2022/09/09 14:03:01 - 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: 433349035 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: 1.9.0+cu111 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.1 - 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_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 - CuDNN 8.0.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, 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 -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.10.0+cu111 OpenCV: 4.6.0 MMEngine: 0.1.0 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None Distributed launcher: slurm Distributed training: True GPU number: 8 ------------------------------------------------------------ 2022/09/09 14:03:01 - mmengine - INFO - Config: preprocess_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], format_shape='NCHW') model = dict( type='Recognizer2D', backbone=dict( type='ResNetTIN', pretrained='torchvision://resnet50', depth=50, norm_eval=False, shift_div=4), cls_head=dict( type='TSMHead', num_classes=174, in_channels=2048, spatial_type='avg', consensus=dict(type='AvgConsensus', dim=1), dropout_ratio=0.8, init_std=0.001, is_shift=False, average_clips=None), data_preprocessor=dict( type='ActionDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], format_shape='NCHW'), train_cfg=None, test_cfg=dict(average_clips=None)) 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'), 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 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' 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), dict(type='Resize', scale=(224, 224), keep_ratio=False), 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, 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') ] train_dataloader = dict( batch_size=24, num_workers=16, 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), dict(type='Resize', scale=(224, 224), keep_ratio=False), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ])) val_dataloader = dict( batch_size=24, 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, 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)) val_evaluator = dict(type='AccMetric') test_evaluator = dict(type='AccMetric') train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=40, val_begin=1, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') optim_wrapper = dict( optimizer=dict(type='SGD', lr=0.04, momentum=0.9, weight_decay=0.0005), constructor='TSMOptimWrapperConstructor', paramwise_cfg=dict(fc_lr5=True), clip_grad=dict(max_norm=20, norm_type=2)) param_scheduler = [ dict( type='LinearLR', start_factor=0.1, by_epoch=True, begin=0, end=1, convert_to_iter_based=True), dict( type='CosineAnnealingLR', T_max=39, eta_min=0, by_epoch=True, convert_to_iter_based=True, begin=1, end=40) ] launcher = 'slurm' work_dir = 'work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4' 2022/09/09 14:03:09 - mmengine - INFO - These parameters in pretrained checkpoint are not loaded: {'fc.weight', 'fc.bias'} 2022/09/09 14:03:11 - mmengine - INFO - Checkpoints will be saved to /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4 by HardDiskBackend. 2022/09/09 14:05:14 - mmengine - INFO - Epoch(train) [1][20/449] lr: 5.5268e-03 eta: 1 day, 6:35:16 time: 6.1380 data_time: 4.4586 memory: 23498 grad_norm: 2.3260 top1_acc: 0.0000 top5_acc: 0.0417 loss_cls: 5.1728 loss: 5.1728 2022/09/09 14:05:33 - mmengine - INFO - Epoch(train) [1][40/449] lr: 7.1339e-03 eta: 17:37:34 time: 0.9439 data_time: 0.2268 memory: 23498 grad_norm: 2.5404 top1_acc: 0.0417 top5_acc: 0.0833 loss_cls: 5.0711 loss: 5.0711 2022/09/09 14:06:02 - mmengine - INFO - Epoch(train) [1][60/449] lr: 8.7411e-03 eta: 14:05:36 time: 1.4214 data_time: 0.0190 memory: 23498 grad_norm: 2.8184 top1_acc: 0.0000 top5_acc: 0.0833 loss_cls: 5.0041 loss: 5.0041 2022/09/09 14:06:11 - mmengine - INFO - Epoch(train) [1][80/449] lr: 1.0348e-02 eta: 11:08:29 time: 0.4697 data_time: 0.0275 memory: 23498 grad_norm: 3.1762 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 4.9529 loss: 4.9529 2022/09/09 14:06:25 - mmengine - INFO - Epoch(train) [1][100/449] lr: 1.1955e-02 eta: 9:35:45 time: 0.6981 data_time: 0.0152 memory: 23498 grad_norm: 3.4123 top1_acc: 0.0417 top5_acc: 0.0833 loss_cls: 4.8147 loss: 4.8147 2022/09/09 14:06:34 - mmengine - INFO - Epoch(train) [1][120/449] lr: 1.3562e-02 eta: 8:21:42 time: 0.4530 data_time: 0.0168 memory: 23498 grad_norm: 3.6335 top1_acc: 0.1250 top5_acc: 0.2083 loss_cls: 4.7397 loss: 4.7397 2022/09/09 14:06:43 - mmengine - INFO - Epoch(train) [1][140/449] lr: 1.5170e-02 eta: 7:29:18 time: 0.4657 data_time: 0.0201 memory: 23498 grad_norm: 3.8294 top1_acc: 0.0417 top5_acc: 0.2083 loss_cls: 4.6805 loss: 4.6805 2022/09/09 14:06:55 - mmengine - INFO - Epoch(train) [1][160/449] lr: 1.6777e-02 eta: 6:54:00 time: 0.5742 data_time: 0.0247 memory: 23498 grad_norm: 3.8971 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.6539 loss: 4.6539 2022/09/09 14:07:21 - mmengine - INFO - Epoch(train) [1][180/449] lr: 1.8384e-02 eta: 6:51:16 time: 1.3271 data_time: 0.0299 memory: 23498 grad_norm: 4.0215 top1_acc: 0.1250 top5_acc: 0.1667 loss_cls: 4.3456 loss: 4.3456 2022/09/09 14:07:34 - mmengine - INFO - Epoch(train) [1][200/449] lr: 1.9991e-02 eta: 6:28:06 time: 0.6206 data_time: 0.0167 memory: 23498 grad_norm: 3.9599 top1_acc: 0.2083 top5_acc: 0.3333 loss_cls: 4.5366 loss: 4.5366 2022/09/09 14:07:57 - mmengine - INFO - Epoch(train) [1][220/449] lr: 2.1598e-02 eta: 6:24:09 time: 1.1803 data_time: 0.0698 memory: 23498 grad_norm: 3.7597 top1_acc: 0.0000 top5_acc: 0.1667 loss_cls: 4.5333 loss: 4.5333 2022/09/09 14:08:13 - mmengine - INFO - Epoch(train) [1][240/449] lr: 2.3205e-02 eta: 6:11:07 time: 0.7877 data_time: 0.0213 memory: 23498 grad_norm: 3.5100 top1_acc: 0.1667 top5_acc: 0.3750 loss_cls: 4.5378 loss: 4.5378 2022/09/09 14:08:25 - mmengine - INFO - Epoch(train) [1][260/449] lr: 2.4813e-02 eta: 5:55:19 time: 0.5785 data_time: 0.0168 memory: 23498 grad_norm: 3.3487 top1_acc: 0.1667 top5_acc: 0.2917 loss_cls: 4.4596 loss: 4.4596 2022/09/09 14:08:48 - mmengine - INFO - Epoch(train) [1][280/449] lr: 2.6420e-02 eta: 5:53:55 time: 1.1574 data_time: 0.0236 memory: 23498 grad_norm: 3.3175 top1_acc: 0.0000 top5_acc: 0.1667 loss_cls: 4.4825 loss: 4.4825 2022/09/09 14:09:01 - mmengine - INFO - Epoch(train) [1][300/449] lr: 2.8027e-02 eta: 5:43:22 time: 0.6833 data_time: 0.0188 memory: 23498 grad_norm: 3.3239 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.3182 loss: 4.3182 2022/09/09 14:09:17 - mmengine - INFO - Epoch(train) [1][320/449] lr: 2.9634e-02 eta: 5:36:00 time: 0.7869 data_time: 0.0235 memory: 23498 grad_norm: 3.1597 top1_acc: 0.0417 top5_acc: 0.2083 loss_cls: 4.2503 loss: 4.2503 2022/09/09 14:09:34 - mmengine - INFO - Epoch(train) [1][340/449] lr: 3.1241e-02 eta: 5:30:37 time: 0.8535 data_time: 0.0392 memory: 23498 grad_norm: 3.0666 top1_acc: 0.0417 top5_acc: 0.3333 loss_cls: 4.3093 loss: 4.3093 2022/09/09 14:09:55 - mmengine - INFO - Epoch(train) [1][360/449] lr: 3.2848e-02 eta: 5:28:40 time: 1.0292 data_time: 0.2069 memory: 23498 grad_norm: 3.0389 top1_acc: 0.0417 top5_acc: 0.1250 loss_cls: 4.4098 loss: 4.4098 2022/09/09 14:10:05 - mmengine - INFO - Epoch(train) [1][380/449] lr: 3.4455e-02 eta: 5:18:33 time: 0.4884 data_time: 0.0445 memory: 23498 grad_norm: 2.9691 top1_acc: 0.0833 top5_acc: 0.2917 loss_cls: 4.2497 loss: 4.2497 2022/09/09 14:10:19 - mmengine - INFO - Epoch(train) [1][400/449] lr: 3.6063e-02 eta: 5:12:29 time: 0.6982 data_time: 0.0121 memory: 23498 grad_norm: 2.9473 top1_acc: 0.2500 top5_acc: 0.4583 loss_cls: 4.1725 loss: 4.1725 2022/09/09 14:10:37 - mmengine - INFO - Epoch(train) [1][420/449] lr: 3.7670e-02 eta: 5:10:07 time: 0.9225 data_time: 0.0236 memory: 23498 grad_norm: 2.7728 top1_acc: 0.1250 top5_acc: 0.3333 loss_cls: 4.2519 loss: 4.2519 2022/09/09 14:10:49 - mmengine - INFO - Epoch(train) [1][440/449] lr: 3.9277e-02 eta: 5:03:45 time: 0.6080 data_time: 0.0163 memory: 23498 grad_norm: 2.9593 top1_acc: 0.1250 top5_acc: 0.2917 loss_cls: 4.1017 loss: 4.1017 2022/09/09 14:11:04 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 14:11:04 - mmengine - INFO - Epoch(train) [1][449/449] lr: 4.0000e-02 eta: 5:03:45 time: 0.9804 data_time: 0.4551 memory: 23498 grad_norm: 3.4273 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 4.3022 loss: 4.3022 2022/09/09 14:12:40 - mmengine - INFO - Epoch(val) [1][20/61] eta: 0:03:18 time: 4.8381 data_time: 4.7184 memory: 2693 2022/09/09 14:13:08 - mmengine - INFO - Epoch(val) [1][40/61] eta: 0:00:28 time: 1.3621 data_time: 1.2437 memory: 2693 2022/09/09 14:13:28 - mmengine - INFO - Epoch(val) [1][60/61] eta: 0:00:01 time: 1.0366 data_time: 0.9176 memory: 2693 2022/09/09 14:13:39 - mmengine - INFO - Epoch(val) [1][61/61] acc/top1: 0.0307 acc/top5: 0.1396 acc/mean1: 0.0285 2022/09/09 14:13:40 - mmengine - INFO - The best checkpoint with 0.0307 acc/top1 at 1 epoch is saved to best_acc/top1_epoch_1.pth. 2022/09/09 14:13:58 - mmengine - INFO - Epoch(train) [2][20/449] lr: 4.0000e-02 eta: 4:55:58 time: 0.9234 data_time: 0.2266 memory: 23498 grad_norm: 2.8969 top1_acc: 0.1667 top5_acc: 0.3750 loss_cls: 4.4198 loss: 4.4198 2022/09/09 14:14:07 - mmengine - INFO - Epoch(train) [2][40/449] lr: 4.0000e-02 eta: 4:48:56 time: 0.4520 data_time: 0.0146 memory: 23498 grad_norm: 2.8184 top1_acc: 0.1667 top5_acc: 0.2500 loss_cls: 4.1278 loss: 4.1278 2022/09/09 14:14:16 - mmengine - INFO - Epoch(train) [2][60/449] lr: 3.9999e-02 eta: 4:42:24 time: 0.4495 data_time: 0.0133 memory: 23498 grad_norm: 2.9166 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.9530 loss: 3.9530 2022/09/09 14:14:26 - mmengine - INFO - Epoch(train) [2][80/449] lr: 3.9998e-02 eta: 4:36:26 time: 0.4586 data_time: 0.0238 memory: 23498 grad_norm: 2.7740 top1_acc: 0.1667 top5_acc: 0.2917 loss_cls: 4.0811 loss: 4.0811 2022/09/09 14:14:40 - mmengine - INFO - Epoch(train) [2][100/449] lr: 3.9997e-02 eta: 4:33:55 time: 0.7433 data_time: 0.0204 memory: 23498 grad_norm: 2.8670 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 3.9662 loss: 3.9662 2022/09/09 14:14:58 - mmengine - INFO - Epoch(train) [2][120/449] lr: 3.9995e-02 eta: 4:32:48 time: 0.8645 data_time: 0.2633 memory: 23498 grad_norm: 2.8479 top1_acc: 0.2083 top5_acc: 0.5417 loss_cls: 3.8830 loss: 3.8830 2022/09/09 14:15:28 - mmengine - INFO - Epoch(train) [2][140/449] lr: 3.9994e-02 eta: 4:38:17 time: 1.5320 data_time: 0.0177 memory: 23498 grad_norm: 2.9618 top1_acc: 0.2500 top5_acc: 0.4167 loss_cls: 3.9604 loss: 3.9604 2022/09/09 14:15:37 - mmengine - INFO - Epoch(train) [2][160/449] lr: 3.9992e-02 eta: 4:33:06 time: 0.4479 data_time: 0.0221 memory: 23498 grad_norm: 2.7843 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.9640 loss: 3.9640 2022/09/09 14:15:51 - mmengine - INFO - Epoch(train) [2][180/449] lr: 3.9990e-02 eta: 4:30:25 time: 0.6875 data_time: 0.0141 memory: 23498 grad_norm: 2.8741 top1_acc: 0.0417 top5_acc: 0.3333 loss_cls: 3.8495 loss: 3.8495 2022/09/09 14:16:10 - mmengine - INFO - Epoch(train) [2][200/449] lr: 3.9987e-02 eta: 4:30:02 time: 0.9281 data_time: 0.0328 memory: 23498 grad_norm: 2.8958 top1_acc: 0.1667 top5_acc: 0.5833 loss_cls: 3.9424 loss: 3.9424 2022/09/09 14:16:26 - mmengine - INFO - Epoch(train) [2][220/449] lr: 3.9985e-02 eta: 4:28:54 time: 0.8399 data_time: 0.0237 memory: 23498 grad_norm: 2.9783 top1_acc: 0.0833 top5_acc: 0.2083 loss_cls: 3.8882 loss: 3.8882 2022/09/09 14:16:36 - mmengine - INFO - Epoch(train) [2][240/449] lr: 3.9982e-02 eta: 4:24:40 time: 0.4639 data_time: 0.0193 memory: 23498 grad_norm: 2.9534 top1_acc: 0.0833 top5_acc: 0.1667 loss_cls: 3.7914 loss: 3.7914 2022/09/09 14:16:49 - mmengine - INFO - Epoch(train) [2][260/449] lr: 3.9978e-02 eta: 4:22:22 time: 0.6737 data_time: 0.0166 memory: 23498 grad_norm: 2.9284 top1_acc: 0.0833 top5_acc: 0.3333 loss_cls: 3.8125 loss: 3.8125 2022/09/09 14:17:05 - mmengine - INFO - Epoch(train) [2][280/449] lr: 3.9975e-02 eta: 4:21:14 time: 0.8073 data_time: 0.0294 memory: 23498 grad_norm: 2.9171 top1_acc: 0.3333 top5_acc: 0.5417 loss_cls: 3.6570 loss: 3.6570 2022/09/09 14:17:19 - mmengine - INFO - Epoch(train) [2][300/449] lr: 3.9971e-02 eta: 4:19:07 time: 0.6721 data_time: 0.0917 memory: 23498 grad_norm: 3.0190 top1_acc: 0.2500 top5_acc: 0.4583 loss_cls: 3.6729 loss: 3.6729 2022/09/09 14:17:36 - mmengine - INFO - Epoch(train) [2][320/449] lr: 3.9967e-02 eta: 4:18:29 time: 0.8597 data_time: 0.0230 memory: 23498 grad_norm: 3.0659 top1_acc: 0.1250 top5_acc: 0.3333 loss_cls: 3.7611 loss: 3.7611 2022/09/09 14:17:51 - mmengine - INFO - Epoch(train) [2][340/449] lr: 3.9963e-02 eta: 4:17:06 time: 0.7536 data_time: 0.3059 memory: 23498 grad_norm: 3.0621 top1_acc: 0.2083 top5_acc: 0.5417 loss_cls: 3.7919 loss: 3.7919 2022/09/09 14:18:07 - mmengine - INFO - Epoch(train) [2][360/449] lr: 3.9959e-02 eta: 4:16:10 time: 0.8071 data_time: 0.3376 memory: 23498 grad_norm: 3.0925 top1_acc: 0.1667 top5_acc: 0.5833 loss_cls: 3.7001 loss: 3.7001 2022/09/09 14:18:17 - mmengine - INFO - Epoch(train) [2][380/449] lr: 3.9954e-02 eta: 4:12:54 time: 0.4673 data_time: 0.0227 memory: 23498 grad_norm: 3.0794 top1_acc: 0.1667 top5_acc: 0.3333 loss_cls: 3.6845 loss: 3.6845 2022/09/09 14:18:33 - mmengine - INFO - Epoch(train) [2][400/449] lr: 3.9949e-02 eta: 4:12:01 time: 0.7984 data_time: 0.0171 memory: 23498 grad_norm: 3.0874 top1_acc: 0.1250 top5_acc: 0.4167 loss_cls: 3.7301 loss: 3.7301 2022/09/09 14:18:50 - mmengine - INFO - Epoch(train) [2][420/449] lr: 3.9944e-02 eta: 4:11:46 time: 0.8883 data_time: 0.0193 memory: 23498 grad_norm: 3.1180 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 3.5950 loss: 3.5950 2022/09/09 14:19:03 - mmengine - INFO - Epoch(train) [2][440/449] lr: 3.9938e-02 eta: 4:09:59 time: 0.6527 data_time: 0.0188 memory: 23498 grad_norm: 3.1201 top1_acc: 0.2083 top5_acc: 0.3750 loss_cls: 3.6959 loss: 3.6959 2022/09/09 14:19:07 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 14:19:07 - mmengine - INFO - Epoch(train) [2][449/449] lr: 3.9935e-02 eta: 4:09:59 time: 0.6389 data_time: 0.0174 memory: 23498 grad_norm: 3.7450 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 3.7089 loss: 3.7089 2022/09/09 14:19:10 - mmengine - INFO - Epoch(val) [2][20/61] eta: 0:00:06 time: 0.1470 data_time: 0.0280 memory: 2693 2022/09/09 14:19:13 - mmengine - INFO - Epoch(val) [2][40/61] eta: 0:00:02 time: 0.1287 data_time: 0.0122 memory: 2693 2022/09/09 14:19:15 - mmengine - INFO - Epoch(val) [2][60/61] eta: 0:00:00 time: 0.1305 data_time: 0.0139 memory: 2693 2022/09/09 14:19:16 - mmengine - INFO - Epoch(val) [2][61/61] acc/top1: 0.0789 acc/top5: 0.2268 acc/mean1: 0.0686 2022/09/09 14:19:16 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_1.pth is removed 2022/09/09 14:19:17 - mmengine - INFO - The best checkpoint with 0.0789 acc/top1 at 2 epoch is saved to best_acc/top1_epoch_2.pth. 2022/09/09 14:19:47 - mmengine - INFO - Epoch(train) [3][20/449] lr: 3.9930e-02 eta: 4:10:41 time: 1.4544 data_time: 0.7161 memory: 23498 grad_norm: 3.2349 top1_acc: 0.1667 top5_acc: 0.5417 loss_cls: 3.8297 loss: 3.8297 2022/09/09 14:19:56 - mmengine - INFO - Epoch(train) [3][40/449] lr: 3.9923e-02 eta: 4:07:54 time: 0.4719 data_time: 0.0283 memory: 23498 grad_norm: 2.9939 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.6926 loss: 3.6926 2022/09/09 14:20:10 - mmengine - INFO - Epoch(train) [3][60/449] lr: 3.9917e-02 eta: 4:06:38 time: 0.7081 data_time: 0.0151 memory: 23498 grad_norm: 3.1659 top1_acc: 0.2500 top5_acc: 0.3333 loss_cls: 3.5903 loss: 3.5903 2022/09/09 14:20:21 - mmengine - INFO - Epoch(train) [3][80/449] lr: 3.9910e-02 eta: 4:04:25 time: 0.5389 data_time: 0.0227 memory: 23498 grad_norm: 3.1182 top1_acc: 0.0000 top5_acc: 0.1667 loss_cls: 3.5153 loss: 3.5153 2022/09/09 14:20:34 - mmengine - INFO - Epoch(train) [3][100/449] lr: 3.9903e-02 eta: 4:03:00 time: 0.6647 data_time: 0.0200 memory: 23498 grad_norm: 3.1601 top1_acc: 0.2500 top5_acc: 0.4167 loss_cls: 3.6111 loss: 3.6111 2022/09/09 14:20:38 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 14:20:46 - mmengine - INFO - Epoch(train) [3][120/449] lr: 3.9896e-02 eta: 4:01:18 time: 0.6041 data_time: 0.0274 memory: 23498 grad_norm: 3.1402 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.6743 loss: 3.6743 2022/09/09 14:20:56 - mmengine - INFO - Epoch(train) [3][140/449] lr: 3.9889e-02 eta: 3:58:53 time: 0.4640 data_time: 0.0154 memory: 23498 grad_norm: 3.1100 top1_acc: 0.1250 top5_acc: 0.5417 loss_cls: 3.6043 loss: 3.6043 2022/09/09 14:21:05 - mmengine - INFO - Epoch(train) [3][160/449] lr: 3.9881e-02 eta: 3:56:30 time: 0.4521 data_time: 0.0171 memory: 23498 grad_norm: 3.1831 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.3751 loss: 3.3751 2022/09/09 14:21:14 - mmengine - INFO - Epoch(train) [3][180/449] lr: 3.9873e-02 eta: 3:54:11 time: 0.4482 data_time: 0.0235 memory: 23498 grad_norm: 3.1569 top1_acc: 0.1667 top5_acc: 0.4583 loss_cls: 3.4111 loss: 3.4111 2022/09/09 14:21:22 - mmengine - INFO - Epoch(train) [3][200/449] lr: 3.9865e-02 eta: 3:51:55 time: 0.4434 data_time: 0.0190 memory: 23498 grad_norm: 3.2152 top1_acc: 0.2500 top5_acc: 0.4583 loss_cls: 3.4571 loss: 3.4571 2022/09/09 14:21:31 - mmengine - INFO - Epoch(train) [3][220/449] lr: 3.9857e-02 eta: 3:49:43 time: 0.4441 data_time: 0.0178 memory: 23498 grad_norm: 3.1110 top1_acc: 0.2500 top5_acc: 0.4167 loss_cls: 3.4236 loss: 3.4236 2022/09/09 14:21:42 - mmengine - INFO - Epoch(train) [3][240/449] lr: 3.9848e-02 eta: 3:48:04 time: 0.5359 data_time: 0.0227 memory: 23498 grad_norm: 3.2572 top1_acc: 0.2500 top5_acc: 0.4583 loss_cls: 3.4074 loss: 3.4074 2022/09/09 14:21:58 - mmengine - INFO - Epoch(train) [3][260/449] lr: 3.9839e-02 eta: 3:47:49 time: 0.8207 data_time: 0.0289 memory: 23498 grad_norm: 3.1821 top1_acc: 0.1667 top5_acc: 0.5417 loss_cls: 3.4237 loss: 3.4237 2022/09/09 14:22:13 - mmengine - INFO - Epoch(train) [3][280/449] lr: 3.9830e-02 eta: 3:47:05 time: 0.7162 data_time: 0.0164 memory: 23498 grad_norm: 3.2394 top1_acc: 0.1250 top5_acc: 0.3333 loss_cls: 3.3478 loss: 3.3478 2022/09/09 14:22:25 - mmengine - INFO - Epoch(train) [3][300/449] lr: 3.9820e-02 eta: 3:45:50 time: 0.6014 data_time: 0.0255 memory: 23498 grad_norm: 3.1257 top1_acc: 0.1667 top5_acc: 0.4583 loss_cls: 3.4859 loss: 3.4859 2022/09/09 14:22:42 - mmengine - INFO - Epoch(train) [3][320/449] lr: 3.9810e-02 eta: 3:45:48 time: 0.8586 data_time: 0.0252 memory: 23498 grad_norm: 3.2662 top1_acc: 0.2917 top5_acc: 0.4583 loss_cls: 3.3687 loss: 3.3687 2022/09/09 14:22:54 - mmengine - INFO - Epoch(train) [3][340/449] lr: 3.9800e-02 eta: 3:44:37 time: 0.6049 data_time: 0.0169 memory: 23498 grad_norm: 3.2323 top1_acc: 0.0833 top5_acc: 0.4167 loss_cls: 3.5112 loss: 3.5112 2022/09/09 14:23:10 - mmengine - INFO - Epoch(train) [3][360/449] lr: 3.9790e-02 eta: 3:44:14 time: 0.7832 data_time: 0.0231 memory: 23498 grad_norm: 3.2092 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.4653 loss: 3.4653 2022/09/09 14:23:26 - mmengine - INFO - Epoch(train) [3][380/449] lr: 3.9780e-02 eta: 3:43:59 time: 0.8088 data_time: 0.0200 memory: 23498 grad_norm: 3.2343 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.4098 loss: 3.4098 2022/09/09 14:23:35 - mmengine - INFO - Epoch(train) [3][400/449] lr: 3.9769e-02 eta: 3:42:16 time: 0.4671 data_time: 0.0213 memory: 23498 grad_norm: 3.3108 top1_acc: 0.1667 top5_acc: 0.3750 loss_cls: 3.4764 loss: 3.4764 2022/09/09 14:23:47 - mmengine - INFO - Epoch(train) [3][420/449] lr: 3.9758e-02 eta: 3:41:05 time: 0.5816 data_time: 0.0148 memory: 23498 grad_norm: 3.2806 top1_acc: 0.2917 top5_acc: 0.6250 loss_cls: 3.3361 loss: 3.3361 2022/09/09 14:24:12 - mmengine - INFO - Epoch(train) [3][440/449] lr: 3.9747e-02 eta: 3:42:45 time: 1.2641 data_time: 0.0220 memory: 23498 grad_norm: 3.1731 top1_acc: 0.1667 top5_acc: 0.4167 loss_cls: 3.6340 loss: 3.6340 2022/09/09 14:24:16 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 14:24:16 - mmengine - INFO - Epoch(train) [3][449/449] lr: 3.9742e-02 eta: 3:42:45 time: 1.0743 data_time: 0.0225 memory: 23498 grad_norm: 3.3780 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 3.4878 loss: 3.4878 2022/09/09 14:24:19 - mmengine - INFO - Epoch(val) [3][20/61] eta: 0:00:06 time: 0.1660 data_time: 0.0439 memory: 2693 2022/09/09 14:24:22 - mmengine - INFO - Epoch(val) [3][40/61] eta: 0:00:02 time: 0.1302 data_time: 0.0130 memory: 2693 2022/09/09 14:24:25 - mmengine - INFO - Epoch(val) [3][60/61] eta: 0:00:00 time: 0.1308 data_time: 0.0141 memory: 2693 2022/09/09 14:24:25 - mmengine - INFO - Epoch(val) [3][61/61] acc/top1: 0.1343 acc/top5: 0.3575 acc/mean1: 0.1142 2022/09/09 14:24:25 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_2.pth is removed 2022/09/09 14:24:26 - mmengine - INFO - The best checkpoint with 0.1343 acc/top1 at 3 epoch is saved to best_acc/top1_epoch_3.pth. 2022/09/09 14:24:45 - mmengine - INFO - Epoch(train) [4][20/449] lr: 3.9730e-02 eta: 3:41:36 time: 0.9778 data_time: 0.1984 memory: 23498 grad_norm: 3.3770 top1_acc: 0.1250 top5_acc: 0.3333 loss_cls: 3.4874 loss: 3.4874 2022/09/09 14:25:03 - mmengine - INFO - Epoch(train) [4][40/449] lr: 3.9718e-02 eta: 3:41:41 time: 0.8877 data_time: 0.0150 memory: 23498 grad_norm: 3.1660 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.4056 loss: 3.4056 2022/09/09 14:25:12 - mmengine - INFO - Epoch(train) [4][60/449] lr: 3.9706e-02 eta: 3:40:02 time: 0.4533 data_time: 0.0148 memory: 23498 grad_norm: 3.4828 top1_acc: 0.2917 top5_acc: 0.4583 loss_cls: 3.3083 loss: 3.3083 2022/09/09 14:25:27 - mmengine - INFO - Epoch(train) [4][80/449] lr: 3.9694e-02 eta: 3:39:34 time: 0.7460 data_time: 0.1001 memory: 23498 grad_norm: 3.4969 top1_acc: 0.1667 top5_acc: 0.6250 loss_cls: 3.2989 loss: 3.2989 2022/09/09 14:25:39 - mmengine - INFO - Epoch(train) [4][100/449] lr: 3.9681e-02 eta: 3:38:27 time: 0.5703 data_time: 0.0227 memory: 23498 grad_norm: 3.5452 top1_acc: 0.2083 top5_acc: 0.4583 loss_cls: 3.3920 loss: 3.3920 2022/09/09 14:25:48 - mmengine - INFO - Epoch(train) [4][120/449] lr: 3.9668e-02 eta: 3:36:55 time: 0.4549 data_time: 0.0225 memory: 23498 grad_norm: 3.3032 top1_acc: 0.2917 top5_acc: 0.4167 loss_cls: 3.3683 loss: 3.3683 2022/09/09 14:26:02 - mmengine - INFO - Epoch(train) [4][140/449] lr: 3.9655e-02 eta: 3:36:23 time: 0.7162 data_time: 0.0156 memory: 23498 grad_norm: 3.2491 top1_acc: 0.2083 top5_acc: 0.4167 loss_cls: 3.2120 loss: 3.2120 2022/09/09 14:26:12 - mmengine - INFO - Epoch(train) [4][160/449] lr: 3.9641e-02 eta: 3:35:06 time: 0.5100 data_time: 0.0193 memory: 23498 grad_norm: 3.2815 top1_acc: 0.2500 top5_acc: 0.5833 loss_cls: 3.3045 loss: 3.3045 2022/09/09 14:26:22 - mmengine - INFO - Epoch(train) [4][180/449] lr: 3.9628e-02 eta: 3:33:44 time: 0.4735 data_time: 0.0253 memory: 23498 grad_norm: 3.5964 top1_acc: 0.2083 top5_acc: 0.4583 loss_cls: 3.1377 loss: 3.1377 2022/09/09 14:26:37 - mmengine - INFO - Epoch(train) [4][200/449] lr: 3.9614e-02 eta: 3:33:24 time: 0.7619 data_time: 0.0157 memory: 23498 grad_norm: 3.6756 top1_acc: 0.1667 top5_acc: 0.4583 loss_cls: 3.2674 loss: 3.2674 2022/09/09 14:26:57 - mmengine - INFO - Epoch(train) [4][220/449] lr: 3.9600e-02 eta: 3:33:53 time: 0.9932 data_time: 0.0142 memory: 23498 grad_norm: 3.4869 top1_acc: 0.1667 top5_acc: 0.2917 loss_cls: 3.4277 loss: 3.4277 2022/09/09 14:27:07 - mmengine - INFO - Epoch(train) [4][240/449] lr: 3.9585e-02 eta: 3:32:37 time: 0.4888 data_time: 0.0227 memory: 23498 grad_norm: 3.4421 top1_acc: 0.1667 top5_acc: 0.3750 loss_cls: 3.3436 loss: 3.3436 2022/09/09 14:27:22 - mmengine - INFO - Epoch(train) [4][260/449] lr: 3.9571e-02 eta: 3:32:15 time: 0.7494 data_time: 0.0165 memory: 23498 grad_norm: 3.3045 top1_acc: 0.3750 top5_acc: 0.7083 loss_cls: 3.2648 loss: 3.2648 2022/09/09 14:27:41 - mmengine - INFO - Epoch(train) [4][280/449] lr: 3.9556e-02 eta: 3:32:42 time: 0.9929 data_time: 0.0208 memory: 23498 grad_norm: 3.2265 top1_acc: 0.2500 top5_acc: 0.5417 loss_cls: 3.3569 loss: 3.3569 2022/09/09 14:27:52 - mmengine - INFO - Epoch(train) [4][300/449] lr: 3.9541e-02 eta: 3:31:41 time: 0.5483 data_time: 0.0235 memory: 23498 grad_norm: 3.4061 top1_acc: 0.2083 top5_acc: 0.5000 loss_cls: 3.2430 loss: 3.2430 2022/09/09 14:28:04 - mmengine - INFO - Epoch(train) [4][320/449] lr: 3.9525e-02 eta: 3:30:45 time: 0.5744 data_time: 0.0214 memory: 23498 grad_norm: 3.6035 top1_acc: 0.1667 top5_acc: 0.6250 loss_cls: 3.2840 loss: 3.2840 2022/09/09 14:28:13 - mmengine - INFO - Epoch(train) [4][340/449] lr: 3.9510e-02 eta: 3:29:26 time: 0.4443 data_time: 0.0214 memory: 23498 grad_norm: 3.6184 top1_acc: 0.2083 top5_acc: 0.5000 loss_cls: 3.2417 loss: 3.2417 2022/09/09 14:28:22 - mmengine - INFO - Epoch(train) [4][360/449] lr: 3.9494e-02 eta: 3:28:10 time: 0.4561 data_time: 0.0199 memory: 23498 grad_norm: 3.3886 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.1898 loss: 3.1898 2022/09/09 14:28:31 - mmengine - INFO - Epoch(train) [4][380/449] lr: 3.9477e-02 eta: 3:26:59 time: 0.4715 data_time: 0.0163 memory: 23498 grad_norm: 3.3014 top1_acc: 0.3333 top5_acc: 0.5417 loss_cls: 3.3051 loss: 3.3051 2022/09/09 14:28:40 - mmengine - INFO - Epoch(train) [4][400/449] lr: 3.9461e-02 eta: 3:25:46 time: 0.4569 data_time: 0.0202 memory: 23498 grad_norm: 3.2100 top1_acc: 0.2917 top5_acc: 0.4583 loss_cls: 3.3924 loss: 3.3924 2022/09/09 14:28:49 - mmengine - INFO - Epoch(train) [4][420/449] lr: 3.9444e-02 eta: 3:24:34 time: 0.4481 data_time: 0.0198 memory: 23498 grad_norm: 3.3426 top1_acc: 0.1250 top5_acc: 0.5417 loss_cls: 3.2794 loss: 3.2794 2022/09/09 14:28:59 - mmengine - INFO - Epoch(train) [4][440/449] lr: 3.9427e-02 eta: 3:23:25 time: 0.4639 data_time: 0.0213 memory: 23498 grad_norm: 3.5626 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.2697 loss: 3.2697 2022/09/09 14:29:02 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 14:29:02 - mmengine - INFO - Epoch(train) [4][449/449] lr: 3.9420e-02 eta: 3:23:25 time: 0.4285 data_time: 0.0166 memory: 23498 grad_norm: 4.0613 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 3.4429 loss: 3.4429 2022/09/09 14:29:06 - mmengine - INFO - Epoch(val) [4][20/61] eta: 0:00:06 time: 0.1493 data_time: 0.0302 memory: 2693 2022/09/09 14:29:08 - mmengine - INFO - Epoch(val) [4][40/61] eta: 0:00:02 time: 0.1296 data_time: 0.0120 memory: 2693 2022/09/09 14:29:11 - mmengine - INFO - Epoch(val) [4][60/61] eta: 0:00:00 time: 0.1306 data_time: 0.0132 memory: 2693 2022/09/09 14:29:11 - mmengine - INFO - Epoch(val) [4][61/61] acc/top1: 0.1274 acc/top5: 0.3290 acc/mean1: 0.1132 2022/09/09 14:29:35 - mmengine - INFO - Epoch(train) [5][20/449] lr: 3.9402e-02 eta: 3:23:24 time: 1.2094 data_time: 0.2459 memory: 23498 grad_norm: 3.6333 top1_acc: 0.2917 top5_acc: 0.4583 loss_cls: 3.5890 loss: 3.5890 2022/09/09 14:29:45 - mmengine - INFO - Epoch(train) [5][40/449] lr: 3.9385e-02 eta: 3:22:16 time: 0.4576 data_time: 0.0190 memory: 23498 grad_norm: 3.4378 top1_acc: 0.0833 top5_acc: 0.4167 loss_cls: 3.3078 loss: 3.3078 2022/09/09 14:29:55 - mmengine - INFO - Epoch(train) [5][60/449] lr: 3.9367e-02 eta: 3:21:20 time: 0.5151 data_time: 0.0215 memory: 23498 grad_norm: 3.4264 top1_acc: 0.2083 top5_acc: 0.5000 loss_cls: 3.1657 loss: 3.1657 2022/09/09 14:30:07 - mmengine - INFO - Epoch(train) [5][80/449] lr: 3.9349e-02 eta: 3:20:37 time: 0.5882 data_time: 0.0166 memory: 23498 grad_norm: 3.1196 top1_acc: 0.3333 top5_acc: 0.5000 loss_cls: 3.4784 loss: 3.4784 2022/09/09 14:30:20 - mmengine - INFO - Epoch(train) [5][100/449] lr: 3.9331e-02 eta: 3:20:11 time: 0.6817 data_time: 0.0534 memory: 23498 grad_norm: 3.3184 top1_acc: 0.2083 top5_acc: 0.5000 loss_cls: 3.1608 loss: 3.1608 2022/09/09 14:30:32 - mmengine - INFO - Epoch(train) [5][120/449] lr: 3.9312e-02 eta: 3:19:27 time: 0.5745 data_time: 0.1376 memory: 23498 grad_norm: 3.3665 top1_acc: 0.2500 top5_acc: 0.5833 loss_cls: 3.1738 loss: 3.1738 2022/09/09 14:30:46 - mmengine - INFO - Epoch(train) [5][140/449] lr: 3.9293e-02 eta: 3:19:07 time: 0.7178 data_time: 0.0162 memory: 23498 grad_norm: 3.4138 top1_acc: 0.2917 top5_acc: 0.5000 loss_cls: 3.2440 loss: 3.2440 2022/09/09 14:30:55 - mmengine - INFO - Epoch(train) [5][160/449] lr: 3.9274e-02 eta: 3:18:04 time: 0.4503 data_time: 0.0168 memory: 23498 grad_norm: 3.6095 top1_acc: 0.1667 top5_acc: 0.4167 loss_cls: 3.3195 loss: 3.3195 2022/09/09 14:31:10 - mmengine - INFO - Epoch(train) [5][180/449] lr: 3.9255e-02 eta: 3:17:50 time: 0.7495 data_time: 0.0197 memory: 23498 grad_norm: 3.5751 top1_acc: 0.3750 top5_acc: 0.6667 loss_cls: 3.1882 loss: 3.1882 2022/09/09 14:31:22 - mmengine - INFO - Epoch(train) [5][200/449] lr: 3.9236e-02 eta: 3:17:10 time: 0.5854 data_time: 0.0178 memory: 23498 grad_norm: 3.4944 top1_acc: 0.1250 top5_acc: 0.5833 loss_cls: 3.2033 loss: 3.2033 2022/09/09 14:31:27 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 14:31:34 - mmengine - INFO - Epoch(train) [5][220/449] lr: 3.9216e-02 eta: 3:16:37 time: 0.6203 data_time: 0.0224 memory: 23498 grad_norm: 3.2877 top1_acc: 0.2500 top5_acc: 0.7083 loss_cls: 3.1559 loss: 3.1559 2022/09/09 14:31:43 - mmengine - INFO - Epoch(train) [5][240/449] lr: 3.9196e-02 eta: 3:15:38 time: 0.4587 data_time: 0.0206 memory: 23498 grad_norm: 3.3578 top1_acc: 0.2917 top5_acc: 0.5417 loss_cls: 3.2390 loss: 3.2390 2022/09/09 14:31:55 - mmengine - INFO - Epoch(train) [5][260/449] lr: 3.9176e-02 eta: 3:14:56 time: 0.5629 data_time: 0.0237 memory: 23498 grad_norm: 3.3884 top1_acc: 0.1667 top5_acc: 0.5833 loss_cls: 3.3798 loss: 3.3798 2022/09/09 14:32:05 - mmengine - INFO - Epoch(train) [5][280/449] lr: 3.9155e-02 eta: 3:14:05 time: 0.4997 data_time: 0.0168 memory: 23498 grad_norm: 3.3046 top1_acc: 0.2083 top5_acc: 0.4583 loss_cls: 3.2607 loss: 