2022/11/28 16:47:27 - 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: 639346033 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 16:47:27 - 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=['jm']), 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=['jm']), 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=['jm']), 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=['jm']), 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=['jm']), 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=['jm']), 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-motion-u100-80e_ntu120-xsub-keypoint-3d' randomness = dict(seed=None, diff_rank_seed=False, deterministic=False) 2022/11/28 16:47:27 - mmengine - INFO - Result has been saved to /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-motion-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 16:49:26 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d. 2022/11/28 16:49:32 - mmengine - INFO - Epoch(train) [1][100/2462] lr: 9.9998e-02 eta: 0:38:48 time: 0.0441 data_time: 0.0065 memory: 1794 loss: 3.8024 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 3.8024 2022/11/28 16:49:36 - mmengine - INFO - Epoch(train) [1][200/2462] lr: 9.9994e-02 eta: 0:34:01 time: 0.0429 data_time: 0.0061 memory: 1794 loss: 2.9377 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.9377 2022/11/28 16:49:41 - mmengine - INFO - Epoch(train) [1][300/2462] lr: 9.9986e-02 eta: 0:32:06 time: 0.0439 data_time: 0.0059 memory: 1794 loss: 2.5568 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 2.5568 2022/11/28 16:49:45 - mmengine - INFO - Epoch(train) [1][400/2462] lr: 9.9975e-02 eta: 0:31:04 time: 0.0431 data_time: 0.0063 memory: 1794 loss: 2.2783 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2783 2022/11/28 16:49:49 - mmengine - INFO - Epoch(train) [1][500/2462] lr: 9.9960e-02 eta: 0:30:26 time: 0.0428 data_time: 0.0059 memory: 1794 loss: 2.1156 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1156 2022/11/28 16:49:54 - mmengine - INFO - Epoch(train) [1][600/2462] lr: 9.9943e-02 eta: 0:29:56 time: 0.0439 data_time: 0.0060 memory: 1794 loss: 2.0560 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0560 2022/11/28 16:49:58 - mmengine - INFO - Epoch(train) [1][700/2462] lr: 9.9922e-02 eta: 0:29:35 time: 0.0432 data_time: 0.0058 memory: 1794 loss: 1.8023 top1_acc: 0.3125 top5_acc: 0.8125 loss_cls: 1.8023 2022/11/28 16:50:02 - mmengine - INFO - Epoch(train) [1][800/2462] lr: 9.9899e-02 eta: 0:29:19 time: 0.0448 data_time: 0.0060 memory: 1794 loss: 1.6628 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 1.6628 2022/11/28 16:50:07 - mmengine - INFO - Epoch(train) [1][900/2462] lr: 9.9872e-02 eta: 0:29:03 time: 0.0431 data_time: 0.0060 memory: 1794 loss: 1.6682 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6682 2022/11/28 16:50:11 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 16:50:11 - mmengine - INFO - Epoch(train) [1][1000/2462] lr: 9.9841e-02 eta: 0:28:53 time: 0.0428 data_time: 0.0059 memory: 1794 loss: 1.4589 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.4589 2022/11/28 16:50:15 - mmengine - INFO - Epoch(train) [1][1100/2462] lr: 9.9808e-02 eta: 0:28:42 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 1.5007 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.5007 2022/11/28 16:50:20 - mmengine - INFO - Epoch(train) [1][1200/2462] lr: 9.9772e-02 eta: 0:28:31 time: 0.0427 data_time: 0.0060 memory: 1794 loss: 1.5565 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5565 2022/11/28 16:50:24 - mmengine - INFO - Epoch(train) [1][1300/2462] lr: 9.9732e-02 eta: 0:28:22 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 1.5497 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5497 2022/11/28 16:50:28 - mmengine - INFO - Epoch(train) [1][1400/2462] lr: 9.9689e-02 eta: 0:28:16 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 1.4619 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.4619 2022/11/28 16:50:33 - mmengine - INFO - Epoch(train) [1][1500/2462] lr: 9.9643e-02 eta: 0:28:09 time: 0.0432 data_time: 0.0060 memory: 1794 loss: 1.3241 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3241 2022/11/28 16:50:37 - mmengine - INFO - Epoch(train) [1][1600/2462] lr: 9.9594e-02 eta: 0:28:01 time: 0.0431 data_time: 0.0059 memory: 1794 loss: 1.5069 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5069 2022/11/28 16:50:41 - mmengine - INFO - Epoch(train) [1][1700/2462] lr: 9.9542e-02 eta: 0:27:55 time: 0.0428 data_time: 0.0060 memory: 1794 loss: 1.5585 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.5585 2022/11/28 16:50:46 - mmengine - INFO - Epoch(train) [1][1800/2462] lr: 9.9486e-02 eta: 0:27:48 time: 0.0428 data_time: 0.0060 memory: 1794 loss: 1.3086 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3086 2022/11/28 16:50:50 - mmengine - INFO - Epoch(train) [1][1900/2462] lr: 9.9428e-02 eta: 0:27:42 time: 0.0447 data_time: 0.0062 memory: 1794 loss: 1.3328 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3328 2022/11/28 16:50:54 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 16:50:54 - mmengine - INFO - Epoch(train) [1][2000/2462] lr: 9.9366e-02 eta: 0:27:37 time: 0.0440 data_time: 0.0069 memory: 1794 loss: 1.2669 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2669 2022/11/28 16:50:59 - mmengine - INFO - Epoch(train) [1][2100/2462] lr: 9.9301e-02 eta: 0:27:31 time: 0.0434 data_time: 0.0061 memory: 1794 loss: 1.2476 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.2476 2022/11/28 16:51:03 - mmengine - INFO - Epoch(train) [1][2200/2462] lr: 9.9233e-02 eta: 0:27:26 time: 0.0443 data_time: 0.0074 memory: 1794 loss: 0.9851 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9851 2022/11/28 16:51:08 - mmengine - INFO - Epoch(train) [1][2300/2462] lr: 9.9162e-02 eta: 0:27:20 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 1.2529 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2529 2022/11/28 16:51:12 - mmengine - INFO - Epoch(train) [1][2400/2462] lr: 9.9088e-02 eta: 0:27:18 time: 0.0483 data_time: 0.0062 memory: 1794 loss: 1.1610 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.1610 2022/11/28 16:51:15 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 16:51:15 - mmengine - INFO - Epoch(train) [1][2462/2462] lr: 9.9040e-02 eta: 0:27:14 time: 0.0434 data_time: 0.0063 memory: 1794 loss: 0.9770 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9770 2022/11/28 16:51:15 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/11/28 16:51:19 - mmengine - INFO - Epoch(val) [1][100/398] eta: 0:00:10 time: 0.0334 data_time: 0.0197 memory: 364 2022/11/28 16:51:22 - mmengine - INFO - Epoch(val) [1][200/398] eta: 0:00:06 time: 0.0242 data_time: 0.0109 memory: 364 2022/11/28 16:51:25 - mmengine - INFO - Epoch(val) [1][300/398] eta: 0:00:03 time: 0.0257 data_time: 0.0130 memory: 364 2022/11/28 16:51:28 - mmengine - INFO - Epoch(val) [1][398/398] acc/top1: 0.4851 acc/top5: 0.8018 acc/mean1: 0.4953 2022/11/28 16:51:29 - mmengine - INFO - The best checkpoint with 0.4851 acc/top1 at 1 epoch is saved to best_acc/top1_epoch_1.pth. 2022/11/28 16:51:33 - mmengine - INFO - Epoch(train) [2][100/2462] lr: 9.8961e-02 eta: 0:27:11 time: 0.0435 data_time: 0.0067 memory: 1794 loss: 1.2321 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2321 2022/11/28 16:51:38 - mmengine - INFO - Epoch(train) [2][200/2462] lr: 9.8878e-02 eta: 0:27:05 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.9424 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9424 2022/11/28 16:51:42 - mmengine - INFO - Epoch(train) [2][300/2462] lr: 9.8793e-02 eta: 0:27:01 time: 0.0433 data_time: 0.0061 memory: 1794 loss: 1.2055 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2055 2022/11/28 16:51:46 - mmengine - INFO - Epoch(train) [2][400/2462] lr: 9.8704e-02 eta: 0:26:56 time: 0.0439 data_time: 0.0067 memory: 1794 loss: 1.0694 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0694 2022/11/28 16:51:51 - mmengine - INFO - Epoch(train) [2][500/2462] lr: 9.8612e-02 eta: 0:26:51 time: 0.0439 data_time: 0.0063 memory: 1794 loss: 1.2535 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.2535 2022/11/28 16:51:52 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 16:51:55 - mmengine - INFO - Epoch(train) [2][600/2462] lr: 9.8518e-02 eta: 0:26:47 time: 0.0441 data_time: 0.0071 memory: 1794 loss: 0.9613 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 0.9613 2022/11/28 16:52:00 - mmengine - INFO - Epoch(train) [2][700/2462] lr: 9.8420e-02 eta: 0:26:42 time: 0.0434 data_time: 0.0061 memory: 1794 loss: 1.0017 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0017 2022/11/28 16:52:04 - mmengine - INFO - Epoch(train) [2][800/2462] lr: 9.8319e-02 eta: 0:26:37 time: 0.0438 data_time: 0.0061 memory: 1794 loss: 0.9225 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9225 2022/11/28 16:52:08 - mmengine - INFO - Epoch(train) [2][900/2462] lr: 9.8215e-02 eta: 0:26:32 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.9819 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 0.9819 2022/11/28 16:52:13 - mmengine - INFO - Epoch(train) [2][1000/2462] lr: 9.8107e-02 eta: 0:26:27 time: 0.0430 data_time: 0.0062 memory: 1794 loss: 0.9782 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9782 2022/11/28 16:52:17 - mmengine - INFO - Epoch(train) [2][1100/2462] lr: 9.7997e-02 eta: 0:26:21 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.9441 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9441 2022/11/28 16:52:21 - mmengine - INFO - Epoch(train) [2][1200/2462] lr: 9.7884e-02 eta: 0:26:17 time: 0.0442 data_time: 0.0063 memory: 1794 loss: 1.1118 top1_acc: 0.3750 top5_acc: 0.9375 loss_cls: 1.1118 2022/11/28 16:52:26 - mmengine - INFO - Epoch(train) [2][1300/2462] lr: 9.7768e-02 eta: 0:26:13 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 1.1378 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1378 2022/11/28 16:52:30 - mmengine - INFO - Epoch(train) [2][1400/2462] lr: 9.7648e-02 eta: 0:26:09 time: 0.0470 data_time: 0.0062 memory: 1794 loss: 0.9802 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9802 2022/11/28 16:52:35 - mmengine - INFO - Epoch(train) [2][1500/2462] lr: 9.7526e-02 eta: 0:26:04 time: 0.0435 data_time: 0.0066 memory: 1794 loss: 1.0150 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0150 2022/11/28 16:52:36 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 16:52:39 - mmengine - INFO - Epoch(train) [2][1600/2462] lr: 9.7400e-02 eta: 0:25:59 time: 0.0439 data_time: 0.0063 memory: 1794 loss: 0.9123 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9123 2022/11/28 16:52:44 - mmengine - INFO - Epoch(train) [2][1700/2462] lr: 9.7272e-02 eta: 0:25:56 time: 0.0441 data_time: 0.0061 memory: 1794 loss: 0.8829 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8829 2022/11/28 16:52:48 - mmengine - INFO - Epoch(train) [2][1800/2462] lr: 9.7141e-02 eta: 0:25:51 time: 0.0433 data_time: 0.0060 memory: 1794 loss: 1.0568 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0568 2022/11/28 16:52:52 - mmengine - INFO - Epoch(train) [2][1900/2462] lr: 9.7006e-02 eta: 0:25:46 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.9073 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9073 2022/11/28 16:52:57 - mmengine - INFO - Epoch(train) [2][2000/2462] lr: 9.6869e-02 eta: 0:25:41 time: 0.0434 data_time: 0.0061 memory: 1794 loss: 0.9254 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9254 2022/11/28 16:53:01 - mmengine - INFO - Epoch(train) [2][2100/2462] lr: 9.6728e-02 eta: 0:25:36 time: 0.0436 data_time: 0.0060 memory: 1794 loss: 1.0061 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.0061 2022/11/28 16:53:05 - mmengine - INFO - Epoch(train) [2][2200/2462] lr: 9.6585e-02 eta: 0:25:31 time: 0.0434 data_time: 0.0066 memory: 1794 loss: 1.1504 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1504 2022/11/28 16:53:10 - mmengine - INFO - Epoch(train) [2][2300/2462] lr: 9.6439e-02 eta: 0:25:27 time: 0.0431 data_time: 0.0061 memory: 1794 loss: 1.0834 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0834 2022/11/28 16:53:14 - mmengine - INFO - Epoch(train) [2][2400/2462] lr: 9.6290e-02 eta: 0:25:22 time: 0.0434 data_time: 0.0062 memory: 1794 loss: 0.8802 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8802 2022/11/28 16:53:17 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 16:53:17 - mmengine - INFO - Epoch(train) [2][2462/2462] lr: 9.6196e-02 eta: 0:25:19 time: 0.0436 data_time: 0.0064 memory: 1794 loss: 1.0059 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0059 