: 1.2138 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2138 2022/11/28 15:36:03 - mmengine - INFO - Epoch(train) [1][2300/2462] lr: 9.9162e-02 eta: 0:28:03 time: 0.0440 data_time: 0.0060 memory: 1794 loss: 1.0407 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0407 2022/11/28 15:36:07 - mmengine - INFO - Epoch(train) [1][2400/2462] lr: 9.9088e-02 eta: 0:27:55 time: 0.0428 data_time: 0.0060 memory: 1794 loss: 1.1022 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1022 2022/11/28 15:36:10 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:36:10 - mmengine - INFO - Epoch(train) [1][2462/2462] lr: 9.9040e-02 eta: 0:27:51 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 1.1990 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1990 2022/11/28 15:36:10 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/11/28 15:36:14 - mmengine - INFO - Epoch(val) [1][100/398] eta: 0:00:10 time: 0.0327 data_time: 0.0195 memory: 364 2022/11/28 15:36:17 - mmengine - INFO - Epoch(val) [1][200/398] eta: 0:00:06 time: 0.0242 data_time: 0.0112 memory: 364 2022/11/28 15:36:19 - mmengine - INFO - Epoch(val) [1][300/398] eta: 0:00:02 time: 0.0253 data_time: 0.0132 memory: 364 2022/11/28 15:36:23 - mmengine - INFO - Epoch(val) [1][398/398] acc/top1: 0.5336 acc/top5: 0.8308 acc/mean1: 0.5777 2022/11/28 15:36:23 - mmengine - INFO - The best checkpoint with 0.5336 acc/top1 at 1 epoch is saved to best_acc/top1_epoch_1.pth. 2022/11/28 15:36:28 - mmengine - INFO - Epoch(train) [2][100/2462] lr: 9.8961e-02 eta: 0:27:47 time: 0.0438 data_time: 0.0064 memory: 1794 loss: 1.0955 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0955 2022/11/28 15:36:32 - mmengine - INFO - Epoch(train) [2][200/2462] lr: 9.8878e-02 eta: 0:27:40 time: 0.0440 data_time: 0.0067 memory: 1794 loss: 1.0857 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0857 2022/11/28 15:36:37 - mmengine - INFO - Epoch(train) [2][300/2462] lr: 9.8793e-02 eta: 0:27:34 time: 0.0435 data_time: 0.0061 memory: 1794 loss: 1.1464 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1464 2022/11/28 15:36:41 - mmengine - INFO - Epoch(train) [2][400/2462] lr: 9.8704e-02 eta: 0:27:28 time: 0.0436 data_time: 0.0063 memory: 1794 loss: 1.0640 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0640 2022/11/28 15:36:45 - mmengine - INFO - Epoch(train) [2][500/2462] lr: 9.8612e-02 eta: 0:27:23 time: 0.0438 data_time: 0.0061 memory: 1794 loss: 1.1601 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1601 2022/11/28 15:36:47 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:36:50 - mmengine - INFO - Epoch(train) [2][600/2462] lr: 9.8518e-02 eta: 0:27:16 time: 0.0433 data_time: 0.0061 memory: 1794 loss: 1.1073 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1073 2022/11/28 15:36:54 - mmengine - INFO - Epoch(train) [2][700/2462] lr: 9.8420e-02 eta: 0:27:11 time: 0.0459 data_time: 0.0066 memory: 1794 loss: 1.0845 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0845 2022/11/28 15:36:59 - mmengine - INFO - Epoch(train) [2][800/2462] lr: 9.8319e-02 eta: 0:27:05 time: 0.0437 data_time: 0.0061 memory: 1794 loss: 1.0259 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0259 2022/11/28 15:37:03 - mmengine - INFO - Epoch(train) [2][900/2462] lr: 9.8215e-02 eta: 0:27:00 time: 0.0439 data_time: 0.0061 memory: 1794 loss: 1.0444 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0444 2022/11/28 15:37:07 - mmengine - INFO - Epoch(train) [2][1000/2462] lr: 9.8107e-02 eta: 0:26:54 time: 0.0437 data_time: 0.0061 memory: 1794 loss: 1.0887 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0887 2022/11/28 15:37:12 - mmengine - INFO - Epoch(train) [2][1100/2462] lr: 9.7997e-02 eta: 0:26:49 time: 0.0442 data_time: 0.0069 memory: 1794 loss: 1.0701 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0701 2022/11/28 15:37:16 - mmengine - INFO - Epoch(train) [2][1200/2462] lr: 9.7884e-02 eta: 0:26:44 time: 0.0439 data_time: 0.0061 memory: 1794 loss: 0.9454 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 0.9454 2022/11/28 15:37:21 - mmengine - INFO - Epoch(train) [2][1300/2462] lr: 9.7768e-02 eta: 0:26:38 time: 0.0431 data_time: 0.0060 memory: 1794 loss: 0.9037 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 0.9037 2022/11/28 15:37:25 - mmengine - INFO - Epoch(train) [2][1400/2462] lr: 9.7648e-02 eta: 0:26:33 time: 0.0434 data_time: 0.0062 memory: 1794 loss: 0.8934 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8934 2022/11/28 15:37:29 - mmengine - INFO - Epoch(train) [2][1500/2462] lr: 9.7526e-02 eta: 0:26:27 time: 0.0430 data_time: 0.0062 memory: 1794 loss: 0.9115 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9115 2022/11/28 15:37:31 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:37:34 - mmengine - INFO - Epoch(train) [2][1600/2462] lr: 9.7400e-02 eta: 0:26:22 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.9661 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9661 2022/11/28 15:37:38 - mmengine - INFO - Epoch(train) [2][1700/2462] lr: 9.7272e-02 eta: 0:26:17 time: 0.0430 data_time: 0.0061 memory: 1794 loss: 0.9590 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9590 2022/11/28 15:37:43 - mmengine - INFO - Epoch(train) [2][1800/2462] lr: 9.7141e-02 eta: 0:26:12 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.7826 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7826 2022/11/28 15:37:47 - mmengine - INFO - Epoch(train) [2][1900/2462] lr: 9.7006e-02 eta: 0:26:07 time: 0.0458 data_time: 0.0077 memory: 1794 loss: 1.0632 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0632 2022/11/28 15:37:51 - mmengine - INFO - Epoch(train) [2][2000/2462] lr: 9.6869e-02 eta: 0:26:02 time: 0.0431 data_time: 0.0061 memory: 1794 loss: 0.8689 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8689 2022/11/28 15:37:56 - mmengine - INFO - Epoch(train) [2][2100/2462] lr: 9.6728e-02 eta: 0:25:57 time: 0.0439 data_time: 0.0061 memory: 1794 loss: 0.8992 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8992 2022/11/28 15:38:00 - mmengine - INFO - Epoch(train) [2][2200/2462] lr: 9.6585e-02 eta: 0:25:52 time: 0.0438 data_time: 0.0060 memory: 1794 loss: 0.9108 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9108 2022/11/28 15:38:05 - mmengine - INFO - Epoch(train) [2][2300/2462] lr: 9.6439e-02 eta: 0:25:47 time: 0.0439 data_time: 0.0063 memory: 1794 loss: 0.9912 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9912 2022/11/28 15:38:09 - mmengine - INFO - Epoch(train) [2][2400/2462] lr: 9.6290e-02 eta: 0:25:42 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.8292 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8292 2022/11/28 15:38:12 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:38:12 - mmengine - INFO - Epoch(train) [2][2462/2462] lr: 9.6196e-02 eta: 0:25:38 time: 0.0432 data_time: 0.0063 memory: 1794 loss: 0.9078 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9078 2022/11/28 15:38:12 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/11/28 15:38:16 - mmengine - INFO - Epoch(val) [2][100/398] eta: 0:00:10 time: 0.0328 data_time: 0.0195 memory: 364 2022/11/28 15:38:18 - mmengine - INFO - Epoch(val) [2][200/398] eta: 0:00:06 time: 0.0238 data_time: 0.0108 memory: 364 2022/11/28 15:38:21 - mmengine - INFO - Epoch(val) [2][300/398] eta: 0:00:02 time: 0.0249 data_time: 0.0126 memory: 364 2022/11/28 15:38:24 - mmengine - INFO - Epoch(val) [2][398/398] acc/top1: 0.5856 acc/top5: 0.8608 acc/mean1: 0.6000 2022/11/28 15:38:24 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_1.pth is removed 2022/11/28 15:38:25 - mmengine - INFO - The best checkpoint with 0.5856 acc/top1 at 2 epoch is saved to best_acc/top1_epoch_2.pth. 2022/11/28 15:38:28 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:38:29 - mmengine - INFO - Epoch(train) [3][100/2462] lr: 9.6041e-02 eta: 0:25:34 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.8282 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8282 2022/11/28 15:38:34 - mmengine - INFO - Epoch(train) [3][200/2462] lr: 9.5884e-02 eta: 0:25:29 time: 0.0442 data_time: 0.0061 memory: 1794 loss: 0.9185 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9185 2022/11/28 15:38:38 - mmengine - INFO - Epoch(train) [3][300/2462] lr: 9.5725e-02 eta: 0:25:24 time: 0.0430 data_time: 0.0061 memory: 1794 loss: 0.9392 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9392 2022/11/28 15:38:42 - mmengine - INFO - Epoch(train) [3][400/2462] lr: 9.5562e-02 eta: 0:25:19 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.8031 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8031 2022/11/28 15:38:47 - mmengine - INFO - Epoch(train) [3][500/2462] lr: 9.5396e-02 eta: 0:25:14 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.7958 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7958 2022/11/28 15:38:51 - mmengine - INFO - Epoch(train) [3][600/2462] lr: 9.5228e-02 eta: 0:25:09 time: 0.0436 data_time: 0.0066 memory: 1794 loss: 0.8350 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8350 2022/11/28 15:38:55 - mmengine - INFO - Epoch(train) [3][700/2462] lr: 9.5056e-02 eta: 0:25:04 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.7739 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7739 2022/11/28 15:39:00 - mmengine - INFO - Epoch(train) [3][800/2462] lr: 9.4882e-02 eta: 0:24:59 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 0.7697 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7697 2022/11/28 15:39:04 - mmengine - INFO - Epoch(train) [3][900/2462] lr: 9.4705e-02 eta: 0:24:54 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.7929 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7929 2022/11/28 15:39:08 - mmengine - INFO - Epoch(train) [3][1000/2462] lr: 9.4525e-02 eta: 0:24:49 time: 0.0450 data_time: 0.0071 memory: 1794 loss: 0.8382 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8382 2022/11/28 15:39:12 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:39:13 - mmengine - INFO - Epoch(train) [3][1100/2462] lr: 9.4342e-02 eta: 0:24:45 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.7739 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7739 2022/11/28 15:39:17 - mmengine - INFO - Epoch(train) [3][1200/2462] lr: 9.4156e-02 eta: 0:24:40 time: 0.0441 data_time: 0.0066 memory: 1794 loss: 0.7271 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7271 2022/11/28 15:39:22 - mmengine - INFO - Epoch(train) [3][1300/2462] lr: 9.3968e-02 eta: 0:24:35 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 0.7482 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7482 2022/11/28 15:39:26 - mmengine - INFO - Epoch(train) [3][1400/2462] lr: 9.3776e-02 eta: 0:24:30 time: 0.0433 data_time: 0.0061 memory: 1794 loss: 0.7347 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.7347 2022/11/28 15:39:30 - mmengine - INFO - Epoch(train) [3][1500/2462] lr: 9.3582e-02 eta: 0:24:25 time: 0.0433 data_time: 0.0061 memory: 1794 loss: 0.8722 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8722 2022/11/28 15:39:35 - mmengine - INFO - Epoch(train) [3][1600/2462] lr: 9.3385e-02 eta: 0:24:21 time: 0.0455 data_time: 0.0068 memory: 1794 loss: 0.8447 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8447 2022/11/28 15:39:39 - mmengine - INFO - Epoch(train) [3][1700/2462] lr: 9.3186e-02 eta: 0:24:16 time: 0.0443 data_time: 0.0067 memory: 1794 loss: 0.8229 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.8229 2022/11/28 15:39:44 - mmengine - INFO - Epoch(train) [3][1800/2462] lr: 9.2983e-02 eta: 0:24:11 time: 0.0454 data_time: 0.0076 memory: 1794 loss: 0.7323 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 0.7323 2022/11/28 15:39:48 - mmengine - INFO - Epoch(train) [3][1900/2462] lr: 9.2778e-02 eta: 0:24:06 time: 0.0437 data_time: 0.0065 memory: 1794 loss: 0.8381 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8381 2022/11/28 15:39:52 - mmengine - INFO - Epoch(train) [3][2000/2462] lr: 9.2571e-02 eta: 0:24:02 time: 0.0441 data_time: 0.0067 memory: 1794 loss: 0.6796 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6796 2022/11/28 15:39:56 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:39:57 - mmengine - INFO - Epoch(train) [3][2100/2462] lr: 9.2360e-02 eta: 0:23:57 time: 0.0436 data_time: 0.0064 memory: 1794 loss: 0.8770 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8770 2022/11/28 15:40:01 - mmengine - INFO - Epoch(train) [3][2200/2462] lr: 9.2147e-02 eta: 0:23:52 time: 0.0438 data_time: 0.0061 memory: 1794 loss: 0.8375 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8375 2022/11/28 15:40:06 - mmengine - INFO - Epoch(train) [3][2300/2462] lr: 9.1931e-02 eta: 0:23:48 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.8461 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8461 2022/11/28 15:40:10 - mmengine - INFO - Epoch(train) [3][2400/2462] lr: 9.1713e-02 eta: 0:23:43 time: 0.0440 data_time: 0.0064 memory: 1794 loss: 0.7468 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7468 2022/11/28 15:40:13 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:40:13 - mmengine - INFO - Epoch(train) [3][2462/2462] lr: 9.1576e-02 eta: 0:23:40 time: 0.0439 data_time: 0.0063 memory: 1794 loss: 0.7862 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7862 2022/11/28 15:40:13 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/11/28 15:40:17 - mmengine - INFO - Epoch(val) [3][100/398] eta: 0:00:10 time: 0.0363 data_time: 0.0230 memory: 364 2022/11/28 15:40:20 - mmengine - INFO - Epoch(val) [3][200/398] eta: 0:00:06 time: 0.0245 data_time: 0.0114 memory: 364 2022/11/28 15:40:22 - mmengine - INFO - Epoch(val) [3][300/398] eta: 0:00:02 time: 0.0253 data_time: 0.0128 memory: 364 2022/11/28 15:40:26 - mmengine - INFO - Epoch(val) [3][398/398] acc/top1: 0.6468 acc/top5: 0.9040 acc/mean1: 0.6694 2022/11/28 15:40:26 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_2.pth is removed 2022/11/28 15:40:26 - mmengine - INFO - The best checkpoint with 0.6468 acc/top1 at 3 epoch is saved to best_acc/top1_epoch_3.pth. 2022/11/28 15:40:31 - mmengine - INFO - Epoch(train) [4][100/2462] lr: 9.1353e-02 eta: 0:23:37 time: 0.0444 data_time: 0.0067 memory: 1794 loss: 0.7010 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7010 2022/11/28 15:40:35 - mmengine - INFO - Epoch(train) [4][200/2462] lr: 9.1127e-02 eta: 0:23:32 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.7803 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7803 2022/11/28 15:40:39 - mmengine - INFO - Epoch(train) [4][300/2462] lr: 9.0899e-02 eta: 0:23:27 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.6859 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6859 2022/11/28 15:40:44 - mmengine - INFO - Epoch(train) [4][400/2462] lr: 9.0669e-02 eta: 0:23:22 time: 0.0431 data_time: 0.0061 memory: 1794 loss: 0.7021 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7021 2022/11/28 15:40:48 - mmengine - INFO - Epoch(train) [4][500/2462] lr: 9.0435e-02 eta: 0:23:18 time: 0.0435 data_time: 0.0065 memory: 1794 loss: 0.6725 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6725 2022/11/28 15:40:52 - mmengine - INFO - Epoch(train) [4][600/2462] lr: 9.0200e-02 eta: 0:23:13 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 0.7025 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7025 2022/11/28 15:40:53 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:40:57 - mmengine - INFO - Epoch(train) [4][700/2462] lr: 8.9961e-02 eta: 0:23:08 time: 0.0429 data_time: 0.0061 memory: 1794 loss: 0.7459 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7459 2022/11/28 15:41:01 - mmengine - INFO - Epoch(train) [4][800/2462] lr: 8.9720e-02 eta: 0:23:03 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 0.8753 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8753 2022/11/28 15:41:05 - mmengine - INFO - Epoch(train) [4][900/2462] lr: 8.9477e-02 eta: 0:22:58 time: 0.0435 data_time: 0.0061 memory: 1794 loss: 0.7530 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7530 2022/11/28 15:41:10 - mmengine - INFO - Epoch(train) [4][1000/2462] lr: 8.9231e-02 eta: 0:22:54 time: 0.0445 data_time: 0.0066 memory: 1794 loss: 0.7767 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7767 2022/11/28 15:41:14 - mmengine - INFO - Epoch(train) [4][1100/2462] lr: 8.8982e-02 eta: 0:22:49 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.7905 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7905 2022/11/28 15:41:18 - mmengine - INFO - Epoch(train) [4][1200/2462] lr: 8.8731e-02 eta: 0:22:44 time: 0.0433 data_time: 0.0061 memory: 1794 loss: 0.7272 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7272 2022/11/28 15:41:23 - mmengine - INFO - Epoch(train) [4][1300/2462] lr: 8.8478e-02 eta: 0:22:40 time: 0.0435 data_time: 0.0061 memory: 1794 loss: 0.7035 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7035 2022/11/28 15:41:27 - mmengine - INFO - Epoch(train) [4][1400/2462] lr: 8.8222e-02 eta: 0:22:35 time: 0.0440 data_time: 0.0067 memory: 1794 loss: 0.7841 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7841 2022/11/28 15:41:32 - mmengine - INFO - Epoch(train) [4][1500/2462] lr: 8.7964e-02 eta: 0:22:31 time: 0.0442 data_time: 0.0067 memory: 1794 loss: 0.7326 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7326 2022/11/28 15:41:36 - mmengine - INFO - Epoch(train) [4][1600/2462] lr: 8.7703e-02 eta: 0:22:26 time: 0.0436 data_time: 0.0067 memory: 1794 loss: 0.6081 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.6081 2022/11/28 15:41:37 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:41:40 - mmengine - INFO - Epoch(train) [4][1700/2462] lr: 8.7440e-02 eta: 0:22:22 time: 0.0431 data_time: 0.0063 memory: 1794 loss: 0.6191 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.6191 2022/11/28 15:41:45 - mmengine - INFO - Epoch(train) [4][1800/2462] lr: 8.7174e-02 eta: 0:22:17 time: 0.0428 data_time: 0.0062 memory: 1794 loss: 0.6990 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6990 2022/11/28 15:41:49 - mmengine - INFO - Epoch(train) [4][1900/2462] lr: 8.6907e-02 eta: 0:22:12 time: 0.0435 data_time: 0.0064 memory: 1794 loss: 0.5806 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5806 2022/11/28 15:41:54 - mmengine - INFO - Epoch(train) [4][2000/2462] lr: 8.6636e-02 eta: 0:22:08 time: 0.0439 data_time: 0.0068 memory: 1794 loss: 0.6461 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6461 2022/11/28 15:41:58 - mmengine - INFO - Epoch(train) [4][2100/2462] lr: 8.6364e-02 eta: 0:22:03 time: 0.0438 data_time: 0.0061 memory: 1794 loss: 0.6752 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6752 2022/11/28 15:42:02 - mmengine - INFO - Epoch(train) [4][2200/2462] lr: 8.6089e-02 eta: 0:21:59 time: 0.0439 data_time: 0.0060 memory: 1794 loss: 0.7459 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7459 2022/11/28 15:42:07 - mmengine - INFO - Epoch(train) [4][2300/2462] lr: 8.5812e-02 eta: 0:21:54 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.6257 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6257 2022/11/28 15:42:11 - mmengine - INFO - Epoch(train) [4][2400/2462] lr: 8.5533e-02 eta: 0:21:50 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.7571 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7571 2022/11/28 15:42:14 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:42:14 - mmengine - INFO - Epoch(train) [4][2462/2462] lr: 8.5358e-02 eta: 0:21:47 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.5972 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5972 2022/11/28 15:42:14 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/11/28 15:42:18 - mmengine - INFO - Epoch(val) [4][100/398] eta: 0:00:10 time: 0.0336 data_time: 0.0198 memory: 364 2022/11/28 15:42:21 - mmengine - INFO - Epoch(val) [4][200/398] eta: 0:00:06 time: 0.0244 data_time: 0.0108 memory: 364 2022/11/28 15:42:23 - mmengine - INFO - Epoch(val) [4][300/398] eta: 0:00:02 time: 0.0251 data_time: 0.0128 memory: 364 2022/11/28 15:42:27 - mmengine - INFO - Epoch(val) [4][398/398] acc/top1: 0.7111 acc/top5: 0.9231 acc/mean1: 0.7387 2022/11/28 15:42:27 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_3.pth is removed 2022/11/28 15:42:27 - mmengine - INFO - The best checkpoint with 0.7111 acc/top1 at 4 epoch is saved to best_acc/top1_epoch_4.pth. 2022/11/28 15:42:32 - mmengine - INFO - Epoch(train) [5][100/2462] lr: 8.5075e-02 eta: 0:21:43 time: 0.0439 data_time: 0.0069 memory: 1794 loss: 0.6257 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6257 2022/11/28 15:42:34 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:42:36 - mmengine - INFO - Epoch(train) [5][200/2462] lr: 8.4790e-02 eta: 0:21:39 time: 0.0463 data_time: 0.0065 memory: 1794 loss: 0.7073 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.7073 2022/11/28 15:42:41 - mmengine - INFO - Epoch(train) [5][300/2462] lr: 8.4502e-02 eta: 0:21:34 time: 0.0440 data_time: 0.0071 memory: 1794 loss: 0.6042 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.6042 2022/11/28 15:42:45 - mmengine - INFO - Epoch(train) [5][400/2462] lr: 8.4213e-02 eta: 0:21:30 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.7031 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7031 2022/11/28 15:42:49 - mmengine - INFO - Epoch(train) [5][500/2462] lr: 8.3921e-02 eta: 0:21:25 time: 0.0437 data_time: 0.0061 memory: 1794 loss: 0.7172 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7172 2022/11/28 15:42:54 - mmengine - INFO - Epoch(train) [5][600/2462] lr: 8.3627e-02 eta: 0:21:21 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.6891 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6891 2022/11/28 15:42:58 - mmengine - INFO - Epoch(train) [5][700/2462] lr: 8.3330e-02 eta: 0:21:16 time: 0.0457 data_time: 0.0062 memory: 1794 loss: 0.5922 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5922 2022/11/28 15:43:03 - mmengine - INFO - Epoch(train) [5][800/2462] lr: 8.3032e-02 eta: 0:21:12 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.7262 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7262 2022/11/28 15:43:07 - mmengine - INFO - Epoch(train) [5][900/2462] lr: 8.2732e-02 eta: 0:21:07 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.6620 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6620 2022/11/28 15:43:11 - mmengine - INFO - Epoch(train) [5][1000/2462] lr: 8.2429e-02 eta: 0:21:03 time: 0.0448 data_time: 0.0075 memory: 1794 loss: 0.5629 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5629 2022/11/28 15:43:16 - mmengine - INFO - Epoch(train) [5][1100/2462] lr: 8.2125e-02 eta: 0:20:58 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.6538 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6538 2022/11/28 15:43:18 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:43:20 - mmengine - INFO - Epoch(train) [5][1200/2462] lr: 8.1818e-02 eta: 0:20:54 time: 0.0433 data_time: 0.0063 memory: 1794 loss: 0.6459 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6459 2022/11/28 15:43:25 - mmengine - INFO - Epoch(train) [5][1300/2462] lr: 8.1510e-02 eta: 0:20:49 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.8605 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.8605 2022/11/28 15:43:29 - mmengine - INFO - Epoch(train) [5][1400/2462] lr: 8.1199e-02 eta: 0:20:45 time: 0.0434 data_time: 0.0061 memory: 1794 loss: 0.5865 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5865 2022/11/28 15:43:33 - mmengine - INFO - Epoch(train) [5][1500/2462] lr: 8.0886e-02 eta: 0:20:40 time: 0.0433 data_time: 0.0063 memory: 1794 loss: 0.6746 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.6746 2022/11/28 15:43:38 - mmengine - INFO - Epoch(train) [5][1600/2462] lr: 8.0572e-02 eta: 0:20:36 time: 0.0436 data_time: 0.0066 memory: 1794 loss: 0.5195 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5195 