3.2607 2022/09/09 14:32:14 - mmengine - INFO - Epoch(train) [5][300/449] lr: 3.9134e-02 eta: 3:13:09 time: 0.4611 data_time: 0.0224 memory: 23498 grad_norm: 3.3011 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.2065 loss: 3.2065 2022/09/09 14:32:26 - mmengine - INFO - Epoch(train) [5][320/449] lr: 3.9113e-02 eta: 3:12:34 time: 0.5922 data_time: 0.0149 memory: 23498 grad_norm: 3.3460 top1_acc: 0.3750 top5_acc: 0.4583 loss_cls: 3.0572 loss: 3.0572 2022/09/09 14:32:35 - mmengine - INFO - Epoch(train) [5][340/449] lr: 3.9092e-02 eta: 3:11:38 time: 0.4488 data_time: 0.0239 memory: 23498 grad_norm: 3.2117 top1_acc: 0.3750 top5_acc: 0.5833 loss_cls: 3.3215 loss: 3.3215 2022/09/09 14:32:48 - mmengine - INFO - Epoch(train) [5][360/449] lr: 3.9071e-02 eta: 3:11:17 time: 0.6831 data_time: 0.0174 memory: 23498 grad_norm: 3.5955 top1_acc: 0.2917 top5_acc: 0.5833 loss_cls: 3.1403 loss: 3.1403 2022/09/09 14:32:57 - mmengine - INFO - Epoch(train) [5][380/449] lr: 3.9049e-02 eta: 3:10:24 time: 0.4586 data_time: 0.0173 memory: 23498 grad_norm: 3.3346 top1_acc: 0.1667 top5_acc: 0.4167 loss_cls: 2.9779 loss: 2.9779 2022/09/09 14:33:08 - mmengine - INFO - Epoch(train) [5][400/449] lr: 3.9027e-02 eta: 3:09:42 time: 0.5354 data_time: 0.0166 memory: 23498 grad_norm: 3.4217 top1_acc: 0.3333 top5_acc: 0.5833 loss_cls: 3.0946 loss: 3.0946 2022/09/09 14:33:27 - mmengine - INFO - Epoch(train) [5][420/449] lr: 3.9005e-02 eta: 3:10:00 time: 0.9526 data_time: 0.0236 memory: 23498 grad_norm: 3.3606 top1_acc: 0.2917 top5_acc: 0.6250 loss_cls: 3.1098 loss: 3.1098 2022/09/09 14:33:36 - mmengine - INFO - Epoch(train) [5][440/449] lr: 3.8982e-02 eta: 3:09:09 time: 0.4583 data_time: 0.0180 memory: 23498 grad_norm: 3.4402 top1_acc: 0.2083 top5_acc: 0.4167 loss_cls: 3.0753 loss: 3.0753 2022/09/09 14:33:42 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 14:33:42 - mmengine - INFO - Epoch(train) [5][449/449] lr: 3.8972e-02 eta: 3:09:09 time: 0.5073 data_time: 0.0902 memory: 23498 grad_norm: 4.3707 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 3.2596 loss: 3.2596 2022/09/09 14:33:45 - mmengine - INFO - Epoch(val) [5][20/61] eta: 0:00:06 time: 0.1491 data_time: 0.0276 memory: 2693 2022/09/09 14:33:47 - mmengine - INFO - Epoch(val) [5][40/61] eta: 0:00:02 time: 0.1299 data_time: 0.0128 memory: 2693 2022/09/09 14:33:50 - mmengine - INFO - Epoch(val) [5][60/61] eta: 0:00:00 time: 0.1319 data_time: 0.0138 memory: 2693 2022/09/09 14:33:50 - mmengine - INFO - Epoch(val) [5][61/61] acc/top1: 0.1020 acc/top5: 0.2756 acc/mean1: 0.0907 2022/09/09 14:34:09 - mmengine - INFO - Epoch(train) [6][20/449] lr: 3.8949e-02 eta: 3:08:34 time: 0.9489 data_time: 0.2870 memory: 23498 grad_norm: 3.6012 top1_acc: 0.2083 top5_acc: 0.4583 loss_cls: 3.1968 loss: 3.1968 2022/09/09 14:34:21 - mmengine - INFO - Epoch(train) [6][40/449] lr: 3.8926e-02 eta: 3:08:00 time: 0.5747 data_time: 0.0179 memory: 23498 grad_norm: 3.7216 top1_acc: 0.3333 top5_acc: 0.6250 loss_cls: 3.2212 loss: 3.2212 2022/09/09 14:34:30 - mmengine - INFO - Epoch(train) [6][60/449] lr: 3.8903e-02 eta: 3:07:10 time: 0.4572 data_time: 0.0237 memory: 23498 grad_norm: 3.4727 top1_acc: 0.3333 top5_acc: 0.4583 loss_cls: 3.3030 loss: 3.3030 2022/09/09 14:34:49 - mmengine - INFO - Epoch(train) [6][80/449] lr: 3.8879e-02 eta: 3:07:26 time: 0.9482 data_time: 0.0182 memory: 23498 grad_norm: 3.6323 top1_acc: 0.3750 top5_acc: 0.7083 loss_cls: 3.1516 loss: 3.1516 2022/09/09 14:35:02 - mmengine - INFO - Epoch(train) [6][100/449] lr: 3.8855e-02 eta: 3:07:00 time: 0.6303 data_time: 0.0198 memory: 23498 grad_norm: 3.4622 top1_acc: 0.1667 top5_acc: 0.3750 loss_cls: 3.1876 loss: 3.1876 2022/09/09 14:35:12 - mmengine - INFO - Epoch(train) [6][120/449] lr: 3.8831e-02 eta: 3:06:18 time: 0.5067 data_time: 0.0254 memory: 23498 grad_norm: 3.3889 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.9761 loss: 2.9761 2022/09/09 14:35:21 - mmengine - INFO - Epoch(train) [6][140/449] lr: 3.8807e-02 eta: 3:05:30 time: 0.4578 data_time: 0.0235 memory: 23498 grad_norm: 3.5038 top1_acc: 0.1667 top5_acc: 0.5417 loss_cls: 3.1768 loss: 3.1768 2022/09/09 14:35:31 - mmengine - INFO - Epoch(train) [6][160/449] lr: 3.8782e-02 eta: 3:04:45 time: 0.4828 data_time: 0.0160 memory: 23498 grad_norm: 3.4225 top1_acc: 0.2500 top5_acc: 0.5833 loss_cls: 3.1370 loss: 3.1370 2022/09/09 14:35:40 - mmengine - INFO - Epoch(train) [6][180/449] lr: 3.8758e-02 eta: 3:03:58 time: 0.4566 data_time: 0.0183 memory: 23498 grad_norm: 3.5216 top1_acc: 0.2500 top5_acc: 0.4167 loss_cls: 3.0710 loss: 3.0710 2022/09/09 14:35:49 - mmengine - INFO - Epoch(train) [6][200/449] lr: 3.8733e-02 eta: 3:03:12 time: 0.4546 data_time: 0.0233 memory: 23498 grad_norm: 3.3817 top1_acc: 0.2917 top5_acc: 0.5833 loss_cls: 2.9902 loss: 2.9902 2022/09/09 14:35:58 - mmengine - INFO - Epoch(train) [6][220/449] lr: 3.8707e-02 eta: 3:02:26 time: 0.4608 data_time: 0.0201 memory: 23498 grad_norm: 3.2978 top1_acc: 0.3333 top5_acc: 0.5833 loss_cls: 3.0040 loss: 3.0040 2022/09/09 14:36:07 - mmengine - INFO - Epoch(train) [6][240/449] lr: 3.8682e-02 eta: 3:01:41 time: 0.4593 data_time: 0.0177 memory: 23498 grad_norm: 3.2279 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.9876 loss: 2.9876 2022/09/09 14:36:17 - mmengine - INFO - Epoch(train) [6][260/449] lr: 3.8656e-02 eta: 3:00:58 time: 0.4658 data_time: 0.0234 memory: 23498 grad_norm: 3.2517 top1_acc: 0.3750 top5_acc: 0.7083 loss_cls: 3.0331 loss: 3.0331 2022/09/09 14:36:26 - mmengine - INFO - Epoch(train) [6][280/449] lr: 3.8630e-02 eta: 3:00:18 time: 0.4916 data_time: 0.0209 memory: 23498 grad_norm: 3.3124 top1_acc: 0.3333 top5_acc: 0.5833 loss_cls: 3.0564 loss: 3.0564 2022/09/09 14:36:39 - mmengine - INFO - Epoch(train) [6][300/449] lr: 3.8604e-02 eta: 2:59:53 time: 0.6147 data_time: 0.0212 memory: 23498 grad_norm: 3.3663 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.9513 loss: 2.9513 2022/09/09 14:36:50 - mmengine - INFO - Epoch(train) [6][320/449] lr: 3.8577e-02 eta: 2:59:23 time: 0.5663 data_time: 0.0195 memory: 23498 grad_norm: 3.2723 top1_acc: 0.2083 top5_acc: 0.5833 loss_cls: 2.9187 loss: 2.9187 2022/09/09 14:37:02 - mmengine - INFO - Epoch(train) [6][340/449] lr: 3.8551e-02 eta: 2:58:56 time: 0.5832 data_time: 0.0259 memory: 23498 grad_norm: 3.3279 top1_acc: 0.3333 top5_acc: 0.5833 loss_cls: 3.0120 loss: 3.0120 2022/09/09 14:37:11 - mmengine - INFO - Epoch(train) [6][360/449] lr: 3.8524e-02 eta: 2:58:14 time: 0.4629 data_time: 0.0266 memory: 23498 grad_norm: 3.3350 top1_acc: 0.2500 top5_acc: 0.5833 loss_cls: 2.9401 loss: 2.9401 2022/09/09 14:37:20 - mmengine - INFO - Epoch(train) [6][380/449] lr: 3.8497e-02 eta: 2:57:33 time: 0.4622 data_time: 0.0180 memory: 23498 grad_norm: 3.3216 top1_acc: 0.3333 top5_acc: 0.6250 loss_cls: 3.0093 loss: 3.0093 2022/09/09 14:37:29 - mmengine - INFO - Epoch(train) [6][400/449] lr: 3.8469e-02 eta: 2:56:52 time: 0.4605 data_time: 0.0226 memory: 23498 grad_norm: 3.3506 top1_acc: 0.3333 top5_acc: 0.4583 loss_cls: 3.0480 loss: 3.0480 2022/09/09 14:37:39 - mmengine - INFO - Epoch(train) [6][420/449] lr: 3.8442e-02 eta: 2:56:11 time: 0.4635 data_time: 0.0184 memory: 23498 grad_norm: 3.4385 top1_acc: 0.3333 top5_acc: 0.5417 loss_cls: 3.0528 loss: 3.0528 2022/09/09 14:37:50 - mmengine - INFO - Epoch(train) [6][440/449] lr: 3.8414e-02 eta: 2:55:46 time: 0.5889 data_time: 0.0181 memory: 23498 grad_norm: 3.5195 top1_acc: 0.1667 top5_acc: 0.5000 loss_cls: 3.0217 loss: 3.0217 2022/09/09 14:37:58 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 14:37:58 - mmengine - INFO - Epoch(train) [6][449/449] lr: 3.8401e-02 eta: 2:55:46 time: 0.7647 data_time: 0.0188 memory: 23498 grad_norm: 3.9295 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 3.3624 loss: 3.3624 2022/09/09 14:38:01 - mmengine - INFO - Epoch(val) [6][20/61] eta: 0:00:06 time: 0.1494 data_time: 0.0286 memory: 2693 2022/09/09 14:38:04 - mmengine - INFO - Epoch(val) [6][40/61] eta: 0:00:02 time: 0.1316 data_time: 0.0134 memory: 2693 2022/09/09 14:38:06 - mmengine - INFO - Epoch(val) [6][60/61] eta: 0:00:00 time: 0.1303 data_time: 0.0133 memory: 2693 2022/09/09 14:38:07 - mmengine - INFO - Epoch(val) [6][61/61] acc/top1: 0.1667 acc/top5: 0.4080 acc/mean1: 0.1496 2022/09/09 14:38:07 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_3.pth is removed 2022/09/09 14:38:08 - mmengine - INFO - The best checkpoint with 0.1667 acc/top1 at 6 epoch is saved to best_acc/top1_epoch_6.pth. 2022/09/09 14:38:24 - mmengine - INFO - Epoch(train) [7][20/449] lr: 3.8373e-02 eta: 2:54:59 time: 0.7654 data_time: 0.2411 memory: 23498 grad_norm: 3.4061 top1_acc: 0.2083 top5_acc: 0.6250 loss_cls: 2.9868 loss: 2.9868 2022/09/09 14:38:33 - mmengine - INFO - Epoch(train) [7][40/449] lr: 3.8344e-02 eta: 2:54:19 time: 0.4537 data_time: 0.0246 memory: 23498 grad_norm: 3.3813 top1_acc: 0.4583 top5_acc: 0.6667 loss_cls: 2.9594 loss: 2.9594 2022/09/09 14:38:42 - mmengine - INFO - Epoch(train) [7][60/449] lr: 3.8316e-02 eta: 2:53:42 time: 0.4716 data_time: 0.0166 memory: 23498 grad_norm: 3.5843 top1_acc: 0.2083 top5_acc: 0.5000 loss_cls: 2.9413 loss: 2.9413 2022/09/09 14:38:51 - mmengine - INFO - Epoch(train) [7][80/449] lr: 3.8287e-02 eta: 2:53:04 time: 0.4636 data_time: 0.0232 memory: 23498 grad_norm: 3.5014 top1_acc: 0.1667 top5_acc: 0.4583 loss_cls: 3.1833 loss: 3.1833 2022/09/09 14:39:01 - mmengine - INFO - Epoch(train) [7][100/449] lr: 3.8257e-02 eta: 2:52:26 time: 0.4627 data_time: 0.0188 memory: 23498 grad_norm: 3.4430 top1_acc: 0.3333 top5_acc: 0.6667 loss_cls: 2.9947 loss: 2.9947 2022/09/09 14:39:10 - mmengine - INFO - Epoch(train) [7][120/449] lr: 3.8228e-02 eta: 2:51:50 time: 0.4721 data_time: 0.0165 memory: 23498 grad_norm: 3.5060 top1_acc: 0.4167 top5_acc: 0.6667 loss_cls: 2.9624 loss: 2.9624 2022/09/09 14:39:19 - mmengine - INFO - Epoch(train) [7][140/449] lr: 3.8198e-02 eta: 2:51:13 time: 0.4656 data_time: 0.0230 memory: 23498 grad_norm: 3.3756 top1_acc: 0.2083 top5_acc: 0.5417 loss_cls: 2.9561 loss: 2.9561 2022/09/09 14:39:29 - mmengine - INFO - Epoch(train) [7][160/449] lr: 3.8169e-02 eta: 2:50:37 time: 0.4648 data_time: 0.0206 memory: 23498 grad_norm: 3.3983 top1_acc: 0.4583 top5_acc: 0.7500 loss_cls: 3.1101 loss: 3.1101 2022/09/09 14:39:38 - mmengine - INFO - Epoch(train) [7][180/449] lr: 3.8138e-02 eta: 2:50:00 time: 0.4573 data_time: 0.0201 memory: 23498 grad_norm: 3.4072 top1_acc: 0.2083 top5_acc: 0.5833 loss_cls: 2.9498 loss: 2.9498 2022/09/09 14:39:47 - mmengine - INFO - Epoch(train) [7][200/449] lr: 3.8108e-02 eta: 2:49:25 time: 0.4690 data_time: 0.0251 memory: 23498 grad_norm: 3.3467 top1_acc: 0.3750 top5_acc: 0.5417 loss_cls: 3.0254 loss: 3.0254 2022/09/09 14:39:59 - mmengine - INFO - Epoch(train) [7][220/449] lr: 3.8077e-02 eta: 2:49:05 time: 0.6071 data_time: 0.0145 memory: 23498 grad_norm: 3.5915 top1_acc: 0.1250 top5_acc: 0.5417 loss_cls: 3.1911 loss: 3.1911 2022/09/09 14:40:08 - mmengine - INFO - Epoch(train) [7][240/449] lr: 3.8047e-02 eta: 2:48:28 time: 0.4498 data_time: 0.0203 memory: 23498 grad_norm: 3.7986 top1_acc: 0.4167 top5_acc: 0.5833 loss_cls: 3.0073 loss: 3.0073 2022/09/09 14:40:17 - mmengine - INFO - Epoch(train) [7][260/449] lr: 3.8016e-02 eta: 2:47:53 time: 0.4616 data_time: 0.0207 memory: 23498 grad_norm: 3.3345 top1_acc: 0.2917 top5_acc: 0.6250 loss_cls: 3.0196 loss: 3.0196 2022/09/09 14:40:28 - mmengine - INFO - Epoch(train) [7][280/449] lr: 3.7984e-02 eta: 2:47:27 time: 0.5424 data_time: 0.0238 memory: 23498 grad_norm: 3.3472 top1_acc: 0.2917 top5_acc: 0.5833 loss_cls: 2.8448 loss: 2.8448 2022/09/09 14:40:37 - mmengine - INFO - Epoch(train) [7][300/449] lr: 3.7953e-02 eta: 2:46:52 time: 0.4542 data_time: 0.0173 memory: 23498 grad_norm: 3.3125 top1_acc: 0.2917 top5_acc: 0.6667 loss_cls: 3.0275 loss: 3.0275 2022/09/09 14:40:40 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 14:40:49 - mmengine - INFO - Epoch(train) [7][320/449] lr: 3.7921e-02 eta: 2:46:28 time: 0.5606 data_time: 0.0245 memory: 23498 grad_norm: 3.4219 top1_acc: 0.2500 top5_acc: 0.5417 loss_cls: 3.0336 loss: 3.0336 2022/09/09 14:40:58 - mmengine - INFO - Epoch(train) [7][340/449] lr: 3.7889e-02 eta: 2:45:55 time: 0.4663 data_time: 0.0196 memory: 23498 grad_norm: 3.4770 top1_acc: 0.2917 top5_acc: 0.5833 loss_cls: 2.8737 loss: 2.8737 2022/09/09 14:41:07 - mmengine - INFO - Epoch(train) [7][360/449] lr: 3.7857e-02 eta: 2:45:21 time: 0.4544 data_time: 0.0156 memory: 23498 grad_norm: 3.2447 top1_acc: 0.2500 top5_acc: 0.5417 loss_cls: 2.9672 loss: 2.9672 2022/09/09 14:41:16 - mmengine - INFO - Epoch(train) [7][380/449] lr: 3.7824e-02 eta: 2:44:47 time: 0.4595 data_time: 0.0178 memory: 23498 grad_norm: 3.3450 top1_acc: 0.3750 top5_acc: 0.7917 loss_cls: 2.7745 loss: 2.7745 2022/09/09 14:41:25 - mmengine - INFO - Epoch(train) [7][400/449] lr: 3.7792e-02 eta: 2:44:14 time: 0.4612 data_time: 0.0225 memory: 23498 grad_norm: 3.3149 top1_acc: 0.4167 top5_acc: 0.6250 loss_cls: 2.9225 loss: 2.9225 2022/09/09 14:41:36 - mmengine - INFO - Epoch(train) [7][420/449] lr: 3.7759e-02 eta: 2:43:47 time: 0.5144 data_time: 0.0776 memory: 23498 grad_norm: 3.4254 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.9656 loss: 2.9656 2022/09/09 14:41:45 - mmengine - INFO - Epoch(train) [7][440/449] lr: 3.7726e-02 eta: 2:43:15 time: 0.4594 data_time: 0.0190 memory: 23498 grad_norm: 3.4005 top1_acc: 0.2917 top5_acc: 0.5833 loss_cls: 2.8945 loss: 2.8945 2022/09/09 14:41:49 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 14:41:49 - mmengine - INFO - Epoch(train) [7][449/449] lr: 3.7711e-02 eta: 2:43:15 time: 0.4431 data_time: 0.0161 memory: 23498 grad_norm: 3.8130 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 3.0054 loss: 3.0054 2022/09/09 14:41:52 - mmengine - INFO - Epoch(val) [7][20/61] eta: 0:00:06 time: 0.1601 data_time: 0.0399 memory: 2693 2022/09/09 14:41:55 - mmengine - INFO - Epoch(val) [7][40/61] eta: 0:00:02 time: 0.1323 data_time: 0.0142 memory: 2693 2022/09/09 14:41:57 - mmengine - INFO - Epoch(val) [7][60/61] eta: 0:00:00 time: 0.1308 data_time: 0.0127 memory: 2693 2022/09/09 14:41:58 - mmengine - INFO - Epoch(val) [7][61/61] acc/top1: 0.1741 acc/top5: 0.4018 acc/mean1: 0.1552 2022/09/09 14:41:58 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_6.pth is removed 2022/09/09 14:41:58 - mmengine - INFO - The best checkpoint with 0.1741 acc/top1 at 7 epoch is saved to best_acc/top1_epoch_7.pth. 2022/09/09 14:42:12 - mmengine - INFO - Epoch(train) [8][20/449] lr: 3.7677e-02 eta: 2:42:30 time: 0.6822 data_time: 0.0903 memory: 23498 grad_norm: 3.1839 top1_acc: 0.2500 top5_acc: 0.5833 loss_cls: 3.0200 loss: 3.0200 2022/09/09 14:42:21 - mmengine - INFO - Epoch(train) [8][40/449] lr: 3.7644e-02 eta: 2:41:57 time: 0.4434 data_time: 0.0195 memory: 23498 grad_norm: 3.4338 top1_acc: 0.2500 top5_acc: 0.4583 loss_cls: 2.9622 loss: 2.9622 2022/09/09 14:42:30 - mmengine - INFO - Epoch(train) [8][60/449] lr: 3.7610e-02 eta: 2:41:24 time: 0.4493 data_time: 0.0241 memory: 23498 grad_norm: 3.2493 top1_acc: 0.3750 top5_acc: 0.5417 loss_cls: 2.8411 loss: 2.8411 2022/09/09 14:42:39 - mmengine - INFO - Epoch(train) [8][80/449] lr: 3.7576e-02 eta: 2:40:52 time: 0.4501 data_time: 0.0192 memory: 23498 grad_norm: 3.3857 top1_acc: 0.4167 top5_acc: 0.6250 loss_cls: 2.7123 loss: 2.7123 2022/09/09 14:42:48 - mmengine - INFO - Epoch(train) [8][100/449] lr: 3.7541e-02 eta: 2:40:20 time: 0.4455 data_time: 0.0197 memory: 23498 grad_norm: 3.4173 top1_acc: 0.4583 top5_acc: 0.6250 loss_cls: 2.9481 loss: 2.9481 2022/09/09 14:42:57 - mmengine - INFO - Epoch(train) [8][120/449] lr: 3.7507e-02 eta: 2:39:49 time: 0.4487 data_time: 0.0242 memory: 23498 grad_norm: 3.4318 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.8470 loss: 2.8470 2022/09/09 14:43:06 - mmengine - INFO - Epoch(train) [8][140/449] lr: 3.7472e-02 eta: 2:39:17 time: 0.4474 data_time: 0.0206 memory: 23498 grad_norm: 3.1864 top1_acc: 0.4583 top5_acc: 0.7083 loss_cls: 2.8211 loss: 2.8211 2022/09/09 14:43:15 - mmengine - INFO - Epoch(train) [8][160/449] lr: 3.7437e-02 eta: 2:38:46 time: 0.4489 data_time: 0.0180 memory: 23498 grad_norm: 3.2742 top1_acc: 0.4583 top5_acc: 0.7083 loss_cls: 2.7324 loss: 2.7324 2022/09/09 14:43:23 - mmengine - INFO - Epoch(train) [8][180/449] lr: 3.7402e-02 eta: 2:38:15 time: 0.4485 data_time: 0.0198 memory: 23498 grad_norm: 3.2335 top1_acc: 0.4167 top5_acc: 0.7083 loss_cls: 2.8478 loss: 2.8478 2022/09/09 14:43:33 - mmengine - INFO - Epoch(train) [8][200/449] lr: 3.7366e-02 eta: 2:37:47 time: 0.4716 data_time: 0.0225 memory: 23498 grad_norm: 3.2351 top1_acc: 0.4167 top5_acc: 0.5833 loss_cls: 2.8375 loss: 2.8375 2022/09/09 14:43:42 - mmengine - INFO - Epoch(train) [8][220/449] lr: 3.7330e-02 eta: 2:37:17 time: 0.4528 data_time: 0.0140 memory: 23498 grad_norm: 3.5102 top1_acc: 0.2917 top5_acc: 0.5833 loss_cls: 2.8841 loss: 2.8841 2022/09/09 14:43:51 - mmengine - INFO - Epoch(train) [8][240/449] lr: 3.7294e-02 eta: 2:36:49 time: 0.4660 data_time: 0.0229 memory: 23498 grad_norm: 3.3119 top1_acc: 0.5417 top5_acc: 0.7083 loss_cls: 2.8801 loss: 2.8801 2022/09/09 14:44:00 - mmengine - INFO - Epoch(train) [8][260/449] lr: 3.7258e-02 eta: 2:36:19 time: 0.4519 data_time: 0.0184 memory: 23498 grad_norm: 3.3721 top1_acc: 0.5417 top5_acc: 0.8333 loss_cls: 2.9255 loss: 2.9255 2022/09/09 14:44:10 - mmengine - INFO - Epoch(train) [8][280/449] lr: 3.7222e-02 eta: 2:35:51 time: 0.4658 data_time: 0.0203 memory: 23498 grad_norm: 3.3941 top1_acc: 0.3750 top5_acc: 0.7083 loss_cls: 2.9056 loss: 2.9056 2022/09/09 14:44:19 - mmengine - INFO - Epoch(train) [8][300/449] lr: 3.7185e-02 eta: 2:35:22 time: 0.4556 data_time: 0.0198 memory: 23498 grad_norm: 3.4347 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6992 loss: 2.6992 2022/09/09 14:44:28 - mmengine - INFO - Epoch(train) [8][320/449] lr: 3.7148e-02 eta: 2:34:55 time: 0.4650 data_time: 0.0190 memory: 23498 grad_norm: 3.4269 top1_acc: 0.3333 top5_acc: 0.7500 loss_cls: 2.8488 loss: 2.8488 2022/09/09 14:44:37 - mmengine - INFO - Epoch(train) [8][340/449] lr: 3.7111e-02 eta: 2:34:27 time: 0.4595 data_time: 0.0184 memory: 23498 grad_norm: 3.4339 top1_acc: 0.3333 top5_acc: 0.5417 loss_cls: 2.7537 loss: 2.7537 2022/09/09 14:44:47 - mmengine - INFO - Epoch(train) [8][360/449] lr: 3.7074e-02 eta: 2:34:00 time: 0.4728 data_time: 0.0219 memory: 23498 grad_norm: 3.4030 top1_acc: 0.4167 top5_acc: 0.7500 loss_cls: 2.8171 loss: 2.8171 2022/09/09 14:44:56 - mmengine - INFO - Epoch(train) [8][380/449] lr: 3.7037e-02 eta: 2:33:32 time: 0.4514 data_time: 0.0158 memory: 23498 grad_norm: 3.3629 top1_acc: 0.1667 top5_acc: 0.5000 loss_cls: 2.7613 loss: 2.7613 2022/09/09 14:45:05 - mmengine - INFO - Epoch(train) [8][400/449] lr: 3.6999e-02 eta: 2:33:05 time: 0.4626 data_time: 0.0193 memory: 23498 grad_norm: 3.4226 top1_acc: 0.2917 top5_acc: 0.6250 loss_cls: 3.0536 loss: 3.0536 2022/09/09 14:45:14 - mmengine - INFO - Epoch(train) [8][420/449] lr: 3.6961e-02 eta: 2:32:38 time: 0.4632 data_time: 0.0240 memory: 23498 grad_norm: 3.2916 top1_acc: 0.4167 top5_acc: 0.6250 loss_cls: 2.7182 loss: 2.7182 2022/09/09 14:45:23 - mmengine - INFO - Epoch(train) [8][440/449] lr: 3.6923e-02 eta: 2:32:10 time: 0.4473 data_time: 0.0125 memory: 23498 grad_norm: 3.5109 top1_acc: 0.3333 top5_acc: 0.5833 loss_cls: 2.7963 loss: 2.7963 2022/09/09 14:45:27 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 14:45:27 - mmengine - INFO - Epoch(train) [8][449/449] lr: 3.6906e-02 eta: 2:32:10 time: 0.4262 data_time: 0.0114 memory: 23498 grad_norm: 3.8152 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.9890 loss: 2.9890 2022/09/09 14:45:30 - mmengine - INFO - Epoch(val) [8][20/61] eta: 0:00:06 time: 0.1495 data_time: 0.0291 memory: 2693 2022/09/09 14:45:33 - mmengine - INFO - Epoch(val) [8][40/61] eta: 0:00:02 time: 0.1347 data_time: 0.0163 memory: 2693 2022/09/09 14:45:35 - mmengine - INFO - Epoch(val) [8][60/61] eta: 0:00:00 time: 0.1289 data_time: 0.0125 memory: 2693 2022/09/09 14:45:36 - mmengine - INFO - Epoch(val) [8][61/61] acc/top1: 0.2203 acc/top5: 0.4888 acc/mean1: 0.1996 2022/09/09 14:45:36 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_7.pth is removed 2022/09/09 14:45:37 - mmengine - INFO - The best checkpoint with 0.2203 acc/top1 at 8 epoch is saved to best_acc/top1_epoch_8.pth. 2022/09/09 14:45:55 - mmengine - INFO - Epoch(train) [9][20/449] lr: 3.6867e-02 eta: 2:31:51 time: 0.9147 data_time: 0.1514 memory: 23498 grad_norm: 3.2115 top1_acc: 0.3333 top5_acc: 0.7083 loss_cls: 3.0027 loss: 3.0027 2022/09/09 14:46:04 - mmengine - INFO - Epoch(train) [9][40/449] lr: 3.6829e-02 eta: 2:31:24 time: 0.4549 data_time: 0.0176 memory: 23498 grad_norm: 3.4594 top1_acc: 0.2083 top5_acc: 0.5417 loss_cls: 2.8878 loss: 2.8878 2022/09/09 14:46:13 - mmengine - INFO - Epoch(train) [9][60/449] lr: 3.6790e-02 eta: 2:30:58 time: 0.4595 data_time: 0.0161 memory: 23498 grad_norm: 3.3496 top1_acc: 0.4583 top5_acc: 0.6250 loss_cls: 2.6953 loss: 2.6953 2022/09/09 14:46:23 - mmengine - INFO - Epoch(train) [9][80/449] lr: 3.6751e-02 eta: 2:30:32 time: 0.4605 data_time: 0.0209 memory: 23498 grad_norm: 3.2919 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.7898 loss: 2.7898 2022/09/09 14:46:32 - mmengine - INFO - Epoch(train) [9][100/449] lr: 3.6711e-02 eta: 2:30:06 time: 0.4597 data_time: 0.0182 memory: 23498 grad_norm: 3.5058 top1_acc: 0.3333 top5_acc: 0.6667 loss_cls: 2.6693 loss: 2.6693 2022/09/09 14:46:41 - mmengine - INFO - Epoch(train) [9][120/449] lr: 3.6672e-02 eta: 2:29:40 time: 0.4561 data_time: 0.0256 memory: 23498 grad_norm: 3.5288 top1_acc: 0.4167 top5_acc: 0.6250 loss_cls: 2.9567 loss: 2.9567 2022/09/09 14:46:50 - mmengine - INFO - Epoch(train) [9][140/449] lr: 3.6632e-02 eta: 2:29:14 time: 0.4496 data_time: 0.0169 memory: 23498 grad_norm: 3.5845 top1_acc: 0.4167 top5_acc: 0.5833 loss_cls: 2.7351 loss: 2.7351 2022/09/09 14:46:59 - mmengine - INFO - Epoch(train) [9][160/449] lr: 3.6592e-02 eta: 2:28:47 time: 0.4502 data_time: 0.0224 memory: 23498 grad_norm: 3.6113 top1_acc: 0.4167 top5_acc: 0.6250 loss_cls: 2.7393 loss: 2.7393 2022/09/09 14:47:08 - mmengine - INFO - Epoch(train) [9][180/449] lr: 3.6552e-02 eta: 2:28:22 time: 0.4522 data_time: 0.0184 memory: 23498 grad_norm: 3.4617 top1_acc: 0.2500 top5_acc: 0.5833 loss_cls: 2.9150 loss: 2.9150 2022/09/09 14:47:17 - mmengine - INFO - Epoch(train) [9][200/449] lr: 3.6511e-02 eta: 2:27:56 time: 0.4557 data_time: 0.0216 memory: 23498 grad_norm: 3.5902 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.7135 loss: 2.7135 2022/09/09 14:47:26 - mmengine - INFO - Epoch(train) [9][220/449] lr: 3.6471e-02 eta: 2:27:32 time: 0.4660 data_time: 0.0218 memory: 23498 grad_norm: 3.5122 top1_acc: 0.5000 top5_acc: 0.6667 loss_cls: 2.7628 loss: 2.7628 2022/09/09 14:47:35 - mmengine - INFO - Epoch(train) [9][240/449] lr: 3.6430e-02 eta: 2:27:07 time: 0.4570 data_time: 0.0178 memory: 23498 grad_norm: 3.5445 top1_acc: 0.2500 top5_acc: 0.5833 loss_cls: 2.7842 loss: 2.7842 2022/09/09 14:47:45 - mmengine - INFO - Epoch(train) [9][260/449] lr: 3.6389e-02 eta: 2:26:42 time: 0.4582 data_time: 0.0159 memory: 23498 grad_norm: 3.5637 top1_acc: 0.2500 top5_acc: 0.6667 loss_cls: 2.7662 loss: 2.7662 2022/09/09 14:47:54 - mmengine - INFO - Epoch(train) [9][280/449] lr: 3.6348e-02 eta: 2:26:17 time: 0.4573 data_time: 0.0225 memory: 23498 grad_norm: 3.5691 top1_acc: 0.4583 top5_acc: 0.6667 loss_cls: 2.8958 loss: 2.8958 2022/09/09 14:48:03 - mmengine - INFO - Epoch(train) [9][300/449] lr: 3.6306e-02 eta: 2:25:53 time: 0.4575 data_time: 0.0157 memory: 23498 grad_norm: 3.6066 top1_acc: 0.3333 top5_acc: 0.4167 loss_cls: 2.8111 loss: 2.8111 2022/09/09 14:48:12 - mmengine - INFO - Epoch(train) [9][320/449] lr: 3.6265e-02 eta: 2:25:28 time: 0.4537 data_time: 0.0272 memory: 23498 grad_norm: 3.4715 top1_acc: 0.4167 top5_acc: 0.5833 loss_cls: 2.6314 loss: 2.6314 2022/09/09 14:48:21 - mmengine - INFO - Epoch(train) [9][340/449] lr: 3.6223e-02 eta: 2:25:05 time: 0.4613 data_time: 0.0152 memory: 23498 grad_norm: 3.2429 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.7373 loss: 2.7373 2022/09/09 14:48:31 - mmengine - INFO - Epoch(train) [9][360/449] lr: 3.6181e-02 eta: 2:24:41 time: 0.4638 data_time: 0.0158 memory: 23498 grad_norm: 3.2693 top1_acc: 0.2917 top5_acc: 0.3750 loss_cls: 2.8052 loss: 2.8052 2022/09/09 14:48:40 - mmengine - INFO - Epoch(train) [9][380/449] lr: 3.6138e-02 eta: 2:24:17 time: 0.4545 data_time: 0.0185 memory: 23498 grad_norm: 3.3704 top1_acc: 0.2083 top5_acc: 0.5417 loss_cls: 2.9903 loss: 2.9903 2022/09/09 14:48:49 - mmengine - INFO - Epoch(train) [9][400/449] lr: 3.6096e-02 eta: 2:23:54 time: 0.4673 data_time: 0.0238 memory: 23498 grad_norm: 3.4172 top1_acc: 0.3333 top5_acc: 0.6667 loss_cls: 2.7811 loss: 2.7811 2022/09/09 14:48:53 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 14:48:58 - mmengine - INFO - Epoch(train) [9][420/449] lr: 3.6053e-02 eta: 2:23:31 time: 0.4612 data_time: 0.0158 memory: 23498 grad_norm: 3.3661 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.6457 loss: 2.6457 2022/09/09 14:49:07 - mmengine - INFO - Epoch(train) [9][440/449] lr: 3.6010e-02 eta: 2:23:07 time: 0.4531 data_time: 0.0170 memory: 23498 grad_norm: 3.4172 top1_acc: 0.1250 top5_acc: 0.2917 loss_cls: 2.8935 loss: 2.8935 2022/09/09 14:49:11 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 14:49:11 - mmengine - INFO - Epoch(train) [9][449/449] lr: 3.5991e-02 eta: 2:23:07 time: 0.4253 data_time: 0.0107 memory: 23498 grad_norm: 3.7153 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.9710 loss: 2.9710 2022/09/09 14:49:14 - mmengine - INFO - Epoch(val) [9][20/61] eta: 0:00:06 time: 0.1499 data_time: 0.0281 memory: 2693 2022/09/09 14:49:16 - mmengine - INFO - Epoch(val) [9][40/61] eta: 0:00:02 time: 0.1311 data_time: 0.0132 memory: 2693 2022/09/09 14:49:19 - mmengine - INFO - Epoch(val) [9][60/61] eta: 0:00:00 time: 0.1288 data_time: 0.0121 memory: 2693 2022/09/09 14:49:19 - mmengine - INFO - Epoch(val) [9][61/61] acc/top1: 0.2043 acc/top5: 0.4619 acc/mean1: 0.1853 2022/09/09 14:49:33 - mmengine - INFO - Epoch(train) [10][20/449] lr: 3.5948e-02 eta: 2:22:35 time: 0.6792 data_time: 0.1853 memory: 23498 grad_norm: 3.3591 top1_acc: 0.2917 top5_acc: 0.5000 loss_cls: 2.7034 loss: 2.7034 2022/09/09 14:49:42 - mmengine - INFO - Epoch(train) [10][40/449] lr: 3.5904e-02 eta: 2:22:12 time: 0.4621 data_time: 0.0120 memory: 23498 grad_norm: 3.4899 top1_acc: 0.2083 top5_acc: 0.6667 loss_cls: 2.7345 loss: 2.7345 2022/09/09 14:49:51 - mmengine - INFO - Epoch(train) [10][60/449] lr: 3.5861e-02 eta: 2:21:49 time: 0.4574 data_time: 0.0193 memory: 23498 grad_norm: 3.4903 top1_acc: 0.3333 top5_acc: 0.6250 loss_cls: 2.7737 loss: 2.7737 2022/09/09 14:50:01 - mmengine - INFO - Epoch(train) [10][80/449] lr: 3.5817e-02 eta: 2:21:26 time: 0.4599 data_time: 0.0235 memory: 23498 grad_norm: 3.4407 top1_acc: 0.4167 top5_acc: 0.7500 loss_cls: 2.7339 loss: 2.7339 2022/09/09 14:50:10 - mmengine - INFO - Epoch(train) [10][100/449] lr: 3.5773e-02 eta: 2:21:04 time: 0.4665 data_time: 0.0183 memory: 23498 grad_norm: 3.3776 top1_acc: 0.3750 top5_acc: 0.4583 loss_cls: 2.8414 loss: 2.8414 2022/09/09 14:50:19 - mmengine - INFO - Epoch(train) [10][120/449] lr: 3.5729e-02 eta: 2:20:42 time: 0.4654 data_time: 0.0216 memory: 23498 grad_norm: 3.3759 top1_acc: 0.5000 top5_acc: 0.5833 loss_cls: 2.5354 loss: 2.5354 2022/09/09 14:50:29 - mmengine - INFO - Epoch(train) [10][140/449] lr: 3.5684e-02 eta: 2:20:20 time: 0.4627 data_time: 0.0193 memory: 23498 grad_norm: 3.4265 top1_acc: 0.3750 top5_acc: 0.6667 loss_cls: 2.6418 loss: 2.6418 2022/09/09 14:50:38 - mmengine - INFO - Epoch(train) [10][160/449] lr: 3.5640e-02 eta: 2:19:58 time: 0.4579 data_time: 0.0231 memory: 23498 grad_norm: 3.3931 top1_acc: 0.3333 top5_acc: 0.6667 loss_cls: 2.7281 loss: 2.7281 2022/09/09 14:50:47 - mmengine - INFO - Epoch(train) [10][180/449] lr: 3.5595e-02 eta: 2:19:36 time: 0.4691 data_time: 0.0196 memory: 23498 grad_norm: 3.4383 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5776 loss: 2.5776 2022/09/09 14:50:56 - mmengine - INFO - Epoch(train) [10][200/449] lr: 3.5550e-02 eta: 2:19:14 time: 0.4557 data_time: 0.0207 memory: 23498 grad_norm: 3.4884 top1_acc: 0.2083 top5_acc: 0.6250 loss_cls: 2.7967 loss: 2.7967 2022/09/09 14:51:05 - mmengine - INFO - Epoch(train) [10][220/449] lr: 3.5505e-02 eta: 2:18:52 time: 0.4585 data_time: 0.0194 memory: 23498 grad_norm: 3.4349 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.8104 loss: 2.8104 2022/09/09 14:51:15 - mmengine - INFO - Epoch(train) [10][240/449] lr: 3.5459e-02 eta: 2:18:31 time: 0.4698 data_time: 0.0203 memory: 23498 grad_norm: 3.4371 top1_acc: 0.4583 top5_acc: 0.7083 loss_cls: 2.6384 loss: 2.6384 2022/09/09 14:51:24 - mmengine - INFO - Epoch(train) [10][260/449] lr: 3.5414e-02 eta: 2:18:10 time: 0.4545 data_time: 0.0189 memory: 23498 grad_norm: 3.3399 top1_acc: 0.5417 top5_acc: 0.8333 loss_cls: 2.5786 loss: 2.5786 2022/09/09 14:51:33 - mmengine - INFO - Epoch(train) [10][280/449] lr: 3.5368e-02 eta: 2:17:48 time: 0.4507 data_time: 0.0227 memory: 23498 grad_norm: 3.4624 top1_acc: 0.3750 top5_acc: 0.5833 loss_cls: 2.6394 loss: 2.6394 2022/09/09 14:51:42 - mmengine - INFO - Epoch(train) [10][300/449] lr: 3.5322e-02 eta: 2:17:26 time: 0.4519 data_time: 0.0192 memory: 23498 grad_norm: 3.5227 top1_acc: 0.3333 top5_acc: 0.5417 loss_cls: 2.8261 loss: 2.8261 2022/09/09 14:51:51 - mmengine - INFO - Epoch(train) [10][320/449] lr: 3.5275e-02 eta: 2:17:04 time: 0.4544 data_time: 0.0199 memory: 23498 grad_norm: 3.3527 top1_acc: 0.3333 top5_acc: 0.6250 loss_cls: 2.7120 loss: 2.7120 2022/09/09 14:52:00 - mmengine - INFO - Epoch(train) [10][340/449] lr: 3.5229e-02 eta: 2:16:43 