2022/11/28 16:53:17 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/11/28 16:53:21 - mmengine - INFO - Epoch(val) [2][100/398] eta: 0:00:10 time: 0.0341 data_time: 0.0208 memory: 364 2022/11/28 16:53:23 - mmengine - INFO - Epoch(val) [2][200/398] eta: 0:00:06 time: 0.0247 data_time: 0.0117 memory: 364 2022/11/28 16:53:26 - mmengine - INFO - Epoch(val) [2][300/398] eta: 0:00:02 time: 0.0256 data_time: 0.0132 memory: 364 2022/11/28 16:53:29 - mmengine - INFO - Epoch(val) [2][398/398] acc/top1: 0.4982 acc/top5: 0.7690 acc/mean1: 0.5319 2022/11/28 16:53:29 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_1.pth is removed 2022/11/28 16:53:30 - mmengine - INFO - The best checkpoint with 0.4982 acc/top1 at 2 epoch is saved to best_acc/top1_epoch_2.pth. 2022/11/28 16:53:33 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 16:53:34 - mmengine - INFO - Epoch(train) [3][100/2462] lr: 9.6041e-02 eta: 0:25:15 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.9317 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9317 2022/11/28 16:53:39 - mmengine - INFO - Epoch(train) [3][200/2462] lr: 9.5884e-02 eta: 0:25:10 time: 0.0448 data_time: 0.0063 memory: 1794 loss: 0.9252 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9252 2022/11/28 16:53:43 - mmengine - INFO - Epoch(train) [3][300/2462] lr: 9.5725e-02 eta: 0:25:06 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.8895 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8895 2022/11/28 16:53:47 - mmengine - INFO - Epoch(train) [3][400/2462] lr: 9.5562e-02 eta: 0:25:01 time: 0.0438 data_time: 0.0061 memory: 1794 loss: 0.9055 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.9055 2022/11/28 16:53:52 - mmengine - INFO - Epoch(train) [3][500/2462] lr: 9.5396e-02 eta: 0:24:56 time: 0.0435 data_time: 0.0063 memory: 1794 loss: 0.8025 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8025 2022/11/28 16:53:56 - mmengine - INFO - Epoch(train) [3][600/2462] lr: 9.5228e-02 eta: 0:24:52 time: 0.0446 data_time: 0.0080 memory: 1794 loss: 0.7608 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7608 2022/11/28 16:54:01 - mmengine - INFO - Epoch(train) [3][700/2462] lr: 9.5056e-02 eta: 0:24:48 time: 0.0429 data_time: 0.0062 memory: 1794 loss: 0.9019 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9019 2022/11/28 16:54:05 - mmengine - INFO - Epoch(train) [3][800/2462] lr: 9.4882e-02 eta: 0:24:43 time: 0.0430 data_time: 0.0062 memory: 1794 loss: 0.7644 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7644 2022/11/28 16:54:09 - mmengine - INFO - Epoch(train) [3][900/2462] lr: 9.4705e-02 eta: 0:24:39 time: 0.0447 data_time: 0.0070 memory: 1794 loss: 0.9817 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9817 2022/11/28 16:54:14 - mmengine - INFO - Epoch(train) [3][1000/2462] lr: 9.4525e-02 eta: 0:24:34 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.8393 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8393 2022/11/28 16:54:17 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 16:54:18 - mmengine - INFO - Epoch(train) [3][1100/2462] lr: 9.4342e-02 eta: 0:24:29 time: 0.0447 data_time: 0.0067 memory: 1794 loss: 0.9660 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9660 2022/11/28 16:54:22 - mmengine - INFO - Epoch(train) [3][1200/2462] lr: 9.4156e-02 eta: 0:24:25 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.8043 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8043 2022/11/28 16:54:27 - mmengine - INFO - Epoch(train) [3][1300/2462] lr: 9.3968e-02 eta: 0:24:20 time: 0.0441 data_time: 0.0065 memory: 1794 loss: 0.8986 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8986 2022/11/28 16:54:31 - mmengine - INFO - Epoch(train) [3][1400/2462] lr: 9.3776e-02 eta: 0:24:16 time: 0.0433 data_time: 0.0061 memory: 1794 loss: 0.9785 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9785 2022/11/28 16:54:36 - mmengine - INFO - Epoch(train) [3][1500/2462] lr: 9.3582e-02 eta: 0:24:11 time: 0.0432 data_time: 0.0061 memory: 1794 loss: 0.6808 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6808 2022/11/28 16:54:40 - mmengine - INFO - Epoch(train) [3][1600/2462] lr: 9.3385e-02 eta: 0:24:07 time: 0.0436 data_time: 0.0061 memory: 1794 loss: 0.9025 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9025 2022/11/28 16:54:44 - mmengine - INFO - Epoch(train) [3][1700/2462] lr: 9.3186e-02 eta: 0:24:02 time: 0.0430 data_time: 0.0062 memory: 1794 loss: 1.0078 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0078 2022/11/28 16:54:49 - mmengine - INFO - Epoch(train) [3][1800/2462] lr: 9.2983e-02 eta: 0:23:58 time: 0.0429 data_time: 0.0062 memory: 1794 loss: 0.9416 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9416 2022/11/28 16:54:53 - mmengine - INFO - Epoch(train) [3][1900/2462] lr: 9.2778e-02 eta: 0:23:53 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 0.8281 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8281 2022/11/28 16:54:58 - mmengine - INFO - Epoch(train) [3][2000/2462] lr: 9.2571e-02 eta: 0:23:49 time: 0.0442 data_time: 0.0069 memory: 1794 loss: 0.8462 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 0.8462 2022/11/28 16:55:01 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 16:55:02 - mmengine - INFO - Epoch(train) [3][2100/2462] lr: 9.2360e-02 eta: 0:23:44 time: 0.0451 data_time: 0.0070 memory: 1794 loss: 0.9707 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9707 2022/11/28 16:55:06 - mmengine - INFO - Epoch(train) [3][2200/2462] lr: 9.2147e-02 eta: 0:23:40 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 0.7253 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7253 2022/11/28 16:55:11 - mmengine - INFO - Epoch(train) [3][2300/2462] lr: 9.1931e-02 eta: 0:23:35 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.7847 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7847 2022/11/28 16:55:15 - mmengine - INFO - Epoch(train) [3][2400/2462] lr: 9.1713e-02 eta: 0:23:30 time: 0.0434 data_time: 0.0062 memory: 1794 loss: 0.9928 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9928 2022/11/28 16:55:18 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 16:55:18 - mmengine - INFO - Epoch(train) [3][2462/2462] lr: 9.1576e-02 eta: 0:23:28 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.7858 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7858 2022/11/28 16:55:18 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/11/28 16:55:22 - mmengine - INFO - Epoch(val) [3][100/398] eta: 0:00:10 time: 0.0329 data_time: 0.0196 memory: 364 2022/11/28 16:55:25 - mmengine - INFO - Epoch(val) [3][200/398] eta: 0:00:06 time: 0.0275 data_time: 0.0141 memory: 364 2022/11/28 16:55:27 - mmengine - INFO - Epoch(val) [3][300/398] eta: 0:00:02 time: 0.0258 data_time: 0.0135 memory: 364 2022/11/28 16:55:31 - mmengine - INFO - Epoch(val) [3][398/398] acc/top1: 0.4969 acc/top5: 0.7841 acc/mean1: 0.5167 2022/11/28 16:55:35 - mmengine - INFO - Epoch(train) [4][100/2462] lr: 9.1353e-02 eta: 0:23:24 time: 0.0427 data_time: 0.0061 memory: 1794 loss: 0.7640 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7640 2022/11/28 16:55:39 - mmengine - INFO - Epoch(train) [4][200/2462] lr: 9.1127e-02 eta: 0:23:19 time: 0.0439 data_time: 0.0065 memory: 1794 loss: 0.9268 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9268 2022/11/28 16:55:44 - mmengine - INFO - Epoch(train) [4][300/2462] lr: 9.0899e-02 eta: 0:23:15 time: 0.0430 data_time: 0.0062 memory: 1794 loss: 0.8491 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8491 2022/11/28 16:55:48 - mmengine - INFO - Epoch(train) [4][400/2462] lr: 9.0669e-02 eta: 0:23:10 time: 0.0433 data_time: 0.0067 memory: 1794 loss: 0.8591 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8591 2022/11/28 16:55:53 - mmengine - INFO - Epoch(train) [4][500/2462] lr: 9.0435e-02 eta: 0:23:05 time: 0.0432 data_time: 0.0060 memory: 1794 loss: 0.8187 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8187 2022/11/28 16:55:57 - mmengine - INFO - Epoch(train) [4][600/2462] lr: 9.0200e-02 eta: 0:23:01 time: 0.0480 data_time: 0.0101 memory: 1794 loss: 0.8470 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8470 2022/11/28 16:55:58 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 16:56:01 - mmengine - INFO - Epoch(train) [4][700/2462] lr: 8.9961e-02 eta: 0:22:57 time: 0.0440 data_time: 0.0066 memory: 1794 loss: 0.8952 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8952 2022/11/28 16:56:06 - mmengine - INFO - Epoch(train) [4][800/2462] lr: 8.9720e-02 eta: 0:22:53 time: 0.0435 data_time: 0.0065 memory: 1794 loss: 0.7584 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7584 2022/11/28 16:56:10 - mmengine - INFO - Epoch(train) [4][900/2462] lr: 8.9477e-02 eta: 0:22:49 time: 0.0456 data_time: 0.0082 memory: 1794 loss: 0.9139 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 0.9139 2022/11/28 16:56:15 - mmengine - INFO - Epoch(train) [4][1000/2462] lr: 8.9231e-02 eta: 0:22:44 time: 0.0433 data_time: 0.0064 memory: 1794 loss: 0.7056 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7056 2022/11/28 16:56:19 - mmengine - INFO - Epoch(train) [4][1100/2462] lr: 8.8982e-02 eta: 0:22:40 time: 0.0433 data_time: 0.0061 memory: 1794 loss: 0.8259 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8259 2022/11/28 16:56:23 - mmengine - INFO - Epoch(train) [4][1200/2462] lr: 8.8731e-02 eta: 0:22:35 time: 0.0436 data_time: 0.0061 memory: 1794 loss: 0.7990 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7990 2022/11/28 16:56:28 - mmengine - INFO - Epoch(train) [4][1300/2462] lr: 8.8478e-02 eta: 0:22:31 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.7454 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7454 2022/11/28 16:56:32 - mmengine - INFO - Epoch(train) [4][1400/2462] lr: 8.8222e-02 eta: 0:22:27 time: 0.0453 data_time: 0.0064 memory: 1794 loss: 0.6386 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6386 2022/11/28 16:56:37 - mmengine - INFO - Epoch(train) [4][1500/2462] lr: 8.7964e-02 eta: 0:22:22 time: 0.0452 data_time: 0.0063 memory: 1794 loss: 0.7377 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7377 2022/11/28 16:56:41 - mmengine - INFO - Epoch(train) [4][1600/2462] lr: 8.7703e-02 eta: 0:22:18 time: 0.0439 data_time: 0.0070 memory: 1794 loss: 0.7568 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7568 2022/11/28 16:56:42 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 16:56:46 - mmengine - INFO - Epoch(train) [4][1700/2462] lr: 8.7440e-02 eta: 0:22:13 time: 0.0443 data_time: 0.0068 memory: 1794 loss: 0.8858 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8858 2022/11/28 16:56:50 - mmengine - INFO - Epoch(train) [4][1800/2462] lr: 8.7174e-02 eta: 0:22:09 time: 0.0433 data_time: 0.0066 memory: 1794 loss: 0.8240 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8240 2022/11/28 16:56:54 - mmengine - INFO - Epoch(train) [4][1900/2462] lr: 8.6907e-02 eta: 0:22:05 time: 0.0458 data_time: 0.0063 memory: 1794 loss: 0.7892 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7892 2022/11/28 16:56:59 - mmengine - INFO - Epoch(train) [4][2000/2462] lr: 8.6636e-02 eta: 0:22:00 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.8465 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8465 2022/11/28 16:57:03 - mmengine - INFO - Epoch(train) [4][2100/2462] lr: 8.6364e-02 eta: 0:21:56 time: 0.0439 data_time: 0.0068 memory: 1794 loss: 0.7570 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7570 2022/11/28 16:57:08 - mmengine - INFO - Epoch(train) [4][2200/2462] lr: 8.6089e-02 eta: 0:21:51 time: 0.0437 data_time: 0.0061 memory: 1794 loss: 0.9014 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9014 2022/11/28 16:57:12 - mmengine - INFO - Epoch(train) [4][2300/2462] lr: 8.5812e-02 eta: 0:21:47 time: 0.0434 data_time: 0.0061 memory: 1794 loss: 0.7108 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7108 2022/11/28 16:57:16 - mmengine - INFO - Epoch(train) [4][2400/2462] lr: 8.5533e-02 eta: 0:21:42 time: 0.0439 data_time: 0.0061 memory: 1794 loss: 0.7373 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7373 2022/11/28 16:57:19 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 16:57:19 - mmengine - INFO - Epoch(train) [4][2462/2462] lr: 8.5358e-02 eta: 0:21:40 time: 0.0440 data_time: 0.0061 memory: 1794 loss: 0.8019 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8019 2022/11/28 16:57:19 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/11/28 16:57:23 - mmengine - INFO - Epoch(val) [4][100/398] eta: 0:00:10 time: 0.0324 data_time: 0.0192 memory: 364 2022/11/28 