2022/09/07 22:16:41 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0] CUDA available: True numpy_random_seed: 2013281687 GPU 0: Tesla V100-DGXS-32GB CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.4, V11.4.100 GCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0 PyTorch: 1.10.0 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 v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740) - 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-variable -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=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.11.0 OpenCV: 4.6.0 MMEngine: 0.1.0 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None Distributed launcher: none Distributed training: False GPU number: 1 ------------------------------------------------------------ 2022/09/07 22:16:41 - mmengine - INFO - Config: model = dict( type='TEM', temporal_dim=100, boundary_ratio=0.1, tem_feat_dim=400, tem_hidden_dim=512, tem_match_threshold=0.5) default_scope = 'mmaction' default_hooks = dict( runtime_info=dict(type='RuntimeInfoHook'), timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=20, ignore_last=False), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict( type='CheckpointHook', interval=1, save_best=None, filename_tmpl='tem_epoch_{}.pth'), sampler_seed=dict(type='DistSamplerSeedHook')) 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 dataset_type = 'ActivityNetDataset' data_root = 'data/ActivityNet/activitynet_feature_cuhk/csv_mean_100/' data_root_val = 'data/ActivityNet/activitynet_feature_cuhk/csv_mean_100/' ann_file_train = 'data/ActivityNet/anet_anno_train.json' ann_file_val = 'data/ActivityNet/anet_anno_val.json' ann_file_test = 'data/ActivityNet/anet_anno_full.json' train_pipeline = [ dict(type='LoadLocalizationFeature'), dict(type='GenerateLocalizationLabels'), dict( type='PackLocalizationInputs', keys=('gt_bbox', ), meta_keys=('video_name', )) ] val_pipeline = [ dict(type='LoadLocalizationFeature'), dict(type='GenerateLocalizationLabels'), dict( type='PackLocalizationInputs', keys=('gt_bbox', ), meta_keys=('video_name', )) ] test_pipeline = [ dict(type='LoadLocalizationFeature'), dict(type='PackLocalizationInputs', meta_keys=('video_name', )) ] train_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='ActivityNetDataset', ann_file='data/ActivityNet/anet_anno_train.json', data_prefix=dict( video='data/ActivityNet/activitynet_feature_cuhk/csv_mean_100/'), pipeline=[ dict(type='LoadLocalizationFeature'), dict(type='GenerateLocalizationLabels'), dict( type='PackLocalizationInputs', keys=('gt_bbox', ), meta_keys=('video_name', )) ])) val_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='ActivityNetDataset', ann_file='data/ActivityNet/anet_anno_val.json', data_prefix=dict( video='data/ActivityNet/activitynet_feature_cuhk/csv_mean_100/'), pipeline=[ dict(type='LoadLocalizationFeature'), dict(type='GenerateLocalizationLabels'), dict( type='PackLocalizationInputs', keys=('gt_bbox', ), meta_keys=('video_name', )) ], test_mode=True)) test_dataloader = dict( batch_size=1, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='ActivityNetDataset', ann_file='data/ActivityNet/anet_anno_full.json', data_prefix=dict( video='data/ActivityNet/activitynet_feature_cuhk/csv_mean_100/'), pipeline=[ dict(type='LoadLocalizationFeature'), dict(type='PackLocalizationInputs', meta_keys=('video_name', )) ], test_mode=True)) train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=20, val_begin=1, val_interval=20) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') optim_wrapper = dict( optimizer=dict(type='Adam', lr=0.001, weight_decay=0.0001), clip_grad=dict(max_norm=40, norm_type=2)) param_scheduler = [ dict( type='MultiStepLR', begin=0, end=20, by_epoch=True, milestones=[7, 14], gamma=0.1) ] work_dir = 'work_dirs/bsn_400x100_20e_1x16_activitynet_feature/' tem_results_dir = 'work_dirs/bsn_400x100_20e_1x16_activitynet_feature//tem_results/' test_evaluator = dict( type='ANetMetric', metric_type='TEM', dump_config=dict( out='work_dirs/bsn_400x100_20e_1x16_activitynet_feature//tem_results/', output_format='csv')) val_evaluator = dict( type='ANetMetric', metric_type='TEM', dump_config=dict( out='work_dirs/bsn_400x100_20e_1x16_activitynet_feature//tem_results/', output_format='csv')) launcher = 'none' 2022/09/07 22:16:45 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used. 2022/09/07 22:16:46 - mmengine - INFO - Checkpoints will be saved to /home/Kai.Hu/bsn/20/mmaction2/work_dirs/bsn_400x100_20e_1x16_activitynet_feature by HardDiskBackend. 2022/09/07 22:16:49 - mmengine - INFO - Epoch(train) [1][20/604] lr: 1.0000e-03 eta: 0:35:48 time: 0.1782 data_time: 0.0845 memory: 43 grad_norm: 0.2800 loss_action: 1.3223 loss_start: 0.6880 loss_end: 0.6885 loss: 2.6988 2022/09/07 22:16:51 - mmengine - INFO - Epoch(train) [1][40/604] lr: 1.0000e-03 eta: 0:25:19 time: 0.0743 data_time: 0.0124 memory: 43 grad_norm: 0.5029 loss_action: 1.2206 loss_start: 0.6677 loss_end: 0.6758 loss: 2.5641 2022/09/07 22:16:52 - mmengine - INFO - Epoch(train) [1][60/604] lr: 1.0000e-03 eta: 0:22:01 time: 0.0775 data_time: 0.0127 memory: 43 grad_norm: 0.6374 loss_action: 1.1991 loss_start: 0.6564 loss_end: 0.6613 loss: 2.5169 2022/09/07 22:16:54 - mmengine - INFO - Epoch(train) [1][80/604] lr: 1.0000e-03 eta: 0:20:17 time: 0.0759 data_time: 0.0127 memory: 43 grad_norm: 0.6391 loss_action: 1.1839 loss_start: 0.6517 loss_end: 0.6536 loss: 2.4892 2022/09/07 22:16:55 - mmengine - INFO - Epoch(train) [1][100/604] lr: 1.0000e-03 eta: 0:19:14 time: 0.0762 data_time: 0.0130 memory: 43 grad_norm: 0.6793 loss_action: 1.2216 loss_start: 0.6427 loss_end: 0.6521 loss: 2.5163 2022/09/07 22:16:57 - mmengine - INFO - Epoch(train) [1][120/604] lr: 1.0000e-03 eta: 0:18:32 time: 0.0763 data_time: 0.0130 memory: 43 grad_norm: 0.6592 loss_action: 1.1360 loss_start: 0.6365 loss_end: 0.6260 loss: 2.3985 2022/09/07 22:16:58 - mmengine - INFO - Epoch(train) [1][140/604] lr: 1.0000e-03 eta: 0:18:02 time: 0.0766 data_time: 0.0131 memory: 43 grad_norm: 0.6332 loss_action: 1.1143 loss_start: 0.6240 loss_end: 0.6301 loss: 2.3684 2022/09/07 22:17:00 - mmengine - INFO - Epoch(train) [1][160/604] lr: 1.0000e-03 eta: 0:17:39 time: 0.0765 data_time: 0.0130 memory: 43 grad_norm: 0.5969 loss_action: 1.1247 loss_start: 0.6244 loss_end: 0.6316 loss: 2.3807 2022/09/07 22:17:02 - mmengine - INFO - Epoch(train) [1][180/604] lr: 1.0000e-03 eta: 0:17:21 time: 0.0766 data_time: 0.0130 memory: 43 grad_norm: 0.6389 loss_action: 1.1197 loss_start: 0.6272 loss_end: 0.6311 loss: 2.3780 2022/09/07 22:17:03 - mmengine - INFO - Epoch(train) [1][200/604] lr: 1.0000e-03 eta: 0:17:06 time: 0.0764 data_time: 0.0130 memory: 43 grad_norm: 0.6496 loss_action: 1.0816 loss_start: 0.6368 loss_end: 0.6363 loss: 2.3546 2022/09/07 22:17:05 - mmengine - INFO - Epoch(train) [1][220/604] lr: 1.0000e-03 eta: 0:16:54 time: 0.0765 data_time: 0.0131 memory: 43 grad_norm: 0.6984 loss_action: 1.0644 loss_start: 0.6175 loss_end: 0.6273 loss: 2.3092 2022/09/07 22:17:06 - mmengine - INFO - Epoch(train) [1][240/604] lr: 1.0000e-03 eta: 0:16:44 time: 0.0769 data_time: 0.0131 memory: 43 grad_norm: 0.6965 loss_action: 1.1556 loss_start: 0.6257 loss_end: 0.6295 loss: 2.4108 2022/09/07 22:17:08 - mmengine - INFO - Epoch(train) [1][260/604] lr: 1.0000e-03 eta: 0:16:35 time: 0.0766 data_time: 0.0130 memory: 43 grad_norm: 0.6547 loss_action: 1.0842 loss_start: 0.6207 loss_end: 0.6195 loss: 2.3244 2022/09/07 22:17:09 - mmengine - INFO - Epoch(train) [1][280/604] lr: 1.0000e-03 eta: 0:16:26 time: 0.0762 data_time: 0.0130 memory: 43 grad_norm: 0.6970 loss_action: 1.1086 loss_start: 0.6235 loss_end: 0.6237 loss: 2.3558 2022/09/07 22:17:11 - mmengine - INFO - Epoch(train) [1][300/604] lr: 1.0000e-03 eta: 0:16:19 time: 0.0763 data_time: 0.0131 memory: 43 grad_norm: 0.6120 loss_action: 1.1079 loss_start: 0.6196 loss_end: 0.6263 loss: 2.3539 2022/09/07 22:17:12 - mmengine - INFO - Epoch(train) [1][320/604] lr: 1.0000e-03 eta: 0:16:12 time: 0.0766 data_time: 0.0132 memory: 43 grad_norm: 0.7808 loss_action: 1.1099 loss_start: 0.6356 loss_end: 0.6218 loss: 2.3673 2022/09/07 22:17:14 - mmengine - INFO - Epoch(train) [1][340/604] lr: 1.0000e-03 eta: 0:16:06 time: 0.0766 data_time: 0.0131 memory: 43 grad_norm: 0.6689 loss_action: 1.1030 loss_start: 0.6327 loss_end: 0.6218 loss: 2.3574 2022/09/07 22:17:15 - mmengine - INFO - Epoch(train) [1][360/604] lr: 1.0000e-03 eta: 0:16:01 time: 0.0763 data_time: 0.0130 memory: 43 grad_norm: 0.7226 loss_action: 1.1419 loss_start: 0.6307 loss_end: 0.6220 loss: 2.3946 2022/09/07 22:17:17 - mmengine - INFO - Epoch(train) [1][380/604] lr: 1.0000e-03 eta: 0:15:56 time: 0.0765 data_time: 0.0132 memory: 43 grad_norm: 0.7325 loss_action: 1.0934 loss_start: 0.6281 loss_end: 0.6245 loss: 2.3460 2022/09/07 22:17:18 - mmengine - INFO - Epoch(train) [1][400/604] lr: 1.0000e-03 eta: 0:15:51 time: 0.0764 data_time: 0.0132 memory: 43 grad_norm: 0.6703 loss_action: 1.1085 loss_start: 0.6261 loss_end: 0.6132 loss: 2.3479 2022/09/07 22:17:20 - mmengine - INFO - Epoch(train) [1][420/604] lr: 1.0000e-03 eta: 0:15:47 time: 0.0767 data_time: 0.0132 memory: 43 grad_norm: 0.7144 loss_action: 1.0793 loss_start: 0.6182 loss_end: 0.6196 loss: 2.3172 2022/09/07 22:17:21 - mmengine - INFO - Epoch(train) [1][440/604] lr: 1.0000e-03 eta: 0:15:42 time: 0.0760 data_time: 0.0131 memory: 43 grad_norm: 0.7201 loss_action: 1.0956 loss_start: 0.6156 loss_end: 0.6080 loss: 2.3192 2022/09/07 22:17:23 - mmengine - INFO - Epoch(train) [1][460/604] lr: 1.0000e-03 eta: 0:15:38 time: 0.0763 data_time: 0.0132 memory: 43 grad_norm: 0.7255 loss_action: 1.0973 loss_start: 0.6127 loss_end: 0.6133 loss: 2.3233 2022/09/07 22:17:24 - mmengine - INFO - Epoch(train) [1][480/604] lr: 1.0000e-03 eta: 0:15:35 time: 0.0765 data_time: 0.0132 memory: 43 grad_norm: 0.8163 loss_action: 1.1343 loss_start: 0.6185 loss_end: 0.6211 loss: 2.3739 2022/09/07 22:17:26 - mmengine - INFO - Epoch(train) [1][500/604] lr: 1.0000e-03 eta: 0:15:32 time: 0.0781 data_time: 0.0134 memory: 43 grad_norm: 0.6491 loss_action: 1.1091 loss_start: 0.6140 loss_end: 0.6163 loss: 2.3394 2022/09/07 22:17:28 - mmengine - INFO - Epoch(train) [1][520/604] lr: 1.0000e-03 eta: 0:15:28 time: 0.0756 data_time: 0.0122 memory: 43 grad_norm: 0.7568 loss_action: 1.1263 loss_start: 0.6397 loss_end: 0.6342 loss: 2.4002 2022/09/07 22:17:29 - mmengine - INFO - Epoch(train) [1][540/604] lr: 1.0000e-03 eta: 0:15:25 time: 0.0768 data_time: 0.0132 memory: 43 grad_norm: 0.7524 loss_action: 1.1139 loss_start: 0.6130 loss_end: 0.6194 loss: 2.3462 2022/09/07 22:17:31 - mmengine - INFO - Epoch(train) [1][560/604] lr: 1.0000e-03 eta: 0:15:22 time: 0.0763 data_time: 0.0131 memory: 43 grad_norm: 0.7373 loss_action: 1.0796 loss_start: 0.6069 loss_end: 0.6148 loss: 2.3013 2022/09/07 22:17:32 - mmengine - INFO - Epoch(train) [1][580/604] lr: 1.0000e-03 eta: 0:15:19 time: 0.0781 data_time: 0.0132 memory: 43 grad_norm: 0.6592 loss_action: 1.1332 loss_start: 0.6287 loss_end: 0.6192 loss: 2.3811 2022/09/07 22:17:34 - mmengine - INFO - Epoch(train) [1][600/604] lr: 1.0000e-03 eta: 0:15:16 time: 0.0757 data_time: 0.0127 memory: 43 grad_norm: 0.8001 loss_action: 1.0538 loss_start: 0.6214 loss_end: 0.6145 loss: 2.2897 2022/09/07 22:17:34 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:17:34 - mmengine - INFO - Epoch(train) [1][604/604] lr: 1.0000e-03 eta: 0:15:16 time: 0.0727 data_time: 0.0121 memory: 43 grad_norm: 0.8727 loss_action: 1.0887 loss_start: 0.6205 loss_end: 0.6321 loss: 2.3413 2022/09/07 22:17:34 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/09/07 22:17:37 - mmengine - INFO - Epoch(train) [2][20/604] lr: 1.0000e-03 eta: 0:15:26 time: 0.1290 data_time: 0.0660 memory: 43 grad_norm: 0.7659 loss_action: 1.0410 loss_start: 0.6128 loss_end: 0.6075 loss: 2.2613 2022/09/07 22:17:38 - mmengine - INFO - Epoch(train) [2][40/604] lr: 1.0000e-03 eta: 0:15:23 time: 0.0758 data_time: 0.0129 memory: 43 grad_norm: 0.7812 loss_action: 1.0661 loss_start: 0.6085 loss_end: 0.6159 loss: 2.2905 2022/09/07 22:17:40 - mmengine - INFO - Epoch(train) [2][60/604] lr: 1.0000e-03 eta: 0:15:20 time: 0.0757 data_time: 0.0129 memory: 43 grad_norm: 0.8029 loss_action: 1.0893 loss_start: 0.6047 loss_end: 0.6182 loss: 2.3123 2022/09/07 22:17:41 - mmengine - INFO - Epoch(train) [2][80/604] lr: 1.0000e-03 eta: 0:15:16 time: 0.0754 data_time: 0.0129 memory: 43 grad_norm: 0.6899 loss_action: 1.0809 loss_start: 0.6146 loss_end: 0.6165 loss: 2.3119 2022/09/07 22:17:43 - mmengine - INFO - Epoch(train) [2][100/604] lr: 1.0000e-03 eta: 0:15:13 time: 0.0755 data_time: 0.0127 memory: 43 grad_norm: 0.6369 loss_action: 1.0485 loss_start: 0.6188 loss_end: 0.6265 loss: 2.2938 2022/09/07 22:17:44 - mmengine - INFO - Epoch(train) [2][120/604] lr: 1.0000e-03 eta: 0:15:10 time: 0.0760 data_time: 0.0129 memory: 43 grad_norm: 0.6518 loss_action: 1.0713 loss_start: 0.6160 loss_end: 0.6093 loss: 2.2965 2022/09/07 22:17:46 - mmengine - INFO - Epoch(train) [2][140/604] lr: 1.0000e-03 eta: 0:15:07 time: 0.0761 data_time: 0.0131 memory: 43 grad_norm: 0.7107 loss_action: 1.0018 loss_start: 0.6077 loss_end: 0.6096 loss: 2.2191 2022/09/07 22:17:47 - mmengine - INFO - Epoch(train) [2][160/604] lr: 1.0000e-03 eta: 0:15:04 time: 0.0759 data_time: 0.0130 memory: 43 grad_norm: 0.7915 loss_action: 1.0659 loss_start: 0.6168 loss_end: 0.6103 loss: 2.2930 2022/09/07 22:17:49 - mmengine - INFO - Epoch(train) [2][180/604] lr: 1.0000e-03 eta: 0:15:02 time: 0.0759 data_time: 0.0131 memory: 43 grad_norm: 0.6932 loss_action: 1.0854 loss_start: 0.6187 loss_end: 0.6197 loss: 2.3237 2022/09/07 22:17:50 - mmengine - INFO - Epoch(train) [2][200/604] lr: 1.0000e-03 eta: 0:14:59 time: 0.0760 data_time: 0.0131 memory: 43 grad_norm: 0.7443 loss_action: 1.0297 loss_start: 0.6262 loss_end: 0.6077 loss: 2.2636 2022/09/07 22:17:52 - mmengine - INFO - Epoch(train) [2][220/604] lr: 1.0000e-03 eta: 0:14:56 time: 0.0760 data_time: 0.0129 memory: 43 grad_norm: 0.7209 loss_action: 1.0536 loss_start: 0.6047 loss_end: 0.6027 loss: 2.2610 2022/09/07 22:17:53 - mmengine - INFO - Epoch(train) [2][240/604] lr: 1.0000e-03 eta: 0:14:54 time: 0.0759 data_time: 0.0129 memory: 43 grad_norm: 0.7774 loss_action: 1.0401 loss_start: 0.6044 loss_end: 0.6145 loss: 2.2591 2022/09/07 22:17:55 - mmengine - INFO - Epoch(train) [2][260/604] lr: 1.0000e-03 eta: 0:14:51 time: 0.0758 data_time: 0.0128 memory: 43 grad_norm: 0.7182 loss_action: 1.0582 loss_start: 0.6002 loss_end: 0.6111 loss: 2.2696 2022/09/07 22:17:57 - mmengine - INFO - Epoch(train) [2][280/604] lr: 1.0000e-03 eta: 0:14:49 time: 0.0753 data_time: 0.0129 memory: 43 grad_norm: 0.7739 loss_action: 1.1643 loss_start: 0.6185 loss_end: 0.6182 loss: 2.4010 2022/09/07 22:17:58 - mmengine - INFO - Epoch(train) [2][300/604] lr: 1.0000e-03 eta: 0:14:46 time: 0.0754 data_time: 0.0128 memory: 43 grad_norm: 0.7182 loss_action: 1.0823 loss_start: 0.6141 loss_end: 0.6099 loss: 2.3064 2022/09/07 22:18:00 - mmengine - INFO - Epoch(train) [2][320/604] lr: 1.0000e-03 eta: 0:14:43 time: 0.0754 data_time: 0.0128 memory: 43 grad_norm: 0.6902 loss_action: 1.0799 loss_start: 0.6237 loss_end: 0.6052 loss: 2.3088 2022/09/07 22:18:01 - mmengine - INFO - Epoch(train) [2][340/604] lr: 1.0000e-03 eta: 0:14:41 time: 0.0771 data_time: 0.0129 memory: 43 grad_norm: 0.7213 loss_action: 1.0188 loss_start: 0.6213 loss_end: 0.6151 loss: 2.2551 2022/09/07 22:18:03 - mmengine - INFO - Epoch(train) [2][360/604] lr: 1.0000e-03 eta: 0:14:39 time: 0.0755 data_time: 0.0127 memory: 43 grad_norm: 0.8701 loss_action: 1.0352 loss_start: 0.6123 loss_end: 0.6181 loss: 2.2656 2022/09/07 22:18:04 - mmengine - INFO - Epoch(train) [2][380/604] lr: 1.0000e-03 eta: 0:14:36 time: 0.0747 data_time: 0.0126 memory: 43 grad_norm: 0.8183 loss_action: 1.0830 loss_start: 0.6179 loss_end: 0.6192 loss: 2.3200 2022/09/07 22:18:05 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:18:06 - mmengine - INFO - Epoch(train) [2][400/604] lr: 1.0000e-03 eta: 0:14:34 time: 0.0761 data_time: 0.0128 memory: 43 grad_norm: 0.6785 loss_action: 1.0657 loss_start: 0.6183 loss_end: 0.6095 loss: 2.2936 2022/09/07 22:18:07 - mmengine - INFO - Epoch(train) [2][420/604] lr: 1.0000e-03 eta: 0:14:32 time: 0.0761 data_time: 0.0132 memory: 43 grad_norm: 0.7395 loss_action: 1.0857 loss_start: 0.6164 loss_end: 0.6135 loss: 2.3156 2022/09/07 22:18:09 - mmengine - INFO - Epoch(train) [2][440/604] lr: 1.0000e-03 eta: 0:14:30 time: 0.0761 data_time: 0.0132 memory: 43 grad_norm: 0.7633 loss_action: 1.0180 loss_start: 0.6120 loss_end: 0.6098 loss: 2.2398 2022/09/07 22:18:10 - mmengine - INFO - Epoch(train) [2][460/604] lr: 1.0000e-03 eta: 0:14:28 time: 0.0765 data_time: 0.0136 memory: 43 grad_norm: 0.7719 loss_action: 1.0581 loss_start: 0.6003 loss_end: 0.6011 loss: 2.2595 2022/09/07 22:18:12 - mmengine - INFO - Epoch(train) [2][480/604] lr: 1.0000e-03 eta: 0:14:26 time: 0.0759 data_time: 0.0131 memory: 43 grad_norm: 0.8181 loss_action: 1.0256 loss_start: 0.6115 loss_end: 0.6128 loss: 2.2500 2022/09/07 22:18:13 - mmengine - INFO - Epoch(train) [2][500/604] lr: 1.0000e-03 eta: 0:14:24 time: 0.0766 data_time: 0.0133 memory: 43 grad_norm: 0.8217 loss_action: 1.0178 loss_start: 0.6048 loss_end: 0.6082 loss: 2.2308 2022/09/07 22:18:15 - mmengine - INFO - Epoch(train) [2][520/604] lr: 1.0000e-03 eta: 0:14:22 time: 0.0773 data_time: 0.0135 memory: 43 grad_norm: 0.7187 loss_action: 1.0823 loss_start: 0.6201 loss_end: 0.6211 loss: 2.3234 2022/09/07 22:18:16 - mmengine - INFO - Epoch(train) [2][540/604] lr: 1.0000e-03 eta: 0:14:20 time: 0.0767 data_time: 0.0133 memory: 43 grad_norm: 0.7195 loss_action: 1.0502 loss_start: 0.6165 loss_end: 0.6129 loss: 2.2796 2022/09/07 22:18:18 - mmengine - INFO - Epoch(train) [2][560/604] lr: 1.0000e-03 eta: 0:14:18 time: 0.0763 data_time: 0.0133 memory: 43 grad_norm: 0.8724 loss_action: 1.0902 loss_start: 0.6124 loss_end: 0.6008 loss: 2.3034 2022/09/07 22:18:19 - mmengine - INFO - Epoch(train) [2][580/604] lr: 1.0000e-03 eta: 0:14:16 time: 0.0770 data_time: 0.0134 memory: 43 grad_norm: 0.9624 loss_action: 1.0505 loss_start: 0.6148 loss_end: 0.6021 loss: 2.2674 2022/09/07 22:18:21 - mmengine - INFO - Epoch(train) [2][600/604] lr: 1.0000e-03 eta: 0:14:14 time: 0.0764 data_time: 0.0131 memory: 43 grad_norm: 0.8308 loss_action: 1.0684 loss_start: 0.6113 loss_end: 0.6157 loss: 2.2954 2022/09/07 22:18:21 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:18:21 - mmengine - INFO - Epoch(train) [2][604/604] lr: 1.0000e-03 eta: 0:14:14 time: 0.0730 data_time: 