2022/09/03 21:43:08 - 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: 611185006 GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/cache/share/cuda-11.1 NVCC: Cuda compilation tools, release 11.1, V11.1.74 GCC: gcc (GCC) 5.4.0 PyTorch: 1.11.0 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.3 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 - CuDNN 8.2 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.12.0 OpenCV: 4.5.5 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: slurm Distributed training: True GPU number: 8 ------------------------------------------------------------ 2022/09/03 21:43:08 - mmengine - INFO - Config: model = dict( type='Recognizer2D', data_preprocessor=dict( type='ActionDataPreprocessor', mean=[123.675, 116.28, 103.5], std=[58.395, 57.12, 57.375], format_shape='NCHW'), backbone=dict( type='TANet', pretrained='torchvision://resnet50', depth=50, num_segments=8, tam_cfg=dict()), cls_head=dict( type='TSMHead', num_classes=174, in_channels=2048, spatial_type='avg', consensus=dict(type='AvgConsensus', dim=1), dropout_ratio=0.6, init_std=0.001, average_clips='prob')) 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='auto', max_keep_ckpts=3), 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 train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=50, val_begin=1, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') param_scheduler = [ dict( type='MultiStepLR', begin=0, end=50, by_epoch=True, milestones=[30, 40, 45], gamma=0.1) ] optim_wrapper = dict( constructor='TSMOptimWrapperConstructor', paramwise_cfg=dict(fc_lr5=True), optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.001), clip_grad=dict(max_norm=20, norm_type=2)) file_client_args = dict( io_backend='petrel', path_mapping=dict( {'data/sthv1': 's204:s3://openmmlab/datasets/action/sthv1'})) dataset_type = 'RawframeDataset' data_root = 'data/sthv1/rawframes' data_root_val = 'data/sthv1/rawframes' ann_file_train = 'data/sthv1/sthv1_train_list_rawframes.txt' ann_file_val = 'data/sthv1/sthv1_val_list_rawframes.txt' ann_file_test = 'data/sthv1/sthv1_val_list_rawframes.txt' sthv1_flip_label_map = dict({2: 4, 4: 2, 30: 41, 41: 30, 52: 66, 66: 52}) train_pipeline = [ dict(type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8), dict( type='RawFrameDecode', io_backend='petrel', path_mapping=dict( {'data/sthv1': 's204:s3://openmmlab/datasets/action/sthv1'})), dict(type='Resize', scale=(-1, 256)), dict( type='MultiScaleCrop', input_size=224, scales=(1, 0.875, 0.75, 0.66), random_crop=False, max_wh_scale_gap=1, num_fixed_crops=13), dict(type='Resize', scale=(224, 224), keep_ratio=False), dict( type='Flip', flip_ratio=0.5, flip_label_map=dict({ 2: 4, 4: 2, 30: 41, 41: 30, 52: 66, 66: 52 })), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ] val_pipeline = [ dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8, test_mode=True), dict( type='RawFrameDecode', io_backend='petrel', path_mapping=dict( {'data/sthv1': 's204:s3://openmmlab/datasets/action/sthv1'})), dict(type='Resize', scale=(-1, 256)), dict(type='CenterCrop', crop_size=224), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ] test_pipeline = [ dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8, twice_sample=True, test_mode=True), dict( type='RawFrameDecode', io_backend='petrel', path_mapping=dict( {'data/sthv1': 's204:s3://openmmlab/datasets/action/sthv1'})), dict(type='Resize', scale=(-1, 256)), dict(type='ThreeCrop', crop_size=256), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ] train_dataloader = dict( batch_size=8, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='RawframeDataset', ann_file='data/sthv1/sthv1_train_list_rawframes.txt', data_prefix=dict(img='data/sthv1/rawframes'), filename_tmpl='{:05}.jpg', pipeline=[ dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8), dict( type='RawFrameDecode', io_backend='petrel', path_mapping=dict({ 'data/sthv1': 's204:s3://openmmlab/datasets/action/sthv1' })), dict(type='Resize', scale=(-1, 256)), dict( type='MultiScaleCrop', input_size=224, scales=(1, 0.875, 0.75, 0.66), random_crop=False, max_wh_scale_gap=1, num_fixed_crops=13), dict(type='Resize', scale=(224, 224), keep_ratio=False), dict( type='Flip', flip_ratio=0.5, flip_label_map=dict({ 2: 4, 4: 2, 30: 41, 41: 30, 52: 66, 66: 52 })), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ])) val_dataloader = dict( batch_size=8, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='RawframeDataset', ann_file='data/sthv1/sthv1_val_list_rawframes.txt', data_prefix=dict(img='data/sthv1/rawframes'), filename_tmpl='{:05}.jpg', pipeline=[ dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8, test_mode=True), dict( type='RawFrameDecode', io_backend='petrel', path_mapping=dict({ 'data/sthv1': 's204:s3://openmmlab/datasets/action/sthv1' })), dict(type='Resize', scale=(-1, 256)), dict(type='CenterCrop', crop_size=224), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ], test_mode=True)) test_dataloader = dict( batch_size=1, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='RawframeDataset', ann_file='data/sthv1/sthv1_val_list_rawframes.txt', data_prefix=dict(img='data/sthv1/rawframes'), filename_tmpl='{:05}.jpg', pipeline=[ dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8, twice_sample=True, test_mode=True), dict( type='RawFrameDecode', io_backend='petrel', path_mapping=dict({ 'data/sthv1': 's204:s3://openmmlab/datasets/action/sthv1' })), dict(type='Resize', scale=(-1, 256)), dict(type='ThreeCrop', crop_size=256), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ], test_mode=True)) val_evaluator = dict(type='AccMetric') test_evaluator = dict(type='AccMetric') launcher = 'slurm' work_dir = './work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb' 2022/09/03 21:43:11 - mmengine - INFO - These parameters in pretrained checkpoint are not loaded: {'fc.bias', 'fc.weight'} 2022/09/03 21:43:12 - mmengine - INFO - Checkpoints will be saved to /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb by HardDiskBackend. 2022/09/03 21:45:17 - mmengine - INFO - Epoch(train) [1][20/1345] lr: 1.0000e-02 eta: 4 days, 20:24:02 time: 6.2330 data_time: 5.7989 memory: 7116 grad_norm: 4.3003 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 5.5747 loss: 5.5747 2022/09/03 21:45:26 - mmengine - INFO - Epoch(train) [1][40/1345] lr: 1.0000e-02 eta: 2 days, 14:25:36 time: 0.4546 data_time: 0.0216 memory: 7116 grad_norm: 4.2797 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 5.3964 loss: 5.3964 2022/09/03 21:45:33 - mmengine - INFO - Epoch(train) [1][60/1345] lr: 1.0000e-02 eta: 1 day, 19:47:44 time: 0.3521 data_time: 0.0406 memory: 7116 grad_norm: 2.4776 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 5.2400 loss: 5.2400 2022/09/03 21:45:40 - mmengine - INFO - Epoch(train) [1][80/1345] lr: 1.0000e-02 eta: 1 day, 10:33:47 time: 0.3701 data_time: 0.0972 memory: 7116 grad_norm: 1.2956 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 5.1147 loss: 5.1147 2022/09/03 21:45:48 - mmengine - INFO - Epoch(train) [1][100/1345] lr: 1.0000e-02 eta: 1 day, 4:59:06 time: 0.3599 data_time: 0.1686 memory: 7116 grad_norm: 1.5294 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 5.1577 loss: 5.1577 2022/09/03 21:45:54 - mmengine - INFO - Epoch(train) [1][120/1345] lr: 1.0000e-02 eta: 1 day, 1:03:33 time: 0.2935 data_time: 0.0978 memory: 7116 grad_norm: 1.1923 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 5.0739 loss: 5.0739 2022/09/03 21:46:01 - mmengine - INFO - Epoch(train) [1][140/1345] lr: 1.0000e-02 eta: 22:28:39 time: 0.3772 data_time: 0.0074 memory: 7116 grad_norm: 1.3544 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 5.0568 loss: 5.0568 2022/09/03 21:46:07 - mmengine - INFO - Epoch(train) [1][160/1345] lr: 1.0000e-02 eta: 20:23:16 time: 0.3116 data_time: 0.0113 memory: 7116 grad_norm: 1.3920 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 5.0616 loss: 5.0616 2022/09/03 21:46:14 - mmengine - INFO - Epoch(train) [1][180/1345] lr: 1.0000e-02 eta: 18:47:37 time: 0.3268 data_time: 0.0108 memory: 7116 grad_norm: 1.3962 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 5.0691 loss: 5.0691 2022/09/03 21:46:24 - mmengine - INFO - Epoch(train) [1][200/1345] lr: 1.0000e-02 eta: 17:50:18 time: 0.4989 data_time: 0.0113 memory: 7116 grad_norm: 1.6676 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 5.0492 loss: 5.0492 2022/09/03 21:46:28 - mmengine - INFO - Epoch(train) [1][220/1345] lr: 1.0000e-02 eta: 16:32:50 time: 0.1983 data_time: 0.0079 memory: 7116 grad_norm: 1.8994 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 5.0487 loss: 5.0487 2022/09/03 21:46:35 - mmengine - INFO - Epoch(train) [1][240/1345] lr: 1.0000e-02 eta: 15:43:11 time: 0.3583 data_time: 0.0117 memory: 7116 grad_norm: 2.0232 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 4.9834 loss: 4.9834 2022/09/03 21:46:40 - mmengine - INFO - Epoch(train) [1][260/1345] lr: 1.0000e-02 eta: 14:52:22 time: 0.2562 data_time: 0.0084 memory: 7116 grad_norm: 2.4177 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 4.8822 loss: 4.8822 2022/09/03 21:46:48 - mmengine - INFO - Epoch(train) [1][280/1345] lr: 1.0000e-02 eta: 14:18:19 time: 0.3754 data_time: 0.0128 memory: 7116 grad_norm: 1.9344 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 5.0295 loss: 5.0295 2022/09/03 21:46:53 - mmengine - INFO - Epoch(train) [1][300/1345] lr: 1.0000e-02 eta: 13:41:16 time: 0.2746 data_time: 0.0074 memory: 7116 grad_norm: 1.6674 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 5.0419 loss: 5.0419 2022/09/03 21:46:59 - mmengine - INFO - Epoch(train) [1][320/1345] lr: 1.0000e-02 eta: 13:09:19 time: 0.2812 data_time: 0.0122 memory: 7116 grad_norm: 1.6901 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 5.0668 loss: 5.0668 2022/09/03 21:47:06 - mmengine - INFO - Epoch(train) [1][340/1345] lr: 1.0000e-02 eta: 12:47:39 time: 0.3810 data_time: 0.0177 memory: 7116 grad_norm: 1.6624 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 5.0869 loss: 5.0869 2022/09/03 21:47:14 - mmengine - INFO - Epoch(train) [1][360/1345] lr: 1.0000e-02 eta: 12:27:44 time: 0.3704 data_time: 0.0108 memory: 7116 grad_norm: 2.0000 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 4.9565 loss: 4.9565 2022/09/03 21:47:20 - mmengine - INFO - Epoch(train) [1][380/1345] lr: 1.0000e-02 eta: 12:05:41 time: 0.2985 data_time: 0.0110 memory: 7116 grad_norm: 2.2080 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 5.0329 loss: 5.0329 2022/09/03 21:47:27 - mmengine - INFO - Epoch(train) [1][400/1345] lr: 1.0000e-02 eta: 11:49:20 time: 0.3616 data_time: 0.0138 memory: 7116 grad_norm: 1.9462 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 4.9006 loss: 4.9006 2022/09/03 21:47:33 - mmengine - INFO - Epoch(train) [1][420/1345] lr: 1.0000e-02 eta: 11:31:58 time: 0.3133 data_time: 0.0081 memory: 7116 grad_norm: 1.8694 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 4.8834 loss: 4.8834 2022/09/03 21:47:38 - mmengine - INFO - Epoch(train) [1][440/1345] lr: 1.0000e-02 eta: 11:13:00 time: 0.2505 data_time: 0.0111 memory: 7116 grad_norm: 2.1637 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.9789 loss: 4.9789 2022/09/03 21:47:44 - mmengine - INFO - Epoch(train) [1][460/1345] lr: 1.0000e-02 eta: 10:58:24 time: 0.3070 data_time: 0.0078 memory: 7116 grad_norm: 1.7785 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.9112 loss: 4.9112 2022/09/03 21:47:51 - mmengine - INFO - Epoch(train) [1][480/1345] lr: 1.0000e-02 eta: 10:45:02 time: 0.3075 data_time: 0.0099 memory: 7116 grad_norm: 2.3299 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.8850 loss: 4.8850 2022/09/03 21:47:55 - mmengine - INFO - Epoch(train) [1][500/1345] lr: 1.0000e-02 eta: 10:28:39 time: 0.2158 data_time: 0.0100 memory: 7116 grad_norm: 2.3910 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 4.8376 loss: 4.8376 2022/09/03 21:48:02 - mmengine - INFO - Epoch(train) [1][520/1345] lr: 1.0000e-02 eta: 10:19:19 time: 0.3513 data_time: 0.0103 memory: 7116 grad_norm: 2.3408 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 4.8370 loss: 4.8370 2022/09/03 21:48:08 - mmengine - INFO - Epoch(train) [1][540/1345] lr: 1.0000e-02 eta: 10:08:45 time: 0.3045 data_time: 0.0085 memory: 7116 grad_norm: 2.4554 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.9543 loss: 4.9543 2022/09/03 21:48:18 - mmengine - INFO - Epoch(train) [1][560/1345] lr: 1.0000e-02 eta: 10:07:02 time: 0.5092 data_time: 0.0112 memory: 7116 grad_norm: 2.2873 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.7178 loss: 4.7178 2022/09/03 21:48:32 - mmengine - INFO - Epoch(train) [1][580/1345] lr: 1.0000e-02 eta: 10:11:57 time: 0.6790 data_time: 0.0101 memory: 7116 grad_norm: 2.5543 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.7329 loss: 4.7329 2022/09/03 21:48:39 - mmengine - INFO - Epoch(train) [1][600/1345] lr: 1.0000e-02 eta: 10:04:52 time: 0.3642 data_time: 0.0130 memory: 7116 grad_norm: 2.2148 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.8538 loss: 4.8538 2022/09/03 21:48:43 - mmengine - INFO - Epoch(train) [1][620/1345] lr: 1.0000e-02 eta: 9:53:06 time: 0.2212 data_time: 0.0102 memory: 7116 grad_norm: 2.4934 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 4.8496 loss: 4.8496 2022/09/03 21:48:50 - mmengine - INFO - Epoch(train) [1][640/1345] lr: 1.0000e-02 eta: 9:45:39 time: 0.3246 data_time: 0.0433 memory: 7116 grad_norm: 2.4889 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.7507 loss: 4.7507 2022/09/03 21:48:55 - mmengine - INFO - Epoch(train) [1][660/1345] lr: 1.0000e-02 eta: 9:35:26 time: 0.2288 data_time: 0.0085 memory: 7116 grad_norm: 2.3693 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.7512 loss: 4.7512 2022/09/03 21:49:00 - mmengine - INFO - Epoch(train) [1][680/1345] lr: 1.0000e-02 eta: 9:27:44 time: 0.2880 data_time: 0.0108 memory: 7116 grad_norm: 2.5458 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.8638 loss: 4.8638 2022/09/03 21:49:05 - mmengine - INFO - Epoch(train) [1][700/1345] lr: 1.0000e-02 eta: 9:18:09 time: 0.2149 data_time: 0.0104 memory: 7116 grad_norm: 2.8654 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.8226 loss: 4.8226 2022/09/03 21:49:10 - mmengine - INFO - Epoch(train) [1][720/1345] lr: 1.0000e-02 eta: 9:10:34 time: 0.2624 data_time: 0.0125 memory: 7116 grad_norm: 2.5117 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.7697 loss: 4.7697 2022/09/03 21:49:18 - mmengine - INFO - Epoch(train) [1][740/1345] lr: 1.0000e-02 eta: 9:07:19 time: 0.3937 data_time: 0.0080 memory: 7116 grad_norm: 2.3954 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.7684 loss: 4.7684 2022/09/03 21:49:23 - mmengine - INFO - Epoch(train) [1][760/1345] lr: 1.0000e-02 eta: 8:59:51 time: 0.2429 data_time: 0.0111 memory: 7116 grad_norm: 2.6878 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 4.5964 loss: 4.5964 2022/09/03 21:49:28 - mmengine - INFO - Epoch(train) [1][780/1345] lr: 1.0000e-02 eta: 8:53:36 time: 0.2728 data_time: 0.0106 memory: 7116 grad_norm: 2.7599 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 4.6942 loss: 4.6942 2022/09/03 21:49:34 - mmengine - INFO - Epoch(train) [1][800/1345] lr: 1.0000e-02 eta: 8:49:04 time: 0.3241 data_time: 0.0100 memory: 7116 grad_norm: 2.7202 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 4.6693 loss: 4.6693 2022/09/03 21:49:41 - mmengine - INFO - Epoch(train) [1][820/1345] lr: 1.0000e-02 eta: 8:44:40 time: 0.3203 data_time: 0.0087 memory: 7116 grad_norm: 3.0428 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.7169 loss: 4.7169 2022/09/03 21:49:50 - mmengine - INFO - Epoch(train) [1][840/1345] lr: 1.0000e-02 eta: 8:43:54 time: 0.4509 data_time: 0.0135 memory: 7116 grad_norm: 3.1188 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 4.5893 loss: 4.5893 2022/09/03 21:49:57 - mmengine - INFO - Epoch(train) [1][860/1345] lr: 1.0000e-02 eta: 8:40:36 time: 0.3511 data_time: 0.0079 memory: 7116 grad_norm: 2.9525 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.7276 loss: 4.7276 2022/09/03 21:50:02 - mmengine - INFO - Epoch(train) [1][880/1345] lr: 1.0000e-02 eta: 8:34:44 time: 0.2434 data_time: 0.0170 memory: 7116 grad_norm: 2.8652 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 4.6437 loss: 4.6437 2022/09/03 21:50:08 - mmengine - INFO - Epoch(train) [1][900/1345] lr: 1.0000e-02 eta: 8:30:31 time: 0.3005 data_time: 0.0089 memory: 7116 grad_norm: 3.1037 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.5875 loss: 4.5875 2022/09/03 21:50:16 - mmengine - INFO - Epoch(train) [1][920/1345] lr: 1.0000e-02 eta: 8:28:35 time: 0.3870 data_time: 0.0129 memory: 7116 grad_norm: 3.0113 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 4.7957 loss: 4.7957 2022/09/03 21:50:20 - mmengine - INFO - Epoch(train) [1][940/1345] lr: 1.0000e-02 eta: 8:22:34 time: 0.2110 data_time: 0.0106 memory: 7116 grad_norm: 3.2731 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.6406 loss: 4.6406 2022/09/03 21:50:24 - mmengine - INFO - Epoch(train) [1][960/1345] lr: 1.0000e-02 eta: 8:16:59 time: 0.2191 data_time: 0.0170 memory: 7116 grad_norm: 3.0637 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 4.6680 loss: 4.6680 2022/09/03 21:50:30 - mmengine - INFO - Epoch(train) [1][980/1345] lr: 1.0000e-02 eta: 8:13:29 time: 0.3011 data_time: 0.0086 memory: 7116 grad_norm: 3.0925 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 4.7010 loss: 4.7010 2022/09/03 21:50:35 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 21:50:35 - mmengine - INFO - Epoch(train) [1][1000/1345] lr: 1.0000e-02 eta: 8:09:08 time: 0.2565 data_time: 0.0117 memory: 7116 grad_norm: 3.3669 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 4.5109 loss: 4.5109 2022/09/03 21:50:42 - mmengine - INFO - Epoch(train) [1][1020/1345] lr: 1.0000e-02 eta: 8:07:08 time: 0.3572 data_time: 0.0088 memory: 7116 grad_norm: 3.0909 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 4.6655 loss: 4.6655 2022/09/03 21:50:47 - mmengine - INFO - Epoch(train) [1][1040/1345] lr: 1.0000e-02 eta: 8:02:11 time: 0.2156 data_time: 0.0117 memory: 7116 grad_norm: 3.2500 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 4.5963 loss: 4.5963 2022/09/03 21:50:51 - mmengine - INFO - Epoch(train) [1][1060/1345] lr: 1.0000e-02 eta: 7:57:27 time: 0.2165 data_time: 0.0243 memory: 7116 grad_norm: 3.3554 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 4.4351 loss: 4.4351 2022/09/03 21:50:57 - mmengine - INFO - Epoch(train) [1][1080/1345] lr: 1.0000e-02 eta: 7:54:35 time: 0.2990 data_time: 0.0098 memory: 7116 grad_norm: 3.4674 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.2399 loss: 4.2399 2022/09/03 21:51:02 - mmengine - INFO - Epoch(train) [1][1100/1345] lr: 1.0000e-02 eta: 7:51:08 time: 0.2657 data_time: 0.0122 memory: 7116 grad_norm: 3.3972 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.4803 loss: 4.4803 2022/09/03 21:51:10 - mmengine - INFO - Epoch(train) [1][1120/1345] lr: 1.0000e-02 eta: 7:49:35 time: 0.3555 data_time: 0.0099 memory: 7116 grad_norm: 3.7446 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.5623 loss: 4.5623 2022/09/03 21:51:14 - mmengine - INFO - Epoch(train) [1][1140/1345] lr: 1.0000e-02 eta: 7:45:20 time: 0.2136 data_time: 0.0107 memory: 7116 grad_norm: 3.4191 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 4.4269 loss: 4.4269 2022/09/03 21:51:19 - mmengine - INFO - Epoch(train) [1][1160/1345] lr: 1.0000e-02 eta: 7:42:04 time: 0.2577 data_time: 0.0106 memory: 7116 grad_norm: 3.8398 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 4.4512 loss: 4.4512 2022/09/03 21:51:26 - mmengine - INFO - Epoch(train) [1][1180/1345] lr: 1.0000e-02 eta: 7:40:13 time: 0.3283 data_time: 0.0404 memory: 7116 grad_norm: 3.4396 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 4.4834 loss: 4.4834 2022/09/03 21:51:32 - mmengine - INFO - Epoch(train) [1][1200/1345] lr: 1.0000e-02 eta: 7:38:00 time: 0.3046 data_time: 0.0168 memory: 7116 grad_norm: 3.4013 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 4.2643 loss: 4.2643 2022/09/03 21:51:36 - mmengine - INFO - Epoch(train) [1][1220/1345] lr: 1.0000e-02 eta: 7:34:44 time: 0.2423 data_time: 0.0094 memory: 7116 grad_norm: 3.8224 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 4.4359 loss: 4.4359 2022/09/03 21:51:44 - mmengine - INFO - Epoch(train) [1][1240/1345] lr: 1.0000e-02 eta: 7:33:33 time: 0.3549 data_time: 0.1586 memory: 7116 grad_norm: 3.6572 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 4.4242 loss: 4.4242 2022/09/03 21:51:49 - mmengine - INFO - Epoch(train) [1][1260/1345] lr: 1.0000e-02 eta: 7:31:13 time: 0.2864 data_time: 0.0233 memory: 7116 grad_norm: 3.4777 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.4104 loss: 4.4104 2022/09/03 21:51:56 - mmengine - INFO - Epoch(train) [1][1280/1345] lr: 1.0000e-02 eta: 7:30:03 time: 0.3500 data_time: 0.0105 memory: 7116 grad_norm: 4.4503 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 4.4083 loss: 4.4083 2022/09/03 21:52:01 - mmengine - INFO - Epoch(train) [1][1300/1345] lr: 1.0000e-02 eta: 7:26:38 time: 0.2155 data_time: 0.0166 memory: 7116 grad_norm: 3.8800 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.3877 loss: 4.3877 2022/09/03 21:52:06 - mmengine - INFO - Epoch(train) [1][1320/1345] lr: 1.0000e-02 eta: 7:24:24 time: 0.2803 data_time: 0.0112 memory: 7116 grad_norm: 3.9458 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.3081 loss: 4.3081 2022/09/03 21:52:10 - mmengine - INFO - Epoch(train) [1][1340/1345] lr: 1.0000e-02 eta: 7:21:09 time: 0.2143 data_time: 0.0090 memory: 7116 grad_norm: 4.0873 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 4.3534 loss: 4.3534 2022/09/03 21:52:12 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 21:52:12 - mmengine - INFO - Epoch(train) [1][1345/1345] lr: 1.0000e-02 eta: 7:21:09 time: 0.2433 data_time: 0.0087 memory: 7116 grad_norm: 4.3130 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 4.3678 loss: 4.3678 2022/09/03 21:52:12 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/09/03 21:53:32 - mmengine - INFO - Epoch(val) [1][20/181] eta: 0:10:26 time: 3.8896 data_time: 3.8521 memory: 1114 2022/09/03 21:53:39 - mmengine - INFO - Epoch(val) [1][40/181] eta: 0:00:46 time: 0.3278 data_time: 0.2898 memory: 1114 2022/09/03 21:53:45 - mmengine - INFO - Epoch(val) [1][60/181] eta: 0:00:36 time: 0.3027 data_time: 0.2643 memory: 1114 2022/09/03 21:53:51 - mmengine - INFO - Epoch(val) [1][80/181] eta: 0:00:29 time: 0.2886 data_time: 0.2508 memory: 1114 2022/09/03 21:53:55 - mmengine - INFO - Epoch(val) [1][100/181] eta: 0:00:19 time: 0.2356 data_time: 0.1980 memory: 1114 2022/09/03 21:54:02 - mmengine - INFO - Epoch(val) [1][120/181] eta: 0:00:21 time: 0.3555 data_time: 0.3176 memory: 1114 2022/09/03 21:54:08 - mmengine - INFO - Epoch(val) [1][140/181] eta: 0:00:12 time: 0.2957 data_time: 0.2580 memory: 1114 2022/09/03 21:54:15 - mmengine - INFO - Epoch(val) [1][160/181] eta: 0:00:07 time: 0.3534 data_time: 0.3161 memory: 1114 2022/09/03 21:54:20 - mmengine - INFO - Epoch(val) [1][180/181] eta: 0:00:00 time: 0.2519 data_time: 0.2149 memory: 1114 2022/09/03 21:54:22 - mmengine - INFO - Epoch(val) [1][181/181] acc/top1: 0.0505 acc/top5: 0.1738 acc/mean1: 0.0445 2022/09/03 21:54:23 - mmengine - INFO - The best checkpoint with 0.0505 acc/top1 at 1 epoch is saved to best_acc/top1_epoch_1.pth. 2022/09/03 21:54:29 - mmengine - INFO - Epoch(train) [2][20/1345] lr: 1.0000e-02 eta: 7:17:58 time: 0.3149 data_time: 0.0444 memory: 7116 grad_norm: 3.5608 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.5054 loss: 4.5054 2022/09/03 21:54:33 - mmengine - INFO - Epoch(train) [2][40/1345] lr: 1.0000e-02 eta: 7:14:41 time: 0.1993 data_time: 0.0093 memory: 7116 grad_norm: 3.8937 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 4.2442 loss: 4.2442 2022/09/03 21:54:37 - mmengine - INFO - Epoch(train) [2][60/1345] lr: 1.0000e-02 eta: 7:11:27 time: 0.1979 data_time: 0.0113 memory: 7116 grad_norm: 4.0718 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 4.4014 loss: 4.4014 2022/09/03 21:54:42 - mmengine - INFO - Epoch(train) [2][80/1345] lr: 1.0000e-02 eta: 7:09:25 time: 0.2692 data_time: 0.0084 memory: 7116 grad_norm: 4.5412 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.4019 loss: 4.4019 2022/09/03 21:54:47 - mmengine - INFO - Epoch(train) [2][100/1345] lr: 1.0000e-02 eta: 7:07:07 time: 0.2490 data_time: 0.0088 memory: 7116 grad_norm: 3.9896 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 4.2121 loss: 4.2121 2022/09/03 21:54:54 - mmengine - INFO - Epoch(train) [2][120/1345] lr: 1.0000e-02 eta: 7:05:48 time: 0.3101 data_time: 0.0125 memory: 7116 grad_norm: 3.9200 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.2654 loss: 4.2654 2022/09/03 21:54:58 - mmengine - INFO - Epoch(train) [2][140/1345] lr: 1.0000e-02 eta: 7:03:09 time: 0.2182 data_time: 0.0095 memory: 7116 grad_norm: 4.0026 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.1998 loss: 4.1998 2022/09/03 21:55:02 - mmengine - INFO - Epoch(train) [2][160/1345] lr: 1.0000e-02 eta: 7:00:26 time: 0.2073 data_time: 0.0110 memory: 7116 grad_norm: 4.2175 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 4.1386 loss: 4.1386 2022/09/03 21:55:06 - mmengine - INFO - Epoch(train) [2][180/1345] lr: 1.0000e-02 eta: 6:57:47 time: 0.2092 data_time: 0.0092 memory: 7116 grad_norm: 4.4352 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 4.1598 loss: 4.1598 2022/09/03 21:55:10 - mmengine - INFO - Epoch(train) [2][200/1345] lr: 1.0000e-02 eta: 6:55:01 time: 0.1952 data_time: 0.0092 memory: 7116 grad_norm: 4.1009 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 4.1020 loss: 4.1020 2022/09/03 21:55:16 - mmengine - INFO - Epoch(train) [2][220/1345] lr: 1.0000e-02 eta: 6:53:32 time: 0.2811 data_time: 0.0114 memory: 7116 grad_norm: 4.5339 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 4.2042 loss: 4.2042 2022/09/03 21:55:23 - mmengine - INFO - Epoch(train) [2][240/1345] lr: 1.0000e-02 eta: 6:52:57 time: 0.3455 data_time: 0.0105 memory: 7116 grad_norm: 4.2019 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 4.2481 loss: 4.2481 2022/09/03 21:55:27 - mmengine - INFO - Epoch(train) [2][260/1345] lr: 1.0000e-02 eta: 6:50:26 time: 0.2017 data_time: 0.0116 memory: 7116 grad_norm: 4.2947 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.1456 loss: 4.1456 2022/09/03 21:55:31 - mmengine - INFO - Epoch(train) [2][280/1345] lr: 1.0000e-02 eta: 6:48:02 time: 0.2058 data_time: 0.0093 memory: 7116 grad_norm: 4.2645 top1_acc: 0.0000 top5_acc: 0.6250 loss_cls: 4.2177 loss: 4.2177 2022/09/03 21:55:36 - mmengine - INFO - Epoch(train) [2][300/1345] lr: 1.0000e-02 eta: 6:46:21 time: 0.2560 data_time: 0.0100 memory: 7116 grad_norm: 4.0926 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.2484 loss: 4.2484 2022/09/03 21:55:40 - mmengine - INFO - Epoch(train) [2][320/1345] lr: 1.0000e-02 eta: 6:44:04 time: 0.2072 data_time: 0.0150 memory: 7116 grad_norm: 4.3823 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 4.3135 loss: 4.3135 2022/09/03 21:55:45 - mmengine - INFO - Epoch(train) [2][340/1345] lr: 1.0000e-02 eta: 6:42:23 time: 0.2492 data_time: 0.0088 memory: 7116 grad_norm: 4.3186 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 4.2875 loss: 4.2875 2022/09/03 21:55:51 - mmengine - INFO - Epoch(train) [2][360/1345] lr: 1.0000e-02 eta: 6:41:07 time: 0.2797 data_time: 0.0087 memory: 7116 grad_norm: 4.0839 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.1492 loss: 4.1492 2022/09/03 21:55:55 - mmengine - INFO - Epoch(train) [2][380/1345] lr: 1.0000e-02 eta: 6:39:04 time: 0.2146 data_time: 0.0103 memory: 7116 grad_norm: 4.2327 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.2529 loss: 4.2529 2022/09/03 21:56:00 - mmengine - INFO - Epoch(train) [2][400/1345] lr: 1.0000e-02 eta: 6:37:15 time: 0.2293 data_time: 0.0100 memory: 7116 grad_norm: 4.0055 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 4.1893 loss: 4.1893 2022/09/03 21:56:04 - mmengine - INFO - Epoch(train) [2][420/1345] lr: 1.0000e-02 eta: 6:35:15 time: 0.2122 data_time: 0.0081 memory: 7116 grad_norm: 4.3410 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 4.1953 loss: 4.1953 2022/09/03 21:56:09 - mmengine - INFO - Epoch(train) [2][440/1345] lr: 1.0000e-02 eta: 6:33:48 time: 0.2538 data_time: 0.0114 memory: 7116 grad_norm: 4.8729 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 3.9906 loss: 3.9906 2022/09/03 21:56:15 - mmengine - INFO - Epoch(train) [2][460/1345] lr: 1.0000e-02 eta: 6:32:59 time: 0.3030 data_time: 0.0083 memory: 7116 grad_norm: 4.7006 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 4.1229 loss: 4.1229 2022/09/03 21:56:19 - mmengine - INFO - Epoch(train) [2][480/1345] lr: 1.0000e-02 eta: 6:31:09 time: 0.2170 data_time: 0.0117 memory: 7116 grad_norm: 4.5831 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 4.1026 loss: 4.1026 2022/09/03 21:56:25 - mmengine - INFO - Epoch(train) [2][500/1345] lr: 1.0000e-02 eta: 6:30:00 time: 0.2712 data_time: 0.0093 memory: 7116 grad_norm: 4.4397 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 4.1687 loss: 4.1687 2022/09/03 21:56:29 - mmengine - INFO - Epoch(train) [2][520/1345] lr: 1.0000e-02 eta: 6:27:59 time: 0.1959 data_time: 0.0099 memory: 7116 grad_norm: 4.3820 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.3059 loss: 4.3059 2022/09/03 21:56:34 - mmengine - INFO - Epoch(train) [2][540/1345] lr: 1.0000e-02 eta: 6:26:38 time: 0.2491 data_time: 0.0273 memory: 7116 grad_norm: 4.5890 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 4.1251 loss: 4.1251 2022/09/03 21:56:38 - mmengine - INFO - Epoch(train) [2][560/1345] lr: 1.0000e-02 eta: 6:25:04 time: 0.2287 data_time: 0.0106 memory: 7116 grad_norm: 4.2881 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 4.0811 loss: 4.0811 2022/09/03 21:56:44 - mmengine - INFO - Epoch(train) [2][580/1345] lr: 1.0000e-02 eta: 6:24:29 time: 0.3129 data_time: 0.0080 memory: 7116 grad_norm: 4.4833 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.9659 loss: 3.9659 2022/09/03 21:56:49 - mmengine - INFO - Epoch(train) [2][600/1345] lr: 1.0000e-02 eta: 6:22:43 time: 0.2062 data_time: 0.0145 memory: 7116 grad_norm: 4.5361 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 4.3400 loss: 4.3400 2022/09/03 21:56:54 - mmengine - INFO - Epoch(train) [2][620/1345] lr: 1.0000e-02 eta: 6:21:28 time: 0.2483 data_time: 0.0100 memory: 7116 grad_norm: 4.4376 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 4.1966 loss: 4.1966 2022/09/03 21:56:59 - mmengine - INFO - Epoch(train) [2][640/1345] lr: 1.0000e-02 eta: 6:20:37 time: 0.2834 data_time: 0.0105 memory: 7116 grad_norm: 4.4765 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 4.0827 loss: 4.0827 2022/09/03 21:57:02 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 21:57:04 - mmengine - INFO - Epoch(train) [2][660/1345] lr: 1.0000e-02 eta: 6:19:03 time: 0.2162 data_time: 0.0130 memory: 7116 grad_norm: 4.3191 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 4.0283 loss: 4.0283 2022/09/03 21:57:08 - mmengine - INFO - Epoch(train) [2][680/1345] lr: 1.0000e-02 eta: 6:17:19 time: 0.1987 data_time: 0.0102 memory: 7116 grad_norm: 4.5148 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 4.0491 loss: 4.0491 2022/09/03 21:57:12 - mmengine - INFO - Epoch(train) [2][700/1345] lr: 1.0000e-02 eta: 6:16:06 time: 0.2430 data_time: 0.0095 memory: 7116 grad_norm: 4.4302 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 4.1782 loss: 4.1782 2022/09/03 21:57:17 - mmengine - INFO - Epoch(train) [2][720/1345] lr: 1.0000e-02 eta: 6:14:43 time: 0.2266 data_time: 0.0455 memory: 7116 grad_norm: 4.6216 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 4.0158 loss: 4.0158 2022/09/03 21:57:21 - mmengine - INFO - Epoch(train) [2][740/1345] lr: 1.0000e-02 eta: 6:13:16 time: 0.2163 data_time: 0.0274 memory: 7116 grad_norm: 4.7399 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 4.1098 loss: 4.1098 2022/09/03 21:57:28 - mmengine - INFO - Epoch(train) [2][760/1345] lr: 1.0000e-02 eta: 6:13:00 time: 0.3296 data_time: 0.1452 memory: 7116 grad_norm: 4.6302 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 3.8306 loss: 3.8306 2022/09/03 21:57:32 - mmengine - INFO - Epoch(train) [2][780/1345] lr: 1.0000e-02 eta: 6:11:27 time: 0.2023 data_time: 0.0092 memory: 7116 grad_norm: 4.7076 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.8368 loss: 3.8368 2022/09/03 21:57:37 - mmengine - INFO - Epoch(train) [2][800/1345] lr: 1.0000e-02 eta: 6:10:32 time: 0.2630 data_time: 0.0084 memory: 7116 grad_norm: 4.5057 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.8799 loss: 3.8799 2022/09/03 21:57:41 - mmengine - INFO - Epoch(train) [2][820/1345] lr: 1.0000e-02 eta: 6:08:57 time: 0.1941 data_time: 0.0095 memory: 7116 grad_norm: 4.7199 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.8976 loss: 3.8976 2022/09/03 21:57:46 - mmengine - INFO - Epoch(train) [2][840/1345] lr: 1.0000e-02 eta: 6:07:43 time: 0.2274 data_time: 0.0465 memory: 7116 grad_norm: 4.7716 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 4.0144 loss: 4.0144 2022/09/03 21:57:51 - mmengine - INFO - Epoch(train) [2][860/1345] lr: 1.0000e-02 eta: 6:06:41 time: 0.2468 data_time: 0.0066 memory: 7116 grad_norm: 4.5650 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 4.0443 loss: 4.0443 2022/09/03 21:57:54 - mmengine - INFO - Epoch(train) [2][880/1345] lr: 1.0000e-02 eta: 6:05:12 time: 0.1971 data_time: 0.0109 memory: 7116 grad_norm: 4.4718 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 3.8698 loss: 3.8698 2022/09/03 21:58:01 - mmengine - INFO - Epoch(train) [2][900/1345] lr: 1.0000e-02 eta: 6:05:13 time: 0.3503 data_time: 0.0116 memory: 7116 grad_norm: 4.7484 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.9784 loss: 3.9784 2022/09/03 21:58:06 - mmengine - INFO - Epoch(train) [2][920/1345] lr: 1.0000e-02 eta: 6:04:02 time: 0.2241 data_time: 0.0082 memory: 7116 grad_norm: 4.7184 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 3.9888 loss: 3.9888 2022/09/03 21:58:10 - mmengine - INFO - Epoch(train) [2][940/1345] lr: 1.0000e-02 eta: 6:02:39 time: 0.2030 data_time: 0.0102 memory: 7116 grad_norm: 4.7768 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.8705 loss: 3.8705 2022/09/03 21:58:15 - mmengine - INFO - Epoch(train) [2][960/1345] lr: 1.0000e-02 eta: 6:01:43 time: 0.2477 data_time: 0.0430 memory: 7116 grad_norm: 4.8871 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.8668 loss: 3.8668 2022/09/03 21:58:20 - mmengine - INFO - Epoch(train) [2][980/1345] lr: 1.0000e-02 eta: 6:00:40 time: 0.2334 data_time: 0.0293 memory: 7116 grad_norm: 4.6386 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.9350 loss: 3.9350 2022/09/03 21:58:24 - mmengine - INFO - Epoch(train) [2][1000/1345] lr: 1.0000e-02 eta: 5:59:34 time: 0.2248 data_time: 0.0101 memory: 7116 grad_norm: 4.7070 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 3.8429 loss: 3.8429 2022/09/03 21:58:28 - mmengine - INFO - Epoch(train) [2][1020/1345] lr: 1.0000e-02 eta: 5:58:24 time: 0.2176 data_time: 0.0298 memory: 7116 grad_norm: 4.8284 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 4.0577 loss: 4.0577 2022/09/03 21:58:33 - mmengine - INFO - Epoch(train) [2][1040/1345] lr: 1.0000e-02 eta: 5:57:09 time: 0.2066 data_time: 0.0092 memory: 7116 grad_norm: 4.7698 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 4.0170 loss: 4.0170 2022/09/03 21:58:38 - mmengine - INFO - Epoch(train) [2][1060/1345] lr: 1.0000e-02 eta: 5:56:26 time: 0.2628 data_time: 0.0156 memory: 7116 grad_norm: 4.7318 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.5898 loss: 3.5898 2022/09/03 21:58:43 - mmengine - INFO - Epoch(train) [2][1080/1345] lr: 1.0000e-02 eta: 5:55:28 time: 0.2328 data_time: 0.0082 memory: 7116 grad_norm: 4.7234 top1_acc: 0.0000 top5_acc: 0.5000 loss_cls: 3.8149 loss: 3.8149 2022/09/03 21:58:48 - mmengine - INFO - Epoch(train) [2][1100/1345] lr: 1.0000e-02 eta: 5:54:55 time: 0.2788 data_time: 0.0105 memory: 7116 grad_norm: 4.5419 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 3.9396 loss: 3.9396 2022/09/03 21:58:52 - mmengine - INFO - Epoch(train) [2][1120/1345] lr: 1.0000e-02 eta: 5:53:39 time: 0.1975 data_time: 0.0107 memory: 7116 grad_norm: 4.8414 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.7814 loss: 3.7814 2022/09/03 21:58:56 - mmengine - INFO - Epoch(train) [2][1140/1345] lr: 1.0000e-02 eta: 5:52:35 time: 0.2174 data_time: 0.0078 memory: 7116 grad_norm: 4.7286 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 3.8787 loss: 3.8787 2022/09/03 21:59:03 - mmengine - INFO - Epoch(train) [2][1160/1345] lr: 1.0000e-02 eta: 5:52:17 time: 0.3045 data_time: 0.0970 memory: 7116 grad_norm: 4.6646 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 3.9240 loss: 3.9240 2022/09/03 21:59:07 - mmengine - INFO - Epoch(train) [2][1180/1345] lr: 1.0000e-02 eta: 5:51:17 time: 0.2219 data_time: 0.0095 memory: 7116 grad_norm: 4.6865 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 3.8840 loss: 3.8840 2022/09/03 21:59:12 - mmengine - INFO - Epoch(train) [2][1200/1345] lr: 1.0000e-02 eta: 5:50:24 time: 0.2333 data_time: 0.0078 memory: 7116 grad_norm: 4.7455 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.7417 loss: 3.7417 2022/09/03 21:59:17 - mmengine - INFO - Epoch(train) [2][1220/1345] lr: 1.0000e-02 eta: 5:49:45 time: 0.2605 data_time: 0.0103 memory: 7116 grad_norm: 4.7677 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.9030 loss: 3.9030 2022/09/03 21:59:22 - mmengine - INFO - Epoch(train) [2][1240/1345] lr: 1.0000e-02 eta: 5:49:06 time: 0.2594 data_time: 0.0104 memory: 7116 grad_norm: 4.7619 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.8722 loss: 3.8722 2022/09/03 21:59:26 - mmengine - INFO - Epoch(train) [2][1260/1345] lr: 1.0000e-02 eta: 5:47:54 time: 0.1917 data_time: 0.0103 memory: 7116 grad_norm: 4.9312 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.1033 loss: 4.1033 2022/09/03 21:59:31 - mmengine - INFO - Epoch(train) [2][1280/1345] lr: 1.0000e-02 eta: 5:47:21 time: 0.2693 data_time: 0.0105 memory: 7116 grad_norm: 4.6983 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 3.6952 loss: 3.6952 2022/09/03 21:59:36 - mmengine - INFO - Epoch(train) [2][1300/1345] lr: 1.0000e-02 eta: 5:46:45 time: 0.2625 data_time: 0.0716 memory: 7116 grad_norm: 4.9861 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 3.8385 loss: 3.8385 2022/09/03 21:59:42 - mmengine - INFO - Epoch(train) [2][1320/1345] lr: 1.0000e-02 eta: 5:46:13 time: 0.2702 data_time: 0.0078 memory: 7116 grad_norm: 4.8107 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.7968 loss: 3.7968 2022/09/03 21:59:51 - mmengine - INFO - Epoch(train) [2][1340/1345] lr: 1.0000e-02 eta: 5:47:04 time: 0.4399 data_time: 0.0117 memory: 7116 grad_norm: 5.0087 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 3.6398 loss: 3.6398 2022/09/03 21:59:52 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 21:59:52 - mmengine - INFO - Epoch(train) [2][1345/1345] lr: 1.0000e-02 eta: 5:47:04 time: 0.4367 data_time: 0.0075 memory: 7116 grad_norm: 5.1510 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 3.7849 loss: 3.7849 2022/09/03 21:59:52 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/09/03 21:59:54 - mmengine - INFO - Epoch(val) [2][20/181] eta: 0:00:07 time: 0.0493 data_time: 0.0118 memory: 1114 2022/09/03 21:59:55 - mmengine - INFO - Epoch(val) [2][40/181] eta: 0:00:06 time: 0.0430 data_time: 0.0071 memory: 1114 2022/09/03 21:59:56 - mmengine - INFO - Epoch(val) [2][60/181] eta: 0:00:05 time: 0.0425 data_time: 0.0068 memory: 1114 2022/09/03 21:59:57 - mmengine - INFO - Epoch(val) [2][80/181] eta: 0:00:04 time: 0.0421 data_time: 0.0064 memory: 1114 2022/09/03 21:59:58 - mmengine - INFO - Epoch(val) [2][100/181] eta: 0:00:03 time: 0.0423 data_time: 0.0065 memory: 1114 2022/09/03 21:59:59 - mmengine - INFO - Epoch(val) [2][120/181] eta: 0:00:02 time: 0.0417 data_time: 0.0063 memory: 1114 2022/09/03 21:59:59 - mmengine - INFO - Epoch(val) [2][140/181] eta: 0:00:01 time: 0.0414 data_time: 0.0060 memory: 1114 2022/09/03 22:00:00 - mmengine - INFO - Epoch(val) [2][160/181] eta: 0:00:00 time: 0.0412 data_time: 0.0061 memory: 1114 2022/09/03 22:00:01 - mmengine - INFO - Epoch(val) [2][180/181] eta: 0:00:00 time: 0.0422 data_time: 0.0070 memory: 1114 2022/09/03 22:00:04 - mmengine - INFO - Epoch(val) [2][181/181] acc/top1: 0.1066 acc/top5: 0.3170 acc/mean1: 0.0986 2022/09/03 22:00:04 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_1.pth is removed 2022/09/03 22:00:05 - mmengine - INFO - The best checkpoint with 0.1066 acc/top1 at 2 epoch is saved to best_acc/top1_epoch_2.pth. 2022/09/03 22:00:10 - mmengine - INFO - Epoch(train) [3][20/1345] lr: 1.0000e-02 eta: 5:45:46 time: 0.2563 data_time: 0.0546 memory: 7116 grad_norm: 4.7618 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.7651 loss: 3.7651 2022/09/03 22:00:15 - mmengine - INFO - Epoch(train) [3][40/1345] lr: 1.0000e-02 eta: 5:45:06 time: 0.2510 data_time: 0.0075 memory: 7116 grad_norm: 4.9198 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 3.5495 loss: 3.5495 2022/09/03 22:00:20 - mmengine - INFO - Epoch(train) [3][60/1345] lr: 1.0000e-02 eta: 5:44:14 time: 0.2227 data_time: 0.0115 memory: 7116 grad_norm: 5.0245 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 3.8348 loss: 3.8348 2022/09/03 22:00:24 - mmengine - INFO - Epoch(train) [3][80/1345] lr: 1.0000e-02 eta: 5:43:21 time: 0.2209 data_time: 0.0341 memory: 7116 grad_norm: 5.2143 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.7245 loss: 3.7245 2022/09/03 22:00:29 - mmengine - INFO - Epoch(train) [3][100/1345] lr: 1.0000e-02 eta: 5:42:29 time: 0.2207 data_time: 0.0341 memory: 7116 grad_norm: 4.8866 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.7045 loss: 3.7045 2022/09/03 22:00:33 - mmengine - INFO - Epoch(train) [3][120/1345] lr: 1.0000e-02 eta: 5:41:31 time: 0.2063 data_time: 0.0110 memory: 7116 grad_norm: 5.0198 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.6730 loss: 3.6730 2022/09/03 22:00:38 - mmengine - INFO - Epoch(train) [3][140/1345] lr: 1.0000e-02 eta: 5:40:50 time: 0.2428 data_time: 0.0095 memory: 7116 grad_norm: 5.0125 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.7214 loss: 3.7214 2022/09/03 22:00:43 - mmengine - INFO - Epoch(train) [3][160/1345] lr: 1.0000e-02 eta: 5:40:12 time: 0.2470 data_time: 0.0084 memory: 7116 grad_norm: 5.2828 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.8742 loss: 3.8742 2022/09/03 22:00:48 - mmengine - INFO - Epoch(train) [3][180/1345] lr: 1.0000e-02 eta: 5:39:55 time: 0.2939 data_time: 0.0119 memory: 7116 grad_norm: 5.4393 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.6482 loss: 3.6482 2022/09/03 22:00:53 - mmengine - INFO - Epoch(train) [3][200/1345] lr: 1.0000e-02 eta: 5:39:08 time: 0.2240 data_time: 0.0094 memory: 7116 grad_norm: 5.0448 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.5417 loss: 3.5417 2022/09/03 22:00:58 - mmengine - INFO - Epoch(train) [3][220/1345] lr: 1.0000e-02 eta: 5:38:30 time: 0.2440 data_time: 0.0113 memory: 7116 grad_norm: 5.3983 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 3.7983 loss: 3.7983 2022/09/03 22:01:03 - mmengine - INFO - Epoch(train) [3][240/1345] lr: 1.0000e-02 eta: 5:38:10 time: 0.2867 data_time: 0.0101 memory: 7116 grad_norm: 5.2743 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.6948 loss: 3.6948 2022/09/03 22:01:08 - mmengine - INFO - Epoch(train) [3][260/1345] lr: 1.0000e-02 eta: 5:37:27 time: 0.2294 data_time: 0.0105 memory: 7116 grad_norm: 5.1493 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 3.5739 loss: 3.5739 2022/09/03 22:01:12 - mmengine - INFO - Epoch(train) [3][280/1345] lr: 1.0000e-02 eta: 5:36:29 time: 0.1961 data_time: 0.0108 memory: 7116 grad_norm: 5.1791 top1_acc: 0.0000 top5_acc: 0.6250 loss_cls: 3.7836 loss: 3.7836 2022/09/03 22:01:16 - mmengine - INFO - Epoch(train) [3][300/1345] lr: 1.0000e-02 eta: 5:35:33 time: 0.1984 data_time: 0.0102 memory: 7116 grad_norm: 5.0177 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.6164 loss: 3.6164 2022/09/03 22:01:18 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:01:20 - mmengine - INFO - Epoch(train) [3][320/1345] lr: 1.0000e-02 eta: 5:34:37 time: 0.1971 data_time: 0.0117 memory: 7116 grad_norm: 5.0985 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.8408 loss: 3.8408 2022/09/03 22:01:24 - mmengine - INFO - Epoch(train) [3][340/1345] lr: 1.0000e-02 eta: 5:33:45 time: 0.2040 data_time: 0.0100 memory: 7116 grad_norm: 5.3261 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 3.5128 loss: 3.5128 2022/09/03 22:01:29 - mmengine - INFO - Epoch(train) [3][360/1345] lr: 1.0000e-02 eta: 5:33:23 time: 0.2739 data_time: 0.0122 memory: 7116 grad_norm: 4.9224 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 3.6845 loss: 3.6845 2022/09/03 22:01:34 - mmengine - INFO - Epoch(train) [3][380/1345] lr: 1.0000e-02 eta: 5:32:46 time: 0.2392 data_time: 0.0102 memory: 7116 grad_norm: 5.1767 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.3389 loss: 3.3389 2022/09/03 22:01:38 - mmengine - INFO - Epoch(train) [3][400/1345] lr: 1.0000e-02 eta: 5:31:54 time: 0.2009 data_time: 0.0088 memory: 7116 grad_norm: 5.1240 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 3.6203 loss: 3.6203 2022/09/03 22:01:43 - mmengine - INFO - Epoch(train) [3][420/1345] lr: 1.0000e-02 eta: 5:31:07 time: 0.2121 data_time: 0.0082 memory: 7116 grad_norm: 5.1313 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.7131 loss: 3.7131 2022/09/03 22:01:46 - mmengine - INFO - Epoch(train) [3][440/1345] lr: 1.0000e-02 eta: 5:30:15 time: 0.1982 data_time: 0.0129 memory: 7116 grad_norm: 4.9940 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 3.7338 loss: 3.7338 2022/09/03 22:01:50 - mmengine - INFO - Epoch(train) [3][460/1345] lr: 1.0000e-02 eta: 5:29:25 time: 0.2004 data_time: 0.0098 memory: 7116 grad_norm: 5.0886 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 3.7401 loss: 3.7401 2022/09/03 22:01:55 - mmengine - INFO - Epoch(train) [3][480/1345] lr: 1.0000e-02 eta: 5:28:50 time: 0.2356 data_time: 0.0093 memory: 7116 grad_norm: 5.0898 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.6578 loss: 3.6578 2022/09/03 22:02:00 - mmengine - INFO - Epoch(train) [3][500/1345] lr: 1.0000e-02 eta: 5:28:10 time: 0.2242 data_time: 0.0407 memory: 7116 grad_norm: 5.1260 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 3.6494 loss: 3.6494 2022/09/03 22:02:04 - mmengine - INFO - Epoch(train) [3][520/1345] lr: 1.0000e-02 eta: 5:27:18 time: 0.1942 data_time: 0.0092 memory: 7116 grad_norm: 5.4690 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 3.6995 loss: 3.6995 2022/09/03 22:02:09 - mmengine - INFO - Epoch(train) [3][540/1345] lr: 1.0000e-02 eta: 5:27:01 time: 0.2771 data_time: 0.0990 memory: 7116 grad_norm: 5.1749 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 3.8293 loss: 3.8293 2022/09/03 22:02:14 - mmengine - INFO - Epoch(train) [3][560/1345] lr: 1.0000e-02 eta: 5:26:20 time: 0.2193 data_time: 0.0282 memory: 7116 grad_norm: 5.2419 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 3.7482 loss: 3.7482 2022/09/03 22:02:17 - mmengine - INFO - Epoch(train) [3][580/1345] lr: 1.0000e-02 eta: 5:25:29 time: 0.1911 data_time: 0.0096 memory: 7116 grad_norm: 4.9829 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.5976 loss: 3.5976 2022/09/03 22:02:21 - mmengine - INFO - Epoch(train) [3][600/1345] lr: 1.0000e-02 eta: 5:24:40 time: 0.1941 data_time: 0.0096 memory: 7116 grad_norm: 5.0939 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.5515 loss: 3.5515 2022/09/03 22:02:25 - mmengine - INFO - Epoch(train) [3][620/1345] lr: 1.0000e-02 eta: 5:23:52 time: 0.1968 data_time: 0.0145 memory: 7116 grad_norm: 5.4515 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.7346 loss: 3.7346 2022/09/03 22:02:30 - mmengine - INFO - Epoch(train) [3][640/1345] lr: 1.0000e-02 eta: 5:23:14 time: 0.2212 data_time: 0.0258 memory: 7116 grad_norm: 5.1951 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.3961 loss: 3.3961 2022/09/03 22:02:34 - mmengine - INFO - Epoch(train) [3][660/1345] lr: 1.0000e-02 eta: 5:22:35 time: 0.2177 data_time: 0.0080 memory: 7116 grad_norm: 5.4027 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.5097 loss: 3.5097 2022/09/03 22:02:38 - mmengine - INFO - Epoch(train) [3][680/1345] lr: 1.0000e-02 eta: 5:21:54 time: 0.2088 data_time: 0.0122 memory: 7116 grad_norm: 5.5297 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 3.4849 loss: 3.4849 2022/09/03 22:02:42 - mmengine - INFO - Epoch(train) [3][700/1345] lr: 1.0000e-02 eta: 5:21:07 time: 0.1942 data_time: 0.0113 memory: 7116 grad_norm: 5.2758 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 3.4523 loss: 3.4523 2022/09/03 22:02:47 - mmengine - INFO - Epoch(train) [3][720/1345] lr: 1.0000e-02 eta: 5:20:46 time: 0.2616 data_time: 0.0075 memory: 7116 grad_norm: 5.5790 top1_acc: 0.0000 top5_acc: 0.7500 loss_cls: 3.5262 loss: 3.5262 2022/09/03 22:02:51 - mmengine - INFO - Epoch(train) [3][740/1345] lr: 1.0000e-02 eta: 5:20:06 time: 0.2104 data_time: 0.0207 memory: 7116 grad_norm: 5.2825 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.7298 loss: 3.7298 2022/09/03 22:02:55 - mmengine - INFO - Epoch(train) [3][760/1345] lr: 1.0000e-02 eta: 5:19:20 time: 0.1942 data_time: 0.0086 memory: 7116 grad_norm: 5.3197 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 3.8698 loss: 3.8698 2022/09/03 22:03:00 - mmengine - INFO - Epoch(train) [3][780/1345] lr: 1.0000e-02 eta: 5:18:49 time: 0.2326 data_time: 0.0456 memory: 7116 grad_norm: 5.1401 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.5567 loss: 3.5567 2022/09/03 22:03:04 - mmengine - INFO - Epoch(train) [3][800/1345] lr: 1.0000e-02 eta: 5:18:06 time: 0.1980 data_time: 0.0097 memory: 7116 grad_norm: 5.2824 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 3.6111 loss: 3.6111 2022/09/03 22:03:08 - mmengine - INFO - Epoch(train) [3][820/1345] lr: 1.0000e-02 eta: 5:17:22 time: 0.1944 data_time: 0.0101 memory: 7116 grad_norm: 6.2964 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 3.5926 loss: 3.5926 2022/09/03 22:03:12 - mmengine - INFO - Epoch(train) [3][840/1345] lr: 1.0000e-02 eta: 5:16:46 time: 0.2140 data_time: 0.0074 memory: 7116 grad_norm: 5.2512 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 3.5899 loss: 3.5899 2022/09/03 22:03:16 - mmengine - INFO - Epoch(train) [3][860/1345] lr: 1.0000e-02 eta: 5:16:03 time: 0.1954 data_time: 0.0126 memory: 7116 grad_norm: 5.3762 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 3.5544 loss: 3.5544 2022/09/03 22:03:20 - mmengine - INFO - Epoch(train) [3][880/1345] lr: 1.0000e-02 eta: 5:15:21 time: 0.1981 data_time: 0.0107 memory: 7116 grad_norm: 5.2084 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.7104 loss: 3.7104 2022/09/03 22:03:24 - mmengine - INFO - Epoch(train) [3][900/1345] lr: 1.0000e-02 eta: 5:14:39 time: 0.1958 data_time: 0.0071 memory: 7116 grad_norm: 5.0935 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 3.2556 loss: 3.2556 2022/09/03 22:03:28 - mmengine - INFO - Epoch(train) [3][920/1345] lr: 1.0000e-02 eta: 5:13:59 time: 0.1983 data_time: 0.0124 memory: 7116 grad_norm: 5.5626 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.5923 loss: 3.5923 2022/09/03 22:03:32 - mmengine - INFO - Epoch(train) [3][940/1345] lr: 1.0000e-02 eta: 5:13:18 time: 0.1968 data_time: 0.0085 memory: 7116 grad_norm: 5.0931 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.6355 loss: 3.6355 2022/09/03 22:03:36 - mmengine - INFO - Epoch(train) [3][960/1345] lr: 1.0000e-02 eta: 5:12:45 time: 0.2178 data_time: 0.0106 memory: 7116 grad_norm: 5.4987 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.5368 loss: 3.5368 2022/09/03 22:03:41 - mmengine - INFO - Epoch(train) [3][980/1345] lr: 1.0000e-02 eta: 5:12:22 time: 0.2462 data_time: 0.0195 memory: 7116 grad_norm: 5.3727 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 3.6266 loss: 3.6266 2022/09/03 22:03:46 - mmengine - INFO - Epoch(train) [3][1000/1345] lr: 1.0000e-02 eta: 5:11:52 time: 0.2229 data_time: 0.0109 memory: 7116 grad_norm: 5.3313 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.5398 loss: 3.5398 2022/09/03 22:03:50 - mmengine - INFO - Epoch(train) [3][1020/1345] lr: 1.0000e-02 eta: 5:11:25 time: 0.2338 data_time: 0.0090 memory: 7116 grad_norm: 5.3708 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 3.2748 loss: 3.2748 2022/09/03 22:03:55 - mmengine - INFO - Epoch(train) [3][1040/1345] lr: 1.0000e-02 eta: 5:10:53 time: 0.2170 data_time: 0.0225 memory: 7116 grad_norm: 5.1706 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.6046 loss: 3.6046 2022/09/03 22:04:00 - mmengine - INFO - Epoch(train) [3][1060/1345] lr: 1.0000e-02 eta: 5:10:37 time: 0.2633 data_time: 0.0096 memory: 7116 grad_norm: 5.3667 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.3540 loss: 3.3540 2022/09/03 22:04:04 - mmengine - INFO - Epoch(train) [3][1080/1345] lr: 1.0000e-02 eta: 5:10:00 time: 0.2011 data_time: 0.0108 memory: 7116 grad_norm: 5.3765 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.7065 loss: 3.7065 2022/09/03 22:04:08 - mmengine - INFO - Epoch(train) [3][1100/1345] lr: 1.0000e-02 eta: 5:09:21 time: 0.1954 data_time: 0.0106 memory: 7116 grad_norm: 5.1262 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 3.4222 loss: 3.4222 2022/09/03 22:04:12 - mmengine - INFO - Epoch(train) [3][1120/1345] lr: 1.0000e-02 eta: 5:08:54 time: 0.2275 data_time: 0.0086 memory: 7116 grad_norm: 5.5538 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 3.4576 loss: 3.4576 2022/09/03 22:04:16 - mmengine - INFO - Epoch(train) [3][1140/1345] lr: 1.0000e-02 eta: 5:08:20 time: 0.2075 data_time: 0.0111 memory: 7116 grad_norm: 5.2882 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 3.6663 loss: 3.6663 2022/09/03 22:04:21 - mmengine - INFO - Epoch(train) [3][1160/1345] lr: 1.0000e-02 eta: 5:07:47 time: 0.2087 data_time: 0.0107 memory: 7116 grad_norm: 5.4005 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.3992 loss: 3.3992 2022/09/03 22:04:25 - mmengine - INFO - Epoch(train) [3][1180/1345] lr: 1.0000e-02 eta: 5:07:13 time: 0.2058 data_time: 0.0158 memory: 7116 grad_norm: 5.5418 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 3.5082 loss: 3.5082 2022/09/03 22:04:30 - mmengine - INFO - Epoch(train) [3][1200/1345] lr: 1.0000e-02 eta: 5:07:03 time: 0.2793 data_time: 0.0078 memory: 7116 grad_norm: 5.5576 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 3.5474 loss: 3.5474 2022/09/03 22:04:36 - mmengine - INFO - Epoch(train) [3][1220/1345] lr: 1.0000e-02 eta: 5:06:52 time: 0.2731 data_time: 0.0114 memory: 7116 grad_norm: 5.2966 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.3188 loss: 3.3188 2022/09/03 22:04:40 - mmengine - INFO - Epoch(train) [3][1240/1345] lr: 1.0000e-02 eta: 5:06:22 time: 0.2158 data_time: 0.0087 memory: 7116 grad_norm: 5.3466 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.7197 loss: 3.7197 2022/09/03 22:04:44 - mmengine - INFO - Epoch(train) [3][1260/1345] lr: 1.0000e-02 eta: 5:05:48 time: 0.2036 data_time: 0.0184 memory: 7116 grad_norm: 5.5223 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.3885 loss: 3.3885 2022/09/03 22:04:48 - mmengine - INFO - Epoch(train) [3][1280/1345] lr: 1.0000e-02 eta: 5:05:12 time: 0.1941 data_time: 0.0112 memory: 7116 grad_norm: 5.5812 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.4718 loss: 3.4718 2022/09/03 22:04:52 - mmengine - INFO - Epoch(train) [3][1300/1345] lr: 1.0000e-02 eta: 5:04:37 time: 0.1972 data_time: 0.0111 memory: 7116 grad_norm: 5.1656 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 3.7959 loss: 3.7959 2022/09/03 22:04:54 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:04:56 - mmengine - INFO - Epoch(train) [3][1320/1345] lr: 1.0000e-02 eta: 5:04:01 time: 0.1950 data_time: 0.0090 memory: 7116 grad_norm: 5.2495 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 3.3586 loss: 3.3586 2022/09/03 22:05:00 - mmengine - INFO - Epoch(train) [3][1340/1345] lr: 1.0000e-02 eta: 5:03:26 time: 0.1933 data_time: 0.0128 memory: 7116 grad_norm: 5.4237 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 3.3809 loss: 3.3809 2022/09/03 22:05:01 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:05:01 - mmengine - INFO - Epoch(train) [3][1345/1345] lr: 1.0000e-02 eta: 5:03:26 time: 0.1875 data_time: 0.0092 memory: 7116 grad_norm: 5.8211 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 3.4776 loss: 3.4776 2022/09/03 22:05:01 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/09/03 22:05:04 - mmengine - INFO - Epoch(val) [3][20/181] eta: 0:00:08 time: 0.0500 data_time: 0.0120 memory: 1114 2022/09/03 22:05:04 - mmengine - INFO - Epoch(val) [3][40/181] eta: 0:00:06 time: 0.0463 data_time: 0.0089 memory: 1114 2022/09/03 22:05:05 - mmengine - INFO - Epoch(val) [3][60/181] eta: 0:00:05 time: 0.0457 data_time: 0.0083 memory: 1114 2022/09/03 22:05:06 - mmengine - INFO - Epoch(val) [3][80/181] eta: 0:00:04 time: 0.0441 data_time: 0.0076 memory: 1114 2022/09/03 22:05:07 - mmengine - INFO - Epoch(val) [3][100/181] eta: 0:00:03 time: 0.0434 data_time: 0.0072 memory: 1114 2022/09/03 22:05:08 - mmengine - INFO - Epoch(val) [3][120/181] eta: 0:00:02 time: 0.0427 data_time: 0.0068 memory: 1114 2022/09/03 22:05:09 - mmengine - INFO - Epoch(val) [3][140/181] eta: 0:00:01 time: 0.0420 data_time: 0.0064 memory: 1114 2022/09/03 22:05:10 - mmengine - INFO - Epoch(val) [3][160/181] eta: 0:00:00 time: 0.0411 data_time: 0.0059 memory: 1114 2022/09/03 22:05:11 - mmengine - INFO - Epoch(val) [3][180/181] eta: 0:00:00 time: 0.0416 data_time: 0.0063 memory: 1114 2022/09/03 22:05:13 - mmengine - INFO - Epoch(val) [3][181/181] acc/top1: 0.1646 acc/top5: 0.4040 acc/mean1: 0.1452 2022/09/03 22:05:13 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_2.pth is removed 2022/09/03 22:05:14 - mmengine - INFO - The best checkpoint with 0.1646 acc/top1 at 3 epoch is saved to best_acc/top1_epoch_3.pth. 2022/09/03 22:05:19 - mmengine - INFO - Epoch(train) [4][20/1345] lr: 1.0000e-02 eta: 5:02:48 time: 0.2617 data_time: 0.0290 memory: 7116 grad_norm: 5.1302 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.4635 loss: 3.4635 2022/09/03 22:05:23 - mmengine - INFO - Epoch(train) [4][40/1345] lr: 1.0000e-02 eta: 5:02:14 time: 0.1969 data_time: 0.0086 memory: 7116 grad_norm: 5.4817 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 3.6021 loss: 3.6021 2022/09/03 22:05:27 - mmengine - INFO - Epoch(train) [4][60/1345] lr: 1.0000e-02 eta: 5:01:40 time: 0.1948 data_time: 0.0108 memory: 7116 grad_norm: 5.5195 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 3.3618 loss: 3.3618 2022/09/03 22:05:31 - mmengine - INFO - Epoch(train) [4][80/1345] lr: 1.0000e-02 eta: 5:01:05 time: 0.1919 data_time: 0.0079 memory: 7116 grad_norm: 5.5699 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.5513 loss: 3.5513 2022/09/03 22:05:35 - mmengine - INFO - Epoch(train) [4][100/1345] lr: 1.0000e-02 eta: 5:00:32 time: 0.1965 data_time: 0.0088 memory: 7116 grad_norm: 5.5803 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 3.4740 loss: 3.4740 2022/09/03 22:05:39 - mmengine - INFO - Epoch(train) [4][120/1345] lr: 1.0000e-02 eta: 5:00:00 time: 0.1990 data_time: 0.0102 memory: 7116 grad_norm: 5.5865 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 3.3760 loss: 3.3760 2022/09/03 22:05:43 - mmengine - INFO - Epoch(train) [4][140/1345] lr: 1.0000e-02 eta: 4:59:28 time: 0.1999 data_time: 0.0098 memory: 7116 grad_norm: 5.7369 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.5161 loss: 3.5161 2022/09/03 22:05:47 - mmengine - INFO - Epoch(train) [4][160/1345] lr: 1.0000e-02 eta: 4:58:57 time: 0.1994 data_time: 0.0084 memory: 7116 grad_norm: 5.7803 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.4644 loss: 3.4644 2022/09/03 22:05:51 - mmengine - INFO - Epoch(train) [4][180/1345] lr: 1.0000e-02 eta: 4:58:25 time: 0.1974 data_time: 0.0096 memory: 7116 grad_norm: 5.5336 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.5585 loss: 3.5585 2022/09/03 22:05:55 - mmengine - INFO - Epoch(train) [4][200/1345] lr: 1.0000e-02 eta: 4:57:55 time: 0.2007 data_time: 0.0090 memory: 7116 grad_norm: 5.4253 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 3.5174 loss: 3.5174 2022/09/03 22:05:59 - mmengine - INFO - Epoch(train) [4][220/1345] lr: 1.0000e-02 eta: 4:57:23 time: 0.1966 data_time: 0.0082 memory: 7116 grad_norm: 5.6406 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.5143 loss: 3.5143 2022/09/03 22:06:03 - mmengine - INFO - Epoch(train) [4][240/1345] lr: 1.0000e-02 eta: 4:56:54 time: 0.2025 data_time: 0.0172 memory: 7116 grad_norm: 5.6745 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.5586 loss: 3.5586 2022/09/03 22:06:07 - mmengine - INFO - Epoch(train) [4][260/1345] lr: 1.0000e-02 eta: 4:56:22 time: 0.1948 data_time: 0.0107 memory: 7116 grad_norm: 5.5221 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.3775 loss: 3.3775 2022/09/03 22:06:11 - mmengine - INFO - Epoch(train) [4][280/1345] lr: 1.0000e-02 eta: 4:55:52 time: 0.1973 data_time: 0.0072 memory: 7116 grad_norm: 5.4578 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.5812 loss: 3.5812 2022/09/03 22:06:15 - mmengine - INFO - Epoch(train) [4][300/1345] lr: 1.0000e-02 eta: 4:55:25 time: 0.2094 data_time: 0.0110 memory: 7116 grad_norm: 5.6778 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.3207 loss: 3.3207 2022/09/03 22:06:19 - mmengine - INFO - Epoch(train) [4][320/1345] lr: 1.0000e-02 eta: 4:54:54 time: 0.1951 data_time: 0.0099 memory: 7116 grad_norm: 5.1144 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.5531 loss: 3.5531 2022/09/03 22:06:23 - mmengine - INFO - Epoch(train) [4][340/1345] lr: 1.0000e-02 eta: 4:54:25 time: 0.1981 data_time: 0.0087 memory: 7116 grad_norm: 5.6147 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.2737 loss: 3.2737 2022/09/03 22:06:27 - mmengine - INFO - Epoch(train) [4][360/1345] lr: 1.0000e-02 eta: 4:53:56 time: 0.2008 data_time: 0.0105 memory: 7116 grad_norm: 5.4900 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.3650 loss: 3.3650 2022/09/03 22:06:31 - mmengine - INFO - Epoch(train) [4][380/1345] lr: 1.0000e-02 eta: 4:53:27 time: 0.1960 data_time: 0.0095 memory: 7116 grad_norm: 5.4265 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.3666 loss: 3.3666 2022/09/03 22:06:35 - mmengine - INFO - Epoch(train) [4][400/1345] lr: 1.0000e-02 eta: 4:52:58 time: 0.1999 data_time: 0.0076 memory: 7116 grad_norm: 5.8348 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 3.4406 loss: 3.4406 2022/09/03 22:06:39 - mmengine - INFO - Epoch(train) [4][420/1345] lr: 1.0000e-02 eta: 4:52:30 time: 0.1999 data_time: 0.0105 memory: 7116 grad_norm: 5.6202 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 3.2152 loss: 3.2152 2022/09/03 22:06:43 - mmengine - INFO - Epoch(train) [4][440/1345] lr: 1.0000e-02 eta: 4:52:02 time: 0.1970 data_time: 0.0094 memory: 7116 grad_norm: 5.7426 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 3.4540 loss: 3.4540 2022/09/03 22:06:47 - mmengine - INFO - Epoch(train) [4][460/1345] lr: 1.0000e-02 eta: 4:51:45 time: 0.2389 data_time: 0.0109 memory: 7116 grad_norm: 5.3879 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.6620 loss: 3.6620 2022/09/03 22:06:51 - mmengine - INFO - Epoch(train) [4][480/1345] lr: 1.0000e-02 eta: 4:51:17 time: 0.1977 data_time: 0.0154 memory: 7116 grad_norm: 5.6515 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.2132 loss: 3.2132 2022/09/03 22:06:55 - mmengine - INFO - Epoch(train) [4][500/1345] lr: 1.0000e-02 eta: 4:50:48 time: 0.1939 data_time: 0.0074 memory: 7116 grad_norm: 5.4661 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 3.3828 loss: 3.3828 2022/09/03 22:06:59 - mmengine - INFO - Epoch(train) [4][520/1345] lr: 1.0000e-02 eta: 4:50:19 time: 0.1950 data_time: 0.0086 memory: 7116 grad_norm: 5.9225 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.1864 loss: 3.1864 2022/09/03 22:07:03 - mmengine - INFO - Epoch(train) [4][540/1345] lr: 1.0000e-02 eta: 4:49:53 time: 0.2046 data_time: 0.0107 memory: 7116 grad_norm: 5.7887 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 3.3696 loss: 3.3696 2022/09/03 22:07:07 - mmengine - INFO - Epoch(train) [4][560/1345] lr: 1.0000e-02 eta: 4:49:27 time: 0.2009 data_time: 0.0106 memory: 7116 grad_norm: 6.0002 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 3.3874 loss: 3.3874 2022/09/03 22:07:11 - mmengine - INFO - Epoch(train) [4][580/1345] lr: 1.0000e-02 eta: 4:49:00 time: 0.1986 data_time: 0.0076 memory: 7116 grad_norm: 6.0578 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.3962 loss: 3.3962 2022/09/03 22:07:16 - mmengine - INFO - Epoch(train) [4][600/1345] lr: 1.0000e-02 eta: 4:48:43 time: 0.2344 data_time: 0.0099 memory: 7116 grad_norm: 9.2268 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 3.5657 loss: 3.5657 2022/09/03 22:07:20 - mmengine - INFO - Epoch(train) [4][620/1345] lr: 1.0000e-02 eta: 4:48:15 time: 0.1941 data_time: 0.0089 memory: 7116 grad_norm: 5.6644 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.3188 loss: 3.3188 2022/09/03 22:07:24 - mmengine - INFO - Epoch(train) [4][640/1345] lr: 1.0000e-02 eta: 4:47:51 time: 0.2051 data_time: 0.0079 memory: 7116 grad_norm: 5.7786 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 3.3260 loss: 3.3260 2022/09/03 22:07:29 - mmengine - INFO - Epoch(train) [4][660/1345] lr: 1.0000e-02 eta: 4:47:43 time: 0.2683 data_time: 0.0096 memory: 7116 grad_norm: 5.6266 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 3.6274 loss: 3.6274 2022/09/03 22:07:34 - mmengine - INFO - Epoch(train) [4][680/1345] lr: 1.0000e-02 eta: 4:47:27 time: 0.2340 data_time: 0.0080 memory: 7116 grad_norm: 5.6122 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.5594 loss: 3.5594 2022/09/03 22:07:39 - mmengine - INFO - Epoch(train) [4][700/1345] lr: 1.0000e-02 eta: 4:47:17 time: 0.2590 data_time: 0.0450 memory: 7116 grad_norm: 5.6167 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 3.3195 loss: 3.3195 2022/09/03 22:07:43 - mmengine - INFO - Epoch(train) [4][720/1345] lr: 1.0000e-02 eta: 4:46:52 time: 0.2015 data_time: 0.0071 memory: 7116 grad_norm: 5.6234 top1_acc: 0.0000 top5_acc: 0.5000 loss_cls: 3.2232 loss: 3.2232 2022/09/03 22:07:47 - mmengine - INFO - Epoch(train) [4][740/1345] lr: 1.0000e-02 eta: 4:46:26 time: 0.1983 data_time: 0.0080 memory: 7116 grad_norm: 5.3400 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.2908 loss: 3.2908 2022/09/03 22:07:51 - mmengine - INFO - Epoch(train) [4][760/1345] lr: 1.0000e-02 eta: 4:46:02 time: 0.2036 data_time: 0.0100 memory: 7116 grad_norm: 5.9172 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.4556 loss: 3.4556 2022/09/03 22:07:55 - mmengine - INFO - Epoch(train) [4][780/1345] lr: 1.0000e-02 eta: 4:45:40 time: 0.2125 data_time: 0.0076 memory: 7116 grad_norm: 5.9135 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.1726 loss: 3.1726 2022/09/03 22:07:59 - mmengine - INFO - Epoch(train) [4][800/1345] lr: 1.0000e-02 eta: 4:45:15 time: 0.2000 data_time: 0.0089 memory: 7116 grad_norm: 5.7171 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 3.4854 loss: 3.4854 2022/09/03 22:08:03 - mmengine - INFO - Epoch(train) [4][820/1345] lr: 1.0000e-02 eta: 4:44:51 time: 0.1993 data_time: 0.0099 memory: 7116 grad_norm: 5.9186 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.3233 loss: 3.3233 2022/09/03 22:08:07 - mmengine - INFO - Epoch(train) [4][840/1345] lr: 1.0000e-02 eta: 4:44:24 time: 0.1915 data_time: 0.0111 memory: 7116 grad_norm: 5.6661 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 3.2652 loss: 3.2652 2022/09/03 22:08:11 - mmengine - INFO - Epoch(train) [4][860/1345] lr: 1.0000e-02 eta: 4:44:03 time: 0.2126 data_time: 0.0087 memory: 7116 grad_norm: 5.6030 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 3.5381 loss: 3.5381 2022/09/03 22:08:15 - mmengine - INFO - Epoch(train) [4][880/1345] lr: 1.0000e-02 eta: 4:43:40 time: 0.2026 data_time: 0.0101 memory: 7116 grad_norm: 5.5576 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 3.3845 loss: 3.3845 2022/09/03 22:08:20 - mmengine - INFO - Epoch(train) [4][900/1345] lr: 1.0000e-02 eta: 4:43:19 time: 0.2111 data_time: 0.0199 memory: 7116 grad_norm: 5.7842 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.4203 loss: 3.4203 2022/09/03 22:08:24 - mmengine - INFO - Epoch(train) [4][920/1345] lr: 1.0000e-02 eta: 4:42:55 time: 0.2008 data_time: 0.0089 memory: 7116 grad_norm: 5.6400 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 3.1628 loss: 3.1628 2022/09/03 22:08:28 - mmengine - INFO - Epoch(train) [4][940/1345] lr: 1.0000e-02 eta: 4:42:30 time: 0.1960 data_time: 0.0102 memory: 7116 grad_norm: 5.4978 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 3.1854 loss: 3.1854 2022/09/03 22:08:32 - mmengine - INFO - Epoch(train) [4][960/1345] lr: 1.0000e-02 eta: 4:42:10 time: 0.2127 data_time: 0.0074 memory: 7116 grad_norm: 5.8496 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.2831 loss: 3.2831 2022/09/03 22:08:33 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:08:36 - mmengine - INFO - Epoch(train) [4][980/1345] lr: 1.0000e-02 eta: 4:41:54 time: 0.2303 data_time: 0.0084 memory: 7116 grad_norm: 5.4336 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 3.1549 loss: 3.1549 2022/09/03 22:08:40 - mmengine - INFO - Epoch(train) [4][1000/1345] lr: 1.0000e-02 eta: 4:41:31 time: 0.2013 data_time: 0.0091 memory: 7116 grad_norm: 5.9391 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.3414 loss: 3.3414 2022/09/03 22:08:45 - mmengine - INFO - Epoch(train) [4][1020/1345] lr: 1.0000e-02 eta: 4:41:20 time: 0.2470 data_time: 0.0073 memory: 7116 grad_norm: 5.5365 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.2660 loss: 3.2660 2022/09/03 22:08:49 - mmengine - INFO - Epoch(train) [4][1040/1345] lr: 1.0000e-02 eta: 4:40:56 time: 0.1967 data_time: 0.0095 memory: 7116 grad_norm: 6.0995 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.1146 loss: 3.1146 2022/09/03 22:08:54 - mmengine - INFO - Epoch(train) [4][1060/1345] lr: 1.0000e-02 eta: 4:40:37 time: 0.2126 data_time: 0.0085 memory: 7116 grad_norm: 5.9986 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 3.1916 loss: 3.1916 2022/09/03 22:08:58 - mmengine - INFO - Epoch(train) [4][1080/1345] lr: 1.0000e-02 eta: 4:40:14 time: 0.2005 data_time: 0.0074 memory: 7116 grad_norm: 5.3770 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.1491 loss: 3.1491 2022/09/03 22:09:02 - mmengine - INFO - Epoch(train) [4][1100/1345] lr: 1.0000e-02 eta: 4:39:57 time: 0.2205 data_time: 0.0084 memory: 7116 grad_norm: 5.6088 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 3.4569 loss: 3.4569 2022/09/03 22:09:06 - mmengine - INFO - Epoch(train) [4][1120/1345] lr: 1.0000e-02 eta: 4:39:34 time: 0.2001 data_time: 0.0106 memory: 7116 grad_norm: 5.5379 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 3.4258 loss: 3.4258 2022/09/03 22:09:10 - mmengine - INFO - Epoch(train) [4][1140/1345] lr: 1.0000e-02 eta: 4:39:12 time: 0.2011 data_time: 0.0062 memory: 7116 grad_norm: 5.6564 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 3.3934 loss: 3.3934 2022/09/03 22:09:14 - mmengine - INFO - Epoch(train) [4][1160/1345] lr: 1.0000e-02 eta: 4:38:52 time: 0.2064 data_time: 0.0089 memory: 7116 grad_norm: 5.6481 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 3.2697 loss: 3.2697 2022/09/03 22:09:18 - mmengine - INFO - Epoch(train) [4][1180/1345] lr: 1.0000e-02 eta: 4:38:30 time: 0.2015 data_time: 0.0091 memory: 7116 grad_norm: 5.8059 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.3213 loss: 3.3213 2022/09/03 22:09:22 - mmengine - INFO - Epoch(train) [4][1200/1345] lr: 1.0000e-02 eta: 4:38:08 time: 0.1972 data_time: 0.0079 memory: 7116 grad_norm: 5.5717 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.1117 loss: 3.1117 2022/09/03 22:09:26 - mmengine - INFO - Epoch(train) [4][1220/1345] lr: 1.0000e-02 eta: 4:37:47 time: 0.2058 data_time: 0.0072 memory: 7116 grad_norm: 6.5492 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 3.0708 loss: 3.0708 2022/09/03 22:09:30 - mmengine - INFO - Epoch(train) [4][1240/1345] lr: 1.0000e-02 eta: 4:37:25 time: 0.1973 data_time: 0.0097 memory: 7116 grad_norm: 5.8142 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 3.6390 loss: 3.6390 2022/09/03 22:09:34 - mmengine - INFO - Epoch(train) [4][1260/1345] lr: 1.0000e-02 eta: 4:37:05 time: 0.2059 data_time: 0.0081 memory: 7116 grad_norm: 5.7464 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.5057 loss: 3.5057 2022/09/03 22:09:38 - mmengine - INFO - Epoch(train) [4][1280/1345] lr: 1.0000e-02 eta: 4:36:44 time: 0.2023 data_time: 0.0093 memory: 7116 grad_norm: 5.8263 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.1436 loss: 3.1436 2022/09/03 22:09:42 - mmengine - INFO - Epoch(train) [4][1300/1345] lr: 1.0000e-02 eta: 4:36:24 time: 0.2026 data_time: 0.0105 memory: 7116 grad_norm: 5.3907 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.3362 loss: 3.3362 2022/09/03 22:09:46 - mmengine - INFO - Epoch(train) [4][1320/1345] lr: 1.0000e-02 eta: 4:36:03 time: 0.2020 data_time: 0.0085 memory: 7116 grad_norm: 5.5663 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.3607 loss: 3.3607 2022/09/03 22:09:51 - mmengine - INFO - Epoch(train) [4][1340/1345] lr: 1.0000e-02 eta: 4:35:44 time: 0.2070 data_time: 0.0066 memory: 7116 grad_norm: 5.5372 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 3.4700 loss: 3.4700 2022/09/03 22:09:52 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:09:52 - mmengine - INFO - Epoch(train) [4][1345/1345] lr: 1.0000e-02 eta: 4:35:44 time: 0.2066 data_time: 0.0096 memory: 7116 grad_norm: 6.8755 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 3.5671 loss: 3.5671 2022/09/03 22:09:52 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/09/03 22:09:55 - mmengine - INFO - Epoch(val) [4][20/181] eta: 0:00:07 time: 0.0446 data_time: 0.0089 memory: 1114 2022/09/03 22:09:56 - mmengine - INFO - Epoch(val) [4][40/181] eta: 0:00:05 time: 0.0408 data_time: 0.0059 memory: 1114 2022/09/03 22:09:56 - mmengine - INFO - Epoch(val) [4][60/181] eta: 0:00:04 time: 0.0412 data_time: 0.0061 memory: 1114 2022/09/03 22:09:57 - mmengine - INFO - Epoch(val) [4][80/181] eta: 0:00:04 time: 0.0421 data_time: 0.0063 memory: 1114 2022/09/03 22:09:58 - mmengine - INFO - Epoch(val) [4][100/181] eta: 0:00:03 time: 0.0408 data_time: 0.0059 memory: 1114 2022/09/03 22:09:59 - mmengine - INFO - Epoch(val) [4][120/181] eta: 0:00:02 time: 0.0406 data_time: 0.0056 memory: 1114 2022/09/03 22:10:00 - mmengine - INFO - Epoch(val) [4][140/181] eta: 0:00:01 time: 0.0407 data_time: 0.0058 memory: 1114 2022/09/03 22:10:01 - mmengine - INFO - Epoch(val) [4][160/181] eta: 0:00:00 time: 0.0405 data_time: 0.0056 memory: 1114 2022/09/03 22:10:01 - mmengine - INFO - Epoch(val) [4][180/181] eta: 0:00:00 time: 0.0403 data_time: 0.0055 memory: 1114 2022/09/03 22:10:04 - mmengine - INFO - Epoch(val) [4][181/181] acc/top1: 0.1831 acc/top5: 0.4373 acc/mean1: 0.1729 2022/09/03 22:10:04 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_3.pth is removed 2022/09/03 22:10:05 - mmengine - INFO - The best checkpoint with 0.1831 acc/top1 at 4 epoch is saved to best_acc/top1_epoch_4.pth. 2022/09/03 22:10:10 - mmengine - INFO - Epoch(train) [5][20/1345] lr: 1.0000e-02 eta: 4:35:16 time: 0.2422 data_time: 0.0440 memory: 7116 grad_norm: 5.6179 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.0853 loss: 3.0853 2022/09/03 22:10:14 - mmengine - INFO - Epoch(train) [5][40/1345] lr: 1.0000e-02 eta: 4:34:56 time: 0.2037 data_time: 0.0058 memory: 7116 grad_norm: 6.0255 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.4101 loss: 3.4101 2022/09/03 22:10:18 - mmengine - INFO - Epoch(train) [5][60/1345] lr: 1.0000e-02 eta: 4:34:37 time: 0.2050 data_time: 0.0090 memory: 7116 grad_norm: 5.6386 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.3304 loss: 3.3304 2022/09/03 22:10:23 - mmengine - INFO - Epoch(train) [5][80/1345] lr: 1.0000e-02 eta: 4:34:19 time: 0.2125 data_time: 0.0082 memory: 7116 grad_norm: 5.7616 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 3.2974 loss: 3.2974 2022/09/03 22:10:27 - mmengine - INFO - Epoch(train) [5][100/1345] lr: 1.0000e-02 eta: 4:34:01 time: 0.2083 data_time: 0.0070 memory: 7116 grad_norm: 5.7493 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.3495 loss: 3.3495 2022/09/03 22:10:31 - mmengine - INFO - Epoch(train) [5][120/1345] lr: 1.0000e-02 eta: 4:33:42 time: 0.2061 data_time: 0.0087 memory: 7116 grad_norm: 5.6958 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 3.1672 loss: 3.1672 2022/09/03 22:10:35 - mmengine - INFO - Epoch(train) [5][140/1345] lr: 1.0000e-02 eta: 4:33:22 time: 0.2017 data_time: 0.0073 memory: 7116 grad_norm: 5.3656 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.0706 loss: 3.0706 2022/09/03 22:10:39 - mmengine - INFO - Epoch(train) [5][160/1345] lr: 1.0000e-02 eta: 4:33:03 time: 0.2044 data_time: 0.0082 memory: 7116 grad_norm: 5.6487 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 3.0935 loss: 3.0935 2022/09/03 22:10:43 - mmengine - INFO - Epoch(train) [5][180/1345] lr: 1.0000e-02 eta: 4:32:45 time: 0.2055 data_time: 0.0086 memory: 7116 grad_norm: 5.4496 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 3.4089 loss: 3.4089 2022/09/03 22:10:47 - mmengine - INFO - Epoch(train) [5][200/1345] lr: 1.0000e-02 eta: 4:32:26 time: 0.2061 data_time: 0.0073 memory: 7116 grad_norm: 5.5146 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.1856 loss: 3.1856 2022/09/03 22:10:51 - mmengine - INFO - Epoch(train) [5][220/1345] lr: 1.0000e-02 eta: 4:32:08 time: 0.2071 data_time: 0.0075 memory: 7116 grad_norm: 5.6605 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.3648 loss: 3.3648 2022/09/03 22:10:56 - mmengine - INFO - Epoch(train) [5][240/1345] lr: 1.0000e-02 eta: 4:31:51 time: 0.2116 data_time: 0.0087 memory: 7116 grad_norm: 5.7162 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 3.2816 loss: 3.2816 2022/09/03 22:11:00 - mmengine - INFO - Epoch(train) [5][260/1345] lr: 1.0000e-02 eta: 4:31:33 time: 0.2049 data_time: 0.0080 memory: 7116 grad_norm: 5.6930 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.0435 loss: 3.0435 2022/09/03 22:11:04 - mmengine - INFO - Epoch(train) [5][280/1345] lr: 1.0000e-02 eta: 4:31:16 time: 0.2089 data_time: 0.0075 memory: 7116 grad_norm: 5.8978 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 3.5221 loss: 3.5221 2022/09/03 22:11:08 - mmengine - INFO - Epoch(train) [5][300/1345] lr: 1.0000e-02 eta: 4:31:00 time: 0.2162 data_time: 0.0092 memory: 7116 grad_norm: 5.4195 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 3.2452 loss: 3.2452 2022/09/03 22:11:13 - mmengine - INFO - Epoch(train) [5][320/1345] lr: 1.0000e-02 eta: 4:30:44 time: 0.2138 data_time: 0.0081 memory: 7116 grad_norm: 5.6817 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 3.3304 loss: 3.3304 2022/09/03 22:11:17 - mmengine - INFO - Epoch(train) [5][340/1345] lr: 1.0000e-02 eta: 4:30:26 time: 0.2048 data_time: 0.0084 memory: 7116 grad_norm: 5.6283 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 3.2931 loss: 3.2931 2022/09/03 22:11:21 - mmengine - INFO - Epoch(train) [5][360/1345] lr: 1.0000e-02 eta: 4:30:09 time: 0.2120 data_time: 0.0083 memory: 7116 grad_norm: 5.4844 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.2216 loss: 3.2216 2022/09/03 22:11:25 - mmengine - INFO - Epoch(train) [5][380/1345] lr: 1.0000e-02 eta: 4:29:52 time: 0.2051 data_time: 0.0082 memory: 7116 grad_norm: 5.6424 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.9720 loss: 2.9720 2022/09/03 22:11:29 - mmengine - INFO - Epoch(train) [5][400/1345] lr: 1.0000e-02 eta: 4:29:34 time: 0.2038 data_time: 0.0076 memory: 7116 grad_norm: 5.6041 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.3944 loss: 3.3944 2022/09/03 22:11:33 - mmengine - INFO - Epoch(train) [5][420/1345] lr: 1.0000e-02 eta: 4:29:17 time: 0.2103 data_time: 0.0079 memory: 7116 grad_norm: 5.8684 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.9932 loss: 2.9932 2022/09/03 22:11:37 - mmengine - INFO - Epoch(train) [5][440/1345] lr: 1.0000e-02 eta: 4:29:00 time: 0.2074 data_time: 0.0074 memory: 7116 grad_norm: 5.7755 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.1165 loss: 3.1165 2022/09/03 22:11:41 - mmengine - INFO - Epoch(train) [5][460/1345] lr: 1.0000e-02 eta: 4:28:42 time: 0.2013 data_time: 0.0070 memory: 7116 grad_norm: 5.6781 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.8946 loss: 2.8946 2022/09/03 22:11:45 - mmengine - INFO - Epoch(train) [5][480/1345] lr: 1.0000e-02 eta: 4:28:24 time: 0.2010 data_time: 0.0110 memory: 7116 grad_norm: 5.6056 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.0679 loss: 3.0679 2022/09/03 22:11:49 - mmengine - INFO - Epoch(train) [5][500/1345] lr: 1.0000e-02 eta: 4:28:06 time: 0.2018 data_time: 0.0066 memory: 7116 grad_norm: 5.4995 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.3028 loss: 3.3028 2022/09/03 22:11:54 - mmengine - INFO - Epoch(train) [5][520/1345] lr: 1.0000e-02 eta: 4:27:52 time: 0.2194 data_time: 0.0071 memory: 7116 grad_norm: 5.8412 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 3.1957 loss: 3.1957 2022/09/03 22:11:58 - mmengine - INFO - Epoch(train) [5][540/1345] lr: 1.0000e-02 eta: 4:27:35 time: 0.2036 data_time: 0.0089 memory: 7116 grad_norm: 6.0288 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.0131 loss: 3.0131 2022/09/03 22:12:02 - mmengine - INFO - Epoch(train) [5][560/1345] lr: 1.0000e-02 eta: 4:27:17 time: 0.2030 data_time: 0.0078 memory: 7116 grad_norm: 5.3685 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 3.0939 loss: 3.0939 2022/09/03 22:12:06 - mmengine - INFO - Epoch(train) [5][580/1345] lr: 1.0000e-02 eta: 4:27:00 time: 0.2009 data_time: 0.0072 memory: 7116 grad_norm: 5.6701 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.9840 loss: 2.9840 2022/09/03 22:12:10 - mmengine - INFO - Epoch(train) [5][600/1345] lr: 1.0000e-02 eta: 4:26:43 time: 0.2041 data_time: 0.0099 memory: 7116 grad_norm: 5.9228 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.2333 loss: 3.2333 2022/09/03 22:12:14 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:12:14 - mmengine - INFO - Epoch(train) [5][620/1345] lr: 1.0000e-02 eta: 4:26:27 time: 0.2097 data_time: 0.0068 memory: 7116 grad_norm: 5.4861 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.3760 loss: 3.3760 2022/09/03 22:12:18 - mmengine - INFO - Epoch(train) [5][640/1345] lr: 1.0000e-02 eta: 4:26:10 time: 0.2037 data_time: 0.0074 memory: 7116 grad_norm: 5.6161 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 3.0274 loss: 3.0274 2022/09/03 22:12:22 - mmengine - INFO - Epoch(train) [5][660/1345] lr: 1.0000e-02 eta: 4:25:53 time: 0.2049 data_time: 0.0100 memory: 7116 grad_norm: 5.6273 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.3688 loss: 3.3688 2022/09/03 22:12:27 - mmengine - INFO - Epoch(train) [5][680/1345] lr: 1.0000e-02 eta: 4:25:37 time: 0.2056 data_time: 0.0078 memory: 7116 grad_norm: 5.9118 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.8551 loss: 2.8551 2022/09/03 22:12:31 - mmengine - INFO - Epoch(train) [5][700/1345] lr: 1.0000e-02 eta: 4:25:21 time: 0.2044 data_time: 0.0064 memory: 7116 grad_norm: 5.5663 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 3.2370 loss: 3.2370 2022/09/03 22:12:35 - mmengine - INFO - Epoch(train) [5][720/1345] lr: 1.0000e-02 eta: 4:25:05 time: 0.2064 data_time: 0.0098 memory: 7116 grad_norm: 5.4187 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 3.1140 loss: 3.1140 2022/09/03 22:12:39 - mmengine - INFO - Epoch(train) [5][740/1345] lr: 1.0000e-02 eta: 4:24:48 time: 0.2011 data_time: 0.0077 memory: 7116 grad_norm: 5.5078 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 3.2710 loss: 3.2710 2022/09/03 22:12:43 - mmengine - INFO - Epoch(train) [5][760/1345] lr: 1.0000e-02 eta: 4:24:34 time: 0.2184 data_time: 0.0066 memory: 7116 grad_norm: 5.9952 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.1454 loss: 3.1454 2022/09/03 22:12:47 - mmengine - INFO - Epoch(train) [5][780/1345] lr: 1.0000e-02 eta: 4:24:18 time: 0.2061 data_time: 0.0087 memory: 7116 grad_norm: 5.7647 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 3.0163 loss: 3.0163 2022/09/03 22:12:51 - mmengine - INFO - Epoch(train) [5][800/1345] lr: 1.0000e-02 eta: 4:24:03 time: 0.2084 data_time: 0.0076 memory: 7116 grad_norm: 5.7130 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.3434 loss: 3.3434 2022/09/03 22:12:56 - mmengine - INFO - Epoch(train) [5][820/1345] lr: 1.0000e-02 eta: 4:23:48 time: 0.2092 data_time: 0.0069 memory: 7116 grad_norm: 5.9886 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 3.1455 loss: 3.1455 2022/09/03 22:13:00 - mmengine - INFO - Epoch(train) [5][840/1345] lr: 1.0000e-02 eta: 4:23:32 time: 0.2046 data_time: 0.0093 memory: 7116 grad_norm: 5.7508 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.0032 loss: 3.0032 2022/09/03 22:13:04 - mmengine - INFO - Epoch(train) [5][860/1345] lr: 1.0000e-02 eta: 4:23:16 time: 0.2034 data_time: 0.0082 memory: 7116 grad_norm: 5.5114 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 3.5118 loss: 3.5118 2022/09/03 22:13:08 - mmengine - INFO - Epoch(train) [5][880/1345] lr: 1.0000e-02 eta: 4:23:00 time: 0.2046 data_time: 0.0076 memory: 7116 grad_norm: 5.8593 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.9171 loss: 2.9171 2022/09/03 22:13:12 - mmengine - INFO - Epoch(train) [5][900/1345] lr: 1.0000e-02 eta: 4:22:45 time: 0.2064 data_time: 0.0088 memory: 7116 grad_norm: 5.6559 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.9589 loss: 2.9589 2022/09/03 22:13:16 - mmengine - INFO - Epoch(train) [5][920/1345] lr: 1.0000e-02 eta: 4:22:30 time: 0.2080 data_time: 0.0078 memory: 7116 grad_norm: 5.6121 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 3.0751 loss: 3.0751 2022/09/03 22:13:20 - mmengine - INFO - Epoch(train) [5][940/1345] lr: 1.0000e-02 eta: 4:22:16 time: 0.2124 data_time: 0.0076 memory: 7116 grad_norm: 5.8690 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.0306 loss: 3.0306 2022/09/03 22:13:25 - mmengine - INFO - Epoch(train) [5][960/1345] lr: 1.0000e-02 eta: 4:22:03 time: 0.2190 data_time: 0.0286 memory: 7116 grad_norm: 5.8309 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.0523 loss: 3.0523 2022/09/03 22:13:29 - mmengine - INFO - Epoch(train) [5][980/1345] lr: 1.0000e-02 eta: 4:21:48 time: 0.2040 data_time: 0.0079 memory: 7116 grad_norm: 5.7647 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.0763 loss: 3.0763 2022/09/03 22:13:33 - mmengine - INFO - Epoch(train) [5][1000/1345] lr: 1.0000e-02 eta: 4:21:33 time: 0.2066 data_time: 0.0076 memory: 7116 grad_norm: 5.5867 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 3.1157 loss: 3.1157 2022/09/03 22:13:37 - mmengine - INFO - Epoch(train) [5][1020/1345] lr: 1.0000e-02 eta: 4:21:19 time: 0.2150 data_time: 0.0086 memory: 7116 grad_norm: 5.8455 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.8974 loss: 2.8974 2022/09/03 22:13:43 - mmengine - INFO - Epoch(train) [5][1040/1345] lr: 1.0000e-02 eta: 4:21:15 time: 0.2621 data_time: 0.0075 memory: 7116 grad_norm: 5.9667 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.9371 loss: 2.9371 2022/09/03 22:13:47 - mmengine - INFO - Epoch(train) [5][1060/1345] lr: 1.0000e-02 eta: 4:21:00 time: 0.2066 data_time: 0.0078 memory: 7116 grad_norm: 6.0203 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.1056 loss: 3.1056 2022/09/03 22:13:51 - mmengine - INFO - Epoch(train) [5][1080/1345] lr: 1.0000e-02 eta: 4:20:45 time: 0.2059 data_time: 0.0090 memory: 7116 grad_norm: 5.7361 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.0270 loss: 3.0270 2022/09/03 22:13:55 - mmengine - INFO - Epoch(train) [5][1100/1345] lr: 1.0000e-02 eta: 4:20:31 time: 0.2096 data_time: 0.0086 memory: 7116 grad_norm: 5.9579 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.9547 loss: 2.9547 2022/09/03 22:13:59 - mmengine - INFO - Epoch(train) [5][1120/1345] lr: 1.0000e-02 eta: 4:20:16 time: 0.2045 data_time: 0.0071 memory: 7116 grad_norm: 5.5391 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.0041 loss: 3.0041 2022/09/03 22:14:03 - mmengine - INFO - Epoch(train) [5][1140/1345] lr: 1.0000e-02 eta: 4:20:01 time: 0.2051 data_time: 0.0095 memory: 7116 grad_norm: 5.8735 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.1919 loss: 3.1919 2022/09/03 22:14:07 - mmengine - INFO - Epoch(train) [5][1160/1345] lr: 1.0000e-02 eta: 4:19:47 time: 0.2062 data_time: 0.0075 memory: 7116 grad_norm: 5.7582 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.8189 loss: 2.8189 2022/09/03 22:14:11 - mmengine - INFO - Epoch(train) [5][1180/1345] lr: 1.0000e-02 eta: 4:19:33 time: 0.2078 data_time: 0.0073 memory: 7116 grad_norm: 5.8802 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.0864 loss: 3.0864 2022/09/03 22:14:16 - mmengine - INFO - Epoch(train) [5][1200/1345] lr: 1.0000e-02 eta: 4:19:19 time: 0.2106 data_time: 0.0093 memory: 7116 grad_norm: 5.5024 top1_acc: 0.0000 top5_acc: 0.6250 loss_cls: 3.3244 loss: 3.3244 2022/09/03 22:14:20 - mmengine - INFO - Epoch(train) [5][1220/1345] lr: 1.0000e-02 eta: 4:19:08 time: 0.2227 data_time: 0.0318 memory: 7116 grad_norm: 6.0011 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.1321 loss: 3.1321 2022/09/03 22:14:24 - mmengine - INFO - Epoch(train) [5][1240/1345] lr: 1.0000e-02 eta: 4:18:53 time: 0.2038 data_time: 0.0082 memory: 7116 grad_norm: 5.4993 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 3.2687 loss: 3.2687 2022/09/03 22:14:28 - mmengine - INFO - Epoch(train) [5][1260/1345] lr: 1.0000e-02 eta: 4:18:38 time: 0.2032 data_time: 0.0083 memory: 7116 grad_norm: 5.7324 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 3.0413 loss: 3.0413 2022/09/03 22:14:33 - mmengine - INFO - Epoch(train) [5][1280/1345] lr: 1.0000e-02 eta: 4:18:26 time: 0.2169 data_time: 0.0080 memory: 7116 grad_norm: 5.8994 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 3.0259 loss: 3.0259 2022/09/03 22:14:37 - mmengine - INFO - Epoch(train) [5][1300/1345] lr: 1.0000e-02 eta: 4:18:11 time: 0.2038 data_time: 0.0078 memory: 7116 grad_norm: 5.5999 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.0579 loss: 3.0579 2022/09/03 22:14:41 - mmengine - INFO - Epoch(train) [5][1320/1345] lr: 1.0000e-02 eta: 4:17:57 time: 0.2054 data_time: 0.0091 memory: 7116 grad_norm: 5.8374 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.8495 loss: 2.8495 2022/09/03 22:14:45 - mmengine - INFO - Epoch(train) [5][1340/1345] lr: 1.0000e-02 eta: 4:17:43 time: 0.2084 data_time: 0.0072 memory: 7116 grad_norm: 5.7128 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 3.1580 loss: 3.1580 2022/09/03 22:14:46 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:14:46 - mmengine - INFO - Epoch(train) [5][1345/1345] lr: 1.0000e-02 eta: 4:17:43 time: 0.2026 data_time: 0.0068 memory: 7116 grad_norm: 5.7509 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 3.2302 loss: 3.2302 2022/09/03 22:14:46 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/09/03 22:14:48 - mmengine - INFO - Epoch(val) [5][20/181] eta: 0:00:07 time: 0.0441 data_time: 0.0094 memory: 1114 2022/09/03 22:14:49 - mmengine - INFO - Epoch(val) [5][40/181] eta: 0:00:05 time: 0.0403 data_time: 0.0055 memory: 1114 2022/09/03 22:14:50 - mmengine - INFO - Epoch(val) [5][60/181] eta: 0:00:04 time: 0.0399 data_time: 0.0054 memory: 1114 2022/09/03 22:14:51 - mmengine - INFO - Epoch(val) [5][80/181] eta: 0:00:04 time: 0.0403 data_time: 0.0058 memory: 1114 2022/09/03 22:14:51 - mmengine - INFO - Epoch(val) [5][100/181] eta: 0:00:03 time: 0.0401 data_time: 0.0055 memory: 1114 2022/09/03 22:14:52 - mmengine - INFO - Epoch(val) [5][120/181] eta: 0:00:02 time: 0.0491 data_time: 0.0145 memory: 1114 2022/09/03 22:14:53 - mmengine - INFO - Epoch(val) [5][140/181] eta: 0:00:01 time: 0.0394 data_time: 0.0050 memory: 1114 2022/09/03 22:14:54 - mmengine - INFO - Epoch(val) [5][160/181] eta: 0:00:00 time: 0.0403 data_time: 0.0057 memory: 1114 2022/09/03 22:14:55 - mmengine - INFO - Epoch(val) [5][180/181] eta: 0:00:00 time: 0.0401 data_time: 0.0055 memory: 1114 2022/09/03 22:14:58 - mmengine - INFO - Epoch(val) [5][181/181] acc/top1: 0.2149 acc/top5: 0.4669 acc/mean1: 0.1896 2022/09/03 22:14:58 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_4.pth is removed 2022/09/03 22:14:59 - mmengine - INFO - The best checkpoint with 0.2149 acc/top1 at 5 epoch is saved to best_acc/top1_epoch_5.pth. 2022/09/03 22:15:05 - mmengine - INFO - Epoch(train) [6][20/1345] lr: 1.0000e-02 eta: 4:17:33 time: 0.2980 data_time: 0.0468 memory: 7116 grad_norm: 5.5974 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.8588 loss: 2.8588 2022/09/03 22:15:09 - mmengine - INFO - Epoch(train) [6][40/1345] lr: 1.0000e-02 eta: 4:17:20 time: 0.2083 data_time: 0.0075 memory: 7116 grad_norm: 5.8034 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 3.1441 loss: 3.1441 2022/09/03 22:15:13 - mmengine - INFO - Epoch(train) [6][60/1345] lr: 1.0000e-02 eta: 4:17:06 time: 0.2093 data_time: 0.0101 memory: 7116 grad_norm: 5.8614 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.9092 loss: 2.9092 2022/09/03 22:15:17 - mmengine - INFO - Epoch(train) [6][80/1345] lr: 1.0000e-02 eta: 4:16:52 time: 0.2007 data_time: 0.0077 memory: 7116 grad_norm: 5.8724 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 2.8314 loss: 2.8314 2022/09/03 22:15:22 - mmengine - INFO - Epoch(train) [6][100/1345] lr: 1.0000e-02 eta: 4:16:38 time: 0.2076 data_time: 0.0070 memory: 7116 grad_norm: 6.0821 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.1583 loss: 3.1583 2022/09/03 22:15:26 - mmengine - INFO - Epoch(train) [6][120/1345] lr: 1.0000e-02 eta: 4:16:25 time: 0.2103 data_time: 0.0115 memory: 7116 grad_norm: 5.6191 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.0570 loss: 3.0570 2022/09/03 22:15:30 - mmengine - INFO - Epoch(train) [6][140/1345] lr: 1.0000e-02 eta: 4:16:11 time: 0.2052 data_time: 0.0061 memory: 7116 grad_norm: 5.9803 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.2017 loss: 3.2017 2022/09/03 22:15:34 - mmengine - INFO - Epoch(train) [6][160/1345] lr: 1.0000e-02 eta: 4:15:59 time: 0.2133 data_time: 0.0088 memory: 7116 grad_norm: 6.0367 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.9602 loss: 2.9602 2022/09/03 22:15:38 - mmengine - INFO - Epoch(train) [6][180/1345] lr: 1.0000e-02 eta: 4:15:45 time: 0.2055 data_time: 0.0089 memory: 7116 grad_norm: 6.0327 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.2173 loss: 3.2173 2022/09/03 22:15:43 - mmengine - INFO - Epoch(train) [6][200/1345] lr: 1.0000e-02 eta: 4:15:34 time: 0.2158 data_time: 0.0071 memory: 7116 grad_norm: 5.9518 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 3.0727 loss: 3.0727 2022/09/03 22:15:47 - mmengine - INFO - Epoch(train) [6][220/1345] lr: 1.0000e-02 eta: 4:15:20 time: 0.2079 data_time: 0.0074 memory: 7116 grad_norm: 5.9811 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.0370 loss: 3.0370 2022/09/03 22:15:51 - mmengine - INFO - Epoch(train) [6][240/1345] lr: 1.0000e-02 eta: 4:15:07 time: 0.2038 data_time: 0.0105 memory: 7116 grad_norm: 5.9793 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 2.9273 loss: 2.9273 2022/09/03 22:15:55 - mmengine - INFO - Epoch(train) [6][260/1345] lr: 1.0000e-02 eta: 4:14:53 time: 0.2031 data_time: 0.0068 memory: 7116 grad_norm: 6.1832 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.1427 loss: 3.1427 2022/09/03 22:15:58 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:15:59 - mmengine - INFO - Epoch(train) [6][280/1345] lr: 1.0000e-02 eta: 4:14:39 time: 0.2048 data_time: 0.0067 memory: 7116 grad_norm: 5.7870 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.1066 loss: 3.1066 2022/09/03 22:16:03 - mmengine - INFO - Epoch(train) [6][300/1345] lr: 1.0000e-02 eta: 4:14:28 time: 0.2192 data_time: 0.0103 memory: 7116 grad_norm: 5.8138 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.8042 loss: 2.8042 2022/09/03 22:16:08 - mmengine - INFO - Epoch(train) [6][320/1345] lr: 1.0000e-02 eta: 4:14:16 time: 0.2084 data_time: 0.0082 memory: 7116 grad_norm: 6.1605 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.0419 loss: 3.0419 2022/09/03 22:16:12 - mmengine - INFO - Epoch(train) [6][340/1345] lr: 1.0000e-02 eta: 4:14:02 time: 0.2060 data_time: 0.0061 memory: 7116 grad_norm: 5.9290 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 2.9981 loss: 2.9981 2022/09/03 22:16:16 - mmengine - INFO - Epoch(train) [6][360/1345] lr: 1.0000e-02 eta: 4:13:49 time: 0.2067 data_time: 0.0096 memory: 7116 grad_norm: 6.1359 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.1707 loss: 3.1707 2022/09/03 22:16:20 - mmengine - INFO - Epoch(train) [6][380/1345] lr: 1.0000e-02 eta: 4:13:37 time: 0.2068 data_time: 0.0064 memory: 7116 grad_norm: 5.7973 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.9738 loss: 2.9738 2022/09/03 22:16:24 - mmengine - INFO - Epoch(train) [6][400/1345] lr: 1.0000e-02 eta: 4:13:26 time: 0.2177 data_time: 0.0072 memory: 7116 grad_norm: 5.8446 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.8763 loss: 2.8763 2022/09/03 22:16:28 - mmengine - INFO - Epoch(train) [6][420/1345] lr: 1.0000e-02 eta: 4:13:13 time: 0.2074 data_time: 0.0093 memory: 7116 grad_norm: 6.1131 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 3.2119 loss: 3.2119 2022/09/03 22:16:33 - mmengine - INFO - Epoch(train) [6][440/1345] lr: 1.0000e-02 eta: 4:13:00 time: 0.2052 data_time: 0.0075 memory: 7116 grad_norm: 5.5547 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.9813 loss: 2.9813 2022/09/03 22:16:37 - mmengine - INFO - Epoch(train) [6][460/1345] lr: 1.0000e-02 eta: 4:12:47 time: 0.2069 data_time: 0.0065 memory: 7116 grad_norm: 5.8126 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.7570 loss: 2.7570 2022/09/03 22:16:41 - mmengine - INFO - Epoch(train) [6][480/1345] lr: 1.0000e-02 eta: 4:12:34 time: 0.2069 data_time: 0.0092 memory: 7116 grad_norm: 6.0874 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.5926 loss: 2.5926 2022/09/03 22:16:45 - mmengine - INFO - Epoch(train) [6][500/1345] lr: 1.0000e-02 eta: 4:12:23 time: 0.2116 data_time: 0.0076 memory: 7116 grad_norm: 5.9108 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 3.2401 loss: 3.2401 2022/09/03 22:16:49 - mmengine - INFO - Epoch(train) [6][520/1345] lr: 1.0000e-02 eta: 4:12:10 time: 0.2062 data_time: 0.0079 memory: 7116 grad_norm: 5.9257 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.2083 loss: 3.2083 2022/09/03 22:16:53 - mmengine - INFO - Epoch(train) [6][540/1345] lr: 1.0000e-02 eta: 4:11:58 time: 0.2093 data_time: 0.0090 memory: 7116 grad_norm: 5.6106 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.9866 loss: 2.9866 2022/09/03 22:16:58 - mmengine - INFO - Epoch(train) [6][560/1345] lr: 1.0000e-02 eta: 4:11:45 time: 0.2062 data_time: 0.0068 memory: 7116 grad_norm: 5.8718 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.9761 loss: 2.9761 2022/09/03 22:17:02 - mmengine - INFO - Epoch(train) [6][580/1345] lr: 1.0000e-02 eta: 4:11:33 time: 0.2061 data_time: 0.0069 memory: 7116 grad_norm: 5.3976 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.2104 loss: 3.2104 2022/09/03 22:17:06 - mmengine - INFO - Epoch(train) [6][600/1345] lr: 1.0000e-02 eta: 4:11:20 time: 0.2052 data_time: 0.0100 memory: 7116 grad_norm: 5.9361 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.2243 loss: 3.2243 2022/09/03 22:17:10 - mmengine - INFO - Epoch(train) [6][620/1345] lr: 1.0000e-02 eta: 4:11:07 time: 0.2040 data_time: 0.0070 memory: 7116 grad_norm: 5.8135 top1_acc: 0.0000 top5_acc: 0.5000 loss_cls: 2.9745 loss: 2.9745 2022/09/03 22:17:14 - mmengine - INFO - Epoch(train) [6][640/1345] lr: 1.0000e-02 eta: 4:10:55 time: 0.2081 data_time: 0.0066 memory: 7116 grad_norm: 5.6277 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.2172 loss: 3.2172 2022/09/03 22:17:18 - mmengine - INFO - Epoch(train) [6][660/1345] lr: 1.0000e-02 eta: 4:10:43 time: 0.2047 data_time: 0.0095 memory: 7116 grad_norm: 6.1063 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.0268 loss: 3.0268 2022/09/03 22:17:22 - mmengine - INFO - Epoch(train) [6][680/1345] lr: 1.0000e-02 eta: 4:10:30 time: 0.2062 data_time: 0.0063 memory: 7116 grad_norm: 5.8671 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.7089 loss: 2.7089 2022/09/03 22:17:26 - mmengine - INFO - Epoch(train) [6][700/1345] lr: 1.0000e-02 eta: 4:10:18 time: 0.2093 data_time: 0.0072 memory: 7116 grad_norm: 6.0016 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.8250 loss: 2.8250 2022/09/03 22:17:31 - mmengine - INFO - Epoch(train) [6][720/1345] lr: 1.0000e-02 eta: 4:10:06 time: 0.2066 data_time: 0.0103 memory: 7116 grad_norm: 6.1876 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.8291 loss: 2.8291 2022/09/03 22:17:35 - mmengine - INFO - Epoch(train) [6][740/1345] lr: 1.0000e-02 eta: 4:09:55 time: 0.2088 data_time: 0.0073 memory: 7116 grad_norm: 5.7768 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.6529 loss: 2.6529 2022/09/03 22:17:39 - mmengine - INFO - Epoch(train) [6][760/1345] lr: 1.0000e-02 eta: 4:09:42 time: 0.2046 data_time: 0.0070 memory: 7116 grad_norm: 6.1217 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.7819 loss: 2.7819 2022/09/03 22:17:43 - mmengine - INFO - Epoch(train) [6][780/1345] lr: 1.0000e-02 eta: 4:09:30 time: 0.2048 data_time: 0.0106 memory: 7116 grad_norm: 5.7011 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.8292 loss: 2.8292 2022/09/03 22:17:47 - mmengine - INFO - Epoch(train) [6][800/1345] lr: 1.0000e-02 eta: 4:09:17 time: 0.2039 data_time: 0.0082 memory: 7116 grad_norm: 5.8095 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.7677 loss: 2.7677 2022/09/03 22:17:51 - mmengine - INFO - Epoch(train) [6][820/1345] lr: 1.0000e-02 eta: 4:09:05 time: 0.2031 data_time: 0.0075 memory: 7116 grad_norm: 5.8324 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.8900 loss: 2.8900 2022/09/03 22:17:55 - mmengine - INFO - Epoch(train) [6][840/1345] lr: 1.0000e-02 eta: 4:08:54 time: 0.2147 data_time: 0.0097 memory: 7116 grad_norm: 5.9755 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 3.1413 loss: 3.1413 2022/09/03 22:17:59 - mmengine - INFO - Epoch(train) [6][860/1345] lr: 1.0000e-02 eta: 4:08:42 time: 0.2053 data_time: 0.0069 memory: 7116 grad_norm: 5.8867 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 3.0223 loss: 3.0223 2022/09/03 22:18:04 - mmengine - INFO - Epoch(train) [6][880/1345] lr: 1.0000e-02 eta: 4:08:30 time: 0.2041 data_time: 0.0089 memory: 7116 grad_norm: 5.5476 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.1370 loss: 3.1370 2022/09/03 22:18:08 - mmengine - INFO - Epoch(train) [6][900/1345] lr: 1.0000e-02 eta: 4:08:18 time: 0.2055 data_time: 0.0087 memory: 7116 grad_norm: 5.7708 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 3.0752 loss: 3.0752 2022/09/03 22:18:12 - mmengine - INFO - Epoch(train) [6][920/1345] lr: 1.0000e-02 eta: 4:08:06 time: 0.2050 data_time: 0.0072 memory: 7116 grad_norm: 5.7187 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.7540 loss: 2.7540 2022/09/03 22:18:16 - mmengine - INFO - Epoch(train) [6][940/1345] lr: 1.0000e-02 eta: 4:07:55 time: 0.2095 data_time: 0.0069 memory: 7116 grad_norm: 5.5799 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.0741 loss: 3.0741 2022/09/03 22:18:20 - mmengine - INFO - Epoch(train) [6][960/1345] lr: 1.0000e-02 eta: 4:07:43 time: 0.2040 data_time: 0.0098 memory: 7116 grad_norm: 5.9702 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.9487 loss: 2.9487 2022/09/03 22:18:24 - mmengine - INFO - Epoch(train) [6][980/1345] lr: 1.0000e-02 eta: 4:07:31 time: 0.2048 data_time: 0.0074 memory: 7116 grad_norm: 5.7277 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.2709 loss: 3.2709 2022/09/03 22:18:28 - mmengine - INFO - Epoch(train) [6][1000/1345] lr: 1.0000e-02 eta: 4:07:18 time: 0.2020 data_time: 0.0070 memory: 7116 grad_norm: 5.7162 top1_acc: 0.0000 top5_acc: 0.5000 loss_cls: 2.8651 loss: 2.8651 2022/09/03 22:18:32 - mmengine - INFO - Epoch(train) [6][1020/1345] lr: 1.0000e-02 eta: 4:07:07 time: 0.2078 data_time: 0.0112 memory: 7116 grad_norm: 6.0163 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.7036 loss: 2.7036 2022/09/03 22:18:36 - mmengine - INFO - Epoch(train) [6][1040/1345] lr: 1.0000e-02 eta: 4:06:55 time: 0.2049 data_time: 0.0077 memory: 7116 grad_norm: 5.9448 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 2.7750 loss: 2.7750 2022/09/03 22:18:40 - mmengine - INFO - Epoch(train) [6][1060/1345] lr: 1.0000e-02 eta: 4:06:44 time: 0.2049 data_time: 0.0073 memory: 7116 grad_norm: 5.8514 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 2.9193 loss: 2.9193 2022/09/03 22:18:45 - mmengine - INFO - Epoch(train) [6][1080/1345] lr: 1.0000e-02 eta: 4:06:32 time: 0.2075 data_time: 0.0086 memory: 7116 grad_norm: 5.7982 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.0238 loss: 3.0238 2022/09/03 22:18:49 - mmengine - INFO - Epoch(train) [6][1100/1345] lr: 1.0000e-02 eta: 4:06:20 time: 0.2020 data_time: 0.0074 memory: 7116 grad_norm: 5.9061 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.8489 loss: 2.8489 2022/09/03 22:18:53 - mmengine - INFO - Epoch(train) [6][1120/1345] lr: 1.0000e-02 eta: 4:06:10 time: 0.2157 data_time: 0.0086 memory: 7116 grad_norm: 6.0131 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.9706 loss: 2.9706 2022/09/03 22:18:57 - mmengine - INFO - Epoch(train) [6][1140/1345] lr: 1.0000e-02 eta: 4:05:59 time: 0.2087 data_time: 0.0085 memory: 7116 grad_norm: 5.8214 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.8897 loss: 2.8897 2022/09/03 22:19:01 - mmengine - INFO - Epoch(train) [6][1160/1345] lr: 1.0000e-02 eta: 4:05:48 time: 0.2049 data_time: 0.0070 memory: 7116 grad_norm: 5.8472 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.9522 loss: 2.9522 2022/09/03 22:19:05 - mmengine - INFO - Epoch(train) [6][1180/1345] lr: 1.0000e-02 eta: 4:05:36 time: 0.2050 data_time: 0.0077 memory: 7116 grad_norm: 6.0955 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.8672 loss: 2.8672 2022/09/03 22:19:10 - mmengine - INFO - Epoch(train) [6][1200/1345] lr: 1.0000e-02 eta: 4:05:25 time: 0.2086 data_time: 0.0092 memory: 7116 grad_norm: 5.9438 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.5270 loss: 2.5270 2022/09/03 22:19:14 - mmengine - INFO - Epoch(train) [6][1220/1345] lr: 1.0000e-02 eta: 4:05:15 time: 0.2128 data_time: 0.0080 memory: 7116 grad_norm: 5.7400 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.9490 loss: 2.9490 2022/09/03 22:19:18 - mmengine - INFO - Epoch(train) [6][1240/1345] lr: 1.0000e-02 eta: 4:05:04 time: 0.2058 data_time: 0.0067 memory: 7116 grad_norm: 5.8486 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.0459 loss: 3.0459 2022/09/03 22:19:22 - mmengine - INFO - Epoch(train) [6][1260/1345] lr: 1.0000e-02 eta: 4:04:53 time: 0.2072 data_time: 0.0087 memory: 7116 grad_norm: 5.8667 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.9697 loss: 2.9697 2022/09/03 22:19:25 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:19:26 - mmengine - INFO - Epoch(train) [6][1280/1345] lr: 1.0000e-02 eta: 4:04:42 time: 0.2095 data_time: 0.0087 memory: 7116 grad_norm: 5.6659 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.8422 loss: 2.8422 2022/09/03 22:19:30 - mmengine - INFO - Epoch(train) [6][1300/1345] lr: 1.0000e-02 eta: 4:04:31 time: 0.2066 data_time: 0.0067 memory: 7116 grad_norm: 6.0264 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.0557 loss: 3.0557 2022/09/03 22:19:35 - mmengine - INFO - Epoch(train) [6][1320/1345] lr: 1.0000e-02 eta: 4:04:21 time: 0.2102 data_time: 0.0095 memory: 7116 grad_norm: 6.0375 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.8016 loss: 2.8016 2022/09/03 22:19:39 - mmengine - INFO - Epoch(train) [6][1340/1345] lr: 1.0000e-02 eta: 4:04:10 time: 0.2064 data_time: 0.0071 memory: 7116 grad_norm: 5.8415 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.8951 loss: 2.8951 2022/09/03 22:19:40 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:19:40 - mmengine - INFO - Epoch(train) [6][1345/1345] lr: 