16:57:26 - mmengine - INFO - Epoch(val) [4][200/398] eta: 0:00:06 time: 0.0236 data_time: 0.0111 memory: 364 2022/11/28 16:57:28 - mmengine - INFO - Epoch(val) [4][300/398] eta: 0:00:02 time: 0.0254 data_time: 0.0131 memory: 364 2022/11/28 16:57:32 - mmengine - INFO - Epoch(val) [4][398/398] acc/top1: 0.6035 acc/top5: 0.8643 acc/mean1: 0.6254 2022/11/28 16:57:32 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_2.pth is removed 2022/11/28 16:57:32 - mmengine - INFO - The best checkpoint with 0.6035 acc/top1 at 4 epoch is saved to best_acc/top1_epoch_4.pth. 2022/11/28 16:57:37 - mmengine - INFO - Epoch(train) [5][100/2462] lr: 8.5075e-02 eta: 0:21:36 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 0.7161 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7161 2022/11/28 16:57:39 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 16:57:41 - mmengine - INFO - Epoch(train) [5][200/2462] lr: 8.4790e-02 eta: 0:21:31 time: 0.0432 data_time: 0.0061 memory: 1794 loss: 0.6646 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6646 2022/11/28 16:57:45 - mmengine - INFO - Epoch(train) [5][300/2462] lr: 8.4502e-02 eta: 0:21:27 time: 0.0433 data_time: 0.0061 memory: 1794 loss: 0.6611 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.6611 2022/11/28 16:57:50 - mmengine - INFO - Epoch(train) [5][400/2462] lr: 8.4213e-02 eta: 0:21:22 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.7713 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7713 2022/11/28 16:57:54 - mmengine - INFO - Epoch(train) [5][500/2462] lr: 8.3921e-02 eta: 0:21:18 time: 0.0444 data_time: 0.0071 memory: 1794 loss: 0.6106 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6106 2022/11/28 16:57:59 - mmengine - INFO - Epoch(train) [5][600/2462] lr: 8.3627e-02 eta: 0:21:14 time: 0.0434 data_time: 0.0059 memory: 1794 loss: 0.7127 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7127 2022/11/28 16:58:03 - mmengine - INFO - Epoch(train) [5][700/2462] lr: 8.3330e-02 eta: 0:21:09 time: 0.0439 data_time: 0.0071 memory: 1794 loss: 0.8171 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8171 2022/11/28 16:58:07 - mmengine - INFO - Epoch(train) [5][800/2462] lr: 8.3032e-02 eta: 0:21:05 time: 0.0432 data_time: 0.0061 memory: 1794 loss: 0.7084 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7084 2022/11/28 16:58:12 - mmengine - INFO - Epoch(train) [5][900/2462] lr: 8.2732e-02 eta: 0:21:00 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.7817 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7817 2022/11/28 16:58:16 - mmengine - INFO - Epoch(train) [5][1000/2462] lr: 8.2429e-02 eta: 0:20:56 time: 0.0432 data_time: 0.0061 memory: 1794 loss: 0.7223 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7223 2022/11/28 16:58:21 - mmengine - INFO - Epoch(train) [5][1100/2462] lr: 8.2125e-02 eta: 0:20:52 time: 0.0432 data_time: 0.0061 memory: 1794 loss: 0.7169 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7169 2022/11/28 16:58:23 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 16:58:25 - mmengine - INFO - Epoch(train) [5][1200/2462] lr: 8.1818e-02 eta: 0:20:47 time: 0.0442 data_time: 0.0069 memory: 1794 loss: 0.6897 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6897 2022/11/28 16:58:29 - mmengine - INFO - Epoch(train) [5][1300/2462] lr: 8.1510e-02 eta: 0:20:43 time: 0.0436 data_time: 0.0061 memory: 1794 loss: 0.7565 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7565 2022/11/28 16:58:34 - mmengine - INFO - Epoch(train) [5][1400/2462] lr: 8.1199e-02 eta: 0:20:38 time: 0.0436 data_time: 0.0066 memory: 1794 loss: 0.6826 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6826 2022/11/28 16:58:38 - mmengine - INFO - Epoch(train) [5][1500/2462] lr: 8.0886e-02 eta: 0:20:34 time: 0.0435 data_time: 0.0067 memory: 1794 loss: 0.6347 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6347 2022/11/28 16:58:42 - mmengine - INFO - Epoch(train) [5][1600/2462] lr: 8.0572e-02 eta: 0:20:29 time: 0.0431 data_time: 0.0061 memory: 1794 loss: 0.7010 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.7010 2022/11/28 16:58:47 - mmengine - INFO - Epoch(train) [5][1700/2462] lr: 8.0255e-02 eta: 0:20:25 time: 0.0437 data_time: 0.0065 memory: 1794 loss: 0.8295 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8295 2022/11/28 16:58:51 - mmengine - INFO - Epoch(train) [5][1800/2462] lr: 7.9937e-02 eta: 0:20:20 time: 0.0435 data_time: 0.0063 memory: 1794 loss: 0.7572 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7572 2022/11/28 16:58:56 - mmengine - INFO - Epoch(train) [5][1900/2462] lr: 7.9617e-02 eta: 0:20:16 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.6743 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6743 2022/11/28 16:59:00 - mmengine - INFO - Epoch(train) [5][2000/2462] lr: 7.9294e-02 eta: 0:20:11 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.7254 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7254 2022/11/28 16:59:04 - mmengine - INFO - Epoch(train) [5][2100/2462] lr: 7.8970e-02 eta: 0:20:07 time: 0.0432 data_time: 0.0060 memory: 1794 loss: 0.6349 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.6349 2022/11/28 16:59:07 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 16:59:09 - mmengine - INFO - Epoch(train) [5][2200/2462] lr: 7.8644e-02 eta: 0:20:02 time: 0.0434 data_time: 0.0064 memory: 1794 loss: 0.8153 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.8153 2022/11/28 16:59:13 - mmengine - INFO - Epoch(train) [5][2300/2462] lr: 7.8317e-02 eta: 0:19:58 time: 0.0435 data_time: 0.0061 memory: 1794 loss: 0.7663 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7663 2022/11/28 16:59:18 - mmengine - INFO - Epoch(train) [5][2400/2462] lr: 7.7987e-02 eta: 0:19:54 time: 0.0436 data_time: 0.0063 memory: 1794 loss: 0.6696 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6696 2022/11/28 16:59:20 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 16:59:20 - mmengine - INFO - Epoch(train) [5][2462/2462] lr: 7.7782e-02 eta: 0:19:51 time: 0.0462 data_time: 0.0062 memory: 1794 loss: 0.6475 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6475 2022/11/28 16:59:20 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/11/28 16:59:24 - mmengine - INFO - Epoch(val) [5][100/398] eta: 0:00:10 time: 0.0347 data_time: 0.0212 memory: 364 2022/11/28 16:59:27 - mmengine - INFO - Epoch(val) [5][200/398] eta: 0:00:06 time: 0.0245 data_time: 0.0115 memory: 364 2022/11/28 16:59:29 - mmengine - INFO - Epoch(val) [5][300/398] eta: 0:00:02 time: 0.0248 data_time: 0.0125 memory: 364 2022/11/28 16:59:33 - mmengine - INFO - Epoch(val) [5][398/398] acc/top1: 0.6390 acc/top5: 0.8745 acc/mean1: 0.6506 2022/11/28 16:59:33 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_4.pth is removed 2022/11/28 16:59:33 - mmengine - INFO - The best checkpoint with 0.6390 acc/top1 at 5 epoch is saved to best_acc/top1_epoch_5.pth. 2022/11/28 16:59:38 - mmengine - INFO - Epoch(train) [6][100/2462] lr: 7.7449e-02 eta: 0:19:47 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.6904 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6904 2022/11/28 16:59:42 - mmengine - INFO - Epoch(train) [6][200/2462] lr: 7.7115e-02 eta: 0:19:42 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.7553 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.7553 2022/11/28 16:59:47 - mmengine - INFO - Epoch(train) [6][300/2462] lr: 7.6779e-02 eta: 0:19:38 time: 0.0447 data_time: 0.0062 memory: 1794 loss: 0.7282 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.7282 2022/11/28 16:59:51 - mmengine - INFO - Epoch(train) [6][400/2462] lr: 7.6442e-02 eta: 0:19:34 time: 0.0448 data_time: 0.0060 memory: 1794 loss: 0.7841 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7841 2022/11/28 16:59:56 - mmengine - INFO - Epoch(train) [6][500/2462] lr: 7.6102e-02 eta: 0:19:30 time: 0.0442 data_time: 0.0067 memory: 1794 loss: 0.7673 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7673 2022/11/28 17:00:00 - mmengine - INFO - Epoch(train) [6][600/2462] lr: 7.5762e-02 eta: 0:19:25 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.6256 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6256 2022/11/28 17:00:04 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:00:05 - mmengine - INFO - Epoch(train) [6][700/2462] lr: 7.5419e-02 eta: 0:19:21 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.7101 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7101 2022/11/28 17:00:09 - mmengine - INFO - Epoch(train) [6][800/2462] lr: 7.5075e-02 eta: 0:19:17 time: 0.0461 data_time: 0.0076 memory: 1794 loss: 0.6573 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6573 2022/11/28 17:00:13 - mmengine - INFO - Epoch(train) [6][900/2462] lr: 7.4729e-02 eta: 0:19:12 time: 0.0440 data_time: 0.0063 memory: 1794 loss: 0.6995 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.6995 2022/11/28 17:00:18 - mmengine - INFO - Epoch(train) [6][1000/2462] lr: 7.4382e-02 eta: 0:19:08 time: 0.0444 data_time: 0.0061 memory: 1794 loss: 0.6694 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6694 2022/11/28 17:00:22 - mmengine - INFO - Epoch(train) [6][1100/2462] lr: 7.4033e-02 eta: 0:19:03 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.7090 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7090 2022/11/28 17:00:27 - mmengine - INFO - Epoch(train) [6][1200/2462] lr: 7.3682e-02 eta: 0:18:59 time: 0.0440 data_time: 0.0061 memory: 1794 loss: 0.7122 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7122 2022/11/28 17:00:31 - mmengine - INFO - Epoch(train) [6][1300/2462] lr: 7.3330e-02 eta: 0:18:54 time: 0.0436 data_time: 0.0061 memory: 1794 loss: 0.8471 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8471 2022/11/28 17:00:35 - mmengine - INFO - Epoch(train) [6][1400/2462] lr: 7.2977e-02 eta: 0:18:50 time: 0.0445 data_time: 0.0067 memory: 1794 loss: 0.6427 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6427 2022/11/28 17:00:40 - mmengine - INFO - Epoch(train) [6][1500/2462] lr: 7.2622e-02 eta: 0:18:46 time: 0.0436 data_time: 0.0064 memory: 1794 loss: 0.5242 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5242 2022/11/28 17:00:44 - mmengine - INFO - Epoch(train) [6][1600/2462] lr: 7.2266e-02 eta: 0:18:42 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.7245 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7245 2022/11/28 17:00:48 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:00:49 - mmengine - INFO - Epoch(train) [6][1700/2462] lr: 7.1908e-02 eta: 0:18:37 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.6881 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6881 2022/11/28 17:00:53 - mmengine - INFO - Epoch(train) [6][1800/2462] lr: 7.1549e-02 eta: 0:18:33 time: 0.0435 data_time: 0.0064 memory: 1794 loss: 0.7006 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7006 2022/11/28 17:00:58 - mmengine - INFO - Epoch(train) [6][1900/2462] lr: 7.1188e-02 eta: 0:18:28 time: 0.0439 data_time: 0.0061 memory: 1794 loss: 0.6621 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6621 2022/11/28 17:01:02 - mmengine - INFO - Epoch(train) [6][2000/2462] lr: 7.0826e-02 eta: 0:18:24 time: 0.0442 data_time: 0.0063 memory: 1794 loss: 0.6983 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6983 2022/11/28 17:01:06 - mmengine - INFO - Epoch(train) [6][2100/2462] lr: 7.0463e-02 eta: 0:18:20 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.5744 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5744 2022/11/28 17:01:11 - mmengine - INFO - Epoch(train) [6][2200/2462] lr: 7.0099e-02 eta: 0:18:15 time: 0.0443 data_time: 0.0062 memory: 1794 loss: 0.6811 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6811 2022/11/28 17:01:15 - mmengine - INFO - Epoch(train) [6][2300/2462] lr: 6.9733e-02 eta: 0:18:11 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.7014 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7014 2022/11/28 17:01:20 - mmengine - INFO - Epoch(train) [6][2400/2462] lr: 6.9366e-02 eta: 0:18:07 time: 0.0435 data_time: 0.0059 memory: 1794 loss: 0.6298 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6298 2022/11/28 17:01:23 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:01:23 - mmengine - INFO - Epoch(train) [6][2462/2462] lr: 6.9138e-02 eta: 0:18:04 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.6569 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.6569 2022/11/28 17:01:23 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/11/28 17:01:27 - mmengine - INFO - Epoch(val) [6][100/398] eta: 0:00:10 time: 0.0319 data_time: 0.0187 memory: 364 2022/11/28 17:01:29 - mmengine - INFO - Epoch(val) [6][200/398] eta: 0:00:06 time: 0.0245 data_time: 0.0114 memory: 364 2022/11/28 17:01:32 - mmengine - INFO - Epoch(val) [6][300/398] eta: 0:00:02 time: 0.0273 data_time: 0.0148 memory: 364 2022/11/28 17:01:35 - mmengine - INFO - Epoch(val) [6][398/398] acc/top1: 0.6782 acc/top5: 0.8958 acc/mean1: 0.7008 2022/11/28 17:01:35 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_5.pth is removed 2022/11/28 17:01:36 - mmengine - INFO - The best checkpoint with 0.6782 acc/top1 at 6 epoch is saved to best_acc/top1_epoch_6.pth. 2022/11/28 17:01:40 - mmengine - INFO - Epoch(train) [7][100/2462] lr: 6.8769e-02 eta: 0:18:00 time: 0.0442 data_time: 0.0061 memory: 1794 loss: 0.6531 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6531 2022/11/28 17:01:45 - mmengine - INFO - Epoch(train) [7][200/2462] lr: 6.8399e-02 eta: 0:17:55 time: 0.0469 data_time: 0.0062 memory: 1794 loss: 0.7201 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7201 2022/11/28 17:01:46 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:01:49 - mmengine - INFO - Epoch(train) [7][300/2462] lr: 6.8027e-02 eta: 0:17:51 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.6679 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6679 2022/11/28 17:01:54 - mmengine - INFO - Epoch(train) [7][400/2462] lr: 6.7655e-02 eta: 0:17:47 time: 0.0446 data_time: 0.0064 memory: 1794 loss: 0.5208 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.5208 2022/11/28 17:01:58 - mmengine - INFO - Epoch(train) [7][500/2462] lr: 6.7281e-02 eta: 0:17:42 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.6003 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6003 2022/11/28 17:02:03 - mmengine - INFO - Epoch(train) [7][600/2462] lr: 6.6906e-02 eta: 0:17:38 time: 0.0437 data_time: 0.0065 memory: 1794 loss: 0.6217 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.6217 2022/11/28 17:02:07 - mmengine - INFO - Epoch(train) [7][700/2462] lr: 6.6531e-02 eta: 0:17:33 time: 0.0437 data_time: 0.0064 memory: 1794 loss: 0.5161 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5161 2022/11/28 17:02:11 - mmengine - INFO - Epoch(train) [7][800/2462] lr: 6.6154e-02 eta: 0:17:29 time: 0.0434 data_time: 0.0062 memory: 1794 loss: 0.6571 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6571 2022/11/28 17:02:16 - mmengine - INFO - Epoch(train) [7][900/2462] lr: 6.5776e-02 eta: 0:17:24 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.6683 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.6683 2022/11/28 17:02:20 - mmengine - INFO - Epoch(train) [7][1000/2462] lr: 6.5397e-02 eta: 0:17:20 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.5533 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5533 2022/11/28 17:02:25 - mmengine - INFO - Epoch(train) [7][1100/2462] lr: 6.5017e-02 eta: 0:17:16 time: 0.0442 data_time: 0.0064 memory: 1794 loss: 0.6911 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.6911 2022/11/28 17:02:29 - mmengine - INFO - Epoch(train) [7][1200/2462] lr: 6.4636e-02 eta: 0:17:11 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.6497 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6497 2022/11/28 17:02:30 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:02:33 - mmengine - INFO - Epoch(train) [7][1300/2462] lr: 6.4255e-02 eta: 0:17:07 time: 0.0465 data_time: 0.0077 memory: 1794 loss: 0.6733 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6733 2022/11/28 17:02:38 - mmengine - INFO - Epoch(train) [7][1400/2462] lr: 6.3872e-02 eta: 0:17:03 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 0.7077 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7077 2022/11/28 17:02:42 - mmengine - INFO - Epoch(train) [7][1500/2462] lr: 6.3488e-02 eta: 0:16:58 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 0.6239 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.6239 2022/11/28 17:02:47 - mmengine - INFO - Epoch(train) [7][1600/2462] lr: 6.3104e-02 eta: 0:16:54 time: 0.0434 data_time: 0.0061 memory: 1794 loss: 0.5809 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5809 2022/11/28 17:02:51 - mmengine - INFO - Epoch(train) [7][1700/2462] lr: 6.2719e-02 eta: 0:16:49 time: 0.0434 data_time: 0.0063 memory: 1794 loss: 0.6370 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6370 2022/11/28 17:02:55 - mmengine - INFO - Epoch(train) [7][1800/2462] lr: 6.2333e-02 eta: 0:16:45 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.5849 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5849 2022/11/28 17:03:00 - mmengine - INFO - Epoch(train) [7][1900/2462] lr: 6.1946e-02 eta: 0:16:40 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.5375 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5375 2022/11/28 17:03:04 - mmengine - INFO - Epoch(train) [7][2000/2462] lr: 6.1558e-02 eta: 0:16:36 time: 0.0434 data_time: 0.0061 memory: 1794 loss: 0.5799 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5799 2022/11/28 17:03:09 - mmengine - INFO - Epoch(train) [7][2100/2462] lr: 6.1170e-02 eta: 0:16:31 time: 0.0441 data_time: 0.0069 memory: 1794 loss: 0.6879 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6879 2022/11/28 17:03:13 - mmengine - INFO - Epoch(train) [7][2200/2462] lr: 6.0781e-02 eta: 0:16:27 time: 0.0449 data_time: 0.0062 memory: 1794 loss: 0.6703 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6703 2022/11/28 17:03:14 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:03:17 - mmengine - INFO - Epoch(train) [7][2300/2462] lr: 6.0391e-02 eta: 0:16:23 time: 0.0447 data_time: 0.0061 memory: 1794 loss: 0.5839 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5839 2022/11/28 17:03:22 - mmengine - INFO - Epoch(train) [7][2400/2462] lr: 6.0001e-02 eta: 0:16:18 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.6643 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6643 2022/11/28 17:03:25 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:03:25 - mmengine - INFO - Epoch(train) [7][2462/2462] lr: 5.9758e-02 eta: 0:16:16 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.6046 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6046 2022/11/28 17:03:25 - mmengine - INFO - Saving checkpoint at 7 epochs 2022/11/28 17:03:28 - mmengine - INFO - Epoch(val) [7][100/398] eta: 0:00:10 time: 0.0326 data_time: 0.0193 memory: 364 2022/11/28 17:03:31 - mmengine - INFO - Epoch(val) [7][200/398] eta: 0:00:06 time: 0.0237 data_time: 0.0110 memory: 364 2022/11/28 17:03:34 - mmengine - INFO - Epoch(val) [7][300/398] eta: 0:00:02 time: 0.0250 data_time: 0.0129 memory: 364 2022/11/28 17:03:37 - mmengine - INFO - Epoch(val) [7][398/398] acc/top1: 0.6747 acc/top5: 0.9071 acc/mean1: 0.6971 2022/11/28 17:03:42 - mmengine - INFO - Epoch(train) [8][100/2462] lr: 5.9367e-02 eta: 0:16:11 time: 0.0445 data_time: 0.0074 memory: 1794 loss: 0.4967 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.4967 2022/11/28 17:03:46 - mmengine - INFO - Epoch(train) [8][200/2462] lr: 5.8975e-02 eta: 0:16:07 time: 0.0445 data_time: 0.0079 memory: 1794 loss: 0.6051 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6051 2022/11/28 17:03:51 - mmengine - INFO - Epoch(train) [8][300/2462] lr: 5.8582e-02 eta: 0:16:03 time: 0.0432 data_time: 0.0061 memory: 1794 loss: 0.5798 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5798 2022/11/28 17:03:55 - mmengine - INFO - Epoch(train) [8][400/2462] lr: 5.8189e-02 eta: 0:15:58 time: 0.0434 data_time: 0.0062 memory: 1794 loss: 0.6066 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6066 2022/11/28 17:03:59 - mmengine - INFO - Epoch(train) [8][500/2462] lr: 5.7796e-02 eta: 0:15:54 time: 0.0443 data_time: 0.0061 memory: 1794 loss: 0.5236 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5236 2022/11/28 17:04:04 - mmengine - INFO - Epoch(train) [8][600/2462] lr: 5.7402e-02 eta: 0:15:50 time: 0.0457 data_time: 0.0073 memory: 1794 loss: 0.5671 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5671 2022/11/28 17:04:08 - mmengine - INFO - Epoch(train) [8][700/2462] lr: 5.7007e-02 eta: 0:15:45 time: 0.0440 data_time: 0.0061 memory: 1794 loss: 0.6605 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6605 2022/11/28 17:04:11 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:04:13 - mmengine - INFO - Epoch(train) [8][800/2462] lr: 5.6612e-02 eta: 0:15:41 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.5214 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5214 2022/11/28 17:04:17 - mmengine - INFO - Epoch(train) [8][900/2462] lr: 5.6216e-02 eta: 0:15:36 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.4802 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.4802 2022/11/28 17:04:22 - mmengine - INFO - Epoch(train) [8][1000/2462] lr: 5.5821e-02 eta: 0:15:32 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.6162 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6162 2022/11/28 17:04:26 - mmengine - INFO - Epoch(train) [8][1100/2462] lr: 5.5424e-02 eta: 0:15:28 time: 0.0449 data_time: 0.0079 memory: 1794 loss: 0.5792 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5792 2022/11/28 17:04:31 - mmengine - INFO - Epoch(train) [8][1200/2462] lr: 5.5028e-02 eta: 0:15:23 time: 0.0441 data_time: 0.0066 memory: 1794 loss: 0.5959 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.5959 2022/11/28 17:04:35 - mmengine - INFO - Epoch(train) [8][1300/2462] lr: 5.4631e-02 eta: 0:15:19 time: 0.0436 data_time: 0.0063 memory: 1794 loss: 0.4385 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4385 2022/11/28 17:04:39 - mmengine - INFO - Epoch(train) [8][1400/2462] lr: 5.4234e-02 eta: 0:15:15 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.5613 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.5613 2022/11/28 17:04:44 - mmengine - INFO - Epoch(train) [8][1500/2462] lr: 5.3836e-02 eta: 0:15:10 time: 0.0444 data_time: 0.0062 memory: 1794 loss: 0.5677 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.5677 2022/11/28 17:04:48 - mmengine - INFO - Epoch(train) [8][1600/2462] lr: 5.3439e-02 eta: 0:15:06 time: 0.0444 data_time: 0.0063 memory: 1794 loss: 0.4635 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4635 2022/11/28 17:04:53 - mmengine - INFO - Epoch(train) [8][1700/2462] lr: 5.3041e-02 eta: 0:15:01 time: 0.0446 data_time: 0.0061 memory: 1794 loss: 0.5635 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5635 2022/11/28 17:04:56 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:04:57 - mmengine - INFO - Epoch(train) [8][1800/2462] lr: 5.2643e-02 eta: 0:14:57 time: 0.0443 data_time: 0.0062 memory: 1794 loss: 0.5215 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.5215 2022/11/28 17:05:02 - mmengine - INFO - Epoch(train) [8][1900/2462] lr: 5.2244e-02 eta: 0:14:53 time: 0.0437 data_time: 0.0063 memory: 1794 loss: 0.5769 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.5769 2022/11/28 17:05:06 - mmengine - INFO - Epoch(train) [8][2000/2462] lr: 5.1846e-02 eta: 0:14:48 time: 0.0432 data_time: 0.0061 memory: 1794 loss: 0.5954 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.5954 2022/11/28 17:05:10 - mmengine - INFO - Epoch(train) [8][2100/2462] lr: 5.1447e-02 eta: 0:14:44 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.4794 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.4794 2022/11/28 17:05:15 - mmengine - INFO - Epoch(train) [8][2200/2462] lr: 5.1049e-02 eta: 0:14:39 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.5008 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5008 2022/11/28 17:05:19 - mmengine - INFO - Epoch(train) [8][2300/2462] lr: 5.0650e-02 eta: 0:14:35 time: 0.0430 data_time: 0.0061 memory: 1794 loss: 0.5265 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5265 2022/11/28 17:05:24 - mmengine - INFO - Epoch(train) [8][2400/2462] lr: 5.0251e-02 eta: 0:14:31 time: 0.0449 data_time: 0.0064 memory: 1794 loss: 0.4470 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4470 2022/11/28 17:05:27 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:05:27 - mmengine - INFO - Epoch(train) [8][2462/2462] lr: 5.0004e-02 eta: 0:14:28 time: 0.0442 data_time: 0.0063 memory: 1794 loss: 0.4981 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4981 2022/11/28 17:05:27 - mmengine - INFO - Saving checkpoint at 8 epochs 2022/11/28 17:05:30 - mmengine - INFO - Epoch(val) [8][100/398] eta: 0:00:09 time: 0.0321 data_time: 0.0188 memory: 364 2022/11/28 17:05:33 - mmengine - INFO - Epoch(val) [8][200/398] eta: 0:00:06 time: 0.0241 data_time: 0.0112 memory: 364 2022/11/28 17:05:36 - mmengine - INFO - Epoch(val) [8][300/398] eta: 0:00:02 time: 0.0245 data_time: 0.0121 memory: 364 