time: 0.4579 data_time: 0.0178 memory: 23498 grad_norm: 3.4099 top1_acc: 0.5000 top5_acc: 0.7083 loss_cls: 2.6062 loss: 2.6062 2022/09/09 14:52:09 - mmengine - INFO - Epoch(train) [10][360/449] lr: 3.5182e-02 eta: 2:16:22 time: 0.4552 data_time: 0.0218 memory: 23498 grad_norm: 3.6159 top1_acc: 0.2917 top5_acc: 0.5833 loss_cls: 2.6570 loss: 2.6570 2022/09/09 14:52:19 - mmengine - INFO - Epoch(train) [10][380/449] lr: 3.5136e-02 eta: 2:16:01 time: 0.4674 data_time: 0.0202 memory: 23498 grad_norm: 3.6276 top1_acc: 0.4583 top5_acc: 0.7083 loss_cls: 2.7169 loss: 2.7169 2022/09/09 14:52:28 - mmengine - INFO - Epoch(train) [10][400/449] lr: 3.5089e-02 eta: 2:15:40 time: 0.4536 data_time: 0.0215 memory: 23498 grad_norm: 3.5208 top1_acc: 0.4167 top5_acc: 0.7083 loss_cls: 2.7494 loss: 2.7494 2022/09/09 14:52:37 - mmengine - INFO - Epoch(train) [10][420/449] lr: 3.5041e-02 eta: 2:15:20 time: 0.4645 data_time: 0.0177 memory: 23498 grad_norm: 3.2723 top1_acc: 0.2917 top5_acc: 0.7083 loss_cls: 2.7589 loss: 2.7589 2022/09/09 14:52:46 - mmengine - INFO - Epoch(train) [10][440/449] lr: 3.4994e-02 eta: 2:14:59 time: 0.4560 data_time: 0.0224 memory: 23498 grad_norm: 3.5462 top1_acc: 0.3333 top5_acc: 0.6250 loss_cls: 2.8232 loss: 2.8232 2022/09/09 14:52:50 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 14:52:50 - mmengine - INFO - Epoch(train) [10][449/449] lr: 3.4973e-02 eta: 2:14:59 time: 0.4295 data_time: 0.0178 memory: 23498 grad_norm: 4.7605 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.9838 loss: 2.9838 2022/09/09 14:52:53 - mmengine - INFO - Epoch(val) [10][20/61] eta: 0:00:06 time: 0.1490 data_time: 0.0284 memory: 2693 2022/09/09 14:52:55 - mmengine - INFO - Epoch(val) [10][40/61] eta: 0:00:02 time: 0.1354 data_time: 0.0159 memory: 2693 2022/09/09 14:52:58 - mmengine - INFO - Epoch(val) [10][60/61] eta: 0:00:00 time: 0.1288 data_time: 0.0128 memory: 2693 2022/09/09 14:52:58 - mmengine - INFO - Epoch(val) [10][61/61] acc/top1: 0.2148 acc/top5: 0.4805 acc/mean1: 0.1916 2022/09/09 14:53:13 - mmengine - INFO - Epoch(train) [11][20/449] lr: 3.4925e-02 eta: 2:14:34 time: 0.7368 data_time: 0.2177 memory: 23498 grad_norm: 3.8726 top1_acc: 0.2500 top5_acc: 0.5833 loss_cls: 2.9648 loss: 2.9648 2022/09/09 14:53:22 - mmengine - INFO - Epoch(train) [11][40/449] lr: 3.4877e-02 eta: 2:14:13 time: 0.4481 data_time: 0.0196 memory: 23498 grad_norm: 3.6647 top1_acc: 0.3333 top5_acc: 0.5833 loss_cls: 2.7636 loss: 2.7636 2022/09/09 14:53:31 - mmengine - INFO - Epoch(train) [11][60/449] lr: 3.4829e-02 eta: 2:13:52 time: 0.4472 data_time: 0.0197 memory: 23498 grad_norm: 3.5570 top1_acc: 0.2083 top5_acc: 0.5000 loss_cls: 2.7202 loss: 2.7202 2022/09/09 14:53:40 - mmengine - INFO - Epoch(train) [11][80/449] lr: 3.4781e-02 eta: 2:13:31 time: 0.4555 data_time: 0.0208 memory: 23498 grad_norm: 3.5330 top1_acc: 0.2917 top5_acc: 0.6250 loss_cls: 2.7471 loss: 2.7471 2022/09/09 14:53:49 - mmengine - INFO - Epoch(train) [11][100/449] lr: 3.4732e-02 eta: 2:13:11 time: 0.4588 data_time: 0.0266 memory: 23498 grad_norm: 3.5458 top1_acc: 0.3750 top5_acc: 0.7083 loss_cls: 2.6608 loss: 2.6608 2022/09/09 14:53:59 - mmengine - INFO - Epoch(train) [11][120/449] lr: 3.4684e-02 eta: 2:12:51 time: 0.4631 data_time: 0.0248 memory: 23498 grad_norm: 3.5022 top1_acc: 0.4167 top5_acc: 0.7083 loss_cls: 2.6854 loss: 2.6854 2022/09/09 14:54:08 - mmengine - INFO - Epoch(train) [11][140/449] lr: 3.4635e-02 eta: 2:12:31 time: 0.4551 data_time: 0.0185 memory: 23498 grad_norm: 3.5271 top1_acc: 0.4167 top5_acc: 0.5833 loss_cls: 2.6996 loss: 2.6996 2022/09/09 14:54:17 - mmengine - INFO - Epoch(train) [11][160/449] lr: 3.4586e-02 eta: 2:12:11 time: 0.4539 data_time: 0.0166 memory: 23498 grad_norm: 3.5032 top1_acc: 0.4167 top5_acc: 0.6250 loss_cls: 2.7791 loss: 2.7791 2022/09/09 14:54:26 - mmengine - INFO - Epoch(train) [11][180/449] lr: 3.4537e-02 eta: 2:11:51 time: 0.4541 data_time: 0.0195 memory: 23498 grad_norm: 3.5033 top1_acc: 0.3750 top5_acc: 0.7083 loss_cls: 2.6519 loss: 2.6519 2022/09/09 14:54:35 - mmengine - INFO - Epoch(train) [11][200/449] lr: 3.4487e-02 eta: 2:11:31 time: 0.4551 data_time: 0.0243 memory: 23498 grad_norm: 3.5011 top1_acc: 0.3333 top5_acc: 0.5417 loss_cls: 2.8236 loss: 2.8236 2022/09/09 14:54:44 - mmengine - INFO - Epoch(train) [11][220/449] lr: 3.4438e-02 eta: 2:11:11 time: 0.4540 data_time: 0.0175 memory: 23498 grad_norm: 3.4416 top1_acc: 0.4167 top5_acc: 0.6250 loss_cls: 2.7014 loss: 2.7014 2022/09/09 14:54:53 - mmengine - INFO - Epoch(train) [11][240/449] lr: 3.4388e-02 eta: 2:10:51 time: 0.4475 data_time: 0.0195 memory: 23498 grad_norm: 3.3203 top1_acc: 0.2083 top5_acc: 0.5000 loss_cls: 2.7810 loss: 2.7810 2022/09/09 14:55:02 - mmengine - INFO - Epoch(train) [11][260/449] lr: 3.4338e-02 eta: 2:10:32 time: 0.4512 data_time: 0.0199 memory: 23498 grad_norm: 3.5388 top1_acc: 0.2917 top5_acc: 0.5417 loss_cls: 2.7650 loss: 2.7650 2022/09/09 14:55:11 - mmengine - INFO - Epoch(train) [11][280/449] lr: 3.4288e-02 eta: 2:10:12 time: 0.4511 data_time: 0.0206 memory: 23498 grad_norm: 3.4295 top1_acc: 0.3333 top5_acc: 0.7083 loss_cls: 2.5661 loss: 2.5661 2022/09/09 14:55:20 - mmengine - INFO - Epoch(train) [11][300/449] lr: 3.4238e-02 eta: 2:09:52 time: 0.4538 data_time: 0.0177 memory: 23498 grad_norm: 3.3571 top1_acc: 0.3333 top5_acc: 0.6250 loss_cls: 2.7202 loss: 2.7202 2022/09/09 14:55:29 - mmengine - INFO - Epoch(train) [11][320/449] lr: 3.4187e-02 eta: 2:09:34 time: 0.4621 data_time: 0.0203 memory: 23498 grad_norm: 3.5111 top1_acc: 0.2917 top5_acc: 0.6250 loss_cls: 2.4900 loss: 2.4900 2022/09/09 14:55:39 - mmengine - INFO - Epoch(train) [11][340/449] lr: 3.4136e-02 eta: 2:09:14 time: 0.4566 data_time: 0.0151 memory: 23498 grad_norm: 3.5469 top1_acc: 0.2917 top5_acc: 0.5417 loss_cls: 2.7079 loss: 2.7079 2022/09/09 14:55:48 - mmengine - INFO - Epoch(train) [11][360/449] lr: 3.4086e-02 eta: 2:08:55 time: 0.4566 data_time: 0.0207 memory: 23498 grad_norm: 3.4287 top1_acc: 0.4583 top5_acc: 0.6667 loss_cls: 2.5498 loss: 2.5498 2022/09/09 14:55:57 - mmengine - INFO - Epoch(train) [11][380/449] lr: 3.4035e-02 eta: 2:08:37 time: 0.4665 data_time: 0.0164 memory: 23498 grad_norm: 3.5405 top1_acc: 0.4167 top5_acc: 0.6667 loss_cls: 2.5995 loss: 2.5995 2022/09/09 14:56:06 - mmengine - INFO - Epoch(train) [11][400/449] lr: 3.3983e-02 eta: 2:08:18 time: 0.4549 data_time: 0.0213 memory: 23498 grad_norm: 3.5157 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.7465 loss: 2.7465 2022/09/09 14:56:15 - mmengine - INFO - Epoch(train) [11][420/449] lr: 3.3932e-02 eta: 2:07:59 time: 0.4511 data_time: 0.0212 memory: 23498 grad_norm: 3.8765 top1_acc: 0.2917 top5_acc: 0.5417 loss_cls: 2.7197 loss: 2.7197 2022/09/09 14:56:24 - mmengine - INFO - Epoch(train) [11][440/449] lr: 3.3880e-02 eta: 2:07:39 time: 0.4427 data_time: 0.0180 memory: 23498 grad_norm: 3.6424 top1_acc: 0.3333 top5_acc: 0.5417 loss_cls: 2.6638 loss: 2.6638 2022/09/09 14:56:28 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 14:56:28 - mmengine - INFO - Epoch(train) [11][449/449] lr: 3.3857e-02 eta: 2:07:39 time: 0.4244 data_time: 0.0140 memory: 23498 grad_norm: 3.8390 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.9070 loss: 2.9070 2022/09/09 14:56:31 - mmengine - INFO - Epoch(val) [11][20/61] eta: 0:00:06 time: 0.1481 data_time: 0.0275 memory: 2693 2022/09/09 14:56:33 - mmengine - INFO - Epoch(val) [11][40/61] eta: 0:00:02 time: 0.1313 data_time: 0.0135 memory: 2693 2022/09/09 14:56:36 - mmengine - INFO - Epoch(val) [11][60/61] eta: 0:00:00 time: 0.1305 data_time: 0.0130 memory: 2693 2022/09/09 14:56:36 - mmengine - INFO - Epoch(val) [11][61/61] acc/top1: 0.2192 acc/top5: 0.4815 acc/mean1: 0.2028 2022/09/09 14:56:50 - mmengine - INFO - Epoch(train) [12][20/449] lr: 3.3805e-02 eta: 2:07:14 time: 0.6964 data_time: 0.0460 memory: 23498 grad_norm: 3.4438 top1_acc: 0.3333 top5_acc: 0.5833 loss_cls: 2.6321 loss: 2.6321 2022/09/09 14:56:59 - mmengine - INFO - Epoch(train) [12][40/449] lr: 3.3753e-02 eta: 2:06:55 time: 0.4423 data_time: 0.0147 memory: 23498 grad_norm: 3.4084 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5923 loss: 2.5923 2022/09/09 14:57:08 - mmengine - INFO - Epoch(train) [12][60/449] lr: 3.3701e-02 eta: 2:06:36 time: 0.4545 data_time: 0.0165 memory: 23498 grad_norm: 3.5085 top1_acc: 0.3333 top5_acc: 0.5833 loss_cls: 2.7535 loss: 2.7535 2022/09/09 14:57:09 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 14:57:17 - mmengine - INFO - Epoch(train) [12][80/449] lr: 3.3649e-02 eta: 2:06:18 time: 0.4544 data_time: 0.0207 memory: 23498 grad_norm: 3.5071 top1_acc: 0.2917 top5_acc: 0.7500 loss_cls: 2.6053 loss: 2.6053 2022/09/09 14:57:26 - mmengine - INFO - Epoch(train) [12][100/449] lr: 3.3596e-02 eta: 2:05:59 time: 0.4468 data_time: 0.0204 memory: 23498 grad_norm: 3.4637 top1_acc: 0.3333 top5_acc: 0.7083 loss_cls: 2.5041 loss: 2.5041 2022/09/09 14:57:35 - mmengine - INFO - Epoch(train) [12][120/449] lr: 3.3543e-02 eta: 2:05:41 time: 0.4552 data_time: 0.0172 memory: 23498 grad_norm: 3.4725 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5611 loss: 2.5611 2022/09/09 14:57:44 - mmengine - INFO - Epoch(train) [12][140/449] lr: 3.3491e-02 eta: 2:05:22 time: 0.4494 data_time: 0.0164 memory: 23498 grad_norm: 3.4299 top1_acc: 0.3750 top5_acc: 0.6667 loss_cls: 2.5666 loss: 2.5666 2022/09/09 14:57:53 - mmengine - INFO - Epoch(train) [12][160/449] lr: 3.3437e-02 eta: 2:05:03 time: 0.4479 data_time: 0.0223 memory: 23498 grad_norm: 3.4862 top1_acc: 0.6250 top5_acc: 0.8333 loss_cls: 2.5235 loss: 2.5235 2022/09/09 14:58:02 - mmengine - INFO - Epoch(train) [12][180/449] lr: 3.3384e-02 eta: 2:04:45 time: 0.4473 data_time: 0.0212 memory: 23498 grad_norm: 3.6406 top1_acc: 0.4167 top5_acc: 0.6250 loss_cls: 2.5316 loss: 2.5316 2022/09/09 14:58:12 - mmengine - INFO - Epoch(train) [12][200/449] lr: 3.3331e-02 eta: 2:04:27 time: 0.4607 data_time: 0.0187 memory: 23498 grad_norm: 3.5152 top1_acc: 0.2500 top5_acc: 0.6667 loss_cls: 2.6180 loss: 2.6180 2022/09/09 14:58:21 - mmengine - INFO - Epoch(train) [12][220/449] lr: 3.3277e-02 eta: 2:04:10 time: 0.4613 data_time: 0.0216 memory: 23498 grad_norm: 3.4601 top1_acc: 0.5000 top5_acc: 0.7917 loss_cls: 2.6036 loss: 2.6036 2022/09/09 14:58:30 - mmengine - INFO - Epoch(train) [12][240/449] lr: 3.3223e-02 eta: 2:03:52 time: 0.4569 data_time: 0.0203 memory: 23498 grad_norm: 3.4129 top1_acc: 0.5417 top5_acc: 0.6250 loss_cls: 2.4407 loss: 2.4407 2022/09/09 14:58:39 - mmengine - INFO - Epoch(train) [12][260/449] lr: 3.3170e-02 eta: 2:03:34 time: 0.4612 data_time: 0.0184 memory: 23498 grad_norm: 3.5409 top1_acc: 0.3333 top5_acc: 0.7083 loss_cls: 2.6395 loss: 2.6395 2022/09/09 14:58:48 - mmengine - INFO - Epoch(train) [12][280/449] lr: 3.3115e-02 eta: 2:03:16 time: 0.4488 data_time: 0.0182 memory: 23498 grad_norm: 3.4894 top1_acc: 0.5000 top5_acc: 0.6667 loss_cls: 2.6875 loss: 2.6875 2022/09/09 14:58:57 - mmengine - INFO - Epoch(train) [12][300/449] lr: 3.3061e-02 eta: 2:02:58 time: 0.4569 data_time: 0.0207 memory: 23498 grad_norm: 3.4996 top1_acc: 0.3333 top5_acc: 0.6667 loss_cls: 2.5661 loss: 2.5661 2022/09/09 14:59:06 - mmengine - INFO - Epoch(train) [12][320/449] lr: 3.3007e-02 eta: 2:02:41 time: 0.4504 data_time: 0.0187 memory: 23498 grad_norm: 3.5148 top1_acc: 0.4583 top5_acc: 0.7083 loss_cls: 2.6675 loss: 2.6675 2022/09/09 14:59:15 - mmengine - INFO - Epoch(train) [12][340/449] lr: 3.2952e-02 eta: 2:02:23 time: 0.4526 data_time: 0.0218 memory: 23498 grad_norm: 3.4322 top1_acc: 0.4583 top5_acc: 0.6250 loss_cls: 2.5730 loss: 2.5730 2022/09/09 14:59:25 - mmengine - INFO - Epoch(train) [12][360/449] lr: 3.2897e-02 eta: 2:02:06 time: 0.4630 data_time: 0.0278 memory: 23498 grad_norm: 3.4931 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.6901 loss: 2.6901 2022/09/09 14:59:34 - mmengine - INFO - Epoch(train) [12][380/449] lr: 3.2842e-02 eta: 2:01:48 time: 0.4475 data_time: 0.0191 memory: 23498 grad_norm: 3.4529 top1_acc: 0.4167 top5_acc: 0.6667 loss_cls: 2.6791 loss: 2.6791 2022/09/09 14:59:43 - mmengine - INFO - Epoch(train) [12][400/449] lr: 3.2787e-02 eta: 2:01:30 time: 0.4514 data_time: 0.0207 memory: 23498 grad_norm: 3.4895 top1_acc: 0.5833 top5_acc: 0.7917 loss_cls: 2.5485 loss: 2.5485 2022/09/09 14:59:51 - mmengine - INFO - Epoch(train) [12][420/449] lr: 3.2732e-02 eta: 2:01:13 time: 0.4462 data_time: 0.0173 memory: 23498 grad_norm: 3.7495 top1_acc: 0.4583 top5_acc: 0.7083 loss_cls: 2.5895 loss: 2.5895 2022/09/09 15:00:00 - mmengine - INFO - Epoch(train) [12][440/449] lr: 3.2677e-02 eta: 2:00:55 time: 0.4495 data_time: 0.0227 memory: 23498 grad_norm: 3.5570 top1_acc: 0.2917 top5_acc: 0.7083 loss_cls: 2.4191 loss: 2.4191 2022/09/09 15:00:04 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:00:04 - mmengine - INFO - Epoch(train) [12][449/449] lr: 3.2652e-02 eta: 2:00:55 time: 0.4247 data_time: 0.0141 memory: 23498 grad_norm: 3.6193 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.7568 loss: 2.7568 2022/09/09 15:00:07 - mmengine - INFO - Epoch(val) [12][20/61] eta: 0:00:06 time: 0.1543 data_time: 0.0349 memory: 2693 2022/09/09 15:00:10 - mmengine - INFO - Epoch(val) [12][40/61] eta: 0:00:02 time: 0.1295 data_time: 0.0121 memory: 2693 2022/09/09 15:00:12 - mmengine - INFO - Epoch(val) [12][60/61] eta: 0:00:00 time: 0.1301 data_time: 0.0132 memory: 2693 2022/09/09 15:00:13 - mmengine - INFO - Epoch(val) [12][61/61] acc/top1: 0.2325 acc/top5: 0.5079 acc/mean1: 0.2019 2022/09/09 15:00:13 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_8.pth is removed 2022/09/09 15:00:14 - mmengine - INFO - The best checkpoint with 0.2325 acc/top1 at 12 epoch is saved to best_acc/top1_epoch_12.pth. 2022/09/09 15:00:26 - mmengine - INFO - Epoch(train) [13][20/449] lr: 3.2596e-02 eta: 2:00:28 time: 0.6154 data_time: 0.0659 memory: 23498 grad_norm: 3.3378 top1_acc: 0.4583 top5_acc: 0.7917 loss_cls: 2.4957 loss: 2.4957 2022/09/09 15:00:35 - mmengine - INFO - Epoch(train) [13][40/449] lr: 3.2540e-02 eta: 2:00:11 time: 0.4499 data_time: 0.0194 memory: 23498 grad_norm: 3.5332 top1_acc: 0.5000 top5_acc: 0.7917 loss_cls: 2.3769 loss: 2.3769 2022/09/09 15:00:44 - mmengine - INFO - Epoch(train) [13][60/449] lr: 3.2484e-02 eta: 1:59:54 time: 0.4596 data_time: 0.0206 memory: 23498 grad_norm: 3.4750 top1_acc: 0.1667 top5_acc: 0.5417 loss_cls: 2.4683 loss: 2.4683 2022/09/09 15:00:53 - mmengine - INFO - Epoch(train) [13][80/449] lr: 3.2428e-02 eta: 1:59:37 time: 0.4509 data_time: 0.0201 memory: 23498 grad_norm: 3.3752 top1_acc: 0.4167 top5_acc: 0.7500 loss_cls: 2.4035 loss: 2.4035 2022/09/09 15:01:02 - mmengine - INFO - Epoch(train) [13][100/449] lr: 3.2372e-02 eta: 1:59:20 time: 0.4546 data_time: 0.0168 memory: 23498 grad_norm: 3.5390 top1_acc: 0.3750 top5_acc: 0.7083 loss_cls: 2.5536 loss: 2.5536 2022/09/09 15:01:12 - mmengine - INFO - Epoch(train) [13][120/449] lr: 3.2315e-02 eta: 1:59:03 time: 0.4560 data_time: 0.0233 memory: 23498 grad_norm: 3.6451 top1_acc: 0.3750 top5_acc: 0.5833 loss_cls: 2.6262 loss: 2.6262 2022/09/09 15:01:21 - mmengine - INFO - Epoch(train) [13][140/449] lr: 3.2259e-02 eta: 1:58:46 time: 0.4527 data_time: 0.0151 memory: 23498 grad_norm: 3.5525 top1_acc: 0.4167 top5_acc: 0.7500 loss_cls: 2.6238 loss: 2.6238 2022/09/09 15:01:30 - mmengine - INFO - Epoch(train) [13][160/449] lr: 3.2202e-02 eta: 1:58:29 time: 0.4538 data_time: 0.0228 memory: 23498 grad_norm: 3.6468 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.7023 loss: 2.7023 2022/09/09 15:01:39 - mmengine - INFO - Epoch(train) [13][180/449] lr: 3.2145e-02 eta: 1:58:13 time: 0.4603 data_time: 0.0196 memory: 23498 grad_norm: 3.5359 top1_acc: 0.3333 top5_acc: 0.6250 loss_cls: 2.5992 loss: 2.5992 2022/09/09 15:01:48 - mmengine - INFO - Epoch(train) [13][200/449] lr: 3.2088e-02 eta: 1:57:57 time: 0.4664 data_time: 0.0234 memory: 23498 grad_norm: 3.7942 top1_acc: 0.4167 top5_acc: 0.5417 loss_cls: 2.4689 loss: 2.4689 2022/09/09 15:01:57 - mmengine - INFO - Epoch(train) [13][220/449] lr: 3.2031e-02 eta: 1:57:40 time: 0.4580 data_time: 0.0178 memory: 23498 grad_norm: 3.4682 top1_acc: 0.2917 top5_acc: 0.7500 loss_cls: 2.5792 loss: 2.5792 2022/09/09 15:02:07 - mmengine - INFO - Epoch(train) [13][240/449] lr: 3.1973e-02 eta: 1:57:25 time: 0.4743 data_time: 0.0247 memory: 23498 grad_norm: 3.4958 top1_acc: 0.3333 top5_acc: 0.6667 loss_cls: 2.4261 loss: 2.4261 2022/09/09 15:02:16 - mmengine - INFO - Epoch(train) [13][260/449] lr: 3.1916e-02 eta: 1:57:09 time: 0.4711 data_time: 0.0171 memory: 23498 grad_norm: 3.5487 top1_acc: 0.2917 top5_acc: 0.7083 loss_cls: 2.4500 loss: 2.4500 2022/09/09 15:02:25 - mmengine - INFO - Epoch(train) [13][280/449] lr: 3.1858e-02 eta: 1:56:52 time: 0.4575 data_time: 0.0211 memory: 23498 grad_norm: 3.4821 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5417 loss: 2.5417 2022/09/09 15:02:34 - mmengine - INFO - Epoch(train) [13][300/449] lr: 3.1800e-02 eta: 1:56:36 time: 0.4530 data_time: 0.0162 memory: 23498 grad_norm: 3.5431 top1_acc: 0.3333 top5_acc: 0.5000 loss_cls: 2.5897 loss: 2.5897 2022/09/09 15:02:44 - mmengine - INFO - Epoch(train) [13][320/449] lr: 3.1742e-02 eta: 1:56:20 time: 0.4597 data_time: 0.0185 memory: 23498 grad_norm: 3.4071 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.6765 loss: 2.6765 2022/09/09 15:02:53 - mmengine - INFO - Epoch(train) [13][340/449] lr: 3.1684e-02 eta: 1:56:04 time: 0.4560 data_time: 0.0165 memory: 23498 grad_norm: 3.4430 top1_acc: 0.2083 top5_acc: 0.5000 loss_cls: 2.4364 loss: 2.4364 2022/09/09 15:03:02 - mmengine - INFO - Epoch(train) [13][360/449] lr: 3.1626e-02 eta: 1:55:47 time: 0.4581 data_time: 0.0212 memory: 23498 grad_norm: 3.4835 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3497 loss: 2.3497 2022/09/09 15:03:11 - mmengine - INFO - Epoch(train) [13][380/449] lr: 3.1567e-02 eta: 1:55:31 time: 0.4597 data_time: 0.0178 memory: 23498 grad_norm: 3.4624 top1_acc: 0.4583 top5_acc: 0.6667 loss_cls: 2.5426 loss: 2.5426 2022/09/09 15:03:20 - mmengine - INFO - Epoch(train) [13][400/449] lr: 3.1508e-02 eta: 1:55:16 time: 0.4638 data_time: 0.0186 memory: 23498 grad_norm: 3.6417 top1_acc: 0.6667 top5_acc: 0.8750 loss_cls: 2.4577 loss: 2.4577 2022/09/09 15:03:30 - mmengine - INFO - Epoch(train) [13][420/449] lr: 3.1450e-02 eta: 1:55:00 time: 0.4583 data_time: 0.0171 memory: 23498 grad_norm: 3.7130 top1_acc: 0.5000 top5_acc: 0.7083 loss_cls: 2.5443 loss: 2.5443 2022/09/09 15:03:39 - mmengine - INFO - Epoch(train) [13][440/449] lr: 3.1391e-02 eta: 1:54:44 time: 0.4598 data_time: 0.0264 memory: 23498 grad_norm: 3.5951 top1_acc: 0.4583 top5_acc: 0.7500 loss_cls: 2.4213 loss: 2.4213 2022/09/09 15:03:42 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:03:42 - mmengine - INFO - Epoch(train) [13][449/449] lr: 3.1364e-02 eta: 1:54:44 time: 0.4362 data_time: 0.0184 memory: 23498 grad_norm: 3.9648 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.4445 loss: 2.4445 2022/09/09 15:03:46 - mmengine - INFO - Epoch(val) [13][20/61] eta: 0:00:06 time: 0.1495 data_time: 0.0280 memory: 2693 2022/09/09 15:03:48 - mmengine - INFO - Epoch(val) [13][40/61] eta: 0:00:02 time: 0.1292 data_time: 0.0122 memory: 2693 2022/09/09 15:03:51 - mmengine - INFO - Epoch(val) [13][60/61] eta: 0:00:00 time: 0.1301 data_time: 0.0127 memory: 2693 2022/09/09 15:03:51 - mmengine - INFO - Epoch(val) [13][61/61] acc/top1: 0.2540 acc/top5: 0.5330 acc/mean1: 0.2312 2022/09/09 15:03:51 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_12.pth is removed 2022/09/09 15:03:52 - mmengine - INFO - The best checkpoint with 0.2540 acc/top1 at 13 epoch is saved to best_acc/top1_epoch_13.pth. 2022/09/09 15:04:03 - mmengine - INFO - Epoch(train) [14][20/449] lr: 3.1305e-02 eta: 1:54:16 time: 0.5464 data_time: 0.0377 memory: 23498 grad_norm: 3.7555 top1_acc: 0.2500 top5_acc: 0.6667 loss_cls: 2.6936 loss: 2.6936 2022/09/09 15:04:12 - mmengine - INFO - Epoch(train) [14][40/449] lr: 3.1246e-02 eta: 1:54:00 time: 0.4546 data_time: 0.0164 memory: 23498 grad_norm: 3.4560 top1_acc: 0.5417 top5_acc: 0.8333 loss_cls: 2.5606 loss: 2.5606 2022/09/09 15:04:21 - mmengine - INFO - Epoch(train) [14][60/449] lr: 3.1186e-02 eta: 1:53:44 time: 0.4572 data_time: 0.0231 memory: 23498 grad_norm: 3.5258 top1_acc: 0.4583 top5_acc: 0.7917 loss_cls: 2.4240 loss: 2.4240 2022/09/09 15:04:31 - mmengine - INFO - Epoch(train) [14][80/449] lr: 3.1127e-02 eta: 1:53:29 time: 0.4716 data_time: 0.0262 memory: 23498 grad_norm: 3.6801 top1_acc: 0.5000 top5_acc: 0.7083 loss_cls: 2.5266 loss: 2.5266 2022/09/09 15:04:40 - mmengine - INFO - Epoch(train) [14][100/449] lr: 3.1067e-02 eta: 1:53:13 time: 0.4567 data_time: 0.0177 memory: 23498 grad_norm: 3.5940 top1_acc: 0.4167 top5_acc: 0.7083 loss_cls: 2.4885 loss: 2.4885 2022/09/09 15:04:50 - mmengine - INFO - Epoch(train) [14][120/449] lr: 3.1007e-02 eta: 1:52:59 time: 0.4760 data_time: 0.0274 memory: 23498 grad_norm: 3.6274 top1_acc: 0.4167 top5_acc: 0.7083 loss_cls: 2.5099 loss: 2.5099 2022/09/09 15:04:59 - mmengine - INFO - Epoch(train) [14][140/449] lr: 3.0947e-02 eta: 1:52:43 time: 0.4570 data_time: 0.0155 memory: 23498 grad_norm: 3.6164 top1_acc: 0.5833 top5_acc: 0.7500 loss_cls: 2.4897 loss: 2.4897 2022/09/09 15:05:08 - mmengine - INFO - Epoch(train) [14][160/449] lr: 3.0887e-02 eta: 1:52:28 time: 0.4680 data_time: 0.0248 memory: 23498 grad_norm: 3.5582 top1_acc: 0.5000 top5_acc: 0.7083 loss_cls: 2.4244 loss: 2.4244 2022/09/09 15:05:09 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:05:17 - mmengine - INFO - Epoch(train) [14][180/449] lr: 3.0827e-02 eta: 1:52:13 time: 0.4690 data_time: 0.0183 memory: 23498 grad_norm: 3.7404 top1_acc: 0.2917 top5_acc: 0.6667 loss_cls: 2.6241 loss: 2.6241 2022/09/09 15:05:27 - mmengine - INFO - Epoch(train) [14][200/449] lr: 3.0767e-02 eta: 1:51:58 time: 0.4640 data_time: 0.0253 memory: 23498 grad_norm: 3.7156 top1_acc: 0.4583 top5_acc: 0.7083 loss_cls: 2.3909 loss: 2.3909 2022/09/09 15:05:36 - mmengine - INFO - Epoch(train) [14][220/449] lr: 3.0706e-02 eta: 1:51:42 time: 0.4546 data_time: 0.0181 memory: 23498 grad_norm: 3.5629 top1_acc: 0.2500 top5_acc: 0.6667 loss_cls: 2.3937 loss: 2.3937 2022/09/09 15:05:45 - mmengine - INFO - Epoch(train) [14][240/449] lr: 3.0645e-02 eta: 1:51:27 time: 0.4701 data_time: 0.0223 memory: 23498 grad_norm: 3.6741 top1_acc: 0.2917 top5_acc: 0.5833 loss_cls: 2.4995 loss: 2.4995 2022/09/09 15:05:54 - mmengine - INFO - Epoch(train) [14][260/449] lr: 3.0584e-02 eta: 1:51:12 time: 0.4630 data_time: 0.0265 memory: 23498 grad_norm: 3.8030 top1_acc: 0.4583 top5_acc: 0.7083 loss_cls: 2.3927 loss: 2.3927 2022/09/09 15:06:04 - mmengine - INFO - Epoch(train) [14][280/449] lr: 3.0524e-02 eta: 1:50:56 time: 0.4545 data_time: 0.0237 memory: 23498 grad_norm: 3.7339 top1_acc: 0.3333 top5_acc: 0.7083 loss_cls: 2.5096 loss: 2.5096 2022/09/09 15:06:12 - mmengine - INFO - Epoch(train) [14][300/449] lr: 3.0462e-02 eta: 1:50:41 time: 0.4463 data_time: 0.0170 memory: 23498 grad_norm: 3.6950 top1_acc: 0.2083 top5_acc: 0.3750 loss_cls: 2.6089 loss: 2.6089 2022/09/09 15:06:22 - mmengine - INFO - Epoch(train) [14][320/449] lr: 3.0401e-02 eta: 1:50:25 time: 0.4560 data_time: 0.0225 memory: 23498 grad_norm: 3.7117 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5956 loss: 2.5956 2022/09/09 15:06:31 - mmengine - INFO - Epoch(train) [14][340/449] lr: 3.0340e-02 eta: 1:50:11 time: 0.4721 data_time: 0.0165 memory: 23498 grad_norm: 3.5379 top1_acc: 0.2917 top5_acc: 0.5417 loss_cls: 2.5708 loss: 2.5708 2022/09/09 15:06:40 - mmengine - INFO - Epoch(train) [14][360/449] lr: 3.0278e-02 eta: 1:49:56 time: 0.4598 data_time: 0.0232 memory: 23498 grad_norm: 3.6943 top1_acc: 0.2083 top5_acc: 0.6250 loss_cls: 2.4450 loss: 2.4450 2022/09/09 15:06:50 - mmengine - INFO - Epoch(train) [14][380/449] lr: 3.0217e-02 eta: 1:49:41 time: 0.4687 data_time: 0.0162 memory: 23498 grad_norm: 3.7282 top1_acc: 0.2917 top5_acc: 0.5000 loss_cls: 2.5349 loss: 2.5349 2022/09/09 15:06:59 - mmengine - INFO - Epoch(train) [14][400/449] lr: 3.0155e-02 eta: 1:49:26 time: 0.4637 data_time: 0.0253 memory: 23498 grad_norm: 3.6961 top1_acc: 0.4167 top5_acc: 0.7917 loss_cls: 2.3945 loss: 2.3945 2022/09/09 15:07:08 - mmengine - INFO - Epoch(train) [14][420/449] lr: 3.0093e-02 eta: 1:49:11 time: 0.4610 data_time: 0.0179 memory: 23498 grad_norm: 3.6700 top1_acc: 0.5000 top5_acc: 0.6667 loss_cls: 2.4328 loss: 2.4328 2022/09/09 15:07:17 - mmengine - INFO - Epoch(train) [14][440/449] lr: 3.0031e-02 eta: 1:48:56 time: 0.4484 data_time: 0.0178 memory: 23498 grad_norm: 3.5755 top1_acc: 0.4167 top5_acc: 0.7917 loss_cls: 2.4423 loss: 2.4423 2022/09/09 15:07:21 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:07:21 - mmengine - INFO - Epoch(train) [14][449/449] lr: 3.0003e-02 eta: 1:48:56 time: 0.4431 data_time: 0.0163 memory: 23498 grad_norm: 3.8494 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.5459 loss: 2.5459 2022/09/09 15:07:24 - mmengine - INFO - Epoch(val) [14][20/61] eta: 0:00:06 time: 0.1481 data_time: 0.0283 memory: 2693 2022/09/09 15:07:27 - mmengine - INFO - Epoch(val) [14][40/61] eta: 0:00:02 time: 0.1312 data_time: 0.0132 memory: 2693 2022/09/09 15:07:29 - mmengine - INFO - Epoch(val) [14][60/61] eta: 0:00:00 time: 0.1300 data_time: 0.0134 memory: 2693 2022/09/09 15:07:30 - mmengine - INFO - Epoch(val) [14][61/61] acc/top1: 0.2703 acc/top5: 0.5461 acc/mean1: 0.2363 2022/09/09 15:07:30 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_13.pth is removed 2022/09/09 15:07:31 - mmengine - INFO - The best checkpoint with 0.2703 acc/top1 at 14 epoch is saved to best_acc/top1_epoch_14.pth. 2022/09/09 15:07:41 - mmengine - INFO - Epoch(train) [15][20/449] lr: 2.9941e-02 eta: 1:48:29 time: 0.5169 data_time: 0.0522 memory: 23498 grad_norm: 3.5889 top1_acc: 0.5417 top5_acc: 0.8750 loss_cls: 2.4439 loss: 2.4439 2022/09/09 15:07:50 - mmengine - INFO - Epoch(train) [15][40/449] lr: 2.9879e-02 eta: 1:48:15 time: 0.4718 data_time: 0.0172 memory: 23498 grad_norm: 3.7240 top1_acc: 0.4167 top5_acc: 0.7083 loss_cls: 2.4938 loss: 2.4938 2022/09/09 15:07:59 - mmengine - INFO - Epoch(train) [15][60/449] lr: 2.9816e-02 eta: 1:48:00 time: 0.4526 data_time: 0.0202 memory: 23498 grad_norm: 3.7300 top1_acc: 0.4167 top5_acc: 0.6667 loss_cls: 2.3340 loss: 2.3340 2022/09/09 15:08:09 - mmengine - INFO - Epoch(train) [15][80/449] lr: 2.9754e-02 eta: 1:47:45 time: 0.4561 data_time: 0.0171 memory: 23498 grad_norm: 3.7635 top1_acc: 0.2917 top5_acc: 0.6250 loss_cls: 2.5450 loss: 2.5450 2022/09/09 15:08:18 - mmengine - INFO - Epoch(train) [15][100/449] lr: 2.9691e-02 eta: 1:47:30 time: 0.4565 data_time: 0.0156 memory: 23498 grad_norm: 3.7426 top1_acc: 0.3750 top5_acc: 0.6667 loss_cls: 2.4006 loss: 2.4006 2022/09/09 15:08:27 - mmengine - INFO - Epoch(train) [15][120/449] lr: 2.9628e-02 eta: 1:47:16 time: 0.4713 data_time: 0.0188 memory: 23498 grad_norm: 3.7184 top1_acc: 0.4583 top5_acc: 0.8333 loss_cls: 2.3742 loss: 2.3742 2022/09/09 15:08:36 - mmengine - INFO - Epoch(train) [15][140/449] lr: 2.9565e-02 eta: 1:47:01 time: 0.4621 data_time: 0.0230 memory: 23498 grad_norm: 3.6884 top1_acc: 0.4167 top5_acc: 0.7083 loss_cls: 2.4537 loss: 2.4537 2022/09/09 15:08:46 - mmengine - INFO - Epoch(train) [15][160/449] lr: 2.9502e-02 eta: 1:46:46 time: 0.4572 data_time: 0.0170 memory: 23498 grad_norm: 3.7451 top1_acc: 0.2917 top5_acc: 0.5833 loss_cls: 2.5651 loss: 2.5651 2022/09/09 15:08:55 - mmengine - INFO - Epoch(train) [15][180/449] lr: 2.9439e-02 eta: 1:46:32 time: 0.4608 data_time: 0.0234 memory: 23498 grad_norm: 3.8673 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4791 loss: 2.4791 2022/09/09 15:09:04 - mmengine - INFO - Epoch(train) [15][200/449] lr: 2.9375e-02 eta: 1:46:17 time: 0.4636 data_time: 0.0183 memory: 23498 grad_norm: 3.5046 top1_acc: 0.5833 top5_acc: 0.8750 loss_cls: 2.4152 loss: 2.4152 2022/09/09 15:09:13 - mmengine - INFO - Epoch(train) [15][220/449] lr: 2.9312e-02 eta: 1:46:03 time: 0.4577 data_time: 0.0196 memory: 23498 grad_norm: 3.5597 top1_acc: 0.4167 top5_acc: 0.7083 loss_cls: 2.2566 loss: 2.2566 2022/09/09 15:09:22 - mmengine - INFO - Epoch(train) [15][240/449] lr: 2.9248e-02 eta: 1:45:48 time: 0.4598 data_time: 0.0241 memory: 23498 grad_norm: 3.8314 top1_acc: 0.4583 top5_acc: 0.7917 loss_cls: 2.4171 loss: 2.4171 2022/09/09 15:09:32 - mmengine - INFO - Epoch(train) [15][260/449] lr: 2.9185e-02 eta: 1:45:34 time: 0.4571 data_time: 0.0240 memory: 23498 grad_norm: 3.7513 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3479 loss: 2.3479 2022/09/09 15:09:41 - mmengine - INFO - Epoch(train) [15][280/449] lr: 2.9121e-02 eta: 1:45:19 time: 0.4611 data_time: 0.0192 memory: 23498 grad_norm: 3.5299 top1_acc: 0.5833 top5_acc: 0.7917 loss_cls: 2.2953 loss: 2.2953 2022/09/09 15:09:50 - mmengine - INFO - Epoch(train) [15][300/449] lr: 2.9057e-02 eta: 1:45:05 time: 0.4535 data_time: 0.0197 memory: 23498 grad_norm: 3.7159 top1_acc: 0.3333 top5_acc: 0.7917 loss_cls: 2.4875 loss: 2.4875 2022/09/09 15:09:59 - mmengine - INFO - Epoch(train) [15][320/449] lr: 2.8993e-02 eta: 1:44:50 time: 0.4553 data_time: 0.0254 memory: 23498 grad_norm: 3.5560 top1_acc: 0.2500 top5_acc: 0.5417 loss_cls: 2.4329 loss: 2.4329 2022/09/09 15:10:08 - mmengine - INFO - Epoch(train) [15][340/449] lr: 2.8929e-02 eta: 1:44:36 time: 0.4564 data_time: 0.0241 memory: 23498 grad_norm: 3.4785 top1_acc: 0.5417 top5_acc: 0.7917 loss_cls: 2.4289 loss: 2.4289 2022/09/09 15:10:17 - mmengine - INFO - Epoch(train) [15][360/449] lr: 2.8864e-02 eta: 1:44:22 time: 0.4647 data_time: 0.0210 memory: 23498 grad_norm: 3.5849 top1_acc: 0.5000 top5_acc: 0.6667 loss_cls: 2.2151 loss: 2.2151 2022/09/09 15:10:27 - mmengine - INFO - Epoch(train) [15][380/449] lr: 2.8800e-02 eta: 1:44:08 time: 0.4590 