2022/11/28 15:43:42 - mmengine - INFO - Epoch(train) [5][1700/2462] lr: 8.0255e-02 eta: 0:20:31 time: 0.0464 data_time: 0.0074 memory: 1794 loss: 0.6108 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6108 2022/11/28 15:43:47 - mmengine - INFO - Epoch(train) [5][1800/2462] lr: 7.9937e-02 eta: 0:20:27 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.5562 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5562 2022/11/28 15:43:51 - mmengine - INFO - Epoch(train) [5][1900/2462] lr: 7.9617e-02 eta: 0:20:22 time: 0.0430 data_time: 0.0061 memory: 1794 loss: 0.6397 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6397 2022/11/28 15:43:55 - mmengine - INFO - Epoch(train) [5][2000/2462] lr: 7.9294e-02 eta: 0:20:18 time: 0.0454 data_time: 0.0078 memory: 1794 loss: 0.7059 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7059 2022/11/28 15:44:00 - mmengine - INFO - Epoch(train) [5][2100/2462] lr: 7.8970e-02 eta: 0:20:13 time: 0.0434 data_time: 0.0062 memory: 1794 loss: 0.5690 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5690 2022/11/28 15:44:02 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:44:04 - mmengine - INFO - Epoch(train) [5][2200/2462] lr: 7.8644e-02 eta: 0:20:09 time: 0.0438 data_time: 0.0065 memory: 1794 loss: 0.5888 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.5888 2022/11/28 15:44:09 - mmengine - INFO - Epoch(train) [5][2300/2462] lr: 7.8317e-02 eta: 0:20:04 time: 0.0437 data_time: 0.0063 memory: 1794 loss: 0.5374 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5374 2022/11/28 15:44:13 - mmengine - INFO - Epoch(train) [5][2400/2462] lr: 7.7987e-02 eta: 0:20:00 time: 0.0436 data_time: 0.0061 memory: 1794 loss: 0.6909 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6909 2022/11/28 15:44:16 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:44:16 - mmengine - INFO - Epoch(train) [5][2462/2462] lr: 7.7782e-02 eta: 0:19:57 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.6075 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6075 2022/11/28 15:44:16 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/11/28 15:44:19 - mmengine - INFO - Epoch(val) [5][100/398] eta: 0:00:10 time: 0.0322 data_time: 0.0190 memory: 364 2022/11/28 15:44:22 - mmengine - INFO - Epoch(val) [5][200/398] eta: 0:00:06 time: 0.0236 data_time: 0.0107 memory: 364 2022/11/28 15:44:25 - mmengine - INFO - Epoch(val) [5][300/398] eta: 0:00:02 time: 0.0247 data_time: 0.0124 memory: 364 2022/11/28 15:44:28 - mmengine - INFO - Epoch(val) [5][398/398] acc/top1: 0.7048 acc/top5: 0.9222 acc/mean1: 0.7186 2022/11/28 15:44:33 - mmengine - INFO - Epoch(train) [6][100/2462] lr: 7.7449e-02 eta: 0:19:53 time: 0.0440 data_time: 0.0064 memory: 1794 loss: 0.5492 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5492 2022/11/28 15:44:37 - mmengine - INFO - Epoch(train) [6][200/2462] lr: 7.7115e-02 eta: 0:19:48 time: 0.0435 data_time: 0.0065 memory: 1794 loss: 0.6241 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.6241 2022/11/28 15:44:41 - mmengine - INFO - Epoch(train) [6][300/2462] lr: 7.6779e-02 eta: 0:19:44 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.6344 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6344 2022/11/28 15:44:46 - mmengine - INFO - Epoch(train) [6][400/2462] lr: 7.6442e-02 eta: 0:19:39 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.6248 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6248 2022/11/28 15:44:50 - mmengine - INFO - Epoch(train) [6][500/2462] lr: 7.6102e-02 eta: 0:19:35 time: 0.0446 data_time: 0.0064 memory: 1794 loss: 0.6913 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6913 2022/11/28 15:44:55 - mmengine - INFO - Epoch(train) [6][600/2462] lr: 7.5762e-02 eta: 0:19:31 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.5824 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5824 2022/11/28 15:44:59 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:44:59 - mmengine - INFO - Epoch(train) [6][700/2462] lr: 7.5419e-02 eta: 0:19:26 time: 0.0437 data_time: 0.0061 memory: 1794 loss: 0.6848 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6848 2022/11/28 15:45:04 - mmengine - INFO - Epoch(train) [6][800/2462] lr: 7.5075e-02 eta: 0:19:22 time: 0.0431 data_time: 0.0061 memory: 1794 loss: 0.7628 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7628 2022/11/28 15:45:08 - mmengine - INFO - Epoch(train) [6][900/2462] lr: 7.4729e-02 eta: 0:19:17 time: 0.0433 data_time: 0.0061 memory: 1794 loss: 0.6609 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6609 2022/11/28 15:45:13 - mmengine - INFO - Epoch(train) [6][1000/2462] lr: 7.4382e-02 eta: 0:19:13 time: 0.0438 data_time: 0.0068 memory: 1794 loss: 0.6382 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.6382 2022/11/28 15:45:17 - mmengine - INFO - Epoch(train) [6][1100/2462] lr: 7.4033e-02 eta: 0:19:09 time: 0.0439 data_time: 0.0066 memory: 1794 loss: 0.5627 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5627 2022/11/28 15:45:21 - mmengine - INFO - Epoch(train) [6][1200/2462] lr: 7.3682e-02 eta: 0:19:04 time: 0.0457 data_time: 0.0067 memory: 1794 loss: 0.6048 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6048 2022/11/28 15:45:26 - mmengine - INFO - Epoch(train) [6][1300/2462] lr: 7.3330e-02 eta: 0:19:00 time: 0.0451 data_time: 0.0071 memory: 1794 loss: 0.6056 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.6056 2022/11/28 15:45:30 - mmengine - INFO - Epoch(train) [6][1400/2462] lr: 7.2977e-02 eta: 0:18:56 time: 0.0439 data_time: 0.0072 memory: 1794 loss: 0.7164 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7164 2022/11/28 15:45:35 - mmengine - INFO - Epoch(train) [6][1500/2462] lr: 7.2622e-02 eta: 0:18:51 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.5457 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5457 2022/11/28 15:45:39 - mmengine - INFO - Epoch(train) [6][1600/2462] lr: 7.2266e-02 eta: 0:18:47 time: 0.0442 data_time: 0.0066 memory: 1794 loss: 0.5406 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5406 2022/11/28 15:45:43 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:45:44 - mmengine - INFO - Epoch(train) [6][1700/2462] lr: 7.1908e-02 eta: 0:18:42 time: 0.0436 data_time: 0.0068 memory: 1794 loss: 0.6183 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6183 2022/11/28 15:45:48 - mmengine - INFO - Epoch(train) [6][1800/2462] lr: 7.1549e-02 eta: 0:18:38 time: 0.0442 data_time: 0.0064 memory: 1794 loss: 0.5476 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5476 2022/11/28 15:45:52 - mmengine - INFO - Epoch(train) [6][1900/2462] lr: 7.1188e-02 eta: 0:18:33 time: 0.0435 data_time: 0.0060 memory: 1794 loss: 0.5901 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5901 2022/11/28 15:45:57 - mmengine - INFO - Epoch(train) [6][2000/2462] lr: 7.0826e-02 eta: 0:18:29 time: 0.0443 data_time: 0.0062 memory: 1794 loss: 0.6310 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.6310 2022/11/28 15:46:01 - mmengine - INFO - Epoch(train) [6][2100/2462] lr: 7.0463e-02 eta: 0:18:24 time: 0.0436 data_time: 0.0066 memory: 1794 loss: 0.5954 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.5954 2022/11/28 15:46:06 - mmengine - INFO - Epoch(train) [6][2200/2462] lr: 7.0099e-02 eta: 0:18:20 time: 0.0436 data_time: 0.0063 memory: 1794 loss: 0.6055 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6055 2022/11/28 15:46:10 - mmengine - INFO - Epoch(train) [6][2300/2462] lr: 6.9733e-02 eta: 0:18:16 time: 0.0435 data_time: 0.0061 memory: 1794 loss: 0.5326 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5326 2022/11/28 15:46:14 - mmengine - INFO - Epoch(train) [6][2400/2462] lr: 6.9366e-02 eta: 0:18:11 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.6661 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6661 2022/11/28 15:46:17 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:46:17 - mmengine - INFO - Epoch(train) [6][2462/2462] lr: 6.9138e-02 eta: 0:18:08 time: 0.0448 data_time: 0.0070 memory: 1794 loss: 0.4943 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4943 2022/11/28 15:46:17 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/11/28 15:46:21 - mmengine - INFO - Epoch(val) [6][100/398] eta: 0:00:09 time: 0.0325 data_time: 0.0191 memory: 364 2022/11/28 15:46:24 - mmengine - INFO - Epoch(val) [6][200/398] eta: 0:00:06 time: 0.0241 data_time: 0.0110 memory: 364 2022/11/28 15:46:26 - mmengine - INFO - Epoch(val) [6][300/398] eta: 0:00:02 time: 0.0242 data_time: 0.0121 memory: 364 2022/11/28 15:46:30 - mmengine - INFO - Epoch(val) [6][398/398] acc/top1: 0.7222 acc/top5: 0.9261 acc/mean1: 0.7413 2022/11/28 15:46:30 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_4.pth is removed 2022/11/28 15:46:30 - mmengine - INFO - The best checkpoint with 0.7222 acc/top1 at 6 epoch is saved to best_acc/top1_epoch_6.pth. 2022/11/28 15:46:35 - mmengine - INFO - Epoch(train) [7][100/2462] lr: 6.8769e-02 eta: 0:18:04 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.5763 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5763 2022/11/28 15:46:39 - mmengine - INFO - Epoch(train) [7][200/2462] lr: 6.8399e-02 eta: 0:18:00 time: 0.0447 data_time: 0.0066 memory: 1794 loss: 0.4999 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4999 2022/11/28 15:46:40 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:46:43 - mmengine - INFO - Epoch(train) [7][300/2462] lr: 6.8027e-02 eta: 0:17:55 time: 0.0445 data_time: 0.0065 memory: 1794 loss: 0.5424 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5424 2022/11/28 15:46:48 - mmengine - INFO - Epoch(train) [7][400/2462] lr: 6.7655e-02 eta: 0:17:51 time: 0.0431 data_time: 0.0061 memory: 1794 loss: 0.5526 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5526 2022/11/28 15:46:52 - mmengine - INFO - Epoch(train) [7][500/2462] lr: 6.7281e-02 eta: 0:17:46 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.4985 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4985 2022/11/28 15:46:57 - mmengine - INFO - Epoch(train) [7][600/2462] lr: 6.6906e-02 eta: 0:17:42 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.6489 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6489 2022/11/28 15:47:01 - mmengine - INFO - Epoch(train) [7][700/2462] lr: 6.6531e-02 eta: 0:17:37 time: 0.0439 data_time: 0.0061 memory: 1794 loss: 0.5626 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5626 2022/11/28 15:47:05 - mmengine - INFO - Epoch(train) [7][800/2462] lr: 6.6154e-02 eta: 0:17:33 time: 0.0452 data_time: 0.0061 memory: 1794 loss: 0.6483 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6483 2022/11/28 15:47:10 - mmengine - INFO - Epoch(train) [7][900/2462] lr: 6.5776e-02 eta: 0:17:29 time: 0.0444 data_time: 0.0065 memory: 1794 loss: 0.5730 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5730 2022/11/28 15:47:14 - mmengine - INFO - Epoch(train) [7][1000/2462] lr: 6.5397e-02 eta: 0:17:24 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.4642 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4642 2022/11/28 15:47:19 - mmengine - INFO - Epoch(train) [7][1100/2462] lr: 6.5017e-02 eta: 0:17:20 time: 0.0436 data_time: 0.0060 memory: 1794 loss: 0.4770 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.4770 2022/11/28 15:47:23 - mmengine - INFO - Epoch(train) [7][1200/2462] lr: 6.4636e-02 eta: 0:17:15 time: 0.0435 data_time: 0.0061 memory: 1794 loss: 0.5788 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5788 2022/11/28 15:47:24 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:47:28 - mmengine - INFO - Epoch(train) [7][1300/2462] lr: 6.4255e-02 eta: 0:17:11 time: 0.0445 data_time: 0.0062 memory: 1794 loss: 0.4865 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.4865 2022/11/28 15:47:32 - mmengine - INFO - Epoch(train) [7][1400/2462] lr: 6.3872e-02 eta: 0:17:07 time: 0.0459 data_time: 0.0063 memory: 1794 loss: 0.4922 