2022/11/28 18:46:49 - 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: 891217338 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 18:46:49 - mmengine - INFO - Config: default_scope = 'mmaction' default_hooks = dict( runtime_info=dict(type='RuntimeInfoHook'), timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=100, ignore_last=False), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict(type='CheckpointHook', interval=1, save_best='auto'), sampler_seed=dict(type='DistSamplerSeedHook'), sync_buffers=dict(type='SyncBuffersHook')) env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) log_processor = dict(type='LogProcessor', window_size=20, by_epoch=True) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='ActionVisualizer', vis_backends=[dict(type='LocalVisBackend')]) log_level = 'INFO' load_from = None resume = False model = dict( type='RecognizerGCN', backbone=dict( type='STGCN', graph_cfg=dict(layout='coco', mode='stgcn_spatial')), cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) dataset_type = 'PoseDataset' ann_file = 'data/skeleton/ntu120_2d.pkl' train_pipeline = [ dict(type='PreNormalize2D'), dict(type='GenSkeFeat', dataset='coco', feats=['b']), dict(type='UniformSampleFrames', clip_len=100), dict(type='PoseDecode'), dict(type='FormatGCNInput', num_person=2), dict(type='PackActionInputs') ] val_pipeline = [ dict(type='PreNormalize2D'), dict(type='GenSkeFeat', dataset='coco', feats=['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='PreNormalize2D'), dict(type='GenSkeFeat', dataset='coco', 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_2d.pkl', pipeline=[ dict(type='PreNormalize2D'), dict(type='GenSkeFeat', dataset='coco', 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_2d.pkl', pipeline=[ dict(type='PreNormalize2D'), dict(type='GenSkeFeat', dataset='coco', 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_2d.pkl', pipeline=[ dict(type='PreNormalize2D'), dict(type='GenSkeFeat', dataset='coco', 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-2d' randomness = dict(seed=None, diff_rank_seed=False, deterministic=False) 2022/11/28 18:46:49 - mmengine - INFO - Result has been saved to /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d/modules_statistic_results.json Name of parameter - Initialization information data_bn.weight - torch.Size([51]): The value is the same before and after calling `init_weights` of STGCN data_bn.bias - torch.Size([51]): The value is the same before and after calling `init_weights` of STGCN gcn.0.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.0.gcn.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.0.gcn.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.0.gcn.conv.weight - torch.Size([192, 3, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.0.gcn.conv.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of STGCN gcn.0.tcn.conv.weight - torch.Size([64, 64, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.0.tcn.conv.bias - torch.Size([64]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.0.tcn.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.0.tcn.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.1.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.1.gcn.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.1.gcn.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.1.gcn.conv.weight - torch.Size([192, 64, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.1.gcn.conv.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of STGCN gcn.1.tcn.conv.weight - torch.Size([64, 64, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.1.tcn.conv.bias - torch.Size([64]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.1.tcn.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.1.tcn.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.2.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.2.gcn.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.2.gcn.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.2.gcn.conv.weight - torch.Size([192, 64, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.2.gcn.conv.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of STGCN gcn.2.tcn.conv.weight - torch.Size([64, 64, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.2.tcn.conv.bias - torch.Size([64]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.2.tcn.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.2.tcn.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.3.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.3.gcn.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.3.gcn.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.3.gcn.conv.weight - torch.Size([192, 64, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.3.gcn.conv.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of STGCN gcn.3.tcn.conv.weight - torch.Size([64, 64, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.3.tcn.conv.bias - torch.Size([64]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.3.tcn.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.3.tcn.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of STGCN gcn.4.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.4.gcn.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.4.gcn.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.4.gcn.conv.weight - torch.Size([384, 64, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.4.gcn.conv.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of STGCN gcn.4.tcn.conv.weight - torch.Size([128, 128, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.4.tcn.conv.bias - torch.Size([128]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.4.tcn.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.4.tcn.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.4.residual.conv.weight - torch.Size([128, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.4.residual.conv.bias - torch.Size([128]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.4.residual.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.4.residual.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.5.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.5.gcn.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.5.gcn.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.5.gcn.conv.weight - torch.Size([384, 128, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.5.gcn.conv.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of STGCN gcn.5.tcn.conv.weight - torch.Size([128, 128, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.5.tcn.conv.bias - torch.Size([128]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.5.tcn.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.5.tcn.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.6.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.6.gcn.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.6.gcn.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.6.gcn.conv.weight - torch.Size([384, 128, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.6.gcn.conv.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of STGCN gcn.6.tcn.conv.weight - torch.Size([128, 128, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.6.tcn.conv.bias - torch.Size([128]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.6.tcn.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.6.tcn.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of STGCN gcn.7.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.7.gcn.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.7.gcn.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.7.gcn.conv.weight - torch.Size([768, 128, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.7.gcn.conv.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of STGCN gcn.7.tcn.conv.weight - torch.Size([256, 256, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.7.tcn.conv.bias - torch.Size([256]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.7.tcn.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.7.tcn.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.7.residual.conv.weight - torch.Size([256, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.7.residual.conv.bias - torch.Size([256]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.7.residual.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.7.residual.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.8.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.8.gcn.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.8.gcn.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.8.gcn.conv.weight - torch.Size([768, 256, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.8.gcn.conv.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of STGCN gcn.8.tcn.conv.weight - torch.Size([256, 256, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.8.tcn.conv.bias - torch.Size([256]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.8.tcn.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.8.tcn.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.9.gcn.PA - torch.Size([3, 17, 17]): The value is the same before and after calling `init_weights` of STGCN gcn.9.gcn.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.9.gcn.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.9.gcn.conv.weight - torch.Size([768, 256, 1, 1]): The value is the same before and after calling `init_weights` of STGCN gcn.9.gcn.conv.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of STGCN gcn.9.tcn.conv.weight - torch.Size([256, 256, 9, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.9.tcn.conv.bias - torch.Size([256]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 gcn.9.tcn.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN gcn.9.tcn.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of STGCN Name of parameter - Initialization information fc.weight - torch.Size([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 18:48:34 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d. 2022/11/28 18:48:41 - mmengine - INFO - Epoch(train) [1][100/2462] lr: 9.9998e-02 eta: 0:43:42 time: 0.0351 data_time: 0.0060 memory: 1253 loss: 3.6014 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.6014 2022/11/28 18:48:44 - mmengine - INFO - Epoch(train) [1][200/2462] lr: 9.9994e-02 eta: 0:33:00 time: 0.0331 data_time: 0.0059 memory: 1253 loss: 2.6856 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.6856 2022/11/28 18:48:47 - mmengine - INFO - Epoch(train) [1][300/2462] lr: 9.9986e-02 eta: 0:29:16 time: 0.0340 data_time: 0.0061 memory: 1253 loss: 2.3334 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.3334 2022/11/28 18:48:51 - mmengine - INFO - Epoch(train) [1][400/2462] lr: 9.9975e-02 eta: 0:27:22 time: 0.0330 data_time: 0.0058 memory: 1253 loss: 1.9583 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9583 2022/11/28 18:48:54 - mmengine - INFO - Epoch(train) [1][500/2462] lr: 9.9960e-02 eta: 0:26:09 time: 0.0336 data_time: 0.0061 memory: 1253 loss: 1.6592 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.6592 2022/11/28 18:48:58 - mmengine - INFO - Epoch(train) [1][600/2462] lr: 9.9943e-02 eta: 0:25:20 time: 0.0331 data_time: 0.0058 memory: 1253 loss: 1.6871 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6871 2022/11/28 18:49:01 - mmengine - INFO - Epoch(train) [1][700/2462] lr: 9.9922e-02 eta: 0:24:43 time: 0.0333 data_time: 0.0058 memory: 1253 loss: 1.5338 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5338 2022/11/28 18:49:04 - mmengine - INFO - Epoch(train) [1][800/2462] lr: 9.9899e-02 eta: 0:24:15 time: 0.0336 data_time: 0.0060 memory: 1253 loss: 1.3637 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3637 2022/11/28 18:49:08 - mmengine - INFO - Epoch(train) [1][900/2462] lr: 9.9872e-02 eta: 0:23:54 time: 0.0332 data_time: 0.0063 memory: 1253 loss: 1.3499 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3499 2022/11/28 18:49:11 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:49:11 - mmengine - INFO - Epoch(train) [1][1000/2462] lr: 9.9841e-02 eta: 0:23:37 time: 0.0336 data_time: 0.0059 memory: 1253 loss: 1.1387 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.1387 2022/11/28 18:49:14 - mmengine - INFO - Epoch(train) [1][1100/2462] lr: 9.9808e-02 eta: 0:23:22 time: 0.0350 data_time: 0.0059 memory: 1253 loss: 0.9525 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9525 2022/11/28 18:49:18 - mmengine - INFO - Epoch(train) [1][1200/2462] lr: 9.9772e-02 eta: 0:23:12 time: 0.0343 data_time: 0.0058 memory: 1253 loss: 0.9898 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9898 2022/11/28 18:49:21 - mmengine - INFO - Epoch(train) [1][1300/2462] lr: 9.9732e-02 eta: 0:22:59 time: 0.0331 data_time: 0.0059 memory: 1253 loss: 1.1035 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1035 2022/11/28 18:49:24 - mmengine - INFO - Epoch(train) [1][1400/2462] lr: 9.9689e-02 eta: 0:22:49 time: 0.0362 data_time: 0.0059 memory: 1253 loss: 1.0816 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0816 2022/11/28 18:49:28 - mmengine - INFO - Epoch(train) [1][1500/2462] lr: 9.9643e-02 eta: 0:22:42 time: 0.0353 data_time: 0.0059 memory: 1253 loss: 0.8952 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8952 2022/11/28 18:49:31 - mmengine - INFO - Epoch(train) [1][1600/2462] lr: 9.9594e-02 eta: 0:22:33 time: 0.0339 data_time: 0.0058 memory: 1253 loss: 0.9490 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9490 2022/11/28 18:49:35 - mmengine - INFO - Epoch(train) [1][1700/2462] lr: 9.9542e-02 eta: 0:22:26 time: 0.0337 data_time: 0.0059 memory: 1253 loss: 0.9763 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9763 2022/11/28 18:49:38 - mmengine - INFO - Epoch(train) [1][1800/2462] lr: 9.9486e-02 eta: 0:22:19 time: 0.0347 data_time: 0.0062 memory: 1253 loss: 0.9949 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9949 2022/11/28 18:49:42 - mmengine - INFO - Epoch(train) [1][1900/2462] lr: 9.9428e-02 eta: 0:22:13 time: 0.0333 data_time: 0.0059 memory: 1253 loss: 0.9291 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9291 2022/11/28 18:49:45 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:49:45 - mmengine - INFO - Epoch(train) [1][2000/2462] lr: 9.9366e-02 eta: 0:22:05 time: 0.0335 data_time: 0.0059 memory: 1253 loss: 1.0011 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0011 2022/11/28 18:49:48 - mmengine - INFO - Epoch(train) [1][2100/2462] lr: 9.9301e-02 eta: 0:21:58 time: 0.0333 data_time: 0.0059 memory: 1253 loss: 1.0591 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0591 2022/11/28 18:49:52 - mmengine - INFO - Epoch(train) [1][2200/2462] lr: 9.9233e-02 eta: 0:21:51 time: 0.0332 data_time: 0.0058 memory: 1253 loss: 0.9213 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9213 2022/11/28 18:49:55 - mmengine - INFO - Epoch(train) [1][2300/2462] lr: 9.9162e-02 eta: 0:21:45 time: 0.0351 data_time: 0.0067 memory: 1253 loss: 0.8286 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8286 2022/11/28 18:49:58 - mmengine - INFO - Epoch(train) [1][2400/2462] lr: 9.9088e-02 eta: 0:21:40 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.7752 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7752 2022/11/28 18:50:00 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:50:00 - mmengine - INFO - Epoch(train) [1][2462/2462] lr: 9.9040e-02 eta: 0:21:36 time: 0.0337 data_time: 0.0060 memory: 1253 loss: 0.8046 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8046 2022/11/28 18:50:00 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/11/28 18:50:03 - mmengine - INFO - Epoch(val) [1][100/398] eta: 0:00:05 time: 0.0148 data_time: 0.0057 memory: 262 2022/11/28 18:50:04 - mmengine - INFO - Epoch(val) [1][200/398] eta: 0:00:03 time: 0.0146 data_time: 0.0056 memory: 262 2022/11/28 18:50:06 - mmengine - INFO - Epoch(val) [1][300/398] eta: 0:00:01 time: 0.0145 data_time: 0.0056 memory: 262 2022/11/28 18:50:08 - mmengine - INFO - Epoch(val) [1][398/398] acc/top1: 0.6109 acc/top5: 0.8909 acc/mean1: 0.6281 2022/11/28 18:50:08 - mmengine - INFO - The best checkpoint with 0.6109 acc/top1 at 1 epoch is saved to best_acc/top1_epoch_1.pth. 2022/11/28 18:50:12 - mmengine - INFO - Epoch(train) [2][100/2462] lr: 9.8961e-02 eta: 0:21:32 time: 0.0338 data_time: 0.0059 memory: 1253 loss: 0.9099 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9099 2022/11/28 18:50:15 - mmengine - INFO - Epoch(train) [2][200/2462] lr: 9.8878e-02 eta: 0:21:27 time: 0.0342 data_time: 0.0062 memory: 1253 loss: 0.8461 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 0.8461 2022/11/28 18:50:19 - mmengine - INFO - Epoch(train) [2][300/2462] lr: 9.8793e-02 eta: 0:21:22 time: 0.0332 data_time: 0.0059 memory: 1253 loss: 0.8195 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8195 2022/11/28 18:50:22 - mmengine - INFO - Epoch(train) [2][400/2462] lr: 9.8704e-02 eta: 0:21:16 time: 0.0331 data_time: 0.0059 memory: 1253 loss: 0.8558 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8558 2022/11/28 18:50:25 - mmengine - INFO - Epoch(train) [2][500/2462] lr: 9.8612e-02 eta: 0:21:11 time: 0.0340 data_time: 0.0059 memory: 1253 loss: 0.7868 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7868 2022/11/28 18:50:27 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:50:29 - mmengine - INFO - Epoch(train) [2][600/2462] lr: 9.8518e-02 eta: 0:21:07 time: 0.0334 data_time: 0.0060 memory: 1253 loss: 0.7916 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7916 2022/11/28 18:50:32 - mmengine - INFO - Epoch(train) [2][700/2462] lr: 9.8420e-02 eta: 0:21:04 time: 0.0359 data_time: 0.0060 memory: 1253 loss: 0.7202 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7202 2022/11/28 18:50:36 - mmengine - INFO - Epoch(train) [2][800/2462] lr: 9.8319e-02 eta: 0:21:00 time: 0.0349 data_time: 0.0060 memory: 1253 loss: 0.7858 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7858 2022/11/28 18:50:39 - mmengine - INFO - Epoch(train) [2][900/2462] lr: 9.8215e-02 eta: 0:20:56 time: 0.0346 data_time: 0.0068 memory: 1253 loss: 0.8883 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8883 2022/11/28 18:50:43 - mmengine - INFO - Epoch(train) [2][1000/2462] lr: 9.8107e-02 eta: 0:20:51 time: 0.0342 data_time: 0.0058 memory: 1253 loss: 0.7334 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.7334 2022/11/28 18:50:46 - mmengine - INFO - Epoch(train) [2][1100/2462] lr: 9.7997e-02 eta: 0:20:47 time: 0.0345 data_time: 0.0060 memory: 1253 loss: 0.7144 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7144 2022/11/28 18:50:49 - mmengine - INFO - Epoch(train) [2][1200/2462] lr: 9.7884e-02 eta: 0:20:44 time: 0.0348 data_time: 0.0059 memory: 1253 loss: 0.8149 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8149 2022/11/28 18:50:53 - mmengine - INFO - Epoch(train) [2][1300/2462] lr: 9.7768e-02 eta: 0:20:39 time: 0.0346 data_time: 0.0059 memory: 1253 loss: 0.6774 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6774 2022/11/28 18:50:56 - mmengine - INFO - Epoch(train) [2][1400/2462] lr: 9.7648e-02 eta: 0:20:35 time: 0.0330 data_time: 0.0060 memory: 1253 loss: 0.7362 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7362 2022/11/28 18:51:00 - mmengine - INFO - Epoch(train) [2][1500/2462] lr: 9.7526e-02 eta: 0:20:30 time: 0.0330 data_time: 0.0060 memory: 1253 loss: 0.6985 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.6985 2022/11/28 18:51:01 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:51:03 - mmengine - INFO - Epoch(train) [2][1600/2462] lr: 9.7400e-02 eta: 0:20:26 time: 0.0353 data_time: 0.0059 memory: 1253 loss: 0.6004 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6004 2022/11/28 18:51:06 - mmengine - INFO - Epoch(train) [2][1700/2462] lr: 9.7272e-02 eta: 0:20:22 time: 0.0332 data_time: 0.0060 memory: 1253 loss: 0.6587 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6587 2022/11/28 18:51:10 - mmengine - INFO - Epoch(train) [2][1800/2462] lr: 9.7141e-02 eta: 0:20:18 time: 0.0343 data_time: 0.0060 memory: 1253 loss: 0.6892 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6892 2022/11/28 18:51:13 - mmengine - INFO - Epoch(train) [2][1900/2462] lr: 9.7006e-02 eta: 0:20:13 time: 0.0331 data_time: 0.0059 memory: 1253 loss: 0.7416 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7416 2022/11/28 18:51:16 - mmengine - INFO - Epoch(train) [2][2000/2462] lr: 9.6869e-02 eta: 0:20:08 time: 0.0331 data_time: 0.0060 memory: 1253 loss: 0.6428 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6428 2022/11/28 18:51:20 - mmengine - INFO - Epoch(train) [2][2100/2462] lr: 9.6728e-02 eta: 0:20:04 time: 0.0331 data_time: 0.0060 memory: 1253 loss: 0.6966 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6966 2022/11/28 18:51:23 - mmengine - INFO - Epoch(train) [2][2200/2462] lr: 9.6585e-02 eta: 0:19:59 time: 0.0328 data_time: 0.0059 memory: 1253 loss: 0.7596 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7596 2022/11/28 18:51:26 - mmengine - INFO - Epoch(train) [2][2300/2462] lr: 9.6439e-02 eta: 0:19:55 time: 0.0337 data_time: 0.0059 memory: 1253 loss: 0.7361 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7361 2022/11/28 18:51:30 - mmengine - INFO - Epoch(train) [2][2400/2462] lr: 9.6290e-02 eta: 0:19:52 time: 0.0342 data_time: 0.0072 memory: 1253 loss: 0.6852 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6852 2022/11/28 18:51:32 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:51:32 - mmengine - INFO - Epoch(train) [2][2462/2462] lr: 9.6196e-02 eta: 0:19:49 time: 0.0343 data_time: 0.0064 memory: 1253 loss: 0.7676 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.7676 2022/11/28 18:51:32 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/11/28 18:51:34 - mmengine - INFO - Epoch(val) [2][100/398] eta: 0:00:04 time: 0.0145 data_time: 0.0056 memory: 262 2022/11/28 18:51:35 - mmengine - INFO - Epoch(val) [2][200/398] eta: 0:00:02 time: 0.0146 data_time: 0.0056 memory: 262 2022/11/28 18:51:37 - mmengine - INFO - Epoch(val) [2][300/398] eta: 0:00:01 time: 0.0145 data_time: 0.0056 memory: 262 2022/11/28 18:51:39 - mmengine - INFO - Epoch(val) [2][398/398] acc/top1: 0.6802 acc/top5: 0.9327 acc/mean1: 0.6956 2022/11/28 18:51:39 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_1.pth is removed 2022/11/28 18:51:39 - mmengine - INFO - The best checkpoint with 0.6802 acc/top1 at 2 epoch is saved to best_acc/top1_epoch_2.pth. 2022/11/28 18:51:42 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:51:43 - mmengine - INFO - Epoch(train) [3][100/2462] lr: 9.6041e-02 eta: 0:19:46 time: 0.0337 data_time: 0.0059 memory: 1253 loss: 0.6609 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.6609 2022/11/28 18:51:46 - mmengine - INFO - Epoch(train) [3][200/2462] lr: 9.5884e-02 eta: 0:19:42 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.6885 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6885 2022/11/28 18:51:49 - mmengine - INFO - Epoch(train) [3][300/2462] lr: 9.5725e-02 eta: 0:19:38 time: 0.0334 data_time: 0.0060 memory: 1253 loss: 0.6593 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6593 2022/11/28 18:51:53 - mmengine - INFO - Epoch(train) [3][400/2462] lr: 9.5562e-02 eta: 0:19:34 time: 0.0332 data_time: 0.0060 memory: 1253 loss: 0.6652 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.6652 2022/11/28 18:51:56 - mmengine - INFO - Epoch(train) [3][500/2462] lr: 9.5396e-02 eta: 0:19:30 time: 0.0333 data_time: 0.0059 memory: 1253 loss: 0.6021 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6021 2022/11/28 18:52:00 - mmengine - INFO - Epoch(train) [3][600/2462] lr: 9.5228e-02 eta: 0:19:26 time: 0.0333 data_time: 0.0060 memory: 1253 loss: 0.6412 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6412 2022/11/28 18:52:03 - mmengine - INFO - Epoch(train) [3][700/2462] lr: 9.5056e-02 eta: 0:19:22 time: 0.0333 data_time: 0.0059 memory: 1253 loss: 0.6914 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6914 2022/11/28 18:52:06 - mmengine - INFO - Epoch(train) [3][800/2462] lr: 9.4882e-02 eta: 0:19:18 time: 0.0349 data_time: 0.0060 memory: 1253 loss: 0.6503 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6503 2022/11/28 18:52:10 - mmengine - INFO - Epoch(train) [3][900/2462] lr: 9.4705e-02 eta: 0:19:15 time: 0.0350 data_time: 0.0068 memory: 1253 loss: 0.5429 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.5429 2022/11/28 18:52:13 - mmengine - INFO - Epoch(train) [3][1000/2462] lr: 9.4525e-02 eta: 0:19:11 time: 0.0334 data_time: 0.0059 memory: 1253 loss: 0.5692 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5692 2022/11/28 18:52:16 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:52:17 - mmengine - INFO - Epoch(train) [3][1100/2462] lr: 9.4342e-02 eta: 0:19:07 time: 0.0341 data_time: 0.0062 memory: 1253 loss: 0.6641 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6641 2022/11/28 18:52:20 - mmengine - INFO - Epoch(train) [3][1200/2462] lr: 9.4156e-02 eta: 0:19:04 time: 0.0342 data_time: 0.0060 memory: 1253 loss: 0.6090 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6090 2022/11/28 18:52:23 - mmengine - INFO - Epoch(train) [3][1300/2462] lr: 9.3968e-02 eta: 0:19:00 time: 0.0331 data_time: 0.0060 memory: 1253 loss: 0.6203 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6203 2022/11/28 18:52:27 - mmengine - INFO - Epoch(train) [3][1400/2462] lr: 9.3776e-02 eta: 0:18:56 time: 0.0347 data_time: 0.0059 memory: 1253 loss: 0.5758 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5758 2022/11/28 18:52:30 - mmengine - INFO - Epoch(train) [3][1500/2462] lr: 9.3582e-02 eta: 0:18:53 time: 0.0338 data_time: 0.0068 memory: 1253 loss: 0.7092 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7092 2022/11/28 18:52:34 - mmengine - INFO - Epoch(train) [3][1600/2462] lr: 9.3385e-02 eta: 0:18:49 time: 0.0335 data_time: 0.0059 memory: 1253 loss: 0.7903 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7903 2022/11/28 18:52:37 - mmengine - INFO - Epoch(train) [3][1700/2462] lr: 9.3186e-02 eta: 0:18:46 time: 0.0347 data_time: 0.0060 memory: 1253 loss: 0.5758 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5758 2022/11/28 18:52:40 - mmengine - INFO - Epoch(train) [3][1800/2462] lr: 9.2983e-02 eta: 0:18:42 time: 0.0339 data_time: 0.0068 memory: 1253 loss: 0.5837 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.5837 2022/11/28 18:52:44 - mmengine - INFO - Epoch(train) [3][1900/2462] lr: 9.2778e-02 eta: 0:18:38 time: 0.0331 data_time: 0.0060 memory: 1253 loss: 0.5175 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5175 2022/11/28 18:52:47 - mmengine - INFO - Epoch(train) [3][2000/2462] lr: 9.2571e-02 eta: 0:18:35 time: 0.0337 data_time: 0.0060 memory: 1253 loss: 0.5230 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5230 2022/11/28 18:52:50 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:52:51 - mmengine - INFO - Epoch(train) [3][2100/2462] lr: 9.2360e-02 eta: 0:18:31 time: 0.0348 data_time: 0.0060 memory: 1253 loss: 0.4542 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4542 2022/11/28 18:52:54 - mmengine - INFO - Epoch(train) [3][2200/2462] lr: 9.2147e-02 eta: 0:18:27 time: 0.0336 data_time: 0.0061 memory: 1253 loss: 0.6004 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6004 2022/11/28 18:52:57 - mmengine - INFO - Epoch(train) [3][2300/2462] lr: 9.1931e-02 eta: 0:18:23 time: 0.0330 data_time: 0.0060 memory: 1253 loss: 0.4643 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.4643 