2022/12/08 12:57:55 - 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: 1028560551 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: 1.10.0+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 v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX512 - 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 -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.11.0+cu111 OpenCV: 4.5.5 MMEngine: 0.3.2 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None diff_rank_seed: False deterministic: False Distributed launcher: pytorch Distributed training: True GPU number: 8 ------------------------------------------------------------ 2022/12/08 12:57:56 - mmengine - INFO - Config: model = dict( type='Recognizer2D', backbone=dict( type='mmcls.ResNeXt', depth=101, num_stages=4, out_indices=(3, ), groups=32, width_per_group=4, style='pytorch', init_cfg=dict( type='Pretrained', checkpoint= 'https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth', prefix='backbone')), cls_head=dict( type='TSNHead', num_classes=400, in_channels=2048, spatial_type='avg', consensus=dict(type='AvgConsensus', dim=1), dropout_ratio=0.4, init_std=0.01, average_clips='prob'), 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=None) train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=100, val_begin=1, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') param_scheduler = [ dict( type='MultiStepLR', begin=0, end=100, by_epoch=True, milestones=[40, 80], gamma=0.1) ] optim_wrapper = dict( optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001), clip_grad=dict(max_norm=40, norm_type=2)) 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=3, save_best='auto', max_keep_ckpts=3), sampler_seed=dict(type='DistSamplerSeedHook'), sync_buffers=dict(type='SyncBuffersHook')) env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) log_processor = dict(type='LogProcessor', window_size=20, by_epoch=True) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='ActionVisualizer', vis_backends=[dict(type='LocalVisBackend')]) log_level = 'INFO' load_from = None resume = False dataset_type = 'VideoDataset' data_root = 'data/kinetics400/videos_train' data_root_val = 'data/kinetics400/videos_val' ann_file_train = 'data/kinetics400/kinetics400_train_list_videos.txt' ann_file_val = 'data/kinetics400/kinetics400_val_list_videos.txt' file_client_args = dict( io_backend='petrel', path_mapping=dict( {'data/kinetics400': 's3://openmmlab/datasets/action/Kinetics400'})) train_pipeline = [ dict( type='DecordInit', io_backend='petrel', path_mapping=dict( {'data/kinetics400': 's3://openmmlab/datasets/action/Kinetics400'})), dict(type='SampleFrames', clip_len=1, frame_interval=1, num_clips=3), dict(type='DecordDecode'), 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='Flip', flip_ratio=0.5), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ] val_pipeline = [ dict( type='DecordInit', io_backend='petrel', path_mapping=dict( {'data/kinetics400': 's3://openmmlab/datasets/action/Kinetics400'})), dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=3, test_mode=True), dict(type='DecordDecode'), 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='DecordInit', io_backend='petrel', path_mapping=dict( {'data/kinetics400': 's3://openmmlab/datasets/action/Kinetics400'})), dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=25, test_mode=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='TenCrop', crop_size=224), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ] train_dataloader = dict( batch_size=32, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='VideoDataset', ann_file='data/kinetics400/kinetics400_train_list_videos.txt', data_prefix=dict(video='data/kinetics400/videos_train'), pipeline=[ dict( type='DecordInit', io_backend='petrel', path_mapping=dict({ 'data/kinetics400': 's3://openmmlab/datasets/action/Kinetics400' })), dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=3), dict(type='DecordDecode'), 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='Flip', flip_ratio=0.5), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ])) val_dataloader = dict( batch_size=32, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='VideoDataset', ann_file='data/kinetics400/kinetics400_val_list_videos.txt', data_prefix=dict(video='data/kinetics400/videos_val'), pipeline=[ dict( type='DecordInit', io_backend='petrel', path_mapping=dict({ 'data/kinetics400': 's3://openmmlab/datasets/action/Kinetics400' })), dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=3, test_mode=True), dict(type='DecordDecode'), 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='VideoDataset', ann_file='data/kinetics400/kinetics400_val_list_videos.txt', data_prefix=dict(video='data/kinetics400/videos_val'), pipeline=[ dict( type='DecordInit', io_backend='petrel', path_mapping=dict({ 'data/kinetics400': 's3://openmmlab/datasets/action/Kinetics400' })), dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=25, test_mode=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='TenCrop', 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') checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth' launcher = 'pytorch' work_dir = './work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb' randomness = dict(seed=None, diff_rank_seed=False, deterministic=False) 2022/12/08 12:57:56 - mmengine - INFO - Result has been saved to /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/modules_statistic_results.json 2022/12/08 12:58:00 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook (BELOW_NORMAL) LoggerHook -------------------- before_train: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook -------------------- before_train_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) DistSamplerSeedHook -------------------- before_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook -------------------- after_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (NORMAL ) IterTimerHook (NORMAL ) SyncBuffersHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_val_epoch: (NORMAL ) IterTimerHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (VERY_LOW ) CheckpointHook -------------------- before_test_epoch: (NORMAL ) IterTimerHook -------------------- before_test_iter: (NORMAL ) IterTimerHook -------------------- after_test_iter: (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_run: (BELOW_NORMAL) LoggerHook -------------------- Name of parameter - Initialization information backbone.conv1.weight - torch.Size([64, 3, 7, 7]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.0.conv1.weight - torch.Size([128, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.0.bn1.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.0.bn1.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.0.conv2.weight - torch.Size([128, 4, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.0.bn2.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.0.bn2.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.0.conv3.weight - torch.Size([256, 128, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.0.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.0.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.0.downsample.1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.0.downsample.1.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.1.conv1.weight - torch.Size([128, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.1.bn1.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.1.bn1.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.1.conv2.weight - torch.Size([128, 4, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.1.bn2.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.1.bn2.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.1.conv3.weight - torch.Size([256, 128, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.1.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.1.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.2.conv1.weight - torch.Size([128, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.2.bn1.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.2.bn1.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.2.conv2.weight - torch.Size([128, 4, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.2.bn2.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.2.bn2.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.2.conv3.weight - torch.Size([256, 128, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.2.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer1.2.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.0.conv1.weight - torch.Size([256, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.0.bn1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.0.bn1.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.0.conv2.weight - torch.Size([256, 8, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.0.bn2.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.0.bn2.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.0.conv3.weight - torch.Size([512, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.0.bn3.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.0.bn3.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.0.downsample.1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.0.downsample.1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.1.conv1.weight - torch.Size([256, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.1.bn1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.1.bn1.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.1.conv2.weight - torch.Size([256, 8, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.1.bn2.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.1.bn2.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.1.conv3.weight - torch.Size([512, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer2.1.bn3.weight - torch.Size([512]): PretrainedInit: load from 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https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.5.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.6.conv1.weight - torch.Size([512, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.6.bn1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.6.bn1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.6.conv2.weight - torch.Size([512, 16, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.6.bn2.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.6.bn2.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.6.conv3.weight - torch.Size([1024, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.6.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.6.bn3.bias - torch.Size([1024]): PretrainedInit: load from 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https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.12.conv1.weight - torch.Size([512, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.12.bn1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.12.bn1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.12.conv2.weight - torch.Size([512, 16, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.12.bn2.weight - torch.Size([512]): PretrainedInit: load from 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https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.14.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.14.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.15.conv1.weight - torch.Size([512, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.15.bn1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.15.bn1.bias - torch.Size([512]): PretrainedInit: load from 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https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.15.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.16.conv1.weight - torch.Size([512, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.16.bn1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.16.bn1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.16.conv2.weight - torch.Size([512, 16, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.16.bn2.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.16.bn2.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.16.conv3.weight - torch.Size([1024, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.16.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.16.bn3.bias - torch.Size([1024]): PretrainedInit: load from 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https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.17.bn2.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.17.conv3.weight - torch.Size([1024, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.17.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.17.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.18.conv1.weight - torch.Size([512, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.18.bn1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.18.bn1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.18.conv2.weight - torch.Size([512, 16, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.18.bn2.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.18.bn2.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.18.conv3.weight - torch.Size([1024, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.18.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.18.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.19.conv1.weight - torch.Size([512, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.19.bn1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.19.bn1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.19.conv2.weight - torch.Size([512, 16, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.19.bn2.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.19.bn2.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.19.conv3.weight - torch.Size([1024, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.19.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.19.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.20.conv1.weight - torch.Size([512, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.20.bn1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.20.bn1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.20.conv2.weight - torch.Size([512, 16, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.20.bn2.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.20.bn2.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.20.conv3.weight - torch.Size([1024, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.20.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.20.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.21.conv1.weight - torch.Size([512, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.21.bn1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.21.bn1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.21.conv2.weight - torch.Size([512, 16, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.21.bn2.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.21.bn2.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.21.conv3.weight - torch.Size([1024, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.21.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.21.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.22.conv1.weight - torch.Size([512, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.22.bn1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.22.bn1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.22.conv2.weight - torch.Size([512, 16, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.22.bn2.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.22.bn2.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.22.conv3.weight - torch.Size([1024, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.22.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer3.22.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.0.conv1.weight - torch.Size([1024, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.0.bn1.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.0.bn1.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.0.conv2.weight - torch.Size([1024, 32, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.0.bn2.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.0.bn2.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.0.conv3.weight - torch.Size([2048, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.0.bn3.weight - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.0.bn3.bias - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.0.downsample.1.weight - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.0.downsample.1.bias - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.1.conv1.weight - torch.Size([1024, 2048, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.1.bn1.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.1.bn1.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.1.conv2.weight - torch.Size([1024, 32, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.1.bn2.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.1.bn2.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.1.conv3.weight - torch.Size([2048, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.1.bn3.weight - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.1.bn3.bias - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.2.conv1.weight - torch.Size([1024, 2048, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.2.bn1.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.2.bn1.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.2.conv2.weight - torch.Size([1024, 32, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.2.bn2.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.2.bn2.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.2.conv3.weight - torch.Size([2048, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.2.bn3.weight - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth backbone.layer4.2.bn3.bias - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth cls_head.fc_cls.weight - torch.Size([400, 2048]): Initialized by user-defined `init_weights` in TSNHead cls_head.fc_cls.bias - torch.Size([400]): Initialized by user-defined `init_weights` in TSNHead 2022/12/08 12:58:05 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb. 2022/12/08 12:58:27 - mmengine - INFO - Epoch(train) [1][ 20/940] lr: 1.0000e-02 eta: 1 day, 5:01:29 time: 1.1118 data_time: 0.6797 memory: 16095 grad_norm: 2.6390 loss: 5.9186 top1_acc: 0.0312 top5_acc: 0.0312 loss_cls: 5.9186 2022/12/08 12:58:39 - mmengine - INFO - Epoch(train) [1][ 40/940] lr: 1.0000e-02 eta: 22:12:13 time: 0.5896 data_time: 0.1259 memory: 16095 grad_norm: 2.7116 loss: 5.6331 top1_acc: 0.1562 top5_acc: 0.2812 loss_cls: 5.6331 2022/12/08 12:58:52 - mmengine - INFO - Epoch(train) [1][ 60/940] lr: 1.0000e-02 eta: 20:38:53 time: 0.6724 data_time: 0.1498 memory: 16095 grad_norm: 2.8582 loss: 5.2878 top1_acc: 0.1562 top5_acc: 0.2188 loss_cls: 5.2878 2022/12/08 12:59:03 - mmengine - INFO - Epoch(train) [1][ 80/940] lr: 1.0000e-02 eta: 19:00:29 time: 0.5405 data_time: 0.1237 memory: 16095 grad_norm: 2.9932 loss: 4.7466 top1_acc: 0.1562 top5_acc: 0.4062 loss_cls: 4.7466 2022/12/08 12:59:17 - mmengine - INFO - Epoch(train) [1][100/940] lr: 1.0000e-02 eta: 18:55:02 time: 0.7120 data_time: 0.3301 memory: 16095 grad_norm: 3.0882 loss: 4.3296 top1_acc: 0.1875 top5_acc: 0.4062 loss_cls: 4.3296 2022/12/08 12:59:28 - mmengine - INFO - Epoch(train) [1][120/940] lr: 1.0000e-02 eta: 18:01:23 time: 0.5205 data_time: 0.1896 memory: 16095 grad_norm: 3.1926 loss: 4.1455 top1_acc: 0.3438 top5_acc: 0.4688 loss_cls: 4.1455 2022/12/08 12:59:41 - mmengine - INFO - Epoch(train) [1][140/940] lr: 1.0000e-02 eta: 17:55:11 time: 0.6644 data_time: 0.2416 memory: 16095 grad_norm: 3.3024 loss: 3.9362 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 3.9362 2022/12/08 12:59:52 - mmengine - INFO - Epoch(train) [1][160/940] lr: 1.0000e-02 eta: 17:29:45 time: 0.5584 data_time: 0.1022 memory: 16095 grad_norm: 3.3942 loss: 3.7418 top1_acc: 0.2188 top5_acc: 0.4688 loss_cls: 3.7418 2022/12/08 13:00:06 - mmengine - INFO - Epoch(train) [1][180/940] lr: 1.0000e-02 eta: 17:35:21 time: 0.7047 data_time: 0.1147 memory: 16095 grad_norm: 3.4930 loss: 3.6838 top1_acc: 0.2812 top5_acc: 0.5312 loss_cls: 3.6838 2022/12/08 13:00:17 - mmengine - INFO - Epoch(train) [1][200/940] lr: 1.0000e-02 eta: 17:12:02 time: 0.5272 data_time: 0.0695 memory: 16095 grad_norm: 3.5570 loss: 3.5396 top1_acc: 0.3438 top5_acc: 0.5625 loss_cls: 3.5396 2022/12/08 13:00:30 - mmengine - INFO - Epoch(train) [1][220/940] lr: 1.0000e-02 eta: 17:11:33 time: 0.6583 data_time: 0.0261 memory: 16095 grad_norm: 3.6229 loss: 3.4369 top1_acc: 0.3125 top5_acc: 0.4688 loss_cls: 3.4369 2022/12/08 13:00:42 - mmengine - INFO - Epoch(train) [1][240/940] lr: 1.0000e-02 eta: 17:00:43 time: 0.5784 data_time: 0.0187 memory: 16095 grad_norm: 3.7576 loss: 3.4767 top1_acc: 0.1250 top5_acc: 0.5625 loss_cls: 3.4767 2022/12/08 13:00:54 - mmengine - INFO - Epoch(train) [1][260/940] lr: 1.0000e-02 eta: 16:56:56 time: 0.6236 data_time: 0.0258 memory: 16095 grad_norm: 3.7997 loss: 3.3694 top1_acc: 0.3125 top5_acc: 0.5312 loss_cls: 3.3694 2022/12/08 13:01:06 - mmengine - INFO - Epoch(train) [1][280/940] lr: 1.0000e-02 eta: 16:53:17 time: 0.6202 data_time: 0.0179 memory: 16095 grad_norm: 3.8154 loss: 3.1589 top1_acc: 0.2812 top5_acc: 0.5938 loss_cls: 3.1589 2022/12/08 13:01:18 - mmengine - INFO - Epoch(train) [1][300/940] lr: 1.0000e-02 eta: 16:45:00 time: 0.5713 data_time: 0.0257 memory: 16095 grad_norm: 3.8907 loss: 3.2380 top1_acc: 0.3438 top5_acc: 0.5625 loss_cls: 3.2380 2022/12/08 13:01:31 - mmengine - INFO - Epoch(train) [1][320/940] lr: 1.0000e-02 eta: 16:44:19 time: 0.6387 data_time: 0.0208 memory: 16095 grad_norm: 3.8974 loss: 3.1935 top1_acc: 0.3125 top5_acc: 0.5312 loss_cls: 3.1935 2022/12/08 13:01:43 - mmengine - INFO - Epoch(train) [1][340/940] lr: 1.0000e-02 eta: 16:42:58 time: 0.6310 data_time: 0.0227 memory: 16095 grad_norm: 3.9711 loss: 2.9556 top1_acc: 0.2500 top5_acc: 0.5312 loss_cls: 2.9556 2022/12/08 13:01:55 - mmengine - INFO - Epoch(train) [1][360/940] lr: 1.0000e-02 eta: 16:39:48 time: 0.6085 data_time: 0.0206 memory: 16095 grad_norm: 4.0188 loss: 3.0723 top1_acc: 0.3750 top5_acc: 0.6562 loss_cls: 3.0723 2022/12/08 13:02:07 - mmengine - INFO - Epoch(train) [1][380/940] lr: 1.0000e-02 eta: 16:36:39 time: 0.6049 data_time: 0.0361 memory: 16095 grad_norm: 4.0974 loss: 2.9995 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 2.9995 2022/12/08 13:02:19 - mmengine - INFO - Epoch(train) [1][400/940] lr: 1.0000e-02 eta: 16:31:27 time: 0.5748 data_time: 0.0249 memory: 16095 grad_norm: 4.1036 loss: 3.0022 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.0022 2022/12/08 13:02:31 - mmengine - INFO - Epoch(train) [1][420/940] lr: 1.0000e-02 eta: 16:30:11 time: 0.6213 data_time: 0.0899 memory: 16095 grad_norm: 4.1555 loss: 3.0562 top1_acc: 0.4062 top5_acc: 0.5938 loss_cls: 3.0562 2022/12/08 13:02:42 - mmengine - INFO - Epoch(train) [1][440/940] lr: 1.0000e-02 eta: 16:24:03 time: 0.5513 data_time: 0.1443 memory: 16095 grad_norm: 4.1821 loss: 2.8931 top1_acc: 0.2500 top5_acc: 0.5938 loss_cls: 2.8931 2022/12/08 13:02:56 - mmengine - INFO - Epoch(train) [1][460/940] lr: 1.0000e-02 eta: 16:27:31 time: 0.6854 data_time: 0.2778 memory: 16095 grad_norm: 4.1486 loss: 2.8803 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.8803 2022/12/08 13:03:08 - mmengine - INFO - Epoch(train) [1][480/940] lr: 1.0000e-02 eta: 16:24:08 time: 0.5846 data_time: 0.2445 memory: 16095 grad_norm: 4.2258 loss: 2.8962 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 2.8962 2022/12/08 13:03:21 - mmengine - INFO - Epoch(train) [1][500/940] lr: 1.0000e-02 eta: 16:24:19 time: 0.6377 data_time: 0.1834 memory: 16095 grad_norm: 4.2183 loss: 2.7974 top1_acc: 0.4375 top5_acc: 0.6562 loss_cls: 2.7974 2022/12/08 13:03:32 - mmengine - INFO - Epoch(train) [1][520/940] lr: 1.0000e-02 eta: 16:21:12 time: 0.5831 data_time: 0.1843 memory: 16095 grad_norm: 4.2599 loss: 2.9128 top1_acc: 0.3125 top5_acc: 0.6562 loss_cls: 2.9128 2022/12/08 13:03:45 - mmengine - INFO - Epoch(train) [1][540/940] lr: 1.0000e-02 eta: 16:22:13 time: 0.6510 data_time: 0.2937 memory: 16095 grad_norm: 4.2901 loss: 2.9623 top1_acc: 0.2500 top5_acc: 0.6562 loss_cls: 2.9623 2022/12/08 13:03:56 - mmengine - INFO - Epoch(train) [1][560/940] lr: 1.0000e-02 eta: 16:16:24 time: 0.5298 data_time: 0.1923 memory: 16095 grad_norm: 4.2891 loss: 2.6361 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.6361 2022/12/08 13:04:09 - mmengine - INFO - Epoch(train) [1][580/940] lr: 1.0000e-02 eta: 16:17:02 time: 0.6428 data_time: 0.1727 memory: 16095 grad_norm: 4.3623 loss: 2.7670 top1_acc: 0.4062 top5_acc: 0.6562 loss_cls: 2.7670 2022/12/08 13:04:21 - mmengine - INFO - Epoch(train) [1][600/940] lr: 1.0000e-02 eta: 16:15:45 time: 0.6069 data_time: 0.0609 memory: 16095 grad_norm: 4.3661 loss: 2.7792 top1_acc: 0.5938 top5_acc: 0.7188 loss_cls: 2.7792 2022/12/08 13:04:34 - mmengine - INFO - Epoch(train) [1][620/940] lr: 1.0000e-02 eta: 16:17:10 time: 0.6592 data_time: 0.2458 memory: 16095 grad_norm: 4.4562 loss: 2.6862 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 2.6862 2022/12/08 13:04:45 - mmengine - INFO - Epoch(train) [1][640/940] lr: 1.0000e-02 eta: 16:13:53 time: 0.5644 data_time: 0.1386 memory: 16095 grad_norm: 4.4026 loss: 2.7877 top1_acc: 0.3750 top5_acc: 0.6562 loss_cls: 2.7877 2022/12/08 13:04:59 - mmengine - INFO - Epoch(train) [1][660/940] lr: 1.0000e-02 eta: 16:17:02 time: 0.6973 data_time: 0.1666 memory: 16095 grad_norm: 4.4274 loss: 2.7879 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 2.7879 2022/12/08 13:05:11 - mmengine - INFO - Epoch(train) [1][680/940] lr: 1.0000e-02 eta: 16:13:57 time: 0.5652 data_time: 0.0470 memory: 16095 grad_norm: 4.4768 loss: 2.7274 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 2.7274 2022/12/08 13:05:23 - mmengine - INFO - Epoch(train) [1][700/940] lr: 1.0000e-02 eta: 16:13:46 time: 0.6267 data_time: 0.1217 memory: 16095 grad_norm: 4.4437 loss: 2.7812 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.7812 2022/12/08 13:05:34 - mmengine - INFO - Epoch(train) [1][720/940] lr: 1.0000e-02 eta: 16:10:10 time: 0.5477 data_time: 0.1271 memory: 16095 grad_norm: 4.5721 loss: 2.7963 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.7963 2022/12/08 13:05:47 - mmengine - INFO - Epoch(train) [1][740/940] lr: 1.0000e-02 eta: 16:10:51 time: 0.6454 data_time: 0.1761 memory: 16095 grad_norm: 4.5469 loss: 2.6831 top1_acc: 0.3438 top5_acc: 0.7500 loss_cls: 2.6831 2022/12/08 13:05:58 - mmengine - INFO - Epoch(train) [1][760/940] lr: 1.0000e-02 eta: 16:06:41 time: 0.5275 data_time: 0.1324 memory: 16095 grad_norm: 4.4904 loss: 2.7508 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.7508 2022/12/08 13:06:11 - mmengine - INFO - Epoch(train) [1][780/940] lr: 1.0000e-02 eta: 16:07:50 time: 0.6564 data_time: 0.1652 memory: 16095 grad_norm: 4.5014 loss: 2.5259 top1_acc: 0.3750 top5_acc: 0.8438 loss_cls: 2.5259 2022/12/08 13:06:23 - mmengine - INFO - Epoch(train) [1][800/940] lr: 1.0000e-02 eta: 16:07:34 time: 0.6212 data_time: 0.2408 memory: 16095 grad_norm: 4.5160 loss: 2.5623 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.5623 2022/12/08 13:06:35 - mmengine - INFO - Epoch(train) [1][820/940] lr: 1.0000e-02 eta: 16:06:27 time: 0.5990 data_time: 0.1914 memory: 16095 grad_norm: 4.6207 loss: 2.6654 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.6654 2022/12/08 13:06:47 - mmengine - INFO - Epoch(train) [1][840/940] lr: 1.0000e-02 eta: 16:05:17 time: 0.5964 data_time: 0.1897 memory: 16095 grad_norm: 4.5152 loss: 2.4874 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.4874 2022/12/08 13:07:00 - mmengine - INFO - Epoch(train) [1][860/940] lr: 1.0000e-02 eta: 16:06:03 time: 0.6488 data_time: 0.2916 memory: 16095 grad_norm: 4.5466 loss: 2.5271 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5271 2022/12/08 13:07:11 - mmengine - INFO - Epoch(train) [1][880/940] lr: 1.0000e-02 eta: 16:03:59 time: 0.5697 data_time: 0.2116 memory: 16095 grad_norm: 4.5832 loss: 2.7302 top1_acc: 0.3125 top5_acc: 0.6562 loss_cls: 2.7302 2022/12/08 13:07:25 - mmengine - INFO - Epoch(train) [1][900/940] lr: 1.0000e-02 eta: 16:05:11 time: 0.6620 data_time: 0.2659 memory: 16095 grad_norm: 4.5313 loss: 2.5888 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 2.5888 2022/12/08 13:07:36 - mmengine - INFO - Epoch(train) [1][920/940] lr: 1.0000e-02 eta: 16:02:53 time: 0.5600 data_time: 0.2087 memory: 16095 grad_norm: 4.5886 loss: 2.6482 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 2.6482 2022/12/08 13:07:47 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 13:07:47 - mmengine - INFO - Epoch(train) [1][940/940] lr: 1.0000e-02 eta: 16:00:50 time: 0.5644 data_time: 0.1483 memory: 16095 grad_norm: 4.7537 loss: 2.6444 top1_acc: 0.1429 top5_acc: 0.7143 loss_cls: 2.6444 2022/12/08 13:08:08 - mmengine - INFO - Epoch(val) [1][20/78] eta: 0:01:00 time: 1.0443 data_time: 0.9501 memory: 1686 2022/12/08 13:08:17 - mmengine - INFO - Epoch(val) [1][40/78] eta: 0:00:28 time: 0.4601 data_time: 0.3688 memory: 1686 2022/12/08 13:08:31 - mmengine - INFO - Epoch(val) [1][60/78] eta: 0:00:13 time: 0.6892 data_time: 0.5943 memory: 1686 2022/12/08 13:08:42 - mmengine - INFO - Epoch(val) [1][78/78] acc/top1: 0.4740 acc/top5: 0.7459 acc/mean1: 0.4738 2022/12/08 13:08:45 - mmengine - INFO - The best checkpoint with 0.4740 acc/top1 at 1 epoch is saved to best_acc/top1_epoch_1.pth. 2022/12/08 13:09:00 - mmengine - INFO - Epoch(train) [2][ 20/940] lr: 1.0000e-02 eta: 16:05:31 time: 0.7712 data_time: 0.4666 memory: 16095 grad_norm: 4.5589 loss: 2.4930 top1_acc: 0.4688 top5_acc: 0.6562 loss_cls: 2.4930 2022/12/08 13:09:11 - mmengine - INFO - Epoch(train) [2][ 40/940] lr: 1.0000e-02 eta: 16:03:23 time: 0.5619 data_time: 0.2619 memory: 16095 grad_norm: 4.5987 loss: 2.5617 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 2.5617 2022/12/08 13:09:25 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 13:09:25 - mmengine - INFO - Epoch(train) [2][ 60/940] lr: 1.0000e-02 eta: 16:04:48 time: 0.6734 data_time: 0.3590 memory: 16095 grad_norm: 4.6291 loss: 2.5275 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.5275 2022/12/08 13:09:35 - mmengine - INFO - Epoch(train) [2][ 80/940] lr: 1.0000e-02 eta: 16:01:48 time: 0.5310 data_time: 0.2174 memory: 16095 grad_norm: 4.6301 loss: 2.4272 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.4272 2022/12/08 13:09:48 - mmengine - INFO - Epoch(train) [2][100/940] lr: 1.0000e-02 eta: 16:02:29 time: 0.6503 data_time: 0.3003 memory: 16095 grad_norm: 4.6332 loss: 2.3658 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.3658 2022/12/08 13:09:59 - mmengine - INFO - Epoch(train) [2][120/940] lr: 1.0000e-02 eta: 16:00:12 time: 0.5504 data_time: 0.2209 memory: 16095 grad_norm: 4.6052 loss: 2.4879 top1_acc: 0.3750 top5_acc: 0.7812 loss_cls: 2.4879 2022/12/08 13:10:12 - mmengine - INFO - Epoch(train) [2][140/940] lr: 1.0000e-02 eta: 16:00:52 time: 0.6500 data_time: 0.2070 memory: 16095 grad_norm: 4.5787 loss: 2.3915 top1_acc: 0.5000 top5_acc: 0.9062 loss_cls: 2.3915 2022/12/08 13:10:24 - mmengine - INFO - Epoch(train) [2][160/940] lr: 1.0000e-02 eta: 15:59:20 time: 0.5731 data_time: 0.0656 memory: 16095 grad_norm: 4.7161 loss: 2.4609 top1_acc: 0.4375 top5_acc: 0.6562 loss_cls: 2.4609 2022/12/08 13:10:37 - mmengine - INFO - Epoch(train) [2][180/940] lr: 1.0000e-02 eta: 15:59:49 time: 0.6446 data_time: 0.1223 memory: 16095 grad_norm: 4.7103 loss: 2.4221 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 2.4221 2022/12/08 13:10:48 - mmengine - INFO - Epoch(train) [2][200/940] lr: 1.0000e-02 eta: 15:57:28 time: 0.5410 data_time: 0.2223 memory: 16095 grad_norm: 4.7535 loss: 2.3664 top1_acc: 0.3438 top5_acc: 0.6562 loss_cls: 2.3664 2022/12/08 13:11:01 - mmengine - INFO - Epoch(train) [2][220/940] lr: 1.0000e-02 eta: 15:58:42 time: 0.6728 data_time: 0.3464 memory: 16095 grad_norm: 4.7125 loss: 2.4119 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 2.4119 2022/12/08 13:11:12 - mmengine - INFO - Epoch(train) [2][240/940] lr: 1.0000e-02 eta: 15:57:04 time: 0.5654 data_time: 0.2162 memory: 16095 grad_norm: 4.8088 loss: 2.5450 top1_acc: 0.5312 top5_acc: 0.6562 loss_cls: 2.5450 2022/12/08 13:11:25 - mmengine - INFO - Epoch(train) [2][260/940] lr: 1.0000e-02 eta: 15:57:41 time: 0.6508 data_time: 0.2580 memory: 16095 grad_norm: 4.7220 loss: 2.6190 top1_acc: 0.3438 top5_acc: 0.6562 loss_cls: 2.6190 2022/12/08 13:11:37 - mmengine - INFO - Epoch(train) [2][280/940] lr: 1.0000e-02 eta: 15:56:11 time: 0.5679 data_time: 0.1096 memory: 16095 grad_norm: 4.7341 loss: 2.3971 top1_acc: 0.3750 top5_acc: 0.6562 loss_cls: 2.3971 2022/12/08 13:11:50 - mmengine - INFO - Epoch(train) [2][300/940] lr: 1.0000e-02 eta: 15:56:48 time: 0.6516 data_time: 0.1293 memory: 16095 grad_norm: 4.6998 loss: 2.6127 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 2.6127 2022/12/08 13:12:02 - mmengine - INFO - Epoch(train) [2][320/940] lr: 1.0000e-02 eta: 15:56:16 time: 0.6055 data_time: 0.1025 memory: 16095 grad_norm: 4.8162 loss: 2.5390 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 2.5390 2022/12/08 13:12:14 - mmengine - INFO - Epoch(train) [2][340/940] lr: 1.0000e-02 eta: 15:55:52 time: 0.6107 data_time: 0.0613 memory: 16095 grad_norm: 4.7522 loss: 2.3946 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3946 2022/12/08 13:12:26 - mmengine - INFO - Epoch(train) [2][360/940] lr: 1.0000e-02 eta: 15:55:32 time: 0.6129 data_time: 0.0714 memory: 16095 grad_norm: 4.7417 loss: 2.4592 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 2.4592 2022/12/08 13:12:39 - mmengine - INFO - Epoch(train) [2][380/940] lr: 1.0000e-02 eta: 15:55:13 time: 0.6136 data_time: 0.0714 memory: 16095 grad_norm: 4.7298 loss: 2.4519 top1_acc: 0.4688 top5_acc: 0.6562 loss_cls: 2.4519 2022/12/08 13:12:51 - mmengine - INFO - Epoch(train) [2][400/940] lr: 1.0000e-02 eta: 15:54:51 time: 0.6114 data_time: 0.0526 memory: 16095 grad_norm: 4.7750 loss: 2.5997 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 2.5997 2022/12/08 13:13:02 - mmengine - INFO - Epoch(train) [2][420/940] lr: 1.0000e-02 eta: 15:53:49 time: 0.5819 data_time: 0.0620 memory: 16095 grad_norm: 4.7217 loss: 2.4564 top1_acc: 0.3438 top5_acc: 0.6875 loss_cls: 2.4564 2022/12/08 13:13:14 - mmengine - INFO - Epoch(train) [2][440/940] lr: 1.0000e-02 eta: 15:52:43 time: 0.5778 data_time: 0.0796 memory: 16095 grad_norm: 4.8145 loss: 2.4784 top1_acc: 0.3750 top5_acc: 0.6562 loss_cls: 2.4784 2022/12/08 13:13:27 - mmengine - INFO - Epoch(train) [2][460/940] lr: 1.0000e-02 eta: 15:53:28 time: 0.6605 data_time: 0.0452 memory: 16095 grad_norm: 4.8048 loss: 2.3838 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 2.3838 2022/12/08 13:13:39 - mmengine - INFO - Epoch(train) [2][480/940] lr: 1.0000e-02 eta: 15:52:39 time: 0.5895 data_time: 0.0495 memory: 16095 grad_norm: 4.8090 loss: 2.2915 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2915 2022/12/08 13:13:53 - mmengine - INFO - Epoch(train) [2][500/940] lr: 1.0000e-02 eta: 15:53:45 time: 0.6789 data_time: 0.0263 memory: 16095 grad_norm: 4.8080 loss: 2.2692 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 2.2692 2022/12/08 13:14:03 - mmengine - INFO - Epoch(train) [2][520/940] lr: 1.0000e-02 eta: 15:51:25 time: 0.5172 data_time: 0.0219 memory: 16095 grad_norm: 4.7943 loss: 2.5579 top1_acc: 0.4688 top5_acc: 0.5938 loss_cls: 2.5579 2022/12/08 13:14:16 - mmengine - INFO - Epoch(train) [2][540/940] lr: 1.0000e-02 eta: 15:51:36 time: 0.6355 data_time: 0.0235 memory: 16095 grad_norm: 4.7672 loss: 2.4522 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 2.4522 2022/12/08 13:14:27 - mmengine - INFO - Epoch(train) [2][560/940] lr: 1.0000e-02 eta: 15:49:57 time: 0.5475 data_time: 0.0253 memory: 16095 grad_norm: 4.8380 loss: 2.4528 top1_acc: 0.5625 top5_acc: 0.7188 loss_cls: 2.4528 2022/12/08 13:14:40 - mmengine - INFO - Epoch(train) [2][580/940] lr: 1.0000e-02 eta: 15:50:42 time: 0.6630 data_time: 0.0218 memory: 16095 grad_norm: 4.7826 loss: 2.3595 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 2.3595 2022/12/08 13:14:52 - mmengine - INFO - Epoch(train) [2][600/940] lr: 1.0000e-02 eta: 15:49:47 time: 0.5815 data_time: 0.0229 memory: 16095 grad_norm: 4.8343 loss: 2.3743 top1_acc: 0.4062 top5_acc: 0.8125 loss_cls: 2.3743 2022/12/08 13:15:04 - mmengine - INFO - Epoch(train) [2][620/940] lr: 1.0000e-02 eta: 15:49:52 time: 0.6309 data_time: 0.0285 memory: 16095 grad_norm: 4.7670 loss: 2.3831 top1_acc: 0.4688 top5_acc: 0.6562 loss_cls: 2.3831 2022/12/08 13:15:15 - mmengine - INFO - Epoch(train) [2][640/940] lr: 1.0000e-02 eta: 15:48:04 time: 0.5344 data_time: 0.0256 memory: 16095 grad_norm: 4.8244 loss: 2.3366 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 2.3366 2022/12/08 13:15:27 - mmengine - INFO - Epoch(train) [2][660/940] lr: 1.0000e-02 eta: 15:47:44 time: 0.6091 data_time: 0.0240 memory: 16095 grad_norm: 4.8175 loss: 2.1843 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1843 2022/12/08 13:15:40 - mmengine - INFO - Epoch(train) [2][680/940] lr: 1.0000e-02 eta: 15:47:51 time: 0.6322 data_time: 0.0267 memory: 16095 grad_norm: 4.7961 loss: 2.5016 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.5016 2022/12/08 13:15:51 - mmengine - INFO - Epoch(train) [2][700/940] lr: 1.0000e-02 eta: 15:46:57 time: 0.5788 data_time: 0.0189 memory: 16095 grad_norm: 4.8282 loss: 2.3576 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3576 2022/12/08 13:16:03 - mmengine - INFO - Epoch(train) [2][720/940] lr: 1.0000e-02 eta: 15:46:43 time: 0.6133 data_time: 0.0269 memory: 16095 grad_norm: 4.8227 loss: 2.4381 top1_acc: 0.4062 top5_acc: 0.6562 loss_cls: 2.4381 2022/12/08 13:16:16 - mmengine - INFO - Epoch(train) [2][740/940] lr: 1.0000e-02 eta: 15:46:56 time: 0.6384 data_time: 0.0195 memory: 16095 grad_norm: 4.8027 loss: 2.4340 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.4340 2022/12/08 13:16:28 - mmengine - INFO - Epoch(train) [2][760/940] lr: 1.0000e-02 eta: 15:46:30 time: 0.6027 data_time: 0.0443 memory: 16095 grad_norm: 4.8622 loss: 2.3295 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 2.3295 2022/12/08 13:16:41 - mmengine - INFO - Epoch(train) [2][780/940] lr: 1.0000e-02 eta: 15:46:59 time: 0.6539 data_time: 0.0201 memory: 16095 grad_norm: 4.9156 loss: 2.4212 top1_acc: 0.4688 top5_acc: 0.6250 loss_cls: 2.4212 2022/12/08 13:16:54 - mmengine - INFO - Epoch(train) [2][800/940] lr: 1.0000e-02 eta: 15:47:07 time: 0.6353 data_time: 0.0270 memory: 16095 grad_norm: 4.8907 loss: 2.1890 top1_acc: 0.4062 top5_acc: 0.7188 loss_cls: 2.1890 2022/12/08 13:17:06 - mmengine - INFO - Epoch(train) [2][820/940] lr: 1.0000e-02 eta: 15:46:41 time: 0.6028 data_time: 0.0194 memory: 16095 grad_norm: 4.9139 loss: 2.2503 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 2.2503 2022/12/08 13:17:17 - mmengine - INFO - Epoch(train) [2][840/940] lr: 1.0000e-02 eta: 15:45:16 time: 0.5452 data_time: 0.0248 memory: 16095 grad_norm: 4.8128 loss: 2.3282 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.3282 2022/12/08 13:17:30 - mmengine - INFO - Epoch(train) [2][860/940] lr: 1.0000e-02 eta: 15:45:14 time: 0.6256 data_time: 0.0758 memory: 16095 grad_norm: 4.8355 loss: 2.4066 top1_acc: 0.3438 top5_acc: 0.6875 loss_cls: 2.4066 2022/12/08 13:17:42 - mmengine - INFO - Epoch(train) [2][880/940] lr: 1.0000e-02 eta: 15:45:04 time: 0.6170 data_time: 0.1479 memory: 16095 grad_norm: 4.8037 loss: 2.2958 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 2.2958 2022/12/08 13:17:54 - mmengine - INFO - Epoch(train) [2][900/940] lr: 1.0000e-02 eta: 15:44:35 time: 0.5989 data_time: 0.0226 memory: 16095 grad_norm: 4.9442 loss: 2.2278 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 2.2278 2022/12/08 13:18:06 - mmengine - INFO - Epoch(train) [2][920/940] lr: 1.0000e-02 eta: 15:43:58 time: 0.5895 data_time: 0.0298 memory: 16095 grad_norm: 4.9052 loss: 2.2362 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 2.2362 2022/12/08 13:18:16 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 13:18:16 - mmengine - INFO - Epoch(train) [2][940/940] lr: 1.0000e-02 eta: 15:42:14 time: 0.5217 data_time: 0.0158 memory: 16095 grad_norm: 5.0483 loss: 2.4149 top1_acc: 0.4286 top5_acc: 0.5714 loss_cls: 2.4149 2022/12/08 13:18:30 - mmengine - INFO - Epoch(val) [2][20/78] eta: 0:00:41 time: 0.7090 data_time: 0.6153 memory: 1686 2022/12/08 13:18:40 - mmengine - INFO - Epoch(val) [2][40/78] eta: 0:00:22 time: 0.4656 data_time: 0.3732 memory: 1686 2022/12/08 13:18:53 - mmengine - INFO - Epoch(val) [2][60/78] eta: 0:00:11 time: 0.6940 data_time: 0.5991 memory: 1686 2022/12/08 13:19:04 - mmengine - INFO - Epoch(val) [2][78/78] acc/top1: 0.5272 acc/top5: 0.7841 acc/mean1: 0.5271 2022/12/08 13:19:04 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_1.pth is removed 2022/12/08 13:19:06 - mmengine - INFO - The best checkpoint with 0.5272 acc/top1 at 2 epoch is saved to best_acc/top1_epoch_2.pth. 2022/12/08 13:19:22 - mmengine - INFO - Epoch(train) [3][ 20/940] lr: 1.0000e-02 eta: 15:44:59 time: 0.7957 data_time: 0.4792 memory: 16095 grad_norm: 4.8511 loss: 2.1909 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1909 2022/12/08 13:19:34 - mmengine - INFO - Epoch(train) [3][ 40/940] lr: 1.0000e-02 eta: 15:44:19 time: 0.5870 data_time: 0.2771 memory: 16095 grad_norm: 4.7650 loss: 2.3030 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.3030 2022/12/08 13:19:47 - mmengine - INFO - Epoch(train) [3][ 60/940] lr: 1.0000e-02 eta: 15:45:02 time: 0.6734 data_time: 0.3486 memory: 16095 grad_norm: 4.8354 loss: 2.2443 top1_acc: 0.3438 top5_acc: 0.6562 loss_cls: 2.2443 2022/12/08 13:19:59 - mmengine - INFO - Epoch(train) [3][ 80/940] lr: 1.0000e-02 eta: 15:44:02 time: 0.5661 data_time: 0.2419 memory: 16095 grad_norm: 4.8187 loss: 2.2679 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.2679 2022/12/08 13:20:12 - mmengine - INFO - Epoch(train) [3][100/940] lr: 1.0000e-02 eta: 15:44:33 time: 0.6611 data_time: 0.3382 memory: 16095 grad_norm: 4.8149 loss: 2.2368 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.2368 2022/12/08 13:20:23 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 13:20:23 - mmengine - INFO - Epoch(train) [3][120/940] lr: 1.0000e-02 eta: 15:43:13 time: 0.5422 data_time: 0.2135 memory: 16095 grad_norm: 4.8439 loss: 2.1150 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 2.1150 2022/12/08 13:20:36 - mmengine - INFO - Epoch(train) [3][140/940] lr: 1.0000e-02 eta: 15:43:54 time: 0.6745 data_time: 0.3350 memory: 16095 grad_norm: 4.8859 loss: 2.3485 top1_acc: 0.3750 top5_acc: 0.5938 loss_cls: 2.3485 2022/12/08 13:20:47 - mmengine - INFO - Epoch(train) [3][160/940] lr: 1.0000e-02 eta: 15:42:31 time: 0.5365 data_time: 0.2065 memory: 16095 grad_norm: 4.8994 loss: 2.3197 top1_acc: 0.4688 top5_acc: 0.6562 loss_cls: 2.3197 2022/12/08 13:21:00 - mmengine - INFO - Epoch(train) [3][180/940] lr: 1.0000e-02 eta: 15:42:52 time: 0.6530 data_time: 0.3290 memory: 16095 grad_norm: 4.9022 loss: 2.2308 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.2308 2022/12/08 13:21:11 - mmengine - INFO - Epoch(train) [3][200/940] lr: 1.0000e-02 eta: 15:41:53 time: 0.5627 data_time: 0.2341 memory: 16095 grad_norm: 4.8963 loss: 2.1192 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1192 2022/12/08 13:21:24 - mmengine - INFO - Epoch(train) [3][220/940] lr: 1.0000e-02 eta: 15:42:04 time: 0.6408 data_time: 0.3117 memory: 16095 grad_norm: 4.9612 loss: 2.2899 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.2899 2022/12/08 13:21:35 - mmengine - INFO - Epoch(train) [3][240/940] lr: 1.0000e-02 eta: 15:40:38 time: 0.5303 data_time: 0.2227 memory: 16095 grad_norm: 4.9073 loss: 2.2210 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2210 2022/12/08 13:21:48 - mmengine - INFO - Epoch(train) [3][260/940] lr: 1.0000e-02 eta: 15:40:54 time: 0.6465 data_time: 0.3303 memory: 16095 grad_norm: 4.9410 loss: 2.1405 top1_acc: 0.3438 top5_acc: 0.6562 loss_cls: 2.1405 2022/12/08 13:21:59 - mmengine - INFO - Epoch(train) [3][280/940] lr: 1.0000e-02 eta: 15:39:49 time: 0.5527 data_time: 0.2313 memory: 16095 grad_norm: 4.9783 loss: 2.0969 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 2.0969 2022/12/08 13:22:13 - mmengine - INFO - Epoch(train) [3][300/940] lr: 1.0000e-02 eta: 15:40:47 time: 0.6979 data_time: 0.3537 memory: 16095 grad_norm: 4.9403 loss: 2.3363 top1_acc: 0.4375 top5_acc: 0.5938 loss_cls: 2.3363 2022/12/08 13:22:24 - mmengine - INFO - Epoch(train) [3][320/940] lr: 1.0000e-02 eta: 15:39:50 time: 0.5610 data_time: 0.2386 memory: 16095 grad_norm: 4.8350 loss: 2.2017 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 2.2017 2022/12/08 13:22:38 - mmengine - INFO - Epoch(train) [3][340/940] lr: 1.0000e-02 eta: 15:40:33 time: 0.6807 data_time: 0.3452 memory: 16095 grad_norm: 4.8414 loss: 2.1818 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1818 2022/12/08 13:22:48 - mmengine - INFO - Epoch(train) [3][360/940] lr: 1.0000e-02 eta: 15:39:06 time: 0.5244 data_time: 0.1833 memory: 16095 grad_norm: 4.9038 loss: 2.0820 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.0820 2022/12/08 13:23:02 - mmengine - INFO - Epoch(train) [3][380/940] lr: 1.0000e-02 eta: 15:39:53 time: 0.6871 data_time: 0.3272 memory: 16095 grad_norm: 4.8797 loss: 2.1967 top1_acc: 0.5312 top5_acc: 0.6875 loss_cls: 2.1967 2022/12/08 13:23:13 - mmengine - INFO - Epoch(train) [3][400/940] lr: 1.0000e-02 eta: 15:38:40 time: 0.5386 data_time: 0.1974 memory: 16095 grad_norm: 4.9294 loss: 2.3517 top1_acc: 0.4062 top5_acc: 0.7188 loss_cls: 2.3517 2022/12/08 13:23:26 - mmengine - INFO - Epoch(train) [3][420/940] lr: 1.0000e-02 eta: 15:39:21 time: 0.6810 data_time: 0.1942 memory: 16095 grad_norm: 5.0100 loss: 2.2543 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 2.2543 2022/12/08 13:23:37 - mmengine - INFO - Epoch(train) [3][440/940] lr: 1.0000e-02 eta: 15:38:22 time: 0.5556 data_time: 0.0425 memory: 16095 grad_norm: 4.8889 loss: 2.1326 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1326 2022/12/08 13:23:50 - mmengine - INFO - Epoch(train) [3][460/940] lr: 1.0000e-02 eta: 15:38:26 time: 0.6346 data_time: 0.1875 memory: 16095 grad_norm: 4.8738 loss: 2.1241 top1_acc: 0.3438 top5_acc: 0.7188 loss_cls: 2.1241 2022/12/08 13:24:02 - mmengine - INFO - Epoch(train) [3][480/940] lr: 1.0000e-02 eta: 15:37:55 time: 0.5907 data_time: 0.2331 memory: 16095 grad_norm: 4.9919 loss: 2.3634 top1_acc: 0.4062 top5_acc: 0.5625 loss_cls: 2.3634 2022/12/08 13:24:15 - mmengine - INFO - Epoch(train) [3][500/940] lr: 1.0000e-02 eta: 15:38:31 time: 0.6768 data_time: 0.3475 memory: 16095 grad_norm: 5.0266 loss: 2.3039 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 2.3039 2022/12/08 13:24:27 - mmengine - INFO - Epoch(train) [3][520/940] lr: 1.0000e-02 eta: 15:37:46 time: 0.5717 data_time: 0.2260 memory: 16095 grad_norm: 5.0415 loss: 2.2437 top1_acc: 0.4062 top5_acc: 0.7188 loss_cls: 2.2437 2022/12/08 13:24:40 - mmengine - INFO - Epoch(train) [3][540/940] lr: 1.0000e-02 eta: 15:38:15 time: 0.6694 data_time: 0.3352 memory: 16095 grad_norm: 4.9696 loss: 2.2512 top1_acc: 0.3438 top5_acc: 0.7500 loss_cls: 2.2512 2022/12/08 13:24:51 - mmengine - INFO - Epoch(train) [3][560/940] lr: 1.0000e-02 eta: 15:37:04 time: 0.5365 data_time: 0.2046 memory: 16095 grad_norm: 4.9845 loss: 2.3973 top1_acc: 0.4688 top5_acc: 0.6562 loss_cls: 2.3973 2022/12/08 13:25:05 - mmengine - INFO - Epoch(train) [3][580/940] lr: 1.0000e-02 eta: 15:38:01 time: 0.7073 data_time: 0.2879 memory: 16095 grad_norm: 4.9694 loss: 2.1512 top1_acc: 0.2812 top5_acc: 0.5938 loss_cls: 2.1512 2022/12/08 13:25:16 - mmengine - INFO - Epoch(train) [3][600/940] lr: 1.0000e-02 eta: 15:37:01 time: 0.5496 data_time: 0.1084 memory: 16095 grad_norm: 5.0815 loss: 2.2861 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 2.2861 2022/12/08 13:25:28 - mmengine - INFO - Epoch(train) [3][620/940] lr: 1.0000e-02 eta: 15:36:42 time: 0.6051 data_time: 0.1610 memory: 16095 grad_norm: 4.9379 loss: 2.1891 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 2.1891 2022/12/08 13:25:40 - mmengine - INFO - Epoch(train) [3][640/940] lr: 1.0000e-02 eta: 15:36:03 time: 0.5776 data_time: 0.1281 memory: 16095 grad_norm: 4.9797 loss: 2.1488 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1488 2022/12/08 13:25:53 - mmengine - INFO - Epoch(train) [3][660/940] lr: 1.0000e-02 eta: 15:36:35 time: 0.6746 data_time: 0.2750 memory: 16095 grad_norm: 5.0036 loss: 2.1540 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1540 2022/12/08 13:26:05 - mmengine - INFO - Epoch(train) [3][680/940] lr: 1.0000e-02 eta: 15:35:57 time: 0.5793 data_time: 0.1426 memory: 16095 grad_norm: 4.9610 loss: 2.2114 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.2114 2022/12/08 13:26:18 - mmengine - INFO - Epoch(train) [3][700/940] lr: 1.0000e-02 eta: 15:36:00 time: 0.6360 data_time: 0.2098 memory: 16095 grad_norm: 5.0419 loss: 2.2142 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2142 2022/12/08 13:26:29 - mmengine - INFO - Epoch(train) [3][720/940] lr: 1.0000e-02 eta: 15:35:23 time: 0.5785 data_time: 0.2230 memory: 16095 grad_norm: 4.9904 loss: 2.3038 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 2.3038 2022/12/08 13:26:43 - mmengine - INFO - Epoch(train) [3][740/940] lr: 1.0000e-02 eta: 15:35:56 time: 0.6785 data_time: 0.2688 memory: 16095 grad_norm: 4.9354 loss: 2.2559 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2559 2022/12/08 13:26:54 - mmengine - INFO - Epoch(train) [3][760/940] lr: 1.0000e-02 eta: 15:35:02 time: 0.5543 data_time: 0.0381 memory: 16095 grad_norm: 5.0477 loss: 2.2631 top1_acc: 0.3750 top5_acc: 0.7188 loss_cls: 2.2631 2022/12/08 13:27:07 - mmengine - INFO - Epoch(train) [3][780/940] lr: 1.0000e-02 eta: 15:35:21 time: 0.6600 data_time: 0.0232 memory: 16095 grad_norm: 4.9603 loss: 2.1433 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 2.1433 2022/12/08 13:27:18 - mmengine - INFO - Epoch(train) [3][800/940] lr: 1.0000e-02 eta: 15:34:20 time: 0.5427 data_time: 0.0249 memory: 16095 grad_norm: 4.8519 loss: 2.1844 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 2.1844 2022/12/08 13:27:31 - mmengine - INFO - Epoch(train) [3][820/940] lr: 1.0000e-02 eta: 15:34:48 time: 0.6733 data_time: 0.0266 memory: 16095 grad_norm: 4.9597 loss: 2.1008 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1008 2022/12/08 13:27:43 - mmengine - INFO - Epoch(train) [3][840/940] lr: 1.0000e-02 eta: 15:34:13 time: 0.5811 data_time: 0.0187 memory: 16095 grad_norm: 4.9440 loss: 2.2684 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.2684 2022/12/08 13:27:57 - mmengine - INFO - Epoch(train) [3][860/940] lr: 1.0000e-02 eta: 15:34:52 time: 0.6910 data_time: 0.0235 memory: 16095 grad_norm: 4.8210 loss: 2.2643 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.2643 2022/12/08 13:28:08 - mmengine - INFO - Epoch(train) [3][880/940] lr: 1.0000e-02 eta: 15:33:55 time: 0.5463 data_time: 0.0198 memory: 16095 grad_norm: 5.0093 loss: 2.1832 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1832 2022/12/08 13:28:20 - mmengine - INFO - Epoch(train) [3][900/940] lr: 1.0000e-02 eta: 15:33:49 time: 0.6241 data_time: 0.0235 memory: 16095 grad_norm: 4.9486 loss: 2.2390 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 2.2390 2022/12/08 13:28:32 - mmengine - INFO - Epoch(train) [3][920/940] lr: 1.0000e-02 eta: 15:33:16 time: 0.5835 data_time: 0.0231 memory: 16095 grad_norm: 4.9353 loss: 2.2391 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 2.2391 2022/12/08 13:28:43 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 13:28:43 - mmengine - INFO - Epoch(train) [3][940/940] lr: 1.0000e-02 eta: 15:32:37 time: 0.5717 data_time: 0.0163 memory: 16095 grad_norm: 5.1505 loss: 2.2526 top1_acc: 0.2857 top5_acc: 0.4286 loss_cls: 2.2526 2022/12/08 13:28:43 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/12/08 13:29:00 - mmengine - INFO - Epoch(val) [3][20/78] eta: 0:00:41 time: 0.7127 data_time: 0.6171 memory: 1686 2022/12/08 13:29:10 - mmengine - INFO - Epoch(val) [3][40/78] eta: 0:00:22 time: 0.4676 data_time: 0.3723 memory: 1686 2022/12/08 13:29:23 - mmengine - INFO - Epoch(val) [3][60/78] eta: 0:00:11 time: 0.6835 data_time: 0.5890 memory: 1686 2022/12/08 13:29:34 - mmengine - INFO - Epoch(val) [3][78/78] acc/top1: 0.5442 acc/top5: 0.7953 acc/mean1: 0.5440 2022/12/08 13:29:34 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_2.pth is removed 2022/12/08 13:29:36 - mmengine - INFO - The best checkpoint with 0.5442 acc/top1 at 3 epoch is saved to best_acc/top1_epoch_3.pth. 2022/12/08 13:29:52 - mmengine - INFO - Epoch(train) [4][ 20/940] lr: 1.0000e-02 eta: 15:34:29 time: 0.8082 data_time: 0.5016 memory: 16095 grad_norm: 4.8331 loss: 2.1111 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 2.1111 2022/12/08 13:30:03 - mmengine - INFO - Epoch(train) [4][ 40/940] lr: 1.0000e-02 eta: 15:33:21 time: 0.5277 data_time: 0.2280 memory: 16095 grad_norm: 4.9291 loss: 2.0297 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.0297 2022/12/08 13:30:17 - mmengine - INFO - Epoch(train) [4][ 60/940] lr: 1.0000e-02 eta: 15:34:03 time: 0.7002 data_time: 0.3859 memory: 16095 grad_norm: 4.9130 loss: 2.1996 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 2.1996 2022/12/08 13:30:27 - mmengine - INFO - Epoch(train) [4][ 80/940] lr: 1.0000e-02 eta: 15:33:04 time: 0.5400 data_time: 0.2205 memory: 16095 grad_norm: 4.9420 loss: 2.1959 top1_acc: 0.4375 top5_acc: 0.7188 loss_cls: 2.1959 2022/12/08 13:30:40 - mmengine - INFO - Epoch(train) [4][100/940] lr: 1.0000e-02 eta: 15:33:06 time: 0.6368 data_time: 0.3097 memory: 16095 grad_norm: 4.9427 loss: 2.1958 top1_acc: 0.4375 top5_acc: 0.6562 loss_cls: 2.1958 2022/12/08 13:30:51 - mmengine - INFO - Epoch(train) [4][120/940] lr: 1.0000e-02 eta: 15:32:06 time: 0.5391 data_time: 0.2122 memory: 16095 grad_norm: 4.9721 loss: 1.9276 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9276 2022/12/08 13:31:05 - mmengine - INFO - Epoch(train) [4][140/940] lr: 1.0000e-02 eta: 15:32:44 time: 0.6960 data_time: 0.3307 memory: 16095 grad_norm: 4.9854 loss: 2.2918 top1_acc: 0.4062 top5_acc: 0.6562 loss_cls: 2.2918 2022/12/08 13:31:15 - mmengine - INFO - Epoch(train) [4][160/940] lr: 1.0000e-02 eta: 15:31:36 time: 0.5232 data_time: 0.1449 memory: 16095 grad_norm: 5.0252 loss: 2.2343 top1_acc: 0.3438 top5_acc: 0.5938 loss_cls: 2.2343 2022/12/08 13:31:29 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 13:31:29 - mmengine - INFO - Epoch(train) [4][180/940] lr: 1.0000e-02 eta: 15:31:55 time: 0.6649 data_time: 0.2191 memory: 16095 grad_norm: 5.0105 loss: 2.1784 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 2.1784 2022/12/08 13:31:41 - mmengine - INFO - Epoch(train) [4][200/940] lr: 1.0000e-02 eta: 15:31:33 time: 0.5994 data_time: 0.1096 memory: 16095 grad_norm: 4.9932 loss: 2.2269 top1_acc: 0.5000 top5_acc: 0.6562 loss_cls: 2.2269 2022/12/08 13:31:54 - mmengine - INFO - Epoch(train) [4][220/940] lr: 1.0000e-02 eta: 15:31:57 time: 0.6749 data_time: 0.0633 memory: 16095 grad_norm: 4.9835 loss: 2.0244 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0244 2022/12/08 13:32:05 - mmengine - INFO - Epoch(train) [4][240/940] lr: 1.0000e-02 eta: 15:31:11 time: 0.5575 data_time: 0.0207 memory: 16095 grad_norm: 4.9919 loss: 2.1226 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.1226 2022/12/08 13:32:19 - mmengine - INFO - Epoch(train) [4][260/940] lr: 1.0000e-02 eta: 15:31:32 time: 0.6704 data_time: 0.0239 memory: 16095 grad_norm: 5.0504 loss: 2.1616 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1616 2022/12/08 13:32:29 - mmengine - INFO - Epoch(train) [4][280/940] lr: 1.0000e-02 eta: 15:30:25 time: 0.5218 data_time: 0.0227 memory: 16095 grad_norm: 5.0235 loss: 2.0401 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 2.0401 2022/12/08 13:32:42 - mmengine - INFO - Epoch(train) [4][300/940] lr: 1.0000e-02 eta: 15:30:32 time: 0.6471 data_time: 0.0268 memory: 16095 grad_norm: 4.9602 loss: 2.1482 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1482 2022/12/08 13:32:53 - mmengine - INFO - Epoch(train) [4][320/940] lr: 1.0000e-02 eta: 15:29:54 time: 0.5696 data_time: 0.0210 memory: 16095 grad_norm: 5.0713 loss: 2.1385 top1_acc: 0.5312 top5_acc: 0.6562 loss_cls: 2.1385 2022/12/08 13:33:06 - mmengine - INFO - Epoch(train) [4][340/940] lr: 1.0000e-02 eta: 15:29:43 time: 0.6158 data_time: 0.0762 memory: 16095 grad_norm: 5.0413 loss: 2.2305 top1_acc: 0.5000 top5_acc: 0.6562 loss_cls: 2.2305 2022/12/08 13:33:17 - mmengine - INFO - Epoch(train) [4][360/940] lr: 1.0000e-02 eta: 15:29:15 time: 0.5877 data_time: 0.1596 memory: 16095 grad_norm: 4.9384 loss: 2.1521 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 2.1521 2022/12/08 13:33:32 - mmengine - INFO - Epoch(train) [4][380/940] lr: 1.0000e-02 eta: 15:29:54 time: 0.7040 data_time: 0.2908 memory: 16095 grad_norm: 4.9352 loss: 2.0693 top1_acc: 0.4688 top5_acc: 0.5938 loss_cls: 2.0693 2022/12/08 13:33:42 - mmengine - INFO - Epoch(train) [4][400/940] lr: 1.0000e-02 eta: 15:28:46 time: 0.5152 data_time: 0.0748 memory: 16095 grad_norm: 4.9685 loss: 2.0717 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0717 2022/12/08 13:33:55 - mmengine - INFO - Epoch(train) [4][420/940] lr: 1.0000e-02 eta: 15:28:53 time: 0.6481 data_time: 0.1567 memory: 16095 grad_norm: 5.0654 loss: 2.1727 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 2.1727 2022/12/08 13:34:06 - mmengine - INFO - Epoch(train) [4][440/940] lr: 1.0000e-02 eta: 15:28:19 time: 0.5749 data_time: 0.0769 memory: 16095 grad_norm: 5.0131 loss: 1.9523 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9523 2022/12/08 13:34:19 - mmengine - INFO - Epoch(train) [4][460/940] lr: 1.0000e-02 eta: 15:28:20 time: 0.6394 data_time: 0.0249 memory: 16095 grad_norm: 4.9982 loss: 2.0267 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 2.0267 2022/12/08 13:34:30 - mmengine - INFO - Epoch(train) [4][480/940] lr: 1.0000e-02 eta: 15:27:37 time: 0.5569 data_time: 0.0365 memory: 16095 grad_norm: 4.9149 loss: 1.8620 top1_acc: 0.4375 top5_acc: 0.5000 loss_cls: 1.8620 2022/12/08 13:34:43 - mmengine - INFO - Epoch(train) [4][500/940] lr: 1.0000e-02 eta: 15:27:42 time: 0.6466 data_time: 0.0843 memory: 16095 grad_norm: 5.0389 loss: 2.1993 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 2.1993 2022/12/08 13:34:55 - mmengine - INFO - Epoch(train) [4][520/940] lr: 1.0000e-02 eta: 15:27:19 time: 0.5930 data_time: 0.0477 memory: 16095 grad_norm: 5.0180 loss: 2.1460 top1_acc: 0.4688 top5_acc: 0.6250 loss_cls: 2.1460 2022/12/08 13:35:09 - mmengine - INFO - Epoch(train) [4][540/940] lr: 1.0000e-02 eta: 15:27:46 time: 0.6860 data_time: 0.0242 memory: 16095 grad_norm: 4.9237 loss: 1.9947 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9947 2022/12/08 13:35:19 - mmengine - INFO - Epoch(train) [4][560/940] lr: 1.0000e-02 eta: 15:26:45 time: 0.5236 data_time: 0.0179 memory: 16095 grad_norm: 4.9265 loss: 2.0490 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 2.0490 2022/12/08 13:35:32 - mmengine - INFO - Epoch(train) [4][580/940] lr: 1.0000e-02 eta: 15:27:00 time: 0.6645 data_time: 0.0834 memory: 16095 grad_norm: 4.9784 loss: 2.0739 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0739 2022/12/08 13:35:45 - mmengine - INFO - Epoch(train) [4][600/940] lr: 1.0000e-02 eta: 15:26:47 time: 0.6140 data_time: 0.0691 memory: 16095 grad_norm: 4.9635 loss: 2.1277 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 2.1277 2022/12/08 13:35:58 - mmengine - INFO - Epoch(train) [4][620/940] lr: 1.0000e-02 eta: 15:27:05 time: 0.6698 data_time: 0.0185 memory: 16095 grad_norm: 5.0567 loss: 2.1854 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 2.1854 2022/12/08 13:36:10 - mmengine - INFO - Epoch(train) [4][640/940] lr: 1.0000e-02 eta: 15:26:31 time: 0.5736 data_time: 0.0220 memory: 16095 grad_norm: 5.0206 loss: 2.2212 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.2212 2022/12/08 13:36:23 - mmengine - INFO - Epoch(train) [4][660/940] lr: 1.0000e-02 eta: 15:26:51 time: 0.6754 data_time: 0.0216 memory: 16095 grad_norm: 5.1491 loss: 2.2939 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2939 2022/12/08 13:36:35 - mmengine - INFO - Epoch(train) [4][680/940] lr: 1.0000e-02 eta: 15:26:22 time: 0.5820 data_time: 0.0208 memory: 16095 grad_norm: 4.9710 loss: 2.0528 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 2.0528 2022/12/08 13:36:47 - mmengine - INFO - Epoch(train) [4][700/940] lr: 1.0000e-02 eta: 15:26:09 time: 0.6127 data_time: 0.0323 memory: 16095 grad_norm: 4.9257 loss: 2.0871 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0871 2022/12/08 13:36:58 - mmengine - INFO - Epoch(train) [4][720/940] lr: 1.0000e-02 eta: 15:25:21 time: 0.5442 data_time: 0.0215 memory: 16095 grad_norm: 5.0641 loss: 2.0890 top1_acc: 0.3750 top5_acc: 0.7812 loss_cls: 2.0890 2022/12/08 13:37:11 - mmengine - INFO - Epoch(train) [4][740/940] lr: 1.0000e-02 eta: 15:25:24 time: 0.6446 data_time: 0.0226 memory: 16095 grad_norm: 5.0456 loss: 2.1623 top1_acc: 0.4375 top5_acc: 0.5938 loss_cls: 2.1623 2022/12/08 13:37:22 - mmengine - INFO - Epoch(train) [4][760/940] lr: 1.0000e-02 eta: 15:24:56 time: 0.5832 data_time: 0.0237 memory: 16095 grad_norm: 4.9630 loss: 2.0424 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0424 2022/12/08 13:37:35 - mmengine - INFO - Epoch(train) [4][780/940] lr: 1.0000e-02 eta: 15:24:56 time: 0.6377 data_time: 0.0263 memory: 16095 grad_norm: 4.9947 loss: 1.9895 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 1.9895 2022/12/08 13:37:46 - mmengine - INFO - Epoch(train) [4][800/940] lr: 1.0000e-02 eta: 15:24:16 time: 0.5576 data_time: 0.0188 memory: 16095 grad_norm: 5.0276 loss: 2.0334 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 2.0334 2022/12/08 13:38:00 - mmengine - INFO - Epoch(train) [4][820/940] lr: 1.0000e-02 eta: 15:24:28 time: 0.6640 data_time: 0.0252 memory: 16095 grad_norm: 4.9928 loss: 2.0459 top1_acc: 0.5312 top5_acc: 0.6875 loss_cls: 2.0459 2022/12/08 13:38:11 - mmengine - INFO - Epoch(train) [4][840/940] lr: 1.0000e-02 eta: 15:23:58 time: 0.5770 data_time: 0.0808 memory: 16095 grad_norm: 5.0853 loss: 2.0433 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0433 2022/12/08 13:38:25 - mmengine - INFO - Epoch(train) [4][860/940] lr: 1.0000e-02 eta: 15:24:12 time: 0.6680 data_time: 0.2240 memory: 16095 grad_norm: 5.0579 loss: 2.1281 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1281 2022/12/08 13:38:36 - mmengine - INFO - Epoch(train) [4][880/940] lr: 1.0000e-02 eta: 15:23:35 time: 0.5626 data_time: 0.1489 memory: 16095 grad_norm: 5.1520 loss: 2.1169 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1169 2022/12/08 13:38:49 - mmengine - INFO - Epoch(train) [4][900/940] lr: 1.0000e-02 eta: 15:23:48 time: 0.6662 data_time: 0.1122 memory: 16095 grad_norm: 4.9388 loss: 2.1466 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 2.1466 2022/12/08 13:39:01 - mmengine - INFO - Epoch(train) [4][920/940] lr: 1.0000e-02 eta: 15:23:27 time: 0.5964 data_time: 0.1244 memory: 16095 grad_norm: 4.9326 loss: 2.0231 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 2.0231 2022/12/08 13:39:11 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 13:39:11 - mmengine - INFO - Epoch(train) [4][940/940] lr: 1.0000e-02 eta: 15:22:14 time: 0.4878 data_time: 0.1613 memory: 16095 grad_norm: 5.3155 loss: 2.2013 top1_acc: 0.2857 top5_acc: 0.7143 loss_cls: 2.2013 2022/12/08 13:39:25 - mmengine - INFO - Epoch(val) [4][20/78] eta: 0:00:40 time: 0.7052 data_time: 0.6106 memory: 1686 2022/12/08 13:39:34 - mmengine - INFO - Epoch(val) [4][40/78] eta: 0:00:22 time: 0.4613 data_time: 0.3658 memory: 1686 2022/12/08 13:39:48 - mmengine - INFO - Epoch(val) [4][60/78] eta: 0:00:11 time: 0.6807 data_time: 0.5847 memory: 1686 2022/12/08 13:39:59 - mmengine - INFO - Epoch(val) [4][78/78] acc/top1: 0.5699 acc/top5: 0.8104 acc/mean1: 0.5697 2022/12/08 13:39:59 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_3.pth is removed 2022/12/08 13:40:01 - mmengine - INFO - The best checkpoint with 0.5699 acc/top1 at 4 epoch is saved to best_acc/top1_epoch_4.pth. 2022/12/08 13:40:17 - mmengine - INFO - Epoch(train) [5][ 20/940] lr: 1.0000e-02 eta: 15:23:40 time: 0.8173 data_time: 0.5160 memory: 16095 grad_norm: 5.0937 loss: 2.1698 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 2.1698 2022/12/08 13:40:29 - mmengine - INFO - Epoch(train) [5][ 40/940] lr: 1.0000e-02 eta: 15:23:06 time: 0.5689 data_time: 0.2546 memory: 16095 grad_norm: 4.9508 loss: 2.1362 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 2.1362 2022/12/08 13:40:42 - mmengine - INFO - Epoch(train) [5][ 60/940] lr: 1.0000e-02 eta: 15:23:11 time: 0.6512 data_time: 0.3375 memory: 16095 grad_norm: 4.8832 loss: 1.9047 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.9047 2022/12/08 13:40:52 - mmengine - INFO - Epoch(train) [5][ 80/940] lr: 1.0000e-02 eta: 15:22:21 time: 0.5338 data_time: 0.2224 memory: 16095 grad_norm: 4.9498 loss: 2.0007 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0007 2022/12/08 13:41:06 - mmengine - INFO - Epoch(train) [5][100/940] lr: 1.0000e-02 eta: 15:22:43 time: 0.6866 data_time: 0.3488 memory: 16095 grad_norm: 5.0476 loss: 2.1471 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1471 2022/12/08 13:41:17 - mmengine - INFO - Epoch(train) [5][120/940] lr: 1.0000e-02 eta: 15:22:03 time: 0.5543 data_time: 0.2189 memory: 16095 grad_norm: 4.9727 loss: 1.9917 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9917 2022/12/08 13:41:31 - mmengine - INFO - Epoch(train) [5][140/940] lr: 1.0000e-02 eta: 15:22:19 time: 0.6755 data_time: 0.3549 memory: 16095 grad_norm: 4.9966 loss: 1.9259 top1_acc: 0.5312 top5_acc: 0.6875 loss_cls: 1.9259 2022/12/08 13:41:41 - mmengine - INFO - Epoch(train) [5][160/940] lr: 1.0000e-02 eta: 15:21:29 time: 0.5319 data_time: 0.2116 memory: 16095 grad_norm: 5.0332 loss: 2.0507 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 2.0507 2022/12/08 13:41:54 - mmengine - INFO - Epoch(train) [5][180/940] lr: 1.0000e-02 eta: 15:21:26 time: 0.6342 data_time: 0.3051 memory: 16095 grad_norm: 4.9250 loss: 2.1174 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 2.1174 2022/12/08 13:42:06 - mmengine - INFO - Epoch(train) [5][200/940] lr: 1.0000e-02 eta: 15:20:58 time: 0.5787 data_time: 0.2651 memory: 16095 grad_norm: 5.0616 loss: 1.8195 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8195 2022/12/08 13:42:19 - mmengine - INFO - Epoch(train) [5][220/940] lr: 1.0000e-02 eta: 15:21:16 time: 0.6827 data_time: 0.3497 memory: 16095 grad_norm: 5.0603 loss: 2.0443 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0443 2022/12/08 13:42:30 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 13:42:30 - mmengine - INFO - Epoch(train) [5][240/940] lr: 1.0000e-02 eta: 15:20:31 time: 0.5406 data_time: 0.2123 memory: 16095 grad_norm: 5.0907 loss: 2.0692 top1_acc: 0.4688 top5_acc: 0.6562 loss_cls: 2.0692 2022/12/08 13:42:44 - mmengine - INFO - Epoch(train) [5][260/940] lr: 1.0000e-02 eta: 15:20:59 time: 0.7044 data_time: 0.3709 memory: 16095 grad_norm: 5.0051 loss: 1.9417 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9417 2022/12/08 13:42:56 - mmengine - INFO - Epoch(train) [5][280/940] lr: 1.0000e-02 eta: 15:20:28 time: 0.5705 data_time: 0.1519 memory: 16095 grad_norm: 5.0354 loss: 2.0635 top1_acc: 0.3125 top5_acc: 0.7188 loss_cls: 2.0635 2022/12/08 13:43:09 - mmengine - INFO - Epoch(train) [5][300/940] lr: 1.0000e-02 eta: 15:20:46 time: 0.6829 data_time: 0.1637 memory: 16095 grad_norm: 5.0762 loss: 2.1408 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 2.1408 2022/12/08 13:43:20 - mmengine - INFO - Epoch(train) [5][320/940] lr: 1.0000e-02 eta: 15:19:52 time: 0.5199 data_time: 0.1267 memory: 16095 grad_norm: 5.0789 loss: 2.1037 top1_acc: 0.4062 top5_acc: 0.5938 loss_cls: 2.1037 2022/12/08 13:43:33 - mmengine - INFO - Epoch(train) [5][340/940] lr: 1.0000e-02 eta: 15:19:59 time: 0.6583 data_time: 0.1346 memory: 16095 grad_norm: 5.0460 loss: 2.0054 top1_acc: 0.5938 top5_acc: 0.6875 loss_cls: 2.0054 2022/12/08 13:43:43 - mmengine - INFO - Epoch(train) [5][360/940] lr: 1.0000e-02 eta: 15:19:05 time: 0.5184 data_time: 0.1261 memory: 16095 grad_norm: 4.9595 loss: 2.1654 top1_acc: 0.3438 top5_acc: 0.6875 loss_cls: 2.1654 2022/12/08 13:43:57 - mmengine - INFO - Epoch(train) [5][380/940] lr: 1.0000e-02 eta: 15:19:24 time: 0.6853 data_time: 0.3507 memory: 16095 grad_norm: 5.0353 loss: 2.0555 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 2.0555 2022/12/08 13:44:08 - mmengine - INFO - Epoch(train) [5][400/940] lr: 1.0000e-02 eta: 15:18:56 time: 0.5768 data_time: 0.2344 memory: 16095 grad_norm: 5.0172 loss: 2.2145 top1_acc: 0.4688 top5_acc: 0.6562 loss_cls: 2.2145 2022/12/08 13:44:21 - mmengine - INFO - Epoch(train) [5][420/940] lr: 1.0000e-02 eta: 15:18:52 time: 0.6326 data_time: 0.3007 memory: 16095 grad_norm: 4.9438 loss: 2.1281 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 2.1281 2022/12/08 13:44:33 - mmengine - INFO - Epoch(train) [5][440/940] lr: 1.0000e-02 eta: 15:18:30 time: 0.5916 data_time: 0.1593 memory: 16095 grad_norm: 5.0255 loss: 2.1961 top1_acc: 0.2812 top5_acc: 0.5625 loss_cls: 2.1961 2022/12/08 13:44:44 - mmengine - INFO - Epoch(train) [5][460/940] lr: 1.0000e-02 eta: 15:18:04 time: 0.5815 data_time: 0.0992 memory: 16095 grad_norm: 5.0701 loss: 1.9694 top1_acc: 0.5312 top5_acc: 0.6875 loss_cls: 1.9694 2022/12/08 13:44:56 - mmengine - INFO - Epoch(train) [5][480/940] lr: 1.0000e-02 eta: 15:17:46 time: 0.5989 data_time: 0.1381 memory: 16095 grad_norm: 5.0986 loss: 2.0893 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 2.0893 2022/12/08 13:45:10 - mmengine - INFO - Epoch(train) [5][500/940] lr: 1.0000e-02 eta: 15:18:04 time: 0.6859 data_time: 0.3411 memory: 16095 grad_norm: 4.9971 loss: 2.0089 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 2.0089 2022/12/08 13:45:22 - mmengine - INFO - Epoch(train) [5][520/940] lr: 1.0000e-02 eta: 15:17:39 time: 0.5840 data_time: 0.2619 memory: 16095 grad_norm: 4.9609 loss: 2.0771 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0771 2022/12/08 13:45:35 - mmengine - INFO - Epoch(train) [5][540/940] lr: 1.0000e-02 eta: 15:17:47 time: 0.6622 data_time: 0.3240 memory: 16095 grad_norm: 4.9793 loss: 2.0153 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0153 2022/12/08 13:45:46 - mmengine - INFO - Epoch(train) [5][560/940] lr: 1.0000e-02 eta: 15:17:10 time: 0.5549 data_time: 0.2340 memory: 16095 grad_norm: 5.0632 loss: 1.9977 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9977 2022/12/08 13:46:01 - mmengine - INFO - Epoch(train) [5][580/940] lr: 1.0000e-02 eta: 15:17:41 time: 0.7165 data_time: 0.3804 memory: 16095 grad_norm: 5.0519 loss: 1.9532 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9532 2022/12/08 13:46:11 - mmengine - INFO - Epoch(train) [5][600/940] lr: 1.0000e-02 eta: 15:16:53 time: 0.5284 data_time: 0.1957 memory: 16095 grad_norm: 5.0411 loss: 1.9857 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.9857 2022/12/08 13:46:25 - mmengine - INFO - Epoch(train) [5][620/940] lr: 1.0000e-02 eta: 15:17:07 time: 0.6789 data_time: 0.3445 memory: 16095 grad_norm: 4.9530 loss: 2.0687 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 2.0687 2022/12/08 13:46:36 - mmengine - INFO - Epoch(train) [5][640/940] lr: 1.0000e-02 eta: 15:16:26 time: 0.5427 data_time: 0.2160 memory: 16095 grad_norm: 4.9714 loss: 1.8785 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8785 2022/12/08 13:46:50 - mmengine - INFO - Epoch(train) [5][660/940] lr: 1.0000e-02 eta: 15:16:56 time: 0.7175 data_time: 0.3782 memory: 16095 grad_norm: 4.9580 loss: 1.9667 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.9667 2022/12/08 13:47:00 - mmengine - INFO - Epoch(train) [5][680/940] lr: 1.0000e-02 eta: 15:16:05 time: 0.5176 data_time: 0.1903 memory: 16095 grad_norm: 5.0297 loss: 2.0274 top1_acc: 0.5625 top5_acc: 0.7188 loss_cls: 2.0274 2022/12/08 13:47:13 - mmengine - INFO - Epoch(train) [5][700/940] lr: 1.0000e-02 eta: 15:16:05 time: 0.6453 data_time: 0.3125 memory: 16095 grad_norm: 5.0741 loss: 2.0521 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 2.0521 2022/12/08 13:47:24 - mmengine - INFO - Epoch(train) [5][720/940] lr: 1.0000e-02 eta: 15:15:31 time: 0.5595 data_time: 0.2181 memory: 16095 grad_norm: 5.0342 loss: 2.0653 top1_acc: 0.4375 top5_acc: 0.6562 loss_cls: 2.0653 2022/12/08 13:47:38 - mmengine - INFO - Epoch(train) [5][740/940] lr: 1.0000e-02 eta: 15:15:46 time: 0.6826 data_time: 0.3408 memory: 16095 grad_norm: 4.9917 loss: 1.9047 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9047 2022/12/08 13:47:49 - mmengine - INFO - Epoch(train) [5][760/940] lr: 1.0000e-02 eta: 15:15:07 time: 0.5446 data_time: 0.2083 memory: 16095 grad_norm: 5.0186 loss: 2.0111 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.0111 2022/12/08 13:48:02 - mmengine - INFO - Epoch(train) [5][780/940] lr: 1.0000e-02 eta: 15:15:08 time: 0.6491 data_time: 0.3193 memory: 16095 grad_norm: 5.0286 loss: 2.1624 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 2.1624 2022/12/08 13:48:13 - mmengine - INFO - Epoch(train) [5][800/940] lr: 1.0000e-02 eta: 15:14:29 time: 0.5462 data_time: 0.2217 memory: 16095 grad_norm: 5.0535 loss: 2.0255 top1_acc: 0.3438 top5_acc: 0.6875 loss_cls: 2.0255 2022/12/08 13:48:27 - mmengine - INFO - Epoch(train) [5][820/940] lr: 1.0000e-02 eta: 15:14:54 time: 0.7083 data_time: 0.3865 memory: 16095 grad_norm: 4.9877 loss: 2.0722 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0722 2022/12/08 13:48:38 - mmengine - INFO - Epoch(train) [5][840/940] lr: 1.0000e-02 eta: 15:14:10 time: 0.5323 data_time: 0.2025 memory: 16095 grad_norm: 5.0307 loss: 1.8598 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8598 2022/12/08 13:48:52 - mmengine - INFO - Epoch(train) [5][860/940] lr: 1.0000e-02 eta: 15:14:31 time: 0.6984 data_time: 0.3767 memory: 16095 grad_norm: 5.0558 loss: 2.0533 top1_acc: 0.3750 top5_acc: 0.6562 loss_cls: 2.0533 2022/12/08 13:49:02 - mmengine - INFO - Epoch(train) [5][880/940] lr: 1.0000e-02 eta: 15:13:40 time: 0.5134 data_time: 0.1844 memory: 16095 grad_norm: 5.1039 loss: 2.0275 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 2.0275 2022/12/08 13:49:16 - mmengine - INFO - Epoch(train) [5][900/940] lr: 1.0000e-02 eta: 15:13:56 time: 0.6867 data_time: 0.3519 memory: 16095 grad_norm: 5.0094 loss: 1.9480 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.9480 2022/12/08 13:49:27 - mmengine - INFO - Epoch(train) [5][920/940] lr: 1.0000e-02 eta: 15:13:28 time: 0.5725 data_time: 0.2445 memory: 16095 grad_norm: 5.0754 loss: 1.9176 top1_acc: 0.4062 top5_acc: 0.5312 loss_cls: 1.9176 2022/12/08 13:49:38 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 13:49:38 - mmengine - INFO - Epoch(train) [5][940/940] lr: 1.0000e-02 eta: 15:12:57 time: 0.5665 data_time: 0.2723 memory: 16095 grad_norm: 5.2640 loss: 1.9319 top1_acc: 0.5714 top5_acc: 1.0000 loss_cls: 1.9319 2022/12/08 13:49:53 - mmengine - INFO - Epoch(val) [5][20/78] eta: 0:00:41 time: 0.7114 data_time: 0.6152 memory: 1686 2022/12/08 13:50:02 - mmengine - INFO - Epoch(val) [5][40/78] eta: 0:00:22 time: 0.4534 data_time: 0.3581 memory: 1686 2022/12/08 13:50:15 - mmengine - INFO - Epoch(val) [5][60/78] eta: 0:00:11 time: 0.6707 data_time: 0.5766 memory: 1686 2022/12/08 13:50:26 - mmengine - INFO - Epoch(val) [5][78/78] acc/top1: 0.5831 acc/top5: 0.8189 acc/mean1: 0.5830 2022/12/08 13:50:26 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_4.pth is removed 2022/12/08 13:50:29 - mmengine - INFO - The best checkpoint with 0.5831 acc/top1 at 5 epoch is saved to best_acc/top1_epoch_5.pth. 2022/12/08 13:50:45 - mmengine - INFO - Epoch(train) [6][ 20/940] lr: 1.0000e-02 eta: 15:13:54 time: 0.7956 data_time: 0.4662 memory: 16095 grad_norm: 5.1176 loss: 2.1448 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 2.1448 2022/12/08 13:50:56 - mmengine - INFO - Epoch(train) [6][ 40/940] lr: 1.0000e-02 eta: 15:13:23 time: 0.5649 data_time: 0.2519 memory: 16095 grad_norm: 4.9791 loss: 1.8509 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.8509 2022/12/08 13:51:08 - mmengine - INFO - Epoch(train) [6][ 60/940] lr: 1.0000e-02 eta: 15:13:15 time: 0.6260 data_time: 0.3092 memory: 16095 grad_norm: 4.9734 loss: 1.8215 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.8215 2022/12/08 13:51:20 - mmengine - INFO - Epoch(train) [6][ 80/940] lr: 1.0000e-02 eta: 15:12:55 time: 0.5914 data_time: 0.2906 memory: 16095 grad_norm: 5.1092 loss: 1.9368 top1_acc: 0.4062 top5_acc: 0.7812 loss_cls: 1.9368 2022/12/08 13:51:33 - mmengine - INFO - Epoch(train) [6][100/940] lr: 1.0000e-02 eta: 15:13:00 time: 0.6624 data_time: 0.3542 memory: 16095 grad_norm: 5.0946 loss: 2.0095 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 2.0095 2022/12/08 13:51:45 - mmengine - INFO - Epoch(train) [6][120/940] lr: 1.0000e-02 eta: 15:12:30 time: 0.5663 data_time: 0.2631 memory: 16095 grad_norm: 5.0640 loss: 1.7909 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.7909 2022/12/08 13:51:57 - mmengine - INFO - Epoch(train) [6][140/940] lr: 1.0000e-02 eta: 15:12:23 time: 0.6266 data_time: 0.3166 memory: 16095 grad_norm: 5.0443 loss: 1.8324 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8324 2022/12/08 13:52:09 - mmengine - INFO - Epoch(train) [6][160/940] lr: 1.0000e-02 eta: 15:11:56 time: 0.5734 data_time: 0.2694 memory: 16095 grad_norm: 5.0715 loss: 2.1048 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 2.1048 2022/12/08 13:52:22 - mmengine - INFO - Epoch(train) [6][180/940] lr: 1.0000e-02 eta: 15:12:07 time: 0.6776 data_time: 0.3578 memory: 16095 grad_norm: 5.0063 loss: 1.9247 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.9247 2022/12/08 13:52:34 - mmengine - INFO - Epoch(train) [6][200/940] lr: 1.0000e-02 eta: 15:11:47 time: 0.5949 data_time: 0.2666 memory: 16095 grad_norm: 5.0521 loss: 1.9839 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.9839 2022/12/08 13:52:47 - mmengine - INFO - Epoch(train) [6][220/940] lr: 1.0000e-02 eta: 15:11:53 time: 0.6639 data_time: 0.2821 memory: 16095 grad_norm: 5.0532 loss: 2.0864 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0864 2022/12/08 13:52:58 - mmengine - INFO - Epoch(train) [6][240/940] lr: 1.0000e-02 eta: 15:11:08 time: 0.5222 data_time: 0.1435 memory: 16095 grad_norm: 4.9602 loss: 1.8701 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8701 2022/12/08 13:53:11 - mmengine - INFO - Epoch(train) [6][260/940] lr: 1.0000e-02 eta: 15:11:17 time: 0.6726 data_time: 0.2083 memory: 16095 grad_norm: 5.0472 loss: 1.9376 top1_acc: 0.4062 top5_acc: 0.6562 loss_cls: 1.9376 2022/12/08 13:53:23 - mmengine - INFO - Epoch(train) [6][280/940] lr: 1.0000e-02 eta: 15:10:57 time: 0.5937 data_time: 0.1066 memory: 16095 grad_norm: 5.0929 loss: 1.8793 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.8793 2022/12/08 13:53:36 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 13:53:36 - mmengine - INFO - Epoch(train) [6][300/940] lr: 1.0000e-02 eta: 15:10:51 time: 0.6330 data_time: 0.0596 memory: 16095 grad_norm: 4.9974 loss: 1.8876 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 1.8876 2022/12/08 13:53:48 - mmengine - INFO - Epoch(train) [6][320/940] lr: 1.0000e-02 eta: 15:10:29 time: 0.5852 data_time: 0.0707 memory: 16095 grad_norm: 4.9601 loss: 1.9857 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9857 2022/12/08 13:54:01 - mmengine - INFO - Epoch(train) [6][340/940] lr: 1.0000e-02 eta: 15:10:29 time: 0.6486 data_time: 0.0741 memory: 16095 grad_norm: 5.1004 loss: 2.0140 top1_acc: 0.4062 top5_acc: 0.6250 loss_cls: 2.0140 2022/12/08 13:54:11 - mmengine - INFO - Epoch(train) [6][360/940] lr: 1.0000e-02 eta: 15:09:52 time: 0.5452 data_time: 0.0587 memory: 16095 grad_norm: 4.9664 loss: 1.8820 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.8820 2022/12/08 13:54:24 - mmengine - INFO - Epoch(train) [6][380/940] lr: 1.0000e-02 eta: 15:09:41 time: 0.6150 data_time: 0.0801 memory: 16095 grad_norm: 4.9940 loss: 2.0158 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 2.0158 2022/12/08 13:54:36 - mmengine - INFO - Epoch(train) [6][400/940] lr: 1.0000e-02 eta: 15:09:24 time: 0.6015 data_time: 0.1947 memory: 16095 grad_norm: 5.0076 loss: 2.0210 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0210 2022/12/08 13:54:49 - mmengine - INFO - Epoch(train) [6][420/940] lr: 1.0000e-02 eta: 15:09:19 time: 0.6353 data_time: 0.0874 memory: 16095 grad_norm: 5.0368 loss: 1.9953 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.9953 2022/12/08 13:55:01 - mmengine - INFO - Epoch(train) [6][440/940] lr: 1.0000e-02 eta: 15:09:07 time: 0.6134 data_time: 0.1815 memory: 16095 grad_norm: 4.9448 loss: 1.8895 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.8895 2022/12/08 13:55:12 - mmengine - INFO - Epoch(train) [6][460/940] lr: 1.0000e-02 eta: 15:08:44 time: 0.5847 data_time: 0.1155 memory: 16095 grad_norm: 4.9860 loss: 1.8303 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 1.8303 2022/12/08 13:55:25 - mmengine - INFO - Epoch(train) [6][480/940] lr: 1.0000e-02 eta: 15:08:43 time: 0.6457 data_time: 0.3255 memory: 16095 grad_norm: 5.0671 loss: 2.0451 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 2.0451 2022/12/08 13:55:37 - mmengine - INFO - Epoch(train) [6][500/940] lr: 1.0000e-02 eta: 15:08:17 time: 0.5720 data_time: 0.2281 memory: 16095 grad_norm: 5.1464 loss: 1.9920 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.9920 2022/12/08 13:55:51 - mmengine - INFO - Epoch(train) [6][520/940] lr: 1.0000e-02 eta: 15:08:31 time: 0.6930 data_time: 0.3619 memory: 16095 grad_norm: 5.0323 loss: 1.9067 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.9067 2022/12/08 13:56:02 - mmengine - INFO - Epoch(train) [6][540/940] lr: 1.0000e-02 eta: 15:08:07 time: 0.5790 data_time: 0.2497 memory: 16095 grad_norm: 5.1037 loss: 1.8766 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 1.8766 2022/12/08 13:56:16 - mmengine - INFO - Epoch(train) [6][560/940] lr: 1.0000e-02 eta: 15:08:15 time: 0.6741 data_time: 0.3507 memory: 16095 grad_norm: 5.0061 loss: 1.9437 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 1.9437 2022/12/08 13:56:27 - mmengine - INFO - Epoch(train) [6][580/940] lr: 1.0000e-02 eta: 15:07:46 time: 0.5644 data_time: 0.2314 memory: 16095 grad_norm: 5.0298 loss: 1.9910 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 1.9910 2022/12/08 13:56:40 - mmengine - INFO - Epoch(train) [6][600/940] lr: 1.0000e-02 eta: 15:07:48 time: 0.6550 data_time: 0.3270 memory: 16095 grad_norm: 5.0211 loss: 1.9178 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9178 2022/12/08 13:56:51 - mmengine - INFO - Epoch(train) [6][620/940] lr: 1.0000e-02 eta: 15:07:03 time: 0.5172 data_time: 0.1888 memory: 16095 grad_norm: 5.0954 loss: 2.0590 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 2.0590 2022/12/08 13:57:04 - mmengine - INFO - Epoch(train) [6][640/940] lr: 1.0000e-02 eta: 15:07:07 time: 0.6615 data_time: 0.3235 memory: 16095 grad_norm: 5.0246 loss: 1.9226 top1_acc: 0.5938 top5_acc: 0.6875 loss_cls: 1.9226 2022/12/08 13:57:15 - mmengine - INFO - Epoch(train) [6][660/940] lr: 1.0000e-02 eta: 15:06:36 time: 0.5582 data_time: 0.2088 memory: 16095 grad_norm: 4.9797 loss: 2.1598 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1598 2022/12/08 13:57:28 - mmengine - INFO - Epoch(train) [6][680/940] lr: 1.0000e-02 eta: 15:06:33 time: 0.6428 data_time: 0.2694 memory: 16095 grad_norm: 5.0645 loss: 2.0204 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.0204 2022/12/08 13:57:40 - mmengine - INFO - Epoch(train) [6][700/940] lr: 1.0000e-02 eta: 15:06:15 time: 0.5956 data_time: 0.1347 memory: 16095 grad_norm: 5.2009 loss: 2.0707 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 2.0707 2022/12/08 13:57:51 - mmengine - INFO - Epoch(train) [6][720/940] lr: 1.0000e-02 eta: 15:05:52 time: 0.5810 data_time: 0.1103 memory: 16095 grad_norm: 5.0717 loss: 1.9278 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.9278 2022/12/08 13:58:04 - mmengine - INFO - Epoch(train) [6][740/940] lr: 1.0000e-02 eta: 15:05:44 time: 0.6279 data_time: 0.1297 memory: 16095 grad_norm: 5.1144 loss: 1.9735 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.9735 2022/12/08 13:58:16 - mmengine - INFO - Epoch(train) [6][760/940] lr: 1.0000e-02 eta: 15:05:33 time: 0.6155 data_time: 0.2152 memory: 16095 grad_norm: 5.0477 loss: 1.8614 top1_acc: 0.3750 top5_acc: 0.7812 loss_cls: 1.8614 2022/12/08 13:58:29 - mmengine - INFO - Epoch(train) [6][780/940] lr: 1.0000e-02 eta: 15:05:27 time: 0.6331 data_time: 0.0987 memory: 16095 grad_norm: 5.0564 loss: 2.0462 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 2.0462 2022/12/08 13:58:40 - mmengine - INFO - Epoch(train) [6][800/940] lr: 1.0000e-02 eta: 15:05:03 time: 0.5783 data_time: 0.1508 memory: 16095 grad_norm: 5.0139 loss: 2.0159 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 2.0159 2022/12/08 13:58:52 - mmengine - INFO - Epoch(train) [6][820/940] lr: 1.0000e-02 eta: 15:04:40 time: 0.5794 data_time: 0.1431 memory: 16095 grad_norm: 5.1137 loss: 1.9972 top1_acc: 0.4375 top5_acc: 0.7188 loss_cls: 1.9972 2022/12/08 13:59:05 - mmengine - INFO - Epoch(train) [6][840/940] lr: 1.0000e-02 eta: 15:04:45 time: 0.6677 data_time: 0.2459 memory: 16095 grad_norm: 5.0948 loss: 1.9259 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9259 2022/12/08 13:59:17 - mmengine - INFO - Epoch(train) [6][860/940] lr: 1.0000e-02 eta: 15:04:17 time: 0.5648 data_time: 0.1190 memory: 16095 grad_norm: 5.1977 loss: 2.1520 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 2.1520 2022/12/08 13:59:29 - mmengine - INFO - Epoch(train) [6][880/940] lr: 1.0000e-02 eta: 15:04:12 time: 0.6353 data_time: 0.1589 memory: 16095 grad_norm: 5.0179 loss: 2.0175 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 2.0175 2022/12/08 13:59:42 - mmengine - INFO - Epoch(train) [6][900/940] lr: 1.0000e-02 eta: 15:03:59 time: 0.6122 data_time: 0.0890 memory: 16095 grad_norm: 5.1158 loss: 2.0106 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 