1.0000e-02 eta: 4:04:10 time: 0.1994 data_time: 0.0064 memory: 7116 grad_norm: 6.4048 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 3.0357 loss: 3.0357 2022/09/03 22:19:40 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/09/03 22:19:42 - mmengine - INFO - Epoch(val) [6][20/181] eta: 0:00:07 time: 0.0441 data_time: 0.0085 memory: 1114 2022/09/03 22:19:43 - mmengine - INFO - Epoch(val) [6][40/181] eta: 0:00:07 time: 0.0534 data_time: 0.0186 memory: 1114 2022/09/03 22:19:44 - mmengine - INFO - Epoch(val) [6][60/181] eta: 0:00:04 time: 0.0398 data_time: 0.0053 memory: 1114 2022/09/03 22:19:45 - mmengine - INFO - Epoch(val) [6][80/181] eta: 0:00:04 time: 0.0400 data_time: 0.0053 memory: 1114 2022/09/03 22:19:46 - mmengine - INFO - Epoch(val) [6][100/181] eta: 0:00:03 time: 0.0402 data_time: 0.0055 memory: 1114 2022/09/03 22:19:46 - mmengine - INFO - Epoch(val) [6][120/181] eta: 0:00:02 time: 0.0406 data_time: 0.0057 memory: 1114 2022/09/03 22:19:47 - mmengine - INFO - Epoch(val) [6][140/181] eta: 0:00:01 time: 0.0406 data_time: 0.0056 memory: 1114 2022/09/03 22:19:48 - mmengine - INFO - Epoch(val) [6][160/181] eta: 0:00:00 time: 0.0402 data_time: 0.0054 memory: 1114 2022/09/03 22:19:49 - mmengine - INFO - Epoch(val) [6][180/181] eta: 0:00:00 time: 0.0400 data_time: 0.0053 memory: 1114 2022/09/03 22:19:52 - mmengine - INFO - Epoch(val) [6][181/181] acc/top1: 0.2404 acc/top5: 0.5156 acc/mean1: 0.2184 2022/09/03 22:19:52 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_5.pth is removed 2022/09/03 22:19:53 - mmengine - INFO - The best checkpoint with 0.2404 acc/top1 at 6 epoch is saved to best_acc/top1_epoch_6.pth. 2022/09/03 22:19:57 - mmengine - INFO - Epoch(train) [7][20/1345] lr: 1.0000e-02 eta: 4:03:52 time: 0.2281 data_time: 0.0242 memory: 7116 grad_norm: 5.8634 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.8183 loss: 2.8183 2022/09/03 22:20:02 - mmengine - INFO - Epoch(train) [7][40/1345] lr: 1.0000e-02 eta: 4:03:41 time: 0.2078 data_time: 0.0067 memory: 7116 grad_norm: 6.0478 top1_acc: 0.0000 top5_acc: 0.5000 loss_cls: 3.0876 loss: 3.0876 2022/09/03 22:20:06 - mmengine - INFO - Epoch(train) [7][60/1345] lr: 1.0000e-02 eta: 4:03:30 time: 0.2057 data_time: 0.0073 memory: 7116 grad_norm: 5.9758 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.8612 loss: 2.8612 2022/09/03 22:20:10 - mmengine - INFO - Epoch(train) [7][80/1345] lr: 1.0000e-02 eta: 4:03:19 time: 0.2065 data_time: 0.0099 memory: 7116 grad_norm: 5.7583 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.8896 loss: 2.8896 2022/09/03 22:20:14 - mmengine - INFO - Epoch(train) [7][100/1345] lr: 1.0000e-02 eta: 4:03:11 time: 0.2240 data_time: 0.0079 memory: 7116 grad_norm: 5.7690 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.7807 loss: 2.7807 2022/09/03 22:20:18 - mmengine - INFO - Epoch(train) [7][120/1345] lr: 1.0000e-02 eta: 4:03:00 time: 0.2080 data_time: 0.0054 memory: 7116 grad_norm: 5.9876 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.6956 loss: 2.6956 2022/09/03 22:20:23 - mmengine - INFO - Epoch(train) [7][140/1345] lr: 1.0000e-02 eta: 4:02:50 time: 0.2080 data_time: 0.0091 memory: 7116 grad_norm: 5.9137 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.8757 loss: 2.8757 2022/09/03 22:20:27 - mmengine - INFO - Epoch(train) [7][160/1345] lr: 1.0000e-02 eta: 4:02:39 time: 0.2062 data_time: 0.0072 memory: 7116 grad_norm: 5.6910 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.9860 loss: 2.9860 2022/09/03 22:20:31 - mmengine - INFO - Epoch(train) [7][180/1345] lr: 1.0000e-02 eta: 4:02:28 time: 0.2067 data_time: 0.0070 memory: 7116 grad_norm: 6.2239 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.6599 loss: 2.6599 2022/09/03 22:20:35 - mmengine - INFO - Epoch(train) [7][200/1345] lr: 1.0000e-02 eta: 4:02:19 time: 0.2163 data_time: 0.0092 memory: 7116 grad_norm: 6.4367 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5869 loss: 2.5869 2022/09/03 22:20:39 - mmengine - INFO - Epoch(train) [7][220/1345] lr: 1.0000e-02 eta: 4:02:08 time: 0.2067 data_time: 0.0070 memory: 7116 grad_norm: 6.1805 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 3.0421 loss: 3.0421 2022/09/03 22:20:44 - mmengine - INFO - Epoch(train) [7][240/1345] lr: 1.0000e-02 eta: 4:01:58 time: 0.2091 data_time: 0.0067 memory: 7116 grad_norm: 5.7998 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.8945 loss: 2.8945 2022/09/03 22:20:48 - mmengine - INFO - Epoch(train) [7][260/1345] lr: 1.0000e-02 eta: 4:01:48 time: 0.2092 data_time: 0.0094 memory: 7116 grad_norm: 5.6353 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.8216 loss: 2.8216 2022/09/03 22:20:52 - mmengine - INFO - Epoch(train) [7][280/1345] lr: 1.0000e-02 eta: 4:01:37 time: 0.2061 data_time: 0.0068 memory: 7116 grad_norm: 6.1271 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.8774 loss: 2.8774 2022/09/03 22:20:56 - mmengine - INFO - Epoch(train) [7][300/1345] lr: 1.0000e-02 eta: 4:01:27 time: 0.2103 data_time: 0.0076 memory: 7116 grad_norm: 5.7880 top1_acc: 0.0000 top5_acc: 0.6250 loss_cls: 2.7741 loss: 2.7741 2022/09/03 22:21:00 - mmengine - INFO - Epoch(train) [7][320/1345] lr: 1.0000e-02 eta: 4:01:17 time: 0.2079 data_time: 0.0088 memory: 7116 grad_norm: 5.8426 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.7007 loss: 2.7007 2022/09/03 22:21:04 - mmengine - INFO - Epoch(train) [7][340/1345] lr: 1.0000e-02 eta: 4:01:06 time: 0.1997 data_time: 0.0075 memory: 7116 grad_norm: 5.8688 top1_acc: 0.0000 top5_acc: 0.7500 loss_cls: 2.9948 loss: 2.9948 2022/09/03 22:21:08 - mmengine - INFO - Epoch(train) [7][360/1345] lr: 1.0000e-02 eta: 4:00:56 time: 0.2087 data_time: 0.0067 memory: 7116 grad_norm: 6.0374 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 3.0046 loss: 3.0046 2022/09/03 22:21:13 - mmengine - INFO - Epoch(train) [7][380/1345] lr: 1.0000e-02 eta: 4:00:46 time: 0.2089 data_time: 0.0097 memory: 7116 grad_norm: 5.7991 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.9277 loss: 2.9277 2022/09/03 22:21:17 - mmengine - INFO - Epoch(train) [7][400/1345] lr: 1.0000e-02 eta: 4:00:35 time: 0.2078 data_time: 0.0076 memory: 7116 grad_norm: 5.9977 top1_acc: 0.0000 top5_acc: 0.5000 loss_cls: 2.8938 loss: 2.8938 2022/09/03 22:21:21 - mmengine - INFO - Epoch(train) [7][420/1345] lr: 1.0000e-02 eta: 4:00:25 time: 0.2035 data_time: 0.0072 memory: 7116 grad_norm: 5.8690 top1_acc: 0.0000 top5_acc: 0.5000 loss_cls: 2.8264 loss: 2.8264 2022/09/03 22:21:25 - mmengine - INFO - Epoch(train) [7][440/1345] lr: 1.0000e-02 eta: 4:00:15 time: 0.2085 data_time: 0.0090 memory: 7116 grad_norm: 6.1253 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.6707 loss: 2.6707 2022/09/03 22:21:29 - mmengine - INFO - Epoch(train) [7][460/1345] lr: 1.0000e-02 eta: 4:00:05 time: 0.2078 data_time: 0.0073 memory: 7116 grad_norm: 6.2043 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.7252 loss: 2.7252 2022/09/03 22:21:33 - mmengine - INFO - Epoch(train) [7][480/1345] lr: 1.0000e-02 eta: 3:59:54 time: 0.2068 data_time: 0.0074 memory: 7116 grad_norm: 5.9542 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.7489 loss: 2.7489 2022/09/03 22:21:37 - mmengine - INFO - Epoch(train) [7][500/1345] lr: 1.0000e-02 eta: 3:59:44 time: 0.2067 data_time: 0.0099 memory: 7116 grad_norm: 5.8640 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.9935 loss: 2.9935 2022/09/03 22:21:41 - mmengine - INFO - Epoch(train) [7][520/1345] lr: 1.0000e-02 eta: 3:59:34 time: 0.2042 data_time: 0.0072 memory: 7116 grad_norm: 6.0131 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.7488 loss: 2.7488 2022/09/03 22:21:46 - mmengine - INFO - Epoch(train) [7][540/1345] lr: 1.0000e-02 eta: 3:59:24 time: 0.2131 data_time: 0.0078 memory: 7116 grad_norm: 5.9825 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.8268 loss: 2.8268 2022/09/03 22:21:50 - mmengine - INFO - Epoch(train) [7][560/1345] lr: 1.0000e-02 eta: 3:59:15 time: 0.2102 data_time: 0.0090 memory: 7116 grad_norm: 5.9509 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.7625 loss: 2.7625 2022/09/03 22:21:54 - mmengine - INFO - Epoch(train) [7][580/1345] lr: 1.0000e-02 eta: 3:59:05 time: 0.2087 data_time: 0.0073 memory: 7116 grad_norm: 6.2725 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.8211 loss: 2.8211 2022/09/03 22:21:58 - mmengine - INFO - Epoch(train) [7][600/1345] lr: 1.0000e-02 eta: 3:58:55 time: 0.2082 data_time: 0.0070 memory: 7116 grad_norm: 6.0096 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.7095 loss: 2.7095 2022/09/03 22:22:03 - mmengine - INFO - Epoch(train) [7][620/1345] lr: 1.0000e-02 eta: 3:58:46 time: 0.2151 data_time: 0.0093 memory: 7116 grad_norm: 6.0458 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.1464 loss: 3.1464 2022/09/03 22:22:07 - mmengine - INFO - Epoch(train) [7][640/1345] lr: 1.0000e-02 eta: 3:58:37 time: 0.2115 data_time: 0.0079 memory: 7116 grad_norm: 5.9258 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.9468 loss: 2.9468 2022/09/03 22:22:11 - mmengine - INFO - Epoch(train) [7][660/1345] lr: 1.0000e-02 eta: 3:58:27 time: 0.2069 data_time: 0.0059 memory: 7116 grad_norm: 6.2102 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.9593 loss: 2.9593 2022/09/03 22:22:15 - mmengine - INFO - Epoch(train) [7][680/1345] lr: 1.0000e-02 eta: 3:58:17 time: 0.2096 data_time: 0.0094 memory: 7116 grad_norm: 5.8857 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.7882 loss: 2.7882 2022/09/03 22:22:19 - mmengine - INFO - Epoch(train) [7][700/1345] lr: 1.0000e-02 eta: 3:58:08 time: 0.2104 data_time: 0.0067 memory: 7116 grad_norm: 5.8872 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.8743 loss: 2.8743 2022/09/03 22:22:24 - mmengine - INFO - Epoch(train) [7][720/1345] lr: 1.0000e-02 eta: 3:57:59 time: 0.2141 data_time: 0.0065 memory: 7116 grad_norm: 6.2439 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.7335 loss: 2.7335 2022/09/03 22:22:28 - mmengine - INFO - Epoch(train) [7][740/1345] lr: 1.0000e-02 eta: 3:57:50 time: 0.2110 data_time: 0.0094 memory: 7116 grad_norm: 5.9906 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.8064 loss: 2.8064 2022/09/03 22:22:32 - mmengine - INFO - Epoch(train) [7][760/1345] lr: 1.0000e-02 eta: 3:57:40 time: 0.2095 data_time: 0.0069 memory: 7116 grad_norm: 5.9799 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6260 loss: 2.6260 2022/09/03 22:22:36 - mmengine - INFO - Epoch(train) [7][780/1345] lr: 1.0000e-02 eta: 3:57:31 time: 0.2134 data_time: 0.0081 memory: 7116 grad_norm: 5.9263 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.0393 loss: 3.0393 2022/09/03 22:22:40 - mmengine - INFO - Epoch(train) [7][800/1345] lr: 1.0000e-02 eta: 3:57:22 time: 0.2098 data_time: 0.0088 memory: 7116 grad_norm: 6.2653 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 2.7819 loss: 2.7819 2022/09/03 22:22:45 - mmengine - INFO - Epoch(train) [7][820/1345] lr: 1.0000e-02 eta: 3:57:13 time: 0.2140 data_time: 0.0078 memory: 7116 grad_norm: 6.3261 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.8557 loss: 2.8557 2022/09/03 22:22:49 - mmengine - INFO - Epoch(train) [7][840/1345] lr: 1.0000e-02 eta: 3:57:04 time: 0.2121 data_time: 0.0063 memory: 7116 grad_norm: 6.0643 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.6553 loss: 2.6553 2022/09/03 22:22:53 - mmengine - INFO - Epoch(train) [7][860/1345] lr: 1.0000e-02 eta: 3:56:56 time: 0.2181 data_time: 0.0096 memory: 7116 grad_norm: 6.2051 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 3.0696 loss: 3.0696 2022/09/03 22:22:58 - mmengine - INFO - Epoch(train) [7][880/1345] lr: 1.0000e-02 eta: 3:56:48 time: 0.2170 data_time: 0.0073 memory: 7116 grad_norm: 5.9206 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.7934 loss: 2.7934 2022/09/03 22:23:02 - mmengine - INFO - Epoch(train) [7][900/1345] lr: 1.0000e-02 eta: 3:56:38 time: 0.2111 data_time: 0.0070 memory: 7116 grad_norm: 6.1303 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.8066 loss: 2.8066 2022/09/03 22:23:06 - mmengine - INFO - Epoch(train) [7][920/1345] lr: 1.0000e-02 eta: 3:56:30 time: 0.2148 data_time: 0.0092 memory: 7116 grad_norm: 5.8566 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.0825 loss: 3.0825 2022/09/03 22:23:08 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:23:11 - mmengine - INFO - Epoch(train) [7][940/1345] lr: 1.0000e-02 eta: 3:56:21 time: 0.2141 data_time: 0.0068 memory: 7116 grad_norm: 6.1060 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.0404 loss: 3.0404 2022/09/03 22:23:15 - mmengine - INFO - Epoch(train) [7][960/1345] lr: 1.0000e-02 eta: 3:56:13 time: 0.2185 data_time: 0.0073 memory: 7116 grad_norm: 6.0909 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.7169 loss: 2.7169 2022/09/03 22:23:19 - mmengine - INFO - Epoch(train) [7][980/1345] lr: 1.0000e-02 eta: 3:56:04 time: 0.2132 data_time: 0.0088 memory: 7116 grad_norm: 6.0834 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.7598 loss: 2.7598 2022/09/03 22:23:23 - mmengine - INFO - Epoch(train) [7][1000/1345] lr: 1.0000e-02 eta: 3:55:56 time: 0.2133 data_time: 0.0067 memory: 7116 grad_norm: 6.0068 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.9632 loss: 2.9632 2022/09/03 22:23:28 - mmengine - INFO - Epoch(train) [7][1020/1345] lr: 1.0000e-02 eta: 3:55:47 time: 0.2127 data_time: 0.0070 memory: 7116 grad_norm: 6.1632 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 2.9590 loss: 2.9590 2022/09/03 22:23:32 - mmengine - INFO - Epoch(train) [7][1040/1345] lr: 1.0000e-02 eta: 3:55:38 time: 0.2160 data_time: 0.0084 memory: 7116 grad_norm: 5.8552 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.9309 loss: 2.9309 2022/09/03 22:23:36 - mmengine - INFO - Epoch(train) [7][1060/1345] lr: 1.0000e-02 eta: 3:55:31 time: 0.2248 data_time: 0.0077 memory: 7116 grad_norm: 5.9442 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.8010 loss: 2.8010 2022/09/03 22:23:41 - mmengine - INFO - Epoch(train) [7][1080/1345] lr: 1.0000e-02 eta: 3:55:22 time: 0.2113 data_time: 0.0072 memory: 7116 grad_norm: 5.9046 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.6091 loss: 2.6091 2022/09/03 22:23:45 - mmengine - INFO - Epoch(train) [7][1100/1345] lr: 1.0000e-02 eta: 3:55:14 time: 0.2157 data_time: 0.0087 memory: 7116 grad_norm: 5.7764 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.7469 loss: 2.7469 2022/09/03 22:23:49 - mmengine - INFO - Epoch(train) [7][1120/1345] lr: 1.0000e-02 eta: 3:55:05 time: 0.2127 data_time: 0.0065 memory: 7116 grad_norm: 6.1743 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.8247 loss: 2.8247 2022/09/03 22:23:54 - mmengine - INFO - Epoch(train) [7][1140/1345] lr: 1.0000e-02 eta: 3:54:57 time: 0.2158 data_time: 0.0078 memory: 7116 grad_norm: 5.8218 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.8862 loss: 2.8862 2022/09/03 22:23:58 - mmengine - INFO - Epoch(train) [7][1160/1345] lr: 1.0000e-02 eta: 3:54:49 time: 0.2148 data_time: 0.0097 memory: 7116 grad_norm: 6.0632 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.8407 loss: 2.8407 2022/09/03 22:24:02 - mmengine - INFO - Epoch(train) [7][1180/1345] lr: 1.0000e-02 eta: 3:54:40 time: 0.2141 data_time: 0.0062 memory: 7116 grad_norm: 5.6641 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.8740 loss: 2.8740 2022/09/03 22:24:07 - mmengine - INFO - Epoch(train) [7][1200/1345] lr: 1.0000e-02 eta: 3:54:32 time: 0.2169 data_time: 0.0073 memory: 7116 grad_norm: 5.8948 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.8232 loss: 2.8232 2022/09/03 22:24:11 - mmengine - INFO - Epoch(train) [7][1220/1345] lr: 1.0000e-02 eta: 3:54:24 time: 0.2145 data_time: 0.0087 memory: 7116 grad_norm: 5.9178 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.8906 loss: 2.8906 2022/09/03 22:24:15 - mmengine - INFO - Epoch(train) [7][1240/1345] lr: 1.0000e-02 eta: 3:54:17 time: 0.2240 data_time: 0.0079 memory: 7116 grad_norm: 5.9881 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.7446 loss: 2.7446 2022/09/03 22:24:20 - mmengine - INFO - Epoch(train) [7][1260/1345] lr: 1.0000e-02 eta: 3:54:08 time: 0.2124 data_time: 0.0067 memory: 7116 grad_norm: 6.0040 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.6463 loss: 2.6463 2022/09/03 22:24:24 - mmengine - INFO - Epoch(train) [7][1280/1345] lr: 1.0000e-02 eta: 3:54:00 time: 0.2144 data_time: 0.0092 memory: 7116 grad_norm: 6.0677 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.6515 loss: 2.6515 2022/09/03 22:24:28 - mmengine - INFO - Epoch(train) [7][1300/1345] lr: 1.0000e-02 eta: 3:53:51 time: 0.2144 data_time: 0.0069 memory: 7116 grad_norm: 6.2020 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4667 loss: 2.4667 2022/09/03 22:24:32 - mmengine - INFO - Epoch(train) [7][1320/1345] lr: 1.0000e-02 eta: 3:53:43 time: 0.2176 data_time: 0.0066 memory: 7116 grad_norm: 6.0785 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.9499 loss: 2.9499 2022/09/03 22:24:37 - mmengine - INFO - Epoch(train) [7][1340/1345] lr: 1.0000e-02 eta: 3:53:35 time: 0.2169 data_time: 0.0114 memory: 7116 grad_norm: 6.0276 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 2.9574 loss: 2.9574 2022/09/03 22:24:38 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:24:38 - mmengine - INFO - Epoch(train) [7][1345/1345] lr: 1.0000e-02 eta: 3:53:35 time: 0.2076 data_time: 0.0056 memory: 7116 grad_norm: 9.0622 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.9584 loss: 2.9584 2022/09/03 22:24:38 - mmengine - INFO - Saving checkpoint at 7 epochs 2022/09/03 22:24:40 - mmengine - INFO - Epoch(val) [7][20/181] eta: 0:00:06 time: 0.0431 data_time: 0.0089 memory: 1114 2022/09/03 22:24:41 - mmengine - INFO - Epoch(val) [7][40/181] eta: 0:00:05 time: 0.0396 data_time: 0.0053 memory: 1114 2022/09/03 22:24:42 - mmengine - INFO - Epoch(val) [7][60/181] eta: 0:00:04 time: 0.0398 data_time: 0.0054 memory: 1114 2022/09/03 22:24:42 - mmengine - INFO - Epoch(val) [7][80/181] eta: 0:00:04 time: 0.0402 data_time: 0.0057 memory: 1114 2022/09/03 22:24:43 - mmengine - INFO - Epoch(val) [7][100/181] eta: 0:00:03 time: 0.0397 data_time: 0.0054 memory: 1114 2022/09/03 22:24:44 - mmengine - INFO - Epoch(val) [7][120/181] eta: 0:00:02 time: 0.0413 data_time: 0.0064 memory: 1114 2022/09/03 22:24:45 - mmengine - INFO - Epoch(val) [7][140/181] eta: 0:00:01 time: 0.0401 data_time: 0.0057 memory: 1114 2022/09/03 22:24:46 - mmengine - INFO - Epoch(val) [7][160/181] eta: 0:00:00 time: 0.0400 data_time: 0.0056 memory: 1114 2022/09/03 22:24:46 - mmengine - INFO - Epoch(val) [7][180/181] eta: 0:00:00 time: 0.0399 data_time: 0.0054 memory: 1114 2022/09/03 22:24:50 - mmengine - INFO - Epoch(val) [7][181/181] acc/top1: 0.2401 acc/top5: 0.5226 acc/mean1: 0.2131 2022/09/03 22:24:56 - mmengine - INFO - Epoch(train) [8][20/1345] lr: 1.0000e-02 eta: 3:53:28 time: 0.2893 data_time: 0.0248 memory: 7116 grad_norm: 6.2735 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.6522 loss: 2.6522 2022/09/03 22:25:00 - mmengine - INFO - Epoch(train) [8][40/1345] lr: 1.0000e-02 eta: 3:53:20 time: 0.2151 data_time: 0.0067 memory: 7116 grad_norm: 6.1612 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.7990 loss: 2.7990 2022/09/03 22:25:05 - mmengine - INFO - Epoch(train) [8][60/1345] lr: 1.0000e-02 eta: 3:53:12 time: 0.2196 data_time: 0.0098 memory: 7116 grad_norm: 5.8627 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.7954 loss: 2.7954 2022/09/03 22:25:09 - mmengine - INFO - Epoch(train) [8][80/1345] lr: 1.0000e-02 eta: 3:53:04 time: 0.2157 data_time: 0.0075 memory: 7116 grad_norm: 6.1929 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.7563 loss: 2.7563 2022/09/03 22:25:14 - mmengine - INFO - Epoch(train) [8][100/1345] lr: 1.0000e-02 eta: 3:52:57 time: 0.2235 data_time: 0.0073 memory: 7116 grad_norm: 6.1617 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.9687 loss: 2.9687 2022/09/03 22:25:18 - mmengine - INFO - Epoch(train) [8][120/1345] lr: 1.0000e-02 eta: 3:52:49 time: 0.2148 data_time: 0.0085 memory: 7116 grad_norm: 5.9012 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.8067 loss: 2.8067 2022/09/03 22:25:22 - mmengine - INFO - Epoch(train) [8][140/1345] lr: 1.0000e-02 eta: 3:52:41 time: 0.2156 data_time: 0.0073 memory: 7116 grad_norm: 5.9548 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.7759 loss: 2.7759 2022/09/03 22:25:26 - mmengine - INFO - Epoch(train) [8][160/1345] lr: 1.0000e-02 eta: 3:52:32 time: 0.2135 data_time: 0.0070 memory: 7116 grad_norm: 6.2605 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.9945 loss: 2.9945 2022/09/03 22:25:31 - mmengine - INFO - Epoch(train) [8][180/1345] lr: 1.0000e-02 eta: 3:52:24 time: 0.2142 data_time: 0.0086 memory: 7116 grad_norm: 5.9031 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.8105 loss: 2.8105 2022/09/03 22:25:35 - mmengine - INFO - Epoch(train) [8][200/1345] lr: 1.0000e-02 eta: 3:52:16 time: 0.2161 data_time: 0.0071 memory: 7116 grad_norm: 6.2072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.7405 loss: 2.7405 2022/09/03 22:25:39 - mmengine - INFO - Epoch(train) [8][220/1345] lr: 1.0000e-02 eta: 3:52:08 time: 0.2158 data_time: 0.0074 memory: 7116 grad_norm: 5.7867 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.9081 loss: 2.9081 2022/09/03 22:25:44 - mmengine - INFO - Epoch(train) [8][240/1345] lr: 1.0000e-02 eta: 3:52:00 time: 0.2154 data_time: 0.0089 memory: 7116 grad_norm: 6.2092 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.9030 loss: 2.9030 2022/09/03 22:25:48 - mmengine - INFO - Epoch(train) [8][260/1345] lr: 1.0000e-02 eta: 3:51:52 time: 0.2123 data_time: 0.0064 memory: 7116 grad_norm: 5.9036 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.6645 loss: 2.6645 2022/09/03 22:25:52 - mmengine - INFO - Epoch(train) [8][280/1345] lr: 1.0000e-02 eta: 3:51:44 time: 0.2151 data_time: 0.0071 memory: 7116 grad_norm: 6.1647 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.8536 loss: 2.8536 2022/09/03 22:25:57 - mmengine - INFO - Epoch(train) [8][300/1345] lr: 1.0000e-02 eta: 3:51:36 time: 0.2183 data_time: 0.0097 memory: 7116 grad_norm: 6.0927 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.7578 loss: 2.7578 2022/09/03 22:26:01 - mmengine - INFO - Epoch(train) [8][320/1345] lr: 1.0000e-02 eta: 3:51:28 time: 0.2129 data_time: 0.0057 memory: 7116 grad_norm: 5.9194 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.8053 loss: 2.8053 2022/09/03 22:26:05 - mmengine - INFO - Epoch(train) [8][340/1345] lr: 1.0000e-02 eta: 3:51:20 time: 0.2168 data_time: 0.0086 memory: 7116 grad_norm: 6.0500 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.8680 loss: 2.8680 2022/09/03 22:26:10 - mmengine - INFO - Epoch(train) [8][360/1345] lr: 1.0000e-02 eta: 3:51:13 time: 0.2155 data_time: 0.0091 memory: 7116 grad_norm: 5.9704 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5304 loss: 2.5304 2022/09/03 22:26:14 - mmengine - INFO - Epoch(train) [8][380/1345] lr: 1.0000e-02 eta: 3:51:05 time: 0.2175 data_time: 0.0068 memory: 7116 grad_norm: 5.9731 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.5139 loss: 2.5139 2022/09/03 22:26:18 - mmengine - INFO - Epoch(train) [8][400/1345] lr: 1.0000e-02 eta: 3:50:57 time: 0.2151 data_time: 0.0071 memory: 7116 grad_norm: 5.8908 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 3.0848 loss: 3.0848 2022/09/03 22:26:22 - mmengine - INFO - Epoch(train) [8][420/1345] lr: 1.0000e-02 eta: 3:50:49 time: 0.2151 data_time: 0.0095 memory: 7116 grad_norm: 5.9246 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.7289 loss: 2.7289 2022/09/03 22:26:27 - mmengine - INFO - Epoch(train) [8][440/1345] lr: 1.0000e-02 eta: 3:50:41 time: 0.2145 data_time: 0.0065 memory: 7116 grad_norm: 6.2254 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.9362 loss: 2.9362 2022/09/03 22:26:31 - mmengine - INFO - Epoch(train) [8][460/1345] lr: 1.0000e-02 eta: 3:50:33 time: 0.2128 data_time: 0.0066 memory: 7116 grad_norm: 6.2164 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.7672 loss: 2.7672 2022/09/03 22:26:35 - mmengine - INFO - Epoch(train) [8][480/1345] lr: 1.0000e-02 eta: 3:50:26 time: 0.2193 data_time: 0.0089 memory: 7116 grad_norm: 6.1787 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.4768 loss: 2.4768 2022/09/03 22:26:40 - mmengine - INFO - Epoch(train) [8][500/1345] lr: 1.0000e-02 eta: 3:50:18 time: 0.2161 data_time: 0.0067 memory: 7116 grad_norm: 6.0111 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.9354 loss: 2.9354 2022/09/03 22:26:44 - mmengine - INFO - Epoch(train) [8][520/1345] lr: 1.0000e-02 eta: 3:50:10 time: 0.2172 data_time: 0.0071 memory: 7116 grad_norm: 6.0815 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.8240 loss: 2.8240 2022/09/03 22:26:48 - mmengine - INFO - Epoch(train) [8][540/1345] lr: 1.0000e-02 eta: 3:50:03 time: 0.2161 data_time: 0.0089 memory: 7116 grad_norm: 6.1250 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.8641 loss: 2.8641 2022/09/03 22:26:53 - mmengine - INFO - Epoch(train) [8][560/1345] lr: 1.0000e-02 eta: 3:49:55 time: 0.2126 data_time: 0.0067 memory: 7116 grad_norm: 5.9509 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.7283 loss: 2.7283 2022/09/03 22:26:57 - mmengine - INFO - Epoch(train) [8][580/1345] lr: 1.0000e-02 eta: 3:49:47 time: 0.2181 data_time: 0.0076 memory: 7116 grad_norm: 5.9909 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6026 loss: 2.6026 2022/09/03 22:26:58 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:27:01 - mmengine - INFO - Epoch(train) [8][600/1345] lr: 1.0000e-02 eta: 3:49:39 time: 0.2133 data_time: 0.0086 memory: 7116 grad_norm: 6.3641 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.6360 loss: 2.6360 2022/09/03 22:27:06 - mmengine - INFO - Epoch(train) [8][620/1345] lr: 1.0000e-02 eta: 3:49:32 time: 0.2180 data_time: 0.0068 memory: 7116 grad_norm: 6.0800 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.7141 loss: 2.7141 2022/09/03 22:27:10 - mmengine - INFO - Epoch(train) [8][640/1345] lr: 1.0000e-02 eta: 3:49:24 time: 0.2142 data_time: 0.0070 memory: 7116 grad_norm: 5.9891 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.7401 loss: 2.7401 2022/09/03 22:27:14 - mmengine - INFO - Epoch(train) [8][660/1345] lr: 1.0000e-02 eta: 3:49:16 time: 0.2146 data_time: 0.0106 memory: 7116 grad_norm: 5.9580 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.9294 loss: 2.9294 2022/09/03 22:27:18 - mmengine - INFO - Epoch(train) [8][680/1345] lr: 1.0000e-02 eta: 3:49:08 time: 0.2113 data_time: 0.0062 memory: 7116 grad_norm: 6.2889 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.8374 loss: 2.8374 2022/09/03 22:27:23 - mmengine - INFO - Epoch(train) [8][700/1345] lr: 1.0000e-02 eta: 3:49:00 time: 0.2116 data_time: 0.0067 memory: 7116 grad_norm: 6.2759 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.3818 loss: 2.3818 2022/09/03 22:27:27 - mmengine - INFO - Epoch(train) [8][720/1345] lr: 1.0000e-02 eta: 3:48:53 time: 0.2183 data_time: 0.0098 memory: 7116 grad_norm: 6.0659 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.9048 loss: 2.9048 2022/09/03 22:27:31 - mmengine - INFO - Epoch(train) [8][740/1345] lr: 1.0000e-02 eta: 3:48:45 time: 0.2128 data_time: 0.0067 memory: 7116 grad_norm: 5.9047 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.8060 loss: 2.8060 2022/09/03 22:27:35 - mmengine - INFO - Epoch(train) [8][760/1345] lr: 1.0000e-02 eta: 3:48:36 time: 0.2057 data_time: 0.0074 memory: 7116 grad_norm: 6.1338 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5363 loss: 2.5363 2022/09/03 22:27:40 - mmengine - INFO - Epoch(train) [8][780/1345] lr: 1.0000e-02 eta: 3:48:28 time: 0.2095 data_time: 0.0100 memory: 7116 grad_norm: 6.0600 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4941 loss: 2.4941 2022/09/03 22:27:44 - mmengine - INFO - Epoch(train) [8][800/1345] lr: 1.0000e-02 eta: 3:48:20 time: 0.2091 data_time: 0.0072 memory: 7116 grad_norm: 5.9083 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.9215 loss: 2.9215 2022/09/03 22:27:48 - mmengine - INFO - Epoch(train) [8][820/1345] lr: 1.0000e-02 eta: 3:48:12 time: 0.2136 data_time: 0.0064 memory: 7116 grad_norm: 6.3392 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.8858 loss: 2.8858 2022/09/03 22:27:52 - mmengine - INFO - Epoch(train) [8][840/1345] lr: 1.0000e-02 eta: 3:48:04 time: 0.2115 data_time: 0.0089 memory: 7116 grad_norm: 5.8606 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.7256 loss: 2.7256 2022/09/03 22:27:57 - mmengine - INFO - Epoch(train) [8][860/1345] lr: 1.0000e-02 eta: 3:47:56 time: 0.2135 data_time: 0.0071 memory: 7116 grad_norm: 6.1399 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.7018 loss: 2.7018 2022/09/03 22:28:01 - mmengine - INFO - Epoch(train) [8][880/1345] lr: 1.0000e-02 eta: 3:47:48 time: 0.2136 data_time: 0.0066 memory: 7116 grad_norm: 5.9024 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.9271 loss: 2.9271 2022/09/03 22:28:05 - mmengine - INFO - Epoch(train) [8][900/1345] lr: 1.0000e-02 eta: 3:47:41 time: 0.2164 data_time: 0.0092 memory: 7116 grad_norm: 5.9109 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.7229 loss: 2.7229 2022/09/03 22:28:09 - mmengine - INFO - Epoch(train) [8][920/1345] lr: 1.0000e-02 eta: 3:47:33 time: 0.2141 data_time: 0.0065 memory: 7116 grad_norm: 6.0448 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.6702 loss: 2.6702 2022/09/03 22:28:14 - mmengine - INFO - Epoch(train) [8][940/1345] lr: 1.0000e-02 eta: 3:47:26 time: 0.2164 data_time: 0.0075 memory: 7116 grad_norm: 5.9790 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.6536 loss: 2.6536 2022/09/03 22:28:18 - mmengine - INFO - Epoch(train) [8][960/1345] lr: 1.0000e-02 eta: 3:47:18 time: 0.2131 data_time: 0.0090 memory: 7116 grad_norm: 6.0426 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.8448 loss: 2.8448 2022/09/03 22:28:22 - mmengine - INFO - Epoch(train) [8][980/1345] lr: 1.0000e-02 eta: 3:47:10 time: 0.2128 data_time: 0.0069 memory: 7116 grad_norm: 6.0903 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.7175 loss: 2.7175 2022/09/03 22:28:27 - mmengine - INFO - Epoch(train) [8][1000/1345] lr: 1.0000e-02 eta: 3:47:03 time: 0.2143 data_time: 0.0070 memory: 7116 grad_norm: 6.4156 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.5795 loss: 2.5795 2022/09/03 22:28:31 - mmengine - INFO - Epoch(train) [8][1020/1345] lr: 1.0000e-02 eta: 3:46:56 time: 0.2178 data_time: 0.0098 memory: 7116 grad_norm: 6.1171 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.8757 loss: 2.8757 2022/09/03 22:28:35 - mmengine - INFO - Epoch(train) [8][1040/1345] lr: 1.0000e-02 eta: 3:46:48 time: 0.2157 data_time: 0.0067 memory: 7116 grad_norm: 6.1961 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.8551 loss: 2.8551 2022/09/03 22:28:40 - mmengine - INFO - Epoch(train) [8][1060/1345] lr: 1.0000e-02 eta: 3:46:41 time: 0.2131 data_time: 0.0070 memory: 7116 grad_norm: 5.6723 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.8154 loss: 2.8154 2022/09/03 22:28:44 - mmengine - INFO - Epoch(train) [8][1080/1345] lr: 1.0000e-02 eta: 3:46:34 time: 0.2191 data_time: 0.0096 memory: 7116 grad_norm: 6.3055 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.7680 loss: 2.7680 2022/09/03 22:28:48 - mmengine - INFO - Epoch(train) [8][1100/1345] lr: 1.0000e-02 eta: 3:46:26 time: 0.2131 data_time: 0.0067 memory: 7116 grad_norm: 5.7956 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.8170 loss: 2.8170 2022/09/03 22:28:52 - mmengine - INFO - Epoch(train) [8][1120/1345] lr: 1.0000e-02 eta: 3:46:18 time: 0.2129 data_time: 0.0074 memory: 7116 grad_norm: 5.9550 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5314 loss: 2.5314 2022/09/03 22:28:57 - mmengine - INFO - Epoch(train) [8][1140/1345] lr: 1.0000e-02 eta: 3:46:11 time: 0.2159 data_time: 0.0088 memory: 7116 grad_norm: 6.1701 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.8193 loss: 2.8193 2022/09/03 22:29:01 - mmengine - INFO - Epoch(train) [8][1160/1345] lr: 1.0000e-02 eta: 3:46:04 time: 0.2169 data_time: 0.0069 memory: 7116 grad_norm: 6.1407 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.6128 loss: 2.6128 2022/09/03 22:29:05 - mmengine - INFO - Epoch(train) [8][1180/1345] lr: 1.0000e-02 eta: 3:45:57 time: 0.2168 data_time: 0.0075 memory: 7116 grad_norm: 6.1723 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.8509 loss: 2.8509 2022/09/03 22:29:10 - mmengine - INFO - Epoch(train) [8][1200/1345] lr: 1.0000e-02 eta: 3:45:50 time: 0.2210 data_time: 0.0094 memory: 7116 grad_norm: 6.3584 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.8740 loss: 2.8740 2022/09/03 22:29:14 - mmengine - INFO - Epoch(train) [8][1220/1345] lr: 1.0000e-02 eta: 3:45:43 time: 0.2216 data_time: 0.0067 memory: 7116 grad_norm: 6.1007 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 2.6932 loss: 2.6932 2022/09/03 22:29:19 - mmengine - INFO - Epoch(train) [8][1240/1345] lr: 1.0000e-02 eta: 3:45:36 time: 0.2172 data_time: 0.0075 memory: 7116 grad_norm: 5.7922 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 3.0231 loss: 3.0231 2022/09/03 22:29:23 - mmengine - INFO - Epoch(train) [8][1260/1345] lr: 1.0000e-02 eta: 3:45:30 time: 0.2282 data_time: 0.0087 memory: 7116 grad_norm: 6.0827 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.8837 loss: 2.8837 2022/09/03 22:29:28 - mmengine - INFO - Epoch(train) [8][1280/1345] lr: 1.0000e-02 eta: 3:45:24 time: 0.2217 data_time: 0.0069 memory: 7116 grad_norm: 6.1153 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.8056 loss: 2.8056 2022/09/03 22:29:32 - mmengine - INFO - Epoch(train) [8][1300/1345] lr: 1.0000e-02 eta: 3:45:17 time: 0.2234 data_time: 0.0069 memory: 7116 grad_norm: 5.9754 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.7329 loss: 2.7329 2022/09/03 22:29:36 - mmengine - INFO - Epoch(train) [8][1320/1345] lr: 1.0000e-02 eta: 3:45:10 time: 0.2172 data_time: 0.0087 memory: 7116 grad_norm: 6.1118 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.9006 loss: 2.9006 2022/09/03 22:29:41 - mmengine - INFO - Epoch(train) [8][1340/1345] lr: 1.0000e-02 eta: 3:45:03 time: 0.2207 data_time: 0.0077 memory: 7116 grad_norm: 5.9117 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.5025 loss: 2.5025 2022/09/03 22:29:42 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:29:42 - mmengine - INFO - Epoch(train) [8][1345/1345] lr: 1.0000e-02 eta: 3:45:03 time: 0.2156 data_time: 0.0070 memory: 7116 grad_norm: 6.1008 top1_acc: 0.0000 top5_acc: 1.0000 loss_cls: 2.5869 loss: 2.5869 2022/09/03 22:29:42 - mmengine - INFO - Saving checkpoint at 8 epochs 2022/09/03 22:29:44 - mmengine - INFO - Epoch(val) [8][20/181] eta: 0:00:07 time: 0.0438 data_time: 0.0085 memory: 1114 2022/09/03 22:29:45 - mmengine - INFO - Epoch(val) [8][40/181] eta: 0:00:05 time: 0.0398 data_time: 0.0053 memory: 1114 2022/09/03 22:29:46 - mmengine - INFO - Epoch(val) [8][60/181] eta: 0:00:04 time: 0.0403 data_time: 0.0056 memory: 1114 2022/09/03 22:29:47 - mmengine - INFO - Epoch(val) [8][80/181] eta: 0:00:04 time: 0.0398 data_time: 0.0050 memory: 1114 2022/09/03 22:29:48 - mmengine - INFO - Epoch(val) [8][100/181] eta: 0:00:03 time: 0.0406 data_time: 0.0055 memory: 1114 2022/09/03 22:29:48 - mmengine - INFO - Epoch(val) [8][120/181] eta: 0:00:02 time: 0.0401 data_time: 0.0055 memory: 1114 2022/09/03 22:29:49 - mmengine - INFO - Epoch(val) [8][140/181] eta: 0:00:01 time: 0.0402 data_time: 0.0055 memory: 1114 2022/09/03 22:29:50 - mmengine - INFO - Epoch(val) [8][160/181] eta: 0:00:00 time: 0.0402 data_time: 0.0056 memory: 1114 2022/09/03 22:29:51 - mmengine - INFO - Epoch(val) [8][180/181] eta: 0:00:00 time: 0.0397 data_time: 0.0053 memory: 1114 2022/09/03 22:29:55 - mmengine - INFO - Epoch(val) [8][181/181] acc/top1: 0.2552 acc/top5: 0.5375 acc/mean1: 0.2280 2022/09/03 22:29:55 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_6.pth is removed 2022/09/03 22:29:56 - mmengine - INFO - The best checkpoint with 0.2552 acc/top1 at 8 epoch is saved to best_acc/top1_epoch_8.pth. 2022/09/03 22:30:00 - mmengine - INFO - Epoch(train) [9][20/1345] lr: 1.0000e-02 eta: 3:44:50 time: 0.2269 data_time: 0.0232 memory: 7116 grad_norm: 6.0407 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4529 loss: 2.4529 2022/09/03 22:30:05 - mmengine - INFO - Epoch(train) [9][40/1345] lr: 1.0000e-02 eta: 3:44:43 time: 0.2162 data_time: 0.0063 memory: 7116 grad_norm: 5.8884 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.6570 loss: 2.6570 2022/09/03 22:30:09 - mmengine - INFO - Epoch(train) [9][60/1345] lr: 1.0000e-02 eta: 3:44:36 time: 0.2212 data_time: 0.0067 memory: 7116 grad_norm: 5.9947 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.9285 loss: 2.9285 2022/09/03 22:30:13 - mmengine - INFO - Epoch(train) [9][80/1345] lr: 1.0000e-02 eta: 3:44:30 time: 0.2235 data_time: 0.0097 memory: 7116 grad_norm: 5.6826 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5327 loss: 2.5327 2022/09/03 22:30:18 - mmengine - INFO - Epoch(train) [9][100/1345] lr: 1.0000e-02 eta: 3:44:23 time: 0.2174 data_time: 0.0067 memory: 7116 grad_norm: 6.3799 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5987 loss: 2.5987 2022/09/03 22:30:22 - mmengine - INFO - Epoch(train) [9][120/1345] lr: 1.0000e-02 eta: 3:44:16 time: 0.2212 data_time: 0.0070 memory: 7116 grad_norm: 6.2810 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.7714 loss: 2.7714 2022/09/03 22:30:27 - mmengine - INFO - Epoch(train) [9][140/1345] lr: 1.0000e-02 eta: 3:44:10 time: 0.2218 data_time: 0.0090 memory: 7116 grad_norm: 6.0144 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.6561 loss: 2.6561 2022/09/03 22:30:31 - mmengine - INFO - Epoch(train) [9][160/1345] lr: 1.0000e-02 eta: 3:44:03 time: 0.2193 data_time: 0.0071 memory: 7116 grad_norm: 6.1259 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4010 loss: 2.4010 2022/09/03 22:30:36 - mmengine - INFO - Epoch(train) [9][180/1345] lr: 1.0000e-02 eta: 3:43:56 time: 0.2221 data_time: 0.0071 memory: 7116 grad_norm: 6.3265 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.8288 loss: 2.8288 2022/09/03 22:30:40 - mmengine - INFO - Epoch(train) [9][200/1345] lr: 1.0000e-02 eta: 3:43:50 time: 0.2222 data_time: 0.0092 memory: 7116 grad_norm: 6.2456 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.5309 loss: 2.5309 2022/09/03 22:30:44 - mmengine - INFO - Epoch(train) [9][220/1345] lr: 1.0000e-02 eta: 3:43:44 time: 0.2247 data_time: 0.0065 memory: 7116 grad_norm: 6.0503 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.6718 loss: 2.6718 2022/09/03 22:30:49 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:30:49 - mmengine - INFO - Epoch(train) [9][240/1345] lr: 1.0000e-02 eta: 3:43:37 time: 0.2188 data_time: 0.0067 memory: 7116 grad_norm: 5.8029 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 2.9097 loss: 2.9097 2022/09/03 22:30:53 - mmengine - INFO - Epoch(train) [9][260/1345] lr: 1.0000e-02 eta: 3:43:30 time: 0.2212 data_time: 0.0086 memory: 7116 grad_norm: 6.2305 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.9647 loss: 2.9647 2022/09/03 22:30:58 - mmengine - INFO - Epoch(train) [9][280/1345] lr: 1.0000e-02 eta: 3:43:24 time: 0.2226 data_time: 0.0073 memory: 7116 grad_norm: 6.3930 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.8805 loss: 2.8805 2022/09/03 22:31:02 - mmengine - INFO - Epoch(train) [9][300/1345] lr: 1.0000e-02 eta: 3:43:17 time: 0.2188 data_time: 0.0067 memory: 7116 grad_norm: 6.2424 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5214 loss: 2.5214 2022/09/03 22:31:07 - mmengine - INFO - Epoch(train) [9][320/1345] lr: 1.0000e-02 eta: 3:43:11 time: 0.2239 data_time: 0.0091 memory: 7116 grad_norm: 6.2366 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.4377 loss: 2.4377 2022/09/03 22:31:11 - mmengine - INFO - Epoch(train) [9][340/1345] lr: 1.0000e-02 eta: 3:43:05 time: 0.2232 data_time: 0.0067 memory: 7116 grad_norm: 6.2225 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.6549 loss: 2.6549 2022/09/03 22:31:16 - mmengine - INFO - Epoch(train) [9][360/1345] lr: 1.0000e-02 eta: 3:42:59 time: 0.2270 data_time: 0.0071 memory: 7116 grad_norm: 5.9645 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.7629 loss: 2.7629 2022/09/03 22:31:20 - mmengine - INFO - Epoch(train) [9][380/1345] lr: 1.0000e-02 eta: 3:42:52 time: 0.2199 data_time: 0.0094 memory: 7116 grad_norm: 6.2877 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 3.0191 loss: 3.0191 2022/09/03 22:31:24 - mmengine - INFO - Epoch(train) [9][400/1345] lr: 1.0000e-02 eta: 3:42:46 time: 0.2223 data_time: 0.0066 memory: 7116 grad_norm: 6.2365 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6976 loss: 2.6976 2022/09/03 22:31:29 - mmengine - INFO - Epoch(train) [9][420/1345] lr: 1.0000e-02 eta: 3:42:40 time: 0.2241 data_time: 0.0067 memory: 7116 grad_norm: 6.2867 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.8527 loss: 2.8527 2022/09/03 22:31:33 - mmengine - INFO - Epoch(train) [9][440/1345] lr: 1.0000e-02 eta: 3:42:33 time: 0.2236 data_time: 0.0085 memory: 7116 grad_norm: 6.2216 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.8153 loss: 2.8153 2022/09/03 22:31:38 - mmengine - INFO - Epoch(train) [9][460/1345] lr: 1.0000e-02 eta: 3:42:27 time: 0.2224 data_time: 0.0065 memory: 7116 grad_norm: 6.0937 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.6405 loss: 2.6405 2022/09/03 22:31:42 - mmengine - INFO - Epoch(train) [9][480/1345] lr: 1.0000e-02 eta: 3:42:21 time: 0.2217 data_time: 0.0068 memory: 7116 grad_norm: 6.3423 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.5248 loss: 2.5248 2022/09/03 22:31:47 - mmengine - INFO - Epoch(train) [9][500/1345] lr: 1.0000e-02 eta: 3:42:14 time: 0.2217 data_time: 0.0089 memory: 7116 grad_norm: 6.3433 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6097 loss: 2.6097 2022/09/03 22:31:51 - mmengine - INFO - Epoch(train) [9][520/1345] lr: 1.0000e-02 eta: 3:42:07 time: 0.2168 data_time: 0.0071 memory: 7116 grad_norm: 6.2112 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.6434 loss: 2.6434 2022/09/03 22:31:55 - mmengine - INFO - Epoch(train) [9][540/1345] lr: 1.0000e-02 eta: 3:42:01 time: 0.2220 data_time: 0.0062 memory: 7116 grad_norm: 6.1798 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.8703 loss: 2.8703 2022/09/03 22:32:00 - mmengine - INFO - Epoch(train) [9][560/1345] lr: 1.0000e-02 eta: 3:41:54 time: 0.2167 data_time: 0.0086 memory: 7116 grad_norm: 5.9988 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 2.8025 loss: 2.8025 2022/09/03 22:32:04 - mmengine - INFO - Epoch(train) [9][580/1345] lr: 1.0000e-02 eta: 3:41:47 time: 0.2182 data_time: 0.0076 memory: 7116 grad_norm: 6.3670 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.9558 loss: 2.9558 2022/09/03 22:32:09 - mmengine - INFO - Epoch(train) [9][600/1345] lr: 1.0000e-02 eta: 3:41:40 time: 0.2175 data_time: 0.0063 memory: 7116 grad_norm: 6.0612 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.6725 loss: 2.6725 2022/09/03 22:32:13 - mmengine - INFO - Epoch(train) [9][620/1345] lr: 1.0000e-02 eta: 3:41:34 time: 0.2241 data_time: 0.0169 memory: 7116 grad_norm: 6.4702 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.7373 loss: 2.7373 2022/09/03 22:32:17 - mmengine - INFO - Epoch(train) [9][640/1345] lr: 1.0000e-02 eta: 3:41:27 time: 0.2119 data_time: 0.0074 memory: 7116 grad_norm: 5.8324 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.6026 loss: 2.6026 2022/09/03 22:32:22 - mmengine - INFO - Epoch(train) [9][660/1345] lr: 1.0000e-02 eta: 3:41:20 time: 0.2149 data_time: 0.0066 memory: 7116 grad_norm: 5.9861 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.6251 loss: 2.6251 2022/09/03 22:32:26 - mmengine - INFO - Epoch(train) [9][680/1345] lr: 1.0000e-02 eta: 3:41:14 time: 0.2263 data_time: 0.0207 memory: 7116 grad_norm: 6.1515 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.7276 loss: 2.7276 2022/09/03 22:32:30 - mmengine - INFO - Epoch(train) [9][700/1345] lr: 1.0000e-02 eta: 3:41:08 time: 0.2192 data_time: 0.0086 memory: 7116 grad_norm: 6.1013 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6638 loss: 2.6638 2022/09/03 22:32:34 - mmengine - INFO - Epoch(train) [9][720/1345] lr: 1.0000e-02 eta: 3:40:59 time: 0.1947 data_time: 0.0115 memory: 7116 grad_norm: 6.4169 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 3.0296 loss: 3.0296 2022/09/03 22:32:38 - mmengine - INFO - Epoch(train) [9][740/1345] lr: 1.0000e-02 eta: 3:40:49 time: 0.1905 data_time: 0.0123 memory: 7116 grad_norm: 6.3956 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.7152 loss: 2.7152 2022/09/03 22:32:42 - mmengine - INFO - Epoch(train) [9][760/1345] lr: 1.0000e-02 eta: 3:40:40 time: 0.1941 data_time: 0.0102 memory: 7116 grad_norm: 6.2828 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.7104 loss: 2.7104 2022/09/03 22:32:46 - mmengine - INFO - Epoch(train) [9][780/1345] lr: 1.0000e-02 eta: 3:40:32 time: 0.1989 data_time: 0.0102 memory: 7116 grad_norm: 6.3349 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.7267 loss: 2.7267 2022/09/03 22:32:50 - mmengine - INFO - Epoch(train) [9][800/1345] lr: 1.0000e-02 eta: 3:40:23 time: 0.1939 data_time: 0.0142 memory: 7116 grad_norm: 6.2521 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.5932 loss: 2.5932 2022/09/03 22:32:54 - mmengine - INFO - Epoch(train) [9][820/1345] lr: 1.0000e-02 eta: 3:40:14 time: 0.1982 data_time: 0.0095 memory: 7116 grad_norm: 5.8661 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.9780 loss: 2.9780 2022/09/03 22:32:58 - mmengine - INFO - Epoch(train) [9][840/1345] lr: 1.0000e-02 eta: 3:40:06 time: 0.1971 data_time: 0.0096 memory: 7116 grad_norm: 6.1581 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.7966 loss: 2.7966 2022/09/03 22:33:02 - mmengine - INFO - Epoch(train) [9][860/1345] lr: 1.0000e-02 eta: 3:39:57 time: 0.1920 data_time: 0.0132 memory: 7116 grad_norm: 5.9725 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.7096 loss: 2.7096 2022/09/03 22:33:05 - mmengine - INFO - Epoch(train) [9][880/1345] lr: 1.0000e-02 eta: 3:39:47 time: 0.1862 data_time: 0.0097 memory: 7116 grad_norm: 6.0919 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.7152 loss: 2.7152 2022/09/03 22:33:09 - mmengine - INFO - Epoch(train) [9][900/1345] lr: 1.0000e-02 eta: 3:39:38 time: 0.1914 data_time: 0.0103 memory: 7116 grad_norm: 6.0889 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.6680 loss: 2.6680 2022/09/03 22:33:13 - mmengine - INFO - Epoch(train) [9][920/1345] lr: 1.0000e-02 eta: 3:39:29 time: 0.1901 data_time: 0.0127 memory: 7116 grad_norm: 6.1227 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.8803 loss: 2.8803 2022/09/03 22:33:17 - mmengine - INFO - Epoch(train) [9][940/1345] lr: 1.0000e-02 eta: 3:39:20 time: 0.1908 data_time: 0.0122 memory: 7116 grad_norm: 6.0534 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.6287 loss: 2.6287 2022/09/03 22:33:21 - mmengine - INFO - Epoch(train) [9][960/1345] lr: 1.0000e-02 eta: 3:39:10 time: 0.1894 data_time: 0.0123 memory: 7116 grad_norm: 5.7894 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.5236 loss: 2.5236 2022/09/03 22:33:25 - mmengine - INFO - Epoch(train) [9][980/1345] lr: 1.0000e-02 eta: 3:39:02 time: 0.2009 data_time: 0.0115 memory: 7116 grad_norm: 6.1695 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4975 loss: 2.4975 2022/09/03 22:33:29 - mmengine - INFO - Epoch(train) [9][1000/1345] lr: 1.0000e-02 eta: 3:38:54 time: 0.1964 data_time: 0.0107 memory: 7116 grad_norm: 5.9827 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.5970 loss: 2.5970 2022/09/03 22:33:32 - mmengine - INFO - Epoch(train) [9][1020/1345] lr: 1.0000e-02 eta: 3:38:44 time: 0.1858 data_time: 0.0110 memory: 7116 grad_norm: 5.9124 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.6560 loss: 2.6560 2022/09/03 22:33:36 - mmengine - INFO - Epoch(train) [9][1040/1345] lr: 1.0000e-02 eta: 3:38:35 time: 0.1870 data_time: 0.0121 memory: 7116 grad_norm: 6.5233 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.9928 loss: 2.9928 2022/09/03 22:33:40 - mmengine - INFO - Epoch(train) [9][1060/1345] lr: 1.0000e-02 eta: 3:38:26 time: 0.1920 data_time: 0.0114 memory: 7116 grad_norm: 6.0216 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.7894 loss: 2.7894 2022/09/03 22:33:44 - mmengine - INFO - Epoch(train) [9][1080/1345] lr: 1.0000e-02 eta: 3:38:17 time: 0.1940 data_time: 0.0109 memory: 7116 grad_norm: 5.9988 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.7659 loss: 2.7659 2022/09/03 22:33:48 - mmengine - INFO - Epoch(train) [9][1100/1345] lr: 1.0000e-02 eta: 3:38:08 time: 0.1889 data_time: 0.0125 memory: 7116 grad_norm: 6.0067 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3471 loss: 2.3471 2022/09/03 22:33:51 - mmengine - INFO - Epoch(train) [9][1120/1345] lr: 1.0000e-02 eta: 3:37:59 time: 0.1935 data_time: 0.0110 memory: 7116 grad_norm: 6.1725 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.8726 loss: 2.8726 2022/09/03 22:33:55 - mmengine - INFO - Epoch(train) [9][1140/1345] lr: 1.0000e-02 eta: 3:37:50 time: 0.1890 data_time: 0.0105 memory: 7116 grad_norm: 5.8874 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.4673 loss: 2.4673 2022/09/03 22:33:59 - mmengine - INFO - Epoch(train) [9][1160/1345] lr: 1.0000e-02 eta: 3:37:42 time: 0.1941 data_time: 0.0134 memory: 7116 grad_norm: 6.2183 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.8126 loss: 2.8126 2022/09/03 22:34:03 - mmengine - INFO - Epoch(train) [9][1180/1345] lr: 1.0000e-02 eta: 3:37:33 time: 0.1908 data_time: 0.0097 memory: 7116 grad_norm: 6.0634 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.6669 loss: 2.6669 2022/09/03 22:34:07 - mmengine - INFO - Epoch(train) [9][1200/1345] lr: 1.0000e-02 eta: 3:37:24 time: 0.1897 data_time: 0.0106 memory: 7116 grad_norm: 5.9091 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6477 loss: 2.6477 2022/09/03 22:34:11 - mmengine - INFO - Epoch(train) [9][1220/1345] lr: 1.0000e-02 eta: 3:37:15 time: 0.1918 data_time: 0.0129 memory: 7116 grad_norm: 6.2832 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.6274 loss: 2.6274 2022/09/03 22:34:14 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:34:14 - mmengine - INFO - Epoch(train) [9][1240/1345] lr: 1.0000e-02 eta: 3:37:07 time: 0.1977 data_time: 0.0102 memory: 7116 grad_norm: 6.1478 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.6245 loss: 2.6245 2022/09/03 22:34:18 - mmengine - INFO - Epoch(train) [9][1260/1345] lr: 1.0000e-02 eta: 3:36:58 time: 0.1980 data_time: 0.0097 memory: 7116 grad_norm: 5.8684 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6703 loss: 2.6703 2022/09/03 22:34:22 - mmengine - INFO - Epoch(train) [9][1280/1345] lr: 1.0000e-02 eta: 3:36:50 time: 0.1919 data_time: 0.0128 memory: 7116 grad_norm: 5.7550 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.8357 loss: 2.8357 2022/09/03 22:34:26 - mmengine - INFO - Epoch(train) [9][1300/1345] lr: 1.0000e-02 eta: 3:36:41 time: 0.1923 data_time: 0.0101 memory: 7116 grad_norm: 5.6770 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.7651 loss: 2.7651 2022/09/03 22:34:30 - mmengine - INFO - Epoch(train) [9][1320/1345] lr: 1.0000e-02 eta: 3:36:32 time: 0.1899 data_time: 0.0111 memory: 7116 grad_norm: 6.1754 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.4093 loss: 2.4093 2022/09/03 22:34:34 - mmengine - INFO - Epoch(train) [9][1340/1345] lr: 1.0000e-02 eta: 3:36:25 time: 0.2079 data_time: 0.0131 memory: 7116 grad_norm: 5.8589 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.9991 loss: 2.9991 2022/09/03 22:34:35 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:34:35 - mmengine - INFO - Epoch(train) [9][1345/1345] lr: 1.0000e-02 eta: 3:36:25 time: 0.2031 data_time: 0.0105 memory: 7116 grad_norm: 7.2596 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.9728 loss: 2.9728 2022/09/03 22:34:35 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/09/03 22:34:38 - mmengine - INFO - Epoch(val) [9][20/181] eta: 0:00:07 time: 0.0486 data_time: 0.0113 memory: 1114 2022/09/03 22:34:39 - mmengine - INFO - Epoch(val) [9][40/181] eta: 0:00:06 time: 0.0467 data_time: 0.0089 memory: 1114 2022/09/03 22:34:40 - mmengine - INFO - Epoch(val) [9][60/181] eta: 0:00:05 time: 0.0470 data_time: 0.0084 memory: 1114 2022/09/03 22:34:41 - mmengine - INFO - Epoch(val) [9][80/181] eta: 0:00:04 time: 0.0470 data_time: 0.0090 memory: 1114 2022/09/03 22:34:42 - mmengine - INFO - Epoch(val) [9][100/181] eta: 0:00:03 time: 0.0479 data_time: 0.0093 memory: 1114 2022/09/03 22:34:43 - mmengine - INFO - Epoch(val) [9][120/181] eta: 0:00:02 time: 0.0473 data_time: 0.0086 memory: 1114 2022/09/03 22:34:44 - mmengine - INFO - Epoch(val) [9][140/181] eta: 0:00:02 time: 0.0494 data_time: 0.0095 memory: 1114 2022/09/03 22:34:45 - mmengine - INFO - Epoch(val) [9][160/181] eta: 0:00:01 time: 0.0543 data_time: 0.0200 memory: 1114 2022/09/03 22:34:45 - mmengine - INFO - Epoch(val) [9][180/181] eta: 0:00:00 time: 0.0408 data_time: 0.0056 memory: 1114 2022/09/03 22:34:47 - mmengine - INFO - Epoch(val) [9][181/181] acc/top1: 0.2663 acc/top5: 0.5619 acc/mean1: 0.2442 2022/09/03 22:34:47 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_8.pth is removed 2022/09/03 22:34:49 - mmengine - INFO - The best checkpoint with 0.2663 acc/top1 at 9 epoch is saved to best_acc/top1_epoch_9.pth. 2022/09/03 22:34:53 - mmengine - INFO - Epoch(train) [10][20/1345] lr: 1.0000e-02 eta: 3:36:12 time: 0.2150 data_time: 0.0340 memory: 7116 grad_norm: 5.9765 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5538 loss: 2.5538 2022/09/03 22:34:57 - mmengine - INFO - Epoch(train) [10][40/1345] lr: 1.0000e-02 eta: 3:36:03 time: 0.1898 data_time: 0.0085 memory: 7116 grad_norm: 6.4936 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.6743 loss: 2.6743 2022/09/03 22:35:00 - mmengine - INFO - Epoch(train) [10][60/1345] lr: 1.0000e-02 eta: 3:35:54 time: 0.1893 data_time: 0.0109 memory: 7116 grad_norm: 6.3308 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.0559 loss: 3.0559 2022/09/03 22:35:05 - mmengine - INFO - Epoch(train) [10][80/1345] lr: 1.0000e-02 eta: 3:35:47 time: 0.2126 data_time: 0.0108 memory: 7116 grad_norm: 6.3698 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.5535 loss: 2.5535 2022/09/03 22:35:08 - mmengine - INFO - Epoch(train) [10][100/1345] lr: 1.0000e-02 eta: 3:35:38 time: 0.1864 data_time: 0.0110 memory: 7116 grad_norm: 6.1774 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.8122 loss: 2.8122 2022/09/03 22:35:12 - mmengine - INFO - Epoch(train) [10][120/1345] lr: 1.0000e-02 eta: 3:35:29 time: 0.1895 data_time: 0.0104 memory: 7116 grad_norm: 6.1441 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.6512 loss: 2.6512 2022/09/03 22:35:16 - mmengine - INFO - Epoch(train) [10][140/1345] lr: 1.0000e-02 eta: 3:35:21 time: 0.1955 data_time: 0.0134 memory: 7116 grad_norm: 5.8736 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4740 loss: 2.4740 2022/09/03 22:35:20 - mmengine - INFO - Epoch(train) [10][160/1345] lr: 1.0000e-02 eta: 3:35:12 time: 0.1867 data_time: 0.0110 memory: 7116 grad_norm: 6.1770 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.5822 loss: 2.5822 2022/09/03 22:35:24 - mmengine - INFO - Epoch(train) [10][180/1345] lr: 1.0000e-02 eta: 3:35:04 time: 0.1909 data_time: 0.0096 memory: 7116 grad_norm: 6.1040 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.6744 loss: 2.6744 2022/09/03 22:35:27 - mmengine - INFO - Epoch(train) [10][200/1345] lr: 1.0000e-02 eta: 3:34:55 time: 0.1892 data_time: 0.0128 memory: 7116 grad_norm: 6.2386 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.7277 loss: 2.7277 2022/09/03 22:35:31 - mmengine - INFO - Epoch(train) [10][220/1345] lr: 1.0000e-02 eta: 3:34:46 time: 0.1885 data_time: 0.0104 memory: 7116 grad_norm: 6.3024 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.7290 loss: 2.7290 2022/09/03 22:35:35 - mmengine - INFO - Epoch(train) [10][240/1345] lr: 1.0000e-02 eta: 3:34:39 time: 0.2121 data_time: 0.0093 memory: 7116 grad_norm: 6.1676 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.8666 loss: 2.8666 2022/09/03 22:35:39 - mmengine - INFO - Epoch(train) [10][260/1345] lr: 1.0000e-02 eta: 3:34:31 time: 0.1886 data_time: 0.0117 memory: 7116 grad_norm: 6.1014 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.6022 loss: 2.6022 2022/09/03 22:35:43 - mmengine - INFO - Epoch(train) [10][280/1345] lr: 1.0000e-02 eta: 3:34:22 time: 0.1874 data_time: 0.0097 memory: 7116 grad_norm: 6.0589 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5551 loss: 2.5551 2022/09/03 22:35:47 - mmengine - INFO - Epoch(train) [10][300/1345] lr: 1.0000e-02 eta: 3:34:13 time: 0.1923 data_time: 0.0098 memory: 7116 grad_norm: 6.3292 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.7508 loss: 2.7508 2022/09/03 22:35:51 - mmengine - INFO - Epoch(train) [10][320/1345] lr: 1.0000e-02 eta: 3:34:05 time: 0.1889 data_time: 0.0126 memory: 7116 grad_norm: 6.2948 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.8773 loss: 2.8773 2022/09/03 22:35:55 - mmengine - INFO - Epoch(train) [10][340/1345] lr: 1.0000e-02 eta: 3:33:58 time: 0.2079 data_time: 0.0253 memory: 7116 grad_norm: 5.9401 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.7208 loss: 2.7208 2022/09/03 22:35:59 - mmengine - INFO - Epoch(train) [10][360/1345] lr: 1.0000e-02 eta: 3:33:50 time: 0.2020 data_time: 0.0093 memory: 7116 grad_norm: 6.0242 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.4534 loss: 2.4534 2022/09/03 22:36:03 - mmengine - INFO - Epoch(train) [10][380/1345] lr: 1.0000e-02 eta: 3:33:42 time: 0.1957 data_time: 0.0139 memory: 7116 grad_norm: 6.4645 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.7718 loss: 2.7718 2022/09/03 22:36:07 - mmengine - INFO - Epoch(train) [10][400/1345] lr: 1.0000e-02 eta: 3:33:33 time: 0.1899 data_time: 0.0096 memory: 7116 grad_norm: 6.3221 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.8792 loss: 2.8792 2022/09/03 22:36:10 - mmengine - INFO - Epoch(train) [10][420/1345] lr: 1.0000e-02 eta: 3:33:25 time: 0.1894 data_time: 0.0108 memory: 7116 grad_norm: 6.2695 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.8524 loss: 2.8524 2022/09/03 22:36:14 - mmengine - INFO - Epoch(train) [10][440/1345] lr: 1.0000e-02 eta: 3:33:17 time: 0.1939 data_time: 0.0138 memory: 7116 grad_norm: 6.2668 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3528 loss: 2.3528 2022/09/03 22:36:18 - mmengine - INFO - Epoch(train) [10][460/1345] lr: 1.0000e-02 eta: 3:33:08 time: 0.1897 data_time: 0.0099 memory: 7116 grad_norm: 6.2190 top1_acc: 0.1250 top5_acc: 0.8750 loss_cls: 2.8658 loss: 2.8658 2022/09/03 22:36:22 - mmengine - INFO - Epoch(train) [10][480/1345] lr: 1.0000e-02 eta: 3:33:00 time: 0.1897 data_time: 0.0101 memory: 7116 grad_norm: 6.3060 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.7868 loss: 2.7868 2022/09/03 22:36:26 - mmengine - INFO - Epoch(train) [10][500/1345] lr: 1.0000e-02 eta: 3:32:52 time: 0.1946 data_time: 0.0116 memory: 7116 grad_norm: 6.1868 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4357 loss: 2.4357 2022/09/03 22:36:29 - mmengine - INFO - Epoch(train) [10][520/1345] lr: 1.0000e-02 eta: 3:32:43 time: 0.1882 data_time: 0.0106 memory: 7116 grad_norm: 6.1904 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.7869 loss: 2.7869 2022/09/03 22:36:33 - mmengine - INFO - Epoch(train) [10][540/1345] lr: 1.0000e-02 eta: 3:32:34 time: 0.1882 data_time: 0.0103 memory: 7116 grad_norm: 6.0721 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.7342 loss: 2.7342 2022/09/03 22:36:37 - mmengine - INFO - Epoch(train) [10][560/1345] lr: 1.0000e-02 eta: 3:32:26 time: 0.1904 data_time: 0.0143 memory: 7116 grad_norm: 6.1187 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.6132 loss: 2.6132 2022/09/03 22:36:41 - mmengine - INFO - Epoch(train) [10][580/1345] lr: 1.0000e-02 eta: 3:32:17 time: 0.1880 data_time: 0.0094 memory: 7116 grad_norm: 6.4008 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.7048 loss: 2.7048 2022/09/03 22:36:45 - mmengine - INFO - Epoch(train) [10][600/1345] lr: 1.0000e-02 eta: 3:32:09 time: 0.1893 data_time: 0.0103 memory: 7116 grad_norm: 6.2565 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 2.8111 loss: 2.8111 2022/09/03 22:36:48 - mmengine - INFO - Epoch(train) [10][620/1345] lr: 1.0000e-02 eta: 3:32:01 time: 0.1921 data_time: 0.0135 memory: 7116 grad_norm: 5.9408 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.6264 loss: 2.6264 2022/09/03 22:36:52 - mmengine - INFO - Epoch(train) [10][640/1345] lr: 1.0000e-02 eta: 3:31:52 time: 0.1910 data_time: 0.0109 memory: 7116 grad_norm: 6.0631 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.7628 loss: 2.7628 2022/09/03 22:36:56 - mmengine - INFO - Epoch(train) [10][660/1345] lr: 1.0000e-02 eta: 3:31:45 time: 0.1990 data_time: 0.0100 memory: 7116 grad_norm: 6.0116 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.6770 loss: 2.6770 2022/09/03 22:37:00 - mmengine - INFO - Epoch(train) [10][680/1345] lr: 1.0000e-02 eta: 3:31:36 time: 0.1890 data_time: 0.0123 memory: 7116 grad_norm: 8.1361 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.8164 loss: 2.8164 2022/09/03 22:37:04 - mmengine - INFO - Epoch(train) [10][700/1345] lr: 1.0000e-02 eta: 3:31:28 time: 0.1933 data_time: 0.0100 memory: 7116 grad_norm: 6.3135 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.5250 loss: 2.5250 2022/09/03 22:37:08 - mmengine - INFO - Epoch(train) [10][720/1345] lr: 1.0000e-02 eta: 3:31:20 time: 0.1897 data_time: 0.0106 memory: 7116 grad_norm: 5.8368 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.7729 loss: 2.7729 2022/09/03 22:37:12 - mmengine - INFO - Epoch(train) [10][740/1345] lr: 1.0000e-02 eta: 3:31:12 time: 0.1934 data_time: 0.0127 memory: 7116 grad_norm: 6.3524 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.7429 loss: 2.7429 2022/09/03 22:37:15 - mmengine - INFO - Epoch(train) [10][760/1345] lr: 1.0000e-02 eta: 3:31:04 time: 0.1919 data_time: 0.0107 memory: 7116 grad_norm: 6.1078 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.7858 loss: 2.7858 2022/09/03 22:37:19 - mmengine - INFO - Epoch(train) [10][780/1345] lr: 1.0000e-02 eta: 3:30:56 time: 0.1901 data_time: 0.0096 memory: 7116 grad_norm: 6.0317 top1_acc: 0.0000 top5_acc: 0.6250 loss_cls: 2.4786 loss: 2.4786 2022/09/03 22:37:23 - mmengine - INFO - Epoch(train) [10][800/1345] lr: 1.0000e-02 eta: 3:30:48 time: 0.1938 data_time: 0.0118 memory: 7116 grad_norm: 6.2286 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.5834 loss: 2.5834 2022/09/03 22:37:27 - mmengine - INFO - Epoch(train) [10][820/1345] lr: 1.0000e-02 eta: 3:30:39 time: 0.1903 data_time: 0.0105 memory: 7116 grad_norm: 6.3664 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5367 loss: 2.5367 2022/09/03 22:37:31 - mmengine - INFO - Epoch(train) [10][840/1345] lr: 1.0000e-02 eta: 3:30:31 time: 0.1918 data_time: 0.0093 memory: 7116 grad_norm: 5.9043 top1_acc: 0.3750 top5_acc: 1.0000 loss_cls: 2.6208 loss: 2.6208 2022/09/03 22:37:35 - mmengine - INFO - Epoch(train) [10][860/1345] lr: 1.0000e-02 eta: 3:30:24 time: 0.1999 data_time: 0.0123 memory: 7116 grad_norm: 6.4011 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5764 loss: 2.5764 2022/09/03 22:37:39 - mmengine - INFO - Epoch(train) [10][880/1345] lr: 1.0000e-02 eta: 3:30:16 time: 0.1963 data_time: 0.0091 memory: 7116 grad_norm: 6.0784 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.8199 loss: 2.8199 2022/09/03 22:37:42 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:37:42 - mmengine - INFO - Epoch(train) [10][900/1345] lr: 1.0000e-02 eta: 3:30:08 time: 0.1896 data_time: 0.0109 memory: 7116 grad_norm: 5.9645 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.9650 loss: 2.9650 2022/09/03 22:37:47 - mmengine - INFO - Epoch(train) [10][920/1345] lr: 1.0000e-02 eta: 3:30:01 time: 0.2051 data_time: 0.0121 memory: 7116 grad_norm: 6.2965 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6107 loss: 2.6107 2022/09/03 22:37:50 - mmengine - INFO - Epoch(train) [10][940/1345] lr: 1.0000e-02 eta: 3:29:53 time: 0.1905 data_time: 0.0110 memory: 7116 grad_norm: 6.1435 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.7456 loss: 2.7456 2022/09/03 22:37:54 - mmengine - INFO - Epoch(train) [10][960/1345] lr: 1.0000e-02 eta: 3:29:45 time: 0.1914 data_time: 0.0095 memory: 7116 grad_norm: 6.1499 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.6291 loss: 2.6291 2022/09/03 22:37:58 - mmengine - INFO - Epoch(train) [10][980/1345] lr: 1.0000e-02 eta: 3:29:37 time: 0.1909 data_time: 0.0119 memory: 7116 grad_norm: 6.0471 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 2.8230 loss: 2.8230 2022/09/03 22:38:02 - mmengine - INFO - Epoch(train) [10][1000/1345] lr: 1.0000e-02 eta: 3:29:29 time: 0.1911 data_time: 0.0111 memory: 7116 grad_norm: 6.3327 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.7780 loss: 2.7780 2022/09/03 22:38:06 - mmengine - INFO - Epoch(train) [10][1020/1345] lr: 1.0000e-02 eta: 3:29:21 time: 0.1953 data_time: 0.0105 memory: 7116 grad_norm: 6.1266 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.7404 loss: 2.7404 2022/09/03 22:38:10 - mmengine - INFO - Epoch(train) [10][1040/1345] lr: 1.0000e-02 eta: 3:29:13 time: 0.1904 data_time: 0.0134 memory: 7116 grad_norm: 6.3907 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.2967 loss: 2.2967 2022/09/03 22:38:13 - mmengine - INFO - Epoch(train) [10][1060/1345] lr: 1.0000e-02 eta: 3:29:05 time: 0.1895 data_time: 0.0099 memory: 7116 grad_norm: 6.2335 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.8008 loss: 2.8008 2022/09/03 22:38:17 - mmengine - INFO - Epoch(train) [10][1080/1345] lr: 1.0000e-02 eta: 3:28:57 time: 0.1921 data_time: 0.0089 memory: 7116 grad_norm: 6.2082 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.7529 loss: 2.7529 2022/09/03 22:38:21 - mmengine - INFO - Epoch(train) [10][1100/1345] lr: 1.0000e-02 eta: 3:28:49 time: 0.1922 data_time: 0.0127 memory: 7116 grad_norm: 6.4233 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.8183 loss: 2.8183 2022/09/03 22:38:25 - mmengine - INFO - Epoch(train) [10][1120/1345] lr: 1.0000e-02 eta: 3:28:41 time: 0.1917 data_time: 0.0097 memory: 7116 grad_norm: 5.9619 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.7382 loss: 2.7382 2022/09/03 22:38:29 - mmengine - INFO - Epoch(train) [10][1140/1345] lr: 1.0000e-02 eta: 3:28:34 time: 0.2005 data_time: 0.0102 memory: 7116 grad_norm: 6.4706 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4699 loss: 2.4699 2022/09/03 22:38:33 - mmengine - INFO - Epoch(train) [10][1160/1345] lr: 1.0000e-02 eta: 3:28:26 time: 0.1935 data_time: 0.0140 memory: 7116 grad_norm: 5.9388 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5491 loss: 2.5491 2022/09/03 22:38:37 - mmengine - INFO - Epoch(train) [10][1180/1345] lr: 1.0000e-02 eta: 3:28:19 time: 0.1960 data_time: 0.0091 memory: 7116 grad_norm: 6.0644 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3426 loss: 2.3426 2022/09/03 22:38:40 - mmengine - INFO - Epoch(train) [10][1200/1345] lr: 1.0000e-02 eta: 3:28:11 time: 0.1895 data_time: 0.0112 memory: 7116 grad_norm: 6.0951 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4983 loss: 2.4983 2022/09/03 22:38:44 - mmengine - INFO - Epoch(train) [10][1220/1345] lr: 1.0000e-02 eta: 3:28:03 time: 0.1920 data_time: 0.0133 memory: 7116 grad_norm: 6.2799 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 2.4631 loss: 2.4631 2022/09/03 22:38:48 - mmengine - INFO - Epoch(train) [10][1240/1345] lr: 1.0000e-02 eta: 3:27:55 time: 0.1916 data_time: 0.0101 memory: 7116 grad_norm: 6.1352 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.9365 loss: 2.9365 2022/09/03 22:38:52 - mmengine - INFO - Epoch(train) [10][1260/1345] lr: 1.0000e-02 eta: 3:27:47 time: 0.1923 data_time: 0.0119 memory: 7116 grad_norm: 6.2211 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.7180 loss: 2.7180 2022/09/03 22:38:56 - mmengine - INFO - Epoch(train) [10][1280/1345] lr: 1.0000e-02 eta: 3:27:40 time: 0.1958 data_time: 0.0121 memory: 7116 grad_norm: 6.1641 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.6063 loss: 2.6063 2022/09/03 22:39:00 - mmengine - INFO - Epoch(train) [10][1300/1345] lr: 1.0000e-02 eta: 3:27:32 time: 0.1903 data_time: 0.0105 memory: 7116 grad_norm: 5.9591 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.6802 loss: 2.6802 2022/09/03 22:39:04 - mmengine - INFO - Epoch(train) [10][1320/1345] lr: 1.0000e-02 eta: 3:27:24 time: 0.1919 data_time: 0.0106 memory: 7116 grad_norm: 6.1088 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.5058 loss: 2.5058 2022/09/03 22:39:07 - mmengine - INFO - Epoch(train) [10][1340/1345] lr: 1.0000e-02 eta: 3:27:17 time: 0.1952 data_time: 0.0123 memory: 7116 grad_norm: 6.0672 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.6238 loss: 2.6238 2022/09/03 22:39:08 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:39:08 - mmengine - INFO - Epoch(train) [10][1345/1345] lr: 1.0000e-02 eta: 3:27:17 time: 0.1893 data_time: 0.0095 memory: 7116 grad_norm: 6.4688 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.5950 loss: 2.5950 2022/09/03 22:39:08 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/09/03 22:39:12 - mmengine - INFO - Epoch(val) [10][20/181] eta: 0:00:08 time: 0.0509 data_time: 0.0111 memory: 1114 2022/09/03 22:39:13 - mmengine - INFO - Epoch(val) [10][40/181] eta: 0:00:06 time: 0.0445 data_time: 0.0077 memory: 1114 2022/09/03 22:39:14 - mmengine - INFO - Epoch(val) [10][60/181] eta: 0:00:05 time: 0.0478 data_time: 0.0087 memory: 1114 2022/09/03 22:39:15 - mmengine - INFO - Epoch(val) [10][80/181] eta: 0:00:04 time: 0.0404 data_time: 0.0056 memory: 1114 2022/09/03 22:39:16 - mmengine - INFO - Epoch(val) [10][100/181] eta: 0:00:03 time: 0.0441 data_time: 0.0076 memory: 1114 2022/09/03 22:39:17 - mmengine - INFO - Epoch(val) [10][120/181] eta: 0:00:02 time: 0.0463 data_time: 0.0082 memory: 1114 2022/09/03 22:39:17 - mmengine - INFO - Epoch(val) [10][140/181] eta: 0:00:01 time: 0.0452 data_time: 0.0079 memory: 1114 2022/09/03 22:39:18 - mmengine - INFO - Epoch(val) [10][160/181] eta: 0:00:00 time: 0.0445 data_time: 0.0076 memory: 1114 2022/09/03 22:39:19 - mmengine - INFO - Epoch(val) [10][180/181] eta: 0:00:00 time: 0.0430 data_time: 0.0066 memory: 1114 2022/09/03 22:39:20 - mmengine - INFO - Epoch(val) [10][181/181] acc/top1: 0.2583 acc/top5: 0.5496 acc/mean1: 0.2315 2022/09/03 22:39:25 - mmengine - INFO - Epoch(train) [11][20/1345] lr: 1.0000e-02 eta: 3:27:07 time: 0.2412 data_time: 0.0139 memory: 7116 grad_norm: 6.1502 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.5382 loss: 2.5382 2022/09/03 22:39:29 - mmengine - INFO - Epoch(train) [11][40/1345] lr: 1.0000e-02 eta: 3:26:59 time: 0.1926 data_time: 0.0112 memory: 7116 grad_norm: 6.0980 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.7492 loss: 2.7492 2022/09/03 22:39:33 - mmengine - INFO - Epoch(train) [11][60/1345] lr: 1.0000e-02 eta: 3:26:51 time: 0.1883 data_time: 0.0094 memory: 7116 grad_norm: 6.2310 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.5930 loss: 2.5930 2022/09/03 22:39:37 - mmengine - INFO - Epoch(train) [11][80/1345] lr: 1.0000e-02 eta: 3:26:44 time: 0.1957 data_time: 0.0130 memory: 7116 grad_norm: 6.0365 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.5465 loss: 2.5465 2022/09/03 22:39:40 - mmengine - INFO - Epoch(train) [11][100/1345] lr: 1.0000e-02 eta: 3:26:36 time: 0.1895 data_time: 0.0104 memory: 7116 grad_norm: 6.3877 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4399 loss: 2.4399 2022/09/03 22:39:44 - mmengine - INFO - Epoch(train) [11][120/1345] lr: 1.0000e-02 eta: 3:26:28 time: 0.1907 data_time: 0.0103 memory: 7116 grad_norm: 6.0871 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.6307 loss: 2.6307 2022/09/03 22:39:48 - mmengine - INFO - Epoch(train) [11][140/1345] lr: 1.0000e-02 eta: 3:26:21 time: 0.2036 data_time: 0.0117 memory: 7116 grad_norm: 6.2534 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.5475 loss: 2.5475 2022/09/03 22:39:52 - mmengine - INFO - Epoch(train) [11][160/1345] lr: 1.0000e-02 eta: 3:26:14 time: 0.1926 data_time: 0.0099 memory: 7116 grad_norm: 6.1741 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5676 loss: 2.5676 2022/09/03 22:39:56 - mmengine - INFO - Epoch(train) [11][180/1345] lr: 1.0000e-02 eta: 3:26:06 time: 0.1906 data_time: 0.0107 memory: 7116 grad_norm: 6.0381 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5840 loss: 2.5840 2022/09/03 22:40:00 - mmengine - INFO - Epoch(train) [11][200/1345] lr: 1.0000e-02 eta: 3:25:58 time: 0.1924 data_time: 0.0138 memory: 7116 grad_norm: 6.3507 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4699 loss: 2.4699 2022/09/03 22:40:04 - mmengine - INFO - Epoch(train) [11][220/1345] lr: 1.0000e-02 eta: 3:25:50 time: 0.1886 data_time: 0.0112 memory: 7116 grad_norm: 6.0877 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.6993 loss: 2.6993 2022/09/03 22:40:07 - mmengine - INFO - Epoch(train) [11][240/1345] lr: 1.0000e-02 eta: 3:25:43 time: 0.1910 data_time: 0.0105 memory: 7116 grad_norm: 6.1988 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.4954 loss: 2.4954 2022/09/03 22:40:12 - mmengine - INFO - Epoch(train) [11][260/1345] lr: 1.0000e-02 eta: 3:25:36 time: 0.2053 data_time: 0.0098 memory: 7116 grad_norm: 6.1750 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.8019 loss: 2.8019 2022/09/03 22:40:15 - mmengine - INFO - Epoch(train) [11][280/1345] lr: 1.0000e-02 eta: 3:25:28 time: 0.1904 data_time: 0.0106 memory: 7116 grad_norm: 6.1768 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.2960 loss: 2.2960 2022/09/03 22:40:19 - mmengine - INFO - Epoch(train) [11][300/1345] lr: 1.0000e-02 eta: 3:25:20 time: 0.1874 data_time: 0.0096 memory: 7116 grad_norm: 6.1484 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.6142 loss: 2.6142 2022/09/03 22:40:23 - mmengine - INFO - Epoch(train) [11][320/1345] lr: 1.0000e-02 eta: 3:25:13 time: 0.1898 data_time: 0.0122 memory: 7116 grad_norm: 6.0934 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.6804 loss: 2.6804 2022/09/03 22:40:27 - mmengine - INFO - Epoch(train) [11][340/1345] lr: 1.0000e-02 eta: 3:25:05 time: 0.1912 data_time: 0.0097 memory: 7116 grad_norm: 6.2260 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.7746 loss: 2.7746 2022/09/03 22:40:30 - mmengine - INFO - Epoch(train) [11][360/1345] lr: 1.0000e-02 eta: 3:24:57 time: 0.1860 data_time: 0.0098 memory: 7116 grad_norm: 6.2360 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.5949 loss: 2.5949 2022/09/03 22:40:34 - mmengine - INFO - Epoch(train) [11][380/1345] lr: 1.0000e-02 eta: 3:24:50 time: 0.1923 data_time: 0.0127 memory: 7116 grad_norm: 6.1895 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2689 loss: 2.2689 2022/09/03 22:40:38 - mmengine - INFO - Epoch(train) [11][400/1345] lr: 1.0000e-02 eta: 3:24:42 time: 0.1898 data_time: 0.0100 memory: 7116 grad_norm: 6.1082 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.5997 loss: 2.5997 2022/09/03 22:40:42 - mmengine - INFO - Epoch(train) [11][420/1345] lr: 1.0000e-02 eta: 3:24:34 time: 0.1900 data_time: 0.0109 memory: 7116 grad_norm: 6.2714 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3842 loss: 2.3842 2022/09/03 22:40:46 - mmengine - INFO - Epoch(train) [11][440/1345] lr: 1.0000e-02 eta: 3:24:27 time: 0.1925 data_time: 0.0137 memory: 7116 grad_norm: 6.1534 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4065 loss: 2.4065 2022/09/03 22:40:49 - mmengine - INFO - Epoch(train) [11][460/1345] lr: 1.0000e-02 eta: 3:24:19 time: 0.1893 data_time: 0.0112 memory: 7116 grad_norm: 6.0850 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.4279 loss: 2.4279 2022/09/03 22:40:53 - mmengine - INFO - Epoch(train) [11][480/1345] lr: 1.0000e-02 eta: 3:24:11 time: 0.1871 data_time: 0.0100 memory: 7116 grad_norm: 6.3799 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.5368 loss: 2.5368 2022/09/03 22:40:57 - mmengine - INFO - Epoch(train) [11][500/1345] lr: 1.0000e-02 eta: 3:24:04 time: 0.1973 data_time: 0.0115 memory: 7116 grad_norm: 6.0277 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3465 loss: 2.3465 2022/09/03 22:41:01 - mmengine - INFO - Epoch(train) [11][520/1345] lr: 1.0000e-02 eta: 3:23:56 time: 0.1885 data_time: 0.0104 memory: 7116 grad_norm: 6.1302 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.7554 loss: 2.7554 2022/09/03 22:41:05 - mmengine - INFO - Epoch(train) [11][540/1345] lr: 1.0000e-02 eta: 3:23:49 time: 0.1901 data_time: 0.0103 memory: 7116 grad_norm: 6.2395 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.8850 loss: 2.8850 2022/09/03 22:41:07 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:41:09 - mmengine - INFO - Epoch(train) [11][560/1345] lr: 1.0000e-02 eta: 3:23:41 time: 0.1902 data_time: 0.0118 memory: 7116 grad_norm: 6.3206 top1_acc: 0.1250 top5_acc: 0.8750 loss_cls: 3.0288 loss: 3.0288 2022/09/03 22:41:12 - mmengine - INFO - Epoch(train) [11][580/1345] lr: 1.0000e-02 eta: 3:23:34 time: 0.1914 data_time: 0.0097 memory: 7116 grad_norm: 6.1676 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.3913 loss: 2.3913 2022/09/03 22:41:17 - mmengine - INFO - Epoch(train) [11][600/1345] lr: 1.0000e-02 eta: 3:23:28 time: 0.2115 data_time: 0.0105 memory: 7116 grad_norm: 6.1396 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.7425 loss: 2.7425 2022/09/03 22:41:20 - mmengine - INFO - Epoch(train) [11][620/1345] lr: 1.0000e-02 eta: 3:23:20 time: 0.1900 data_time: 0.0122 memory: 7116 grad_norm: 6.6218 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.5187 loss: 2.5187 2022/09/03 22:41:24 - mmengine - INFO - Epoch(train) [11][640/1345] lr: 1.0000e-02 eta: 3:23:13 time: 0.1905 data_time: 0.0096 memory: 7116 grad_norm: 6.2374 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.7494 loss: 2.7494 2022/09/03 22:41:28 - mmengine - INFO - Epoch(train) [11][660/1345] lr: 1.0000e-02 eta: 3:23:05 time: 0.1877 data_time: 0.0105 memory: 7116 grad_norm: 6.3479 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.8402 loss: 2.8402 2022/09/03 22:41:32 - mmengine - INFO - Epoch(train) [11][680/1345] lr: 1.0000e-02 eta: 3:22:57 time: 0.1905 data_time: 0.0122 memory: 7116 grad_norm: 6.0999 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.7490 loss: 2.7490 2022/09/03 22:41:36 - mmengine - INFO - Epoch(train) [11][700/1345] lr: 1.0000e-02 eta: 3:22:50 time: 0.1900 data_time: 0.0103 memory: 7116 grad_norm: 6.1844 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5888 loss: 2.5888 2022/09/03 22:41:39 - mmengine - INFO - Epoch(train) [11][720/1345] lr: 1.0000e-02 eta: 3:22:42 time: 0.1906 data_time: 0.0106 memory: 7116 grad_norm: 6.2666 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.5384 loss: 2.5384 2022/09/03 22:41:43 - mmengine - INFO - Epoch(train) [11][740/1345] lr: 1.0000e-02 eta: 3:22:35 time: 0.1909 data_time: 0.0115 memory: 7116 grad_norm: 6.2227 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.6188 loss: 2.6188 2022/09/03 22:41:47 - mmengine - INFO - Epoch(train) [11][760/1345] lr: 1.0000e-02 eta: 3:22:28 time: 0.1981 data_time: 0.0093 memory: 7116 grad_norm: 6.2417 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.7335 loss: 2.7335 2022/09/03 22:41:51 - mmengine - INFO - Epoch(train) [11][780/1345] lr: 1.0000e-02 eta: 3:22:20 time: 0.1867 data_time: 0.0109 memory: 7116 grad_norm: 6.1241 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4578 loss: 2.4578 2022/09/03 22:41:55 - mmengine - INFO - Epoch(train) [11][800/1345] lr: 1.0000e-02 eta: 3:22:13 time: 0.1955 data_time: 0.0118 memory: 7116 grad_norm: 6.4089 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.3661 loss: 2.3661 2022/09/03 22:41:59 - mmengine - INFO - Epoch(train) [11][820/1345] lr: 1.0000e-02 eta: 3:22:06 time: 0.1897 data_time: 0.0102 memory: 7116 grad_norm: 6.3612 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4023 loss: 2.4023 2022/09/03 22:42:03 - mmengine - INFO - Epoch(train) [11][840/1345] lr: 1.0000e-02 eta: 3:21:59 time: 0.1941 data_time: 0.0099 memory: 7116 grad_norm: 6.2826 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.5669 loss: 2.5669 2022/09/03 22:42:06 - mmengine - INFO - Epoch(train) [11][860/1345] lr: 1.0000e-02 eta: 3:21:51 time: 0.1924 data_time: 0.0117 memory: 7116 grad_norm: 6.0751 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.5592 loss: 2.5592 2022/09/03 22:42:10 - mmengine - INFO - Epoch(train) [11][880/1345] lr: 1.0000e-02 eta: 3:21:44 time: 0.1901 data_time: 0.0112 memory: 7116 grad_norm: 6.1418 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.6403 loss: 2.6403 2022/09/03 22:42:14 - mmengine - INFO - Epoch(train) [11][900/1345] lr: 1.0000e-02 eta: 3:21:37 time: 0.1921 data_time: 0.0094 memory: 7116 grad_norm: 6.1452 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5591 loss: 2.5591 2022/09/03 22:42:18 - mmengine - INFO - Epoch(train) [11][920/1345] lr: 1.0000e-02 eta: 3:21:29 time: 0.1930 data_time: 0.0124 memory: 7116 grad_norm: 6.1908 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.5405 loss: 2.5405 2022/09/03 22:42:22 - mmengine - INFO - Epoch(train) [11][940/1345] lr: 1.0000e-02 eta: 3:21:22 time: 0.1914 data_time: 0.0108 memory: 7116 grad_norm: 6.2561 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.7211 loss: 2.7211 2022/09/03 22:42:26 - mmengine - INFO - Epoch(train) [11][960/1345] lr: 1.0000e-02 eta: 3:21:15 time: 0.1907 data_time: 0.0100 memory: 7116 grad_norm: 6.2370 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.8009 loss: 2.8009 2022/09/03 22:42:29 - mmengine - INFO - Epoch(train) [11][980/1345] lr: 1.0000e-02 eta: 3:21:07 time: 0.1902 data_time: 0.0108 memory: 7116 grad_norm: 6.1267 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4714 loss: 2.4714 2022/09/03 22:42:33 - mmengine - INFO - Epoch(train) [11][1000/1345] lr: 1.0000e-02 eta: 3:21:00 time: 0.1903 data_time: 0.0102 memory: 7116 grad_norm: 6.1837 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.3828 loss: 2.3828 2022/09/03 22:42:37 - mmengine - INFO - Epoch(train) [11][1020/1345] lr: 1.0000e-02 eta: 3:20:53 time: 0.1975 data_time: 0.0105 memory: 7116 grad_norm: 6.1944 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 2.9046 loss: 2.9046 2022/09/03 22:42:41 - mmengine - INFO - Epoch(train) [11][1040/1345] lr: 1.0000e-02 eta: 3:20:46 time: 0.1942 data_time: 0.0115 memory: 7116 grad_norm: 6.3677 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.5537 loss: 2.5537 2022/09/03 22:42:45 - mmengine - INFO - Epoch(train) [11][1060/1345] lr: 1.0000e-02 eta: 3:20:39 time: 0.1955 data_time: 0.0100 memory: 7116 grad_norm: 6.2613 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.7798 loss: 2.7798 2022/09/03 22:42:49 - mmengine - INFO - Epoch(train) [11][1080/1345] lr: 1.0000e-02 eta: 3:20:32 time: 0.1922 data_time: 0.0085 memory: 7116 grad_norm: 6.0569 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.5154 loss: 2.5154 2022/09/03 22:42:53 - mmengine - INFO - Epoch(train) [11][1100/1345] lr: 1.0000e-02 eta: 3:20:25 time: 0.1945 data_time: 0.0121 memory: 7116 grad_norm: 6.2245 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.5133 loss: 2.5133 2022/09/03 22:42:57 - mmengine - INFO - Epoch(train) [11][1120/1345] lr: 1.0000e-02 eta: 3:20:18 time: 0.1971 data_time: 0.0090 memory: 7116 grad_norm: 6.2117 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.5812 loss: 2.5812 2022/09/03 22:43:00 - mmengine - INFO - Epoch(train) [11][1140/1345] lr: 1.0000e-02 eta: 3:20:11 time: 0.1929 data_time: 0.0096 memory: 7116 grad_norm: 6.1363 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6556 loss: 2.6556 2022/09/03 22:43:04 - mmengine - INFO - Epoch(train) [11][1160/1345] lr: 1.0000e-02 eta: 3:20:04 time: 0.1892 data_time: 0.0123 memory: 7116 grad_norm: 6.3915 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5715 loss: 2.5715 2022/09/03 22:43:08 - mmengine - INFO - Epoch(train) [11][1180/1345] lr: 1.0000e-02 eta: 3:19:57 time: 0.1937 data_time: 0.0103 memory: 7116 grad_norm: 5.9762 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2858 loss: 2.2858 2022/09/03 22:43:12 - mmengine - INFO - Epoch(train) [11][1200/1345] lr: 1.0000e-02 eta: 3:19:49 time: 0.1913 data_time: 0.0094 memory: 7116 grad_norm: 6.1118 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.5187 loss: 2.5187 2022/09/03 22:43:16 - mmengine - INFO - Epoch(train) [11][1220/1345] lr: 1.0000e-02 eta: 3:19:43 time: 0.1974 data_time: 0.0126 memory: 7116 grad_norm: 6.6961 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6919 loss: 2.6919 2022/09/03 22:43:20 - mmengine - INFO - Epoch(train) [11][1240/1345] lr: 1.0000e-02 eta: 3:19:36 time: 0.1917 data_time: 0.0091 memory: 7116 grad_norm: 6.3187 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4994 loss: 2.4994 2022/09/03 22:43:24 - mmengine - INFO - Epoch(train) [11][1260/1345] lr: 1.0000e-02 eta: 3:19:29 time: 0.1934 data_time: 0.0092 memory: 7116 grad_norm: 6.3541 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5860 loss: 2.5860 2022/09/03 22:43:27 - mmengine - INFO - Epoch(train) [11][1280/1345] lr: 1.0000e-02 eta: 3:19:22 time: 0.1951 data_time: 0.0117 memory: 7116 grad_norm: 6.3718 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.7402 loss: 2.7402 2022/09/03 22:43:31 - mmengine - INFO - Epoch(train) [11][1300/1345] lr: 1.0000e-02 eta: 3:19:15 time: 0.1960 data_time: 0.0083 memory: 7116 grad_norm: 6.0904 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.9660 loss: 2.9660 2022/09/03 22:43:35 - mmengine - INFO - Epoch(train) [11][1320/1345] lr: 1.0000e-02 eta: 3:19:08 time: 0.1904 data_time: 0.0093 memory: 7116 grad_norm: 6.2495 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5276 loss: 2.5276 2022/09/03 22:43:39 - mmengine - INFO - Epoch(train) [11][1340/1345] lr: 1.0000e-02 eta: 3:19:01 time: 0.1934 data_time: 0.0127 memory: 7116 grad_norm: 5.8695 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.7977 loss: 2.7977 2022/09/03 22:43:40 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:43:40 - mmengine - INFO - Epoch(train) [11][1345/1345] lr: 1.0000e-02 eta: 3:19:01 time: 0.1887 data_time: 0.0101 memory: 7116 grad_norm: 6.2284 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.6603 loss: 2.6603 2022/09/03 22:43:40 - mmengine - INFO - Saving checkpoint at 11 epochs 2022/09/03 22:43:43 - mmengine - INFO - Epoch(val) [11][20/181] eta: 0:00:08 time: 0.0555 data_time: 0.0174 memory: 1114 2022/09/03 22:43:44 - mmengine - INFO - Epoch(val) [11][40/181] eta: 0:00:06 time: 0.0432 data_time: 0.0073 memory: 1114 2022/09/03 22:43:44 - mmengine - INFO - Epoch(val) [11][60/181] eta: 0:00:05 time: 0.0429 data_time: 0.0072 memory: 1114 2022/09/03 22:43:46 - mmengine - INFO - Epoch(val) [11][80/181] eta: 0:00:05 time: 0.0593 data_time: 0.0242 memory: 1114 2022/09/03 22:43:46 - mmengine - INFO - Epoch(val) [11][100/181] eta: 0:00:03 time: 0.0408 data_time: 0.0059 memory: 1114 2022/09/03 22:43:47 - mmengine - INFO - Epoch(val) [11][120/181] eta: 0:00:02 time: 0.0414 data_time: 0.0061 memory: 1114 2022/09/03 22:43:48 - mmengine - INFO - Epoch(val) [11][140/181] eta: 0:00:01 time: 0.0422 data_time: 0.0067 memory: 1114 2022/09/03 22:43:49 - mmengine - INFO - Epoch(val) [11][160/181] eta: 0:00:00 time: 0.0436 data_time: 0.0073 memory: 1114 2022/09/03 22:43:50 - mmengine - INFO - Epoch(val) [11][180/181] eta: 0:00:00 time: 0.0428 data_time: 0.0071 memory: 1114 2022/09/03 22:43:52 - mmengine - INFO - Epoch(val) [11][181/181] acc/top1: 0.2912 acc/top5: 0.5713 acc/mean1: 0.2633 2022/09/03 22:43:52 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_9.pth is removed 2022/09/03 22:43:53 - mmengine - INFO - The best checkpoint with 0.2912 acc/top1 at 11 epoch is saved to best_acc/top1_epoch_11.pth. 2022/09/03 22:43:57 - mmengine - INFO - Epoch(train) [12][20/1345] lr: 1.0000e-02 eta: 3:18:49 time: 0.1953 data_time: 0.0138 memory: 7116 grad_norm: 6.0728 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.3624 loss: 2.3624 2022/09/03 22:44:01 - mmengine - INFO - Epoch(train) [12][40/1345] lr: 1.0000e-02 eta: 3:18:42 time: 0.1951 data_time: 0.0092 memory: 7116 grad_norm: 6.5215 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5209 loss: 2.5209 2022/09/03 22:44:05 - mmengine - INFO - Epoch(train) [12][60/1345] lr: 1.0000e-02 eta: 3:18:35 time: 0.1997 data_time: 0.0094 memory: 7116 grad_norm: 6.2034 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.5991 loss: 2.5991 2022/09/03 22:44:09 - mmengine - INFO - Epoch(train) [12][80/1345] lr: 1.0000e-02 eta: 3:18:28 time: 0.1949 data_time: 0.0120 memory: 7116 grad_norm: 6.3601 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4288 loss: 2.4288 2022/09/03 22:44:13 - mmengine - INFO - Epoch(train) [12][100/1345] lr: 1.0000e-02 eta: 3:18:22 time: 0.1996 data_time: 0.0089 memory: 7116 grad_norm: 6.4400 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.6213 loss: 2.6213 2022/09/03 22:44:17 - mmengine - INFO - Epoch(train) [12][120/1345] lr: 1.0000e-02 eta: 3:18:15 time: 0.1989 data_time: 0.0097 memory: 7116 grad_norm: 6.3536 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.2670 loss: 2.2670 2022/09/03 22:44:21 - mmengine - INFO - Epoch(train) [12][140/1345] lr: 1.0000e-02 eta: 3:18:09 time: 0.2035 data_time: 0.0112 memory: 7116 grad_norm: 6.4661 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.5012 loss: 2.5012 2022/09/03 22:44:25 - mmengine - INFO - Epoch(train) [12][160/1345] lr: 1.0000e-02 eta: 3:18:02 time: 0.1965 data_time: 0.0090 memory: 7116 grad_norm: 6.0782 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4731 loss: 2.4731 2022/09/03 22:44:29 - mmengine - INFO - Epoch(train) [12][180/1345] lr: 1.0000e-02 eta: 3:17:56 time: 0.1960 data_time: 0.0100 memory: 7116 grad_norm: 6.4374 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2457 loss: 2.2457 2022/09/03 22:44:33 - mmengine - INFO - Epoch(train) [12][200/1345] lr: 1.0000e-02 eta: 3:17:49 time: 0.2009 data_time: 0.0118 memory: 7116 grad_norm: 6.2645 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3166 loss: 2.3166 2022/09/03 22:44:34 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:44:37 - mmengine - INFO - Epoch(train) [12][220/1345] lr: 1.0000e-02 eta: 3:17:43 time: 0.1957 data_time: 0.0104 memory: 7116 grad_norm: 6.2505 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.5082 loss: 2.5082 2022/09/03 22:44:41 - mmengine - INFO - Epoch(train) [12][240/1345] lr: 1.0000e-02 eta: 3:17:36 time: 0.1956 data_time: 0.0087 memory: 7116 grad_norm: 6.2490 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.7409 loss: 2.7409 2022/09/03 22:44:45 - mmengine - INFO - Epoch(train) [12][260/1345] lr: 1.0000e-02 eta: 3:17:29 time: 0.1965 data_time: 0.0124 memory: 7116 grad_norm: 6.2495 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.6758 loss: 2.6758 2022/09/03 22:44:49 - mmengine - INFO - Epoch(train) [12][280/1345] lr: 1.0000e-02 eta: 3:17:23 time: 0.2026 data_time: 0.0085 memory: 7116 grad_norm: 6.0751 top1_acc: 0.0000 top5_acc: 0.5000 loss_cls: 2.7951 loss: 2.7951 2022/09/03 22:44:53 - mmengine - INFO - Epoch(train) [12][300/1345] lr: 1.0000e-02 eta: 3:17:16 time: 0.1946 data_time: 0.0087 memory: 7116 grad_norm: 6.2081 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.4311 loss: 2.4311 2022/09/03 22:44:57 - mmengine - INFO - Epoch(train) [12][320/1345] lr: 1.0000e-02 eta: 3:17:10 time: 0.1979 data_time: 0.0112 memory: 7116 grad_norm: 6.3772 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.4362 loss: 2.4362 2022/09/03 22:45:00 - mmengine - INFO - Epoch(train) [12][340/1345] lr: 1.0000e-02 eta: 3:17:03 time: 0.1927 data_time: 0.0088 memory: 7116 grad_norm: 6.1172 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4095 loss: 2.4095 2022/09/03 22:45:04 - mmengine - INFO - Epoch(train) [12][360/1345] lr: 1.0000e-02 eta: 3:16:57 time: 0.2039 data_time: 0.0073 memory: 7116 grad_norm: 6.4038 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4569 loss: 2.4569 2022/09/03 22:45:08 - mmengine - INFO - Epoch(train) [12][380/1345] lr: 1.0000e-02 eta: 3:16:50 time: 0.1972 data_time: 0.0121 memory: 7116 grad_norm: 6.3628 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.8097 loss: 2.8097 2022/09/03 22:45:12 - mmengine - INFO - Epoch(train) [12][400/1345] lr: 1.0000e-02 eta: 3:16:43 time: 0.1957 data_time: 0.0087 memory: 7116 grad_norm: 6.2189 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4841 loss: 2.4841 2022/09/03 22:45:16 - mmengine - INFO - Epoch(train) [12][420/1345] lr: 1.0000e-02 eta: 3:16:37 time: 0.1936 data_time: 0.0084 memory: 7116 grad_norm: 6.4430 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4626 loss: 2.4626 2022/09/03 22:45:20 - mmengine - INFO - Epoch(train) [12][440/1345] lr: 1.0000e-02 eta: 3:16:30 time: 0.1935 data_time: 0.0140 memory: 7116 grad_norm: 6.2673 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.5757 loss: 2.5757 2022/09/03 22:45:24 - mmengine - INFO - Epoch(train) [12][460/1345] lr: 1.0000e-02 eta: 3:16:23 time: 0.1949 data_time: 0.0084 memory: 7116 grad_norm: 6.4115 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.4022 loss: 2.4022 2022/09/03 22:45:28 - mmengine - INFO - Epoch(train) [12][480/1345] lr: 1.0000e-02 eta: 3:16:17 time: 0.1989 data_time: 0.0092 memory: 7116 grad_norm: 6.4012 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.7267 loss: 2.7267 2022/09/03 22:45:32 - mmengine - INFO - Epoch(train) [12][500/1345] lr: 1.0000e-02 eta: 3:16:10 time: 0.1969 data_time: 0.0109 memory: 7116 grad_norm: 6.5276 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6061 loss: 2.6061 2022/09/03 22:45:36 - mmengine - INFO - Epoch(train) [12][520/1345] lr: 1.0000e-02 eta: 3:16:04 time: 0.1967 data_time: 0.0100 memory: 7116 grad_norm: 6.5493 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.6055 loss: 2.6055 2022/09/03 22:45:40 - mmengine - INFO - Epoch(train) [12][540/1345] lr: 1.0000e-02 eta: 3:15:57 time: 0.1966 data_time: 0.0100 memory: 7116 grad_norm: 7.3686 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5130 loss: 2.5130 2022/09/03 22:45:44 - mmengine - INFO - Epoch(train) [12][560/1345] lr: 1.0000e-02 eta: 3:15:50 time: 0.1958 data_time: 0.0108 memory: 7116 grad_norm: 6.4038 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.7975 loss: 2.7975 2022/09/03 22:45:48 - mmengine - INFO - Epoch(train) [12][580/1345] lr: 1.0000e-02 eta: 3:15:44 time: 0.1991 data_time: 0.0093 memory: 7116 grad_norm: 6.4332 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.6048 loss: 2.6048 2022/09/03 22:45:52 - mmengine - INFO - Epoch(train) [12][600/1345] lr: 1.0000e-02 eta: 3:15:37 time: 0.1948 data_time: 0.0098 memory: 7116 grad_norm: 6.1313 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.5053 loss: 2.5053 2022/09/03 22:45:56 - mmengine - INFO - Epoch(train) [12][620/1345] lr: 1.0000e-02 eta: 3:15:32 time: 0.2107 data_time: 0.0152 memory: 7116 grad_norm: 6.1604 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.5225 loss: 2.5225 2022/09/03 22:46:00 - mmengine - INFO - Epoch(train) [12][640/1345] lr: 1.0000e-02 eta: 3:15:25 time: 0.1941 data_time: 0.0078 memory: 7116 grad_norm: 6.0515 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5249 loss: 2.5249 2022/09/03 22:46:03 - mmengine - INFO - Epoch(train) [12][660/1345] lr: 1.0000e-02 eta: 3:15:18 time: 0.1926 data_time: 0.0093 memory: 7116 grad_norm: 6.0335 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.6764 loss: 2.6764 2022/09/03 22:46:07 - mmengine - INFO - Epoch(train) [12][680/1345] lr: 1.0000e-02 eta: 3:15:12 time: 0.1971 data_time: 0.0110 memory: 7116 grad_norm: 6.3008 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.8424 loss: 2.8424 2022/09/03 22:46:11 - mmengine - INFO - Epoch(train) [12][700/1345] lr: 1.0000e-02 eta: 3:15:05 time: 0.1940 data_time: 0.0087 memory: 7116 grad_norm: 6.1847 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.8699 loss: 2.8699 2022/09/03 22:46:15 - mmengine - INFO - Epoch(train) [12][720/1345] lr: 1.0000e-02 eta: 3:14:59 time: 0.1990 data_time: 0.0094 memory: 7116 grad_norm: 6.1100 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 2.8395 loss: 2.8395 2022/09/03 22:46:19 - mmengine - INFO - Epoch(train) [12][740/1345] lr: 1.0000e-02 eta: 3:14:52 time: 0.1927 data_time: 0.0111 memory: 7116 grad_norm: 6.0307 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.5438 loss: 2.5438 2022/09/03 22:46:23 - mmengine - INFO - Epoch(train) [12][760/1345] lr: 1.0000e-02 eta: 3:14:45 time: 0.1943 data_time: 0.0089 memory: 7116 grad_norm: 6.3552 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4933 loss: 2.4933 2022/09/03 22:46:27 - mmengine - INFO - Epoch(train) [12][780/1345] lr: 1.0000e-02 eta: 3:14:39 time: 0.1891 data_time: 0.0100 memory: 7116 grad_norm: 6.1089 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4691 loss: 2.4691 2022/09/03 22:46:31 - mmengine - INFO - Epoch(train) [12][800/1345] lr: 1.0000e-02 eta: 3:14:32 time: 0.1925 data_time: 0.0105 memory: 7116 grad_norm: 6.1527 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.5786 loss: 2.5786 2022/09/03 22:46:34 - mmengine - INFO - Epoch(train) [12][820/1345] lr: 1.0000e-02 eta: 3:14:25 time: 0.1900 data_time: 0.0094 memory: 7116 grad_norm: 6.5614 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.5231 loss: 2.5231 2022/09/03 22:46:39 - mmengine - INFO - Epoch(train) [12][840/1345] lr: 1.0000e-02 eta: 3:14:19 time: 0.2039 data_time: 0.0098 memory: 7116 grad_norm: 6.3199 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.6378 loss: 2.6378 2022/09/03 22:46:42 - mmengine - INFO - Epoch(train) [12][860/1345] lr: 1.0000e-02 eta: 3:14:12 time: 0.1962 data_time: 0.0109 memory: 7116 grad_norm: 6.1195 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.6272 loss: 2.6272 2022/09/03 22:46:46 - mmengine - INFO - Epoch(train) [12][880/1345] lr: 1.0000e-02 eta: 3:14:06 time: 0.2006 data_time: 0.0084 memory: 7116 grad_norm: 6.3628 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4959 loss: 2.4959 2022/09/03 22:46:50 - mmengine - INFO - Epoch(train) [12][900/1345] lr: 1.0000e-02 eta: 3:14:00 time: 0.1973 data_time: 0.0091 memory: 7116 grad_norm: 6.1803 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.6769 loss: 2.6769 2022/09/03 22:46:54 - mmengine - INFO - Epoch(train) [12][920/1345] lr: 1.0000e-02 eta: 3:13:53 time: 0.1937 data_time: 0.0116 memory: 7116 grad_norm: 6.0955 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3970 loss: 2.3970 2022/09/03 22:46:58 - mmengine - INFO - Epoch(train) [12][940/1345] lr: 1.0000e-02 eta: 3:13:47 time: 0.2016 data_time: 0.0086 memory: 7116 grad_norm: 6.2931 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3895 loss: 2.3895 2022/09/03 22:47:02 - mmengine - INFO - Epoch(train) [12][960/1345] lr: 1.0000e-02 eta: 3:13:41 time: 0.1968 data_time: 0.0096 memory: 7116 grad_norm: 6.2378 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1350 loss: 2.1350 2022/09/03 22:47:06 - mmengine - INFO - Epoch(train) [12][980/1345] lr: 1.0000e-02 eta: 3:13:34 time: 0.1966 data_time: 0.0108 memory: 7116 grad_norm: 6.2658 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.4508 loss: 2.4508 2022/09/03 22:47:10 - mmengine - INFO - Epoch(train) [12][1000/1345] lr: 1.0000e-02 eta: 3:13:28 time: 0.1948 data_time: 0.0082 memory: 7116 grad_norm: 5.9719 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.6730 loss: 2.6730 2022/09/03 22:47:14 - mmengine - INFO - Epoch(train) [12][1020/1345] lr: 1.0000e-02 eta: 3:13:22 time: 0.1985 data_time: 0.0091 memory: 7116 grad_norm: 6.5814 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.7168 loss: 2.7168 2022/09/03 22:47:18 - mmengine - INFO - Epoch(train) [12][1040/1345] lr: 1.0000e-02 eta: 3:13:15 time: 0.1980 data_time: 0.0111 memory: 7116 grad_norm: 6.3506 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.7155 loss: 2.7155 2022/09/03 22:47:22 - mmengine - INFO - Epoch(train) [12][1060/1345] lr: 1.0000e-02 eta: 3:13:09 time: 0.1940 data_time: 0.0090 memory: 7116 grad_norm: 6.2968 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.5358 loss: 2.5358 2022/09/03 22:47:26 - mmengine - INFO - Epoch(train) [12][1080/1345] lr: 1.0000e-02 eta: 3:13:03 time: 0.2025 data_time: 0.0090 memory: 7116 grad_norm: 6.1077 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.7783 loss: 2.7783 2022/09/03 22:47:30 - mmengine - INFO - Epoch(train) [12][1100/1345] lr: 1.0000e-02 eta: 3:12:56 time: 0.1986 data_time: 0.0108 memory: 7116 grad_norm: 6.3236 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4414 loss: 2.4414 2022/09/03 22:47:34 - mmengine - INFO - Epoch(train) [12][1120/1345] lr: 1.0000e-02 eta: 3:12:50 time: 0.1965 data_time: 0.0093 memory: 7116 grad_norm: 6.4355 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0340 loss: 2.0340 2022/09/03 22:47:38 - mmengine - INFO - Epoch(train) [12][1140/1345] lr: 1.0000e-02 eta: 3:12:44 time: 0.1996 data_time: 0.0089 memory: 7116 grad_norm: 6.2950 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.5482 loss: 2.5482 2022/09/03 22:47:42 - mmengine - INFO - Epoch(train) [12][1160/1345] lr: 1.0000e-02 eta: 3:12:38 time: 0.1996 data_time: 0.0117 memory: 7116 grad_norm: 6.4453 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.5474 loss: 2.5474 2022/09/03 22:47:46 - mmengine - INFO - Epoch(train) [12][1180/1345] lr: 1.0000e-02 eta: 3:12:32 time: 0.1987 data_time: 0.0088 memory: 7116 grad_norm: 6.3315 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4366 loss: 2.4366 2022/09/03 22:47:50 - mmengine - INFO - Epoch(train) [12][1200/1345] lr: 1.0000e-02 eta: 3:12:25 time: 0.1967 data_time: 0.0091 memory: 7116 grad_norm: 6.4838 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4594 loss: 2.4594 2022/09/03 22:47:51 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:47:54 - mmengine - INFO - Epoch(train) [12][1220/1345] lr: 1.0000e-02 eta: 3:12:19 time: 0.1994 data_time: 0.0108 memory: 7116 grad_norm: 6.1379 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.5092 loss: 2.5092 2022/09/03 22:47:58 - mmengine - INFO - Epoch(train) [12][1240/1345] lr: 1.0000e-02 eta: 3:12:13 time: 0.2006 data_time: 0.0093 memory: 7116 grad_norm: 6.3063 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.3922 loss: 2.3922 2022/09/03 22:48:02 - mmengine - INFO - Epoch(train) [12][1260/1345] lr: 1.0000e-02 eta: 3:12:07 time: 0.2053 data_time: 0.0099 memory: 7116 grad_norm: 6.3556 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.8085 loss: 2.8085 2022/09/03 22:48:06 - mmengine - INFO - Epoch(train) [12][1280/1345] lr: 1.0000e-02 eta: 3:12:01 time: 0.1979 data_time: 0.0116 memory: 7116 grad_norm: 6.2617 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.6344 loss: 2.6344 2022/09/03 22:48:10 - mmengine - INFO - Epoch(train) [12][1300/1345] lr: 1.0000e-02 eta: 3:11:54 time: 0.1938 data_time: 0.0094 memory: 7116 grad_norm: 6.1298 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.8018 loss: 2.8018 2022/09/03 22:48:14 - mmengine - INFO - Epoch(train) [12][1320/1345] lr: 1.0000e-02 eta: 3:11:48 time: 0.1951 data_time: 0.0082 memory: 7116 grad_norm: 6.0212 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 2.3366 loss: 2.3366 2022/09/03 22:48:18 - mmengine - INFO - Epoch(train) [12][1340/1345] lr: 1.0000e-02 eta: 3:11:42 time: 0.2011 data_time: 0.0108 memory: 7116 grad_norm: 6.6685 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.1543 loss: 2.1543 2022/09/03 22:48:19 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:48:19 - mmengine - INFO - Epoch(train) [12][1345/1345] lr: 1.0000e-02 eta: 3:11:42 time: 0.1940 data_time: 0.0084 memory: 7116 grad_norm: 6.4875 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.3858 loss: 2.3858 2022/09/03 22:48:19 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/09/03 22:48:22 - mmengine - INFO - Epoch(val) [12][20/181] eta: 0:00:07 time: 0.0460 data_time: 0.0100 memory: 1114 2022/09/03 22:48:22 - mmengine - INFO - Epoch(val) [12][40/181] eta: 0:00:06 time: 0.0432 data_time: 0.0068 memory: 1114 2022/09/03 22:48:23 - mmengine - INFO - Epoch(val) [12][60/181] eta: 0:00:05 time: 0.0425 data_time: 0.0067 memory: 1114 2022/09/03 22:48:24 - mmengine - INFO - Epoch(val) [12][80/181] eta: 0:00:04 time: 0.0428 data_time: 0.0069 memory: 1114 2022/09/03 22:48:25 - mmengine - INFO - Epoch(val) [12][100/181] eta: 0:00:03 time: 0.0429 data_time: 0.0070 memory: 1114 2022/09/03 22:48:26 - mmengine - INFO - Epoch(val) [12][120/181] eta: 0:00:02 time: 0.0429 data_time: 0.0069 memory: 1114 2022/09/03 22:48:27 - mmengine - INFO - Epoch(val) [12][140/181] eta: 0:00:01 time: 0.0415 data_time: 0.0060 memory: 1114 2022/09/03 22:48:28 - mmengine - INFO - Epoch(val) [12][160/181] eta: 0:00:00 time: 0.0437 data_time: 0.0075 memory: 1114 2022/09/03 22:48:28 - mmengine - INFO - Epoch(val) [12][180/181] eta: 0:00:00 time: 0.0455 data_time: 0.0081 memory: 1114 2022/09/03 22:48:30 - mmengine - INFO - Epoch(val) [12][181/181] acc/top1: 0.2752 acc/top5: 0.5787 acc/mean1: 0.2440 2022/09/03 22:48:35 - mmengine - INFO - Epoch(train) [13][20/1345] lr: 1.0000e-02 eta: 3:11:32 time: 0.2110 data_time: 0.0219 memory: 7116 grad_norm: 5.9911 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2508 loss: 2.2508 2022/09/03 22:48:39 - mmengine - INFO - Epoch(train) [13][40/1345] lr: 1.0000e-02 eta: 3:11:26 time: 0.1997 data_time: 0.0084 memory: 7116 grad_norm: 6.2861 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.6359 loss: 2.6359 2022/09/03 22:48:43 - mmengine - INFO - Epoch(train) [13][60/1345] lr: 1.0000e-02 eta: 3:11:19 time: 0.1950 data_time: 0.0096 memory: 7116 grad_norm: 6.5197 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3705 loss: 2.3705 2022/09/03 22:48:47 - mmengine - INFO - Epoch(train) [13][80/1345] lr: 1.0000e-02 eta: 3:11:13 time: 0.1967 data_time: 0.0111 memory: 7116 grad_norm: 6.1565 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.5896 loss: 2.5896 2022/09/03 22:48:51 - mmengine - INFO - Epoch(train) [13][100/1345] lr: 1.0000e-02 eta: 3:11:07 time: 0.1973 data_time: 0.0085 memory: 7116 grad_norm: 6.4723 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 2.3229 loss: 2.3229 2022/09/03 22:48:54 - mmengine - INFO - Epoch(train) [13][120/1345] lr: 1.0000e-02 eta: 3:11:01 time: 0.1947 data_time: 0.0092 memory: 7116 grad_norm: 6.4323 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4961 loss: 2.4961 2022/09/03 22:48:58 - mmengine - INFO - Epoch(train) [13][140/1345] lr: 1.0000e-02 eta: 3:10:55 time: 0.2016 data_time: 0.0117 memory: 7116 grad_norm: 6.3388 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4014 loss: 2.4014 2022/09/03 22:49:02 - mmengine - INFO - Epoch(train) [13][160/1345] lr: 1.0000e-02 eta: 3:10:48 time: 0.1964 data_time: 0.0085 memory: 7116 grad_norm: 6.3071 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.5957 loss: 2.5957 2022/09/03 22:49:06 - mmengine - INFO - Epoch(train) [13][180/1345] lr: 1.0000e-02 eta: 3:10:42 time: 0.1975 data_time: 0.0091 memory: 7116 grad_norm: 6.4930 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2632 loss: 2.2632 2022/09/03 22:49:10 - mmengine - INFO - Epoch(train) [13][200/1345] lr: 1.0000e-02 eta: 3:10:36 time: 0.1975 data_time: 0.0108 memory: 7116 grad_norm: 6.3419 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.3317 loss: 2.3317 2022/09/03 22:49:14 - mmengine - INFO - Epoch(train) [13][220/1345] lr: 1.0000e-02 eta: 3:10:30 time: 0.1986 data_time: 0.0080 memory: 7116 grad_norm: 6.4203 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1562 loss: 2.1562 2022/09/03 22:49:18 - mmengine - INFO - Epoch(train) [13][240/1345] lr: 1.0000e-02 eta: 3:10:24 time: 0.1954 data_time: 0.0097 memory: 7116 grad_norm: 6.6046 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3427 loss: 2.3427 2022/09/03 22:49:22 - mmengine - INFO - Epoch(train) [13][260/1345] lr: 1.0000e-02 eta: 3:10:17 time: 0.1953 data_time: 0.0117 memory: 7116 grad_norm: 6.3401 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.6671 loss: 2.6671 2022/09/03 22:49:26 - mmengine - INFO - Epoch(train) [13][280/1345] lr: 1.0000e-02 eta: 3:10:11 time: 0.1984 data_time: 0.0085 memory: 7116 grad_norm: 6.4024 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.7972 loss: 2.7972 2022/09/03 22:49:30 - mmengine - INFO - Epoch(train) [13][300/1345] lr: 1.0000e-02 eta: 3:10:05 time: 0.2017 data_time: 0.0090 memory: 7116 grad_norm: 6.4076 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.6393 loss: 2.6393 2022/09/03 22:49:34 - mmengine - INFO - Epoch(train) [13][320/1345] lr: 1.0000e-02 eta: 3:09:59 time: 0.1982 data_time: 0.0115 