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.4922 2022/11/28 15:47:37 - mmengine - INFO - Epoch(train) [7][1500/2462] lr: 6.3488e-02 eta: 0:17:02 time: 0.0458 data_time: 0.0065 memory: 1794 loss: 0.4693 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4693 2022/11/28 15:47:41 - mmengine - INFO - Epoch(train) [7][1600/2462] lr: 6.3104e-02 eta: 0:16:58 time: 0.0456 data_time: 0.0069 memory: 1794 loss: 0.4618 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.4618 2022/11/28 15:47:46 - mmengine - INFO - Epoch(train) [7][1700/2462] lr: 6.2719e-02 eta: 0:16:54 time: 0.0438 data_time: 0.0061 memory: 1794 loss: 0.6528 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.6528 2022/11/28 15:47:50 - mmengine - INFO - Epoch(train) [7][1800/2462] lr: 6.2333e-02 eta: 0:16:49 time: 0.0437 data_time: 0.0061 memory: 1794 loss: 0.6583 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6583 2022/11/28 15:47:55 - mmengine - INFO - Epoch(train) [7][1900/2462] lr: 6.1946e-02 eta: 0:16:45 time: 0.0440 data_time: 0.0068 memory: 1794 loss: 0.4001 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4001 2022/11/28 15:47:59 - mmengine - INFO - Epoch(train) [7][2000/2462] lr: 6.1558e-02 eta: 0:16:40 time: 0.0433 data_time: 0.0061 memory: 1794 loss: 0.3943 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.3943 2022/11/28 15:48:03 - mmengine - INFO - Epoch(train) [7][2100/2462] lr: 6.1170e-02 eta: 0:16:36 time: 0.0436 data_time: 0.0064 memory: 1794 loss: 0.4685 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.4685 2022/11/28 15:48:08 - mmengine - INFO - Epoch(train) [7][2200/2462] lr: 6.0781e-02 eta: 0:16:31 time: 0.0442 data_time: 0.0063 memory: 1794 loss: 0.5239 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5239 2022/11/28 15:48:09 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:48:12 - mmengine - INFO - Epoch(train) [7][2300/2462] lr: 6.0391e-02 eta: 0:16:27 time: 0.0471 data_time: 0.0066 memory: 1794 loss: 0.3617 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3617 2022/11/28 15:48:17 - mmengine - INFO - Epoch(train) [7][2400/2462] lr: 6.0001e-02 eta: 0:16:23 time: 0.0460 data_time: 0.0063 memory: 1794 loss: 0.4785 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4785 2022/11/28 15:48:19 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:48:19 - mmengine - INFO - Epoch(train) [7][2462/2462] lr: 5.9758e-02 eta: 0:16:20 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.4975 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4975 2022/11/28 15:48:19 - mmengine - INFO - Saving checkpoint at 7 epochs 2022/11/28 15:48:23 - mmengine - INFO - Epoch(val) [7][100/398] eta: 0:00:10 time: 0.0321 data_time: 0.0189 memory: 364 2022/11/28 15:48:26 - mmengine - INFO - Epoch(val) [7][200/398] eta: 0:00:06 time: 0.0242 data_time: 0.0114 memory: 364 2022/11/28 15:48:28 - mmengine - INFO - Epoch(val) [7][300/398] eta: 0:00:02 time: 0.0244 data_time: 0.0120 memory: 364 2022/11/28 15:48:32 - mmengine - INFO - Epoch(val) [7][398/398] acc/top1: 0.7365 acc/top5: 0.9285 acc/mean1: 0.7520 2022/11/28 15:48:32 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_6.pth is removed 2022/11/28 15:48:32 - mmengine - INFO - The best checkpoint with 0.7365 acc/top1 at 7 epoch is saved to best_acc/top1_epoch_7.pth. 2022/11/28 15:48:37 - mmengine - INFO - Epoch(train) [8][100/2462] lr: 5.9367e-02 eta: 0:16:16 time: 0.0444 data_time: 0.0062 memory: 1794 loss: 0.4025 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4025 2022/11/28 15:48:42 - mmengine - INFO - Epoch(train) [8][200/2462] lr: 5.8975e-02 eta: 0:16:11 time: 0.0440 data_time: 0.0063 memory: 1794 loss: 0.5492 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5492 2022/11/28 15:48:46 - mmengine - INFO - Epoch(train) [8][300/2462] lr: 5.8582e-02 eta: 0:16:07 time: 0.0446 data_time: 0.0061 memory: 1794 loss: 0.4641 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4641 2022/11/28 15:48:50 - mmengine - INFO - Epoch(train) [8][400/2462] lr: 5.8189e-02 eta: 0:16:03 time: 0.0444 data_time: 0.0070 memory: 1794 loss: 0.4164 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4164 2022/11/28 15:48:55 - mmengine - INFO - Epoch(train) [8][500/2462] lr: 5.7796e-02 eta: 0:15:58 time: 0.0441 data_time: 0.0061 memory: 1794 loss: 0.4726 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.4726 2022/11/28 15:48:59 - mmengine - INFO - Epoch(train) [8][600/2462] lr: 5.7402e-02 eta: 0:15:54 time: 0.0440 data_time: 0.0061 memory: 1794 loss: 0.5309 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.5309 2022/11/28 15:49:04 - mmengine - INFO - Epoch(train) [8][700/2462] lr: 5.7007e-02 eta: 0:15:49 time: 0.0475 data_time: 0.0061 memory: 1794 loss: 0.5526 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5526 2022/11/28 15:49:07 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:49:08 - mmengine - INFO - Epoch(train) [8][800/2462] lr: 5.6612e-02 eta: 0:15:45 time: 0.0435 data_time: 0.0061 memory: 1794 loss: 0.5316 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.5316 2022/11/28 15:49:13 - mmengine - INFO - Epoch(train) [8][900/2462] lr: 5.6216e-02 eta: 0:15:40 time: 0.0443 data_time: 0.0062 memory: 1794 loss: 0.4965 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4965 2022/11/28 15:49:17 - mmengine - INFO - Epoch(train) [8][1000/2462] lr: 5.5821e-02 eta: 0:15:36 time: 0.0437 data_time: 0.0063 memory: 1794 loss: 0.5357 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.5357 2022/11/28 15:49:21 - mmengine - INFO - Epoch(train) [8][1100/2462] lr: 5.5424e-02 eta: 0:15:31 time: 0.0441 data_time: 0.0073 memory: 1794 loss: 0.5195 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5195 2022/11/28 15:49:26 - mmengine - INFO - Epoch(train) [8][1200/2462] lr: 5.5028e-02 eta: 0:15:27 time: 0.0440 data_time: 0.0061 memory: 1794 loss: 0.4915 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4915 2022/11/28 15:49:30 - mmengine - INFO - Epoch(train) [8][1300/2462] lr: 5.4631e-02 eta: 0:15:23 time: 0.0439 data_time: 0.0067 memory: 1794 loss: 0.3677 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3677 2022/11/28 15:50:06 - mmengine - INFO - Epoch(train) [8][1400/2462] lr: 5.4234e-02 eta: 0:15:53 time: 0.0432 data_time: 0.0061 memory: 1794 loss: 0.4694 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4694 2022/11/28 15:50:10 - mmengine - INFO - Epoch(train) [8][1500/2462] lr: 5.3836e-02 eta: 0:15:48 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.4075 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4075 2022/11/28 15:50:15 - mmengine - INFO - Epoch(train) [8][1600/2462] lr: 5.3439e-02 eta: 0:15:43 time: 0.0446 data_time: 0.0065 memory: 1794 loss: 0.3479 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.3479 2022/11/28 15:50:19 - mmengine - INFO - Epoch(train) [8][1700/2462] lr: 5.3041e-02 eta: 0:15:38 time: 0.0444 data_time: 0.0061 memory: 1794 loss: 0.3810 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3810 2022/11/28 15:50:22 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:50:24 - mmengine - INFO - Epoch(train) [8][1800/2462] lr: 5.2643e-02 eta: 0:15:34 time: 0.0438 data_time: 0.0061 memory: 1794 loss: 0.4789 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4789 2022/11/28 15:50:28 - mmengine - INFO - Epoch(train) [8][1900/2462] lr: 5.2244e-02 eta: 0:15:29 time: 0.0444 data_time: 0.0069 memory: 1794 loss: 0.4152 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4152 2022/11/28 15:50:32 - mmengine - INFO - Epoch(train) [8][2000/2462] lr: 5.1846e-02 eta: 0:15:24 time: 0.0440 data_time: 0.0061 memory: 1794 loss: 0.5281 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5281 2022/11/28 15:50:37 - mmengine - INFO - Epoch(train) [8][2100/2462] lr: 5.1447e-02 eta: 0:15:19 time: 0.0444 data_time: 0.0061 memory: 1794 loss: 0.5036 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5036 2022/11/28 15:50:41 - mmengine - INFO - Epoch(train) [8][2200/2462] lr: 5.1049e-02 eta: 0:15:15 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.4317 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4317 2022/11/28 15:50:46 - mmengine - INFO - Epoch(train) [8][2300/2462] lr: 5.0650e-02 eta: 0:15:10 time: 0.0440 data_time: 0.0061 memory: 1794 loss: 0.3182 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.3182 2022/11/28 15:50:50 - mmengine - INFO - Epoch(train) [8][2400/2462] lr: 5.0251e-02 eta: 0:15:05 time: 0.0440 data_time: 0.0067 memory: 1794 loss: 0.4958 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.4958 2022/11/28 15:50:53 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:50:53 - mmengine - INFO - Epoch(train) [8][2462/2462] lr: 5.0004e-02 eta: 0:15:02 time: 0.0443 data_time: 0.0063 memory: 1794 loss: 0.5049 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5049 2022/11/28 15:50:53 - mmengine - INFO - Saving checkpoint at 8 epochs 2022/11/28 15:50:57 - mmengine - INFO - Epoch(val) [8][100/398] eta: 0:00:10 time: 0.0323 data_time: 0.0190 memory: 364 2022/11/28 15:50:59 - mmengine - INFO - Epoch(val) [8][200/398] eta: 0:00:06 time: 0.0236 data_time: 0.0107 memory: 364 2022/11/28 15:51:02 - mmengine - INFO - Epoch(val) [8][300/398] eta: 0:00:02 time: 0.0265 data_time: 0.0142 memory: 364 2022/11/28 15:51:05 - mmengine - INFO - Epoch(val) [8][398/398] acc/top1: 0.7420 acc/top5: 0.9363 acc/mean1: 0.7678 2022/11/28 15:51:05 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_7.pth is removed 2022/11/28 15:51:06 - mmengine - INFO - The best checkpoint with 0.7420 acc/top1 at 8 epoch is saved to best_acc/top1_epoch_8.pth. 2022/11/28 15:51:10 - mmengine - INFO - Epoch(train) [9][100/2462] lr: 4.9605e-02 eta: 0:14:58 time: 0.0444 data_time: 0.0062 memory: 1794 loss: 0.4932 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4932 2022/11/28 15:51:15 - mmengine - INFO - Epoch(train) [9][200/2462] lr: 4.9207e-02 eta: 0:14:53 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.4157 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4157 2022/11/28 15:51:19 - mmengine - INFO - Epoch(train) [9][300/2462] lr: 4.8808e-02 eta: 0:14:48 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.4068 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.4068 2022/11/28 15:51:19 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:51:23 - mmengine - INFO - Epoch(train) [9][400/2462] lr: 4.8409e-02 eta: 0:14:43 time: 0.0431 data_time: 0.0061 memory: 1794 loss: 0.4010 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.4010 2022/11/28 15:51:28 - mmengine - INFO - Epoch(train) [9][500/2462] lr: 4.8011e-02 eta: 0:14:39 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.4684 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.4684 2022/11/28 15:51:32 - mmengine - INFO - Epoch(train) [9][600/2462] lr: 4.7612e-02 eta: 0:14:34 time: 0.0448 data_time: 0.0063 memory: 1794 loss: 0.4425 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4425 2022/11/28 15:51:37 - mmengine - INFO - Epoch(train) [9][700/2462] lr: 4.7214e-02 eta: 0:14:29 time: 0.0434 data_time: 0.0061 memory: 1794 loss: 0.3652 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.3652 2022/11/28 15:51:41 - mmengine - INFO - Epoch(train) [9][800/2462] lr: 4.6816e-02 eta: 0:14:24 time: 0.0442 data_time: 0.0073 memory: 1794 loss: 0.4684 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4684 2022/11/28 15:51:46 - mmengine - INFO - Epoch(train) [9][900/2462] lr: 4.6418e-02 eta: 0:14:20 time: 0.0435 data_time: 0.0067 memory: 1794 loss: 0.4478 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4478 2022/11/28 15:51:50 - mmengine - INFO - Epoch(train) [9][1000/2462] lr: 4.6021e-02 eta: 0:14:15 time: 0.0440 data_time: 0.0066 memory: 1794 loss: 0.3576 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3576 2022/11/28 15:51:54 - mmengine - INFO - Epoch(train) [9][1100/2462] lr: 4.5623e-02 eta: 0:14:10 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.3303 