0.0124 memory: 43 grad_norm: 0.8616 loss_action: 1.0896 loss_start: 0.6112 loss_end: 0.6128 loss: 2.3135 2022/09/07 22:18:21 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/09/07 22:18:24 - mmengine - INFO - Epoch(train) [3][20/604] lr: 1.0000e-03 eta: 0:14:20 time: 0.1396 data_time: 0.0739 memory: 43 grad_norm: 0.6725 loss_action: 1.0256 loss_start: 0.6142 loss_end: 0.6184 loss: 2.2582 2022/09/07 22:18:26 - mmengine - INFO - Epoch(train) [3][40/604] lr: 1.0000e-03 eta: 0:14:18 time: 0.0757 data_time: 0.0131 memory: 43 grad_norm: 0.7821 loss_action: 0.9835 loss_start: 0.5963 loss_end: 0.5977 loss: 2.1775 2022/09/07 22:18:27 - mmengine - INFO - Epoch(train) [3][60/604] lr: 1.0000e-03 eta: 0:14:16 time: 0.0755 data_time: 0.0128 memory: 43 grad_norm: 0.7559 loss_action: 1.0653 loss_start: 0.6123 loss_end: 0.6001 loss: 2.2777 2022/09/07 22:18:29 - mmengine - INFO - Epoch(train) [3][80/604] lr: 1.0000e-03 eta: 0:14:13 time: 0.0758 data_time: 0.0128 memory: 43 grad_norm: 0.7932 loss_action: 1.0535 loss_start: 0.6070 loss_end: 0.6173 loss: 2.2779 2022/09/07 22:18:30 - mmengine - INFO - Epoch(train) [3][100/604] lr: 1.0000e-03 eta: 0:14:11 time: 0.0760 data_time: 0.0130 memory: 43 grad_norm: 0.7789 loss_action: 1.0229 loss_start: 0.6010 loss_end: 0.6019 loss: 2.2258 2022/09/07 22:18:32 - mmengine - INFO - Epoch(train) [3][120/604] lr: 1.0000e-03 eta: 0:14:09 time: 0.0756 data_time: 0.0128 memory: 43 grad_norm: 0.6944 loss_action: 1.0490 loss_start: 0.6035 loss_end: 0.5988 loss: 2.2513 2022/09/07 22:18:33 - mmengine - INFO - Epoch(train) [3][140/604] lr: 1.0000e-03 eta: 0:14:07 time: 0.0758 data_time: 0.0129 memory: 43 grad_norm: 0.6499 loss_action: 1.0608 loss_start: 0.5999 loss_end: 0.6099 loss: 2.2706 2022/09/07 22:18:35 - mmengine - INFO - Epoch(train) [3][160/604] lr: 1.0000e-03 eta: 0:14:05 time: 0.0777 data_time: 0.0134 memory: 43 grad_norm: 0.7405 loss_action: 1.0412 loss_start: 0.6098 loss_end: 0.6066 loss: 2.2575 2022/09/07 22:18:36 - mmengine - INFO - Epoch(train) [3][180/604] lr: 1.0000e-03 eta: 0:14:03 time: 0.0770 data_time: 0.0133 memory: 43 grad_norm: 0.6713 loss_action: 1.0427 loss_start: 0.5971 loss_end: 0.6055 loss: 2.2453 2022/09/07 22:18:38 - mmengine - INFO - Epoch(train) [3][200/604] lr: 1.0000e-03 eta: 0:14:02 time: 0.0776 data_time: 0.0133 memory: 43 grad_norm: 0.7414 loss_action: 1.0625 loss_start: 0.6001 loss_end: 0.6075 loss: 2.2701 2022/09/07 22:18:40 - mmengine - INFO - Epoch(train) [3][220/604] lr: 1.0000e-03 eta: 0:14:05 time: 0.1151 data_time: 0.0158 memory: 43 grad_norm: 0.8724 loss_action: 1.0957 loss_start: 0.6099 loss_end: 0.6078 loss: 2.3135 2022/09/07 22:18:43 - mmengine - INFO - Epoch(train) [3][240/604] lr: 1.0000e-03 eta: 0:14:15 time: 0.1571 data_time: 0.0248 memory: 43 grad_norm: 0.8045 loss_action: 1.0516 loss_start: 0.5972 loss_end: 0.6065 loss: 2.2553 2022/09/07 22:18:47 - mmengine - INFO - Epoch(train) [3][260/604] lr: 1.0000e-03 eta: 0:14:26 time: 0.1627 data_time: 0.0282 memory: 43 grad_norm: 0.6814 loss_action: 1.0500 loss_start: 0.6172 loss_end: 0.6062 loss: 2.2734 2022/09/07 22:18:51 - mmengine - INFO - Epoch(train) [3][280/604] lr: 1.0000e-03 eta: 0:14:40 time: 0.1926 data_time: 0.0322 memory: 43 grad_norm: 0.6911 loss_action: 1.0307 loss_start: 0.6143 loss_end: 0.6048 loss: 2.2498 2022/09/07 22:18:54 - mmengine - INFO - Epoch(train) [3][300/604] lr: 1.0000e-03 eta: 0:14:50 time: 0.1691 data_time: 0.0315 memory: 43 grad_norm: 0.6785 loss_action: 1.0131 loss_start: 0.6096 loss_end: 0.6057 loss: 2.2284 2022/09/07 22:18:58 - mmengine - INFO - Epoch(train) [3][320/604] lr: 1.0000e-03 eta: 0:15:02 time: 0.1819 data_time: 0.0300 memory: 43 grad_norm: 0.7444 loss_action: 0.9943 loss_start: 0.6074 loss_end: 0.5973 loss: 2.1990 2022/09/07 22:19:02 - mmengine - INFO - Epoch(train) [3][340/604] lr: 1.0000e-03 eta: 0:15:17 time: 0.2089 data_time: 0.0371 memory: 43 grad_norm: 0.6578 loss_action: 1.0847 loss_start: 0.6118 loss_end: 0.6086 loss: 2.3051 2022/09/07 22:19:05 - mmengine - INFO - Epoch(train) [3][360/604] lr: 1.0000e-03 eta: 0:15:26 time: 0.1659 data_time: 0.0263 memory: 43 grad_norm: 0.7664 loss_action: 1.0360 loss_start: 0.6123 loss_end: 0.6092 loss: 2.2575 2022/09/07 22:19:08 - mmengine - INFO - Epoch(train) [3][380/604] lr: 1.0000e-03 eta: 0:15:34 time: 0.1657 data_time: 0.0299 memory: 43 grad_norm: 0.7200 loss_action: 1.0150 loss_start: 0.6102 loss_end: 0.6086 loss: 2.2337 2022/09/07 22:19:12 - mmengine - INFO - Epoch(train) [3][400/604] lr: 1.0000e-03 eta: 0:15:44 time: 0.1738 data_time: 0.0270 memory: 43 grad_norm: 0.7738 loss_action: 1.0122 loss_start: 0.5978 loss_end: 0.5995 loss: 2.2095 2022/09/07 22:19:15 - mmengine - INFO - Epoch(train) [3][420/604] lr: 1.0000e-03 eta: 0:15:51 time: 0.1640 data_time: 0.0296 memory: 43 grad_norm: 0.6724 loss_action: 1.0499 loss_start: 0.6207 loss_end: 0.6149 loss: 2.2856 2022/09/07 22:19:19 - mmengine - INFO - Epoch(train) [3][440/604] lr: 1.0000e-03 eta: 0:16:00 time: 0.1714 data_time: 0.0309 memory: 43 grad_norm: 0.6473 loss_action: 1.0158 loss_start: 0.5975 loss_end: 0.6003 loss: 2.2135 2022/09/07 22:19:22 - mmengine - INFO - Epoch(train) [3][460/604] lr: 1.0000e-03 eta: 0:16:09 time: 0.1854 data_time: 0.0310 memory: 43 grad_norm: 0.8856 loss_action: 1.0530 loss_start: 0.6111 loss_end: 0.5999 loss: 2.2640 2022/09/07 22:19:26 - mmengine - INFO - Epoch(train) [3][480/604] lr: 1.0000e-03 eta: 0:16:17 time: 0.1674 data_time: 0.0305 memory: 43 grad_norm: 0.7750 loss_action: 1.0267 loss_start: 0.6027 loss_end: 0.6157 loss: 2.2451 2022/09/07 22:19:28 - mmengine - INFO - Epoch(train) [3][500/604] lr: 1.0000e-03 eta: 0:16:17 time: 0.1152 data_time: 0.0168 memory: 43 grad_norm: 0.6765 loss_action: 1.0391 loss_start: 0.6159 loss_end: 0.6069 loss: 2.2620 2022/09/07 22:19:31 - mmengine - INFO - Epoch(train) [3][520/604] lr: 1.0000e-03 eta: 0:16:21 time: 0.1361 data_time: 0.0197 memory: 43 grad_norm: 0.6374 loss_action: 1.0233 loss_start: 0.6022 loss_end: 0.5933 loss: 2.2189 2022/09/07 22:19:33 - mmengine - INFO - Epoch(train) [3][540/604] lr: 1.0000e-03 eta: 0:16:24 time: 0.1413 data_time: 0.0179 memory: 43 grad_norm: 0.8137 loss_action: 1.0359 loss_start: 0.5986 loss_end: 0.6044 loss: 2.2389 2022/09/07 22:19:36 - mmengine - INFO - Epoch(train) [3][560/604] lr: 1.0000e-03 eta: 0:16:28 time: 0.1434 data_time: 0.0201 memory: 43 grad_norm: 0.8817 loss_action: 1.0604 loss_start: 0.6142 loss_end: 0.6159 loss: 2.2905 2022/09/07 22:19:40 - mmengine - INFO - Epoch(train) [3][580/604] lr: 1.0000e-03 eta: 0:16:33 time: 0.1600 data_time: 0.0271 memory: 43 grad_norm: 0.9467 loss_action: 1.0609 loss_start: 0.6062 loss_end: 0.5967 loss: 2.2637 2022/09/07 22:19:43 - mmengine - INFO - Epoch(train) [3][600/604] lr: 1.0000e-03 eta: 0:16:41 time: 0.1780 data_time: 0.0296 memory: 43 grad_norm: 0.7990 loss_action: 1.0324 loss_start: 0.6011 loss_end: 0.6025 loss: 2.2361 2022/09/07 22:19:44 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:19:44 - mmengine - INFO - Epoch(train) [3][604/604] lr: 1.0000e-03 eta: 0:16:41 time: 0.1668 data_time: 0.0270 memory: 43 grad_norm: 0.9080 loss_action: 1.0364 loss_start: 0.6095 loss_end: 0.5940 loss: 2.2399 2022/09/07 22:19:44 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/09/07 22:19:50 - mmengine - INFO - Epoch(train) [4][20/604] lr: 1.0000e-03 eta: 0:16:55 time: 0.2684 data_time: 0.1154 memory: 43 grad_norm: 0.6652 loss_action: 1.0446 loss_start: 0.6055 loss_end: 0.6075 loss: 2.2576 2022/09/07 22:19:53 - mmengine - INFO - Epoch(train) [4][40/604] lr: 1.0000e-03 eta: 0:17:01 time: 0.1729 data_time: 0.0304 memory: 43 grad_norm: 0.6799 loss_action: 0.9862 loss_start: 0.5826 loss_end: 0.5955 loss: 2.1643 2022/09/07 22:19:56 - mmengine - INFO - Epoch(train) [4][60/604] lr: 1.0000e-03 eta: 0:17:06 time: 0.1592 data_time: 0.0253 memory: 43 grad_norm: 0.8482 loss_action: 1.0028 loss_start: 0.5919 loss_end: 0.6025 loss: 2.1972 2022/09/07 22:20:00 - mmengine - INFO - Epoch(train) [4][80/604] lr: 1.0000e-03 eta: 0:17:13 time: 0.1809 data_time: 0.0329 memory: 43 grad_norm: 0.7808 loss_action: 1.0232 loss_start: 0.6027 loss_end: 0.6015 loss: 2.2273 2022/09/07 22:20:04 - mmengine - INFO - Epoch(train) [4][100/604] lr: 1.0000e-03 eta: 0:17:19 time: 0.1837 data_time: 0.0340 memory: 43 grad_norm: 0.7304 loss_action: 1.0282 loss_start: 0.6015 loss_end: 0.5934 loss: 2.2231 2022/09/07 22:20:07 - mmengine - INFO - Epoch(train) [4][120/604] lr: 1.0000e-03 eta: 0:17:25 time: 0.1749 data_time: 0.0438 memory: 43 grad_norm: 0.8004 loss_action: 1.0083 loss_start: 0.6030 loss_end: 0.5953 loss: 2.2066 2022/09/07 22:20:11 - mmengine - INFO - Epoch(train) [4][140/604] lr: 1.0000e-03 eta: 0:17:34 time: 0.2122 data_time: 0.0354 memory: 43 grad_norm: 0.8176 loss_action: 1.0434 loss_start: 0.6085 loss_end: 0.6102 loss: 2.2620 2022/09/07 22:20:15 - mmengine - INFO - Epoch(train) [4][160/604] lr: 1.0000e-03 eta: 0:17:38 time: 0.1659 data_time: 0.0249 memory: 43 grad_norm: 0.8601 loss_action: 1.0136 loss_start: 0.6046 loss_end: 0.6079 loss: 2.2261 2022/09/07 22:20:18 - mmengine - INFO - Epoch(train) [4][180/604] lr: 1.0000e-03 eta: 0:17:42 time: 0.1658 data_time: 0.0301 memory: 43 grad_norm: 0.6769 loss_action: 1.0583 loss_start: 0.6225 loss_end: 0.6136 loss: 2.2944 2022/09/07 22:20:20 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:20:22 - mmengine - INFO - Epoch(train) [4][200/604] lr: 1.0000e-03 eta: 0:17:47 time: 0.1766 data_time: 0.0254 memory: 43 grad_norm: 0.8319 loss_action: 1.0245 loss_start: 0.5994 loss_end: 0.5916 loss: 2.2155 2022/09/07 22:20:25 - mmengine - INFO - Epoch(train) [4][220/604] lr: 1.0000e-03 eta: 0:17:52 time: 0.1762 data_time: 0.0301 memory: 43 grad_norm: 0.6792 loss_action: 0.9993 loss_start: 0.6087 loss_end: 0.6065 loss: 2.2146 2022/09/07 22:20:28 - mmengine - INFO - Epoch(train) [4][240/604] lr: 1.0000e-03 eta: 0:17:55 time: 0.1589 data_time: 0.0335 memory: 43 grad_norm: 0.7345 loss_action: 0.9706 loss_start: 0.6100 loss_end: 0.5949 loss: 2.1754 2022/09/07 22:20:32 - mmengine - INFO - Epoch(train) [4][260/604] lr: 1.0000e-03 eta: 0:18:01 time: 0.1944 data_time: 0.0391 memory: 43 grad_norm: 0.7524 loss_action: 1.0182 loss_start: 0.6054 loss_end: 0.6094 loss: 2.2329 2022/09/07 22:20:36 - mmengine - INFO - Epoch(train) [4][280/604] lr: 1.0000e-03 eta: 0:18:07 time: 0.1886 data_time: 0.0307 memory: 43 grad_norm: 0.6710 loss_action: 1.0422 loss_start: 0.6185 loss_end: 0.6034 loss: 2.2640 2022/09/07 22:20:40 - mmengine - INFO - Epoch(train) [4][300/604] lr: 1.0000e-03 eta: 0:18:14 time: 0.2062 data_time: 0.0322 memory: 43 grad_norm: 0.8126 loss_action: 1.0658 loss_start: 0.6073 loss_end: 0.6030 loss: 2.2761 2022/09/07 22:20:44 - mmengine - INFO - Epoch(train) [4][320/604] lr: 1.0000e-03 eta: 0:18:21 time: 0.2056 data_time: 0.0330 memory: 43 grad_norm: 0.7665 loss_action: 0.9842 loss_start: 0.5974 loss_end: 0.5916 loss: 2.1732 2022/09/07 22:20:48 - mmengine - INFO - Epoch(train) [4][340/604] lr: 1.0000e-03 eta: 0:18:24 time: 0.1686 data_time: 0.0308 memory: 43 grad_norm: 0.7314 loss_action: 0.9685 loss_start: 0.5956 loss_end: 0.5993 loss: 2.1634 2022/09/07 22:20:51 - mmengine - INFO - Epoch(train) [4][360/604] lr: 1.0000e-03 eta: 0:18:27 time: 0.1687 data_time: 0.0337 memory: 43 grad_norm: 0.7550 loss_action: 0.9902 loss_start: 0.5954 loss_end: 0.5937 loss: 2.1793 2022/09/07 22:20:54 - mmengine - INFO - Epoch(train) [4][380/604] lr: 1.0000e-03 eta: 0:18:30 time: 0.1659 data_time: 0.0270 memory: 43 grad_norm: 0.8365 loss_action: 0.9986 loss_start: 0.5986 loss_end: 0.5961 loss: 2.1933 2022/09/07 22:20:58 - mmengine - INFO - Epoch(train) [4][400/604] lr: 1.0000e-03 eta: 0:18:32 time: 0.1669 data_time: 0.0308 memory: 43 grad_norm: 0.7464 loss_action: 1.0649 loss_start: 0.6068 loss_end: 0.5954 loss: 2.2671 2022/09/07 22:21:01 - mmengine - INFO - Epoch(train) [4][420/604] lr: 1.0000e-03 eta: 0:18:34 time: 0.1611 data_time: 0.0285 memory: 43 grad_norm: 0.8382 loss_action: 1.0623 loss_start: 0.6127 loss_end: 0.6011 loss: 2.2761 2022/09/07 22:21:03 - mmengine - INFO - Epoch(train) [4][440/604] lr: 1.0000e-03 eta: 0:18:34 time: 0.1344 data_time: 0.0186 memory: 43 grad_norm: 0.7094 loss_action: 1.0349 loss_start: 0.6026 loss_end: 0.5901 loss: 2.2276 2022/09/07 22:21:06 - mmengine - INFO - Epoch(train) [4][460/604] lr: 1.0000e-03 eta: 0:18:34 time: 0.1409 data_time: 0.0189 memory: 43 grad_norm: 0.8599 loss_action: 1.0488 loss_start: 0.6098 loss_end: 0.6004 loss: 2.2591 2022/09/07 22:21:09 - mmengine - INFO - Epoch(train) [4][480/604] lr: 1.0000e-03 eta: 0:18:33 time: 0.1260 data_time: 0.0155 memory: 43 grad_norm: 0.7158 loss_action: 1.0653 loss_start: 0.6013 loss_end: 0.6150 loss: 2.2816 2022/09/07 22:21:11 - mmengine - INFO - Epoch(train) [4][500/604] lr: 1.0000e-03 eta: 0:18:30 time: 0.1143 data_time: 0.0158 memory: 43 grad_norm: 0.7110 loss_action: 0.9797 loss_start: 0.6074 loss_end: 0.6062 loss: 2.1933 2022/09/07 22:21:13 - mmengine - INFO - Epoch(train) [4][520/604] lr: 1.0000e-03 eta: 0:18:27 time: 0.1010 data_time: 0.0247 memory: 43 grad_norm: 0.9085 loss_action: 1.0916 loss_start: 0.6096 loss_end: 0.6070 loss: 2.3082 2022/09/07 22:21:15 - mmengine - INFO - Epoch(train) [4][540/604] lr: 1.0000e-03 eta: 0:18:22 time: 0.0789 data_time: 0.0132 memory: 43 grad_norm: 0.7873 loss_action: 1.0299 loss_start: 0.6086 loss_end: 0.6087 loss: 2.2473 2022/09/07 22:21:16 - mmengine - INFO - Epoch(train) [4][560/604] lr: 1.0000e-03 eta: 0:18:17 time: 0.0753 data_time: 0.0125 memory: 43 grad_norm: 0.7947 loss_action: 1.0282 loss_start: 0.6063 loss_end: 0.6118 loss: 2.2462 2022/09/07 22:21:18 - mmengine - INFO - Epoch(train) [4][580/604] lr: 1.0000e-03 eta: 0:18:11 time: 0.0755 data_time: 0.0128 memory: 43 grad_norm: 0.6256 loss_action: 1.0081 loss_start: 0.6091 loss_end: 0.5910 loss: 2.2082 2022/09/07 22:21:19 - mmengine - INFO - Epoch(train) [4][600/604] lr: 1.0000e-03 eta: 0:18:06 time: 0.0760 data_time: 0.0129 memory: 43 grad_norm: 0.8100 loss_action: 1.0438 loss_start: 0.5935 loss_end: 0.6039 loss: 2.2412 2022/09/07 22:21:19 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:21:19 - mmengine - INFO - Epoch(train) [4][604/604] lr: 1.0000e-03 eta: 0:18:06 time: 0.0728 data_time: 0.0123 memory: 43 grad_norm: 0.8545 loss_action: 1.0456 loss_start: 0.5992 loss_end: 0.5976 loss: 2.2425 2022/09/07 22:21:19 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/09/07 22:21:23 - mmengine - INFO - Epoch(train) [5][20/604] lr: 1.0000e-03 eta: 0:18:03 time: 0.1351 data_time: 0.0708 memory: 43 grad_norm: 0.6945 loss_action: 1.0058 loss_start: 0.6015 loss_end: 0.5965 loss: 2.2038 2022/09/07 22:21:24 - mmengine - INFO - Epoch(train) [5][40/604] lr: 1.0000e-03 eta: 0:17:58 time: 0.0773 data_time: 0.0133 memory: 43 grad_norm: 0.8266 loss_action: 1.0518 loss_start: 0.5932 loss_end: 0.5942 loss: 2.2391 2022/09/07 22:21:26 - mmengine - INFO - Epoch(train) [5][60/604] lr: 1.0000e-03 eta: 0:17:54 time: 0.0779 data_time: 0.0133 memory: 43 grad_norm: 0.6602 loss_action: 1.0132 loss_start: 0.6053 loss_end: 0.6064 loss: 2.2249 2022/09/07 22:21:27 - mmengine - INFO - Epoch(train) [5][80/604] lr: 1.0000e-03 eta: 0:17:49 time: 0.0771 data_time: 0.0131 memory: 43 grad_norm: 0.7750 loss_action: 1.0404 loss_start: 0.5907 loss_end: 0.6048 loss: 2.2359 2022/09/07 22:21:29 - mmengine - INFO - Epoch(train) [5][100/604] lr: 1.0000e-03 eta: 0:17:44 time: 0.0767 data_time: 0.0130 memory: 43 grad_norm: 0.8277 loss_action: 0.9877 loss_start: 0.6068 loss_end: 0.5930 loss: 2.1875 2022/09/07 22:21:30 - mmengine - INFO - Epoch(train) [5][120/604] lr: 1.0000e-03 eta: 0:17:39 time: 0.0760 data_time: 0.0129 memory: 43 grad_norm: 0.8316 loss_action: 1.0009 loss_start: 0.6006 loss_end: 0.5935 loss: 2.1950 2022/09/07 22:21:32 - mmengine - INFO - Epoch(train) [5][140/604] lr: 1.0000e-03 eta: 0:17:35 time: 0.0843 data_time: 0.0206 memory: 43 grad_norm: 0.7530 loss_action: 0.9935 loss_start: 0.6123 loss_end: 0.5995 loss: 2.2053 2022/09/07 22:21:34 - mmengine - INFO - Epoch(train) [5][160/604] lr: 1.0000e-03 eta: 0:17:30 time: 0.0764 data_time: 0.0127 memory: 43 grad_norm: 0.6700 loss_action: 1.0223 loss_start: 0.6004 loss_end: 0.5959 loss: 2.2187 2022/09/07 22:21:36 - mmengine - INFO - Epoch(train) [5][180/604] lr: 1.0000e-03 eta: 0:17:27 time: 0.1013 data_time: 0.0381 memory: 43 grad_norm: 0.7524 loss_action: 0.9801 loss_start: 0.6029 loss_end: 0.6069 loss: 2.1899 2022/09/07 22:21:37 - mmengine - INFO - Epoch(train) [5][200/604] lr: 1.0000e-03 eta: 0:17:22 time: 0.0763 data_time: 0.0127 memory: 43 grad_norm: 0.6583 loss_action: 0.9927 loss_start: 0.6088 loss_end: 0.5941 loss: 2.1957 2022/09/07 22:21:39 - mmengine - INFO - Epoch(train) [5][220/604] lr: 1.0000e-03 eta: 0:17:19 time: 0.1000 data_time: 0.0372 memory: 43 grad_norm: 0.8152 loss_action: 1.0078 loss_start: 0.6009 loss_end: 0.5946 loss: 2.2033 2022/09/07 22:21:41 - mmengine - INFO - Epoch(train) [5][240/604] lr: 1.0000e-03 eta: 0:17:15 time: 0.0757 data_time: 0.0127 memory: 43 grad_norm: 0.7227 loss_action: 1.0454 loss_start: 0.6075 loss_end: 0.5995 loss: 2.2523 2022/09/07 22:21:43 - mmengine - INFO - Epoch(train) [5][260/604] lr: 1.0000e-03 eta: 0:17:12 time: 0.0998 data_time: 0.0373 memory: 43 grad_norm: 0.8402 loss_action: 1.0533 loss_start: 0.5968 loss_end: 0.6028 loss: 2.2529 2022/09/07 22:21:44 - mmengine - INFO - Epoch(train) [5][280/604] lr: 1.0000e-03 eta: 0:17:07 time: 0.0772 data_time: 0.0130 memory: 43 grad_norm: 0.7021 loss_action: 0.9609 loss_start: 0.5956 loss_end: 0.5889 loss: 2.1453 2022/09/07 22:21:46 - mmengine - INFO - Epoch(train) [5][300/604] lr: 1.0000e-03 eta: 0:17:05 time: 0.1040 data_time: 0.0391 memory: 43 grad_norm: 0.7051 loss_action: 0.9749 loss_start: 0.6058 loss_end: 0.5890 loss: 2.1696 2022/09/07 22:21:48 - mmengine - INFO - Epoch(train) [5][320/604] lr: 1.0000e-03 eta: 0:17:00 time: 0.0760 data_time: 0.0126 memory: 43 grad_norm: 0.7169 loss_action: 0.9727 loss_start: 0.5946 loss_end: 0.5925 loss: 2.1598 2022/09/07 22:21:50 - mmengine - INFO - Epoch(train) [5][340/604] lr: 1.0000e-03 eta: 0:16:57 time: 0.0987 data_time: 0.0353 memory: 43 grad_norm: 0.6552 loss_action: 1.0234 loss_start: 0.6005 loss_end: 0.5938 loss: 2.2176 2022/09/07 22:21:51 - mmengine - INFO - Epoch(train) [5][360/604] lr: 1.0000e-03 eta: 0:16:53 time: 0.0755 data_time: 0.0125 memory: 43 grad_norm: 0.7965 loss_action: 1.0061 loss_start: 0.5990 loss_end: 0.5903 loss: 2.1954 2022/09/07 22:21:53 - mmengine - INFO - Epoch(train) [5][380/604] lr: 1.0000e-03 eta: 0:16:50 time: 0.1036 data_time: 0.0400 memory: 43 grad_norm: 0.6887 loss_action: 1.0213 loss_start: 0.6058 loss_end: 