2022/11/28 18:17:27 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.9.13 (main, Aug 25 2022, 23:26:10) [GCC 11.2.0] CUDA available: True numpy_random_seed: 238556737 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 18:17:27 - mmengine - INFO - Config: default_scope = 'mmaction' default_hooks = dict( runtime_info=dict(type='RuntimeInfoHook'), timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=100, ignore_last=False), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict(type='CheckpointHook', interval=1, save_best='auto'), sampler_seed=dict(type='DistSamplerSeedHook'), sync_buffers=dict(type='SyncBuffersHook')) env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) log_processor = dict(type='LogProcessor', window_size=20, by_epoch=True) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='ActionVisualizer', vis_backends=[dict(type='LocalVisBackend')]) log_level = 'INFO' load_from = None resume = False model = dict( type='RecognizerGCN', backbone=dict( type='STGCN', graph_cfg=dict(layout='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=['j']), 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=['j']), dict( type='UniformSampleFrames', clip_len=100, num_clips=1, test_mode=True), dict(type='PoseDecode'), dict(type='FormatGCNInput', num_person=2), dict(type='PackActionInputs') ] test_pipeline = [ dict(type='PreNormalize2D'), dict(type='GenSkeFeat', dataset='coco', feats=['j']), dict( type='UniformSampleFrames', clip_len=100, num_clips=10, test_mode=True), dict(type='PoseDecode'), dict(type='FormatGCNInput', num_person=2), dict(type='PackActionInputs') ] train_dataloader = dict( batch_size=16, num_workers=2, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='RepeatDataset', times=5, dataset=dict( type='PoseDataset', ann_file='data/skeleton/ntu120_2d.pkl', pipeline=[ dict(type='PreNormalize2D'), dict(type='GenSkeFeat', dataset='coco', feats=['j']), dict(type='UniformSampleFrames', clip_len=100), dict(type='PoseDecode'), dict(type='FormatGCNInput', num_person=2), dict(type='PackActionInputs') ], split='xsub_train'))) val_dataloader = dict( batch_size=16, num_workers=2, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='PoseDataset', ann_file='data/skeleton/ntu120_2d.pkl', pipeline=[ dict(type='PreNormalize2D'), dict(type='GenSkeFeat', dataset='coco', feats=['j']), dict( type='UniformSampleFrames', clip_len=100, num_clips=1, test_mode=True), dict(type='PoseDecode'), dict(type='FormatGCNInput', num_person=2), dict(type='PackActionInputs') ], split='xsub_val', test_mode=True)) test_dataloader = dict( batch_size=1, num_workers=2, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='PoseDataset', ann_file='data/skeleton/ntu120_2d.pkl', pipeline=[ dict(type='PreNormalize2D'), dict(type='GenSkeFeat', dataset='coco', feats=['j']), dict( type='UniformSampleFrames', clip_len=100, num_clips=10, test_mode=True), dict(type='PoseDecode'), dict(type='FormatGCNInput', num_person=2), dict(type='PackActionInputs') ], split='xsub_val', test_mode=True)) val_evaluator = [dict(type='AccMetric')] test_evaluator = [dict(type='AccMetric')] train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=16, val_begin=1, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') param_scheduler = [ dict( type='CosineAnnealingLR', eta_min=0, T_max=16, by_epoch=True, convert_to_iter_based=True) ] optim_wrapper = dict( optimizer=dict( type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True)) auto_scale_lr = dict(enable=False, base_batch_size=128) launcher = 'pytorch' work_dir = './work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d' randomness = dict(seed=None, diff_rank_seed=False, deterministic=False) 2022/11/28 18:17:27 - mmengine - INFO - Result has been saved to /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-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 18:19:14 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d. 2022/11/28 18:19:20 - mmengine - INFO - Epoch(train) [1][100/2462] lr: 9.9998e-02 eta: 0:39:54 time: 0.0345 data_time: 0.0062 memory: 1253 loss: 3.9529 top1_acc: 0.1875 top5_acc: 0.3125 loss_cls: 3.9529 2022/11/28 18:19:24 - mmengine - INFO - Epoch(train) [1][200/2462] lr: 9.9994e-02 eta: 0:31:02 time: 0.0350 data_time: 0.0064 memory: 1253 loss: 3.2595 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 3.2595 2022/11/28 18:19:27 - mmengine - INFO - Epoch(train) [1][300/2462] lr: 9.9986e-02 eta: 0:28:12 time: 0.0348 data_time: 0.0058 memory: 1253 loss: 2.7333 top1_acc: 0.1875 top5_acc: 0.6875 loss_cls: 2.7333 2022/11/28 18:19:31 - mmengine - INFO - Epoch(train) [1][400/2462] lr: 9.9975e-02 eta: 0:26:38 time: 0.0336 data_time: 0.0060 memory: 1253 loss: 2.2664 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2664 2022/11/28 18:19:34 - mmengine - INFO - Epoch(train) [1][500/2462] lr: 9.9960e-02 eta: 0:25:44 time: 0.0342 data_time: 0.0059 memory: 1253 loss: 2.0644 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0644 2022/11/28 18:19:37 - mmengine - INFO - Epoch(train) [1][600/2462] lr: 9.9943e-02 eta: 0:25:01 time: 0.0333 data_time: 0.0059 memory: 1253 loss: 1.9081 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9081 2022/11/28 18:19:41 - mmengine - INFO - Epoch(train) [1][700/2462] lr: 9.9922e-02 eta: 0:24:31 time: 0.0338 data_time: 0.0058 memory: 1253 loss: 1.9146 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9146 2022/11/28 18:19:44 - mmengine - INFO - Epoch(train) [1][800/2462] lr: 9.9899e-02 eta: 0:24:09 time: 0.0348 data_time: 0.0059 memory: 1253 loss: 1.6136 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.6136 2022/11/28 18:19:48 - mmengine - INFO - Epoch(train) [1][900/2462] lr: 9.9872e-02 eta: 0:23:55 time: 0.0351 data_time: 0.0065 memory: 1253 loss: 1.3759 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3759 2022/11/28 18:19:51 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:19:51 - mmengine - INFO - Epoch(train) [1][1000/2462] lr: 9.9841e-02 eta: 0:23:41 time: 0.0340 data_time: 0.0066 memory: 1253 loss: 1.4812 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.4812 2022/11/28 18:19:55 - mmengine - INFO - Epoch(train) [1][1100/2462] lr: 9.9808e-02 eta: 0:23:29 time: 0.0340 data_time: 0.0059 memory: 1253 loss: 1.3806 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.3806 2022/11/28 18:19:58 - mmengine - INFO - Epoch(train) [1][1200/2462] lr: 9.9772e-02 eta: 0:23:17 time: 0.0350 data_time: 0.0059 memory: 1253 loss: 1.3092 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3092 2022/11/28 18:20:02 - mmengine - INFO - Epoch(train) [1][1300/2462] lr: 9.9732e-02 eta: 0:23:06 time: 0.0345 data_time: 0.0062 memory: 1253 loss: 1.2753 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2753 2022/11/28 18:20:05 - mmengine - INFO - Epoch(train) [1][1400/2462] lr: 9.9689e-02 eta: 0:22:56 time: 0.0334 data_time: 0.0059 memory: 1253 loss: 1.1827 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1827 2022/11/28 18:20:08 - mmengine - INFO - Epoch(train) [1][1500/2462] lr: 9.9643e-02 eta: 0:22:46 time: 0.0335 data_time: 0.0059 memory: 1253 loss: 1.3532 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3532 2022/11/28 18:20:12 - mmengine - INFO - Epoch(train) [1][1600/2462] lr: 9.9594e-02 eta: 0:22:41 time: 0.0354 data_time: 0.0058 memory: 1253 loss: 1.0721 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0721 2022/11/28 18:20:15 - mmengine - INFO - Epoch(train) [1][1700/2462] lr: 9.9542e-02 eta: 0:22:33 time: 0.0337 data_time: 0.0059 memory: 1253 loss: 1.1776 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1776 2022/11/28 18:20:19 - mmengine - INFO - Epoch(train) [1][1800/2462] lr: 9.9486e-02 eta: 0:22:26 time: 0.0348 data_time: 0.0058 memory: 1253 loss: 1.1553 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1553 2022/11/28 18:20:22 - mmengine - INFO - Epoch(train) [1][1900/2462] lr: 9.9428e-02 eta: 0:22:21 time: 0.0357 data_time: 0.0059 memory: 1253 loss: 1.0502 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0502 2022/11/28 18:20:26 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:20:26 - mmengine - INFO - Epoch(train) [1][2000/2462] lr: 9.9366e-02 eta: 0:22:15 time: 0.0333 data_time: 0.0059 memory: 1253 loss: 1.0404 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0404 2022/11/28 18:20:29 - mmengine - INFO - Epoch(train) [1][2100/2462] lr: 9.9301e-02 eta: 0:22:09 time: 0.0337 data_time: 0.0059 memory: 1253 loss: 1.0249 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0249 2022/11/28 18:20:33 - mmengine - INFO - Epoch(train) [1][2200/2462] lr: 9.9233e-02 eta: 0:22:03 time: 0.0348 data_time: 0.0060 memory: 1253 loss: 0.9270 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9270 2022/11/28 18:20:36 - mmengine - INFO - Epoch(train) [1][2300/2462] lr: 9.9162e-02 eta: 0:21:57 time: 0.0343 data_time: 0.0060 memory: 1253 loss: 1.0577 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0577 2022/11/28 18:20:39 - mmengine - INFO - Epoch(train) [1][2400/2462] lr: 9.9088e-02 eta: 0:21:52 time: 0.0341 data_time: 0.0066 memory: 1253 loss: 0.8807 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8807 2022/11/28 18:20:42 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:20:42 - mmengine - INFO - Epoch(train) [1][2462/2462] lr: 9.9040e-02 eta: 0:21:49 time: 0.0348 data_time: 0.0065 memory: 1253 loss: 0.9367 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9367 2022/11/28 18:20:42 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/11/28 18:20:44 - mmengine - INFO - Epoch(val) [1][100/398] eta: 0:00:05 time: 0.0149 data_time: 0.0058 memory: 262 2022/11/28 18:20:45 - mmengine - INFO - Epoch(val) [1][200/398] eta: 0:00:03 time: 0.0153 data_time: 0.0062 memory: 262 2022/11/28 18:20:47 - mmengine - INFO - Epoch(val) [1][300/398] eta: 0:00:01 time: 0.0179 data_time: 0.0070 memory: 262 2022/11/28 18:20:49 - mmengine - INFO - Epoch(val) [1][398/398] acc/top1: 0.5873 acc/top5: 0.8948 acc/mean1: 0.6126 2022/11/28 18:20:50 - mmengine - INFO - The best checkpoint with 0.5873 acc/top1 at 1 epoch is saved to best_acc/top1_epoch_1.pth. 2022/11/28 18:20:53 - mmengine - INFO - Epoch(train) [2][100/2462] lr: 9.8961e-02 eta: 0:21:47 time: 0.0364 data_time: 0.0061 memory: 1253 loss: 0.9216 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9216 2022/11/28 18:20:57 - mmengine - INFO - Epoch(train) [2][200/2462] lr: 9.8878e-02 eta: 0:21:42 time: 0.0345 data_time: 0.0063 memory: 1253 loss: 1.0723 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0723 2022/11/28 18:21:00 - mmengine - INFO - Epoch(train) [2][300/2462] lr: 9.8793e-02 eta: 0:21:38 time: 0.0346 data_time: 0.0062 memory: 1253 loss: 0.9282 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9282 2022/11/28 18:21:04 - mmengine - INFO - Epoch(train) [2][400/2462] lr: 9.8704e-02 eta: 0:21:33 time: 0.0351 data_time: 0.0065 memory: 1253 loss: 0.8566 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8566 2022/11/28 18:21:07 - mmengine - INFO - Epoch(train) [2][500/2462] lr: 9.8612e-02 eta: 0:21:29 time: 0.0344 data_time: 0.0060 memory: 1253 loss: 0.8798 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8798 2022/11/28 18:21:09 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:21:11 - mmengine - INFO - Epoch(train) [2][600/2462] lr: 9.8518e-02 eta: 0:21:25 time: 0.0349 data_time: 0.0060 memory: 1253 loss: 0.9491 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 0.9491 2022/11/28 18:21:14 - mmengine - INFO - Epoch(train) [2][700/2462] lr: 9.8420e-02 eta: 0:21:21 time: 0.0347 data_time: 0.0068 memory: 1253 loss: 0.8421 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8421 2022/11/28 18:21:18 - mmengine - INFO - Epoch(train) [2][800/2462] lr: 9.8319e-02 eta: 0:21:16 time: 0.0357 data_time: 0.0062 memory: 1253 loss: 0.9698 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9698 2022/11/28 18:21:21 - mmengine - INFO - Epoch(train) [2][900/2462] lr: 9.8215e-02 eta: 0:21:12 time: 0.0348 data_time: 0.0061 memory: 1253 loss: 0.9060 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9060 2022/11/28 18:21:25 - mmengine - INFO - Epoch(train) [2][1000/2462] lr: 9.8107e-02 eta: 0:21:08 time: 0.0343 data_time: 0.0060 memory: 1253 loss: 0.8624 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8624 2022/11/28 18:21:28 - mmengine - INFO - Epoch(train) [2][1100/2462] lr: 9.7997e-02 eta: 0:21:05 time: 0.0361 data_time: 0.0061 memory: 1253 loss: 0.8803 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8803 2022/11/28 18:21:32 - mmengine - INFO - Epoch(train) [2][1200/2462] lr: 9.7884e-02 eta: 0:21:02 time: 0.0363 data_time: 0.0063 memory: 1253 loss: 0.9024 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9024 2022/11/28 18:21:35 - mmengine - INFO - Epoch(train) [2][1300/2462] lr: 9.7768e-02 eta: 0:20:58 time: 0.0353 data_time: 0.0060 memory: 1253 loss: 0.7383 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7383 2022/11/28 18:21:39 - mmengine - INFO - Epoch(train) [2][1400/2462] lr: 