2022/11/28 18:53:01 - mmengine - INFO - Epoch(train) [3][2400/2462] lr: 9.1713e-02 eta: 0:18:20 time: 0.0331 data_time: 0.0060 memory: 1253 loss: 0.5789 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.5789 2022/11/28 18:53:03 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:53:03 - mmengine - INFO - Epoch(train) [3][2462/2462] lr: 9.1576e-02 eta: 0:18:18 time: 0.0353 data_time: 0.0061 memory: 1253 loss: 0.5961 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.5961 2022/11/28 18:53:03 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/11/28 18:53:05 - mmengine - INFO - Epoch(val) [3][100/398] eta: 0:00:04 time: 0.0145 data_time: 0.0056 memory: 262 2022/11/28 18:53:06 - mmengine - INFO - Epoch(val) [3][200/398] eta: 0:00:02 time: 0.0144 data_time: 0.0055 memory: 262 2022/11/28 18:53:08 - mmengine - INFO - Epoch(val) [3][300/398] eta: 0:00:01 time: 0.0145 data_time: 0.0056 memory: 262 2022/11/28 18:53:10 - mmengine - INFO - Epoch(val) [3][398/398] acc/top1: 0.6367 acc/top5: 0.9120 acc/mean1: 0.6787 2022/11/28 18:53:14 - mmengine - INFO - Epoch(train) [4][100/2462] lr: 9.1353e-02 eta: 0:18:14 time: 0.0344 data_time: 0.0059 memory: 1253 loss: 0.6019 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6019 2022/11/28 18:53:17 - mmengine - INFO - Epoch(train) [4][200/2462] lr: 9.1127e-02 eta: 0:18:11 time: 0.0359 data_time: 0.0062 memory: 1253 loss: 0.5598 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5598 2022/11/28 18:53:20 - mmengine - INFO - Epoch(train) [4][300/2462] lr: 9.0899e-02 eta: 0:18:08 time: 0.0343 data_time: 0.0066 memory: 1253 loss: 0.4791 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4791 2022/11/28 18:53:24 - mmengine - INFO - Epoch(train) [4][400/2462] lr: 9.0669e-02 eta: 0:18:05 time: 0.0361 data_time: 0.0061 memory: 1253 loss: 0.6638 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6638 2022/11/28 18:53:27 - mmengine - INFO - Epoch(train) [4][500/2462] lr: 9.0435e-02 eta: 0:18:01 time: 0.0337 data_time: 0.0063 memory: 1253 loss: 0.5986 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.5986 2022/11/28 18:53:31 - mmengine - INFO - Epoch(train) [4][600/2462] lr: 9.0200e-02 eta: 0:17:58 time: 0.0351 data_time: 0.0066 memory: 1253 loss: 0.4864 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4864 2022/11/28 18:53:31 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:53:34 - mmengine - INFO - Epoch(train) [4][700/2462] lr: 8.9961e-02 eta: 0:17:55 time: 0.0332 data_time: 0.0059 memory: 1253 loss: 0.4570 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4570 2022/11/28 18:53:38 - mmengine - INFO - Epoch(train) [4][800/2462] lr: 8.9720e-02 eta: 0:17:52 time: 0.0368 data_time: 0.0060 memory: 1253 loss: 0.5519 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5519 2022/11/28 18:53:42 - mmengine - INFO - Epoch(train) [4][900/2462] lr: 8.9477e-02 eta: 0:17:49 time: 0.0358 data_time: 0.0068 memory: 1253 loss: 0.5310 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5310 2022/11/28 18:53:45 - mmengine - INFO - Epoch(train) [4][1000/2462] lr: 8.9231e-02 eta: 0:17:46 time: 0.0359 data_time: 0.0059 memory: 1253 loss: 0.6367 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6367 2022/11/28 18:53:49 - mmengine - INFO - Epoch(train) [4][1100/2462] lr: 8.8982e-02 eta: 0:17:43 time: 0.0347 data_time: 0.0060 memory: 1253 loss: 0.5223 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5223 2022/11/28 18:53:52 - mmengine - INFO - Epoch(train) [4][1200/2462] lr: 8.8731e-02 eta: 0:17:39 time: 0.0347 data_time: 0.0060 memory: 1253 loss: 0.5175 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5175 2022/11/28 18:53:56 - mmengine - INFO - Epoch(train) [4][1300/2462] lr: 8.8478e-02 eta: 0:17:36 time: 0.0342 data_time: 0.0067 memory: 1253 loss: 0.5096 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5096 2022/11/28 18:53:59 - mmengine - INFO - Epoch(train) [4][1400/2462] lr: 8.8222e-02 eta: 0:17:32 time: 0.0335 data_time: 0.0065 memory: 1253 loss: 0.6685 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6685 2022/11/28 18:54:02 - mmengine - INFO - Epoch(train) [4][1500/2462] lr: 8.7964e-02 eta: 0:17:29 time: 0.0339 data_time: 0.0066 memory: 1253 loss: 0.5910 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.5910 2022/11/28 18:54:06 - mmengine - INFO - Epoch(train) [4][1600/2462] lr: 8.7703e-02 eta: 0:17:25 time: 0.0333 data_time: 0.0059 memory: 1253 loss: 0.5159 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5159 2022/11/28 18:54:06 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:54:09 - mmengine - INFO - Epoch(train) [4][1700/2462] lr: 8.7440e-02 eta: 0:17:21 time: 0.0344 data_time: 0.0065 memory: 1253 loss: 0.5197 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5197 2022/11/28 18:54:12 - mmengine - INFO - Epoch(train) [4][1800/2462] lr: 8.7174e-02 eta: 0:17:18 time: 0.0341 data_time: 0.0063 memory: 1253 loss: 0.5428 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5428 2022/11/28 18:54:16 - mmengine - INFO - Epoch(train) [4][1900/2462] lr: 8.6907e-02 eta: 0:17:14 time: 0.0330 data_time: 0.0061 memory: 1253 loss: 0.4900 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.4900 2022/11/28 18:54:19 - mmengine - INFO - Epoch(train) [4][2000/2462] lr: 8.6636e-02 eta: 0:17:11 time: 0.0331 data_time: 0.0059 memory: 1253 loss: 0.5397 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5397 2022/11/28 18:54:23 - mmengine - INFO - Epoch(train) [4][2100/2462] lr: 8.6364e-02 eta: 0:17:07 time: 0.0336 data_time: 0.0065 memory: 1253 loss: 0.5720 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.5720 2022/11/28 18:54:26 - mmengine - INFO - Epoch(train) [4][2200/2462] lr: 8.6089e-02 eta: 0:17:03 time: 0.0335 data_time: 0.0059 memory: 1253 loss: 0.5431 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5431 2022/11/28 18:54:29 - mmengine - INFO - Epoch(train) [4][2300/2462] lr: 8.5812e-02 eta: 0:17:00 time: 0.0332 data_time: 0.0059 memory: 1253 loss: 0.5631 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5631 2022/11/28 18:54:33 - mmengine - INFO - Epoch(train) [4][2400/2462] lr: 8.5533e-02 eta: 0:16:56 time: 0.0345 data_time: 0.0061 memory: 1253 loss: 0.4304 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4304 2022/11/28 18:54:35 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:54:35 - mmengine - INFO - Epoch(train) [4][2462/2462] lr: 8.5358e-02 eta: 0:16:54 time: 0.0339 data_time: 0.0060 memory: 1253 loss: 0.6231 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6231 2022/11/28 18:54:35 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/11/28 18:54:37 - mmengine - INFO - Epoch(val) [4][100/398] eta: 0:00:04 time: 0.0155 data_time: 0.0066 memory: 262 2022/11/28 18:54:38 - mmengine - INFO - Epoch(val) [4][200/398] eta: 0:00:02 time: 0.0148 data_time: 0.0057 memory: 262 2022/11/28 18:54:40 - mmengine - INFO - Epoch(val) [4][300/398] eta: 0:00:01 time: 0.0145 data_time: 0.0056 memory: 262 2022/11/28 18:54:42 - mmengine - INFO - Epoch(val) [4][398/398] acc/top1: 0.7037 acc/top5: 0.9303 acc/mean1: 0.7333 2022/11/28 18:54:42 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_2.pth is removed 2022/11/28 18:54:42 - 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 18:54:46 - mmengine - INFO - Epoch(train) [5][100/2462] lr: 8.5075e-02 eta: 0:16:50 time: 0.0334 data_time: 0.0059 memory: 1253 loss: 0.5570 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5570 2022/11/28 18:54:48 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:54:49 - mmengine - INFO - Epoch(train) [5][200/2462] lr: 8.4790e-02 eta: 0:16:47 time: 0.0333 data_time: 0.0060 memory: 1253 loss: 0.4689 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4689 2022/11/28 18:54:53 - mmengine - INFO - Epoch(train) [5][300/2462] lr: 8.4502e-02 eta: 0:16:43 time: 0.0334 data_time: 0.0060 memory: 1253 loss: 0.5839 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5839 2022/11/28 18:54:56 - mmengine - INFO - Epoch(train) [5][400/2462] lr: 8.4213e-02 eta: 0:16:40 time: 0.0342 data_time: 0.0059 memory: 1253 loss: 0.4455 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4455 2022/11/28 18:55:00 - mmengine - INFO - Epoch(train) [5][500/2462] lr: 8.3921e-02 eta: 0:16:37 time: 0.0339 data_time: 0.0060 memory: 1253 loss: 0.5153 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5153 2022/11/28 18:55:03 - mmengine - INFO - Epoch(train) [5][600/2462] lr: 8.3627e-02 eta: 0:16:33 time: 0.0334 data_time: 0.0060 memory: 1253 loss: 0.5788 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5788 2022/11/28 18:55:06 - mmengine - INFO - Epoch(train) [5][700/2462] lr: 8.3330e-02 eta: 0:16:29 time: 0.0334 data_time: 0.0059 memory: 1253 loss: 0.4936 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4936 2022/11/28 18:55:10 - mmengine - INFO - Epoch(train) [5][800/2462] lr: 8.3032e-02 eta: 0:16:26 time: 0.0341 data_time: 0.0059 memory: 1253 loss: 0.4451 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.4451 2022/11/28 18:55:13 - mmengine - INFO - Epoch(train) [5][900/2462] lr: 8.2732e-02 eta: 0:16:22 time: 0.0336 data_time: 0.0060 memory: 1253 loss: 0.5062 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5062 2022/11/28 18:55:16 - mmengine - INFO - Epoch(train) [5][1000/2462] lr: 8.2429e-02 eta: 0:16:19 time: 0.0337 data_time: 0.0060 memory: 1253 loss: 0.5096 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5096 2022/11/28 18:55:20 - mmengine - INFO - Epoch(train) [5][1100/2462] lr: 8.2125e-02 eta: 0:16:15 time: 0.0348 data_time: 0.0062 memory: 1253 loss: 0.5299 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5299 2022/11/28 18:55:22 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:55:23 - mmengine - INFO - Epoch(train) [5][1200/2462] lr: 8.1818e-02 eta: 0:16:12 time: 0.0334 data_time: 0.0060 memory: 1253 loss: 0.4484 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4484 2022/11/28 18:55:27 - mmengine - INFO - Epoch(train) [5][1300/2462] lr: 8.1510e-02 eta: 0:16:08 time: 0.0344 data_time: 0.0064 memory: 1253 loss: 0.4858 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4858 2022/11/28 18:55:30 - mmengine - INFO - Epoch(train) [5][1400/2462] lr: 8.1199e-02 eta: 0:16:05 time: 0.0343 data_time: 0.0060 memory: 1253 loss: 0.5224 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5224 2022/11/28 18:55:34 - mmengine - INFO - Epoch(train) [5][1500/2462] lr: 8.0886e-02 eta: 0:16:01 time: 0.0332 data_time: 0.0060 memory: 1253 loss: 0.4628 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4628 2022/11/28 18:55:37 - mmengine - INFO - Epoch(train) [5][1600/2462] lr: 8.0572e-02 eta: 0:15:58 time: 0.0331 data_time: 0.0060 memory: 1253 loss: 0.4208 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4208 2022/11/28 18:55:40 - mmengine - INFO - Epoch(train) [5][1700/2462] lr: 8.0255e-02 eta: 0:15:54 time: 0.0331 data_time: 0.0060 memory: 1253 loss: 0.4555 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4555 2022/11/28 18:55:44 - mmengine - INFO - Epoch(train) [5][1800/2462] lr: 7.9937e-02 eta: 0:15:51 time: 0.0336 data_time: 0.0059 memory: 1253 loss: 0.5368 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5368 2022/11/28 18:55:47 - mmengine - INFO - Epoch(train) [5][1900/2462] lr: 7.9617e-02 eta: 0:15:47 time: 0.0342 data_time: 0.0061 memory: 1253 loss: 0.5626 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5626 2022/11/28 18:55:50 - mmengine - INFO - Epoch(train) [5][2000/2462] lr: 7.9294e-02 eta: 0:15:43 time: 0.0331 data_time: 0.0059 memory: 1253 loss: 0.4691 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4691 2022/11/28 18:55:54 - mmengine - INFO - Epoch(train) [5][2100/2462] lr: 7.8970e-02 eta: 0:15:40 time: 0.0339 data_time: 0.0062 memory: 1253 loss: 0.4932 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4932 2022/11/28 18:55:55 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:55:57 - mmengine - INFO - Epoch(train) [5][2200/2462] lr: 7.8644e-02 eta: 0:15:36 time: 0.0341 data_time: 0.0059 memory: 1253 loss: 0.4508 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4508 2022/11/28 18:56:01 - mmengine - INFO - Epoch(train) [5][2300/2462] lr: 7.8317e-02 eta: 0:15:33 time: 0.0342 data_time: 0.0060 memory: 1253 loss: 0.5442 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5442 2022/11/28 18:56:04 - mmengine - INFO - Epoch(train) [5][2400/2462] lr: 7.7987e-02 eta: 0:15:30 time: 0.0355 data_time: 0.0060 memory: 1253 loss: 0.4679 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4679 2022/11/28 18:56:06 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:56:06 - mmengine - INFO - Epoch(train) [5][2462/2462] lr: 7.7782e-02 eta: 0:15:27 time: 0.0342 data_time: 0.0060 memory: 1253 loss: 0.5275 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5275 2022/11/28 18:56:06 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/11/28 18:56:08 - mmengine - INFO - Epoch(val) [5][100/398] eta: 0:00:04 time: 0.0146 data_time: 0.0056 memory: 262 2022/11/28 18:56:10 - mmengine - INFO - Epoch(val) [5][200/398] eta: 0:00:02 time: 0.0146 data_time: 0.0056 memory: 262 2022/11/28 18:56:11 - mmengine - INFO - Epoch(val) [5][300/398] eta: 0:00:01 time: 0.0148 data_time: 0.0057 memory: 262 2022/11/28 18:56:13 - mmengine - INFO - Epoch(val) [5][398/398] acc/top1: 0.7459 acc/top5: 0.9464 acc/mean1: 0.7725 2022/11/28 18:56:13 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_4.pth is removed 2022/11/28 18:56:14 - mmengine - INFO - The best checkpoint with 0.7459 acc/top1 at 5 epoch is saved to best_acc/top1_epoch_5.pth. 2022/11/28 18:56:17 - mmengine - INFO - Epoch(train) [6][100/2462] lr: 7.7449e-02 eta: 0:15:24 time: 0.0350 data_time: 0.0060 memory: 1253 loss: 0.4741 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4741 