0.5902 loss: 2.2173 2022/09/07 22:21:55 - mmengine - INFO - Epoch(train) [5][400/604] lr: 1.0000e-03 eta: 0:16:46 time: 0.0756 data_time: 0.0128 memory: 43 grad_norm: 0.6510 loss_action: 1.0526 loss_start: 0.6085 loss_end: 0.6058 loss: 2.2669 2022/09/07 22:21:56 - mmengine - INFO - Epoch(train) [5][420/604] lr: 1.0000e-03 eta: 0:16:42 time: 0.0765 data_time: 0.0129 memory: 43 grad_norm: 0.7420 loss_action: 1.0688 loss_start: 0.6112 loss_end: 0.6010 loss: 2.2810 2022/09/07 22:21:58 - mmengine - INFO - Epoch(train) [5][440/604] lr: 1.0000e-03 eta: 0:16:39 time: 0.0940 data_time: 0.0302 memory: 43 grad_norm: 0.8541 loss_action: 1.0413 loss_start: 0.6128 loss_end: 0.6109 loss: 2.2650 2022/09/07 22:22:00 - mmengine - INFO - Epoch(train) [5][460/604] lr: 1.0000e-03 eta: 0:16:35 time: 0.0766 data_time: 0.0128 memory: 43 grad_norm: 0.8168 loss_action: 1.0138 loss_start: 0.6057 loss_end: 0.5947 loss: 2.2142 2022/09/07 22:22:02 - mmengine - INFO - Epoch(train) [5][480/604] lr: 1.0000e-03 eta: 0:16:32 time: 0.0998 data_time: 0.0361 memory: 43 grad_norm: 0.8288 loss_action: 0.9686 loss_start: 0.5872 loss_end: 0.5887 loss: 2.1445 2022/09/07 22:22:03 - mmengine - INFO - Epoch(train) [5][500/604] lr: 1.0000e-03 eta: 0:16:28 time: 0.0768 data_time: 0.0129 memory: 43 grad_norm: 0.7089 loss_action: 1.0500 loss_start: 0.6028 loss_end: 0.6020 loss: 2.2548 2022/09/07 22:22:05 - mmengine - INFO - Epoch(train) [5][520/604] lr: 1.0000e-03 eta: 0:16:25 time: 0.1044 data_time: 0.0405 memory: 43 grad_norm: 0.8031 loss_action: 0.9822 loss_start: 0.5794 loss_end: 0.5902 loss: 2.1518 2022/09/07 22:22:07 - mmengine - INFO - Epoch(train) [5][540/604] lr: 1.0000e-03 eta: 0:16:21 time: 0.0809 data_time: 0.0167 memory: 43 grad_norm: 0.7326 loss_action: 0.9429 loss_start: 0.5975 loss_end: 0.5989 loss: 2.1394 2022/09/07 22:22:09 - mmengine - INFO - Epoch(train) [5][560/604] lr: 1.0000e-03 eta: 0:16:17 time: 0.0765 data_time: 0.0130 memory: 43 grad_norm: 0.8945 loss_action: 1.0153 loss_start: 0.6064 loss_end: 0.5890 loss: 2.2106 2022/09/07 22:22:10 - mmengine - INFO - Epoch(train) [5][580/604] lr: 1.0000e-03 eta: 0:16:13 time: 0.0762 data_time: 0.0128 memory: 43 grad_norm: 0.6803 loss_action: 0.9746 loss_start: 0.5968 loss_end: 0.6056 loss: 2.1769 2022/09/07 22:22:11 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:22:13 - mmengine - INFO - Epoch(train) [5][600/604] lr: 1.0000e-03 eta: 0:16:14 time: 0.1533 data_time: 0.0893 memory: 43 grad_norm: 0.6750 loss_action: 1.0542 loss_start: 0.6125 loss_end: 0.6060 loss: 2.2726 2022/09/07 22:22:14 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:22:14 - mmengine - INFO - Epoch(train) [5][604/604] lr: 1.0000e-03 eta: 0:16:14 time: 0.1426 data_time: 0.0813 memory: 43 grad_norm: 0.7934 loss_action: 1.0233 loss_start: 0.6045 loss_end: 0.6017 loss: 2.2295 2022/09/07 22:22:14 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/09/07 22:22:18 - mmengine - INFO - Epoch(train) [6][20/604] lr: 1.0000e-03 eta: 0:16:12 time: 0.1387 data_time: 0.0745 memory: 43 grad_norm: 0.7260 loss_action: 0.9677 loss_start: 0.5925 loss_end: 0.5974 loss: 2.1577 2022/09/07 22:22:20 - mmengine - INFO - Epoch(train) [6][40/604] lr: 1.0000e-03 eta: 0:16:09 time: 0.0920 data_time: 0.0282 memory: 43 grad_norm: 0.8179 loss_action: 1.0411 loss_start: 0.6015 loss_end: 0.6045 loss: 2.2471 2022/09/07 22:22:22 - mmengine - INFO - Epoch(train) [6][60/604] lr: 1.0000e-03 eta: 0:16:07 time: 0.1082 data_time: 0.0446 memory: 43 grad_norm: 0.7201 loss_action: 1.0307 loss_start: 0.5934 loss_end: 0.6035 loss: 2.2276 2022/09/07 22:22:24 - mmengine - INFO - Epoch(train) [6][80/604] lr: 1.0000e-03 eta: 0:16:03 time: 0.0770 data_time: 0.0131 memory: 43 grad_norm: 0.7785 loss_action: 0.9618 loss_start: 0.5956 loss_end: 0.5874 loss: 2.1448 2022/09/07 22:22:26 - mmengine - INFO - Epoch(train) [6][100/604] lr: 1.0000e-03 eta: 0:16:00 time: 0.1023 data_time: 0.0390 memory: 43 grad_norm: 0.8351 loss_action: 1.0460 loss_start: 0.5995 loss_end: 0.5949 loss: 2.2404 2022/09/07 22:22:27 - mmengine - INFO - Epoch(train) [6][120/604] lr: 1.0000e-03 eta: 0:15:57 time: 0.0761 data_time: 0.0129 memory: 43 grad_norm: 0.6912 loss_action: 1.0206 loss_start: 0.6054 loss_end: 0.5862 loss: 2.2122 2022/09/07 22:22:29 - mmengine - INFO - Epoch(train) [6][140/604] lr: 1.0000e-03 eta: 0:15:54 time: 0.1031 data_time: 0.0369 memory: 43 grad_norm: 0.7716 loss_action: 1.0314 loss_start: 0.5995 loss_end: 0.6020 loss: 2.2329 2022/09/07 22:22:31 - mmengine - INFO - Epoch(train) [6][160/604] lr: 1.0000e-03 eta: 0:15:50 time: 0.0772 data_time: 0.0125 memory: 43 grad_norm: 0.7909 loss_action: 1.0315 loss_start: 0.6044 loss_end: 0.5973 loss: 2.2333 2022/09/07 22:22:33 - mmengine - INFO - Epoch(train) [6][180/604] lr: 1.0000e-03 eta: 0:15:48 time: 0.0980 data_time: 0.0353 memory: 43 grad_norm: 0.6211 loss_action: 0.9886 loss_start: 0.6029 loss_end: 0.5914 loss: 2.1830 2022/09/07 22:22:34 - mmengine - INFO - Epoch(train) [6][200/604] lr: 1.0000e-03 eta: 0:15:44 time: 0.0761 data_time: 0.0128 memory: 43 grad_norm: 0.7337 loss_action: 0.9561 loss_start: 0.5950 loss_end: 0.5917 loss: 2.1429 2022/09/07 22:22:36 - mmengine - INFO - Epoch(train) [6][220/604] lr: 1.0000e-03 eta: 0:15:42 time: 0.1008 data_time: 0.0378 memory: 43 grad_norm: 0.7335 loss_action: 1.0007 loss_start: 0.5909 loss_end: 0.5912 loss: 2.1828 2022/09/07 22:22:38 - mmengine - INFO - Epoch(train) [6][240/604] lr: 1.0000e-03 eta: 0:15:38 time: 0.0768 data_time: 0.0131 memory: 43 grad_norm: 0.6956 loss_action: 1.0039 loss_start: 0.5915 loss_end: 0.5943 loss: 2.1897 2022/09/07 22:22:40 - mmengine - INFO - Epoch(train) [6][260/604] lr: 1.0000e-03 eta: 0:15:35 time: 0.0991 data_time: 0.0358 memory: 43 grad_norm: 0.6896 loss_action: 0.9857 loss_start: 0.5981 loss_end: 0.5961 loss: 2.1800 2022/09/07 22:22:41 - mmengine - INFO - Epoch(train) [6][280/604] lr: 1.0000e-03 eta: 0:15:32 time: 0.0764 data_time: 0.0130 memory: 43 grad_norm: 0.7980 loss_action: 1.0396 loss_start: 0.6103 loss_end: 0.5850 loss: 2.2349 2022/09/07 22:22:43 - mmengine - INFO - Epoch(train) [6][300/604] lr: 1.0000e-03 eta: 0:15:29 time: 0.1031 data_time: 0.0372 memory: 43 grad_norm: 0.7371 loss_action: 0.9815 loss_start: 0.5993 loss_end: 0.5944 loss: 2.1752 2022/09/07 22:22:45 - mmengine - INFO - Epoch(train) [6][320/604] lr: 1.0000e-03 eta: 0:15:26 time: 0.0787 data_time: 0.0133 memory: 43 grad_norm: 0.8047 loss_action: 1.0071 loss_start: 0.5965 loss_end: 0.5920 loss: 2.1955 2022/09/07 22:22:47 - mmengine - INFO - Epoch(train) [6][340/604] lr: 1.0000e-03 eta: 0:15:23 time: 0.0977 data_time: 0.0330 memory: 43 grad_norm: 0.7367 loss_action: 0.9932 loss_start: 0.5977 loss_end: 0.6056 loss: 2.1965 2022/09/07 22:22:48 - mmengine - INFO - Epoch(train) [6][360/604] lr: 1.0000e-03 eta: 0:15:20 time: 0.0770 data_time: 0.0132 memory: 43 grad_norm: 0.6200 loss_action: 0.9868 loss_start: 0.5986 loss_end: 0.5958 loss: 2.1812 2022/09/07 22:22:50 - mmengine - INFO - Epoch(train) [6][380/604] lr: 1.0000e-03 eta: 0:15:16 time: 0.0864 data_time: 0.0221 memory: 43 grad_norm: 0.7508 loss_action: 0.9826 loss_start: 0.5899 loss_end: 0.5979 loss: 2.1705 2022/09/07 22:22:52 - mmengine - INFO - Epoch(train) [6][400/604] lr: 1.0000e-03 eta: 0:15:13 time: 0.0790 data_time: 0.0152 memory: 43 grad_norm: 0.7839 loss_action: 1.0059 loss_start: 0.6071 loss_end: 0.6034 loss: 2.2163 2022/09/07 22:22:54 - mmengine - INFO - Epoch(train) [6][420/604] lr: 1.0000e-03 eta: 0:15:11 time: 0.1068 data_time: 0.0426 memory: 43 grad_norm: 0.8090 loss_action: 0.9969 loss_start: 0.6075 loss_end: 0.5862 loss: 2.1906 2022/09/07 22:22:55 - mmengine - INFO - Epoch(train) [6][440/604] lr: 1.0000e-03 eta: 0:15:07 time: 0.0764 data_time: 0.0129 memory: 43 grad_norm: 0.7623 loss_action: 0.9981 loss_start: 0.6021 loss_end: 0.6020 loss: 2.2022 2022/09/07 22:22:57 - mmengine - INFO - Epoch(train) [6][460/604] lr: 1.0000e-03 eta: 0:15:05 time: 0.1008 data_time: 0.0370 memory: 43 grad_norm: 0.7639 loss_action: 0.9825 loss_start: 0.5989 loss_end: 0.5901 loss: 2.1715 2022/09/07 22:22:59 - mmengine - INFO - Epoch(train) [6][480/604] lr: 1.0000e-03 eta: 0:15:02 time: 0.0771 data_time: 0.0132 memory: 43 grad_norm: 0.7631 loss_action: 0.9753 loss_start: 0.5955 loss_end: 0.5968 loss: 2.1677 2022/09/07 22:23:01 - mmengine - INFO - Epoch(train) [6][500/604] lr: 1.0000e-03 eta: 0:14:59 time: 0.1006 data_time: 0.0348 memory: 43 grad_norm: 0.7019 loss_action: 0.9847 loss_start: 0.6042 loss_end: 0.6013 loss: 2.1902 2022/09/07 22:23:03 - mmengine - INFO - Epoch(train) [6][520/604] lr: 1.0000e-03 eta: 0:14:56 time: 0.0766 data_time: 0.0132 memory: 43 grad_norm: 0.7002 loss_action: 0.9756 loss_start: 0.6062 loss_end: 0.5860 loss: 2.1678 2022/09/07 22:23:05 - mmengine - INFO - Epoch(train) [6][540/604] lr: 1.0000e-03 eta: 0:14:53 time: 0.1019 data_time: 0.0367 memory: 43 grad_norm: 0.7712 loss_action: 0.9744 loss_start: 0.5945 loss_end: 0.5841 loss: 2.1530 2022/09/07 22:23:06 - mmengine - INFO - Epoch(train) [6][560/604] lr: 1.0000e-03 eta: 0:14:50 time: 0.0757 data_time: 0.0128 memory: 43 grad_norm: 0.6687 loss_action: 0.9969 loss_start: 0.5836 loss_end: 0.6012 loss: 2.1817 2022/09/07 22:23:08 - mmengine - INFO - Epoch(train) [6][580/604] lr: 1.0000e-03 eta: 0:14:48 time: 0.1009 data_time: 0.0377 memory: 43 grad_norm: 0.7977 loss_action: 1.0093 loss_start: 0.5955 loss_end: 0.5992 loss: 2.2039 2022/09/07 22:23:10 - mmengine - INFO - Epoch(train) [6][600/604] lr: 1.0000e-03 eta: 0:14:44 time: 0.0759 data_time: 0.0129 memory: 43 grad_norm: 0.8206 loss_action: 1.0808 loss_start: 0.5997 loss_end: 0.5852 loss: 2.2658 2022/09/07 22:23:10 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:23:10 - mmengine - INFO - Epoch(train) [6][604/604] lr: 1.0000e-03 eta: 0:14:44 time: 0.0974 data_time: 0.0369 memory: 43 grad_norm: 0.9694 loss_action: 1.0734 loss_start: 0.5906 loss_end: 0.5734 loss: 2.2374 2022/09/07 22:23:10 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/09/07 22:23:14 - mmengine - INFO - Epoch(train) [7][20/604] lr: 1.0000e-03 eta: 0:14:42 time: 0.1280 data_time: 0.0646 memory: 43 grad_norm: 0.7277 loss_action: 0.9555 loss_start: 0.5923 loss_end: 0.5938 loss: 2.1416 2022/09/07 22:23:17 - mmengine - INFO - Epoch(train) [7][40/604] lr: 1.0000e-03 eta: 0:14:41 time: 0.1216 data_time: 0.0586 memory: 43 grad_norm: 0.7182 loss_action: 1.0048 loss_start: 0.5993 loss_end: 0.5859 loss: 2.1901 2022/09/07 22:23:19 - mmengine - INFO - Epoch(train) [7][60/604] lr: 1.0000e-03 eta: 0:14:39 time: 0.1060 data_time: 0.0427 memory: 43 grad_norm: 0.6840 loss_action: 0.9621 loss_start: 0.5882 loss_end: 0.5919 loss: 2.1422 2022/09/07 22:23:20 - mmengine - INFO - Epoch(train) [7][80/604] lr: 1.0000e-03 eta: 0:14:35 time: 0.0760 data_time: 0.0128 memory: 43 grad_norm: 0.6940 loss_action: 0.9847 loss_start: 0.5853 loss_end: 0.5931 loss: 2.1631 2022/09/07 22:23:22 - mmengine - INFO - Epoch(train) [7][100/604] lr: 1.0000e-03 eta: 0:14:32 time: 0.0759 data_time: 0.0131 memory: 43 grad_norm: 0.6888 loss_action: 0.9722 loss_start: 0.6041 loss_end: 0.5847 loss: 2.1610 2022/09/07 22:23:23 - mmengine - INFO - Epoch(train) [7][120/604] lr: 1.0000e-03 eta: 0:14:28 time: 0.0765 data_time: 0.0132 memory: 43 grad_norm: 0.7356 loss_action: 0.9950 loss_start: 0.5888 loss_end: 0.5934 loss: 2.1772 2022/09/07 22:23:25 - mmengine - INFO - Epoch(train) [7][140/604] lr: 1.0000e-03 eta: 0:14:25 time: 0.0769 data_time: 0.0135 memory: 43 grad_norm: 0.7347 loss_action: 1.0134 loss_start: 0.6088 loss_end: 0.5812 loss: 2.2034 2022/09/07 22:23:26 - mmengine - INFO - Epoch(train) [7][160/604] lr: 1.0000e-03 eta: 0:14:22 time: 0.0760 data_time: 0.0128 memory: 43 grad_norm: 0.7305 loss_action: 1.0009 loss_start: 0.5963 loss_end: 0.6042 loss: 2.2014 2022/09/07 22:23:28 - mmengine - INFO - Epoch(train) [7][180/604] lr: 1.0000e-03 eta: 0:14:19 time: 0.0762 data_time: 0.0129 memory: 43 grad_norm: 0.7032 loss_action: 0.9685 loss_start: 0.5799 loss_end: 0.5861 loss: 2.1346 2022/09/07 22:23:29 - mmengine - INFO - Epoch(train) [7][200/604] lr: 1.0000e-03 eta: 0:14:15 time: 0.0762 data_time: 0.0130 memory: 43 grad_norm: 0.7865 loss_action: 1.0343 loss_start: 0.6038 loss_end: 0.5935 loss: 2.2315 2022/09/07 22:23:31 - mmengine - INFO - Epoch(train) [7][220/604] lr: 1.0000e-03 eta: 0:14:12 time: 0.0763 data_time: 0.0129 memory: 43 grad_norm: 0.8071 loss_action: 0.9234 loss_start: 0.5915 loss_end: 0.5692 loss: 2.0841 2022/09/07 22:23:32 - mmengine - INFO - Epoch(train) [7][240/604] lr: 1.0000e-03 eta: 0:14:09 time: 0.0762 data_time: 0.0129 memory: 43 grad_norm: 0.6837 loss_action: 1.0148 loss_start: 0.5984 loss_end: 0.5944 loss: 2.2077 2022/09/07 22:23:34 - mmengine - INFO - Epoch(train) [7][260/604] lr: 1.0000e-03 eta: 0:14:06 time: 0.0760 data_time: 0.0129 memory: 43 grad_norm: 0.7012 loss_action: 0.9913 loss_start: 0.5845 loss_end: 0.5863 loss: 2.1621 2022/09/07 22:23:35 - mmengine - INFO - Epoch(train) [7][280/604] lr: 1.0000e-03 eta: 0:14:02 time: 0.0761 data_time: 0.0131 memory: 43 grad_norm: 0.6701 loss_action: 1.0141 loss_start: 0.5893 loss_end: 0.6104 loss: 2.2138 2022/09/07 22:23:37 - mmengine - INFO - Epoch(train) [7][300/604] lr: 1.0000e-03 eta: 0:13:59 time: 0.0758 data_time: 0.0130 memory: 43 grad_norm: 0.7895 loss_action: 1.0020 loss_start: 0.5937 loss_end: 0.5841 loss: 2.1797 2022/09/07 22:23:38 - mmengine - INFO - Epoch(train) [7][320/604] lr: 1.0000e-03 eta: 0:13:56 time: 0.0762 data_time: 0.0131 memory: 43 grad_norm: 0.7311 loss_action: 0.9741 loss_start: 0.5971 loss_end: 0.5929 loss: 2.1641 2022/09/07 22:23:40 - mmengine - INFO - Epoch(train) [7][340/604] lr: 1.0000e-03 eta: 0:13:53 time: 0.0792 data_time: 0.0132 memory: 43 grad_norm: 0.6551 loss_action: 1.0056 loss_start: 0.5984 loss_end: 0.6093 loss: 2.2133 2022/09/07 22:23:42 - mmengine - INFO - Epoch(train) [7][360/604] lr: 1.0000e-03 eta: 0:13:50 time: 0.0806 data_time: 0.0134 memory: 43 grad_norm: 0.7569 loss_action: 0.9717 loss_start: 0.5912 loss_end: 0.5948 loss: 2.1578 2022/09/07 22:23:43 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:23:44 - mmengine - INFO - Epoch(train) [7][380/604] lr: 1.0000e-03 eta: 0:13:48 time: 0.1031 data_time: 0.0149 memory: 43 grad_norm: 0.7471 loss_action: 0.9807 loss_start: 0.5938 loss_end: 0.5835 loss: 2.1581 2022/09/07 22:23:46 - mmengine - INFO - Epoch(train) [7][400/604] lr: 1.0000e-03 eta: 0:13:47 time: 0.1207 data_time: 0.0234 memory: 43 grad_norm: 0.8773 loss_action: 1.0228 loss_start: 0.5952 loss_end: 0.5911 loss: 2.2090 2022/09/07 22:23:49 - mmengine - INFO - Epoch(train) [7][420/604] lr: 1.0000e-03 eta: 0:13:46 time: 0.1487 data_time: 0.0194 memory: 43 grad_norm: 0.7576 loss_action: 0.9845 loss_start: 0.6031 loss_end: 0.5900 loss: 2.1776 2022/09/07 22:23:52 - mmengine - INFO - Epoch(train) [7][440/604] lr: 1.0000e-03 eta: 0:13:47 time: 0.1559 data_time: 0.0212 memory: 43 grad_norm: 0.7054 loss_action: 0.9662 loss_start: 0.5854 loss_end: 0.5902 loss: 2.1418 2022/09/07 22:23:55 - mmengine - INFO - Epoch(train) [7][460/604] lr: 1.0000e-03 eta: 0:13:47 time: 0.1643 data_time: 0.0295 memory: 43 grad_norm: 0.7361 loss_action: 0.9249 loss_start: 0.5976 loss_end: 0.5902 loss: 2.1127 2022/09/07 22:24:00 - mmengine - INFO - Epoch(train) [7][480/604] lr: 1.0000e-03 eta: 0:13:49 time: 0.2011 data_time: 0.0342 memory: 43 grad_norm: 0.8377 loss_action: 1.0184 loss_start: 0.5903 loss_end: 0.5994 loss: 2.2080 2022/09/07 22:24:03 - mmengine - INFO - Epoch(train) [7][500/604] lr: 1.0000e-03 eta: 0:13:49 time: 0.1628 data_time: 0.0289 memory: 43 grad_norm: 0.8257 loss_action: 1.0732 loss_start: 0.5941 loss_end: 0.5939 loss: 2.2613 2022/09/07 22:24:04 - mmengine - INFO - Epoch(train) [7][520/604] lr: 1.0000e-03 eta: 0:13:46 time: 0.0751 data_time: 0.0120 memory: 43 grad_norm: 0.7004 loss_action: 1.0124 loss_start: 0.5977 loss_end: 0.5983 loss: 2.2084 2022/09/07 22:24:06 - mmengine - INFO - Epoch(train) [7][540/604] lr: 1.0000e-03 eta: 0:13:42 time: 0.0758 data_time: 0.0130 memory: 43 grad_norm: 0.8227 loss_action: 0.9827 loss_start: 0.5926 loss_end: 0.5929 loss: 2.1683 2022/09/07 22:24:09 - mmengine - INFO - Epoch(train) [7][560/604] lr: 1.0000e-03 eta: 0:13:42 time: 0.1540 data_time: 0.0343 memory: 43 grad_norm: 0.7750 loss_action: 0.9859 loss_start: 0.6017 loss_end: 0.6081 loss: 2.1957 2022/09/07 22:24:11 - mmengine - INFO - Epoch(train) [7][580/604] lr: 1.0000e-03 eta: 0:13:39 time: 0.0839 data_time: 0.0217 memory: 43 grad_norm: 0.7415 loss_action: 1.0288 loss_start: 0.6005 loss_end: 0.5884 loss: 2.2177 2022/09/07 22:24:12 - mmengine - INFO - Epoch(train) [7][600/604] lr: 1.0000e-03 eta: 0:13:37 time: 0.0830 data_time: 0.0207 memory: 43 grad_norm: 0.7238 loss_action: 0.9895 loss_start: 0.5998 loss_end: 0.5929 loss: 2.1822 2022/09/07 22:24:12 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:24:12 - mmengine - INFO - Epoch(train) [7][604/604] lr: 1.0000e-03 eta: 0:13:37 time: 0.0717 data_time: 0.0121 memory: 43 grad_norm: 0.7471 loss_action: 0.9996 loss_start: 0.5982 loss_end: 0.5826 loss: 2.1804 2022/09/07 22:24:12 - mmengine - INFO - Saving checkpoint at 7 epochs 2022/09/07 22:24:15 - mmengine - INFO - Epoch(train) [8][20/604] lr: 1.0000e-04 eta: 0:13:34 time: 0.1286 data_time: 0.0665 memory: 43 grad_norm: 0.8352 loss_action: 0.9911 loss_start: 0.5855 loss_end: 0.5891 loss: 2.1657 2022/09/07 22:24:18 - mmengine - INFO - Epoch(train) [8][40/604] lr: 1.0000e-04 eta: 0:13:32 time: 0.1124 data_time: 0.0499 memory: 43 grad_norm: 0.6472 loss_action: 0.9423 loss_start: 0.5960 loss_end: 0.5843 loss: 2.1226 2022/09/07 22:24:19 - mmengine - INFO - Epoch(train) [8][60/604] lr: 1.0000e-04 eta: 0:13:29 time: 0.0747 data_time: 0.0126 memory: 43 grad_norm: 0.6658 loss_action: 0.9709 loss_start: 0.5815 loss_end: 0.5786 loss: 2.1310 2022/09/07 22:24:21 - mmengine - INFO - Epoch(train) [8][80/604] lr: 1.0000e-04 eta: 0:13:26 time: 0.0744 data_time: 0.0126 memory: 43 grad_norm: 0.7564 loss_action: 0.9893 loss_start: 0.5953 loss_end: 0.5874 loss: 2.1721 2022/09/07 22:24:22 - mmengine - INFO - Epoch(train) [8][100/604] lr: 1.0000e-04 eta: 0:13:23 time: 0.0750 data_time: 0.0132 memory: 43 grad_norm: 0.7443 loss_action: 0.9091 loss_start: 0.5831 loss_end: 0.5633 loss: 2.0554 2022/09/07 22:24:24 - mmengine - INFO - Epoch(train) [8][120/604] lr: 1.0000e-04 eta: 0:13:20 time: 0.0742 data_time: 0.0125 memory: 43 grad_norm: 0.6321 loss_action: 0.9466 loss_start: 0.5815 loss_end: 0.5703 loss: 2.0984 2022/09/07 22:24:25 - mmengine - INFO - Epoch(train) [8][140/604] lr: 1.0000e-04 eta: 0:13:17 time: 0.0776 data_time: 0.0130 memory: 43 grad_norm: 0.6784 loss_action: 