2022/11/28 17:05:39 - mmengine - INFO - Epoch(val) [8][398/398] acc/top1: 0.7008 acc/top5: 0.9154 acc/mean1: 0.7112 2022/11/28 17:05:39 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_6.pth is removed 2022/11/28 17:05:39 - mmengine - INFO - The best checkpoint with 0.7008 acc/top1 at 8 epoch is saved to best_acc/top1_epoch_8.pth. 2022/11/28 17:05:44 - mmengine - INFO - Epoch(train) [9][100/2462] lr: 4.9605e-02 eta: 0:14:24 time: 0.0447 data_time: 0.0064 memory: 1794 loss: 0.4955 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.4955 2022/11/28 17:05:49 - mmengine - INFO - Epoch(train) [9][200/2462] lr: 4.9207e-02 eta: 0:14:19 time: 0.0439 data_time: 0.0063 memory: 1794 loss: 0.4862 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.4862 2022/11/28 17:05:53 - mmengine - INFO - Epoch(train) [9][300/2462] lr: 4.8808e-02 eta: 0:14:15 time: 0.0444 data_time: 0.0062 memory: 1794 loss: 0.6415 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.6415 2022/11/28 17:05:53 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:05:57 - mmengine - INFO - Epoch(train) [9][400/2462] lr: 4.8409e-02 eta: 0:14:11 time: 0.0443 data_time: 0.0063 memory: 1794 loss: 0.5103 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5103 2022/11/28 17:06:02 - mmengine - INFO - Epoch(train) [9][500/2462] lr: 4.8011e-02 eta: 0:14:06 time: 0.0434 data_time: 0.0064 memory: 1794 loss: 0.3857 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3857 2022/11/28 17:06:06 - mmengine - INFO - Epoch(train) [9][600/2462] lr: 4.7612e-02 eta: 0:14:02 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.5444 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5444 2022/11/28 17:06:11 - mmengine - INFO - Epoch(train) [9][700/2462] lr: 4.7214e-02 eta: 0:13:57 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.4995 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4995 2022/11/28 17:06:15 - mmengine - INFO - Epoch(train) [9][800/2462] lr: 4.6816e-02 eta: 0:13:53 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.4099 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4099 2022/11/28 17:06:19 - mmengine - INFO - Epoch(train) [9][900/2462] lr: 4.6418e-02 eta: 0:13:48 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.4873 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4873 2022/11/28 17:06:24 - mmengine - INFO - Epoch(train) [9][1000/2462] lr: 4.6021e-02 eta: 0:13:44 time: 0.0442 data_time: 0.0065 memory: 1794 loss: 0.4819 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4819 2022/11/28 17:06:28 - mmengine - INFO - Epoch(train) [9][1100/2462] lr: 4.5623e-02 eta: 0:13:40 time: 0.0444 data_time: 0.0063 memory: 1794 loss: 0.6197 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6197 2022/11/28 17:06:33 - mmengine - INFO - Epoch(train) [9][1200/2462] lr: 4.5226e-02 eta: 0:13:35 time: 0.0443 data_time: 0.0063 memory: 1794 loss: 0.4129 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4129 2022/11/28 17:06:37 - mmengine - INFO - Epoch(train) [9][1300/2462] lr: 4.4829e-02 eta: 0:13:31 time: 0.0449 data_time: 0.0068 memory: 1794 loss: 0.4576 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.4576 2022/11/28 17:06:37 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:06:42 - mmengine - INFO - Epoch(train) [9][1400/2462] lr: 4.4433e-02 eta: 0:13:26 time: 0.0442 data_time: 0.0066 memory: 1794 loss: 0.4267 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4267 2022/11/28 17:06:46 - mmengine - INFO - Epoch(train) [9][1500/2462] lr: 4.4037e-02 eta: 0:13:22 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.5391 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.5391 2022/11/28 17:06:50 - mmengine - INFO - Epoch(train) [9][1600/2462] lr: 4.3641e-02 eta: 0:13:18 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.4342 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.4342 2022/11/28 17:06:55 - mmengine - INFO - Epoch(train) [9][1700/2462] lr: 4.3246e-02 eta: 0:13:13 time: 0.0439 data_time: 0.0065 memory: 1794 loss: 0.4819 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4819 2022/11/28 17:06:59 - mmengine - INFO - Epoch(train) [9][1800/2462] lr: 4.2851e-02 eta: 0:13:09 time: 0.0450 data_time: 0.0070 memory: 1794 loss: 0.4404 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4404 2022/11/28 17:07:04 - mmengine - INFO - Epoch(train) [9][1900/2462] lr: 4.2456e-02 eta: 0:13:04 time: 0.0445 data_time: 0.0070 memory: 1794 loss: 0.5361 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5361 2022/11/28 17:07:08 - mmengine - INFO - Epoch(train) [9][2000/2462] lr: 4.2063e-02 eta: 0:13:00 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.5361 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5361 2022/11/28 17:07:13 - mmengine - INFO - Epoch(train) [9][2100/2462] lr: 4.1669e-02 eta: 0:12:56 time: 0.0440 data_time: 0.0069 memory: 1794 loss: 0.4943 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4943 2022/11/28 17:07:17 - mmengine - INFO - Epoch(train) [9][2200/2462] lr: 4.1276e-02 eta: 0:12:51 time: 0.0449 data_time: 0.0069 memory: 1794 loss: 0.3989 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.3989 2022/11/28 17:07:22 - mmengine - INFO - Epoch(train) [9][2300/2462] lr: 4.0884e-02 eta: 0:12:47 time: 0.0437 data_time: 0.0063 memory: 1794 loss: 0.4124 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4124 2022/11/28 17:07:22 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:07:26 - mmengine - INFO - Epoch(train) [9][2400/2462] lr: 4.0492e-02 eta: 0:12:43 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.5399 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5399 2022/11/28 17:07:29 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:07:29 - mmengine - INFO - Epoch(train) [9][2462/2462] lr: 4.0249e-02 eta: 0:12:40 time: 0.0446 data_time: 0.0069 memory: 1794 loss: 0.4066 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.4066 2022/11/28 17:07:29 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/11/28 17:07:33 - mmengine - INFO - Epoch(val) [9][100/398] eta: 0:00:10 time: 0.0343 data_time: 0.0206 memory: 364 2022/11/28 17:07:36 - mmengine - INFO - Epoch(val) [9][200/398] eta: 0:00:06 time: 0.0237 data_time: 0.0109 memory: 364 2022/11/28 17:07:38 - mmengine - INFO - Epoch(val) [9][300/398] eta: 0:00:02 time: 0.0270 data_time: 0.0149 memory: 364 2022/11/28 17:07:42 - mmengine - INFO - Epoch(val) [9][398/398] acc/top1: 0.7002 acc/top5: 0.9153 acc/mean1: 0.7144 2022/11/28 17:07:46 - mmengine - INFO - Epoch(train) [10][100/2462] lr: 3.9859e-02 eta: 0:12:36 time: 0.0432 data_time: 0.0061 memory: 1794 loss: 0.3479 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3479 2022/11/28 17:07:51 - mmengine - INFO - Epoch(train) [10][200/2462] lr: 3.9468e-02 eta: 0:12:31 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.4971 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.4971 2022/11/28 17:07:55 - mmengine - INFO - Epoch(train) [10][300/2462] lr: 3.9079e-02 eta: 0:12:27 time: 0.0448 data_time: 0.0065 memory: 1794 loss: 0.4511 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4511 2022/11/28 17:08:00 - mmengine - INFO - Epoch(train) [10][400/2462] lr: 3.8690e-02 eta: 0:12:22 time: 0.0437 data_time: 0.0064 memory: 1794 loss: 0.4410 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.4410 2022/11/28 17:08:04 - mmengine - INFO - Epoch(train) [10][500/2462] lr: 3.8302e-02 eta: 0:12:18 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.4083 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.4083 2022/11/28 17:08:09 - mmengine - INFO - Epoch(train) [10][600/2462] lr: 3.7915e-02 eta: 0:12:14 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.4602 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4602 2022/11/28 17:08:13 - mmengine - INFO - Epoch(train) [10][700/2462] lr: 3.7528e-02 eta: 0:12:09 time: 0.0451 data_time: 0.0065 memory: 1794 loss: 0.4685 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4685 2022/11/28 17:08:17 - mmengine - INFO - Epoch(train) [10][800/2462] lr: 3.7143e-02 eta: 0:12:05 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.3219 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3219 2022/11/28 17:08:19 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:08:22 - mmengine - INFO - Epoch(train) [10][900/2462] lr: 3.6758e-02 eta: 0:12:00 time: 0.0441 data_time: 0.0061 memory: 1794 loss: 0.4615 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4615 2022/11/28 17:08:26 - mmengine - INFO - Epoch(train) [10][1000/2462] lr: 3.6373e-02 eta: 0:11:56 time: 0.0439 data_time: 0.0061 memory: 1794 loss: 0.3897 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.3897 2022/11/28 17:08:31 - mmengine - INFO - Epoch(train) [10][1100/2462] lr: 3.5990e-02 eta: 0:11:51 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.4203 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4203 2022/11/28 17:08:35 - mmengine - INFO - Epoch(train) [10][1200/2462] lr: 3.5608e-02 eta: 0:11:47 time: 0.0444 data_time: 0.0061 memory: 1794 loss: 0.5367 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5367 2022/11/28 17:08:39 - mmengine - INFO - Epoch(train) [10][1300/2462] lr: 3.5226e-02 eta: 0:11:43 time: 0.0440 data_time: 0.0063 memory: 1794 loss: 0.4535 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.4535 2022/11/28 17:08:44 - mmengine - INFO - Epoch(train) [10][1400/2462] lr: 3.4846e-02 eta: 0:11:38 time: 0.0442 data_time: 0.0064 memory: 1794 loss: 0.4342 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4342 2022/11/28 17:08:48 - mmengine - INFO - Epoch(train) [10][1500/2462] lr: 3.4466e-02 eta: 0:11:34 time: 0.0448 data_time: 0.0064 memory: 1794 loss: 0.4014 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4014 2022/11/28 17:08:53 - mmengine - INFO - Epoch(train) [10][1600/2462] lr: 3.4088e-02 eta: 0:11:29 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.4201 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4201 2022/11/28 17:08:57 - mmengine - INFO - Epoch(train) [10][1700/2462] lr: 3.3710e-02 eta: 0:11:25 time: 0.0448 data_time: 0.0062 memory: 1794 loss: 0.3295 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3295 2022/11/28 17:09:01 - mmengine - INFO - Epoch(train) [10][1800/2462] lr: 3.3334e-02 eta: 0:11:21 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.4861 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4861 2022/11/28 17:09:03 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:09:06 - mmengine - INFO - Epoch(train) [10][1900/2462] lr: 3.2959e-02 eta: 0:11:16 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.4340 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4340 2022/11/28 17:09:10 - mmengine - INFO - Epoch(train) [10][2000/2462] lr: 3.2584e-02 eta: 0:11:12 time: 0.0431 data_time: 0.0061 memory: 1794 loss: 0.3781 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.3781 2022/11/28 17:09:15 - mmengine - INFO - Epoch(train) [10][2100/2462] lr: 3.2211e-02 eta: 0:11:07 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.4502 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.4502 2022/11/28 17:09:19 - mmengine - INFO - Epoch(train) [10][2200/2462] lr: 3.1839e-02 eta: 0:11:03 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.4086 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.4086 2022/11/28 17:09:24 - mmengine - INFO - Epoch(train) [10][2300/2462] lr: 3.1468e-02 eta: 0:10:58 time: 0.0447 data_time: 0.0066 memory: 1794 loss: 0.3986 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3986 2022/11/28 17:09:28 - mmengine - INFO - Epoch(train) [10][2400/2462] lr: 3.1098e-02 eta: 0:10:54 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.3551 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3551 2022/11/28 17:09:31 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:09:31 - mmengine - INFO - Epoch(train) [10][2462/2462] lr: 3.0870e-02 eta: 0:10:51 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.3284 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.3284 2022/11/28 17:09:31 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/11/28 17:09:35 - mmengine - INFO - Epoch(val) [10][100/398] eta: 0:00:10 time: 0.0325 data_time: 0.0193 memory: 364 2022/11/28 17:09:37 - mmengine - INFO - Epoch(val) [10][200/398] eta: 0:00:06 time: 0.0240 data_time: 0.0111 memory: 364 2022/11/28 17:09:40 - mmengine - INFO - Epoch(val) [10][300/398] eta: 0:00:02 time: 0.0248 data_time: 0.0125 memory: 364 2022/11/28 17:09:44 - mmengine - INFO - Epoch(val) [10][398/398] acc/top1: 0.7187 acc/top5: 0.9244 acc/mean1: 0.7321 2022/11/28 17:09:44 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_8.pth