2.0106 2022/12/08 13:59:55 - mmengine - INFO - Epoch(train) [6][920/940] lr: 1.0000e-02 eta: 15:03:58 time: 0.6500 data_time: 0.2530 memory: 16095 grad_norm: 5.0203 loss: 1.9987 top1_acc: 0.6562 top5_acc: 0.7500 loss_cls: 1.9987 2022/12/08 14:00:04 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 14:00:04 - mmengine - INFO - Epoch(train) [6][940/940] lr: 1.0000e-02 eta: 15:02:57 time: 0.4563 data_time: 0.1564 memory: 16095 grad_norm: 5.3666 loss: 2.0168 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 2.0168 2022/12/08 14:00:04 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/12/08 14:00:21 - mmengine - INFO - Epoch(val) [6][20/78] eta: 0:00:41 time: 0.7226 data_time: 0.6279 memory: 1686 2022/12/08 14:00:30 - mmengine - INFO - Epoch(val) [6][40/78] eta: 0:00:22 time: 0.4590 data_time: 0.3629 memory: 1686 2022/12/08 14:00:44 - mmengine - INFO - Epoch(val) [6][60/78] eta: 0:00:11 time: 0.6769 data_time: 0.5812 memory: 1686 2022/12/08 14:00:53 - mmengine - INFO - Epoch(val) [6][78/78] acc/top1: 0.5948 acc/top5: 0.8304 acc/mean1: 0.5947 2022/12/08 14:00:53 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_5.pth is removed 2022/12/08 14:00:56 - mmengine - INFO - The best checkpoint with 0.5948 acc/top1 at 6 epoch is saved to best_acc/top1_epoch_6.pth. 2022/12/08 14:01:12 - mmengine - INFO - Epoch(train) [7][ 20/940] lr: 1.0000e-02 eta: 15:03:47 time: 0.8121 data_time: 0.5051 memory: 16095 grad_norm: 4.9292 loss: 1.8719 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8719 2022/12/08 14:01:23 - mmengine - INFO - Epoch(train) [7][ 40/940] lr: 1.0000e-02 eta: 15:03:21 time: 0.5715 data_time: 0.2695 memory: 16095 grad_norm: 5.0254 loss: 1.9430 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 1.9430 2022/12/08 14:01:36 - mmengine - INFO - Epoch(train) [7][ 60/940] lr: 1.0000e-02 eta: 15:03:21 time: 0.6532 data_time: 0.3451 memory: 16095 grad_norm: 5.0002 loss: 1.9594 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 1.9594 2022/12/08 14:01:48 - mmengine - INFO - Epoch(train) [7][ 80/940] lr: 1.0000e-02 eta: 15:02:55 time: 0.5693 data_time: 0.2665 memory: 16095 grad_norm: 5.0668 loss: 1.8038 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.8038 2022/12/08 14:02:01 - mmengine - INFO - Epoch(train) [7][100/940] lr: 1.0000e-02 eta: 15:02:57 time: 0.6587 data_time: 0.3382 memory: 16095 grad_norm: 5.1527 loss: 1.9860 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.9860 2022/12/08 14:02:12 - mmengine - INFO - Epoch(train) [7][120/940] lr: 1.0000e-02 eta: 15:02:24 time: 0.5487 data_time: 0.2356 memory: 16095 grad_norm: 5.0453 loss: 1.8821 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 1.8821 2022/12/08 14:02:25 - mmengine - INFO - Epoch(train) [7][140/940] lr: 1.0000e-02 eta: 15:02:21 time: 0.6435 data_time: 0.3240 memory: 16095 grad_norm: 5.0466 loss: 2.0764 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 2.0764 2022/12/08 14:02:37 - mmengine - INFO - Epoch(train) [7][160/940] lr: 1.0000e-02 eta: 15:02:05 time: 0.5995 data_time: 0.1916 memory: 16095 grad_norm: 4.9886 loss: 1.8616 top1_acc: 0.5312 top5_acc: 0.6562 loss_cls: 1.8616 2022/12/08 14:02:51 - mmengine - INFO - Epoch(train) [7][180/940] lr: 1.0000e-02 eta: 15:02:16 time: 0.6919 data_time: 0.1067 memory: 16095 grad_norm: 4.9860 loss: 1.9064 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.9064 2022/12/08 14:03:01 - mmengine - INFO - Epoch(train) [7][200/940] lr: 1.0000e-02 eta: 15:01:39 time: 0.5319 data_time: 0.0192 memory: 16095 grad_norm: 5.1414 loss: 1.9121 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 1.9121 2022/12/08 14:03:14 - mmengine - INFO - Epoch(train) [7][220/940] lr: 1.0000e-02 eta: 15:01:35 time: 0.6423 data_time: 0.0531 memory: 16095 grad_norm: 5.0519 loss: 1.7945 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.7945 2022/12/08 14:03:26 - mmengine - INFO - Epoch(train) [7][240/940] lr: 1.0000e-02 eta: 15:01:14 time: 0.5836 data_time: 0.0257 memory: 16095 grad_norm: 5.0778 loss: 1.9921 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.9921 2022/12/08 14:03:39 - mmengine - INFO - Epoch(train) [7][260/940] lr: 1.0000e-02 eta: 15:01:18 time: 0.6669 data_time: 0.0276 memory: 16095 grad_norm: 5.0493 loss: 1.9410 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9410 2022/12/08 14:03:50 - mmengine - INFO - Epoch(train) [7][280/940] lr: 1.0000e-02 eta: 15:00:52 time: 0.5693 data_time: 0.0221 memory: 16095 grad_norm: 5.0233 loss: 1.7594 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 1.7594 2022/12/08 14:04:02 - mmengine - INFO - Epoch(train) [7][300/940] lr: 1.0000e-02 eta: 15:00:36 time: 0.6007 data_time: 0.0273 memory: 16095 grad_norm: 5.0271 loss: 1.8943 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.8943 2022/12/08 14:04:14 - mmengine - INFO - Epoch(train) [7][320/940] lr: 1.0000e-02 eta: 15:00:12 time: 0.5740 data_time: 0.0940 memory: 16095 grad_norm: 4.9907 loss: 1.9020 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.9020 2022/12/08 14:04:27 - mmengine - INFO - Epoch(train) [7][340/940] lr: 1.0000e-02 eta: 15:00:17 time: 0.6709 data_time: 0.1271 memory: 16095 grad_norm: 5.0425 loss: 1.9457 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.9457 2022/12/08 14:04:39 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 14:04:39 - mmengine - INFO - Epoch(train) [7][360/940] lr: 1.0000e-02 eta: 14:59:59 time: 0.5946 data_time: 0.0527 memory: 16095 grad_norm: 5.0890 loss: 1.7729 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.7729 2022/12/08 14:04:53 - mmengine - INFO - Epoch(train) [7][380/940] lr: 1.0000e-02 eta: 15:00:11 time: 0.6983 data_time: 0.0411 memory: 16095 grad_norm: 5.2562 loss: 1.9843 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9843 2022/12/08 14:05:05 - mmengine - INFO - Epoch(train) [7][400/940] lr: 1.0000e-02 eta: 14:59:47 time: 0.5719 data_time: 0.0276 memory: 16095 grad_norm: 5.2648 loss: 1.9658 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9658 2022/12/08 14:05:17 - mmengine - INFO - Epoch(train) [7][420/940] lr: 1.0000e-02 eta: 14:59:40 time: 0.6341 data_time: 0.0280 memory: 16095 grad_norm: 5.0253 loss: 1.9564 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9564 2022/12/08 14:05:29 - mmengine - INFO - Epoch(train) [7][440/940] lr: 1.0000e-02 eta: 14:59:15 time: 0.5676 data_time: 0.0269 memory: 16095 grad_norm: 5.0772 loss: 2.0357 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 2.0357 2022/12/08 14:05:41 - mmengine - INFO - Epoch(train) [7][460/940] lr: 1.0000e-02 eta: 14:59:09 time: 0.6372 data_time: 0.1035 memory: 16095 grad_norm: 5.0605 loss: 1.8750 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.8750 2022/12/08 14:05:52 - mmengine - INFO - Epoch(train) [7][480/940] lr: 1.0000e-02 eta: 14:58:32 time: 0.5265 data_time: 0.1251 memory: 16095 grad_norm: 5.0076 loss: 1.8952 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.8952 2022/12/08 14:06:05 - mmengine - INFO - Epoch(train) [7][500/940] lr: 1.0000e-02 eta: 14:58:28 time: 0.6420 data_time: 0.2335 memory: 16095 grad_norm: 5.1600 loss: 1.8652 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.8652 2022/12/08 14:06:17 - mmengine - INFO - Epoch(train) [7][520/940] lr: 1.0000e-02 eta: 14:58:07 time: 0.5849 data_time: 0.2139 memory: 16095 grad_norm: 5.1081 loss: 2.1189 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.1189 2022/12/08 14:06:30 - mmengine - INFO - Epoch(train) [7][540/940] lr: 1.0000e-02 eta: 14:58:11 time: 0.6692 data_time: 0.1295 memory: 16095 grad_norm: 4.9808 loss: 1.8954 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.8954 2022/12/08 14:06:41 - mmengine - INFO - Epoch(train) [7][560/940] lr: 1.0000e-02 eta: 14:57:37 time: 0.5383 data_time: 0.1573 memory: 16095 grad_norm: 4.9778 loss: 1.9143 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9143 2022/12/08 14:06:54 - mmengine - INFO - Epoch(train) [7][580/940] lr: 1.0000e-02 eta: 14:57:43 time: 0.6787 data_time: 0.2828 memory: 16095 grad_norm: 5.0568 loss: 1.9200 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 1.9200 2022/12/08 14:07:05 - mmengine - INFO - Epoch(train) [7][600/940] lr: 1.0000e-02 eta: 14:57:07 time: 0.5292 data_time: 0.1838 memory: 16095 grad_norm: 5.1548 loss: 1.8655 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.8655 2022/12/08 14:07:20 - mmengine - INFO - Epoch(train) [7][620/940] lr: 1.0000e-02 eta: 14:57:37 time: 0.7614 data_time: 0.4220 memory: 16095 grad_norm: 5.0738 loss: 1.9099 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.9099 2022/12/08 14:07:30 - mmengine - INFO - Epoch(train) [7][640/940] lr: 1.0000e-02 eta: 14:56:59 time: 0.5213 data_time: 0.1966 memory: 16095 grad_norm: 5.0500 loss: 1.9860 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9860 2022/12/08 14:07:44 - mmengine - INFO - Epoch(train) [7][660/940] lr: 1.0000e-02 eta: 14:57:08 time: 0.6918 data_time: 0.3566 memory: 16095 grad_norm: 5.0175 loss: 1.9125 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9125 2022/12/08 14:07:55 - mmengine - INFO - Epoch(train) [7][680/940] lr: 1.0000e-02 eta: 14:56:39 time: 0.5549 data_time: 0.2402 memory: 16095 grad_norm: 5.1332 loss: 1.9081 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.9081 2022/12/08 14:08:08 - mmengine - INFO - Epoch(train) [7][700/940] lr: 1.0000e-02 eta: 14:56:35 time: 0.6435 data_time: 0.3273 memory: 16095 grad_norm: 5.1278 loss: 1.8545 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.8545 2022/12/08 14:08:19 - mmengine - INFO - Epoch(train) [7][720/940] lr: 1.0000e-02 eta: 14:55:58 time: 0.5234 data_time: 0.2029 memory: 16095 grad_norm: 5.0209 loss: 2.0389 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0389 2022/12/08 14:08:32 - mmengine - INFO - Epoch(train) [7][740/940] lr: 1.0000e-02 eta: 14:56:05 time: 0.6840 data_time: 0.3091 memory: 16095 grad_norm: 5.2108 loss: 1.9024 top1_acc: 0.4375 top5_acc: 0.7188 loss_cls: 1.9024 2022/12/08 14:08:43 - mmengine - INFO - Epoch(train) [7][760/940] lr: 1.0000e-02 eta: 14:55:34 time: 0.5448 data_time: 0.2015 memory: 16095 grad_norm: 5.1293 loss: 2.0464 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 2.0464 2022/12/08 14:08:57 - mmengine - INFO - Epoch(train) [7][780/940] lr: 1.0000e-02 eta: 14:55:39 time: 0.6772 data_time: 0.3088 memory: 16095 grad_norm: 5.0578 loss: 2.0169 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 2.0169 2022/12/08 14:09:09 - mmengine - INFO - Epoch(train) [7][800/940] lr: 1.0000e-02 eta: 14:55:19 time: 0.5834 data_time: 0.1241 memory: 16095 grad_norm: 5.1261 loss: 1.8602 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.8602 2022/12/08 14:09:20 - mmengine - INFO - Epoch(train) [7][820/940] lr: 1.0000e-02 eta: 14:55:01 time: 0.5933 data_time: 0.1497 memory: 16095 grad_norm: 5.0511 loss: 1.8487 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 1.8487 2022/12/08 14:09:32 - mmengine - INFO - Epoch(train) [7][840/940] lr: 1.0000e-02 eta: 14:54:44 time: 0.5957 data_time: 0.0920 memory: 16095 grad_norm: 5.1548 loss: 1.9067 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9067 2022/12/08 14:09:45 - mmengine - INFO - Epoch(train) [7][860/940] lr: 1.0000e-02 eta: 14:54:36 time: 0.6279 data_time: 0.1296 memory: 16095 grad_norm: 5.0476 loss: 1.7963 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.7963 2022/12/08 14:09:57 - mmengine - INFO - Epoch(train) [7][880/940] lr: 1.0000e-02 eta: 14:54:28 time: 0.6295 data_time: 0.0302 memory: 16095 grad_norm: 4.9680 loss: 1.9418 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.9418 2022/12/08 14:10:10 - mmengine - INFO - Epoch(train) [7][900/940] lr: 1.0000e-02 eta: 14:54:15 time: 0.6107 data_time: 0.0252 memory: 16095 grad_norm: 5.1663 loss: 1.9315 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9315 2022/12/08 14:10:23 - mmengine - INFO - Epoch(train) [7][920/940] lr: 1.0000e-02 eta: 14:54:18 time: 0.6705 data_time: 0.0223 memory: 16095 grad_norm: 5.0476 loss: 2.0012 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 2.0012 2022/12/08 14:10:32 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 14:10:32 - mmengine - INFO - Epoch(train) [7][940/940] lr: 1.0000e-02 eta: 14:53:27 time: 0.4685 data_time: 0.0183 memory: 16095 grad_norm: 5.3209 loss: 2.0517 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 2.0517 2022/12/08 14:10:47 - mmengine - INFO - Epoch(val) [7][20/78] eta: 0:00:41 time: 0.7175 data_time: 0.6244 memory: 1686 2022/12/08 14:10:56 - mmengine - INFO - Epoch(val) [7][40/78] eta: 0:00:21 time: 0.4339 data_time: 0.3399 memory: 1686 2022/12/08 14:11:09 - mmengine - INFO - Epoch(val) [7][60/78] eta: 0:00:11 time: 0.6993 data_time: 0.6054 memory: 1686 2022/12/08 14:11:20 - mmengine - INFO - Epoch(val) [7][78/78] acc/top1: 0.5981 acc/top5: 0.8309 acc/mean1: 0.5978 2022/12/08 14:11:20 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_6.pth is removed 2022/12/08 14:11:23 - mmengine - INFO - The best checkpoint with 0.5981 acc/top1 at 7 epoch is saved to best_acc/top1_epoch_7.pth. 2022/12/08 14:11:39 - mmengine - INFO - Epoch(train) [8][ 20/940] lr: 1.0000e-02 eta: 14:54:11 time: 0.8244 data_time: 0.5178 memory: 16095 grad_norm: 5.1024 loss: 1.7007 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.7007 2022/12/08 14:11:50 - mmengine - INFO - Epoch(train) [8][ 40/940] lr: 1.0000e-02 eta: 14:53:40 time: 0.5457 data_time: 0.2353 memory: 16095 grad_norm: 5.0715 loss: 1.8591 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.8591 2022/12/08 14:12:03 - mmengine - INFO - Epoch(train) [8][ 60/940] lr: 1.0000e-02 eta: 14:53:43 time: 0.6693 data_time: 0.3452 memory: 16095 grad_norm: 5.0683 loss: 1.9418 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.9418 2022/12/08 14:12:15 - mmengine - INFO - Epoch(train) [8][ 80/940] lr: 1.0000e-02 eta: 14:53:17 time: 0.5628 data_time: 0.2380 memory: 16095 grad_norm: 5.0458 loss: 1.9128 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 1.9128 2022/12/08 14:12:28 - mmengine - INFO - Epoch(train) [8][100/940] lr: 1.0000e-02 eta: 14:53:12 time: 0.6413 data_time: 0.3133 memory: 16095 grad_norm: 5.0882 loss: 1.9469 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9469 2022/12/08 14:12:39 - mmengine - INFO - Epoch(train) [8][120/940] lr: 1.0000e-02 eta: 14:52:42 time: 0.5476 data_time: 0.2190 memory: 16095 grad_norm: 4.9608 loss: 1.8088 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.8088 2022/12/08 14:12:52 - mmengine - INFO - Epoch(train) [8][140/940] lr: 1.0000e-02 eta: 14:52:44 time: 0.6687 data_time: 0.3337 memory: 16095 grad_norm: 5.0472 loss: 1.8404 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8404 2022/12/08 14:13:03 - mmengine - INFO - Epoch(train) [8][160/940] lr: 1.0000e-02 eta: 14:52:20 time: 0.5671 data_time: 0.2391 memory: 16095 grad_norm: 5.0670 loss: 1.6873 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.6873 2022/12/08 14:13:17 - mmengine - INFO - Epoch(train) [8][180/940] lr: 1.0000e-02 eta: 14:52:25 time: 0.6811 data_time: 0.3419 memory: 16095 grad_norm: 5.0781 loss: 1.8242 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8242 2022/12/08 14:13:28 - mmengine - INFO - Epoch(train) [8][200/940] lr: 1.0000e-02 eta: 14:52:01 time: 0.5673 data_time: 0.2351 memory: 16095 grad_norm: 5.0364 loss: 1.7811 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.7811 2022/12/08 14:13:42 - mmengine - INFO - Epoch(train) [8][220/940] lr: 1.0000e-02 eta: 14:52:13 time: 0.7095 data_time: 0.3793 memory: 16095 grad_norm: 5.0228 loss: 1.8008 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.8008 2022/12/08 14:13:53 - mmengine - INFO - Epoch(train) [8][240/940] lr: 1.0000e-02 eta: 14:51:38 time: 0.5252 data_time: 0.2023 memory: 16095 grad_norm: 5.1009 loss: 1.9361 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.9361 2022/12/08 14:14:07 - mmengine - INFO - Epoch(train) [8][260/940] lr: 1.0000e-02 eta: 14:51:48 time: 0.6995 data_time: 0.3743 memory: 16095 grad_norm: 5.1469 loss: 1.8536 top1_acc: 0.5625 top5_acc: 0.7188 loss_cls: 1.8536 2022/12/08 14:14:18 - mmengine - INFO - Epoch(train) [8][280/940] lr: 1.0000e-02 eta: 14:51:26 time: 0.5775 data_time: 0.2539 memory: 16095 grad_norm: 5.1350 loss: 1.9679 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.9679 2022/12/08 14:14:32 - mmengine - INFO - Epoch(train) [8][300/940] lr: 1.0000e-02 eta: 14:51:31 time: 0.6795 data_time: 0.3510 memory: 16095 grad_norm: 5.0009 loss: 1.9260 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.9260 2022/12/08 14:14:42 - mmengine - INFO - Epoch(train) [8][320/940] lr: 1.0000e-02 eta: 14:50:53 time: 0.5108 data_time: 0.1957 memory: 16095 grad_norm: 5.1677 loss: 1.9511 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 1.9511 2022/12/08 14:14:55 - mmengine - INFO - Epoch(train) [8][340/940] lr: 1.0000e-02 eta: 14:50:48 time: 0.6452 data_time: 0.3296 memory: 16095 grad_norm: 5.1489 loss: 2.0361 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 2.0361 2022/12/08 14:15:07 - mmengine - INFO - Epoch(train) [8][360/940] lr: 1.0000e-02 eta: 14:50:25 time: 0.5721 data_time: 0.2465 memory: 16095 grad_norm: 5.1215 loss: 1.9327 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.9327 2022/12/08 14:15:20 - mmengine - INFO - Epoch(train) [8][380/940] lr: 1.0000e-02 eta: 14:50:27 time: 0.6699 data_time: 0.3465 memory: 16095 grad_norm: 5.0176 loss: 1.8588 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.8588 2022/12/08 14:15:31 - mmengine - INFO - Epoch(train) [8][400/940] lr: 1.0000e-02 eta: 14:49:56 time: 0.5391 data_time: 0.2178 memory: 16095 grad_norm: 5.0415 loss: 2.1171 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 2.1171 2022/12/08 14:15:44 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 14:15:44 - mmengine - INFO - Epoch(train) [8][420/940] lr: 1.0000e-02 eta: 14:49:59 time: 0.6742 data_time: 0.3401 memory: 16095 grad_norm: 5.1039 loss: 1.7481 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.7481 2022/12/08 14:15:56 - mmengine - INFO - Epoch(train) [8][440/940] lr: 1.0000e-02 eta: 14:49:37 time: 0.5740 data_time: 0.2435 memory: 16095 grad_norm: 5.0442 loss: 1.9239 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9239 2022/12/08 14:16:09 - mmengine - INFO - Epoch(train) [8][460/940] lr: 1.0000e-02 eta: 14:49:37 time: 0.6623 data_time: 0.3294 memory: 16095 grad_norm: 5.1183 loss: 1.8936 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.8936 2022/12/08 14:16:20 - mmengine - INFO - Epoch(train) [8][480/940] lr: 1.0000e-02 eta: 14:49:08 time: 0.5468 data_time: 0.1925 memory: 16095 grad_norm: 5.1528 loss: 1.9692 top1_acc: 0.4062 top5_acc: 0.6562 loss_cls: 1.9692 2022/12/08 14:16:33 - mmengine - INFO - Epoch(train) [8][500/940] lr: 1.0000e-02 eta: 14:49:06 time: 0.6564 data_time: 0.2986 memory: 16095 grad_norm: 5.0154 loss: 1.9583 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.9583 2022/12/08 14:16:45 - mmengine - INFO - Epoch(train) [8][520/940] lr: 1.0000e-02 eta: 14:48:49 time: 0.5949 data_time: 0.2409 memory: 16095 grad_norm: 5.0957 loss: 1.9224 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 1.9224 2022/12/08 14:16:58 - mmengine - INFO - Epoch(train) [8][540/940] lr: 1.0000e-02 eta: 14:48:41 time: 0.6314 data_time: 0.1556 memory: 16095 grad_norm: 5.1266 loss: 1.9624 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.9624 2022/12/08 14:17:10 - mmengine - INFO - Epoch(train) [8][560/940] lr: 1.0000e-02 eta: 14:48:27 time: 0.6074 data_time: 0.0600 memory: 16095 grad_norm: 5.2129 loss: 1.8235 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.8235 2022/12/08 14:17:22 - mmengine - INFO - Epoch(train) [8][580/940] lr: 1.0000e-02 eta: 14:48:12 time: 0.6014 data_time: 0.0258 memory: 16095 grad_norm: 5.0571 loss: 1.8930 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 1.8930 2022/12/08 14:17:34 - mmengine - INFO - Epoch(train) [8][600/940] lr: 1.0000e-02 eta: 14:47:54 time: 0.5890 data_time: 0.0342 memory: 16095 grad_norm: 5.0983 loss: 1.9430 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.9430 2022/12/08 14:17:46 - mmengine - INFO - Epoch(train) [8][620/940] lr: 1.0000e-02 eta: 14:47:42 time: 0.6152 data_time: 0.2214 memory: 16095 grad_norm: 5.0485 loss: 1.8948 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.8948 2022/12/08 14:17:58 - mmengine - INFO - Epoch(train) [8][640/940] lr: 1.0000e-02 eta: 14:47:25 time: 0.5924 data_time: 0.1703 memory: 16095 grad_norm: 4.9978 loss: 2.0747 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 2.0747 2022/12/08 14:18:11 - mmengine - INFO - Epoch(train) [8][660/940] lr: 1.0000e-02 eta: 14:47:21 time: 0.6499 data_time: 0.3162 memory: 16095 grad_norm: 5.0033 loss: 1.9333 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9333 2022/12/08 14:18:21 - mmengine - INFO - Epoch(train) [8][680/940] lr: 1.0000e-02 eta: 14:46:49 time: 0.5311 data_time: 0.1827 memory: 16095 grad_norm: 5.1468 loss: 1.9414 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.9414 2022/12/08 14:18:34 - mmengine - INFO - Epoch(train) [8][700/940] lr: 1.0000e-02 eta: 14:46:39 time: 0.6225 data_time: 0.2459 memory: 16095 grad_norm: 5.0424 loss: 1.9414 top1_acc: 0.7188 top5_acc: 0.7812 loss_cls: 1.9414 2022/12/08 14:18:46 - mmengine - INFO - Epoch(train) [8][720/940] lr: 1.0000e-02 eta: 14:46:24 time: 0.6035 data_time: 0.1545 memory: 16095 grad_norm: 5.1160 loss: 1.9459 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.9459 2022/12/08 14:18:58 - mmengine - INFO - Epoch(train) [8][740/940] lr: 1.0000e-02 eta: 14:46:16 time: 0.6317 data_time: 0.2615 memory: 16095 grad_norm: 5.0394 loss: 2.0587 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 2.0587 2022/12/08 14:19:12 - mmengine - INFO - Epoch(train) [8][760/940] lr: 1.0000e-02 eta: 14:46:18 time: 0.6707 data_time: 0.0854 memory: 16095 grad_norm: 5.0546 loss: 1.8964 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.8964 2022/12/08 14:19:23 - mmengine - INFO - Epoch(train) [8][780/940] lr: 1.0000e-02 eta: 14:45:52 time: 0.5578 data_time: 0.0392 memory: 16095 grad_norm: 5.0999 loss: 1.8394 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.8394 2022/12/08 14:19:36 - mmengine - INFO - Epoch(train) [8][800/940] lr: 1.0000e-02 eta: 14:45:45 time: 0.6330 data_time: 0.0507 memory: 16095 grad_norm: 5.0400 loss: 1.9785 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.9785 2022/12/08 14:19:47 - mmengine - INFO - Epoch(train) [8][820/940] lr: 1.0000e-02 eta: 14:45:20 time: 0.5631 data_time: 0.0262 memory: 16095 grad_norm: 5.0768 loss: 1.8917 top1_acc: 0.3438 top5_acc: 0.7500 loss_cls: 1.8917 2022/12/08 14:20:00 - mmengine - INFO - Epoch(train) [8][840/940] lr: 1.0000e-02 eta: 14:45:23 time: 0.6752 data_time: 0.0221 memory: 16095 grad_norm: 5.0716 loss: 1.8930 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.8930 2022/12/08 14:20:12 - mmengine - INFO - Epoch(train) [8][860/940] lr: 1.0000e-02 eta: 14:44:56 time: 0.5525 data_time: 0.0507 memory: 16095 grad_norm: 5.0326 loss: 1.7999 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7999 2022/12/08 14:20:25 - mmengine - INFO - Epoch(train) [8][880/940] lr: 1.0000e-02 eta: 14:44:54 time: 0.6564 data_time: 0.0787 memory: 16095 grad_norm: 5.1576 loss: 1.8924 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 1.8924 2022/12/08 14:20:36 - mmengine - INFO - Epoch(train) [8][900/940] lr: 1.0000e-02 eta: 14:44:32 time: 0.5735 data_time: 0.0269 memory: 16095 grad_norm: 4.9284 loss: 1.8686 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 1.8686 2022/12/08 14:20:49 - mmengine - INFO - Epoch(train) [8][920/940] lr: 1.0000e-02 eta: 14:44:33 time: 0.6675 data_time: 0.0228 memory: 16095 grad_norm: 4.9724 loss: 1.8934 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.8934 2022/12/08 14:20:59 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 14:20:59 - mmengine - INFO - Epoch(train) [8][940/940] lr: 1.0000e-02 eta: 14:43:46 time: 0.4666 data_time: 0.0174 memory: 16095 grad_norm: 5.1568 loss: 1.7652 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 1.7652 2022/12/08 14:21:13 - mmengine - INFO - Epoch(val) [8][20/78] eta: 0:00:41 time: 0.7155 data_time: 0.6210 memory: 1686 2022/12/08 14:21:22 - mmengine - INFO - Epoch(val) [8][40/78] eta: 0:00:22 time: 0.4633 data_time: 0.3691 memory: 1686 2022/12/08 14:21:36 - mmengine - INFO - Epoch(val) [8][60/78] eta: 0:00:11 time: 0.6855 data_time: 0.5902 memory: 1686 2022/12/08 14:21:47 - mmengine - INFO - Epoch(val) [8][78/78] acc/top1: 0.6057 acc/top5: 0.8331 acc/mean1: 0.6055 2022/12/08 14:21:47 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_7.pth is removed 2022/12/08 14:21:49 - mmengine - INFO - The best checkpoint with 0.6057 acc/top1 at 8 epoch is saved to best_acc/top1_epoch_8.pth. 2022/12/08 14:22:05 - mmengine - INFO - Epoch(train) [9][ 20/940] lr: 1.0000e-02 eta: 14:44:20 time: 0.8135 data_time: 0.5137 memory: 16095 grad_norm: 4.9100 loss: 1.8666 top1_acc: 0.5625 top5_acc: 0.7188 loss_cls: 1.8666 2022/12/08 14:22:16 - mmengine - INFO - Epoch(train) [9][ 40/940] lr: 1.0000e-02 eta: 14:43:52 time: 0.5422 data_time: 0.2397 memory: 16095 grad_norm: 5.0199 loss: 1.7235 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7235 2022/12/08 14:22:30 - mmengine - INFO - Epoch(train) [9][ 60/940] lr: 1.0000e-02 eta: 14:43:56 time: 0.6857 data_time: 0.3749 memory: 16095 grad_norm: 5.0137 loss: 1.7619 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7619 2022/12/08 14:22:41 - mmengine - INFO - Epoch(train) [9][ 80/940] lr: 1.0000e-02 eta: 14:43:30 time: 0.5553 data_time: 0.2395 memory: 16095 grad_norm: 5.0883 loss: 1.8411 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 1.8411 2022/12/08 14:22:54 - mmengine - INFO - Epoch(train) [9][100/940] lr: 1.0000e-02 eta: 14:43:25 time: 0.6447 data_time: 0.3279 memory: 16095 grad_norm: 5.0012 loss: 1.8845 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.8845 2022/12/08 14:23:05 - mmengine - INFO - Epoch(train) [9][120/940] lr: 1.0000e-02 eta: 14:43:01 time: 0.5619 data_time: 0.2416 memory: 16095 grad_norm: 5.1071 loss: 1.9423 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.9423 2022/12/08 14:23:19 - mmengine - INFO - Epoch(train) [9][140/940] lr: 1.0000e-02 eta: 14:43:02 time: 0.6719 data_time: 0.3294 memory: 16095 grad_norm: 4.9665 loss: 1.8821 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8821 2022/12/08 14:23:29 - mmengine - INFO - Epoch(train) [9][160/940] lr: 1.0000e-02 eta: 14:42:31 time: 0.5298 data_time: 0.2122 memory: 16095 grad_norm: 5.0900 loss: 1.7926 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.7926 2022/12/08 14:23:43 - mmengine - INFO - Epoch(train) [9][180/940] lr: 1.0000e-02 eta: 14:42:40 time: 0.7090 data_time: 0.3849 memory: 16095 grad_norm: 5.0043 loss: 1.7377 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.7377 2022/12/08 14:23:54 - mmengine - INFO - Epoch(train) [9][200/940] lr: 1.0000e-02 eta: 14:42:07 time: 0.5223 data_time: 0.1979 memory: 16095 grad_norm: 4.9830 loss: 1.8192 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8192 2022/12/08 14:24:07 - mmengine - INFO - Epoch(train) [9][220/940] lr: 1.0000e-02 eta: 14:42:07 time: 0.6694 data_time: 0.3519 memory: 16095 grad_norm: 4.9995 loss: 1.8662 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8662 2022/12/08 14:24:19 - mmengine - INFO - Epoch(train) [9][240/940] lr: 1.0000e-02 eta: 14:41:49 time: 0.5855 data_time: 0.2760 memory: 16095 grad_norm: 5.0544 loss: 1.8802 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8802 2022/12/08 14:24:33 - mmengine - INFO - Epoch(train) [9][260/940] lr: 1.0000e-02 eta: 14:41:57 time: 0.7044 data_time: 0.4030 memory: 16095 grad_norm: 5.0424 loss: 1.7792 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.7792 2022/12/08 14:24:44 - mmengine - INFO - Epoch(train) [9][280/940] lr: 1.0000e-02 eta: 14:41:29 time: 0.5437 data_time: 0.2334 memory: 16095 grad_norm: 5.0116 loss: 1.7128 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.7128 2022/12/08 14:24:57 - mmengine - INFO - Epoch(train) [9][300/940] lr: 1.0000e-02 eta: 14:41:29 time: 0.6698 data_time: 0.3599 memory: 16095 grad_norm: 5.0600 loss: 1.7526 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7526 2022/12/08 14:25:08 - mmengine - INFO - Epoch(train) [9][320/940] lr: 1.0000e-02 eta: 14:40:59 time: 0.5315 data_time: 0.2092 memory: 16095 grad_norm: 4.9444 loss: 1.7363 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.7363 2022/12/08 14:25:21 - mmengine - INFO - Epoch(train) [9][340/940] lr: 1.0000e-02 eta: 14:40:54 time: 0.6494 data_time: 0.3211 memory: 16095 grad_norm: 5.1679 loss: 1.9441 top1_acc: 0.4062 top5_acc: 0.7500 loss_cls: 1.9441 2022/12/08 14:25:32 - mmengine - INFO - Epoch(train) [9][360/940] lr: 1.0000e-02 eta: 14:40:27 time: 0.5460 data_time: 0.1852 memory: 16095 grad_norm: 5.0208 loss: 2.0106 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 2.0106 2022/12/08 14:25:44 - mmengine - INFO - Epoch(train) [9][380/940] lr: 1.0000e-02 eta: 14:40:18 time: 0.6275 data_time: 0.2627 memory: 16095 grad_norm: 5.0050 loss: 1.9206 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.9206 2022/12/08 14:25:56 - mmengine - INFO - Epoch(train) [9][400/940] lr: 1.0000e-02 eta: 14:39:59 time: 0.5812 data_time: 0.1175 memory: 16095 grad_norm: 5.0825 loss: 1.7795 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.7795 2022/12/08 14:26:09 - mmengine - INFO - Epoch(train) [9][420/940] lr: 1.0000e-02 eta: 14:39:49 time: 0.6264 data_time: 0.1129 memory: 16095 grad_norm: 5.0404 loss: 1.8465 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8465 2022/12/08 14:26:20 - mmengine - INFO - Epoch(train) [9][440/940] lr: 1.0000e-02 eta: 14:39:33 time: 0.5936 data_time: 0.1731 memory: 16095 grad_norm: 5.0373 loss: 1.9607 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.9607 2022/12/08 14:26:34 - mmengine - INFO - Epoch(train) [9][460/940] lr: 1.0000e-02 eta: 14:39:31 time: 0.6633 data_time: 0.3172 memory: 16095 grad_norm: 5.0512 loss: 1.8305 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.8305 2022/12/08 14:26:45 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 14:26:45 - mmengine - INFO - Epoch(train) [9][480/940] lr: 1.0000e-02 eta: 14:39:07 time: 0.5570 data_time: 0.1802 memory: 16095 grad_norm: 5.0745 loss: 1.7957 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.7957 2022/12/08 14:26:59 - mmengine - INFO - Epoch(train) [9][500/940] lr: 1.0000e-02 eta: 14:39:14 time: 0.7013 data_time: 0.3156 memory: 16095 grad_norm: 5.0374 loss: 1.8959 top1_acc: 0.5625 top5_acc: 0.7188 loss_cls: 1.8959 2022/12/08 14:27:10 - mmengine - INFO - Epoch(train) [9][520/940] lr: 1.0000e-02 eta: 14:38:53 time: 0.5756 data_time: 0.2387 memory: 16095 grad_norm: 5.0468 loss: 1.7926 top1_acc: 0.7500 top5_acc: 0.7812 loss_cls: 1.7926 2022/12/08 14:27:24 - mmengine - INFO - Epoch(train) [9][540/940] lr: 1.0000e-02 eta: 14:38:54 time: 0.6766 data_time: 0.3475 memory: 16095 grad_norm: 5.1594 loss: 1.8116 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8116 2022/12/08 14:27:36 - mmengine - INFO - Epoch(train) [9][560/940] lr: 1.0000e-02 eta: 14:38:39 time: 0.6008 data_time: 0.2688 memory: 16095 grad_norm: 5.0227 loss: 1.8365 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.8365 2022/12/08 14:27:49 - mmengine - INFO - Epoch(train) [9][580/940] lr: 1.0000e-02 eta: 14:38:32 time: 0.6377 data_time: 0.3267 memory: 16095 grad_norm: 5.0064 loss: 1.9922 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.9922 2022/12/08 14:28:00 - mmengine - INFO - Epoch(train) [9][600/940] lr: 1.0000e-02 eta: 14:38:13 time: 0.5820 data_time: 0.2627 memory: 16095 grad_norm: 5.0216 loss: 1.7705 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.7705 2022/12/08 14:28:13 - mmengine - INFO - Epoch(train) [9][620/940] lr: 1.0000e-02 eta: 14:38:01 time: 0.6140 data_time: 0.2906 memory: 16095 grad_norm: 4.9973 loss: 1.8364 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.8364 2022/12/08 14:28:24 - mmengine - INFO - Epoch(train) [9][640/940] lr: 1.0000e-02 eta: 14:37:42 time: 0.5823 data_time: 0.2449 memory: 16095 grad_norm: 5.0195 loss: 1.9514 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.9514 2022/12/08 14:28:37 - mmengine - INFO - Epoch(train) [9][660/940] lr: 1.0000e-02 eta: 14:37:32 time: 0.6223 data_time: 0.3000 memory: 16095 grad_norm: 5.1311 loss: 1.8707 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.8707 2022/12/08 14:28:48 - mmengine - INFO - Epoch(train) [9][680/940] lr: 1.0000e-02 eta: 14:37:10 time: 0.5697 data_time: 0.2474 memory: 16095 grad_norm: 5.0414 loss: 1.7635 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.7635 2022/12/08 14:29:01 - mmengine - INFO - Epoch(train) [9][700/940] lr: 1.0000e-02 eta: 14:37:02 time: 0.6302 data_time: 0.3114 memory: 16095 grad_norm: 5.1004 loss: 1.9267 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.9267 2022/12/08 14:29:13 - mmengine - INFO - Epoch(train) [9][720/940] lr: 1.0000e-02 eta: 14:36:47 time: 0.6005 data_time: 0.1401 memory: 16095 grad_norm: 5.1245 loss: 1.8427 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8427 2022/12/08 14:29:25 - mmengine - INFO - Epoch(train) [9][740/940] lr: 1.0000e-02 eta: 14:36:33 time: 0.6082 data_time: 0.1922 memory: 16095 grad_norm: 5.1388 loss: 1.9230 top1_acc: 0.5938 top5_acc: 0.7188 loss_cls: 1.9230 2022/12/08 14:29:37 - mmengine - INFO - Epoch(train) [9][760/940] lr: 1.0000e-02 eta: 14:36:15 time: 0.5864 data_time: 0.1598 memory: 16095 grad_norm: 5.1404 loss: 1.8698 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.8698 2022/12/08 14:29:49 - mmengine - INFO - Epoch(train) [9][780/940] lr: 1.0000e-02 eta: 14:36:04 time: 0.6155 data_time: 0.1827 memory: 16095 grad_norm: 5.1192 loss: 1.8535 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.8535 2022/12/08 14:30:01 - mmengine - INFO - Epoch(train) [9][800/940] lr: 1.0000e-02 eta: 14:35:45 time: 0.5804 data_time: 0.0406 memory: 16095 grad_norm: 5.1091 loss: 1.9189 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.9189 2022/12/08 14:30:13 - mmengine - INFO - Epoch(train) [9][820/940] lr: 1.0000e-02 eta: 14:35:36 time: 0.6329 data_time: 0.0247 memory: 16095 grad_norm: 5.1117 loss: 1.9591 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.9591 2022/12/08 14:30:25 - mmengine - INFO - Epoch(train) [9][840/940] lr: 1.0000e-02 eta: 14:35:23 time: 0.6065 data_time: 0.0234 memory: 16095 grad_norm: 5.1615 loss: 1.8351 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.8351 2022/12/08 14:30:38 - mmengine - INFO - Epoch(train) [9][860/940] lr: 1.0000e-02 eta: 14:35:16 time: 0.6400 data_time: 0.0249 memory: 16095 grad_norm: 5.1629 loss: 1.8268 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.8268 2022/12/08 14:30:50 - mmengine - INFO - Epoch(train) [9][880/940] lr: 1.0000e-02 eta: 14:34:58 time: 0.5848 data_time: 0.0295 memory: 16095 grad_norm: 5.0710 loss: 1.8011 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.8011 2022/12/08 14:31:04 - mmengine - INFO - Epoch(train) [9][900/940] lr: 1.0000e-02 eta: 14:35:01 time: 0.6906 data_time: 0.0453 memory: 16095 grad_norm: 4.9481 loss: 1.9174 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9174 2022/12/08 14:31:15 - mmengine - INFO - Epoch(train) [9][920/940] lr: 1.0000e-02 eta: 14:34:44 time: 0.5875 data_time: 0.0420 memory: 16095 grad_norm: 5.0762 loss: 1.9568 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9568 2022/12/08 14:31:26 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 14:31:26 - mmengine - INFO - Epoch(train) [9][940/940] lr: 1.0000e-02 eta: 14:34:12 time: 0.5193 data_time: 0.0274 memory: 16095 grad_norm: 5.2937 loss: 1.8272 top1_acc: 0.7143 top5_acc: 1.0000 loss_cls: 1.8272 2022/12/08 14:31:26 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/12/08 14:31:43 - mmengine - INFO - Epoch(val) [9][20/78] eta: 0:00:41 time: 0.7234 data_time: 0.6285 memory: 1686 2022/12/08 14:31:52 - mmengine - INFO - Epoch(val) [9][40/78] eta: 0:00:22 time: 0.4628 data_time: 0.3683 memory: 1686 2022/12/08 14:32:06 - mmengine - INFO - Epoch(val) [9][60/78] eta: 0:00:11 time: 0.6728 data_time: 0.5770 memory: 1686 2022/12/08 14:32:15 - mmengine - INFO - Epoch(val) [9][78/78] acc/top1: 0.6116 acc/top5: 0.8395 acc/mean1: 0.6115 2022/12/08 14:32:15 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_8.pth is removed 2022/12/08 14:32:18 - mmengine - INFO - The best checkpoint with 0.6116 acc/top1 at 9 epoch is saved to best_acc/top1_epoch_9.pth. 2022/12/08 14:32:34 - mmengine - INFO - Epoch(train) [10][ 20/940] lr: 1.0000e-02 eta: 14:34:38 time: 0.8024 data_time: 0.4790 memory: 16095 grad_norm: 4.9729 loss: 1.7096 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7096 2022/12/08 14:32:46 - mmengine - INFO - Epoch(train) [10][ 40/940] lr: 1.0000e-02 eta: 14:34:25 time: 0.6101 data_time: 0.2973 memory: 16095 grad_norm: 5.0070 loss: 1.8834 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8834 2022/12/08 14:32:58 - mmengine - INFO - Epoch(train) [10][ 60/940] lr: 1.0000e-02 eta: 14:34:13 time: 0.6127 data_time: 0.3004 memory: 16095 grad_norm: 4.9001 loss: 1.7170 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.7170 2022/12/08 14:33:09 - mmengine - INFO - Epoch(train) [10][ 80/940] lr: 1.0000e-02 eta: 14:33:46 time: 0.5397 data_time: 0.2235 memory: 16095 grad_norm: 5.0193 loss: 1.9013 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9013 2022/12/08 14:33:22 - mmengine - INFO - Epoch(train) [10][100/940] lr: 1.0000e-02 eta: 14:33:44 time: 0.6638 data_time: 0.3448 memory: 16095 grad_norm: 5.1192 loss: 1.8423 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.8423 2022/12/08 14:33:34 - mmengine - INFO - Epoch(train) [10][120/940] lr: 1.0000e-02 eta: 14:33:23 time: 0.5697 data_time: 0.2473 memory: 16095 grad_norm: 5.0572 loss: 1.7821 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7821 2022/12/08 14:33:48 - mmengine - INFO - Epoch(train) [10][140/940] lr: 1.0000e-02 eta: 14:33:30 time: 0.7138 data_time: 0.3750 memory: 16095 grad_norm: 5.0591 loss: 1.7299 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.7299 2022/12/08 14:34:00 - mmengine - INFO - Epoch(train) [10][160/940] lr: 1.0000e-02 eta: 14:33:14 time: 0.5926 data_time: 0.2696 memory: 16095 grad_norm: 5.0825 loss: 1.7298 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.7298 2022/12/08 14:34:13 - mmengine - INFO - Epoch(train) [10][180/940] lr: 1.0000e-02 eta: 14:33:12 time: 0.6665 data_time: 0.3537 memory: 16095 grad_norm: 5.0630 loss: 1.8176 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.8176 2022/12/08 14:34:24 - mmengine - INFO - Epoch(train) [10][200/940] lr: 1.0000e-02 eta: 14:32:41 time: 0.5170 data_time: 0.2027 memory: 16095 grad_norm: 5.0143 loss: 1.8495 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.8495 2022/12/08 14:34:37 - mmengine - INFO - Epoch(train) [10][220/940] lr: 1.0000e-02 eta: 14:32:39 time: 0.6661 data_time: 0.3328 memory: 16095 grad_norm: 5.0377 loss: 1.7413 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.7413 2022/12/08 14:34:48 - mmengine - INFO - Epoch(train) [10][240/940] lr: 1.0000e-02 eta: 14:32:18 time: 0.5733 data_time: 0.2363 memory: 16095 grad_norm: 5.0741 loss: 1.8312 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.8312 2022/12/08 14:35:01 - mmengine - INFO - Epoch(train) [10][260/940] lr: 1.0000e-02 eta: 14:32:14 time: 0.6531 data_time: 0.3198 memory: 16095 grad_norm: 4.9834 loss: 1.7873 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.7873 2022/12/08 14:35:13 - mmengine - INFO - Epoch(train) [10][280/940] lr: 1.0000e-02 eta: 14:31:51 time: 0.5581 data_time: 0.2291 memory: 16095 grad_norm: 5.1028 loss: 1.9096 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9096 2022/12/08 14:35:26 - mmengine - INFO - Epoch(train) [10][300/940] lr: 1.0000e-02 eta: 14:31:49 time: 0.6661 data_time: 0.3254 memory: 16095 grad_norm: 5.1841 loss: 1.8704 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8704 2022/12/08 14:35:37 - mmengine - INFO - Epoch(train) [10][320/940] lr: 1.0000e-02 eta: 14:31:21 time: 0.5357 data_time: 0.1999 memory: 16095 grad_norm: 5.0913 loss: 1.7631 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.7631 2022/12/08 14:35:50 - mmengine - INFO - Epoch(train) [10][340/940] lr: 1.0000e-02 eta: 14:31:21 time: 0.6777 data_time: 0.3407 memory: 16095 grad_norm: 5.1293 loss: 1.8967 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.8967 2022/12/08 14:36:02 - mmengine - INFO - Epoch(train) [10][360/940] lr: 1.0000e-02 eta: 14:31:02 time: 0.5790 data_time: 0.1921 memory: 16095 grad_norm: 5.0611 loss: 1.8400 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.8400 2022/12/08 14:36:14 - mmengine - INFO - Epoch(train) [10][380/940] lr: 1.0000e-02 eta: 14:30:52 time: 0.6240 data_time: 0.1545 memory: 16095 grad_norm: 5.0747 loss: 1.8537 top1_acc: 0.5312 top5_acc: 0.6562 loss_cls: 1.8537 2022/12/08 14:36:27 - mmengine - INFO - Epoch(train) [10][400/940] lr: 1.0000e-02 eta: 14:30:45 time: 0.6404 data_time: 0.0655 memory: 16095 grad_norm: 5.0605 loss: 1.8411 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.8411 2022/12/08 14:36:39 - mmengine - INFO - Epoch(train) [10][420/940] lr: 1.0000e-02 eta: 14:30:27 time: 0.5849 data_time: 0.0495 memory: 16095 grad_norm: 5.1040 loss: 1.7915 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.7915 2022/12/08 14:36:52 - mmengine - INFO - Epoch(train) [10][440/940] lr: 1.0000e-02 eta: 14:30:23 time: 0.6534 data_time: 0.0308 memory: 16095 grad_norm: 5.1255 loss: 1.9034 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.9034 2022/12/08 14:37:03 - mmengine - INFO - Epoch(train) [10][460/940] lr: 1.0000e-02 eta: 14:30:05 time: 0.5844 data_time: 0.0275 memory: 16095 grad_norm: 5.0803 loss: 1.7652 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.7652 2022/12/08 14:37:16 - mmengine - INFO - Epoch(train) [10][480/940] lr: 1.0000e-02 eta: 14:29:53 time: 0.6179 data_time: 0.0443 memory: 16095 grad_norm: 5.1168 loss: 1.7851 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.7851 2022/12/08 14:37:28 - mmengine - INFO - Epoch(train) [10][500/940] lr: 1.0000e-02 eta: 14:29:42 time: 0.6171 data_time: 0.2108 memory: 16095 grad_norm: 5.1034 loss: 1.8652 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.8652 2022/12/08 14:37:40 - mmengine - INFO - Epoch(train) [10][520/940] lr: 1.0000e-02 eta: 14:29:25 time: 0.5878 data_time: 0.1195 memory: 16095 grad_norm: 5.0837 loss: 1.8244 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8244 2022/12/08 14:37:53 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 14:37:53 - mmengine - INFO - Epoch(train) [10][540/940] lr: 1.0000e-02 eta: 14:29:24 time: 0.6739 data_time: 0.2808 memory: 16095 grad_norm: 5.0721 loss: 1.9012 top1_acc: 0.5000 top5_acc: 0.6562 loss_cls: 1.9012 2022/12/08 14:38:04 - mmengine - INFO - Epoch(train) [10][560/940] lr: 1.0000e-02 eta: 14:28:58 time: 0.5403 data_time: 0.2145 memory: 16095 grad_norm: 5.0504 loss: 1.8848 top1_acc: 0.5938 top5_acc: 0.7188 loss_cls: 1.8848 2022/12/08 14:38:17 - mmengine - INFO - Epoch(train) [10][580/940] lr: 1.0000e-02 eta: 14:28:52 time: 0.6490 data_time: 0.2844 memory: 16095 grad_norm: 5.1402 loss: 1.7825 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.7825 2022/12/08 14:38:29 - mmengine - INFO - Epoch(train) [10][600/940] lr: 1.0000e-02 eta: 14:28:38 time: 0.6019 data_time: 0.1715 memory: 16095 grad_norm: 5.1770 loss: 1.8080 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.8080 2022/12/08 14:38:41 - mmengine - INFO - Epoch(train) [10][620/940] lr: 1.0000e-02 eta: 14:28:23 time: 0.6010 data_time: 0.2022 memory: 16095 grad_norm: 5.0616 loss: 1.8315 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.8315 2022/12/08 14:38:52 - mmengine - INFO - Epoch(train) [10][640/940] lr: 1.0000e-02 eta: 14:28:01 time: 0.5592 data_time: 0.1566 memory: 16095 grad_norm: 5.0317 loss: 1.8960 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8960 2022/12/08 14:39:06 - mmengine - INFO - Epoch(train) [10][660/940] lr: 1.0000e-02 eta: 14:27:56 time: 0.6550 data_time: 0.3183 memory: 16095 grad_norm: 5.0820 loss: 1.8694 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.8694 2022/12/08 14:39:17 - mmengine - INFO - Epoch(train) [10][680/940] lr: 1.0000e-02 eta: 14:27:34 time: 0.5635 data_time: 0.2290 memory: 16095 grad_norm: 5.0293 loss: 1.6934 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 1.6934 2022/12/08 14:39:30 - mmengine - INFO - Epoch(train) [10][700/940] lr: 1.0000e-02 eta: 14:27:33 time: 0.6718 data_time: 0.3158 memory: 16095 grad_norm: 5.0777 loss: 1.8330 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.8330 2022/12/08 14:39:42 - mmengine - INFO - Epoch(train) [10][720/940] lr: 1.0000e-02 eta: 14:27:12 time: 0.5639 data_time: 0.2174 memory: 16095 grad_norm: 4.9646 loss: 1.8231 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.8231 2022/12/08 14:39:55 - mmengine - INFO - Epoch(train) [10][740/940] lr: 1.0000e-02 eta: 14:27:14 time: 0.6942 data_time: 0.2400 memory: 16095 grad_norm: 5.2357 loss: 1.9939 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.9939 2022/12/08 14:40:07 - mmengine - INFO - Epoch(train) [10][760/940] lr: 1.0000e-02 eta: 14:26:52 time: 0.5617 data_time: 0.0694 memory: 16095 grad_norm: 5.0794 loss: 1.7583 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.7583 2022/12/08 14:40:20 - mmengine - INFO - Epoch(train) [10][780/940] lr: 1.0000e-02 eta: 14:26:50 time: 0.6670 data_time: 0.0455 memory: 16095 grad_norm: 5.2544 loss: 1.9362 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.9362 2022/12/08 14:40:32 - mmengine - INFO - Epoch(train) [10][800/940] lr: 1.0000e-02 eta: 14:26:32 time: 0.5822 data_time: 0.0228 memory: 16095 grad_norm: 4.9546 loss: 1.8172 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.8172 2022/12/08 14:40:45 - mmengine - INFO - Epoch(train) [10][820/940] lr: 1.0000e-02 eta: 14:26:30 time: 0.6716 data_time: 0.0251 memory: 16095 grad_norm: 5.0348 loss: 1.8728 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.8728 2022/12/08 14:40:56 - mmengine - INFO - Epoch(train) [10][840/940] lr: 1.0000e-02 eta: 14:26:09 time: 0.5646 data_time: 0.0219 memory: 16095 grad_norm: 5.1002 loss: 1.7767 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.7767 2022/12/08 14:41:09 - mmengine - INFO - Epoch(train) [10][860/940] lr: 1.0000e-02 eta: 14:26:01 time: 0.6348 data_time: 0.0238 memory: 16095 grad_norm: 5.0878 loss: 1.7589 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.7589 2022/12/08 14:41:19 - mmengine - INFO - Epoch(train) [10][880/940] lr: 1.0000e-02 eta: 14:25:28 time: 0.4994 data_time: 0.0253 memory: 16095 grad_norm: 5.0956 loss: 1.9089 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9089 2022/12/08 14:41:32 - mmengine - INFO - Epoch(train) [10][900/940] lr: 1.0000e-02 eta: 14:25:25 time: 0.6644 data_time: 0.0283 memory: 16095 grad_norm: 5.2041 loss: 2.0815 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 2.0815 2022/12/08 14:41:44 - mmengine - INFO - Epoch(train) [10][920/940] lr: 1.0000e-02 eta: 14:25:08 time: 0.5883 data_time: 0.0211 memory: 16095 grad_norm: 5.1066 loss: 1.8367 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.8367 2022/12/08 14:41:56 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 14:41:56 - mmengine - INFO - Epoch(train) [10][940/940] lr: 1.0000e-02 eta: 14:24:49 time: 0.5772 data_time: 0.0182 memory: 16095 grad_norm: 5.3581 loss: 1.7617 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.7617 2022/12/08 14:42:09 - mmengine - INFO - Epoch(val) [10][20/78] eta: 0:00:40 time: 0.6901 data_time: 0.5963 memory: 1686 2022/12/08 14:42:19 - mmengine - INFO - Epoch(val) [10][40/78] eta: 0:00:22 time: 0.4773 data_time: 0.3829 memory: 1686 2022/12/08 14:42:33 - mmengine - INFO - Epoch(val) [10][60/78] eta: 0:00:11 time: 0.6749 data_time: 0.5793 memory: 1686 2022/12/08 14:42:43 - mmengine - INFO - Epoch(val) [10][78/78] acc/top1: 0.5891 acc/top5: 0.8230 acc/mean1: 0.5889 2022/12/08 14:43:00 - mmengine - INFO - Epoch(train) [11][ 20/940] lr: 1.0000e-02 eta: 14:25:19 time: 0.8467 data_time: 0.2417 memory: 16095 grad_norm: 5.0973 loss: 1.7677 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7677 2022/12/08 14:43:11 - mmengine - INFO - Epoch(train) [11][ 40/940] lr: 1.0000e-02 eta: 14:24:58 time: 0.5652 data_time: 0.0211 memory: 16095 grad_norm: 5.0542 loss: 1.7879 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.7879 2022/12/08 14:43:25 - mmengine - INFO - Epoch(train) [11][ 60/940] lr: 1.0000e-02 eta: 14:24:54 time: 0.6619 data_time: 0.0271 memory: 16095 grad_norm: 5.0015 loss: 1.7383 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7383 2022/12/08 14:43:36 - mmengine - INFO - Epoch(train) [11][ 80/940] lr: 1.0000e-02 eta: 14:24:31 time: 0.5532 data_time: 0.0222 memory: 16095 grad_norm: 5.0127 loss: 1.6451 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6451 2022/12/08 14:43:49 - mmengine - INFO - Epoch(train) [11][100/940] lr: 1.0000e-02 eta: 14:24:31 time: 0.6835 data_time: 0.0268 memory: 16095 grad_norm: 4.9895 loss: 1.8117 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8117 2022/12/08 14:44:00 - mmengine - INFO - Epoch(train) [11][120/940] lr: 1.0000e-02 eta: 14:24:04 time: 0.5319 data_time: 0.0304 memory: 16095 grad_norm: 5.0755 loss: 1.6948 top1_acc: 0.6250 top5_acc: 0.7188 loss_cls: 1.6948 2022/12/08 14:44:14 - mmengine - INFO - Epoch(train) [11][140/940] lr: 1.0000e-02 eta: 14:24:10 time: 0.7149 data_time: 0.0291 memory: 16095 grad_norm: 4.9701 loss: 1.6869 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.6869 2022/12/08 14:44:25 - mmengine - INFO - Epoch(train) [11][160/940] lr: 1.0000e-02 eta: 14:23:43 time: 0.5317 data_time: 0.0206 memory: 16095 grad_norm: 5.1723 loss: 1.6466 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6466 2022/12/08 14:44:38 - mmengine - INFO - Epoch(train) [11][180/940] lr: 1.0000e-02 eta: 14:23:41 time: 0.6709 data_time: 0.0275 memory: 16095 grad_norm: 5.0219 loss: 1.7700 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.7700 2022/12/08 14:44:50 - mmengine - INFO - Epoch(train) [11][200/940] lr: 1.0000e-02 eta: 14:23:25 time: 0.5917 data_time: 0.0230 memory: 16095 grad_norm: 5.0436 loss: 1.7110 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.7110 2022/12/08 14:45:04 - mmengine - INFO - Epoch(train) [11][220/940] lr: 1.0000e-02 eta: 14:23:22 time: 0.6706 data_time: 0.0228 memory: 16095 grad_norm: 4.9391 loss: 1.6671 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6671 2022/12/08 14:45:14 - mmengine - INFO - Epoch(train) [11][240/940] lr: 1.0000e-02 eta: 14:22:55 time: 0.5310 data_time: 0.0246 memory: 16095 grad_norm: 5.0805 loss: 1.5681 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.5681 2022/12/08 14:45:28 - mmengine - INFO - Epoch(train) [11][260/940] lr: 1.0000e-02 eta: 14:22:52 time: 0.6657 data_time: 0.0261 memory: 16095 grad_norm: 5.1381 loss: 1.6072 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.6072 2022/12/08 14:45:38 - mmengine - INFO - Epoch(train) [11][280/940] lr: 1.0000e-02 eta: 14:22:25 time: 0.5297 data_time: 0.0245 memory: 16095 grad_norm: 5.0919 loss: 1.9112 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.9112 2022/12/08 14:45:52 - mmengine - INFO - Epoch(train) [11][300/940] lr: 1.0000e-02 eta: 14:22:29 time: 0.7053 data_time: 0.0236 memory: 16095 grad_norm: 5.1621 loss: 1.6882 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6882 2022/12/08 14:46:03 - mmengine - INFO - Epoch(train) [11][320/940] lr: 1.0000e-02 eta: 14:22:08 time: 0.5623 data_time: 0.0260 memory: 16095 grad_norm: 4.9805 loss: 1.6825 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6825 2022/12/08 14:46:17 - mmengine - INFO - Epoch(train) [11][340/940] lr: 1.0000e-02 eta: 14:22:03 time: 0.6576 data_time: 0.0255 memory: 16095 grad_norm: 5.1413 loss: 1.9534 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.9534 2022/12/08 14:46:28 - mmengine - INFO - Epoch(train) [11][360/940] lr: 1.0000e-02 eta: 14:21:41 time: 0.5542 data_time: 0.0262 memory: 16095 grad_norm: 5.1425 loss: 1.8675 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.8675 2022/12/08 14:46:41 - mmengine - INFO - Epoch(train) [11][380/940] lr: 1.0000e-02 eta: 14:21:37 time: 0.6619 data_time: 0.0241 memory: 16095 grad_norm: 5.1767 loss: 1.7626 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.7626 2022/12/08 14:46:52 - mmengine - INFO - Epoch(train) [11][400/940] lr: 1.0000e-02 eta: 14:21:10 time: 0.5313 data_time: 0.0250 memory: 16095 grad_norm: 5.2005 loss: 1.6673 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 1.6673 2022/12/08 14:47:05 - mmengine - INFO - Epoch(train) [11][420/940] lr: 1.0000e-02 eta: 14:21:06 time: 0.6633 data_time: 0.0235 memory: 16095 grad_norm: 5.2101 loss: 1.9551 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.9551 2022/12/08 14:47:16 - mmengine - INFO - Epoch(train) [11][440/940] lr: 1.0000e-02 eta: 14:20:46 time: 0.5645 data_time: 0.0230 memory: 16095 grad_norm: 5.0263 loss: 1.8762 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.8762 2022/12/08 14:47:29 - mmengine - INFO - Epoch(train) [11][460/940] lr: 1.0000e-02 eta: 14:20:41 time: 0.6590 data_time: 0.0252 memory: 16095 grad_norm: 5.0117 loss: 1.9643 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.9643 2022/12/08 14:47:40 - mmengine - INFO - Epoch(train) [11][480/940] lr: 1.0000e-02 eta: 14:20:17 time: 0.5473 data_time: 0.0241 memory: 16095 grad_norm: 5.1259 loss: 1.6172 top1_acc: 0.6562 top5_acc: 0.7188 loss_cls: 1.6172 2022/12/08 14:47:54 - mmengine - INFO - Epoch(train) [11][500/940] lr: 1.0000e-02 eta: 14:20:16 time: 0.6749 data_time: 0.0258 memory: 16095 grad_norm: 4.9786 loss: 1.7004 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.7004 2022/12/08 14:48:05 - mmengine - INFO - Epoch(train) [11][520/940] lr: 1.0000e-02 eta: 14:19:55 time: 0.5626 data_time: 0.0233 memory: 16095 grad_norm: 5.1043 loss: 1.8607 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.8607 2022/12/08 14:48:18 - mmengine - INFO - Epoch(train) [11][540/940] lr: 1.0000e-02 eta: 14:19:51 time: 0.6649 data_time: 0.0245 memory: 16095 grad_norm: 5.1724 loss: 1.7903 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.7903 2022/12/08 14:48:29 - mmengine - INFO - Epoch(train) [11][560/940] lr: 1.0000e-02 eta: 14:19:28 time: 0.5496 data_time: 0.0885 memory: 16095 grad_norm: 5.0019 loss: 1.8493 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.8493 2022/12/08 14:48:41 - mmengine - INFO - Epoch(train) [11][580/940] lr: 1.0000e-02 eta: 14:19:13 time: 0.5959 data_time: 0.1214 memory: 16095 grad_norm: 4.8986 loss: 1.5860 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.5860 2022/12/08 14:48:54 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 14:48:54 - mmengine - INFO - Epoch(train) [11][600/940] lr: 1.0000e-02 eta: 14:19:01 time: 0.6194 data_time: 0.1965 memory: 16095 grad_norm: 5.0001 loss: 1.9419 top1_acc: 0.5312 top5_acc: 0.6875 loss_cls: 1.9419 2022/12/08 14:49:06 - mmengine - INFO - Epoch(train) [11][620/940] lr: 1.0000e-02 eta: 14:18:48 time: 0.6055 data_time: 0.1026 memory: 16095 grad_norm: 5.0345 loss: 1.7891 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.7891 2022/12/08 14:49:17 - mmengine - INFO - Epoch(train) [11][640/940] lr: 1.0000e-02 eta: 14:18:27 time: 0.5631 data_time: 0.0624 memory: 16095 grad_norm: 5.1452 loss: 1.8468 top1_acc: 0.4062 top5_acc: 0.6562 loss_cls: 1.8468 2022/12/08 14:49:31 - mmengine - INFO - Epoch(train) [11][660/940] lr: 1.0000e-02 eta: 14:18:25 time: 0.6771 data_time: 0.0231 memory: 16095 grad_norm: 5.0537 loss: 1.8493 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8493 2022/12/08 14:49:41 - mmengine - INFO - Epoch(train) [11][680/940] lr: 1.0000e-02 eta: 14:18:01 time: 0.5426 data_time: 0.0238 memory: 16095 grad_norm: 5.1298 loss: 1.8145 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.8145 2022/12/08 14:49:55 - mmengine - INFO - Epoch(train) [11][700/940] lr: 1.0000e-02 eta: 14:18:01 time: 0.6871 data_time: 0.0246 memory: 16095 grad_norm: 5.0785 loss: 1.8010 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.8010 2022/12/08 14:50:07 - mmengine - INFO - Epoch(train) [11][720/940] lr: 1.0000e-02 eta: 14:17:46 time: 0.5962 data_time: 0.0255 memory: 16095 grad_norm: 5.1670 loss: 1.8705 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.8705 2022/12/08 14:50:20 - mmengine - INFO - Epoch(train) [11][740/940] lr: 1.0000e-02 eta: 14:17:38 time: 0.6390 data_time: 0.0230 memory: 16095 grad_norm: 5.0460 loss: 1.6368 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6368 2022/12/08 14:50:31 - mmengine - INFO - Epoch(train) [11][760/940] lr: 1.0000e-02 eta: 14:17:13 time: 0.5365 data_time: 0.0247 memory: 16095 grad_norm: 5.1417 loss: 1.8264 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.8264 2022/12/08 14:50:44 - mmengine - INFO - Epoch(train) [11][780/940] lr: 1.0000e-02 eta: 14:17:13 time: 0.6849 data_time: 0.0251 memory: 16095 grad_norm: 5.0918 loss: 1.8570 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.8570 2022/12/08 14:50:55 - mmengine - INFO - Epoch(train) [11][800/940] lr: 1.0000e-02 eta: 14:16:51 time: 0.5588 data_time: 0.0240 memory: 16095 grad_norm: 5.1147 loss: 1.7540 top1_acc: 0.5938 top5_acc: 0.7188 loss_cls: 1.7540 2022/12/08 14:51:09 - mmengine - INFO - Epoch(train) [11][820/940] lr: 1.0000e-02 eta: 14:16:48 time: 0.6677 data_time: 0.0272 memory: 16095 grad_norm: 5.0007 loss: 1.8613 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.8613 2022/12/08 14:51:20 - mmengine - INFO - Epoch(train) [11][840/940] lr: 1.0000e-02 eta: 14:16:30 time: 0.5785 data_time: 0.0234 memory: 16095 grad_norm: 5.0398 loss: 1.8787 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8787 2022/12/08 14:51:34 - mmengine - INFO - Epoch(train) [11][860/940] lr: 1.0000e-02 eta: 14:16:27 time: 0.6722 data_time: 0.0229 memory: 16095 grad_norm: 5.1639 loss: 1.7777 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.7777 2022/12/08 14:51:44 - mmengine - INFO - Epoch(train) [11][880/940] lr: 1.0000e-02 eta: 14:16:00 time: 0.5244 data_time: 0.0249 memory: 16095 grad_norm: 5.1641 loss: 1.9299 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.9299 2022/12/08 14:51:58 - mmengine - INFO - Epoch(train) [11][900/940] lr: 1.0000e-02 eta: 14:15:56 time: 0.6628 data_time: 0.0240 memory: 16095 grad_norm: 5.2397 loss: 1.9156 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9156 2022/12/08 14:52:09 - mmengine - INFO - Epoch(train) [11][920/940] lr: 1.0000e-02 eta: 14:15:38 time: 0.5751 data_time: 0.0267 memory: 16095 grad_norm: 5.1459 loss: 1.7369 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.7369 2022/12/08 14:52:20 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 14:52:20 - mmengine - INFO - Epoch(train) [11][940/940] lr: 1.0000e-02 eta: 14:15:15 time: 0.5485 data_time: 0.0165 memory: 16095 grad_norm: 5.5008 loss: 1.8481 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.8481 2022/12/08 14:52:34 - mmengine - INFO - Epoch(val) [11][20/78] eta: 0:00:41 time: 0.7090 data_time: 0.6129 memory: 1686 2022/12/08 14:52:44 - mmengine - INFO - Epoch(val) [11][40/78] eta: 0:00:22 time: 0.4738 data_time: 0.3811 memory: 1686 2022/12/08 14:52:57 - mmengine - INFO - Epoch(val) [11][60/78] eta: 0:00:11 time: 0.6635 data_time: 0.5685 memory: 1686 2022/12/08 14:53:07 - mmengine - INFO - Epoch(val) [11][78/78] acc/top1: 0.6118 acc/top5: 0.8398 acc/mean1: 0.6117 2022/12/08 14:53:07 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_9.pth is removed 2022/12/08 14:53:10 - mmengine - INFO - The best checkpoint with 0.6118 acc/top1 at 11 epoch is saved to best_acc/top1_epoch_11.pth. 2022/12/08 14:53:25 - mmengine - INFO - Epoch(train) [12][ 20/940] lr: 1.0000e-02 eta: 14:15:29 time: 0.7757 data_time: 0.4798 memory: 16095 grad_norm: 4.9209 loss: 1.7279 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 1.7279 2022/12/08 14:53:36 - mmengine - INFO - Epoch(train) [12][ 40/940] lr: 1.0000e-02 eta: 14:15:06 time: 0.5463 data_time: 0.2430 memory: 16095 grad_norm: 4.9325 loss: 1.7260 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7260 2022/12/08 14:53:50 - mmengine - INFO - Epoch(train) [12][ 60/940] lr: 1.0000e-02 eta: 14:15:03 time: 0.6716 data_time: 0.3472 memory: 16095 grad_norm: 4.9085 loss: 1.6498 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6498 2022/12/08 14:54:01 - mmengine - INFO - Epoch(train) [12][ 80/940] lr: 1.0000e-02 eta: 14:14:45 time: 0.5824 data_time: 0.2828 memory: 16095 grad_norm: 5.1530 loss: 1.7608 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.7608 2022/12/08 14:54:16 - mmengine - INFO - Epoch(train) [12][100/940] lr: 1.0000e-02 eta: 14:14:48 time: 0.7092 data_time: 0.3924 memory: 16095 grad_norm: 5.1099 loss: 1.8723 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 1.8723 2022/12/08 14:54:26 - mmengine - INFO - Epoch(train) [12][120/940] lr: 1.0000e-02 eta: 14:14:25 time: 0.5456 data_time: 0.2356 memory: 16095 grad_norm: 5.1451 loss: 1.8132 top1_acc: 0.4688 top5_acc: 0.6562 loss_cls: 1.8132 2022/12/08 14:54:40 - mmengine - INFO - Epoch(train) [12][140/940] lr: 1.0000e-02 eta: 14:14:21 time: 0.6612 data_time: 0.3456 memory: 16095 grad_norm: 4.9172 loss: 1.7291 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.7291 2022/12/08 14:54:51 - mmengine - INFO - Epoch(train) [12][160/940] lr: 1.0000e-02 eta: 14:13:58 time: 0.5476 data_time: 0.2337 memory: 16095 grad_norm: 5.0333 loss: 1.8535 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.8535 2022/12/08 14:55:04 - mmengine - INFO - Epoch(train) [12][180/940] lr: 1.0000e-02 eta: 14:13:56 time: 0.6794 data_time: 0.3490 memory: 16095 grad_norm: 5.2185 loss: 1.7102 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.7102 2022/12/08 14:55:15 - mmengine - INFO - Epoch(train) [12][200/940] lr: 1.0000e-02 eta: 14:13:29 time: 0.5220 data_time: 0.2023 memory: 16095 grad_norm: 5.0505 loss: 1.6259 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.6259 2022/12/08 14:55:28 - mmengine - INFO - Epoch(train) [12][220/940] lr: 1.0000e-02 eta: 14:13:26 time: 0.6733 data_time: 0.3429 memory: 16095 grad_norm: 5.1119 loss: 1.7571 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.7571 2022/12/08 14:55:39 - mmengine - INFO - Epoch(train) [12][240/940] lr: 1.0000e-02 eta: 14:12:59 time: 0.5213 data_time: 0.1984 memory: 16095 grad_norm: 5.2271 loss: 1.7940 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.7940 2022/12/08 14:55:53 - mmengine - INFO - Epoch(train) [12][260/940] lr: 1.0000e-02 eta: 14:13:02 time: 0.7075 data_time: 0.3887 memory: 16095 grad_norm: 5.1267 loss: 1.8263 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.8263 2022/12/08 14:56:04 - mmengine - INFO - Epoch(train) [12][280/940] lr: 1.0000e-02 eta: 14:12:41 time: 0.5584 data_time: 0.2262 memory: 16095 grad_norm: 5.0551 loss: 1.6541 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 1.6541 2022/12/08 14:56:17 - mmengine - INFO - Epoch(train) [12][300/940] lr: 1.0000e-02 eta: 14:12:36 time: 0.6593 data_time: 0.3289 memory: 16095 grad_norm: 5.0518 loss: 1.7383 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7383 2022/12/08 14:56:29 - mmengine - INFO - Epoch(train) [12][320/940] lr: 1.0000e-02 eta: 14:12:18 time: 0.5767 data_time: 0.2509 memory: 16095 grad_norm: 5.1308 loss: 1.7790 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 1.7790 2022/12/08 14:56:42 - mmengine - INFO - Epoch(train) [12][340/940] lr: 1.0000e-02 eta: 14:12:16 time: 0.6791 data_time: 0.3572 memory: 16095 grad_norm: 5.1292 loss: 1.7482 top1_acc: 0.5312 top5_acc: 0.6562 loss_cls: 1.7482 2022/12/08 14:56:53 - mmengine - INFO - Epoch(train) [12][360/940] lr: 1.0000e-02 eta: 14:11:50 time: 0.5259 data_time: 0.1952 memory: 16095 grad_norm: 5.0498 loss: 1.7270 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.7270 2022/12/08 14:57:06 - mmengine - INFO - Epoch(train) [12][380/940] lr: 1.0000e-02 eta: 14:11:44 time: 0.6575 data_time: 0.3288 memory: 16095 grad_norm: 4.9714 loss: 1.7333 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.7333 2022/12/08 14:57:16 - mmengine - INFO - Epoch(train) [12][400/940] lr: 1.0000e-02 eta: 14:11:19 time: 0.5298 data_time: 0.1968 memory: 16095 grad_norm: 5.0431 loss: 1.7528 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.7528 2022/12/08 14:57:31 - mmengine - INFO - Epoch(train) [12][420/940] lr: 1.0000e-02 eta: 14:11:22 time: 0.7113 data_time: 0.3759 memory: 16095 grad_norm: 5.0366 loss: 1.7709 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.7709 2022/12/08 14:57:42 - mmengine - INFO - Epoch(train) [12][440/940] lr: 1.0000e-02 eta: 14:11:05 time: 0.5818 data_time: 0.2594 memory: 16095 grad_norm: 5.1199 loss: 1.6144 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.6144 2022/12/08 14:57:54 - mmengine - INFO - Epoch(train) [12][460/940] lr: 1.0000e-02 eta: 14:10:48 time: 0.5861 data_time: 0.2694 memory: 16095 grad_norm: 5.0845 loss: 1.7897 top1_acc: 0.5625 top5_acc: 0.7188 loss_cls: 1.7897 2022/12/08 14:58:05 - mmengine - INFO - Epoch(train) [12][480/940] lr: 1.0000e-02 eta: 14:10:22 time: 0.5260 data_time: 0.1931 memory: 16095 grad_norm: 5.1539 loss: 1.6349 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.6349 2022/12/08 14:58:17 - mmengine - INFO - Epoch(train) [12][500/940] lr: 1.0000e-02 eta: 14:10:15 time: 0.6445 data_time: 0.2840 memory: 16095 grad_norm: 5.1728 loss: 1.7580 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7580 2022/12/08 14:58:29 - mmengine - INFO - Epoch(train) [12][520/940] lr: 1.0000e-02 eta: 14:10:00 time: 0.5955 data_time: 0.1438 memory: 16095 grad_norm: 5.1357 loss: 1.8357 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.8357 2022/12/08 14:58:42 - mmengine - INFO - Epoch(train) [12][540/940] lr: 1.0000e-02 eta: 14:09:52 time: 0.6418 data_time: 0.1597 memory: 16095 grad_norm: 5.1274 loss: 1.9367 top1_acc: 0.4375 top5_acc: 0.5938 loss_cls: 1.9367 2022/12/08 14:58:55 - mmengine - INFO - Epoch(train) [12][560/940] lr: 1.0000e-02 eta: 14:09:40 time: 0.6139 data_time: 0.0369 memory: 16095 grad_norm: 5.1382 loss: 1.6573 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.6573 2022/12/08 14:59:06 - mmengine - INFO - Epoch(train) [12][580/940] lr: 1.0000e-02 eta: 14:09:24 time: 0.5922 data_time: 0.0278 memory: 16095 grad_norm: 5.1550 loss: 1.7040 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.7040 2022/12/08 14:59:19 - mmengine - INFO - Epoch(train) [12][600/940] lr: 1.0000e-02 eta: 14:09:15 time: 0.6318 data_time: 0.0244 memory: 16095 grad_norm: 5.2355 loss: 1.8335 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.8335 2022/12/08 14:59:31 - mmengine - INFO - Epoch(train) [12][620/940] lr: 1.0000e-02 eta: 14:08:58 time: 0.5809 data_time: 0.0257 memory: 16095 grad_norm: 5.1322 loss: 1.7308 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.7308 2022/12/08 14:59:45 - mmengine - INFO - Epoch(train) [12][640/940] lr: 1.0000e-02 eta: 14:08:58 time: 0.6956 data_time: 0.0255 memory: 16095 grad_norm: 5.0863 loss: 1.7776 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7776 2022/12/08 14:59:56 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 14:59:56 - mmengine - INFO - Epoch(train) [12][660/940] lr: 1.0000e-02 eta: 14:08:41 time: 0.5840 data_time: 0.0347 memory: 16095 grad_norm: 5.1749 loss: 1.8776 top1_acc: 0.6562 top5_acc: 0.7500 loss_cls: 1.8776 2022/12/08 15:00:09 - mmengine - INFO - Epoch(train) [12][680/940] lr: 1.0000e-02 eta: 14:08:36 time: 0.6599 data_time: 0.0222 memory: 16095 grad_norm: 5.1096 loss: 1.7137 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7137 2022/12/08 15:00:21 - mmengine - INFO - Epoch(train) [12][700/940] lr: 1.0000e-02 eta: 14:08:17 time: 0.5684 data_time: 0.0242 memory: 16095 grad_norm: 5.1924 loss: 1.8694 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.8694 2022/12/08 15:00:35 - mmengine - INFO - Epoch(train) [12][720/940] lr: 1.0000e-02 eta: 14:08:17 time: 0.6949 data_time: 0.0244 memory: 16095 grad_norm: 5.0392 loss: 1.7251 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.7251 2022/12/08 15:00:46 - mmengine - INFO - Epoch(train) [12][740/940] lr: 1.0000e-02 eta: 14:07:59 time: 0.5811 data_time: 0.0243 memory: 16095 grad_norm: 5.1389 loss: 1.7989 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7989 2022/12/08 15:01:00 - mmengine - INFO - Epoch(train) [12][760/940] lr: 1.0000e-02 eta: 14:07:55 time: 0.6680 data_time: 0.0245 memory: 16095 grad_norm: 5.1775 loss: 1.8339 top1_acc: 0.5938 top5_acc: 0.7188 loss_cls: 1.8339 2022/12/08 15:01:11 - mmengine - INFO - Epoch(train) [12][780/940] lr: 1.0000e-02 eta: 14:07:34 time: 0.5561 data_time: 0.0241 memory: 16095 grad_norm: 5.1722 loss: 1.7684 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.7684 2022/12/08 15:01:24 - mmengine - INFO - Epoch(train) [12][800/940] lr: 1.0000e-02 eta: 14:07:29 time: 0.6587 data_time: 0.0226 memory: 16095 grad_norm: 5.0451 loss: 1.9214 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9214 2022/12/08 15:01:35 - mmengine - INFO - Epoch(train) [12][820/940] lr: 1.0000e-02 eta: 14:07:10 time: 0.5709 data_time: 0.0267 memory: 16095 grad_norm: 5.1082 loss: 1.7533 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.7533 2022/12/08 15:01:47 - mmengine - INFO - Epoch(train) [12][840/940] lr: 1.0000e-02 eta: 14:06:56 time: 0.6015 data_time: 0.0206 memory: 16095 grad_norm: 5.2323 loss: 1.8457 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8457 2022/12/08 15:01:58 - mmengine - INFO - Epoch(train) [12][860/940] lr: 1.0000e-02 eta: 14:06:32 time: 0.5327 data_time: 0.0275 memory: 16095 grad_norm: 5.1034 loss: 1.7449 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7449 2022/12/08 15:02:12 - mmengine - INFO - Epoch(train) [12][880/940] lr: 1.0000e-02 eta: 14:06:34 time: 0.7105 data_time: 0.0212 memory: 16095 grad_norm: 5.0680 loss: 1.7742 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.7742 2022/12/08 15:02:23 - mmengine - INFO - Epoch(train) [12][900/940] lr: 1.0000e-02 eta: 14:06:10 time: 0.5358 data_time: 0.0285 memory: 16095 grad_norm: 5.1572 loss: 1.8497 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 1.8497 2022/12/08 15:02:37 - mmengine - INFO - Epoch(train) [12][920/940] lr: 1.0000e-02 eta: 14:06:12 time: 0.7114 data_time: 0.0220 memory: 16095 grad_norm: 5.0922 loss: 1.7037 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.7037 2022/12/08 15:02:46 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 15:02:46 - mmengine - INFO - Epoch(train) [12][940/940] lr: 1.0000e-02 eta: 14:05:34 time: 0.4329 data_time: 0.0165 memory: 16095 grad_norm: 5.4049 loss: 1.7916 top1_acc: 0.4286 top5_acc: 0.5714 loss_cls: 1.7916 2022/12/08 15:02:46 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/12/08 15:03:03 - mmengine - INFO - Epoch(val) [12][20/78] eta: 0:00:40 time: 0.7026 data_time: 0.6064 memory: 1686 2022/12/08 15:03:12 - mmengine - INFO - Epoch(val) [12][40/78] eta: 0:00:22 time: 0.4642 data_time: 0.3697 memory: 1686 2022/12/08 15:03:26 - mmengine - INFO - Epoch(val) [12][60/78] eta: 0:00:11 time: 0.6877 data_time: 0.5931 memory: 1686 2022/12/08 15:03:35 - mmengine - INFO - Epoch(val) [12][78/78] acc/top1: 0.6119 acc/top5: 0.8380 acc/mean1: 0.6118 2022/12/08 15:03:35 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_11.pth is removed 2022/12/08 15:03:38 - mmengine - INFO - The best checkpoint with 0.6119 acc/top1 at 12 epoch is saved to best_acc/top1_epoch_12.pth. 2022/12/08 15:03:53 - mmengine - INFO - Epoch(train) [13][ 20/940] lr: 1.0000e-02 eta: 14:05:45 time: 0.7718 data_time: 0.4632 memory: 16095 grad_norm: 5.0173 loss: 1.6769 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.6769 2022/12/08 15:04:04 - mmengine - INFO - Epoch(train) [13][ 40/940] lr: 1.0000e-02 eta: 14:05:25 time: 0.5602 data_time: 0.2522 memory: 16095 grad_norm: 5.0313 loss: 1.6083 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.6083 2022/12/08 15:04:17 - mmengine - INFO - Epoch(train) [13][ 60/940] lr: 1.0000e-02 eta: 14:05:19 time: 0.6578 data_time: 0.3379 memory: 16095 grad_norm: 5.0120 loss: 1.7032 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7032 2022/12/08 15:04:29 - mmengine - INFO - Epoch(train) [13][ 80/940] lr: 1.0000e-02 eta: 14:04:58 time: 0.5565 data_time: 0.2540 memory: 16095 grad_norm: 5.0566 loss: 1.7757 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7757 2022/12/08 15:04:42 - mmengine - INFO - Epoch(train) [13][100/940] lr: 1.0000e-02 eta: 14:04:55 time: 0.6791 data_time: 0.3822 memory: 16095 grad_norm: 5.0058 loss: 1.5063 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.5063 2022/12/08 15:04:54 - mmengine - INFO - Epoch(train) [13][120/940] lr: 1.0000e-02 eta: 14:04:43 time: 0.6111 data_time: 0.2937 memory: 16095 grad_norm: 5.0504 loss: 1.7019 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.7019 2022/12/08 15:05:06 - mmengine - INFO - Epoch(train) [13][140/940] lr: 1.0000e-02 eta: 14:04:26 time: 0.5855 data_time: 0.2524 memory: 16095 grad_norm: 5.0761 loss: 1.6613 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.6613 2022/12/08 15:05:19 - mmengine - INFO - Epoch(train) [13][160/940] lr: 1.0000e-02 eta: 14:04:21 time: 0.6629 data_time: 0.3303 memory: 16095 grad_norm: 5.0975 loss: 1.6681 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.6681 2022/12/08 15:05:31 - mmengine - INFO - Epoch(train) [13][180/940] lr: 1.0000e-02 eta: 14:04:03 time: 0.5724 data_time: 0.2561 memory: 16095 grad_norm: 5.0721 loss: 1.6809 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6809 2022/12/08 15:05:44 - mmengine - INFO - Epoch(train) [13][200/940] lr: 1.0000e-02 eta: 14:03:58 time: 0.6671 data_time: 0.3517 memory: 16095 grad_norm: 5.0285 loss: 1.6955 top1_acc: 0.5312 top5_acc: 0.6875 loss_cls: 1.6955 2022/12/08 15:05:55 - mmengine - INFO - Epoch(train) [13][220/940] lr: 1.0000e-02 eta: 14:03:36 time: 0.5414 data_time: 0.2241 memory: 16095 grad_norm: 5.1270 loss: 1.9685 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9685 2022/12/08 15:06:07 - mmengine - INFO - Epoch(train) [13][240/940] lr: 1.0000e-02 eta: 14:03:25 time: 0.6231 data_time: 0.3074 memory: 16095 grad_norm: 5.0612 loss: 1.8188 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 1.8188 2022/12/08 15:06:20 - mmengine - INFO - Epoch(train) [13][260/940] lr: 1.0000e-02 eta: 14:03:11 time: 0.6041 data_time: 0.2709 memory: 16095 grad_norm: 5.0227 loss: 1.7868 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.7868 2022/12/08 15:06:31 - mmengine - INFO - Epoch(train) [13][280/940] lr: 1.0000e-02 eta: 14:02:56 time: 0.5948 data_time: 0.2674 memory: 16095 grad_norm: 5.0216 loss: 1.5279 top1_acc: 0.4688 top5_acc: 0.8750 loss_cls: 1.5279 2022/12/08 15:06:43 - mmengine - INFO - Epoch(train) [13][300/940] lr: 1.0000e-02 eta: 14:02:42 time: 0.6003 data_time: 0.1681 memory: 16095 grad_norm: 5.1030 loss: 1.6442 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.6442 2022/12/08 15:06:56 - mmengine - INFO - Epoch(train) [13][320/940] lr: 1.0000e-02 eta: 14:02:34 time: 0.6399 data_time: 0.1654 memory: 16095 grad_norm: 5.0264 loss: 1.6797 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.6797 2022/12/08 15:07:07 - mmengine - INFO - Epoch(train) [13][340/940] lr: 1.0000e-02 eta: 14:02:12 time: 0.5476 data_time: 0.1347 memory: 16095 grad_norm: 5.0400 loss: 1.7408 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.7408 2022/12/08 15:07:19 - mmengine - INFO - Epoch(train) [13][360/940] lr: 1.0000e-02 eta: 14:01:58 time: 0.5999 data_time: 0.2290 memory: 16095 grad_norm: 5.1007 loss: 1.5976 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.5976 2022/12/08 15:07:32 - mmengine - INFO - Epoch(train) [13][380/940] lr: 1.0000e-02 eta: 14:01:47 time: 0.6224 data_time: 0.1208 memory: 16095 grad_norm: 5.1085 loss: 1.7888 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7888 2022/12/08 15:07:43 - mmengine - INFO - Epoch(train) [13][400/940] lr: 1.0000e-02 eta: 14:01:31 time: 0.5847 data_time: 0.1066 memory: 16095 grad_norm: 5.1916 loss: 1.6406 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.6406 2022/12/08 15:07:56 - mmengine - INFO - Epoch(train) [13][420/940] lr: 1.0000e-02 eta: 14:01:22 time: 0.6417 data_time: 0.0369 memory: 16095 grad_norm: 5.1331 loss: 1.7089 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7089 2022/12/08 15:08:08 - mmengine - INFO - Epoch(train) [13][440/940] lr: 1.0000e-02 eta: 14:01:07 time: 0.5892 data_time: 0.0865 memory: 16095 grad_norm: 5.1768 loss: 1.8684 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.8684 2022/12/08 15:08:21 - mmengine - INFO - Epoch(train) [13][460/940] lr: 1.0000e-02 eta: 14:00:57 time: 0.6326 data_time: 0.0655 memory: 16095 grad_norm: 5.1551 loss: 1.6245 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6245 2022/12/08 15:08:32 - mmengine - INFO - Epoch(train) [13][480/940] lr: 1.0000e-02 eta: 14:00:41 time: 0.5840 data_time: 0.0227 memory: 16095 grad_norm: 5.1536 loss: 1.7745 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.7745 2022/12/08 15:08:45 - mmengine - INFO - Epoch(train) [13][500/940] lr: 1.0000e-02 eta: 14:00:34 time: 0.6498 data_time: 0.0272 memory: 16095 grad_norm: 5.0330 loss: 1.7334 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.7334 2022/12/08 15:08:57 - mmengine - INFO - Epoch(train) [13][520/940] lr: 1.0000e-02 eta: 14:00:17 time: 0.5797 data_time: 0.0219 memory: 16095 grad_norm: 5.1568 loss: 1.7992 top1_acc: 0.4375 top5_acc: 0.6562 loss_cls: 1.7992 2022/12/08 15:09:10 - mmengine - INFO - Epoch(train) [13][540/940] lr: 1.0000e-02 eta: 14:00:09 time: 0.6436 data_time: 0.0846 memory: 16095 grad_norm: 5.1994 loss: 1.7875 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7875 2022/12/08 15:09:22 - mmengine - INFO - Epoch(train) [13][560/940] lr: 1.0000e-02 eta: 13:59:53 time: 0.5931 data_time: 0.1913 memory: 16095 grad_norm: 5.1142 loss: 1.8138 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 1.8138 2022/12/08 15:09:34 - mmengine - INFO - Epoch(train) [13][580/940] lr: 1.0000e-02 eta: 13:59:40 time: 0.6082 data_time: 0.1939 memory: 16095 grad_norm: 5.0716 loss: 1.8085 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 1.8085 2022/12/08 15:09:46 - mmengine - INFO - Epoch(train) [13][600/940] lr: 1.0000e-02 eta: 13:59:30 time: 0.6298 data_time: 0.2870 memory: 16095 grad_norm: 5.1562 loss: 1.5601 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.5601 2022/12/08 15:09:58 - mmengine - INFO - Epoch(train) [13][620/940] lr: 1.0000e-02 eta: 13:59:14 time: 0.5819 data_time: 0.2474 memory: 16095 grad_norm: 5.1560 loss: 1.7135 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.7135 2022/12/08 15:10:10 - mmengine - INFO - Epoch(train) [13][640/940] lr: 1.0000e-02 eta: 13:59:02 time: 0.6159 data_time: 0.2366 memory: 16095 grad_norm: 5.1749 loss: 1.6643 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.6643 2022/12/08 15:10:23 - mmengine - INFO - Epoch(train) [13][660/940] lr: 1.0000e-02 eta: 13:58:50 time: 0.6129 data_time: 0.2510 memory: 16095 grad_norm: 5.1086 loss: 1.7448 top1_acc: 0.5312 top5_acc: 0.6875 loss_cls: 1.7448 2022/12/08 15:10:36 - mmengine - INFO - Epoch(train) [13][680/940] lr: 1.0000e-02 eta: 13:58:42 time: 0.6462 data_time: 0.0555 memory: 16095 grad_norm: 5.1019 loss: 1.6329 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 1.6329 2022/12/08 15:10:47 - mmengine - INFO - Epoch(train) [13][700/940] lr: 1.0000e-02 eta: 13:58:22 time: 0.5555 data_time: 0.0595 memory: 16095 grad_norm: 5.0241 loss: 1.7625 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.7625 2022/12/08 15:10:59 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 15:10:59 - mmengine - INFO - Epoch(train) [13][720/940] lr: 1.0000e-02 eta: 13:58:12 time: 0.6356 data_time: 0.0607 memory: 16095 grad_norm: 4.9520 loss: 1.7674 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 1.7674 2022/12/08 15:11:11 - mmengine - INFO - Epoch(train) [13][740/940] lr: 1.0000e-02 eta: 13:57:55 time: 0.5747 data_time: 0.0703 memory: 16095 grad_norm: 5.1012 loss: 1.7054 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7054 2022/12/08 15:11:24 - mmengine - INFO - Epoch(train) [13][760/940] lr: 1.0000e-02 eta: 13:57:49 time: 0.6622 data_time: 0.0365 memory: 16095 grad_norm: 5.0684 loss: 1.7183 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.7183 2022/12/08 15:11:35 - mmengine - INFO - Epoch(train) [13][780/940] lr: 1.0000e-02 eta: 13:57:29 time: 0.5564 data_time: 0.0399 memory: 16095 grad_norm: 5.2228 loss: 1.6721 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.6721 2022/12/08 15:11:47 - mmengine - INFO - Epoch(train) [13][800/940] lr: 1.0000e-02 eta: 13:57:16 time: 0.6091 data_time: 0.0575 memory: 16095 grad_norm: 5.0443 loss: 1.6787 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6787 2022/12/08 15:12:00 - mmengine - INFO - Epoch(train) [13][820/940] lr: 1.0000e-02 eta: 13:57:09 time: 0.6468 data_time: 0.2686 memory: 16095 grad_norm: 5.1267 loss: 1.7058 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7058 2022/12/08 15:12:11 - mmengine - INFO - Epoch(train) [13][840/940] lr: 1.0000e-02 eta: 13:56:46 time: 0.5369 data_time: 0.2138 memory: 16095 grad_norm: 5.0805 loss: 1.7202 top1_acc: 0.4375 top5_acc: 0.6562 loss_cls: 1.7202 2022/12/08 15:12:25 - mmengine - INFO - Epoch(train) [13][860/940] lr: 1.0000e-02 eta: 13:56:47 time: 0.7085 data_time: 0.3736 memory: 16095 grad_norm: 5.1174 loss: 1.6943 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.6943 2022/12/08 15:12:35 - mmengine - INFO - Epoch(train) [13][880/940] lr: 1.0000e-02 eta: 13:56:20 time: 0.5031 data_time: 0.1783 memory: 16095 grad_norm: 5.1393 loss: 1.6809 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6809 2022/12/08 15:12:48 - mmengine - INFO - Epoch(train) [13][900/940] lr: 1.0000e-02 eta: 13:56:09 time: 0.6280 data_time: 0.2564 memory: 16095 grad_norm: 5.1246 loss: 1.8039 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8039 2022/12/08 15:13:00 - mmengine - INFO - Epoch(train) [13][920/940] lr: 1.0000e-02 eta: 13:55:54 time: 0.5937 data_time: 0.2115 memory: 16095 grad_norm: 5.1380 loss: 1.7337 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7337 2022/12/08 15:13:12 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 15:13:12 - mmengine - INFO - Epoch(train) [13][940/940] lr: 1.0000e-02 eta: 13:55:41 time: 0.6055 data_time: 0.3088 memory: 16095 grad_norm: 5.3567 loss: 1.8069 top1_acc: 0.4286 top5_acc: 0.4286 loss_cls: 1.8069 2022/12/08 15:13:26 - mmengine - INFO - Epoch(val) [13][20/78] eta: 0:00:40 time: 0.6937 data_time: 0.5996 memory: 1686 2022/12/08 15:13:35 - mmengine - INFO - Epoch(val) [13][40/78] eta: 0:00:22 time: 0.4753 data_time: 0.3797 memory: 1686 2022/12/08 15:13:49 - mmengine - INFO - Epoch(val) [13][60/78] eta: 0:00:11 time: 0.6994 data_time: 0.6029 memory: 1686 2022/12/08 15:13:59 - mmengine - INFO - Epoch(val) [13][78/78] acc/top1: 0.6259 acc/top5: 0.8463 acc/mean1: 0.6258 2022/12/08 15:13:59 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_12.pth is removed 2022/12/08 15:14:02 - mmengine - INFO - The best checkpoint with 0.6259 acc/top1 at 13 epoch is saved to best_acc/top1_epoch_13.pth. 2022/12/08 15:14:17 - mmengine - INFO - Epoch(train) [14][ 20/940] lr: 1.0000e-02 eta: 13:55:52 time: 0.7828 data_time: 0.4877 memory: 16095 grad_norm: 5.1176 loss: 1.6954 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.6954 2022/12/08 15:14:29 - mmengine - INFO - Epoch(train) [14][ 40/940] lr: 1.0000e-02 eta: 13:55:36 time: 0.5846 data_time: 0.2771 memory: 16095 grad_norm: 5.0655 loss: 1.5977 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.5977 2022/12/08 15:14:42 - mmengine - INFO - Epoch(train) [14][ 60/940] lr: 1.0000e-02 eta: 13:55:29 time: 0.6584 data_time: 0.3523 memory: 16095 grad_norm: 4.9340 loss: 1.5911 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5911 2022/12/08 15:14:53 - mmengine - INFO - Epoch(train) [14][ 80/940] lr: 1.0000e-02 eta: 13:55:08 time: 0.5438 data_time: 0.2540 memory: 16095 grad_norm: 5.0715 loss: 1.8111 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.8111 2022/12/08 15:15:06 - mmengine - INFO - Epoch(train) [14][100/940] lr: 1.0000e-02 eta: 13:54:57 time: 0.6282 data_time: 0.3241 memory: 16095 grad_norm: 5.1658 loss: 1.7429 top1_acc: 0.3438 top5_acc: 0.7812 loss_cls: 1.7429 2022/12/08 15:15:16 - mmengine - INFO - Epoch(train) [14][120/940] lr: 1.0000e-02 eta: 13:54:35 time: 0.5367 data_time: 0.2203 memory: 16095 grad_norm: 4.9268 loss: 1.6044 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.6044 2022/12/08 15:15:30 - mmengine - INFO - Epoch(train) [14][140/940] lr: 1.0000e-02 eta: 13:54:29 time: 0.6619 data_time: 0.3332 memory: 16095 grad_norm: 5.0886 loss: 1.7448 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7448 2022/12/08 15:15:41 - mmengine - INFO - Epoch(train) [14][160/940] lr: 1.0000e-02 eta: 13:54:10 time: 0.5646 data_time: 0.2334 memory: 16095 grad_norm: 4.9906 loss: 1.6887 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6887 2022/12/08 15:15:54 - mmengine - INFO - Epoch(train) [14][180/940] lr: 1.0000e-02 eta: 13:54:07 time: 0.6769 data_time: 0.2536 memory: 16095 grad_norm: 5.0502 loss: 1.7049 top1_acc: 0.6562 top5_acc: 0.7500 loss_cls: 1.7049 2022/12/08 15:16:05 - mmengine - INFO - Epoch(train) [14][200/940] lr: 1.0000e-02 eta: 13:53:47 time: 0.5554 data_time: 0.1689 memory: 16095 grad_norm: 5.1890 loss: 1.7304 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7304 2022/12/08 15:16:19 - mmengine - INFO - Epoch(train) [14][220/940] lr: 1.0000e-02 eta: 13:53:45 time: 0.6942 data_time: 0.2782 memory: 16095 grad_norm: 5.1303 loss: 1.6728 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.6728 2022/12/08 15:16:30 - mmengine - INFO - Epoch(train) [14][240/940] lr: 1.0000e-02 eta: 13:53:22 time: 0.5333 data_time: 0.0901 memory: 16095 grad_norm: 5.1090 loss: 1.6247 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6247 2022/12/08 15:16:44 - mmengine - INFO - Epoch(train) [14][260/940] lr: 1.0000e-02 eta: 13:53:20 time: 0.6915 data_time: 0.1877 memory: 16095 grad_norm: 5.0685 loss: 1.6584 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.6584 2022/12/08 15:16:54 - mmengine - INFO - Epoch(train) [14][280/940] lr: 1.0000e-02 eta: 13:52:57 time: 0.5312 data_time: 0.0543 memory: 16095 grad_norm: 5.1318 loss: 1.6227 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 1.6227 2022/12/08 15:17:08 - mmengine - INFO - Epoch(train) [14][300/940] lr: 1.0000e-02 eta: 13:52:53 time: 0.6757 data_time: 0.1375 memory: 16095 grad_norm: 4.9864 loss: 1.6058 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.6058 2022/12/08 15:17:20 - mmengine - INFO - Epoch(train) [14][320/940] lr: 