memory: 7116 grad_norm: 6.2084 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4174 loss: 2.4174 2022/09/03 22:49:39 - mmengine - INFO - Epoch(train) [13][340/1345] lr: 1.0000e-02 eta: 3:09:55 time: 0.2334 data_time: 0.0088 memory: 7116 grad_norm: 6.1495 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.6148 loss: 2.6148 2022/09/03 22:49:43 - mmengine - INFO - Epoch(train) [13][360/1345] lr: 1.0000e-02 eta: 3:09:49 time: 0.1961 data_time: 0.0093 memory: 7116 grad_norm: 6.2780 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.7877 loss: 2.7877 2022/09/03 22:49:47 - mmengine - INFO - Epoch(train) [13][380/1345] lr: 1.0000e-02 eta: 3:09:43 time: 0.1964 data_time: 0.0108 memory: 7116 grad_norm: 5.9928 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.3209 loss: 2.3209 2022/09/03 22:49:51 - mmengine - INFO - Epoch(train) [13][400/1345] lr: 1.0000e-02 eta: 3:09:37 time: 0.2005 data_time: 0.0086 memory: 7116 grad_norm: 6.1746 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3479 loss: 2.3479 2022/09/03 22:49:54 - mmengine - INFO - Epoch(train) [13][420/1345] lr: 1.0000e-02 eta: 3:09:31 time: 0.1948 data_time: 0.0081 memory: 7116 grad_norm: 6.0751 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4959 loss: 2.4959 2022/09/03 22:49:59 - mmengine - INFO - Epoch(train) [13][440/1345] lr: 1.0000e-02 eta: 3:09:25 time: 0.2074 data_time: 0.0101 memory: 7116 grad_norm: 6.2128 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2787 loss: 2.2787 2022/09/03 22:50:03 - mmengine - INFO - Epoch(train) [13][460/1345] lr: 1.0000e-02 eta: 3:09:19 time: 0.2026 data_time: 0.0096 memory: 7116 grad_norm: 6.4734 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.3892 loss: 2.3892 2022/09/03 22:50:07 - mmengine - INFO - Epoch(train) [13][480/1345] lr: 1.0000e-02 eta: 3:09:13 time: 0.1952 data_time: 0.0083 memory: 7116 grad_norm: 6.0440 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3410 loss: 2.3410 2022/09/03 22:50:11 - mmengine - INFO - Epoch(train) [13][500/1345] lr: 1.0000e-02 eta: 3:09:07 time: 0.1992 data_time: 0.0095 memory: 7116 grad_norm: 6.2389 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.6793 loss: 2.6793 2022/09/03 22:50:15 - mmengine - INFO - Epoch(train) [13][520/1345] lr: 1.0000e-02 eta: 3:09:01 time: 0.1978 data_time: 0.0089 memory: 7116 grad_norm: 6.4107 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4016 loss: 2.4016 2022/09/03 22:50:19 - mmengine - INFO - Epoch(train) [13][540/1345] lr: 1.0000e-02 eta: 3:08:55 time: 0.2020 data_time: 0.0087 memory: 7116 grad_norm: 6.1711 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5671 loss: 2.5671 2022/09/03 22:50:23 - mmengine - INFO - Epoch(train) [13][560/1345] lr: 1.0000e-02 eta: 3:08:49 time: 0.2051 data_time: 0.0095 memory: 7116 grad_norm: 6.3481 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4257 loss: 2.4257 2022/09/03 22:50:27 - mmengine - INFO - Epoch(train) [13][580/1345] lr: 1.0000e-02 eta: 3:08:44 time: 0.2030 data_time: 0.0083 memory: 7116 grad_norm: 7.5178 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1933 loss: 2.1933 2022/09/03 22:50:31 - mmengine - INFO - Epoch(train) [13][600/1345] lr: 1.0000e-02 eta: 3:08:37 time: 0.1941 data_time: 0.0083 memory: 7116 grad_norm: 6.5496 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6251 loss: 2.6251 2022/09/03 22:50:35 - mmengine - INFO - Epoch(train) [13][620/1345] lr: 1.0000e-02 eta: 3:08:31 time: 0.2014 data_time: 0.0097 memory: 7116 grad_norm: 6.3909 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4198 loss: 2.4198 2022/09/03 22:50:40 - mmengine - INFO - Epoch(train) [13][640/1345] lr: 1.0000e-02 eta: 3:08:30 time: 0.2726 data_time: 0.0103 memory: 7116 grad_norm: 6.0917 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6027 loss: 2.6027 2022/09/03 22:50:44 - mmengine - INFO - Epoch(train) [13][660/1345] lr: 1.0000e-02 eta: 3:08:24 time: 0.2015 data_time: 0.0079 memory: 7116 grad_norm: 6.3193 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 3.0527 loss: 3.0527 2022/09/03 22:50:48 - mmengine - INFO - Epoch(train) [13][680/1345] lr: 1.0000e-02 eta: 3:08:18 time: 0.2011 data_time: 0.0111 memory: 7116 grad_norm: 6.2216 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.8290 loss: 2.8290 2022/09/03 22:50:52 - mmengine - INFO - Epoch(train) [13][700/1345] lr: 1.0000e-02 eta: 3:08:12 time: 0.2026 data_time: 0.0090 memory: 7116 grad_norm: 6.6855 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.5186 loss: 2.5186 2022/09/03 22:50:56 - mmengine - INFO - Epoch(train) [13][720/1345] lr: 1.0000e-02 eta: 3:08:07 time: 0.2006 data_time: 0.0082 memory: 7116 grad_norm: 6.4124 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.3541 loss: 2.3541 2022/09/03 22:51:00 - mmengine - INFO - Epoch(train) [13][740/1345] lr: 1.0000e-02 eta: 3:08:00 time: 0.1966 data_time: 0.0104 memory: 7116 grad_norm: 6.2480 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6556 loss: 2.6556 2022/09/03 22:51:04 - mmengine - INFO - Epoch(train) [13][760/1345] lr: 1.0000e-02 eta: 3:07:54 time: 0.1975 data_time: 0.0085 memory: 7116 grad_norm: 6.2355 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.5585 loss: 2.5585 2022/09/03 22:51:08 - mmengine - INFO - Epoch(train) [13][780/1345] lr: 1.0000e-02 eta: 3:07:48 time: 0.1955 data_time: 0.0081 memory: 7116 grad_norm: 5.9001 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3705 loss: 2.3705 2022/09/03 22:51:12 - mmengine - INFO - Epoch(train) [13][800/1345] lr: 1.0000e-02 eta: 3:07:42 time: 0.1983 data_time: 0.0109 memory: 7116 grad_norm: 6.1636 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6905 loss: 2.6905 2022/09/03 22:51:16 - mmengine - INFO - Epoch(train) [13][820/1345] lr: 1.0000e-02 eta: 3:07:36 time: 0.1990 data_time: 0.0084 memory: 7116 grad_norm: 6.4441 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.5721 loss: 2.5721 2022/09/03 22:51:20 - mmengine - INFO - Epoch(train) [13][840/1345] lr: 1.0000e-02 eta: 3:07:30 time: 0.1999 data_time: 0.0084 memory: 7116 grad_norm: 6.4740 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.5523 loss: 2.5523 2022/09/03 22:51:24 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:51:24 - mmengine - INFO - Epoch(train) [13][860/1345] lr: 1.0000e-02 eta: 3:07:24 time: 0.1984 data_time: 0.0101 memory: 7116 grad_norm: 6.4267 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.6801 loss: 2.6801 2022/09/03 22:51:28 - mmengine - INFO - Epoch(train) [13][880/1345] lr: 1.0000e-02 eta: 3:07:19 time: 0.2004 data_time: 0.0079 memory: 7116 grad_norm: 6.7936 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.6560 loss: 2.6560 2022/09/03 22:51:32 - mmengine - INFO - Epoch(train) [13][900/1345] lr: 1.0000e-02 eta: 3:07:13 time: 0.1984 data_time: 0.0086 memory: 7116 grad_norm: 6.3414 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.4599 loss: 2.4599 2022/09/03 22:51:36 - mmengine - INFO - Epoch(train) [13][920/1345] lr: 1.0000e-02 eta: 3:07:07 time: 0.2025 data_time: 0.0106 memory: 7116 grad_norm: 6.4278 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.5554 loss: 2.5554 2022/09/03 22:51:40 - mmengine - INFO - Epoch(train) [13][940/1345] lr: 1.0000e-02 eta: 3:07:01 time: 0.2012 data_time: 0.0080 memory: 7116 grad_norm: 6.2797 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.6288 loss: 2.6288 2022/09/03 22:51:44 - mmengine - INFO - Epoch(train) [13][960/1345] lr: 1.0000e-02 eta: 3:06:55 time: 0.1913 data_time: 0.0081 memory: 7116 grad_norm: 6.1694 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5333 loss: 2.5333 2022/09/03 22:51:48 - mmengine - INFO - Epoch(train) [13][980/1345] lr: 1.0000e-02 eta: 3:06:49 time: 0.2017 data_time: 0.0093 memory: 7116 grad_norm: 6.1639 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.2425 loss: 2.2425 2022/09/03 22:51:52 - mmengine - INFO - Epoch(train) [13][1000/1345] lr: 1.0000e-02 eta: 3:06:43 time: 0.1982 data_time: 0.0099 memory: 7116 grad_norm: 6.5702 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.3900 loss: 2.3900 2022/09/03 22:51:56 - mmengine - INFO - Epoch(train) [13][1020/1345] lr: 1.0000e-02 eta: 3:06:38 time: 0.2085 data_time: 0.0087 memory: 7116 grad_norm: 6.5591 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.7158 loss: 2.7158 2022/09/03 22:52:00 - mmengine - INFO - Epoch(train) [13][1040/1345] lr: 1.0000e-02 eta: 3:06:32 time: 0.2098 data_time: 0.0086 memory: 7116 grad_norm: 6.2790 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4203 loss: 2.4203 2022/09/03 22:52:04 - mmengine - INFO - Epoch(train) [13][1060/1345] lr: 1.0000e-02 eta: 3:06:27 time: 0.2033 data_time: 0.0081 memory: 7116 grad_norm: 6.3100 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.6813 loss: 2.6813 2022/09/03 22:52:09 - mmengine - INFO - Epoch(train) [13][1080/1345] lr: 1.0000e-02 eta: 3:06:22 time: 0.2132 data_time: 0.0071 memory: 7116 grad_norm: 6.3522 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4955 loss: 2.4955 2022/09/03 22:52:13 - mmengine - INFO - Epoch(train) [13][1100/1345] lr: 1.0000e-02 eta: 3:06:16 time: 0.2082 data_time: 0.0090 memory: 7116 grad_norm: 6.2129 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.7980 loss: 2.7980 2022/09/03 22:52:17 - mmengine - INFO - Epoch(train) [13][1120/1345] lr: 1.0000e-02 eta: 3:06:11 time: 0.2059 data_time: 0.0086 memory: 7116 grad_norm: 6.2114 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.6064 loss: 2.6064 2022/09/03 22:52:21 - mmengine - INFO - Epoch(train) [13][1140/1345] lr: 1.0000e-02 eta: 3:06:05 time: 0.1997 data_time: 0.0085 memory: 7116 grad_norm: 5.9816 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6306 loss: 2.6306 2022/09/03 22:52:25 - mmengine - INFO - Epoch(train) [13][1160/1345] lr: 1.0000e-02 eta: 3:05:59 time: 0.2024 data_time: 0.0092 memory: 7116 grad_norm: 6.4015 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.4043 loss: 2.4043 2022/09/03 22:52:29 - mmengine - INFO - Epoch(train) [13][1180/1345] lr: 1.0000e-02 eta: 3:05:54 time: 0.2020 data_time: 0.0091 memory: 7116 grad_norm: 6.2424 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.7636 loss: 2.7636 2022/09/03 22:52:33 - mmengine - INFO - Epoch(train) [13][1200/1345] lr: 1.0000e-02 eta: 3:05:48 time: 0.2016 data_time: 0.0073 memory: 7116 grad_norm: 6.5448 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.7255 loss: 2.7255 2022/09/03 22:52:37 - mmengine - INFO - Epoch(train) [13][1220/1345] lr: 1.0000e-02 eta: 3:05:42 time: 0.2006 data_time: 0.0093 memory: 7116 grad_norm: 7.0482 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6944 loss: 2.6944 2022/09/03 22:52:41 - mmengine - INFO - Epoch(train) [13][1240/1345] lr: 1.0000e-02 eta: 3:05:36 time: 0.2035 data_time: 0.0073 memory: 7116 grad_norm: 6.5819 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.5540 loss: 2.5540 2022/09/03 22:52:45 - mmengine - INFO - Epoch(train) [13][1260/1345] lr: 1.0000e-02 eta: 3:05:31 time: 0.2063 data_time: 0.0076 memory: 7116 grad_norm: 6.3034 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.4102 loss: 2.4102 2022/09/03 22:52:49 - mmengine - INFO - Epoch(train) [13][1280/1345] lr: 1.0000e-02 eta: 3:05:25 time: 0.2033 data_time: 0.0103 memory: 7116 grad_norm: 6.3501 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.8378 loss: 2.8378 2022/09/03 22:52:53 - mmengine - INFO - Epoch(train) [13][1300/1345] lr: 1.0000e-02 eta: 3:05:20 time: 0.2063 data_time: 0.0081 memory: 7116 grad_norm: 6.1565 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4863 loss: 2.4863 2022/09/03 22:52:57 - mmengine - INFO - Epoch(train) [13][1320/1345] lr: 1.0000e-02 eta: 3:05:15 time: 0.2058 data_time: 0.0070 memory: 7116 grad_norm: 6.4248 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.7148 loss: 2.7148 2022/09/03 22:53:02 - mmengine - INFO - Epoch(train) [13][1340/1345] lr: 1.0000e-02 eta: 3:05:09 time: 0.2075 data_time: 0.0098 memory: 7116 grad_norm: 6.4687 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5861 loss: 2.5861 2022/09/03 22:53:03 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:53:03 - mmengine - INFO - Epoch(train) [13][1345/1345] lr: 1.0000e-02 eta: 3:05:09 time: 0.2024 data_time: 0.0066 memory: 7116 grad_norm: 6.5145 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.7797 loss: 2.7797 2022/09/03 22:53:03 - mmengine - INFO - Saving checkpoint at 13 epochs 2022/09/03 22:53:05 - mmengine - INFO - Epoch(val) [13][20/181] eta: 0:00:07 time: 0.0461 data_time: 0.0098 memory: 1114 2022/09/03 22:53:06 - mmengine - INFO - Epoch(val) [13][40/181] eta: 0:00:05 time: 0.0410 data_time: 0.0061 memory: 1114 2022/09/03 22:53:07 - mmengine - INFO - Epoch(val) [13][60/181] eta: 0:00:04 time: 0.0401 data_time: 0.0053 memory: 1114 2022/09/03 22:53:08 - mmengine - INFO - Epoch(val) [13][80/181] eta: 0:00:04 time: 0.0405 data_time: 0.0056 memory: 1114 2022/09/03 22:53:08 - mmengine - INFO - Epoch(val) [13][100/181] eta: 0:00:03 time: 0.0402 data_time: 0.0054 memory: 1114 2022/09/03 22:53:09 - mmengine - INFO - Epoch(val) [13][120/181] eta: 0:00:02 time: 0.0411 data_time: 0.0059 memory: 1114 2022/09/03 22:53:10 - mmengine - INFO - Epoch(val) [13][140/181] eta: 0:00:01 time: 0.0402 data_time: 0.0053 memory: 1114 2022/09/03 22:53:11 - mmengine - INFO - Epoch(val) [13][160/181] eta: 0:00:00 time: 0.0400 data_time: 0.0053 memory: 1114 2022/09/03 22:53:12 - mmengine - INFO - Epoch(val) [13][180/181] eta: 0:00:00 time: 0.0400 data_time: 0.0053 memory: 1114 2022/09/03 22:53:15 - mmengine - INFO - Epoch(val) [13][181/181] acc/top1: 0.2894 acc/top5: 0.5725 acc/mean1: 0.2550 2022/09/03 22:53:19 - mmengine - INFO - Epoch(train) [14][20/1345] lr: 1.0000e-02 eta: 3:05:00 time: 0.2139 data_time: 0.0170 memory: 7116 grad_norm: 5.7243 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.5797 loss: 2.5797 2022/09/03 22:53:23 - mmengine - INFO - Epoch(train) [14][40/1345] lr: 1.0000e-02 eta: 3:04:54 time: 0.2015 data_time: 0.0072 memory: 7116 grad_norm: 6.2724 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3102 loss: 2.3102 2022/09/03 22:53:27 - mmengine - INFO - Epoch(train) [14][60/1345] lr: 1.0000e-02 eta: 3:04:48 time: 0.1999 data_time: 0.0082 memory: 7116 grad_norm: 6.2972 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.4801 loss: 2.4801 2022/09/03 22:53:31 - mmengine - INFO - Epoch(train) [14][80/1345] lr: 1.0000e-02 eta: 3:04:43 time: 0.2017 data_time: 0.0092 memory: 7116 grad_norm: 6.1373 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.5169 loss: 2.5169 2022/09/03 22:53:35 - mmengine - INFO - Epoch(train) [14][100/1345] lr: 1.0000e-02 eta: 3:04:37 time: 0.2055 data_time: 0.0080 memory: 7116 grad_norm: 6.2723 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.4683 loss: 2.4683 2022/09/03 22:53:39 - mmengine - INFO - Epoch(train) [14][120/1345] lr: 1.0000e-02 eta: 3:04:32 time: 0.2048 data_time: 0.0070 memory: 7116 grad_norm: 6.3234 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.5428 loss: 2.5428 2022/09/03 22:53:43 - mmengine - INFO - Epoch(train) [14][140/1345] lr: 1.0000e-02 eta: 3:04:26 time: 0.2047 data_time: 0.0096 memory: 7116 grad_norm: 6.2741 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2854 loss: 2.2854 2022/09/03 22:53:47 - mmengine - INFO - Epoch(train) [14][160/1345] lr: 1.0000e-02 eta: 3:04:21 time: 0.2072 data_time: 0.0083 memory: 7116 grad_norm: 6.3456 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.2382 loss: 2.2382 2022/09/03 22:53:52 - mmengine - INFO - Epoch(train) [14][180/1345] lr: 1.0000e-02 eta: 3:04:15 time: 0.2042 data_time: 0.0074 memory: 7116 grad_norm: 6.6445 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6990 loss: 2.6990 2022/09/03 22:53:56 - mmengine - INFO - Epoch(train) [14][200/1345] lr: 1.0000e-02 eta: 3:04:10 time: 0.2039 data_time: 0.0094 memory: 7116 grad_norm: 6.3139 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 2.3804 loss: 2.3804 2022/09/03 22:54:00 - mmengine - INFO - Epoch(train) [14][220/1345] lr: 1.0000e-02 eta: 3:04:04 time: 0.2025 data_time: 0.0078 memory: 7116 grad_norm: 6.5045 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4915 loss: 2.4915 2022/09/03 22:54:04 - mmengine - INFO - Epoch(train) [14][240/1345] lr: 1.0000e-02 eta: 3:03:59 time: 0.2046 data_time: 0.0081 memory: 7116 grad_norm: 6.2148 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.3604 loss: 2.3604 2022/09/03 22:54:08 - mmengine - INFO - Epoch(train) [14][260/1345] lr: 1.0000e-02 eta: 3:03:53 time: 0.2061 data_time: 0.0096 memory: 7116 grad_norm: 5.9826 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.3829 loss: 2.3829 2022/09/03 22:54:12 - mmengine - INFO - Epoch(train) [14][280/1345] lr: 1.0000e-02 eta: 3:03:48 time: 0.2001 data_time: 0.0076 memory: 7116 grad_norm: 6.0998 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2978 loss: 2.2978 2022/09/03 22:54:16 - mmengine - INFO - Epoch(train) [14][300/1345] lr: 1.0000e-02 eta: 3:03:42 time: 0.2028 data_time: 0.0082 memory: 7116 grad_norm: 6.2414 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4351 loss: 2.4351 2022/09/03 22:54:20 - mmengine - INFO - Epoch(train) [14][320/1345] lr: 1.0000e-02 eta: 3:03:36 time: 0.1975 data_time: 0.0103 memory: 7116 grad_norm: 6.4116 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.6269 loss: 2.6269 2022/09/03 22:54:24 - mmengine - INFO - Epoch(train) [14][340/1345] lr: 1.0000e-02 eta: 3:03:31 time: 0.2037 data_time: 0.0073 memory: 7116 grad_norm: 6.5048 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.5076 loss: 2.5076 2022/09/03 22:54:28 - mmengine - INFO - Epoch(train) [14][360/1345] lr: 1.0000e-02 eta: 3:03:26 time: 0.2101 data_time: 0.0075 memory: 7116 grad_norm: 6.3449 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.3151 loss: 2.3151 2022/09/03 22:54:32 - mmengine - INFO - Epoch(train) [14][380/1345] lr: 1.0000e-02 eta: 3:03:20 time: 0.2023 data_time: 0.0105 memory: 7116 grad_norm: 6.2058 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2302 loss: 2.2302 2022/09/03 22:54:36 - mmengine - INFO - Epoch(train) [14][400/1345] lr: 1.0000e-02 eta: 3:03:14 time: 0.2029 data_time: 0.0078 memory: 7116 grad_norm: 6.1980 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2371 loss: 2.2371 2022/09/03 22:54:40 - mmengine - INFO - Epoch(train) [14][420/1345] lr: 1.0000e-02 eta: 3:03:09 time: 0.2029 data_time: 0.0084 memory: 7116 grad_norm: 6.3849 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.5167 loss: 2.5167 2022/09/03 22:54:44 - mmengine - INFO - Epoch(train) [14][440/1345] lr: 1.0000e-02 eta: 3:03:03 time: 0.2057 data_time: 0.0098 memory: 7116 grad_norm: 6.3660 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.6016 loss: 2.6016 2022/09/03 22:54:49 - mmengine - INFO - Epoch(train) [14][460/1345] lr: 1.0000e-02 eta: 3:02:58 time: 0.2113 data_time: 0.0080 memory: 7116 grad_norm: 6.3823 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.3726 loss: 2.3726 2022/09/03 22:54:53 - mmengine - INFO - Epoch(train) [14][480/1345] lr: 1.0000e-02 eta: 3:02:53 time: 0.2036 data_time: 0.0071 memory: 7116 grad_norm: 6.4388 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.4469 loss: 2.4469 2022/09/03 22:54:57 - mmengine - INFO - Epoch(train) [14][500/1345] lr: 1.0000e-02 eta: 3:02:48 time: 0.2084 data_time: 0.0099 memory: 7116 grad_norm: 6.4915 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.6096 loss: 2.6096 2022/09/03 22:55:00 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:55:01 - mmengine - INFO - Epoch(train) [14][520/1345] lr: 1.0000e-02 eta: 3:02:42 time: 0.2026 data_time: 0.0094 memory: 7116 grad_norm: 6.4168 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3820 loss: 2.3820 2022/09/03 22:55:05 - mmengine - INFO - Epoch(train) [14][540/1345] lr: 1.0000e-02 eta: 3:02:36 time: 0.1993 data_time: 0.0082 memory: 7116 grad_norm: 6.4321 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 2.4625 loss: 2.4625 2022/09/03 22:55:09 - mmengine - INFO - Epoch(train) [14][560/1345] lr: 1.0000e-02 eta: 3:02:31 time: 0.2061 data_time: 0.0090 memory: 7116 grad_norm: 6.3552 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4756 loss: 2.4756 2022/09/03 22:55:13 - mmengine - INFO - Epoch(train) [14][580/1345] lr: 1.0000e-02 eta: 3:02:26 time: 0.2094 data_time: 0.0073 memory: 7116 grad_norm: 6.1809 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.4888 loss: 2.4888 2022/09/03 22:55:18 - mmengine - INFO - Epoch(train) [14][600/1345] lr: 1.0000e-02 eta: 3:02:21 time: 0.2151 data_time: 0.0078 memory: 7116 grad_norm: 6.5213 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.4968 loss: 2.4968 2022/09/03 22:55:22 - mmengine - INFO - Epoch(train) [14][620/1345] lr: 1.0000e-02 eta: 3:02:16 time: 0.2074 data_time: 0.0093 memory: 7116 grad_norm: 6.4652 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.5358 loss: 2.5358 2022/09/03 22:55:26 - mmengine - INFO - Epoch(train) [14][640/1345] lr: 1.0000e-02 eta: 3:02:11 time: 0.2114 data_time: 0.0081 memory: 7116 grad_norm: 6.1934 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5654 loss: 2.5654 2022/09/03 22:55:30 - mmengine - INFO - Epoch(train) [14][660/1345] lr: 1.0000e-02 eta: 3:02:05 time: 0.2004 data_time: 0.0073 memory: 7116 grad_norm: 6.4904 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6169 loss: 2.6169 2022/09/03 22:55:34 - mmengine - INFO - Epoch(train) [14][680/1345] lr: 1.0000e-02 eta: 3:02:00 time: 0.2073 data_time: 0.0095 memory: 7116 grad_norm: 6.3153 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4533 loss: 2.4533 2022/09/03 22:55:38 - mmengine - INFO - Epoch(train) [14][700/1345] lr: 1.0000e-02 eta: 3:01:55 time: 0.2113 data_time: 0.0077 memory: 7116 grad_norm: 6.3667 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.6741 loss: 2.6741 2022/09/03 22:55:42 - mmengine - INFO - Epoch(train) [14][720/1345] lr: 1.0000e-02 eta: 3:01:49 time: 0.2053 data_time: 0.0073 memory: 7116 grad_norm: 6.2606 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.3684 loss: 2.3684 2022/09/03 22:55:47 - mmengine - INFO - Epoch(train) [14][740/1345] lr: 1.0000e-02 eta: 3:01:44 time: 0.2080 data_time: 0.0093 memory: 7116 grad_norm: 6.2218 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.7720 loss: 2.7720 2022/09/03 22:55:51 - mmengine - INFO - Epoch(train) [14][760/1345] lr: 1.0000e-02 eta: 3:01:39 time: 0.2087 data_time: 0.0079 memory: 7116 grad_norm: 6.3599 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6414 loss: 2.6414 2022/09/03 22:55:55 - mmengine - INFO - Epoch(train) [14][780/1345] lr: 1.0000e-02 eta: 3:01:34 time: 0.2081 data_time: 0.0067 memory: 7116 grad_norm: 6.2895 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2947 loss: 2.2947 2022/09/03 22:55:59 - mmengine - INFO - Epoch(train) [14][800/1345] lr: 1.0000e-02 eta: 3:01:29 time: 0.2118 data_time: 0.0093 memory: 7116 grad_norm: 6.3179 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4189 loss: 2.4189 2022/09/03 22:56:03 - mmengine - INFO - Epoch(train) [14][820/1345] lr: 1.0000e-02 eta: 3:01:23 time: 0.2049 data_time: 0.0069 memory: 7116 grad_norm: 6.2489 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.7355 loss: 2.7355 2022/09/03 22:56:07 - mmengine - INFO - Epoch(train) [14][840/1345] lr: 1.0000e-02 eta: 3:01:18 time: 0.2081 data_time: 0.0078 memory: 7116 grad_norm: 6.5609 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.5504 loss: 2.5504 2022/09/03 22:56:12 - mmengine - INFO - Epoch(train) [14][860/1345] lr: 1.0000e-02 eta: 3:01:13 time: 0.2108 data_time: 0.0096 memory: 7116 grad_norm: 6.2137 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.2830 loss: 2.2830 2022/09/03 22:56:16 - mmengine - INFO - Epoch(train) [14][880/1345] lr: 1.0000e-02 eta: 3:01:08 time: 0.2192 data_time: 0.0074 memory: 7116 grad_norm: 6.3163 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.5479 loss: 2.5479 2022/09/03 22:56:20 - mmengine - INFO - Epoch(train) [14][900/1345] lr: 1.0000e-02 eta: 3:01:03 time: 0.2053 data_time: 0.0077 memory: 7116 grad_norm: 6.0708 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.6046 loss: 2.6046 2022/09/03 22:56:24 - mmengine - INFO - Epoch(train) [14][920/1345] lr: 1.0000e-02 eta: 3:00:58 time: 0.2047 data_time: 0.0096 memory: 7116 grad_norm: 6.3561 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5297 loss: 2.5297 2022/09/03 22:56:29 - mmengine - INFO - Epoch(train) [14][940/1345] lr: 1.0000e-02 eta: 3:00:53 time: 0.2129 data_time: 0.0071 memory: 7116 grad_norm: 6.2907 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.3782 loss: 2.3782 2022/09/03 22:56:33 - mmengine - INFO - Epoch(train) [14][960/1345] lr: 1.0000e-02 eta: 3:00:47 time: 0.2057 data_time: 0.0068 memory: 7116 grad_norm: 6.0858 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3974 loss: 2.3974 2022/09/03 22:56:37 - mmengine - INFO - Epoch(train) [14][980/1345] lr: 1.0000e-02 eta: 3:00:42 time: 0.2073 data_time: 0.0095 memory: 7116 grad_norm: 6.1997 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.7473 loss: 2.7473 2022/09/03 22:56:41 - mmengine - INFO - Epoch(train) [14][1000/1345] lr: 1.0000e-02 eta: 3:00:37 time: 0.2104 data_time: 0.0071 memory: 7116 grad_norm: 6.3056 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4248 loss: 2.4248 2022/09/03 22:56:45 - mmengine - INFO - Epoch(train) [14][1020/1345] lr: 1.0000e-02 eta: 3:00:32 time: 0.2077 data_time: 0.0073 memory: 7116 grad_norm: 6.2549 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.6079 loss: 2.6079 2022/09/03 22:56:49 - mmengine - INFO - Epoch(train) [14][1040/1345] lr: 1.0000e-02 eta: 3:00:27 time: 0.2062 data_time: 0.0096 memory: 7116 grad_norm: 6.5197 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5618 loss: 2.5618 2022/09/03 22:56:53 - mmengine - INFO - Epoch(train) [14][1060/1345] lr: 1.0000e-02 eta: 3:00:21 time: 0.2057 data_time: 0.0075 memory: 7116 grad_norm: 6.2925 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.5389 loss: 2.5389 2022/09/03 22:56:58 - mmengine - INFO - Epoch(train) [14][1080/1345] lr: 1.0000e-02 eta: 3:00:16 time: 0.2081 data_time: 0.0067 memory: 7116 grad_norm: 6.2148 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4054 loss: 2.4054 2022/09/03 22:57:02 - mmengine - INFO - Epoch(train) [14][1100/1345] lr: 1.0000e-02 eta: 3:00:11 time: 0.2091 data_time: 0.0099 memory: 7116 grad_norm: 6.1716 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.6708 loss: 2.6708 2022/09/03 22:57:06 - mmengine - INFO - Epoch(train) [14][1120/1345] lr: 1.0000e-02 eta: 3:00:06 time: 0.2063 data_time: 0.0080 memory: 7116 grad_norm: 6.4990 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4154 loss: 2.4154 2022/09/03 22:57:10 - mmengine - INFO - Epoch(train) [14][1140/1345] lr: 1.0000e-02 eta: 3:00:01 time: 0.2079 data_time: 0.0073 memory: 7116 grad_norm: 6.2553 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6704 loss: 2.6704 2022/09/03 22:57:14 - mmengine - INFO - Epoch(train) [14][1160/1345] lr: 1.0000e-02 eta: 2:59:55 time: 0.2090 data_time: 0.0092 memory: 7116 grad_norm: 6.4419 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.5202 loss: 2.5202 2022/09/03 22:57:19 - mmengine - INFO - Epoch(train) [14][1180/1345] lr: 1.0000e-02 eta: 2:59:51 time: 0.2274 data_time: 0.0087 memory: 7116 grad_norm: 6.4584 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.2755 loss: 2.2755 2022/09/03 22:57:23 - mmengine - INFO - Epoch(train) [14][1200/1345] lr: 1.0000e-02 eta: 2:59:46 time: 0.2082 data_time: 0.0080 memory: 7116 grad_norm: 6.0955 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.7071 loss: 2.7071 2022/09/03 22:57:27 - mmengine - INFO - Epoch(train) [14][1220/1345] lr: 1.0000e-02 eta: 2:59:41 time: 0.2092 data_time: 0.0090 memory: 7116 grad_norm: 6.4497 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.5437 loss: 2.5437 2022/09/03 22:57:31 - mmengine - INFO - Epoch(train) [14][1240/1345] lr: 1.0000e-02 eta: 2:59:36 time: 0.2074 data_time: 0.0074 memory: 7116 grad_norm: 6.3007 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3802 loss: 2.3802 2022/09/03 22:57:35 - mmengine - INFO - Epoch(train) [14][1260/1345] lr: 1.0000e-02 eta: 2:59:30 time: 0.2051 data_time: 0.0076 memory: 7116 grad_norm: 6.3173 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.5440 loss: 2.5440 2022/09/03 22:57:40 - mmengine - INFO - Epoch(train) [14][1280/1345] lr: 1.0000e-02 eta: 2:59:26 time: 0.2177 data_time: 0.0102 memory: 7116 grad_norm: 6.3174 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5911 loss: 2.5911 2022/09/03 22:57:44 - mmengine - INFO - Epoch(train) [14][1300/1345] lr: 1.0000e-02 eta: 2:59:21 time: 0.2061 data_time: 0.0070 memory: 7116 grad_norm: 6.3719 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.5323 loss: 2.5323 2022/09/03 22:57:48 - mmengine - INFO - Epoch(train) [14][1320/1345] lr: 1.0000e-02 eta: 2:59:15 time: 0.2083 data_time: 0.0069 memory: 7116 grad_norm: 6.8729 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5811 loss: 2.5811 2022/09/03 22:57:52 - mmengine - INFO - Epoch(train) [14][1340/1345] lr: 1.0000e-02 eta: 2:59:10 time: 0.2071 data_time: 0.0092 memory: 7116 grad_norm: 6.3520 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.3172 loss: 2.3172 2022/09/03 22:57:53 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:57:53 - mmengine - INFO - Epoch(train) [14][1345/1345] lr: 1.0000e-02 eta: 2:59:10 time: 0.2021 data_time: 0.0067 memory: 7116 grad_norm: 6.6806 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.4013 loss: 2.4013 2022/09/03 22:57:53 - mmengine - INFO - Saving checkpoint at 14 epochs 2022/09/03 22:57:55 - mmengine - INFO - Epoch(val) [14][20/181] eta: 0:00:07 time: 0.0436 data_time: 0.0089 memory: 1114 2022/09/03 22:57:56 - mmengine - INFO - Epoch(val) [14][40/181] eta: 0:00:05 time: 0.0408 data_time: 0.0058 memory: 1114 2022/09/03 22:57:57 - mmengine - INFO - Epoch(val) [14][60/181] eta: 0:00:04 time: 0.0401 data_time: 0.0055 memory: 1114 2022/09/03 22:57:58 - mmengine - INFO - Epoch(val) [14][80/181] eta: 0:00:04 time: 0.0405 data_time: 0.0058 memory: 1114 2022/09/03 22:57:59 - mmengine - INFO - Epoch(val) [14][100/181] eta: 0:00:03 time: 0.0403 data_time: 0.0055 memory: 1114 2022/09/03 22:57:59 - mmengine - INFO - Epoch(val) [14][120/181] eta: 0:00:02 time: 0.0401 data_time: 0.0054 memory: 1114 2022/09/03 22:58:00 - mmengine - INFO - Epoch(val) [14][140/181] eta: 0:00:01 time: 0.0404 data_time: 0.0057 memory: 1114 2022/09/03 22:58:01 - mmengine - INFO - Epoch(val) [14][160/181] eta: 0:00:00 time: 0.0403 data_time: 0.0055 memory: 1114 2022/09/03 22:58:02 - mmengine - INFO - Epoch(val) [14][180/181] eta: 0:00:00 time: 0.0400 data_time: 0.0055 memory: 1114 2022/09/03 22:58:06 - mmengine - INFO - Epoch(val) [14][181/181] acc/top1: 0.2717 acc/top5: 0.5633 acc/mean1: 0.2560 2022/09/03 22:58:11 - mmengine - INFO - Epoch(train) [15][20/1345] lr: 1.0000e-02 eta: 2:59:03 time: 0.2405 data_time: 0.0385 memory: 7116 grad_norm: 6.4492 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4771 loss: 2.4771 2022/09/03 22:58:15 - mmengine - INFO - Epoch(train) [15][40/1345] lr: 1.0000e-02 eta: 2:58:58 time: 0.2085 data_time: 0.0070 memory: 7116 grad_norm: 6.1127 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 2.5640 loss: 2.5640 2022/09/03 22:58:19 - mmengine - INFO - Epoch(train) [15][60/1345] lr: 1.0000e-02 eta: 2:58:52 time: 0.2064 data_time: 0.0069 memory: 7116 grad_norm: 6.4981 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4338 loss: 2.4338 2022/09/03 22:58:23 - mmengine - INFO - Epoch(train) [15][80/1345] lr: 1.0000e-02 eta: 2:58:47 time: 0.2094 data_time: 0.0097 memory: 7116 grad_norm: 6.2166 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.1439 loss: 2.1439 2022/09/03 22:58:27 - mmengine - INFO - Epoch(train) [15][100/1345] lr: 1.0000e-02 eta: 2:58:42 time: 0.2116 data_time: 0.0081 memory: 7116 grad_norm: 6.5515 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3238 loss: 2.3238 2022/09/03 22:58:32 - mmengine - INFO - Epoch(train) [15][120/1345] lr: 1.0000e-02 eta: 2:58:37 time: 0.2070 data_time: 0.0086 memory: 7116 grad_norm: 6.3490 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4616 loss: 2.4616 2022/09/03 22:58:36 - mmengine - INFO - Epoch(train) [15][140/1345] lr: 1.0000e-02 eta: 2:58:32 time: 0.2084 data_time: 0.0101 memory: 7116 grad_norm: 6.5362 top1_acc: 0.0000 top5_acc: 0.6250 loss_cls: 2.4861 loss: 2.4861 2022/09/03 22:58:40 - mmengine - INFO - Epoch(train) [15][160/1345] lr: 1.0000e-02 eta: 2:58:27 time: 0.2090 data_time: 0.0076 memory: 7116 grad_norm: 6.4217 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4791 loss: 2.4791 2022/09/03 22:58:42 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 22:58:44 - mmengine - INFO - Epoch(train) [15][180/1345] lr: 1.0000e-02 eta: 2:58:22 time: 0.2099 data_time: 0.0072 memory: 7116 grad_norm: 6.1246 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3935 loss: 2.3935 2022/09/03 22:58:48 - mmengine - INFO - Epoch(train) [15][200/1345] lr: 1.0000e-02 eta: 2:58:17 time: 0.2092 data_time: 0.0094 memory: 7116 grad_norm: 6.5841 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.3987 loss: 2.3987 2022/09/03 22:58:52 - mmengine - INFO - Epoch(train) [15][220/1345] lr: 1.0000e-02 eta: 2:58:12 time: 0.2098 data_time: 0.0070 memory: 7116 grad_norm: 6.4629 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2815 loss: 2.2815 2022/09/03 22:58:57 - mmengine - INFO - Epoch(train) [15][240/1345] lr: 1.0000e-02 eta: 2:58:07 time: 0.2072 data_time: 0.0073 memory: 7116 grad_norm: 6.2998 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.4535 loss: 2.4535 2022/09/03 22:59:01 - mmengine - INFO - Epoch(train) [15][260/1345] lr: 1.0000e-02 eta: 2:58:01 time: 0.2091 data_time: 0.0098 memory: 7116 grad_norm: 6.3566 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3297 loss: 2.3297 2022/09/03 22:59:05 - mmengine - INFO - Epoch(train) [15][280/1345] lr: 1.0000e-02 eta: 2:57:56 time: 0.2111 data_time: 0.0072 memory: 7116 grad_norm: 6.5034 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.2315 loss: 2.2315 2022/09/03 22:59:09 - mmengine - INFO - Epoch(train) [15][300/1345] lr: 1.0000e-02 eta: 2:57:52 time: 0.2125 data_time: 0.0087 memory: 7116 grad_norm: 6.1149 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4414 loss: 2.4414 2022/09/03 22:59:14 - mmengine - INFO - Epoch(train) [15][320/1345] lr: 1.0000e-02 eta: 2:57:47 time: 0.2223 data_time: 0.0260 memory: 7116 grad_norm: 6.1033 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3920 loss: 2.3920 2022/09/03 22:59:18 - mmengine - INFO - Epoch(train) [15][340/1345] lr: 1.0000e-02 eta: 2:57:42 time: 0.2074 data_time: 0.0077 memory: 7116 grad_norm: 6.4307 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1630 loss: 2.1630 2022/09/03 22:59:22 - mmengine - INFO - Epoch(train) [15][360/1345] lr: 1.0000e-02 eta: 2:57:37 time: 0.2116 data_time: 0.0164 memory: 7116 grad_norm: 6.3013 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.7870 loss: 2.7870 2022/09/03 22:59:26 - mmengine - INFO - Epoch(train) [15][380/1345] lr: 1.0000e-02 eta: 2:57:32 time: 0.2019 data_time: 0.0133 memory: 7116 grad_norm: 6.5652 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.6059 loss: 2.6059 2022/09/03 22:59:30 - mmengine - INFO - Epoch(train) [15][400/1345] lr: 1.0000e-02 eta: 2:57:26 time: 0.1929 data_time: 0.0097 memory: 7116 grad_norm: 6.4185 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.5486 loss: 2.5486 2022/09/03 22:59:34 - mmengine - INFO - Epoch(train) [15][420/1345] lr: 1.0000e-02 eta: 2:57:20 time: 0.1910 data_time: 0.0110 memory: 7116 grad_norm: 6.3841 top1_acc: 0.1250 top5_acc: 0.8750 loss_cls: 2.4936 loss: 2.4936 2022/09/03 22:59:38 - mmengine - INFO - Epoch(train) [15][440/1345] lr: 1.0000e-02 eta: 2:57:14 time: 0.2034 data_time: 0.0121 memory: 7116 grad_norm: 6.1870 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5133 loss: 2.5133 2022/09/03 22:59:42 - mmengine - INFO - Epoch(train) [15][460/1345] lr: 1.0000e-02 eta: 2:57:09 time: 0.1948 data_time: 0.0098 memory: 7116 grad_norm: 6.4004 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4418 loss: 2.4418 2022/09/03 22:59:46 - mmengine - INFO - Epoch(train) [15][480/1345] lr: 1.0000e-02 eta: 2:57:03 time: 0.1948 data_time: 0.0099 memory: 7116 grad_norm: 6.2300 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.5049 loss: 2.5049 2022/09/03 22:59:50 - mmengine - INFO - Epoch(train) [15][500/1345] lr: 1.0000e-02 eta: 2:56:57 time: 0.1951 data_time: 0.0123 memory: 7116 grad_norm: 6.3859 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.6320 loss: 2.6320 2022/09/03 22:59:53 - mmengine - INFO - Epoch(train) [15][520/1345] lr: 1.0000e-02 eta: 2:56:51 time: 0.1942 data_time: 0.0100 memory: 7116 grad_norm: 6.2701 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.5630 loss: 2.5630 2022/09/03 22:59:57 - mmengine - INFO - Epoch(train) [15][540/1345] lr: 1.0000e-02 eta: 2:56:46 time: 0.2000 data_time: 0.0098 memory: 7116 grad_norm: 6.2587 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.5502 loss: 2.5502 2022/09/03 23:00:01 - mmengine - INFO - Epoch(train) [15][560/1345] lr: 1.0000e-02 eta: 2:56:40 time: 0.1965 data_time: 0.0111 memory: 7116 grad_norm: 6.5729 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5398 loss: 2.5398 2022/09/03 23:00:06 - mmengine - INFO - Epoch(train) [15][580/1345] lr: 1.0000e-02 eta: 2:56:35 time: 0.2052 data_time: 0.0094 memory: 7116 grad_norm: 6.2824 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.4261 loss: 2.4261 2022/09/03 23:00:09 - mmengine - INFO - Epoch(train) [15][600/1345] lr: 1.0000e-02 eta: 2:56:29 time: 0.1933 data_time: 0.0094 memory: 7116 grad_norm: 6.5213 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.5146 loss: 2.5146 2022/09/03 23:00:13 - mmengine - INFO - Epoch(train) [15][620/1345] lr: 1.0000e-02 eta: 2:56:23 time: 0.1934 data_time: 0.0119 memory: 7116 grad_norm: 6.1225 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.5455 loss: 2.5455 2022/09/03 23:00:17 - mmengine - INFO - Epoch(train) [15][640/1345] lr: 1.0000e-02 eta: 2:56:18 time: 0.2013 data_time: 0.0101 memory: 7116 grad_norm: 6.1727 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4096 loss: 2.4096 2022/09/03 23:00:21 - mmengine - INFO - Epoch(train) [15][660/1345] lr: 1.0000e-02 eta: 2:56:12 time: 0.1923 data_time: 0.0096 memory: 7116 grad_norm: 6.4701 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 2.5389 loss: 2.5389 2022/09/03 23:00:25 - mmengine - INFO - Epoch(train) [15][680/1345] lr: 1.0000e-02 eta: 2:56:06 time: 0.1953 data_time: 0.0122 memory: 7116 grad_norm: 6.4278 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4185 loss: 2.4185 2022/09/03 23:00:29 - mmengine - INFO - Epoch(train) [15][700/1345] lr: 1.0000e-02 eta: 2:56:01 time: 0.1943 data_time: 0.0100 memory: 7116 grad_norm: 6.3546 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4784 loss: 2.4784 2022/09/03 23:00:33 - mmengine - INFO - Epoch(train) [15][720/1345] lr: 1.0000e-02 eta: 2:55:55 time: 0.1947 data_time: 0.0106 memory: 7116 grad_norm: 6.3135 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.5048 loss: 2.5048 2022/09/03 23:00:37 - mmengine - INFO - Epoch(train) [15][740/1345] lr: 1.0000e-02 eta: 2:55:49 time: 0.1884 data_time: 0.0127 memory: 7116 grad_norm: 6.3579 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3770 loss: 2.3770 2022/09/03 23:00:40 - mmengine - INFO - Epoch(train) [15][760/1345] lr: 1.0000e-02 eta: 2:55:43 time: 0.1945 data_time: 0.0095 memory: 7116 grad_norm: 6.2385 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.2475 loss: 2.2475 2022/09/03 23:00:45 - mmengine - INFO - Epoch(train) [15][780/1345] lr: 1.0000e-02 eta: 2:55:38 time: 0.2022 data_time: 0.0081 memory: 7116 grad_norm: 6.3677 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2045 loss: 2.2045 2022/09/03 23:00:49 - mmengine - INFO - Epoch(train) [15][800/1345] lr: 1.0000e-02 eta: 2:55:32 time: 0.1997 data_time: 0.0135 memory: 7116 grad_norm: 6.4318 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5745 loss: 2.5745 2022/09/03 23:00:52 - mmengine - INFO - Epoch(train) [15][820/1345] lr: 1.0000e-02 eta: 2:55:27 time: 0.1932 data_time: 0.0111 memory: 7116 grad_norm: 6.5435 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.7066 loss: 2.7066 2022/09/03 23:00:56 - mmengine - INFO - Epoch(train) [15][840/1345] lr: 1.0000e-02 eta: 2:55:21 time: 0.1936 data_time: 0.0108 memory: 7116 grad_norm: 6.3865 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.5654 loss: 2.5654 2022/09/03 23:01:00 - mmengine - INFO - Epoch(train) [15][860/1345] lr: 1.0000e-02 eta: 2:55:15 time: 0.1939 data_time: 0.0128 memory: 7116 grad_norm: 6.3103 top1_acc: 0.1250 top5_acc: 0.8750 loss_cls: 2.5197 loss: 2.5197 2022/09/03 23:01:04 - mmengine - INFO - Epoch(train) [15][880/1345] lr: 1.0000e-02 eta: 2:55:09 time: 0.1965 data_time: 0.0095 memory: 7116 grad_norm: 6.0743 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.2601 loss: 2.2601 2022/09/03 23:01:08 - mmengine - INFO - Epoch(train) [15][900/1345] lr: 1.0000e-02 eta: 2:55:04 time: 0.2059 data_time: 0.0098 memory: 7116 grad_norm: 6.3412 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2083 loss: 2.2083 2022/09/03 23:01:12 - mmengine - INFO - Epoch(train) [15][920/1345] lr: 1.0000e-02 eta: 2:54:59 time: 0.1924 data_time: 0.0114 memory: 7116 grad_norm: 6.5642 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.3524 loss: 2.3524 2022/09/03 23:01:16 - mmengine - INFO - Epoch(train) [15][940/1345] lr: 1.0000e-02 eta: 2:54:53 time: 0.1893 data_time: 0.0091 memory: 7116 grad_norm: 6.4094 top1_acc: 0.0000 top5_acc: 0.5000 loss_cls: 2.5932 loss: 2.5932 2022/09/03 23:01:20 - mmengine - INFO - Epoch(train) [15][960/1345] lr: 1.0000e-02 eta: 2:54:47 time: 0.1936 data_time: 0.0105 memory: 7116 grad_norm: 6.3811 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.4396 loss: 2.4396 2022/09/03 23:01:24 - mmengine - INFO - Epoch(train) [15][980/1345] lr: 1.0000e-02 eta: 2:54:41 time: 0.1940 data_time: 0.0126 memory: 7116 grad_norm: 6.5888 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.4222 loss: 2.4222 2022/09/03 23:01:27 - mmengine - INFO - Epoch(train) [15][1000/1345] lr: 1.0000e-02 eta: 2:54:35 time: 0.1927 data_time: 0.0114 memory: 7116 grad_norm: 6.3277 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3119 loss: 2.3119 2022/09/03 23:01:31 - mmengine - INFO - Epoch(train) [15][1020/1345] lr: 1.0000e-02 eta: 2:54:30 time: 0.1942 data_time: 0.0103 memory: 7116 grad_norm: 6.4550 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2576 loss: 2.2576 2022/09/03 23:01:35 - mmengine - INFO - Epoch(train) [15][1040/1345] lr: 1.0000e-02 eta: 2:54:24 time: 0.1941 data_time: 0.0130 memory: 7116 grad_norm: 6.3742 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1359 loss: 2.1359 2022/09/03 23:01:39 - mmengine - INFO - Epoch(train) [15][1060/1345] lr: 1.0000e-02 eta: 2:54:18 time: 0.1912 data_time: 0.0094 memory: 7116 grad_norm: 6.2216 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.3291 loss: 2.3291 2022/09/03 23:01:43 - mmengine - INFO - Epoch(train) [15][1080/1345] lr: 1.0000e-02 eta: 2:54:13 time: 0.1980 data_time: 0.0110 memory: 7116 grad_norm: 6.0763 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.6871 loss: 2.6871 2022/09/03 23:01:47 - mmengine - INFO - Epoch(train) [15][1100/1345] lr: 1.0000e-02 eta: 2:54:07 time: 0.1999 data_time: 0.0130 memory: 7116 grad_norm: 6.2920 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.5556 loss: 2.5556 2022/09/03 23:01:51 - mmengine - INFO - Epoch(train) [15][1120/1345] lr: 1.0000e-02 eta: 2:54:02 time: 0.1966 data_time: 0.0093 memory: 7116 grad_norm: 6.3323 top1_acc: 0.0000 top5_acc: 0.5000 loss_cls: 2.5740 loss: 2.5740 2022/09/03 23:01:55 - mmengine - INFO - Epoch(train) [15][1140/1345] lr: 1.0000e-02 eta: 2:53:56 time: 0.1878 data_time: 0.0105 memory: 7116 grad_norm: 6.2169 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.5758 loss: 2.5758 2022/09/03 23:01:59 - mmengine - INFO - Epoch(train) [15][1160/1345] lr: 1.0000e-02 eta: 2:53:50 time: 0.1954 data_time: 0.0133 memory: 7116 grad_norm: 6.4887 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1365 loss: 2.1365 2022/09/03 23:02:00 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:02:02 - mmengine - INFO - Epoch(train) [15][1180/1345] lr: 1.0000e-02 eta: 2:53:44 time: 0.1892 data_time: 0.0100 memory: 7116 grad_norm: 6.7778 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.6727 loss: 2.6727 2022/09/03 23:02:07 - mmengine - INFO - Epoch(train) [15][1200/1345] lr: 1.0000e-02 eta: 2:53:39 time: 0.2095 data_time: 0.0116 memory: 7116 grad_norm: 6.3540 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.3844 loss: 2.3844 2022/09/03 23:02:10 - mmengine - INFO - Epoch(train) [15][1220/1345] lr: 1.0000e-02 eta: 2:53:34 time: 0.1949 data_time: 0.0125 memory: 7116 grad_norm: 6.3707 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.4096 loss: 2.4096 2022/09/03 23:02:14 - mmengine - INFO - Epoch(train) [15][1240/1345] lr: 1.0000e-02 eta: 2:53:28 time: 0.1901 data_time: 0.0101 memory: 7116 grad_norm: 6.5221 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.6315 loss: 2.6315 2022/09/03 23:02:18 - mmengine - INFO - Epoch(train) [15][1260/1345] lr: 1.0000e-02 eta: 2:53:22 time: 0.1933 data_time: 0.0106 memory: 7116 grad_norm: 6.4033 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.6894 loss: 2.6894 2022/09/03 23:02:22 - mmengine - INFO - Epoch(train) [15][1280/1345] lr: 1.0000e-02 eta: 2:53:16 time: 0.1898 data_time: 0.0129 memory: 7116 grad_norm: 6.3307 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.4597 loss: 2.4597 2022/09/03 23:02:26 - mmengine - INFO - Epoch(train) [15][1300/1345] lr: 1.0000e-02 eta: 2:53:11 time: 0.1944 data_time: 0.0101 memory: 7116 grad_norm: 6.6500 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.2183 loss: 2.2183 2022/09/03 23:02:30 - mmengine - INFO - Epoch(train) [15][1320/1345] lr: 1.0000e-02 eta: 2:53:05 time: 0.1917 data_time: 0.0098 memory: 7116 grad_norm: 6.6711 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5384 loss: 2.5384 2022/09/03 23:02:34 - mmengine - INFO - Epoch(train) [15][1340/1345] lr: 1.0000e-02 eta: 2:52:59 time: 0.1969 data_time: 0.0131 memory: 7116 grad_norm: 6.4113 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.3434 loss: 2.3434 2022/09/03 23:02:35 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:02:35 - mmengine - INFO - Epoch(train) [15][1345/1345] lr: 1.0000e-02 eta: 2:52:59 time: 0.1910 data_time: 0.0105 memory: 7116 grad_norm: 6.7738 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.6143 loss: 2.6143 2022/09/03 23:02:35 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/09/03 23:02:38 - mmengine - INFO - Epoch(val) [15][20/181] eta: 0:00:07 time: 0.0474 data_time: 0.0106 memory: 1114 2022/09/03 23:02:39 - mmengine - INFO - Epoch(val) [15][40/181] eta: 0:00:06 time: 0.0462 data_time: 0.0090 memory: 1114 2022/09/03 23:02:39 - mmengine - INFO - Epoch(val) [15][60/181] eta: 0:00:05 time: 0.0474 data_time: 0.0087 memory: 1114 2022/09/03 23:02:40 - mmengine - INFO - Epoch(val) [15][80/181] eta: 0:00:04 time: 0.0469 data_time: 0.0086 memory: 1114 2022/09/03 23:02:41 - mmengine - INFO - Epoch(val) [15][100/181] eta: 0:00:03 time: 0.0474 data_time: 0.0089 memory: 1114 2022/09/03 23:02:42 - mmengine - INFO - Epoch(val) [15][120/181] eta: 0:00:02 time: 0.0463 data_time: 0.0088 memory: 1114 2022/09/03 23:02:43 - mmengine - INFO - Epoch(val) [15][140/181] eta: 0:00:01 time: 0.0461 data_time: 0.0078 memory: 1114 2022/09/03 23:02:44 - mmengine - INFO - Epoch(val) [15][160/181] eta: 0:00:00 time: 0.0443 data_time: 0.0073 memory: 1114 2022/09/03 23:02:45 - mmengine - INFO - Epoch(val) [15][180/181] eta: 0:00:00 time: 0.0433 data_time: 0.0070 memory: 1114 2022/09/03 23:02:46 - mmengine - INFO - Epoch(val) [15][181/181] acc/top1: 0.2905 acc/top5: 0.5766 acc/mean1: 0.2655 2022/09/03 23:02:51 - mmengine - INFO - Epoch(train) [16][20/1345] lr: 1.0000e-02 eta: 2:52:51 time: 0.2253 data_time: 0.0217 memory: 7116 grad_norm: 6.2950 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2766 loss: 2.2766 2022/09/03 23:02:55 - mmengine - INFO - Epoch(train) [16][40/1345] lr: 1.0000e-02 eta: 2:52:46 time: 0.1913 data_time: 0.0104 memory: 7116 grad_norm: 6.2742 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.5254 loss: 2.5254 2022/09/03 23:02:59 - mmengine - INFO - Epoch(train) [16][60/1345] lr: 1.0000e-02 eta: 2:52:40 time: 0.1972 data_time: 0.0098 memory: 7116 grad_norm: 6.3653 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.5010 loss: 2.5010 2022/09/03 23:03:03 - mmengine - INFO - Epoch(train) [16][80/1345] lr: 1.0000e-02 eta: 2:52:35 time: 0.1949 data_time: 0.0123 memory: 7116 grad_norm: 6.3732 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.3994 loss: 2.3994 2022/09/03 23:03:07 - mmengine - INFO - Epoch(train) [16][100/1345] lr: 1.0000e-02 eta: 2:52:29 time: 0.1946 data_time: 0.0104 memory: 7116 grad_norm: 6.4209 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3141 loss: 2.3141 2022/09/03 23:03:11 - mmengine - INFO - Epoch(train) [16][120/1345] lr: 1.0000e-02 eta: 2:52:23 time: 0.1959 data_time: 0.0107 memory: 7116 grad_norm: 6.1369 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.6129 loss: 2.6129 2022/09/03 23:03:15 - mmengine - INFO - Epoch(train) [16][140/1345] lr: 1.0000e-02 eta: 2:52:18 time: 0.1989 data_time: 0.0117 memory: 7116 grad_norm: 6.7476 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.1505 loss: 2.1505 2022/09/03 23:03:18 - mmengine - INFO - Epoch(train) [16][160/1345] lr: 1.0000e-02 eta: 2:52:12 time: 0.1942 data_time: 0.0103 memory: 7116 grad_norm: 6.2677 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.7170 loss: 2.7170 2022/09/03 23:03:22 - mmengine - INFO - Epoch(train) [16][180/1345] lr: 1.0000e-02 eta: 2:52:07 time: 0.1924 data_time: 0.0101 memory: 7116 grad_norm: 6.2264 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.4052 loss: 2.4052 2022/09/03 23:03:26 - mmengine - INFO - Epoch(train) [16][200/1345] lr: 1.0000e-02 eta: 2:52:01 time: 0.1914 data_time: 0.0120 memory: 7116 grad_norm: 6.4843 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.3887 loss: 2.3887 2022/09/03 23:03:30 - mmengine - INFO - Epoch(train) [16][220/1345] lr: 1.0000e-02 eta: 2:51:56 time: 0.1976 data_time: 0.0104 memory: 7116 grad_norm: 6.6256 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6792 loss: 2.6792 2022/09/03 23:03:34 - mmengine - INFO - Epoch(train) [16][240/1345] lr: 1.0000e-02 eta: 2:51:50 time: 0.1931 data_time: 0.0111 memory: 7116 grad_norm: 6.5791 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2491 loss: 2.2491 2022/09/03 23:03:38 - mmengine - INFO - Epoch(train) [16][260/1345] lr: 1.0000e-02 eta: 2:51:44 time: 0.1932 data_time: 0.0133 memory: 7116 grad_norm: 6.6785 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.6722 loss: 2.6722 2022/09/03 23:03:42 - mmengine - INFO - Epoch(train) [16][280/1345] lr: 1.0000e-02 eta: 2:51:39 time: 0.1927 data_time: 0.0104 memory: 7116 grad_norm: 6.4229 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3649 loss: 2.3649 2022/09/03 23:03:46 - mmengine - INFO - Epoch(train) [16][300/1345] lr: 1.0000e-02 eta: 2:51:33 time: 0.1973 data_time: 0.0108 memory: 7116 grad_norm: 6.2629 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2825 loss: 2.2825 2022/09/03 23:03:49 - mmengine - INFO - Epoch(train) [16][320/1345] lr: 1.0000e-02 eta: 2:51:27 time: 0.1878 data_time: 0.0127 memory: 7116 grad_norm: 6.2115 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5692 loss: 2.5692 2022/09/03 23:03:53 - mmengine - INFO - Epoch(train) [16][340/1345] lr: 1.0000e-02 eta: 2:51:22 time: 0.1893 data_time: 0.0110 memory: 7116 grad_norm: 6.3619 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.3732 loss: 2.3732 2022/09/03 23:03:57 - mmengine - INFO - Epoch(train) [16][360/1345] lr: 1.0000e-02 eta: 2:51:16 time: 0.1957 data_time: 0.0108 memory: 7116 grad_norm: 6.3997 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2707 loss: 2.2707 2022/09/03 23:04:01 - mmengine - INFO - Epoch(train) [16][380/1345] lr: 1.0000e-02 eta: 2:51:10 time: 0.1923 data_time: 0.0115 memory: 7116 grad_norm: 6.3935 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.7095 loss: 2.7095 2022/09/03 23:04:05 - mmengine - INFO - Epoch(train) [16][400/1345] lr: 1.0000e-02 eta: 2:51:05 time: 0.1912 data_time: 0.0111 memory: 7116 grad_norm: 6.0698 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4295 loss: 2.4295 2022/09/03 23:04:09 - mmengine - INFO - Epoch(train) [16][420/1345] lr: 1.0000e-02 eta: 2:50:59 time: 0.1901 data_time: 0.0121 memory: 7116 grad_norm: 6.4546 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.4047 loss: 2.4047 2022/09/03 23:04:12 - mmengine - INFO - Epoch(train) [16][440/1345] lr: 1.0000e-02 eta: 2:50:53 time: 0.1895 data_time: 0.0117 memory: 7116 grad_norm: 6.2789 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.4640 loss: 2.4640 2022/09/03 23:04:16 - mmengine - INFO - Epoch(train) [16][460/1345] lr: 1.0000e-02 eta: 2:50:47 time: 0.1902 data_time: 0.0114 memory: 7116 grad_norm: 6.1535 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.4662 loss: 2.4662 2022/09/03 23:04:20 - mmengine - INFO - Epoch(train) [16][480/1345] lr: 1.0000e-02 eta: 2:50:42 time: 0.1933 data_time: 0.0112 memory: 7116 grad_norm: 6.5651 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.5324 loss: 2.5324 2022/09/03 23:04:24 - mmengine - INFO - Epoch(train) [16][500/1345] lr: 1.0000e-02 eta: 2:50:36 time: 0.1933 data_time: 0.0113 memory: 7116 grad_norm: 6.2645 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.5308 loss: 2.5308 2022/09/03 23:04:28 - mmengine - INFO - Epoch(train) [16][520/1345] lr: 1.0000e-02 eta: 2:50:30 time: 0.1880 data_time: 0.0109 memory: 7116 grad_norm: 6.3108 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4859 loss: 2.4859 2022/09/03 23:04:31 - mmengine - INFO - Epoch(train) [16][540/1345] lr: 1.0000e-02 eta: 2:50:25 time: 0.1879 data_time: 0.0113 memory: 7116 grad_norm: 6.3977 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3387 loss: 2.3387 2022/09/03 23:04:35 - mmengine - INFO - Epoch(train) [16][560/1345] lr: 1.0000e-02 eta: 2:50:19 time: 0.1909 data_time: 0.0121 memory: 7116 grad_norm: 6.5436 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.3168 loss: 2.3168 2022/09/03 23:04:39 - mmengine - INFO - Epoch(train) [16][580/1345] lr: 1.0000e-02 eta: 2:50:13 time: 0.1938 data_time: 0.0124 memory: 7116 grad_norm: 6.3029 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3580 loss: 2.3580 2022/09/03 23:04:43 - mmengine - INFO - Epoch(train) [16][600/1345] lr: 1.0000e-02 eta: 2:50:08 time: 0.1925 data_time: 0.0107 memory: 7116 grad_norm: 6.3996 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4870 loss: 2.4870 2022/09/03 23:04:47 - mmengine - INFO - Epoch(train) [16][620/1345] lr: 1.0000e-02 eta: 2:50:02 time: 0.1933 data_time: 0.0120 memory: 7116 grad_norm: 6.3211 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.5346 loss: 2.5346 2022/09/03 23:04:51 - mmengine - INFO - Epoch(train) [16][640/1345] lr: 1.0000e-02 eta: 2:49:57 time: 0.1931 data_time: 0.0118 memory: 7116 grad_norm: 6.3801 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4316 loss: 2.4316 2022/09/03 23:04:54 - mmengine - INFO - Epoch(train) [16][660/1345] lr: 1.0000e-02 eta: 2:49:51 time: 0.1914 data_time: 0.0113 memory: 7116 grad_norm: 6.3405 top1_acc: 0.0000 top5_acc: 0.7500 loss_cls: 2.4668 loss: 2.4668 2022/09/03 23:04:58 - mmengine - INFO - Epoch(train) [16][680/1345] lr: 1.0000e-02 eta: 2:49:45 time: 0.1936 data_time: 0.0122 memory: 7116 grad_norm: 6.5988 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.2449 loss: 2.2449 2022/09/03 23:05:02 - mmengine - INFO - Epoch(train) [16][700/1345] lr: 1.0000e-02 eta: 2:49:40 time: 0.1915 data_time: 0.0132 memory: 7116 grad_norm: 6.2047 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 2.2664 loss: 2.2664 2022/09/03 23:05:06 - mmengine - INFO - Epoch(train) [16][720/1345] lr: 1.0000e-02 eta: 2:49:34 time: 0.1897 data_time: 0.0113 memory: 7116 grad_norm: 6.3645 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3153 loss: 2.3153 2022/09/03 23:05:10 - mmengine - INFO - Epoch(train) [16][740/1345] lr: 1.0000e-02 eta: 2:49:29 time: 0.1941 data_time: 0.0125 memory: 7116 grad_norm: 6.3511 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.3855 loss: 2.3855 2022/09/03 23:05:14 - mmengine - INFO - Epoch(train) [16][760/1345] lr: 1.0000e-02 eta: 2:49:23 time: 0.1905 data_time: 0.0116 memory: 7116 grad_norm: 6.7347 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2016 loss: 2.2016 2022/09/03 23:05:18 - mmengine - INFO - Epoch(train) [16][780/1345] lr: 1.0000e-02 eta: 2:49:17 time: 0.1934 data_time: 0.0125 memory: 7116 grad_norm: 6.2079 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.6003 loss: 2.6003 2022/09/03 23:05:21 - mmengine - INFO - Epoch(train) [16][800/1345] lr: 1.0000e-02 eta: 2:49:12 time: 0.1920 data_time: 0.0117 memory: 7116 grad_norm: 6.2085 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0693 loss: 2.0693 2022/09/03 23:05:25 - mmengine - INFO - Epoch(train) [16][820/1345] lr: 1.0000e-02 eta: 2:49:06 time: 0.1899 data_time: 0.0108 memory: 7116 grad_norm: 6.2808 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.6109 loss: 2.6109 2022/09/03 23:05:26 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:05:29 - mmengine - INFO - Epoch(train) [16][840/1345] lr: 1.0000e-02 eta: 2:49:00 time: 0.1910 data_time: 0.0117 memory: 7116 grad_norm: 6.4743 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5041 loss: 2.5041 2022/09/03 23:05:33 - mmengine - INFO - Epoch(train) [16][860/1345] lr: 1.0000e-02 eta: 2:48:55 time: 0.1925 data_time: 0.0131 memory: 7116 grad_norm: 6.5778 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3185 loss: 2.3185 2022/09/03 23:05:37 - mmengine - INFO - Epoch(train) [16][880/1345] lr: 1.0000e-02 eta: 2:48:49 time: 0.1947 data_time: 0.0108 memory: 7116 grad_norm: 6.4860 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3730 loss: 2.3730 2022/09/03 23:05:41 - mmengine - INFO - Epoch(train) [16][900/1345] lr: 1.0000e-02 eta: 2:48:44 time: 0.1885 data_time: 0.0108 memory: 7116 grad_norm: 6.4418 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3836 loss: 2.3836 2022/09/03 23:05:44 - mmengine - INFO - Epoch(train) [16][920/1345] lr: 1.0000e-02 eta: 2:48:38 time: 0.1914 data_time: 0.0115 memory: 7116 grad_norm: 6.1934 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5744 loss: 2.5744 2022/09/03 23:05:48 - mmengine - INFO - Epoch(train) [16][940/1345] lr: 1.0000e-02 eta: 2:48:33 time: 0.2017 data_time: 0.0113 memory: 7116 grad_norm: 6.4755 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.6573 loss: 2.6573 2022/09/03 23:05:52 - mmengine - INFO - Epoch(train) [16][960/1345] lr: 1.0000e-02 eta: 2:48:27 time: 0.1903 data_time: 0.0108 memory: 7116 grad_norm: 6.5983 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.4862 loss: 2.4862 2022/09/03 23:05:56 - mmengine - INFO - Epoch(train) [16][980/1345] lr: 1.0000e-02 eta: 2:48:22 time: 0.1908 data_time: 0.0121 memory: 7116 grad_norm: 6.3829 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.6003 loss: 2.6003 2022/09/03 23:06:00 - mmengine - INFO - Epoch(train) [16][1000/1345] lr: 1.0000e-02 eta: 2:48:17 time: 0.2075 data_time: 0.0087 memory: 7116 grad_norm: 6.2281 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3476 loss: 2.3476 2022/09/03 23:06:04 - mmengine - INFO - Epoch(train) [16][1020/1345] lr: 1.0000e-02 eta: 2:48:11 time: 0.1893 data_time: 0.0112 memory: 7116 grad_norm: 6.8497 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4736 loss: 2.4736 2022/09/03 23:06:08 - mmengine - INFO - Epoch(train) [16][1040/1345] lr: 1.0000e-02 eta: 2:48:06 time: 0.1926 data_time: 0.0129 memory: 7116 grad_norm: 6.5412 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.5060 loss: 2.5060 2022/09/03 23:06:12 - mmengine - INFO - Epoch(train) [16][1060/1345] lr: 1.0000e-02 eta: 2:48:00 time: 0.1897 data_time: 0.0097 memory: 7116 grad_norm: 6.9077 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.4083 loss: 2.4083 2022/09/03 23:06:15 - mmengine - INFO - Epoch(train) [16][1080/1345] lr: 1.0000e-02 eta: 2:47:54 time: 0.1864 data_time: 0.0107 memory: 7116 grad_norm: 6.4086 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5474 loss: 2.5474 2022/09/03 23:06:19 - mmengine - INFO - Epoch(train) [16][1100/1345] lr: 1.0000e-02 eta: 2:47:48 time: 0.1892 data_time: 0.0114 memory: 7116 grad_norm: 6.3786 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.7285 loss: 2.7285 2022/09/03 23:06:23 - mmengine - INFO - Epoch(train) [16][1120/1345] lr: 1.0000e-02 eta: 2:47:43 time: 0.1900 data_time: 0.0132 memory: 7116 grad_norm: 6.3937 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3398 loss: 2.3398 2022/09/03 23:06:27 - mmengine - INFO - Epoch(train) [16][1140/1345] lr: 1.0000e-02 eta: 2:47:37 time: 0.1909 data_time: 0.0103 memory: 7116 grad_norm: 6.4057 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.4027 loss: 2.4027 2022/09/03 23:06:31 - mmengine - INFO - Epoch(train) [16][1160/1345] lr: 1.0000e-02 eta: 2:47:32 time: 0.1916 data_time: 0.0124 memory: 7116 grad_norm: 6.6210 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1908 loss: 2.1908 2022/09/03 23:06:34 - mmengine - INFO - Epoch(train) [16][1180/1345] lr: 1.0000e-02 eta: 2:47:26 time: 0.1891 data_time: 0.0113 memory: 7116 grad_norm: 6.3020 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.5111 loss: 2.5111 2022/09/03 23:06:39 - mmengine - INFO - Epoch(train) [16][1200/1345] lr: 1.0000e-02 eta: 2:47:21 time: 0.2056 data_time: 0.0098 memory: 7116 grad_norm: 6.3308 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 2.3709 loss: 2.3709 2022/09/03 23:06:42 - mmengine - INFO - Epoch(train) [16][1220/1345] lr: 1.0000e-02 eta: 2:47:16 time: 0.1952 data_time: 0.0119 memory: 7116 grad_norm: 6.5825 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.4549 loss: 2.4549 2022/09/03 23:06:46 - mmengine - INFO - Epoch(train) [16][1240/1345] lr: 1.0000e-02 eta: 2:47:10 time: 0.1912 data_time: 0.0113 memory: 7116 grad_norm: 6.2945 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3280 loss: 2.3280 2022/09/03 23:06:50 - mmengine - INFO - Epoch(train) [16][1260/1345] lr: 1.0000e-02 eta: 2:47:05 time: 0.1917 data_time: 0.0115 memory: 7116 grad_norm: 6.4389 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.8551 loss: 2.8551 2022/09/03 23:06:54 - mmengine - INFO - Epoch(train) [16][1280/1345] lr: 1.0000e-02 eta: 2:46:59 time: 0.2024 data_time: 0.0107 memory: 7116 grad_norm: 6.5562 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.6408 loss: 2.6408 2022/09/03 23:06:58 - mmengine - INFO - Epoch(train) [16][1300/1345] lr: 1.0000e-02 eta: 2:46:54 time: 0.1916 data_time: 0.0120 memory: 7116 grad_norm: 6.5731 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.4261 loss: 2.4261 2022/09/03 23:07:02 - mmengine - INFO - Epoch(train) [16][1320/1345] lr: 1.0000e-02 eta: 2:46:48 time: 0.1895 data_time: 0.0114 memory: 7116 grad_norm: 6.4271 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3564 loss: 2.3564 2022/09/03 23:07:06 - mmengine - INFO - Epoch(train) [16][1340/1345] lr: 1.0000e-02 eta: 2:46:43 time: 0.1891 data_time: 0.0146 memory: 7116 grad_norm: 6.2469 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.5901 loss: 2.5901 2022/09/03 23:07:06 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:07:06 - mmengine - INFO - Epoch(train) [16][1345/1345] lr: 1.0000e-02 eta: 2:46:43 time: 0.1830 data_time: 0.0104 memory: 7116 grad_norm: 6.7277 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.5716 loss: 2.5716 2022/09/03 23:07:06 - mmengine - INFO - Saving checkpoint at 16 epochs 2022/09/03 23:07:09 - mmengine - INFO - Epoch(val) [16][20/181] eta: 0:00:07 time: 0.0488 data_time: 0.0108 memory: 1114 2022/09/03 23:07:10 - mmengine - INFO - Epoch(val) [16][40/181] eta: 0:00:06 time: 0.0454 data_time: 0.0084 memory: 1114 2022/09/03 23:07:11 - mmengine - INFO - Epoch(val) [16][60/181] eta: 0:00:05 time: 0.0480 data_time: 0.0092 memory: 1114 2022/09/03 23:07:12 - mmengine - INFO - Epoch(val) [16][80/181] eta: 0:00:04 time: 0.0451 data_time: 0.0080 memory: 1114 2022/09/03 23:07:13 - mmengine - INFO - Epoch(val) [16][100/181] eta: 0:00:03 time: 0.0474 data_time: 0.0086 memory: 1114 2022/09/03 23:07:14 - mmengine - INFO - Epoch(val) [16][120/181] eta: 0:00:02 time: 0.0471 data_time: 0.0083 memory: 1114 2022/09/03 23:07:15 - mmengine - INFO - Epoch(val) [16][140/181] eta: 0:00:01 time: 0.0448 data_time: 0.0079 memory: 1114 2022/09/03 23:07:16 - mmengine - INFO - Epoch(val) [16][160/181] eta: 0:00:00 time: 0.0469 data_time: 0.0084 memory: 1114 2022/09/03 23:07:17 - mmengine - INFO - Epoch(val) [16][180/181] eta: 0:00:00 time: 0.0457 data_time: 0.0084 memory: 1114 2022/09/03 23:07:18 - mmengine - INFO - Epoch(val) [16][181/181] acc/top1: 0.2850 acc/top5: 0.5744 acc/mean1: 0.2627 2022/09/03 23:07:23 - mmengine - INFO - Epoch(train) [17][20/1345] lr: 1.0000e-02 eta: 2:46:36 time: 0.2448 data_time: 0.0470 memory: 7116 grad_norm: 6.4246 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2912 loss: 2.2912 2022/09/03 23:07:27 - mmengine - INFO - Epoch(train) [17][40/1345] lr: 1.0000e-02 eta: 2:46:31 time: 0.1945 data_time: 0.0098 memory: 7116 grad_norm: 6.3491 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.4451 loss: 2.4451 2022/09/03 23:07:31 - mmengine - INFO - Epoch(train) [17][60/1345] lr: 1.0000e-02 eta: 2:46:25 time: 0.1884 data_time: 0.0100 memory: 7116 grad_norm: 6.3413 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1841 loss: 2.1841 2022/09/03 23:07:35 - mmengine - INFO - Epoch(train) [17][80/1345] lr: 1.0000e-02 eta: 2:46:19 time: 0.1910 data_time: 0.0137 memory: 7116 grad_norm: 6.4796 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.3371 loss: 2.3371 2022/09/03 23:07:38 - mmengine - INFO - Epoch(train) [17][100/1345] lr: 1.0000e-02 eta: 2:46:14 time: 0.1902 data_time: 0.0106 memory: 7116 grad_norm: 6.4349 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.3407 loss: 2.3407 2022/09/03 23:07:42 - mmengine - INFO - Epoch(train) [17][120/1345] lr: 1.0000e-02 eta: 2:46:08 time: 0.1921 data_time: 0.0107 memory: 7116 grad_norm: 6.4765 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 2.4800 loss: 2.4800 2022/09/03 23:07:46 - mmengine - INFO - Epoch(train) [17][140/1345] lr: 1.0000e-02 eta: 2:46:03 time: 0.1933 data_time: 0.0125 memory: 7116 grad_norm: 6.1821 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.2640 loss: 2.2640 2022/09/03 23:07:50 - mmengine - INFO - Epoch(train) [17][160/1345] lr: 1.0000e-02 eta: 2:45:58 time: 0.2140 data_time: 0.0125 memory: 7116 grad_norm: 6.3263 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4376 loss: 2.4376 2022/09/03 23:07:54 - mmengine - INFO - Epoch(train) [17][180/1345] lr: 1.0000e-02 eta: 2:45:53 time: 0.1880 data_time: 0.0099 memory: 7116 grad_norm: 6.4308 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5881 loss: 2.5881 2022/09/03 23:07:58 - mmengine - INFO - Epoch(train) [17][200/1345] lr: 1.0000e-02 eta: 2:45:47 time: 0.1899 data_time: 0.0117 memory: 7116 grad_norm: 6.3235 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.1853 loss: 2.1853 2022/09/03 23:08:02 - mmengine - INFO - Epoch(train) [17][220/1345] lr: 1.0000e-02 eta: 2:45:42 time: 0.1899 data_time: 0.0123 memory: 7116 grad_norm: 6.4523 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4142 loss: 2.4142 2022/09/03 23:08:06 - mmengine - INFO - Epoch(train) [17][240/1345] lr: 1.0000e-02 eta: 2:45:36 time: 0.1895 data_time: 0.0101 memory: 7116 grad_norm: 6.6853 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0766 loss: 2.0766 2022/09/03 23:08:09 - mmengine - INFO - Epoch(train) [17][260/1345] lr: 1.0000e-02 eta: 2:45:30 time: 0.1924 data_time: 0.0123 memory: 7116 grad_norm: 6.3722 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.1217 loss: 2.1217 2022/09/03 23:08:13 - mmengine - INFO - Epoch(train) [17][280/1345] lr: 1.0000e-02 eta: 2:45:25 time: 0.1918 data_time: 0.0113 memory: 7116 grad_norm: 6.3656 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4849 loss: 2.4849 2022/09/03 23:08:17 - mmengine - INFO - Epoch(train) [17][300/1345] lr: 1.0000e-02 eta: 2:45:20 time: 0.1934 data_time: 0.0113 memory: 7116 grad_norm: 6.4964 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.4414 loss: 2.4414 2022/09/03 23:08:21 - mmengine - INFO - Epoch(train) [17][320/1345] lr: 1.0000e-02 eta: 2:45:14 time: 0.1898 data_time: 0.0123 memory: 7116 grad_norm: 6.5161 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.2671 loss: 2.2671 2022/09/03 23:08:25 - mmengine - INFO - Epoch(train) [17][340/1345] lr: 1.0000e-02 eta: 2:45:08 time: 0.1885 data_time: 0.0104 memory: 7116 grad_norm: 6.3287 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.1849 loss: 2.1849 2022/09/03 23:08:29 - mmengine - INFO - Epoch(train) [17][360/1345] lr: 1.0000e-02 eta: 2:45:04 time: 0.2092 data_time: 0.0109 memory: 7116 grad_norm: 6.5048 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.2792 loss: 2.2792 2022/09/03 23:08:33 - mmengine - INFO - Epoch(train) [17][380/1345] lr: 1.0000e-02 eta: 2:44:58 time: 0.1942 data_time: 0.0128 memory: 7116 grad_norm: 6.6823 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3559 loss: 2.3559 2022/09/03 23:08:37 - mmengine - INFO - Epoch(train) [17][400/1345] lr: 1.0000e-02 eta: 2:44:53 time: 0.1887 data_time: 0.0107 memory: 7116 grad_norm: 6.5230 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4383 loss: 2.4383 2022/09/03 23:08:40 - mmengine - INFO - Epoch(train) [17][420/1345] lr: 1.0000e-02 eta: 2:44:47 time: 0.1915 data_time: 0.0108 memory: 7116 grad_norm: 6.5464 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3677 loss: 2.3677 2022/09/03 23:08:44 - mmengine - INFO - Epoch(train) [17][440/1345] lr: 1.0000e-02 eta: 2:44:42 time: 0.1904 data_time: 0.0126 memory: 7116 grad_norm: 6.6720 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.3153 loss: 2.3153 2022/09/03 23:08:48 - mmengine - INFO - Epoch(train) [17][460/1345] lr: 1.0000e-02 eta: 2:44:37 time: 0.2073 data_time: 0.0102 memory: 7116 grad_norm: 6.4535 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1811 loss: 2.1811 2022/09/03 23:08:52 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:08:52 - mmengine - INFO - Epoch(train) [17][480/1345] lr: 1.0000e-02 eta: 2:44:31 time: 0.1914 data_time: 0.0101 memory: 7116 grad_norm: 6.5735 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2100 loss: 2.2100 2022/09/03 23:08:56 - mmengine - INFO - Epoch(train) [17][500/1345] lr: 1.0000e-02 eta: 2:44:26 time: 0.1882 data_time: 0.0130 memory: 7116 grad_norm: 6.6942 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.2933 loss: 2.2933 2022/09/03 23:09:00 - mmengine - INFO - Epoch(train) [17][520/1345] lr: 1.0000e-02 eta: 2:44:20 time: 0.1904 data_time: 0.0119 memory: 7116 grad_norm: 6.6185 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3947 loss: 2.3947 2022/09/03 23:09:04 - mmengine - INFO - Epoch(train) [17][540/1345] lr: 1.0000e-02 eta: 2:44:15 time: 0.1855 data_time: 0.0117 memory: 7116 grad_norm: 6.2595 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.5282 loss: 2.5282 2022/09/03 23:09:07 - mmengine - INFO - Epoch(train) [17][560/1345] lr: 1.0000e-02 eta: 2:44:10 time: 0.1981 data_time: 0.0132 memory: 7116 grad_norm: 6.5724 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.1388 loss: 2.1388 2022/09/03 23:09:12 - mmengine - INFO - Epoch(train) [17][580/1345] lr: 1.0000e-02 eta: 2:44:04 time: 0.2020 data_time: 0.0097 memory: 7116 grad_norm: 6.6917 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5384 loss: 2.5384 2022/09/03 23:09:15 - mmengine - INFO - Epoch(train) [17][600/1345] lr: 1.0000e-02 eta: 2:43:59 time: 0.1910 data_time: 0.0099 memory: 7116 grad_norm: 6.4478 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5041 loss: 2.5041 2022/09/03 23:09:19 - mmengine - INFO - Epoch(train) [17][620/1345] lr: 1.0000e-02 eta: 2:43:54 time: 0.1906 data_time: 0.0112 memory: 7116 grad_norm: 6.4059 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 2.4705 loss: 2.4705 2022/09/03 23:09:23 - mmengine - INFO - Epoch(train) [17][640/1345] lr: 1.0000e-02 eta: 2:43:48 time: 0.2001 data_time: 0.0110 memory: 7116 grad_norm: 6.5635 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.5488 loss: 2.5488 2022/09/03 23:09:27 - mmengine - INFO - Epoch(train) [17][660/1345] lr: 1.0000e-02 eta: 2:43:43 time: 0.1897 data_time: 0.0099 memory: 7116 grad_norm: 6.2620 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4839 loss: 2.4839 2022/09/03 23:09:31 - mmengine - INFO - Epoch(train) [17][680/1345] lr: 1.0000e-02 eta: 2:43:37 time: 0.1881 data_time: 0.0118 memory: 7116 grad_norm: 6.1421 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5539 loss: 2.5539 2022/09/03 23:09:35 - mmengine - INFO - Epoch(train) [17][700/1345] lr: 1.0000e-02 eta: 2:43:32 time: 0.1923 data_time: 0.0109 memory: 7116 grad_norm: 6.2068 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.6794 loss: 2.6794 2022/09/03 23:09:39 - mmengine - INFO - Epoch(train) [17][720/1345] lr: 1.0000e-02 eta: 2:43:27 time: 0.2084 data_time: 0.0115 memory: 7116 grad_norm: 6.0885 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.2725 loss: 2.2725 2022/09/03 23:09:43 - mmengine - INFO - Epoch(train) [17][740/1345] lr: 1.0000e-02 eta: 2:43:22 time: 0.1925 data_time: 0.0120 memory: 7116 grad_norm: 6.2614 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.2052 loss: 2.2052 2022/09/03 23:09:46 - mmengine - INFO - Epoch(train) [17][760/1345] lr: 1.0000e-02 eta: 2:43:16 time: 0.1875 data_time: 0.0109 memory: 7116 grad_norm: 6.2683 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4134 loss: 2.4134 2022/09/03 23:09:50 - mmengine - INFO - Epoch(train) [17][780/1345] lr: 1.0000e-02 eta: 2:43:11 time: 0.1927 data_time: 0.0104 memory: 7116 grad_norm: 6.4316 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4141 loss: 2.4141 2022/09/03 23:09:54 - mmengine - INFO - Epoch(train) [17][800/1345] lr: 1.0000e-02 eta: 2:43:05 time: 0.1913 data_time: 0.0118 memory: 7116 grad_norm: 6.0655 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.2285 loss: 2.2285 2022/09/03 23:09:58 - mmengine - INFO - Epoch(train) [17][820/1345] lr: 1.0000e-02 eta: 2:43:00 time: 0.1944 data_time: 0.0129 memory: 7116 grad_norm: 6.4430 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.3110 loss: 2.3110 2022/09/03 23:10:02 - mmengine - INFO - Epoch(train) [17][840/1345] lr: 1.0000e-02 eta: 2:42:55 time: 0.1878 data_time: 0.0095 memory: 7116 grad_norm: 6.4713 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.2487 loss: 2.2487 2022/09/03 23:10:06 - mmengine - INFO - Epoch(train) [17][860/1345] lr: 1.0000e-02 eta: 2:42:49 time: 0.1925 data_time: 0.0143 memory: 7116 grad_norm: 6.3280 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.5850 loss: 2.5850 2022/09/03 23:10:09 - mmengine - INFO - Epoch(train) [17][880/1345] lr: 1.0000e-02 eta: 2:42:44 time: 0.1901 data_time: 0.0106 memory: 7116 grad_norm: 6.4463 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.5429 loss: 2.5429 2022/09/03 23:10:13 - mmengine - INFO - Epoch(train) [17][900/1345] lr: 1.0000e-02 eta: 2:42:38 time: 0.1892 data_time: 0.0110 memory: 7116 grad_norm: 6.3283 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4185 loss: 2.4185 2022/09/03 23:10:17 - mmengine - INFO - Epoch(train) [17][920/1345] lr: 1.0000e-02 eta: 2:42:33 time: 0.1958 data_time: 0.0122 memory: 7116 grad_norm: 6.5341 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.7266 loss: 2.7266 2022/09/03 23:10:21 - mmengine - INFO - Epoch(train) [17][940/1345] lr: 1.0000e-02 eta: 2:42:28 time: 0.1905 data_time: 0.0099 memory: 7116 grad_norm: 6.5210 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3665 loss: 2.3665 2022/09/03 23:10:25 - mmengine - INFO - Epoch(train) [17][960/1345] lr: 1.0000e-02 eta: 2:42:22 time: 0.1873 data_time: 0.0106 memory: 7116 grad_norm: 6.5261 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4827 loss: 2.4827 2022/09/03 23:10:29 - mmengine - INFO - Epoch(train) [17][980/1345] lr: 1.0000e-02 eta: 2:42:17 time: 0.1972 data_time: 0.0112 memory: 7116 grad_norm: 6.1628 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.2032 loss: 2.2032 2022/09/03 23:10:32 - mmengine - INFO - Epoch(train) [17][1000/1345] lr: 1.0000e-02 eta: 2:42:11 time: 0.1906 data_time: 0.0110 memory: 7116 grad_norm: 6.2631 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.5591 loss: 2.5591 2022/09/03 23:10:36 - mmengine - INFO - Epoch(train) [17][1020/1345] lr: 1.0000e-02 eta: 2:42:06 time: 0.1888 data_time: 0.0117 memory: 7116 grad_norm: 6.4297 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.4131 loss: 2.4131 2022/09/03 23:10:40 - mmengine - INFO - Epoch(train) [17][1040/1345] lr: 1.0000e-02 eta: 2:42:01 time: 0.1958 data_time: 0.0129 memory: 7116 grad_norm: 6.2273 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3757 loss: 2.3757 2022/09/03 23:10:44 - mmengine - INFO - Epoch(train) [17][1060/1345] lr: 1.0000e-02 eta: 2:41:55 time: 0.1878 data_time: 0.0110 memory: 7116 grad_norm: 6.5799 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.6561 loss: 2.6561 2022/09/03 23:10:48 - mmengine - INFO - Epoch(train) [17][1080/1345] lr: 1.0000e-02 eta: 2:41:50 time: 0.1892 data_time: 0.0108 memory: 7116 grad_norm: 6.4668 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.5151 loss: 2.5151 2022/09/03 23:10:51 - mmengine - INFO - Epoch(train) [17][1100/1345] lr: 1.0000e-02 eta: 2:41:44 time: 0.1894 data_time: 0.0136 memory: 7116 grad_norm: 6.5150 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.9873 loss: 1.9873 2022/09/03 23:10:55 - mmengine - INFO - Epoch(train) [17][1120/1345] lr: 1.0000e-02 eta: 2:41:39 time: 0.1891 data_time: 0.0101 memory: 7116 grad_norm: 6.4944 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.5349 loss: 2.5349 2022/09/03 23:10:59 - mmengine - INFO - Epoch(train) [17][1140/1345] lr: 1.0000e-02 eta: 2:41:34 time: 0.1973 data_time: 0.0118 memory: 7116 grad_norm: 6.6510 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.5300 loss: 2.5300 2022/09/03 23:11:03 - mmengine - INFO - Epoch(train) [17][1160/1345] lr: 1.0000e-02 eta: 2:41:28 time: 0.1862 data_time: 0.0120 memory: 7116 grad_norm: 6.3283 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3683 loss: 2.3683 2022/09/03 23:11:07 - mmengine - INFO - Epoch(train) [17][1180/1345] lr: 1.0000e-02 eta: 2:41:23 time: 0.1912 data_time: 0.0113 memory: 7116 grad_norm: 6.3742 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.3704 loss: 2.3704 2022/09/03 23:11:11 - mmengine - INFO - Epoch(train) [17][1200/1345] lr: 1.0000e-02 eta: 2:41:17 time: 0.1912 data_time: 0.0105 memory: 7116 grad_norm: 6.4507 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1624 loss: 2.1624 2022/09/03 23:11:14 - mmengine - INFO - Epoch(train) [17][1220/1345] lr: 1.0000e-02 eta: 2:41:12 time: 0.1896 data_time: 0.0127 memory: 7116 grad_norm: 6.5086 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3626 loss: 2.3626 2022/09/03 23:11:18 - mmengine - INFO - Epoch(train) [17][1240/1345] lr: 1.0000e-02 eta: 2:41:07 time: 0.1986 data_time: 0.0120 memory: 7116 grad_norm: 6.6493 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4303 loss: 2.4303 2022/09/03 23:11:22 - mmengine - INFO - Epoch(train) [17][1260/1345] lr: 1.0000e-02 eta: 2:41:02 time: 0.1920 data_time: 0.0104 memory: 7116 grad_norm: 6.3422 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5597 loss: 2.5597 2022/09/03 23:11:26 - mmengine - INFO - Epoch(train) [17][1280/1345] lr: 1.0000e-02 eta: 2:40:56 time: 0.1947 data_time: 0.0141 memory: 7116 grad_norm: 6.9873 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1535 loss: 2.1535 2022/09/03 23:11:30 - mmengine - INFO - Epoch(train) [17][1300/1345] lr: 1.0000e-02 eta: 2:40:51 time: 0.1936 data_time: 0.0116 memory: 7116 grad_norm: 6.7041 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4432 loss: 2.4432 2022/09/03 23:11:34 - mmengine - INFO - Epoch(train) [17][1320/1345] lr: 1.0000e-02 eta: 2:40:46 time: 0.1911 data_time: 0.0106 memory: 7116 grad_norm: 6.4549 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1801 loss: 2.1801 2022/09/03 23:11:38 - mmengine - INFO - Epoch(train) [17][1340/1345] lr: 1.0000e-02 eta: 2:40:41 time: 0.1978 data_time: 0.0128 memory: 7116 grad_norm: 6.5339 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.1908 loss: 2.1908 2022/09/03 23:11:39 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:11:39 - mmengine - INFO - Epoch(train) [17][1345/1345] lr: 1.0000e-02 eta: 2:40:41 time: 0.1921 data_time: 0.0107 memory: 7116 grad_norm: 6.6434 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.4743 loss: 2.4743 2022/09/03 23:11:39 - mmengine - INFO - Saving checkpoint at 17 epochs 2022/09/03 23:11:41 - mmengine - INFO - Epoch(val) [17][20/181] eta: 0:00:07 time: 0.0475 data_time: 0.0103 memory: 1114 2022/09/03 23:11:42 - mmengine - INFO - Epoch(val) [17][40/181] eta: 0:00:06 time: 0.0463 data_time: 0.0087 memory: 1114 2022/09/03 23:11:43 - mmengine - INFO - Epoch(val) [17][60/181] eta: 0:00:05 time: 0.0449 data_time: 0.0076 memory: 1114 2022/09/03 23:11:44 - mmengine - INFO - Epoch(val) [17][80/181] eta: 0:00:04 time: 0.0466 data_time: 0.0083 memory: 1114 2022/09/03 23:11:45 - mmengine - INFO - Epoch(val) [17][100/181] eta: 0:00:03 time: 0.0450 data_time: 0.0078 memory: 1114 2022/09/03 23:11:46 - mmengine - INFO - Epoch(val) [17][120/181] eta: 0:00:02 time: 0.0442 data_time: 0.0076 memory: 1114 2022/09/03 23:11:47 - mmengine - INFO - Epoch(val) [17][140/181] eta: 0:00:01 time: 0.0450 data_time: 0.0083 memory: 1114 2022/09/03 23:11:48 - mmengine - INFO - Epoch(val) [17][160/181] eta: 0:00:01 time: 0.0477 data_time: 0.0126 memory: 1114 2022/09/03 23:11:49 - mmengine - INFO - Epoch(val) [17][180/181] eta: 0:00:00 time: 0.0418 data_time: 0.0055 memory: 1114 2022/09/03 23:11:51 - mmengine - INFO - Epoch(val) [17][181/181] acc/top1: 0.2920 acc/top5: 0.5807 acc/mean1: 0.2538 2022/09/03 23:11:51 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_11.pth is removed 2022/09/03 23:11:52 - mmengine - INFO - The best checkpoint with 0.2920 acc/top1 at 17 epoch is saved to best_acc/top1_epoch_17.pth. 2022/09/03 23:11:56 - mmengine - INFO - Epoch(train) [18][20/1345] lr: 1.0000e-02 eta: 2:40:32 time: 0.1994 data_time: 0.0156 memory: 7116 grad_norm: 6.2636 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3830 loss: 2.3830 2022/09/03 23:12:00 - mmengine - INFO - Epoch(train) [18][40/1345] lr: 1.0000e-02 eta: 2:40:27 time: 0.1902 data_time: 0.0103 memory: 7116 grad_norm: 6.5768 top1_acc: 0.3750 top5_acc: 1.0000 loss_cls: 2.4460 loss: 2.4460 2022/09/03 23:12:04 - mmengine - INFO - Epoch(train) [18][60/1345] lr: 1.0000e-02 eta: 2:40:22 time: 0.1968 data_time: 0.0116 memory: 7116 grad_norm: 6.5568 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4346 loss: 2.4346 2022/09/03 23:12:08 - mmengine - INFO - Epoch(train) [18][80/1345] lr: 1.0000e-02 eta: 2:40:17 time: 0.1957 data_time: 0.0138 memory: 7116 grad_norm: 6.6407 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2086 loss: 2.2086 2022/09/03 23:12:11 - mmengine - INFO - Epoch(train) [18][100/1345] lr: 1.0000e-02 eta: 2:40:11 time: 0.1911 data_time: 0.0108 memory: 7116 grad_norm: 6.5355 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.7414 loss: 2.7414 2022/09/03 23:12:15 - mmengine - INFO - Epoch(train) [18][120/1345] lr: 1.0000e-02 eta: 2:40:06 time: 0.1925 data_time: 0.0102 memory: 7116 grad_norm: 6.3067 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.4793 loss: 2.4793 2022/09/03 23:12:18 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:12:19 - mmengine - INFO - Epoch(train) [18][140/1345] lr: 1.0000e-02 eta: 2:40:01 time: 0.2007 data_time: 0.0118 memory: 7116 grad_norm: 6.2780 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.2728 loss: 2.2728 2022/09/03 23:12:23 - mmengine - INFO - Epoch(train) [18][160/1345] lr: 1.0000e-02 eta: 2:39:56 time: 0.1895 data_time: 0.0108 memory: 7116 grad_norm: 6.6922 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.4746 loss: 2.4746 2022/09/03 23:12:27 - mmengine - INFO - Epoch(train) [18][180/1345] lr: 1.0000e-02 eta: 2:39:50 time: 0.1918 data_time: 0.0105 memory: 7116 grad_norm: 6.3741 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.3620 loss: 2.3620 2022/09/03 23:12:31 - mmengine - INFO - Epoch(train) [18][200/1345] lr: 1.0000e-02 eta: 2:39:45 time: 0.1920 data_time: 0.0117 memory: 7116 grad_norm: 6.4331 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.4610 loss: 2.4610 2022/09/03 23:12:35 - mmengine - INFO - Epoch(train) [18][220/1345] lr: 1.0000e-02 eta: 2:39:40 time: 0.1887 data_time: 0.0102 memory: 7116 grad_norm: 7.2709 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.5797 loss: 2.5797 2022/09/03 23:12:38 - mmengine - INFO - Epoch(train) [18][240/1345] lr: 1.0000e-02 eta: 2:39:34 time: 0.1942 data_time: 0.0100 memory: 7116 grad_norm: 6.4912 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4555 loss: 2.4555 2022/09/03 23:12:42 - mmengine - INFO - Epoch(train) [18][260/1345] lr: 1.0000e-02 eta: 2:39:29 time: 0.1893 data_time: 0.0127 memory: 7116 grad_norm: 6.4278 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1342 loss: 2.1342 2022/09/03 23:12:46 - mmengine - INFO - Epoch(train) [18][280/1345] lr: 1.0000e-02 eta: 2:39:24 time: 0.1987 data_time: 0.0095 memory: 7116 grad_norm: 6.4657 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2762 loss: 2.2762 2022/09/03 23:12:50 - mmengine - INFO - Epoch(train) [18][300/1345] lr: 1.0000e-02 eta: 2:39:19 time: 0.1896 data_time: 0.0109 memory: 7116 grad_norm: 6.4881 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1409 loss: 2.1409 2022/09/03 23:12:54 - mmengine - INFO - Epoch(train) [18][320/1345] lr: 1.0000e-02 eta: 2:39:13 time: 0.1933 data_time: 0.0121 memory: 7116 grad_norm: 6.5085 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.4692 loss: 2.4692 2022/09/03 23:12:58 - mmengine - INFO - Epoch(train) [18][340/1345] lr: 1.0000e-02 eta: 2:39:08 time: 0.1886 data_time: 0.0102 memory: 7116 grad_norm: 6.5342 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.6268 loss: 2.6268 2022/09/03 23:13:01 - mmengine - INFO - Epoch(train) [18][360/1345] lr: 1.0000e-02 eta: 2:39:03 time: 0.1904 data_time: 0.0096 memory: 7116 grad_norm: 6.3069 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3822 loss: 2.3822 2022/09/03 23:13:05 - mmengine - INFO - Epoch(train) [18][380/1345] lr: 1.0000e-02 eta: 2:38:57 time: 0.1886 data_time: 0.0122 memory: 7116 grad_norm: 6.3047 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3234 loss: 2.3234 2022/09/03 23:13:09 - mmengine - INFO - Epoch(train) [18][400/1345] lr: 1.0000e-02 eta: 2:38:53 time: 0.2063 data_time: 0.0113 memory: 7116 grad_norm: 6.5809 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.4232 loss: 2.4232 2022/09/03 23:13:13 - mmengine - INFO - Epoch(train) [18][420/1345] lr: 1.0000e-02 eta: 2:38:47 time: 0.1895 data_time: 0.0101 memory: 7116 grad_norm: 6.3639 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.3659 loss: 2.3659 2022/09/03 23:13:17 - mmengine - INFO - Epoch(train) [18][440/1345] lr: 1.0000e-02 eta: 2:38:42 time: 0.1912 data_time: 0.0122 memory: 7116 grad_norm: 6.2934 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2473 loss: 2.2473 2022/09/03 23:13:21 - mmengine - INFO - Epoch(train) [18][460/1345] lr: 1.0000e-02 eta: 2:38:37 time: 0.1928 data_time: 0.0107 memory: 7116 grad_norm: 6.3762 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.4453 loss: 2.4453 2022/09/03 23:13:25 - mmengine - INFO - Epoch(train) [18][480/1345] lr: 1.0000e-02 eta: 2:38:31 time: 0.1918 data_time: 0.0097 memory: 7116 grad_norm: 6.5652 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1611 loss: 2.1611 2022/09/03 23:13:29 - mmengine - INFO - Epoch(train) [18][500/1345] lr: 1.0000e-02 eta: 2:38:26 time: 0.1942 data_time: 0.0138 memory: 7116 grad_norm: 6.5109 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1008 loss: 2.1008 2022/09/03 23:13:32 - mmengine - INFO - Epoch(train) [18][520/1345] lr: 1.0000e-02 eta: 2:38:21 time: 0.1873 data_time: 0.0098 memory: 7116 grad_norm: 6.4487 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.2640 loss: 2.2640 2022/09/03 23:13:36 - mmengine - INFO - Epoch(train) [18][540/1345] lr: 1.0000e-02 eta: 2:38:15 time: 0.1897 data_time: 0.0105 memory: 7116 grad_norm: 6.2543 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4232 loss: 2.4232 2022/09/03 23:13:40 - mmengine - INFO - Epoch(train) [18][560/1345] lr: 1.0000e-02 eta: 2:38:10 time: 0.1913 data_time: 0.0112 memory: 7116 grad_norm: 6.5887 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4454 loss: 2.4454 2022/09/03 23:13:44 - mmengine - INFO - Epoch(train) [18][580/1345] lr: 1.0000e-02 eta: 2:38:05 time: 0.1932 data_time: 0.0100 memory: 7116 grad_norm: 6.5362 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2102 loss: 2.2102 2022/09/03 23:13:48 - mmengine - INFO - Epoch(train) [18][600/1345] lr: 1.0000e-02 eta: 2:38:00 time: 0.1980 data_time: 0.0107 memory: 7116 grad_norm: 6.4704 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3211 loss: 2.3211 2022/09/03 23:13:52 - mmengine - INFO - Epoch(train) [18][620/1345] lr: 1.0000e-02 eta: 2:37:55 time: 0.1934 data_time: 0.0127 memory: 7116 grad_norm: 6.5462 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.3436 loss: 2.3436 2022/09/03 23:13:56 - mmengine - INFO - Epoch(train) [18][640/1345] lr: 1.0000e-02 eta: 2:37:50 time: 0.1924 data_time: 0.0098 memory: 7116 grad_norm: 6.4744 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5236 loss: 2.5236 2022/09/03 23:13:59 - mmengine - INFO - Epoch(train) [18][660/1345] lr: 1.0000e-02 eta: 2:37:44 time: 0.1932 data_time: 0.0100 memory: 7116 grad_norm: 6.4022 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2431 loss: 2.2431 2022/09/03 23:14:03 - mmengine - INFO - Epoch(train) [18][680/1345] lr: 1.0000e-02 eta: 2:37:39 time: 0.1929 data_time: 0.0116 memory: 7116 grad_norm: 6.3418 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6732 loss: 2.6732 2022/09/03 23:14:07 - mmengine - INFO - Epoch(train) [18][700/1345] lr: 1.0000e-02 eta: 2:37:34 time: 0.1911 data_time: 0.0099 memory: 7116 grad_norm: 6.3126 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.4581 loss: 2.4581 2022/09/03 23:14:11 - mmengine - INFO - Epoch(train) [18][720/1345] lr: 1.0000e-02 eta: 2:37:29 time: 0.1961 data_time: 0.0119 memory: 7116 grad_norm: 6.5389 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3175 loss: 2.3175 2022/09/03 23:14:15 - mmengine - INFO - Epoch(train) [18][740/1345] lr: 1.0000e-02 eta: 2:37:23 time: 0.1897 data_time: 0.0125 memory: 7116 grad_norm: 6.5655 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4257 loss: 2.4257 2022/09/03 23:14:19 - mmengine - INFO - Epoch(train) [18][760/1345] lr: 1.0000e-02 eta: 2:37:18 time: 0.1957 data_time: 0.0092 memory: 7116 grad_norm: 6.5425 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.3266 loss: 2.3266 2022/09/03 23:14:23 - mmengine - INFO - Epoch(train) [18][780/1345] lr: 1.0000e-02 eta: 2:37:13 time: 0.1923 data_time: 0.0100 memory: 7116 grad_norm: 6.5727 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3855 loss: 2.3855 2022/09/03 23:14:26 - mmengine - INFO - Epoch(train) [18][800/1345] lr: 1.0000e-02 eta: 2:37:08 time: 0.1952 data_time: 0.0123 memory: 7116 grad_norm: 6.1136 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4096 loss: 2.4096 2022/09/03 23:14:32 - mmengine - INFO - Epoch(train) [18][820/1345] lr: 1.0000e-02 eta: 2:37:05 time: 0.2600 data_time: 0.0163 memory: 7116 grad_norm: 6.3932 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5202 loss: 2.5202 2022/09/03 23:14:36 - mmengine - INFO - Epoch(train) [18][840/1345] lr: 1.0000e-02 eta: 2:37:00 time: 0.1923 data_time: 0.0100 memory: 7116 grad_norm: 6.4901 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1795 loss: 2.1795 2022/09/03 23:14:39 - mmengine - INFO - Epoch(train) [18][860/1345] lr: 1.0000e-02 eta: 2:36:55 time: 0.1980 data_time: 0.0160 memory: 7116 grad_norm: 6.7312 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.2518 loss: 2.2518 2022/09/03 23:14:43 - mmengine - INFO - Epoch(train) [18][880/1345] lr: 1.0000e-02 eta: 2:36:50 time: 0.1944 data_time: 0.0106 memory: 7116 grad_norm: 6.5274 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3817 loss: 2.3817 2022/09/03 23:14:47 - mmengine - INFO - Epoch(train) [18][900/1345] lr: 1.0000e-02 eta: 2:36:45 time: 0.1927 data_time: 0.0097 memory: 7116 grad_norm: 6.2955 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2680 loss: 2.2680 2022/09/03 23:14:51 - mmengine - INFO - Epoch(train) [18][920/1345] lr: 1.0000e-02 eta: 2:36:40 time: 0.1945 data_time: 0.0129 memory: 7116 grad_norm: 6.4457 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.3312 loss: 2.3312 2022/09/03 23:14:55 - mmengine - INFO - Epoch(train) [18][940/1345] lr: 1.0000e-02 eta: 2:36:34 time: 0.1915 data_time: 0.0105 memory: 7116 grad_norm: 6.2593 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4514 loss: 2.4514 2022/09/03 23:14:59 - mmengine - INFO - Epoch(train) [18][960/1345] lr: 1.0000e-02 eta: 2:36:29 time: 0.1962 data_time: 0.0099 memory: 7116 grad_norm: 6.7515 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5928 loss: 2.5928 2022/09/03 23:15:03 - mmengine - INFO - Epoch(train) [18][980/1345] lr: 1.0000e-02 eta: 2:36:24 time: 0.2000 data_time: 0.0124 memory: 7116 grad_norm: 6.6668 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.1486 loss: 2.1486 2022/09/03 23:15:07 - mmengine - INFO - Epoch(train) [18][1000/1345] lr: 1.0000e-02 eta: 2:36:19 time: 0.1908 data_time: 0.0103 memory: 7116 grad_norm: 6.3266 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4542 loss: 2.4542 2022/09/03 23:15:10 - mmengine - INFO - Epoch(train) [18][1020/1345] lr: 1.0000e-02 eta: 2:36:14 time: 0.1878 data_time: 0.0100 memory: 7116 grad_norm: 6.3270 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1489 loss: 2.1489 2022/09/03 23:15:14 - mmengine - INFO - Epoch(train) [18][1040/1345] lr: 1.0000e-02 eta: 2:36:09 time: 0.1927 data_time: 0.0113 memory: 7116 grad_norm: 6.4459 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4957 loss: 2.4957 2022/09/03 23:15:18 - mmengine - INFO - Epoch(train) [18][1060/1345] lr: 1.0000e-02 eta: 2:36:04 time: 0.1964 data_time: 0.0104 memory: 7116 grad_norm: 6.4819 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4170 loss: 2.4170 2022/09/03 23:15:22 - mmengine - INFO - Epoch(train) [18][1080/1345] lr: 1.0000e-02 eta: 2:35:58 time: 0.1908 data_time: 0.0101 memory: 7116 grad_norm: 6.3959 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1375 loss: 2.1375 2022/09/03 23:15:26 - mmengine - INFO - Epoch(train) [18][1100/1345] lr: 1.0000e-02 eta: 2:35:53 time: 0.1931 data_time: 0.0115 memory: 7116 grad_norm: 6.5632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.4618 loss: 2.4618 2022/09/03 23:15:30 - mmengine - INFO - Epoch(train) [18][1120/1345] lr: 1.0000e-02 eta: 2:35:48 time: 0.1922 data_time: 0.0098 memory: 7116 grad_norm: 6.7578 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3118 loss: 2.3118 2022/09/03 23:15:33 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:15:34 - mmengine - INFO - Epoch(train) [18][1140/1345] lr: 1.0000e-02 eta: 2:35:43 time: 0.1930 data_time: 0.0100 memory: 7116 grad_norm: 6.5263 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.7166 loss: 2.7166 2022/09/03 23:15:38 - mmengine - INFO - Epoch(train) [18][1160/1345] lr: 1.0000e-02 eta: 2:35:38 time: 0.1932 data_time: 0.0112 memory: 7116 grad_norm: 6.2430 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.1546 loss: 2.1546 2022/09/03 23:15:41 - mmengine - INFO - Epoch(train) [18][1180/1345] lr: 1.0000e-02 eta: 2:35:33 time: 0.1933 data_time: 0.0106 memory: 7116 grad_norm: 6.3408 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2369 loss: 2.2369 2022/09/03 23:15:45 - mmengine - INFO - Epoch(train) [18][1200/1345] lr: 1.0000e-02 eta: 2:35:27 time: 0.1905 data_time: 0.0095 memory: 7116 grad_norm: 6.4124 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.5545 loss: 2.5545 2022/09/03 23:15:49 - mmengine - INFO - Epoch(train) [18][1220/1345] lr: 1.0000e-02 eta: 2:35:22 time: 0.1940 data_time: 0.0120 memory: 7116 grad_norm: 6.3908 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.4888 loss: 2.4888 2022/09/03 23:15:53 - mmengine - INFO - Epoch(train) [18][1240/1345] lr: 1.0000e-02 eta: 2:35:17 time: 0.1956 data_time: 0.0096 memory: 7116 grad_norm: 6.3074 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.2948 loss: 2.2948 2022/09/03 23:15:57 - mmengine - INFO - Epoch(train) [18][1260/1345] lr: 1.0000e-02 eta: 2:35:12 time: 0.1918 data_time: 0.0103 memory: 7116 grad_norm: 6.4213 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.5890 loss: 2.5890 2022/09/03 23:16:01 - mmengine - INFO - Epoch(train) [18][1280/1345] lr: 1.0000e-02 eta: 2:35:07 time: 0.1968 data_time: 0.0122 memory: 7116 grad_norm: 6.2400 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5775 loss: 2.5775 2022/09/03 23:16:05 - mmengine - INFO - Epoch(train) [18][1300/1345] lr: 1.0000e-02 eta: 2:35:02 time: 0.1880 data_time: 0.0102 memory: 7116 grad_norm: 6.5359 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2675 loss: 2.2675 2022/09/03 23:16:08 - mmengine - INFO - Epoch(train) [18][1320/1345] lr: 1.0000e-02 eta: 2:34:57 time: 0.1971 data_time: 0.0099 memory: 7116 grad_norm: 6.4108 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2558 loss: 2.2558 2022/09/03 23:16:12 - mmengine - INFO - Epoch(train) [18][1340/1345] lr: 1.0000e-02 eta: 2:34:52 time: 0.1971 data_time: 0.0115 memory: 7116 grad_norm: 6.5706 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4415 loss: 2.4415 2022/09/03 23:16:13 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:16:13 - mmengine - INFO - Epoch(train) [18][1345/1345] lr: 1.0000e-02 eta: 2:34:52 time: 0.1894 data_time: 0.0090 memory: 7116 grad_norm: 6.9644 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.7057 loss: 2.7057 2022/09/03 23:16:13 - mmengine - INFO - Saving checkpoint at 18 epochs 2022/09/03 23:16:16 - mmengine - INFO - Epoch(val) [18][20/181] eta: 0:00:07 time: 0.0471 data_time: 0.0100 memory: 1114 2022/09/03 23:16:17 - mmengine - INFO - Epoch(val) [18][40/181] eta: 0:00:06 time: 0.0429 data_time: 0.0069 memory: 1114 2022/09/03 23:16:18 - mmengine - INFO - Epoch(val) [18][60/181] eta: 0:00:05 time: 0.0426 data_time: 0.0069 memory: 1114 2022/09/03 23:16:18 - mmengine - INFO - Epoch(val) [18][80/181] eta: 0:00:04 time: 0.0431 data_time: 0.0072 memory: 1114 2022/09/03 23:16:19 - mmengine - INFO - Epoch(val) [18][100/181] eta: 0:00:03 time: 0.0425 data_time: 0.0068 memory: 1114 2022/09/03 23:16:20 - mmengine - INFO - Epoch(val) [18][120/181] eta: 0:00:02 time: 0.0433 data_time: 0.0074 memory: 1114 2022/09/03 23:16:21 - mmengine - INFO - Epoch(val) [18][140/181] eta: 0:00:01 time: 0.0438 data_time: 0.0074 memory: 1114 2022/09/03 23:16:22 - mmengine - INFO - Epoch(val) [18][160/181] eta: 0:00:00 time: 0.0446 data_time: 0.0074 memory: 1114 2022/09/03 23:16:23 - mmengine - INFO - Epoch(val) [18][180/181] eta: 0:00:00 time: 0.0433 data_time: 0.0069 memory: 1114 2022/09/03 23:16:25 - mmengine - INFO - Epoch(val) [18][181/181] acc/top1: 0.3008 acc/top5: 0.5897 acc/mean1: 0.2661 2022/09/03 23:16:25 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_17.pth is removed 2022/09/03 23:16:26 - mmengine - INFO - The best checkpoint with 0.3008 acc/top1 at 18 epoch is saved to best_acc/top1_epoch_18.pth. 2022/09/03 23:16:30 - mmengine - INFO - Epoch(train) [19][20/1345] lr: 1.0000e-02 eta: 2:34:44 time: 0.1947 data_time: 0.0123 memory: 7116 grad_norm: 6.2763 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.2302 loss: 2.2302 2022/09/03 23:16:34 - mmengine - INFO - Epoch(train) [19][40/1345] lr: 1.0000e-02 eta: 2:34:39 time: 0.1909 data_time: 0.0098 memory: 7116 grad_norm: 6.7836 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4755 loss: 2.4755 2022/09/03 23:16:38 - mmengine - INFO - Epoch(train) [19][60/1345] lr: 1.0000e-02 eta: 2:34:33 time: 0.1923 data_time: 0.0096 memory: 7116 grad_norm: 6.3811 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5204 loss: 2.5204 2022/09/03 23:16:42 - mmengine - INFO - Epoch(train) [19][80/1345] lr: 1.0000e-02 eta: 2:34:28 time: 0.1909 data_time: 0.0127 memory: 7116 grad_norm: 6.5320 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1489 loss: 2.1489 2022/09/03 23:16:45 - mmengine - INFO - Epoch(train) [19][100/1345] lr: 1.0000e-02 eta: 2:34:23 time: 0.1901 data_time: 0.0104 memory: 7116 grad_norm: 6.4657 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3530 loss: 2.3530 2022/09/03 23:16:49 - mmengine - INFO - Epoch(train) [19][120/1345] lr: 1.0000e-02 eta: 2:34:18 time: 0.1964 data_time: 0.0095 memory: 7116 grad_norm: 6.6046 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4919 loss: 2.4919 2022/09/03 23:16:53 - mmengine - INFO - Epoch(train) [19][140/1345] lr: 1.0000e-02 eta: 2:34:13 time: 0.1962 data_time: 0.0119 memory: 7116 grad_norm: 6.4027 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 2.5229 loss: 2.5229 2022/09/03 23:16:57 - mmengine - INFO - Epoch(train) [19][160/1345] lr: 1.0000e-02 eta: 2:34:08 time: 0.1935 data_time: 0.0094 memory: 7116 grad_norm: 6.3718 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.3222 loss: 2.3222 2022/09/03 23:17:01 - mmengine - INFO - Epoch(train) [19][180/1345] lr: 1.0000e-02 eta: 2:34:03 time: 0.2074 data_time: 0.0095 memory: 7116 grad_norm: 6.4439 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0245 loss: 2.0245 2022/09/03 23:17:05 - mmengine - INFO - Epoch(train) [19][200/1345] lr: 1.0000e-02 eta: 2:33:58 time: 0.1933 data_time: 0.0120 memory: 7116 grad_norm: 6.2256 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3258 loss: 2.3258 2022/09/03 23:17:09 - mmengine - INFO - Epoch(train) [19][220/1345] lr: 1.0000e-02 eta: 2:33:53 time: 0.2001 data_time: 0.0087 memory: 7116 grad_norm: 6.7266 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5860 loss: 2.5860 2022/09/03 23:17:13 - mmengine - INFO - Epoch(train) [19][240/1345] lr: 1.0000e-02 eta: 2:33:48 time: 0.1940 data_time: 0.0106 memory: 7116 grad_norm: 6.2070 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3641 loss: 2.3641 2022/09/03 23:17:17 - mmengine - INFO - Epoch(train) [19][260/1345] lr: 1.0000e-02 eta: 2:33:43 time: 0.1941 data_time: 0.0112 memory: 7116 grad_norm: 6.3030 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2525 loss: 2.2525 2022/09/03 23:17:21 - mmengine - INFO - Epoch(train) [19][280/1345] lr: 1.0000e-02 eta: 2:33:38 time: 0.1922 data_time: 0.0092 memory: 7116 grad_norm: 6.6265 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3864 loss: 2.3864 2022/09/03 23:17:25 - mmengine - INFO - Epoch(train) [19][300/1345] lr: 1.0000e-02 eta: 2:33:33 time: 0.2001 data_time: 0.0090 memory: 7116 grad_norm: 6.3269 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3811 loss: 2.3811 2022/09/03 23:17:29 - mmengine - INFO - Epoch(train) [19][320/1345] lr: 1.0000e-02 eta: 2:33:28 time: 0.1967 data_time: 0.0127 memory: 7116 grad_norm: 6.4672 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3924 loss: 2.3924 2022/09/03 23:17:33 - mmengine - INFO - Epoch(train) [19][340/1345] lr: 1.0000e-02 eta: 2:33:23 time: 0.1929 data_time: 0.0086 memory: 7116 grad_norm: 6.6465 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3035 loss: 2.3035 2022/09/03 23:17:37 - mmengine - INFO - Epoch(train) [19][360/1345] lr: 1.0000e-02 eta: 2:33:18 time: 0.1942 data_time: 0.0089 memory: 7116 grad_norm: 6.7221 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2983 loss: 2.2983 2022/09/03 23:17:40 - mmengine - INFO - Epoch(train) [19][380/1345] lr: 1.0000e-02 eta: 2:33:13 time: 0.1965 data_time: 0.0139 memory: 7116 grad_norm: 6.5213 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0591 loss: 2.0591 2022/09/03 23:17:44 - mmengine - INFO - Epoch(train) [19][400/1345] lr: 1.0000e-02 eta: 2:33:08 time: 0.1954 data_time: 0.0095 memory: 7116 grad_norm: 6.6387 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3761 loss: 2.3761 2022/09/03 23:17:48 - mmengine - INFO - Epoch(train) [19][420/1345] lr: 1.0000e-02 eta: 2:33:03 time: 0.1921 data_time: 0.0095 memory: 7116 grad_norm: 6.5363 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5612 loss: 2.5612 2022/09/03 23:17:52 - mmengine - INFO - Epoch(train) [19][440/1345] lr: 1.0000e-02 eta: 2:32:58 time: 0.1975 data_time: 0.0130 memory: 7116 grad_norm: 6.6796 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.5836 loss: 2.5836 2022/09/03 23:17:56 - mmengine - INFO - Epoch(train) [19][460/1345] lr: 1.0000e-02 eta: 2:32:53 time: 0.1960 data_time: 0.0098 memory: 7116 grad_norm: 6.4574 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1670 loss: 2.1670 2022/09/03 23:18:00 - mmengine - INFO - Epoch(train) [19][480/1345] lr: 1.0000e-02 eta: 2:32:48 time: 0.1994 data_time: 0.0087 memory: 7116 grad_norm: 6.4491 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6977 loss: 2.6977 2022/09/03 23:18:04 - mmengine - INFO - Epoch(train) [19][500/1345] lr: 1.0000e-02 eta: 2:32:43 time: 0.1912 data_time: 0.0112 memory: 7116 grad_norm: 6.7731 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5998 loss: 2.5998 2022/09/03 23:18:08 - mmengine - INFO - Epoch(train) [19][520/1345] lr: 1.0000e-02 eta: 2:32:38 time: 0.1928 data_time: 0.0098 memory: 7116 grad_norm: 6.5836 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4156 loss: 2.4156 2022/09/03 23:18:12 - mmengine - INFO - Epoch(train) [19][540/1345] lr: 1.0000e-02 eta: 2:32:33 time: 0.1969 data_time: 0.0097 memory: 7116 grad_norm: 6.7141 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4862 loss: 2.4862 2022/09/03 23:18:16 - mmengine - INFO - Epoch(train) [19][560/1345] lr: 1.0000e-02 eta: 2:32:28 time: 0.1956 data_time: 0.0115 memory: 7116 grad_norm: 6.5353 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 2.4790 loss: 2.4790 2022/09/03 23:18:20 - mmengine - INFO - Epoch(train) [19][580/1345] lr: 1.0000e-02 eta: 2:32:23 time: 0.2000 data_time: 0.0111 memory: 7116 grad_norm: 6.7399 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.3074 loss: 2.3074 2022/09/03 23:18:23 - mmengine - INFO - Epoch(train) [19][600/1345] lr: 1.0000e-02 eta: 2:32:18 time: 0.1944 data_time: 0.0094 memory: 7116 grad_norm: 6.0394 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3543 loss: 2.3543 2022/09/03 23:18:27 - mmengine - INFO - Epoch(train) [19][620/1345] lr: 1.0000e-02 eta: 2:32:13 time: 0.1961 data_time: 0.0114 memory: 7116 grad_norm: 6.4727 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 2.1688 loss: 2.1688 2022/09/03 23:18:31 - mmengine - INFO - Epoch(train) [19][640/1345] lr: 1.0000e-02 eta: 2:32:08 time: 0.1986 data_time: 0.0091 memory: 7116 grad_norm: 6.5274 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5112 loss: 2.5112 2022/09/03 23:18:35 - mmengine - INFO - Epoch(train) [19][660/1345] lr: 1.0000e-02 eta: 2:32:03 time: 0.1959 data_time: 0.0090 memory: 7116 grad_norm: 6.5450 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 2.1209 loss: 2.1209 2022/09/03 23:18:39 - mmengine - INFO - Epoch(train) [19][680/1345] lr: 1.0000e-02 eta: 2:31:59 time: 0.2004 data_time: 0.0121 memory: 7116 grad_norm: 6.5599 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.4850 loss: 2.4850 2022/09/03 23:18:43 - mmengine - INFO - Epoch(train) [19][700/1345] lr: 1.0000e-02 eta: 2:31:54 time: 0.1955 data_time: 0.0105 memory: 7116 grad_norm: 6.2002 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2916 loss: 2.2916 2022/09/03 23:18:47 - mmengine - INFO - Epoch(train) [19][720/1345] lr: 1.0000e-02 eta: 2:31:49 time: 0.1946 data_time: 0.0092 memory: 7116 grad_norm: 6.2648 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.5000 loss: 2.5000 2022/09/03 23:18:51 - mmengine - INFO - Epoch(train) [19][740/1345] lr: 1.0000e-02 eta: 2:31:44 time: 0.1968 data_time: 0.0114 memory: 7116 grad_norm: 6.3078 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.3166 loss: 2.3166 2022/09/03 23:18:55 - mmengine - INFO - Epoch(train) [19][760/1345] lr: 1.0000e-02 eta: 2:31:39 time: 0.1967 data_time: 0.0095 memory: 7116 grad_norm: 6.1572 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5915 loss: 2.5915 2022/09/03 23:18:59 - mmengine - INFO - Epoch(train) [19][780/1345] lr: 1.0000e-02 eta: 2:31:34 time: 0.1988 data_time: 0.0094 memory: 7116 grad_norm: 