2022/11/28 16:10:11 - 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: 1814560694 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 16:10:11 - 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=['b']), 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=['b']), 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=['b']), 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=['b']), 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=['b']), 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=['b']), dict( type='UniformSampleFrames', clip_len=100, num_clips=10, test_mode=True), dict(type='PoseDecode'), dict(type='FormatGCNInput', num_person=2), dict(type='PackActionInputs') ], split='xsub_val', test_mode=True)) val_evaluator = [dict(type='AccMetric')] test_evaluator = [dict(type='AccMetric')] train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=16, val_begin=1, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') param_scheduler = [ dict( type='CosineAnnealingLR', eta_min=0, T_max=16, by_epoch=True, convert_to_iter_based=True) ] optim_wrapper = dict( optimizer=dict( type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True)) auto_scale_lr = dict(enable=False, base_batch_size=128) launcher = 'pytorch' work_dir = './work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d' randomness = dict(seed=None, diff_rank_seed=False, deterministic=False) 2022/11/28 16:10:11 - mmengine - INFO - Result has been saved to /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-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 16:12:10 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d. 2022/11/28 16:12:16 - mmengine - INFO - Epoch(train) [1][100/2462] lr: 9.9998e-02 eta: 0:38:39 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 3.6806 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 3.6806 2022/11/28 16:12:20 - mmengine - INFO - Epoch(train) [1][200/2462] lr: 9.9994e-02 eta: 0:33:35 time: 0.0436 data_time: 0.0061 memory: 1794 loss: 3.0462 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 3.0462 2022/11/28 16:12:25 - mmengine - INFO - Epoch(train) [1][300/2462] lr: 9.9986e-02 eta: 0:31:56 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 2.6802 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.6802 2022/11/28 16:12:29 - mmengine - INFO - Epoch(train) [1][400/2462] lr: 9.9975e-02 eta: 0:30:59 time: 0.0439 data_time: 0.0064 memory: 1794 loss: 2.2672 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 2.2672 2022/11/28 16:12:34 - mmengine - INFO - Epoch(train) [1][500/2462] lr: 9.9960e-02 eta: 0:30:28 time: 0.0451 data_time: 0.0061 memory: 1794 loss: 2.2037 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2037 2022/11/28 16:12:38 - mmengine - INFO - Epoch(train) [1][600/2462] lr: 9.9943e-02 eta: 0:30:02 time: 0.0428 data_time: 0.0061 memory: 1794 loss: 1.9545 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9545 2022/11/28 16:12:42 - mmengine - INFO - Epoch(train) [1][700/2462] lr: 9.9922e-02 eta: 0:29:41 time: 0.0432 data_time: 0.0061 memory: 1794 loss: 1.6315 top1_acc: 0.2500 top5_acc: 0.9375 loss_cls: 1.6315 2022/11/28 16:12:47 - mmengine - INFO - Epoch(train) [1][800/2462] lr: 9.9899e-02 eta: 0:29:26 time: 0.0434 data_time: 0.0062 memory: 1794 loss: 1.5585 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.5585 2022/11/28 16:12:51 - mmengine - INFO - Epoch(train) [1][900/2462] lr: 9.9872e-02 eta: 0:29:12 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 1.5310 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5310 2022/11/28 16:12:55 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:12:55 - mmengine - INFO - Epoch(train) [1][1000/2462] lr: 9.9841e-02 eta: 0:29:00 time: 0.0436 data_time: 0.0061 memory: 1794 loss: 1.4412 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4412 2022/11/28 16:13:00 - mmengine - INFO - Epoch(train) [1][1100/2462] lr: 9.9808e-02 eta: 0:28:51 time: 0.0432 data_time: 0.0061 memory: 1794 loss: 1.5277 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.5277 2022/11/28 16:13:06 - mmengine - INFO - Epoch(train) [1][1200/2462] lr: 9.9772e-02 eta: 0:29:36 time: 0.0439 data_time: 0.0060 memory: 1794 loss: 1.3323 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3323 2022/11/28 16:13:10 - mmengine - INFO - Epoch(train) [1][1300/2462] lr: 9.9732e-02 eta: 0:29:22 time: 0.0432 data_time: 0.0060 memory: 1794 loss: 1.3648 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3648 2022/11/28 16:13:15 - mmengine - INFO - Epoch(train) [1][1400/2462] lr: 9.9689e-02 eta: 0:29:09 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 1.3803 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3803 2022/11/28 16:13:19 - mmengine - INFO - Epoch(train) [1][1500/2462] lr: 9.9643e-02 eta: 0:28:57 time: 0.0428 data_time: 0.0061 memory: 1794 loss: 1.2930 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2930 2022/11/28 16:13:23 - mmengine - INFO - Epoch(train) [1][1600/2462] lr: 9.9594e-02 eta: 0:28:46 time: 0.0428 data_time: 0.0060 memory: 1794 loss: 1.2790 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2790 2022/11/28 16:13:27 - mmengine - INFO - Epoch(train) [1][1700/2462] lr: 9.9542e-02 eta: 0:28:36 time: 0.0428 data_time: 0.0062 memory: 1794 loss: 1.1799 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1799 2022/11/28 16:13:32 - mmengine - INFO - Epoch(train) [1][1800/2462] lr: 9.9486e-02 eta: 0:28:26 time: 0.0433 data_time: 0.0065 memory: 1794 loss: 1.2681 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2681 2022/11/28 16:13:36 - mmengine - INFO - Epoch(train) [1][1900/2462] lr: 9.9428e-02 eta: 0:28:17 time: 0.0433 data_time: 0.0061 memory: 1794 loss: 1.1230 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1230 2022/11/28 16:13:41 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:13:41 - mmengine - INFO - Epoch(train) [1][2000/2462] lr: 9.9366e-02 eta: 0:28:11 time: 0.0443 data_time: 0.0061 memory: 1794 loss: 1.1758 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.1758 2022/11/28 16:13:45 - mmengine - INFO - Epoch(train) [1][2100/2462] lr: 9.9301e-02 eta: 0:28:04 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 1.0120 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0120 2022/11/28 16:13:49 - mmengine - INFO - Epoch(train) [1][2200/2462] lr: 9.9233e-02 eta: 0:27:58 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 1.2293 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2293 2022/11/28 16:13:54 - mmengine - INFO - Epoch(train) [1][2300/2462] lr: 9.9162e-02 eta: 0:27:51 time: 0.0445 data_time: 0.0066 memory: 1794 loss: 1.0477 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0477 2022/11/28 16:13:58 - mmengine - INFO - Epoch(train) [1][2400/2462] lr: 9.9088e-02 eta: 0:27:45 time: 0.0437 data_time: 0.0063 memory: 1794 loss: 1.0370 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0370 2022/11/28 16:14:01 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:14:01 - mmengine - INFO - Epoch(train) [1][2462/2462] lr: 9.9040e-02 eta: 0:27:41 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 1.0709 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0709 2022/11/28 16:14:01 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/11/28 16:14:06 - mmengine - INFO - Epoch(val) [1][100/398] eta: 0:00:11 time: 0.0341 data_time: 0.0207 memory: 364 2022/11/28 16:14:09 - mmengine - INFO - Epoch(val) [1][200/398] eta: 0:00:06 time: 0.0261 data_time: 0.0128 memory: 364 2022/11/28 16:14:12 - mmengine - INFO - Epoch(val) [1][300/398] eta: 0:00:03 time: 0.0277 data_time: 0.0148 memory: 364 2022/11/28 16:14:16 - mmengine - INFO - Epoch(val) [1][398/398] acc/top1: 0.6202 acc/top5: 0.8978 acc/mean1: 0.6381 2022/11/28 16:14:16 - mmengine - INFO - The best checkpoint with 0.6202 acc/top1 at 1 epoch is saved to best_acc/top1_epoch_1.pth. 2022/11/28 16:14:21 - mmengine - INFO - Epoch(train) [2][100/2462] lr: 9.8961e-02 eta: 0:27:37 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 1.1476 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1476 2022/11/28 16:14:25 - mmengine - INFO - Epoch(train) [2][200/2462] lr: 9.8878e-02 eta: 0:27:31 time: 0.0437 data_time: 0.0070 memory: 1794 loss: 1.0326 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0326 2022/11/28 16:14:29 - mmengine - INFO - Epoch(train) [2][300/2462] lr: 9.8793e-02 eta: 0:27:25 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 1.1002 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1002 2022/11/28 16:14:34 - mmengine - INFO - Epoch(train) [2][400/2462] lr: 9.8704e-02 eta: 0:27:19 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.8947 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8947 2022/11/28 16:14:38 - mmengine - INFO - Epoch(train) [2][500/2462] lr: 9.8612e-02 eta: 0:27:13 time: 0.0436 data_time: 0.0066 memory: 1794 loss: 1.1754 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1754 2022/11/28 16:14:40 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:14:43 - mmengine - INFO - Epoch(train) [2][600/2462] lr: 9.8518e-02 eta: 0:27:08 time: 0.0437 data_time: 0.0067 memory: 1794 loss: 1.0417 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0417 2022/11/28 16:14:47 - mmengine - INFO - Epoch(train) [2][700/2462] lr: 9.8420e-02 eta: 0:27:02 time: 0.0433 data_time: 0.0066 memory: 1794 loss: 0.9460 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.9460 2022/11/28 16:14:51 - mmengine - INFO - Epoch(train) [2][800/2462] lr: 9.8319e-02 eta: 0:26:57 time: 0.0430 data_time: 0.0062 memory: 1794 loss: 0.8774 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8774 2022/11/28 16:14:56 - mmengine - INFO - Epoch(train) [2][900/2462] lr: 9.8215e-02 eta: 0:26:52 time: 0.0435 data_time: 0.0063 memory: 1794 loss: 0.9484 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9484 2022/11/28 16:15:00 - mmengine - INFO - Epoch(train) [2][1000/2462] lr: 9.8107e-02 eta: 0:26:46 time: 0.0430 data_time: 0.0064 memory: 1794 loss: 0.9008 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9008 2022/11/28 16:15:05 - mmengine - INFO - Epoch(train) [2][1100/2462] lr: 9.7997e-02 eta: 0:26:40 time: 0.0429 data_time: 0.0062 memory: 1794 loss: 1.0288 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0288 2022/11/28 16:15:09 - mmengine - INFO - Epoch(train) [2][1200/2462] lr: 9.7884e-02 eta: 0:26:34 time: 0.0445 data_time: 0.0066 memory: 1794 loss: 0.9728 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9728 2022/11/28 16:15:13 - mmengine - INFO - Epoch(train) [2][1300/2462] lr: 9.7768e-02 eta: 0:26:29 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 1.0083 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0083 2022/11/28 16:15:18 - mmengine - INFO - Epoch(train) [2][1400/2462] lr: 9.7648e-02 eta: 0:26:23 time: 0.0430 data_time: 0.0061 memory: 1794 loss: 0.8672 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8672 2022/11/28 16:15:22 - mmengine - INFO - Epoch(train) [2][1500/2462] lr: 9.7526e-02 eta: 0:26:17 time: 0.0429 data_time: 0.0062 memory: 1794 loss: 0.8192 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8192 2022/11/28 16:15:23 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:15:26 - mmengine - INFO - Epoch(train) [2][1600/2462] lr: 9.7400e-02 eta: 0:26:12 time: 0.0432 data_time: 0.0065 memory: 1794 loss: 1.0740 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0740 2022/11/28 16:15:30 - mmengine - INFO - Epoch(train) [2][1700/2462] lr: 9.7272e-02 eta: 0:26:06 time: 0.0428 data_time: 0.0062 memory: 1794 loss: 0.8610 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8610 2022/11/28 16:15:35 - mmengine - INFO - Epoch(train) [2][1800/2462] lr: 9.7141e-02 eta: 0:26:01 time: 0.0432 data_time: 0.0063 memory: 1794 loss: 0.9171 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9171 2022/11/28 16:15:39 - mmengine - INFO - Epoch(train) [2][1900/2462] lr: 9.7006e-02 eta: 0:25:56 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.8302 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8302 2022/11/28 16:15:45 - mmengine - INFO - Epoch(train) [2][2000/2462] lr: 9.6869e-02 eta: 0:25:59 time: 0.0433 data_time: 0.0061 memory: 1794 loss: 0.8131 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8131 2022/11/28 16:15:49 - mmengine - INFO - Epoch(train) [2][2100/2462] lr: 9.6728e-02 eta: 0:25:53 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.7745 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7745 2022/11/28 16:15:53 - mmengine - INFO - Epoch(train) [2][2200/2462] lr: 9.6585e-02 eta: 0:25:48 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 0.9664 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9664 2022/11/28 16:15:58 - mmengine - INFO - Epoch(train) [2][2300/2462] lr: 9.6439e-02 eta: 0:25:43 time: 