1.0000e-02 eta: 13:52:39 time: 0.5956 data_time: 0.1315 memory: 16095 grad_norm: 5.0849 loss: 1.7392 top1_acc: 0.4062 top5_acc: 0.7188 loss_cls: 1.7392 2022/12/08 15:17:32 - mmengine - INFO - Epoch(train) [14][340/940] lr: 1.0000e-02 eta: 13:52:26 time: 0.6090 data_time: 0.1271 memory: 16095 grad_norm: 5.1525 loss: 1.7114 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.7114 2022/12/08 15:17:44 - mmengine - INFO - Epoch(train) [14][360/940] lr: 1.0000e-02 eta: 13:52:11 time: 0.5907 data_time: 0.0335 memory: 16095 grad_norm: 5.0665 loss: 1.7339 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.7339 2022/12/08 15:17:58 - mmengine - INFO - Epoch(train) [14][380/940] lr: 1.0000e-02 eta: 13:52:07 time: 0.6803 data_time: 0.0272 memory: 16095 grad_norm: 5.2449 loss: 1.8421 top1_acc: 0.5938 top5_acc: 0.7188 loss_cls: 1.8421 2022/12/08 15:18:09 - mmengine - INFO - Epoch(train) [14][400/940] lr: 1.0000e-02 eta: 13:51:48 time: 0.5592 data_time: 0.0221 memory: 16095 grad_norm: 5.0509 loss: 1.6413 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6413 2022/12/08 15:18:21 - mmengine - INFO - Epoch(train) [14][420/940] lr: 1.0000e-02 eta: 13:51:38 time: 0.6352 data_time: 0.0354 memory: 16095 grad_norm: 5.1754 loss: 1.6828 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.6828 2022/12/08 15:18:33 - mmengine - INFO - Epoch(train) [14][440/940] lr: 1.0000e-02 eta: 13:51:21 time: 0.5726 data_time: 0.0202 memory: 16095 grad_norm: 5.0450 loss: 1.7197 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.7197 2022/12/08 15:18:46 - mmengine - INFO - Epoch(train) [14][460/940] lr: 1.0000e-02 eta: 13:51:14 time: 0.6549 data_time: 0.0307 memory: 16095 grad_norm: 5.1211 loss: 1.6557 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.6557 2022/12/08 15:18:57 - mmengine - INFO - Epoch(train) [14][480/940] lr: 1.0000e-02 eta: 13:50:55 time: 0.5613 data_time: 0.0208 memory: 16095 grad_norm: 5.0254 loss: 1.6581 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.6581 2022/12/08 15:19:10 - mmengine - INFO - Epoch(train) [14][500/940] lr: 1.0000e-02 eta: 13:50:49 time: 0.6622 data_time: 0.0293 memory: 16095 grad_norm: 5.2612 loss: 1.6885 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.6885 2022/12/08 15:19:22 - mmengine - INFO - Epoch(train) [14][520/940] lr: 1.0000e-02 eta: 13:50:32 time: 0.5758 data_time: 0.0208 memory: 16095 grad_norm: 5.1209 loss: 1.7179 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.7179 2022/12/08 15:19:37 - mmengine - INFO - Epoch(train) [14][540/940] lr: 1.0000e-02 eta: 13:50:34 time: 0.7299 data_time: 0.0285 memory: 16095 grad_norm: 5.1284 loss: 1.6481 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.6481 2022/12/08 15:19:47 - mmengine - INFO - Epoch(train) [14][560/940] lr: 1.0000e-02 eta: 13:50:11 time: 0.5236 data_time: 0.0206 memory: 16095 grad_norm: 5.1479 loss: 1.6488 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.6488 2022/12/08 15:20:00 - mmengine - INFO - Epoch(train) [14][580/940] lr: 1.0000e-02 eta: 13:50:04 time: 0.6549 data_time: 0.0275 memory: 16095 grad_norm: 5.1226 loss: 1.7668 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.7668 2022/12/08 15:20:11 - mmengine - INFO - Epoch(train) [14][600/940] lr: 1.0000e-02 eta: 13:49:45 time: 0.5622 data_time: 0.0226 memory: 16095 grad_norm: 5.1695 loss: 1.7827 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.7827 2022/12/08 15:20:25 - mmengine - INFO - Epoch(train) [14][620/940] lr: 1.0000e-02 eta: 13:49:43 time: 0.6955 data_time: 0.0245 memory: 16095 grad_norm: 5.2605 loss: 1.9029 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.9029 2022/12/08 15:20:36 - mmengine - INFO - Epoch(train) [14][640/940] lr: 1.0000e-02 eta: 13:49:21 time: 0.5358 data_time: 0.0201 memory: 16095 grad_norm: 5.2075 loss: 1.8050 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.8050 2022/12/08 15:20:49 - mmengine - INFO - Epoch(train) [14][660/940] lr: 1.0000e-02 eta: 13:49:13 time: 0.6445 data_time: 0.0292 memory: 16095 grad_norm: 5.2242 loss: 1.8479 top1_acc: 0.3750 top5_acc: 0.7188 loss_cls: 1.8479 2022/12/08 15:20:59 - mmengine - INFO - Epoch(train) [14][680/940] lr: 1.0000e-02 eta: 13:48:50 time: 0.5283 data_time: 0.0212 memory: 16095 grad_norm: 5.1367 loss: 1.6503 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.6503 2022/12/08 15:21:14 - mmengine - INFO - Epoch(train) [14][700/940] lr: 1.0000e-02 eta: 13:48:49 time: 0.7029 data_time: 0.0286 memory: 16095 grad_norm: 5.0609 loss: 1.7909 top1_acc: 0.4375 top5_acc: 0.7188 loss_cls: 1.7909 2022/12/08 15:21:25 - mmengine - INFO - Epoch(train) [14][720/940] lr: 1.0000e-02 eta: 13:48:30 time: 0.5591 data_time: 0.1134 memory: 16095 grad_norm: 5.1152 loss: 1.8036 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8036 2022/12/08 15:21:38 - mmengine - INFO - Epoch(train) [14][740/940] lr: 1.0000e-02 eta: 13:48:26 time: 0.6825 data_time: 0.0765 memory: 16095 grad_norm: 5.1111 loss: 1.9122 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 1.9122 2022/12/08 15:21:50 - mmengine - INFO - Epoch(train) [14][760/940] lr: 1.0000e-02 eta: 13:48:08 time: 0.5661 data_time: 0.1312 memory: 16095 grad_norm: 5.1255 loss: 1.7376 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 1.7376 2022/12/08 15:22:04 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 15:22:04 - mmengine - INFO - Epoch(train) [14][780/940] lr: 1.0000e-02 eta: 13:48:06 time: 0.6971 data_time: 0.1098 memory: 16095 grad_norm: 5.0925 loss: 1.8564 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.8564 2022/12/08 15:22:14 - mmengine - INFO - Epoch(train) [14][800/940] lr: 1.0000e-02 eta: 13:47:42 time: 0.5214 data_time: 0.0417 memory: 16095 grad_norm: 5.0602 loss: 1.6611 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.6611 2022/12/08 15:22:27 - mmengine - INFO - Epoch(train) [14][820/940] lr: 1.0000e-02 eta: 13:47:36 time: 0.6567 data_time: 0.0257 memory: 16095 grad_norm: 5.1189 loss: 1.8261 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.8261 2022/12/08 15:22:39 - mmengine - INFO - Epoch(train) [14][840/940] lr: 1.0000e-02 eta: 13:47:18 time: 0.5740 data_time: 0.0227 memory: 16095 grad_norm: 5.1802 loss: 1.7477 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.7477 2022/12/08 15:22:52 - mmengine - INFO - Epoch(train) [14][860/940] lr: 1.0000e-02 eta: 13:47:10 time: 0.6480 data_time: 0.0530 memory: 16095 grad_norm: 5.1219 loss: 1.7607 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7607 2022/12/08 15:23:02 - mmengine - INFO - Epoch(train) [14][880/940] lr: 1.0000e-02 eta: 13:46:48 time: 0.5310 data_time: 0.0340 memory: 16095 grad_norm: 5.0282 loss: 1.6801 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.6801 2022/12/08 15:23:16 - mmengine - INFO - Epoch(train) [14][900/940] lr: 1.0000e-02 eta: 13:46:45 time: 0.6853 data_time: 0.0267 memory: 16095 grad_norm: 5.1798 loss: 1.6120 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6120 2022/12/08 15:23:27 - mmengine - INFO - Epoch(train) [14][920/940] lr: 1.0000e-02 eta: 13:46:26 time: 0.5576 data_time: 0.0209 memory: 16095 grad_norm: 5.2072 loss: 1.8035 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.8035 2022/12/08 15:23:39 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 15:23:39 - mmengine - INFO - Epoch(train) [14][940/940] lr: 1.0000e-02 eta: 13:46:10 time: 0.5826 data_time: 0.0177 memory: 16095 grad_norm: 5.4767 loss: 1.7552 top1_acc: 0.5714 top5_acc: 1.0000 loss_cls: 1.7552 2022/12/08 15:23:53 - mmengine - INFO - Epoch(val) [14][20/78] eta: 0:00:40 time: 0.6939 data_time: 0.5995 memory: 1686 2022/12/08 15:24:02 - mmengine - INFO - Epoch(val) [14][40/78] eta: 0:00:22 time: 0.4675 data_time: 0.3732 memory: 1686 2022/12/08 15:24:16 - mmengine - INFO - Epoch(val) [14][60/78] eta: 0:00:11 time: 0.6947 data_time: 0.6005 memory: 1686 2022/12/08 15:24:26 - mmengine - INFO - Epoch(val) [14][78/78] acc/top1: 0.6252 acc/top5: 0.8456 acc/mean1: 0.6250 2022/12/08 15:24:42 - mmengine - INFO - Epoch(train) [15][ 20/940] lr: 1.0000e-02 eta: 13:46:22 time: 0.8117 data_time: 0.4368 memory: 16095 grad_norm: 5.0274 loss: 1.6903 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6903 2022/12/08 15:24:54 - mmengine - INFO - Epoch(train) [15][ 40/940] lr: 1.0000e-02 eta: 13:46:05 time: 0.5812 data_time: 0.2342 memory: 16095 grad_norm: 5.1308 loss: 1.6872 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6872 2022/12/08 15:25:07 - mmengine - INFO - Epoch(train) [15][ 60/940] lr: 1.0000e-02 eta: 13:45:55 time: 0.6263 data_time: 0.2898 memory: 16095 grad_norm: 4.9428 loss: 1.5452 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.5452 2022/12/08 15:25:18 - mmengine - INFO - Epoch(train) [15][ 80/940] lr: 1.0000e-02 eta: 13:45:37 time: 0.5675 data_time: 0.1922 memory: 16095 grad_norm: 4.9939 loss: 1.6910 top1_acc: 0.4062 top5_acc: 0.7188 loss_cls: 1.6910 2022/12/08 15:25:32 - mmengine - INFO - Epoch(train) [15][100/940] lr: 1.0000e-02 eta: 13:45:33 time: 0.6828 data_time: 0.1391 memory: 16095 grad_norm: 4.9976 loss: 1.5475 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.5475 2022/12/08 15:25:43 - mmengine - INFO - Epoch(train) [15][120/940] lr: 1.0000e-02 eta: 13:45:13 time: 0.5512 data_time: 0.0541 memory: 16095 grad_norm: 5.1456 loss: 1.7071 top1_acc: 0.4688 top5_acc: 0.8750 loss_cls: 1.7071 2022/12/08 15:25:56 - mmengine - INFO - Epoch(train) [15][140/940] lr: 1.0000e-02 eta: 13:45:10 time: 0.6898 data_time: 0.0834 memory: 16095 grad_norm: 5.0518 loss: 1.5855 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5855 2022/12/08 15:26:07 - mmengine - INFO - Epoch(train) [15][160/940] lr: 1.0000e-02 eta: 13:44:50 time: 0.5506 data_time: 0.0211 memory: 16095 grad_norm: 5.1377 loss: 1.6367 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.6367 2022/12/08 15:26:20 - mmengine - INFO - Epoch(train) [15][180/940] lr: 1.0000e-02 eta: 13:44:43 time: 0.6558 data_time: 0.0276 memory: 16095 grad_norm: 5.0625 loss: 1.7060 top1_acc: 0.4062 top5_acc: 0.7812 loss_cls: 1.7060 2022/12/08 15:26:31 - mmengine - INFO - Epoch(train) [15][200/940] lr: 1.0000e-02 eta: 13:44:23 time: 0.5443 data_time: 0.0215 memory: 16095 grad_norm: 5.0470 loss: 1.7660 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.7660 2022/12/08 15:26:45 - mmengine - INFO - Epoch(train) [15][220/940] lr: 1.0000e-02 eta: 13:44:20 time: 0.6958 data_time: 0.0294 memory: 16095 grad_norm: 5.1078 loss: 1.7702 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.7702 2022/12/08 15:26:56 - mmengine - INFO - Epoch(train) [15][240/940] lr: 1.0000e-02 eta: 13:44:01 time: 0.5550 data_time: 0.0205 memory: 16095 grad_norm: 4.9906 loss: 1.5344 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.5344 2022/12/08 15:27:10 - mmengine - INFO - Epoch(train) [15][260/940] lr: 1.0000e-02 eta: 13:43:55 time: 0.6630 data_time: 0.0297 memory: 16095 grad_norm: 5.1397 loss: 1.6846 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.6846 2022/12/08 15:27:20 - mmengine - INFO - Epoch(train) [15][280/940] lr: 1.0000e-02 eta: 13:43:30 time: 0.5123 data_time: 0.0832 memory: 16095 grad_norm: 5.0835 loss: 1.5673 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 1.5673 2022/12/08 15:27:34 - mmengine - INFO - Epoch(train) [15][300/940] lr: 1.0000e-02 eta: 13:43:27 time: 0.6889 data_time: 0.2546 memory: 16095 grad_norm: 5.1164 loss: 1.7356 top1_acc: 0.5312 top5_acc: 0.9062 loss_cls: 1.7356 2022/12/08 15:27:45 - mmengine - INFO - Epoch(train) [15][320/940] lr: 1.0000e-02 eta: 13:43:07 time: 0.5467 data_time: 0.1683 memory: 16095 grad_norm: 5.1728 loss: 1.7013 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7013 2022/12/08 15:27:58 - mmengine - INFO - Epoch(train) [15][340/940] lr: 1.0000e-02 eta: 13:43:04 time: 0.6935 data_time: 0.1485 memory: 16095 grad_norm: 5.0555 loss: 1.7596 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.7596 2022/12/08 15:28:09 - mmengine - INFO - Epoch(train) [15][360/940] lr: 1.0000e-02 eta: 13:42:42 time: 0.5322 data_time: 0.0560 memory: 16095 grad_norm: 5.0507 loss: 1.6582 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6582 2022/12/08 15:28:22 - mmengine - INFO - Epoch(train) [15][380/940] lr: 1.0000e-02 eta: 13:42:32 time: 0.6356 data_time: 0.0997 memory: 16095 grad_norm: 5.0367 loss: 1.6170 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.6170 2022/12/08 15:28:33 - mmengine - INFO - Epoch(train) [15][400/940] lr: 1.0000e-02 eta: 13:42:15 time: 0.5741 data_time: 0.0585 memory: 16095 grad_norm: 5.1018 loss: 1.6467 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.6467 2022/12/08 15:28:47 - mmengine - INFO - Epoch(train) [15][420/940] lr: 1.0000e-02 eta: 13:42:10 time: 0.6717 data_time: 0.0261 memory: 16095 grad_norm: 5.1535 loss: 1.6457 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6457 2022/12/08 15:28:58 - mmengine - INFO - Epoch(train) [15][440/940] lr: 1.0000e-02 eta: 13:41:53 time: 0.5736 data_time: 0.0390 memory: 16095 grad_norm: 5.0398 loss: 1.6932 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.6932 2022/12/08 15:29:10 - mmengine - INFO - Epoch(train) [15][460/940] lr: 1.0000e-02 eta: 13:41:40 time: 0.6022 data_time: 0.0853 memory: 16095 grad_norm: 5.0860 loss: 1.7246 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7246 2022/12/08 15:29:22 - mmengine - INFO - Epoch(train) [15][480/940] lr: 1.0000e-02 eta: 13:41:22 time: 0.5698 data_time: 0.1924 memory: 16095 grad_norm: 5.0788 loss: 1.7793 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 1.7793 2022/12/08 15:29:34 - mmengine - INFO - Epoch(train) [15][500/940] lr: 1.0000e-02 eta: 13:41:12 time: 0.6324 data_time: 0.3008 memory: 16095 grad_norm: 5.2232 loss: 1.7846 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7846 2022/12/08 15:29:47 - mmengine - INFO - Epoch(train) [15][520/940] lr: 1.0000e-02 eta: 13:41:05 time: 0.6525 data_time: 0.3076 memory: 16095 grad_norm: 4.9590 loss: 1.6956 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.6956 2022/12/08 15:29:59 - mmengine - INFO - Epoch(train) [15][540/940] lr: 1.0000e-02 eta: 13:40:47 time: 0.5728 data_time: 0.1564 memory: 16095 grad_norm: 5.0541 loss: 1.7427 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.7427 2022/12/08 15:30:10 - mmengine - INFO - Epoch(train) [15][560/940] lr: 1.0000e-02 eta: 13:40:30 time: 0.5645 data_time: 0.1367 memory: 16095 grad_norm: 5.1936 loss: 1.6543 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.6543 2022/12/08 15:30:24 - mmengine - INFO - Epoch(train) [15][580/940] lr: 1.0000e-02 eta: 13:40:28 time: 0.7080 data_time: 0.2763 memory: 16095 grad_norm: 5.1752 loss: 1.6870 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.6870 2022/12/08 15:30:35 - mmengine - INFO - Epoch(train) [15][600/940] lr: 1.0000e-02 eta: 13:40:10 time: 0.5575 data_time: 0.2272 memory: 16095 grad_norm: 5.1262 loss: 1.7606 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.7606 2022/12/08 15:30:48 - mmengine - INFO - Epoch(train) [15][620/940] lr: 1.0000e-02 eta: 13:40:00 time: 0.6368 data_time: 0.3151 memory: 16095 grad_norm: 5.1028 loss: 1.5933 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5933 2022/12/08 15:30:59 - mmengine - INFO - Epoch(train) [15][640/940] lr: 1.0000e-02 eta: 13:39:42 time: 0.5661 data_time: 0.2414 memory: 16095 grad_norm: 5.2589 loss: 1.8426 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.8426 2022/12/08 15:31:13 - mmengine - INFO - Epoch(train) [15][660/940] lr: 1.0000e-02 eta: 13:39:39 time: 0.6936 data_time: 0.3770 memory: 16095 grad_norm: 5.1871 loss: 1.6995 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6995 2022/12/08 15:31:26 - mmengine - INFO - Epoch(train) [15][680/940] lr: 1.0000e-02 eta: 13:39:26 time: 0.6083 data_time: 0.2768 memory: 16095 grad_norm: 5.2163 loss: 1.7983 top1_acc: 0.4688 top5_acc: 0.6250 loss_cls: 1.7983 2022/12/08 15:31:41 - mmengine - INFO - Epoch(train) [15][700/940] lr: 1.0000e-02 eta: 13:39:30 time: 0.7524 data_time: 0.4057 memory: 16095 grad_norm: 5.1264 loss: 1.6062 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.6062 2022/12/08 15:31:52 - mmengine - INFO - Epoch(train) [15][720/940] lr: 1.0000e-02 eta: 13:39:13 time: 0.5717 data_time: 0.2501 memory: 16095 grad_norm: 5.0883 loss: 1.7250 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.7250 2022/12/08 15:32:05 - mmengine - INFO - Epoch(train) [15][740/940] lr: 1.0000e-02 eta: 13:39:07 time: 0.6654 data_time: 0.3536 memory: 16095 grad_norm: 5.2001 loss: 1.7466 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.7466 2022/12/08 15:32:17 - mmengine - INFO - Epoch(train) [15][760/940] lr: 1.0000e-02 eta: 13:38:49 time: 0.5615 data_time: 0.1898 memory: 16095 grad_norm: 5.1369 loss: 1.6975 top1_acc: 0.5938 top5_acc: 0.9688 loss_cls: 1.6975 2022/12/08 15:32:30 - mmengine - INFO - Epoch(train) [15][780/940] lr: 1.0000e-02 eta: 13:38:46 time: 0.6940 data_time: 0.1627 memory: 16095 grad_norm: 5.1235 loss: 1.6234 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.6234 2022/12/08 15:32:46 - mmengine - INFO - Epoch(train) [15][800/940] lr: 1.0000e-02 eta: 13:38:51 time: 0.7692 data_time: 0.0238 memory: 16095 grad_norm: 5.0856 loss: 1.6792 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6792 2022/12/08 15:33:01 - mmengine - INFO - Epoch(train) [15][820/940] lr: 1.0000e-02 eta: 13:38:54 time: 0.7492 data_time: 0.0289 memory: 16095 grad_norm: 5.0371 loss: 1.6501 top1_acc: 0.5938 top5_acc: 0.7188 loss_cls: 1.6501 2022/12/08 15:33:15 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 15:33:15 - mmengine - INFO - Epoch(train) [15][840/940] lr: 1.0000e-02 eta: 13:38:55 time: 0.7286 data_time: 0.0217 memory: 16095 grad_norm: 5.0865 loss: 1.6120 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.6120 2022/12/08 15:33:30 - mmengine - INFO - Epoch(train) [15][860/940] lr: 1.0000e-02 eta: 13:38:55 time: 0.7159 data_time: 0.0238 memory: 16095 grad_norm: 5.1311 loss: 1.6921 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.6921 2022/12/08 15:33:44 - mmengine - INFO - Epoch(train) [15][880/940] lr: 1.0000e-02 eta: 13:38:54 time: 0.7177 data_time: 0.0245 memory: 16095 grad_norm: 5.1554 loss: 1.7133 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.7133 2022/12/08 15:33:59 - mmengine - INFO - Epoch(train) [15][900/940] lr: 1.0000e-02 eta: 13:38:54 time: 0.7267 data_time: 0.0244 memory: 16095 grad_norm: 5.0675 loss: 1.6813 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6813 2022/12/08 15:34:13 - mmengine - INFO - Epoch(train) [15][920/940] lr: 1.0000e-02 eta: 13:38:54 time: 0.7211 data_time: 0.0253 memory: 16095 grad_norm: 5.2515 loss: 1.6872 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.6872 2022/12/08 15:34:27 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 15:34:27 - mmengine - INFO - Epoch(train) [15][940/940] lr: 1.0000e-02 eta: 13:38:49 time: 0.6778 data_time: 0.0163 memory: 16095 grad_norm: 5.5216 loss: 1.6938 top1_acc: 0.4286 top5_acc: 0.8571 loss_cls: 1.6938 2022/12/08 15:34:27 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/12/08 15:34:47 - mmengine - INFO - Epoch(val) [15][20/78] eta: 0:00:48 time: 0.8375 data_time: 0.6010 memory: 1686 2022/12/08 15:34:58 - mmengine - INFO - Epoch(val) [15][40/78] eta: 0:00:26 time: 0.5665 data_time: 0.3183 memory: 1686 2022/12/08 15:35:13 - mmengine - INFO - Epoch(val) [15][60/78] eta: 0:00:12 time: 0.7520 data_time: 0.5108 memory: 1686 2022/12/08 15:35:24 - mmengine - INFO - Epoch(val) [15][78/78] acc/top1: 0.6289 acc/top5: 0.8465 acc/mean1: 0.6288 2022/12/08 15:35:24 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_13.pth is removed 2022/12/08 15:35:27 - mmengine - INFO - The best checkpoint with 0.6289 acc/top1 at 15 epoch is saved to best_acc/top1_epoch_15.pth. 2022/12/08 15:35:47 - mmengine - INFO - Epoch(train) [16][ 20/940] lr: 1.0000e-02 eta: 13:39:20 time: 0.9995 data_time: 0.2898 memory: 16095 grad_norm: 5.1178 loss: 1.7621 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.7621 2022/12/08 15:36:01 - mmengine - INFO - Epoch(train) [16][ 40/940] lr: 1.0000e-02 eta: 13:39:21 time: 0.7316 data_time: 0.0186 memory: 16095 grad_norm: 5.1581 loss: 1.6822 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.6822 2022/12/08 15:36:16 - mmengine - INFO - Epoch(train) [16][ 60/940] lr: 1.0000e-02 eta: 13:39:21 time: 0.7207 data_time: 0.0259 memory: 16095 grad_norm: 4.9395 loss: 1.5658 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.5658 2022/12/08 15:36:30 - mmengine - INFO - Epoch(train) [16][ 80/940] lr: 1.0000e-02 eta: 13:39:21 time: 0.7265 data_time: 0.0241 memory: 16095 grad_norm: 5.0857 loss: 1.6504 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.6504 2022/12/08 15:36:45 - mmengine - INFO - Epoch(train) [16][100/940] lr: 1.0000e-02 eta: 13:39:20 time: 0.7162 data_time: 0.0237 memory: 16095 grad_norm: 5.0309 loss: 1.6235 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.6235 2022/12/08 15:36:59 - mmengine - INFO - Epoch(train) [16][120/940] lr: 1.0000e-02 eta: 13:39:19 time: 0.7155 data_time: 0.0251 memory: 16095 grad_norm: 5.1980 loss: 1.6709 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.6709 2022/12/08 15:37:13 - mmengine - INFO - Epoch(train) [16][140/940] lr: 1.0000e-02 eta: 13:39:18 time: 0.7164 data_time: 0.0265 memory: 16095 grad_norm: 5.0421 loss: 1.4655 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.4655 2022/12/08 15:37:28 - mmengine - INFO - Epoch(train) [16][160/940] lr: 1.0000e-02 eta: 13:39:17 time: 0.7186 data_time: 0.0248 memory: 16095 grad_norm: 5.1222 loss: 1.5968 top1_acc: 0.5312 top5_acc: 0.6875 loss_cls: 1.5968 2022/12/08 15:37:42 - mmengine - INFO - Epoch(train) [16][180/940] lr: 1.0000e-02 eta: 13:39:17 time: 0.7305 data_time: 0.0369 memory: 16095 grad_norm: 5.1397 loss: 1.7185 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.7185 2022/12/08 15:37:57 - mmengine - INFO - Epoch(train) [16][200/940] lr: 1.0000e-02 eta: 13:39:16 time: 0.7145 data_time: 0.0243 memory: 16095 grad_norm: 5.1775 loss: 1.5200 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.5200 2022/12/08 15:38:11 - mmengine - INFO - Epoch(train) [16][220/940] lr: 1.0000e-02 eta: 13:39:15 time: 0.7192 data_time: 0.0260 memory: 16095 grad_norm: 5.0594 loss: 1.6421 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.6421 2022/12/08 15:38:25 - mmengine - INFO - Epoch(train) [16][240/940] lr: 1.0000e-02 eta: 13:39:14 time: 0.7177 data_time: 0.0242 memory: 16095 grad_norm: 5.0341 loss: 1.5156 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.5156 2022/12/08 15:38:40 - mmengine - INFO - Epoch(train) [16][260/940] lr: 1.0000e-02 eta: 13:39:13 time: 0.7220 data_time: 0.0246 memory: 16095 grad_norm: 5.0653 loss: 1.5897 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5897 2022/12/08 15:38:54 - mmengine - INFO - Epoch(train) [16][280/940] lr: 1.0000e-02 eta: 13:39:12 time: 0.7181 data_time: 0.0247 memory: 16095 grad_norm: 5.1594 loss: 1.6737 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.6737 2022/12/08 15:39:09 - mmengine - INFO - Epoch(train) [16][300/940] lr: 1.0000e-02 eta: 13:39:11 time: 0.7211 data_time: 0.0244 memory: 16095 grad_norm: 5.0255 loss: 1.7259 top1_acc: 0.4062 top5_acc: 0.7188 loss_cls: 1.7259 2022/12/08 15:39:23 - mmengine - INFO - Epoch(train) [16][320/940] lr: 1.0000e-02 eta: 13:39:09 time: 0.7147 data_time: 0.0243 memory: 16095 grad_norm: 5.0667 loss: 1.6947 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.6947 2022/12/08 15:39:37 - mmengine - INFO - Epoch(train) [16][340/940] lr: 1.0000e-02 eta: 13:39:08 time: 0.7182 data_time: 0.0239 memory: 16095 grad_norm: 4.9953 loss: 1.4639 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.4639 2022/12/08 15:39:52 - mmengine - INFO - Epoch(train) [16][360/940] lr: 1.0000e-02 eta: 13:39:07 time: 0.7187 data_time: 0.0239 memory: 16095 grad_norm: 5.0988 loss: 1.6424 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 1.6424 2022/12/08 15:40:06 - mmengine - INFO - Epoch(train) [16][380/940] lr: 1.0000e-02 eta: 13:39:05 time: 0.7175 data_time: 0.0244 memory: 16095 grad_norm: 5.0837 loss: 1.5958 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.5958 2022/12/08 15:40:20 - mmengine - INFO - Epoch(train) [16][400/940] lr: 1.0000e-02 eta: 13:39:04 time: 0.7214 data_time: 0.0240 memory: 16095 grad_norm: 5.0660 loss: 1.5915 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.5915 2022/12/08 15:40:35 - mmengine - INFO - Epoch(train) [16][420/940] lr: 1.0000e-02 eta: 13:39:03 time: 0.7239 data_time: 0.0230 memory: 16095 grad_norm: 5.1052 loss: 1.6066 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.6066 2022/12/08 15:40:49 - mmengine - INFO - Epoch(train) [16][440/940] lr: 1.0000e-02 eta: 13:39:02 time: 0.7171 data_time: 0.0244 memory: 16095 grad_norm: 5.0145 loss: 1.6381 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.6381 2022/12/08 15:41:04 - mmengine - INFO - Epoch(train) [16][460/940] lr: 1.0000e-02 eta: 13:39:00 time: 0.7180 data_time: 0.0248 memory: 16095 grad_norm: 5.1312 loss: 1.5237 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.5237 2022/12/08 15:41:18 - mmengine - INFO - Epoch(train) [16][480/940] lr: 1.0000e-02 eta: 13:38:59 time: 0.7241 data_time: 0.0249 memory: 16095 grad_norm: 5.1593 loss: 1.5993 top1_acc: 0.7188 top5_acc: 1.0000 loss_cls: 1.5993 2022/12/08 15:41:32 - mmengine - INFO - Epoch(train) [16][500/940] lr: 1.0000e-02 eta: 13:38:58 time: 0.7229 data_time: 0.0245 memory: 16095 grad_norm: 5.0699 loss: 1.5922 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.5922 2022/12/08 15:41:47 - mmengine - INFO - Epoch(train) [16][520/940] lr: 1.0000e-02 eta: 13:38:57 time: 0.7205 data_time: 0.0275 memory: 16095 grad_norm: 5.0483 loss: 1.7352 top1_acc: 0.6562 top5_acc: 0.7500 loss_cls: 1.7352 2022/12/08 15:42:01 - mmengine - INFO - Epoch(train) [16][540/940] lr: 1.0000e-02 eta: 13:38:55 time: 0.7171 data_time: 0.0253 memory: 16095 grad_norm: 5.0146 loss: 1.7339 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7339 2022/12/08 15:42:16 - mmengine - INFO - Epoch(train) [16][560/940] lr: 1.0000e-02 eta: 13:38:53 time: 0.7152 data_time: 0.0250 memory: 16095 grad_norm: 5.1139 loss: 1.7629 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 1.7629 2022/12/08 15:42:30 - mmengine - INFO - Epoch(train) [16][580/940] lr: 1.0000e-02 eta: 13:38:52 time: 0.7183 data_time: 0.0259 memory: 16095 grad_norm: 5.0701 loss: 1.7264 top1_acc: 0.4062 top5_acc: 0.7812 loss_cls: 1.7264 2022/12/08 15:42:44 - mmengine - INFO - Epoch(train) [16][600/940] lr: 1.0000e-02 eta: 13:38:51 time: 0.7259 data_time: 0.0241 memory: 16095 grad_norm: 5.0597 loss: 1.6195 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6195 2022/12/08 15:42:59 - mmengine - INFO - Epoch(train) [16][620/940] lr: 1.0000e-02 eta: 13:38:49 time: 0.7159 data_time: 0.0243 memory: 16095 grad_norm: 5.1929 loss: 1.7425 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.7425 2022/12/08 15:43:13 - mmengine - INFO - Epoch(train) [16][640/940] lr: 1.0000e-02 eta: 13:38:49 time: 0.7364 data_time: 0.0260 memory: 16095 grad_norm: 5.0737 loss: 1.4947 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.4947 2022/12/08 15:43:28 - mmengine - INFO - Epoch(train) [16][660/940] lr: 1.0000e-02 eta: 13:38:47 time: 0.7142 data_time: 0.0239 memory: 16095 grad_norm: 5.1817 loss: 1.6361 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.6361 2022/12/08 15:43:42 - mmengine - INFO - Epoch(train) [16][680/940] lr: 1.0000e-02 eta: 13:38:45 time: 0.7191 data_time: 0.0248 memory: 16095 grad_norm: 5.1969 loss: 1.7881 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.7881 2022/12/08 15:43:57 - mmengine - INFO - Epoch(train) [16][700/940] lr: 1.0000e-02 eta: 13:38:43 time: 0.7196 data_time: 0.0286 memory: 16095 grad_norm: 5.0893 loss: 1.6380 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.6380 2022/12/08 15:44:11 - mmengine - INFO - Epoch(train) [16][720/940] lr: 1.0000e-02 eta: 13:38:41 time: 0.7146 data_time: 0.0257 memory: 16095 grad_norm: 5.0615 loss: 1.6662 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6662 2022/12/08 15:44:25 - mmengine - INFO - Epoch(train) [16][740/940] lr: 1.0000e-02 eta: 13:38:39 time: 0.7168 data_time: 0.0249 memory: 16095 grad_norm: 5.0884 loss: 1.6725 top1_acc: 0.4688 top5_acc: 0.9062 loss_cls: 1.6725 2022/12/08 15:44:40 - mmengine - INFO - Epoch(train) [16][760/940] lr: 1.0000e-02 eta: 13:38:38 time: 0.7285 data_time: 0.0362 memory: 16095 grad_norm: 5.1877 loss: 1.7041 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.7041 2022/12/08 15:44:54 - mmengine - INFO - Epoch(train) [16][780/940] lr: 1.0000e-02 eta: 13:38:35 time: 0.7143 data_time: 0.0249 memory: 16095 grad_norm: 5.2480 loss: 1.8014 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8014 2022/12/08 15:45:08 - mmengine - INFO - Epoch(train) [16][800/940] lr: 1.0000e-02 eta: 13:38:33 time: 0.7174 data_time: 0.0236 memory: 16095 grad_norm: 5.1443 loss: 1.8002 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.8002 2022/12/08 15:45:23 - mmengine - INFO - Epoch(train) [16][820/940] lr: 1.0000e-02 eta: 13:38:31 time: 0.7159 data_time: 0.0238 memory: 16095 grad_norm: 5.1477 loss: 1.6967 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.6967 2022/12/08 15:45:37 - mmengine - INFO - Epoch(train) [16][840/940] lr: 1.0000e-02 eta: 13:38:29 time: 0.7238 data_time: 0.0235 memory: 16095 grad_norm: 5.0383 loss: 1.6561 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.6561 2022/12/08 15:45:52 - mmengine - INFO - Epoch(train) [16][860/940] lr: 1.0000e-02 eta: 13:38:28 time: 0.7306 data_time: 0.0251 memory: 16095 grad_norm: 5.1591 loss: 1.6919 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.6919 2022/12/08 15:46:06 - mmengine - INFO - Epoch(train) [16][880/940] lr: 1.0000e-02 eta: 13:38:26 time: 0.7210 data_time: 0.0251 memory: 16095 grad_norm: 5.2140 loss: 1.7131 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.7131 2022/12/08 15:46:21 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 15:46:21 - mmengine - INFO - Epoch(train) [16][900/940] lr: 1.0000e-02 eta: 13:38:26 time: 0.7323 data_time: 0.0241 memory: 16095 grad_norm: 5.1385 loss: 1.7417 top1_acc: 0.5312 top5_acc: 0.6875 loss_cls: 1.7417 2022/12/08 15:46:35 - mmengine - INFO - Epoch(train) [16][920/940] lr: 1.0000e-02 eta: 13:38:23 time: 0.7170 data_time: 0.0259 memory: 16095 grad_norm: 5.0692 loss: 1.7279 top1_acc: 0.5938 top5_acc: 0.7188 loss_cls: 1.7279 2022/12/08 15:46:49 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 15:46:49 - mmengine - INFO - Epoch(train) [16][940/940] lr: 1.0000e-02 eta: 13:38:17 time: 0.6818 data_time: 0.0173 memory: 16095 grad_norm: 5.4674 loss: 1.8509 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.8509 2022/12/08 15:47:06 - mmengine - INFO - Epoch(val) [16][20/78] eta: 0:00:48 time: 0.8420 data_time: 0.6032 memory: 1686 2022/12/08 15:47:17 - mmengine - INFO - Epoch(val) [16][40/78] eta: 0:00:26 time: 0.5648 data_time: 0.3283 memory: 1686 2022/12/08 15:47:33 - mmengine - INFO - Epoch(val) [16][60/78] eta: 0:00:13 time: 0.7883 data_time: 0.5391 memory: 1686 2022/12/08 15:47:45 - mmengine - INFO - Epoch(val) [16][78/78] acc/top1: 0.6318 acc/top5: 0.8456 acc/mean1: 0.6316 2022/12/08 15:47:45 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_15.pth is removed 2022/12/08 15:47:47 - mmengine - INFO - The best checkpoint with 0.6318 acc/top1 at 16 epoch is saved to best_acc/top1_epoch_16.pth. 2022/12/08 15:48:06 - mmengine - INFO - Epoch(train) [17][ 20/940] lr: 1.0000e-02 eta: 13:38:41 time: 0.9694 data_time: 0.2642 memory: 16095 grad_norm: 5.0047 loss: 1.6059 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.6059 2022/12/08 15:48:21 - mmengine - INFO - Epoch(train) [17][ 40/940] lr: 1.0000e-02 eta: 13:38:40 time: 0.7318 data_time: 0.0205 memory: 16095 grad_norm: 5.0033 loss: 1.6131 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6131 2022/12/08 15:48:36 - mmengine - INFO - Epoch(train) [17][ 60/940] lr: 1.0000e-02 eta: 13:38:39 time: 0.7288 data_time: 0.0310 memory: 16095 grad_norm: 5.0478 loss: 1.6094 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 1.6094 2022/12/08 15:48:50 - mmengine - INFO - Epoch(train) [17][ 80/940] lr: 1.0000e-02 eta: 13:38:36 time: 0.7173 data_time: 0.0208 memory: 16095 grad_norm: 5.0248 loss: 1.5824 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.5824 2022/12/08 15:49:04 - mmengine - INFO - Epoch(train) [17][100/940] lr: 1.0000e-02 eta: 13:38:34 time: 0.7221 data_time: 0.0239 memory: 16095 grad_norm: 5.0243 loss: 1.6806 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.6806 2022/12/08 15:49:19 - mmengine - INFO - Epoch(train) [17][120/940] lr: 1.0000e-02 eta: 13:38:31 time: 0.7163 data_time: 0.0241 memory: 16095 grad_norm: 5.0550 loss: 1.5946 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.5946 2022/12/08 15:49:33 - mmengine - INFO - Epoch(train) [17][140/940] lr: 1.0000e-02 eta: 13:38:29 time: 0.7197 data_time: 0.0250 memory: 16095 grad_norm: 5.0342 loss: 1.7186 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7186 2022/12/08 15:49:48 - mmengine - INFO - Epoch(train) [17][160/940] lr: 1.0000e-02 eta: 13:38:26 time: 0.7168 data_time: 0.0264 memory: 16095 grad_norm: 4.9449 loss: 1.6458 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.6458 2022/12/08 15:50:02 - mmengine - INFO - Epoch(train) [17][180/940] lr: 1.0000e-02 eta: 13:38:24 time: 0.7226 data_time: 0.0278 memory: 16095 grad_norm: 5.0644 loss: 1.5711 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.5711 2022/12/08 15:50:16 - mmengine - INFO - Epoch(train) [17][200/940] lr: 1.0000e-02 eta: 13:38:21 time: 0.7153 data_time: 0.0249 memory: 16095 grad_norm: 5.0375 loss: 1.5559 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.5559 2022/12/08 15:50:31 - mmengine - INFO - Epoch(train) [17][220/940] lr: 1.0000e-02 eta: 13:38:19 time: 0.7274 data_time: 0.0349 memory: 16095 grad_norm: 4.9570 loss: 1.6733 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.6733 2022/12/08 15:50:45 - mmengine - INFO - Epoch(train) [17][240/940] lr: 1.0000e-02 eta: 13:38:16 time: 0.7131 data_time: 0.0255 memory: 16095 grad_norm: 5.0075 loss: 1.5483 top1_acc: 0.5312 top5_acc: 0.9062 loss_cls: 1.5483 2022/12/08 15:50:59 - mmengine - INFO - Epoch(train) [17][260/940] lr: 1.0000e-02 eta: 13:38:13 time: 0.7163 data_time: 0.0250 memory: 16095 grad_norm: 5.1609 loss: 1.4332 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4332 2022/12/08 15:51:14 - mmengine - INFO - Epoch(train) [17][280/940] lr: 1.0000e-02 eta: 13:38:11 time: 0.7188 data_time: 0.0249 memory: 16095 grad_norm: 5.1361 loss: 1.6491 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.6491 2022/12/08 15:51:28 - mmengine - INFO - Epoch(train) [17][300/940] lr: 1.0000e-02 eta: 13:38:07 time: 0.7147 data_time: 0.0237 memory: 16095 grad_norm: 5.1421 loss: 1.7459 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7459 2022/12/08 15:51:42 - mmengine - INFO - Epoch(train) [17][320/940] lr: 1.0000e-02 eta: 13:38:04 time: 0.7129 data_time: 0.0251 memory: 16095 grad_norm: 5.1338 loss: 1.5714 top1_acc: 0.5312 top5_acc: 0.6875 loss_cls: 1.5714 2022/12/08 15:51:57 - mmengine - INFO - Epoch(train) [17][340/940] lr: 1.0000e-02 eta: 13:38:01 time: 0.7138 data_time: 0.0251 memory: 16095 grad_norm: 5.1213 loss: 1.4940 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.4940 2022/12/08 15:52:11 - mmengine - INFO - Epoch(train) [17][360/940] lr: 1.0000e-02 eta: 13:37:57 time: 0.7131 data_time: 0.0254 memory: 16095 grad_norm: 5.1974 loss: 1.6992 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.6992 2022/12/08 15:52:25 - mmengine - INFO - Epoch(train) [17][380/940] lr: 1.0000e-02 eta: 13:37:55 time: 0.7222 data_time: 0.0234 memory: 16095 grad_norm: 5.0678 loss: 1.5889 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5889 2022/12/08 15:52:40 - mmengine - INFO - Epoch(train) [17][400/940] lr: 1.0000e-02 eta: 13:37:52 time: 0.7183 data_time: 0.0245 memory: 16095 grad_norm: 5.1984 loss: 1.7284 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 1.7284 2022/12/08 15:52:54 - mmengine - INFO - Epoch(train) [17][420/940] lr: 1.0000e-02 eta: 13:37:49 time: 0.7156 data_time: 0.0245 memory: 16095 grad_norm: 5.1531 loss: 1.7252 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.7252 2022/12/08 15:53:08 - mmengine - INFO - Epoch(train) [17][440/940] lr: 1.0000e-02 eta: 13:37:45 time: 0.7144 data_time: 0.0248 memory: 16095 grad_norm: 5.1952 loss: 1.6208 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.6208 2022/12/08 15:53:23 - mmengine - INFO - Epoch(train) [17][460/940] lr: 1.0000e-02 eta: 13:37:41 time: 0.7097 data_time: 0.0230 memory: 16095 grad_norm: 5.0972 loss: 1.6137 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.6137 2022/12/08 15:53:37 - mmengine - INFO - Epoch(train) [17][480/940] lr: 1.0000e-02 eta: 13:37:39 time: 0.7238 data_time: 0.0255 memory: 16095 grad_norm: 5.1180 loss: 1.5745 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.5745 2022/12/08 15:53:51 - mmengine - INFO - Epoch(train) [17][500/940] lr: 1.0000e-02 eta: 13:37:35 time: 0.7148 data_time: 0.0247 memory: 16095 grad_norm: 5.1582 loss: 1.6445 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.6445 2022/12/08 15:54:05 - mmengine - INFO - Epoch(train) [17][520/940] lr: 1.0000e-02 eta: 13:37:31 time: 0.7041 data_time: 0.0240 memory: 16095 grad_norm: 5.2039 loss: 1.7149 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.7149 2022/12/08 15:54:20 - mmengine - INFO - Epoch(train) [17][540/940] lr: 1.0000e-02 eta: 13:37:28 time: 0.7186 data_time: 0.0241 memory: 16095 grad_norm: 5.1116 loss: 1.6045 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.6045 2022/12/08 15:54:34 - mmengine - INFO - Epoch(train) [17][560/940] lr: 1.0000e-02 eta: 13:37:24 time: 0.7174 data_time: 0.0246 memory: 16095 grad_norm: 5.1331 loss: 1.6750 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6750 2022/12/08 15:54:48 - mmengine - INFO - Epoch(train) [17][580/940] lr: 1.0000e-02 eta: 13:37:21 time: 0.7176 data_time: 0.0256 memory: 16095 grad_norm: 5.1220 loss: 1.5958 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.5958 2022/12/08 15:55:03 - mmengine - INFO - Epoch(train) [17][600/940] lr: 1.0000e-02 eta: 13:37:18 time: 0.7155 data_time: 0.0242 memory: 16095 grad_norm: 5.1833 loss: 1.5029 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5029 2022/12/08 15:55:17 - mmengine - INFO - Epoch(train) [17][620/940] lr: 1.0000e-02 eta: 13:37:14 time: 0.7176 data_time: 0.0239 memory: 16095 grad_norm: 5.2092 loss: 1.6173 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.6173 2022/12/08 15:55:31 - mmengine - INFO - Epoch(train) [17][640/940] lr: 1.0000e-02 eta: 13:37:11 time: 0.7154 data_time: 0.0241 memory: 16095 grad_norm: 5.1986 loss: 1.5603 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5603 2022/12/08 15:55:46 - mmengine - INFO - Epoch(train) [17][660/940] lr: 1.0000e-02 eta: 13:37:08 time: 0.7198 data_time: 0.0256 memory: 16095 grad_norm: 5.1966 loss: 1.6812 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.6812 2022/12/08 15:56:00 - mmengine - INFO - Epoch(train) [17][680/940] lr: 1.0000e-02 eta: 13:37:03 time: 0.7094 data_time: 0.0264 memory: 16095 grad_norm: 5.1079 loss: 1.5406 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.5406 2022/12/08 15:56:14 - mmengine - INFO - Epoch(train) [17][700/940] lr: 1.0000e-02 eta: 13:37:00 time: 0.7181 data_time: 0.0259 memory: 16095 grad_norm: 5.2137 loss: 1.5950 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.5950 2022/12/08 15:56:29 - mmengine - INFO - Epoch(train) [17][720/940] lr: 1.0000e-02 eta: 13:36:57 time: 0.7239 data_time: 0.0237 memory: 16095 grad_norm: 5.2605 loss: 1.7371 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.7371 2022/12/08 15:56:43 - mmengine - INFO - Epoch(train) [17][740/940] lr: 1.0000e-02 eta: 13:36:54 time: 0.7182 data_time: 0.0232 memory: 16095 grad_norm: 5.1770 loss: 1.6693 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.6693 2022/12/08 15:56:58 - mmengine - INFO - Epoch(train) [17][760/940] lr: 1.0000e-02 eta: 13:36:50 time: 0.7164 data_time: 0.0247 memory: 16095 grad_norm: 5.1751 loss: 1.6368 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.6368 2022/12/08 15:57:12 - mmengine - INFO - Epoch(train) [17][780/940] lr: 1.0000e-02 eta: 13:36:46 time: 0.7134 data_time: 0.0268 memory: 16095 grad_norm: 5.1506 loss: 1.7852 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.7852 2022/12/08 15:57:26 - mmengine - INFO - Epoch(train) [17][800/940] lr: 1.0000e-02 eta: 13:36:43 time: 0.7244 data_time: 0.0313 memory: 16095 grad_norm: 5.1159 loss: 1.5794 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.5794 2022/12/08 15:57:41 - mmengine - INFO - Epoch(train) [17][820/940] lr: 1.0000e-02 eta: 13:36:40 time: 0.7193 data_time: 0.0256 memory: 16095 grad_norm: 5.2091 loss: 1.5767 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5767 2022/12/08 15:57:55 - mmengine - INFO - Epoch(train) [17][840/940] lr: 1.0000e-02 eta: 13:36:36 time: 0.7208 data_time: 0.0248 memory: 16095 grad_norm: 5.1633 loss: 1.7399 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.7399 2022/12/08 15:58:09 - mmengine - INFO - Epoch(train) [17][860/940] lr: 1.0000e-02 eta: 13:36:33 time: 0.7175 data_time: 0.0252 memory: 16095 grad_norm: 5.0980 loss: 1.6032 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.6032 2022/12/08 15:58:24 - mmengine - INFO - Epoch(train) [17][880/940] lr: 1.0000e-02 eta: 13:36:29 time: 0.7179 data_time: 0.0257 memory: 16095 grad_norm: 5.0208 loss: 1.6847 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6847 2022/12/08 15:58:38 - mmengine - INFO - Epoch(train) [17][900/940] lr: 1.0000e-02 eta: 13:36:25 time: 0.7142 data_time: 0.0263 memory: 16095 grad_norm: 5.0375 loss: 1.5448 top1_acc: 0.5938 top5_acc: 0.7188 loss_cls: 1.5448 2022/12/08 15:58:53 - mmengine - INFO - Epoch(train) [17][920/940] lr: 1.0000e-02 eta: 13:36:22 time: 0.7272 data_time: 0.0265 memory: 16095 grad_norm: 5.0062 loss: 1.6104 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6104 2022/12/08 15:59:06 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 15:59:06 - mmengine - INFO - Epoch(train) [17][940/940] lr: 1.0000e-02 eta: 13:36:15 time: 0.6812 data_time: 0.0173 memory: 16095 grad_norm: 5.6455 loss: 1.6184 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.6184 2022/12/08 15:59:23 - mmengine - INFO - Epoch(val) [17][20/78] eta: 0:00:49 time: 0.8454 data_time: 0.6241 memory: 1686 2022/12/08 15:59:35 - mmengine - INFO - Epoch(val) [17][40/78] eta: 0:00:26 time: 0.5716 data_time: 0.3222 memory: 1686 2022/12/08 15:59:49 - mmengine - INFO - Epoch(val) [17][60/78] eta: 0:00:12 time: 0.7396 data_time: 0.5056 memory: 1686 2022/12/08 16:00:02 - mmengine - INFO - Epoch(val) [17][78/78] acc/top1: 0.6273 acc/top5: 0.8460 acc/mean1: 0.6272 2022/12/08 16:00:23 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 16:00:23 - mmengine - INFO - Epoch(train) [18][ 20/940] lr: 1.0000e-02 eta: 13:36:41 time: 1.0291 data_time: 0.3111 memory: 16095 grad_norm: 5.1204 loss: 1.6222 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.6222 2022/12/08 16:00:37 - mmengine - INFO - Epoch(train) [18][ 40/940] lr: 1.0000e-02 eta: 13:36:40 time: 0.7405 data_time: 0.0205 memory: 16095 grad_norm: 5.0363 loss: 1.5185 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.5185 2022/12/08 16:00:52 - mmengine - INFO - Epoch(train) [18][ 60/940] lr: 1.0000e-02 eta: 13:36:38 time: 0.7392 data_time: 0.0262 memory: 16095 grad_norm: 5.1389 loss: 1.5820 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.5820 2022/12/08 16:01:07 - mmengine - INFO - Epoch(train) [18][ 80/940] lr: 1.0000e-02 eta: 13:36:35 time: 0.7270 data_time: 0.0215 memory: 16095 grad_norm: 4.9343 loss: 1.4022 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.4022 2022/12/08 16:01:21 - mmengine - INFO - Epoch(train) [18][100/940] lr: 1.0000e-02 eta: 13:36:31 time: 0.7215 data_time: 0.0244 memory: 16095 grad_norm: 5.0336 loss: 1.5046 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.5046 2022/12/08 16:01:36 - mmengine - INFO - Epoch(train) [18][120/940] lr: 1.0000e-02 eta: 13:36:27 time: 0.7180 data_time: 0.0242 memory: 16095 grad_norm: 5.0249 loss: 1.4807 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.4807 2022/12/08 16:01:50 - mmengine - INFO - Epoch(train) [18][140/940] lr: 1.0000e-02 eta: 13:36:24 time: 0.7190 data_time: 0.0238 memory: 16095 grad_norm: 5.1735 loss: 1.7279 top1_acc: 0.4062 top5_acc: 0.5625 loss_cls: 1.7279 2022/12/08 16:02:04 - mmengine - INFO - Epoch(train) [18][160/940] lr: 1.0000e-02 eta: 13:36:20 time: 0.7233 data_time: 0.0242 memory: 16095 grad_norm: 5.0855 loss: 1.5321 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5321 2022/12/08 16:02:19 - mmengine - INFO - Epoch(train) [18][180/940] lr: 1.0000e-02 eta: 13:36:16 time: 0.7201 data_time: 0.0249 memory: 16095 grad_norm: 5.1285 loss: 1.5273 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.5273 2022/12/08 16:02:33 - mmengine - INFO - Epoch(train) [18][200/940] lr: 1.0000e-02 eta: 13:36:12 time: 0.7202 data_time: 0.0252 memory: 16095 grad_norm: 5.0763 loss: 1.6247 top1_acc: 0.4688 top5_acc: 0.6875 loss_cls: 1.6247 2022/12/08 16:02:48 - mmengine - INFO - Epoch(train) [18][220/940] lr: 1.0000e-02 eta: 13:36:08 time: 0.7142 data_time: 0.0237 memory: 16095 grad_norm: 5.0462 loss: 1.6653 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.6653 2022/12/08 16:03:02 - mmengine - INFO - Epoch(train) [18][240/940] lr: 1.0000e-02 eta: 13:36:05 time: 0.7307 data_time: 0.0255 memory: 16095 grad_norm: 5.2577 loss: 1.6047 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.6047 2022/12/08 16:03:17 - mmengine - INFO - Epoch(train) [18][260/940] lr: 1.0000e-02 eta: 13:36:01 time: 0.7200 data_time: 0.0241 memory: 16095 grad_norm: 5.1129 loss: 1.5262 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.5262 2022/12/08 16:03:31 - mmengine - INFO - Epoch(train) [18][280/940] lr: 1.0000e-02 eta: 13:35:58 time: 0.7284 data_time: 0.0334 memory: 16095 grad_norm: 5.1187 loss: 1.6582 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.6582 2022/12/08 16:03:46 - mmengine - INFO - Epoch(train) [18][300/940] lr: 1.0000e-02 eta: 13:35:54 time: 0.7212 data_time: 0.0256 memory: 16095 grad_norm: 5.1742 loss: 1.4929 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.4929 2022/12/08 16:04:00 - mmengine - INFO - Epoch(train) [18][320/940] lr: 1.0000e-02 eta: 13:35:50 time: 0.7167 data_time: 0.0255 memory: 16095 grad_norm: 5.1594 loss: 1.5562 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5562 2022/12/08 16:04:14 - mmengine - INFO - Epoch(train) [18][340/940] lr: 1.0000e-02 eta: 13:35:46 time: 0.7228 data_time: 0.0253 memory: 16095 grad_norm: 5.2493 loss: 1.6952 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.6952 2022/12/08 16:04:29 - mmengine - INFO - Epoch(train) [18][360/940] lr: 1.0000e-02 eta: 13:35:43 time: 0.7291 data_time: 0.0233 memory: 16095 grad_norm: 5.0636 loss: 1.6510 top1_acc: 0.6250 top5_acc: 0.7188 loss_cls: 1.6510 2022/12/08 16:04:43 - mmengine - INFO - Epoch(train) [18][380/940] lr: 1.0000e-02 eta: 13:35:38 time: 0.7150 data_time: 0.0264 memory: 16095 grad_norm: 5.2200 loss: 1.6630 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6630 2022/12/08 16:04:58 - mmengine - INFO - Epoch(train) [18][400/940] lr: 1.0000e-02 eta: 13:35:35 time: 0.7260 data_time: 0.0231 memory: 16095 grad_norm: 5.2346 loss: 1.7107 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.7107 2022/12/08 16:05:12 - mmengine - INFO - Epoch(train) [18][420/940] lr: 1.0000e-02 eta: 13:35:31 time: 0.7200 data_time: 0.0251 memory: 16095 grad_norm: 5.1297 loss: 1.6454 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.6454 2022/12/08 16:05:26 - mmengine - INFO - Epoch(train) [18][440/940] lr: 1.0000e-02 eta: 13:35:26 time: 0.7155 data_time: 0.0236 memory: 16095 grad_norm: 5.1447 loss: 1.5757 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5757 2022/12/08 16:05:41 - mmengine - INFO - Epoch(train) [18][460/940] lr: 1.0000e-02 eta: 13:35:22 time: 0.7180 data_time: 0.0250 memory: 16095 grad_norm: 5.1350 loss: 1.5883 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.5883 2022/12/08 16:05:55 - mmengine - INFO - Epoch(train) [18][480/940] lr: 1.0000e-02 eta: 13:35:18 time: 0.7266 data_time: 0.0244 memory: 16095 grad_norm: 5.1413 loss: 1.5680 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.5680 2022/12/08 16:06:10 - mmengine - INFO - Epoch(train) [18][500/940] lr: 1.0000e-02 eta: 13:35:14 time: 0.7193 data_time: 0.0232 memory: 16095 grad_norm: 5.2018 loss: 1.7312 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7312 2022/12/08 16:06:24 - mmengine - INFO - Epoch(train) [18][520/940] lr: 1.0000e-02 eta: 13:35:09 time: 0.7186 data_time: 0.0268 memory: 16095 grad_norm: 5.2301 loss: 1.5730 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.5730 2022/12/08 16:06:39 - mmengine - INFO - Epoch(train) [18][540/940] lr: 1.0000e-02 eta: 13:35:05 time: 0.7176 data_time: 0.0235 memory: 16095 grad_norm: 5.1391 loss: 1.4918 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 1.4918 2022/12/08 16:06:53 - mmengine - INFO - Epoch(train) [18][560/940] lr: 1.0000e-02 eta: 13:35:01 time: 0.7275 data_time: 0.0240 memory: 16095 grad_norm: 5.1065 loss: 1.6003 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.6003 2022/12/08 16:07:07 - mmengine - INFO - Epoch(train) [18][580/940] lr: 1.0000e-02 eta: 13:34:56 time: 0.7142 data_time: 0.0247 memory: 16095 grad_norm: 5.1090 loss: 1.6905 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.6905 2022/12/08 16:07:22 - mmengine - INFO - Epoch(train) [18][600/940] lr: 1.0000e-02 eta: 13:34:52 time: 0.7206 data_time: 0.0256 memory: 16095 grad_norm: 5.1272 loss: 1.8008 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.8008 2022/12/08 16:07:36 - mmengine - INFO - Epoch(train) [18][620/940] lr: 1.0000e-02 eta: 13:34:48 time: 0.7251 data_time: 0.0255 memory: 16095 grad_norm: 5.0995 loss: 1.6935 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.6935 2022/12/08 16:07:51 - mmengine - INFO - Epoch(train) [18][640/940] lr: 1.0000e-02 eta: 13:34:44 time: 0.7214 data_time: 0.0249 memory: 16095 grad_norm: 5.1232 loss: 1.6085 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6085 2022/12/08 16:08:05 - mmengine - INFO - Epoch(train) [18][660/940] lr: 1.0000e-02 eta: 13:34:39 time: 0.7176 data_time: 0.0241 memory: 16095 grad_norm: 5.1322 loss: 1.5812 top1_acc: 0.7188 top5_acc: 1.0000 loss_cls: 1.5812 2022/12/08 16:08:19 - mmengine - INFO - Epoch(train) [18][680/940] lr: 1.0000e-02 eta: 13:34:35 time: 0.7187 data_time: 0.0237 memory: 16095 grad_norm: 5.1087 loss: 1.7365 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7365 2022/12/08 16:08:34 - mmengine - INFO - Epoch(train) [18][700/940] lr: 1.0000e-02 eta: 13:34:31 time: 0.7242 data_time: 0.0252 memory: 16095 grad_norm: 5.0549 loss: 1.4851 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.4851 2022/12/08 16:08:48 - mmengine - INFO - Epoch(train) [18][720/940] lr: 1.0000e-02 eta: 13:34:27 time: 0.7245 data_time: 0.0240 memory: 16095 grad_norm: 5.1771 loss: 1.5779 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.5779 2022/12/08 16:09:03 - mmengine - INFO - Epoch(train) [18][740/940] lr: 1.0000e-02 eta: 13:34:22 time: 0.7166 data_time: 0.0255 memory: 16095 grad_norm: 5.0812 loss: 1.5356 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.5356 2022/12/08 16:09:17 - mmengine - INFO - Epoch(train) [18][760/940] lr: 1.0000e-02 eta: 13:34:17 time: 0.7214 data_time: 0.0254 memory: 16095 grad_norm: 5.1182 loss: 1.6028 top1_acc: 0.6562 top5_acc: 0.6875 loss_cls: 1.6028 2022/12/08 16:09:32 - mmengine - INFO - Epoch(train) [18][780/940] lr: 1.0000e-02 eta: 13:34:13 time: 0.7273 data_time: 0.0363 memory: 16095 grad_norm: 5.0822 loss: 1.6188 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.6188 2022/12/08 16:09:46 - mmengine - INFO - Epoch(train) [18][800/940] lr: 1.0000e-02 eta: 13:34:10 time: 0.7283 data_time: 0.0249 memory: 16095 grad_norm: 5.1487 loss: 1.6209 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.6209 2022/12/08 16:10:01 - mmengine - INFO - Epoch(train) [18][820/940] lr: 1.0000e-02 eta: 13:34:05 time: 0.7216 data_time: 0.0240 memory: 16095 grad_norm: 5.1076 loss: 1.5724 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.5724 2022/12/08 16:10:15 - mmengine - INFO - Epoch(train) [18][840/940] lr: 1.0000e-02 eta: 13:34:01 time: 0.7222 data_time: 0.0257 memory: 16095 grad_norm: 5.1652 loss: 1.7275 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7275 2022/12/08 16:10:30 - mmengine - INFO - Epoch(train) [18][860/940] lr: 1.0000e-02 eta: 13:33:57 time: 0.7277 data_time: 0.0340 memory: 16095 grad_norm: 5.2038 loss: 1.5989 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.5989 2022/12/08 16:10:44 - mmengine - INFO - Epoch(train) [18][880/940] lr: 1.0000e-02 eta: 13:33:51 time: 0.7130 data_time: 0.0247 memory: 16095 grad_norm: 5.1620 loss: 1.7989 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7989 2022/12/08 16:10:58 - mmengine - INFO - Epoch(train) [18][900/940] lr: 1.0000e-02 eta: 13:33:47 time: 0.7191 data_time: 0.0242 memory: 16095 grad_norm: 5.0545 loss: 1.6320 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6320 2022/12/08 16:11:13 - mmengine - INFO - Epoch(train) [18][920/940] lr: 1.0000e-02 eta: 13:33:42 time: 0.7205 data_time: 0.0267 memory: 16095 grad_norm: 5.0558 loss: 1.7214 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.7214 2022/12/08 16:11:26 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 16:11:26 - mmengine - INFO - Epoch(train) [18][940/940] lr: 1.0000e-02 eta: 13:33:34 time: 0.6840 data_time: 0.0169 memory: 16095 grad_norm: 5.4372 loss: 1.5438 top1_acc: 0.7143 top5_acc: 1.0000 loss_cls: 1.5438 2022/12/08 16:11:26 - mmengine - INFO - Saving checkpoint at 18 epochs 2022/12/08 16:11:47 - mmengine - INFO - Epoch(val) [18][20/78] eta: 0:00:49 time: 0.8459 data_time: 0.6087 memory: 1686 2022/12/08 16:11:58 - mmengine - INFO - Epoch(val) [18][40/78] eta: 0:00:26 time: 0.5562 data_time: 0.3059 memory: 1686 2022/12/08 16:12:13 - mmengine - INFO - Epoch(val) [18][60/78] eta: 0:00:13 time: 0.7804 data_time: 0.5339 memory: 1686 2022/12/08 16:12:24 - mmengine - INFO - Epoch(val) [18][78/78] acc/top1: 0.6359 acc/top5: 0.8523 acc/mean1: 0.6357 2022/12/08 16:12:24 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_16.pth is removed 2022/12/08 16:12:27 - mmengine - INFO - The best checkpoint with 0.6359 acc/top1 at 18 epoch is saved to best_acc/top1_epoch_18.pth. 2022/12/08 16:12:46 - mmengine - INFO - Epoch(train) [19][ 20/940] lr: 1.0000e-02 eta: 13:33:52 time: 0.9678 data_time: 0.2667 memory: 16095 grad_norm: 5.1656 loss: 1.6611 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 1.6611 2022/12/08 16:13:01 - mmengine - INFO - Epoch(train) [19][ 40/940] lr: 1.0000e-02 eta: 13:33:47 time: 0.7247 data_time: 0.0238 memory: 16095 grad_norm: 5.1035 loss: 1.6720 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.6720 2022/12/08 16:13:15 - mmengine - INFO - Epoch(train) [19][ 60/940] lr: 1.0000e-02 eta: 13:33:43 time: 0.7231 data_time: 0.0247 memory: 16095 grad_norm: 5.0773 loss: 1.5629 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5629 2022/12/08 16:13:30 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 16:13:30 - mmengine - INFO - Epoch(train) [19][ 80/940] lr: 1.0000e-02 eta: 13:33:39 time: 0.7293 data_time: 0.0224 memory: 16095 grad_norm: 5.1408 loss: 1.5632 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5632 2022/12/08 16:13:44 - mmengine - INFO - Epoch(train) [19][100/940] lr: 1.0000e-02 eta: 13:33:34 time: 0.7182 data_time: 0.0249 memory: 16095 grad_norm: 5.1906 loss: 1.5740 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5740 2022/12/08 16:13:58 - mmengine - INFO - Epoch(train) [19][120/940] lr: 1.0000e-02 eta: 13:33:29 time: 0.7246 data_time: 0.0251 memory: 16095 grad_norm: 4.9411 loss: 1.6675 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.6675 2022/12/08 16:14:13 - mmengine - INFO - Epoch(train) [19][140/940] lr: 1.0000e-02 eta: 13:33:26 time: 0.7367 data_time: 0.0251 memory: 16095 grad_norm: 5.0040 loss: 1.5621 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 1.5621 2022/12/08 16:14:27 - mmengine - INFO - Epoch(train) [19][160/940] lr: 1.0000e-02 eta: 13:33:20 time: 0.7152 data_time: 0.0236 memory: 16095 grad_norm: 5.1127 loss: 1.6070 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.6070 2022/12/08 16:14:42 - mmengine - INFO - Epoch(train) [19][180/940] lr: 1.0000e-02 eta: 13:33:15 time: 0.7185 data_time: 0.0249 memory: 16095 grad_norm: 5.0578 loss: 1.4923 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.4923 2022/12/08 16:14:56 - mmengine - INFO - Epoch(train) [19][200/940] lr: 1.0000e-02 eta: 13:33:10 time: 0.7163 data_time: 0.0237 memory: 16095 grad_norm: 5.2471 loss: 1.5454 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.5454 2022/12/08 16:15:10 - mmengine - INFO - Epoch(train) [19][220/940] lr: 1.0000e-02 eta: 13:33:04 time: 0.7146 data_time: 0.0266 memory: 16095 grad_norm: 5.1427 loss: 1.5311 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.5311 2022/12/08 16:15:25 - mmengine - INFO - Epoch(train) [19][240/940] lr: 1.0000e-02 eta: 13:32:59 time: 0.7155 data_time: 0.0247 memory: 16095 grad_norm: 5.1155 loss: 1.5697 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5697 2022/12/08 16:15:39 - mmengine - INFO - Epoch(train) [19][260/940] lr: 1.0000e-02 eta: 13:32:53 time: 0.7167 data_time: 0.0236 memory: 16095 grad_norm: 5.0907 loss: 1.5978 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.5978 2022/12/08 16:15:53 - mmengine - INFO - Epoch(train) [19][280/940] lr: 1.0000e-02 eta: 13:32:48 time: 0.7153 data_time: 0.0246 memory: 16095 grad_norm: 5.1062 loss: 1.5607 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.5607 2022/12/08 16:16:08 - mmengine - INFO - Epoch(train) [19][300/940] lr: 1.0000e-02 eta: 13:32:43 time: 0.7267 data_time: 0.0245 memory: 16095 grad_norm: 5.1401 loss: 1.5463 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5463 2022/12/08 16:16:22 - mmengine - INFO - Epoch(train) [19][320/940] lr: 1.0000e-02 eta: 13:32:38 time: 0.7180 data_time: 0.0246 memory: 16095 grad_norm: 5.2259 loss: 1.6158 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.6158 2022/12/08 16:16:37 - mmengine - INFO - Epoch(train) [19][340/940] lr: 1.0000e-02 eta: 13:32:33 time: 0.7269 data_time: 0.0346 memory: 16095 grad_norm: 5.0621 loss: 1.4330 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.4330 2022/12/08 16:16:50 - mmengine - INFO - Epoch(train) [19][360/940] lr: 1.0000e-02 eta: 13:32:20 time: 0.6317 data_time: 0.0320 memory: 16095 grad_norm: 5.2260 loss: 1.4877 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.4877 2022/12/08 16:16:59 - mmengine - INFO - Epoch(train) [19][380/940] lr: 1.0000e-02 eta: 13:31:52 time: 0.4553 data_time: 0.0782 memory: 16095 grad_norm: 5.2189 loss: 1.6269 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.6269 2022/12/08 16:17:11 - mmengine - INFO - Epoch(train) [19][400/940] lr: 1.0000e-02 eta: 13:31:39 time: 0.6358 data_time: 0.1190 memory: 16095 grad_norm: 5.2755 loss: 1.5136 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5136 2022/12/08 16:17:24 - mmengine - INFO - Epoch(train) [19][420/940] lr: 1.0000e-02 eta: 13:31:26 time: 0.6331 data_time: 0.1081 memory: 16095 grad_norm: 5.1091 loss: 1.4128 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.4128 2022/12/08 16:17:36 - mmengine - INFO - Epoch(train) [19][440/940] lr: 1.0000e-02 eta: 13:31:12 time: 0.6142 data_time: 0.0227 memory: 16095 grad_norm: 5.1553 loss: 1.6544 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.6544 2022/12/08 16:17:48 - mmengine - INFO - Epoch(train) [19][460/940] lr: 1.0000e-02 eta: 13:30:56 time: 0.5976 data_time: 0.0275 memory: 16095 grad_norm: 5.1346 loss: 1.5321 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.5321 2022/12/08 16:18:01 - mmengine - INFO - Epoch(train) [19][480/940] lr: 1.0000e-02 eta: 13:30:42 time: 0.6200 data_time: 0.0222 memory: 16095 grad_norm: 5.0674 loss: 1.5000 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.5000 2022/12/08 16:18:13 - mmengine - INFO - Epoch(train) [19][500/940] lr: 1.0000e-02 eta: 13:30:26 time: 0.5973 data_time: 0.0257 memory: 16095 grad_norm: 5.1218 loss: 1.7102 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.7102 2022/12/08 16:18:25 - mmengine - INFO - Epoch(train) [19][520/940] lr: 1.0000e-02 eta: 13:30:11 time: 0.6182 data_time: 0.0233 memory: 16095 grad_norm: 5.1884 loss: 1.6378 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.6378 2022/12/08 16:18:37 - mmengine - INFO - Epoch(train) [19][540/940] lr: 1.0000e-02 eta: 13:29:54 time: 0.5850 data_time: 0.0247 memory: 16095 grad_norm: 5.1931 loss: 1.5904 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.5904 2022/12/08 16:18:49 - mmengine - INFO - Epoch(train) [19][560/940] lr: 1.0000e-02 eta: 13:29:42 time: 0.6398 data_time: 0.0250 memory: 16095 grad_norm: 5.1029 loss: 1.5227 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 1.5227 2022/12/08 16:19:02 - mmengine - INFO - Epoch(train) [19][580/940] lr: 1.0000e-02 eta: 13:29:29 time: 0.6311 data_time: 0.0240 memory: 16095 grad_norm: 5.1564 loss: 1.4961 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4961 2022/12/08 16:19:14 - mmengine - INFO - Epoch(train) [19][600/940] lr: 1.0000e-02 eta: 13:29:14 time: 0.6022 data_time: 0.0235 memory: 16095 grad_norm: 5.2304 loss: 1.6027 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6027 2022/12/08 16:19:26 - mmengine - INFO - Epoch(train) [19][620/940] lr: 1.0000e-02 eta: 13:28:57 time: 0.5882 data_time: 0.0244 memory: 16095 grad_norm: 5.2638 loss: 1.5965 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 1.5965 2022/12/08 16:19:38 - mmengine - INFO - Epoch(train) [19][640/940] lr: 1.0000e-02 eta: 13:28:42 time: 0.6113 data_time: 0.0387 memory: 16095 grad_norm: 5.1572 loss: 1.5175 top1_acc: 0.7188 top5_acc: 0.7812 loss_cls: 1.5175 2022/12/08 16:19:50 - mmengine - INFO - Epoch(train) [19][660/940] lr: 1.0000e-02 eta: 13:28:27 time: 0.6085 data_time: 0.0857 memory: 16095 grad_norm: 5.2329 loss: 1.5403 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5403 2022/12/08 16:20:02 - mmengine - INFO - Epoch(train) [19][680/940] lr: 1.0000e-02 eta: 13:28:10 time: 0.5855 data_time: 0.0730 memory: 16095 grad_norm: 5.1160 loss: 1.4979 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.4979 2022/12/08 16:20:15 - mmengine - INFO - Epoch(train) [19][700/940] lr: 1.0000e-02 eta: 13:27:58 time: 0.6400 data_time: 0.0925 memory: 16095 grad_norm: 5.1701 loss: 1.5558 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.5558 2022/12/08 16:20:27 - mmengine - INFO - Epoch(train) [19][720/940] lr: 1.0000e-02 eta: 13:27:44 time: 0.6198 data_time: 0.1654 memory: 16095 grad_norm: 5.1792 loss: 1.5506 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.5506 2022/12/08 16:20:40 - mmengine - INFO - Epoch(train) [19][740/940] lr: 1.0000e-02 eta: 13:27:30 time: 0.6268 data_time: 0.2013 memory: 16095 grad_norm: 5.0521 loss: 1.5117 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.5117 2022/12/08 16:20:51 - mmengine - INFO - Epoch(train) [19][760/940] lr: 1.0000e-02 eta: 13:27:12 time: 0.5672 data_time: 0.1156 memory: 16095 grad_norm: 5.0948 loss: 1.6194 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6194 2022/12/08 16:21:05 - mmengine - INFO - Epoch(train) [19][780/940] lr: 1.0000e-02 eta: 13:27:05 time: 0.6957 data_time: 0.2831 memory: 16095 grad_norm: 5.2230 loss: 1.5990 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5990 2022/12/08 16:21:17 - mmengine - INFO - Epoch(train) [19][800/940] lr: 1.0000e-02 eta: 13:26:48 time: 0.5919 data_time: 0.2583 memory: 16095 grad_norm: 5.2273 loss: 1.6096 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.6096 2022/12/08 16:21:29 - mmengine - INFO - Epoch(train) [19][820/940] lr: 1.0000e-02 eta: 13:26:35 time: 0.6275 data_time: 0.3018 memory: 16095 grad_norm: 5.2837 loss: 1.6585 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.6585 2022/12/08 16:21:40 - mmengine - INFO - Epoch(train) [19][840/940] lr: 1.0000e-02 eta: 13:26:13 time: 0.5303 data_time: 0.2042 memory: 16095 grad_norm: 5.2178 loss: 1.6043 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6043 2022/12/08 16:21:53 - mmengine - INFO - Epoch(train) [19][860/940] lr: 1.0000e-02 eta: 13:26:01 time: 0.6419 data_time: 0.2817 memory: 16095 grad_norm: 5.1403 loss: 1.6087 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.6087 2022/12/08 16:22:05 - mmengine - INFO - Epoch(train) [19][880/940] lr: 1.0000e-02 eta: 13:25:45 time: 0.5911 data_time: 0.2537 memory: 16095 grad_norm: 5.0957 loss: 1.6580 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.6580 2022/12/08 16:22:18 - mmengine - INFO - Epoch(train) [19][900/940] lr: 1.0000e-02 eta: 13:25:36 time: 0.6755 data_time: 0.3417 memory: 16095 grad_norm: 5.2163 loss: 1.6956 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 1.6956 2022/12/08 16:22:30 - mmengine - INFO - Epoch(train) [19][920/940] lr: 1.0000e-02 eta: 13:25:17 time: 0.5692 data_time: 0.2411 memory: 16095 grad_norm: 5.2885 loss: 1.7628 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.7628 2022/12/08 16:22:40 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 16:22:40 - mmengine - INFO - Epoch(train) [19][940/940] lr: 1.0000e-02 eta: 13:24:57 time: 0.5402 data_time: 0.2386 memory: 16095 grad_norm: 5.5069 loss: 1.6666 top1_acc: 0.2857 top5_acc: 0.8571 loss_cls: 1.6666 2022/12/08 16:22:55 - mmengine - INFO - Epoch(val) [19][20/78] eta: 0:00:41 time: 0.7197 data_time: 0.6254 memory: 1686 2022/12/08 16:23:04 - mmengine - INFO - Epoch(val) [19][40/78] eta: 0:00:22 time: 0.4606 data_time: 0.3660 memory: 1686 2022/12/08 16:23:18 - mmengine - INFO - Epoch(val) [19][60/78] eta: 0:00:11 time: 0.6833 data_time: 0.5874 memory: 1686 2022/12/08 16:23:28 - mmengine - INFO - Epoch(val) [19][78/78] acc/top1: 0.6384 acc/top5: 0.8537 acc/mean1: 0.6382 2022/12/08 16:23:28 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_18.pth is removed 2022/12/08 16:23:30 - mmengine - INFO - The best checkpoint with 0.6384 acc/top1 at 19 epoch is saved to best_acc/top1_epoch_19.pth. 2022/12/08 16:23:46 - mmengine - INFO - Epoch(train) [20][ 20/940] lr: 1.0000e-02 eta: 13:24:58 time: 0.7958 data_time: 0.4905 memory: 16095 grad_norm: 5.0259 loss: 1.6088 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.6088 2022/12/08 16:23:57 - mmengine - INFO - Epoch(train) [20][ 40/940] lr: 1.0000e-02 eta: 13:24:36 time: 0.5256 data_time: 0.2271 memory: 16095 grad_norm: 5.0699 loss: 1.6085 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.6085 2022/12/08 16:24:11 - mmengine - INFO - Epoch(train) [20][ 60/940] lr: 1.0000e-02 eta: 13:24:27 time: 0.6833 data_time: 0.3944 memory: 16095 grad_norm: 5.1545 loss: 1.4578 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4578 2022/12/08 16:24:22 - mmengine - INFO - Epoch(train) [20][ 80/940] lr: 1.0000e-02 eta: 13:24:09 time: 0.5654 data_time: 0.2597 memory: 16095 grad_norm: 5.0381 loss: 1.5724 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.5724 2022/12/08 16:24:35 - mmengine - INFO - Epoch(train) [20][100/940] lr: 1.0000e-02 eta: 13:23:59 time: 0.6679 data_time: 0.3631 memory: 16095 grad_norm: 4.9419 loss: 1.4400 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 1.4400 2022/12/08 16:24:47 - mmengine - INFO - Epoch(train) [20][120/940] lr: 1.0000e-02 eta: 13:23:41 time: 0.5789 data_time: 0.2711 memory: 16095 grad_norm: 5.0668 loss: 1.5820 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5820 2022/12/08 16:25:00 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 16:25:00 - mmengine - INFO - Epoch(train) [20][140/940] lr: 1.0000e-02 eta: 13:23:31 time: 0.6628 data_time: 0.3007 memory: 16095 grad_norm: 5.1684 loss: 1.6178 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.6178 2022/12/08 16:25:10 - mmengine - INFO - Epoch(train) [20][160/940] lr: 1.0000e-02 eta: 13:23:08 time: 0.5102 data_time: 0.1783 memory: 16095 grad_norm: 5.2625 loss: 1.5161 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5161 2022/12/08 16:25:24 - mmengine - INFO - Epoch(train) [20][180/940] lr: 1.0000e-02 eta: 13:22:58 time: 0.6723 data_time: 0.3191 memory: 16095 grad_norm: 5.2021 loss: 1.5092 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.5092 2022/12/08 16:25:34 - mmengine - INFO - Epoch(train) [20][200/940] lr: 1.0000e-02 eta: 13:22:37 time: 0.5299 data_time: 0.2046 memory: 16095 grad_norm: 5.1287 loss: 1.6651 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6651 2022/12/08 16:25:47 - mmengine - INFO - Epoch(train) [20][220/940] lr: 1.0000e-02 eta: 13:22:26 time: 0.6576 data_time: 0.2140 memory: 16095 grad_norm: 5.0455 loss: 1.3670 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.3670 2022/12/08 16:25:59 - mmengine - INFO - Epoch(train) [20][240/940] lr: 1.0000e-02 eta: 13:22:10 time: 0.5948 data_time: 0.0841 memory: 16095 grad_norm: 5.1149 loss: 1.6611 top1_acc: 0.4375 top5_acc: 0.5938 loss_cls: 1.6611 2022/12/08 16:26:13 - mmengine - INFO - Epoch(train) [20][260/940] lr: 1.0000e-02 eta: 13:22:01 time: 0.6777 data_time: 0.1630 memory: 16095 grad_norm: 5.0931 loss: 1.5285 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5285 2022/12/08 16:26:24 - mmengine - INFO - Epoch(train) [20][280/940] lr: 1.0000e-02 eta: 13:21:42 time: 0.5506 data_time: 0.1834 memory: 16095 grad_norm: 5.1353 loss: 1.5335 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.5335 2022/12/08 16:26:37 - mmengine - INFO - Epoch(train) [20][300/940] lr: 1.0000e-02 eta: 13:21:32 time: 0.6688 data_time: 0.0822 memory: 16095 grad_norm: 5.1348 loss: 1.4738 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.4738 2022/12/08 16:26:48 - mmengine - INFO - Epoch(train) [20][320/940] lr: 1.0000e-02 eta: 13:21:11 time: 0.5376 data_time: 0.0384 memory: 16095 grad_norm: 5.1852 loss: 1.6512 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.6512 2022/12/08 16:27:01 - mmengine - INFO - Epoch(train) [20][340/940] lr: 1.0000e-02 eta: 13:21:01 time: 0.6703 data_time: 0.1605 memory: 16095 grad_norm: 5.3090 loss: 1.4532 top1_acc: 0.5312 top5_acc: 0.9062 loss_cls: 1.4532 2022/12/08 16:27:12 - mmengine - INFO - Epoch(train) [20][360/940] lr: 1.0000e-02 eta: 13:20:40 time: 0.5320 data_time: 0.0920 memory: 16095 grad_norm: 5.2199 loss: 1.6601 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.6601 2022/12/08 16:27:25 - mmengine - INFO - Epoch(train) [20][380/940] lr: 1.0000e-02 eta: 13:20:28 time: 0.6400 data_time: 0.1141 memory: 16095 grad_norm: 5.1915 loss: 1.5852 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.5852 2022/12/08 16:27:36 - mmengine - INFO - Epoch(train) [20][400/940] lr: 1.0000e-02 eta: 13:20:09 time: 0.5573 data_time: 0.0821 memory: 16095 grad_norm: 5.1752 loss: 1.6481 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.6481 2022/12/08 16:27:50 - mmengine - INFO - Epoch(train) [20][420/940] lr: 1.0000e-02 eta: 13:20:03 time: 0.7120 data_time: 0.0374 memory: 16095 grad_norm: 5.0958 loss: 1.5760 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.5760 2022/12/08 16:28:01 - mmengine - INFO - Epoch(train) [20][440/940] lr: 1.0000e-02 eta: 13:19:43 time: 0.5481 data_time: 0.0202 memory: 16095 grad_norm: 5.1475 loss: 1.6096 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 1.6096 2022/12/08 16:28:15 - mmengine - INFO - Epoch(train) [20][460/940] lr: 1.0000e-02 eta: 13:19:36 time: 0.7046 data_time: 0.0283 memory: 16095 grad_norm: 5.1455 loss: 1.5851 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.5851 2022/12/08 16:28:26 - mmengine - INFO - Epoch(train) [20][480/940] lr: 1.0000e-02 eta: 13:19:16 time: 0.5411 data_time: 0.0211 memory: 16095 grad_norm: 5.1253 loss: 1.5105 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.5105 2022/12/08 16:28:39 - mmengine - INFO - Epoch(train) [20][500/940] lr: 1.0000e-02 eta: 13:19:02 time: 0.6219 data_time: 0.0254 memory: 16095 grad_norm: 5.0811 loss: 1.5877 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.5877 2022/12/08 16:28:49 - mmengine - INFO - Epoch(train) [20][520/940] lr: 1.0000e-02 eta: 13:18:41 time: 0.5293 data_time: 0.1326 memory: 16095 grad_norm: 5.0917 loss: 1.6087 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.6087 2022/12/08 16:29:04 - mmengine - INFO - Epoch(train) [20][540/940] lr: 1.0000e-02 eta: 13:18:36 time: 0.7282 data_time: 0.2685 memory: 16095 grad_norm: 5.1638 loss: 1.6246 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6246 2022/12/08 16:29:15 - mmengine - INFO - Epoch(train) [20][560/940] lr: 1.0000e-02 eta: 13:18:16 time: 0.5421 data_time: 0.1531 memory: 16095 grad_norm: 5.0948 loss: 1.4458 top1_acc: 0.4688 top5_acc: 0.9062 loss_cls: 1.4458 2022/12/08 16:29:28 - mmengine - INFO - Epoch(train) [20][580/940] lr: 1.0000e-02 eta: 13:18:05 time: 0.6546 data_time: 0.3008 memory: 16095 grad_norm: 5.1627 loss: 1.7288 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.7288 2022/12/08 16:29:39 - mmengine - INFO - Epoch(train) [20][600/940] lr: 1.0000e-02 eta: 13:17:45 time: 0.5429 data_time: 0.2058 memory: 16095 grad_norm: 5.1037 loss: 1.5311 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5311 2022/12/08 16:29:52 - mmengine - INFO - Epoch(train) [20][620/940] lr: 1.0000e-02 eta: 13:17:36 time: 0.6760 data_time: 0.3399 memory: 16095 grad_norm: 5.0859 loss: 1.5050 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.5050 2022/12/08 16:30:04 - mmengine - INFO - Epoch(train) [20][640/940] lr: 1.0000e-02 eta: 13:17:18 time: 0.5739 data_time: 0.2524 memory: 16095 grad_norm: 5.1298 loss: 1.5878 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.5878 2022/12/08 16:30:17 - mmengine - INFO - Epoch(train) [20][660/940] lr: 1.0000e-02 eta: 13:17:10 time: 0.6875 data_time: 0.3106 memory: 16095 grad_norm: 5.2404 loss: 1.5981 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.5981 2022/12/08 16:30:28 - mmengine - INFO - Epoch(train) [20][680/940] lr: 1.0000e-02 eta: 13:16:49 time: 0.5389 data_time: 0.1142 memory: 16095 grad_norm: 5.0838 loss: 1.5471 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.5471 2022/12/08 16:30:42 - mmengine - INFO - Epoch(train) [20][700/940] lr: 1.0000e-02 eta: 13:16:42 time: 0.7018 data_time: 0.1969 memory: 16095 grad_norm: 5.1926 loss: 1.6294 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.6294 2022/12/08 16:30:53 - mmengine - INFO - Epoch(train) [20][720/940] lr: 1.0000e-02 eta: 13:16:24 time: 0.5597 data_time: 0.2273 memory: 16095 grad_norm: 5.1691 loss: 1.5088 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.5088 2022/12/08 16:31:06 - mmengine - INFO - Epoch(train) [20][740/940] lr: 1.0000e-02 eta: 13:16:12 time: 0.6444 data_time: 0.3146 memory: 16095 grad_norm: 5.2027 loss: 1.5795 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.5795 2022/12/08 16:31:18 - mmengine - INFO - Epoch(train) [20][760/940] lr: 1.0000e-02 eta: 13:15:56 time: 0.5945 data_time: 0.2563 memory: 16095 grad_norm: 5.1251 loss: 1.5529 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.5529 2022/12/08 16:31:31 - mmengine - INFO - Epoch(train) [20][780/940] lr: 1.0000e-02 eta: 13:15:43 time: 0.6291 data_time: 0.3065 memory: 16095 grad_norm: 5.1107 loss: 1.5050 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.5050 2022/12/08 16:31:42 - mmengine - INFO - Epoch(train) [20][800/940] lr: 1.0000e-02 eta: 13:15:23 time: 0.5398 data_time: 0.2047 memory: 16095 grad_norm: 5.0500 loss: 1.6014 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 1.6014 2022/12/08 16:31:54 - mmengine - INFO - Epoch(train) [20][820/940] lr: 1.0000e-02 eta: 13:15:11 time: 0.6405 data_time: 0.3200 memory: 16095 grad_norm: 5.0672 loss: 1.5425 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.5425 2022/12/08 16:32:06 - mmengine - INFO - Epoch(train) [20][840/940] lr: 1.0000e-02 eta: 13:14:54 time: 0.5799 data_time: 0.2648 memory: 16095 grad_norm: 5.0458 loss: 1.7455 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.7455 2022/12/08 16:32:19 - mmengine - INFO - Epoch(train) [20][860/940] lr: 1.0000e-02 eta: 13:14:43 time: 0.6576 data_time: 0.3363 memory: 16095 grad_norm: 5.1037 loss: 1.4469 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4469 2022/12/08 16:32:30 - mmengine - INFO - Epoch(train) [20][880/940] lr: 1.0000e-02 eta: 13:14:23 time: 0.5444 data_time: 0.2171 memory: 16095 grad_norm: 5.1566 loss: 1.5451 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.5451 2022/12/08 16:32:43 - mmengine - INFO - Epoch(train) [20][900/940] lr: 1.0000e-02 eta: 13:14:13 time: 0.6613 data_time: 0.2980 memory: 16095 grad_norm: 5.2374 loss: 1.5235 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.5235 2022/12/08 16:32:55 - mmengine - INFO - Epoch(train) [20][920/940] lr: 1.0000e-02 eta: 13:13:57 time: 0.5998 data_time: 0.0783 memory: 16095 grad_norm: 5.1173 loss: 1.6172 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.6172 2022/12/08 16:33:06 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 16:33:06 - mmengine - INFO - Epoch(train) [20][940/940] lr: 1.0000e-02 eta: 13:13:39 time: 0.5618 data_time: 0.2096 memory: 16095 grad_norm: 5.3850 loss: 1.5875 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.5875 2022/12/08 16:33:20 - mmengine - INFO - Epoch(val) [20][20/78] eta: 0:00:40 time: 0.7004 data_time: 0.6062 memory: 1686 2022/12/08 16:33:30 - mmengine - INFO - Epoch(val) [20][40/78] eta: 0:00:22 time: 0.4625 data_time: 0.3685 memory: 1686 2022/12/08 16:33:44 - mmengine - INFO - Epoch(val) [20][60/78] eta: 0:00:11 time: 0.6918 data_time: 0.5972 memory: 1686 2022/12/08 16:33:54 - mmengine - INFO - Epoch(val) [20][78/78] acc/top1: 0.6295 acc/top5: 0.8471 acc/mean1: 0.6293 2022/12/08 16:34:10 - mmengine - INFO - Epoch(train) [21][ 20/940] lr: 1.0000e-02 eta: 13:13:40 time: 0.8005 data_time: 0.4055 memory: 16095 grad_norm: 4.9897 loss: 1.5856 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.5856 2022/12/08 16:34:21 - mmengine - INFO - Epoch(train) [21][ 40/940] lr: 1.0000e-02 eta: 13:13:21 time: 0.5577 data_time: 0.2130 memory: 16095 grad_norm: 5.0487 loss: 1.4742 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.4742 2022/12/08 16:34:34 - mmengine - INFO - Epoch(train) [21][ 60/940] lr: 1.0000e-02 eta: 13:13:11 time: 0.6638 data_time: 0.2664 memory: 16095 grad_norm: 5.1125 loss: 1.5380 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5380 2022/12/08 16:34:45 - mmengine - INFO - Epoch(train) [21][ 80/940] lr: 1.0000e-02 eta: 13:12:51 time: 0.5446 data_time: 0.1669 memory: 16095 grad_norm: 4.9167 loss: 1.4437 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.4437 2022/12/08 16:34:59 - mmengine - INFO - Epoch(train) [21][100/940] lr: 1.0000e-02 eta: 13:12:42 time: 0.6765 data_time: 0.1641 memory: 16095 grad_norm: 5.0700 loss: 1.4989 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4989 2022/12/08 16:35:10 - mmengine - INFO - Epoch(train) [21][120/940] lr: 1.0000e-02 eta: 13:12:22 time: 0.5437 data_time: 0.0482 memory: 16095 grad_norm: 5.0876 loss: 1.5262 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.5262 2022/12/08 16:35:24 - mmengine - INFO - Epoch(train) [21][140/940] lr: 1.0000e-02 eta: 13:12:15 time: 0.7025 data_time: 0.0329 memory: 16095 grad_norm: 5.2265 loss: 1.5196 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.5196 2022/12/08 16:35:35 - mmengine - INFO - Epoch(train) [21][160/940] lr: 1.0000e-02 eta: 13:11:58 time: 0.5803 data_time: 0.0220 memory: 16095 grad_norm: 5.0187 loss: 1.4410 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.4410 2022/12/08 16:35:48 - mmengine - INFO - Epoch(train) [21][180/940] lr: 1.0000e-02 eta: 13:11:46 time: 0.6432 data_time: 0.0274 memory: 16095 grad_norm: 5.0855 loss: 1.5697 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.5697 2022/12/08 16:35:59 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 16:35:59 - mmengine - INFO - Epoch(train) [21][200/940] lr: 1.0000e-02 eta: 13:11:28 time: 0.5621 data_time: 0.0229 memory: 16095 grad_norm: 5.1712 loss: 1.4919 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.4919 2022/12/08 16:36:12 - mmengine - INFO - Epoch(train) [21][220/940] lr: 1.0000e-02 eta: 13:11:16 time: 0.6512 data_time: 0.0240 memory: 16095 grad_norm: 5.1494 loss: 1.5320 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.5320 2022/12/08 16:36:23 - mmengine - INFO - Epoch(train) [21][240/940] lr: 1.0000e-02 eta: 13:10:57 time: 0.5531 data_time: 0.0341 memory: 16095 grad_norm: 5.1171 loss: 1.5974 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.5974 2022/12/08 16:36:37 - mmengine - INFO - Epoch(train) [21][260/940] lr: 1.0000e-02 eta: 13:10:50 time: 0.7008 data_time: 0.0260 memory: 16095 grad_norm: 5.0774 loss: 1.4995 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.4995 2022/12/08 16:36:48 - mmengine - INFO - Epoch(train) [21][280/940] lr: 1.0000e-02 eta: 13:10:28 time: 0.5106 data_time: 0.0229 memory: 16095 grad_norm: 5.0808 loss: 1.4825 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4825 2022/12/08 16:37:00 - mmengine - INFO - Epoch(train) [21][300/940] lr: 1.0000e-02 eta: 13:10:16 time: 0.6405 data_time: 0.0275 memory: 16095 grad_norm: 5.1468 loss: 1.5604 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 1.5604 2022/12/08 16:37:11 - mmengine - INFO - Epoch(train) [21][320/940] lr: 1.0000e-02 eta: 13:09:57 time: 0.5541 data_time: 0.0222 memory: 16095 grad_norm: 5.2404 loss: 1.7143 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 1.7143 2022/12/08 16:37:25 - mmengine - INFO - Epoch(train) [21][340/940] lr: 1.0000e-02 eta: 13:09:47 time: 0.6725 data_time: 0.0263 memory: 16095 grad_norm: 5.1521 loss: 1.7262 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.7262 2022/12/08 16:37:36 - mmengine - INFO - Epoch(train) [21][360/940] lr: 1.0000e-02 eta: 13:09:27 time: 0.5317 data_time: 0.0235 memory: 16095 grad_norm: 5.1213 loss: 1.6566 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6566 2022/12/08 16:37:49 - mmengine - INFO - Epoch(train) [21][380/940] lr: 1.0000e-02 eta: 13:09:17 time: 0.6728 data_time: 0.0263 memory: 16095 grad_norm: 5.1521 loss: 1.6619 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6619 2022/12/08 16:38:00 - mmengine - INFO - Epoch(train) [21][400/940] lr: 1.0000e-02 eta: 13:08:59 time: 0.5602 data_time: 0.0236 memory: 16095 grad_norm: 5.1337 loss: 1.6381 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.6381 2022/12/08 16:38:14 - mmengine - INFO - Epoch(train) [21][420/940] lr: 1.0000e-02 eta: 13:08:49 time: 0.6645 data_time: 0.0258 memory: 16095 grad_norm: 5.2906 loss: 1.6912 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6912 2022/12/08 16:38:25 - mmengine - INFO - Epoch(train) [21][440/940] lr: 1.0000e-02 eta: 13:08:32 time: 0.5827 data_time: 0.0252 memory: 16095 grad_norm: 5.0605 loss: 1.5399 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.5399 2022/12/08 16:38:39 - mmengine - INFO - Epoch(train) [21][460/940] lr: 1.0000e-02 eta: 13:08:23 time: 0.6726 data_time: 0.0253 memory: 16095 grad_norm: 5.1948 loss: 1.5013 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.5013 2022/12/08 16:38:50 - mmengine - INFO - Epoch(train) [21][480/940] lr: 1.0000e-02 eta: 13:08:06 time: 0.5840 data_time: 0.0224 memory: 16095 grad_norm: 5.1782 loss: 1.5399 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5399 2022/12/08 16:39:03 - mmengine - INFO - Epoch(train) [21][500/940] lr: 1.0000e-02 eta: 13:07:54 time: 0.6466 data_time: 0.0229 memory: 16095 grad_norm: 5.1949 loss: 1.5416 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.5416 2022/12/08 16:39:15 - mmengine - INFO - Epoch(train) [21][520/940] lr: 1.0000e-02 eta: 13:07:37 time: 0.5653 data_time: 0.0263 memory: 16095 grad_norm: 5.1863 loss: 1.5659 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.5659 2022/12/08 16:39:28 - mmengine - INFO - Epoch(train) [21][540/940] lr: 1.0000e-02 eta: 13:07:27 time: 0.6707 data_time: 0.0238 memory: 16095 grad_norm: 5.1694 loss: 1.5474 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5474 2022/12/08 16:39:39 - mmengine - INFO - Epoch(train) [21][560/940] lr: 1.0000e-02 eta: 13:07:07 time: 0.5431 data_time: 0.0238 memory: 16095 grad_norm: 5.0973 loss: 1.6593 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6593 2022/12/08 16:39:51 - mmengine - INFO - Epoch(train) [21][580/940] lr: 1.0000e-02 eta: 13:06:55 time: 0.6314 data_time: 0.0280 memory: 16095 grad_norm: 5.2687 loss: 1.5824 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.5824 2022/12/08 16:40:04 - mmengine - INFO - Epoch(train) [21][600/940] lr: 1.0000e-02 eta: 13:06:42 time: 0.6294 data_time: 0.0214 memory: 16095 grad_norm: 5.2037 loss: 1.5556 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.5556 2022/12/08 16:40:17 - mmengine - INFO - Epoch(train) [21][620/940] lr: 1.0000e-02 eta: 13:06:29 time: 0.6342 data_time: 0.0255 memory: 16095 grad_norm: 5.1242 loss: 1.4871 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.4871 2022/12/08 16:40:27 - mmengine - INFO - Epoch(train) [21][640/940] lr: 1.0000e-02 eta: 13:06:07 time: 0.5134 data_time: 0.0250 memory: 16095 grad_norm: 5.1103 loss: 1.4746 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.4746 2022/12/08 16:40:41 - mmengine - INFO - Epoch(train) [21][660/940] lr: 1.0000e-02 eta: 13:06:00 time: 0.6982 data_time: 0.0254 memory: 16095 grad_norm: 5.1242 loss: 1.4893 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 1.4893 2022/12/08 16:40:52 - mmengine - INFO - Epoch(train) [21][680/940] lr: 1.0000e-02 eta: 13:05:40 time: 0.5382 data_time: 0.0263 memory: 16095 grad_norm: 5.1165 loss: 1.4532 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4532 2022/12/08 16:41:05 - mmengine - INFO - Epoch(train) [21][700/940] lr: 1.0000e-02 eta: 13:05:30 time: 0.6723 data_time: 0.0349 memory: 16095 grad_norm: 5.1640 loss: 1.7004 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.7004 2022/12/08 16:41:16 - mmengine - INFO - Epoch(train) [21][720/940] lr: 1.0000e-02 eta: 13:05:10 time: 0.5325 data_time: 0.0253 memory: 16095 grad_norm: 5.0601 loss: 1.4198 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.4198 2022/12/08 16:41:29 - mmengine - INFO - Epoch(train) [21][740/940] lr: 1.0000e-02 eta: 13:05:00 time: 0.6644 data_time: 0.0277 memory: 16095 grad_norm: 5.1933 loss: 1.6748 top1_acc: 0.4688 top5_acc: 0.6562 loss_cls: 1.6748 2022/12/08 16:41:41 - mmengine - INFO - Epoch(train) [21][760/940] lr: 1.0000e-02 eta: 13:04:43 time: 0.5826 data_time: 