data_time: 0.0201 memory: 23498 grad_norm: 3.6219 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4812 loss: 2.4812 2022/09/09 15:10:36 - mmengine - INFO - Epoch(train) [15][400/449] lr: 2.8736e-02 eta: 1:43:53 time: 0.4585 data_time: 0.0265 memory: 23498 grad_norm: 3.5599 top1_acc: 0.3333 top5_acc: 0.6250 loss_cls: 2.5574 loss: 2.5574 2022/09/09 15:10:45 - mmengine - INFO - Epoch(train) [15][420/449] lr: 2.8671e-02 eta: 1:43:39 time: 0.4651 data_time: 0.0225 memory: 23498 grad_norm: 3.5585 top1_acc: 0.4167 top5_acc: 0.6250 loss_cls: 2.3145 loss: 2.3145 2022/09/09 15:10:54 - mmengine - INFO - Epoch(train) [15][440/449] lr: 2.8606e-02 eta: 1:43:25 time: 0.4552 data_time: 0.0200 memory: 23498 grad_norm: 3.5728 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3982 loss: 2.3982 2022/09/09 15:10:58 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:10:58 - mmengine - INFO - Epoch(train) [15][449/449] lr: 2.8577e-02 eta: 1:43:25 time: 0.4288 data_time: 0.0183 memory: 23498 grad_norm: 3.8357 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.6401 loss: 2.6401 2022/09/09 15:11:01 - mmengine - INFO - Epoch(val) [15][20/61] eta: 0:00:06 time: 0.1539 data_time: 0.0344 memory: 2693 2022/09/09 15:11:03 - mmengine - INFO - Epoch(val) [15][40/61] eta: 0:00:02 time: 0.1310 data_time: 0.0133 memory: 2693 2022/09/09 15:11:06 - mmengine - INFO - Epoch(val) [15][60/61] eta: 0:00:00 time: 0.1302 data_time: 0.0125 memory: 2693 2022/09/09 15:11:07 - mmengine - INFO - Epoch(val) [15][61/61] acc/top1: 0.2703 acc/top5: 0.5461 acc/mean1: 0.2408 2022/09/09 15:11:18 - mmengine - INFO - Epoch(train) [16][20/449] lr: 2.8512e-02 eta: 1:43:01 time: 0.5596 data_time: 0.0736 memory: 23498 grad_norm: 3.4869 top1_acc: 0.3333 top5_acc: 0.5417 loss_cls: 2.3477 loss: 2.3477 2022/09/09 15:11:27 - mmengine - INFO - Epoch(train) [16][40/449] lr: 2.8447e-02 eta: 1:42:47 time: 0.4710 data_time: 0.0250 memory: 23498 grad_norm: 3.5950 top1_acc: 0.4167 top5_acc: 0.5833 loss_cls: 2.4755 loss: 2.4755 2022/09/09 15:11:37 - mmengine - INFO - Epoch(train) [16][60/449] lr: 2.8382e-02 eta: 1:42:34 time: 0.4740 data_time: 0.0267 memory: 23498 grad_norm: 3.8319 top1_acc: 0.4167 top5_acc: 0.7500 loss_cls: 2.1511 loss: 2.1511 2022/09/09 15:11:46 - mmengine - INFO - Epoch(train) [16][80/449] lr: 2.8317e-02 eta: 1:42:20 time: 0.4595 data_time: 0.0192 memory: 23498 grad_norm: 3.8863 top1_acc: 0.5833 top5_acc: 0.9583 loss_cls: 2.3160 loss: 2.3160 2022/09/09 15:11:55 - mmengine - INFO - Epoch(train) [16][100/449] lr: 2.8252e-02 eta: 1:42:06 time: 0.4606 data_time: 0.0197 memory: 23498 grad_norm: 3.8724 top1_acc: 0.5417 top5_acc: 0.7083 loss_cls: 2.4974 loss: 2.4974 2022/09/09 15:12:05 - mmengine - INFO - Epoch(train) [16][120/449] lr: 2.8186e-02 eta: 1:41:52 time: 0.4723 data_time: 0.0302 memory: 23498 grad_norm: 3.8868 top1_acc: 0.4583 top5_acc: 0.7083 loss_cls: 2.2336 loss: 2.2336 2022/09/09 15:12:14 - mmengine - INFO - Epoch(train) [16][140/449] lr: 2.8121e-02 eta: 1:41:38 time: 0.4584 data_time: 0.0232 memory: 23498 grad_norm: 3.9545 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2707 loss: 2.2707 2022/09/09 15:12:23 - mmengine - INFO - Epoch(train) [16][160/449] lr: 2.8055e-02 eta: 1:41:25 time: 0.4687 data_time: 0.0236 memory: 23498 grad_norm: 3.7195 top1_acc: 0.5833 top5_acc: 0.6250 loss_cls: 2.2602 loss: 2.2602 2022/09/09 15:12:32 - mmengine - INFO - Epoch(train) [16][180/449] lr: 2.7989e-02 eta: 1:41:10 time: 0.4528 data_time: 0.0202 memory: 23498 grad_norm: 3.6664 top1_acc: 0.3750 top5_acc: 0.6667 loss_cls: 2.3086 loss: 2.3086 2022/09/09 15:12:41 - mmengine - INFO - Epoch(train) [16][200/449] lr: 2.7923e-02 eta: 1:40:56 time: 0.4548 data_time: 0.0198 memory: 23498 grad_norm: 3.7658 top1_acc: 0.7083 top5_acc: 0.8750 loss_cls: 2.2866 loss: 2.2866 2022/09/09 15:12:50 - mmengine - INFO - Epoch(train) [16][220/449] lr: 2.7857e-02 eta: 1:40:43 time: 0.4574 data_time: 0.0205 memory: 23498 grad_norm: 3.9353 top1_acc: 0.5000 top5_acc: 0.7917 loss_cls: 2.5768 loss: 2.5768 2022/09/09 15:13:00 - mmengine - INFO - Epoch(train) [16][240/449] lr: 2.7791e-02 eta: 1:40:29 time: 0.4559 data_time: 0.0223 memory: 23498 grad_norm: 3.8024 top1_acc: 0.5417 top5_acc: 0.6667 loss_cls: 2.3218 loss: 2.3218 2022/09/09 15:13:09 - mmengine - INFO - Epoch(train) [16][260/449] lr: 2.7725e-02 eta: 1:40:15 time: 0.4554 data_time: 0.0213 memory: 23498 grad_norm: 3.7790 top1_acc: 0.5000 top5_acc: 0.6667 loss_cls: 2.4702 loss: 2.4702 2022/09/09 15:13:11 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:13:18 - mmengine - INFO - Epoch(train) [16][280/449] lr: 2.7659e-02 eta: 1:40:01 time: 0.4530 data_time: 0.0223 memory: 23498 grad_norm: 3.8642 top1_acc: 0.5000 top5_acc: 0.7083 loss_cls: 2.3192 loss: 2.3192 2022/09/09 15:13:27 - mmengine - INFO - Epoch(train) [16][300/449] lr: 2.7593e-02 eta: 1:39:47 time: 0.4581 data_time: 0.0231 memory: 23498 grad_norm: 3.7488 top1_acc: 0.4167 top5_acc: 0.6250 loss_cls: 2.3171 loss: 2.3171 2022/09/09 15:13:36 - mmengine - INFO - Epoch(train) [16][320/449] lr: 2.7526e-02 eta: 1:39:33 time: 0.4504 data_time: 0.0208 memory: 23498 grad_norm: 3.9061 top1_acc: 0.3333 top5_acc: 0.6250 loss_cls: 2.5556 loss: 2.5556 2022/09/09 15:13:45 - mmengine - INFO - Epoch(train) [16][340/449] lr: 2.7460e-02 eta: 1:39:20 time: 0.4638 data_time: 0.0234 memory: 23498 grad_norm: 3.7752 top1_acc: 0.4167 top5_acc: 0.6250 loss_cls: 2.3888 loss: 2.3888 2022/09/09 15:13:54 - mmengine - INFO - Epoch(train) [16][360/449] lr: 2.7393e-02 eta: 1:39:06 time: 0.4513 data_time: 0.0222 memory: 23498 grad_norm: 3.7491 top1_acc: 0.3333 top5_acc: 0.6667 loss_cls: 2.2544 loss: 2.2544 2022/09/09 15:14:03 - mmengine - INFO - Epoch(train) [16][380/449] lr: 2.7326e-02 eta: 1:38:52 time: 0.4522 data_time: 0.0225 memory: 23498 grad_norm: 3.9086 top1_acc: 0.5417 top5_acc: 0.8333 loss_cls: 2.3184 loss: 2.3184 2022/09/09 15:14:12 - mmengine - INFO - Epoch(train) [16][400/449] lr: 2.7260e-02 eta: 1:38:38 time: 0.4593 data_time: 0.0217 memory: 23498 grad_norm: 3.8356 top1_acc: 0.6250 top5_acc: 0.7917 loss_cls: 2.3417 loss: 2.3417 2022/09/09 15:14:22 - mmengine - INFO - Epoch(train) [16][420/449] lr: 2.7193e-02 eta: 1:38:24 time: 0.4561 data_time: 0.0215 memory: 23498 grad_norm: 3.7493 top1_acc: 0.4167 top5_acc: 0.5833 loss_cls: 2.5687 loss: 2.5687 2022/09/09 15:14:30 - mmengine - INFO - Epoch(train) [16][440/449] lr: 2.7126e-02 eta: 1:38:11 time: 0.4448 data_time: 0.0203 memory: 23498 grad_norm: 3.6155 top1_acc: 0.5417 top5_acc: 0.7083 loss_cls: 2.4004 loss: 2.4004 2022/09/09 15:14:34 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:14:34 - mmengine - INFO - Epoch(train) [16][449/449] lr: 2.7095e-02 eta: 1:38:11 time: 0.4276 data_time: 0.0135 memory: 23498 grad_norm: 3.8148 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.4494 loss: 2.4494 2022/09/09 15:14:37 - mmengine - INFO - Epoch(val) [16][20/61] eta: 0:00:06 time: 0.1514 data_time: 0.0323 memory: 2693 2022/09/09 15:14:40 - mmengine - INFO - Epoch(val) [16][40/61] eta: 0:00:02 time: 0.1297 data_time: 0.0123 memory: 2693 2022/09/09 15:14:42 - mmengine - INFO - Epoch(val) [16][60/61] eta: 0:00:00 time: 0.1296 data_time: 0.0131 memory: 2693 2022/09/09 15:14:43 - mmengine - INFO - Epoch(val) [16][61/61] acc/top1: 0.2582 acc/top5: 0.5350 acc/mean1: 0.2250 2022/09/09 15:14:56 - mmengine - INFO - Epoch(train) [17][20/449] lr: 2.7028e-02 eta: 1:37:50 time: 0.6516 data_time: 0.1456 memory: 23498 grad_norm: 3.5617 top1_acc: 0.3333 top5_acc: 0.7917 loss_cls: 2.4749 loss: 2.4749 2022/09/09 15:15:05 - mmengine - INFO - Epoch(train) [17][40/449] lr: 2.6961e-02 eta: 1:37:37 time: 0.4519 data_time: 0.0205 memory: 23498 grad_norm: 3.6785 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0960 loss: 2.0960 2022/09/09 15:15:14 - mmengine - INFO - Epoch(train) [17][60/449] lr: 2.6894e-02 eta: 1:37:23 time: 0.4564 data_time: 0.0218 memory: 23498 grad_norm: 3.7451 top1_acc: 0.4583 top5_acc: 0.6667 loss_cls: 2.3160 loss: 2.3160 2022/09/09 15:15:23 - mmengine - INFO - Epoch(train) [17][80/449] lr: 2.6826e-02 eta: 1:37:09 time: 0.4481 data_time: 0.0159 memory: 23498 grad_norm: 3.7911 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3082 loss: 2.3082 2022/09/09 15:15:32 - mmengine - INFO - Epoch(train) [17][100/449] lr: 2.6759e-02 eta: 1:36:56 time: 0.4561 data_time: 0.0213 memory: 23498 grad_norm: 3.6633 top1_acc: 0.2500 top5_acc: 0.5833 loss_cls: 2.3559 loss: 2.3559 2022/09/09 15:15:41 - mmengine - INFO - Epoch(train) [17][120/449] lr: 2.6691e-02 eta: 1:36:42 time: 0.4508 data_time: 0.0171 memory: 23498 grad_norm: 3.8260 top1_acc: 0.5417 top5_acc: 0.7500 loss_cls: 2.3730 loss: 2.3730 2022/09/09 15:15:50 - mmengine - INFO - Epoch(train) [17][140/449] lr: 2.6624e-02 eta: 1:36:29 time: 0.4561 data_time: 0.0225 memory: 23498 grad_norm: 3.9535 top1_acc: 0.5417 top5_acc: 0.8750 loss_cls: 2.2432 loss: 2.2432 2022/09/09 15:15:59 - mmengine - INFO - Epoch(train) [17][160/449] lr: 2.6556e-02 eta: 1:36:15 time: 0.4568 data_time: 0.0188 memory: 23498 grad_norm: 3.7808 top1_acc: 0.4167 top5_acc: 0.6250 loss_cls: 2.4283 loss: 2.4283 2022/09/09 15:16:09 - mmengine - INFO - Epoch(train) [17][180/449] lr: 2.6488e-02 eta: 1:36:02 time: 0.4562 data_time: 0.0183 memory: 23498 grad_norm: 3.9426 top1_acc: 0.4167 top5_acc: 0.7083 loss_cls: 2.2466 loss: 2.2466 2022/09/09 15:16:18 - mmengine - INFO - Epoch(train) [17][200/449] lr: 2.6420e-02 eta: 1:35:49 time: 0.4575 data_time: 0.0200 memory: 23498 grad_norm: 4.0762 top1_acc: 0.3333 top5_acc: 0.7500 loss_cls: 2.1867 loss: 2.1867 2022/09/09 15:16:27 - mmengine - INFO - Epoch(train) [17][220/449] lr: 2.6352e-02 eta: 1:35:35 time: 0.4504 data_time: 0.0212 memory: 23498 grad_norm: 3.8252 top1_acc: 0.4167 top5_acc: 0.7500 loss_cls: 2.2224 loss: 2.2224 2022/09/09 15:16:36 - mmengine - INFO - Epoch(train) [17][240/449] lr: 2.6284e-02 eta: 1:35:22 time: 0.4517 data_time: 0.0205 memory: 23498 grad_norm: 3.7422 top1_acc: 0.4583 top5_acc: 0.5833 loss_cls: 2.3703 loss: 2.3703 2022/09/09 15:16:45 - mmengine - INFO - Epoch(train) [17][260/449] lr: 2.6216e-02 eta: 1:35:08 time: 0.4553 data_time: 0.0159 memory: 23498 grad_norm: 3.8228 top1_acc: 0.4167 top5_acc: 0.5417 loss_cls: 2.2619 loss: 2.2619 2022/09/09 15:16:54 - mmengine - INFO - Epoch(train) [17][280/449] lr: 2.6148e-02 eta: 1:34:55 time: 0.4599 data_time: 0.0172 memory: 23498 grad_norm: 4.0572 top1_acc: 0.4583 top5_acc: 0.6667 loss_cls: 2.2191 loss: 2.2191 2022/09/09 15:17:03 - mmengine - INFO - Epoch(train) [17][300/449] lr: 2.6079e-02 eta: 1:34:42 time: 0.4554 data_time: 0.0192 memory: 23498 grad_norm: 4.0847 top1_acc: 0.6250 top5_acc: 0.8333 loss_cls: 2.3267 loss: 2.3267 2022/09/09 15:17:12 - mmengine - INFO - Epoch(train) [17][320/449] lr: 2.6011e-02 eta: 1:34:28 time: 0.4470 data_time: 0.0161 memory: 23498 grad_norm: 4.0149 top1_acc: 0.4167 top5_acc: 0.7083 loss_cls: 2.3787 loss: 2.3787 2022/09/09 15:17:21 - mmengine - INFO - Epoch(train) [17][340/449] lr: 2.5942e-02 eta: 1:34:15 time: 0.4589 data_time: 0.0154 memory: 23498 grad_norm: 4.0201 top1_acc: 0.4167 top5_acc: 0.5833 loss_cls: 2.3569 loss: 2.3569 2022/09/09 15:17:30 - mmengine - INFO - Epoch(train) [17][360/449] lr: 2.5874e-02 eta: 1:34:02 time: 0.4530 data_time: 0.0202 memory: 23498 grad_norm: 3.8177 top1_acc: 0.4583 top5_acc: 0.7083 loss_cls: 2.2573 loss: 2.2573 2022/09/09 15:17:39 - mmengine - INFO - Epoch(train) [17][380/449] lr: 2.5805e-02 eta: 1:33:49 time: 0.4552 data_time: 0.0204 memory: 23498 grad_norm: 4.0835 top1_acc: 0.3750 top5_acc: 0.4583 loss_cls: 2.3643 loss: 2.3643 2022/09/09 15:17:49 - mmengine - INFO - Epoch(train) [17][400/449] lr: 2.5736e-02 eta: 1:33:35 time: 0.4542 data_time: 0.0178 memory: 23498 grad_norm: 3.8491 top1_acc: 0.4583 top5_acc: 0.8333 loss_cls: 2.2965 loss: 2.2965 2022/09/09 15:17:58 - mmengine - INFO - Epoch(train) [17][420/449] lr: 2.5668e-02 eta: 1:33:22 time: 0.4542 data_time: 0.0172 memory: 23498 grad_norm: 3.8508 top1_acc: 0.6250 top5_acc: 0.7917 loss_cls: 2.2170 loss: 2.2170 2022/09/09 15:18:06 - mmengine - INFO - Epoch(train) [17][440/449] lr: 2.5599e-02 eta: 1:33:09 time: 0.4449 data_time: 0.0173 memory: 23498 grad_norm: 3.9137 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2973 loss: 2.2973 2022/09/09 15:18:10 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:18:10 - mmengine - INFO - Epoch(train) [17][449/449] lr: 2.5568e-02 eta: 1:33:09 time: 0.4295 data_time: 0.0125 memory: 23498 grad_norm: 4.1964 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.4903 loss: 2.4903 2022/09/09 15:18:13 - mmengine - INFO - Epoch(val) [17][20/61] eta: 0:00:06 time: 0.1479 data_time: 0.0281 memory: 2693 2022/09/09 15:18:16 - mmengine - INFO - Epoch(val) [17][40/61] eta: 0:00:02 time: 0.1295 data_time: 0.0116 memory: 2693 2022/09/09 15:18:18 - mmengine - INFO - Epoch(val) [17][60/61] eta: 0:00:00 time: 0.1288 data_time: 0.0124 memory: 2693 2022/09/09 15:18:19 - mmengine - INFO - Epoch(val) [17][61/61] acc/top1: 0.2566 acc/top5: 0.5423 acc/mean1: 0.2227 2022/09/09 15:18:30 - mmengine - INFO - Epoch(train) [18][20/449] lr: 2.5499e-02 eta: 1:32:47 time: 0.5672 data_time: 0.0408 memory: 23498 grad_norm: 3.7594 top1_acc: 0.5833 top5_acc: 0.8750 loss_cls: 2.3717 loss: 2.3717 2022/09/09 15:18:39 - mmengine - INFO - Epoch(train) [18][40/449] lr: 2.5430e-02 eta: 1:32:34 time: 0.4566 data_time: 0.0240 memory: 23498 grad_norm: 3.8192 top1_acc: 0.5833 top5_acc: 0.7917 loss_cls: 2.1790 loss: 2.1790 2022/09/09 15:18:49 - mmengine - INFO - Epoch(train) [18][60/449] lr: 2.5361e-02 eta: 1:32:21 time: 0.4640 data_time: 0.0156 memory: 23498 grad_norm: 3.7865 top1_acc: 0.4583 top5_acc: 0.7917 loss_cls: 2.3806 loss: 2.3806 2022/09/09 15:18:58 - mmengine - INFO - Epoch(train) [18][80/449] lr: 2.5292e-02 eta: 1:32:09 time: 0.4598 data_time: 0.0172 memory: 23498 grad_norm: 3.8577 top1_acc: 0.6250 top5_acc: 0.7917 loss_cls: 2.1140 loss: 2.1140 2022/09/09 15:19:07 - mmengine - INFO - Epoch(train) [18][100/449] lr: 2.5222e-02 eta: 1:31:56 time: 0.4697 data_time: 0.0199 memory: 23498 grad_norm: 4.0616 top1_acc: 0.4167 top5_acc: 0.7083 loss_cls: 2.3815 loss: 2.3815 2022/09/09 15:19:17 - mmengine - INFO - Epoch(train) [18][120/449] lr: 2.5153e-02 eta: 1:31:43 time: 0.4708 data_time: 0.0278 memory: 23498 grad_norm: 3.9172 top1_acc: 0.6250 top5_acc: 0.7917 loss_cls: 2.1910 loss: 2.1910 2022/09/09 15:19:26 - mmengine - INFO - Epoch(train) [18][140/449] lr: 2.5084e-02 eta: 1:31:30 time: 0.4630 data_time: 0.0165 memory: 23498 grad_norm: 3.9349 top1_acc: 0.5417 top5_acc: 0.6667 loss_cls: 2.3161 loss: 2.3161 2022/09/09 15:19:35 - mmengine - INFO - Epoch(train) [18][160/449] lr: 2.5014e-02 eta: 1:31:18 time: 0.4685 data_time: 0.0191 memory: 23498 grad_norm: 3.8667 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2211 loss: 2.2211 2022/09/09 15:19:45 - mmengine - INFO - Epoch(train) [18][180/449] lr: 2.4945e-02 eta: 1:31:05 time: 0.4698 data_time: 0.0161 memory: 23498 grad_norm: 3.9817 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3465 loss: 2.3465 2022/09/09 15:19:54 - mmengine - INFO - Epoch(train) [18][200/449] lr: 2.4875e-02 eta: 1:30:53 time: 0.4635 data_time: 0.0257 memory: 23498 grad_norm: 3.9265 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2234 loss: 2.2234 2022/09/09 15:20:03 - mmengine - INFO - Epoch(train) [18][220/449] lr: 2.4805e-02 eta: 1:30:40 time: 0.4644 data_time: 0.0143 memory: 23498 grad_norm: 4.0376 top1_acc: 0.4583 top5_acc: 0.6250 loss_cls: 2.4193 loss: 2.4193 2022/09/09 15:20:13 - mmengine - INFO - Epoch(train) [18][240/449] lr: 2.4736e-02 eta: 1:30:27 time: 0.4598 data_time: 0.0178 memory: 23498 grad_norm: 3.9478 top1_acc: 0.6250 top5_acc: 0.7917 loss_cls: 2.2207 loss: 2.2207 2022/09/09 15:20:22 - mmengine - INFO - Epoch(train) [18][260/449] lr: 2.4666e-02 eta: 1:30:14 time: 0.4637 data_time: 0.0185 memory: 23498 grad_norm: 3.9608 top1_acc: 0.3750 top5_acc: 0.5833 loss_cls: 2.1745 loss: 2.1745 2022/09/09 15:20:31 - mmengine - INFO - Epoch(train) [18][280/449] lr: 2.4596e-02 eta: 1:30:02 time: 0.4818 data_time: 0.0313 memory: 23498 grad_norm: 3.9902 top1_acc: 0.4167 top5_acc: 0.6250 loss_cls: 2.2317 loss: 2.2317 2022/09/09 15:20:41 - mmengine - INFO - Epoch(train) [18][300/449] lr: 2.4526e-02 eta: 1:29:50 time: 0.4618 data_time: 0.0164 memory: 23498 grad_norm: 3.9930 top1_acc: 0.3333 top5_acc: 0.5833 loss_cls: 2.4497 loss: 2.4497 2022/09/09 15:20:50 - mmengine - INFO - Epoch(train) [18][320/449] lr: 2.4456e-02 eta: 1:29:37 time: 0.4565 data_time: 0.0183 memory: 23498 grad_norm: 4.0180 top1_acc: 0.2500 top5_acc: 0.6667 loss_cls: 2.3163 loss: 2.3163 2022/09/09 15:20:59 - mmengine - INFO - Epoch(train) [18][340/449] lr: 2.4386e-02 eta: 1:29:24 time: 0.4615 data_time: 0.0187 memory: 23498 grad_norm: 4.0567 top1_acc: 0.2083 top5_acc: 0.5833 loss_cls: 2.3126 loss: 2.3126 2022/09/09 15:21:09 - mmengine - INFO - Epoch(train) [18][360/449] lr: 2.4316e-02 eta: 1:29:12 time: 0.4781 data_time: 0.0260 memory: 23498 grad_norm: 4.0120 top1_acc: 0.4167 top5_acc: 0.7917 loss_cls: 2.1622 loss: 2.1622 2022/09/09 15:21:12 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:21:18 - mmengine - INFO - Epoch(train) [18][380/449] lr: 2.4246e-02 eta: 1:28:59 time: 0.4575 data_time: 0.0167 memory: 23498 grad_norm: 3.9728 top1_acc: 0.6250 top5_acc: 0.8333 loss_cls: 2.2142 loss: 2.2142 2022/09/09 15:21:27 - mmengine - INFO - Epoch(train) [18][400/449] lr: 2.4176e-02 eta: 1:28:46 time: 0.4509 data_time: 0.0174 memory: 23498 grad_norm: 3.8345 top1_acc: 0.5000 top5_acc: 0.7083 loss_cls: 2.2946 loss: 2.2946 2022/09/09 15:21:36 - mmengine - INFO - Epoch(train) [18][420/449] lr: 2.4106e-02 eta: 1:28:34 time: 0.4542 data_time: 0.0165 memory: 23498 grad_norm: 3.7962 top1_acc: 0.3333 top5_acc: 0.5833 loss_cls: 2.2982 loss: 2.2982 2022/09/09 15:21:45 - mmengine - INFO - Epoch(train) [18][440/449] lr: 2.4036e-02 eta: 1:28:21 time: 0.4532 data_time: 0.0225 memory: 23498 grad_norm: 3.8975 top1_acc: 0.4167 top5_acc: 0.6667 loss_cls: 2.1992 loss: 2.1992 2022/09/09 15:21:49 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:21:49 - mmengine - INFO - Epoch(train) [18][449/449] lr: 2.4004e-02 eta: 1:28:21 time: 0.4271 data_time: 0.0166 memory: 23498 grad_norm: 4.1675 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.3209 loss: 2.3209 2022/09/09 15:21:52 - mmengine - INFO - Epoch(val) [18][20/61] eta: 0:00:06 time: 0.1479 data_time: 0.0291 memory: 2693 2022/09/09 15:21:54 - mmengine - INFO - Epoch(val) [18][40/61] eta: 0:00:02 time: 0.1315 data_time: 0.0128 memory: 2693 2022/09/09 15:21:57 - mmengine - INFO - Epoch(val) [18][60/61] eta: 0:00:00 time: 0.1331 data_time: 0.0157 memory: 2693 2022/09/09 15:21:57 - mmengine - INFO - Epoch(val) [18][61/61] acc/top1: 0.2855 acc/top5: 0.5796 acc/mean1: 0.2508 2022/09/09 15:21:57 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_14.pth is removed 2022/09/09 15:21:58 - mmengine - INFO - The best checkpoint with 0.2855 acc/top1 at 18 epoch is saved to best_acc/top1_epoch_18.pth. 2022/09/09 15:22:10 - mmengine - INFO - Epoch(train) [19][20/449] lr: 2.3934e-02 eta: 1:28:01 time: 0.5916 data_time: 0.0326 memory: 23498 grad_norm: 3.9131 top1_acc: 0.5417 top5_acc: 0.6667 loss_cls: 2.4133 loss: 2.4133 2022/09/09 15:22:19 - mmengine - INFO - Epoch(train) [19][40/449] lr: 2.3863e-02 eta: 1:27:48 time: 0.4588 data_time: 0.0170 memory: 23498 grad_norm: 3.7952 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 2.1325 loss: 2.1325 2022/09/09 15:22:28 - mmengine - INFO - Epoch(train) [19][60/449] lr: 2.3793e-02 eta: 1:27:36 time: 0.4543 data_time: 0.0183 memory: 23498 grad_norm: 3.7623 top1_acc: 0.2917 top5_acc: 0.7083 loss_cls: 2.0880 loss: 2.0880 2022/09/09 15:22:38 - mmengine - INFO - Epoch(train) [19][80/449] lr: 2.3722e-02 eta: 1:27:23 time: 0.4668 data_time: 0.0235 memory: 23498 grad_norm: 3.9901 top1_acc: 0.3333 top5_acc: 0.6250 loss_cls: 1.9198 loss: 1.9198 2022/09/09 15:22:47 - mmengine - INFO - Epoch(train) [19][100/449] lr: 2.3652e-02 eta: 1:27:10 time: 0.4505 data_time: 0.0190 memory: 23498 grad_norm: 4.0295 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3013 loss: 2.3013 2022/09/09 15:22:56 - mmengine - INFO - Epoch(train) [19][120/449] lr: 2.3581e-02 eta: 1:26:58 time: 0.4668 data_time: 0.0276 memory: 23498 grad_norm: 3.8723 top1_acc: 0.4167 top5_acc: 0.7083 loss_cls: 2.2950 loss: 2.2950 2022/09/09 15:23:05 - mmengine - INFO - Epoch(train) [19][140/449] lr: 2.3511e-02 eta: 1:26:46 time: 0.4556 data_time: 0.0189 memory: 23498 grad_norm: 4.0854 top1_acc: 0.5000 top5_acc: 0.6667 loss_cls: 2.1938 loss: 2.1938 2022/09/09 15:23:14 - mmengine - INFO - Epoch(train) [19][160/449] lr: 2.3440e-02 eta: 1:26:33 time: 0.4557 data_time: 0.0207 memory: 23498 grad_norm: 4.0980 top1_acc: 0.3333 top5_acc: 0.7917 loss_cls: 2.1699 loss: 2.1699 2022/09/09 15:23:23 - mmengine - INFO - Epoch(train) [19][180/449] lr: 2.3369e-02 eta: 1:26:20 time: 0.4563 data_time: 0.0196 memory: 23498 grad_norm: 4.1284 top1_acc: 0.5417 top5_acc: 0.8333 loss_cls: 2.2538 loss: 2.2538 2022/09/09 15:23:33 - mmengine - INFO - Epoch(train) [19][200/449] lr: 2.3299e-02 eta: 1:26:08 time: 0.4750 data_time: 0.0217 memory: 23498 grad_norm: 4.0460 top1_acc: 0.4167 top5_acc: 0.7083 loss_cls: 2.1624 loss: 2.1624 2022/09/09 15:23:42 - mmengine - INFO - Epoch(train) [19][220/449] lr: 2.3228e-02 eta: 1:25:56 time: 0.4620 data_time: 0.0231 memory: 23498 grad_norm: 3.9455 top1_acc: 0.3750 top5_acc: 0.8333 loss_cls: 2.1065 loss: 2.1065 2022/09/09 15:23:51 - mmengine - INFO - Epoch(train) [19][240/449] lr: 2.3157e-02 eta: 1:25:44 time: 0.4644 data_time: 0.0238 memory: 23498 grad_norm: 3.9416 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3011 loss: 2.3011 2022/09/09 15:24:01 - mmengine - INFO - Epoch(train) [19][260/449] lr: 2.3086e-02 eta: 1:25:31 time: 0.4581 data_time: 0.0162 memory: 23498 grad_norm: 4.0621 top1_acc: 0.3750 top5_acc: 0.7083 loss_cls: 2.2966 loss: 2.2966 2022/09/09 15:24:10 - mmengine - INFO - Epoch(train) [19][280/449] lr: 2.3015e-02 eta: 1:25:19 time: 0.4690 data_time: 0.0232 memory: 23498 grad_norm: 3.9430 top1_acc: 0.2500 top5_acc: 0.4167 loss_cls: 2.1983 loss: 2.1983 2022/09/09 15:24:19 - mmengine - INFO - Epoch(train) [19][300/449] lr: 2.2944e-02 eta: 1:25:07 time: 0.4565 data_time: 0.0166 memory: 23498 grad_norm: 3.9413 top1_acc: 0.5000 top5_acc: 0.7083 loss_cls: 2.2475 loss: 2.2475 2022/09/09 15:24:28 - mmengine - INFO - Epoch(train) [19][320/449] lr: 2.2873e-02 eta: 1:24:55 time: 0.4658 data_time: 0.0269 memory: 23498 grad_norm: 4.0137 top1_acc: 0.6250 top5_acc: 0.7917 loss_cls: 2.2349 loss: 2.2349 2022/09/09 15:24:38 - mmengine - INFO - Epoch(train) [19][340/449] lr: 2.2802e-02 eta: 1:24:42 time: 0.4555 data_time: 0.0169 memory: 23498 grad_norm: 3.9313 top1_acc: 0.3750 top5_acc: 0.7917 loss_cls: 2.1845 loss: 2.1845 2022/09/09 15:24:47 - mmengine - INFO - Epoch(train) [19][360/449] lr: 2.2731e-02 eta: 1:24:30 time: 0.4610 data_time: 0.0185 memory: 23498 grad_norm: 4.0184 top1_acc: 0.4167 top5_acc: 0.5833 loss_cls: 2.2377 loss: 2.2377 2022/09/09 15:24:56 - mmengine - INFO - Epoch(train) [19][380/449] lr: 2.2660e-02 eta: 1:24:18 time: 0.4577 data_time: 0.0176 memory: 23498 grad_norm: 3.9424 top1_acc: 0.4583 top5_acc: 0.7917 loss_cls: 2.4563 loss: 2.4563 2022/09/09 15:25:05 - mmengine - INFO - Epoch(train) [19][400/449] lr: 2.2589e-02 eta: 1:24:05 time: 0.4603 data_time: 0.0241 memory: 23498 grad_norm: 4.1627 top1_acc: 0.3750 top5_acc: 0.7917 loss_cls: 2.3187 loss: 2.3187 2022/09/09 15:25:14 - mmengine - INFO - Epoch(train) [19][420/449] lr: 2.2518e-02 eta: 1:23:53 time: 0.4607 data_time: 0.0155 memory: 23498 grad_norm: 4.0005 top1_acc: 0.5417 top5_acc: 0.7083 loss_cls: 2.3082 loss: 2.3082 2022/09/09 15:25:23 - mmengine - INFO - Epoch(train) [19][440/449] lr: 2.2446e-02 eta: 1:23:41 time: 0.4508 data_time: 0.0170 memory: 23498 grad_norm: 4.0856 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2622 loss: 2.2622 2022/09/09 15:25:27 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:25:27 - mmengine - INFO - Epoch(train) [19][449/449] lr: 2.2414e-02 eta: 1:23:41 time: 0.4452 data_time: 0.0175 memory: 23498 grad_norm: 4.3934 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.3923 loss: 2.3923 2022/09/09 15:25:30 - mmengine - INFO - Epoch(val) [19][20/61] eta: 0:00:06 time: 0.1552 data_time: 0.0363 memory: 2693 2022/09/09 15:25:33 - mmengine - INFO - Epoch(val) [19][40/61] eta: 0:00:02 time: 0.1289 data_time: 0.0115 memory: 2693 2022/09/09 15:25:36 - mmengine - INFO - Epoch(val) [19][60/61] eta: 0:00:00 time: 0.1296 data_time: 0.0127 memory: 2693 2022/09/09 15:25:36 - mmengine - INFO - Epoch(val) [19][61/61] acc/top1: 0.2862 acc/top5: 0.5596 acc/mean1: 0.2495 2022/09/09 15:25:36 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_18.pth is removed 2022/09/09 15:25:38 - mmengine - INFO - The best checkpoint with 0.2862 acc/top1 at 19 epoch is saved to best_acc/top1_epoch_19.pth. 2022/09/09 15:25:48 - mmengine - INFO - Epoch(train) [20][20/449] lr: 2.2343e-02 eta: 1:23:20 time: 0.5177 data_time: 0.0824 memory: 23498 grad_norm: 4.1078 top1_acc: 0.5000 top5_acc: 0.8333 loss_cls: 2.2395 loss: 2.2395 2022/09/09 15:25:58 - mmengine - INFO - Epoch(train) [20][40/449] lr: 2.2272e-02 eta: 1:23:07 time: 0.4528 data_time: 0.0152 memory: 23498 grad_norm: 4.3183 top1_acc: 0.3333 top5_acc: 0.7500 loss_cls: 2.1868 loss: 2.1868 2022/09/09 15:26:06 - mmengine - INFO - Epoch(train) [20][60/449] lr: 2.2200e-02 eta: 1:22:55 time: 0.4450 data_time: 0.0212 memory: 23498 grad_norm: 4.4304 top1_acc: 0.4583 top5_acc: 0.7083 loss_cls: 2.3173 loss: 2.3173 2022/09/09 15:26:15 - mmengine - INFO - Epoch(train) [20][80/449] lr: 2.2129e-02 eta: 1:22:42 time: 0.4481 data_time: 0.0169 memory: 23498 grad_norm: 4.4198 top1_acc: 0.5000 top5_acc: 0.7083 loss_cls: 2.2209 loss: 2.2209 2022/09/09 15:26:24 - mmengine - INFO - Epoch(train) [20][100/449] lr: 2.2058e-02 eta: 1:22:30 time: 0.4450 data_time: 0.0182 memory: 23498 grad_norm: 4.2352 top1_acc: 0.4167 top5_acc: 0.8333 loss_cls: 1.9722 loss: 1.9722 2022/09/09 15:26:33 - mmengine - INFO - Epoch(train) [20][120/449] lr: 2.1986e-02 eta: 1:22:18 time: 0.4566 data_time: 0.0205 memory: 23498 grad_norm: 4.1191 top1_acc: 0.5417 top5_acc: 0.9167 loss_cls: 2.1558 loss: 2.1558 2022/09/09 15:26:42 - mmengine - INFO - Epoch(train) [20][140/449] lr: 2.1915e-02 eta: 1:22:05 time: 0.4492 data_time: 0.0218 memory: 23498 grad_norm: 3.9529 top1_acc: 0.2500 top5_acc: 0.6667 loss_cls: 2.2696 loss: 2.2696 2022/09/09 15:26:52 - mmengine - INFO - Epoch(train) [20][160/449] lr: 2.1843e-02 eta: 1:21:53 time: 0.4570 data_time: 0.0185 memory: 23498 grad_norm: 3.9486 top1_acc: 0.4167 top5_acc: 0.6667 loss_cls: 2.0926 loss: 2.0926 2022/09/09 15:27:01 - mmengine - INFO - Epoch(train) [20][180/449] lr: 2.1772e-02 eta: 1:21:41 time: 0.4496 data_time: 0.0187 memory: 23498 grad_norm: 4.0424 top1_acc: 0.5417 top5_acc: 0.7917 loss_cls: 2.2729 loss: 2.2729 2022/09/09 15:27:10 - mmengine - INFO - Epoch(train) [20][200/449] lr: 2.1701e-02 eta: 1:21:29 time: 0.4599 data_time: 0.0212 memory: 23498 grad_norm: 4.1602 top1_acc: 0.5000 top5_acc: 0.7917 loss_cls: 2.0984 loss: 2.0984 2022/09/09 15:27:19 - mmengine - INFO - Epoch(train) [20][220/449] lr: 2.1629e-02 eta: 1:21:16 time: 0.4512 data_time: 0.0203 memory: 23498 grad_norm: 4.2030 top1_acc: 0.4167 top5_acc: 0.7917 loss_cls: 2.2048 loss: 2.2048 2022/09/09 15:27:28 - mmengine - INFO - Epoch(train) [20][240/449] lr: 2.1557e-02 eta: 1:21:04 time: 0.4572 data_time: 0.0181 memory: 23498 grad_norm: 4.2175 top1_acc: 0.5417 top5_acc: 0.7500 loss_cls: 2.1904 loss: 2.1904 2022/09/09 15:27:37 - mmengine - INFO - Epoch(train) [20][260/449] lr: 2.1486e-02 eta: 1:20:52 time: 0.4510 data_time: 0.0202 memory: 23498 grad_norm: 4.1604 top1_acc: 0.2083 top5_acc: 0.5417 loss_cls: 2.1395 loss: 2.1395 2022/09/09 15:27:46 - mmengine - INFO - Epoch(train) [20][280/449] lr: 2.1414e-02 eta: 1:20:40 time: 0.4610 data_time: 0.0274 memory: 23498 grad_norm: 4.0624 top1_acc: 0.3750 top5_acc: 0.7917 loss_cls: 2.1309 loss: 2.1309 2022/09/09 15:27:56 - mmengine - INFO - Epoch(train) [20][300/449] lr: 2.1343e-02 eta: 1:20:28 time: 0.4730 data_time: 0.0162 memory: 23498 grad_norm: 4.1709 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 2.3213 loss: 2.3213 2022/09/09 15:28:05 - mmengine - INFO - Epoch(train) [20][320/449] lr: 2.1271e-02 eta: 1:20:16 time: 0.4658 data_time: 0.0170 memory: 23498 grad_norm: 4.0702 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2084 loss: 2.2084 2022/09/09 15:28:14 - mmengine - INFO - Epoch(train) [20][340/449] lr: 2.1200e-02 eta: 1:20:05 time: 0.4659 data_time: 0.0187 memory: 23498 grad_norm: 4.1830 top1_acc: 0.4167 top5_acc: 0.7500 loss_cls: 2.1495 loss: 2.1495 2022/09/09 15:28:24 - mmengine - INFO - Epoch(train) [20][360/449] lr: 2.1128e-02 eta: 1:19:53 time: 0.4739 data_time: 0.0240 memory: 23498 grad_norm: 4.1314 top1_acc: 0.3333 top5_acc: 0.5833 loss_cls: 2.2242 loss: 2.2242 2022/09/09 15:28:33 - mmengine - INFO - Epoch(train) [20][380/449] lr: 2.1056e-02 eta: 1:19:41 time: 0.4584 data_time: 0.0156 memory: 23498 grad_norm: 4.2751 top1_acc: 0.4583 top5_acc: 0.7500 loss_cls: 2.1342 loss: 2.1342 2022/09/09 15:28:42 - mmengine - INFO - Epoch(train) [20][400/449] lr: 2.0985e-02 eta: 1:19:29 time: 0.4534 data_time: 0.0190 memory: 23498 grad_norm: 4.3639 top1_acc: 0.3750 top5_acc: 0.5833 loss_cls: 2.0664 loss: 2.0664 2022/09/09 15:28:51 - mmengine - INFO - Epoch(train) [20][420/449] lr: 2.0913e-02 eta: 1:19:17 time: 0.4582 data_time: 0.0185 memory: 23498 grad_norm: 4.4820 top1_acc: 0.2917 top5_acc: 0.7083 loss_cls: 2.2383 loss: 2.2383 2022/09/09 15:29:00 - mmengine - INFO - Epoch(train) [20][440/449] lr: 2.0841e-02 eta: 1:19:05 time: 