0.0453 data_time: 0.0062 memory: 1794 loss: 0.8335 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8335 2022/11/28 16:16:02 - mmengine - INFO - Epoch(train) [2][2400/2462] lr: 9.6290e-02 eta: 0:25:38 time: 0.0441 data_time: 0.0067 memory: 1794 loss: 0.9363 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9363 2022/11/28 16:16:05 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:16:05 - mmengine - INFO - Epoch(train) [2][2462/2462] lr: 9.6196e-02 eta: 0:25:35 time: 0.0436 data_time: 0.0063 memory: 1794 loss: 0.8158 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.8158 2022/11/28 16:16:05 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/11/28 16:16:09 - mmengine - INFO - Epoch(val) [2][100/398] eta: 0:00:10 time: 0.0343 data_time: 0.0210 memory: 364 2022/11/28 16:16:12 - mmengine - INFO - Epoch(val) [2][200/398] eta: 0:00:06 time: 0.0246 data_time: 0.0118 memory: 364 2022/11/28 16:16:14 - mmengine - INFO - Epoch(val) [2][300/398] eta: 0:00:02 time: 0.0261 data_time: 0.0132 memory: 364 2022/11/28 16:16:18 - mmengine - INFO - Epoch(val) [2][398/398] acc/top1: 0.6577 acc/top5: 0.9039 acc/mean1: 0.6819 2022/11/28 16:16:18 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_1.pth is removed 2022/11/28 16:16:18 - mmengine - INFO - The best checkpoint with 0.6577 acc/top1 at 2 epoch is saved to best_acc/top1_epoch_2.pth. 2022/11/28 16:16:22 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:16:23 - mmengine - INFO - Epoch(train) [3][100/2462] lr: 9.6041e-02 eta: 0:25:31 time: 0.0430 data_time: 0.0065 memory: 1794 loss: 0.7566 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7566 2022/11/28 16:16:27 - mmengine - INFO - Epoch(train) [3][200/2462] lr: 9.5884e-02 eta: 0:25:26 time: 0.0434 data_time: 0.0071 memory: 1794 loss: 0.7690 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7690 2022/11/28 16:16:32 - mmengine - INFO - Epoch(train) [3][300/2462] lr: 9.5725e-02 eta: 0:25:21 time: 0.0432 data_time: 0.0061 memory: 1794 loss: 0.7568 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.7568 2022/11/28 16:16:36 - mmengine - INFO - Epoch(train) [3][400/2462] lr: 9.5562e-02 eta: 0:25:16 time: 0.0429 data_time: 0.0061 memory: 1794 loss: 0.8722 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8722 2022/11/28 16:16:40 - mmengine - INFO - Epoch(train) [3][500/2462] lr: 9.5396e-02 eta: 0:25:11 time: 0.0429 data_time: 0.0062 memory: 1794 loss: 0.8506 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8506 2022/11/28 16:16:44 - mmengine - INFO - Epoch(train) [3][600/2462] lr: 9.5228e-02 eta: 0:25:06 time: 0.0431 data_time: 0.0061 memory: 1794 loss: 0.7350 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7350 2022/11/28 16:16:49 - mmengine - INFO - Epoch(train) [3][700/2462] lr: 9.5056e-02 eta: 0:25:01 time: 0.0443 data_time: 0.0062 memory: 1794 loss: 0.8099 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8099 2022/11/28 16:16:53 - mmengine - INFO - Epoch(train) [3][800/2462] lr: 9.4882e-02 eta: 0:24:56 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 0.7769 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7769 2022/11/28 16:16:58 - mmengine - INFO - Epoch(train) [3][900/2462] lr: 9.4705e-02 eta: 0:24:51 time: 0.0436 data_time: 0.0061 memory: 1794 loss: 0.8710 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8710 2022/11/28 16:17:02 - mmengine - INFO - Epoch(train) [3][1000/2462] lr: 9.4525e-02 eta: 0:24:46 time: 0.0443 data_time: 0.0063 memory: 1794 loss: 0.8697 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8697 2022/11/28 16:17:05 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:17:06 - mmengine - INFO - Epoch(train) [3][1100/2462] lr: 9.4342e-02 eta: 0:24:41 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.7159 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7159 2022/11/28 16:17:11 - mmengine - INFO - Epoch(train) [3][1200/2462] lr: 9.4156e-02 eta: 0:24:36 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.6908 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 0.6908 2022/11/28 16:17:15 - mmengine - INFO - Epoch(train) [3][1300/2462] lr: 9.3968e-02 eta: 0:24:32 time: 0.0459 data_time: 0.0063 memory: 1794 loss: 0.7279 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7279 2022/11/28 16:17:20 - mmengine - INFO - Epoch(train) [3][1400/2462] lr: 9.3776e-02 eta: 0:24:27 time: 0.0434 data_time: 0.0062 memory: 1794 loss: 0.8341 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8341 2022/11/28 16:17:24 - mmengine - INFO - Epoch(train) [3][1500/2462] lr: 9.3582e-02 eta: 0:24:22 time: 0.0446 data_time: 0.0065 memory: 1794 loss: 0.8042 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8042 2022/11/28 16:17:28 - mmengine - INFO - Epoch(train) [3][1600/2462] lr: 9.3385e-02 eta: 0:24:18 time: 0.0439 data_time: 0.0068 memory: 1794 loss: 0.7158 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7158 2022/11/28 16:17:33 - mmengine - INFO - Epoch(train) [3][1700/2462] lr: 9.3186e-02 eta: 0:24:13 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.8715 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8715 2022/11/28 16:17:37 - mmengine - INFO - Epoch(train) [3][1800/2462] lr: 9.2983e-02 eta: 0:24:08 time: 0.0435 data_time: 0.0061 memory: 1794 loss: 0.8478 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8478 2022/11/28 16:17:41 - mmengine - INFO - Epoch(train) [3][1900/2462] lr: 9.2778e-02 eta: 0:24:03 time: 0.0437 data_time: 0.0063 memory: 1794 loss: 0.7367 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7367 2022/11/28 16:17:46 - mmengine - INFO - Epoch(train) [3][2000/2462] lr: 9.2571e-02 eta: 0:23:59 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 0.7427 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7427 2022/11/28 16:17:49 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:17:50 - mmengine - INFO - Epoch(train) [3][2100/2462] lr: 9.2360e-02 eta: 0:23:54 time: 0.0431 data_time: 0.0063 memory: 1794 loss: 0.7658 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7658 2022/11/28 16:17:55 - mmengine - INFO - Epoch(train) [3][2200/2462] lr: 9.2147e-02 eta: 0:23:49 time: 0.0500 data_time: 0.0062 memory: 1794 loss: 0.6616 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6616 2022/11/28 16:17:59 - mmengine - INFO - Epoch(train) [3][2300/2462] lr: 9.1931e-02 eta: 0:23:45 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.7342 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7342 2022/11/28 16:18:03 - mmengine - INFO - Epoch(train) [3][2400/2462] lr: 9.1713e-02 eta: 0:23:40 time: 0.0431 data_time: 0.0061 memory: 1794 loss: 0.7669 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7669 2022/11/28 16:18:06 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:18:06 - mmengine - INFO - Epoch(train) [3][2462/2462] lr: 9.1576e-02 eta: 0:23:37 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 0.6734 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6734 2022/11/28 16:18:06 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/11/28 16:18:10 - mmengine - INFO - Epoch(val) [3][100/398] eta: 0:00:10 time: 0.0336 data_time: 0.0203 memory: 364 2022/11/28 16:18:13 - mmengine - INFO - Epoch(val) [3][200/398] eta: 0:00:06 time: 0.0246 data_time: 0.0118 memory: 364 2022/11/28 16:18:15 - mmengine - INFO - Epoch(val) [3][300/398] eta: 0:00:02 time: 0.0253 data_time: 0.0130 memory: 364 2022/11/28 16:18:19 - mmengine - INFO - Epoch(val) [3][398/398] acc/top1: 0.7004 acc/top5: 0.9192 acc/mean1: 0.7198 2022/11/28 16:18:19 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_2.pth is removed 2022/11/28 16:18:19 - mmengine - INFO - The best checkpoint with 0.7004 acc/top1 at 3 epoch is saved to best_acc/top1_epoch_3.pth. 2022/11/28 16:18:24 - mmengine - INFO - Epoch(train) [4][100/2462] lr: 9.1353e-02 eta: 0:23:33 time: 0.0447 data_time: 0.0070 memory: 1794 loss: 0.7371 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.7371 2022/11/28 16:18:28 - mmengine - INFO - Epoch(train) [4][200/2462] lr: 9.1127e-02 eta: 0:23:28 time: 0.0435 data_time: 0.0061 memory: 1794 loss: 0.6506 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6506 2022/11/28 16:18:33 - mmengine - INFO - Epoch(train) [4][300/2462] lr: 9.0899e-02 eta: 0:23:23 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.7598 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7598 2022/11/28 16:18:37 - mmengine - INFO - Epoch(train) [4][400/2462] lr: 9.0669e-02 eta: 0:23:19 time: 0.0430 data_time: 0.0061 memory: 1794 loss: 0.6116 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6116 2022/11/28 16:18:41 - mmengine - INFO - Epoch(train) [4][500/2462] lr: 9.0435e-02 eta: 0:23:14 time: 0.0440 data_time: 0.0063 memory: 1794 loss: 0.6662 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6662 2022/11/28 16:18:46 - mmengine - INFO - Epoch(train) [4][600/2462] lr: 9.0200e-02 eta: 0:23:09 time: 0.0430 data_time: 0.0063 memory: 1794 loss: 0.6237 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6237 2022/11/28 16:18:46 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:18:50 - mmengine - INFO - Epoch(train) [4][700/2462] lr: 8.9961e-02 eta: 0:23:05 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.6182 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6182 2022/11/28 16:18:54 - mmengine - INFO - Epoch(train) [4][800/2462] lr: 8.9720e-02 eta: 0:23:00 time: 0.0436 data_time: 0.0069 memory: 1794 loss: 0.7331 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7331 2022/11/28 16:18:59 - mmengine - INFO - Epoch(train) [4][900/2462] lr: 8.9477e-02 eta: 0:22:55 time: 0.0430 data_time: 0.0061 memory: 1794 loss: 0.7537 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 0.7537 2022/11/28 16:19:03 - mmengine - INFO - Epoch(train) [4][1000/2462] lr: 8.9231e-02 eta: 0:22:50 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.6723 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6723 2022/11/28 16:19:07 - mmengine - INFO - Epoch(train) [4][1100/2462] lr: 8.8982e-02 eta: 0:22:45 time: 0.0431 data_time: 0.0061 memory: 1794 loss: 0.6666 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.6666 2022/11/28 16:19:12 - mmengine - INFO - Epoch(train) [4][1200/2462] lr: 8.8731e-02 eta: 0:22:41 time: 0.0432 data_time: 0.0068 memory: 1794 loss: 0.6930 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6930 2022/11/28 16:19:16 - mmengine - INFO - Epoch(train) [4][1300/2462] lr: 8.8478e-02 eta: 0:22:36 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.6304 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6304 2022/11/28 16:19:20 - mmengine - INFO - Epoch(train) [4][1400/2462] lr: 8.8222e-02 eta: 0:22:31 time: 0.0431 data_time: 0.0061 memory: 1794 loss: 0.6830 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6830 2022/11/28 16:19:25 - mmengine - INFO - Epoch(train) [4][1500/2462] lr: 8.7964e-02 eta: 0:22:27 time: 0.0440 data_time: 0.0069 memory: 1794 loss: 0.7876 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.7876 2022/11/28 16:19:29 - mmengine - INFO - Epoch(train) [4][1600/2462] lr: 8.7703e-02 eta: 0:22:22 time: 0.0449 data_time: 0.0062 memory: 1794 loss: 0.7257 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7257 2022/11/28 16:19:30 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:19:34 - mmengine - INFO - Epoch(train) [4][1700/2462] lr: 8.7440e-02 eta: 0:22:18 time: 0.0433 data_time: 0.0061 memory: 1794 loss: 0.7162 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7162 2022/11/28 16:19:38 - mmengine - INFO - Epoch(train) [4][1800/2462] lr: 8.7174e-02 eta: 0:22:13 time: 0.0432 data_time: 0.0061 memory: 1794 loss: 0.7551 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7551 2022/11/28 16:19:42 - mmengine - INFO - Epoch(train) [4][1900/2462] lr: 8.6907e-02 eta: 0:22:09 time: 0.0436 data_time: 0.0066 memory: 1794 loss: 0.7509 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7509 2022/11/28 16:19:47 - mmengine - INFO - Epoch(train) [4][2000/2462] lr: 8.6636e-02 eta: 0:22:04 time: 0.0458 data_time: 0.0062 memory: 1794 loss: 0.8219 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8219 2022/11/28 16:19:51 - mmengine - INFO - Epoch(train) [4][2100/2462] lr: 8.6364e-02 eta: 0:22:00 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.6531 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.6531 2022/11/28 16:19:55 - mmengine - INFO - Epoch(train) [4][2200/2462] lr: 8.6089e-02 eta: 0:21:55 time: 0.0442 data_time: 0.0065 memory: 1794 loss: 0.6671 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6671 2022/11/28 16:20:00 - mmengine - INFO - Epoch(train) [4][2300/2462] lr: 8.5812e-02 eta: 0:21:50 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.7724 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7724 2022/11/28 16:20:04 - mmengine - INFO - Epoch(train) [4][2400/2462] lr: 8.5533e-02 eta: 0:21:46 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.6165 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6165 2022/11/28 16:20:07 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:20:07 - mmengine - INFO - Epoch(train) [4][2462/2462] lr: 8.5358e-02 eta: 0:21:43 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.7122 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7122 2022/11/28 16:20:07 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/11/28 16:20:11 - mmengine - INFO - Epoch(val) [4][100/398] eta: 0:00:10 time: 0.0335 data_time: 0.0203 memory: 364 2022/11/28 16:20:14 - mmengine - INFO - Epoch(val) [4][200/398] eta: 0:00:06 time: 0.0251 data_time: 0.0121 memory: 364 2022/11/28 16:20:17 - mmengine - INFO - Epoch(val) [4][300/398] eta: 0:00:03 time: 0.0258 data_time: 0.0136 memory: 364 2022/11/28 16:20:20 - mmengine - INFO - Epoch(val) [4][398/398] acc/top1: 0.7037 acc/top5: 0.9280 acc/mean1: 0.7209 2022/11/28 16:20:20 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_3.pth is removed 2022/11/28 16:20:20 - mmengine - INFO - The best checkpoint with 0.7037 acc/top1 at 4 epoch is saved to best_acc/top1_epoch_4.pth. 2022/11/28 16:20:25 - mmengine - INFO - Epoch(train) [5][100/2462] lr: 8.5075e-02 eta: 0:21:39 time: 0.0446 data_time: 0.0070 memory: 1794 loss: 0.6074 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6074 2022/11/28 16:20:27 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:20:29 - mmengine - INFO - Epoch(train) [5][200/2462] lr: 8.4790e-02 eta: 0:21:34 time: 0.0437 data_time: 0.0063 memory: 1794 loss: 0.7214 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7214 2022/11/28 16:20:34 - mmengine - INFO - Epoch(train) [5][300/2462] lr: 8.4502e-02 eta: 0:21:30 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.7626 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7626 2022/11/28 16:20:38 - mmengine - INFO - Epoch(train) [5][400/2462] lr: 8.4213e-02 eta: 0:21:26 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.5920 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5920 2022/11/28 16:20:43 - mmengine - INFO - Epoch(train) [5][500/2462] lr: 8.3921e-02 eta: 0:21:21 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.6616 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6616 2022/11/28 16:20:47 - mmengine - INFO - Epoch(train) [5][600/2462] lr: 8.3627e-02 eta: 0:21:17 time: 0.0442 data_time: 0.0064 memory: 1794 loss: 0.6899 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6899 2022/11/28 16:20:51 - mmengine - INFO - Epoch(train) [5][700/2462] lr: 8.3330e-02 eta: 0:21:12 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.5981 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5981 2022/11/28 16:20:56 - mmengine - INFO - Epoch(train) [5][800/2462] lr: 8.3032e-02 eta: 0:21:08 time: 0.0428 data_time: 0.0062 memory: 1794 loss: 0.7254 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7254 2022/11/28 16:21:00 - mmengine - INFO - Epoch(train) [5][900/2462] lr: 8.2732e-02 eta: 0:21:03 time: 0.0431 data_time: 0.0061 memory: 1794 loss: 0.6472 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.6472 2022/11/28 16:21:04 - mmengine - INFO - Epoch(train) [5][1000/2462] lr: 8.2429e-02 eta: 0:20:59 time: 0.0440 data_time: 0.0071 memory: 1794 loss: 0.7266 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7266 2022/11/28 16:21:09 - mmengine - INFO - Epoch(train) [5][1100/2462] lr: 8.2125e-02 eta: 0:20:54 time: 0.0437 data_time: 0.0066 memory: 1794 loss: 0.6646 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6646 2022/11/28 16:21:11 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:21:13 - mmengine - INFO - Epoch(train) [5][1200/2462] lr: 8.1818e-02 eta: 0:20:50 time: 0.0436 data_time: 0.0066 memory: 1794 loss: 0.6573 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.6573 2022/11/28 16:21:18 - mmengine - INFO - Epoch(train) [5][1300/2462] lr: 8.1510e-02 eta: 0:20:45 time: 0.0433 data_time: 0.0061 memory: 1794 loss: 0.6257 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6257 2022/11/28 16:21:22 - mmengine - INFO - Epoch(train) [5][1400/2462] lr: 8.1199e-02 eta: 0:20:41 time: 0.0430 data_time: 0.0062 memory: 1794 loss: 0.5481 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5481 2022/11/28 16:21:26 - mmengine - INFO - Epoch(train) [5][1500/2462] lr: 8.0886e-02 eta: 0:20:36 time: 0.0429 data_time: 0.0062 memory: 1794 loss: 0.6814 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6814 2022/11/28 16:21:31 - mmengine - INFO - Epoch(train) [5][1600/2462] lr: 8.0572e-02 eta: 0:20:31 time: 0.0432 data_time: 0.0061 memory: 1794 loss: 0.6243 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6243 2022/11/28 16:21:35 - mmengine - INFO - Epoch(train) [5][1700/2462] lr: 8.0255e-02 eta: 0:20:27 time: 0.0436 data_time: 0.0061 memory: 1794 loss: 0.6205 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6205 2022/11/28 16:21:39 - mmengine - INFO - Epoch(train) [5][1800/2462] lr: 7.9937e-02 eta: 0:20:22 time: 0.0449 data_time: 0.0066 memory: 1794 loss: 0.6308 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6308 2022/11/28 16:21:44 - mmengine - INFO - Epoch(train) [5][1900/2462] lr: 7.9617e-02 eta: 0:20:18 time: 0.0438 data_time: 0.0064 memory: 1794 loss: 0.5278 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.5278 2022/11/28 16:21:48 - mmengine - INFO - Epoch(train) [5][2000/2462] lr: 7.9294e-02 eta: 0:20:13 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.7233 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7233 2022/11/28 16:21:52 - mmengine - INFO - Epoch(train) [5][2100/2462] lr: 7.8970e-02 eta: 0:20:09 time: 0.0438 data_time: 0.0061 memory: 1794 loss: 0.5801 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5801 2022/11/28 16:21:55 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:21:57 - mmengine - INFO - Epoch(train) [5][2200/2462] lr: 7.8644e-02 eta: 0:20:04 time: 0.0430 data_time: 0.0062 memory: 1794 loss: 0.6491 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6491 2022/11/28 16:22:01 - mmengine - INFO - Epoch(train) [5][2300/2462] lr: 7.8317e-02 eta: 0:20:00 time: 0.0431 data_time: 0.0064 memory: 1794 loss: 0.6793 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6793 2022/11/28 16:22:06 - mmengine - INFO - Epoch(train) [5][2400/2462] lr: 7.7987e-02 eta: 0:19:55 time: 0.0432 data_time: 0.0064 memory: 1794 loss: 0.7178 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7178 2022/11/28 16:22:08 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:22:08 - mmengine - INFO - Epoch(train) [5][2462/2462] lr: 7.7782e-02 eta: 0:19:53 time: 0.0432 data_time: 0.0063 memory: 1794 loss: 0.5587 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5587 2022/11/28 16:22:08 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/11/28 16:22:12 - mmengine - INFO - Epoch(val) [5][100/398] eta: 0:00:10 time: 0.0333 data_time: 0.0202 memory: 364 2022/11/28 16:22:15 - mmengine - INFO - Epoch(val) [5][200/398] eta: 0:00:06 time: 0.0245 data_time: 0.0116 memory: 364 2022/11/28 16:22:18 - mmengine - INFO - Epoch(val) [5][300/398] eta: 0:00:02 time: 0.0255 data_time: 0.0132 memory: 364 2022/11/28 16:22:21 - mmengine - INFO - Epoch(val) [5][398/398] acc/top1: 0.7197 acc/top5: 0.9279 acc/mean1: 0.7375 2022/11/28 16:22:21 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_4.pth is removed 2022/11/28 16:22:21 - mmengine - INFO - The best checkpoint with 0.7197 acc/top1 at 5 epoch is saved to best_acc/top1_epoch_5.pth. 2022/11/28 16:22:26 - mmengine - INFO - Epoch(train) [6][100/2462] lr: 7.7449e-02 eta: 0:19:49 time: 0.0442 data_time: 0.0063 memory: 1794 loss: 0.4968 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4968 2022/11/28 16:22:30 - mmengine - INFO - Epoch(train) [6][200/2462] lr: 7.7115e-02 eta: 0:19:44 time: 0.0443 data_time: 0.0062 memory: 1794 loss: 0.5844 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5844 2022/11/28 16:22:35 - mmengine - INFO - Epoch(train) [6][300/2462] lr: 7.6779e-02 eta: 0:19:40 time: 0.0438 data_time: 0.0068 memory: 1794 loss: 0.5429 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5429 2022/11/28 16:22:39 - mmengine - INFO - Epoch(train) [6][400/2462] lr: 7.6442e-02 eta: 0:19:36 time: 0.0452 data_time: 0.0065 memory: 1794 loss: 0.6244 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6244 2022/11/28 16:22:44 - mmengine - INFO - Epoch(train) [6][500/2462] lr: 7.6102e-02 eta: 0:19:31 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.6078 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6078 2022/11/28 16:22:48 - mmengine - INFO - Epoch(train) [6][600/2462] lr: 7.5762e-02 eta: 0:19:27 time: 0.0444 data_time: 0.0062 memory: 1794 loss: 0.7285 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7285 2022/11/28 16:22:52 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:22:53 - mmengine - INFO - Epoch(train) [6][700/2462] lr: 7.5419e-02 eta: 0:19:22 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.6672 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6672 2022/11/28 16:22:57 - mmengine - INFO - Epoch(train) [6][800/2462] lr: 7.5075e-02 eta: 0:19:18 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.6166 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6166 2022/11/28 16:23:01 - mmengine - INFO - Epoch(train) [6][900/2462] lr: 7.4729e-02 eta: 0:19:13 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.4959 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4959 2022/11/28 16:23:06 - mmengine - INFO - Epoch(train) [6][1000/2462] lr: 7.4382e-02 eta: 0:19:09 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.4835 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.4835 2022/11/28 16:23:10 - mmengine - INFO - Epoch(train) [6][1100/2462] lr: 7.4033e-02 eta: 0:19:05 time: 0.0447 data_time: 0.0077 memory: 1794 loss: 0.5829 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5829 2022/11/28 16:23:15 - mmengine - INFO - Epoch(train) [6][1200/2462] lr: 7.3682e-02 eta: 0:19:00 time: 0.0442 data_time: 0.0064 memory: 1794 loss: 0.5766 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5766 2022/11/28 16:23:19 - mmengine - INFO - Epoch(train) [6][1300/2462] lr: 7.3330e-02 eta: 0:18:56 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.4418 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.4418 2022/11/28 16:23:23 - mmengine - INFO - Epoch(train) [6][1400/2462] lr: 7.2977e-02 eta: 0:18:51 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.5261 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5261 2022/11/28 16:23:28 - mmengine - INFO - Epoch(train) [6][1500/2462] lr: 7.2622e-02 eta: 0:18:47 time: 0.0435 data_time: 0.0063 memory: 1794 loss: 0.5806 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5806 2022/11/28 16:23:32 - mmengine - INFO - Epoch(train) [6][1600/2462] lr: 7.2266e-02 eta: 0:18:43 time: 0.0472 data_time: 0.0067 memory: 1794 loss: 0.5832 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5832 2022/11/28 16:23:36 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:23:37 - mmengine - INFO - Epoch(train) [6][1700/2462] lr: 7.1908e-02 eta: 0:18:39 time: 0.0437 data_time: 0.0066 memory: 1794 loss: 0.5538 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5538 2022/11/28 16:23:41 - mmengine - INFO - Epoch(train) [6][1800/2462] lr: 7.1549e-02 eta: 0:18:34 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.5607 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5607 2022/11/28 16:23:46 - mmengine - INFO - Epoch(train) [6][1900/2462] lr: 7.1188e-02 eta: 0:18:30 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.6625 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6625 2022/11/28 16:23:50 - mmengine - INFO - Epoch(train) [6][2000/2462] lr: 7.0826e-02 eta: 0:18:25 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.5825 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.5825 2022/11/28 16:23:55 - mmengine - INFO - Epoch(train) [6][2100/2462] lr: 7.0463e-02 eta: 0:18:21 time: 0.0448 data_time: 0.0063 memory: 1794 loss: 0.5821 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5821 2022/11/28 16:23:59 - mmengine - INFO - Epoch(train) [6][2200/2462] lr: 7.0099e-02 eta: 0:18:17 time: 0.0435 data_time: 0.0063 memory: 1794 loss: 0.6690 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.6690 2022/11/28 16:24:03 - mmengine - INFO - Epoch(train) [6][2300/2462] lr: 6.9733e-02 eta: 0:18:12 time: 0.0437 data_time: 0.0063 memory: 1794 loss: 0.4700 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4700 2022/11/28 16:24:08 - mmengine - INFO - Epoch(train) [6][2400/2462] lr: 6.9366e-02 eta: 0:18:08 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.4588 