2022/11/28 18:56:21 - mmengine - INFO - Epoch(train) [6][200/2462] lr: 7.7115e-02 eta: 0:15:21 time: 0.0339 data_time: 0.0060 memory: 1253 loss: 0.3984 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.3984 2022/11/28 18:56:24 - mmengine - INFO - Epoch(train) [6][300/2462] lr: 7.6779e-02 eta: 0:15:17 time: 0.0347 data_time: 0.0062 memory: 1253 loss: 0.5241 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5241 2022/11/28 18:56:28 - mmengine - INFO - Epoch(train) [6][400/2462] lr: 7.6442e-02 eta: 0:15:14 time: 0.0352 data_time: 0.0060 memory: 1253 loss: 0.4354 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4354 2022/11/28 18:56:31 - mmengine - INFO - Epoch(train) [6][500/2462] lr: 7.6102e-02 eta: 0:15:11 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.4779 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4779 2022/11/28 18:56:34 - mmengine - INFO - Epoch(train) [6][600/2462] lr: 7.5762e-02 eta: 0:15:07 time: 0.0345 data_time: 0.0061 memory: 1253 loss: 0.4703 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4703 2022/11/28 18:56:37 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:56:38 - mmengine - INFO - Epoch(train) [6][700/2462] lr: 7.5419e-02 eta: 0:15:04 time: 0.0336 data_time: 0.0060 memory: 1253 loss: 0.4939 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4939 2022/11/28 18:56:41 - mmengine - INFO - Epoch(train) [6][800/2462] lr: 7.5075e-02 eta: 0:15:00 time: 0.0344 data_time: 0.0061 memory: 1253 loss: 0.5192 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5192 2022/11/28 18:56:45 - mmengine - INFO - Epoch(train) [6][900/2462] lr: 7.4729e-02 eta: 0:14:57 time: 0.0347 data_time: 0.0061 memory: 1253 loss: 0.4770 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4770 2022/11/28 18:56:48 - mmengine - INFO - Epoch(train) [6][1000/2462] lr: 7.4382e-02 eta: 0:14:54 time: 0.0350 data_time: 0.0060 memory: 1253 loss: 0.3868 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3868 2022/11/28 18:56:52 - mmengine - INFO - Epoch(train) [6][1100/2462] lr: 7.4033e-02 eta: 0:14:50 time: 0.0337 data_time: 0.0060 memory: 1253 loss: 0.4452 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4452 2022/11/28 18:56:55 - mmengine - INFO - Epoch(train) [6][1200/2462] lr: 7.3682e-02 eta: 0:14:47 time: 0.0346 data_time: 0.0060 memory: 1253 loss: 0.5594 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5594 2022/11/28 18:56:59 - mmengine - INFO - Epoch(train) [6][1300/2462] lr: 7.3330e-02 eta: 0:14:44 time: 0.0339 data_time: 0.0060 memory: 1253 loss: 0.4774 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4774 2022/11/28 18:57:02 - mmengine - INFO - Epoch(train) [6][1400/2462] lr: 7.2977e-02 eta: 0:14:40 time: 0.0339 data_time: 0.0062 memory: 1253 loss: 0.3306 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3306 2022/11/28 18:57:05 - mmengine - INFO - Epoch(train) [6][1500/2462] lr: 7.2622e-02 eta: 0:14:37 time: 0.0336 data_time: 0.0060 memory: 1253 loss: 0.4296 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4296 2022/11/28 18:57:09 - mmengine - INFO - Epoch(train) [6][1600/2462] lr: 7.2266e-02 eta: 0:14:33 time: 0.0348 data_time: 0.0062 memory: 1253 loss: 0.4855 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.4855 2022/11/28 18:57:12 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:57:12 - mmengine - INFO - Epoch(train) [6][1700/2462] lr: 7.1908e-02 eta: 0:14:30 time: 0.0342 data_time: 0.0060 memory: 1253 loss: 0.5024 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5024 2022/11/28 18:57:16 - mmengine - INFO - Epoch(train) [6][1800/2462] lr: 7.1549e-02 eta: 0:14:26 time: 0.0338 data_time: 0.0060 memory: 1253 loss: 0.3886 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3886 2022/11/28 18:57:19 - mmengine - INFO - Epoch(train) [6][1900/2462] lr: 7.1188e-02 eta: 0:14:23 time: 0.0337 data_time: 0.0060 memory: 1253 loss: 0.4483 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4483 2022/11/28 18:57:22 - mmengine - INFO - Epoch(train) [6][2000/2462] lr: 7.0826e-02 eta: 0:14:19 time: 0.0338 data_time: 0.0061 memory: 1253 loss: 0.3867 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3867 2022/11/28 18:57:26 - mmengine - INFO - Epoch(train) [6][2100/2462] lr: 7.0463e-02 eta: 0:14:16 time: 0.0336 data_time: 0.0061 memory: 1253 loss: 0.4473 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4473 2022/11/28 18:57:29 - mmengine - INFO - Epoch(train) [6][2200/2462] lr: 7.0099e-02 eta: 0:14:12 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.5266 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.5266 2022/11/28 18:57:33 - mmengine - INFO - Epoch(train) [6][2300/2462] lr: 6.9733e-02 eta: 0:14:09 time: 0.0338 data_time: 0.0061 memory: 1253 loss: 0.4183 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4183 2022/11/28 18:57:36 - mmengine - INFO - Epoch(train) [6][2400/2462] lr: 6.9366e-02 eta: 0:14:05 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.5067 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.5067 2022/11/28 18:57:38 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:57:38 - mmengine - INFO - Epoch(train) [6][2462/2462] lr: 6.9138e-02 eta: 0:14:03 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.3739 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3739 2022/11/28 18:57:38 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/11/28 18:57:40 - mmengine - INFO - Epoch(val) [6][100/398] eta: 0:00:04 time: 0.0144 data_time: 0.0056 memory: 262 2022/11/28 18:57:42 - mmengine - INFO - Epoch(val) [6][200/398] eta: 0:00:02 time: 0.0147 data_time: 0.0057 memory: 262 2022/11/28 18:57:43 - mmengine - INFO - Epoch(val) [6][300/398] eta: 0:00:01 time: 0.0145 data_time: 0.0056 memory: 262 2022/11/28 18:57:45 - mmengine - INFO - Epoch(val) [6][398/398] acc/top1: 0.7368 acc/top5: 0.9442 acc/mean1: 0.7574 2022/11/28 18:57:49 - mmengine - INFO - Epoch(train) [7][100/2462] lr: 6.8769e-02 eta: 0:14:00 time: 0.0343 data_time: 0.0059 memory: 1253 loss: 0.4109 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4109 2022/11/28 18:57:52 - mmengine - INFO - Epoch(train) [7][200/2462] lr: 6.8399e-02 eta: 0:13:56 time: 0.0340 data_time: 0.0060 memory: 1253 loss: 0.5475 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5475 2022/11/28 18:57:53 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:57:56 - mmengine - INFO - Epoch(train) [7][300/2462] lr: 6.8027e-02 eta: 0:13:53 time: 0.0340 data_time: 0.0061 memory: 1253 loss: 0.4371 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4371 2022/11/28 18:57:59 - mmengine - INFO - Epoch(train) [7][400/2462] lr: 6.7655e-02 eta: 0:13:50 time: 0.0341 data_time: 0.0060 memory: 1253 loss: 0.4068 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4068 2022/11/28 18:58:03 - mmengine - INFO - Epoch(train) [7][500/2462] lr: 6.7281e-02 eta: 0:13:46 time: 0.0339 data_time: 0.0061 memory: 1253 loss: 0.4727 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4727 2022/11/28 18:58:06 - mmengine - INFO - Epoch(train) [7][600/2462] lr: 6.6906e-02 eta: 0:13:43 time: 0.0338 data_time: 0.0060 memory: 1253 loss: 0.3684 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3684 2022/11/28 18:58:09 - mmengine - INFO - Epoch(train) [7][700/2462] lr: 6.6531e-02 eta: 0:13:39 time: 0.0341 data_time: 0.0059 memory: 1253 loss: 0.3887 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3887 2022/11/28 18:58:13 - mmengine - INFO - Epoch(train) [7][800/2462] lr: 6.6154e-02 eta: 0:13:36 time: 0.0350 data_time: 0.0062 memory: 1253 loss: 0.3136 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3136 2022/11/28 18:58:16 - mmengine - INFO - Epoch(train) [7][900/2462] lr: 6.5776e-02 eta: 0:13:32 time: 0.0340 data_time: 0.0063 memory: 1253 loss: 0.5002 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.5002 2022/11/28 18:58:20 - mmengine - INFO - Epoch(train) [7][1000/2462] lr: 6.5397e-02 eta: 0:13:29 time: 0.0334 data_time: 0.0060 memory: 1253 loss: 0.4637 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.4637 2022/11/28 18:58:23 - mmengine - INFO - Epoch(train) [7][1100/2462] lr: 6.5017e-02 eta: 0:13:26 time: 0.0342 data_time: 0.0060 memory: 1253 loss: 0.3972 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3972 2022/11/28 18:58:27 - mmengine - INFO - Epoch(train) [7][1200/2462] lr: 6.4636e-02 eta: 0:13:22 time: 0.0366 data_time: 0.0060 memory: 1253 loss: 0.3898 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3898 2022/11/28 18:58:28 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:58:30 - mmengine - INFO - Epoch(train) [7][1300/2462] lr: 6.4255e-02 eta: 0:13:19 time: 0.0339 data_time: 0.0061 memory: 1253 loss: 0.3936 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3936 2022/11/28 18:58:34 - mmengine - INFO - Epoch(train) [7][1400/2462] lr: 6.3872e-02 eta: 0:13:16 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.3616 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3616 2022/11/28 18:58:37 - mmengine - INFO - Epoch(train) [7][1500/2462] lr: 6.3488e-02 eta: 0:13:12 time: 0.0348 data_time: 0.0060 memory: 1253 loss: 0.3256 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3256 2022/11/28 18:58:41 - mmengine - INFO - Epoch(train) [7][1600/2462] lr: 6.3104e-02 eta: 0:13:09 time: 0.0354 data_time: 0.0065 memory: 1253 loss: 0.3440 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3440 2022/11/28 18:58:44 - mmengine - INFO - Epoch(train) [7][1700/2462] lr: 6.2719e-02 eta: 0:13:05 time: 0.0337 data_time: 0.0060 memory: 1253 loss: 0.3329 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3329 2022/11/28 18:58:48 - mmengine - INFO - Epoch(train) [7][1800/2462] lr: 6.2333e-02 eta: 0:13:02 time: 0.0345 data_time: 0.0060 memory: 1253 loss: 0.4902 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.4902 2022/11/28 18:58:51 - mmengine - INFO - Epoch(train) [7][1900/2462] lr: 6.1946e-02 eta: 0:12:59 time: 0.0353 data_time: 0.0060 memory: 1253 loss: 0.3692 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3692 2022/11/28 18:58:55 - mmengine - INFO - Epoch(train) [7][2000/2462] lr: 6.1558e-02 eta: 0:12:55 time: 0.0342 data_time: 0.0060 memory: 1253 loss: 0.3740 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.3740 2022/11/28 18:58:58 - mmengine - INFO - Epoch(train) [7][2100/2462] lr: 6.1170e-02 eta: 0:12:52 time: 0.0341 data_time: 0.0059 memory: 1253 loss: 0.4386 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4386 2022/11/28 18:59:01 - mmengine - INFO - Epoch(train) [7][2200/2462] lr: 6.0781e-02 eta: 0:12:48 time: 0.0346 data_time: 0.0065 memory: 1253 loss: 0.3693 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.3693 2022/11/28 18:59:02 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:59:05 - mmengine - INFO - Epoch(train) [7][2300/2462] lr: 6.0391e-02 eta: 0:12:45 time: 0.0339 data_time: 0.0060 memory: 1253 loss: 0.4165 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4165 2022/11/28 18:59:08 - mmengine - INFO - Epoch(train) [7][2400/2462] lr: 6.0001e-02 eta: 0:12:41 time: 0.0333 data_time: 0.0060 memory: 1253 loss: 0.4194 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4194 2022/11/28 18:59:10 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:59:10 - mmengine - INFO - Epoch(train) [7][2462/2462] lr: 5.9758e-02 eta: 0:12:39 time: 0.0337 data_time: 0.0062 memory: 1253 loss: 0.2866 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2866 2022/11/28 18:59:10 - mmengine - INFO - Saving checkpoint at 7 epochs 2022/11/28 18:59:12 - mmengine - INFO - Epoch(val) [7][100/398] eta: 0:00:04 time: 0.0145 data_time: 0.0056 memory: 262 2022/11/28 18:59:14 - mmengine - INFO - Epoch(val) [7][200/398] eta: 0:00:02 time: 0.0145 data_time: 0.0056 memory: 262 2022/11/28 18:59:15 - mmengine - INFO - Epoch(val) [7][300/398] eta: 0:00:01 time: 0.0145 data_time: 0.0056 memory: 262 2022/11/28 18:59:17 - mmengine - INFO - Epoch(val) [7][398/398] acc/top1: 0.7585 acc/top5: 0.9527 acc/mean1: 0.7774 2022/11/28 18:59:17 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_5.pth is removed 2022/11/28 18:59:18 - mmengine - INFO - The best checkpoint with 0.7585 acc/top1 at 7 epoch is saved to best_acc/top1_epoch_7.pth. 2022/11/28 18:59:21 - mmengine - INFO - Epoch(train) [8][100/2462] lr: 5.9367e-02 eta: 0:12:36 time: 0.0344 data_time: 0.0066 memory: 1253 loss: 0.4589 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4589 2022/11/28 18:59:25 - mmengine - INFO - Epoch(train) [8][200/2462] lr: 5.8975e-02 eta: 0:12:33 time: 0.0340 data_time: 0.0060 memory: 1253 loss: 0.3658 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3658 2022/11/28 18:59:28 - mmengine - INFO - Epoch(train) [8][300/2462] lr: 5.8582e-02 eta: 0:12:29 time: 0.0341 data_time: 0.0060 memory: 1253 loss: 0.4360 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4360 2022/11/28 18:59:32 - mmengine - INFO - Epoch(train) [8][400/2462] lr: 5.8189e-02 eta: 0:12:26 time: 0.0360 data_time: 0.0061 memory: 1253 loss: 0.4900 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4900 2022/11/28 18:59:35 - mmengine - INFO - Epoch(train) [8][500/2462] lr: 5.7796e-02 eta: 0:12:22 time: 0.0368 data_time: 0.0060 memory: 1253 loss: 0.4834 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.4834 2022/11/28 18:59:39 - mmengine - INFO - Epoch(train) [8][600/2462] lr: 5.7402e-02 eta: 0:12:19 time: 0.0353 data_time: 0.0064 memory: 1253 loss: 0.3830 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3830 2022/11/28 18:59:42 - mmengine - INFO - Epoch(train) [8][700/2462] lr: 5.7007e-02 eta: 0:12:16 time: 0.0351 data_time: 0.0060 memory: 1253 loss: 0.3564 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3564 2022/11/28 18:59:45 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 18:59:46 - mmengine - INFO - Epoch(train) [8][800/2462] lr: 