0.4569 data_time: 0.0207 memory: 23498 grad_norm: 4.2101 top1_acc: 0.4583 top5_acc: 0.6667 loss_cls: 2.0951 loss: 2.0951 2022/09/09 15:29:04 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:29:04 - mmengine - INFO - Epoch(train) [20][449/449] lr: 2.0809e-02 eta: 1:19:05 time: 0.4313 data_time: 0.0147 memory: 23498 grad_norm: 4.3865 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.3312 loss: 2.3312 2022/09/09 15:29:07 - mmengine - INFO - Epoch(val) [20][20/61] eta: 0:00:06 time: 0.1495 data_time: 0.0296 memory: 2693 2022/09/09 15:29:09 - mmengine - INFO - Epoch(val) [20][40/61] eta: 0:00:02 time: 0.1282 data_time: 0.0114 memory: 2693 2022/09/09 15:29:12 - mmengine - INFO - Epoch(val) [20][60/61] eta: 0:00:00 time: 0.1301 data_time: 0.0132 memory: 2693 2022/09/09 15:29:13 - mmengine - INFO - Epoch(val) [20][61/61] acc/top1: 0.2878 acc/top5: 0.5681 acc/mean1: 0.2686 2022/09/09 15:29:13 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_19.pth is removed 2022/09/09 15:29:14 - mmengine - INFO - The best checkpoint with 0.2878 acc/top1 at 20 epoch is saved to best_acc/top1_epoch_20.pth. 2022/09/09 15:29:24 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:29:24 - mmengine - INFO - Epoch(train) [21][20/449] lr: 2.0737e-02 eta: 1:18:45 time: 0.5240 data_time: 0.0332 memory: 23498 grad_norm: 4.1611 top1_acc: 0.4167 top5_acc: 0.6250 loss_cls: 2.3333 loss: 2.3333 2022/09/09 15:29:34 - mmengine - INFO - Epoch(train) [21][40/449] lr: 2.0665e-02 eta: 1:18:33 time: 0.4776 data_time: 0.0150 memory: 23498 grad_norm: 4.2716 top1_acc: 0.5000 top5_acc: 0.7917 loss_cls: 1.9821 loss: 1.9821 2022/09/09 15:29:43 - mmengine - INFO - Epoch(train) [21][60/449] lr: 2.0594e-02 eta: 1:18:21 time: 0.4537 data_time: 0.0167 memory: 23498 grad_norm: 4.3349 top1_acc: 0.5417 top5_acc: 0.7917 loss_cls: 2.1944 loss: 2.1944 2022/09/09 15:29:52 - mmengine - INFO - Epoch(train) [21][80/449] lr: 2.0522e-02 eta: 1:18:09 time: 0.4635 data_time: 0.0313 memory: 23498 grad_norm: 4.1252 top1_acc: 0.5000 top5_acc: 0.7083 loss_cls: 2.1568 loss: 2.1568 2022/09/09 15:30:01 - mmengine - INFO - Epoch(train) [21][100/449] lr: 2.0450e-02 eta: 1:17:57 time: 0.4526 data_time: 0.0156 memory: 23498 grad_norm: 4.3198 top1_acc: 0.5000 top5_acc: 0.6667 loss_cls: 2.1667 loss: 2.1667 2022/09/09 15:30:10 - mmengine - INFO - Epoch(train) [21][120/449] lr: 2.0379e-02 eta: 1:17:46 time: 0.4606 data_time: 0.0168 memory: 23498 grad_norm: 4.0986 top1_acc: 0.4167 top5_acc: 0.7500 loss_cls: 2.2425 loss: 2.2425 2022/09/09 15:30:19 - mmengine - INFO - Epoch(train) [21][140/449] lr: 2.0307e-02 eta: 1:17:34 time: 0.4484 data_time: 0.0160 memory: 23498 grad_norm: 4.1934 top1_acc: 0.5417 top5_acc: 0.8333 loss_cls: 2.1734 loss: 2.1734 2022/09/09 15:30:28 - mmengine - INFO - Epoch(train) [21][160/449] lr: 2.0235e-02 eta: 1:17:22 time: 0.4585 data_time: 0.0244 memory: 23498 grad_norm: 4.0931 top1_acc: 0.3333 top5_acc: 0.8333 loss_cls: 2.0475 loss: 2.0475 2022/09/09 15:30:38 - mmengine - INFO - Epoch(train) [21][180/449] lr: 2.0163e-02 eta: 1:17:10 time: 0.4544 data_time: 0.0175 memory: 23498 grad_norm: 4.0634 top1_acc: 0.2917 top5_acc: 0.8333 loss_cls: 2.0559 loss: 2.0559 2022/09/09 15:30:47 - mmengine - INFO - Epoch(train) [21][200/449] lr: 2.0091e-02 eta: 1:16:58 time: 0.4601 data_time: 0.0188 memory: 23498 grad_norm: 4.0318 top1_acc: 0.5417 top5_acc: 0.7500 loss_cls: 2.2120 loss: 2.2120 2022/09/09 15:30:56 - mmengine - INFO - Epoch(train) [21][220/449] lr: 2.0020e-02 eta: 1:16:46 time: 0.4501 data_time: 0.0169 memory: 23498 grad_norm: 4.0767 top1_acc: 0.6667 top5_acc: 0.8333 loss_cls: 2.1343 loss: 2.1343 2022/09/09 15:31:05 - mmengine - INFO - Epoch(train) [21][240/449] lr: 1.9948e-02 eta: 1:16:34 time: 0.4568 data_time: 0.0236 memory: 23498 grad_norm: 4.0451 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 2.2199 loss: 2.2199 2022/09/09 15:31:14 - mmengine - INFO - Epoch(train) [21][260/449] lr: 1.9876e-02 eta: 1:16:22 time: 0.4587 data_time: 0.0183 memory: 23498 grad_norm: 4.0729 top1_acc: 0.5417 top5_acc: 0.9167 loss_cls: 1.9637 loss: 1.9637 2022/09/09 15:31:23 - mmengine - INFO - Epoch(train) [21][280/449] lr: 1.9804e-02 eta: 1:16:11 time: 0.4578 data_time: 0.0189 memory: 23498 grad_norm: 4.2732 top1_acc: 0.4583 top5_acc: 0.7083 loss_cls: 2.0608 loss: 2.0608 2022/09/09 15:31:32 - mmengine - INFO - Epoch(train) [21][300/449] lr: 1.9733e-02 eta: 1:15:59 time: 0.4572 data_time: 0.0165 memory: 23498 grad_norm: 4.1371 top1_acc: 0.7083 top5_acc: 0.8333 loss_cls: 2.1084 loss: 2.1084 2022/09/09 15:31:42 - mmengine - INFO - Epoch(train) [21][320/449] lr: 1.9661e-02 eta: 1:15:47 time: 0.4649 data_time: 0.0248 memory: 23498 grad_norm: 4.2831 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1316 loss: 2.1316 2022/09/09 15:31:51 - mmengine - INFO - Epoch(train) [21][340/449] lr: 1.9589e-02 eta: 1:15:36 time: 0.4629 data_time: 0.0189 memory: 23498 grad_norm: 4.2663 top1_acc: 0.2917 top5_acc: 0.5417 loss_cls: 2.0629 loss: 2.0629 2022/09/09 15:32:00 - mmengine - INFO - Epoch(train) [21][360/449] lr: 1.9517e-02 eta: 1:15:24 time: 0.4584 data_time: 0.0188 memory: 23498 grad_norm: 4.2813 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9164 loss: 1.9164 2022/09/09 15:32:09 - mmengine - INFO - Epoch(train) [21][380/449] lr: 1.9446e-02 eta: 1:15:12 time: 0.4514 data_time: 0.0175 memory: 23498 grad_norm: 4.1442 top1_acc: 0.6250 top5_acc: 0.8333 loss_cls: 2.1135 loss: 2.1135 2022/09/09 15:32:18 - mmengine - INFO - Epoch(train) [21][400/449] lr: 1.9374e-02 eta: 1:15:00 time: 0.4569 data_time: 0.0218 memory: 23498 grad_norm: 4.1566 top1_acc: 0.6250 top5_acc: 0.8333 loss_cls: 1.9501 loss: 1.9501 2022/09/09 15:32:28 - mmengine - INFO - Epoch(train) [21][420/449] lr: 1.9302e-02 eta: 1:14:49 time: 0.4664 data_time: 0.0160 memory: 23498 grad_norm: 4.3107 top1_acc: 0.3750 top5_acc: 0.7083 loss_cls: 2.1878 loss: 2.1878 2022/09/09 15:32:37 - mmengine - INFO - Epoch(train) [21][440/449] lr: 1.9231e-02 eta: 1:14:37 time: 0.4611 data_time: 0.0210 memory: 23498 grad_norm: 4.2208 top1_acc: 0.5000 top5_acc: 0.7083 loss_cls: 2.1232 loss: 2.1232 2022/09/09 15:32:41 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:32:41 - mmengine - INFO - Epoch(train) [21][449/449] lr: 1.9198e-02 eta: 1:14:37 time: 0.4533 data_time: 0.0179 memory: 23498 grad_norm: 4.4964 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.2694 loss: 2.2694 2022/09/09 15:32:44 - mmengine - INFO - Epoch(val) [21][20/61] eta: 0:00:05 time: 0.1460 data_time: 0.0260 memory: 2693 2022/09/09 15:32:46 - mmengine - INFO - Epoch(val) [21][40/61] eta: 0:00:02 time: 0.1328 data_time: 0.0131 memory: 2693 2022/09/09 15:32:49 - mmengine - INFO - Epoch(val) [21][60/61] eta: 0:00:00 time: 0.1295 data_time: 0.0127 memory: 2693 2022/09/09 15:32:49 - mmengine - INFO - Epoch(val) [21][61/61] acc/top1: 0.3044 acc/top5: 0.6035 acc/mean1: 0.2721 2022/09/09 15:32:49 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_20.pth is removed 2022/09/09 15:32:50 - mmengine - INFO - The best checkpoint with 0.3044 acc/top1 at 21 epoch is saved to best_acc/top1_epoch_21.pth. 2022/09/09 15:33:00 - mmengine - INFO - Epoch(train) [22][20/449] lr: 1.9127e-02 eta: 1:14:17 time: 0.5033 data_time: 0.0375 memory: 23498 grad_norm: 4.2225 top1_acc: 0.4167 top5_acc: 0.7083 loss_cls: 2.1022 loss: 2.1022 2022/09/09 15:33:10 - mmengine - INFO - Epoch(train) [22][40/449] lr: 1.9055e-02 eta: 1:14:06 time: 0.4769 data_time: 0.0223 memory: 23498 grad_norm: 4.0493 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1091 loss: 2.1091 2022/09/09 15:33:19 - mmengine - INFO - Epoch(train) [22][60/449] lr: 1.8983e-02 eta: 1:13:55 time: 0.4683 data_time: 0.0169 memory: 23498 grad_norm: 4.0011 top1_acc: 0.6667 top5_acc: 0.8750 loss_cls: 1.9625 loss: 1.9625 2022/09/09 15:33:29 - mmengine - INFO - Epoch(train) [22][80/449] lr: 1.8912e-02 eta: 1:13:43 time: 0.4764 data_time: 0.0167 memory: 23498 grad_norm: 4.0695 top1_acc: 0.4583 top5_acc: 0.6250 loss_cls: 1.9643 loss: 1.9643 2022/09/09 15:33:38 - mmengine - INFO - Epoch(train) [22][100/449] lr: 1.8840e-02 eta: 1:13:32 time: 0.4766 data_time: 0.0168 memory: 23498 grad_norm: 4.1423 top1_acc: 0.4167 top5_acc: 0.7083 loss_cls: 2.0621 loss: 2.0621 2022/09/09 15:33:48 - mmengine - INFO - Epoch(train) [22][120/449] lr: 1.8768e-02 eta: 1:13:21 time: 0.4762 data_time: 0.0237 memory: 23498 grad_norm: 4.2967 top1_acc: 0.5833 top5_acc: 0.7917 loss_cls: 2.0264 loss: 2.0264 2022/09/09 15:33:57 - mmengine - INFO - Epoch(train) [22][140/449] lr: 1.8697e-02 eta: 1:13:09 time: 0.4734 data_time: 0.0156 memory: 23498 grad_norm: 4.1989 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 1.7838 loss: 1.7838 2022/09/09 15:34:06 - mmengine - INFO - Epoch(train) [22][160/449] lr: 1.8625e-02 eta: 1:12:58 time: 0.4666 data_time: 0.0174 memory: 23498 grad_norm: 4.2659 top1_acc: 0.4583 top5_acc: 0.7917 loss_cls: 2.0088 loss: 2.0088 2022/09/09 15:34:16 - mmengine - INFO - Epoch(train) [22][180/449] lr: 1.8553e-02 eta: 1:12:47 time: 0.4773 data_time: 0.0217 memory: 23498 grad_norm: 4.3324 top1_acc: 0.4167 top5_acc: 0.7917 loss_cls: 2.0438 loss: 2.0438 2022/09/09 15:34:25 - mmengine - INFO - Epoch(train) [22][200/449] lr: 1.8482e-02 eta: 1:12:35 time: 0.4677 data_time: 0.0257 memory: 23498 grad_norm: 4.1381 top1_acc: 0.3750 top5_acc: 0.7083 loss_cls: 2.0531 loss: 2.0531 2022/09/09 15:34:35 - mmengine - INFO - Epoch(train) [22][220/449] lr: 1.8410e-02 eta: 1:12:24 time: 0.4651 data_time: 0.0173 memory: 23498 grad_norm: 4.2501 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0771 loss: 2.0771 2022/09/09 15:34:44 - mmengine - INFO - Epoch(train) [22][240/449] lr: 1.8339e-02 eta: 1:12:12 time: 0.4626 data_time: 0.0194 memory: 23498 grad_norm: 4.3895 top1_acc: 0.4167 top5_acc: 0.7917 loss_cls: 1.9550 loss: 1.9550 2022/09/09 15:34:53 - mmengine - INFO - Epoch(train) [22][260/449] lr: 1.8267e-02 eta: 1:12:01 time: 0.4603 data_time: 0.0233 memory: 23498 grad_norm: 4.3220 top1_acc: 0.5000 top5_acc: 0.7917 loss_cls: 1.9520 loss: 1.9520 2022/09/09 15:35:02 - mmengine - INFO - Epoch(train) [22][280/449] lr: 1.8196e-02 eta: 1:11:49 time: 0.4636 data_time: 0.0214 memory: 23498 grad_norm: 4.4131 top1_acc: 0.4583 top5_acc: 0.6250 loss_cls: 2.1655 loss: 2.1655 2022/09/09 15:35:12 - mmengine - INFO - Epoch(train) [22][300/449] lr: 1.8124e-02 eta: 1:11:38 time: 0.4860 data_time: 0.0158 memory: 23498 grad_norm: 4.4313 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1145 loss: 2.1145 2022/09/09 15:35:21 - mmengine - INFO - Epoch(train) [22][320/449] lr: 1.8053e-02 eta: 1:11:27 time: 0.4655 data_time: 0.0199 memory: 23498 grad_norm: 4.4579 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 1.9915 loss: 1.9915 2022/09/09 15:35:31 - mmengine - INFO - Epoch(train) [22][340/449] lr: 1.7982e-02 eta: 1:11:15 time: 0.4615 data_time: 0.0258 memory: 23498 grad_norm: 4.3157 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 1.8512 loss: 1.8512 2022/09/09 15:35:40 - mmengine - INFO - Epoch(train) [22][360/449] lr: 1.7910e-02 eta: 1:11:04 time: 0.4829 data_time: 0.0236 memory: 23498 grad_norm: 4.2783 top1_acc: 0.3333 top5_acc: 0.6250 loss_cls: 2.1455 loss: 2.1455 2022/09/09 15:35:50 - mmengine - INFO - Epoch(train) [22][380/449] lr: 1.7839e-02 eta: 1:10:53 time: 0.4633 data_time: 0.0187 memory: 23498 grad_norm: 4.3059 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9633 loss: 1.9633 2022/09/09 15:35:59 - mmengine - INFO - Epoch(train) [22][400/449] lr: 1.7767e-02 eta: 1:10:42 time: 0.4771 data_time: 0.0262 memory: 23498 grad_norm: 4.1958 top1_acc: 0.4167 top5_acc: 0.8333 loss_cls: 1.8667 loss: 1.8667 2022/09/09 15:36:08 - mmengine - INFO - Epoch(train) [22][420/449] lr: 1.7696e-02 eta: 1:10:30 time: 0.4664 data_time: 0.0244 memory: 23498 grad_norm: 4.2846 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1106 loss: 2.1106 2022/09/09 15:36:18 - mmengine - INFO - Epoch(train) [22][440/449] lr: 1.7625e-02 eta: 1:10:19 time: 0.4756 data_time: 0.0216 memory: 23498 grad_norm: 4.3094 top1_acc: 0.4167 top5_acc: 0.7500 loss_cls: 1.8983 loss: 1.8983 2022/09/09 15:36:22 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:36:22 - mmengine - INFO - Epoch(train) [22][449/449] lr: 1.7593e-02 eta: 1:10:19 time: 0.4529 data_time: 0.0190 memory: 23498 grad_norm: 4.5757 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.0015 loss: 2.0015 2022/09/09 15:36:25 - mmengine - INFO - Epoch(val) [22][20/61] eta: 0:00:06 time: 0.1533 data_time: 0.0340 memory: 2693 2022/09/09 15:36:27 - mmengine - INFO - Epoch(val) [22][40/61] eta: 0:00:02 time: 0.1363 data_time: 0.0162 memory: 2693 2022/09/09 15:36:30 - mmengine - INFO - Epoch(val) [22][60/61] eta: 0:00:00 time: 0.1331 data_time: 0.0140 memory: 2693 2022/09/09 15:36:31 - mmengine - INFO - Epoch(val) [22][61/61] acc/top1: 0.2876 acc/top5: 0.5815 acc/mean1: 0.2589 2022/09/09 15:36:42 - mmengine - INFO - Epoch(train) [23][20/449] lr: 1.7522e-02 eta: 1:10:01 time: 0.5676 data_time: 0.0531 memory: 23498 grad_norm: 4.2552 top1_acc: 0.5833 top5_acc: 0.7917 loss_cls: 1.9848 loss: 1.9848 2022/09/09 15:36:51 - mmengine - INFO - Epoch(train) [23][40/449] lr: 1.7450e-02 eta: 1:09:50 time: 0.4620 data_time: 0.0223 memory: 23498 grad_norm: 4.1742 top1_acc: 0.5833 top5_acc: 0.7917 loss_cls: 1.8631 loss: 1.8631 2022/09/09 15:37:00 - mmengine - INFO - Epoch(train) [23][60/449] lr: 1.7379e-02 eta: 1:09:38 time: 0.4541 data_time: 0.0189 memory: 23498 grad_norm: 4.3986 top1_acc: 0.5833 top5_acc: 0.7917 loss_cls: 1.9776 loss: 1.9776 2022/09/09 15:37:09 - mmengine - INFO - Epoch(train) [23][80/449] lr: 1.7308e-02 eta: 1:09:27 time: 0.4531 data_time: 0.0204 memory: 23498 grad_norm: 4.3731 top1_acc: 0.6667 top5_acc: 0.7917 loss_cls: 1.7947 loss: 1.7947 2022/09/09 15:37:18 - mmengine - INFO - Epoch(train) [23][100/449] lr: 1.7237e-02 eta: 1:09:15 time: 0.4530 data_time: 0.0210 memory: 23498 grad_norm: 4.4101 top1_acc: 0.3750 top5_acc: 0.6667 loss_cls: 1.9195 loss: 1.9195 2022/09/09 15:37:27 - mmengine - INFO - Epoch(train) [23][120/449] lr: 1.7166e-02 eta: 1:09:04 time: 0.4558 data_time: 0.0187 memory: 23498 grad_norm: 4.2342 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 1.8900 loss: 1.8900 2022/09/09 15:37:28 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:37:37 - mmengine - INFO - Epoch(train) [23][140/449] lr: 1.7095e-02 eta: 1:08:52 time: 0.4555 data_time: 0.0212 memory: 23498 grad_norm: 4.2116 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 1.8891 loss: 1.8891 2022/09/09 15:37:46 - mmengine - INFO - Epoch(train) [23][160/449] lr: 1.7024e-02 eta: 1:08:41 time: 0.4521 data_time: 0.0180 memory: 23498 grad_norm: 4.2620 top1_acc: 0.5000 top5_acc: 0.6667 loss_cls: 1.9326 loss: 1.9326 2022/09/09 15:37:55 - mmengine - INFO - Epoch(train) [23][180/449] lr: 1.6953e-02 eta: 1:08:29 time: 0.4571 data_time: 0.0251 memory: 23498 grad_norm: 4.1501 top1_acc: 0.5833 top5_acc: 0.7500 loss_cls: 1.8421 loss: 1.8421 2022/09/09 15:38:04 - mmengine - INFO - Epoch(train) [23][200/449] lr: 1.6882e-02 eta: 1:08:18 time: 0.4519 data_time: 0.0182 memory: 23498 grad_norm: 4.3276 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.9508 loss: 1.9508 2022/09/09 15:38:13 - mmengine - INFO - Epoch(train) [23][220/449] lr: 1.6811e-02 eta: 1:08:06 time: 0.4539 data_time: 0.0210 memory: 23498 grad_norm: 4.3432 top1_acc: 0.7083 top5_acc: 0.8750 loss_cls: 2.0308 loss: 2.0308 2022/09/09 15:38:22 - mmengine - INFO - Epoch(train) [23][240/449] lr: 1.6740e-02 eta: 1:07:55 time: 0.4551 data_time: 0.0185 memory: 23498 grad_norm: 4.1938 top1_acc: 0.6250 top5_acc: 0.9167 loss_cls: 1.9312 loss: 1.9312 2022/09/09 15:38:31 - mmengine - INFO - Epoch(train) [23][260/449] lr: 1.6670e-02 eta: 1:07:44 time: 0.4558 data_time: 0.0207 memory: 23498 grad_norm: 4.2370 top1_acc: 0.5417 top5_acc: 0.7083 loss_cls: 1.8529 loss: 1.8529 2022/09/09 15:38:40 - mmengine - INFO - Epoch(train) [23][280/449] lr: 1.6599e-02 eta: 1:07:32 time: 0.4554 data_time: 0.0206 memory: 23498 grad_norm: 4.4926 top1_acc: 0.4167 top5_acc: 0.8333 loss_cls: 1.8217 loss: 1.8217 2022/09/09 15:38:49 - mmengine - INFO - Epoch(train) [23][300/449] lr: 1.6528e-02 eta: 1:07:21 time: 0.4532 data_time: 0.0213 memory: 23498 grad_norm: 4.4523 top1_acc: 0.4583 top5_acc: 0.8750 loss_cls: 2.1466 loss: 2.1466 2022/09/09 15:38:58 - mmengine - INFO - Epoch(train) [23][320/449] lr: 1.6458e-02 eta: 1:07:09 time: 0.4505 data_time: 0.0191 memory: 23498 grad_norm: 4.3609 top1_acc: 0.4167 top5_acc: 0.6250 loss_cls: 1.9039 loss: 1.9039 2022/09/09 15:39:07 - mmengine - INFO - Epoch(train) [23][340/449] lr: 1.6387e-02 eta: 1:06:58 time: 0.4534 data_time: 0.0213 memory: 23498 grad_norm: 4.1381 top1_acc: 0.5417 top5_acc: 0.7500 loss_cls: 1.9242 loss: 1.9242 2022/09/09 15:39:17 - mmengine - INFO - Epoch(train) [23][360/449] lr: 1.6316e-02 eta: 1:06:47 time: 0.4659 data_time: 0.0200 memory: 23498 grad_norm: 4.2657 top1_acc: 0.5833 top5_acc: 0.9167 loss_cls: 1.9585 loss: 1.9585 2022/09/09 15:39:26 - mmengine - INFO - Epoch(train) [23][380/449] lr: 1.6246e-02 eta: 1:06:35 time: 0.4511 data_time: 0.0212 memory: 23498 grad_norm: 4.2554 top1_acc: 0.3750 top5_acc: 0.7083 loss_cls: 1.8627 loss: 1.8627 2022/09/09 15:39:35 - mmengine - INFO - Epoch(train) [23][400/449] lr: 1.6175e-02 eta: 1:06:24 time: 0.4598 data_time: 0.0197 memory: 23498 grad_norm: 4.2454 top1_acc: 0.7917 top5_acc: 0.9167 loss_cls: 2.0909 loss: 2.0909 2022/09/09 15:39:44 - mmengine - INFO - Epoch(train) [23][420/449] lr: 1.6105e-02 eta: 1:06:13 time: 0.4531 data_time: 0.0206 memory: 23498 grad_norm: 4.2251 top1_acc: 0.5000 top5_acc: 0.7917 loss_cls: 1.9957 loss: 1.9957 2022/09/09 15:39:53 - mmengine - INFO - Epoch(train) [23][440/449] lr: 1.6035e-02 eta: 1:06:02 time: 0.4577 data_time: 0.0175 memory: 23498 grad_norm: 4.3471 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8815 loss: 1.8815 2022/09/09 15:39:57 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:39:57 - mmengine - INFO - Epoch(train) [23][449/449] lr: 1.6003e-02 eta: 1:06:02 time: 0.4318 data_time: 0.0152 memory: 23498 grad_norm: 4.7933 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.1329 loss: 2.1329 2022/09/09 15:40:00 - mmengine - INFO - Epoch(val) [23][20/61] eta: 0:00:06 time: 0.1515 data_time: 0.0313 memory: 2693 2022/09/09 15:40:02 - mmengine - INFO - Epoch(val) [23][40/61] eta: 0:00:02 time: 0.1308 data_time: 0.0125 memory: 2693 2022/09/09 15:40:05 - mmengine - INFO - Epoch(val) [23][60/61] eta: 0:00:00 time: 0.1301 data_time: 0.0127 memory: 2693 2022/09/09 15:40:05 - mmengine - INFO - Epoch(val) [23][61/61] acc/top1: 0.3174 acc/top5: 0.6105 acc/mean1: 0.2909 2022/09/09 15:40:05 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_21.pth is removed 2022/09/09 15:40:07 - mmengine - INFO - The best checkpoint with 0.3174 acc/top1 at 23 epoch is saved to best_acc/top1_epoch_23.pth. 2022/09/09 15:40:16 - mmengine - INFO - Epoch(train) [24][20/449] lr: 1.5933e-02 eta: 1:05:43 time: 0.4887 data_time: 0.0514 memory: 23498 grad_norm: 4.4285 top1_acc: 0.6250 top5_acc: 0.8333 loss_cls: 1.9305 loss: 1.9305 2022/09/09 15:40:25 - mmengine - INFO - Epoch(train) [24][40/449] lr: 1.5862e-02 eta: 1:05:31 time: 0.4501 data_time: 0.0145 memory: 23498 grad_norm: 4.3679 top1_acc: 0.5833 top5_acc: 0.7917 loss_cls: 1.9334 loss: 1.9334 2022/09/09 15:40:34 - mmengine - INFO - Epoch(train) [24][60/449] lr: 1.5792e-02 eta: 1:05:20 time: 0.4514 data_time: 0.0171 memory: 23498 grad_norm: 4.3407 top1_acc: 0.5833 top5_acc: 0.7917 loss_cls: 1.9151 loss: 1.9151 2022/09/09 15:40:44 - mmengine - INFO - Epoch(train) [24][80/449] lr: 1.5722e-02 eta: 1:05:09 time: 0.4630 data_time: 0.0196 memory: 23498 grad_norm: 4.3487 top1_acc: 0.4583 top5_acc: 0.8750 loss_cls: 2.0414 loss: 2.0414 2022/09/09 15:40:53 - mmengine - INFO - Epoch(train) [24][100/449] lr: 1.5652e-02 eta: 1:04:57 time: 0.4459 data_time: 0.0170 memory: 23498 grad_norm: 4.4291 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7577 loss: 1.7577 2022/09/09 15:41:02 - mmengine - INFO - Epoch(train) [24][120/449] lr: 1.5582e-02 eta: 1:04:46 time: 0.4555 data_time: 0.0192 memory: 23498 grad_norm: 4.5383 top1_acc: 0.6250 top5_acc: 0.7917 loss_cls: 1.8425 loss: 1.8425 2022/09/09 15:41:11 - mmengine - INFO - Epoch(train) [24][140/449] lr: 1.5512e-02 eta: 1:04:35 time: 0.4514 data_time: 0.0172 memory: 23498 grad_norm: 4.5957 top1_acc: 0.5417 top5_acc: 0.7083 loss_cls: 1.9508 loss: 1.9508 2022/09/09 15:41:20 - mmengine - INFO - Epoch(train) [24][160/449] lr: 1.5442e-02 eta: 1:04:24 time: 0.4619 data_time: 0.0202 memory: 23498 grad_norm: 4.5787 top1_acc: 0.5000 top5_acc: 0.8333 loss_cls: 1.8250 loss: 1.8250 2022/09/09 15:41:29 - mmengine - INFO - Epoch(train) [24][180/449] lr: 1.5372e-02 eta: 1:04:13 time: 0.4540 data_time: 0.0210 memory: 23498 grad_norm: 4.3658 top1_acc: 0.4583 top5_acc: 0.7083 loss_cls: 1.9003 loss: 1.9003 2022/09/09 15:41:38 - mmengine - INFO - Epoch(train) [24][200/449] lr: 1.5303e-02 eta: 1:04:01 time: 0.4571 data_time: 0.0179 memory: 23498 grad_norm: 4.4208 top1_acc: 0.3750 top5_acc: 0.7083 loss_cls: 1.7628 loss: 1.7628 2022/09/09 15:41:47 - mmengine - INFO - Epoch(train) [24][220/449] lr: 1.5233e-02 eta: 1:03:50 time: 0.4584 data_time: 0.0167 memory: 23498 grad_norm: 4.4355 top1_acc: 0.4583 top5_acc: 0.7083 loss_cls: 1.9037 loss: 1.9037 2022/09/09 15:41:56 - mmengine - INFO - Epoch(train) [24][240/449] lr: 1.5163e-02 eta: 1:03:39 time: 0.4552 data_time: 0.0170 memory: 23498 grad_norm: 4.4951 top1_acc: 0.3750 top5_acc: 0.7083 loss_cls: 1.7770 loss: 1.7770 2022/09/09 15:42:05 - mmengine - INFO - Epoch(train) [24][260/449] lr: 1.5094e-02 eta: 1:03:28 time: 0.4495 data_time: 0.0199 memory: 23498 grad_norm: 4.5747 top1_acc: 0.4583 top5_acc: 0.7500 loss_cls: 1.9706 loss: 1.9706 2022/09/09 15:42:15 - mmengine - INFO - Epoch(train) [24][280/449] lr: 1.5024e-02 eta: 1:03:17 time: 0.4558 data_time: 0.0192 memory: 23498 grad_norm: 4.3596 top1_acc: 0.6250 top5_acc: 0.8333 loss_cls: 1.8594 loss: 1.8594 2022/09/09 15:42:23 - mmengine - INFO - Epoch(train) [24][300/449] lr: 1.4955e-02 eta: 1:03:05 time: 0.4449 data_time: 0.0172 memory: 23498 grad_norm: 4.4690 top1_acc: 0.5833 top5_acc: 0.7083 loss_cls: 2.0277 loss: 2.0277 2022/09/09 15:42:32 - mmengine - INFO - Epoch(train) [24][320/449] lr: 1.4885e-02 eta: 1:02:54 time: 0.4513 data_time: 0.0187 memory: 23498 grad_norm: 4.5354 top1_acc: 0.5417 top5_acc: 0.9167 loss_cls: 1.7017 loss: 1.7017 2022/09/09 15:42:41 - mmengine - INFO - Epoch(train) [24][340/449] lr: 1.4816e-02 eta: 1:02:43 time: 0.4487 data_time: 0.0200 memory: 23498 grad_norm: 4.6309 top1_acc: 0.6667 top5_acc: 0.8333 loss_cls: 1.8862 loss: 1.8862 2022/09/09 15:42:51 - mmengine - INFO - Epoch(train) [24][360/449] lr: 1.4747e-02 eta: 1:02:32 time: 0.4558 data_time: 0.0196 memory: 23498 grad_norm: 4.5001 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8801 loss: 1.8801 2022/09/09 15:42:59 - mmengine - INFO - Epoch(train) [24][380/449] lr: 1.4677e-02 eta: 1:02:20 time: 0.4452 data_time: 0.0186 memory: 23498 grad_norm: 4.7081 top1_acc: 0.4583 top5_acc: 0.6250 loss_cls: 1.9155 loss: 1.9155 2022/09/09 15:43:09 - mmengine - INFO - Epoch(train) [24][400/449] lr: 1.4608e-02 eta: 1:02:09 time: 0.4525 data_time: 0.0210 memory: 23498 grad_norm: 4.5376 top1_acc: 0.4167 top5_acc: 0.7500 loss_cls: 1.8493 loss: 1.8493 2022/09/09 15:43:17 - mmengine - INFO - Epoch(train) [24][420/449] lr: 1.4539e-02 eta: 1:01:58 time: 0.4433 data_time: 0.0195 memory: 23498 grad_norm: 4.5806 top1_acc: 0.4583 top5_acc: 0.7500 loss_cls: 1.8691 loss: 1.8691 2022/09/09 15:43:26 - mmengine - INFO - Epoch(train) [24][440/449] lr: 1.4470e-02 eta: 1:01:47 time: 0.4499 data_time: 0.0164 memory: 23498 grad_norm: 4.5400 top1_acc: 0.5417 top5_acc: 0.8333 loss_cls: 1.8780 loss: 1.8780 2022/09/09 15:43:30 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:43:30 - mmengine - INFO - Epoch(train) [24][449/449] lr: 1.4439e-02 eta: 1:01:47 time: 0.4229 data_time: 0.0167 memory: 23498 grad_norm: 4.8843 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 2.1117 loss: 2.1117 2022/09/09 15:43:33 - mmengine - INFO - Epoch(val) [24][20/61] eta: 0:00:06 time: 0.1587 data_time: 0.0375 memory: 2693 2022/09/09 15:43:36 - mmengine - INFO - Epoch(val) [24][40/61] eta: 0:00:02 time: 0.1292 data_time: 0.0117 memory: 2693 2022/09/09 15:43:38 - mmengine - INFO - Epoch(val) [24][60/61] eta: 0:00:00 time: 0.1297 data_time: 0.0129 memory: 2693 2022/09/09 15:43:39 - mmengine - INFO - Epoch(val) [24][61/61] acc/top1: 0.3322 acc/top5: 0.6230 acc/mean1: 0.2893 2022/09/09 15:43:39 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_23.pth is removed 2022/09/09 15:43:40 - mmengine - INFO - The best checkpoint with 0.3322 acc/top1 at 24 epoch is saved to best_acc/top1_epoch_24.pth. 2022/09/09 15:43:50 - mmengine - INFO - Epoch(train) [25][20/449] lr: 1.4370e-02 eta: 1:01:29 time: 0.5233 data_time: 0.0398 memory: 23498 grad_norm: 4.5716 top1_acc: 0.2917 top5_acc: 0.6667 loss_cls: 1.9573 loss: 1.9573 2022/09/09 15:43:59 - mmengine - INFO - Epoch(train) [25][40/449] lr: 1.4301e-02 eta: 1:01:18 time: 0.4558 data_time: 0.0222 memory: 23498 grad_norm: 4.7027 top1_acc: 0.5000 top5_acc: 0.8333 loss_cls: 1.9193 loss: 1.9193 2022/09/09 15:44:08 - mmengine - INFO - Epoch(train) [25][60/449] lr: 1.4233e-02 eta: 1:01:07 time: 0.4580 data_time: 0.0194 memory: 23498 grad_norm: 4.6748 top1_acc: 0.5000 top5_acc: 0.6667 loss_cls: 1.8519 loss: 1.8519 2022/09/09 15:44:17 - mmengine - INFO - Epoch(train) [25][80/449] lr: 1.4164e-02 eta: 1:00:56 time: 0.4524 data_time: 0.0208 memory: 23498 grad_norm: 4.5673 top1_acc: 0.7500 top5_acc: 0.8333 loss_cls: 1.8664 loss: 1.8664 2022/09/09 15:44:27 - mmengine - INFO - Epoch(train) [25][100/449] lr: 1.4095e-02 eta: 1:00:45 time: 0.4640 data_time: 0.0228 memory: 23498 grad_norm: 4.7503 top1_acc: 0.5833 top5_acc: 0.7500 loss_cls: 1.8471 loss: 1.8471 2022/09/09 15:44:36 - mmengine - INFO - Epoch(train) [25][120/449] lr: 1.4027e-02 eta: 1:00:34 time: 0.4609 data_time: 0.0241 memory: 23498 grad_norm: 4.5770 top1_acc: 0.5417 top5_acc: 0.6667 loss_cls: 1.9330 loss: 1.9330 2022/09/09 15:44:45 - mmengine - INFO - Epoch(train) [25][140/449] lr: 1.3958e-02 eta: 1:00:23 time: 0.4602 data_time: 0.0173 memory: 23498 grad_norm: 4.5737 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 1.8253 loss: 1.8253 2022/09/09 15:44:54 - mmengine - INFO - Epoch(train) [25][160/449] lr: 1.3890e-02 eta: 1:00:12 time: 0.4505 data_time: 0.0185 memory: 23498 grad_norm: 4.5990 top1_acc: 0.4583 top5_acc: 0.7917 loss_cls: 1.9542 loss: 1.9542 2022/09/09 15:45:03 - mmengine - INFO - Epoch(train) [25][180/449] lr: 1.3822e-02 eta: 1:00:01 time: 0.4682 data_time: 0.0257 memory: 23498 grad_norm: 4.6367 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.8945 loss: 1.8945 2022/09/09 15:45:13 - mmengine - INFO - Epoch(train) [25][200/449] lr: 1.3753e-02 eta: 0:59:50 time: 0.4562 data_time: 0.0207 memory: 23498 grad_norm: 4.6337 top1_acc: 0.6667 top5_acc: 0.7500 loss_cls: 1.8885 loss: 1.8885 2022/09/09 15:45:22 - mmengine - INFO - Epoch(train) [25][220/449] lr: 1.3685e-02 eta: 0:59:39 time: 0.4538 data_time: 0.0171 memory: 23498 grad_norm: 4.6834 top1_acc: 0.5000 top5_acc: 0.7917 loss_cls: 1.8712 loss: 1.8712 2022/09/09 15:45:23 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:45:31 - mmengine - INFO - Epoch(train) [25][240/449] lr: 1.3617e-02 eta: 0:59:27 time: 0.4499 data_time: 0.0172 memory: 23498 grad_norm: 4.6919 top1_acc: 0.5000 top5_acc: 0.6667 loss_cls: 1.9466 loss: 1.9466 2022/09/09 15:45:40 - mmengine - INFO - Epoch(train) [25][260/449] lr: 1.3549e-02 eta: 0:59:17 time: 0.4591 data_time: 0.0190 memory: 23498 grad_norm: 4.5346 top1_acc: 0.6667 top5_acc: 0.8333 loss_cls: 1.8490 loss: 1.8490 2022/09/09 15:45:49 - mmengine - INFO - Epoch(train) [25][280/449] lr: 1.3481e-02 eta: 0:59:06 time: 0.4569 data_time: 0.0219 memory: 23498 grad_norm: 4.5668 top1_acc: 0.5417 top5_acc: 0.7500 loss_cls: 1.6708 loss: 1.6708 2022/09/09 15:45:58 - mmengine - INFO - Epoch(train) [25][300/449] lr: 1.3414e-02 eta: 0:58:54 time: 0.4505 data_time: 0.0163 memory: 23498 grad_norm: 4.7208 top1_acc: 0.4583 top5_acc: 0.7500 loss_cls: 1.8378 loss: 1.8378 2022/09/09 15:46:07 - mmengine - INFO - Epoch(train) [25][320/449] lr: 1.3346e-02 eta: 0:58:43 time: 0.4514 data_time: 0.0183 memory: 23498 grad_norm: 4.8181 top1_acc: 0.5417 top5_acc: 0.8333 loss_cls: 1.8211 loss: 1.8211 2022/09/09 15:46:16 - mmengine - INFO - Epoch(train) [25][340/449] lr: 1.3278e-02 eta: 0:58:33 time: 0.4636 data_time: 0.0173 memory: 23498 grad_norm: 4.7698 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7863 loss: 1.7863 2022/09/09 15:46:25 - mmengine - INFO - Epoch(train) [25][360/449] lr: 1.3211e-02 eta: 0:58:22 time: 0.4540 data_time: 0.0243 memory: 23498 grad_norm: 4.7550 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8522 loss: 1.8522 2022/09/09 15:46:34 - mmengine - INFO - Epoch(train) [25][380/449] lr: 1.3143e-02 eta: 0:58:11 time: 0.4485 data_time: 0.0182 memory: 23498 grad_norm: 4.6133 top1_acc: 0.5000 top5_acc: 0.6667 loss_cls: 1.8147 loss: 1.8147 2022/09/09 15:46:43 - mmengine - INFO - Epoch(train) [25][400/449] lr: 1.3076e-02 eta: 0:57:59 time: 0.4512 data_time: 0.0226 memory: 23498 grad_norm: 4.5569 top1_acc: 0.4167 top5_acc: 0.8333 loss_cls: 1.7153 loss: 1.7153 2022/09/09 15:46:52 - mmengine - INFO - Epoch(train) [25][420/449] lr: 1.3009e-02 eta: 0:57:49 time: 0.4571 data_time: 0.0162 memory: 23498 grad_norm: 4.6641 top1_acc: 0.4583 top5_acc: 0.7500 loss_cls: 1.7723 loss: 1.7723 2022/09/09 15:47:01 - mmengine - INFO - Epoch(train) [25][440/449] lr: 1.2941e-02 eta: 0:57:38 time: 0.4482 data_time: 0.0219 memory: 23498 grad_norm: 4.7363 top1_acc: 0.4583 top5_acc: 0.7500 