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4588 2022/11/28 16:24:11 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:24:11 - mmengine - INFO - Epoch(train) [6][2462/2462] lr: 6.9138e-02 eta: 0:18:05 time: 0.0456 data_time: 0.0067 memory: 1794 loss: 0.6051 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.6051 2022/11/28 16:24:11 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/11/28 16:24:14 - mmengine - INFO - Epoch(val) [6][100/398] eta: 0:00:10 time: 0.0334 data_time: 0.0202 memory: 364 2022/11/28 16:24:17 - mmengine - INFO - Epoch(val) [6][200/398] eta: 0:00:06 time: 0.0250 data_time: 0.0117 memory: 364 2022/11/28 16:24:20 - mmengine - INFO - Epoch(val) [6][300/398] eta: 0:00:02 time: 0.0255 data_time: 0.0132 memory: 364 2022/11/28 16:24:23 - mmengine - INFO - Epoch(val) [6][398/398] acc/top1: 0.7300 acc/top5: 0.9340 acc/mean1: 0.7397 2022/11/28 16:24:23 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_5.pth is removed 2022/11/28 16:24:24 - mmengine - INFO - The best checkpoint with 0.7300 acc/top1 at 6 epoch is saved to best_acc/top1_epoch_6.pth. 2022/11/28 16:24:29 - mmengine - INFO - Epoch(train) [7][100/2462] lr: 6.8769e-02 eta: 0:18:01 time: 0.0457 data_time: 0.0080 memory: 1794 loss: 0.4896 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4896 2022/11/28 16:24:33 - mmengine - INFO - Epoch(train) [7][200/2462] lr: 6.8399e-02 eta: 0:17:57 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.5981 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5981 2022/11/28 16:24:34 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:24:37 - mmengine - INFO - Epoch(train) [7][300/2462] lr: 6.8027e-02 eta: 0:17:52 time: 0.0437 data_time: 0.0063 memory: 1794 loss: 0.4883 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4883 2022/11/28 16:24:42 - mmengine - INFO - Epoch(train) [7][400/2462] lr: 6.7655e-02 eta: 0:17:48 time: 0.0453 data_time: 0.0064 memory: 1794 loss: 0.5175 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.5175 2022/11/28 16:24:46 - mmengine - INFO - Epoch(train) [7][500/2462] lr: 6.7281e-02 eta: 0:17:44 time: 0.0436 data_time: 0.0061 memory: 1794 loss: 0.4091 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4091 2022/11/28 16:24:51 - mmengine - INFO - Epoch(train) [7][600/2462] lr: 6.6906e-02 eta: 0:17:39 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.5493 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5493 2022/11/28 16:24:55 - mmengine - INFO - Epoch(train) [7][700/2462] lr: 6.6531e-02 eta: 0:17:35 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.5710 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5710 2022/11/28 16:24:59 - mmengine - INFO - Epoch(train) [7][800/2462] lr: 6.6154e-02 eta: 0:17:30 time: 0.0436 data_time: 0.0061 memory: 1794 loss: 0.6226 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6226 2022/11/28 16:25:04 - mmengine - INFO - Epoch(train) [7][900/2462] lr: 6.5776e-02 eta: 0:17:26 time: 0.0441 data_time: 0.0069 memory: 1794 loss: 0.5940 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5940 2022/11/28 16:25:08 - mmengine - INFO - Epoch(train) [7][1000/2462] lr: 6.5397e-02 eta: 0:17:21 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.5391 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5391 2022/11/28 16:25:13 - mmengine - INFO - Epoch(train) [7][1100/2462] lr: 6.5017e-02 eta: 0:17:17 time: 0.0455 data_time: 0.0063 memory: 1794 loss: 0.5722 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5722 2022/11/28 16:25:17 - mmengine - INFO - Epoch(train) [7][1200/2462] lr: 6.4636e-02 eta: 0:17:13 time: 0.0453 data_time: 0.0062 memory: 1794 loss: 0.7161 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7161 2022/11/28 16:25:18 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:25:22 - mmengine - INFO - Epoch(train) [7][1300/2462] lr: 6.4255e-02 eta: 0:17:08 time: 0.0429 data_time: 0.0062 memory: 1794 loss: 0.4502 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4502 2022/11/28 16:25:26 - mmengine - INFO - Epoch(train) [7][1400/2462] lr: 6.3872e-02 eta: 0:17:04 time: 0.0436 data_time: 0.0063 memory: 1794 loss: 0.5854 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.5854 2022/11/28 16:25:30 - mmengine - INFO - Epoch(train) [7][1500/2462] lr: 6.3488e-02 eta: 0:16:59 time: 0.0434 data_time: 0.0066 memory: 1794 loss: 0.4159 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.4159 2022/11/28 16:25:35 - mmengine - INFO - Epoch(train) [7][1600/2462] lr: 6.3104e-02 eta: 0:16:55 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.4566 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4566 2022/11/28 16:25:39 - mmengine - INFO - Epoch(train) [7][1700/2462] lr: 6.2719e-02 eta: 0:16:51 time: 0.0460 data_time: 0.0068 memory: 1794 loss: 0.5034 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.5034 2022/11/28 16:25:44 - mmengine - INFO - Epoch(train) [7][1800/2462] lr: 6.2333e-02 eta: 0:16:46 time: 0.0444 data_time: 0.0072 memory: 1794 loss: 0.5840 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5840 2022/11/28 16:25:48 - mmengine - INFO - Epoch(train) [7][1900/2462] lr: 6.1946e-02 eta: 0:16:42 time: 0.0472 data_time: 0.0066 memory: 1794 loss: 0.4242 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.4242 2022/11/28 16:25:53 - mmengine - INFO - Epoch(train) [7][2000/2462] lr: 6.1558e-02 eta: 0:16:37 time: 0.0434 data_time: 0.0062 memory: 1794 loss: 0.5202 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5202 2022/11/28 16:25:57 - mmengine - INFO - Epoch(train) [7][2100/2462] lr: 6.1170e-02 eta: 0:16:33 time: 0.0435 data_time: 0.0069 memory: 1794 loss: 0.4774 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4774 2022/11/28 16:26:01 - mmengine - INFO - Epoch(train) [7][2200/2462] lr: 6.0781e-02 eta: 0:16:29 time: 0.0447 data_time: 0.0061 memory: 1794 loss: 0.6178 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6178 2022/11/28 16:26:03 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:26:06 - mmengine - INFO - Epoch(train) [7][2300/2462] lr: 6.0391e-02 eta: 0:16:24 time: 0.0434 data_time: 0.0062 memory: 1794 loss: 0.5962 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5962 2022/11/28 16:26:10 - mmengine - INFO - Epoch(train) [7][2400/2462] lr: 6.0001e-02 eta: 0:16:20 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.5979 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5979 2022/11/28 16:26:13 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:26:13 - mmengine - INFO - Epoch(train) [7][2462/2462] lr: 5.9758e-02 eta: 0:16:17 time: 0.0433 data_time: 0.0063 memory: 1794 loss: 0.5077 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5077 2022/11/28 16:26:13 - mmengine - INFO - Saving checkpoint at 7 epochs 2022/11/28 16:26:17 - mmengine - INFO - Epoch(val) [7][100/398] eta: 0:00:10 time: 0.0337 data_time: 0.0204 memory: 364 2022/11/28 16:26:20 - mmengine - INFO - Epoch(val) [7][200/398] eta: 0:00:06 time: 0.0244 data_time: 0.0114 memory: 364 2022/11/28 16:26:22 - mmengine - INFO - Epoch(val) [7][300/398] eta: 0:00:02 time: 0.0253 data_time: 0.0133 memory: 364 2022/11/28 16:26:26 - mmengine - INFO - Epoch(val) [7][398/398] acc/top1: 0.7478 acc/top5: 0.9381 acc/mean1: 0.7580 2022/11/28 16:26:26 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_6.pth is removed 2022/11/28 16:26:26 - mmengine - INFO - The best checkpoint with 0.7478 acc/top1 at 7 epoch is saved to best_acc/top1_epoch_7.pth. 2022/11/28 16:26:31 - mmengine - INFO - Epoch(train) [8][100/2462] lr: 5.9367e-02 eta: 0:16:13 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.5084 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5084 2022/11/28 16:26:35 - mmengine - INFO - Epoch(train) [8][200/2462] lr: 5.8975e-02 eta: 0:16:08 time: 0.0435 data_time: 0.0061 memory: 1794 loss: 0.4555 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4555 2022/11/28 16:26:39 - mmengine - INFO - Epoch(train) [8][300/2462] lr: 5.8582e-02 eta: 0:16:04 time: 0.0445 data_time: 0.0064 memory: 1794 loss: 0.5534 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5534 2022/11/28 16:26:44 - mmengine - INFO - Epoch(train) [8][400/2462] lr: 5.8189e-02 eta: 0:15:59 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.5533 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.5533 2022/11/28 16:26:48 - mmengine - INFO - Epoch(train) [8][500/2462] lr: 5.7796e-02 eta: 0:15:55 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 0.4296 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.4296 2022/11/28 16:26:53 - mmengine - INFO - Epoch(train) [8][600/2462] lr: 5.7402e-02 eta: 0:15:50 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.4482 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.4482 2022/11/28 16:26:57 - mmengine - INFO - Epoch(train) [8][700/2462] lr: 5.7007e-02 eta: 0:15:46 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.4124 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4124 2022/11/28 16:27:00 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:27:01 - mmengine - INFO - Epoch(train) [8][800/2462] lr: 5.6612e-02 eta: 0:15:42 time: 0.0438 data_time: 0.0061 memory: 1794 loss: 0.4260 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4260 2022/11/28 16:27:06 - mmengine - INFO - Epoch(train) [8][900/2462] lr: 5.6216e-02 eta: 0:15:37 time: 0.0430 data_time: 0.0062 memory: 1794 loss: 0.4869 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4869 2022/11/28 16:27:10 - mmengine - INFO - Epoch(train) [8][1000/2462] lr: 5.5821e-02 eta: 0:15:33 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.4953 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4953 2022/11/28 16:27:15 - mmengine - INFO - Epoch(train) [8][1100/2462] lr: 5.5424e-02 eta: 0:15:28 time: 0.0436 data_time: 0.0066 memory: 1794 loss: 0.4116 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.4116 2022/11/28 16:27:19 - mmengine - INFO - Epoch(train) [8][1200/2462] lr: 5.5028e-02 eta: 0:15:24 time: 0.0433 data_time: 0.0061 memory: 1794 loss: 0.4589 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4589 2022/11/28 16:27:23 - mmengine - INFO - Epoch(train) [8][1300/2462] lr: 5.4631e-02 eta: 0:15:19 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.4632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4632 2022/11/28 16:27:28 - mmengine - INFO - Epoch(train) [8][1400/2462] lr: 5.4234e-02 eta: 0:15:15 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.4802 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4802 2022/11/28 16:27:32 - mmengine - INFO - Epoch(train) [8][1500/2462] lr: 5.3836e-02 eta: 0:15:10 time: 0.0436 data_time: 0.0061 memory: 1794 loss: 0.4518 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4518 2022/11/28 16:27:37 - mmengine - INFO - Epoch(train) [8][1600/2462] lr: 5.3439e-02 eta: 0:15:06 time: 0.0438 data_time: 0.0062 memory: 1794 loss: 0.4732 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4732 2022/11/28 16:27:41 - mmengine - INFO - Epoch(train) [8][1700/2462] lr: 5.3041e-02 eta: 0:15:02 time: 0.0440 data_time: 0.0071 memory: 1794 loss: 0.3575 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.3575 2022/11/28 16:27:44 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:27:45 - mmengine - INFO - Epoch(train) [8][1800/2462] lr: 5.2643e-02 eta: 0:14:57 time: 0.0439 data_time: 0.0066 memory: 1794 loss: 0.4541 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4541 2022/11/28 16:27:50 - mmengine - INFO - Epoch(train) [8][1900/2462] lr: 5.2244e-02 eta: 0:14:53 time: 0.0434 data_time: 0.0062 memory: 1794 loss: 0.5001 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5001 2022/11/28 16:27:54 - mmengine - INFO - Epoch(train) [8][2000/2462] lr: 5.1846e-02 eta: 0:14:48 time: 0.0456 data_time: 0.0066 memory: 1794 loss: 0.5083 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5083 2022/11/28 16:27:59 - mmengine - INFO - Epoch(train) [8][2100/2462] lr: 5.1447e-02 eta: 0:14:44 time: 0.0437 data_time: 0.0061 memory: 1794 loss: 0.4512 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.4512 2022/11/28 16:28:03 - mmengine - INFO - Epoch(train) [8][2200/2462] lr: 5.1049e-02 eta: 0:14:40 time: 0.0445 data_time: 0.0062 memory: 1794 loss: 0.4562 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4562 2022/11/28 16:28:08 - mmengine - INFO - Epoch(train) [8][2300/2462] lr: 5.0650e-02 eta: 0:14:35 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.3713 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.3713 2022/11/28 16:28:12 - mmengine - INFO - Epoch(train) [8][2400/2462] lr: 5.0251e-02 eta: 0:14:31 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.3857 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3857 2022/11/28 16:28:15 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:28:15 - mmengine - INFO - Epoch(train) [8][2462/2462] lr: 5.0004e-02 eta: 0:14:28 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.4205 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4205 2022/11/28 16:28:15 - mmengine - INFO - Saving checkpoint at 8 epochs 2022/11/28 16:28:19 - mmengine - INFO - Epoch(val) [8][100/398] eta: 0:00:10 time: 0.0333 data_time: 0.0201 memory: 364 2022/11/28 16:28:21 - mmengine - INFO - Epoch(val) [8][200/398] eta: 0:00:06 time: 0.0245 data_time: 0.0116 memory: 364 2022/11/28 16:28:24 - mmengine - INFO - Epoch(val) [8][300/398] eta: 0:00:02 time: 0.0279 data_time: 0.0156 memory: 364 2022/11/28 16:28:27 - mmengine - INFO - Epoch(val) [8][398/398] acc/top1: 0.7396 acc/top5: 0.9304 acc/mean1: 0.7583 2022/11/28 16:28:32 - mmengine - INFO - Epoch(train) [9][100/2462] lr: 4.9605e-02 eta: 0:14:24 time: 0.0445 data_time: 0.0067 memory: 1794 loss: 0.4302 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4302 2022/11/28 16:28:37 - mmengine - INFO - Epoch(train) [9][200/2462] lr: 4.9207e-02 eta: 0:14:20 time: 0.0466 data_time: 0.0062 memory: 1794 loss: 0.3818 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3818 2022/11/28 16:28:41 - mmengine - INFO - Epoch(train) [9][300/2462] lr: 4.8808e-02 eta: 0:14:15 time: 0.0432 data_time: 0.0062 memory: 1794 loss: 0.5023 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5023 2022/11/28 16:28:41 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:28:45 - mmengine - INFO - Epoch(train) [9][400/2462] lr: 4.8409e-02 eta: 0:14:11 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.4411 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.4411 2022/11/28 16:28:50 - mmengine - INFO - Epoch(train) [9][500/2462] lr: 4.8011e-02 eta: 0:14:06 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.4155 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.4155 2022/11/28 16:28:54 - mmengine - INFO - Epoch(train) [9][600/2462] lr: 4.7612e-02 eta: 0:14:02 time: 0.0442 data_time: 0.0064 memory: 1794 loss: 0.5703 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5703 2022/11/28 16:28:59 - mmengine - INFO - Epoch(train) [9][700/2462] lr: 4.7214e-02 eta: 0:13:57 time: 0.0438 data_time: 0.0066 memory: 1794 loss: 0.4115 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4115 2022/11/28 16:29:03 - mmengine - INFO - Epoch(train) [9][800/2462] lr: 4.6816e-02 eta: 0:13:53 time: 0.0442 data_time: 0.0070 memory: 1794 loss: 0.3318 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3318 2022/11/28 16:29:08 - mmengine - INFO - Epoch(train) [9][900/2462] lr: 4.6418e-02 eta: 0:13:49 time: 0.0435 data_time: 0.0066 memory: 1794 loss: 0.4153 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4153 2022/11/28 16:29:12 - mmengine - INFO - Epoch(train) [9][1000/2462] lr: 4.6021e-02 eta: 0:13:44 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.4186 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4186 2022/11/28 16:29:16 - mmengine - INFO - Epoch(train) [9][1100/2462] lr: 4.5623e-02 eta: 0:13:40 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.3847 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3847 2022/11/28 16:29:21 - mmengine - INFO - Epoch(train) [9][1200/2462] lr: 4.5226e-02 eta: 0:13:35 time: 0.0437 data_time: 0.0070 memory: 1794 loss: 0.4757 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4757 2022/11/28 16:29:25 - mmengine - INFO - Epoch(train) [9][1300/2462] lr: 4.4829e-02 eta: 0:13:31 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.4238 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4238 2022/11/28 16:29:25 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:29:29 - mmengine - INFO - Epoch(train) [9][1400/2462] lr: 4.4433e-02 eta: 0:13:26 time: 0.0430 data_time: 0.0061 memory: 1794 loss: 0.3673 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3673 2022/11/28 16:29:34 - mmengine - INFO - Epoch(train) [9][1500/2462] lr: 4.4037e-02 eta: 0:13:22 time: 0.0431 data_time: 0.0062 memory: 1794 loss: 0.3737 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3737 2022/11/28 16:29:38 - mmengine - INFO - Epoch(train) [9][1600/2462] lr: 4.3641e-02 eta: 0:13:18 time: 0.0447 data_time: 0.0074 memory: 1794 loss: 0.3742 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.3742 2022/11/28 16:29:43 - mmengine - INFO - Epoch(train) [9][1700/2462] lr: 4.3246e-02 eta: 0:13:13 time: 0.0455 data_time: 0.0063 memory: 1794 loss: 0.3095 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3095 2022/11/28 16:29:47 - mmengine - INFO - Epoch(train) [9][1800/2462] lr: 4.2851e-02 eta: 0:13:09 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.3612 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3612 2022/11/28 16:29:51 - mmengine - INFO - Epoch(train) [9][1900/2462] lr: 4.2456e-02 eta: 0:13:04 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.4240 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4240 2022/11/28 16:29:56 - mmengine - INFO - Epoch(train) [9][2000/2462] lr: 4.2063e-02 eta: 0:13:00 time: 0.0448 data_time: 0.0069 memory: 1794 loss: 0.3872 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.3872 2022/11/28 16:30:00 - mmengine - INFO - Epoch(train) [9][2100/2462] lr: 4.1669e-02 eta: 0:12:56 time: 0.0433 data_time: 0.0062 memory: 1794 loss: 0.3422 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3422 2022/11/28 16:30:05 - mmengine - INFO - Epoch(train) [9][2200/2462] lr: 4.1276e-02 eta: 0:12:51 time: 0.0437 data_time: 0.0062 memory: 1794 loss: 0.3003 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3003 2022/11/28 16:30:09 - mmengine - INFO - Epoch(train) [9][2300/2462] lr: 4.0884e-02 eta: 0:12:47 time: 0.0460 data_time: 0.0064 memory: 1794 loss: 0.3217 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3217 2022/11/28 16:30:09 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:30:14 - mmengine - INFO - Epoch(train) [9][2400/2462] lr: 4.0492e-02 eta: 0:12:42 time: 0.0445 data_time: 0.0062 memory: 1794 loss: 0.4346 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4346 2022/11/28 16:30:16 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:30:16 - mmengine - INFO - Epoch(train) [9][2462/2462] lr: 4.0249e-02 eta: 0:12:40 time: 0.0434 data_time: 0.0062 memory: 1794 loss: 0.3919 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3919 2022/11/28 16:30:16 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/11/28 16:30:20 - mmengine - INFO - Epoch(val) [9][100/398] eta: 0:00:10 time: 0.0331 data_time: 0.0199 memory: 364 2022/11/28 16:30:23 - mmengine - INFO - Epoch(val) [9][200/398] eta: 0:00:06 time: 0.0243 data_time: 0.0116 memory: 364 2022/11/28 16:30:26 - mmengine - INFO - Epoch(val) [9][300/398] eta: 0:00:02 time: 0.0253 data_time: 0.0131 memory: 364 2022/11/28 16:30:29 - mmengine - INFO - Epoch(val) [9][398/398] acc/top1: 0.7662 acc/top5: 0.9439 acc/mean1: 0.7845 2022/11/28 16:30:29 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_7.pth is removed 2022/11/28 16:30:30 - mmengine - INFO - The best checkpoint with 0.7662 acc/top1 at 9 epoch is saved to best_acc/top1_epoch_9.pth. 2022/11/28 16:30:34 - mmengine - INFO - Epoch(train) [10][100/2462] lr: 3.9859e-02 eta: 0:12:35 time: 0.0446 data_time: 0.0073 memory: 1794 loss: 0.3361 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3361 2022/11/28 16:30:39 - mmengine - INFO - Epoch(train) [10][200/2462] lr: 3.9468e-02 eta: 0:12:31 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.3171 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3171 2022/11/28 16:30:43 - mmengine - INFO - Epoch(train) [10][300/2462] lr: 3.9079e-02 eta: 0:12:27 time: 0.0441 data_time: 0.0067 memory: 1794 loss: 0.3615 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3615 2022/11/28 16:30:47 - mmengine - INFO - Epoch(train) [10][400/2462] lr: 3.8690e-02 eta: 0:12:22 time: 0.0441 data_time: 0.0068 memory: 1794 loss: 0.3214 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3214 2022/11/28 16:30:52 - mmengine - INFO - Epoch(train) [10][500/2462] lr: 3.8302e-02 eta: 0:12:18 time: 0.0448 data_time: 0.0066 memory: 1794 loss: 0.3101 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3101 2022/11/28 16:30:56 - mmengine - INFO - Epoch(train) [10][600/2462] lr: 3.7915e-02 eta: 0:12:13 time: 0.0443 data_time: 0.0070 memory: 1794 loss: 0.3316 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3316 2022/11/28 16:31:01 - mmengine - INFO - Epoch(train) [10][700/2462] lr: 3.7528e-02 eta: 0:12:09 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.3423 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3423 2022/11/28 16:31:05 - mmengine - INFO - Epoch(train) [10][800/2462] lr: 3.7143e-02 eta: 0:12:05 time: 0.0441 data_time: 0.0061 memory: 1794 loss: 0.3531 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3531 2022/11/28 16:31:07 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:31:10 - mmengine - INFO - Epoch(train) [10][900/2462] lr: 3.6758e-02 eta: 0:12:00 time: 0.0446 data_time: 0.0066 memory: 1794 loss: 0.3457 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3457 2022/11/28 16:31:14 - mmengine - INFO - Epoch(train) [10][1000/2462] lr: 3.6373e-02 eta: 0:11:56 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.3714 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.3714 2022/11/28 16:31:19 - 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: 0.2743 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2743 2022/11/28 16:31:23 - mmengine - INFO - Epoch(train) [10][1200/2462] lr: 3.5608e-02 eta: 0:11:47 time: 0.0439 data_time: 0.0063 memory: 1794 loss: 0.3465 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3465 2022/11/28 16:31:27 - mmengine - INFO - Epoch(train) [10][1300/2462] lr: 3.5226e-02 eta: 0:11:43 time: 0.0444 data_time: 0.0065 memory: 1794 loss: 0.3762 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3762 2022/11/28 16:31:32 - mmengine - INFO - Epoch(train) [10][1400/2462] lr: 3.4846e-02 eta: 0:11:38 time: 0.0445 data_time: 0.0064 memory: 1794 loss: 0.3198 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.3198 2022/11/28 16:31:36 - mmengine - INFO - Epoch(train) [10][1500/2462] lr: 3.4466e-02 eta: 0:11:34 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.3534 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3534 2022/11/28 16:31:41 - mmengine - INFO - Epoch(train) [10][1600/2462] lr: 3.4088e-02 eta: 0:11:29 time: 0.0438 data_time: 0.0069 memory: 1794 loss: 0.2997 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.2997 2022/11/28 16:31:45 - mmengine - INFO - Epoch(train) [10][1700/2462] lr: 3.3710e-02 eta: 0:11:25 time: 0.0434 data_time: 0.0062 memory: 1794 loss: 0.3275 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.3275 2022/11/28 16:31:49 - mmengine - INFO - Epoch(train) [10][1800/2462] lr: 3.3334e-02 eta: 0:11:20 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.3218 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.3218 2022/11/28 16:31:51 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:31:54 - mmengine - INFO - Epoch(train) [10][1900/2462] lr: 3.2959e-02 eta: 0:11:16 time: 0.0461 data_time: 0.0068 memory: 1794 loss: 0.2355 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.2355 2022/11/28 16:31:58 - mmengine - INFO - Epoch(train) [10][2000/2462] lr: 3.2584e-02 eta: 0:11:12 time: 0.0437 data_time: 0.0067 memory: 1794 loss: 0.3573 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3573 2022/11/28 16:32:03 - mmengine - INFO - Epoch(train) [10][2100/2462] lr: 3.2211e-02 eta: 0:11:07 time: 0.0478 data_time: 0.0085 memory: 1794 loss: 0.3350 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3350 2022/11/28 16:32:07 - mmengine - INFO - Epoch(train) [10][2200/2462] lr: 3.1839e-02 eta: 0:11:03 time: 0.0447 data_time: 0.0063 memory: 1794 loss: 0.2930 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2930 2022/11/28 16:32:12 - mmengine - INFO - Epoch(train) [10][2300/2462] lr: 3.1468e-02 eta: 0:10:58 time: 0.0443 data_time: 0.0071 memory: 1794 loss: 0.3296 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3296 2022/11/28 16:32:16 - mmengine - INFO - Epoch(train) [10][2400/2462] lr: 3.1098e-02 eta: 0:10:54 time: 0.0435 data_time: 0.0062 memory: 1794 loss: 0.3679 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3679 2022/11/28 16:32:19 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:32:19 - mmengine - INFO - Epoch(train) [10][2462/2462] lr: 3.0870e-02 eta: 0:10:51 time: 0.0436 data_time: 0.0064 memory: 1794 loss: 0.2801 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2801 2022/11/28 16:32:19 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/11/28 16:32:23 - mmengine - INFO - Epoch(val) [10][100/398] eta: 0:00:10 time: 0.0335 data_time: 0.0203 memory: 364 2022/11/28 16:32:26 - mmengine - INFO - Epoch(val) [10][200/398] eta: 0:00:06 time: 0.0252 data_time: 0.0120 memory: 364 2022/11/28 16:32:28 - mmengine - INFO - Epoch(val) [10][300/398] eta: 0:00:02 time: 0.0257 data_time: 0.0129 memory: 364 2022/11/28 16:32:32 - mmengine - INFO - Epoch(val) [10][398/398] acc/top1: 0.7699 acc/top5: 0.9468 acc/mean1: 0.7814 2022/11/28 16:32:32 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_9.pth is removed 2022/11/28 16:32:32 - mmengine - INFO - The best checkpoint with 0.7699 acc/top1 at 10 epoch is saved to best_acc/top1_epoch_10.pth. 2022/11/28 16:32:37 - mmengine - INFO - Epoch(train) [11][100/2462] lr: 3.0502e-02 eta: 0:10:47 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.3097 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3097 2022/11/28 16:32:41 - mmengine - INFO - Epoch(train) [11][200/2462] lr: 3.0135e-02 eta: 0:10:43 time: 0.0445 data_time: 0.0070 memory: 1794 loss: 0.2838 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2838 2022/11/28 16:32:46 - mmengine - INFO - Epoch(train) [11][300/2462] lr: 2.9770e-02 eta: 0:10:38 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.1841 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1841 2022/11/28 16:32:49 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:32:50 - mmengine - INFO - Epoch(train) [11][400/2462] lr: 2.9406e-02 eta: 0:10:34 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.1938 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1938 2022/11/28 16:32:55 - mmengine - INFO - Epoch(train) [11][500/2462] lr: 2.9043e-02 eta: 0:10:29 time: 0.0443 data_time: 0.0067 memory: 1794 loss: 0.2265 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2265 2022/11/28 16:32:59 - 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: 0.2673 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2673 2022/11/28 16:33:03 - mmengine - INFO - Epoch(train) [11][700/2462] lr: 2.8322e-02 eta: 0:10:21 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.2458 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.2458 2022/11/28 16:33:08 - mmengine - INFO - Epoch(train) [11][800/2462] lr: 2.7963e-02 eta: 0:10:16 time: 0.0440 data_time: 0.0063 memory: 1794 loss: 0.2421 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2421 2022/11/28 16:33:12 - mmengine - INFO - Epoch(train) [11][900/2462] lr: 2.7606e-02 eta: 0:10:12 time: 0.0456 data_time: 0.0067 memory: 1794 loss: 0.2704 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.2704 2022/11/28 16:33:17 - mmengine - INFO - Epoch(train) [11][1000/2462] lr: 2.7250e-02 eta: 0:10:07 time: 0.0449 data_time: 0.0063 memory: 1794 loss: 0.3098 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.3098 2022/11/28 16:33:21 - mmengine - INFO - Epoch(train) [11][1100/2462] lr: 2.6896e-02 eta: 0:10:03 time: 0.0446 data_time: 0.0062 memory: 1794 loss: 0.2050 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2050 2022/11/28 16:33:26 - mmengine - INFO - Epoch(train) [11][1200/2462] lr: 2.6543e-02 eta: 0:09:59 time: 0.0448 data_time: 0.0065 memory: 1794 loss: 0.2571 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2571 2022/11/28 16:33:30 - mmengine - INFO - Epoch(train) [11][1300/2462] lr: 2.6191e-02 eta: 0:09:54 time: 0.0442 data_time: 0.0063 memory: 1794 loss: 0.2040 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2040 2022/11/28 16:33:34 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:33:35 - mmengine - INFO - Epoch(train) [11][1400/2462] lr: 2.5841e-02 eta: 0:09:50 time: 0.0439 data_time: 0.0064 memory: 1794 loss: 0.2721 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2721 2022/11/28 16:33:39 - mmengine - INFO - Epoch(train) [11][1500/2462] lr: 2.5493e-02 eta: 0:09:45 time: 0.0440 data_time: 0.0062 memory: 1794 loss: 0.2400 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2400 2022/11/28 16:33:44 - mmengine - INFO - Epoch(train) [11][1600/2462] lr: 2.5146e-02 eta: 0:09:41 time: 0.0436 data_time: 0.0064 memory: 1794 loss: 0.2301 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2301 2022/11/28 16:33:48 - mmengine - INFO - Epoch(train) [11][1700/2462] lr: 2.4801e-02 eta: 0:09:37 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.2404 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2404 2022/11/28 16:33:53 - mmengine - INFO - Epoch(train) [11][1800/2462] lr: 2.4458e-02 eta: 0:09:32 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.1822 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1822 2022/11/28 16:33:57 - mmengine - INFO - Epoch(train) [11][1900/2462] lr: 2.4116e-02 eta: 0:09:28 time: 0.0467 data_time: 0.0065 memory: 1794 loss: 0.1917 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1917 2022/11/28 16:34:02 - mmengine - INFO - Epoch(train) [11][2000/2462] lr: 2.3775e-02 eta: 0:09:23 time: 0.0446 data_time: 0.0068 memory: 1794 loss: 0.2495 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2495 2022/11/28 16:34:06 - mmengine - INFO - Epoch(train) [11][2100/2462] lr: 2.3437e-02 eta: 0:09:19 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.2004 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2004 2022/11/28 16:34:10 - mmengine - INFO - Epoch(train) [11][2200/2462] lr: 2.3100e-02 eta: 0:09:15 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.1595 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1595 2022/11/28 16:34:15 - mmengine - INFO - Epoch(train) [11][2300/2462] lr: 2.2764e-02 eta: 0:09:10 time: 0.0443 data_time: 0.0063 memory: 1794 loss: 0.2587 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2587 2022/11/28 16:34:18 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:34:19 - mmengine - INFO - Epoch(train) [11][2400/2462] lr: 2.2431e-02 eta: 0:09:06 time: 0.0444 data_time: 0.0062 memory: 1794 loss: 0.1979 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1979 2022/11/28 16:34:22 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:34:22 - mmengine - INFO - Epoch(train) [11][2462/2462] lr: 2.2225e-02 eta: 0:09:03 time: 0.0443 data_time: 0.0063 memory: 1794 loss: 0.2276 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2276 2022/11/28 16:34:22 - mmengine - INFO - Saving checkpoint at 11 epochs 2022/11/28 16:34:26 - mmengine - INFO - Epoch(val) [11][100/398] eta: 0:00:10 time: 0.0335 data_time: 0.0198 memory: 364 2022/11/28 16:34:29 - mmengine - INFO - Epoch(val) [11][200/398] eta: 0:00:06 time: 0.0264 data_time: 0.0133 memory: 364 2022/11/28 16:34:32 - mmengine - INFO - Epoch(val) [11][300/398] eta: 0:00:02 time: 0.0261 data_time: 0.0134 memory: 364 2022/11/28 16:34:35 - mmengine - INFO - Epoch(val) [11][398/398] acc/top1: 0.7992 acc/top5: 0.9542 acc/mean1: 0.8117 2022/11/28 16:34:35 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_10.pth is removed 2022/11/28 16:34:36 - mmengine - INFO - The best checkpoint with 0.7992 acc/top1 at 11 epoch is saved to best_acc/top1_epoch_11.pth. 2022/11/28 16:34:40 - mmengine - INFO - Epoch(train) [12][100/2462] lr: 2.1894e-02 eta: 0:08:59 time: 0.0448 data_time: 0.0070 memory: 1794 loss: 0.1605 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1605 2022/11/28 16:34:45 - mmengine - INFO - Epoch(train) [12][200/2462] lr: 2.1565e-02 eta: 0:08:54 time: 0.0434 data_time: 0.0063 memory: 1794 loss: 0.2306 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2306 2022/11/28 16:34:49 - mmengine - INFO - Epoch(train) [12][300/2462] lr: 2.1238e-02 eta: 0:08:50 time: 0.0449 data_time: 0.0069 memory: 1794 loss: 0.1469 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1469 2022/11/28 16:34:53 - mmengine - INFO - Epoch(train) [12][400/2462] lr: 2.0913e-02 eta: 0:08:46 time: 0.0436 data_time: 0.0063 memory: 1794 loss: 0.1878 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1878 2022/11/28 16:34:58 - mmengine - INFO - Epoch(train) [12][500/2462] lr: 2.0589e-02 eta: 0:08:41 time: 0.0442 data_time: 0.0063 memory: 1794 loss: 0.1600 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1600 2022/11/28 16:35:02 - mmengine - INFO - Epoch(train) [12][600/2462] lr: 2.0268e-02 eta: 0:08:37 time: 0.0443 data_time: 0.0064 memory: 1794 loss: 0.1742 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1742 2022/11/28 16:35:07 - mmengine - INFO - Epoch(train) [12][700/2462] lr: 1.9948e-02 eta: 0:08:32 time: 0.0443 data_time: 0.0064 memory: 1794 loss: 0.1859 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1859 2022/11/28 16:35:11 - mmengine - INFO - Epoch(train) [12][800/2462] lr: 1.9631e-02 eta: 0:08:28 time: 0.0440 data_time: 0.0063 memory: 1794 loss: 0.2162 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2162 2022/11/28 16:35:16 - mmengine - INFO - Epoch(train) [12][900/2462] lr: 1.9315e-02 eta: 0:08:24 time: 0.0435 data_time: 0.0063 memory: 1794 loss: 0.1103 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1103 2022/11/28 16:35:16 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:35:20 - mmengine - INFO - Epoch(train) [12][1000/2462] lr: 1.9001e-02 eta: 0:08:19 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.1981 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1981 2022/11/28 16:35:25 - mmengine - INFO - Epoch(train) [12][1100/2462] lr: 1.8689e-02 eta: 0:08:15 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.2159 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2159 2022/11/28 16:35:29 - mmengine - INFO - Epoch(train) [12][1200/2462] lr: 1.8379e-02 eta: 0:08:10 time: 0.0443 data_time: 0.0063 memory: 1794 loss: 0.1317 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1317 2022/11/28 16:35:34 - mmengine - INFO - Epoch(train) [12][1300/2462] lr: 1.8071e-02 eta: 0:08:06 time: 0.0458 data_time: 0.0064 memory: 1794 loss: 0.1188 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1188 2022/11/28 16:35:38 - mmengine - INFO - Epoch(train) [12][1400/2462] lr: 1.7765e-02 eta: 0:08:01 time: 0.0437 data_time: 0.0063 memory: 1794 loss: 0.1503 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1503 2022/11/28 16:35:42 - mmengine - INFO - Epoch(train) [12][1500/2462] lr: 1.7462e-02 eta: 0:07:57 time: 0.0445 data_time: 0.0064 memory: 1794 loss: 0.1143 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1143 2022/11/28 16:35:47 - mmengine - INFO - Epoch(train) [12][1600/2462] lr: 1.7160e-02 eta: 0:07:53 time: 0.0446 data_time: 0.0064 memory: 1794 loss: 0.1437 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1437 2022/11/28 16:35:51 - mmengine - INFO - Epoch(train) [12][1700/2462] lr: 1.6860e-02 eta: 0:07:48 time: 0.0460 data_time: 0.0077 memory: 1794 loss: 0.1779 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1779 2022/11/28 16:35:56 - mmengine - INFO - Epoch(train) [12][1800/2462] lr: 1.6563e-02 eta: 0:07:44 time: 0.0445 data_time: 0.0066 memory: 1794 loss: 0.1314 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1314 2022/11/28 16:36:00 - mmengine - INFO - Epoch(train) [12][1900/2462] lr: 1.6267e-02 eta: 0:07:40 time: 0.0445 data_time: 0.0064 memory: 1794 loss: 0.1593 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1593 2022/11/28 16:36:01 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:36:05 - mmengine - INFO - Epoch(train) [12][2000/2462] lr: 1.5974e-02 eta: 0:07:35 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.1166 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1166 2022/11/28 16:36:09 - mmengine - INFO - Epoch(train) [12][2100/2462] lr: 1.5683e-02 eta: 0:07:31 time: 0.0448 data_time: 0.0066 memory: 1794 loss: 0.1416 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1416 2022/11/28 16:36:14 - mmengine - INFO - Epoch(train) [12][2200/2462] lr: 1.5394e-02 eta: 0:07:26 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.1619 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1619 2022/11/28 16:36:18 - mmengine - INFO - Epoch(train) [12][2300/2462] lr: 1.5107e-02 eta: 0:07:22 time: 0.0443 data_time: 0.0071 memory: 1794 loss: 0.1737 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1737 2022/11/28 16:36:23 - mmengine - INFO - Epoch(train) [12][2400/2462] lr: 1.4823e-02 eta: 0:07:17 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.0942 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0942 2022/11/28 16:36:25 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:36:25 - mmengine - INFO - Epoch(train) [12][2462/2462] lr: 1.4647e-02 eta: 0:07:15 time: 0.0448 data_time: 0.0072 memory: 1794 loss: 0.1312 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1312 2022/11/28 16:36:25 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/11/28 16:36:29 - mmengine - INFO - Epoch(val) [12][100/398] eta: 0:00:10 time: 0.0337 data_time: 0.0201 memory: 364 2022/11/28 16:36:32 - mmengine - INFO - Epoch(val) [12][200/398] eta: 0:00:06 time: 0.0246 data_time: 0.0119 memory: 364 2022/11/28 16:36:35 - mmengine - INFO - Epoch(val) [12][300/398] eta: 0:00:02 