0.9205 loss_start: 0.5761 loss_end: 0.5738 loss: 2.0704 2022/09/07 22:24:27 - mmengine - INFO - Epoch(train) [8][160/604] lr: 1.0000e-04 eta: 0:13:14 time: 0.0763 data_time: 0.0128 memory: 43 grad_norm: 0.6426 loss_action: 0.9333 loss_start: 0.5767 loss_end: 0.5655 loss: 2.0755 2022/09/07 22:24:28 - mmengine - INFO - Epoch(train) [8][180/604] lr: 1.0000e-04 eta: 0:13:11 time: 0.0766 data_time: 0.0128 memory: 43 grad_norm: 0.6982 loss_action: 0.9345 loss_start: 0.5837 loss_end: 0.5732 loss: 2.0914 2022/09/07 22:24:30 - mmengine - INFO - Epoch(train) [8][200/604] lr: 1.0000e-04 eta: 0:13:08 time: 0.0761 data_time: 0.0128 memory: 43 grad_norm: 0.6777 loss_action: 0.9631 loss_start: 0.5825 loss_end: 0.5873 loss: 2.1329 2022/09/07 22:24:31 - mmengine - INFO - Epoch(train) [8][220/604] lr: 1.0000e-04 eta: 0:13:05 time: 0.0768 data_time: 0.0128 memory: 43 grad_norm: 0.7686 loss_action: 0.9714 loss_start: 0.5874 loss_end: 0.5882 loss: 2.1471 2022/09/07 22:24:33 - mmengine - INFO - Epoch(train) [8][240/604] lr: 1.0000e-04 eta: 0:13:02 time: 0.0767 data_time: 0.0129 memory: 43 grad_norm: 0.7367 loss_action: 0.9565 loss_start: 0.5933 loss_end: 0.5794 loss: 2.1292 2022/09/07 22:24:34 - mmengine - INFO - Epoch(train) [8][260/604] lr: 1.0000e-04 eta: 0:12:59 time: 0.0767 data_time: 0.0128 memory: 43 grad_norm: 0.6885 loss_action: 0.9184 loss_start: 0.5671 loss_end: 0.5672 loss: 2.0527 2022/09/07 22:24:36 - mmengine - INFO - Epoch(train) [8][280/604] lr: 1.0000e-04 eta: 0:12:56 time: 0.0770 data_time: 0.0129 memory: 43 grad_norm: 0.7237 loss_action: 0.9716 loss_start: 0.5777 loss_end: 0.5710 loss: 2.1203 2022/09/07 22:24:37 - mmengine - INFO - Epoch(train) [8][300/604] lr: 1.0000e-04 eta: 0:12:53 time: 0.0767 data_time: 0.0128 memory: 43 grad_norm: 0.6933 loss_action: 0.9070 loss_start: 0.5859 loss_end: 0.5732 loss: 2.0660 2022/09/07 22:24:39 - mmengine - INFO - Epoch(train) [8][320/604] lr: 1.0000e-04 eta: 0:12:50 time: 0.0766 data_time: 0.0129 memory: 43 grad_norm: 0.7526 loss_action: 0.9714 loss_start: 0.5904 loss_end: 0.5788 loss: 2.1406 2022/09/07 22:24:40 - mmengine - INFO - Epoch(train) [8][340/604] lr: 1.0000e-04 eta: 0:12:47 time: 0.0765 data_time: 0.0129 memory: 43 grad_norm: 0.8017 loss_action: 0.9500 loss_start: 0.5802 loss_end: 0.6013 loss: 2.1316 2022/09/07 22:24:42 - mmengine - INFO - Epoch(train) [8][360/604] lr: 1.0000e-04 eta: 0:12:44 time: 0.0764 data_time: 0.0129 memory: 43 grad_norm: 0.6595 loss_action: 0.9541 loss_start: 0.5765 loss_end: 0.5694 loss: 2.1000 2022/09/07 22:24:43 - mmengine - INFO - Epoch(train) [8][380/604] lr: 1.0000e-04 eta: 0:12:42 time: 0.0769 data_time: 0.0128 memory: 43 grad_norm: 0.6699 loss_action: 0.9569 loss_start: 0.5791 loss_end: 0.5742 loss: 2.1102 2022/09/07 22:24:45 - mmengine - INFO - Epoch(train) [8][400/604] lr: 1.0000e-04 eta: 0:12:39 time: 0.0767 data_time: 0.0129 memory: 43 grad_norm: 0.7539 loss_action: 0.9959 loss_start: 0.5775 loss_end: 0.5885 loss: 2.1620 2022/09/07 22:24:47 - mmengine - INFO - Epoch(train) [8][420/604] lr: 1.0000e-04 eta: 0:12:36 time: 0.0765 data_time: 0.0128 memory: 43 grad_norm: 0.7011 loss_action: 0.9280 loss_start: 0.5657 loss_end: 0.5700 loss: 2.0637 2022/09/07 22:24:48 - mmengine - INFO - Epoch(train) [8][440/604] lr: 1.0000e-04 eta: 0:12:33 time: 0.0766 data_time: 0.0130 memory: 43 grad_norm: 0.6839 loss_action: 0.9239 loss_start: 0.5793 loss_end: 0.5736 loss: 2.0769 2022/09/07 22:24:50 - mmengine - INFO - Epoch(train) [8][460/604] lr: 1.0000e-04 eta: 0:12:30 time: 0.0764 data_time: 0.0129 memory: 43 grad_norm: 0.6637 loss_action: 0.9338 loss_start: 0.5770 loss_end: 0.5799 loss: 2.0907 2022/09/07 22:24:51 - mmengine - INFO - Epoch(train) [8][480/604] lr: 1.0000e-04 eta: 0:12:27 time: 0.0768 data_time: 0.0130 memory: 43 grad_norm: 0.6958 loss_action: 0.9380 loss_start: 0.5829 loss_end: 0.5808 loss: 2.1016 2022/09/07 22:24:53 - mmengine - INFO - Epoch(train) [8][500/604] lr: 1.0000e-04 eta: 0:12:25 time: 0.0761 data_time: 0.0129 memory: 43 grad_norm: 0.6964 loss_action: 0.9669 loss_start: 0.5712 loss_end: 0.5598 loss: 2.0979 2022/09/07 22:24:54 - mmengine - INFO - Epoch(train) [8][520/604] lr: 1.0000e-04 eta: 0:12:22 time: 0.0765 data_time: 0.0129 memory: 43 grad_norm: 0.7318 loss_action: 0.9991 loss_start: 0.5900 loss_end: 0.5893 loss: 2.1784 2022/09/07 22:24:56 - mmengine - INFO - Epoch(train) [8][540/604] lr: 1.0000e-04 eta: 0:12:19 time: 0.0759 data_time: 0.0127 memory: 43 grad_norm: 0.6748 loss_action: 0.9093 loss_start: 0.5779 loss_end: 0.5746 loss: 2.0617 2022/09/07 22:24:57 - mmengine - INFO - Epoch(train) [8][560/604] lr: 1.0000e-04 eta: 0:12:16 time: 0.0751 data_time: 0.0127 memory: 43 grad_norm: 0.7672 loss_action: 0.9592 loss_start: 0.5725 loss_end: 0.5735 loss: 2.1052 2022/09/07 22:24:59 - mmengine - INFO - Epoch(train) [8][580/604] lr: 1.0000e-04 eta: 0:12:13 time: 0.0745 data_time: 0.0125 memory: 43 grad_norm: 0.6719 loss_action: 0.9446 loss_start: 0.5755 loss_end: 0.5763 loss: 2.0965 2022/09/07 22:25:00 - mmengine - INFO - Epoch(train) [8][600/604] lr: 1.0000e-04 eta: 0:12:11 time: 0.0781 data_time: 0.0128 memory: 43 grad_norm: 0.6801 loss_action: 0.9588 loss_start: 0.5885 loss_end: 0.5847 loss: 2.1320 2022/09/07 22:25:00 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:25:00 - mmengine - INFO - Epoch(train) [8][604/604] lr: 1.0000e-04 eta: 0:12:11 time: 0.0722 data_time: 0.0122 memory: 43 grad_norm: 0.7442 loss_action: 0.9062 loss_start: 0.5783 loss_end: 0.5817 loss: 2.0661 2022/09/07 22:25:00 - mmengine - INFO - Saving checkpoint at 8 epochs 2022/09/07 22:25:03 - mmengine - INFO - Epoch(train) [9][20/604] lr: 1.0000e-04 eta: 0:12:09 time: 0.1325 data_time: 0.0672 memory: 43 grad_norm: 0.8113 loss_action: 0.9547 loss_start: 0.5828 loss_end: 0.5832 loss: 2.1206 2022/09/07 22:25:05 - mmengine - INFO - Epoch(train) [9][40/604] lr: 1.0000e-04 eta: 0:12:06 time: 0.0767 data_time: 0.0132 memory: 43 grad_norm: 0.6866 loss_action: 0.9477 loss_start: 0.5641 loss_end: 0.5723 loss: 2.0841 2022/09/07 22:25:06 - mmengine - INFO - Epoch(train) [9][60/604] lr: 1.0000e-04 eta: 0:12:03 time: 0.0768 data_time: 0.0134 memory: 43 grad_norm: 0.7026 loss_action: 0.9236 loss_start: 0.5694 loss_end: 0.5677 loss: 2.0606 2022/09/07 22:25:08 - mmengine - INFO - Epoch(train) [9][80/604] lr: 1.0000e-04 eta: 0:12:00 time: 0.0768 data_time: 0.0131 memory: 43 grad_norm: 0.7568 loss_action: 0.9678 loss_start: 0.5869 loss_end: 0.5691 loss: 2.1237 2022/09/07 22:25:10 - mmengine - INFO - Epoch(train) [9][100/604] lr: 1.0000e-04 eta: 0:11:58 time: 0.0771 data_time: 0.0134 memory: 43 grad_norm: 0.6923 loss_action: 0.9470 loss_start: 0.5824 loss_end: 0.5796 loss: 2.1091 2022/09/07 22:25:11 - mmengine - INFO - Epoch(train) [9][120/604] lr: 1.0000e-04 eta: 0:11:55 time: 0.0778 data_time: 0.0131 memory: 43 grad_norm: 0.6843 loss_action: 0.9172 loss_start: 0.5794 loss_end: 0.5668 loss: 2.0634 2022/09/07 22:25:13 - mmengine - INFO - Epoch(train) [9][140/604] lr: 1.0000e-04 eta: 0:11:52 time: 0.0764 data_time: 0.0127 memory: 43 grad_norm: 0.6448 loss_action: 0.9322 loss_start: 0.5884 loss_end: 0.5697 loss: 2.0903 2022/09/07 22:25:14 - mmengine - INFO - Epoch(train) [9][160/604] lr: 1.0000e-04 eta: 0:11:50 time: 0.0766 data_time: 0.0126 memory: 43 grad_norm: 0.7030 loss_action: 0.9083 loss_start: 0.5714 loss_end: 0.5584 loss: 2.0381 2022/09/07 22:25:15 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:25:16 - mmengine - INFO - Epoch(train) [9][180/604] lr: 1.0000e-04 eta: 0:11:47 time: 0.0762 data_time: 0.0125 memory: 43 grad_norm: 0.6740 loss_action: 0.9044 loss_start: 0.5717 loss_end: 0.5686 loss: 2.0446 2022/09/07 22:25:17 - mmengine - INFO - Epoch(train) [9][200/604] lr: 1.0000e-04 eta: 0:11:44 time: 0.0763 data_time: 0.0126 memory: 43 grad_norm: 0.6748 loss_action: 0.9530 loss_start: 0.5681 loss_end: 0.5755 loss: 2.0966 2022/09/07 22:25:19 - mmengine - INFO - Epoch(train) [9][220/604] lr: 1.0000e-04 eta: 0:11:42 time: 0.0763 data_time: 0.0126 memory: 43 grad_norm: 0.8093 loss_action: 0.9416 loss_start: 0.5764 loss_end: 0.5719 loss: 2.0899 2022/09/07 22:25:20 - mmengine - INFO - Epoch(train) [9][240/604] lr: 1.0000e-04 eta: 0:11:39 time: 0.0764 data_time: 0.0126 memory: 43 grad_norm: 0.7164 loss_action: 0.9194 loss_start: 0.5758 loss_end: 0.5692 loss: 2.0644 2022/09/07 22:25:22 - mmengine - INFO - Epoch(train) [9][260/604] lr: 1.0000e-04 eta: 0:11:36 time: 0.0763 data_time: 0.0128 memory: 43 grad_norm: 0.7038 loss_action: 0.9241 loss_start: 0.5732 loss_end: 0.5638 loss: 2.0611 2022/09/07 22:25:23 - mmengine - INFO - Epoch(train) [9][280/604] lr: 1.0000e-04 eta: 0:11:34 time: 0.0773 data_time: 0.0131 memory: 43 grad_norm: 0.8262 loss_action: 0.9987 loss_start: 0.5827 loss_end: 0.5774 loss: 2.1589 2022/09/07 22:25:25 - mmengine - INFO - Epoch(train) [9][300/604] lr: 1.0000e-04 eta: 0:11:31 time: 0.0773 data_time: 0.0130 memory: 43 grad_norm: 0.7257 loss_action: 0.9184 loss_start: 0.5810 loss_end: 0.5625 loss: 2.0619 2022/09/07 22:25:26 - mmengine - INFO - Epoch(train) [9][320/604] lr: 1.0000e-04 eta: 0:11:29 time: 0.0774 data_time: 0.0131 memory: 43 grad_norm: 0.7311 loss_action: 0.9328 loss_start: 0.5734 loss_end: 0.5736 loss: 2.0799 2022/09/07 22:25:28 - mmengine - INFO - Epoch(train) [9][340/604] lr: 1.0000e-04 eta: 0:11:26 time: 0.0772 data_time: 0.0131 memory: 43 grad_norm: 0.7112 loss_action: 0.8697 loss_start: 0.5586 loss_end: 0.5640 loss: 1.9923 2022/09/07 22:25:30 - mmengine - INFO - Epoch(train) [9][360/604] lr: 1.0000e-04 eta: 0:11:23 time: 0.0775 data_time: 0.0132 memory: 43 grad_norm: 0.7129 loss_action: 0.9381 loss_start: 0.5757 loss_end: 0.5740 loss: 2.0878 2022/09/07 22:25:31 - mmengine - INFO - Epoch(train) [9][380/604] lr: 1.0000e-04 eta: 0:11:21 time: 0.0777 data_time: 0.0135 memory: 43 grad_norm: 0.7565 loss_action: 0.9505 loss_start: 0.5763 loss_end: 0.5678 loss: 2.0946 2022/09/07 22:25:33 - mmengine - INFO - Epoch(train) [9][400/604] lr: 1.0000e-04 eta: 0:11:19 time: 0.1088 data_time: 0.0151 memory: 43 grad_norm: 0.6764 loss_action: 0.9577 loss_start: 0.5709 loss_end: 0.5712 loss: 2.0998 2022/09/07 22:25:35 - mmengine - INFO - Epoch(train) [9][420/604] lr: 1.0000e-04 eta: 0:11:17 time: 0.0752 data_time: 0.0129 memory: 43 grad_norm: 0.7242 loss_action: 0.9281 loss_start: 0.5782 loss_end: 0.5649 loss: 2.0712 2022/09/07 22:25:36 - mmengine - INFO - Epoch(train) [9][440/604] lr: 1.0000e-04 eta: 0:11:14 time: 0.0796 data_time: 0.0129 memory: 43 grad_norm: 0.6684 loss_action: 0.9104 loss_start: 0.5901 loss_end: 0.5709 loss: 2.0714 2022/09/07 22:25:38 - mmengine - INFO - Epoch(train) [9][460/604] lr: 1.0000e-04 eta: 0:11:12 time: 0.0773 data_time: 0.0131 memory: 43 grad_norm: 0.8286 loss_action: 0.9470 loss_start: 0.5697 loss_end: 0.5736 loss: 2.0903 2022/09/07 22:25:39 - mmengine - INFO - Epoch(train) [9][480/604] lr: 1.0000e-04 eta: 0:11:09 time: 0.0773 data_time: 0.0132 memory: 43 grad_norm: 0.7056 loss_action: 0.9396 loss_start: 0.5783 loss_end: 0.5740 loss: 2.0919 2022/09/07 22:25:41 - mmengine - INFO - Epoch(train) [9][500/604] lr: 1.0000e-04 eta: 0:11:06 time: 0.0763 data_time: 0.0128 memory: 43 grad_norm: 0.7726 loss_action: 0.9518 loss_start: 0.5806 loss_end: 0.5745 loss: 2.1069 2022/09/07 22:25:43 - mmengine - INFO - Epoch(train) [9][520/604] lr: 1.0000e-04 eta: 0:11:04 time: 0.0761 data_time: 0.0130 memory: 43 grad_norm: 0.7339 loss_action: 0.9533 loss_start: 0.5857 loss_end: 0.5911 loss: 2.1302 2022/09/07 22:25:44 - mmengine - INFO - Epoch(train) [9][540/604] lr: 1.0000e-04 eta: 0:11:01 time: 0.0752 data_time: 0.0124 memory: 43 grad_norm: 0.6663 loss_action: 0.9555 loss_start: 0.5697 loss_end: 0.5716 loss: 2.0968 2022/09/07 22:25:46 - mmengine - INFO - Epoch(train) [9][560/604] lr: 1.0000e-04 eta: 0:10:59 time: 0.0760 data_time: 0.0128 memory: 43 grad_norm: 0.7380 loss_action: 0.9484 loss_start: 0.5793 loss_end: 0.5895 loss: 2.1172 2022/09/07 22:25:47 - mmengine - INFO - Epoch(train) [9][580/604] lr: 1.0000e-04 eta: 0:10:56 time: 0.0753 data_time: 0.0122 memory: 43 grad_norm: 0.7342 loss_action: 0.9292 loss_start: 0.5797 loss_end: 0.5745 loss: 2.0834 2022/09/07 22:25:49 - mmengine - INFO - Epoch(train) [9][600/604] lr: 1.0000e-04 eta: 0:10:54 time: 0.0753 data_time: 0.0125 memory: 43 grad_norm: 0.7428 loss_action: 0.9270 loss_start: 0.5714 loss_end: 0.5662 loss: 2.0646 2022/09/07 22:25:49 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:25:49 - mmengine - INFO - Epoch(train) [9][604/604] lr: 1.0000e-04 eta: 0:10:54 time: 0.0720 data_time: 0.0118 memory: 43 grad_norm: 0.7695 loss_action: 0.9168 loss_start: 0.5734 loss_end: 0.5603 loss: 2.0505 2022/09/07 22:25:49 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/09/07 22:25:52 - mmengine - INFO - Epoch(train) [10][20/604] lr: 1.0000e-04 eta: 0:10:52 time: 0.1275 data_time: 0.0641 memory: 43 grad_norm: 0.8631 loss_action: 0.9506 loss_start: 0.5738 loss_end: 0.5654 loss: 2.0898 2022/09/07 22:25:53 - mmengine - INFO - Epoch(train) [10][40/604] lr: 1.0000e-04 eta: 0:10:49 time: 0.0741 data_time: 0.0125 memory: 43 grad_norm: 0.6655 loss_action: 0.8986 loss_start: 0.5582 loss_end: 0.5537 loss: 2.0105 2022/09/07 22:25:55 - mmengine - INFO - Epoch(train) [10][60/604] lr: 1.0000e-04 eta: 0:10:47 time: 0.0741 data_time: 0.0125 memory: 43 grad_norm: 0.7153 loss_action: 0.9465 loss_start: 0.5834 loss_end: 0.5694 loss: 2.0993 2022/09/07 22:25:56 - mmengine - INFO - Epoch(train) [10][80/604] lr: 1.0000e-04 eta: 0:10:44 time: 0.0753 data_time: 0.0124 memory: 43 grad_norm: 0.6820 loss_action: 0.9302 loss_start: 0.5679 loss_end: 0.5651 loss: 2.0632 2022/09/07 22:25:58 - mmengine - INFO - Epoch(train) [10][100/604] lr: 1.0000e-04 eta: 0:10:41 time: 0.0742 data_time: 0.0126 memory: 43 grad_norm: 0.6627 loss_action: 0.9205 loss_start: 0.5721 loss_end: 0.5586 loss: 2.0512 2022/09/07 22:25:59 - mmengine - INFO - Epoch(train) [10][120/604] lr: 1.0000e-04 eta: 0:10:39 time: 0.0739 data_time: 0.0125 memory: 43 grad_norm: 0.7542 loss_action: 0.9625 loss_start: 0.5563 loss_end: 0.5703 loss: 2.0890 2022/09/07 22:26:01 - mmengine - INFO - Epoch(train) [10][140/604] lr: 1.0000e-04 eta: 0:10:36 time: 0.0753 data_time: 0.0127 memory: 43 grad_norm: 0.7666 loss_action: 0.9233 loss_start: 0.5652 loss_end: 0.5710 loss: 2.0596 2022/09/07 22:26:02 - mmengine - INFO - Epoch(train) [10][160/604] lr: 1.0000e-04 eta: 0:10:34 time: 0.0744 data_time: 0.0125 memory: 43 grad_norm: 0.7093 loss_action: 0.9051 loss_start: 0.5781 loss_end: 0.5659 loss: 2.0491 2022/09/07 22:26:04 - mmengine - INFO - Epoch(train) [10][180/604] lr: 1.0000e-04 eta: 0:10:31 time: 0.0744 data_time: 0.0126 memory: 43 grad_norm: 0.7266 loss_action: 0.9319 loss_start: 0.5712 loss_end: 0.5654 loss: 2.0685 2022/09/07 22:26:05 - mmengine - INFO - Epoch(train) [10][200/604] lr: 1.0000e-04 eta: 0:10:29 time: 0.0739 data_time: 0.0125 memory: 43 grad_norm: 0.6562 loss_action: 0.9588 loss_start: 0.5639 loss_end: 0.5745 loss: 2.0972 2022/09/07 22:26:07 - mmengine - INFO - Epoch(train) [10][220/604] lr: 1.0000e-04 eta: 0:10:26 time: 0.0752 data_time: 0.0126 memory: 43 grad_norm: 0.7034 loss_action: 0.9008 loss_start: 0.5832 loss_end: 0.5754 loss: 2.0593 2022/09/07 22:26:08 - mmengine - INFO - Epoch(train) [10][240/604] lr: 1.0000e-04 eta: 0:10:24 time: 0.0742 data_time: 0.0125 memory: 43 grad_norm: 0.7184 loss_action: 0.9428 loss_start: 0.5844 loss_end: 0.5794 loss: 2.1065 2022/09/07 22:26:09 - mmengine - INFO - Epoch(train) [10][260/604] lr: 1.0000e-04 eta: 0:10:22 time: 0.0741 data_time: 0.0125 memory: 43 grad_norm: 0.7112 loss_action: 0.9121 loss_start: 0.5712 loss_end: 0.5652 loss: 2.0485 2022/09/07 22:26:11 - mmengine - INFO - Epoch(train) [10][280/604] lr: 1.0000e-04 eta: 0:10:19 time: 0.0751 data_time: 0.0124 memory: 43 grad_norm: 0.6730 loss_action: 0.9651 loss_start: 0.5766 loss_end: 0.5629 loss: 2.1047 2022/09/07 22:26:12 - mmengine - INFO - Epoch(train) [10][300/604] lr: 1.0000e-04 eta: 0:10:17 time: 0.0745 data_time: 0.0126 memory: 43 grad_norm: 0.7380 loss_action: 0.9437 loss_start: 0.5816 loss_end: 0.5626 loss: 2.0879 2022/09/07 22:26:14 - mmengine - INFO - Epoch(train) [10][320/604] lr: 1.0000e-04 eta: 0:10:14 time: 0.0741 data_time: 0.0125 memory: 43 grad_norm: 0.7208 loss_action: 0.9685 loss_start: 0.5757 loss_end: 0.5712 loss: 2.1155 2022/09/07 22:26:15 - mmengine - INFO - Epoch(train) [10][340/604] lr: 1.0000e-04 eta: 0:10:12 time: 0.0743 data_time: 0.0127 memory: 43 grad_norm: 0.7089 loss_action: 0.9006 loss_start: 0.5784 loss_end: 0.5587 loss: 2.0378 2022/09/07 22:26:17 - mmengine - INFO - Epoch(train) [10][360/604] lr: 1.0000e-04 eta: 0:10:09 time: 0.0750 data_time: 0.0125 memory: 43 grad_norm: 0.7883 loss_action: 0.9240 loss_start: 0.5671 loss_end: 0.5774 loss: 2.0685 2022/09/07 22:26:18 - mmengine - INFO - Epoch(train) [10][380/604] lr: 1.0000e-04 eta: 0:10:07 time: 0.0738 data_time: 0.0125 memory: 43 grad_norm: 0.6964 loss_action: 0.9683 loss_start: 0.5758 loss_end: 0.5758 loss: 2.1198 2022/09/07 22:26:20 - mmengine - INFO - Epoch(train) [10][400/604] lr: 1.0000e-04 eta: 0:10:04 time: 0.0749 data_time: 0.0128 memory: 43 grad_norm: 0.7255 loss_action: 0.8938 loss_start: 0.5780 loss_end: 0.5720 loss: 2.0438 2022/09/07 22:26:21 - mmengine - INFO - Epoch(train) [10][420/604] lr: 1.0000e-04 eta: 0:10:02 time: 0.0773 data_time: 0.0130 memory: 43 grad_norm: 0.7483 loss_action: 0.9544 loss_start: 0.5668 loss_end: 0.5808 loss: 2.1020 2022/09/07 22:26:23 - mmengine - INFO - Epoch(train) [10][440/604] lr: 1.0000e-04 eta: 0:10:00 time: 0.0753 data_time: 0.0130 memory: 43 grad_norm: 0.8225 loss_action: 0.9449 loss_start: 0.5769 loss_end: 0.5718 loss: 2.0936 2022/09/07 22:26:24 - mmengine - INFO - Epoch(train) [10][460/604] lr: 1.0000e-04 eta: 0:09:57 time: 0.0753 data_time: 0.0130 memory: 43 grad_norm: 0.7136 loss_action: 0.9138 loss_start: 0.5635 loss_end: 0.5682 loss: 2.0455 2022/09/07 22:26:26 - mmengine - INFO - Epoch(train) [10][480/604] lr: 1.0000e-04 eta: 0:09:55 time: 0.0769 data_time: 0.0130 memory: 43 grad_norm: 0.7505 loss_action: 0.9289 loss_start: 0.5740 loss_end: 0.5740 loss: 2.0769 2022/09/07 22:26:28 - mmengine - INFO - Epoch(train) [10][500/604] lr: 1.0000e-04 eta: 0:09:53 time: 0.0760 data_time: 0.0131 memory: 43 grad_norm: 0.7119 loss_action: 0.8884 loss_start: 0.5793 loss_end: 0.5730 loss: 2.0407 2022/09/07 22:26:29 - mmengine - INFO - Epoch(train) [10][520/604] lr: 1.0000e-04 eta: 0:09:50 time: 0.0753 data_time: 0.0130 memory: 43 grad_norm: 0.7084 loss_action: 0.9498 loss_start: 0.5556 loss_end: 0.5755 loss: 2.0809 2022/09/07 22:26:31 - mmengine - INFO - Epoch(train) [10][540/604] lr: 