0.0219 memory: 16095 grad_norm: 5.2384 loss: 1.6703 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.6703 2022/12/08 16:41:54 - mmengine - INFO - Epoch(train) [21][780/940] lr: 1.0000e-02 eta: 13:04:34 time: 0.6777 data_time: 0.0302 memory: 16095 grad_norm: 5.1277 loss: 1.5148 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 1.5148 2022/12/08 16:42:05 - mmengine - INFO - Epoch(train) [21][800/940] lr: 1.0000e-02 eta: 13:04:12 time: 0.5138 data_time: 0.0213 memory: 16095 grad_norm: 5.2424 loss: 1.5546 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5546 2022/12/08 16:42:18 - mmengine - INFO - Epoch(train) [21][820/940] lr: 1.0000e-02 eta: 13:04:02 time: 0.6619 data_time: 0.0281 memory: 16095 grad_norm: 5.1288 loss: 1.6639 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.6639 2022/12/08 16:42:29 - mmengine - INFO - Epoch(train) [21][840/940] lr: 1.0000e-02 eta: 13:03:43 time: 0.5445 data_time: 0.0213 memory: 16095 grad_norm: 5.2396 loss: 1.6360 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.6360 2022/12/08 16:42:43 - mmengine - INFO - Epoch(train) [21][860/940] lr: 1.0000e-02 eta: 13:03:34 time: 0.6902 data_time: 0.1562 memory: 16095 grad_norm: 5.1385 loss: 1.6023 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 1.6023 2022/12/08 16:42:53 - mmengine - INFO - Epoch(train) [21][880/940] lr: 1.0000e-02 eta: 13:03:14 time: 0.5370 data_time: 0.0719 memory: 16095 grad_norm: 5.1187 loss: 1.6582 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6582 2022/12/08 16:43:07 - mmengine - INFO - Epoch(train) [21][900/940] lr: 1.0000e-02 eta: 13:03:06 time: 0.6838 data_time: 0.1706 memory: 16095 grad_norm: 5.1053 loss: 1.5056 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.5056 2022/12/08 16:43:19 - mmengine - INFO - Epoch(train) [21][920/940] lr: 1.0000e-02 eta: 13:02:50 time: 0.5929 data_time: 0.1908 memory: 16095 grad_norm: 5.1992 loss: 1.5823 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.5823 2022/12/08 16:43:30 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 16:43:30 - mmengine - INFO - Epoch(train) [21][940/940] lr: 1.0000e-02 eta: 13:02:32 time: 0.5539 data_time: 0.2536 memory: 16095 grad_norm: 5.3817 loss: 1.6912 top1_acc: 0.5714 top5_acc: 1.0000 loss_cls: 1.6912 2022/12/08 16:43:30 - mmengine - INFO - Saving checkpoint at 21 epochs 2022/12/08 16:43:47 - mmengine - INFO - Epoch(val) [21][20/78] eta: 0:00:41 time: 0.7117 data_time: 0.6177 memory: 1686 2022/12/08 16:43:56 - mmengine - INFO - Epoch(val) [21][40/78] eta: 0:00:22 time: 0.4585 data_time: 0.3644 memory: 1686 2022/12/08 16:44:10 - mmengine - INFO - Epoch(val) [21][60/78] eta: 0:00:11 time: 0.6835 data_time: 0.5881 memory: 1686 2022/12/08 16:44:20 - mmengine - INFO - Epoch(val) [21][78/78] acc/top1: 0.6359 acc/top5: 0.8506 acc/mean1: 0.6358 2022/12/08 16:44:36 - mmengine - INFO - Epoch(train) [22][ 20/940] lr: 1.0000e-02 eta: 13:02:34 time: 0.8394 data_time: 0.4278 memory: 16095 grad_norm: 5.0776 loss: 1.5219 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5219 2022/12/08 16:44:48 - mmengine - INFO - Epoch(train) [22][ 40/940] lr: 1.0000e-02 eta: 13:02:16 time: 0.5585 data_time: 0.1082 memory: 16095 grad_norm: 5.0886 loss: 1.5076 top1_acc: 0.7188 top5_acc: 0.7812 loss_cls: 1.5076 2022/12/08 16:45:02 - mmengine - INFO - Epoch(train) [22][ 60/940] lr: 1.0000e-02 eta: 13:02:10 time: 0.7207 data_time: 0.0403 memory: 16095 grad_norm: 5.1189 loss: 1.5744 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.5744 2022/12/08 16:45:13 - mmengine - INFO - Epoch(train) [22][ 80/940] lr: 1.0000e-02 eta: 13:01:50 time: 0.5291 data_time: 0.0219 memory: 16095 grad_norm: 5.0617 loss: 1.5043 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 1.5043 2022/12/08 16:45:26 - mmengine - INFO - Epoch(train) [22][100/940] lr: 1.0000e-02 eta: 13:01:40 time: 0.6668 data_time: 0.0378 memory: 16095 grad_norm: 5.0887 loss: 1.4329 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 1.4329 2022/12/08 16:45:37 - mmengine - INFO - Epoch(train) [22][120/940] lr: 1.0000e-02 eta: 13:01:22 time: 0.5700 data_time: 0.0298 memory: 16095 grad_norm: 5.0547 loss: 1.4393 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.4393 2022/12/08 16:45:51 - mmengine - INFO - Epoch(train) [22][140/940] lr: 1.0000e-02 eta: 13:01:12 time: 0.6662 data_time: 0.0368 memory: 16095 grad_norm: 5.1962 loss: 1.5522 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5522 2022/12/08 16:46:02 - mmengine - INFO - Epoch(train) [22][160/940] lr: 1.0000e-02 eta: 13:00:54 time: 0.5534 data_time: 0.0220 memory: 16095 grad_norm: 5.1609 loss: 1.4325 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4325 2022/12/08 16:46:15 - mmengine - INFO - Epoch(train) [22][180/940] lr: 1.0000e-02 eta: 13:00:45 time: 0.6851 data_time: 0.0290 memory: 16095 grad_norm: 5.1348 loss: 1.5558 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.5558 2022/12/08 16:46:27 - mmengine - INFO - Epoch(train) [22][200/940] lr: 1.0000e-02 eta: 13:00:28 time: 0.5768 data_time: 0.0252 memory: 16095 grad_norm: 5.1298 loss: 1.4790 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.4790 2022/12/08 16:46:40 - mmengine - INFO - Epoch(train) [22][220/940] lr: 1.0000e-02 eta: 13:00:17 time: 0.6449 data_time: 0.0263 memory: 16095 grad_norm: 5.1949 loss: 1.5979 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 1.5979 2022/12/08 16:46:51 - mmengine - INFO - Epoch(train) [22][240/940] lr: 1.0000e-02 eta: 12:59:58 time: 0.5495 data_time: 0.0230 memory: 16095 grad_norm: 5.1998 loss: 1.5416 top1_acc: 0.5938 top5_acc: 0.6562 loss_cls: 1.5416 2022/12/08 16:47:03 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 16:47:03 - mmengine - INFO - Epoch(train) [22][260/940] lr: 1.0000e-02 eta: 12:59:45 time: 0.6291 data_time: 0.0245 memory: 16095 grad_norm: 5.1271 loss: 1.5430 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.5430 2022/12/08 16:47:14 - mmengine - INFO - Epoch(train) [22][280/940] lr: 1.0000e-02 eta: 12:59:26 time: 0.5448 data_time: 0.0242 memory: 16095 grad_norm: 5.1062 loss: 1.5054 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.5054 2022/12/08 16:47:28 - mmengine - INFO - Epoch(train) [22][300/940] lr: 1.0000e-02 eta: 12:59:19 time: 0.7092 data_time: 0.0275 memory: 16095 grad_norm: 5.1228 loss: 1.5438 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.5438 2022/12/08 16:47:39 - mmengine - INFO - Epoch(train) [22][320/940] lr: 1.0000e-02 eta: 12:58:59 time: 0.5341 data_time: 0.0228 memory: 16095 grad_norm: 5.1941 loss: 1.4445 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.4445 2022/12/08 16:47:52 - mmengine - INFO - Epoch(train) [22][340/940] lr: 1.0000e-02 eta: 12:58:48 time: 0.6539 data_time: 0.0293 memory: 16095 grad_norm: 5.1551 loss: 1.6183 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6183 2022/12/08 16:48:03 - mmengine - INFO - Epoch(train) [22][360/940] lr: 1.0000e-02 eta: 12:58:29 time: 0.5508 data_time: 0.0199 memory: 16095 grad_norm: 5.1368 loss: 1.4084 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.4084 2022/12/08 16:48:16 - mmengine - INFO - Epoch(train) [22][380/940] lr: 1.0000e-02 eta: 12:58:19 time: 0.6615 data_time: 0.0370 memory: 16095 grad_norm: 5.1243 loss: 1.5507 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5507 2022/12/08 16:48:28 - mmengine - INFO - Epoch(train) [22][400/940] lr: 1.0000e-02 eta: 12:58:04 time: 0.5999 data_time: 0.0209 memory: 16095 grad_norm: 5.1976 loss: 1.4861 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4861 2022/12/08 16:48:41 - mmengine - INFO - Epoch(train) [22][420/940] lr: 1.0000e-02 eta: 12:57:51 time: 0.6356 data_time: 0.0284 memory: 16095 grad_norm: 5.2390 loss: 1.5803 top1_acc: 0.5625 top5_acc: 0.7188 loss_cls: 1.5803 2022/12/08 16:48:52 - mmengine - INFO - Epoch(train) [22][440/940] lr: 1.0000e-02 eta: 12:57:33 time: 0.5553 data_time: 0.0214 memory: 16095 grad_norm: 5.0451 loss: 1.6270 top1_acc: 0.5625 top5_acc: 0.7188 loss_cls: 1.6270 2022/12/08 16:49:06 - mmengine - INFO - Epoch(train) [22][460/940] lr: 1.0000e-02 eta: 12:57:24 time: 0.6863 data_time: 0.0304 memory: 16095 grad_norm: 5.1634 loss: 1.5613 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.5613 2022/12/08 16:49:16 - mmengine - INFO - Epoch(train) [22][480/940] lr: 1.0000e-02 eta: 12:57:02 time: 0.5038 data_time: 0.0208 memory: 16095 grad_norm: 5.2229 loss: 1.6219 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6219 2022/12/08 16:49:30 - mmengine - INFO - Epoch(train) [22][500/940] lr: 1.0000e-02 eta: 12:56:54 time: 0.6854 data_time: 0.0277 memory: 16095 grad_norm: 5.2135 loss: 1.4040 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.4040 2022/12/08 16:49:41 - mmengine - INFO - Epoch(train) [22][520/940] lr: 1.0000e-02 eta: 12:56:35 time: 0.5500 data_time: 0.0479 memory: 16095 grad_norm: 5.0719 loss: 1.4759 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4759 2022/12/08 16:49:53 - mmengine - INFO - Epoch(train) [22][540/940] lr: 1.0000e-02 eta: 12:56:22 time: 0.6282 data_time: 0.0789 memory: 16095 grad_norm: 5.2076 loss: 1.6231 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.6231 2022/12/08 16:50:06 - mmengine - INFO - Epoch(train) [22][560/940] lr: 1.0000e-02 eta: 12:56:10 time: 0.6411 data_time: 0.0758 memory: 16095 grad_norm: 5.2361 loss: 1.5683 top1_acc: 0.5312 top5_acc: 0.6875 loss_cls: 1.5683 2022/12/08 16:50:19 - mmengine - INFO - Epoch(train) [22][580/940] lr: 1.0000e-02 eta: 12:55:59 time: 0.6573 data_time: 0.2058 memory: 16095 grad_norm: 5.1755 loss: 1.5929 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.5929 2022/12/08 16:50:31 - mmengine - INFO - Epoch(train) [22][600/940] lr: 1.0000e-02 eta: 12:55:45 time: 0.6079 data_time: 0.2183 memory: 16095 grad_norm: 5.1966 loss: 1.5762 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.5762 2022/12/08 16:50:45 - mmengine - INFO - Epoch(train) [22][620/940] lr: 1.0000e-02 eta: 12:55:35 time: 0.6640 data_time: 0.3087 memory: 16095 grad_norm: 5.1288 loss: 1.5970 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5970 2022/12/08 16:50:56 - mmengine - INFO - Epoch(train) [22][640/940] lr: 1.0000e-02 eta: 12:55:18 time: 0.5757 data_time: 0.2298 memory: 16095 grad_norm: 5.2365 loss: 1.6281 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6281 2022/12/08 16:51:09 - mmengine - INFO - Epoch(train) [22][660/940] lr: 1.0000e-02 eta: 12:55:04 time: 0.6126 data_time: 0.2614 memory: 16095 grad_norm: 5.1963 loss: 1.5721 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5721 2022/12/08 16:51:20 - mmengine - INFO - Epoch(train) [22][680/940] lr: 1.0000e-02 eta: 12:54:46 time: 0.5588 data_time: 0.2205 memory: 16095 grad_norm: 5.0383 loss: 1.4824 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.4824 2022/12/08 16:51:32 - mmengine - INFO - Epoch(train) [22][700/940] lr: 1.0000e-02 eta: 12:54:33 time: 0.6271 data_time: 0.2469 memory: 16095 grad_norm: 5.0732 loss: 1.5761 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5761 2022/12/08 16:51:44 - mmengine - INFO - Epoch(train) [22][720/940] lr: 1.0000e-02 eta: 12:54:18 time: 0.5977 data_time: 0.1880 memory: 16095 grad_norm: 5.1605 loss: 1.6613 top1_acc: 0.4688 top5_acc: 0.6562 loss_cls: 1.6613 2022/12/08 16:51:57 - mmengine - INFO - Epoch(train) [22][740/940] lr: 1.0000e-02 eta: 12:54:07 time: 0.6478 data_time: 0.2627 memory: 16095 grad_norm: 5.2395 loss: 1.6156 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.6156 2022/12/08 16:53:39 - mmengine - INFO - Epoch(train) [22][760/940] lr: 1.0000e-02 eta: 12:59:15 time: 5.1098 data_time: 0.1222 memory: 16095 grad_norm: 5.3034 loss: 1.4726 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.4726 2022/12/08 16:53:52 - mmengine - INFO - Epoch(train) [22][780/940] lr: 1.0000e-02 eta: 12:59:01 time: 0.6235 data_time: 0.1610 memory: 16095 grad_norm: 5.0940 loss: 1.4917 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4917 2022/12/08 16:54:05 - mmengine - INFO - Epoch(train) [22][800/940] lr: 1.0000e-02 eta: 12:58:49 time: 0.6429 data_time: 0.0792 memory: 16095 grad_norm: 5.1990 loss: 1.5691 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.5691 2022/12/08 16:54:17 - mmengine - INFO - Epoch(train) [22][820/940] lr: 1.0000e-02 eta: 12:58:34 time: 0.5988 data_time: 0.0844 memory: 16095 grad_norm: 5.2629 loss: 1.5578 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5578 2022/12/08 16:54:29 - mmengine - INFO - Epoch(train) [22][840/940] lr: 1.0000e-02 eta: 12:58:18 time: 0.5917 data_time: 0.0626 memory: 16095 grad_norm: 5.0845 loss: 1.5516 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5516 2022/12/08 16:54:41 - mmengine - INFO - Epoch(train) [22][860/940] lr: 1.0000e-02 eta: 12:58:06 time: 0.6400 data_time: 0.3095 memory: 16095 grad_norm: 5.1273 loss: 1.4718 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.4718 2022/12/08 16:54:53 - mmengine - INFO - Epoch(train) [22][880/940] lr: 1.0000e-02 eta: 12:57:49 time: 0.5851 data_time: 0.1947 memory: 16095 grad_norm: 5.0692 loss: 1.7212 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7212 2022/12/08 16:55:05 - mmengine - INFO - Epoch(train) [22][900/940] lr: 1.0000e-02 eta: 12:57:35 time: 0.6106 data_time: 0.1506 memory: 16095 grad_norm: 5.1129 loss: 1.5215 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5215 2022/12/08 16:55:19 - mmengine - INFO - Epoch(train) [22][920/940] lr: 1.0000e-02 eta: 12:57:24 time: 0.6701 data_time: 0.0659 memory: 16095 grad_norm: 5.1583 loss: 1.3964 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.3964 2022/12/08 16:55:28 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 16:55:28 - mmengine - INFO - Epoch(train) [22][940/940] lr: 1.0000e-02 eta: 12:57:01 time: 0.4812 data_time: 0.0772 memory: 16095 grad_norm: 5.4967 loss: 1.5647 top1_acc: 0.4286 top5_acc: 0.7143 loss_cls: 1.5647 2022/12/08 16:55:43 - mmengine - INFO - Epoch(val) [22][20/78] eta: 0:00:41 time: 0.7224 data_time: 0.6279 memory: 1686 2022/12/08 16:55:52 - mmengine - INFO - Epoch(val) [22][40/78] eta: 0:00:22 time: 0.4574 data_time: 0.3641 memory: 1686 2022/12/08 16:56:06 - mmengine - INFO - Epoch(val) [22][60/78] eta: 0:00:11 time: 0.6864 data_time: 0.5916 memory: 1686 2022/12/08 16:56:16 - mmengine - INFO - Epoch(val) [22][78/78] acc/top1: 0.6398 acc/top5: 0.8521 acc/mean1: 0.6396 2022/12/08 16:56:16 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_19.pth is removed 2022/12/08 16:56:18 - mmengine - INFO - The best checkpoint with 0.6398 acc/top1 at 22 epoch is saved to best_acc/top1_epoch_22.pth. 2022/12/08 16:56:35 - mmengine - INFO - Epoch(train) [23][ 20/940] lr: 1.0000e-02 eta: 12:57:02 time: 0.8386 data_time: 0.5295 memory: 16095 grad_norm: 5.1386 loss: 1.5067 top1_acc: 0.7188 top5_acc: 0.7812 loss_cls: 1.5067 2022/12/08 16:56:46 - mmengine - INFO - Epoch(train) [23][ 40/940] lr: 1.0000e-02 eta: 12:56:44 time: 0.5515 data_time: 0.2374 memory: 16095 grad_norm: 4.9981 loss: 1.3765 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.3765 2022/12/08 16:57:00 - mmengine - INFO - Epoch(train) [23][ 60/940] lr: 1.0000e-02 eta: 12:56:34 time: 0.6788 data_time: 0.3526 memory: 16095 grad_norm: 5.0082 loss: 1.4712 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.4712 2022/12/08 16:57:10 - mmengine - INFO - Epoch(train) [23][ 80/940] lr: 1.0000e-02 eta: 12:56:11 time: 0.4928 data_time: 0.1874 memory: 16095 grad_norm: 5.1151 loss: 1.5263 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5263 2022/12/08 16:57:23 - mmengine - INFO - Epoch(train) [23][100/940] lr: 1.0000e-02 eta: 12:56:00 time: 0.6562 data_time: 0.3391 memory: 16095 grad_norm: 5.0388 loss: 1.4225 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.4225 2022/12/08 16:57:34 - mmengine - INFO - Epoch(train) [23][120/940] lr: 1.0000e-02 eta: 12:55:43 time: 0.5719 data_time: 0.2744 memory: 16095 grad_norm: 5.1538 loss: 1.5415 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.5415 2022/12/08 16:57:48 - mmengine - INFO - Epoch(train) [23][140/940] lr: 1.0000e-02 eta: 12:55:32 time: 0.6685 data_time: 0.3673 memory: 16095 grad_norm: 5.0885 loss: 1.5247 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.5247 2022/12/08 16:57:59 - mmengine - INFO - Epoch(train) [23][160/940] lr: 1.0000e-02 eta: 12:55:15 time: 0.5784 data_time: 0.2773 memory: 16095 grad_norm: 5.2268 loss: 1.4900 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.4900 2022/12/08 16:58:12 - mmengine - INFO - Epoch(train) [23][180/940] lr: 1.0000e-02 eta: 12:55:04 time: 0.6544 data_time: 0.3349 memory: 16095 grad_norm: 5.2761 loss: 1.4654 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.4654 2022/12/08 16:58:24 - mmengine - INFO - Epoch(train) [23][200/940] lr: 1.0000e-02 eta: 12:54:47 time: 0.5793 data_time: 0.2703 memory: 16095 grad_norm: 5.3006 loss: 1.6184 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.6184 2022/12/08 16:58:37 - mmengine - INFO - Epoch(train) [23][220/940] lr: 1.0000e-02 eta: 12:54:35 time: 0.6404 data_time: 0.3162 memory: 16095 grad_norm: 5.0930 loss: 1.5779 top1_acc: 0.5625 top5_acc: 0.7188 loss_cls: 1.5779 2022/12/08 16:58:49 - mmengine - INFO - Epoch(train) [23][240/940] lr: 1.0000e-02 eta: 12:54:22 time: 0.6263 data_time: 0.3160 memory: 16095 grad_norm: 5.2485 loss: 1.4790 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4790 2022/12/08 16:59:01 - mmengine - INFO - Epoch(train) [23][260/940] lr: 1.0000e-02 eta: 12:54:06 time: 0.5956 data_time: 0.2569 memory: 16095 grad_norm: 5.1954 loss: 1.4262 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4262 2022/12/08 16:59:14 - mmengine - INFO - Epoch(train) [23][280/940] lr: 1.0000e-02 eta: 12:53:54 time: 0.6412 data_time: 0.1963 memory: 16095 grad_norm: 5.1319 loss: 1.4121 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.4121 2022/12/08 16:59:25 - mmengine - INFO - Epoch(train) [23][300/940] lr: 1.0000e-02 eta: 12:53:36 time: 0.5671 data_time: 0.2045 memory: 16095 grad_norm: 5.1942 loss: 1.5122 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5122 2022/12/08 16:59:39 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 16:59:39 - mmengine - INFO - Epoch(train) [23][320/940] lr: 1.0000e-02 eta: 12:53:26 time: 0.6697 data_time: 0.3113 memory: 16095 grad_norm: 5.1576 loss: 1.6707 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.6707 2022/12/08 16:59:50 - mmengine - INFO - Epoch(train) [23][340/940] lr: 1.0000e-02 eta: 12:53:08 time: 0.5640 data_time: 0.1787 memory: 16095 grad_norm: 5.1600 loss: 1.5575 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.5575 2022/12/08 17:00:02 - mmengine - INFO - Epoch(train) [23][360/940] lr: 1.0000e-02 eta: 12:52:54 time: 0.6214 data_time: 0.2384 memory: 16095 grad_norm: 5.0763 loss: 1.5107 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.5107 2022/12/08 17:00:14 - mmengine - INFO - Epoch(train) [23][380/940] lr: 1.0000e-02 eta: 12:52:37 time: 0.5676 data_time: 0.2428 memory: 16095 grad_norm: 5.1440 loss: 1.4225 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4225 2022/12/08 17:00:26 - mmengine - INFO - Epoch(train) [23][400/940] lr: 1.0000e-02 eta: 12:52:24 time: 0.6341 data_time: 0.2875 memory: 16095 grad_norm: 5.1296 loss: 1.4770 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.4770 2022/12/08 17:00:39 - mmengine - INFO - Epoch(train) [23][420/940] lr: 1.0000e-02 eta: 12:52:11 time: 0.6317 data_time: 0.1719 memory: 16095 grad_norm: 5.1342 loss: 1.4631 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.4631 2022/12/08 17:00:51 - mmengine - INFO - Epoch(train) [23][440/940] lr: 1.0000e-02 eta: 12:51:56 time: 0.6018 data_time: 0.2002 memory: 16095 grad_norm: 5.2956 loss: 1.5953 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.5953 2022/12/08 17:01:03 - mmengine - INFO - Epoch(train) [23][460/940] lr: 1.0000e-02 eta: 12:51:41 time: 0.5995 data_time: 0.1473 memory: 16095 grad_norm: 5.2302 loss: 1.4658 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.4658 2022/12/08 17:01:15 - mmengine - INFO - Epoch(train) [23][480/940] lr: 1.0000e-02 eta: 12:51:26 time: 0.6026 data_time: 0.1652 memory: 16095 grad_norm: 5.2423 loss: 1.6607 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.6607 2022/12/08 17:01:28 - mmengine - INFO - Epoch(train) [23][500/940] lr: 1.0000e-02 eta: 12:51:12 time: 0.6191 data_time: 0.0635 memory: 16095 grad_norm: 5.1034 loss: 1.3560 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3560 2022/12/08 17:01:41 - mmengine - INFO - Epoch(train) [23][520/940] lr: 1.0000e-02 eta: 12:51:01 time: 0.6604 data_time: 0.1976 memory: 16095 grad_norm: 5.2018 loss: 1.6295 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6295 2022/12/08 17:01:52 - mmengine - INFO - Epoch(train) [23][540/940] lr: 1.0000e-02 eta: 12:50:43 time: 0.5518 data_time: 0.0679 memory: 16095 grad_norm: 5.2535 loss: 1.6293 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.6293 2022/12/08 17:02:05 - mmengine - INFO - Epoch(train) [23][560/940] lr: 1.0000e-02 eta: 12:50:31 time: 0.6493 data_time: 0.1605 memory: 16095 grad_norm: 5.2758 loss: 1.5326 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.5326 2022/12/08 17:02:17 - mmengine - INFO - Epoch(train) [23][580/940] lr: 1.0000e-02 eta: 12:50:17 time: 0.6108 data_time: 0.0905 memory: 16095 grad_norm: 5.1284 loss: 1.5440 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.5440 2022/12/08 17:02:30 - mmengine - INFO - Epoch(train) [23][600/940] lr: 1.0000e-02 eta: 12:50:05 time: 0.6480 data_time: 0.0247 memory: 16095 grad_norm: 5.1123 loss: 1.6539 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.6539 2022/12/08 17:02:41 - mmengine - INFO - Epoch(train) [23][620/940] lr: 1.0000e-02 eta: 12:49:48 time: 0.5739 data_time: 0.0264 memory: 16095 grad_norm: 5.2090 loss: 1.3993 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.3993 2022/12/08 17:02:55 - mmengine - INFO - Epoch(train) [23][640/940] lr: 1.0000e-02 eta: 12:49:38 time: 0.6842 data_time: 0.0221 memory: 16095 grad_norm: 5.1685 loss: 1.6223 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.6223 2022/12/08 17:03:06 - mmengine - INFO - Epoch(train) [23][660/940] lr: 1.0000e-02 eta: 12:49:18 time: 0.5247 data_time: 0.0265 memory: 16095 grad_norm: 5.2803 loss: 1.4781 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.4781 2022/12/08 17:03:19 - mmengine - INFO - Epoch(train) [23][680/940] lr: 1.0000e-02 eta: 12:49:08 time: 0.6755 data_time: 0.0258 memory: 16095 grad_norm: 5.1506 loss: 1.5236 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.5236 2022/12/08 17:03:30 - mmengine - INFO - Epoch(train) [23][700/940] lr: 1.0000e-02 eta: 12:48:50 time: 0.5529 data_time: 0.0222 memory: 16095 grad_norm: 5.1667 loss: 1.5049 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5049 2022/12/08 17:03:43 - mmengine - INFO - Epoch(train) [23][720/940] lr: 1.0000e-02 eta: 12:48:37 time: 0.6380 data_time: 0.0720 memory: 16095 grad_norm: 5.1590 loss: 1.5205 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.5205 2022/12/08 17:03:55 - mmengine - INFO - Epoch(train) [23][740/940] lr: 1.0000e-02 eta: 12:48:21 time: 0.5827 data_time: 0.0463 memory: 16095 grad_norm: 5.1454 loss: 1.5125 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.5125 2022/12/08 17:04:08 - mmengine - INFO - Epoch(train) [23][760/940] lr: 1.0000e-02 eta: 12:48:11 time: 0.6748 data_time: 0.1395 memory: 16095 grad_norm: 5.1966 loss: 1.6067 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.6067 2022/12/08 17:04:19 - mmengine - INFO - Epoch(train) [23][780/940] lr: 1.0000e-02 eta: 12:47:53 time: 0.5517 data_time: 0.1161 memory: 16095 grad_norm: 5.2020 loss: 1.7303 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.7303 2022/12/08 17:04:32 - mmengine - INFO - Epoch(train) [23][800/940] lr: 1.0000e-02 eta: 12:47:42 time: 0.6579 data_time: 0.2522 memory: 16095 grad_norm: 5.2259 loss: 1.5818 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.5818 2022/12/08 17:04:45 - mmengine - INFO - Epoch(train) [23][820/940] lr: 1.0000e-02 eta: 12:47:28 time: 0.6174 data_time: 0.0703 memory: 16095 grad_norm: 5.1707 loss: 1.5681 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5681 2022/12/08 17:04:58 - mmengine - INFO - Epoch(train) [23][840/940] lr: 1.0000e-02 eta: 12:47:17 time: 0.6710 data_time: 0.0263 memory: 16095 grad_norm: 5.0328 loss: 1.5079 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5079 2022/12/08 17:05:10 - mmengine - INFO - Epoch(train) [23][860/940] lr: 1.0000e-02 eta: 12:47:00 time: 0.5716 data_time: 0.0216 memory: 16095 grad_norm: 5.1351 loss: 1.3894 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.3894 2022/12/08 17:05:22 - mmengine - INFO - Epoch(train) [23][880/940] lr: 1.0000e-02 eta: 12:46:48 time: 0.6314 data_time: 0.0247 memory: 16095 grad_norm: 5.1834 loss: 1.5381 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5381 2022/12/08 17:05:34 - mmengine - INFO - Epoch(train) [23][900/940] lr: 1.0000e-02 eta: 12:46:31 time: 0.5733 data_time: 0.0238 memory: 16095 grad_norm: 5.2191 loss: 1.5246 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.5246 2022/12/08 17:05:47 - mmengine - INFO - Epoch(train) [23][920/940] lr: 1.0000e-02 eta: 12:46:20 time: 0.6604 data_time: 0.0261 memory: 16095 grad_norm: 5.1634 loss: 1.4969 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.4969 2022/12/08 17:05:57 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 17:05:57 - mmengine - INFO - Epoch(train) [23][940/940] lr: 1.0000e-02 eta: 12:45:58 time: 0.5037 data_time: 0.0156 memory: 16095 grad_norm: 5.5840 loss: 1.6478 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.6478 2022/12/08 17:06:11 - mmengine - INFO - Epoch(val) [23][20/78] eta: 0:00:40 time: 0.7039 data_time: 0.6072 memory: 1686 2022/12/08 17:06:20 - mmengine - INFO - Epoch(val) [23][40/78] eta: 0:00:22 time: 0.4686 data_time: 0.3753 memory: 1686 2022/12/08 17:06:34 - mmengine - INFO - Epoch(val) [23][60/78] eta: 0:00:11 time: 0.6753 data_time: 0.5797 memory: 1686 2022/12/08 17:06:44 - mmengine - INFO - Epoch(val) [23][78/78] acc/top1: 0.6470 acc/top5: 0.8544 acc/mean1: 0.6468 2022/12/08 17:06:44 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_22.pth is removed 2022/12/08 17:06:47 - mmengine - INFO - The best checkpoint with 0.6470 acc/top1 at 23 epoch is saved to best_acc/top1_epoch_23.pth. 2022/12/08 17:07:02 - mmengine - INFO - Epoch(train) [24][ 20/940] lr: 1.0000e-02 eta: 12:45:54 time: 0.7692 data_time: 0.4753 memory: 16095 grad_norm: 5.1441 loss: 1.5413 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.5413 2022/12/08 17:07:14 - mmengine - INFO - Epoch(train) [24][ 40/940] lr: 1.0000e-02 eta: 12:45:39 time: 0.6011 data_time: 0.3077 memory: 16095 grad_norm: 5.1644 loss: 1.5039 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.5039 2022/12/08 17:07:27 - mmengine - INFO - Epoch(train) [24][ 60/940] lr: 1.0000e-02 eta: 12:45:28 time: 0.6570 data_time: 0.3342 memory: 16095 grad_norm: 5.0336 loss: 1.4243 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.4243 2022/12/08 17:07:39 - mmengine - INFO - Epoch(train) [24][ 80/940] lr: 1.0000e-02 eta: 12:45:11 time: 0.5658 data_time: 0.2443 memory: 16095 grad_norm: 5.1952 loss: 1.4397 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.4397 2022/12/08 17:07:52 - mmengine - INFO - Epoch(train) [24][100/940] lr: 1.0000e-02 eta: 12:45:00 time: 0.6691 data_time: 0.3522 memory: 16095 grad_norm: 5.0537 loss: 1.5000 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.5000 2022/12/08 17:08:03 - mmengine - INFO - Epoch(train) [24][120/940] lr: 1.0000e-02 eta: 12:44:41 time: 0.5336 data_time: 0.2202 memory: 16095 grad_norm: 5.1014 loss: 1.5747 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.5747 2022/12/08 17:08:16 - mmengine - INFO - Epoch(train) [24][140/940] lr: 1.0000e-02 eta: 12:44:31 time: 0.6684 data_time: 0.3559 memory: 16095 grad_norm: 5.2604 loss: 1.5272 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.5272 2022/12/08 17:08:26 - mmengine - INFO - Epoch(train) [24][160/940] lr: 1.0000e-02 eta: 12:44:10 time: 0.5203 data_time: 0.2063 memory: 16095 grad_norm: 5.0316 loss: 1.3606 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.3606 2022/12/08 17:08:39 - mmengine - INFO - Epoch(train) [24][180/940] lr: 1.0000e-02 eta: 12:43:57 time: 0.6248 data_time: 0.2703 memory: 16095 grad_norm: 5.1259 loss: 1.4768 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.4768 2022/12/08 17:08:51 - mmengine - INFO - Epoch(train) [24][200/940] lr: 1.0000e-02 eta: 12:43:41 time: 0.5895 data_time: 0.2225 memory: 16095 grad_norm: 5.2235 loss: 1.5221 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.5221 2022/12/08 17:09:04 - mmengine - INFO - Epoch(train) [24][220/940] lr: 1.0000e-02 eta: 12:43:30 time: 0.6590 data_time: 0.2514 memory: 16095 grad_norm: 5.1803 loss: 1.5017 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5017 2022/12/08 17:09:16 - mmengine - INFO - Epoch(train) [24][240/940] lr: 1.0000e-02 eta: 12:43:15 time: 0.5963 data_time: 0.2502 memory: 16095 grad_norm: 5.2597 loss: 1.5380 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5380 2022/12/08 17:09:29 - mmengine - INFO - Epoch(train) [24][260/940] lr: 1.0000e-02 eta: 12:43:03 time: 0.6492 data_time: 0.3063 memory: 16095 grad_norm: 5.2053 loss: 1.4913 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.4913 2022/12/08 17:09:40 - mmengine - INFO - Epoch(train) [24][280/940] lr: 1.0000e-02 eta: 12:42:46 time: 0.5671 data_time: 0.2206 memory: 16095 grad_norm: 5.1906 loss: 1.4453 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.4453 2022/12/08 17:09:53 - mmengine - INFO - Epoch(train) [24][300/940] lr: 1.0000e-02 eta: 12:42:33 time: 0.6340 data_time: 0.2570 memory: 16095 grad_norm: 5.1591 loss: 1.4757 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.4757 2022/12/08 17:10:04 - mmengine - INFO - Epoch(train) [24][320/940] lr: 1.0000e-02 eta: 12:42:16 time: 0.5625 data_time: 0.1359 memory: 16095 grad_norm: 5.2376 loss: 1.4732 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.4732 2022/12/08 17:10:17 - mmengine - INFO - Epoch(train) [24][340/940] lr: 1.0000e-02 eta: 12:42:04 time: 0.6562 data_time: 0.0971 memory: 16095 grad_norm: 5.1375 loss: 1.5525 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.5525 2022/12/08 17:10:28 - mmengine - INFO - Epoch(train) [24][360/940] lr: 1.0000e-02 eta: 12:41:47 time: 0.5573 data_time: 0.0392 memory: 16095 grad_norm: 5.2117 loss: 1.4513 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.4513 2022/12/08 17:10:42 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 17:10:42 - mmengine - INFO - Epoch(train) [24][380/940] lr: 1.0000e-02 eta: 12:41:38 time: 0.6967 data_time: 0.0267 memory: 16095 grad_norm: 5.1223 loss: 1.4207 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.4207 2022/12/08 17:10:53 - mmengine - INFO - Epoch(train) [24][400/940] lr: 1.0000e-02 eta: 12:41:18 time: 0.5264 data_time: 0.0229 memory: 16095 grad_norm: 5.2387 loss: 1.5252 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.5252 2022/12/08 17:11:06 - mmengine - INFO - Epoch(train) [24][420/940] lr: 1.0000e-02 eta: 12:41:09 time: 0.6816 data_time: 0.0258 memory: 16095 grad_norm: 5.2156 loss: 1.4436 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.4436 2022/12/08 17:11:18 - mmengine - INFO - Epoch(train) [24][440/940] lr: 1.0000e-02 eta: 12:40:52 time: 0.5767 data_time: 0.0238 memory: 16095 grad_norm: 5.1733 loss: 1.4958 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.4958 2022/12/08 17:11:31 - mmengine - INFO - Epoch(train) [24][460/940] lr: 1.0000e-02 eta: 12:40:41 time: 0.6539 data_time: 0.0255 memory: 16095 grad_norm: 5.2310 loss: 1.3785 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.3785 2022/12/08 17:11:42 - mmengine - INFO - Epoch(train) [24][480/940] lr: 1.0000e-02 eta: 12:40:22 time: 0.5382 data_time: 0.0233 memory: 16095 grad_norm: 5.2159 loss: 1.4545 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.4545 2022/12/08 17:11:55 - mmengine - INFO - Epoch(train) [24][500/940] lr: 1.0000e-02 eta: 12:40:10 time: 0.6555 data_time: 0.0531 memory: 16095 grad_norm: 5.1328 loss: 1.5282 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.5282 2022/12/08 17:12:06 - mmengine - INFO - Epoch(train) [24][520/940] lr: 1.0000e-02 eta: 12:39:53 time: 0.5551 data_time: 0.0414 memory: 16095 grad_norm: 5.1453 loss: 1.5987 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.5987 2022/12/08 17:12:19 - mmengine - INFO - Epoch(train) [24][540/940] lr: 1.0000e-02 eta: 12:39:41 time: 0.6542 data_time: 0.0290 memory: 16095 grad_norm: 5.1773 loss: 1.5107 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5107 2022/12/08 17:12:32 - mmengine - INFO - Epoch(train) [24][560/940] lr: 1.0000e-02 eta: 12:39:28 time: 0.6277 data_time: 0.0219 memory: 16095 grad_norm: 5.2868 loss: 1.5504 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.5504 2022/12/08 17:12:44 - mmengine - INFO - Epoch(train) [24][580/940] lr: 1.0000e-02 eta: 12:39:14 time: 0.6195 data_time: 0.0257 memory: 16095 grad_norm: 5.2276 loss: 1.4351 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4351 2022/12/08 17:12:56 - mmengine - INFO - Epoch(train) [24][600/940] lr: 1.0000e-02 eta: 12:38:58 time: 0.5834 data_time: 0.0251 memory: 16095 grad_norm: 5.2452 loss: 1.5991 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5991 2022/12/08 17:13:09 - mmengine - INFO - Epoch(train) [24][620/940] lr: 1.0000e-02 eta: 12:38:47 time: 0.6599 data_time: 0.0260 memory: 16095 grad_norm: 5.1038 loss: 1.4501 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.4501 2022/12/08 17:13:21 - mmengine - INFO - Epoch(train) [24][640/940] lr: 1.0000e-02 eta: 12:38:32 time: 0.5848 data_time: 0.0233 memory: 16095 grad_norm: 5.2454 loss: 1.5689 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5689 2022/12/08 17:13:34 - mmengine - INFO - Epoch(train) [24][660/940] lr: 1.0000e-02 eta: 12:38:20 time: 0.6532 data_time: 0.0291 memory: 16095 grad_norm: 5.2839 loss: 1.3432 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.3432 2022/12/08 17:13:45 - mmengine - INFO - Epoch(train) [24][680/940] lr: 1.0000e-02 eta: 12:38:04 time: 0.5879 data_time: 0.0213 memory: 16095 grad_norm: 5.1357 loss: 1.4078 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.4078 2022/12/08 17:13:57 - mmengine - INFO - Epoch(train) [24][700/940] lr: 1.0000e-02 eta: 12:37:48 time: 0.5839 data_time: 0.0263 memory: 16095 grad_norm: 5.1526 loss: 1.5799 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.5799 2022/12/08 17:14:09 - mmengine - INFO - Epoch(train) [24][720/940] lr: 1.0000e-02 eta: 12:37:32 time: 0.5837 data_time: 0.0228 memory: 16095 grad_norm: 5.1691 loss: 1.5065 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5065 2022/12/08 17:14:21 - mmengine - INFO - Epoch(train) [24][740/940] lr: 1.0000e-02 eta: 12:37:19 time: 0.6285 data_time: 0.0287 memory: 16095 grad_norm: 5.1824 loss: 1.5602 top1_acc: 0.5312 top5_acc: 0.9688 loss_cls: 1.5602 2022/12/08 17:14:35 - mmengine - INFO - Epoch(train) [24][760/940] lr: 1.0000e-02 eta: 12:37:09 time: 0.6703 data_time: 0.0188 memory: 16095 grad_norm: 5.1774 loss: 1.4949 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.4949 2022/12/08 17:14:46 - mmengine - INFO - Epoch(train) [24][780/940] lr: 1.0000e-02 eta: 12:36:52 time: 0.5728 data_time: 0.0319 memory: 16095 grad_norm: 5.1266 loss: 1.5010 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5010 2022/12/08 17:14:59 - mmengine - INFO - Epoch(train) [24][800/940] lr: 1.0000e-02 eta: 12:36:38 time: 0.6140 data_time: 0.0224 memory: 16095 grad_norm: 5.2010 loss: 1.5039 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5039 2022/12/08 17:15:11 - mmengine - INFO - Epoch(train) [24][820/940] lr: 1.0000e-02 eta: 12:36:23 time: 0.5975 data_time: 0.0261 memory: 16095 grad_norm: 5.1128 loss: 1.5923 top1_acc: 0.6562 top5_acc: 0.7500 loss_cls: 1.5923 2022/12/08 17:15:23 - mmengine - INFO - Epoch(train) [24][840/940] lr: 1.0000e-02 eta: 12:36:09 time: 0.6107 data_time: 0.0293 memory: 16095 grad_norm: 5.2983 loss: 1.5441 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.5441 2022/12/08 17:15:35 - mmengine - INFO - Epoch(train) [24][860/940] lr: 1.0000e-02 eta: 12:35:56 time: 0.6307 data_time: 0.0306 memory: 16095 grad_norm: 5.1773 loss: 1.6090 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.6090 2022/12/08 17:15:48 - mmengine - INFO - Epoch(train) [24][880/940] lr: 1.0000e-02 eta: 12:35:42 time: 0.6124 data_time: 0.0297 memory: 16095 grad_norm: 5.2127 loss: 1.5335 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 1.5335 2022/12/08 17:16:00 - mmengine - INFO - Epoch(train) [24][900/940] lr: 1.0000e-02 eta: 12:35:29 time: 0.6309 data_time: 0.0258 memory: 16095 grad_norm: 5.2688 loss: 1.5936 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.5936 2022/12/08 17:16:12 - mmengine - INFO - Epoch(train) [24][920/940] lr: 1.0000e-02 eta: 12:35:14 time: 0.5974 data_time: 0.0219 memory: 16095 grad_norm: 5.1764 loss: 1.4478 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.4478 2022/12/08 17:18:51 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 17:18:51 - mmengine - INFO - Epoch(train) [24][940/940] lr: 1.0000e-02 eta: 12:42:44 time: 7.9354 data_time: 0.0174 memory: 16095 grad_norm: 5.4394 loss: 1.5112 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.5112 2022/12/08 17:18:51 - mmengine - INFO - Saving checkpoint at 24 epochs 2022/12/08 17:22:38 - mmengine - INFO - Epoch(val) [24][20/78] eta: 0:00:42 time: 0.7259 data_time: 0.6140 memory: 1686 2022/12/08 17:22:45 - mmengine - INFO - Epoch(val) [24][40/78] eta: 0:00:20 time: 0.3745 data_time: 0.2862 memory: 1686 2022/12/08 17:22:57 - mmengine - INFO - Epoch(val) [24][60/78] eta: 0:00:10 time: 0.5847 data_time: 0.4951 memory: 1686 2022/12/08 17:23:08 - mmengine - INFO - Epoch(val) [24][78/78] acc/top1: 0.6349 acc/top5: 0.8500 acc/mean1: 0.6348 2022/12/08 17:23:25 - mmengine - INFO - Epoch(train) [25][ 20/940] lr: 1.0000e-02 eta: 12:42:45 time: 0.8535 data_time: 0.3295 memory: 16095 grad_norm: 5.1417 loss: 1.5260 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5260 2022/12/08 17:23:36 - mmengine - INFO - Epoch(train) [25][ 40/940] lr: 1.0000e-02 eta: 12:42:26 time: 0.5404 data_time: 0.0353 memory: 16095 grad_norm: 5.0688 loss: 1.3560 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.3560 2022/12/08 17:23:51 - mmengine - INFO - Epoch(train) [25][ 60/940] lr: 1.0000e-02 eta: 12:42:20 time: 0.7591 data_time: 0.1067 memory: 16095 grad_norm: 5.0661 loss: 1.4258 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4258 2022/12/08 17:24:01 - mmengine - INFO - Epoch(train) [25][ 80/940] lr: 1.0000e-02 eta: 12:42:00 time: 0.5266 data_time: 0.0221 memory: 16095 grad_norm: 5.1394 loss: 1.5495 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.5495 2022/12/08 17:24:16 - mmengine - INFO - Epoch(train) [25][100/940] lr: 1.0000e-02 eta: 12:41:52 time: 0.7118 data_time: 0.0388 memory: 16095 grad_norm: 5.0946 loss: 1.4885 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.4885 2022/12/08 17:24:27 - mmengine - INFO - Epoch(train) [25][120/940] lr: 1.0000e-02 eta: 12:41:34 time: 0.5542 data_time: 0.0311 memory: 16095 grad_norm: 5.1858 loss: 1.4202 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.4202 2022/12/08 17:24:40 - mmengine - INFO - Epoch(train) [25][140/940] lr: 1.0000e-02 eta: 12:41:21 time: 0.6512 data_time: 0.0912 memory: 16095 grad_norm: 5.0969 loss: 1.4197 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4197 2022/12/08 17:24:52 - mmengine - INFO - Epoch(train) [25][160/940] lr: 1.0000e-02 eta: 12:41:05 time: 0.5905 data_time: 0.0779 memory: 16095 grad_norm: 5.0953 loss: 1.5157 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5157 2022/12/08 17:25:05 - mmengine - INFO - Epoch(train) [25][180/940] lr: 1.0000e-02 eta: 12:40:54 time: 0.6559 data_time: 0.0982 memory: 16095 grad_norm: 5.1900 loss: 1.5223 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.5223 2022/12/08 17:25:16 - mmengine - INFO - Epoch(train) [25][200/940] lr: 1.0000e-02 eta: 12:40:35 time: 0.5554 data_time: 0.0348 memory: 16095 grad_norm: 5.1974 loss: 1.5870 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.5870 2022/12/08 17:25:29 - mmengine - INFO - Epoch(train) [25][220/940] lr: 1.0000e-02 eta: 12:40:23 time: 0.6382 data_time: 0.1160 memory: 16095 grad_norm: 5.1850 loss: 1.4676 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.4676 2022/12/08 17:25:40 - mmengine - INFO - Epoch(train) [25][240/940] lr: 1.0000e-02 eta: 12:40:04 time: 0.5488 data_time: 0.2207 memory: 16095 grad_norm: 5.2113 loss: 1.4902 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.4902 2022/12/08 17:25:53 - mmengine - INFO - Epoch(train) [25][260/940] lr: 1.0000e-02 eta: 12:39:55 time: 0.6950 data_time: 0.3544 memory: 16095 grad_norm: 5.1807 loss: 1.4624 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.4624 2022/12/08 17:26:05 - mmengine - INFO - Epoch(train) [25][280/940] lr: 1.0000e-02 eta: 12:39:37 time: 0.5712 data_time: 0.2481 memory: 16095 grad_norm: 5.2741 loss: 1.5111 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.5111 2022/12/08 17:26:18 - mmengine - INFO - Epoch(train) [25][300/940] lr: 1.0000e-02 eta: 12:39:26 time: 0.6646 data_time: 0.3354 memory: 16095 grad_norm: 5.1814 loss: 1.4501 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.4501 2022/12/08 17:26:29 - mmengine - INFO - Epoch(train) [25][320/940] lr: 1.0000e-02 eta: 12:39:06 time: 0.5298 data_time: 0.2103 memory: 16095 grad_norm: 5.1155 loss: 1.4551 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.4551 2022/12/08 17:26:43 - mmengine - INFO - Epoch(train) [25][340/940] lr: 1.0000e-02 eta: 12:38:58 time: 0.7186 data_time: 0.3986 memory: 16095 grad_norm: 5.2711 loss: 1.3926 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.3926 2022/12/08 17:26:54 - mmengine - INFO - Epoch(train) [25][360/940] lr: 1.0000e-02 eta: 12:38:41 time: 0.5640 data_time: 0.2388 memory: 16095 grad_norm: 5.2027 loss: 1.5023 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.5023 2022/12/08 17:27:07 - mmengine - INFO - Epoch(train) [25][380/940] lr: 1.0000e-02 eta: 12:38:28 time: 0.6449 data_time: 0.3209 memory: 16095 grad_norm: 5.1719 loss: 1.5112 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.5112 2022/12/08 17:27:19 - mmengine - INFO - Epoch(train) [25][400/940] lr: 1.0000e-02 eta: 12:38:11 time: 0.5622 data_time: 0.2426 memory: 16095 grad_norm: 5.1906 loss: 1.4635 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.4635 2022/12/08 17:27:31 - mmengine - INFO - Epoch(train) [25][420/940] lr: 1.0000e-02 eta: 12:37:58 time: 0.6421 data_time: 0.3220 memory: 16095 grad_norm: 5.2791 loss: 1.6240 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.6240 2022/12/08 17:27:42 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 17:27:42 - mmengine - INFO - Epoch(train) [25][440/940] lr: 1.0000e-02 eta: 12:37:40 time: 0.5481 data_time: 0.2327 memory: 16095 grad_norm: 5.3231 loss: 1.3696 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 1.3696 2022/12/08 17:27:57 - mmengine - INFO - Epoch(train) [25][460/940] lr: 1.0000e-02 eta: 12:37:31 time: 0.7111 data_time: 0.3656 memory: 16095 grad_norm: 5.2322 loss: 1.5089 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.5089 2022/12/08 17:28:08 - mmengine - INFO - Epoch(train) [25][480/940] lr: 1.0000e-02 eta: 12:37:13 time: 0.5510 data_time: 0.2364 memory: 16095 grad_norm: 5.1454 loss: 1.5375 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.5375 2022/12/08 17:28:21 - mmengine - INFO - Epoch(train) [25][500/940] lr: 1.0000e-02 eta: 12:37:03 time: 0.6841 data_time: 0.3660 memory: 16095 grad_norm: 5.3176 loss: 1.5783 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.5783 2022/12/08 17:28:32 - mmengine - INFO - Epoch(train) [25][520/940] lr: 1.0000e-02 eta: 12:36:43 time: 0.5331 data_time: 0.2140 memory: 16095 grad_norm: 5.1663 loss: 1.4159 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.4159 2022/12/08 17:28:45 - mmengine - INFO - Epoch(train) [25][540/940] lr: 1.0000e-02 eta: 12:36:32 time: 0.6716 data_time: 0.3475 memory: 16095 grad_norm: 5.1325 loss: 1.5290 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 1.5290 2022/12/08 17:28:56 - mmengine - INFO - Epoch(train) [25][560/940] lr: 1.0000e-02 eta: 12:36:14 time: 0.5508 data_time: 0.2307 memory: 16095 grad_norm: 5.2397 loss: 1.4825 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.4825 2022/12/08 17:29:10 - mmengine - INFO - Epoch(train) [25][580/940] lr: 1.0000e-02 eta: 12:36:05 time: 0.6948 data_time: 0.3718 memory: 16095 grad_norm: 5.1967 loss: 1.4252 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4252 2022/12/08 17:29:20 - mmengine - INFO - Epoch(train) [25][600/940] lr: 1.0000e-02 eta: 12:35:44 time: 0.5056 data_time: 0.1771 memory: 16095 grad_norm: 5.1936 loss: 1.5496 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.5496 2022/12/08 17:29:34 - mmengine - INFO - Epoch(train) [25][620/940] lr: 1.0000e-02 eta: 12:35:34 time: 0.6871 data_time: 0.3567 memory: 16095 grad_norm: 5.2468 loss: 1.5347 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.5347 2022/12/08 17:29:45 - mmengine - INFO - Epoch(train) [25][640/940] lr: 1.0000e-02 eta: 12:35:16 time: 0.5572 data_time: 0.2324 memory: 16095 grad_norm: 5.1911 loss: 1.4370 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.4370 2022/12/08 17:29:58 - mmengine - INFO - Epoch(train) [25][660/940] lr: 1.0000e-02 eta: 12:35:02 time: 0.6169 data_time: 0.2199 memory: 16095 grad_norm: 5.2150 loss: 1.4370 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.4370 2022/12/08 17:30:09 - mmengine - INFO - Epoch(train) [25][680/940] lr: 1.0000e-02 eta: 12:34:45 time: 0.5792 data_time: 0.0924 memory: 16095 grad_norm: 5.4077 loss: 1.5869 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.5869 2022/12/08 17:30:21 - mmengine - INFO - Epoch(train) [25][700/940] lr: 1.0000e-02 eta: 12:34:30 time: 0.5977 data_time: 0.0564 memory: 16095 grad_norm: 5.3239 loss: 1.4743 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.4743 2022/12/08 17:30:35 - mmengine - INFO - Epoch(train) [25][720/940] lr: 1.0000e-02 eta: 12:34:19 time: 0.6660 data_time: 0.0236 memory: 16095 grad_norm: 5.2082 loss: 1.5051 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.5051 2022/12/08 17:30:47 - mmengine - INFO - Epoch(train) [25][740/940] lr: 1.0000e-02 eta: 12:34:04 time: 0.6094 data_time: 0.0278 memory: 16095 grad_norm: 5.2217 loss: 1.6403 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6403 2022/12/08 17:31:00 - mmengine - INFO - Epoch(train) [25][760/940] lr: 1.0000e-02 eta: 12:33:52 time: 0.6558 data_time: 0.0246 memory: 16095 grad_norm: 5.2028 loss: 1.4946 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.4946 2022/12/08 17:31:10 - mmengine - INFO - Epoch(train) [25][780/940] lr: 1.0000e-02 eta: 12:33:31 time: 0.5044 data_time: 0.0261 memory: 16095 grad_norm: 5.1484 loss: 1.5756 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.5756 2022/12/08 17:31:23 - mmengine - INFO - Epoch(train) [25][800/940] lr: 1.0000e-02 eta: 12:33:20 time: 0.6617 data_time: 0.0240 memory: 16095 grad_norm: 5.1533 loss: 1.4569 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.4569 2022/12/08 17:31:35 - mmengine - INFO - Epoch(train) [25][820/940] lr: 1.0000e-02 eta: 12:33:04 time: 0.5906 data_time: 0.0459 memory: 16095 grad_norm: 5.3128 loss: 1.5061 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5061 2022/12/08 17:31:48 - mmengine - INFO - Epoch(train) [25][840/940] lr: 1.0000e-02 eta: 12:32:52 time: 0.6600 data_time: 0.0367 memory: 16095 grad_norm: 5.2209 loss: 1.5278 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 1.5278 2022/12/08 17:31:59 - mmengine - INFO - Epoch(train) [25][860/940] lr: 1.0000e-02 eta: 12:32:34 time: 0.5491 data_time: 0.0258 memory: 16095 grad_norm: 5.1639 loss: 1.4979 top1_acc: 0.6875 top5_acc: 0.9688 loss_cls: 1.4979 2022/12/08 17:32:11 - mmengine - INFO - Epoch(train) [25][880/940] lr: 1.0000e-02 eta: 12:32:17 time: 0.5760 data_time: 0.0886 memory: 16095 grad_norm: 5.1948 loss: 1.4707 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.4707 2022/12/08 17:32:24 - mmengine - INFO - Epoch(train) [25][900/940] lr: 1.0000e-02 eta: 12:32:05 time: 0.6473 data_time: 0.3108 memory: 16095 grad_norm: 5.0478 loss: 1.3964 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3964 2022/12/08 17:32:35 - mmengine - INFO - Epoch(train) [25][920/940] lr: 1.0000e-02 eta: 12:31:48 time: 0.5674 data_time: 0.1817 memory: 16095 grad_norm: 5.2937 loss: 1.4790 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.4790 2022/12/08 17:32:47 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 17:32:47 - mmengine - INFO - Epoch(train) [25][940/940] lr: 1.0000e-02 eta: 12:31:32 time: 0.5914 data_time: 0.2860 memory: 16095 grad_norm: 5.6424 loss: 1.3993 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.3993 2022/12/08 17:33:01 - mmengine - INFO - Epoch(val) [25][20/78] eta: 0:00:41 time: 0.7131 data_time: 0.6204 memory: 1686 2022/12/08 17:33:11 - mmengine - INFO - Epoch(val) [25][40/78] eta: 0:00:22 time: 0.4721 data_time: 0.3786 memory: 1686 2022/12/08 17:33:24 - mmengine - INFO - Epoch(val) [25][60/78] eta: 0:00:11 time: 0.6602 data_time: 0.5646 memory: 1686 2022/12/08 17:33:34 - mmengine - INFO - Epoch(val) [25][78/78] acc/top1: 0.6413 acc/top5: 0.8486 acc/mean1: 0.6412 2022/12/08 17:33:51 - mmengine - INFO - Epoch(train) [26][ 20/940] lr: 1.0000e-02 eta: 12:31:30 time: 0.8095 data_time: 0.3347 memory: 16095 grad_norm: 5.1636 loss: 1.4691 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4691 2022/12/08 17:34:01 - mmengine - INFO - Epoch(train) [26][ 40/940] lr: 1.0000e-02 eta: 12:31:11 time: 0.5406 data_time: 0.1558 memory: 16095 grad_norm: 5.0418 loss: 1.2915 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.2915 2022/12/08 17:34:15 - mmengine - INFO - Epoch(train) [26][ 60/940] lr: 1.0000e-02 eta: 12:31:01 time: 0.6977 data_time: 0.1389 memory: 16095 grad_norm: 5.1251 loss: 1.3990 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.3990 2022/12/08 17:34:26 - mmengine - INFO - Epoch(train) [26][ 80/940] lr: 1.0000e-02 eta: 12:30:42 time: 0.5329 data_time: 0.0679 memory: 16095 grad_norm: 5.1725 loss: 1.4032 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.4032 2022/12/08 17:34:40 - mmengine - INFO - Epoch(train) [26][100/940] lr: 1.0000e-02 eta: 12:30:32 time: 0.6767 data_time: 0.0924 memory: 16095 grad_norm: 5.1678 loss: 1.4297 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.4297 2022/12/08 17:34:50 - mmengine - INFO - Epoch(train) [26][120/940] lr: 1.0000e-02 eta: 12:30:12 time: 0.5252 data_time: 0.0465 memory: 16095 grad_norm: 5.2169 loss: 1.3787 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.3787 2022/12/08 17:35:04 - mmengine - INFO - Epoch(train) [26][140/940] lr: 1.0000e-02 eta: 12:30:02 time: 0.6865 data_time: 0.1654 memory: 16095 grad_norm: 5.2523 loss: 1.5483 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.5483 2022/12/08 17:35:15 - mmengine - INFO - Epoch(train) [26][160/940] lr: 1.0000e-02 eta: 12:29:46 time: 0.5763 data_time: 0.0770 memory: 16095 grad_norm: 5.1497 loss: 1.3996 top1_acc: 0.4688 top5_acc: 0.7188 loss_cls: 1.3996 2022/12/08 17:35:28 - mmengine - INFO - Epoch(train) [26][180/940] lr: 1.0000e-02 eta: 12:29:31 time: 0.6152 data_time: 0.0258 memory: 16095 grad_norm: 5.1900 loss: 1.5384 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.5384 2022/12/08 17:35:39 - mmengine - INFO - Epoch(train) [26][200/940] lr: 1.0000e-02 eta: 12:29:16 time: 0.5915 data_time: 0.0844 memory: 16095 grad_norm: 5.1231 loss: 1.4773 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.4773 2022/12/08 17:35:53 - mmengine - INFO - Epoch(train) [26][220/940] lr: 1.0000e-02 eta: 12:29:04 time: 0.6560 data_time: 0.3140 memory: 16095 grad_norm: 5.1288 loss: 1.4396 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.4396 2022/12/08 17:36:05 - mmengine - INFO - Epoch(train) [26][240/940] lr: 1.0000e-02 eta: 12:28:49 time: 0.6019 data_time: 0.2397 memory: 16095 grad_norm: 5.2464 loss: 1.4599 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.4599 2022/12/08 17:36:19 - mmengine - INFO - Epoch(train) [26][260/940] lr: 1.0000e-02 eta: 12:28:39 time: 0.6966 data_time: 0.2671 memory: 16095 grad_norm: 5.2187 loss: 1.4185 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 1.4185 2022/12/08 17:36:29 - mmengine - INFO - Epoch(train) [26][280/940] lr: 1.0000e-02 eta: 12:28:20 time: 0.5281 data_time: 0.0915 memory: 16095 grad_norm: 5.3379 loss: 1.4748 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.4748 2022/12/08 17:36:44 - mmengine - INFO - Epoch(train) [26][300/940] lr: 1.0000e-02 eta: 12:28:12 time: 0.7253 data_time: 0.1511 memory: 16095 grad_norm: 5.1382 loss: 1.5193 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 1.5193 2022/12/08 17:36:54 - mmengine - INFO - Epoch(train) [26][320/940] lr: 1.0000e-02 eta: 12:27:51 time: 0.4976 data_time: 0.0361 memory: 16095 grad_norm: 5.1215 loss: 1.4457 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.4457 2022/12/08 17:37:07 - mmengine - INFO - Epoch(train) [26][340/940] lr: 1.0000e-02 eta: 12:27:42 time: 0.6933 data_time: 0.0831 memory: 16095 grad_norm: 5.1749 loss: 1.4636 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.4636 2022/12/08 17:37:19 - mmengine - INFO - Epoch(train) [26][360/940] lr: 1.0000e-02 eta: 12:27:24 time: 0.5631 data_time: 0.0245 memory: 16095 grad_norm: 5.3931 loss: 1.5001 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.5001 2022/12/08 17:37:32 - mmengine - INFO - Epoch(train) [26][380/940] lr: 1.0000e-02 eta: 12:27:14 time: 0.6893 data_time: 0.0260 memory: 16095 grad_norm: 5.2758 loss: 1.3630 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.3630 2022/12/08 17:37:44 - mmengine - INFO - Epoch(train) [26][400/940] lr: 1.0000e-02 eta: 12:26:59 time: 0.5912 data_time: 0.0258 memory: 16095 grad_norm: 5.2148 loss: 1.3832 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3832 2022/12/08 17:37:58 - mmengine - INFO - Epoch(train) [26][420/940] lr: 1.0000e-02 eta: 12:26:49 time: 0.6840 data_time: 0.0232 memory: 16095 grad_norm: 5.2489 loss: 1.4641 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.4641 2022/12/08 17:38:09 - mmengine - INFO - Epoch(train) [26][440/940] lr: 1.0000e-02 eta: 12:26:30 time: 0.5345 data_time: 0.0259 memory: 16095 grad_norm: 5.2869 loss: 1.6217 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 1.6217 2022/12/08 17:38:21 - mmengine - INFO - Epoch(train) [26][460/940] lr: 1.0000e-02 eta: 12:26:17 time: 0.6335 data_time: 0.0239 memory: 16095 grad_norm: 5.2063 loss: 1.4864 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.4864 2022/12/08 17:38:32 - mmengine - INFO - Epoch(train) [26][480/940] lr: 1.0000e-02 eta: 12:25:57 time: 0.5279 data_time: 0.0250 memory: 16095 grad_norm: 5.1553 loss: 1.4539 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.4539 2022/12/08 17:38:45 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 17:38:45 - mmengine - INFO - Epoch(train) [26][500/940] lr: 1.0000e-02 eta: 12:25:45 time: 0.6441 data_time: 0.0258 memory: 16095 grad_norm: 5.2295 loss: 1.4742 top1_acc: 0.6250 top5_acc: 0.7188 loss_cls: 1.4742 2022/12/08 17:38:57 - mmengine - INFO - Epoch(train) [26][520/940] lr: 1.0000e-02 eta: 12:25:31 time: 0.6235 data_time: 0.0243 memory: 16095 grad_norm: 5.1595 loss: 1.4094 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.4094 2022/12/08 17:39:09 - mmengine - INFO - Epoch(train) [26][540/940] lr: 1.0000e-02 eta: 12:25:16 time: 0.5965 data_time: 0.0274 memory: 16095 grad_norm: 5.1854 loss: 1.4619 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4619 2022/12/08 17:39:22 - mmengine - INFO - Epoch(train) [26][560/940] lr: 1.0000e-02 eta: 12:25:03 time: 0.6441 data_time: 0.0215 memory: 16095 grad_norm: 5.2643 loss: 1.4950 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.4950 2022/12/08 17:39:34 - mmengine - INFO - Epoch(train) [26][580/940] lr: 1.0000e-02 eta: 12:24:47 time: 0.5824 data_time: 0.0270 memory: 16095 grad_norm: 5.2044 loss: 1.4425 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4425 2022/12/08 17:39:45 - mmengine - INFO - Epoch(train) [26][600/940] lr: 1.0000e-02 eta: 12:24:31 time: 0.5791 data_time: 0.0217 memory: 16095 grad_norm: 5.1938 loss: 1.4909 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4909 2022/12/08 17:39:58 - mmengine - INFO - Epoch(train) [26][620/940] lr: 1.0000e-02 eta: 12:24:17 time: 0.6228 data_time: 0.0380 memory: 16095 grad_norm: 5.2670 loss: 1.3521 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.3521 2022/12/08 17:40:10 - mmengine - INFO - Epoch(train) [26][640/940] lr: 1.0000e-02 eta: 12:24:03 time: 0.6045 data_time: 0.0219 memory: 16095 grad_norm: 5.1876 loss: 1.4693 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.4693 2022/12/08 17:40:22 - mmengine - INFO - Epoch(train) [26][660/940] lr: 1.0000e-02 eta: 12:23:49 time: 0.6289 data_time: 0.0282 memory: 16095 grad_norm: 5.2658 loss: 1.4393 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.4393 2022/12/08 17:40:35 - mmengine - INFO - Epoch(train) [26][680/940] lr: 1.0000e-02 eta: 12:23:34 time: 0.6051 data_time: 0.0367 memory: 16095 grad_norm: 5.2817 loss: 1.5605 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.5605 2022/12/08 17:40:47 - mmengine - INFO - Epoch(train) [26][700/940] lr: 1.0000e-02 eta: 12:23:21 time: 0.6351 data_time: 0.0961 memory: 16095 grad_norm: 5.1714 loss: 1.4351 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.4351 2022/12/08 17:40:58 - mmengine - INFO - Epoch(train) [26][720/940] lr: 1.0000e-02 eta: 12:23:03 time: 0.5487 data_time: 0.0764 memory: 16095 grad_norm: 5.2390 loss: 1.4689 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 1.4689 2022/12/08 17:41:11 - mmengine - INFO - Epoch(train) [26][740/940] lr: 1.0000e-02 eta: 12:22:52 time: 0.6609 data_time: 0.1997 memory: 16095 grad_norm: 5.2507 loss: 1.5490 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5490 2022/12/08 17:41:23 - mmengine - INFO - Epoch(train) [26][760/940] lr: 1.0000e-02 eta: 12:22:36 time: 0.5856 data_time: 0.1019 memory: 16095 grad_norm: 5.2958 loss: 1.4511 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.4511 2022/12/08 17:41:35 - mmengine - INFO - Epoch(train) [26][780/940] lr: 1.0000e-02 eta: 12:22:22 time: 0.6129 data_time: 0.0805 memory: 16095 grad_norm: 5.0709 loss: 1.5627 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 1.5627 2022/12/08 17:41:48 - mmengine - INFO - Epoch(train) [26][800/940] lr: 1.0000e-02 eta: 12:22:08 time: 0.6185 data_time: 0.0218 memory: 16095 grad_norm: 5.2384 loss: 1.6105 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6105 2022/12/08 17:42:00 - mmengine - INFO - Epoch(train) [26][820/940] lr: 1.0000e-02 eta: 12:21:53 time: 0.6039 data_time: 0.0267 memory: 16095 grad_norm: 5.2643 loss: 1.6257 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.6257 2022/12/08 17:42:13 - mmengine - INFO - Epoch(train) [26][840/940] lr: 1.0000e-02 eta: 12:21:40 time: 0.6379 data_time: 0.0225 memory: 16095 grad_norm: 5.1329 loss: 1.4818 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.4818 2022/12/08 17:42:24 - mmengine - INFO - Epoch(train) [26][860/940] lr: 1.0000e-02 eta: 12:21:25 time: 0.5885 data_time: 0.0237 memory: 16095 grad_norm: 5.1434 loss: 1.5734 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.5734 2022/12/08 17:42:38 - mmengine - INFO - Epoch(train) [26][880/940] lr: 1.0000e-02 eta: 12:21:13 time: 0.6610 data_time: 0.0253 memory: 16095 grad_norm: 5.0587 loss: 1.4657 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.4657 2022/12/08 17:42:49 - mmengine - INFO - Epoch(train) [26][900/940] lr: 1.0000e-02 eta: 12:20:55 time: 0.5526 data_time: 0.0236 memory: 16095 grad_norm: 5.1622 loss: 1.4589 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4589 2022/12/08 17:43:03 - mmengine - INFO - Epoch(train) [26][920/940] lr: 1.0000e-02 eta: 12:20:46 time: 0.6976 data_time: 0.0239 memory: 16095 grad_norm: 5.2550 loss: 1.5404 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.5404 2022/12/08 17:43:12 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 17:43:12 - mmengine - INFO - Epoch(train) [26][940/940] lr: 1.0000e-02 eta: 12:20:24 time: 0.4709 data_time: 0.0183 memory: 16095 grad_norm: 5.4468 loss: 1.5589 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.5589 2022/12/08 17:43:26 - mmengine - INFO - Epoch(val) [26][20/78] eta: 0:00:41 time: 0.7107 data_time: 0.6168 memory: 1686 2022/12/08 17:43:36 - mmengine - INFO - Epoch(val) [26][40/78] eta: 0:00:22 time: 0.4635 data_time: 0.3678 memory: 1686 2022/12/08 17:43:49 - mmengine - INFO - Epoch(val) [26][60/78] eta: 0:00:11 time: 0.6693 data_time: 0.5733 memory: 1686 2022/12/08 17:44:00 - mmengine - INFO - Epoch(val) [26][78/78] acc/top1: 0.6454 acc/top5: 0.8569 acc/mean1: 0.6452 2022/12/08 17:44:16 - mmengine - INFO - Epoch(train) [27][ 20/940] lr: 1.0000e-02 eta: 12:20:21 time: 0.8234 data_time: 0.4226 memory: 16095 grad_norm: 5.0904 loss: 1.5806 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.5806 2022/12/08 17:44:27 - mmengine - INFO - Epoch(train) [27][ 40/940] lr: 1.0000e-02 eta: 12:20:03 time: 0.5480 data_time: 0.1230 memory: 16095 grad_norm: 5.1634 loss: 1.4367 top1_acc: 0.4688 top5_acc: 0.8438 loss_cls: 1.4367 2022/12/08 17:44:42 - mmengine - INFO - Epoch(train) [27][ 60/940] lr: 1.0000e-02 eta: 12:19:56 time: 0.7350 data_time: 0.0769 memory: 16095 grad_norm: 5.2448 loss: 1.4355 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.4355 2022/12/08 17:44:53 - mmengine - INFO - Epoch(train) [27][ 80/940] lr: 1.0000e-02 eta: 12:19:39 time: 0.5704 data_time: 0.0225 memory: 16095 grad_norm: 5.1620 loss: 1.5021 top1_acc: 0.6875 top5_acc: 0.9688 loss_cls: 1.5021 2022/12/08 17:45:07 - mmengine - INFO - Epoch(train) [27][100/940] lr: 1.0000e-02 eta: 12:19:29 time: 0.6765 data_time: 0.0252 memory: 16095 grad_norm: 5.0893 loss: 1.4541 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.4541 2022/12/08 17:45:17 - mmengine - INFO - Epoch(train) [27][120/940] lr: 1.0000e-02 eta: 12:19:10 time: 0.5339 data_time: 0.0240 memory: 16095 grad_norm: 5.1378 loss: 1.4690 top1_acc: 0.4688 top5_acc: 0.9062 loss_cls: 1.4690 2022/12/08 17:45:31 - mmengine - INFO - Epoch(train) [27][140/940] lr: 1.0000e-02 eta: 12:18:59 time: 0.6737 data_time: 0.0274 memory: 16095 grad_norm: 5.1523 loss: 1.4330 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4330 2022/12/08 17:45:42 - mmengine - INFO - Epoch(train) [27][160/940] lr: 1.0000e-02 eta: 12:18:42 time: 0.5619 data_time: 0.0222 memory: 16095 grad_norm: 5.0861 loss: 1.4832 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.4832 2022/12/08 17:45:55 - mmengine - INFO - Epoch(train) [27][180/940] lr: 1.0000e-02 eta: 12:18:31 time: 0.6712 data_time: 0.0280 memory: 16095 grad_norm: 5.0292 loss: 1.3767 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3767 2022/12/08 17:46:07 - mmengine - INFO - Epoch(train) [27][200/940] lr: 1.0000e-02 eta: 12:18:16 time: 0.5920 data_time: 0.0210 memory: 16095 grad_norm: 5.1631 loss: 1.4724 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.4724 2022/12/08 17:46:20 - mmengine - INFO - Epoch(train) [27][220/940] lr: 1.0000e-02 eta: 12:18:03 time: 0.6440 data_time: 0.0301 memory: 16095 grad_norm: 5.1300 loss: 1.5705 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.5705 2022/12/08 17:46:31 - mmengine - INFO - Epoch(train) [27][240/940] lr: 1.0000e-02 eta: 12:17:46 time: 0.5570 data_time: 0.0367 memory: 16095 grad_norm: 5.1872 loss: 1.3980 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3980 2022/12/08 17:46:45 - mmengine - INFO - Epoch(train) [27][260/940] lr: 1.0000e-02 eta: 12:17:36 time: 0.6873 data_time: 0.1317 memory: 16095 grad_norm: 5.2157 loss: 1.4417 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.4417 2022/12/08 17:46:57 - mmengine - INFO - Epoch(train) [27][280/940] lr: 1.0000e-02 eta: 12:17:20 time: 0.5784 data_time: 0.1862 memory: 16095 grad_norm: 5.1715 loss: 1.3708 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.3708 2022/12/08 17:47:10 - mmengine - INFO - Epoch(train) [27][300/940] lr: 1.0000e-02 eta: 12:17:08 time: 0.6473 data_time: 0.3188 memory: 16095 grad_norm: 5.2697 loss: 1.4829 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.4829 2022/12/08 17:47:21 - mmengine - INFO - Epoch(train) [27][320/940] lr: 1.0000e-02 eta: 12:16:50 time: 0.5538 data_time: 0.1510 memory: 16095 grad_norm: 5.1929 loss: 1.6007 top1_acc: 0.6562 top5_acc: 0.7500 loss_cls: 1.6007 2022/12/08 17:47:33 - mmengine - INFO - Epoch(train) [27][340/940] lr: 1.0000e-02 eta: 12:16:37 time: 0.6345 data_time: 0.1890 memory: 16095 grad_norm: 5.0972 loss: 1.4964 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.4964 2022/12/08 17:47:44 - mmengine - INFO - Epoch(train) [27][360/940] lr: 1.0000e-02 eta: 12:16:20 time: 0.5547 data_time: 0.1290 memory: 16095 grad_norm: 5.2452 loss: 1.4024 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.4024 2022/12/08 17:47:58 - mmengine - INFO - Epoch(train) [27][380/940] lr: 1.0000e-02 eta: 12:16:09 time: 0.6750 data_time: 0.1223 memory: 16095 grad_norm: 5.2136 loss: 1.3359 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.3359 2022/12/08 17:48:09 - mmengine - INFO - Epoch(train) [27][400/940] lr: 1.0000e-02 eta: 12:15:52 time: 0.5691 data_time: 0.1134 memory: 16095 grad_norm: 5.1890 loss: 1.4717 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.4717 2022/12/08 17:48:24 - mmengine - INFO - Epoch(train) [27][420/940] lr: 1.0000e-02 eta: 12:15:44 time: 0.7162 data_time: 0.3070 memory: 16095 grad_norm: 5.2443 loss: 1.3155 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.3155 2022/12/08 17:48:34 - mmengine - INFO - Epoch(train) [27][440/940] lr: 1.0000e-02 eta: 12:15:25 time: 0.5337 data_time: 0.2058 memory: 16095 grad_norm: 5.2576 loss: 1.5123 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.5123 2022/12/08 17:48:47 - mmengine - INFO - Epoch(train) [27][460/940] lr: 1.0000e-02 eta: 12:15:13 time: 0.6407 data_time: 0.3043 memory: 16095 grad_norm: 5.2540 loss: 1.6299 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.6299 2022/12/08 17:48:59 - mmengine - INFO - Epoch(train) [27][480/940] lr: 1.0000e-02 eta: 12:14:57 time: 0.5846 data_time: 0.2618 memory: 16095 grad_norm: 5.1955 loss: 1.4039 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.4039 2022/12/08 17:49:12 - mmengine - INFO - Epoch(train) [27][500/940] lr: 1.0000e-02 eta: 12:14:45 time: 0.6525 data_time: 0.3329 memory: 16095 grad_norm: 5.1750 loss: 1.5061 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.5061 2022/12/08 17:49:24 - mmengine - INFO - Epoch(train) [27][520/940] lr: 1.0000e-02 eta: 12:14:31 time: 0.6216 data_time: 0.2949 memory: 16095 grad_norm: 5.1486 loss: 1.3969 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.3969 2022/12/08 17:49:37 - mmengine - INFO - Epoch(train) [27][540/940] lr: 1.0000e-02 eta: 12:14:19 time: 0.6559 data_time: 0.3321 memory: 16095 grad_norm: 5.1971 loss: 1.4149 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.4149 2022/12/08 17:49:48 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 17:49:48 - mmengine - INFO - Epoch(train) [27][560/940] lr: 1.0000e-02 eta: 12:14:01 time: 0.5459 data_time: 0.2206 memory: 16095 grad_norm: 5.1209 loss: 1.4510 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.4510 2022/12/08 17:50:01 - mmengine - INFO - Epoch(train) [27][580/940] lr: 1.0000e-02 eta: 12:13:49 time: 0.6520 data_time: 0.3000 memory: 16095 grad_norm: 5.1663 loss: 1.4674 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.4674 2022/12/08 17:50:13 - mmengine - INFO - Epoch(train) [27][600/940] lr: 1.0000e-02 eta: 12:13:33 time: 0.5755 data_time: 0.2474 memory: 16095 grad_norm: 5.3630 loss: 1.5420 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5420 2022/12/08 17:50:26 - mmengine - INFO - Epoch(train) [27][620/940] lr: 1.0000e-02 eta: 12:13:20 time: 0.6367 data_time: 0.3132 memory: 16095 grad_norm: 5.1762 loss: 1.4862 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.4862 2022/12/08 17:50:38 - mmengine - INFO - Epoch(train) [27][640/940] lr: 1.0000e-02 eta: 12:13:06 time: 0.6030 data_time: 0.2144 memory: 16095 grad_norm: 5.2350 loss: 1.4248 top1_acc: 0.4375 top5_acc: 0.8438 loss_cls: 1.4248 2022/12/08 17:50:50 - mmengine - INFO - Epoch(train) [27][660/940] lr: 1.0000e-02 eta: 12:12:52 time: 0.6150 data_time: 0.2046 memory: 16095 grad_norm: 5.2531 loss: 1.5060 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.5060 2022/12/08 17:51:01 - mmengine - INFO - Epoch(train) [27][680/940] lr: 1.0000e-02 eta: 12:12:35 time: 0.5705 data_time: 0.2020 memory: 16095 grad_norm: 5.1818 loss: 1.5033 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.5033 2022/12/08 17:51:15 - mmengine - INFO - Epoch(train) [27][700/940] lr: 1.0000e-02 eta: 12:12:25 time: 0.6873 data_time: 0.3530 memory: 16095 grad_norm: 5.2530 loss: 1.4973 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.4973 2022/12/08 17:51:26 - mmengine - INFO - Epoch(train) [27][720/940] lr: 1.0000e-02 eta: 12:12:06 time: 0.5175 data_time: 0.1662 memory: 16095 grad_norm: 5.1799 loss: 1.4134 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.4134 2022/12/08 17:51:39 - mmengine - INFO - Epoch(train) [27][740/940] lr: 1.0000e-02 eta: 12:11:54 time: 0.6607 data_time: 0.3061 memory: 16095 grad_norm: 5.2233 loss: 1.4054 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.4054 2022/12/08 17:51:51 - mmengine - INFO - Epoch(train) [27][760/940] lr: 1.0000e-02 eta: 12:11:39 time: 0.5898 data_time: 0.2523 memory: 16095 grad_norm: 5.2391 loss: 1.3601 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.3601 2022/12/08 17:52:04 - mmengine - INFO - Epoch(train) [27][780/940] lr: 1.0000e-02 eta: 12:11:28 time: 0.6709 data_time: 0.3380 memory: 16095 grad_norm: 5.1913 loss: 1.3749 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 1.3749 2022/12/08 17:52:14 - mmengine - INFO - Epoch(train) [27][800/940] lr: 1.0000e-02 eta: 12:11:08 time: 0.5040 data_time: 0.1704 memory: 16095 grad_norm: 5.2540 loss: 1.4883 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.4883 2022/12/08 17:52:28 - mmengine - INFO - Epoch(train) [27][820/940] lr: 1.0000e-02 eta: 12:10:58 time: 0.6855 data_time: 0.3691 memory: 16095 grad_norm: 5.1439 loss: 1.5863 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.5863 2022/12/08 17:52:39 - mmengine - INFO - Epoch(train) [27][840/940] lr: 1.0000e-02 eta: 12:10:39 time: 0.5362 data_time: 0.2153 memory: 16095 grad_norm: 5.0774 loss: 1.3899 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.3899 2022/12/08 17:52:51 - mmengine - INFO - Epoch(train) [27][860/940] lr: 1.0000e-02 eta: 12:10:27 time: 0.6445 data_time: 0.3170 memory: 16095 grad_norm: 5.3009 loss: 1.6273 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6273 2022/12/08 17:53:03 - mmengine - INFO - Epoch(train) [27][880/940] lr: 1.0000e-02 eta: 12:10:10 time: 0.5681 data_time: 0.1958 memory: 16095 grad_norm: 5.2905 loss: 1.4405 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.4405 2022/12/08 17:53:16 - mmengine - INFO - Epoch(train) [27][900/940] lr: 1.0000e-02 eta: 12:09:57 time: 0.6355 data_time: 0.1942 memory: 16095 grad_norm: 5.2989 loss: 1.4057 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4057 2022/12/08 17:53:27 - mmengine - INFO - Epoch(train) [27][920/940] lr: 1.0000e-02 eta: 12:09:41 time: 0.5711 data_time: 0.1369 memory: 16095 grad_norm: 5.1732 loss: 1.4372 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 1.4372 2022/12/08 17:53:38 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 17:53:38 - mmengine - INFO - Epoch(train) [27][940/940] lr: 1.0000e-02 eta: 12:09:25 time: 0.5763 data_time: 0.1544 memory: 16095 grad_norm: 5.5433 loss: 1.6029 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.6029 2022/12/08 17:53:38 - mmengine - INFO - Saving checkpoint at 27 epochs 2022/12/08 17:53:56 - mmengine - INFO - Epoch(val) [27][20/78] eta: 0:00:43 time: 0.7575 data_time: 0.6539 memory: 1686 2022/12/08 17:54:05 - mmengine - INFO - Epoch(val) [27][40/78] eta: 0:00:23 time: 0.4540 data_time: 0.3510 memory: 1686 2022/12/08 17:54:19 - mmengine - INFO - Epoch(val) [27][60/78] eta: 0:00:11 time: 0.6899 data_time: 0.5933 memory: 1686 2022/12/08 17:54:29 - mmengine - INFO - Epoch(val) [27][78/78] acc/top1: 0.6400 acc/top5: 0.8530 acc/mean1: 0.6399 2022/12/08 17:54:47 - mmengine - INFO - Epoch(train) [28][ 20/940] lr: 1.0000e-02 eta: 12:09:25 time: 0.8729 data_time: 0.3159 memory: 16095 grad_norm: 5.1697 loss: 1.5933 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5933 2022/12/08 17:54:57 - mmengine - INFO - Epoch(train) [28][ 40/940] lr: 1.0000e-02 eta: 12:09:06 time: 0.5298 data_time: 0.1106 memory: 16095 grad_norm: 5.1249 loss: 1.4596 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 1.4596 2022/12/08 17:55:11 - mmengine - INFO - Epoch(train) [28][ 60/940] lr: 1.0000e-02 eta: 12:08:57 time: 0.6981 data_time: 0.2962 memory: 16095 grad_norm: 5.2178 loss: 1.3648 top1_acc: 0.4062 top5_acc: 0.6875 loss_cls: 1.3648 2022/12/08 17:55:23 - mmengine - INFO - Epoch(train) [28][ 80/940] lr: 1.0000e-02 eta: 12:08:41 time: 0.5805 data_time: 0.2054 memory: 16095 grad_norm: 5.2473 loss: 1.5462 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.5462 2022/12/08 17:55:36 - mmengine - INFO - Epoch(train) [28][100/940] lr: 1.0000e-02 eta: 12:08:30 time: 0.6723 data_time: 0.3647 memory: 16095 grad_norm: 5.0836 loss: 1.3727 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 1.3727 2022/12/08 17:55:47 - mmengine - INFO - Epoch(train) [28][120/940] lr: 1.0000e-02 eta: 12:08:13 time: 0.5598 data_time: 0.2224 memory: 16095 grad_norm: 5.1104 loss: 1.3461 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.3461 2022/12/08 17:56:01 - mmengine - INFO - Epoch(train) [28][140/940] lr: 1.0000e-02 eta: 12:08:03 time: 0.6845 data_time: 0.3712 memory: 16095 grad_norm: 5.1761 loss: 1.4647 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.4647 2022/12/08 17:56:11 - mmengine - INFO - Epoch(train) [28][160/940] lr: 1.0000e-02 eta: 12:07:43 time: 0.5035 data_time: 0.1952 memory: 16095 grad_norm: 5.0656 loss: 1.3036 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.3036 2022/12/08 17:56:24 - mmengine - INFO - Epoch(train) [28][180/940] lr: 1.0000e-02 eta: 12:07:30 time: 0.6367 data_time: 0.3278 memory: 16095 grad_norm: 5.1710 loss: 1.4408 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.4408 2022/12/08 17:56:36 - mmengine - INFO - Epoch(train) [28][200/940] lr: 1.0000e-02 eta: 12:07:15 time: 0.5958 data_time: 0.2728 memory: 16095 grad_norm: 5.1692 loss: 1.4464 top1_acc: 0.5000 top5_acc: 0.6562 loss_cls: 1.4464 2022/12/08 17:56:49 - mmengine - INFO - Epoch(train) [28][220/940] lr: 1.0000e-02 eta: 12:07:04 time: 0.6673 data_time: 0.3442 memory: 16095 grad_norm: 5.0959 loss: 1.4213 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4213 2022/12/08 17:57:00 - mmengine - INFO - Epoch(train) [28][240/940] lr: 1.0000e-02 eta: 12:06:46 time: 0.5479 data_time: 0.2316 memory: 16095 grad_norm: 5.0511 loss: 1.3139 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3139 2022/12/08 17:57:13 - mmengine - INFO - Epoch(train) [28][260/940] lr: 1.0000e-02 eta: 12:06:35 time: 0.6569 data_time: 0.3389 memory: 16095 grad_norm: 5.1171 loss: 1.3779 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.3779 2022/12/08 17:57:24 - mmengine - INFO - Epoch(train) [28][280/940] lr: 1.0000e-02 eta: 12:06:17 time: 0.5540 data_time: 0.2357 memory: 16095 grad_norm: 5.2554 loss: 1.4287 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.4287 2022/12/08 17:57:38 - mmengine - INFO - Epoch(train) [28][300/940] lr: 1.0000e-02 eta: 12:06:06 time: 0.6577 data_time: 0.3352 memory: 16095 grad_norm: 5.2092 loss: 1.4235 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.4235 2022/12/08 17:57:49 - mmengine - INFO - Epoch(train) [28][320/940] lr: 1.0000e-02 eta: 12:05:49 time: 0.5686 data_time: 0.1432 memory: 16095 grad_norm: 5.3321 loss: 1.4841 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 1.4841 2022/12/08 17:58:02 - mmengine - INFO - Epoch(train) [28][340/940] lr: 1.0000e-02 eta: 12:05:37 time: 0.6401 data_time: 0.2061 memory: 16095 grad_norm: 5.2890 loss: 1.4210 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.4210 2022/12/08 17:58:13 - mmengine - INFO - Epoch(train) [28][360/940] lr: 1.0000e-02 eta: 12:05:19 time: 0.5456 data_time: 0.1484 memory: 16095 grad_norm: 5.1167 loss: 1.3601 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.3601 2022/12/08 17:58:27 - mmengine - INFO - Epoch(train) [28][380/940] lr: 1.0000e-02 eta: 12:05:11 time: 0.7307 data_time: 0.3556 memory: 16095 grad_norm: 5.2060 loss: 1.5050 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.5050 2022/12/08 17:58:38 - mmengine - INFO - Epoch(train) [28][400/940] lr: 1.0000e-02 eta: 12:04:53 time: 0.5279 data_time: 0.1936 memory: 16095 grad_norm: 5.3116 loss: 1.4120 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.4120 2022/12/08 17:58:51 - mmengine - INFO - Epoch(train) [28][420/940] lr: 1.0000e-02 eta: 12:04:42 time: 0.6746 data_time: 0.3408 memory: 16095 grad_norm: 5.2061 loss: 1.4878 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.4878 2022/12/08 17:59:02 - mmengine - INFO - Epoch(train) [28][440/940] lr: 1.0000e-02 eta: 12:04:25 time: 0.5548 data_time: 0.1949 memory: 16095 grad_norm: 5.2005 loss: 1.4909 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 1.4909 2022/12/08 17:59:16 - mmengine - INFO - Epoch(train) [28][460/940] lr: 1.0000e-02 eta: 12:04:13 time: 0.6654 data_time: 0.1891 memory: 16095 grad_norm: 5.1619 loss: 1.2722 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2722 2022/12/08 17:59:27 - mmengine - INFO - Epoch(train) [28][480/940] lr: 1.0000e-02 eta: 12:03:57 time: 0.5644 data_time: 0.1353 memory: 16095 grad_norm: 5.1723 loss: 1.3717 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3717 2022/12/08 17:59:40 - mmengine - INFO - Epoch(train) [28][500/940] lr: 1.0000e-02 eta: 12:03:45 time: 0.6543 data_time: 0.3401 memory: 16095 grad_norm: 5.2342 loss: 1.4460 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4460 2022/12/08 17:59:51 - mmengine - INFO - Epoch(train) [28][520/940] lr: 1.0000e-02 eta: 12:03:28 time: 0.5523 data_time: 0.2223 memory: 16095 grad_norm: 5.1457 loss: 1.3320 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.3320 2022/12/08 18:00:05 - mmengine - INFO - Epoch(train) [28][540/940] lr: 1.0000e-02 eta: 12:03:19 time: 0.7197 data_time: 0.3573 memory: 16095 grad_norm: 5.1521 loss: 1.5600 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.5600 2022/12/08 18:00:17 - mmengine - INFO - Epoch(train) [28][560/940] lr: 1.0000e-02 eta: 12:03:04 time: 0.5881 data_time: 0.1476 memory: 16095 grad_norm: 5.1659 loss: 1.4293 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.4293 2022/12/08 18:00:31 - mmengine - INFO - Epoch(train) [28][580/940] lr: 1.0000e-02 eta: 12:02:53 time: 0.6696 data_time: 0.2075 memory: 16095 grad_norm: 5.1377 loss: 1.3790 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3790 2022/12/08 18:00:42 - mmengine - INFO - Epoch(train) [28][600/940] lr: 1.0000e-02 eta: 12:02:36 time: 0.5632 data_time: 0.1793 memory: 16095 grad_norm: 5.3130 loss: 1.4837 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.4837 2022/12/08 18:00:55 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 18:00:55 - mmengine - INFO - Epoch(train) [28][620/940] lr: 1.0000e-02 eta: 12:02:25 time: 0.6691 data_time: 0.3615 memory: 16095 grad_norm: 5.2014 loss: 1.4282 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.4282 2022/12/08 18:01:07 - mmengine - INFO - Epoch(train) [28][640/940] lr: 1.0000e-02 eta: 12:02:09 time: 0.5765 data_time: 0.2602 memory: 16095 grad_norm: 5.1502 loss: 1.3359 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.3359 2022/12/08 18:01:19 - mmengine - INFO - Epoch(train) [28][660/940] lr: 1.0000e-02 eta: 12:01:56 time: 0.6245 data_time: 0.2952 memory: 16095 grad_norm: 5.2223 loss: 1.5182 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.5182 2022/12/08 18:01:31 - mmengine - INFO - Epoch(train) [28][680/940] lr: 1.0000e-02 eta: 12:01:39 time: 0.5619 data_time: 0.2459 memory: 16095 grad_norm: 5.2594 loss: 1.4530 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4530 2022/12/08 18:01:45 - mmengine - INFO - Epoch(train) [28][700/940] lr: 1.0000e-02 eta: 12:01:30 time: 0.6977 data_time: 0.3771 memory: 16095 grad_norm: 5.1478 loss: 1.4203 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 1.4203 2022/12/08 18:01:56 - mmengine - INFO - Epoch(train) [28][720/940] lr: 1.0000e-02 eta: 12:01:12 time: 0.5495 data_time: 0.2238 memory: 16095 grad_norm: 5.1915 loss: 1.5707 top1_acc: 0.7500 top5_acc: 0.7812 loss_cls: 1.5707 2022/12/08 18:02:09 - mmengine - INFO - Epoch(train) [28][740/940] lr: 1.0000e-02 eta: 12:01:01 time: 0.6566 data_time: 0.2575 memory: 16095 grad_norm: 5.3196 loss: 1.4779 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.4779 2022/12/08 18:02:20 - mmengine - INFO - Epoch(train) [28][760/940] lr: 1.0000e-02 eta: 12:00:44 time: 0.5663 data_time: 0.2516 memory: 16095 grad_norm: 5.2878 loss: 1.4214 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 1.4214 2022/12/08 18:02:34 - mmengine - INFO - Epoch(train) [28][780/940] lr: 1.0000e-02 eta: 12:00:34 time: 0.6977 data_time: 0.3787 memory: 16095 grad_norm: 5.4074 loss: 1.5522 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5522 2022/12/08 18:02:45 - mmengine - INFO - Epoch(train) [28][800/940] lr: 1.0000e-02 eta: 12:00:16 time: 0.5316 data_time: 0.2146 memory: 16095 grad_norm: 5.2204 loss: 1.4243 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.4243 2022/12/08 18:02:58 - mmengine - INFO - Epoch(train) [28][820/940] lr: 1.0000e-02 eta: 12:00:04 time: 0.6519 data_time: 0.3371 memory: 16095 grad_norm: 5.2922 loss: 1.4116 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.4116 2022/12/08 18:03:08 - mmengine - INFO - Epoch(train) [28][840/940] lr: 1.0000e-02 eta: 11:59:47 time: 0.5424 data_time: 0.2263 memory: 16095 grad_norm: 5.2393 loss: 1.4012 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 1.4012 2022/12/08 18:03:23 - mmengine - INFO - Epoch(train) [28][860/940] lr: 1.0000e-02 eta: 11:59:38 time: 0.7150 data_time: 0.3965 memory: 16095 grad_norm: 5.3302 loss: 1.5421 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.5421 2022/12/08 18:03:35 - mmengine - INFO - Epoch(train) [28][880/940] lr: 1.0000e-02 eta: 11:59:23 time: 0.5965 data_time: 0.2713 memory: 16095 grad_norm: 5.3361 loss: 1.4142 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4142 2022/12/08 18:03:48 - mmengine - INFO - Epoch(train) [28][900/940] lr: 1.0000e-02 eta: 11:59:12 time: 0.6745 data_time: 0.3529 memory: 16095 grad_norm: 5.4052 loss: 1.4496 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.4496 2022/12/08 18:03:59 - mmengine - INFO - Epoch(train) [28][920/940] lr: 1.0000e-02 eta: 11:58:55 time: 0.5595 data_time: 0.2323 memory: 16095 grad_norm: 5.3522 loss: 1.5768 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.5768 2022/12/08 18:04:11 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 18:04:11 - mmengine - INFO - Epoch(train) [28][940/940] lr: 1.0000e-02 eta: 11:58:40 time: 0.5909 data_time: 0.3076 memory: 16095 grad_norm: 5.5975 loss: 1.3609 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 1.3609 2022/12/08 18:04:25 - mmengine - INFO - Epoch(val) [28][20/78] eta: 0:00:40 time: 0.7053 data_time: 0.6117 memory: 1686 2022/12/08 18:04:35 - mmengine - INFO - Epoch(val) [28][40/78] eta: 0:00:22 time: 0.4724 data_time: 0.3780 memory: 1686 2022/12/08 18:04:49 - mmengine - INFO - Epoch(val) [28][60/78] eta: 0:00:11 time: 0.6920 data_time: 0.5976 memory: 1686 2022/12/08 18:04:59 - mmengine - INFO - Epoch(val) [28][78/78] acc/top1: 0.6390 acc/top5: 0.8521 acc/mean1: 0.6390 2022/12/08 18:05:16 - mmengine - INFO - Epoch(train) [29][ 20/940] lr: 1.0000e-02 eta: 11:58:38 time: 0.8449 data_time: 0.4713 memory: 16095 grad_norm: 5.1353 loss: 1.4886 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.4886 2022/12/08 18:05:26 - mmengine - INFO - Epoch(train) [29][ 40/940] lr: 1.0000e-02 eta: 11:58:20 time: 0.5299 data_time: 0.1497 memory: 16095 grad_norm: 5.1525 loss: 1.3810 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 1.3810 2022/12/08 18:05:39 - mmengine - INFO - Epoch(train) [29][ 60/940] lr: 1.0000e-02 eta: 11:58:08 time: 0.6475 data_time: 0.0795 memory: 16095 grad_norm: 5.3074 loss: 1.3464 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.3464 2022/12/08 18:05:50 - mmengine - INFO - Epoch(train) [29][ 80/940] lr: 1.0000e-02 eta: 11:57:51 time: 0.5561 data_time: 0.0491 memory: 16095 grad_norm: 5.2074 loss: 1.3455 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 1.3455 2022/12/08 18:06:05 - mmengine - INFO - Epoch(train) [29][100/940] lr: 1.0000e-02 eta: 11:57:42 time: 0.7093 data_time: 0.0276 memory: 16095 grad_norm: 5.2368 loss: 1.4223 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.4223 2022/12/08 18:06:16 - mmengine - INFO - Epoch(train) [29][120/940] lr: 1.0000e-02 eta: 11:57:25 time: 0.5643 data_time: 0.0210 memory: 16095 grad_norm: 5.0994 loss: 1.2076 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.2076 2022/12/08 18:06:29 - mmengine - INFO - Epoch(train) [29][140/940] lr: 1.0000e-02 eta: 11:57:14 time: 0.6609 data_time: 0.0281 memory: 16095 grad_norm: 5.1208 loss: 1.3457 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 1.3457 2022/12/08 18:06:41 - mmengine - INFO - Epoch(train) [29][160/940] lr: 1.0000e-02 eta: 11:56:58 time: 0.5809 data_time: 0.0209 memory: 16095 grad_norm: 5.1276 loss: 1.3790 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3790 2022/12/08 18:06:54 - mmengine - INFO - Epoch(train) [29][180/940] lr: 1.0000e-02 eta: 11:56:46 time: 0.6561 data_time: 0.0278 memory: 16095 grad_norm: 5.1819 loss: 1.3977 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3977 2022/12/08 18:07:05 - mmengine - INFO - Epoch(train) [29][200/940] lr: 1.0000e-02 eta: 11:56:30 time: 0.5611 data_time: 0.0211 memory: 16095 grad_norm: 5.0493 loss: 1.4631 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.4631 2022/12/08 18:07:18 - mmengine - INFO - Epoch(train) [29][220/940] lr: 1.0000e-02 eta: 11:56:18 time: 0.6625 data_time: 0.0281 memory: 16095 grad_norm: 5.1153 loss: 1.3776 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.3776 2022/12/08 18:07:29 - mmengine - INFO - Epoch(train) [29][240/940] lr: 1.0000e-02 eta: 11:56:02 time: 0.5588 data_time: 0.0239 memory: 