5.6612e-02 eta: 0:12:12 time: 0.0337 data_time: 0.0060 memory: 1253 loss: 0.4006 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4006 2022/11/28 18:59:49 - mmengine - INFO - Epoch(train) [8][900/2462] lr: 5.6216e-02 eta: 0:12:09 time: 0.0342 data_time: 0.0060 memory: 1253 loss: 0.3291 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3291 2022/11/28 18:59:53 - mmengine - INFO - Epoch(train) [8][1000/2462] lr: 5.5821e-02 eta: 0:12:06 time: 0.0369 data_time: 0.0060 memory: 1253 loss: 0.3610 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3610 2022/11/28 18:59:56 - mmengine - INFO - Epoch(train) [8][1100/2462] lr: 5.5424e-02 eta: 0:12:03 time: 0.0352 data_time: 0.0060 memory: 1253 loss: 0.3678 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3678 2022/11/28 19:00:00 - mmengine - INFO - Epoch(train) [8][1200/2462] lr: 5.5028e-02 eta: 0:11:59 time: 0.0344 data_time: 0.0060 memory: 1253 loss: 0.4308 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4308 2022/11/28 19:00:03 - mmengine - INFO - Epoch(train) [8][1300/2462] lr: 5.4631e-02 eta: 0:11:56 time: 0.0361 data_time: 0.0067 memory: 1253 loss: 0.3693 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3693 2022/11/28 19:00:07 - mmengine - INFO - Epoch(train) [8][1400/2462] lr: 5.4234e-02 eta: 0:11:52 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.3079 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3079 2022/11/28 19:00:10 - mmengine - INFO - Epoch(train) [8][1500/2462] lr: 5.3836e-02 eta: 0:11:49 time: 0.0344 data_time: 0.0060 memory: 1253 loss: 0.2741 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.2741 2022/11/28 19:00:14 - mmengine - INFO - Epoch(train) [8][1600/2462] lr: 5.3439e-02 eta: 0:11:45 time: 0.0340 data_time: 0.0059 memory: 1253 loss: 0.3132 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3132 2022/11/28 19:00:17 - mmengine - INFO - Epoch(train) [8][1700/2462] lr: 5.3041e-02 eta: 0:11:42 time: 0.0351 data_time: 0.0061 memory: 1253 loss: 0.2905 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2905 2022/11/28 19:00:20 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:00:21 - mmengine - INFO - Epoch(train) [8][1800/2462] lr: 5.2643e-02 eta: 0:11:39 time: 0.0340 data_time: 0.0060 memory: 1253 loss: 0.3166 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3166 2022/11/28 19:00:24 - mmengine - INFO - Epoch(train) [8][1900/2462] lr: 5.2244e-02 eta: 0:11:35 time: 0.0352 data_time: 0.0061 memory: 1253 loss: 0.2574 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2574 2022/11/28 19:00:27 - mmengine - INFO - Epoch(train) [8][2000/2462] lr: 5.1846e-02 eta: 0:11:32 time: 0.0336 data_time: 0.0060 memory: 1253 loss: 0.2853 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2853 2022/11/28 19:00:31 - mmengine - INFO - Epoch(train) [8][2100/2462] lr: 5.1447e-02 eta: 0:11:28 time: 0.0337 data_time: 0.0060 memory: 1253 loss: 0.2977 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2977 2022/11/28 19:00:34 - mmengine - INFO - Epoch(train) [8][2200/2462] lr: 5.1049e-02 eta: 0:11:25 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.3480 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.3480 2022/11/28 19:00:38 - mmengine - INFO - Epoch(train) [8][2300/2462] lr: 5.0650e-02 eta: 0:11:21 time: 0.0339 data_time: 0.0060 memory: 1253 loss: 0.3119 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3119 2022/11/28 19:00:41 - mmengine - INFO - Epoch(train) [8][2400/2462] lr: 5.0251e-02 eta: 0:11:18 time: 0.0366 data_time: 0.0061 memory: 1253 loss: 0.3648 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3648 2022/11/28 19:00:43 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:00:43 - mmengine - INFO - Epoch(train) [8][2462/2462] lr: 5.0004e-02 eta: 0:11:16 time: 0.0338 data_time: 0.0061 memory: 1253 loss: 0.3194 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3194 2022/11/28 19:00:43 - mmengine - INFO - Saving checkpoint at 8 epochs 2022/11/28 19:00:45 - mmengine - INFO - Epoch(val) [8][100/398] eta: 0:00:04 time: 0.0146 data_time: 0.0056 memory: 262 2022/11/28 19:00:47 - mmengine - INFO - Epoch(val) [8][200/398] eta: 0:00:02 time: 0.0149 data_time: 0.0058 memory: 262 2022/11/28 19:00:48 - mmengine - INFO - Epoch(val) [8][300/398] eta: 0:00:01 time: 0.0146 data_time: 0.0056 memory: 262 2022/11/28 19:00:50 - mmengine - INFO - Epoch(val) [8][398/398] acc/top1: 0.7713 acc/top5: 0.9558 acc/mean1: 0.7889 2022/11/28 19:00:50 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_7.pth is removed 2022/11/28 19:00:51 - mmengine - INFO - The best checkpoint with 0.7713 acc/top1 at 8 epoch is saved to best_acc/top1_epoch_8.pth. 2022/11/28 19:00:54 - mmengine - INFO - Epoch(train) [9][100/2462] lr: 4.9605e-02 eta: 0:11:12 time: 0.0348 data_time: 0.0061 memory: 1253 loss: 0.3266 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3266 2022/11/28 19:00:58 - mmengine - INFO - Epoch(train) [9][200/2462] lr: 4.9207e-02 eta: 0:11:09 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.3148 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3148 2022/11/28 19:01:01 - mmengine - INFO - Epoch(train) [9][300/2462] lr: 4.8808e-02 eta: 0:11:06 time: 0.0354 data_time: 0.0062 memory: 1253 loss: 0.3163 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.3163 2022/11/28 19:01:01 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:01:05 - mmengine - INFO - Epoch(train) [9][400/2462] lr: 4.8409e-02 eta: 0:11:02 time: 0.0357 data_time: 0.0061 memory: 1253 loss: 0.2303 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.2303 2022/11/28 19:01:08 - mmengine - INFO - Epoch(train) [9][500/2462] lr: 4.8011e-02 eta: 0:10:59 time: 0.0350 data_time: 0.0061 memory: 1253 loss: 0.3285 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3285 2022/11/28 19:01:12 - mmengine - INFO - Epoch(train) [9][600/2462] lr: 4.7612e-02 eta: 0:10:56 time: 0.0365 data_time: 0.0061 memory: 1253 loss: 0.2490 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2490 2022/11/28 19:01:16 - mmengine - INFO - Epoch(train) [9][700/2462] lr: 4.7214e-02 eta: 0:10:52 time: 0.0351 data_time: 0.0068 memory: 1253 loss: 0.2671 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2671 2022/11/28 19:01:19 - mmengine - INFO - Epoch(train) [9][800/2462] lr: 4.6816e-02 eta: 0:10:49 time: 0.0337 data_time: 0.0061 memory: 1253 loss: 0.3182 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3182 2022/11/28 19:01:22 - mmengine - INFO - Epoch(train) [9][900/2462] lr: 4.6418e-02 eta: 0:10:45 time: 0.0337 data_time: 0.0061 memory: 1253 loss: 0.2776 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2776 2022/11/28 19:01:26 - mmengine - INFO - Epoch(train) [9][1000/2462] lr: 4.6021e-02 eta: 0:10:42 time: 0.0336 data_time: 0.0060 memory: 1253 loss: 0.2463 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2463 2022/11/28 19:01:29 - mmengine - INFO - Epoch(train) [9][1100/2462] lr: 4.5623e-02 eta: 0:10:38 time: 0.0336 data_time: 0.0060 memory: 1253 loss: 0.3132 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.3132 2022/11/28 19:01:33 - mmengine - INFO - Epoch(train) [9][1200/2462] lr: 4.5226e-02 eta: 0:10:35 time: 0.0356 data_time: 0.0062 memory: 1253 loss: 0.2529 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2529 2022/11/28 19:01:36 - mmengine - INFO - Epoch(train) [9][1300/2462] lr: 4.4829e-02 eta: 0:10:32 time: 0.0352 data_time: 0.0060 memory: 1253 loss: 0.3798 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3798 2022/11/28 19:01:36 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:01:40 - mmengine - INFO - Epoch(train) [9][1400/2462] lr: 4.4433e-02 eta: 0:10:28 time: 0.0359 data_time: 0.0061 memory: 1253 loss: 0.3627 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3627 2022/11/28 19:01:43 - mmengine - INFO - Epoch(train) [9][1500/2462] lr: 4.4037e-02 eta: 0:10:25 time: 0.0336 data_time: 0.0061 memory: 1253 loss: 0.2641 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2641 2022/11/28 19:01:47 - mmengine - INFO - Epoch(train) [9][1600/2462] lr: 4.3641e-02 eta: 0:10:22 time: 0.0345 data_time: 0.0061 memory: 1253 loss: 0.3159 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3159 2022/11/28 19:01:50 - mmengine - INFO - Epoch(train) [9][1700/2462] lr: 4.3246e-02 eta: 0:10:18 time: 0.0340 data_time: 0.0061 memory: 1253 loss: 0.2756 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2756 2022/11/28 19:01:53 - mmengine - INFO - Epoch(train) [9][1800/2462] lr: 4.2851e-02 eta: 0:10:15 time: 0.0337 data_time: 0.0061 memory: 1253 loss: 0.2963 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2963 2022/11/28 19:01:57 - mmengine - INFO - Epoch(train) [9][1900/2462] lr: 4.2456e-02 eta: 0:10:11 time: 0.0347 data_time: 0.0059 memory: 1253 loss: 0.2768 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2768 2022/11/28 19:02:00 - mmengine - INFO - Epoch(train) [9][2000/2462] lr: 4.2063e-02 eta: 0:10:08 time: 0.0347 data_time: 0.0060 memory: 1253 loss: 0.2389 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2389 2022/11/28 19:02:04 - mmengine - INFO - Epoch(train) [9][2100/2462] lr: 4.1669e-02 eta: 0:10:04 time: 0.0350 data_time: 0.0061 memory: 1253 loss: 0.3442 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3442 2022/11/28 19:02:07 - mmengine - INFO - Epoch(train) [9][2200/2462] lr: 4.1276e-02 eta: 0:10:01 time: 0.0344 data_time: 0.0070 memory: 1253 loss: 0.2822 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2822 2022/11/28 19:02:11 - mmengine - INFO - Epoch(train) [9][2300/2462] lr: 4.0884e-02 eta: 0:09:57 time: 0.0346 data_time: 0.0068 memory: 1253 loss: 0.2762 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2762 2022/11/28 19:02:11 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:02:14 - mmengine - INFO - Epoch(train) [9][2400/2462] lr: 4.0492e-02 eta: 0:09:54 time: 0.0340 data_time: 0.0061 memory: 1253 loss: 0.2198 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2198 2022/11/28 19:02:16 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:02:16 - mmengine - INFO - Epoch(train) [9][2462/2462] lr: 4.0249e-02 eta: 0:09:52 time: 0.0338 data_time: 0.0061 memory: 1253 loss: 0.2663 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2663 2022/11/28 19:02:16 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/11/28 19:02:18 - mmengine - INFO - Epoch(val) [9][100/398] eta: 0:00:04 time: 0.0148 data_time: 0.0056 memory: 262 2022/11/28 19:02:20 - mmengine - INFO - Epoch(val) [9][200/398] eta: 0:00:03 time: 0.0149 data_time: 0.0057 memory: 262 2022/11/28 19:02:21 - mmengine - INFO - Epoch(val) [9][300/398] eta: 0:00:01 time: 0.0148 data_time: 0.0058 memory: 262 2022/11/28 19:02:24 - mmengine - INFO - Epoch(val) [9][398/398] acc/top1: 0.7753 acc/top5: 0.9550 acc/mean1: 0.7912 2022/11/28 19:02:24 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_8.pth is removed 2022/11/28 19:02:24 - mmengine - INFO - The best checkpoint with 0.7753 acc/top1 at 9 epoch is saved to best_acc/top1_epoch_9.pth. 2022/11/28 19:02:27 - mmengine - INFO - Epoch(train) [10][100/2462] lr: 3.9859e-02 eta: 0:09:48 time: 0.0338 data_time: 0.0060 memory: 1253 loss: 0.3292 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3292 2022/11/28 19:02:31 - mmengine - INFO - Epoch(train) [10][200/2462] lr: 3.9468e-02 eta: 0:09:45 time: 0.0340 data_time: 0.0061 memory: 1253 loss: 0.2342 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2342 2022/11/28 19:02:34 - mmengine - INFO - Epoch(train) [10][300/2462] lr: 3.9079e-02 eta: 0:09:42 time: 0.0359 data_time: 0.0061 memory: 1253 loss: 0.3072 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3072 2022/11/28 19:02:38 - mmengine - INFO - Epoch(train) [10][400/2462] lr: 3.8690e-02 eta: 0:09:38 time: 0.0339 data_time: 0.0060 memory: 1253 loss: 0.3917 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3917 2022/11/28 19:02:41 - mmengine - INFO - Epoch(train) [10][500/2462] lr: 3.8302e-02 eta: 0:09:35 time: 0.0340 data_time: 0.0062 memory: 1253 loss: 0.3040 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3040 2022/11/28 19:02:45 - mmengine - INFO - Epoch(train) [10][600/2462] lr: 3.7915e-02 eta: 0:09:31 time: 0.0337 data_time: 0.0061 memory: 1253 loss: 0.2276 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2276 2022/11/28 19:02:48 - mmengine - INFO - Epoch(train) [10][700/2462] lr: 3.7528e-02 eta: 0:09:28 time: 0.0348 data_time: 0.0060 memory: 1253 loss: 0.1957 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1957 2022/11/28 19:02:52 - mmengine - INFO - Epoch(train) [10][800/2462] lr: 3.7143e-02 eta: 0:09:24 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.2315 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2315 2022/11/28 19:02:53 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:02:55 - mmengine - INFO - Epoch(train) [10][900/2462] lr: 3.6758e-02 eta: 0:09:21 time: 0.0356 data_time: 0.0060 memory: 1253 loss: 0.2300 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2300 2022/11/28 19:02:59 - mmengine - INFO - Epoch(train) [10][1000/2462] lr: 3.6373e-02 eta: 0:09:18 time: 0.0340 data_time: 0.0060 memory: 1253 loss: 0.2257 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2257 2022/11/28 19:03:02 - mmengine - INFO - Epoch(train) [10][1100/2462] lr: 3.5990e-02 eta: 0:09:14 time: 0.0348 data_time: 0.0061 memory: 1253 loss: 0.2634 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2634 2022/11/28 19:03:06 - mmengine - INFO - Epoch(train) [10][1200/2462] lr: 3.5608e-02 eta: 0:09:11 time: 0.0341 data_time: 0.0060 memory: 1253 loss: 0.2779 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.2779 2022/11/28 19:03:09 - mmengine - INFO - Epoch(train) [10][1300/2462] lr: 3.5226e-02 eta: 0:09:07 time: 0.0357 data_time: 0.0064 memory: 1253 loss: 0.2474 