9.7648e-02 eta: 0:20:54 time: 0.0348 data_time: 0.0067 memory: 1253 loss: 0.7395 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7395 2022/11/28 18:21:42 - mmengine - INFO - Epoch(train) [2][1500/2462] lr: 9.7526e-02 eta: 0:20:51 time: 0.0358 data_time: 0.0060 memory: 1253 loss: 0.9190 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9190 2022/11/28 18:21:44 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:21:46 - mmengine - INFO - Epoch(train) [2][1600/2462] lr: 9.7400e-02 eta: 0:20:48 time: 0.0345 data_time: 0.0060 memory: 1253 loss: 0.7563 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7563 2022/11/28 18:21:50 - mmengine - INFO - Epoch(train) [2][1700/2462] lr: 9.7272e-02 eta: 0:20:44 time: 0.0342 data_time: 0.0061 memory: 1253 loss: 0.7706 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7706 2022/11/28 18:21:53 - mmengine - INFO - Epoch(train) [2][1800/2462] lr: 9.7141e-02 eta: 0:20:41 time: 0.0342 data_time: 0.0061 memory: 1253 loss: 0.8613 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8613 2022/11/28 18:21:57 - mmengine - INFO - Epoch(train) [2][1900/2462] lr: 9.7006e-02 eta: 0:20:37 time: 0.0349 data_time: 0.0074 memory: 1253 loss: 0.7875 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7875 2022/11/28 18:22:00 - mmengine - INFO - Epoch(train) [2][2000/2462] lr: 9.6869e-02 eta: 0:20:34 time: 0.0356 data_time: 0.0061 memory: 1253 loss: 0.7477 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7477 2022/11/28 18:22:04 - mmengine - INFO - Epoch(train) [2][2100/2462] lr: 9.6728e-02 eta: 0:20:31 time: 0.0345 data_time: 0.0061 memory: 1253 loss: 0.8525 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8525 2022/11/28 18:22:07 - mmengine - INFO - Epoch(train) [2][2200/2462] lr: 9.6585e-02 eta: 0:20:27 time: 0.0348 data_time: 0.0064 memory: 1253 loss: 0.7751 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7751 2022/11/28 18:22:11 - mmengine - INFO - Epoch(train) [2][2300/2462] lr: 9.6439e-02 eta: 0:20:23 time: 0.0366 data_time: 0.0061 memory: 1253 loss: 0.7049 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7049 2022/11/28 18:22:14 - mmengine - INFO - Epoch(train) [2][2400/2462] lr: 9.6290e-02 eta: 0:20:20 time: 0.0356 data_time: 0.0061 memory: 1253 loss: 0.7960 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7960 2022/11/28 18:22:17 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:22:17 - mmengine - INFO - Epoch(train) [2][2462/2462] lr: 9.6196e-02 eta: 0:20:18 time: 0.0346 data_time: 0.0062 memory: 1253 loss: 0.6974 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6974 2022/11/28 18:22:17 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/11/28 18:22:18 - mmengine - INFO - Epoch(val) [2][100/398] eta: 0:00:04 time: 0.0149 data_time: 0.0058 memory: 262 2022/11/28 18:22:20 - mmengine - INFO - Epoch(val) [2][200/398] eta: 0:00:03 time: 0.0150 data_time: 0.0058 memory: 262 2022/11/28 18:22:22 - mmengine - INFO - Epoch(val) [2][300/398] eta: 0:00:01 time: 0.0149 data_time: 0.0058 memory: 262 2022/11/28 18:22:24 - mmengine - INFO - Epoch(val) [2][398/398] acc/top1: 0.6521 acc/top5: 0.9083 acc/mean1: 0.6835 2022/11/28 18:22:24 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_1.pth is removed 2022/11/28 18:22:24 - mmengine - INFO - The best checkpoint with 0.6521 acc/top1 at 2 epoch is saved to best_acc/top1_epoch_2.pth. 2022/11/28 18:22:27 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:22:28 - mmengine - INFO - Epoch(train) [3][100/2462] lr: 9.6041e-02 eta: 0:20:15 time: 0.0358 data_time: 0.0061 memory: 1253 loss: 0.7287 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7287 2022/11/28 18:22:31 - mmengine - INFO - Epoch(train) [3][200/2462] lr: 9.5884e-02 eta: 0:20:12 time: 0.0362 data_time: 0.0062 memory: 1253 loss: 0.7585 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7585 2022/11/28 18:22:35 - mmengine - INFO - Epoch(train) [3][300/2462] lr: 9.5725e-02 eta: 0:20:09 time: 0.0359 data_time: 0.0061 memory: 1253 loss: 0.7504 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.7504 2022/11/28 18:22:39 - mmengine - INFO - Epoch(train) [3][400/2462] lr: 9.5562e-02 eta: 0:20:06 time: 0.0364 data_time: 0.0063 memory: 1253 loss: 0.7206 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7206 2022/11/28 18:22:42 - mmengine - INFO - Epoch(train) [3][500/2462] lr: 9.5396e-02 eta: 0:20:03 time: 0.0373 data_time: 0.0061 memory: 1253 loss: 0.7257 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7257 2022/11/28 18:22:46 - mmengine - INFO - Epoch(train) [3][600/2462] lr: 9.5228e-02 eta: 0:20:00 time: 0.0363 data_time: 0.0061 memory: 1253 loss: 0.7664 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7664 2022/11/28 18:22:50 - mmengine - INFO - Epoch(train) [3][700/2462] lr: 9.5056e-02 eta: 0:19:57 time: 0.0343 data_time: 0.0060 memory: 1253 loss: 0.6778 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.6778 2022/11/28 18:22:53 - mmengine - INFO - Epoch(train) [3][800/2462] lr: 9.4882e-02 eta: 0:19:53 time: 0.0343 data_time: 0.0062 memory: 1253 loss: 0.7071 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.7071 2022/11/28 18:22:57 - mmengine - INFO - Epoch(train) [3][900/2462] lr: 9.4705e-02 eta: 0:19:49 time: 0.0353 data_time: 0.0061 memory: 1253 loss: 0.7853 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.7853 2022/11/28 18:23:00 - mmengine - INFO - Epoch(train) [3][1000/2462] lr: 9.4525e-02 eta: 0:19:46 time: 0.0352 data_time: 0.0063 memory: 1253 loss: 0.6937 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6937 2022/11/28 18:23:03 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:23:04 - mmengine - INFO - Epoch(train) [3][1100/2462] lr: 9.4342e-02 eta: 0:19:43 time: 0.0375 data_time: 0.0061 memory: 1253 loss: 0.6181 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6181 2022/11/28 18:23:07 - mmengine - INFO - Epoch(train) [3][1200/2462] lr: 9.4156e-02 eta: 0:19:39 time: 0.0350 data_time: 0.0062 memory: 1253 loss: 0.7060 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.7060 2022/11/28 18:23:11 - mmengine - INFO - Epoch(train) [3][1300/2462] lr: 9.3968e-02 eta: 0:19:36 time: 0.0353 data_time: 0.0067 memory: 1253 loss: 0.7804 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7804 2022/11/28 18:23:14 - mmengine - INFO - Epoch(train) [3][1400/2462] lr: 9.3776e-02 eta: 0:19:32 time: 0.0364 data_time: 0.0061 memory: 1253 loss: 0.8012 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8012 2022/11/28 18:23:18 - mmengine - INFO - Epoch(train) [3][1500/2462] lr: 9.3582e-02 eta: 0:19:29 time: 0.0354 data_time: 0.0068 memory: 1253 loss: 0.8494 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.8494 2022/11/28 18:23:22 - mmengine - INFO - Epoch(train) [3][1600/2462] lr: 9.3385e-02 eta: 0:19:25 time: 0.0361 data_time: 0.0061 memory: 1253 loss: 0.7770 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7770 2022/11/28 18:23:25 - mmengine - INFO - Epoch(train) [3][1700/2462] lr: 9.3186e-02 eta: 0:19:22 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.7326 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.7326 2022/11/28 18:23:29 - mmengine - INFO - Epoch(train) [3][1800/2462] lr: 9.2983e-02 eta: 0:19:18 time: 0.0352 data_time: 0.0061 memory: 1253 loss: 0.6229 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6229 2022/11/28 18:23:32 - mmengine - INFO - Epoch(train) [3][1900/2462] lr: 9.2778e-02 eta: 0:19:15 time: 0.0371 data_time: 0.0063 memory: 1253 loss: 0.7172 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7172 2022/11/28 18:23:36 - mmengine - INFO - Epoch(train) [3][2000/2462] lr: 9.2571e-02 eta: 0:19:11 time: 0.0345 data_time: 0.0061 memory: 1253 loss: 0.6761 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6761 2022/11/28 18:23:38 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:23:39 - mmengine - INFO - Epoch(train) [3][2100/2462] lr: 9.2360e-02 eta: 0:19:07 time: 0.0352 data_time: 0.0062 memory: 1253 loss: 0.6064 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6064 2022/11/28 18:23:43 - mmengine - INFO - Epoch(train) [3][2200/2462] lr: 9.2147e-02 eta: 0:19:04 time: 0.0348 data_time: 0.0067 memory: 1253 loss: 0.6486 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6486 2022/11/28 18:23:46 - mmengine - INFO - Epoch(train) [3][2300/2462] lr: 9.1931e-02 eta: 0:19:00 time: 0.0346 data_time: 0.0067 memory: 1253 loss: 0.5465 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.5465 2022/11/28 18:23:50 - mmengine - INFO - Epoch(train) [3][2400/2462] lr: 9.1713e-02 eta: 0:18:56 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.5883 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.5883 2022/11/28 18:23:52 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:23:52 - mmengine - INFO - Epoch(train) [3][2462/2462] lr: 9.1576e-02 eta: 0:18:54 time: 0.0352 data_time: 0.0062 memory: 1253 loss: 0.7341 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.7341 2022/11/28 18:23:52 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/11/28 18:23:54 - mmengine - INFO - Epoch(val) [3][100/398] eta: 0:00:04 time: 0.0149 data_time: 0.0058 memory: 262 2022/11/28 18:23:55 - mmengine - INFO - Epoch(val) [3][200/398] eta: 0:00:03 time: 0.0150 data_time: 0.0058 memory: 262 2022/11/28 18:23:57 - mmengine - INFO - Epoch(val) [3][300/398] eta: 0:00:01 time: 0.0156 data_time: 0.0064 memory: 262 2022/11/28 18:23:59 - mmengine - INFO - Epoch(val) [3][398/398] acc/top1: 0.6853 acc/top5: 0.9228 acc/mean1: 0.7109 2022/11/28 18:23:59 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_2.pth is removed 2022/11/28 18:24:00 - mmengine - INFO - The best checkpoint with 0.6853 acc/top1 at 3 epoch is saved to best_acc/top1_epoch_3.pth. 2022/11/28 18:24:03 - mmengine - INFO - Epoch(train) [4][100/2462] lr: 9.1353e-02 eta: 0:18:50 time: 0.0360 data_time: 0.0060 memory: 1253 loss: 0.6160 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6160 2022/11/28 18:24:07 - mmengine - INFO - Epoch(train) [4][200/2462] lr: 9.1127e-02 eta: 0:18:47 time: 0.0359 data_time: 0.0061 memory: 1253 loss: 0.6513 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.6513 2022/11/28 18:24:10 - mmengine - INFO - Epoch(train) [4][300/2462] lr: 9.0899e-02 eta: 0:18:43 time: 0.0356 data_time: 0.0061 memory: 1253 loss: 0.5801 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5801 2022/11/28 18:24:14 - mmengine - INFO - Epoch(train) [4][400/2462] lr: 9.0669e-02 eta: 0:18:40 time: 0.0358 data_time: 0.0060 memory: 1253 loss: 0.6744 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6744 2022/11/28 18:24:17 - mmengine - INFO - Epoch(train) [4][500/2462] lr: 9.0435e-02 eta: 0:18:36 time: 0.0363 data_time: 0.0066 memory: 1253 loss: 0.7292 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7292 2022/11/28 18:24:21 - mmengine - INFO - Epoch(train) [4][600/2462] lr: 9.0200e-02 eta: 0:18:33 time: 0.0357 data_time: 0.0061 memory: 1253 loss: 0.6453 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6453 2022/11/28 18:24:22 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:24:25 - mmengine - INFO - Epoch(train) [4][700/2462] lr: 8.9961e-02 eta: 0:18:29 time: 0.0350 data_time: 0.0068 memory: 1253 loss: 0.6570 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.6570 2022/11/28 18:24:28 - mmengine - INFO - Epoch(train) [4][800/2462] lr: 8.9720e-02 eta: 0:18:25 time: 0.0348 data_time: 0.0072 memory: 1253 loss: 0.6073 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6073 2022/11/28 18:24:31 - mmengine - INFO - Epoch(train) [4][900/2462] lr: 8.9477e-02 eta: 0:18:21 time: 0.0342 data_time: 0.0061 memory: 1253 loss: 0.6143 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6143 2022/11/28 18:24:35 - mmengine - INFO - Epoch(train) [4][1000/2462] lr: 8.9231e-02 eta: 0:18:17 time: 0.0342 data_time: 0.0060 memory: 1253 loss: 0.6234 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6234 2022/11/28 18:24:38 - mmengine - INFO - Epoch(train) [4][1100/2462] lr: 8.8982e-02 eta: 0:18:14 time: 0.0356 data_time: 0.0060 memory: 1253 loss: 0.6447 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6447 2022/11/28 18:24:42 - mmengine - INFO - Epoch(train) [4][1200/2462] lr: 8.8731e-02 eta: 0:18:10 time: 0.0339 data_time: 0.0061 memory: 1253 loss: 0.6708 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 0.6708 2022/11/28 18:24:45 - mmengine - INFO - Epoch(train) [4][1300/2462] lr: 8.8478e-02 eta: 0:18:06 time: 0.0338 data_time: 0.0060 memory: 1253 loss: 0.5611 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.5611 2022/11/28 18:24:49 - mmengine - INFO - Epoch(train) [4][1400/2462] lr: 8.8222e-02 eta: 0:18:02 time: 0.0357 data_time: 0.0065 memory: 1253 loss: 0.5962 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.5962 2022/11/28 18:24:52 - mmengine - INFO - Epoch(train) [4][1500/2462] lr: 8.7964e-02 eta: 0:17:58 time: 0.0348 data_time: 0.0064 memory: 1253 loss: 0.6026 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6026 2022/11/28 18:24:56 - mmengine - INFO - Epoch(train) [4][1600/2462] lr: 8.7703e-02 eta: 0:17:54 time: 0.0338 data_time: 0.0060 memory: 1253 loss: 0.5563 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5563 2022/11/28 18:24:56 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:24:59 - mmengine - INFO - Epoch(train) [4][1700/2462] lr: 8.7440e-02 eta: 0:17:51 time: 0.0351 data_time: 0.0060 memory: 1253 loss: 0.6759 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6759 2022/11/28 18:25:03 - mmengine - INFO - Epoch(train) [4][1800/2462] lr: 8.7174e-02 eta: 0:17:47 time: 0.0340 data_time: 0.0061 memory: 1253 loss: 0.4474 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4474 2022/11/28 18:25:06 - mmengine - INFO - Epoch(train) [4][1900/2462] lr: 8.6907e-02 eta: 0:17:43 time: 0.0339 data_time: 0.0060 memory: 1253 loss: 0.6544 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6544 2022/11/28 18:25:09 - mmengine - INFO - Epoch(train) [4][2000/2462] lr: 8.6636e-02 eta: 0:17:39 time: 0.0343 data_time: 0.0060 memory: 1253 loss: 0.5731 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5731 2022/11/28 18:25:13 - mmengine - INFO - Epoch(train) [4][2100/2462] lr: 8.6364e-02 eta: 0:17:35 time: 0.0344 data_time: 0.0063 memory: 1253 loss: 0.6101 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6101 2022/11/28 18:25:16 - mmengine - INFO - Epoch(train) [4][2200/2462] lr: 8.6089e-02 eta: 0:17:31 time: 0.0342 data_time: 0.0060 memory: 1253 loss: 0.5989 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5989 2022/11/28 18:25:20 - mmengine - INFO - Epoch(train) [4][2300/2462] lr: 8.5812e-02 eta: 0:17:28 time: 0.0344 data_time: 0.0060 memory: 1253 loss: 0.6128 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.6128 2022/11/28 18:25:23 - mmengine - INFO - Epoch(train) [4][2400/2462] lr: 8.5533e-02 eta: 0:17:24 time: 0.0338 data_time: 0.0061 memory: 1253 loss: 0.6411 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.6411 2022/11/28 18:25:25 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:25:25 - mmengine - INFO - Epoch(train) [4][2462/2462] lr: 8.5358e-02 eta: 0:17:21 time: 0.0339 data_time: 0.0062 memory: 1253 loss: 0.6006 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6006 2022/11/28 18:25:25 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/11/28 18:25:27 - mmengine - INFO - Epoch(val) [4][100/398] eta: 0:00:04 time: 0.0149 data_time: 0.0058 memory: 262 2022/11/28 18:25:29 - mmengine - INFO - Epoch(val) [4][200/398] eta: 0:00:03 time: 0.0147 data_time: 0.0057 memory: 262 2022/11/28 18:25:30 - mmengine - INFO - Epoch(val) [4][300/398] eta: 0:00:01 time: 0.0148 data_time: 0.0058 memory: 262 2022/11/28 18:25:33 - mmengine - INFO - Epoch(val) [4][398/398] acc/top1: 0.6533 acc/top5: 0.9205 acc/mean1: 0.6783 2022/11/28 18:25:36 - mmengine - INFO - Epoch(train) [5][100/2462] lr: 8.5075e-02 eta: 0:17:18 time: 0.0344 data_time: 0.0059 memory: 1253 loss: 0.5409 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5409 2022/11/28 18:25:38 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:25:40 - mmengine - INFO - Epoch(train) [5][200/2462] lr: 8.4790e-02 eta: 0:17:14 time: 0.0350 data_time: 0.0059 memory: 1253 loss: 0.4910 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 0.4910 2022/11/28 18:25:43 - mmengine - INFO - Epoch(train) [5][300/2462] lr: 8.4502e-02 eta: 0:17:11 time: 0.0340 data_time: 0.0060 memory: 1253 loss: 0.5069 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5069 2022/11/28 18:25:47 - mmengine - INFO - Epoch(train) [5][400/2462] lr: 8.4213e-02 eta: 0:17:07 time: 0.0342 data_time: 0.0064 memory: 1253 loss: 0.6148 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6148 2022/11/28 18:25:50 - mmengine - INFO - Epoch(train) [5][500/2462] lr: 8.3921e-02 eta: 0:17:03 time: 0.0337 data_time: 0.0060 memory: 1253 loss: 0.5423 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5423 2022/11/28 18:25:53 - mmengine - INFO - Epoch(train) [5][600/2462] lr: 8.3627e-02 eta: 0:16:59 time: 0.0351 data_time: 0.0060 memory: 1253 loss: 0.4311 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4311 2022/11/28 18:25:57 - mmengine - INFO - Epoch(train) [5][700/2462] lr: 8.3330e-02 eta: 0:16:55 time: 0.0341 data_time: 0.0059 memory: 1253 loss: 0.6760 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.6760 2022/11/28 18:26:00 - mmengine - INFO - Epoch(train) [5][800/2462] lr: 8.3032e-02 eta: 0:16:52 time: 0.0345 data_time: 0.0066 memory: 1253 loss: 0.5605 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.5605 2022/11/28 18:26:04 - mmengine - INFO - Epoch(train) [5][900/2462] lr: 8.2732e-02 eta: 0:16:48 time: 0.0350 data_time: 0.0060 memory: 1253 loss: 0.5246 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5246 2022/11/28 18:26:07 - mmengine - INFO - Epoch(train) [5][1000/2462] lr: 8.2429e-02 eta: 0:16:44 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.5515 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.5515 2022/11/28 18:26:11 - mmengine - INFO - Epoch(train) [5][1100/2462] lr: 8.2125e-02 eta: 0:16:41 time: 0.0339 data_time: 0.0064 memory: 1253 loss: 0.5057 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5057 2022/11/28 18:26:12 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:26:14 - mmengine - INFO - Epoch(train) [5][1200/2462] lr: 8.1818e-02 eta: 0:16:37 time: 0.0337 data_time: 0.0060 memory: 1253 loss: 0.4660 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4660 2022/11/28 18:26:17 - mmengine - INFO - Epoch(train) [5][1300/2462] lr: 8.1510e-02 eta: 0:16:33 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.5121 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5121 2022/11/28 18:26:21 - mmengine - INFO - Epoch(train) [5][1400/2462] lr: 8.1199e-02 eta: 0:16:29 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.5735 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5735 2022/11/28 18:26:24 - mmengine - INFO - Epoch(train) [5][1500/2462] lr: 8.0886e-02 eta: 0:16:25 time: 0.0357 data_time: 0.0060 memory: 1253 loss: 0.5415 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.5415 2022/11/28 18:26:28 - mmengine - INFO - Epoch(train) [5][1600/2462] lr: 8.0572e-02 eta: 0:16:22 time: 0.0353 data_time: 0.0064 memory: 1253 loss: 0.5154 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5154 2022/11/28 18:26:31 - mmengine - INFO - Epoch(train) [5][1700/2462] lr: 8.0255e-02 eta: 0:16:18 time: 0.0340 data_time: 0.0060 memory: 1253 loss: 0.5352 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5352 2022/11/28 18:26:35 - mmengine - INFO - Epoch(train) [5][1800/2462] lr: 7.9937e-02 eta: 0:16:14 time: 0.0337 data_time: 0.0060 memory: 1253 loss: 0.4647 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4647 2022/11/28 18:26:38 - mmengine - INFO - Epoch(train) [5][1900/2462] lr: 7.9617e-02 eta: 0:16:11 time: 0.0337 data_time: 0.0060 memory: 1253 loss: 0.5200 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 0.5200 2022/11/28 18:26:41 - mmengine - INFO - Epoch(train) [5][2000/2462] lr: 7.9294e-02 eta: 0:16:07 time: 0.0337 data_time: 0.0060 memory: 1253 loss: 0.4797 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.4797 2022/11/28 18:26:45 - mmengine - INFO - Epoch(train) [5][2100/2462] lr: 7.8970e-02 eta: 0:16:03 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.4237 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4237 2022/11/28 18:26:47 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:26:48 - mmengine - INFO - Epoch(train) [5][2200/2462] lr: 7.8644e-02 eta: 0:15:59 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.5352 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5352 2022/11/28 18:26:52 - mmengine - INFO - Epoch(train) [5][2300/2462] lr: 7.8317e-02 eta: 0:15:55 time: 0.0337 data_time: 0.0060 memory: 1253 loss: 0.5086 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5086 2022/11/28 18:26:55 - mmengine - INFO - Epoch(train) [5][2400/2462] lr: 7.7987e-02 eta: 0:15:51 time: 0.0339 data_time: 0.0060 memory: 1253 loss: 0.5055 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5055 2022/11/28 18:26:57 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:26:57 - mmengine - INFO - Epoch(train) [5][2462/2462] lr: 7.7782e-02 eta: 0:15:49 time: 0.0341 data_time: 0.0067 memory: 1253 loss: 0.5064 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5064 2022/11/28 18:26:57 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/11/28 18:26:59 - mmengine - INFO - Epoch(val) [5][100/398] eta: 0:00:04 time: 0.0148 data_time: 0.0057 memory: 262 2022/11/28 18:27:01 - mmengine - INFO - Epoch(val) [5][200/398] eta: 0:00:02 time: 0.0151 data_time: 0.0058 memory: 262 2022/11/28 18:27:02 - mmengine - INFO - Epoch(val) [5][300/398] eta: 0:00:01 time: 0.0148 data_time: 0.0058 memory: 262 2022/11/28 18:27:04 - mmengine - INFO - Epoch(val) [5][398/398] acc/top1: 0.6980 acc/top5: 0.9258 acc/mean1: 0.7232 2022/11/28 18:27:04 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_3.pth is removed 2022/11/28 18:27:05 - mmengine - INFO - The best checkpoint with 0.6980 acc/top1 at 5 epoch is saved to best_acc/top1_epoch_5.pth. 2022/11/28 18:27:08 - mmengine - INFO - Epoch(train) [6][100/2462] lr: 7.7449e-02 eta: 0:15:45 time: 0.0343 data_time: 0.0067 memory: 1253 loss: 0.5171 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5171 2022/11/28 18:27:12 - mmengine - INFO - Epoch(train) [6][200/2462] lr: 7.7115e-02 eta: 0:15:42 time: 0.0358 data_time: 0.0061 memory: 1253 loss: 0.4930 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4930 2022/11/28 18:27:15 - mmengine - INFO - Epoch(train) [6][300/2462] lr: 7.6779e-02 eta: 0:15:39 time: 0.0338 data_time: 0.0060 memory: 1253 loss: 0.4634 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4634 2022/11/28 18:27:19 - mmengine - INFO - Epoch(train) [6][400/2462] lr: 7.6442e-02 eta: 0:15:35 time: 0.0336 data_time: 0.0060 memory: 1253 loss: 0.5414 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.5414 2022/11/28 18:27:22 - mmengine - INFO - Epoch(train) [6][500/2462] lr: 7.6102e-02 eta: 0:15:31 time: 0.0336 data_time: 0.0060 memory: 1253 loss: 0.5409 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.5409 2022/11/28 18:27:26 - mmengine - INFO - Epoch(train) [6][600/2462] lr: 7.5762e-02 eta: 0:15:27 time: 0.0339 data_time: 0.0061 memory: 1253 loss: 0.5094 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5094 2022/11/28 18:27:29 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:27:29 - mmengine - INFO - Epoch(train) [6][700/2462] lr: 7.5419e-02 eta: 0:15:24 time: 0.0372 data_time: 0.0060 memory: 1253 loss: 0.4777 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4777 2022/11/28 18:27:33 - mmengine - INFO - Epoch(train) [6][800/2462] lr: 7.5075e-02 eta: 0:15:21 time: 0.0360 data_time: 0.0064 memory: 1253 loss: 0.4478 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4478 2022/11/28 18:27:36 - mmengine - INFO - Epoch(train) [6][900/2462] lr: 7.4729e-02 eta: 0:15:17 time: 0.0345 data_time: 0.0062 memory: 1253 loss: 0.5184 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5184 2022/11/28 18:27:40 - mmengine - INFO - Epoch(train) [6][1000/2462] lr: 7.4382e-02 eta: 0:15:14 time: 0.0345 data_time: 0.0062 memory: 1253 loss: 0.4798 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4798 2022/11/28 18:27:43 - mmengine - INFO - Epoch(train) [6][1100/2462] lr: 7.4033e-02 eta: 0:15:10 time: 0.0347 data_time: 0.0067 memory: 1253 loss: 0.4597 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.4597 2022/11/28 18:27:47 - mmengine - INFO - Epoch(train) [6][1200/2462] lr: 7.3682e-02 eta: 0:15:07 time: 0.0342 data_time: 0.0061 memory: 1253 loss: 0.4999 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4999 2022/11/28 18:27:50 - mmengine - INFO - Epoch(train) [6][1300/2462] lr: 7.3330e-02 eta: 0:15:03 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.4116 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4116 2022/11/28 18:27:53 - mmengine - INFO - Epoch(train) [6][1400/2462] lr: 7.2977e-02 eta: 0:14:59 time: 0.0343 data_time: 0.0060 memory: 1253 loss: 0.4751 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4751 2022/11/28 18:27:57 - mmengine - INFO - Epoch(train) [6][1500/2462] lr: 7.2622e-02 eta: 0:14:55 time: 0.0342 data_time: 0.0060 memory: 1253 loss: 0.3917 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3917 2022/11/28 18:28:00 - mmengine - INFO - Epoch(train) [6][1600/2462] lr: 7.2266e-02 eta: 0:14:52 time: 0.0341 data_time: 0.0059 memory: 1253 loss: 0.4578 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4578 2022/11/28 18:28:03 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:28:04 - mmengine - INFO - Epoch(train) [6][1700/2462] lr: 7.1908e-02 eta: 0:14:48 time: 0.0343 data_time: 0.0060 memory: 1253 loss: 0.4446 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4446 2022/11/28 18:28:07 - mmengine - INFO - Epoch(train) [6][1800/2462] lr: 7.1549e-02 eta: 0:14:45 time: 0.0346 data_time: 0.0060 memory: 