6.3076 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.4104 loss: 2.4104 2022/09/03 23:19:01 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:19:03 - mmengine - INFO - Epoch(train) [19][800/1345] lr: 1.0000e-02 eta: 2:31:29 time: 0.1951 data_time: 0.0115 memory: 7116 grad_norm: 6.3528 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3051 loss: 2.3051 2022/09/03 23:19:07 - mmengine - INFO - Epoch(train) [19][820/1345] lr: 1.0000e-02 eta: 2:31:24 time: 0.1956 data_time: 0.0095 memory: 7116 grad_norm: 6.6212 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3156 loss: 2.3156 2022/09/03 23:19:11 - mmengine - INFO - Epoch(train) [19][840/1345] lr: 1.0000e-02 eta: 2:31:19 time: 0.1961 data_time: 0.0103 memory: 7116 grad_norm: 6.2578 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4654 loss: 2.4654 2022/09/03 23:19:15 - mmengine - INFO - Epoch(train) [19][860/1345] lr: 1.0000e-02 eta: 2:31:14 time: 0.1937 data_time: 0.0120 memory: 7116 grad_norm: 6.8295 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.3428 loss: 2.3428 2022/09/03 23:19:19 - mmengine - INFO - Epoch(train) [19][880/1345] lr: 1.0000e-02 eta: 2:31:09 time: 0.2016 data_time: 0.0107 memory: 7116 grad_norm: 6.1471 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3758 loss: 2.3758 2022/09/03 23:19:23 - mmengine - INFO - Epoch(train) [19][900/1345] lr: 1.0000e-02 eta: 2:31:04 time: 0.1998 data_time: 0.0095 memory: 7116 grad_norm: 6.3473 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4454 loss: 2.4454 2022/09/03 23:19:27 - mmengine - INFO - Epoch(train) [19][920/1345] lr: 1.0000e-02 eta: 2:30:59 time: 0.1957 data_time: 0.0121 memory: 7116 grad_norm: 6.2694 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4186 loss: 2.4186 2022/09/03 23:19:30 - mmengine - INFO - Epoch(train) [19][940/1345] lr: 1.0000e-02 eta: 2:30:54 time: 0.1946 data_time: 0.0092 memory: 7116 grad_norm: 6.2415 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4096 loss: 2.4096 2022/09/03 23:19:34 - mmengine - INFO - Epoch(train) [19][960/1345] lr: 1.0000e-02 eta: 2:30:49 time: 0.1944 data_time: 0.0101 memory: 7116 grad_norm: 6.3014 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4813 loss: 2.4813 2022/09/03 23:19:38 - mmengine - INFO - Epoch(train) [19][980/1345] lr: 1.0000e-02 eta: 2:30:44 time: 0.1966 data_time: 0.0117 memory: 7116 grad_norm: 6.6828 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.4818 loss: 2.4818 2022/09/03 23:19:42 - mmengine - INFO - Epoch(train) [19][1000/1345] lr: 1.0000e-02 eta: 2:30:40 time: 0.1976 data_time: 0.0092 memory: 7116 grad_norm: 6.2756 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1306 loss: 2.1306 2022/09/03 23:19:46 - mmengine - INFO - Epoch(train) [19][1020/1345] lr: 1.0000e-02 eta: 2:30:35 time: 0.1951 data_time: 0.0088 memory: 7116 grad_norm: 6.5068 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1487 loss: 2.1487 2022/09/03 23:19:50 - mmengine - INFO - Epoch(train) [19][1040/1345] lr: 1.0000e-02 eta: 2:30:30 time: 0.1985 data_time: 0.0119 memory: 7116 grad_norm: 6.3593 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.3030 loss: 2.3030 2022/09/03 23:19:54 - mmengine - INFO - Epoch(train) [19][1060/1345] lr: 1.0000e-02 eta: 2:30:25 time: 0.1946 data_time: 0.0095 memory: 7116 grad_norm: 6.4115 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.5552 loss: 2.5552 2022/09/03 23:19:58 - mmengine - INFO - Epoch(train) [19][1080/1345] lr: 1.0000e-02 eta: 2:30:20 time: 0.1956 data_time: 0.0093 memory: 7116 grad_norm: 6.3058 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3928 loss: 2.3928 2022/09/03 23:20:02 - mmengine - INFO - Epoch(train) [19][1100/1345] lr: 1.0000e-02 eta: 2:30:15 time: 0.1966 data_time: 0.0131 memory: 7116 grad_norm: 6.3973 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3108 loss: 2.3108 2022/09/03 23:20:06 - mmengine - INFO - Epoch(train) [19][1120/1345] lr: 1.0000e-02 eta: 2:30:10 time: 0.1935 data_time: 0.0107 memory: 7116 grad_norm: 6.3272 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.7147 loss: 2.7147 2022/09/03 23:20:10 - mmengine - INFO - Epoch(train) [19][1140/1345] lr: 1.0000e-02 eta: 2:30:05 time: 0.2045 data_time: 0.0113 memory: 7116 grad_norm: 6.2919 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4274 loss: 2.4274 2022/09/03 23:20:14 - mmengine - INFO - Epoch(train) [19][1160/1345] lr: 1.0000e-02 eta: 2:30:00 time: 0.1960 data_time: 0.0114 memory: 7116 grad_norm: 6.7527 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.6171 loss: 2.6171 2022/09/03 23:20:18 - mmengine - INFO - Epoch(train) [19][1180/1345] lr: 1.0000e-02 eta: 2:29:55 time: 0.1944 data_time: 0.0095 memory: 7116 grad_norm: 6.2541 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.4766 loss: 2.4766 2022/09/03 23:20:21 - mmengine - INFO - Epoch(train) [19][1200/1345] lr: 1.0000e-02 eta: 2:29:50 time: 0.1930 data_time: 0.0095 memory: 7116 grad_norm: 6.2699 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4634 loss: 2.4634 2022/09/03 23:20:25 - mmengine - INFO - Epoch(train) [19][1220/1345] lr: 1.0000e-02 eta: 2:29:45 time: 0.1962 data_time: 0.0116 memory: 7116 grad_norm: 6.3349 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.4390 loss: 2.4390 2022/09/03 23:20:29 - mmengine - INFO - Epoch(train) [19][1240/1345] lr: 1.0000e-02 eta: 2:29:41 time: 0.2036 data_time: 0.0108 memory: 7116 grad_norm: 6.3843 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2597 loss: 2.2597 2022/09/03 23:20:33 - mmengine - INFO - Epoch(train) [19][1260/1345] lr: 1.0000e-02 eta: 2:29:36 time: 0.1958 data_time: 0.0103 memory: 7116 grad_norm: 6.1211 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2555 loss: 2.2555 2022/09/03 23:20:37 - mmengine - INFO - Epoch(train) [19][1280/1345] lr: 1.0000e-02 eta: 2:29:31 time: 0.1935 data_time: 0.0123 memory: 7116 grad_norm: 6.9994 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.4632 loss: 2.4632 2022/09/03 23:20:41 - mmengine - INFO - Epoch(train) [19][1300/1345] lr: 1.0000e-02 eta: 2:29:26 time: 0.1959 data_time: 0.0088 memory: 7116 grad_norm: 6.1603 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 2.0545 loss: 2.0545 2022/09/03 23:20:45 - mmengine - INFO - Epoch(train) [19][1320/1345] lr: 1.0000e-02 eta: 2:29:21 time: 0.1954 data_time: 0.0094 memory: 7116 grad_norm: 6.3925 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2557 loss: 2.2557 2022/09/03 23:20:49 - mmengine - INFO - Epoch(train) [19][1340/1345] lr: 1.0000e-02 eta: 2:29:16 time: 0.1939 data_time: 0.0127 memory: 7116 grad_norm: 6.1809 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.5200 loss: 2.5200 2022/09/03 23:20:50 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:20:50 - mmengine - INFO - Epoch(train) [19][1345/1345] lr: 1.0000e-02 eta: 2:29:16 time: 0.1905 data_time: 0.0105 memory: 7116 grad_norm: 6.4586 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.5738 loss: 2.5738 2022/09/03 23:20:50 - mmengine - INFO - Saving checkpoint at 19 epochs 2022/09/03 23:20:53 - mmengine - INFO - Epoch(val) [19][20/181] eta: 0:00:07 time: 0.0457 data_time: 0.0091 memory: 1114 2022/09/03 23:20:53 - mmengine - INFO - Epoch(val) [19][40/181] eta: 0:00:05 time: 0.0425 data_time: 0.0067 memory: 1114 2022/09/03 23:20:54 - mmengine - INFO - Epoch(val) [19][60/181] eta: 0:00:05 time: 0.0434 data_time: 0.0073 memory: 1114 2022/09/03 23:20:55 - mmengine - INFO - Epoch(val) [19][80/181] eta: 0:00:04 time: 0.0433 data_time: 0.0072 memory: 1114 2022/09/03 23:20:56 - mmengine - INFO - Epoch(val) [19][100/181] eta: 0:00:03 time: 0.0436 data_time: 0.0073 memory: 1114 2022/09/03 23:20:57 - mmengine - INFO - Epoch(val) [19][120/181] eta: 0:00:02 time: 0.0428 data_time: 0.0070 memory: 1114 2022/09/03 23:20:58 - mmengine - INFO - Epoch(val) [19][140/181] eta: 0:00:01 time: 0.0432 data_time: 0.0073 memory: 1114 2022/09/03 23:20:59 - mmengine - INFO - Epoch(val) [19][160/181] eta: 0:00:00 time: 0.0456 data_time: 0.0102 memory: 1114 2022/09/03 23:21:00 - mmengine - INFO - Epoch(val) [19][180/181] eta: 0:00:00 time: 0.0409 data_time: 0.0058 memory: 1114 2022/09/03 23:21:02 - mmengine - INFO - Epoch(val) [19][181/181] acc/top1: 0.3153 acc/top5: 0.5991 acc/mean1: 0.2817 2022/09/03 23:21:02 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_18.pth is removed 2022/09/03 23:21:03 - mmengine - INFO - The best checkpoint with 0.3153 acc/top1 at 19 epoch is saved to best_acc/top1_epoch_19.pth. 2022/09/03 23:21:06 - mmengine - INFO - Epoch(train) [20][20/1345] lr: 1.0000e-02 eta: 2:29:08 time: 0.1917 data_time: 0.0115 memory: 7116 grad_norm: 6.3933 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4832 loss: 2.4832 2022/09/03 23:21:10 - mmengine - INFO - Epoch(train) [20][40/1345] lr: 1.0000e-02 eta: 2:29:03 time: 0.1953 data_time: 0.0096 memory: 7116 grad_norm: 6.7305 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.4244 loss: 2.4244 2022/09/03 23:21:14 - mmengine - INFO - Epoch(train) [20][60/1345] lr: 1.0000e-02 eta: 2:28:58 time: 0.1939 data_time: 0.0096 memory: 7116 grad_norm: 6.4899 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0992 loss: 2.0992 2022/09/03 23:21:18 - mmengine - INFO - Epoch(train) [20][80/1345] lr: 1.0000e-02 eta: 2:28:53 time: 0.1987 data_time: 0.0116 memory: 7116 grad_norm: 6.5573 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2249 loss: 2.2249 2022/09/03 23:21:22 - mmengine - INFO - Epoch(train) [20][100/1345] lr: 1.0000e-02 eta: 2:28:49 time: 0.1983 data_time: 0.0097 memory: 7116 grad_norm: 6.6315 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.2302 loss: 2.2302 2022/09/03 23:21:26 - mmengine - INFO - Epoch(train) [20][120/1345] lr: 1.0000e-02 eta: 2:28:44 time: 0.1973 data_time: 0.0099 memory: 7116 grad_norm: 6.5752 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3065 loss: 2.3065 2022/09/03 23:21:30 - mmengine - INFO - Epoch(train) [20][140/1345] lr: 1.0000e-02 eta: 2:28:39 time: 0.1949 data_time: 0.0103 memory: 7116 grad_norm: 6.3979 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.5980 loss: 2.5980 2022/09/03 23:21:34 - mmengine - INFO - Epoch(train) [20][160/1345] lr: 1.0000e-02 eta: 2:28:34 time: 0.1918 data_time: 0.0096 memory: 7116 grad_norm: 6.5966 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4705 loss: 2.4705 2022/09/03 23:21:38 - mmengine - INFO - Epoch(train) [20][180/1345] lr: 1.0000e-02 eta: 2:28:29 time: 0.1931 data_time: 0.0092 memory: 7116 grad_norm: 6.5980 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6925 loss: 2.6925 2022/09/03 23:21:42 - mmengine - INFO - Epoch(train) [20][200/1345] lr: 1.0000e-02 eta: 2:28:24 time: 0.2014 data_time: 0.0116 memory: 7116 grad_norm: 6.7258 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.5064 loss: 2.5064 2022/09/03 23:21:46 - mmengine - INFO - Epoch(train) [20][220/1345] lr: 1.0000e-02 eta: 2:28:19 time: 0.1940 data_time: 0.0092 memory: 7116 grad_norm: 6.3910 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3547 loss: 2.3547 2022/09/03 23:21:50 - mmengine - INFO - Epoch(train) [20][240/1345] lr: 1.0000e-02 eta: 2:28:14 time: 0.1952 data_time: 0.0087 memory: 7116 grad_norm: 6.3394 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5734 loss: 2.5734 2022/09/03 23:21:54 - mmengine - INFO - Epoch(train) [20][260/1345] lr: 1.0000e-02 eta: 2:28:09 time: 0.1981 data_time: 0.0116 memory: 7116 grad_norm: 6.4220 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.2959 loss: 2.2959 2022/09/03 23:21:58 - mmengine - INFO - Epoch(train) [20][280/1345] lr: 1.0000e-02 eta: 2:28:05 time: 0.1996 data_time: 0.0091 memory: 7116 grad_norm: 6.3525 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.2731 loss: 2.2731 2022/09/03 23:22:01 - mmengine - INFO - Epoch(train) [20][300/1345] lr: 1.0000e-02 eta: 2:28:00 time: 0.1951 data_time: 0.0086 memory: 7116 grad_norm: 6.5405 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4627 loss: 2.4627 2022/09/03 23:22:05 - mmengine - INFO - Epoch(train) [20][320/1345] lr: 1.0000e-02 eta: 2:27:55 time: 0.1944 data_time: 0.0105 memory: 7116 grad_norm: 6.5555 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4305 loss: 2.4305 2022/09/03 23:22:09 - mmengine - INFO - Epoch(train) [20][340/1345] lr: 1.0000e-02 eta: 2:27:50 time: 0.2020 data_time: 0.0109 memory: 7116 grad_norm: 6.4415 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1182 loss: 2.1182 2022/09/03 23:22:13 - mmengine - INFO - Epoch(train) [20][360/1345] lr: 1.0000e-02 eta: 2:27:45 time: 0.1954 data_time: 0.0090 memory: 7116 grad_norm: 6.5662 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4424 loss: 2.4424 2022/09/03 23:22:17 - mmengine - INFO - Epoch(train) [20][380/1345] lr: 1.0000e-02 eta: 2:27:40 time: 0.1945 data_time: 0.0110 memory: 7116 grad_norm: 6.5805 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3249 loss: 2.3249 2022/09/03 23:22:21 - mmengine - INFO - Epoch(train) [20][400/1345] lr: 1.0000e-02 eta: 2:27:35 time: 0.1950 data_time: 0.0087 memory: 7116 grad_norm: 6.5341 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.4041 loss: 2.4041 2022/09/03 23:22:25 - mmengine - INFO - Epoch(train) [20][420/1345] lr: 1.0000e-02 eta: 2:27:30 time: 0.1935 data_time: 0.0089 memory: 7116 grad_norm: 6.5722 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.3919 loss: 2.3919 2022/09/03 23:22:29 - mmengine - INFO - Epoch(train) [20][440/1345] lr: 1.0000e-02 eta: 2:27:26 time: 0.2023 data_time: 0.0117 memory: 7116 grad_norm: 6.6841 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.6074 loss: 2.6074 2022/09/03 23:22:30 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:22:33 - mmengine - INFO - Epoch(train) [20][460/1345] lr: 1.0000e-02 eta: 2:27:21 time: 0.1961 data_time: 0.0089 memory: 7116 grad_norm: 6.4668 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3989 loss: 2.3989 2022/09/03 23:22:37 - mmengine - INFO - Epoch(train) [20][480/1345] lr: 1.0000e-02 eta: 2:27:16 time: 0.1966 data_time: 0.0086 memory: 7116 grad_norm: 6.5441 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0907 loss: 2.0907 2022/09/03 23:22:41 - mmengine - INFO - Epoch(train) [20][500/1345] lr: 1.0000e-02 eta: 2:27:11 time: 0.1975 data_time: 0.0105 memory: 7116 grad_norm: 6.3641 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2739 loss: 2.2739 2022/09/03 23:22:45 - mmengine - INFO - Epoch(train) [20][520/1345] lr: 1.0000e-02 eta: 2:27:06 time: 0.1935 data_time: 0.0095 memory: 7116 grad_norm: 6.5824 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2194 loss: 2.2194 2022/09/03 23:22:49 - mmengine - INFO - Epoch(train) [20][540/1345] lr: 1.0000e-02 eta: 2:27:02 time: 0.2085 data_time: 0.0107 memory: 7116 grad_norm: 6.6419 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.2268 loss: 2.2268 2022/09/03 23:22:53 - mmengine - INFO - Epoch(train) [20][560/1345] lr: 1.0000e-02 eta: 2:26:57 time: 0.1951 data_time: 0.0114 memory: 7116 grad_norm: 6.7533 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4765 loss: 2.4765 2022/09/03 23:22:57 - mmengine - INFO - Epoch(train) [20][580/1345] lr: 1.0000e-02 eta: 2:26:52 time: 0.1928 data_time: 0.0095 memory: 7116 grad_norm: 6.6485 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.9959 loss: 1.9959 2022/09/03 23:23:01 - mmengine - INFO - Epoch(train) [20][600/1345] lr: 1.0000e-02 eta: 2:26:47 time: 0.1990 data_time: 0.0104 memory: 7116 grad_norm: 6.5453 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2020 loss: 2.2020 2022/09/03 23:23:04 - mmengine - INFO - Epoch(train) [20][620/1345] lr: 1.0000e-02 eta: 2:26:42 time: 0.1928 data_time: 0.0111 memory: 7116 grad_norm: 6.7136 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.5425 loss: 2.5425 2022/09/03 23:23:09 - mmengine - INFO - Epoch(train) [20][640/1345] lr: 1.0000e-02 eta: 2:26:38 time: 0.2224 data_time: 0.0087 memory: 7116 grad_norm: 6.6469 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.1706 loss: 2.1706 2022/09/03 23:23:13 - mmengine - INFO - Epoch(train) [20][660/1345] lr: 1.0000e-02 eta: 2:26:33 time: 0.1955 data_time: 0.0089 memory: 7116 grad_norm: 6.4717 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.1734 loss: 2.1734 2022/09/03 23:23:17 - mmengine - INFO - Epoch(train) [20][680/1345] lr: 1.0000e-02 eta: 2:26:28 time: 0.1926 data_time: 0.0109 memory: 7116 grad_norm: 6.2680 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0902 loss: 2.0902 2022/09/03 23:23:21 - mmengine - INFO - Epoch(train) [20][700/1345] lr: 1.0000e-02 eta: 2:26:23 time: 0.1945 data_time: 0.0094 memory: 7116 grad_norm: 6.6369 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4464 loss: 2.4464 2022/09/03 23:23:24 - mmengine - INFO - Epoch(train) [20][720/1345] lr: 1.0000e-02 eta: 2:26:19 time: 0.1958 data_time: 0.0085 memory: 7116 grad_norm: 6.3865 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3593 loss: 2.3593 2022/09/03 23:23:29 - mmengine - INFO - Epoch(train) [20][740/1345] lr: 1.0000e-02 eta: 2:26:14 time: 0.2007 data_time: 0.0117 memory: 7116 grad_norm: 6.5410 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3652 loss: 2.3652 2022/09/03 23:23:32 - mmengine - INFO - Epoch(train) [20][760/1345] lr: 1.0000e-02 eta: 2:26:09 time: 0.1939 data_time: 0.0091 memory: 7116 grad_norm: 6.4939 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4243 loss: 2.4243 2022/09/03 23:23:36 - mmengine - INFO - Epoch(train) [20][780/1345] lr: 1.0000e-02 eta: 2:26:04 time: 0.1975 data_time: 0.0086 memory: 7116 grad_norm: 6.6232 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.2784 loss: 2.2784 2022/09/03 23:23:40 - mmengine - INFO - Epoch(train) [20][800/1345] lr: 1.0000e-02 eta: 2:25:59 time: 0.1976 data_time: 0.0116 memory: 7116 grad_norm: 6.5362 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.1238 loss: 2.1238 2022/09/03 23:23:44 - mmengine - INFO - Epoch(train) [20][820/1345] lr: 1.0000e-02 eta: 2:25:54 time: 0.1948 data_time: 0.0091 memory: 7116 grad_norm: 6.3831 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.4311 loss: 2.4311 2022/09/03 23:23:48 - mmengine - INFO - Epoch(train) [20][840/1345] lr: 1.0000e-02 eta: 2:25:50 time: 0.1943 data_time: 0.0087 memory: 7116 grad_norm: 6.3606 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3513 loss: 2.3513 2022/09/03 23:23:52 - mmengine - INFO - Epoch(train) [20][860/1345] lr: 1.0000e-02 eta: 2:25:45 time: 0.2060 data_time: 0.0127 memory: 7116 grad_norm: 6.5255 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.4751 loss: 2.4751 2022/09/03 23:23:56 - mmengine - INFO - Epoch(train) [20][880/1345] lr: 1.0000e-02 eta: 2:25:40 time: 0.1945 data_time: 0.0090 memory: 7116 grad_norm: 6.4340 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.4837 loss: 2.4837 2022/09/03 23:24:00 - mmengine - INFO - Epoch(train) [20][900/1345] lr: 1.0000e-02 eta: 2:25:35 time: 0.1944 data_time: 0.0092 memory: 7116 grad_norm: 6.2508 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.2005 loss: 2.2005 2022/09/03 23:24:04 - mmengine - INFO - Epoch(train) [20][920/1345] lr: 1.0000e-02 eta: 2:25:30 time: 0.1962 data_time: 0.0120 memory: 7116 grad_norm: 6.3059 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.3079 loss: 2.3079 2022/09/03 23:24:08 - mmengine - INFO - Epoch(train) [20][940/1345] lr: 1.0000e-02 eta: 2:25:25 time: 0.1931 data_time: 0.0096 memory: 7116 grad_norm: 6.2869 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3524 loss: 2.3524 2022/09/03 23:24:12 - mmengine - INFO - Epoch(train) [20][960/1345] lr: 1.0000e-02 eta: 2:25:21 time: 0.2057 data_time: 0.0096 memory: 7116 grad_norm: 6.4729 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4763 loss: 2.4763 2022/09/03 23:24:16 - mmengine - INFO - Epoch(train) [20][980/1345] lr: 1.0000e-02 eta: 2:25:16 time: 0.1957 data_time: 0.0109 memory: 7116 grad_norm: 6.2314 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 2.5348 loss: 2.5348 2022/09/03 23:24:20 - mmengine - INFO - Epoch(train) [20][1000/1345] lr: 1.0000e-02 eta: 2:25:11 time: 0.1927 data_time: 0.0092 memory: 7116 grad_norm: 6.5107 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.4147 loss: 2.4147 2022/09/03 23:24:24 - mmengine - INFO - Epoch(train) [20][1020/1345] lr: 1.0000e-02 eta: 2:25:06 time: 0.1947 data_time: 0.0094 memory: 7116 grad_norm: 6.4655 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.2859 loss: 2.2859 2022/09/03 23:24:27 - mmengine - INFO - Epoch(train) [20][1040/1345] lr: 1.0000e-02 eta: 2:25:01 time: 0.1950 data_time: 0.0106 memory: 7116 grad_norm: 6.6461 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4532 loss: 2.4532 2022/09/03 23:24:32 - mmengine - INFO - Epoch(train) [20][1060/1345] lr: 1.0000e-02 eta: 2:24:57 time: 0.2094 data_time: 0.0095 memory: 7116 grad_norm: 6.4234 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3719 loss: 2.3719 2022/09/03 23:24:36 - mmengine - INFO - Epoch(train) [20][1080/1345] lr: 1.0000e-02 eta: 2:24:52 time: 0.1933 data_time: 0.0089 memory: 7116 grad_norm: 6.7619 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1929 loss: 2.1929 2022/09/03 23:24:39 - mmengine - INFO - Epoch(train) [20][1100/1345] lr: 1.0000e-02 eta: 2:24:47 time: 0.1947 data_time: 0.0112 memory: 7116 grad_norm: 6.3927 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4802 loss: 2.4802 2022/09/03 23:24:43 - mmengine - INFO - Epoch(train) [20][1120/1345] lr: 1.0000e-02 eta: 2:24:42 time: 0.1957 data_time: 0.0099 memory: 7116 grad_norm: 6.3576 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.7189 loss: 2.7189 2022/09/03 23:24:47 - mmengine - INFO - Epoch(train) [20][1140/1345] lr: 1.0000e-02 eta: 2:24:37 time: 0.1943 data_time: 0.0094 memory: 7116 grad_norm: 6.3769 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2310 loss: 2.2310 2022/09/03 23:24:51 - mmengine - INFO - Epoch(train) [20][1160/1345] lr: 1.0000e-02 eta: 2:24:33 time: 0.2003 data_time: 0.0119 memory: 7116 grad_norm: 6.7313 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4916 loss: 2.4916 2022/09/03 23:24:55 - mmengine - INFO - Epoch(train) [20][1180/1345] lr: 1.0000e-02 eta: 2:24:28 time: 0.1938 data_time: 0.0093 memory: 7116 grad_norm: 6.6729 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4361 loss: 2.4361 2022/09/03 23:24:59 - mmengine - INFO - Epoch(train) [20][1200/1345] lr: 1.0000e-02 eta: 2:24:23 time: 0.1981 data_time: 0.0087 memory: 7116 grad_norm: 6.3968 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2250 loss: 2.2250 2022/09/03 23:25:03 - mmengine - INFO - Epoch(train) [20][1220/1345] lr: 1.0000e-02 eta: 2:24:18 time: 0.1925 data_time: 0.0116 memory: 7116 grad_norm: 6.6117 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.4275 loss: 2.4275 2022/09/03 23:25:07 - mmengine - INFO - Epoch(train) [20][1240/1345] lr: 1.0000e-02 eta: 2:24:13 time: 0.1924 data_time: 0.0087 memory: 7116 grad_norm: 6.5026 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4054 loss: 2.4054 2022/09/03 23:25:11 - mmengine - INFO - Epoch(train) [20][1260/1345] lr: 1.0000e-02 eta: 2:24:09 time: 0.1997 data_time: 0.0110 memory: 7116 grad_norm: 6.4810 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2946 loss: 2.2946 2022/09/03 23:25:15 - mmengine - INFO - Epoch(train) [20][1280/1345] lr: 1.0000e-02 eta: 2:24:04 time: 0.1987 data_time: 0.0107 memory: 7116 grad_norm: 6.4480 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.5540 loss: 2.5540 2022/09/03 23:25:19 - mmengine - INFO - Epoch(train) [20][1300/1345] lr: 1.0000e-02 eta: 2:23:59 time: 0.1955 data_time: 0.0096 memory: 7116 grad_norm: 6.6669 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4715 loss: 2.4715 2022/09/03 23:25:23 - mmengine - INFO - Epoch(train) [20][1320/1345] lr: 1.0000e-02 eta: 2:23:54 time: 0.1974 data_time: 0.0086 memory: 7116 grad_norm: 6.3838 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3761 loss: 2.3761 2022/09/03 23:25:27 - mmengine - INFO - Epoch(train) [20][1340/1345] lr: 1.0000e-02 eta: 2:23:50 time: 0.1989 data_time: 0.0120 memory: 7116 grad_norm: 6.2544 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1876 loss: 2.1876 2022/09/03 23:25:28 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:25:28 - mmengine - INFO - Epoch(train) [20][1345/1345] lr: 1.0000e-02 eta: 2:23:50 time: 0.1969 data_time: 0.0096 memory: 7116 grad_norm: 6.7478 top1_acc: 0.0000 top5_acc: 1.0000 loss_cls: 2.2977 loss: 2.2977 2022/09/03 23:25:28 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/09/03 23:25:30 - mmengine - INFO - Epoch(val) [20][20/181] eta: 0:00:07 time: 0.0442 data_time: 0.0086 memory: 1114 2022/09/03 23:25:31 - mmengine - INFO - Epoch(val) [20][40/181] eta: 0:00:05 time: 0.0409 data_time: 0.0060 memory: 1114 2022/09/03 23:25:32 - mmengine - INFO - Epoch(val) [20][60/181] eta: 0:00:05 time: 0.0417 data_time: 0.0061 memory: 1114 2022/09/03 23:25:33 - mmengine - INFO - Epoch(val) [20][80/181] eta: 0:00:04 time: 0.0424 data_time: 0.0066 memory: 1114 2022/09/03 23:25:34 - mmengine - INFO - Epoch(val) [20][100/181] eta: 0:00:03 time: 0.0421 data_time: 0.0064 memory: 1114 2022/09/03 23:25:34 - mmengine - INFO - Epoch(val) [20][120/181] eta: 0:00:02 time: 0.0415 data_time: 0.0061 memory: 1114 2022/09/03 23:25:35 - mmengine - INFO - Epoch(val) [20][140/181] eta: 0:00:01 time: 0.0407 data_time: 0.0056 memory: 1114 2022/09/03 23:25:36 - mmengine - INFO - Epoch(val) [20][160/181] eta: 0:00:00 time: 0.0418 data_time: 0.0064 memory: 1114 2022/09/03 23:25:37 - mmengine - INFO - Epoch(val) [20][180/181] eta: 0:00:00 time: 0.0407 data_time: 0.0057 memory: 1114 2022/09/03 23:25:40 - mmengine - INFO - Epoch(val) [20][181/181] acc/top1: 0.3078 acc/top5: 0.6022 acc/mean1: 0.2800 2022/09/03 23:25:44 - mmengine - INFO - Epoch(train) [21][20/1345] lr: 1.0000e-02 eta: 2:23:42 time: 0.2096 data_time: 0.0131 memory: 7116 grad_norm: 6.2890 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3336 loss: 2.3336 2022/09/03 23:25:48 - mmengine - INFO - Epoch(train) [21][40/1345] lr: 1.0000e-02 eta: 2:23:38 time: 0.1946 data_time: 0.0086 memory: 7116 grad_norm: 6.3394 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.5759 loss: 2.5759 2022/09/03 23:25:52 - mmengine - INFO - Epoch(train) [21][60/1345] lr: 1.0000e-02 eta: 2:23:33 time: 0.1928 data_time: 0.0084 memory: 7116 grad_norm: 6.5040 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3038 loss: 2.3038 2022/09/03 23:25:56 - mmengine - INFO - Epoch(train) [21][80/1345] lr: 1.0000e-02 eta: 2:23:28 time: 0.1962 data_time: 0.0112 memory: 7116 grad_norm: 6.7833 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.2187 loss: 2.2187 2022/09/03 23:26:00 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:26:00 - mmengine - INFO - Epoch(train) [21][100/1345] lr: 1.0000e-02 eta: 2:23:23 time: 0.2050 data_time: 0.0086 memory: 7116 grad_norm: 6.3621 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2593 loss: 2.2593 2022/09/03 23:26:04 - mmengine - INFO - Epoch(train) [21][120/1345] lr: 1.0000e-02 eta: 2:23:19 time: 0.1973 data_time: 0.0089 memory: 7116 grad_norm: 6.7441 top1_acc: 0.3750 top5_acc: 1.0000 loss_cls: 2.2053 loss: 2.2053 2022/09/03 23:26:08 - mmengine - INFO - Epoch(train) [21][140/1345] lr: 1.0000e-02 eta: 2:23:14 time: 0.1946 data_time: 0.0107 memory: 7116 grad_norm: 6.2580 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3188 loss: 2.3188 2022/09/03 23:26:11 - mmengine - INFO - Epoch(train) [21][160/1345] lr: 1.0000e-02 eta: 2:23:09 time: 0.1930 data_time: 0.0081 memory: 7116 grad_norm: 6.7782 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4390 loss: 2.4390 2022/09/03 23:26:15 - mmengine - INFO - Epoch(train) [21][180/1345] lr: 1.0000e-02 eta: 2:23:04 time: 0.1963 data_time: 0.0081 memory: 7116 grad_norm: 6.4449 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.1069 loss: 2.1069 2022/09/03 23:26:20 - mmengine - INFO - Epoch(train) [21][200/1345] lr: 1.0000e-02 eta: 2:23:00 time: 0.2106 data_time: 0.0119 memory: 7116 grad_norm: 6.4013 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4168 loss: 2.4168 2022/09/03 23:26:23 - mmengine - INFO - Epoch(train) [21][220/1345] lr: 1.0000e-02 eta: 2:22:55 time: 0.1926 data_time: 0.0089 memory: 7116 grad_norm: 6.3723 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.9356 loss: 1.9356 2022/09/03 23:26:27 - mmengine - INFO - Epoch(train) [21][240/1345] lr: 1.0000e-02 eta: 2:22:50 time: 0.1953 data_time: 0.0085 memory: 7116 grad_norm: 6.6898 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4190 loss: 2.4190 2022/09/03 23:26:31 - mmengine - INFO - Epoch(train) [21][260/1345] lr: 1.0000e-02 eta: 2:22:45 time: 0.1979 data_time: 0.0127 memory: 7116 grad_norm: 6.5445 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4022 loss: 2.4022 2022/09/03 23:26:35 - mmengine - INFO - Epoch(train) [21][280/1345] lr: 1.0000e-02 eta: 2:22:40 time: 0.1954 data_time: 0.0082 memory: 7116 grad_norm: 6.6181 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2738 loss: 2.2738 2022/09/03 23:26:39 - mmengine - INFO - Epoch(train) [21][300/1345] lr: 1.0000e-02 eta: 2:22:36 time: 0.2014 data_time: 0.0079 memory: 7116 grad_norm: 6.3652 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4119 loss: 2.4119 2022/09/03 23:26:43 - mmengine - INFO - Epoch(train) [21][320/1345] lr: 1.0000e-02 eta: 2:22:31 time: 0.2010 data_time: 0.0104 memory: 7116 grad_norm: 6.5835 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3017 loss: 2.3017 2022/09/03 23:26:47 - mmengine - INFO - Epoch(train) [21][340/1345] lr: 1.0000e-02 eta: 2:22:26 time: 0.2003 data_time: 0.0079 memory: 7116 grad_norm: 6.3365 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2279 loss: 2.2279 2022/09/03 23:26:51 - mmengine - INFO - Epoch(train) [21][360/1345] lr: 1.0000e-02 eta: 2:22:22 time: 0.1997 data_time: 0.0078 memory: 7116 grad_norm: 6.5528 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2602 loss: 2.2602 2022/09/03 23:26:55 - mmengine - INFO - Epoch(train) [21][380/1345] lr: 1.0000e-02 eta: 2:22:17 time: 0.1987 data_time: 0.0114 memory: 7116 grad_norm: 6.5311 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.5153 loss: 2.5153 2022/09/03 23:26:59 - mmengine - INFO - Epoch(train) [21][400/1345] lr: 1.0000e-02 eta: 2:22:12 time: 0.1998 data_time: 0.0085 memory: 7116 grad_norm: 6.3467 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2548 loss: 2.2548 2022/09/03 23:27:03 - mmengine - INFO - Epoch(train) [21][420/1345] lr: 1.0000e-02 eta: 2:22:08 time: 0.2050 data_time: 0.0082 memory: 7116 grad_norm: 6.6499 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.0637 loss: 2.0637 2022/09/03 23:27:07 - mmengine - INFO - Epoch(train) [21][440/1345] lr: 1.0000e-02 eta: 2:22:03 time: 0.2016 data_time: 0.0097 memory: 7116 grad_norm: 6.5169 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2733 loss: 2.2733 2022/09/03 23:27:11 - mmengine - INFO - Epoch(train) [21][460/1345] lr: 1.0000e-02 eta: 2:21:59 time: 0.2030 data_time: 0.0080 memory: 7116 grad_norm: 6.4877 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 2.4661 loss: 2.4661 2022/09/03 23:27:16 - mmengine - INFO - Epoch(train) [21][480/1345] lr: 1.0000e-02 eta: 2:21:54 time: 0.2015 data_time: 0.0083 memory: 7116 grad_norm: 6.6143 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4162 loss: 2.4162 2022/09/03 23:27:20 - mmengine - INFO - Epoch(train) [21][500/1345] lr: 1.0000e-02 eta: 2:21:49 time: 0.2022 data_time: 0.0103 memory: 7116 grad_norm: 6.5055 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1335 loss: 2.1335 2022/09/03 23:27:24 - mmengine - INFO - Epoch(train) [21][520/1345] lr: 1.0000e-02 eta: 2:21:45 time: 0.2031 data_time: 0.0074 memory: 7116 grad_norm: 6.5454 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9359 loss: 1.9359 2022/09/03 23:27:28 - mmengine - INFO - Epoch(train) [21][540/1345] lr: 1.0000e-02 eta: 2:21:40 time: 0.1992 data_time: 0.0074 memory: 7116 grad_norm: 6.4148 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.0646 loss: 2.0646 2022/09/03 23:27:32 - mmengine - INFO - Epoch(train) [21][560/1345] lr: 1.0000e-02 eta: 2:21:36 time: 0.2016 data_time: 0.0108 memory: 7116 grad_norm: 6.4007 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4963 loss: 2.4963 2022/09/03 23:27:36 - mmengine - INFO - Epoch(train) [21][580/1345] lr: 1.0000e-02 eta: 2:21:31 time: 0.1941 data_time: 0.0081 memory: 7116 grad_norm: 6.3140 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.3505 loss: 2.3505 2022/09/03 23:27:40 - mmengine - INFO - Epoch(train) [21][600/1345] lr: 1.0000e-02 eta: 2:21:26 time: 0.2009 data_time: 0.0081 memory: 7116 grad_norm: 6.3081 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2053 loss: 2.2053 2022/09/03 23:27:44 - mmengine - INFO - Epoch(train) [21][620/1345] lr: 1.0000e-02 eta: 2:21:22 time: 0.2027 data_time: 0.0101 memory: 7116 grad_norm: 6.5672 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2829 loss: 2.2829 2022/09/03 23:27:48 - mmengine - INFO - Epoch(train) [21][640/1345] lr: 1.0000e-02 eta: 2:21:17 time: 0.2003 data_time: 0.0073 memory: 7116 grad_norm: 6.2002 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.5297 loss: 2.5297 2022/09/03 23:27:52 - mmengine - INFO - Epoch(train) [21][660/1345] lr: 1.0000e-02 eta: 2:21:12 time: 0.2036 data_time: 0.0084 memory: 7116 grad_norm: 6.4700 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.5522 loss: 2.5522 2022/09/03 23:27:56 - mmengine - INFO - Epoch(train) [21][680/1345] lr: 1.0000e-02 eta: 2:21:08 time: 0.2081 data_time: 0.0101 memory: 7116 grad_norm: 6.3557 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.1343 loss: 2.1343 2022/09/03 23:28:00 - mmengine - INFO - Epoch(train) [21][700/1345] lr: 1.0000e-02 eta: 2:21:03 time: 0.2021 data_time: 0.0087 memory: 7116 grad_norm: 6.6832 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4399 loss: 2.4399 2022/09/03 23:28:04 - mmengine - INFO - Epoch(train) [21][720/1345] lr: 1.0000e-02 eta: 2:20:59 time: 0.2021 data_time: 0.0076 memory: 7116 grad_norm: 6.5120 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.3280 loss: 2.3280 2022/09/03 23:28:08 - mmengine - INFO - Epoch(train) [21][740/1345] lr: 1.0000e-02 eta: 2:20:54 time: 0.2029 data_time: 0.0098 memory: 7116 grad_norm: 6.3814 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.2572 loss: 2.2572 2022/09/03 23:28:12 - mmengine - INFO - Epoch(train) [21][760/1345] lr: 1.0000e-02 eta: 2:20:50 time: 0.2015 data_time: 0.0079 memory: 7116 grad_norm: 6.4856 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1064 loss: 2.1064 2022/09/03 23:28:16 - mmengine - INFO - Epoch(train) [21][780/1345] lr: 1.0000e-02 eta: 2:20:45 time: 0.2022 data_time: 0.0076 memory: 7116 grad_norm: 6.5574 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3352 loss: 2.3352 2022/09/03 23:28:20 - mmengine - INFO - Epoch(train) [21][800/1345] lr: 1.0000e-02 eta: 2:20:40 time: 0.2013 data_time: 0.0101 memory: 7116 grad_norm: 6.2850 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2095 loss: 2.2095 2022/09/03 23:28:24 - mmengine - INFO - Epoch(train) [21][820/1345] lr: 1.0000e-02 eta: 2:20:36 time: 0.1992 data_time: 0.0077 memory: 7116 grad_norm: 6.1805 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.1662 loss: 2.1662 2022/09/03 23:28:28 - mmengine - INFO - Epoch(train) [21][840/1345] lr: 1.0000e-02 eta: 2:20:31 time: 0.2019 data_time: 0.0082 memory: 7116 grad_norm: 6.5861 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3713 loss: 2.3713 2022/09/03 23:28:32 - mmengine - INFO - Epoch(train) [21][860/1345] lr: 1.0000e-02 eta: 2:20:27 time: 0.2096 data_time: 0.0104 memory: 7116 grad_norm: 6.5021 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.3399 loss: 2.3399 2022/09/03 23:28:36 - mmengine - INFO - Epoch(train) [21][880/1345] lr: 1.0000e-02 eta: 2:20:22 time: 0.1985 data_time: 0.0078 memory: 7116 grad_norm: 6.6933 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4105 loss: 2.4105 2022/09/03 23:28:40 - mmengine - INFO - Epoch(train) [21][900/1345] lr: 1.0000e-02 eta: 2:20:17 time: 0.2047 data_time: 0.0089 memory: 7116 grad_norm: 6.1648 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.4063 loss: 2.4063 2022/09/03 23:28:44 - mmengine - INFO - Epoch(train) [21][920/1345] lr: 1.0000e-02 eta: 2:20:13 time: 0.2006 data_time: 0.0102 memory: 7116 grad_norm: 6.3006 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.1613 loss: 2.1613 2022/09/03 23:28:48 - mmengine - INFO - Epoch(train) [21][940/1345] lr: 1.0000e-02 eta: 2:20:08 time: 0.2002 data_time: 0.0080 memory: 7116 grad_norm: 6.5227 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3557 loss: 2.3557 2022/09/03 23:28:52 - mmengine - INFO - Epoch(train) [21][960/1345] lr: 1.0000e-02 eta: 2:20:04 time: 0.1997 data_time: 0.0080 memory: 7116 grad_norm: 6.5052 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5620 loss: 2.5620 2022/09/03 23:28:56 - mmengine - INFO - Epoch(train) [21][980/1345] lr: 1.0000e-02 eta: 2:19:59 time: 0.1979 data_time: 0.0110 memory: 7116 grad_norm: 6.5945 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1040 loss: 2.1040 2022/09/03 23:29:01 - mmengine - INFO - Epoch(train) [21][1000/1345] lr: 1.0000e-02 eta: 2:19:54 time: 0.2078 data_time: 0.0084 memory: 7116 grad_norm: 6.6805 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2464 loss: 2.2464 2022/09/03 23:29:05 - mmengine - INFO - Epoch(train) [21][1020/1345] lr: 1.0000e-02 eta: 2:19:50 time: 0.2039 data_time: 0.0075 memory: 7116 grad_norm: 6.0484 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.4501 loss: 2.4501 2022/09/03 23:29:09 - mmengine - INFO - Epoch(train) [21][1040/1345] lr: 1.0000e-02 eta: 2:19:46 time: 0.2243 data_time: 0.0105 memory: 7116 grad_norm: 6.6046 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.3668 loss: 2.3668 2022/09/03 23:29:13 - mmengine - INFO - Epoch(train) [21][1060/1345] lr: 1.0000e-02 eta: 2:19:41 time: 0.2013 data_time: 0.0082 memory: 7116 grad_norm: 6.4478 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3419 loss: 2.3419 2022/09/03 23:29:17 - mmengine - INFO - Epoch(train) [21][1080/1345] lr: 1.0000e-02 eta: 2:19:37 time: 0.1987 data_time: 0.0075 memory: 7116 grad_norm: 6.2776 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4487 loss: 2.4487 2022/09/03 23:29:21 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:29:21 - mmengine - INFO - Epoch(train) [21][1100/1345] lr: 1.0000e-02 eta: 2:19:32 time: 0.2017 data_time: 0.0094 memory: 7116 grad_norm: 6.4136 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3084 loss: 2.3084 2022/09/03 23:29:25 - mmengine - INFO - Epoch(train) [21][1120/1345] lr: 1.0000e-02 eta: 2:19:28 time: 0.2034 data_time: 0.0072 memory: 7116 grad_norm: 6.3607 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2807 loss: 2.2807 2022/09/03 23:29:29 - mmengine - INFO - Epoch(train) [21][1140/1345] lr: 1.0000e-02 eta: 2:19:23 time: 0.2099 data_time: 0.0092 memory: 7116 grad_norm: 6.3426 top1_acc: 0.0000 top5_acc: 0.5000 loss_cls: 2.5534 loss: 2.5534 2022/09/03 23:29:33 - mmengine - INFO - Epoch(train) [21][1160/1345] lr: 1.0000e-02 eta: 2:19:19 time: 0.2003 data_time: 0.0108 memory: 7116 grad_norm: 6.5446 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1465 loss: 2.1465 2022/09/03 23:29:37 - mmengine - INFO - Epoch(train) [21][1180/1345] lr: 1.0000e-02 eta: 2:19:14 time: 0.1973 data_time: 0.0076 memory: 7116 grad_norm: 6.4551 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2740 loss: 2.2740 2022/09/03 23:29:41 - mmengine - INFO - Epoch(train) [21][1200/1345] lr: 1.0000e-02 eta: 2:19:09 time: 0.2044 data_time: 0.0077 memory: 7116 grad_norm: 6.5036 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0924 loss: 2.0924 2022/09/03 23:29:46 - mmengine - INFO - Epoch(train) [21][1220/1345] lr: 1.0000e-02 eta: 2:19:05 time: 0.2025 data_time: 0.0102 memory: 7116 grad_norm: 6.5265 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.1333 loss: 2.1333 2022/09/03 23:29:50 - mmengine - INFO - Epoch(train) [21][1240/1345] lr: 1.0000e-02 eta: 2:19:00 time: 0.2100 data_time: 0.0083 memory: 7116 grad_norm: 6.6091 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.3755 loss: 2.3755 2022/09/03 23:29:54 - mmengine - INFO - Epoch(train) [21][1260/1345] lr: 1.0000e-02 eta: 2:18:56 time: 0.2033 data_time: 0.0077 memory: 7116 grad_norm: 6.4213 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.1673 loss: 2.1673 2022/09/03 23:29:58 - mmengine - INFO - Epoch(train) [21][1280/1345] lr: 1.0000e-02 eta: 2:18:51 time: 0.2035 data_time: 0.0097 memory: 7116 grad_norm: 6.5601 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.3468 loss: 2.3468 2022/09/03 23:30:02 - mmengine - INFO - Epoch(train) [21][1300/1345] lr: 1.0000e-02 eta: 2:18:47 time: 0.2014 data_time: 0.0080 memory: 7116 grad_norm: 6.5667 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2238 loss: 2.2238 2022/09/03 23:30:06 - mmengine - INFO - Epoch(train) [21][1320/1345] lr: 1.0000e-02 eta: 2:18:42 time: 0.2027 data_time: 0.0097 memory: 7116 grad_norm: 6.3211 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.3019 loss: 2.3019 2022/09/03 23:30:10 - mmengine - INFO - Epoch(train) [21][1340/1345] lr: 1.0000e-02 eta: 2:18:37 time: 0.1943 data_time: 0.0117 memory: 7116 grad_norm: 6.3979 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4527 loss: 2.4527 2022/09/03 23:30:11 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:30:11 - mmengine - INFO - Epoch(train) [21][1345/1345] lr: 1.0000e-02 eta: 2:18:37 time: 0.1925 data_time: 0.0085 memory: 7116 grad_norm: 6.4920 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.4598 loss: 2.4598 2022/09/03 23:30:11 - mmengine - INFO - Saving checkpoint at 21 epochs 2022/09/03 23:30:14 - mmengine - INFO - Epoch(val) [21][20/181] eta: 0:00:07 time: 0.0445 data_time: 0.0092 memory: 1114 2022/09/03 23:30:14 - mmengine - INFO - Epoch(val) [21][40/181] eta: 0:00:05 time: 0.0405 data_time: 0.0056 memory: 1114 2022/09/03 23:30:15 - mmengine - INFO - Epoch(val) [21][60/181] eta: 0:00:04 time: 0.0413 data_time: 0.0062 memory: 1114 2022/09/03 23:30:16 - mmengine - INFO - Epoch(val) [21][80/181] eta: 0:00:04 time: 0.0405 data_time: 0.0055 memory: 1114 2022/09/03 23:30:17 - mmengine - INFO - Epoch(val) [21][100/181] eta: 0:00:03 time: 0.0405 data_time: 0.0057 memory: 1114 2022/09/03 23:30:18 - mmengine - INFO - Epoch(val) [21][120/181] eta: 0:00:02 time: 0.0403 data_time: 0.0055 memory: 1114 2022/09/03 23:30:19 - mmengine - INFO - Epoch(val) [21][140/181] eta: 0:00:01 time: 0.0403 data_time: 0.0055 memory: 1114 2022/09/03 23:30:19 - mmengine - INFO - Epoch(val) [21][160/181] eta: 0:00:00 time: 0.0413 data_time: 0.0064 memory: 1114 2022/09/03 23:30:20 - mmengine - INFO - Epoch(val) [21][180/181] eta: 0:00:00 time: 0.0397 data_time: 0.0052 memory: 1114 2022/09/03 23:30:23 - mmengine - INFO - Epoch(val) [21][181/181] acc/top1: 0.3153 acc/top5: 0.6120 acc/mean1: 0.2905 2022/09/03 23:30:27 - mmengine - INFO - Epoch(train) [22][20/1345] lr: 1.0000e-02 eta: 2:18:30 time: 0.2057 data_time: 0.0118 memory: 7116 grad_norm: 6.1202 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.2923 loss: 2.2923 2022/09/03 23:30:31 - mmengine - INFO - Epoch(train) [22][40/1345] lr: 1.0000e-02 eta: 2:18:26 time: 0.2050 data_time: 0.0077 memory: 7116 grad_norm: 6.4118 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2281 loss: 2.2281 2022/09/03 23:30:35 - mmengine - INFO - Epoch(train) [22][60/1345] lr: 1.0000e-02 eta: 2:18:21 time: 0.2053 data_time: 0.0070 memory: 7116 grad_norm: 6.3066 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.4236 loss: 2.4236 2022/09/03 23:30:40 - mmengine - INFO - Epoch(train) [22][80/1345] lr: 1.0000e-02 eta: 2:18:17 time: 0.2084 data_time: 0.0099 memory: 7116 grad_norm: 6.2281 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1440 loss: 2.1440 2022/09/03 23:30:44 - mmengine - INFO - Epoch(train) [22][100/1345] lr: 1.0000e-02 eta: 2:18:13 time: 0.2057 data_time: 0.0074 memory: 7116 grad_norm: 6.5711 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.3401 loss: 2.3401 2022/09/03 23:30:48 - mmengine - INFO - Epoch(train) [22][120/1345] lr: 1.0000e-02 eta: 2:18:08 time: 0.2034 data_time: 0.0076 memory: 7116 grad_norm: 6.7412 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.2861 loss: 2.2861 2022/09/03 23:30:52 - mmengine - INFO - Epoch(train) [22][140/1345] lr: 1.0000e-02 eta: 2:18:03 time: 0.2047 data_time: 0.0100 memory: 7116 grad_norm: 6.6582 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.1730 loss: 2.1730 2022/09/03 23:30:56 - mmengine - INFO - Epoch(train) [22][160/1345] lr: 1.0000e-02 eta: 2:17:59 time: 0.2044 data_time: 0.0075 memory: 7116 grad_norm: 6.5191 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3569 loss: 2.3569 2022/09/03 23:31:00 - mmengine - INFO - Epoch(train) [22][180/1345] lr: 1.0000e-02 eta: 2:17:54 time: 0.2044 data_time: 0.0083 memory: 7116 grad_norm: 6.6653 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2354 loss: 2.2354 2022/09/03 23:31:04 - mmengine - INFO - Epoch(train) [22][200/1345] lr: 1.0000e-02 eta: 2:17:50 time: 0.2061 data_time: 0.0095 memory: 7116 grad_norm: 6.5502 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.4415 loss: 2.4415 2022/09/03 23:31:08 - mmengine - INFO - Epoch(train) [22][220/1345] lr: 1.0000e-02 eta: 2:17:46 time: 0.2141 data_time: 0.0073 memory: 7116 grad_norm: 6.5652 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1875 loss: 2.1875 2022/09/03 23:31:13 - mmengine - INFO - Epoch(train) [22][240/1345] lr: 1.0000e-02 eta: 2:17:41 time: 0.2094 data_time: 0.0089 memory: 7116 grad_norm: 6.5434 top1_acc: 0.0000 top5_acc: 0.5000 loss_cls: 2.1990 loss: 2.1990 2022/09/03 23:31:17 - mmengine - INFO - Epoch(train) [22][260/1345] lr: 1.0000e-02 eta: 2:17:37 time: 0.2070 data_time: 0.0093 memory: 7116 grad_norm: 6.3903 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3722 loss: 2.3722 2022/09/03 23:31:21 - mmengine - INFO - Epoch(train) [22][280/1345] lr: 1.0000e-02 eta: 2:17:33 time: 0.2065 data_time: 0.0073 memory: 7116 grad_norm: 6.6184 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1328 loss: 2.1328 2022/09/03 23:31:25 - mmengine - INFO - Epoch(train) [22][300/1345] lr: 1.0000e-02 eta: 2:17:28 time: 0.2084 data_time: 0.0072 memory: 7116 grad_norm: 6.2806 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.3959 loss: 2.3959 2022/09/03 23:31:29 - mmengine - INFO - Epoch(train) [22][320/1345] lr: 1.0000e-02 eta: 2:17:24 time: 0.2122 data_time: 0.0110 memory: 7116 grad_norm: 6.6728 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.0552 loss: 2.0552 2022/09/03 23:31:33 - mmengine - INFO - Epoch(train) [22][340/1345] lr: 1.0000e-02 eta: 2:17:19 time: 0.2082 data_time: 0.0070 memory: 7116 grad_norm: 6.7232 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.3997 loss: 2.3997 2022/09/03 23:31:38 - mmengine - INFO - Epoch(train) [22][360/1345] lr: 1.0000e-02 eta: 2:17:15 time: 0.2061 data_time: 0.0070 memory: 7116 grad_norm: 6.5024 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 2.0167 loss: 2.0167 2022/09/03 23:31:42 - mmengine - INFO - Epoch(train) [22][380/1345] lr: 1.0000e-02 eta: 2:17:11 time: 0.2094 data_time: 0.0097 memory: 7116 grad_norm: 6.7694 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3156 loss: 2.3156 2022/09/03 23:31:46 - mmengine - INFO - Epoch(train) [22][400/1345] lr: 1.0000e-02 eta: 2:17:06 time: 0.2055 data_time: 0.0069 memory: 7116 grad_norm: 6.6211 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0175 loss: 2.0175 2022/09/03 23:31:50 - mmengine - INFO - Epoch(train) [22][420/1345] lr: 1.0000e-02 eta: 2:17:02 time: 0.2129 data_time: 0.0080 memory: 7116 grad_norm: 6.7891 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.3755 loss: 2.3755 2022/09/03 23:31:54 - mmengine - INFO - Epoch(train) [22][440/1345] lr: 1.0000e-02 eta: 2:16:58 time: 0.2073 data_time: 0.0100 memory: 7116 grad_norm: 6.6777 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2825 loss: 2.2825 2022/09/03 23:31:58 - mmengine - INFO - Epoch(train) [22][460/1345] lr: 1.0000e-02 eta: 2:16:53 time: 0.2058 data_time: 0.0074 memory: 7116 grad_norm: 6.7580 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.5597 loss: 2.5597 2022/09/03 23:32:03 - mmengine - INFO - Epoch(train) [22][480/1345] lr: 1.0000e-02 eta: 2:16:49 time: 0.2103 data_time: 0.0074 memory: 7116 grad_norm: 6.6523 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4030 loss: 2.4030 2022/09/03 23:32:07 - mmengine - INFO - Epoch(train) [22][500/1345] lr: 1.0000e-02 eta: 2:16:44 time: 0.2069 data_time: 0.0094 memory: 7116 grad_norm: 6.3471 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 2.1882 loss: 2.1882 2022/09/03 23:32:11 - mmengine - INFO - Epoch(train) [22][520/1345] lr: 1.0000e-02 eta: 2:16:40 time: 0.2125 data_time: 0.0080 memory: 7116 grad_norm: 6.6611 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.3003 loss: 2.3003 2022/09/03 23:32:15 - mmengine - INFO - Epoch(train) [22][540/1345] lr: 1.0000e-02 eta: 2:16:36 time: 0.2065 data_time: 0.0074 memory: 7116 grad_norm: 6.5484 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.3398 loss: 2.3398 2022/09/03 23:32:19 - mmengine - INFO - Epoch(train) [22][560/1345] lr: 1.0000e-02 eta: 2:16:31 time: 0.2077 data_time: 0.0097 memory: 7116 grad_norm: 6.6772 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2331 loss: 2.2331 2022/09/03 23:32:23 - mmengine - INFO - Epoch(train) [22][580/1345] lr: 1.0000e-02 eta: 2:16:27 time: 0.2047 data_time: 0.0069 memory: 7116 grad_norm: 6.9324 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.3665 loss: 2.3665 2022/09/03 23:32:28 - mmengine - INFO - Epoch(train) [22][600/1345] lr: 1.0000e-02 eta: 2:16:22 time: 0.2062 data_time: 0.0072 memory: 7116 grad_norm: 6.6045 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.3936 loss: 2.3936 2022/09/03 23:32:32 - mmengine - INFO - Epoch(train) [22][620/1345] lr: 1.0000e-02 eta: 2:16:18 time: 0.2169 data_time: 0.0100 memory: 7116 grad_norm: 6.5761 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1621 loss: 2.1621 2022/09/03 23:32:36 - mmengine - INFO - Epoch(train) [22][640/1345] lr: 1.0000e-02 eta: 2:16:14 time: 0.2063 data_time: 0.0077 memory: 7116 grad_norm: 6.4384 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.2555 loss: 2.2555 2022/09/03 23:32:40 - mmengine - INFO - Epoch(train) [22][660/1345] lr: 1.0000e-02 eta: 2:16:09 time: 0.2083 data_time: 0.0072 memory: 7116 grad_norm: 6.6529 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.1839 loss: 2.1839 2022/09/03 23:32:44 - mmengine - INFO - Epoch(train) [22][680/1345] lr: 1.0000e-02 eta: 2:16:05 time: 0.2070 data_time: 0.0094 memory: 7116 grad_norm: 6.4515 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 2.2934 loss: 2.2934 2022/09/03 23:32:48 - mmengine - INFO - Epoch(train) [22][700/1345] lr: 1.0000e-02 eta: 2:16:00 time: 0.2062 data_time: 0.0074 memory: 7116 grad_norm: 6.3616 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.0180 loss: 2.0180 2022/09/03 23:32:53 - mmengine - INFO - Epoch(train) [22][720/1345] lr: 1.0000e-02 eta: 2:15:56 time: 0.2158 data_time: 0.0087 memory: 7116 grad_norm: 6.7416 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1903 loss: 2.1903 2022/09/03 23:32:57 - mmengine - INFO - Epoch(train) [22][740/1345] lr: 1.0000e-02 eta: 2:15:52 time: 0.2080 data_time: 0.0096 memory: 7116 grad_norm: 6.5101 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.1512 loss: 2.1512 2022/09/03 23:33:00 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:33:01 - mmengine - INFO - Epoch(train) [22][760/1345] lr: 1.0000e-02 eta: 2:15:47 time: 0.2064 data_time: 0.0070 memory: 7116 grad_norm: 6.5297 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3094 loss: 2.3094 2022/09/03 23:33:05 - mmengine - INFO - Epoch(train) [22][780/1345] lr: 1.0000e-02 eta: 2:15:43 time: 0.2084 data_time: 0.0074 memory: 7116 grad_norm: 6.3856 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.2564 loss: 2.2564 2022/09/03 23:33:09 - mmengine - INFO - Epoch(train) [22][800/1345] lr: 1.0000e-02 eta: 2:15:39 time: 0.2104 data_time: 0.0101 memory: 7116 grad_norm: 6.8143 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.2960 loss: 2.2960 2022/09/03 23:33:14 - mmengine - INFO - Epoch(train) [22][820/1345] lr: 1.0000e-02 eta: 2:15:34 time: 0.2100 data_time: 0.0069 memory: 7116 grad_norm: 6.6676 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3557 loss: 2.3557 2022/09/03 23:33:18 - mmengine - INFO - Epoch(train) [22][840/1345] lr: 1.0000e-02 eta: 2:15:30 time: 0.2063 data_time: 0.0071 memory: 7116 grad_norm: 6.6600 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3878 loss: 2.3878 2022/09/03 23:33:22 - mmengine - INFO - Epoch(train) [22][860/1345] lr: 1.0000e-02 eta: 2:15:26 time: 0.2187 data_time: 0.0105 memory: 7116 grad_norm: 6.6787 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2613 loss: 2.2613 2022/09/03 23:33:26 - mmengine - INFO - Epoch(train) [22][880/1345] lr: 1.0000e-02 eta: 2:15:21 time: 0.2070 data_time: 0.0070 memory: 7116 grad_norm: 6.5383 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4350 loss: 2.4350 2022/09/03 23:33:31 - mmengine - INFO - Epoch(train) [22][900/1345] lr: 1.0000e-02 eta: 2:15:17 time: 0.2134 data_time: 0.0072 memory: 7116 grad_norm: 6.3995 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9195 loss: 1.9195 2022/09/03 23:33:35 - mmengine - INFO - Epoch(train) [22][920/1345] lr: 1.0000e-02 eta: 2:15:13 time: 0.2066 data_time: 0.0104 memory: 7116 grad_norm: 6.4728 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3266 loss: 2.3266 2022/09/03 23:33:39 - mmengine - INFO - Epoch(train) [22][940/1345] lr: 1.0000e-02 eta: 2:15:08 time: 0.2046 data_time: 0.0071 memory: 7116 grad_norm: 6.5154 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0251 loss: 2.0251 2022/09/03 23:33:43 - mmengine - INFO - Epoch(train) [22][960/1345] lr: 1.0000e-02 eta: 2:15:04 time: 0.2258 data_time: 0.0078 memory: 7116 grad_norm: 6.6172 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.9677 loss: 1.9677 2022/09/03 23:33:47 - mmengine - INFO - Epoch(train) [22][980/1345] lr: 1.0000e-02 eta: 2:15:00 time: 0.2074 data_time: 0.0096 memory: 7116 grad_norm: 6.4450 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3297 loss: 2.3297 2022/09/03 23:33:52 - mmengine - INFO - Epoch(train) [22][1000/1345] lr: 1.0000e-02 eta: 2:14:56 time: 0.2350 data_time: 0.0081 memory: 7116 grad_norm: 6.3714 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1356 loss: 2.1356 2022/09/03 23:33:56 - mmengine - INFO - Epoch(train) [22][1020/1345] lr: 1.0000e-02 eta: 2:14:52 time: 0.2057 data_time: 0.0071 memory: 7116 grad_norm: 6.5632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0564 loss: 2.0564 2022/09/03 23:34:00 - mmengine - INFO - Epoch(train) [22][1040/1345] lr: 1.0000e-02 eta: 2:14:47 time: 0.2088 data_time: 0.0098 memory: 7116 grad_norm: 6.3586 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.5477 loss: 2.5477 2022/09/03 23:34:05 - mmengine - INFO - Epoch(train) [22][1060/1345] lr: 1.0000e-02 eta: 2:14:43 time: 0.2066 data_time: 0.0075 memory: 7116 grad_norm: 6.6989 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.2417 loss: 2.2417 2022/09/03 23:34:09 - mmengine - INFO - Epoch(train) [22][1080/1345] lr: 1.0000e-02 eta: 2:14:39 time: 0.2108 data_time: 0.0070 memory: 7116 grad_norm: 6.4338 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4483 loss: 2.4483 2022/09/03 23:34:13 - mmengine - INFO - Epoch(train) [22][1100/1345] lr: 1.0000e-02 eta: 2:14:35 time: 0.2295 data_time: 0.0105 memory: 7116 grad_norm: 6.6419 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.4033 loss: 2.4033 2022/09/03 23:34:18 - mmengine - INFO - Epoch(train) [22][1120/1345] lr: 1.0000e-02 eta: 2:14:30 time: 0.2069 data_time: 0.0071 memory: 7116 grad_norm: 6.3426 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2600 loss: 2.2600 2022/09/03 23:34:22 - mmengine - INFO - Epoch(train) [22][1140/1345] lr: 1.0000e-02 eta: 2:14:26 time: 0.2056 data_time: 0.0070 memory: 7116 grad_norm: 6.5142 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3715 loss: 2.3715 2022/09/03 23:34:26 - mmengine - INFO - Epoch(train) [22][1160/1345] lr: 1.0000e-02 eta: 2:14:21 time: 0.2060 data_time: 0.0102 memory: 7116 grad_norm: 6.4375 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.5765 loss: 2.5765 2022/09/03 23:34:30 - mmengine - INFO - Epoch(train) [22][1180/1345] lr: 1.0000e-02 eta: 2:14:17 time: 0.2132 data_time: 0.0081 memory: 7116 grad_norm: 6.4743 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1740 loss: 2.1740 2022/09/03 23:34:34 - mmengine - INFO - Epoch(train) [22][1200/1345] lr: 1.0000e-02 eta: 2:14:13 time: 0.2094 data_time: 0.0071 memory: 7116 grad_norm: 6.4294 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4455 loss: 2.4455 2022/09/03 23:34:38 - mmengine - INFO - Epoch(train) [22][1220/1345] lr: 1.0000e-02 eta: 2:14:08 time: 0.2069 data_time: 0.0093 memory: 7116 grad_norm: 6.2681 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2886 loss: 2.2886 2022/09/03 23:34:43 - mmengine - INFO - Epoch(train) [22][1240/1345] lr: 1.0000e-02 eta: 2:14:04 time: 0.2069 data_time: 0.0077 memory: 7116 grad_norm: 6.5263 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.5562 loss: 2.5562 2022/09/03 23:34:47 - mmengine - INFO - Epoch(train) [22][1260/1345] lr: 1.0000e-02 eta: 2:14:00 time: 0.2130 data_time: 0.0072 memory: 7116 grad_norm: 6.6517 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1876 loss: 2.1876 2022/09/03 23:34:51 - mmengine - INFO - Epoch(train) [22][1280/1345] lr: 1.0000e-02 eta: 2:13:55 time: 0.2095 data_time: 0.0101 memory: 7116 grad_norm: 6.4999 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.4268 loss: 2.4268 2022/09/03 23:34:55 - mmengine - INFO - Epoch(train) [22][1300/1345] lr: 1.0000e-02 eta: 2:13:51 time: 0.2055 data_time: 0.0071 memory: 7116 grad_norm: 6.5503 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.9273 loss: 1.9273 2022/09/03 23:34:59 - mmengine - INFO - Epoch(train) [22][1320/1345] lr: 1.0000e-02 eta: 2:13:47 time: 0.2074 data_time: 0.0071 memory: 7116 grad_norm: 6.4892 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4717 loss: 2.4717 2022/09/03 23:35:04 - mmengine - INFO - Epoch(train) [22][1340/1345] lr: 1.0000e-02 eta: 2:13:42 time: 0.2175 data_time: 0.0101 memory: 7116 grad_norm: 6.3884 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3598 loss: 2.3598 2022/09/03 23:35:05 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:35:05 - mmengine - INFO - Epoch(train) [22][1345/1345] lr: 1.0000e-02 eta: 2:13:42 time: 0.2112 data_time: 0.0069 memory: 7116 grad_norm: 6.6600 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.4226 loss: 2.4226 2022/09/03 23:35:05 - mmengine - INFO - Saving checkpoint at 22 epochs 2022/09/03 23:35:07 - mmengine - INFO - Epoch(val) [22][20/181] eta: 0:00:07 time: 0.0443 data_time: 0.0091 memory: 1114 2022/09/03 23:35:08 - mmengine - INFO - Epoch(val) [22][40/181] eta: 0:00:05 time: 0.0404 data_time: 0.0056 memory: 1114 2022/09/03 23:35:08 - mmengine - INFO - Epoch(val) [22][60/181] eta: 0:00:04 time: 0.0410 data_time: 0.0063 memory: 1114 2022/09/03 23:35:09 - mmengine - INFO - Epoch(val) [22][80/181] eta: 0:00:03 time: 0.0395 data_time: 0.0047 memory: 1114 2022/09/03 23:35:10 - mmengine - INFO - Epoch(val) [22][100/181] eta: 0:00:03 time: 0.0407 data_time: 0.0056 memory: 1114 2022/09/03 23:35:11 - mmengine - INFO - Epoch(val) [22][120/181] eta: 0:00:02 time: 0.0405 data_time: 0.0057 memory: 1114 2022/09/03 23:35:12 - mmengine - INFO - Epoch(val) [22][140/181] eta: 0:00:01 time: 0.0402 data_time: 0.0055 memory: 1114 2022/09/03 23:35:12 - mmengine - INFO - Epoch(val) [22][160/181] eta: 0:00:00 time: 0.0400 data_time: 0.0053 memory: 1114 2022/09/03 23:35:13 - mmengine - INFO - Epoch(val) [22][180/181] eta: 0:00:00 time: 0.0398 data_time: 0.0052 memory: 1114 2022/09/03 23:35:17 - mmengine - INFO - Epoch(val) [22][181/181] acc/top1: 0.3207 acc/top5: 0.6113 acc/mean1: 0.2842 2022/09/03 23:35:17 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_19.pth is removed 2022/09/03 23:35:18 - mmengine - INFO - The best checkpoint with 0.3207 acc/top1 at 22 epoch is saved to best_acc/top1_epoch_22.pth. 2022/09/03 23:35:22 - mmengine - INFO - Epoch(train) [23][20/1345] lr: 1.0000e-02 eta: 2:13:36 time: 0.2099 data_time: 0.0113 memory: 7116 grad_norm: 6.4052 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1608 loss: 2.1608 2022/09/03 23:35:26 - mmengine - INFO - Epoch(train) [23][40/1345] lr: 1.0000e-02 eta: 2:13:31 time: 0.2094 data_time: 0.0066 memory: 7116 grad_norm: 6.5901 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.9663 loss: 1.9663 2022/09/03 23:35:30 - mmengine - INFO - Epoch(train) [23][60/1345] lr: 1.0000e-02 eta: 2:13:27 time: 0.2133 data_time: 0.0076 memory: 7116 grad_norm: 6.6250 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0325 loss: 2.0325 2022/09/03 23:35:35 - mmengine - INFO - Epoch(train) [23][80/1345] lr: 1.0000e-02 eta: 2:13:23 time: 0.2154 data_time: 0.0095 memory: 7116 grad_norm: 6.7839 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 2.1531 loss: 2.1531 2022/09/03 23:35:39 - mmengine - INFO - Epoch(train) [23][100/1345] lr: 1.0000e-02 eta: 2:13:19 time: 0.2167 data_time: 0.0075 memory: 7116 grad_norm: 6.6947 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.1384 loss: 2.1384 2022/09/03 23:35:43 - mmengine - INFO - Epoch(train) [23][120/1345] lr: 1.0000e-02 eta: 2:13:14 time: 0.2148 data_time: 0.0073 memory: 7116 grad_norm: 6.6508 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2841 loss: 2.2841 2022/09/03 23:35:48 - mmengine - INFO - Epoch(train) [23][140/1345] lr: 1.0000e-02 eta: 2:13:10 time: 0.2101 data_time: 0.0103 memory: 7116 grad_norm: 6.3946 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4645 loss: 2.4645 2022/09/03 23:35:52 - mmengine - INFO - Epoch(train) [23][160/1345] lr: 1.0000e-02 eta: 2:13:06 time: 0.2141 data_time: 0.0089 memory: 7116 grad_norm: 6.5576 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3392 loss: 2.3392 2022/09/03 23:35:56 - mmengine - INFO - Epoch(train) [23][180/1345] lr: 1.0000e-02 eta: 2:13:02 time: 0.2112 data_time: 0.0075 memory: 7116 grad_norm: 6.4370 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.5123 loss: 2.5123 2022/09/03 23:36:00 - mmengine - INFO - Epoch(train) [23][200/1345] lr: 1.0000e-02 eta: 2:12:57 time: 0.2159 data_time: 0.0101 memory: 7116 grad_norm: 6.4039 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0390 loss: 2.0390 2022/09/03 23:36:05 - mmengine - INFO - Epoch(train) [23][220/1345] lr: 1.0000e-02 eta: 2:12:53 time: 0.2101 data_time: 0.0072 memory: 7116 grad_norm: 6.5809 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.2597 loss: 2.2597 2022/09/03 23:36:09 - mmengine - INFO - Epoch(train) [23][240/1345] lr: 1.0000e-02 eta: 2:12:49 time: 0.2098 data_time: 0.0071 memory: 7116 grad_norm: 6.8043 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0429 loss: 2.0429 2022/09/03 23:36:13 - mmengine - INFO - Epoch(train) [23][260/1345] lr: 1.0000e-02 eta: 2:12:45 time: 0.2190 data_time: 0.0103 memory: 7116 grad_norm: 6.6211 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2086 loss: 2.2086 2022/09/03 23:36:17 - mmengine - INFO - Epoch(train) [23][280/1345] lr: 1.0000e-02 eta: 2:12:40 time: 0.2036 data_time: 0.0074 memory: 7116 grad_norm: 6.7444 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1157 loss: 2.1157 2022/09/03 23:36:21 - mmengine - INFO - Epoch(train) [23][300/1345] lr: 1.0000e-02 eta: 2:12:36 time: 0.2085 data_time: 0.0083 memory: 7116 grad_norm: 6.4629 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3385 loss: 2.3385 2022/09/03 23:36:26 - mmengine - INFO - Epoch(train) [23][320/1345] lr: 1.0000e-02 eta: 2:12:32 time: 0.2148 data_time: 0.0091 memory: 7116 grad_norm: 6.6829 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0244 loss: 2.0244 2022/09/03 23:36:30 - mmengine - INFO - Epoch(train) [23][340/1345] lr: 1.0000e-02 eta: 2:12:27 time: 0.2158 data_time: 0.0074 memory: 7116 grad_norm: 6.6420 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3133 loss: 2.3133 2022/09/03 23:36:34 - mmengine - INFO - Epoch(train) [23][360/1345] lr: 1.0000e-02 eta: 2:12:23 time: 0.2122 data_time: 0.0068 memory: 7116 grad_norm: 6.3191 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2499 loss: 2.2499 2022/09/03 23:36:38 - mmengine - INFO - Epoch(train) [23][380/1345] lr: 1.0000e-02 eta: 2:12:19 time: 0.2090 data_time: 0.0091 memory: 7116 grad_norm: 6.4086 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.2826 loss: 2.2826 2022/09/03 23:36:43 - mmengine - INFO - Epoch(train) [23][400/1345] lr: 1.0000e-02 eta: 2:12:14 time: 0.2121 data_time: 0.0077 memory: 7116 grad_norm: 6.6686 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3462 loss: 2.3462 2022/09/03 23:36:45 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:36:47 - mmengine - INFO - Epoch(train) [23][420/1345] lr: 1.0000e-02 eta: 2:12:10 time: 0.2114 data_time: 0.0073 memory: 7116 grad_norm: 6.4733 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.1922 loss: 2.1922 2022/09/03 23:36:51 - mmengine - INFO - Epoch(train) [23][440/1345] lr: 1.0000e-02 eta: 2:12:06 time: 0.2263 data_time: 0.0104 memory: 7116 grad_norm: 6.4117 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.1435 loss: 2.1435 2022/09/03 23:36:56 - mmengine - INFO - Epoch(train) [23][460/1345] lr: 1.0000e-02 eta: 2:12:02 time: 0.2115 data_time: 0.0070 memory: 7116 grad_norm: 6.3051 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1113 loss: 2.1113 2022/09/03 23:37:00 - mmengine - INFO - Epoch(train) [23][480/1345] lr: 1.0000e-02 eta: 2:11:58 time: 0.2134 data_time: 0.0072 memory: 7116 grad_norm: 6.3833 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2637 loss: 2.2637 2022/09/03 23:37:04 - mmengine - INFO - Epoch(train) [23][500/1345] lr: 1.0000e-02 eta: 2:11:53 time: 0.2131 data_time: 0.0093 memory: 7116 grad_norm: 6.2071 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9351 loss: 1.9351 2022/09/03 23:37:08 - mmengine - INFO - Epoch(train) [23][520/1345] lr: 1.0000e-02 eta: 2:11:49 time: 0.2122 data_time: 0.0069 memory: 7116 grad_norm: 6.4161 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.4382 loss: 2.4382 2022/09/03 23:37:13 - mmengine - INFO - Epoch(train) [23][540/1345] lr: 1.0000e-02 eta: 2:11:45 time: 0.2190 data_time: 0.0085 memory: 7116 grad_norm: 6.9145 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.4571 loss: 2.4571 2022/09/03 23:37:17 - mmengine - INFO - Epoch(train) [23][560/1345] lr: 1.0000e-02 eta: 2:11:41 time: 0.2131 data_time: 0.0097 memory: 7116 grad_norm: 6.4064 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.5360 loss: 2.5360 2022/09/03 23:37:21 - mmengine - INFO - Epoch(train) [23][580/1345] lr: 1.0000e-02 eta: 2:11:36 time: 0.2079 data_time: 0.0073 memory: 7116 grad_norm: 6.5178 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.2250 loss: 2.2250 2022/09/03 23:37:26 - mmengine - INFO - Epoch(train) [23][600/1345] lr: 1.0000e-02 eta: 2:11:32 time: 0.2159 data_time: 0.0080 memory: 7116 grad_norm: 6.4098 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2733 loss: 2.2733 2022/09/03 23:37:30 - mmengine - INFO - Epoch(train) [23][620/1345] lr: 1.0000e-02 eta: 2:11:28 time: 0.2123 data_time: 0.0098 memory: 7116 grad_norm: 6.5558 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2867 loss: 2.2867 2022/09/03 23:37:34 - mmengine - INFO - Epoch(train) [23][640/1345] lr: 1.0000e-02 eta: 2:11:24 time: 0.2187 data_time: 0.0095 memory: 7116 grad_norm: 6.2350 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9940 loss: 1.9940 2022/09/03 23:37:39 - mmengine - INFO - Epoch(train) [23][660/1345] lr: 1.0000e-02 eta: 2:11:20 time: 0.2193 data_time: 0.0079 memory: 7116 grad_norm: 6.5968 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.2447 loss: 2.2447 2022/09/03 23:37:43 - mmengine - INFO - Epoch(train) [23][680/1345] lr: 1.0000e-02 eta: 2:11:16 time: 0.2161 data_time: 0.0102 memory: 7116 grad_norm: 6.2995 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3376 loss: 2.3376 2022/09/03 23:37:47 - mmengine - INFO - Epoch(train) [23][700/1345] lr: 1.0000e-02 eta: 2:11:11 time: 0.2152 data_time: 0.0080 memory: 7116 grad_norm: 6.3796 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3941 loss: 2.3941 2022/09/03 23:37:52 - mmengine - INFO - Epoch(train) [23][720/1345] lr: 1.0000e-02 eta: 2:11:07 time: 0.2164 data_time: 0.0141 memory: 7116 grad_norm: 6.4774 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.2550 loss: 2.2550 2022/09/03 23:37:56 - mmengine - INFO - Epoch(train) [23][740/1345] lr: 1.0000e-02 eta: 2:11:03 time: 0.2197 data_time: 0.0266 memory: 7116 grad_norm: 6.7068 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3199 loss: 2.3199 2022/09/03 23:38:00 - mmengine - INFO - Epoch(train) [23][760/1345] lr: 1.0000e-02 eta: 2:10:58 time: 0.1962 data_time: 0.0111 memory: 7116 grad_norm: 6.4814 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4318 loss: 2.4318 2022/09/03 23:38:04 - mmengine - INFO - Epoch(train) [23][780/1345] lr: 1.0000e-02 eta: 2:10:54 time: 0.1949 data_time: 0.0096 memory: 7116 grad_norm: 6.2134 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.4158 loss: 2.4158 2022/09/03 23:38:08 - mmengine - INFO - Epoch(train) [23][800/1345] lr: 1.0000e-02 eta: 2:10:49 time: 0.1952 data_time: 0.0122 memory: 7116 grad_norm: 6.7069 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.5329 loss: 2.5329 2022/09/03 23:38:12 - mmengine - INFO - Epoch(train) [23][820/1345] lr: 1.0000e-02 eta: 2:10:44 time: 0.1959 data_time: 0.0093 memory: 7116 grad_norm: 6.4462 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2248 loss: 2.2248 2022/09/03 23:38:16 - mmengine - INFO - Epoch(train) [23][840/1345] lr: 1.0000e-02 eta: 2:10:40 time: 0.1964 data_time: 0.0100 memory: 7116 grad_norm: 6.5181 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2931 loss: 2.2931 2022/09/03 23:38:19 - mmengine - INFO - Epoch(train) [23][860/1345] lr: 1.0000e-02 eta: 2:10:35 time: 0.1963 data_time: 0.0119 memory: 7116 grad_norm: 6.4348 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.3556 loss: 2.3556 2022/09/03 23:38:23 - mmengine - INFO - Epoch(train) [23][880/1345] lr: 1.0000e-02 eta: 2:10:30 time: 0.1929 data_time: 0.0101 memory: 7116 grad_norm: 6.7011 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1392 loss: 2.1392 2022/09/03 23:38:27 - mmengine - INFO - Epoch(train) [23][900/1345] lr: 1.0000e-02 eta: 2:10:25 time: 0.1921 data_time: 0.0115 memory: 7116 grad_norm: 6.5319 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4752 loss: 2.4752 2022/09/03 23:38:31 - mmengine - INFO - Epoch(train) [23][920/1345] lr: 1.0000e-02 eta: 2:10:21 time: 0.1987 data_time: 0.0123 memory: 7116 grad_norm: 6.4212 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.4749 loss: 2.4749 2022/09/03 23:38:35 - mmengine - INFO - Epoch(train) [23][940/1345] lr: 1.0000e-02 eta: 2:10:16 time: 0.1938 data_time: 0.0104 memory: 7116 grad_norm: 6.6401 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.4747 loss: 2.4747 2022/09/03 23:38:39 - mmengine - INFO - Epoch(train) [23][960/1345] lr: 1.0000e-02 eta: 2:10:11 time: 0.1935 data_time: 0.0104 memory: 7116 grad_norm: 6.7265 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.2942 loss: 2.2942 2022/09/03 23:38:43 - mmengine - INFO - Epoch(train) [23][980/1345] lr: 1.0000e-02 eta: 2:10:07 time: 0.1942 data_time: 0.0124 memory: 7116 grad_norm: 6.6760 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3628 loss: 2.3628 2022/09/03 23:38:47 - mmengine - INFO - Epoch(train) [23][1000/1345] lr: 1.0000e-02 eta: 2:10:02 time: 0.1918 data_time: 0.0116 memory: 7116 grad_norm: 6.5681 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.2384 loss: 2.2384 2022/09/03 23:38:51 - mmengine - INFO - Epoch(train) [23][1020/1345] lr: 1.0000e-02 eta: 2:09:57 time: 0.1966 data_time: 0.0097 memory: 7116 grad_norm: 6.5094 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.1894 loss: 2.1894 2022/09/03 23:38:54 - mmengine - INFO - Epoch(train) [23][1040/1345] lr: 1.0000e-02 eta: 2:09:53 time: 0.1953 data_time: 0.0111 memory: 7116 grad_norm: 6.4975 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3398 loss: 2.3398 2022/09/03 23:38:58 - mmengine - INFO - Epoch(train) [23][1060/1345] lr: 1.0000e-02 eta: 2:09:48 time: 0.1966 data_time: 0.0110 memory: 7116 grad_norm: 6.7907 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1865 loss: 2.1865 2022/09/03 23:39:02 - mmengine - INFO - Epoch(train) [23][1080/1345] lr: 1.0000e-02 eta: 2:09:43 time: 0.1961 data_time: 0.0094 memory: 7116 grad_norm: 6.4308 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.0413 loss: 2.0413 2022/09/03 23:39:06 - mmengine - INFO - Epoch(train) [23][1100/1345] lr: 1.0000e-02 eta: 2:09:39 time: 0.1957 data_time: 0.0120 memory: 7116 grad_norm: 6.2109 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.0744 loss: 2.0744 2022/09/03 23:39:10 - mmengine - INFO - Epoch(train) [23][1120/1345] lr: 1.0000e-02 eta: 2:09:34 time: 0.1974 data_time: 0.0114 memory: 7116 grad_norm: 6.4492 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1109 loss: 2.1109 2022/09/03 23:39:14 - mmengine - INFO - Epoch(train) [23][1140/1345] lr: 1.0000e-02 eta: 2:09:29 time: 0.1966 data_time: 0.0101 memory: 7116 grad_norm: 6.1171 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2107 loss: 2.2107 2022/09/03 23:39:18 - mmengine - INFO - Epoch(train) [23][1160/1345] lr: 1.0000e-02 eta: 2:09:25 time: 0.1960 data_time: 0.0120 memory: 7116 grad_norm: 6.6353 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.1027 loss: 2.1027 2022/09/03 23:39:22 - mmengine - INFO - Epoch(train) [23][1180/1345] lr: 1.0000e-02 eta: 2:09:20 time: 0.2079 data_time: 0.0098 memory: 7116 grad_norm: 6.5657 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.3330 loss: 2.3330 2022/09/03 23:39:26 - mmengine - INFO - Epoch(train) [23][1200/1345] lr: 1.0000e-02 eta: 2:09:16 time: 0.1940 data_time: 0.0097 memory: 7116 grad_norm: 6.5360 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.5218 loss: 2.5218 2022/09/03 23:39:30 - mmengine - INFO - Epoch(train) [23][1220/1345] lr: 1.0000e-02 eta: 2:09:11 time: 0.1979 data_time: 0.0122 memory: 7116 grad_norm: 7.0345 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3066 loss: 2.3066 2022/09/03 23:39:34 - mmengine - INFO - Epoch(train) [23][1240/1345] lr: 1.0000e-02 eta: 2:09:07 time: 0.1982 data_time: 0.0096 memory: 7116 grad_norm: 6.6957 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2806 loss: 2.2806 2022/09/03 23:39:38 - mmengine - INFO - Epoch(train) [23][1260/1345] lr: 1.0000e-02 eta: 2:09:02 time: 0.1914 data_time: 0.0107 memory: 7116 grad_norm: 6.6391 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.2212 loss: 2.2212 2022/09/03 23:39:42 - mmengine - INFO - Epoch(train) [23][1280/1345] lr: 1.0000e-02 eta: 2:08:57 time: 0.1981 data_time: 0.0136 memory: 7116 grad_norm: 6.4769 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3590 loss: 2.3590 2022/09/03 23:39:46 - mmengine - INFO - Epoch(train) [23][1300/1345] lr: 1.0000e-02 eta: 2:08:52 time: 0.1909 data_time: 0.0109 memory: 7116 grad_norm: 6.5248 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.0956 loss: 2.0956 2022/09/03 23:39:49 - mmengine - INFO - Epoch(train) [23][1320/1345] lr: 1.0000e-02 eta: 2:08:48 time: 0.1881 data_time: 0.0103 memory: 7116 grad_norm: 6.5871 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2964 loss: 2.2964 2022/09/03 23:39:53 - mmengine - INFO - Epoch(train) [23][1340/1345] lr: 1.0000e-02 eta: 2:08:43 time: 0.2010 data_time: 0.0112 memory: 7116 grad_norm: 6.6533 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.1237 loss: 2.1237 2022/09/03 23:39:54 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:39:54 - mmengine - INFO - Epoch(train) [23][1345/1345] lr: 1.0000e-02 eta: 2:08:43 time: 0.1925 data_time: 0.0094 memory: 7116 grad_norm: 7.4970 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.3492 loss: 2.3492 2022/09/03 23:39:54 - mmengine - INFO - Saving checkpoint at 23 epochs 2022/09/03 23:39:57 - mmengine - INFO - Epoch(val) [23][20/181] eta: 0:00:07 time: 0.0496 data_time: 0.0120 memory: 1114 2022/09/03 23:39:58 - mmengine - INFO - Epoch(val) [23][40/181] eta: 0:00:05 time: 0.0419 data_time: 0.0064 memory: 1114 2022/09/03 23:39:59 - mmengine - INFO - Epoch(val) [23][60/181] eta: 0:00:05 time: 0.0457 data_time: 0.0082 memory: 1114 2022/09/03 23:40:00 - mmengine - INFO - Epoch(val) [23][80/181] eta: 0:00:04 time: 0.0458 data_time: 0.0081 memory: 1114 2022/09/03 23:40:01 - mmengine - INFO - Epoch(val) [23][100/181] eta: 0:00:03 time: 0.0462 data_time: 0.0091 memory: 1114 2022/09/03 23:40:02 - mmengine - INFO - Epoch(val) [23][120/181] eta: 0:00:02 time: 0.0466 data_time: 0.0086 memory: 1114 2022/09/03 23:40:03 - mmengine - INFO - Epoch(val) [23][140/181] eta: 0:00:01 time: 0.0443 data_time: 0.0076 memory: 1114 2022/09/03 23:40:04 - mmengine - INFO - Epoch(val) [23][160/181] eta: 0:00:01 time: 0.0479 data_time: 0.0087 memory: 1114 2022/09/03 23:40:05 - mmengine - INFO - Epoch(val) [23][180/181] eta: 0:00:00 time: 0.0451 data_time: 0.0082 memory: 1114 2022/09/03 23:40:07 - mmengine - INFO - Epoch(val) [23][181/181] acc/top1: 0.3175 acc/top5: 0.6153 acc/mean1: 0.2844 2022/09/03 23:40:11 - mmengine - INFO - Epoch(train) [24][20/1345] lr: 1.0000e-02 eta: 2:08:36 time: 0.2107 data_time: 0.0166 memory: 7116 grad_norm: 6.7151 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3513 loss: 2.3513 2022/09/03 23:40:15 - mmengine - INFO - Epoch(train) [24][40/1345] lr: 1.0000e-02 eta: 2:08:32 time: 0.1951 data_time: 0.0108 memory: 7116 grad_norm: 6.4664 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3548 loss: 2.3548 2022/09/03 23:40:19 - mmengine - INFO - Epoch(train) [24][60/1345] lr: 1.0000e-02 eta: 2:08:27 time: 0.2083 data_time: 0.0093 memory: 7116 grad_norm: 6.6946 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 2.2257 loss: 2.2257 2022/09/03 23:40:20 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:40:23 - mmengine - INFO - Epoch(train) [24][80/1345] lr: 1.0000e-02 eta: 2:08:23 time: 0.2006 data_time: 0.0122 memory: 7116 grad_norm: 6.6905 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2150 loss: 2.2150 2022/09/03 23:40:27 - mmengine - INFO - Epoch(train) [24][100/1345] lr: 1.0000e-02 eta: 2:08:18 time: 0.1946 data_time: 0.0098 memory: 7116 grad_norm: 6.7646 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0965 loss: 2.0965 2022/09/03 23:40:31 - mmengine - INFO - Epoch(train) [24][120/1345] lr: 1.0000e-02 eta: 2:08:14 time: 0.2017 data_time: 0.0101 memory: 7116 grad_norm: 6.6277 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.3541 loss: 2.3541 2022/09/03 23:40:35 - mmengine - INFO - Epoch(train) [24][140/1345] lr: 1.0000e-02 eta: 2:08:09 time: 0.1983 data_time: 0.0121 memory: 7116 grad_norm: 6.4275 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.3335 loss: 2.3335 2022/09/03 23:40:39 - mmengine - INFO - Epoch(train) [24][160/1345] lr: 1.0000e-02 eta: 2:08:04 time: 0.1908 data_time: 0.0112 memory: 7116 grad_norm: 6.8218 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2213 loss: 2.2213 2022/09/03 23:40:43 - mmengine - INFO - Epoch(train) [24][180/1345] lr: 1.0000e-02 eta: 2:08:00 time: 0.1945 data_time: 0.0110 memory: 7116 grad_norm: 6.6834 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1036 loss: 2.1036 2022/09/03 23:40:46 - mmengine - INFO - Epoch(train) [24][200/1345] lr: 1.0000e-02 eta: 2:07:55 time: 0.1954 data_time: 0.0116 memory: 7116 grad_norm: 6.7729 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2336 loss: 2.2336 2022/09/03 23:40:50 - mmengine - INFO - Epoch(train) [24][220/1345] lr: 1.0000e-02 eta: 2:07:50 time: 0.1960 data_time: 0.0099 memory: 7116 grad_norm: 6.6525 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.5570 loss: 2.5570 2022/09/03 23:40:54 - mmengine - INFO - Epoch(train) [24][240/1345] lr: 1.0000e-02 eta: 2:07:46 time: 0.1921 data_time: 0.0108 memory: 7116 grad_norm: 6.6371 top1_acc: 0.0000 top5_acc: 0.5000 loss_cls: 2.4406 loss: 2.4406 2022/09/03 23:40:58 - mmengine - INFO - Epoch(train) [24][260/1345] lr: 1.0000e-02 eta: 2:07:41 time: 0.1922 data_time: 0.0138 memory: 7116 grad_norm: 6.4784 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.1601 loss: 2.1601 2022/09/03 23:41:02 - mmengine - INFO - Epoch(train) [24][280/1345] lr: 1.0000e-02 eta: 2:07:36 time: 0.1971 data_time: 0.0096 memory: 7116 grad_norm: 6.5370 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2192 loss: 2.2192 2022/09/03 23:41:06 - mmengine - INFO - Epoch(train) [24][300/1345] lr: 1.0000e-02 eta: 2:07:32 time: 0.1938 data_time: 0.0114 memory: 7116 grad_norm: 6.7420 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.2885 loss: 2.2885 2022/09/03 23:41:10 - mmengine - INFO - Epoch(train) [24][320/1345] lr: 1.0000e-02 eta: 2:07:27 time: 0.1954 data_time: 0.0126 memory: 7116 grad_norm: 6.4868 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.9963 loss: 1.9963 2022/09/03 23:41:14 - mmengine - INFO - Epoch(train) [24][340/1345] lr: 1.0000e-02 eta: 2:07:22 time: 0.1938 data_time: 0.0104 memory: 7116 grad_norm: 6.4032 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.2292 loss: 2.2292 2022/09/03 23:41:18 - mmengine - INFO - Epoch(train) [24][360/1345] lr: 1.0000e-02 eta: 2:07:18 time: 0.1913 data_time: 0.0103 memory: 7116 grad_norm: 6.5979 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.3944 loss: 2.3944 2022/09/03 23:41:21 - mmengine - INFO - Epoch(train) [24][380/1345] lr: 1.0000e-02 eta: 2:07:13 time: 0.1973 data_time: 0.0125 memory: 7116 grad_norm: 6.6009 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.3186 loss: 2.3186 2022/09/03 23:41:25 - mmengine - INFO - Epoch(train) [24][400/1345] lr: 1.0000e-02 eta: 2:07:08 time: 0.1924 data_time: 0.0102 memory: 7116 grad_norm: 6.5468 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.1073 loss: 2.1073 2022/09/03 23:41:29 - mmengine - INFO - Epoch(train) [24][420/1345] lr: 1.0000e-02 eta: 2:07:04 time: 0.1882 data_time: 0.0098 memory: 7116 grad_norm: 6.4504 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.1427 loss: 2.1427 2022/09/03 23:41:33 - mmengine - INFO - Epoch(train) [24][440/1345] lr: 1.0000e-02 eta: 2:06:59 time: 0.1953 data_time: 0.0127 memory: 7116 grad_norm: 6.6110 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3191 loss: 2.3191 2022/09/03 23:41:37 - mmengine - INFO - Epoch(train) [24][460/1345] lr: 1.0000e-02 eta: 2:06:54 time: 0.1872 data_time: 0.0104 memory: 7116 grad_norm: 6.4222 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2190 loss: 2.2190 2022/09/03 23:41:41 - mmengine - INFO - Epoch(train) [24][480/1345] lr: 1.0000e-02 eta: 2:06:50 time: 0.2041 data_time: 0.0108 memory: 7116 grad_norm: 6.5856 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.4248 loss: 2.4248 2022/09/03 23:41:45 - mmengine - INFO - Epoch(train) [24][500/1345] lr: 1.0000e-02 eta: 2:06:45 time: 0.1885 data_time: 0.0130 memory: 7116 grad_norm: 6.4347 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2092 loss: 2.2092 2022/09/03 23:41:48 - mmengine - INFO - Epoch(train) [24][520/1345] lr: 1.0000e-02 eta: 2:06:40 time: 0.1933 data_time: 0.0111 memory: 7116 grad_norm: 6.5399 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3286 loss: 2.3286 2022/09/03 23:41:53 - mmengine - INFO - Epoch(train) [24][540/1345] lr: 1.0000e-02 eta: 2:06:36 time: 0.2061 data_time: 0.0098 memory: 7116 grad_norm: 6.7310 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 2.1656 loss: 2.1656 2022/09/03 23:41:56 - mmengine - INFO - Epoch(train) [24][560/1345] lr: 1.0000e-02 eta: 2:06:31 time: 0.1951 data_time: 0.0118 memory: 7116 grad_norm: 6.5922 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3124 loss: 2.3124 2022/09/03 23:42:00 - mmengine - INFO - Epoch(train) [24][580/1345] lr: 1.0000e-02 eta: 2:06:26 time: 0.1914 data_time: 0.0100 memory: 7116 grad_norm: 6.7444 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4527 loss: 2.4527 2022/09/03 23:42:04 - mmengine - INFO - Epoch(train) [24][600/1345] lr: 1.0000e-02 eta: 2:06:22 time: 0.1933 data_time: 0.0118 memory: 7116 grad_norm: 6.4698 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.3132 loss: 2.3132 2022/09/03 23:42:08 - mmengine - INFO - Epoch(train) [24][620/1345] lr: 1.0000e-02 eta: 2:06:17 time: 0.1877 data_time: 0.0131 memory: 7116 grad_norm: 6.5859 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2955 loss: 2.2955 2022/09/03 23:42:12 - mmengine - INFO - Epoch(train) [24][640/1345] lr: 1.0000e-02 eta: 2:06:12 time: 0.2008 data_time: 0.0107 memory: 7116 grad_norm: 6.6006 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9905 loss: 1.9905 2022/09/03 23:42:16 - mmengine - INFO - Epoch(train) [24][660/1345] lr: 1.0000e-02 eta: 2:06:08 time: 0.1926 data_time: 0.0110 memory: 7116 grad_norm: 6.4015 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3124 loss: 2.3124 2022/09/03 23:42:20 - mmengine - INFO - Epoch(train) [24][680/1345] lr: 1.0000e-02 eta: 2:06:03 time: 0.1894 data_time: 0.0129 memory: 7116 grad_norm: 6.6399 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.3351 loss: 2.3351 2022/09/03 23:42:24 - mmengine - INFO - Epoch(train) [24][700/1345] lr: 1.0000e-02 eta: 2:05:58 time: 0.1944 data_time: 0.0103 memory: 7116 grad_norm: 6.4650 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.8491 loss: 2.8491 2022/09/03 23:42:27 - mmengine - INFO - Epoch(train) [24][720/1345] lr: 1.0000e-02 eta: 2:05:54 time: 0.1926 data_time: 0.0104 memory: 7116 grad_norm: 6.5546 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1396 loss: 2.1396 2022/09/03 23:42:32 - mmengine - INFO - Epoch(train) [24][740/1345] lr: 1.0000e-02 eta: 2:05:49 time: 0.2085 data_time: 0.0145 memory: 7116 grad_norm: 6.5264 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.2368 loss: 2.2368 2022/09/03 23:42:35 - mmengine - INFO - Epoch(train) [24][760/1345] lr: 1.0000e-02 eta: 2:05:45 time: 0.1921 data_time: 0.0104 memory: 7116 grad_norm: 6.4973 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.4564 loss: 2.4564 2022/09/03 23:42:39 - mmengine - INFO - Epoch(train) [24][780/1345] lr: 1.0000e-02 eta: 2:05:40 time: 0.1916 data_time: 0.0101 memory: 7116 grad_norm: 6.5643 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3397 loss: 2.3397 2022/09/03 23:42:43 - mmengine - INFO - Epoch(train) [24][800/1345] lr: 1.0000e-02 eta: 2:05:35 time: 0.1980 data_time: 0.0124 memory: 7116 grad_norm: 6.5036 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.1626 loss: 2.1626 2022/09/03 23:42:47 - mmengine - INFO - Epoch(train) [24][820/1345] lr: 1.0000e-02 eta: 2:05:31 time: 0.1910 data_time: 0.0104 memory: 7116 grad_norm: 6.5728 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1736 loss: 2.1736 2022/09/03 23:42:51 - mmengine - INFO - Epoch(train) [24][840/1345] lr: 1.0000e-02 eta: 2:05:26 time: 0.2031 data_time: 0.0114 memory: 7116 grad_norm: 6.6002 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2465 loss: 2.2465 2022/09/03 23:42:55 - mmengine - INFO - Epoch(train) [24][860/1345] lr: 1.0000e-02 eta: 2:05:21 time: 0.1925 data_time: 0.0131 memory: 7116 grad_norm: 6.4865 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.6083 loss: 2.6083 2022/09/03 23:42:59 - mmengine - INFO - Epoch(train) [24][880/1345] lr: 1.0000e-02 eta: 2:05:17 time: 0.1917 data_time: 0.0102 memory: 7116 grad_norm: 6.7489 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2187 loss: 2.2187 2022/09/03 23:43:03 - mmengine - INFO - Epoch(train) [24][900/1345] lr: 1.0000e-02 eta: 2:05:12 time: 0.1962 data_time: 0.0103 memory: 7116 grad_norm: 6.7739 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2913 loss: 2.2913 2022/09/03 23:43:07 - mmengine - INFO - Epoch(train) [24][920/1345] lr: 1.0000e-02 eta: 2:05:08 time: 0.1996 data_time: 0.0132 memory: 7116 grad_norm: 6.4361 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4607 loss: 2.4607 2022/09/03 23:43:11 - mmengine - INFO - Epoch(train) [24][940/1345] lr: 1.0000e-02 eta: 2:05:03 time: 0.1986 data_time: 0.0100 memory: 7116 grad_norm: 6.3308 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3336 loss: 2.3336 2022/09/03 23:43:15 - mmengine - INFO - Epoch(train) [24][960/1345] lr: 1.0000e-02 eta: 2:04:58 time: 0.1941 data_time: 0.0114 memory: 7116 grad_norm: 6.5555 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.2242 loss: 2.2242 2022/09/03 23:43:18 - mmengine - INFO - Epoch(train) [24][980/1345] lr: 1.0000e-02 eta: 2:04:54 time: 0.1927 data_time: 0.0130 memory: 7116 grad_norm: 6.7257 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.3547 loss: 2.3547 2022/09/03 23:43:22 - mmengine - INFO - Epoch(train) [24][1000/1345] lr: 1.0000e-02 eta: 2:04:49 time: 0.1961 data_time: 0.0114 memory: 7116 grad_norm: 6.5289 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.1627 loss: 2.1627 2022/09/03 23:43:26 - mmengine - INFO - Epoch(train) [24][1020/1345] lr: 1.0000e-02 eta: 2:04:45 time: 0.1934 data_time: 0.0097 memory: 7116 grad_norm: 6.7820 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.1586 loss: 2.1586 2022/09/03 23:43:30 - mmengine - INFO - Epoch(train) [24][1040/1345] lr: 1.0000e-02 eta: 2:04:40 time: 0.1987 data_time: 0.0136 memory: 7116 grad_norm: 7.0298 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2311 loss: 2.2311 2022/09/03 23:43:34 - mmengine - INFO - Epoch(train) [24][1060/1345] lr: 1.0000e-02 eta: 2:04:35 time: 0.1984 data_time: 0.0112 memory: 7116 grad_norm: 6.4003 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.5542 loss: 2.5542 2022/09/03 23:43:35 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:43:38 - mmengine - INFO - Epoch(train) [24][1080/1345] lr: 1.0000e-02 eta: 2:04:31 time: 0.1895 data_time: 0.0111 memory: 7116 grad_norm: 6.8960 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4213 loss: 2.4213 2022/09/03 23:43:42 - mmengine - INFO - Epoch(train) [24][1100/1345] lr: 1.0000e-02 eta: 2:04:26 time: 0.1955 data_time: 0.0121 memory: 7116 grad_norm: 6.6617 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.5091 loss: 2.5091 2022/09/03 23:43:46 - mmengine - INFO - Epoch(train) [24][1120/1345] lr: 1.0000e-02 eta: 2:04:21 time: 0.1920 data_time: 0.0100 memory: 7116 grad_norm: 6.4624 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2346 loss: 2.2346 2022/09/03 23:43:50 - mmengine - INFO - Epoch(train) [24][1140/1345] lr: 1.0000e-02 eta: 2:04:17 time: 0.1941 data_time: 0.0110 memory: 7116 grad_norm: 6.4578 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.0940 loss: 2.0940 2022/09/03 23:43:53 - mmengine - INFO - Epoch(train) [24][1160/1345] lr: 1.0000e-02 eta: 2:04:12 time: 0.1935 data_time: 0.0121 memory: 7116 grad_norm: 6.5726 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1431 loss: 2.1431 2022/09/03 23:43:57 - mmengine - INFO - Epoch(train) [24][1180/1345] lr: 1.0000e-02 eta: 2:04:07 time: 0.1905 data_time: 0.0094 memory: 7116 grad_norm: 6.5665 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.1059 loss: 2.1059 2022/09/03 23:44:01 - mmengine - INFO - Epoch(train) [24][1200/1345] lr: 1.0000e-02 eta: 2:04:03 time: 0.1989 data_time: 0.0101 memory: 7116 grad_norm: 6.5131 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9136 loss: 1.9136 2022/09/03 23:44:05 - mmengine - INFO - Epoch(train) [24][1220/1345] lr: 1.0000e-02 eta: 2:03:58 time: 0.1911 data_time: 0.0134 memory: 7116 grad_norm: 6.5388 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 2.2855 loss: 2.2855 2022/09/03 23:44:09 - mmengine - INFO - Epoch(train) [24][1240/1345] lr: 1.0000e-02 eta: 2:03:54 time: 0.1897 data_time: 0.0108 memory: 7116 grad_norm: 6.3370 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.2324 loss: 2.2324 2022/09/03 23:44:13 - mmengine - INFO - Epoch(train) [24][1260/1345] lr: 1.0000e-02 eta: 2:03:49 time: 0.1943 data_time: 0.0106 memory: 7116 grad_norm: 6.5587 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3583 loss: 2.3583 2022/09/03 23:44:17 - mmengine - INFO - Epoch(train) [24][1280/1345] lr: 1.0000e-02 eta: 2:03:44 time: 0.1907 data_time: 0.0118 memory: 7116 grad_norm: 6.2891 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5398 loss: 2.5398 2022/09/03 23:44:21 - mmengine - INFO - Epoch(train) [24][1300/1345] lr: 1.0000e-02 eta: 2:03:40 time: 0.1996 data_time: 0.0093 memory: 7116 grad_norm: 6.7337 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2034 loss: 2.2034 2022/09/03 23:44:25 - mmengine - INFO - Epoch(train) [24][1320/1345] lr: 1.0000e-02 eta: 2:03:35 time: 0.1972 data_time: 0.0089 memory: 7116 grad_norm: 6.3978 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.4248 loss: 2.4248 2022/09/03 23:44:28 - mmengine - INFO - Epoch(train) [24][1340/1345] lr: 1.0000e-02 eta: 2:03:30 time: 0.1868 data_time: 0.0124 memory: 7116 grad_norm: 6.3104 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2533 loss: 2.2533 2022/09/03 23:44:29 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:44:29 - mmengine - INFO - Epoch(train) [24][1345/1345] lr: 1.0000e-02 eta: 2:03:30 time: 0.1862 data_time: 0.0102 memory: 7116 grad_norm: 6.7164 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.2887 loss: 2.2887 2022/09/03 23:44:29 - mmengine - INFO - Saving checkpoint at 24 epochs 2022/09/03 23:44:32 - mmengine - INFO - Epoch(val) [24][20/181] eta: 0:00:08 time: 0.0509 data_time: 0.0132 memory: 1114 2022/09/03 23:44:33 - mmengine - INFO - Epoch(val) [24][40/181] eta: 0:00:06 time: 0.0470 data_time: 0.0090 memory: 1114 2022/09/03 23:44:34 - mmengine - INFO - Epoch(val) [24][60/181] eta: 0:00:05 time: 0.0471 data_time: 0.0090 memory: 1114 2022/09/03 23:44:35 - mmengine - INFO - Epoch(val) [24][80/181] eta: 0:00:04 time: 0.0468 data_time: 0.0091 memory: 1114 2022/09/03 23:44:36 - mmengine - INFO - Epoch(val) [24][100/181] eta: 0:00:03 time: 0.0456 data_time: 0.0088 memory: 1114 2022/09/03 23:44:37 - mmengine - INFO - Epoch(val) [24][120/181] eta: 0:00:02 time: 0.0482 data_time: 0.0094 memory: 1114 2022/09/03 23:44:38 - mmengine - INFO - Epoch(val) [24][140/181] eta: 0:00:01 time: 0.0451 data_time: 0.0083 memory: 1114 2022/09/03 23:44:39 - mmengine - INFO - Epoch(val) [24][160/181] eta: 0:00:00 time: 0.0455 data_time: 0.0086 memory: 1114 2022/09/03 23:44:40 - mmengine - INFO - Epoch(val) [24][180/181] eta: 0:00:00 time: 0.0462 data_time: 0.0086 memory: 1114 2022/09/03 23:44:41 - mmengine - INFO - Epoch(val) [24][181/181] acc/top1: 0.3276 acc/top5: 0.6187 acc/mean1: 0.2876 2022/09/03 23:44:41 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_22.pth is removed 2022/09/03 23:44:43 - mmengine - INFO - The best checkpoint with 0.3276 acc/top1 at 24 epoch is saved to best_acc/top1_epoch_24.pth. 2022/09/03 23:44:47 - mmengine - INFO - Epoch(train) [25][20/1345] lr: 1.0000e-02 eta: 2:03:24 time: 0.1947 data_time: 0.0134 memory: 7116 grad_norm: 6.8989 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.3468 loss: 2.3468 2022/09/03 23:44:51 - mmengine - INFO - Epoch(train) [25][40/1345] lr: 1.0000e-02 eta: 2:03:19 time: 0.1920 data_time: 0.0098 memory: 7116 grad_norm: 6.6781 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2218 loss: 2.2218 2022/09/03 23:44:55 - mmengine - INFO - Epoch(train) [25][60/1345] lr: 1.0000e-02 eta: 2:03:14 time: 0.2020 data_time: 0.0111 memory: 7116 grad_norm: 6.6803 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4467 loss: 2.4467 2022/09/03 23:44:59 - mmengine - INFO - Epoch(train) [25][80/1345] lr: 1.0000e-02 eta: 2:03:10 time: 0.1926 data_time: 0.0120 memory: 7116 grad_norm: 6.6407 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3417 loss: 2.3417 2022/09/03 23:45:03 - mmengine - INFO - Epoch(train) [25][100/1345] lr: 1.0000e-02 eta: 2:03:05 time: 0.1994 data_time: 0.0101 memory: 7116 grad_norm: 6.5795 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3879 loss: 2.3879 2022/09/03 23:45:06 - mmengine - INFO - Epoch(train) [25][120/1345] lr: 1.0000e-02 eta: 2:03:01 time: 0.1914 data_time: 0.0103 memory: 7116 grad_norm: 6.5992 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.0871 loss: 2.0871 2022/09/03 23:45:10 - mmengine - INFO - Epoch(train) [25][140/1345] lr: 1.0000e-02 eta: 2:02:56 time: 0.1962 data_time: 0.0139 memory: 7116 grad_norm: 6.7206 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0659 loss: 2.0659 2022/09/03 23:45:14 - mmengine - INFO - Epoch(train) [25][160/1345] lr: 1.0000e-02 eta: 2:02:52 time: 0.2038 data_time: 0.0115 memory: 7116 grad_norm: 6.7291 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.3230 loss: 2.3230 2022/09/03 23:45:18 - mmengine - INFO - Epoch(train) [25][180/1345] lr: 1.0000e-02 eta: 2:02:47 time: 0.1929 data_time: 0.0099 memory: 7116 grad_norm: 6.8455 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.1446 loss: 2.1446 2022/09/03 23:45:22 - mmengine - INFO - Epoch(train) [25][200/1345] lr: 1.0000e-02 eta: 2:02:42 time: 0.1931 data_time: 0.0120 memory: 7116 grad_norm: 6.5029 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 2.3832 loss: 2.3832 2022/09/03 23:45:26 - mmengine - INFO - Epoch(train) [25][220/1345] lr: 1.0000e-02 eta: 2:02:38 time: 0.1914 data_time: 0.0097 memory: 7116 grad_norm: 6.6755 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1821 loss: 2.1821 2022/09/03 23:45:30 - mmengine - INFO - Epoch(train) [25][240/1345] lr: 1.0000e-02 eta: 2:02:33 time: 0.2052 data_time: 0.0087 memory: 7116 grad_norm: 6.2792 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1032 loss: 2.1032 2022/09/03 23:45:34 - mmengine - INFO - Epoch(train) [25][260/1345] lr: 1.0000e-02 eta: 2:02:29 time: 0.2026 data_time: 0.0134 memory: 7116 grad_norm: 6.9391 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2823 loss: 2.2823 2022/09/03 23:45:38 - mmengine - INFO - Epoch(train) [25][280/1345] lr: 1.0000e-02 eta: 2:02:24 time: 0.1919 data_time: 0.0103 memory: 7116 grad_norm: 6.6295 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2471 loss: 2.2471 2022/09/03 23:45:42 - mmengine - INFO - Epoch(train) [25][300/1345] lr: 1.0000e-02 eta: 2:02:20 time: 0.1895 data_time: 0.0103 memory: 7116 grad_norm: 6.4801 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9622 loss: 1.9622 2022/09/03 23:45:46 - mmengine - INFO - Epoch(train) [25][320/1345] lr: 1.0000e-02 eta: 2:02:15 time: 0.1960 data_time: 0.0127 memory: 7116 grad_norm: 6.5246 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.0331 loss: 2.0331 2022/09/03 23:45:50 - mmengine - INFO - Epoch(train) [25][340/1345] lr: 1.0000e-02 eta: 2:02:10 time: 0.1929 data_time: 0.0095 memory: 7116 grad_norm: 6.5656 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4751 loss: 2.4751 2022/09/03 23:45:53 - mmengine - INFO - Epoch(train) [25][360/1345] lr: 1.0000e-02 eta: 2:02:06 time: 0.1933 data_time: 0.0098 memory: 7116 grad_norm: 6.8216 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2547 loss: 2.2547 2022/09/03 23:45:57 - mmengine - INFO - Epoch(train) [25][380/1345] lr: 1.0000e-02 eta: 2:02:01 time: 0.1948 data_time: 0.0127 memory: 7116 grad_norm: 6.1379 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 2.4345 loss: 2.4345 2022/09/03 23:46:01 - mmengine - INFO - Epoch(train) [25][400/1345] lr: 1.0000e-02 eta: 2:01:56 time: 0.1895 data_time: 0.0109 memory: 7116 grad_norm: 6.8670 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.2741 loss: 2.2741 2022/09/03 23:46:05 - mmengine - INFO - Epoch(train) [25][420/1345] lr: 1.0000e-02 eta: 2:01:52 time: 0.1918 data_time: 0.0096 memory: 7116 grad_norm: 6.3692 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0353 loss: 2.0353 2022/09/03 23:46:09 - mmengine - INFO - Epoch(train) [25][440/1345] lr: 1.0000e-02 eta: 2:01:47 time: 0.1956 data_time: 0.0114 memory: 7116 grad_norm: 6.5885 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.5567 loss: 2.5567 2022/09/03 23:46:13 - mmengine - INFO - Epoch(train) [25][460/1345] lr: 1.0000e-02 eta: 2:01:43 time: 0.1984 data_time: 0.0108 memory: 7116 grad_norm: 6.7425 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1654 loss: 2.1654 2022/09/03 23:46:17 - mmengine - INFO - Epoch(train) [25][480/1345] lr: 1.0000e-02 eta: 2:01:38 time: 0.1890 data_time: 0.0100 memory: 7116 grad_norm: 6.7243 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.3682 loss: 2.3682 2022/09/03 23:46:20 - mmengine - INFO - Epoch(train) [25][500/1345] lr: 1.0000e-02 eta: 2:01:33 time: 0.1912 data_time: 0.0128 memory: 7116 grad_norm: 6.5205 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.3880 loss: 2.3880 2022/09/03 23:46:24 - mmengine - INFO - Epoch(train) [25][520/1345] lr: 1.0000e-02 eta: 2:01:29 time: 0.1932 data_time: 0.0105 memory: 7116 grad_norm: 6.8085 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1317 loss: 2.1317 2022/09/03 23:46:28 - mmengine - INFO - Epoch(train) [25][540/1345] lr: 1.0000e-02 eta: 2:01:24 time: 0.1911 data_time: 0.0103 memory: 7116 grad_norm: 6.5902 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2324 loss: 2.2324 2022/09/03 23:46:32 - mmengine - INFO - Epoch(train) [25][560/1345] lr: 1.0000e-02 eta: 2:01:19 time: 0.1909 data_time: 0.0118 memory: 7116 grad_norm: 6.8073 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.3004 loss: 2.3004 2022/09/03 23:46:36 - mmengine - INFO - Epoch(train) [25][580/1345] lr: 1.0000e-02 eta: 2:01:15 time: 0.1938 data_time: 0.0100 memory: 7116 grad_norm: 6.5795 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2762 loss: 2.2762 2022/09/03 23:46:40 - mmengine - INFO - Epoch(train) [25][600/1345] lr: 1.0000e-02 eta: 2:01:10 time: 0.1932 data_time: 0.0091 memory: 7116 grad_norm: 6.5751 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.3924 loss: 2.3924 2022/09/03 23:46:44 - mmengine - INFO - Epoch(train) [25][620/1345] lr: 1.0000e-02 eta: 2:01:06 time: 0.1962 data_time: 0.0125 memory: 7116 grad_norm: 6.5763 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2439 loss: 2.2439 2022/09/03 23:46:47 - mmengine - INFO - Epoch(train) [25][640/1345] lr: 1.0000e-02 eta: 2:01:01 time: 0.1910 data_time: 0.0100 memory: 7116 grad_norm: 6.6309 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3490 loss: 2.3490 2022/09/03 23:46:51 - mmengine - INFO - Epoch(train) [25][660/1345] lr: 1.0000e-02 eta: 2:00:56 time: 0.1929 data_time: 0.0107 memory: 7116 grad_norm: 6.6151 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2891 loss: 2.2891 2022/09/03 23:46:55 - mmengine - INFO - Epoch(train) [25][680/1345] lr: 1.0000e-02 eta: 2:00:52 time: 0.1933 data_time: 0.0130 memory: 7116 grad_norm: 6.6878 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.3273 loss: 2.3273 2022/09/03 23:46:59 - mmengine - INFO - Epoch(train) [25][700/1345] lr: 1.0000e-02 eta: 2:00:47 time: 0.1917 data_time: 0.0103 memory: 7116 grad_norm: 6.5624 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2554 loss: 2.2554 2022/09/03 23:47:03 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:47:03 - mmengine - INFO - Epoch(train) [25][720/1345] lr: 1.0000e-02 eta: 2:00:42 time: 0.1930 data_time: 0.0105 memory: 7116 grad_norm: 6.7107 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0386 loss: 2.0386 2022/09/03 23:47:07 - mmengine - INFO - Epoch(train) [25][740/1345] lr: 1.0000e-02 eta: 2:00:38 time: 0.1923 data_time: 0.0131 memory: 7116 grad_norm: 6.4576 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1600 loss: 2.1600 2022/09/03 23:47:11 - mmengine - INFO - Epoch(train) [25][760/1345] lr: 1.0000e-02 eta: 2:00:33 time: 0.1894 data_time: 0.0109 memory: 7116 grad_norm: 6.4290 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0136 loss: 2.0136 2022/09/03 23:47:14 - mmengine - INFO - Epoch(train) [25][780/1345] lr: 1.0000e-02 eta: 2:00:28 time: 0.1913 data_time: 0.0099 memory: 7116 grad_norm: 6.4429 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3196 loss: 2.3196 2022/09/03 23:47:18 - mmengine - INFO - Epoch(train) [25][800/1345] lr: 1.0000e-02 eta: 2:00:24 time: 0.1937 data_time: 0.0130 memory: 7116 grad_norm: 6.4648 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2400 loss: 2.2400 2022/09/03 23:47:22 - mmengine - INFO - Epoch(train) [25][820/1345] lr: 1.0000e-02 eta: 2:00:19 time: 0.1917 data_time: 0.0113 memory: 7116 grad_norm: 6.5718 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.2777 loss: 2.2777 2022/09/03 23:47:26 - mmengine - INFO - Epoch(train) [25][840/1345] lr: 1.0000e-02 eta: 2:00:15 time: 0.1929 data_time: 0.0109 memory: 7116 grad_norm: 6.5260 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3808 loss: 2.3808 2022/09/03 23:47:30 - mmengine - INFO - Epoch(train) [25][860/1345] lr: 1.0000e-02 eta: 2:00:10 time: 0.2028 data_time: 0.0130 memory: 7116 grad_norm: 6.4345 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2165 loss: 2.2165 2022/09/03 23:47:34 - mmengine - INFO - Epoch(train) [25][880/1345] lr: 1.0000e-02 eta: 2:00:06 time: 0.1955 data_time: 0.0101 memory: 7116 grad_norm: 6.7431 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.0894 loss: 2.0894 2022/09/03 23:47:38 - mmengine - INFO - Epoch(train) [25][900/1345] lr: 1.0000e-02 eta: 2:00:01 time: 0.1925 data_time: 0.0112 memory: 7116 grad_norm: 6.5199 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1258 loss: 2.1258 2022/09/03 23:47:42 - mmengine - INFO - Epoch(train) [25][920/1345] lr: 1.0000e-02 eta: 1:59:56 time: 0.1944 data_time: 0.0130 memory: 7116 grad_norm: 6.7584 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0517 loss: 2.0517 2022/09/03 23:47:46 - mmengine - INFO - Epoch(train) [25][940/1345] lr: 1.0000e-02 eta: 1:59:52 time: 0.1935 data_time: 0.0105 memory: 7116 grad_norm: 6.5055 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.3913 loss: 2.3913 2022/09/03 23:47:49 - mmengine - INFO - Epoch(train) [25][960/1345] lr: 1.0000e-02 eta: 1:59:47 time: 0.1914 data_time: 0.0094 memory: 7116 grad_norm: 6.8061 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2568 loss: 2.2568 2022/09/03 23:47:53 - mmengine - INFO - Epoch(train) [25][980/1345] lr: 1.0000e-02 eta: 1:59:43 time: 0.1951 data_time: 0.0122 memory: 7116 grad_norm: 6.5908 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.0783 loss: 2.0783 2022/09/03 23:47:57 - mmengine - INFO - Epoch(train) [25][1000/1345] lr: 1.0000e-02 eta: 1:59:38 time: 0.1925 data_time: 0.0096 memory: 7116 grad_norm: 6.6542 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.2333 loss: 2.2333 2022/09/03 23:48:01 - mmengine - INFO - Epoch(train) [25][1020/1345] lr: 1.0000e-02 eta: 1:59:34 time: 0.1942 data_time: 0.0096 memory: 7116 grad_norm: 6.5008 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.2437 loss: 2.2437 2022/09/03 23:48:05 - mmengine - INFO - Epoch(train) [25][1040/1345] lr: 1.0000e-02 eta: 1:59:29 time: 0.1929 data_time: 0.0124 memory: 7116 grad_norm: 6.6416 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.5207 loss: 2.5207 2022/09/03 23:48:09 - mmengine - INFO - Epoch(train) [25][1060/1345] lr: 1.0000e-02 eta: 1:59:24 time: 0.1909 data_time: 0.0096 memory: 7116 grad_norm: 6.5489 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.4175 loss: 2.4175 2022/09/03 23:48:13 - mmengine - INFO - Epoch(train) [25][1080/1345] lr: 1.0000e-02 eta: 1:59:20 time: 0.1961 data_time: 0.0094 memory: 7116 grad_norm: 6.4780 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0285 loss: 2.0285 2022/09/03 23:48:16 - mmengine - INFO - Epoch(train) [25][1100/1345] lr: 1.0000e-02 eta: 1:59:15 time: 0.1924 data_time: 0.0129 memory: 7116 grad_norm: 6.7414 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1282 loss: 2.1282 2022/09/03 23:48:20 - mmengine - INFO - Epoch(train) [25][1120/1345] lr: 1.0000e-02 eta: 1:59:11 time: 0.1927 data_time: 0.0092 memory: 7116 grad_norm: 6.5695 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3065 loss: 2.3065 2022/09/03 23:48:24 - mmengine - INFO - Epoch(train) [25][1140/1345] lr: 1.0000e-02 eta: 1:59:06 time: 0.1961 data_time: 0.0098 memory: 7116 grad_norm: 6.6615 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.9991 loss: 1.9991 2022/09/03 23:48:28 - mmengine - INFO - Epoch(train) [25][1160/1345] lr: 1.0000e-02 eta: 1:59:01 time: 0.1937 data_time: 0.0126 memory: 7116 grad_norm: 6.6298 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.5075 loss: 2.5075 2022/09/03 23:48:32 - mmengine - INFO - Epoch(train) [25][1180/1345] lr: 1.0000e-02 eta: 1:58:57 time: 0.1965 data_time: 0.0117 memory: 7116 grad_norm: 6.4465 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.2627 loss: 2.2627 2022/09/03 23:48:36 - mmengine - INFO - Epoch(train) [25][1200/1345] lr: 1.0000e-02 eta: 1:58:52 time: 0.1904 data_time: 0.0113 memory: 7116 grad_norm: 6.4997 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.1240 loss: 2.1240 2022/09/03 23:48:40 - mmengine - INFO - Epoch(train) [25][1220/1345] lr: 1.0000e-02 eta: 1:58:48 time: 0.1916 data_time: 0.0132 