16095 grad_norm: 5.1860 loss: 1.4168 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.4168 2022/12/08 18:07:43 - mmengine - INFO - Epoch(train) [29][260/940] lr: 1.0000e-02 eta: 11:55:50 time: 0.6589 data_time: 0.0383 memory: 16095 grad_norm: 5.2277 loss: 1.3187 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3187 2022/12/08 18:07:54 - mmengine - INFO - Epoch(train) [29][280/940] lr: 1.0000e-02 eta: 11:55:33 time: 0.5582 data_time: 0.0206 memory: 16095 grad_norm: 5.2232 loss: 1.4123 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.4123 2022/12/08 18:08:08 - mmengine - INFO - Epoch(train) [29][300/940] lr: 1.0000e-02 eta: 11:55:24 time: 0.6965 data_time: 0.0285 memory: 16095 grad_norm: 5.2245 loss: 1.4990 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.4990 2022/12/08 18:08:19 - mmengine - INFO - Epoch(train) [29][320/940] lr: 1.0000e-02 eta: 11:55:06 time: 0.5498 data_time: 0.0213 memory: 16095 grad_norm: 5.1765 loss: 1.4700 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4700 2022/12/08 18:11:03 - mmengine - INFO - Epoch(train) [29][340/940] lr: 1.0000e-02 eta: 12:01:17 time: 8.2288 data_time: 0.0280 memory: 16095 grad_norm: 5.1228 loss: 1.3402 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3402 2022/12/08 18:11:14 - mmengine - INFO - Epoch(train) [29][360/940] lr: 1.0000e-02 eta: 12:01:00 time: 0.5499 data_time: 0.0232 memory: 16095 grad_norm: 5.1985 loss: 1.3056 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.3056 2022/12/08 18:11:28 - mmengine - INFO - Epoch(train) [29][380/940] lr: 1.0000e-02 eta: 12:00:48 time: 0.6626 data_time: 0.0275 memory: 16095 grad_norm: 5.1492 loss: 1.2942 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.2942 2022/12/08 18:11:39 - mmengine - INFO - Epoch(train) [29][400/940] lr: 1.0000e-02 eta: 12:00:31 time: 0.5586 data_time: 0.0231 memory: 16095 grad_norm: 5.2787 loss: 1.5481 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.5481 2022/12/08 18:11:52 - mmengine - INFO - Epoch(train) [29][420/940] lr: 1.0000e-02 eta: 12:00:20 time: 0.6816 data_time: 0.0258 memory: 16095 grad_norm: 5.0852 loss: 1.3660 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 1.3660 2022/12/08 18:12:03 - mmengine - INFO - Epoch(train) [29][440/940] lr: 1.0000e-02 eta: 12:00:02 time: 0.5346 data_time: 0.0230 memory: 16095 grad_norm: 5.2457 loss: 1.5172 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.5172 2022/12/08 18:12:16 - mmengine - INFO - Epoch(train) [29][460/940] lr: 1.0000e-02 eta: 11:59:50 time: 0.6606 data_time: 0.0246 memory: 16095 grad_norm: 5.1552 loss: 1.4326 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.4326 2022/12/08 18:12:28 - mmengine - INFO - Epoch(train) [29][480/940] lr: 1.0000e-02 eta: 11:59:33 time: 0.5632 data_time: 0.0672 memory: 16095 grad_norm: 5.1705 loss: 1.4298 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.4298 2022/12/08 18:12:41 - mmengine - INFO - Epoch(train) [29][500/940] lr: 1.0000e-02 eta: 11:59:21 time: 0.6558 data_time: 0.1620 memory: 16095 grad_norm: 5.2890 loss: 1.3625 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3625 2022/12/08 18:12:52 - mmengine - INFO - Epoch(train) [29][520/940] lr: 1.0000e-02 eta: 11:59:04 time: 0.5641 data_time: 0.0448 memory: 16095 grad_norm: 5.2254 loss: 1.4246 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.4246 2022/12/08 18:13:06 - mmengine - INFO - Epoch(train) [29][540/940] lr: 1.0000e-02 eta: 11:58:53 time: 0.6882 data_time: 0.0259 memory: 16095 grad_norm: 5.2675 loss: 1.5534 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.5534 2022/12/08 18:13:16 - mmengine - INFO - Epoch(train) [29][560/940] lr: 1.0000e-02 eta: 11:58:35 time: 0.5369 data_time: 0.0476 memory: 16095 grad_norm: 5.2641 loss: 1.4014 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4014 2022/12/08 18:13:30 - mmengine - INFO - Epoch(train) [29][580/940] lr: 1.0000e-02 eta: 11:58:24 time: 0.6842 data_time: 0.0524 memory: 16095 grad_norm: 5.1616 loss: 1.4158 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.4158 2022/12/08 18:13:41 - mmengine - INFO - Epoch(train) [29][600/940] lr: 1.0000e-02 eta: 11:58:08 time: 0.5662 data_time: 0.0210 memory: 16095 grad_norm: 5.2785 loss: 1.3836 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3836 2022/12/08 18:13:55 - mmengine - INFO - Epoch(train) [29][620/940] lr: 1.0000e-02 eta: 11:57:57 time: 0.6915 data_time: 0.0280 memory: 16095 grad_norm: 5.3086 loss: 1.5683 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5683 2022/12/08 18:14:07 - mmengine - INFO - Epoch(train) [29][640/940] lr: 1.0000e-02 eta: 11:57:42 time: 0.5815 data_time: 0.0238 memory: 16095 grad_norm: 5.2379 loss: 1.3495 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.3495 2022/12/08 18:14:20 - mmengine - INFO - Epoch(train) [29][660/940] lr: 1.0000e-02 eta: 11:57:30 time: 0.6669 data_time: 0.0243 memory: 16095 grad_norm: 5.2070 loss: 1.4595 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 1.4595 2022/12/08 18:14:31 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 18:14:31 - mmengine - INFO - Epoch(train) [29][680/940] lr: 1.0000e-02 eta: 11:57:12 time: 0.5339 data_time: 0.0252 memory: 16095 grad_norm: 5.1920 loss: 1.4674 top1_acc: 0.5625 top5_acc: 0.7188 loss_cls: 1.4674 2022/12/08 18:14:44 - mmengine - INFO - Epoch(train) [29][700/940] lr: 1.0000e-02 eta: 11:56:59 time: 0.6378 data_time: 0.0234 memory: 16095 grad_norm: 5.1336 loss: 1.4914 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.4914 2022/12/08 18:14:55 - mmengine - INFO - Epoch(train) [29][720/940] lr: 1.0000e-02 eta: 11:56:42 time: 0.5691 data_time: 0.0228 memory: 16095 grad_norm: 5.1512 loss: 1.4344 top1_acc: 0.4688 top5_acc: 0.6562 loss_cls: 1.4344 2022/12/08 18:15:08 - mmengine - INFO - Epoch(train) [29][740/940] lr: 1.0000e-02 eta: 11:56:28 time: 0.6254 data_time: 0.0251 memory: 16095 grad_norm: 5.2953 loss: 1.5607 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.5607 2022/12/08 18:15:19 - mmengine - INFO - Epoch(train) [29][760/940] lr: 1.0000e-02 eta: 11:56:11 time: 0.5484 data_time: 0.0243 memory: 16095 grad_norm: 5.2438 loss: 1.4300 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 1.4300 2022/12/08 18:15:32 - mmengine - INFO - Epoch(train) [29][780/940] lr: 1.0000e-02 eta: 11:56:00 time: 0.6741 data_time: 0.0253 memory: 16095 grad_norm: 5.1553 loss: 1.4812 top1_acc: 0.5000 top5_acc: 0.7812 loss_cls: 1.4812 2022/12/08 18:15:44 - mmengine - INFO - Epoch(train) [29][800/940] lr: 1.0000e-02 eta: 11:55:43 time: 0.5739 data_time: 0.0255 memory: 16095 grad_norm: 5.2428 loss: 1.3400 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3400 2022/12/08 18:15:58 - mmengine - INFO - Epoch(train) [29][820/940] lr: 1.0000e-02 eta: 11:55:34 time: 0.7079 data_time: 0.0224 memory: 16095 grad_norm: 5.2724 loss: 1.4639 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 1.4639 2022/12/08 18:16:09 - mmengine - INFO - Epoch(train) [29][840/940] lr: 1.0000e-02 eta: 11:55:16 time: 0.5482 data_time: 0.0252 memory: 16095 grad_norm: 5.2251 loss: 1.3822 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.3822 2022/12/08 18:16:22 - mmengine - INFO - Epoch(train) [29][860/940] lr: 1.0000e-02 eta: 11:55:04 time: 0.6408 data_time: 0.0235 memory: 16095 grad_norm: 5.3164 loss: 1.4269 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.4269 2022/12/08 18:16:32 - mmengine - INFO - Epoch(train) [29][880/940] lr: 1.0000e-02 eta: 11:54:45 time: 0.5282 data_time: 0.0253 memory: 16095 grad_norm: 5.2384 loss: 1.4418 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4418 2022/12/08 18:16:46 - mmengine - INFO - Epoch(train) [29][900/940] lr: 1.0000e-02 eta: 11:54:35 time: 0.6975 data_time: 0.0211 memory: 16095 grad_norm: 5.3706 loss: 1.4797 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.4797 2022/12/08 18:16:57 - mmengine - INFO - Epoch(train) [29][920/940] lr: 1.0000e-02 eta: 11:54:18 time: 0.5556 data_time: 0.0374 memory: 16095 grad_norm: 5.0810 loss: 1.5062 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.5062 2022/12/08 18:17:08 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 18:17:08 - mmengine - INFO - Epoch(train) [29][940/940] lr: 1.0000e-02 eta: 11:53:59 time: 0.5285 data_time: 0.0150 memory: 16095 grad_norm: 5.6062 loss: 1.5651 top1_acc: 0.4286 top5_acc: 0.5714 loss_cls: 1.5651 2022/12/08 18:17:22 - mmengine - INFO - Epoch(val) [29][20/78] eta: 0:00:40 time: 0.6985 data_time: 0.6047 memory: 1686 2022/12/08 18:17:31 - mmengine - INFO - Epoch(val) [29][40/78] eta: 0:00:22 time: 0.4662 data_time: 0.3721 memory: 1686 2022/12/08 18:17:45 - mmengine - INFO - Epoch(val) [29][60/78] eta: 0:00:11 time: 0.6850 data_time: 0.5894 memory: 1686 2022/12/08 18:17:55 - mmengine - INFO - Epoch(val) [29][78/78] acc/top1: 0.6447 acc/top5: 0.8565 acc/mean1: 0.6446 2022/12/08 18:18:12 - mmengine - INFO - Epoch(train) [30][ 20/940] lr: 1.0000e-02 eta: 11:53:56 time: 0.8351 data_time: 0.4275 memory: 16095 grad_norm: 5.0687 loss: 1.3718 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.3718 2022/12/08 18:18:23 - mmengine - INFO - Epoch(train) [30][ 40/940] lr: 1.0000e-02 eta: 11:53:38 time: 0.5337 data_time: 0.2288 memory: 16095 grad_norm: 5.2393 loss: 1.5201 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.5201 2022/12/08 18:18:36 - mmengine - INFO - Epoch(train) [30][ 60/940] lr: 1.0000e-02 eta: 11:53:28 time: 0.6928 data_time: 0.2237 memory: 16095 grad_norm: 5.1667 loss: 1.3274 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.3274 2022/12/08 18:18:48 - mmengine - INFO - Epoch(train) [30][ 80/940] lr: 1.0000e-02 eta: 11:53:11 time: 0.5691 data_time: 0.1254 memory: 16095 grad_norm: 5.2360 loss: 1.4055 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.4055 2022/12/08 18:19:01 - mmengine - INFO - Epoch(train) [30][100/940] lr: 1.0000e-02 eta: 11:52:59 time: 0.6476 data_time: 0.1044 memory: 16095 grad_norm: 5.2184 loss: 1.3032 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.3032 2022/12/08 18:19:12 - mmengine - INFO - Epoch(train) [30][120/940] lr: 1.0000e-02 eta: 11:52:42 time: 0.5674 data_time: 0.1303 memory: 16095 grad_norm: 5.1708 loss: 1.3658 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.3658 2022/12/08 18:19:25 - mmengine - INFO - Epoch(train) [30][140/940] lr: 1.0000e-02 eta: 11:52:29 time: 0.6325 data_time: 0.0421 memory: 16095 grad_norm: 5.2591 loss: 1.3896 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 1.3896 2022/12/08 18:19:36 - mmengine - INFO - Epoch(train) [30][160/940] lr: 1.0000e-02 eta: 11:52:12 time: 0.5636 data_time: 0.0841 memory: 16095 grad_norm: 5.1873 loss: 1.4199 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.4199 2022/12/08 18:19:50 - mmengine - INFO - Epoch(train) [30][180/940] lr: 1.0000e-02 eta: 11:52:02 time: 0.7014 data_time: 0.1928 memory: 16095 grad_norm: 5.0584 loss: 1.3489 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3489 2022/12/08 18:20:01 - mmengine - INFO - Epoch(train) [30][200/940] lr: 1.0000e-02 eta: 11:51:45 time: 0.5578 data_time: 0.1613 memory: 16095 grad_norm: 5.2353 loss: 1.4370 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.4370 2022/12/08 18:20:14 - mmengine - INFO - Epoch(train) [30][220/940] lr: 1.0000e-02 eta: 11:51:32 time: 0.6365 data_time: 0.1248 memory: 16095 grad_norm: 5.2298 loss: 1.3771 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.3771 2022/12/08 18:20:25 - mmengine - INFO - Epoch(train) [30][240/940] lr: 1.0000e-02 eta: 11:51:16 time: 0.5667 data_time: 0.0230 memory: 16095 grad_norm: 5.1889 loss: 1.3521 top1_acc: 0.6250 top5_acc: 0.9688 loss_cls: 1.3521 2022/12/08 18:20:39 - mmengine - INFO - Epoch(train) [30][260/940] lr: 1.0000e-02 eta: 11:51:05 time: 0.6799 data_time: 0.0337 memory: 16095 grad_norm: 5.2375 loss: 1.3944 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 1.3944 2022/12/08 18:20:51 - mmengine - INFO - Epoch(train) [30][280/940] lr: 1.0000e-02 eta: 11:50:49 time: 0.5868 data_time: 0.0236 memory: 16095 grad_norm: 5.2762 loss: 1.3732 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.3732 2022/12/08 18:21:04 - mmengine - INFO - Epoch(train) [30][300/940] lr: 1.0000e-02 eta: 11:50:37 time: 0.6581 data_time: 0.0251 memory: 16095 grad_norm: 5.2086 loss: 1.4312 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4312 2022/12/08 18:21:15 - mmengine - INFO - Epoch(train) [30][320/940] lr: 1.0000e-02 eta: 11:50:20 time: 0.5399 data_time: 0.0233 memory: 16095 grad_norm: 5.1762 loss: 1.2916 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2916 2022/12/08 18:21:28 - mmengine - INFO - Epoch(train) [30][340/940] lr: 1.0000e-02 eta: 11:50:07 time: 0.6515 data_time: 0.0441 memory: 16095 grad_norm: 5.2705 loss: 1.5160 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.5160 2022/12/08 18:21:38 - mmengine - INFO - Epoch(train) [30][360/940] lr: 1.0000e-02 eta: 11:49:49 time: 0.5229 data_time: 0.0862 memory: 16095 grad_norm: 5.2377 loss: 1.4299 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.4299 2022/12/08 18:21:52 - mmengine - INFO - Epoch(train) [30][380/940] lr: 1.0000e-02 eta: 11:49:38 time: 0.6964 data_time: 0.2542 memory: 16095 grad_norm: 5.1040 loss: 1.3099 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.3099 2022/12/08 18:22:03 - mmengine - INFO - Epoch(train) [30][400/940] lr: 1.0000e-02 eta: 11:49:21 time: 0.5546 data_time: 0.1866 memory: 16095 grad_norm: 5.2871 loss: 1.4598 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.4598 2022/12/08 18:22:16 - mmengine - INFO - Epoch(train) [30][420/940] lr: 1.0000e-02 eta: 11:49:10 time: 0.6603 data_time: 0.2152 memory: 16095 grad_norm: 5.2229 loss: 1.4671 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.4671 2022/12/08 18:22:28 - mmengine - INFO - Epoch(train) [30][440/940] lr: 1.0000e-02 eta: 11:48:53 time: 0.5660 data_time: 0.1227 memory: 16095 grad_norm: 5.2896 loss: 1.3508 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.3508 2022/12/08 18:22:41 - mmengine - INFO - Epoch(train) [30][460/940] lr: 1.0000e-02 eta: 11:48:41 time: 0.6601 data_time: 0.2396 memory: 16095 grad_norm: 5.2318 loss: 1.5348 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.5348 2022/12/08 18:22:53 - mmengine - INFO - Epoch(train) [30][480/940] lr: 1.0000e-02 eta: 11:48:26 time: 0.6037 data_time: 0.1270 memory: 16095 grad_norm: 5.2389 loss: 1.4907 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 1.4907 2022/12/08 18:23:06 - mmengine - INFO - Epoch(train) [30][500/940] lr: 1.0000e-02 eta: 11:48:15 time: 0.6639 data_time: 0.2284 memory: 16095 grad_norm: 5.1515 loss: 1.3149 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.3149 2022/12/08 18:23:18 - mmengine - INFO - Epoch(train) [30][520/940] lr: 1.0000e-02 eta: 11:47:59 time: 0.5883 data_time: 0.1808 memory: 16095 grad_norm: 5.1764 loss: 1.2893 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.2893 2022/12/08 18:23:30 - mmengine - INFO - Epoch(train) [30][540/940] lr: 1.0000e-02 eta: 11:47:45 time: 0.6163 data_time: 0.1455 memory: 16095 grad_norm: 5.3013 loss: 1.4845 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.4845 2022/12/08 18:23:42 - mmengine - INFO - Epoch(train) [30][560/940] lr: 1.0000e-02 eta: 11:47:29 time: 0.5645 data_time: 0.0741 memory: 16095 grad_norm: 5.4008 loss: 1.5367 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.5367 2022/12/08 18:23:55 - mmengine - INFO - Epoch(train) [30][580/940] lr: 1.0000e-02 eta: 11:47:17 time: 0.6621 data_time: 0.2567 memory: 16095 grad_norm: 5.2010 loss: 1.4225 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.4225 2022/12/08 18:24:06 - mmengine - INFO - Epoch(train) [30][600/940] lr: 1.0000e-02 eta: 11:47:00 time: 0.5553 data_time: 0.1925 memory: 16095 grad_norm: 5.3337 loss: 1.4233 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.4233 2022/12/08 18:24:19 - mmengine - INFO - Epoch(train) [30][620/940] lr: 1.0000e-02 eta: 11:46:47 time: 0.6348 data_time: 0.1609 memory: 16095 grad_norm: 5.1509 loss: 1.5508 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.5508 2022/12/08 18:24:30 - mmengine - INFO - Epoch(train) [30][640/940] lr: 1.0000e-02 eta: 11:46:29 time: 0.5443 data_time: 0.1433 memory: 16095 grad_norm: 5.3009 loss: 1.4501 top1_acc: 0.4375 top5_acc: 0.7188 loss_cls: 1.4501 2022/12/08 18:24:42 - mmengine - INFO - Epoch(train) [30][660/940] lr: 1.0000e-02 eta: 11:46:17 time: 0.6404 data_time: 0.2862 memory: 16095 grad_norm: 5.3772 loss: 1.4184 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 1.4184 2022/12/08 18:24:54 - mmengine - INFO - Epoch(train) [30][680/940] lr: 1.0000e-02 eta: 11:46:01 time: 0.5875 data_time: 0.2558 memory: 16095 grad_norm: 5.3381 loss: 1.5206 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5206 2022/12/08 18:25:07 - mmengine - INFO - Epoch(train) [30][700/940] lr: 1.0000e-02 eta: 11:45:49 time: 0.6637 data_time: 0.3227 memory: 16095 grad_norm: 5.2437 loss: 1.2636 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.2636 2022/12/08 18:25:19 - mmengine - INFO - Epoch(train) [30][720/940] lr: 1.0000e-02 eta: 11:45:33 time: 0.5631 data_time: 0.2237 memory: 16095 grad_norm: 5.0724 loss: 1.4302 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.4302 2022/12/08 18:25:32 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 18:25:32 - mmengine - INFO - Epoch(train) [30][740/940] lr: 1.0000e-02 eta: 11:45:21 time: 0.6597 data_time: 0.3136 memory: 16095 grad_norm: 5.3047 loss: 1.6135 top1_acc: 0.5625 top5_acc: 0.7188 loss_cls: 1.6135 2022/12/08 18:25:43 - mmengine - INFO - Epoch(train) [30][760/940] lr: 1.0000e-02 eta: 11:45:05 time: 0.5645 data_time: 0.2039 memory: 16095 grad_norm: 5.2201 loss: 1.4521 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.4521 2022/12/08 18:25:57 - mmengine - INFO - Epoch(train) [30][780/940] lr: 1.0000e-02 eta: 11:44:54 time: 0.6859 data_time: 0.3578 memory: 16095 grad_norm: 5.2401 loss: 1.5140 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.5140 2022/12/08 18:26:07 - mmengine - INFO - Epoch(train) [30][800/940] lr: 1.0000e-02 eta: 11:44:36 time: 0.5264 data_time: 0.2031 memory: 16095 grad_norm: 5.2758 loss: 1.5033 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5033 2022/12/08 18:26:21 - mmengine - INFO - Epoch(train) [30][820/940] lr: 1.0000e-02 eta: 11:44:24 time: 0.6630 data_time: 0.3382 memory: 16095 grad_norm: 5.3688 loss: 1.4448 top1_acc: 0.4688 top5_acc: 0.7812 loss_cls: 1.4448 2022/12/08 18:26:31 - mmengine - INFO - Epoch(train) [30][840/940] lr: 1.0000e-02 eta: 11:44:06 time: 0.5366 data_time: 0.2123 memory: 16095 grad_norm: 5.1806 loss: 1.3087 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3087 2022/12/08 18:26:46 - mmengine - INFO - Epoch(train) [30][860/940] lr: 1.0000e-02 eta: 11:43:57 time: 0.7255 data_time: 0.2941 memory: 16095 grad_norm: 5.2691 loss: 1.4855 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.4855 2022/12/08 18:26:56 - mmengine - INFO - Epoch(train) [30][880/940] lr: 1.0000e-02 eta: 11:43:39 time: 0.5192 data_time: 0.1439 memory: 16095 grad_norm: 5.3309 loss: 1.4347 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 1.4347 2022/12/08 18:27:10 - mmengine - INFO - Epoch(train) [30][900/940] lr: 1.0000e-02 eta: 11:43:29 time: 0.6970 data_time: 0.1959 memory: 16095 grad_norm: 5.2567 loss: 1.4991 top1_acc: 0.6250 top5_acc: 0.7188 loss_cls: 1.4991 2022/12/08 18:27:21 - mmengine - INFO - Epoch(train) [30][920/940] lr: 1.0000e-02 eta: 11:43:12 time: 0.5553 data_time: 0.0805 memory: 16095 grad_norm: 5.2548 loss: 1.4410 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.4410 2022/12/08 18:27:32 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 18:27:32 - mmengine - INFO - Epoch(train) [30][940/940] lr: 1.0000e-02 eta: 11:42:54 time: 0.5336 data_time: 0.0261 memory: 16095 grad_norm: 5.6389 loss: 1.4017 top1_acc: 0.2857 top5_acc: 0.7143 loss_cls: 1.4017 2022/12/08 18:27:32 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/12/08 18:27:49 - mmengine - INFO - Epoch(val) [30][20/78] eta: 0:00:41 time: 0.7125 data_time: 0.6177 memory: 1686 2022/12/08 18:27:59 - mmengine - INFO - Epoch(val) [30][40/78] eta: 0:00:22 time: 0.4703 data_time: 0.3759 memory: 1686 2022/12/08 18:28:12 - mmengine - INFO - Epoch(val) [30][60/78] eta: 0:00:11 time: 0.6634 data_time: 0.5674 memory: 1686 2022/12/08 18:28:21 - mmengine - INFO - Epoch(val) [30][78/78] acc/top1: 0.6442 acc/top5: 0.8541 acc/mean1: 0.6440 2022/12/08 18:28:38 - mmengine - INFO - Epoch(train) [31][ 20/940] lr: 1.0000e-02 eta: 11:42:49 time: 0.8167 data_time: 0.4628 memory: 16095 grad_norm: 5.1024 loss: 1.4322 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.4322 2022/12/08 18:28:49 - mmengine - INFO - Epoch(train) [31][ 40/940] lr: 1.0000e-02 eta: 11:42:33 time: 0.5627 data_time: 0.1290 memory: 16095 grad_norm: 5.1272 loss: 1.4514 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4514 2022/12/08 18:29:02 - mmengine - INFO - Epoch(train) [31][ 60/940] lr: 1.0000e-02 eta: 11:42:21 time: 0.6613 data_time: 0.1887 memory: 16095 grad_norm: 5.1824 loss: 1.3481 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.3481 2022/12/08 18:29:13 - mmengine - INFO - Epoch(train) [31][ 80/940] lr: 1.0000e-02 eta: 11:42:04 time: 0.5599 data_time: 0.1673 memory: 16095 grad_norm: 5.2416 loss: 1.4587 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4587 2022/12/08 18:29:27 - mmengine - INFO - Epoch(train) [31][100/940] lr: 1.0000e-02 eta: 11:41:52 time: 0.6589 data_time: 0.3105 memory: 16095 grad_norm: 5.2367 loss: 1.4071 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.4071 2022/12/08 18:29:39 - mmengine - INFO - Epoch(train) [31][120/940] lr: 1.0000e-02 eta: 11:41:38 time: 0.5996 data_time: 0.2225 memory: 16095 grad_norm: 5.1242 loss: 1.4117 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.4117 2022/12/08 18:29:52 - mmengine - INFO - Epoch(train) [31][140/940] lr: 1.0000e-02 eta: 11:41:26 time: 0.6728 data_time: 0.1228 memory: 16095 grad_norm: 5.1005 loss: 1.4407 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.4407 2022/12/08 18:30:03 - mmengine - INFO - Epoch(train) [31][160/940] lr: 1.0000e-02 eta: 11:41:09 time: 0.5521 data_time: 0.0957 memory: 16095 grad_norm: 5.0325 loss: 1.3632 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.3632 2022/12/08 18:30:16 - mmengine - INFO - Epoch(train) [31][180/940] lr: 1.0000e-02 eta: 11:40:57 time: 0.6625 data_time: 0.2403 memory: 16095 grad_norm: 5.1094 loss: 1.3074 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.3074 2022/12/08 18:30:27 - mmengine - INFO - Epoch(train) [31][200/940] lr: 1.0000e-02 eta: 11:40:40 time: 0.5377 data_time: 0.1676 memory: 16095 grad_norm: 5.2722 loss: 1.3014 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3014 2022/12/08 18:30:41 - mmengine - INFO - Epoch(train) [31][220/940] lr: 1.0000e-02 eta: 11:40:30 time: 0.6939 data_time: 0.3395 memory: 16095 grad_norm: 5.2066 loss: 1.3544 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.3544 2022/12/08 18:30:52 - mmengine - INFO - Epoch(train) [31][240/940] lr: 1.0000e-02 eta: 11:40:13 time: 0.5597 data_time: 0.2312 memory: 16095 grad_norm: 5.1526 loss: 1.2819 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.2819 2022/12/08 18:31:06 - mmengine - INFO - Epoch(train) [31][260/940] lr: 1.0000e-02 eta: 11:40:02 time: 0.6834 data_time: 0.3617 memory: 16095 grad_norm: 5.2916 loss: 1.4432 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 1.4432 2022/12/08 18:31:17 - mmengine - INFO - Epoch(train) [31][280/940] lr: 1.0000e-02 eta: 11:39:46 time: 0.5688 data_time: 0.2494 memory: 16095 grad_norm: 5.1606 loss: 1.4256 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.4256 2022/12/08 18:31:29 - mmengine - INFO - Epoch(train) [31][300/940] lr: 1.0000e-02 eta: 11:39:31 time: 0.5980 data_time: 0.2793 memory: 16095 grad_norm: 5.1902 loss: 1.3697 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3697 2022/12/08 18:31:40 - mmengine - INFO - Epoch(train) [31][320/940] lr: 1.0000e-02 eta: 11:39:14 time: 0.5358 data_time: 0.2193 memory: 16095 grad_norm: 5.1923 loss: 1.4131 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4131 2022/12/08 18:31:53 - mmengine - INFO - Epoch(train) [31][340/940] lr: 1.0000e-02 eta: 11:39:01 time: 0.6574 data_time: 0.3356 memory: 16095 grad_norm: 5.3275 loss: 1.3691 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3691 2022/12/08 18:32:05 - mmengine - INFO - Epoch(train) [31][360/940] lr: 1.0000e-02 eta: 11:38:46 time: 0.5812 data_time: 0.1938 memory: 16095 grad_norm: 5.1292 loss: 1.3869 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.3869 2022/12/08 18:32:19 - mmengine - INFO - Epoch(train) [31][380/940] lr: 1.0000e-02 eta: 11:38:36 time: 0.6956 data_time: 0.1903 memory: 16095 grad_norm: 5.2380 loss: 1.3003 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.3003 2022/12/08 18:32:30 - mmengine - INFO - Epoch(train) [31][400/940] lr: 1.0000e-02 eta: 11:38:19 time: 0.5472 data_time: 0.0239 memory: 16095 grad_norm: 5.2101 loss: 1.4362 top1_acc: 0.6562 top5_acc: 1.0000 loss_cls: 1.4362 2022/12/08 18:32:43 - mmengine - INFO - Epoch(train) [31][420/940] lr: 1.0000e-02 eta: 11:38:07 time: 0.6764 data_time: 0.0251 memory: 16095 grad_norm: 5.2064 loss: 1.3898 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.3898 2022/12/08 18:32:54 - mmengine - INFO - Epoch(train) [31][440/940] lr: 1.0000e-02 eta: 11:37:49 time: 0.5262 data_time: 0.0233 memory: 16095 grad_norm: 5.3082 loss: 1.4261 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.4261 2022/12/08 18:33:07 - mmengine - INFO - Epoch(train) [31][460/940] lr: 1.0000e-02 eta: 11:37:38 time: 0.6693 data_time: 0.0397 memory: 16095 grad_norm: 5.2522 loss: 1.4635 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.4635 2022/12/08 18:33:19 - mmengine - INFO - Epoch(train) [31][480/940] lr: 1.0000e-02 eta: 11:37:22 time: 0.5813 data_time: 0.0460 memory: 16095 grad_norm: 5.3090 loss: 1.5295 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.5295 2022/12/08 18:33:32 - mmengine - INFO - Epoch(train) [31][500/940] lr: 1.0000e-02 eta: 11:37:11 time: 0.6755 data_time: 0.0252 memory: 16095 grad_norm: 5.3905 loss: 1.4950 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.4950 2022/12/08 18:33:42 - mmengine - INFO - Epoch(train) [31][520/940] lr: 1.0000e-02 eta: 11:36:52 time: 0.4967 data_time: 0.0247 memory: 16095 grad_norm: 5.3887 loss: 1.4496 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.4496 2022/12/08 18:33:56 - mmengine - INFO - Epoch(train) [31][540/940] lr: 1.0000e-02 eta: 11:36:42 time: 0.7030 data_time: 0.0264 memory: 16095 grad_norm: 5.3014 loss: 1.3930 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 1.3930 2022/12/08 18:34:07 - mmengine - INFO - Epoch(train) [31][560/940] lr: 1.0000e-02 eta: 11:36:25 time: 0.5461 data_time: 0.0236 memory: 16095 grad_norm: 5.3135 loss: 1.4492 top1_acc: 0.5000 top5_acc: 0.8438 loss_cls: 1.4492 2022/12/08 18:34:20 - mmengine - INFO - Epoch(train) [31][580/940] lr: 1.0000e-02 eta: 11:36:13 time: 0.6653 data_time: 0.0251 memory: 16095 grad_norm: 5.1507 loss: 1.3985 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 1.3985 2022/12/08 18:34:32 - mmengine - INFO - Epoch(train) [31][600/940] lr: 1.0000e-02 eta: 11:35:57 time: 0.5615 data_time: 0.0259 memory: 16095 grad_norm: 5.2797 loss: 1.4148 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4148 2022/12/08 18:34:45 - mmengine - INFO - Epoch(train) [31][620/940] lr: 1.0000e-02 eta: 11:35:45 time: 0.6740 data_time: 0.0376 memory: 16095 grad_norm: 5.3138 loss: 1.4370 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.4370 2022/12/08 18:34:56 - mmengine - INFO - Epoch(train) [31][640/940] lr: 1.0000e-02 eta: 11:35:28 time: 0.5494 data_time: 0.0214 memory: 16095 grad_norm: 5.2509 loss: 1.4524 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.4524 2022/12/08 18:35:09 - mmengine - INFO - Epoch(train) [31][660/940] lr: 1.0000e-02 eta: 11:35:15 time: 0.6313 data_time: 0.0287 memory: 16095 grad_norm: 5.2715 loss: 1.3569 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3569 2022/12/08 18:35:19 - mmengine - INFO - Epoch(train) [31][680/940] lr: 1.0000e-02 eta: 11:34:57 time: 0.5194 data_time: 0.0203 memory: 16095 grad_norm: 5.2367 loss: 1.3905 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.3905 2022/12/08 18:35:32 - mmengine - INFO - Epoch(train) [31][700/940] lr: 1.0000e-02 eta: 11:34:45 time: 0.6671 data_time: 0.0250 memory: 16095 grad_norm: 5.3033 loss: 1.3669 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 1.3669 2022/12/08 18:35:44 - mmengine - INFO - Epoch(train) [31][720/940] lr: 1.0000e-02 eta: 11:34:30 time: 0.5822 data_time: 0.0221 memory: 16095 grad_norm: 5.1950 loss: 1.4009 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.4009 2022/12/08 18:35:57 - mmengine - INFO - Epoch(train) [31][740/940] lr: 1.0000e-02 eta: 11:34:17 time: 0.6433 data_time: 0.0285 memory: 16095 grad_norm: 5.2767 loss: 1.4464 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.4464 2022/12/08 18:36:09 - mmengine - INFO - Epoch(train) [31][760/940] lr: 1.0000e-02 eta: 11:34:02 time: 0.5787 data_time: 0.0214 memory: 16095 grad_norm: 5.2696 loss: 1.3472 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.3472 2022/12/08 18:36:21 - mmengine - INFO - Epoch(train) [31][780/940] lr: 1.0000e-02 eta: 11:33:49 time: 0.6455 data_time: 0.0291 memory: 16095 grad_norm: 5.2975 loss: 1.4639 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.4639 2022/12/08 18:36:34 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 18:36:34 - mmengine - INFO - Epoch(train) [31][800/940] lr: 1.0000e-02 eta: 11:33:35 time: 0.6029 data_time: 0.0370 memory: 16095 grad_norm: 5.2245 loss: 1.4187 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.4187 2022/12/08 18:36:47 - mmengine - INFO - Epoch(train) [31][820/940] lr: 1.0000e-02 eta: 11:33:23 time: 0.6608 data_time: 0.0515 memory: 16095 grad_norm: 5.2885 loss: 1.4193 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.4193 2022/12/08 18:36:58 - mmengine - INFO - Epoch(train) [31][840/940] lr: 1.0000e-02 eta: 11:33:05 time: 0.5408 data_time: 0.0702 memory: 16095 grad_norm: 5.3622 loss: 1.4082 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.4082 2022/12/08 18:37:11 - mmengine - INFO - Epoch(train) [31][860/940] lr: 1.0000e-02 eta: 11:32:54 time: 0.6639 data_time: 0.1901 memory: 16095 grad_norm: 5.2790 loss: 1.4353 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4353 2022/12/08 18:37:22 - mmengine - INFO - Epoch(train) [31][880/940] lr: 1.0000e-02 eta: 11:32:38 time: 0.5713 data_time: 0.1405 memory: 16095 grad_norm: 5.2535 loss: 1.4436 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.4436 2022/12/08 18:37:36 - mmengine - INFO - Epoch(train) [31][900/940] lr: 1.0000e-02 eta: 11:32:28 time: 0.7040 data_time: 0.2008 memory: 16095 grad_norm: 5.3371 loss: 1.4479 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4479 2022/12/08 18:37:48 - mmengine - INFO - Epoch(train) [31][920/940] lr: 1.0000e-02 eta: 11:32:11 time: 0.5597 data_time: 0.1067 memory: 16095 grad_norm: 5.1790 loss: 1.2965 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2965 2022/12/08 18:37:58 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 18:37:58 - mmengine - INFO - Epoch(train) [31][940/940] lr: 1.0000e-02 eta: 11:31:52 time: 0.4990 data_time: 0.1321 memory: 16095 grad_norm: 5.6765 loss: 1.4489 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.4489 2022/12/08 18:38:12 - mmengine - INFO - Epoch(val) [31][20/78] eta: 0:00:40 time: 0.7058 data_time: 0.6109 memory: 1686 2022/12/08 18:38:21 - mmengine - INFO - Epoch(val) [31][40/78] eta: 0:00:22 time: 0.4791 data_time: 0.3851 memory: 1686 2022/12/08 18:38:35 - mmengine - INFO - Epoch(val) [31][60/78] eta: 0:00:11 time: 0.6807 data_time: 0.5852 memory: 1686 2022/12/08 18:38:45 - mmengine - INFO - Epoch(val) [31][78/78] acc/top1: 0.6358 acc/top5: 0.8507 acc/mean1: 0.6357 2022/12/08 18:39:02 - mmengine - INFO - Epoch(train) [32][ 20/940] lr: 1.0000e-02 eta: 11:31:49 time: 0.8476 data_time: 0.2656 memory: 16095 grad_norm: 5.1853 loss: 1.2972 top1_acc: 0.6875 top5_acc: 0.9688 loss_cls: 1.2972 2022/12/08 18:39:13 - mmengine - INFO - Epoch(train) [32][ 40/940] lr: 1.0000e-02 eta: 11:31:32 time: 0.5411 data_time: 0.0645 memory: 16095 grad_norm: 5.1523 loss: 1.4062 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.4062 2022/12/08 18:39:27 - mmengine - INFO - Epoch(train) [32][ 60/940] lr: 1.0000e-02 eta: 11:31:21 time: 0.6877 data_time: 0.0746 memory: 16095 grad_norm: 5.1512 loss: 1.3428 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.3428 2022/12/08 18:39:38 - mmengine - INFO - Epoch(train) [32][ 80/940] lr: 1.0000e-02 eta: 11:31:04 time: 0.5392 data_time: 0.0363 memory: 16095 grad_norm: 5.1906 loss: 1.4084 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4084 2022/12/08 18:39:51 - mmengine - INFO - Epoch(train) [32][100/940] lr: 1.0000e-02 eta: 11:30:51 time: 0.6530 data_time: 0.0253 memory: 16095 grad_norm: 5.0552 loss: 1.3268 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3268 2022/12/08 18:40:02 - mmengine - INFO - Epoch(train) [32][120/940] lr: 1.0000e-02 eta: 11:30:36 time: 0.5859 data_time: 0.0520 memory: 16095 grad_norm: 5.0824 loss: 1.3710 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.3710 2022/12/08 18:40:17 - mmengine - INFO - Epoch(train) [32][140/940] lr: 1.0000e-02 eta: 11:30:27 time: 0.7116 data_time: 0.0328 memory: 16095 grad_norm: 5.1465 loss: 1.3382 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3382 2022/12/08 18:40:28 - mmengine - INFO - Epoch(train) [32][160/940] lr: 1.0000e-02 eta: 11:30:10 time: 0.5638 data_time: 0.0209 memory: 16095 grad_norm: 5.1869 loss: 1.3271 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.3271 2022/12/08 18:40:41 - mmengine - INFO - Epoch(train) [32][180/940] lr: 1.0000e-02 eta: 11:29:57 time: 0.6249 data_time: 0.0267 memory: 16095 grad_norm: 5.2123 loss: 1.3522 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.3522 2022/12/08 18:40:51 - mmengine - INFO - Epoch(train) [32][200/940] lr: 1.0000e-02 eta: 11:29:38 time: 0.5070 data_time: 0.0200 memory: 16095 grad_norm: 5.2993 loss: 1.4592 top1_acc: 0.5000 top5_acc: 0.7188 loss_cls: 1.4592 2022/12/08 18:41:05 - mmengine - INFO - Epoch(train) [32][220/940] lr: 1.0000e-02 eta: 11:29:28 time: 0.6949 data_time: 0.0281 memory: 16095 grad_norm: 5.2250 loss: 1.4378 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4378 2022/12/08 18:41:16 - mmengine - INFO - Epoch(train) [32][240/940] lr: 1.0000e-02 eta: 11:29:13 time: 0.5902 data_time: 0.0203 memory: 16095 grad_norm: 5.1793 loss: 1.4133 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4133 2022/12/08 18:41:30 - mmengine - INFO - Epoch(train) [32][260/940] lr: 1.0000e-02 eta: 11:29:01 time: 0.6641 data_time: 0.0269 memory: 16095 grad_norm: 5.2112 loss: 1.3998 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3998 2022/12/08 18:41:40 - mmengine - INFO - Epoch(train) [32][280/940] lr: 1.0000e-02 eta: 11:28:44 time: 0.5336 data_time: 0.0199 memory: 16095 grad_norm: 5.1401 loss: 1.2194 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.2194 2022/12/08 18:41:54 - mmengine - INFO - Epoch(train) [32][300/940] lr: 1.0000e-02 eta: 11:28:32 time: 0.6679 data_time: 0.1464 memory: 16095 grad_norm: 5.1641 loss: 1.4065 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.4065 2022/12/08 18:42:05 - mmengine - INFO - Epoch(train) [32][320/940] lr: 1.0000e-02 eta: 11:28:16 time: 0.5542 data_time: 0.1169 memory: 16095 grad_norm: 5.2411 loss: 1.3097 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3097 2022/12/08 18:42:18 - mmengine - INFO - Epoch(train) [32][340/940] lr: 1.0000e-02 eta: 11:28:04 time: 0.6775 data_time: 0.1409 memory: 16095 grad_norm: 5.2265 loss: 1.4296 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4296 2022/12/08 18:42:30 - mmengine - INFO - Epoch(train) [32][360/940] lr: 1.0000e-02 eta: 11:27:49 time: 0.5773 data_time: 0.2077 memory: 16095 grad_norm: 5.1463 loss: 1.3265 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3265 2022/12/08 18:42:43 - mmengine - INFO - Epoch(train) [32][380/940] lr: 1.0000e-02 eta: 11:27:36 time: 0.6441 data_time: 0.2097 memory: 16095 grad_norm: 5.2202 loss: 1.3448 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.3448 2022/12/08 18:42:54 - mmengine - INFO - Epoch(train) [32][400/940] lr: 1.0000e-02 eta: 11:27:19 time: 0.5399 data_time: 0.1314 memory: 16095 grad_norm: 5.2932 loss: 1.3105 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3105 2022/12/08 18:43:07 - mmengine - INFO - Epoch(train) [32][420/940] lr: 1.0000e-02 eta: 11:27:08 time: 0.6849 data_time: 0.2124 memory: 16095 grad_norm: 5.2948 loss: 1.4946 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.4946 2022/12/08 18:43:18 - mmengine - INFO - Epoch(train) [32][440/940] lr: 1.0000e-02 eta: 11:26:52 time: 0.5493 data_time: 0.1440 memory: 16095 grad_norm: 5.2975 loss: 1.3235 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.3235 2022/12/08 18:43:31 - mmengine - INFO - Epoch(train) [32][460/940] lr: 1.0000e-02 eta: 11:26:39 time: 0.6535 data_time: 0.0822 memory: 16095 grad_norm: 5.1625 loss: 1.4472 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.4472 2022/12/08 18:43:42 - mmengine - INFO - Epoch(train) [32][480/940] lr: 1.0000e-02 eta: 11:26:22 time: 0.5248 data_time: 0.0757 memory: 16095 grad_norm: 5.2336 loss: 1.3734 top1_acc: 0.6562 top5_acc: 0.9688 loss_cls: 1.3734 2022/12/08 18:43:55 - mmengine - INFO - Epoch(train) [32][500/940] lr: 1.0000e-02 eta: 11:26:10 time: 0.6711 data_time: 0.2891 memory: 16095 grad_norm: 5.3406 loss: 1.4291 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.4291 2022/12/08 18:44:06 - mmengine - INFO - Epoch(train) [32][520/940] lr: 1.0000e-02 eta: 11:25:53 time: 0.5457 data_time: 0.2001 memory: 16095 grad_norm: 5.1407 loss: 1.1926 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.1926 2022/12/08 18:44:19 - mmengine - INFO - Epoch(train) [32][540/940] lr: 1.0000e-02 eta: 11:25:41 time: 0.6616 data_time: 0.2430 memory: 16095 grad_norm: 5.2908 loss: 1.5394 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.5394 2022/12/08 18:44:31 - mmengine - INFO - Epoch(train) [32][560/940] lr: 1.0000e-02 eta: 11:25:26 time: 0.5807 data_time: 0.2011 memory: 16095 grad_norm: 5.2539 loss: 1.5168 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.5168 2022/12/08 18:44:45 - mmengine - INFO - Epoch(train) [32][580/940] lr: 1.0000e-02 eta: 11:25:15 time: 0.6752 data_time: 0.3311 memory: 16095 grad_norm: 5.1948 loss: 1.2916 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.2916 2022/12/08 18:44:56 - mmengine - INFO - Epoch(train) [32][600/940] lr: 1.0000e-02 eta: 11:24:59 time: 0.5753 data_time: 0.2452 memory: 16095 grad_norm: 5.3403 loss: 1.2699 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.2699 2022/12/08 18:45:10 - mmengine - INFO - Epoch(train) [32][620/940] lr: 1.0000e-02 eta: 11:24:48 time: 0.6800 data_time: 0.3287 memory: 16095 grad_norm: 5.2949 loss: 1.4422 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4422 2022/12/08 18:45:20 - mmengine - INFO - Epoch(train) [32][640/940] lr: 1.0000e-02 eta: 11:24:30 time: 0.5080 data_time: 0.1547 memory: 16095 grad_norm: 5.3153 loss: 1.4409 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.4409 2022/12/08 18:45:33 - mmengine - INFO - Epoch(train) [32][660/940] lr: 1.0000e-02 eta: 11:24:17 time: 0.6425 data_time: 0.2601 memory: 16095 grad_norm: 5.2557 loss: 1.5134 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.5134 2022/12/08 18:45:44 - mmengine - INFO - Epoch(train) [32][680/940] lr: 1.0000e-02 eta: 11:24:02 time: 0.5806 data_time: 0.1726 memory: 16095 grad_norm: 5.2145 loss: 1.3827 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.3827 2022/12/08 18:45:58 - mmengine - INFO - Epoch(train) [32][700/940] lr: 1.0000e-02 eta: 11:23:51 time: 0.6844 data_time: 0.1436 memory: 16095 grad_norm: 5.1715 loss: 1.4206 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 1.4206 2022/12/08 18:46:10 - mmengine - INFO - Epoch(train) [32][720/940] lr: 1.0000e-02 eta: 11:23:37 time: 0.6127 data_time: 0.0324 memory: 16095 grad_norm: 5.3698 loss: 1.4548 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.4548 2022/12/08 18:46:21 - mmengine - INFO - Epoch(train) [32][740/940] lr: 1.0000e-02 eta: 11:23:21 time: 0.5632 data_time: 0.0269 memory: 16095 grad_norm: 5.2510 loss: 1.4849 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4849 2022/12/08 18:46:35 - mmengine - INFO - Epoch(train) [32][760/940] lr: 1.0000e-02 eta: 11:23:11 time: 0.6994 data_time: 0.0224 memory: 16095 grad_norm: 5.3171 loss: 1.3867 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.3867 2022/12/08 18:46:46 - mmengine - INFO - Epoch(train) [32][780/940] lr: 1.0000e-02 eta: 11:22:54 time: 0.5483 data_time: 0.0247 memory: 16095 grad_norm: 5.2748 loss: 1.4024 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.4024 2022/12/08 18:46:59 - mmengine - INFO - Epoch(train) [32][800/940] lr: 1.0000e-02 eta: 11:22:41 time: 0.6422 data_time: 0.0231 memory: 16095 grad_norm: 5.1999 loss: 1.4331 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 1.4331 2022/12/08 18:47:11 - mmengine - INFO - Epoch(train) [32][820/940] lr: 1.0000e-02 eta: 11:22:25 time: 0.5683 data_time: 0.0247 memory: 16095 grad_norm: 5.2219 loss: 1.3347 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.3347 2022/12/08 18:47:24 - mmengine - INFO - Epoch(train) [32][840/940] lr: 1.0000e-02 eta: 11:22:14 time: 0.6622 data_time: 0.0236 memory: 16095 grad_norm: 5.3211 loss: 1.3305 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3305 2022/12/08 18:47:36 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 18:47:36 - mmengine - INFO - Epoch(train) [32][860/940] lr: 1.0000e-02 eta: 11:21:59 time: 0.5948 data_time: 0.0220 memory: 16095 grad_norm: 5.2304 loss: 1.3236 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3236 2022/12/08 18:47:49 - mmengine - INFO - Epoch(train) [32][880/940] lr: 1.0000e-02 eta: 11:21:48 time: 0.6722 data_time: 0.0268 memory: 16095 grad_norm: 5.3115 loss: 1.4568 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4568 2022/12/08 18:48:01 - mmengine - INFO - Epoch(train) [32][900/940] lr: 1.0000e-02 eta: 11:21:32 time: 0.5680 data_time: 0.0289 memory: 16095 grad_norm: 5.3155 loss: 1.4715 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4715 2022/12/08 18:48:13 - mmengine - INFO - Epoch(train) [32][920/940] lr: 1.0000e-02 eta: 11:21:19 time: 0.6286 data_time: 0.0257 memory: 16095 grad_norm: 5.2915 loss: 1.4953 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.4953 2022/12/08 18:48:23 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 18:48:23 - mmengine - INFO - Epoch(train) [32][940/940] lr: 1.0000e-02 eta: 11:21:00 time: 0.5002 data_time: 0.0175 memory: 16095 grad_norm: 5.6346 loss: 1.5009 top1_acc: 0.2857 top5_acc: 0.7143 loss_cls: 1.5009 2022/12/08 18:48:38 - mmengine - INFO - Epoch(val) [32][20/78] eta: 0:00:41 time: 0.7194 data_time: 0.6270 memory: 1686 2022/12/08 18:48:47 - mmengine - INFO - Epoch(val) [32][40/78] eta: 0:00:22 time: 0.4588 data_time: 0.3643 memory: 1686 2022/12/08 18:49:00 - mmengine - INFO - Epoch(val) [32][60/78] eta: 0:00:11 time: 0.6813 data_time: 0.5856 memory: 1686 2022/12/08 18:49:11 - mmengine - INFO - Epoch(val) [32][78/78] acc/top1: 0.6452 acc/top5: 0.8502 acc/mean1: 0.6451 2022/12/08 18:49:27 - mmengine - INFO - Epoch(train) [33][ 20/940] lr: 1.0000e-02 eta: 11:20:55 time: 0.8357 data_time: 0.2861 memory: 16095 grad_norm: 5.1321 loss: 1.3073 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.3073 2022/12/08 18:49:39 - mmengine - INFO - Epoch(train) [33][ 40/940] lr: 1.0000e-02 eta: 11:20:40 time: 0.5722 data_time: 0.1321 memory: 16095 grad_norm: 5.1934 loss: 1.3712 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.3712 2022/12/08 18:49:53 - mmengine - INFO - Epoch(train) [33][ 60/940] lr: 1.0000e-02 eta: 11:20:29 time: 0.6881 data_time: 0.0448 memory: 16095 grad_norm: 5.2039 loss: 1.3221 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.3221 2022/12/08 18:50:03 - mmengine - INFO - Epoch(train) [33][ 80/940] lr: 1.0000e-02 eta: 11:20:11 time: 0.5273 data_time: 0.0225 memory: 16095 grad_norm: 5.1343 loss: 1.4342 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.4342 2022/12/08 18:50:17 - mmengine - INFO - Epoch(train) [33][100/940] lr: 1.0000e-02 eta: 11:20:01 time: 0.6824 data_time: 0.0878 memory: 16095 grad_norm: 5.1477 loss: 1.5072 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 1.5072 2022/12/08 18:50:28 - mmengine - INFO - Epoch(train) [33][120/940] lr: 1.0000e-02 eta: 11:19:44 time: 0.5578 data_time: 0.0872 memory: 16095 grad_norm: 5.0181 loss: 1.3473 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.3473 2022/12/08 18:50:41 - mmengine - INFO - Epoch(train) [33][140/940] lr: 1.0000e-02 eta: 11:19:33 time: 0.6727 data_time: 0.2691 memory: 16095 grad_norm: 5.2782 loss: 1.3686 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.3686 2022/12/08 18:50:53 - mmengine - INFO - Epoch(train) [33][160/940] lr: 1.0000e-02 eta: 11:19:18 time: 0.5828 data_time: 0.1942 memory: 16095 grad_norm: 5.1741 loss: 1.2593 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.2593 2022/12/08 18:51:06 - mmengine - INFO - Epoch(train) [33][180/940] lr: 1.0000e-02 eta: 11:19:06 time: 0.6567 data_time: 0.2170 memory: 16095 grad_norm: 5.2226 loss: 1.3944 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3944 2022/12/08 18:51:18 - mmengine - INFO - Epoch(train) [33][200/940] lr: 1.0000e-02 eta: 11:18:50 time: 0.5686 data_time: 0.1864 memory: 16095 grad_norm: 5.1559 loss: 1.3496 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3496 2022/12/08 18:51:32 - mmengine - INFO - Epoch(train) [33][220/940] lr: 1.0000e-02 eta: 11:18:40 time: 0.7024 data_time: 0.0909 memory: 16095 grad_norm: 5.2642 loss: 1.3698 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3698 2022/12/08 18:51:42 - mmengine - INFO - Epoch(train) [33][240/940] lr: 1.0000e-02 eta: 11:18:22 time: 0.5248 data_time: 0.0248 memory: 16095 grad_norm: 5.1936 loss: 1.2802 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.2802 2022/12/08 18:51:56 - mmengine - INFO - Epoch(train) [33][260/940] lr: 1.0000e-02 eta: 11:18:11 time: 0.6689 data_time: 0.1133 memory: 16095 grad_norm: 5.2235 loss: 1.2983 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 1.2983 2022/12/08 18:52:08 - mmengine - INFO - Epoch(train) [33][280/940] lr: 1.0000e-02 eta: 11:17:56 time: 0.6008 data_time: 0.0420 memory: 16095 grad_norm: 5.3099 loss: 1.3891 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.3891 2022/12/08 18:52:20 - mmengine - INFO - Epoch(train) [33][300/940] lr: 1.0000e-02 eta: 11:17:43 time: 0.6276 data_time: 0.0243 memory: 16095 grad_norm: 5.2941 loss: 1.4329 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.4329 2022/12/08 18:52:32 - mmengine - INFO - Epoch(train) [33][320/940] lr: 1.0000e-02 eta: 11:17:28 time: 0.5802 data_time: 0.0232 memory: 16095 grad_norm: 5.2316 loss: 1.4083 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.4083 2022/12/08 18:52:45 - mmengine - INFO - Epoch(train) [33][340/940] lr: 1.0000e-02 eta: 11:17:16 time: 0.6633 data_time: 0.0246 memory: 16095 grad_norm: 5.1805 loss: 1.3427 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.3427 2022/12/08 18:52:57 - mmengine - INFO - Epoch(train) [33][360/940] lr: 1.0000e-02 eta: 11:17:01 time: 0.5827 data_time: 0.0235 memory: 16095 grad_norm: 5.2241 loss: 1.4186 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4186 2022/12/08 18:53:10 - mmengine - INFO - Epoch(train) [33][380/940] lr: 1.0000e-02 eta: 11:16:49 time: 0.6555 data_time: 0.0254 memory: 16095 grad_norm: 5.2933 loss: 1.3692 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.3692 2022/12/08 18:53:21 - mmengine - INFO - Epoch(train) [33][400/940] lr: 1.0000e-02 eta: 11:16:32 time: 0.5377 data_time: 0.0255 memory: 16095 grad_norm: 5.3777 loss: 1.3012 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.3012 2022/12/08 18:53:34 - mmengine - INFO - Epoch(train) [33][420/940] lr: 1.0000e-02 eta: 11:16:20 time: 0.6615 data_time: 0.0249 memory: 16095 grad_norm: 5.2120 loss: 1.4509 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.4509 2022/12/08 18:53:45 - mmengine - INFO - Epoch(train) [33][440/940] lr: 1.0000e-02 eta: 11:16:04 time: 0.5696 data_time: 0.0241 memory: 16095 grad_norm: 5.3152 loss: 1.4328 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4328 2022/12/08 18:53:58 - mmengine - INFO - Epoch(train) [33][460/940] lr: 1.0000e-02 eta: 11:15:52 time: 0.6496 data_time: 0.0241 memory: 16095 grad_norm: 5.3223 loss: 1.3420 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.3420 2022/12/08 18:54:09 - mmengine - INFO - Epoch(train) [33][480/940] lr: 1.0000e-02 eta: 11:15:34 time: 0.5224 data_time: 0.0265 memory: 16095 grad_norm: 5.3850 loss: 1.3771 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.3771 2022/12/08 18:54:22 - mmengine - INFO - Epoch(train) [33][500/940] lr: 1.0000e-02 eta: 11:15:23 time: 0.6858 data_time: 0.0250 memory: 16095 grad_norm: 5.2582 loss: 1.3321 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.3321 2022/12/08 18:54:33 - mmengine - INFO - Epoch(train) [33][520/940] lr: 1.0000e-02 eta: 11:15:07 time: 0.5548 data_time: 0.0247 memory: 16095 grad_norm: 5.3217 loss: 1.3754 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.3754 2022/12/08 18:54:47 - mmengine - INFO - Epoch(train) [33][540/940] lr: 1.0000e-02 eta: 11:14:56 time: 0.6681 data_time: 0.0240 memory: 16095 grad_norm: 5.3638 loss: 1.3817 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3817 2022/12/08 18:54:58 - mmengine - INFO - Epoch(train) [33][560/940] lr: 1.0000e-02 eta: 11:14:40 time: 0.5734 data_time: 0.0241 memory: 16095 grad_norm: 5.2503 loss: 1.4627 top1_acc: 0.4375 top5_acc: 0.8438 loss_cls: 1.4627 2022/12/08 18:55:11 - mmengine - INFO - Epoch(train) [33][580/940] lr: 1.0000e-02 eta: 11:14:28 time: 0.6501 data_time: 0.0239 memory: 16095 grad_norm: 5.1330 loss: 1.2952 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 1.2952 2022/12/08 18:55:23 - mmengine - INFO - Epoch(train) [33][600/940] lr: 1.0000e-02 eta: 11:14:12 time: 0.5715 data_time: 0.0263 memory: 16095 grad_norm: 5.2835 loss: 1.3425 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 1.3425 2022/12/08 18:55:36 - mmengine - INFO - Epoch(train) [33][620/940] lr: 1.0000e-02 eta: 11:14:00 time: 0.6630 data_time: 0.0244 memory: 16095 grad_norm: 5.2237 loss: 1.2562 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.2562 2022/12/08 18:55:48 - mmengine - INFO - Epoch(train) [33][640/940] lr: 1.0000e-02 eta: 11:13:45 time: 0.5803 data_time: 0.0246 memory: 16095 grad_norm: 5.2819 loss: 1.4098 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.4098 2022/12/08 18:56:01 - mmengine - INFO - Epoch(train) [33][660/940] lr: 1.0000e-02 eta: 11:13:34 time: 0.6726 data_time: 0.0247 memory: 16095 grad_norm: 5.2409 loss: 1.3702 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3702 2022/12/08 18:56:12 - mmengine - INFO - Epoch(train) [33][680/940] lr: 1.0000e-02 eta: 11:13:18 time: 0.5645 data_time: 0.0239 memory: 16095 grad_norm: 5.4180 loss: 1.3349 top1_acc: 0.6875 top5_acc: 0.9688 loss_cls: 1.3349 2022/12/08 18:56:26 - mmengine - INFO - Epoch(train) [33][700/940] lr: 1.0000e-02 eta: 11:13:07 time: 0.6718 data_time: 0.0329 memory: 16095 grad_norm: 5.3439 loss: 1.5321 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5321 2022/12/08 18:56:37 - mmengine - INFO - Epoch(train) [33][720/940] lr: 1.0000e-02 eta: 11:12:50 time: 0.5484 data_time: 0.0280 memory: 16095 grad_norm: 5.2644 loss: 1.3979 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.3979 2022/12/08 18:56:50 - mmengine - INFO - Epoch(train) [33][740/940] lr: 1.0000e-02 eta: 11:12:37 time: 0.6404 data_time: 0.0214 memory: 16095 grad_norm: 5.3007 loss: 1.4770 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.4770 2022/12/08 18:57:00 - mmengine - INFO - Epoch(train) [33][760/940] lr: 1.0000e-02 eta: 11:12:21 time: 0.5399 data_time: 0.0257 memory: 16095 grad_norm: 5.2662 loss: 1.3996 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3996 2022/12/08 18:57:14 - mmengine - INFO - Epoch(train) [33][780/940] lr: 1.0000e-02 eta: 11:12:10 time: 0.6944 data_time: 0.0226 memory: 16095 grad_norm: 5.2979 loss: 1.3595 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3595 2022/12/08 18:57:26 - mmengine - INFO - Epoch(train) [33][800/940] lr: 1.0000e-02 eta: 11:11:55 time: 0.5773 data_time: 0.0265 memory: 16095 grad_norm: 5.2941 loss: 1.4429 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.4429 2022/12/08 18:57:38 - mmengine - INFO - Epoch(train) [33][820/940] lr: 1.0000e-02 eta: 11:11:42 time: 0.6286 data_time: 0.0204 memory: 16095 grad_norm: 5.2982 loss: 1.3616 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3616 2022/12/08 18:57:50 - mmengine - INFO - Epoch(train) [33][840/940] lr: 1.0000e-02 eta: 11:11:27 time: 0.5836 data_time: 0.0262 memory: 16095 grad_norm: 5.3272 loss: 1.4757 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.4757 2022/12/08 18:58:03 - mmengine - INFO - Epoch(train) [33][860/940] lr: 1.0000e-02 eta: 11:11:13 time: 0.6254 data_time: 0.0238 memory: 16095 grad_norm: 5.3685 loss: 1.4712 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4712 2022/12/08 18:58:14 - mmengine - INFO - Epoch(train) [33][880/940] lr: 1.0000e-02 eta: 11:10:57 time: 0.5638 data_time: 0.0258 memory: 16095 grad_norm: 5.2379 loss: 1.3961 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.3961 2022/12/08 18:58:27 - mmengine - INFO - Epoch(train) [33][900/940] lr: 1.0000e-02 eta: 11:10:45 time: 0.6518 data_time: 0.0233 memory: 16095 grad_norm: 5.3711 loss: 1.4615 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.4615 2022/12/08 18:58:39 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 18:58:39 - mmengine - INFO - Epoch(train) [33][920/940] lr: 1.0000e-02 eta: 11:10:30 time: 0.5862 data_time: 0.0250 memory: 16095 grad_norm: 5.3126 loss: 1.3414 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.3414 2022/12/08 18:58:48 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 18:58:48 - mmengine - INFO - Epoch(train) [33][940/940] lr: 1.0000e-02 eta: 11:10:11 time: 0.4799 data_time: 0.0181 memory: 16095 grad_norm: 5.5604 loss: 1.4428 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 1.4428 2022/12/08 18:58:48 - mmengine - INFO - Saving checkpoint at 33 epochs 2022/12/08 18:59:06 - mmengine - INFO - Epoch(val) [33][20/78] eta: 0:00:41 time: 0.7147 data_time: 0.6201 memory: 1686 2022/12/08 18:59:15 - mmengine - INFO - Epoch(val) [33][40/78] eta: 0:00:22 time: 0.4646 data_time: 0.3705 memory: 1686 2022/12/08 18:59:29 - mmengine - INFO - Epoch(val) [33][60/78] eta: 0:00:11 time: 0.6686 data_time: 0.5731 memory: 1686 2022/12/08 18:59:38 - mmengine - INFO - Epoch(val) [33][78/78] acc/top1: 0.6393 acc/top5: 0.8550 acc/mean1: 0.6392 2022/12/08 18:59:55 - mmengine - INFO - Epoch(train) [34][ 20/940] lr: 1.0000e-02 eta: 11:10:07 time: 0.8455 data_time: 0.3678 memory: 16095 grad_norm: 5.1142 loss: 1.3642 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 1.3642 2022/12/08 19:00:06 - mmengine - INFO - Epoch(train) [34][ 40/940] lr: 1.0000e-02 eta: 11:09:50 time: 0.5293 data_time: 0.0322 memory: 16095 grad_norm: 5.1854 loss: 1.3457 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 1.3457 2022/12/08 19:00:20 - mmengine - INFO - Epoch(train) [34][ 60/940] lr: 1.0000e-02 eta: 11:09:40 time: 0.7087 data_time: 0.0627 memory: 16095 grad_norm: 5.1700 loss: 1.4859 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.4859 2022/12/08 19:00:31 - mmengine - INFO - Epoch(train) [34][ 80/940] lr: 1.0000e-02 eta: 11:09:23 time: 0.5486 data_time: 0.0222 memory: 16095 grad_norm: 5.2478 loss: 1.4343 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.4343 2022/12/08 19:00:44 - mmengine - INFO - Epoch(train) [34][100/940] lr: 1.0000e-02 eta: 11:09:11 time: 0.6623 data_time: 0.0273 memory: 16095 grad_norm: 5.1604 loss: 1.3166 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.3166 2022/12/08 19:00:55 - mmengine - INFO - Epoch(train) [34][120/940] lr: 1.0000e-02 eta: 11:08:54 time: 0.5197 data_time: 0.0215 memory: 16095 grad_norm: 5.0314 loss: 1.3079 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 1.3079 2022/12/08 19:01:09 - mmengine - INFO - Epoch(train) [34][140/940] lr: 1.0000e-02 eta: 11:08:43 time: 0.6980 data_time: 0.1292 memory: 16095 grad_norm: 5.1563 loss: 1.4199 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.4199 2022/12/08 19:01:19 - mmengine - INFO - Epoch(train) [34][160/940] lr: 1.0000e-02 eta: 11:08:26 time: 0.5223 data_time: 0.0981 memory: 16095 grad_norm: 5.2038 loss: 1.4143 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.4143 2022/12/08 19:01:33 - mmengine - INFO - Epoch(train) [34][180/940] lr: 1.0000e-02 eta: 11:08:15 time: 0.6862 data_time: 0.0587 memory: 16095 grad_norm: 5.3256 loss: 1.3151 top1_acc: 0.6562 top5_acc: 0.7500 loss_cls: 1.3151 2022/12/08 19:01:43 - mmengine - INFO - Epoch(train) [34][200/940] lr: 1.0000e-02 eta: 11:07:58 time: 0.5194 data_time: 0.0716 memory: 16095 grad_norm: 5.2263 loss: 1.3132 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.3132 2022/12/08 19:01:57 - mmengine - INFO - Epoch(train) [34][220/940] lr: 1.0000e-02 eta: 11:07:47 time: 0.6861 data_time: 0.1560 memory: 16095 grad_norm: 5.3050 loss: 1.3236 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.3236 2022/12/08 19:02:08 - mmengine - INFO - Epoch(train) [34][240/940] lr: 1.0000e-02 eta: 11:07:30 time: 0.5399 data_time: 0.1230 memory: 16095 grad_norm: 5.1645 loss: 1.2862 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.2862 2022/12/08 19:02:21 - mmengine - INFO - Epoch(train) [34][260/940] lr: 1.0000e-02 eta: 11:07:19 time: 0.6794 data_time: 0.2558 memory: 16095 grad_norm: 5.1697 loss: 1.3144 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3144 2022/12/08 19:02:32 - mmengine - INFO - Epoch(train) [34][280/940] lr: 1.0000e-02 eta: 11:07:01 time: 0.5176 data_time: 0.1654 memory: 16095 grad_norm: 5.3515 loss: 1.2724 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.2724 2022/12/08 19:02:44 - mmengine - INFO - Epoch(train) [34][300/940] lr: 1.0000e-02 eta: 11:06:49 time: 0.6389 data_time: 0.2846 memory: 16095 grad_norm: 5.2455 loss: 1.4008 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.4008 2022/12/08 19:02:56 - mmengine - INFO - Epoch(train) [34][320/940] lr: 1.0000e-02 eta: 11:06:33 time: 0.5599 data_time: 0.2227 memory: 16095 grad_norm: 5.2618 loss: 1.3661 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3661 2022/12/08 19:03:09 - mmengine - INFO - Epoch(train) [34][340/940] lr: 1.0000e-02 eta: 11:06:21 time: 0.6675 data_time: 0.1472 memory: 16095 grad_norm: 5.2598 loss: 1.2933 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.2933 2022/12/08 19:03:20 - mmengine - INFO - Epoch(train) [34][360/940] lr: 1.0000e-02 eta: 11:06:05 time: 0.5674 data_time: 0.0855 memory: 16095 grad_norm: 5.2453 loss: 1.3086 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3086 2022/12/08 19:03:33 - mmengine - INFO - Epoch(train) [34][380/940] lr: 1.0000e-02 eta: 11:05:53 time: 0.6503 data_time: 0.0385 memory: 16095 grad_norm: 5.2900 loss: 1.3560 top1_acc: 0.7188 top5_acc: 0.7812 loss_cls: 1.3560 2022/12/08 19:03:44 - mmengine - INFO - Epoch(train) [34][400/940] lr: 1.0000e-02 eta: 11:05:37 time: 0.5521 data_time: 0.0253 memory: 16095 grad_norm: 5.3064 loss: 1.4372 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4372 2022/12/08 19:03:57 - mmengine - INFO - Epoch(train) [34][420/940] lr: 1.0000e-02 eta: 11:05:25 time: 0.6526 data_time: 0.0690 memory: 16095 grad_norm: 5.3245 loss: 1.3939 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.3939 2022/12/08 19:04:10 - mmengine - INFO - Epoch(train) [34][440/940] lr: 1.0000e-02 eta: 11:05:11 time: 0.6179 data_time: 0.0534 memory: 16095 grad_norm: 5.2787 loss: 1.2825 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.2825 2022/12/08 19:04:23 - mmengine - INFO - Epoch(train) [34][460/940] lr: 1.0000e-02 eta: 11:05:00 time: 0.6803 data_time: 0.0335 memory: 16095 grad_norm: 5.2724 loss: 1.3417 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.3417 2022/12/08 19:04:34 - mmengine - INFO - Epoch(train) [34][480/940] lr: 1.0000e-02 eta: 11:04:43 time: 0.5163 data_time: 0.0244 memory: 16095 grad_norm: 5.2988 loss: 1.4016 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 1.4016 2022/12/08 19:04:47 - mmengine - INFO - Epoch(train) [34][500/940] lr: 1.0000e-02 eta: 11:04:30 time: 0.6537 data_time: 0.0260 memory: 16095 grad_norm: 5.2911 loss: 1.3341 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.3341 2022/12/08 19:04:58 - mmengine - INFO - Epoch(train) [34][520/940] lr: 1.0000e-02 eta: 11:04:14 time: 0.5411 data_time: 0.0235 memory: 16095 grad_norm: 5.2748 loss: 1.4917 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.4917 2022/12/08 19:05:12 - mmengine - INFO - Epoch(train) [34][540/940] lr: 1.0000e-02 eta: 11:04:03 time: 0.6996 data_time: 0.0291 memory: 16095 grad_norm: 5.2503 loss: 1.3954 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.3954 2022/12/08 19:05:23 - mmengine - INFO - Epoch(train) [34][560/940] lr: 1.0000e-02 eta: 11:03:48 time: 0.5700 data_time: 0.0198 memory: 16095 grad_norm: 5.3120 loss: 1.3651 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.3651 2022/12/08 19:05:36 - mmengine - INFO - Epoch(train) [34][580/940] lr: 1.0000e-02 eta: 11:03:35 time: 0.6445 data_time: 0.0259 memory: 16095 grad_norm: 5.2690 loss: 1.5338 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 1.5338 2022/12/08 19:05:47 - mmengine - INFO - Epoch(train) [34][600/940] lr: 1.0000e-02 eta: 11:03:20 time: 0.5636 data_time: 0.0213 memory: 16095 grad_norm: 5.3784 loss: 1.5182 top1_acc: 0.5938 top5_acc: 0.9688 loss_cls: 1.5182 2022/12/08 19:06:00 - mmengine - INFO - Epoch(train) [34][620/940] lr: 1.0000e-02 eta: 11:03:08 time: 0.6666 data_time: 0.0278 memory: 16095 grad_norm: 5.2742 loss: 1.4562 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.4562 2022/12/08 19:06:13 - mmengine - INFO - Epoch(train) [34][640/940] lr: 1.0000e-02 eta: 11:02:54 time: 0.6036 data_time: 0.0227 memory: 16095 grad_norm: 5.2682 loss: 1.2420 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 1.2420 2022/12/08 19:06:26 - mmengine - INFO - Epoch(train) [34][660/940] lr: 1.0000e-02 eta: 11:02:42 time: 0.6527 data_time: 0.0263 memory: 16095 grad_norm: 5.3455 loss: 1.4859 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.4859 2022/12/08 19:06:36 - mmengine - INFO - Epoch(train) [34][680/940] lr: 1.0000e-02 eta: 11:02:25 time: 0.5393 data_time: 0.0201 memory: 16095 grad_norm: 5.2993 loss: 1.4179 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.4179 2022/12/08 19:06:49 - mmengine - INFO - Epoch(train) [34][700/940] lr: 1.0000e-02 eta: 11:02:13 time: 0.6549 data_time: 0.0283 memory: 16095 grad_norm: 5.1856 loss: 1.3508 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.3508 2022/12/08 19:07:01 - mmengine - INFO - Epoch(train) [34][720/940] lr: 1.0000e-02 eta: 11:01:57 time: 0.5522 data_time: 0.0215 memory: 16095 grad_norm: 5.2255 loss: 1.2581 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.2581 2022/12/08 19:07:13 - mmengine - INFO - Epoch(train) [34][740/940] lr: 1.0000e-02 eta: 11:01:44 time: 0.6371 data_time: 0.0276 memory: 16095 grad_norm: 5.1136 loss: 1.3918 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.3918 2022/12/08 19:07:24 - mmengine - INFO - Epoch(train) [34][760/940] lr: 1.0000e-02 eta: 11:01:27 time: 0.5262 data_time: 0.0204 memory: 16095 grad_norm: 5.2540 loss: 1.4000 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.4000 2022/12/08 19:07:38 - mmengine - INFO - Epoch(train) [34][780/940] lr: 1.0000e-02 eta: 11:01:16 time: 0.6882 data_time: 0.0260 memory: 16095 grad_norm: 5.2348 loss: 1.3052 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.3052 2022/12/08 19:07:49 - mmengine - INFO - Epoch(train) [34][800/940] lr: 1.0000e-02 eta: 11:01:00 time: 0.5488 data_time: 0.0259 memory: 16095 grad_norm: 5.2640 loss: 1.3036 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3036 2022/12/08 19:08:03 - mmengine - INFO - Epoch(train) [34][820/940] lr: 1.0000e-02 eta: 11:00:51 time: 0.7320 data_time: 0.0553 memory: 16095 grad_norm: 5.2451 loss: 1.3532 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3532 2022/12/08 19:08:15 - mmengine - INFO - Epoch(train) [34][840/940] lr: 1.0000e-02 eta: 11:00:36 time: 0.5834 data_time: 0.0217 memory: 16095 grad_norm: 5.2943 loss: 1.2779 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2779 2022/12/08 19:08:28 - mmengine - INFO - Epoch(train) [34][860/940] lr: 1.0000e-02 eta: 11:00:24 time: 0.6489 data_time: 0.0268 memory: 16095 grad_norm: 5.2672 loss: 1.4177 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.4177 2022/12/08 19:08:40 - mmengine - INFO - Epoch(train) [34][880/940] lr: 1.0000e-02 eta: 11:00:09 time: 0.5842 data_time: 0.0207 memory: 16095 grad_norm: 5.1934 loss: 1.3986 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.3986 2022/12/08 19:08:52 - mmengine - INFO - Epoch(train) [34][900/940] lr: 1.0000e-02 eta: 10:59:55 time: 0.6183 data_time: 0.0268 memory: 16095 grad_norm: 5.3142 loss: 1.3904 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3904 2022/12/08 19:09:03 - mmengine - INFO - Epoch(train) [34][920/940] lr: 1.0000e-02 eta: 10:59:39 time: 0.5549 data_time: 0.0219 memory: 16095 grad_norm: 5.3346 loss: 1.5071 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5071 2022/12/08 19:09:14 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 19:09:14 - mmengine - INFO - Epoch(train) [34][940/940] lr: 1.0000e-02 eta: 10:59:23 time: 0.5322 data_time: 0.0216 memory: 16095 grad_norm: 5.4648 loss: 1.3024 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.3024 2022/12/08 19:09:28 - mmengine - INFO - Epoch(val) [34][20/78] eta: 0:00:41 time: 0.7077 data_time: 0.6125 memory: 1686 2022/12/08 19:09:37 - mmengine - INFO - Epoch(val) [34][40/78] eta: 0:00:22 time: 0.4549 data_time: 0.3618 memory: 1686 2022/12/08 19:09:50 - mmengine - INFO - Epoch(val) [34][60/78] eta: 0:00:10 time: 0.6670 data_time: 0.5715 memory: 1686 2022/12/08 19:10:01 - mmengine - INFO - Epoch(val) [34][78/78] acc/top1: 0.6509 acc/top5: 0.8577 acc/mean1: 0.6508 2022/12/08 19:10:01 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_23.pth is removed 2022/12/08 19:10:03 - mmengine - INFO - The best checkpoint with 0.6509 acc/top1 at 34 epoch is saved to best_acc/top1_epoch_34.pth. 2022/12/08 19:10:20 - mmengine - INFO - Epoch(train) [35][ 20/940] lr: 1.0000e-02 eta: 10:59:18 time: 0.8363 data_time: 0.5325 memory: 16095 grad_norm: 5.1324 loss: 1.3497 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3497 2022/12/08 19:10:30 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 19:10:30 - mmengine - INFO - Epoch(train) [35][ 40/940] lr: 1.0000e-02 eta: 10:58:59 time: 0.4994 data_time: 0.2035 memory: 16095 grad_norm: 5.3702 loss: 1.6113 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6113 2022/12/08 19:10:44 - mmengine - INFO - Epoch(train) [35][ 60/940] lr: 1.0000e-02 eta: 10:58:49 time: 0.7053 data_time: 0.3806 memory: 16095 grad_norm: 5.2895 loss: 1.3227 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.3227 2022/12/08 19:10:56 - mmengine - INFO - Epoch(train) [35][ 80/940] lr: 1.0000e-02 eta: 10:58:35 time: 0.5873 data_time: 0.2686 memory: 16095 grad_norm: 5.3113 loss: 1.3277 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.3277 2022/12/08 19:11:09 - mmengine - INFO - Epoch(train) [35][100/940] lr: 1.0000e-02 eta: 10:58:23 time: 0.6727 data_time: 0.3485 memory: 16095 grad_norm: 5.2945 loss: 1.2977 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.2977 2022/12/08 19:11:21 - mmengine - INFO - Epoch(train) [35][120/940] lr: 1.0000e-02 eta: 10:58:08 time: 0.5650 data_time: 0.2510 memory: 16095 grad_norm: 5.1710 loss: 1.4571 top1_acc: 0.6250 top5_acc: 0.6562 loss_cls: 1.4571 2022/12/08 19:11:34 - mmengine - INFO - Epoch(train) [35][140/940] lr: 1.0000e-02 eta: 10:57:55 time: 0.6448 data_time: 0.3231 memory: 16095 grad_norm: 5.1918 loss: 1.3339 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3339 2022/12/08 19:11:45 - mmengine - INFO - Epoch(train) [35][160/940] lr: 1.0000e-02 eta: 10:57:40 time: 0.5814 data_time: 0.2624 memory: 16095 grad_norm: 5.2556 loss: 1.2913 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.2913 2022/12/08 19:11:58 - mmengine - INFO - Epoch(train) [35][180/940] lr: 1.0000e-02 eta: 10:57:27 time: 0.6392 data_time: 0.3137 memory: 16095 grad_norm: 5.2454 loss: 1.1890 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.1890 2022/12/08 19:12:09 - mmengine - INFO - Epoch(train) [35][200/940] lr: 1.0000e-02 eta: 10:57:12 time: 0.5651 data_time: 0.2456 memory: 16095 grad_norm: 5.1203 loss: 1.1645 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.1645 2022/12/08 19:12:23 - mmengine - INFO - Epoch(train) [35][220/940] lr: 1.0000e-02 eta: 10:57:00 time: 0.6706 data_time: 0.3460 memory: 16095 grad_norm: 5.2217 loss: 1.3665 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3665 2022/12/08 19:12:34 - mmengine - INFO - Epoch(train) [35][240/940] lr: 1.0000e-02 eta: 10:56:45 time: 0.5602 data_time: 0.2351 memory: 16095 grad_norm: 5.1845 loss: 1.3241 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.3241 2022/12/08 19:12:47 - mmengine - INFO - Epoch(train) [35][260/940] lr: 1.0000e-02 eta: 10:56:32 time: 0.6508 data_time: 0.3347 memory: 16095 grad_norm: 5.2759 loss: 1.4653 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.4653 2022/12/08 19:12:58 - mmengine - INFO - Epoch(train) [35][280/940] lr: 1.0000e-02 eta: 10:56:17 time: 0.5637 data_time: 0.2458 memory: 16095 grad_norm: 5.3033 loss: 1.3844 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.3844 2022/12/08 19:13:12 - mmengine - INFO - Epoch(train) [35][300/940] lr: 1.0000e-02 eta: 10:56:06 time: 0.6831 data_time: 0.3652 memory: 16095 grad_norm: 5.2280 loss: 1.3658 top1_acc: 0.5938 top5_acc: 0.7188 loss_cls: 1.3658 2022/12/08 19:13:23 - mmengine - INFO - Epoch(train) [35][320/940] lr: 1.0000e-02 eta: 10:55:50 time: 0.5687 data_time: 0.2508 memory: 16095 grad_norm: 5.3853 loss: 1.4189 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.4189 2022/12/08 19:13:36 - mmengine - INFO - Epoch(train) [35][340/940] lr: 1.0000e-02 eta: 10:55:37 time: 0.6315 data_time: 0.3049 memory: 16095 grad_norm: 5.2157 loss: 1.4912 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.4912 2022/12/08 19:13:47 - mmengine - INFO - Epoch(train) [35][360/940] lr: 1.0000e-02 eta: 10:55:21 time: 0.5427 data_time: 0.2159 memory: 16095 grad_norm: 5.1614 loss: 1.3860 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.3860 2022/12/08 19:14:00 - mmengine - INFO - Epoch(train) [35][380/940] lr: 1.0000e-02 eta: 10:55:09 time: 0.6609 data_time: 0.3255 memory: 16095 grad_norm: 5.1620 loss: 1.2636 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2636 2022/12/08 19:14:11 - mmengine - INFO - Epoch(train) [35][400/940] lr: 1.0000e-02 eta: 10:54:54 time: 0.5692 data_time: 0.2432 memory: 16095 grad_norm: 5.2645 loss: 1.3807 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.3807 2022/12/08 19:14:24 - mmengine - INFO - Epoch(train) [35][420/940] lr: 1.0000e-02 eta: 10:54:40 time: 0.6107 data_time: 0.2718 memory: 16095 grad_norm: 5.4183 loss: 1.4648 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.4648 2022/12/08 19:14:36 - mmengine - INFO - Epoch(train) [35][440/940] lr: 1.0000e-02 eta: 10:54:27 time: 0.6235 data_time: 0.1709 memory: 16095 grad_norm: 5.3302 loss: 1.3986 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.3986 2022/12/08 19:14:49 - mmengine - INFO - Epoch(train) [35][460/940] lr: 1.0000e-02 eta: 10:54:14 time: 0.6311 data_time: 0.1526 memory: 16095 grad_norm: 5.3263 loss: 1.3102 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 1.3102 2022/12/08 19:15:01 - mmengine - INFO - Epoch(train) [35][480/940] lr: 1.0000e-02 eta: 10:54:00 time: 0.6174 data_time: 0.0419 memory: 16095 grad_norm: 5.2902 loss: 1.4171 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4171 2022/12/08 19:15:13 - mmengine - INFO - Epoch(train) [35][500/940] lr: 1.0000e-02 eta: 10:53:46 time: 0.5877 data_time: 0.0529 memory: 16095 grad_norm: 5.2541 loss: 1.3008 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.3008 2022/12/08 19:15:26 - mmengine - INFO - Epoch(train) [35][520/940] lr: 1.0000e-02 eta: 10:53:33 time: 0.6415 data_time: 0.0303 memory: 16095 grad_norm: 5.3453 loss: 1.3208 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3208 2022/12/08 19:15:37 - mmengine - INFO - Epoch(train) [35][540/940] lr: 1.0000e-02 eta: 10:53:19 time: 0.5900 data_time: 0.0303 memory: 16095 grad_norm: 5.3594 loss: 1.3707 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.3707 2022/12/08 19:15:50 - mmengine - INFO - Epoch(train) [35][560/940] lr: 1.0000e-02 eta: 10:53:06 time: 0.6347 data_time: 0.0211 memory: 16095 grad_norm: 5.4597 loss: 1.3290 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.3290 2022/12/08 19:16:01 - mmengine - INFO - Epoch(train) [35][580/940] lr: 1.0000e-02 eta: 10:52:50 time: 0.5487 data_time: 0.0277 memory: 16095 grad_norm: 5.4149 loss: 1.3063 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.3063 2022/12/08 19:16:13 - mmengine - INFO - Epoch(train) [35][600/940] lr: 1.0000e-02 eta: 10:52:36 time: 0.6188 data_time: 0.0208 memory: 16095 grad_norm: 5.2189 loss: 1.3637 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.3637 2022/12/08 19:16:26 - mmengine - INFO - Epoch(train) [35][620/940] lr: 1.0000e-02 eta: 10:52:23 time: 0.6164 data_time: 0.0845 memory: 16095 grad_norm: 5.4414 loss: 1.3794 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3794 2022/12/08 19:16:38 - mmengine - INFO - Epoch(train) [35][640/940] lr: 1.0000e-02 eta: 10:52:08 time: 0.5964 data_time: 0.1105 memory: 16095 grad_norm: 5.4243 loss: 1.3982 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 1.3982 2022/12/08 19:16:51 - mmengine - INFO - Epoch(train) [35][660/940] lr: 1.0000e-02 eta: 10:51:56 time: 0.6553 data_time: 0.2490 memory: 16095 grad_norm: 5.3414 loss: 1.3572 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.3572 2022/12/08 19:17:02 - mmengine - INFO - Epoch(train) [35][680/940] lr: 1.0000e-02 eta: 10:51:40 time: 0.5451 data_time: 0.1737 memory: 16095 grad_norm: 5.2473 loss: 1.4078 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.4078 2022/12/08 19:17:14 - mmengine - INFO - Epoch(train) [35][700/940] lr: 1.0000e-02 eta: 10:51:27 time: 0.6277 data_time: 0.2958 memory: 16095 grad_norm: 5.2809 loss: 1.3580 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.3580 2022/12/08 19:17:26 - mmengine - INFO - Epoch(train) [35][720/940] lr: 1.0000e-02 eta: 10:51:13 time: 0.5921 data_time: 0.2160 memory: 16095 grad_norm: 5.3033 loss: 1.3355 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.3355 2022/12/08 19:17:39 - mmengine - INFO - Epoch(train) [35][740/940] lr: 1.0000e-02 eta: 10:51:00 time: 0.6518 data_time: 0.3196 memory: 16095 grad_norm: 5.2859 loss: 1.4092 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4092 2022/12/08 19:17:51 - mmengine - INFO - Epoch(train) [35][760/940] lr: 1.0000e-02 eta: 10:50:46 time: 0.5825 data_time: 0.2186 memory: 16095 grad_norm: 5.1766 loss: 1.2559 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.2559 2022/12/08 19:18:05 - mmengine - INFO - Epoch(train) [35][780/940] lr: 1.0000e-02 eta: 10:50:35 time: 0.6890 data_time: 0.2127 memory: 16095 grad_norm: 5.3013 loss: 1.3113 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.3113 2022/12/08 19:18:16 - mmengine - INFO - Epoch(train) [35][800/940] lr: 1.0000e-02 eta: 10:50:19 time: 0.5541 data_time: 0.1285 memory: 16095 grad_norm: 5.2043 loss: 1.3837 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.3837 2022/12/08 19:18:28 - mmengine - INFO - Epoch(train) [35][820/940] lr: 1.0000e-02 eta: 10:50:05 time: 0.5960 data_time: 0.1005 memory: 16095 grad_norm: 5.3469 loss: 1.2849 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.2849 2022/12/08 19:18:40 - mmengine - INFO - Epoch(train) [35][840/940] lr: 1.0000e-02 eta: 10:49:51 time: 0.6066 data_time: 0.0240 memory: 16095 grad_norm: 5.3073 loss: 1.3229 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.3229 2022/12/08 19:18:53 - mmengine - INFO - Epoch(train) [35][860/940] lr: 1.0000e-02 eta: 10:49:39 time: 0.6551 data_time: 0.0474 memory: 16095 grad_norm: 5.4451 loss: 1.4122 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.4122 2022/12/08 19:19:04 - mmengine - INFO - Epoch(train) [35][880/940] lr: 1.0000e-02 eta: 10:49:24 time: 0.5737 data_time: 0.0342 memory: 16095 grad_norm: 5.3320 loss: 1.3767 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.3767 2022/12/08 19:19:17 - mmengine - INFO - Epoch(train) [35][900/940] lr: 1.0000e-02 eta: 10:49:10 time: 0.6282 data_time: 0.0402 memory: 16095 grad_norm: 5.2432 loss: 1.3970 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.3970 2022/12/08 19:19:28 - mmengine - INFO - Epoch(train) [35][920/940] lr: 1.0000e-02 eta: 10:48:55 time: 0.5708 data_time: 0.0480 memory: 16095 grad_norm: 5.1725 loss: 1.3209 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 1.3209 2022/12/08 19:19:39 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 19:19:39 - mmengine - INFO - Epoch(train) [35][940/940] lr: 1.0000e-02 eta: 10:48:38 time: 0.5227 data_time: 0.0426 memory: 16095 grad_norm: 5.6448 loss: 1.4178 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.4178 2022/12/08 19:19:53 - mmengine - INFO - Epoch(val) [35][20/78] eta: 0:00:40 time: 0.7005 data_time: 0.6048 memory: 1686 2022/12/08 19:20:02 - mmengine - INFO - Epoch(val) [35][40/78] eta: 0:00:22 time: 0.4665 data_time: 0.3709 memory: 1686 2022/12/08 19:20:16 - mmengine - INFO - Epoch(val) [35][60/78] eta: 0:00:11 time: 0.6717 data_time: 0.5761 memory: 1686 2022/12/08 19:20:26 - mmengine - INFO - Epoch(val) [35][78/78] acc/top1: 0.6449 acc/top5: 0.8555 acc/mean1: 0.6448 2022/12/08 19:20:43 - mmengine - INFO - Epoch(train) [36][ 20/940] lr: 1.0000e-02 eta: 10:48:32 time: 0.8155 data_time: 0.3116 memory: 16095 grad_norm: 5.2329 loss: 1.2806 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.2806 2022/12/08 19:20:54 - mmengine - INFO - Epoch(train) [36][ 40/940] lr: 1.0000e-02 eta: 10:48:16 time: 0.5478 data_time: 0.0623 memory: 16095 grad_norm: 5.0896 loss: 1.2022 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.2022 2022/12/08 19:21:07 - mmengine - INFO - Epoch(train) [36][ 60/940] lr: 1.0000e-02 eta: 10:48:04 time: 0.6520 data_time: 0.0983 memory: 16095 grad_norm: 5.2068 loss: 1.1754 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.1754 2022/12/08 19:21:18 - mmengine - INFO - Epoch(train) [36][ 80/940] lr: 1.0000e-02 eta: 10:47:48 time: 0.5551 data_time: 0.0317 memory: 16095 grad_norm: 5.2497 loss: 1.3174 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.3174 2022/12/08 19:21:31 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 19:21:31 - mmengine - INFO - Epoch(train) [36][100/940] lr: 1.0000e-02 eta: 10:47:37 time: 0.6756 data_time: 0.0253 memory: 16095 grad_norm: 5.3080 loss: 1.1873 top1_acc: 0.4688 top5_acc: 0.9375 loss_cls: 1.1873 2022/12/08 19:21:42 - mmengine - INFO - Epoch(train) [36][120/940] lr: 1.0000e-02 eta: 10:47:21 time: 0.5615 data_time: 0.0234 memory: 16095 grad_norm: 5.1366 loss: 1.1528 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.1528 2022/12/08 19:21:55 - mmengine - INFO - Epoch(train) [36][140/940] lr: 1.0000e-02 eta: 10:47:09 time: 0.6445 data_time: 0.0257 memory: 16095 grad_norm: 5.2895 loss: 1.3127 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 1.3127 2022/12/08 19:22:07 - mmengine - INFO - Epoch(train) [36][160/940] lr: 1.0000e-02 eta: 10:46:54 time: 0.5882 data_time: 0.0848 memory: 16095 grad_norm: 5.3319 loss: 1.4168 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4168 2022/12/08 19:22:20 - mmengine - INFO - Epoch(train) [36][180/940] lr: 1.0000e-02 eta: 10:46:41 time: 0.6248 data_time: 0.0605 memory: 16095 grad_norm: 5.2406 loss: 1.3268 top1_acc: 0.7812 top5_acc: 0.8125 loss_cls: 1.3268 2022/12/08 19:22:31 - mmengine - INFO - Epoch(train) [36][200/940] lr: 1.0000e-02 eta: 10:46:26 time: 0.5693 data_time: 0.0235 memory: 16095 grad_norm: 5.3089 loss: 1.4896 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 1.4896 2022/12/08 19:22:44 - mmengine - INFO - Epoch(train) [36][220/940] lr: 1.0000e-02 eta: 10:46:13 time: 0.6440 data_time: 0.0253 memory: 16095 grad_norm: 5.2309 loss: 1.3613 top1_acc: 0.7188 top5_acc: 0.7812 loss_cls: 1.3613 2022/12/08 19:22:56 - mmengine - INFO - Epoch(train) [36][240/940] lr: 1.0000e-02 eta: 10:45:59 time: 0.5998 data_time: 0.0223 memory: 16095 grad_norm: 5.2464 loss: 1.5121 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.5121 2022/12/08 19:23:08 - mmengine - INFO - Epoch(train) [36][260/940] lr: 1.0000e-02 eta: 10:45:46 time: 0.6169 data_time: 0.0296 memory: 16095 grad_norm: 5.2740 loss: 1.2896 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 1.2896 2022/12/08 19:23:20 - mmengine - INFO - Epoch(train) [36][280/940] lr: 1.0000e-02 eta: 10:45:32 time: 0.6050 data_time: 0.0206 memory: 16095 grad_norm: 5.2428 loss: 1.3137 top1_acc: 0.5312 top5_acc: 0.8125 loss_cls: 1.3137 2022/12/08 19:23:33 - mmengine - INFO - Epoch(train) [36][300/940] lr: 1.0000e-02 eta: 10:45:19 time: 0.6271 data_time: 0.0260 memory: 16095 grad_norm: 5.2278 loss: 1.2949 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 1.2949 2022/12/08 19:23:45 - mmengine - INFO - Epoch(train) [36][320/940] lr: 1.0000e-02 eta: 10:45:06 time: 0.6284 data_time: 0.0211 memory: 16095 grad_norm: 5.3062 loss: 1.3635 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 1.3635 2022/12/08 19:23:58 - mmengine - INFO - Epoch(train) [36][340/940] lr: 1.0000e-02 eta: 10:44:52 time: 0.6055 data_time: 0.0256 memory: 16095 grad_norm: 5.2630 loss: 1.2254 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2254 2022/12/08 19:24:11 - mmengine - INFO - Epoch(train) [36][360/940] lr: 1.0000e-02 eta: 10:44:40 time: 0.6453 data_time: 0.0222 memory: 16095 grad_norm: 5.3338 loss: 1.2468 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.2468 2022/12/08 19:24:22 - mmengine - INFO - Epoch(train) [36][380/940] lr: 1.0000e-02 eta: 10:44:24 time: 0.5592 data_time: 0.0250 memory: 16095 grad_norm: 5.3879 loss: 1.4628 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.4628 2022/12/08 19:24:34 - mmengine - INFO - Epoch(train) [36][400/940] lr: 1.0000e-02 eta: 10:44:11 time: 0.6258 data_time: 0.0226 memory: 16095 grad_norm: 5.3353 loss: 1.3333 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.3333 2022/12/08 19:24:46 - mmengine - INFO - Epoch(train) [36][420/940] lr: 1.0000e-02 eta: 10:43:57 time: 0.6019 data_time: 0.0270 memory: 16095 grad_norm: 5.2733 loss: 1.2652 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.2652 2022/12/08 19:24:59 - mmengine - INFO - Epoch(train) [36][440/940] lr: 1.0000e-02 eta: 10:43:44 time: 0.6221 data_time: 0.0208 memory: 16095 grad_norm: 5.2009 loss: 1.3995 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.3995 2022/12/08 19:25:12 - mmengine - INFO - Epoch(train) [36][460/940] lr: 1.0000e-02 eta: 10:43:31 time: 0.6469 data_time: 0.0314 memory: 16095 grad_norm: 5.4101 loss: 1.4468 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.4468 2022/12/08 19:25:23 - mmengine - INFO - Epoch(train) [36][480/940] lr: 1.0000e-02 eta: 10:43:16 time: 0.5584 data_time: 0.0255 memory: 16095 grad_norm: 5.3165 loss: 1.4197 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4197 2022/12/08 19:25:35 - mmengine - INFO - Epoch(train) [36][500/940] lr: 1.0000e-02 eta: 10:43:02 time: 0.6105 data_time: 0.0600 memory: 16095 grad_norm: 5.1987 loss: 1.3574 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.3574 2022/12/08 19:25:47 - mmengine - INFO - Epoch(train) [36][520/940] lr: 1.0000e-02 eta: 10:42:47 time: 0.5878 data_time: 0.0728 memory: 16095 grad_norm: 5.4582 loss: 1.3735 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.3735 2022/12/08 19:26:00 - mmengine - INFO - Epoch(train) [36][540/940] lr: 1.0000e-02 eta: 10:42:35 time: 0.6415 data_time: 0.1268 memory: 16095 grad_norm: 5.2848 loss: 1.3920 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.3920 2022/12/08 19:26:11 - mmengine - INFO - Epoch(train) [36][560/940] lr: 1.0000e-02 eta: 10:42:20 time: 0.5915 data_time: 0.2000 memory: 16095 grad_norm: 5.2194 loss: 1.4855 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4855 2022/12/08 19:26:24 - mmengine - INFO - Epoch(train) [36][580/940] lr: 1.0000e-02 eta: 10:42:08 time: 0.6399 data_time: 0.2976 memory: 16095 grad_norm: 5.3753 loss: 1.3352 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 1.3352 2022/12/08 19:26:35 - mmengine - INFO - Epoch(train) [36][600/940] lr: 1.0000e-02 eta: 10:41:52 time: 0.5477 data_time: 0.1970 memory: 16095 grad_norm: 5.4076 loss: 1.3233 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.3233 2022/12/08 19:26:49 - mmengine - INFO - Epoch(train) [36][620/940] lr: 1.0000e-02 eta: 10:41:41 time: 0.6741 data_time: 0.3017 memory: 16095 grad_norm: 5.3620 loss: 1.3553 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3553 2022/12/08 19:27:00 - mmengine - INFO - Epoch(train) [36][640/940] lr: 1.0000e-02 eta: 10:41:25 time: 0.5584 data_time: 0.1810 memory: 16095 grad_norm: 5.3393 loss: 1.3873 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3873 2022/12/08 19:27:14 - mmengine - INFO - Epoch(train) [36][660/940] lr: 1.0000e-02 eta: 10:41:15 time: 0.7086 data_time: 0.3697 memory: 16095 grad_norm: 5.2725 loss: 1.2841 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 1.2841 2022/12/08 19:27:25 - mmengine - INFO - Epoch(train) [36][680/940] lr: 1.0000e-02 eta: 10:41:00 time: 0.5682 data_time: 0.2410 memory: 16095 grad_norm: 5.3559 loss: 1.3849 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 1.3849 2022/12/08 19:27:39 - mmengine - INFO - Epoch(train) [36][700/940] lr: 1.0000e-02 eta: 10:40:48 time: 0.6631 data_time: 0.3286 memory: 16095 grad_norm: 5.3750 loss: 1.4318 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 1.4318 2022/12/08 19:27:50 - mmengine - INFO - Epoch(train) [36][720/940] lr: 1.0000e-02 eta: 10:40:32 time: 0.5478 data_time: 0.2211 memory: 16095 grad_norm: 5.2495 loss: 1.3678 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.3678 2022/12/08 19:28:03 - mmengine - INFO - Epoch(train) [36][740/940] lr: 