1.0000e-04 eta: 0:09:48 time: 0.0747 data_time: 0.0128 memory: 43 grad_norm: 0.7301 loss_action: 0.8843 loss_start: 0.5763 loss_end: 0.5548 loss: 2.0154 2022/09/07 22:26:32 - mmengine - INFO - Epoch(train) [10][560/604] lr: 1.0000e-04 eta: 0:09:46 time: 0.0762 data_time: 0.0139 memory: 43 grad_norm: 0.6912 loss_action: 0.9268 loss_start: 0.5851 loss_end: 0.5650 loss: 2.0769 2022/09/07 22:26:33 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:26:35 - mmengine - INFO - Epoch(train) [10][580/604] lr: 1.0000e-04 eta: 0:09:44 time: 0.1288 data_time: 0.0592 memory: 43 grad_norm: 0.7583 loss_action: 0.8999 loss_start: 0.5670 loss_end: 0.5665 loss: 2.0334 2022/09/07 22:26:37 - mmengine - INFO - Epoch(train) [10][600/604] lr: 1.0000e-04 eta: 0:09:43 time: 0.1170 data_time: 0.0526 memory: 43 grad_norm: 0.7902 loss_action: 0.9468 loss_start: 0.5746 loss_end: 0.5692 loss: 2.0906 2022/09/07 22:26:37 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:26:38 - mmengine - INFO - Epoch(train) [10][604/604] lr: 1.0000e-04 eta: 0:09:43 time: 0.0718 data_time: 0.0123 memory: 43 grad_norm: 0.7787 loss_action: 0.9069 loss_start: 0.5723 loss_end: 0.5553 loss: 2.0345 2022/09/07 22:26:38 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/09/07 22:26:41 - mmengine - INFO - Epoch(train) [11][20/604] lr: 1.0000e-04 eta: 0:09:41 time: 0.1222 data_time: 0.0578 memory: 43 grad_norm: 0.6463 loss_action: 0.8711 loss_start: 0.5627 loss_end: 0.5721 loss: 2.0059 2022/09/07 22:26:43 - mmengine - INFO - Epoch(train) [11][40/604] lr: 1.0000e-04 eta: 0:09:38 time: 0.0763 data_time: 0.0130 memory: 43 grad_norm: 0.7630 loss_action: 0.9067 loss_start: 0.5644 loss_end: 0.5657 loss: 2.0368 2022/09/07 22:26:45 - mmengine - INFO - Epoch(train) [11][60/604] lr: 1.0000e-04 eta: 0:09:36 time: 0.0885 data_time: 0.0252 memory: 43 grad_norm: 0.7261 loss_action: 0.8810 loss_start: 0.5587 loss_end: 0.5653 loss: 2.0050 2022/09/07 22:26:46 - mmengine - INFO - Epoch(train) [11][80/604] lr: 1.0000e-04 eta: 0:09:34 time: 0.0763 data_time: 0.0131 memory: 43 grad_norm: 0.7211 loss_action: 0.8520 loss_start: 0.5613 loss_end: 0.5544 loss: 1.9678 2022/09/07 22:26:48 - mmengine - INFO - Epoch(train) [11][100/604] lr: 1.0000e-04 eta: 0:09:32 time: 0.0764 data_time: 0.0131 memory: 43 grad_norm: 0.8212 loss_action: 0.9847 loss_start: 0.5820 loss_end: 0.5661 loss: 2.1328 2022/09/07 22:26:50 - mmengine - INFO - Epoch(train) [11][120/604] lr: 1.0000e-04 eta: 0:09:30 time: 0.1022 data_time: 0.0390 memory: 43 grad_norm: 0.7868 loss_action: 0.9487 loss_start: 0.5757 loss_end: 0.5724 loss: 2.0968 2022/09/07 22:26:51 - mmengine - INFO - Epoch(train) [11][140/604] lr: 1.0000e-04 eta: 0:09:27 time: 0.0766 data_time: 0.0132 memory: 43 grad_norm: 0.6640 loss_action: 0.8548 loss_start: 0.5542 loss_end: 0.5510 loss: 1.9600 2022/09/07 22:26:53 - mmengine - INFO - Epoch(train) [11][160/604] lr: 1.0000e-04 eta: 0:09:25 time: 0.0764 data_time: 0.0130 memory: 43 grad_norm: 0.7878 loss_action: 0.9236 loss_start: 0.5777 loss_end: 0.5710 loss: 2.0722 2022/09/07 22:26:55 - mmengine - INFO - Epoch(train) [11][180/604] lr: 1.0000e-04 eta: 0:09:24 time: 0.1202 data_time: 0.0566 memory: 43 grad_norm: 0.7416 loss_action: 0.9162 loss_start: 0.5684 loss_end: 0.5669 loss: 2.0515 2022/09/07 22:26:57 - mmengine - INFO - Epoch(train) [11][200/604] lr: 1.0000e-04 eta: 0:09:21 time: 0.0773 data_time: 0.0133 memory: 43 grad_norm: 0.7527 loss_action: 0.8984 loss_start: 0.5508 loss_end: 0.5522 loss: 2.0014 2022/09/07 22:26:58 - mmengine - INFO - Epoch(train) [11][220/604] lr: 1.0000e-04 eta: 0:09:19 time: 0.0761 data_time: 0.0129 memory: 43 grad_norm: 0.7292 loss_action: 0.9280 loss_start: 0.5665 loss_end: 0.5685 loss: 2.0630 2022/09/07 22:27:00 - mmengine - INFO - Epoch(train) [11][240/604] lr: 1.0000e-04 eta: 0:09:17 time: 0.0938 data_time: 0.0290 memory: 43 grad_norm: 0.6825 loss_action: 0.9299 loss_start: 0.5726 loss_end: 0.5525 loss: 2.0550 2022/09/07 22:27:02 - mmengine - INFO - Epoch(train) [11][260/604] lr: 1.0000e-04 eta: 0:09:15 time: 0.1103 data_time: 0.0467 memory: 43 grad_norm: 0.7866 loss_action: 0.9499 loss_start: 0.5691 loss_end: 0.5716 loss: 2.0905 2022/09/07 22:27:04 - mmengine - INFO - Epoch(train) [11][280/604] lr: 1.0000e-04 eta: 0:09:13 time: 0.0760 data_time: 0.0127 memory: 43 grad_norm: 0.7480 loss_action: 0.9617 loss_start: 0.5648 loss_end: 0.5580 loss: 2.0845 2022/09/07 22:27:06 - mmengine - INFO - Epoch(train) [11][300/604] lr: 1.0000e-04 eta: 0:09:11 time: 0.1009 data_time: 0.0376 memory: 43 grad_norm: 0.7428 loss_action: 0.9235 loss_start: 0.5603 loss_end: 0.5628 loss: 2.0466 2022/09/07 22:27:08 - mmengine - INFO - Epoch(train) [11][320/604] lr: 1.0000e-04 eta: 0:09:09 time: 0.0764 data_time: 0.0127 memory: 43 grad_norm: 0.7058 loss_action: 0.8741 loss_start: 0.5793 loss_end: 0.5638 loss: 2.0173 2022/09/07 22:27:10 - mmengine - INFO - Epoch(train) [11][340/604] lr: 1.0000e-04 eta: 0:09:07 time: 0.1040 data_time: 0.0404 memory: 43 grad_norm: 0.8213 loss_action: 0.9368 loss_start: 0.5787 loss_end: 0.5710 loss: 2.0865 2022/09/07 22:27:11 - mmengine - INFO - Epoch(train) [11][360/604] lr: 1.0000e-04 eta: 0:09:05 time: 0.0764 data_time: 0.0128 memory: 43 grad_norm: 0.7055 loss_action: 0.9555 loss_start: 0.5680 loss_end: 0.5787 loss: 2.1023 2022/09/07 22:27:13 - mmengine - INFO - Epoch(train) [11][380/604] lr: 1.0000e-04 eta: 0:09:03 time: 0.0985 data_time: 0.0349 memory: 43 grad_norm: 0.7225 loss_action: 0.9367 loss_start: 0.5654 loss_end: 0.5700 loss: 2.0721 2022/09/07 22:27:15 - mmengine - INFO - Epoch(train) [11][400/604] lr: 1.0000e-04 eta: 0:09:01 time: 0.0763 data_time: 0.0127 memory: 43 grad_norm: 0.8053 loss_action: 0.9092 loss_start: 0.5697 loss_end: 0.5669 loss: 2.0459 2022/09/07 22:27:17 - mmengine - INFO - Epoch(train) [11][420/604] lr: 1.0000e-04 eta: 0:08:59 time: 0.1256 data_time: 0.0618 memory: 43 grad_norm: 0.7733 loss_action: 0.9130 loss_start: 0.5733 loss_end: 0.5675 loss: 2.0538 2022/09/07 22:27:19 - mmengine - INFO - Epoch(train) [11][440/604] lr: 1.0000e-04 eta: 0:08:57 time: 0.0760 data_time: 0.0125 memory: 43 grad_norm: 0.7036 loss_action: 0.9012 loss_start: 0.5662 loss_end: 0.5680 loss: 2.0354 2022/09/07 22:27:21 - mmengine - INFO - Epoch(train) [11][460/604] lr: 1.0000e-04 eta: 0:08:55 time: 0.1059 data_time: 0.0420 memory: 43 grad_norm: 0.8308 loss_action: 0.9772 loss_start: 0.5779 loss_end: 0.5754 loss: 2.1305 2022/09/07 22:27:22 - mmengine - INFO - Epoch(train) [11][480/604] lr: 1.0000e-04 eta: 0:08:53 time: 0.0750 data_time: 0.0121 memory: 43 grad_norm: 0.7968 loss_action: 0.9863 loss_start: 0.5751 loss_end: 0.5723 loss: 2.1337 2022/09/07 22:27:24 - mmengine - INFO - Epoch(train) [11][500/604] lr: 1.0000e-04 eta: 0:08:51 time: 0.0983 data_time: 0.0353 memory: 43 grad_norm: 0.8195 loss_action: 0.9839 loss_start: 0.5664 loss_end: 0.5677 loss: 2.1180 2022/09/07 22:27:26 - mmengine - INFO - Epoch(train) [11][520/604] lr: 1.0000e-04 eta: 0:08:49 time: 0.0748 data_time: 0.0121 memory: 43 grad_norm: 0.7438 loss_action: 0.8951 loss_start: 0.5749 loss_end: 0.5549 loss: 2.0250 2022/09/07 22:27:27 - mmengine - INFO - Epoch(train) [11][540/604] lr: 1.0000e-04 eta: 0:08:47 time: 0.0751 data_time: 0.0121 memory: 43 grad_norm: 0.7967 loss_action: 0.9453 loss_start: 0.5775 loss_end: 0.5766 loss: 2.0994 2022/09/07 22:27:29 - mmengine - INFO - Epoch(train) [11][560/604] lr: 1.0000e-04 eta: 0:08:44 time: 0.0751 data_time: 0.0122 memory: 43 grad_norm: 0.7404 loss_action: 0.9185 loss_start: 0.5794 loss_end: 0.5584 loss: 2.0563 2022/09/07 22:27:31 - mmengine - INFO - Epoch(train) [11][580/604] lr: 1.0000e-04 eta: 0:08:43 time: 0.1109 data_time: 0.0474 memory: 43 grad_norm: 0.7675 loss_action: 0.9675 loss_start: 0.5845 loss_end: 0.5617 loss: 2.1137 2022/09/07 22:27:32 - mmengine - INFO - Epoch(train) [11][600/604] lr: 1.0000e-04 eta: 0:08:41 time: 0.0752 data_time: 0.0122 memory: 43 grad_norm: 0.7469 loss_action: 0.9352 loss_start: 0.5793 loss_end: 0.5632 loss: 2.0777 2022/09/07 22:27:33 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:27:33 - mmengine - INFO - Epoch(train) [11][604/604] lr: 1.0000e-04 eta: 0:08:41 time: 0.0999 data_time: 0.0395 memory: 43 grad_norm: 0.8138 loss_action: 0.9564 loss_start: 0.5806 loss_end: 0.5704 loss: 2.1074 2022/09/07 22:27:33 - mmengine - INFO - Saving checkpoint at 11 epochs 2022/09/07 22:27:37 - mmengine - INFO - Epoch(train) [12][20/604] lr: 1.0000e-04 eta: 0:08:39 time: 0.1458 data_time: 0.0824 memory: 43 grad_norm: 0.7531 loss_action: 0.8796 loss_start: 0.5636 loss_end: 0.5515 loss: 1.9947 2022/09/07 22:27:39 - mmengine - INFO - Epoch(train) [12][40/604] lr: 1.0000e-04 eta: 0:08:37 time: 0.0910 data_time: 0.0288 memory: 43 grad_norm: 0.7517 loss_action: 0.8645 loss_start: 0.5634 loss_end: 0.5620 loss: 1.9900 2022/09/07 22:27:41 - mmengine - INFO - Epoch(train) [12][60/604] lr: 1.0000e-04 eta: 0:08:35 time: 0.1126 data_time: 0.0479 memory: 43 grad_norm: 0.7608 loss_action: 0.9152 loss_start: 0.5642 loss_end: 0.5602 loss: 2.0395 2022/09/07 22:27:43 - mmengine - INFO - Epoch(train) [12][80/604] lr: 1.0000e-04 eta: 0:08:33 time: 0.0750 data_time: 0.0124 memory: 43 grad_norm: 0.7047 loss_action: 0.9059 loss_start: 0.5610 loss_end: 0.5536 loss: 2.0205 2022/09/07 22:27:45 - mmengine - INFO - Epoch(train) [12][100/604] lr: 1.0000e-04 eta: 0:08:31 time: 0.1012 data_time: 0.0387 memory: 43 grad_norm: 0.8712 loss_action: 0.9358 loss_start: 0.5363 loss_end: 0.5535 loss: 2.0256 2022/09/07 22:27:46 - mmengine - INFO - Epoch(train) [12][120/604] lr: 1.0000e-04 eta: 0:08:29 time: 0.0751 data_time: 0.0122 memory: 43 grad_norm: 0.8177 loss_action: 0.9053 loss_start: 0.5691 loss_end: 0.5565 loss: 2.0309 2022/09/07 22:27:49 - mmengine - INFO - Epoch(train) [12][140/604] lr: 1.0000e-04 eta: 0:08:27 time: 0.1050 data_time: 0.0424 memory: 43 grad_norm: 0.7157 loss_action: 0.8994 loss_start: 0.5658 loss_end: 0.5554 loss: 2.0207 2022/09/07 22:27:50 - mmengine - INFO - Epoch(train) [12][160/604] lr: 1.0000e-04 eta: 0:08:25 time: 0.0747 data_time: 0.0124 memory: 43 grad_norm: 0.7433 loss_action: 0.9173 loss_start: 0.5548 loss_end: 0.5785 loss: 2.0506 2022/09/07 22:27:52 - mmengine - INFO - Epoch(train) [12][180/604] lr: 1.0000e-04 eta: 0:08:23 time: 0.1040 data_time: 0.0415 memory: 43 grad_norm: 0.7340 loss_action: 0.9400 loss_start: 0.5775 loss_end: 0.5676 loss: 2.0852 2022/09/07 22:27:54 - mmengine - INFO - Epoch(train) [12][200/604] lr: 1.0000e-04 eta: 0:08:21 time: 0.0750 data_time: 0.0126 memory: 43 grad_norm: 0.8975 loss_action: 0.9304 loss_start: 0.5669 loss_end: 0.5776 loss: 2.0749 2022/09/07 22:27:56 - mmengine - INFO - Epoch(train) [12][220/604] lr: 1.0000e-04 eta: 0:08:19 time: 0.1036 data_time: 0.0412 memory: 43 grad_norm: 0.7958 loss_action: 0.9409 loss_start: 0.5684 loss_end: 0.5598 loss: 2.0690 2022/09/07 22:27:58 - mmengine - INFO - Epoch(train) [12][240/604] lr: 1.0000e-04 eta: 0:08:17 time: 0.1173 data_time: 0.0543 memory: 43 grad_norm: 0.7260 loss_action: 0.8853 loss_start: 0.5712 loss_end: 0.5615 loss: 2.0180 2022/09/07 22:28:00 - mmengine - INFO - Epoch(train) [12][260/604] lr: 1.0000e-04 eta: 0:08:15 time: 0.0941 data_time: 0.0315 memory: 43 grad_norm: 0.7201 loss_action: 0.9061 loss_start: 0.5739 loss_end: 0.5564 loss: 2.0364 2022/09/07 22:28:02 - mmengine - INFO - Epoch(train) [12][280/604] lr: 1.0000e-04 eta: 0:08:13 time: 0.0803 data_time: 0.0175 memory: 43 grad_norm: 0.7072 loss_action: 0.8891 loss_start: 0.5654 loss_end: 0.5594 loss: 2.0140 2022/09/07 22:28:04 - mmengine - INFO - Epoch(train) [12][300/604] lr: 1.0000e-04 eta: 0:08:11 time: 0.1079 data_time: 0.0456 memory: 43 grad_norm: 0.6817 loss_action: 0.8913 loss_start: 0.5754 loss_end: 0.5616 loss: 2.0283 2022/09/07 22:28:05 - mmengine - INFO - Epoch(train) [12][320/604] lr: 1.0000e-04 eta: 0:08:09 time: 0.0767 data_time: 0.0129 memory: 43 grad_norm: 0.7052 loss_action: 0.8654 loss_start: 0.5583 loss_end: 0.5560 loss: 1.9798 2022/09/07 22:28:07 - mmengine - INFO - Epoch(train) [12][340/604] lr: 1.0000e-04 eta: 0:08:07 time: 0.1022 data_time: 0.0389 memory: 43 grad_norm: 0.8509 loss_action: 0.9557 loss_start: 0.5674 loss_end: 0.5620 loss: 2.0850 2022/09/07 22:28:09 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:28:09 - mmengine - INFO - Epoch(train) [12][360/604] lr: 1.0000e-04 eta: 0:08:05 time: 0.0751 data_time: 0.0126 memory: 43 grad_norm: 0.8114 loss_action: 0.9590 loss_start: 0.5803 loss_end: 0.5760 loss: 2.1153 2022/09/07 22:28:11 - mmengine - INFO - Epoch(train) [12][380/604] lr: 1.0000e-04 eta: 0:08:03 time: 0.1024 data_time: 0.0400 memory: 43 grad_norm: 0.7568 loss_action: 0.9926 loss_start: 0.5762 loss_end: 0.5681 loss: 2.1369 2022/09/07 22:28:12 - mmengine - INFO - Epoch(train) [12][400/604] lr: 1.0000e-04 eta: 0:08:01 time: 0.0754 data_time: 0.0125 memory: 43 grad_norm: 0.6992 loss_action: 0.8789 loss_start: 0.5698 loss_end: 0.5602 loss: 2.0090 2022/09/07 22:28:14 - mmengine - INFO - Epoch(train) [12][420/604] lr: 1.0000e-04 eta: 0:07:59 time: 0.1008 data_time: 0.0380 memory: 43 grad_norm: 0.6965 loss_action: 0.9055 loss_start: 0.5662 loss_end: 0.5774 loss: 2.0491 2022/09/07 22:28:16 - mmengine - INFO - Epoch(train) [12][440/604] lr: 1.0000e-04 eta: 0:07:57 time: 0.0750 data_time: 0.0125 memory: 43 grad_norm: 0.7758 loss_action: 0.9306 loss_start: 0.5709 loss_end: 0.5578 loss: 2.0593 2022/09/07 22:28:18 - mmengine - INFO - Epoch(train) [12][460/604] lr: 1.0000e-04 eta: 0:07:55 time: 0.1037 data_time: 0.0412 memory: 43 grad_norm: 0.7018 loss_action: 0.9377 loss_start: 0.5877 loss_end: 0.5688 loss: 2.0943 2022/09/07 22:28:19 - mmengine - INFO - Epoch(train) [12][480/604] lr: 1.0000e-04 eta: 0:07:53 time: 0.0750 data_time: 0.0124 memory: 43 grad_norm: 0.8108 loss_action: 0.9101 loss_start: 0.5694 loss_end: 0.5653 loss: 2.0448 2022/09/07 22:28:22 - mmengine - INFO - Epoch(train) [12][500/604] lr: 1.0000e-04 eta: 0:07:51 time: 0.1034 data_time: 0.0409 memory: 43 grad_norm: 0.8415 loss_action: 0.9496 loss_start: 0.5679 loss_end: 0.5770 loss: 2.0944 2022/09/07 22:28:23 - mmengine - INFO - Epoch(train) [12][520/604] lr: 1.0000e-04 eta: 0:07:49 time: 0.0747 data_time: 0.0125 memory: 43 grad_norm: 0.7681 loss_action: 0.9367 loss_start: 0.5689 loss_end: 0.5650 loss: 2.0706 2022/09/07 22:28:25 - mmengine - INFO - Epoch(train) [12][540/604] lr: 1.0000e-04 eta: 0:07:47 time: 0.1057 data_time: 0.0436 memory: 43 grad_norm: 0.7563 loss_action: 0.9158 loss_start: 0.5604 loss_end: 0.5487 loss: 2.0249 2022/09/07 22:28:27 - mmengine - INFO - Epoch(train) [12][560/604] lr: 1.0000e-04 eta: 0:07:45 time: 0.0743 data_time: 0.0123 memory: 43 grad_norm: 0.8105 loss_action: 0.9015 loss_start: 0.5593 loss_end: 0.5540 loss: 2.0148 2022/09/07 22:28:28 - mmengine - INFO - Epoch(train) [12][580/604] lr: 1.0000e-04 eta: 0:07:43 time: 0.0741 data_time: 0.0125 memory: 43 grad_norm: 0.7239 loss_action: 0.9305 loss_start: 0.5663 loss_end: 0.5678 loss: 2.0645 2022/09/07 22:28:30 - mmengine - INFO - Epoch(train) [12][600/604] lr: 1.0000e-04 eta: 0:07:41 time: 0.0740 data_time: 0.0123 memory: 43 grad_norm: 0.7319 loss_action: 0.9015 loss_start: 0.5667 loss_end: 0.5607 loss: 2.0290 2022/09/07 22:28:30 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:28:30 - mmengine - INFO - Epoch(train) [12][604/604] lr: 1.0000e-04 eta: 0:07:41 time: 0.0707 data_time: 0.0117 memory: 43 grad_norm: 0.8879 loss_action: 0.9406 loss_start: 0.5699 loss_end: 0.5574 loss: 2.0679 2022/09/07 22:28:30 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/09/07 22:28:33 - mmengine - INFO - Epoch(train) [13][20/604] lr: 1.0000e-04 eta: 0:07:39 time: 0.1293 data_time: 0.0666 memory: 43 grad_norm: 0.7031 loss_action: 0.8658 loss_start: 0.5688 loss_end: 0.5463 loss: 1.9810 2022/09/07 22:28:34 - mmengine - INFO - Epoch(train) [13][40/604] lr: 1.0000e-04 eta: 0:07:37 time: 0.0746 data_time: 0.0124 memory: 43 grad_norm: 0.7415 loss_action: 0.8607 loss_start: 0.5521 loss_end: 0.5458 loss: 1.9586 2022/09/07 22:28:36 - mmengine - INFO - Epoch(train) [13][60/604] lr: 1.0000e-04 eta: 0:07:34 time: 0.0747 data_time: 0.0123 memory: 43 grad_norm: 0.8050 loss_action: 0.9398 loss_start: 0.5660 loss_end: 0.5665 loss: 2.0723 2022/09/07 22:28:37 - mmengine - INFO - Epoch(train) [13][80/604] lr: 1.0000e-04 eta: 0:07:32 time: 0.0748 data_time: 0.0122 memory: 43 grad_norm: 0.7660 loss_action: 0.9004 loss_start: 0.5755 loss_end: 0.5577 loss: 2.0337 2022/09/07 22:28:39 - mmengine - INFO - Epoch(train) [13][100/604] lr: 1.0000e-04 eta: 0:07:30 time: 0.0747 data_time: 0.0122 memory: 43 grad_norm: 0.7228 loss_action: 0.8920 loss_start: 0.5605 loss_end: 0.5628 loss: 2.0152 2022/09/07 22:28:40 - mmengine - INFO - Epoch(train) [13][120/604] lr: 1.0000e-04 eta: 0:07:28 time: 0.0747 data_time: 0.0123 memory: 43 grad_norm: 0.8333 loss_action: 0.9346 loss_start: 0.5547 loss_end: 0.5551 loss: 2.0444 2022/09/07 22:28:42 - mmengine - INFO - Epoch(train) [13][140/604] lr: 1.0000e-04 eta: 0:07:26 time: 0.0750 data_time: 0.0125 memory: 43 grad_norm: 0.7554 loss_action: 0.9051 loss_start: 0.5580 loss_end: 0.5520 loss: 2.0150 2022/09/07 22:28:43 - mmengine - INFO - Epoch(train) [13][160/604] lr: 1.0000e-04 eta: 0:07:24 time: 0.0774 data_time: 0.0130 memory: 43 grad_norm: 0.8070 loss_action: 0.9285 loss_start: 0.5657 loss_end: 0.5673 loss: 2.0615 2022/09/07 22:28:45 - mmengine - INFO - Epoch(train) [13][180/604] lr: 1.0000e-04 eta: 0:07:21 time: 0.0782 data_time: 0.0129 memory: 43 grad_norm: 0.8009 loss_action: 0.8900 loss_start: 0.5565 loss_end: 0.5532 loss: 1.9997 2022/09/07 22:28:46 - mmengine - INFO - Epoch(train) [13][200/604] lr: 1.0000e-04 eta: 0:07:19 time: 0.0782 data_time: 0.0132 memory: 43 grad_norm: 0.8799 loss_action: 0.9226 loss_start: 0.5649 loss_end: 0.5572 loss: 2.0447 2022/09/07 22:28:48 - mmengine - INFO - Epoch(train) [13][220/604] lr: 1.0000e-04 eta: 0:07:17 time: 0.0777 data_time: 0.0132 memory: 43 grad_norm: 0.7644 loss_action: 0.9331 loss_start: 0.5733 loss_end: 0.5695 loss: 2.0759 2022/09/07 22:28:49 - mmengine - INFO - Epoch(train) [13][240/604] lr: 1.0000e-04 eta: 0:07:15 time: 0.0770 data_time: 0.0131 memory: 43 grad_norm: 0.7358 loss_action: 0.8824 loss_start: 0.5515 loss_end: 0.5528 loss: 1.9866 2022/09/07 22:28:51 - mmengine - INFO - Epoch(train) [13][260/604] lr: 1.0000e-04 eta: 0:07:13 time: 0.0776 data_time: 0.0132 memory: 43 grad_norm: 0.8184 loss_action: 0.8972 loss_start: 0.5547 loss_end: 0.5534 loss: 2.0053 2022/09/07 22:28:53 - mmengine - INFO - Epoch(train) [13][280/604] lr: 1.0000e-04 eta: 0:07:11 time: 0.0793 data_time: 0.0132 memory: 43 grad_norm: 0.8761 loss_action: 0.9233 