1253 loss: 0.4571 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4571 2022/11/28 18:28:11 - mmengine - INFO - Epoch(train) [6][1900/2462] lr: 7.1188e-02 eta: 0:14:41 time: 0.0350 data_time: 0.0060 memory: 1253 loss: 0.4587 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4587 2022/11/28 18:28:14 - mmengine - INFO - Epoch(train) [6][2000/2462] lr: 7.0826e-02 eta: 0:14:38 time: 0.0342 data_time: 0.0060 memory: 1253 loss: 0.4348 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.4348 2022/11/28 18:28:18 - mmengine - INFO - Epoch(train) [6][2100/2462] lr: 7.0463e-02 eta: 0:14:34 time: 0.0339 data_time: 0.0061 memory: 1253 loss: 0.4659 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.4659 2022/11/28 18:28:21 - mmengine - INFO - Epoch(train) [6][2200/2462] lr: 7.0099e-02 eta: 0:14:30 time: 0.0335 data_time: 0.0060 memory: 1253 loss: 0.4710 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4710 2022/11/28 18:28:24 - mmengine - INFO - Epoch(train) [6][2300/2462] lr: 6.9733e-02 eta: 0:14:27 time: 0.0355 data_time: 0.0060 memory: 1253 loss: 0.4027 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.4027 2022/11/28 18:28:28 - mmengine - INFO - Epoch(train) [6][2400/2462] lr: 6.9366e-02 eta: 0:14:23 time: 0.0336 data_time: 0.0060 memory: 1253 loss: 0.4549 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4549 2022/11/28 18:28:30 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:28:30 - mmengine - INFO - Epoch(train) [6][2462/2462] lr: 6.9138e-02 eta: 0:14:21 time: 0.0337 data_time: 0.0061 memory: 1253 loss: 0.4678 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4678 2022/11/28 18:28:30 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/11/28 18:28:32 - mmengine - INFO - Epoch(val) [6][100/398] eta: 0:00:04 time: 0.0152 data_time: 0.0060 memory: 262 2022/11/28 18:28:33 - mmengine - INFO - Epoch(val) [6][200/398] eta: 0:00:03 time: 0.0147 data_time: 0.0057 memory: 262 2022/11/28 18:28:35 - mmengine - INFO - Epoch(val) [6][300/398] eta: 0:00:01 time: 0.0148 data_time: 0.0057 memory: 262 2022/11/28 18:28:37 - mmengine - INFO - Epoch(val) [6][398/398] acc/top1: 0.7203 acc/top5: 0.9385 acc/mean1: 0.7481 2022/11/28 18:28:37 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_5.pth is removed 2022/11/28 18:28:38 - mmengine - INFO - The best checkpoint with 0.7203 acc/top1 at 6 epoch is saved to best_acc/top1_epoch_6.pth. 2022/11/28 18:28:41 - mmengine - INFO - Epoch(train) [7][100/2462] lr: 6.8769e-02 eta: 0:14:17 time: 0.0343 data_time: 0.0060 memory: 1253 loss: 0.4046 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4046 2022/11/28 18:28:45 - mmengine - INFO - Epoch(train) [7][200/2462] lr: 6.8399e-02 eta: 0:14:14 time: 0.0342 data_time: 0.0060 memory: 1253 loss: 0.4554 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.4554 2022/11/28 18:28:46 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:28:48 - mmengine - INFO - Epoch(train) [7][300/2462] lr: 6.8027e-02 eta: 0:14:10 time: 0.0343 data_time: 0.0060 memory: 1253 loss: 0.4571 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4571 2022/11/28 18:28:52 - mmengine - INFO - Epoch(train) [7][400/2462] lr: 6.7655e-02 eta: 0:14:06 time: 0.0339 data_time: 0.0060 memory: 1253 loss: 0.4054 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4054 2022/11/28 18:28:55 - mmengine - INFO - Epoch(train) [7][500/2462] lr: 6.7281e-02 eta: 0:14:03 time: 0.0341 data_time: 0.0060 memory: 1253 loss: 0.4794 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4794 2022/11/28 18:28:58 - mmengine - INFO - Epoch(train) [7][600/2462] lr: 6.6906e-02 eta: 0:13:59 time: 0.0345 data_time: 0.0066 memory: 1253 loss: 0.4196 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4196 2022/11/28 18:29:02 - mmengine - INFO - Epoch(train) [7][700/2462] lr: 6.6531e-02 eta: 0:13:55 time: 0.0346 data_time: 0.0065 memory: 1253 loss: 0.4357 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4357 2022/11/28 18:29:05 - mmengine - INFO - Epoch(train) [7][800/2462] lr: 6.6154e-02 eta: 0:13:52 time: 0.0338 data_time: 0.0060 memory: 1253 loss: 0.4459 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4459 2022/11/28 18:29:09 - mmengine - INFO - Epoch(train) [7][900/2462] lr: 6.5776e-02 eta: 0:13:48 time: 0.0344 data_time: 0.0062 memory: 1253 loss: 0.4016 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4016 2022/11/28 18:29:12 - mmengine - INFO - Epoch(train) [7][1000/2462] lr: 6.5397e-02 eta: 0:13:45 time: 0.0365 data_time: 0.0060 memory: 1253 loss: 0.4045 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4045 2022/11/28 18:29:16 - mmengine - INFO - Epoch(train) [7][1100/2462] lr: 6.5017e-02 eta: 0:13:41 time: 0.0336 data_time: 0.0060 memory: 1253 loss: 0.4122 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4122 2022/11/28 18:29:19 - mmengine - INFO - Epoch(train) [7][1200/2462] lr: 6.4636e-02 eta: 0:13:38 time: 0.0342 data_time: 0.0060 memory: 1253 loss: 0.4234 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4234 2022/11/28 18:29:20 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:29:23 - mmengine - INFO - Epoch(train) [7][1300/2462] lr: 6.4255e-02 eta: 0:13:34 time: 0.0357 data_time: 0.0060 memory: 1253 loss: 0.4600 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4600 2022/11/28 18:29:26 - mmengine - INFO - Epoch(train) [7][1400/2462] lr: 6.3872e-02 eta: 0:13:31 time: 0.0346 data_time: 0.0059 memory: 1253 loss: 0.4959 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4959 2022/11/28 18:29:29 - mmengine - INFO - Epoch(train) [7][1500/2462] lr: 6.3488e-02 eta: 0:13:27 time: 0.0338 data_time: 0.0060 memory: 1253 loss: 0.3746 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3746 2022/11/28 18:29:33 - mmengine - INFO - Epoch(train) [7][1600/2462] lr: 6.3104e-02 eta: 0:13:23 time: 0.0341 data_time: 0.0060 memory: 1253 loss: 0.4479 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4479 2022/11/28 18:29:36 - mmengine - INFO - Epoch(train) [7][1700/2462] lr: 6.2719e-02 eta: 0:13:20 time: 0.0341 data_time: 0.0060 memory: 1253 loss: 0.3950 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.3950 2022/11/28 18:29:40 - mmengine - INFO - Epoch(train) [7][1800/2462] lr: 6.2333e-02 eta: 0:13:16 time: 0.0346 data_time: 0.0060 memory: 1253 loss: 0.3870 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3870 2022/11/28 18:29:43 - mmengine - INFO - Epoch(train) [7][1900/2462] lr: 6.1946e-02 eta: 0:13:13 time: 0.0345 data_time: 0.0067 memory: 1253 loss: 0.4441 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.4441 2022/11/28 18:29:47 - mmengine - INFO - Epoch(train) [7][2000/2462] lr: 6.1558e-02 eta: 0:13:09 time: 0.0351 data_time: 0.0068 memory: 1253 loss: 0.3739 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3739 2022/11/28 18:29:50 - mmengine - INFO - Epoch(train) [7][2100/2462] lr: 6.1170e-02 eta: 0:13:06 time: 0.0343 data_time: 0.0068 memory: 1253 loss: 0.3888 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3888 2022/11/28 18:29:54 - mmengine - INFO - Epoch(train) [7][2200/2462] lr: 6.0781e-02 eta: 0:13:02 time: 0.0346 data_time: 0.0069 memory: 1253 loss: 0.3426 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3426 2022/11/28 18:29:55 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:29:57 - mmengine - INFO - Epoch(train) [7][2300/2462] lr: 6.0391e-02 eta: 0:12:59 time: 0.0350 data_time: 0.0075 memory: 1253 loss: 0.4984 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4984 2022/11/28 18:30:01 - mmengine - INFO - Epoch(train) [7][2400/2462] lr: 6.0001e-02 eta: 0:12:55 time: 0.0344 data_time: 0.0073 memory: 1253 loss: 0.4682 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.4682 2022/11/28 18:30:03 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:30:03 - mmengine - INFO - Epoch(train) [7][2462/2462] lr: 5.9758e-02 eta: 0:12:53 time: 0.0347 data_time: 0.0065 memory: 1253 loss: 0.4181 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4181 2022/11/28 18:30:03 - mmengine - INFO - Saving checkpoint at 7 epochs 2022/11/28 18:30:05 - mmengine - INFO - Epoch(val) [7][100/398] eta: 0:00:04 time: 0.0146 data_time: 0.0056 memory: 262 2022/11/28 18:30:06 - mmengine - INFO - Epoch(val) [7][200/398] eta: 0:00:02 time: 0.0145 data_time: 0.0056 memory: 262 2022/11/28 18:30:08 - mmengine - INFO - Epoch(val) [7][300/398] eta: 0:00:01 time: 0.0148 data_time: 0.0057 memory: 262 2022/11/28 18:30:10 - mmengine - INFO - Epoch(val) [7][398/398] acc/top1: 0.7508 acc/top5: 0.9498 acc/mean1: 0.7631 2022/11/28 18:30:10 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_6.pth is removed 2022/11/28 18:30:11 - mmengine - INFO - The best checkpoint with 0.7508 acc/top1 at 7 epoch is saved to best_acc/top1_epoch_7.pth. 2022/11/28 18:30:14 - mmengine - INFO - Epoch(train) [8][100/2462] lr: 5.9367e-02 eta: 0:12:50 time: 0.0354 data_time: 0.0067 memory: 1253 loss: 0.3916 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3916 2022/11/28 18:30:18 - mmengine - INFO - Epoch(train) [8][200/2462] lr: 5.8975e-02 eta: 0:12:46 time: 0.0357 data_time: 0.0060 memory: 1253 loss: 0.4399 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4399 2022/11/28 18:30:21 - mmengine - INFO - Epoch(train) [8][300/2462] lr: 5.8582e-02 eta: 0:12:43 time: 0.0352 data_time: 0.0061 memory: 1253 loss: 0.3828 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3828 2022/11/28 18:30:25 - mmengine - INFO - Epoch(train) [8][400/2462] lr: 5.8189e-02 eta: 0:12:39 time: 0.0347 data_time: 0.0066 memory: 1253 loss: 0.3555 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3555 2022/11/28 18:30:28 - mmengine - INFO - Epoch(train) [8][500/2462] lr: 5.7796e-02 eta: 0:12:36 time: 0.0342 data_time: 0.0066 memory: 1253 loss: 0.3685 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3685 2022/11/28 18:30:32 - mmengine - INFO - Epoch(train) [8][600/2462] lr: 5.7402e-02 eta: 0:12:32 time: 0.0354 data_time: 0.0068 memory: 1253 loss: 0.3574 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3574 2022/11/28 18:30:35 - mmengine - INFO - Epoch(train) [8][700/2462] lr: 5.7007e-02 eta: 0:12:29 time: 0.0348 data_time: 0.0059 memory: 1253 loss: 0.3845 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3845 2022/11/28 18:30:38 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:30:39 - mmengine - INFO - Epoch(train) [8][800/2462] lr: 5.6612e-02 eta: 0:12:25 time: 0.0356 data_time: 0.0063 memory: 1253 loss: 0.4400 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.4400 2022/11/28 18:30:42 - mmengine - INFO - Epoch(train) [8][900/2462] lr: 5.6216e-02 eta: 0:12:22 time: 0.0347 data_time: 0.0064 memory: 1253 loss: 0.3640 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3640 2022/11/28 18:30:46 - mmengine - INFO - Epoch(train) [8][1000/2462] lr: 5.5821e-02 eta: 0:12:18 time: 0.0343 data_time: 0.0065 memory: 1253 loss: 0.3455 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.3455 2022/11/28 18:30:49 - mmengine - INFO - Epoch(train) [8][1100/2462] lr: 5.5424e-02 eta: 0:12:15 time: 0.0338 data_time: 0.0060 memory: 1253 loss: 0.3671 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.3671 2022/11/28 18:30:53 - mmengine - INFO - Epoch(train) [8][1200/2462] lr: 5.5028e-02 eta: 0:12:11 time: 0.0348 data_time: 0.0061 memory: 1253 loss: 0.4117 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.4117 2022/11/28 18:30:56 - mmengine - INFO - Epoch(train) [8][1300/2462] lr: 5.4631e-02 eta: 0:12:08 time: 0.0343 data_time: 0.0060 memory: 1253 loss: 0.4483 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.4483 2022/11/28 18:30:59 - mmengine - INFO - Epoch(train) [8][1400/2462] lr: 5.4234e-02 eta: 0:12:04 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.3183 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.3183 2022/11/28 18:31:03 - mmengine - INFO - Epoch(train) [8][1500/2462] lr: 5.3836e-02 eta: 0:12:01 time: 0.0359 data_time: 0.0061 memory: 1253 loss: 0.3681 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3681 2022/11/28 18:31:07 - mmengine - INFO - Epoch(train) [8][1600/2462] lr: 5.3439e-02 eta: 0:11:57 time: 0.0354 data_time: 0.0060 memory: 1253 loss: 0.3314 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3314 2022/11/28 18:31:10 - mmengine - INFO - Epoch(train) [8][1700/2462] lr: 5.3041e-02 eta: 0:11:54 time: 0.0355 data_time: 0.0061 memory: 1253 loss: 0.3612 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3612 2022/11/28 18:31:13 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:31:14 - mmengine - INFO - Epoch(train) [8][1800/2462] lr: 5.2643e-02 eta: 0:11:50 time: 0.0345 data_time: 0.0066 memory: 1253 loss: 0.2873 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2873 2022/11/28 18:31:17 - mmengine - INFO - Epoch(train) [8][1900/2462] lr: 5.2244e-02 eta: 0:11:47 time: 0.0358 data_time: 0.0062 memory: 1253 loss: 0.3298 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3298 2022/11/28 18:31:21 - mmengine - INFO - Epoch(train) [8][2000/2462] lr: 5.1846e-02 eta: 0:11:43 time: 