memory: 7116 grad_norm: 6.4931 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.0894 loss: 2.0894 2022/09/03 23:48:43 - mmengine - INFO - Epoch(train) [25][1240/1345] lr: 1.0000e-02 eta: 1:58:43 time: 0.1890 data_time: 0.0104 memory: 7116 grad_norm: 6.8735 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2674 loss: 2.2674 2022/09/03 23:48:47 - mmengine - INFO - Epoch(train) [25][1260/1345] lr: 1.0000e-02 eta: 1:58:38 time: 0.1924 data_time: 0.0123 memory: 7116 grad_norm: 6.4038 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.2593 loss: 2.2593 2022/09/03 23:48:51 - mmengine - INFO - Epoch(train) [25][1280/1345] lr: 1.0000e-02 eta: 1:58:34 time: 0.1918 data_time: 0.0135 memory: 7116 grad_norm: 6.7648 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3732 loss: 2.3732 2022/09/03 23:48:55 - mmengine - INFO - Epoch(train) [25][1300/1345] lr: 1.0000e-02 eta: 1:58:29 time: 0.1904 data_time: 0.0096 memory: 7116 grad_norm: 6.3753 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2364 loss: 2.2364 2022/09/03 23:48:59 - mmengine - INFO - Epoch(train) [25][1320/1345] lr: 1.0000e-02 eta: 1:58:24 time: 0.1890 data_time: 0.0099 memory: 7116 grad_norm: 6.6474 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.2189 loss: 2.2189 2022/09/03 23:49:03 - mmengine - INFO - Epoch(train) [25][1340/1345] lr: 1.0000e-02 eta: 1:58:20 time: 0.1932 data_time: 0.0127 memory: 7116 grad_norm: 6.5759 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.3002 loss: 2.3002 2022/09/03 23:49:04 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:49:04 - mmengine - INFO - Epoch(train) [25][1345/1345] lr: 1.0000e-02 eta: 1:58:20 time: 0.1892 data_time: 0.0108 memory: 7116 grad_norm: 6.6234 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.3155 loss: 2.3155 2022/09/03 23:49:04 - mmengine - INFO - Saving checkpoint at 25 epochs 2022/09/03 23:49:06 - mmengine - INFO - Epoch(val) [25][20/181] eta: 0:00:07 time: 0.0493 data_time: 0.0117 memory: 1114 2022/09/03 23:49:07 - mmengine - INFO - Epoch(val) [25][40/181] eta: 0:00:06 time: 0.0452 data_time: 0.0084 memory: 1114 2022/09/03 23:49:08 - mmengine - INFO - Epoch(val) [25][60/181] eta: 0:00:05 time: 0.0456 data_time: 0.0087 memory: 1114 2022/09/03 23:49:09 - mmengine - INFO - Epoch(val) [25][80/181] eta: 0:00:04 time: 0.0432 data_time: 0.0072 memory: 1114 2022/09/03 23:49:10 - mmengine - INFO - Epoch(val) [25][100/181] eta: 0:00:03 time: 0.0449 data_time: 0.0080 memory: 1114 2022/09/03 23:49:11 - mmengine - INFO - Epoch(val) [25][120/181] eta: 0:00:02 time: 0.0470 data_time: 0.0085 memory: 1114 2022/09/03 23:49:12 - mmengine - INFO - Epoch(val) [25][140/181] eta: 0:00:01 time: 0.0431 data_time: 0.0073 memory: 1114 2022/09/03 23:49:13 - mmengine - INFO - Epoch(val) [25][160/181] eta: 0:00:00 time: 0.0437 data_time: 0.0073 memory: 1114 2022/09/03 23:49:14 - mmengine - INFO - Epoch(val) [25][180/181] eta: 0:00:00 time: 0.0433 data_time: 0.0072 memory: 1114 2022/09/03 23:49:16 - mmengine - INFO - Epoch(val) [25][181/181] acc/top1: 0.3114 acc/top5: 0.5948 acc/mean1: 0.2810 2022/09/03 23:49:20 - mmengine - INFO - Epoch(train) [26][20/1345] lr: 1.0000e-02 eta: 1:58:14 time: 0.2340 data_time: 0.0398 memory: 7116 grad_norm: 6.4928 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4858 loss: 2.4858 2022/09/03 23:49:24 - mmengine - INFO - Epoch(train) [26][40/1345] lr: 1.0000e-02 eta: 1:58:09 time: 0.1960 data_time: 0.0103 memory: 7116 grad_norm: 6.5439 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2874 loss: 2.2874 2022/09/03 23:49:28 - mmengine - INFO - Epoch(train) [26][60/1345] lr: 1.0000e-02 eta: 1:58:05 time: 0.1939 data_time: 0.0094 memory: 7116 grad_norm: 6.7073 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.2242 loss: 2.2242 2022/09/03 23:49:32 - mmengine - INFO - Epoch(train) [26][80/1345] lr: 1.0000e-02 eta: 1:58:00 time: 0.1942 data_time: 0.0122 memory: 7116 grad_norm: 6.6113 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.2841 loss: 2.2841 2022/09/03 23:49:36 - mmengine - INFO - Epoch(train) [26][100/1345] lr: 1.0000e-02 eta: 1:57:56 time: 0.1918 data_time: 0.0101 memory: 7116 grad_norm: 6.8299 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.3644 loss: 2.3644 2022/09/03 23:49:40 - mmengine - INFO - Epoch(train) [26][120/1345] lr: 1.0000e-02 eta: 1:57:51 time: 0.1942 data_time: 0.0097 memory: 7116 grad_norm: 6.7043 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3758 loss: 2.3758 2022/09/03 23:49:44 - mmengine - INFO - Epoch(train) [26][140/1345] lr: 1.0000e-02 eta: 1:57:47 time: 0.1969 data_time: 0.0143 memory: 7116 grad_norm: 6.6584 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3508 loss: 2.3508 2022/09/03 23:49:48 - mmengine - INFO - Epoch(train) [26][160/1345] lr: 1.0000e-02 eta: 1:57:42 time: 0.1914 data_time: 0.0104 memory: 7116 grad_norm: 6.6676 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2499 loss: 2.2499 2022/09/03 23:49:51 - mmengine - INFO - Epoch(train) [26][180/1345] lr: 1.0000e-02 eta: 1:57:37 time: 0.1908 data_time: 0.0107 memory: 7116 grad_norm: 6.4009 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0198 loss: 2.0198 2022/09/03 23:49:55 - mmengine - INFO - Epoch(train) [26][200/1345] lr: 1.0000e-02 eta: 1:57:33 time: 0.1976 data_time: 0.0119 memory: 7116 grad_norm: 6.3880 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3053 loss: 2.3053 2022/09/03 23:49:59 - mmengine - INFO - Epoch(train) [26][220/1345] lr: 1.0000e-02 eta: 1:57:28 time: 0.1948 data_time: 0.0104 memory: 7116 grad_norm: 6.5151 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3850 loss: 2.3850 2022/09/03 23:50:03 - mmengine - INFO - Epoch(train) [26][240/1345] lr: 1.0000e-02 eta: 1:57:24 time: 0.2021 data_time: 0.0106 memory: 7116 grad_norm: 6.8360 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.0643 loss: 2.0643 2022/09/03 23:50:07 - mmengine - INFO - Epoch(train) [26][260/1345] lr: 1.0000e-02 eta: 1:57:19 time: 0.1926 data_time: 0.0133 memory: 7116 grad_norm: 6.4922 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0968 loss: 2.0968 2022/09/03 23:50:11 - mmengine - INFO - Epoch(train) [26][280/1345] lr: 1.0000e-02 eta: 1:57:15 time: 0.1946 data_time: 0.0107 memory: 7116 grad_norm: 6.4408 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.2633 loss: 2.2633 2022/09/03 23:50:15 - mmengine - INFO - Epoch(train) [26][300/1345] lr: 1.0000e-02 eta: 1:57:10 time: 0.1954 data_time: 0.0099 memory: 7116 grad_norm: 6.4910 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.0634 loss: 2.0634 2022/09/03 23:50:19 - mmengine - INFO - Epoch(train) [26][320/1345] lr: 1.0000e-02 eta: 1:57:06 time: 0.1929 data_time: 0.0128 memory: 7116 grad_norm: 6.6512 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3418 loss: 2.3418 2022/09/03 23:50:23 - mmengine - INFO - Epoch(train) [26][340/1345] lr: 1.0000e-02 eta: 1:57:01 time: 0.2005 data_time: 0.0114 memory: 7116 grad_norm: 6.4726 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5883 loss: 2.5883 2022/09/03 23:50:27 - mmengine - INFO - Epoch(train) [26][360/1345] lr: 1.0000e-02 eta: 1:56:57 time: 0.1927 data_time: 0.0103 memory: 7116 grad_norm: 6.5170 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1829 loss: 2.1829 2022/09/03 23:50:30 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:50:30 - mmengine - INFO - Epoch(train) [26][380/1345] lr: 1.0000e-02 eta: 1:56:52 time: 0.1928 data_time: 0.0137 memory: 7116 grad_norm: 6.7604 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.9239 loss: 1.9239 2022/09/03 23:50:34 - mmengine - INFO - Epoch(train) [26][400/1345] lr: 1.0000e-02 eta: 1:56:48 time: 0.1911 data_time: 0.0110 memory: 7116 grad_norm: 6.7890 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.4813 loss: 2.4813 2022/09/03 23:50:38 - mmengine - INFO - Epoch(train) [26][420/1345] lr: 1.0000e-02 eta: 1:56:43 time: 0.1933 data_time: 0.0121 memory: 7116 grad_norm: 6.5542 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3744 loss: 2.3744 2022/09/03 23:50:42 - mmengine - INFO - Epoch(train) [26][440/1345] lr: 1.0000e-02 eta: 1:56:39 time: 0.2019 data_time: 0.0122 memory: 7116 grad_norm: 6.7358 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2661 loss: 2.2661 2022/09/03 23:50:46 - mmengine - INFO - Epoch(train) [26][460/1345] lr: 1.0000e-02 eta: 1:56:34 time: 0.1931 data_time: 0.0099 memory: 7116 grad_norm: 6.5151 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1420 loss: 2.1420 2022/09/03 23:50:50 - mmengine - INFO - Epoch(train) [26][480/1345] lr: 1.0000e-02 eta: 1:56:30 time: 0.1938 data_time: 0.0106 memory: 7116 grad_norm: 6.3663 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2083 loss: 2.2083 2022/09/03 23:50:54 - mmengine - INFO - Epoch(train) [26][500/1345] lr: 1.0000e-02 eta: 1:56:25 time: 0.1969 data_time: 0.0144 memory: 7116 grad_norm: 6.7559 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2097 loss: 2.2097 2022/09/03 23:50:58 - mmengine - INFO - Epoch(train) [26][520/1345] lr: 1.0000e-02 eta: 1:56:20 time: 0.1922 data_time: 0.0103 memory: 7116 grad_norm: 6.8002 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.2361 loss: 2.2361 2022/09/03 23:51:02 - mmengine - INFO - Epoch(train) [26][540/1345] lr: 1.0000e-02 eta: 1:56:16 time: 0.1974 data_time: 0.0098 memory: 7116 grad_norm: 7.0568 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.2654 loss: 2.2654 2022/09/03 23:51:06 - mmengine - INFO - Epoch(train) [26][560/1345] lr: 1.0000e-02 eta: 1:56:12 time: 0.1991 data_time: 0.0120 memory: 7116 grad_norm: 6.5430 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.1487 loss: 2.1487 2022/09/03 23:51:10 - mmengine - INFO - Epoch(train) [26][580/1345] lr: 1.0000e-02 eta: 1:56:07 time: 0.1945 data_time: 0.0094 memory: 7116 grad_norm: 6.9115 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6110 loss: 2.6110 2022/09/03 23:51:14 - mmengine - INFO - Epoch(train) [26][600/1345] lr: 1.0000e-02 eta: 1:56:03 time: 0.1967 data_time: 0.0110 memory: 7116 grad_norm: 6.5538 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3283 loss: 2.3283 2022/09/03 23:51:17 - mmengine - INFO - Epoch(train) [26][620/1345] lr: 1.0000e-02 eta: 1:55:58 time: 0.1930 data_time: 0.0130 memory: 7116 grad_norm: 6.6575 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3972 loss: 2.3972 2022/09/03 23:51:21 - mmengine - INFO - Epoch(train) [26][640/1345] lr: 1.0000e-02 eta: 1:55:53 time: 0.1915 data_time: 0.0099 memory: 7116 grad_norm: 6.6149 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0017 loss: 2.0017 2022/09/03 23:51:25 - mmengine - INFO - Epoch(train) [26][660/1345] lr: 1.0000e-02 eta: 1:55:49 time: 0.1916 data_time: 0.0101 memory: 7116 grad_norm: 6.6451 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.2908 loss: 2.2908 2022/09/03 23:51:29 - mmengine - INFO - Epoch(train) [26][680/1345] lr: 1.0000e-02 eta: 1:55:44 time: 0.1946 data_time: 0.0118 memory: 7116 grad_norm: 6.5627 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.9865 loss: 1.9865 2022/09/03 23:51:33 - mmengine - INFO - Epoch(train) [26][700/1345] lr: 1.0000e-02 eta: 1:55:40 time: 0.1943 data_time: 0.0108 memory: 7116 grad_norm: 6.7790 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.5075 loss: 2.5075 2022/09/03 23:51:37 - mmengine - INFO - Epoch(train) [26][720/1345] lr: 1.0000e-02 eta: 1:55:35 time: 0.1920 data_time: 0.0120 memory: 7116 grad_norm: 6.4281 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.1954 loss: 2.1954 2022/09/03 23:51:41 - mmengine - INFO - Epoch(train) [26][740/1345] lr: 1.0000e-02 eta: 1:55:31 time: 0.1949 data_time: 0.0131 memory: 7116 grad_norm: 6.5311 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.5425 loss: 2.5425 2022/09/03 23:51:45 - mmengine - INFO - Epoch(train) [26][760/1345] lr: 1.0000e-02 eta: 1:55:26 time: 0.1956 data_time: 0.0107 memory: 7116 grad_norm: 6.4995 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1876 loss: 2.1876 2022/09/03 23:51:48 - mmengine - INFO - Epoch(train) [26][780/1345] lr: 1.0000e-02 eta: 1:55:22 time: 0.1957 data_time: 0.0101 memory: 7116 grad_norm: 6.6253 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.2932 loss: 2.2932 2022/09/03 23:51:52 - mmengine - INFO - Epoch(train) [26][800/1345] lr: 1.0000e-02 eta: 1:55:17 time: 0.1968 data_time: 0.0115 memory: 7116 grad_norm: 6.8048 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1322 loss: 2.1322 2022/09/03 23:51:56 - mmengine - INFO - Epoch(train) [26][820/1345] lr: 1.0000e-02 eta: 1:55:13 time: 0.1919 data_time: 0.0104 memory: 7116 grad_norm: 6.7123 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2655 loss: 2.2655 2022/09/03 23:52:00 - mmengine - INFO - Epoch(train) [26][840/1345] lr: 1.0000e-02 eta: 1:55:08 time: 0.1933 data_time: 0.0104 memory: 7116 grad_norm: 6.9229 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.1765 loss: 2.1765 2022/09/03 23:52:04 - mmengine - INFO - Epoch(train) [26][860/1345] lr: 1.0000e-02 eta: 1:55:04 time: 0.1964 data_time: 0.0134 memory: 7116 grad_norm: 6.3872 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4218 loss: 2.4218 2022/09/03 23:52:08 - mmengine - INFO - Epoch(train) [26][880/1345] lr: 1.0000e-02 eta: 1:54:59 time: 0.1951 data_time: 0.0098 memory: 7116 grad_norm: 6.8932 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1977 loss: 2.1977 2022/09/03 23:52:12 - mmengine - INFO - Epoch(train) [26][900/1345] lr: 1.0000e-02 eta: 1:54:54 time: 0.1939 data_time: 0.0104 memory: 7116 grad_norm: 6.4461 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.3065 loss: 2.3065 2022/09/03 23:52:16 - mmengine - INFO - Epoch(train) [26][920/1345] lr: 1.0000e-02 eta: 1:54:50 time: 0.1927 data_time: 0.0134 memory: 7116 grad_norm: 6.5853 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2422 loss: 2.2422 2022/09/03 23:52:20 - mmengine - INFO - Epoch(train) [26][940/1345] lr: 1.0000e-02 eta: 1:54:45 time: 0.1936 data_time: 0.0099 memory: 7116 grad_norm: 6.4924 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.2205 loss: 2.2205 2022/09/03 23:52:23 - mmengine - INFO - Epoch(train) [26][960/1345] lr: 1.0000e-02 eta: 1:54:41 time: 0.1979 data_time: 0.0103 memory: 7116 grad_norm: 6.5124 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5248 loss: 2.5248 2022/09/03 23:52:27 - mmengine - INFO - Epoch(train) [26][980/1345] lr: 1.0000e-02 eta: 1:54:36 time: 0.1923 data_time: 0.0129 memory: 7116 grad_norm: 6.8065 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.2348 loss: 2.2348 2022/09/03 23:52:31 - mmengine - INFO - Epoch(train) [26][1000/1345] lr: 1.0000e-02 eta: 1:54:32 time: 0.1960 data_time: 0.0105 memory: 7116 grad_norm: 6.7993 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.1873 loss: 2.1873 2022/09/03 23:52:35 - mmengine - INFO - Epoch(train) [26][1020/1345] lr: 1.0000e-02 eta: 1:54:27 time: 0.1954 data_time: 0.0110 memory: 7116 grad_norm: 6.4527 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.2714 loss: 2.2714 2022/09/03 23:52:39 - mmengine - INFO - Epoch(train) [26][1040/1345] lr: 1.0000e-02 eta: 1:54:23 time: 0.1946 data_time: 0.0140 memory: 7116 grad_norm: 6.7707 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.1490 loss: 2.1490 2022/09/03 23:52:43 - mmengine - INFO - Epoch(train) [26][1060/1345] lr: 1.0000e-02 eta: 1:54:18 time: 0.2004 data_time: 0.0120 memory: 7116 grad_norm: 6.6146 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.2301 loss: 2.2301 2022/09/03 23:52:47 - mmengine - INFO - Epoch(train) [26][1080/1345] lr: 1.0000e-02 eta: 1:54:14 time: 0.1906 data_time: 0.0104 memory: 7116 grad_norm: 6.7123 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4346 loss: 2.4346 2022/09/03 23:52:51 - mmengine - INFO - Epoch(train) [26][1100/1345] lr: 1.0000e-02 eta: 1:54:09 time: 0.1954 data_time: 0.0119 memory: 7116 grad_norm: 6.3911 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3003 loss: 2.3003 2022/09/03 23:52:55 - mmengine - INFO - Epoch(train) [26][1120/1345] lr: 1.0000e-02 eta: 1:54:05 time: 0.1960 data_time: 0.0098 memory: 7116 grad_norm: 6.3616 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3704 loss: 2.3704 2022/09/03 23:52:58 - mmengine - INFO - Epoch(train) [26][1140/1345] lr: 1.0000e-02 eta: 1:54:00 time: 0.1861 data_time: 0.0115 memory: 7116 grad_norm: 6.5186 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9627 loss: 1.9627 2022/09/03 23:53:02 - mmengine - INFO - Epoch(train) [26][1160/1345] lr: 1.0000e-02 eta: 1:53:56 time: 0.1968 data_time: 0.0121 memory: 7116 grad_norm: 6.8334 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2829 loss: 2.2829 2022/09/03 23:53:06 - mmengine - INFO - Epoch(train) [26][1180/1345] lr: 1.0000e-02 eta: 1:53:51 time: 0.1940 data_time: 0.0119 memory: 7116 grad_norm: 6.4750 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.6586 loss: 2.6586 2022/09/03 23:53:10 - mmengine - INFO - Epoch(train) [26][1200/1345] lr: 1.0000e-02 eta: 1:53:47 time: 0.1899 data_time: 0.0100 memory: 7116 grad_norm: 6.4496 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2142 loss: 2.2142 2022/09/03 23:53:14 - mmengine - INFO - Epoch(train) [26][1220/1345] lr: 1.0000e-02 eta: 1:53:42 time: 0.1960 data_time: 0.0116 memory: 7116 grad_norm: 6.7846 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6115 loss: 2.6115 2022/09/03 23:53:18 - mmengine - INFO - Epoch(train) [26][1240/1345] lr: 1.0000e-02 eta: 1:53:38 time: 0.1907 data_time: 0.0109 memory: 7116 grad_norm: 6.6398 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3627 loss: 2.3627 2022/09/03 23:53:22 - mmengine - INFO - Epoch(train) [26][1260/1345] lr: 1.0000e-02 eta: 1:53:33 time: 0.1958 data_time: 0.0137 memory: 7116 grad_norm: 6.5235 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.4751 loss: 2.4751 2022/09/03 23:53:26 - mmengine - INFO - Epoch(train) [26][1280/1345] lr: 1.0000e-02 eta: 1:53:29 time: 0.1895 data_time: 0.0126 memory: 7116 grad_norm: 6.4214 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.0932 loss: 2.0932 2022/09/03 23:53:29 - mmengine - INFO - Epoch(train) [26][1300/1345] lr: 1.0000e-02 eta: 1:53:24 time: 0.1924 data_time: 0.0103 memory: 7116 grad_norm: 6.3107 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.2891 loss: 2.2891 2022/09/03 23:53:33 - mmengine - INFO - Epoch(train) [26][1320/1345] lr: 1.0000e-02 eta: 1:53:19 time: 0.1947 data_time: 0.0110 memory: 7116 grad_norm: 6.5269 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3773 loss: 2.3773 2022/09/03 23:53:37 - mmengine - INFO - Epoch(train) [26][1340/1345] lr: 1.0000e-02 eta: 1:53:15 time: 0.1991 data_time: 0.0120 memory: 7116 grad_norm: 6.8514 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.2999 loss: 2.2999 2022/09/03 23:53:38 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:53:38 - mmengine - INFO - Epoch(train) [26][1345/1345] lr: 1.0000e-02 eta: 1:53:15 time: 0.1929 data_time: 0.0087 memory: 7116 grad_norm: 7.8811 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.3504 loss: 2.3504 2022/09/03 23:53:38 - mmengine - INFO - Saving checkpoint at 26 epochs 2022/09/03 23:53:41 - mmengine - INFO - Epoch(val) [26][20/181] eta: 0:00:07 time: 0.0482 data_time: 0.0110 memory: 1114 2022/09/03 23:53:42 - mmengine - INFO - Epoch(val) [26][40/181] eta: 0:00:06 time: 0.0449 data_time: 0.0077 memory: 1114 2022/09/03 23:53:43 - mmengine - INFO - Epoch(val) [26][60/181] eta: 0:00:05 time: 0.0440 data_time: 0.0073 memory: 1114 2022/09/03 23:53:44 - mmengine - INFO - Epoch(val) [26][80/181] eta: 0:00:04 time: 0.0459 data_time: 0.0083 memory: 1114 2022/09/03 23:53:45 - mmengine - INFO - Epoch(val) [26][100/181] eta: 0:00:03 time: 0.0460 data_time: 0.0082 memory: 1114 2022/09/03 23:53:46 - mmengine - INFO - Epoch(val) [26][120/181] eta: 0:00:02 time: 0.0437 data_time: 0.0073 memory: 1114 2022/09/03 23:53:47 - mmengine - INFO - Epoch(val) [26][140/181] eta: 0:00:01 time: 0.0429 data_time: 0.0068 memory: 1114 2022/09/03 23:53:47 - mmengine - INFO - Epoch(val) [26][160/181] eta: 0:00:00 time: 0.0467 data_time: 0.0083 memory: 1114 2022/09/03 23:53:48 - mmengine - INFO - Epoch(val) [26][180/181] eta: 0:00:00 time: 0.0454 data_time: 0.0080 memory: 1114 2022/09/03 23:53:50 - mmengine - INFO - Epoch(val) [26][181/181] acc/top1: 0.3037 acc/top5: 0.6034 acc/mean1: 0.2758 2022/09/03 23:53:54 - mmengine - INFO - Epoch(train) [27][20/1345] lr: 1.0000e-02 eta: 1:53:09 time: 0.2018 data_time: 0.0152 memory: 7116 grad_norm: 6.4368 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0187 loss: 2.0187 2022/09/03 23:53:56 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:53:58 - mmengine - INFO - Epoch(train) [27][40/1345] lr: 1.0000e-02 eta: 1:53:04 time: 0.1945 data_time: 0.0091 memory: 7116 grad_norm: 6.6923 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.9770 loss: 1.9770 2022/09/03 23:54:02 - mmengine - INFO - Epoch(train) [27][60/1345] lr: 1.0000e-02 eta: 1:53:00 time: 0.2002 data_time: 0.0083 memory: 7116 grad_norm: 6.9734 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3024 loss: 2.3024 2022/09/03 23:54:06 - mmengine - INFO - Epoch(train) [27][80/1345] lr: 1.0000e-02 eta: 1:52:55 time: 0.1969 data_time: 0.0120 memory: 7116 grad_norm: 6.6688 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.2112 loss: 2.2112 2022/09/03 23:54:10 - mmengine - INFO - Epoch(train) [27][100/1345] lr: 1.0000e-02 eta: 1:52:51 time: 0.1935 data_time: 0.0112 memory: 7116 grad_norm: 6.4343 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3391 loss: 2.3391 2022/09/03 23:54:14 - mmengine - INFO - Epoch(train) [27][120/1345] lr: 1.0000e-02 eta: 1:52:46 time: 0.1985 data_time: 0.0117 memory: 7116 grad_norm: 6.5708 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2621 loss: 2.2621 2022/09/03 23:54:18 - mmengine - INFO - Epoch(train) [27][140/1345] lr: 1.0000e-02 eta: 1:52:42 time: 0.1934 data_time: 0.0117 memory: 7116 grad_norm: 6.9373 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2898 loss: 2.2898 2022/09/03 23:54:22 - mmengine - INFO - Epoch(train) [27][160/1345] lr: 1.0000e-02 eta: 1:52:37 time: 0.1971 data_time: 0.0095 memory: 7116 grad_norm: 6.6495 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.2516 loss: 2.2516 2022/09/03 23:54:26 - mmengine - INFO - Epoch(train) [27][180/1345] lr: 1.0000e-02 eta: 1:52:33 time: 0.1984 data_time: 0.0100 memory: 7116 grad_norm: 6.4669 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0585 loss: 2.0585 2022/09/03 23:54:30 - mmengine - INFO - Epoch(train) [27][200/1345] lr: 1.0000e-02 eta: 1:52:28 time: 0.1946 data_time: 0.0118 memory: 7116 grad_norm: 6.6130 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3235 loss: 2.3235 2022/09/03 23:54:34 - mmengine - INFO - Epoch(train) [27][220/1345] lr: 1.0000e-02 eta: 1:52:24 time: 0.1988 data_time: 0.0114 memory: 7116 grad_norm: 6.7219 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0903 loss: 2.0903 2022/09/03 23:54:38 - mmengine - INFO - Epoch(train) [27][240/1345] lr: 1.0000e-02 eta: 1:52:20 time: 0.1954 data_time: 0.0110 memory: 7116 grad_norm: 6.5888 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4565 loss: 2.4565 2022/09/03 23:54:41 - mmengine - INFO - Epoch(train) [27][260/1345] lr: 1.0000e-02 eta: 1:52:15 time: 0.1960 data_time: 0.0128 memory: 7116 grad_norm: 6.8829 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2797 loss: 2.2797 2022/09/03 23:54:45 - mmengine - INFO - Epoch(train) [27][280/1345] lr: 1.0000e-02 eta: 1:52:11 time: 0.1954 data_time: 0.0108 memory: 7116 grad_norm: 6.6101 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.3518 loss: 2.3518 2022/09/03 23:54:49 - mmengine - INFO - Epoch(train) [27][300/1345] lr: 1.0000e-02 eta: 1:52:06 time: 0.1928 data_time: 0.0092 memory: 7116 grad_norm: 6.7028 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.2829 loss: 2.2829 2022/09/03 23:54:53 - mmengine - INFO - Epoch(train) [27][320/1345] lr: 1.0000e-02 eta: 1:52:02 time: 0.2072 data_time: 0.0128 memory: 7116 grad_norm: 6.5928 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.1106 loss: 2.1106 2022/09/03 23:54:57 - mmengine - INFO - Epoch(train) [27][340/1345] lr: 1.0000e-02 eta: 1:51:57 time: 0.1953 data_time: 0.0097 memory: 7116 grad_norm: 6.6568 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.3288 loss: 2.3288 2022/09/03 23:55:01 - mmengine - INFO - Epoch(train) [27][360/1345] lr: 1.0000e-02 eta: 1:51:53 time: 0.1958 data_time: 0.0103 memory: 7116 grad_norm: 6.4446 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.0438 loss: 2.0438 2022/09/03 23:55:05 - mmengine - INFO - Epoch(train) [27][380/1345] lr: 1.0000e-02 eta: 1:51:48 time: 0.1964 data_time: 0.0142 memory: 7116 grad_norm: 6.4155 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1620 loss: 2.1620 2022/09/03 23:55:09 - mmengine - INFO - Epoch(train) [27][400/1345] lr: 1.0000e-02 eta: 1:51:44 time: 0.1953 data_time: 0.0101 memory: 7116 grad_norm: 6.8195 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.2503 loss: 2.2503 2022/09/03 23:55:13 - mmengine - INFO - Epoch(train) [27][420/1345] lr: 1.0000e-02 eta: 1:51:40 time: 0.2002 data_time: 0.0097 memory: 7116 grad_norm: 6.5628 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.9899 loss: 1.9899 2022/09/03 23:55:17 - mmengine - INFO - Epoch(train) [27][440/1345] lr: 1.0000e-02 eta: 1:51:35 time: 0.1940 data_time: 0.0122 memory: 7116 grad_norm: 6.7867 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.0718 loss: 2.0718 2022/09/03 23:55:21 - mmengine - INFO - Epoch(train) [27][460/1345] lr: 1.0000e-02 eta: 1:51:31 time: 0.1954 data_time: 0.0103 memory: 7116 grad_norm: 6.6075 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.3446 loss: 2.3446 2022/09/03 23:55:25 - mmengine - INFO - Epoch(train) [27][480/1345] lr: 1.0000e-02 eta: 1:51:26 time: 0.1962 data_time: 0.0106 memory: 7116 grad_norm: 6.9978 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3361 loss: 2.3361 2022/09/03 23:55:29 - mmengine - INFO - Epoch(train) [27][500/1345] lr: 1.0000e-02 eta: 1:51:22 time: 0.1926 data_time: 0.0122 memory: 7116 grad_norm: 6.8634 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3721 loss: 2.3721 2022/09/03 23:55:33 - mmengine - INFO - Epoch(train) [27][520/1345] lr: 1.0000e-02 eta: 1:51:17 time: 0.1970 data_time: 0.0092 memory: 7116 grad_norm: 6.9273 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 2.2358 loss: 2.2358 2022/09/03 23:55:36 - mmengine - INFO - Epoch(train) [27][540/1345] lr: 1.0000e-02 eta: 1:51:13 time: 0.1962 data_time: 0.0103 memory: 7116 grad_norm: 6.9817 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.1845 loss: 2.1845 2022/09/03 23:55:40 - mmengine - INFO - Epoch(train) [27][560/1345] lr: 1.0000e-02 eta: 1:51:08 time: 0.1971 data_time: 0.0125 memory: 7116 grad_norm: 6.5393 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3020 loss: 2.3020 2022/09/03 23:55:44 - mmengine - INFO - Epoch(train) [27][580/1345] lr: 1.0000e-02 eta: 1:51:04 time: 0.2028 data_time: 0.0100 memory: 7116 grad_norm: 6.3017 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3570 loss: 2.3570 2022/09/03 23:55:48 - mmengine - INFO - Epoch(train) [27][600/1345] lr: 1.0000e-02 eta: 1:51:00 time: 0.2000 data_time: 0.0100 memory: 7116 grad_norm: 6.5007 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.2099 loss: 2.2099 2022/09/03 23:55:52 - mmengine - INFO - Epoch(train) [27][620/1345] lr: 1.0000e-02 eta: 1:50:55 time: 0.1991 data_time: 0.0123 memory: 7116 grad_norm: 7.0639 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.2138 loss: 2.2138 2022/09/03 23:55:56 - mmengine - INFO - Epoch(train) [27][640/1345] lr: 1.0000e-02 eta: 1:50:51 time: 0.2001 data_time: 0.0103 memory: 7116 grad_norm: 6.8944 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2206 loss: 2.2206 2022/09/03 23:56:00 - mmengine - INFO - Epoch(train) [27][660/1345] lr: 1.0000e-02 eta: 1:50:46 time: 0.1986 data_time: 0.0094 memory: 7116 grad_norm: 7.0453 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 2.1894 loss: 2.1894 2022/09/03 23:56:05 - mmengine - INFO - Epoch(train) [27][680/1345] lr: 1.0000e-02 eta: 1:50:42 time: 0.2047 data_time: 0.0111 memory: 7116 grad_norm: 6.8435 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 2.1193 loss: 2.1193 2022/09/03 23:56:09 - mmengine - INFO - Epoch(train) [27][700/1345] lr: 1.0000e-02 eta: 1:50:38 time: 0.2023 data_time: 0.0101 memory: 7116 grad_norm: 6.5698 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2618 loss: 2.2618 2022/09/03 23:56:13 - mmengine - INFO - Epoch(train) [27][720/1345] lr: 1.0000e-02 eta: 1:50:33 time: 0.2033 data_time: 0.0107 memory: 7116 grad_norm: 6.5149 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.1860 loss: 2.1860 2022/09/03 23:56:17 - mmengine - INFO - Epoch(train) [27][740/1345] lr: 1.0000e-02 eta: 1:50:29 time: 0.2006 data_time: 0.0116 memory: 7116 grad_norm: 6.6262 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.2571 loss: 2.2571 2022/09/03 23:56:21 - mmengine - INFO - Epoch(train) [27][760/1345] lr: 1.0000e-02 eta: 1:50:25 time: 0.1987 data_time: 0.0100 memory: 7116 grad_norm: 6.4764 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1710 loss: 2.1710 2022/09/03 23:56:25 - mmengine - INFO - Epoch(train) [27][780/1345] lr: 1.0000e-02 eta: 1:50:20 time: 0.1973 data_time: 0.0095 memory: 7116 grad_norm: 6.6416 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4074 loss: 2.4074 2022/09/03 23:56:29 - mmengine - INFO - Epoch(train) [27][800/1345] lr: 1.0000e-02 eta: 1:50:16 time: 0.1996 data_time: 0.0117 memory: 7116 grad_norm: 6.7095 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.0762 loss: 2.0762 2022/09/03 23:56:33 - mmengine - INFO - Epoch(train) [27][820/1345] lr: 1.0000e-02 eta: 1:50:11 time: 0.2023 data_time: 0.0098 memory: 7116 grad_norm: 6.5642 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.1481 loss: 2.1481 2022/09/03 23:56:37 - mmengine - INFO - Epoch(train) [27][840/1345] lr: 1.0000e-02 eta: 1:50:07 time: 0.1964 data_time: 0.0096 memory: 7116 grad_norm: 6.5904 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5147 loss: 2.5147 2022/09/03 23:56:41 - mmengine - INFO - Epoch(train) [27][860/1345] lr: 1.0000e-02 eta: 1:50:03 time: 0.1965 data_time: 0.0121 memory: 7116 grad_norm: 6.5381 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.5208 loss: 2.5208 2022/09/03 23:56:45 - mmengine - INFO - Epoch(train) [27][880/1345] lr: 1.0000e-02 eta: 1:49:58 time: 0.2014 data_time: 0.0111 memory: 7116 grad_norm: 6.8718 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.1249 loss: 2.1249 2022/09/03 23:56:48 - mmengine - INFO - Epoch(train) [27][900/1345] lr: 1.0000e-02 eta: 1:49:54 time: 0.1945 data_time: 0.0092 memory: 7116 grad_norm: 6.5451 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.1955 loss: 2.1955 2022/09/03 23:56:52 - mmengine - INFO - Epoch(train) [27][920/1345] lr: 1.0000e-02 eta: 1:49:49 time: 0.1936 data_time: 0.0110 memory: 7116 grad_norm: 6.6881 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.5572 loss: 2.5572 2022/09/03 23:56:56 - mmengine - INFO - Epoch(train) [27][940/1345] lr: 1.0000e-02 eta: 1:49:45 time: 0.1966 data_time: 0.0112 memory: 7116 grad_norm: 6.2012 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.3542 loss: 2.3542 2022/09/03 23:57:00 - mmengine - INFO - Epoch(train) [27][960/1345] lr: 1.0000e-02 eta: 1:49:40 time: 0.1967 data_time: 0.0101 memory: 7116 grad_norm: 6.5365 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4948 loss: 2.4948 2022/09/03 23:57:04 - mmengine - INFO - Epoch(train) [27][980/1345] lr: 1.0000e-02 eta: 1:49:36 time: 0.2100 data_time: 0.0113 memory: 7116 grad_norm: 6.8222 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.3622 loss: 2.3622 2022/09/03 23:57:08 - mmengine - INFO - Epoch(train) [27][1000/1345] lr: 1.0000e-02 eta: 1:49:32 time: 0.1944 data_time: 0.0089 memory: 7116 grad_norm: 6.6379 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2881 loss: 2.2881 2022/09/03 23:57:12 - mmengine - INFO - Epoch(train) [27][1020/1345] lr: 1.0000e-02 eta: 1:49:27 time: 0.2051 data_time: 0.0099 memory: 7116 grad_norm: 6.8975 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.2118 loss: 2.2118 2022/09/03 23:57:14 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:57:16 - mmengine - INFO - Epoch(train) [27][1040/1345] lr: 1.0000e-02 eta: 1:49:23 time: 0.1958 data_time: 0.0115 memory: 7116 grad_norm: 6.5181 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2074 loss: 2.2074 2022/09/03 23:57:20 - mmengine - INFO - Epoch(train) [27][1060/1345] lr: 1.0000e-02 eta: 1:49:18 time: 0.1971 data_time: 0.0103 memory: 7116 grad_norm: 6.4699 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.3273 loss: 2.3273 2022/09/03 23:57:24 - mmengine - INFO - Epoch(train) [27][1080/1345] lr: 1.0000e-02 eta: 1:49:14 time: 0.1957 data_time: 0.0093 memory: 7116 grad_norm: 6.5545 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3732 loss: 2.3732 2022/09/03 23:57:28 - mmengine - INFO - Epoch(train) [27][1100/1345] lr: 1.0000e-02 eta: 1:49:10 time: 0.1968 data_time: 0.0106 memory: 7116 grad_norm: 6.7326 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0937 loss: 2.0937 2022/09/03 23:57:32 - mmengine - INFO - Epoch(train) [27][1120/1345] lr: 1.0000e-02 eta: 1:49:05 time: 0.2041 data_time: 0.0097 memory: 7116 grad_norm: 6.7603 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.3007 loss: 2.3007 2022/09/03 23:57:36 - mmengine - INFO - Epoch(train) [27][1140/1345] lr: 1.0000e-02 eta: 1:49:01 time: 0.1985 data_time: 0.0086 memory: 7116 grad_norm: 6.3681 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3123 loss: 2.3123 2022/09/03 23:57:40 - mmengine - INFO - Epoch(train) [27][1160/1345] lr: 1.0000e-02 eta: 1:48:56 time: 0.1982 data_time: 0.0113 memory: 7116 grad_norm: 6.6718 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3783 loss: 2.3783 2022/09/03 23:57:44 - mmengine - INFO - Epoch(train) [27][1180/1345] lr: 1.0000e-02 eta: 1:48:52 time: 0.1985 data_time: 0.0098 memory: 7116 grad_norm: 6.3991 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.0814 loss: 2.0814 2022/09/03 23:57:48 - mmengine - INFO - Epoch(train) [27][1200/1345] lr: 1.0000e-02 eta: 1:48:48 time: 0.1975 data_time: 0.0086 memory: 7116 grad_norm: 6.4559 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3811 loss: 2.3811 2022/09/03 23:57:53 - mmengine - INFO - Epoch(train) [27][1220/1345] lr: 1.0000e-02 eta: 1:48:44 time: 0.2341 data_time: 0.0107 memory: 7116 grad_norm: 6.6554 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1413 loss: 2.1413 2022/09/03 23:57:57 - mmengine - INFO - Epoch(train) [27][1240/1345] lr: 1.0000e-02 eta: 1:48:39 time: 0.1962 data_time: 0.0096 memory: 7116 grad_norm: 6.8326 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.3276 loss: 2.3276 2022/09/03 23:58:01 - mmengine - INFO - Epoch(train) [27][1260/1345] lr: 1.0000e-02 eta: 1:48:35 time: 0.2001 data_time: 0.0088 memory: 7116 grad_norm: 6.5017 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.1008 loss: 2.1008 2022/09/03 23:58:05 - mmengine - INFO - Epoch(train) [27][1280/1345] lr: 1.0000e-02 eta: 1:48:31 time: 0.2032 data_time: 0.0114 memory: 7116 grad_norm: 6.9362 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.2662 loss: 2.2662 2022/09/03 23:58:09 - mmengine - INFO - Epoch(train) [27][1300/1345] lr: 1.0000e-02 eta: 1:48:26 time: 0.1986 data_time: 0.0092 memory: 7116 grad_norm: 6.3865 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9860 loss: 1.9860 2022/09/03 23:58:13 - mmengine - INFO - Epoch(train) [27][1320/1345] lr: 1.0000e-02 eta: 1:48:22 time: 0.2003 data_time: 0.0100 memory: 7116 grad_norm: 6.6742 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2828 loss: 2.2828 2022/09/03 23:58:17 - mmengine - INFO - Epoch(train) [27][1340/1345] lr: 1.0000e-02 eta: 1:48:18 time: 0.1972 data_time: 0.0108 memory: 7116 grad_norm: 6.6424 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1969 loss: 2.1969 2022/09/03 23:58:18 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/03 23:58:18 - mmengine - INFO - Epoch(train) [27][1345/1345] lr: 1.0000e-02 eta: 1:48:18 time: 0.1915 data_time: 0.0088 memory: 7116 grad_norm: 9.2657 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.2679 loss: 2.2679 2022/09/03 23:58:18 - mmengine - INFO - Saving checkpoint at 27 epochs 2022/09/03 23:58:20 - mmengine - INFO - Epoch(val) [27][20/181] eta: 0:00:07 time: 0.0495 data_time: 0.0120 memory: 1114 2022/09/03 23:58:21 - mmengine - INFO - Epoch(val) [27][40/181] eta: 0:00:05 time: 0.0413 data_time: 0.0058 memory: 1114 2022/09/03 23:58:22 - mmengine - INFO - Epoch(val) [27][60/181] eta: 0:00:04 time: 0.0406 data_time: 0.0053 memory: 1114 2022/09/03 23:58:23 - mmengine - INFO - Epoch(val) [27][80/181] eta: 0:00:05 time: 0.0572 data_time: 0.0225 memory: 1114 2022/09/03 23:58:24 - mmengine - INFO - Epoch(val) [27][100/181] eta: 0:00:03 time: 0.0413 data_time: 0.0061 memory: 1114 2022/09/03 23:58:25 - mmengine - INFO - Epoch(val) [27][120/181] eta: 0:00:02 time: 0.0421 data_time: 0.0066 memory: 1114 2022/09/03 23:58:25 - mmengine - INFO - Epoch(val) [27][140/181] eta: 0:00:01 time: 0.0399 data_time: 0.0053 memory: 1114 2022/09/03 23:58:26 - mmengine - INFO - Epoch(val) [27][160/181] eta: 0:00:00 time: 0.0417 data_time: 0.0062 memory: 1114 2022/09/03 23:58:27 - mmengine - INFO - Epoch(val) [27][180/181] eta: 0:00:00 time: 0.0419 data_time: 0.0064 memory: 1114 2022/09/03 23:58:30 - mmengine - INFO - Epoch(val) [27][181/181] acc/top1: 0.3308 acc/top5: 0.6200 acc/mean1: 0.2835 2022/09/03 23:58:30 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_24.pth is removed 2022/09/03 23:58:31 - mmengine - INFO - The best checkpoint with 0.3308 acc/top1 at 27 epoch is saved to best_acc/top1_epoch_27.pth. 2022/09/03 23:58:35 - mmengine - INFO - Epoch(train) [28][20/1345] lr: 1.0000e-02 eta: 1:48:11 time: 0.1954 data_time: 0.0111 memory: 7116 grad_norm: 6.9066 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2891 loss: 2.2891 2022/09/03 23:58:39 - mmengine - INFO - Epoch(train) [28][40/1345] lr: 1.0000e-02 eta: 1:48:07 time: 0.1978 data_time: 0.0084 memory: 7116 grad_norm: 6.4530 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.3212 loss: 2.3212 2022/09/03 23:58:43 - mmengine - INFO - Epoch(train) [28][60/1345] lr: 1.0000e-02 eta: 1:48:02 time: 0.2029 data_time: 0.0091 memory: 7116 grad_norm: 6.9551 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.2178 loss: 2.2178 2022/09/03 23:58:47 - mmengine - INFO - Epoch(train) [28][80/1345] lr: 1.0000e-02 eta: 1:47:58 time: 0.2010 data_time: 0.0113 memory: 7116 grad_norm: 6.6997 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3609 loss: 2.3609 2022/09/03 23:58:51 - mmengine - INFO - Epoch(train) [28][100/1345] lr: 1.0000e-02 eta: 1:47:54 time: 0.1980 data_time: 0.0094 memory: 7116 grad_norm: 6.7052 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.9504 loss: 1.9504 2022/09/03 23:58:55 - mmengine - INFO - Epoch(train) [28][120/1345] lr: 1.0000e-02 eta: 1:47:49 time: 0.1939 data_time: 0.0085 memory: 7116 grad_norm: 6.9497 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.0500 loss: 2.0500 2022/09/03 23:58:59 - mmengine - INFO - Epoch(train) [28][140/1345] lr: 1.0000e-02 eta: 1:47:45 time: 0.1983 data_time: 0.0108 memory: 7116 grad_norm: 6.6566 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.2107 loss: 2.2107 2022/09/03 23:59:03 - mmengine - INFO - Epoch(train) [28][160/1345] lr: 1.0000e-02 eta: 1:47:41 time: 0.2028 data_time: 0.0098 memory: 7116 grad_norm: 6.7071 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8416 loss: 1.8416 2022/09/03 23:59:07 - mmengine - INFO - Epoch(train) [28][180/1345] lr: 1.0000e-02 eta: 1:47:36 time: 0.1963 data_time: 0.0087 memory: 7116 grad_norm: 6.4979 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.2469 loss: 2.2469 2022/09/03 23:59:11 - mmengine - INFO - Epoch(train) [28][200/1345] lr: 1.0000e-02 eta: 1:47:32 time: 0.1978 data_time: 0.0111 memory: 7116 grad_norm: 6.5652 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.3996 loss: 2.3996 2022/09/03 23:59:15 - mmengine - INFO - Epoch(train) [28][220/1345] lr: 1.0000e-02 eta: 1:47:27 time: 0.2005 data_time: 0.0094 memory: 7116 grad_norm: 6.6699 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9939 loss: 1.9939 2022/09/03 23:59:19 - mmengine - INFO - Epoch(train) [28][240/1345] lr: 1.0000e-02 eta: 1:47:23 time: 0.1999 data_time: 0.0091 memory: 7116 grad_norm: 6.9675 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9781 loss: 1.9781 2022/09/03 23:59:23 - mmengine - INFO - Epoch(train) [28][260/1345] lr: 1.0000e-02 eta: 1:47:19 time: 0.2017 data_time: 0.0107 memory: 7116 grad_norm: 6.8036 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8257 loss: 1.8257 2022/09/03 23:59:27 - mmengine - INFO - Epoch(train) [28][280/1345] lr: 1.0000e-02 eta: 1:47:14 time: 0.2041 data_time: 0.0089 memory: 7116 grad_norm: 6.6352 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2337 loss: 2.2337 2022/09/03 23:59:31 - mmengine - INFO - Epoch(train) [28][300/1345] lr: 1.0000e-02 eta: 1:47:10 time: 0.1990 data_time: 0.0089 memory: 7116 grad_norm: 6.6649 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3297 loss: 2.3297 2022/09/03 23:59:35 - mmengine - INFO - Epoch(train) [28][320/1345] lr: 1.0000e-02 eta: 1:47:06 time: 0.1972 data_time: 0.0115 memory: 7116 grad_norm: 6.6094 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2478 loss: 2.2478 2022/09/03 23:59:39 - mmengine - INFO - Epoch(train) [28][340/1345] lr: 1.0000e-02 eta: 1:47:01 time: 0.1948 data_time: 0.0093 memory: 7116 grad_norm: 6.9964 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0290 loss: 2.0290 2022/09/03 23:59:43 - mmengine - INFO - Epoch(train) [28][360/1345] lr: 1.0000e-02 eta: 1:46:57 time: 0.2013 data_time: 0.0095 memory: 7116 grad_norm: 6.5658 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3136 loss: 2.3136 2022/09/03 23:59:47 - mmengine - INFO - Epoch(train) [28][380/1345] lr: 1.0000e-02 eta: 1:46:52 time: 0.1991 data_time: 0.0108 memory: 7116 grad_norm: 6.5034 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.0932 loss: 2.0932 2022/09/03 23:59:51 - mmengine - INFO - Epoch(train) [28][400/1345] lr: 1.0000e-02 eta: 1:46:48 time: 0.1971 data_time: 0.0093 memory: 7116 grad_norm: 6.8652 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1773 loss: 2.1773 2022/09/03 23:59:55 - mmengine - INFO - Epoch(train) [28][420/1345] lr: 1.0000e-02 eta: 1:46:44 time: 0.2007 data_time: 0.0093 memory: 7116 grad_norm: 6.7368 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1505 loss: 2.1505 2022/09/03 23:59:59 - mmengine - INFO - Epoch(train) [28][440/1345] lr: 1.0000e-02 eta: 1:46:39 time: 0.1989 data_time: 0.0111 memory: 7116 grad_norm: 6.6309 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.0996 loss: 2.0996 2022/09/04 00:00:05 - mmengine - INFO - Epoch(train) [28][460/1345] lr: 1.0000e-02 eta: 1:46:37 time: 0.3307 data_time: 0.0105 memory: 7116 grad_norm: 6.6430 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3465 loss: 2.3465 2022/09/04 00:00:09 - mmengine - INFO - Epoch(train) [28][480/1345] lr: 1.0000e-02 eta: 1:46:33 time: 0.2072 data_time: 0.0132 memory: 7116 grad_norm: 6.4697 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.1373 loss: 2.1373 2022/09/04 00:00:13 - mmengine - INFO - Epoch(train) [28][500/1345] lr: 1.0000e-02 eta: 1:46:28 time: 0.1994 data_time: 0.0093 memory: 7116 grad_norm: 6.5171 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1729 loss: 2.1729 2022/09/04 00:00:17 - mmengine - INFO - Epoch(train) [28][520/1345] lr: 1.0000e-02 eta: 1:46:24 time: 0.1972 data_time: 0.0089 memory: 7116 grad_norm: 6.5953 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9974 loss: 1.9974 2022/09/04 00:00:21 - mmengine - INFO - Epoch(train) [28][540/1345] lr: 1.0000e-02 eta: 1:46:20 time: 0.1989 data_time: 0.0113 memory: 7116 grad_norm: 6.6623 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 1.9959 loss: 1.9959 2022/09/04 00:00:25 - mmengine - INFO - Epoch(train) [28][560/1345] lr: 1.0000e-02 eta: 1:46:15 time: 0.2029 data_time: 0.0095 memory: 7116 grad_norm: 6.5296 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2814 loss: 2.2814 2022/09/04 00:00:29 - mmengine - INFO - Epoch(train) [28][580/1345] lr: 1.0000e-02 eta: 1:46:11 time: 0.1945 data_time: 0.0087 memory: 7116 grad_norm: 6.6324 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1639 loss: 2.1639 2022/09/04 00:00:33 - mmengine - INFO - Epoch(train) [28][600/1345] lr: 1.0000e-02 eta: 1:46:06 time: 0.1935 data_time: 0.0108 memory: 7116 grad_norm: 6.6131 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1038 loss: 2.1038 2022/09/04 00:00:37 - mmengine - INFO - Epoch(train) [28][620/1345] lr: 1.0000e-02 eta: 1:46:02 time: 0.1976 data_time: 0.0084 memory: 7116 grad_norm: 6.4661 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.5279 loss: 2.5279 2022/09/04 00:00:41 - mmengine - INFO - Epoch(train) [28][640/1345] lr: 1.0000e-02 eta: 1:45:58 time: 0.1981 data_time: 0.0082 memory: 7116 grad_norm: 6.6213 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.6723 loss: 2.6723 2022/09/04 00:00:45 - mmengine - INFO - Epoch(train) [28][660/1345] lr: 1.0000e-02 eta: 1:45:53 time: 0.2099 data_time: 0.0120 memory: 7116 grad_norm: 6.7666 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.4746 loss: 2.4746 2022/09/04 00:00:49 - mmengine - INFO - Epoch(train) [28][680/1345] lr: 1.0000e-02 eta: 1:45:49 time: 0.2000 data_time: 0.0088 memory: 7116 grad_norm: 6.5492 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.1486 loss: 2.1486 2022/09/04 00:00:50 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:00:53 - mmengine - INFO - Epoch(train) [28][700/1345] lr: 1.0000e-02 eta: 1:45:45 time: 0.1937 data_time: 0.0093 memory: 7116 grad_norm: 6.2873 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2215 loss: 2.2215 2022/09/04 00:00:57 - mmengine - INFO - Epoch(train) [28][720/1345] lr: 1.0000e-02 eta: 1:45:40 time: 0.1965 data_time: 0.0125 memory: 7116 grad_norm: 6.8092 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1142 loss: 2.1142 2022/09/04 00:01:01 - mmengine - INFO - Epoch(train) [28][740/1345] lr: 1.0000e-02 eta: 1:45:36 time: 0.1994 data_time: 0.0099 memory: 7116 grad_norm: 6.5510 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.2281 loss: 2.2281 2022/09/04 00:01:05 - mmengine - INFO - Epoch(train) [28][760/1345] lr: 1.0000e-02 eta: 1:45:31 time: 0.1947 data_time: 0.0097 memory: 7116 grad_norm: 6.6311 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0835 loss: 2.0835 2022/09/04 00:01:09 - mmengine - INFO - Epoch(train) [28][780/1345] lr: 1.0000e-02 eta: 1:45:27 time: 0.1953 data_time: 0.0115 memory: 7116 grad_norm: 6.6835 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4732 loss: 2.4732 2022/09/04 00:01:13 - mmengine - INFO - Epoch(train) [28][800/1345] lr: 1.0000e-02 eta: 1:45:23 time: 0.2000 data_time: 0.0095 memory: 7116 grad_norm: 6.6680 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.5644 loss: 2.5644 2022/09/04 00:01:17 - mmengine - INFO - Epoch(train) [28][820/1345] lr: 1.0000e-02 eta: 1:45:18 time: 0.1987 data_time: 0.0095 memory: 7116 grad_norm: 6.4167 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1352 loss: 2.1352 2022/09/04 00:01:21 - mmengine - INFO - Epoch(train) [28][840/1345] lr: 1.0000e-02 eta: 1:45:14 time: 0.1986 data_time: 0.0113 memory: 7116 grad_norm: 6.4561 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0259 loss: 2.0259 2022/09/04 00:01:25 - mmengine - INFO - Epoch(train) [28][860/1345] lr: 1.0000e-02 eta: 1:45:09 time: 0.1975 data_time: 0.0085 memory: 7116 grad_norm: 6.4260 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3310 loss: 2.3310 2022/09/04 00:01:29 - mmengine - INFO - Epoch(train) [28][880/1345] lr: 1.0000e-02 eta: 1:45:05 time: 0.1974 data_time: 0.0088 memory: 7116 grad_norm: 6.4339 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.9398 loss: 1.9398 2022/09/04 00:01:33 - mmengine - INFO - Epoch(train) [28][900/1345] lr: 1.0000e-02 eta: 1:45:01 time: 0.2026 data_time: 0.0128 memory: 7116 grad_norm: 6.7211 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4297 loss: 2.4297 2022/09/04 00:01:37 - mmengine - INFO - Epoch(train) [28][920/1345] lr: 1.0000e-02 eta: 1:44:56 time: 0.1968 data_time: 0.0085 memory: 7116 grad_norm: 6.6401 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.2662 loss: 2.2662 2022/09/04 00:01:41 - mmengine - INFO - Epoch(train) [28][940/1345] lr: 1.0000e-02 eta: 1:44:52 time: 0.1955 data_time: 0.0098 memory: 7116 grad_norm: 6.5841 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.2959 loss: 2.2959 2022/09/04 00:01:45 - mmengine - INFO - Epoch(train) [28][960/1345] lr: 1.0000e-02 eta: 1:44:48 time: 0.1997 data_time: 0.0115 memory: 7116 grad_norm: 6.8745 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.6290 loss: 2.6290 2022/09/04 00:01:49 - mmengine - INFO - Epoch(train) [28][980/1345] lr: 1.0000e-02 eta: 1:44:43 time: 0.1967 data_time: 0.0092 memory: 7116 grad_norm: 6.5173 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1538 loss: 2.1538 2022/09/04 00:01:53 - mmengine - INFO - Epoch(train) [28][1000/1345] lr: 1.0000e-02 eta: 1:44:39 time: 0.2004 data_time: 0.0107 memory: 7116 grad_norm: 6.3917 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.1656 loss: 2.1656 2022/09/04 00:01:56 - mmengine - INFO - Epoch(train) [28][1020/1345] lr: 1.0000e-02 eta: 1:44:34 time: 0.1975 data_time: 0.0105 memory: 7116 grad_norm: 6.6071 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1501 loss: 2.1501 2022/09/04 00:02:00 - mmengine - INFO - Epoch(train) [28][1040/1345] lr: 1.0000e-02 eta: 1:44:30 time: 0.1974 data_time: 0.0087 memory: 7116 grad_norm: 6.6891 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3787 loss: 2.3787 2022/09/04 00:02:05 - mmengine - INFO - Epoch(train) [28][1060/1345] lr: 1.0000e-02 eta: 1:44:26 time: 0.2040 data_time: 0.0097 memory: 7116 grad_norm: 6.5824 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0063 loss: 2.0063 2022/09/04 00:02:08 - mmengine - INFO - Epoch(train) [28][1080/1345] lr: 1.0000e-02 eta: 1:44:21 time: 0.1978 data_time: 0.0108 memory: 7116 grad_norm: 6.6511 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1395 loss: 2.1395 2022/09/04 00:02:12 - mmengine - INFO - Epoch(train) [28][1100/1345] lr: 1.0000e-02 eta: 1:44:17 time: 0.1974 data_time: 0.0094 memory: 7116 grad_norm: 6.7859 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3738 loss: 2.3738 2022/09/04 00:02:16 - mmengine - INFO - Epoch(train) [28][1120/1345] lr: 1.0000e-02 eta: 1:44:13 time: 0.1977 data_time: 0.0092 memory: 7116 grad_norm: 6.7236 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.9801 loss: 1.9801 2022/09/04 00:02:20 - mmengine - INFO - Epoch(train) [28][1140/1345] lr: 1.0000e-02 eta: 1:44:08 time: 0.1974 data_time: 0.0115 memory: 7116 grad_norm: 6.7610 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1160 loss: 2.1160 2022/09/04 00:02:25 - mmengine - INFO - Epoch(train) [28][1160/1345] lr: 1.0000e-02 eta: 1:44:04 time: 0.2092 data_time: 0.0104 memory: 7116 grad_norm: 6.6582 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2691 loss: 2.2691 2022/09/04 00:02:28 - mmengine - INFO - Epoch(train) [28][1180/1345] lr: 1.0000e-02 eta: 1:44:00 time: 0.1963 data_time: 0.0094 memory: 7116 grad_norm: 6.7089 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4211 loss: 2.4211 2022/09/04 00:02:33 - mmengine - INFO - Epoch(train) [28][1200/1345] lr: 1.0000e-02 eta: 1:43:55 time: 0.2019 data_time: 0.0115 memory: 7116 grad_norm: 6.5813 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.0856 loss: 2.0856 2022/09/04 00:02:36 - mmengine - INFO - Epoch(train) [28][1220/1345] lr: 1.0000e-02 eta: 1:43:51 time: 0.1955 data_time: 0.0098 memory: 7116 grad_norm: 6.8155 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2750 loss: 2.2750 2022/09/04 00:02:40 - mmengine - INFO - Epoch(train) [28][1240/1345] lr: 1.0000e-02 eta: 1:43:46 time: 0.1932 data_time: 0.0098 memory: 7116 grad_norm: 6.5853 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3407 loss: 2.3407 2022/09/04 00:02:44 - mmengine - INFO - Epoch(train) [28][1260/1345] lr: 1.0000e-02 eta: 1:43:42 time: 0.2006 data_time: 0.0120 memory: 7116 grad_norm: 6.9156 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.2963 loss: 2.2963 2022/09/04 00:02:48 - mmengine - INFO - Epoch(train) [28][1280/1345] lr: 1.0000e-02 eta: 1:43:38 time: 0.1968 data_time: 0.0088 memory: 7116 grad_norm: 6.3618 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2769 loss: 2.2769 2022/09/04 00:02:52 - mmengine - INFO - Epoch(train) [28][1300/1345] lr: 1.0000e-02 eta: 1:43:33 time: 0.1998 data_time: 0.0101 memory: 7116 grad_norm: 6.4268 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.2568 loss: 2.2568 2022/09/04 00:02:56 - mmengine - INFO - Epoch(train) [28][1320/1345] lr: 1.0000e-02 eta: 1:43:29 time: 0.2077 data_time: 0.0111 memory: 7116 grad_norm: 6.3164 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.2729 loss: 2.2729 2022/09/04 00:03:00 - mmengine - INFO - Epoch(train) [28][1340/1345] lr: 1.0000e-02 eta: 1:43:25 time: 0.1977 data_time: 0.0085 memory: 7116 grad_norm: 6.7678 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1932 loss: 2.1932 2022/09/04 00:03:01 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:03:01 - mmengine - INFO - Epoch(train) [28][1345/1345] lr: 1.0000e-02 eta: 1:43:25 time: 0.1968 data_time: 0.0079 memory: 7116 grad_norm: 7.0070 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.1274 loss: 2.1274 2022/09/04 00:03:01 - mmengine - INFO - Saving checkpoint at 28 epochs 2022/09/04 00:03:04 - mmengine - INFO - Epoch(val) [28][20/181] eta: 0:00:07 time: 0.0493 data_time: 0.0116 memory: 1114 2022/09/04 00:03:05 - mmengine - INFO - Epoch(val) [28][40/181] eta: 0:00:06 time: 0.0430 data_time: 0.0072 memory: 1114 2022/09/04 00:03:06 - mmengine - INFO - Epoch(val) [28][60/181] eta: 0:00:05 time: 0.0426 data_time: 0.0073 memory: 1114 2022/09/04 00:03:07 - mmengine - INFO - Epoch(val) [28][80/181] eta: 0:00:04 time: 0.0414 data_time: 0.0064 memory: 1114 2022/09/04 00:03:08 - mmengine - INFO - Epoch(val) [28][100/181] eta: 0:00:03 time: 0.0432 data_time: 0.0074 memory: 1114 2022/09/04 00:03:08 - mmengine - INFO - Epoch(val) [28][120/181] eta: 0:00:02 time: 0.0413 data_time: 0.0061 memory: 1114 2022/09/04 00:03:09 - mmengine - INFO - Epoch(val) [28][140/181] eta: 0:00:01 time: 0.0415 data_time: 0.0065 memory: 1114 2022/09/04 00:03:10 - mmengine - INFO - Epoch(val) [28][160/181] eta: 0:00:00 time: 0.0431 data_time: 0.0071 memory: 1114 2022/09/04 00:03:11 - mmengine - INFO - Epoch(val) [28][180/181] eta: 0:00:00 time: 0.0409 data_time: 0.0060 memory: 1114 2022/09/04 00:03:14 - mmengine - INFO - Epoch(val) [28][181/181] acc/top1: 0.3203 acc/top5: 0.6133 acc/mean1: 0.2893 2022/09/04 00:03:18 - mmengine - INFO - Epoch(train) [29][20/1345] lr: 1.0000e-02 eta: 1:43:19 time: 0.2080 data_time: 0.0129 memory: 7116 grad_norm: 6.7059 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.3050 loss: 2.3050 2022/09/04 00:03:22 - mmengine - INFO - Epoch(train) [29][40/1345] lr: 1.0000e-02 eta: 1:43:14 time: 0.1985 data_time: 0.0094 memory: 7116 grad_norm: 6.6248 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.1243 loss: 2.1243 2022/09/04 00:03:26 - mmengine - INFO - Epoch(train) [29][60/1345] lr: 1.0000e-02 eta: 1:43:10 time: 0.2008 data_time: 0.0094 memory: 7116 grad_norm: 6.4340 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4014 loss: 2.4014 2022/09/04 00:03:30 - mmengine - INFO - Epoch(train) [29][80/1345] lr: 1.0000e-02 eta: 1:43:05 time: 0.1977 data_time: 0.0109 memory: 7116 grad_norm: 6.7435 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.4008 loss: 2.4008 2022/09/04 00:03:34 - mmengine - INFO - Epoch(train) [29][100/1345] lr: 1.0000e-02 eta: 1:43:01 time: 0.2054 data_time: 0.0088 memory: 7116 grad_norm: 6.4990 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2671 loss: 2.2671 2022/09/04 00:03:38 - mmengine - INFO - Epoch(train) [29][120/1345] lr: 1.0000e-02 eta: 1:42:57 time: 0.2010 data_time: 0.0087 memory: 7116 grad_norm: 6.3710 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.1276 loss: 2.1276 2022/09/04 00:03:42 - mmengine - INFO - Epoch(train) [29][140/1345] lr: 1.0000e-02 eta: 1:42:53 time: 0.2009 data_time: 0.0116 memory: 7116 grad_norm: 6.6851 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.3582 loss: 2.3582 2022/09/04 00:03:46 - mmengine - INFO - Epoch(train) [29][160/1345] lr: 1.0000e-02 eta: 1:42:48 time: 0.2056 data_time: 0.0088 memory: 7116 grad_norm: 6.5669 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.9315 loss: 1.9315 2022/09/04 00:03:50 - mmengine - INFO - Epoch(train) [29][180/1345] lr: 1.0000e-02 eta: 1:42:44 time: 0.2003 data_time: 0.0091 memory: 7116 grad_norm: 6.8513 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.0911 loss: 2.0911 2022/09/04 00:03:54 - mmengine - INFO - Epoch(train) [29][200/1345] lr: 1.0000e-02 eta: 1:42:40 time: 0.2004 data_time: 0.0105 memory: 7116 grad_norm: 7.0216 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1553 loss: 2.1553 2022/09/04 00:03:58 - mmengine - INFO - Epoch(train) [29][220/1345] lr: 1.0000e-02 eta: 1:42:35 time: 0.2051 data_time: 0.0076 memory: 7116 grad_norm: 6.8165 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 2.2904 loss: 2.2904 2022/09/04 00:04:02 - mmengine - INFO - Epoch(train) [29][240/1345] lr: 1.0000e-02 eta: 1:42:31 time: 0.2034 data_time: 0.0086 memory: 7116 grad_norm: 6.8813 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.9807 loss: 1.9807 2022/09/04 00:04:06 - mmengine - INFO - Epoch(train) [29][260/1345] lr: 1.0000e-02 eta: 1:42:27 time: 0.2042 data_time: 0.0122 memory: 7116 grad_norm: 6.7750 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4964 loss: 2.4964 2022/09/04 00:04:10 - mmengine - INFO - Epoch(train) [29][280/1345] lr: 1.0000e-02 eta: 1:42:23 time: 0.2031 data_time: 0.0085 memory: 7116 grad_norm: 6.9089 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3416 loss: 2.3416 2022/09/04 00:04:14 - mmengine - INFO - Epoch(train) [29][300/1345] lr: 1.0000e-02 eta: 1:42:18 time: 0.1992 data_time: 0.0083 memory: 7116 grad_norm: 6.8307 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.2633 loss: 2.2633 2022/09/04 00:04:18 - mmengine - INFO - Epoch(train) [29][320/1345] lr: 1.0000e-02 eta: 1:42:14 time: 0.2000 data_time: 0.0103 memory: 7116 grad_norm: 6.8321 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.3841 loss: 2.3841 2022/09/04 00:04:23 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:04:23 - mmengine - INFO - Epoch(train) [29][340/1345] lr: 1.0000e-02 eta: 1:42:10 time: 0.2093 data_time: 0.0105 memory: 7116 grad_norm: 6.7999 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.1791 loss: 2.1791 2022/09/04 00:04:27 - mmengine - INFO - Epoch(train) [29][360/1345] lr: 1.0000e-02 eta: 1:42:05 time: 0.2032 data_time: 0.0079 memory: 7116 grad_norm: 6.7635 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.5078 loss: 2.5078 2022/09/04 00:04:31 - mmengine - INFO - Epoch(train) [29][380/1345] lr: 1.0000e-02 eta: 1:42:01 time: 0.2011 data_time: 0.0107 memory: 7116 grad_norm: 6.8426 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.3012 loss: 2.3012 2022/09/04 00:04:35 - mmengine - INFO - Epoch(train) [29][400/1345] lr: 1.0000e-02 eta: 1:41:57 time: 0.1967 data_time: 0.0079 memory: 7116 grad_norm: 6.7024 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4416 loss: 2.4416 2022/09/04 00:04:38 - mmengine - INFO - Epoch(train) [29][420/1345] lr: 1.0000e-02 eta: 1:41:52 time: 0.1972 data_time: 0.0083 memory: 7116 grad_norm: 6.5705 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.1962 loss: 2.1962 2022/09/04 00:04:43 - mmengine - INFO - Epoch(train) [29][440/1345] lr: 1.0000e-02 eta: 1:41:48 time: 0.2072 data_time: 0.0106 memory: 7116 grad_norm: 6.7752 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.3090 loss: 2.3090 2022/09/04 00:04:47 - mmengine - INFO - Epoch(train) [29][460/1345] lr: 1.0000e-02 eta: 1:41:44 time: 0.2049 data_time: 0.0073 memory: 7116 grad_norm: 6.6478 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.1460 loss: 2.1460 2022/09/04 00:04:51 - mmengine - INFO - Epoch(train) [29][480/1345] lr: 1.0000e-02 eta: 1:41:39 time: 0.2005 data_time: 0.0082 memory: 7116 grad_norm: 6.6172 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0210 loss: 2.0210 2022/09/04 00:04:55 - mmengine - INFO - Epoch(train) [29][500/1345] lr: 1.0000e-02 eta: 1:41:35 time: 0.1991 data_time: 0.0109 memory: 7116 grad_norm: 6.5892 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1641 loss: 2.1641 2022/09/04 00:04:59 - mmengine - INFO - Epoch(train) [29][520/1345] lr: 1.0000e-02 eta: 1:41:31 time: 0.1996 data_time: 0.0090 memory: 7116 grad_norm: 6.6275 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.2931 loss: 2.2931 2022/09/04 00:05:03 - mmengine - INFO - Epoch(train) [29][540/1345] lr: 1.0000e-02 eta: 1:41:26 time: 0.1990 data_time: 0.0090 memory: 7116 grad_norm: 6.6189 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2761 loss: 2.2761 2022/09/04 00:05:07 - mmengine - INFO - Epoch(train) [29][560/1345] lr: 1.0000e-02 eta: 1:41:22 time: 0.1987 data_time: 0.0121 memory: 7116 grad_norm: 6.5797 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.3045 loss: 2.3045 2022/09/04 00:05:11 - mmengine - INFO - Epoch(train) [29][580/1345] lr: 1.0000e-02 eta: 1:41:18 time: 0.1994 data_time: 0.0083 memory: 7116 grad_norm: 6.6975 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1245 loss: 2.1245 2022/09/04 00:05:15 - mmengine - INFO - Epoch(train) [29][600/1345] lr: 1.0000e-02 eta: 1:41:13 time: 0.1989 data_time: 0.0089 memory: 7116 grad_norm: 6.8440 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.3817 loss: 2.3817 2022/09/04 00:05:19 - mmengine - INFO - Epoch(train) [29][620/1345] lr: 1.0000e-02 eta: 1:41:09 time: 0.2001 data_time: 0.0106 memory: 7116 grad_norm: 6.8832 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0321 loss: 2.0321 2022/09/04 00:05:23 - mmengine - INFO - Epoch(train) [29][640/1345] lr: 1.0000e-02 eta: 1:41:05 time: 0.1989 data_time: 0.0093 memory: 7116 grad_norm: 6.7183 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3524 loss: 2.3524 2022/09/04 00:05:27 - mmengine - INFO - Epoch(train) [29][660/1345] lr: 1.0000e-02 eta: 1:41:00 time: 0.1967 data_time: 0.0080 memory: 7116 grad_norm: 6.8840 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5626 loss: 2.5626 2022/09/04 00:05:31 - mmengine - INFO - Epoch(train) [29][680/1345] lr: 1.0000e-02 eta: 1:40:56 time: 0.2020 data_time: 0.0099 memory: 7116 grad_norm: 6.5220 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.0630 loss: 2.0630 2022/09/04 00:05:35 - mmengine - INFO - Epoch(train) [29][700/1345] lr: 1.0000e-02 eta: 1:40:52 time: 0.2023 data_time: 0.0087 memory: 7116 grad_norm: 6.4450 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.9726 loss: 1.9726 2022/09/04 00:05:39 - mmengine - INFO - Epoch(train) [29][720/1345] lr: 1.0000e-02 eta: 1:40:47 time: 0.2045 data_time: 0.0092 memory: 7116 grad_norm: 7.0097 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.2404 loss: 2.2404 2022/09/04 00:05:43 - mmengine - INFO - Epoch(train) [29][740/1345] lr: 1.0000e-02 eta: 1:40:43 time: 0.2043 data_time: 0.0102 memory: 7116 grad_norm: 6.4993 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.9503 loss: 1.9503 2022/09/04 00:05:47 - mmengine - INFO - Epoch(train) [29][760/1345] lr: 1.0000e-02 eta: 1:40:39 time: 0.2026 data_time: 0.0077 memory: 7116 grad_norm: 6.7264 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1290 loss: 2.1290 2022/09/04 00:05:51 - mmengine - INFO - Epoch(train) [29][780/1345] lr: 1.0000e-02 eta: 1:40:35 time: 0.1999 data_time: 0.0084 memory: 7116 grad_norm: 6.4322 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.2397 loss: 2.2397 2022/09/04 00:05:55 - mmengine - INFO - Epoch(train) [29][800/1345] lr: 1.0000e-02 eta: 1:40:30 time: 0.2034 data_time: 0.0111 memory: 7116 grad_norm: 6.4652 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2651 loss: 2.2651 2022/09/04 00:05:59 - mmengine - INFO - Epoch(train) [29][820/1345] lr: 1.0000e-02 eta: 1:40:26 time: 0.1985 data_time: 0.0083 memory: 7116 grad_norm: 6.8679 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2131 loss: 2.2131 2022/09/04 00:06:03 - mmengine - INFO - Epoch(train) [29][840/1345] lr: 1.0000e-02 eta: 1:40:21 time: 0.1960 data_time: 0.0086 memory: 7116 grad_norm: 6.8533 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2126 loss: 2.2126 2022/09/04 00:06:07 - mmengine - INFO - Epoch(train) [29][860/1345] lr: 1.0000e-02 eta: 1:40:17 time: 0.2017 data_time: 0.0114 memory: 7116 grad_norm: 6.5921 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.2463 loss: 2.2463 2022/09/04 00:06:11 - mmengine - INFO - Epoch(train) [29][880/1345] lr: 1.0000e-02 eta: 1:40:13 time: 0.1994 data_time: 0.0081 memory: 7116 grad_norm: 6.5444 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.1657 loss: 2.1657 2022/09/04 00:06:15 - mmengine - INFO - Epoch(train) [29][900/1345] lr: 1.0000e-02 eta: 1:40:09 time: 0.2016 data_time: 0.0082 memory: 7116 grad_norm: 6.5863 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.0735 loss: 2.0735 2022/09/04 00:06:19 - mmengine - INFO - Epoch(train) [29][920/1345] lr: 1.0000e-02 eta: 1:40:04 time: 0.1980 data_time: 0.0116 memory: 7116 grad_norm: 6.6691 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1971 loss: 2.1971 2022/09/04 00:06:23 - mmengine - INFO - Epoch(train) [29][940/1345] lr: 1.0000e-02 eta: 1:40:00 time: 0.2035 data_time: 0.0104 memory: 7116 grad_norm: 6.4185 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.2506 loss: 2.2506 2022/09/04 00:06:27 - mmengine - INFO - Epoch(train) [29][960/1345] lr: 1.0000e-02 eta: 1:39:56 time: 0.1994 data_time: 0.0079 memory: 7116 grad_norm: 6.5850 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2132 loss: 2.2132 2022/09/04 00:06:31 - mmengine - INFO - Epoch(train) [29][980/1345] lr: 1.0000e-02 eta: 1:39:51 time: 0.2025 data_time: 0.0109 memory: 7116 grad_norm: 6.7232 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.2131 loss: 2.2131 2022/09/04 00:06:35 - mmengine - INFO - Epoch(train) [29][1000/1345] lr: 1.0000e-02 eta: 1:39:47 time: 0.1987 data_time: 0.0085 memory: 7116 grad_norm: 6.5775 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2191 loss: 2.2191 2022/09/04 00:06:39 - mmengine - INFO - Epoch(train) [29][1020/1345] lr: 1.0000e-02 eta: 1:39:43 time: 0.2030 data_time: 0.0082 memory: 7116 grad_norm: 6.6439 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2656 loss: 2.2656 2022/09/04 00:06:43 - mmengine - INFO - Epoch(train) [29][1040/1345] lr: 1.0000e-02 eta: 1:39:38 time: 0.2089 data_time: 0.0121 memory: 7116 grad_norm: 6.7173 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2058 loss: 2.2058 2022/09/04 00:06:47 - mmengine - INFO - Epoch(train) [29][1060/1345] lr: 1.0000e-02 eta: 1:39:34 time: 0.2022 data_time: 0.0086 memory: 7116 grad_norm: 6.7486 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1978 loss: 2.1978 2022/09/04 00:06:51 - mmengine - INFO - Epoch(train) [29][1080/1345] lr: 1.0000e-02 eta: 1:39:30 time: 0.2012 data_time: 0.0088 memory: 7116 grad_norm: 6.6061 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1287 loss: 2.1287 2022/09/04 00:06:55 - mmengine - INFO - Epoch(train) [29][1100/1345] lr: 1.0000e-02 eta: 1:39:26 time: 0.2030 data_time: 0.0103 memory: 7116 grad_norm: 6.5863 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1807 loss: 2.1807 2022/09/04 00:06:59 - mmengine - INFO - Epoch(train) [29][1120/1345] lr: 1.0000e-02 eta: 1:39:21 time: 0.1995 data_time: 0.0085 memory: 7116 grad_norm: 6.6518 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.9907 loss: 1.9907 2022/09/04 00:07:04 - mmengine - INFO - Epoch(train) [29][1140/1345] lr: 1.0000e-02 eta: 1:39:17 time: 0.2379 data_time: 0.0097 memory: 7116 grad_norm: 6.6481 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3042 loss: 2.3042 2022/09/04 00:07:08 - mmengine - INFO - Epoch(train) [29][1160/1345] lr: 1.0000e-02 eta: 1:39:13 time: 0.2044 data_time: 0.0103 memory: 7116 grad_norm: 6.6482 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.9293 loss: 1.9293 2022/09/04 00:07:12 - mmengine - INFO - Epoch(train) [29][1180/1345] lr: 1.0000e-02 eta: 1:39:09 time: 0.2037 data_time: 0.0086 memory: 7116 grad_norm: 6.5068 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.1706 loss: 2.1706 2022/09/04 00:07:16 - mmengine - INFO - Epoch(train) [29][1200/1345] lr: 1.0000e-02 eta: 1:39:05 time: 0.1969 data_time: 0.0085 memory: 7116 grad_norm: 6.7775 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3870 loss: 2.3870 2022/09/04 00:07:20 - mmengine - INFO - Epoch(train) [29][1220/1345] lr: 1.0000e-02 eta: 1:39:00 time: 0.2021 data_time: 0.0102 memory: 7116 grad_norm: 6.7102 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1868 loss: 2.1868 2022/09/04 00:07:24 - mmengine - INFO - Epoch(train) [29][1240/1345] lr: 1.0000e-02 eta: 1:38:56 time: 0.2075 data_time: 0.0097 memory: 7116 grad_norm: 6.3401 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.3984 loss: 2.3984 2022/09/04 00:07:29 - mmengine - INFO - Epoch(train) [29][1260/1345] lr: 1.0000e-02 eta: 1:38:52 time: 0.2041 data_time: 0.0083 memory: 7116 grad_norm: 6.7844 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.9570 loss: 1.9570 2022/09/04 00:07:33 - mmengine - INFO - Epoch(train) [29][1280/1345] lr: 1.0000e-02 eta: 1:38:47 time: 0.2019 data_time: 0.0096 memory: 7116 grad_norm: 6.6794 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.2183 loss: 2.2183 2022/09/04 00:07:37 - mmengine - INFO - Epoch(train) [29][1300/1345] lr: 1.0000e-02 eta: 1:38:43 time: 0.2090 data_time: 0.0081 memory: 7116 grad_norm: 6.9962 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1637 loss: 2.1637 2022/09/04 00:07:41 - mmengine - INFO - Epoch(train) [29][1320/1345] lr: 1.0000e-02 eta: 1:38:39 time: 0.2038 data_time: 0.0088 memory: 7116 grad_norm: 6.6780 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2325 loss: 2.2325 2022/09/04 00:07:45 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:07:45 - mmengine - INFO - Epoch(train) [29][1340/1345] lr: 1.0000e-02 eta: 1:38:35 time: 0.2056 data_time: 0.0096 memory: 7116 grad_norm: 6.7633 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2478 loss: 2.2478 2022/09/04 00:07:46 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:07:46 - mmengine - INFO - Epoch(train) [29][1345/1345] lr: 1.0000e-02 eta: 1:38:35 time: 0.2077 data_time: 0.0078 memory: 7116 grad_norm: 7.0436 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.4047 loss: 2.4047 2022/09/04 00:07:46 - mmengine - INFO - Saving checkpoint at 29 epochs 2022/09/04 00:07:48 - mmengine - INFO - Epoch(val) [29][20/181] eta: 0:00:07 time: 0.0440 data_time: 0.0083 memory: 1114 2022/09/04 00:07:49 - mmengine - INFO - Epoch(val) [29][40/181] eta: 0:00:05 time: 0.0420 data_time: 0.0066 memory: 1114 2022/09/04 00:07:50 - mmengine - INFO - Epoch(val) [29][60/181] eta: 0:00:05 time: 0.0415 data_time: 0.0064 memory: 1114 2022/09/04 00:07:51 - mmengine - INFO - Epoch(val) [29][80/181] eta: 0:00:04 time: 0.0415 data_time: 0.0066 memory: 1114 2022/09/04 00:07:52 - mmengine - INFO - Epoch(val) [29][100/181] eta: 0:00:03 time: 0.0411 data_time: 0.0061 memory: 1114 2022/09/04 00:07:53 - mmengine - INFO - Epoch(val) [29][120/181] eta: 0:00:02 time: 0.0434 data_time: 0.0074 memory: 1114 2022/09/04 00:07:53 - mmengine - INFO - Epoch(val) [29][140/181] eta: 0:00:01 time: 0.0426 data_time: 0.0071 memory: 1114 2022/09/04 00:07:54 - mmengine - INFO - Epoch(val) [29][160/181] eta: 0:00:00 time: 0.0414 data_time: 0.0062 memory: 1114 2022/09/04 00:07:55 - mmengine - INFO - Epoch(val) [29][180/181] eta: 0:00:00 time: 0.0419 data_time: 0.0066 memory: 1114 2022/09/04 00:07:58 - mmengine - INFO - Epoch(val) [29][181/181] acc/top1: 0.3312 acc/top5: 0.6020 acc/mean1: 0.2895 2022/09/04 00:07:58 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_27.pth is removed 2022/09/04 00:07:59 - mmengine - INFO - The best checkpoint with 0.3312 acc/top1 at 29 epoch is saved to best_acc/top1_epoch_29.pth. 2022/09/04 00:08:03 - mmengine - INFO - Epoch(train) [30][20/1345] lr: 1.0000e-02 eta: 1:38:29 time: 0.2008 data_time: 0.0102 memory: 7116 grad_norm: 6.1461 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.0522 loss: 2.0522 2022/09/04 00:08:07 - mmengine - INFO - Epoch(train) [30][40/1345] lr: 1.0000e-02 eta: 1:38:24 time: 0.2040 data_time: 0.0084 memory: 7116 grad_norm: 6.6877 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2814 loss: 2.2814 2022/09/04 00:08:11 - mmengine - INFO - Epoch(train) [30][60/1345] lr: 1.0000e-02 eta: 1:38:20 time: 0.1969 data_time: 0.0085 memory: 7116 grad_norm: 6.5771 top1_acc: 0.3750 top5_acc: 1.0000 loss_cls: 2.1708 loss: 2.1708 2022/09/04 00:08:15 - mmengine - INFO - Epoch(train) [30][80/1345] lr: 1.0000e-02 eta: 1:38:16 time: 0.2087 data_time: 0.0111 memory: 7116 grad_norm: 6.7027 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1548 loss: 2.1548 2022/09/04 00:08:19 - mmengine - INFO - Epoch(train) [30][100/1345] lr: 1.0000e-02 eta: 1:38:12 time: 0.2002 data_time: 0.0080 memory: 7116 grad_norm: 6.8903 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2751 loss: 2.2751 2022/09/04 00:08:23 - mmengine - INFO - Epoch(train) [30][120/1345] lr: 1.0000e-02 eta: 1:38:07 time: 0.2015 data_time: 0.0091 memory: 7116 grad_norm: 6.5600 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9922 loss: 1.9922 2022/09/04 00:08:27 - mmengine - INFO - Epoch(train) [30][140/1345] lr: 1.0000e-02 eta: 1:38:03 time: 0.2031 data_time: 0.0102 memory: 7116 grad_norm: 6.8839 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9769 loss: 1.9769 2022/09/04 00:08:31 - mmengine - INFO - Epoch(train) [30][160/1345] lr: 1.0000e-02 eta: 1:37:59 time: 0.2011 data_time: 0.0079 memory: 7116 grad_norm: 6.6365 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.9972 loss: 1.9972 2022/09/04 00:08:35 - mmengine - INFO - Epoch(train) [30][180/1345] lr: 1.0000e-02 eta: 1:37:54 time: 0.2046 data_time: 0.0084 memory: 7116 grad_norm: 6.7488 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.1430 loss: 2.1430 2022/09/04 00:08:39 - mmengine - INFO - Epoch(train) [30][200/1345] lr: 1.0000e-02 eta: 1:37:50 time: 0.2041 data_time: 0.0103 memory: 7116 grad_norm: 6.6855 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1747 loss: 2.1747 2022/09/04 00:08:44 - mmengine - INFO - Epoch(train) [30][220/1345] lr: 1.0000e-02 eta: 1:37:46 time: 0.2137 data_time: 0.0079 memory: 7116 grad_norm: 6.6771 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.3590 loss: 2.3590 2022/09/04 00:08:48 - mmengine - INFO - Epoch(train) [30][240/1345] lr: 1.0000e-02 eta: 1:37:42 time: 0.2032 data_time: 0.0082 memory: 7116 grad_norm: 6.5263 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1111 loss: 2.1111 2022/09/04 00:08:52 - mmengine - INFO - Epoch(train) [30][260/1345] lr: 1.0000e-02 eta: 1:37:37 time: 0.2035 data_time: 0.0101 memory: 7116 grad_norm: 6.7318 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3221 loss: 2.3221 2022/09/04 00:08:56 - mmengine - INFO - Epoch(train) [30][280/1345] lr: 1.0000e-02 eta: 1:37:33 time: 0.2104 data_time: 0.0094 memory: 7116 grad_norm: 6.6870 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 1.9956 loss: 1.9956 2022/09/04 00:09:00 - mmengine - INFO - Epoch(train) [30][300/1345] lr: 1.0000e-02 eta: 1:37:29 time: 0.2048 data_time: 0.0074 memory: 7116 grad_norm: 6.6469 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0382 loss: 2.0382 2022/09/04 00:09:04 - mmengine - INFO - Epoch(train) [30][320/1345] lr: 1.0000e-02 eta: 1:37:25 time: 0.2066 data_time: 0.0103 memory: 7116 grad_norm: 6.5349 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1445 loss: 2.1445 2022/09/04 00:09:08 - mmengine - INFO - Epoch(train) [30][340/1345] lr: 1.0000e-02 eta: 1:37:21 time: 0.2049 data_time: 0.0075 memory: 7116 grad_norm: 6.5713 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.0345 loss: 2.0345 2022/09/04 00:09:12 - mmengine - INFO - Epoch(train) [30][360/1345] lr: 1.0000e-02 eta: 1:37:16 time: 0.2040 data_time: 0.0080 memory: 7116 grad_norm: 6.7272 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0076 loss: 2.0076 2022/09/04 00:09:17 - mmengine - INFO - Epoch(train) [30][380/1345] lr: 1.0000e-02 eta: 1:37:12 time: 0.2255 data_time: 0.0106 memory: 7116 grad_norm: 6.7313 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1185 loss: 2.1185 2022/09/04 00:09:21 - mmengine - INFO - Epoch(train) [30][400/1345] lr: 1.0000e-02 eta: 1:37:08 time: 0.2018 data_time: 0.0076 memory: 7116 grad_norm: 6.8472 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4281 loss: 2.4281 2022/09/04 00:09:25 - mmengine - INFO - Epoch(train) [30][420/1345] lr: 1.0000e-02 eta: 1:37:04 time: 0.2042 data_time: 0.0086 memory: 7116 grad_norm: 6.9335 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3210 loss: 2.3210 2022/09/04 00:09:29 - mmengine - INFO - Epoch(train) [30][440/1345] lr: 1.0000e-02 eta: 1:37:00 time: 0.2045 data_time: 0.0100 memory: 7116 grad_norm: 6.6938 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0475 loss: 2.0475 2022/09/04 00:09:33 - mmengine - INFO - Epoch(train) [30][460/1345] lr: 1.0000e-02 eta: 1:36:55 time: 0.2026 data_time: 0.0081 memory: 7116 grad_norm: 6.7188 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.4581 loss: 2.4581 2022/09/04 00:09:37 - mmengine - INFO - Epoch(train) [30][480/1345] lr: 1.0000e-02 eta: 1:36:51 time: 0.2042 data_time: 0.0079 memory: 7116 grad_norm: 6.7270 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.1184 loss: 2.1184 2022/09/04 00:09:41 - mmengine - INFO - Epoch(train) [30][500/1345] lr: 1.0000e-02 eta: 1:36:47 time: 0.2044 data_time: 0.0107 memory: 7116 grad_norm: 6.6505 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0475 loss: 2.0475 2022/09/04 00:09:45 - mmengine - INFO - Epoch(train) [30][520/1345] lr: 1.0000e-02 eta: 1:36:42 time: 0.2017 data_time: 0.0078 memory: 7116 grad_norm: 6.6889 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 2.3176 loss: 2.3176 2022/09/04 00:09:49 - mmengine - INFO - Epoch(train) [30][540/1345] lr: 1.0000e-02 eta: 1:36:38 time: 0.2006 data_time: 0.0078 memory: 7116 grad_norm: 6.8265 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1634 loss: 2.1634 2022/09/04 00:09:54 - mmengine - INFO - Epoch(train) [30][560/1345] lr: 1.0000e-02 eta: 1:36:34 time: 0.2267 data_time: 0.0118 memory: 7116 grad_norm: 6.5894 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8590 loss: 1.8590 2022/09/04 00:09:58 - mmengine - INFO - Epoch(train) [30][580/1345] lr: 1.0000e-02 eta: 1:36:30 time: 0.2034 data_time: 0.0080 memory: 7116 grad_norm: 6.7325 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1173 loss: 2.1173 2022/09/04 00:10:02 - mmengine - INFO - Epoch(train) [30][600/1345] lr: 1.0000e-02 eta: 1:36:26 time: 0.2057 data_time: 0.0078 memory: 7116 grad_norm: 6.5931 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0959 loss: 2.0959 2022/09/04 00:10:06 - mmengine - INFO - Epoch(train) [30][620/1345] lr: 1.0000e-02 eta: 1:36:21 time: 0.2065 data_time: 0.0101 memory: 7116 grad_norm: 6.4390 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0179 loss: 2.0179 2022/09/04 00:10:10 - mmengine - INFO - Epoch(train) [30][640/1345] lr: 1.0000e-02 eta: 1:36:17 time: 0.2050 data_time: 0.0081 memory: 7116 grad_norm: 6.6228 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1431 loss: 2.1431 2022/09/04 00:10:15 - mmengine - INFO - Epoch(train) [30][660/1345] lr: 1.0000e-02 eta: 1:36:13 time: 0.2056 data_time: 0.0074 memory: 7116 grad_norm: 6.4819 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0480 loss: 2.0480 2022/09/04 00:10:19 - mmengine - INFO - Epoch(train) [30][680/1345] lr: 1.0000e-02 eta: 1:36:09 time: 0.2068 data_time: 0.0099 memory: 7116 grad_norm: 6.6719 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.4683 loss: 2.4683 2022/09/04 00:10:23 - mmengine - INFO - Epoch(train) [30][700/1345] lr: 1.0000e-02 eta: 1:36:05 time: 0.2136 data_time: 0.0075 memory: 7116 grad_norm: 6.7019 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.2508 loss: 2.2508 2022/09/04 00:10:27 - mmengine - INFO - Epoch(train) [30][720/1345] lr: 1.0000e-02 eta: 1:36:01 time: 0.2169 data_time: 0.0085 memory: 7116 grad_norm: 6.6281 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.1509 loss: 2.1509 2022/09/04 00:10:31 - mmengine - INFO - Epoch(train) [30][740/1345] lr: 1.0000e-02 eta: 1:35:56 time: 0.2068 data_time: 0.0095 memory: 7116 grad_norm: 6.4517 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3303 loss: 2.3303 2022/09/04 00:10:36 - mmengine - INFO - Epoch(train) [30][760/1345] lr: 1.0000e-02 eta: 1:35:52 time: 0.2060 data_time: 0.0078 memory: 7116 grad_norm: 6.8233 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.1465 loss: 2.1465 2022/09/04 00:10:40 - mmengine - INFO - Epoch(train) [30][780/1345] lr: 1.0000e-02 eta: 1:35:48 time: 0.2072 data_time: 0.0080 memory: 7116 grad_norm: 6.5535 top1_acc: 0.2500 top5_acc: 1.0000 loss_cls: 2.2508 loss: 2.2508 2022/09/04 00:10:44 - mmengine - INFO - Epoch(train) [30][800/1345] lr: 1.0000e-02 eta: 1:35:44 time: 0.2141 data_time: 0.0108 memory: 7116 grad_norm: 6.7950 top1_acc: 0.3750 top5_acc: 1.0000 loss_cls: 2.0627 loss: 2.0627 2022/09/04 00:10:48 - mmengine - INFO - Epoch(train) [30][820/1345] lr: 1.0000e-02 eta: 1:35:40 time: 0.2062 data_time: 0.0075 memory: 7116 grad_norm: 6.8538 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1557 loss: 2.1557 2022/09/04 00:10:52 - mmengine - INFO - Epoch(train) [30][840/1345] lr: 1.0000e-02 eta: 1:35:35 time: 0.2072 data_time: 0.0077 memory: 7116 grad_norm: 6.6565 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.0719 loss: 2.0719 2022/09/04 00:10:56 - mmengine - INFO - Epoch(train) [30][860/1345] lr: 1.0000e-02 eta: 1:35:31 time: 0.2100 data_time: 0.0104 memory: 7116 grad_norm: 6.4096 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.1787 loss: 2.1787 2022/09/04 00:11:01 - mmengine - INFO - Epoch(train) [30][880/1345] lr: 1.0000e-02 eta: 1:35:27 time: 0.2100 data_time: 0.0070 memory: 7116 grad_norm: 6.6181 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0806 loss: 2.0806 2022/09/04 00:11:05 - mmengine - INFO - Epoch(train) [30][900/1345] lr: 1.0000e-02 eta: 1:35:23 time: 0.2160 data_time: 0.0087 memory: 7116 grad_norm: 7.0382 top1_acc: 0.0000 top5_acc: 0.7500 loss_cls: 2.2103 loss: 2.2103 2022/09/04 00:11:09 - mmengine - INFO - Epoch(train) [30][920/1345] lr: 1.0000e-02 eta: 1:35:19 time: 0.2073 data_time: 0.0107 memory: 7116 grad_norm: 7.0018 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1559 loss: 2.1559 2022/09/04 00:11:13 - mmengine - INFO - Epoch(train) [30][940/1345] lr: 1.0000e-02 eta: 1:35:14 time: 0.2062 data_time: 0.0076 memory: 7116 grad_norm: 6.4993 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2743 loss: 2.2743 2022/09/04 00:11:17 - mmengine - INFO - Epoch(train) [30][960/1345] lr: 1.0000e-02 eta: 1:35:10 time: 0.2085 data_time: 0.0077 memory: 7116 grad_norm: 6.5893 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5417 loss: 2.5417 2022/09/04 00:11:22 - mmengine - INFO - Epoch(train) [30][980/1345] lr: 1.0000e-02 eta: 1:35:06 time: 0.2074 data_time: 0.0103 memory: 7116 grad_norm: 6.5277 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2696 loss: 2.2696 2022/09/04 00:11:25 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:11:26 - mmengine - INFO - Epoch(train) [30][1000/1345] lr: 1.0000e-02 eta: 1:35:02 time: 0.2098 data_time: 0.0077 memory: 7116 grad_norm: 6.6546 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3577 loss: 2.3577 2022/09/04 00:11:30 - mmengine - INFO - Epoch(train) [30][1020/1345] lr: 1.0000e-02 eta: 1:34:58 time: 0.2073 data_time: 0.0073 memory: 7116 grad_norm: 6.5537 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1856 loss: 2.1856 2022/09/04 00:11:34 - mmengine - INFO - Epoch(train) [30][1040/1345] lr: 1.0000e-02 eta: 1:34:53 time: 0.2097 data_time: 0.0114 memory: 7116 grad_norm: 6.6488 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0894 loss: 2.0894 2022/09/04 00:11:38 - mmengine - INFO - Epoch(train) [30][1060/1345] lr: 1.0000e-02 eta: 1:34:49 time: 0.2068 data_time: 0.0073 memory: 7116 grad_norm: 6.3210 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.2951 loss: 2.2951 2022/09/04 00:11:42 - mmengine - INFO - Epoch(train) [30][1080/1345] lr: 1.0000e-02 eta: 1:34:45 time: 0.2064 data_time: 0.0078 memory: 7116 grad_norm: 6.9090 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2376 loss: 2.2376 2022/09/04 00:11:47 - mmengine - INFO - Epoch(train) [30][1100/1345] lr: 1.0000e-02 eta: 1:34:41 time: 0.2048 data_time: 0.0100 memory: 7116 grad_norm: 6.5738 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.9886 loss: 1.9886 2022/09/04 00:11:51 - mmengine - INFO - Epoch(train) [30][1120/1345] lr: 1.0000e-02 eta: 1:34:37 time: 0.2064 data_time: 0.0074 memory: 7116 grad_norm: 6.7030 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.3528 loss: 2.3528 2022/09/04 00:11:55 - mmengine - INFO - Epoch(train) [30][1140/1345] lr: 1.0000e-02 eta: 1:34:32 time: 0.2114 data_time: 0.0086 memory: 7116 grad_norm: 6.8665 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.9554 loss: 1.9554 2022/09/04 00:11:59 - mmengine - INFO - Epoch(train) [30][1160/1345] lr: 1.0000e-02 eta: 1:34:28 time: 0.2056 data_time: 0.0095 memory: 7116 grad_norm: 6.5697 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4200 loss: 2.4200 2022/09/04 00:12:03 - mmengine - INFO - Epoch(train) [30][1180/1345] lr: 1.0000e-02 eta: 1:34:24 time: 0.2013 data_time: 0.0086 memory: 7116 grad_norm: 6.5766 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2132 loss: 2.2132 2022/09/04 00:12:07 - mmengine - INFO - Epoch(train) [30][1200/1345] lr: 1.0000e-02 eta: 1:34:20 time: 0.2049 data_time: 0.0079 memory: 7116 grad_norm: 6.4711 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.1132 loss: 2.1132 2022/09/04 00:12:11 - mmengine - INFO - Epoch(train) [30][1220/1345] lr: 1.0000e-02 eta: 1:34:15 time: 0.2062 data_time: 0.0105 memory: 7116 grad_norm: 6.4343 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.2646 loss: 2.2646 2022/09/04 00:12:15 - mmengine - INFO - Epoch(train) [30][1240/1345] lr: 1.0000e-02 eta: 1:34:11 time: 0.2114 data_time: 0.0079 memory: 7116 grad_norm: 6.3900 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2333 loss: 2.2333 2022/09/04 00:12:20 - mmengine - INFO - Epoch(train) [30][1260/1345] lr: 1.0000e-02 eta: 1:34:07 time: 0.2041 data_time: 0.0086 memory: 7116 grad_norm: 6.5107 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.2393 loss: 2.2393 2022/09/04 00:12:24 - mmengine - INFO - Epoch(train) [30][1280/1345] lr: 1.0000e-02 eta: 1:34:03 time: 0.2041 data_time: 0.0099 memory: 7116 grad_norm: 6.6653 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2254 loss: 2.2254 2022/09/04 00:12:28 - mmengine - INFO - Epoch(train) [30][1300/1345] lr: 1.0000e-02 eta: 1:33:58 time: 0.2032 data_time: 0.0076 memory: 7116 grad_norm: 6.3531 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0836 loss: 2.0836 2022/09/04 00:12:32 - mmengine - INFO - Epoch(train) [30][1320/1345] lr: 1.0000e-02 eta: 1:33:54 time: 0.2087 data_time: 0.0074 memory: 7116 grad_norm: 6.3608 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0221 loss: 2.0221 2022/09/04 00:12:37 - mmengine - INFO - Epoch(train) [30][1340/1345] lr: 1.0000e-02 eta: 1:33:51 time: 0.2512 data_time: 0.0113 memory: 7116 grad_norm: 6.5043 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1984 loss: 2.1984 2022/09/04 00:12:38 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:12:38 - mmengine - INFO - Epoch(train) [30][1345/1345] lr: 1.0000e-02 eta: 1:33:51 time: 0.2482 data_time: 0.0087 memory: 7116 grad_norm: 6.6261 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.2555 loss: 2.2555 2022/09/04 00:12:38 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/09/04 00:12:40 - mmengine - INFO - Epoch(val) [30][20/181] eta: 0:00:06 time: 0.0428 data_time: 0.0084 memory: 1114 2022/09/04 00:12:41 - mmengine - INFO - Epoch(val) [30][40/181] eta: 0:00:05 time: 0.0406 data_time: 0.0059 memory: 1114 2022/09/04 00:12:42 - mmengine - INFO - Epoch(val) [30][60/181] eta: 0:00:04 time: 0.0408 data_time: 0.0059 memory: 1114 2022/09/04 00:12:43 - mmengine - INFO - Epoch(val) [30][80/181] eta: 0:00:04 time: 0.0402 data_time: 0.0055 memory: 1114 2022/09/04 00:12:43 - mmengine - INFO - Epoch(val) [30][100/181] eta: 0:00:03 time: 0.0402 data_time: 0.0055 memory: 1114 2022/09/04 00:12:44 - mmengine - INFO - Epoch(val) [30][120/181] eta: 0:00:02 time: 0.0403 data_time: 0.0056 memory: 1114 2022/09/04 00:12:45 - mmengine - INFO - Epoch(val) [30][140/181] eta: 0:00:01 time: 0.0405 data_time: 0.0058 memory: 1114 2022/09/04 00:12:46 - mmengine - INFO - Epoch(val) [30][160/181] eta: 0:00:00 time: 0.0402 data_time: 0.0056 memory: 1114 2022/09/04 00:12:47 - mmengine - INFO - Epoch(val) [30][180/181] eta: 0:00:00 time: 0.0401 data_time: 0.0056 memory: 1114 2022/09/04 00:12:50 - mmengine - INFO - Epoch(val) [30][181/181] acc/top1: 0.3334 acc/top5: 0.6230 acc/mean1: 0.2995 2022/09/04 00:12:50 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_29.pth is removed 2022/09/04 00:12:51 - mmengine - INFO - The best checkpoint with 0.3334 acc/top1 at 30 epoch is saved to best_acc/top1_epoch_30.pth. 2022/09/04 00:12:55 - mmengine - INFO - Epoch(train) [31][20/1345] lr: 1.0000e-03 eta: 1:33:45 time: 0.2074 data_time: 0.0098 memory: 7116 grad_norm: 6.7050 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.2071 loss: 2.2071 2022/09/04 00:12:59 - mmengine - INFO - Epoch(train) [31][40/1345] lr: 1.0000e-03 eta: 1:33:40 time: 0.2049 data_time: 0.0076 memory: 7116 grad_norm: 6.1705 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0045 loss: 2.0045 2022/09/04 00:13:04 - mmengine - INFO - Epoch(train) [31][60/1345] lr: 1.0000e-03 eta: 1:33:36 time: 0.2080 data_time: 0.0082 memory: 7116 grad_norm: 6.1859 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.6487 loss: 1.6487 2022/09/04 00:13:08 - mmengine - INFO - Epoch(train) [31][80/1345] lr: 1.0000e-03 eta: 1:33:32 time: 0.2070 data_time: 0.0101 memory: 7116 grad_norm: 6.5191 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7562 loss: 1.7562 2022/09/04 00:13:12 - mmengine - INFO - Epoch(train) [31][100/1345] lr: 1.0000e-03 eta: 1:33:28 time: 0.2072 data_time: 0.0078 memory: 7116 grad_norm: 6.2102 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.7798 loss: 1.7798 2022/09/04 00:13:16 - mmengine - INFO - Epoch(train) [31][120/1345] lr: 1.0000e-03 eta: 1:33:24 time: 0.2062 data_time: 0.0075 memory: 7116 grad_norm: 6.0408 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7636 loss: 1.7636 2022/09/04 00:13:20 - mmengine - INFO - Epoch(train) [31][140/1345] lr: 1.0000e-03 eta: 1:33:19 time: 0.2064 data_time: 0.0100 memory: 7116 grad_norm: 6.2318 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 1.6102 loss: 1.6102 2022/09/04 00:13:24 - mmengine - INFO - Epoch(train) [31][160/1345] lr: 1.0000e-03 eta: 1:33:15 time: 0.2096 data_time: 0.0085 memory: 7116 grad_norm: 6.1394 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9726 loss: 1.9726 2022/09/04 00:13:28 - mmengine - INFO - Epoch(train) [31][180/1345] lr: 1.0000e-03 eta: 1:33:11 time: 0.2072 data_time: 0.0077 memory: 7116 grad_norm: 6.2100 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6905 loss: 1.6905 2022/09/04 00:13:33 - mmengine - INFO - Epoch(train) [31][200/1345] lr: 1.0000e-03 eta: 1:33:07 time: 0.2072 data_time: 0.0098 memory: 7116 grad_norm: 6.1673 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8955 loss: 1.8955 2022/09/04 00:13:37 - mmengine - INFO - Epoch(train) [31][220/1345] lr: 1.0000e-03 eta: 1:33:02 time: 0.2045 data_time: 0.0082 memory: 7116 grad_norm: 6.3137 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9582 loss: 1.9582 2022/09/04 00:13:41 - mmengine - INFO - Epoch(train) [31][240/1345] lr: 1.0000e-03 eta: 1:32:58 time: 0.2085 data_time: 0.0071 memory: 7116 grad_norm: 6.2535 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8982 loss: 1.8982 2022/09/04 00:13:45 - mmengine - INFO - Epoch(train) [31][260/1345] lr: 1.0000e-03 eta: 1:32:54 time: 0.2130 data_time: 0.0105 memory: 7116 grad_norm: 6.1988 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7044 loss: 1.7044 2022/09/04 00:13:49 - mmengine - INFO - Epoch(train) [31][280/1345] lr: 1.0000e-03 eta: 1:32:50 time: 0.2076 data_time: 0.0076 memory: 7116 grad_norm: 6.2428 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.9850 loss: 1.9850 2022/09/04 00:13:53 - mmengine - INFO - Epoch(train) [31][300/1345] lr: 1.0000e-03 eta: 1:32:46 time: 0.2081 data_time: 0.0073 memory: 7116 grad_norm: 6.3886 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.8350 loss: 1.8350 2022/09/04 00:13:58 - mmengine - INFO - Epoch(train) [31][320/1345] lr: 1.0000e-03 eta: 1:32:42 time: 0.2056 data_time: 0.0096 memory: 7116 grad_norm: 6.2455 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8103 loss: 1.8103 2022/09/04 00:14:02 - mmengine - INFO - Epoch(train) [31][340/1345] lr: 1.0000e-03 eta: 1:32:37 time: 0.2085 data_time: 0.0078 memory: 7116 grad_norm: 6.4019 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7622 loss: 1.7622 2022/09/04 00:14:06 - mmengine - INFO - Epoch(train) [31][360/1345] lr: 1.0000e-03 eta: 1:32:33 time: 0.2084 data_time: 0.0088 memory: 7116 grad_norm: 6.4548 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.8028 loss: 1.8028 2022/09/04 00:14:10 - mmengine - INFO - Epoch(train) [31][380/1345] lr: 1.0000e-03 eta: 1:32:29 time: 0.2088 data_time: 0.0094 memory: 7116 grad_norm: 6.3821 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8774 loss: 1.8774 2022/09/04 00:14:14 - mmengine - INFO - Epoch(train) [31][400/1345] lr: 1.0000e-03 eta: 1:32:25 time: 0.2086 data_time: 0.0085 memory: 7116 grad_norm: 6.2623 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7052 loss: 1.7052 2022/09/04 00:14:18 - mmengine - INFO - Epoch(train) [31][420/1345] lr: 1.0000e-03 eta: 1:32:21 time: 0.2086 data_time: 0.0072 memory: 7116 grad_norm: 6.3653 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4403 loss: 1.4403 2022/09/04 00:14:23 - mmengine - INFO - Epoch(train) [31][440/1345] lr: 1.0000e-03 eta: 1:32:16 time: 0.2056 data_time: 0.0102 memory: 7116 grad_norm: 6.3337 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7367 loss: 1.7367 2022/09/04 00:14:27 - mmengine - INFO - Epoch(train) [31][460/1345] lr: 1.0000e-03 eta: 1:32:12 time: 0.2134 data_time: 0.0090 memory: 7116 grad_norm: 6.4325 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8498 loss: 1.8498 2022/09/04 00:14:31 - mmengine - INFO - Epoch(train) [31][480/1345] lr: 1.0000e-03 eta: 1:32:08 time: 0.2072 data_time: 0.0071 memory: 7116 grad_norm: 6.3543 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7241 loss: 1.7241 2022/09/04 00:14:35 - mmengine - INFO - Epoch(train) [31][500/1345] lr: 1.0000e-03 eta: 1:32:04 time: 0.2061 data_time: 0.0100 memory: 7116 grad_norm: 6.5804 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.7138 loss: 1.7138 2022/09/04 00:14:39 - mmengine - INFO - Epoch(train) [31][520/1345] lr: 1.0000e-03 eta: 1:31:59 time: 0.2045 data_time: 0.0079 memory: 7116 grad_norm: 6.5840 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5142 loss: 1.5142 2022/09/04 00:14:43 - mmengine - INFO - Epoch(train) [31][540/1345] lr: 1.0000e-03 eta: 1:31:55 time: 0.2105 data_time: 0.0086 memory: 7116 grad_norm: 6.4333 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7395 loss: 1.7395 2022/09/04 00:14:48 - mmengine - INFO - Epoch(train) [31][560/1345] lr: 1.0000e-03 eta: 1:31:51 time: 0.2096 data_time: 0.0093 memory: 7116 grad_norm: 6.3312 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7016 loss: 1.7016 2022/09/04 00:14:52 - mmengine - INFO - Epoch(train) [31][580/1345] lr: 1.0000e-03 eta: 1:31:47 time: 0.2078 data_time: 0.0076 memory: 7116 grad_norm: 6.4847 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5990 loss: 1.5990 2022/09/04 00:14:56 - mmengine - INFO - Epoch(train) [31][600/1345] lr: 1.0000e-03 eta: 1:31:43 time: 0.2146 data_time: 0.0094 memory: 7116 grad_norm: 6.5008 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3164 loss: 1.3164 2022/09/04 00:15:00 - mmengine - INFO - Epoch(train) [31][620/1345] lr: 1.0000e-03 eta: 1:31:39 time: 0.2071 data_time: 0.0103 memory: 7116 grad_norm: 6.5833 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7791 loss: 1.7791 2022/09/04 00:15:04 - mmengine - INFO - Epoch(train) [31][640/1345] lr: 1.0000e-03 eta: 1:31:34 time: 0.2136 data_time: 0.0098 memory: 7116 grad_norm: 6.5106 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.6992 loss: 1.6992 2022/09/04 00:15:07 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:15:09 - mmengine - INFO - Epoch(train) [31][660/1345] lr: 1.0000e-03 eta: 1:31:30 time: 0.2047 data_time: 0.0083 memory: 7116 grad_norm: 6.3586 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.6630 loss: 1.6630 2022/09/04 00:15:13 - mmengine - INFO - Epoch(train) [31][680/1345] lr: 1.0000e-03 eta: 1:31:26 time: 0.2059 data_time: 0.0096 memory: 7116 grad_norm: 6.3253 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6074 loss: 1.6074 2022/09/04 00:15:17 - mmengine - INFO - Epoch(train) [31][700/1345] lr: 1.0000e-03 eta: 1:31:22 time: 0.1982 data_time: 0.0149 memory: 7116 grad_norm: 6.8508 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.8244 loss: 1.8244 2022/09/04 00:15:21 - mmengine - INFO - Epoch(train) [31][720/1345] lr: 1.0000e-03 eta: 1:31:17 time: 0.1928 data_time: 0.0123 memory: 7116 grad_norm: 6.2999 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6773 loss: 1.6773 2022/09/04 00:15:24 - mmengine - INFO - Epoch(train) [31][740/1345] lr: 1.0000e-03 eta: 1:31:13 time: 0.1952 data_time: 0.0128 memory: 7116 grad_norm: 6.5323 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5549 loss: 1.5549 2022/09/04 00:15:28 - mmengine - INFO - Epoch(train) [31][760/1345] lr: 1.0000e-03 eta: 1:31:08 time: 0.1890 data_time: 0.0114 memory: 7116 grad_norm: 6.3754 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.6444 loss: 1.6444 2022/09/04 00:15:32 - mmengine - INFO - Epoch(train) [31][780/1345] lr: 1.0000e-03 eta: 1:31:04 time: 0.1902 data_time: 0.0111 memory: 7116 grad_norm: 6.4344 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6864 loss: 1.6864 2022/09/04 00:15:36 - mmengine - INFO - Epoch(train) [31][800/1345] lr: 1.0000e-03 eta: 1:31:00 time: 0.1939 data_time: 0.0132 memory: 7116 grad_norm: 6.5988 top1_acc: 0.1250 top5_acc: 1.0000 loss_cls: 1.7895 loss: 1.7895 2022/09/04 00:15:40 - mmengine - INFO - Epoch(train) [31][820/1345] lr: 1.0000e-03 eta: 1:30:55 time: 0.1867 data_time: 0.0114 memory: 7116 grad_norm: 6.3602 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5115 loss: 1.5115 2022/09/04 00:15:43 - mmengine - INFO - Epoch(train) [31][840/1345] lr: 1.0000e-03 eta: 1:30:51 time: 0.1882 data_time: 0.0108 memory: 7116 grad_norm: 6.4014 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 1.7109 loss: 1.7109 2022/09/04 00:15:47 - mmengine - INFO - Epoch(train) [31][860/1345] lr: 1.0000e-03 eta: 1:30:46 time: 0.1901 data_time: 0.0125 memory: 7116 grad_norm: 6.3102 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.7078 loss: 1.7078 2022/09/04 00:15:51 - mmengine - INFO - Epoch(train) [31][880/1345] lr: 1.0000e-03 eta: 1:30:42 time: 0.1871 data_time: 0.0116 memory: 7116 grad_norm: 6.5110 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6627 loss: 1.6627 2022/09/04 00:15:55 - mmengine - INFO - Epoch(train) [31][900/1345] lr: 1.0000e-03 eta: 1:30:38 time: 0.2004 data_time: 0.0107 memory: 7116 grad_norm: 6.5446 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7830 loss: 1.7830 2022/09/04 00:15:59 - mmengine - INFO - Epoch(train) [31][920/1345] lr: 1.0000e-03 eta: 1:30:33 time: 0.1893 data_time: 0.0132 memory: 7116 grad_norm: 6.3073 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6213 loss: 1.6213 2022/09/04 00:16:03 - mmengine - INFO - Epoch(train) [31][940/1345] lr: 1.0000e-03 eta: 1:30:29 time: 0.1909 data_time: 0.0113 memory: 7116 grad_norm: 6.3629 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3638 loss: 1.3638 2022/09/04 00:16:06 - mmengine - INFO - Epoch(train) [31][960/1345] lr: 1.0000e-03 eta: 1:30:24 time: 0.1882 data_time: 0.0118 memory: 7116 grad_norm: 6.5125 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7819 loss: 1.7819 2022/09/04 00:16:10 - mmengine - INFO - Epoch(train) [31][980/1345] lr: 1.0000e-03 eta: 1:30:20 time: 0.1894 data_time: 0.0128 memory: 7116 grad_norm: 6.5410 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5602 loss: 1.5602 2022/09/04 00:16:14 - mmengine - INFO - Epoch(train) [31][1000/1345] lr: 1.0000e-03 eta: 1:30:16 time: 0.1965 data_time: 0.0120 memory: 7116 grad_norm: 6.4224 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.4933 loss: 1.4933 2022/09/04 00:16:18 - mmengine - INFO - Epoch(train) [31][1020/1345] lr: 1.0000e-03 eta: 1:30:11 time: 0.1874 data_time: 0.0114 memory: 7116 grad_norm: 6.4077 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7723 loss: 1.7723 2022/09/04 00:16:22 - mmengine - INFO - Epoch(train) [31][1040/1345] lr: 1.0000e-03 eta: 1:30:07 time: 0.1868 data_time: 0.0122 memory: 7116 grad_norm: 6.3186 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6369 loss: 1.6369 2022/09/04 00:16:25 - mmengine - INFO - Epoch(train) [31][1060/1345] lr: 1.0000e-03 eta: 1:30:02 time: 0.1939 data_time: 0.0112 memory: 7116 grad_norm: 6.5113 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5267 loss: 1.5267 2022/09/04 00:16:29 - mmengine - INFO - Epoch(train) [31][1080/1345] lr: 1.0000e-03 eta: 1:29:58 time: 0.1930 data_time: 0.0114 memory: 7116 grad_norm: 6.6964 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6999 loss: 1.6999 2022/09/04 00:16:33 - mmengine - INFO - Epoch(train) [31][1100/1345] lr: 1.0000e-03 eta: 1:29:53 time: 0.1860 data_time: 0.0119 memory: 7116 grad_norm: 6.4249 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.6769 loss: 1.6769 2022/09/04 00:16:37 - mmengine - INFO - Epoch(train) [31][1120/1345] lr: 1.0000e-03 eta: 1:29:49 time: 0.1896 data_time: 0.0118 memory: 7116 grad_norm: 6.4891 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6218 loss: 1.6218 2022/09/04 00:16:41 - mmengine - INFO - Epoch(train) [31][1140/1345] lr: 1.0000e-03 eta: 1:29:45 time: 0.1907 data_time: 0.0113 memory: 7116 grad_norm: 6.3820 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 1.7081 loss: 1.7081 2022/09/04 00:16:45 - mmengine - INFO - Epoch(train) [31][1160/1345] lr: 1.0000e-03 eta: 1:29:40 time: 0.1953 data_time: 0.0134 memory: 7116 grad_norm: 6.6705 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.7766 loss: 1.7766 2022/09/04 00:16:48 - mmengine - INFO - Epoch(train) [31][1180/1345] lr: 1.0000e-03 eta: 1:29:36 time: 0.1929 data_time: 0.0115 memory: 7116 grad_norm: 6.4718 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6509 loss: 1.6509 2022/09/04 00:16:52 - mmengine - INFO - Epoch(train) [31][1200/1345] lr: 1.0000e-03 eta: 1:29:31 time: 0.1942 data_time: 0.0112 memory: 7116 grad_norm: 6.4617 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6792 loss: 1.6792 2022/09/04 00:16:56 - mmengine - INFO - Epoch(train) [31][1220/1345] lr: 1.0000e-03 eta: 1:29:27 time: 0.1930 data_time: 0.0125 memory: 7116 grad_norm: 6.6557 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.7533 loss: 1.7533 2022/09/04 00:17:00 - mmengine - INFO - Epoch(train) [31][1240/1345] lr: 1.0000e-03 eta: 1:29:23 time: 0.2070 data_time: 0.0117 memory: 7116 grad_norm: 6.4985 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 1.8144 loss: 1.8144 2022/09/04 00:17:04 - mmengine - INFO - Epoch(train) [31][1260/1345] lr: 1.0000e-03 eta: 1:29:18 time: 0.1898 data_time: 0.0112 memory: 7116 grad_norm: 6.6249 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4914 loss: 1.4914 2022/09/04 00:17:08 - mmengine - INFO - Epoch(train) [31][1280/1345] lr: 1.0000e-03 eta: 1:29:14 time: 0.1935 data_time: 0.0132 memory: 7116 grad_norm: 6.5989 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8388 loss: 1.8388 2022/09/04 00:17:12 - mmengine - INFO - Epoch(train) [31][1300/1345] lr: 1.0000e-03 eta: 1:29:10 time: 0.1920 data_time: 0.0117 memory: 7116 grad_norm: 6.6524 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9493 loss: 1.9493 2022/09/04 00:17:16 - mmengine - INFO - Epoch(train) [31][1320/1345] lr: 1.0000e-03 eta: 1:29:05 time: 0.1902 data_time: 0.0119 memory: 7116 grad_norm: 6.4558 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5129 loss: 1.5129 2022/09/04 00:17:19 - mmengine - INFO - Epoch(train) [31][1340/1345] lr: 1.0000e-03 eta: 1:29:01 time: 0.1874 data_time: 0.0130 memory: 7116 grad_norm: 6.6096 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6387 loss: 1.6387 2022/09/04 00:17:20 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:17:20 - mmengine - INFO - Epoch(train) [31][1345/1345] lr: 1.0000e-03 eta: 1:29:01 time: 0.1837 data_time: 0.0104 memory: 7116 grad_norm: 7.1399 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.8167 loss: 1.8167 2022/09/04 00:17:20 - mmengine - INFO - Saving checkpoint at 31 epochs 2022/09/04 00:17:26 - mmengine - INFO - Epoch(val) [31][20/181] eta: 0:00:07 time: 0.0484 data_time: 0.0111 memory: 1114 2022/09/04 00:17:27 - mmengine - INFO - Epoch(val) [31][40/181] eta: 0:00:06 time: 0.0461 data_time: 0.0083 memory: 1114 2022/09/04 00:17:27 - mmengine - INFO - Epoch(val) [31][60/181] eta: 0:00:05 time: 0.0457 data_time: 0.0080 memory: 1114 2022/09/04 00:17:28 - mmengine - INFO - Epoch(val) [31][80/181] eta: 0:00:04 time: 0.0484 data_time: 0.0092 memory: 1114 2022/09/04 00:17:29 - mmengine - INFO - Epoch(val) [31][100/181] eta: 0:00:03 time: 0.0461 data_time: 0.0086 memory: 1114 2022/09/04 00:17:30 - mmengine - INFO - Epoch(val) [31][120/181] eta: 0:00:03 time: 0.0510 data_time: 0.0095 memory: 1114 2022/09/04 00:17:31 - mmengine - INFO - Epoch(val) [31][140/181] eta: 0:00:01 time: 0.0452 data_time: 0.0080 memory: 1114 2022/09/04 00:17:32 - mmengine - INFO - Epoch(val) [31][160/181] eta: 0:00:00 time: 0.0476 data_time: 0.0090 memory: 1114 2022/09/04 00:17:33 - mmengine - INFO - Epoch(val) [31][180/181] eta: 0:00:00 time: 0.0455 data_time: 0.0083 memory: 1114 2022/09/04 00:17:34 - mmengine - INFO - Epoch(val) [31][181/181] acc/top1: 0.4320 acc/top5: 0.7274 acc/mean1: 0.3923 2022/09/04 00:17:34 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_30.pth is removed 2022/09/04 00:17:35 - mmengine - INFO - The best checkpoint with 0.4320 acc/top1 at 31 epoch is saved to best_acc/top1_epoch_31.pth. 2022/09/04 00:17:39 - mmengine - INFO - Epoch(train) [32][20/1345] lr: 1.0000e-03 eta: 1:28:55 time: 0.1967 data_time: 0.0157 memory: 7116 grad_norm: 6.3040 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4816 loss: 1.4816 2022/09/04 00:17:43 - mmengine - INFO - Epoch(train) [32][40/1345] lr: 1.0000e-03 eta: 1:28:50 time: 0.1894 data_time: 0.0114 memory: 7116 grad_norm: 6.6913 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7511 loss: 1.7511 2022/09/04 00:17:47 - mmengine - INFO - Epoch(train) [32][60/1345] lr: 1.0000e-03 eta: 1:28:46 time: 0.1908 data_time: 0.0105 memory: 7116 grad_norm: 6.3198 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8059 loss: 1.8059 2022/09/04 00:17:51 - mmengine - INFO - Epoch(train) [32][80/1345] lr: 1.0000e-03 eta: 1:28:42 time: 0.1986 data_time: 0.0133 memory: 7116 grad_norm: 6.4663 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4694 loss: 1.4694 2022/09/04 00:17:55 - mmengine - INFO - Epoch(train) [32][100/1345] lr: 1.0000e-03 eta: 1:28:37 time: 0.1926 data_time: 0.0105 memory: 7116 grad_norm: 6.7667 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4419 loss: 1.4419 2022/09/04 00:17:59 - mmengine - INFO - Epoch(train) [32][120/1345] lr: 1.0000e-03 eta: 1:28:33 time: 0.1920 data_time: 0.0119 memory: 7116 grad_norm: 6.5259 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8385 loss: 1.8385 2022/09/04 00:18:02 - mmengine - INFO - Epoch(train) [32][140/1345] lr: 1.0000e-03 eta: 1:28:29 time: 0.1913 data_time: 0.0136 memory: 7116 grad_norm: 6.2931 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6019 loss: 1.6019 2022/09/04 00:18:06 - mmengine - INFO - Epoch(train) [32][160/1345] lr: 1.0000e-03 eta: 1:28:24 time: 0.1983 data_time: 0.0095 memory: 7116 grad_norm: 6.7054 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5960 