is removed 2022/11/28 17:09:44 - mmengine - INFO - The best checkpoint with 0.7187 acc/top1 at 10 epoch is saved to best_acc/top1_epoch_10.pth. 2022/11/28 17:09:48 - mmengine - INFO - Epoch(train) [11][100/2462] lr: 3.0502e-02 eta: 0:10:47 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.3642 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3642 2022/11/28 17:09:53 - mmengine - INFO - Epoch(train) [11][200/2462] lr: 3.0135e-02 eta: 0:10:43 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.3328 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3328 2022/11/28 17:09:57 - mmengine - INFO - Epoch(train) [11][300/2462] lr: 2.9770e-02 eta: 0:10:38 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.3713 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3713 2022/11/28 17:10:01 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:10:02 - mmengine - INFO - Epoch(train) [11][400/2462] lr: 2.9406e-02 eta: 0:10:34 time: 0.0443 data_time: 0.0063 memory: 1794 loss: 0.3677 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.3677 2022/11/28 17:10:06 - mmengine - INFO - Epoch(train) [11][500/2462] lr: 2.9043e-02 eta: 0:10:29 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.4253 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4253 2022/11/28 17:10:11 - mmengine - INFO - Epoch(train) [11][600/2462] lr: 2.8682e-02 eta: 0:10:25 time: 0.0447 data_time: 0.0063 memory: 1794 loss: 0.3008 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3008 2022/11/28 17:10:15 - mmengine - INFO - Epoch(train) [11][700/2462] lr: 2.8322e-02 eta: 0:10:21 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.4007 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4007 2022/11/28 17:10:20 - mmengine - INFO - Epoch(train) [11][800/2462] lr: 2.7963e-02 eta: 0:10:16 time: 0.0441 data_time: 0.0064 memory: 1794 loss: 0.3965 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3965 2022/11/28 17:10:24 - mmengine - INFO - Epoch(train) [11][900/2462] lr: 2.7606e-02 eta: 0:10:12 time: 0.0446 data_time: 0.0065 memory: 1794 loss: 0.3433 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3433 2022/11/28 17:10:29 - mmengine - INFO - Epoch(train) [11][1000/2462] lr: 2.7250e-02 eta: 0:10:07 time: 0.0441 data_time: 0.0064 memory: 1794 loss: 0.3789 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3789 2022/11/28 17:10:33 - mmengine - INFO - Epoch(train) [11][1100/2462] lr: 2.6896e-02 eta: 0:10:03 time: 0.0449 data_time: 0.0067 memory: 1794 loss: 0.3758 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.3758 2022/11/28 17:10:37 - mmengine - INFO - Epoch(train) [11][1200/2462] lr: 2.6543e-02 eta: 0:09:59 time: 0.0446 data_time: 0.0064 memory: 1794 loss: 0.2696 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2696 2022/11/28 17:10:42 - mmengine - INFO - Epoch(train) [11][1300/2462] lr: 2.6191e-02 eta: 0:09:54 time: 0.0439 data_time: 0.0063 memory: 1794 loss: 0.2937 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2937 2022/11/28 17:10:45 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:10:46 - mmengine - INFO - Epoch(train) [11][1400/2462] lr: 2.5841e-02 eta: 0:09:50 time: 0.0436 data_time: 0.0063 memory: 1794 loss: 0.3727 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3727 2022/11/28 17:10:51 - mmengine - INFO - Epoch(train) [11][1500/2462] lr: 2.5493e-02 eta: 0:09:45 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.2951 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.2951 2022/11/28 17:10:55 - mmengine - INFO - Epoch(train) [11][1600/2462] lr: 2.5146e-02 eta: 0:09:41 time: 0.0447 data_time: 0.0062 memory: 1794 loss: 0.3147 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3147 2022/11/28 17:11:00 - mmengine - INFO - Epoch(train) [11][1700/2462] lr: 2.4801e-02 eta: 0:09:37 time: 0.0441 data_time: 0.0064 memory: 1794 loss: 0.3420 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3420 2022/11/28 17:11:04 - mmengine - INFO - Epoch(train) [11][1800/2462] lr: 2.4458e-02 eta: 0:09:32 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.2951 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2951 2022/11/28 17:11:08 - mmengine - INFO - Epoch(train) [11][1900/2462] lr: 2.4116e-02 eta: 0:09:28 time: 0.0440 data_time: 0.0063 memory: 1794 loss: 0.2977 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.2977 2022/11/28 17:11:13 - mmengine - INFO - Epoch(train) [11][2000/2462] lr: 2.3775e-02 eta: 0:09:23 time: 0.0439 data_time: 0.0064 memory: 1794 loss: 0.2639 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2639 2022/11/28 17:11:17 - mmengine - INFO - Epoch(train) [11][2100/2462] lr: 2.3437e-02 eta: 0:09:19 time: 0.0441 data_time: 0.0064 memory: 1794 loss: 0.2455 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2455 2022/11/28 17:11:22 - mmengine - INFO - Epoch(train) [11][2200/2462] lr: 2.3100e-02 eta: 0:09:14 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.2667 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.2667 2022/11/28 17:11:26 - mmengine - INFO - Epoch(train) [11][2300/2462] lr: 2.2764e-02 eta: 0:09:10 time: 0.0440 data_time: 0.0063 memory: 1794 loss: 0.2732 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2732 2022/11/28 17:11:30 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:11:31 - mmengine - INFO - Epoch(train) [11][2400/2462] lr: 2.2431e-02 eta: 0:09:06 time: 0.0463 data_time: 0.0090 memory: 1794 loss: 0.3109 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3109 2022/11/28 17:11:33 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:11:33 - mmengine - INFO - Epoch(train) [11][2462/2462] lr: 2.2225e-02 eta: 0:09:03 time: 0.0439 data_time: 0.0064 memory: 1794 loss: 0.3629 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3629 2022/11/28 17:11:33 - mmengine - INFO - Saving checkpoint at 11 epochs 2022/11/28 17:11:37 - mmengine - INFO - Epoch(val) [11][100/398] eta: 0:00:09 time: 0.0322 data_time: 0.0189 memory: 364 2022/11/28 17:11:40 - mmengine - INFO - Epoch(val) [11][200/398] eta: 0:00:06 time: 0.0244 data_time: 0.0113 memory: 364 2022/11/28 17:11:43 - mmengine - INFO - Epoch(val) [11][300/398] eta: 0:00:02 time: 0.0287 data_time: 0.0164 memory: 364 2022/11/28 17:11:46 - mmengine - INFO - Epoch(val) [11][398/398] acc/top1: 0.7326 acc/top5: 0.9311 acc/mean1: 0.7407 2022/11/28 17:11:46 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_10.pth is removed 2022/11/28 17:11:47 - mmengine - INFO - The best checkpoint with 0.7326 acc/top1 at 11 epoch is saved to best_acc/top1_epoch_11.pth. 2022/11/28 17:11:51 - mmengine - INFO - Epoch(train) [12][100/2462] lr: 2.1894e-02 eta: 0:08:59 time: 0.0443 data_time: 0.0062 memory: 1794 loss: 0.1873 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1873 2022/11/28 17:11:56 - mmengine - INFO - Epoch(train) [12][200/2462] lr: 2.1565e-02 eta: 0:08:54 time: 0.0443 data_time: 0.0063 memory: 1794 loss: 0.2506 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2506 2022/11/28 17:12:00 - mmengine - INFO - Epoch(train) [12][300/2462] lr: 2.1238e-02 eta: 0:08:50 time: 0.0442 data_time: 0.0063 memory: 1794 loss: 0.2436 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2436 2022/11/28 17:12:05 - mmengine - INFO - Epoch(train) [12][400/2462] lr: 2.0913e-02 eta: 0:08:45 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.2280 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2280 2022/11/28 17:12:09 - mmengine - INFO - Epoch(train) [12][500/2462] lr: 2.0589e-02 eta: 0:08:41 time: 0.0450 data_time: 0.0067 memory: 1794 loss: 0.2725 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.2725 2022/11/28 17:12:14 - mmengine - INFO - Epoch(train) [12][600/2462] lr: 2.0268e-02 eta: 0:08:37 time: 0.0446 data_time: 0.0062 memory: 1794 loss: 0.2390 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2390 2022/11/28 17:12:18 - mmengine - INFO - Epoch(train) [12][700/2462] lr: 1.9948e-02 eta: 0:08:32 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.2316 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2316 2022/11/28 17:12:22 - mmengine - INFO - Epoch(train) [12][800/2462] lr: 1.9631e-02 eta: 0:08:28 time: 0.0442 data_time: 0.0071 memory: 1794 loss: 0.3197 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3197 2022/11/28 17:12:27 - mmengine - INFO - Epoch(train) [12][900/2462] lr: 1.9315e-02 eta: 0:08:23 time: 0.0445 data_time: 0.0072 memory: 1794 loss: 0.2411 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2411 2022/11/28 17:12:28 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:12:31 - mmengine - INFO - Epoch(train) [12][1000/2462] lr: 1.9001e-02 eta: 0:08:19 time: 0.0442 data_time: 0.0065 memory: 1794 loss: 0.2284 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2284 2022/11/28 17:12:36 - mmengine - INFO - Epoch(train) [12][1100/2462] lr: 1.8689e-02 eta: 0:08:15 time: 0.0439 data_time: 0.0064 memory: 1794 loss: 0.2514 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2514 2022/11/28 17:12:40 - mmengine - INFO - Epoch(train) [12][1200/2462] lr: 1.8379e-02 eta: 0:08:10 time: 0.0440 data_time: 0.0063 memory: 1794 loss: 0.1758 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1758 2022/11/28 17:12:45 - mmengine - INFO - Epoch(train) [12][1300/2462] lr: 1.8071e-02 eta: 0:08:06 time: 0.0443 data_time: 0.0063 memory: 1794 loss: 0.1936 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1936 2022/11/28 17:12:49 - mmengine - INFO - Epoch(train) [12][1400/2462] lr: 1.7765e-02 eta: 0:08:01 time: 0.0439 data_time: 0.0064 memory: 1794 loss: 0.1880 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1880 2022/11/28 17:12:54 - mmengine - INFO - Epoch(train) [12][1500/2462] lr: 1.7462e-02 eta: 0:07:57 time: 0.0443 data_time: 0.0066 memory: 1794 loss: 0.2046 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2046 2022/11/28 17:12:58 - mmengine - INFO - Epoch(train) [12][1600/2462] lr: 1.7160e-02 eta: 0:07:53 time: 0.0439 data_time: 0.0064 memory: 1794 loss: 0.1914 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1914 2022/11/28 17:13:03 - mmengine - INFO - Epoch(train) [12][1700/2462] lr: 1.6860e-02 eta: 0:07:48 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.2474 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.2474 2022/11/28 17:13:07 - mmengine - INFO - Epoch(train) [12][1800/2462] lr: 1.6563e-02 eta: 0:07:44 time: 0.0460 data_time: 0.0079 memory: 1794 loss: 0.1657 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1657 2022/11/28 17:13:12 - mmengine - INFO - Epoch(train) [12][1900/2462] lr: 1.6267e-02 eta: 0:07:39 time: 0.0443 data_time: 0.0063 memory: 1794 loss: 0.2418 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2418 2022/11/28 17:13:12 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:13:16 - mmengine - INFO - Epoch(train) [12][2000/2462] lr: 1.5974e-02 eta: 0:07:35 time: 0.0449 data_time: 0.0064 memory: 1794 loss: 0.2514 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2514 2022/11/28 17:13:21 - mmengine - INFO - Epoch(train) [12][2100/2462] lr: 1.5683e-02 eta: 0:07:31 time: 0.0440 data_time: 0.0064 memory: 1794 loss: 0.1942 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1942 2022/11/28 17:13:25 - mmengine - INFO - Epoch(train) [12][2200/2462] lr: 1.5394e-02 eta: 0:07:26 time: 0.0448 data_time: 0.0075 memory: 1794 loss: 0.1701 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1701 2022/11/28 17:13:29 - mmengine - INFO - Epoch(train) [12][2300/2462] lr: 1.5107e-02 eta: 0:07:22 time: 0.0443 data_time: 0.0061 memory: 1794 loss: 0.2129 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2129 2022/11/28 17:13:34 - mmengine - INFO - Epoch(train) [12][2400/2462] lr: 1.4823e-02 eta: 0:07:17 time: 0.0439 data_time: 0.0063 memory: 1794 loss: 0.1623 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1623 2022/11/28 17:13:37 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:13:37 - mmengine - INFO - Epoch(train) [12][2462/2462] lr: 1.4647e-02 eta: 0:07:15 time: 0.0446 data_time: 0.0066 memory: 1794 loss: 0.1802 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1802 2022/11/28 17:13:37 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/11/28 17:13:40 - mmengine - INFO - Epoch(val) [12][100/398] eta: 0:00:10 time: 0.0324 data_time: 0.0190 memory: 364 2022/11/28 17:13:43 - mmengine - INFO - Epoch(val) [12][200/398] eta: 0:00:06 time: 0.0237 data_time: 0.0109 memory: 364 2022/11/28 17:13:46 - mmengine - INFO - Epoch(val) [12][300/398] eta: 0:00:02 time: 0.0250 data_time: 0.0127 memory: 364 2022/11/28 17:13:49 - mmengine - INFO - Epoch(val) [12][398/398] acc/top1: 0.7365 acc/top5: 0.9213 acc/mean1: 0.7524 2022/11/28 17:13:49 