0.0336 data_time: 0.0060 memory: 1253 loss: 0.4004 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.4004 2022/11/28 18:31:24 - mmengine - INFO - Epoch(train) [8][2100/2462] lr: 5.1447e-02 eta: 0:11:40 time: 0.0340 data_time: 0.0061 memory: 1253 loss: 0.3453 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3453 2022/11/28 18:31:27 - mmengine - INFO - Epoch(train) [8][2200/2462] lr: 5.1049e-02 eta: 0:11:36 time: 0.0337 data_time: 0.0059 memory: 1253 loss: 0.4527 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.4527 2022/11/28 18:31:31 - mmengine - INFO - Epoch(train) [8][2300/2462] lr: 5.0650e-02 eta: 0:11:33 time: 0.0336 data_time: 0.0060 memory: 1253 loss: 0.3429 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3429 2022/11/28 18:31:34 - mmengine - INFO - Epoch(train) [8][2400/2462] lr: 5.0251e-02 eta: 0:11:29 time: 0.0350 data_time: 0.0061 memory: 1253 loss: 0.4072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.4072 2022/11/28 18:31:36 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:31:36 - mmengine - INFO - Epoch(train) [8][2462/2462] lr: 5.0004e-02 eta: 0:11:27 time: 0.0339 data_time: 0.0061 memory: 1253 loss: 0.3400 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3400 2022/11/28 18:31:36 - mmengine - INFO - Saving checkpoint at 8 epochs 2022/11/28 18:31:38 - mmengine - INFO - Epoch(val) [8][100/398] eta: 0:00:04 time: 0.0149 data_time: 0.0057 memory: 262 2022/11/28 18:31:40 - mmengine - INFO - Epoch(val) [8][200/398] eta: 0:00:03 time: 0.0152 data_time: 0.0061 memory: 262 2022/11/28 18:31:41 - mmengine - INFO - Epoch(val) [8][300/398] eta: 0:00:01 time: 0.0149 data_time: 0.0058 memory: 262 2022/11/28 18:31:44 - mmengine - INFO - Epoch(val) [8][398/398] acc/top1: 0.7551 acc/top5: 0.9558 acc/mean1: 0.7765 2022/11/28 18:31:44 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_7.pth is removed 2022/11/28 18:31:44 - mmengine - INFO - The best checkpoint with 0.7551 acc/top1 at 8 epoch is saved to best_acc/top1_epoch_8.pth. 2022/11/28 18:31:48 - mmengine - INFO - Epoch(train) [9][100/2462] lr: 4.9605e-02 eta: 0:11:23 time: 0.0345 data_time: 0.0060 memory: 1253 loss: 0.3258 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3258 2022/11/28 18:31:51 - mmengine - INFO - Epoch(train) [9][200/2462] lr: 4.9207e-02 eta: 0:11:20 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.3206 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3206 2022/11/28 18:31:54 - mmengine - INFO - Epoch(train) [9][300/2462] lr: 4.8808e-02 eta: 0:11:16 time: 0.0347 data_time: 0.0060 memory: 1253 loss: 0.4000 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.4000 2022/11/28 18:31:55 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:31:58 - mmengine - INFO - Epoch(train) [9][400/2462] lr: 4.8409e-02 eta: 0:11:13 time: 0.0339 data_time: 0.0060 memory: 1253 loss: 0.3193 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3193 2022/11/28 18:32:01 - mmengine - INFO - Epoch(train) [9][500/2462] lr: 4.8011e-02 eta: 0:11:09 time: 0.0342 data_time: 0.0061 memory: 1253 loss: 0.3811 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3811 2022/11/28 18:32:05 - mmengine - INFO - Epoch(train) [9][600/2462] lr: 4.7612e-02 eta: 0:11:06 time: 0.0341 data_time: 0.0060 memory: 1253 loss: 0.3277 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3277 2022/11/28 18:32:08 - mmengine - INFO - Epoch(train) [9][700/2462] lr: 4.7214e-02 eta: 0:11:02 time: 0.0338 data_time: 0.0060 memory: 1253 loss: 0.3436 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3436 2022/11/28 18:32:12 - mmengine - INFO - Epoch(train) [9][800/2462] lr: 4.6816e-02 eta: 0:10:59 time: 0.0342 data_time: 0.0060 memory: 1253 loss: 0.3366 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.3366 2022/11/28 18:32:15 - mmengine - INFO - Epoch(train) [9][900/2462] lr: 4.6418e-02 eta: 0:10:55 time: 0.0342 data_time: 0.0066 memory: 1253 loss: 0.2866 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2866 2022/11/28 18:32:18 - mmengine - INFO - Epoch(train) [9][1000/2462] lr: 4.6021e-02 eta: 0:10:52 time: 0.0344 data_time: 0.0066 memory: 1253 loss: 0.3817 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3817 2022/11/28 18:32:22 - mmengine - INFO - Epoch(train) [9][1100/2462] lr: 4.5623e-02 eta: 0:10:48 time: 0.0340 data_time: 0.0060 memory: 1253 loss: 0.2682 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.2682 2022/11/28 18:32:25 - mmengine - INFO - Epoch(train) [9][1200/2462] lr: 4.5226e-02 eta: 0:10:44 time: 0.0347 data_time: 0.0060 memory: 1253 loss: 0.3051 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3051 2022/11/28 18:32:29 - mmengine - INFO - Epoch(train) [9][1300/2462] lr: 4.4829e-02 eta: 0:10:41 time: 0.0347 data_time: 0.0060 memory: 1253 loss: 0.3030 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3030 2022/11/28 18:32:29 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:32:32 - mmengine - INFO - Epoch(train) [9][1400/2462] lr: 4.4433e-02 eta: 0:10:37 time: 0.0346 data_time: 0.0066 memory: 1253 loss: 0.3538 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3538 2022/11/28 18:32:36 - mmengine - INFO - Epoch(train) [9][1500/2462] lr: 4.4037e-02 eta: 0:10:34 time: 0.0342 data_time: 0.0061 memory: 1253 loss: 0.2769 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.2769 2022/11/28 18:32:39 - mmengine - INFO - Epoch(train) [9][1600/2462] lr: 4.3641e-02 eta: 0:10:30 time: 0.0354 data_time: 0.0066 memory: 1253 loss: 0.3289 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3289 2022/11/28 18:32:43 - mmengine - INFO - Epoch(train) [9][1700/2462] lr: 4.3246e-02 eta: 0:10:27 time: 0.0345 data_time: 0.0060 memory: 1253 loss: 0.2637 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.2637 2022/11/28 18:32:46 - mmengine - INFO - Epoch(train) [9][1800/2462] lr: 4.2851e-02 eta: 0:10:23 time: 0.0344 data_time: 0.0061 memory: 1253 loss: 0.3556 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.3556 2022/11/28 18:32:50 - mmengine - INFO - Epoch(train) [9][1900/2462] lr: 4.2456e-02 eta: 0:10:20 time: 0.0352 data_time: 0.0062 memory: 1253 loss: 0.3150 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3150 2022/11/28 18:32:53 - mmengine - INFO - Epoch(train) [9][2000/2462] lr: 4.2063e-02 eta: 0:10:17 time: 0.0353 data_time: 0.0062 memory: 1253 loss: 0.3200 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3200 2022/11/28 18:32:57 - mmengine - INFO - Epoch(train) [9][2100/2462] lr: 4.1669e-02 eta: 0:10:13 time: 0.0339 data_time: 0.0060 memory: 1253 loss: 0.3600 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.3600 2022/11/28 18:33:00 - mmengine - INFO - Epoch(train) [9][2200/2462] lr: 4.1276e-02 eta: 0:10:09 time: 0.0341 data_time: 0.0060 memory: 1253 loss: 0.4007 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.4007 2022/11/28 18:33:04 - mmengine - INFO - Epoch(train) [9][2300/2462] lr: 4.0884e-02 eta: 0:10:06 time: 0.0346 data_time: 0.0060 memory: 1253 loss: 0.3185 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3185 2022/11/28 18:33:04 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:33:07 - mmengine - INFO - Epoch(train) [9][2400/2462] lr: 4.0492e-02 eta: 0:10:02 time: 0.0338 data_time: 0.0061 memory: 1253 loss: 0.3379 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.3379 2022/11/28 18:33:09 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:33:09 - mmengine - INFO - Epoch(train) [9][2462/2462] lr: 4.0249e-02 eta: 0:10:00 time: 0.0340 data_time: 0.0062 memory: 1253 loss: 0.3021 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.3021 2022/11/28 18:33:09 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/11/28 18:33:11 - mmengine - INFO - Epoch(val) [9][100/398] eta: 0:00:04 time: 0.0149 data_time: 0.0058 memory: 262 2022/11/28 18:33:13 - mmengine - INFO - Epoch(val) [9][200/398] eta: 0:00:03 time: 0.0152 data_time: 0.0058 memory: 262 2022/11/28 18:33:14 - mmengine - INFO - Epoch(val) [9][300/398] eta: 0:00:01 time: 0.0156 data_time: 0.0061 memory: 262 2022/11/28 18:33:17 - mmengine - INFO - Epoch(val) [9][398/398] acc/top1: 0.7732 acc/top5: 0.9554 acc/mean1: 0.7945 2022/11/28 18:33:17 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_8.pth is removed 2022/11/28 18:33:17 - mmengine - INFO - The best checkpoint with 0.7732 acc/top1 at 9 epoch is saved to best_acc/top1_epoch_9.pth. 2022/11/28 18:33:21 - mmengine - INFO - Epoch(train) [10][100/2462] lr: 3.9859e-02 eta: 0:09:57 time: 0.0346 data_time: 0.0060 memory: 1253 loss: 0.3051 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.3051 2022/11/28 18:33:24 - mmengine - INFO - Epoch(train) [10][200/2462] lr: 3.9468e-02 eta: 0:09:53 time: 0.0355 data_time: 0.0062 memory: 1253 loss: 0.2625 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2625 2022/11/28 18:33:28 - mmengine - INFO - Epoch(train) [10][300/2462] lr: 3.9079e-02 eta: 0:09:50 time: 0.0347 data_time: 0.0067 memory: 1253 loss: 0.2559 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2559 2022/11/28 18:33:31 - mmengine - INFO - Epoch(train) [10][400/2462] lr: 3.8690e-02 eta: 0:09:46 time: 0.0342 data_time: 0.0065 memory: 1253 loss: 0.2709 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2709 2022/11/28 18:33:35 - mmengine - INFO - Epoch(train) [10][500/2462] lr: 3.8302e-02 eta: 0:09:43 time: 0.0345 data_time: 0.0060 memory: 1253 loss: 0.2651 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.2651 2022/11/28 18:33:38 - mmengine - INFO - Epoch(train) [10][600/2462] lr: 3.7915e-02 eta: 0:09:39 time: 0.0341 data_time: 0.0061 memory: 1253 loss: 0.2964 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2964 2022/11/28 18:33:41 - mmengine - INFO - Epoch(train) [10][700/2462] lr: 3.7528e-02 eta: 0:09:36 time: 0.0342 data_time: 0.0059 memory: 1253 loss: 0.2495 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2495 2022/11/28 18:33:45 - mmengine - INFO - Epoch(train) [10][800/2462] lr: 3.7143e-02 eta: 0:09:32 time: 0.0339 data_time: 0.0061 memory: 1253 loss: 0.2840 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2840 2022/11/28 18:33:46 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:33:48 - mmengine - INFO - Epoch(train) [10][900/2462] lr: 3.6758e-02 eta: 0:09:29 time: 0.0344 data_time: 0.0060 memory: 1253 loss: 0.3042 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.3042 2022/11/28 18:33:52 - mmengine - INFO - Epoch(train) [10][1000/2462] lr: 3.6373e-02 eta: 0:09:25 time: 0.0349 data_time: 0.0060 memory: 1253 loss: 0.2649 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2649 2022/11/28 18:33:55 - mmengine - INFO - Epoch(train) [10][1100/2462] lr: 3.5990e-02 eta: 0:09:22 time: 0.0358 data_time: 0.0060 memory: 1253 loss: 0.2581 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2581 2022/11/28 18:33:59 - mmengine - INFO - Epoch(train) [10][1200/2462] lr: 3.5608e-02 eta: 0:09:18 time: 0.0346 data_time: 0.0060 memory: 1253 loss: 0.2933 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2933 2022/11/28 18:34:02 - mmengine - INFO - Epoch(train) [10][1300/2462] lr: 3.5226e-02 eta: 0:09:15 time: 0.0349 data_time: 0.0060 memory: 1253 loss: 0.2746 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.2746 2022/11/28 18:34:06 - mmengine - INFO - Epoch(train) [10][1400/2462] lr: 3.4846e-02 eta: 0:09:11 time: 0.0346 data_time: 0.0060 memory: 1253 loss: 0.2194 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2194 2022/11/28 18:34:09 - mmengine - INFO - Epoch(train) [10][1500/2462] lr: 3.4466e-02 eta: 0:09:08 time: 0.0346 data_time: 0.0067 memory: 1253 loss: 0.3256 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.3256 2022/11/28 18:34:13 - mmengine - INFO - Epoch(train) [10][1600/2462] lr: 3.4088e-02 eta: 0:09:04 time: 0.0348 data_time: 0.0060 memory: 1253 loss: 0.2508 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2508 2022/11/28 18:34:16 - mmengine - INFO - Epoch(train) [10][1700/2462] lr: 3.3710e-02 eta: 0:09:01 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.2680 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2680 2022/11/28 18:34:20 - mmengine - INFO - Epoch(train) [10][1800/2462] lr: 3.3334e-02 eta: 0:08:57 time: 0.0342 data_time: 0.0060 memory: 1253 loss: 0.2677 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2677 2022/11/28 18:34:21 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:34:23 - mmengine - INFO - Epoch(train) [10][1900/2462] lr: 3.2959e-02 eta: 0:08:54 time: 0.0357 data_time: 0.0060 memory: 1253 loss: 0.2332 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2332 2022/11/28 18:34:27 - mmengine - INFO - Epoch(train) [10][2000/2462] lr: 3.2584e-02 eta: 0:08:50 time: 0.0362 data_time: 0.0068 memory: 1253 loss: 0.2482 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2482 2022/11/28 18:34:30 - mmengine - INFO - Epoch(train) [10][2100/2462] lr: 3.2211e-02 eta: 0:08:47 time: 0.0360 data_time: 0.0065 memory: 1253 loss: 0.2726 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2726 2022/11/28 18:34:34 - mmengine - INFO - Epoch(train) [10][2200/2462] lr: 3.1839e-02 eta: 0:08:43 time: 0.0341 data_time: 0.0060 memory: 1253 loss: 0.1957 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1957 2022/11/28 18:34:37 - mmengine - INFO - Epoch(train) [10][2300/2462] lr: 3.1468e-02 eta: 0:08:40 time: 0.0339 data_time: 0.0060 memory: 1253 loss: 0.2038 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2038 2022/11/28 18:34:41 - mmengine - INFO - Epoch(train) [10][2400/2462] lr: 3.1098e-02 eta: 0:08:36 time: 0.0341 data_time: 0.0060 memory: 1253 loss: 0.2418 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2418 2022/11/28 18:34:43 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:34:43 - mmengine - INFO - Epoch(train) [10][2462/2462] lr: 3.0870e-02 eta: 0:08:34 time: 0.0341 data_time: 0.0061 memory: 1253 loss: 0.1782 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1782 2022/11/28 18:34:43 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/11/28 18:34:45 - mmengine - INFO - Epoch(val) [10][100/398] eta: 0:00:04 time: 0.0149 data_time: 0.0058 memory: 262 2022/11/28 18:34:46 - mmengine - INFO - Epoch(val) [10][200/398] eta: 0:00:03 time: 0.0155 data_time: 0.0059 memory: 262 2022/11/28 18:34:48 - mmengine - INFO - Epoch(val) [10][300/398] eta: 0:00:01 time: 0.0151 data_time: 0.0059 memory: 262 2022/11/28 18:34:50 - mmengine - INFO - Epoch(val) [10][398/398] acc/top1: 0.7860 acc/top5: 0.9624 acc/mean1: 0.8015 2022/11/28 18:34:50 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_9.pth is removed 2022/11/28 18:34:51 - mmengine - INFO - The best checkpoint with 0.7860 acc/top1 at 10 epoch is saved to best_acc/top1_epoch_10.pth. 2022/11/28 18:34:54 - mmengine - INFO - Epoch(train) [11][100/2462] lr: 3.0502e-02 eta: 0:08:31 time: 0.0367 data_time: 0.0066 memory: 1253 loss: 0.2193 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2193 2022/11/28 18:34:58 - mmengine - INFO - Epoch(train) [11][200/2462] lr: 3.0135e-02 eta: 0:08:27 time: 0.0344 data_time: 0.0061 memory: 1253 loss: 0.1824 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1824 2022/11/28 18:35:01 - mmengine - INFO - Epoch(train) [11][300/2462] lr: 2.9770e-02 eta: 0:08:24 time: 0.0347 data_time: 0.0062 memory: 1253 loss: 0.2191 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2191 2022/11/28 18:35:04 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:35:05 - mmengine - INFO - Epoch(train) [11][400/2462] lr: 2.9406e-02 eta: 0:08:20 time: 0.0354 data_time: 0.0061 memory: 1253 loss: 0.2115 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2115 2022/11/28 18:35:08 - mmengine - INFO - Epoch(train) [11][500/2462] lr: 2.9043e-02 eta: 0:08:17 time: 0.0365 data_time: 0.0067 memory: 1253 loss: 0.2892 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.2892 2022/11/28 18:35:12 - mmengine - INFO - Epoch(train) [11][600/2462] lr: 2.8682e-02 eta: 0:08:14 time: 0.0359 data_time: 0.0062 memory: 1253 loss: 0.2120 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2120 2022/11/28 18:35:16 - mmengine - INFO - Epoch(train) [11][700/2462] lr: 2.8322e-02 eta: 0:08:10 time: 0.0377 data_time: 0.0066 memory: 1253 loss: 0.1590 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1590 2022/11/28 18:35:19 - mmengine - INFO - Epoch(train) [11][800/2462] lr: 2.7963e-02 eta: 0:08:07 time: 0.0361 data_time: 0.0063 memory: 1253 loss: 0.1872 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1872 2022/11/28 18:35:23 - mmengine - INFO - Epoch(train) [11][900/2462] lr: 2.7606e-02 eta: 0:08:03 time: 0.0379 data_time: 0.0061 memory: 1253 loss: 0.2280 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2280 2022/11/28 18:35:27 - mmengine - INFO - Epoch(train) [11][1000/2462] lr: 2.7250e-02 eta: 0:08:00 time: 0.0368 data_time: 0.0063 memory: 1253 loss: 0.2270 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2270 2022/11/28 18:35:30 - mmengine - INFO - Epoch(train) [11][1100/2462] lr: 2.6896e-02 eta: 0:07:57 time: 0.0347 data_time: 0.0062 memory: 1253 loss: 0.1648 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1648 2022/11/28 18:35:34 - mmengine - INFO - Epoch(train) [11][1200/2462] lr: 2.6543e-02 eta: 0:07:53 time: 0.0351 data_time: 0.0070 memory: 1253 loss: 0.2384 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.2384 2022/11/28 18:35:37 - mmengine - INFO - Epoch(train) [11][1300/2462] lr: 2.6191e-02 eta: 0:07:50 time: 0.0365 data_time: 0.0061 memory: 1253 loss: 0.1567 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1567 2022/11/28 18:35:40 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:35:41 - mmengine - INFO - Epoch(train) [11][1400/2462] lr: 2.5841e-02 eta: 0:07:46 time: 0.0365 data_time: 0.0063 memory: 1253 loss: 0.1830 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1830 2022/11/28 18:35:45 - mmengine - INFO - Epoch(train) [11][1500/2462] lr: 2.5493e-02 eta: 0:07:43 time: 0.0354 data_time: 0.0071 memory: 1253 loss: 0.2012 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.2012 2022/11/28 18:35:48 - mmengine - INFO - Epoch(train) [11][1600/2462] lr: 2.5146e-02 eta: 0:07:39 time: 0.0353 data_time: 0.0060 memory: 1253 loss: 0.1300 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1300 2022/11/28 18:35:52 - mmengine - INFO - Epoch(train) [11][1700/2462] lr: 2.4801e-02 eta: 0:07:36 time: 0.0359 data_time: 0.0068 memory: 1253 loss: 0.1610 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1610 2022/11/28 18:35:55 - mmengine - INFO - Epoch(train) [11][1800/2462] lr: 2.4458e-02 eta: 0:07:32 time: 0.0349 data_time: 0.0062 memory: 1253 loss: 0.1661 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1661 2022/11/28 18:35:59 - mmengine - INFO - Epoch(train) [11][1900/2462] lr: 2.4116e-02 eta: 0:07:29 time: 0.0363 data_time: 0.0067 memory: 1253 loss: 0.1714 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1714 2022/11/28 18:36:03 - mmengine - INFO - Epoch(train) [11][2000/2462] lr: 2.3775e-02 eta: 0:07:26 time: 0.0363 data_time: 0.0064 memory: 1253 loss: 0.2051 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.2051 2022/11/28 18:36:06 - mmengine - INFO - Epoch(train) [11][2100/2462] lr: 2.3437e-02 eta: 0:07:22 time: 0.0361 data_time: 0.0063 memory: 1253 loss: 0.1271 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1271 2022/11/28 18:36:10 - mmengine - INFO - Epoch(train) [11][2200/2462] lr: 2.3100e-02 eta: 0:07:19 time: 0.0367 data_time: 0.0069 memory: 1253 loss: 0.1909 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1909 2022/11/28 18:36:13 - mmengine - INFO - Epoch(train) [11][2300/2462] lr: 2.2764e-02 eta: 0:07:15 time: 0.0353 data_time: 0.0069 memory: 1253 loss: 0.1705 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1705 2022/11/28 18:36:16 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:36:17 - mmengine - INFO - Epoch(train) [11][2400/2462] lr: 2.2431e-02 eta: 0:07:12 time: 0.0364 data_time: 0.0067 memory: 1253 loss: 0.1809 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1809 2022/11/28 18:36:19 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:36:19 - mmengine - INFO - Epoch(train) [11][2462/2462] lr: 2.2225e-02 eta: 0:07:10 time: 0.0374 data_time: 0.0063 memory: 1253 loss: 0.1327 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1327 2022/11/28 18:36:19 - mmengine - INFO - Saving checkpoint at 11 epochs 2022/11/28 18:36:21 - mmengine - INFO - Epoch(val) [11][100/398] eta: 0:00:04 time: 0.0150 data_time: 0.0059 memory: 262 2022/11/28 18:36:23 - mmengine - INFO - Epoch(val) [11][200/398] eta: 0:00:03 time: 0.0149 data_time: 0.0058 memory: 262 2022/11/28 18:36:24 - mmengine - INFO - Epoch(val) [11][300/398] eta: 0:00:01 time: 0.0164 data_time: 0.0068 memory: 262 2022/11/28 18:36:27 - mmengine - INFO - Epoch(val) [11][398/398] acc/top1: 0.7748 acc/top5: 0.9529 acc/mean1: 0.7984 2022/11/28 18:36:31 - mmengine - INFO - Epoch(train) [12][100/2462] lr: 2.1894e-02 eta: 0:07:06 time: 0.0351 data_time: 0.0063 memory: 1253 loss: 0.1408 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1408 2022/11/28 18:36:34 - mmengine - INFO - Epoch(train) [12][200/2462] lr: 2.1565e-02 eta: 0:07:03 time: 0.0368 data_time: 0.0064 memory: 1253 loss: 0.1180 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1180 2022/11/28 18:36:38 - mmengine - INFO - Epoch(train) [12][300/2462] lr: 2.1238e-02 eta: 0:06:59 time: 0.0348 data_time: 0.0061 memory: 1253 loss: 0.1148 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1148 2022/11/28 18:36:41 - mmengine - INFO - Epoch(train) [12][400/2462] lr: 2.0913e-02 eta: 0:06:56 time: 0.0355 data_time: 0.0061 memory: 1253 loss: 0.1630 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1630 2022/11/28 18:36:45 - mmengine - INFO - Epoch(train) [12][500/2462] lr: 2.0589e-02 eta: 0:06:52 time: 0.0353 data_time: 0.0062 memory: 1253 loss: 0.1258 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1258 2022/11/28 18:36:48 - mmengine - INFO - Epoch(train) [12][600/2462] lr: 2.0268e-02 eta: 0:06:49 time: 0.0352 data_time: 0.0062 memory: 1253 loss: 0.1141 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1141 2022/11/28 18:36:52 - mmengine - INFO - Epoch(train) [12][700/2462] lr: 1.9948e-02 eta: 0:06:45 time: 0.0348 data_time: 0.0061 memory: 1253 loss: 0.1277 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1277 2022/11/28 18:36:55 - mmengine - INFO - Epoch(train) [12][800/2462] lr: 1.9631e-02 eta: 0:06:42 time: 0.0366 data_time: 0.0061 memory: 1253 loss: 0.1500 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1500 2022/11/28 18:36:59 - mmengine - INFO - Epoch(train) [12][900/2462] lr: 1.9315e-02 eta: 0:06:38 time: 0.0346 data_time: 0.0062 memory: 1253 loss: 0.1241 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1241 2022/11/28 18:37:00 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:37:03 - mmengine - INFO - Epoch(train) [12][1000/2462] lr: 1.9001e-02 eta: 0:06:35 time: 0.0348 data_time: 0.0068 memory: 1253 loss: 0.1342 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1342 2022/11/28 18:37:06 - mmengine - INFO - Epoch(train) [12][1100/2462] lr: 1.8689e-02 eta: 0:06:32 time: 0.0352 data_time: 0.0062 memory: 1253 loss: 0.1456 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1456 2022/11/28 18:37:10 - mmengine - INFO - Epoch(train) [12][1200/2462] lr: 1.8379e-02 eta: 0:06:28 time: 0.0382 data_time: 0.0069 memory: 1253 loss: 0.1172 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1172 2022/11/28 18:37:14 - mmengine - INFO - Epoch(train) [12][1300/2462] lr: 1.8071e-02 eta: 0:06:25 time: 0.0349 data_time: 0.0061 memory: 1253 loss: 0.1401 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1401 2022/11/28 18:37:17 - mmengine - INFO - Epoch(train) [12][1400/2462] lr: 1.7765e-02 eta: 0:06:21 time: 0.0369 data_time: 0.0062 memory: 1253 loss: 0.1069 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1069 2022/11/28 18:37:21 - mmengine - INFO - Epoch(train) [12][1500/2462] lr: 1.7462e-02 eta: 0:06:18 time: 0.0369 data_time: 0.0063 memory: 1253 loss: 0.1337 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1337 2022/11/28 18:37:24 - mmengine - INFO - Epoch(train) [12][1600/2462] lr: 1.7160e-02 eta: 0:06:14 time: 0.0362 data_time: 0.0061 memory: 1253 loss: 0.1277 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1277 2022/11/28 18:37:28 - mmengine - INFO - Epoch(train) [12][1700/2462] lr: 1.6860e-02 eta: 0:06:11 time: 0.0376 data_time: 0.0067 memory: 1253 loss: 0.1095 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1095 2022/11/28 18:37:32 - mmengine - INFO - Epoch(train) [12][1800/2462] lr: 1.6563e-02 eta: 0:06:07 time: 0.0365 data_time: 0.0062 memory: 1253 loss: 0.1047 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.1047 2022/11/28 18:37:35 - mmengine - INFO - Epoch(train) [12][1900/2462] lr: 1.6267e-02 eta: 0:06:04 time: 0.0357 data_time: 0.0062 memory: 1253 loss: 0.1061 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1061 2022/11/28 18:37:36 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:37:39 - mmengine - INFO - Epoch(train) [12][2000/2462] lr: 1.5974e-02 eta: 0:06:01 time: 0.0362 data_time: 0.0070 memory: 1253 loss: 0.0892 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0892 2022/11/28 18:37:43 - mmengine - INFO - Epoch(train) [12][2100/2462] lr: 1.5683e-02 eta: 0:05:57 time: 0.0378 data_time: 0.0062 memory: 1253 loss: 0.1195 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1195 2022/11/28 18:37:46 - mmengine - INFO - Epoch(train) [12][2200/2462] lr: 1.5394e-02 eta: 0:05:54 time: 0.0363 data_time: 0.0062 memory: 1253 loss: 0.0810 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0810 2022/11/28 18:37:50 - mmengine - INFO - Epoch(train) [12][2300/2462] lr: 1.5107e-02 eta: 0:05:50 time: 0.0358 data_time: 0.0062 memory: 1253 loss: 0.1160 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.1160 2022/11/28 18:37:54 - mmengine - INFO - Epoch(train) [12][2400/2462] lr: 1.4823e-02 eta: 0:05:47 time: 0.0355 data_time: 0.0068 memory: 1253 loss: 0.1029 