1.0000e-02 eta: 10:40:21 time: 0.6922 data_time: 0.3641 memory: 16095 grad_norm: 5.2618 loss: 1.2638 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.2638 2022/12/08 19:28:13 - mmengine - INFO - Epoch(train) [36][760/940] lr: 1.0000e-02 eta: 10:40:02 time: 0.4669 data_time: 0.1381 memory: 16095 grad_norm: 5.1971 loss: 1.3826 top1_acc: 0.7188 top5_acc: 0.7812 loss_cls: 1.3826 2022/12/08 19:28:26 - mmengine - INFO - Epoch(train) [36][780/940] lr: 1.0000e-02 eta: 10:39:51 time: 0.6810 data_time: 0.3570 memory: 16095 grad_norm: 5.3771 loss: 1.3691 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.3691 2022/12/08 19:28:37 - mmengine - INFO - Epoch(train) [36][800/940] lr: 1.0000e-02 eta: 10:39:35 time: 0.5504 data_time: 0.2180 memory: 16095 grad_norm: 5.2113 loss: 1.3211 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 1.3211 2022/12/08 19:28:50 - mmengine - INFO - Epoch(train) [36][820/940] lr: 1.0000e-02 eta: 10:39:22 time: 0.6111 data_time: 0.1770 memory: 16095 grad_norm: 5.3375 loss: 1.4135 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.4135 2022/12/08 19:29:03 - mmengine - INFO - Epoch(train) [36][840/940] lr: 1.0000e-02 eta: 10:39:10 time: 0.6545 data_time: 0.0228 memory: 16095 grad_norm: 5.3248 loss: 1.3021 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3021 2022/12/08 19:29:15 - mmengine - INFO - Epoch(train) [36][860/940] lr: 1.0000e-02 eta: 10:38:56 time: 0.6027 data_time: 0.0218 memory: 16095 grad_norm: 5.2502 loss: 1.3490 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.3490 2022/12/08 19:29:28 - mmengine - INFO - Epoch(train) [36][880/940] lr: 1.0000e-02 eta: 10:38:44 time: 0.6473 data_time: 0.0244 memory: 16095 grad_norm: 5.2440 loss: 1.3226 top1_acc: 0.4688 top5_acc: 0.8125 loss_cls: 1.3226 2022/12/08 19:29:39 - mmengine - INFO - Epoch(train) [36][900/940] lr: 1.0000e-02 eta: 10:38:28 time: 0.5563 data_time: 0.0234 memory: 16095 grad_norm: 5.4795 loss: 1.3912 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.3912 2022/12/08 19:29:52 - mmengine - INFO - Epoch(train) [36][920/940] lr: 1.0000e-02 eta: 10:38:16 time: 0.6472 data_time: 0.0235 memory: 16095 grad_norm: 5.3942 loss: 1.4186 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.4186 2022/12/08 19:30:02 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 19:30:02 - mmengine - INFO - Epoch(train) [36][940/940] lr: 1.0000e-02 eta: 10:37:59 time: 0.5263 data_time: 0.0154 memory: 16095 grad_norm: 5.6773 loss: 1.4039 top1_acc: 0.7143 top5_acc: 0.7143 loss_cls: 1.4039 2022/12/08 19:30:02 - mmengine - INFO - Saving checkpoint at 36 epochs 2022/12/08 19:30:19 - mmengine - INFO - Epoch(val) [36][20/78] eta: 0:00:40 time: 0.7061 data_time: 0.6120 memory: 1686 2022/12/08 19:30:29 - mmengine - INFO - Epoch(val) [36][40/78] eta: 0:00:22 time: 0.4779 data_time: 0.3845 memory: 1686 2022/12/08 19:30:42 - mmengine - INFO - Epoch(val) [36][60/78] eta: 0:00:11 time: 0.6599 data_time: 0.5647 memory: 1686 2022/12/08 19:30:52 - mmengine - INFO - Epoch(val) [36][78/78] acc/top1: 0.6401 acc/top5: 0.8535 acc/mean1: 0.6400 2022/12/08 19:31:08 - mmengine - INFO - Epoch(train) [37][ 20/940] lr: 1.0000e-02 eta: 10:37:52 time: 0.8047 data_time: 0.3667 memory: 16095 grad_norm: 5.3375 loss: 1.3740 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3740 2022/12/08 19:31:20 - mmengine - INFO - Epoch(train) [37][ 40/940] lr: 1.0000e-02 eta: 10:37:37 time: 0.5751 data_time: 0.1493 memory: 16095 grad_norm: 5.2584 loss: 1.2735 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2735 2022/12/08 19:31:34 - mmengine - INFO - Epoch(train) [37][ 60/940] lr: 1.0000e-02 eta: 10:37:27 time: 0.7163 data_time: 0.0938 memory: 16095 grad_norm: 5.1920 loss: 1.3173 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.3173 2022/12/08 19:31:45 - mmengine - INFO - Epoch(train) [37][ 80/940] lr: 1.0000e-02 eta: 10:37:11 time: 0.5391 data_time: 0.0568 memory: 16095 grad_norm: 5.1435 loss: 1.4237 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.4237 2022/12/08 19:31:59 - mmengine - INFO - Epoch(train) [37][100/940] lr: 1.0000e-02 eta: 10:37:01 time: 0.6951 data_time: 0.2116 memory: 16095 grad_norm: 5.2500 loss: 1.3390 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3390 2022/12/08 19:32:09 - mmengine - INFO - Epoch(train) [37][120/940] lr: 1.0000e-02 eta: 10:36:44 time: 0.5164 data_time: 0.1211 memory: 16095 grad_norm: 5.2678 loss: 1.2858 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.2858 2022/12/08 19:32:23 - mmengine - INFO - Epoch(train) [37][140/940] lr: 1.0000e-02 eta: 10:36:33 time: 0.6857 data_time: 0.1422 memory: 16095 grad_norm: 5.2336 loss: 1.2477 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.2477 2022/12/08 19:32:34 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 19:32:34 - mmengine - INFO - Epoch(train) [37][160/940] lr: 1.0000e-02 eta: 10:36:17 time: 0.5477 data_time: 0.0814 memory: 16095 grad_norm: 5.2044 loss: 1.3151 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3151 2022/12/08 19:32:46 - mmengine - INFO - Epoch(train) [37][180/940] lr: 1.0000e-02 eta: 10:36:04 time: 0.6422 data_time: 0.1340 memory: 16095 grad_norm: 5.3593 loss: 1.3122 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.3122 2022/12/08 19:32:57 - mmengine - INFO - Epoch(train) [37][200/940] lr: 1.0000e-02 eta: 10:35:49 time: 0.5517 data_time: 0.1491 memory: 16095 grad_norm: 5.3197 loss: 1.4004 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.4004 2022/12/08 19:33:12 - mmengine - INFO - Epoch(train) [37][220/940] lr: 1.0000e-02 eta: 10:35:39 time: 0.7178 data_time: 0.1888 memory: 16095 grad_norm: 5.1946 loss: 1.2930 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.2930 2022/12/08 19:33:23 - mmengine - INFO - Epoch(train) [37][240/940] lr: 1.0000e-02 eta: 10:35:23 time: 0.5451 data_time: 0.0453 memory: 16095 grad_norm: 5.3028 loss: 1.2513 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2513 2022/12/08 19:33:37 - mmengine - INFO - Epoch(train) [37][260/940] lr: 1.0000e-02 eta: 10:35:12 time: 0.6995 data_time: 0.0385 memory: 16095 grad_norm: 5.3026 loss: 1.3617 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.3617 2022/12/08 19:33:47 - mmengine - INFO - Epoch(train) [37][280/940] lr: 1.0000e-02 eta: 10:34:56 time: 0.5254 data_time: 0.0216 memory: 16095 grad_norm: 5.1544 loss: 1.3582 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.3582 2022/12/08 19:34:01 - mmengine - INFO - Epoch(train) [37][300/940] lr: 1.0000e-02 eta: 10:34:45 time: 0.6862 data_time: 0.0387 memory: 16095 grad_norm: 5.2463 loss: 1.3345 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.3345 2022/12/08 19:34:12 - mmengine - INFO - Epoch(train) [37][320/940] lr: 1.0000e-02 eta: 10:34:29 time: 0.5442 data_time: 0.0203 memory: 16095 grad_norm: 5.2906 loss: 1.4280 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.4280 2022/12/08 19:34:24 - mmengine - INFO - Epoch(train) [37][340/940] lr: 1.0000e-02 eta: 10:34:15 time: 0.6208 data_time: 0.0318 memory: 16095 grad_norm: 5.3212 loss: 1.4033 top1_acc: 0.5312 top5_acc: 0.8750 loss_cls: 1.4033 2022/12/08 19:34:36 - mmengine - INFO - Epoch(train) [37][360/940] lr: 1.0000e-02 eta: 10:34:01 time: 0.5828 data_time: 0.0372 memory: 16095 grad_norm: 5.2563 loss: 1.2569 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 1.2569 2022/12/08 19:34:49 - mmengine - INFO - Epoch(train) [37][380/940] lr: 1.0000e-02 eta: 10:33:48 time: 0.6384 data_time: 0.0242 memory: 16095 grad_norm: 5.2399 loss: 1.4367 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4367 2022/12/08 19:34:59 - mmengine - INFO - Epoch(train) [37][400/940] lr: 1.0000e-02 eta: 10:33:32 time: 0.5363 data_time: 0.0253 memory: 16095 grad_norm: 5.2302 loss: 1.2341 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.2341 2022/12/08 19:35:12 - mmengine - INFO - Epoch(train) [37][420/940] lr: 1.0000e-02 eta: 10:33:20 time: 0.6490 data_time: 0.0284 memory: 16095 grad_norm: 5.3567 loss: 1.4214 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.4214 2022/12/08 19:35:24 - mmengine - INFO - Epoch(train) [37][440/940] lr: 1.0000e-02 eta: 10:33:06 time: 0.6021 data_time: 0.0207 memory: 16095 grad_norm: 5.3524 loss: 1.4175 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.4175 2022/12/08 19:35:37 - mmengine - INFO - Epoch(train) [37][460/940] lr: 1.0000e-02 eta: 10:32:53 time: 0.6262 data_time: 0.0280 memory: 16095 grad_norm: 5.2837 loss: 1.2339 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2339 2022/12/08 19:35:48 - mmengine - INFO - Epoch(train) [37][480/940] lr: 1.0000e-02 eta: 10:32:38 time: 0.5751 data_time: 0.0222 memory: 16095 grad_norm: 5.3141 loss: 1.4631 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.4631 2022/12/08 19:36:01 - mmengine - INFO - Epoch(train) [37][500/940] lr: 1.0000e-02 eta: 10:32:25 time: 0.6444 data_time: 0.0268 memory: 16095 grad_norm: 5.3617 loss: 1.3117 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 1.3117 2022/12/08 19:36:12 - mmengine - INFO - Epoch(train) [37][520/940] lr: 1.0000e-02 eta: 10:32:10 time: 0.5466 data_time: 0.0293 memory: 16095 grad_norm: 5.2198 loss: 1.3480 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.3480 2022/12/08 19:36:26 - mmengine - INFO - Epoch(train) [37][540/940] lr: 1.0000e-02 eta: 10:31:59 time: 0.7057 data_time: 0.0277 memory: 16095 grad_norm: 5.3773 loss: 1.3908 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 1.3908 2022/12/08 19:36:37 - mmengine - INFO - Epoch(train) [37][560/940] lr: 1.0000e-02 eta: 10:31:43 time: 0.5385 data_time: 0.0235 memory: 16095 grad_norm: 5.2632 loss: 1.3701 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.3701 2022/12/08 19:36:52 - mmengine - INFO - Epoch(train) [37][580/940] lr: 1.0000e-02 eta: 10:31:33 time: 0.7169 data_time: 0.0262 memory: 16095 grad_norm: 5.4051 loss: 1.3898 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3898 2022/12/08 19:37:02 - mmengine - INFO - Epoch(train) [37][600/940] lr: 1.0000e-02 eta: 10:31:17 time: 0.5169 data_time: 0.0226 memory: 16095 grad_norm: 5.3618 loss: 1.5112 top1_acc: 0.5000 top5_acc: 0.6562 loss_cls: 1.5112 2022/12/08 19:37:16 - mmengine - INFO - Epoch(train) [37][620/940] lr: 1.0000e-02 eta: 10:31:06 time: 0.6943 data_time: 0.0258 memory: 16095 grad_norm: 5.2966 loss: 1.4260 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4260 2022/12/08 19:37:26 - mmengine - INFO - Epoch(train) [37][640/940] lr: 1.0000e-02 eta: 10:30:49 time: 0.5164 data_time: 0.0208 memory: 16095 grad_norm: 5.2072 loss: 1.4161 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.4161 2022/12/08 19:37:40 - mmengine - INFO - Epoch(train) [37][660/940] lr: 1.0000e-02 eta: 10:30:38 time: 0.6913 data_time: 0.0234 memory: 16095 grad_norm: 5.2692 loss: 1.2857 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.2857 2022/12/08 19:37:51 - mmengine - INFO - Epoch(train) [37][680/940] lr: 1.0000e-02 eta: 10:30:22 time: 0.5354 data_time: 0.0266 memory: 16095 grad_norm: 5.3795 loss: 1.3032 top1_acc: 0.5312 top5_acc: 0.9062 loss_cls: 1.3032 2022/12/08 19:38:03 - mmengine - INFO - Epoch(train) [37][700/940] lr: 1.0000e-02 eta: 10:30:09 time: 0.6229 data_time: 0.0242 memory: 16095 grad_norm: 5.4730 loss: 1.5902 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 1.5902 2022/12/08 19:38:14 - mmengine - INFO - Epoch(train) [37][720/940] lr: 1.0000e-02 eta: 10:29:53 time: 0.5552 data_time: 0.0243 memory: 16095 grad_norm: 5.2593 loss: 1.3461 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.3461 2022/12/08 19:38:27 - mmengine - INFO - Epoch(train) [37][740/940] lr: 1.0000e-02 eta: 10:29:41 time: 0.6410 data_time: 0.0245 memory: 16095 grad_norm: 5.2866 loss: 1.4784 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.4784 2022/12/08 19:38:39 - mmengine - INFO - Epoch(train) [37][760/940] lr: 1.0000e-02 eta: 10:29:27 time: 0.5924 data_time: 0.0249 memory: 16095 grad_norm: 5.4003 loss: 1.3246 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3246 2022/12/08 19:38:52 - mmengine - INFO - Epoch(train) [37][780/940] lr: 1.0000e-02 eta: 10:29:14 time: 0.6493 data_time: 0.0276 memory: 16095 grad_norm: 5.4090 loss: 1.4259 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4259 2022/12/08 19:39:02 - mmengine - INFO - Epoch(train) [37][800/940] lr: 1.0000e-02 eta: 10:28:58 time: 0.5191 data_time: 0.0214 memory: 16095 grad_norm: 5.3270 loss: 1.3112 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3112 2022/12/08 19:39:16 - mmengine - INFO - Epoch(train) [37][820/940] lr: 1.0000e-02 eta: 10:28:46 time: 0.6803 data_time: 0.0915 memory: 16095 grad_norm: 5.2992 loss: 1.3350 top1_acc: 0.5312 top5_acc: 0.7500 loss_cls: 1.3350 2022/12/08 19:39:27 - mmengine - INFO - Epoch(train) [37][840/940] lr: 1.0000e-02 eta: 10:28:31 time: 0.5698 data_time: 0.0302 memory: 16095 grad_norm: 5.3875 loss: 1.3484 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3484 2022/12/08 19:39:41 - mmengine - INFO - Epoch(train) [37][860/940] lr: 1.0000e-02 eta: 10:28:20 time: 0.6796 data_time: 0.0442 memory: 16095 grad_norm: 5.4462 loss: 1.3499 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.3499 2022/12/08 19:39:52 - mmengine - INFO - Epoch(train) [37][880/940] lr: 1.0000e-02 eta: 10:28:05 time: 0.5663 data_time: 0.0265 memory: 16095 grad_norm: 5.4142 loss: 1.2915 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.2915 2022/12/08 19:40:05 - mmengine - INFO - Epoch(train) [37][900/940] lr: 1.0000e-02 eta: 10:27:53 time: 0.6574 data_time: 0.0218 memory: 16095 grad_norm: 5.2121 loss: 1.3592 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.3592 2022/12/08 19:40:17 - mmengine - INFO - Epoch(train) [37][920/940] lr: 1.0000e-02 eta: 10:27:39 time: 0.5933 data_time: 0.0272 memory: 16095 grad_norm: 5.3263 loss: 1.4357 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.4357 2022/12/08 19:40:28 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 19:40:28 - mmengine - INFO - Epoch(train) [37][940/940] lr: 1.0000e-02 eta: 10:27:24 time: 0.5564 data_time: 0.0147 memory: 16095 grad_norm: 5.6671 loss: 1.3774 top1_acc: 0.2857 top5_acc: 0.5714 loss_cls: 1.3774 2022/12/08 19:40:42 - mmengine - INFO - Epoch(val) [37][20/78] eta: 0:00:40 time: 0.7026 data_time: 0.6092 memory: 1686 2022/12/08 19:40:52 - mmengine - INFO - Epoch(val) [37][40/78] eta: 0:00:22 time: 0.4637 data_time: 0.3701 memory: 1686 2022/12/08 19:41:05 - mmengine - INFO - Epoch(val) [37][60/78] eta: 0:00:11 time: 0.6788 data_time: 0.5843 memory: 1686 2022/12/08 19:41:16 - mmengine - INFO - Epoch(val) [37][78/78] acc/top1: 0.6454 acc/top5: 0.8540 acc/mean1: 0.6452 2022/12/08 19:41:32 - mmengine - INFO - Epoch(train) [38][ 20/940] lr: 1.0000e-02 eta: 10:27:17 time: 0.8182 data_time: 0.4406 memory: 16095 grad_norm: 5.2144 loss: 1.2615 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.2615 2022/12/08 19:41:43 - mmengine - INFO - Epoch(train) [38][ 40/940] lr: 1.0000e-02 eta: 10:27:02 time: 0.5597 data_time: 0.1557 memory: 16095 grad_norm: 5.1563 loss: 1.2406 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2406 2022/12/08 19:41:56 - mmengine - INFO - Epoch(train) [38][ 60/940] lr: 1.0000e-02 eta: 10:26:50 time: 0.6655 data_time: 0.1417 memory: 16095 grad_norm: 5.1979 loss: 1.2844 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.2844 2022/12/08 19:42:08 - mmengine - INFO - Epoch(train) [38][ 80/940] lr: 1.0000e-02 eta: 10:26:35 time: 0.5680 data_time: 0.1523 memory: 16095 grad_norm: 5.1411 loss: 1.1586 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 1.1586 2022/12/08 19:42:20 - mmengine - INFO - Epoch(train) [38][100/940] lr: 1.0000e-02 eta: 10:26:22 time: 0.6284 data_time: 0.2510 memory: 16095 grad_norm: 5.2508 loss: 1.2457 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 1.2457 2022/12/08 19:42:31 - mmengine - INFO - Epoch(train) [38][120/940] lr: 1.0000e-02 eta: 10:26:07 time: 0.5535 data_time: 0.1833 memory: 16095 grad_norm: 5.1856 loss: 1.2180 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.2180 2022/12/08 19:42:45 - mmengine - INFO - Epoch(train) [38][140/940] lr: 1.0000e-02 eta: 10:25:56 time: 0.6899 data_time: 0.3011 memory: 16095 grad_norm: 5.2577 loss: 1.3459 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.3459 2022/12/08 19:42:56 - mmengine - INFO - Epoch(train) [38][160/940] lr: 1.0000e-02 eta: 10:25:39 time: 0.5234 data_time: 0.0938 memory: 16095 grad_norm: 5.2066 loss: 1.2026 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.2026 2022/12/08 19:43:09 - mmengine - INFO - Epoch(train) [38][180/940] lr: 1.0000e-02 eta: 10:25:27 time: 0.6556 data_time: 0.2343 memory: 16095 grad_norm: 5.2488 loss: 1.0909 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.0909 2022/12/08 19:43:20 - mmengine - INFO - Epoch(train) [38][200/940] lr: 1.0000e-02 eta: 10:25:11 time: 0.5321 data_time: 0.1845 memory: 16095 grad_norm: 5.3052 loss: 1.4213 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.4213 2022/12/08 19:43:34 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 19:43:34 - mmengine - INFO - Epoch(train) [38][220/940] lr: 1.0000e-02 eta: 10:25:01 time: 0.7026 data_time: 0.3675 memory: 16095 grad_norm: 5.3543 loss: 1.3514 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.3514 2022/12/08 19:43:45 - mmengine - INFO - Epoch(train) [38][240/940] lr: 1.0000e-02 eta: 10:24:45 time: 0.5633 data_time: 0.2462 memory: 16095 grad_norm: 5.3181 loss: 1.3869 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.3869 2022/12/08 19:43:58 - mmengine - INFO - Epoch(train) [38][260/940] lr: 1.0000e-02 eta: 10:24:33 time: 0.6570 data_time: 0.3332 memory: 16095 grad_norm: 5.2251 loss: 1.4002 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.4002 2022/12/08 19:44:10 - mmengine - INFO - Epoch(train) [38][280/940] lr: 1.0000e-02 eta: 10:24:19 time: 0.5770 data_time: 0.2645 memory: 16095 grad_norm: 5.1663 loss: 1.3521 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.3521 2022/12/08 19:44:23 - mmengine - INFO - Epoch(train) [38][300/940] lr: 1.0000e-02 eta: 10:24:08 time: 0.6828 data_time: 0.3575 memory: 16095 grad_norm: 5.3895 loss: 1.3370 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 1.3370 2022/12/08 19:44:35 - mmengine - INFO - Epoch(train) [38][320/940] lr: 1.0000e-02 eta: 10:23:53 time: 0.5840 data_time: 0.2653 memory: 16095 grad_norm: 5.3113 loss: 1.3609 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.3609 2022/12/08 19:44:47 - mmengine - INFO - Epoch(train) [38][340/940] lr: 1.0000e-02 eta: 10:23:39 time: 0.5915 data_time: 0.2586 memory: 16095 grad_norm: 5.3021 loss: 1.4765 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.4765 2022/12/08 19:44:58 - mmengine - INFO - Epoch(train) [38][360/940] lr: 1.0000e-02 eta: 10:23:23 time: 0.5491 data_time: 0.2111 memory: 16095 grad_norm: 5.3428 loss: 1.3867 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.3867 2022/12/08 19:45:11 - mmengine - INFO - Epoch(train) [38][380/940] lr: 1.0000e-02 eta: 10:23:12 time: 0.6901 data_time: 0.3559 memory: 16095 grad_norm: 5.2746 loss: 1.4090 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.4090 2022/12/08 19:45:23 - mmengine - INFO - Epoch(train) [38][400/940] lr: 1.0000e-02 eta: 10:22:57 time: 0.5592 data_time: 0.1770 memory: 16095 grad_norm: 5.3672 loss: 1.3583 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.3583 2022/12/08 19:45:36 - mmengine - INFO - Epoch(train) [38][420/940] lr: 1.0000e-02 eta: 10:22:45 time: 0.6591 data_time: 0.1912 memory: 16095 grad_norm: 5.1920 loss: 1.3830 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 1.3830 2022/12/08 19:45:47 - mmengine - INFO - Epoch(train) [38][440/940] lr: 1.0000e-02 eta: 10:22:30 time: 0.5603 data_time: 0.1243 memory: 16095 grad_norm: 5.3522 loss: 1.4087 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.4087 2022/12/08 19:46:01 - mmengine - INFO - Epoch(train) [38][460/940] lr: 1.0000e-02 eta: 10:22:19 time: 0.6783 data_time: 0.2773 memory: 16095 grad_norm: 5.3580 loss: 1.2671 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2671 2022/12/08 19:46:12 - mmengine - INFO - Epoch(train) [38][480/940] lr: 1.0000e-02 eta: 10:22:03 time: 0.5539 data_time: 0.2284 memory: 16095 grad_norm: 5.2747 loss: 1.3284 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3284 2022/12/08 19:46:25 - mmengine - INFO - Epoch(train) [38][500/940] lr: 1.0000e-02 eta: 10:21:51 time: 0.6459 data_time: 0.2476 memory: 16095 grad_norm: 5.2462 loss: 1.3315 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.3315 2022/12/08 19:46:36 - mmengine - INFO - Epoch(train) [38][520/940] lr: 1.0000e-02 eta: 10:21:36 time: 0.5545 data_time: 0.2138 memory: 16095 grad_norm: 5.3511 loss: 1.3217 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3217 2022/12/08 19:46:50 - mmengine - INFO - Epoch(train) [38][540/940] lr: 1.0000e-02 eta: 10:21:25 time: 0.7064 data_time: 0.3660 memory: 16095 grad_norm: 5.2888 loss: 1.3428 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3428 2022/12/08 19:47:01 - mmengine - INFO - Epoch(train) [38][560/940] lr: 1.0000e-02 eta: 10:21:10 time: 0.5454 data_time: 0.2140 memory: 16095 grad_norm: 5.4441 loss: 1.3927 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.3927 2022/12/08 19:47:15 - mmengine - INFO - Epoch(train) [38][580/940] lr: 1.0000e-02 eta: 10:20:59 time: 0.6999 data_time: 0.3413 memory: 16095 grad_norm: 5.2870 loss: 1.3657 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3657 2022/12/08 19:47:26 - mmengine - INFO - Epoch(train) [38][600/940] lr: 1.0000e-02 eta: 10:20:43 time: 0.5370 data_time: 0.1950 memory: 16095 grad_norm: 5.2759 loss: 1.2982 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.2982 2022/12/08 19:47:39 - mmengine - INFO - Epoch(train) [38][620/940] lr: 1.0000e-02 eta: 10:20:31 time: 0.6665 data_time: 0.3059 memory: 16095 grad_norm: 5.3792 loss: 1.4893 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 1.4893 2022/12/08 19:47:50 - mmengine - INFO - Epoch(train) [38][640/940] lr: 1.0000e-02 eta: 10:20:17 time: 0.5715 data_time: 0.2405 memory: 16095 grad_norm: 5.3939 loss: 1.3648 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.3648 2022/12/08 19:48:04 - mmengine - INFO - Epoch(train) [38][660/940] lr: 1.0000e-02 eta: 10:20:06 time: 0.6949 data_time: 0.3110 memory: 16095 grad_norm: 5.2782 loss: 1.5062 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 1.5062 2022/12/08 19:48:15 - mmengine - INFO - Epoch(train) [38][680/940] lr: 1.0000e-02 eta: 10:19:50 time: 0.5377 data_time: 0.1577 memory: 16095 grad_norm: 5.3685 loss: 1.3255 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3255 2022/12/08 19:48:28 - mmengine - INFO - Epoch(train) [38][700/940] lr: 1.0000e-02 eta: 10:19:38 time: 0.6497 data_time: 0.2764 memory: 16095 grad_norm: 5.4072 loss: 1.3137 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.3137 2022/12/08 19:48:39 - mmengine - INFO - Epoch(train) [38][720/940] lr: 1.0000e-02 eta: 10:19:23 time: 0.5741 data_time: 0.1718 memory: 16095 grad_norm: 5.4274 loss: 1.2669 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2669 2022/12/08 19:48:53 - mmengine - INFO - Epoch(train) [38][740/940] lr: 1.0000e-02 eta: 10:19:12 time: 0.6860 data_time: 0.0944 memory: 16095 grad_norm: 5.3692 loss: 1.4059 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4059 2022/12/08 19:49:04 - mmengine - INFO - Epoch(train) [38][760/940] lr: 1.0000e-02 eta: 10:18:57 time: 0.5645 data_time: 0.0243 memory: 16095 grad_norm: 5.5016 loss: 1.3867 top1_acc: 0.5312 top5_acc: 0.7188 loss_cls: 1.3867 2022/12/08 19:49:18 - mmengine - INFO - Epoch(train) [38][780/940] lr: 1.0000e-02 eta: 10:18:45 time: 0.6589 data_time: 0.0288 memory: 16095 grad_norm: 5.3657 loss: 1.4093 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.4093 2022/12/08 19:49:29 - mmengine - INFO - Epoch(train) [38][800/940] lr: 1.0000e-02 eta: 10:18:31 time: 0.5927 data_time: 0.0214 memory: 16095 grad_norm: 5.3101 loss: 1.2863 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2863 2022/12/08 19:49:43 - mmengine - INFO - Epoch(train) [38][820/940] lr: 1.0000e-02 eta: 10:18:19 time: 0.6732 data_time: 0.0258 memory: 16095 grad_norm: 5.2818 loss: 1.4210 top1_acc: 0.5312 top5_acc: 0.7812 loss_cls: 1.4210 2022/12/08 19:49:54 - mmengine - INFO - Epoch(train) [38][840/940] lr: 1.0000e-02 eta: 10:18:04 time: 0.5519 data_time: 0.0234 memory: 16095 grad_norm: 5.4248 loss: 1.3587 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3587 2022/12/08 19:50:07 - mmengine - INFO - Epoch(train) [38][860/940] lr: 1.0000e-02 eta: 10:17:52 time: 0.6547 data_time: 0.0218 memory: 16095 grad_norm: 5.3862 loss: 1.4723 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.4723 2022/12/08 19:50:18 - mmengine - INFO - Epoch(train) [38][880/940] lr: 1.0000e-02 eta: 10:17:37 time: 0.5618 data_time: 0.0247 memory: 16095 grad_norm: 5.3551 loss: 1.2808 top1_acc: 0.4375 top5_acc: 0.7812 loss_cls: 1.2808 2022/12/08 19:50:31 - mmengine - INFO - Epoch(train) [38][900/940] lr: 1.0000e-02 eta: 10:17:24 time: 0.6291 data_time: 0.0234 memory: 16095 grad_norm: 5.3499 loss: 1.2789 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 1.2789 2022/12/08 19:50:43 - mmengine - INFO - Epoch(train) [38][920/940] lr: 1.0000e-02 eta: 10:17:10 time: 0.6146 data_time: 0.0274 memory: 16095 grad_norm: 5.4370 loss: 1.4261 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4261 2022/12/08 19:50:53 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 19:50:53 - mmengine - INFO - Epoch(train) [38][940/940] lr: 1.0000e-02 eta: 10:16:53 time: 0.4906 data_time: 0.0144 memory: 16095 grad_norm: 5.8197 loss: 1.3680 top1_acc: 0.1429 top5_acc: 0.7143 loss_cls: 1.3680 2022/12/08 19:51:07 - mmengine - INFO - Epoch(val) [38][20/78] eta: 0:00:40 time: 0.6930 data_time: 0.5981 memory: 1686 2022/12/08 19:51:16 - mmengine - INFO - Epoch(val) [38][40/78] eta: 0:00:22 time: 0.4651 data_time: 0.3721 memory: 1686 2022/12/08 19:51:30 - mmengine - INFO - Epoch(val) [38][60/78] eta: 0:00:10 time: 0.6656 data_time: 0.5698 memory: 1686 2022/12/08 19:51:40 - mmengine - INFO - Epoch(val) [38][78/78] acc/top1: 0.6463 acc/top5: 0.8555 acc/mean1: 0.6462 2022/12/08 19:51:57 - mmengine - INFO - Epoch(train) [39][ 20/940] lr: 1.0000e-02 eta: 10:16:46 time: 0.8176 data_time: 0.4898 memory: 16095 grad_norm: 5.2819 loss: 1.2728 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.2728 2022/12/08 19:52:08 - mmengine - INFO - Epoch(train) [39][ 40/940] lr: 1.0000e-02 eta: 10:16:31 time: 0.5536 data_time: 0.2215 memory: 16095 grad_norm: 5.2314 loss: 1.2515 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.2515 2022/12/08 19:52:22 - mmengine - INFO - Epoch(train) [39][ 60/940] lr: 1.0000e-02 eta: 10:16:20 time: 0.6993 data_time: 0.3936 memory: 16095 grad_norm: 5.2948 loss: 1.4170 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4170 2022/12/08 19:52:32 - mmengine - INFO - Epoch(train) [39][ 80/940] lr: 1.0000e-02 eta: 10:16:04 time: 0.5300 data_time: 0.1689 memory: 16095 grad_norm: 5.1571 loss: 1.3242 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.3242 2022/12/08 19:52:46 - mmengine - INFO - Epoch(train) [39][100/940] lr: 1.0000e-02 eta: 10:15:53 time: 0.6957 data_time: 0.3824 memory: 16095 grad_norm: 5.2527 loss: 1.2978 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.2978 2022/12/08 19:52:57 - mmengine - INFO - Epoch(train) [39][120/940] lr: 1.0000e-02 eta: 10:15:37 time: 0.5331 data_time: 0.2291 memory: 16095 grad_norm: 5.2141 loss: 1.2756 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.2756 2022/12/08 19:53:11 - mmengine - INFO - Epoch(train) [39][140/940] lr: 1.0000e-02 eta: 10:15:27 time: 0.6985 data_time: 0.3987 memory: 16095 grad_norm: 5.1758 loss: 1.2459 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2459 2022/12/08 19:53:22 - mmengine - INFO - Epoch(train) [39][160/940] lr: 1.0000e-02 eta: 10:15:12 time: 0.5638 data_time: 0.2642 memory: 16095 grad_norm: 5.2010 loss: 1.2545 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.2545 2022/12/08 19:53:36 - mmengine - INFO - Epoch(train) [39][180/940] lr: 1.0000e-02 eta: 10:15:00 time: 0.6756 data_time: 0.3086 memory: 16095 grad_norm: 5.3606 loss: 1.3898 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.3898 2022/12/08 19:53:47 - mmengine - INFO - Epoch(train) [39][200/940] lr: 1.0000e-02 eta: 10:14:45 time: 0.5666 data_time: 0.2471 memory: 16095 grad_norm: 5.3449 loss: 1.2826 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.2826 2022/12/08 19:54:00 - mmengine - INFO - Epoch(train) [39][220/940] lr: 1.0000e-02 eta: 10:14:33 time: 0.6402 data_time: 0.3027 memory: 16095 grad_norm: 5.1749 loss: 1.2515 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 1.2515 2022/12/08 19:54:11 - mmengine - INFO - Epoch(train) [39][240/940] lr: 1.0000e-02 eta: 10:14:18 time: 0.5693 data_time: 0.1750 memory: 16095 grad_norm: 5.3998 loss: 1.4108 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.4108 2022/12/08 19:54:24 - mmengine - INFO - Epoch(train) [39][260/940] lr: 1.0000e-02 eta: 10:14:05 time: 0.6339 data_time: 0.1607 memory: 16095 grad_norm: 5.2558 loss: 1.2016 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 1.2016 2022/12/08 19:54:35 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 19:54:35 - mmengine - INFO - Epoch(train) [39][280/940] lr: 1.0000e-02 eta: 10:13:51 time: 0.5686 data_time: 0.0676 memory: 16095 grad_norm: 5.3082 loss: 1.3915 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 1.3915 2022/12/08 19:54:48 - mmengine - INFO - Epoch(train) [39][300/940] lr: 1.0000e-02 eta: 10:13:39 time: 0.6562 data_time: 0.1595 memory: 16095 grad_norm: 5.2720 loss: 1.3713 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 1.3713 2022/12/08 19:55:00 - mmengine - INFO - Epoch(train) [39][320/940] lr: 1.0000e-02 eta: 10:13:24 time: 0.5672 data_time: 0.1058 memory: 16095 grad_norm: 5.2841 loss: 1.2824 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2824 2022/12/08 19:55:13 - mmengine - INFO - Epoch(train) [39][340/940] lr: 1.0000e-02 eta: 10:13:12 time: 0.6784 data_time: 0.2135 memory: 16095 grad_norm: 5.3609 loss: 1.2628 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.2628 2022/12/08 19:55:25 - mmengine - INFO - Epoch(train) [39][360/940] lr: 1.0000e-02 eta: 10:12:58 time: 0.5898 data_time: 0.0901 memory: 16095 grad_norm: 5.4133 loss: 1.3750 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 1.3750 2022/12/08 19:55:38 - mmengine - INFO - Epoch(train) [39][380/940] lr: 1.0000e-02 eta: 10:12:46 time: 0.6448 data_time: 0.0450 memory: 16095 grad_norm: 5.2483 loss: 1.2720 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.2720 2022/12/08 19:55:50 - mmengine - INFO - Epoch(train) [39][400/940] lr: 1.0000e-02 eta: 10:12:32 time: 0.5885 data_time: 0.0222 memory: 16095 grad_norm: 5.3446 loss: 1.2346 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 1.2346 2022/12/08 19:56:02 - mmengine - INFO - Epoch(train) [39][420/940] lr: 1.0000e-02 eta: 10:12:18 time: 0.6153 data_time: 0.0278 memory: 16095 grad_norm: 5.2458 loss: 1.4448 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.4448 2022/12/08 19:56:14 - mmengine - INFO - Epoch(train) [39][440/940] lr: 1.0000e-02 eta: 10:12:05 time: 0.6067 data_time: 0.0219 memory: 16095 grad_norm: 5.3379 loss: 1.3204 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.3204 2022/12/08 19:56:26 - mmengine - INFO - Epoch(train) [39][460/940] lr: 1.0000e-02 eta: 10:11:51 time: 0.5956 data_time: 0.0270 memory: 16095 grad_norm: 5.3423 loss: 1.2462 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.2462 2022/12/08 19:56:38 - mmengine - INFO - Epoch(train) [39][480/940] lr: 1.0000e-02 eta: 10:11:36 time: 0.5723 data_time: 0.0320 memory: 16095 grad_norm: 5.3075 loss: 1.2894 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.2894 2022/12/08 19:56:50 - mmengine - INFO - Epoch(train) [39][500/940] lr: 1.0000e-02 eta: 10:11:24 time: 0.6442 data_time: 0.0296 memory: 16095 grad_norm: 5.3175 loss: 1.2003 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2003 2022/12/08 19:57:02 - mmengine - INFO - Epoch(train) [39][520/940] lr: 1.0000e-02 eta: 10:11:10 time: 0.6037 data_time: 0.0230 memory: 16095 grad_norm: 5.3937 loss: 1.2797 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.2797 2022/12/08 19:57:14 - mmengine - INFO - Epoch(train) [39][540/940] lr: 1.0000e-02 eta: 10:10:56 time: 0.5882 data_time: 0.0251 memory: 16095 grad_norm: 5.3735 loss: 1.3388 top1_acc: 0.5938 top5_acc: 0.9375 loss_cls: 1.3388 2022/12/08 19:57:26 - mmengine - INFO - Epoch(train) [39][560/940] lr: 1.0000e-02 eta: 10:10:42 time: 0.5938 data_time: 0.0227 memory: 16095 grad_norm: 5.3569 loss: 1.2679 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.2679 2022/12/08 19:57:38 - mmengine - INFO - Epoch(train) [39][580/940] lr: 1.0000e-02 eta: 10:10:28 time: 0.6153 data_time: 0.0246 memory: 16095 grad_norm: 5.3871 loss: 1.2773 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.2773 2022/12/08 19:57:50 - mmengine - INFO - Epoch(train) [39][600/940] lr: 1.0000e-02 eta: 10:10:14 time: 0.5686 data_time: 0.0244 memory: 16095 grad_norm: 5.3180 loss: 1.2571 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.2571 2022/12/08 19:58:03 - mmengine - INFO - Epoch(train) [39][620/940] lr: 1.0000e-02 eta: 10:10:01 time: 0.6446 data_time: 0.0253 memory: 16095 grad_norm: 5.3678 loss: 1.3048 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.3048 2022/12/08 19:58:14 - mmengine - INFO - Epoch(train) [39][640/940] lr: 1.0000e-02 eta: 10:09:46 time: 0.5578 data_time: 0.0241 memory: 16095 grad_norm: 5.2657 loss: 1.2688 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2688 2022/12/08 19:58:27 - mmengine - INFO - Epoch(train) [39][660/940] lr: 1.0000e-02 eta: 10:09:34 time: 0.6394 data_time: 0.0265 memory: 16095 grad_norm: 5.4305 loss: 1.2563 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.2563 2022/12/08 19:58:39 - mmengine - INFO - Epoch(train) [39][680/940] lr: 1.0000e-02 eta: 10:09:20 time: 0.6064 data_time: 0.0237 memory: 16095 grad_norm: 5.4561 loss: 1.3588 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.3588 2022/12/08 19:58:51 - mmengine - INFO - Epoch(train) [39][700/940] lr: 1.0000e-02 eta: 10:09:06 time: 0.6083 data_time: 0.0249 memory: 16095 grad_norm: 5.3280 loss: 1.2595 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 1.2595 2022/12/08 19:59:04 - mmengine - INFO - Epoch(train) [39][720/940] lr: 1.0000e-02 eta: 10:08:54 time: 0.6479 data_time: 0.0344 memory: 16095 grad_norm: 5.2824 loss: 1.2057 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.2057 2022/12/08 19:59:15 - mmengine - INFO - Epoch(train) [39][740/940] lr: 1.0000e-02 eta: 10:08:39 time: 0.5679 data_time: 0.0246 memory: 16095 grad_norm: 5.4207 loss: 1.3353 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.3353 2022/12/08 19:59:29 - mmengine - INFO - Epoch(train) [39][760/940] lr: 1.0000e-02 eta: 10:08:28 time: 0.6834 data_time: 0.0232 memory: 16095 grad_norm: 5.3425 loss: 1.2600 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.2600 2022/12/08 19:59:39 - mmengine - INFO - Epoch(train) [39][780/940] lr: 1.0000e-02 eta: 10:08:12 time: 0.5195 data_time: 0.0241 memory: 16095 grad_norm: 5.2289 loss: 1.3075 top1_acc: 0.5938 top5_acc: 0.7500 loss_cls: 1.3075 2022/12/08 19:59:53 - mmengine - INFO - Epoch(train) [39][800/940] lr: 1.0000e-02 eta: 10:08:01 time: 0.6922 data_time: 0.0260 memory: 16095 grad_norm: 5.3399 loss: 1.4114 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.4114 2022/12/08 20:00:05 - mmengine - INFO - Epoch(train) [39][820/940] lr: 1.0000e-02 eta: 10:07:46 time: 0.5693 data_time: 0.0249 memory: 16095 grad_norm: 5.4545 loss: 1.2921 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2921 2022/12/08 20:00:17 - mmengine - INFO - Epoch(train) [39][840/940] lr: 1.0000e-02 eta: 10:07:33 time: 0.6178 data_time: 0.0237 memory: 16095 grad_norm: 5.4025 loss: 1.3886 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3886 2022/12/08 20:00:29 - mmengine - INFO - Epoch(train) [39][860/940] lr: 1.0000e-02 eta: 10:07:19 time: 0.5877 data_time: 0.0236 memory: 16095 grad_norm: 5.4268 loss: 1.4701 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4701 2022/12/08 20:00:42 - mmengine - INFO - Epoch(train) [39][880/940] lr: 1.0000e-02 eta: 10:07:07 time: 0.6479 data_time: 0.0241 memory: 16095 grad_norm: 5.3194 loss: 1.3617 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.3617 2022/12/08 20:00:52 - mmengine - INFO - Epoch(train) [39][900/940] lr: 1.0000e-02 eta: 10:06:51 time: 0.5340 data_time: 0.0243 memory: 16095 grad_norm: 5.3783 loss: 1.4074 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.4074 2022/12/08 20:01:06 - mmengine - INFO - Epoch(train) [39][920/940] lr: 1.0000e-02 eta: 10:06:39 time: 0.6717 data_time: 0.0250 memory: 16095 grad_norm: 5.4379 loss: 1.2818 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2818 2022/12/08 20:01:15 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 20:01:15 - mmengine - INFO - Epoch(train) [39][940/940] lr: 1.0000e-02 eta: 10:06:22 time: 0.4767 data_time: 0.0179 memory: 16095 grad_norm: 5.8532 loss: 1.4959 top1_acc: 0.4286 top5_acc: 0.5714 loss_cls: 1.4959 2022/12/08 20:01:15 - mmengine - INFO - Saving checkpoint at 39 epochs 2022/12/08 20:01:32 - mmengine - INFO - Epoch(val) [39][20/78] eta: 0:00:41 time: 0.7182 data_time: 0.6232 memory: 1686 2022/12/08 20:01:41 - mmengine - INFO - Epoch(val) [39][40/78] eta: 0:00:22 time: 0.4451 data_time: 0.3520 memory: 1686 2022/12/08 20:01:55 - mmengine - INFO - Epoch(val) [39][60/78] eta: 0:00:11 time: 0.6962 data_time: 0.6009 memory: 1686 2022/12/08 20:02:05 - mmengine - INFO - Epoch(val) [39][78/78] acc/top1: 0.6384 acc/top5: 0.8461 acc/mean1: 0.6383 2022/12/08 20:02:22 - mmengine - INFO - Epoch(train) [40][ 20/940] lr: 1.0000e-02 eta: 10:06:16 time: 0.8476 data_time: 0.4494 memory: 16095 grad_norm: 5.3548 loss: 1.3492 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 1.3492 2022/12/08 20:02:33 - mmengine - INFO - Epoch(train) [40][ 40/940] lr: 1.0000e-02 eta: 10:06:00 time: 0.5531 data_time: 0.1488 memory: 16095 grad_norm: 5.1404 loss: 1.1820 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1820 2022/12/08 20:02:47 - mmengine - INFO - Epoch(train) [40][ 60/940] lr: 1.0000e-02 eta: 10:05:49 time: 0.6866 data_time: 0.1962 memory: 16095 grad_norm: 5.1654 loss: 1.2353 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2353 2022/12/08 20:02:58 - mmengine - INFO - Epoch(train) [40][ 80/940] lr: 1.0000e-02 eta: 10:05:34 time: 0.5429 data_time: 0.0256 memory: 16095 grad_norm: 5.2381 loss: 1.2220 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2220 2022/12/08 20:03:12 - mmengine - INFO - Epoch(train) [40][100/940] lr: 1.0000e-02 eta: 10:05:23 time: 0.7020 data_time: 0.0262 memory: 16095 grad_norm: 5.2266 loss: 1.2928 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2928 2022/12/08 20:03:23 - mmengine - INFO - Epoch(train) [40][120/940] lr: 1.0000e-02 eta: 10:05:09 time: 0.5763 data_time: 0.0249 memory: 16095 grad_norm: 5.2463 loss: 1.2750 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 1.2750 2022/12/08 20:03:37 - mmengine - INFO - Epoch(train) [40][140/940] lr: 1.0000e-02 eta: 10:04:57 time: 0.6683 data_time: 0.0243 memory: 16095 grad_norm: 5.2081 loss: 1.2223 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2223 2022/12/08 20:03:49 - mmengine - INFO - Epoch(train) [40][160/940] lr: 1.0000e-02 eta: 10:04:43 time: 0.5909 data_time: 0.0340 memory: 16095 grad_norm: 5.2320 loss: 1.3515 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3515 2022/12/08 20:04:02 - mmengine - INFO - Epoch(train) [40][180/940] lr: 1.0000e-02 eta: 10:04:31 time: 0.6483 data_time: 0.0279 memory: 16095 grad_norm: 5.2268 loss: 1.1870 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 1.1870 2022/12/08 20:04:13 - mmengine - INFO - Epoch(train) [40][200/940] lr: 1.0000e-02 eta: 10:04:15 time: 0.5479 data_time: 0.0247 memory: 16095 grad_norm: 5.1831 loss: 1.2546 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 1.2546 2022/12/08 20:04:26 - mmengine - INFO - Epoch(train) [40][220/940] lr: 1.0000e-02 eta: 10:04:04 time: 0.6757 data_time: 0.0227 memory: 16095 grad_norm: 5.2339 loss: 1.3514 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.3514 2022/12/08 20:04:37 - mmengine - INFO - Epoch(train) [40][240/940] lr: 1.0000e-02 eta: 10:03:49 time: 0.5646 data_time: 0.0257 memory: 16095 grad_norm: 5.2672 loss: 1.2461 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.2461 2022/12/08 20:04:50 - mmengine - INFO - Epoch(train) [40][260/940] lr: 1.0000e-02 eta: 10:03:37 time: 0.6539 data_time: 0.0250 memory: 16095 grad_norm: 5.4268 loss: 1.2213 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.2213 2022/12/08 20:05:02 - mmengine - INFO - Epoch(train) [40][280/940] lr: 1.0000e-02 eta: 10:03:22 time: 0.5699 data_time: 0.0224 memory: 16095 grad_norm: 5.2873 loss: 1.3196 top1_acc: 0.5938 top5_acc: 0.8125 loss_cls: 1.3196 2022/12/08 20:05:15 - mmengine - INFO - Epoch(train) [40][300/940] lr: 1.0000e-02 eta: 10:03:10 time: 0.6478 data_time: 0.0288 memory: 16095 grad_norm: 5.2654 loss: 1.3164 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.3164 2022/12/08 20:05:26 - mmengine - INFO - Epoch(train) [40][320/940] lr: 1.0000e-02 eta: 10:02:56 time: 0.5824 data_time: 0.0210 memory: 16095 grad_norm: 5.3152 loss: 1.3331 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.3331 2022/12/08 20:05:40 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 20:05:40 - mmengine - INFO - Epoch(train) [40][340/940] lr: 1.0000e-02 eta: 10:02:44 time: 0.6589 data_time: 0.0235 memory: 16095 grad_norm: 5.2893 loss: 1.2489 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 1.2489 2022/12/08 20:05:50 - mmengine - INFO - Epoch(train) [40][360/940] lr: 1.0000e-02 eta: 10:02:28 time: 0.5330 data_time: 0.0258 memory: 16095 grad_norm: 5.2997 loss: 1.2409 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.2409 2022/12/08 20:06:03 - mmengine - INFO - Epoch(train) [40][380/940] lr: 1.0000e-02 eta: 10:02:15 time: 0.6374 data_time: 0.0249 memory: 16095 grad_norm: 5.1782 loss: 1.3160 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.3160 2022/12/08 20:06:14 - mmengine - INFO - Epoch(train) [40][400/940] lr: 1.0000e-02 eta: 10:02:00 time: 0.5387 data_time: 0.0275 memory: 16095 grad_norm: 5.2892 loss: 1.3012 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.3012 2022/12/08 20:06:26 - mmengine - INFO - Epoch(train) [40][420/940] lr: 1.0000e-02 eta: 10:01:47 time: 0.6327 data_time: 0.0241 memory: 16095 grad_norm: 5.3496 loss: 1.2593 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.2593 2022/12/08 20:06:38 - mmengine - INFO - Epoch(train) [40][440/940] lr: 1.0000e-02 eta: 10:01:33 time: 0.5949 data_time: 0.0257 memory: 16095 grad_norm: 5.4232 loss: 1.3340 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3340 2022/12/08 20:06:53 - mmengine - INFO - Epoch(train) [40][460/940] lr: 1.0000e-02 eta: 10:01:23 time: 0.7236 data_time: 0.0269 memory: 16095 grad_norm: 5.4179 loss: 1.3764 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 1.3764 2022/12/08 20:07:04 - mmengine - INFO - Epoch(train) [40][480/940] lr: 1.0000e-02 eta: 10:01:08 time: 0.5410 data_time: 0.0248 memory: 16095 grad_norm: 5.3074 loss: 1.3232 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.3232 2022/12/08 20:07:17 - mmengine - INFO - Epoch(train) [40][500/940] lr: 1.0000e-02 eta: 10:00:55 time: 0.6484 data_time: 0.0247 memory: 16095 grad_norm: 5.3115 loss: 1.2593 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.2593 2022/12/08 20:07:28 - mmengine - INFO - Epoch(train) [40][520/940] lr: 1.0000e-02 eta: 10:00:40 time: 0.5482 data_time: 0.0233 memory: 16095 grad_norm: 5.4375 loss: 1.4465 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.4465 2022/12/08 20:07:41 - mmengine - INFO - Epoch(train) [40][540/940] lr: 1.0000e-02 eta: 10:00:28 time: 0.6462 data_time: 0.0244 memory: 16095 grad_norm: 5.4030 loss: 1.2224 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 1.2224 2022/12/08 20:07:52 - mmengine - INFO - Epoch(train) [40][560/940] lr: 1.0000e-02 eta: 10:00:12 time: 0.5520 data_time: 0.0256 memory: 16095 grad_norm: 5.3883 loss: 1.2845 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.2845 2022/12/08 20:08:04 - mmengine - INFO - Epoch(train) [40][580/940] lr: 1.0000e-02 eta: 9:59:59 time: 0.6250 data_time: 0.0285 memory: 16095 grad_norm: 5.3304 loss: 1.4480 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4480 2022/12/08 20:08:16 - mmengine - INFO - Epoch(train) [40][600/940] lr: 1.0000e-02 eta: 9:59:46 time: 0.6200 data_time: 0.0251 memory: 16095 grad_norm: 5.3912 loss: 1.4326 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4326 2022/12/08 20:08:29 - mmengine - INFO - Epoch(train) [40][620/940] lr: 1.0000e-02 eta: 9:59:33 time: 0.6155 data_time: 0.0231 memory: 16095 grad_norm: 5.4132 loss: 1.3382 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 1.3382 2022/12/08 20:08:40 - mmengine - INFO - Epoch(train) [40][640/940] lr: 1.0000e-02 eta: 9:59:19 time: 0.5831 data_time: 0.0328 memory: 16095 grad_norm: 5.3335 loss: 1.4166 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.4166 2022/12/08 20:08:54 - mmengine - INFO - Epoch(train) [40][660/940] lr: 1.0000e-02 eta: 9:59:07 time: 0.6578 data_time: 0.0279 memory: 16095 grad_norm: 5.3216 loss: 1.3371 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.3371 2022/12/08 20:09:05 - mmengine - INFO - Epoch(train) [40][680/940] lr: 1.0000e-02 eta: 9:58:52 time: 0.5693 data_time: 0.0237 memory: 16095 grad_norm: 5.3366 loss: 1.3813 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3813 2022/12/08 20:09:17 - mmengine - INFO - Epoch(train) [40][700/940] lr: 1.0000e-02 eta: 9:58:39 time: 0.6058 data_time: 0.0242 memory: 16095 grad_norm: 5.2746 loss: 1.3827 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3827 2022/12/08 20:09:30 - mmengine - INFO - Epoch(train) [40][720/940] lr: 1.0000e-02 eta: 9:58:26 time: 0.6196 data_time: 0.0251 memory: 16095 grad_norm: 5.3055 loss: 1.3133 top1_acc: 0.8125 top5_acc: 0.8438 loss_cls: 1.3133 2022/12/08 20:09:42 - mmengine - INFO - Epoch(train) [40][740/940] lr: 1.0000e-02 eta: 9:58:13 time: 0.6371 data_time: 0.0241 memory: 16095 grad_norm: 5.3444 loss: 1.2923 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.2923 2022/12/08 20:09:54 - mmengine - INFO - Epoch(train) [40][760/940] lr: 1.0000e-02 eta: 9:57:59 time: 0.6077 data_time: 0.0259 memory: 16095 grad_norm: 5.3914 loss: 1.2779 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.2779 2022/12/08 20:10:06 - mmengine - INFO - Epoch(train) [40][780/940] lr: 1.0000e-02 eta: 9:57:45 time: 0.5759 data_time: 0.0247 memory: 16095 grad_norm: 5.2717 loss: 1.3670 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 1.3670 2022/12/08 20:10:19 - mmengine - INFO - Epoch(train) [40][800/940] lr: 1.0000e-02 eta: 9:57:34 time: 0.6760 data_time: 0.0253 memory: 16095 grad_norm: 5.2473 loss: 1.3308 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.3308 2022/12/08 20:10:30 - mmengine - INFO - Epoch(train) [40][820/940] lr: 1.0000e-02 eta: 9:57:18 time: 0.5347 data_time: 0.0225 memory: 16095 grad_norm: 5.3479 loss: 1.3011 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.3011 2022/12/08 20:10:43 - mmengine - INFO - Epoch(train) [40][840/940] lr: 1.0000e-02 eta: 9:57:05 time: 0.6204 data_time: 0.0274 memory: 16095 grad_norm: 5.3683 loss: 1.3103 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.3103 2022/12/08 20:10:55 - mmengine - INFO - Epoch(train) [40][860/940] lr: 1.0000e-02 eta: 9:56:52 time: 0.6376 data_time: 0.0211 memory: 16095 grad_norm: 5.3454 loss: 1.3353 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3353 2022/12/08 20:11:07 - mmengine - INFO - Epoch(train) [40][880/940] lr: 1.0000e-02 eta: 9:56:38 time: 0.5868 data_time: 0.0234 memory: 16095 grad_norm: 5.4224 loss: 1.2938 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 1.2938 2022/12/08 20:11:20 - mmengine - INFO - Epoch(train) [40][900/940] lr: 1.0000e-02 eta: 9:56:26 time: 0.6361 data_time: 0.0258 memory: 16095 grad_norm: 5.3869 loss: 1.3101 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 1.3101 2022/12/08 20:11:32 - mmengine - INFO - Epoch(train) [40][920/940] lr: 1.0000e-02 eta: 9:56:11 time: 0.5843 data_time: 0.0246 memory: 16095 grad_norm: 5.3034 loss: 1.3034 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.3034 2022/12/08 20:11:42 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 20:11:42 - mmengine - INFO - Epoch(train) [40][940/940] lr: 1.0000e-02 eta: 9:55:56 time: 0.5337 data_time: 0.0213 memory: 16095 grad_norm: 5.6491 loss: 1.2723 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.2723 2022/12/08 20:11:57 - mmengine - INFO - Epoch(val) [40][20/78] eta: 0:00:42 time: 0.7261 data_time: 0.6314 memory: 1686 2022/12/08 20:12:06 - mmengine - INFO - Epoch(val) [40][40/78] eta: 0:00:22 time: 0.4703 data_time: 0.3767 memory: 1686 2022/12/08 20:12:19 - mmengine - INFO - Epoch(val) [40][60/78] eta: 0:00:11 time: 0.6576 data_time: 0.5620 memory: 1686 2022/12/08 20:12:30 - mmengine - INFO - Epoch(val) [40][78/78] acc/top1: 0.6500 acc/top5: 0.8564 acc/mean1: 0.6500 2022/12/08 20:12:46 - mmengine - INFO - Epoch(train) [41][ 20/940] lr: 1.0000e-03 eta: 9:55:49 time: 0.8207 data_time: 0.4965 memory: 16095 grad_norm: 5.0755 loss: 1.2048 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 1.2048 2022/12/08 20:12:58 - mmengine - INFO - Epoch(train) [41][ 40/940] lr: 1.0000e-03 eta: 9:55:34 time: 0.5564 data_time: 0.2117 memory: 16095 grad_norm: 5.0929 loss: 1.3026 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 1.3026 2022/12/08 20:13:12 - mmengine - INFO - Epoch(train) [41][ 60/940] lr: 1.0000e-03 eta: 9:55:23 time: 0.7063 data_time: 0.2675 memory: 16095 grad_norm: 5.1116 loss: 1.2633 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2633 2022/12/08 20:13:23 - mmengine - INFO - Epoch(train) [41][ 80/940] lr: 1.0000e-03 eta: 9:55:08 time: 0.5520 data_time: 0.0900 memory: 16095 grad_norm: 4.8216 loss: 1.1941 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1941 2022/12/08 20:13:36 - mmengine - INFO - Epoch(train) [41][100/940] lr: 1.0000e-03 eta: 9:54:57 time: 0.6797 data_time: 0.0732 memory: 16095 grad_norm: 4.8772 loss: 1.1987 top1_acc: 0.6562 top5_acc: 0.7500 loss_cls: 1.1987 2022/12/08 20:13:47 - mmengine - INFO - Epoch(train) [41][120/940] lr: 1.0000e-03 eta: 9:54:42 time: 0.5560 data_time: 0.0313 memory: 16095 grad_norm: 4.9251 loss: 1.1326 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 1.1326 2022/12/08 20:14:00 - mmengine - INFO - Epoch(train) [41][140/940] lr: 1.0000e-03 eta: 9:54:29 time: 0.6445 data_time: 0.0472 memory: 16095 grad_norm: 4.7910 loss: 1.1853 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.1853 2022/12/08 20:14:11 - mmengine - INFO - Epoch(train) [41][160/940] lr: 1.0000e-03 eta: 9:54:14 time: 0.5473 data_time: 0.0856 memory: 16095 grad_norm: 4.9489 loss: 1.2815 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.2815 2022/12/08 20:14:25 - mmengine - INFO - Epoch(train) [41][180/940] lr: 1.0000e-03 eta: 9:54:03 time: 0.6774 data_time: 0.1371 memory: 16095 grad_norm: 4.8836 loss: 1.1597 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.1597 2022/12/08 20:14:36 - mmengine - INFO - Epoch(train) [41][200/940] lr: 1.0000e-03 eta: 9:53:47 time: 0.5462 data_time: 0.0632 memory: 16095 grad_norm: 4.8336 loss: 1.1367 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.1367 2022/12/08 20:14:49 - mmengine - INFO - Epoch(train) [41][220/940] lr: 1.0000e-03 eta: 9:53:35 time: 0.6479 data_time: 0.0859 memory: 16095 grad_norm: 5.0519 loss: 1.2402 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.2402 2022/12/08 20:15:00 - mmengine - INFO - Epoch(train) [41][240/940] lr: 1.0000e-03 eta: 9:53:21 time: 0.5803 data_time: 0.0384 memory: 16095 grad_norm: 4.9770 loss: 1.1709 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 1.1709 2022/12/08 20:15:14 - mmengine - INFO - Epoch(train) [41][260/940] lr: 1.0000e-03 eta: 9:53:10 time: 0.6978 data_time: 0.0269 memory: 16095 grad_norm: 4.9155 loss: 1.1846 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.1846 2022/12/08 20:15:25 - mmengine - INFO - Epoch(train) [41][280/940] lr: 1.0000e-03 eta: 9:52:54 time: 0.5351 data_time: 0.0581 memory: 16095 grad_norm: 4.9796 loss: 1.2124 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 1.2124 2022/12/08 20:15:38 - mmengine - INFO - Epoch(train) [41][300/940] lr: 1.0000e-03 eta: 9:52:43 time: 0.6647 data_time: 0.0776 memory: 16095 grad_norm: 5.0182 loss: 1.1824 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.1824 2022/12/08 20:15:49 - mmengine - INFO - Epoch(train) [41][320/940] lr: 1.0000e-03 eta: 9:52:27 time: 0.5409 data_time: 0.0232 memory: 16095 grad_norm: 4.9088 loss: 1.1903 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.1903 2022/12/08 20:16:03 - mmengine - INFO - Epoch(train) [41][340/940] lr: 1.0000e-03 eta: 9:52:16 time: 0.6927 data_time: 0.0355 memory: 16095 grad_norm: 4.7574 loss: 1.0491 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.0491 2022/12/08 20:16:14 - mmengine - INFO - Epoch(train) [41][360/940] lr: 1.0000e-03 eta: 9:52:02 time: 0.5654 data_time: 0.0226 memory: 16095 grad_norm: 4.9574 loss: 1.1363 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.1363 2022/12/08 20:16:27 - mmengine - INFO - Epoch(train) [41][380/940] lr: 1.0000e-03 eta: 9:51:49 time: 0.6461 data_time: 0.0261 memory: 16095 grad_norm: 4.9452 loss: 1.2236 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 1.2236 2022/12/08 20:16:39 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 20:16:39 - mmengine - INFO - Epoch(train) [41][400/940] lr: 1.0000e-03 eta: 9:51:35 time: 0.5810 data_time: 0.0220 memory: 16095 grad_norm: 4.8454 loss: 1.0481 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 1.0481 2022/12/08 20:16:53 - mmengine - INFO - Epoch(train) [41][420/940] lr: 1.0000e-03 eta: 9:51:24 time: 0.6843 data_time: 0.0231 memory: 16095 grad_norm: 4.8382 loss: 1.0844 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.0844 2022/12/08 20:17:04 - mmengine - INFO - Epoch(train) [41][440/940] lr: 1.0000e-03 eta: 9:51:09 time: 0.5488 data_time: 0.0237 memory: 16095 grad_norm: 4.7805 loss: 1.0063 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0063 2022/12/08 20:17:17 - mmengine - INFO - Epoch(train) [41][460/940] lr: 1.0000e-03 eta: 9:50:57 time: 0.6725 data_time: 0.0320 memory: 16095 grad_norm: 4.7320 loss: 1.1246 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1246 2022/12/08 20:17:28 - mmengine - INFO - Epoch(train) [41][480/940] lr: 1.0000e-03 eta: 9:50:42 time: 0.5384 data_time: 0.0275 memory: 16095 grad_norm: 4.8843 loss: 1.0850 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.0850 2022/12/08 20:17:41 - mmengine - INFO - Epoch(train) [41][500/940] lr: 1.0000e-03 eta: 9:50:29 time: 0.6388 data_time: 0.0249 memory: 16095 grad_norm: 4.9405 loss: 1.1295 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 1.1295 2022/12/08 20:17:51 - mmengine - INFO - Epoch(train) [41][520/940] lr: 1.0000e-03 eta: 9:50:13 time: 0.5039 data_time: 0.0265 memory: 16095 grad_norm: 4.9189 loss: 1.0966 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0966 2022/12/08 20:18:05 - mmengine - INFO - Epoch(train) [41][540/940] lr: 1.0000e-03 eta: 9:50:02 time: 0.7021 data_time: 0.0237 memory: 16095 grad_norm: 4.8557 loss: 1.0476 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0476 2022/12/08 20:18:16 - mmengine - INFO - Epoch(train) [41][560/940] lr: 1.0000e-03 eta: 9:49:47 time: 0.5539 data_time: 0.0250 memory: 16095 grad_norm: 4.7581 loss: 1.1284 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.1284 2022/12/08 20:18:28 - mmengine - INFO - Epoch(train) [41][580/940] lr: 1.0000e-03 eta: 9:49:34 time: 0.6303 data_time: 0.0251 memory: 16095 grad_norm: 4.7673 loss: 1.1285 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 1.1285 2022/12/08 20:18:39 - mmengine - INFO - Epoch(train) [41][600/940] lr: 1.0000e-03 eta: 9:49:19 time: 0.5434 data_time: 0.0235 memory: 16095 grad_norm: 4.8510 loss: 1.1728 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1728 2022/12/08 20:18:53 - mmengine - INFO - Epoch(train) [41][620/940] lr: 1.0000e-03 eta: 9:49:07 time: 0.6804 data_time: 0.0282 memory: 16095 grad_norm: 4.9231 loss: 1.0275 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 1.0275 2022/12/08 20:19:04 - mmengine - INFO - Epoch(train) [41][640/940] lr: 1.0000e-03 eta: 9:48:53 time: 0.5610 data_time: 0.0214 memory: 16095 grad_norm: 4.8000 loss: 1.1324 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.1324 2022/12/08 20:19:18 - mmengine - INFO - Epoch(train) [41][660/940] lr: 1.0000e-03 eta: 9:48:41 time: 0.6755 data_time: 0.0260 memory: 16095 grad_norm: 4.8358 loss: 0.9687 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.9687 2022/12/08 20:19:28 - mmengine - INFO - Epoch(train) [41][680/940] lr: 1.0000e-03 eta: 9:48:26 time: 0.5388 data_time: 0.0221 memory: 16095 grad_norm: 4.8751 loss: 1.0431 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.0431 2022/12/08 20:19:41 - mmengine - INFO - Epoch(train) [41][700/940] lr: 1.0000e-03 eta: 9:48:14 time: 0.6513 data_time: 0.0278 memory: 16095 grad_norm: 4.8205 loss: 1.1030 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.1030 2022/12/08 20:19:53 - mmengine - INFO - Epoch(train) [41][720/940] lr: 1.0000e-03 eta: 9:47:59 time: 0.5640 data_time: 0.0231 memory: 16095 grad_norm: 4.9064 loss: 1.0567 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.0567 2022/12/08 20:20:07 - mmengine - INFO - Epoch(train) [41][740/940] lr: 1.0000e-03 eta: 9:47:48 time: 0.7056 data_time: 0.0273 memory: 16095 grad_norm: 4.9580 loss: 1.0228 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.0228 2022/12/08 20:20:18 - mmengine - INFO - Epoch(train) [41][760/940] lr: 1.0000e-03 eta: 9:47:34 time: 0.5807 data_time: 0.0206 memory: 16095 grad_norm: 4.9187 loss: 1.1933 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1933 2022/12/08 20:20:31 - mmengine - INFO - Epoch(train) [41][780/940] lr: 1.0000e-03 eta: 9:47:22 time: 0.6496 data_time: 0.0276 memory: 16095 grad_norm: 4.8401 loss: 1.0245 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 1.0245 2022/12/08 20:20:42 - mmengine - INFO - Epoch(train) [41][800/940] lr: 1.0000e-03 eta: 9:47:07 time: 0.5535 data_time: 0.0209 memory: 16095 grad_norm: 4.9186 loss: 1.0069 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 1.0069 2022/12/08 20:20:56 - mmengine - INFO - Epoch(train) [41][820/940] lr: 1.0000e-03 eta: 9:46:55 time: 0.6668 data_time: 0.0297 memory: 16095 grad_norm: 4.9809 loss: 1.1013 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 1.1013 2022/12/08 20:21:07 - mmengine - INFO - Epoch(train) [41][840/940] lr: 1.0000e-03 eta: 9:46:40 time: 0.5561 data_time: 0.0197 memory: 16095 grad_norm: 4.9416 loss: 1.1143 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 1.1143 2022/12/08 20:21:20 - mmengine - INFO - Epoch(train) [41][860/940] lr: 1.0000e-03 eta: 9:46:28 time: 0.6619 data_time: 0.0286 memory: 16095 grad_norm: 4.7602 loss: 0.9354 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9354 2022/12/08 20:21:31 - mmengine - INFO - Epoch(train) [41][880/940] lr: 1.0000e-03 eta: 9:46:13 time: 0.5300 data_time: 0.0201 memory: 16095 grad_norm: 4.9754 loss: 1.1235 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1235 2022/12/08 20:21:44 - mmengine - INFO - Epoch(train) [41][900/940] lr: 1.0000e-03 eta: 9:46:01 time: 0.6463 data_time: 0.0264 memory: 16095 grad_norm: 4.9256 loss: 1.0322 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0322 2022/12/08 20:21:55 - mmengine - INFO - Epoch(train) [41][920/940] lr: 1.0000e-03 eta: 9:45:45 time: 0.5492 data_time: 0.0302 memory: 16095 grad_norm: 4.8085 loss: 1.0313 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.0313 2022/12/08 20:22:06 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 20:22:06 - mmengine - INFO - Epoch(train) [41][940/940] lr: 1.0000e-03 eta: 9:45:31 time: 0.5740 data_time: 0.0959 memory: 16095 grad_norm: 5.0803 loss: 1.0065 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.0065 2022/12/08 20:22:20 - mmengine - INFO - Epoch(val) [41][20/78] eta: 0:00:40 time: 0.7025 data_time: 0.6076 memory: 1686 2022/12/08 20:22:29 - mmengine - INFO - Epoch(val) [41][40/78] eta: 0:00:22 time: 0.4611 data_time: 0.3655 memory: 1686 2022/12/08 20:22:43 - mmengine - INFO - Epoch(val) [41][60/78] eta: 0:00:10 time: 0.6604 data_time: 0.5638 memory: 1686 2022/12/08 20:22:54 - mmengine - INFO - Epoch(val) [41][78/78] acc/top1: 0.6868 acc/top5: 0.8759 acc/mean1: 0.6867 2022/12/08 20:22:54 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_34.pth is removed 2022/12/08 20:22:56 - mmengine - INFO - The best checkpoint with 0.6868 acc/top1 at 41 epoch is saved to best_acc/top1_epoch_41.pth. 2022/12/08 20:23:12 - mmengine - INFO - Epoch(train) [42][ 20/940] lr: 1.0000e-03 eta: 9:45:23 time: 0.8023 data_time: 0.4914 memory: 16095 grad_norm: 4.9984 loss: 1.1364 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.1364 2022/12/08 20:23:23 - mmengine - INFO - Epoch(train) [42][ 40/940] lr: 1.0000e-03 eta: 9:45:08 time: 0.5487 data_time: 0.2575 memory: 16095 grad_norm: 4.8418 loss: 1.0665 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0665 2022/12/08 20:23:36 - mmengine - INFO - Epoch(train) [42][ 60/940] lr: 1.0000e-03 eta: 9:44:56 time: 0.6507 data_time: 0.3316 memory: 16095 grad_norm: 4.8622 loss: 1.0343 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 1.0343 2022/12/08 20:23:47 - mmengine - INFO - Epoch(train) [42][ 80/940] lr: 1.0000e-03 eta: 9:44:41 time: 0.5600 data_time: 0.2180 memory: 16095 grad_norm: 4.8063 loss: 1.0879 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0879 2022/12/08 20:24:01 - mmengine - INFO - Epoch(train) [42][100/940] lr: 1.0000e-03 eta: 9:44:29 time: 0.6535 data_time: 0.3462 memory: 16095 grad_norm: 4.7598 loss: 0.9725 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9725 2022/12/08 20:24:11 - mmengine - INFO - Epoch(train) [42][120/940] lr: 1.0000e-03 eta: 9:44:14 time: 0.5377 data_time: 0.2324 memory: 16095 grad_norm: 4.7810 loss: 1.0431 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.0431 2022/12/08 20:24:25 - mmengine - INFO - Epoch(train) [42][140/940] lr: 1.0000e-03 eta: 9:44:02 time: 0.6779 data_time: 0.3590 memory: 16095 grad_norm: 4.9112 loss: 1.0496 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.0496 2022/12/08 20:24:36 - mmengine - INFO - Epoch(train) [42][160/940] lr: 1.0000e-03 eta: 9:43:47 time: 0.5388 data_time: 0.2295 memory: 16095 grad_norm: 4.8014 loss: 1.0433 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.0433 2022/12/08 20:24:49 - mmengine - INFO - Epoch(train) [42][180/940] lr: 1.0000e-03 eta: 9:43:35 time: 0.6579 data_time: 0.3210 memory: 16095 grad_norm: 4.6737 loss: 0.9261 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9261 2022/12/08 20:25:00 - mmengine - INFO - Epoch(train) [42][200/940] lr: 1.0000e-03 eta: 9:43:20 time: 0.5520 data_time: 0.2344 memory: 16095 grad_norm: 4.7765 loss: 1.0411 top1_acc: 0.7812 top5_acc: 0.8125 loss_cls: 1.0411 2022/12/08 20:25:14 - mmengine - INFO - Epoch(train) [42][220/940] lr: 1.0000e-03 eta: 9:43:10 time: 0.7137 data_time: 0.4047 memory: 16095 grad_norm: 4.8899 loss: 1.0870 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.0870 2022/12/08 20:25:25 - mmengine - INFO - Epoch(train) [42][240/940] lr: 1.0000e-03 eta: 9:42:54 time: 0.5393 data_time: 0.2221 memory: 16095 grad_norm: 4.8571 loss: 1.0695 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0695 2022/12/08 20:25:38 - mmengine - INFO - Epoch(train) [42][260/940] lr: 1.0000e-03 eta: 9:42:43 time: 0.6696 data_time: 0.3394 memory: 16095 grad_norm: 4.8662 loss: 0.9850 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9850 2022/12/08 20:25:49 - mmengine - INFO - Epoch(train) [42][280/940] lr: 1.0000e-03 eta: 9:42:27 time: 0.5410 data_time: 0.2293 memory: 16095 grad_norm: 4.7817 loss: 0.9600 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9600 2022/12/08 20:26:03 - mmengine - INFO - Epoch(train) [42][300/940] lr: 1.0000e-03 eta: 9:42:17 time: 0.7068 data_time: 0.3800 memory: 16095 grad_norm: 4.8764 loss: 0.9626 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9626 2022/12/08 20:26:15 - mmengine - INFO - Epoch(train) [42][320/940] lr: 1.0000e-03 eta: 9:42:02 time: 0.5623 data_time: 0.2424 memory: 16095 grad_norm: 5.0300 loss: 1.0299 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 1.0299 2022/12/08 20:26:28 - mmengine - INFO - Epoch(train) [42][340/940] lr: 1.0000e-03 eta: 9:41:50 time: 0.6622 data_time: 0.3405 memory: 16095 grad_norm: 4.9253 loss: 1.0436 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 1.0436 2022/12/08 20:26:38 - mmengine - INFO - Epoch(train) [42][360/940] lr: 1.0000e-03 eta: 9:41:34 time: 0.5251 data_time: 0.2097 memory: 16095 grad_norm: 4.8237 loss: 0.9678 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 0.9678 2022/12/08 20:26:51 - mmengine - INFO - Epoch(train) [42][380/940] lr: 1.0000e-03 eta: 9:41:22 time: 0.6391 data_time: 0.3214 memory: 16095 grad_norm: 4.8869 loss: 1.0270 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.0270 2022/12/08 20:27:02 - mmengine - INFO - Epoch(train) [42][400/940] lr: 1.0000e-03 eta: 9:41:07 time: 0.5695 data_time: 0.1748 memory: 16095 grad_norm: 4.8770 loss: 1.0727 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0727 2022/12/08 20:27:16 - mmengine - INFO - Epoch(train) [42][420/940] lr: 1.0000e-03 eta: 9:40:56 time: 0.6694 data_time: 0.2762 memory: 16095 grad_norm: 4.9189 loss: 1.0416 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 1.0416 2022/12/08 20:27:27 - mmengine - INFO - Epoch(train) [42][440/940] lr: 1.0000e-03 eta: 9:40:41 time: 0.5619 data_time: 0.1885 memory: 16095 grad_norm: 4.8230 loss: 0.9375 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.9375 2022/12/08 20:27:40 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 20:27:40 - mmengine - INFO - Epoch(train) [42][460/940] lr: 1.0000e-03 eta: 9:40:29 time: 0.6600 data_time: 0.3409 memory: 16095 grad_norm: 4.8897 loss: 1.0527 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0527 2022/12/08 20:27:51 - mmengine - INFO - Epoch(train) [42][480/940] lr: 1.0000e-03 eta: 9:40:14 time: 0.5508 data_time: 0.2209 memory: 16095 grad_norm: 4.9042 loss: 1.0772 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.0772 2022/12/08 20:28:04 - mmengine - INFO - Epoch(train) [42][500/940] lr: 1.0000e-03 eta: 9:40:02 time: 0.6529 data_time: 0.3447 memory: 16095 grad_norm: 4.8103 loss: 0.9769 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.9769 2022/12/08 20:28:16 - mmengine - INFO - Epoch(train) [42][520/940] lr: 1.0000e-03 eta: 9:39:47 time: 0.5633 data_time: 0.2495 memory: 16095 grad_norm: 4.9605 loss: 1.0541 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0541 2022/12/08 20:28:29 - mmengine - INFO - Epoch(train) [42][540/940] lr: 1.0000e-03 eta: 9:39:36 time: 0.6767 data_time: 0.3661 memory: 16095 grad_norm: 4.7993 loss: 1.0842 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 1.0842 2022/12/08 20:28:40 - mmengine - INFO - Epoch(train) [42][560/940] lr: 1.0000e-03 eta: 9:39:21 time: 0.5472 data_time: 0.2364 memory: 16095 grad_norm: 4.7937 loss: 0.9960 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 0.9960 2022/12/08 20:28:53 - mmengine - INFO - Epoch(train) [42][580/940] lr: 1.0000e-03 eta: 9:39:09 time: 0.6651 data_time: 0.3597 memory: 16095 grad_norm: 4.9762 loss: 1.0554 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.0554 2022/12/08 20:29:05 - mmengine - INFO - Epoch(train) [42][600/940] lr: 1.0000e-03 eta: 9:38:55 time: 0.5721 data_time: 0.2520 memory: 16095 grad_norm: 4.9631 loss: 0.8482 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8482 2022/12/08 20:29:18 - mmengine - INFO - Epoch(train) [42][620/940] lr: 1.0000e-03 eta: 9:38:43 time: 0.6582 data_time: 0.3314 memory: 16095 grad_norm: 4.9176 loss: 1.0437 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.0437 2022/12/08 20:29:29 - mmengine - INFO - Epoch(train) [42][640/940] lr: 1.0000e-03 eta: 9:38:28 time: 0.5605 data_time: 0.2212 memory: 16095 grad_norm: 4.9540 loss: 1.0959 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.0959 2022/12/08 20:29:42 - mmengine - INFO - Epoch(train) [42][660/940] lr: 1.0000e-03 eta: 9:38:16 time: 0.6368 data_time: 0.2041 memory: 16095 grad_norm: 5.0403 loss: 1.0057 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 1.0057 2022/12/08 20:29:54 - mmengine - INFO - Epoch(train) [42][680/940] lr: 1.0000e-03 eta: 9:38:02 time: 0.6047 data_time: 0.0451 memory: 16095 grad_norm: 4.9670 loss: 0.9470 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.9470 2022/12/08 20:30:06 - mmengine - INFO - Epoch(train) [42][700/940] lr: 1.0000e-03 eta: 9:37:49 time: 0.6114 data_time: 0.2198 memory: 16095 grad_norm: 4.9467 loss: 1.0107 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.0107 2022/12/08 20:30:19 - mmengine - INFO - Epoch(train) [42][720/940] lr: 1.0000e-03 eta: 9:37:36 time: 0.6115 data_time: 0.1181 memory: 16095 grad_norm: 4.9228 loss: 0.9862 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.9862 2022/12/08 20:30:30 - mmengine - INFO - Epoch(train) [42][740/940] lr: 1.0000e-03 eta: 9:37:21 time: 0.5770 data_time: 0.1089 memory: 16095 grad_norm: 4.9429 loss: 1.0186 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.0186 2022/12/08 20:30:44 - mmengine - INFO - Epoch(train) [42][760/940] lr: 1.0000e-03 eta: 9:37:10 time: 0.6706 data_time: 0.0347 memory: 16095 grad_norm: 4.9609 loss: 1.1008 top1_acc: 0.4688 top5_acc: 0.7500 loss_cls: 1.1008 2022/12/08 20:30:55 - mmengine - INFO - Epoch(train) [42][780/940] lr: 1.0000e-03 eta: 9:36:56 time: 0.5892 data_time: 0.0476 memory: 16095 grad_norm: 4.8801 loss: 1.0208 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.0208 2022/12/08 20:31:08 - mmengine - INFO - Epoch(train) [42][800/940] lr: 1.0000e-03 eta: 9:36:43 time: 0.6278 data_time: 0.0328 memory: 16095 grad_norm: 4.8864 loss: 0.9727 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9727 2022/12/08 20:31:20 - mmengine - INFO - Epoch(train) [42][820/940] lr: 1.0000e-03 eta: 9:36:30 time: 0.6108 data_time: 0.0492 memory: 16095 grad_norm: 4.9615 loss: 1.0458 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.0458 2022/12/08 20:31:32 - mmengine - INFO - Epoch(train) [42][840/940] lr: 1.0000e-03 eta: 9:36:16 time: 0.6091 data_time: 0.0200 memory: 16095 grad_norm: 4.9687 loss: 0.9843 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9843 2022/12/08 20:31:45 - mmengine - INFO - Epoch(train) [42][860/940] lr: 1.0000e-03 eta: 9:36:03 time: 0.6110 data_time: 0.0267 memory: 16095 grad_norm: 5.0234 loss: 0.9853 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9853 2022/12/08 20:31:56 - mmengine - INFO - Epoch(train) [42][880/940] lr: 1.0000e-03 eta: 9:35:49 time: 0.5805 data_time: 0.0554 memory: 16095 grad_norm: 4.8986 loss: 1.0122 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 1.0122 2022/12/08 20:32:09 - mmengine - INFO - Epoch(train) [42][900/940] lr: 1.0000e-03 eta: 9:35:36 time: 0.6344 data_time: 0.1075 memory: 16095 grad_norm: 4.7957 loss: 0.9276 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 0.9276 2022/12/08 20:32:21 - mmengine - INFO - Epoch(train) [42][920/940] lr: 1.0000e-03 eta: 9:35:23 time: 0.5926 data_time: 0.0270 memory: 16095 grad_norm: 4.8995 loss: 0.9891 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.9891 2022/12/08 20:32:33 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 20:32:33 - mmengine - INFO - Epoch(train) [42][940/940] lr: 1.0000e-03 eta: 9:35:09 time: 0.6119 data_time: 0.0137 memory: 16095 grad_norm: 5.2636 loss: 0.9487 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 0.9487 2022/12/08 20:32:33 - mmengine - INFO - Saving checkpoint at 42 epochs 2022/12/08 20:32:50 - mmengine - INFO - Epoch(val) [42][20/78] eta: 0:00:40 time: 0.6973 data_time: 0.6024 memory: 1686 2022/12/08 20:32:59 - mmengine - INFO - Epoch(val) [42][40/78] eta: 0:00:22 time: 0.4781 data_time: 0.3847 memory: 1686 2022/12/08 20:33:13 - mmengine - INFO - Epoch(val) [42][60/78] eta: 0:00:11 time: 0.6846 data_time: 0.5891 memory: 1686 2022/12/08 20:33:23 - mmengine - INFO - Epoch(val) [42][78/78] acc/top1: 0.6897 acc/top5: 0.8793 acc/mean1: 0.6896 2022/12/08 20:33:23 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_41.pth is removed 2022/12/08 20:33:25 - mmengine - INFO - The best checkpoint with 0.6897 acc/top1 at 42 epoch is saved to best_acc/top1_epoch_42.pth. 2022/12/08 20:33:42 - mmengine - INFO - Epoch(train) [43][ 20/940] lr: 1.0000e-03 eta: 9:35:02 time: 0.8196 data_time: 0.5163 memory: 16095 grad_norm: 4.8293 loss: 1.0303 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 1.0303 2022/12/08 20:33:53 - mmengine - INFO - Epoch(train) [43][ 40/940] lr: 1.0000e-03 eta: 9:34:47 time: 0.5570 data_time: 0.2376 memory: 16095 grad_norm: 4.8738 loss: 1.0152 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.0152 2022/12/08 20:34:07 - mmengine - INFO - Epoch(train) [43][ 60/940] lr: 1.0000e-03 eta: 9:34:36 time: 0.6977 data_time: 0.3755 memory: 16095 grad_norm: 4.8269 loss: 1.0375 top1_acc: 0.6875 top5_acc: 0.9688 loss_cls: 1.0375 2022/12/08 20:34:18 - mmengine - INFO - Epoch(train) [43][ 80/940] lr: 1.0000e-03 eta: 9:34:22 time: 0.5670 data_time: 0.2466 memory: 16095 grad_norm: 4.8565 loss: 0.9772 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.9772 2022/12/08 20:34:32 - mmengine - INFO - Epoch(train) [43][100/940] lr: 1.0000e-03 eta: 9:34:10 time: 0.6766 data_time: 0.3524 memory: 16095 grad_norm: 4.8324 loss: 1.0004 top1_acc: 0.6250 top5_acc: 0.9688 loss_cls: 1.0004 2022/12/08 20:34:43 - mmengine - INFO - Epoch(train) [43][120/940] lr: 1.0000e-03 eta: 9:33:55 time: 0.5539 data_time: 0.2397 memory: 16095 grad_norm: 4.9492 loss: 1.0675 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0675 2022/12/08 20:34:56 - mmengine - INFO - Epoch(train) [43][140/940] lr: 1.0000e-03 eta: 9:33:43 time: 0.6520 data_time: 0.3294 memory: 16095 grad_norm: 4.9531 loss: 1.0204 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.0204 2022/12/08 20:35:07 - mmengine - INFO - Epoch(train) [43][160/940] lr: 1.0000e-03 eta: 9:33:29 time: 0.5542 data_time: 0.2263 memory: 16095 grad_norm: 4.7468 loss: 0.9935 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.9935 2022/12/08 20:35:20 - mmengine - INFO - Epoch(train) [43][180/940] lr: 1.0000e-03 eta: 9:33:17 time: 0.6663 data_time: 0.3310 memory: 16095 grad_norm: 4.9232 loss: 1.0568 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.0568 2022/12/08 20:35:31 - mmengine - INFO - Epoch(train) [43][200/940] lr: 1.0000e-03 eta: 9:33:01 time: 0.5350 data_time: 0.1917 memory: 16095 grad_norm: 4.7463 loss: 1.0103 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.0103 2022/12/08 20:35:44 - mmengine - INFO - Epoch(train) [43][220/940] lr: 1.0000e-03 eta: 9:32:50 time: 0.6759 data_time: 0.3161 memory: 16095 grad_norm: 4.9458 loss: 0.9936 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 0.9936 2022/12/08 20:35:56 - mmengine - INFO - Epoch(train) [43][240/940] lr: 1.0000e-03 eta: 9:32:36 time: 0.5925 data_time: 0.2701 memory: 16095 grad_norm: 4.9105 loss: 0.9033 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9033 2022/12/08 20:36:10 - mmengine - INFO - Epoch(train) [43][260/940] lr: 1.0000e-03 eta: 9:32:25 time: 0.6795 data_time: 0.3447 memory: 16095 grad_norm: 4.8754 loss: 0.9781 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9781 2022/12/08 20:36:21 - mmengine - INFO - Epoch(train) [43][280/940] lr: 1.0000e-03 eta: 9:32:10 time: 0.5583 data_time: 0.2339 memory: 16095 grad_norm: 4.8222 loss: 0.8712 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8712 2022/12/08 20:36:34 - mmengine - INFO - Epoch(train) [43][300/940] lr: 1.0000e-03 eta: 9:31:58 time: 0.6383 data_time: 0.3184 memory: 16095 grad_norm: 4.9147 loss: 0.8662 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8662 2022/12/08 20:36:44 - mmengine - INFO - Epoch(train) [43][320/940] lr: 1.0000e-03 eta: 9:31:42 time: 0.5237 data_time: 0.2023 memory: 16095 grad_norm: 4.8397 loss: 1.0060 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0060 2022/12/08 20:36:58 - mmengine - INFO - Epoch(train) [43][340/940] lr: 1.0000e-03 eta: 9:31:31 time: 0.6855 data_time: 0.3499 memory: 16095 grad_norm: 4.8729 loss: 1.0110 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.0110 2022/12/08 20:37:09 - mmengine - INFO - Epoch(train) [43][360/940] lr: 1.0000e-03 eta: 9:31:16 time: 0.5533 data_time: 0.2185 memory: 16095 grad_norm: 4.9564 loss: 0.9759 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9759 2022/12/08 20:37:21 - mmengine - INFO - Epoch(train) [43][380/940] lr: 1.0000e-03 eta: 9:31:03 time: 0.6087 data_time: 0.2337 memory: 16095 grad_norm: 4.9197 loss: 0.9868 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9868 2022/12/08 20:37:33 - mmengine - INFO - Epoch(train) [43][400/940] lr: 1.0000e-03 eta: 9:30:48 time: 0.5682 data_time: 0.1388 memory: 16095 grad_norm: 4.8535 loss: 0.9949 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9949 2022/12/08 20:37:46 - mmengine - INFO - Epoch(train) [43][420/940] lr: 1.0000e-03 eta: 9:30:36 time: 0.6583 data_time: 0.3069 memory: 16095 grad_norm: 4.8397 loss: 1.0430 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.0430 2022/12/08 20:37:57 - mmengine - INFO - Epoch(train) [43][440/940] lr: 1.0000e-03 eta: 9:30:21 time: 0.5533 data_time: 0.1496 memory: 16095 grad_norm: 4.8363 loss: 0.8981 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8981 2022/12/08 20:38:11 - mmengine - INFO - Epoch(train) [43][460/940] lr: 1.0000e-03 eta: 9:30:11 time: 0.7017 data_time: 0.1917 memory: 16095 grad_norm: 4.8323 loss: 0.9205 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.9205 2022/12/08 20:38:23 - mmengine - INFO - Epoch(train) [43][480/940] lr: 1.0000e-03 eta: 9:29:57 time: 0.5928 data_time: 0.0336 memory: 16095 grad_norm: 4.8922 loss: 1.0458 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.0458 2022/12/08 20:38:36 - mmengine - INFO - Epoch(train) [43][500/940] lr: 1.0000e-03 eta: 9:29:45 time: 0.6483 data_time: 0.0416 memory: 16095 grad_norm: 4.9069 loss: 1.0704 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0704 2022/12/08 20:38:47 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 20:38:47 - mmengine - INFO - Epoch(train) [43][520/940] lr: 1.0000e-03 eta: 9:29:31 time: 0.5786 data_time: 0.0282 memory: 16095 grad_norm: 4.9337 loss: 0.8978 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8978 2022/12/08 20:39:01 - mmengine - INFO - Epoch(train) [43][540/940] lr: 1.0000e-03 eta: 9:29:19 time: 0.6788 data_time: 0.0253 memory: 16095 grad_norm: 4.9608 loss: 1.0611 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.0611 2022/12/08 20:39:12 - mmengine - INFO - Epoch(train) [43][560/940] lr: 1.0000e-03 eta: 9:29:05 time: 0.5666 data_time: 0.0238 memory: 16095 grad_norm: 4.8923 loss: 1.0289 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.0289 2022/12/08 20:39:26 - mmengine - INFO - Epoch(train) [43][580/940] lr: 1.0000e-03 eta: 9:28:54 time: 0.6999 data_time: 0.0227 memory: 16095 grad_norm: 4.9033 loss: 1.0457 top1_acc: 0.6562 top5_acc: 0.9688 loss_cls: 1.0457 2022/12/08 20:39:36 - mmengine - INFO - Epoch(train) [43][600/940] lr: 1.0000e-03 eta: 9:28:38 time: 0.5174 data_time: 0.0245 memory: 16095 grad_norm: 5.0109 loss: 1.0112 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.0112 2022/12/08 20:39:50 - mmengine - INFO - Epoch(train) [43][620/940] lr: 1.0000e-03 eta: 9:28:27 time: 0.6756 data_time: 0.0333 memory: 16095 grad_norm: 4.9417 loss: 0.9645 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.9645 2022/12/08 20:40:02 - mmengine - INFO - Epoch(train) [43][640/940] lr: 1.0000e-03 eta: 9:28:13 time: 0.5870 data_time: 0.0236 memory: 16095 grad_norm: 4.8982 loss: 1.0103 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0103 2022/12/08 20:40:15 - mmengine - INFO - Epoch(train) [43][660/940] lr: 1.0000e-03 eta: 9:28:00 time: 0.6390 data_time: 0.0251 memory: 16095 grad_norm: 4.8145 loss: 0.9488 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.9488 2022/12/08 20:40:26 - mmengine - INFO - Epoch(train) [43][680/940] lr: 1.0000e-03 eta: 9:27:45 time: 0.5541 data_time: 0.0249 memory: 16095 grad_norm: 4.9857 loss: 1.0594 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0594 2022/12/08 20:40:39 - mmengine - INFO - Epoch(train) [43][700/940] lr: 1.0000e-03 eta: 9:27:33 time: 0.6563 data_time: 0.0244 memory: 16095 grad_norm: 4.9540 loss: 1.1329 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 1.1329 2022/12/08 20:40:50 - mmengine - INFO - Epoch(train) [43][720/940] lr: 1.0000e-03 eta: 9:27:19 time: 0.5604 data_time: 0.0256 memory: 16095 grad_norm: 4.8739 loss: 0.9936 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.9936 2022/12/08 20:41:03 - mmengine - INFO - Epoch(train) [43][740/940] lr: 1.0000e-03 eta: 9:27:07 time: 0.6482 data_time: 0.0248 memory: 16095 grad_norm: 4.8481 loss: 1.0205 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.0205 2022/12/08 20:41:14 - mmengine - INFO - Epoch(train) [43][760/940] lr: 1.0000e-03 eta: 9:26:52 time: 0.5688 data_time: 0.0233 memory: 16095 grad_norm: 4.9136 loss: 0.9873 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9873 2022/12/08 20:41:28 - mmengine - INFO - Epoch(train) [43][780/940] lr: 1.0000e-03 eta: 9:26:41 time: 0.6703 data_time: 0.0259 memory: 16095 grad_norm: 4.9482 loss: 0.9483 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.9483 2022/12/08 20:41:39 - mmengine - INFO - Epoch(train) [43][800/940] lr: 1.0000e-03 eta: 9:26:26 time: 0.5574 data_time: 0.0241 memory: 16095 grad_norm: 4.8707 loss: 0.9599 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.9599 2022/12/08 20:41:51 - mmengine - INFO - Epoch(train) [43][820/940] lr: 1.0000e-03 eta: 9:26:13 time: 0.6191 data_time: 0.0243 memory: 16095 grad_norm: 5.0499 loss: 1.1374 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1374 2022/12/08 20:42:04 - mmengine - INFO - Epoch(train) [43][840/940] lr: 1.0000e-03 eta: 9:26:00 time: 0.6281 data_time: 0.0251 memory: 16095 grad_norm: 4.9853 loss: 0.9438 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9438 2022/12/08 20:42:15 - mmengine - INFO - Epoch(train) [43][860/940] lr: 1.0000e-03 eta: 9:25:46 time: 0.5738 data_time: 0.0262 memory: 16095 grad_norm: 4.8940 loss: 0.9144 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9144 2022/12/08 20:42:28 - mmengine - INFO - Epoch(train) [43][880/940] lr: 1.0000e-03 eta: 9:25:33 time: 0.6404 data_time: 0.0239 memory: 16095 grad_norm: 4.8992 loss: 0.9354 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.9354 2022/12/08 20:42:39 - mmengine - INFO - Epoch(train) [43][900/940] lr: 1.0000e-03 eta: 9:25:19 time: 0.5666 data_time: 0.0268 memory: 16095 grad_norm: 4.9128 loss: 0.9284 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.9284 2022/12/08 20:42:52 - mmengine - INFO - Epoch(train) [43][920/940] lr: 1.0000e-03 eta: 9:25:06 time: 0.6106 data_time: 0.0229 memory: 16095 grad_norm: 4.9203 loss: 0.9727 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.9727 2022/12/08 20:43:03 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 20:43:03 - mmengine - INFO - Epoch(train) [43][940/940] lr: 1.0000e-03 eta: 9:24:51 time: 0.5534 data_time: 0.0182 memory: 16095 grad_norm: 5.0530 loss: 1.0777 top1_acc: 0.4286 top5_acc: 1.0000 loss_cls: 1.0777 2022/12/08 20:43:17 - mmengine - INFO - Epoch(val) [43][20/78] eta: 0:00:40 time: 0.6987 data_time: 0.6038 memory: 1686 2022/12/08 20:43:26 - mmengine - INFO - Epoch(val) [43][40/78] eta: 0:00:22 time: 0.4654 data_time: 0.3691 memory: 1686 2022/12/08 20:43:39 - mmengine - INFO - Epoch(val) [43][60/78] eta: 0:00:11 time: 0.6722 data_time: 0.5780 memory: 1686 2022/12/08 20:43:50 - mmengine - INFO - Epoch(val) [43][78/78] acc/top1: 0.6908 acc/top5: 0.8781 acc/mean1: 0.6907 2022/12/08 20:43:50 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_42.pth is removed 2022/12/08 20:43:53 - mmengine - INFO - The best checkpoint with 0.6908 acc/top1 at 43 epoch is saved to best_acc/top1_epoch_43.pth. 2022/12/08 20:44:09 - mmengine - INFO - Epoch(train) [44][ 20/940] lr: 1.0000e-03 eta: 9:24:43 time: 0.8004 data_time: 0.4969 memory: 16095 grad_norm: 4.8639 loss: 1.0709 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 1.0709 2022/12/08 20:44:19 - mmengine - INFO - Epoch(train) [44][ 40/940] lr: 1.0000e-03 eta: 9:24:27 time: 0.5124 data_time: 0.2126 memory: 16095 grad_norm: 4.8855 loss: 0.9633 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9633 2022/12/08 20:44:33 - mmengine - INFO - Epoch(train) [44][ 60/940] lr: 1.0000e-03 eta: 9:24:16 time: 0.6984 data_time: 0.3892 memory: 16095 grad_norm: 4.8146 loss: 0.9034 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 0.9034 2022/12/08 20:44:44 - mmengine - INFO - Epoch(train) [44][ 80/940] lr: 1.0000e-03 eta: 9:24:02 time: 0.5664 data_time: 0.2589 memory: 16095 grad_norm: 4.8260 loss: 0.9644 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.9644 2022/12/08 20:44:58 - mmengine - INFO - Epoch(train) [44][100/940] lr: 1.0000e-03 eta: 9:23:51 time: 0.7136 data_time: 0.4024 memory: 16095 grad_norm: 4.7984 loss: 0.8306 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 0.8306 2022/12/08 20:45:10 - mmengine - INFO - Epoch(train) [44][120/940] lr: 1.0000e-03 eta: 9:23:37 time: 0.5827 data_time: 0.2731 memory: 16095 grad_norm: 4.7770 loss: 0.8953 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8953 2022/12/08 20:45:22 - mmengine - INFO - Epoch(train) [44][140/940] lr: 1.0000e-03 eta: 9:23:24 time: 0.6201 data_time: 0.3063 memory: 16095 grad_norm: 4.8953 loss: 0.9858 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.9858 2022/12/08 20:45:34 - mmengine - INFO - Epoch(train) [44][160/940] lr: 1.0000e-03 eta: 9:23:10 time: 0.5727 data_time: 0.2491 memory: 16095 grad_norm: 4.8172 loss: 0.8769 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8769 2022/12/08 20:45:47 - mmengine - INFO - Epoch(train) [44][180/940] lr: 1.0000e-03 eta: 9:22:58 time: 0.6654 data_time: 0.3380 memory: 16095 grad_norm: 4.9483 loss: 1.0523 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 1.0523 2022/12/08 20:45:58 - mmengine - INFO - Epoch(train) [44][200/940] lr: 1.0000e-03 eta: 9:22:43 time: 0.5504 data_time: 0.2129 memory: 16095 grad_norm: 4.8194 loss: 0.8753 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8753 