loss_cls: 1.7098 loss: 1.7098 2022/09/09 15:47:05 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:47:05 - mmengine - INFO - Epoch(train) [25][449/449] lr: 1.2911e-02 eta: 0:57:38 time: 0.4250 data_time: 0.0171 memory: 23498 grad_norm: 5.0852 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.8380 loss: 1.8380 2022/09/09 15:47:08 - mmengine - INFO - Epoch(val) [25][20/61] eta: 0:00:06 time: 0.1474 data_time: 0.0283 memory: 2693 2022/09/09 15:47:11 - mmengine - INFO - Epoch(val) [25][40/61] eta: 0:00:02 time: 0.1319 data_time: 0.0133 memory: 2693 2022/09/09 15:47:13 - mmengine - INFO - Epoch(val) [25][60/61] eta: 0:00:00 time: 0.1340 data_time: 0.0165 memory: 2693 2022/09/09 15:47:14 - mmengine - INFO - Epoch(val) [25][61/61] acc/top1: 0.3176 acc/top5: 0.6153 acc/mean1: 0.2878 2022/09/09 15:47:24 - mmengine - INFO - Epoch(train) [26][20/449] lr: 1.2844e-02 eta: 0:57:20 time: 0.5343 data_time: 0.0463 memory: 23498 grad_norm: 4.6876 top1_acc: 0.7083 top5_acc: 0.8333 loss_cls: 1.6528 loss: 1.6528 2022/09/09 15:47:34 - mmengine - INFO - Epoch(train) [26][40/449] lr: 1.2777e-02 eta: 0:57:09 time: 0.4538 data_time: 0.0199 memory: 23498 grad_norm: 4.6707 top1_acc: 0.7917 top5_acc: 0.8750 loss_cls: 1.8438 loss: 1.8438 2022/09/09 15:47:43 - mmengine - INFO - Epoch(train) [26][60/449] lr: 1.2710e-02 eta: 0:56:59 time: 0.4762 data_time: 0.0151 memory: 23498 grad_norm: 4.6195 top1_acc: 0.4167 top5_acc: 0.8750 loss_cls: 1.6047 loss: 1.6047 2022/09/09 15:47:52 - mmengine - INFO - Epoch(train) [26][80/449] lr: 1.2644e-02 eta: 0:56:48 time: 0.4568 data_time: 0.0179 memory: 23498 grad_norm: 4.6167 top1_acc: 0.5833 top5_acc: 0.7917 loss_cls: 1.6511 loss: 1.6511 2022/09/09 15:48:01 - mmengine - INFO - Epoch(train) [26][100/449] lr: 1.2577e-02 eta: 0:56:37 time: 0.4591 data_time: 0.0167 memory: 23498 grad_norm: 4.5975 top1_acc: 0.4167 top5_acc: 0.8750 loss_cls: 1.5995 loss: 1.5995 2022/09/09 15:48:11 - mmengine - INFO - Epoch(train) [26][120/449] lr: 1.2510e-02 eta: 0:56:26 time: 0.4648 data_time: 0.0239 memory: 23498 grad_norm: 4.6761 top1_acc: 0.5417 top5_acc: 0.7917 loss_cls: 1.7729 loss: 1.7729 2022/09/09 15:48:20 - mmengine - INFO - Epoch(train) [26][140/449] lr: 1.2444e-02 eta: 0:56:15 time: 0.4764 data_time: 0.0163 memory: 23498 grad_norm: 4.7399 top1_acc: 0.4583 top5_acc: 0.6667 loss_cls: 1.7869 loss: 1.7869 2022/09/09 15:48:29 - mmengine - INFO - Epoch(train) [26][160/449] lr: 1.2377e-02 eta: 0:56:04 time: 0.4563 data_time: 0.0183 memory: 23498 grad_norm: 4.8571 top1_acc: 0.6667 top5_acc: 0.8750 loss_cls: 1.8837 loss: 1.8837 2022/09/09 15:48:39 - mmengine - INFO - Epoch(train) [26][180/449] lr: 1.2311e-02 eta: 0:55:54 time: 0.4637 data_time: 0.0202 memory: 23498 grad_norm: 4.8505 top1_acc: 0.6667 top5_acc: 0.8333 loss_cls: 1.6687 loss: 1.6687 2022/09/09 15:48:48 - mmengine - INFO - Epoch(train) [26][200/449] lr: 1.2245e-02 eta: 0:55:43 time: 0.4695 data_time: 0.0259 memory: 23498 grad_norm: 4.8861 top1_acc: 0.3750 top5_acc: 0.7083 loss_cls: 1.8199 loss: 1.8199 2022/09/09 15:48:57 - mmengine - INFO - Epoch(train) [26][220/449] lr: 1.2179e-02 eta: 0:55:32 time: 0.4714 data_time: 0.0159 memory: 23498 grad_norm: 4.8419 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7322 loss: 1.7322 2022/09/09 15:49:07 - mmengine - INFO - Epoch(train) [26][240/449] lr: 1.2113e-02 eta: 0:55:21 time: 0.4586 data_time: 0.0176 memory: 23498 grad_norm: 4.8024 top1_acc: 0.3333 top5_acc: 0.5833 loss_cls: 1.6950 loss: 1.6950 2022/09/09 15:49:16 - mmengine - INFO - Epoch(train) [26][260/449] lr: 1.2047e-02 eta: 0:55:11 time: 0.4618 data_time: 0.0246 memory: 23498 grad_norm: 4.9809 top1_acc: 0.5417 top5_acc: 0.9167 loss_cls: 1.7382 loss: 1.7382 2022/09/09 15:49:25 - mmengine - INFO - Epoch(train) [26][280/449] lr: 1.1981e-02 eta: 0:55:00 time: 0.4625 data_time: 0.0232 memory: 23498 grad_norm: 4.6424 top1_acc: 0.5833 top5_acc: 0.9167 loss_cls: 1.7591 loss: 1.7591 2022/09/09 15:49:34 - mmengine - INFO - Epoch(train) [26][300/449] lr: 1.1915e-02 eta: 0:54:49 time: 0.4611 data_time: 0.0185 memory: 23498 grad_norm: 4.8232 top1_acc: 0.7083 top5_acc: 0.9583 loss_cls: 1.8428 loss: 1.8428 2022/09/09 15:49:43 - mmengine - INFO - Epoch(train) [26][320/449] lr: 1.1850e-02 eta: 0:54:38 time: 0.4511 data_time: 0.0191 memory: 23498 grad_norm: 4.8733 top1_acc: 0.5833 top5_acc: 0.7500 loss_cls: 1.6454 loss: 1.6454 2022/09/09 15:49:52 - mmengine - INFO - Epoch(train) [26][340/449] lr: 1.1784e-02 eta: 0:54:27 time: 0.4573 data_time: 0.0230 memory: 23498 grad_norm: 4.7046 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8323 loss: 1.8323 2022/09/09 15:50:02 - mmengine - INFO - Epoch(train) [26][360/449] lr: 1.1719e-02 eta: 0:54:17 time: 0.4594 data_time: 0.0237 memory: 23498 grad_norm: 4.9644 top1_acc: 0.5417 top5_acc: 0.8333 loss_cls: 2.0234 loss: 2.0234 2022/09/09 15:50:11 - mmengine - INFO - Epoch(train) [26][380/449] lr: 1.1654e-02 eta: 0:54:06 time: 0.4745 data_time: 0.0157 memory: 23498 grad_norm: 4.7582 top1_acc: 0.4167 top5_acc: 0.6250 loss_cls: 1.6535 loss: 1.6535 2022/09/09 15:50:20 - mmengine - INFO - Epoch(train) [26][400/449] lr: 1.1589e-02 eta: 0:53:55 time: 0.4663 data_time: 0.0279 memory: 23498 grad_norm: 4.7761 top1_acc: 0.6250 top5_acc: 0.8333 loss_cls: 1.6873 loss: 1.6873 2022/09/09 15:50:30 - mmengine - INFO - Epoch(train) [26][420/449] lr: 1.1524e-02 eta: 0:53:44 time: 0.4552 data_time: 0.0120 memory: 23498 grad_norm: 4.6474 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 1.6158 loss: 1.6158 2022/09/09 15:50:39 - mmengine - INFO - Epoch(train) [26][440/449] lr: 1.1459e-02 eta: 0:53:34 time: 0.4608 data_time: 0.0219 memory: 23498 grad_norm: 4.6812 top1_acc: 0.7917 top5_acc: 0.9583 loss_cls: 1.7169 loss: 1.7169 2022/09/09 15:50:43 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:50:43 - mmengine - INFO - Epoch(train) [26][449/449] lr: 1.1429e-02 eta: 0:53:34 time: 0.4298 data_time: 0.0132 memory: 23498 grad_norm: 5.1548 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.8224 loss: 1.8224 2022/09/09 15:50:46 - mmengine - INFO - Epoch(val) [26][20/61] eta: 0:00:06 time: 0.1516 data_time: 0.0317 memory: 2693 2022/09/09 15:50:48 - mmengine - INFO - Epoch(val) [26][40/61] eta: 0:00:02 time: 0.1313 data_time: 0.0141 memory: 2693 2022/09/09 15:50:51 - mmengine - INFO - Epoch(val) [26][60/61] eta: 0:00:00 time: 0.1301 data_time: 0.0129 memory: 2693 2022/09/09 15:50:51 - mmengine - INFO - Epoch(val) [26][61/61] acc/top1: 0.3450 acc/top5: 0.6330 acc/mean1: 0.3111 2022/09/09 15:50:51 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_24.pth is removed 2022/09/09 15:50:52 - mmengine - INFO - The best checkpoint with 0.3450 acc/top1 at 26 epoch is saved to best_acc/top1_epoch_26.pth. 2022/09/09 15:51:03 - mmengine - INFO - Epoch(train) [27][20/449] lr: 1.1365e-02 eta: 0:53:17 time: 0.5424 data_time: 0.0998 memory: 23498 grad_norm: 4.7958 top1_acc: 0.5833 top5_acc: 0.8750 loss_cls: 1.8169 loss: 1.8169 2022/09/09 15:51:12 - mmengine - INFO - Epoch(train) [27][40/449] lr: 1.1300e-02 eta: 0:53:06 time: 0.4389 data_time: 0.0124 memory: 23498 grad_norm: 4.7041 top1_acc: 0.6667 top5_acc: 0.8750 loss_cls: 1.7022 loss: 1.7022 2022/09/09 15:51:21 - mmengine - INFO - Epoch(train) [27][60/449] lr: 1.1235e-02 eta: 0:52:55 time: 0.4424 data_time: 0.0163 memory: 23498 grad_norm: 4.7117 top1_acc: 0.5000 top5_acc: 0.8333 loss_cls: 1.7086 loss: 1.7086 2022/09/09 15:51:30 - mmengine - INFO - Epoch(train) [27][80/449] lr: 1.1171e-02 eta: 0:52:44 time: 0.4422 data_time: 0.0188 memory: 23498 grad_norm: 4.7064 top1_acc: 0.7083 top5_acc: 0.9167 loss_cls: 1.7994 loss: 1.7994 2022/09/09 15:51:39 - mmengine - INFO - Epoch(train) [27][100/449] lr: 1.1107e-02 eta: 0:52:33 time: 0.4616 data_time: 0.0173 memory: 23498 grad_norm: 4.7886 top1_acc: 0.6667 top5_acc: 0.8750 loss_cls: 1.6682 loss: 1.6682 2022/09/09 15:51:48 - mmengine - INFO - Epoch(train) [27][120/449] lr: 1.1042e-02 eta: 0:52:22 time: 0.4442 data_time: 0.0184 memory: 23498 grad_norm: 4.9604 top1_acc: 0.6667 top5_acc: 0.8333 loss_cls: 1.6010 loss: 1.6010 2022/09/09 15:51:57 - mmengine - INFO - Epoch(train) [27][140/449] lr: 1.0978e-02 eta: 0:52:12 time: 0.4527 data_time: 0.0186 memory: 23498 grad_norm: 5.0001 top1_acc: 0.7083 top5_acc: 0.8750 loss_cls: 1.6626 loss: 1.6626 2022/09/09 15:52:06 - mmengine - INFO - Epoch(train) [27][160/449] lr: 1.0914e-02 eta: 0:52:01 time: 0.4526 data_time: 0.0201 memory: 23498 grad_norm: 5.1011 top1_acc: 0.5417 top5_acc: 0.7500 loss_cls: 1.9147 loss: 1.9147 2022/09/09 15:52:15 - mmengine - INFO - Epoch(train) [27][180/449] lr: 1.0850e-02 eta: 0:51:50 time: 0.4638 data_time: 0.0181 memory: 23498 grad_norm: 5.0935 top1_acc: 0.7083 top5_acc: 0.9167 loss_cls: 1.9017 loss: 1.9017 2022/09/09 15:52:24 - mmengine - INFO - Epoch(train) [27][200/449] lr: 1.0787e-02 eta: 0:51:39 time: 0.4482 data_time: 0.0194 memory: 23498 grad_norm: 4.9361 top1_acc: 0.4583 top5_acc: 0.7083 loss_cls: 1.6839 loss: 1.6839 2022/09/09 15:52:33 - mmengine - INFO - Epoch(train) [27][220/449] lr: 1.0723e-02 eta: 0:51:28 time: 0.4467 data_time: 0.0174 memory: 23498 grad_norm: 4.9209 top1_acc: 0.4583 top5_acc: 0.7917 loss_cls: 1.6724 loss: 1.6724 2022/09/09 15:52:42 - mmengine - INFO - Epoch(train) [27][240/449] lr: 1.0659e-02 eta: 0:51:18 time: 0.4525 data_time: 0.0199 memory: 23498 grad_norm: 4.8995 top1_acc: 0.5417 top5_acc: 0.7917 loss_cls: 1.6215 loss: 1.6215 2022/09/09 15:52:51 - mmengine - INFO - Epoch(train) [27][260/449] lr: 1.0596e-02 eta: 0:51:07 time: 0.4677 data_time: 0.0168 memory: 23498 grad_norm: 5.0250 top1_acc: 0.3333 top5_acc: 0.7500 loss_cls: 1.8617 loss: 1.8617 2022/09/09 15:53:01 - mmengine - INFO - Epoch(train) [27][280/449] lr: 1.0533e-02 eta: 0:50:56 time: 0.4550 data_time: 0.0177 memory: 23498 grad_norm: 5.1155 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 1.7113 loss: 1.7113 2022/09/09 15:53:10 - mmengine - INFO - Epoch(train) [27][300/449] lr: 1.0470e-02 eta: 0:50:46 time: 0.4549 data_time: 0.0186 memory: 23498 grad_norm: 5.0214 top1_acc: 0.5833 top5_acc: 0.8750 loss_cls: 1.6975 loss: 1.6975 2022/09/09 15:53:19 - mmengine - INFO - Epoch(train) [27][320/449] lr: 1.0407e-02 eta: 0:50:35 time: 0.4544 data_time: 0.0180 memory: 23498 grad_norm: 4.8970 top1_acc: 0.5833 top5_acc: 0.7083 loss_cls: 1.7232 loss: 1.7232 2022/09/09 15:53:22 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:53:28 - mmengine - INFO - Epoch(train) [27][340/449] lr: 1.0344e-02 eta: 0:50:24 time: 0.4658 data_time: 0.0187 memory: 23498 grad_norm: 4.7772 top1_acc: 0.5000 top5_acc: 0.8333 loss_cls: 1.6459 loss: 1.6459 2022/09/09 15:53:37 - mmengine - INFO - Epoch(train) [27][360/449] lr: 1.0281e-02 eta: 0:50:14 time: 0.4488 data_time: 0.0192 memory: 23498 grad_norm: 4.7990 top1_acc: 0.7500 top5_acc: 0.7917 loss_cls: 1.5242 loss: 1.5242 2022/09/09 15:53:46 - mmengine - INFO - Epoch(train) [27][380/449] lr: 1.0218e-02 eta: 0:50:03 time: 0.4541 data_time: 0.0172 memory: 23498 grad_norm: 4.9160 top1_acc: 0.5417 top5_acc: 0.8750 loss_cls: 1.7506 loss: 1.7506 2022/09/09 15:53:55 - mmengine - INFO - Epoch(train) [27][400/449] lr: 1.0156e-02 eta: 0:49:52 time: 0.4606 data_time: 0.0180 memory: 23498 grad_norm: 5.0275 top1_acc: 0.5833 top5_acc: 0.7083 loss_cls: 1.7907 loss: 1.7907 2022/09/09 15:54:04 - mmengine - INFO - Epoch(train) [27][420/449] lr: 1.0093e-02 eta: 0:49:41 time: 0.4438 data_time: 0.0179 memory: 23498 grad_norm: 4.7834 top1_acc: 0.6250 top5_acc: 0.8333 loss_cls: 1.6006 loss: 1.6006 2022/09/09 15:54:13 - mmengine - INFO - Epoch(train) [27][440/449] lr: 1.0031e-02 eta: 0:49:31 time: 0.4392 data_time: 0.0185 memory: 23498 grad_norm: 4.8994 top1_acc: 0.5417 top5_acc: 0.9167 loss_cls: 1.7062 loss: 1.7062 2022/09/09 15:54:17 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:54:17 - mmengine - INFO - Epoch(train) [27][449/449] lr: 1.0003e-02 eta: 0:49:31 time: 0.4276 data_time: 0.0186 memory: 23498 grad_norm: 5.2992 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.9921 loss: 1.9921 2022/09/09 15:54:20 - mmengine - INFO - Epoch(val) [27][20/61] eta: 0:00:06 time: 0.1605 data_time: 0.0417 memory: 2693 2022/09/09 15:54:23 - mmengine - INFO - Epoch(val) [27][40/61] eta: 0:00:02 time: 0.1303 data_time: 0.0114 memory: 2693 2022/09/09 15:54:25 - mmengine - INFO - Epoch(val) [27][60/61] eta: 0:00:00 time: 0.1286 data_time: 0.0121 memory: 2693 2022/09/09 15:54:26 - mmengine - INFO - Epoch(val) [27][61/61] acc/top1: 0.3440 acc/top5: 0.6375 acc/mean1: 0.3080 2022/09/09 15:54:36 - mmengine - INFO - Epoch(train) [28][20/449] lr: 9.9410e-03 eta: 0:49:14 time: 0.5372 data_time: 0.0526 memory: 23498 grad_norm: 4.8422 top1_acc: 0.4583 top5_acc: 0.7917 loss_cls: 1.6709 loss: 1.6709 2022/09/09 15:54:45 - mmengine - INFO - Epoch(train) [28][40/449] lr: 9.8791e-03 eta: 0:49:03 time: 0.4449 data_time: 0.0175 memory: 23498 grad_norm: 4.8779 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 1.7429 loss: 1.7429 2022/09/09 15:54:54 - mmengine - INFO - Epoch(train) [28][60/449] lr: 9.8172e-03 eta: 0:48:53 time: 0.4489 data_time: 0.0184 memory: 23498 grad_norm: 4.9541 top1_acc: 0.5000 top5_acc: 0.7917 loss_cls: 1.6861 loss: 1.6861 2022/09/09 15:55:04 - mmengine - INFO - Epoch(train) [28][80/449] lr: 9.7555e-03 eta: 0:48:42 time: 0.4712 data_time: 0.0178 memory: 23498 grad_norm: 5.0804 top1_acc: 0.5000 top5_acc: 0.8333 loss_cls: 1.5411 loss: 1.5411 2022/09/09 15:55:13 - mmengine - INFO - Epoch(train) [28][100/449] lr: 9.6940e-03 eta: 0:48:31 time: 0.4648 data_time: 0.0201 memory: 23498 grad_norm: 4.9973 top1_acc: 0.6667 top5_acc: 0.7500 loss_cls: 1.5967 loss: 1.5967 2022/09/09 15:55:22 - mmengine - INFO - Epoch(train) [28][120/449] lr: 9.6325e-03 eta: 0:48:21 time: 0.4470 data_time: 0.0160 memory: 23498 grad_norm: 4.9576 top1_acc: 0.6667 top5_acc: 0.8750 loss_cls: 1.6104 loss: 1.6104 2022/09/09 15:55:31 - mmengine - INFO - Epoch(train) [28][140/449] lr: 9.5712e-03 eta: 0:48:10 time: 0.4593 data_time: 0.0219 memory: 23498 grad_norm: 4.9077 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.7335 loss: 1.7335 2022/09/09 15:55:40 - mmengine - INFO - Epoch(train) [28][160/449] lr: 9.5101e-03 eta: 0:48:00 time: 0.4597 data_time: 0.0163 memory: 23498 grad_norm: 5.0337 top1_acc: 0.6250 top5_acc: 0.8333 loss_cls: 1.6187 loss: 1.6187 2022/09/09 15:55:50 - mmengine - INFO - Epoch(train) [28][180/449] lr: 9.4490e-03 eta: 0:47:49 time: 0.4667 data_time: 0.0162 memory: 23498 grad_norm: 4.9813 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6395 loss: 1.6395 2022/09/09 15:55:59 - mmengine - INFO - Epoch(train) [28][200/449] lr: 9.3881e-03 eta: 0:47:38 time: 0.4540 data_time: 0.0182 memory: 23498 grad_norm: 5.0486 top1_acc: 0.5000 top5_acc: 0.8333 loss_cls: 1.5842 loss: 1.5842 2022/09/09 15:56:08 - mmengine - INFO - Epoch(train) [28][220/449] lr: 9.3274e-03 eta: 0:47:28 time: 0.4619 data_time: 0.0218 memory: 23498 grad_norm: 5.1482 top1_acc: 0.6250 top5_acc: 0.8333 loss_cls: 1.5483 loss: 1.5483 2022/09/09 15:56:17 - mmengine - INFO - Epoch(train) [28][240/449] lr: 9.2667e-03 eta: 0:47:17 time: 0.4586 data_time: 0.0184 memory: 23498 grad_norm: 5.0391 top1_acc: 0.6250 top5_acc: 0.8333 loss_cls: 1.6191 loss: 1.6191 2022/09/09 15:56:26 - mmengine - INFO - Epoch(train) [28][260/449] lr: 9.2063e-03 eta: 0:47:07 time: 0.4648 data_time: 0.0185 memory: 23498 grad_norm: 4.9674 top1_acc: 0.5833 top5_acc: 0.7500 loss_cls: 1.7270 loss: 1.7270 2022/09/09 15:56:36 - mmengine - INFO - Epoch(train) [28][280/449] lr: 9.1459e-03 eta: 0:46:56 time: 0.4660 data_time: 0.0183 memory: 23498 grad_norm: 5.0645 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 1.6307 loss: 1.6307 2022/09/09 15:56:45 - mmengine - INFO - Epoch(train) [28][300/449] lr: 9.0857e-03 eta: 0:46:46 time: 0.4584 data_time: 0.0245 memory: 23498 grad_norm: 5.0388 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5812 loss: 1.5812 2022/09/09 15:56:54 - mmengine - INFO - Epoch(train) [28][320/449] lr: 9.0256e-03 eta: 0:46:35 time: 0.4547 data_time: 0.0178 memory: 23498 grad_norm: 5.1908 top1_acc: 0.3750 top5_acc: 0.5833 loss_cls: 1.5849 loss: 1.5849 2022/09/09 15:57:03 - mmengine - INFO - Epoch(train) [28][340/449] lr: 8.9657e-03 eta: 0:46:25 time: 0.4635 data_time: 0.0180 memory: 23498 grad_norm: 5.0325 top1_acc: 0.5833 top5_acc: 0.8750 loss_cls: 1.6510 loss: 1.6510 2022/09/09 15:57:12 - mmengine - INFO - Epoch(train) [28][360/449] lr: 8.9059e-03 eta: 0:46:14 time: 0.4558 data_time: 0.0225 memory: 23498 grad_norm: 4.9973 top1_acc: 0.5000 top5_acc: 0.7083 loss_cls: 1.7030 loss: 1.7030 2022/09/09 15:57:22 - mmengine - INFO - Epoch(train) [28][380/449] lr: 8.8463e-03 eta: 0:46:03 time: 0.4561 data_time: 0.0233 memory: 23498 grad_norm: 5.0694 top1_acc: 0.5417 top5_acc: 0.7083 loss_cls: 1.5305 loss: 1.5305 2022/09/09 15:57:31 - mmengine - INFO - Epoch(train) [28][400/449] lr: 8.7868e-03 eta: 0:45:53 time: 0.4633 data_time: 0.0181 memory: 23498 grad_norm: 5.1659 top1_acc: 0.4583 top5_acc: 0.7500 loss_cls: 1.6081 loss: 1.6081 2022/09/09 15:57:40 - mmengine - INFO - Epoch(train) [28][420/449] lr: 8.7275e-03 eta: 0:45:42 time: 0.4655 data_time: 0.0175 memory: 23498 grad_norm: 5.0477 top1_acc: 0.7083 top5_acc: 0.9167 loss_cls: 1.5182 loss: 1.5182 2022/09/09 15:57:49 - mmengine - INFO - Epoch(train) [28][440/449] lr: 8.6683e-03 eta: 0:45:32 time: 0.4463 data_time: 0.0201 memory: 23498 grad_norm: 5.1636 top1_acc: 0.4583 top5_acc: 0.8333 loss_cls: 1.6552 loss: 1.6552 2022/09/09 15:57:53 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 15:57:53 - mmengine - INFO - Epoch(train) [28][449/449] lr: 8.6417e-03 eta: 0:45:32 time: 0.4405 data_time: 0.0127 memory: 23498 grad_norm: 5.5675 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.7579 loss: 1.7579 2022/09/09 15:57:56 - mmengine - INFO - Epoch(val) [28][20/61] eta: 0:00:06 time: 0.1511 data_time: 0.0306 memory: 2693 2022/09/09 15:57:59 - mmengine - INFO - Epoch(val) [28][40/61] eta: 0:00:02 time: 0.1297 data_time: 0.0122 memory: 2693 2022/09/09 15:58:01 - mmengine - INFO - Epoch(val) [28][60/61] eta: 0:00:00 time: 0.1303 data_time: 0.0127 memory: 2693 2022/09/09 15:58:02 - mmengine - INFO - Epoch(val) [28][61/61] acc/top1: 0.3621 acc/top5: 0.6575 acc/mean1: 0.3259 2022/09/09 15:58:02 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_26.pth is removed 2022/09/09 15:58:03 - mmengine - INFO - The best checkpoint with 0.3621 acc/top1 at 28 epoch is saved to best_acc/top1_epoch_28.pth. 2022/09/09 15:58:12 - mmengine - INFO - Epoch(train) [29][20/449] lr: 8.5827e-03 eta: 0:45:15 time: 0.4758 data_time: 0.0317 memory: 23498 grad_norm: 5.1860 top1_acc: 0.4583 top5_acc: 0.8750 loss_cls: 1.5954 loss: 1.5954 2022/09/09 15:58:22 - mmengine - INFO - Epoch(train) [29][40/449] lr: 8.5238e-03 eta: 0:45:04 time: 0.4719 data_time: 0.0215 memory: 23498 grad_norm: 5.1860 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4903 loss: 1.4903 2022/09/09 15:58:31 - mmengine - INFO - Epoch(train) [29][60/449] lr: 8.4651e-03 eta: 0:44:54 time: 0.4671 data_time: 0.0225 memory: 23498 grad_norm: 5.1550 top1_acc: 0.6667 top5_acc: 0.8750 loss_cls: 1.4671 loss: 1.4671 2022/09/09 15:58:40 - mmengine - INFO - Epoch(train) [29][80/449] lr: 8.4066e-03 eta: 0:44:43 time: 0.4529 data_time: 0.0168 memory: 23498 grad_norm: 5.2774 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6588 loss: 1.6588 2022/09/09 15:58:49 - mmengine - INFO - Epoch(train) [29][100/449] lr: 8.3482e-03 eta: 0:44:33 time: 0.4567 data_time: 0.0152 memory: 23498 grad_norm: 5.1848 top1_acc: 0.7500 top5_acc: 0.9167 loss_cls: 1.5929 loss: 1.5929 2022/09/09 15:58:58 - mmengine - INFO - Epoch(train) [29][120/449] lr: 8.2899e-03 eta: 0:44:23 time: 0.4676 data_time: 0.0252 memory: 23498 grad_norm: 5.1275 top1_acc: 0.6250 top5_acc: 0.8333 loss_cls: 1.5408 loss: 1.5408 2022/09/09 15:59:08 - mmengine - INFO - Epoch(train) [29][140/449] lr: 8.2318e-03 eta: 0:44:12 time: 0.4640 data_time: 0.0147 memory: 23498 grad_norm: 5.2144 top1_acc: 0.5833 top5_acc: 0.7500 loss_cls: 1.5901 loss: 1.5901 2022/09/09 15:59:17 - mmengine - INFO - Epoch(train) [29][160/449] lr: 8.1739e-03 eta: 0:44:02 time: 0.4650 data_time: 0.0239 memory: 23498 grad_norm: 5.2183 top1_acc: 0.7083 top5_acc: 0.8333 loss_cls: 1.5634 loss: 1.5634 2022/09/09 15:59:26 - mmengine - INFO - Epoch(train) [29][180/449] lr: 8.1161e-03 eta: 0:43:51 time: 0.4738 data_time: 0.0253 memory: 23498 grad_norm: 5.4434 top1_acc: 0.2917 top5_acc: 0.7500 loss_cls: 1.4483 loss: 1.4483 2022/09/09 15:59:36 - mmengine - INFO - Epoch(train) [29][200/449] lr: 8.0584e-03 eta: 0:43:41 time: 0.4699 data_time: 0.0238 memory: 23498 grad_norm: 5.1331 top1_acc: 0.4583 top5_acc: 0.6250 loss_cls: 1.4919 loss: 1.4919 2022/09/09 15:59:45 - mmengine - INFO - Epoch(train) [29][220/449] lr: 8.0009e-03 eta: 0:43:30 time: 0.4613 data_time: 0.0161 memory: 23498 grad_norm: 5.3190 top1_acc: 0.6667 top5_acc: 0.7917 loss_cls: 1.6119 loss: 1.6119 2022/09/09 15:59:54 - mmengine - INFO - Epoch(train) [29][240/449] lr: 7.9436e-03 eta: 0:43:20 time: 0.4595 data_time: 0.0175 memory: 23498 grad_norm: 5.1199 top1_acc: 0.4583 top5_acc: 0.7500 loss_cls: 1.5538 loss: 1.5538 2022/09/09 16:00:04 - mmengine - INFO - Epoch(train) [29][260/449] lr: 7.8864e-03 eta: 0:43:10 time: 0.4726 data_time: 0.0170 memory: 23498 grad_norm: 5.2864 top1_acc: 0.4167 top5_acc: 0.8750 loss_cls: 1.6523 loss: 1.6523 2022/09/09 16:00:13 - mmengine - INFO - Epoch(train) [29][280/449] lr: 7.8294e-03 eta: 0:42:59 time: 0.4709 data_time: 0.0239 memory: 23498 grad_norm: 5.2883 top1_acc: 0.7083 top5_acc: 1.0000 loss_cls: 1.4721 loss: 1.4721 2022/09/09 16:00:23 - mmengine - INFO - Epoch(train) [29][300/449] lr: 7.7725e-03 eta: 0:42:49 time: 0.4703 data_time: 0.0156 memory: 23498 grad_norm: 5.2722 top1_acc: 0.5833 top5_acc: 0.8750 loss_cls: 1.5007 loss: 1.5007 2022/09/09 16:00:32 - mmengine - INFO - Epoch(train) [29][320/449] lr: 7.7158e-03 eta: 0:42:38 time: 0.4607 data_time: 0.0173 memory: 23498 grad_norm: 5.3201 top1_acc: 0.5000 top5_acc: 0.8333 loss_cls: 1.4816 loss: 1.4816 2022/09/09 16:00:41 - mmengine - INFO - Epoch(train) [29][340/449] lr: 7.6593e-03 eta: 0:42:28 time: 0.4650 data_time: 0.0183 memory: 23498 grad_norm: 5.4894 top1_acc: 0.5417 top5_acc: 0.7083 loss_cls: 1.6280 loss: 1.6280 2022/09/09 16:00:50 - mmengine - INFO - Epoch(train) [29][360/449] lr: 7.6029e-03 eta: 0:42:18 time: 0.4601 data_time: 0.0240 memory: 23498 grad_norm: 5.2977 top1_acc: 0.5417 top5_acc: 0.8750 loss_cls: 1.4737 loss: 1.4737 2022/09/09 16:00:59 - mmengine - INFO - Epoch(train) [29][380/449] lr: 7.5466e-03 eta: 0:42:07 time: 0.4510 data_time: 0.0152 memory: 23498 grad_norm: 5.3763 top1_acc: 0.5833 top5_acc: 0.8750 loss_cls: 1.5411 loss: 1.5411 2022/09/09 16:01:08 - mmengine - INFO - Epoch(train) [29][400/449] lr: 7.4906e-03 eta: 0:41:57 time: 0.4569 data_time: 0.0181 memory: 23498 grad_norm: 5.3750 top1_acc: 0.7083 top5_acc: 0.9583 loss_cls: 1.4649 loss: 1.4649 2022/09/09 16:01:18 - mmengine - INFO - Epoch(train) [29][420/449] lr: 7.4347e-03 eta: 0:41:46 time: 0.4769 data_time: 0.0171 memory: 23498 grad_norm: 5.2931 top1_acc: 0.5417 top5_acc: 0.9583 loss_cls: 1.5144 loss: 1.5144 2022/09/09 16:01:22 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 16:01:28 - mmengine - INFO - Epoch(train) [29][440/449] lr: 7.3789e-03 eta: 0:41:36 time: 0.4849 data_time: 0.0215 memory: 23498 grad_norm: 5.2133 top1_acc: 0.5833 top5_acc: 0.7083 loss_cls: 1.6383 loss: 1.6383 2022/09/09 16:01:32 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 16:01:32 - mmengine - INFO - Epoch(train) [29][449/449] lr: 7.3539e-03 eta: 0:41:36 time: 0.4481 data_time: 0.0117 memory: 23498 grad_norm: 5.8010 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.9087 loss: 1.9087 2022/09/09 16:01:35 - mmengine - INFO - Epoch(val) [29][20/61] eta: 0:00:06 time: 0.1518 data_time: 0.0329 memory: 2693 2022/09/09 16:01:37 - mmengine - INFO - Epoch(val) [29][40/61] eta: 0:00:02 time: 0.1322 data_time: 0.0133 memory: 2693 2022/09/09 16:01:40 - mmengine - INFO - Epoch(val) [29][60/61] eta: 0:00:00 time: 0.1292 data_time: 0.0123 memory: 2693 2022/09/09 16:01:40 - mmengine - INFO - Epoch(val) [29][61/61] acc/top1: 0.3657 acc/top5: 0.6517 acc/mean1: 0.3294 2022/09/09 16:01:41 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_28.pth is removed 2022/09/09 16:01:42 - mmengine - INFO - The best checkpoint with 0.3657 acc/top1 at 29 epoch is saved to best_acc/top1_epoch_29.pth. 2022/09/09 16:01:52 - mmengine - INFO - Epoch(train) [30][20/449] lr: 7.2984e-03 eta: 0:41:19 time: 0.4750 data_time: 0.0341 memory: 23498 grad_norm: 5.2927 top1_acc: 0.7917 top5_acc: 0.9583 loss_cls: 1.4594 loss: 1.4594 2022/09/09 16:02:01 - mmengine - INFO - Epoch(train) [30][40/449] lr: 7.2430e-03 eta: 0:41:09 time: 0.4560 data_time: 0.0230 memory: 23498 grad_norm: 5.3232 top1_acc: 0.6667 top5_acc: 0.7083 loss_cls: 1.4816 loss: 1.4816 2022/09/09 16:02:10 - mmengine - INFO - Epoch(train) [30][60/449] lr: 7.1878e-03 eta: 0:40:59 time: 0.4558 data_time: 0.0159 memory: 23498 grad_norm: 5.3412 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5340 loss: 1.5340 2022/09/09 16:02:19 - mmengine - INFO - Epoch(train) [30][80/449] lr: 7.1328e-03 eta: 0:40:48 time: 0.4466 data_time: 0.0169 memory: 23498 grad_norm: 5.2684 top1_acc: 0.5833 top5_acc: 0.7917 loss_cls: 1.4962 loss: 1.4962 2022/09/09 16:02:28 - mmengine - INFO - Epoch(train) [30][100/449] lr: 7.0779e-03 eta: 0:40:38 time: 0.4541 data_time: 0.0168 memory: 23498 grad_norm: 5.2958 top1_acc: 0.5417 top5_acc: 0.8750 loss_cls: 1.4673 loss: 1.4673 2022/09/09 16:02:37 - mmengine - INFO - Epoch(train) [30][120/449] lr: 7.0233e-03 eta: 0:40:27 time: 0.4575 data_time: 0.0232 memory: 23498 grad_norm: 5.2876 top1_acc: 0.7083 top5_acc: 0.8750 loss_cls: 1.3983 loss: 1.3983 2022/09/09 16:02:46 - mmengine - INFO - Epoch(train) [30][140/449] lr: 6.9687e-03 eta: 0:40:17 time: 0.4527 data_time: 0.0156 memory: 23498 grad_norm: 5.3559 top1_acc: 0.6667 top5_acc: 0.9583 loss_cls: 1.5187 loss: 1.5187 2022/09/09 16:02:55 - mmengine - INFO - Epoch(train) [30][160/449] lr: 6.9144e-03 eta: 0:40:06 time: 0.4446 data_time: 0.0175 memory: 23498 grad_norm: 5.4118 top1_acc: 0.5417 top5_acc: 0.7917 loss_cls: 1.4728 loss: 1.4728 2022/09/09 16:03:04 - mmengine - INFO - Epoch(train) [30][180/449] lr: 6.8602e-03 eta: 0:39:56 time: 0.4543 data_time: 0.0172 memory: 23498 grad_norm: 5.3606 top1_acc: 0.6250 top5_acc: 0.8333 loss_cls: 1.4007 loss: 1.4007 2022/09/09 16:03:14 - mmengine - INFO - Epoch(train) [30][200/449] lr: 6.8062e-03 eta: 0:39:45 time: 0.4605 data_time: 0.0263 memory: 23498 grad_norm: 5.4340 top1_acc: 0.7917 top5_acc: 0.9167 loss_cls: 1.5341 loss: 1.5341 2022/09/09 16:03:23 - mmengine - INFO - Epoch(train) [30][220/449] lr: 6.7523e-03 eta: 0:39:35 time: 0.4573 data_time: 0.0156 memory: 23498 grad_norm: 5.2880 top1_acc: 0.6250 top5_acc: 0.7917 loss_cls: 1.5346 loss: 1.5346 2022/09/09 16:03:32 - mmengine - INFO - Epoch(train) [30][240/449] lr: 6.6986e-03 eta: 0:39:25 time: 0.4486 data_time: 0.0173 memory: 23498 grad_norm: 5.5657 top1_acc: 0.7083 top5_acc: 0.8750 loss_cls: 1.5718 loss: 1.5718 2022/09/09 16:03:41 - mmengine - INFO - Epoch(train) [30][260/449] lr: 6.6451e-03 eta: 0:39:14 time: 0.4585 data_time: 0.0185 memory: 23498 grad_norm: 5.3998 top1_acc: 0.7500 top5_acc: 0.9583 loss_cls: 1.5054 loss: 1.5054 2022/09/09 16:03:50 - mmengine - INFO - Epoch(train) [30][280/449] lr: 6.5918e-03 eta: 0:39:04 time: 0.4680 data_time: 0.0300 memory: 23498 grad_norm: 5.4881 top1_acc: 0.5000 top5_acc: 0.6667 loss_cls: 1.5804 loss: 1.5804 2022/09/09 16:03:59 - mmengine - INFO - Epoch(train) [30][300/449] lr: 6.5386e-03 eta: 0:38:53 time: 0.4529 data_time: 0.0159 memory: 23498 grad_norm: 5.5473 top1_acc: 0.5417 top5_acc: 0.8333 loss_cls: 1.5293 loss: 1.5293 2022/09/09 16:04:08 - mmengine - INFO - Epoch(train) [30][320/449] lr: 6.4857e-03 eta: 0:38:43 time: 0.4488 data_time: 0.0181 memory: 23498 grad_norm: 5.4402 top1_acc: 0.5833 top5_acc: 0.7917 loss_cls: 1.4109 loss: 1.4109 2022/09/09 16:04:17 - mmengine - INFO - Epoch(train) [30][340/449] lr: 6.4328e-03 eta: 0:38:33 time: 0.4530 data_time: 0.0220 memory: 23498 grad_norm: 5.4913 top1_acc: 0.5417 top5_acc: 0.7083 loss_cls: 1.4466 loss: 1.4466 2022/09/09 16:04:26 - mmengine - INFO - Epoch(train) [30][360/449] lr: 6.3802e-03 eta: 0:38:22 time: 0.4510 data_time: 0.0240 memory: 23498 grad_norm: 5.4171 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 1.4459 loss: 1.4459 2022/09/09 16:04:35 - mmengine - INFO - Epoch(train) [30][380/449] lr: 6.3277e-03 eta: 0:38:12 time: 0.4571 data_time: 0.0172 memory: 23498 grad_norm: 5.6903 top1_acc: 0.6667 top5_acc: 0.9167 loss_cls: 1.4481 loss: 1.4481 2022/09/09 16:04:44 - mmengine - INFO - Epoch(train) [30][400/449] lr: 6.2755e-03 eta: 0:38:01 time: 0.4514 data_time: 0.0185 memory: 23498 grad_norm: 5.5230 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5903 loss: 1.5903 2022/09/09 16:04:54 - mmengine - INFO - Epoch(train) [30][420/449] lr: 6.2233e-03 eta: 0:37:51 time: 0.4539 data_time: 0.0234 memory: 23498 grad_norm: 5.6599 top1_acc: 0.4583 top5_acc: 0.7917 loss_cls: 1.5671 loss: 1.5671 2022/09/09 16:05:03 - mmengine - INFO - Epoch(train) [30][440/449] lr: 6.1714e-03 eta: 0:37:41 time: 0.4560 data_time: 0.0214 memory: 23498 grad_norm: 5.6788 top1_acc: 0.5000 top5_acc: 0.7917 loss_cls: 1.4768 loss: 1.4768 2022/09/09 16:05:06 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 16:05:06 - mmengine - INFO - Epoch(train) [30][449/449] lr: 6.1481e-03 eta: 0:37:41 time: 0.4246 data_time: 0.0151 memory: 23498 grad_norm: 5.9190 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.5888 loss: 1.5888 2022/09/09 16:05:09 - mmengine - INFO - Epoch(val) [30][20/61] eta: 0:00:06 time: 0.1552 data_time: 