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.2474 2022/11/28 19:03:13 - mmengine - INFO - Epoch(train) [10][1400/2462] lr: 3.4846e-02 eta: 0:09:04 time: 0.0345 data_time: 0.0060 memory: 1253 loss: 0.2381 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2381 2022/11/28 19:03:16 - mmengine - INFO - Epoch(train) [10][1500/2462] lr: 3.4466e-02 eta: 0:09:01 time: 0.0348 data_time: 0.0060 memory: 1253 loss: 0.2497 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2497 2022/11/28 19:03:19 - mmengine - INFO - Epoch(train) [10][1600/2462] lr: 3.4088e-02 eta: 0:08:57 time: 0.0340 data_time: 0.0060 memory: 1253 loss: 0.2036 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2036 2022/11/28 19:03:23 - mmengine - INFO - Epoch(train) [10][1700/2462] lr: 3.3710e-02 eta: 0:08:54 time: 0.0346 data_time: 0.0060 memory: 1253 loss: 0.2116 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2116 2022/11/28 19:03:26 - mmengine - INFO - Epoch(train) [10][1800/2462] lr: 3.3334e-02 eta: 0:08:50 time: 0.0346 data_time: 0.0061 memory: 1253 loss: 0.1867 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1867 2022/11/28 19:03:28 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:03:30 - mmengine - INFO - Epoch(train) [10][1900/2462] lr: 3.2959e-02 eta: 0:08:47 time: 0.0348 data_time: 0.0060 memory: 1253 loss: 0.2315 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2315 2022/11/28 19:03:33 - mmengine - INFO - Epoch(train) [10][2000/2462] lr: 3.2584e-02 eta: 0:08:43 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.2150 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2150 2022/11/28 19:03:37 - mmengine - INFO - Epoch(train) [10][2100/2462] lr: 3.2211e-02 eta: 0:08:40 time: 0.0347 data_time: 0.0061 memory: 1253 loss: 0.2063 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2063 2022/11/28 19:03:40 - mmengine - INFO - Epoch(train) [10][2200/2462] lr: 3.1839e-02 eta: 0:08:37 time: 0.0341 data_time: 0.0060 memory: 1253 loss: 0.2300 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2300 2022/11/28 19:03:44 - mmengine - INFO - Epoch(train) [10][2300/2462] lr: 3.1468e-02 eta: 0:08:33 time: 0.0338 data_time: 0.0060 memory: 1253 loss: 0.2087 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2087 2022/11/28 19:03:47 - mmengine - INFO - Epoch(train) [10][2400/2462] lr: 3.1098e-02 eta: 0:08:30 time: 0.0349 data_time: 0.0068 memory: 1253 loss: 0.2012 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2012 2022/11/28 19:03:49 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:03:49 - mmengine - INFO - Epoch(train) [10][2462/2462] lr: 3.0870e-02 eta: 0:08:28 time: 0.0338 data_time: 0.0061 memory: 1253 loss: 0.2078 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.2078 2022/11/28 19:03:49 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/11/28 19:03:51 - mmengine - INFO - Epoch(val) [10][100/398] eta: 0:00:04 time: 0.0146 data_time: 0.0056 memory: 262 2022/11/28 19:03:53 - mmengine - INFO - Epoch(val) [10][200/398] eta: 0:00:02 time: 0.0149 data_time: 0.0057 memory: 262 2022/11/28 19:03:54 - mmengine - INFO - Epoch(val) [10][300/398] eta: 0:00:01 time: 0.0145 data_time: 0.0056 memory: 262 2022/11/28 19:03:57 - mmengine - INFO - Epoch(val) [10][398/398] acc/top1: 0.7775 acc/top5: 0.9563 acc/mean1: 0.7990 2022/11/28 19:03:57 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_9.pth is removed 2022/11/28 19:03:57 - mmengine - INFO - The best checkpoint with 0.7775 acc/top1 at 10 epoch is saved to best_acc/top1_epoch_10.pth. 2022/11/28 19:04:00 - mmengine - INFO - Epoch(train) [11][100/2462] lr: 3.0502e-02 eta: 0:08:24 time: 0.0342 data_time: 0.0061 memory: 1253 loss: 0.1736 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.1736 2022/11/28 19:04:04 - mmengine - INFO - Epoch(train) [11][200/2462] lr: 3.0135e-02 eta: 0:08:21 time: 0.0344 data_time: 0.0061 memory: 1253 loss: 0.1862 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.1862 2022/11/28 19:04:07 - mmengine - INFO - Epoch(train) [11][300/2462] lr: 2.9770e-02 eta: 0:08:17 time: 0.0342 data_time: 0.0061 memory: 1253 loss: 0.1957 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1957 2022/11/28 19:04:10 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:04:11 - mmengine - INFO - Epoch(train) [11][400/2462] lr: 2.9406e-02 eta: 0:08:14 time: 0.0355 data_time: 0.0067 memory: 1253 loss: 0.1951 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1951 2022/11/28 19:04:15 - mmengine - INFO - Epoch(train) [11][500/2462] lr: 2.9043e-02 eta: 0:08:11 time: 0.0355 data_time: 0.0060 memory: 1253 loss: 0.2125 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.2125 2022/11/28 19:04:18 - mmengine - INFO - Epoch(train) [11][600/2462] lr: 2.8682e-02 eta: 0:08:07 time: 0.0350 data_time: 0.0061 memory: 1253 loss: 0.2094 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2094 2022/11/28 19:04:21 - mmengine - INFO - Epoch(train) [11][700/2462] lr: 2.8322e-02 eta: 0:08:04 time: 0.0337 data_time: 0.0060 memory: 1253 loss: 0.1905 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1905 2022/11/28 19:04:25 - mmengine - INFO - Epoch(train) [11][800/2462] lr: 2.7963e-02 eta: 0:08:00 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.1803 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1803 2022/11/28 19:04:28 - mmengine - INFO - Epoch(train) [11][900/2462] lr: 2.7606e-02 eta: 0:07:57 time: 0.0347 data_time: 0.0061 memory: 1253 loss: 0.1976 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.1976 2022/11/28 19:04:32 - mmengine - INFO - Epoch(train) [11][1000/2462] lr: 2.7250e-02 eta: 0:07:53 time: 0.0345 data_time: 0.0061 memory: 1253 loss: 0.1563 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1563 2022/11/28 19:04:35 - mmengine - INFO - Epoch(train) [11][1100/2462] lr: 2.6896e-02 eta: 0:07:50 time: 0.0355 data_time: 0.0061 memory: 1253 loss: 0.2026 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2026 2022/11/28 19:04:39 - mmengine - INFO - Epoch(train) [11][1200/2462] lr: 2.6543e-02 eta: 0:07:47 time: 0.0347 data_time: 0.0067 memory: 1253 loss: 0.1655 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1655 2022/11/28 19:04:42 - mmengine - INFO - Epoch(train) [11][1300/2462] lr: 2.6191e-02 eta: 0:07:43 time: 0.0354 data_time: 0.0060 memory: 1253 loss: 0.1837 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1837 2022/11/28 19:04:45 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:04:46 - mmengine - INFO - Epoch(train) [11][1400/2462] lr: 2.5841e-02 eta: 0:07:40 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.1371 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1371 2022/11/28 19:04:49 - mmengine - INFO - Epoch(train) [11][1500/2462] lr: 2.5493e-02 eta: 0:07:36 time: 0.0345 data_time: 0.0061 memory: 1253 loss: 0.1655 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1655 2022/11/28 19:04:53 - mmengine - INFO - Epoch(train) [11][1600/2462] lr: 2.5146e-02 eta: 0:07:33 time: 0.0370 data_time: 0.0060 memory: 1253 loss: 0.1326 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1326 2022/11/28 19:04:56 - mmengine - INFO - Epoch(train) [11][1700/2462] lr: 2.4801e-02 eta: 0:07:30 time: 0.0347 data_time: 0.0061 memory: 1253 loss: 0.1748 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1748 2022/11/28 19:05:00 - mmengine - INFO - Epoch(train) [11][1800/2462] lr: 2.4458e-02 eta: 0:07:26 time: 0.0346 data_time: 0.0061 memory: 1253 loss: 0.2351 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2351 2022/11/28 19:05:03 - mmengine - INFO - Epoch(train) [11][1900/2462] lr: 2.4116e-02 eta: 0:07:23 time: 0.0340 data_time: 0.0060 memory: 1253 loss: 0.1896 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1896 2022/11/28 19:05:07 - mmengine - INFO - Epoch(train) [11][2000/2462] lr: 2.3775e-02 eta: 0:07:19 time: 0.0346 data_time: 0.0061 memory: 1253 loss: 0.1692 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1692 2022/11/28 19:05:10 - mmengine - INFO - Epoch(train) [11][2100/2462] lr: 2.3437e-02 eta: 0:07:16 time: 0.0352 data_time: 0.0061 memory: 1253 loss: 0.1705 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1705 2022/11/28 19:05:14 - mmengine - INFO - Epoch(train) [11][2200/2462] lr: 2.3100e-02 eta: 0:07:12 time: 0.0361 data_time: 0.0061 memory: 1253 loss: 0.1546 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1546 2022/11/28 19:05:18 - mmengine - INFO - Epoch(train) [11][2300/2462] lr: 2.2764e-02 eta: 0:07:09 time: 0.0342 data_time: 0.0061 memory: 1253 loss: 0.1689 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1689 2022/11/28 19:05:20 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:05:21 - mmengine - INFO - Epoch(train) [11][2400/2462] lr: 2.2431e-02 eta: 0:07:06 time: 0.0348 data_time: 0.0062 memory: 1253 loss: 0.1456 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1456 2022/11/28 19:05:23 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:05:23 - mmengine - INFO - Epoch(train) [11][2462/2462] lr: 2.2225e-02 eta: 0:07:04 time: 0.0349 data_time: 0.0062 memory: 1253 loss: 0.1669 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1669 2022/11/28 19:05:23 - mmengine - INFO - Saving checkpoint at 11 epochs 2022/11/28 19:05:25 - mmengine - INFO - Epoch(val) [11][100/398] eta: 0:00:04 time: 0.0149 data_time: 0.0058 memory: 262 2022/11/28 19:05:27 - mmengine - INFO - Epoch(val) [11][200/398] eta: 0:00:02 time: 0.0147 data_time: 0.0056 memory: 262 2022/11/28 19:05:28 - mmengine - INFO - Epoch(val) [11][300/398] eta: 0:00:01 time: 0.0148 data_time: 0.0057 memory: 262 2022/11/28 19:05:30 - mmengine - INFO - Epoch(val) [11][398/398] acc/top1: 0.7929 acc/top5: 0.9591 acc/mean1: 0.8155 2022/11/28 19:05:31 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_10.pth is removed 2022/11/28 19:05:31 - mmengine - INFO - The best checkpoint with 0.7929 acc/top1 at 11 epoch is saved to best_acc/top1_epoch_11.pth. 2022/11/28 19:05:34 - mmengine - INFO - Epoch(train) [12][100/2462] lr: 2.1894e-02 eta: 0:07:00 time: 0.0350 data_time: 0.0061 memory: 1253 loss: 0.1007 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1007 2022/11/28 19:05:38 - mmengine - INFO - Epoch(train) [12][200/2462] lr: 2.1565e-02 eta: 0:06:57 time: 0.0356 data_time: 0.0061 memory: 1253 loss: 0.1304 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1304 2022/11/28 19:05:42 - mmengine - INFO - Epoch(train) [12][300/2462] lr: 2.1238e-02 eta: 0:06:53 time: 0.0361 data_time: 0.0068 memory: 1253 loss: 0.1338 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1338 2022/11/28 19:05:45 - mmengine - INFO - Epoch(train) [12][400/2462] lr: 2.0913e-02 eta: 0:06:50 time: 0.0349 data_time: 0.0061 memory: 1253 loss: 0.1002 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1002 2022/11/28 19:05:49 - mmengine - INFO - Epoch(train) [12][500/2462] lr: 2.0589e-02 eta: 0:06:47 time: 0.0352 data_time: 0.0069 memory: 1253 loss: 0.1358 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1358 2022/11/28 19:05:52 - mmengine - INFO - Epoch(train) [12][600/2462] lr: 2.0268e-02 eta: 0:06:43 time: 0.0356 data_time: 0.0061 memory: 1253 loss: 0.1260 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1260 2022/11/28 19:05:56 - mmengine - INFO - Epoch(train) [12][700/2462] lr: 1.9948e-02 eta: 0:06:40 time: 0.0376 data_time: 0.0062 memory: 1253 loss: 0.1622 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1622 2022/11/28 19:05:59 - mmengine - INFO - Epoch(train) [12][800/2462] lr: 1.9631e-02 eta: 0:06:36 time: 0.0343 data_time: 0.0062 memory: 1253 loss: 0.1289 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1289 2022/11/28 19:06:03 - mmengine - INFO - Epoch(train) [12][900/2462] lr: 1.9315e-02 eta: 0:06:33 time: 0.0350 data_time: 0.0062 memory: 1253 loss: 0.1255 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1255 2022/11/28 19:06:04 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:06:07 - mmengine - INFO - Epoch(train) [12][1000/2462] lr: 1.9001e-02 eta: 0:06:30 time: 0.0356 data_time: 0.0068 memory: 1253 loss: 0.1399 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1399 2022/11/28 19:06:10 - mmengine - INFO - Epoch(train) [12][1100/2462] lr: 1.8689e-02 eta: 0:06:26 time: 0.0351 data_time: 0.0062 memory: 1253 loss: 0.0886 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0886 2022/11/28 19:06:14 - mmengine - INFO - Epoch(train) [12][1200/2462] lr: 1.8379e-02 eta: 0:06:23 time: 0.0348 data_time: 0.0062 memory: 1253 loss: 0.0777 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0777 2022/11/28 19:06:17 - mmengine - INFO - Epoch(train) [12][1300/2462] lr: 1.8071e-02 eta: 0:06:19 time: 0.0355 data_time: 0.0062 memory: 1253 loss: 0.1120 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1120 2022/11/28 19:06:21 - mmengine - INFO - Epoch(train) [12][1400/2462] lr: 1.7765e-02 eta: 0:06:16 time: 0.0344 data_time: 0.0062 memory: 1253 loss: 0.1073 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1073 2022/11/28 19:06:24 - mmengine - INFO - Epoch(train) [12][1500/2462] lr: 1.7462e-02 eta: 0:06:13 time: 0.0354 data_time: 0.0062 memory: 1253 loss: 0.1038 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1038 2022/11/28 19:06:28 - mmengine - INFO - Epoch(train) [12][1600/2462] lr: 1.7160e-02 eta: 0:06:09 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.0855 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0855 2022/11/28 19:06:31 - mmengine - INFO - Epoch(train) [12][1700/2462] lr: 1.6860e-02 eta: 0:06:06 time: 0.0362 data_time: 0.0062 memory: 1253 loss: 0.1111 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1111 2022/11/28 19:06:35 - mmengine - INFO - Epoch(train) [12][1800/2462] lr: 1.6563e-02 eta: 0:06:02 time: 0.0366 data_time: 0.0061 memory: 1253 loss: 0.1027 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1027 