time: 0.0259 data_time: 0.0133 memory: 364 2022/11/28 16:36:38 - mmengine - INFO - Epoch(val) [12][398/398] acc/top1: 0.8193 acc/top5: 0.9575 acc/mean1: 0.8289 2022/11/28 16:36:38 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_11.pth is removed 2022/11/28 16:36:39 - mmengine - INFO - The best checkpoint with 0.8193 acc/top1 at 12 epoch is saved to best_acc/top1_epoch_12.pth. 2022/11/28 16:36:43 - mmengine - INFO - Epoch(train) [13][100/2462] lr: 1.4367e-02 eta: 0:07:10 time: 0.0446 data_time: 0.0063 memory: 1794 loss: 0.0888 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0888 2022/11/28 16:36:48 - mmengine - INFO - Epoch(train) [13][200/2462] lr: 1.4088e-02 eta: 0:07:06 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.0992 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0992 2022/11/28 16:36:52 - mmengine - INFO - Epoch(train) [13][300/2462] lr: 1.3812e-02 eta: 0:07:02 time: 0.0437 data_time: 0.0063 memory: 1794 loss: 0.0769 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0769 2022/11/28 16:36:57 - mmengine - INFO - Epoch(train) [13][400/2462] lr: 1.3538e-02 eta: 0:06:57 time: 0.0449 data_time: 0.0074 memory: 1794 loss: 0.0959 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.0959 2022/11/28 16:36:59 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:37:01 - mmengine - INFO - Epoch(train) [13][500/2462] lr: 1.3266e-02 eta: 0:06:53 time: 0.0462 data_time: 0.0064 memory: 1794 loss: 0.1097 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1097 2022/11/28 16:37:06 - mmengine - INFO - Epoch(train) [13][600/2462] lr: 1.2997e-02 eta: 0:06:48 time: 0.0441 data_time: 0.0064 memory: 1794 loss: 0.1060 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1060 2022/11/28 16:37:10 - mmengine - INFO - Epoch(train) [13][700/2462] lr: 1.2730e-02 eta: 0:06:44 time: 0.0449 data_time: 0.0071 memory: 1794 loss: 0.0948 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0948 2022/11/28 16:37:15 - mmengine - INFO - Epoch(train) [13][800/2462] lr: 1.2465e-02 eta: 0:06:39 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.1007 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1007 2022/11/28 16:37:19 - mmengine - INFO - Epoch(train) [13][900/2462] lr: 1.2203e-02 eta: 0:06:35 time: 0.0454 data_time: 0.0063 memory: 1794 loss: 0.0680 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0680 2022/11/28 16:37:23 - mmengine - INFO - Epoch(train) [13][1000/2462] lr: 1.1943e-02 eta: 0:06:31 time: 0.0447 data_time: 0.0063 memory: 1794 loss: 0.0783 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0783 2022/11/28 16:37:28 - mmengine - INFO - Epoch(train) [13][1100/2462] lr: 1.1686e-02 eta: 0:06:26 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.0651 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0651 2022/11/28 16:37:32 - mmengine - INFO - Epoch(train) [13][1200/2462] lr: 1.1431e-02 eta: 0:06:22 time: 0.0445 data_time: 0.0063 memory: 1794 loss: 0.0669 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0669 2022/11/28 16:37:37 - mmengine - INFO - Epoch(train) [13][1300/2462] lr: 1.1178e-02 eta: 0:06:17 time: 0.0444 data_time: 0.0064 memory: 1794 loss: 0.0584 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0584 2022/11/28 16:37:41 - mmengine - INFO - Epoch(train) [13][1400/2462] lr: 1.0928e-02 eta: 0:06:13 time: 0.0449 data_time: 0.0063 memory: 1794 loss: 0.0764 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0764 2022/11/28 16:37:44 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:37:46 - mmengine - INFO - Epoch(train) [13][1500/2462] lr: 1.0680e-02 eta: 0:06:09 time: 0.0440 data_time: 0.0064 memory: 1794 loss: 0.0608 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0608 2022/11/28 16:37:50 - mmengine - INFO - Epoch(train) [13][1600/2462] lr: 1.0435e-02 eta: 0:06:04 time: 0.0447 data_time: 0.0062 memory: 1794 loss: 0.0665 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0665 2022/11/28 16:37:55 - mmengine - INFO - Epoch(train) [13][1700/2462] lr: 1.0193e-02 eta: 0:06:00 time: 0.0449 data_time: 0.0065 memory: 1794 loss: 0.0504 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0504 2022/11/28 16:37:59 - mmengine - INFO - Epoch(train) [13][1800/2462] lr: 9.9527e-03 eta: 0:05:55 time: 0.0450 data_time: 0.0064 memory: 1794 loss: 0.0618 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0618 2022/11/28 16:38:04 - mmengine - INFO - Epoch(train) [13][1900/2462] lr: 9.7153e-03 eta: 0:05:51 time: 0.0446 data_time: 0.0065 memory: 1794 loss: 0.0463 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0463 2022/11/28 16:38:08 - mmengine - INFO - Epoch(train) [13][2000/2462] lr: 9.4804e-03 eta: 0:05:47 time: 0.0442 data_time: 0.0063 memory: 1794 loss: 0.0621 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0621 2022/11/28 16:38:13 - mmengine - INFO - Epoch(train) [13][2100/2462] lr: 9.2480e-03 eta: 0:05:42 time: 0.0443 data_time: 0.0068 memory: 1794 loss: 0.0491 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0491 2022/11/28 16:38:17 - mmengine - INFO - Epoch(train) [13][2200/2462] lr: 9.0183e-03 eta: 0:05:38 time: 0.0449 data_time: 0.0067 memory: 1794 loss: 0.0527 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0527 2022/11/28 16:38:22 - mmengine - INFO - Epoch(train) [13][2300/2462] lr: 8.7911e-03 eta: 0:05:33 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.0615 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0615 2022/11/28 16:38:26 - mmengine - INFO - Epoch(train) [13][2400/2462] lr: 8.5666e-03 eta: 0:05:29 time: 0.0443 data_time: 0.0062 memory: 1794 loss: 0.0465 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0465 2022/11/28 16:38:29 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:38:29 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:38:29 - mmengine - INFO - Epoch(train) [13][2462/2462] lr: 8.4287e-03 eta: 0:05:26 time: 0.0438 data_time: 0.0063 memory: 1794 loss: 0.0535 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0535 2022/11/28 16:38:29 - mmengine - INFO - Saving checkpoint at 13 epochs 2022/11/28 16:38:33 - mmengine - INFO - Epoch(val) [13][100/398] eta: 0:00:10 time: 0.0366 data_time: 0.0234 memory: 364 2022/11/28 16:38:36 - mmengine - INFO - Epoch(val) [13][200/398] eta: 0:00:06 time: 0.0245 data_time: 0.0116 memory: 364 2022/11/28 16:38:39 - mmengine - INFO - Epoch(val) [13][300/398] eta: 0:00:02 time: 0.0256 data_time: 0.0130 memory: 364 2022/11/28 16:38:42 - mmengine - INFO - Epoch(val) [13][398/398] acc/top1: 0.8256 acc/top5: 0.9584 acc/mean1: 0.8362 2022/11/28 16:38:42 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_12.pth is removed 2022/11/28 16:38:42 - mmengine - INFO - The best checkpoint with 0.8256 acc/top1 at 13 epoch is saved to best_acc/top1_epoch_13.pth. 2022/11/28 16:38:47 - mmengine - INFO - Epoch(train) [14][100/2462] lr: 8.2085e-03 eta: 0:05:22 time: 0.0454 data_time: 0.0073 memory: 1794 loss: 0.0300 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0300 2022/11/28 16:38:51 - mmengine - INFO - Epoch(train) [14][200/2462] lr: 7.9909e-03 eta: 0:05:17 time: 0.0456 data_time: 0.0068 memory: 1794 loss: 0.0263 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0263 2022/11/28 16:38:56 - mmengine - INFO - Epoch(train) [14][300/2462] lr: 7.7760e-03 eta: 0:05:13 time: 0.0446 data_time: 0.0063 memory: 1794 loss: 0.0271 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0271 2022/11/28 16:39:01 - mmengine - INFO - Epoch(train) [14][400/2462] lr: 7.5638e-03 eta: 0:05:09 time: 0.0455 data_time: 0.0066 memory: 1794 loss: 0.0293 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0293 2022/11/28 16:39:05 - mmengine - INFO - Epoch(train) [14][500/2462] lr: 7.3542e-03 eta: 0:05:04 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.0360 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0360 2022/11/28 16:39:09 - mmengine - INFO - Epoch(train) [14][600/2462] lr: 7.1474e-03 eta: 0:05:00 time: 0.0448 data_time: 0.0065 memory: 1794 loss: 0.0446 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0446 2022/11/28 16:39:14 - mmengine - INFO - Epoch(train) [14][700/2462] lr: 6.9433e-03 eta: 0:04:55 time: 0.0441 data_time: 0.0067 memory: 1794 loss: 0.0436 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0436 2022/11/28 16:39:18 - mmengine - INFO - Epoch(train) [14][800/2462] lr: 6.7420e-03 eta: 0:04:51 time: 0.0444 data_time: 0.0067 memory: 1794 loss: 0.0292 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0292 2022/11/28 16:39:23 - mmengine - INFO - Epoch(train) [14][900/2462] lr: 6.5434e-03 eta: 0:04:47 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.0290 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0290 2022/11/28 16:39:27 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:39:27 - mmengine - INFO - Epoch(train) [14][1000/2462] lr: 6.3476e-03 eta: 0:04:42 time: 0.0448 data_time: 0.0064 memory: 1794 loss: 0.0192 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0192 2022/11/28 16:39:32 - mmengine - INFO - Epoch(train) [14][1100/2462] lr: 6.1545e-03 eta: 0:04:38 time: 0.0444 data_time: 0.0067 memory: 1794 loss: 0.0237 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0237 2022/11/28 16:39:36 - mmengine - INFO - Epoch(train) [14][1200/2462] lr: 5.9642e-03 eta: 0:04:33 time: 0.0436 data_time: 0.0062 memory: 1794 loss: 0.0332 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0332 2022/11/28 16:39:41 - mmengine - INFO - Epoch(train) [14][1300/2462] lr: 5.7768e-03 eta: 0:04:29 time: 0.0440 data_time: 0.0063 memory: 1794 loss: 0.0369 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0369 2022/11/28 16:39:45 - mmengine - INFO - Epoch(train) [14][1400/2462] lr: 5.5921e-03 eta: 0:04:24 time: 0.0439 data_time: 0.0062 memory: 1794 loss: 0.0243 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0243 2022/11/28 16:39:50 - mmengine - INFO - Epoch(train) [14][1500/2462] lr: 5.4103e-03 eta: 0:04:20 time: 0.0439 data_time: 0.0063 memory: 1794 loss: 0.0314 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0314 2022/11/28 16:39:54 - mmengine - INFO - Epoch(train) [14][1600/2462] lr: 5.2313e-03 eta: 0:04:16 time: 0.0460 data_time: 0.0063 memory: 1794 loss: 0.0269 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0269 2022/11/28 16:39:59 - mmengine - INFO - Epoch(train) [14][1700/2462] lr: 5.0551e-03 eta: 0:04:11 time: 0.0441 data_time: 0.0064 memory: 1794 loss: 0.0243 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0243 2022/11/28 16:40:03 - mmengine - INFO - Epoch(train) [14][1800/2462] lr: 4.8818e-03 eta: 0:04:07 time: 0.0449 data_time: 0.0063 memory: 1794 loss: 0.0223 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0223 2022/11/28 16:40:07 - mmengine - INFO - Epoch(train) [14][1900/2462] lr: 4.7114e-03 eta: 0:04:02 time: 0.0444 data_time: 0.0063 memory: 1794 loss: 0.0200 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0200 2022/11/28 16:40:12 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:40:12 - mmengine - INFO - Epoch(train) [14][2000/2462] lr: 4.5439e-03 eta: 0:03:58 time: 0.0448 data_time: 0.0064 memory: 1794 loss: 0.0217 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0217 2022/11/28 16:40:16 - mmengine - INFO - Epoch(train) [14][2100/2462] lr: 4.3792e-03 eta: 0:03:53 time: 0.0442 data_time: 0.0063 memory: 1794 loss: 0.0228 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0228 2022/11/28 16:40:21 - mmengine - INFO - Epoch(train) [14][2200/2462] lr: 4.2175e-03 eta: 0:03:49 time: 0.0439 data_time: 0.0063 memory: 1794 loss: 0.0256 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0256 2022/11/28 16:40:25 - mmengine - INFO - Epoch(train) [14][2300/2462] lr: 4.0587e-03 eta: 0:03:45 time: 0.0441 data_time: 0.0062 memory: 1794 loss: 0.0226 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0226 2022/11/28 16:40:30 - mmengine - INFO - Epoch(train) [14][2400/2462] lr: 3.9027e-03 eta: 0:03:40 time: 0.0439 data_time: 0.0063 memory: 1794 loss: 0.0270 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0270 2022/11/28 16:40:33 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:40:33 - mmengine - INFO - Epoch(train) [14][2462/2462] lr: 3.8075e-03 eta: 0:03:37 time: 0.0443 data_time: 0.0063 memory: 1794 loss: 0.0158 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0158 2022/11/28 16:40:33 - mmengine - INFO - Saving checkpoint at 14 epochs 2022/11/28 16:40:36 - mmengine - INFO - Epoch(val) [14][100/398] eta: 0:00:10 time: 0.0330 data_time: 0.0197 memory: 364 2022/11/28 16:40:39 - mmengine - INFO - Epoch(val) [14][200/398] eta: 0:00:06 time: 0.0245 data_time: 0.0115 memory: 364 2022/11/28 16:40:42 - mmengine - INFO - Epoch(val) [14][300/398] eta: 0:00:02 time: 0.0266 data_time: 0.0143 memory: 364 2022/11/28 16:40:45 - mmengine - INFO - Epoch(val) [14][398/398] acc/top1: 0.8357 acc/top5: 0.9625 acc/mean1: 0.8476 2022/11/28 16:40:45 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_13.pth is removed 2022/11/28 16:40:46 - mmengine - INFO - The best checkpoint with 0.8357 acc/top1 at 14 epoch is saved to best_acc/top1_epoch_14.pth. 2022/11/28 16:40:50 - mmengine - INFO - Epoch(train) [15][100/2462] lr: 3.6564e-03 eta: 0:03:33 time: 0.0440 data_time: 0.0063 memory: 1794 loss: 0.0203 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0203 2022/11/28 16:40:55 - mmengine - INFO - Epoch(train) [15][200/2462] lr: 3.5082e-03 eta: 0:03:29 time: 0.0443 data_time: 0.0063 memory: 1794 loss: 0.0188 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0188 2022/11/28 16:40:59 - mmengine - INFO - Epoch(train) [15][300/2462] lr: 3.3629e-03 eta: 0:03:24 time: 0.0439 data_time: 0.0063 memory: 1794 loss: 0.0220 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0220 2022/11/28 16:41:04 - mmengine - INFO - Epoch(train) [15][400/2462] lr: 3.2206e-03 eta: 0:03:20 time: 0.0450 data_time: 0.0065 memory: 1794 loss: 0.0204 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0204 2022/11/28 16:41:08 - mmengine - INFO - Epoch(train) [15][500/2462] lr: 3.0813e-03 eta: 0:03:15 time: 0.0439 data_time: 0.0063 memory: 1794 loss: 0.0205 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0205 2022/11/28 16:41:10 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:41:13 - mmengine - INFO - Epoch(train) [15][600/2462] lr: 2.9450e-03 eta: 0:03:11 time: 0.0439 data_time: 0.0063 memory: 1794 loss: 0.0217 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0217 2022/11/28 16:41:17 - mmengine - INFO - Epoch(train) [15][700/2462] lr: 2.8117e-03 eta: 0:03:06 time: 0.0457 data_time: 0.0065 memory: 1794 loss: 0.0239 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0239 2022/11/28 16:41:21 - mmengine - INFO - Epoch(train) [15][800/2462] lr: 2.6813e-03 eta: 0:03:02 time: 0.0435 data_time: 0.0063 memory: 1794 loss: 0.0189 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0189 2022/11/28 16:41:26 - mmengine - INFO - Epoch(train) [15][900/2462] lr: 2.5540e-03 eta: 0:02:58 time: 0.0446 data_time: 0.0067 memory: 1794 loss: 0.0229 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0229 2022/11/28 16:41:30 - mmengine - INFO - Epoch(train) [15][1000/2462] lr: 2.4297e-03 eta: 0:02:53 time: 0.0442 data_time: 0.0064 memory: 1794 loss: 0.0148 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0148 2022/11/28 16:41:35 - mmengine - INFO - Epoch(train) [15][1100/2462] lr: 2.3084e-03 eta: 0:02:49 time: 0.0442 data_time: 0.0069 memory: 1794 loss: 0.0182 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0182 2022/11/28 16:41:39 - mmengine - INFO - Epoch(train) [15][1200/2462] lr: 2.1902e-03 eta: 0:02:44 time: 0.0443 data_time: 0.0063 memory: 1794 loss: 0.0166 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0166 2022/11/28 16:41:44 - mmengine - INFO - Epoch(train) [15][1300/2462] lr: 2.0750e-03 eta: 0:02:40 time: 0.0441 data_time: 0.0064 memory: 1794 loss: 0.0140 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0140 2022/11/28 16:41:48 - mmengine - INFO - Epoch(train) [15][1400/2462] lr: 1.9628e-03 eta: 0:02:35 time: 0.0440 data_time: 0.0067 memory: 1794 loss: 0.0174 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0174 2022/11/28 16:41:53 - mmengine - INFO - Epoch(train) [15][1500/2462] lr: 1.8537e-03 eta: 0:02:31 time: 0.0443 data_time: 0.0064 memory: 1794 loss: 0.0200 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0200 2022/11/28 16:41:54 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:41:57 - mmengine - INFO - Epoch(train) [15][1600/2462] lr: 1.7477e-03 eta: 0:02:27 time: 0.0442 data_time: 0.0069 memory: 1794 loss: 0.0216 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0216 2022/11/28 16:42:01 - mmengine - INFO - Epoch(train) [15][1700/2462] lr: 1.6447e-03 eta: 0:02:22 time: 0.0444 data_time: 0.0067 memory: 1794 loss: 0.0161 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0161 2022/11/28 16:42:06 - mmengine - INFO - Epoch(train) [15][1800/2462] lr: 1.5448e-03 eta: 0:02:18 time: 0.0440 data_time: 0.0065 memory: 1794 loss: 0.0190 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0190 2022/11/28 16:42:10 - mmengine - INFO - Epoch(train) [15][1900/2462] lr: 1.4480e-03 eta: 0:02:13 time: 0.0445 data_time: 0.0065 memory: 1794 loss: 0.0196 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0196 2022/11/28 16:42:15 - mmengine - INFO - Epoch(train) [15][2000/2462] lr: 1.3543e-03 eta: 0:02:09 time: 0.0442 data_time: 0.0062 memory: 1794 loss: 0.0164 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0164 2022/11/28 16:42:19 - mmengine - INFO - Epoch(train) [15][2100/2462] lr: 1.2636e-03 eta: 0:02:05 time: 0.0453 data_time: 0.0067 memory: 1794 loss: 0.0152 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0152 2022/11/28 16:42:24 - mmengine - INFO - Epoch(train) [15][2200/2462] lr: 1.1761e-03 eta: 0:02:00 time: 0.0444 data_time: 0.0066 memory: 1794 loss: 0.0149 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0149 2022/11/28 16:42:28 - mmengine - INFO - Epoch(train) [15][2300/2462] lr: 1.0917e-03 eta: 0:01:56 time: 0.0447 data_time: 0.0065 memory: 1794 loss: 0.0155 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0155 2022/11/28 16:42:33 - mmengine - INFO - Epoch(train) [15][2400/2462] lr: 1.0104e-03 eta: 0:01:51 time: 0.0444 data_time: 0.0063 memory: 1794 loss: 0.0222 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0222 2022/11/28 16:42:35 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:42:35 - mmengine - INFO - Epoch(train) [15][2462/2462] lr: 9.6151e-04 eta: 0:01:49 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.0189 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0189 2022/11/28 16:42:35 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/11/28 16:42:39 - mmengine - INFO - Epoch(val) [15][100/398] eta: 0:00:10 time: 0.0331 data_time: 0.0199 memory: 364 2022/11/28 16:42:42 - mmengine - INFO - Epoch(val) [15][200/398] eta: 0:00:06 time: 0.0256 data_time: 0.0125 memory: 364 2022/11/28 16:42:45 - mmengine - INFO - Epoch(val) [15][300/398] eta: 0:00:02 time: 0.0253 data_time: 0.0132 memory: 364 2022/11/28 16:42:48 - mmengine - INFO - Epoch(val) [15][398/398] acc/top1: 0.8373 acc/top5: 0.9628 acc/mean1: 0.8489 2022/11/28 16:42:48 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d/best_acc/top1_epoch_14.pth is removed 2022/11/28 16:42:49 - mmengine - INFO - The best checkpoint with 0.8373 acc/top1 at 15 epoch is saved to best_acc/top1_epoch_15.pth. 2022/11/28 16:42:52 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:42:53 - mmengine - INFO - Epoch(train) [16][100/2462] lr: 8.8525e-04 eta: 0:01:44 time: 0.0440 data_time: 0.0063 memory: 1794 loss: 0.0198 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0198 2022/11/28 16:42:58 - mmengine - INFO - Epoch(train) [16][200/2462] lr: 8.1211e-04 eta: 0:01:40 time: 0.0443 data_time: 0.0063 memory: 1794 loss: 0.0245 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0245 2022/11/28 16:43:02 - mmengine - INFO - Epoch(train) [16][300/2462] lr: 7.4209e-04 eta: 0:01:35 time: 0.0459 data_time: 0.0064 memory: 1794 loss: 0.0175 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0175 2022/11/28 16:43:07 - mmengine - INFO - Epoch(train) [16][400/2462] lr: 6.7522e-04 eta: 0:01:31 time: 0.0444 data_time: 0.0063 memory: 1794 loss: 0.0176 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0176 2022/11/28 16:43:11 - mmengine - INFO - Epoch(train) [16][500/2462] lr: 6.1147e-04 eta: 0:01:26 time: 0.0472 data_time: 0.0097 memory: 1794 loss: 0.0180 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0180 2022/11/28 16:43:16 - mmengine - INFO - Epoch(train) [16][600/2462] lr: 5.5087e-04 eta: 0:01:22 time: 0.0446 data_time: 0.0063 memory: 1794 loss: 0.0230 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0230 2022/11/28 16:43:20 - mmengine - INFO - Epoch(train) [16][700/2462] lr: 4.9342e-04 eta: 0:01:18 time: 0.0443 data_time: 0.0068 memory: 1794 loss: 0.0196 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0196 2022/11/28 16:43:25 - mmengine - INFO - Epoch(train) [16][800/2462] lr: 4.3911e-04 eta: 0:01:13 time: 0.0453 data_time: 0.0068 memory: 1794 loss: 0.0262 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0262 2022/11/28 16:43:30 - mmengine - INFO - Epoch(train) [16][900/2462] lr: 3.8795e-04 eta: 0:01:09 time: 0.0444 data_time: 0.0062 memory: 1794 loss: 0.0150 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0150 2022/11/28 16:43:34 - mmengine - INFO - Epoch(train) [16][1000/2462] lr: 3.3995e-04 eta: 0:01:04 time: 0.0449 data_time: 0.0064 memory: 1794 loss: 0.0189 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0189 2022/11/28 16:43:37 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:43:38 - mmengine - INFO - Epoch(train) [16][1100/2462] lr: 2.9511e-04 eta: 0:01:00 time: 0.0438 data_time: 0.0064 memory: 1794 loss: 0.0129 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0129 2022/11/28 16:43:43 - mmengine - INFO - Epoch(train) [16][1200/2462] lr: 2.5343e-04 eta: 0:00:55 time: 0.0444 data_time: 0.0068 memory: 1794 loss: 0.0186 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0186 2022/11/28 16:43:47 - mmengine - INFO - Epoch(train) [16][1300/2462] lr: 2.1492e-04 eta: 0:00:51 time: 0.0443 data_time: 0.0064 memory: 1794 loss: 0.0184 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0184 2022/11/28 16:43:52 - mmengine - INFO - Epoch(train) [16][1400/2462] lr: 1.7957e-04 eta: 0:00:47 time: 0.0437 data_time: 0.0063 memory: 1794 loss: 0.0188 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0188 2022/11/28 16:43:56 - mmengine - INFO - Epoch(train) [16][1500/2462] lr: 1.4739e-04 eta: 0:00:42 time: 0.0444 data_time: 0.0066 memory: 1794 loss: 0.0154 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0154 2022/11/28 16:44:01 - mmengine - INFO - Epoch(train) [16][1600/2462] lr: 1.1838e-04 eta: 0:00:38 time: 0.0437 data_time: 0.0063 memory: 1794 loss: 0.0192 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0192 2022/11/28 16:44:05 - mmengine - INFO - Epoch(train) [16][1700/2462] lr: 9.2542e-05 eta: 0:00:33 time: 0.0441 data_time: 0.0063 memory: 1794 loss: 0.0205 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0205 2022/11/28 16:44:10 - mmengine - INFO - Epoch(train) [16][1800/2462] lr: 6.9879e-05 eta: 0:00:29 time: 0.0449 data_time: 0.0064 memory: 1794 loss: 0.0184 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0184 2022/11/28 16:44:14 - mmengine - INFO - Epoch(train) [16][1900/2462] lr: 5.0393e-05 eta: 0:00:24 time: 0.0442 data_time: 0.0064 memory: 1794 loss: 0.0190 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0190 2022/11/28 16:44:19 - mmengine - INFO - Epoch(train) [16][2000/2462] lr: 3.4083e-05 eta: 0:00:20 time: 0.0443 data_time: 0.0071 memory: 1794 loss: 0.0174 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0174 2022/11/28 16:44:22 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:44:23 - mmengine - INFO - Epoch(train) [16][2100/2462] lr: 2.0951e-05 eta: 0:00:16 time: 0.0445 data_time: 0.0069 memory: 1794 loss: 0.0185 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0185 2022/11/28 16:44:28 - mmengine - INFO - Epoch(train) [16][2200/2462] lr: 1.0998e-05 eta: 0:00:11 time: 0.0469 data_time: 0.0063 memory: 1794 loss: 0.0154 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0154 2022/11/28 16:44:32 - mmengine - INFO - Epoch(train) [16][2300/2462] lr: 4.2247e-06 eta: 0:00:07 time: 0.0454 data_time: 0.0070 memory: 1794 loss: 0.0183 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0183 2022/11/28 16:44:37 - mmengine - INFO - Epoch(train) [16][2400/2462] lr: 6.3111e-07 eta: 0:00:02 time: 0.0442 data_time: 0.0063 memory: 1794 loss: 0.0183 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0183 2022/11/28 16:44:39 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-3d_20221128_161004 2022/11/28 16:44:39 - mmengine - INFO - Epoch(train) [16][2462/2462] lr: 1.5901e-10 eta: 0:00:00 time: 0.0454 data_time: 0.0068 memory: 1794 loss: 0.0157 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0157 2022/11/28 16:44:39 - mmengine - INFO - Saving checkpoint at 16 epochs 2022/11/28 16:44:43 - mmengine - INFO - Epoch(val) [16][100/398] eta: 0:00:10 time: 0.0332 data_time: 0.0199 memory: 364 2022/11/28 16:44:46 - mmengine - INFO - Epoch(val) [16][200/398] eta: 0:00:06 time: 0.0245 data_time: 0.0115 memory: 364 2022/11/28 16:44:49 - mmengine - INFO - Epoch(val) [16][300/398] eta: 0:00:02 time: 0.0256 data_time: 0.0126 memory: 364 2022/11/28 16:44:52 - mmengine - INFO - Epoch(val) [16][398/398] acc/top1: 0.8370 acc/top5: 0.9629 acc/mean1: 0.8492