0.0358 memory: 2693 2022/09/09 16:05:12 - mmengine - INFO - Epoch(val) [30][40/61] eta: 0:00:02 time: 0.1302 data_time: 0.0122 memory: 2693 2022/09/09 16:05:15 - mmengine - INFO - Epoch(val) [30][60/61] eta: 0:00:00 time: 0.1298 data_time: 0.0129 memory: 2693 2022/09/09 16:05:15 - mmengine - INFO - Epoch(val) [30][61/61] acc/top1: 0.3797 acc/top5: 0.6672 acc/mean1: 0.3373 2022/09/09 16:05:15 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_29.pth is removed 2022/09/09 16:05:16 - mmengine - INFO - The best checkpoint with 0.3797 acc/top1 at 30 epoch is saved to best_acc/top1_epoch_30.pth. 2022/09/09 16:05:26 - mmengine - INFO - Epoch(train) [31][20/449] lr: 6.0964e-03 eta: 0:37:25 time: 0.4951 data_time: 0.0462 memory: 23498 grad_norm: 5.3899 top1_acc: 0.6667 top5_acc: 0.9167 loss_cls: 1.4268 loss: 1.4268 2022/09/09 16:05:35 - mmengine - INFO - Epoch(train) [31][40/449] lr: 6.0449e-03 eta: 0:37:14 time: 0.4503 data_time: 0.0136 memory: 23498 grad_norm: 5.4511 top1_acc: 0.5417 top5_acc: 0.8333 loss_cls: 1.3465 loss: 1.3465 2022/09/09 16:05:44 - mmengine - INFO - Epoch(train) [31][60/449] lr: 5.9936e-03 eta: 0:37:04 time: 0.4709 data_time: 0.0144 memory: 23498 grad_norm: 5.4443 top1_acc: 0.6667 top5_acc: 0.8750 loss_cls: 1.4682 loss: 1.4682 2022/09/09 16:05:53 - mmengine - INFO - Epoch(train) [31][80/449] lr: 5.9425e-03 eta: 0:36:54 time: 0.4615 data_time: 0.0198 memory: 23498 grad_norm: 5.7192 top1_acc: 0.6667 top5_acc: 0.8333 loss_cls: 1.3617 loss: 1.3617 2022/09/09 16:06:03 - mmengine - INFO - Epoch(train) [31][100/449] lr: 5.8915e-03 eta: 0:36:43 time: 0.4597 data_time: 0.0223 memory: 23498 grad_norm: 5.6853 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4397 loss: 1.4397 2022/09/09 16:06:12 - mmengine - INFO - Epoch(train) [31][120/449] lr: 5.8407e-03 eta: 0:36:33 time: 0.4706 data_time: 0.0177 memory: 23498 grad_norm: 5.7124 top1_acc: 0.5833 top5_acc: 0.9167 loss_cls: 1.4721 loss: 1.4721 2022/09/09 16:06:21 - mmengine - INFO - Epoch(train) [31][140/449] lr: 5.7902e-03 eta: 0:36:23 time: 0.4683 data_time: 0.0198 memory: 23498 grad_norm: 5.5794 top1_acc: 0.5417 top5_acc: 0.6667 loss_cls: 1.5180 loss: 1.5180 2022/09/09 16:06:31 - mmengine - INFO - Epoch(train) [31][160/449] lr: 5.7397e-03 eta: 0:36:13 time: 0.4646 data_time: 0.0214 memory: 23498 grad_norm: 5.6056 top1_acc: 0.5417 top5_acc: 0.6667 loss_cls: 1.4977 loss: 1.4977 2022/09/09 16:06:40 - mmengine - INFO - Epoch(train) [31][180/449] lr: 5.6895e-03 eta: 0:36:02 time: 0.4561 data_time: 0.0228 memory: 23498 grad_norm: 5.5167 top1_acc: 0.7083 top5_acc: 0.8333 loss_cls: 1.3182 loss: 1.3182 2022/09/09 16:06:49 - mmengine - INFO - Epoch(train) [31][200/449] lr: 5.6395e-03 eta: 0:35:52 time: 0.4612 data_time: 0.0188 memory: 23498 grad_norm: 5.5759 top1_acc: 0.6250 top5_acc: 0.8333 loss_cls: 1.4273 loss: 1.4273 2022/09/09 16:06:58 - mmengine - INFO - Epoch(train) [31][220/449] lr: 5.5896e-03 eta: 0:35:42 time: 0.4577 data_time: 0.0215 memory: 23498 grad_norm: 5.6268 top1_acc: 0.7500 top5_acc: 0.9583 loss_cls: 1.4519 loss: 1.4519 2022/09/09 16:07:08 - mmengine - INFO - Epoch(train) [31][240/449] lr: 5.5400e-03 eta: 0:35:31 time: 0.4726 data_time: 0.0195 memory: 23498 grad_norm: 5.5123 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4338 loss: 1.4338 2022/09/09 16:07:17 - mmengine - INFO - Epoch(train) [31][260/449] lr: 5.4905e-03 eta: 0:35:21 time: 0.4648 data_time: 0.0229 memory: 23498 grad_norm: 5.6074 top1_acc: 0.5417 top5_acc: 0.8750 loss_cls: 1.3730 loss: 1.3730 2022/09/09 16:07:26 - mmengine - INFO - Epoch(train) [31][280/449] lr: 5.4412e-03 eta: 0:35:11 time: 0.4605 data_time: 0.0198 memory: 23498 grad_norm: 5.5188 top1_acc: 0.7083 top5_acc: 0.7500 loss_cls: 1.3660 loss: 1.3660 2022/09/09 16:07:36 - mmengine - INFO - Epoch(train) [31][300/449] lr: 5.3921e-03 eta: 0:35:01 time: 0.4654 data_time: 0.0207 memory: 23498 grad_norm: 5.7851 top1_acc: 0.6667 top5_acc: 0.9167 loss_cls: 1.2544 loss: 1.2544 2022/09/09 16:07:45 - mmengine - INFO - Epoch(train) [31][320/449] lr: 5.3431e-03 eta: 0:34:50 time: 0.4620 data_time: 0.0226 memory: 23498 grad_norm: 5.6878 top1_acc: 0.6667 top5_acc: 0.8333 loss_cls: 1.3714 loss: 1.3714 2022/09/09 16:07:54 - mmengine - INFO - Epoch(train) [31][340/449] lr: 5.2944e-03 eta: 0:34:40 time: 0.4625 data_time: 0.0321 memory: 23498 grad_norm: 5.5795 top1_acc: 0.8333 top5_acc: 0.9167 loss_cls: 1.4684 loss: 1.4684 2022/09/09 16:08:03 - mmengine - INFO - Epoch(train) [31][360/449] lr: 5.2459e-03 eta: 0:34:30 time: 0.4589 data_time: 0.0175 memory: 23498 grad_norm: 5.9286 top1_acc: 0.5417 top5_acc: 0.8333 loss_cls: 1.3257 loss: 1.3257 2022/09/09 16:08:12 - mmengine - INFO - Epoch(train) [31][380/449] lr: 5.1975e-03 eta: 0:34:19 time: 0.4551 data_time: 0.0211 memory: 23498 grad_norm: 5.7395 top1_acc: 0.7083 top5_acc: 0.8750 loss_cls: 1.2899 loss: 1.2899 2022/09/09 16:08:21 - mmengine - INFO - Epoch(train) [31][400/449] lr: 5.1494e-03 eta: 0:34:09 time: 0.4521 data_time: 0.0236 memory: 23498 grad_norm: 5.6475 top1_acc: 0.4583 top5_acc: 0.7500 loss_cls: 1.4394 loss: 1.4394 2022/09/09 16:08:30 - mmengine - INFO - Epoch(train) [31][420/449] lr: 5.1014e-03 eta: 0:33:59 time: 0.4500 data_time: 0.0183 memory: 23498 grad_norm: 5.8339 top1_acc: 0.6667 top5_acc: 0.7500 loss_cls: 1.3705 loss: 1.3705 2022/09/09 16:08:39 - mmengine - INFO - Epoch(train) [31][440/449] lr: 5.0536e-03 eta: 0:33:49 time: 0.4458 data_time: 0.0196 memory: 23498 grad_norm: 5.6076 top1_acc: 0.5417 top5_acc: 0.7083 loss_cls: 1.5874 loss: 1.5874 2022/09/09 16:08:43 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 16:08:43 - mmengine - INFO - Epoch(train) [31][449/449] lr: 5.0322e-03 eta: 0:33:49 time: 0.4312 data_time: 0.0212 memory: 23498 grad_norm: 6.0449 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.7026 loss: 1.7026 2022/09/09 16:08:46 - mmengine - INFO - Epoch(val) [31][20/61] eta: 0:00:06 time: 0.1553 data_time: 0.0362 memory: 2693 2022/09/09 16:08:49 - mmengine - INFO - Epoch(val) [31][40/61] eta: 0:00:02 time: 0.1295 data_time: 0.0126 memory: 2693 2022/09/09 16:08:51 - mmengine - INFO - Epoch(val) [31][60/61] eta: 0:00:00 time: 0.1301 data_time: 0.0131 memory: 2693 2022/09/09 16:08:52 - mmengine - INFO - Epoch(val) [31][61/61] acc/top1: 0.3698 acc/top5: 0.6565 acc/mean1: 0.3271 2022/09/09 16:09:02 - mmengine - INFO - Epoch(train) [32][20/449] lr: 4.9847e-03 eta: 0:33:33 time: 0.4955 data_time: 0.0526 memory: 23498 grad_norm: 5.5692 top1_acc: 0.7083 top5_acc: 0.9583 loss_cls: 1.3876 loss: 1.3876 2022/09/09 16:09:11 - mmengine - INFO - Epoch(train) [32][40/449] lr: 4.9374e-03 eta: 0:33:22 time: 0.4657 data_time: 0.0180 memory: 23498 grad_norm: 5.3382 top1_acc: 0.5000 top5_acc: 0.7917 loss_cls: 1.2719 loss: 1.2719 2022/09/09 16:09:20 - mmengine - INFO - Epoch(train) [32][60/449] lr: 4.8902e-03 eta: 0:33:12 time: 0.4611 data_time: 0.0181 memory: 23498 grad_norm: 5.5267 top1_acc: 0.7917 top5_acc: 1.0000 loss_cls: 1.3976 loss: 1.3976 2022/09/09 16:09:30 - mmengine - INFO - Epoch(train) [32][80/449] lr: 4.8433e-03 eta: 0:33:02 time: 0.4635 data_time: 0.0216 memory: 23498 grad_norm: 5.5130 top1_acc: 0.5833 top5_acc: 0.8750 loss_cls: 1.2569 loss: 1.2569 2022/09/09 16:09:30 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 16:09:39 - mmengine - INFO - Epoch(train) [32][100/449] lr: 4.7966e-03 eta: 0:32:52 time: 0.4738 data_time: 0.0232 memory: 23498 grad_norm: 5.6691 top1_acc: 0.7083 top5_acc: 0.9583 loss_cls: 1.2877 loss: 1.2877 2022/09/09 16:09:48 - mmengine - INFO - Epoch(train) [32][120/449] lr: 4.7501e-03 eta: 0:32:42 time: 0.4685 data_time: 0.0190 memory: 23498 grad_norm: 5.7510 top1_acc: 0.7500 top5_acc: 0.9167 loss_cls: 1.3672 loss: 1.3672 2022/09/09 16:09:58 - mmengine - INFO - Epoch(train) [32][140/449] lr: 4.7037e-03 eta: 0:32:31 time: 0.4576 data_time: 0.0178 memory: 23498 grad_norm: 5.7489 top1_acc: 0.7917 top5_acc: 0.8750 loss_cls: 1.3137 loss: 1.3137 2022/09/09 16:10:07 - mmengine - INFO - Epoch(train) [32][160/449] lr: 4.6576e-03 eta: 0:32:21 time: 0.4649 data_time: 0.0191 memory: 23498 grad_norm: 5.7552 top1_acc: 0.5417 top5_acc: 0.7917 loss_cls: 1.3640 loss: 1.3640 2022/09/09 16:10:17 - mmengine - INFO - Epoch(train) [32][180/449] lr: 4.6117e-03 eta: 0:32:11 time: 0.4797 data_time: 0.0258 memory: 23498 grad_norm: 5.7373 top1_acc: 0.5833 top5_acc: 0.8750 loss_cls: 1.3406 loss: 1.3406 2022/09/09 16:10:26 - mmengine - INFO - Epoch(train) [32][200/449] lr: 4.5659e-03 eta: 0:32:01 time: 0.4747 data_time: 0.0175 memory: 23498 grad_norm: 5.9158 top1_acc: 0.6250 top5_acc: 0.7917 loss_cls: 1.4644 loss: 1.4644 2022/09/09 16:10:35 - mmengine - INFO - Epoch(train) [32][220/449] lr: 4.5204e-03 eta: 0:31:51 time: 0.4589 data_time: 0.0167 memory: 23498 grad_norm: 5.7006 top1_acc: 0.5833 top5_acc: 0.7500 loss_cls: 1.3494 loss: 1.3494 2022/09/09 16:10:44 - mmengine - INFO - Epoch(train) [32][240/449] lr: 4.4750e-03 eta: 0:31:41 time: 0.4655 data_time: 0.0227 memory: 23498 grad_norm: 5.8106 top1_acc: 0.6667 top5_acc: 0.7500 loss_cls: 1.4695 loss: 1.4695 2022/09/09 16:10:54 - mmengine - INFO - Epoch(train) [32][260/449] lr: 4.4299e-03 eta: 0:31:30 time: 0.4715 data_time: 0.0209 memory: 23498 grad_norm: 5.9609 top1_acc: 0.7083 top5_acc: 0.8750 loss_cls: 1.3888 loss: 1.3888 2022/09/09 16:11:03 - mmengine - INFO - Epoch(train) [32][280/449] lr: 4.3850e-03 eta: 0:31:20 time: 0.4703 data_time: 0.0185 memory: 23498 grad_norm: 5.8162 top1_acc: 0.5417 top5_acc: 0.7500 loss_cls: 1.3515 loss: 1.3515 2022/09/09 16:11:12 - mmengine - INFO - Epoch(train) [32][300/449] lr: 4.3402e-03 eta: 0:31:10 time: 0.4573 data_time: 0.0156 memory: 23498 grad_norm: 5.8136 top1_acc: 0.6250 top5_acc: 0.7917 loss_cls: 1.3998 loss: 1.3998 2022/09/09 16:11:22 - mmengine - INFO - Epoch(train) [32][320/449] lr: 4.2957e-03 eta: 0:31:00 time: 0.4592 data_time: 0.0192 memory: 23498 grad_norm: 5.9679 top1_acc: 0.7083 top5_acc: 0.9583 loss_cls: 1.4109 loss: 1.4109 2022/09/09 16:11:31 - mmengine - INFO - Epoch(train) [32][340/449] lr: 4.2514e-03 eta: 0:30:50 time: 0.4715 data_time: 0.0234 memory: 23498 grad_norm: 6.0162 top1_acc: 0.6667 top5_acc: 0.9167 loss_cls: 1.4047 loss: 1.4047 2022/09/09 16:11:40 - mmengine - INFO - Epoch(train) [32][360/449] lr: 4.2072e-03 eta: 0:30:39 time: 0.4695 data_time: 0.0196 memory: 23498 grad_norm: 5.9466 top1_acc: 0.6250 top5_acc: 0.7917 loss_cls: 1.4055 loss: 1.4055 2022/09/09 16:11:50 - mmengine - INFO - Epoch(train) [32][380/449] lr: 4.1633e-03 eta: 0:30:29 time: 0.4672 data_time: 0.0198 memory: 23498 grad_norm: 5.8452 top1_acc: 0.6667 top5_acc: 0.7917 loss_cls: 1.3687 loss: 1.3687 2022/09/09 16:11:59 - mmengine - INFO - Epoch(train) [32][400/449] lr: 4.1196e-03 eta: 0:30:19 time: 0.4631 data_time: 0.0184 memory: 23498 grad_norm: 5.7039 top1_acc: 0.5833 top5_acc: 1.0000 loss_cls: 1.2723 loss: 1.2723 2022/09/09 16:12:08 - mmengine - INFO - Epoch(train) [32][420/449] lr: 4.0760e-03 eta: 0:30:09 time: 0.4700 data_time: 0.0158 memory: 23498 grad_norm: 5.7344 top1_acc: 0.6667 top5_acc: 0.8750 loss_cls: 1.2792 loss: 1.2792 2022/09/09 16:12:18 - mmengine - INFO - Epoch(train) [32][440/449] lr: 4.0327e-03 eta: 0:29:59 time: 0.4671 data_time: 0.0218 memory: 23498 grad_norm: 5.9285 top1_acc: 0.5417 top5_acc: 0.7917 loss_cls: 1.3914 loss: 1.3914 2022/09/09 16:12:22 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 16:12:22 - mmengine - INFO - Epoch(train) [32][449/449] lr: 4.0133e-03 eta: 0:29:59 time: 0.4685 data_time: 0.0143 memory: 23498 grad_norm: 6.4187 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.5411 loss: 1.5411 2022/09/09 16:12:25 - mmengine - INFO - Epoch(val) [32][20/61] eta: 0:00:06 time: 0.1553 data_time: 0.0359 memory: 2693 2022/09/09 16:12:28 - mmengine - INFO - Epoch(val) [32][40/61] eta: 0:00:02 time: 0.1308 data_time: 0.0120 memory: 2693 2022/09/09 16:12:30 - mmengine - INFO - Epoch(val) [32][60/61] eta: 0:00:00 time: 0.1302 data_time: 0.0128 memory: 2693 2022/09/09 16:12:31 - mmengine - INFO - Epoch(val) [32][61/61] acc/top1: 0.3717 acc/top5: 0.6698 acc/mean1: 0.3379 2022/09/09 16:12:42 - mmengine - INFO - Epoch(train) [33][20/449] lr: 3.9703e-03 eta: 0:29:43 time: 0.5202 data_time: 0.0465 memory: 23498 grad_norm: 5.8323 top1_acc: 0.7917 top5_acc: 0.8333 loss_cls: 1.3374 loss: 1.3374 2022/09/09 16:12:51 - mmengine - INFO - Epoch(train) [33][40/449] lr: 3.9275e-03 eta: 0:29:33 time: 0.4648 data_time: 0.0225 memory: 23498 grad_norm: 5.9015 top1_acc: 0.6667 top5_acc: 0.8333 loss_cls: 1.1938 loss: 1.1938 2022/09/09 16:13:00 - mmengine - INFO - Epoch(train) [33][60/449] lr: 3.8849e-03 eta: 0:29:23 time: 0.4691 data_time: 0.0152 memory: 23498 grad_norm: 5.8498 top1_acc: 0.6250 top5_acc: 0.7917 loss_cls: 1.2178 loss: 1.2178 2022/09/09 16:13:10 - mmengine - INFO - Epoch(train) [33][80/449] lr: 3.8425e-03 eta: 0:29:13 time: 0.4713 data_time: 0.0173 memory: 23498 grad_norm: 6.0268 top1_acc: 0.6667 top5_acc: 0.8750 loss_cls: 1.2331 loss: 1.2331 2022/09/09 16:13:19 - mmengine - INFO - Epoch(train) [33][100/449] lr: 3.8003e-03 eta: 0:29:03 time: 0.4772 data_time: 0.0161 memory: 23498 grad_norm: 6.1265 top1_acc: 0.3750 top5_acc: 0.7917 loss_cls: 1.3585 loss: 1.3585 2022/09/09 16:13:29 - mmengine - INFO - Epoch(train) [33][120/449] lr: 3.7583e-03 eta: 0:28:53 time: 0.4776 data_time: 0.0267 memory: 23498 grad_norm: 5.9961 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2522 loss: 1.2522 2022/09/09 16:13:38 - mmengine - INFO - Epoch(train) [33][140/449] lr: 3.7165e-03 eta: 0:28:43 time: 0.4745 data_time: 0.0227 memory: 23498 grad_norm: 6.1555 top1_acc: 0.7083 top5_acc: 1.0000 loss_cls: 1.2604 loss: 1.2604 2022/09/09 16:13:48 - mmengine - INFO - Epoch(train) [33][160/449] lr: 3.6750e-03 eta: 0:28:32 time: 0.4704 data_time: 0.0191 memory: 23498 grad_norm: 6.1903 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 1.4446 loss: 1.4446 2022/09/09 16:13:57 - mmengine - INFO - Epoch(train) [33][180/449] lr: 3.6336e-03 eta: 0:28:22 time: 0.4671 data_time: 0.0153 memory: 23498 grad_norm: 6.0305 top1_acc: 0.6667 top5_acc: 0.8333 loss_cls: 1.2653 loss: 1.2653 2022/09/09 16:14:06 - mmengine - INFO - Epoch(train) [33][200/449] lr: 3.5925e-03 eta: 0:28:12 time: 0.4698 data_time: 0.0254 memory: 23498 grad_norm: 6.0702 top1_acc: 0.5417 top5_acc: 0.7083 loss_cls: 1.3886 loss: 1.3886 2022/09/09 16:14:16 - mmengine - INFO - Epoch(train) [33][220/449] lr: 3.5515e-03 eta: 0:28:02 time: 0.4635 data_time: 0.0176 memory: 23498 grad_norm: 5.9820 top1_acc: 0.6667 top5_acc: 0.8333 loss_cls: 1.3091 loss: 1.3091 2022/09/09 16:14:25 - mmengine - INFO - Epoch(train) [33][240/449] lr: 3.5108e-03 eta: 0:27:52 time: 0.4665 data_time: 0.0189 memory: 23498 grad_norm: 5.8810 top1_acc: 0.8333 top5_acc: 0.9583 loss_cls: 1.3733 loss: 1.3733 2022/09/09 16:14:34 - mmengine - INFO - Epoch(train) [33][260/449] lr: 3.4703e-03 eta: 0:27:42 time: 0.4671 data_time: 0.0182 memory: 23498 grad_norm: 5.9678 top1_acc: 0.7083 top5_acc: 0.9583 loss_cls: 1.3080 loss: 1.3080 2022/09/09 16:14:44 - mmengine - INFO - Epoch(train) [33][280/449] lr: 3.4300e-03 eta: 0:27:32 time: 0.4742 data_time: 0.0259 memory: 23498 grad_norm: 5.9184 top1_acc: 0.5417 top5_acc: 0.8750 loss_cls: 1.3462 loss: 1.3462 2022/09/09 16:14:53 - mmengine - INFO - Epoch(train) [33][300/449] lr: 3.3899e-03 eta: 0:27:22 time: 0.4799 data_time: 0.0150 memory: 23498 grad_norm: 5.8914 top1_acc: 0.6667 top5_acc: 0.9583 loss_cls: 1.0836 loss: 1.0836 2022/09/09 16:15:03 - mmengine - INFO - Epoch(train) [33][320/449] lr: 3.3501e-03 eta: 0:27:12 time: 0.4759 data_time: 0.0185 memory: 23498 grad_norm: 5.7985 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2843 loss: 1.2843 2022/09/09 16:15:12 - mmengine - INFO - Epoch(train) [33][340/449] lr: 3.3104e-03 eta: 0:27:02 time: 0.4707 data_time: 0.0177 memory: 23498 grad_norm: 5.9956 top1_acc: 0.4167 top5_acc: 0.7083 loss_cls: 1.3091 loss: 1.3091 2022/09/09 16:15:22 - mmengine - INFO - Epoch(train) [33][360/449] lr: 3.2710e-03 eta: 0:26:51 time: 0.4666 data_time: 0.0252 memory: 23498 grad_norm: 6.0554 top1_acc: 0.6667 top5_acc: 0.8750 loss_cls: 1.4853 loss: 1.4853 2022/09/09 16:15:31 - mmengine - INFO - Epoch(train) [33][380/449] lr: 3.2318e-03 eta: 0:26:41 time: 0.4678 data_time: 0.0163 memory: 23498 grad_norm: 5.9503 top1_acc: 0.5417 top5_acc: 0.8333 loss_cls: 1.3882 loss: 1.3882 2022/09/09 16:15:40 - mmengine - INFO - Epoch(train) [33][400/449] lr: 3.1928e-03 eta: 0:26:31 time: 0.4721 data_time: 0.0230 memory: 23498 grad_norm: 6.0357 top1_acc: 0.8750 top5_acc: 0.9583 loss_cls: 1.2699 loss: 1.2699 2022/09/09 16:15:50 - mmengine - INFO - Epoch(train) [33][420/449] lr: 3.1540e-03 eta: 0:26:21 time: 0.4762 data_time: 0.0139 memory: 23498 grad_norm: 6.0233 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2634 loss: 1.2634 2022/09/09 16:15:59 - mmengine - INFO - Epoch(train) [33][440/449] lr: 3.1154e-03 eta: 0:26:11 time: 0.4553 data_time: 0.0174 memory: 23498 grad_norm: 5.8473 top1_acc: 0.7083 top5_acc: 0.8750 loss_cls: 1.1905 loss: 1.1905 2022/09/09 16:16:03 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 16:16:03 - mmengine - INFO - Epoch(train) [33][449/449] lr: 3.0981e-03 eta: 0:26:11 time: 0.4369 data_time: 0.0151 memory: 23498 grad_norm: 6.3384 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.4655 loss: 1.4655 2022/09/09 16:16:06 - mmengine - INFO - Epoch(val) [33][20/61] eta: 0:00:06 time: 0.1532 data_time: 0.0337 memory: 2693 2022/09/09 16:16:09 - mmengine - INFO - Epoch(val) [33][40/61] eta: 0:00:02 time: 0.1325 data_time: 0.0134 memory: 2693 2022/09/09 16:16:11 - mmengine - INFO - Epoch(val) [33][60/61] eta: 0:00:00 time: 0.1303 data_time: 0.0133 memory: 2693 2022/09/09 16:16:12 - mmengine - INFO - Epoch(val) [33][61/61] acc/top1: 0.3879 acc/top5: 0.6722 acc/mean1: 0.3520 2022/09/09 16:16:12 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_30.pth is removed 2022/09/09 16:16:13 - mmengine - INFO - The best checkpoint with 0.3879 acc/top1 at 33 epoch is saved to best_acc/top1_epoch_33.pth. 2022/09/09 16:16:23 - mmengine - INFO - Epoch(train) [34][20/449] lr: 3.0599e-03 eta: 0:25:56 time: 0.5043 data_time: 0.0458 memory: 23498 grad_norm: 6.0042 top1_acc: 0.7083 top5_acc: 0.8750 loss_cls: 1.3379 loss: 1.3379 2022/09/09 16:16:32 - mmengine - INFO - Epoch(train) [34][40/449] lr: 3.0218e-03 eta: 0:25:45 time: 0.4588 data_time: 0.0216 memory: 23498 grad_norm: 5.9036 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1442 loss: 1.1442 2022/09/09 16:16:41 - mmengine - INFO - Epoch(train) [34][60/449] lr: 2.9840e-03 eta: 0:25:35 time: 0.4606 data_time: 0.0152 memory: 23498 grad_norm: 6.0239 top1_acc: 0.6667 top5_acc: 0.9583 loss_cls: 1.2529 loss: 1.2529 2022/09/09 16:16:51 - mmengine - INFO - Epoch(train) [34][80/449] lr: 2.9464e-03 eta: 0:25:25 time: 0.4627 data_time: 0.0188 memory: 23498 grad_norm: 5.9806 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3415 loss: 1.3415 2022/09/09 16:17:00 - mmengine - INFO - Epoch(train) [34][100/449] lr: 2.9090e-03 eta: 0:25:15 time: 0.4961 data_time: 0.0156 memory: 23498 grad_norm: 5.8765 top1_acc: 0.8750 top5_acc: 0.9167 loss_cls: 1.2941 loss: 1.2941 2022/09/09 16:17:10 - mmengine - INFO - Epoch(train) [34][120/449] lr: 2.8719e-03 eta: 0:25:05 time: 0.4653 data_time: 0.0232 memory: 23498 grad_norm: 5.9942 top1_acc: 0.7917 top5_acc: 1.0000 loss_cls: 1.2033 loss: 1.2033 2022/09/09 16:17:19 - mmengine - INFO - Epoch(train) [34][140/449] lr: 2.8349e-03 eta: 0:24:55 time: 0.4635 data_time: 0.0214 memory: 23498 grad_norm: 5.9298 top1_acc: 0.6667 top5_acc: 0.9167 loss_cls: 1.2904 loss: 1.2904 2022/09/09 16:17:28 - mmengine - INFO - Epoch(train) [34][160/449] lr: 2.7982e-03 eta: 0:24:45 time: 0.4682 data_time: 0.0171 memory: 23498 grad_norm: 6.1406 top1_acc: 0.7917 top5_acc: 0.9167 loss_cls: 1.2033 loss: 1.2033 2022/09/09 16:17:38 - mmengine - INFO - Epoch(train) [34][180/449] lr: 2.7617e-03 eta: 0:24:35 time: 0.4678 data_time: 0.0213 memory: 23498 grad_norm: 5.9874 top1_acc: 0.5833 top5_acc: 0.7500 loss_cls: 1.1630 loss: 1.1630 2022/09/09 16:17:39 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 16:17:47 - mmengine - INFO - Epoch(train) [34][200/449] lr: 2.7254e-03 eta: 0:24:25 time: 0.4735 data_time: 0.0248 memory: 23498 grad_norm: 6.1773 top1_acc: 0.7500 top5_acc: 0.8333 loss_cls: 1.2295 loss: 1.2295 2022/09/09 16:17:56 - mmengine - INFO - Epoch(train) [34][220/449] lr: 2.6894e-03 eta: 0:24:15 time: 0.4611 data_time: 0.0217 memory: 23498 grad_norm: 6.0358 top1_acc: 0.6667 top5_acc: 0.9167 loss_cls: 1.1631 loss: 1.1631 2022/09/09 16:18:06 - mmengine - INFO - Epoch(train) [34][240/449] lr: 2.6535e-03 eta: 0:24:05 time: 0.4712 data_time: 0.0179 memory: 23498 grad_norm: 6.1357 top1_acc: 0.6667 top5_acc: 0.8750 loss_cls: 1.3407 loss: 1.3407 2022/09/09 16:18:15 - mmengine - INFO - Epoch(train) [34][260/449] lr: 2.6179e-03 eta: 0:23:55 time: 0.4635 data_time: 0.0209 memory: 23498 grad_norm: 6.3128 top1_acc: 0.5833 top5_acc: 0.9583 loss_cls: 1.3282 loss: 1.3282 2022/09/09 16:18:24 - mmengine - INFO - Epoch(train) [34][280/449] lr: 2.5825e-03 eta: 0:23:45 time: 0.4633 data_time: 0.0219 memory: 23498 grad_norm: 6.4006 top1_acc: 0.7917 top5_acc: 0.9583 loss_cls: 1.2629 loss: 1.2629 2022/09/09 16:18:34 - mmengine - INFO - Epoch(train) [34][300/449] lr: 2.5474e-03 eta: 0:23:34 time: 0.4596 data_time: 0.0167 memory: 23498 grad_norm: 6.3672 top1_acc: 0.5417 top5_acc: 0.9583 loss_cls: 1.1958 loss: 1.1958 2022/09/09 16:18:43 - mmengine - INFO - Epoch(train) [34][320/449] lr: 2.5124e-03 eta: 0:23:24 time: 0.4562 data_time: 0.0177 memory: 23498 grad_norm: 6.0842 top1_acc: 0.5833 top5_acc: 1.0000 loss_cls: 1.1373 loss: 1.1373 2022/09/09 16:18:52 - mmengine - INFO - Epoch(train) [34][340/449] lr: 2.4777e-03 eta: 0:23:14 time: 0.4554 data_time: 0.0202 memory: 23498 grad_norm: 6.1183 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3310 loss: 1.3310 2022/09/09 16:19:01 - mmengine - INFO - Epoch(train) [34][360/449] lr: 2.4433e-03 eta: 0:23:04 time: 0.4605 data_time: 0.0220 memory: 23498 grad_norm: 6.1114 top1_acc: 0.6250 top5_acc: 0.9167 loss_cls: 1.1837 loss: 1.1837 2022/09/09 16:19:10 - mmengine - INFO - Epoch(train) [34][380/449] lr: 2.4090e-03 eta: 0:22:54 time: 0.4538 data_time: 0.0176 memory: 23498 grad_norm: 6.0811 top1_acc: 0.7500 top5_acc: 0.9167 loss_cls: 1.1144 loss: 1.1144 2022/09/09 16:19:19 - mmengine - INFO - Epoch(train) [34][400/449] lr: 2.3750e-03 eta: 0:22:44 time: 0.4596 data_time: 0.0170 memory: 23498 grad_norm: 6.1474 top1_acc: 0.5833 top5_acc: 0.8750 loss_cls: 1.2184 loss: 1.2184 2022/09/09 16:19:29 - mmengine - INFO - Epoch(train) [34][420/449] lr: 2.3412e-03 eta: 0:22:34 time: 0.4618 data_time: 0.0208 memory: 23498 grad_norm: 5.9103 top1_acc: 0.6250 top5_acc: 0.7917 loss_cls: 1.2172 loss: 1.2172 2022/09/09 16:19:38 - mmengine - INFO - Epoch(train) [34][440/449] lr: 2.3076e-03 eta: 0:22:24 time: 0.4565 data_time: 0.0184 memory: 23498 grad_norm: 6.1341 top1_acc: 0.7917 top5_acc: 0.8750 loss_cls: 1.2028 loss: 1.2028 2022/09/09 16:19:41 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 16:19:41 - mmengine - INFO - Epoch(train) [34][449/449] lr: 2.2925e-03 eta: 0:22:24 time: 0.4360 data_time: 0.0134 memory: 23498 grad_norm: 6.8239 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.3562 loss: 1.3562 2022/09/09 16:19:45 - mmengine - INFO - Epoch(val) [34][20/61] eta: 0:00:06 time: 0.1556 data_time: 0.0357 memory: 2693 2022/09/09 16:19:47 - mmengine - INFO - Epoch(val) [34][40/61] eta: 0:00:02 time: 0.1302 data_time: 0.0111 memory: 2693 2022/09/09 16:19:50 - mmengine - INFO - Epoch(val) [34][60/61] eta: 0:00:00 time: 0.1303 data_time: 0.0127 memory: 2693 2022/09/09 16:19:50 - mmengine - INFO - Epoch(val) [34][61/61] acc/top1: 0.3913 acc/top5: 0.6874 acc/mean1: 0.3532 2022/09/09 16:19:50 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_33.pth is removed 2022/09/09 16:19:51 - mmengine - INFO - The best checkpoint with 0.3913 acc/top1 at 34 epoch is saved to best_acc/top1_epoch_34.pth. 2022/09/09 16:20:01 - mmengine - INFO - Epoch(train) [35][20/449] lr: 2.2593e-03 eta: 0:22:08 time: 0.4828 data_time: 0.0320 memory: 23498 grad_norm: 5.9606 top1_acc: 0.7083 top5_acc: 0.9167 loss_cls: 1.1051 loss: 1.1051 2022/09/09 16:20:10 - mmengine - INFO - Epoch(train) [35][40/449] lr: 2.2263e-03 eta: 0:21:58 time: 0.4584 data_time: 0.0160 memory: 23498 grad_norm: 5.9638 top1_acc: 0.7500 top5_acc: 0.9167 loss_cls: 1.1477 loss: 1.1477 2022/09/09 16:20:20 - mmengine - INFO - Epoch(train) [35][60/449] lr: 2.1935e-03 eta: 0:21:48 time: 0.4678 data_time: 0.0226 memory: 23498 grad_norm: 6.1054 top1_acc: 0.7500 top5_acc: 0.8333 loss_cls: 1.2319 loss: 1.2319 2022/09/09 16:20:29 - mmengine - INFO - Epoch(train) [35][80/449] lr: 2.1609e-03 eta: 0:21:38 time: 0.4796 data_time: 0.0168 memory: 23498 grad_norm: 5.9892 top1_acc: 0.8333 top5_acc: 0.9583 loss_cls: 0.9890 loss: 0.9890 2022/09/09 16:20:39 - mmengine - INFO - Epoch(train) [35][100/449] lr: 2.1286e-03 eta: 0:21:28 time: 0.4745 data_time: 0.0161 memory: 23498 grad_norm: 6.1207 top1_acc: 0.6250 top5_acc: 0.9167 loss_cls: 1.0769 loss: 1.0769 2022/09/09 16:20:48 - mmengine - INFO - Epoch(train) [35][120/449] lr: 2.0965e-03 eta: 0:21:18 time: 0.4689 data_time: 0.0172 memory: 23498 grad_norm: 6.0278 top1_acc: 0.7917 top5_acc: 1.0000 loss_cls: 1.1571 loss: 1.1571 2022/09/09 16:20:58 - mmengine - INFO - Epoch(train) [35][140/449] lr: 2.0646e-03 eta: 0:21:08 time: 0.4781 data_time: 0.0260 memory: 23498 grad_norm: 6.1896 top1_acc: 0.7083 top5_acc: 0.9583 loss_cls: 1.2250 loss: 1.2250 2022/09/09 16:21:07 - mmengine - INFO - Epoch(train) [35][160/449] lr: 2.0330e-03 eta: 0:20:58 time: 0.4729 data_time: 0.0173 memory: 23498 grad_norm: 6.1903 top1_acc: 0.6667 top5_acc: 0.7917 loss_cls: 1.2046 loss: 1.2046 2022/09/09 16:21:17 - mmengine - INFO - Epoch(train) [35][180/449] lr: 2.0016e-03 eta: 0:20:48 time: 0.4833 data_time: 0.0222 memory: 23498 grad_norm: 6.4041 top1_acc: 0.5833 top5_acc: 0.8750 loss_cls: 1.2630 loss: 1.2630 2022/09/09 16:21:26 - mmengine - INFO - Epoch(train) [35][200/449] lr: 1.9704e-03 eta: 0:20:38 time: 0.4649 data_time: 0.0163 memory: 23498 grad_norm: 6.3660 top1_acc: 0.7083 top5_acc: 1.0000 loss_cls: 1.1994 loss: 1.1994 2022/09/09 16:21:36 - mmengine - INFO - Epoch(train) [35][220/449] lr: 1.9395e-03 eta: 0:20:28 time: 0.4755 data_time: 0.0231 memory: 23498 grad_norm: 6.2162 top1_acc: 0.6250 top5_acc: 0.7917 loss_cls: 1.1595 loss: 1.1595 2022/09/09 16:21:45 - mmengine - INFO - Epoch(train) [35][240/449] lr: 1.9087e-03 eta: 0:20:18 time: 0.4726 data_time: 0.0166 memory: 23498 grad_norm: 6.1916 top1_acc: 0.7917 top5_acc: 0.9583 loss_cls: 1.1393 loss: 1.1393 2022/09/09 16:21:55 - mmengine - INFO - Epoch(train) [35][260/449] lr: 1.8783e-03 eta: 0:20:08 time: 0.4798 data_time: 0.0151 memory: 23498 grad_norm: 6.3469 top1_acc: 0.6667 top5_acc: 0.8750 loss_cls: 1.1313 loss: 1.1313 2022/09/09 16:22:04 - mmengine - INFO - Epoch(train) [35][280/449] lr: 1.8480e-03 eta: 0:19:58 time: 0.4665 data_time: 0.0163 memory: 23498 grad_norm: 6.3758 top1_acc: 0.7500 top5_acc: 0.9583 loss_cls: 1.1051 loss: 1.1051 2022/09/09 16:22:13 - mmengine - INFO - Epoch(train) [35][300/449] lr: 1.8180e-03 eta: 0:19:48 time: 0.4754 data_time: 0.0291 memory: 23498 grad_norm: 6.3232 top1_acc: 0.7500 top5_acc: 0.8333 loss_cls: 1.1380 loss: 1.1380 2022/09/09 16:22:23 - mmengine - INFO - Epoch(train) [35][320/449] lr: 1.7882e-03 eta: 0:19:38 time: 0.4716 data_time: 0.0175 memory: 23498 grad_norm: 6.1135 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1393 loss: 1.1393 2022/09/09 16:22:32 - mmengine - INFO - Epoch(train) [35][340/449] lr: 1.7587e-03 eta: 0:19:28 time: 0.4757 data_time: 0.0153 memory: 23498 grad_norm: 6.4564 top1_acc: 0.6667 top5_acc: 0.9167 loss_cls: 1.1751 loss: 1.1751 2022/09/09 16:22:42 - mmengine - INFO - Epoch(train) [35][360/449] lr: 1.7294e-03 eta: 0:19:18 time: 0.4688 data_time: 0.0165 memory: 23498 grad_norm: 6.3255 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1932 loss: 1.1932 2022/09/09 16:22:51 - mmengine - INFO - Epoch(train) [35][380/449] lr: 1.7003e-03 eta: 0:19:08 time: 0.4758 data_time: 0.0237 memory: 23498 grad_norm: 6.3789 top1_acc: 0.7083 top5_acc: 0.8750 loss_cls: 1.1292 loss: 1.1292 2022/09/09 16:23:01 - mmengine - INFO - Epoch(train) [35][400/449] lr: 1.6715e-03 eta: 0:18:58 time: 0.4749 data_time: 0.0175 memory: 23498 grad_norm: 6.1727 top1_acc: 0.7500 top5_acc: 0.9167 loss_cls: 1.0840 loss: 1.0840 2022/09/09 16:23:10 - mmengine - INFO - Epoch(train) [35][420/449] lr: 1.6429e-03 eta: 0:18:48 time: 0.4739 data_time: 0.0157 memory: 23498 grad_norm: 6.4516 top1_acc: 0.6250 top5_acc: 0.8333 loss_cls: 1.2043 loss: 1.2043 2022/09/09 16:23:20 - mmengine - INFO - Epoch(train) [35][440/449] lr: 1.6145e-03 eta: 0:18:38 time: 0.4722 data_time: 0.0176 memory: 23498 grad_norm: 6.1312 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 1.2217 loss: 1.2217 2022/09/09 16:23:24 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 16:23:24 - mmengine - INFO - Epoch(train) [35][449/449] lr: 1.6018e-03 eta: 0:18:38 time: 0.4518 data_time: 0.0118 memory: 23498 grad_norm: 6.5905 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.3363 loss: 1.3363 2022/09/09 16:23:27 - mmengine - INFO - Epoch(val) [35][20/61] eta: 0:00:06 time: 0.1654 data_time: 0.0469 memory: 2693 2022/09/09 16:23:30 - mmengine - INFO - Epoch(val) [35][40/61] eta: 0:00:02 time: 0.1315 data_time: 0.0141 memory: 2693 2022/09/09 16:23:32 - mmengine - INFO - Epoch(val) [35][60/61] eta: 0:00:00 time: 0.1303 data_time: 0.0132 memory: 2693 2022/09/09 16:23:33 - mmengine - INFO - Epoch(val) [35][61/61] acc/top1: 0.3917 acc/top5: 0.6841 acc/mean1: 0.3511 2022/09/09 16:23:33 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_34.pth