2022/12/08 20:46:12 - mmengine - INFO - Epoch(train) [44][220/940] lr: 1.0000e-03 eta: 9:22:32 time: 0.6865 data_time: 0.3462 memory: 16095 grad_norm: 4.9715 loss: 0.9367 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 0.9367 2022/12/08 20:46:23 - mmengine - INFO - Epoch(train) [44][240/940] lr: 1.0000e-03 eta: 9:22:18 time: 0.5570 data_time: 0.2267 memory: 16095 grad_norm: 4.8847 loss: 0.8949 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.8949 2022/12/08 20:46:36 - mmengine - INFO - Epoch(train) [44][260/940] lr: 1.0000e-03 eta: 9:22:05 time: 0.6278 data_time: 0.2161 memory: 16095 grad_norm: 4.9397 loss: 1.0280 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 1.0280 2022/12/08 20:46:48 - mmengine - INFO - Epoch(train) [44][280/940] lr: 1.0000e-03 eta: 9:21:51 time: 0.6042 data_time: 0.0394 memory: 16095 grad_norm: 4.8241 loss: 0.9201 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.9201 2022/12/08 20:47:00 - mmengine - INFO - Epoch(train) [44][300/940] lr: 1.0000e-03 eta: 9:21:38 time: 0.5988 data_time: 0.1207 memory: 16095 grad_norm: 4.8544 loss: 0.9103 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.9103 2022/12/08 20:47:12 - mmengine - INFO - Epoch(train) [44][320/940] lr: 1.0000e-03 eta: 9:21:25 time: 0.6108 data_time: 0.1352 memory: 16095 grad_norm: 4.9202 loss: 1.0299 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0299 2022/12/08 20:47:24 - mmengine - INFO - Epoch(train) [44][340/940] lr: 1.0000e-03 eta: 9:21:11 time: 0.5925 data_time: 0.0863 memory: 16095 grad_norm: 5.0102 loss: 0.9940 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.9940 2022/12/08 20:47:38 - mmengine - INFO - Epoch(train) [44][360/940] lr: 1.0000e-03 eta: 9:21:00 time: 0.6986 data_time: 0.0212 memory: 16095 grad_norm: 4.9712 loss: 0.8792 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.8792 2022/12/08 20:47:48 - mmengine - INFO - Epoch(train) [44][380/940] lr: 1.0000e-03 eta: 9:20:45 time: 0.5297 data_time: 0.0256 memory: 16095 grad_norm: 4.8578 loss: 1.0555 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0555 2022/12/08 20:48:02 - mmengine - INFO - Epoch(train) [44][400/940] lr: 1.0000e-03 eta: 9:20:34 time: 0.6901 data_time: 0.0228 memory: 16095 grad_norm: 5.0148 loss: 1.0395 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 1.0395 2022/12/08 20:48:13 - mmengine - INFO - Epoch(train) [44][420/940] lr: 1.0000e-03 eta: 9:20:19 time: 0.5627 data_time: 0.0250 memory: 16095 grad_norm: 4.9208 loss: 0.9411 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.9411 2022/12/08 20:48:27 - mmengine - INFO - Epoch(train) [44][440/940] lr: 1.0000e-03 eta: 9:20:08 time: 0.6931 data_time: 0.0248 memory: 16095 grad_norm: 4.9086 loss: 1.0695 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.0695 2022/12/08 20:48:38 - mmengine - INFO - Epoch(train) [44][460/940] lr: 1.0000e-03 eta: 9:19:53 time: 0.5285 data_time: 0.0244 memory: 16095 grad_norm: 5.0017 loss: 0.8963 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 0.8963 2022/12/08 20:48:50 - mmengine - INFO - Epoch(train) [44][480/940] lr: 1.0000e-03 eta: 9:19:40 time: 0.6143 data_time: 0.0229 memory: 16095 grad_norm: 4.8924 loss: 0.9235 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.9235 2022/12/08 20:49:02 - mmengine - INFO - Epoch(train) [44][500/940] lr: 1.0000e-03 eta: 9:19:25 time: 0.5769 data_time: 0.0262 memory: 16095 grad_norm: 4.8275 loss: 0.9329 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.9329 2022/12/08 20:49:16 - mmengine - INFO - Epoch(train) [44][520/940] lr: 1.0000e-03 eta: 9:19:14 time: 0.6913 data_time: 0.0233 memory: 16095 grad_norm: 4.8078 loss: 0.9129 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.9129 2022/12/08 20:49:27 - mmengine - INFO - Epoch(train) [44][540/940] lr: 1.0000e-03 eta: 9:19:00 time: 0.5516 data_time: 0.0260 memory: 16095 grad_norm: 4.9092 loss: 0.8512 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8512 2022/12/08 20:49:39 - mmengine - INFO - Epoch(train) [44][560/940] lr: 1.0000e-03 eta: 9:18:47 time: 0.6304 data_time: 0.0243 memory: 16095 grad_norm: 4.9361 loss: 1.0383 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.0383 2022/12/08 20:49:50 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 20:49:50 - mmengine - INFO - Epoch(train) [44][580/940] lr: 1.0000e-03 eta: 9:18:32 time: 0.5396 data_time: 0.0251 memory: 16095 grad_norm: 4.9911 loss: 1.0138 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.0138 2022/12/08 20:50:03 - mmengine - INFO - Epoch(train) [44][600/940] lr: 1.0000e-03 eta: 9:18:19 time: 0.6381 data_time: 0.0218 memory: 16095 grad_norm: 4.9230 loss: 0.9622 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 0.9622 2022/12/08 20:50:15 - mmengine - INFO - Epoch(train) [44][620/940] lr: 1.0000e-03 eta: 9:18:06 time: 0.5967 data_time: 0.0271 memory: 16095 grad_norm: 5.0265 loss: 0.9394 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.9394 2022/12/08 20:50:28 - mmengine - INFO - Epoch(train) [44][640/940] lr: 1.0000e-03 eta: 9:17:54 time: 0.6587 data_time: 0.0212 memory: 16095 grad_norm: 4.9472 loss: 1.0616 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0616 2022/12/08 20:50:39 - mmengine - INFO - Epoch(train) [44][660/940] lr: 1.0000e-03 eta: 9:17:39 time: 0.5598 data_time: 0.0256 memory: 16095 grad_norm: 4.8722 loss: 1.1580 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 1.1580 2022/12/08 20:50:52 - mmengine - INFO - Epoch(train) [44][680/940] lr: 1.0000e-03 eta: 9:17:27 time: 0.6316 data_time: 0.0337 memory: 16095 grad_norm: 4.9968 loss: 1.0413 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 1.0413 2022/12/08 20:51:03 - mmengine - INFO - Epoch(train) [44][700/940] lr: 1.0000e-03 eta: 9:17:12 time: 0.5626 data_time: 0.0258 memory: 16095 grad_norm: 4.8902 loss: 0.9065 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9065 2022/12/08 20:51:16 - mmengine - INFO - Epoch(train) [44][720/940] lr: 1.0000e-03 eta: 9:17:00 time: 0.6665 data_time: 0.0233 memory: 16095 grad_norm: 4.8231 loss: 0.9075 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9075 2022/12/08 20:51:29 - mmengine - INFO - Epoch(train) [44][740/940] lr: 1.0000e-03 eta: 9:16:47 time: 0.6138 data_time: 0.0272 memory: 16095 grad_norm: 5.0125 loss: 0.9652 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.9652 2022/12/08 20:51:40 - mmengine - INFO - Epoch(train) [44][760/940] lr: 1.0000e-03 eta: 9:16:33 time: 0.5786 data_time: 0.0233 memory: 16095 grad_norm: 5.0299 loss: 0.9340 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.9340 2022/12/08 20:51:54 - mmengine - INFO - Epoch(train) [44][780/940] lr: 1.0000e-03 eta: 9:16:22 time: 0.6902 data_time: 0.0286 memory: 16095 grad_norm: 4.9965 loss: 0.9407 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.9407 2022/12/08 20:52:05 - mmengine - INFO - Epoch(train) [44][800/940] lr: 1.0000e-03 eta: 9:16:08 time: 0.5640 data_time: 0.0209 memory: 16095 grad_norm: 5.0356 loss: 0.9711 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 0.9711 2022/12/08 20:52:19 - mmengine - INFO - Epoch(train) [44][820/940] lr: 1.0000e-03 eta: 9:15:56 time: 0.6642 data_time: 0.0259 memory: 16095 grad_norm: 4.9618 loss: 1.1063 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.1063 2022/12/08 20:52:30 - mmengine - INFO - Epoch(train) [44][840/940] lr: 1.0000e-03 eta: 9:15:42 time: 0.5837 data_time: 0.0256 memory: 16095 grad_norm: 5.0369 loss: 1.0155 top1_acc: 0.8125 top5_acc: 0.8438 loss_cls: 1.0155 2022/12/08 20:52:42 - mmengine - INFO - Epoch(train) [44][860/940] lr: 1.0000e-03 eta: 9:15:28 time: 0.5947 data_time: 0.0249 memory: 16095 grad_norm: 5.0312 loss: 0.9823 top1_acc: 0.5938 top5_acc: 0.7812 loss_cls: 0.9823 2022/12/08 20:52:54 - mmengine - INFO - Epoch(train) [44][880/940] lr: 1.0000e-03 eta: 9:15:15 time: 0.5988 data_time: 0.0224 memory: 16095 grad_norm: 4.8802 loss: 0.9425 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.9425 2022/12/08 20:53:06 - mmengine - INFO - Epoch(train) [44][900/940] lr: 1.0000e-03 eta: 9:15:01 time: 0.5968 data_time: 0.0250 memory: 16095 grad_norm: 4.9735 loss: 0.9438 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9438 2022/12/08 20:53:19 - mmengine - INFO - Epoch(train) [44][920/940] lr: 1.0000e-03 eta: 9:14:49 time: 0.6276 data_time: 0.0239 memory: 16095 grad_norm: 5.0248 loss: 1.0298 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0298 2022/12/08 20:53:29 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 20:53:29 - mmengine - INFO - Epoch(train) [44][940/940] lr: 1.0000e-03 eta: 9:14:33 time: 0.5051 data_time: 0.0190 memory: 16095 grad_norm: 5.2205 loss: 0.9585 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 0.9585 2022/12/08 20:53:43 - mmengine - INFO - Epoch(val) [44][20/78] eta: 0:00:40 time: 0.7065 data_time: 0.6115 memory: 1686 2022/12/08 20:53:52 - mmengine - INFO - Epoch(val) [44][40/78] eta: 0:00:22 time: 0.4525 data_time: 0.3583 memory: 1686 2022/12/08 20:54:05 - mmengine - INFO - Epoch(val) [44][60/78] eta: 0:00:11 time: 0.6776 data_time: 0.5829 memory: 1686 2022/12/08 20:54:16 - mmengine - INFO - Epoch(val) [44][78/78] acc/top1: 0.6924 acc/top5: 0.8786 acc/mean1: 0.6922 2022/12/08 20:54:16 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_43.pth is removed 2022/12/08 20:54:18 - mmengine - INFO - The best checkpoint with 0.6924 acc/top1 at 44 epoch is saved to best_acc/top1_epoch_44.pth. 2022/12/08 20:54:33 - mmengine - INFO - Epoch(train) [45][ 20/940] lr: 1.0000e-03 eta: 9:14:23 time: 0.7397 data_time: 0.4415 memory: 16095 grad_norm: 4.9022 loss: 1.0450 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0450 2022/12/08 20:54:45 - mmengine - INFO - Epoch(train) [45][ 40/940] lr: 1.0000e-03 eta: 9:14:09 time: 0.5902 data_time: 0.2763 memory: 16095 grad_norm: 4.9593 loss: 0.9150 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.9150 2022/12/08 20:54:58 - mmengine - INFO - Epoch(train) [45][ 60/940] lr: 1.0000e-03 eta: 9:13:56 time: 0.6302 data_time: 0.3328 memory: 16095 grad_norm: 4.9525 loss: 0.8502 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.8502 2022/12/08 20:55:09 - mmengine - INFO - Epoch(train) [45][ 80/940] lr: 1.0000e-03 eta: 9:13:42 time: 0.5691 data_time: 0.2358 memory: 16095 grad_norm: 4.7897 loss: 0.8159 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8159 2022/12/08 20:55:23 - mmengine - INFO - Epoch(train) [45][100/940] lr: 1.0000e-03 eta: 9:13:31 time: 0.6930 data_time: 0.1902 memory: 16095 grad_norm: 4.8639 loss: 0.9479 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9479 2022/12/08 20:55:34 - mmengine - INFO - Epoch(train) [45][120/940] lr: 1.0000e-03 eta: 9:13:16 time: 0.5329 data_time: 0.1635 memory: 16095 grad_norm: 4.9823 loss: 0.9743 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.9743 2022/12/08 20:55:47 - mmengine - INFO - Epoch(train) [45][140/940] lr: 1.0000e-03 eta: 9:13:05 time: 0.6976 data_time: 0.3693 memory: 16095 grad_norm: 5.0445 loss: 1.0243 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 1.0243 2022/12/08 20:55:59 - mmengine - INFO - Epoch(train) [45][160/940] lr: 1.0000e-03 eta: 9:12:50 time: 0.5540 data_time: 0.2125 memory: 16095 grad_norm: 4.9573 loss: 0.9972 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9972 2022/12/08 20:56:12 - mmengine - INFO - Epoch(train) [45][180/940] lr: 1.0000e-03 eta: 9:12:39 time: 0.6902 data_time: 0.1934 memory: 16095 grad_norm: 5.0054 loss: 0.9262 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.9262 2022/12/08 20:56:23 - mmengine - INFO - Epoch(train) [45][200/940] lr: 1.0000e-03 eta: 9:12:24 time: 0.5364 data_time: 0.0739 memory: 16095 grad_norm: 4.9013 loss: 0.9452 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.9452 2022/12/08 20:56:37 - mmengine - INFO - Epoch(train) [45][220/940] lr: 1.0000e-03 eta: 9:12:13 time: 0.7003 data_time: 0.0377 memory: 16095 grad_norm: 4.9399 loss: 1.0037 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.0037 2022/12/08 20:56:48 - mmengine - INFO - Epoch(train) [45][240/940] lr: 1.0000e-03 eta: 9:11:58 time: 0.5373 data_time: 0.0246 memory: 16095 grad_norm: 4.8734 loss: 1.0696 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.0696 2022/12/08 20:57:02 - mmengine - INFO - Epoch(train) [45][260/940] lr: 1.0000e-03 eta: 9:11:48 time: 0.7103 data_time: 0.0273 memory: 16095 grad_norm: 4.9103 loss: 0.8390 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8390 2022/12/08 20:57:13 - mmengine - INFO - Epoch(train) [45][280/940] lr: 1.0000e-03 eta: 9:11:32 time: 0.5306 data_time: 0.0208 memory: 16095 grad_norm: 4.9484 loss: 0.8990 top1_acc: 0.7188 top5_acc: 1.0000 loss_cls: 0.8990 2022/12/08 20:57:26 - mmengine - INFO - Epoch(train) [45][300/940] lr: 1.0000e-03 eta: 9:11:20 time: 0.6459 data_time: 0.0255 memory: 16095 grad_norm: 4.9080 loss: 0.9165 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.9165 2022/12/08 20:57:36 - mmengine - INFO - Epoch(train) [45][320/940] lr: 1.0000e-03 eta: 9:11:05 time: 0.5358 data_time: 0.0235 memory: 16095 grad_norm: 4.8872 loss: 0.9377 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.9377 2022/12/08 20:57:50 - mmengine - INFO - Epoch(train) [45][340/940] lr: 1.0000e-03 eta: 9:10:54 time: 0.6862 data_time: 0.0260 memory: 16095 grad_norm: 5.0393 loss: 1.0025 top1_acc: 0.7188 top5_acc: 0.7812 loss_cls: 1.0025 2022/12/08 20:58:01 - mmengine - INFO - Epoch(train) [45][360/940] lr: 1.0000e-03 eta: 9:10:38 time: 0.5273 data_time: 0.0245 memory: 16095 grad_norm: 4.9281 loss: 1.0115 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.0115 2022/12/08 20:58:14 - mmengine - INFO - Epoch(train) [45][380/940] lr: 1.0000e-03 eta: 9:10:27 time: 0.6932 data_time: 0.0275 memory: 16095 grad_norm: 5.0199 loss: 0.9493 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.9493 2022/12/08 20:58:25 - mmengine - INFO - Epoch(train) [45][400/940] lr: 1.0000e-03 eta: 9:10:12 time: 0.5374 data_time: 0.0236 memory: 16095 grad_norm: 4.8642 loss: 0.9021 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.9021 2022/12/08 20:58:37 - mmengine - INFO - Epoch(train) [45][420/940] lr: 1.0000e-03 eta: 9:09:59 time: 0.6090 data_time: 0.0222 memory: 16095 grad_norm: 4.9288 loss: 0.8969 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.8969 2022/12/08 20:58:48 - mmengine - INFO - Epoch(train) [45][440/940] lr: 1.0000e-03 eta: 9:09:44 time: 0.5424 data_time: 0.0261 memory: 16095 grad_norm: 5.0822 loss: 0.9529 top1_acc: 0.6875 top5_acc: 0.9688 loss_cls: 0.9529 2022/12/08 20:59:01 - mmengine - INFO - Epoch(train) [45][460/940] lr: 1.0000e-03 eta: 9:09:32 time: 0.6400 data_time: 0.0236 memory: 16095 grad_norm: 5.0053 loss: 0.9841 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.9841 2022/12/08 20:59:14 - mmengine - INFO - Epoch(train) [45][480/940] lr: 1.0000e-03 eta: 9:09:20 time: 0.6695 data_time: 0.0254 memory: 16095 grad_norm: 5.0606 loss: 1.0274 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 1.0274 2022/12/08 20:59:26 - mmengine - INFO - Epoch(train) [45][500/940] lr: 1.0000e-03 eta: 9:09:06 time: 0.5657 data_time: 0.0227 memory: 16095 grad_norm: 5.0229 loss: 0.9587 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9587 2022/12/08 20:59:39 - mmengine - INFO - Epoch(train) [45][520/940] lr: 1.0000e-03 eta: 9:08:54 time: 0.6807 data_time: 0.0305 memory: 16095 grad_norm: 4.8860 loss: 1.0457 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.0457 2022/12/08 20:59:51 - mmengine - INFO - Epoch(train) [45][540/940] lr: 1.0000e-03 eta: 9:08:40 time: 0.5627 data_time: 0.0287 memory: 16095 grad_norm: 4.9724 loss: 0.9635 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 0.9635 2022/12/08 21:00:04 - mmengine - INFO - Epoch(train) [45][560/940] lr: 1.0000e-03 eta: 9:08:28 time: 0.6470 data_time: 0.0228 memory: 16095 grad_norm: 4.9488 loss: 0.9782 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.9782 2022/12/08 21:00:15 - mmengine - INFO - Epoch(train) [45][580/940] lr: 1.0000e-03 eta: 9:08:13 time: 0.5497 data_time: 0.0272 memory: 16095 grad_norm: 4.9641 loss: 0.9207 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.9207 2022/12/08 21:00:28 - mmengine - INFO - Epoch(train) [45][600/940] lr: 1.0000e-03 eta: 9:08:01 time: 0.6496 data_time: 0.0258 memory: 16095 grad_norm: 5.0132 loss: 1.0135 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 1.0135 2022/12/08 21:00:38 - mmengine - INFO - Epoch(train) [45][620/940] lr: 1.0000e-03 eta: 9:07:46 time: 0.5255 data_time: 0.0266 memory: 16095 grad_norm: 4.9345 loss: 0.8763 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8763 2022/12/08 21:00:52 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 21:00:52 - mmengine - INFO - Epoch(train) [45][640/940] lr: 1.0000e-03 eta: 9:07:34 time: 0.6765 data_time: 0.0227 memory: 16095 grad_norm: 4.9654 loss: 0.9081 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9081 2022/12/08 21:01:03 - mmengine - INFO - Epoch(train) [45][660/940] lr: 1.0000e-03 eta: 9:07:20 time: 0.5622 data_time: 0.0261 memory: 16095 grad_norm: 5.1058 loss: 0.9887 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9887 2022/12/08 21:01:16 - mmengine - INFO - Epoch(train) [45][680/940] lr: 1.0000e-03 eta: 9:07:08 time: 0.6726 data_time: 0.0223 memory: 16095 grad_norm: 5.0040 loss: 1.0883 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.0883 2022/12/08 21:01:29 - mmengine - INFO - Epoch(train) [45][700/940] lr: 1.0000e-03 eta: 9:06:55 time: 0.6130 data_time: 0.0298 memory: 16095 grad_norm: 4.9212 loss: 0.8681 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8681 2022/12/08 21:01:42 - mmengine - INFO - Epoch(train) [45][720/940] lr: 1.0000e-03 eta: 9:06:43 time: 0.6459 data_time: 0.0198 memory: 16095 grad_norm: 4.9214 loss: 0.9788 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.9788 2022/12/08 21:01:53 - mmengine - INFO - Epoch(train) [45][740/940] lr: 1.0000e-03 eta: 9:06:29 time: 0.5880 data_time: 0.0231 memory: 16095 grad_norm: 4.9939 loss: 0.9365 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.9365 2022/12/08 21:02:07 - mmengine - INFO - Epoch(train) [45][760/940] lr: 1.0000e-03 eta: 9:06:18 time: 0.6865 data_time: 0.0233 memory: 16095 grad_norm: 4.9579 loss: 0.9991 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.9991 2022/12/08 21:02:18 - mmengine - INFO - Epoch(train) [45][780/940] lr: 1.0000e-03 eta: 9:06:03 time: 0.5602 data_time: 0.0226 memory: 16095 grad_norm: 4.9595 loss: 0.9085 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9085 2022/12/08 21:02:32 - mmengine - INFO - Epoch(train) [45][800/940] lr: 1.0000e-03 eta: 9:05:52 time: 0.6950 data_time: 0.0251 memory: 16095 grad_norm: 5.1070 loss: 1.1001 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1001 2022/12/08 21:02:43 - mmengine - INFO - Epoch(train) [45][820/940] lr: 1.0000e-03 eta: 9:05:37 time: 0.5224 data_time: 0.0228 memory: 16095 grad_norm: 4.9927 loss: 1.0045 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0045 2022/12/08 21:02:55 - mmengine - INFO - Epoch(train) [45][840/940] lr: 1.0000e-03 eta: 9:05:23 time: 0.5965 data_time: 0.0250 memory: 16095 grad_norm: 4.9023 loss: 0.9288 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.9288 2022/12/08 21:03:06 - mmengine - INFO - Epoch(train) [45][860/940] lr: 1.0000e-03 eta: 9:05:09 time: 0.5771 data_time: 0.0244 memory: 16095 grad_norm: 4.9206 loss: 0.9089 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.9089 2022/12/08 21:03:19 - mmengine - INFO - Epoch(train) [45][880/940] lr: 1.0000e-03 eta: 9:04:58 time: 0.6667 data_time: 0.0232 memory: 16095 grad_norm: 4.9748 loss: 0.9385 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9385 2022/12/08 21:03:30 - mmengine - INFO - Epoch(train) [45][900/940] lr: 1.0000e-03 eta: 9:04:43 time: 0.5488 data_time: 0.0245 memory: 16095 grad_norm: 5.0011 loss: 1.1076 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.1076 2022/12/08 21:03:45 - mmengine - INFO - Epoch(train) [45][920/940] lr: 1.0000e-03 eta: 9:04:33 time: 0.7296 data_time: 0.0223 memory: 16095 grad_norm: 5.0963 loss: 1.0586 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 1.0586 2022/12/08 21:03:54 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 21:03:54 - mmengine - INFO - Epoch(train) [45][940/940] lr: 1.0000e-03 eta: 9:04:15 time: 0.4327 data_time: 0.0168 memory: 16095 grad_norm: 5.1924 loss: 0.9728 top1_acc: 0.7143 top5_acc: 0.7143 loss_cls: 0.9728 2022/12/08 21:03:54 - mmengine - INFO - Saving checkpoint at 45 epochs 2022/12/08 21:04:11 - mmengine - INFO - Epoch(val) [45][20/78] eta: 0:00:41 time: 0.7189 data_time: 0.6247 memory: 1686 2022/12/08 21:04:20 - mmengine - INFO - Epoch(val) [45][40/78] eta: 0:00:22 time: 0.4557 data_time: 0.3615 memory: 1686 2022/12/08 21:04:34 - mmengine - INFO - Epoch(val) [45][60/78] eta: 0:00:11 time: 0.6672 data_time: 0.5709 memory: 1686 2022/12/08 21:04:43 - mmengine - INFO - Epoch(val) [45][78/78] acc/top1: 0.6936 acc/top5: 0.8792 acc/mean1: 0.6935 2022/12/08 21:04:43 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_44.pth is removed 2022/12/08 21:04:46 - mmengine - INFO - The best checkpoint with 0.6936 acc/top1 at 45 epoch is saved to best_acc/top1_epoch_45.pth. 2022/12/08 21:05:01 - mmengine - INFO - Epoch(train) [46][ 20/940] lr: 1.0000e-03 eta: 9:04:06 time: 0.7734 data_time: 0.4579 memory: 16095 grad_norm: 4.9297 loss: 1.0049 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.0049 2022/12/08 21:05:13 - mmengine - INFO - Epoch(train) [46][ 40/940] lr: 1.0000e-03 eta: 9:03:52 time: 0.5706 data_time: 0.2761 memory: 16095 grad_norm: 5.0120 loss: 0.9055 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.9055 2022/12/08 21:05:26 - mmengine - INFO - Epoch(train) [46][ 60/940] lr: 1.0000e-03 eta: 9:03:40 time: 0.6714 data_time: 0.3832 memory: 16095 grad_norm: 4.9218 loss: 0.9414 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.9414 2022/12/08 21:05:37 - mmengine - INFO - Epoch(train) [46][ 80/940] lr: 1.0000e-03 eta: 9:03:26 time: 0.5649 data_time: 0.2676 memory: 16095 grad_norm: 5.0234 loss: 0.9604 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9604 2022/12/08 21:05:50 - mmengine - INFO - Epoch(train) [46][100/940] lr: 1.0000e-03 eta: 9:03:14 time: 0.6552 data_time: 0.3558 memory: 16095 grad_norm: 5.0600 loss: 0.9486 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9486 2022/12/08 21:06:02 - mmengine - INFO - Epoch(train) [46][120/940] lr: 1.0000e-03 eta: 9:02:59 time: 0.5529 data_time: 0.2364 memory: 16095 grad_norm: 5.0077 loss: 1.0134 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0134 2022/12/08 21:06:15 - mmengine - INFO - Epoch(train) [46][140/940] lr: 1.0000e-03 eta: 9:02:48 time: 0.6727 data_time: 0.2882 memory: 16095 grad_norm: 4.8683 loss: 0.8422 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8422 2022/12/08 21:06:25 - mmengine - INFO - Epoch(train) [46][160/940] lr: 1.0000e-03 eta: 9:02:32 time: 0.5071 data_time: 0.1244 memory: 16095 grad_norm: 4.8592 loss: 0.8877 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8877 2022/12/08 21:06:40 - mmengine - INFO - Epoch(train) [46][180/940] lr: 1.0000e-03 eta: 9:02:22 time: 0.7215 data_time: 0.0481 memory: 16095 grad_norm: 5.0337 loss: 0.9655 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.9655 2022/12/08 21:06:51 - mmengine - INFO - Epoch(train) [46][200/940] lr: 1.0000e-03 eta: 9:02:07 time: 0.5570 data_time: 0.0251 memory: 16095 grad_norm: 5.0527 loss: 0.8470 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8470 2022/12/08 21:07:04 - mmengine - INFO - Epoch(train) [46][220/940] lr: 1.0000e-03 eta: 9:01:56 time: 0.6847 data_time: 0.0292 memory: 16095 grad_norm: 5.0214 loss: 1.0389 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 1.0389 2022/12/08 21:07:15 - mmengine - INFO - Epoch(train) [46][240/940] lr: 1.0000e-03 eta: 9:01:41 time: 0.5478 data_time: 0.0206 memory: 16095 grad_norm: 4.9805 loss: 1.0120 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0120 2022/12/08 21:07:29 - mmengine - INFO - Epoch(train) [46][260/940] lr: 1.0000e-03 eta: 9:01:29 time: 0.6659 data_time: 0.0258 memory: 16095 grad_norm: 4.8868 loss: 0.9777 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.9777 2022/12/08 21:07:39 - mmengine - INFO - Epoch(train) [46][280/940] lr: 1.0000e-03 eta: 9:01:15 time: 0.5370 data_time: 0.0210 memory: 16095 grad_norm: 4.9680 loss: 0.9313 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.9313 2022/12/08 21:07:53 - mmengine - INFO - Epoch(train) [46][300/940] lr: 1.0000e-03 eta: 9:01:03 time: 0.6639 data_time: 0.0391 memory: 16095 grad_norm: 5.0234 loss: 0.9785 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 0.9785 2022/12/08 21:08:04 - mmengine - INFO - Epoch(train) [46][320/940] lr: 1.0000e-03 eta: 9:00:48 time: 0.5545 data_time: 0.0222 memory: 16095 grad_norm: 5.0010 loss: 0.9783 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.9783 2022/12/08 21:08:17 - mmengine - INFO - Epoch(train) [46][340/940] lr: 1.0000e-03 eta: 9:00:37 time: 0.6786 data_time: 0.0370 memory: 16095 grad_norm: 5.0279 loss: 0.9630 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.9630 2022/12/08 21:08:29 - mmengine - INFO - Epoch(train) [46][360/940] lr: 1.0000e-03 eta: 9:00:23 time: 0.6018 data_time: 0.0213 memory: 16095 grad_norm: 4.9962 loss: 0.8720 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.8720 2022/12/08 21:08:41 - mmengine - INFO - Epoch(train) [46][380/940] lr: 1.0000e-03 eta: 9:00:09 time: 0.5644 data_time: 0.0278 memory: 16095 grad_norm: 4.9513 loss: 0.9261 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9261 2022/12/08 21:08:52 - mmengine - INFO - Epoch(train) [46][400/940] lr: 1.0000e-03 eta: 8:59:55 time: 0.5830 data_time: 0.0238 memory: 16095 grad_norm: 5.0374 loss: 0.9156 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.9156 2022/12/08 21:09:06 - mmengine - INFO - Epoch(train) [46][420/940] lr: 1.0000e-03 eta: 8:59:44 time: 0.6688 data_time: 0.0246 memory: 16095 grad_norm: 5.1102 loss: 0.8440 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8440 2022/12/08 21:09:18 - mmengine - INFO - Epoch(train) [46][440/940] lr: 1.0000e-03 eta: 8:59:30 time: 0.6143 data_time: 0.0203 memory: 16095 grad_norm: 4.8992 loss: 0.9333 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.9333 2022/12/08 21:09:31 - mmengine - INFO - Epoch(train) [46][460/940] lr: 1.0000e-03 eta: 8:59:19 time: 0.6604 data_time: 0.0253 memory: 16095 grad_norm: 4.8921 loss: 1.0350 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.0350 2022/12/08 21:09:41 - mmengine - INFO - Epoch(train) [46][480/940] lr: 1.0000e-03 eta: 8:59:03 time: 0.5030 data_time: 0.0217 memory: 16095 grad_norm: 5.0638 loss: 0.9016 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9016 2022/12/08 21:09:53 - mmengine - INFO - Epoch(train) [46][500/940] lr: 1.0000e-03 eta: 8:58:49 time: 0.5999 data_time: 0.0253 memory: 16095 grad_norm: 5.0122 loss: 1.0507 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 1.0507 2022/12/08 21:10:05 - mmengine - INFO - Epoch(train) [46][520/940] lr: 1.0000e-03 eta: 8:58:36 time: 0.5977 data_time: 0.0262 memory: 16095 grad_norm: 4.9574 loss: 1.0086 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.0086 2022/12/08 21:10:18 - mmengine - INFO - Epoch(train) [46][540/940] lr: 1.0000e-03 eta: 8:58:23 time: 0.6334 data_time: 0.0746 memory: 16095 grad_norm: 5.0565 loss: 0.8231 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8231 2022/12/08 21:10:29 - mmengine - INFO - Epoch(train) [46][560/940] lr: 1.0000e-03 eta: 8:58:10 time: 0.5774 data_time: 0.1089 memory: 16095 grad_norm: 4.9904 loss: 0.9620 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 0.9620 2022/12/08 21:10:43 - mmengine - INFO - Epoch(train) [46][580/940] lr: 1.0000e-03 eta: 8:57:58 time: 0.6935 data_time: 0.2626 memory: 16095 grad_norm: 4.9547 loss: 0.8641 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 0.8641 2022/12/08 21:10:55 - mmengine - INFO - Epoch(train) [46][600/940] lr: 1.0000e-03 eta: 8:57:45 time: 0.5884 data_time: 0.1719 memory: 16095 grad_norm: 4.8893 loss: 0.9044 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9044 2022/12/08 21:11:08 - mmengine - INFO - Epoch(train) [46][620/940] lr: 1.0000e-03 eta: 8:57:32 time: 0.6355 data_time: 0.1561 memory: 16095 grad_norm: 4.9543 loss: 0.9315 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.9315 2022/12/08 21:11:19 - mmengine - INFO - Epoch(train) [46][640/940] lr: 1.0000e-03 eta: 8:57:18 time: 0.5581 data_time: 0.1115 memory: 16095 grad_norm: 4.9201 loss: 0.9756 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9756 2022/12/08 21:11:32 - mmengine - INFO - Epoch(train) [46][660/940] lr: 1.0000e-03 eta: 8:57:05 time: 0.6431 data_time: 0.2297 memory: 16095 grad_norm: 5.0314 loss: 0.9361 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9361 2022/12/08 21:11:43 - mmengine - INFO - Epoch(train) [46][680/940] lr: 1.0000e-03 eta: 8:56:51 time: 0.5390 data_time: 0.0962 memory: 16095 grad_norm: 4.8980 loss: 0.9072 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.9072 2022/12/08 21:11:56 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 21:11:56 - mmengine - INFO - Epoch(train) [46][700/940] lr: 1.0000e-03 eta: 8:56:38 time: 0.6508 data_time: 0.0668 memory: 16095 grad_norm: 4.8932 loss: 0.8272 top1_acc: 0.9688 top5_acc: 0.9688 loss_cls: 0.8272 2022/12/08 21:12:07 - mmengine - INFO - Epoch(train) [46][720/940] lr: 1.0000e-03 eta: 8:56:24 time: 0.5567 data_time: 0.0757 memory: 16095 grad_norm: 4.9855 loss: 0.8764 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8764 2022/12/08 21:12:20 - mmengine - INFO - Epoch(train) [46][740/940] lr: 1.0000e-03 eta: 8:56:12 time: 0.6592 data_time: 0.1631 memory: 16095 grad_norm: 4.9640 loss: 0.8205 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8205 2022/12/08 21:12:32 - mmengine - INFO - Epoch(train) [46][760/940] lr: 1.0000e-03 eta: 8:55:59 time: 0.5986 data_time: 0.0973 memory: 16095 grad_norm: 5.0144 loss: 0.9830 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.9830 2022/12/08 21:12:45 - mmengine - INFO - Epoch(train) [46][780/940] lr: 1.0000e-03 eta: 8:55:46 time: 0.6267 data_time: 0.2667 memory: 16095 grad_norm: 5.0072 loss: 0.8774 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8774 2022/12/08 21:12:56 - mmengine - INFO - Epoch(train) [46][800/940] lr: 1.0000e-03 eta: 8:55:32 time: 0.5681 data_time: 0.1492 memory: 16095 grad_norm: 4.8974 loss: 1.0052 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.0052 2022/12/08 21:13:09 - mmengine - INFO - Epoch(train) [46][820/940] lr: 1.0000e-03 eta: 8:55:20 time: 0.6515 data_time: 0.2142 memory: 16095 grad_norm: 4.9096 loss: 0.8220 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8220 2022/12/08 21:13:21 - mmengine - INFO - Epoch(train) [46][840/940] lr: 1.0000e-03 eta: 8:55:06 time: 0.6045 data_time: 0.0651 memory: 16095 grad_norm: 4.9620 loss: 1.0012 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 1.0012 2022/12/08 21:13:32 - mmengine - INFO - Epoch(train) [46][860/940] lr: 1.0000e-03 eta: 8:54:52 time: 0.5400 data_time: 0.0280 memory: 16095 grad_norm: 5.0181 loss: 1.0011 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 1.0011 2022/12/08 21:13:45 - mmengine - INFO - Epoch(train) [46][880/940] lr: 1.0000e-03 eta: 8:54:40 time: 0.6622 data_time: 0.0218 memory: 16095 grad_norm: 5.0114 loss: 0.9092 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9092 2022/12/08 21:13:57 - mmengine - INFO - Epoch(train) [46][900/940] lr: 1.0000e-03 eta: 8:54:26 time: 0.5806 data_time: 0.0255 memory: 16095 grad_norm: 5.0335 loss: 0.9778 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.9778 2022/12/08 21:14:10 - mmengine - INFO - Epoch(train) [46][920/940] lr: 1.0000e-03 eta: 8:54:14 time: 0.6732 data_time: 0.0239 memory: 16095 grad_norm: 5.0133 loss: 0.9654 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9654 2022/12/08 21:14:19 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 21:14:19 - mmengine - INFO - Epoch(train) [46][940/940] lr: 1.0000e-03 eta: 8:53:58 time: 0.4612 data_time: 0.0163 memory: 16095 grad_norm: 5.2489 loss: 1.0544 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.0544 2022/12/08 21:14:33 - mmengine - INFO - Epoch(val) [46][20/78] eta: 0:00:40 time: 0.6909 data_time: 0.5950 memory: 1686 2022/12/08 21:14:43 - mmengine - INFO - Epoch(val) [46][40/78] eta: 0:00:22 time: 0.4750 data_time: 0.3814 memory: 1686 2022/12/08 21:14:56 - mmengine - INFO - Epoch(val) [46][60/78] eta: 0:00:11 time: 0.6822 data_time: 0.5862 memory: 1686 2022/12/08 21:15:07 - mmengine - INFO - Epoch(val) [46][78/78] acc/top1: 0.6948 acc/top5: 0.8794 acc/mean1: 0.6947 2022/12/08 21:15:07 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_45.pth is removed 2022/12/08 21:15:09 - mmengine - INFO - The best checkpoint with 0.6948 acc/top1 at 46 epoch is saved to best_acc/top1_epoch_46.pth. 2022/12/08 21:15:24 - mmengine - INFO - Epoch(train) [47][ 20/940] lr: 1.0000e-03 eta: 8:53:49 time: 0.7797 data_time: 0.4765 memory: 16095 grad_norm: 4.8515 loss: 0.9138 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9138 2022/12/08 21:15:36 - mmengine - INFO - Epoch(train) [47][ 40/940] lr: 1.0000e-03 eta: 8:53:34 time: 0.5621 data_time: 0.2675 memory: 16095 grad_norm: 4.9481 loss: 0.8869 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8869 2022/12/08 21:15:49 - mmengine - INFO - Epoch(train) [47][ 60/940] lr: 1.0000e-03 eta: 8:53:22 time: 0.6479 data_time: 0.3358 memory: 16095 grad_norm: 4.9200 loss: 1.0156 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0156 2022/12/08 21:16:00 - mmengine - INFO - Epoch(train) [47][ 80/940] lr: 1.0000e-03 eta: 8:53:08 time: 0.5596 data_time: 0.2636 memory: 16095 grad_norm: 5.0644 loss: 1.0434 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0434 2022/12/08 21:16:14 - mmengine - INFO - Epoch(train) [47][100/940] lr: 1.0000e-03 eta: 8:52:57 time: 0.6893 data_time: 0.3852 memory: 16095 grad_norm: 4.9657 loss: 0.9518 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.9518 2022/12/08 21:16:25 - mmengine - INFO - Epoch(train) [47][120/940] lr: 1.0000e-03 eta: 8:52:43 time: 0.5720 data_time: 0.2581 memory: 16095 grad_norm: 4.9767 loss: 0.8865 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8865 2022/12/08 21:16:38 - mmengine - INFO - Epoch(train) [47][140/940] lr: 1.0000e-03 eta: 8:52:31 time: 0.6691 data_time: 0.3487 memory: 16095 grad_norm: 5.0327 loss: 1.0108 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 1.0108 2022/12/08 21:16:50 - mmengine - INFO - Epoch(train) [47][160/940] lr: 1.0000e-03 eta: 8:52:17 time: 0.5940 data_time: 0.2769 memory: 16095 grad_norm: 4.9525 loss: 0.9234 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.9234 2022/12/08 21:17:04 - mmengine - INFO - Epoch(train) [47][180/940] lr: 1.0000e-03 eta: 8:52:06 time: 0.6856 data_time: 0.3523 memory: 16095 grad_norm: 4.8258 loss: 0.8444 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.8444 2022/12/08 21:17:15 - mmengine - INFO - Epoch(train) [47][200/940] lr: 1.0000e-03 eta: 8:51:51 time: 0.5226 data_time: 0.2061 memory: 16095 grad_norm: 4.9894 loss: 0.9273 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.9273 2022/12/08 21:17:28 - mmengine - INFO - Epoch(train) [47][220/940] lr: 1.0000e-03 eta: 8:51:39 time: 0.6754 data_time: 0.3475 memory: 16095 grad_norm: 4.8857 loss: 1.0370 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 1.0370 2022/12/08 21:17:39 - mmengine - INFO - Epoch(train) [47][240/940] lr: 1.0000e-03 eta: 8:51:24 time: 0.5394 data_time: 0.2161 memory: 16095 grad_norm: 4.8941 loss: 0.9169 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9169 2022/12/08 21:17:51 - mmengine - INFO - Epoch(train) [47][260/940] lr: 1.0000e-03 eta: 8:51:11 time: 0.6125 data_time: 0.2886 memory: 16095 grad_norm: 4.9470 loss: 0.9744 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.9744 2022/12/08 21:18:02 - mmengine - INFO - Epoch(train) [47][280/940] lr: 1.0000e-03 eta: 8:50:57 time: 0.5634 data_time: 0.2275 memory: 16095 grad_norm: 5.1560 loss: 0.9499 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.9499 2022/12/08 21:18:16 - mmengine - INFO - Epoch(train) [47][300/940] lr: 1.0000e-03 eta: 8:50:46 time: 0.6932 data_time: 0.3556 memory: 16095 grad_norm: 5.0462 loss: 0.8679 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.8679 2022/12/08 21:18:28 - mmengine - INFO - Epoch(train) [47][320/940] lr: 1.0000e-03 eta: 8:50:32 time: 0.5833 data_time: 0.2412 memory: 16095 grad_norm: 5.0681 loss: 0.9167 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.9167 2022/12/08 21:18:41 - mmengine - INFO - Epoch(train) [47][340/940] lr: 1.0000e-03 eta: 8:50:20 time: 0.6326 data_time: 0.2932 memory: 16095 grad_norm: 4.9288 loss: 0.7970 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7970 2022/12/08 21:18:53 - mmengine - INFO - Epoch(train) [47][360/940] lr: 1.0000e-03 eta: 8:50:06 time: 0.5973 data_time: 0.2557 memory: 16095 grad_norm: 5.0186 loss: 0.9212 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9212 2022/12/08 21:19:05 - mmengine - INFO - Epoch(train) [47][380/940] lr: 1.0000e-03 eta: 8:49:54 time: 0.6481 data_time: 0.3192 memory: 16095 grad_norm: 4.8811 loss: 0.9269 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.9269 2022/12/08 21:19:17 - mmengine - INFO - Epoch(train) [47][400/940] lr: 1.0000e-03 eta: 8:49:40 time: 0.5765 data_time: 0.2409 memory: 16095 grad_norm: 4.9793 loss: 0.8533 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8533 2022/12/08 21:19:30 - mmengine - INFO - Epoch(train) [47][420/940] lr: 1.0000e-03 eta: 8:49:28 time: 0.6617 data_time: 0.3305 memory: 16095 grad_norm: 5.0178 loss: 0.8996 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8996 2022/12/08 21:19:41 - mmengine - INFO - Epoch(train) [47][440/940] lr: 1.0000e-03 eta: 8:49:14 time: 0.5516 data_time: 0.2153 memory: 16095 grad_norm: 4.9730 loss: 0.9173 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.9173 2022/12/08 21:19:54 - mmengine - INFO - Epoch(train) [47][460/940] lr: 1.0000e-03 eta: 8:49:01 time: 0.6420 data_time: 0.3015 memory: 16095 grad_norm: 5.0235 loss: 0.8306 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8306 2022/12/08 21:20:05 - mmengine - INFO - Epoch(train) [47][480/940] lr: 1.0000e-03 eta: 8:48:47 time: 0.5602 data_time: 0.2120 memory: 16095 grad_norm: 4.9372 loss: 0.8731 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8731 2022/12/08 21:20:18 - mmengine - INFO - Epoch(train) [47][500/940] lr: 1.0000e-03 eta: 8:48:35 time: 0.6290 data_time: 0.2688 memory: 16095 grad_norm: 4.9883 loss: 0.8676 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8676 2022/12/08 21:20:29 - mmengine - INFO - Epoch(train) [47][520/940] lr: 1.0000e-03 eta: 8:48:20 time: 0.5625 data_time: 0.2307 memory: 16095 grad_norm: 5.1161 loss: 0.9915 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.9915 2022/12/08 21:20:42 - mmengine - INFO - Epoch(train) [47][540/940] lr: 1.0000e-03 eta: 8:48:08 time: 0.6605 data_time: 0.3080 memory: 16095 grad_norm: 5.0997 loss: 0.8592 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8592 2022/12/08 21:20:53 - mmengine - INFO - Epoch(train) [47][560/940] lr: 1.0000e-03 eta: 8:47:54 time: 0.5407 data_time: 0.2070 memory: 16095 grad_norm: 4.8364 loss: 0.7850 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7850 2022/12/08 21:21:06 - mmengine - INFO - Epoch(train) [47][580/940] lr: 1.0000e-03 eta: 8:47:41 time: 0.6305 data_time: 0.2084 memory: 16095 grad_norm: 5.0171 loss: 0.9700 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.9700 2022/12/08 21:21:17 - mmengine - INFO - Epoch(train) [47][600/940] lr: 1.0000e-03 eta: 8:47:27 time: 0.5783 data_time: 0.0225 memory: 16095 grad_norm: 4.9648 loss: 0.9648 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9648 2022/12/08 21:21:31 - mmengine - INFO - Epoch(train) [47][620/940] lr: 1.0000e-03 eta: 8:47:16 time: 0.6802 data_time: 0.0427 memory: 16095 grad_norm: 4.9834 loss: 0.9247 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.9247 2022/12/08 21:21:42 - mmengine - INFO - Epoch(train) [47][640/940] lr: 1.0000e-03 eta: 8:47:02 time: 0.5679 data_time: 0.0200 memory: 16095 grad_norm: 5.1059 loss: 0.9955 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9955 2022/12/08 21:21:56 - mmengine - INFO - Epoch(train) [47][660/940] lr: 1.0000e-03 eta: 8:46:50 time: 0.6756 data_time: 0.0536 memory: 16095 grad_norm: 5.0251 loss: 0.8765 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8765 2022/12/08 21:22:07 - mmengine - INFO - Epoch(train) [47][680/940] lr: 1.0000e-03 eta: 8:46:36 time: 0.5595 data_time: 0.0207 memory: 16095 grad_norm: 5.1618 loss: 0.8735 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8735 2022/12/08 21:22:20 - mmengine - INFO - Epoch(train) [47][700/940] lr: 1.0000e-03 eta: 8:46:23 time: 0.6402 data_time: 0.0606 memory: 16095 grad_norm: 5.0042 loss: 0.9718 top1_acc: 0.5625 top5_acc: 0.8438 loss_cls: 0.9718 2022/12/08 21:22:31 - mmengine - INFO - Epoch(train) [47][720/940] lr: 1.0000e-03 eta: 8:46:09 time: 0.5643 data_time: 0.0168 memory: 16095 grad_norm: 5.1563 loss: 0.9624 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.9624 2022/12/08 21:22:45 - mmengine - INFO - Epoch(train) [47][740/940] lr: 1.0000e-03 eta: 8:45:58 time: 0.6985 data_time: 0.0277 memory: 16095 grad_norm: 4.8846 loss: 0.9452 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9452 2022/12/08 21:22:56 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 21:22:56 - mmengine - INFO - Epoch(train) [47][760/940] lr: 1.0000e-03 eta: 8:45:44 time: 0.5441 data_time: 0.0181 memory: 16095 grad_norm: 5.0717 loss: 0.9174 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9174 2022/12/08 21:23:10 - mmengine - INFO - Epoch(train) [47][780/940] lr: 1.0000e-03 eta: 8:45:32 time: 0.6931 data_time: 0.0288 memory: 16095 grad_norm: 5.0047 loss: 0.8700 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8700 2022/12/08 21:23:21 - mmengine - INFO - Epoch(train) [47][800/940] lr: 1.0000e-03 eta: 8:45:18 time: 0.5493 data_time: 0.0203 memory: 16095 grad_norm: 5.0981 loss: 0.9571 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.9571 2022/12/08 21:23:35 - mmengine - INFO - Epoch(train) [47][820/940] lr: 1.0000e-03 eta: 8:45:06 time: 0.6811 data_time: 0.0286 memory: 16095 grad_norm: 5.0106 loss: 0.9166 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.9166 2022/12/08 21:23:44 - mmengine - INFO - Epoch(train) [47][840/940] lr: 1.0000e-03 eta: 8:44:51 time: 0.4907 data_time: 0.0201 memory: 16095 grad_norm: 5.1099 loss: 0.8471 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8471 2022/12/08 21:23:59 - mmengine - INFO - Epoch(train) [47][860/940] lr: 1.0000e-03 eta: 8:44:40 time: 0.7150 data_time: 0.0279 memory: 16095 grad_norm: 5.0009 loss: 0.8680 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8680 2022/12/08 21:24:11 - mmengine - INFO - Epoch(train) [47][880/940] lr: 1.0000e-03 eta: 8:44:27 time: 0.6036 data_time: 0.0296 memory: 16095 grad_norm: 4.9996 loss: 0.8690 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8690 2022/12/08 21:24:23 - mmengine - INFO - Epoch(train) [47][900/940] lr: 1.0000e-03 eta: 8:44:14 time: 0.6267 data_time: 0.0314 memory: 16095 grad_norm: 4.8575 loss: 0.8975 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.8975 2022/12/08 21:24:34 - mmengine - INFO - Epoch(train) [47][920/940] lr: 1.0000e-03 eta: 8:43:59 time: 0.5453 data_time: 0.0212 memory: 16095 grad_norm: 5.0771 loss: 0.9359 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.9359 2022/12/08 21:24:45 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 21:24:45 - mmengine - INFO - Epoch(train) [47][940/940] lr: 1.0000e-03 eta: 8:43:45 time: 0.5585 data_time: 0.0178 memory: 16095 grad_norm: 5.3023 loss: 0.9089 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 0.9089 2022/12/08 21:24:59 - mmengine - INFO - Epoch(val) [47][20/78] eta: 0:00:40 time: 0.7049 data_time: 0.6100 memory: 1686 2022/12/08 21:25:09 - mmengine - INFO - Epoch(val) [47][40/78] eta: 0:00:22 time: 0.4695 data_time: 0.3757 memory: 1686 2022/12/08 21:25:22 - mmengine - INFO - Epoch(val) [47][60/78] eta: 0:00:11 time: 0.6702 data_time: 0.5746 memory: 1686 2022/12/08 21:25:33 - mmengine - INFO - Epoch(val) [47][78/78] acc/top1: 0.6939 acc/top5: 0.8794 acc/mean1: 0.6938 2022/12/08 21:25:49 - mmengine - INFO - Epoch(train) [48][ 20/940] lr: 1.0000e-03 eta: 8:43:37 time: 0.8321 data_time: 0.4772 memory: 16095 grad_norm: 5.1998 loss: 0.9112 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.9112 2022/12/08 21:26:00 - mmengine - INFO - Epoch(train) [48][ 40/940] lr: 1.0000e-03 eta: 8:43:23 time: 0.5494 data_time: 0.2413 memory: 16095 grad_norm: 5.0410 loss: 0.9795 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 0.9795 2022/12/08 21:26:13 - mmengine - INFO - Epoch(train) [48][ 60/940] lr: 1.0000e-03 eta: 8:43:11 time: 0.6559 data_time: 0.3322 memory: 16095 grad_norm: 4.9209 loss: 0.9533 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.9533 2022/12/08 21:26:24 - mmengine - INFO - Epoch(train) [48][ 80/940] lr: 1.0000e-03 eta: 8:42:56 time: 0.5327 data_time: 0.2105 memory: 16095 grad_norm: 5.0395 loss: 0.8788 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.8788 2022/12/08 21:26:37 - mmengine - INFO - Epoch(train) [48][100/940] lr: 1.0000e-03 eta: 8:42:43 time: 0.6390 data_time: 0.2833 memory: 16095 grad_norm: 5.0512 loss: 1.0141 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 1.0141 2022/12/08 21:26:48 - mmengine - INFO - Epoch(train) [48][120/940] lr: 1.0000e-03 eta: 8:42:29 time: 0.5711 data_time: 0.1857 memory: 16095 grad_norm: 4.9185 loss: 0.8463 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8463 2022/12/08 21:27:02 - mmengine - INFO - Epoch(train) [48][140/940] lr: 1.0000e-03 eta: 8:42:18 time: 0.6652 data_time: 0.1923 memory: 16095 grad_norm: 5.0025 loss: 0.9474 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.9474 2022/12/08 21:27:12 - mmengine - INFO - Epoch(train) [48][160/940] lr: 1.0000e-03 eta: 8:42:03 time: 0.5240 data_time: 0.0545 memory: 16095 grad_norm: 5.1259 loss: 0.8433 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.8433 2022/12/08 21:27:26 - mmengine - INFO - Epoch(train) [48][180/940] lr: 1.0000e-03 eta: 8:41:51 time: 0.6903 data_time: 0.1295 memory: 16095 grad_norm: 5.0425 loss: 0.8893 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.8893 2022/12/08 21:27:37 - mmengine - INFO - Epoch(train) [48][200/940] lr: 1.0000e-03 eta: 8:41:37 time: 0.5473 data_time: 0.0227 memory: 16095 grad_norm: 5.0477 loss: 0.8019 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8019 2022/12/08 21:27:50 - mmengine - INFO - Epoch(train) [48][220/940] lr: 1.0000e-03 eta: 8:41:25 time: 0.6772 data_time: 0.0261 memory: 16095 grad_norm: 4.9226 loss: 0.9201 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.9201 2022/12/08 21:28:01 - mmengine - INFO - Epoch(train) [48][240/940] lr: 1.0000e-03 eta: 8:41:11 time: 0.5480 data_time: 0.0220 memory: 16095 grad_norm: 5.0377 loss: 0.9160 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.9160 2022/12/08 21:28:14 - mmengine - INFO - Epoch(train) [48][260/940] lr: 1.0000e-03 eta: 8:40:58 time: 0.6275 data_time: 0.0236 memory: 16095 grad_norm: 5.1503 loss: 0.8797 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8797 2022/12/08 21:28:25 - mmengine - INFO - Epoch(train) [48][280/940] lr: 1.0000e-03 eta: 8:40:43 time: 0.5350 data_time: 0.0244 memory: 16095 grad_norm: 5.0653 loss: 0.8373 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8373 2022/12/08 21:28:38 - mmengine - INFO - Epoch(train) [48][300/940] lr: 1.0000e-03 eta: 8:40:32 time: 0.6658 data_time: 0.0239 memory: 16095 grad_norm: 5.0128 loss: 0.9464 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.9464 2022/12/08 21:28:49 - mmengine - INFO - Epoch(train) [48][320/940] lr: 1.0000e-03 eta: 8:40:17 time: 0.5598 data_time: 0.0255 memory: 16095 grad_norm: 5.1003 loss: 0.9821 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9821 2022/12/08 21:29:03 - mmengine - INFO - Epoch(train) [48][340/940] lr: 1.0000e-03 eta: 8:40:06 time: 0.6973 data_time: 0.0261 memory: 16095 grad_norm: 5.0794 loss: 0.8460 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.8460 2022/12/08 21:29:14 - mmengine - INFO - Epoch(train) [48][360/940] lr: 1.0000e-03 eta: 8:39:52 time: 0.5470 data_time: 0.0230 memory: 16095 grad_norm: 5.1082 loss: 1.0246 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 1.0246 2022/12/08 21:29:27 - mmengine - INFO - Epoch(train) [48][380/940] lr: 1.0000e-03 eta: 8:39:40 time: 0.6614 data_time: 0.0236 memory: 16095 grad_norm: 4.9720 loss: 0.9475 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.9475 2022/12/08 21:29:39 - mmengine - INFO - Epoch(train) [48][400/940] lr: 1.0000e-03 eta: 8:39:26 time: 0.5676 data_time: 0.0243 memory: 16095 grad_norm: 5.1420 loss: 0.7603 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7603 2022/12/08 21:29:53 - mmengine - INFO - Epoch(train) [48][420/940] lr: 1.0000e-03 eta: 8:39:15 time: 0.6996 data_time: 0.0247 memory: 16095 grad_norm: 5.0730 loss: 1.0263 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0263 2022/12/08 21:30:03 - mmengine - INFO - Epoch(train) [48][440/940] lr: 1.0000e-03 eta: 8:39:00 time: 0.5258 data_time: 0.0250 memory: 16095 grad_norm: 5.1547 loss: 0.8992 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8992 2022/12/08 21:30:16 - mmengine - INFO - Epoch(train) [48][460/940] lr: 1.0000e-03 eta: 8:38:47 time: 0.6432 data_time: 0.0261 memory: 16095 grad_norm: 5.1445 loss: 0.9154 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.9154 2022/12/08 21:30:27 - mmengine - INFO - Epoch(train) [48][480/940] lr: 1.0000e-03 eta: 8:38:33 time: 0.5673 data_time: 0.0238 memory: 16095 grad_norm: 5.0118 loss: 0.8278 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8278 2022/12/08 21:30:41 - mmengine - INFO - Epoch(train) [48][500/940] lr: 1.0000e-03 eta: 8:38:22 time: 0.6780 data_time: 0.0251 memory: 16095 grad_norm: 5.0175 loss: 0.8986 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8986 2022/12/08 21:30:52 - mmengine - INFO - Epoch(train) [48][520/940] lr: 1.0000e-03 eta: 8:38:08 time: 0.5602 data_time: 0.0235 memory: 16095 grad_norm: 5.0931 loss: 0.8197 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8197 2022/12/08 21:31:05 - mmengine - INFO - Epoch(train) [48][540/940] lr: 1.0000e-03 eta: 8:37:55 time: 0.6459 data_time: 0.0243 memory: 16095 grad_norm: 5.0523 loss: 0.9070 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.9070 2022/12/08 21:31:16 - mmengine - INFO - Epoch(train) [48][560/940] lr: 1.0000e-03 eta: 8:37:41 time: 0.5690 data_time: 0.0252 memory: 16095 grad_norm: 5.1046 loss: 0.9781 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9781 2022/12/08 21:31:30 - mmengine - INFO - Epoch(train) [48][580/940] lr: 1.0000e-03 eta: 8:37:30 time: 0.6833 data_time: 0.0269 memory: 16095 grad_norm: 4.9508 loss: 0.8596 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8596 2022/12/08 21:31:41 - mmengine - INFO - Epoch(train) [48][600/940] lr: 1.0000e-03 eta: 8:37:15 time: 0.5295 data_time: 0.0229 memory: 16095 grad_norm: 4.9786 loss: 1.0338 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 1.0338 2022/12/08 21:31:54 - mmengine - INFO - Epoch(train) [48][620/940] lr: 1.0000e-03 eta: 8:37:03 time: 0.6533 data_time: 0.0347 memory: 16095 grad_norm: 5.0956 loss: 0.9676 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.9676 2022/12/08 21:32:04 - mmengine - INFO - Epoch(train) [48][640/940] lr: 1.0000e-03 eta: 8:36:48 time: 0.5291 data_time: 0.0216 memory: 16095 grad_norm: 5.1616 loss: 0.9419 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.9419 2022/12/08 21:32:17 - mmengine - INFO - Epoch(train) [48][660/940] lr: 1.0000e-03 eta: 8:36:36 time: 0.6454 data_time: 0.0271 memory: 16095 grad_norm: 5.0687 loss: 0.8066 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8066 2022/12/08 21:32:29 - mmengine - INFO - Epoch(train) [48][680/940] lr: 1.0000e-03 eta: 8:36:23 time: 0.6057 data_time: 0.0216 memory: 16095 grad_norm: 5.0359 loss: 0.9637 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.9637 2022/12/08 21:32:43 - mmengine - INFO - Epoch(train) [48][700/940] lr: 1.0000e-03 eta: 8:36:11 time: 0.6791 data_time: 0.0256 memory: 16095 grad_norm: 5.0137 loss: 0.8403 top1_acc: 0.6875 top5_acc: 0.9688 loss_cls: 0.8403 2022/12/08 21:32:54 - mmengine - INFO - Epoch(train) [48][720/940] lr: 1.0000e-03 eta: 8:35:56 time: 0.5335 data_time: 0.0231 memory: 16095 grad_norm: 5.0696 loss: 0.9507 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.9507 2022/12/08 21:33:06 - mmengine - INFO - Epoch(train) [48][740/940] lr: 1.0000e-03 eta: 8:35:44 time: 0.6388 data_time: 0.0269 memory: 16095 grad_norm: 5.1179 loss: 0.8554 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.8554 2022/12/08 21:33:18 - mmengine - INFO - Epoch(train) [48][760/940] lr: 1.0000e-03 eta: 8:35:30 time: 0.5558 data_time: 0.0220 memory: 16095 grad_norm: 5.0699 loss: 0.8896 top1_acc: 0.9688 top5_acc: 0.9688 loss_cls: 0.8896 2022/12/08 21:33:31 - mmengine - INFO - Epoch(train) [48][780/940] lr: 1.0000e-03 eta: 8:35:18 time: 0.6487 data_time: 0.0285 memory: 16095 grad_norm: 5.0296 loss: 0.9535 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.9535 2022/12/08 21:33:42 - mmengine - INFO - Epoch(train) [48][800/940] lr: 1.0000e-03 eta: 8:35:04 time: 0.5680 data_time: 0.0229 memory: 16095 grad_norm: 5.0971 loss: 0.9590 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.9590 2022/12/08 21:33:54 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 21:33:54 - mmengine - INFO - Epoch(train) [48][820/940] lr: 1.0000e-03 eta: 8:34:51 time: 0.6206 data_time: 0.0243 memory: 16095 grad_norm: 5.0415 loss: 0.8883 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.8883 2022/12/08 21:34:06 - mmengine - INFO - Epoch(train) [48][840/940] lr: 1.0000e-03 eta: 8:34:37 time: 0.5879 data_time: 0.0255 memory: 16095 grad_norm: 5.0055 loss: 0.9324 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.9324 2022/12/08 21:34:19 - mmengine - INFO - Epoch(train) [48][860/940] lr: 1.0000e-03 eta: 8:34:25 time: 0.6499 data_time: 0.0229 memory: 16095 grad_norm: 5.0069 loss: 0.9899 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.9899 2022/12/08 21:34:31 - mmengine - INFO - Epoch(train) [48][880/940] lr: 1.0000e-03 eta: 8:34:12 time: 0.6064 data_time: 0.0252 memory: 16095 grad_norm: 4.8098 loss: 0.8857 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8857 2022/12/08 21:34:45 - mmengine - INFO - Epoch(train) [48][900/940] lr: 1.0000e-03 eta: 8:34:01 time: 0.6855 data_time: 0.0269 memory: 16095 grad_norm: 5.0441 loss: 1.0024 top1_acc: 0.7188 top5_acc: 0.7812 loss_cls: 1.0024 2022/12/08 21:34:56 - mmengine - INFO - Epoch(train) [48][920/940] lr: 1.0000e-03 eta: 8:33:47 time: 0.5672 data_time: 0.0234 memory: 16095 grad_norm: 5.1225 loss: 0.8712 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8712 2022/12/08 21:35:07 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 21:35:07 - mmengine - INFO - Epoch(train) [48][940/940] lr: 1.0000e-03 eta: 8:33:32 time: 0.5412 data_time: 0.0174 memory: 16095 grad_norm: 5.4312 loss: 0.9189 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 0.9189 2022/12/08 21:35:07 - mmengine - INFO - Saving checkpoint at 48 epochs 2022/12/08 21:35:24 - mmengine - INFO - Epoch(val) [48][20/78] eta: 0:00:40 time: 0.7061 data_time: 0.6119 memory: 1686 2022/12/08 21:35:33 - mmengine - INFO - Epoch(val) [48][40/78] eta: 0:00:22 time: 0.4668 data_time: 0.3730 memory: 1686 2022/12/08 21:35:47 - mmengine - INFO - Epoch(val) [48][60/78] eta: 0:00:11 time: 0.6710 data_time: 0.5752 memory: 1686 2022/12/08 21:35:56 - mmengine - INFO - Epoch(val) [48][78/78] acc/top1: 0.6949 acc/top5: 0.8783 acc/mean1: 0.6948 2022/12/08 21:35:56 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_46.pth is removed 2022/12/08 21:35:59 - mmengine - INFO - The best checkpoint with 0.6949 acc/top1 at 48 epoch is saved to best_acc/top1_epoch_48.pth. 2022/12/08 21:36:15 - mmengine - INFO - Epoch(train) [49][ 20/940] lr: 1.0000e-03 eta: 8:33:23 time: 0.7823 data_time: 0.4788 memory: 16095 grad_norm: 5.0435 loss: 0.9478 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 0.9478 2022/12/08 21:36:26 - mmengine - INFO - Epoch(train) [49][ 40/940] lr: 1.0000e-03 eta: 8:33:08 time: 0.5499 data_time: 0.2392 memory: 16095 grad_norm: 4.9852 loss: 0.8293 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8293 2022/12/08 21:36:39 - mmengine - INFO - Epoch(train) [49][ 60/940] lr: 1.0000e-03 eta: 8:32:56 time: 0.6498 data_time: 0.3540 memory: 16095 grad_norm: 4.9526 loss: 0.9859 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.9859 2022/12/08 21:36:50 - mmengine - INFO - Epoch(train) [49][ 80/940] lr: 1.0000e-03 eta: 8:32:42 time: 0.5526 data_time: 0.2547 memory: 16095 grad_norm: 5.0680 loss: 0.8794 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.8794 2022/12/08 21:37:02 - mmengine - INFO - Epoch(train) [49][100/940] lr: 1.0000e-03 eta: 8:32:29 time: 0.6343 data_time: 0.3107 memory: 16095 grad_norm: 5.0389 loss: 0.9884 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.9884 2022/12/08 21:37:14 - mmengine - INFO - Epoch(train) [49][120/940] lr: 1.0000e-03 eta: 8:32:16 time: 0.5982 data_time: 0.2945 memory: 16095 grad_norm: 5.1812 loss: 0.9236 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 0.9236 2022/12/08 21:37:27 - mmengine - INFO - Epoch(train) [49][140/940] lr: 1.0000e-03 eta: 8:32:03 time: 0.6311 data_time: 0.3199 memory: 16095 grad_norm: 4.9654 loss: 0.8675 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.8675 2022/12/08 21:37:38 - mmengine - INFO - Epoch(train) [49][160/940] lr: 1.0000e-03 eta: 8:31:50 time: 0.5695 data_time: 0.2697 memory: 16095 grad_norm: 5.0226 loss: 0.8492 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8492 2022/12/08 21:37:51 - mmengine - INFO - Epoch(train) [49][180/940] lr: 1.0000e-03 eta: 8:31:37 time: 0.6473 data_time: 0.2374 memory: 16095 grad_norm: 4.9287 loss: 0.8429 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8429 2022/12/08 21:38:03 - mmengine - INFO - Epoch(train) [49][200/940] lr: 1.0000e-03 eta: 8:31:24 time: 0.5848 data_time: 0.2037 memory: 16095 grad_norm: 5.0236 loss: 0.9031 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9031 2022/12/08 21:38:16 - mmengine - INFO - Epoch(train) [49][220/940] lr: 1.0000e-03 eta: 8:31:11 time: 0.6422 data_time: 0.1144 memory: 16095 grad_norm: 5.0169 loss: 0.9583 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.9583 2022/12/08 21:38:27 - mmengine - INFO - Epoch(train) [49][240/940] lr: 1.0000e-03 eta: 8:30:58 time: 0.5717 data_time: 0.1077 memory: 16095 grad_norm: 5.0796 loss: 0.9054 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.9054 2022/12/08 21:38:41 - mmengine - INFO - Epoch(train) [49][260/940] lr: 1.0000e-03 eta: 8:30:46 time: 0.6959 data_time: 0.1763 memory: 16095 grad_norm: 5.0088 loss: 0.8884 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 0.8884 2022/12/08 21:38:53 - mmengine - INFO - Epoch(train) [49][280/940] lr: 1.0000e-03 eta: 8:30:33 time: 0.5769 data_time: 0.1022 memory: 16095 grad_norm: 5.0091 loss: 0.8814 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8814 2022/12/08 21:39:06 - mmengine - INFO - Epoch(train) [49][300/940] lr: 1.0000e-03 eta: 8:30:20 time: 0.6411 data_time: 0.2235 memory: 16095 grad_norm: 5.0349 loss: 1.0040 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 1.0040 2022/12/08 21:39:16 - mmengine - INFO - Epoch(train) [49][320/940] lr: 1.0000e-03 eta: 8:30:05 time: 0.5324 data_time: 0.1260 memory: 16095 grad_norm: 5.0684 loss: 0.9774 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9774 2022/12/08 21:39:30 - mmengine - INFO - Epoch(train) [49][340/940] lr: 1.0000e-03 eta: 8:29:55 time: 0.7088 data_time: 0.2395 memory: 16095 grad_norm: 5.1152 loss: 0.8891 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8891 2022/12/08 21:39:41 - mmengine - INFO - Epoch(train) [49][360/940] lr: 1.0000e-03 eta: 8:29:40 time: 0.5398 data_time: 0.0562 memory: 16095 grad_norm: 5.1701 loss: 0.8873 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8873 2022/12/08 21:39:55 - mmengine - INFO - Epoch(train) [49][380/940] lr: 1.0000e-03 eta: 8:29:28 time: 0.6680 data_time: 0.0358 memory: 16095 grad_norm: 4.9301 loss: 0.9828 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9828 2022/12/08 21:40:06 - mmengine - INFO - Epoch(train) [49][400/940] lr: 1.0000e-03 eta: 8:29:14 time: 0.5555 data_time: 0.0234 memory: 16095 grad_norm: 5.1027 loss: 0.8666 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.8666 2022/12/08 21:40:18 - mmengine - INFO - Epoch(train) [49][420/940] lr: 1.0000e-03 eta: 8:29:02 time: 0.6352 data_time: 0.0259 memory: 16095 grad_norm: 5.0626 loss: 0.8578 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8578 2022/12/08 21:40:29 - mmengine - INFO - Epoch(train) [49][440/940] lr: 1.0000e-03 eta: 8:28:46 time: 0.5086 data_time: 0.1058 memory: 16095 grad_norm: 5.0873 loss: 0.8778 top1_acc: 0.8438 top5_acc: 0.8438 loss_cls: 0.8778 2022/12/08 21:40:42 - mmengine - INFO - Epoch(train) [49][460/940] lr: 1.0000e-03 eta: 8:28:35 time: 0.6696 data_time: 0.1401 memory: 16095 grad_norm: 4.9722 loss: 0.9347 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.9347 2022/12/08 21:40:54 - mmengine - INFO - Epoch(train) [49][480/940] lr: 1.0000e-03 eta: 8:28:21 time: 0.5916 data_time: 0.1315 memory: 16095 grad_norm: 5.0335 loss: 0.8899 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8899 2022/12/08 21:41:07 - mmengine - INFO - Epoch(train) [49][500/940] lr: 1.0000e-03 eta: 8:28:09 time: 0.6582 data_time: 0.0983 memory: 16095 grad_norm: 5.0075 loss: 0.9287 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.9287 2022/12/08 21:41:18 - mmengine - INFO - Epoch(train) [49][520/940] lr: 1.0000e-03 eta: 8:27:55 time: 0.5546 data_time: 0.1308 memory: 16095 grad_norm: 5.1419 loss: 0.9705 top1_acc: 0.5625 top5_acc: 0.6562 loss_cls: 0.9705 2022/12/08 21:41:31 - mmengine - INFO - Epoch(train) [49][540/940] lr: 1.0000e-03 eta: 8:27:42 time: 0.6384 data_time: 0.2056 memory: 16095 grad_norm: 5.0974 loss: 0.8673 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8673 2022/12/08 21:41:43 - mmengine - INFO - Epoch(train) [49][560/940] lr: 1.0000e-03 eta: 8:27:29 time: 0.6036 data_time: 0.1492 memory: 16095 grad_norm: 5.1385 loss: 0.8966 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.8966 2022/12/08 21:41:56 - mmengine - INFO - Epoch(train) [49][580/940] lr: 1.0000e-03 eta: 8:27:18 time: 0.6751 data_time: 0.0738 memory: 16095 grad_norm: 4.9816 loss: 0.8622 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8622 2022/12/08 21:42:07 - mmengine - INFO - Epoch(train) [49][600/940] lr: 1.0000e-03 eta: 8:27:03 time: 0.5349 data_time: 0.0277 memory: 16095 grad_norm: 5.0686 loss: 0.9245 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.9245 2022/12/08 21:42:19 - mmengine - INFO - Epoch(train) [49][620/940] lr: 1.0000e-03 eta: 8:26:50 time: 0.6074 data_time: 0.1301 memory: 16095 grad_norm: 5.1045 loss: 0.9282 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.9282 2022/12/08 21:42:31 - mmengine - INFO - Epoch(train) [49][640/940] lr: 1.0000e-03 eta: 8:26:37 time: 0.5958 data_time: 0.1236 memory: 16095 grad_norm: 5.0996 loss: 0.9701 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.9701 2022/12/08 21:42:44 - mmengine - INFO - Epoch(train) [49][660/940] lr: 1.0000e-03 eta: 8:26:24 time: 0.6257 data_time: 0.0684 memory: 16095 grad_norm: 5.0040 loss: 0.8165 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8165 2022/12/08 21:42:57 - mmengine - INFO - Epoch(train) [49][680/940] lr: 1.0000e-03 eta: 8:26:12 time: 0.6430 data_time: 0.1571 memory: 16095 grad_norm: 5.2048 loss: 0.8634 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 0.8634 2022/12/08 21:43:09 - mmengine - INFO - Epoch(train) [49][700/940] lr: 1.0000e-03 eta: 8:25:58 time: 0.6036 data_time: 0.0715 memory: 16095 grad_norm: 5.1666 loss: 0.9468 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.9468 2022/12/08 21:43:21 - mmengine - INFO - Epoch(train) [49][720/940] lr: 1.0000e-03 eta: 8:25:46 time: 0.6163 data_time: 0.1450 memory: 16095 grad_norm: 5.0832 loss: 0.9049 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.9049 2022/12/08 21:43:33 - mmengine - INFO - Epoch(train) [49][740/940] lr: 1.0000e-03 eta: 8:25:32 time: 0.5777 data_time: 0.0565 memory: 16095 grad_norm: 5.0262 loss: 0.8897 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8897 2022/12/08 21:43:45 - mmengine - INFO - Epoch(train) [49][760/940] lr: 1.0000e-03 eta: 8:25:19 time: 0.6222 data_time: 0.0258 memory: 16095 grad_norm: 5.1080 loss: 0.8640 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8640 2022/12/08 21:43:56 - mmengine - INFO - Epoch(train) [49][780/940] lr: 1.0000e-03 eta: 8:25:04 time: 0.5272 data_time: 0.0256 memory: 16095 grad_norm: 4.9644 loss: 0.8994 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.8994 2022/12/08 21:44:09 - mmengine - INFO - Epoch(train) [49][800/940] lr: 1.0000e-03 eta: 8:24:53 time: 0.6783 data_time: 0.0209 memory: 16095 grad_norm: 4.9949 loss: 0.9252 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.9252 2022/12/08 21:44:20 - mmengine - INFO - Epoch(train) [49][820/940] lr: 1.0000e-03 eta: 8:24:39 time: 0.5561 data_time: 0.0261 memory: 16095 grad_norm: 4.9593 loss: 0.8415 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8415 2022/12/08 21:44:32 - mmengine - INFO - Epoch(train) [49][840/940] lr: 1.0000e-03 eta: 8:24:25 time: 0.5904 data_time: 0.0227 memory: 16095 grad_norm: 5.1563 loss: 0.9065 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 0.9065 2022/12/08 21:44:44 - mmengine - INFO - Epoch(train) [49][860/940] lr: 1.0000e-03 eta: 8:24:12 time: 0.6103 data_time: 0.0267 memory: 16095 grad_norm: 5.0824 loss: 0.8446 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8446 2022/12/08 21:44:56 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 21:44:56 - mmengine - INFO - Epoch(train) [49][880/940] lr: 1.0000e-03 eta: 8:23:58 time: 0.5761 data_time: 0.0331 memory: 16095 grad_norm: 5.2294 loss: 0.8684 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 0.8684 2022/12/08 21:45:09 - mmengine - INFO - Epoch(train) [49][900/940] lr: 1.0000e-03 eta: 8:23:46 time: 0.6490 data_time: 0.0505 memory: 16095 grad_norm: 5.1650 loss: 0.9506 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.9506 2022/12/08 21:45:21 - mmengine - INFO - Epoch(train) [49][920/940] lr: 1.0000e-03 eta: 8:23:33 time: 0.6143 data_time: 0.0305 memory: 16095 grad_norm: 5.0995 loss: 0.8697 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.8697 2022/12/08 21:45:31 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 21:45:31 - mmengine - INFO - Epoch(train) [49][940/940] lr: 1.0000e-03 eta: 8:23:18 time: 0.5067 data_time: 0.0175 memory: 16095 grad_norm: 5.3990 loss: 1.0176 top1_acc: 0.5714 top5_acc: 0.5714 loss_cls: 1.0176 2022/12/08 21:45:46 - mmengine - INFO - Epoch(val) [49][20/78] eta: 0:00:41 time: 0.7161 data_time: 0.6216 memory: 1686 2022/12/08 21:45:54 - mmengine - INFO - Epoch(val) [49][40/78] eta: 0:00:22 time: 0.4444 data_time: 0.3507 memory: 1686 2022/12/08 21:46:09 - mmengine - INFO - Epoch(val) [49][60/78] eta: 0:00:11 time: 0.7030 data_time: 0.6090 memory: 1686 2022/12/08 21:46:19 - mmengine - INFO - Epoch(val) [49][78/78] acc/top1: 0.6939 acc/top5: 0.8796 acc/mean1: 0.6938 2022/12/08 21:46:35 - mmengine - INFO - Epoch(train) [50][ 20/940] lr: 1.0000e-03 eta: 8:23:09 time: 0.8042 data_time: 0.4254 memory: 16095 grad_norm: 5.1949 loss: 0.9539 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.9539 2022/12/08 21:46:46 - mmengine - INFO - Epoch(train) [50][ 40/940] lr: 1.0000e-03 eta: 8:22:55 time: 0.5624 data_time: 0.2133 memory: 16095 grad_norm: 5.1576 loss: 0.8781 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8781 2022/12/08 21:47:00 - mmengine - INFO - Epoch(train) [50][ 60/940] lr: 1.0000e-03 eta: 8:22:44 time: 0.7013 data_time: 0.2376 memory: 16095 grad_norm: 5.2082 loss: 0.9882 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 0.9882 2022/12/08 21:47:12 - mmengine - INFO - Epoch(train) [50][ 80/940] lr: 1.0000e-03 eta: 8:22:30 time: 0.5775 data_time: 0.1203 memory: 16095 grad_norm: 5.0334 loss: 0.9519 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9519 2022/12/08 21:47:25 - mmengine - INFO - Epoch(train) [50][100/940] lr: 1.0000e-03 eta: 8:22:18 time: 0.6542 data_time: 0.1094 memory: 16095 grad_norm: 5.1033 loss: 0.9784 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9784 2022/12/08 21:47:36 - mmengine - INFO - Epoch(train) [50][120/940] lr: 1.0000e-03 eta: 8:22:04 time: 0.5653 data_time: 0.0898 memory: 16095 grad_norm: 5.0665 loss: 0.8147 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 0.8147 2022/12/08 21:47:49 - mmengine - INFO - Epoch(train) [50][140/940] lr: 1.0000e-03 eta: 8:21:52 time: 0.6287 data_time: 0.0280 memory: 16095 grad_norm: 5.0207 loss: 0.8563 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.8563 2022/12/08 21:48:00 - mmengine - INFO - Epoch(train) [50][160/940] lr: 1.0000e-03 eta: 8:21:38 time: 0.5611 data_time: 0.0261 memory: 16095 grad_norm: 5.0413 loss: 0.8151 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.8151 2022/12/08 21:48:14 - mmengine - INFO - Epoch(train) [50][180/940] lr: 1.0000e-03 eta: 8:21:27 time: 0.7047 data_time: 0.0294 memory: 16095 grad_norm: 5.0179 loss: 0.9147 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.9147 2022/12/08 21:48:26 - mmengine - INFO - Epoch(train) [50][200/940] lr: 1.0000e-03 eta: 8:21:13 time: 0.5874 data_time: 0.0216 memory: 16095 grad_norm: 5.1563 loss: 0.7951 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7951 2022/12/08 21:48:39 - mmengine - INFO - Epoch(train) [50][220/940] lr: 1.0000e-03 eta: 8:21:01 time: 0.6428 data_time: 0.0303 memory: 16095 grad_norm: 5.1141 loss: 0.9270 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.9270 2022/12/08 21:48:50 - mmengine - INFO - Epoch(train) [50][240/940] lr: 1.0000e-03 eta: 8:20:47 time: 0.5937 data_time: 0.0201 memory: 16095 grad_norm: 5.0779 loss: 0.8552 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.8552 2022/12/08 21:49:04 - mmengine - INFO - Epoch(train) [50][260/940] lr: 1.0000e-03 eta: 8:20:36 time: 0.6760 data_time: 0.0276 memory: 16095 grad_norm: 5.0806 loss: 0.8640 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 0.8640 2022/12/08 21:49:15 - mmengine - INFO - Epoch(train) [50][280/940] lr: 1.0000e-03 eta: 8:20:22 time: 0.5669 data_time: 0.0224 memory: 16095 grad_norm: 5.1060 loss: 0.8046 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8046 2022/12/08 21:49:29 - mmengine - INFO - Epoch(train) [50][300/940] lr: 1.0000e-03 eta: 8:20:10 time: 0.6748 data_time: 0.0243 memory: 16095 grad_norm: 4.9761 loss: 1.0087 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 1.0087 2022/12/08 21:49:40 - mmengine - INFO - Epoch(train) [50][320/940] lr: 1.0000e-03 eta: 8:19:56 time: 0.5492 data_time: 0.0239 memory: 16095 grad_norm: 5.1715 loss: 0.8855 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8855 2022/12/08 21:49:53 - mmengine - INFO - Epoch(train) [50][340/940] lr: 1.0000e-03 eta: 8:19:44 time: 0.6456 data_time: 0.0248 memory: 16095 grad_norm: 5.0980 loss: 0.8327 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8327 2022/12/08 21:50:03 - mmengine - INFO - Epoch(train) [50][360/940] lr: 1.0000e-03 eta: 8:19:29 time: 0.5285 data_time: 0.0333 memory: 16095 grad_norm: 5.0694 loss: 1.0841 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 1.0841 2022/12/08 21:50:15 - mmengine - INFO - Epoch(train) [50][380/940] lr: 1.0000e-03 eta: 8:19:16 time: 0.6097 data_time: 0.0285 memory: 16095 grad_norm: 5.1098 loss: 0.8560 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8560 2022/12/08 21:50:27 - mmengine - INFO - Epoch(train) [50][400/940] lr: 1.0000e-03 eta: 8:19:03 time: 0.5932 data_time: 0.0197 memory: 16095 grad_norm: 5.1616 loss: 1.0808 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 1.0808 2022/12/08 21:50:41 - mmengine - INFO - Epoch(train) [50][420/940] lr: 1.0000e-03 eta: 8:18:51 time: 0.6919 data_time: 0.0342 memory: 16095 grad_norm: 4.9985 loss: 0.9425 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9425 2022/12/08 21:50:53 - mmengine - INFO - Epoch(train) [50][440/940] lr: 1.0000e-03 eta: 8:18:38 time: 0.5923 data_time: 0.0235 memory: 16095 grad_norm: 5.2421 loss: 0.9746 top1_acc: 0.6250 top5_acc: 0.9062 loss_cls: 0.9746 2022/12/08 21:51:06 - mmengine - INFO - Epoch(train) [50][460/940] lr: 1.0000e-03 eta: 8:18:26 time: 0.6735 data_time: 0.0256 memory: 16095 grad_norm: 5.1518 loss: 1.0116 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 1.0116 2022/12/08 21:51:18 - mmengine - INFO - Epoch(train) [50][480/940] lr: 1.0000e-03 eta: 8:18:12 time: 0.5539 data_time: 0.0239 memory: 16095 grad_norm: 5.1914 loss: 0.8081 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.8081 2022/12/08 21:51:30 - mmengine - INFO - Epoch(train) [50][500/940] lr: 1.0000e-03 eta: 8:18:00 time: 0.6331 data_time: 0.0261 memory: 16095 grad_norm: 5.1053 loss: 0.9137 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.9137 2022/12/08 21:51:41 - mmengine - INFO - Epoch(train) [50][520/940] lr: 1.0000e-03 eta: 8:17:45 time: 0.5355 data_time: 0.0204 memory: 16095 grad_norm: 5.0450 loss: 1.0135 top1_acc: 0.7500 top5_acc: 0.7812 loss_cls: 1.0135 2022/12/08 21:51:54 - mmengine - INFO - Epoch(train) [50][540/940] lr: 1.0000e-03 eta: 8:17:33 time: 0.6476 data_time: 0.0277 memory: 16095 grad_norm: 5.0494 loss: 0.8925 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.8925 2022/12/08 21:52:05 - mmengine - INFO - Epoch(train) [50][560/940] lr: 1.0000e-03 eta: 8:17:19 time: 0.5542 data_time: 0.0220 memory: 16095 grad_norm: 5.1316 loss: 0.8927 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8927 2022/12/08 21:52:18 - mmengine - INFO - Epoch(train) [50][580/940] lr: 1.0000e-03 eta: 8:17:07 time: 0.6628 data_time: 0.0274 memory: 16095 grad_norm: 4.9857 loss: 0.9355 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.9355 2022/12/08 21:52:30 - mmengine - INFO - Epoch(train) [50][600/940] lr: 1.0000e-03 eta: 8:16:54 time: 0.6087 data_time: 0.0221 memory: 16095 grad_norm: 5.0883 loss: 0.8147 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8147 2022/12/08 21:52:42 - mmengine - INFO - Epoch(train) [50][620/940] lr: 1.0000e-03 eta: 8:16:40 time: 0.5972 data_time: 0.0268 memory: 16095 grad_norm: 5.2022 loss: 0.9948 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.9948 2022/12/08 21:52:54 - mmengine - INFO - Epoch(train) [50][640/940] lr: 1.0000e-03 eta: 8:16:27 time: 0.5749 data_time: 0.0229 memory: 16095 grad_norm: 5.1172 loss: 0.8573 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8573 2022/12/08 21:53:06 - mmengine - INFO - Epoch(train) [50][660/940] lr: 1.0000e-03 eta: 8:16:14 time: 0.6310 data_time: 0.0271 memory: 16095 grad_norm: 5.1370 loss: 0.8554 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8554 2022/12/08 21:53:18 - mmengine - INFO - Epoch(train) [50][680/940] lr: 1.0000e-03 eta: 8:16:01 time: 0.5902 data_time: 0.0201 memory: 16095 grad_norm: 5.1687 loss: 0.9334 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.9334 2022/12/08 21:53:32 - mmengine - INFO - Epoch(train) [50][700/940] lr: 1.0000e-03 eta: 8:15:49 time: 0.6860 data_time: 0.2291 memory: 16095 grad_norm: 5.0772 loss: 0.9265 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9265 2022/12/08 21:53:43 - mmengine - INFO - Epoch(train) [50][720/940] lr: 1.0000e-03 eta: 8:15:35 time: 0.5395 data_time: 0.1719 memory: 16095 grad_norm: 5.0839 loss: 0.9003 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.9003 2022/12/08 21:53:58 - mmengine - INFO - Epoch(train) [50][740/940] lr: 1.0000e-03 eta: 8:15:25 time: 0.7423 data_time: 0.3691 memory: 16095 grad_norm: 5.1164 loss: 0.9535 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.9535 2022/12/08 21:54:08 - mmengine - INFO - Epoch(train) [50][760/940] lr: 1.0000e-03 eta: 8:15:10 time: 0.5111 data_time: 0.1856 memory: 16095 grad_norm: 5.1372 loss: 0.8901 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8901 2022/12/08 21:54:21 - mmengine - INFO - Epoch(train) [50][780/940] lr: 1.0000e-03 eta: 8:14:57 time: 0.6473 data_time: 0.3231 memory: 16095 grad_norm: 5.0914 loss: 0.9037 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9037 2022/12/08 21:54:32 - mmengine - INFO - Epoch(train) [50][800/940] lr: 1.0000e-03 eta: 8:14:43 time: 0.5592 data_time: 0.2374 memory: 16095 grad_norm: 5.1279 loss: 0.9707 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.9707 2022/12/08 21:54:44 - mmengine - INFO - Epoch(train) [50][820/940] lr: 1.0000e-03 eta: 8:14:30 time: 0.5972 data_time: 0.2735 memory: 16095 grad_norm: 5.1138 loss: 0.9472 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9472 2022/12/08 21:54:56 - mmengine - INFO - Epoch(train) [50][840/940] lr: 1.0000e-03 eta: 8:14:17 time: 0.6011 data_time: 0.2176 memory: 16095 grad_norm: 5.1353 loss: 0.9009 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9009 2022/12/08 21:55:09 - mmengine - INFO - Epoch(train) [50][860/940] lr: 1.0000e-03 eta: 8:14:05 time: 0.6641 data_time: 0.3347 memory: 16095 grad_norm: 5.0973 loss: 0.9695 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.9695 2022/12/08 21:55:20 - mmengine - INFO - Epoch(train) [50][880/940] lr: 1.0000e-03 eta: 8:13:51 time: 0.5437 data_time: 0.2199 memory: 16095 grad_norm: 5.1236 loss: 0.9136 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.9136 2022/12/08 21:55:33 - mmengine - INFO - Epoch(train) [50][900/940] lr: 1.0000e-03 eta: 8:13:39 time: 0.6585 data_time: 0.3308 memory: 16095 grad_norm: 5.1480 loss: 0.8054 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8054 2022/12/08 21:55:45 - mmengine - INFO - Epoch(train) [50][920/940] lr: 1.0000e-03 eta: 8:13:26 time: 0.6056 data_time: 0.1486 memory: 16095 grad_norm: 5.2312 loss: 0.9241 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9241 2022/12/08 21:55:56 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 21:55:56 - mmengine - INFO - Epoch(train) [50][940/940] lr: 1.0000e-03 eta: 8:13:11 time: 0.5175 data_time: 0.1120 memory: 16095 grad_norm: 5.5491 loss: 0.9555 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 0.9555 2022/12/08 21:56:10 - mmengine - INFO - Epoch(val) [50][20/78] eta: 0:00:40 time: 0.6965 data_time: 0.6015 memory: 1686 2022/12/08 21:56:19 - mmengine - INFO - Epoch(val) [50][40/78] eta: 0:00:22 time: 0.4729 data_time: 0.3785 memory: 1686 2022/12/08 21:56:33 - mmengine - INFO - Epoch(val) [50][60/78] eta: 0:00:11 time: 0.6860 data_time: 0.5921 memory: 1686 2022/12/08 21:56:43 - mmengine - INFO - Epoch(val) [50][78/78] acc/top1: 0.6952 acc/top5: 0.8800 acc/mean1: 0.6951 2022/12/08 21:56:43 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_48.pth is removed 2022/12/08 21:56:46 - mmengine - INFO - The best checkpoint with 0.6952 acc/top1 at 50 epoch is saved to best_acc/top1_epoch_50.pth. 2022/12/08 21:57:02 - mmengine - INFO - Epoch(train) [51][ 20/940] lr: 1.0000e-03 eta: 8:13:01 time: 0.7917 data_time: 0.4828 memory: 16095 grad_norm: 5.0818 loss: 0.8846 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8846 2022/12/08 21:57:12 - mmengine - INFO - Epoch(train) [51][ 40/940] lr: 1.0000e-03 eta: 8:12:47 time: 0.5346 data_time: 0.2302 memory: 16095 grad_norm: 5.1842 loss: 0.8975 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.8975 2022/12/08 21:57:25 - mmengine - INFO - Epoch(train) [51][ 60/940] lr: 1.0000e-03 eta: 8:12:35 time: 0.6518 data_time: 0.3348 memory: 16095 grad_norm: 5.0607 loss: 0.8967 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8967 2022/12/08 21:57:37 - mmengine - INFO - Epoch(train) [51][ 80/940] lr: 1.0000e-03 eta: 8:12:21 time: 0.5904 data_time: 0.2756 memory: 16095 grad_norm: 5.1640 loss: 0.8188 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 0.8188 2022/12/08 21:57:50 - mmengine - INFO - Epoch(train) [51][100/940] lr: 1.0000e-03 eta: 8:12:09 time: 0.6202 data_time: 0.3097 memory: 16095 grad_norm: 5.1743 loss: 0.9202 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9202 2022/12/08 21:58:01 - mmengine - INFO - Epoch(train) [51][120/940] lr: 1.0000e-03 eta: 8:11:55 time: 0.5723 data_time: 0.2473 memory: 16095 grad_norm: 5.1928 loss: 0.9722 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.9722 2022/12/08 21:58:15 - mmengine - INFO - Epoch(train) [51][140/940] lr: 1.0000e-03 eta: 8:11:43 time: 0.6774 data_time: 0.3300 memory: 16095 grad_norm: 5.1496 loss: 0.9394 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.9394 2022/12/08 21:58:26 - mmengine - INFO - Epoch(train) [51][160/940] lr: 1.0000e-03 eta: 8:11:29 time: 0.5457 data_time: 0.1729 memory: 16095 grad_norm: 4.9943 loss: 0.8952 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8952 2022/12/08 21:58:39 - mmengine - INFO - Epoch(train) [51][180/940] lr: 1.0000e-03 eta: 8:11:17 time: 0.6799 data_time: 0.3297 memory: 16095 grad_norm: 5.1838 loss: 0.8179 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8179 2022/12/08 21:58:50 - mmengine - INFO - Epoch(train) [51][200/940] lr: 1.0000e-03 eta: 8:11:03 time: 0.5195 data_time: 0.2022 memory: 16095 grad_norm: 5.0841 loss: 0.8179 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.8179 2022/12/08 21:59:03 - mmengine - INFO - Epoch(train) [51][220/940] lr: 1.0000e-03 eta: 8:10:51 time: 0.6561 data_time: 0.3414 memory: 16095 grad_norm: 5.1580 loss: 0.9476 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 0.9476 2022/12/08 21:59:15 - mmengine - INFO - Epoch(train) [51][240/940] lr: 1.0000e-03 eta: 8:10:37 time: 0.5921 data_time: 0.2599 memory: 16095 grad_norm: 5.0994 loss: 0.9813 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.9813 2022/12/08 21:59:27 - mmengine - INFO - Epoch(train) [51][260/940] lr: 1.0000e-03 eta: 8:10:24 time: 0.6096 data_time: 0.2884 memory: 16095 grad_norm: 5.2808 loss: 0.9546 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9546 2022/12/08 21:59:38 - mmengine - INFO - Epoch(train) [51][280/940] lr: 1.0000e-03 eta: 8:10:10 time: 0.5561 data_time: 0.2356 memory: 16095 grad_norm: 5.1589 loss: 0.9302 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.9302 2022/12/08 21:59:51 - mmengine - INFO - Epoch(train) [51][300/940] lr: 1.0000e-03 eta: 8:09:58 time: 0.6610 data_time: 0.3474 memory: 16095 grad_norm: 5.1437 loss: 0.8852 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 0.8852 2022/12/08 22:00:02 - mmengine - INFO - Epoch(train) [51][320/940] lr: 1.0000e-03 eta: 8:09:44 time: 0.5561 data_time: 0.2466 memory: 16095 grad_norm: 5.1057 loss: 0.9065 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.9065 2022/12/08 22:00:16 - mmengine - INFO - Epoch(train) [51][340/940] lr: 1.0000e-03 eta: 8:09:33 time: 0.6734 data_time: 0.2772 memory: 16095 grad_norm: 5.1001 loss: 0.8997 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8997 2022/12/08 22:00:28 - mmengine - INFO - Epoch(train) [51][360/940] lr: 1.0000e-03 eta: 8:09:19 time: 0.5976 data_time: 0.1468 memory: 16095 grad_norm: 5.0371 loss: 0.8459 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8459 2022/12/08 22:00:40 - mmengine - INFO - Epoch(train) [51][380/940] lr: 1.0000e-03 eta: 8:09:06 time: 0.6114 data_time: 0.2395 memory: 16095 grad_norm: 5.1343 loss: 0.9301 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.9301 2022/12/08 22:00:51 - mmengine - INFO - Epoch(train) [51][400/940] lr: 1.0000e-03 eta: 8:08:53 time: 0.5795 data_time: 0.1101 memory: 16095 grad_norm: 5.2199 loss: 0.8827 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.8827 2022/12/08 22:01:05 - mmengine - INFO - Epoch(train) [51][420/940] lr: 1.0000e-03 eta: 8:08:41 time: 0.6792 data_time: 0.1951 memory: 16095 grad_norm: 5.0904 loss: 0.8748 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 0.8748 2022/12/08 22:01:16 - mmengine - INFO - Epoch(train) [51][440/940] lr: 1.0000e-03 eta: 8:08:27 time: 0.5459 data_time: 0.1410 memory: 16095 grad_norm: 5.0863 loss: 0.8624 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8624 2022/12/08 22:01:28 - mmengine - INFO - Epoch(train) [51][460/940] lr: 1.0000e-03 eta: 8:08:14 time: 0.6014 data_time: 0.0857 memory: 16095 grad_norm: 5.1370 loss: 0.8578 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.8578 2022/12/08 22:01:41 - mmengine - INFO - Epoch(train) [51][480/940] lr: 1.0000e-03 eta: 8:08:01 time: 0.6423 data_time: 0.1000 memory: 16095 grad_norm: 5.1734 loss: 0.8208 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8208 2022/12/08 22:01:52 - mmengine - INFO - Epoch(train) [51][500/940] lr: 1.0000e-03 eta: 8:07:48 time: 0.5768 data_time: 0.1513 memory: 16095 grad_norm: 5.1820 loss: 0.9219 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9219 2022/12/08 22:02:05 - mmengine - INFO - Epoch(train) [51][520/940] lr: 1.0000e-03 eta: 8:07:35 time: 0.6252 data_time: 0.1238 memory: 16095 grad_norm: 5.1470 loss: 0.9362 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.9362 2022/12/08 22:02:17 - mmengine - INFO - Epoch(train) [51][540/940] lr: 1.0000e-03 eta: 8:07:22 time: 0.5896 data_time: 0.0370 memory: 16095 grad_norm: 5.0871 loss: 0.9001 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.9001 2022/12/08 22:02:29 - mmengine - INFO - Epoch(train) [51][560/940] lr: 1.0000e-03 eta: 8:07:09 time: 0.6097 data_time: 0.1443 memory: 16095 grad_norm: 5.2488 loss: 0.8629 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8629 2022/12/08 22:02:42 - mmengine - INFO - Epoch(train) [51][580/940] lr: 1.0000e-03 eta: 8:06:56 time: 0.6375 data_time: 0.0680 memory: 16095 grad_norm: 5.1378 loss: 0.8604 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8604 2022/12/08 22:02:53 - mmengine - INFO - Epoch(train) [51][600/940] lr: 1.0000e-03 eta: 8:06:43 time: 0.5681 data_time: 0.0331 memory: 16095 grad_norm: 5.1613 loss: 1.0653 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0653 2022/12/08 22:03:06 - mmengine - INFO - Epoch(train) [51][620/940] lr: 1.0000e-03 eta: 8:06:30 time: 0.6533 data_time: 0.0261 memory: 16095 grad_norm: 5.2402 loss: 0.9351 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9351 2022/12/08 22:03:17 - mmengine - INFO - Epoch(train) [51][640/940] lr: 1.0000e-03 eta: 8:06:17 time: 0.5689 data_time: 0.0512 memory: 16095 grad_norm: 5.1527 loss: 0.8852 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8852 2022/12/08 22:03:31 - mmengine - INFO - Epoch(train) [51][660/940] lr: 1.0000e-03 eta: 8:06:05 