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_11.pth is removed 2022/11/28 17:13:50 - mmengine - INFO - The best checkpoint with 0.7365 acc/top1 at 12 epoch is saved to best_acc/top1_epoch_12.pth. 2022/11/28 17:13:54 - mmengine - INFO - Epoch(train) [13][100/2462] lr: 1.4367e-02 eta: 0:07:10 time: 0.0464 data_time: 0.0082 memory: 1794 loss: 0.1963 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1963 2022/11/28 17:13:59 - mmengine - INFO - Epoch(train) [13][200/2462] lr: 1.4088e-02 eta: 0:07:06 time: 0.0450 data_time: 0.0067 memory: 1794 loss: 0.1932 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.1932 2022/11/28 17:14:03 - mmengine - INFO - Epoch(train) [13][300/2462] lr: 1.3812e-02 eta: 0:07:01 time: 0.0443 data_time: 0.0063 memory: 1794 loss: 0.1309 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1309 2022/11/28 17:14:08 - mmengine - INFO - Epoch(train) [13][400/2462] lr: 1.3538e-02 eta: 0:06:57 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.1647 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.1647 2022/11/28 17:14:10 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:14:12 - mmengine - INFO - Epoch(train) [13][500/2462] lr: 1.3266e-02 eta: 0:06:53 time: 0.0444 data_time: 0.0063 memory: 1794 loss: 0.1446 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1446 2022/11/28 17:14:17 - mmengine - INFO - Epoch(train) [13][600/2462] lr: 1.2997e-02 eta: 0:06:48 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.1366 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1366 2022/11/28 17:14:21 - mmengine - INFO - Epoch(train) [13][700/2462] lr: 1.2730e-02 eta: 0:06:44 time: 0.0440 data_time: 0.0065 memory: 1794 loss: 0.1647 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1647 2022/11/28 17:14:26 - mmengine - INFO - Epoch(train) [13][800/2462] lr: 1.2465e-02 eta: 0:06:39 time: 0.0482 data_time: 0.0089 memory: 1794 loss: 0.1525 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1525 2022/11/28 17:14:30 - mmengine - INFO - Epoch(train) [13][900/2462] lr: 1.2203e-02 eta: 0:06:35 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.1431 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1431 2022/11/28 17:14:34 - mmengine - INFO - Epoch(train) [13][1000/2462] lr: 1.1943e-02 eta: 0:06:31 time: 0.0435 data_time: 0.0063 memory: 1794 loss: 0.1339 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1339 2022/11/28 17:14:39 - mmengine - INFO - Epoch(train) [13][1100/2462] lr: 1.1686e-02 eta: 0:06:26 time: 0.0453 data_time: 0.0065 memory: 1794 loss: 0.1258 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1258 2022/11/28 17:14:43 - mmengine - INFO - Epoch(train) [13][1200/2462] lr: 1.1431e-02 eta: 0:06:22 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.1099 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1099 2022/11/28 17:14:48 - mmengine - INFO - Epoch(train) [13][1300/2462] lr: 1.1178e-02 eta: 0:06:17 time: 0.0443 data_time: 0.0062 memory: 1794 loss: 0.1642 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1642 2022/11/28 17:14:52 - mmengine - INFO - Epoch(train) [13][1400/2462] lr: 1.0928e-02 eta: 0:06:13 time: 0.0443 data_time: 0.0063 memory: 1794 loss: 0.1403 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1403 2022/11/28 17:14:55 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:14:57 - mmengine - INFO - Epoch(train) [13][1500/2462] lr: 1.0680e-02 eta: 0:06:08 time: 0.0435 data_time: 0.0063 memory: 1794 loss: 0.0924 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0924 2022/11/28 17:15:01 - mmengine - INFO - Epoch(train) [13][1600/2462] lr: 1.0435e-02 eta: 0:06:04 time: 0.0434 data_time: 0.0062 memory: 1794 loss: 0.1271 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.1271 2022/11/28 17:15:05 - mmengine - INFO - Epoch(train) [13][1700/2462] lr: 1.0193e-02 eta: 0:06:00 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.1307 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1307 2022/11/28 17:15:10 - mmengine - INFO - Epoch(train) [13][1800/2462] lr: 9.9527e-03 eta: 0:05:55 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.1237 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1237 2022/11/28 17:15:14 - mmengine - INFO - Epoch(train) [13][1900/2462] lr: 9.7153e-03 eta: 0:05:51 time: 0.0445 data_time: 0.0069 memory: 1794 loss: 0.1077 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1077 2022/11/28 17:15:19 - mmengine - INFO - Epoch(train) [13][2000/2462] lr: 9.4804e-03 eta: 0:05:46 time: 0.0434 data_time: 0.0062 memory: 1794 loss: 0.1278 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1278 2022/11/28 17:15:23 - mmengine - INFO - Epoch(train) [13][2100/2462] lr: 9.2480e-03 eta: 0:05:42 time: 0.0433 data_time: 0.0063 memory: 1794 loss: 0.1217 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1217 2022/11/28 17:15:27 - mmengine - INFO - Epoch(train) [13][2200/2462] lr: 9.0183e-03 eta: 0:05:37 time: 0.0440 data_time: 0.0063 memory: 1794 loss: 0.1063 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1063 2022/11/28 17:15:32 - mmengine - INFO - Epoch(train) [13][2300/2462] lr: 8.7911e-03 eta: 0:05:33 time: 0.0445 data_time: 0.0068 memory: 1794 loss: 0.0883 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0883 2022/11/28 17:15:36 - mmengine - INFO - Epoch(train) [13][2400/2462] lr: 8.5666e-03 eta: 0:05:29 time: 0.0439 data_time: 0.0064 memory: 1794 loss: 0.0840 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0840 2022/11/28 17:15:39 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:15:39 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:15:39 - mmengine - INFO - Epoch(train) [13][2462/2462] lr: 8.4287e-03 eta: 0:05:26 time: 0.0442 data_time: 0.0065 memory: 1794 loss: 0.0801 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0801 2022/11/28 17:15:39 - mmengine - INFO - Saving checkpoint at 13 epochs 2022/11/28 17:15:43 - mmengine - INFO - Epoch(val) [13][100/398] eta: 0:00:10 time: 0.0324 data_time: 0.0189 memory: 364 2022/11/28 17:15:46 - mmengine - INFO - Epoch(val) [13][200/398] eta: 0:00:06 time: 0.0238 data_time: 0.0112 memory: 364 2022/11/28 17:15:48 - mmengine - INFO - Epoch(val) [13][300/398] eta: 0:00:02 time: 0.0262 data_time: 0.0139 memory: 364 2022/11/28 17:15:52 - mmengine - INFO - Epoch(val) [13][398/398] acc/top1: 0.7551 acc/top5: 0.9353 acc/mean1: 0.7736 2022/11/28 17:15:52 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_12.pth is removed 2022/11/28 17:15:52 - mmengine - INFO - The best checkpoint with 0.7551 acc/top1 at 13 epoch is saved to best_acc/top1_epoch_13.pth. 2022/11/28 17:15:57 - mmengine - INFO - Epoch(train) [14][100/2462] lr: 8.2085e-03 eta: 0:05:21 time: 0.0452 data_time: 0.0062 memory: 1794 loss: 0.0600 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0600 2022/11/28 17:16:01 - mmengine - INFO - Epoch(train) [14][200/2462] lr: 7.9909e-03 eta: 0:05:17 time: 0.0449 data_time: 0.0068 memory: 1794 loss: 0.0899 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0899 2022/11/28 17:16:06 - mmengine - INFO - Epoch(train) [14][300/2462] lr: 7.7760e-03 eta: 0:05:13 time: 0.0449 data_time: 0.0064 memory: 1794 loss: 0.0707 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0707 2022/11/28 17:16:10 - mmengine - INFO - Epoch(train) [14][400/2462] lr: 7.5638e-03 eta: 0:05:08 time: 0.0454 data_time: 0.0062 memory: 1794 loss: 0.1042 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1042 2022/11/28 17:16:15 - mmengine - INFO - Epoch(train) [14][500/2462] lr: 7.3542e-03 eta: 0:05:04 time: 0.0444 data_time: 0.0063 memory: 1794 loss: 0.0618 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0618 2022/11/28 17:16:19 - mmengine - INFO - Epoch(train) [14][600/2462] lr: 7.1474e-03 eta: 0:04:59 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.0653 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0653 2022/11/28 17:16:24 - mmengine - INFO - Epoch(train) [14][700/2462] lr: 6.9433e-03 eta: 0:04:55 time: 0.0450 data_time: 0.0066 memory: 1794 loss: 0.1065 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1065 2022/11/28 17:16:28 - mmengine - INFO - Epoch(train) [14][800/2462] lr: 6.7420e-03 eta: 0:04:51 time: 0.0439 data_time: 0.0065 memory: 1794 loss: 0.0604 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0604 2022/11/28 17:16:33 - mmengine - INFO - Epoch(train) [14][900/2462] lr: 6.5434e-03 eta: 0:04:46 time: 0.0444 data_time: 0.0063 memory: 1794 loss: 0.0759 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0759 2022/11/28 17:16:37 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:16:37 - mmengine - INFO - Epoch(train) [14][1000/2462] lr: 6.3476e-03 eta: 0:04:42 time: 0.0460 data_time: 0.0064 memory: 1794 loss: 0.1018 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1018 2022/11/28 17:16:42 - mmengine - INFO - Epoch(train) [14][1100/2462] lr: 6.1545e-03 eta: 0:04:37 time: 0.0442 data_time: 0.0063 memory: 1794 loss: 0.0834 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0834 2022/11/28 17:16:46 - mmengine - INFO - Epoch(train) [14][1200/2462] lr: 5.9642e-03 eta: 0:04:33 time: 0.0462 data_time: 0.0063 memory: 1794 loss: 0.0720 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0720 2022/11/28 17:16:51 - mmengine - INFO - Epoch(train) [14][1300/2462] lr: 5.7768e-03 eta: 0:04:29 time: 0.0439 data_time: 0.0063 memory: 1794 loss: 0.0550 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0550 2022/11/28 17:16:55 - mmengine - INFO - Epoch(train) [14][1400/2462] lr: 5.5921e-03 eta: 0:04:24 time: 0.0443 data_time: 0.0075 memory: 1794 loss: 0.0658 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0658 2022/11/28 17:17:00 - mmengine - INFO - Epoch(train) [14][1500/2462] lr: 5.4103e-03 eta: 0:04:20 time: 0.0437 data_time: 0.0063 memory: 1794 loss: 0.0688 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0688 2022/11/28 17:17:04 - mmengine - INFO - Epoch(train) [14][1600/2462] lr: 5.2313e-03 eta: 0:04:15 time: 0.0439 data_time: 0.0064 memory: 1794 loss: 0.0864 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0864 2022/11/28 17:17:09 - mmengine - INFO - Epoch(train) [14][1700/2462] lr: 5.0551e-03 eta: 0:04:11 time: 0.0452 data_time: 0.0062 memory: 1794 loss: 0.0502 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0502 2022/11/28 17:17:13 - mmengine - INFO - Epoch(train) [14][1800/2462] lr: 4.8818e-03 eta: 0:04:07 time: 0.0447 data_time: 0.0064 memory: 1794 loss: 0.0654 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0654 2022/11/28 17:17:18 - mmengine - INFO - Epoch(train) [14][1900/2462] lr: 4.7114e-03 eta: 0:04:02 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.0493 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0493 2022/11/28 17:17:22 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:17:22 - mmengine - INFO - Epoch(train) [14][2000/2462] lr: 4.5439e-03 eta: 0:03:58 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.0476 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0476 2022/11/28 17:17:27 - mmengine - INFO - Epoch(train) [14][2100/2462] lr: 4.3792e-03 eta: 0:03:53 time: 0.0480 data_time: 0.0088 memory: 1794 loss: 0.0548 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0548 2022/11/28 17:17:31 - mmengine - INFO - Epoch(train) [14][2200/2462] lr: 4.2175e-03 eta: 0:03:49 time: 0.0454 data_time: 0.0065 memory: 1794 loss: 0.0468 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0468 2022/11/28 17:17:36 - mmengine - INFO - Epoch(train) [14][2300/2462] lr: 4.0587e-03 eta: 0:03:44 time: 0.0441 data_time: 0.0065 memory: 1794 loss: 0.0429 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0429 2022/11/28 17:17:40 - mmengine - INFO - Epoch(train) [14][2400/2462] lr: 3.9027e-03 eta: 0:03:40 time: 0.0450 data_time: 0.0068 memory: 1794 loss: 0.0758 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0758 2022/11/28 17:17:43 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:17:43 - mmengine - INFO - Epoch(train) [14][2462/2462] lr: 3.8075e-03 eta: 0:03:37 time: 0.0443 data_time: 0.0064 memory: 1794 loss: 0.0605 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0605 2022/11/28 17:17:43 - mmengine - INFO - Saving checkpoint at 14 epochs 2022/11/28 17:17:47 - mmengine - INFO - Epoch(val) [14][100/398] eta: 0:00:09 time: 0.0323 data_time: 0.0190 memory: 364 2022/11/28 17:17:50 - mmengine - INFO - Epoch(val) [14][200/398] eta: 0:00:06 time: 0.0242 data_time: 0.0107 memory: 364 2022/11/28 17:17:52 - mmengine - INFO - Epoch(val) [14][300/398] eta: 0:00:02 time: 0.0245 data_time: 0.0123 memory: 364 2022/11/28 17:17:55 - mmengine - INFO - Epoch(val) [14][398/398] acc/top1: 0.7762 acc/top5: 0.9407 acc/mean1: 0.7936 2022/11/28 