2022/11/28 19:06:39 - mmengine - INFO - Epoch(train) [12][1900/2462] lr: 1.6267e-02 eta: 0:05:59 time: 0.0369 data_time: 0.0062 memory: 1253 loss: 0.0872 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0872 2022/11/28 19:06:39 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:06:42 - mmengine - INFO - Epoch(train) [12][2000/2462] lr: 1.5974e-02 eta: 0:05:55 time: 0.0358 data_time: 0.0062 memory: 1253 loss: 0.0763 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0763 2022/11/28 19:06:46 - mmengine - INFO - Epoch(train) [12][2100/2462] lr: 1.5683e-02 eta: 0:05:52 time: 0.0361 data_time: 0.0062 memory: 1253 loss: 0.0885 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0885 2022/11/28 19:06:49 - mmengine - INFO - Epoch(train) [12][2200/2462] lr: 1.5394e-02 eta: 0:05:49 time: 0.0361 data_time: 0.0065 memory: 1253 loss: 0.1105 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1105 2022/11/28 19:06:53 - mmengine - INFO - Epoch(train) [12][2300/2462] lr: 1.5107e-02 eta: 0:05:45 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.0630 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0630 2022/11/28 19:06:57 - mmengine - INFO - Epoch(train) [12][2400/2462] lr: 1.4823e-02 eta: 0:05:42 time: 0.0351 data_time: 0.0064 memory: 1253 loss: 0.0957 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0957 2022/11/28 19:06:59 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:06:59 - mmengine - INFO - Epoch(train) [12][2462/2462] lr: 1.4647e-02 eta: 0:05:40 time: 0.0348 data_time: 0.0063 memory: 1253 loss: 0.1431 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1431 2022/11/28 19:06:59 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/11/28 19:07:01 - mmengine - INFO - Epoch(val) [12][100/398] eta: 0:00:04 time: 0.0146 data_time: 0.0056 memory: 262 2022/11/28 19:07:02 - mmengine - INFO - Epoch(val) [12][200/398] eta: 0:00:02 time: 0.0148 data_time: 0.0057 memory: 262 2022/11/28 19:07:04 - mmengine - INFO - Epoch(val) [12][300/398] eta: 0:00:01 time: 0.0147 data_time: 0.0057 memory: 262 2022/11/28 19:07:06 - mmengine - INFO - Epoch(val) [12][398/398] acc/top1: 0.8036 acc/top5: 0.9640 acc/mean1: 0.8234 2022/11/28 19:07:06 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_11.pth is removed 2022/11/28 19:07:06 - mmengine - INFO - The best checkpoint with 0.8036 acc/top1 at 12 epoch is saved to best_acc/top1_epoch_12.pth. 2022/11/28 19:07:10 - mmengine - INFO - Epoch(train) [13][100/2462] lr: 1.4367e-02 eta: 0:05:36 time: 0.0354 data_time: 0.0061 memory: 1253 loss: 0.0708 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0708 2022/11/28 19:07:14 - mmengine - INFO - Epoch(train) [13][200/2462] lr: 1.4088e-02 eta: 0:05:33 time: 0.0357 data_time: 0.0062 memory: 1253 loss: 0.0684 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0684 2022/11/28 19:07:17 - mmengine - INFO - Epoch(train) [13][300/2462] lr: 1.3812e-02 eta: 0:05:29 time: 0.0352 data_time: 0.0062 memory: 1253 loss: 0.0750 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0750 2022/11/28 19:07:21 - mmengine - INFO - Epoch(train) [13][400/2462] lr: 1.3538e-02 eta: 0:05:26 time: 0.0362 data_time: 0.0061 memory: 1253 loss: 0.0927 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0927 2022/11/28 19:07:23 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:07:24 - mmengine - INFO - Epoch(train) [13][500/2462] lr: 1.3266e-02 eta: 0:05:23 time: 0.0377 data_time: 0.0068 memory: 1253 loss: 0.0691 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0691 2022/11/28 19:07:28 - mmengine - INFO - Epoch(train) [13][600/2462] lr: 1.2997e-02 eta: 0:05:19 time: 0.0363 data_time: 0.0061 memory: 1253 loss: 0.0977 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0977 2022/11/28 19:07:32 - mmengine - INFO - Epoch(train) [13][700/2462] lr: 1.2730e-02 eta: 0:05:16 time: 0.0368 data_time: 0.0061 memory: 1253 loss: 0.0503 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0503 2022/11/28 19:07:35 - mmengine - INFO - Epoch(train) [13][800/2462] lr: 1.2465e-02 eta: 0:05:12 time: 0.0349 data_time: 0.0061 memory: 1253 loss: 0.0541 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0541 2022/11/28 19:07:39 - mmengine - INFO - Epoch(train) [13][900/2462] lr: 1.2203e-02 eta: 0:05:09 time: 0.0354 data_time: 0.0063 memory: 1253 loss: 0.0844 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0844 2022/11/28 19:07:42 - mmengine - INFO - Epoch(train) [13][1000/2462] lr: 1.1943e-02 eta: 0:05:06 time: 0.0373 data_time: 0.0062 memory: 1253 loss: 0.0561 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0561 2022/11/28 19:07:46 - mmengine - INFO - Epoch(train) [13][1100/2462] lr: 1.1686e-02 eta: 0:05:02 time: 0.0350 data_time: 0.0062 memory: 1253 loss: 0.0469 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0469 2022/11/28 19:07:50 - mmengine - INFO - Epoch(train) [13][1200/2462] lr: 1.1431e-02 eta: 0:04:59 time: 0.0345 data_time: 0.0062 memory: 1253 loss: 0.0461 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0461 2022/11/28 19:07:53 - mmengine - INFO - Epoch(train) [13][1300/2462] lr: 1.1178e-02 eta: 0:04:55 time: 0.0345 data_time: 0.0061 memory: 1253 loss: 0.0718 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0718 2022/11/28 19:07:57 - mmengine - INFO - Epoch(train) [13][1400/2462] lr: 1.0928e-02 eta: 0:04:52 time: 0.0348 data_time: 0.0062 memory: 1253 loss: 0.0490 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0490 2022/11/28 19:07:59 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:08:00 - mmengine - INFO - Epoch(train) [13][1500/2462] lr: 1.0680e-02 eta: 0:04:48 time: 0.0349 data_time: 0.0062 memory: 1253 loss: 0.0278 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0278 2022/11/28 19:08:04 - mmengine - INFO - Epoch(train) [13][1600/2462] lr: 1.0435e-02 eta: 0:04:45 time: 0.0359 data_time: 0.0061 memory: 1253 loss: 0.0424 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0424 2022/11/28 19:08:07 - mmengine - INFO - Epoch(train) [13][1700/2462] lr: 1.0193e-02 eta: 0:04:42 time: 0.0348 data_time: 0.0062 memory: 1253 loss: 0.0391 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0391 2022/11/28 19:08:11 - mmengine - INFO - Epoch(train) [13][1800/2462] lr: 9.9527e-03 eta: 0:04:38 time: 0.0352 data_time: 0.0061 memory: 1253 loss: 0.0668 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0668 2022/11/28 19:08:14 - mmengine - INFO - Epoch(train) [13][1900/2462] lr: 9.7153e-03 eta: 0:04:35 time: 0.0350 data_time: 0.0062 memory: 1253 loss: 0.0473 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0473 2022/11/28 19:08:18 - mmengine - INFO - Epoch(train) [13][2000/2462] lr: 9.4804e-03 eta: 0:04:31 time: 0.0347 data_time: 0.0062 memory: 1253 loss: 0.0281 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0281 2022/11/28 19:08:22 - mmengine - INFO - Epoch(train) [13][2100/2462] lr: 9.2480e-03 eta: 0:04:28 time: 0.0362 data_time: 0.0061 memory: 1253 loss: 0.0412 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0412 2022/11/28 19:08:25 - mmengine - INFO - Epoch(train) [13][2200/2462] lr: 9.0183e-03 eta: 0:04:24 time: 0.0358 data_time: 0.0070 memory: 1253 loss: 0.0467 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.0467 2022/11/28 19:08:29 - mmengine - INFO - Epoch(train) [13][2300/2462] lr: 8.7911e-03 eta: 0:04:21 time: 0.0372 data_time: 0.0065 memory: 1253 loss: 0.0426 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0426 2022/11/28 19:08:32 - mmengine - INFO - Epoch(train) [13][2400/2462] lr: 8.5666e-03 eta: 0:04:17 time: 0.0358 data_time: 0.0064 memory: 1253 loss: 0.0359 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0359 2022/11/28 19:08:34 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:08:35 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:08:35 - mmengine - INFO - Epoch(train) [13][2462/2462] lr: 8.4287e-03 eta: 0:04:15 time: 0.0352 data_time: 0.0063 memory: 1253 loss: 0.0534 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0534 2022/11/28 19:08:35 - mmengine - INFO - Saving checkpoint at 13 epochs 2022/11/28 19:08:37 - mmengine - INFO - Epoch(val) [13][100/398] eta: 0:00:04 time: 0.0150 data_time: 0.0059 memory: 262 2022/11/28 19:08:38 - mmengine - INFO - Epoch(val) [13][200/398] eta: 0:00:03 time: 0.0150 data_time: 0.0058 memory: 262 2022/11/28 19:08:40 - mmengine - INFO - Epoch(val) [13][300/398] eta: 0:00:01 time: 0.0151 data_time: 0.0058 memory: 262 2022/11/28 19:08:42 - mmengine - INFO - Epoch(val) [13][398/398] acc/top1: 0.8072 acc/top5: 0.9611 acc/mean1: 0.8301 2022/11/28 19:08:42 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_12.pth is removed 2022/11/28 19:08:42 - mmengine - INFO - The best checkpoint with 0.8072 acc/top1 at 13 epoch is saved to best_acc/top1_epoch_13.pth. 2022/11/28 19:08:46 - mmengine - INFO - Epoch(train) [14][100/2462] lr: 8.2085e-03 eta: 0:04:12 time: 0.0368 data_time: 0.0062 memory: 1253 loss: 0.0398 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0398 2022/11/28 19:08:50 - mmengine - INFO - Epoch(train) [14][200/2462] lr: 7.9909e-03 eta: 0:04:08 time: 0.0350 data_time: 0.0062 memory: 1253 loss: 0.0258 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0258 2022/11/28 19:08:53 - mmengine - INFO - Epoch(train) [14][300/2462] lr: 7.7760e-03 eta: 0:04:05 time: 0.0350 data_time: 0.0063 memory: 1253 loss: 0.0379 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0379 2022/11/28 19:08:57 - mmengine - INFO - Epoch(train) [14][400/2462] lr: 7.5638e-03 eta: 0:04:02 time: 0.0360 data_time: 0.0062 memory: 1253 loss: 0.0237 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0237 2022/11/28 19:09:00 - mmengine - INFO - Epoch(train) [14][500/2462] lr: 7.3542e-03 eta: 0:03:58 time: 0.0356 data_time: 0.0062 memory: 1253 loss: 0.0341 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0341 2022/11/28 19:09:04 - mmengine - INFO - Epoch(train) [14][600/2462] lr: 7.1474e-03 eta: 0:03:55 time: 0.0350 data_time: 0.0067 memory: 1253 loss: 0.0272 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0272 2022/11/28 19:09:07 - mmengine - INFO - Epoch(train) [14][700/2462] lr: 6.9433e-03 eta: 0:03:51 time: 0.0364 data_time: 0.0063 memory: 1253 loss: 0.0288 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0288 2022/11/28 19:09:11 - mmengine - INFO - Epoch(train) [14][800/2462] lr: 6.7420e-03 eta: 0:03:48 time: 0.0353 data_time: 0.0062 memory: 1253 loss: 0.0227 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0227 2022/11/28 19:09:15 - mmengine - INFO - Epoch(train) [14][900/2462] lr: 6.5434e-03 eta: 0:03:44 time: 0.0352 data_time: 0.0067 memory: 1253 loss: 0.0280 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0280 2022/11/28 19:09:18 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:09:18 - mmengine - INFO - Epoch(train) [14][1000/2462] lr: 6.3476e-03 eta: 0:03:41 time: 0.0355 data_time: 0.0061 memory: 1253 loss: 0.0210 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0210 2022/11/28 19:09:22 - mmengine - INFO - Epoch(train) [14][1100/2462] lr: 6.1545e-03 eta: 0:03:37 time: 0.0349 data_time: 0.0062 memory: 1253 loss: 0.0261 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0261 2022/11/28 19:09:25 - mmengine - INFO - Epoch(train) [14][1200/2462] lr: 5.9642e-03 eta: 0:03:34 time: 0.0350 data_time: 0.0063 memory: 1253 loss: 0.0207 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0207 2022/11/28 19:09:29 - mmengine - INFO - Epoch(train) [14][1300/2462] lr: 5.7768e-03 eta: 0:03:31 time: 0.0358 data_time: 0.0061 memory: 1253 loss: 0.0247 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0247 2022/11/28 19:09:33 - mmengine - INFO - Epoch(train) [14][1400/2462] lr: 5.5921e-03 eta: 0:03:27 time: 0.0349 data_time: 0.0061 memory: 1253 loss: 0.0120 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0120 2022/11/28 19:09:36 - mmengine - INFO - Epoch(train) [14][1500/2462] lr: 5.4103e-03 eta: 0:03:24 time: 0.0351 data_time: 0.0061 memory: 1253 loss: 0.0189 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0189 2022/11/28 19:09:40 - mmengine - INFO - Epoch(train) [14][1600/2462] lr: 5.2313e-03 eta: 0:03:20 time: 0.0355 data_time: 0.0061 memory: 1253 loss: 0.0195 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0195 2022/11/28 19:09:43 - mmengine - INFO - Epoch(train) [14][1700/2462] lr: 5.0551e-03 eta: 0:03:17 time: 0.0351 data_time: 0.0062 memory: 1253 loss: 0.0277 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0277 2022/11/28 19:09:47 - mmengine - INFO - Epoch(train) [14][1800/2462] lr: 4.8818e-03 eta: 0:03:13 time: 0.0364 data_time: 0.0063 memory: 1253 loss: 0.0186 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0186 2022/11/28 19:09:50 - mmengine - INFO - Epoch(train) [14][1900/2462] lr: 4.7114e-03 eta: 0:03:10 time: 0.0373 data_time: 0.0061 memory: 1253 loss: 0.0166 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0166 2022/11/28 19:09:54 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:09:54 - mmengine - INFO - Epoch(train) [14][2000/2462] lr: 4.5439e-03 eta: 0:03:06 time: 0.0357 data_time: 0.0065 memory: 1253 loss: 0.0163 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0163 2022/11/28 19:09:58 - mmengine - INFO - Epoch(train) [14][2100/2462] lr: 4.3792e-03 eta: 0:03:03 time: 0.0353 data_time: 0.0063 memory: 1253 loss: 0.0183 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0183 2022/11/28 19:10:01 - mmengine - INFO - Epoch(train) [14][2200/2462] lr: 4.2175e-03 eta: 0:02:59 time: 0.0350 data_time: 0.0063 memory: 1253 loss: 0.0146 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0146 2022/11/28 19:10:05 - mmengine - INFO - Epoch(train) [14][2300/2462] lr: 4.0587e-03 eta: 0:02:56 time: 0.0359 data_time: 0.0062 memory: 1253 loss: 0.0141 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0141 