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3303 2022/11/28 15:51:59 - mmengine - INFO - Epoch(train) [9][1200/2462] lr: 4.5226e-02 eta: 0:14:05 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.3661 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3661 2022/11/28 15:52:03 - mmengine - INFO - Epoch(train) [9][1300/2462] lr: 4.4829e-02 eta: 0:14:01 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.3244 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3244 2022/11/28 15:52:03 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:52:07 - mmengine - INFO - Epoch(train) [9][1400/2462] lr: 4.4433e-02 eta: 0:13:56 time: 0.0437 data_time: 0.0066 memory: 1794 loss: 0.3269 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.3269 2022/11/28 15:52:12 - mmengine - INFO - Epoch(train) [9][1500/2462] lr: 4.4037e-02 eta: 0:13:51 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.4602 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4602 2022/11/28 15:52:16 - mmengine - INFO - Epoch(train) [9][1600/2462] lr: 4.3641e-02 eta: 0:13:46 time: 0.0429 data_time: 0.0061 memory: 1794 loss: 0.3977 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3977 2022/11/28 15:52:21 - mmengine - INFO - Epoch(train) [9][1700/2462] lr: 4.3246e-02 eta: 0:13:42 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.3342 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3342 2022/11/28 15:52:25 - mmengine - INFO - Epoch(train) [9][1800/2462] lr: 4.2851e-02 eta: 0:13:37 time: 0.0434 data_time: 0.0062 memory: 1794 loss: 0.4726 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.4726 2022/11/28 15:52:30 - mmengine - INFO - Epoch(train) [9][1900/2462] lr: 4.2456e-02 eta: 0:13:32 time: 0.0432 data_time: 0.0061 memory: 1794 loss: 0.4014 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4014 2022/11/28 15:52:34 - mmengine - INFO - Epoch(train) [9][2000/2462] lr: 4.2063e-02 eta: 0:13:28 time: 0.0433 data_time: 0.0064 memory: 1794 loss: 0.3188 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3188 2022/11/28 15:52:38 - mmengine - INFO - Epoch(train) [9][2100/2462] lr: 4.1669e-02 eta: 0:13:23 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.4446 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4446 2022/11/28 15:52:43 - mmengine - INFO - Epoch(train) [9][2200/2462] lr: 4.1276e-02 eta: 0:13:18 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.2892 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2892 2022/11/28 15:52:47 - mmengine - INFO - Epoch(train) [9][2300/2462] lr: 4.0884e-02 eta: 0:13:13 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.4630 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4630 2022/11/28 15:52:47 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:52:51 - mmengine - INFO - Epoch(train) [9][2400/2462] lr: 4.0492e-02 eta: 0:13:09 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 0.4554 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4554 2022/11/28 15:52:54 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:52:54 - mmengine - INFO - Epoch(train) [9][2462/2462] lr: 4.0249e-02 eta: 0:13:06 time: 0.0448 data_time: 0.0065 memory: 1794 loss: 0.3841 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.3841 2022/11/28 15:52:54 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/11/28 15:52:58 - mmengine - INFO - Epoch(val) [9][100/398] eta: 0:00:09 time: 0.0320 data_time: 0.0185 memory: 364 2022/11/28 15:53:01 - mmengine - INFO - Epoch(val) [9][200/398] eta: 0:00:06 time: 0.0242 data_time: 0.0115 memory: 364 2022/11/28 15:53:03 - mmengine - INFO - Epoch(val) [9][300/398] eta: 0:00:02 time: 0.0246 data_time: 0.0124 memory: 364 2022/11/28 15:53:07 - mmengine - INFO - Epoch(val) [9][398/398] acc/top1: 0.7284 acc/top5: 0.9318 acc/mean1: 0.7485 2022/11/28 15:53:11 - mmengine - INFO - Epoch(train) [10][100/2462] lr: 3.9859e-02 eta: 0:13:01 time: 0.0457 data_time: 0.0063 memory: 1794 loss: 0.4165 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.4165 2022/11/28 15:53:16 - mmengine - INFO - Epoch(train) [10][200/2462] lr: 3.9468e-02 eta: 0:12:57 time: 0.0434 data_time: 0.0061 memory: 1794 loss: 0.3499 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.3499 2022/11/28 15:53:20 - mmengine - INFO - Epoch(train) [10][300/2462] lr: 3.9079e-02 eta: 0:12:52 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.2765 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2765 2022/11/28 15:53:25 - mmengine - INFO - Epoch(train) [10][400/2462] lr: 3.8690e-02 eta: 0:12:47 time: 0.0442 data_time: 0.0069 memory: 1794 loss: 0.3161 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3161 2022/11/28 15:53:29 - mmengine - INFO - Epoch(train) [10][500/2462] lr: 3.8302e-02 eta: 0:12:43 time: 0.0448 data_time: 0.0061 memory: 1794 loss: 0.3769 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3769 2022/11/28 15:53:34 - mmengine - INFO - Epoch(train) [10][600/2462] lr: 3.7915e-02 eta: 0:12:38 time: 0.0445 data_time: 0.0066 memory: 1794 loss: 0.2582 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2582 2022/11/28 15:53:38 - mmengine - INFO - Epoch(train) [10][700/2462] lr: 3.7528e-02 eta: 0:12:33 time: 0.0444 data_time: 0.0066 memory: 1794 loss: 0.3204 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3204 2022/11/28 15:53:43 - mmengine - INFO - Epoch(train) [10][800/2462] lr: 3.7143e-02 eta: 0:12:29 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.3517 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3517 2022/11/28 15:53:44 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:53:47 - mmengine - INFO - Epoch(train) [10][900/2462] lr: 3.6758e-02 eta: 0:12:24 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.2894 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2894 2022/11/28 15:53:51 - mmengine - INFO - Epoch(train) [10][1000/2462] lr: 3.6373e-02 eta: 0:12:19 time: 0.0437 data_time: 0.0061 memory: 1794 loss: 0.3398 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3398 2022/11/28 15:53:56 - mmengine - INFO - Epoch(train) [10][1100/2462] lr: 3.5990e-02 eta: 0:12:15 time: 0.0437 data_time: 0.0061 memory: 1794 loss: 0.3512 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3512 2022/11/28 15:54:00 - mmengine - INFO - Epoch(train) [10][1200/2462] lr: 3.5608e-02 eta: 0:12:10 time: 0.0447 data_time: 0.0076 memory: 1794 loss: 0.3675 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.3675 2022/11/28 15:54:05 - mmengine - INFO - Epoch(train) [10][1300/2462] lr: 3.5226e-02 eta: 0:12:05 time: 0.0434 data_time: 0.0061 memory: 1794 loss: 0.3335 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3335 2022/11/28 15:54:09 - mmengine - INFO - Epoch(train) [10][1400/2462] lr: 3.4846e-02 eta: 0:12:01 time: 0.0471 data_time: 0.0067 memory: 1794 loss: 0.2503 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2503 2022/11/28 15:54:14 - mmengine - INFO - Epoch(train) [10][1500/2462] lr: 3.4466e-02 eta: 0:11:56 time: 0.0442 data_time: 0.0068 memory: 1794 loss: 0.3441 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3441 2022/11/28 15:54:18 - mmengine - INFO - Epoch(train) [10][1600/2462] lr: 3.4088e-02 eta: 0:11:51 time: 0.0437 data_time: 0.0061 memory: 1794 loss: 0.3847 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3847 2022/11/28 15:54:22 - mmengine - INFO - Epoch(train) [10][1700/2462] lr: 3.3710e-02 eta: 0:11:47 time: 0.0456 data_time: 0.0062 memory: 1794 loss: 0.2861 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2861 2022/11/28 15:54:27 - mmengine - INFO - Epoch(train) [10][1800/2462] lr: 3.3334e-02 eta: 0:11:42 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.3147 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3147 2022/11/28 15:54:29 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:54:31 - mmengine - INFO - Epoch(train) [10][1900/2462] lr: 3.2959e-02 eta: 0:11:38 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.3249 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3249 2022/11/28 15:54:36 - mmengine - INFO - Epoch(train) [10][2000/2462] lr: 3.2584e-02 eta: 0:11:33 time: 0.0439 data_time: 0.0061 memory: 1794 loss: 0.2632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2632 2022/11/28 15:54:40 - mmengine - INFO - Epoch(train) [10][2100/2462] lr: 3.2211e-02 eta: 0:11:28 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.3363 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3363 2022/11/28 15:54:45 - mmengine - INFO - Epoch(train) [10][2200/2462] lr: 3.1839e-02 eta: 0:11:24 time: 0.0437 data_time: 0.0061 memory: 1794 loss: 0.3608 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3608 2022/11/28 15:54:50 - mmengine - INFO - Epoch(train) [10][2300/2462] lr: 3.1468e-02 eta: 0:11:19 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.2781 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2781 2022/11/28 15:54:54 - mmengine - INFO - Epoch(train) [10][2400/2462] lr: 3.1098e-02 eta: 0:11:15 time: 0.0446 data_time: 0.0068 memory: 1794 loss: 0.3201 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3201 2022/11/28 15:54:57 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:54:57 - mmengine - INFO - Epoch(train) [10][2462/2462] lr: 3.0870e-02 eta: 0:11:12 time: 0.0447 data_time: 0.0063 memory: 1794 loss: 0.2617 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2617 2022/11/28 15:54:57 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/11/28 15:55:01 - mmengine - INFO - Epoch(val) [10][100/398] eta: 0:00:10 time: 0.0320 data_time: 0.0188 memory: 364 2022/11/28 15:55:04 - mmengine - INFO - Epoch(val) [10][200/398] eta: 0:00:06 time: 0.0274 data_time: 0.0141 memory: 364 2022/11/28 15:55:06 - mmengine - INFO - Epoch(val) [10][300/398] eta: 0:00:02 time: 0.0250 data_time: 0.0123 memory: 364 2022/11/28 15:55:10 - mmengine - INFO - Epoch(val) [10][398/398] acc/top1: 0.7682 acc/top5: 0.9406 acc/mean1: 0.7885 2022/11/28 15:55:10 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_8.pth is removed 2022/11/28 15:55:10 - mmengine - INFO - The best checkpoint with 0.7682 acc/top1 at 10 epoch is saved to best_acc/top1_epoch_10.pth. 2022/11/28 15:55:14 - mmengine - INFO - Epoch(train) [11][100/2462] lr: 3.0502e-02 eta: 0:11:07 time: 0.0438 data_time: 0.0061 memory: 1794 loss: 0.2318 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2318 2022/11/28 15:55:19 - mmengine - INFO - Epoch(train) [11][200/2462] lr: 3.0135e-02 eta: 0:11:03 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.2383 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2383 2022/11/28 15:55:23 - mmengine - INFO - Epoch(train) [11][300/2462] lr: 2.9770e-02 eta: 0:10:58 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.2648 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2648 2022/11/28 15:55:27 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:55:28 - mmengine - INFO - Epoch(train) [11][400/2462] lr: 2.9406e-02 eta: 0:10:54 time: 0.0451 data_time: 0.0073 memory: 1794 loss: 0.2501 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2501 2022/11/28 15:55:32 - mmengine - INFO - Epoch(train) [11][500/2462] lr: 2.9043e-02 eta: 0:10:49 time: 0.0442 data_time: 0.0063 memory: 1794 loss: 0.2265 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2265 2022/11/28 15:55:37 - mmengine - INFO - Epoch(train) [11][600/2462] lr: 2.8682e-02 eta: 0:10:44 time: 0.0439 data_time: 0.0069 memory: 1794 loss: 0.2749 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2749 2022/11/28 15:55:41 - mmengine - INFO - Epoch(train) [11][700/2462] lr: 2.8322e-02 eta: 0:10:40 time: 0.0452 data_time: 0.0069 memory: 1794 loss: 0.2363 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.2363 2022/11/28 15:55:46 - mmengine - INFO - Epoch(train) [11][800/2462] lr: 2.7963e-02 eta: 0:10:35 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.2785 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2785 2022/11/28 15:55:50 - mmengine - INFO - Epoch(train) [11][900/2462] lr: 2.7606e-02 