loss_start: 0.5599 loss_end: 0.5648 loss: 2.0480 2022/09/07 22:28:54 - mmengine - INFO - Epoch(train) [13][300/604] lr: 1.0000e-04 eta: 0:07:09 time: 0.0837 data_time: 0.0136 memory: 43 grad_norm: 0.7502 loss_action: 0.8788 loss_start: 0.5628 loss_end: 0.5675 loss: 2.0090 2022/09/07 22:28:57 - mmengine - INFO - Epoch(train) [13][320/604] lr: 1.0000e-04 eta: 0:07:07 time: 0.1351 data_time: 0.0126 memory: 43 grad_norm: 0.7533 loss_action: 0.9065 loss_start: 0.5595 loss_end: 0.5609 loss: 2.0268 2022/09/07 22:29:00 - mmengine - INFO - Epoch(train) [13][340/604] lr: 1.0000e-04 eta: 0:07:07 time: 0.1722 data_time: 0.0133 memory: 43 grad_norm: 0.7969 loss_action: 0.9624 loss_start: 0.5709 loss_end: 0.5637 loss: 2.0970 2022/09/07 22:29:04 - mmengine - INFO - Epoch(train) [13][360/604] lr: 1.0000e-04 eta: 0:07:05 time: 0.1676 data_time: 0.0132 memory: 43 grad_norm: 0.8363 loss_action: 0.9463 loss_start: 0.5749 loss_end: 0.5649 loss: 2.0861 2022/09/07 22:29:06 - mmengine - INFO - Epoch(train) [13][380/604] lr: 1.0000e-04 eta: 0:07:04 time: 0.1357 data_time: 0.0128 memory: 43 grad_norm: 0.7558 loss_action: 0.9165 loss_start: 0.5700 loss_end: 0.5592 loss: 2.0457 2022/09/07 22:29:09 - mmengine - INFO - Epoch(train) [13][400/604] lr: 1.0000e-04 eta: 0:07:03 time: 0.1290 data_time: 0.0123 memory: 43 grad_norm: 0.8337 loss_action: 0.9421 loss_start: 0.5687 loss_end: 0.5582 loss: 2.0691 2022/09/07 22:29:12 - mmengine - INFO - Epoch(train) [13][420/604] lr: 1.0000e-04 eta: 0:07:01 time: 0.1331 data_time: 0.0125 memory: 43 grad_norm: 0.7384 loss_action: 0.9133 loss_start: 0.5735 loss_end: 0.5632 loss: 2.0500 2022/09/07 22:29:14 - mmengine - INFO - Epoch(train) [13][440/604] lr: 1.0000e-04 eta: 0:07:00 time: 0.1354 data_time: 0.0124 memory: 43 grad_norm: 0.7606 loss_action: 0.9494 loss_start: 0.5728 loss_end: 0.5588 loss: 2.0810 2022/09/07 22:29:17 - mmengine - INFO - Epoch(train) [13][460/604] lr: 1.0000e-04 eta: 0:06:58 time: 0.1416 data_time: 0.0126 memory: 43 grad_norm: 0.7859 loss_action: 0.9534 loss_start: 0.5747 loss_end: 0.5699 loss: 2.0980 2022/09/07 22:29:20 - mmengine - INFO - Epoch(train) [13][480/604] lr: 1.0000e-04 eta: 0:06:57 time: 0.1386 data_time: 0.0126 memory: 43 grad_norm: 0.7633 loss_action: 0.8833 loss_start: 0.5623 loss_end: 0.5448 loss: 1.9904 2022/09/07 22:29:23 - mmengine - INFO - Epoch(train) [13][500/604] lr: 1.0000e-04 eta: 0:06:55 time: 0.1395 data_time: 0.0124 memory: 43 grad_norm: 0.8251 loss_action: 0.9383 loss_start: 0.5614 loss_end: 0.5687 loss: 2.0685 2022/09/07 22:29:26 - mmengine - INFO - Epoch(train) [13][520/604] lr: 1.0000e-04 eta: 0:06:54 time: 0.1348 data_time: 0.0124 memory: 43 grad_norm: 0.8100 loss_action: 0.9261 loss_start: 0.5689 loss_end: 0.5612 loss: 2.0562 2022/09/07 22:29:28 - mmengine - INFO - Epoch(train) [13][540/604] lr: 1.0000e-04 eta: 0:06:52 time: 0.1381 data_time: 0.0125 memory: 43 grad_norm: 0.8091 loss_action: 0.9071 loss_start: 0.5586 loss_end: 0.5589 loss: 2.0246 2022/09/07 22:29:31 - mmengine - INFO - Epoch(train) [13][560/604] lr: 1.0000e-04 eta: 0:06:51 time: 0.1427 data_time: 0.0129 memory: 43 grad_norm: 0.7732 loss_action: 0.8794 loss_start: 0.5556 loss_end: 0.5651 loss: 2.0002 2022/09/07 22:29:34 - mmengine - INFO - Epoch(train) [13][580/604] lr: 1.0000e-04 eta: 0:06:50 time: 0.1441 data_time: 0.0129 memory: 43 grad_norm: 0.7570 loss_action: 0.8825 loss_start: 0.5589 loss_end: 0.5580 loss: 1.9994 2022/09/07 22:29:37 - mmengine - INFO - Epoch(train) [13][600/604] lr: 1.0000e-04 eta: 0:06:48 time: 0.1414 data_time: 0.0126 memory: 43 grad_norm: 0.7931 loss_action: 0.9506 loss_start: 0.5737 loss_end: 0.5734 loss: 2.0977 2022/09/07 22:29:37 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:29:37 - mmengine - INFO - Epoch(train) [13][604/604] lr: 1.0000e-04 eta: 0:06:48 time: 0.1348 data_time: 0.0120 memory: 43 grad_norm: 1.0195 loss_action: 0.9734 loss_start: 0.5786 loss_end: 0.5800 loss: 2.1319 2022/09/07 22:29:37 - mmengine - INFO - Saving checkpoint at 13 epochs 2022/09/07 22:29:41 - mmengine - INFO - Epoch(train) [14][20/604] lr: 1.0000e-04 eta: 0:06:46 time: 0.1766 data_time: 0.0570 memory: 43 grad_norm: 0.7537 loss_action: 0.9303 loss_start: 0.5604 loss_end: 0.5516 loss: 2.0422 2022/09/07 22:29:44 - mmengine - INFO - Epoch(train) [14][40/604] lr: 1.0000e-04 eta: 0:06:45 time: 0.1464 data_time: 0.0127 memory: 43 grad_norm: 0.7985 loss_action: 0.9105 loss_start: 0.5471 loss_end: 0.5483 loss: 2.0059 2022/09/07 22:29:47 - mmengine - INFO - Epoch(train) [14][60/604] lr: 1.0000e-04 eta: 0:06:44 time: 0.1457 data_time: 0.0125 memory: 43 grad_norm: 0.7561 loss_action: 0.9156 loss_start: 0.5674 loss_end: 0.5445 loss: 2.0275 2022/09/07 22:29:50 - mmengine - INFO - Epoch(train) [14][80/604] lr: 1.0000e-04 eta: 0:06:42 time: 0.1455 data_time: 0.0131 memory: 43 grad_norm: 0.8741 loss_action: 0.8798 loss_start: 0.5592 loss_end: 0.5588 loss: 1.9978 2022/09/07 22:29:53 - mmengine - INFO - Epoch(train) [14][100/604] lr: 1.0000e-04 eta: 0:06:41 time: 0.1475 data_time: 0.0128 memory: 43 grad_norm: 0.8441 loss_action: 0.9028 loss_start: 0.5668 loss_end: 0.5433 loss: 2.0129 2022/09/07 22:29:56 - mmengine - INFO - Epoch(train) [14][120/604] lr: 1.0000e-04 eta: 0:06:39 time: 0.1398 data_time: 0.0125 memory: 43 grad_norm: 0.8198 loss_action: 0.9634 loss_start: 0.5505 loss_end: 0.5597 loss: 2.0736 2022/09/07 22:29:58 - mmengine - INFO - Epoch(train) [14][140/604] lr: 1.0000e-04 eta: 0:06:38 time: 0.1333 data_time: 0.0125 memory: 43 grad_norm: 0.8090 loss_action: 0.9184 loss_start: 0.5587 loss_end: 0.5618 loss: 2.0389 2022/09/07 22:29:59 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:30:01 - mmengine - INFO - Epoch(train) [14][160/604] lr: 1.0000e-04 eta: 0:06:36 time: 0.1385 data_time: 0.0125 memory: 43 grad_norm: 0.8053 loss_action: 0.9006 loss_start: 0.5536 loss_end: 0.5599 loss: 2.0141 2022/09/07 22:30:04 - mmengine - INFO - Epoch(train) [14][180/604] lr: 1.0000e-04 eta: 0:06:35 time: 0.1392 data_time: 0.0125 memory: 43 grad_norm: 0.7808 loss_action: 0.9051 loss_start: 0.5558 loss_end: 0.5499 loss: 2.0108 2022/09/07 22:30:07 - mmengine - INFO - Epoch(train) [14][200/604] lr: 1.0000e-04 eta: 0:06:33 time: 0.1399 data_time: 0.0128 memory: 43 grad_norm: 0.7815 loss_action: 0.8743 loss_start: 0.5548 loss_end: 0.5441 loss: 1.9732 2022/09/07 22:30:09 - mmengine - INFO - Epoch(train) [14][220/604] lr: 1.0000e-04 eta: 0:06:32 time: 0.1415 data_time: 0.0126 memory: 43 grad_norm: 0.7386 loss_action: 0.8536 loss_start: 0.5510 loss_end: 0.5436 loss: 1.9482 2022/09/07 22:30:12 - mmengine - INFO - Epoch(train) [14][240/604] lr: 1.0000e-04 eta: 0:06:30 time: 0.1429 data_time: 0.0127 memory: 43 grad_norm: 0.7491 loss_action: 0.8613 loss_start: 0.5700 loss_end: 0.5558 loss: 1.9872 2022/09/07 22:30:15 - mmengine - INFO - Epoch(train) [14][260/604] lr: 1.0000e-04 eta: 0:06:29 time: 0.1389 data_time: 0.0127 memory: 43 grad_norm: 0.8140 loss_action: 0.9650 loss_start: 0.5673 loss_end: 0.5654 loss: 2.0978 2022/09/07 22:30:18 - mmengine - INFO - Epoch(train) [14][280/604] lr: 1.0000e-04 eta: 0:06:27 time: 0.1433 data_time: 0.0127 memory: 43 grad_norm: 0.8989 loss_action: 0.8914 loss_start: 0.5561 loss_end: 0.5463 loss: 1.9938 2022/09/07 22:30:21 - mmengine - INFO - Epoch(train) [14][300/604] lr: 1.0000e-04 eta: 0:06:25 time: 0.1373 data_time: 0.0124 memory: 43 grad_norm: 0.7460 loss_action: 0.9150 loss_start: 0.5526 loss_end: 0.5534 loss: 2.0211 2022/09/07 22:30:24 - mmengine - INFO - Epoch(train) [14][320/604] lr: 1.0000e-04 eta: 0:06:24 time: 0.1411 data_time: 0.0126 memory: 43 grad_norm: 0.7767 loss_action: 0.8959 loss_start: 0.5783 loss_end: 0.5559 loss: 2.0300 2022/09/07 22:30:26 - mmengine - INFO - Epoch(train) [14][340/604] lr: 1.0000e-04 eta: 0:06:22 time: 0.1401 data_time: 0.0124 memory: 43 grad_norm: 0.8053 loss_action: 0.8992 loss_start: 0.5647 loss_end: 0.5480 loss: 2.0119 2022/09/07 22:30:29 - mmengine - INFO - Epoch(train) [14][360/604] lr: 1.0000e-04 eta: 0:06:21 time: 0.1429 data_time: 0.0126 memory: 43 grad_norm: 0.8197 loss_action: 0.9179 loss_start: 0.5541 loss_end: 0.5660 loss: 2.0381 2022/09/07 22:30:32 - mmengine - INFO - Epoch(train) [14][380/604] lr: 1.0000e-04 eta: 0:06:19 time: 0.1389 data_time: 0.0127 memory: 43 grad_norm: 0.7877 loss_action: 0.9162 loss_start: 0.5554 loss_end: 0.5656 loss: 2.0373 2022/09/07 22:30:35 - mmengine - INFO - Epoch(train) [14][400/604] lr: 1.0000e-04 eta: 0:06:18 time: 0.1463 data_time: 0.0130 memory: 43 grad_norm: 0.8097 loss_action: 0.8668 loss_start: 0.5613 loss_end: 0.5595 loss: 1.9876 2022/09/07 22:30:38 - mmengine - INFO - Epoch(train) [14][420/604] lr: 1.0000e-04 eta: 0:06:16 time: 0.1408 data_time: 0.0124 memory: 43 grad_norm: 0.8311 loss_action: 0.9314 loss_start: 0.5674 loss_end: 0.5587 loss: 2.0575 2022/09/07 22:30:40 - mmengine - INFO - Epoch(train) [14][440/604] lr: 1.0000e-04 eta: 0:06:14 time: 0.1351 data_time: 0.0124 memory: 43 grad_norm: 0.8363 loss_action: 0.9653 loss_start: 0.5782 loss_end: 0.5708 loss: 2.1143 2022/09/07 22:30:43 - mmengine - INFO - Epoch(train) [14][460/604] lr: 1.0000e-04 eta: 0:06:13 time: 0.1344 data_time: 0.0123 memory: 43 grad_norm: 0.8100 loss_action: 0.9308 loss_start: 0.5518 loss_end: 0.5711 loss: 2.0537 2022/09/07 22:30:46 - mmengine - INFO - Epoch(train) [14][480/604] lr: 1.0000e-04 eta: 0:06:11 time: 0.1377 data_time: 0.0124 memory: 43 grad_norm: 0.7596 loss_action: 0.8876 loss_start: 0.5603 loss_end: 0.5617 loss: 2.0096 2022/09/07 22:30:48 - mmengine - INFO - Epoch(train) [14][500/604] lr: 1.0000e-04 eta: 0:06:09 time: 0.1323 data_time: 0.0123 memory: 43 grad_norm: 0.7857 loss_action: 0.8767 loss_start: 0.5488 loss_end: 0.5478 loss: 1.9734 2022/09/07 22:30:51 - mmengine - INFO - Epoch(train) [14][520/604] lr: 1.0000e-04 eta: 0:06:08 time: 0.1380 data_time: 0.0126 memory: 43 grad_norm: 0.8454 loss_action: 0.9334 loss_start: 0.5620 loss_end: 0.5618 loss: 2.0572 2022/09/07 22:30:54 - mmengine - INFO - Epoch(train) [14][540/604] lr: 1.0000e-04 eta: 0:06:06 time: 0.1419 data_time: 0.0125 memory: 43 grad_norm: 0.8175 loss_action: 0.9444 loss_start: 0.5661 loss_end: 0.5609 loss: 2.0713 2022/09/07 22:30:57 - mmengine - INFO - Epoch(train) [14][560/604] lr: 1.0000e-04 eta: 0:06:04 time: 0.1320 data_time: 0.0123 memory: 43 grad_norm: 0.8727 loss_action: 0.9357 loss_start: 0.5747 loss_end: 0.5659 loss: 2.0762 2022/09/07 22:30:59 - mmengine - INFO - Epoch(train) [14][580/604] lr: 1.0000e-04 eta: 0:06:03 time: 0.1335 data_time: 0.0123 memory: 43 grad_norm: 0.8754 loss_action: 0.9400 loss_start: 0.5854 loss_end: 0.5614 loss: 2.0867 2022/09/07 22:31:02 - mmengine - INFO - Epoch(train) [14][600/604] lr: 1.0000e-04 eta: 0:06:01 time: 0.1366 data_time: 0.0123 memory: 43 grad_norm: 0.7899 loss_action: 0.9176 loss_start: 0.5590 loss_end: 0.5663 loss: 2.0429 2022/09/07 22:31:03 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:31:03 - mmengine - INFO - Epoch(train) [14][604/604] lr: 1.0000e-04 eta: 0:06:01 time: 0.1331 data_time: 0.0117 memory: 43 grad_norm: 0.8437 loss_action: 0.8913 loss_start: 0.5461 loss_end: 0.5562 loss: 1.9936 2022/09/07 22:31:03 - mmengine - INFO - Saving checkpoint at 14 epochs 2022/09/07 22:31:06 - mmengine - INFO - Epoch(train) [15][20/604] lr: 1.0000e-05 eta: 0:05:59 time: 0.1771 data_time: 0.0504 memory: 43 grad_norm: 0.7438 loss_action: 0.8930 loss_start: 0.5670 loss_end: 0.5543 loss: 2.0143 2022/09/07 22:31:09 - mmengine - INFO - Epoch(train) [15][40/604] lr: 1.0000e-05 eta: 0:05:58 time: 0.1409 data_time: 0.0127 memory: 43 grad_norm: 0.7847 loss_action: 0.9204 loss_start: 0.5633 loss_end: 0.5571 loss: 2.0408 2022/09/07 22:31:12 - mmengine - INFO - Epoch(train) [15][60/604] lr: 1.0000e-05 eta: 0:05:56 time: 0.1347 data_time: 0.0125 memory: 43 grad_norm: 0.7167 loss_action: 0.8946 loss_start: 0.5550 loss_end: 0.5506 loss: 2.0003 2022/09/07 22:31:15 - mmengine - INFO - Epoch(train) [15][80/604] lr: 1.0000e-05 eta: 0:05:54 time: 0.1352 data_time: 0.0122 memory: 43 grad_norm: 0.7537 loss_action: 0.9068 loss_start: 0.5577 loss_end: 0.5504 loss: 2.0149 2022/09/07 22:31:17 - mmengine - INFO - Epoch(train) [15][100/604] lr: 1.0000e-05 eta: 0:05:52 time: 0.1341 data_time: 0.0124 memory: 43 grad_norm: 0.8112 loss_action: 0.8932 loss_start: 0.5532 loss_end: 0.5576 loss: 2.0040 2022/09/07 22:31:20 - mmengine - INFO - Epoch(train) [15][120/604] lr: 1.0000e-05 eta: 0:05:51 time: 0.1318 data_time: 0.0124 memory: 43 grad_norm: 0.7753 loss_action: 0.8776 loss_start: 0.5621 loss_end: 0.5599 loss: 1.9995 2022/09/07 22:31:23 - mmengine - INFO - Epoch(train) [15][140/604] lr: 1.0000e-05 eta: 0:05:49 time: 0.1364 data_time: 0.0124 memory: 43 grad_norm: 0.7728 loss_action: 0.8801 loss_start: 0.5695 loss_end: 0.5520 loss: 2.0017 2022/09/07 22:31:25 - mmengine - INFO - Epoch(train) [15][160/604] lr: 1.0000e-05 eta: 0:05:47 time: 0.1378 data_time: 0.0122 memory: 43 grad_norm: 0.8258 loss_action: 0.8987 loss_start: 0.5605 loss_end: 0.5508 loss: 2.0101 2022/09/07 22:31:28 - mmengine - INFO - Epoch(train) [15][180/604] lr: 1.0000e-05 eta: 0:05:45 time: 0.1366 data_time: 0.0124 memory: 43 grad_norm: 0.7749 loss_action: 0.8863 loss_start: 0.5465 loss_end: 0.5435 loss: 1.9764 2022/09/07 22:31:31 - mmengine - INFO - Epoch(train) [15][200/604] lr: 1.0000e-05 eta: 0:05:44 time: 0.1333 data_time: 0.0124 memory: 43 grad_norm: 0.8719 loss_action: 0.9262 loss_start: 0.5423 loss_end: 0.5573 loss: 2.0259 2022/09/07 22:31:34 - mmengine - INFO - Epoch(train) [15][220/604] lr: 1.0000e-05 eta: 0:05:42 time: 0.1332 data_time: 0.0123 memory: 43 grad_norm: 0.7415 loss_action: 0.8520 loss_start: 0.5498 loss_end: 0.5585 loss: 1.9603 2022/09/07 22:31:36 - mmengine - INFO - Epoch(train) [15][240/604] lr: 1.0000e-05 eta: 0:05:40 time: 0.1359 data_time: 0.0125 memory: 43 grad_norm: 0.7670 loss_action: 0.8857 loss_start: 0.5535 loss_end: 0.5624 loss: 2.0016 2022/09/07 22:31:39 - mmengine - INFO - Epoch(train) [15][260/604] lr: 1.0000e-05 eta: 0:05:39 time: 0.1415 data_time: 0.0128 memory: 43 grad_norm: 0.8162 loss_action: 0.8945 loss_start: 0.5645 loss_end: 0.5496 loss: 2.0087 2022/09/07 22:31:42 - mmengine - INFO - Epoch(train) [15][280/604] lr: 1.0000e-05 eta: 0:05:37 time: 0.1411 data_time: 0.0126 memory: 43 grad_norm: 0.7848 loss_action: 0.9164 loss_start: 0.5502 loss_end: 0.5483 loss: 2.0149 2022/09/07 22:31:45 - mmengine - INFO - Epoch(train) [15][300/604] lr: 1.0000e-05 eta: 0:05:35 time: 0.1373 data_time: 0.0127 memory: 43 grad_norm: 0.8299 loss_action: 0.8876 loss_start: 0.5550 loss_end: 0.5511 loss: 1.9937 2022/09/07 22:31:47 - mmengine - INFO - Epoch(train) [15][320/604] lr: 1.0000e-05 eta: 0:05:33 time: 0.1372 data_time: 0.0124 memory: 43 grad_norm: 0.8277 loss_action: 0.9094 loss_start: 0.5571 loss_end: 0.5441 loss: 2.0106 2022/09/07 22:31:50 - mmengine - INFO - Epoch(train) [15][340/604] lr: 1.0000e-05 eta: 0:05:32 time: 0.1335 data_time: 0.0125 memory: 43 grad_norm: 0.7547 loss_action: 0.9201 loss_start: 0.5576 loss_end: 0.5546 loss: 2.0324 2022/09/07 22:31:53 - mmengine - INFO - Epoch(train) [15][360/604] lr: 1.0000e-05 eta: 0:05:30 time: 0.1376 data_time: 0.0123 memory: 43 grad_norm: 0.8193 loss_action: 0.9059 loss_start: 0.5580 loss_end: 0.5528 loss: 2.0167 2022/09/07 22:31:56 - mmengine - INFO - Epoch(train) [15][380/604] lr: 1.0000e-05 eta: 0:05:28 time: 0.1341 data_time: 0.0124 memory: 43 grad_norm: 0.8480 loss_action: 0.8952 loss_start: 0.5472 loss_end: 0.5384 loss: 1.9808 2022/09/07 22:31:58 - mmengine - INFO - Epoch(train) [15][400/604] lr: 1.0000e-05 eta: 0:05:26 time: 0.1345 data_time: 0.0123 memory: 43 grad_norm: 0.8523 loss_action: 0.8775 loss_start: 0.5488 loss_end: 0.5471 loss: 1.9735 2022/09/07 22:32:01 - mmengine - INFO - Epoch(train) [15][420/604] lr: 1.0000e-05 eta: 0:05:25 time: 0.1391 data_time: 0.0129 memory: 43 grad_norm: 0.7577 loss_action: 0.8852 loss_start: 0.5525 loss_end: 0.5475 loss: 1.9852 2022/09/07 22:32:04 - mmengine - INFO - Epoch(train) [15][440/604] lr: 1.0000e-05 eta: 0:05:23 time: 0.1336 data_time: 0.0125 memory: 43 grad_norm: 0.8011 loss_action: 0.8970 loss_start: 0.5516 loss_end: 0.5433 loss: 1.9919 2022/09/07 22:32:06 - mmengine - INFO - Epoch(train) [15][460/604] lr: 1.0000e-05 eta: 0:05:21 time: 0.1374 data_time: 0.0124 memory: 43 grad_norm: 0.8279 loss_action: 0.9492 loss_start: 0.5534 loss_end: 0.5562 loss: 2.0589 2022/09/07 22:32:09 - mmengine - INFO - Epoch(train) [15][480/604] lr: 1.0000e-05 eta: 0:05:19 time: 0.1346 data_time: 0.0125 memory: 43 grad_norm: 0.7855 loss_action: 0.8793 loss_start: 0.5560 loss_end: 0.5545 loss: 1.9897 2022/09/07 22:32:12 - mmengine - INFO - Epoch(train) [15][500/604] lr: 1.0000e-05 eta: 0:05:17 time: 0.1371 data_time: 0.0123 memory: 43 grad_norm: 0.7607 loss_action: 0.9032 loss_start: 0.5676 loss_end: 0.5558 loss: 2.0266 2022/09/07 22:32:15 - mmengine - INFO - Epoch(train) [15][520/604] lr: 1.0000e-05 eta: 0:05:16 time: 0.1364 data_time: 0.0126 memory: 43 grad_norm: 0.8897 loss_action: 0.9384 loss_start: 0.5592 loss_end: 0.5513 loss: 2.0489 2022/09/07 22:32:17 - mmengine - INFO - Epoch(train) [15][540/604] lr: 1.0000e-05 eta: 0:05:14 time: 0.1292 data_time: 0.0123 memory: 43 grad_norm: 0.7679 loss_action: 0.9094 loss_start: 0.5585 loss_end: 0.5593 loss: 2.0272 2022/09/07 22:32:18 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:32:20 - mmengine - INFO - Epoch(train) [15][560/604] lr: 1.0000e-05 eta: 0:05:12 time: 0.1397 data_time: 0.0124 memory: 43 grad_norm: 0.8206 loss_action: 0.9166 loss_start: 0.5520 loss_end: 0.5537 loss: 2.0223 2022/09/07 22:32:23 - mmengine - INFO - Epoch(train) [15][580/604] lr: 1.0000e-05 eta: 0:05:10 time: 0.1294 data_time: 0.0123 memory: 43 grad_norm: 0.7552 loss_action: 0.8679 loss_start: 0.5523 loss_end: 0.5279 loss: 1.9480 2022/09/07 22:32:25 - mmengine - INFO - Epoch(train) [15][600/604] lr: 1.0000e-05 eta: 0:05:08 time: 0.1280 data_time: 0.0121 memory: 43 grad_norm: 0.7842 loss_action: 0.9051 loss_start: 0.5524 loss_end: 0.5522 loss: 2.0096 2022/09/07 22:32:25 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:32:25 - mmengine - INFO - Epoch(train) [15][604/604] lr: 1.0000e-05 eta: 0:05:08 time: 0.1198 data_time: 0.0115 memory: 43 grad_norm: 0.8643 loss_action: 0.9083 loss_start: 0.5679 loss_end: 0.5536 loss: 2.0298 2022/09/07 22:32:26 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/09/07 22:32:29 - mmengine - INFO - Epoch(train) [16][20/604] lr: 1.0000e-05 eta: 0:05:06 time: 0.1739 data_time: 0.0588 memory: 43 grad_norm: 0.8776 loss_action: 0.9135 loss_start: 0.5517 loss_end: 0.5638 loss: 2.0290 2022/09/07 22:32:32 - mmengine - INFO - Epoch(train) [16][40/604] lr: 