2022/11/28 19:10:09 - mmengine - INFO - Epoch(train) [14][2400/2462] lr: 3.9027e-03 eta: 0:02:53 time: 0.0367 data_time: 0.0063 memory: 1253 loss: 0.0192 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0192 2022/11/28 19:10:11 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:10:11 - mmengine - INFO - Epoch(train) [14][2462/2462] lr: 3.8075e-03 eta: 0:02:50 time: 0.0365 data_time: 0.0064 memory: 1253 loss: 0.0180 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0180 2022/11/28 19:10:11 - mmengine - INFO - Saving checkpoint at 14 epochs 2022/11/28 19:10:13 - mmengine - INFO - Epoch(val) [14][100/398] eta: 0:00:04 time: 0.0150 data_time: 0.0057 memory: 262 2022/11/28 19:10:14 - mmengine - INFO - Epoch(val) [14][200/398] eta: 0:00:02 time: 0.0150 data_time: 0.0059 memory: 262 2022/11/28 19:10:16 - mmengine - INFO - Epoch(val) [14][300/398] eta: 0:00:01 time: 0.0150 data_time: 0.0058 memory: 262 2022/11/28 19:10:18 - mmengine - INFO - Epoch(val) [14][398/398] acc/top1: 0.8281 acc/top5: 0.9689 acc/mean1: 0.8467 2022/11/28 19:10:18 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_13.pth is removed 2022/11/28 19:10:19 - mmengine - INFO - The best checkpoint with 0.8281 acc/top1 at 14 epoch is saved to best_acc/top1_epoch_14.pth. 2022/11/28 19:10:22 - mmengine - INFO - Epoch(train) [15][100/2462] lr: 3.6564e-03 eta: 0:02:47 time: 0.0348 data_time: 0.0061 memory: 1253 loss: 0.0144 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0144 2022/11/28 19:10:26 - mmengine - INFO - Epoch(train) [15][200/2462] lr: 3.5082e-03 eta: 0:02:44 time: 0.0365 data_time: 0.0062 memory: 1253 loss: 0.0166 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0166 2022/11/28 19:10:29 - mmengine - INFO - Epoch(train) [15][300/2462] lr: 3.3629e-03 eta: 0:02:40 time: 0.0347 data_time: 0.0062 memory: 1253 loss: 0.0147 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0147 2022/11/28 19:10:33 - mmengine - INFO - Epoch(train) [15][400/2462] lr: 3.2206e-03 eta: 0:02:37 time: 0.0365 data_time: 0.0070 memory: 1253 loss: 0.0136 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0136 2022/11/28 19:10:37 - mmengine - INFO - Epoch(train) [15][500/2462] lr: 3.0813e-03 eta: 0:02:33 time: 0.0352 data_time: 0.0062 memory: 1253 loss: 0.0141 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0141 2022/11/28 19:10:38 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:10:40 - mmengine - INFO - Epoch(train) [15][600/2462] lr: 2.9450e-03 eta: 0:02:30 time: 0.0360 data_time: 0.0063 memory: 1253 loss: 0.0148 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0148 2022/11/28 19:10:44 - mmengine - INFO - Epoch(train) [15][700/2462] lr: 2.8117e-03 eta: 0:02:26 time: 0.0355 data_time: 0.0061 memory: 1253 loss: 0.0112 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0112 2022/11/28 19:10:47 - mmengine - INFO - Epoch(train) [15][800/2462] lr: 2.6813e-03 eta: 0:02:23 time: 0.0348 data_time: 0.0062 memory: 1253 loss: 0.0136 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0136 2022/11/28 19:10:51 - mmengine - INFO - Epoch(train) [15][900/2462] lr: 2.5540e-03 eta: 0:02:19 time: 0.0345 data_time: 0.0062 memory: 1253 loss: 0.0137 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0137 2022/11/28 19:10:54 - mmengine - INFO - Epoch(train) [15][1000/2462] lr: 2.4297e-03 eta: 0:02:16 time: 0.0352 data_time: 0.0073 memory: 1253 loss: 0.0313 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0313 2022/11/28 19:10:58 - mmengine - INFO - Epoch(train) [15][1100/2462] lr: 2.3084e-03 eta: 0:02:12 time: 0.0346 data_time: 0.0061 memory: 1253 loss: 0.0144 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0144 2022/11/28 19:11:02 - mmengine - INFO - Epoch(train) [15][1200/2462] lr: 2.1902e-03 eta: 0:02:09 time: 0.0366 data_time: 0.0062 memory: 1253 loss: 0.0159 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0159 2022/11/28 19:11:05 - mmengine - INFO - Epoch(train) [15][1300/2462] lr: 2.0750e-03 eta: 0:02:05 time: 0.0351 data_time: 0.0061 memory: 1253 loss: 0.0147 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0147 2022/11/28 19:11:09 - mmengine - INFO - Epoch(train) [15][1400/2462] lr: 1.9628e-03 eta: 0:02:02 time: 0.0356 data_time: 0.0062 memory: 1253 loss: 0.0107 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0107 2022/11/28 19:11:12 - mmengine - INFO - Epoch(train) [15][1500/2462] lr: 1.8537e-03 eta: 0:01:59 time: 0.0353 data_time: 0.0061 memory: 1253 loss: 0.0171 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0171 2022/11/28 19:11:13 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:11:16 - mmengine - INFO - Epoch(train) [15][1600/2462] lr: 1.7477e-03 eta: 0:01:55 time: 0.0359 data_time: 0.0062 memory: 1253 loss: 0.0160 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0160 2022/11/28 19:11:19 - mmengine - INFO - Epoch(train) [15][1700/2462] lr: 1.6447e-03 eta: 0:01:52 time: 0.0349 data_time: 0.0060 memory: 1253 loss: 0.0130 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0130 2022/11/28 19:11:23 - mmengine - INFO - Epoch(train) [15][1800/2462] lr: 1.5448e-03 eta: 0:01:48 time: 0.0349 data_time: 0.0061 memory: 1253 loss: 0.0115 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0115 2022/11/28 19:11:26 - mmengine - INFO - Epoch(train) [15][1900/2462] lr: 1.4480e-03 eta: 0:01:45 time: 0.0346 data_time: 0.0060 memory: 1253 loss: 0.0119 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0119 2022/11/28 19:11:30 - mmengine - INFO - Epoch(train) [15][2000/2462] lr: 1.3543e-03 eta: 0:01:41 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.0132 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0132 2022/11/28 19:11:33 - mmengine - INFO - Epoch(train) [15][2100/2462] lr: 1.2636e-03 eta: 0:01:38 time: 0.0345 data_time: 0.0061 memory: 1253 loss: 0.0161 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0161 2022/11/28 19:11:37 - mmengine - INFO - Epoch(train) [15][2200/2462] lr: 1.1761e-03 eta: 0:01:34 time: 0.0376 data_time: 0.0062 memory: 1253 loss: 0.0211 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0211 2022/11/28 19:11:41 - mmengine - INFO - Epoch(train) [15][2300/2462] lr: 1.0917e-03 eta: 0:01:31 time: 0.0341 data_time: 0.0061 memory: 1253 loss: 0.0116 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0116 2022/11/28 19:11:44 - mmengine - INFO - Epoch(train) [15][2400/2462] lr: 1.0104e-03 eta: 0:01:27 time: 0.0356 data_time: 0.0061 memory: 1253 loss: 0.0114 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0114 2022/11/28 19:11:46 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:11:46 - mmengine - INFO - Epoch(train) [15][2462/2462] lr: 9.6151e-04 eta: 0:01:25 time: 0.0345 data_time: 0.0062 memory: 1253 loss: 0.0142 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0142 2022/11/28 19:11:46 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/11/28 19:11:48 - mmengine - INFO - Epoch(val) [15][100/398] eta: 0:00:04 time: 0.0148 data_time: 0.0057 memory: 262 2022/11/28 19:11:50 - mmengine - INFO - Epoch(val) [15][200/398] eta: 0:00:03 time: 0.0150 data_time: 0.0058 memory: 262 2022/11/28 19:11:51 - mmengine - INFO - Epoch(val) [15][300/398] eta: 0:00:01 time: 0.0150 data_time: 0.0058 memory: 262 2022/11/28 19:11:54 - mmengine - INFO - Epoch(val) [15][398/398] acc/top1: 0.8273 acc/top5: 0.9679 acc/mean1: 0.8473 2022/11/28 19:11:56 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:11:57 - mmengine - INFO - Epoch(train) [16][100/2462] lr: 8.8525e-04 eta: 0:01:22 time: 0.0341 data_time: 0.0061 memory: 1253 loss: 0.0192 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0192 2022/11/28 19:12:01 - mmengine - INFO - Epoch(train) [16][200/2462] lr: 8.1211e-04 eta: 0:01:18 time: 0.0344 data_time: 0.0061 memory: 1253 loss: 0.0148 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0148 2022/11/28 19:12:04 - mmengine - INFO - Epoch(train) [16][300/2462] lr: 7.4209e-04 eta: 0:01:15 time: 0.0348 data_time: 0.0061 memory: 1253 loss: 0.0132 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0132 2022/11/28 19:12:08 - mmengine - INFO - Epoch(train) [16][400/2462] lr: 6.7522e-04 eta: 0:01:11 time: 0.0346 data_time: 0.0061 memory: 1253 loss: 0.0134 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0134 2022/11/28 19:12:11 - mmengine - INFO - Epoch(train) [16][500/2462] lr: 6.1147e-04 eta: 0:01:08 time: 0.0356 data_time: 0.0061 memory: 1253 loss: 0.0102 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0102 2022/11/28 19:12:15 - mmengine - INFO - Epoch(train) [16][600/2462] lr: 5.5087e-04 eta: 0:01:04 time: 0.0348 data_time: 0.0061 memory: 1253 loss: 0.0152 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0152 2022/11/28 19:12:18 - mmengine - INFO - Epoch(train) [16][700/2462] lr: 4.9342e-04 eta: 0:01:01 time: 0.0348 data_time: 0.0061 memory: 1253 loss: 0.0116 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0116 2022/11/28 19:12:22 - mmengine - INFO - Epoch(train) [16][800/2462] lr: 4.3911e-04 eta: 0:00:57 time: 0.0347 data_time: 0.0062 memory: 1253 loss: 0.0132 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0132 2022/11/28 19:12:25 - mmengine - INFO - Epoch(train) [16][900/2462] lr: 3.8795e-04 eta: 0:00:54 time: 0.0343 data_time: 0.0062 memory: 1253 loss: 0.0110 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0110 2022/11/28 19:12:29 - mmengine - INFO - Epoch(train) [16][1000/2462] lr: 3.3995e-04 eta: 0:00:50 time: 0.0344 data_time: 0.0061 memory: 1253 loss: 0.0116 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0116 2022/11/28 19:12:31 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:12:32 - mmengine - INFO - Epoch(train) [16][1100/2462] lr: 2.9511e-04 eta: 0:00:47 time: 0.0345 data_time: 0.0061 memory: 1253 loss: 0.0167 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0167 2022/11/28 19:12:36 - mmengine - INFO - Epoch(train) [16][1200/2462] lr: 2.5343e-04 eta: 0:00:43 time: 0.0343 data_time: 0.0062 memory: 1253 loss: 0.0104 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0104 2022/11/28 19:12:39 - mmengine - INFO - Epoch(train) [16][1300/2462] lr: 2.1492e-04 eta: 0:00:40 time: 0.0348 data_time: 0.0068 memory: 1253 loss: 0.0131 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0131 2022/11/28 19:12:43 - mmengine - INFO - Epoch(train) [16][1400/2462] lr: 1.7957e-04 eta: 0:00:36 time: 0.0356 data_time: 0.0068 memory: 1253 loss: 0.0117 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0117 2022/11/28 19:12:46 - mmengine - INFO - Epoch(train) [16][1500/2462] lr: 1.4739e-04 eta: 0:00:33 time: 0.0342 data_time: 0.0062 memory: 1253 loss: 0.0128 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0128 2022/11/28 19:12:50 - mmengine - INFO - Epoch(train) [16][1600/2462] lr: 1.1838e-04 eta: 0:00:29 time: 0.0341 data_time: 0.0061 memory: 1253 loss: 0.0133 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0133 2022/11/28 19:12:53 - mmengine - INFO - Epoch(train) [16][1700/2462] lr: 9.2542e-05 eta: 0:00:26 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.0140 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0140 2022/11/28 19:12:57 - mmengine - INFO - Epoch(train) [16][1800/2462] lr: 6.9879e-05 eta: 0:00:23 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.0132 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0132 2022/11/28 19:13:00 - mmengine - INFO - Epoch(train) [16][1900/2462] lr: 5.0393e-05 eta: 0:00:19 time: 0.0344 data_time: 0.0060 memory: 1253 loss: 0.0165 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0165 2022/11/28 19:13:04 - mmengine - INFO - Epoch(train) [16][2000/2462] lr: 3.4083e-05 eta: 0:00:16 time: 0.0353 data_time: 0.0069 memory: 1253 loss: 0.0145 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0145 2022/11/28 19:13:06 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:13:07 - mmengine - INFO - Epoch(train) [16][2100/2462] lr: 2.0951e-05 eta: 0:00:12 time: 0.0347 data_time: 0.0062 memory: 1253 loss: 0.0136 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0136 2022/11/28 19:13:11 - mmengine - INFO - Epoch(train) [16][2200/2462] lr: 1.0998e-05 eta: 0:00:09 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.0214 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0214 2022/11/28 19:13:14 - mmengine - INFO - Epoch(train) [16][2300/2462] lr: 4.2247e-06 eta: 0:00:05 time: 0.0348 data_time: 0.0061 memory: 1253 loss: 0.0148 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0148 2022/11/28 19:13:18 - mmengine - INFO - Epoch(train) [16][2400/2462] lr: 6.3111e-07 eta: 0:00:02 time: 0.0359 data_time: 0.0067 memory: 1253 loss: 0.0152 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0152 2022/11/28 19:13:20 - mmengine - INFO - Exp name: stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d_20221128_184642 2022/11/28 19:13:20 - mmengine - INFO - Epoch(train) [16][2462/2462] lr: 1.5901e-10 eta: 0:00:00 time: 0.0357 data_time: 0.0063 memory: 1253 loss: 0.0114 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0114 2022/11/28 19:13:20 - mmengine - INFO - Saving checkpoint at 16 epochs 2022/11/28 19:13:22 - mmengine - INFO - Epoch(val) [16][100/398] eta: 0:00:04 time: 0.0149 data_time: 0.0057 memory: 262 2022/11/28 19:13:24 - mmengine - INFO - Epoch(val) [16][200/398] eta: 0:00:03 time: 0.0155 data_time: 0.0061 memory: 262 2022/11/28 19:13:25 - mmengine - INFO - Epoch(val) [16][300/398] eta: 0:00:01 time: 0.0148 data_time: 0.0057 memory: 262 2022/11/28 19:13:28 - mmengine - INFO - Epoch(val) [16][398/398] acc/top1: 0.8289 acc/top5: 0.9684 acc/mean1: 0.8486 2022/11/28 19:13:28 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_14.pth is removed 2022/11/28 19:13:28 - mmengine - INFO - The best checkpoint with 0.8289 acc/top1 at 16 epoch is saved to best_acc/top1_epoch_16.pth.