eta: 0:10:31 time: 0.0434 data_time: 0.0061 memory: 1794 loss: 0.2257 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2257 2022/11/28 15:55:55 - mmengine - INFO - Epoch(train) [11][1000/2462] lr: 2.7250e-02 eta: 0:10:26 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.3128 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3128 2022/11/28 15:55:59 - mmengine - INFO - Epoch(train) [11][1100/2462] lr: 2.6896e-02 eta: 0:10:21 time: 0.0444 data_time: 0.0072 memory: 1794 loss: 0.2456 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2456 2022/11/28 15:56:03 - mmengine - INFO - Epoch(train) [11][1200/2462] lr: 2.6543e-02 eta: 0:10:17 time: 0.0439 data_time: 0.0067 memory: 1794 loss: 0.2535 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2535 2022/11/28 15:56:08 - mmengine - INFO - Epoch(train) [11][1300/2462] lr: 2.6191e-02 eta: 0:10:12 time: 0.0436 data_time: 0.0065 memory: 1794 loss: 0.2057 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2057 2022/11/28 15:56:11 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:56:12 - mmengine - INFO - Epoch(train) [11][1400/2462] lr: 2.5841e-02 eta: 0:10:08 time: 0.0439 data_time: 0.0068 memory: 1794 loss: 0.2706 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2706 2022/11/28 15:56:17 - mmengine - INFO - Epoch(train) [11][1500/2462] lr: 2.5493e-02 eta: 0:10:03 time: 0.0448 data_time: 0.0066 memory: 1794 loss: 0.2559 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2559 2022/11/28 15:56:21 - mmengine - INFO - Epoch(train) [11][1600/2462] lr: 2.5146e-02 eta: 0:09:58 time: 0.0439 data_time: 0.0061 memory: 1794 loss: 0.2831 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2831 2022/11/28 15:56:26 - mmengine - INFO - Epoch(train) [11][1700/2462] lr: 2.4801e-02 eta: 0:09:54 time: 0.0444 data_time: 0.0070 memory: 1794 loss: 0.2306 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2306 2022/11/28 15:56:30 - mmengine - INFO - Epoch(train) [11][1800/2462] lr: 2.4458e-02 eta: 0:09:49 time: 0.0447 data_time: 0.0065 memory: 1794 loss: 0.3092 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3092 2022/11/28 15:56:34 - mmengine - INFO - Epoch(train) [11][1900/2462] lr: 2.4116e-02 eta: 0:09:44 time: 0.0445 data_time: 0.0067 memory: 1794 loss: 0.1662 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1662 2022/11/28 15:56:39 - mmengine - INFO - Epoch(train) [11][2000/2462] lr: 2.3775e-02 eta: 0:09:40 time: 0.0448 data_time: 0.0069 memory: 1794 loss: 0.2407 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2407 2022/11/28 15:56:43 - mmengine - INFO - Epoch(train) [11][2100/2462] lr: 2.3437e-02 eta: 0:09:35 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.2648 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2648 2022/11/28 15:56:48 - mmengine - INFO - Epoch(train) [11][2200/2462] lr: 2.3100e-02 eta: 0:09:31 time: 0.0437 data_time: 0.0061 memory: 1794 loss: 0.2724 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.2724 2022/11/28 15:56:52 - mmengine - INFO - Epoch(train) [11][2300/2462] lr: 2.2764e-02 eta: 0:09:26 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.2178 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2178 2022/11/28 15:56:56 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:56:57 - mmengine - INFO - Epoch(train) [11][2400/2462] lr: 2.2431e-02 eta: 0:09:21 time: 0.0445 data_time: 0.0063 memory: 1794 loss: 0.2315 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.2315 2022/11/28 15:56:59 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:56:59 - mmengine - INFO - Epoch(train) [11][2462/2462] lr: 2.2225e-02 eta: 0:09:19 time: 0.0447 data_time: 0.0063 memory: 1794 loss: 0.2626 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2626 2022/11/28 15:56:59 - mmengine - INFO - Saving checkpoint at 11 epochs 2022/11/28 15:57:03 - mmengine - INFO - Epoch(val) [11][100/398] eta: 0:00:10 time: 0.0316 data_time: 0.0183 memory: 364 2022/11/28 15:57:06 - mmengine - INFO - Epoch(val) [11][200/398] eta: 0:00:06 time: 0.0238 data_time: 0.0112 memory: 364 2022/11/28 15:57:09 - mmengine - INFO - Epoch(val) [11][300/398] eta: 0:00:02 time: 0.0278 data_time: 0.0131 memory: 364 2022/11/28 15:57:12 - mmengine - INFO - Epoch(val) [11][398/398] acc/top1: 0.7811 acc/top5: 0.9478 acc/mean1: 0.7947 2022/11/28 15:57:12 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_10.pth is removed 2022/11/28 15:57:12 - mmengine - INFO - The best checkpoint with 0.7811 acc/top1 at 11 epoch is saved to best_acc/top1_epoch_11.pth. 2022/11/28 15:57:17 - mmengine - INFO - Epoch(train) [12][100/2462] lr: 2.1894e-02 eta: 0:09:14 time: 0.0450 data_time: 0.0063 memory: 1794 loss: 0.1444 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1444 2022/11/28 15:57:21 - mmengine - INFO - Epoch(train) [12][200/2462] lr: 2.1565e-02 eta: 0:09:09 time: 0.0440 data_time: 0.0061 memory: 1794 loss: 0.2146 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2146 2022/11/28 15:57:26 - mmengine - INFO - Epoch(train) [12][300/2462] lr: 2.1238e-02 eta: 0:09:05 time: 0.0443 data_time: 0.0065 memory: 1794 loss: 0.1553 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1553 2022/11/28 15:57:30 - mmengine - INFO - Epoch(train) [12][400/2462] lr: 2.0913e-02 eta: 0:09:00 time: 0.0440 data_time: 0.0066 memory: 1794 loss: 0.1614 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1614 2022/11/28 15:57:35 - mmengine - INFO - Epoch(train) [12][500/2462] lr: 2.0589e-02 eta: 0:08:56 time: 0.0442 data_time: 0.0064 memory: 1794 loss: 0.2128 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.2128 2022/11/28 15:57:39 - mmengine - INFO - Epoch(train) [12][600/2462] lr: 2.0268e-02 eta: 0:08:51 time: 0.0448 data_time: 0.0070 memory: 1794 loss: 0.2173 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2173 2022/11/28 15:57:44 - mmengine - INFO - Epoch(train) [12][700/2462] lr: 1.9948e-02 eta: 0:08:47 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.1819 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1819 2022/11/28 15:57:48 - mmengine - INFO - Epoch(train) [12][800/2462] lr: 1.9631e-02 eta: 0:08:42 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.1637 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1637 2022/11/28 15:57:53 - mmengine - INFO - Epoch(train) [12][900/2462] lr: 1.9315e-02 eta: 0:08:37 time: 0.0459 data_time: 0.0080 memory: 1794 loss: 0.1851 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1851 2022/11/28 15:57:53 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:57:57 - mmengine - INFO - Epoch(train) [12][1000/2462] lr: 1.9001e-02 eta: 0:08:33 time: 0.0439 data_time: 0.0061 memory: 1794 loss: 0.2153 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2153 2022/11/28 15:58:02 - mmengine - INFO - Epoch(train) [12][1100/2462] lr: 1.8689e-02 eta: 0:08:28 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.1644 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1644 2022/11/28 15:58:06 - mmengine - INFO - Epoch(train) [12][1200/2462] lr: 1.8379e-02 eta: 0:08:24 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.1887 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1887 2022/11/28 15:58:11 - mmengine - INFO - Epoch(train) [12][1300/2462] lr: 1.8071e-02 eta: 0:08:19 time: 0.0443 data_time: 0.0062 memory: 1794 loss: 0.1743 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1743 2022/11/28 15:58:15 - mmengine - INFO - Epoch(train) [12][1400/2462] lr: 1.7765e-02 eta: 0:08:15 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.1911 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1911 2022/11/28 15:58:19 - mmengine - INFO - Epoch(train) [12][1500/2462] lr: 1.7462e-02 eta: 0:08:10 time: 0.0442 data_time: 0.0063 memory: 1794 loss: 0.1283 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1283 2022/11/28 15:58:24 - mmengine - INFO - Epoch(train) [12][1600/2462] lr: 1.7160e-02 eta: 0:08:05 time: 0.0445 data_time: 0.0067 memory: 1794 loss: 0.1790 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1790 2022/11/28 15:58:28 - mmengine - INFO - Epoch(train) [12][1700/2462] lr: 1.6860e-02 eta: 0:08:01 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.1852 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1852 2022/11/28 15:58:33 - mmengine - INFO - Epoch(train) [12][1800/2462] lr: 1.6563e-02 eta: 0:07:56 time: 0.0450 data_time: 0.0072 memory: 1794 loss: 0.1035 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1035 2022/11/28 15:58:37 - mmengine - INFO - Epoch(train) [12][1900/2462] lr: 1.6267e-02 eta: 0:07:52 time: 0.0447 data_time: 0.0062 memory: 1794 loss: 0.0984 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0984 2022/11/28 15:58:38 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:58:42 - mmengine - INFO - Epoch(train) [12][2000/2462] lr: 1.5974e-02 eta: 0:07:47 time: 0.0444 data_time: 0.0064 memory: 1794 loss: 0.1485 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1485 2022/11/28 15:58:46 - mmengine - INFO - Epoch(train) [12][2100/2462] lr: 1.5683e-02 eta: 0:07:43 time: 0.0448 data_time: 0.0063 memory: 1794 loss: 0.1522 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1522 2022/11/28 15:58:51 - mmengine - INFO - Epoch(train) [12][2200/2462] lr: 1.5394e-02 eta: 0:07:38 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.1801 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1801 2022/11/28 15:58:55 - mmengine - INFO - Epoch(train) [12][2300/2462] lr: 1.5107e-02 eta: 0:07:34 time: 0.0443 data_time: 0.0062 memory: 1794 loss: 0.1268 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1268 2022/11/28 15:59:00 - mmengine - INFO - Epoch(train) [12][2400/2462] lr: 1.4823e-02 eta: 0:07:29 time: 0.0442 data_time: 0.0068 memory: 1794 loss: 0.1162 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1162 2022/11/28 15:59:03 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:59:03 - mmengine - INFO - Epoch(train) [12][2462/2462] lr: 1.4647e-02 eta: 0:07:26 time: 0.0444 data_time: 0.0063 memory: 1794 loss: 0.1257 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1257 2022/11/28 15:59:03 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/11/28 15:59:07 - mmengine - INFO - Epoch(val) [12][100/398] eta: 0:00:10 time: 0.0319 data_time: 0.0186 memory: 364 2022/11/28 15:59:09 - mmengine - INFO - Epoch(val) [12][200/398] eta: 0:00:06 time: 0.0236 data_time: 0.0107 memory: 364 2022/11/28 15:59:12 - mmengine - INFO - Epoch(val) [12][300/398] eta: 0:00:02 time: 0.0243 data_time: 0.0123 memory: 364 2022/11/28 15:59:15 - mmengine - INFO - Epoch(val) [12][398/398] acc/top1: 0.7788 acc/top5: 0.9428 acc/mean1: 0.7967 2022/11/28 15:59:20 - mmengine - INFO - Epoch(train) [13][100/2462] lr: 1.4367e-02 eta: 0:07:22 time: 0.0459 data_time: 0.0062 memory: 1794 loss: 0.0838 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0838 2022/11/28 15:59:24 - mmengine - INFO - Epoch(train) [13][200/2462] lr: 1.4088e-02 eta: 0:07:17 time: 0.0460 data_time: 0.0067 memory: 1794 loss: 0.0896 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0896 2022/11/28 15:59:29 - mmengine - INFO - Epoch(train) [13][300/2462] lr: 1.3812e-02 eta: 0:07:13 time: 0.0449 data_time: 0.0069 memory: 1794 loss: 0.1061 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1061 2022/11/28 15:59:33 - mmengine - INFO - Epoch(train) [13][400/2462] lr: 1.3538e-02 eta: 0:07:08 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.1210 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1210 2022/11/28 15:59:36 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 15:59:38 - mmengine - INFO - Epoch(train) [13][500/2462] lr: 1.3266e-02 eta: 0:07:03 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.0755 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0755 2022/11/28 15:59:42 - mmengine - INFO - Epoch(train) [13][600/2462] lr: 1.2997e-02 eta: 0:06:59 time: 0.0444 data_time: 0.0067 memory: 1794 loss: 0.0773 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0773 