is removed 2022/09/09 16:23:35 - mmengine - INFO - The best checkpoint with 0.3917 acc/top1 at 35 epoch is saved to best_acc/top1_epoch_35.pth. 2022/09/09 16:23:44 - mmengine - INFO - Epoch(train) [36][20/449] lr: 1.5738e-03 eta: 0:18:23 time: 0.4637 data_time: 0.0343 memory: 23498 grad_norm: 6.2317 top1_acc: 0.5833 top5_acc: 0.8750 loss_cls: 1.1362 loss: 1.1362 2022/09/09 16:23:54 - mmengine - INFO - Epoch(train) [36][40/449] lr: 1.5460e-03 eta: 0:18:13 time: 0.4649 data_time: 0.0226 memory: 23498 grad_norm: 6.1652 top1_acc: 0.9167 top5_acc: 0.9583 loss_cls: 1.0747 loss: 1.0747 2022/09/09 16:24:03 - mmengine - INFO - Epoch(train) [36][60/449] lr: 1.5185e-03 eta: 0:18:03 time: 0.4533 data_time: 0.0226 memory: 23498 grad_norm: 6.4191 top1_acc: 0.7500 top5_acc: 0.9167 loss_cls: 1.1945 loss: 1.1945 2022/09/09 16:24:12 - mmengine - INFO - Epoch(train) [36][80/449] lr: 1.4912e-03 eta: 0:17:53 time: 0.4556 data_time: 0.0243 memory: 23498 grad_norm: 6.1419 top1_acc: 0.8333 top5_acc: 1.0000 loss_cls: 1.0827 loss: 1.0827 2022/09/09 16:24:21 - mmengine - INFO - Epoch(train) [36][100/449] lr: 1.4641e-03 eta: 0:17:43 time: 0.4636 data_time: 0.0168 memory: 23498 grad_norm: 6.4585 top1_acc: 0.6667 top5_acc: 0.7917 loss_cls: 1.1726 loss: 1.1726 2022/09/09 16:24:30 - mmengine - INFO - Epoch(train) [36][120/449] lr: 1.4373e-03 eta: 0:17:33 time: 0.4492 data_time: 0.0186 memory: 23498 grad_norm: 6.1230 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1767 loss: 1.1767 2022/09/09 16:24:39 - mmengine - INFO - Epoch(train) [36][140/449] lr: 1.4107e-03 eta: 0:17:23 time: 0.4561 data_time: 0.0217 memory: 23498 grad_norm: 6.3607 top1_acc: 0.7917 top5_acc: 0.9583 loss_cls: 1.0430 loss: 1.0430 2022/09/09 16:24:48 - mmengine - INFO - Epoch(train) [36][160/449] lr: 1.3843e-03 eta: 0:17:13 time: 0.4499 data_time: 0.0175 memory: 23498 grad_norm: 6.3016 top1_acc: 0.7083 top5_acc: 0.8333 loss_cls: 1.2326 loss: 1.2326 2022/09/09 16:24:58 - mmengine - INFO - Epoch(train) [36][180/449] lr: 1.3582e-03 eta: 0:17:03 time: 0.4676 data_time: 0.0171 memory: 23498 grad_norm: 6.4449 top1_acc: 0.6667 top5_acc: 0.7500 loss_cls: 1.1500 loss: 1.1500 2022/09/09 16:25:07 - mmengine - INFO - Epoch(train) [36][200/449] lr: 1.3323e-03 eta: 0:16:53 time: 0.4586 data_time: 0.0176 memory: 23498 grad_norm: 6.3667 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0762 loss: 1.0762 2022/09/09 16:25:17 - mmengine - INFO - Epoch(train) [36][220/449] lr: 1.3067e-03 eta: 0:16:43 time: 0.4909 data_time: 0.0241 memory: 23498 grad_norm: 6.4211 top1_acc: 0.7917 top5_acc: 0.9583 loss_cls: 1.1488 loss: 1.1488 2022/09/09 16:25:26 - mmengine - INFO - Epoch(train) [36][240/449] lr: 1.2813e-03 eta: 0:16:33 time: 0.4614 data_time: 0.0191 memory: 23498 grad_norm: 6.4773 top1_acc: 0.7083 top5_acc: 0.9167 loss_cls: 1.2279 loss: 1.2279 2022/09/09 16:25:35 - mmengine - INFO - Epoch(train) [36][260/449] lr: 1.2561e-03 eta: 0:16:23 time: 0.4578 data_time: 0.0166 memory: 23498 grad_norm: 6.5200 top1_acc: 0.7083 top5_acc: 0.9167 loss_cls: 1.1200 loss: 1.1200 2022/09/09 16:25:45 - mmengine - INFO - Epoch(train) [36][280/449] lr: 1.2312e-03 eta: 0:16:13 time: 0.4816 data_time: 0.0180 memory: 23498 grad_norm: 6.4800 top1_acc: 0.5833 top5_acc: 0.9167 loss_cls: 1.0525 loss: 1.0525 2022/09/09 16:25:47 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 16:25:54 - mmengine - INFO - Epoch(train) [36][300/449] lr: 1.2066e-03 eta: 0:16:03 time: 0.4671 data_time: 0.0301 memory: 23498 grad_norm: 6.3893 top1_acc: 0.7500 top5_acc: 0.9167 loss_cls: 1.0597 loss: 1.0597 2022/09/09 16:26:03 - mmengine - INFO - Epoch(train) [36][320/449] lr: 1.1821e-03 eta: 0:15:53 time: 0.4695 data_time: 0.0176 memory: 23498 grad_norm: 6.3967 top1_acc: 0.7917 top5_acc: 0.8750 loss_cls: 1.0672 loss: 1.0672 2022/09/09 16:26:13 - mmengine - INFO - Epoch(train) [36][340/449] lr: 1.1579e-03 eta: 0:15:43 time: 0.4616 data_time: 0.0176 memory: 23498 grad_norm: 6.4467 top1_acc: 0.6667 top5_acc: 0.8333 loss_cls: 1.1171 loss: 1.1171 2022/09/09 16:26:22 - mmengine - INFO - Epoch(train) [36][360/449] lr: 1.1340e-03 eta: 0:15:33 time: 0.4636 data_time: 0.0175 memory: 23498 grad_norm: 6.2929 top1_acc: 0.6667 top5_acc: 0.8750 loss_cls: 1.1602 loss: 1.1602 2022/09/09 16:26:31 - mmengine - INFO - Epoch(train) [36][380/449] lr: 1.1103e-03 eta: 0:15:23 time: 0.4690 data_time: 0.0246 memory: 23498 grad_norm: 6.4686 top1_acc: 0.7083 top5_acc: 0.8750 loss_cls: 1.1146 loss: 1.1146 2022/09/09 16:26:40 - mmengine - INFO - Epoch(train) [36][400/449] lr: 1.0868e-03 eta: 0:15:13 time: 0.4602 data_time: 0.0242 memory: 23498 grad_norm: 6.3934 top1_acc: 0.8333 top5_acc: 0.8750 loss_cls: 1.1521 loss: 1.1521 2022/09/09 16:26:50 - mmengine - INFO - Epoch(train) [36][420/449] lr: 1.0636e-03 eta: 0:15:03 time: 0.4763 data_time: 0.0182 memory: 23498 grad_norm: 6.5301 top1_acc: 0.7083 top5_acc: 1.0000 loss_cls: 1.1041 loss: 1.1041 2022/09/09 16:26:59 - mmengine - INFO - Epoch(train) [36][440/449] lr: 1.0407e-03 eta: 0:14:53 time: 0.4588 data_time: 0.0252 memory: 23498 grad_norm: 6.5535 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2184 loss: 1.2184 2022/09/09 16:27:03 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 16:27:03 - mmengine - INFO - Epoch(train) [36][449/449] lr: 1.0304e-03 eta: 0:14:53 time: 0.4321 data_time: 0.0148 memory: 23498 grad_norm: 7.0167 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.2498 loss: 1.2498 2022/09/09 16:27:06 - mmengine - INFO - Epoch(val) [36][20/61] eta: 0:00:06 time: 0.1572 data_time: 0.0369 memory: 2693 2022/09/09 16:27:09 - mmengine - INFO - Epoch(val) [36][40/61] eta: 0:00:02 time: 0.1302 data_time: 0.0121 memory: 2693 2022/09/09 16:27:11 - mmengine - INFO - Epoch(val) [36][60/61] eta: 0:00:00 time: 0.1303 data_time: 0.0131 memory: 2693 2022/09/09 16:27:12 - mmengine - INFO - Epoch(val) [36][61/61] acc/top1: 0.3919 acc/top5: 0.6873 acc/mean1: 0.3528 2022/09/09 16:27:12 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_35.pth is removed 2022/09/09 16:27:14 - mmengine - INFO - The best checkpoint with 0.3919 acc/top1 at 36 epoch is saved to best_acc/top1_epoch_36.pth. 2022/09/09 16:27:23 - mmengine - INFO - Epoch(train) [37][20/449] lr: 1.0078e-03 eta: 0:14:38 time: 0.4687 data_time: 0.0321 memory: 23498 grad_norm: 6.3177 top1_acc: 0.6667 top5_acc: 0.8750 loss_cls: 1.0648 loss: 1.0648 2022/09/09 16:27:32 - mmengine - INFO - Epoch(train) [37][40/449] lr: 9.8542e-04 eta: 0:14:28 time: 0.4519 data_time: 0.0183 memory: 23498 grad_norm: 6.5325 top1_acc: 0.7083 top5_acc: 0.9167 loss_cls: 1.1936 loss: 1.1936 2022/09/09 16:27:42 - mmengine - INFO - Epoch(train) [37][60/449] lr: 9.6330e-04 eta: 0:14:18 time: 0.4697 data_time: 0.0182 memory: 23498 grad_norm: 6.4216 top1_acc: 0.7500 top5_acc: 0.9583 loss_cls: 1.1492 loss: 1.1492 2022/09/09 16:27:51 - mmengine - INFO - Epoch(train) [37][80/449] lr: 9.4142e-04 eta: 0:14:09 time: 0.4643 data_time: 0.0239 memory: 23498 grad_norm: 6.3422 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 1.0653 loss: 1.0653 2022/09/09 16:28:00 - mmengine - INFO - Epoch(train) [37][100/449] lr: 9.1978e-04 eta: 0:13:59 time: 0.4709 data_time: 0.0191 memory: 23498 grad_norm: 6.5509 top1_acc: 0.7917 top5_acc: 0.9167 loss_cls: 1.0297 loss: 1.0297 2022/09/09 16:28:09 - mmengine - INFO - Epoch(train) [37][120/449] lr: 8.9839e-04 eta: 0:13:49 time: 0.4592 data_time: 0.0218 memory: 23498 grad_norm: 6.5728 top1_acc: 0.7083 top5_acc: 0.9583 loss_cls: 1.1049 loss: 1.1049 2022/09/09 16:28:19 - mmengine - INFO - Epoch(train) [37][140/449] lr: 8.7725e-04 eta: 0:13:39 time: 0.4673 data_time: 0.0182 memory: 23498 grad_norm: 6.6830 top1_acc: 0.7917 top5_acc: 0.9583 loss_cls: 1.0361 loss: 1.0361 2022/09/09 16:28:28 - mmengine - INFO - Epoch(train) [37][160/449] lr: 8.5635e-04 eta: 0:13:29 time: 0.4716 data_time: 0.0240 memory: 23498 grad_norm: 6.5624 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0235 loss: 1.0235 2022/09/09 16:28:38 - mmengine - INFO - Epoch(train) [37][180/449] lr: 8.3570e-04 eta: 0:13:19 time: 0.4732 data_time: 0.0191 memory: 23498 grad_norm: 6.6583 top1_acc: 0.7083 top5_acc: 0.8750 loss_cls: 1.2102 loss: 1.2102 2022/09/09 16:28:47 - mmengine - INFO - Epoch(train) [37][200/449] lr: 8.1529e-04 eta: 0:13:09 time: 0.4598 data_time: 0.0175 memory: 23498 grad_norm: 6.6481 top1_acc: 0.7083 top5_acc: 0.8750 loss_cls: 1.0554 loss: 1.0554 2022/09/09 16:28:56 - mmengine - INFO - Epoch(train) [37][220/449] lr: 7.9514e-04 eta: 0:12:59 time: 0.4711 data_time: 0.0175 memory: 23498 grad_norm: 6.5377 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1742 loss: 1.1742 2022/09/09 16:29:06 - mmengine - INFO - Epoch(train) [37][240/449] lr: 7.7523e-04 eta: 0:12:49 time: 0.4767 data_time: 0.0309 memory: 23498 grad_norm: 6.3981 top1_acc: 0.7500 top5_acc: 0.9167 loss_cls: 1.1893 loss: 1.1893 2022/09/09 16:29:15 - mmengine - INFO - Epoch(train) [37][260/449] lr: 7.5557e-04 eta: 0:12:39 time: 0.4658 data_time: 0.0193 memory: 23498 grad_norm: 6.3778 top1_acc: 0.7083 top5_acc: 0.8750 loss_cls: 1.1641 loss: 1.1641 2022/09/09 16:29:25 - mmengine - INFO - Epoch(train) [37][280/449] lr: 7.3615e-04 eta: 0:12:29 time: 0.4752 data_time: 0.0185 memory: 23498 grad_norm: 6.5638 top1_acc: 0.7083 top5_acc: 0.9583 loss_cls: 1.1857 loss: 1.1857 2022/09/09 16:29:34 - mmengine - INFO - Epoch(train) [37][300/449] lr: 7.1698e-04 eta: 0:12:19 time: 0.4633 data_time: 0.0168 memory: 23498 grad_norm: 6.6970 top1_acc: 0.7917 top5_acc: 0.8750 loss_cls: 1.0374 loss: 1.0374 2022/09/09 16:29:43 - mmengine - INFO - Epoch(train) [37][320/449] lr: 6.9807e-04 eta: 0:12:09 time: 0.4578 data_time: 0.0241 memory: 23498 grad_norm: 6.4847 top1_acc: 0.9167 top5_acc: 1.0000 loss_cls: 1.0249 loss: 1.0249 2022/09/09 16:29:52 - mmengine - INFO - Epoch(train) [37][340/449] lr: 6.7940e-04 eta: 0:11:59 time: 0.4635 data_time: 0.0181 memory: 23498 grad_norm: 6.4899 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0113 loss: 1.0113 2022/09/09 16:30:02 - mmengine - INFO - Epoch(train) [37][360/449] lr: 6.6097e-04 eta: 0:11:49 time: 0.4763 data_time: 0.0194 memory: 23498 grad_norm: 6.5645 top1_acc: 0.7500 top5_acc: 0.8333 loss_cls: 1.0378 loss: 1.0378 2022/09/09 16:30:11 - mmengine - INFO - Epoch(train) [37][380/449] lr: 6.4280e-04 eta: 0:11:39 time: 0.4563 data_time: 0.0196 memory: 23498 grad_norm: 6.5563 top1_acc: 0.7083 top5_acc: 0.8333 loss_cls: 1.0971 loss: 1.0971 2022/09/09 16:30:20 - mmengine - INFO - Epoch(train) [37][400/449] lr: 6.2488e-04 eta: 0:11:29 time: 0.4575 data_time: 0.0265 memory: 23498 grad_norm: 6.4871 top1_acc: 0.8333 top5_acc: 1.0000 loss_cls: 1.1281 loss: 1.1281 2022/09/09 16:30:29 - mmengine - INFO - Epoch(train) [37][420/449] lr: 6.0721e-04 eta: 0:11:19 time: 0.4603 data_time: 0.0188 memory: 23498 grad_norm: 6.5531 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 1.0984 loss: 1.0984 2022/09/09 16:30:38 - mmengine - INFO - Epoch(train) [37][440/449] lr: 5.8978e-04 eta: 0:11:10 time: 0.4505 data_time: 0.0171 memory: 23498 grad_norm: 6.5478 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9787 loss: 0.9787 2022/09/09 16:30:42 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 16:30:42 - mmengine - INFO - Epoch(train) [37][449/449] lr: 5.8202e-04 eta: 0:11:10 time: 0.4341 data_time: 0.0184 memory: 23498 grad_norm: 7.2049 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.1668 loss: 1.1668 2022/09/09 16:30:45 - mmengine - INFO - Epoch(val) [37][20/61] eta: 0:00:06 time: 0.1524 data_time: 0.0325 memory: 2693 2022/09/09 16:30:48 - mmengine - INFO - Epoch(val) [37][40/61] eta: 0:00:02 time: 0.1306 data_time: 0.0123 memory: 2693 2022/09/09 16:30:50 - mmengine - INFO - Epoch(val) [37][60/61] eta: 0:00:00 time: 0.1285 data_time: 0.0125 memory: 2693 2022/09/09 16:30:51 - mmengine - INFO - Epoch(val) [37][61/61] acc/top1: 0.3894 acc/top5: 0.6853 acc/mean1: 0.3515 2022/09/09 16:31:02 - mmengine - INFO - Epoch(train) [38][20/449] lr: 5.6496e-04 eta: 0:10:55 time: 0.5728 data_time: 0.0516 memory: 23498 grad_norm: 6.5845 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1031 loss: 1.1031 2022/09/09 16:31:12 - mmengine - INFO - Epoch(train) [38][40/449] lr: 5.4815e-04 eta: 0:10:45 time: 0.4595 data_time: 0.0281 memory: 23498 grad_norm: 6.4683 top1_acc: 0.8333 top5_acc: 0.9583 loss_cls: 1.0138 loss: 1.0138 2022/09/09 16:31:21 - mmengine - INFO - Epoch(train) [38][60/449] lr: 5.3159e-04 eta: 0:10:35 time: 0.4545 data_time: 0.0147 memory: 23498 grad_norm: 6.4275 top1_acc: 0.7917 top5_acc: 0.9167 loss_cls: 1.0800 loss: 1.0800 2022/09/09 16:31:30 - mmengine - INFO - Epoch(train) [38][80/449] lr: 5.1528e-04 eta: 0:10:25 time: 0.4636 data_time: 0.0186 memory: 23498 grad_norm: 6.5781 top1_acc: 0.6667 top5_acc: 0.9167 loss_cls: 0.9871 loss: 0.9871 2022/09/09 16:31:39 - mmengine - INFO - Epoch(train) [38][100/449] lr: 4.9922e-04 eta: 0:10:15 time: 0.4570 data_time: 0.0213 memory: 23498 grad_norm: 6.5999 top1_acc: 0.6667 top5_acc: 0.9167 loss_cls: 0.9961 loss: 0.9961 2022/09/09 16:31:48 - mmengine - INFO - Epoch(train) [38][120/449] lr: 4.8341e-04 eta: 0:10:05 time: 0.4505 data_time: 0.0184 memory: 23498 grad_norm: 6.5161 top1_acc: 0.7500 top5_acc: 0.9583 loss_cls: 1.0220 loss: 1.0220 2022/09/09 16:31:57 - mmengine - INFO - Epoch(train) [38][140/449] lr: 4.6785e-04 eta: 0:09:55 time: 0.4562 data_time: 0.0163 memory: 23498 grad_norm: 6.7215 top1_acc: 0.7083 top5_acc: 0.9167 loss_cls: 1.0705 loss: 1.0705 2022/09/09 16:32:07 - mmengine - INFO - Epoch(train) [38][160/449] lr: 4.5255e-04 eta: 0:09:45 time: 0.4640 data_time: 0.0167 memory: 23498 grad_norm: 6.7600 top1_acc: 0.8750 top5_acc: 0.9583 loss_cls: 1.0682 loss: 1.0682 2022/09/09 16:32:16 - mmengine - INFO - Epoch(train) [38][180/449] lr: 4.3750e-04 eta: 0:09:36 time: 0.4594 data_time: 0.0206 memory: 23498 grad_norm: 6.5015 top1_acc: 0.7083 top5_acc: 0.8750 loss_cls: 0.9565 loss: 0.9565 2022/09/09 16:32:25 - mmengine - INFO - Epoch(train) [38][200/449] lr: 4.2269e-04 eta: 0:09:26 time: 0.4474 data_time: 0.0168 memory: 23498 grad_norm: 6.5706 top1_acc: 0.5833 top5_acc: 0.8750 loss_cls: 1.0308 loss: 1.0308 2022/09/09 16:32:34 - mmengine - INFO - Epoch(train) [38][220/449] lr: 4.0814e-04 eta: 0:09:16 time: 0.4580 data_time: 0.0164 memory: 23498 grad_norm: 6.4976 top1_acc: 0.6667 top5_acc: 0.7917 loss_cls: 1.0867 loss: 1.0867 2022/09/09 16:32:43 - mmengine - INFO - Epoch(train) [38][240/449] lr: 3.9385e-04 eta: 0:09:06 time: 0.4506 data_time: 0.0180 memory: 23498 grad_norm: 6.4460 top1_acc: 0.7500 top5_acc: 0.8333 loss_cls: 1.0877 loss: 1.0877 2022/09/09 16:32:52 - mmengine - INFO - Epoch(train) [38][260/449] lr: 3.7980e-04 eta: 0:08:56 time: 0.4528 data_time: 0.0248 memory: 23498 grad_norm: 6.8646 top1_acc: 0.7083 top5_acc: 0.9583 loss_cls: 0.9730 loss: 0.9730 2022/09/09 16:33:01 - mmengine - INFO - Epoch(train) [38][280/449] lr: 3.6601e-04 eta: 0:08:46 time: 0.4479 data_time: 0.0184 memory: 23498 grad_norm: 6.5042 top1_acc: 0.9167 top5_acc: 1.0000 loss_cls: 1.0336 loss: 1.0336 2022/09/09 16:33:10 - mmengine - INFO - Epoch(train) [38][300/449] lr: 3.5247e-04 eta: 0:08:36 time: 0.4503 data_time: 0.0171 memory: 23498 grad_norm: 6.7915 top1_acc: 0.8333 top5_acc: 0.9583 loss_cls: 1.0926 loss: 1.0926 2022/09/09 16:33:19 - mmengine - INFO - Epoch(train) [38][320/449] lr: 3.3918e-04 eta: 0:08:26 time: 0.4533 data_time: 0.0176 memory: 23498 grad_norm: 6.4789 top1_acc: 0.7500 top5_acc: 0.8333 loss_cls: 1.0911 loss: 1.0911 2022/09/09 16:33:28 - mmengine - INFO - Epoch(train) [38][340/449] lr: 3.2615e-04 eta: 0:08:16 time: 0.4596 data_time: 0.0240 memory: 23498 grad_norm: 6.7051 top1_acc: 0.6667 top5_acc: 0.8750 loss_cls: 1.1090 loss: 1.1090 2022/09/09 16:33:37 - mmengine - INFO - Epoch(train) [38][360/449] lr: 3.1337e-04 eta: 0:08:06 time: 0.4473 data_time: 0.0180 memory: 23498 grad_norm: 7.0316 top1_acc: 0.7917 top5_acc: 0.8750 loss_cls: 1.1066 loss: 1.1066 2022/09/09 16:33:46 - mmengine - INFO - Epoch(train) [38][380/449] lr: 3.0084e-04 eta: 0:07:56 time: 0.4492 data_time: 0.0186 memory: 23498 grad_norm: 6.9075 top1_acc: 0.7917 top5_acc: 0.9583 loss_cls: 1.0592 loss: 1.0592 2022/09/09 16:33:49 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 16:33:55 - mmengine - INFO - Epoch(train) [38][400/449] lr: 2.8857e-04 eta: 0:07:46 time: 0.4469 data_time: 0.0191 memory: 23498 grad_norm: 6.6134 top1_acc: 0.8333 top5_acc: 1.0000 loss_cls: 1.0257 loss: 1.0257 2022/09/09 16:34:04 - mmengine - INFO - Epoch(train) [38][420/449] lr: 2.7655e-04 eta: 0:07:37 time: 0.4525 data_time: 0.0281 memory: 23498 grad_norm: 6.7187 top1_acc: 0.7500 top5_acc: 0.9167 loss_cls: 0.9857 loss: 0.9857 2022/09/09 16:34:13 - mmengine - INFO - Epoch(train) [38][440/449] lr: 2.6478e-04 eta: 0:07:27 time: 0.4425 data_time: 0.0171 memory: 23498 grad_norm: 6.6423 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0831 loss: 1.0831 2022/09/09 16:34:16 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 16:34:16 - mmengine - INFO - Epoch(train) [38][449/449] lr: 2.5957e-04 eta: 0:07:27 time: 0.4202 data_time: 0.0158 memory: 23498 grad_norm: 7.1389 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.2866 loss: 1.2866 2022/09/09 16:34:20 - mmengine - INFO - Epoch(val) [38][20/61] eta: 0:00:06 time: 0.1491 data_time: 0.0285 memory: 2693 2022/09/09 16:34:22 - mmengine - INFO - Epoch(val) [38][40/61] eta: 0:00:02 time: 0.1289 data_time: 0.0112 memory: 2693 2022/09/09 16:34:25 - mmengine - INFO - Epoch(val) [38][60/61] eta: 0:00:00 time: 0.1298 data_time: 0.0123 memory: 2693 2022/09/09 16:34:25 - mmengine - INFO - Epoch(val) [38][61/61] acc/top1: 0.3974 acc/top5: 0.6893 acc/mean1: 0.3589 2022/09/09 16:34:25 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_36.pth is removed 2022/09/09 16:34:28 - mmengine - INFO - The best checkpoint with 0.3974 acc/top1 at 38 epoch is saved to best_acc/top1_epoch_38.pth. 2022/09/09 16:34:37 - mmengine - INFO - Epoch(train) [39][20/449] lr: 2.4817e-04 eta: 0:07:12 time: 0.4763 data_time: 0.0323 memory: 23498 grad_norm: 6.5866 top1_acc: 0.7917 top5_acc: 0.9167 loss_cls: 0.9884 loss: 0.9884 2022/09/09 16:34:46 - mmengine - INFO - Epoch(train) [39][40/449] lr: 2.3703e-04 eta: 0:07:02 time: 0.4576 data_time: 0.0213 memory: 23498 grad_norm: 6.5521 top1_acc: 0.7917 top5_acc: 0.9167 loss_cls: 1.0405 loss: 1.0405 2022/09/09 16:34:55 - mmengine - INFO - Epoch(train) [39][60/449] lr: 2.2614e-04 eta: 0:06:52 time: 0.4504 data_time: 0.0151 memory: 23498 grad_norm: 6.6238 top1_acc: 0.7083 top5_acc: 0.7917 loss_cls: 1.0789 loss: 1.0789 2022/09/09 16:35:04 - mmengine - INFO - Epoch(train) [39][80/449] lr: 2.1551e-04 eta: 0:06:42 time: 0.4519 data_time: 0.0188 memory: 23498 grad_norm: 7.0414 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0170 loss: 1.0170 2022/09/09 16:35:14 - mmengine - INFO - Epoch(train) [39][100/449] lr: 2.0513e-04 eta: 0:06:33 time: 0.4636 data_time: 0.0238 memory: 23498 grad_norm: 6.6866 top1_acc: 0.7500 top5_acc: 0.9167 loss_cls: 0.9772 loss: 0.9772 2022/09/09 16:35:23 - mmengine - INFO - Epoch(train) [39][120/449] lr: 1.9501e-04 eta: 0:06:23 time: 0.4490 data_time: 0.0177 memory: 23498 grad_norm: 6.6545 top1_acc: 0.7917 top5_acc: 0.9167 loss_cls: 1.0332 loss: 1.0332 2022/09/09 16:35:32 - mmengine - INFO - Epoch(train) [39][140/449] lr: 1.8514e-04 eta: 0:06:13 time: 0.4501 data_time: 0.0156 memory: 23498 grad_norm: 6.5759 top1_acc: 0.6667 top5_acc: 0.9167 loss_cls: 1.0005 loss: 1.0005 2022/09/09 16:35:41 - mmengine - INFO - Epoch(train) [39][160/449] lr: 1.7552e-04 eta: 0:06:03 time: 0.4625 data_time: 0.0241 memory: 23498 grad_norm: 6.7210 top1_acc: 0.8333 top5_acc: 0.8750 loss_cls: 1.0997 loss: 1.0997 2022/09/09 16:35:50 - mmengine - INFO - Epoch(train) [39][180/449] lr: 1.6616e-04 eta: 0:05:53 time: 0.4660 data_time: 0.0240 memory: 23498 grad_norm: 6.6980 top1_acc: 0.7083 top5_acc: 0.8750 loss_cls: 1.0068 loss: 1.0068 2022/09/09 16:35:59 - mmengine - INFO - Epoch(train) [39][200/449] lr: 1.5706e-04 eta: 0:05:43 time: 0.4487 data_time: 0.0169 memory: 23498 grad_norm: 6.6418 top1_acc: 0.8333 top5_acc: 0.9583 loss_cls: 0.9932 loss: 0.9932 2022/09/09 16:36:08 - mmengine - INFO - Epoch(train) [39][220/449] lr: 1.4821e-04 eta: 0:05:33 time: 0.4500 data_time: 0.0149 memory: 23498 grad_norm: 6.6600 top1_acc: 0.5000 top5_acc: 0.8333 loss_cls: 1.0890 loss: 1.0890 2022/09/09 16:36:17 - mmengine - INFO - Epoch(train) [39][240/449] lr: 1.3962e-04 eta: 0:05:23 time: 0.4548 data_time: 0.0187 memory: 23498 grad_norm: 6.4628 top1_acc: 0.7500 top5_acc: 0.9167 loss_cls: 1.0266 loss: 1.0266 2022/09/09 16:36:26 - mmengine - INFO - Epoch(train) [39][260/449] lr: 1.3128e-04 eta: 0:05:14 time: 0.4639 data_time: 0.0228 memory: 23498 grad_norm: 7.0354 top1_acc: 0.7500 top5_acc: 0.9583 loss_cls: 1.0193 loss: 1.0193 2022/09/09 16:36:36 - mmengine - INFO - Epoch(train) [39][280/449] lr: 1.2320e-04 eta: 0:05:04 time: 0.4537 data_time: 0.0174 memory: 23498 grad_norm: 6.7368 top1_acc: 0.7500 top5_acc: 0.9167 loss_cls: 1.1173 loss: 1.1173 2022/09/09 16:36:45 - mmengine - INFO - Epoch(train) [39][300/449] lr: 1.1538e-04 eta: 0:04:54 time: 0.4519 data_time: 0.0204 memory: 23498 grad_norm: 6.7091 top1_acc: 0.5417 top5_acc: 0.8333 loss_cls: 1.0639 loss: 1.0639 2022/09/09 16:36:54 - mmengine - INFO - Epoch(train) [39][320/449] lr: 1.0781e-04 eta: 0:04:44 time: 0.4483 data_time: 0.0159 memory: 23498 grad_norm: 6.6991 top1_acc: 0.5833 top5_acc: 0.8333 loss_cls: 0.9409 loss: 0.9409 2022/09/09 16:37:03 - mmengine - INFO - Epoch(train) [39][340/449] lr: 1.0049e-04 eta: 0:04:34 time: 0.4607 data_time: 0.0205 memory: 23498 grad_norm: 6.7277 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1133 loss: 1.1133 2022/09/09 16:37:12 - mmengine - INFO - Epoch(train) [39][360/449] lr: 9.3437e-05 eta: 0:04:24 time: 0.4503 data_time: 0.0191 memory: 23498 grad_norm: 6.6101 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0729 loss: 1.0729 2022/09/09 16:37:21 - mmengine - INFO - Epoch(train) [39][380/449] lr: 8.6636e-05 eta: 0:04:14 time: 0.4515 data_time: 0.0200 memory: 23498 grad_norm: 6.6124 top1_acc: 0.7917 top5_acc: 0.8750 loss_cls: 1.1055 loss: 1.1055 2022/09/09 16:37:30 - mmengine - INFO - Epoch(train) [39][400/449] lr: 8.0092e-05 eta: 0:04:04 time: 0.4518 data_time: 0.0187 memory: 23498 grad_norm: 6.6207 top1_acc: 0.7917 top5_acc: 0.8750 loss_cls: 0.9617 loss: 0.9617 2022/09/09 16:37:39 - mmengine - INFO - Epoch(train) [39][420/449] lr: 7.3804e-05 eta: 0:03:55 time: 0.4759 data_time: 0.0230 memory: 23498 grad_norm: 6.5940 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9409 loss: 0.9409 2022/09/09 16:37:48 - mmengine - INFO - Epoch(train) [39][440/449] lr: 6.7773e-05 eta: 0:03:45 time: 0.4407 data_time: 0.0162 memory: 23498 grad_norm: 6.5263 top1_acc: 0.6667 top5_acc: 0.8333 loss_cls: 0.9662 loss: 0.9662 2022/09/09 16:37:52 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 16:37:52 - mmengine - INFO - Epoch(train) [39][449/449] lr: 6.5143e-05 eta: 0:03:45 time: 0.4368 data_time: 0.0217 memory: 23498 grad_norm: 7.1175 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 1.2819 loss: 1.2819 2022/09/09 16:37:55 - mmengine - INFO - Epoch(val) [39][20/61] eta: 0:00:06 time: 0.1498 data_time: 0.0288 memory: 2693 2022/09/09 16:37:58 - mmengine - INFO - Epoch(val) [39][40/61] eta: 0:00:02 time: 0.1308 data_time: 0.0129 memory: 2693 2022/09/09 16:38:00 - mmengine - INFO - Epoch(val) [39][60/61] eta: 0:00:00 time: 0.1305 data_time: 0.0129 memory: 2693 2022/09/09 16:38:01 - mmengine - INFO - Epoch(val) [39][61/61] acc/top1: 0.3960 acc/top5: 0.6872 acc/mean1: 0.3555 2022/09/09 16:38:11 - mmengine - INFO - Epoch(train) [40][20/449] lr: 5.9484e-05 eta: 0:03:30 time: 0.4899 data_time: 0.0513 memory: 23498 grad_norm: 6.9098 top1_acc: 0.8333 top5_acc: 0.9583 loss_cls: 1.0682 loss: 1.0682 2022/09/09 16:38:20 - mmengine - INFO - Epoch(train) [40][40/449] lr: 5.4082e-05 eta: 0:03:21 time: 0.4535 data_time: 0.0258 memory: 23498 grad_norm: 6.7445 top1_acc: 0.7083 top5_acc: 0.8333 loss_cls: 0.9939 loss: 0.9939 2022/09/09 16:38:29 - mmengine - INFO - Epoch(train) [40][60/449] lr: 4.8936e-05 eta: 0:03:11 time: 0.4449 data_time: 0.0182 memory: 23498 grad_norm: 6.6864 top1_acc: 0.7083 top5_acc: 0.9167 loss_cls: 0.9828 loss: 0.9828 2022/09/09 16:38:38 - mmengine - INFO - Epoch(train) [40][80/449] lr: 4.4048e-05 eta: 0:03:01 time: 0.4596 data_time: 0.0201 memory: 23498 grad_norm: 6.7040 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9467 loss: 0.9467 2022/09/09 16:38:47 - mmengine - INFO - Epoch(train) [40][100/449] lr: 3.9416e-05 eta: 0:02:51 time: 0.4503 data_time: 0.0233 memory: 23498 grad_norm: 6.7379 top1_acc: 0.8750 top5_acc: 0.9583 loss_cls: 1.0447 loss: 1.0447 2022/09/09 16:38:56 - mmengine - INFO - Epoch(train) [40][120/449] lr: 3.5041e-05 eta: 0:02:41 time: 0.4713 data_time: 0.0247 memory: 23498 grad_norm: 6.6714 top1_acc: 0.7500 top5_acc: 0.9167 loss_cls: 1.0068 loss: 1.0068 2022/09/09 16:39:05 - mmengine - INFO - Epoch(train) [40][140/449] lr: 3.0924e-05 eta: 0:02:31 time: 0.4464 data_time: 0.0174 memory: 23498 grad_norm: 6.6489 top1_acc: 0.7500 top5_acc: 0.9167 loss_cls: 1.0203 loss: 1.0203 2022/09/09 16:39:14 - mmengine - INFO - Epoch(train) [40][160/449] lr: 2.7063e-05 eta: 0:02:21 time: 0.4523 data_time: 0.0304 memory: 23498 grad_norm: 6.6751 top1_acc: 0.7500 top5_acc: 0.9167 loss_cls: 1.0934 loss: 1.0934 2022/09/09 16:39:23 - mmengine - INFO - Epoch(train) [40][180/449] lr: 2.3460e-05 eta: 0:02:12 time: 0.4464 data_time: 0.0233 memory: 23498 grad_norm: 6.5266 top1_acc: 0.9167 top5_acc: 0.9583 loss_cls: 0.9488 loss: 0.9488 2022/09/09 16:39:32 - mmengine - INFO - Epoch(train) [40][200/449] lr: 2.0113e-05 eta: 0:02:02 time: 0.4509 data_time: 0.0174 memory: 23498 grad_norm: 6.5708 top1_acc: 0.7917 top5_acc: 0.9167 loss_cls: 1.0306 loss: 1.0306 2022/09/09 16:39:41 - mmengine - INFO - Epoch(train) [40][220/449] lr: 1.7024e-05 eta: 0:01:52 time: 0.4469 data_time: 0.0165 memory: 23498 grad_norm: 6.6189 top1_acc: 0.7083 top5_acc: 0.9583 loss_cls: 0.8788 loss: 0.8788 2022/09/09 16:39:50 - mmengine - INFO - Epoch(train) [40][240/449] lr: 1.4193e-05 eta: 0:01:42 time: 0.4486 data_time: 0.0177 memory: 23498 grad_norm: 6.6034 top1_acc: 0.8333 top5_acc: 0.9167 loss_cls: 1.0188 loss: 1.0188 2022/09/09 16:39:59 - mmengine - INFO - Epoch(train) [40][260/449] lr: 1.1618e-05 eta: 0:01:32 time: 0.4548 data_time: 0.0229 memory: 23498 grad_norm: 6.6015 top1_acc: 0.7917 top5_acc: 0.9167 loss_cls: 1.1922 loss: 1.1922 2022/09/09 16:40:08 - mmengine - INFO - Epoch(train) [40][280/449] lr: 9.3013e-06 eta: 0:01:22 time: 0.4624 data_time: 0.0177 memory: 23498 grad_norm: 6.4829 top1_acc: 0.7083 top5_acc: 0.9167 loss_cls: 1.0190 loss: 1.0190 2022/09/09 16:40:17 - mmengine - INFO - Epoch(train) [40][300/449] lr: 7.2416e-06 eta: 0:01:13 time: 0.4510 data_time: 0.0173 memory: 23498 grad_norm: 6.8841 top1_acc: 0.6250 top5_acc: 0.8333 loss_cls: 1.0994 loss: 1.0994 2022/09/09 16:40:26 - mmengine - INFO - Epoch(train) [40][320/449] lr: 5.4393e-06 eta: 0:01:03 time: 0.4491 data_time: 0.0173 memory: 23498 grad_norm: 6.6694 top1_acc: 0.8333 top5_acc: 0.9583 loss_cls: 0.8462 loss: 0.8462 2022/09/09 16:40:36 - mmengine - INFO - Epoch(train) [40][340/449] lr: 3.8945e-06 eta: 0:00:53 time: 0.4618 data_time: 0.0224 memory: 23498 grad_norm: 6.4236 top1_acc: 0.8333 top5_acc: 0.8750 loss_cls: 0.9372 loss: 0.9372 2022/09/09 16:40:45 - mmengine - INFO - Epoch(train) [40][360/449] lr: 2.6071e-06 eta: 0:00:43 time: 0.4628 data_time: 0.0167 memory: 23498 grad_norm: 6.6444 top1_acc: 0.8333 top5_acc: 0.9583 loss_cls: 1.0477 loss: 1.0477 2022/09/09 16:40:54 - mmengine - INFO - Epoch(train) [40][380/449] lr: 1.5771e-06 eta: 0:00:33 time: 0.4584 data_time: 0.0168 memory: 23498 grad_norm: 6.7810 top1_acc: 0.7500 top5_acc: 0.9583 loss_cls: 0.9089 loss: 0.9089 2022/09/09 16:41:03 - mmengine - INFO - Epoch(train) [40][400/449] lr: 8.0466e-07 eta: 0:00:24 time: 0.4496 data_time: 0.0190 memory: 23498 grad_norm: 6.7572 top1_acc: 0.7917 top5_acc: 0.9583 loss_cls: 1.0061 loss: 1.0061 2022/09/09 16:41:12 - mmengine - INFO - Epoch(train) [40][420/449] lr: 2.8968e-07 eta: 0:00:14 time: 0.4562 data_time: 0.0222 memory: 23498 grad_norm: 6.5147 top1_acc: 0.7500 top5_acc: 0.8333 loss_cls: 0.9140 loss: 0.9140 2022/09/09 16:41:22 - mmengine - INFO - Epoch(train) [40][440/449] lr: 3.2187e-08 eta: 0:00:04 time: 0.4603 data_time: 0.0167 memory: 23498 grad_norm: 6.6338 top1_acc: 0.4583 top5_acc: 0.8333 loss_cls: 1.0056 loss: 1.0056 2022/09/09 16:41:25 - mmengine - INFO - Exp name: tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_20220909_140251 2022/09/09 16:41:25 - mmengine - INFO - Epoch(train) [40][449/449] lr: 3.2187e-10 eta: 0:00:04 time: 0.4330 data_time: 0.0264 memory: 23498 grad_norm: 7.2653 top1_acc: 0.0000 top5_acc: 1.0000 loss_cls: 1.1247 loss: 1.1247 2022/09/09 16:41:25 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/09/09 16:41:30 - mmengine - INFO - Epoch(val) [40][20/61] eta: 0:00:05 time: 0.1439 data_time: 0.0245 memory: 2693 2022/09/09 16:41:32 - mmengine - INFO - Epoch(val) [40][40/61] eta: 0:00:02 time: 0.1304 data_time: 0.0120 memory: 2693 2022/09/09 16:41:35 - mmengine - INFO - Epoch(val) [40][60/61] eta: 0:00:00 time: 0.1283 data_time: 0.0122 memory: 2693 2022/09/09 16:41:35 - mmengine - INFO - Epoch(val) [40][61/61] acc/top1: 0.3983 acc/top5: 0.6901 acc/mean1: 0.3587 2022/09/09 16:41:36 - mmengine - INFO - The previous best checkpoint /mnt/cache/lilin/Repos/mmaction2/work_dirs/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv1-rgb_lr0.01*4/best_acc/top1_epoch_38.pth is removed 2022/09/09 16:41:37 - mmengine - INFO - The best checkpoint with 0.3983 acc/top1 at 40 epoch is saved to best_acc/top1_epoch_40.pth.