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1029 2022/11/28 18:37:56 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:37:56 - mmengine - INFO - Epoch(train) [12][2462/2462] lr: 1.4647e-02 eta: 0:05:45 time: 0.0363 data_time: 0.0062 memory: 1253 loss: 0.1029 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.1029 2022/11/28 18:37:56 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/11/28 18:37:58 - mmengine - INFO - Epoch(val) [12][100/398] eta: 0:00:04 time: 0.0150 data_time: 0.0059 memory: 262 2022/11/28 18:37:59 - mmengine - INFO - Epoch(val) [12][200/398] eta: 0:00:03 time: 0.0151 data_time: 0.0058 memory: 262 2022/11/28 18:38:01 - mmengine - INFO - Epoch(val) [12][300/398] eta: 0:00:01 time: 0.0152 data_time: 0.0059 memory: 262 2022/11/28 18:38:03 - mmengine - INFO - Epoch(val) [12][398/398] acc/top1: 0.8024 acc/top5: 0.9637 acc/mean1: 0.8227 2022/11/28 18:38:03 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_10.pth is removed 2022/11/28 18:38:04 - mmengine - INFO - The best checkpoint with 0.8024 acc/top1 at 12 epoch is saved to best_acc/top1_epoch_12.pth. 2022/11/28 18:38:07 - mmengine - INFO - Epoch(train) [13][100/2462] lr: 1.4367e-02 eta: 0:05:41 time: 0.0347 data_time: 0.0061 memory: 1253 loss: 0.0912 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0912 2022/11/28 18:38:11 - mmengine - INFO - Epoch(train) [13][200/2462] lr: 1.4088e-02 eta: 0:05:38 time: 0.0372 data_time: 0.0070 memory: 1253 loss: 0.0763 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0763 2022/11/28 18:38:14 - mmengine - INFO - Epoch(train) [13][300/2462] lr: 1.3812e-02 eta: 0:05:34 time: 0.0363 data_time: 0.0071 memory: 1253 loss: 0.0648 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0648 2022/11/28 18:38:18 - mmengine - INFO - Epoch(train) [13][400/2462] lr: 1.3538e-02 eta: 0:05:31 time: 0.0370 data_time: 0.0062 memory: 1253 loss: 0.0700 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0700 2022/11/28 18:38:20 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:38:22 - mmengine - INFO - Epoch(train) [13][500/2462] lr: 1.3266e-02 eta: 0:05:27 time: 0.0349 data_time: 0.0062 memory: 1253 loss: 0.0721 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0721 2022/11/28 18:38:25 - mmengine - INFO - Epoch(train) [13][600/2462] lr: 1.2997e-02 eta: 0:05:24 time: 0.0365 data_time: 0.0063 memory: 1253 loss: 0.0805 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0805 2022/11/28 18:38:29 - mmengine - INFO - Epoch(train) [13][700/2462] lr: 1.2730e-02 eta: 0:05:20 time: 0.0354 data_time: 0.0062 memory: 1253 loss: 0.0753 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0753 2022/11/28 18:38:32 - mmengine - INFO - Epoch(train) [13][800/2462] lr: 1.2465e-02 eta: 0:05:17 time: 0.0368 data_time: 0.0062 memory: 1253 loss: 0.0675 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0675 2022/11/28 18:38:36 - mmengine - INFO - Epoch(train) [13][900/2462] lr: 1.2203e-02 eta: 0:05:13 time: 0.0349 data_time: 0.0068 memory: 1253 loss: 0.0645 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0645 2022/11/28 18:38:40 - mmengine - INFO - Epoch(train) [13][1000/2462] lr: 1.1943e-02 eta: 0:05:10 time: 0.0374 data_time: 0.0069 memory: 1253 loss: 0.0514 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0514 2022/11/28 18:38:43 - mmengine - INFO - Epoch(train) [13][1100/2462] lr: 1.1686e-02 eta: 0:05:06 time: 0.0368 data_time: 0.0062 memory: 1253 loss: 0.0560 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0560 2022/11/28 18:38:47 - mmengine - INFO - Epoch(train) [13][1200/2462] lr: 1.1431e-02 eta: 0:05:03 time: 0.0367 data_time: 0.0064 memory: 1253 loss: 0.0623 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0623 2022/11/28 18:38:51 - mmengine - INFO - Epoch(train) [13][1300/2462] lr: 1.1178e-02 eta: 0:04:59 time: 0.0347 data_time: 0.0061 memory: 1253 loss: 0.0696 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0696 2022/11/28 18:38:54 - mmengine - INFO - Epoch(train) [13][1400/2462] lr: 1.0928e-02 eta: 0:04:56 time: 0.0359 data_time: 0.0063 memory: 1253 loss: 0.0942 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0942 2022/11/28 18:38:56 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:38:58 - mmengine - INFO - Epoch(train) [13][1500/2462] lr: 1.0680e-02 eta: 0:04:52 time: 0.0380 data_time: 0.0062 memory: 1253 loss: 0.0706 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0706 2022/11/28 18:39:02 - mmengine - INFO - Epoch(train) [13][1600/2462] lr: 1.0435e-02 eta: 0:04:49 time: 0.0362 data_time: 0.0062 memory: 1253 loss: 0.0615 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0615 2022/11/28 18:39:05 - mmengine - INFO - Epoch(train) [13][1700/2462] lr: 1.0193e-02 eta: 0:04:45 time: 0.0348 data_time: 0.0062 memory: 1253 loss: 0.0446 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0446 2022/11/28 18:39:09 - mmengine - INFO - Epoch(train) [13][1800/2462] lr: 9.9527e-03 eta: 0:04:42 time: 0.0360 data_time: 0.0064 memory: 1253 loss: 0.0465 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0465 2022/11/28 18:39:12 - mmengine - INFO - Epoch(train) [13][1900/2462] lr: 9.7153e-03 eta: 0:04:38 time: 0.0362 data_time: 0.0067 memory: 1253 loss: 0.0340 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0340 2022/11/28 18:39:16 - mmengine - INFO - Epoch(train) [13][2000/2462] lr: 9.4804e-03 eta: 0:04:35 time: 0.0346 data_time: 0.0062 memory: 1253 loss: 0.0613 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0613 2022/11/28 18:39:19 - mmengine - INFO - Epoch(train) [13][2100/2462] lr: 9.2480e-03 eta: 0:04:31 time: 0.0367 data_time: 0.0066 memory: 1253 loss: 0.0651 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0651 2022/11/28 18:39:23 - mmengine - INFO - Epoch(train) [13][2200/2462] lr: 9.0183e-03 eta: 0:04:28 time: 0.0355 data_time: 0.0061 memory: 1253 loss: 0.0420 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0420 2022/11/28 18:39:27 - mmengine - INFO - Epoch(train) [13][2300/2462] lr: 8.7911e-03 eta: 0:04:25 time: 0.0353 data_time: 0.0064 memory: 1253 loss: 0.0455 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0455 2022/11/28 18:39:30 - mmengine - INFO - Epoch(train) [13][2400/2462] lr: 8.5666e-03 eta: 0:04:21 time: 0.0371 data_time: 0.0067 memory: 1253 loss: 0.0372 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0372 2022/11/28 18:39:32 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:39:33 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:39:33 - mmengine - INFO - Epoch(train) [13][2462/2462] lr: 8.4287e-03 eta: 0:04:19 time: 0.0380 data_time: 0.0069 memory: 1253 loss: 0.0303 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0303 2022/11/28 18:39:33 - mmengine - INFO - Saving checkpoint at 13 epochs 2022/11/28 18:39:35 - mmengine - INFO - Epoch(val) [13][100/398] eta: 0:00:04 time: 0.0153 data_time: 0.0061 memory: 262 2022/11/28 18:39:36 - mmengine - INFO - Epoch(val) [13][200/398] eta: 0:00:03 time: 0.0150 data_time: 0.0057 memory: 262 2022/11/28 18:39:38 - mmengine - INFO - Epoch(val) [13][300/398] eta: 0:00:01 time: 0.0151 data_time: 0.0059 memory: 262 2022/11/28 18:39:40 - mmengine - INFO - Epoch(val) [13][398/398] acc/top1: 0.8185 acc/top5: 0.9654 acc/mean1: 0.8372 2022/11/28 18:39:40 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_12.pth is removed 2022/11/28 18:39:41 - mmengine - INFO - The best checkpoint with 0.8185 acc/top1 at 13 epoch is saved to best_acc/top1_epoch_13.pth. 2022/11/28 18:39:44 - mmengine - INFO - Epoch(train) [14][100/2462] lr: 8.2085e-03 eta: 0:04:15 time: 0.0357 data_time: 0.0062 memory: 1253 loss: 0.0341 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0341 2022/11/28 18:39:48 - mmengine - INFO - Epoch(train) [14][200/2462] lr: 7.9909e-03 eta: 0:04:12 time: 0.0366 data_time: 0.0072 memory: 1253 loss: 0.0321 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0321 2022/11/28 18:39:51 - mmengine - INFO - Epoch(train) [14][300/2462] lr: 7.7760e-03 eta: 0:04:08 time: 0.0357 data_time: 0.0066 memory: 1253 loss: 0.0367 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0367 2022/11/28 18:39:55 - mmengine - INFO - Epoch(train) [14][400/2462] lr: 7.5638e-03 eta: 0:04:05 time: 0.0356 data_time: 0.0061 memory: 1253 loss: 0.0279 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0279 2022/11/28 18:39:59 - mmengine - INFO - Epoch(train) [14][500/2462] lr: 7.3542e-03 eta: 0:04:01 time: 0.0348 data_time: 0.0062 memory: 1253 loss: 0.0428 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0428 2022/11/28 18:40:02 - mmengine - INFO - Epoch(train) [14][600/2462] lr: 7.1474e-03 eta: 0:03:58 time: 0.0366 data_time: 0.0062 memory: 1253 loss: 0.0308 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0308 2022/11/28 18:40:06 - mmengine - INFO - Epoch(train) [14][700/2462] lr: 6.9433e-03 eta: 0:03:54 time: 0.0343 data_time: 0.0060 memory: 1253 loss: 0.0351 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0351 2022/11/28 18:40:09 - mmengine - INFO - Epoch(train) [14][800/2462] lr: 6.7420e-03 eta: 0:03:51 time: 0.0369 data_time: 0.0066 memory: 1253 loss: 0.0212 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.0212 2022/11/28 18:40:13 - mmengine - INFO - Epoch(train) [14][900/2462] lr: 6.5434e-03 eta: 0:03:47 time: 0.0367 data_time: 0.0062 memory: 1253 loss: 0.0289 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0289 2022/11/28 18:40:16 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:40:16 - mmengine - INFO - Epoch(train) [14][1000/2462] lr: 6.3476e-03 eta: 0:03:44 time: 0.0361 data_time: 0.0061 memory: 1253 loss: 0.0187 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0187 2022/11/28 18:40:20 - mmengine - INFO - Epoch(train) [14][1100/2462] lr: 6.1545e-03 eta: 0:03:40 time: 0.0356 data_time: 0.0062 memory: 1253 loss: 0.0275 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0275 2022/11/28 18:40:24 - mmengine - INFO - Epoch(train) [14][1200/2462] lr: 5.9642e-03 eta: 0:03:37 time: 0.0366 data_time: 0.0064 memory: 1253 loss: 0.0295 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0295 2022/11/28 18:40:27 - mmengine - INFO - Epoch(train) [14][1300/2462] lr: 5.7768e-03 eta: 0:03:33 time: 0.0363 data_time: 0.0062 memory: 1253 loss: 0.0192 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0192 2022/11/28 18:40:31 - mmengine - INFO - Epoch(train) [14][1400/2462] lr: 5.5921e-03 eta: 0:03:30 time: 0.0346 data_time: 0.0062 memory: 1253 loss: 0.0242 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0242 2022/11/28 18:40:34 - mmengine - INFO - Epoch(train) [14][1500/2462] lr: 5.4103e-03 eta: 0:03:26 time: 0.0349 data_time: 0.0062 memory: 1253 loss: 0.0139 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0139 2022/11/28 18:40:38 - mmengine - INFO - Epoch(train) [14][1600/2462] lr: 5.2313e-03 eta: 0:03:23 time: 0.0351 data_time: 0.0061 memory: 1253 loss: 0.0241 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0241 2022/11/28 18:40:42 - mmengine - INFO - Epoch(train) [14][1700/2462] lr: 5.0551e-03 eta: 0:03:19 time: 0.0358 data_time: 0.0061 memory: 1253 loss: 0.0256 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0256 2022/11/28 18:40:45 - mmengine - INFO - Epoch(train) [14][1800/2462] lr: 4.8818e-03 eta: 0:03:16 time: 0.0348 data_time: 0.0061 memory: 1253 loss: 0.0122 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0122 2022/11/28 18:40:49 - mmengine - INFO - Epoch(train) [14][1900/2462] lr: 4.7114e-03 eta: 0:03:12 time: 0.0349 data_time: 0.0067 memory: 1253 loss: 0.0113 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0113 2022/11/28 18:40:52 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:40:52 - mmengine - INFO - Epoch(train) [14][2000/2462] lr: 4.5439e-03 eta: 0:03:09 time: 0.0348 data_time: 0.0062 memory: 1253 loss: 0.0157 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0157 2022/11/28 18:40:56 - mmengine - INFO - Epoch(train) [14][2100/2462] lr: 4.3792e-03 eta: 0:03:05 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 18:40:59 - mmengine - INFO - Epoch(train) [14][2200/2462] lr: 4.2175e-03 eta: 0:03:02 time: 0.0346 data_time: 0.0061 memory: 1253 loss: 0.0129 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0129 2022/11/28 18:41:03 - mmengine - INFO - Epoch(train) [14][2300/2462] lr: 4.0587e-03 eta: 0:02:58 time: 0.0353 data_time: 0.0062 memory: 1253 loss: 0.0118 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0118 2022/11/28 18:41:06 - mmengine - INFO - Epoch(train) [14][2400/2462] lr: 3.9027e-03 eta: 0:02:55 time: 0.0349 data_time: 0.0062 memory: 1253 loss: 0.0187 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0187 2022/11/28 18:41:08 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:41:08 - mmengine - INFO - Epoch(train) [14][2462/2462] lr: 3.8075e-03 eta: 0:02:53 time: 0.0350 data_time: 0.0063 memory: 1253 loss: 0.0224 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0224 2022/11/28 18:41:08 - mmengine - INFO - Saving checkpoint at 14 epochs 2022/11/28 18:41:10 - mmengine - INFO - Epoch(val) [14][100/398] eta: 0:00:04 time: 0.0150 data_time: 0.0058 memory: 262 2022/11/28 18:41:12 - mmengine - INFO - Epoch(val) [14][200/398] eta: 0:00:03 time: 0.0156 data_time: 0.0062 memory: 262 2022/11/28 18:41:14 - mmengine - INFO - Epoch(val) [14][300/398] eta: 0:00:01 time: 0.0151 data_time: 0.0060 memory: 262 2022/11/28 18:41:16 - mmengine - INFO - Epoch(val) [14][398/398] acc/top1: 0.8269 acc/top5: 0.9676 acc/mean1: 0.8465 2022/11/28 18:41:16 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/daiwenxun/mmlab/mmaction2/work_dirs/stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d/best_acc/top1_epoch_13.pth is removed 2022/11/28 18:41:16 - mmengine - INFO - The best checkpoint with 0.8269 acc/top1 at 14 epoch is saved to best_acc/top1_epoch_14.pth. 2022/11/28 18:41:20 - mmengine - INFO - Epoch(train) [15][100/2462] lr: 3.6564e-03 eta: 0:02:49 time: 0.0355 data_time: 0.0061 memory: 1253 loss: 0.0159 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0159 2022/11/28 18:41:23 - mmengine - INFO - Epoch(train) [15][200/2462] lr: 3.5082e-03 eta: 0:02:46 time: 0.0347 data_time: 0.0062 memory: 1253 loss: 0.0197 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0197 2022/11/28 18:41:27 - mmengine - INFO - Epoch(train) [15][300/2462] lr: 3.3629e-03 eta: 0:02:42 time: 0.0368 data_time: 0.0069 memory: 1253 loss: 0.0142 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0142 2022/11/28 18:41:31 - mmengine - INFO - Epoch(train) [15][400/2462] lr: 3.2206e-03 eta: 0:02:39 time: 0.0364 data_time: 0.0061 memory: 1253 loss: 0.0125 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0125 2022/11/28 18:41:34 - mmengine - INFO - Epoch(train) [15][500/2462] lr: 3.0813e-03 eta: 0:02:35 time: 0.0377 data_time: 0.0062 memory: 1253 loss: 0.0168 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0168 2022/11/28 18:41:36 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:41:38 - mmengine - INFO - Epoch(train) [15][600/2462] lr: 2.9450e-03 eta: 0:02:32 time: 0.0348 data_time: 0.0066 memory: 1253 loss: 0.0203 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0203 2022/11/28 18:41:42 - mmengine - INFO - Epoch(train) [15][700/2462] lr: 2.8117e-03 eta: 0:02:28 time: 0.0360 data_time: 0.0061 memory: 1253 loss: 0.0148 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0148 2022/11/28 18:41:45 - mmengine - INFO - Epoch(train) [15][800/2462] lr: 2.6813e-03 eta: 0:02:25 time: 0.0346 data_time: 0.0062 memory: 1253 loss: 0.0143 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0143 2022/11/28 18:41:49 - mmengine - INFO - Epoch(train) [15][900/2462] lr: 2.5540e-03 eta: 0:02:21 time: 0.0354 data_time: 0.0061 memory: 1253 loss: 0.0166 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0166 2022/11/28 18:41:52 - mmengine - INFO - Epoch(train) [15][1000/2462] lr: 2.4297e-03 eta: 0:02:17 time: 0.0344 data_time: 0.0061 memory: 1253 loss: 0.0151 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0151 2022/11/28 18:41:56 - mmengine - INFO - Epoch(train) [15][1100/2462] lr: 2.3084e-03 eta: 0:02:14 time: 0.0347 data_time: 0.0061 memory: 1253 loss: 0.0164 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0164 2022/11/28 18:41:59 - mmengine - INFO - Epoch(train) [15][1200/2462] lr: 2.1902e-03 eta: 0:02:10 time: 0.0348 data_time: 0.0061 memory: 1253 loss: 0.0130 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0130 2022/11/28 18:42:03 - mmengine - INFO - Epoch(train) [15][1300/2462] lr: 2.0750e-03 eta: 0:02:07 time: 0.0361 data_time: 0.0065 memory: 1253 loss: 0.0133 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0133 2022/11/28 18:42:06 - mmengine - INFO - Epoch(train) [15][1400/2462] lr: 1.9628e-03 eta: 0:02:03 time: 0.0358 data_time: 0.0062 memory: 1253 loss: 0.0119 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0119 2022/11/28 18:42:10 - mmengine - INFO - Epoch(train) [15][1500/2462] lr: 1.8537e-03 eta: 0:02:00 time: 0.0345 data_time: 0.0067 memory: 1253 loss: 0.0143 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0143 2022/11/28 18:42:11 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:42:13 - mmengine - INFO - Epoch(train) [15][1600/2462] lr: 1.7477e-03 eta: 0:01:56 time: 0.0345 data_time: 0.0066 memory: 1253 loss: 0.0133 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0133 2022/11/28 18:42:17 - mmengine - INFO - Epoch(train) [15][1700/2462] lr: 1.6447e-03 eta: 0:01:53 time: 0.0349 data_time: 0.0068 memory: 1253 loss: 0.0146 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0146 2022/11/28 18:42:20 - mmengine - INFO - Epoch(train) [15][1800/2462] lr: 1.5448e-03 eta: 0:01:49 time: 0.0344 data_time: 0.0066 memory: 1253 loss: 0.0137 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0137 2022/11/28 18:42:24 - mmengine - INFO - Epoch(train) [15][1900/2462] lr: 1.4480e-03 eta: 0:01:46 time: 0.0349 data_time: 0.0062 memory: 1253 loss: 0.0164 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0164 2022/11/28 18:42:27 - mmengine - INFO - Epoch(train) [15][2000/2462] lr: 1.3543e-03 eta: 0:01:42 time: 0.0344 data_time: 0.0061 memory: 1253 loss: 0.0140 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0140 2022/11/28 18:42:31 - mmengine - INFO - Epoch(train) [15][2100/2462] lr: 1.2636e-03 eta: 0:01:39 time: 0.0341 data_time: 0.0061 memory: 1253 loss: 0.0127 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0127 2022/11/28 18:42:34 - mmengine - INFO - Epoch(train) [15][2200/2462] lr: 1.1761e-03 eta: 0:01:35 time: 0.0343 data_time: 0.0061 memory: 1253 loss: 0.0135 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0135 2022/11/28 18:42:38 - mmengine - INFO - Epoch(train) [15][2300/2462] lr: 1.0917e-03 eta: 0:01:32 time: 0.0341 data_time: 0.0061 memory: 1253 loss: 0.0117 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0117 2022/11/28 18:42:41 - mmengine - INFO - Epoch(train) [15][2400/2462] lr: 1.0104e-03 eta: 0:01:28 time: 0.0365 data_time: 0.0067 memory: 1253 loss: 0.0133 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0133 2022/11/28 18:42:44 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:42:44 - mmengine - INFO - Epoch(train) [15][2462/2462] lr: 9.6151e-04 eta: 0:01:26 time: 0.0356 data_time: 0.0062 memory: 1253 loss: 0.0145 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0145 2022/11/28 18:42:44 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/11/28 18:42:46 - mmengine - INFO - Epoch(val) [15][100/398] eta: 0:00:04 time: 0.0151 data_time: 0.0059 memory: 262 2022/11/28 18:42:47 - mmengine - INFO - Epoch(val) [15][200/398] eta: 0:00:03 time: 0.0153 data_time: 0.0058 memory: 262 2022/11/28 18:42:49 - mmengine - INFO - Epoch(val) [15][300/398] eta: 0:00:01 time: 0.0153 data_time: 0.0060 memory: 262 2022/11/28 18:42:51 - mmengine - INFO - Epoch(val) [15][398/398] acc/top1: 0.8260 acc/top5: 0.9671 acc/mean1: 0.8468 2022/11/28 18:42:54 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:42:55 - mmengine - INFO - Epoch(train) [16][100/2462] lr: 8.8525e-04 eta: 0:01:23 time: 0.0373 data_time: 0.0061 memory: 1253 loss: 0.0155 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0155 2022/11/28 18:42:59 - mmengine - INFO - Epoch(train) [16][200/2462] lr: 8.1211e-04 eta: 0:01:19 time: 0.0350 data_time: 0.0061 memory: 1253 loss: 0.0137 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0137 2022/11/28 18:43:02 - mmengine - INFO - Epoch(train) [16][300/2462] lr: 7.4209e-04 eta: 0:01:16 time: 0.0356 data_time: 0.0064 memory: 1253 loss: 0.0176 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0176 2022/11/28 18:43:06 - mmengine - INFO - Epoch(train) [16][400/2462] lr: 6.7522e-04 eta: 0:01:12 time: 0.0351 data_time: 0.0067 memory: 1253 loss: 0.0145 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0145 2022/11/28 18:43:09 - mmengine - INFO - Epoch(train) [16][500/2462] lr: 6.1147e-04 eta: 0:01:09 time: 0.0357 data_time: 0.0067 memory: 1253 loss: 0.0199 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0199 2022/11/28 18:43:13 - mmengine - INFO - Epoch(train) [16][600/2462] lr: 5.5087e-04 eta: 0:01:05 time: 0.0352 data_time: 0.0063 memory: 1253 loss: 0.0153 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0153 2022/11/28 18:43:16 - mmengine - INFO - Epoch(train) [16][700/2462] lr: 4.9342e-04 eta: 0:01:01 time: 0.0370 data_time: 0.0061 memory: 1253 loss: 0.0112 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0112 2022/11/28 18:43:20 - mmengine - INFO - Epoch(train) [16][800/2462] lr: 4.3911e-04 eta: 0:00:58 time: 0.0347 data_time: 0.0062 memory: 1253 loss: 0.0115 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0115 2022/11/28 18:43:23 - mmengine - INFO - Epoch(train) [16][900/2462] lr: 3.8795e-04 eta: 0:00:54 time: 0.0346 data_time: 0.0061 memory: 1253 loss: 0.0106 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0106 2022/11/28 18:43:27 - mmengine - INFO - Epoch(train) [16][1000/2462] lr: 3.3995e-04 eta: 0:00:51 time: 0.0359 data_time: 0.0062 memory: 1253 loss: 0.0139 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0139 2022/11/28 18:43:29 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:43:30 - mmengine - INFO - Epoch(train) [16][1100/2462] lr: 2.9511e-04 eta: 0:00:47 time: 0.0350 data_time: 0.0062 memory: 1253 loss: 0.0195 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0195 2022/11/28 18:43:34 - mmengine - INFO - Epoch(train) [16][1200/2462] lr: 2.5343e-04 eta: 0:00:44 time: 0.0348 data_time: 0.0062 memory: 1253 loss: 0.0148 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0148 2022/11/28 18:43:37 - mmengine - INFO - Epoch(train) [16][1300/2462] lr: 2.1492e-04 eta: 0:00:40 time: 0.0347 data_time: 0.0061 memory: 1253 loss: 0.0153 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0153 2022/11/28 18:43:41 - mmengine - INFO - Epoch(train) [16][1400/2462] lr: 1.7957e-04 eta: 0:00:37 time: 0.0370 data_time: 0.0061 memory: 1253 loss: 0.0117 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0117 2022/11/28 18:43:45 - mmengine - INFO - Epoch(train) [16][1500/2462] lr: 1.4739e-04 eta: 0:00:33 time: 0.0362 data_time: 0.0061 memory: 1253 loss: 0.0145 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0145 2022/11/28 18:43:48 - mmengine - INFO - Epoch(train) [16][1600/2462] lr: 1.1838e-04 eta: 0:00:30 time: 0.0345 data_time: 0.0066 memory: 1253 loss: 0.0133 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0133 2022/11/28 18:43:52 - mmengine - INFO - Epoch(train) [16][1700/2462] lr: 9.2542e-05 eta: 0:00:26 time: 0.0347 data_time: 0.0064 memory: 1253 loss: 0.0147 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0147 2022/11/28 18:43:55 - mmengine - INFO - Epoch(train) [16][1800/2462] lr: 6.9879e-05 eta: 0:00:23 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 18:43:59 - mmengine - INFO - Epoch(train) [16][1900/2462] lr: 5.0393e-05 eta: 0:00:19 time: 0.0365 data_time: 0.0061 memory: 1253 loss: 0.0108 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0108 2022/11/28 18:44:03 - mmengine - INFO - Epoch(train) [16][2000/2462] lr: 3.4083e-05 eta: 0:00:16 time: 0.0355 data_time: 0.0062 memory: 1253 loss: 0.0151 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0151 2022/11/28 18:44:05 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:44:06 - mmengine - INFO - Epoch(train) [16][2100/2462] lr: 2.0951e-05 eta: 0:00:12 time: 0.0357 data_time: 0.0061 memory: 1253 loss: 0.0109 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0109 2022/11/28 18:44:10 - mmengine - INFO - Epoch(train) [16][2200/2462] lr: 1.0998e-05 eta: 0:00:09 time: 0.0362 data_time: 0.0061 memory: 1253 loss: 0.0123 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0123 2022/11/28 18:44:13 - mmengine - INFO - Epoch(train) [16][2300/2462] lr: 4.2247e-06 eta: 0:00:05 time: 0.0374 data_time: 0.0062 memory: 1253 loss: 0.0155 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0155 2022/11/28 18:44:17 - mmengine - INFO - Epoch(train) [16][2400/2462] lr: 6.3111e-07 eta: 0:00:02 time: 0.0363 data_time: 0.0068 memory: 1253 loss: 0.0149 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0149 2022/11/28 18:44:19 - mmengine - INFO - Exp name: stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d_20221128_181721 2022/11/28 18:44:19 - mmengine - INFO - Epoch(train) [16][2462/2462] lr: 1.5901e-10 eta: 0:00:00 time: 0.0356 data_time: 0.0062 memory: 1253 loss: 0.0141 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.0141 2022/11/28 18:44:19 - mmengine - INFO - Saving checkpoint at 16 epochs 2022/11/28 18:44:21 - mmengine - INFO - Epoch(val) [16][100/398] eta: 0:00:04 time: 0.0149 data_time: 0.0058 memory: 262 2022/11/28 18:44:23 - mmengine - INFO - Epoch(val) [16][200/398] eta: 0:00:03 time: 0.0148 data_time: 0.0057 memory: 262 2022/11/28 18:44:24 - mmengine - INFO - Epoch(val) [16][300/398] eta: 0:00:01 time: 0.0150 data_time: 0.0059 memory: 262 2022/11/28 18:44:26 - mmengine - INFO - Epoch(val) [16][398/398] acc/top1: 0.8265 acc/top5: 0.9670 acc/mean1: 0.8476