2022/11/28 15:59:47 - mmengine - INFO - Epoch(train) [13][700/2462] lr: 1.2730e-02 eta: 0:06:54 time: 0.0440 data_time: 0.0064 memory: 1794 loss: 0.0869 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0869 2022/11/28 15:59:51 - mmengine - INFO - Epoch(train) [13][800/2462] lr: 1.2465e-02 eta: 0:06:50 time: 0.0443 data_time: 0.0061 memory: 1794 loss: 0.0750 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0750 2022/11/28 15:59:56 - mmengine - INFO - Epoch(train) [13][900/2462] lr: 1.2203e-02 eta: 0:06:45 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.0793 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0793 2022/11/28 16:00:00 - mmengine - INFO - Epoch(train) [13][1000/2462] lr: 1.1943e-02 eta: 0:06:41 time: 0.0445 data_time: 0.0062 memory: 1794 loss: 0.0685 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0685 2022/11/28 16:00:05 - mmengine - INFO - Epoch(train) [13][1100/2462] lr: 1.1686e-02 eta: 0:06:36 time: 0.0450 data_time: 0.0063 memory: 1794 loss: 0.1031 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1031 2022/11/28 16:00:09 - mmengine - INFO - Epoch(train) [13][1200/2462] lr: 1.1431e-02 eta: 0:06:32 time: 0.0460 data_time: 0.0067 memory: 1794 loss: 0.0748 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0748 2022/11/28 16:00:14 - mmengine - INFO - Epoch(train) [13][1300/2462] lr: 1.1178e-02 eta: 0:06:27 time: 0.0453 data_time: 0.0062 memory: 1794 loss: 0.0806 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0806 2022/11/28 16:00:18 - mmengine - INFO - Epoch(train) [13][1400/2462] lr: 1.0928e-02 eta: 0:06:22 time: 0.0449 data_time: 0.0062 memory: 1794 loss: 0.0613 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0613 2022/11/28 16:00:21 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 16:00:23 - mmengine - INFO - Epoch(train) [13][1500/2462] lr: 1.0680e-02 eta: 0:06:18 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.0895 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0895 2022/11/28 16:00:27 - mmengine - INFO - Epoch(train) [13][1600/2462] lr: 1.0435e-02 eta: 0:06:13 time: 0.0443 data_time: 0.0063 memory: 1794 loss: 0.0625 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0625 2022/11/28 16:00:31 - mmengine - INFO - Epoch(train) [13][1700/2462] lr: 1.0193e-02 eta: 0:06:09 time: 0.0446 data_time: 0.0065 memory: 1794 loss: 0.0534 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0534 2022/11/28 16:00:36 - mmengine - INFO - Epoch(train) [13][1800/2462] lr: 9.9527e-03 eta: 0:06:04 time: 0.0450 data_time: 0.0071 memory: 1794 loss: 0.0532 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0532 2022/11/28 16:00:41 - mmengine - INFO - Epoch(train) [13][1900/2462] lr: 9.7153e-03 eta: 0:06:00 time: 0.0443 data_time: 0.0061 memory: 1794 loss: 0.0477 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0477 2022/11/28 16:00:45 - mmengine - INFO - Epoch(train) [13][2000/2462] lr: 9.4804e-03 eta: 0:05:55 time: 0.0442 data_time: 0.0063 memory: 1794 loss: 0.0583 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0583 2022/11/28 16:00:49 - mmengine - INFO - Epoch(train) [13][2100/2462] lr: 9.2480e-03 eta: 0:05:51 time: 0.0449 data_time: 0.0062 memory: 1794 loss: 0.0413 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0413 2022/11/28 16:00:54 - mmengine - INFO - Epoch(train) [13][2200/2462] lr: 9.0183e-03 eta: 0:05:46 time: 0.0448 data_time: 0.0066 memory: 1794 loss: 0.0523 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0523 2022/11/28 16:00:58 - mmengine - INFO - Epoch(train) [13][2300/2462] lr: 8.7911e-03 eta: 0:05:42 time: 0.0449 data_time: 0.0066 memory: 1794 loss: 0.0479 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0479 2022/11/28 16:01:03 - mmengine - INFO - Epoch(train) [13][2400/2462] lr: 8.5666e-03 eta: 0:05:37 time: 0.0465 data_time: 0.0065 memory: 1794 loss: 0.0394 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0394 2022/11/28 16:01:05 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 16:01:06 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 16:01:06 - mmengine - INFO - Epoch(train) [13][2462/2462] lr: 8.4287e-03 eta: 0:05:34 time: 0.0465 data_time: 0.0078 memory: 1794 loss: 0.0522 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0522 2022/11/28 16:01:06 - mmengine - INFO - Saving checkpoint at 13 epochs 2022/11/28 16:01:10 - mmengine - INFO - Epoch(val) [13][100/398] eta: 0:00:09 time: 0.0325 data_time: 0.0192 memory: 364 2022/11/28 16:01:12 - mmengine - INFO - Epoch(val) [13][200/398] eta: 0:00:06 time: 0.0240 data_time: 0.0111 memory: 364 2022/11/28 16:01:15 - mmengine - INFO - Epoch(val) [13][300/398] eta: 0:00:02 time: 0.0243 data_time: 0.0123 memory: 364 2022/11/28 16:01:18 - mmengine - INFO - Epoch(val) [13][398/398] acc/top1: 0.8013 acc/top5: 0.9517 acc/mean1: 0.8169 2022/11/28 16:01:18 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_11.pth is removed 2022/11/28 16:01:19 - mmengine - INFO - The best checkpoint with 0.8013 acc/top1 at 13 epoch is saved to best_acc/top1_epoch_13.pth. 2022/11/28 16:01:23 - mmengine - INFO - Epoch(train) [14][100/2462] lr: 8.2085e-03 eta: 0:05:30 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.0297 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0297 2022/11/28 16:01:28 - mmengine - INFO - Epoch(train) [14][200/2462] lr: 7.9909e-03 eta: 0:05:25 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.0548 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0548 2022/11/28 16:01:32 - mmengine - INFO - Epoch(train) [14][300/2462] lr: 7.7760e-03 eta: 0:05:21 time: 0.0446 data_time: 0.0067 memory: 1794 loss: 0.0333 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0333 2022/11/28 16:01:37 - mmengine - INFO - Epoch(train) [14][400/2462] lr: 7.5638e-03 eta: 0:05:16 time: 0.0451 data_time: 0.0063 memory: 1794 loss: 0.0391 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0391 2022/11/28 16:01:41 - mmengine - INFO - Epoch(train) [14][500/2462] lr: 7.3542e-03 eta: 0:05:12 time: 0.0461 data_time: 0.0065 memory: 1794 loss: 0.0386 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0386 2022/11/28 16:01:46 - mmengine - INFO - Epoch(train) [14][600/2462] lr: 7.1474e-03 eta: 0:05:07 time: 0.0452 data_time: 0.0063 memory: 1794 loss: 0.0377 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0377 2022/11/28 16:01:50 - mmengine - INFO - Epoch(train) [14][700/2462] lr: 6.9433e-03 eta: 0:05:02 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.0368 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0368 2022/11/28 16:01:55 - mmengine - INFO - Epoch(train) [14][800/2462] lr: 6.7420e-03 eta: 0:04:58 time: 0.0452 data_time: 0.0062 memory: 1794 loss: 0.0270 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0270 2022/11/28 16:01:59 - mmengine - INFO - Epoch(train) [14][900/2462] lr: 6.5434e-03 eta: 0:04:53 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.0275 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0275 2022/11/28 16:02:03 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 16:02:04 - mmengine - INFO - Epoch(train) [14][1000/2462] lr: 6.3476e-03 eta: 0:04:49 time: 0.0445 data_time: 0.0065 memory: 1794 loss: 0.0314 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0314 2022/11/28 16:02:08 - mmengine - INFO - Epoch(train) [14][1100/2462] lr: 6.1545e-03 eta: 0:04:44 time: 0.0443 data_time: 0.0061 memory: 1794 loss: 0.0372 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0372 2022/11/28 16:02:12 - mmengine - INFO - Epoch(train) [14][1200/2462] lr: 5.9642e-03 eta: 0:04:40 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.0249 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0249 2022/11/28 16:02:17 - mmengine - INFO - Epoch(train) [14][1300/2462] lr: 5.7768e-03 eta: 0:04:35 time: 0.0441 data_time: 0.0064 memory: 1794 loss: 0.0293 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0293 2022/11/28 16:02:21 - mmengine - INFO - Epoch(train) [14][1400/2462] lr: 5.5921e-03 eta: 0:04:31 time: 0.0441 data_time: 0.0065 memory: 1794 loss: 0.0416 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0416 2022/11/28 16:02:26 - mmengine - INFO - Epoch(train) [14][1500/2462] lr: 5.4103e-03 eta: 0:04:26 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.0236 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0236 2022/11/28 16:02:30 - mmengine - INFO - Epoch(train) [14][1600/2462] lr: 5.2313e-03 eta: 0:04:22 time: 0.0442 data_time: 0.0068 memory: 1794 loss: 0.0272 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0272 2022/11/28 16:02:35 - mmengine - INFO - Epoch(train) [14][1700/2462] lr: 5.0551e-03 eta: 0:04:17 time: 0.0444 data_time: 0.0066 memory: 1794 loss: 0.0241 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0241 2022/11/28 16:02:39 - mmengine - INFO - Epoch(train) [14][1800/2462] lr: 4.8818e-03 eta: 0:04:12 time: 0.0448 data_time: 0.0063 memory: 1794 loss: 0.0298 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0298 2022/11/28 16:02:44 - mmengine - INFO - Epoch(train) [14][1900/2462] lr: 4.7114e-03 eta: 0:04:08 time: 0.0443 data_time: 0.0062 memory: 1794 loss: 0.0246 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0246 2022/11/28 16:02:48 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 16:02:48 - mmengine - INFO - Epoch(train) [14][2000/2462] lr: 4.5439e-03 eta: 0:04:03 time: 0.0448 data_time: 0.0065 memory: 1794 loss: 0.0176 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0176 2022/11/28 16:02:53 - mmengine - INFO - Epoch(train) [14][2100/2462] lr: 4.3792e-03 eta: 0:03:59 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.0235 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0235 2022/11/28 16:02:57 - mmengine - INFO - Epoch(train) [14][2200/2462] lr: 4.2175e-03 eta: 0:03:54 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.0286 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0286 2022/11/28 16:03:02 - mmengine - INFO - Epoch(train) [14][2300/2462] lr: 4.0587e-03 eta: 0:03:50 time: 0.0446 data_time: 0.0062 memory: 1794 loss: 0.0256 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0256 2022/11/28 16:03:06 - mmengine - INFO - Epoch(train) [14][2400/2462] lr: 3.9027e-03 eta: 0:03:45 time: 0.0444 data_time: 0.0064 memory: 1794 loss: 0.0268 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0268 2022/11/28 16:03:09 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 16:03:09 - mmengine - INFO - Epoch(train) [14][2462/2462] lr: 3.8075e-03 eta: 0:03:42 time: 0.0460 data_time: 0.0066 memory: 1794 loss: 0.0242 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0242 2022/11/28 16:03:09 - mmengine - INFO - Saving checkpoint at 14 epochs 2022/11/28 16:03:13 - mmengine - INFO - Epoch(val) [14][100/398] eta: 0:00:09 time: 0.0321 data_time: 0.0187 memory: 364 2022/11/28 16:03:15 - mmengine - INFO - Epoch(val) [14][200/398] eta: 0:00:06 time: 0.0237 data_time: 0.0109 memory: 364 2022/11/28 16:03:18 - mmengine - INFO - Epoch(val) [14][300/398] eta: 0:00:02 time: 0.0242 data_time: 0.0118 memory: 364 2022/11/28 16:03:21 - mmengine - INFO - Epoch(val) [14][398/398] acc/top1: 0.8144 acc/top5: 0.9562 acc/mean1: 0.8318 2022/11/28 16:03:21 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_13.pth is removed 2022/11/28 16:03:22 - mmengine - INFO - The best checkpoint with 0.8144 acc/top1 at 14 epoch is saved to best_acc/top1_epoch_14.pth. 2022/11/28 16:03:26 - mmengine - INFO - Epoch(train) [15][100/2462] lr: 3.6564e-03 eta: 0:03:38 time: 0.0449 data_time: 0.0070 memory: 1794 loss: 0.0266 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0266 2022/11/28 16:03:31 - mmengine - INFO - Epoch(train) [15][200/2462] lr: 3.5082e-03 eta: 0:03:33 time: 0.0439 data_time: 0.0063 memory: 1794 loss: 0.0175 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0175 2022/11/28 16:03:35 - mmengine - INFO - Epoch(train) [15][300/2462] lr: 3.3629e-03 eta: 0:03:29 time: 0.0487 data_time: 0.0071 memory: 