loss: 1.5960 2022/09/04 00:18:10 - mmengine - INFO - Epoch(train) [32][180/1345] lr: 1.0000e-03 eta: 1:28:20 time: 0.1900 data_time: 0.0115 memory: 7116 grad_norm: 6.6304 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5467 loss: 1.5467 2022/09/04 00:18:14 - mmengine - INFO - Epoch(train) [32][200/1345] lr: 1.0000e-03 eta: 1:28:15 time: 0.1933 data_time: 0.0124 memory: 7116 grad_norm: 6.5970 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4912 loss: 1.4912 2022/09/04 00:18:18 - mmengine - INFO - Epoch(train) [32][220/1345] lr: 1.0000e-03 eta: 1:28:11 time: 0.1871 data_time: 0.0107 memory: 7116 grad_norm: 6.5027 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5554 loss: 1.5554 2022/09/04 00:18:22 - mmengine - INFO - Epoch(train) [32][240/1345] lr: 1.0000e-03 eta: 1:28:07 time: 0.1874 data_time: 0.0108 memory: 7116 grad_norm: 6.2182 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6128 loss: 1.6128 2022/09/04 00:18:26 - mmengine - INFO - Epoch(train) [32][260/1345] lr: 1.0000e-03 eta: 1:28:02 time: 0.2048 data_time: 0.0123 memory: 7116 grad_norm: 6.4474 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.6322 loss: 1.6322 2022/09/04 00:18:30 - mmengine - INFO - Epoch(train) [32][280/1345] lr: 1.0000e-03 eta: 1:27:58 time: 0.1924 data_time: 0.0099 memory: 7116 grad_norm: 6.7547 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8164 loss: 1.8164 2022/09/04 00:18:33 - mmengine - INFO - Epoch(train) [32][300/1345] lr: 1.0000e-03 eta: 1:27:54 time: 0.1873 data_time: 0.0103 memory: 7116 grad_norm: 6.7426 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.7766 loss: 1.7766 2022/09/04 00:18:34 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:18:37 - mmengine - INFO - Epoch(train) [32][320/1345] lr: 1.0000e-03 eta: 1:27:49 time: 0.1918 data_time: 0.0130 memory: 7116 grad_norm: 6.6605 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3980 loss: 1.3980 2022/09/04 00:18:41 - mmengine - INFO - Epoch(train) [32][340/1345] lr: 1.0000e-03 eta: 1:27:45 time: 0.1876 data_time: 0.0106 memory: 7116 grad_norm: 6.6158 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7969 loss: 1.7969 2022/09/04 00:18:45 - mmengine - INFO - Epoch(train) [32][360/1345] lr: 1.0000e-03 eta: 1:27:40 time: 0.1940 data_time: 0.0107 memory: 7116 grad_norm: 6.6346 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.6233 loss: 1.6233 2022/09/04 00:18:49 - mmengine - INFO - Epoch(train) [32][380/1345] lr: 1.0000e-03 eta: 1:27:36 time: 0.1919 data_time: 0.0134 memory: 7116 grad_norm: 7.0795 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.7916 loss: 1.7916 2022/09/04 00:18:52 - mmengine - INFO - Epoch(train) [32][400/1345] lr: 1.0000e-03 eta: 1:27:32 time: 0.1904 data_time: 0.0112 memory: 7116 grad_norm: 6.4736 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4392 loss: 1.4392 2022/09/04 00:18:57 - mmengine - INFO - Epoch(train) [32][420/1345] lr: 1.0000e-03 eta: 1:27:28 time: 0.2216 data_time: 0.0115 memory: 7116 grad_norm: 6.4887 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3445 loss: 1.3445 2022/09/04 00:19:01 - mmengine - INFO - Epoch(train) [32][440/1345] lr: 1.0000e-03 eta: 1:27:23 time: 0.1930 data_time: 0.0122 memory: 7116 grad_norm: 6.6500 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.5536 loss: 1.5536 2022/09/04 00:19:05 - mmengine - INFO - Epoch(train) [32][460/1345] lr: 1.0000e-03 eta: 1:27:19 time: 0.1949 data_time: 0.0111 memory: 7116 grad_norm: 6.6589 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5288 loss: 1.5288 2022/09/04 00:19:08 - mmengine - INFO - Epoch(train) [32][480/1345] lr: 1.0000e-03 eta: 1:27:14 time: 0.1891 data_time: 0.0122 memory: 7116 grad_norm: 6.9692 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.6148 loss: 1.6148 2022/09/04 00:19:12 - mmengine - INFO - Epoch(train) [32][500/1345] lr: 1.0000e-03 eta: 1:27:10 time: 0.1890 data_time: 0.0137 memory: 7116 grad_norm: 6.4825 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8007 loss: 1.8007 2022/09/04 00:19:16 - mmengine - INFO - Epoch(train) [32][520/1345] lr: 1.0000e-03 eta: 1:27:06 time: 0.1920 data_time: 0.0109 memory: 7116 grad_norm: 6.5264 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.5343 loss: 1.5343 2022/09/04 00:19:20 - mmengine - INFO - Epoch(train) [32][540/1345] lr: 1.0000e-03 eta: 1:27:01 time: 0.1912 data_time: 0.0111 memory: 7116 grad_norm: 6.8111 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6181 loss: 1.6181 2022/09/04 00:19:24 - mmengine - INFO - Epoch(train) [32][560/1345] lr: 1.0000e-03 eta: 1:26:57 time: 0.1896 data_time: 0.0126 memory: 7116 grad_norm: 6.9103 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.6388 loss: 1.6388 2022/09/04 00:19:28 - mmengine - INFO - Epoch(train) [32][580/1345] lr: 1.0000e-03 eta: 1:26:53 time: 0.2052 data_time: 0.0114 memory: 7116 grad_norm: 6.5957 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2124 loss: 1.2124 2022/09/04 00:19:32 - mmengine - INFO - Epoch(train) [32][600/1345] lr: 1.0000e-03 eta: 1:26:48 time: 0.1937 data_time: 0.0115 memory: 7116 grad_norm: 6.6401 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5900 loss: 1.5900 2022/09/04 00:19:35 - mmengine - INFO - Epoch(train) [32][620/1345] lr: 1.0000e-03 eta: 1:26:44 time: 0.1926 data_time: 0.0140 memory: 7116 grad_norm: 6.9538 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5640 loss: 1.5640 2022/09/04 00:19:39 - mmengine - INFO - Epoch(train) [32][640/1345] lr: 1.0000e-03 eta: 1:26:40 time: 0.1870 data_time: 0.0111 memory: 7116 grad_norm: 6.9122 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4629 loss: 1.4629 2022/09/04 00:19:43 - mmengine - INFO - Epoch(train) [32][660/1345] lr: 1.0000e-03 eta: 1:26:35 time: 0.1899 data_time: 0.0113 memory: 7116 grad_norm: 6.3693 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6135 loss: 1.6135 2022/09/04 00:19:47 - mmengine - INFO - Epoch(train) [32][680/1345] lr: 1.0000e-03 eta: 1:26:31 time: 0.2064 data_time: 0.0125 memory: 7116 grad_norm: 6.7896 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8361 loss: 1.8361 2022/09/04 00:19:51 - mmengine - INFO - Epoch(train) [32][700/1345] lr: 1.0000e-03 eta: 1:26:27 time: 0.1915 data_time: 0.0121 memory: 7116 grad_norm: 6.8376 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.5727 loss: 1.5727 2022/09/04 00:19:55 - mmengine - INFO - Epoch(train) [32][720/1345] lr: 1.0000e-03 eta: 1:26:22 time: 0.1893 data_time: 0.0115 memory: 7116 grad_norm: 7.0734 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8607 loss: 1.8607 2022/09/04 00:19:59 - mmengine - INFO - Epoch(train) [32][740/1345] lr: 1.0000e-03 eta: 1:26:18 time: 0.1938 data_time: 0.0146 memory: 7116 grad_norm: 7.0681 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9056 loss: 1.9056 2022/09/04 00:20:03 - mmengine - INFO - Epoch(train) [32][760/1345] lr: 1.0000e-03 eta: 1:26:13 time: 0.1918 data_time: 0.0101 memory: 7116 grad_norm: 6.5630 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5285 loss: 1.5285 2022/09/04 00:20:06 - mmengine - INFO - Epoch(train) [32][780/1345] lr: 1.0000e-03 eta: 1:26:09 time: 0.1904 data_time: 0.0117 memory: 7116 grad_norm: 6.9878 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.7892 loss: 1.7892 2022/09/04 00:20:10 - mmengine - INFO - Epoch(train) [32][800/1345] lr: 1.0000e-03 eta: 1:26:05 time: 0.1962 data_time: 0.0152 memory: 7116 grad_norm: 6.7341 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.5653 loss: 1.5653 2022/09/04 00:20:14 - mmengine - INFO - Epoch(train) [32][820/1345] lr: 1.0000e-03 eta: 1:26:00 time: 0.1933 data_time: 0.0102 memory: 7116 grad_norm: 6.5659 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.4922 loss: 1.4922 2022/09/04 00:20:18 - mmengine - INFO - Epoch(train) [32][840/1345] lr: 1.0000e-03 eta: 1:25:56 time: 0.1907 data_time: 0.0106 memory: 7116 grad_norm: 6.6077 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4525 loss: 1.4525 2022/09/04 00:20:22 - mmengine - INFO - Epoch(train) [32][860/1345] lr: 1.0000e-03 eta: 1:25:52 time: 0.1937 data_time: 0.0139 memory: 7116 grad_norm: 6.8704 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.6822 loss: 1.6822 2022/09/04 00:20:26 - mmengine - INFO - Epoch(train) [32][880/1345] lr: 1.0000e-03 eta: 1:25:47 time: 0.1925 data_time: 0.0114 memory: 7116 grad_norm: 6.7077 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5034 loss: 1.5034 2022/09/04 00:20:29 - mmengine - INFO - Epoch(train) [32][900/1345] lr: 1.0000e-03 eta: 1:25:43 time: 0.1875 data_time: 0.0106 memory: 7116 grad_norm: 6.6047 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 1.5518 loss: 1.5518 2022/09/04 00:20:33 - mmengine - INFO - Epoch(train) [32][920/1345] lr: 1.0000e-03 eta: 1:25:38 time: 0.1880 data_time: 0.0127 memory: 7116 grad_norm: 6.6637 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5515 loss: 1.5515 2022/09/04 00:20:37 - mmengine - INFO - Epoch(train) [32][940/1345] lr: 1.0000e-03 eta: 1:25:34 time: 0.1944 data_time: 0.0120 memory: 7116 grad_norm: 6.7806 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5607 loss: 1.5607 2022/09/04 00:20:41 - mmengine - INFO - Epoch(train) [32][960/1345] lr: 1.0000e-03 eta: 1:25:30 time: 0.1873 data_time: 0.0110 memory: 7116 grad_norm: 6.8664 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.5007 loss: 1.5007 2022/09/04 00:20:45 - mmengine - INFO - Epoch(train) [32][980/1345] lr: 1.0000e-03 eta: 1:25:25 time: 0.1894 data_time: 0.0132 memory: 7116 grad_norm: 6.8887 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.3231 loss: 1.3231 2022/09/04 00:20:48 - mmengine - INFO - Epoch(train) [32][1000/1345] lr: 1.0000e-03 eta: 1:25:21 time: 0.1912 data_time: 0.0111 memory: 7116 grad_norm: 7.0034 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7843 loss: 1.7843 2022/09/04 00:20:52 - mmengine - INFO - Epoch(train) [32][1020/1345] lr: 1.0000e-03 eta: 1:25:17 time: 0.1934 data_time: 0.0111 memory: 7116 grad_norm: 6.6050 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2634 loss: 1.2634 2022/09/04 00:20:56 - mmengine - INFO - Epoch(train) [32][1040/1345] lr: 1.0000e-03 eta: 1:25:12 time: 0.1957 data_time: 0.0134 memory: 7116 grad_norm: 6.8209 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5373 loss: 1.5373 2022/09/04 00:21:00 - mmengine - INFO - Epoch(train) [32][1060/1345] lr: 1.0000e-03 eta: 1:25:08 time: 0.1913 data_time: 0.0101 memory: 7116 grad_norm: 6.7750 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3241 loss: 1.3241 2022/09/04 00:21:04 - mmengine - INFO - Epoch(train) [32][1080/1345] lr: 1.0000e-03 eta: 1:25:04 time: 0.1967 data_time: 0.0110 memory: 7116 grad_norm: 6.7714 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.6206 loss: 1.6206 2022/09/04 00:21:08 - mmengine - INFO - Epoch(train) [32][1100/1345] lr: 1.0000e-03 eta: 1:24:59 time: 0.1947 data_time: 0.0130 memory: 7116 grad_norm: 6.7427 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.4680 loss: 1.4680 2022/09/04 00:21:12 - mmengine - INFO - Epoch(train) [32][1120/1345] lr: 1.0000e-03 eta: 1:24:55 time: 0.1981 data_time: 0.0105 memory: 7116 grad_norm: 6.7331 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.8185 loss: 1.8185 2022/09/04 00:21:16 - mmengine - INFO - Epoch(train) [32][1140/1345] lr: 1.0000e-03 eta: 1:24:51 time: 0.2008 data_time: 0.0102 memory: 7116 grad_norm: 6.7673 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5817 loss: 1.5817 2022/09/04 00:21:20 - mmengine - INFO - Epoch(train) [32][1160/1345] lr: 1.0000e-03 eta: 1:24:46 time: 0.1988 data_time: 0.0131 memory: 7116 grad_norm: 6.6136 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.7280 loss: 1.7280 2022/09/04 00:21:24 - mmengine - INFO - Epoch(train) [32][1180/1345] lr: 1.0000e-03 eta: 1:24:42 time: 0.1957 data_time: 0.0098 memory: 7116 grad_norm: 6.6797 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6471 loss: 1.6471 2022/09/04 00:21:28 - mmengine - INFO - Epoch(train) [32][1200/1345] lr: 1.0000e-03 eta: 1:24:38 time: 0.1981 data_time: 0.0111 memory: 7116 grad_norm: 6.7767 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5793 loss: 1.5793 2022/09/04 00:21:32 - mmengine - INFO - Epoch(train) [32][1220/1345] lr: 1.0000e-03 eta: 1:24:34 time: 0.1999 data_time: 0.0130 memory: 7116 grad_norm: 6.7726 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.6908 loss: 1.6908 2022/09/04 00:21:36 - mmengine - INFO - Epoch(train) [32][1240/1345] lr: 1.0000e-03 eta: 1:24:29 time: 0.2003 data_time: 0.0101 memory: 7116 grad_norm: 6.8436 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.6803 loss: 1.6803 2022/09/04 00:21:40 - mmengine - INFO - Epoch(train) [32][1260/1345] lr: 1.0000e-03 eta: 1:24:25 time: 0.1989 data_time: 0.0105 memory: 7116 grad_norm: 6.4020 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.5954 loss: 1.5954 2022/09/04 00:21:44 - mmengine - INFO - Epoch(train) [32][1280/1345] lr: 1.0000e-03 eta: 1:24:21 time: 0.1959 data_time: 0.0133 memory: 7116 grad_norm: 6.9477 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5145 loss: 1.5145 2022/09/04 00:21:48 - mmengine - INFO - Epoch(train) [32][1300/1345] lr: 1.0000e-03 eta: 1:24:16 time: 0.1971 data_time: 0.0105 memory: 7116 grad_norm: 6.8375 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.5482 loss: 1.5482 2022/09/04 00:21:49 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:21:52 - mmengine - INFO - Epoch(train) [32][1320/1345] lr: 1.0000e-03 eta: 1:24:12 time: 0.1976 data_time: 0.0121 memory: 7116 grad_norm: 6.8954 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3674 loss: 1.3674 2022/09/04 00:21:56 - mmengine - INFO - Epoch(train) [32][1340/1345] lr: 1.0000e-03 eta: 1:24:08 time: 0.2000 data_time: 0.0128 memory: 7116 grad_norm: 6.7955 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5536 loss: 1.5536 2022/09/04 00:21:57 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:21:57 - mmengine - INFO - Epoch(train) [32][1345/1345] lr: 1.0000e-03 eta: 1:24:08 time: 0.2020 data_time: 0.0111 memory: 7116 grad_norm: 7.1588 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.6598 loss: 1.6598 2022/09/04 00:21:57 - mmengine - INFO - Saving checkpoint at 32 epochs 2022/09/04 00:22:00 - mmengine - INFO - Epoch(val) [32][20/181] eta: 0:00:07 time: 0.0486 data_time: 0.0113 memory: 1114 2022/09/04 00:22:01 - mmengine - INFO - Epoch(val) [32][40/181] eta: 0:00:06 time: 0.0476 data_time: 0.0088 memory: 1114 2022/09/04 00:22:01 - mmengine - INFO - Epoch(val) [32][60/181] eta: 0:00:05 time: 0.0457 data_time: 0.0084 memory: 1114 2022/09/04 00:22:02 - mmengine - INFO - Epoch(val) [32][80/181] eta: 0:00:04 time: 0.0456 data_time: 0.0082 memory: 1114 2022/09/04 00:22:03 - mmengine - INFO - Epoch(val) [32][100/181] eta: 0:00:03 time: 0.0489 data_time: 0.0098 memory: 1114 2022/09/04 00:22:04 - mmengine - INFO - Epoch(val) [32][120/181] eta: 0:00:02 time: 0.0448 data_time: 0.0078 memory: 1114 2022/09/04 00:22:05 - mmengine - INFO - Epoch(val) [32][140/181] eta: 0:00:01 time: 0.0474 data_time: 0.0084 memory: 1114 2022/09/04 00:22:06 - mmengine - INFO - Epoch(val) [32][160/181] eta: 0:00:00 time: 0.0452 data_time: 0.0083 memory: 1114 2022/09/04 00:22:07 - mmengine - INFO - Epoch(val) [32][180/181] eta: 0:00:00 time: 0.0456 data_time: 0.0082 memory: 1114 2022/09/04 00:22:09 - mmengine - INFO - Epoch(val) [32][181/181] acc/top1: 0.4427 acc/top5: 0.7317 acc/mean1: 0.4022 2022/09/04 00:22:09 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_31.pth is removed 2022/09/04 00:22:10 - mmengine - INFO - The best checkpoint with 0.4427 acc/top1 at 32 epoch is saved to best_acc/top1_epoch_32.pth. 2022/09/04 00:22:14 - mmengine - INFO - Epoch(train) [33][20/1345] lr: 1.0000e-03 eta: 1:24:02 time: 0.1959 data_time: 0.0121 memory: 7116 grad_norm: 6.7525 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3551 loss: 1.3551 2022/09/04 00:22:18 - mmengine - INFO - Epoch(train) [33][40/1345] lr: 1.0000e-03 eta: 1:23:58 time: 0.1950 data_time: 0.0112 memory: 7116 grad_norm: 6.8066 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.5609 loss: 1.5609 2022/09/04 00:22:22 - mmengine - INFO - Epoch(train) [33][60/1345] lr: 1.0000e-03 eta: 1:23:53 time: 0.1911 data_time: 0.0107 memory: 7116 grad_norm: 6.9567 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.8220 loss: 1.8220 2022/09/04 00:22:26 - mmengine - INFO - Epoch(train) [33][80/1345] lr: 1.0000e-03 eta: 1:23:49 time: 0.2003 data_time: 0.0124 memory: 7116 grad_norm: 6.6377 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5544 loss: 1.5544 2022/09/04 00:22:29 - mmengine - INFO - Epoch(train) [33][100/1345] lr: 1.0000e-03 eta: 1:23:45 time: 0.1917 data_time: 0.0107 memory: 7116 grad_norm: 6.8911 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3624 loss: 1.3624 2022/09/04 00:22:33 - mmengine - INFO - Epoch(train) [33][120/1345] lr: 1.0000e-03 eta: 1:23:40 time: 0.1901 data_time: 0.0112 memory: 7116 grad_norm: 6.9877 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5579 loss: 1.5579 2022/09/04 00:22:37 - mmengine - INFO - Epoch(train) [33][140/1345] lr: 1.0000e-03 eta: 1:23:36 time: 0.1976 data_time: 0.0133 memory: 7116 grad_norm: 6.9479 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6275 loss: 1.6275 2022/09/04 00:22:41 - mmengine - INFO - Epoch(train) [33][160/1345] lr: 1.0000e-03 eta: 1:23:32 time: 0.1888 data_time: 0.0097 memory: 7116 grad_norm: 7.0861 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5699 loss: 1.5699 2022/09/04 00:22:45 - mmengine - INFO - Epoch(train) [33][180/1345] lr: 1.0000e-03 eta: 1:23:27 time: 0.2007 data_time: 0.0114 memory: 7116 grad_norm: 6.8835 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.6193 loss: 1.6193 2022/09/04 00:22:49 - mmengine - INFO - Epoch(train) [33][200/1345] lr: 1.0000e-03 eta: 1:23:23 time: 0.1987 data_time: 0.0128 memory: 7116 grad_norm: 6.4035 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7301 loss: 1.7301 2022/09/04 00:22:53 - mmengine - INFO - Epoch(train) [33][220/1345] lr: 1.0000e-03 eta: 1:23:19 time: 0.1910 data_time: 0.0111 memory: 7116 grad_norm: 6.9310 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2819 loss: 1.2819 2022/09/04 00:22:57 - mmengine - INFO - Epoch(train) [33][240/1345] lr: 1.0000e-03 eta: 1:23:14 time: 0.1955 data_time: 0.0110 memory: 7116 grad_norm: 7.2490 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.7681 loss: 1.7681 2022/09/04 00:23:01 - mmengine - INFO - Epoch(train) [33][260/1345] lr: 1.0000e-03 eta: 1:23:10 time: 0.1919 data_time: 0.0137 memory: 7116 grad_norm: 7.0022 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4597 loss: 1.4597 2022/09/04 00:23:05 - mmengine - INFO - Epoch(train) [33][280/1345] lr: 1.0000e-03 eta: 1:23:06 time: 0.1956 data_time: 0.0124 memory: 7116 grad_norm: 6.7898 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.6636 loss: 1.6636 2022/09/04 00:23:08 - mmengine - INFO - Epoch(train) [33][300/1345] lr: 1.0000e-03 eta: 1:23:01 time: 0.1925 data_time: 0.0107 memory: 7116 grad_norm: 7.0244 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8895 loss: 1.8895 2022/09/04 00:23:12 - mmengine - INFO - Epoch(train) [33][320/1345] lr: 1.0000e-03 eta: 1:22:57 time: 0.1911 data_time: 0.0133 memory: 7116 grad_norm: 6.8560 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4346 loss: 1.4346 2022/09/04 00:23:16 - mmengine - INFO - Epoch(train) [33][340/1345] lr: 1.0000e-03 eta: 1:22:53 time: 0.1942 data_time: 0.0110 memory: 7116 grad_norm: 6.7159 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5909 loss: 1.5909 2022/09/04 00:23:20 - mmengine - INFO - Epoch(train) [33][360/1345] lr: 1.0000e-03 eta: 1:22:48 time: 0.1918 data_time: 0.0108 memory: 7116 grad_norm: 7.0293 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5693 loss: 1.5693 2022/09/04 00:23:24 - mmengine - INFO - Epoch(train) [33][380/1345] lr: 1.0000e-03 eta: 1:22:44 time: 0.1929 data_time: 0.0129 memory: 7116 grad_norm: 6.8618 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.4934 loss: 1.4934 2022/09/04 00:23:28 - mmengine - INFO - Epoch(train) [33][400/1345] lr: 1.0000e-03 eta: 1:22:40 time: 0.1988 data_time: 0.0133 memory: 7116 grad_norm: 6.7691 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4021 loss: 1.4021 2022/09/04 00:23:32 - mmengine - INFO - Epoch(train) [33][420/1345] lr: 1.0000e-03 eta: 1:22:35 time: 0.1909 data_time: 0.0111 memory: 7116 grad_norm: 6.9391 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5988 loss: 1.5988 2022/09/04 00:23:35 - mmengine - INFO - Epoch(train) [33][440/1345] lr: 1.0000e-03 eta: 1:22:31 time: 0.1926 data_time: 0.0130 memory: 7116 grad_norm: 7.0668 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4065 loss: 1.4065 2022/09/04 00:23:39 - mmengine - INFO - Epoch(train) [33][460/1345] lr: 1.0000e-03 eta: 1:22:27 time: 0.1907 data_time: 0.0108 memory: 7116 grad_norm: 6.8320 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.6091 loss: 1.6091 2022/09/04 00:23:43 - mmengine - INFO - Epoch(train) [33][480/1345] lr: 1.0000e-03 eta: 1:22:22 time: 0.1922 data_time: 0.0106 memory: 7116 grad_norm: 6.9461 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4785 loss: 1.4785 2022/09/04 00:23:47 - mmengine - INFO - Epoch(train) [33][500/1345] lr: 1.0000e-03 eta: 1:22:18 time: 0.1935 data_time: 0.0130 memory: 7116 grad_norm: 7.1122 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7420 loss: 1.7420 2022/09/04 00:23:51 - mmengine - INFO - Epoch(train) [33][520/1345] lr: 1.0000e-03 eta: 1:22:14 time: 0.1895 data_time: 0.0112 memory: 7116 grad_norm: 7.0177 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.5598 loss: 1.5598 2022/09/04 00:23:55 - mmengine - INFO - Epoch(train) [33][540/1345] lr: 1.0000e-03 eta: 1:22:09 time: 0.1906 data_time: 0.0114 memory: 7116 grad_norm: 7.0104 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5783 loss: 1.5783 2022/09/04 00:23:58 - mmengine - INFO - Epoch(train) [33][560/1345] lr: 1.0000e-03 eta: 1:22:05 time: 0.1906 data_time: 0.0120 memory: 7116 grad_norm: 6.9226 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.5745 loss: 1.5745 2022/09/04 00:24:02 - mmengine - INFO - Epoch(train) [33][580/1345] lr: 1.0000e-03 eta: 1:22:01 time: 0.1892 data_time: 0.0105 memory: 7116 grad_norm: 6.9324 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.5797 loss: 1.5797 2022/09/04 00:24:06 - mmengine - INFO - Epoch(train) [33][600/1345] lr: 1.0000e-03 eta: 1:21:56 time: 0.2006 data_time: 0.0099 memory: 7116 grad_norm: 6.9511 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.4958 loss: 1.4958 2022/09/04 00:24:10 - mmengine - INFO - Epoch(train) [33][620/1345] lr: 1.0000e-03 eta: 1:21:52 time: 0.1924 data_time: 0.0131 memory: 7116 grad_norm: 6.8454 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.5102 loss: 1.5102 2022/09/04 00:24:14 - mmengine - INFO - Epoch(train) [33][640/1345] lr: 1.0000e-03 eta: 1:21:48 time: 0.1923 data_time: 0.0107 memory: 7116 grad_norm: 7.1861 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6820 loss: 1.6820 2022/09/04 00:24:18 - mmengine - INFO - Epoch(train) [33][660/1345] lr: 1.0000e-03 eta: 1:21:43 time: 0.1871 data_time: 0.0102 memory: 7116 grad_norm: 7.1697 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2470 loss: 1.2470 2022/09/04 00:24:22 - mmengine - INFO - Epoch(train) [33][680/1345] lr: 1.0000e-03 eta: 1:21:39 time: 0.1950 data_time: 0.0136 memory: 7116 grad_norm: 7.0823 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4968 loss: 1.4968 2022/09/04 00:24:25 - mmengine - INFO - Epoch(train) [33][700/1345] lr: 1.0000e-03 eta: 1:21:35 time: 0.1924 data_time: 0.0102 memory: 7116 grad_norm: 6.7855 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2998 loss: 1.2998 2022/09/04 00:24:29 - mmengine - INFO - Epoch(train) [33][720/1345] lr: 1.0000e-03 eta: 1:21:30 time: 0.1914 data_time: 0.0110 memory: 7116 grad_norm: 6.9999 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.5055 loss: 1.5055 2022/09/04 00:24:33 - mmengine - INFO - Epoch(train) [33][740/1345] lr: 1.0000e-03 eta: 1:21:26 time: 0.1886 data_time: 0.0139 memory: 7116 grad_norm: 7.1444 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.5888 loss: 1.5888 2022/09/04 00:24:37 - mmengine - INFO - Epoch(train) [33][760/1345] lr: 1.0000e-03 eta: 1:21:22 time: 0.1900 data_time: 0.0112 memory: 7116 grad_norm: 7.0778 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6589 loss: 1.6589 2022/09/04 00:24:41 - mmengine - INFO - Epoch(train) [33][780/1345] lr: 1.0000e-03 eta: 1:21:17 time: 0.1982 data_time: 0.0162 memory: 7116 grad_norm: 7.1031 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.5868 loss: 1.5868 2022/09/04 00:24:45 - mmengine - INFO - Epoch(train) [33][800/1345] lr: 1.0000e-03 eta: 1:21:13 time: 0.1963 data_time: 0.0131 memory: 7116 grad_norm: 7.2117 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.5146 loss: 1.5146 2022/09/04 00:24:49 - mmengine - INFO - Epoch(train) [33][820/1345] lr: 1.0000e-03 eta: 1:21:09 time: 0.1921 data_time: 0.0108 memory: 7116 grad_norm: 6.8737 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4454 loss: 1.4454 2022/09/04 00:24:52 - mmengine - INFO - Epoch(train) [33][840/1345] lr: 1.0000e-03 eta: 1:21:04 time: 0.1889 data_time: 0.0104 memory: 7116 grad_norm: 7.1127 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5668 loss: 1.5668 2022/09/04 00:24:56 - mmengine - INFO - Epoch(train) [33][860/1345] lr: 1.0000e-03 eta: 1:21:00 time: 0.1942 data_time: 0.0136 memory: 7116 grad_norm: 6.8723 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3503 loss: 1.3503 2022/09/04 00:25:00 - mmengine - INFO - Epoch(train) [33][880/1345] lr: 1.0000e-03 eta: 1:20:56 time: 0.1859 data_time: 0.0106 memory: 7116 grad_norm: 7.0536 top1_acc: 0.3750 top5_acc: 1.0000 loss_cls: 1.5398 loss: 1.5398 2022/09/04 00:25:04 - mmengine - INFO - Epoch(train) [33][900/1345] lr: 1.0000e-03 eta: 1:20:51 time: 0.1938 data_time: 0.0101 memory: 7116 grad_norm: 6.9732 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.7158 loss: 1.7158 2022/09/04 00:25:08 - mmengine - INFO - Epoch(train) [33][920/1345] lr: 1.0000e-03 eta: 1:20:47 time: 0.1896 data_time: 0.0129 memory: 7116 grad_norm: 7.1532 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.6042 loss: 1.6042 2022/09/04 00:25:12 - mmengine - INFO - Epoch(train) [33][940/1345] lr: 1.0000e-03 eta: 1:20:43 time: 0.1967 data_time: 0.0113 memory: 7116 grad_norm: 6.8665 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4423 loss: 1.4423 2022/09/04 00:25:16 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:25:16 - mmengine - INFO - Epoch(train) [33][960/1345] lr: 1.0000e-03 eta: 1:20:38 time: 0.2025 data_time: 0.0101 memory: 7116 grad_norm: 7.0773 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5262 loss: 1.5262 2022/09/04 00:25:20 - mmengine - INFO - Epoch(train) [33][980/1345] lr: 1.0000e-03 eta: 1:20:34 time: 0.1923 data_time: 0.0126 memory: 7116 grad_norm: 6.7857 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5102 loss: 1.5102 2022/09/04 00:25:23 - mmengine - INFO - Epoch(train) [33][1000/1345] lr: 1.0000e-03 eta: 1:20:30 time: 0.1933 data_time: 0.0126 memory: 7116 grad_norm: 6.8776 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2824 loss: 1.2824 2022/09/04 00:25:27 - mmengine - INFO - Epoch(train) [33][1020/1345] lr: 1.0000e-03 eta: 1:20:25 time: 0.1958 data_time: 0.0099 memory: 7116 grad_norm: 6.8539 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.4358 loss: 1.4358 2022/09/04 00:25:31 - mmengine - INFO - Epoch(train) [33][1040/1345] lr: 1.0000e-03 eta: 1:20:21 time: 0.1919 data_time: 0.0122 memory: 7116 grad_norm: 6.8316 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2416 loss: 1.2416 2022/09/04 00:25:35 - mmengine - INFO - Epoch(train) [33][1060/1345] lr: 1.0000e-03 eta: 1:20:17 time: 0.1940 data_time: 0.0117 memory: 7116 grad_norm: 6.8912 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.3360 loss: 1.3360 2022/09/04 00:25:39 - mmengine - INFO - Epoch(train) [33][1080/1345] lr: 1.0000e-03 eta: 1:20:12 time: 0.1915 data_time: 0.0098 memory: 7116 grad_norm: 7.0525 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5767 loss: 1.5767 2022/09/04 00:25:43 - mmengine - INFO - Epoch(train) [33][1100/1345] lr: 1.0000e-03 eta: 1:20:08 time: 0.1907 data_time: 0.0120 memory: 7116 grad_norm: 7.0636 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7895 loss: 1.7895 2022/09/04 00:25:47 - mmengine - INFO - Epoch(train) [33][1120/1345] lr: 1.0000e-03 eta: 1:20:04 time: 0.1943 data_time: 0.0104 memory: 7116 grad_norm: 6.8726 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 1.5335 loss: 1.5335 2022/09/04 00:25:50 - mmengine - INFO - Epoch(train) [33][1140/1345] lr: 1.0000e-03 eta: 1:19:59 time: 0.1924 data_time: 0.0109 memory: 7116 grad_norm: 7.0976 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6052 loss: 1.6052 2022/09/04 00:25:54 - mmengine - INFO - Epoch(train) [33][1160/1345] lr: 1.0000e-03 eta: 1:19:55 time: 0.1960 data_time: 0.0129 memory: 7116 grad_norm: 7.2948 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5715 loss: 1.5715 2022/09/04 00:25:58 - mmengine - INFO - Epoch(train) [33][1180/1345] lr: 1.0000e-03 eta: 1:19:51 time: 0.1939 data_time: 0.0111 memory: 7116 grad_norm: 7.2858 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5354 loss: 1.5354 2022/09/04 00:26:02 - mmengine - INFO - Epoch(train) [33][1200/1345] lr: 1.0000e-03 eta: 1:19:47 time: 0.1912 data_time: 0.0108 memory: 7116 grad_norm: 7.0945 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.5504 loss: 1.5504 2022/09/04 00:26:06 - mmengine - INFO - Epoch(train) [33][1220/1345] lr: 1.0000e-03 eta: 1:19:42 time: 0.1969 data_time: 0.0130 memory: 7116 grad_norm: 6.9754 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.4717 loss: 1.4717 2022/09/04 00:26:10 - mmengine - INFO - Epoch(train) [33][1240/1345] lr: 1.0000e-03 eta: 1:19:38 time: 0.1911 data_time: 0.0113 memory: 7116 grad_norm: 6.7799 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8524 loss: 1.8524 2022/09/04 00:26:14 - mmengine - INFO - Epoch(train) [33][1260/1345] lr: 1.0000e-03 eta: 1:19:34 time: 0.1902 data_time: 0.0104 memory: 7116 grad_norm: 7.0843 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3723 loss: 1.3723 2022/09/04 00:26:18 - mmengine - INFO - Epoch(train) [33][1280/1345] lr: 1.0000e-03 eta: 1:19:29 time: 0.1936 data_time: 0.0134 memory: 7116 grad_norm: 7.1287 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5742 loss: 1.5742 2022/09/04 00:26:21 - mmengine - INFO - Epoch(train) [33][1300/1345] lr: 1.0000e-03 eta: 1:19:25 time: 0.1926 data_time: 0.0111 memory: 7116 grad_norm: 7.0941 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.6031 loss: 1.6031 2022/09/04 00:26:25 - mmengine - INFO - Epoch(train) [33][1320/1345] lr: 1.0000e-03 eta: 1:19:21 time: 0.1920 data_time: 0.0113 memory: 7116 grad_norm: 7.0790 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.4118 loss: 1.4118 2022/09/04 00:26:29 - mmengine - INFO - Epoch(train) [33][1340/1345] lr: 1.0000e-03 eta: 1:19:16 time: 0.1947 data_time: 0.0148 memory: 7116 grad_norm: 7.2202 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7461 loss: 1.7461 2022/09/04 00:26:30 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:26:30 - mmengine - INFO - Epoch(train) [33][1345/1345] lr: 1.0000e-03 eta: 1:19:16 time: 0.1881 data_time: 0.0108 memory: 7116 grad_norm: 7.8882 top1_acc: 0.0000 top5_acc: 1.0000 loss_cls: 1.7565 loss: 1.7565 2022/09/04 00:26:30 - mmengine - INFO - Saving checkpoint at 33 epochs 2022/09/04 00:26:33 - mmengine - INFO - Epoch(val) [33][20/181] eta: 0:00:08 time: 0.0513 data_time: 0.0122 memory: 1114 2022/09/04 00:26:34 - mmengine - INFO - Epoch(val) [33][40/181] eta: 0:00:06 time: 0.0480 data_time: 0.0092 memory: 1114 2022/09/04 00:26:35 - mmengine - INFO - Epoch(val) [33][60/181] eta: 0:00:05 time: 0.0482 data_time: 0.0089 memory: 1114 2022/09/04 00:26:36 - mmengine - INFO - Epoch(val) [33][80/181] eta: 0:00:04 time: 0.0460 data_time: 0.0087 memory: 1114 2022/09/04 00:26:37 - mmengine - INFO - Epoch(val) [33][100/181] eta: 0:00:03 time: 0.0466 data_time: 0.0090 memory: 1114 2022/09/04 00:26:38 - mmengine - INFO - Epoch(val) [33][120/181] eta: 0:00:02 time: 0.0450 data_time: 0.0078 memory: 1114 2022/09/04 00:26:39 - mmengine - INFO - Epoch(val) [33][140/181] eta: 0:00:01 time: 0.0450 data_time: 0.0079 memory: 1114 2022/09/04 00:26:40 - mmengine - INFO - Epoch(val) [33][160/181] eta: 0:00:00 time: 0.0443 data_time: 0.0078 memory: 1114 2022/09/04 00:26:40 - mmengine - INFO - Epoch(val) [33][180/181] eta: 0:00:00 time: 0.0435 data_time: 0.0075 memory: 1114 2022/09/04 00:26:42 - mmengine - INFO - Epoch(val) [33][181/181] acc/top1: 0.4405 acc/top5: 0.7336 acc/mean1: 0.4026 2022/09/04 00:26:46 - mmengine - INFO - Epoch(train) [34][20/1345] lr: 1.0000e-03 eta: 1:19:11 time: 0.2114 data_time: 0.0270 memory: 7116 grad_norm: 7.0469 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5781 loss: 1.5781 2022/09/04 00:26:50 - mmengine - INFO - Epoch(train) [34][40/1345] lr: 1.0000e-03 eta: 1:19:06 time: 0.1947 data_time: 0.0106 memory: 7116 grad_norm: 7.0352 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.4277 loss: 1.4277 2022/09/04 00:26:54 - mmengine - INFO - Epoch(train) [34][60/1345] lr: 1.0000e-03 eta: 1:19:02 time: 0.1918 data_time: 0.0102 memory: 7116 grad_norm: 7.3287 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.3682 loss: 1.3682 2022/09/04 00:26:58 - mmengine - INFO - Epoch(train) [34][80/1345] lr: 1.0000e-03 eta: 1:18:58 time: 0.2063 data_time: 0.0122 memory: 7116 grad_norm: 7.1205 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3639 loss: 1.3639 2022/09/04 00:27:02 - mmengine - INFO - Epoch(train) [34][100/1345] lr: 1.0000e-03 eta: 1:18:54 time: 0.1917 data_time: 0.0102 memory: 7116 grad_norm: 6.8295 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4173 loss: 1.4173 2022/09/04 00:27:06 - mmengine - INFO - Epoch(train) [34][120/1345] lr: 1.0000e-03 eta: 1:18:49 time: 0.1961 data_time: 0.0108 memory: 7116 grad_norm: 7.0855 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5293 loss: 1.5293 2022/09/04 00:27:10 - mmengine - INFO - Epoch(train) [34][140/1345] lr: 1.0000e-03 eta: 1:18:45 time: 0.2020 data_time: 0.0118 memory: 7116 grad_norm: 7.0174 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.5846 loss: 1.5846 2022/09/04 00:27:14 - mmengine - INFO - Epoch(train) [34][160/1345] lr: 1.0000e-03 eta: 1:18:41 time: 0.1886 data_time: 0.0108 memory: 7116 grad_norm: 7.3034 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.5931 loss: 1.5931 2022/09/04 00:27:18 - mmengine - INFO - Epoch(train) [34][180/1345] lr: 1.0000e-03 eta: 1:18:36 time: 0.1923 data_time: 0.0117 memory: 7116 grad_norm: 6.8519 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4714 loss: 1.4714 2022/09/04 00:27:22 - mmengine - INFO - Epoch(train) [34][200/1345] lr: 1.0000e-03 eta: 1:18:32 time: 0.1937 data_time: 0.0127 memory: 7116 grad_norm: 7.1532 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3366 loss: 1.3366 2022/09/04 00:27:25 - mmengine - INFO - Epoch(train) [34][220/1345] lr: 1.0000e-03 eta: 1:18:28 time: 0.1900 data_time: 0.0106 memory: 7116 grad_norm: 7.1364 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.5624 loss: 1.5624 2022/09/04 00:27:29 - mmengine - INFO - Epoch(train) [34][240/1345] lr: 1.0000e-03 eta: 1:18:23 time: 0.1887 data_time: 0.0112 memory: 7116 grad_norm: 6.9644 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4167 loss: 1.4167 2022/09/04 00:27:33 - mmengine - INFO - Epoch(train) [34][260/1345] lr: 1.0000e-03 eta: 1:18:19 time: 0.1906 data_time: 0.0120 memory: 7116 grad_norm: 7.0503 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2407 loss: 1.2407 2022/09/04 00:27:37 - mmengine - INFO - Epoch(train) [34][280/1345] lr: 1.0000e-03 eta: 1:18:15 time: 0.1939 data_time: 0.0106 memory: 7116 grad_norm: 7.1073 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3052 loss: 1.3052 2022/09/04 00:27:41 - mmengine - INFO - Epoch(train) [34][300/1345] lr: 1.0000e-03 eta: 1:18:10 time: 0.1916 data_time: 0.0102 memory: 7116 grad_norm: 7.3607 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5102 loss: 1.5102 2022/09/04 00:27:45 - mmengine - INFO - Epoch(train) [34][320/1345] lr: 1.0000e-03 eta: 1:18:06 time: 0.1939 data_time: 0.0126 memory: 7116 grad_norm: 7.5175 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6942 loss: 1.6942 2022/09/04 00:27:48 - mmengine - INFO - Epoch(train) [34][340/1345] lr: 1.0000e-03 eta: 1:18:02 time: 0.1876 data_time: 0.0100 memory: 7116 grad_norm: 6.9743 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4740 loss: 1.4740 2022/09/04 00:27:52 - mmengine - INFO - Epoch(train) [34][360/1345] lr: 1.0000e-03 eta: 1:17:57 time: 0.1927 data_time: 0.0104 memory: 7116 grad_norm: 7.3815 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5118 loss: 1.5118 2022/09/04 00:27:56 - mmengine - INFO - Epoch(train) [34][380/1345] lr: 1.0000e-03 eta: 1:17:53 time: 0.1942 data_time: 0.0114 memory: 7116 grad_norm: 7.0624 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4159 loss: 1.4159 2022/09/04 00:28:00 - mmengine - INFO - Epoch(train) [34][400/1345] lr: 1.0000e-03 eta: 1:17:49 time: 0.1902 data_time: 0.0106 memory: 7116 grad_norm: 7.4119 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7375 loss: 1.7375 2022/09/04 00:28:04 - mmengine - INFO - Epoch(train) [34][420/1345] lr: 1.0000e-03 eta: 1:17:44 time: 0.1916 data_time: 0.0103 memory: 7116 grad_norm: 7.2561 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.3279 loss: 1.3279 2022/09/04 00:28:08 - mmengine - INFO - Epoch(train) [34][440/1345] lr: 1.0000e-03 eta: 1:17:40 time: 0.1988 data_time: 0.0133 memory: 7116 grad_norm: 7.3655 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1961 loss: 1.1961 2022/09/04 00:28:12 - mmengine - INFO - Epoch(train) [34][460/1345] lr: 1.0000e-03 eta: 1:17:36 time: 0.1922 data_time: 0.0096 memory: 7116 grad_norm: 7.2582 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4951 loss: 1.4951 2022/09/04 00:28:15 - mmengine - INFO - Epoch(train) [34][480/1345] lr: 1.0000e-03 eta: 1:17:32 time: 0.1986 data_time: 0.0101 memory: 7116 grad_norm: 7.0878 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.3107 loss: 1.3107 2022/09/04 00:28:19 - mmengine - INFO - Epoch(train) [34][500/1345] lr: 1.0000e-03 eta: 1:17:27 time: 0.1926 data_time: 0.0131 memory: 7116 grad_norm: 7.3566 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4875 loss: 1.4875 2022/09/04 00:28:23 - mmengine - INFO - Epoch(train) [34][520/1345] lr: 1.0000e-03 eta: 1:17:23 time: 0.1908 data_time: 0.0098 memory: 7116 grad_norm: 7.1686 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3075 loss: 1.3075 2022/09/04 00:28:27 - mmengine - INFO - Epoch(train) [34][540/1345] lr: 1.0000e-03 eta: 1:17:19 time: 0.1926 data_time: 0.0103 memory: 7116 grad_norm: 7.1174 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4688 loss: 1.4688 2022/09/04 00:28:31 - mmengine - INFO - Epoch(train) [34][560/1345] lr: 1.0000e-03 eta: 1:17:14 time: 0.1951 data_time: 0.0123 memory: 7116 grad_norm: 6.9906 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.5067 loss: 1.5067 2022/09/04 00:28:35 - mmengine - INFO - Epoch(train) [34][580/1345] lr: 1.0000e-03 eta: 1:17:10 time: 0.1943 data_time: 0.0104 memory: 7116 grad_norm: 7.1955 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.5965 loss: 1.5965 2022/09/04 00:28:39 - mmengine - INFO - Epoch(train) [34][600/1345] lr: 1.0000e-03 eta: 1:17:06 time: 0.1889 data_time: 0.0095 memory: 7116 grad_norm: 7.1427 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2379 loss: 1.2379 2022/09/04 00:28:41 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:28:42 - mmengine - INFO - Epoch(train) [34][620/1345] lr: 1.0000e-03 eta: 1:17:02 time: 0.1916 data_time: 0.0129 memory: 7116 grad_norm: 6.9951 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4295 loss: 1.4295 2022/09/04 00:28:46 - mmengine - INFO - Epoch(train) [34][640/1345] lr: 1.0000e-03 eta: 1:16:57 time: 0.1912 data_time: 0.0107 memory: 7116 grad_norm: 7.1977 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.5405 loss: 1.5405 2022/09/04 00:28:50 - mmengine - INFO - Epoch(train) [34][660/1345] lr: 1.0000e-03 eta: 1:16:53 time: 0.1904 data_time: 0.0113 memory: 7116 grad_norm: 7.1417 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3975 loss: 1.3975 2022/09/04 00:28:54 - mmengine - INFO - Epoch(train) [34][680/1345] lr: 1.0000e-03 eta: 1:16:49 time: 0.1891 data_time: 0.0127 memory: 7116 grad_norm: 7.1298 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3485 loss: 1.3485 2022/09/04 00:28:58 - mmengine - INFO - Epoch(train) [34][700/1345] lr: 1.0000e-03 eta: 1:16:44 time: 0.1917 data_time: 0.0111 memory: 7116 grad_norm: 7.1966 top1_acc: 0.3750 top5_acc: 1.0000 loss_cls: 1.3330 loss: 1.3330 2022/09/04 00:29:02 - mmengine - INFO - Epoch(train) [34][720/1345] lr: 1.0000e-03 eta: 1:16:40 time: 0.1918 data_time: 0.0110 memory: 7116 grad_norm: 7.2986 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7026 loss: 1.7026 2022/09/04 00:29:05 - mmengine - INFO - Epoch(train) [34][740/1345] lr: 1.0000e-03 eta: 1:16:36 time: 0.1940 data_time: 0.0134 memory: 7116 grad_norm: 7.2549 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.3900 loss: 1.3900 2022/09/04 00:29:09 - mmengine - INFO - Epoch(train) [34][760/1345] lr: 1.0000e-03 eta: 1:16:31 time: 0.1916 data_time: 0.0100 memory: 7116 grad_norm: 7.4538 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5839 loss: 1.5839 2022/09/04 00:29:13 - mmengine - INFO - Epoch(train) [34][780/1345] lr: 1.0000e-03 eta: 1:16:27 time: 0.1898 data_time: 0.0114 memory: 7116 grad_norm: 7.2004 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3563 loss: 1.3563 2022/09/04 00:29:17 - mmengine - INFO - Epoch(train) [34][800/1345] lr: 1.0000e-03 eta: 1:16:23 time: 0.1954 data_time: 0.0127 memory: 7116 grad_norm: 7.2629 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5521 loss: 1.5521 2022/09/04 00:29:21 - mmengine - INFO - Epoch(train) [34][820/1345] lr: 1.0000e-03 eta: 1:16:18 time: 0.1940 data_time: 0.0116 memory: 7116 grad_norm: 7.3233 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5532 loss: 1.5532 2022/09/04 00:29:25 - mmengine - INFO - Epoch(train) [34][840/1345] lr: 1.0000e-03 eta: 1:16:14 time: 0.1906 data_time: 0.0104 memory: 7116 grad_norm: 7.4695 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.5351 loss: 1.5351 2022/09/04 00:29:29 - mmengine - INFO - Epoch(train) [34][860/1345] lr: 1.0000e-03 eta: 1:16:10 time: 0.1936 data_time: 0.0137 memory: 7116 grad_norm: 7.1630 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3734 loss: 1.3734 2022/09/04 00:29:32 - mmengine - INFO - Epoch(train) [34][880/1345] lr: 1.0000e-03 eta: 1:16:05 time: 0.1909 data_time: 0.0108 memory: 7116 grad_norm: 7.4258 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4815 loss: 1.4815 2022/09/04 00:29:36 - mmengine - INFO - Epoch(train) [34][900/1345] lr: 1.0000e-03 eta: 1:16:01 time: 0.1978 data_time: 0.0102 memory: 7116 grad_norm: 7.2678 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.3271 loss: 1.3271 2022/09/04 00:29:40 - mmengine - INFO - Epoch(train) [34][920/1345] lr: 1.0000e-03 eta: 1:15:57 time: 0.1935 data_time: 0.0113 memory: 7116 grad_norm: 7.5776 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6998 loss: 1.6998 2022/09/04 00:29:44 - mmengine - INFO - Epoch(train) [34][940/1345] lr: 1.0000e-03 eta: 1:15:53 time: 0.1912 data_time: 0.0107 memory: 7116 grad_norm: 7.3343 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3965 loss: 1.3965 2022/09/04 00:29:48 - mmengine - INFO - Epoch(train) [34][960/1345] lr: 1.0000e-03 eta: 1:15:48 time: 0.1955 data_time: 0.0130 memory: 7116 grad_norm: 7.3273 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.4508 loss: 1.4508 2022/09/04 00:29:52 - mmengine - INFO - Epoch(train) [34][980/1345] lr: 1.0000e-03 eta: 1:15:44 time: 0.1894 data_time: 0.0121 memory: 7116 grad_norm: 7.3740 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.4665 loss: 1.4665 2022/09/04 00:29:56 - mmengine - INFO - Epoch(train) [34][1000/1345] lr: 1.0000e-03 eta: 1:15:40 time: 0.1964 data_time: 0.0100 memory: 7116 grad_norm: 7.1352 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3289 loss: 1.3289 2022/09/04 00:30:00 - mmengine - INFO - Epoch(train) [34][1020/1345] lr: 1.0000e-03 eta: 1:15:36 time: 0.1972 data_time: 0.0104 memory: 7116 grad_norm: 7.9055 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.4825 loss: 1.4825 2022/09/04 00:30:04 - mmengine - INFO - Epoch(train) [34][1040/1345] lr: 1.0000e-03 eta: 1:15:31 time: 0.1938 data_time: 0.0124 memory: 7116 grad_norm: 7.3034 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5916 loss: 1.5916 2022/09/04 00:30:07 - mmengine - INFO - Epoch(train) [34][1060/1345] lr: 1.0000e-03 eta: 1:15:27 time: 0.1905 data_time: 0.0104 memory: 7116 grad_norm: 7.3600 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.5154 loss: 1.5154 2022/09/04 00:30:11 - mmengine - INFO - Epoch(train) [34][1080/1345] lr: 1.0000e-03 eta: 1:15:23 time: 0.1893 data_time: 0.0106 memory: 7116 grad_norm: 7.1622 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4226 loss: 1.4226 2022/09/04 00:30:15 - mmengine - INFO - Epoch(train) [34][1100/1345] lr: 1.0000e-03 eta: 1:15:18 time: 0.1911 data_time: 0.0132 memory: 7116 grad_norm: 7.1950 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1300 loss: 1.1300 2022/09/04 00:30:19 - mmengine - INFO - Epoch(train) [34][1120/1345] lr: 1.0000e-03 eta: 1:15:14 time: 0.2002 data_time: 0.0092 memory: 7116 grad_norm: 7.2278 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5606 loss: 1.5606 2022/09/04 00:30:23 - mmengine - INFO - Epoch(train) [34][1140/1345] lr: 1.0000e-03 eta: 1:15:10 time: 0.1886 data_time: 0.0114 memory: 7116 grad_norm: 7.5965 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.3934 loss: 1.3934 2022/09/04 00:30:27 - mmengine - INFO - Epoch(train) [34][1160/1345] lr: 1.0000e-03 eta: 1:15:05 time: 0.1930 data_time: 0.0128 memory: 7116 grad_norm: 7.2558 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3684 loss: 1.3684 2022/09/04 00:30:30 - mmengine - INFO - Epoch(train) [34][1180/1345] lr: 1.0000e-03 eta: 1:15:01 time: 0.1936 data_time: 0.0104 memory: 7116 grad_norm: 7.5781 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.4062 loss: 1.4062 2022/09/04 00:30:34 - mmengine - INFO - Epoch(train) [34][1200/1345] lr: 1.0000e-03 eta: 1:14:57 time: 0.1937 data_time: 0.0104 memory: 7116 grad_norm: 7.2773 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.4595 loss: 1.4595 2022/09/04 00:30:38 - mmengine - INFO - Epoch(train) [34][1220/1345] lr: 1.0000e-03 eta: 1:14:53 time: 0.1983 data_time: 0.0121 memory: 7116 grad_norm: 7.3720 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4314 loss: 1.4314 2022/09/04 00:30:42 - mmengine - INFO - Epoch(train) [34][1240/1345] lr: 1.0000e-03 eta: 1:14:48 time: 0.1908 data_time: 0.0099 memory: 7116 grad_norm: 7.2766 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3811 loss: 1.3811 2022/09/04 00:30:46 - mmengine - INFO - Epoch(train) [34][1260/1345] lr: 1.0000e-03 eta: 1:14:44 time: 0.1954 data_time: 0.0108 memory: 7116 grad_norm: 7.5888 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.6359 loss: 1.6359 2022/09/04 00:30:50 - mmengine - INFO - Epoch(train) [34][1280/1345] lr: 1.0000e-03 eta: 1:14:40 time: 0.1927 data_time: 0.0119 memory: 7116 grad_norm: 7.3050 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.4382 loss: 1.4382 2022/09/04 00:30:54 - mmengine - INFO - Epoch(train) [34][1300/1345] lr: 1.0000e-03 eta: 1:14:35 time: 0.1945 data_time: 0.0099 memory: 7116 grad_norm: 7.3380 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2008 loss: 1.2008 2022/09/04 00:30:58 - mmengine - INFO - Epoch(train) [34][1320/1345] lr: 1.0000e-03 eta: 1:14:31 time: 0.1959 data_time: 0.0114 memory: 7116 grad_norm: 7.3882 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.6668 loss: 1.6668 2022/09/04 00:31:02 - mmengine - INFO - Epoch(train) [34][1340/1345] lr: 1.0000e-03 eta: 1:14:27 time: 0.1933 data_time: 0.0110 memory: 7116 grad_norm: 7.1319 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4457 loss: 1.4457 2022/09/04 00:31:03 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:31:03 - mmengine - INFO - Epoch(train) [34][1345/1345] lr: 1.0000e-03 eta: 1:14:27 time: 0.1875 data_time: 0.0090 memory: 7116 grad_norm: 7.5323 top1_acc: 0.0000 top5_acc: 1.0000 loss_cls: 1.5137 loss: 1.5137 2022/09/04 00:31:03 - mmengine - INFO - Saving checkpoint at 34 epochs 2022/09/04 00:31:06 - mmengine - INFO - Epoch(val) [34][20/181] eta: 0:00:12 time: 0.0801 data_time: 0.0425 memory: 1114 2022/09/04 00:31:07 - mmengine - INFO - Epoch(val) [34][40/181] eta: 0:00:06 time: 0.0460 data_time: 0.0084 memory: 1114 2022/09/04 00:31:08 - mmengine - INFO - Epoch(val) [34][60/181] eta: 0:00:05 time: 0.0451 data_time: 0.0079 memory: 1114 2022/09/04 00:31:09 - mmengine - INFO - Epoch(val) [34][80/181] eta: 0:00:04 time: 0.0458 data_time: 0.0082 memory: 1114 2022/09/04 00:31:09 - mmengine - INFO - Epoch(val) [34][100/181] eta: 0:00:03 time: 0.0453 data_time: 0.0081 memory: 1114 2022/09/04 00:31:10 - mmengine - INFO - Epoch(val) [34][120/181] eta: 0:00:02 time: 0.0454 data_time: 0.0083 memory: 1114 2022/09/04 00:31:11 - mmengine - INFO - Epoch(val) [34][140/181] eta: 0:00:01 time: 0.0450 data_time: 0.0079 memory: 1114 2022/09/04 00:31:12 - mmengine - INFO - Epoch(val) [34][160/181] eta: 0:00:00 time: 0.0433 data_time: 0.0071 memory: 1114 2022/09/04 00:31:13 - mmengine - INFO - Epoch(val) [34][180/181] eta: 0:00:00 time: 0.0426 data_time: 0.0065 memory: 1114 2022/09/04 00:31:15 - mmengine - INFO - Epoch(val) [34][181/181] acc/top1: 0.4510 acc/top5: 0.7409 acc/mean1: 0.4074 2022/09/04 00:31:15 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_32.pth is removed 2022/09/04 00:31:16 - mmengine - INFO - The best checkpoint with 0.4510 acc/top1 at 34 epoch is saved to best_acc/top1_epoch_34.pth. 2022/09/04 00:31:20 - mmengine - INFO - Epoch(train) [35][20/1345] lr: 1.0000e-03 eta: 1:14:21 time: 0.1987 data_time: 0.0134 memory: 7116 grad_norm: 7.3361 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4551 loss: 1.4551 2022/09/04 00:31:24 - mmengine - INFO - Epoch(train) [35][40/1345] lr: 1.0000e-03 eta: 1:14:17 time: 0.1972 data_time: 0.0100 memory: 7116 grad_norm: 7.4330 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.3657 loss: 1.3657 2022/09/04 00:31:28 - mmengine - INFO - Epoch(train) [35][60/1345] lr: 1.0000e-03 eta: 1:14:13 time: 0.2036 data_time: 0.0093 memory: 7116 grad_norm: 7.5375 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3858 loss: 1.3858 2022/09/04 00:31:32 - mmengine - INFO - Epoch(train) [35][80/1345] lr: 1.0000e-03 eta: 1:14:08 time: 0.1961 data_time: 0.0115 memory: 7116 grad_norm: 7.3897 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4224 loss: 1.4224 2022/09/04 00:31:36 - mmengine - INFO - Epoch(train) [35][100/1345] lr: 1.0000e-03 eta: 1:14:04 time: 0.1991 data_time: 0.0110 memory: 7116 grad_norm: 7.1870 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3113 loss: 1.3113 2022/09/04 00:31:40 - mmengine - INFO - Epoch(train) [35][120/1345] lr: 1.0000e-03 eta: 1:14:00 time: 0.1956 data_time: 0.0092 memory: 7116 grad_norm: 7.3275 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.4033 loss: 1.4033 2022/09/04 00:31:44 - mmengine - INFO - Epoch(train) [35][140/1345] lr: 1.0000e-03 eta: 1:13:56 time: 0.1974 data_time: 0.0113 memory: 7116 grad_norm: 7.3595 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4985 loss: 1.4985 2022/09/04 00:31:48 - mmengine - INFO - Epoch(train) [35][160/1345] lr: 1.0000e-03 eta: 1:13:51 time: 0.1957 data_time: 0.0103 memory: 7116 grad_norm: 7.5205 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4976 loss: 1.4976 2022/09/04 00:31:52 - mmengine - INFO - Epoch(train) [35][180/1345] lr: 1.0000e-03 eta: 1:13:47 time: 0.1959 data_time: 0.0110 memory: 7116 grad_norm: 7.7361 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4903 loss: 1.4903 2022/09/04 00:31:56 - mmengine - INFO - Epoch(train) [35][200/1345] lr: 1.0000e-03 eta: 1:13:43 time: 0.1976 data_time: 0.0117 memory: 7116 grad_norm: 7.3838 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5406 loss: 1.5406 2022/09/04 00:32:00 - mmengine - INFO - Epoch(train) [35][220/1345] lr: 1.0000e-03 eta: 1:13:39 time: 0.1919 data_time: 0.0099 memory: 7116 grad_norm: 7.3903 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3612 loss: 1.3612 2022/09/04 00:32:04 - mmengine - INFO - Epoch(train) [35][240/1345] lr: 1.0000e-03 eta: 1:13:34 time: 0.1964 data_time: 0.0105 memory: 7116 grad_norm: 7.5208 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3914 loss: 1.3914 2022/09/04 00:32:08 - mmengine - INFO - Epoch(train) [35][260/1345] lr: 1.0000e-03 eta: 1:13:30 time: 0.2008 data_time: 0.0157 memory: 7116 grad_norm: 7.4335 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3767 loss: 1.3767 2022/09/04 00:32:10 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:32:12 - mmengine - INFO - Epoch(train) [35][280/1345] lr: 1.0000e-03 eta: 1:13:26 time: 0.1975 data_time: 0.0099 memory: 7116 grad_norm: 7.4098 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.4073 loss: 1.4073 2022/09/04 00:32:16 - mmengine - INFO - Epoch(train) [35][300/1345] lr: 1.0000e-03 eta: 1:13:22 time: 0.1979 data_time: 0.0102 memory: 7116 grad_norm: 7.2746 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4232 loss: 1.4232 2022/09/04 00:32:20 - mmengine - INFO - Epoch(train) [35][320/1345] lr: 1.0000e-03 eta: 1:13:18 time: 0.2024 data_time: 0.0113 memory: 7116 grad_norm: 7.6122 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4813 loss: 1.4813 2022/09/04 00:32:24 - mmengine - INFO - Epoch(train) [35][340/1345] lr: 1.0000e-03 eta: 1:13:13 time: 0.1948 data_time: 0.0089 memory: 7116 grad_norm: 7.5713 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3432 loss: 1.3432 2022/09/04 00:32:28 - mmengine - INFO - Epoch(train) [35][360/1345] lr: 1.0000e-03 eta: 1:13:09 time: 0.2024 data_time: 0.0092 memory: 7116 grad_norm: 7.3619 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5381 loss: 1.5381 2022/09/04 00:32:32 - mmengine - INFO - Epoch(train) [35][380/1345] lr: 1.0000e-03 eta: 1:13:05 time: 0.1963 data_time: 0.0123 memory: 7116 grad_norm: 7.1466 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1976 loss: 1.1976 2022/09/04 00:32:36 - mmengine - INFO - Epoch(train) [35][400/1345] lr: 1.0000e-03 eta: 1:13:01 time: 0.2230 data_time: 0.0094 memory: 7116 grad_norm: 7.2568 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.5129 loss: 1.5129 2022/09/04 00:32:40 - mmengine - INFO - Epoch(train) [35][420/1345] lr: 1.0000e-03 eta: 1:12:57 time: 0.1963 data_time: 0.0090 memory: 7116 grad_norm: 7.4014 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4628 loss: 1.4628 2022/09/04 00:32:44 - mmengine - INFO - Epoch(train) [35][440/1345] lr: 1.0000e-03 eta: 1:12:52 time: 0.1977 data_time: 0.0121 memory: 7116 grad_norm: 7.3602 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6329 loss: 1.6329 2022/09/04 00:32:48 - mmengine - INFO - Epoch(train) [35][460/1345] lr: 1.0000e-03 eta: 1:12:48 time: 0.2032 data_time: 0.0099 memory: 7116 grad_norm: 7.5489 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.2226 loss: 1.2226 2022/09/04 00:32:52 - mmengine - INFO - Epoch(train) [35][480/1345] lr: 1.0000e-03 eta: 1:12:44 time: 0.1988 data_time: 0.0083 memory: 7116 grad_norm: 7.4920 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5027 loss: 1.5027 2022/09/04 00:32:56 - mmengine - INFO - Epoch(train) [35][500/1345] lr: 1.0000e-03 eta: 1:12:40 time: 0.1983 data_time: 0.0117 memory: 7116 grad_norm: 7.4316 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2488 loss: 1.2488 2022/09/04 00:33:00 - mmengine - INFO - Epoch(train) [35][520/1345] lr: 1.0000e-03 eta: 1:12:35 time: 0.2006 data_time: 0.0092 memory: 7116 grad_norm: 7.4218 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.6414 loss: 1.6414 2022/09/04 00:33:04 - mmengine - INFO - Epoch(train) [35][540/1345] lr: 1.0000e-03 eta: 1:12:31 time: 0.1973 data_time: 0.0100 memory: 7116 grad_norm: 7.3952 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4510 loss: 1.4510 2022/09/04 00:33:08 - mmengine - INFO - Epoch(train) [35][560/1345] lr: 1.0000e-03 eta: 1:12:27 time: 0.2147 data_time: 0.0112 memory: 7116 grad_norm: 7.5876 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3851 loss: 1.3851 2022/09/04 00:33:12 - mmengine - INFO - Epoch(train) [35][580/1345] lr: 1.0000e-03 eta: 1:12:23 time: 0.1979 data_time: 0.0091 memory: 7116 grad_norm: 7.0471 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2527 loss: 1.2527 2022/09/04 00:33:16 - mmengine - INFO - Epoch(train) [35][600/1345] lr: 1.0000e-03 eta: 1:12:19 time: 0.2015 data_time: 0.0103 memory: 7116 grad_norm: 7.4219 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.4304 loss: 1.4304 2022/09/04 00:33:20 - mmengine - INFO - Epoch(train) [35][620/1345] lr: 1.0000e-03 eta: 1:12:14 time: 0.1995 data_time: 0.0121 memory: 7116 grad_norm: 7.6171 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3870 loss: 1.3870 2022/09/04 00:33:24 - mmengine - INFO - Epoch(train) [35][640/1345] lr: 1.0000e-03 eta: 1:12:10 time: 0.1976 data_time: 0.0089 memory: 7116 grad_norm: 7.2041 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3874 loss: 1.3874 2022/09/04 00:33:28 - mmengine - INFO - Epoch(train) [35][660/1345] lr: 1.0000e-03 eta: 1:12:06 time: 0.2006 data_time: 0.0091 memory: 7116 grad_norm: 7.4318 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3650 loss: 1.3650 2022/09/04 00:33:32 - mmengine - INFO - Epoch(train) [35][680/1345] lr: 1.0000e-03 eta: 1:12:02 time: 0.2014 data_time: 0.0119 memory: 7116 grad_norm: 7.3350 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.4674 loss: 1.4674 2022/09/04 00:33:36 - mmengine - INFO - Epoch(train) [35][700/1345] lr: 1.0000e-03 eta: 1:11:58 time: 0.2064 data_time: 0.0100 memory: 7116 grad_norm: 7.4302 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4666 loss: 1.4666 2022/09/04 00:33:40 - mmengine - INFO - Epoch(train) [35][720/1345] lr: 1.0000e-03 eta: 1:11:53 time: 0.1968 data_time: 0.0088 memory: 7116 grad_norm: 7.8968 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8822 loss: 1.8822 2022/09/04 00:33:44 - mmengine - INFO - Epoch(train) [35][740/1345] lr: 1.0000e-03 eta: 1:11:49 time: 0.1965 data_time: 0.0124 memory: 7116 grad_norm: 7.5170 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3553 loss: 1.3553 2022/09/04 00:33:48 - mmengine - INFO - Epoch(train) [35][760/1345] lr: 1.0000e-03 eta: 1:11:45 time: 0.2119 data_time: 0.0094 memory: 7116 grad_norm: 7.6750 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3259 loss: 1.3259 2022/09/04 00:33:52 - mmengine - INFO - Epoch(train) [35][780/1345] lr: 1.0000e-03 eta: 1:11:41 time: 0.1974 data_time: 0.0091 memory: 7116 grad_norm: 7.7432 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1749 loss: 1.1749 2022/09/04 00:33:56 - mmengine - INFO - Epoch(train) [35][800/1345] lr: 1.0000e-03 eta: 1:11:37 time: 0.2027 data_time: 0.0124 memory: 7116 grad_norm: 7.8133 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.7484 loss: 1.7484 2022/09/04 00:34:00 - mmengine - INFO - Epoch(train) [35][820/1345] lr: 1.0000e-03 eta: 1:11:32 time: 0.1967 data_time: 0.0102 memory: 7116 grad_norm: 7.4345 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2573 loss: 1.2573 2022/09/04 00:34:04 - mmengine - INFO - Epoch(train) [35][840/1345] lr: 1.0000e-03 eta: 1:11:28 time: 0.1966 data_time: 0.0096 memory: 7116 grad_norm: 7.5721 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5405 loss: 1.5405 2022/09/04 00:34:08 - mmengine - INFO - Epoch(train) [35][860/1345] lr: 1.0000e-03 eta: 1:11:24 time: 0.1963 data_time: 0.0115 memory: 7116 grad_norm: 7.3888 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3454 loss: 1.3454 2022/09/04 00:34:12 - mmengine - INFO - Epoch(train) [35][880/1345] lr: 1.0000e-03 eta: 1:11:20 time: 0.1963 data_time: 0.0090 memory: 7116 grad_norm: 7.5951 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1685 loss: 1.1685 2022/09/04 00:34:16 - mmengine - INFO - Epoch(train) [35][900/1345] lr: 1.0000e-03 eta: 1:11:15 time: 0.1976 data_time: 0.0099 memory: 7116 grad_norm: 7.5675 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1962 loss: 1.1962 2022/09/04 00:34:20 - mmengine - INFO - Epoch(train) [35][920/1345] lr: 1.0000e-03 eta: 1:11:11 time: 0.1971 data_time: 0.0119 memory: 7116 grad_norm: 7.5656 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3262 loss: 1.3262 2022/09/04 00:34:24 - mmengine - INFO - Epoch(train) [35][940/1345] lr: 1.0000e-03 eta: 1:11:07 time: 0.1959 data_time: 0.0092 memory: 7116 grad_norm: 7.4275 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3889 loss: 1.3889 2022/09/04 00:34:29 - mmengine - INFO - Epoch(train) [35][960/1345] lr: 1.0000e-03 eta: 1:11:03 time: 0.2268 data_time: 0.0098 memory: 7116 grad_norm: 7.4742 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6344 loss: 1.6344 2022/09/04 00:34:33 - mmengine - INFO - Epoch(train) [35][980/1345] lr: 1.0000e-03 eta: 1:10:59 time: 0.1994 data_time: 0.0111 memory: 7116 grad_norm: 7.3945 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5102 loss: 1.5102 2022/09/04 00:34:37 - mmengine - INFO - Epoch(train) [35][1000/1345] lr: 1.0000e-03 eta: 1:10:55 time: 0.1986 data_time: 0.0101 memory: 7116 grad_norm: 7.8339 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2994 loss: 1.2994 2022/09/04 00:34:40 - mmengine - INFO - Epoch(train) [35][1020/1345] lr: 1.0000e-03 eta: 1:10:50 time: 0.1968 data_time: 0.0090 memory: 7116 grad_norm: 7.5700 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.4671 loss: 1.4671 2022/09/04 00:34:44 - mmengine - INFO - Epoch(train) [35][1040/1345] lr: 1.0000e-03 eta: 1:10:46 time: 0.1984 data_time: 0.0129 memory: 7116 grad_norm: 7.3421 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4774 loss: 1.4774 2022/09/04 00:34:48 - mmengine - INFO - Epoch(train) [35][1060/1345] lr: 1.0000e-03 eta: 1:10:42 time: 0.1971 data_time: 0.0091 memory: 7116 grad_norm: 7.5338 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3845 loss: 1.3845 2022/09/04 00:34:52 - mmengine - INFO - Epoch(train) [35][1080/1345] lr: 1.0000e-03 eta: 1:10:38 time: 0.1960 data_time: 0.0097 memory: 7116 grad_norm: 7.8405 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5642 loss: 1.5642 2022/09/04 00:34:56 - mmengine - INFO - Epoch(train) [35][1100/1345] lr: 1.0000e-03 eta: 1:10:33 time: 0.2022 data_time: 0.0119 memory: 7116 grad_norm: 7.4258 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2698 loss: 1.2698 2022/09/04 00:35:00 - mmengine - INFO - Epoch(train) [35][1120/1345] lr: 1.0000e-03 eta: 1:10:29 time: 0.1987 data_time: 0.0096 memory: 7116 grad_norm: 7.6192 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4248 loss: 1.4248 2022/09/04 00:35:04 - mmengine - INFO - Epoch(train) [35][1140/1345] lr: 1.0000e-03 eta: 1:10:25 time: 0.1976 data_time: 0.0092 memory: 7116 grad_norm: 7.5412 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2285 loss: 1.2285 2022/09/04 00:35:09 - mmengine - INFO - Epoch(train) [35][1160/1345] lr: 1.0000e-03 eta: 1:10:21 time: 0.2257 data_time: 0.0127 memory: 7116 grad_norm: 7.6835 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2883 loss: 1.2883 2022/09/04 00:35:13 - mmengine - INFO - Epoch(train) [35][1180/1345] lr: 1.0000e-03 eta: 1:10:17 time: 0.1953 data_time: 0.0102 memory: 7116 grad_norm: 7.9569 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5122 loss: 1.5122 2022/09/04 00:35:17 - mmengine - INFO - Epoch(train) [35][1200/1345] lr: 1.0000e-03 eta: 1:10:13 time: 0.2054 data_time: 0.0077 memory: 7116 grad_norm: 8.0213 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3676 loss: 1.3676 2022/09/04 00:35:21 - mmengine - INFO - Epoch(train) [35][1220/1345] lr: 1.0000e-03 eta: 1:10:08 time: 0.1989 data_time: 0.0113 memory: 7116 grad_norm: 7.9071 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.6278 loss: 1.6278 2022/09/04 00:35:25 - mmengine - INFO - Epoch(train) [35][1240/1345] lr: 1.0000e-03 eta: 1:10:04 time: 0.1992 data_time: 0.0087 memory: 7116 grad_norm: 7.7738 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4474 loss: 1.4474 2022/09/04 00:35:29 - mmengine - INFO - Epoch(train) [35][1260/1345] lr: 1.0000e-03 eta: 1:10:00 time: 0.1983 data_time: 0.0101 memory: 7116 grad_norm: 7.7751 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3968 loss: 1.3968 2022/09/04 00:35:31 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:35:33 - mmengine - INFO - Epoch(train) [35][1280/1345] lr: 1.0000e-03 eta: 1:09:56 time: 0.1962 data_time: 0.0120 memory: 7116 grad_norm: 7.9881 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5588 loss: 1.5588 2022/09/04 00:35:37 - mmengine - INFO - Epoch(train) [35][1300/1345] lr: 1.0000e-03 eta: 1:09:51 time: 0.2024 data_time: 0.0096 memory: 7116 grad_norm: 7.5234 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4884 loss: 1.4884 2022/09/04 00:35:41 - mmengine - INFO - Epoch(train) [35][1320/1345] lr: 1.0000e-03 eta: 1:09:47 time: 0.1961 data_time: 0.0098 memory: 7116 grad_norm: 7.7039 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3822 loss: 1.3822 2022/09/04 00:35:45 - mmengine - INFO - Epoch(train) [35][1340/1345] lr: 1.0000e-03 eta: 1:09:43 time: 0.1951 data_time: 0.0116 memory: 7116 grad_norm: 7.7493 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6666 loss: 1.6666 2022/09/04 00:35:46 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:35:46 - mmengine - INFO - Epoch(train) [35][1345/1345] lr: 1.0000e-03 eta: 1:09:43 time: 0.2126 data_time: 0.0099 memory: 7116 grad_norm: 8.0544 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.7998 loss: 1.7998 2022/09/04 00:35:46 - mmengine - INFO - Saving checkpoint at 35 epochs 2022/09/04 00:35:49 - mmengine - INFO - Epoch(val) [35][20/181] eta: 0:00:08 time: 0.0513 data_time: 0.0141 memory: 1114 2022/09/04 00:35:49 - mmengine - INFO - Epoch(val) [35][40/181] eta: 0:00:06 time: 0.0438 data_time: 0.0075 memory: 1114 2022/09/04 00:35:50 - mmengine - INFO - Epoch(val) [35][60/181] eta: 0:00:05 time: 0.0439 data_time: 0.0076 memory: 1114 2022/09/04 00:35:51 - mmengine - INFO - Epoch(val) [35][80/181] eta: 0:00:04 time: 0.0453 data_time: 0.0085 memory: 1114 2022/09/04 00:35:52 - mmengine - INFO - Epoch(val) [35][100/181] eta: 0:00:03 time: 0.0445 data_time: 0.0081 memory: 1114 2022/09/04 00:35:53 - mmengine - INFO - Epoch(val) [35][120/181] eta: 0:00:02 time: 0.0430 data_time: 0.0071 memory: 1114 2022/09/04 00:35:54 - mmengine - INFO - Epoch(val) [35][140/181] eta: 0:00:01 time: 0.0422 data_time: 0.0066 memory: 1114 2022/09/04 00:35:55 - mmengine - INFO - Epoch(val) [35][160/181] eta: 0:00:00 time: 0.0420 data_time: 0.0067 memory: 1114 2022/09/04 00:35:56 - mmengine - INFO - Epoch(val) [35][180/181] eta: 0:00:00 time: 0.0436 data_time: 0.0069 memory: 1114 2022/09/04 00:35:58 - mmengine - INFO - Epoch(val) [35][181/181] acc/top1: 0.4451 acc/top5: 0.7395 acc/mean1: 0.4074 2022/09/04 00:36:02 - mmengine - INFO - Epoch(train) [36][20/1345] lr: 1.0000e-03 eta: 1:09:37 time: 0.2081 data_time: 0.0148 memory: 7116 grad_norm: 7.4674 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3870 loss: 1.3870 2022/09/04 00:36:06 - mmengine - INFO - Epoch(train) [36][40/1345] lr: 1.0000e-03 eta: 1:09:33 time: 0.2007 data_time: 0.0111 memory: 7116 grad_norm: 7.5043 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3667 loss: 1.3667 2022/09/04 00:36:10 - mmengine - INFO - Epoch(train) [36][60/1345] lr: 1.0000e-03 eta: 1:09:29 time: 0.2003 data_time: 0.0098 memory: 7116 grad_norm: 7.4082 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 1.4497 loss: 1.4497 2022/09/04 00:36:14 - mmengine - INFO - Epoch(train) [36][80/1345] lr: 1.0000e-03 eta: 1:09:25 time: 0.1984 data_time: 0.0124 memory: 7116 grad_norm: 7.8427 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5219 loss: 1.5219 2022/09/04 00:36:18 - mmengine - INFO - Epoch(train) [36][100/1345] lr: 1.0000e-03 eta: 1:09:21 time: 0.2017 data_time: 0.0108 memory: 7116 grad_norm: 7.6677 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2865 loss: 1.2865 2022/09/04 00:36:22 - mmengine - INFO - Epoch(train) [36][120/1345] lr: 1.0000e-03 eta: 1:09:16 time: 0.1989 data_time: 0.0088 memory: 7116 grad_norm: 7.3129 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1585 loss: 1.1585 2022/09/04 00:36:26 - mmengine - INFO - Epoch(train) [36][140/1345] lr: 1.0000e-03 eta: 1:09:12 time: 0.1992 data_time: 0.0125 memory: 7116 grad_norm: 7.6649 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4228 loss: 1.4228 2022/09/04 00:36:30 - mmengine - INFO - Epoch(train) [36][160/1345] lr: 1.0000e-03 eta: 1:09:08 time: 0.1969 data_time: 0.0095 memory: 7116 grad_norm: 7.7033 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.4248 loss: 1.4248 2022/09/04 00:36:34 - mmengine - INFO - Epoch(train) [36][180/1345] lr: 1.0000e-03 eta: 1:09:04 time: 0.1993 data_time: 0.0095 memory: 7116 grad_norm: 7.4043 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3024 loss: 1.3024 2022/09/04 00:36:38 - mmengine - INFO - Epoch(train) [36][200/1345] lr: 1.0000e-03 eta: 1:08:59 time: 0.2061 data_time: 0.0119 memory: 7116 grad_norm: 8.0270 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.6258 loss: 1.6258 2022/09/04 00:36:43 - mmengine - INFO - Epoch(train) [36][220/1345] lr: 1.0000e-03 eta: 1:08:55 time: 0.2030 data_time: 0.0095 memory: 7116 grad_norm: 7.8226 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4806 loss: 1.4806 2022/09/04 00:36:47 - mmengine - INFO - Epoch(train) [36][240/1345] lr: 1.0000e-03 eta: 1:08:51 time: 0.2034 data_time: 0.0085 memory: 7116 grad_norm: 7.8254 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.3642 loss: 1.3642 2022/09/04 00:36:51 - mmengine - INFO - Epoch(train) [36][260/1345] lr: 1.0000e-03 eta: 1:08:47 time: 0.2038 data_time: 0.0110 memory: 7116 grad_norm: 7.7315 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5371 loss: 1.5371 2022/09/04 00:36:55 - mmengine - INFO - Epoch(train) [36][280/1345] lr: 1.0000e-03 eta: 1:08:43 time: 0.2056 data_time: 0.0090 memory: 7116 grad_norm: 7.8600 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4886 loss: 1.4886 2022/09/04 00:36:59 - mmengine - INFO - Epoch(train) [36][300/1345] lr: 1.0000e-03 eta: 1:08:39 time: 0.2074 data_time: 0.0094 memory: 7116 grad_norm: 7.4634 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2120 loss: 1.2120 2022/09/04 00:37:03 - mmengine - INFO - Epoch(train) [36][320/1345] lr: 1.0000e-03 eta: 1:08:35 time: 0.2063 data_time: 0.0113 memory: 7116 grad_norm: 7.7287 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.3383 loss: 1.3383 2022/09/04 00:37:07 - mmengine - INFO - Epoch(train) [36][340/1345] lr: 1.0000e-03 eta: 1:08:30 time: 0.2070 data_time: 0.0097 memory: 7116 grad_norm: 7.6969 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3651 loss: 1.3651 2022/09/04 00:37:11 - mmengine - INFO - Epoch(train) [36][360/1345] lr: 1.0000e-03 eta: 1:08:26 time: 0.1986 data_time: 0.0091 memory: 7116 grad_norm: 7.7659 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3656 loss: 1.3656 2022/09/04 00:37:16 - mmengine - INFO - Epoch(train) [36][380/1345] lr: 1.0000e-03 eta: 1:08:22 time: 0.2230 data_time: 0.0114 memory: 7116 grad_norm: 7.8600 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5780 loss: 1.5780 2022/09/04 00:37:20 - mmengine - INFO - Epoch(train) [36][400/1345] lr: 1.0000e-03 eta: 1:08:18 time: 0.2013 data_time: 0.0090 memory: 7116 grad_norm: 7.4339 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4172 loss: 1.4172 2022/09/04 00:37:24 - mmengine - INFO - Epoch(train) [36][420/1345] lr: 1.0000e-03 eta: 1:08:14 time: 0.2042 data_time: 0.0088 memory: 7116 grad_norm: 7.8594 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3285 loss: 1.3285 2022/09/04 00:37:28 - mmengine - INFO - Epoch(train) [36][440/1345] lr: 1.0000e-03 eta: 1:08:10 time: 0.2066 data_time: 0.0120 memory: 7116 grad_norm: 7.9143 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.6923 loss: 1.6923 2022/09/04 00:37:32 - mmengine - INFO - Epoch(train) [36][460/1345] lr: 1.0000e-03 eta: 1:08:05 time: 0.2026 data_time: 0.0098 memory: 7116 grad_norm: 7.8034 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5429 loss: 1.5429 2022/09/04 00:37:36 - mmengine - INFO - Epoch(train) [36][480/1345] lr: 1.0000e-03 eta: 1:08:01 time: 0.2224 data_time: 0.0112 memory: 7116 grad_norm: 7.9370 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3825 loss: 1.3825 2022/09/04 00:37:41 - mmengine - INFO - Epoch(train) [36][500/1345] lr: 1.0000e-03 eta: 1:07:57 time: 0.2040 data_time: 0.0117 memory: 7116 grad_norm: 7.5650 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4879 loss: 1.4879 2022/09/04 00:37:45 - mmengine - INFO - Epoch(train) [36][520/1345] lr: 1.0000e-03 eta: 1:07:53 time: 0.2043 data_time: 0.0092 memory: 7116 grad_norm: 7.8402 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4994 loss: 1.4994 2022/09/04 00:37:49 - mmengine - INFO - Epoch(train) [36][540/1345] lr: 1.0000e-03 eta: 1:07:49 time: 0.2106 data_time: 0.0094 memory: 7116 grad_norm: 7.9536 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6194 loss: 1.6194 2022/09/04 00:37:53 - mmengine - INFO - Epoch(train) [36][560/1345] lr: 1.0000e-03 eta: 1:07:45 time: 0.2058 data_time: 0.0111 memory: 7116 grad_norm: 7.6339 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2777 loss: 1.2777 2022/09/04 00:37:57 - mmengine - INFO - Epoch(train) [36][580/1345] lr: 1.0000e-03 eta: 1:07:41 time: 0.2083 data_time: 0.0087 memory: 7116 grad_norm: 7.7983 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3357 loss: 1.3357 2022/09/04 00:38:01 - mmengine - INFO - Epoch(train) [36][600/1345] lr: 1.0000e-03 eta: 1:07:37 time: 0.2063 data_time: 0.0088 memory: 7116 grad_norm: 7.5998 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2801 loss: 1.2801 2022/09/04 00:38:05 - mmengine - INFO - Epoch(train) [36][620/1345] lr: 1.0000e-03 eta: 1:07:32 time: 0.2058 data_time: 0.0119 memory: 7116 grad_norm: 7.5679 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9950 loss: 0.9950 2022/09/04 00:38:10 - mmengine - INFO - Epoch(train) [36][640/1345] lr: 1.0000e-03 eta: 1:07:28 time: 0.2204 data_time: 0.0114 memory: 7116 grad_norm: 7.7010 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.2938 loss: 1.2938 2022/09/04 00:38:14 - mmengine - INFO - Epoch(train) [36][660/1345] lr: 1.0000e-03 eta: 1:07:24 time: 0.2041 data_time: 0.0088 memory: 7116 grad_norm: 8.2726 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5311 loss: 1.5311 2022/09/04 00:38:18 - mmengine - INFO - Epoch(train) [36][680/1345] lr: 1.0000e-03 eta: 1:07:20 time: 0.2018 data_time: 0.0122 memory: 7116 grad_norm: 7.9484 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1887 loss: 1.1887 2022/09/04 00:38:22 - mmengine - INFO - Epoch(train) [36][700/1345] lr: 1.0000e-03 eta: 1:07:16 time: 0.2020 data_time: 0.0104 memory: 7116 grad_norm: 7.6664 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5578 loss: 1.5578 2022/09/04 00:38:27 - mmengine - INFO - Epoch(train) [36][720/1345] lr: 1.0000e-03 eta: 1:07:12 time: 0.2392 data_time: 0.0100 memory: 7116 grad_norm: 7.8154 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4800 loss: 1.4800 2022/09/04 00:38:31 - mmengine - INFO - Epoch(train) [36][740/1345] lr: 1.0000e-03 eta: 1:07:08 time: 0.1976 data_time: 0.0120 memory: 7116 grad_norm: 7.9481 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5455 loss: 1.5455 2022/09/04 00:38:35 - mmengine - INFO - Epoch(train) [36][760/1345] lr: 1.0000e-03 eta: 1:07:03 time: 0.2015 data_time: 0.0095 memory: 7116 grad_norm: 7.8974 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3976 loss: 1.3976 2022/09/04 00:38:39 - mmengine - INFO - Epoch(train) [36][780/1345] lr: 1.0000e-03 eta: 1:06:59 time: 0.1997 data_time: 0.0092 memory: 7116 grad_norm: 8.1863 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.2992 loss: 1.2992 2022/09/04 00:38:43 - mmengine - INFO - Epoch(train) [36][800/1345] lr: 1.0000e-03 eta: 1:06:55 time: 0.2034 data_time: 0.0115 memory: 7116 grad_norm: 7.8786 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4856 loss: 1.4856 2022/09/04 00:38:47 - mmengine - INFO - Epoch(train) [36][820/1345] lr: 1.0000e-03 eta: 1:06:51 time: 0.2287 data_time: 0.0091 memory: 7116 grad_norm: 8.1234 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3837 loss: 1.3837 2022/09/04 00:38:52 - mmengine - INFO - Epoch(train) [36][840/1345] lr: 1.0000e-03 eta: 1:06:47 time: 0.2062 data_time: 0.0089 memory: 7116 grad_norm: 8.3318 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3177 loss: 1.3177 2022/09/04 00:38:56 - mmengine - INFO - Epoch(train) [36][860/1345] lr: 1.0000e-03 eta: 1:06:43 time: 0.2026 data_time: 0.0115 memory: 7116 grad_norm: 7.9582 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.5724 loss: 1.5724 2022/09/04 00:39:00 - mmengine - INFO - Epoch(train) [36][880/1345] lr: 1.0000e-03 eta: 1:06:39 time: 0.2044 data_time: 0.0093 memory: 7116 grad_norm: 7.9247 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2011 loss: 1.2011 2022/09/04 00:39:04 - mmengine - INFO - Epoch(train) [36][900/1345] lr: 1.0000e-03 eta: 1:06:34 time: 0.2018 data_time: 0.0094 memory: 7116 grad_norm: 8.0845 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3264 loss: 1.3264 2022/09/04 00:39:08 - mmengine - INFO - Epoch(train) [36][920/1345] lr: 1.0000e-03 eta: 1:06:30 time: 0.2107 data_time: 0.0119 memory: 7116 grad_norm: 8.0506 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2903 loss: 1.2903 2022/09/04 00:39:09 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:39:12 - mmengine - INFO - Epoch(train) [36][940/1345] lr: 1.0000e-03 eta: 1:06:26 time: 0.2044 data_time: 0.0090 memory: 7116 grad_norm: 7.7891 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4463 loss: 1.4463 2022/09/04 00:39:16 - mmengine - INFO - Epoch(train) [36][960/1345] lr: 1.0000e-03 eta: 1:06:22 time: 0.2011 data_time: 0.0092 memory: 7116 grad_norm: 7.6403 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.5301 loss: 1.5301 2022/09/04 00:39:20 - mmengine - INFO - Epoch(train) [36][980/1345] lr: 1.0000e-03 eta: 1:06:18 time: 0.2069 data_time: 0.0112 memory: 7116 grad_norm: 7.6510 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1856 loss: 1.1856 2022/09/04 00:39:24 - mmengine - INFO - Epoch(train) [36][1000/1345] lr: 1.0000e-03 eta: 1:06:14 time: 0.2059 data_time: 0.0088 memory: 7116 grad_norm: 7.8862 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.2351 loss: 1.2351 2022/09/04 00:39:29 - mmengine - INFO - Epoch(train) [36][1020/1345] lr: 1.0000e-03 eta: 1:06:09 time: 0.2093 data_time: 0.0091 memory: 7116 grad_norm: 7.7876 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2904 loss: 1.2904 2022/09/04 00:39:33 - mmengine - INFO - Epoch(train) [36][1040/1345] lr: 1.0000e-03 eta: 1:06:05 time: 0.2046 data_time: 0.0125 memory: 7116 grad_norm: 8.0600 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4626 loss: 1.4626 2022/09/04 00:39:37 - mmengine - INFO - Epoch(train) [36][1060/1345] lr: 1.0000e-03 eta: 1:06:01 time: 0.2048 data_time: 0.0091 memory: 7116 grad_norm: 7.9343 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3144 loss: 1.3144 2022/09/04 00:39:41 - mmengine - INFO - Epoch(train) [36][1080/1345] lr: 1.0000e-03 eta: 1:05:57 time: 0.2027 data_time: 0.0092 memory: 7116 grad_norm: 7.9594 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3787 loss: 1.3787 2022/09/04 00:39:45 - mmengine - INFO - Epoch(train) [36][1100/1345] lr: 1.0000e-03 eta: 1:05:53 time: 0.2031 data_time: 0.0110 memory: 7116 grad_norm: 8.0523 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5862 loss: 1.5862 2022/09/04 00:39:49 - mmengine - INFO - Epoch(train) [36][1120/1345] lr: 1.0000e-03 eta: 1:05:49 time: 0.2086 data_time: 0.0096 memory: 7116 grad_norm: 7.8109 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3172 loss: 1.3172 2022/09/04 00:39:53 - mmengine - INFO - Epoch(train) [36][1140/1345] lr: 1.0000e-03 eta: 1:05:45 time: 0.2071 data_time: 0.0088 memory: 7116 grad_norm: 8.0666 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4934 loss: 1.4934 2022/09/04 00:39:57 - mmengine - INFO - Epoch(train) [36][1160/1345] lr: 1.0000e-03 eta: 1:05:40 time: 0.2054 data_time: 0.0112 memory: 7116 grad_norm: 7.9652 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3775 loss: 1.3775 2022/09/04 00:40:01 - mmengine - INFO - Epoch(train) [36][1180/1345] lr: 1.0000e-03 eta: 1:05:36 time: 0.2106 data_time: 0.0089 memory: 7116 grad_norm: 7.9033 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2450 loss: 1.2450 2022/09/04 00:40:06 - mmengine - INFO - Epoch(train) [36][1200/1345] lr: 1.0000e-03 eta: 1:05:32 time: 0.2063 data_time: 0.0102 memory: 7116 grad_norm: 8.1068 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3081 loss: 1.3081 2022/09/04 00:40:10 - mmengine - INFO - Epoch(train) [36][1220/1345] lr: 1.0000e-03 eta: 1:05:28 time: 0.2165 data_time: 0.0116 memory: 7116 grad_norm: 8.0769 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4020 loss: 1.4020 2022/09/04 00:40:14 - mmengine - INFO - Epoch(train) [36][1240/1345] lr: 1.0000e-03 eta: 1:05:24 time: 0.2066 data_time: 0.0095 memory: 7116 grad_norm: 7.8219 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5228 loss: 1.5228 2022/09/04 00:40:18 - mmengine - INFO - Epoch(train) [36][1260/1345] lr: 1.0000e-03 eta: 1:05:20 time: 0.2086 data_time: 0.0100 memory: 7116 grad_norm: 8.0377 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4683 loss: 1.4683 2022/09/04 00:40:22 - mmengine - INFO - Epoch(train) [36][1280/1345] lr: 1.0000e-03 eta: 1:05:16 time: 0.2029 data_time: 0.0133 memory: 7116 grad_norm: 7.8208 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.5979 loss: 1.5979 2022/09/04 00:40:26 - mmengine - INFO - Epoch(train) [36][1300/1345] lr: 1.0000e-03 eta: 1:05:11 time: 0.2006 data_time: 0.0095 memory: 7116 grad_norm: 8.0073 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.5288 loss: 1.5288 2022/09/04 00:40:31 - mmengine - INFO - Epoch(train) [36][1320/1345] lr: 1.0000e-03 eta: 1:05:07 time: 0.2104 data_time: 0.0085 memory: 7116 grad_norm: 8.0269 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6206 loss: 1.6206 2022/09/04 00:40:35 - mmengine - INFO - Epoch(train) [36][1340/1345] lr: 1.0000e-03 eta: 1:05:03 time: 0.2043 data_time: 0.0109 memory: 7116 grad_norm: 8.0342 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4648 loss: 1.4648 2022/09/04 00:40:36 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:40:36 - mmengine - INFO - Epoch(train) [36][1345/1345] lr: 1.0000e-03 eta: 1:05:03 time: 0.2034 data_time: 0.0082 memory: 7116 grad_norm: 8.4294 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.7777 loss: 1.7777 2022/09/04 00:40:36 - mmengine - INFO - Saving checkpoint at 36 epochs 2022/09/04 00:40:41 - mmengine - INFO - Epoch(val) [36][20/181] eta: 0:00:07 time: 0.0497 data_time: 0.0117 memory: 1114 2022/09/04 00:40:42 - mmengine - INFO - Epoch(val) [36][40/181] eta: 0:00:06 time: 0.0433 data_time: 0.0075 memory: 1114 2022/09/04 00:40:42 - mmengine - INFO - Epoch(val) [36][60/181] eta: 0:00:05 time: 0.0421 data_time: 0.0067 memory: 1114 2022/09/04 00:40:43 - mmengine - INFO - Epoch(val) [36][80/181] eta: 0:00:04 time: 0.0420 data_time: 0.0065 memory: 1114 2022/09/04 00:40:44 - mmengine - INFO - Epoch(val) [36][100/181] eta: 0:00:03 time: 0.0451 data_time: 0.0083 memory: 1114 2022/09/04 00:40:45 - mmengine - INFO - Epoch(val) [36][120/181] eta: 0:00:02 time: 0.0438 data_time: 0.0076 memory: 1114 2022/09/04 00:40:46 - mmengine - INFO - Epoch(val) [36][140/181] eta: 0:00:01 time: 0.0443 data_time: 0.0079 memory: 1114 2022/09/04 00:40:47 - mmengine - INFO - Epoch(val) [36][160/181] eta: 0:00:00 time: 0.0471 data_time: 0.0080 memory: 1114 2022/09/04 00:40:48 - mmengine - INFO - Epoch(val) [36][180/181] eta: 0:00:00 time: 0.0424 data_time: 0.0067 memory: 1114 2022/09/04 00:40:49 - mmengine - INFO - Epoch(val) [36][181/181] acc/top1: 0.4519 acc/top5: 0.7412 acc/mean1: 0.4095 2022/09/04 00:40:49 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_34.pth is removed 2022/09/04 00:40:50 - mmengine - INFO - The best checkpoint with 0.4519 acc/top1 at 36 epoch is saved to best_acc/top1_epoch_36.pth. 2022/09/04 00:40:54 - mmengine - INFO - Epoch(train) [37][20/1345] lr: 1.0000e-03 eta: 1:04:57 time: 0.2049 data_time: 0.0113 memory: 7116 grad_norm: 7.7757 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1928 loss: 1.1928 2022/09/04 00:40:58 - mmengine - INFO - Epoch(train) [37][40/1345] lr: 1.0000e-03 eta: 1:04:53 time: 0.2024 data_time: 0.0090 memory: 7116 grad_norm: 7.5062 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5127 loss: 1.5127 2022/09/04 00:41:02 - mmengine - INFO - Epoch(train) [37][60/1345] lr: 1.0000e-03 eta: 1:04:49 time: 0.2069 data_time: 0.0101 memory: 7116 grad_norm: 7.7122 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.5255 loss: 1.5255 2022/09/04 00:41:06 - mmengine - INFO - Epoch(train) [37][80/1345] lr: 1.0000e-03 eta: 1:04:45 time: 0.2003 data_time: 0.0118 memory: 7116 grad_norm: 7.9465 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3962 loss: 1.3962 2022/09/04 00:41:10 - mmengine - INFO - Epoch(train) [37][100/1345] lr: 1.0000e-03 eta: 1:04:41 time: 0.2024 data_time: 0.0093 memory: 7116 grad_norm: 8.1136 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.4412 loss: 1.4412 2022/09/04 00:41:14 - mmengine - INFO - Epoch(train) [37][120/1345] lr: 1.0000e-03 eta: 1:04:37 time: 0.2043 data_time: 0.0082 memory: 7116 grad_norm: 7.9482 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3037 loss: 1.3037 2022/09/04 00:41:18 - mmengine - INFO - Epoch(train) [37][140/1345] lr: 1.0000e-03 eta: 1:04:32 time: 0.2100 data_time: 0.0120 memory: 7116 grad_norm: 7.9118 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3819 loss: 1.3819 2022/09/04 00:41:23 - mmengine - INFO - Epoch(train) [37][160/1345] lr: 1.0000e-03 eta: 1:04:28 time: 0.2065 data_time: 0.0088 memory: 7116 grad_norm: 8.0017 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4583 loss: 1.4583 2022/09/04 00:41:27 - mmengine - INFO - Epoch(train) [37][180/1345] lr: 1.0000e-03 eta: 1:04:24 time: 0.2036 data_time: 0.0085 memory: 7116 grad_norm: 7.9485 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1260 loss: 1.1260 2022/09/04 00:41:31 - mmengine - INFO - Epoch(train) [37][200/1345] lr: 1.0000e-03 eta: 1:04:20 time: 0.2083 data_time: 0.0108 memory: 7116 grad_norm: 7.8582 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2435 loss: 1.2435 2022/09/04 00:41:35 - mmengine - INFO - Epoch(train) [37][220/1345] lr: 1.0000e-03 eta: 1:04:16 time: 0.2060 data_time: 0.0089 memory: 7116 grad_norm: 7.9896 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4693 loss: 1.4693 2022/09/04 00:41:39 - mmengine - INFO - Epoch(train) [37][240/1345] lr: 1.0000e-03 eta: 1:04:12 time: 0.2093 data_time: 0.0092 memory: 7116 grad_norm: 7.8014 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1540 loss: 1.1540 2022/09/04 00:41:43 - mmengine - INFO - Epoch(train) [37][260/1345] lr: 1.0000e-03 eta: 1:04:08 time: 0.2033 data_time: 0.0111 memory: 7116 grad_norm: 8.1316 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3973 loss: 1.3973 2022/09/04 00:41:47 - mmengine - INFO - Epoch(train) [37][280/1345] lr: 1.0000e-03 eta: 1:04:03 time: 0.2115 data_time: 0.0103 memory: 7116 grad_norm: 8.1754 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.7154 loss: 1.7154 2022/09/04 00:41:52 - mmengine - INFO - Epoch(train) [37][300/1345] lr: 1.0000e-03 eta: 1:03:59 time: 0.2052 data_time: 0.0093 memory: 7116 grad_norm: 7.9109 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2681 loss: 1.2681 2022/09/04 00:41:56 - mmengine - INFO - Epoch(train) [37][320/1345] lr: 1.0000e-03 eta: 1:03:55 time: 0.1998 data_time: 0.0109 memory: 7116 grad_norm: 7.7066 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2905 loss: 1.2905 2022/09/04 00:42:00 - mmengine - INFO - Epoch(train) [37][340/1345] lr: 1.0000e-03 eta: 1:03:51 time: 0.2017 data_time: 0.0086 memory: 7116 grad_norm: 7.9661 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5353 loss: 1.5353 2022/09/04 00:42:04 - mmengine - INFO - Epoch(train) [37][360/1345] lr: 1.0000e-03 eta: 1:03:47 time: 0.2054 data_time: 0.0093 memory: 7116 grad_norm: 8.0404 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3227 loss: 1.3227 2022/09/04 00:42:08 - mmengine - INFO - Epoch(train) [37][380/1345] lr: 1.0000e-03 eta: 1:03:43 time: 0.2058 data_time: 0.0109 memory: 7116 grad_norm: 7.9560 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3351 loss: 1.3351 2022/09/04 00:42:12 - mmengine - INFO - Epoch(train) [37][400/1345] lr: 1.0000e-03 eta: 1:03:38 time: 0.2051 data_time: 0.0098 memory: 7116 grad_norm: 8.0093 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2252 loss: 1.2252 2022/09/04 00:42:16 - mmengine - INFO - Epoch(train) [37][420/1345] lr: 1.0000e-03 eta: 1:03:34 time: 0.2057 data_time: 0.0086 memory: 7116 grad_norm: 7.9202 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3517 loss: 1.3517 2022/09/04 00:42:20 - mmengine - INFO - Epoch(train) [37][440/1345] lr: 1.0000e-03 eta: 1:03:30 time: 0.2063 data_time: 0.0102 memory: 7116 grad_norm: 8.0145 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3786 loss: 1.3786 2022/09/04 00:42:24 - mmengine - INFO - Epoch(train) [37][460/1345] lr: 1.0000e-03 eta: 1:03:26 time: 0.2039 data_time: 0.0094 memory: 7116 grad_norm: 8.1175 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3996 loss: 1.3996 2022/09/04 00:42:28 - mmengine - INFO - Epoch(train) [37][480/1345] lr: 1.0000e-03 eta: 1:03:22 time: 0.2047 data_time: 0.0098 memory: 7116 grad_norm: 7.8022 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3971 loss: 1.3971 2022/09/04 00:42:33 - mmengine - INFO - Epoch(train) [37][500/1345] lr: 1.0000e-03 eta: 1:03:18 time: 0.2089 data_time: 0.0115 memory: 7116 grad_norm: 8.2092 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2860 loss: 1.2860 2022/09/04 00:42:37 - mmengine - INFO - Epoch(train) [37][520/1345] lr: 1.0000e-03 eta: 1:03:14 time: 0.2082 data_time: 0.0091 memory: 7116 grad_norm: 8.0464 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.3647 loss: 1.3647 2022/09/04 00:42:41 - mmengine - INFO - Epoch(train) [37][540/1345] lr: 1.0000e-03 eta: 1:03:09 time: 0.2040 data_time: 0.0085 memory: 7116 grad_norm: 7.9353 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.5882 loss: 1.5882 2022/09/04 00:42:45 - mmengine - INFO - Epoch(train) [37][560/1345] lr: 1.0000e-03 eta: 1:03:05 time: 0.2054 data_time: 0.0108 memory: 7116 grad_norm: 7.9471 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3111 loss: 1.3111 2022/09/04 00:42:49 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:42:49 - mmengine - INFO - Epoch(train) [37][580/1345] lr: 1.0000e-03 eta: 1:03:01 time: 0.2026 data_time: 0.0091 memory: 7116 grad_norm: 8.0710 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2227 loss: 1.2227 2022/09/04 00:42:53 - mmengine - INFO - Epoch(train) [37][600/1345] lr: 1.0000e-03 eta: 1:02:57 time: 0.2060 data_time: 0.0086 memory: 7116 grad_norm: 8.1265 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3221 loss: 1.3221 2022/09/04 00:42:57 - mmengine - INFO - Epoch(train) [37][620/1345] lr: 1.0000e-03 eta: 1:02:53 time: 0.2132 data_time: 0.0111 memory: 7116 grad_norm: 8.2554 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5771 loss: 1.5771 2022/09/04 00:43:02 - mmengine - INFO - Epoch(train) [37][640/1345] lr: 1.0000e-03 eta: 1:02:49 time: 0.2092 data_time: 0.0087 memory: 7116 grad_norm: 8.2941 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3654 loss: 1.3654 2022/09/04 00:43:06 - mmengine - INFO - Epoch(train) [37][660/1345] lr: 1.0000e-03 eta: 1:02:44 time: 0.2081 data_time: 0.0081 memory: 7116 grad_norm: 7.8914 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3920 loss: 1.3920 2022/09/04 00:43:10 - mmengine - INFO - Epoch(train) [37][680/1345] lr: 1.0000e-03 eta: 1:02:40 time: 0.2088 data_time: 0.0108 memory: 7116 grad_norm: 7.8469 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.5351 loss: 1.5351 2022/09/04 00:43:14 - mmengine - INFO - Epoch(train) [37][700/1345] lr: 1.0000e-03 eta: 1:02:36 time: 0.2057 data_time: 0.0081 memory: 7116 grad_norm: 8.0845 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4825 loss: 1.4825 2022/09/04 00:43:18 - mmengine - INFO - Epoch(train) [37][720/1345] lr: 1.0000e-03 eta: 1:02:32 time: 0.2126 data_time: 0.0094 memory: 7116 grad_norm: 8.0870 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6060 loss: 1.6060 2022/09/04 00:43:22 - mmengine - INFO - Epoch(train) [37][740/1345] lr: 1.0000e-03 eta: 1:02:28 time: 0.2013 data_time: 0.0101 memory: 7116 grad_norm: 8.0131 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3033 loss: 1.3033 2022/09/04 00:43:26 - mmengine - INFO - Epoch(train) [37][760/1345] lr: 1.0000e-03 eta: 1:02:24 time: 0.2047 data_time: 0.0082 memory: 7116 grad_norm: 8.0826 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3799 loss: 1.3799 2022/09/04 00:43:31 - mmengine - INFO - Epoch(train) [37][780/1345] lr: 1.0000e-03 eta: 1:02:20 time: 0.2118 data_time: 0.0097 memory: 7116 grad_norm: 8.1859 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.3724 loss: 1.3724 2022/09/04 00:43:35 - mmengine - INFO - Epoch(train) [37][800/1345] lr: 1.0000e-03 eta: 1:02:16 time: 0.2084 data_time: 0.0103 memory: 7116 grad_norm: 8.2478 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3901 loss: 1.3901 2022/09/04 00:43:39 - mmengine - INFO - Epoch(train) [37][820/1345] lr: 1.0000e-03 eta: 1:02:11 time: 0.2156 data_time: 0.0079 memory: 7116 grad_norm: 8.1218 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3412 loss: 1.3412 2022/09/04 00:43:43 - mmengine - INFO - Epoch(train) [37][840/1345] lr: 1.0000e-03 eta: 1:02:07 time: 0.2100 data_time: 0.0081 memory: 7116 grad_norm: 8.0598 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4133 loss: 1.4133 2022/09/04 00:43:48 - mmengine - INFO - Epoch(train) [37][860/1345] lr: 1.0000e-03 eta: 1:02:03 time: 0.2317 data_time: 0.0107 memory: 7116 grad_norm: 8.1011 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3876 loss: 1.3876 2022/09/04 00:43:52 - mmengine - INFO - Epoch(train) [37][880/1345] lr: 1.0000e-03 eta: 1:01:59 time: 0.2102 data_time: 0.0076 memory: 7116 grad_norm: 7.7950 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3814 loss: 1.3814 2022/09/04 00:43:56 - mmengine - INFO - Epoch(train) [37][900/1345] lr: 1.0000e-03 eta: 1:01:55 time: 0.2143 data_time: 0.0089 memory: 7116 grad_norm: 8.0296 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4780 loss: 1.4780 2022/09/04 00:44:01 - mmengine - INFO - Epoch(train) [37][920/1345] lr: 1.0000e-03 eta: 1:01:51 time: 0.2143 data_time: 0.0103 memory: 7116 grad_norm: 8.1051 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2740 loss: 1.2740 2022/09/04 00:44:05 - mmengine - INFO - Epoch(train) [37][940/1345] lr: 1.0000e-03 eta: 1:01:47 time: 0.2067 data_time: 0.0085 memory: 7116 grad_norm: 8.2698 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3594 loss: 1.3594 2022/09/04 00:44:10 - mmengine - INFO - Epoch(train) [37][960/1345] lr: 1.0000e-03 eta: 1:01:43 time: 0.2340 data_time: 0.0085 memory: 7116 grad_norm: 8.0971 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2681 loss: 1.2681 2022/09/04 00:44:14 - mmengine - INFO - Epoch(train) [37][980/1345] lr: 1.0000e-03 eta: 1:01:39 time: 0.2100 data_time: 0.0104 memory: 7116 grad_norm: 8.1266 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3334 loss: 1.3334 2022/09/04 00:44:18 - mmengine - INFO - Epoch(train) [37][1000/1345] lr: 1.0000e-03 eta: 1:01:35 time: 0.2046 data_time: 0.0081 memory: 7116 grad_norm: 7.9790 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5058 loss: 1.5058 2022/09/04 00:44:22 - mmengine - INFO - Epoch(train) [37][1020/1345] lr: 1.0000e-03 eta: 1:01:31 time: 0.2129 data_time: 0.0080 memory: 7116 grad_norm: 8.2893 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.2152 loss: 1.2152 2022/09/04 00:44:27 - mmengine - INFO - Epoch(train) [37][1040/1345] lr: 1.0000e-03 eta: 1:01:27 time: 0.2343 data_time: 0.0110 memory: 7116 grad_norm: 8.0434 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3788 loss: 1.3788 2022/09/04 00:44:31 - mmengine - INFO - Epoch(train) [37][1060/1345] lr: 1.0000e-03 eta: 1:01:22 time: 0.2127 data_time: 0.0077 memory: 7116 grad_norm: 8.2252 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3967 loss: 1.3967 2022/09/04 00:44:35 - mmengine - INFO - Epoch(train) [37][1080/1345] lr: 1.0000e-03 eta: 1:01:18 time: 0.2092 data_time: 0.0086 memory: 7116 grad_norm: 8.4092 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4704 loss: 1.4704 2022/09/04 00:44:40 - mmengine - INFO - Epoch(train) [37][1100/1345] lr: 1.0000e-03 eta: 1:01:14 time: 0.2136 data_time: 0.0102 memory: 7116 grad_norm: 8.2273 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.5146 loss: 1.5146 2022/09/04 00:44:44 - mmengine - INFO - Epoch(train) [37][1120/1345] lr: 1.0000e-03 eta: 1:01:10 time: 0.2100 data_time: 0.0080 memory: 7116 grad_norm: 8.3184 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5518 loss: 1.5518 2022/09/04 00:44:48 - mmengine - INFO - Epoch(train) [37][1140/1345] lr: 1.0000e-03 eta: 1:01:06 time: 0.2180 data_time: 0.0105 memory: 7116 grad_norm: 8.1108 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2690 loss: 1.2690 2022/09/04 00:44:52 - mmengine - INFO - Epoch(train) [37][1160/1345] lr: 1.0000e-03 eta: 1:01:02 time: 0.2130 data_time: 0.0101 memory: 7116 grad_norm: 8.3195 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.3422 loss: 1.3422 2022/09/04 00:44:57 - mmengine - INFO - Epoch(train) [37][1180/1345] lr: 1.0000e-03 eta: 1:00:58 time: 0.2146 data_time: 0.0085 memory: 7116 grad_norm: 7.7944 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1595 loss: 1.1595 2022/09/04 00:45:01 - mmengine - INFO - Epoch(train) [37][1200/1345] lr: 1.0000e-03 eta: 1:00:54 time: 0.2140 data_time: 0.0081 memory: 7116 grad_norm: 8.3200 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2461 loss: 1.2461 2022/09/04 00:45:05 - mmengine - INFO - Epoch(train) [37][1220/1345] lr: 1.0000e-03 eta: 1:00:50 time: 0.2136 data_time: 0.0118 memory: 7116 grad_norm: 8.1125 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3211 loss: 1.3211 2022/09/04 00:45:10 - mmengine - INFO - Epoch(train) [37][1240/1345] lr: 1.0000e-03 eta: 1:00:46 time: 0.2236 data_time: 0.0085 memory: 7116 grad_norm: 8.1453 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3778 loss: 1.3778 2022/09/04 00:45:14 - mmengine - INFO - Epoch(train) [37][1260/1345] lr: 1.0000e-03 eta: 1:00:42 time: 0.2148 data_time: 0.0083 memory: 7116 grad_norm: 8.3249 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.3940 loss: 1.3940 2022/09/04 00:45:18 - mmengine - INFO - Epoch(train) [37][1280/1345] lr: 1.0000e-03 eta: 1:00:37 time: 0.2178 data_time: 0.0104 memory: 7116 grad_norm: 8.1077 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3044 loss: 1.3044 2022/09/04 00:45:23 - mmengine - INFO - Epoch(train) [37][1300/1345] lr: 1.0000e-03 eta: 1:00:33 time: 0.2088 data_time: 0.0081 memory: 7116 grad_norm: 8.2798 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4520 loss: 1.4520 2022/09/04 00:45:27 - mmengine - INFO - Epoch(train) [37][1320/1345] lr: 1.0000e-03 eta: 1:00:29 time: 0.2223 data_time: 0.0085 memory: 7116 grad_norm: 8.5895 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.5277 loss: 1.5277 2022/09/04 00:45:31 - mmengine - INFO - Epoch(train) [37][1340/1345] lr: 1.0000e-03 eta: 1:00:25 time: 0.2192 data_time: 0.0098 memory: 7116 grad_norm: 8.4503 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0216 loss: 1.0216 2022/09/04 00:45:32 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:45:32 - mmengine - INFO - Epoch(train) [37][1345/1345] lr: 1.0000e-03 eta: 1:00:25 time: 0.2118 data_time: 0.0082 memory: 7116 grad_norm: 8.6125 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1110 loss: 1.1110 2022/09/04 00:45:32 - mmengine - INFO - Saving checkpoint at 37 epochs 2022/09/04 00:45:35 - mmengine - INFO - Epoch(val) [37][20/181] eta: 0:00:07 time: 0.0440 data_time: 0.0090 memory: 1114 2022/09/04 00:45:36 - mmengine - INFO - Epoch(val) [37][40/181] eta: 0:00:05 time: 0.0409 data_time: 0.0058 memory: 1114 2022/09/04 00:45:36 - mmengine - INFO - Epoch(val) [37][60/181] eta: 0:00:04 time: 0.0405 data_time: 0.0057 memory: 1114 2022/09/04 00:45:37 - mmengine - INFO - Epoch(val) [37][80/181] eta: 0:00:04 time: 0.0409 data_time: 0.0060 memory: 1114 2022/09/04 00:45:38 - mmengine - INFO - Epoch(val) [37][100/181] eta: 0:00:03 time: 0.0410 data_time: 0.0060 memory: 1114 2022/09/04 00:45:39 - mmengine - INFO - Epoch(val) [37][120/181] eta: 0:00:02 time: 0.0406 data_time: 0.0058 memory: 1114 2022/09/04 00:45:40 - mmengine - INFO - Epoch(val) [37][140/181] eta: 0:00:01 time: 0.0404 data_time: 0.0057 memory: 1114 2022/09/04 00:45:41 - mmengine - INFO - Epoch(val) [37][160/181] eta: 0:00:00 time: 0.0404 data_time: 0.0056 memory: 1114 2022/09/04 00:45:41 - mmengine - INFO - Epoch(val) [37][180/181] eta: 0:00:00 time: 0.0405 data_time: 0.0055 memory: 1114 2022/09/04 00:45:45 - mmengine - INFO - Epoch(val) [37][181/181] acc/top1: 0.4543 acc/top5: 0.7458 acc/mean1: 0.4175 2022/09/04 00:45:45 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_36.pth is removed 2022/09/04 00:45:48 - mmengine - INFO - The best checkpoint with 0.4543 acc/top1 at 37 epoch is saved to best_acc/top1_epoch_37.pth. 2022/09/04 00:45:53 - mmengine - INFO - Epoch(train) [38][20/1345] lr: 1.0000e-03 eta: 1:00:20 time: 0.2334 data_time: 0.0309 memory: 7116 grad_norm: 7.7766 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5082 loss: 1.5082 2022/09/04 00:45:57 - mmengine - INFO - Epoch(train) [38][40/1345] lr: 1.0000e-03 eta: 1:00:16 time: 0.2125 data_time: 0.0081 memory: 7116 grad_norm: 8.0265 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1637 loss: 1.1637 2022/09/04 00:46:01 - mmengine - INFO - Epoch(train) [38][60/1345] lr: 1.0000e-03 eta: 1:00:12 time: 0.2124 data_time: 0.0101 memory: 7116 grad_norm: 8.1101 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2902 loss: 1.2902 2022/09/04 00:46:05 - mmengine - INFO - Epoch(train) [38][80/1345] lr: 1.0000e-03 eta: 1:00:08 time: 0.2133 data_time: 0.0078 memory: 7116 grad_norm: 8.0667 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3863 loss: 1.3863 2022/09/04 00:46:10 - mmengine - INFO - Epoch(train) [38][100/1345] lr: 1.0000e-03 eta: 1:00:03 time: 0.2208 data_time: 0.0092 memory: 7116 grad_norm: 8.1462 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3415 loss: 1.3415 2022/09/04 00:46:14 - mmengine - INFO - Epoch(train) [38][120/1345] lr: 1.0000e-03 eta: 0:59:59 time: 0.2124 data_time: 0.0102 memory: 7116 grad_norm: 8.1392 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2677 loss: 1.2677 2022/09/04 00:46:18 - mmengine - INFO - Epoch(train) [38][140/1345] lr: 1.0000e-03 eta: 0:59:55 time: 0.2153 data_time: 0.0096 memory: 7116 grad_norm: 8.0544 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2864 loss: 1.2864 2022/09/04 00:46:23 - mmengine - INFO - Epoch(train) [38][160/1345] lr: 1.0000e-03 eta: 0:59:51 time: 0.2116 data_time: 0.0086 memory: 7116 grad_norm: 8.1360 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4095 loss: 1.4095 2022/09/04 00:46:27 - mmengine - INFO - Epoch(train) [38][180/1345] lr: 1.0000e-03 eta: 0:59:47 time: 0.2191 data_time: 0.0107 memory: 7116 grad_norm: 8.2286 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2731 loss: 1.2731 2022/09/04 00:46:31 - mmengine - INFO - Epoch(train) [38][200/1345] lr: 1.0000e-03 eta: 0:59:43 time: 0.2146 data_time: 0.0079 memory: 7116 grad_norm: 8.4205 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7330 loss: 1.7330 2022/09/04 00:46:36 - mmengine - INFO - Epoch(train) [38][220/1345] lr: 1.0000e-03 eta: 0:59:39 time: 0.2112 data_time: 0.0081 memory: 7116 grad_norm: 8.1881 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.3420 loss: 1.3420 2022/09/04 00:46:39 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:46:40 - mmengine - INFO - Epoch(train) [38][240/1345] lr: 1.0000e-03 eta: 0:59:35 time: 0.2163 data_time: 0.0102 memory: 7116 grad_norm: 8.3338 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2614 loss: 1.2614 2022/09/04 00:46:44 - mmengine - INFO - Epoch(train) [38][260/1345] lr: 1.0000e-03 eta: 0:59:31 time: 0.2125 data_time: 0.0086 memory: 7116 grad_norm: 8.3793 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4389 loss: 1.4389 2022/09/04 00:46:48 - mmengine - INFO - Epoch(train) [38][280/1345] lr: 1.0000e-03 eta: 0:59:27 time: 0.2139 data_time: 0.0082 memory: 7116 grad_norm: 8.2846 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3034 loss: 1.3034 2022/09/04 00:46:53 - mmengine - INFO - Epoch(train) [38][300/1345] lr: 1.0000e-03 eta: 0:59:22 time: 0.2122 data_time: 0.0107 memory: 7116 grad_norm: 8.3622 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5399 loss: 1.5399 2022/09/04 00:46:57 - mmengine - INFO - Epoch(train) [38][320/1345] lr: 1.0000e-03 eta: 0:59:18 time: 0.2218 data_time: 0.0075 memory: 7116 grad_norm: 8.3556 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.4892 loss: 1.4892 2022/09/04 00:47:02 - mmengine - INFO - Epoch(train) [38][340/1345] lr: 1.0000e-03 eta: 0:59:14 time: 0.2270 data_time: 0.0083 memory: 7116 grad_norm: 8.0279 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3705 loss: 1.3705 2022/09/04 00:47:06 - mmengine - INFO - Epoch(train) [38][360/1345] lr: 1.0000e-03 eta: 0:59:10 time: 0.2159 data_time: 0.0107 memory: 7116 grad_norm: 8.1703 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3396 loss: 1.3396 2022/09/04 00:47:10 - mmengine - INFO - Epoch(train) [38][380/1345] lr: 1.0000e-03 eta: 0:59:06 time: 0.2132 data_time: 0.0080 memory: 7116 grad_norm: 8.1150 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.3729 loss: 1.3729 2022/09/04 00:47:15 - mmengine - INFO - Epoch(train) [38][400/1345] lr: 1.0000e-03 eta: 0:59:02 time: 0.2135 data_time: 0.0077 memory: 7116 grad_norm: 7.8386 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3917 loss: 1.3917 2022/09/04 00:47:19 - mmengine - INFO - Epoch(train) [38][420/1345] lr: 1.0000e-03 eta: 0:58:58 time: 0.2214 data_time: 0.0105 memory: 7116 grad_norm: 8.0745 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3368 loss: 1.3368 2022/09/04 00:47:23 - mmengine - INFO - Epoch(train) [38][440/1345] lr: 1.0000e-03 eta: 0:58:54 time: 0.2121 data_time: 0.0083 memory: 7116 grad_norm: 8.0393 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3716 loss: 1.3716 2022/09/04 00:47:27 - mmengine - INFO - Epoch(train) [38][460/1345] lr: 1.0000e-03 eta: 0:58:50 time: 0.2154 data_time: 0.0098 memory: 7116 grad_norm: 8.1319 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2932 loss: 1.2932 2022/09/04 00:47:32 - mmengine - INFO - Epoch(train) [38][480/1345] lr: 1.0000e-03 eta: 0:58:46 time: 0.2185 data_time: 0.0104 memory: 7116 grad_norm: 8.3317 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.3760 loss: 1.3760 2022/09/04 00:47:36 - mmengine - INFO - Epoch(train) [38][500/1345] lr: 1.0000e-03 eta: 0:58:42 time: 0.2116 data_time: 0.0086 memory: 7116 grad_norm: 8.2634 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4797 loss: 1.4797 2022/09/04 00:47:40 - mmengine - INFO - Epoch(train) [38][520/1345] lr: 1.0000e-03 eta: 0:58:38 time: 0.2188 data_time: 0.0076 memory: 7116 grad_norm: 8.7353 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.4207 loss: 1.4207 2022/09/04 00:47:45 - mmengine - INFO - Epoch(train) [38][540/1345] lr: 1.0000e-03 eta: 0:58:34 time: 0.2168 data_time: 0.0100 memory: 7116 grad_norm: 8.2798 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3422 loss: 1.3422 2022/09/04 00:47:49 - mmengine - INFO - Epoch(train) [38][560/1345] lr: 1.0000e-03 eta: 0:58:29 time: 0.2269 data_time: 0.0089 memory: 7116 grad_norm: 8.3927 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3766 loss: 1.3766 2022/09/04 00:47:54 - mmengine - INFO - Epoch(train) [38][580/1345] lr: 1.0000e-03 eta: 0:58:25 time: 0.2157 data_time: 0.0072 memory: 7116 grad_norm: 8.4244 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2178 loss: 1.2178 2022/09/04 00:47:58 - mmengine - INFO - Epoch(train) [38][600/1345] lr: 