1.0000e-05 eta: 0:05:04 time: 0.1333 data_time: 0.0125 memory: 43 grad_norm: 0.7752 loss_action: 0.9218 loss_start: 0.5505 loss_end: 0.5571 loss: 2.0294 2022/09/07 22:32:35 - mmengine - INFO - Epoch(train) [16][60/604] lr: 1.0000e-05 eta: 0:05:02 time: 0.1373 data_time: 0.0124 memory: 43 grad_norm: 0.8375 loss_action: 0.9086 loss_start: 0.5546 loss_end: 0.5517 loss: 2.0149 2022/09/07 22:32:37 - mmengine - INFO - Epoch(train) [16][80/604] lr: 1.0000e-05 eta: 0:05:01 time: 0.1373 data_time: 0.0124 memory: 43 grad_norm: 0.8146 loss_action: 0.8858 loss_start: 0.5458 loss_end: 0.5351 loss: 1.9666 2022/09/07 22:32:40 - mmengine - INFO - Epoch(train) [16][100/604] lr: 1.0000e-05 eta: 0:04:59 time: 0.1328 data_time: 0.0122 memory: 43 grad_norm: 0.8363 loss_action: 0.8856 loss_start: 0.5345 loss_end: 0.5479 loss: 1.9680 2022/09/07 22:32:43 - mmengine - INFO - Epoch(train) [16][120/604] lr: 1.0000e-05 eta: 0:04:57 time: 0.1324 data_time: 0.0124 memory: 43 grad_norm: 0.8714 loss_action: 0.9537 loss_start: 0.5590 loss_end: 0.5506 loss: 2.0633 2022/09/07 22:32:45 - mmengine - INFO - Epoch(train) [16][140/604] lr: 1.0000e-05 eta: 0:04:55 time: 0.1323 data_time: 0.0123 memory: 43 grad_norm: 0.7902 loss_action: 0.9070 loss_start: 0.5579 loss_end: 0.5456 loss: 2.0105 2022/09/07 22:32:48 - mmengine - INFO - Epoch(train) [16][160/604] lr: 1.0000e-05 eta: 0:04:53 time: 0.1361 data_time: 0.0124 memory: 43 grad_norm: 0.7552 loss_action: 0.8621 loss_start: 0.5612 loss_end: 0.5418 loss: 1.9652 2022/09/07 22:32:52 - mmengine - INFO - Epoch(train) [16][180/604] lr: 1.0000e-05 eta: 0:04:52 time: 0.1845 data_time: 0.0124 memory: 43 grad_norm: 0.7995 loss_action: 0.8716 loss_start: 0.5479 loss_end: 0.5490 loss: 1.9685 2022/09/07 22:32:56 - mmengine - INFO - Epoch(train) [16][200/604] lr: 1.0000e-05 eta: 0:04:50 time: 0.1872 data_time: 0.0125 memory: 43 grad_norm: 0.7753 loss_action: 0.9132 loss_start: 0.5567 loss_end: 0.5600 loss: 2.0298 2022/09/07 22:32:59 - mmengine - INFO - Epoch(train) [16][220/604] lr: 1.0000e-05 eta: 0:04:49 time: 0.1866 data_time: 0.0124 memory: 43 grad_norm: 0.7696 loss_action: 0.8922 loss_start: 0.5723 loss_end: 0.5533 loss: 2.0179 2022/09/07 22:33:03 - mmengine - INFO - Epoch(train) [16][240/604] lr: 1.0000e-05 eta: 0:04:47 time: 0.1877 data_time: 0.0124 memory: 43 grad_norm: 0.8362 loss_action: 0.9222 loss_start: 0.5727 loss_end: 0.5570 loss: 2.0520 2022/09/07 22:33:07 - mmengine - INFO - Epoch(train) [16][260/604] lr: 1.0000e-05 eta: 0:04:45 time: 0.1870 data_time: 0.0125 memory: 43 grad_norm: 0.8342 loss_action: 0.9200 loss_start: 0.5570 loss_end: 0.5576 loss: 2.0345 2022/09/07 22:33:11 - mmengine - INFO - Epoch(train) [16][280/604] lr: 1.0000e-05 eta: 0:04:44 time: 0.1869 data_time: 0.0124 memory: 43 grad_norm: 0.7461 loss_action: 0.8602 loss_start: 0.5552 loss_end: 0.5308 loss: 1.9463 2022/09/07 22:33:14 - mmengine - INFO - Epoch(train) [16][300/604] lr: 1.0000e-05 eta: 0:04:42 time: 0.1877 data_time: 0.0124 memory: 43 grad_norm: 0.7638 loss_action: 0.8930 loss_start: 0.5559 loss_end: 0.5565 loss: 2.0054 2022/09/07 22:33:18 - mmengine - INFO - Epoch(train) [16][320/604] lr: 1.0000e-05 eta: 0:04:41 time: 0.1864 data_time: 0.0125 memory: 43 grad_norm: 0.7954 loss_action: 0.8833 loss_start: 0.5490 loss_end: 0.5447 loss: 1.9769 2022/09/07 22:33:22 - mmengine - INFO - Epoch(train) [16][340/604] lr: 1.0000e-05 eta: 0:04:39 time: 0.1855 data_time: 0.0127 memory: 43 grad_norm: 0.7988 loss_action: 0.8855 loss_start: 0.5476 loss_end: 0.5489 loss: 1.9820 2022/09/07 22:33:26 - mmengine - INFO - Epoch(train) [16][360/604] lr: 1.0000e-05 eta: 0:04:37 time: 0.1879 data_time: 0.0126 memory: 43 grad_norm: 0.8012 loss_action: 0.9270 loss_start: 0.5500 loss_end: 0.5632 loss: 2.0402 2022/09/07 22:33:29 - mmengine - INFO - Epoch(train) [16][380/604] lr: 1.0000e-05 eta: 0:04:36 time: 0.1850 data_time: 0.0125 memory: 43 grad_norm: 0.8503 loss_action: 0.8593 loss_start: 0.5508 loss_end: 0.5367 loss: 1.9469 2022/09/07 22:33:33 - mmengine - INFO - Epoch(train) [16][400/604] lr: 1.0000e-05 eta: 0:04:34 time: 0.1857 data_time: 0.0124 memory: 43 grad_norm: 0.7862 loss_action: 0.8489 loss_start: 0.5577 loss_end: 0.5631 loss: 1.9697 2022/09/07 22:33:37 - mmengine - INFO - Epoch(train) [16][420/604] lr: 1.0000e-05 eta: 0:04:32 time: 0.1852 data_time: 0.0125 memory: 43 grad_norm: 0.8502 loss_action: 0.9155 loss_start: 0.5545 loss_end: 0.5556 loss: 2.0256 2022/09/07 22:33:40 - mmengine - INFO - Epoch(train) [16][440/604] lr: 1.0000e-05 eta: 0:04:31 time: 0.1846 data_time: 0.0124 memory: 43 grad_norm: 0.7895 loss_action: 0.8708 loss_start: 0.5594 loss_end: 0.5451 loss: 1.9753 2022/09/07 22:33:44 - mmengine - INFO - Epoch(train) [16][460/604] lr: 1.0000e-05 eta: 0:04:29 time: 0.1833 data_time: 0.0124 memory: 43 grad_norm: 0.9271 loss_action: 0.9469 loss_start: 0.5548 loss_end: 0.5462 loss: 2.0479 2022/09/07 22:33:48 - mmengine - INFO - Epoch(train) [16][480/604] lr: 1.0000e-05 eta: 0:04:27 time: 0.1843 data_time: 0.0125 memory: 43 grad_norm: 0.8014 loss_action: 0.9438 loss_start: 0.5684 loss_end: 0.5453 loss: 2.0574 2022/09/07 22:33:51 - mmengine - INFO - Epoch(train) [16][500/604] lr: 1.0000e-05 eta: 0:04:26 time: 0.1859 data_time: 0.0125 memory: 43 grad_norm: 0.8066 loss_action: 0.8580 loss_start: 0.5581 loss_end: 0.5576 loss: 1.9737 2022/09/07 22:33:55 - mmengine - INFO - Epoch(train) [16][520/604] lr: 1.0000e-05 eta: 0:04:24 time: 0.1860 data_time: 0.0125 memory: 43 grad_norm: 0.8162 loss_action: 0.9162 loss_start: 0.5649 loss_end: 0.5590 loss: 2.0402 2022/09/07 22:33:59 - mmengine - INFO - Epoch(train) [16][540/604] lr: 1.0000e-05 eta: 0:04:22 time: 0.1860 data_time: 0.0124 memory: 43 grad_norm: 0.7488 loss_action: 0.8742 loss_start: 0.5522 loss_end: 0.5578 loss: 1.9842 2022/09/07 22:34:03 - mmengine - INFO - Epoch(train) [16][560/604] lr: 1.0000e-05 eta: 0:04:21 time: 0.1848 data_time: 0.0126 memory: 43 grad_norm: 0.8090 loss_action: 0.8964 loss_start: 0.5529 loss_end: 0.5404 loss: 1.9897 2022/09/07 22:34:06 - mmengine - INFO - Epoch(train) [16][580/604] lr: 1.0000e-05 eta: 0:04:19 time: 0.1854 data_time: 0.0127 memory: 43 grad_norm: 0.7996 loss_action: 0.8590 loss_start: 0.5478 loss_end: 0.5470 loss: 1.9537 2022/09/07 22:34:10 - mmengine - INFO - Epoch(train) [16][600/604] lr: 1.0000e-05 eta: 0:04:17 time: 0.1858 data_time: 0.0125 memory: 43 grad_norm: 0.7474 loss_action: 0.8782 loss_start: 0.5510 loss_end: 0.5480 loss: 1.9772 2022/09/07 22:34:11 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:34:11 - mmengine - INFO - Epoch(train) [16][604/604] lr: 1.0000e-05 eta: 0:04:17 time: 0.1801 data_time: 0.0120 memory: 43 grad_norm: 0.8402 loss_action: 0.8713 loss_start: 0.5432 loss_end: 0.5412 loss: 1.9557 2022/09/07 22:34:11 - mmengine - INFO - Saving checkpoint at 16 epochs 2022/09/07 22:34:16 - mmengine - INFO - Epoch(train) [17][20/604] lr: 1.0000e-05 eta: 0:04:15 time: 0.2377 data_time: 0.0609 memory: 43 grad_norm: 0.8295 loss_action: 0.9280 loss_start: 0.5574 loss_end: 0.5581 loss: 2.0436 2022/09/07 22:34:19 - mmengine - INFO - Epoch(train) [17][40/604] lr: 1.0000e-05 eta: 0:04:13 time: 0.1864 data_time: 0.0125 memory: 43 grad_norm: 0.8092 loss_action: 0.8743 loss_start: 0.5550 loss_end: 0.5458 loss: 1.9752 2022/09/07 22:34:23 - mmengine - INFO - Epoch(train) [17][60/604] lr: 1.0000e-05 eta: 0:04:12 time: 0.1877 data_time: 0.0125 memory: 43 grad_norm: 0.8219 loss_action: 0.9163 loss_start: 0.5623 loss_end: 0.5455 loss: 2.0241 2022/09/07 22:34:27 - mmengine - INFO - Epoch(train) [17][80/604] lr: 1.0000e-05 eta: 0:04:10 time: 0.1874 data_time: 0.0125 memory: 43 grad_norm: 0.8163 loss_action: 0.9151 loss_start: 0.5465 loss_end: 0.5565 loss: 2.0180 2022/09/07 22:34:31 - mmengine - INFO - Epoch(train) [17][100/604] lr: 1.0000e-05 eta: 0:04:08 time: 0.1844 data_time: 0.0125 memory: 43 grad_norm: 0.8208 loss_action: 0.8695 loss_start: 0.5542 loss_end: 0.5466 loss: 1.9703 2022/09/07 22:34:34 - mmengine - INFO - Epoch(train) [17][120/604] lr: 1.0000e-05 eta: 0:04:06 time: 0.1865 data_time: 0.0125 memory: 43 grad_norm: 0.7804 loss_action: 0.9111 loss_start: 0.5389 loss_end: 0.5363 loss: 1.9863 2022/09/07 22:34:38 - mmengine - INFO - Epoch(train) [17][140/604] lr: 1.0000e-05 eta: 0:04:05 time: 0.1879 data_time: 0.0125 memory: 43 grad_norm: 0.8082 loss_action: 0.8865 loss_start: 0.5454 loss_end: 0.5459 loss: 1.9778 2022/09/07 22:34:42 - mmengine - INFO - Epoch(train) [17][160/604] lr: 1.0000e-05 eta: 0:04:03 time: 0.1859 data_time: 0.0125 memory: 43 grad_norm: 0.8004 loss_action: 0.9345 loss_start: 0.5743 loss_end: 0.5619 loss: 2.0707 2022/09/07 22:34:46 - mmengine - INFO - Epoch(train) [17][180/604] lr: 1.0000e-05 eta: 0:04:01 time: 0.1865 data_time: 0.0125 memory: 43 grad_norm: 0.8189 loss_action: 0.8727 loss_start: 0.5523 loss_end: 0.5494 loss: 1.9744 2022/09/07 22:34:49 - mmengine - INFO - Epoch(train) [17][200/604] lr: 1.0000e-05 eta: 0:03:59 time: 0.1849 data_time: 0.0126 memory: 43 grad_norm: 0.8231 loss_action: 0.8951 loss_start: 0.5588 loss_end: 0.5557 loss: 2.0096 2022/09/07 22:34:53 - mmengine - INFO - Epoch(train) [17][220/604] lr: 1.0000e-05 eta: 0:03:57 time: 0.1879 data_time: 0.0127 memory: 43 grad_norm: 0.8441 loss_action: 0.9614 loss_start: 0.5559 loss_end: 0.5502 loss: 2.0674 2022/09/07 22:34:57 - mmengine - INFO - Epoch(train) [17][240/604] lr: 1.0000e-05 eta: 0:03:56 time: 0.1870 data_time: 0.0126 memory: 43 grad_norm: 0.8054 loss_action: 0.8848 loss_start: 0.5486 loss_end: 0.5544 loss: 1.9878 2022/09/07 22:35:01 - mmengine - INFO - Epoch(train) [17][260/604] lr: 1.0000e-05 eta: 0:03:54 time: 0.1902 data_time: 0.0127 memory: 43 grad_norm: 0.8536 loss_action: 0.9142 loss_start: 0.5584 loss_end: 0.5557 loss: 2.0283 2022/09/07 22:35:04 - mmengine - INFO - Epoch(train) [17][280/604] lr: 1.0000e-05 eta: 0:03:52 time: 0.1862 data_time: 0.0125 memory: 43 grad_norm: 0.7910 loss_action: 0.8436 loss_start: 0.5489 loss_end: 0.5327 loss: 1.9252 2022/09/07 22:35:08 - mmengine - INFO - Epoch(train) [17][300/604] lr: 1.0000e-05 eta: 0:03:50 time: 0.1870 data_time: 0.0125 memory: 43 grad_norm: 0.8377 loss_action: 0.8562 loss_start: 0.5574 loss_end: 0.5407 loss: 1.9542 2022/09/07 22:35:12 - mmengine - INFO - Epoch(train) [17][320/604] lr: 1.0000e-05 eta: 0:03:48 time: 0.1887 data_time: 0.0125 memory: 43 grad_norm: 0.8379 loss_action: 0.8960 loss_start: 0.5512 loss_end: 0.5460 loss: 1.9932 2022/09/07 22:35:15 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:35:16 - mmengine - INFO - Epoch(train) [17][340/604] lr: 1.0000e-05 eta: 0:03:46 time: 0.1889 data_time: 0.0125 memory: 43 grad_norm: 0.7886 loss_action: 0.9079 loss_start: 0.5511 loss_end: 0.5579 loss: 2.0169 2022/09/07 22:35:19 - mmengine - INFO - Epoch(train) [17][360/604] lr: 1.0000e-05 eta: 0:03:45 time: 0.1895 data_time: 0.0125 memory: 43 grad_norm: 0.8316 loss_action: 0.9128 loss_start: 0.5499 loss_end: 0.5543 loss: 2.0170 2022/09/07 22:35:23 - mmengine - INFO - Epoch(train) [17][380/604] lr: 1.0000e-05 eta: 0:03:43 time: 0.1886 data_time: 0.0126 memory: 43 grad_norm: 0.7400 loss_action: 0.8771 loss_start: 0.5585 loss_end: 0.5563 loss: 1.9918 2022/09/07 22:35:27 - mmengine - INFO - Epoch(train) [17][400/604] lr: 1.0000e-05 eta: 0:03:41 time: 0.1884 data_time: 0.0126 memory: 43 grad_norm: 0.7929 loss_action: 0.8678 loss_start: 0.5506 loss_end: 0.5501 loss: 1.9686 2022/09/07 22:35:31 - mmengine - INFO - Epoch(train) [17][420/604] lr: 1.0000e-05 eta: 0:03:39 time: 0.1889 data_time: 0.0126 memory: 43 grad_norm: 0.8072 loss_action: 0.8902 loss_start: 0.5471 loss_end: 0.5513 loss: 1.9887 2022/09/07 22:35:34 - mmengine - INFO - Epoch(train) [17][440/604] lr: 1.0000e-05 eta: 0:03:37 time: 0.1890 data_time: 0.0125 memory: 43 grad_norm: 0.7700 loss_action: 0.8857 loss_start: 0.5621 loss_end: 0.5539 loss: 2.0016 2022/09/07 22:35:38 - mmengine - INFO - Epoch(train) [17][460/604] lr: 1.0000e-05 eta: 0:03:35 time: 0.1853 data_time: 0.0125 memory: 43 grad_norm: 0.8047 loss_action: 0.8852 loss_start: 0.5602 loss_end: 0.5468 loss: 1.9922 2022/09/07 22:35:42 - mmengine - INFO - Epoch(train) [17][480/604] lr: 1.0000e-05 eta: 0:03:33 time: 0.1870 data_time: 0.0128 memory: 43 grad_norm: 0.7977 loss_action: 0.9245 loss_start: 0.5680 loss_end: 0.5522 loss: 2.0447 2022/09/07 22:35:46 - mmengine - INFO - Epoch(train) [17][500/604] lr: 1.0000e-05 eta: 0:03:31 time: 0.1848 data_time: 0.0126 memory: 43 grad_norm: 0.8172 loss_action: 0.8709 loss_start: 0.5473 loss_end: 0.5416 loss: 1.9598 2022/09/07 22:35:49 - mmengine - INFO - Epoch(train) [17][520/604] lr: 1.0000e-05 eta: 0:03:29 time: 0.1874 data_time: 0.0128 memory: 43 grad_norm: 0.8243 loss_action: 0.9282 loss_start: 0.5642 loss_end: 0.5620 loss: 2.0544 2022/09/07 22:35:53 - mmengine - INFO - Epoch(train) [17][540/604] lr: 1.0000e-05 eta: 0:03:27 time: 0.1859 data_time: 0.0126 memory: 43 grad_norm: 0.8452 loss_action: 0.8975 loss_start: 0.5461 loss_end: 0.5421 loss: 1.9856 2022/09/07 22:35:57 - mmengine - INFO - Epoch(train) [17][560/604] lr: 1.0000e-05 eta: 0:03:25 time: 0.1876 data_time: 0.0125 memory: 43 grad_norm: 0.8867 loss_action: 0.9155 loss_start: 0.5588 loss_end: 0.5593 loss: 2.0336 2022/09/07 22:36:01 - mmengine - INFO - Epoch(train) [17][580/604] lr: 1.0000e-05 eta: 0:03:23 time: 0.1883 data_time: 0.0125 memory: 43 grad_norm: 0.8344 loss_action: 0.8614 loss_start: 0.5658 loss_end: 0.5406 loss: 1.9678 2022/09/07 22:36:04 - mmengine - INFO - Epoch(train) [17][600/604] lr: 1.0000e-05 eta: 0:03:22 time: 0.1871 data_time: 0.0125 memory: 43 grad_norm: 0.8467 loss_action: 0.8864 loss_start: 0.5420 loss_end: 0.5393 loss: 1.9678 2022/09/07 22:36:05 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:36:05 - mmengine - INFO - Epoch(train) [17][604/604] lr: 1.0000e-05 eta: 0:03:22 time: 0.1808 data_time: 0.0119 memory: 43 grad_norm: 0.9240 loss_action: 0.9116 loss_start: 0.5449 loss_end: 0.5403 loss: 1.9967 2022/09/07 22:36:05 - mmengine - INFO - Saving checkpoint at 17 epochs 2022/09/07 22:36:10 - mmengine - INFO - Epoch(train) [18][20/604] lr: 1.0000e-05 eta: 0:03:19 time: 0.2345 data_time: 0.0626 memory: 43 grad_norm: 0.7381 loss_action: 0.8490 loss_start: 0.5444 loss_end: 0.5551 loss: 1.9485 2022/09/07 22:36:14 - mmengine - INFO - Epoch(train) [18][40/604] lr: 1.0000e-05 eta: 0:03:17 time: 0.1854 data_time: 0.0127 memory: 43 grad_norm: 0.7878 loss_action: 0.8704 loss_start: 0.5537 loss_end: 0.5362 loss: 1.9603 2022/09/07 22:36:17 - mmengine - INFO - Epoch(train) [18][60/604] lr: 1.0000e-05 eta: 0:03:15 time: 0.1867 data_time: 0.0127 memory: 43 grad_norm: 0.9088 loss_action: 0.9066 loss_start: 0.5504 loss_end: 0.5508 loss: 2.0079 2022/09/07 22:36:21 - mmengine - INFO - Epoch(train) [18][80/604] lr: 1.0000e-05 eta: 0:03:13 time: 0.1852 data_time: 0.0126 memory: 43 grad_norm: 0.8387 loss_action: 0.8999 loss_start: 0.5630 loss_end: 0.5436 loss: 2.0065 2022/09/07 22:36:25 - mmengine - INFO - Epoch(train) [18][100/604] lr: 1.0000e-05 eta: 0:03:11 time: 0.1851 data_time: 0.0125 memory: 43 grad_norm: 0.7656 loss_action: 0.8517 loss_start: 0.5469 loss_end: 0.5479 loss: 1.9465 2022/09/07 22:36:28 - mmengine - INFO - Epoch(train) [18][120/604] lr: 1.0000e-05 eta: 0:03:09 time: 0.1863 data_time: 0.0126 memory: 43 grad_norm: 0.8131 loss_action: 0.9281 loss_start: 0.5664 loss_end: 0.5567 loss: 2.0512 2022/09/07 22:36:32 - mmengine - INFO - Epoch(train) [18][140/604] lr: 1.0000e-05 eta: 0:03:07 time: 0.1870 data_time: 0.0126 memory: 43 grad_norm: 0.8298 loss_action: 0.9257 loss_start: 0.5680 loss_end: 0.5654 loss: 2.0590 2022/09/07 22:36:36 - mmengine - INFO - Epoch(train) [18][160/604] lr: 1.0000e-05 eta: 0:03:05 time: 0.1854 data_time: 0.0125 memory: 43 grad_norm: 0.8340 loss_action: 0.8802 loss_start: 0.5452 loss_end: 0.5396 loss: 1.9650 2022/09/07 22:36:40 - mmengine - INFO - Epoch(train) [18][180/604] lr: 1.0000e-05 eta: 0:03:03 time: 0.1855 data_time: 0.0128 memory: 43 grad_norm: 0.8609 loss_action: 0.8916 loss_start: 0.5591 loss_end: 0.5456 loss: 1.9962 2022/09/07 22:36:43 - mmengine - INFO - Epoch(train) [18][200/604] lr: 1.0000e-05 eta: 0:03:01 time: 0.1880 data_time: 0.0129 memory: 43 grad_norm: 0.8249 loss_action: 0.9025 loss_start: 0.5626 loss_end: 0.5597 loss: 2.0248 2022/09/07 22:36:47 - mmengine - INFO - Epoch(train) [18][220/604] lr: 1.0000e-05 eta: 0:02:59 time: 0.1886 data_time: 0.0128 memory: 43 grad_norm: 0.7699 loss_action: 0.8813 loss_start: 0.5595 loss_end: 0.5484 loss: 1.9892 2022/09/07 22:36:51 - mmengine - INFO - Epoch(train) [18][240/604] lr: 1.0000e-05 eta: 0:02:57 time: 0.1844 data_time: 0.0127 memory: 43 grad_norm: 0.8668 loss_action: 0.8840 loss_start: 0.5464 loss_end: 0.5386 loss: 1.9691 2022/09/07 22:36:55 - mmengine - INFO - Epoch(train) [18][260/604] lr: 1.0000e-05 eta: 0:02:55 time: 0.1856 data_time: 0.0128 memory: 43 grad_norm: 0.7753 loss_action: 0.9059 loss_start: 0.5598 loss_end: 0.5533 loss: 2.0191 2022/09/07 22:36:57 - mmengine - INFO - Epoch(train) [18][280/604] lr: 1.0000e-05 eta: 0:02:53 time: 0.0995 data_time: 0.0130 memory: 43 grad_norm: 0.8202 loss_action: 0.8665 loss_start: 0.5585 loss_end: 0.5636 loss: 1.9886 2022/09/07 22:36:58 - mmengine - INFO - Epoch(train) [18][300/604] lr: 1.0000e-05 eta: 0:02:50 time: 0.0748 data_time: 0.0130 memory: 43 grad_norm: 0.8043 loss_action: 0.8568 loss_start: 0.5482 loss_end: 0.5477 loss: 1.9527 2022/09/07 22:37:00 - mmengine - INFO - Epoch(train) [18][320/604] lr: 1.0000e-05 eta: 0:02:48 time: 0.0749 data_time: 0.0130 memory: 43 grad_norm: 0.9023 loss_action: 0.9307 loss_start: 0.5506 loss_end: 0.5465 loss: 2.0278 2022/09/07 22:37:01 - mmengine - INFO - Epoch(train) [18][340/604] lr: 1.0000e-05 eta: 0:02:46 time: 0.0750 data_time: 0.0129 memory: 43 grad_norm: 0.7933 loss_action: 0.9082 loss_start: 0.5454 loss_end: 0.5647 loss: 2.0183 2022/09/07 22:37:03 - mmengine - INFO - Epoch(train) [18][360/604] lr: 1.0000e-05 eta: 0:02:43 time: 0.0752 data_time: 0.0129 memory: 43 grad_norm: 0.8405 loss_action: 0.8995 loss_start: 0.5564 loss_end: 0.5433 loss: 1.9993 2022/09/07 22:37:04 - mmengine - INFO - Epoch(train) [18][380/604] lr: 1.0000e-05 eta: 0:02:41 time: 0.0755 data_time: 0.0130 memory: 43 grad_norm: 0.7753 loss_action: 0.8948 loss_start: 0.5648 loss_end: 0.5437 loss: 2.0033 2022/09/07 22:37:06 - mmengine - INFO - Epoch(train) [18][400/604] lr: 1.0000e-05 eta: 0:02:39 time: 0.0752 data_time: 0.0129 memory: 43 grad_norm: 0.8539 loss_action: 0.9047 loss_start: 0.5489 loss_end: 0.5454 loss: 1.9990 2022/09/07 22:37:07 - mmengine - INFO - Epoch(train) [18][420/604] lr: 1.0000e-05 eta: 0:02:36 time: 0.0752 data_time: 0.0129 