1794 loss: 0.0322 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0322 2022/11/28 16:03:40 - mmengine - INFO - Epoch(train) [15][400/2462] lr: 3.2206e-03 eta: 0:03:24 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.0156 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0156 2022/11/28 16:03:44 - mmengine - INFO - Epoch(train) [15][500/2462] lr: 3.0813e-03 eta: 0:03:20 time: 0.0462 data_time: 0.0063 memory: 1794 loss: 0.0183 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0183 2022/11/28 16:03:46 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 16:03:49 - mmengine - INFO - Epoch(train) [15][600/2462] lr: 2.9450e-03 eta: 0:03:15 time: 0.0452 data_time: 0.0067 memory: 1794 loss: 0.0217 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0217 2022/11/28 16:03:53 - mmengine - INFO - Epoch(train) [15][700/2462] lr: 2.8117e-03 eta: 0:03:11 time: 0.0442 data_time: 0.0063 memory: 1794 loss: 0.0249 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0249 2022/11/28 16:03:58 - mmengine - INFO - Epoch(train) [15][800/2462] lr: 2.6813e-03 eta: 0:03:06 time: 0.0443 data_time: 0.0062 memory: 1794 loss: 0.0182 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0182 2022/11/28 16:04:02 - mmengine - INFO - Epoch(train) [15][900/2462] lr: 2.5540e-03 eta: 0:03:02 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.0221 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0221 2022/11/28 16:04:07 - mmengine - INFO - Epoch(train) [15][1000/2462] lr: 2.4297e-03 eta: 0:02:57 time: 0.0485 data_time: 0.0062 memory: 1794 loss: 0.0212 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0212 2022/11/28 16:04:11 - mmengine - INFO - Epoch(train) [15][1100/2462] lr: 2.3084e-03 eta: 0:02:53 time: 0.0453 data_time: 0.0064 memory: 1794 loss: 0.0161 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0161 2022/11/28 16:04:16 - mmengine - INFO - Epoch(train) [15][1200/2462] lr: 2.1902e-03 eta: 0:02:48 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.0185 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0185 2022/11/28 16:04:20 - mmengine - INFO - Epoch(train) [15][1300/2462] lr: 2.0750e-03 eta: 0:02:43 time: 0.0444 data_time: 0.0066 memory: 1794 loss: 0.0232 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0232 2022/11/28 16:04:25 - mmengine - INFO - Epoch(train) [15][1400/2462] lr: 1.9628e-03 eta: 0:02:39 time: 0.0448 data_time: 0.0070 memory: 1794 loss: 0.0136 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0136 2022/11/28 16:04:29 - mmengine - INFO - Epoch(train) [15][1500/2462] lr: 1.8537e-03 eta: 0:02:34 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.0207 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0207 2022/11/28 16:04:31 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 16:04:34 - mmengine - INFO - Epoch(train) [15][1600/2462] lr: 1.7477e-03 eta: 0:02:30 time: 0.0452 data_time: 0.0068 memory: 1794 loss: 0.0167 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0167 2022/11/28 16:04:38 - mmengine - INFO - Epoch(train) [15][1700/2462] lr: 1.6447e-03 eta: 0:02:25 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.0232 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0232 2022/11/28 16:04:43 - mmengine - INFO - Epoch(train) [15][1800/2462] lr: 1.5448e-03 eta: 0:02:21 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.0182 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0182 2022/11/28 16:04:47 - mmengine - INFO - Epoch(train) [15][1900/2462] lr: 1.4480e-03 eta: 0:02:16 time: 0.0451 data_time: 0.0065 memory: 1794 loss: 0.0240 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0240 2022/11/28 16:04:52 - mmengine - INFO - Epoch(train) [15][2000/2462] lr: 1.3543e-03 eta: 0:02:12 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.0178 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0178 2022/11/28 16:04:56 - mmengine - INFO - Epoch(train) [15][2100/2462] lr: 1.2636e-03 eta: 0:02:07 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.0155 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0155 2022/11/28 16:05:01 - mmengine - INFO - Epoch(train) [15][2200/2462] lr: 1.1761e-03 eta: 0:02:03 time: 0.0445 data_time: 0.0066 memory: 1794 loss: 0.0206 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0206 2022/11/28 16:05:05 - mmengine - INFO - Epoch(train) [15][2300/2462] lr: 1.0917e-03 eta: 0:01:58 time: 0.0445 data_time: 0.0066 memory: 1794 loss: 0.0176 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0176 2022/11/28 16:05:10 - mmengine - INFO - Epoch(train) [15][2400/2462] lr: 1.0104e-03 eta: 0:01:54 time: 0.0453 data_time: 0.0074 memory: 1794 loss: 0.0206 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0206 2022/11/28 16:05:13 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 16:05:13 - mmengine - INFO - Epoch(train) [15][2462/2462] lr: 9.6151e-04 eta: 0:01:51 time: 0.0444 data_time: 0.0063 memory: 1794 loss: 0.0212 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0212 2022/11/28 16:05:13 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/11/28 16:05:16 - mmengine - INFO - Epoch(val) [15][100/398] eta: 0:00:09 time: 0.0318 data_time: 0.0182 memory: 364 2022/11/28 16:05:19 - mmengine - INFO - Epoch(val) [15][200/398] eta: 0:00:06 time: 0.0235 data_time: 0.0106 memory: 364 2022/11/28 16:05:22 - mmengine - INFO - Epoch(val) [15][300/398] eta: 0:00:02 time: 0.0241 data_time: 0.0115 memory: 364 2022/11/28 16:05:25 - mmengine - INFO - Epoch(val) [15][398/398] acc/top1: 0.8160 acc/top5: 0.9561 acc/mean1: 0.8324 2022/11/28 16:05:25 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_14.pth is removed 2022/11/28 16:05:26 - mmengine - INFO - The best checkpoint with 0.8160 acc/top1 at 15 epoch is saved to best_acc/top1_epoch_15.pth. 2022/11/28 16:05:29 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 16:05:30 - mmengine - INFO - Epoch(train) [16][100/2462] lr: 8.8525e-04 eta: 0:01:46 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.0217 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0217 2022/11/28 16:05:35 - mmengine - INFO - Epoch(train) [16][200/2462] lr: 8.1211e-04 eta: 0:01:42 time: 0.0469 data_time: 0.0068 memory: 1794 loss: 0.0152 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0152 2022/11/28 16:05:39 - mmengine - INFO - Epoch(train) [16][300/2462] lr: 7.4209e-04 eta: 0:01:37 time: 0.0448 data_time: 0.0063 memory: 1794 loss: 0.0178 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0178 2022/11/28 16:05:44 - mmengine - INFO - Epoch(train) [16][400/2462] lr: 6.7522e-04 eta: 0:01:33 time: 0.0451 data_time: 0.0067 memory: 1794 loss: 0.0200 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0200 2022/11/28 16:05:48 - mmengine - INFO - Epoch(train) [16][500/2462] lr: 6.1147e-04 eta: 0:01:28 time: 0.0453 data_time: 0.0071 memory: 1794 loss: 0.0163 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0163 2022/11/28 16:05:53 - mmengine - INFO - Epoch(train) [16][600/2462] lr: 5.5087e-04 eta: 0:01:24 time: 0.0440 data_time: 0.0063 memory: 1794 loss: 0.0156 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0156 2022/11/28 16:05:57 - mmengine - INFO - Epoch(train) [16][700/2462] lr: 4.9342e-04 eta: 0:01:19 time: 0.0443 data_time: 0.0064 memory: 1794 loss: 0.0167 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0167 2022/11/28 16:06:02 - mmengine - INFO - Epoch(train) [16][800/2462] lr: 4.3911e-04 eta: 0:01:15 time: 0.0452 data_time: 0.0067 memory: 1794 loss: 0.0145 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0145 2022/11/28 16:06:06 - mmengine - INFO - Epoch(train) [16][900/2462] lr: 3.8795e-04 eta: 0:01:10 time: 0.0456 data_time: 0.0068 memory: 1794 loss: 0.0156 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0156 2022/11/28 16:06:11 - mmengine - INFO - Epoch(train) [16][1000/2462] lr: 3.3995e-04 eta: 0:01:06 time: 0.0448 data_time: 0.0068 memory: 1794 loss: 0.0141 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0141 2022/11/28 16:06:14 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 16:06:15 - mmengine - INFO - Epoch(train) [16][1100/2462] lr: 2.9511e-04 eta: 0:01:01 time: 0.0452 data_time: 0.0071 memory: 1794 loss: 0.0212 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0212 2022/11/28 16:06:20 - mmengine - INFO - Epoch(train) [16][1200/2462] lr: 2.5343e-04 eta: 0:00:57 time: 0.0443 data_time: 0.0062 memory: 1794 loss: 0.0186 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0186 2022/11/28 16:06:24 - mmengine - INFO - Epoch(train) [16][1300/2462] lr: 2.1492e-04 eta: 0:00:52 time: 0.0455 data_time: 0.0067 memory: 1794 loss: 0.0177 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0177 2022/11/28 16:06:29 - mmengine - INFO - Epoch(train) [16][1400/2462] lr: 1.7957e-04 eta: 0:00:48 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.0174 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0174 2022/11/28 16:06:33 - mmengine - INFO - Epoch(train) [16][1500/2462] lr: 1.4739e-04 eta: 0:00:43 time: 0.0444 data_time: 0.0070 memory: 1794 loss: 0.0181 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0181 2022/11/28 16:06:38 - mmengine - INFO - Epoch(train) [16][1600/2462] lr: 1.1838e-04 eta: 0:00:38 time: 0.0448 data_time: 0.0062 memory: 1794 loss: 0.0196 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0196 2022/11/28 16:06:42 - mmengine - INFO - Epoch(train) [16][1700/2462] lr: 9.2542e-05 eta: 0:00:34 time: 0.0454 data_time: 0.0067 memory: 1794 loss: 0.0191 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0191 2022/11/28 16:06:47 - mmengine - INFO - Epoch(train) [16][1800/2462] lr: 6.9879e-05 eta: 0:00:29 time: 0.0446 data_time: 0.0067 memory: 1794 loss: 0.0182 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0182 2022/11/28 16:06:51 - mmengine - INFO - Epoch(train) [16][1900/2462] lr: 5.0393e-05 eta: 0:00:25 time: 0.0451 data_time: 0.0067 memory: 1794 loss: 0.0187 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0187 2022/11/28 16:06:56 - mmengine - INFO - Epoch(train) [16][2000/2462] lr: 3.4083e-05 eta: 0:00:20 time: 0.0447 data_time: 0.0062 memory: 1794 loss: 0.0212 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0212 2022/11/28 16:06:59 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 16:07:00 - mmengine - INFO - Epoch(train) [16][2100/2462] lr: 2.0951e-05 eta: 0:00:16 time: 0.0443 data_time: 0.0064 memory: 1794 loss: 0.0173 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0173 2022/11/28 16:07:05 - mmengine - INFO - Epoch(train) [16][2200/2462] lr: 1.0998e-05 eta: 0:00:11 time: 0.0448 data_time: 0.0069 memory: 1794 loss: 0.0182 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0182 2022/11/28 16:07:09 - mmengine - INFO - Epoch(train) [16][2300/2462] lr: 4.2247e-06 eta: 0:00:07 time: 0.0458 data_time: 0.0065 memory: 1794 loss: 0.0192 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0192 2022/11/28 16:07:14 - mmengine - INFO - Epoch(train) [16][2400/2462] lr: 6.3111e-07 eta: 0:00:02 time: 0.0458 data_time: 0.0065 memory: 1794 loss: 0.0264 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0264 2022/11/28 16:07:16 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-3d_20221128_153217 2022/11/28 16:07:16 - mmengine - INFO - Epoch(train) [16][2462/2462] lr: 1.5901e-10 eta: 0:00:00 time: 0.0445 data_time: 0.0064 memory: 1794 loss: 0.0158 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0158 2022/11/28 16:07:16 - mmengine - INFO - Saving checkpoint at 16 epochs 2022/11/28 16:07:20 - mmengine - INFO - Epoch(val) [16][100/398] eta: 0:00:10 time: 0.0323 data_time: 0.0186 memory: 364 2022/11/28 16:07:23 - mmengine - INFO - Epoch(val) [16][200/398] eta: 0:00:06 time: 0.0236 data_time: 0.0108 memory: 364 2022/11/28 16:07:26 - mmengine - INFO - Epoch(val) [16][300/398] eta: 0:00:02 time: 0.0247 data_time: 0.0119 memory: 364 2022/11/28 16:07:29 - mmengine - INFO - Epoch(val) [16][398/398] acc/top1: 0.8157 acc/top5: 0.9561 acc/mean1: 0.8324