17:17:56 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_13.pth is removed 2022/11/28 17:17:56 - mmengine - INFO - The best checkpoint with 0.7762 acc/top1 at 14 epoch is saved to best_acc/top1_epoch_14.pth. 2022/11/28 17:18:00 - mmengine - INFO - Epoch(train) [15][100/2462] lr: 3.6564e-03 eta: 0:03:33 time: 0.0439 data_time: 0.0064 memory: 1794 loss: 0.0564 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0564 2022/11/28 17:18:05 - mmengine - INFO - Epoch(train) [15][200/2462] lr: 3.5082e-03 eta: 0:03:28 time: 0.0449 data_time: 0.0063 memory: 1794 loss: 0.0486 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0486 2022/11/28 17:18:09 - mmengine - INFO - Epoch(train) [15][300/2462] lr: 3.3629e-03 eta: 0:03:24 time: 0.0456 data_time: 0.0072 memory: 1794 loss: 0.0442 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0442 2022/11/28 17:18:14 - mmengine - INFO - Epoch(train) [15][400/2462] lr: 3.2206e-03 eta: 0:03:20 time: 0.0442 data_time: 0.0065 memory: 1794 loss: 0.0453 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0453 2022/11/28 17:18:18 - mmengine - INFO - Epoch(train) [15][500/2462] lr: 3.0813e-03 eta: 0:03:15 time: 0.0447 data_time: 0.0064 memory: 1794 loss: 0.0378 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0378 2022/11/28 17:18:20 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:18:23 - mmengine - INFO - Epoch(train) [15][600/2462] lr: 2.9450e-03 eta: 0:03:11 time: 0.0444 data_time: 0.0063 memory: 1794 loss: 0.0500 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0500 2022/11/28 17:18:28 - mmengine - INFO - Epoch(train) [15][700/2462] lr: 2.8117e-03 eta: 0:03:06 time: 0.0461 data_time: 0.0073 memory: 1794 loss: 0.0547 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.0547 2022/11/28 17:18:32 - mmengine - INFO - Epoch(train) [15][800/2462] lr: 2.6813e-03 eta: 0:03:02 time: 0.0452 data_time: 0.0067 memory: 1794 loss: 0.0469 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0469 2022/11/28 17:18:36 - mmengine - INFO - Epoch(train) [15][900/2462] lr: 2.5540e-03 eta: 0:02:58 time: 0.0439 data_time: 0.0063 memory: 1794 loss: 0.0420 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0420 2022/11/28 17:18:41 - mmengine - INFO - Epoch(train) [15][1000/2462] lr: 2.4297e-03 eta: 0:02:53 time: 0.0442 data_time: 0.0063 memory: 1794 loss: 0.0296 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0296 2022/11/28 17:18:45 - mmengine - INFO - Epoch(train) [15][1100/2462] lr: 2.3084e-03 eta: 0:02:49 time: 0.0443 data_time: 0.0062 memory: 1794 loss: 0.0196 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0196 2022/11/28 17:18:50 - mmengine - INFO - Epoch(train) [15][1200/2462] lr: 2.1902e-03 eta: 0:02:44 time: 0.0447 data_time: 0.0066 memory: 1794 loss: 0.0331 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0331 2022/11/28 17:18:54 - mmengine - INFO - Epoch(train) [15][1300/2462] lr: 2.0750e-03 eta: 0:02:40 time: 0.0452 data_time: 0.0068 memory: 1794 loss: 0.0433 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0433 2022/11/28 17:18:59 - mmengine - INFO - Epoch(train) [15][1400/2462] lr: 1.9628e-03 eta: 0:02:35 time: 0.0458 data_time: 0.0066 memory: 1794 loss: 0.0361 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0361 2022/11/28 17:19:03 - mmengine - INFO - Epoch(train) [15][1500/2462] lr: 1.8537e-03 eta: 0:02:31 time: 0.0450 data_time: 0.0073 memory: 1794 loss: 0.0339 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0339 2022/11/28 17:19:05 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:19:08 - mmengine - INFO - Epoch(train) [15][1600/2462] lr: 1.7477e-03 eta: 0:02:27 time: 0.0453 data_time: 0.0069 memory: 1794 loss: 0.0361 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0361 2022/11/28 17:19:12 - mmengine - INFO - Epoch(train) [15][1700/2462] lr: 1.6447e-03 eta: 0:02:22 time: 0.0453 data_time: 0.0066 memory: 1794 loss: 0.0439 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0439 2022/11/28 17:19:17 - mmengine - INFO - Epoch(train) [15][1800/2462] lr: 1.5448e-03 eta: 0:02:18 time: 0.0456 data_time: 0.0069 memory: 1794 loss: 0.0351 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0351 2022/11/28 17:19:21 - mmengine - INFO - Epoch(train) [15][1900/2462] lr: 1.4480e-03 eta: 0:02:13 time: 0.0469 data_time: 0.0062 memory: 1794 loss: 0.0301 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0301 2022/11/28 17:19:26 - mmengine - INFO - Epoch(train) [15][2000/2462] lr: 1.3543e-03 eta: 0:02:09 time: 0.0446 data_time: 0.0063 memory: 1794 loss: 0.0578 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0578 2022/11/28 17:19:30 - mmengine - INFO - Epoch(train) [15][2100/2462] lr: 1.2636e-03 eta: 0:02:05 time: 0.0439 data_time: 0.0063 memory: 1794 loss: 0.0358 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0358 2022/11/28 17:19:35 - mmengine - INFO - Epoch(train) [15][2200/2462] lr: 1.1761e-03 eta: 0:02:00 time: 0.0442 data_time: 0.0063 memory: 1794 loss: 0.0212 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0212 2022/11/28 17:19:39 - mmengine - INFO - Epoch(train) [15][2300/2462] lr: 1.0917e-03 eta: 0:01:56 time: 0.0453 data_time: 0.0065 memory: 1794 loss: 0.0442 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0442 2022/11/28 17:19:44 - mmengine - INFO - Epoch(train) [15][2400/2462] lr: 1.0104e-03 eta: 0:01:51 time: 0.0443 data_time: 0.0066 memory: 1794 loss: 0.0412 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0412 2022/11/28 17:19:47 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:19:47 - mmengine - INFO - Epoch(train) [15][2462/2462] lr: 9.6151e-04 eta: 0:01:49 time: 0.0448 data_time: 0.0067 memory: 1794 loss: 0.0364 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0364 2022/11/28 17:19:47 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/11/28 17:19:51 - mmengine - INFO - Epoch(val) [15][100/398] eta: 0:00:11 time: 0.0321 data_time: 0.0188 memory: 364 2022/11/28 17:19:54 - mmengine - INFO - Epoch(val) [15][200/398] eta: 0:00:06 time: 0.0243 data_time: 0.0112 memory: 364 2022/11/28 17:19:56 - mmengine - INFO - Epoch(val) [15][300/398] eta: 0:00:02 time: 0.0248 data_time: 0.0126 memory: 364 2022/11/28 17:20:00 - mmengine - INFO - Epoch(val) [15][398/398] acc/top1: 0.7881 acc/top5: 0.9462 acc/mean1: 0.8036 2022/11/28 17:20:00 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_14.pth is removed 2022/11/28 17:20:00 - mmengine - INFO - The best checkpoint with 0.7881 acc/top1 at 15 epoch is saved to best_acc/top1_epoch_15.pth. 2022/11/28 17:20:04 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:20:05 - mmengine - INFO - Epoch(train) [16][100/2462] lr: 8.8525e-04 eta: 0:01:44 time: 0.0445 data_time: 0.0065 memory: 1794 loss: 0.0199 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0199 2022/11/28 17:20:10 - mmengine - INFO - Epoch(train) [16][200/2462] lr: 8.1211e-04 eta: 0:01:40 time: 0.0454 data_time: 0.0065 memory: 1794 loss: 0.0314 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0314 2022/11/28 17:20:14 - mmengine - INFO - Epoch(train) [16][300/2462] lr: 7.4209e-04 eta: 0:01:35 time: 0.0445 data_time: 0.0063 memory: 1794 loss: 0.0241 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0241 2022/11/28 17:20:19 - mmengine - INFO - Epoch(train) [16][400/2462] lr: 6.7522e-04 eta: 0:01:31 time: 0.0444 data_time: 0.0064 memory: 1794 loss: 0.0454 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0454 2022/11/28 17:20:23 - mmengine - INFO - Epoch(train) [16][500/2462] lr: 6.1147e-04 eta: 0:01:26 time: 0.0448 data_time: 0.0063 memory: 1794 loss: 0.0320 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0320 2022/11/28 17:20:28 - mmengine - INFO - Epoch(train) [16][600/2462] lr: 5.5087e-04 eta: 0:01:22 time: 0.0443 data_time: 0.0064 memory: 1794 loss: 0.0251 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0251 2022/11/28 17:20:32 - mmengine - INFO - Epoch(train) [16][700/2462] lr: 4.9342e-04 eta: 0:01:18 time: 0.0447 data_time: 0.0069 memory: 1794 loss: 0.0329 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0329 2022/11/28 17:20:37 - mmengine - INFO - Epoch(train) [16][800/2462] lr: 4.3911e-04 eta: 0:01:13 time: 0.0444 data_time: 0.0064 memory: 1794 loss: 0.0270 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0270 2022/11/28 17:20:41 - mmengine - INFO - Epoch(train) [16][900/2462] lr: 3.8795e-04 eta: 0:01:09 time: 0.0449 data_time: 0.0065 memory: 1794 loss: 0.0202 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0202 2022/11/28 17:20:46 - mmengine - INFO - Epoch(train) [16][1000/2462] lr: 3.3995e-04 eta: 0:01:04 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.0303 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0303 2022/11/28 17:20:49 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:20:51 - mmengine - INFO - Epoch(train) [16][1100/2462] lr: 2.9511e-04 eta: 0:01:00 time: 0.0437 data_time: 0.0063 memory: 1794 loss: 0.0256 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0256 2022/11/28 17:20:55 - mmengine - INFO - Epoch(train) [16][1200/2462] lr: 2.5343e-04 eta: 0:00:55 time: 0.0454 data_time: 0.0072 memory: 1794 loss: 0.0239 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0239 2022/11/28 17:21:00 - mmengine - INFO - Epoch(train) [16][1300/2462] lr: 2.1492e-04 eta: 0:00:51 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.0342 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0342 2022/11/28 17:21:04 - mmengine - INFO - Epoch(train) [16][1400/2462] lr: 1.7957e-04 eta: 0:00:47 time: 0.0450 data_time: 0.0072 memory: 1794 loss: 0.0290 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0290 2022/11/28 17:21:09 - mmengine - INFO - Epoch(train) [16][1500/2462] lr: 1.4739e-04 eta: 0:00:42 time: 0.0444 data_time: 0.0064 memory: 1794 loss: 0.0221 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0221 2022/11/28 17:21:13 - mmengine - INFO - Epoch(train) [16][1600/2462] lr: 1.1838e-04 eta: 0:00:38 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.0242 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0242 2022/11/28 17:21:17 - mmengine - INFO - Epoch(train) [16][1700/2462] lr: 9.2542e-05 eta: 0:00:33 time: 0.0450 data_time: 0.0068 memory: 1794 loss: 0.0251 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0251 2022/11/28 17:21:22 - 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: 0.0265 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0265 2022/11/28 17:21:26 - mmengine - INFO - Epoch(train) [16][1900/2462] lr: 5.0393e-05 eta: 0:00:24 time: 0.0460 data_time: 0.0062 memory: 1794 loss: 0.0344 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0344 2022/11/28 17:21:31 - mmengine - INFO - Epoch(train) [16][2000/2462] lr: 3.4083e-05 eta: 0:00:20 time: 0.0440 data_time: 0.0063 memory: 1794 loss: 0.0314 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0314 2022/11/28 17:21:34 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:21:36 - mmengine - INFO - Epoch(train) [16][2100/2462] lr: 2.0951e-05 eta: 0:00:16 time: 0.0452 data_time: 0.0067 memory: 1794 loss: 0.0228 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0228 2022/11/28 17:21:40 - mmengine - INFO - Epoch(train) [16][2200/2462] lr: 1.0998e-05 eta: 0:00:11 time: 0.0450 data_time: 0.0064 memory: 1794 loss: 0.0388 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0388 2022/11/28 17:21:45 - mmengine - INFO - Epoch(train) [16][2300/2462] lr: 4.2247e-06 eta: 0:00:07 time: 0.0445 data_time: 0.0062 memory: 1794 loss: 0.0412 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0412 2022/11/28 17:21:49 - mmengine - INFO - Epoch(train) [16][2400/2462] lr: 6.3111e-07 eta: 0:00:02 time: 0.0462 data_time: 0.0078 memory: 1794 loss: 0.0372 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0372 2022/11/28 17:21:52 - mmengine - INFO - Exp name: stgcn_8xb16-joint-motion-u100-80e_ntu120-xsub-keypoint-3d_20221128_164721 2022/11/28 17:21:52 - mmengine - INFO - Epoch(train) [16][2462/2462] lr: 1.5901e-10 eta: 0:00:00 time: 0.0445 data_time: 0.0068 memory: 1794 loss: 0.0221 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0221 2022/11/28 17:21:52 - mmengine - INFO - Saving checkpoint at 16 epochs 2022/11/28 17:21:56 - mmengine - INFO - Epoch(val) [16][100/398] eta: 0:00:10 time: 0.0327 data_time: 0.0194 memory: 364 2022/11/28 17:21:59 - mmengine - INFO - Epoch(val) [16][200/398] eta: 0:00:06 time: 0.0237 data_time: 0.0108 memory: 364 2022/11/28 17:22:01 - mmengine - INFO - Epoch(val) [16][300/398] eta: 0:00:02 time: 0.0245 data_time: 0.0124 memory: 364 2022/11/28 17:22:05 - mmengine - INFO - Epoch(val) [16][398/398] acc/top1: 0.7842 acc/top5: 0.9455 acc/mean1: 0.7998