memory: 43 grad_norm: 0.8484 loss_action: 0.8950 loss_start: 0.5506 loss_end: 0.5466 loss: 1.9922 2022/09/07 22:37:09 - mmengine - INFO - Epoch(train) [18][440/604] lr: 1.0000e-05 eta: 0:02:34 time: 0.0752 data_time: 0.0128 memory: 43 grad_norm: 0.8066 loss_action: 0.8745 loss_start: 0.5530 loss_end: 0.5456 loss: 1.9731 2022/09/07 22:37:10 - mmengine - INFO - Epoch(train) [18][460/604] lr: 1.0000e-05 eta: 0:02:32 time: 0.0751 data_time: 0.0129 memory: 43 grad_norm: 0.7643 loss_action: 0.9144 loss_start: 0.5571 loss_end: 0.5615 loss: 2.0330 2022/09/07 22:37:12 - mmengine - INFO - Epoch(train) [18][480/604] lr: 1.0000e-05 eta: 0:02:29 time: 0.0754 data_time: 0.0128 memory: 43 grad_norm: 0.7755 loss_action: 0.8677 loss_start: 0.5541 loss_end: 0.5480 loss: 1.9698 2022/09/07 22:37:13 - mmengine - INFO - Epoch(train) [18][500/604] lr: 1.0000e-05 eta: 0:02:27 time: 0.0773 data_time: 0.0134 memory: 43 grad_norm: 0.8361 loss_action: 0.8722 loss_start: 0.5481 loss_end: 0.5419 loss: 1.9622 2022/09/07 22:37:15 - mmengine - INFO - Epoch(train) [18][520/604] lr: 1.0000e-05 eta: 0:02:25 time: 0.0786 data_time: 0.0137 memory: 43 grad_norm: 0.8244 loss_action: 0.8643 loss_start: 0.5427 loss_end: 0.5521 loss: 1.9591 2022/09/07 22:37:16 - mmengine - INFO - Epoch(train) [18][540/604] lr: 1.0000e-05 eta: 0:02:22 time: 0.0792 data_time: 0.0139 memory: 43 grad_norm: 0.7885 loss_action: 0.8650 loss_start: 0.5503 loss_end: 0.5409 loss: 1.9561 2022/09/07 22:37:18 - mmengine - INFO - Epoch(train) [18][560/604] lr: 1.0000e-05 eta: 0:02:20 time: 0.0787 data_time: 0.0135 memory: 43 grad_norm: 0.8468 loss_action: 0.9337 loss_start: 0.5473 loss_end: 0.5344 loss: 2.0154 2022/09/07 22:37:19 - mmengine - INFO - Epoch(train) [18][580/604] lr: 1.0000e-05 eta: 0:02:18 time: 0.0782 data_time: 0.0136 memory: 43 grad_norm: 0.8190 loss_action: 0.9460 loss_start: 0.5656 loss_end: 0.5515 loss: 2.0632 2022/09/07 22:37:21 - mmengine - INFO - Epoch(train) [18][600/604] lr: 1.0000e-05 eta: 0:02:15 time: 0.0774 data_time: 0.0130 memory: 43 grad_norm: 0.8261 loss_action: 0.9147 loss_start: 0.5526 loss_end: 0.5555 loss: 2.0228 2022/09/07 22:37:21 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:37:21 - mmengine - INFO - Epoch(train) [18][604/604] lr: 1.0000e-05 eta: 0:02:15 time: 0.0743 data_time: 0.0125 memory: 43 grad_norm: 0.9514 loss_action: 0.9116 loss_start: 0.5525 loss_end: 0.5733 loss: 2.0373 2022/09/07 22:37:21 - mmengine - INFO - Saving checkpoint at 18 epochs 2022/09/07 22:37:24 - mmengine - INFO - Epoch(train) [19][20/604] lr: 1.0000e-05 eta: 0:02:13 time: 0.1475 data_time: 0.0777 memory: 43 grad_norm: 0.8078 loss_action: 0.8918 loss_start: 0.5571 loss_end: 0.5441 loss: 1.9929 2022/09/07 22:37:27 - mmengine - INFO - Epoch(train) [19][40/604] lr: 1.0000e-05 eta: 0:02:10 time: 0.1507 data_time: 0.0130 memory: 43 grad_norm: 0.8730 loss_action: 0.9084 loss_start: 0.5574 loss_end: 0.5482 loss: 2.0140 2022/09/07 22:37:31 - mmengine - INFO - Epoch(train) [19][60/604] lr: 1.0000e-05 eta: 0:02:08 time: 0.1734 data_time: 0.0125 memory: 43 grad_norm: 0.8120 loss_action: 0.8971 loss_start: 0.5569 loss_end: 0.5500 loss: 2.0040 2022/09/07 22:37:34 - mmengine - INFO - Epoch(train) [19][80/604] lr: 1.0000e-05 eta: 0:02:06 time: 0.1616 data_time: 0.0130 memory: 43 grad_norm: 0.7996 loss_action: 0.8837 loss_start: 0.5576 loss_end: 0.5403 loss: 1.9816 2022/09/07 22:37:37 - mmengine - INFO - Epoch(train) [19][100/604] lr: 1.0000e-05 eta: 0:02:04 time: 0.1363 data_time: 0.0128 memory: 43 grad_norm: 0.8625 loss_action: 0.9127 loss_start: 0.5565 loss_end: 0.5493 loss: 2.0184 2022/09/07 22:37:40 - mmengine - INFO - Epoch(train) [19][120/604] lr: 1.0000e-05 eta: 0:02:02 time: 0.1367 data_time: 0.0128 memory: 43 grad_norm: 0.8292 loss_action: 0.9313 loss_start: 0.5449 loss_end: 0.5548 loss: 2.0311 2022/09/07 22:37:41 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:37:42 - mmengine - INFO - Epoch(train) [19][140/604] lr: 1.0000e-05 eta: 0:02:00 time: 0.1361 data_time: 0.0126 memory: 43 grad_norm: 0.7968 loss_action: 0.8800 loss_start: 0.5583 loss_end: 0.5518 loss: 1.9901 2022/09/07 22:37:45 - mmengine - INFO - Epoch(train) [19][160/604] lr: 1.0000e-05 eta: 0:01:57 time: 0.1386 data_time: 0.0126 memory: 43 grad_norm: 0.7958 loss_action: 0.8707 loss_start: 0.5430 loss_end: 0.5408 loss: 1.9545 2022/09/07 22:37:48 - mmengine - INFO - Epoch(train) [19][180/604] lr: 1.0000e-05 eta: 0:01:55 time: 0.1353 data_time: 0.0125 memory: 43 grad_norm: 0.8368 loss_action: 0.8964 loss_start: 0.5514 loss_end: 0.5585 loss: 2.0063 2022/09/07 22:37:51 - mmengine - INFO - Epoch(train) [19][200/604] lr: 1.0000e-05 eta: 0:01:53 time: 0.1355 data_time: 0.0127 memory: 43 grad_norm: 0.7827 loss_action: 0.8682 loss_start: 0.5546 loss_end: 0.5493 loss: 1.9722 2022/09/07 22:37:53 - mmengine - INFO - Epoch(train) [19][220/604] lr: 1.0000e-05 eta: 0:01:51 time: 0.1374 data_time: 0.0127 memory: 43 grad_norm: 0.7453 loss_action: 0.8997 loss_start: 0.5498 loss_end: 0.5538 loss: 2.0033 2022/09/07 22:37:56 - mmengine - INFO - Epoch(train) [19][240/604] lr: 1.0000e-05 eta: 0:01:49 time: 0.1407 data_time: 0.0129 memory: 43 grad_norm: 0.8566 loss_action: 0.9042 loss_start: 0.5514 loss_end: 0.5412 loss: 1.9968 2022/09/07 22:37:59 - mmengine - INFO - Epoch(train) [19][260/604] lr: 1.0000e-05 eta: 0:01:46 time: 0.1411 data_time: 0.0128 memory: 43 grad_norm: 0.8733 loss_action: 0.8926 loss_start: 0.5598 loss_end: 0.5554 loss: 2.0078 2022/09/07 22:38:02 - mmengine - INFO - Epoch(train) [19][280/604] lr: 1.0000e-05 eta: 0:01:44 time: 0.1393 data_time: 0.0127 memory: 43 grad_norm: 0.8533 loss_action: 0.8923 loss_start: 0.5532 loss_end: 0.5508 loss: 1.9963 2022/09/07 22:38:05 - mmengine - INFO - Epoch(train) [19][300/604] lr: 1.0000e-05 eta: 0:01:42 time: 0.1390 data_time: 0.0128 memory: 43 grad_norm: 0.9063 loss_action: 0.9372 loss_start: 0.5540 loss_end: 0.5434 loss: 2.0346 2022/09/07 22:38:07 - mmengine - INFO - Epoch(train) [19][320/604] lr: 1.0000e-05 eta: 0:01:40 time: 0.1377 data_time: 0.0127 memory: 43 grad_norm: 0.7899 loss_action: 0.8637 loss_start: 0.5601 loss_end: 0.5438 loss: 1.9675 2022/09/07 22:38:10 - mmengine - INFO - Epoch(train) [19][340/604] lr: 1.0000e-05 eta: 0:01:38 time: 0.1462 data_time: 0.0128 memory: 43 grad_norm: 0.8040 loss_action: 0.8797 loss_start: 0.5560 loss_end: 0.5550 loss: 1.9908 2022/09/07 22:38:13 - mmengine - INFO - Epoch(train) [19][360/604] lr: 1.0000e-05 eta: 0:01:35 time: 0.1453 data_time: 0.0130 memory: 43 grad_norm: 0.7927 loss_action: 0.8693 loss_start: 0.5574 loss_end: 0.5441 loss: 1.9708 2022/09/07 22:38:16 - mmengine - INFO - Epoch(train) [19][380/604] lr: 1.0000e-05 eta: 0:01:33 time: 0.1354 data_time: 0.0127 memory: 43 grad_norm: 0.9115 loss_action: 0.9244 loss_start: 0.5518 loss_end: 0.5501 loss: 2.0262 2022/09/07 22:38:19 - mmengine - INFO - Epoch(train) [19][400/604] lr: 1.0000e-05 eta: 0:01:31 time: 0.1388 data_time: 0.0129 memory: 43 grad_norm: 0.8345 loss_action: 0.9238 loss_start: 0.5567 loss_end: 0.5601 loss: 2.0406 2022/09/07 22:38:21 - mmengine - INFO - Epoch(train) [19][420/604] lr: 1.0000e-05 eta: 0:01:29 time: 0.1418 data_time: 0.0129 memory: 43 grad_norm: 0.8918 loss_action: 0.8907 loss_start: 0.5445 loss_end: 0.5446 loss: 1.9797 2022/09/07 22:38:24 - mmengine - INFO - Epoch(train) [19][440/604] lr: 1.0000e-05 eta: 0:01:26 time: 0.1434 data_time: 0.0129 memory: 43 grad_norm: 0.7908 loss_action: 0.8750 loss_start: 0.5603 loss_end: 0.5458 loss: 1.9811 2022/09/07 22:38:27 - mmengine - INFO - Epoch(train) [19][460/604] lr: 1.0000e-05 eta: 0:01:24 time: 0.1370 data_time: 0.0129 memory: 43 grad_norm: 0.8283 loss_action: 0.9106 loss_start: 0.5527 loss_end: 0.5637 loss: 2.0270 2022/09/07 22:38:30 - mmengine - INFO - Epoch(train) [19][480/604] lr: 1.0000e-05 eta: 0:01:22 time: 0.1374 data_time: 0.0129 memory: 43 grad_norm: 0.8089 loss_action: 0.9081 loss_start: 0.5459 loss_end: 0.5418 loss: 1.9958 2022/09/07 22:38:33 - mmengine - INFO - Epoch(train) [19][500/604] lr: 1.0000e-05 eta: 0:01:20 time: 0.1370 data_time: 0.0131 memory: 43 grad_norm: 0.8639 loss_action: 0.8919 loss_start: 0.5554 loss_end: 0.5389 loss: 1.9861 2022/09/07 22:38:35 - mmengine - INFO - Epoch(train) [19][520/604] lr: 1.0000e-05 eta: 0:01:18 time: 0.1449 data_time: 0.0133 memory: 43 grad_norm: 0.8146 loss_action: 0.8725 loss_start: 0.5466 loss_end: 0.5409 loss: 1.9600 2022/09/07 22:38:38 - mmengine - INFO - Epoch(train) [19][540/604] lr: 1.0000e-05 eta: 0:01:15 time: 0.1377 data_time: 0.0129 memory: 43 grad_norm: 0.8625 loss_action: 0.8826 loss_start: 0.5505 loss_end: 0.5584 loss: 1.9914 2022/09/07 22:38:41 - mmengine - INFO - Epoch(train) [19][560/604] lr: 1.0000e-05 eta: 0:01:13 time: 0.1422 data_time: 0.0131 memory: 43 grad_norm: 0.7995 loss_action: 0.9006 loss_start: 0.5452 loss_end: 0.5462 loss: 1.9921 2022/09/07 22:38:44 - mmengine - INFO - Epoch(train) [19][580/604] lr: 1.0000e-05 eta: 0:01:11 time: 0.1439 data_time: 0.0130 memory: 43 grad_norm: 0.7899 loss_action: 0.8587 loss_start: 0.5538 loss_end: 0.5423 loss: 1.9548 2022/09/07 22:38:47 - mmengine - INFO - Epoch(train) [19][600/604] lr: 1.0000e-05 eta: 0:01:09 time: 0.1333 data_time: 0.0128 memory: 43 grad_norm: 0.8522 loss_action: 0.9312 loss_start: 0.5631 loss_end: 0.5532 loss: 2.0475 2022/09/07 22:38:47 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:38:47 - mmengine - INFO - Epoch(train) [19][604/604] lr: 1.0000e-05 eta: 0:01:09 time: 0.1296 data_time: 0.0123 memory: 43 grad_norm: 0.9148 loss_action: 0.9246 loss_start: 0.5648 loss_end: 0.5428 loss: 2.0322 2022/09/07 22:38:47 - mmengine - INFO - Saving checkpoint at 19 epochs 2022/09/07 22:38:51 - mmengine - INFO - Epoch(train) [20][20/604] lr: 1.0000e-05 eta: 0:01:06 time: 0.1762 data_time: 0.0553 memory: 43 grad_norm: 0.8075 loss_action: 0.8749 loss_start: 0.5736 loss_end: 0.5490 loss: 1.9975 2022/09/07 22:38:54 - mmengine - INFO - Epoch(train) [20][40/604] lr: 1.0000e-05 eta: 0:01:04 time: 0.1460 data_time: 0.0130 memory: 43 grad_norm: 0.8137 loss_action: 0.8669 loss_start: 0.5567 loss_end: 0.5419 loss: 1.9655 2022/09/07 22:38:57 - mmengine - INFO - Epoch(train) [20][60/604] lr: 1.0000e-05 eta: 0:01:01 time: 0.1425 data_time: 0.0130 memory: 43 grad_norm: 0.8105 loss_action: 0.8835 loss_start: 0.5583 loss_end: 0.5534 loss: 1.9952 2022/09/07 22:38:59 - mmengine - INFO - Epoch(train) [20][80/604] lr: 1.0000e-05 eta: 0:00:59 time: 0.1379 data_time: 0.0129 memory: 43 grad_norm: 0.8674 loss_action: 0.9014 loss_start: 0.5558 loss_end: 0.5484 loss: 2.0056 2022/09/07 22:39:02 - mmengine - INFO - Epoch(train) [20][100/604] lr: 1.0000e-05 eta: 0:00:57 time: 0.1418 data_time: 0.0129 memory: 43 grad_norm: 0.7835 loss_action: 0.8887 loss_start: 0.5408 loss_end: 0.5485 loss: 1.9780 2022/09/07 22:39:05 - mmengine - INFO - Epoch(train) [20][120/604] lr: 1.0000e-05 eta: 0:00:55 time: 0.1418 data_time: 0.0130 memory: 43 grad_norm: 0.7924 loss_action: 0.8905 loss_start: 0.5543 loss_end: 0.5551 loss: 1.9999 2022/09/07 22:39:08 - mmengine - INFO - Epoch(train) [20][140/604] lr: 1.0000e-05 eta: 0:00:52 time: 0.1447 data_time: 0.0130 memory: 43 grad_norm: 0.8286 loss_action: 0.9040 loss_start: 0.5520 loss_end: 0.5455 loss: 2.0014 2022/09/07 22:39:11 - mmengine - INFO - Epoch(train) [20][160/604] lr: 1.0000e-05 eta: 0:00:50 time: 0.1385 data_time: 0.0129 memory: 43 grad_norm: 0.7766 loss_action: 0.8654 loss_start: 0.5578 loss_end: 0.5617 loss: 1.9849 2022/09/07 22:39:14 - mmengine - INFO - Epoch(train) [20][180/604] lr: 1.0000e-05 eta: 0:00:48 time: 0.1442 data_time: 0.0130 memory: 43 grad_norm: 0.8557 loss_action: 0.8744 loss_start: 0.5503 loss_end: 0.5360 loss: 1.9607 2022/09/07 22:39:16 - mmengine - INFO - Epoch(train) [20][200/604] lr: 1.0000e-05 eta: 0:00:46 time: 0.1397 data_time: 0.0129 memory: 43 grad_norm: 0.8660 loss_action: 0.9556 loss_start: 0.5539 loss_end: 0.5701 loss: 2.0796 2022/09/07 22:39:19 - mmengine - INFO - Epoch(train) [20][220/604] lr: 1.0000e-05 eta: 0:00:43 time: 0.1407 data_time: 0.0130 memory: 43 grad_norm: 0.8267 loss_action: 0.8763 loss_start: 0.5435 loss_end: 0.5545 loss: 1.9743 2022/09/07 22:39:22 - mmengine - INFO - Epoch(train) [20][240/604] lr: 1.0000e-05 eta: 0:00:41 time: 0.1378 data_time: 0.0130 memory: 43 grad_norm: 0.8040 loss_action: 0.9000 loss_start: 0.5575 loss_end: 0.5532 loss: 2.0107 2022/09/07 22:39:25 - mmengine - INFO - Epoch(train) [20][260/604] lr: 1.0000e-05 eta: 0:00:39 time: 0.1414 data_time: 0.0130 memory: 43 grad_norm: 0.8499 loss_action: 0.8706 loss_start: 0.5566 loss_end: 0.5487 loss: 1.9759 2022/09/07 22:39:28 - mmengine - INFO - Epoch(train) [20][280/604] lr: 1.0000e-05 eta: 0:00:37 time: 0.1377 data_time: 0.0131 memory: 43 grad_norm: 0.8965 loss_action: 0.9242 loss_start: 0.5561 loss_end: 0.5511 loss: 2.0315 2022/09/07 22:39:30 - mmengine - INFO - Epoch(train) [20][300/604] lr: 1.0000e-05 eta: 0:00:34 time: 0.1448 data_time: 0.0131 memory: 43 grad_norm: 0.8470 loss_action: 0.9009 loss_start: 0.5541 loss_end: 0.5409 loss: 1.9959 2022/09/07 22:39:33 - mmengine - INFO - Epoch(train) [20][320/604] lr: 1.0000e-05 eta: 0:00:32 time: 0.1404 data_time: 0.0129 memory: 43 grad_norm: 0.7918 loss_action: 0.8733 loss_start: 0.5598 loss_end: 0.5505 loss: 1.9836 2022/09/07 22:39:36 - mmengine - INFO - Epoch(train) [20][340/604] lr: 1.0000e-05 eta: 0:00:30 time: 0.1408 data_time: 0.0129 memory: 43 grad_norm: 0.8219 loss_action: 0.8664 loss_start: 0.5478 loss_end: 0.5507 loss: 1.9649 2022/09/07 22:39:39 - mmengine - INFO - Epoch(train) [20][360/604] lr: 1.0000e-05 eta: 0:00:27 time: 0.1364 data_time: 0.0128 memory: 43 grad_norm: 0.8297 loss_action: 0.9115 loss_start: 0.5590 loss_end: 0.5440 loss: 2.0146 2022/09/07 22:39:42 - mmengine - INFO - Epoch(train) [20][380/604] lr: 1.0000e-05 eta: 0:00:25 time: 0.1385 data_time: 0.0129 memory: 43 grad_norm: 0.8835 loss_action: 0.9377 loss_start: 0.5580 loss_end: 0.5453 loss: 2.0410 2022/09/07 22:39:44 - mmengine - INFO - Epoch(train) [20][400/604] lr: 1.0000e-05 eta: 0:00:23 time: 0.1414 data_time: 0.0129 memory: 43 grad_norm: 0.8082 loss_action: 0.8523 loss_start: 0.5381 loss_end: 0.5332 loss: 1.9235 2022/09/07 22:39:47 - mmengine - INFO - Epoch(train) [20][420/604] lr: 1.0000e-05 eta: 0:00:21 time: 0.1410 data_time: 0.0129 memory: 43 grad_norm: 0.8649 loss_action: 0.8443 loss_start: 0.5406 loss_end: 0.5363 loss: 1.9212 2022/09/07 22:39:50 - mmengine - INFO - Epoch(train) [20][440/604] lr: 1.0000e-05 eta: 0:00:18 time: 0.1454 data_time: 0.0132 memory: 43 grad_norm: 0.7933 loss_action: 0.8829 loss_start: 0.5522 loss_end: 0.5429 loss: 1.9780 2022/09/07 22:39:53 - mmengine - INFO - Epoch(train) [20][460/604] lr: 1.0000e-05 eta: 0:00:16 time: 0.1429 data_time: 0.0129 memory: 43 grad_norm: 0.8965 loss_action: 0.9436 loss_start: 0.5556 loss_end: 0.5532 loss: 2.0523 2022/09/07 22:39:56 - mmengine - INFO - Epoch(train) [20][480/604] lr: 1.0000e-05 eta: 0:00:14 time: 0.1378 data_time: 0.0129 memory: 43 grad_norm: 0.8662 loss_action: 0.9236 loss_start: 0.5505 loss_end: 0.5548 loss: 2.0290 2022/09/07 22:39:59 - mmengine - INFO - Epoch(train) [20][500/604] lr: 1.0000e-05 eta: 0:00:11 time: 0.1418 data_time: 0.0128 memory: 43 grad_norm: 0.8513 loss_action: 0.9254 loss_start: 0.5686 loss_end: 0.5619 loss: 2.0558 2022/09/07 22:40:01 - mmengine - INFO - Epoch(train) [20][520/604] lr: 1.0000e-05 eta: 0:00:09 time: 0.1435 data_time: 0.0128 memory: 43 grad_norm: 0.7768 loss_action: 0.8761 loss_start: 0.5460 loss_end: 0.5395 loss: 1.9616 2022/09/07 22:40:02 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:40:04 - mmengine - INFO - Epoch(train) [20][540/604] lr: 1.0000e-05 eta: 0:00:07 time: 0.1420 data_time: 0.0129 memory: 43 grad_norm: 0.7988 loss_action: 0.9158 loss_start: 0.5562 loss_end: 0.5663 loss: 2.0383 2022/09/07 22:40:07 - mmengine - INFO - Epoch(train) [20][560/604] lr: 1.0000e-05 eta: 0:00:05 time: 0.1420 data_time: 0.0128 memory: 43 grad_norm: 0.8369 loss_action: 0.8840 loss_start: 0.5480 loss_end: 0.5377 loss: 1.9696 2022/09/07 22:40:10 - mmengine - INFO - Epoch(train) [20][580/604] lr: 1.0000e-05 eta: 0:00:02 time: 0.1422 data_time: 0.0126 memory: 43 grad_norm: 0.8730 loss_action: 0.8974 loss_start: 0.5484 loss_end: 0.5304 loss: 1.9762 2022/09/07 22:40:13 - mmengine - INFO - Epoch(train) [20][600/604] lr: 1.0000e-05 eta: 0:00:00 time: 0.1369 data_time: 0.0126 memory: 43 grad_norm: 0.7977 loss_action: 0.8881 loss_start: 0.5498 loss_end: 0.5547 loss: 1.9925 2022/09/07 22:40:13 - mmengine - INFO - Exp name: bsn_tem_400x100_1x16_20e_activitynet_feature_20220907_221641 2022/09/07 22:40:13 - mmengine - INFO - Epoch(train) [20][604/604] lr: 1.0000e-05 eta: 0:00:00 time: 0.1324 data_time: 0.0111 memory: 43 grad_norm: 1.0533 loss_action: 0.9181 loss_start: 0.5490 loss_end: 0.5550 loss: 2.0222 2022/09/07 22:40:13 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/09/07 22:40:16 - mmengine - INFO - Epoch(val) [20][20/296] eta: 0:00:38 time: 0.1402 data_time: 0.1102 memory: 40 2022/09/07 22:40:17 - mmengine - INFO - Epoch(val) [20][40/296] eta: 0:00:15 time: 0.0587 data_time: 0.0153 memory: 40 2022/09/07 22:40:19 - mmengine - INFO - Epoch(val) [20][60/296] eta: 0:00:13 time: 0.0565 data_time: 0.0138 memory: 40 2022/09/07 22:40:20 - mmengine - INFO - Epoch(val) [20][80/296] eta: 0:00:13 time: 0.0619 data_time: 0.0133 memory: 40 2022/09/07 22:40:21 - mmengine - INFO - Epoch(val) [20][100/296] eta: 0:00:10 time: 0.0557 data_time: 0.0127 memory: 40 2022/09/07 22:40:22 - mmengine - INFO - Epoch(val) [20][120/296] eta: 0:00:09 time: 0.0541 data_time: 0.0123 memory: 40 2022/09/07 22:40:23 - mmengine - INFO - Epoch(val) [20][140/296] eta: 0:00:07 time: 0.0489 data_time: 0.0123 memory: 40 2022/09/07 22:40:24 - mmengine - INFO - Epoch(val) [20][160/296] eta: 0:00:07 time: 0.0519 data_time: 0.0120 memory: 40 2022/09/07 22:40:25 - mmengine - INFO - Epoch(val) [20][180/296] eta: 0:00:05 time: 0.0498 data_time: 0.0118 memory: 40 2022/09/07 22:40:26 - mmengine - INFO - Epoch(val) [20][200/296] eta: 0:00:04 time: 0.0518 data_time: 0.0122 memory: 40 2022/09/07 22:40:27 - mmengine - INFO - Epoch(val) [20][220/296] eta: 0:00:03 time: 0.0499 data_time: 0.0120 memory: 40 2022/09/07 22:40:28 - mmengine - INFO - Epoch(val) [20][240/296] eta: 0:00:02 time: 0.0513 data_time: 0.0120 memory: 40 2022/09/07 22:40:29 - mmengine - INFO - Epoch(val) [20][260/296] eta: 0:00:01 time: 0.0543 data_time: 0.0144 memory: 40 2022/09/07 22:40:30 - mmengine - INFO - Epoch(val) [20][280/296] eta: 0:00:00 time: 0.0558 data_time: 0.0185 memory: 40 2022/09/07 22:40:35 - mmengine - INFO - Epoch(val) [20][296/296]