1.0000e-03 eta: 0:58:21 time: 0.2188 data_time: 0.0095 memory: 7116 grad_norm: 8.1456 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4637 loss: 1.4637 2022/09/04 00:48:02 - mmengine - INFO - Epoch(train) [38][620/1345] lr: 1.0000e-03 eta: 0:58:17 time: 0.2168 data_time: 0.0078 memory: 7116 grad_norm: 8.3975 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6899 loss: 1.6899 2022/09/04 00:48:07 - mmengine - INFO - Epoch(train) [38][640/1345] lr: 1.0000e-03 eta: 0:58:13 time: 0.2175 data_time: 0.0084 memory: 7116 grad_norm: 8.5117 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.2753 loss: 1.2753 2022/09/04 00:48:11 - mmengine - INFO - Epoch(train) [38][660/1345] lr: 1.0000e-03 eta: 0:58:09 time: 0.2214 data_time: 0.0099 memory: 7116 grad_norm: 8.2371 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3246 loss: 1.3246 2022/09/04 00:48:16 - mmengine - INFO - Epoch(train) [38][680/1345] lr: 1.0000e-03 eta: 0:58:05 time: 0.2165 data_time: 0.0073 memory: 7116 grad_norm: 8.6075 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4723 loss: 1.4723 2022/09/04 00:48:20 - mmengine - INFO - Epoch(train) [38][700/1345] lr: 1.0000e-03 eta: 0:58:01 time: 0.2212 data_time: 0.0075 memory: 7116 grad_norm: 8.1536 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.6699 loss: 1.6699 2022/09/04 00:48:24 - mmengine - INFO - Epoch(train) [38][720/1345] lr: 1.0000e-03 eta: 0:57:57 time: 0.2194 data_time: 0.0108 memory: 7116 grad_norm: 8.0297 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1019 loss: 1.1019 2022/09/04 00:48:29 - mmengine - INFO - Epoch(train) [38][740/1345] lr: 1.0000e-03 eta: 0:57:53 time: 0.2188 data_time: 0.0075 memory: 7116 grad_norm: 8.1562 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3220 loss: 1.3220 2022/09/04 00:48:33 - mmengine - INFO - Epoch(train) [38][760/1345] lr: 1.0000e-03 eta: 0:57:49 time: 0.2137 data_time: 0.0079 memory: 7116 grad_norm: 8.1583 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0713 loss: 1.0713 2022/09/04 00:48:37 - mmengine - INFO - Epoch(train) [38][780/1345] lr: 1.0000e-03 eta: 0:57:45 time: 0.2189 data_time: 0.0105 memory: 7116 grad_norm: 8.0815 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.3466 loss: 1.3466 2022/09/04 00:48:42 - mmengine - INFO - Epoch(train) [38][800/1345] lr: 1.0000e-03 eta: 0:57:41 time: 0.2166 data_time: 0.0078 memory: 7116 grad_norm: 8.4404 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3619 loss: 1.3619 2022/09/04 00:48:46 - mmengine - INFO - Epoch(train) [38][820/1345] lr: 1.0000e-03 eta: 0:57:36 time: 0.2163 data_time: 0.0080 memory: 7116 grad_norm: 8.3050 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3717 loss: 1.3717 2022/09/04 00:48:50 - mmengine - INFO - Epoch(train) [38][840/1345] lr: 1.0000e-03 eta: 0:57:32 time: 0.2185 data_time: 0.0102 memory: 7116 grad_norm: 8.4035 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4826 loss: 1.4826 2022/09/04 00:48:55 - mmengine - INFO - Epoch(train) [38][860/1345] lr: 1.0000e-03 eta: 0:57:28 time: 0.2181 data_time: 0.0077 memory: 7116 grad_norm: 8.2741 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.4996 loss: 1.4996 2022/09/04 00:48:59 - mmengine - INFO - Epoch(train) [38][880/1345] lr: 1.0000e-03 eta: 0:57:24 time: 0.2237 data_time: 0.0085 memory: 7116 grad_norm: 8.0876 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1321 loss: 1.1321 2022/09/04 00:49:04 - mmengine - INFO - Epoch(train) [38][900/1345] lr: 1.0000e-03 eta: 0:57:20 time: 0.2163 data_time: 0.0101 memory: 7116 grad_norm: 8.3400 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2854 loss: 1.2854 2022/09/04 00:49:08 - mmengine - INFO - Epoch(train) [38][920/1345] lr: 1.0000e-03 eta: 0:57:16 time: 0.2181 data_time: 0.0074 memory: 7116 grad_norm: 8.3279 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3869 loss: 1.3869 2022/09/04 00:49:12 - mmengine - INFO - Epoch(train) [38][940/1345] lr: 1.0000e-03 eta: 0:57:12 time: 0.2166 data_time: 0.0074 memory: 7116 grad_norm: 8.4960 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2979 loss: 1.2979 2022/09/04 00:49:17 - mmengine - INFO - Epoch(train) [38][960/1345] lr: 1.0000e-03 eta: 0:57:08 time: 0.2097 data_time: 0.0109 memory: 7116 grad_norm: 8.3137 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4171 loss: 1.4171 2022/09/04 00:49:21 - mmengine - INFO - Epoch(train) [38][980/1345] lr: 1.0000e-03 eta: 0:57:04 time: 0.2252 data_time: 0.0083 memory: 7116 grad_norm: 8.3912 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4527 loss: 1.4527 2022/09/04 00:49:25 - mmengine - INFO - Epoch(train) [38][1000/1345] lr: 1.0000e-03 eta: 0:57:00 time: 0.2156 data_time: 0.0080 memory: 7116 grad_norm: 8.3129 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.4188 loss: 1.4188 2022/09/04 00:49:30 - mmengine - INFO - Epoch(train) [38][1020/1345] lr: 1.0000e-03 eta: 0:56:56 time: 0.2164 data_time: 0.0095 memory: 7116 grad_norm: 8.6715 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4289 loss: 1.4289 2022/09/04 00:49:34 - mmengine - INFO - Epoch(train) [38][1040/1345] lr: 1.0000e-03 eta: 0:56:52 time: 0.2157 data_time: 0.0076 memory: 7116 grad_norm: 8.5477 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7126 loss: 1.7126 2022/09/04 00:49:38 - mmengine - INFO - Epoch(train) [38][1060/1345] lr: 1.0000e-03 eta: 0:56:47 time: 0.2202 data_time: 0.0088 memory: 7116 grad_norm: 8.5596 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2265 loss: 1.2265 2022/09/04 00:49:43 - mmengine - INFO - Epoch(train) [38][1080/1345] lr: 1.0000e-03 eta: 0:56:43 time: 0.2105 data_time: 0.0114 memory: 7116 grad_norm: 8.9047 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5936 loss: 1.5936 2022/09/04 00:49:47 - mmengine - INFO - Epoch(train) [38][1100/1345] lr: 1.0000e-03 eta: 0:56:39 time: 0.2181 data_time: 0.0071 memory: 7116 grad_norm: 8.5628 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3230 loss: 1.3230 2022/09/04 00:49:51 - mmengine - INFO - Epoch(train) [38][1120/1345] lr: 1.0000e-03 eta: 0:56:35 time: 0.2187 data_time: 0.0078 memory: 7116 grad_norm: 8.1882 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3196 loss: 1.3196 2022/09/04 00:49:56 - mmengine - INFO - Epoch(train) [38][1140/1345] lr: 1.0000e-03 eta: 0:56:31 time: 0.2194 data_time: 0.0099 memory: 7116 grad_norm: 8.3593 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4256 loss: 1.4256 2022/09/04 00:50:00 - mmengine - INFO - Epoch(train) [38][1160/1345] lr: 1.0000e-03 eta: 0:56:27 time: 0.2205 data_time: 0.0084 memory: 7116 grad_norm: 8.7023 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2451 loss: 1.2451 2022/09/04 00:50:04 - mmengine - INFO - Epoch(train) [38][1180/1345] lr: 1.0000e-03 eta: 0:56:23 time: 0.2167 data_time: 0.0076 memory: 7116 grad_norm: 8.4255 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.3771 loss: 1.3771 2022/09/04 00:50:09 - mmengine - INFO - Epoch(train) [38][1200/1345] lr: 1.0000e-03 eta: 0:56:19 time: 0.2125 data_time: 0.0110 memory: 7116 grad_norm: 8.8347 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3448 loss: 1.3448 2022/09/04 00:50:13 - mmengine - INFO - Epoch(train) [38][1220/1345] lr: 1.0000e-03 eta: 0:56:15 time: 0.2192 data_time: 0.0083 memory: 7116 grad_norm: 8.4521 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3296 loss: 1.3296 2022/09/04 00:50:16 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:50:17 - mmengine - INFO - Epoch(train) [38][1240/1345] lr: 1.0000e-03 eta: 0:56:11 time: 0.2153 data_time: 0.0080 memory: 7116 grad_norm: 8.1130 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2499 loss: 1.2499 2022/09/04 00:50:22 - mmengine - INFO - Epoch(train) [38][1260/1345] lr: 1.0000e-03 eta: 0:56:07 time: 0.2167 data_time: 0.0100 memory: 7116 grad_norm: 8.4877 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2887 loss: 1.2887 2022/09/04 00:50:26 - mmengine - INFO - Epoch(train) [38][1280/1345] lr: 1.0000e-03 eta: 0:56:02 time: 0.2146 data_time: 0.0082 memory: 7116 grad_norm: 8.4542 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5735 loss: 1.5735 2022/09/04 00:50:30 - mmengine - INFO - Epoch(train) [38][1300/1345] lr: 1.0000e-03 eta: 0:55:58 time: 0.2176 data_time: 0.0090 memory: 7116 grad_norm: 8.4449 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3204 loss: 1.3204 2022/09/04 00:50:35 - mmengine - INFO - Epoch(train) [38][1320/1345] lr: 1.0000e-03 eta: 0:55:54 time: 0.2116 data_time: 0.0107 memory: 7116 grad_norm: 8.6799 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3245 loss: 1.3245 2022/09/04 00:50:39 - mmengine - INFO - Epoch(train) [38][1340/1345] lr: 1.0000e-03 eta: 0:55:50 time: 0.2162 data_time: 0.0080 memory: 7116 grad_norm: 8.5173 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3350 loss: 1.3350 2022/09/04 00:50:40 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:50:40 - mmengine - INFO - Epoch(train) [38][1345/1345] lr: 1.0000e-03 eta: 0:55:50 time: 0.2100 data_time: 0.0077 memory: 7116 grad_norm: 8.9482 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.4100 loss: 1.4100 2022/09/04 00:50:40 - mmengine - INFO - Saving checkpoint at 38 epochs 2022/09/04 00:50:45 - mmengine - INFO - Epoch(val) [38][20/181] eta: 0:00:07 time: 0.0489 data_time: 0.0117 memory: 1114 2022/09/04 00:50:46 - mmengine - INFO - Epoch(val) [38][40/181] eta: 0:00:05 time: 0.0412 data_time: 0.0061 memory: 1114 2022/09/04 00:50:47 - mmengine - INFO - Epoch(val) [38][60/181] eta: 0:00:05 time: 0.0421 data_time: 0.0064 memory: 1114 2022/09/04 00:50:48 - mmengine - INFO - Epoch(val) [38][80/181] eta: 0:00:04 time: 0.0405 data_time: 0.0057 memory: 1114 2022/09/04 00:50:49 - mmengine - INFO - Epoch(val) [38][100/181] eta: 0:00:03 time: 0.0439 data_time: 0.0081 memory: 1114 2022/09/04 00:50:49 - mmengine - INFO - Epoch(val) [38][120/181] eta: 0:00:02 time: 0.0436 data_time: 0.0092 memory: 1114 2022/09/04 00:50:50 - mmengine - INFO - Epoch(val) [38][140/181] eta: 0:00:01 time: 0.0404 data_time: 0.0056 memory: 1114 2022/09/04 00:50:51 - mmengine - INFO - Epoch(val) [38][160/181] eta: 0:00:00 time: 0.0402 data_time: 0.0055 memory: 1114 2022/09/04 00:50:52 - mmengine - INFO - Epoch(val) [38][180/181] eta: 0:00:00 time: 0.0397 data_time: 0.0052 memory: 1114 2022/09/04 00:50:53 - mmengine - INFO - Epoch(val) [38][181/181] acc/top1: 0.4583 acc/top5: 0.7494 acc/mean1: 0.4184 2022/09/04 00:50:53 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_37.pth is removed 2022/09/04 00:50:54 - mmengine - INFO - The best checkpoint with 0.4583 acc/top1 at 38 epoch is saved to best_acc/top1_epoch_38.pth. 2022/09/04 00:50:58 - mmengine - INFO - Epoch(train) [39][20/1345] lr: 1.0000e-03 eta: 0:55:45 time: 0.2142 data_time: 0.0101 memory: 7116 grad_norm: 8.2898 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2163 loss: 1.2163 2022/09/04 00:51:02 - mmengine - INFO - Epoch(train) [39][40/1345] lr: 1.0000e-03 eta: 0:55:41 time: 0.2139 data_time: 0.0080 memory: 7116 grad_norm: 8.2751 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2961 loss: 1.2961 2022/09/04 00:51:07 - mmengine - INFO - Epoch(train) [39][60/1345] lr: 1.0000e-03 eta: 0:55:37 time: 0.2214 data_time: 0.0071 memory: 7116 grad_norm: 8.3841 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.6294 loss: 1.6294 2022/09/04 00:51:11 - mmengine - INFO - Epoch(train) [39][80/1345] lr: 1.0000e-03 eta: 0:55:32 time: 0.2169 data_time: 0.0119 memory: 7116 grad_norm: 8.2451 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4124 loss: 1.4124 2022/09/04 00:51:15 - mmengine - INFO - Epoch(train) [39][100/1345] lr: 1.0000e-03 eta: 0:55:28 time: 0.2131 data_time: 0.0075 memory: 7116 grad_norm: 8.3613 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2130 loss: 1.2130 2022/09/04 00:51:20 - mmengine - INFO - Epoch(train) [39][120/1345] lr: 1.0000e-03 eta: 0:55:24 time: 0.2158 data_time: 0.0086 memory: 7116 grad_norm: 8.1261 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0197 loss: 1.0197 2022/09/04 00:51:24 - mmengine - INFO - Epoch(train) [39][140/1345] lr: 1.0000e-03 eta: 0:55:20 time: 0.2127 data_time: 0.0109 memory: 7116 grad_norm: 8.4595 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4638 loss: 1.4638 2022/09/04 00:51:28 - mmengine - INFO - Epoch(train) [39][160/1345] lr: 1.0000e-03 eta: 0:55:16 time: 0.2174 data_time: 0.0078 memory: 7116 grad_norm: 8.2522 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2995 loss: 1.2995 2022/09/04 00:51:33 - mmengine - INFO - Epoch(train) [39][180/1345] lr: 1.0000e-03 eta: 0:55:12 time: 0.2161 data_time: 0.0074 memory: 7116 grad_norm: 8.6404 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.5146 loss: 1.5146 2022/09/04 00:51:37 - mmengine - INFO - Epoch(train) [39][200/1345] lr: 1.0000e-03 eta: 0:55:08 time: 0.2154 data_time: 0.0105 memory: 7116 grad_norm: 8.8028 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.5024 loss: 1.5024 2022/09/04 00:51:41 - mmengine - INFO - Epoch(train) [39][220/1345] lr: 1.0000e-03 eta: 0:55:04 time: 0.2159 data_time: 0.0077 memory: 7116 grad_norm: 8.4244 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3120 loss: 1.3120 2022/09/04 00:51:46 - mmengine - INFO - Epoch(train) [39][240/1345] lr: 1.0000e-03 eta: 0:55:00 time: 0.2184 data_time: 0.0072 memory: 7116 grad_norm: 8.4117 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4449 loss: 1.4449 2022/09/04 00:51:50 - mmengine - INFO - Epoch(train) [39][260/1345] lr: 1.0000e-03 eta: 0:54:56 time: 0.2184 data_time: 0.0103 memory: 7116 grad_norm: 8.3255 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2286 loss: 1.2286 2022/09/04 00:51:54 - mmengine - INFO - Epoch(train) [39][280/1345] lr: 1.0000e-03 eta: 0:54:51 time: 0.2188 data_time: 0.0078 memory: 7116 grad_norm: 8.4728 top1_acc: 0.3750 top5_acc: 1.0000 loss_cls: 1.4574 loss: 1.4574 2022/09/04 00:51:59 - mmengine - INFO - Epoch(train) [39][300/1345] lr: 1.0000e-03 eta: 0:54:47 time: 0.2198 data_time: 0.0089 memory: 7116 grad_norm: 8.7082 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3644 loss: 1.3644 2022/09/04 00:52:03 - mmengine - INFO - Epoch(train) [39][320/1345] lr: 1.0000e-03 eta: 0:54:43 time: 0.2152 data_time: 0.0100 memory: 7116 grad_norm: 8.4562 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4327 loss: 1.4327 2022/09/04 00:52:08 - mmengine - INFO - Epoch(train) [39][340/1345] lr: 1.0000e-03 eta: 0:54:39 time: 0.2183 data_time: 0.0078 memory: 7116 grad_norm: 8.0534 top1_acc: 0.3750 top5_acc: 1.0000 loss_cls: 1.1392 loss: 1.1392 2022/09/04 00:52:12 - mmengine - INFO - Epoch(train) [39][360/1345] lr: 1.0000e-03 eta: 0:54:35 time: 0.2179 data_time: 0.0077 memory: 7116 grad_norm: 8.6694 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.3105 loss: 1.3105 2022/09/04 00:52:16 - mmengine - INFO - Epoch(train) [39][380/1345] lr: 1.0000e-03 eta: 0:54:31 time: 0.2180 data_time: 0.0101 memory: 7116 grad_norm: 8.1054 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4139 loss: 1.4139 2022/09/04 00:52:21 - mmengine - INFO - Epoch(train) [39][400/1345] lr: 1.0000e-03 eta: 0:54:27 time: 0.2162 data_time: 0.0077 memory: 7116 grad_norm: 8.3578 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.4395 loss: 1.4395 2022/09/04 00:52:25 - mmengine - INFO - Epoch(train) [39][420/1345] lr: 1.0000e-03 eta: 0:54:23 time: 0.2235 data_time: 0.0077 memory: 7116 grad_norm: 8.4798 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3498 loss: 1.3498 2022/09/04 00:52:29 - mmengine - INFO - Epoch(train) [39][440/1345] lr: 1.0000e-03 eta: 0:54:19 time: 0.2209 data_time: 0.0097 memory: 7116 grad_norm: 9.0217 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.3877 loss: 1.3877 2022/09/04 00:52:34 - mmengine - INFO - Epoch(train) [39][460/1345] lr: 1.0000e-03 eta: 0:54:15 time: 0.2165 data_time: 0.0075 memory: 7116 grad_norm: 8.7358 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0636 loss: 1.0636 2022/09/04 00:52:38 - mmengine - INFO - Epoch(train) [39][480/1345] lr: 1.0000e-03 eta: 0:54:11 time: 0.2196 data_time: 0.0074 memory: 7116 grad_norm: 8.6673 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5654 loss: 1.5654 2022/09/04 00:52:43 - mmengine - INFO - Epoch(train) [39][500/1345] lr: 1.0000e-03 eta: 0:54:07 time: 0.2195 data_time: 0.0107 memory: 7116 grad_norm: 8.4853 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2589 loss: 1.2589 2022/09/04 00:52:47 - mmengine - INFO - Epoch(train) [39][520/1345] lr: 1.0000e-03 eta: 0:54:02 time: 0.2171 data_time: 0.0077 memory: 7116 grad_norm: 8.1638 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4827 loss: 1.4827 2022/09/04 00:52:51 - mmengine - INFO - Epoch(train) [39][540/1345] lr: 1.0000e-03 eta: 0:53:58 time: 0.2199 data_time: 0.0083 memory: 7116 grad_norm: 8.5107 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4135 loss: 1.4135 2022/09/04 00:52:56 - mmengine - INFO - Epoch(train) [39][560/1345] lr: 1.0000e-03 eta: 0:53:54 time: 0.2173 data_time: 0.0097 memory: 7116 grad_norm: 8.5823 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.4638 loss: 1.4638 2022/09/04 00:53:00 - mmengine - INFO - Epoch(train) [39][580/1345] lr: 1.0000e-03 eta: 0:53:50 time: 0.2250 data_time: 0.0076 memory: 7116 grad_norm: 8.6497 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.3270 loss: 1.3270 2022/09/04 00:53:05 - mmengine - INFO - Epoch(train) [39][600/1345] lr: 1.0000e-03 eta: 0:53:46 time: 0.2149 data_time: 0.0080 memory: 7116 grad_norm: 8.6628 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0615 loss: 1.0615 2022/09/04 00:53:09 - mmengine - INFO - Epoch(train) [39][620/1345] lr: 1.0000e-03 eta: 0:53:42 time: 0.2196 data_time: 0.0097 memory: 7116 grad_norm: 8.7841 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5945 loss: 1.5945 2022/09/04 00:53:13 - mmengine - INFO - Epoch(train) [39][640/1345] lr: 1.0000e-03 eta: 0:53:38 time: 0.2194 data_time: 0.0080 memory: 7116 grad_norm: 8.8291 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5198 loss: 1.5198 2022/09/04 00:53:18 - mmengine - INFO - Epoch(train) [39][660/1345] lr: 1.0000e-03 eta: 0:53:34 time: 0.2211 data_time: 0.0076 memory: 7116 grad_norm: 8.6496 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4147 loss: 1.4147 2022/09/04 00:53:22 - mmengine - INFO - Epoch(train) [39][680/1345] lr: 1.0000e-03 eta: 0:53:30 time: 0.2208 data_time: 0.0095 memory: 7116 grad_norm: 8.8581 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2929 loss: 1.2929 2022/09/04 00:53:27 - mmengine - INFO - Epoch(train) [39][700/1345] lr: 1.0000e-03 eta: 0:53:26 time: 0.2234 data_time: 0.0076 memory: 7116 grad_norm: 8.6129 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2539 loss: 1.2539 2022/09/04 00:53:31 - mmengine - INFO - Epoch(train) [39][720/1345] lr: 1.0000e-03 eta: 0:53:22 time: 0.2353 data_time: 0.0084 memory: 7116 grad_norm: 8.4746 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3143 loss: 1.3143 2022/09/04 00:53:36 - mmengine - INFO - Epoch(train) [39][740/1345] lr: 1.0000e-03 eta: 0:53:18 time: 0.2185 data_time: 0.0102 memory: 7116 grad_norm: 8.6012 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4287 loss: 1.4287 2022/09/04 00:53:40 - mmengine - INFO - Epoch(train) [39][760/1345] lr: 1.0000e-03 eta: 0:53:14 time: 0.2175 data_time: 0.0072 memory: 7116 grad_norm: 8.7820 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4280 loss: 1.4280 2022/09/04 00:53:45 - mmengine - INFO - Epoch(train) [39][780/1345] lr: 1.0000e-03 eta: 0:53:10 time: 0.2221 data_time: 0.0072 memory: 7116 grad_norm: 8.6183 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2837 loss: 1.2837 2022/09/04 00:53:49 - mmengine - INFO - Epoch(train) [39][800/1345] lr: 1.0000e-03 eta: 0:53:05 time: 0.2249 data_time: 0.0111 memory: 7116 grad_norm: 8.4630 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6457 loss: 1.6457 2022/09/04 00:53:53 - mmengine - INFO - Epoch(train) [39][820/1345] lr: 1.0000e-03 eta: 0:53:01 time: 0.2173 data_time: 0.0078 memory: 7116 grad_norm: 8.4570 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3977 loss: 1.3977 2022/09/04 00:53:58 - mmengine - INFO - Epoch(train) [39][840/1345] lr: 1.0000e-03 eta: 0:52:57 time: 0.2130 data_time: 0.0079 memory: 7116 grad_norm: 8.5648 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.5885 loss: 1.5885 2022/09/04 00:54:02 - mmengine - INFO - Epoch(train) [39][860/1345] lr: 1.0000e-03 eta: 0:52:53 time: 0.2117 data_time: 0.0108 memory: 7116 grad_norm: 8.8010 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1639 loss: 1.1639 2022/09/04 00:54:06 - mmengine - INFO - Epoch(train) [39][880/1345] lr: 1.0000e-03 eta: 0:52:49 time: 0.2129 data_time: 0.0086 memory: 7116 grad_norm: 8.7014 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4789 loss: 1.4789 2022/09/04 00:54:08 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:54:10 - mmengine - INFO - Epoch(train) [39][900/1345] lr: 1.0000e-03 eta: 0:52:45 time: 0.2170 data_time: 0.0083 memory: 7116 grad_norm: 8.6474 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6119 loss: 1.6119 2022/09/04 00:54:15 - mmengine - INFO - Epoch(train) [39][920/1345] lr: 1.0000e-03 eta: 0:52:41 time: 0.2136 data_time: 0.0102 memory: 7116 grad_norm: 8.4257 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.4205 loss: 1.4205 2022/09/04 00:54:19 - mmengine - INFO - Epoch(train) [39][940/1345] lr: 1.0000e-03 eta: 0:52:37 time: 0.2160 data_time: 0.0090 memory: 7116 grad_norm: 8.8789 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2584 loss: 1.2584 2022/09/04 00:54:23 - mmengine - INFO - Epoch(train) [39][960/1345] lr: 1.0000e-03 eta: 0:52:33 time: 0.2110 data_time: 0.0081 memory: 7116 grad_norm: 8.9204 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5058 loss: 1.5058 2022/09/04 00:54:28 - mmengine - INFO - Epoch(train) [39][980/1345] lr: 1.0000e-03 eta: 0:52:28 time: 0.2219 data_time: 0.0113 memory: 7116 grad_norm: 8.8705 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3335 loss: 1.3335 2022/09/04 00:54:32 - mmengine - INFO - Epoch(train) [39][1000/1345] lr: 1.0000e-03 eta: 0:52:24 time: 0.2143 data_time: 0.0083 memory: 7116 grad_norm: 8.5823 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.2434 loss: 1.2434 2022/09/04 00:54:36 - mmengine - INFO - Epoch(train) [39][1020/1345] lr: 1.0000e-03 eta: 0:52:20 time: 0.2139 data_time: 0.0081 memory: 7116 grad_norm: 8.3138 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2783 loss: 1.2783 2022/09/04 00:54:41 - mmengine - INFO - Epoch(train) [39][1040/1345] lr: 1.0000e-03 eta: 0:52:16 time: 0.2180 data_time: 0.0102 memory: 7116 grad_norm: 8.7057 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4124 loss: 1.4124 2022/09/04 00:54:45 - mmengine - INFO - Epoch(train) [39][1060/1345] lr: 1.0000e-03 eta: 0:52:12 time: 0.2168 data_time: 0.0086 memory: 7116 grad_norm: 8.9213 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5332 loss: 1.5332 2022/09/04 00:54:49 - mmengine - INFO - Epoch(train) [39][1080/1345] lr: 1.0000e-03 eta: 0:52:08 time: 0.2190 data_time: 0.0092 memory: 7116 grad_norm: 8.7777 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.2196 loss: 1.2196 2022/09/04 00:54:54 - mmengine - INFO - Epoch(train) [39][1100/1345] lr: 1.0000e-03 eta: 0:52:04 time: 0.2154 data_time: 0.0098 memory: 7116 grad_norm: 8.9295 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4933 loss: 1.4933 2022/09/04 00:54:58 - mmengine - INFO - Epoch(train) [39][1120/1345] lr: 1.0000e-03 eta: 0:52:00 time: 0.2179 data_time: 0.0086 memory: 7116 grad_norm: 8.7281 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2667 loss: 1.2667 2022/09/04 00:55:02 - mmengine - INFO - Epoch(train) [39][1140/1345] lr: 1.0000e-03 eta: 0:51:56 time: 0.2132 data_time: 0.0078 memory: 7116 grad_norm: 8.8893 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4237 loss: 1.4237 2022/09/04 00:55:07 - mmengine - INFO - Epoch(train) [39][1160/1345] lr: 1.0000e-03 eta: 0:51:51 time: 0.2150 data_time: 0.0105 memory: 7116 grad_norm: 8.3837 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.2687 loss: 1.2687 2022/09/04 00:55:11 - mmengine - INFO - Epoch(train) [39][1180/1345] lr: 1.0000e-03 eta: 0:51:47 time: 0.2228 data_time: 0.0092 memory: 7116 grad_norm: 8.6864 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3081 loss: 1.3081 2022/09/04 00:55:15 - mmengine - INFO - Epoch(train) [39][1200/1345] lr: 1.0000e-03 eta: 0:51:43 time: 0.2164 data_time: 0.0090 memory: 7116 grad_norm: 9.0066 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3700 loss: 1.3700 2022/09/04 00:55:20 - mmengine - INFO - Epoch(train) [39][1220/1345] lr: 1.0000e-03 eta: 0:51:39 time: 0.2145 data_time: 0.0104 memory: 7116 grad_norm: 8.9197 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3401 loss: 1.3401 2022/09/04 00:55:24 - mmengine - INFO - Epoch(train) [39][1240/1345] lr: 1.0000e-03 eta: 0:51:35 time: 0.2169 data_time: 0.0079 memory: 7116 grad_norm: 8.3773 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2700 loss: 1.2700 2022/09/04 00:55:29 - mmengine - INFO - Epoch(train) [39][1260/1345] lr: 1.0000e-03 eta: 0:51:31 time: 0.2322 data_time: 0.0082 memory: 7116 grad_norm: 8.9083 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2908 loss: 1.2908 2022/09/04 00:55:33 - mmengine - INFO - Epoch(train) [39][1280/1345] lr: 1.0000e-03 eta: 0:51:27 time: 0.2167 data_time: 0.0103 memory: 7116 grad_norm: 8.9424 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5885 loss: 1.5885 2022/09/04 00:55:38 - mmengine - INFO - Epoch(train) [39][1300/1345] lr: 1.0000e-03 eta: 0:51:23 time: 0.2260 data_time: 0.0070 memory: 7116 grad_norm: 8.6439 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1716 loss: 1.1716 2022/09/04 00:55:42 - mmengine - INFO - Epoch(train) [39][1320/1345] lr: 1.0000e-03 eta: 0:51:19 time: 0.2315 data_time: 0.0086 memory: 7116 grad_norm: 8.9941 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.4364 loss: 1.4364 2022/09/04 00:55:47 - mmengine - INFO - Epoch(train) [39][1340/1345] lr: 1.0000e-03 eta: 0:51:15 time: 0.2253 data_time: 0.0095 memory: 7116 grad_norm: 9.0029 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3723 loss: 1.3723 2022/09/04 00:55:48 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:55:48 - mmengine - INFO - Epoch(train) [39][1345/1345] lr: 1.0000e-03 eta: 0:51:15 time: 0.2178 data_time: 0.0072 memory: 7116 grad_norm: 9.2983 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.5357 loss: 1.5357 2022/09/04 00:55:48 - mmengine - INFO - Saving checkpoint at 39 epochs 2022/09/04 00:55:50 - mmengine - INFO - Epoch(val) [39][20/181] eta: 0:00:07 time: 0.0436 data_time: 0.0090 memory: 1114 2022/09/04 00:55:51 - mmengine - INFO - Epoch(val) [39][40/181] eta: 0:00:05 time: 0.0400 data_time: 0.0054 memory: 1114 2022/09/04 00:55:52 - mmengine - INFO - Epoch(val) [39][60/181] eta: 0:00:04 time: 0.0400 data_time: 0.0055 memory: 1114 2022/09/04 00:55:52 - mmengine - INFO - Epoch(val) [39][80/181] eta: 0:00:04 time: 0.0399 data_time: 0.0053 memory: 1114 2022/09/04 00:55:53 - mmengine - INFO - Epoch(val) [39][100/181] eta: 0:00:03 time: 0.0396 data_time: 0.0052 memory: 1114 2022/09/04 00:55:54 - mmengine - INFO - Epoch(val) [39][120/181] eta: 0:00:02 time: 0.0399 data_time: 0.0053 memory: 1114 2022/09/04 00:55:55 - mmengine - INFO - Epoch(val) [39][140/181] eta: 0:00:01 time: 0.0396 data_time: 0.0053 memory: 1114 2022/09/04 00:55:56 - mmengine - INFO - Epoch(val) [39][160/181] eta: 0:00:00 time: 0.0398 data_time: 0.0052 memory: 1114 2022/09/04 00:55:56 - mmengine - INFO - Epoch(val) [39][180/181] eta: 0:00:00 time: 0.0397 data_time: 0.0052 memory: 1114 2022/09/04 00:56:00 - mmengine - INFO - Epoch(val) [39][181/181] acc/top1: 0.4462 acc/top5: 0.7412 acc/mean1: 0.4092 2022/09/04 00:56:06 - mmengine - INFO - Epoch(train) [40][20/1345] lr: 1.0000e-03 eta: 0:51:10 time: 0.2560 data_time: 0.0238 memory: 7116 grad_norm: 8.4287 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2635 loss: 1.2635 2022/09/04 00:56:10 - mmengine - INFO - Epoch(train) [40][40/1345] lr: 1.0000e-03 eta: 0:51:06 time: 0.2220 data_time: 0.0073 memory: 7116 grad_norm: 8.6023 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3788 loss: 1.3788 2022/09/04 00:56:14 - mmengine - INFO - Epoch(train) [40][60/1345] lr: 1.0000e-03 eta: 0:51:01 time: 0.2226 data_time: 0.0071 memory: 7116 grad_norm: 8.7856 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2358 loss: 1.2358 2022/09/04 00:56:19 - mmengine - INFO - Epoch(train) [40][80/1345] lr: 1.0000e-03 eta: 0:50:57 time: 0.2262 data_time: 0.0106 memory: 7116 grad_norm: 8.8618 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4215 loss: 1.4215 2022/09/04 00:56:23 - mmengine - INFO - Epoch(train) [40][100/1345] lr: 1.0000e-03 eta: 0:50:53 time: 0.2229 data_time: 0.0075 memory: 7116 grad_norm: 8.6762 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2744 loss: 1.2744 2022/09/04 00:56:28 - mmengine - INFO - Epoch(train) [40][120/1345] lr: 1.0000e-03 eta: 0:50:49 time: 0.2225 data_time: 0.0075 memory: 7116 grad_norm: 8.9960 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4850 loss: 1.4850 2022/09/04 00:56:32 - mmengine - INFO - Epoch(train) [40][140/1345] lr: 1.0000e-03 eta: 0:50:45 time: 0.2223 data_time: 0.0093 memory: 7116 grad_norm: 8.5958 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2561 loss: 1.2561 2022/09/04 00:56:37 - mmengine - INFO - Epoch(train) [40][160/1345] lr: 1.0000e-03 eta: 0:50:41 time: 0.2196 data_time: 0.0070 memory: 7116 grad_norm: 8.9161 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.4597 loss: 1.4597 2022/09/04 00:56:41 - mmengine - INFO - Epoch(train) [40][180/1345] lr: 1.0000e-03 eta: 0:50:37 time: 0.2279 data_time: 0.0074 memory: 7116 grad_norm: 8.8503 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3017 loss: 1.3017 2022/09/04 00:56:46 - mmengine - INFO - Epoch(train) [40][200/1345] lr: 1.0000e-03 eta: 0:50:33 time: 0.2226 data_time: 0.0095 memory: 7116 grad_norm: 8.3675 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3374 loss: 1.3374 2022/09/04 00:56:50 - mmengine - INFO - Epoch(train) [40][220/1345] lr: 1.0000e-03 eta: 0:50:29 time: 0.2227 data_time: 0.0080 memory: 7116 grad_norm: 8.6727 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.4426 loss: 1.4426 2022/09/04 00:56:55 - mmengine - INFO - Epoch(train) [40][240/1345] lr: 1.0000e-03 eta: 0:50:25 time: 0.2206 data_time: 0.0070 memory: 7116 grad_norm: 8.8905 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2838 loss: 1.2838 2022/09/04 00:56:59 - mmengine - INFO - Epoch(train) [40][260/1345] lr: 1.0000e-03 eta: 0:50:21 time: 0.2264 data_time: 0.0108 memory: 7116 grad_norm: 8.9056 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.6748 loss: 1.6748 2022/09/04 00:57:04 - mmengine - INFO - Epoch(train) [40][280/1345] lr: 1.0000e-03 eta: 0:50:17 time: 0.2162 data_time: 0.0076 memory: 7116 grad_norm: 8.8046 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1971 loss: 1.1971 2022/09/04 00:57:08 - mmengine - INFO - Epoch(train) [40][300/1345] lr: 1.0000e-03 eta: 0:50:13 time: 0.2191 data_time: 0.0072 memory: 7116 grad_norm: 8.6687 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2532 loss: 1.2532 2022/09/04 00:57:12 - mmengine - INFO - Epoch(train) [40][320/1345] lr: 1.0000e-03 eta: 0:50:08 time: 0.2222 data_time: 0.0096 memory: 7116 grad_norm: 8.5885 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3160 loss: 1.3160 2022/09/04 00:57:17 - mmengine - INFO - Epoch(train) [40][340/1345] lr: 1.0000e-03 eta: 0:50:04 time: 0.2195 data_time: 0.0071 memory: 7116 grad_norm: 8.5398 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3953 loss: 1.3953 2022/09/04 00:57:21 - mmengine - INFO - Epoch(train) [40][360/1345] lr: 1.0000e-03 eta: 0:50:00 time: 0.2272 data_time: 0.0080 memory: 7116 grad_norm: 8.9959 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3365 loss: 1.3365 2022/09/04 00:57:26 - mmengine - INFO - Epoch(train) [40][380/1345] lr: 1.0000e-03 eta: 0:49:56 time: 0.2205 data_time: 0.0096 memory: 7116 grad_norm: 9.0027 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.3224 loss: 1.3224 2022/09/04 00:57:30 - mmengine - INFO - Epoch(train) [40][400/1345] lr: 1.0000e-03 eta: 0:49:52 time: 0.2218 data_time: 0.0074 memory: 7116 grad_norm: 8.9215 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6217 loss: 1.6217 2022/09/04 00:57:35 - mmengine - INFO - Epoch(train) [40][420/1345] lr: 1.0000e-03 eta: 0:49:48 time: 0.2200 data_time: 0.0074 memory: 7116 grad_norm: 8.7523 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5229 loss: 1.5229 2022/09/04 00:57:39 - mmengine - INFO - Epoch(train) [40][440/1345] lr: 1.0000e-03 eta: 0:49:44 time: 0.2387 data_time: 0.0117 memory: 7116 grad_norm: 8.9840 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.5902 loss: 1.5902 2022/09/04 00:57:44 - mmengine - INFO - Epoch(train) [40][460/1345] lr: 1.0000e-03 eta: 0:49:40 time: 0.2210 data_time: 0.0070 memory: 7116 grad_norm: 8.4640 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3366 loss: 1.3366 2022/09/04 00:57:48 - mmengine - INFO - Epoch(train) [40][480/1345] lr: 1.0000e-03 eta: 0:49:36 time: 0.2219 data_time: 0.0079 memory: 7116 grad_norm: 8.6879 top1_acc: 0.3750 top5_acc: 1.0000 loss_cls: 1.3212 loss: 1.3212 2022/09/04 00:57:53 - mmengine - INFO - Epoch(train) [40][500/1345] lr: 1.0000e-03 eta: 0:49:32 time: 0.2212 data_time: 0.0097 memory: 7116 grad_norm: 8.6738 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0504 loss: 1.0504 2022/09/04 00:57:57 - mmengine - INFO - Epoch(train) [40][520/1345] lr: 1.0000e-03 eta: 0:49:28 time: 0.2204 data_time: 0.0074 memory: 7116 grad_norm: 8.9587 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3405 loss: 1.3405 2022/09/04 00:58:01 - mmengine - INFO - Epoch(train) [40][540/1345] lr: 1.0000e-03 eta: 0:49:24 time: 0.2223 data_time: 0.0076 memory: 7116 grad_norm: 8.6261 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1515 loss: 1.1515 2022/09/04 00:58:03 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 00:58:06 - mmengine - INFO - Epoch(train) [40][560/1345] lr: 1.0000e-03 eta: 0:49:20 time: 0.2173 data_time: 0.0094 memory: 7116 grad_norm: 8.8656 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1986 loss: 1.1986 2022/09/04 00:58:10 - mmengine - INFO - Epoch(train) [40][580/1345] lr: 1.0000e-03 eta: 0:49:15 time: 0.2224 data_time: 0.0082 memory: 7116 grad_norm: 8.3085 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1230 loss: 1.1230 2022/09/04 00:58:15 - mmengine - INFO - Epoch(train) [40][600/1345] lr: 1.0000e-03 eta: 0:49:11 time: 0.2169 data_time: 0.0071 memory: 7116 grad_norm: 8.3902 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2353 loss: 1.2353 2022/09/04 00:58:19 - mmengine - INFO - Epoch(train) [40][620/1345] lr: 1.0000e-03 eta: 0:49:07 time: 0.2231 data_time: 0.0104 memory: 7116 grad_norm: 8.9626 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6619 loss: 1.6619 2022/09/04 00:58:24 - mmengine - INFO - Epoch(train) [40][640/1345] lr: 1.0000e-03 eta: 0:49:03 time: 0.2209 data_time: 0.0073 memory: 7116 grad_norm: 9.0847 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2057 loss: 1.2057 2022/09/04 00:58:28 - mmengine - INFO - Epoch(train) [40][660/1345] lr: 1.0000e-03 eta: 0:48:59 time: 0.2252 data_time: 0.0081 memory: 7116 grad_norm: 8.6947 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3151 loss: 1.3151 2022/09/04 00:58:33 - mmengine - INFO - Epoch(train) [40][680/1345] lr: 1.0000e-03 eta: 0:48:55 time: 0.2281 data_time: 0.0133 memory: 7116 grad_norm: 9.0148 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.3905 loss: 1.3905 2022/09/04 00:58:37 - mmengine - INFO - Epoch(train) [40][700/1345] lr: 1.0000e-03 eta: 0:48:51 time: 0.2229 data_time: 0.0093 memory: 7116 grad_norm: 8.7483 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1401 loss: 1.1401 2022/09/04 00:58:42 - mmengine - INFO - Epoch(train) [40][720/1345] lr: 1.0000e-03 eta: 0:48:47 time: 0.2244 data_time: 0.0082 memory: 7116 grad_norm: 8.7248 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5570 loss: 1.5570 2022/09/04 00:58:46 - mmengine - INFO - Epoch(train) [40][740/1345] lr: 1.0000e-03 eta: 0:48:43 time: 0.2248 data_time: 0.0106 memory: 7116 grad_norm: 8.9275 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4424 loss: 1.4424 2022/09/04 00:58:51 - mmengine - INFO - Epoch(train) [40][760/1345] lr: 1.0000e-03 eta: 0:48:39 time: 0.2308 data_time: 0.0112 memory: 7116 grad_norm: 8.8817 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3326 loss: 1.3326 2022/09/04 00:58:55 - mmengine - INFO - Epoch(train) [40][780/1345] lr: 1.0000e-03 eta: 0:48:35 time: 0.2335 data_time: 0.0121 memory: 7116 grad_norm: 8.5575 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2276 loss: 1.2276 2022/09/04 01:10:31 - mmengine - INFO - Epoch(train) [40][800/1345] lr: 1.0000e-03 eta: 0:51:32 time: 34.7990 data_time: 0.2410 memory: 7116 grad_norm: 8.8162 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1397 loss: 1.1397 2022/09/04 01:10:38 - mmengine - INFO - Epoch(train) [40][820/1345] lr: 1.0000e-03 eta: 0:51:29 time: 0.3560 data_time: 0.0329 memory: 7116 grad_norm: 9.4437 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3424 loss: 1.3424 2022/09/04 04:50:27 - mmengine - INFO - Epoch(train) [40][840/1345] lr: 1.0000e-03 eta: 1:48:56 time: 659.3067 data_time: 15.3593 memory: 7116 grad_norm: 8.7620 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3888 loss: 1.3888 2022/09/04 04:57:47 - mmengine - INFO - Epoch(train) [40][860/1345] lr: 1.0000e-03 eta: 1:50:39 time: 21.9963 data_time: 0.0327 memory: 7116 grad_norm: 8.8541 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1749 loss: 1.1749 2022/09/04 04:58:10 - mmengine - INFO - Epoch(train) [40][880/1345] lr: 1.0000e-03 eta: 1:50:33 time: 1.1528 data_time: 0.0262 memory: 7116 grad_norm: 8.2873 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1853 loss: 1.1853 2022/09/04 04:58:26 - mmengine - INFO - Epoch(train) [40][900/1345] lr: 1.0000e-03 eta: 1:50:25 time: 0.8110 data_time: 0.1642 memory: 7116 grad_norm: 8.8321 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.2065 loss: 1.2065 2022/09/04 04:58:45 - mmengine - INFO - Epoch(train) [40][920/1345] lr: 1.0000e-03 eta: 1:50:18 time: 0.9071 data_time: 0.0148 memory: 7116 grad_norm: 8.8634 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3065 loss: 1.3065 2022/09/04 04:58:57 - mmengine - INFO - Epoch(train) [40][940/1345] lr: 1.0000e-03 eta: 1:50:09 time: 0.6374 data_time: 0.0564 memory: 7116 grad_norm: 9.0584 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5131 loss: 1.5131 2022/09/04 04:59:07 - mmengine - INFO - Epoch(train) [40][960/1345] lr: 1.0000e-03 eta: 1:50:00 time: 0.5053 data_time: 0.0202 memory: 7116 grad_norm: 8.8474 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2020 loss: 1.2020 2022/09/04 04:59:19 - mmengine - INFO - Epoch(train) [40][980/1345] lr: 1.0000e-03 eta: 1:49:51 time: 0.6203 data_time: 0.0370 memory: 7116 grad_norm: 9.0589 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3017 loss: 1.3017 2022/09/04 04:59:29 - mmengine - INFO - Epoch(train) [40][1000/1345] lr: 1.0000e-03 eta: 1:49:42 time: 0.5098 data_time: 0.0341 memory: 7116 grad_norm: 8.7619 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2889 loss: 1.2889 2022/09/04 04:59:56 - mmengine - INFO - Epoch(train) [40][1020/1345] lr: 1.0000e-03 eta: 1:49:36 time: 1.3093 data_time: 0.0160 memory: 7116 grad_norm: 8.8476 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1501 loss: 1.1501 2022/09/04 05:00:09 - mmengine - INFO - Epoch(train) [40][1040/1345] lr: 1.0000e-03 eta: 1:49:28 time: 0.6513 data_time: 0.0937 memory: 7116 grad_norm: 9.4073 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4981 loss: 1.4981 2022/09/04 05:00:18 - mmengine - INFO - Epoch(train) [40][1060/1345] lr: 1.0000e-03 eta: 1:49:18 time: 0.4533 data_time: 0.0188 memory: 7116 grad_norm: 8.9322 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2633 loss: 1.2633 2022/09/04 05:00:23 - mmengine - INFO - Epoch(train) [40][1080/1345] lr: 1.0000e-03 eta: 1:49:08 time: 0.2868 data_time: 0.0208 memory: 7116 grad_norm: 9.3274 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2587 loss: 1.2587 2022/09/04 05:00:29 - mmengine - INFO - Epoch(train) [40][1100/1345] lr: 1.0000e-03 eta: 1:48:57 time: 0.2977 data_time: 0.0173 memory: 7116 grad_norm: 9.4046 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.4051 loss: 1.4051 2022/09/04 05:01:23 - mmengine - INFO - Epoch(train) [40][1120/1345] lr: 1.0000e-03 eta: 1:48:59 time: 2.6924 data_time: 0.0311 memory: 7116 grad_norm: 8.9819 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3414 loss: 1.3414 2022/09/04 05:03:46 - mmengine - INFO - Epoch(train) [40][1140/1345] lr: 1.0000e-03 eta: 1:49:23 time: 7.1500 data_time: 0.0998 memory: 7116 grad_norm: 8.9278 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2676 loss: 1.2676 2022/09/04 05:05:14 - mmengine - INFO - Epoch(train) [40][1160/1345] lr: 1.0000e-03 eta: 1:49:34 time: 4.4060 data_time: 0.0191 memory: 7116 grad_norm: 8.9682 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 1.6068 loss: 1.6068 2022/09/04 05:06:09 - mmengine - INFO - Epoch(train) [40][1180/1345] lr: 1.0000e-03 eta: 1:49:35 time: 2.7238 data_time: 0.0549 memory: 7116 grad_norm: 9.0430 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4404 loss: 1.4404 2022/09/04 05:07:20 - mmengine - INFO - Epoch(train) [40][1200/1345] lr: 1.0000e-03 eta: 1:49:41 time: 3.5368 data_time: 0.0178 memory: 7116 grad_norm: 9.0760 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4795 loss: 1.4795 2022/09/04 05:10:14 - mmengine - INFO - Epoch(train) [40][1220/1345] lr: 1.0000e-03 eta: 1:50:13 time: 8.7018 data_time: 0.2929 memory: 7116 grad_norm: 8.9766 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5563 loss: 1.5563 2022/09/04 05:14:48 - mmengine - INFO - Epoch(train) [40][1240/1345] lr: 1.0000e-03 eta: 1:51:10 time: 13.7254 data_time: 0.0393 memory: 7116 grad_norm: 8.7052 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.4849 loss: 1.4849 2022/09/04 05:14:54 - mmengine - INFO - Epoch(train) [40][1260/1345] lr: 1.0000e-03 eta: 1:50:59 time: 0.2723 data_time: 0.0349 memory: 7116 grad_norm: 9.0156 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2092 loss: 1.2092 2022/09/04 05:14:57 - mmengine - INFO - Epoch(train) [40][1280/1345] lr: 1.0000e-03 eta: 1:50:48 time: 0.1834 data_time: 0.0111 memory: 7116 grad_norm: 8.9782 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3813 loss: 1.3813 2022/09/04 05:15:01 - mmengine - INFO - Epoch(train) [40][1300/1345] lr: 1.0000e-03 eta: 1:50:36 time: 0.1790 data_time: 0.0094 memory: 7116 grad_norm: 9.2700 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3260 loss: 1.3260 2022/09/04 05:29:40 - mmengine - INFO - Epoch(train) [40][1320/1345] lr: 1.0000e-03 eta: 1:54:04 time: 43.9611 data_time: 1.6310 memory: 7116 grad_norm: 9.1750 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3861 loss: 1.3861 2022/09/04 06:00:40 - mmengine - INFO - Epoch(train) [40][1340/1345] lr: 1.0000e-03 eta: 2:01:37 time: 92.9376 data_time: 1.3726 memory: 7116 grad_norm: 8.9776 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.3238 loss: 1.3238 2022/09/04 06:04:36 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 06:04:36 - mmengine - INFO - Epoch(train) [40][1345/1345] lr: 1.0000e-03 eta: 2:01:37 time: 74.9158 data_time: 1.3186 memory: 7116 grad_norm: 9.9864 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.6542 loss: 1.6542 2022/09/04 06:04:36 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/09/04 06:08:48 - mmengine - INFO - Epoch(val) [40][20/181] eta: 0:33:19 time: 12.4172 data_time: 0.3419 memory: 1114 2022/09/04 06:08:49 - mmengine - INFO - Epoch(val) [40][40/181] eta: 0:00:05 time: 0.0398 data_time: 0.0055 memory: 1114 2022/09/04 06:08:50 - mmengine - INFO - Epoch(val) [40][60/181] eta: 0:00:04 time: 0.0401 data_time: 0.0057 memory: 1114 2022/09/04 06:08:51 - mmengine - INFO - Epoch(val) [40][80/181] eta: 0:00:04 time: 0.0434 data_time: 0.0066 memory: 1114 2022/09/04 06:08:57 - mmengine - INFO - Epoch(val) [40][100/181] eta: 0:00:27 time: 0.3451 data_time: 0.2412 memory: 1114 2022/09/04 06:09:05 - mmengine - INFO - Epoch(val) [40][120/181] eta: 0:00:21 time: 0.3585 data_time: 0.0966 memory: 1114 2022/09/04 06:09:05 - mmengine - INFO - Epoch(val) [40][140/181] eta: 0:00:01 time: 0.0384 data_time: 0.0040 memory: 1114 2022/09/04 06:09:06 - mmengine - INFO - Epoch(val) [40][160/181] eta: 0:00:00 time: 0.0432 data_time: 0.0071 memory: 1114 2022/09/04 06:09:07 - mmengine - INFO - Epoch(val) [40][180/181] eta: 0:00:00 time: 0.0431 data_time: 0.0065 memory: 1114 2022/09/04 06:09:13 - mmengine - INFO - Epoch(val) [40][181/181] acc/top1: 0.4559 acc/top5: 0.7470 acc/mean1: 0.4148 2022/09/04 06:12:16 - mmengine - INFO - Epoch(train) [41][20/1345] lr: 1.0000e-04 eta: 2:02:05 time: 9.1659 data_time: 0.1176 memory: 7116 grad_norm: 8.5976 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3401 loss: 1.3401 2022/09/04 06:20:06 - mmengine - INFO - Epoch(train) [41][40/1345] lr: 1.0000e-04 eta: 2:03:49 time: 23.4519 data_time: 0.3425 memory: 7116 grad_norm: 8.8204 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5155 loss: 1.5155 2022/09/04 06:20:09 - mmengine - INFO - Epoch(train) [41][60/1345] lr: 1.0000e-04 eta: 2:03:36 time: 0.1885 data_time: 0.0164 memory: 7116 grad_norm: 8.9315 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3891 loss: 1.3891 2022/09/04 06:28:07 - mmengine - INFO - Epoch(train) [41][80/1345] lr: 1.0000e-04 eta: 2:05:20 time: 23.8976 data_time: 0.3019 memory: 7116 grad_norm: 8.7271 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2352 loss: 1.2352 2022/09/04 06:33:10 - mmengine - INFO - Epoch(train) [41][100/1345] lr: 1.0000e-04 eta: 2:06:21 time: 15.1393 data_time: 0.6104 memory: 7116 grad_norm: 9.1081 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4073 loss: 1.4073 2022/09/04 06:33:15 - mmengine - INFO - Epoch(train) [41][120/1345] lr: 1.0000e-04 eta: 2:06:08 time: 0.2392 data_time: 0.0213 memory: 7116 grad_norm: 8.9223 top1_acc: 0.3750 top5_acc: 1.0000 loss_cls: 1.4579 loss: 1.4579 2022/09/04 06:33:20 - mmengine - INFO - Epoch(train) [41][140/1345] lr: 1.0000e-04 eta: 2:05:56 time: 0.2686 data_time: 0.0232 memory: 7116 grad_norm: 8.6448 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1336 loss: 1.1336 2022/09/04 06:33:25 - mmengine - INFO - Epoch(train) [41][160/1345] lr: 1.0000e-04 eta: 2:05:43 time: 0.2181 data_time: 0.0214 memory: 7116 grad_norm: 8.7284 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0745 loss: 1.0745 2022/09/04 06:33:28 - mmengine - INFO - Epoch(train) [41][180/1345] lr: 1.0000e-04 eta: 2:05:29 time: 0.1892 data_time: 0.0106 memory: 7116 grad_norm: 8.5573 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3992 loss: 1.3992 2022/09/04 06:33:32 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 06:33:32 - mmengine - INFO - Epoch(train) [41][200/1345] lr: 1.0000e-04 eta: 2:05:16 time: 0.1908 data_time: 0.0099 memory: 7116 grad_norm: 8.5668 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1659 loss: 1.1659 2022/09/04 06:33:36 - mmengine - INFO - Epoch(train) [41][220/1345] lr: 1.0000e-04 eta: 2:05:03 time: 0.1905 data_time: 0.0121 memory: 7116 grad_norm: 8.5979 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3867 loss: 1.3867 2022/09/04 06:33:40 - mmengine - INFO - Epoch(train) [41][240/1345] lr: 1.0000e-04 eta: 2:04:50 time: 0.1951 data_time: 0.0089 memory: 7116 grad_norm: 8.6758 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.2483 loss: 1.2483 2022/09/04 06:33:44 - mmengine - INFO - Epoch(train) [41][260/1345] lr: 1.0000e-04 eta: 2:04:37 time: 0.1956 data_time: 0.0086 memory: 7116 grad_norm: 8.6883 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2300 loss: 1.2300 2022/09/04 06:33:48 - mmengine - INFO - Epoch(train) [41][280/1345] lr: 1.0000e-04 eta: 2:04:23 time: 0.1894 data_time: 0.0110 memory: 7116 grad_norm: 8.5264 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1784 loss: 1.1784 2022/09/04 06:33:51 - mmengine - INFO - Epoch(train) [41][300/1345] lr: 1.0000e-04 eta: 2:04:10 time: 0.1921 data_time: 0.0093 memory: 7116 grad_norm: 9.0799 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4179 loss: 1.4179 2022/09/04 06:33:55 - mmengine - INFO - Epoch(train) [41][320/1345] lr: 1.0000e-04 eta: 2:03:57 time: 0.1962 data_time: 0.0090 memory: 7116 grad_norm: 8.7356 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1805 loss: 1.1805 2022/09/04 06:33:59 - mmengine - INFO - Epoch(train) [41][340/1345] lr: 1.0000e-04 eta: 2:03:44 time: 0.2017 data_time: 0.0112 memory: 7116 grad_norm: 8.6328 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1605 loss: 1.1605 2022/09/04 06:34:04 - mmengine - INFO - Epoch(train) [41][360/1345] lr: 1.0000e-04 eta: 2:03:31 time: 0.2123 data_time: 0.0109 memory: 7116 grad_norm: 8.7259 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.6249 loss: 1.6249 2022/09/04 06:34:08 - mmengine - INFO - Epoch(train) [41][380/1345] lr: 1.0000e-04 eta: 2:03:18 time: 0.1936 data_time: 0.0096 memory: 7116 grad_norm: 8.8664 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4186 loss: 1.4186 2022/09/04 06:34:12 - mmengine - INFO - Epoch(train) [41][400/1345] lr: 1.0000e-04 eta: 2:03:05 time: 0.2003 data_time: 0.0106 memory: 7116 grad_norm: 8.4884 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0553 loss: 1.0553 2022/09/04 06:34:16 - mmengine - INFO - Epoch(train) [41][420/1345] lr: 1.0000e-04 eta: 2:02:52 time: 0.1960 data_time: 0.0090 memory: 7116 grad_norm: 8.9615 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.4215 loss: 1.4215 2022/09/04 06:34:19 - mmengine - INFO - Epoch(train) [41][440/1345] lr: 1.0000e-04 eta: 2:02:39 time: 0.1931 data_time: 0.0112 memory: 7116 grad_norm: 8.5141 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2986 loss: 1.2986 2022/09/04 06:34:23 - mmengine - INFO - Epoch(train) [41][460/1345] lr: 1.0000e-04 eta: 2:02:26 time: 0.1986 data_time: 0.0125 memory: 7116 grad_norm: 8.6815 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.2272 loss: 1.2272 2022/09/04 06:34:27 - mmengine - INFO - Epoch(train) [41][480/1345] lr: 1.0000e-04 eta: 2:02:12 time: 0.1959 data_time: 0.0089 memory: 7116 grad_norm: 8.8455 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2482 loss: 1.2482 2022/09/04 06:34:31 - mmengine - INFO - Epoch(train) [41][500/1345] lr: 1.0000e-04 eta: 2:01:59 time: 0.1977 data_time: 0.0092 memory: 7116 grad_norm: 8.6577 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3307 loss: 1.3307 2022/09/04 06:34:35 - mmengine - INFO - Epoch(train) [41][520/1345] lr: 1.0000e-04 eta: 2:01:46 time: 0.1956 data_time: 0.0121 memory: 7116 grad_norm: 8.6049 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3479 loss: 1.3479 2022/09/04 06:34:39 - mmengine - INFO - Epoch(train) [41][540/1345] lr: 1.0000e-04 eta: 2:01:33 time: 0.1972 data_time: 0.0111 memory: 7116 grad_norm: 8.9606 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0850 loss: 1.0850 2022/09/04 06:34:43 - mmengine - INFO - Epoch(train) [41][560/1345] lr: 1.0000e-04 eta: 2:01:20 time: 0.1968 data_time: 0.0086 memory: 7116 grad_norm: 8.7873 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3729 loss: 1.3729 2022/09/04 06:34:47 - mmengine - INFO - Epoch(train) [41][580/1345] lr: 1.0000e-04 eta: 2:01:07 time: 0.1967 data_time: 0.0115 memory: 7116 grad_norm: 8.9562 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2135 loss: 1.2135 2022/09/04 06:34:51 - mmengine - INFO - Epoch(train) [41][600/1345] lr: 1.0000e-04 eta: 2:00:54 time: 0.2069 data_time: 0.0197 memory: 7116 grad_norm: 8.5163 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1806 loss: 1.1806 2022/09/04 06:34:55 - mmengine - INFO - Epoch(train) [41][620/1345] lr: 1.0000e-04 eta: 2:00:41 time: 0.1989 data_time: 0.0095 memory: 7116 grad_norm: 8.7752 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2860 loss: 1.2860 2022/09/04 06:34:59 - mmengine - INFO - Epoch(train) [41][640/1345] lr: 1.0000e-04 eta: 2:00:28 time: 0.1964 data_time: 0.0133 memory: 7116 grad_norm: 8.4161 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0704 loss: 1.0704 2022/09/04 06:35:03 - mmengine - INFO - Epoch(train) [41][660/1345] lr: 1.0000e-04 eta: 2:00:15 time: 0.1974 data_time: 0.0090 memory: 7116 grad_norm: 8.9694 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3043 loss: 1.3043 2022/09/04 06:35:07 - mmengine - INFO - Epoch(train) [41][680/1345] lr: 1.0000e-04 eta: 2:00:02 time: 0.1972 data_time: 0.0093 memory: 7116 grad_norm: 8.3711 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0628 loss: 1.0628 2022/09/04 06:35:11 - mmengine - INFO - Epoch(train) [41][700/1345] lr: 1.0000e-04 eta: 1:59:49 time: 0.1940 data_time: 0.0125 memory: 7116 grad_norm: 9.0422 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3998 loss: 1.3998 2022/09/04 06:35:15 - mmengine - INFO - Epoch(train) [41][720/1345] lr: 1.0000e-04 eta: 1:59:36 time: 0.1949 data_time: 0.0107 memory: 7116 grad_norm: 8.9584 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3022 loss: 1.3022 2022/09/04 06:35:19 - mmengine - INFO - Epoch(train) [41][740/1345] lr: 1.0000e-04 eta: 1:59:23 time: 0.1961 data_time: 0.0096 memory: 7116 grad_norm: 8.8990 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2673 loss: 1.2673 2022/09/04 06:35:23 - mmengine - INFO - Epoch(train) [41][760/1345] lr: 1.0000e-04 eta: 1:59:10 time: 0.2066 data_time: 0.0109 memory: 7116 grad_norm: 8.6691 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4234 loss: 1.4234 2022/09/04 06:35:27 - mmengine - INFO - Epoch(train) [41][780/1345] lr: 1.0000e-04 eta: 1:58:57 time: 0.1940 data_time: 0.0087 memory: 7116 grad_norm: 8.9462 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 1.4709 loss: 1.4709 2022/09/04 06:35:31 - mmengine - INFO - Epoch(train) [41][800/1345] lr: 1.0000e-04 eta: 1:58:44 time: 0.1974 data_time: 0.0094 memory: 7116 grad_norm: 8.5040 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.3015 loss: 1.3015 2022/09/04 06:35:35 - mmengine - INFO - Epoch(train) [41][820/1345] lr: 1.0000e-04 eta: 1:58:31 time: 0.1933 data_time: 0.0114 memory: 7116 grad_norm: 8.9856 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1873 loss: 1.1873 2022/09/04 06:35:38 - mmengine - INFO - Epoch(train) [41][840/1345] lr: 1.0000e-04 eta: 1:58:18 time: 0.1956 data_time: 0.0097 memory: 7116 grad_norm: 8.2050 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0462 loss: 1.0462 2022/09/04 06:35:42 - mmengine - INFO - Epoch(train) [41][860/1345] lr: 1.0000e-04 eta: 1:58:05 time: 0.2009 data_time: 0.0098 memory: 7116 grad_norm: 8.8974 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3307 loss: 1.3307 2022/09/04 06:35:46 - mmengine - INFO - Epoch(train) [41][880/1345] lr: 1.0000e-04 eta: 1:57:53 time: 0.1984 data_time: 0.0121 memory: 7116 grad_norm: 8.9601 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.3058 loss: 1.3058 2022/09/04 06:35:50 - mmengine - INFO - Epoch(train) [41][900/1345] lr: 1.0000e-04 eta: 1:57:40 time: 0.1966 data_time: 0.0104 memory: 7116 grad_norm: 8.5966 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1009 loss: 1.1009 2022/09/04 06:35:54 - mmengine - INFO - Epoch(train) [41][920/1345] lr: 1.0000e-04 eta: 1:57:27 time: 0.1977 data_time: 0.0093 memory: 7116 grad_norm: 8.6225 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2649 loss: 1.2649 2022/09/04 06:35:58 - mmengine - INFO - Epoch(train) [41][940/1345] lr: 1.0000e-04 eta: 1:57:14 time: 0.1957 data_time: 0.0122 memory: 7116 grad_norm: 8.8313 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2211 loss: 1.2211 2022/09/04 06:36:02 - mmengine - INFO - Epoch(train) [41][960/1345] lr: 1.0000e-04 eta: 1:57:01 time: 0.1955 data_time: 0.0109 memory: 7116 grad_norm: 8.6516 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3318 loss: 1.3318 2022/09/04 06:36:06 - mmengine - INFO - Epoch(train) [41][980/1345] lr: 1.0000e-04 eta: 1:56:48 time: 0.1961 data_time: 0.0101 memory: 7116 grad_norm: 8.4000 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1845 loss: 1.1845 2022/09/04 06:36:10 - mmengine - INFO - Epoch(train) [41][1000/1345] lr: 1.0000e-04 eta: 1:56:35 time: 0.1975 data_time: 0.0114 memory: 7116 grad_norm: 8.6557 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2942 loss: 1.2942 2022/09/04 06:36:14 - mmengine - INFO - Epoch(train) [41][1020/1345] lr: 1.0000e-04 eta: 1:56:22 time: 0.1970 data_time: 0.0095 memory: 7116 grad_norm: 9.1745 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5325 loss: 1.5325 2022/09/04 06:36:18 - mmengine - INFO - Epoch(train) [41][1040/1345] lr: 1.0000e-04 eta: 1:56:09 time: 0.2008 data_time: 0.0091 memory: 7116 grad_norm: 8.5564 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4202 loss: 1.4202 2022/09/04 06:36:22 - mmengine - INFO - Epoch(train) [41][1060/1345] lr: 1.0000e-04 eta: 1:55:56 time: 0.2104 data_time: 0.0106 memory: 7116 grad_norm: 8.8237 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4437 loss: 1.4437 2022/09/04 06:36:26 - mmengine - INFO - Epoch(train) [41][1080/1345] lr: 1.0000e-04 eta: 1:55:44 time: 0.1937 data_time: 0.0103 memory: 7116 grad_norm: 8.7683 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2899 loss: 1.2899 2022/09/04 06:36:30 - mmengine - INFO - Epoch(train) [41][1100/1345] lr: 1.0000e-04 eta: 1:55:31 time: 0.1976 data_time: 0.0090 memory: 7116 grad_norm: 8.7842 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2804 loss: 1.2804 2022/09/04 06:36:34 - mmengine - INFO - Epoch(train) [41][1120/1345] lr: 1.0000e-04 eta: 1:55:18 time: 0.1978 data_time: 0.0141 memory: 7116 grad_norm: 8.7525 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1150 loss: 1.1150 2022/09/04 06:36:38 - mmengine - INFO - Epoch(train) [41][1140/1345] lr: 1.0000e-04 eta: 1:55:05 time: 0.1909 data_time: 0.0084 memory: 7116 grad_norm: 8.7180 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1940 loss: 1.1940 2022/09/04 06:36:42 - mmengine - INFO - Epoch(train) [41][1160/1345] lr: 1.0000e-04 eta: 1:54:52 time: 0.1926 data_time: 0.0100 memory: 7116 grad_norm: 9.3200 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.2927 loss: 1.2927 2022/09/04 06:36:46 - mmengine - INFO - Epoch(train) [41][1180/1345] lr: 1.0000e-04 eta: 1:54:39 time: 0.1991 data_time: 0.0131 memory: 7116 grad_norm: 8.8624 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2627 loss: 1.2627 2022/09/04 06:36:50 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 06:36:50 - mmengine - INFO - Epoch(train) [41][1200/1345] lr: 1.0000e-04 eta: 1:54:26 time: 0.1912 data_time: 0.0114 memory: 7116 grad_norm: 8.9425 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5162 loss: 1.5162 2022/09/04 06:36:53 - mmengine - INFO - Epoch(train) [41][1220/1345] lr: 1.0000e-04 eta: 1:54:14 time: 0.1982 data_time: 0.0105 memory: 7116 grad_norm: 8.8458 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2723 loss: 1.2723 2022/09/04 06:36:57 - mmengine - INFO - Epoch(train) [41][1240/1345] lr: 1.0000e-04 eta: 1:54:01 time: 0.1972 data_time: 0.0125 memory: 7116 grad_norm: 8.8155 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3323 loss: 1.3323 2022/09/04 06:37:01 - mmengine - INFO - Epoch(train) [41][1260/1345] lr: 1.0000e-04 eta: 1:53:48 time: 0.1918 data_time: 0.0101 memory: 7116 grad_norm: 8.7542 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3784 loss: 1.3784 2022/09/04 06:37:05 - mmengine - INFO - Epoch(train) [41][1280/1345] lr: 1.0000e-04 eta: 1:53:35 time: 0.1944 data_time: 0.0102 memory: 7116 grad_norm: 8.4996 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0402 loss: 1.0402 2022/09/04 06:37:09 - mmengine - INFO - Epoch(train) [41][1300/1345] lr: 1.0000e-04 eta: 1:53:22 time: 0.1951 data_time: 0.0120 memory: 7116 grad_norm: 9.0441 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1687 loss: 1.1687 2022/09/04 06:37:13 - mmengine - INFO - Epoch(train) [41][1320/1345] lr: 1.0000e-04 eta: 1:53:09 time: 0.2002 data_time: 0.0101 memory: 7116 grad_norm: 8.6741 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0741 loss: 1.0741 2022/09/04 06:37:17 - mmengine - INFO - Epoch(train) [41][1340/1345] lr: 1.0000e-04 eta: 1:52:57 time: 0.1953 data_time: 0.0103 memory: 7116 grad_norm: 8.5482 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1667 loss: 1.1667 2022/09/04 06:37:18 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 06:37:18 - mmengine - INFO - Epoch(train) [41][1345/1345] lr: 1.0000e-04 eta: 1:52:57 time: 0.2023 data_time: 0.0108 memory: 7116 grad_norm: 8.8621 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.4232 loss: 1.4232 2022/09/04 06:37:18 - mmengine - INFO - Saving checkpoint at 41 epochs 2022/09/04 06:37:21 - mmengine - INFO - Epoch(val) [41][20/181] eta: 0:00:08 time: 0.0501 data_time: 0.0132 memory: 1114 2022/09/04 06:37:22 - mmengine - INFO - Epoch(val) [41][40/181] eta: 0:00:06 time: 0.0467 data_time: 0.0099 memory: 1114 2022/09/04 06:37:23 - mmengine - INFO - Epoch(val) [41][60/181] eta: 0:00:05 time: 0.0452 data_time: 0.0072 memory: 1114 2022/09/04 06:37:24 - mmengine - INFO - Epoch(val) [41][80/181] eta: 0:00:04 time: 0.0438 data_time: 0.0074 memory: 1114 2022/09/04 06:37:25 - mmengine - INFO - Epoch(val) [41][100/181] eta: 0:00:03 time: 0.0436 data_time: 0.0074 memory: 1114 2022/09/04 06:37:26 - mmengine - INFO - Epoch(val) [41][120/181] eta: 0:00:02 time: 0.0443 data_time: 0.0077 memory: 1114 2022/09/04 06:37:27 - mmengine - INFO - Epoch(val) [41][140/181] eta: 0:00:01 time: 0.0441 data_time: 0.0073 memory: 1114 2022/09/04 06:37:28 - mmengine - INFO - Epoch(val) [41][160/181] eta: 0:00:00 time: 0.0432 data_time: 0.0070 memory: 1114 2022/09/04 06:37:28 - mmengine - INFO - Epoch(val) [41][180/181] eta: 0:00:00 time: 0.0433 data_time: 0.0070 memory: 1114 2022/09/04 06:37:30 - mmengine - INFO - Epoch(val) [41][181/181] acc/top1: 0.4621 acc/top5: 0.7546 acc/mean1: 0.4243 2022/09/04 06:37:30 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_38.pth is removed 2022/09/04 06:37:31 - mmengine - INFO - The best checkpoint with 0.4621 acc/top1 at 41 epoch is saved to best_acc/top1_epoch_41.pth. 2022/09/04 06:37:35 - mmengine - INFO - Epoch(train) [42][20/1345] lr: 1.0000e-04 eta: 1:52:41 time: 0.1984 data_time: 0.0157 memory: 7116 grad_norm: 8.6491 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.1996 loss: 1.1996 2022/09/04 06:37:39 - mmengine - INFO - Epoch(train) [42][40/1345] lr: 1.0000e-04 eta: 1:52:28 time: 0.1969 data_time: 0.0096 memory: 7116 grad_norm: 9.0900 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9742 loss: 0.9742 2022/09/04 06:37:43 - mmengine - INFO - Epoch(train) [42][60/1345] lr: 1.0000e-04 eta: 1:52:15 time: 0.2025 data_time: 0.0099 memory: 7116 grad_norm: 8.8464 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.2374 loss: 1.2374 2022/09/04 06:37:47 - mmengine - INFO - Epoch(train) [42][80/1345] lr: 1.0000e-04 eta: 1:52:02 time: 0.1981 data_time: 0.0125 memory: 7116 grad_norm: 8.6625 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1463 loss: 1.1463 2022/09/04 06:37:51 - mmengine - INFO - Epoch(train) [42][100/1345] lr: 1.0000e-04 eta: 1:51:49 time: 0.1987 data_time: 0.0103 memory: 7116 grad_norm: 8.7449 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 1.3165 loss: 1.3165 2022/09/04 06:37:55 - mmengine - INFO - Epoch(train) [42][120/1345] lr: 1.0000e-04 eta: 1:51:37 time: 0.2133 data_time: 0.0105 memory: 7116 grad_norm: 9.3149 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2502 loss: 1.2502 2022/09/04 06:37:59 - mmengine - INFO - Epoch(train) [42][140/1345] lr: 1.0000e-04 eta: 1:51:24 time: 0.1952 data_time: 0.0135 memory: 7116 grad_norm: 8.3398 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0526 loss: 1.0526 2022/09/04 06:38:03 - mmengine - INFO - Epoch(train) [42][160/1345] lr: 1.0000e-04 eta: 1:51:11 time: 0.1950 data_time: 0.0093 memory: 7116 grad_norm: 8.5731 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3149 loss: 1.3149 2022/09/04 06:38:07 - mmengine - INFO - Epoch(train) [42][180/1345] lr: 1.0000e-04 eta: 1:50:59 time: 0.1952 data_time: 0.0103 memory: 7116 grad_norm: 8.8687 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0283 loss: 1.0283 2022/09/04 06:38:11 - mmengine - INFO - Epoch(train) [42][200/1345] lr: 1.0000e-04 eta: 1:50:46 time: 0.1999 data_time: 0.0115 memory: 7116 grad_norm: 8.9062 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1239 loss: 1.1239 2022/09/04 06:38:15 - mmengine - INFO - Epoch(train) [42][220/1345] lr: 1.0000e-04 eta: 1:50:33 time: 0.1939 data_time: 0.0092 memory: 7116 grad_norm: 8.5108 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0189 loss: 1.0189 2022/09/04 06:38:19 - mmengine - INFO - Epoch(train) [42][240/1345] lr: 1.0000e-04 eta: 1:50:20 time: 0.1953 data_time: 0.0099 memory: 7116 grad_norm: 8.6702 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1591 loss: 1.1591 2022/09/04 06:38:23 - mmengine - INFO - Epoch(train) [42][260/1345] lr: 1.0000e-04 eta: 1:50:08 time: 0.1992 data_time: 0.0133 memory: 7116 grad_norm: 8.6968 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.2997 loss: 1.2997 2022/09/04 06:38:27 - mmengine - INFO - Epoch(train) [42][280/1345] lr: 1.0000e-04 eta: 1:49:55 time: 0.1957 data_time: 0.0107 memory: 7116 grad_norm: 8.4099 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2793 loss: 1.2793 2022/09/04 06:38:31 - mmengine - INFO - Epoch(train) [42][300/1345] lr: 1.0000e-04 eta: 1:49:42 time: 0.1983 data_time: 0.0103 memory: 7116 grad_norm: 8.9939 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2872 loss: 1.2872 2022/09/04 06:38:35 - mmengine - INFO - Epoch(train) [42][320/1345] lr: 1.0000e-04 eta: 1:49:30 time: 0.1966 data_time: 0.0144 memory: 7116 grad_norm: 9.0371 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4453 loss: 1.4453 2022/09/04 06:38:39 - mmengine - INFO - Epoch(train) [42][340/1345] lr: 1.0000e-04 eta: 1:49:17 time: 0.1927 data_time: 0.0101 memory: 7116 grad_norm: 8.9554 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4773 loss: 1.4773 2022/09/04 06:38:43 - mmengine - INFO - Epoch(train) [42][360/1345] lr: 1.0000e-04 eta: 1:49:04 time: 0.1957 data_time: 0.0103 memory: 7116 grad_norm: 8.6807 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1955 loss: 1.1955 2022/09/04 06:38:46 - mmengine - INFO - Epoch(train) [42][380/1345] lr: 1.0000e-04 eta: 1:48:52 time: 0.1966 data_time: 0.0130 memory: 7116 grad_norm: 8.8417 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.1139 loss: 1.1139 2022/09/04 06:38:51 - mmengine - INFO - Epoch(train) [42][400/1345] lr: 1.0000e-04 eta: 1:48:39 time: 0.2170 data_time: 0.0121 memory: 7116 grad_norm: 9.0632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3194 loss: 1.3194 2022/09/04 06:38:55 - mmengine - INFO - Epoch(train) [42][420/1345] lr: 1.0000e-04 eta: 1:48:26 time: 0.1931 data_time: 0.0105 memory: 7116 grad_norm: 8.6209 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3448 loss: 1.3448 2022/09/04 06:38:59 - mmengine - INFO - Epoch(train) [42][440/1345] lr: 1.0000e-04 eta: 1:48:14 time: 0.1931 data_time: 0.0127 memory: 7116 grad_norm: 8.9844 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5703 loss: 1.5703 2022/09/04 06:39:02 - mmengine - INFO - Epoch(train) [42][460/1345] lr: 1.0000e-04 eta: 1:48:01 time: 0.1945 data_time: 0.0092 memory: 7116 grad_norm: 8.7755 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0349 loss: 1.0349 2022/09/04 06:39:06 - mmengine - INFO - Epoch(train) [42][480/1345] lr: 1.0000e-04 eta: 1:47:48 time: 0.1925 data_time: 0.0121 memory: 7116 grad_norm: 8.6661 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9813 loss: 0.9813 2022/09/04 06:39:10 - mmengine - INFO - Epoch(train) [42][500/1345] lr: 1.0000e-04 eta: 1:47:36 time: 0.1981 data_time: 0.0132 memory: 7116 grad_norm: 8.9216 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2367 loss: 1.2367 2022/09/04 06:39:14 - mmengine - INFO - Epoch(train) [42][520/1345] lr: 1.0000e-04 eta: 1:47:23 time: 0.1956 data_time: 0.0102 memory: 7116 grad_norm: 8.6821 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.3860 loss: 1.3860 2022/09/04 06:39:18 - mmengine - INFO - Epoch(train) [42][540/1345] lr: 1.0000e-04 eta: 1:47:11 time: 0.1958 data_time: 0.0113 memory: 7116 grad_norm: 8.9842 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4522 loss: 1.4522 2022/09/04 06:39:22 - mmengine - INFO - Epoch(train) [42][560/1345] lr: 1.0000e-04 eta: 1:46:58 time: 0.1975 data_time: 0.0123 memory: 7116 grad_norm: 8.2489 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.9545 loss: 0.9545 2022/09/04 06:39:26 - mmengine - INFO - Epoch(train) [42][580/1345] lr: 1.0000e-04 eta: 1:46:45 time: 0.1946 data_time: 0.0103 memory: 7116 grad_norm: 8.6934 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1265 loss: 1.1265 2022/09/04 06:39:30 - mmengine - INFO - Epoch(train) [42][600/1345] lr: 1.0000e-04 eta: 1:46:33 time: 0.1894 data_time: 0.0105 memory: 7116 grad_norm: 8.7010 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1323 loss: 1.1323 2022/09/04 06:39:34 - mmengine - INFO - Epoch(train) [42][620/1345] lr: 1.0000e-04 eta: 1:46:20 time: 0.1947 data_time: 0.0138 memory: 7116 grad_norm: 8.8660 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3981 loss: 1.3981 2022/09/04 06:39:38 - mmengine - INFO - Epoch(train) [42][640/1345] lr: 1.0000e-04 eta: 1:46:07 time: 0.1929 data_time: 0.0106 memory: 7116 grad_norm: 8.7516 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1626 loss: 1.1626 2022/09/04 06:39:41 - mmengine - INFO - Epoch(train) [42][660/1345] lr: 1.0000e-04 eta: 1:45:55 time: 0.1954 data_time: 0.0111 memory: 7116 grad_norm: 9.0664 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.4446 loss: 1.4446 2022/09/04 06:39:45 - mmengine - INFO - Epoch(train) [42][680/1345] lr: 1.0000e-04 eta: 1:45:42 time: 0.1961 data_time: 0.0119 memory: 7116 grad_norm: 8.9485 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3496 loss: 1.3496 2022/09/04 06:39:49 - mmengine - INFO - Epoch(train) [42][700/1345] lr: 1.0000e-04 eta: 1:45:30 time: 0.1937 data_time: 0.0103 memory: 7116 grad_norm: 8.7297 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.3579 loss: 1.3579 2022/09/04 06:39:53 - mmengine - INFO - Epoch(train) [42][720/1345] lr: 1.0000e-04 eta: 1:45:17 time: 0.1945 data_time: 0.0106 memory: 7116 grad_norm: 8.9260 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4530 loss: 1.4530 2022/09/04 06:39:57 - mmengine - INFO - Epoch(train) [42][740/1345] lr: 1.0000e-04 eta: 1:45:05 time: 0.1990 data_time: 0.0125 memory: 7116 grad_norm: 8.7143 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0203 loss: 1.0203 2022/09/04 06:40:01 - mmengine - INFO - Epoch(train) [42][760/1345] lr: 1.0000e-04 eta: 1:44:52 time: 0.1970 data_time: 0.0103 memory: 7116 grad_norm: 8.7778 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1472 loss: 1.1472 2022/09/04 06:40:05 - mmengine - INFO - Epoch(train) [42][780/1345] lr: 1.0000e-04 eta: 1:44:40 time: 0.1935 data_time: 0.0099 memory: 7116 grad_norm: 8.4014 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2409 loss: 1.2409 2022/09/04 06:40:09 - mmengine - INFO - Epoch(train) [42][800/1345] lr: 1.0000e-04 eta: 1:44:27 time: 0.2020 data_time: 0.0132 memory: 7116 grad_norm: 8.7360 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3574 loss: 1.3574 2022/09/04 06:40:13 - mmengine - INFO - Epoch(train) [42][820/1345] lr: 1.0000e-04 eta: 1:44:14 time: 0.1922 data_time: 0.0106 memory: 7116 grad_norm: 8.7845 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2720 loss: 1.2720 2022/09/04 06:40:17 - mmengine - INFO - Epoch(train) [42][840/1345] lr: 1.0000e-04 eta: 1:44:02 time: 0.1929 data_time: 0.0095 memory: 7116 grad_norm: 8.6624 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1400 loss: 1.1400 2022/09/04 06:40:20 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 06:40:21 - mmengine - INFO - Epoch(train) [42][860/1345] lr: 1.0000e-04 eta: 1:43:49 time: 0.1967 data_time: 0.0126 memory: 7116 grad_norm: 8.6098 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2428 loss: 1.2428 2022/09/04 06:40:25 - mmengine - INFO - Epoch(train) [42][880/1345] lr: 1.0000e-04 eta: 1:43:37 time: 0.1958 data_time: 0.0098 memory: 7116 grad_norm: 8.8397 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1964 loss: 1.1964 2022/09/04 06:40:28 - mmengine - INFO - Epoch(train) [42][900/1345] lr: 1.0000e-04 eta: 1:43:24 time: 0.1967 data_time: 0.0095 memory: 7116 grad_norm: 8.6035 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0559 loss: 1.0559 2022/09/04 06:40:32 - mmengine - INFO - Epoch(train) [42][920/1345] lr: 1.0000e-04 eta: 1:43:12 time: 0.1973 data_time: 0.0122 memory: 7116 grad_norm: 8.6384 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1956 loss: 1.1956 2022/09/04 06:40:36 - mmengine - INFO - Epoch(train) [42][940/1345] lr: 1.0000e-04 eta: 1:42:59 time: 0.1910 data_time: 0.0104 memory: 7116 grad_norm: 8.9563 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3060 loss: 1.3060 2022/09/04 06:40:40 - mmengine - INFO - Epoch(train) [42][960/1345] lr: 1.0000e-04 eta: 1:42:47 time: 0.1954 data_time: 0.0093 memory: 7116 grad_norm: 8.7019 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1806 loss: 1.1806 2022/09/04 06:40:44 - mmengine - INFO - Epoch(train) [42][980/1345] lr: 1.0000e-04 eta: 1:42:34 time: 0.1979 data_time: 0.0121 memory: 7116 grad_norm: 8.8305 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9723 loss: 0.9723 2022/09/04 06:40:48 - mmengine - INFO - Epoch(train) [42][1000/1345] lr: 1.0000e-04 eta: 1:42:22 time: 0.1966 data_time: 0.0097 memory: 7116 grad_norm: 8.9485 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1864 loss: 1.1864 2022/09/04 06:40:52 - mmengine - INFO - Epoch(train) [42][1020/1345] lr: 1.0000e-04 eta: 1:42:09 time: 0.1970 data_time: 0.0093 memory: 7116 grad_norm: 8.4759 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3121 loss: 1.3121 2022/09/04 06:40:56 - mmengine - INFO - Epoch(train) [42][1040/1345] lr: 1.0000e-04 eta: 1:41:57 time: 0.1968 data_time: 0.0121 memory: 7116 grad_norm: 9.0832 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3225 loss: 1.3225 2022/09/04 06:41:00 - mmengine - INFO - Epoch(train) [42][1060/1345] lr: 1.0000e-04 eta: 1:41:45 time: 0.1938 data_time: 0.0096 memory: 7116 grad_norm: 8.6249 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3513 loss: 1.3513 2022/09/04 06:41:04 - mmengine - INFO - Epoch(train) [42][1080/1345] lr: 1.0000e-04 eta: 1:41:32 time: 0.1986 data_time: 0.0092 memory: 7116 grad_norm: 8.6906 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1864 loss: 1.1864 2022/09/04 06:41:08 - mmengine - INFO - Epoch(train) [42][1100/1345] lr: 1.0000e-04 eta: 1:41:20 time: 0.1969 data_time: 0.0127 memory: 7116 grad_norm: 8.6528 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2109 loss: 1.2109 2022/09/04 06:41:12 - mmengine - INFO - Epoch(train) [42][1120/1345] lr: 1.0000e-04 eta: 1:41:07 time: 0.1979 data_time: 0.0106 memory: 7116 grad_norm: 8.8923 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.2838 loss: 1.2838 2022/09/04 06:41:16 - mmengine - INFO - Epoch(train) [42][1140/1345] lr: 1.0000e-04 eta: 1:40:55 time: 0.1949 data_time: 0.0103 memory: 7116 grad_norm: 9.0166 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2117 loss: 1.2117 2022/09/04 06:41:20 - mmengine - INFO - Epoch(train) [42][1160/1345] lr: 1.0000e-04 eta: 1:40:42 time: 0.1962 data_time: 0.0129 memory: 7116 grad_norm: 9.1275 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.2308 loss: 1.2308 2022/09/04 06:41:23 - mmengine - INFO - Epoch(train) [42][1180/1345] lr: 1.0000e-04 eta: 1:40:30 time: 0.1928 data_time: 0.0090 memory: 7116 grad_norm: 8.7031 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9214 loss: 0.9214 2022/09/04 06:41:27 - mmengine - INFO - Epoch(train) [42][1200/1345] lr: 1.0000e-04 eta: 1:40:17 time: 0.1952 data_time: 0.0119 memory: 7116 grad_norm: 8.5464 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1325 loss: 1.1325 2022/09/04 06:41:31 - mmengine - INFO - Epoch(train) [42][1220/1345] lr: 1.0000e-04 eta: 1:40:05 time: 0.1996 data_time: 0.0122 memory: 7116 grad_norm: 9.1984 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2030 loss: 1.2030 2022/09/04 06:41:35 - mmengine - INFO - Epoch(train) [42][1240/1345] lr: 1.0000e-04 eta: 1:39:53 time: 0.1967 data_time: 0.0098 memory: 7116 grad_norm: 8.8709 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.3142 loss: 1.3142 2022/09/04 06:41:39 - mmengine - INFO - Epoch(train) [42][1260/1345] lr: 1.0000e-04 eta: 1:39:40 time: 0.1972 data_time: 0.0094 memory: 7116 grad_norm: 8.8960 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3214 loss: 1.3214 2022/09/04 06:41:43 - mmengine - INFO - Epoch(train) [42][1280/1345] lr: 1.0000e-04 eta: 1:39:28 time: 0.1999 data_time: 0.0120 memory: 7116 grad_norm: 9.2642 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1352 loss: 1.1352 2022/09/04 06:41:47 - mmengine - INFO - Epoch(train) [42][1300/1345] lr: 1.0000e-04 eta: 1:39:16 time: 0.1974 data_time: 0.0098 memory: 7116 grad_norm: 8.9772 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.1651 loss: 1.1651 2022/09/04 06:41:51 - mmengine - INFO - Epoch(train) [42][1320/1345] lr: 1.0000e-04 eta: 1:39:03 time: 0.2055 data_time: 0.0107 memory: 7116 grad_norm: 8.9723 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3295 loss: 1.3295 2022/09/04 06:41:55 - mmengine - INFO - Epoch(train) [42][1340/1345] lr: 1.0000e-04 eta: 1:38:51 time: 0.1969 data_time: 0.0119 memory: 7116 grad_norm: 8.7785 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1983 loss: 1.1983 2022/09/04 06:41:56 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 06:41:56 - mmengine - INFO - Epoch(train) [42][1345/1345] lr: 1.0000e-04 eta: 1:38:51 time: 0.1913 data_time: 0.0092 memory: 7116 grad_norm: 8.6962 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.1935 loss: 1.1935 2022/09/04 06:41:56 - mmengine - INFO - Saving checkpoint at 42 epochs 2022/09/04 06:41:59 - mmengine - INFO - Epoch(val) [42][20/181] eta: 0:00:10 time: 0.0621 data_time: 0.0243 memory: 1114 2022/09/04 06:42:00 - mmengine - INFO - Epoch(val) [42][40/181] eta: 0:00:06 time: 0.0438 data_time: 0.0070 memory: 1114 2022/09/04 06:42:01 - mmengine - INFO - Epoch(val) [42][60/181] eta: 0:00:06 time: 0.0539 data_time: 0.0178 memory: 1114 2022/09/04 06:42:02 - mmengine - INFO - Epoch(val) [42][80/181] eta: 0:00:04 time: 0.0456 data_time: 0.0083 memory: 1114 2022/09/04 06:42:03 - mmengine - INFO - Epoch(val) [42][100/181] eta: 0:00:03 time: 0.0488 data_time: 0.0094 memory: 1114 2022/09/04 06:42:04 - mmengine - INFO - Epoch(val) [42][120/181] eta: 0:00:02 time: 0.0456 data_time: 0.0084 memory: 1114 2022/09/04 06:42:05 - mmengine - INFO - Epoch(val) [42][140/181] eta: 0:00:01 time: 0.0435 data_time: 0.0074 memory: 1114 2022/09/04 06:42:06 - mmengine - INFO - Epoch(val) [42][160/181] eta: 0:00:00 time: 0.0431 data_time: 0.0069 memory: 1114 2022/09/04 06:42:07 - mmengine - INFO - Epoch(val) [42][180/181] eta: 0:00:00 time: 0.0437 data_time: 0.0074 memory: 1114 2022/09/04 06:42:08 - mmengine - INFO - Epoch(val) [42][181/181] acc/top1: 0.4652 acc/top5: 0.7559 acc/mean1: 0.4264 2022/09/04 06:42:08 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_41.pth is removed 2022/09/04 06:42:10 - mmengine - INFO - The best checkpoint with 0.4652 acc/top1 at 42 epoch is saved to best_acc/top1_epoch_42.pth. 2022/09/04 06:42:14 - mmengine - INFO - Epoch(train) [43][20/1345] lr: 1.0000e-04 eta: 1:38:35 time: 0.1996 data_time: 0.0138 memory: 7116 grad_norm: 8.7291 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1854 loss: 1.1854 2022/09/04 06:42:18 - mmengine - INFO - Epoch(train) [43][40/1345] lr: 1.0000e-04 eta: 1:38:23 time: 0.2095 data_time: 0.0109 memory: 7116 grad_norm: 8.6226 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1905 loss: 1.1905 2022/09/04 06:42:22 - mmengine - INFO - Epoch(train) [43][60/1345] lr: 1.0000e-04 eta: 1:38:10 time: 0.1935 data_time: 0.0102 memory: 7116 grad_norm: 9.0143 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2443 loss: 1.2443 2022/09/04 06:42:26 - mmengine - INFO - Epoch(train) [43][80/1345] lr: 1.0000e-04 eta: 1:37:58 time: 0.1991 data_time: 0.0124 memory: 7116 grad_norm: 8.6700 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1015 loss: 1.1015 2022/09/04 06:42:30 - mmengine - INFO - Epoch(train) [43][100/1345] lr: 1.0000e-04 eta: 1:37:46 time: 0.1975 data_time: 0.0092 memory: 7116 grad_norm: 8.8996 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2505 loss: 1.2505 2022/09/04 06:42:34 - mmengine - INFO - Epoch(train) [43][120/1345] lr: 1.0000e-04 eta: 1:37:33 time: 0.1974 data_time: 0.0094 memory: 7116 grad_norm: 8.8409 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3769 loss: 1.3769 2022/09/04 06:42:37 - mmengine - INFO - Epoch(train) [43][140/1345] lr: 1.0000e-04 eta: 1:37:21 time: 0.1948 data_time: 0.0125 memory: 7116 grad_norm: 8.7567 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1265 loss: 1.1265 2022/09/04 06:42:41 - mmengine - INFO - Epoch(train) [43][160/1345] lr: 1.0000e-04 eta: 1:37:09 time: 0.1949 data_time: 0.0119 memory: 7116 grad_norm: 8.9773 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4798 loss: 1.4798 2022/09/04 06:42:45 - mmengine - INFO - Epoch(train) [43][180/1345] lr: 1.0000e-04 eta: 1:36:56 time: 0.1928 data_time: 0.0106 memory: 7116 grad_norm: 8.7521 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1198 loss: 1.1198 2022/09/04 06:42:49 - mmengine - INFO - Epoch(train) [43][200/1345] lr: 1.0000e-04 eta: 1:36:44 time: 0.1925 data_time: 0.0132 memory: 7116 grad_norm: 8.7982 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2009 loss: 1.2009 2022/09/04 06:42:53 - mmengine - INFO - Epoch(train) [43][220/1345] lr: 1.0000e-04 eta: 1:36:32 time: 0.1950 data_time: 0.0091 memory: 7116 grad_norm: 9.1280 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1937 loss: 1.1937 2022/09/04 06:42:57 - mmengine - INFO - Epoch(train) [43][240/1345] lr: 1.0000e-04 eta: 1:36:20 time: 0.1962 data_time: 0.0129 memory: 7116 grad_norm: 8.9211 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2937 loss: 1.2937 2022/09/04 06:43:01 - mmengine - INFO - Epoch(train) [43][260/1345] lr: 1.0000e-04 eta: 1:36:07 time: 0.1989 data_time: 0.0131 memory: 7116 grad_norm: 8.8327 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1506 loss: 1.1506 2022/09/04 06:43:05 - mmengine - INFO - Epoch(train) [43][280/1345] lr: 1.0000e-04 eta: 1:35:55 time: 0.1940 data_time: 0.0104 memory: 7116 grad_norm: 8.9256 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1607 loss: 1.1607 2022/09/04 06:43:09 - mmengine - INFO - Epoch(train) [43][300/1345] lr: 1.0000e-04 eta: 1:35:43 time: 0.1931 data_time: 0.0107 memory: 7116 grad_norm: 9.3400 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3362 loss: 1.3362 2022/09/04 06:43:12 - mmengine - INFO - Epoch(train) [43][320/1345] lr: 1.0000e-04 eta: 1:35:30 time: 0.1896 data_time: 0.0122 memory: 7116 grad_norm: 9.0099 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2425 loss: 1.2425 2022/09/04 06:43:16 - mmengine - INFO - Epoch(train) [43][340/1345] lr: 1.0000e-04 eta: 1:35:18 time: 0.1940 data_time: 0.0111 memory: 7116 grad_norm: 9.0473 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1061 loss: 1.1061 2022/09/04 06:43:20 - mmengine - INFO - Epoch(train) [43][360/1345] lr: 1.0000e-04 eta: 1:35:06 time: 0.1922 data_time: 0.0106 memory: 7116 grad_norm: 8.8453 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1368 loss: 1.1368 2022/09/04 06:43:24 - mmengine - INFO - Epoch(train) [43][380/1345] lr: 1.0000e-04 eta: 1:34:54 time: 0.2117 data_time: 0.0129 memory: 7116 grad_norm: 8.8994 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.3521 loss: 1.3521 2022/09/04 06:43:28 - mmengine - INFO - Epoch(train) [43][400/1345] lr: 1.0000e-04 eta: 1:34:41 time: 0.1925 data_time: 0.0099 memory: 7116 grad_norm: 8.9839 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3286 loss: 1.3286 2022/09/04 06:43:32 - mmengine - INFO - Epoch(train) [43][420/1345] lr: 1.0000e-04 eta: 1:34:29 time: 0.1950 data_time: 0.0101 memory: 7116 grad_norm: 8.7063 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2189 loss: 1.2189 2022/09/04 06:43:36 - mmengine - INFO - Epoch(train) [43][440/1345] lr: 1.0000e-04 eta: 1:34:17 time: 0.1948 data_time: 0.0138 memory: 7116 grad_norm: 9.1006 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1569 loss: 1.1569 2022/09/04 06:43:40 - mmengine - INFO - Epoch(train) [43][460/1345] lr: 1.0000e-04 eta: 1:34:05 time: 0.1948 data_time: 0.0108 memory: 7116 grad_norm: 8.5587 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2212 loss: 1.2212 2022/09/04 06:43:44 - mmengine - INFO - Epoch(train) [43][480/1345] lr: 1.0000e-04 eta: 1:33:52 time: 0.2069 data_time: 0.0114 memory: 7116 grad_norm: 8.9192 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0988 loss: 1.0988 2022/09/04 06:43:48 - mmengine - INFO - Epoch(train) [43][500/1345] lr: 1.0000e-04 eta: 1:33:40 time: 0.1951 data_time: 0.0124 memory: 7116 grad_norm: 8.7805 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0504 loss: 1.0504 2022/09/04 06:43:50 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 06:43:52 - mmengine - INFO - Epoch(train) [43][520/1345] lr: 1.0000e-04 eta: 1:33:28 time: 0.1932 data_time: 0.0101 memory: 7116 grad_norm: 8.9498 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.2458 loss: 1.2458 2022/09/04 06:43:56 - mmengine - INFO - Epoch(train) [43][540/1345] lr: 1.0000e-04 eta: 1:33:16 time: 0.1858 data_time: 0.0104 memory: 7116 grad_norm: 9.2454 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1220 loss: 1.1220 2022/09/04 06:43:59 - mmengine - INFO - Epoch(train) [43][560/1345] lr: 1.0000e-04 eta: 1:33:03 time: 0.1933 data_time: 0.0124 memory: 7116 grad_norm: 9.0560 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1961 loss: 1.1961 2022/09/04 06:44:04 - mmengine - INFO - Epoch(train) [43][580/1345] lr: 1.0000e-04 eta: 1:32:51 time: 0.2024 data_time: 0.0109 memory: 7116 grad_norm: 8.6470 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2689 loss: 1.2689 2022/09/04 06:44:07 - mmengine - INFO - Epoch(train) [43][600/1345] lr: 1.0000e-04 eta: 1:32:39 time: 0.1928 data_time: 0.0096 memory: 7116 grad_norm: 8.6993 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.2180 loss: 1.2180 2022/09/04 06:44:11 - mmengine - INFO - Epoch(train) [43][620/1345] lr: 1.0000e-04 eta: 1:32:27 time: 0.1960 data_time: 0.0126 memory: 7116 grad_norm: 9.1171 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3789 loss: 1.3789 2022/09/04 06:44:15 - mmengine - INFO - Epoch(train) [43][640/1345] lr: 1.0000e-04 eta: 1:32:15 time: 0.1900 data_time: 0.0114 memory: 7116 grad_norm: 8.8490 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1714 loss: 1.1714 2022/09/04 06:44:19 - mmengine - INFO - Epoch(train) [43][660/1345] lr: 1.0000e-04 eta: 1:32:02 time: 0.1964 data_time: 0.0096 memory: 7116 grad_norm: 8.8783 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1019 loss: 1.1019 2022/09/04 06:44:23 - mmengine - INFO - Epoch(train) [43][680/1345] lr: 1.0000e-04 eta: 1:31:50 time: 0.1968 data_time: 0.0119 memory: 7116 grad_norm: 9.2129 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3050 loss: 1.3050 2022/09/04 06:44:27 - mmengine - INFO - Epoch(train) [43][700/1345] lr: 1.0000e-04 eta: 1:31:38 time: 0.1936 data_time: 0.0099 memory: 7116 grad_norm: 8.8978 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 0.9952 loss: 0.9952 2022/09/04 06:44:31 - mmengine - INFO - Epoch(train) [43][720/1345] lr: 1.0000e-04 eta: 1:31:26 time: 0.1941 data_time: 0.0124 memory: 7116 grad_norm: 8.9328 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1853 loss: 1.1853 2022/09/04 06:44:35 - mmengine - INFO - Epoch(train) [43][740/1345] lr: 1.0000e-04 eta: 1:31:14 time: 0.2020 data_time: 0.0112 memory: 7116 grad_norm: 8.9109 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.0261 loss: 1.0261 2022/09/04 06:44:39 - mmengine - INFO - Epoch(train) [43][760/1345] lr: 1.0000e-04 eta: 1:31:02 time: 0.1952 data_time: 0.0107 memory: 7116 grad_norm: 8.9963 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2299 loss: 1.2299 2022/09/04 06:44:43 - mmengine - INFO - Epoch(train) [43][780/1345] lr: 1.0000e-04 eta: 1:30:50 time: 0.1963 data_time: 0.0105 memory: 7116 grad_norm: 8.6623 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9926 loss: 0.9926 2022/09/04 06:44:47 - mmengine - INFO - Epoch(train) [43][800/1345] lr: 1.0000e-04 eta: 1:30:37 time: 0.2099 data_time: 0.0118 memory: 7116 grad_norm: 8.9673 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0881 loss: 1.0881 2022/09/04 06:44:51 - mmengine - INFO - Epoch(train) [43][820/1345] lr: 1.0000e-04 eta: 1:30:25 time: 0.1978 data_time: 0.0115 memory: 7116 grad_norm: 8.8158 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1547 loss: 1.1547 2022/09/04 06:44:55 - mmengine - INFO - Epoch(train) [43][840/1345] lr: 1.0000e-04 eta: 1:30:13 time: 0.1958 data_time: 0.0116 memory: 7116 grad_norm: 8.6139 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9452 loss: 0.9452 2022/09/04 06:44:59 - mmengine - INFO - Epoch(train) [43][860/1345] lr: 1.0000e-04 eta: 1:30:01 time: 0.1891 data_time: 0.0121 memory: 7116 grad_norm: 9.0909 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3170 loss: 1.3170 2022/09/04 06:45:03 - mmengine - INFO - Epoch(train) [43][880/1345] lr: 1.0000e-04 eta: 1:29:49 time: 0.2013 data_time: 0.0105 memory: 7116 grad_norm: 9.1169 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1345 loss: 1.1345 2022/09/04 06:45:07 - mmengine - INFO - Epoch(train) [43][900/1345] lr: 1.0000e-04 eta: 1:29:37 time: 0.1969 data_time: 0.0101 memory: 7116 grad_norm: 8.7278 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0326 loss: 1.0326 2022/09/04 06:45:10 - mmengine - INFO - Epoch(train) [43][920/1345] lr: 1.0000e-04 eta: 1:29:25 time: 0.1946 data_time: 0.0127 memory: 7116 grad_norm: 9.0573 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.2872 loss: 1.2872 2022/09/04 06:45:14 - mmengine - INFO - Epoch(train) [43][940/1345] lr: 1.0000e-04 eta: 1:29:13 time: 0.1967 data_time: 0.0110 memory: 7116 grad_norm: 8.9545 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2660 loss: 1.2660 2022/09/04 06:45:18 - mmengine - INFO - Epoch(train) [43][960/1345] lr: 1.0000e-04 eta: 1:29:01 time: 0.1967 data_time: 0.0106 memory: 7116 grad_norm: 9.0246 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1539 loss: 1.1539 2022/09/04 06:45:22 - mmengine - INFO - Epoch(train) [43][980/1345] lr: 1.0000e-04 eta: 1:28:48 time: 0.2000 data_time: 0.0118 memory: 7116 grad_norm: 8.7492 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2460 loss: 1.2460 2022/09/04 06:45:26 - mmengine - INFO - Epoch(train) [43][1000/1345] lr: 1.0000e-04 eta: 1:28:36 time: 0.1934 data_time: 0.0118 memory: 7116 grad_norm: 8.9934 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1013 loss: 1.1013 2022/09/04 06:45:30 - mmengine - INFO - Epoch(train) [43][1020/1345] lr: 1.0000e-04 eta: 1:28:24 time: 0.1930 data_time: 0.0092 memory: 7116 grad_norm: 8.8353 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3284 loss: 1.3284 2022/09/04 06:45:34 - mmengine - INFO - Epoch(train) [43][1040/1345] lr: 1.0000e-04 eta: 1:28:12 time: 0.1968 data_time: 0.0133 memory: 7116 grad_norm: 9.0161 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2174 loss: 1.2174 2022/09/04 06:45:38 - mmengine - INFO - Epoch(train) [43][1060/1345] lr: 1.0000e-04 eta: 1:28:00 time: 0.1942 data_time: 0.0099 memory: 7116 grad_norm: 8.9259 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3017 loss: 1.3017 2022/09/04 06:45:42 - mmengine - INFO - Epoch(train) [43][1080/1345] lr: 1.0000e-04 eta: 1:27:48 time: 0.1948 data_time: 0.0107 memory: 7116 grad_norm: 8.6437 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3947 loss: 1.3947 2022/09/04 06:45:46 - mmengine - INFO - Epoch(train) [43][1100/1345] lr: 1.0000e-04 eta: 1:27:36 time: 0.1923 data_time: 0.0172 memory: 7116 grad_norm: 8.6648 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1243 loss: 1.1243 2022/09/04 06:45:50 - mmengine - INFO - Epoch(train) [43][1120/1345] lr: 1.0000e-04 eta: 1:27:24 time: 0.1953 data_time: 0.0107 memory: 7116 grad_norm: 8.6426 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1289 loss: 1.1289 2022/09/04 06:45:54 - mmengine - INFO - Epoch(train) [43][1140/1345] lr: 1.0000e-04 eta: 1:27:12 time: 0.1996 data_time: 0.0114 memory: 7116 grad_norm: 8.7119 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2438 loss: 1.2438 2022/09/04 06:45:57 - mmengine - INFO - Epoch(train) [43][1160/1345] lr: 1.0000e-04 eta: 1:27:00 time: 0.1968 data_time: 0.0132 memory: 7116 grad_norm: 9.0004 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2750 loss: 1.2750 2022/09/04 06:46:01 - mmengine - INFO - Epoch(train) [43][1180/1345] lr: 1.0000e-04 eta: 1:26:48 time: 0.1941 data_time: 0.0106 memory: 7116 grad_norm: 9.0635 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1906 loss: 1.1906 2022/09/04 06:46:05 - mmengine - INFO - Epoch(train) [43][1200/1345] lr: 1.0000e-04 eta: 1:26:36 time: 0.1942 data_time: 0.0125 memory: 7116 grad_norm: 9.1047 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2903 loss: 1.2903 2022/09/04 06:46:09 - mmengine - INFO - Epoch(train) [43][1220/1345] lr: 1.0000e-04 eta: 1:26:24 time: 0.1913 data_time: 0.0126 memory: 7116 grad_norm: 9.2583 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3097 loss: 1.3097 2022/09/04 06:46:13 - mmengine - INFO - Epoch(train) [43][1240/1345] lr: 1.0000e-04 eta: 1:26:12 time: 0.1930 data_time: 0.0125 memory: 7116 grad_norm: 8.7054 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1921 loss: 1.1921 2022/09/04 06:46:17 - mmengine - INFO - Epoch(train) [43][1260/1345] lr: 1.0000e-04 eta: 1:26:00 time: 0.1904 data_time: 0.0102 memory: 7116 grad_norm: 8.7260 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9961 loss: 0.9961 2022/09/04 06:46:21 - mmengine - INFO - Epoch(train) [43][1280/1345] lr: 1.0000e-04 eta: 1:25:48 time: 0.1934 data_time: 0.0133 memory: 7116 grad_norm: 9.1927 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1854 loss: 1.1854 2022/09/04 06:46:25 - mmengine - INFO - Epoch(train) [43][1300/1345] lr: 1.0000e-04 eta: 1:25:36 time: 0.2045 data_time: 0.0107 memory: 7116 grad_norm: 8.5508 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1055 loss: 1.1055 2022/09/04 06:46:29 - mmengine - INFO - Epoch(train) [43][1320/1345] lr: 1.0000e-04 eta: 1:25:24 time: 0.1919 data_time: 0.0108 memory: 7116 grad_norm: 8.9190 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3572 loss: 1.3572 2022/09/04 06:46:32 - mmengine - INFO - Epoch(train) [43][1340/1345] lr: 1.0000e-04 eta: 1:25:12 time: 0.1904 data_time: 0.0133 memory: 7116 grad_norm: 9.1096 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3174 loss: 1.3174 2022/09/04 06:46:33 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 06:46:33 - mmengine - INFO - Epoch(train) [43][1345/1345] lr: 1.0000e-04 eta: 1:25:12 time: 0.1890 data_time: 0.0106 memory: 7116 grad_norm: 9.9507 top1_acc: 0.0000 top5_acc: 1.0000 loss_cls: 1.4355 loss: 1.4355 2022/09/04 06:46:33 - mmengine - INFO - Saving checkpoint at 43 epochs 2022/09/04 06:46:36 - mmengine - INFO - Epoch(val) [43][20/181] eta: 0:00:07 time: 0.0457 data_time: 0.0093 memory: 1114 2022/09/04 06:46:37 - mmengine - INFO - Epoch(val) [43][40/181] eta: 0:00:06 time: 0.0431 data_time: 0.0072 memory: 1114 2022/09/04 06:46:38 - mmengine - INFO - Epoch(val) [43][60/181] eta: 0:00:05 time: 0.0452 data_time: 0.0080 memory: 1114 2022/09/04 06:46:39 - mmengine - INFO - Epoch(val) [43][80/181] eta: 0:00:04 time: 0.0449 data_time: 0.0081 memory: 1114 2022/09/04 06:46:40 - mmengine - INFO - Epoch(val) [43][100/181] eta: 0:00:03 time: 0.0448 data_time: 0.0079 memory: 1114 2022/09/04 06:46:41 - mmengine - INFO - Epoch(val) [43][120/181] eta: 0:00:02 time: 0.0438 data_time: 0.0076 memory: 1114 2022/09/04 06:46:42 - mmengine - INFO - Epoch(val) [43][140/181] eta: 0:00:02 time: 0.0566 data_time: 0.0205 memory: 1114 2022/09/04 06:46:43 - mmengine - INFO - Epoch(val) [43][160/181] eta: 0:00:00 time: 0.0441 data_time: 0.0074 memory: 1114 2022/09/04 06:46:44 - mmengine - INFO - Epoch(val) [43][180/181] eta: 0:00:00 time: 0.0442 data_time: 0.0075 memory: 1114 2022/09/04 06:46:45 - mmengine - INFO - Epoch(val) [43][181/181] acc/top1: 0.4662 acc/top5: 0.7559 acc/mean1: 0.4277 2022/09/04 06:46:45 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_42.pth is removed 2022/09/04 06:46:47 - mmengine - INFO - The best checkpoint with 0.4662 acc/top1 at 43 epoch is saved to best_acc/top1_epoch_43.pth. 2022/09/04 06:46:50 - mmengine - INFO - Epoch(train) [44][20/1345] lr: 1.0000e-04 eta: 1:24:57 time: 0.1971 data_time: 0.0145 memory: 7116 grad_norm: 8.8963 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0952 loss: 1.0952 2022/09/04 06:46:54 - mmengine - INFO - Epoch(train) [44][40/1345] lr: 1.0000e-04 eta: 1:24:45 time: 0.1958 data_time: 0.0101 memory: 7116 grad_norm: 8.6559 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2122 loss: 1.2122 2022/09/04 06:46:58 - mmengine - INFO - Epoch(train) [44][60/1345] lr: 1.0000e-04 eta: 1:24:33 time: 0.1963 data_time: 0.0093 memory: 7116 grad_norm: 8.9663 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4378 loss: 1.4378 2022/09/04 06:47:02 - mmengine - INFO - Epoch(train) [44][80/1345] lr: 1.0000e-04 eta: 1:24:21 time: 0.1941 data_time: 0.0127 memory: 7116 grad_norm: 9.1286 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3751 loss: 1.3751 2022/09/04 06:47:06 - mmengine - INFO - Epoch(train) [44][100/1345] lr: 1.0000e-04 eta: 1:24:09 time: 0.1919 data_time: 0.0107 memory: 7116 grad_norm: 9.2344 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2383 loss: 1.2383 2022/09/04 06:47:10 - mmengine - INFO - Epoch(train) [44][120/1345] lr: 1.0000e-04 eta: 1:23:57 time: 0.1934 data_time: 0.0100 memory: 7116 grad_norm: 8.8518 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2835 loss: 1.2835 2022/09/04 06:47:14 - mmengine - INFO - Epoch(train) [44][140/1345] lr: 1.0000e-04 eta: 1:23:45 time: 0.1972 data_time: 0.0125 memory: 7116 grad_norm: 9.2368 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2610 loss: 1.2610 2022/09/04 06:47:18 - mmengine - INFO - Epoch(train) [44][160/1345] lr: 1.0000e-04 eta: 1:23:33 time: 0.1923 data_time: 0.0112 memory: 7116 grad_norm: 9.3089 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1599 loss: 1.1599 2022/09/04 06:47:19 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 06:47:22 - mmengine - INFO - Epoch(train) [44][180/1345] lr: 1.0000e-04 eta: 1:23:21 time: 0.1925 data_time: 0.0096 memory: 7116 grad_norm: 9.0116 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2060 loss: 1.2060 2022/09/04 06:47:25 - mmengine - INFO - Epoch(train) [44][200/1345] lr: 1.0000e-04 eta: 1:23:09 time: 0.1942 data_time: 0.0132 memory: 7116 grad_norm: 9.0002 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1871 loss: 1.1871 2022/09/04 06:47:29 - mmengine - INFO - Epoch(train) [44][220/1345] lr: 1.0000e-04 eta: 1:22:57 time: 0.1932 data_time: 0.0110 memory: 7116 grad_norm: 8.6851 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3138 loss: 1.3138 2022/09/04 06:47:33 - mmengine - INFO - Epoch(train) [44][240/1345] lr: 1.0000e-04 eta: 1:22:45 time: 0.1967 data_time: 0.0115 memory: 7116 grad_norm: 9.0821 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3491 loss: 1.3491 2022/09/04 06:47:37 - mmengine - INFO - Epoch(train) [44][260/1345] lr: 1.0000e-04 eta: 1:22:33 time: 0.1912 data_time: 0.0126 memory: 7116 grad_norm: 9.0654 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0143 loss: 1.0143 2022/09/04 06:47:41 - mmengine - INFO - Epoch(train) [44][280/1345] lr: 1.0000e-04 eta: 1:22:21 time: 0.1936 data_time: 0.0107 memory: 7116 grad_norm: 9.0220 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2197 loss: 1.2197 2022/09/04 06:47:45 - mmengine - INFO - Epoch(train) [44][300/1345] lr: 1.0000e-04 eta: 1:22:09 time: 0.1917 data_time: 0.0112 memory: 7116 grad_norm: 8.6843 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0009 loss: 1.0009 2022/09/04 06:47:49 - mmengine - INFO - Epoch(train) [44][320/1345] lr: 1.0000e-04 eta: 1:21:58 time: 0.1951 data_time: 0.0130 memory: 7116 grad_norm: 9.3659 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2153 loss: 1.2153 2022/09/04 06:47:53 - mmengine - INFO - Epoch(train) [44][340/1345] lr: 1.0000e-04 eta: 1:21:46 time: 0.1906 data_time: 0.0110 memory: 7116 grad_norm: 8.9206 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1648 loss: 1.1648 2022/09/04 06:47:56 - mmengine - INFO - Epoch(train) [44][360/1345] lr: 1.0000e-04 eta: 1:21:34 time: 0.1911 data_time: 0.0118 memory: 7116 grad_norm: 9.1321 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1854 loss: 1.1854 2022/09/04 06:48:00 - mmengine - INFO - Epoch(train) [44][380/1345] lr: 1.0000e-04 eta: 1:21:22 time: 0.1888 data_time: 0.0140 memory: 7116 grad_norm: 8.6544 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1010 loss: 1.1010 2022/09/04 06:48:04 - mmengine - INFO - Epoch(train) [44][400/1345] lr: 1.0000e-04 eta: 1:21:10 time: 0.1981 data_time: 0.0093 memory: 7116 grad_norm: 8.7171 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2779 loss: 1.2779 2022/09/04 06:48:08 - mmengine - INFO - Epoch(train) [44][420/1345] lr: 1.0000e-04 eta: 1:20:58 time: 0.1895 data_time: 0.0105 memory: 7116 grad_norm: 9.0221 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1743 loss: 1.1743 2022/09/04 06:48:12 - mmengine - INFO - Epoch(train) [44][440/1345] lr: 1.0000e-04 eta: 1:20:46 time: 0.1866 data_time: 0.0143 memory: 7116 grad_norm: 8.5887 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0851 loss: 1.0851 2022/09/04 06:48:15 - mmengine - INFO - Epoch(train) [44][460/1345] lr: 1.0000e-04 eta: 1:20:34 time: 0.1898 data_time: 0.0102 memory: 7116 grad_norm: 8.9695 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0691 loss: 1.0691 2022/09/04 06:48:19 - mmengine - INFO - Epoch(train) [44][480/1345] lr: 1.0000e-04 eta: 1:20:22 time: 0.1920 data_time: 0.0115 memory: 7116 grad_norm: 9.1917 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5120 loss: 1.5120 2022/09/04 06:48:23 - mmengine - INFO - Epoch(train) [44][500/1345] lr: 1.0000e-04 eta: 1:20:11 time: 0.2010 data_time: 0.0127 memory: 7116 grad_norm: 9.1386 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2299 loss: 1.2299 2022/09/04 06:48:27 - mmengine - INFO - Epoch(train) [44][520/1345] lr: 1.0000e-04 eta: 1:19:59 time: 0.1921 data_time: 0.0103 memory: 7116 grad_norm: 9.1815 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.0969 loss: 1.0969 2022/09/04 06:48:31 - mmengine - INFO - Epoch(train) [44][540/1345] lr: 1.0000e-04 eta: 1:19:47 time: 0.1902 data_time: 0.0107 memory: 7116 grad_norm: 9.0551 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0544 loss: 1.0544 2022/09/04 06:48:35 - mmengine - INFO - Epoch(train) [44][560/1345] lr: 1.0000e-04 eta: 1:19:35 time: 0.1959 data_time: 0.0129 memory: 7116 grad_norm: 9.0808 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1393 loss: 1.1393 2022/09/04 06:48:39 - mmengine - INFO - Epoch(train) [44][580/1345] lr: 1.0000e-04 eta: 1:19:23 time: 0.1937 data_time: 0.0109 memory: 7116 grad_norm: 8.9090 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4022 loss: 1.4022 2022/09/04 06:48:43 - mmengine - INFO - Epoch(train) [44][600/1345] lr: 1.0000e-04 eta: 1:19:11 time: 0.1987 data_time: 0.0111 memory: 7116 grad_norm: 9.0214 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3383 loss: 1.3383 2022/09/04 06:48:47 - mmengine - INFO - Epoch(train) [44][620/1345] lr: 1.0000e-04 eta: 1:19:00 time: 0.1950 data_time: 0.0130 memory: 7116 grad_norm: 9.4351 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.1524 loss: 1.1524 2022/09/04 06:48:51 - mmengine - INFO - Epoch(train) [44][640/1345] lr: 1.0000e-04 eta: 1:18:48 time: 0.1917 data_time: 0.0112 memory: 7116 grad_norm: 8.6392 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2100 loss: 1.2100 2022/09/04 06:48:54 - mmengine - INFO - Epoch(train) [44][660/1345] lr: 1.0000e-04 eta: 1:18:36 time: 0.1966 data_time: 0.0108 memory: 7116 grad_norm: 9.1117 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1233 loss: 1.1233 2022/09/04 06:48:58 - mmengine - INFO - Epoch(train) [44][680/1345] lr: 1.0000e-04 eta: 1:18:24 time: 0.1969 data_time: 0.0139 memory: 7116 grad_norm: 8.8730 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1228 loss: 1.1228 2022/09/04 06:49:02 - mmengine - INFO - Epoch(train) [44][700/1345] lr: 1.0000e-04 eta: 1:18:12 time: 0.1989 data_time: 0.0091 memory: 7116 grad_norm: 9.0740 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1459 loss: 1.1459 2022/09/04 06:49:06 - mmengine - INFO - Epoch(train) [44][720/1345] lr: 1.0000e-04 eta: 1:18:01 time: 0.1910 data_time: 0.0104 memory: 7116 grad_norm: 8.8984 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1191 loss: 1.1191 2022/09/04 06:49:10 - mmengine - INFO - Epoch(train) [44][740/1345] lr: 1.0000e-04 eta: 1:17:49 time: 0.1880 data_time: 0.0123 memory: 7116 grad_norm: 8.8857 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1514 loss: 1.1514 2022/09/04 06:49:14 - mmengine - INFO - Epoch(train) [44][760/1345] lr: 1.0000e-04 eta: 1:17:37 time: 0.1881 data_time: 0.0098 memory: 7116 grad_norm: 9.0497 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0865 loss: 1.0865 2022/09/04 06:49:18 - mmengine - INFO - Epoch(train) [44][780/1345] lr: 1.0000e-04 eta: 1:17:25 time: 0.1959 data_time: 0.0121 memory: 7116 grad_norm: 9.1392 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2702 loss: 1.2702 2022/09/04 06:49:22 - mmengine - INFO - Epoch(train) [44][800/1345] lr: 1.0000e-04 eta: 1:17:13 time: 0.2005 data_time: 0.0139 memory: 7116 grad_norm: 9.0358 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1127 loss: 1.1127 2022/09/04 06:49:26 - mmengine - INFO - Epoch(train) [44][820/1345] lr: 1.0000e-04 eta: 1:17:02 time: 0.1934 data_time: 0.0106 memory: 7116 grad_norm: 9.3063 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3022 loss: 1.3022 2022/09/04 06:49:29 - mmengine - INFO - Epoch(train) [44][840/1345] lr: 1.0000e-04 eta: 1:16:50 time: 0.1905 data_time: 0.0104 memory: 7116 grad_norm: 9.0171 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3521 loss: 1.3521 2022/09/04 06:49:33 - mmengine - INFO - Epoch(train) [44][860/1345] lr: 1.0000e-04 eta: 1:16:38 time: 0.1956 data_time: 0.0139 memory: 7116 grad_norm: 8.9135 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3886 loss: 1.3886 2022/09/04 06:49:37 - mmengine - INFO - Epoch(train) [44][880/1345] lr: 1.0000e-04 eta: 1:16:26 time: 0.1908 data_time: 0.0115 memory: 7116 grad_norm: 8.8443 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9831 loss: 0.9831 2022/09/04 06:49:41 - mmengine - INFO - Epoch(train) [44][900/1345] lr: 1.0000e-04 eta: 1:16:15 time: 0.1966 data_time: 0.0114 memory: 7116 grad_norm: 9.2321 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3338 loss: 1.3338 2022/09/04 06:49:45 - mmengine - INFO - Epoch(train) [44][920/1345] lr: 1.0000e-04 eta: 1:16:03 time: 0.1988 data_time: 0.0132 memory: 7116 grad_norm: 8.8727 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9462 loss: 0.9462 2022/09/04 06:49:49 - mmengine - INFO - Epoch(train) [44][940/1345] lr: 1.0000e-04 eta: 1:15:51 time: 0.1918 data_time: 0.0098 memory: 7116 grad_norm: 8.7732 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0241 loss: 1.0241 2022/09/04 06:49:53 - mmengine - INFO - Epoch(train) [44][960/1345] lr: 1.0000e-04 eta: 1:15:39 time: 0.1947 data_time: 0.0111 memory: 7116 grad_norm: 9.3347 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3929 loss: 1.3929 2022/09/04 06:49:57 - mmengine - INFO - Epoch(train) [44][980/1345] lr: 1.0000e-04 eta: 1:15:28 time: 0.1943 data_time: 0.0126 memory: 7116 grad_norm: 8.7304 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1946 loss: 1.1946 2022/09/04 06:50:01 - mmengine - INFO - Epoch(train) [44][1000/1345] lr: 1.0000e-04 eta: 1:15:16 time: 0.1960 data_time: 0.0103 memory: 7116 grad_norm: 9.2969 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4962 loss: 1.4962 2022/09/04 06:50:05 - mmengine - INFO - Epoch(train) [44][1020/1345] lr: 1.0000e-04 eta: 1:15:04 time: 0.1952 data_time: 0.0107 memory: 7116 grad_norm: 8.8210 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9998 loss: 0.9998 2022/09/04 06:50:08 - mmengine - INFO - Epoch(train) [44][1040/1345] lr: 1.0000e-04 eta: 1:14:53 time: 0.1932 data_time: 0.0136 memory: 7116 grad_norm: 9.1771 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2229 loss: 1.2229 2022/09/04 06:50:12 - mmengine - INFO - Epoch(train) [44][1060/1345] lr: 1.0000e-04 eta: 1:14:41 time: 0.1951 data_time: 0.0106 memory: 7116 grad_norm: 8.7419 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1397 loss: 1.1397 2022/09/04 06:50:16 - mmengine - INFO - Epoch(train) [44][1080/1345] lr: 1.0000e-04 eta: 1:14:29 time: 0.1933 data_time: 0.0108 memory: 7116 grad_norm: 9.0135 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2340 loss: 1.2340 2022/09/04 06:50:20 - mmengine - INFO - Epoch(train) [44][1100/1345] lr: 1.0000e-04 eta: 1:14:18 time: 0.1929 data_time: 0.0127 memory: 7116 grad_norm: 8.8852 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3695 loss: 1.3695 2022/09/04 06:50:24 - mmengine - INFO - Epoch(train) [44][1120/1345] lr: 1.0000e-04 eta: 1:14:06 time: 0.1928 data_time: 0.0102 memory: 7116 grad_norm: 9.1164 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8174 loss: 0.8174 2022/09/04 06:50:28 - mmengine - INFO - Epoch(train) [44][1140/1345] lr: 1.0000e-04 eta: 1:13:54 time: 0.1947 data_time: 0.0111 memory: 7116 grad_norm: 8.9471 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1838 loss: 1.1838 2022/09/04 06:50:32 - mmengine - INFO - Epoch(train) [44][1160/1345] lr: 1.0000e-04 eta: 1:13:42 time: 0.1960 data_time: 0.0133 memory: 7116 grad_norm: 8.8898 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2491 loss: 1.2491 2022/09/04 06:50:33 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 06:50:36 - mmengine - INFO - Epoch(train) [44][1180/1345] lr: 1.0000e-04 eta: 1:13:31 time: 0.1945 data_time: 0.0110 memory: 7116 grad_norm: 8.7424 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1326 loss: 1.1326 2022/09/04 06:50:39 - mmengine - INFO - Epoch(train) [44][1200/1345] lr: 1.0000e-04 eta: 1:13:19 time: 0.1910 data_time: 0.0102 memory: 7116 grad_norm: 9.1418 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4402 loss: 1.4402 2022/09/04 06:50:43 - mmengine - INFO - Epoch(train) [44][1220/1345] lr: 1.0000e-04 eta: 1:13:08 time: 0.1991 data_time: 0.0131 memory: 7116 grad_norm: 8.9103 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3140 loss: 1.3140 2022/09/04 06:50:47 - mmengine - INFO - Epoch(train) [44][1240/1345] lr: 1.0000e-04 eta: 1:12:56 time: 0.1981 data_time: 0.0126 memory: 7116 grad_norm: 9.1076 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1615 loss: 1.1615 2022/09/04 06:50:51 - mmengine - INFO - Epoch(train) [44][1260/1345] lr: 1.0000e-04 eta: 1:12:44 time: 0.1926 data_time: 0.0089 memory: 7116 grad_norm: 8.9630 top1_acc: 0.0000 top5_acc: 0.8750 loss_cls: 1.2356 loss: 1.2356 2022/09/04 06:50:55 - mmengine - INFO - Epoch(train) [44][1280/1345] lr: 1.0000e-04 eta: 1:12:33 time: 0.1947 data_time: 0.0131 memory: 7116 grad_norm: 9.2376 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9591 loss: 0.9591 2022/09/04 06:50:59 - mmengine - INFO - Epoch(train) [44][1300/1345] lr: 1.0000e-04 eta: 1:12:21 time: 0.1946 data_time: 0.0105 memory: 7116 grad_norm: 8.7381 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3182 loss: 1.3182 2022/09/04 06:51:03 - mmengine - INFO - Epoch(train) [44][1320/1345] lr: 1.0000e-04 eta: 1:12:09 time: 0.1971 data_time: 0.0113 memory: 7116 grad_norm: 9.1860 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2980 loss: 1.2980 2022/09/04 06:51:07 - mmengine - INFO - Epoch(train) [44][1340/1345] lr: 1.0000e-04 eta: 1:11:58 time: 0.1978 data_time: 0.0127 memory: 7116 grad_norm: 8.7428 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9470 loss: 0.9470 2022/09/04 06:51:08 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 06:51:08 - mmengine - INFO - Epoch(train) [44][1345/1345] lr: 1.0000e-04 eta: 1:11:58 time: 0.1924 data_time: 0.0100 memory: 7116 grad_norm: 9.0321 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.1174 loss: 1.1174 2022/09/04 06:51:08 - mmengine - INFO - Saving checkpoint at 44 epochs 2022/09/04 06:51:12 - mmengine - INFO - Epoch(val) [44][20/181] eta: 0:00:08 time: 0.0512 data_time: 0.0131 memory: 1114 2022/09/04 06:51:12 - mmengine - INFO - Epoch(val) [44][40/181] eta: 0:00:06 time: 0.0454 data_time: 0.0081 memory: 1114 2022/09/04 06:51:13 - mmengine - INFO - Epoch(val) [44][60/181] eta: 0:00:05 time: 0.0438 data_time: 0.0078 memory: 1114 2022/09/04 06:51:14 - mmengine - INFO - Epoch(val) [44][80/181] eta: 0:00:04 time: 0.0449 data_time: 0.0081 memory: 1114 2022/09/04 06:51:15 - mmengine - INFO - Epoch(val) [44][100/181] eta: 0:00:03 time: 0.0457 data_time: 0.0083 memory: 1114 2022/09/04 06:51:16 - mmengine - INFO - Epoch(val) [44][120/181] eta: 0:00:02 time: 0.0464 data_time: 0.0098 memory: 1114 2022/09/04 06:51:17 - mmengine - INFO - Epoch(val) [44][140/181] eta: 0:00:01 time: 0.0443 data_time: 0.0068 memory: 1114 2022/09/04 06:51:18 - mmengine - INFO - Epoch(val) [44][160/181] eta: 0:00:00 time: 0.0452 data_time: 0.0083 memory: 1114 2022/09/04 06:51:19 - mmengine - INFO - Epoch(val) [44][180/181] eta: 0:00:00 time: 0.0444 data_time: 0.0083 memory: 1114 2022/09/04 06:51:20 - mmengine - INFO - Epoch(val) [44][181/181] acc/top1: 0.4616 acc/top5: 0.7547 acc/mean1: 0.4237 2022/09/04 06:51:25 - mmengine - INFO - Epoch(train) [45][20/1345] lr: 1.0000e-04 eta: 1:11:43 time: 0.2379 data_time: 0.0222 memory: 7116 grad_norm: 9.0234 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2517 loss: 1.2517 2022/09/04 06:51:29 - mmengine - INFO - Epoch(train) [45][40/1345] lr: 1.0000e-04 eta: 1:11:32 time: 0.1934 data_time: 0.0113 memory: 7116 grad_norm: 9.2909 top1_acc: 0.2500 top5_acc: 1.0000 loss_cls: 1.2842 loss: 1.2842 2022/09/04 06:51:33 - mmengine - INFO - Epoch(train) [45][60/1345] lr: 1.0000e-04 eta: 1:11:20 time: 0.1950 data_time: 0.0111 memory: 7116 grad_norm: 8.9922 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1599 loss: 1.1599 2022/09/04 06:51:37 - mmengine - INFO - Epoch(train) [45][80/1345] lr: 1.0000e-04 eta: 1:11:08 time: 0.1956 data_time: 0.0130 memory: 7116 grad_norm: 8.9975 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0794 loss: 1.0794 2022/09/04 06:51:41 - mmengine - INFO - Epoch(train) [45][100/1345] lr: 1.0000e-04 eta: 1:10:57 time: 0.1941 data_time: 0.0108 memory: 7116 grad_norm: 9.1494 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3430 loss: 1.3430 2022/09/04 06:51:46 - mmengine - INFO - Epoch(train) [45][120/1345] lr: 1.0000e-04 eta: 1:10:45 time: 0.2343 data_time: 0.0106 memory: 7116 grad_norm: 8.8389 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1142 loss: 1.1142 2022/09/04 06:51:49 - mmengine - INFO - Epoch(train) [45][140/1345] lr: 1.0000e-04 eta: 1:10:34 time: 0.1923 data_time: 0.0141 memory: 7116 grad_norm: 8.9069 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8812 loss: 0.8812 2022/09/04 06:51:53 - mmengine - INFO - Epoch(train) [45][160/1345] lr: 1.0000e-04 eta: 1:10:22 time: 0.1943 data_time: 0.0106 memory: 7116 grad_norm: 9.0541 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3831 loss: 1.3831 2022/09/04 06:51:57 - mmengine - INFO - Epoch(train) [45][180/1345] lr: 1.0000e-04 eta: 1:10:10 time: 0.1885 data_time: 0.0099 memory: 7116 grad_norm: 9.2013 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.1540 loss: 1.1540 2022/09/04 06:52:01 - mmengine - INFO - Epoch(train) [45][200/1345] lr: 1.0000e-04 eta: 1:09:59 time: 0.1950 data_time: 0.0135 memory: 7116 grad_norm: 8.9360 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0763 loss: 1.0763 2022/09/04 06:52:05 - mmengine - INFO - Epoch(train) [45][220/1345] lr: 1.0000e-04 eta: 1:09:47 time: 0.1992 data_time: 0.0113 memory: 7116 grad_norm: 8.9591 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1911 loss: 1.1911 2022/09/04 06:52:09 - mmengine - INFO - Epoch(train) [45][240/1345] lr: 1.0000e-04 eta: 1:09:36 time: 0.1946 data_time: 0.0101 memory: 7116 grad_norm: 9.2275 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3042 loss: 1.3042 2022/09/04 06:52:13 - mmengine - INFO - Epoch(train) [45][260/1345] lr: 1.0000e-04 eta: 1:09:24 time: 0.1947 data_time: 0.0121 memory: 7116 grad_norm: 9.2665 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2150 loss: 1.2150 2022/09/04 06:52:17 - mmengine - INFO - Epoch(train) [45][280/1345] lr: 1.0000e-04 eta: 1:09:13 time: 0.1961 data_time: 0.0106 memory: 7116 grad_norm: 9.2306 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0720 loss: 1.0720 2022/09/04 06:52:21 - mmengine - INFO - Epoch(train) [45][300/1345] lr: 1.0000e-04 eta: 1:09:01 time: 0.1970 data_time: 0.0104 memory: 7116 grad_norm: 9.0736 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2060 loss: 1.2060 2022/09/04 06:52:25 - mmengine - INFO - Epoch(train) [45][320/1345] lr: 1.0000e-04 eta: 1:08:50 time: 0.1955 data_time: 0.0128 memory: 7116 grad_norm: 9.3759 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3223 loss: 1.3223 2022/09/04 06:52:28 - mmengine - INFO - Epoch(train) [45][340/1345] lr: 1.0000e-04 eta: 1:08:38 time: 0.1916 data_time: 0.0108 memory: 7116 grad_norm: 9.1354 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4892 loss: 1.4892 2022/09/04 06:52:32 - mmengine - INFO - Epoch(train) [45][360/1345] lr: 1.0000e-04 eta: 1:08:27 time: 0.2042 data_time: 0.0112 memory: 7116 grad_norm: 9.0962 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1762 loss: 1.1762 2022/09/04 06:52:36 - mmengine - INFO - Epoch(train) [45][380/1345] lr: 1.0000e-04 eta: 1:08:15 time: 0.1970 data_time: 0.0120 memory: 7116 grad_norm: 8.7373 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1154 loss: 1.1154 2022/09/04 06:52:40 - mmengine - INFO - Epoch(train) [45][400/1345] lr: 1.0000e-04 eta: 1:08:03 time: 0.1953 data_time: 0.0097 memory: 7116 grad_norm: 9.0443 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3355 loss: 1.3355 2022/09/04 06:52:44 - mmengine - INFO - Epoch(train) [45][420/1345] lr: 1.0000e-04 eta: 1:07:52 time: 0.1950 data_time: 0.0102 memory: 7116 grad_norm: 8.9756 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0626 loss: 1.0626 2022/09/04 06:52:48 - mmengine - INFO - Epoch(train) [45][440/1345] lr: 1.0000e-04 eta: 1:07:40 time: 0.1953 data_time: 0.0124 memory: 7116 grad_norm: 9.4498 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2097 loss: 1.2097 2022/09/04 06:52:52 - mmengine - INFO - Epoch(train) [45][460/1345] lr: 1.0000e-04 eta: 1:07:29 time: 0.2025 data_time: 0.0099 memory: 7116 grad_norm: 9.0263 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0597 loss: 1.0597 2022/09/04 06:52:56 - mmengine - INFO - Epoch(train) [45][480/1345] lr: 1.0000e-04 eta: 1:07:17 time: 0.1971 data_time: 0.0092 memory: 7116 grad_norm: 8.9357 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2385 loss: 1.2385 2022/09/04 06:53:00 - mmengine - INFO - Epoch(train) [45][500/1345] lr: 1.0000e-04 eta: 1:07:06 time: 0.1950 data_time: 0.0133 memory: 7116 grad_norm: 9.0976 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1033 loss: 1.1033 2022/09/04 06:53:04 - mmengine - INFO - Epoch(train) [45][520/1345] lr: 1.0000e-04 eta: 1:06:55 time: 0.1947 data_time: 0.0109 memory: 7116 grad_norm: 9.2901 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4223 loss: 1.4223 2022/09/04 06:53:08 - mmengine - INFO - Epoch(train) [45][540/1345] lr: 1.0000e-04 eta: 1:06:43 time: 0.1952 data_time: 0.0110 memory: 7116 grad_norm: 9.0351 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1375 loss: 1.1375 2022/09/04 06:53:12 - mmengine - INFO - Epoch(train) [45][560/1345] lr: 1.0000e-04 eta: 1:06:32 time: 0.2052 data_time: 0.0123 memory: 7116 grad_norm: 8.9497 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1858 loss: 1.1858 2022/09/04 06:53:16 - mmengine - INFO - Epoch(train) [45][580/1345] lr: 1.0000e-04 eta: 1:06:20 time: 0.1943 data_time: 0.0098 memory: 7116 grad_norm: 8.7845 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3641 loss: 1.3641 2022/09/04 06:53:20 - mmengine - INFO - Epoch(train) [45][600/1345] lr: 1.0000e-04 eta: 1:06:09 time: 0.1915 data_time: 0.0095 memory: 7116 grad_norm: 9.1042 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0593 loss: 1.0593 2022/09/04 06:53:24 - mmengine - INFO - Epoch(train) [45][620/1345] lr: 1.0000e-04 eta: 1:05:57 time: 0.2023 data_time: 0.0111 memory: 7116 grad_norm: 8.9747 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9972 loss: 0.9972 2022/09/04 06:53:28 - mmengine - INFO - Epoch(train) [45][640/1345] lr: 1.0000e-04 eta: 1:05:46 time: 0.1936 data_time: 0.0102 memory: 7116 grad_norm: 8.9999 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0404 loss: 1.0404 2022/09/04 06:53:32 - mmengine - INFO - Epoch(train) [45][660/1345] lr: 1.0000e-04 eta: 1:05:34 time: 0.1991 data_time: 0.0101 memory: 7116 grad_norm: 9.0205 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1328 loss: 1.1328 2022/09/04 06:53:36 - mmengine - INFO - Epoch(train) [45][680/1345] lr: 1.0000e-04 eta: 1:05:23 time: 0.1949 data_time: 0.0125 memory: 7116 grad_norm: 9.1379 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2653 loss: 1.2653 2022/09/04 06:53:39 - mmengine - INFO - Epoch(train) [45][700/1345] lr: 1.0000e-04 eta: 1:05:11 time: 0.1962 data_time: 0.0110 memory: 7116 grad_norm: 9.3700 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2508 loss: 1.2508 2022/09/04 06:53:43 - mmengine - INFO - Epoch(train) [45][720/1345] lr: 1.0000e-04 eta: 1:05:00 time: 0.1958 data_time: 0.0106 memory: 7116 grad_norm: 9.0705 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1207 loss: 1.1207 2022/09/04 06:53:47 - mmengine - INFO - Epoch(train) [45][740/1345] lr: 1.0000e-04 eta: 1:04:48 time: 0.1926 data_time: 0.0126 memory: 7116 grad_norm: 9.0593 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0410 loss: 1.0410 2022/09/04 06:53:51 - mmengine - INFO - Epoch(train) [45][760/1345] lr: 1.0000e-04 eta: 1:04:37 time: 0.1937 data_time: 0.0096 memory: 7116 grad_norm: 9.0378 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.1263 loss: 1.1263 2022/09/04 06:53:55 - mmengine - INFO - Epoch(train) [45][780/1345] lr: 1.0000e-04 eta: 1:04:26 time: 0.2028 data_time: 0.0106 memory: 7116 grad_norm: 9.2763 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0493 loss: 1.0493 2022/09/04 06:53:59 - mmengine - INFO - Epoch(train) [45][800/1345] lr: 1.0000e-04 eta: 1:04:14 time: 0.1904 data_time: 0.0114 memory: 7116 grad_norm: 9.2386 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3191 loss: 1.3191 2022/09/04 06:54:03 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 06:54:03 - mmengine - INFO - Epoch(train) [45][820/1345] lr: 1.0000e-04 eta: 1:04:03 time: 0.1979 data_time: 0.0114 memory: 7116 grad_norm: 8.9222 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1454 loss: 1.1454 2022/09/04 06:54:07 - mmengine - INFO - Epoch(train) [45][840/1345] lr: 1.0000e-04 eta: 1:03:51 time: 0.1961 data_time: 0.0102 memory: 7116 grad_norm: 8.9554 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2496 loss: 1.2496 2022/09/04 06:54:11 - mmengine - INFO - Epoch(train) [45][860/1345] lr: 1.0000e-04 eta: 1:03:40 time: 0.1963 data_time: 0.0117 memory: 7116 grad_norm: 8.7720 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1609 loss: 1.1609 2022/09/04 06:54:15 - mmengine - INFO - Epoch(train) [45][880/1345] lr: 1.0000e-04 eta: 1:03:29 time: 0.1945 data_time: 0.0099 memory: 7116 grad_norm: 9.1670 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.1940 loss: 1.1940 2022/09/04 06:54:19 - mmengine - INFO - Epoch(train) [45][900/1345] lr: 1.0000e-04 eta: 1:03:17 time: 0.1969 data_time: 0.0099 memory: 7116 grad_norm: 9.6677 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0402 loss: 1.0402 2022/09/04 06:54:23 - mmengine - INFO - Epoch(train) [45][920/1345] lr: 1.0000e-04 eta: 1:03:06 time: 0.2004 data_time: 0.0126 memory: 7116 grad_norm: 9.2963 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2593 loss: 1.2593 2022/09/04 06:54:27 - mmengine - INFO - Epoch(train) [45][940/1345] lr: 1.0000e-04 eta: 1:02:54 time: 0.1953 data_time: 0.0100 memory: 7116 grad_norm: 8.9750 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.1773 loss: 1.1773 2022/09/04 06:54:31 - mmengine - INFO - Epoch(train) [45][960/1345] lr: 1.0000e-04 eta: 1:02:43 time: 0.1972 data_time: 0.0107 memory: 7116 grad_norm: 9.2733 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1187 loss: 1.1187 2022/09/04 06:54:34 - mmengine - INFO - Epoch(train) [45][980/1345] lr: 1.0000e-04 eta: 1:02:32 time: 0.1974 data_time: 0.0124 memory: 7116 grad_norm: 9.2092 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2354 loss: 1.2354 2022/09/04 06:54:38 - mmengine - INFO - Epoch(train) [45][1000/1345] lr: 1.0000e-04 eta: 1:02:20 time: 0.1975 data_time: 0.0103 memory: 7116 grad_norm: 9.0784 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1892 loss: 1.1892 2022/09/04 06:54:42 - mmengine - INFO - Epoch(train) [45][1020/1345] lr: 1.0000e-04 eta: 1:02:09 time: 0.1991 data_time: 0.0103 memory: 7116 grad_norm: 8.8276 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0431 loss: 1.0431 2022/09/04 06:54:46 - mmengine - INFO - Epoch(train) [45][1040/1345] lr: 1.0000e-04 eta: 1:01:58 time: 0.1945 data_time: 0.0121 memory: 7116 grad_norm: 8.9033 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9172 loss: 0.9172 2022/09/04 06:54:50 - mmengine - INFO - Epoch(train) [45][1060/1345] lr: 1.0000e-04 eta: 1:01:46 time: 0.1908 data_time: 0.0103 memory: 7116 grad_norm: 8.9047 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3176 loss: 1.3176 2022/09/04 06:54:54 - mmengine - INFO - Epoch(train) [45][1080/1345] lr: 1.0000e-04 eta: 1:01:35 time: 0.1964 data_time: 0.0103 memory: 7116 grad_norm: 9.0180 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1915 loss: 1.1915 2022/09/04 06:54:58 - mmengine - INFO - Epoch(train) [45][1100/1345] lr: 1.0000e-04 eta: 1:01:24 time: 0.1962 data_time: 0.0121 memory: 7116 grad_norm: 9.3829 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2253 loss: 1.2253 2022/09/04 06:55:02 - mmengine - INFO - Epoch(train) [45][1120/1345] lr: 1.0000e-04 eta: 1:01:12 time: 0.2024 data_time: 0.0115 memory: 7116 grad_norm: 8.8241 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1886 loss: 1.1886 2022/09/04 06:55:06 - mmengine - INFO - Epoch(train) [45][1140/1345] lr: 1.0000e-04 eta: 1:01:01 time: 0.1985 data_time: 0.0090 memory: 7116 grad_norm: 9.4737 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2050 loss: 1.2050 2022/09/04 06:55:10 - mmengine - INFO - Epoch(train) [45][1160/1345] lr: 1.0000e-04 eta: 1:00:50 time: 0.1937 data_time: 0.0117 memory: 7116 grad_norm: 9.1835 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0531 loss: 1.0531 2022/09/04 06:55:14 - mmengine - INFO - Epoch(train) [45][1180/1345] lr: 1.0000e-04 eta: 1:00:38 time: 0.1944 data_time: 0.0102 memory: 7116 grad_norm: 9.2241 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3559 loss: 1.3559 2022/09/04 06:55:18 - mmengine - INFO - Epoch(train) [45][1200/1345] lr: 1.0000e-04 eta: 1:00:27 time: 0.1931 data_time: 0.0114 memory: 7116 grad_norm: 8.9672 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2191 loss: 1.2191 2022/09/04 06:55:22 - mmengine - INFO - Epoch(train) [45][1220/1345] lr: 1.0000e-04 eta: 1:00:16 time: 0.1985 data_time: 0.0126 memory: 7116 grad_norm: 9.2551 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1884 loss: 1.1884 2022/09/04 06:55:26 - mmengine - INFO - Epoch(train) [45][1240/1345] lr: 1.0000e-04 eta: 1:00:04 time: 0.1975 data_time: 0.0103 memory: 7116 grad_norm: 9.2554 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1892 loss: 1.1892 2022/09/04 06:55:29 - mmengine - INFO - Epoch(train) [45][1260/1345] lr: 1.0000e-04 eta: 0:59:53 time: 0.1925 data_time: 0.0094 memory: 7116 grad_norm: 9.3266 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2449 loss: 1.2449 2022/09/04 06:55:33 - mmengine - INFO - Epoch(train) [45][1280/1345] lr: 1.0000e-04 eta: 0:59:42 time: 0.1963 data_time: 0.0117 memory: 7116 grad_norm: 9.0577 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1496 loss: 1.1496 2022/09/04 06:55:37 - mmengine - INFO - Epoch(train) [45][1300/1345] lr: 1.0000e-04 eta: 0:59:30 time: 0.1927 data_time: 0.0102 memory: 7116 grad_norm: 9.4540 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1739 loss: 1.1739 2022/09/04 06:55:41 - mmengine - INFO - Epoch(train) [45][1320/1345] lr: 1.0000e-04 eta: 0:59:19 time: 0.1947 data_time: 0.0102 memory: 7116 grad_norm: 9.7322 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5327 loss: 1.5327 2022/09/04 06:55:45 - mmengine - INFO - Epoch(train) [45][1340/1345] lr: 1.0000e-04 eta: 0:59:08 time: 0.1981 data_time: 0.0126 memory: 7116 grad_norm: 9.1180 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9578 loss: 0.9578 2022/09/04 06:55:46 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 06:55:46 - mmengine - INFO - Epoch(train) [45][1345/1345] lr: 1.0000e-04 eta: 0:59:08 time: 0.1955 data_time: 0.0102 memory: 7116 grad_norm: 9.2399 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 0.9554 loss: 0.9554 2022/09/04 06:55:46 - mmengine - INFO - Saving checkpoint at 45 epochs 2022/09/04 06:55:49 - mmengine - INFO - Epoch(val) [45][20/181] eta: 0:00:07 time: 0.0493 data_time: 0.0116 memory: 1114 2022/09/04 06:55:50 - mmengine - INFO - Epoch(val) [45][40/181] eta: 0:00:06 time: 0.0445 data_time: 0.0078 memory: 1114 2022/09/04 06:55:51 - mmengine - INFO - Epoch(val) [45][60/181] eta: 0:00:05 time: 0.0454 data_time: 0.0080 memory: 1114 2022/09/04 06:55:52 - mmengine - INFO - Epoch(val) [45][80/181] eta: 0:00:04 time: 0.0438 data_time: 0.0074 memory: 1114 2022/09/04 06:55:52 - mmengine - INFO - Epoch(val) [45][100/181] eta: 0:00:03 time: 0.0439 data_time: 0.0076 memory: 1114 2022/09/04 06:55:53 - mmengine - INFO - Epoch(val) [45][120/181] eta: 0:00:02 time: 0.0465 data_time: 0.0083 memory: 1114 2022/09/04 06:55:54 - mmengine - INFO - Epoch(val) [45][140/181] eta: 0:00:01 time: 0.0451 data_time: 0.0083 memory: 1114 2022/09/04 06:55:55 - mmengine - INFO - Epoch(val) [45][160/181] eta: 0:00:00 time: 0.0451 data_time: 0.0083 memory: 1114 2022/09/04 06:55:56 - mmengine - INFO - Epoch(val) [45][180/181] eta: 0:00:00 time: 0.0441 data_time: 0.0073 memory: 1114 2022/09/04 06:55:58 - mmengine - INFO - Epoch(val) [45][181/181] acc/top1: 0.4642 acc/top5: 0.7561 acc/mean1: 0.4239 2022/09/04 06:56:02 - mmengine - INFO - Epoch(train) [46][20/1345] lr: 1.0000e-05 eta: 0:58:54 time: 0.2167 data_time: 0.0145 memory: 7116 grad_norm: 9.2966 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1648 loss: 1.1648 2022/09/04 06:56:06 - mmengine - INFO - Epoch(train) [46][40/1345] lr: 1.0000e-05 eta: 0:58:42 time: 0.1954 data_time: 0.0099 memory: 7116 grad_norm: 9.0774 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.3619 loss: 1.3619 2022/09/04 06:56:10 - mmengine - INFO - Epoch(train) [46][60/1345] lr: 1.0000e-05 eta: 0:58:31 time: 0.1960 data_time: 0.0108 memory: 7116 grad_norm: 8.7698 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0514 loss: 1.0514 2022/09/04 06:56:14 - mmengine - INFO - Epoch(train) [46][80/1345] lr: 1.0000e-05 eta: 0:58:20 time: 0.1991 data_time: 0.0120 memory: 7116 grad_norm: 9.1826 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2818 loss: 1.2818 2022/09/04 06:56:18 - mmengine - INFO - Epoch(train) [46][100/1345] lr: 1.0000e-05 eta: 0:58:09 time: 0.1916 data_time: 0.0095 memory: 7116 grad_norm: 9.1201 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1016 loss: 1.1016 2022/09/04 06:56:22 - mmengine - INFO - Epoch(train) [46][120/1345] lr: 1.0000e-05 eta: 0:57:57 time: 0.1977 data_time: 0.0113 memory: 7116 grad_norm: 9.0050 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1310 loss: 1.1310 2022/09/04 06:56:26 - mmengine - INFO - Epoch(train) [46][140/1345] lr: 1.0000e-05 eta: 0:57:46 time: 0.1976 data_time: 0.0122 memory: 7116 grad_norm: 9.0025 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2693 loss: 1.2693 2022/09/04 06:56:30 - mmengine - INFO - Epoch(train) [46][160/1345] lr: 1.0000e-05 eta: 0:57:35 time: 0.1930 data_time: 0.0103 memory: 7116 grad_norm: 8.9934 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4130 loss: 1.4130 2022/09/04 06:56:34 - mmengine - INFO - Epoch(train) [46][180/1345] lr: 1.0000e-05 eta: 0:57:24 time: 0.1969 data_time: 0.0108 memory: 7116 grad_norm: 9.4061 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1030 loss: 1.1030 2022/09/04 06:56:38 - mmengine - INFO - Epoch(train) [46][200/1345] lr: 1.0000e-05 eta: 0:57:12 time: 0.1937 data_time: 0.0122 memory: 7116 grad_norm: 9.3283 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0283 loss: 1.0283 2022/09/04 06:56:42 - mmengine - INFO - Epoch(train) [46][220/1345] lr: 1.0000e-05 eta: 0:57:01 time: 0.1957 data_time: 0.0096 memory: 7116 grad_norm: 9.0337 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9719 loss: 0.9719 2022/09/04 06:56:46 - mmengine - INFO - Epoch(train) [46][240/1345] lr: 1.0000e-05 eta: 0:56:50 time: 0.1950 data_time: 0.0111 memory: 7116 grad_norm: 8.8898 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1477 loss: 1.1477 2022/09/04 06:56:50 - mmengine - INFO - Epoch(train) [46][260/1345] lr: 1.0000e-05 eta: 0:56:39 time: 0.1960 data_time: 0.0122 memory: 7116 grad_norm: 8.6621 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2152 loss: 1.2152 2022/09/04 06:56:53 - mmengine - INFO - Epoch(train) [46][280/1345] lr: 1.0000e-05 eta: 0:56:27 time: 0.1929 data_time: 0.0092 memory: 7116 grad_norm: 9.2230 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1918 loss: 1.1918 2022/09/04 06:56:57 - mmengine - INFO - Epoch(train) [46][300/1345] lr: 1.0000e-05 eta: 0:56:16 time: 0.1966 data_time: 0.0089 memory: 7116 grad_norm: 9.0052 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2089 loss: 1.2089 2022/09/04 06:57:01 - mmengine - INFO - Epoch(train) [46][320/1345] lr: 1.0000e-05 eta: 0:56:05 time: 0.1983 data_time: 0.0130 memory: 7116 grad_norm: 9.1099 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.0695 loss: 1.0695 2022/09/04 06:57:06 - mmengine - INFO - Epoch(train) [46][340/1345] lr: 1.0000e-05 eta: 0:55:54 time: 0.2134 data_time: 0.0180 memory: 7116 grad_norm: 8.8893 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0979 loss: 1.0979 2022/09/04 06:57:09 - mmengine - INFO - Epoch(train) [46][360/1345] lr: 1.0000e-05 eta: 0:55:43 time: 0.1954 data_time: 0.0090 memory: 7116 grad_norm: 8.7284 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2084 loss: 1.2084 2022/09/04 06:57:13 - mmengine - INFO - Epoch(train) [46][380/1345] lr: 1.0000e-05 eta: 0:55:32 time: 0.1996 data_time: 0.0121 memory: 7116 grad_norm: 9.0847 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1429 loss: 1.1429 2022/09/04 06:57:17 - mmengine - INFO - Epoch(train) [46][400/1345] lr: 1.0000e-05 eta: 0:55:20 time: 0.1963 data_time: 0.0097 memory: 7116 grad_norm: 8.6015 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1888 loss: 1.1888 2022/09/04 06:57:21 - mmengine - INFO - Epoch(train) [46][420/1345] lr: 1.0000e-05 eta: 0:55:09 time: 0.1947 data_time: 0.0098 memory: 7116 grad_norm: 9.0092 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.0387 loss: 1.0387 2022/09/04 06:57:25 - mmengine - INFO - Epoch(train) [46][440/1345] lr: 1.0000e-05 eta: 0:54:58 time: 0.2004 data_time: 0.0123 memory: 7116 grad_norm: 8.8808 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0347 loss: 1.0347 2022/09/04 06:57:29 - mmengine - INFO - Epoch(train) [46][460/1345] lr: 1.0000e-05 eta: 0:54:47 time: 0.1958 data_time: 0.0090 memory: 7116 grad_norm: 9.1698 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0853 loss: 1.0853 2022/09/04 06:57:32 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 06:57:33 - mmengine - INFO - Epoch(train) [46][480/1345] lr: 1.0000e-05 eta: 0:54:36 time: 0.1986 data_time: 0.0094 memory: 7116 grad_norm: 9.4051 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3813 loss: 1.3813 2022/09/04 06:57:37 - mmengine - INFO - Epoch(train) [46][500/1345] lr: 1.0000e-05 eta: 0:54:25 time: 0.1953 data_time: 0.0122 memory: 7116 grad_norm: 8.9506 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.0589 loss: 1.0589 2022/09/04 06:57:41 - mmengine - INFO - Epoch(train) [46][520/1345] lr: 1.0000e-05 eta: 0:54:13 time: 0.1934 data_time: 0.0094 memory: 7116 grad_norm: 9.2973 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3054 loss: 1.3054 2022/09/04 06:57:45 - mmengine - INFO - Epoch(train) [46][540/1345] lr: 1.0000e-05 eta: 0:54:02 time: 0.2063 data_time: 0.0109 memory: 7116 grad_norm: 9.2212 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1622 loss: 1.1622 2022/09/04 06:57:49 - mmengine - INFO - Epoch(train) [46][560/1345] lr: 1.0000e-05 eta: 0:53:51 time: 0.1949 data_time: 0.0120 memory: 7116 grad_norm: 9.3867 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1177 loss: 1.1177 2022/09/04 06:57:53 - mmengine - INFO - Epoch(train) [46][580/1345] lr: 1.0000e-05 eta: 0:53:40 time: 0.1976 data_time: 0.0099 memory: 7116 grad_norm: 9.1607 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3154 loss: 1.3154 2022/09/04 06:57:57 - mmengine - INFO - Epoch(train) [46][600/1345] lr: 1.0000e-05 eta: 0:53:29 time: 0.1978 data_time: 0.0101 memory: 7116 grad_norm: 9.4049 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5557 loss: 1.5557 2022/09/04 06:58:01 - mmengine - INFO - Epoch(train) [46][620/1345] lr: 1.0000e-05 eta: 0:53:18 time: 0.1956 data_time: 0.0118 memory: 7116 grad_norm: 9.0318 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0325 loss: 1.0325 2022/09/04 06:58:05 - mmengine - INFO - Epoch(train) [46][640/1345] lr: 1.0000e-05 eta: 0:53:07 time: 0.2004 data_time: 0.0095 memory: 7116 grad_norm: 9.3268 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1115 loss: 1.1115 2022/09/04 06:58:09 - mmengine - INFO - Epoch(train) [46][660/1345] lr: 1.0000e-05 eta: 0:52:55 time: 0.1959 data_time: 0.0101 memory: 7116 grad_norm: 9.1027 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1664 loss: 1.1664 2022/09/04 06:58:13 - mmengine - INFO - Epoch(train) [46][680/1345] lr: 1.0000e-05 eta: 0:52:44 time: 0.2090 data_time: 0.0123 memory: 7116 grad_norm: 9.2822 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.0957 loss: 1.0957 2022/09/04 06:58:17 - mmengine - INFO - Epoch(train) [46][700/1345] lr: 1.0000e-05 eta: 0:52:33 time: 0.1950 data_time: 0.0099 memory: 7116 grad_norm: 9.3794 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0358 loss: 1.0358 2022/09/04 06:58:21 - mmengine - INFO - Epoch(train) [46][720/1345] lr: 1.0000e-05 eta: 0:52:22 time: 0.1950 data_time: 0.0101 memory: 7116 grad_norm: 9.2833 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1070 loss: 1.1070 2022/09/04 06:58:25 - mmengine - INFO - Epoch(train) [46][740/1345] lr: 1.0000e-05 eta: 0:52:11 time: 0.1965 data_time: 0.0123 memory: 7116 grad_norm: 9.2454 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1385 loss: 1.1385 2022/09/04 06:58:29 - mmengine - INFO - Epoch(train) [46][760/1345] lr: 1.0000e-05 eta: 0:52:00 time: 0.1972 data_time: 0.0093 memory: 7116 grad_norm: 9.4710 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0682 loss: 1.0682 2022/09/04 06:58:33 - mmengine - INFO - Epoch(train) [46][780/1345] lr: 1.0000e-05 eta: 0:51:49 time: 0.2055 data_time: 0.0100 memory: 7116 grad_norm: 8.9580 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9673 loss: 0.9673 2022/09/04 06:58:37 - mmengine - INFO - Epoch(train) [46][800/1345] lr: 1.0000e-05 eta: 0:51:38 time: 0.1957 data_time: 0.0111 memory: 7116 grad_norm: 9.6881 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3499 loss: 1.3499 2022/09/04 06:58:41 - mmengine - INFO - Epoch(train) [46][820/1345] lr: 1.0000e-05 eta: 0:51:27 time: 0.2003 data_time: 0.0088 memory: 7116 grad_norm: 9.1897 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1241 loss: 1.1241 2022/09/04 06:58:45 - mmengine - INFO - Epoch(train) [46][840/1345] lr: 1.0000e-05 eta: 0:51:16 time: 0.2084 data_time: 0.0100 memory: 7116 grad_norm: 9.0208 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7860 loss: 0.7860 2022/09/04 06:58:49 - mmengine - INFO - Epoch(train) [46][860/1345] lr: 1.0000e-05 eta: 0:51:04 time: 0.1953 data_time: 0.0116 memory: 7116 grad_norm: 8.8948 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1066 loss: 1.1066 2022/09/04 06:58:53 - mmengine - INFO - Epoch(train) [46][880/1345] lr: 1.0000e-05 eta: 0:50:53 time: 0.1993 data_time: 0.0106 memory: 7116 grad_norm: 8.8800 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3141 loss: 1.3141 2022/09/04 06:58:57 - mmengine - INFO - Epoch(train) [46][900/1345] lr: 1.0000e-05 eta: 0:50:42 time: 0.1959 data_time: 0.0094 memory: 7116 grad_norm: 8.9677 top1_acc: 0.3750 top5_acc: 1.0000 loss_cls: 1.0908 loss: 1.0908 2022/09/04 06:59:01 - mmengine - INFO - Epoch(train) [46][920/1345] lr: 1.0000e-05 eta: 0:50:31 time: 0.1970 data_time: 0.0114 memory: 7116 grad_norm: 9.3064 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3077 loss: 1.3077 2022/09/04 06:59:05 - mmengine - INFO - Epoch(train) [46][940/1345] lr: 1.0000e-05 eta: 0:50:20 time: 0.1970 data_time: 0.0097 memory: 7116 grad_norm: 9.0895 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1974 loss: 1.1974 2022/09/04 06:59:09 - mmengine - INFO - Epoch(train) [46][960/1345] lr: 1.0000e-05 eta: 0:50:09 time: 0.1976 data_time: 0.0085 memory: 7116 grad_norm: 9.2652 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1958 loss: 1.1958 2022/09/04 06:59:13 - mmengine - INFO - Epoch(train) [46][980/1345] lr: 1.0000e-05 eta: 0:49:58 time: 0.2006 data_time: 0.0110 memory: 7116 grad_norm: 8.8930 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2714 loss: 1.2714 2022/09/04 06:59:17 - mmengine - INFO - Epoch(train) [46][1000/1345] lr: 1.0000e-05 eta: 0:49:47 time: 0.2178 data_time: 0.0289 memory: 7116 grad_norm: 8.9532 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.1534 loss: 1.1534 2022/09/04 06:59:21 - mmengine - INFO - Epoch(train) [46][1020/1345] lr: 1.0000e-05 eta: 0:49:36 time: 0.1989 data_time: 0.0106 memory: 7116 grad_norm: 8.7531 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9525 loss: 0.9525 2022/09/04 06:59:25 - mmengine - INFO - Epoch(train) [46][1040/1345] lr: 1.0000e-05 eta: 0:49:25 time: 0.2001 data_time: 0.0110 memory: 7116 grad_norm: 9.0435 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0923 loss: 1.0923 2022/09/04 06:59:29 - mmengine - INFO - Epoch(train) [46][1060/1345] lr: 1.0000e-05 eta: 0:49:14 time: 0.1977 data_time: 0.0092 memory: 7116 grad_norm: 9.3372 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3902 loss: 1.3902 2022/09/04 06:59:33 - mmengine - INFO - Epoch(train) [46][1080/1345] lr: 1.0000e-05 eta: 0:49:03 time: 0.1964 data_time: 0.0096 memory: 7116 grad_norm: 8.4667 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1453 loss: 1.1453 2022/09/04 06:59:37 - mmengine - INFO - Epoch(train) [46][1100/1345] lr: 1.0000e-05 eta: 0:48:52 time: 0.1973 data_time: 0.0111 memory: 7116 grad_norm: 9.2415 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1429 loss: 1.1429 2022/09/04 06:59:41 - mmengine - INFO - Epoch(train) [46][1120/1345] lr: 1.0000e-05 eta: 0:48:41 time: 0.1953 data_time: 0.0090 memory: 7116 grad_norm: 8.8698 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1974 loss: 1.1974 2022/09/04 06:59:45 - mmengine - INFO - Epoch(train) [46][1140/1345] lr: 1.0000e-05 eta: 0:48:30 time: 0.2027 data_time: 0.0090 memory: 7116 grad_norm: 9.2237 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.2295 loss: 1.2295 2022/09/04 06:59:49 - mmengine - INFO - Epoch(train) [46][1160/1345] lr: 1.0000e-05 eta: 0:48:19 time: 0.1994 data_time: 0.0111 memory: 7116 grad_norm: 8.6918 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2595 loss: 1.2595 2022/09/04 06:59:53 - mmengine - INFO - Epoch(train) [46][1180/1345] lr: 1.0000e-05 eta: 0:48:08 time: 0.2020 data_time: 0.0095 memory: 7116 grad_norm: 8.9538 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1216 loss: 1.1216 2022/09/04 06:59:57 - mmengine - INFO - Epoch(train) [46][1200/1345] lr: 1.0000e-05 eta: 0:47:57 time: 0.1995 data_time: 0.0081 memory: 7116 grad_norm: 8.9255 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1265 loss: 1.1265 2022/09/04 07:00:01 - mmengine - INFO - Epoch(train) [46][1220/1345] lr: 1.0000e-05 eta: 0:47:46 time: 0.1985 data_time: 0.0103 memory: 7116 grad_norm: 9.1915 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1052 loss: 1.1052 2022/09/04 07:00:05 - mmengine - INFO - Epoch(train) [46][1240/1345] lr: 1.0000e-05 eta: 0:47:35 time: 0.2166 data_time: 0.0105 memory: 7116 grad_norm: 9.1044 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.1346 loss: 1.1346 2022/09/04 07:00:09 - mmengine - INFO - Epoch(train) [46][1260/1345] lr: 1.0000e-05 eta: 0:47:24 time: 0.1952 data_time: 0.0087 memory: 7116 grad_norm: 9.2301 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2833 loss: 1.2833 2022/09/04 07:00:13 - mmengine - INFO - Epoch(train) [46][1280/1345] lr: 1.0000e-05 eta: 0:47:13 time: 0.1982 data_time: 0.0107 memory: 7116 grad_norm: 9.3297 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4127 loss: 1.4127 2022/09/04 07:00:17 - mmengine - INFO - Epoch(train) [46][1300/1345] lr: 1.0000e-05 eta: 0:47:02 time: 0.2037 data_time: 0.0091 memory: 7116 grad_norm: 9.0175 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9205 loss: 0.9205 2022/09/04 07:00:21 - mmengine - INFO - Epoch(train) [46][1320/1345] lr: 1.0000e-05 eta: 0:46:51 time: 0.2011 data_time: 0.0082 memory: 7116 grad_norm: 9.0427 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2170 loss: 1.2170 2022/09/04 07:00:25 - mmengine - INFO - Epoch(train) [46][1340/1345] lr: 1.0000e-05 eta: 0:46:40 time: 0.1995 data_time: 0.0114 memory: 7116 grad_norm: 8.9470 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0422 loss: 1.0422 2022/09/04 07:00:26 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 07:00:26 - mmengine - INFO - Epoch(train) [46][1345/1345] lr: 1.0000e-05 eta: 0:46:40 time: 0.1952 data_time: 0.0098 memory: 7116 grad_norm: 9.3741 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.2799 loss: 1.2799 2022/09/04 07:00:26 - mmengine - INFO - Saving checkpoint at 46 epochs 2022/09/04 07:00:29 - mmengine - INFO - Epoch(val) [46][20/181] eta: 0:00:07 time: 0.0481 data_time: 0.0110 memory: 1114 2022/09/04 07:00:30 - mmengine - INFO - Epoch(val) [46][40/181] eta: 0:00:06 time: 0.0426 data_time: 0.0069 memory: 1114 2022/09/04 07:00:31 - mmengine - INFO - Epoch(val) [46][60/181] eta: 0:00:05 time: 0.0415 data_time: 0.0063 memory: 1114 2022/09/04 07:00:32 - mmengine - INFO - Epoch(val) [46][80/181] eta: 0:00:04 time: 0.0424 data_time: 0.0065 memory: 1114 2022/09/04 07:00:33 - mmengine - INFO - Epoch(val) [46][100/181] eta: 0:00:04 time: 0.0552 data_time: 0.0194 memory: 1114 2022/09/04 07:00:34 - mmengine - INFO - Epoch(val) [46][120/181] eta: 0:00:02 time: 0.0410 data_time: 0.0058 memory: 1114 2022/09/04 07:00:34 - mmengine - INFO - Epoch(val) [46][140/181] eta: 0:00:01 time: 0.0412 data_time: 0.0061 memory: 1114 2022/09/04 07:00:35 - mmengine - INFO - Epoch(val) [46][160/181] eta: 0:00:00 time: 0.0414 data_time: 0.0062 memory: 1114 2022/09/04 07:00:36 - mmengine - INFO - Epoch(val) [46][180/181] eta: 0:00:00 time: 0.0438 data_time: 0.0076 memory: 1114 2022/09/04 07:00:38 - mmengine - INFO - Epoch(val) [46][181/181] acc/top1: 0.4665 acc/top5: 0.7560 acc/mean1: 0.4277 2022/09/04 07:00:38 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_43.pth is removed 2022/09/04 07:00:39 - mmengine - INFO - The best checkpoint with 0.4665 acc/top1 at 46 epoch is saved to best_acc/top1_epoch_46.pth. 2022/09/04 07:00:44 - mmengine - INFO - Epoch(train) [47][20/1345] lr: 1.0000e-05 eta: 0:46:26 time: 0.2049 data_time: 0.0137 memory: 7116 grad_norm: 9.2406 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1733 loss: 1.1733 2022/09/04 07:00:47 - mmengine - INFO - Epoch(train) [47][40/1345] lr: 1.0000e-05 eta: 0:46:15 time: 0.1958 data_time: 0.0091 memory: 7116 grad_norm: 8.9788 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2173 loss: 1.2173 2022/09/04 07:00:51 - mmengine - INFO - Epoch(train) [47][60/1345] lr: 1.0000e-05 eta: 0:46:05 time: 0.1988 data_time: 0.0090 memory: 7116 grad_norm: 8.9943 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2116 loss: 1.2116 2022/09/04 07:00:56 - mmengine - INFO - Epoch(train) [47][80/1345] lr: 1.0000e-05 eta: 0:45:54 time: 0.2068 data_time: 0.0122 memory: 7116 grad_norm: 8.9362 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.2341 loss: 1.2341 2022/09/04 07:01:00 - mmengine - INFO - Epoch(train) [47][100/1345] lr: 1.0000e-05 eta: 0:45:43 time: 0.1971 data_time: 0.0098 memory: 7116 grad_norm: 9.2548 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.2499 loss: 1.2499 2022/09/04 07:01:03 - mmengine - INFO - Epoch(train) [47][120/1345] lr: 1.0000e-05 eta: 0:45:32 time: 0.1993 data_time: 0.0114 memory: 7116 grad_norm: 9.2738 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2694 loss: 1.2694 2022/09/04 07:01:05 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 07:01:07 - mmengine - INFO - Epoch(train) [47][140/1345] lr: 1.0000e-05 eta: 0:45:21 time: 0.1995 data_time: 0.0113 memory: 7116 grad_norm: 9.0526 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3771 loss: 1.3771 2022/09/04 07:01:12 - mmengine - INFO - Epoch(train) [47][160/1345] lr: 1.0000e-05 eta: 0:45:10 time: 0.2025 data_time: 0.0092 memory: 7116 grad_norm: 9.4122 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2563 loss: 1.2563 2022/09/04 07:01:16 - mmengine - INFO - Epoch(train) [47][180/1345] lr: 1.0000e-05 eta: 0:44:59 time: 0.2023 data_time: 0.0091 memory: 7116 grad_norm: 9.2691 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1791 loss: 1.1791 2022/09/04 07:01:20 - mmengine - INFO - Epoch(train) [47][200/1345] lr: 1.0000e-05 eta: 0:44:48 time: 0.1999 data_time: 0.0108 memory: 7116 grad_norm: 8.9822 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3713 loss: 1.3713 2022/09/04 07:01:24 - mmengine - INFO - Epoch(train) [47][220/1345] lr: 1.0000e-05 eta: 0:44:37 time: 0.2021 data_time: 0.0103 memory: 7116 grad_norm: 9.2906 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2016 loss: 1.2016 2022/09/04 07:01:28 - mmengine - INFO - Epoch(train) [47][240/1345] lr: 1.0000e-05 eta: 0:44:26 time: 0.1993 data_time: 0.0088 memory: 7116 grad_norm: 9.3015 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.2991 loss: 1.2991 2022/09/04 07:01:32 - mmengine - INFO - Epoch(train) [47][260/1345] lr: 1.0000e-05 eta: 0:44:15 time: 0.2021 data_time: 0.0103 memory: 7116 grad_norm: 9.0857 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.2065 loss: 1.2065 2022/09/04 07:01:36 - mmengine - INFO - Epoch(train) [47][280/1345] lr: 1.0000e-05 eta: 0:44:04 time: 0.2017 data_time: 0.0081 memory: 7116 grad_norm: 8.8899 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0361 loss: 1.0361 2022/09/04 07:01:40 - mmengine - INFO - Epoch(train) [47][300/1345] lr: 1.0000e-05 eta: 0:43:54 time: 0.2006 data_time: 0.0083 memory: 7116 grad_norm: 8.8552 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0557 loss: 1.0557 2022/09/04 07:01:44 - mmengine - INFO - Epoch(train) [47][320/1345] lr: 1.0000e-05 eta: 0:43:43 time: 0.2091 data_time: 0.0109 memory: 7116 grad_norm: 9.0760 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1767 loss: 1.1767 2022/09/04 07:01:48 - mmengine - INFO - Epoch(train) [47][340/1345] lr: 1.0000e-05 eta: 0:43:32 time: 0.2019 data_time: 0.0088 memory: 7116 grad_norm: 9.4333 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0537 loss: 1.0537 2022/09/04 07:01:52 - mmengine - INFO - Epoch(train) [47][360/1345] lr: 1.0000e-05 eta: 0:43:21 time: 0.2005 data_time: 0.0077 memory: 7116 grad_norm: 9.1444 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2682 loss: 1.2682 2022/09/04 07:01:56 - mmengine - INFO - Epoch(train) [47][380/1345] lr: 1.0000e-05 eta: 0:43:10 time: 0.2018 data_time: 0.0103 memory: 7116 grad_norm: 9.0597 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3693 loss: 1.3693 2022/09/04 07:02:00 - mmengine - INFO - Epoch(train) [47][400/1345] lr: 1.0000e-05 eta: 0:42:59 time: 0.2001 data_time: 0.0079 memory: 7116 grad_norm: 9.1376 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1654 loss: 1.1654 2022/09/04 07:02:04 - mmengine - INFO - Epoch(train) [47][420/1345] lr: 1.0000e-05 eta: 0:42:48 time: 0.2084 data_time: 0.0084 memory: 7116 grad_norm: 8.7096 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0455 loss: 1.0455 2022/09/04 07:02:08 - mmengine - INFO - Epoch(train) [47][440/1345] lr: 1.0000e-05 eta: 0:42:37 time: 0.2030 data_time: 0.0108 memory: 7116 grad_norm: 9.0234 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3212 loss: 1.3212 2022/09/04 07:02:12 - mmengine - INFO - Epoch(train) [47][460/1345] lr: 1.0000e-05 eta: 0:42:27 time: 0.1999 data_time: 0.0078 memory: 7116 grad_norm: 9.0776 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2527 loss: 1.2527 2022/09/04 07:02:16 - mmengine - INFO - Epoch(train) [47][480/1345] lr: 1.0000e-05 eta: 0:42:16 time: 0.2021 data_time: 0.0084 memory: 7116 grad_norm: 9.1577 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2507 loss: 1.2507 2022/09/04 07:02:20 - mmengine - INFO - Epoch(train) [47][500/1345] lr: 1.0000e-05 eta: 0:42:05 time: 0.2011 data_time: 0.0105 memory: 7116 grad_norm: 8.7414 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8889 loss: 0.8889 2022/09/04 07:02:24 - mmengine - INFO - Epoch(train) [47][520/1345] lr: 1.0000e-05 eta: 0:41:54 time: 0.2070 data_time: 0.0083 memory: 7116 grad_norm: 9.0987 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2525 loss: 1.2525 2022/09/04 07:02:29 - mmengine - INFO - Epoch(train) [47][540/1345] lr: 1.0000e-05 eta: 0:41:43 time: 0.2021 data_time: 0.0086 memory: 7116 grad_norm: 9.1281 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1085 loss: 1.1085 2022/09/04 07:02:33 - mmengine - INFO - Epoch(train) [47][560/1345] lr: 1.0000e-05 eta: 0:41:32 time: 0.2004 data_time: 0.0112 memory: 7116 grad_norm: 8.8279 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0142 loss: 1.0142 2022/09/04 07:02:37 - mmengine - INFO - Epoch(train) [47][580/1345] lr: 1.0000e-05 eta: 0:41:22 time: 0.1999 data_time: 0.0081 memory: 7116 grad_norm: 9.1300 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3077 loss: 1.3077 2022/09/04 07:02:41 - mmengine - INFO - Epoch(train) [47][600/1345] lr: 1.0000e-05 eta: 0:41:11 time: 0.1986 data_time: 0.0079 memory: 7116 grad_norm: 8.9128 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2170 loss: 1.2170 2022/09/04 07:02:45 - mmengine - INFO - Epoch(train) [47][620/1345] lr: 1.0000e-05 eta: 0:41:00 time: 0.2086 data_time: 0.0117 memory: 7116 grad_norm: 9.3677 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1486 loss: 1.1486 2022/09/04 07:02:49 - mmengine - INFO - Epoch(train) [47][640/1345] lr: 1.0000e-05 eta: 0:40:49 time: 0.1994 data_time: 0.0083 memory: 7116 grad_norm: 9.2261 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3409 loss: 1.3409 2022/09/04 07:02:53 - mmengine - INFO - Epoch(train) [47][660/1345] lr: 1.0000e-05 eta: 0:40:38 time: 0.2038 data_time: 0.0088 memory: 7116 grad_norm: 8.8559 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2995 loss: 1.2995 2022/09/04 07:02:57 - mmengine - INFO - Epoch(train) [47][680/1345] lr: 1.0000e-05 eta: 0:40:27 time: 0.2033 data_time: 0.0100 memory: 7116 grad_norm: 9.3903 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1225 loss: 1.1225 2022/09/04 07:03:01 - mmengine - INFO - Epoch(train) [47][700/1345] lr: 1.0000e-05 eta: 0:40:17 time: 0.2027 data_time: 0.0085 memory: 7116 grad_norm: 9.0304 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2196 loss: 1.2196 2022/09/04 07:03:05 - mmengine - INFO - Epoch(train) [47][720/1345] lr: 1.0000e-05 eta: 0:40:06 time: 0.2105 data_time: 0.0093 memory: 7116 grad_norm: 9.2057 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0472 loss: 1.0472 2022/09/04 07:03:09 - mmengine - INFO - Epoch(train) [47][740/1345] lr: 1.0000e-05 eta: 0:39:55 time: 0.1989 data_time: 0.0112 memory: 7116 grad_norm: 9.0248 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3141 loss: 1.3141 2022/09/04 07:03:13 - mmengine - INFO - Epoch(train) [47][760/1345] lr: 1.0000e-05 eta: 0:39:44 time: 0.1993 data_time: 0.0083 memory: 7116 grad_norm: 8.9680 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0649 loss: 1.0649 2022/09/04 07:03:17 - mmengine - INFO - Epoch(train) [47][780/1345] lr: 1.0000e-05 eta: 0:39:33 time: 0.1965 data_time: 0.0084 memory: 7116 grad_norm: 9.0967 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.4214 loss: 1.4214 2022/09/04 07:03:21 - mmengine - INFO - Epoch(train) [47][800/1345] lr: 1.0000e-05 eta: 0:39:23 time: 0.2050 data_time: 0.0115 memory: 7116 grad_norm: 9.1329 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1354 loss: 1.1354 2022/09/04 07:03:25 - mmengine - INFO - Epoch(train) [47][820/1345] lr: 1.0000e-05 eta: 0:39:12 time: 0.2093 data_time: 0.0082 memory: 7116 grad_norm: 9.0444 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.1290 loss: 1.1290 2022/09/04 07:03:29 - mmengine - INFO - Epoch(train) [47][840/1345] lr: 1.0000e-05 eta: 0:39:01 time: 0.1987 data_time: 0.0083 memory: 7116 grad_norm: 9.0999 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1986 loss: 1.1986 2022/09/04 07:03:34 - mmengine - INFO - Epoch(train) [47][860/1345] lr: 1.0000e-05 eta: 0:38:50 time: 0.2139 data_time: 0.0118 memory: 7116 grad_norm: 9.1657 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2234 loss: 1.2234 2022/09/04 07:03:38 - mmengine - INFO - Epoch(train) [47][880/1345] lr: 1.0000e-05 eta: 0:38:40 time: 0.1989 data_time: 0.0080 memory: 7116 grad_norm: 9.0961 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3668 loss: 1.3668 2022/09/04 07:03:42 - mmengine - INFO - Epoch(train) [47][900/1345] lr: 1.0000e-05 eta: 0:38:29 time: 0.1953 data_time: 0.0082 memory: 7116 grad_norm: 8.8244 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3717 loss: 1.3717 2022/09/04 07:03:46 - mmengine - INFO - Epoch(train) [47][920/1345] lr: 1.0000e-05 eta: 0:38:18 time: 0.2056 data_time: 0.0102 memory: 7116 grad_norm: 9.0668 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3062 loss: 1.3062 2022/09/04 07:03:50 - mmengine - INFO - Epoch(train) [47][940/1345] lr: 1.0000e-05 eta: 0:38:07 time: 0.2005 data_time: 0.0090 memory: 7116 grad_norm: 8.7646 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3220 loss: 1.3220 2022/09/04 07:03:54 - mmengine - INFO - Epoch(train) [47][960/1345] lr: 1.0000e-05 eta: 0:37:57 time: 0.2164 data_time: 0.0090 memory: 7116 grad_norm: 9.1255 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1418 loss: 1.1418 2022/09/04 07:03:58 - mmengine - INFO - Epoch(train) [47][980/1345] lr: 1.0000e-05 eta: 0:37:46 time: 0.1990 data_time: 0.0104 memory: 7116 grad_norm: 9.1057 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1013 loss: 1.1013 2022/09/04 07:04:02 - mmengine - INFO - Epoch(train) [47][1000/1345] lr: 1.0000e-05 eta: 0:37:35 time: 0.2003 data_time: 0.0089 memory: 7116 grad_norm: 9.5092 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1191 loss: 1.1191 2022/09/04 07:04:06 - mmengine - INFO - Epoch(train) [47][1020/1345] lr: 1.0000e-05 eta: 0:37:24 time: 0.2053 data_time: 0.0083 memory: 7116 grad_norm: 8.4813 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1523 loss: 1.1523 2022/09/04 07:04:10 - mmengine - INFO - Epoch(train) [47][1040/1345] lr: 1.0000e-05 eta: 0:37:14 time: 0.2046 data_time: 0.0103 memory: 7116 grad_norm: 8.9943 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2906 loss: 1.2906 2022/09/04 07:04:14 - mmengine - INFO - Epoch(train) [47][1060/1345] lr: 1.0000e-05 eta: 0:37:03 time: 0.2009 data_time: 0.0082 memory: 7116 grad_norm: 9.2073 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1648 loss: 1.1648 2022/09/04 07:04:18 - mmengine - INFO - Epoch(train) [47][1080/1345] lr: 1.0000e-05 eta: 0:36:52 time: 0.1973 data_time: 0.0088 memory: 7116 grad_norm: 8.8560 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2087 loss: 1.2087 2022/09/04 07:04:22 - mmengine - INFO - Epoch(train) [47][1100/1345] lr: 1.0000e-05 eta: 0:36:42 time: 0.1972 data_time: 0.0107 memory: 7116 grad_norm: 8.9829 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0058 loss: 1.0058 2022/09/04 07:04:26 - mmengine - INFO - Epoch(train) [47][1120/1345] lr: 1.0000e-05 eta: 0:36:31 time: 0.2001 data_time: 0.0091 memory: 7116 grad_norm: 9.0571 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2997 loss: 1.2997 2022/09/04 07:04:28 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 07:04:30 - mmengine - INFO - Epoch(train) [47][1140/1345] lr: 1.0000e-05 eta: 0:36:20 time: 0.2032 data_time: 0.0076 memory: 7116 grad_norm: 8.6253 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1019 loss: 1.1019 2022/09/04 07:04:34 - mmengine - INFO - Epoch(train) [47][1160/1345] lr: 1.0000e-05 eta: 0:36:09 time: 0.2022 data_time: 0.0103 memory: 7116 grad_norm: 8.9189 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2595 loss: 1.2595 2022/09/04 07:04:38 - mmengine - INFO - Epoch(train) [47][1180/1345] lr: 1.0000e-05 eta: 0:35:59 time: 0.1992 data_time: 0.0085 memory: 7116 grad_norm: 9.0297 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1540 loss: 1.1540 2022/09/04 07:04:42 - mmengine - INFO - Epoch(train) [47][1200/1345] lr: 1.0000e-05 eta: 0:35:48 time: 0.1996 data_time: 0.0083 memory: 7116 grad_norm: 9.3259 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0570 loss: 1.0570 2022/09/04 07:04:46 - mmengine - INFO - Epoch(train) [47][1220/1345] lr: 1.0000e-05 eta: 0:35:37 time: 0.2052 data_time: 0.0111 memory: 7116 grad_norm: 9.2281 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0626 loss: 1.0626 2022/09/04 07:04:50 - mmengine - INFO - Epoch(train) [47][1240/1345] lr: 1.0000e-05 eta: 0:35:27 time: 0.2017 data_time: 0.0082 memory: 7116 grad_norm: 9.3614 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2382 loss: 1.2382 2022/09/04 07:04:54 - mmengine - INFO - Epoch(train) [47][1260/1345] lr: 1.0000e-05 eta: 0:35:16 time: 0.1981 data_time: 0.0083 memory: 7116 grad_norm: 8.9968 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.1695 loss: 1.1695 2022/09/04 07:04:58 - mmengine - INFO - Epoch(train) [47][1280/1345] lr: 1.0000e-05 eta: 0:35:05 time: 0.2002 data_time: 0.0105 memory: 7116 grad_norm: 9.0247 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2754 loss: 1.2754 2022/09/04 07:05:02 - mmengine - INFO - Epoch(train) [47][1300/1345] lr: 1.0000e-05 eta: 0:34:55 time: 0.1965 data_time: 0.0081 memory: 7116 grad_norm: 9.1861 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0514 loss: 1.0514 2022/09/04 07:05:06 - mmengine - INFO - Epoch(train) [47][1320/1345] lr: 1.0000e-05 eta: 0:34:44 time: 0.2026 data_time: 0.0086 memory: 7116 grad_norm: 9.0655 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1483 loss: 1.1483 2022/09/04 07:05:10 - mmengine - INFO - Epoch(train) [47][1340/1345] lr: 1.0000e-05 eta: 0:34:33 time: 0.2026 data_time: 0.0097 memory: 7116 grad_norm: 9.6534 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2475 loss: 1.2475 2022/09/04 07:05:11 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 07:05:11 - mmengine - INFO - Epoch(train) [47][1345/1345] lr: 1.0000e-05 eta: 0:34:33 time: 0.1962 data_time: 0.0071 memory: 7116 grad_norm: 9.6222 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.3958 loss: 1.3958 2022/09/04 07:05:11 - mmengine - INFO - Saving checkpoint at 47 epochs 2022/09/04 07:05:14 - mmengine - INFO - Epoch(val) [47][20/181] eta: 0:00:07 time: 0.0485 data_time: 0.0134 memory: 1114 2022/09/04 07:05:15 - mmengine - INFO - Epoch(val) [47][40/181] eta: 0:00:05 time: 0.0406 data_time: 0.0057 memory: 1114 2022/09/04 07:05:15 - mmengine - INFO - Epoch(val) [47][60/181] eta: 0:00:04 time: 0.0399 data_time: 0.0053 memory: 1114 2022/09/04 07:05:16 - mmengine - INFO - Epoch(val) [47][80/181] eta: 0:00:04 time: 0.0407 data_time: 0.0057 memory: 1114 2022/09/04 07:05:17 - mmengine - INFO - Epoch(val) [47][100/181] eta: 0:00:03 time: 0.0414 data_time: 0.0062 memory: 1114 2022/09/04 07:05:18 - mmengine - INFO - Epoch(val) [47][120/181] eta: 0:00:02 time: 0.0413 data_time: 0.0061 memory: 1114 2022/09/04 07:05:19 - mmengine - INFO - Epoch(val) [47][140/181] eta: 0:00:01 time: 0.0411 data_time: 0.0061 memory: 1114 2022/09/04 07:05:20 - mmengine - INFO - Epoch(val) [47][160/181] eta: 0:00:00 time: 0.0405 data_time: 0.0054 memory: 1114 2022/09/04 07:05:20 - mmengine - INFO - Epoch(val) [47][180/181] eta: 0:00:00 time: 0.0403 data_time: 0.0055 memory: 1114 2022/09/04 07:05:24 - mmengine - INFO - Epoch(val) [47][181/181] acc/top1: 0.4661 acc/top5: 0.7560 acc/mean1: 0.4271 2022/09/04 07:05:28 - mmengine - INFO - Epoch(train) [48][20/1345] lr: 1.0000e-05 eta: 0:34:20 time: 0.2230 data_time: 0.0202 memory: 7116 grad_norm: 9.4076 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4007 loss: 1.4007 2022/09/04 07:05:32 - mmengine - INFO - Epoch(train) [48][40/1345] lr: 1.0000e-05 eta: 0:34:09 time: 0.2013 data_time: 0.0083 memory: 7116 grad_norm: 9.2033 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1293 loss: 1.1293 2022/09/04 07:05:36 - mmengine - INFO - Epoch(train) [48][60/1345] lr: 1.0000e-05 eta: 0:33:59 time: 0.2007 data_time: 0.0095 memory: 7116 grad_norm: 9.1422 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3144 loss: 1.3144 2022/09/04 07:05:40 - mmengine - INFO - Epoch(train) [48][80/1345] lr: 1.0000e-05 eta: 0:33:48 time: 0.2010 data_time: 0.0099 memory: 7116 grad_norm: 9.2196 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1890 loss: 1.1890 2022/09/04 07:05:44 - mmengine - INFO - Epoch(train) [48][100/1345] lr: 1.0000e-05 eta: 0:33:37 time: 0.2103 data_time: 0.0084 memory: 7116 grad_norm: 9.1760 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0229 loss: 1.0229 2022/09/04 07:05:49 - mmengine - INFO - Epoch(train) [48][120/1345] lr: 1.0000e-05 eta: 0:33:27 time: 0.2076 data_time: 0.0081 memory: 7116 grad_norm: 8.6147 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1431 loss: 1.1431 2022/09/04 07:05:53 - mmengine - INFO - Epoch(train) [48][140/1345] lr: 1.0000e-05 eta: 0:33:16 time: 0.2048 data_time: 0.0097 memory: 7116 grad_norm: 9.0886 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.2733 loss: 1.2733 2022/09/04 07:05:57 - mmengine - INFO - Epoch(train) [48][160/1345] lr: 1.0000e-05 eta: 0:33:05 time: 0.2075 data_time: 0.0078 memory: 7116 grad_norm: 9.2295 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2611 loss: 1.2611 2022/09/04 07:06:01 - mmengine - INFO - Epoch(train) [48][180/1345] lr: 1.0000e-05 eta: 0:32:55 time: 0.2040 data_time: 0.0089 memory: 7116 grad_norm: 9.0847 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4650 loss: 1.4650 2022/09/04 07:06:05 - mmengine - INFO - Epoch(train) [48][200/1345] lr: 1.0000e-05 eta: 0:32:44 time: 0.2103 data_time: 0.0129 memory: 7116 grad_norm: 9.2460 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0745 loss: 1.0745 2022/09/04 07:06:09 - mmengine - INFO - Epoch(train) [48][220/1345] lr: 1.0000e-05 eta: 0:32:34 time: 0.2007 data_time: 0.0095 memory: 7116 grad_norm: 9.1457 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3437 loss: 1.3437 2022/09/04 07:06:13 - mmengine - INFO - Epoch(train) [48][240/1345] lr: 1.0000e-05 eta: 0:32:23 time: 0.2025 data_time: 0.0089 memory: 7116 grad_norm: 8.6912 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3358 loss: 1.3358 2022/09/04 07:06:17 - mmengine - INFO - Epoch(train) [48][260/1345] lr: 1.0000e-05 eta: 0:32:12 time: 0.2056 data_time: 0.0122 memory: 7116 grad_norm: 8.9327 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9782 loss: 0.9782 2022/09/04 07:06:21 - mmengine - INFO - Epoch(train) [48][280/1345] lr: 1.0000e-05 eta: 0:32:02 time: 0.1977 data_time: 0.0105 memory: 7116 grad_norm: 8.8662 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.0886 loss: 1.0886 2022/09/04 07:06:25 - mmengine - INFO - Epoch(train) [48][300/1345] lr: 1.0000e-05 eta: 0:31:51 time: 0.2021 data_time: 0.0079 memory: 7116 grad_norm: 9.1051 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2738 loss: 1.2738 2022/09/04 07:06:29 - mmengine - INFO - Epoch(train) [48][320/1345] lr: 1.0000e-05 eta: 0:31:41 time: 0.2076 data_time: 0.0105 memory: 7116 grad_norm: 9.1220 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1989 loss: 1.1989 2022/09/04 07:06:34 - mmengine - INFO - Epoch(train) [48][340/1345] lr: 1.0000e-05 eta: 0:31:30 time: 0.2077 data_time: 0.0077 memory: 7116 grad_norm: 9.1747 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3516 loss: 1.3516 2022/09/04 07:06:38 - mmengine - INFO - Epoch(train) [48][360/1345] lr: 1.0000e-05 eta: 0:31:19 time: 0.2114 data_time: 0.0069 memory: 7116 grad_norm: 8.9467 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1308 loss: 1.1308 2022/09/04 07:06:42 - mmengine - INFO - Epoch(train) [48][380/1345] lr: 1.0000e-05 eta: 0:31:09 time: 0.2126 data_time: 0.0094 memory: 7116 grad_norm: 9.3962 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0858 loss: 1.0858 2022/09/04 07:06:46 - mmengine - INFO - Epoch(train) [48][400/1345] lr: 1.0000e-05 eta: 0:30:58 time: 0.2098 data_time: 0.0085 memory: 7116 grad_norm: 9.2779 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1764 loss: 1.1764 2022/09/04 07:06:50 - mmengine - INFO - Epoch(train) [48][420/1345] lr: 1.0000e-05 eta: 0:30:48 time: 0.2077 data_time: 0.0078 memory: 7116 grad_norm: 9.4464 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1039 loss: 1.1039 2022/09/04 07:06:55 - mmengine - INFO - Epoch(train) [48][440/1345] lr: 1.0000e-05 eta: 0:30:37 time: 0.2125 data_time: 0.0098 memory: 7116 grad_norm: 8.9863 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1990 loss: 1.1990 2022/09/04 07:06:59 - mmengine - INFO - Epoch(train) [48][460/1345] lr: 1.0000e-05 eta: 0:30:27 time: 0.2114 data_time: 0.0076 memory: 7116 grad_norm: 8.7911 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2223 loss: 1.2223 2022/09/04 07:07:03 - mmengine - INFO - Epoch(train) [48][480/1345] lr: 1.0000e-05 eta: 0:30:16 time: 0.2112 data_time: 0.0084 memory: 7116 grad_norm: 9.0216 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2370 loss: 1.2370 2022/09/04 07:07:07 - mmengine - INFO - Epoch(train) [48][500/1345] lr: 1.0000e-05 eta: 0:30:05 time: 0.2140 data_time: 0.0097 memory: 7116 grad_norm: 8.9292 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0431 loss: 1.0431 2022/09/04 07:07:12 - mmengine - INFO - Epoch(train) [48][520/1345] lr: 1.0000e-05 eta: 0:29:55 time: 0.2095 data_time: 0.0069 memory: 7116 grad_norm: 8.9793 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0964 loss: 1.0964 2022/09/04 07:07:16 - mmengine - INFO - Epoch(train) [48][540/1345] lr: 1.0000e-05 eta: 0:29:44 time: 0.2099 data_time: 0.0078 memory: 7116 grad_norm: 9.1437 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0936 loss: 1.0936 2022/09/04 07:07:20 - mmengine - INFO - Epoch(train) [48][560/1345] lr: 1.0000e-05 eta: 0:29:34 time: 0.2070 data_time: 0.0098 memory: 7116 grad_norm: 8.9226 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.2180 loss: 1.2180 2022/09/04 07:07:24 - mmengine - INFO - Epoch(train) [48][580/1345] lr: 1.0000e-05 eta: 0:29:23 time: 0.2155 data_time: 0.0076 memory: 7116 grad_norm: 8.8721 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3277 loss: 1.3277 2022/09/04 07:07:28 - mmengine - INFO - Epoch(train) [48][600/1345] lr: 1.0000e-05 eta: 0:29:13 time: 0.2101 data_time: 0.0070 memory: 7116 grad_norm: 9.3048 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2872 loss: 1.2872 2022/09/04 07:07:33 - mmengine - INFO - Epoch(train) [48][620/1345] lr: 1.0000e-05 eta: 0:29:02 time: 0.2073 data_time: 0.0098 memory: 7116 grad_norm: 9.3975 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1399 loss: 1.1399 2022/09/04 07:07:37 - mmengine - INFO - Epoch(train) [48][640/1345] lr: 1.0000e-05 eta: 0:28:52 time: 0.2128 data_time: 0.0087 memory: 7116 grad_norm: 9.1734 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2691 loss: 1.2691 2022/09/04 07:07:41 - mmengine - INFO - Epoch(train) [48][660/1345] lr: 1.0000e-05 eta: 0:28:41 time: 0.2101 data_time: 0.0071 memory: 7116 grad_norm: 8.9313 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0656 loss: 1.0656 2022/09/04 07:07:45 - mmengine - INFO - Epoch(train) [48][680/1345] lr: 1.0000e-05 eta: 0:28:31 time: 0.2115 data_time: 0.0094 memory: 7116 grad_norm: 9.0755 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2032 loss: 1.2032 2022/09/04 07:07:50 - mmengine - INFO - Epoch(train) [48][700/1345] lr: 1.0000e-05 eta: 0:28:20 time: 0.2106 data_time: 0.0076 memory: 7116 grad_norm: 9.2149 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.0101 loss: 1.0101 2022/09/04 07:07:54 - mmengine - INFO - Epoch(train) [48][720/1345] lr: 1.0000e-05 eta: 0:28:10 time: 0.2117 data_time: 0.0079 memory: 7116 grad_norm: 9.3288 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2331 loss: 1.2331 2022/09/04 07:07:58 - mmengine - INFO - Epoch(train) [48][740/1345] lr: 1.0000e-05 eta: 0:27:59 time: 0.2112 data_time: 0.0098 memory: 7116 grad_norm: 9.4334 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2759 loss: 1.2759 2022/09/04 07:08:02 - mmengine - INFO - Epoch(train) [48][760/1345] lr: 1.0000e-05 eta: 0:27:49 time: 0.2103 data_time: 0.0066 memory: 7116 grad_norm: 9.1109 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1044 loss: 1.1044 2022/09/04 07:08:07 - mmengine - INFO - Epoch(train) [48][780/1345] lr: 1.0000e-05 eta: 0:27:38 time: 0.2193 data_time: 0.0080 memory: 7116 grad_norm: 8.9993 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2685 loss: 1.2685 2022/09/04 07:08:08 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 07:08:11 - mmengine - INFO - Epoch(train) [48][800/1345] lr: 1.0000e-05 eta: 0:27:28 time: 0.2092 data_time: 0.0092 memory: 7116 grad_norm: 9.4355 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1334 loss: 1.1334 2022/09/04 07:08:15 - mmengine - INFO - Epoch(train) [48][820/1345] lr: 1.0000e-05 eta: 0:27:17 time: 0.2107 data_time: 0.0073 memory: 7116 grad_norm: 9.2440 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2846 loss: 1.2846 2022/09/04 07:08:19 - mmengine - INFO - Epoch(train) [48][840/1345] lr: 1.0000e-05 eta: 0:27:07 time: 0.2080 data_time: 0.0075 memory: 7116 grad_norm: 8.9023 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1552 loss: 1.1552 2022/09/04 07:08:23 - mmengine - INFO - Epoch(train) [48][860/1345] lr: 1.0000e-05 eta: 0:26:56 time: 0.2070 data_time: 0.0109 memory: 7116 grad_norm: 9.0361 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1891 loss: 1.1891 2022/09/04 07:08:28 - mmengine - INFO - Epoch(train) [48][880/1345] lr: 1.0000e-05 eta: 0:26:46 time: 0.2115 data_time: 0.0079 memory: 7116 grad_norm: 9.0066 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1577 loss: 1.1577 2022/09/04 07:08:32 - mmengine - INFO - Epoch(train) [48][900/1345] lr: 1.0000e-05 eta: 0:26:35 time: 0.2091 data_time: 0.0082 memory: 7116 grad_norm: 9.1371 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2901 loss: 1.2901 2022/09/04 07:08:36 - mmengine - INFO - Epoch(train) [48][920/1345] lr: 1.0000e-05 eta: 0:26:25 time: 0.2148 data_time: 0.0108 memory: 7116 grad_norm: 9.0670 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4936 loss: 1.4936 2022/09/04 07:08:40 - mmengine - INFO - Epoch(train) [48][940/1345] lr: 1.0000e-05 eta: 0:26:14 time: 0.2095 data_time: 0.0074 memory: 7116 grad_norm: 8.7732 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2427 loss: 1.2427 2022/09/04 07:08:44 - mmengine - INFO - Epoch(train) [48][960/1345] lr: 1.0000e-05 eta: 0:26:04 time: 0.2110 data_time: 0.0070 memory: 7116 grad_norm: 8.9251 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1139 loss: 1.1139 2022/09/04 07:08:49 - mmengine - INFO - Epoch(train) [48][980/1345] lr: 1.0000e-05 eta: 0:25:53 time: 0.2134 data_time: 0.0103 memory: 7116 grad_norm: 8.9156 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9803 loss: 0.9803 2022/09/04 07:08:53 - mmengine - INFO - Epoch(train) [48][1000/1345] lr: 1.0000e-05 eta: 0:25:43 time: 0.2106 data_time: 0.0077 memory: 7116 grad_norm: 8.7930 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1028 loss: 1.1028 2022/09/04 07:08:57 - mmengine - INFO - Epoch(train) [48][1020/1345] lr: 1.0000e-05 eta: 0:25:32 time: 0.2157 data_time: 0.0071 memory: 7116 grad_norm: 8.9293 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9159 loss: 0.9159 2022/09/04 07:09:02 - mmengine - INFO - Epoch(train) [48][1040/1345] lr: 1.0000e-05 eta: 0:25:22 time: 0.2125 data_time: 0.0104 memory: 7116 grad_norm: 9.1146 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2253 loss: 1.2253 2022/09/04 07:09:06 - mmengine - INFO - Epoch(train) [48][1060/1345] lr: 1.0000e-05 eta: 0:25:12 time: 0.2109 data_time: 0.0073 memory: 7116 grad_norm: 9.2011 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1823 loss: 1.1823 2022/09/04 07:09:10 - mmengine - INFO - Epoch(train) [48][1080/1345] lr: 1.0000e-05 eta: 0:25:01 time: 0.2133 data_time: 0.0065 memory: 7116 grad_norm: 9.1314 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0059 loss: 1.0059 2022/09/04 07:09:14 - mmengine - INFO - Epoch(train) [48][1100/1345] lr: 1.0000e-05 eta: 0:24:51 time: 0.2167 data_time: 0.0101 memory: 7116 grad_norm: 9.2385 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2578 loss: 1.2578 2022/09/04 07:09:19 - mmengine - INFO - Epoch(train) [48][1120/1345] lr: 1.0000e-05 eta: 0:24:40 time: 0.2118 data_time: 0.0075 memory: 7116 grad_norm: 9.2565 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3518 loss: 1.3518 2022/09/04 07:09:23 - mmengine - INFO - Epoch(train) [48][1140/1345] lr: 1.0000e-05 eta: 0:24:30 time: 0.2129 data_time: 0.0074 memory: 7116 grad_norm: 8.9118 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2443 loss: 1.2443 2022/09/04 07:09:27 - mmengine - INFO - Epoch(train) [48][1160/1345] lr: 1.0000e-05 eta: 0:24:19 time: 0.2173 data_time: 0.0098 memory: 7116 grad_norm: 9.0684 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1704 loss: 1.1704 2022/09/04 07:09:32 - mmengine - INFO - Epoch(train) [48][1180/1345] lr: 1.0000e-05 eta: 0:24:09 time: 0.2168 data_time: 0.0073 memory: 7116 grad_norm: 9.1548 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1556 loss: 1.1556 2022/09/04 07:09:36 - mmengine - INFO - Epoch(train) [48][1200/1345] lr: 1.0000e-05 eta: 0:23:59 time: 0.2239 data_time: 0.0074 memory: 7116 grad_norm: 9.2337 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1185 loss: 1.1185 2022/09/04 07:09:41 - mmengine - INFO - Epoch(train) [48][1220/1345] lr: 1.0000e-05 eta: 0:23:48 time: 0.2262 data_time: 0.0097 memory: 7116 grad_norm: 9.1541 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0435 loss: 1.0435 2022/09/04 07:09:45 - mmengine - INFO - Epoch(train) [48][1240/1345] lr: 1.0000e-05 eta: 0:23:38 time: 0.2202 data_time: 0.0076 memory: 7116 grad_norm: 9.3480 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3748 loss: 1.3748 2022/09/04 07:09:49 - mmengine - INFO - Epoch(train) [48][1260/1345] lr: 1.0000e-05 eta: 0:23:27 time: 0.2174 data_time: 0.0072 memory: 7116 grad_norm: 9.1825 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2036 loss: 1.2036 2022/09/04 07:09:54 - mmengine - INFO - Epoch(train) [48][1280/1345] lr: 1.0000e-05 eta: 0:23:17 time: 0.2148 data_time: 0.0095 memory: 7116 grad_norm: 9.3345 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1376 loss: 1.1376 2022/09/04 07:09:58 - mmengine - INFO - Epoch(train) [48][1300/1345] lr: 1.0000e-05 eta: 0:23:07 time: 0.2165 data_time: 0.0069 memory: 7116 grad_norm: 8.8373 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1954 loss: 1.1954 2022/09/04 07:10:02 - mmengine - INFO - Epoch(train) [48][1320/1345] lr: 1.0000e-05 eta: 0:22:56 time: 0.2166 data_time: 0.0077 memory: 7116 grad_norm: 9.1070 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0010 loss: 1.0010 2022/09/04 07:10:07 - mmengine - INFO - Epoch(train) [48][1340/1345] lr: 1.0000e-05 eta: 0:22:46 time: 0.2143 data_time: 0.0100 memory: 7116 grad_norm: 8.8284 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0936 loss: 1.0936 2022/09/04 07:10:08 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 07:10:08 - mmengine - INFO - Epoch(train) [48][1345/1345] lr: 1.0000e-05 eta: 0:22:46 time: 0.2102 data_time: 0.0073 memory: 7116 grad_norm: 9.0019 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.2902 loss: 1.2902 2022/09/04 07:10:08 - mmengine - INFO - Saving checkpoint at 48 epochs 2022/09/04 07:10:10 - mmengine - INFO - Epoch(val) [48][20/181] eta: 0:00:07 time: 0.0450 data_time: 0.0104 memory: 1114 2022/09/04 07:10:11 - mmengine - INFO - Epoch(val) [48][40/181] eta: 0:00:05 time: 0.0393 data_time: 0.0047 memory: 1114 2022/09/04 07:10:11 - mmengine - INFO - Epoch(val) [48][60/181] eta: 0:00:04 time: 0.0391 data_time: 0.0047 memory: 1114 2022/09/04 07:10:12 - mmengine - INFO - Epoch(val) [48][80/181] eta: 0:00:04 time: 0.0400 data_time: 0.0053 memory: 1114 2022/09/04 07:10:13 - mmengine - INFO - Epoch(val) [48][100/181] eta: 0:00:03 time: 0.0403 data_time: 0.0057 memory: 1114 2022/09/04 07:10:14 - mmengine - INFO - Epoch(val) [48][120/181] eta: 0:00:02 time: 0.0400 data_time: 0.0054 memory: 1114 2022/09/04 07:10:15 - mmengine - INFO - Epoch(val) [48][140/181] eta: 0:00:01 time: 0.0397 data_time: 0.0052 memory: 1114 2022/09/04 07:10:15 - mmengine - INFO - Epoch(val) [48][160/181] eta: 0:00:00 time: 0.0399 data_time: 0.0053 memory: 1114 2022/09/04 07:10:16 - mmengine - INFO - Epoch(val) [48][180/181] eta: 0:00:00 time: 0.0396 data_time: 0.0050 memory: 1114 2022/09/04 07:10:20 - mmengine - INFO - Epoch(val) [48][181/181] acc/top1: 0.4698 acc/top5: 0.7575 acc/mean1: 0.4301 2022/09/04 07:10:20 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb/best_acc/top1_epoch_46.pth is removed 2022/09/04 07:10:21 - mmengine - INFO - The best checkpoint with 0.4698 acc/top1 at 48 epoch is saved to best_acc/top1_epoch_48.pth. 2022/09/04 07:10:26 - mmengine - INFO - Epoch(train) [49][20/1345] lr: 1.0000e-05 eta: 0:22:33 time: 0.2234 data_time: 0.0112 memory: 7116 grad_norm: 8.7983 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1257 loss: 1.1257 2022/09/04 07:10:30 - mmengine - INFO - Epoch(train) [49][40/1345] lr: 1.0000e-05 eta: 0:22:22 time: 0.2279 data_time: 0.0071 memory: 7116 grad_norm: 8.7617 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7924 loss: 0.7924 2022/09/04 07:10:35 - mmengine - INFO - Epoch(train) [49][60/1345] lr: 1.0000e-05 eta: 0:22:12 time: 0.2205 data_time: 0.0097 memory: 7116 grad_norm: 9.0049 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.1230 loss: 1.1230 2022/09/04 07:10:39 - mmengine - INFO - Epoch(train) [49][80/1345] lr: 1.0000e-05 eta: 0:22:02 time: 0.2149 data_time: 0.0079 memory: 7116 grad_norm: 9.2961 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1534 loss: 1.1534 2022/09/04 07:10:43 - mmengine - INFO - Epoch(train) [49][100/1345] lr: 1.0000e-05 eta: 0:21:51 time: 0.2259 data_time: 0.0075 memory: 7116 grad_norm: 9.1494 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2324 loss: 1.2324 2022/09/04 07:10:48 - mmengine - INFO - Epoch(train) [49][120/1345] lr: 1.0000e-05 eta: 0:21:41 time: 0.2161 data_time: 0.0087 memory: 7116 grad_norm: 8.9809 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1619 loss: 1.1619 2022/09/04 07:10:52 - mmengine - INFO - Epoch(train) [49][140/1345] lr: 1.0000e-05 eta: 0:21:31 time: 0.2183 data_time: 0.0079 memory: 7116 grad_norm: 9.0172 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0583 loss: 1.0583 2022/09/04 07:10:57 - mmengine - INFO - Epoch(train) [49][160/1345] lr: 1.0000e-05 eta: 0:21:20 time: 0.2233 data_time: 0.0073 memory: 7116 grad_norm: 8.9612 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0179 loss: 1.0179 2022/09/04 07:11:01 - mmengine - INFO - Epoch(train) [49][180/1345] lr: 1.0000e-05 eta: 0:21:10 time: 0.2239 data_time: 0.0087 memory: 7116 grad_norm: 9.0442 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2416 loss: 1.2416 2022/09/04 07:11:05 - mmengine - INFO - Epoch(train) [49][200/1345] lr: 1.0000e-05 eta: 0:21:00 time: 0.2134 data_time: 0.0082 memory: 7116 grad_norm: 8.8123 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2015 loss: 1.2015 2022/09/04 07:11:10 - mmengine - INFO - Epoch(train) [49][220/1345] lr: 1.0000e-05 eta: 0:20:49 time: 0.2166 data_time: 0.0075 memory: 7116 grad_norm: 9.3493 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3167 loss: 1.3167 2022/09/04 07:11:14 - mmengine - INFO - Epoch(train) [49][240/1345] lr: 1.0000e-05 eta: 0:20:39 time: 0.2185 data_time: 0.0094 memory: 7116 grad_norm: 9.3379 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1048 loss: 1.1048 2022/09/04 07:11:18 - mmengine - INFO - Epoch(train) [49][260/1345] lr: 1.0000e-05 eta: 0:20:29 time: 0.2177 data_time: 0.0085 memory: 7116 grad_norm: 9.0046 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3115 loss: 1.3115 2022/09/04 07:11:23 - mmengine - INFO - Epoch(train) [49][280/1345] lr: 1.0000e-05 eta: 0:20:18 time: 0.2158 data_time: 0.0077 memory: 7116 grad_norm: 9.2678 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1566 loss: 1.1566 2022/09/04 07:11:27 - mmengine - INFO - Epoch(train) [49][300/1345] lr: 1.0000e-05 eta: 0:20:08 time: 0.2281 data_time: 0.0090 memory: 7116 grad_norm: 9.2345 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2097 loss: 1.2097 2022/09/04 07:11:32 - mmengine - INFO - Epoch(train) [49][320/1345] lr: 1.0000e-05 eta: 0:19:58 time: 0.2200 data_time: 0.0074 memory: 7116 grad_norm: 9.3439 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2587 loss: 1.2587 2022/09/04 07:11:36 - mmengine - INFO - Epoch(train) [49][340/1345] lr: 1.0000e-05 eta: 0:19:47 time: 0.2101 data_time: 0.0078 memory: 7116 grad_norm: 9.1401 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0432 loss: 1.0432 2022/09/04 07:11:40 - mmengine - INFO - Epoch(train) [49][360/1345] lr: 1.0000e-05 eta: 0:19:37 time: 0.2182 data_time: 0.0093 memory: 7116 grad_norm: 9.3442 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9612 loss: 0.9612 2022/09/04 07:11:45 - mmengine - INFO - Epoch(train) [49][380/1345] lr: 1.0000e-05 eta: 0:19:27 time: 0.2171 data_time: 0.0075 memory: 7116 grad_norm: 8.9402 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0123 loss: 1.0123 2022/09/04 07:11:49 - mmengine - INFO - Epoch(train) [49][400/1345] lr: 1.0000e-05 eta: 0:19:16 time: 0.2175 data_time: 0.0074 memory: 7116 grad_norm: 9.2604 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1421 loss: 1.1421 2022/09/04 07:11:53 - mmengine - INFO - Epoch(train) [49][420/1345] lr: 1.0000e-05 eta: 0:19:06 time: 0.2170 data_time: 0.0087 memory: 7116 grad_norm: 9.1646 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3163 loss: 1.3163 2022/09/04 07:11:58 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 07:11:58 - mmengine - INFO - Epoch(train) [49][440/1345] lr: 1.0000e-05 eta: 0:18:56 time: 0.2170 data_time: 0.0075 memory: 7116 grad_norm: 9.1353 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1486 loss: 1.1486 2022/09/04 07:12:02 - mmengine - INFO - Epoch(train) [49][460/1345] lr: 1.0000e-05 eta: 0:18:45 time: 0.2142 data_time: 0.0072 memory: 7116 grad_norm: 8.6555 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2259 loss: 1.2259 2022/09/04 07:12:06 - mmengine - INFO - Epoch(train) [49][480/1345] lr: 1.0000e-05 eta: 0:18:35 time: 0.2177 data_time: 0.0092 memory: 7116 grad_norm: 9.0010 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 0.9854 loss: 0.9854 2022/09/04 07:12:11 - mmengine - INFO - Epoch(train) [49][500/1345] lr: 1.0000e-05 eta: 0:18:25 time: 0.2168 data_time: 0.0077 memory: 7116 grad_norm: 8.8153 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0602 loss: 1.0602 2022/09/04 07:12:15 - mmengine - INFO - Epoch(train) [49][520/1345] lr: 1.0000e-05 eta: 0:18:15 time: 0.2172 data_time: 0.0075 memory: 7116 grad_norm: 9.1151 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9441 loss: 0.9441 2022/09/04 07:12:19 - mmengine - INFO - Epoch(train) [49][540/1345] lr: 1.0000e-05 eta: 0:18:04 time: 0.2162 data_time: 0.0083 memory: 7116 grad_norm: 9.2204 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3379 loss: 1.3379 2022/09/04 07:12:24 - mmengine - INFO - Epoch(train) [49][560/1345] lr: 1.0000e-05 eta: 0:17:54 time: 0.2205 data_time: 0.0086 memory: 7116 grad_norm: 9.5204 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3116 loss: 1.3116 2022/09/04 07:12:28 - mmengine - INFO - Epoch(train) [49][580/1345] lr: 1.0000e-05 eta: 0:17:44 time: 0.2201 data_time: 0.0069 memory: 7116 grad_norm: 8.7702 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0570 loss: 1.0570 2022/09/04 07:12:33 - mmengine - INFO - Epoch(train) [49][600/1345] lr: 1.0000e-05 eta: 0:17:33 time: 0.2208 data_time: 0.0096 memory: 7116 grad_norm: 8.9548 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.2708 loss: 1.2708 2022/09/04 07:12:37 - mmengine - INFO - Epoch(train) [49][620/1345] lr: 1.0000e-05 eta: 0:17:23 time: 0.2171 data_time: 0.0082 memory: 7116 grad_norm: 9.0949 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3146 loss: 1.3146 2022/09/04 07:12:41 - mmengine - INFO - Epoch(train) [49][640/1345] lr: 1.0000e-05 eta: 0:17:13 time: 0.2144 data_time: 0.0071 memory: 7116 grad_norm: 9.0961 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2383 loss: 1.2383 2022/09/04 07:12:46 - mmengine - INFO - Epoch(train) [49][660/1345] lr: 1.0000e-05 eta: 0:17:03 time: 0.2222 data_time: 0.0098 memory: 7116 grad_norm: 9.3991 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2272 loss: 1.2272 2022/09/04 07:12:50 - mmengine - INFO - Epoch(train) [49][680/1345] lr: 1.0000e-05 eta: 0:16:52 time: 0.2246 data_time: 0.0076 memory: 7116 grad_norm: 9.1553 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2378 loss: 1.2378 2022/09/04 07:12:55 - mmengine - INFO - Epoch(train) [49][700/1345] lr: 1.0000e-05 eta: 0:16:42 time: 0.2219 data_time: 0.0069 memory: 7116 grad_norm: 9.3974 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9708 loss: 0.9708 2022/09/04 07:12:59 - mmengine - INFO - Epoch(train) [49][720/1345] lr: 1.0000e-05 eta: 0:16:32 time: 0.2199 data_time: 0.0085 memory: 7116 grad_norm: 8.8722 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4580 loss: 1.4580 2022/09/04 07:13:03 - mmengine - INFO - Epoch(train) [49][740/1345] lr: 1.0000e-05 eta: 0:16:22 time: 0.2175 data_time: 0.0077 memory: 7116 grad_norm: 9.2115 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4233 loss: 1.4233 2022/09/04 07:13:08 - mmengine - INFO - Epoch(train) [49][760/1345] lr: 1.0000e-05 eta: 0:16:11 time: 0.2193 data_time: 0.0073 memory: 7116 grad_norm: 9.2241 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9657 loss: 0.9657 2022/09/04 07:13:12 - mmengine - INFO - Epoch(train) [49][780/1345] lr: 1.0000e-05 eta: 0:16:01 time: 0.2190 data_time: 0.0089 memory: 7116 grad_norm: 9.2706 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.0782 loss: 1.0782 2022/09/04 07:13:17 - mmengine - INFO - Epoch(train) [49][800/1345] lr: 1.0000e-05 eta: 0:15:51 time: 0.2284 data_time: 0.0075 memory: 7116 grad_norm: 8.7162 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0812 loss: 1.0812 2022/09/04 07:13:21 - mmengine - INFO - Epoch(train) [49][820/1345] lr: 1.0000e-05 eta: 0:15:41 time: 0.2168 data_time: 0.0069 memory: 7116 grad_norm: 9.0345 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0282 loss: 1.0282 2022/09/04 07:13:25 - mmengine - INFO - Epoch(train) [49][840/1345] lr: 1.0000e-05 eta: 0:15:31 time: 0.2186 data_time: 0.0092 memory: 7116 grad_norm: 9.0194 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0360 loss: 1.0360 2022/09/04 07:13:30 - mmengine - INFO - Epoch(train) [49][860/1345] lr: 1.0000e-05 eta: 0:15:20 time: 0.2166 data_time: 0.0076 memory: 7116 grad_norm: 9.6234 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.1307 loss: 1.1307 2022/09/04 07:13:34 - mmengine - INFO - Epoch(train) [49][880/1345] lr: 1.0000e-05 eta: 0:15:10 time: 0.2200 data_time: 0.0075 memory: 7116 grad_norm: 8.8749 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4685 loss: 1.4685 2022/09/04 07:13:39 - mmengine - INFO - Epoch(train) [49][900/1345] lr: 1.0000e-05 eta: 0:15:00 time: 0.2201 data_time: 0.0088 memory: 7116 grad_norm: 8.9876 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1360 loss: 1.1360 2022/09/04 07:13:43 - mmengine - INFO - Epoch(train) [49][920/1345] lr: 1.0000e-05 eta: 0:14:50 time: 0.2170 data_time: 0.0075 memory: 7116 grad_norm: 8.9556 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0831 loss: 1.0831 2022/09/04 07:13:47 - mmengine - INFO - Epoch(train) [49][940/1345] lr: 1.0000e-05 eta: 0:14:39 time: 0.2172 data_time: 0.0068 memory: 7116 grad_norm: 8.8510 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1287 loss: 1.1287 2022/09/04 07:13:52 - mmengine - INFO - Epoch(train) [49][960/1345] lr: 1.0000e-05 eta: 0:14:29 time: 0.2170 data_time: 0.0091 memory: 7116 grad_norm: 8.7708 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0987 loss: 1.0987 2022/09/04 07:13:56 - mmengine - INFO - Epoch(train) [49][980/1345] lr: 1.0000e-05 eta: 0:14:19 time: 0.2234 data_time: 0.0087 memory: 7116 grad_norm: 9.1727 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3517 loss: 1.3517 2022/09/04 07:14:00 - mmengine - INFO - Epoch(train) [49][1000/1345] lr: 1.0000e-05 eta: 0:14:09 time: 0.2196 data_time: 0.0068 memory: 7116 grad_norm: 9.0957 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1196 loss: 1.1196 2022/09/04 07:14:05 - mmengine - INFO - Epoch(train) [49][1020/1345] lr: 1.0000e-05 eta: 0:13:59 time: 0.2162 data_time: 0.0087 memory: 7116 grad_norm: 8.9212 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3184 loss: 1.3184 2022/09/04 07:14:09 - mmengine - INFO - Epoch(train) [49][1040/1345] lr: 1.0000e-05 eta: 0:13:48 time: 0.2163 data_time: 0.0076 memory: 7116 grad_norm: 9.2477 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1765 loss: 1.1765 2022/09/04 07:14:14 - mmengine - INFO - Epoch(train) [49][1060/1345] lr: 1.0000e-05 eta: 0:13:38 time: 0.2227 data_time: 0.0071 memory: 7116 grad_norm: 8.9303 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1097 loss: 1.1097 2022/09/04 07:14:18 - mmengine - INFO - Epoch(train) [49][1080/1345] lr: 1.0000e-05 eta: 0:13:28 time: 0.2200 data_time: 0.0092 memory: 7116 grad_norm: 9.2512 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1298 loss: 1.1298 2022/09/04 07:14:22 - mmengine - INFO - Epoch(train) [49][1100/1345] lr: 1.0000e-05 eta: 0:13:18 time: 0.2182 data_time: 0.0079 memory: 7116 grad_norm: 8.8574 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1229 loss: 1.1229 2022/09/04 07:14:27 - mmengine - INFO - Epoch(train) [49][1120/1345] lr: 1.0000e-05 eta: 0:13:08 time: 0.2161 data_time: 0.0067 memory: 7116 grad_norm: 9.3808 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2656 loss: 1.2656 2022/09/04 07:14:31 - mmengine - INFO - Epoch(train) [49][1140/1345] lr: 1.0000e-05 eta: 0:12:58 time: 0.2164 data_time: 0.0087 memory: 7116 grad_norm: 9.2509 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.2891 loss: 1.2891 2022/09/04 07:14:36 - mmengine - INFO - Epoch(train) [49][1160/1345] lr: 1.0000e-05 eta: 0:12:47 time: 0.2245 data_time: 0.0088 memory: 7116 grad_norm: 8.5374 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9112 loss: 0.9112 2022/09/04 07:14:40 - mmengine - INFO - Epoch(train) [49][1180/1345] lr: 1.0000e-05 eta: 0:12:37 time: 0.2179 data_time: 0.0069 memory: 7116 grad_norm: 9.2214 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2398 loss: 1.2398 2022/09/04 07:14:44 - mmengine - INFO - Epoch(train) [49][1200/1345] lr: 1.0000e-05 eta: 0:12:27 time: 0.2174 data_time: 0.0090 memory: 7116 grad_norm: 8.9349 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2064 loss: 1.2064 2022/09/04 07:14:49 - mmengine - INFO - Epoch(train) [49][1220/1345] lr: 1.0000e-05 eta: 0:12:17 time: 0.2148 data_time: 0.0077 memory: 7116 grad_norm: 8.7073 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.9627 loss: 0.9627 2022/09/04 07:14:53 - mmengine - INFO - Epoch(train) [49][1240/1345] lr: 1.0000e-05 eta: 0:12:07 time: 0.2169 data_time: 0.0070 memory: 7116 grad_norm: 9.2814 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1142 loss: 1.1142 2022/09/04 07:14:57 - mmengine - INFO - Epoch(train) [49][1260/1345] lr: 1.0000e-05 eta: 0:11:57 time: 0.2249 data_time: 0.0096 memory: 7116 grad_norm: 9.0273 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1174 loss: 1.1174 2022/09/04 07:15:02 - mmengine - INFO - Epoch(train) [49][1280/1345] lr: 1.0000e-05 eta: 0:11:46 time: 0.2164 data_time: 0.0077 memory: 7116 grad_norm: 9.2224 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.2454 loss: 1.2454 2022/09/04 07:15:06 - mmengine - INFO - Epoch(train) [49][1300/1345] lr: 1.0000e-05 eta: 0:11:36 time: 0.2175 data_time: 0.0074 memory: 7116 grad_norm: 9.2306 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1506 loss: 1.1506 2022/09/04 07:15:10 - mmengine - INFO - Epoch(train) [49][1320/1345] lr: 1.0000e-05 eta: 0:11:26 time: 0.2146 data_time: 0.0086 memory: 7116 grad_norm: 9.0985 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2797 loss: 1.2797 2022/09/04 07:15:15 - mmengine - INFO - Epoch(train) [49][1340/1345] lr: 1.0000e-05 eta: 0:11:16 time: 0.2278 data_time: 0.0084 memory: 7116 grad_norm: 9.2421 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1929 loss: 1.1929 2022/09/04 07:15:16 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 07:15:16 - mmengine - INFO - Epoch(train) [49][1345/1345] lr: 1.0000e-05 eta: 0:11:16 time: 0.2230 data_time: 0.0081 memory: 7116 grad_norm: 9.3960 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.2978 loss: 1.2978 2022/09/04 07:15:16 - mmengine - INFO - Saving checkpoint at 49 epochs 2022/09/04 07:15:18 - mmengine - INFO - Epoch(val) [49][20/181] eta: 0:00:07 time: 0.0439 data_time: 0.0091 memory: 1114 2022/09/04 07:15:19 - mmengine - INFO - Epoch(val) [49][40/181] eta: 0:00:05 time: 0.0399 data_time: 0.0052 memory: 1114 2022/09/04 07:15:20 - mmengine - INFO - Epoch(val) [49][60/181] eta: 0:00:04 time: 0.0398 data_time: 0.0052 memory: 1114 2022/09/04 07:15:21 - mmengine - INFO - Epoch(val) [49][80/181] eta: 0:00:04 time: 0.0406 data_time: 0.0060 memory: 1114 2022/09/04 07:15:21 - mmengine - INFO - Epoch(val) [49][100/181] eta: 0:00:03 time: 0.0399 data_time: 0.0051 memory: 1114 2022/09/04 07:15:22 - mmengine - INFO - Epoch(val) [49][120/181] eta: 0:00:02 time: 0.0394 data_time: 0.0049 memory: 1114 2022/09/04 07:15:23 - mmengine - INFO - Epoch(val) [49][140/181] eta: 0:00:01 time: 0.0399 data_time: 0.0053 memory: 1114 2022/09/04 07:15:24 - mmengine - INFO - Epoch(val) [49][160/181] eta: 0:00:00 time: 0.0409 data_time: 0.0064 memory: 1114 2022/09/04 07:15:25 - mmengine - INFO - Epoch(val) [49][180/181] eta: 0:00:00 time: 0.0400 data_time: 0.0057 memory: 1114 2022/09/04 07:15:29 - mmengine - INFO - Epoch(val) [49][181/181] acc/top1: 0.4668 acc/top5: 0.7586 acc/mean1: 0.4278 2022/09/04 07:15:33 - mmengine - INFO - Epoch(train) [50][20/1345] lr: 1.0000e-05 eta: 0:11:03 time: 0.2235 data_time: 0.0124 memory: 7116 grad_norm: 8.9186 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1162 loss: 1.1162 2022/09/04 07:15:38 - mmengine - INFO - Epoch(train) [50][40/1345] lr: 1.0000e-05 eta: 0:10:53 time: 0.2181 data_time: 0.0078 memory: 7116 grad_norm: 8.9257 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1224 loss: 1.1224 2022/09/04 07:15:42 - mmengine - INFO - Epoch(train) [50][60/1345] lr: 1.0000e-05 eta: 0:10:43 time: 0.2176 data_time: 0.0069 memory: 7116 grad_norm: 9.0928 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1122 loss: 1.1122 2022/09/04 07:15:46 - mmengine - INFO - Epoch(train) [50][80/1345] lr: 1.0000e-05 eta: 0:10:33 time: 0.2238 data_time: 0.0096 memory: 7116 grad_norm: 8.8430 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2136 loss: 1.2136 2022/09/04 07:15:50 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 07:15:51 - mmengine - INFO - Epoch(train) [50][100/1345] lr: 1.0000e-05 eta: 0:10:23 time: 0.2154 data_time: 0.0075 memory: 7116 grad_norm: 8.9169 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1431 loss: 1.1431 2022/09/04 07:15:55 - mmengine - INFO - Epoch(train) [50][120/1345] lr: 1.0000e-05 eta: 0:10:13 time: 0.2146 data_time: 0.0069 memory: 7116 grad_norm: 8.8044 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0982 loss: 1.0982 2022/09/04 07:15:59 - mmengine - INFO - Epoch(train) [50][140/1345] lr: 1.0000e-05 eta: 0:10:03 time: 0.2153 data_time: 0.0092 memory: 7116 grad_norm: 9.0841 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2833 loss: 1.2833 2022/09/04 07:16:04 - mmengine - INFO - Epoch(train) [50][160/1345] lr: 1.0000e-05 eta: 0:09:52 time: 0.2194 data_time: 0.0071 memory: 7116 grad_norm: 9.0298 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1167 loss: 1.1167 2022/09/04 07:16:08 - mmengine - INFO - Epoch(train) [50][180/1345] lr: 1.0000e-05 eta: 0:09:42 time: 0.2195 data_time: 0.0069 memory: 7116 grad_norm: 9.3301 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2828 loss: 1.2828 2022/09/04 07:16:12 - mmengine - INFO - Epoch(train) [50][200/1345] lr: 1.0000e-05 eta: 0:09:32 time: 0.2158 data_time: 0.0094 memory: 7116 grad_norm: 9.0848 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1336 loss: 1.1336 2022/09/04 07:16:17 - mmengine - INFO - Epoch(train) [50][220/1345] lr: 1.0000e-05 eta: 0:09:22 time: 0.2165 data_time: 0.0071 memory: 7116 grad_norm: 8.9715 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1567 loss: 1.1567 2022/09/04 07:16:21 - mmengine - INFO - Epoch(train) [50][240/1345] lr: 1.0000e-05 eta: 0:09:12 time: 0.2175 data_time: 0.0069 memory: 7116 grad_norm: 9.3829 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2450 loss: 1.2450 2022/09/04 07:16:25 - mmengine - INFO - Epoch(train) [50][260/1345] lr: 1.0000e-05 eta: 0:09:02 time: 0.2166 data_time: 0.0099 memory: 7116 grad_norm: 8.7855 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0734 loss: 1.0734 2022/09/04 07:16:30 - mmengine - INFO - Epoch(train) [50][280/1345] lr: 1.0000e-05 eta: 0:08:52 time: 0.2172 data_time: 0.0074 memory: 7116 grad_norm: 9.0327 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0408 loss: 1.0408 2022/09/04 07:16:34 - mmengine - INFO - Epoch(train) [50][300/1345] lr: 1.0000e-05 eta: 0:08:42 time: 0.2308 data_time: 0.0073 memory: 7116 grad_norm: 9.0261 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0410 loss: 1.0410 2022/09/04 07:16:39 - mmengine - INFO - Epoch(train) [50][320/1345] lr: 1.0000e-05 eta: 0:08:32 time: 0.2182 data_time: 0.0096 memory: 7116 grad_norm: 9.1246 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4117 loss: 1.4117 2022/09/04 07:16:43 - mmengine - INFO - Epoch(train) [50][340/1345] lr: 1.0000e-05 eta: 0:08:22 time: 0.2165 data_time: 0.0069 memory: 7116 grad_norm: 9.0969 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1917 loss: 1.1917 2022/09/04 07:16:47 - mmengine - INFO - Epoch(train) [50][360/1345] lr: 1.0000e-05 eta: 0:08:12 time: 0.2187 data_time: 0.0074 memory: 7116 grad_norm: 9.1675 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1202 loss: 1.1202 2022/09/04 07:16:52 - mmengine - INFO - Epoch(train) [50][380/1345] lr: 1.0000e-05 eta: 0:08:01 time: 0.2179 data_time: 0.0091 memory: 7116 grad_norm: 9.2713 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0924 loss: 1.0924 2022/09/04 07:16:56 - mmengine - INFO - Epoch(train) [50][400/1345] lr: 1.0000e-05 eta: 0:07:51 time: 0.2244 data_time: 0.0074 memory: 7116 grad_norm: 9.2117 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2596 loss: 1.2596 2022/09/04 07:17:01 - mmengine - INFO - Epoch(train) [50][420/1345] lr: 1.0000e-05 eta: 0:07:41 time: 0.2171 data_time: 0.0070 memory: 7116 grad_norm: 8.9525 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1938 loss: 1.1938 2022/09/04 07:17:05 - mmengine - INFO - Epoch(train) [50][440/1345] lr: 1.0000e-05 eta: 0:07:31 time: 0.2202 data_time: 0.0102 memory: 7116 grad_norm: 8.9954 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.2019 loss: 1.2019 2022/09/04 07:17:09 - mmengine - INFO - Epoch(train) [50][460/1345] lr: 1.0000e-05 eta: 0:07:21 time: 0.2180 data_time: 0.0072 memory: 7116 grad_norm: 9.0094 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2631 loss: 1.2631 2022/09/04 07:17:14 - mmengine - INFO - Epoch(train) [50][480/1345] lr: 1.0000e-05 eta: 0:07:11 time: 0.2175 data_time: 0.0070 memory: 7116 grad_norm: 9.0977 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0787 loss: 1.0787 2022/09/04 07:17:18 - mmengine - INFO - Epoch(train) [50][500/1345] lr: 1.0000e-05 eta: 0:07:01 time: 0.2200 data_time: 0.0098 memory: 7116 grad_norm: 9.0898 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3261 loss: 1.3261 2022/09/04 07:17:23 - mmengine - INFO - Epoch(train) [50][520/1345] lr: 1.0000e-05 eta: 0:06:51 time: 0.2156 data_time: 0.0073 memory: 7116 grad_norm: 8.9991 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0738 loss: 1.0738 2022/09/04 07:17:27 - mmengine - INFO - Epoch(train) [50][540/1345] lr: 1.0000e-05 eta: 0:06:41 time: 0.2203 data_time: 0.0070 memory: 7116 grad_norm: 8.9154 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1221 loss: 1.1221 2022/09/04 07:17:31 - mmengine - INFO - Epoch(train) [50][560/1345] lr: 1.0000e-05 eta: 0:06:31 time: 0.2190 data_time: 0.0094 memory: 7116 grad_norm: 8.6905 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1613 loss: 1.1613 2022/09/04 07:17:36 - mmengine - INFO - Epoch(train) [50][580/1345] lr: 1.0000e-05 eta: 0:06:21 time: 0.2170 data_time: 0.0081 memory: 7116 grad_norm: 9.1375 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.1710 loss: 1.1710 2022/09/04 07:17:40 - mmengine - INFO - Epoch(train) [50][600/1345] lr: 1.0000e-05 eta: 0:06:11 time: 0.2141 data_time: 0.0072 memory: 7116 grad_norm: 9.1884 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2193 loss: 1.2193 2022/09/04 07:17:44 - mmengine - INFO - Epoch(train) [50][620/1345] lr: 1.0000e-05 eta: 0:06:01 time: 0.2181 data_time: 0.0092 memory: 7116 grad_norm: 9.2031 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1041 loss: 1.1041 2022/09/04 07:17:49 - mmengine - INFO - Epoch(train) [50][640/1345] lr: 1.0000e-05 eta: 0:05:51 time: 0.2206 data_time: 0.0067 memory: 7116 grad_norm: 9.0884 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3480 loss: 1.3480 2022/09/04 07:17:53 - mmengine - INFO - Epoch(train) [50][660/1345] lr: 1.0000e-05 eta: 0:05:41 time: 0.2166 data_time: 0.0074 memory: 7116 grad_norm: 9.1273 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1478 loss: 1.1478 2022/09/04 07:17:58 - mmengine - INFO - Epoch(train) [50][680/1345] lr: 1.0000e-05 eta: 0:05:31 time: 0.2331 data_time: 0.0102 memory: 7116 grad_norm: 9.0751 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2520 loss: 1.2520 2022/09/04 07:18:02 - mmengine - INFO - Epoch(train) [50][700/1345] lr: 1.0000e-05 eta: 0:05:21 time: 0.2163 data_time: 0.0069 memory: 7116 grad_norm: 8.9994 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0315 loss: 1.0315 2022/09/04 07:18:07 - mmengine - INFO - Epoch(train) [50][720/1345] lr: 1.0000e-05 eta: 0:05:11 time: 0.2217 data_time: 0.0072 memory: 7116 grad_norm: 8.9562 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1442 loss: 1.1442 2022/09/04 07:18:11 - mmengine - INFO - Epoch(train) [50][740/1345] lr: 1.0000e-05 eta: 0:05:01 time: 0.2180 data_time: 0.0092 memory: 7116 grad_norm: 9.4516 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1554 loss: 1.1554 2022/09/04 07:18:15 - mmengine - INFO - Epoch(train) [50][760/1345] lr: 1.0000e-05 eta: 0:04:51 time: 0.2174 data_time: 0.0078 memory: 7116 grad_norm: 8.8918 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0115 loss: 1.0115 2022/09/04 07:18:20 - mmengine - INFO - Epoch(train) [50][780/1345] lr: 1.0000e-05 eta: 0:04:41 time: 0.2161 data_time: 0.0069 memory: 7116 grad_norm: 9.5501 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1866 loss: 1.1866 2022/09/04 07:18:24 - mmengine - INFO - Epoch(train) [50][800/1345] lr: 1.0000e-05 eta: 0:04:31 time: 0.2202 data_time: 0.0092 memory: 7116 grad_norm: 9.2433 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0660 loss: 1.0660 2022/09/04 07:18:28 - mmengine - INFO - Epoch(train) [50][820/1345] lr: 1.0000e-05 eta: 0:04:21 time: 0.2188 data_time: 0.0070 memory: 7116 grad_norm: 8.8768 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1404 loss: 1.1404 2022/09/04 07:18:33 - mmengine - INFO - Epoch(train) [50][840/1345] lr: 1.0000e-05 eta: 0:04:11 time: 0.2140 data_time: 0.0071 memory: 7116 grad_norm: 8.7487 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2133 loss: 1.2133 2022/09/04 07:18:37 - mmengine - INFO - Epoch(train) [50][860/1345] lr: 1.0000e-05 eta: 0:04:01 time: 0.2201 data_time: 0.0098 memory: 7116 grad_norm: 9.0770 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2080 loss: 1.2080 2022/09/04 07:18:41 - mmengine - INFO - Epoch(train) [50][880/1345] lr: 1.0000e-05 eta: 0:03:51 time: 0.2166 data_time: 0.0069 memory: 7116 grad_norm: 9.0292 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1119 loss: 1.1119 2022/09/04 07:18:46 - mmengine - INFO - Epoch(train) [50][900/1345] lr: 1.0000e-05 eta: 0:03:41 time: 0.2186 data_time: 0.0084 memory: 7116 grad_norm: 8.9996 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1961 loss: 1.1961 2022/09/04 07:18:50 - mmengine - INFO - Epoch(train) [50][920/1345] lr: 1.0000e-05 eta: 0:03:31 time: 0.2173 data_time: 0.0100 memory: 7116 grad_norm: 9.1027 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1244 loss: 1.1244 2022/09/04 07:18:54 - mmengine - INFO - Epoch(train) [50][940/1345] lr: 1.0000e-05 eta: 0:03:21 time: 0.2167 data_time: 0.0068 memory: 7116 grad_norm: 9.0809 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1139 loss: 1.1139 2022/09/04 07:18:59 - mmengine - INFO - Epoch(train) [50][960/1345] lr: 1.0000e-05 eta: 0:03:11 time: 0.2166 data_time: 0.0068 memory: 7116 grad_norm: 9.0557 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1330 loss: 1.1330 2022/09/04 07:19:03 - mmengine - INFO - Epoch(train) [50][980/1345] lr: 1.0000e-05 eta: 0:03:01 time: 0.2157 data_time: 0.0095 memory: 7116 grad_norm: 9.1328 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0522 loss: 1.0522 2022/09/04 07:19:08 - mmengine - INFO - Epoch(train) [50][1000/1345] lr: 1.0000e-05 eta: 0:02:51 time: 0.2239 data_time: 0.0077 memory: 7116 grad_norm: 9.2098 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0535 loss: 1.0535 2022/09/04 07:19:12 - mmengine - INFO - Epoch(train) [50][1020/1345] lr: 1.0000e-05 eta: 0:02:41 time: 0.2146 data_time: 0.0071 memory: 7116 grad_norm: 9.0574 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0474 loss: 1.0474 2022/09/04 07:19:16 - mmengine - INFO - Epoch(train) [50][1040/1345] lr: 1.0000e-05 eta: 0:02:31 time: 0.2192 data_time: 0.0096 memory: 7116 grad_norm: 9.0952 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1615 loss: 1.1615 2022/09/04 07:19:21 - mmengine - INFO - Epoch(train) [50][1060/1345] lr: 1.0000e-05 eta: 0:02:21 time: 0.2120 data_time: 0.0071 memory: 7116 grad_norm: 8.9688 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3736 loss: 1.3736 2022/09/04 07:19:25 - mmengine - INFO - Epoch(train) [50][1080/1345] lr: 1.0000e-05 eta: 0:02:11 time: 0.2133 data_time: 0.0076 memory: 7116 grad_norm: 8.8400 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8880 loss: 0.8880 2022/09/04 07:19:28 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 07:19:29 - mmengine - INFO - Epoch(train) [50][1100/1345] lr: 1.0000e-05 eta: 0:02:01 time: 0.2175 data_time: 0.0090 memory: 7116 grad_norm: 8.8725 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1192 loss: 1.1192 2022/09/04 07:19:33 - mmengine - INFO - Epoch(train) [50][1120/1345] lr: 1.0000e-05 eta: 0:01:51 time: 0.2151 data_time: 0.0072 memory: 7116 grad_norm: 9.4629 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1477 loss: 1.1477 2022/09/04 07:19:38 - mmengine - INFO - Epoch(train) [50][1140/1345] lr: 1.0000e-05 eta: 0:01:41 time: 0.2234 data_time: 0.0090 memory: 7116 grad_norm: 8.8240 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.2011 loss: 1.2011 2022/09/04 07:19:42 - mmengine - INFO - Epoch(train) [50][1160/1345] lr: 1.0000e-05 eta: 0:01:31 time: 0.2142 data_time: 0.0094 memory: 7116 grad_norm: 8.8317 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2021 loss: 1.2021 2022/09/04 07:19:47 - mmengine - INFO - Epoch(train) [50][1180/1345] lr: 1.0000e-05 eta: 0:01:21 time: 0.2259 data_time: 0.0069 memory: 7116 grad_norm: 9.0896 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2673 loss: 1.2673 2022/09/04 07:19:51 - mmengine - INFO - Epoch(train) [50][1200/1345] lr: 1.0000e-05 eta: 0:01:11 time: 0.2127 data_time: 0.0072 memory: 7116 grad_norm: 9.5223 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1504 loss: 1.1504 2022/09/04 07:19:55 - mmengine - INFO - Epoch(train) [50][1220/1345] lr: 1.0000e-05 eta: 0:01:01 time: 0.2167 data_time: 0.0103 memory: 7116 grad_norm: 8.8699 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1394 loss: 1.1394 2022/09/04 07:20:00 - mmengine - INFO - Epoch(train) [50][1240/1345] lr: 1.0000e-05 eta: 0:00:52 time: 0.2129 data_time: 0.0074 memory: 7116 grad_norm: 9.1652 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3522 loss: 1.3522 2022/09/04 07:20:04 - mmengine - INFO - Epoch(train) [50][1260/1345] lr: 1.0000e-05 eta: 0:00:42 time: 0.2135 data_time: 0.0077 memory: 7116 grad_norm: 9.2606 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1477 loss: 1.1477 2022/09/04 07:20:08 - mmengine - INFO - Epoch(train) [50][1280/1345] lr: 1.0000e-05 eta: 0:00:32 time: 0.2219 data_time: 0.0095 memory: 7116 grad_norm: 9.3397 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 1.0905 loss: 1.0905 2022/09/04 07:20:13 - mmengine - INFO - Epoch(train) [50][1300/1345] lr: 1.0000e-05 eta: 0:00:22 time: 0.2137 data_time: 0.0074 memory: 7116 grad_norm: 9.2927 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3609 loss: 1.3609 2022/09/04 07:20:17 - mmengine - INFO - Epoch(train) [50][1320/1345] lr: 1.0000e-05 eta: 0:00:12 time: 0.2171 data_time: 0.0071 memory: 7116 grad_norm: 9.2006 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.1612 loss: 1.1612 2022/09/04 07:20:21 - mmengine - INFO - Epoch(train) [50][1340/1345] lr: 1.0000e-05 eta: 0:00:02 time: 0.2191 data_time: 0.0118 memory: 7116 grad_norm: 8.9634 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9976 loss: 0.9976 2022/09/04 07:20:22 - mmengine - INFO - Exp name: tanet_imagenet-pretrained-r50_8xb8-1x1x8-50e_sthv1-rgb_20220903_214257 2022/09/04 07:20:22 - mmengine - INFO - Epoch(train) [50][1345/1345] lr: 1.0000e-05 eta: 0:00:02 time: 0.2156 data_time: 0.0070 memory: 7116 grad_norm: 9.3235 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.0504 loss: 1.0504 2022/09/04 07:20:22 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/09/04 07:20:25 - mmengine - INFO - Epoch(val) [50][20/181] eta: 0:00:07 time: 0.0444 data_time: 0.0086 memory: 1114 2022/09/04 07:20:26 - mmengine - INFO - Epoch(val) [50][40/181] eta: 0:00:05 time: 0.0420 data_time: 0.0072 memory: 1114 2022/09/04 07:20:27 - mmengine - INFO - Epoch(val) [50][60/181] eta: 0:00:04 time: 0.0404 data_time: 0.0054 memory: 1114 2022/09/04 07:20:27 - mmengine - INFO - Epoch(val) [50][80/181] eta: 0:00:04 time: 0.0399 data_time: 0.0053 memory: 1114 2022/09/04 07:20:28 - mmengine - INFO - Epoch(val) [50][100/181] eta: 0:00:03 time: 0.0408 data_time: 0.0058 memory: 1114 2022/09/04 07:20:29 - mmengine - INFO - Epoch(val) [50][120/181] eta: 0:00:02 time: 0.0403 data_time: 0.0057 memory: 1114 2022/09/04 07:20:30 - mmengine - INFO - Epoch(val) [50][140/181] eta: 0:00:01 time: 0.0397 data_time: 0.0052 memory: 1114 2022/09/04 07:20:31 - mmengine - INFO - Epoch(val) [50][160/181] eta: 0:00:00 time: 0.0394 data_time: 0.0050 memory: 1114 2022/09/04 07:20:31 - mmengine - INFO - Epoch(val) [50][180/181] eta: 0:00:00 time: 0.0395 data_time: 0.0050 memory: 1114 2022/09/04 07:20:35 - mmengine - INFO - Epoch(val) [50][181/181] acc/top1: 0.4651 acc/top5: 0.7568 acc/mean1: 0.4279