time: 0.6982 data_time: 0.0241 memory: 16095 grad_norm: 4.9826 loss: 0.8665 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8665 2022/12/08 22:03:42 - mmengine - INFO - Epoch(train) [51][680/940] lr: 1.0000e-03 eta: 8:05:51 time: 0.5451 data_time: 0.0315 memory: 16095 grad_norm: 5.1480 loss: 0.8662 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.8662 2022/12/08 22:03:55 - mmengine - INFO - Epoch(train) [51][700/940] lr: 1.0000e-03 eta: 8:05:39 time: 0.6251 data_time: 0.0446 memory: 16095 grad_norm: 5.2965 loss: 0.9857 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.9857 2022/12/08 22:04:07 - mmengine - INFO - Epoch(train) [51][720/940] lr: 1.0000e-03 eta: 8:05:26 time: 0.6076 data_time: 0.1918 memory: 16095 grad_norm: 5.2426 loss: 0.9115 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.9115 2022/12/08 22:04:18 - mmengine - INFO - Epoch(train) [51][740/940] lr: 1.0000e-03 eta: 8:05:12 time: 0.5702 data_time: 0.2168 memory: 16095 grad_norm: 5.2099 loss: 0.9240 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9240 2022/12/08 22:04:31 - mmengine - INFO - Epoch(train) [51][760/940] lr: 1.0000e-03 eta: 8:05:00 time: 0.6490 data_time: 0.2962 memory: 16095 grad_norm: 5.2444 loss: 0.8590 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8590 2022/12/08 22:04:43 - mmengine - INFO - Epoch(train) [51][780/940] lr: 1.0000e-03 eta: 8:04:46 time: 0.5582 data_time: 0.2281 memory: 16095 grad_norm: 5.0738 loss: 0.9016 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.9016 2022/12/08 22:04:55 - mmengine - INFO - Epoch(train) [51][800/940] lr: 1.0000e-03 eta: 8:04:33 time: 0.6357 data_time: 0.2629 memory: 16095 grad_norm: 5.1020 loss: 0.9307 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9307 2022/12/08 22:05:07 - mmengine - INFO - Epoch(train) [51][820/940] lr: 1.0000e-03 eta: 8:04:20 time: 0.6097 data_time: 0.1046 memory: 16095 grad_norm: 5.2396 loss: 0.8921 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8921 2022/12/08 22:05:20 - mmengine - INFO - Epoch(train) [51][840/940] lr: 1.0000e-03 eta: 8:04:07 time: 0.6180 data_time: 0.2262 memory: 16095 grad_norm: 5.1879 loss: 0.8530 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8530 2022/12/08 22:05:32 - mmengine - INFO - Epoch(train) [51][860/940] lr: 1.0000e-03 eta: 8:03:54 time: 0.5888 data_time: 0.0931 memory: 16095 grad_norm: 5.1312 loss: 0.9065 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.9065 2022/12/08 22:05:44 - mmengine - INFO - Epoch(train) [51][880/940] lr: 1.0000e-03 eta: 8:03:41 time: 0.6271 data_time: 0.2078 memory: 16095 grad_norm: 5.2374 loss: 0.8929 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8929 2022/12/08 22:05:55 - mmengine - INFO - Epoch(train) [51][900/940] lr: 1.0000e-03 eta: 8:03:28 time: 0.5642 data_time: 0.1626 memory: 16095 grad_norm: 5.1692 loss: 0.7868 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.7868 2022/12/08 22:06:08 - mmengine - INFO - Epoch(train) [51][920/940] lr: 1.0000e-03 eta: 8:03:15 time: 0.6207 data_time: 0.2146 memory: 16095 grad_norm: 5.1372 loss: 0.8722 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 0.8722 2022/12/08 22:06:18 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 22:06:18 - mmengine - INFO - Epoch(train) [51][940/940] lr: 1.0000e-03 eta: 8:03:00 time: 0.4929 data_time: 0.1569 memory: 16095 grad_norm: 5.3514 loss: 0.8540 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 0.8540 2022/12/08 22:06:18 - mmengine - INFO - Saving checkpoint at 51 epochs 2022/12/08 22:06:35 - mmengine - INFO - Epoch(val) [51][20/78] eta: 0:00:41 time: 0.7078 data_time: 0.6126 memory: 1686 2022/12/08 22:06:44 - mmengine - INFO - Epoch(val) [51][40/78] eta: 0:00:22 time: 0.4719 data_time: 0.3780 memory: 1686 2022/12/08 22:06:58 - mmengine - INFO - Epoch(val) [51][60/78] eta: 0:00:11 time: 0.6812 data_time: 0.5856 memory: 1686 2022/12/08 22:07:08 - mmengine - INFO - Epoch(val) [51][78/78] acc/top1: 0.6929 acc/top5: 0.8792 acc/mean1: 0.6928 2022/12/08 22:07:23 - mmengine - INFO - Epoch(train) [52][ 20/940] lr: 1.0000e-03 eta: 8:02:50 time: 0.7963 data_time: 0.3930 memory: 16095 grad_norm: 5.1344 loss: 0.9309 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.9309 2022/12/08 22:07:35 - mmengine - INFO - Epoch(train) [52][ 40/940] lr: 1.0000e-03 eta: 8:02:37 time: 0.5749 data_time: 0.1390 memory: 16095 grad_norm: 5.1594 loss: 0.7938 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7938 2022/12/08 22:07:48 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 22:07:48 - mmengine - INFO - Epoch(train) [52][ 60/940] lr: 1.0000e-03 eta: 8:02:25 time: 0.6715 data_time: 0.1238 memory: 16095 grad_norm: 5.2648 loss: 0.9694 top1_acc: 0.5938 top5_acc: 0.7188 loss_cls: 0.9694 2022/12/08 22:07:59 - mmengine - INFO - Epoch(train) [52][ 80/940] lr: 1.0000e-03 eta: 8:02:10 time: 0.5293 data_time: 0.0610 memory: 16095 grad_norm: 5.0988 loss: 0.8500 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.8500 2022/12/08 22:08:12 - mmengine - INFO - Epoch(train) [52][100/940] lr: 1.0000e-03 eta: 8:01:58 time: 0.6606 data_time: 0.0254 memory: 16095 grad_norm: 5.1174 loss: 0.9679 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.9679 2022/12/08 22:08:24 - mmengine - INFO - Epoch(train) [52][120/940] lr: 1.0000e-03 eta: 8:01:45 time: 0.5932 data_time: 0.0626 memory: 16095 grad_norm: 5.2134 loss: 0.8476 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8476 2022/12/08 22:08:38 - mmengine - INFO - Epoch(train) [52][140/940] lr: 1.0000e-03 eta: 8:01:34 time: 0.6884 data_time: 0.0265 memory: 16095 grad_norm: 5.1061 loss: 0.9744 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 0.9744 2022/12/08 22:08:48 - mmengine - INFO - Epoch(train) [52][160/940] lr: 1.0000e-03 eta: 8:01:19 time: 0.5239 data_time: 0.0312 memory: 16095 grad_norm: 5.1755 loss: 0.7725 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7725 2022/12/08 22:09:01 - mmengine - INFO - Epoch(train) [52][180/940] lr: 1.0000e-03 eta: 8:01:07 time: 0.6550 data_time: 0.0613 memory: 16095 grad_norm: 5.3179 loss: 0.9503 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9503 2022/12/08 22:09:12 - mmengine - INFO - Epoch(train) [52][200/940] lr: 1.0000e-03 eta: 8:00:53 time: 0.5313 data_time: 0.0222 memory: 16095 grad_norm: 5.1290 loss: 0.8828 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8828 2022/12/08 22:09:26 - mmengine - INFO - Epoch(train) [52][220/940] lr: 1.0000e-03 eta: 8:00:41 time: 0.7055 data_time: 0.0278 memory: 16095 grad_norm: 5.1321 loss: 0.8315 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8315 2022/12/08 22:09:37 - mmengine - INFO - Epoch(train) [52][240/940] lr: 1.0000e-03 eta: 8:00:27 time: 0.5322 data_time: 0.0273 memory: 16095 grad_norm: 5.1924 loss: 0.7879 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7879 2022/12/08 22:09:50 - mmengine - INFO - Epoch(train) [52][260/940] lr: 1.0000e-03 eta: 8:00:15 time: 0.6692 data_time: 0.0419 memory: 16095 grad_norm: 5.1436 loss: 0.8100 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.8100 2022/12/08 22:10:02 - mmengine - INFO - Epoch(train) [52][280/940] lr: 1.0000e-03 eta: 8:00:01 time: 0.5689 data_time: 0.0208 memory: 16095 grad_norm: 5.2098 loss: 0.8000 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8000 2022/12/08 22:10:15 - mmengine - INFO - Epoch(train) [52][300/940] lr: 1.0000e-03 eta: 7:59:50 time: 0.6833 data_time: 0.0746 memory: 16095 grad_norm: 5.1541 loss: 0.8591 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8591 2022/12/08 22:10:26 - mmengine - INFO - Epoch(train) [52][320/940] lr: 1.0000e-03 eta: 7:59:36 time: 0.5461 data_time: 0.1748 memory: 16095 grad_norm: 5.1361 loss: 0.9620 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9620 2022/12/08 22:10:40 - mmengine - INFO - Epoch(train) [52][340/940] lr: 1.0000e-03 eta: 7:59:24 time: 0.7004 data_time: 0.2798 memory: 16095 grad_norm: 5.1258 loss: 0.8021 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8021 2022/12/08 22:10:51 - mmengine - INFO - Epoch(train) [52][360/940] lr: 1.0000e-03 eta: 7:59:10 time: 0.5453 data_time: 0.2021 memory: 16095 grad_norm: 5.1121 loss: 0.9066 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 0.9066 2022/12/08 22:11:04 - mmengine - INFO - Epoch(train) [52][380/940] lr: 1.0000e-03 eta: 7:58:58 time: 0.6329 data_time: 0.2079 memory: 16095 grad_norm: 5.3340 loss: 0.8553 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8553 2022/12/08 22:11:16 - mmengine - INFO - Epoch(train) [52][400/940] lr: 1.0000e-03 eta: 7:58:45 time: 0.5962 data_time: 0.0575 memory: 16095 grad_norm: 5.1891 loss: 0.8245 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.8245 2022/12/08 22:11:28 - mmengine - INFO - Epoch(train) [52][420/940] lr: 1.0000e-03 eta: 7:58:32 time: 0.6050 data_time: 0.0271 memory: 16095 grad_norm: 5.3083 loss: 0.9618 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.9618 2022/12/08 22:11:40 - mmengine - INFO - Epoch(train) [52][440/940] lr: 1.0000e-03 eta: 7:58:19 time: 0.6083 data_time: 0.0500 memory: 16095 grad_norm: 5.2427 loss: 0.9546 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9546 2022/12/08 22:11:53 - mmengine - INFO - Epoch(train) [52][460/940] lr: 1.0000e-03 eta: 7:58:06 time: 0.6394 data_time: 0.1895 memory: 16095 grad_norm: 5.1252 loss: 0.7010 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7010 2022/12/08 22:12:04 - mmengine - INFO - Epoch(train) [52][480/940] lr: 1.0000e-03 eta: 7:57:53 time: 0.5807 data_time: 0.1057 memory: 16095 grad_norm: 5.1155 loss: 0.8121 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8121 2022/12/08 22:12:18 - mmengine - INFO - Epoch(train) [52][500/940] lr: 1.0000e-03 eta: 7:57:41 time: 0.6650 data_time: 0.0530 memory: 16095 grad_norm: 5.1964 loss: 0.8936 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8936 2022/12/08 22:12:29 - mmengine - INFO - Epoch(train) [52][520/940] lr: 1.0000e-03 eta: 7:57:27 time: 0.5595 data_time: 0.0802 memory: 16095 grad_norm: 5.2106 loss: 0.9096 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.9096 2022/12/08 22:12:41 - mmengine - INFO - Epoch(train) [52][540/940] lr: 1.0000e-03 eta: 7:57:14 time: 0.6239 data_time: 0.1849 memory: 16095 grad_norm: 5.0787 loss: 0.8927 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.8927 2022/12/08 22:12:52 - mmengine - INFO - Epoch(train) [52][560/940] lr: 1.0000e-03 eta: 7:57:00 time: 0.5333 data_time: 0.1782 memory: 16095 grad_norm: 5.1510 loss: 0.8703 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.8703 2022/12/08 22:13:05 - mmengine - INFO - Epoch(train) [52][580/940] lr: 1.0000e-03 eta: 7:56:48 time: 0.6547 data_time: 0.2637 memory: 16095 grad_norm: 5.1681 loss: 0.9236 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9236 2022/12/08 22:13:17 - mmengine - INFO - Epoch(train) [52][600/940] lr: 1.0000e-03 eta: 7:56:34 time: 0.5706 data_time: 0.0492 memory: 16095 grad_norm: 5.3272 loss: 0.8866 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8866 2022/12/08 22:13:30 - mmengine - INFO - Epoch(train) [52][620/940] lr: 1.0000e-03 eta: 7:56:23 time: 0.6947 data_time: 0.0480 memory: 16095 grad_norm: 5.3256 loss: 0.9538 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 0.9538 2022/12/08 22:13:42 - mmengine - INFO - Epoch(train) [52][640/940] lr: 1.0000e-03 eta: 7:56:09 time: 0.5546 data_time: 0.0973 memory: 16095 grad_norm: 5.3002 loss: 0.7894 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7894 2022/12/08 22:13:55 - mmengine - INFO - Epoch(train) [52][660/940] lr: 1.0000e-03 eta: 7:55:57 time: 0.6529 data_time: 0.1787 memory: 16095 grad_norm: 5.2472 loss: 0.8888 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 0.8888 2022/12/08 22:14:06 - mmengine - INFO - Epoch(train) [52][680/940] lr: 1.0000e-03 eta: 7:55:43 time: 0.5687 data_time: 0.1236 memory: 16095 grad_norm: 5.0589 loss: 0.8605 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8605 2022/12/08 22:14:20 - mmengine - INFO - Epoch(train) [52][700/940] lr: 1.0000e-03 eta: 7:55:32 time: 0.7073 data_time: 0.0897 memory: 16095 grad_norm: 5.0627 loss: 0.8829 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.8829 2022/12/08 22:14:31 - mmengine - INFO - Epoch(train) [52][720/940] lr: 1.0000e-03 eta: 7:55:18 time: 0.5431 data_time: 0.0242 memory: 16095 grad_norm: 5.3136 loss: 0.8578 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.8578 2022/12/08 22:14:44 - mmengine - INFO - Epoch(train) [52][740/940] lr: 1.0000e-03 eta: 7:55:06 time: 0.6660 data_time: 0.0627 memory: 16095 grad_norm: 5.1661 loss: 0.8512 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8512 2022/12/08 22:14:56 - mmengine - INFO - Epoch(train) [52][760/940] lr: 1.0000e-03 eta: 7:54:52 time: 0.5607 data_time: 0.0896 memory: 16095 grad_norm: 5.1468 loss: 0.9548 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.9548 2022/12/08 22:15:08 - mmengine - INFO - Epoch(train) [52][780/940] lr: 1.0000e-03 eta: 7:54:39 time: 0.6250 data_time: 0.1040 memory: 16095 grad_norm: 5.1163 loss: 0.8299 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8299 2022/12/08 22:15:21 - mmengine - INFO - Epoch(train) [52][800/940] lr: 1.0000e-03 eta: 7:54:27 time: 0.6422 data_time: 0.3015 memory: 16095 grad_norm: 5.1375 loss: 0.7961 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7961 2022/12/08 22:15:32 - mmengine - INFO - Epoch(train) [52][820/940] lr: 1.0000e-03 eta: 7:54:13 time: 0.5330 data_time: 0.2044 memory: 16095 grad_norm: 5.1592 loss: 0.8743 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8743 2022/12/08 22:15:45 - mmengine - INFO - Epoch(train) [52][840/940] lr: 1.0000e-03 eta: 7:54:01 time: 0.6718 data_time: 0.3462 memory: 16095 grad_norm: 5.3048 loss: 0.8770 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8770 2022/12/08 22:15:56 - mmengine - INFO - Epoch(train) [52][860/940] lr: 1.0000e-03 eta: 7:53:46 time: 0.5237 data_time: 0.1803 memory: 16095 grad_norm: 5.2312 loss: 0.9128 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9128 2022/12/08 22:16:09 - mmengine - INFO - Epoch(train) [52][880/940] lr: 1.0000e-03 eta: 7:53:34 time: 0.6658 data_time: 0.3466 memory: 16095 grad_norm: 5.1816 loss: 0.8752 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8752 2022/12/08 22:16:20 - mmengine - INFO - Epoch(train) [52][900/940] lr: 1.0000e-03 eta: 7:53:20 time: 0.5424 data_time: 0.2142 memory: 16095 grad_norm: 5.1743 loss: 0.8692 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8692 2022/12/08 22:16:33 - mmengine - INFO - Epoch(train) [52][920/940] lr: 1.0000e-03 eta: 7:53:08 time: 0.6676 data_time: 0.3359 memory: 16095 grad_norm: 5.1916 loss: 0.8898 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.8898 2022/12/08 22:16:42 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 22:16:42 - mmengine - INFO - Epoch(train) [52][940/940] lr: 1.0000e-03 eta: 7:52:53 time: 0.4710 data_time: 0.1801 memory: 16095 grad_norm: 5.5251 loss: 0.8952 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 0.8952 2022/12/08 22:16:56 - mmengine - INFO - Epoch(val) [52][20/78] eta: 0:00:40 time: 0.6933 data_time: 0.5984 memory: 1686 2022/12/08 22:17:06 - mmengine - INFO - Epoch(val) [52][40/78] eta: 0:00:22 time: 0.4763 data_time: 0.3820 memory: 1686 2022/12/08 22:17:19 - mmengine - INFO - Epoch(val) [52][60/78] eta: 0:00:11 time: 0.6731 data_time: 0.5770 memory: 1686 2022/12/08 22:17:30 - mmengine - INFO - Epoch(val) [52][78/78] acc/top1: 0.6939 acc/top5: 0.8803 acc/mean1: 0.6938 2022/12/08 22:17:46 - mmengine - INFO - Epoch(train) [53][ 20/940] lr: 1.0000e-03 eta: 7:52:43 time: 0.8064 data_time: 0.4676 memory: 16095 grad_norm: 5.0574 loss: 0.8089 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8089 2022/12/08 22:17:57 - mmengine - INFO - Epoch(train) [53][ 40/940] lr: 1.0000e-03 eta: 7:52:30 time: 0.5693 data_time: 0.1830 memory: 16095 grad_norm: 5.1548 loss: 0.8868 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8868 2022/12/08 22:18:11 - mmengine - INFO - Epoch(train) [53][ 60/940] lr: 1.0000e-03 eta: 7:52:18 time: 0.6566 data_time: 0.1361 memory: 16095 grad_norm: 5.2131 loss: 0.8793 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8793 2022/12/08 22:18:22 - mmengine - INFO - Epoch(train) [53][ 80/940] lr: 1.0000e-03 eta: 7:52:04 time: 0.5740 data_time: 0.0426 memory: 16095 grad_norm: 5.3509 loss: 0.8720 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8720 2022/12/08 22:18:36 - mmengine - INFO - Epoch(train) [53][100/940] lr: 1.0000e-03 eta: 7:51:53 time: 0.7015 data_time: 0.0369 memory: 16095 grad_norm: 5.2332 loss: 0.7277 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7277 2022/12/08 22:18:46 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 22:18:46 - mmengine - INFO - Epoch(train) [53][120/940] lr: 1.0000e-03 eta: 7:51:38 time: 0.5142 data_time: 0.0235 memory: 16095 grad_norm: 5.3786 loss: 0.9316 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9316 2022/12/08 22:19:00 - mmengine - INFO - Epoch(train) [53][140/940] lr: 1.0000e-03 eta: 7:51:27 time: 0.6890 data_time: 0.1433 memory: 16095 grad_norm: 5.3855 loss: 0.9055 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.9055 2022/12/08 22:19:12 - mmengine - INFO - Epoch(train) [53][160/940] lr: 1.0000e-03 eta: 7:51:13 time: 0.5758 data_time: 0.0450 memory: 16095 grad_norm: 5.1683 loss: 0.8387 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8387 2022/12/08 22:19:25 - mmengine - INFO - Epoch(train) [53][180/940] lr: 1.0000e-03 eta: 7:51:01 time: 0.6640 data_time: 0.0279 memory: 16095 grad_norm: 5.1132 loss: 0.8635 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.8635 2022/12/08 22:19:36 - mmengine - INFO - Epoch(train) [53][200/940] lr: 1.0000e-03 eta: 7:50:47 time: 0.5331 data_time: 0.0572 memory: 16095 grad_norm: 5.1716 loss: 0.8710 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8710 2022/12/08 22:19:48 - mmengine - INFO - Epoch(train) [53][220/940] lr: 1.0000e-03 eta: 7:50:34 time: 0.6332 data_time: 0.1059 memory: 16095 grad_norm: 5.2475 loss: 0.9450 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9450 2022/12/08 22:20:00 - mmengine - INFO - Epoch(train) [53][240/940] lr: 1.0000e-03 eta: 7:50:21 time: 0.5734 data_time: 0.0681 memory: 16095 grad_norm: 5.1899 loss: 0.8580 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.8580 2022/12/08 22:20:13 - mmengine - INFO - Epoch(train) [53][260/940] lr: 1.0000e-03 eta: 7:50:09 time: 0.6490 data_time: 0.0618 memory: 16095 grad_norm: 5.3048 loss: 0.9463 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9463 2022/12/08 22:20:24 - mmengine - INFO - Epoch(train) [53][280/940] lr: 1.0000e-03 eta: 7:49:55 time: 0.5699 data_time: 0.0923 memory: 16095 grad_norm: 5.3314 loss: 0.8428 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8428 2022/12/08 22:20:37 - mmengine - INFO - Epoch(train) [53][300/940] lr: 1.0000e-03 eta: 7:49:43 time: 0.6633 data_time: 0.0211 memory: 16095 grad_norm: 5.1271 loss: 0.8922 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.8922 2022/12/08 22:20:48 - mmengine - INFO - Epoch(train) [53][320/940] lr: 1.0000e-03 eta: 7:49:29 time: 0.5512 data_time: 0.1300 memory: 16095 grad_norm: 5.2737 loss: 0.9355 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.9355 2022/12/08 22:21:00 - mmengine - INFO - Epoch(train) [53][340/940] lr: 1.0000e-03 eta: 7:49:16 time: 0.5877 data_time: 0.1665 memory: 16095 grad_norm: 5.0869 loss: 0.8462 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 0.8462 2022/12/08 22:21:13 - mmengine - INFO - Epoch(train) [53][360/940] lr: 1.0000e-03 eta: 7:49:03 time: 0.6258 data_time: 0.1924 memory: 16095 grad_norm: 5.1800 loss: 0.9071 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9071 2022/12/08 22:21:25 - mmengine - INFO - Epoch(train) [53][380/940] lr: 1.0000e-03 eta: 7:48:50 time: 0.6240 data_time: 0.1183 memory: 16095 grad_norm: 5.2388 loss: 0.9680 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.9680 2022/12/08 22:21:39 - mmengine - INFO - Epoch(train) [53][400/940] lr: 1.0000e-03 eta: 7:48:39 time: 0.6764 data_time: 0.3501 memory: 16095 grad_norm: 5.1953 loss: 0.7853 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.7853 2022/12/08 22:21:50 - mmengine - INFO - Epoch(train) [53][420/940] lr: 1.0000e-03 eta: 7:48:25 time: 0.5409 data_time: 0.2183 memory: 16095 grad_norm: 5.2090 loss: 0.8471 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.8471 2022/12/08 22:22:03 - mmengine - INFO - Epoch(train) [53][440/940] lr: 1.0000e-03 eta: 7:48:13 time: 0.6599 data_time: 0.3259 memory: 16095 grad_norm: 5.1122 loss: 0.7388 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7388 2022/12/08 22:22:14 - mmengine - INFO - Epoch(train) [53][460/940] lr: 1.0000e-03 eta: 7:47:59 time: 0.5613 data_time: 0.2234 memory: 16095 grad_norm: 5.1079 loss: 0.8855 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.8855 2022/12/08 22:22:27 - mmengine - INFO - Epoch(train) [53][480/940] lr: 1.0000e-03 eta: 7:47:47 time: 0.6531 data_time: 0.3278 memory: 16095 grad_norm: 5.2191 loss: 0.8599 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8599 2022/12/08 22:22:38 - mmengine - INFO - Epoch(train) [53][500/940] lr: 1.0000e-03 eta: 7:47:33 time: 0.5707 data_time: 0.2386 memory: 16095 grad_norm: 5.1837 loss: 0.8369 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8369 2022/12/08 22:22:51 - mmengine - INFO - Epoch(train) [53][520/940] lr: 1.0000e-03 eta: 7:47:20 time: 0.6019 data_time: 0.2881 memory: 16095 grad_norm: 5.1762 loss: 0.7988 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7988 2022/12/08 22:23:02 - mmengine - INFO - Epoch(train) [53][540/940] lr: 1.0000e-03 eta: 7:47:06 time: 0.5685 data_time: 0.1714 memory: 16095 grad_norm: 5.2933 loss: 0.8906 top1_acc: 0.8125 top5_acc: 0.8438 loss_cls: 0.8906 2022/12/08 22:23:15 - mmengine - INFO - Epoch(train) [53][560/940] lr: 1.0000e-03 eta: 7:46:55 time: 0.6744 data_time: 0.3383 memory: 16095 grad_norm: 5.2331 loss: 0.9083 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.9083 2022/12/08 22:23:27 - mmengine - INFO - Epoch(train) [53][580/940] lr: 1.0000e-03 eta: 7:46:41 time: 0.5675 data_time: 0.1186 memory: 16095 grad_norm: 5.1849 loss: 0.9478 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.9478 2022/12/08 22:23:41 - mmengine - INFO - Epoch(train) [53][600/940] lr: 1.0000e-03 eta: 7:46:30 time: 0.7124 data_time: 0.1184 memory: 16095 grad_norm: 5.2340 loss: 0.8779 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8779 2022/12/08 22:23:52 - mmengine - INFO - Epoch(train) [53][620/940] lr: 1.0000e-03 eta: 7:46:16 time: 0.5494 data_time: 0.0379 memory: 16095 grad_norm: 5.1645 loss: 0.8494 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8494 2022/12/08 22:24:05 - mmengine - INFO - Epoch(train) [53][640/940] lr: 1.0000e-03 eta: 7:46:04 time: 0.6558 data_time: 0.0298 memory: 16095 grad_norm: 5.1843 loss: 0.7679 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7679 2022/12/08 22:24:16 - mmengine - INFO - Epoch(train) [53][660/940] lr: 1.0000e-03 eta: 7:45:49 time: 0.5351 data_time: 0.0208 memory: 16095 grad_norm: 5.2057 loss: 0.8698 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8698 2022/12/08 22:24:29 - mmengine - INFO - Epoch(train) [53][680/940] lr: 1.0000e-03 eta: 7:45:37 time: 0.6614 data_time: 0.0510 memory: 16095 grad_norm: 5.3076 loss: 0.8491 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8491 2022/12/08 22:24:40 - mmengine - INFO - Epoch(train) [53][700/940] lr: 1.0000e-03 eta: 7:45:23 time: 0.5477 data_time: 0.0857 memory: 16095 grad_norm: 5.2592 loss: 0.8712 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 0.8712 2022/12/08 22:24:53 - mmengine - INFO - Epoch(train) [53][720/940] lr: 1.0000e-03 eta: 7:45:11 time: 0.6539 data_time: 0.1397 memory: 16095 grad_norm: 5.2813 loss: 0.9130 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.9130 2022/12/08 22:25:04 - mmengine - INFO - Epoch(train) [53][740/940] lr: 1.0000e-03 eta: 7:44:58 time: 0.5665 data_time: 0.1420 memory: 16095 grad_norm: 5.4132 loss: 0.9572 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9572 2022/12/08 22:25:18 - mmengine - INFO - Epoch(train) [53][760/940] lr: 1.0000e-03 eta: 7:44:46 time: 0.6734 data_time: 0.3114 memory: 16095 grad_norm: 5.1042 loss: 0.8490 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8490 2022/12/08 22:25:29 - mmengine - INFO - Epoch(train) [53][780/940] lr: 1.0000e-03 eta: 7:44:32 time: 0.5363 data_time: 0.1191 memory: 16095 grad_norm: 5.2782 loss: 0.9140 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 0.9140 2022/12/08 22:25:41 - mmengine - INFO - Epoch(train) [53][800/940] lr: 1.0000e-03 eta: 7:44:19 time: 0.6258 data_time: 0.1542 memory: 16095 grad_norm: 5.1918 loss: 0.8975 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 0.8975 2022/12/08 22:25:52 - mmengine - INFO - Epoch(train) [53][820/940] lr: 1.0000e-03 eta: 7:44:05 time: 0.5453 data_time: 0.0512 memory: 16095 grad_norm: 5.2108 loss: 0.9076 top1_acc: 0.6562 top5_acc: 1.0000 loss_cls: 0.9076 2022/12/08 22:26:06 - mmengine - INFO - Epoch(train) [53][840/940] lr: 1.0000e-03 eta: 7:43:53 time: 0.6794 data_time: 0.0920 memory: 16095 grad_norm: 5.2916 loss: 0.9134 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9134 2022/12/08 22:26:17 - mmengine - INFO - Epoch(train) [53][860/940] lr: 1.0000e-03 eta: 7:43:40 time: 0.5607 data_time: 0.0999 memory: 16095 grad_norm: 5.2277 loss: 0.8610 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8610 2022/12/08 22:26:31 - mmengine - INFO - Epoch(train) [53][880/940] lr: 1.0000e-03 eta: 7:43:28 time: 0.7040 data_time: 0.0788 memory: 16095 grad_norm: 5.2141 loss: 0.8934 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 0.8934 2022/12/08 22:26:42 - mmengine - INFO - Epoch(train) [53][900/940] lr: 1.0000e-03 eta: 7:43:15 time: 0.5750 data_time: 0.0803 memory: 16095 grad_norm: 5.2918 loss: 0.9438 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.9438 2022/12/08 22:26:55 - mmengine - INFO - Epoch(train) [53][920/940] lr: 1.0000e-03 eta: 7:43:02 time: 0.6234 data_time: 0.1989 memory: 16095 grad_norm: 5.2637 loss: 0.8508 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8508 2022/12/08 22:27:05 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 22:27:05 - mmengine - INFO - Epoch(train) [53][940/940] lr: 1.0000e-03 eta: 7:42:47 time: 0.4891 data_time: 0.1684 memory: 16095 grad_norm: 5.6236 loss: 0.9277 top1_acc: 0.7143 top5_acc: 0.7143 loss_cls: 0.9277 2022/12/08 22:27:19 - mmengine - INFO - Epoch(val) [53][20/78] eta: 0:00:40 time: 0.7057 data_time: 0.6110 memory: 1686 2022/12/08 22:27:28 - mmengine - INFO - Epoch(val) [53][40/78] eta: 0:00:22 time: 0.4654 data_time: 0.3712 memory: 1686 2022/12/08 22:27:41 - mmengine - INFO - Epoch(val) [53][60/78] eta: 0:00:11 time: 0.6666 data_time: 0.5707 memory: 1686 2022/12/08 22:27:52 - mmengine - INFO - Epoch(val) [53][78/78] acc/top1: 0.6937 acc/top5: 0.8788 acc/mean1: 0.6936 2022/12/08 22:28:08 - mmengine - INFO - Epoch(train) [54][ 20/940] lr: 1.0000e-03 eta: 7:42:38 time: 0.8088 data_time: 0.4833 memory: 16095 grad_norm: 5.2609 loss: 1.0327 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 1.0327 2022/12/08 22:28:20 - mmengine - INFO - Epoch(train) [54][ 40/940] lr: 1.0000e-03 eta: 7:42:24 time: 0.5581 data_time: 0.2479 memory: 16095 grad_norm: 5.1148 loss: 0.7732 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7732 2022/12/08 22:28:32 - mmengine - INFO - Epoch(train) [54][ 60/940] lr: 1.0000e-03 eta: 7:42:11 time: 0.6391 data_time: 0.3153 memory: 16095 grad_norm: 5.2734 loss: 0.7539 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.7539 2022/12/08 22:28:44 - mmengine - INFO - Epoch(train) [54][ 80/940] lr: 1.0000e-03 eta: 7:41:58 time: 0.5721 data_time: 0.1780 memory: 16095 grad_norm: 5.1581 loss: 0.8312 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8312 2022/12/08 22:28:57 - mmengine - INFO - Epoch(train) [54][100/940] lr: 1.0000e-03 eta: 7:41:46 time: 0.6685 data_time: 0.1628 memory: 16095 grad_norm: 5.1221 loss: 0.8539 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8539 2022/12/08 22:29:08 - mmengine - INFO - Epoch(train) [54][120/940] lr: 1.0000e-03 eta: 7:41:32 time: 0.5658 data_time: 0.0442 memory: 16095 grad_norm: 5.1372 loss: 0.8874 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8874 2022/12/08 22:29:21 - mmengine - INFO - Epoch(train) [54][140/940] lr: 1.0000e-03 eta: 7:41:20 time: 0.6437 data_time: 0.0250 memory: 16095 grad_norm: 5.1824 loss: 0.7889 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 0.7889 2022/12/08 22:29:32 - mmengine - INFO - Epoch(train) [54][160/940] lr: 1.0000e-03 eta: 7:41:06 time: 0.5201 data_time: 0.0645 memory: 16095 grad_norm: 5.2311 loss: 0.8288 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.8288 2022/12/08 22:29:45 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 22:29:45 - mmengine - INFO - Epoch(train) [54][180/940] lr: 1.0000e-03 eta: 7:40:54 time: 0.6673 data_time: 0.1093 memory: 16095 grad_norm: 5.2648 loss: 0.8536 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8536 2022/12/08 22:29:56 - mmengine - INFO - Epoch(train) [54][200/940] lr: 1.0000e-03 eta: 7:40:40 time: 0.5490 data_time: 0.1770 memory: 16095 grad_norm: 5.3310 loss: 0.7942 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7942 2022/12/08 22:30:10 - mmengine - INFO - Epoch(train) [54][220/940] lr: 1.0000e-03 eta: 7:40:28 time: 0.6891 data_time: 0.2930 memory: 16095 grad_norm: 5.2886 loss: 0.9074 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.9074 2022/12/08 22:30:21 - mmengine - INFO - Epoch(train) [54][240/940] lr: 1.0000e-03 eta: 7:40:14 time: 0.5532 data_time: 0.0479 memory: 16095 grad_norm: 5.2260 loss: 0.9183 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9183 2022/12/08 22:30:35 - mmengine - INFO - Epoch(train) [54][260/940] lr: 1.0000e-03 eta: 7:40:03 time: 0.6937 data_time: 0.0263 memory: 16095 grad_norm: 5.2302 loss: 0.8754 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8754 2022/12/08 22:30:46 - mmengine - INFO - Epoch(train) [54][280/940] lr: 1.0000e-03 eta: 7:39:49 time: 0.5426 data_time: 0.0245 memory: 16095 grad_norm: 5.0703 loss: 0.8182 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8182 2022/12/08 22:30:59 - mmengine - INFO - Epoch(train) [54][300/940] lr: 1.0000e-03 eta: 7:39:37 time: 0.6745 data_time: 0.0266 memory: 16095 grad_norm: 5.1829 loss: 0.8284 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8284 2022/12/08 22:31:11 - mmengine - INFO - Epoch(train) [54][320/940] lr: 1.0000e-03 eta: 7:39:24 time: 0.5735 data_time: 0.0220 memory: 16095 grad_norm: 5.2398 loss: 0.7588 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.7588 2022/12/08 22:31:23 - mmengine - INFO - Epoch(train) [54][340/940] lr: 1.0000e-03 eta: 7:39:11 time: 0.6165 data_time: 0.0247 memory: 16095 grad_norm: 5.2487 loss: 0.8562 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8562 2022/12/08 22:31:35 - mmengine - INFO - Epoch(train) [54][360/940] lr: 1.0000e-03 eta: 7:38:58 time: 0.6038 data_time: 0.0257 memory: 16095 grad_norm: 5.2475 loss: 0.8867 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.8867 2022/12/08 22:31:47 - mmengine - INFO - Epoch(train) [54][380/940] lr: 1.0000e-03 eta: 7:38:45 time: 0.6177 data_time: 0.0238 memory: 16095 grad_norm: 5.1847 loss: 0.8108 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8108 2022/12/08 22:31:59 - mmengine - INFO - Epoch(train) [54][400/940] lr: 1.0000e-03 eta: 7:38:32 time: 0.5935 data_time: 0.0247 memory: 16095 grad_norm: 5.2668 loss: 0.9128 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.9128 2022/12/08 22:32:12 - mmengine - INFO - Epoch(train) [54][420/940] lr: 1.0000e-03 eta: 7:38:19 time: 0.6371 data_time: 0.0348 memory: 16095 grad_norm: 5.2411 loss: 0.9624 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.9624 2022/12/08 22:32:23 - mmengine - INFO - Epoch(train) [54][440/940] lr: 1.0000e-03 eta: 7:38:05 time: 0.5443 data_time: 0.0229 memory: 16095 grad_norm: 5.3248 loss: 0.9715 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.9715 2022/12/08 22:32:36 - mmengine - INFO - Epoch(train) [54][460/940] lr: 1.0000e-03 eta: 7:37:53 time: 0.6637 data_time: 0.0254 memory: 16095 grad_norm: 5.2715 loss: 0.7595 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7595 2022/12/08 22:32:46 - mmengine - INFO - Epoch(train) [54][480/940] lr: 1.0000e-03 eta: 7:37:39 time: 0.5144 data_time: 0.0262 memory: 16095 grad_norm: 5.3275 loss: 0.7952 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7952 2022/12/08 22:33:01 - mmengine - INFO - Epoch(train) [54][500/940] lr: 1.0000e-03 eta: 7:37:28 time: 0.7259 data_time: 0.0242 memory: 16095 grad_norm: 5.3651 loss: 0.8015 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.8015 2022/12/08 22:33:12 - mmengine - INFO - Epoch(train) [54][520/940] lr: 1.0000e-03 eta: 7:37:14 time: 0.5475 data_time: 0.0250 memory: 16095 grad_norm: 5.3492 loss: 0.8249 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8249 2022/12/08 22:33:26 - mmengine - INFO - Epoch(train) [54][540/940] lr: 1.0000e-03 eta: 7:37:03 time: 0.6972 data_time: 0.0238 memory: 16095 grad_norm: 5.2213 loss: 0.8232 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.8232 2022/12/08 22:33:37 - mmengine - INFO - Epoch(train) [54][560/940] lr: 1.0000e-03 eta: 7:36:49 time: 0.5748 data_time: 0.0277 memory: 16095 grad_norm: 5.1668 loss: 0.7360 top1_acc: 0.9062 top5_acc: 0.9062 loss_cls: 0.7360 2022/12/08 22:33:50 - mmengine - INFO - Epoch(train) [54][580/940] lr: 1.0000e-03 eta: 7:36:37 time: 0.6527 data_time: 0.0276 memory: 16095 grad_norm: 5.3323 loss: 0.9288 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 0.9288 2022/12/08 22:34:01 - mmengine - INFO - Epoch(train) [54][600/940] lr: 1.0000e-03 eta: 7:36:23 time: 0.5487 data_time: 0.0230 memory: 16095 grad_norm: 5.2919 loss: 0.8650 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8650 2022/12/08 22:34:14 - mmengine - INFO - Epoch(train) [54][620/940] lr: 1.0000e-03 eta: 7:36:11 time: 0.6469 data_time: 0.0244 memory: 16095 grad_norm: 5.3787 loss: 0.8668 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.8668 2022/12/08 22:34:25 - mmengine - INFO - Epoch(train) [54][640/940] lr: 1.0000e-03 eta: 7:35:57 time: 0.5305 data_time: 0.0232 memory: 16095 grad_norm: 5.3323 loss: 0.8100 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 0.8100 2022/12/08 22:34:38 - mmengine - INFO - Epoch(train) [54][660/940] lr: 1.0000e-03 eta: 7:35:45 time: 0.6671 data_time: 0.0281 memory: 16095 grad_norm: 5.4249 loss: 0.8680 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.8680 2022/12/08 22:34:50 - mmengine - INFO - Epoch(train) [54][680/940] lr: 1.0000e-03 eta: 7:35:32 time: 0.6058 data_time: 0.0221 memory: 16095 grad_norm: 5.2616 loss: 0.8501 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 0.8501 2022/12/08 22:35:03 - mmengine - INFO - Epoch(train) [54][700/940] lr: 1.0000e-03 eta: 7:35:19 time: 0.6215 data_time: 0.0302 memory: 16095 grad_norm: 5.2561 loss: 1.0136 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 1.0136 2022/12/08 22:35:16 - mmengine - INFO - Epoch(train) [54][720/940] lr: 1.0000e-03 eta: 7:35:06 time: 0.6309 data_time: 0.0221 memory: 16095 grad_norm: 5.1969 loss: 0.9434 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.9434 2022/12/08 22:35:27 - mmengine - INFO - Epoch(train) [54][740/940] lr: 1.0000e-03 eta: 7:34:53 time: 0.5957 data_time: 0.0319 memory: 16095 grad_norm: 5.0587 loss: 0.7446 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7446 2022/12/08 22:35:40 - mmengine - INFO - Epoch(train) [54][760/940] lr: 1.0000e-03 eta: 7:34:41 time: 0.6477 data_time: 0.0816 memory: 16095 grad_norm: 5.3341 loss: 0.8972 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8972 2022/12/08 22:35:53 - mmengine - INFO - Epoch(train) [54][780/940] lr: 1.0000e-03 eta: 7:34:28 time: 0.6152 data_time: 0.2005 memory: 16095 grad_norm: 5.2430 loss: 0.9205 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 0.9205 2022/12/08 22:36:04 - mmengine - INFO - Epoch(train) [54][800/940] lr: 1.0000e-03 eta: 7:34:14 time: 0.5438 data_time: 0.1976 memory: 16095 grad_norm: 5.1735 loss: 0.7149 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.7149 2022/12/08 22:36:18 - mmengine - INFO - Epoch(train) [54][820/940] lr: 1.0000e-03 eta: 7:34:03 time: 0.7212 data_time: 0.3651 memory: 16095 grad_norm: 5.1962 loss: 0.9529 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9529 2022/12/08 22:36:29 - mmengine - INFO - Epoch(train) [54][840/940] lr: 1.0000e-03 eta: 7:33:49 time: 0.5531 data_time: 0.2118 memory: 16095 grad_norm: 5.4061 loss: 0.8925 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.8925 2022/12/08 22:36:43 - mmengine - INFO - Epoch(train) [54][860/940] lr: 1.0000e-03 eta: 7:33:38 time: 0.6918 data_time: 0.3555 memory: 16095 grad_norm: 5.4428 loss: 0.8686 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.8686 2022/12/08 22:36:54 - mmengine - INFO - Epoch(train) [54][880/940] lr: 1.0000e-03 eta: 7:33:24 time: 0.5568 data_time: 0.2297 memory: 16095 grad_norm: 5.1665 loss: 0.9257 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9257 2022/12/08 22:37:07 - mmengine - INFO - Epoch(train) [54][900/940] lr: 1.0000e-03 eta: 7:33:12 time: 0.6472 data_time: 0.3230 memory: 16095 grad_norm: 5.2520 loss: 0.9580 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9580 2022/12/08 22:37:19 - mmengine - INFO - Epoch(train) [54][920/940] lr: 1.0000e-03 eta: 7:32:59 time: 0.5904 data_time: 0.2574 memory: 16095 grad_norm: 5.1251 loss: 0.8191 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.8191 2022/12/08 22:37:30 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 22:37:30 - mmengine - INFO - Epoch(train) [54][940/940] lr: 1.0000e-03 eta: 7:32:45 time: 0.5642 data_time: 0.2705 memory: 16095 grad_norm: 5.5917 loss: 0.9283 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 0.9283 2022/12/08 22:37:30 - mmengine - INFO - Saving checkpoint at 54 epochs 2022/12/08 22:37:47 - mmengine - INFO - Epoch(val) [54][20/78] eta: 0:00:40 time: 0.7033 data_time: 0.6097 memory: 1686 2022/12/08 22:37:56 - mmengine - INFO - Epoch(val) [54][40/78] eta: 0:00:22 time: 0.4704 data_time: 0.3780 memory: 1686 2022/12/08 22:38:10 - mmengine - INFO - Epoch(val) [54][60/78] eta: 0:00:11 time: 0.6706 data_time: 0.5759 memory: 1686 2022/12/08 22:38:20 - mmengine - INFO - Epoch(val) [54][78/78] acc/top1: 0.6935 acc/top5: 0.8802 acc/mean1: 0.6934 2022/12/08 22:38:36 - mmengine - INFO - Epoch(train) [55][ 20/940] lr: 1.0000e-03 eta: 7:32:35 time: 0.7944 data_time: 0.4684 memory: 16095 grad_norm: 5.1923 loss: 0.8449 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 0.8449 2022/12/08 22:38:47 - mmengine - INFO - Epoch(train) [55][ 40/940] lr: 1.0000e-03 eta: 7:32:22 time: 0.5589 data_time: 0.1841 memory: 16095 grad_norm: 5.1665 loss: 0.8737 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.8737 2022/12/08 22:39:01 - mmengine - INFO - Epoch(train) [55][ 60/940] lr: 1.0000e-03 eta: 7:32:10 time: 0.6818 data_time: 0.0928 memory: 16095 grad_norm: 5.2865 loss: 0.8181 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.8181 2022/12/08 22:39:12 - mmengine - INFO - Epoch(train) [55][ 80/940] lr: 1.0000e-03 eta: 7:31:56 time: 0.5607 data_time: 0.0325 memory: 16095 grad_norm: 5.1908 loss: 0.8165 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.8165 2022/12/08 22:39:25 - mmengine - INFO - Epoch(train) [55][100/940] lr: 1.0000e-03 eta: 7:31:44 time: 0.6577 data_time: 0.0442 memory: 16095 grad_norm: 5.2177 loss: 0.8243 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.8243 2022/12/08 22:39:36 - mmengine - INFO - Epoch(train) [55][120/940] lr: 1.0000e-03 eta: 7:31:30 time: 0.5554 data_time: 0.0217 memory: 16095 grad_norm: 5.1980 loss: 0.8600 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 0.8600 2022/12/08 22:39:50 - mmengine - INFO - Epoch(train) [55][140/940] lr: 1.0000e-03 eta: 7:31:19 time: 0.6849 data_time: 0.0394 memory: 16095 grad_norm: 5.1558 loss: 0.9525 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.9525 2022/12/08 22:40:01 - mmengine - INFO - Epoch(train) [55][160/940] lr: 1.0000e-03 eta: 7:31:05 time: 0.5442 data_time: 0.0210 memory: 16095 grad_norm: 5.2821 loss: 0.8397 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8397 2022/12/08 22:40:14 - mmengine - INFO - Epoch(train) [55][180/940] lr: 1.0000e-03 eta: 7:30:53 time: 0.6762 data_time: 0.0455 memory: 16095 grad_norm: 5.1730 loss: 0.8649 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 0.8649 2022/12/08 22:40:25 - mmengine - INFO - Epoch(train) [55][200/940] lr: 1.0000e-03 eta: 7:30:39 time: 0.5650 data_time: 0.0212 memory: 16095 grad_norm: 5.2537 loss: 0.8008 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8008 2022/12/08 22:40:40 - mmengine - INFO - Epoch(train) [55][220/940] lr: 1.0000e-03 eta: 7:30:29 time: 0.7281 data_time: 0.0268 memory: 16095 grad_norm: 5.2908 loss: 0.9316 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.9316 2022/12/08 22:40:51 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 22:40:51 - mmengine - INFO - Epoch(train) [55][240/940] lr: 1.0000e-03 eta: 7:30:14 time: 0.5230 data_time: 0.0216 memory: 16095 grad_norm: 5.2694 loss: 0.8558 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.8558 2022/12/08 22:41:04 - mmengine - INFO - Epoch(train) [55][260/940] lr: 1.0000e-03 eta: 7:30:02 time: 0.6611 data_time: 0.0278 memory: 16095 grad_norm: 5.1657 loss: 0.9031 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9031 2022/12/08 22:41:15 - mmengine - INFO - Epoch(train) [55][280/940] lr: 1.0000e-03 eta: 7:29:48 time: 0.5411 data_time: 0.0228 memory: 16095 grad_norm: 5.2580 loss: 0.8929 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8929 2022/12/08 22:41:28 - mmengine - INFO - Epoch(train) [55][300/940] lr: 1.0000e-03 eta: 7:29:36 time: 0.6568 data_time: 0.0248 memory: 16095 grad_norm: 5.3341 loss: 0.9865 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9865 2022/12/08 22:41:39 - mmengine - INFO - Epoch(train) [55][320/940] lr: 1.0000e-03 eta: 7:29:22 time: 0.5416 data_time: 0.0247 memory: 16095 grad_norm: 5.1829 loss: 0.7977 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7977 2022/12/08 22:41:53 - mmengine - INFO - Epoch(train) [55][340/940] lr: 1.0000e-03 eta: 7:29:11 time: 0.7039 data_time: 0.0293 memory: 16095 grad_norm: 5.3731 loss: 0.8340 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.8340 2022/12/08 22:42:03 - mmengine - INFO - Epoch(train) [55][360/940] lr: 1.0000e-03 eta: 7:28:57 time: 0.5324 data_time: 0.0218 memory: 16095 grad_norm: 5.3462 loss: 0.8871 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 0.8871 2022/12/08 22:42:17 - mmengine - INFO - Epoch(train) [55][380/940] lr: 1.0000e-03 eta: 7:28:45 time: 0.6617 data_time: 0.0253 memory: 16095 grad_norm: 5.2735 loss: 0.8766 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 0.8766 2022/12/08 22:42:28 - mmengine - INFO - Epoch(train) [55][400/940] lr: 1.0000e-03 eta: 7:28:31 time: 0.5710 data_time: 0.0207 memory: 16095 grad_norm: 5.3609 loss: 0.8353 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8353 2022/12/08 22:42:40 - mmengine - INFO - Epoch(train) [55][420/940] lr: 1.0000e-03 eta: 7:28:18 time: 0.6203 data_time: 0.0254 memory: 16095 grad_norm: 5.3668 loss: 0.8786 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8786 2022/12/08 22:42:52 - mmengine - INFO - Epoch(train) [55][440/940] lr: 1.0000e-03 eta: 7:28:05 time: 0.5874 data_time: 0.0215 memory: 16095 grad_norm: 5.3573 loss: 0.8127 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8127 2022/12/08 22:43:05 - mmengine - INFO - Epoch(train) [55][460/940] lr: 1.0000e-03 eta: 7:27:53 time: 0.6591 data_time: 0.0256 memory: 16095 grad_norm: 5.3367 loss: 0.8807 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.8807 2022/12/08 22:43:16 - mmengine - INFO - Epoch(train) [55][480/940] lr: 1.0000e-03 eta: 7:27:39 time: 0.5398 data_time: 0.0248 memory: 16095 grad_norm: 5.3554 loss: 0.8192 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8192 2022/12/08 22:43:31 - mmengine - INFO - Epoch(train) [55][500/940] lr: 1.0000e-03 eta: 7:27:28 time: 0.7271 data_time: 0.0259 memory: 16095 grad_norm: 5.2811 loss: 0.8313 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.8313 2022/12/08 22:43:42 - mmengine - INFO - Epoch(train) [55][520/940] lr: 1.0000e-03 eta: 7:27:15 time: 0.5699 data_time: 0.0212 memory: 16095 grad_norm: 5.1778 loss: 0.8947 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8947 2022/12/08 22:43:55 - mmengine - INFO - Epoch(train) [55][540/940] lr: 1.0000e-03 eta: 7:27:02 time: 0.6252 data_time: 0.0340 memory: 16095 grad_norm: 5.3244 loss: 0.8623 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.8623 2022/12/08 22:44:06 - mmengine - INFO - Epoch(train) [55][560/940] lr: 1.0000e-03 eta: 7:26:48 time: 0.5483 data_time: 0.0226 memory: 16095 grad_norm: 5.2290 loss: 0.8757 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8757 2022/12/08 22:44:19 - mmengine - INFO - Epoch(train) [55][580/940] lr: 1.0000e-03 eta: 7:26:36 time: 0.6504 data_time: 0.0250 memory: 16095 grad_norm: 5.2695 loss: 0.8100 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8100 2022/12/08 22:44:30 - mmengine - INFO - Epoch(train) [55][600/940] lr: 1.0000e-03 eta: 7:26:22 time: 0.5745 data_time: 0.0230 memory: 16095 grad_norm: 5.3099 loss: 0.7877 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7877 2022/12/08 22:44:43 - mmengine - INFO - Epoch(train) [55][620/940] lr: 1.0000e-03 eta: 7:26:10 time: 0.6604 data_time: 0.0240 memory: 16095 grad_norm: 5.3307 loss: 0.7817 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7817 2022/12/08 22:44:55 - mmengine - INFO - Epoch(train) [55][640/940] lr: 1.0000e-03 eta: 7:25:57 time: 0.5848 data_time: 0.0244 memory: 16095 grad_norm: 5.2485 loss: 0.8334 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8334 2022/12/08 22:45:08 - mmengine - INFO - Epoch(train) [55][660/940] lr: 1.0000e-03 eta: 7:25:45 time: 0.6569 data_time: 0.0235 memory: 16095 grad_norm: 5.2880 loss: 0.8213 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.8213 2022/12/08 22:45:20 - mmengine - INFO - Epoch(train) [55][680/940] lr: 1.0000e-03 eta: 7:25:32 time: 0.5763 data_time: 0.0251 memory: 16095 grad_norm: 5.2836 loss: 0.8171 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.8171 2022/12/08 22:45:32 - mmengine - INFO - Epoch(train) [55][700/940] lr: 1.0000e-03 eta: 7:25:19 time: 0.6250 data_time: 0.0245 memory: 16095 grad_norm: 5.3073 loss: 0.7784 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7784 2022/12/08 22:45:43 - mmengine - INFO - Epoch(train) [55][720/940] lr: 1.0000e-03 eta: 7:25:05 time: 0.5661 data_time: 0.0231 memory: 16095 grad_norm: 5.2537 loss: 0.8610 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 0.8610 2022/12/08 22:45:56 - mmengine - INFO - Epoch(train) [55][740/940] lr: 1.0000e-03 eta: 7:24:53 time: 0.6092 data_time: 0.0235 memory: 16095 grad_norm: 5.3759 loss: 0.7739 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.7739 2022/12/08 22:46:07 - mmengine - INFO - Epoch(train) [55][760/940] lr: 1.0000e-03 eta: 7:24:39 time: 0.5890 data_time: 0.0247 memory: 16095 grad_norm: 5.3515 loss: 0.8409 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8409 2022/12/08 22:46:20 - mmengine - INFO - Epoch(train) [55][780/940] lr: 1.0000e-03 eta: 7:24:26 time: 0.6074 data_time: 0.0253 memory: 16095 grad_norm: 5.3655 loss: 0.9185 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.9185 2022/12/08 22:46:32 - mmengine - INFO - Epoch(train) [55][800/940] lr: 1.0000e-03 eta: 7:24:14 time: 0.6077 data_time: 0.0257 memory: 16095 grad_norm: 5.4123 loss: 0.8854 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.8854 2022/12/08 22:46:44 - mmengine - INFO - Epoch(train) [55][820/940] lr: 1.0000e-03 eta: 7:24:00 time: 0.5974 data_time: 0.0519 memory: 16095 grad_norm: 5.3371 loss: 0.8442 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8442 2022/12/08 22:46:56 - mmengine - INFO - Epoch(train) [55][840/940] lr: 1.0000e-03 eta: 7:23:48 time: 0.6277 data_time: 0.1247 memory: 16095 grad_norm: 5.3184 loss: 0.8750 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.8750 2022/12/08 22:47:08 - mmengine - INFO - Epoch(train) [55][860/940] lr: 1.0000e-03 eta: 7:23:35 time: 0.5962 data_time: 0.0545 memory: 16095 grad_norm: 5.4147 loss: 0.8486 top1_acc: 0.9062 top5_acc: 0.9062 loss_cls: 0.8486 2022/12/08 22:47:21 - mmengine - INFO - Epoch(train) [55][880/940] lr: 1.0000e-03 eta: 7:23:22 time: 0.6395 data_time: 0.1069 memory: 16095 grad_norm: 5.2956 loss: 0.8897 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8897 2022/12/08 22:47:33 - mmengine - INFO - Epoch(train) [55][900/940] lr: 1.0000e-03 eta: 7:23:10 time: 0.6203 data_time: 0.0224 memory: 16095 grad_norm: 5.3452 loss: 0.9244 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.9244 2022/12/08 22:47:46 - mmengine - INFO - Epoch(train) [55][920/940] lr: 1.0000e-03 eta: 7:22:57 time: 0.6422 data_time: 0.0453 memory: 16095 grad_norm: 5.3972 loss: 0.8503 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8503 2022/12/08 22:47:55 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 22:47:55 - mmengine - INFO - Epoch(train) [55][940/940] lr: 1.0000e-03 eta: 7:22:42 time: 0.4505 data_time: 0.0758 memory: 16095 grad_norm: 5.7620 loss: 1.0422 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.0422 2022/12/08 22:48:10 - mmengine - INFO - Epoch(val) [55][20/78] eta: 0:00:42 time: 0.7264 data_time: 0.6318 memory: 1686 2022/12/08 22:48:19 - mmengine - INFO - Epoch(val) [55][40/78] eta: 0:00:22 time: 0.4423 data_time: 0.3485 memory: 1686 2022/12/08 22:48:32 - mmengine - INFO - Epoch(val) [55][60/78] eta: 0:00:10 time: 0.6637 data_time: 0.5689 memory: 1686 2022/12/08 22:48:43 - mmengine - INFO - Epoch(val) [55][78/78] acc/top1: 0.6920 acc/top5: 0.8803 acc/mean1: 0.6919 2022/12/08 22:48:59 - mmengine - INFO - Epoch(train) [56][ 20/940] lr: 1.0000e-03 eta: 7:22:32 time: 0.8038 data_time: 0.3405 memory: 16095 grad_norm: 5.2537 loss: 0.8173 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8173 2022/12/08 22:49:10 - mmengine - INFO - Epoch(train) [56][ 40/940] lr: 1.0000e-03 eta: 7:22:18 time: 0.5389 data_time: 0.1700 memory: 16095 grad_norm: 5.2972 loss: 0.7852 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7852 2022/12/08 22:49:24 - mmengine - INFO - Epoch(train) [56][ 60/940] lr: 1.0000e-03 eta: 7:22:07 time: 0.6942 data_time: 0.1833 memory: 16095 grad_norm: 5.1729 loss: 0.7842 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7842 2022/12/08 22:49:35 - mmengine - INFO - Epoch(train) [56][ 80/940] lr: 1.0000e-03 eta: 7:21:53 time: 0.5411 data_time: 0.0619 memory: 16095 grad_norm: 5.2599 loss: 0.7809 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7809 2022/12/08 22:49:48 - mmengine - INFO - Epoch(train) [56][100/940] lr: 1.0000e-03 eta: 7:21:41 time: 0.6593 data_time: 0.0462 memory: 16095 grad_norm: 5.3060 loss: 0.7715 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7715 2022/12/08 22:49:59 - mmengine - INFO - Epoch(train) [56][120/940] lr: 1.0000e-03 eta: 7:21:27 time: 0.5692 data_time: 0.0204 memory: 16095 grad_norm: 5.3900 loss: 0.8715 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8715 2022/12/08 22:50:12 - mmengine - INFO - Epoch(train) [56][140/940] lr: 1.0000e-03 eta: 7:21:15 time: 0.6520 data_time: 0.0549 memory: 16095 grad_norm: 5.2535 loss: 0.8502 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8502 2022/12/08 22:50:23 - mmengine - INFO - Epoch(train) [56][160/940] lr: 1.0000e-03 eta: 7:21:01 time: 0.5596 data_time: 0.0206 memory: 16095 grad_norm: 5.2643 loss: 0.8150 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8150 2022/12/08 22:50:36 - mmengine - INFO - Epoch(train) [56][180/940] lr: 1.0000e-03 eta: 7:20:49 time: 0.6304 data_time: 0.0658 memory: 16095 grad_norm: 5.2189 loss: 0.8494 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.8494 2022/12/08 22:50:48 - mmengine - INFO - Epoch(train) [56][200/940] lr: 1.0000e-03 eta: 7:20:35 time: 0.5751 data_time: 0.0589 memory: 16095 grad_norm: 5.3658 loss: 0.8565 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8565 2022/12/08 22:51:01 - mmengine - INFO - Epoch(train) [56][220/940] lr: 1.0000e-03 eta: 7:20:24 time: 0.6867 data_time: 0.0302 memory: 16095 grad_norm: 5.3798 loss: 0.8792 top1_acc: 0.6875 top5_acc: 0.9688 loss_cls: 0.8792 2022/12/08 22:51:13 - mmengine - INFO - Epoch(train) [56][240/940] lr: 1.0000e-03 eta: 7:20:10 time: 0.5712 data_time: 0.0206 memory: 16095 grad_norm: 5.3018 loss: 0.8237 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.8237 2022/12/08 22:51:26 - mmengine - INFO - Epoch(train) [56][260/940] lr: 1.0000e-03 eta: 7:19:59 time: 0.6858 data_time: 0.0263 memory: 16095 grad_norm: 5.4360 loss: 0.8437 top1_acc: 0.8125 top5_acc: 0.8438 loss_cls: 0.8437 2022/12/08 22:51:37 - mmengine - INFO - Epoch(train) [56][280/940] lr: 1.0000e-03 eta: 7:19:45 time: 0.5366 data_time: 0.0209 memory: 16095 grad_norm: 5.2741 loss: 0.8315 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8315 2022/12/08 22:51:50 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 22:51:50 - mmengine - INFO - Epoch(train) [56][300/940] lr: 1.0000e-03 eta: 7:19:32 time: 0.6320 data_time: 0.0277 memory: 16095 grad_norm: 5.2387 loss: 0.7766 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.7766 2022/12/08 22:52:02 - mmengine - INFO - Epoch(train) [56][320/940] lr: 1.0000e-03 eta: 7:19:19 time: 0.5902 data_time: 0.0298 memory: 16095 grad_norm: 5.2758 loss: 0.7696 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7696 2022/12/08 22:52:14 - mmengine - INFO - Epoch(train) [56][340/940] lr: 1.0000e-03 eta: 7:19:07 time: 0.6282 data_time: 0.0290 memory: 16095 grad_norm: 5.3533 loss: 0.8647 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 0.8647 2022/12/08 22:52:26 - mmengine - INFO - Epoch(train) [56][360/940] lr: 1.0000e-03 eta: 7:18:53 time: 0.5651 data_time: 0.0381 memory: 16095 grad_norm: 5.2690 loss: 0.7795 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7795 2022/12/08 22:52:38 - mmengine - INFO - Epoch(train) [56][380/940] lr: 1.0000e-03 eta: 7:18:41 time: 0.6388 data_time: 0.0267 memory: 16095 grad_norm: 5.3386 loss: 0.8115 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8115 2022/12/08 22:52:51 - mmengine - INFO - Epoch(train) [56][400/940] lr: 1.0000e-03 eta: 7:18:28 time: 0.6236 data_time: 0.0222 memory: 16095 grad_norm: 5.3258 loss: 0.8694 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.8694 2022/12/08 22:53:03 - mmengine - INFO - Epoch(train) [56][420/940] lr: 1.0000e-03 eta: 7:18:15 time: 0.5993 data_time: 0.0254 memory: 16095 grad_norm: 5.3363 loss: 0.8676 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8676 2022/12/08 22:53:16 - mmengine - INFO - Epoch(train) [56][440/940] lr: 1.0000e-03 eta: 7:18:02 time: 0.6367 data_time: 0.0245 memory: 16095 grad_norm: 5.3114 loss: 0.9251 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.9251 2022/12/08 22:53:27 - mmengine - INFO - Epoch(train) [56][460/940] lr: 1.0000e-03 eta: 7:17:49 time: 0.5765 data_time: 0.0276 memory: 16095 grad_norm: 5.4398 loss: 0.8660 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8660 2022/12/08 22:53:39 - mmengine - INFO - Epoch(train) [56][480/940] lr: 1.0000e-03 eta: 7:17:36 time: 0.5787 data_time: 0.0246 memory: 16095 grad_norm: 5.3844 loss: 0.8981 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8981 2022/12/08 22:53:52 - mmengine - INFO - Epoch(train) [56][500/940] lr: 1.0000e-03 eta: 7:17:24 time: 0.6860 data_time: 0.0249 memory: 16095 grad_norm: 5.1625 loss: 0.7664 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7664 2022/12/08 22:54:04 - mmengine - INFO - Epoch(train) [56][520/940] lr: 1.0000e-03 eta: 7:17:11 time: 0.5629 data_time: 0.0238 memory: 16095 grad_norm: 5.4211 loss: 0.8418 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.8418 2022/12/08 22:54:17 - mmengine - INFO - Epoch(train) [56][540/940] lr: 1.0000e-03 eta: 7:16:58 time: 0.6438 data_time: 0.0244 memory: 16095 grad_norm: 5.3592 loss: 0.8392 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8392 2022/12/08 22:54:28 - mmengine - INFO - Epoch(train) [56][560/940] lr: 1.0000e-03 eta: 7:16:45 time: 0.5813 data_time: 0.0227 memory: 16095 grad_norm: 5.3263 loss: 0.8288 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8288 2022/12/08 22:54:42 - mmengine - INFO - Epoch(train) [56][580/940] lr: 1.0000e-03 eta: 7:16:33 time: 0.6696 data_time: 0.0246 memory: 16095 grad_norm: 5.2968 loss: 0.8237 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.8237 2022/12/08 22:54:53 - mmengine - INFO - Epoch(train) [56][600/940] lr: 1.0000e-03 eta: 7:16:19 time: 0.5651 data_time: 0.0246 memory: 16095 grad_norm: 5.3115 loss: 0.7531 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7531 2022/12/08 22:55:07 - mmengine - INFO - Epoch(train) [56][620/940] lr: 1.0000e-03 eta: 7:16:08 time: 0.6918 data_time: 0.0240 memory: 16095 grad_norm: 5.4827 loss: 0.8462 top1_acc: 0.6562 top5_acc: 0.9688 loss_cls: 0.8462 2022/12/08 22:55:18 - mmengine - INFO - Epoch(train) [56][640/940] lr: 1.0000e-03 eta: 7:15:54 time: 0.5631 data_time: 0.0239 memory: 16095 grad_norm: 5.2730 loss: 0.8075 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8075 2022/12/08 22:55:32 - mmengine - INFO - Epoch(train) [56][660/940] lr: 1.0000e-03 eta: 7:15:43 time: 0.6918 data_time: 0.0268 memory: 16095 grad_norm: 5.3223 loss: 0.7728 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.7728 2022/12/08 22:55:42 - mmengine - INFO - Epoch(train) [56][680/940] lr: 1.0000e-03 eta: 7:15:28 time: 0.4986 data_time: 0.0256 memory: 16095 grad_norm: 5.3538 loss: 0.8905 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.8905 2022/12/08 22:55:55 - mmengine - INFO - Epoch(train) [56][700/940] lr: 1.0000e-03 eta: 7:15:16 time: 0.6486 data_time: 0.0247 memory: 16095 grad_norm: 5.2822 loss: 0.7247 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7247 2022/12/08 22:56:05 - mmengine - INFO - Epoch(train) [56][720/940] lr: 1.0000e-03 eta: 7:15:02 time: 0.5282 data_time: 0.0248 memory: 16095 grad_norm: 5.4273 loss: 0.8004 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8004 2022/12/08 22:56:19 - mmengine - INFO - Epoch(train) [56][740/940] lr: 1.0000e-03 eta: 7:14:51 time: 0.7081 data_time: 0.0243 memory: 16095 grad_norm: 5.5431 loss: 0.8847 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8847 2022/12/08 22:56:30 - mmengine - INFO - Epoch(train) [56][760/940] lr: 1.0000e-03 eta: 7:14:36 time: 0.5274 data_time: 0.0231 memory: 16095 grad_norm: 5.2324 loss: 0.8705 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.8705 2022/12/08 22:56:43 - mmengine - INFO - Epoch(train) [56][780/940] lr: 1.0000e-03 eta: 7:14:24 time: 0.6565 data_time: 0.0238 memory: 16095 grad_norm: 5.3915 loss: 0.7600 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7600 2022/12/08 22:56:55 - mmengine - INFO - Epoch(train) [56][800/940] lr: 1.0000e-03 eta: 7:14:12 time: 0.6141 data_time: 0.0343 memory: 16095 grad_norm: 5.2436 loss: 0.8592 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8592 2022/12/08 22:57:08 - mmengine - INFO - Epoch(train) [56][820/940] lr: 1.0000e-03 eta: 7:13:59 time: 0.6150 data_time: 0.0254 memory: 16095 grad_norm: 5.4131 loss: 0.8833 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 0.8833 2022/12/08 22:57:19 - mmengine - INFO - Epoch(train) [56][840/940] lr: 1.0000e-03 eta: 7:13:46 time: 0.5804 data_time: 0.0217 memory: 16095 grad_norm: 5.2844 loss: 0.8401 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8401 2022/12/08 22:57:32 - mmengine - INFO - Epoch(train) [56][860/940] lr: 1.0000e-03 eta: 7:13:33 time: 0.6244 data_time: 0.0272 memory: 16095 grad_norm: 5.4484 loss: 0.9906 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.9906 2022/12/08 22:57:45 - mmengine - INFO - Epoch(train) [56][880/940] lr: 1.0000e-03 eta: 7:13:21 time: 0.6768 data_time: 0.0216 memory: 16095 grad_norm: 5.4096 loss: 0.8232 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8232 2022/12/08 22:57:57 - mmengine - INFO - Epoch(train) [56][900/940] lr: 1.0000e-03 eta: 7:13:08 time: 0.5790 data_time: 0.0269 memory: 16095 grad_norm: 5.2471 loss: 0.7717 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7717 2022/12/08 22:58:11 - mmengine - INFO - Epoch(train) [56][920/940] lr: 1.0000e-03 eta: 7:12:56 time: 0.6977 data_time: 0.0233 memory: 16095 grad_norm: 5.2224 loss: 0.8242 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8242 2022/12/08 22:58:20 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 22:58:20 - mmengine - INFO - Epoch(train) [56][940/940] lr: 1.0000e-03 eta: 7:12:41 time: 0.4495 data_time: 0.0170 memory: 16095 grad_norm: 5.5550 loss: 0.8542 top1_acc: 0.7143 top5_acc: 1.0000 loss_cls: 0.8542 2022/12/08 22:58:34 - mmengine - INFO - Epoch(val) [56][20/78] eta: 0:00:40 time: 0.7019 data_time: 0.6083 memory: 1686 2022/12/08 22:58:43 - mmengine - INFO - Epoch(val) [56][40/78] eta: 0:00:22 time: 0.4623 data_time: 0.3684 memory: 1686 2022/12/08 22:58:57 - mmengine - INFO - Epoch(val) [56][60/78] eta: 0:00:11 time: 0.6821 data_time: 0.5880 memory: 1686 2022/12/08 22:59:07 - mmengine - INFO - Epoch(val) [56][78/78] acc/top1: 0.6957 acc/top5: 0.8808 acc/mean1: 0.6956 2022/12/08 22:59:07 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/hukai/test_mm/mmaction2/work_dirs/tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb/best_acc/top1_epoch_50.pth is removed 2022/12/08 22:59:09 - mmengine - INFO - The best checkpoint with 0.6957 acc/top1 at 56 epoch is saved to best_acc/top1_epoch_56.pth. 2022/12/08 22:59:26 - mmengine - INFO - Epoch(train) [57][ 20/940] lr: 1.0000e-03 eta: 7:12:31 time: 0.8225 data_time: 0.5152 memory: 16095 grad_norm: 5.2519 loss: 0.8165 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8165 2022/12/08 22:59:37 - mmengine - INFO - Epoch(train) [57][ 40/940] lr: 1.0000e-03 eta: 7:12:18 time: 0.5590 data_time: 0.2447 memory: 16095 grad_norm: 5.2219 loss: 0.7627 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7627 2022/12/08 22:59:50 - mmengine - INFO - Epoch(train) [57][ 60/940] lr: 1.0000e-03 eta: 7:12:06 time: 0.6455 data_time: 0.3280 memory: 16095 grad_norm: 5.2328 loss: 0.8733 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 0.8733 2022/12/08 23:00:01 - mmengine - INFO - Epoch(train) [57][ 80/940] lr: 1.0000e-03 eta: 7:11:52 time: 0.5390 data_time: 0.2256 memory: 16095 grad_norm: 5.1549 loss: 0.7556 top1_acc: 0.8125 top5_acc: 0.8438 loss_cls: 0.7556 2022/12/08 23:00:13 - mmengine - INFO - Epoch(train) [57][100/940] lr: 1.0000e-03 eta: 7:11:39 time: 0.6361 data_time: 0.3092 memory: 16095 grad_norm: 5.1874 loss: 0.8550 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.8550 2022/12/08 23:00:25 - mmengine - INFO - Epoch(train) [57][120/940] lr: 1.0000e-03 eta: 7:11:26 time: 0.5923 data_time: 0.2910 memory: 16095 grad_norm: 5.1068 loss: 0.7895 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.7895 2022/12/08 23:00:40 - mmengine - INFO - Epoch(train) [57][140/940] lr: 1.0000e-03 eta: 7:11:15 time: 0.7208 data_time: 0.4090 memory: 16095 grad_norm: 5.2591 loss: 0.7645 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.7645 2022/12/08 23:00:51 - mmengine - INFO - Epoch(train) [57][160/940] lr: 1.0000e-03 eta: 7:11:01 time: 0.5493 data_time: 0.2277 memory: 16095 grad_norm: 5.3296 loss: 0.8728 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.8728 2022/12/08 23:01:05 - mmengine - INFO - Epoch(train) [57][180/940] lr: 1.0000e-03 eta: 7:10:50 time: 0.6906 data_time: 0.3804 memory: 16095 grad_norm: 5.4203 loss: 1.0022 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0022 2022/12/08 23:01:15 - mmengine - INFO - Epoch(train) [57][200/940] lr: 1.0000e-03 eta: 7:10:35 time: 0.5238 data_time: 0.2125 memory: 16095 grad_norm: 5.4060 loss: 0.9008 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.9008 2022/12/08 23:01:28 - mmengine - INFO - Epoch(train) [57][220/940] lr: 1.0000e-03 eta: 7:10:24 time: 0.6691 data_time: 0.3552 memory: 16095 grad_norm: 5.2848 loss: 0.8120 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.8120 2022/12/08 23:01:40 - mmengine - INFO - Epoch(train) [57][240/940] lr: 1.0000e-03 eta: 7:10:10 time: 0.5584 data_time: 0.2309 memory: 16095 grad_norm: 5.4239 loss: 0.7365 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7365 2022/12/08 23:01:52 - mmengine - INFO - Epoch(train) [57][260/940] lr: 1.0000e-03 eta: 7:09:57 time: 0.6108 data_time: 0.2922 memory: 16095 grad_norm: 5.3819 loss: 0.8322 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8322 2022/12/08 23:02:04 - mmengine - INFO - Epoch(train) [57][280/940] lr: 1.0000e-03 eta: 7:09:44 time: 0.5837 data_time: 0.2165 memory: 16095 grad_norm: 5.3607 loss: 0.9062 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.9062 2022/12/08 23:02:16 - mmengine - INFO - Epoch(train) [57][300/940] lr: 1.0000e-03 eta: 7:09:32 time: 0.6410 data_time: 0.3113 memory: 16095 grad_norm: 5.2713 loss: 0.8648 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8648 2022/12/08 23:02:27 - mmengine - INFO - Epoch(train) [57][320/940] lr: 1.0000e-03 eta: 7:09:18 time: 0.5487 data_time: 0.1899 memory: 16095 grad_norm: 5.3745 loss: 0.8530 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.8530 2022/12/08 23:02:41 - mmengine - INFO - Epoch(train) [57][340/940] lr: 1.0000e-03 eta: 7:09:06 time: 0.6701 data_time: 0.2413 memory: 16095 grad_norm: 5.5317 loss: 0.9458 top1_acc: 0.6875 top5_acc: 0.7812 loss_cls: 0.9458 2022/12/08 23:02:51 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 23:02:51 - mmengine - INFO - Epoch(train) [57][360/940] lr: 1.0000e-03 eta: 7:08:52 time: 0.5237 data_time: 0.1941 memory: 16095 grad_norm: 5.2844 loss: 0.7845 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7845 2022/12/08 23:03:05 - mmengine - INFO - Epoch(train) [57][380/940] lr: 1.0000e-03 eta: 7:08:40 time: 0.6652 data_time: 0.3219 memory: 16095 grad_norm: 5.2447 loss: 0.7369 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7369 2022/12/08 23:03:16 - mmengine - INFO - Epoch(train) [57][400/940] lr: 1.0000e-03 eta: 7:08:26 time: 0.5641 data_time: 0.1704 memory: 16095 grad_norm: 5.4534 loss: 0.8111 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8111 2022/12/08 23:03:28 - mmengine - INFO - Epoch(train) [57][420/940] lr: 1.0000e-03 eta: 7:08:14 time: 0.6227 data_time: 0.0892 memory: 16095 grad_norm: 5.3626 loss: 0.9048 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.9048 2022/12/08 23:03:42 - mmengine - INFO - Epoch(train) [57][440/940] lr: 1.0000e-03 eta: 7:08:02 time: 0.6654 data_time: 0.0474 memory: 16095 grad_norm: 5.4724 loss: 0.8878 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8878 2022/12/08 23:03:53 - mmengine - INFO - Epoch(train) [57][460/940] lr: 1.0000e-03 eta: 7:07:48 time: 0.5716 data_time: 0.0253 memory: 16095 grad_norm: 5.3757 loss: 0.8755 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.8755 2022/12/08 23:04:06 - mmengine - INFO - Epoch(train) [57][480/940] lr: 1.0000e-03 eta: 7:07:36 time: 0.6299 data_time: 0.0232 memory: 16095 grad_norm: 5.3848 loss: 0.7773 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7773 2022/12/08 23:04:17 - mmengine - INFO - Epoch(train) [57][500/940] lr: 1.0000e-03 eta: 7:07:22 time: 0.5868 data_time: 0.0337 memory: 16095 grad_norm: 5.4538 loss: 0.8572 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8572 2022/12/08 23:04:30 - mmengine - INFO - Epoch(train) [57][520/940] lr: 1.0000e-03 eta: 7:07:10 time: 0.6257 data_time: 0.0243 memory: 16095 grad_norm: 5.3111 loss: 0.8621 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.8621 2022/12/08 23:04:42 - mmengine - INFO - Epoch(train) [57][540/940] lr: 1.0000e-03 eta: 7:06:57 time: 0.5985 data_time: 0.0233 memory: 16095 grad_norm: 5.5251 loss: 0.8075 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 0.8075 2022/12/08 23:04:55 - mmengine - INFO - Epoch(train) [57][560/940] lr: 1.0000e-03 eta: 7:06:44 time: 0.6355 data_time: 0.0244 memory: 16095 grad_norm: 5.2649 loss: 0.8301 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.8301 2022/12/08 23:05:07 - mmengine - INFO - Epoch(train) [57][580/940] lr: 1.0000e-03 eta: 7:06:32 time: 0.6068 data_time: 0.0238 memory: 16095 grad_norm: 5.3944 loss: 0.9318 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9318 2022/12/08 23:05:20 - mmengine - INFO - Epoch(train) [57][600/940] lr: 1.0000e-03 eta: 7:06:19 time: 0.6510 data_time: 0.0263 memory: 16095 grad_norm: 5.2664 loss: 0.8042 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.8042 2022/12/08 23:05:31 - mmengine - INFO - Epoch(train) [57][620/940] lr: 1.0000e-03 eta: 7:06:06 time: 0.5534 data_time: 0.0250 memory: 16095 grad_norm: 5.3157 loss: 0.7252 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7252 2022/12/08 23:05:43 - mmengine - INFO - Epoch(train) [57][640/940] lr: 1.0000e-03 eta: 7:05:53 time: 0.6115 data_time: 0.0250 memory: 16095 grad_norm: 5.4619 loss: 0.8481 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8481 2022/12/08 23:05:56 - mmengine - INFO - Epoch(train) [57][660/940] lr: 1.0000e-03 eta: 7:05:41 time: 0.6635 data_time: 0.0256 memory: 16095 grad_norm: 5.3226 loss: 0.7702 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7702 2022/12/08 23:06:08 - mmengine - INFO - Epoch(train) [57][680/940] lr: 1.0000e-03 eta: 7:05:27 time: 0.5599 data_time: 0.0237 memory: 16095 grad_norm: 5.3269 loss: 0.8592 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8592 2022/12/08 23:06:20 - mmengine - INFO - Epoch(train) [57][700/940] lr: 1.0000e-03 eta: 7:05:15 time: 0.6429 data_time: 0.0269 memory: 16095 grad_norm: 5.3999 loss: 0.9770 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9770 2022/12/08 23:06:32 - mmengine - INFO - Epoch(train) [57][720/940] lr: 1.0000e-03 eta: 7:05:02 time: 0.5829 data_time: 0.0245 memory: 16095 grad_norm: 5.2059 loss: 0.7521 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7521 2022/12/08 23:06:44 - mmengine - INFO - Epoch(train) [57][740/940] lr: 1.0000e-03 eta: 7:04:49 time: 0.6129 data_time: 0.0238 memory: 16095 grad_norm: 5.4078 loss: 0.7396 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7396 2022/12/08 23:06:57 - mmengine - INFO - Epoch(train) [57][760/940] lr: 1.0000e-03 eta: 7:04:37 time: 0.6350 data_time: 0.0258 memory: 16095 grad_norm: 5.5156 loss: 0.9529 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 0.9529 2022/12/08 23:07:08 - mmengine - INFO - Epoch(train) [57][780/940] lr: 1.0000e-03 eta: 7:04:23 time: 0.5453 data_time: 0.0262 memory: 16095 grad_norm: 5.2192 loss: 0.7376 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 0.7376 2022/12/08 23:07:21 - mmengine - INFO - Epoch(train) [57][800/940] lr: 1.0000e-03 eta: 7:04:11 time: 0.6447 data_time: 0.0219 memory: 16095 grad_norm: 5.4140 loss: 0.7682 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7682 2022/12/08 23:07:32 - mmengine - INFO - Epoch(train) [57][820/940] lr: 1.0000e-03 eta: 7:03:57 time: 0.5613 data_time: 0.0248 memory: 16095 grad_norm: 5.3444 loss: 0.8857 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8857 2022/12/08 23:07:44 - mmengine - INFO - Epoch(train) [57][840/940] lr: 1.0000e-03 eta: 7:03:44 time: 0.6135 data_time: 0.0230 memory: 16095 grad_norm: 5.2895 loss: 0.8158 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8158 2022/12/08 23:07:57 - mmengine - INFO - Epoch(train) [57][860/940] lr: 1.0000e-03 eta: 7:03:32 time: 0.6383 data_time: 0.0247 memory: 16095 grad_norm: 5.4060 loss: 0.9007 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.9007 2022/12/08 23:08:09 - mmengine - INFO - Epoch(train) [57][880/940] lr: 1.0000e-03 eta: 7:03:19 time: 0.5802 data_time: 0.0245 memory: 16095 grad_norm: 5.5456 loss: 0.7560 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7560 2022/12/08 23:08:22 - mmengine - INFO - Epoch(train) [57][900/940] lr: 1.0000e-03 eta: 7:03:07 time: 0.6647 data_time: 0.0238 memory: 16095 grad_norm: 5.3106 loss: 0.7663 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.7663 2022/12/08 23:08:33 - mmengine - INFO - Epoch(train) [57][920/940] lr: 1.0000e-03 eta: 7:02:53 time: 0.5529 data_time: 0.0268 memory: 16095 grad_norm: 5.3348 loss: 0.8491 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8491 2022/12/08 23:08:44 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 23:08:44 - mmengine - INFO - Epoch(train) [57][940/940] lr: 1.0000e-03 eta: 7:02:39 time: 0.5446 data_time: 0.0184 memory: 16095 grad_norm: 5.6303 loss: 0.8143 top1_acc: 0.5714 top5_acc: 1.0000 loss_cls: 0.8143 2022/12/08 23:08:44 - mmengine - INFO - Saving checkpoint at 57 epochs 2022/12/08 23:09:01 - mmengine - INFO - Epoch(val) [57][20/78] eta: 0:00:40 time: 0.7008 data_time: 0.6047 memory: 1686 2022/12/08 23:09:10 - mmengine - INFO - Epoch(val) [57][40/78] eta: 0:00:22 time: 0.4791 data_time: 0.3851 memory: 1686 2022/12/08 23:09:24 - mmengine - INFO - Epoch(val) [57][60/78] eta: 0:00:11 time: 0.6576 data_time: 0.5635 memory: 1686 2022/12/08 23:09:33 - mmengine - INFO - Epoch(val) [57][78/78] acc/top1: 0.6947 acc/top5: 0.8788 acc/mean1: 0.6946 2022/12/08 23:09:50 - mmengine - INFO - Epoch(train) [58][ 20/940] lr: 1.0000e-03 eta: 7:02:30 time: 0.8380 data_time: 0.4147 memory: 16095 grad_norm: 5.4260 loss: 0.8616 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8616 2022/12/08 23:10:01 - mmengine - INFO - Epoch(train) [58][ 40/940] lr: 1.0000e-03 eta: 7:02:16 time: 0.5246 data_time: 0.1469 memory: 16095 grad_norm: 5.2678 loss: 0.9219 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.9219 2022/12/08 23:10:15 - mmengine - INFO - Epoch(train) [58][ 60/940] lr: 1.0000e-03 eta: 7:02:04 time: 0.7031 data_time: 0.1722 memory: 16095 grad_norm: 5.2517 loss: 0.8456 top1_acc: 0.5938 top5_acc: 0.9062 loss_cls: 0.8456 2022/12/08 23:10:25 - mmengine - INFO - Epoch(train) [58][ 80/940] lr: 1.0000e-03 eta: 7:01:50 time: 0.5274 data_time: 0.0222 memory: 16095 grad_norm: 5.2348 loss: 0.8039 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8039 2022/12/08 23:10:39 - mmengine - INFO - Epoch(train) [58][100/940] lr: 1.0000e-03 eta: 7:01:39 time: 0.6930 data_time: 0.0268 memory: 16095 grad_norm: 5.4908 loss: 0.9111 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.9111 2022/12/08 23:10:51 - mmengine - INFO - Epoch(train) [58][120/940] lr: 1.0000e-03 eta: 7:01:25 time: 0.5775 data_time: 0.0212 memory: 16095 grad_norm: 5.5065 loss: 0.9019 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.9019 2022/12/08 23:11:04 - mmengine - INFO - Epoch(train) [58][140/940] lr: 1.0000e-03 eta: 7:01:13 time: 0.6765 data_time: 0.0241 memory: 16095 grad_norm: 5.1868 loss: 0.8086 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.8086 2022/12/08 23:11:15 - mmengine - INFO - Epoch(train) [58][160/940] lr: 1.0000e-03 eta: 7:01:00 time: 0.5464 data_time: 0.0254 memory: 16095 grad_norm: 5.1423 loss: 0.8344 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.8344 2022/12/08 23:11:29 - mmengine - INFO - Epoch(train) [58][180/940] lr: 1.0000e-03 eta: 7:00:48 time: 0.6918 data_time: 0.0237 memory: 16095 grad_norm: 5.3820 loss: 0.7813 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.7813 2022/12/08 23:11:39 - mmengine - INFO - Epoch(train) [58][200/940] lr: 1.0000e-03 eta: 7:00:34 time: 0.5210 data_time: 0.0223 memory: 16095 grad_norm: 5.4917 loss: 0.7280 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7280 2022/12/08 23:11:53 - mmengine - INFO - Epoch(train) [58][220/940] lr: 1.0000e-03 eta: 7:00:22 time: 0.6722 data_time: 0.0247 memory: 16095 grad_norm: 5.2558 loss: 0.6822 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6822 2022/12/08 23:12:05 - mmengine - INFO - Epoch(train) [58][240/940] lr: 1.0000e-03 eta: 7:00:09 time: 0.6055 data_time: 0.0243 memory: 16095 grad_norm: 5.4801 loss: 0.8696 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.8696 2022/12/08 23:12:18 - mmengine - INFO - Epoch(train) [58][260/940] lr: 1.0000e-03 eta: 6:59:57 time: 0.6244 data_time: 0.0241 memory: 16095 grad_norm: 5.1843 loss: 0.7323 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7323 2022/12/08 23:12:28 - mmengine - INFO - Epoch(train) [58][280/940] lr: 1.0000e-03 eta: 6:59:42 time: 0.5085 data_time: 0.0255 memory: 16095 grad_norm: 5.2196 loss: 0.8536 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8536 2022/12/08 23:12:42 - mmengine - INFO - Epoch(train) [58][300/940] lr: 1.0000e-03 eta: 6:59:31 time: 0.6919 data_time: 0.0429 memory: 16095 grad_norm: 5.4693 loss: 0.7418 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7418 2022/12/08 23:12:53 - mmengine - INFO - Epoch(train) [58][320/940] lr: 1.0000e-03 eta: 6:59:17 time: 0.5768 data_time: 0.0376 memory: 16095 grad_norm: 5.3802 loss: 0.9198 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9198 2022/12/08 23:13:06 - mmengine - INFO - Epoch(train) [58][340/940] lr: 1.0000e-03 eta: 6:59:05 time: 0.6486 data_time: 0.0240 memory: 16095 grad_norm: 5.4192 loss: 0.8573 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.8573 2022/12/08 23:13:17 - mmengine - INFO - Epoch(train) [58][360/940] lr: 1.0000e-03 eta: 6:58:51 time: 0.5391 data_time: 0.0247 memory: 16095 grad_norm: 5.4592 loss: 0.8790 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8790 2022/12/08 23:13:29 - mmengine - INFO - Epoch(train) [58][380/940] lr: 1.0000e-03 eta: 6:58:39 time: 0.6302 data_time: 0.0244 memory: 16095 grad_norm: 5.3806 loss: 0.8648 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 0.8648 2022/12/08 23:13:41 - mmengine - INFO - Epoch(train) [58][400/940] lr: 1.0000e-03 eta: 6:58:26 time: 0.5993 data_time: 0.0244 memory: 16095 grad_norm: 5.2346 loss: 0.8898 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 0.8898 2022/12/08 23:13:54 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 23:13:54 - mmengine - INFO - Epoch(train) [58][420/940] lr: 1.0000e-03 eta: 6:58:13 time: 0.6225 data_time: 0.0254 memory: 16095 grad_norm: 5.3664 loss: 0.8854 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 0.8854 2022/12/08 23:14:05 - mmengine - INFO - Epoch(train) [58][440/940] lr: 1.0000e-03 eta: 6:58:00 time: 0.5648 data_time: 0.0370 memory: 16095 grad_norm: 5.3384 loss: 0.8020 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.8020 2022/12/08 23:14:19 - mmengine - INFO - Epoch(train) [58][460/940] lr: 1.0000e-03 eta: 6:57:48 time: 0.6697 data_time: 0.0254 memory: 16095 grad_norm: 5.5279 loss: 0.8186 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.8186 2022/12/08 23:14:30 - mmengine - INFO - Epoch(train) [58][480/940] lr: 1.0000e-03 eta: 6:57:34 time: 0.5566 data_time: 0.0255 memory: 16095 grad_norm: 5.4285 loss: 0.7985 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7985 2022/12/08 23:14:44 - mmengine - INFO - Epoch(train) [58][500/940] lr: 1.0000e-03 eta: 6:57:23 time: 0.6975 data_time: 0.0247 memory: 16095 grad_norm: 5.3290 loss: 0.8357 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8357 2022/12/08 23:14:55 - mmengine - INFO - Epoch(train) [58][520/940] lr: 1.0000e-03 eta: 6:57:09 time: 0.5700 data_time: 0.0220 memory: 16095 grad_norm: 5.3257 loss: 0.8727 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8727 2022/12/08 23:15:08 - mmengine - INFO - Epoch(train) [58][540/940] lr: 1.0000e-03 eta: 6:56:57 time: 0.6404 data_time: 0.0251 memory: 16095 grad_norm: 5.4020 loss: 0.7435 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7435 2022/12/08 23:15:19 - mmengine - INFO - Epoch(train) [58][560/940] lr: 1.0000e-03 eta: 6:56:44 time: 0.5692 data_time: 0.0226 memory: 16095 grad_norm: 5.5258 loss: 0.8570 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8570 2022/12/08 23:15:33 - mmengine - INFO - Epoch(train) [58][580/940] lr: 1.0000e-03 eta: 6:56:32 time: 0.6873 data_time: 0.0257 memory: 16095 grad_norm: 5.3399 loss: 0.7003 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7003 2022/12/08 23:15:43 - mmengine - INFO - Epoch(train) [58][600/940] lr: 1.0000e-03 eta: 6:56:18 time: 0.5149 data_time: 0.0220 memory: 16095 grad_norm: 5.6133 loss: 0.8377 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.8377 2022/12/08 23:15:57 - mmengine - INFO - Epoch(train) [58][620/940] lr: 1.0000e-03 eta: 6:56:06 time: 0.6865 data_time: 0.0247 memory: 16095 grad_norm: 5.4486 loss: 0.8783 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8783 2022/12/08 23:16:08 - mmengine - INFO - Epoch(train) [58][640/940] lr: 1.0000e-03 eta: 6:55:53 time: 0.5610 data_time: 0.0246 memory: 16095 grad_norm: 5.4668 loss: 0.8869 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8869 2022/12/08 23:16:21 - mmengine - INFO - Epoch(train) [58][660/940] lr: 1.0000e-03 eta: 6:55:40 time: 0.6454 data_time: 0.0247 memory: 16095 grad_norm: 5.4470 loss: 0.7837 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7837 2022/12/08 23:16:32 - mmengine - INFO - Epoch(train) [58][680/940] lr: 1.0000e-03 eta: 6:55:27 time: 0.5559 data_time: 0.0263 memory: 16095 grad_norm: 5.3705 loss: 0.8971 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.8971 2022/12/08 23:16:46 - mmengine - INFO - Epoch(train) [58][700/940] lr: 1.0000e-03 eta: 6:55:15 time: 0.6980 data_time: 0.0258 memory: 16095 grad_norm: 5.4751 loss: 0.8652 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 0.8652 2022/12/08 23:16:57 - mmengine - INFO - Epoch(train) [58][720/940] lr: 1.0000e-03 eta: 6:55:01 time: 0.5386 data_time: 0.0307 memory: 16095 grad_norm: 5.2917 loss: 0.7859 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.7859 2022/12/08 23:17:10 - mmengine - INFO - Epoch(train) [58][740/940] lr: 1.0000e-03 eta: 6:54:49 time: 0.6277 data_time: 0.0262 memory: 16095 grad_norm: 5.4852 loss: 0.8803 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.8803 2022/12/08 23:17:21 - mmengine - INFO - Epoch(train) [58][760/940] lr: 1.0000e-03 eta: 6:54:35 time: 0.5661 data_time: 0.0231 memory: 16095 grad_norm: 5.5418 loss: 0.8116 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.8116 2022/12/08 23:17:33 - mmengine - INFO - Epoch(train) [58][780/940] lr: 1.0000e-03 eta: 6:54:23 time: 0.6214 data_time: 0.0247 memory: 16095 grad_norm: 5.5070 loss: 0.8361 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.8361 2022/12/08 23:17:45 - mmengine - INFO - Epoch(train) [58][800/940] lr: 1.0000e-03 eta: 6:54:09 time: 0.5667 data_time: 0.0232 memory: 16095 grad_norm: 5.3461 loss: 0.7721 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7721 2022/12/08 23:17:59 - mmengine - INFO - Epoch(train) [58][820/940] lr: 1.0000e-03 eta: 6:53:58 time: 0.6913 data_time: 0.0268 memory: 16095 grad_norm: 5.3760 loss: 0.7964 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7964 2022/12/08 23:18:09 - mmengine - INFO - Epoch(train) [58][840/940] lr: 1.0000e-03 eta: 6:53:44 time: 0.5359 data_time: 0.0226 memory: 16095 grad_norm: 5.2541 loss: 0.7915 top1_acc: 0.8125 top5_acc: 0.8438 loss_cls: 0.7915 2022/12/08 23:18:23 - mmengine - INFO - Epoch(train) [58][860/940] lr: 1.0000e-03 eta: 6:53:32 time: 0.6765 data_time: 0.0259 memory: 16095 grad_norm: 5.5266 loss: 0.8843 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.8843 2022/12/08 23:18:33 - mmengine - INFO - Epoch(train) [58][880/940] lr: 1.0000e-03 eta: 6:53:18 time: 0.5266 data_time: 0.0228 memory: 16095 grad_norm: 5.3975 loss: 0.8026 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.8026 2022/12/08 23:18:47 - mmengine - INFO - Epoch(train) [58][900/940] lr: 1.0000e-03 eta: 6:53:06 time: 0.6960 data_time: 0.0288 memory: 16095 grad_norm: 5.4672 loss: 0.7849 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7849 2022/12/08 23:18:58 - mmengine - INFO - Epoch(train) [58][920/940] lr: 1.0000e-03 eta: 6:52:53 time: 0.5570 data_time: 0.0208 memory: 16095 grad_norm: 5.5134 loss: 0.8618 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8618 2022/12/08 23:19:10 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 23:19:10 - mmengine - INFO - Epoch(train) [58][940/940] lr: 1.0000e-03 eta: 6:52:39 time: 0.5654 data_time: 0.0181 memory: 16095 grad_norm: 5.7871 loss: 0.7368 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7368 2022/12/08 23:19:24 - mmengine - INFO - Epoch(val) [58][20/78] eta: 0:00:42 time: 0.7270 data_time: 0.6324 memory: 1686 2022/12/08 23:19:33 - mmengine - INFO - Epoch(val) [58][40/78] eta: 0:00:22 time: 0.4393 data_time: 0.3456 memory: 1686 2022/12/08 23:19:47 - mmengine - INFO - Epoch(val) [58][60/78] eta: 0:00:11 time: 0.6997 data_time: 0.6022 memory: 1686 2022/12/08 23:19:57 - mmengine - INFO - Epoch(val) [58][78/78] acc/top1: 0.6947 acc/top5: 0.8801 acc/mean1: 0.6945 2022/12/08 23:20:14 - mmengine - INFO - Epoch(train) [59][ 20/940] lr: 1.0000e-03 eta: 6:52:30 time: 0.8303 data_time: 0.3715 memory: 16095 grad_norm: 5.2079 loss: 0.7693 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.7693 2022/12/08 23:20:26 - mmengine - INFO - Epoch(train) [59][ 40/940] lr: 1.0000e-03 eta: 6:52:17 time: 0.5936 data_time: 0.1488 memory: 16095 grad_norm: 5.3033 loss: 0.8574 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.8574 2022/12/08 23:20:40 - mmengine - INFO - Epoch(train) [59][ 60/940] lr: 1.0000e-03 eta: 6:52:05 time: 0.6923 data_time: 0.0517 memory: 16095 grad_norm: 5.4293 loss: 0.9094 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 0.9094 2022/12/08 23:20:51 - mmengine - INFO - Epoch(train) [59][ 80/940] lr: 1.0000e-03 eta: 6:51:52 time: 0.5532 data_time: 0.0502 memory: 16095 grad_norm: 5.4244 loss: 0.8481 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8481 2022/12/08 23:21:04 - mmengine - INFO - Epoch(train) [59][100/940] lr: 1.0000e-03 eta: 6:51:39 time: 0.6427 data_time: 0.0424 memory: 16095 grad_norm: 5.4273 loss: 0.8850 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8850 2022/12/08 23:21:15 - mmengine - INFO - Epoch(train) [59][120/940] lr: 1.0000e-03 eta: 6:51:26 time: 0.5528 data_time: 0.1059 memory: 16095 grad_norm: 5.3401 loss: 0.8528 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8528 2022/12/08 23:21:27 - mmengine - INFO - Epoch(train) [59][140/940] lr: 1.0000e-03 eta: 6:51:13 time: 0.6339 data_time: 0.1828 memory: 16095 grad_norm: 5.3973 loss: 0.8199 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8199 2022/12/08 23:21:39 - mmengine - INFO - Epoch(train) [59][160/940] lr: 1.0000e-03 eta: 6:51:00 time: 0.5707 data_time: 0.1174 memory: 16095 grad_norm: 5.4269 loss: 0.8543 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8543 2022/12/08 23:21:53 - mmengine - INFO - Epoch(train) [59][180/940] lr: 1.0000e-03 eta: 6:50:49 time: 0.7225 data_time: 0.0980 memory: 16095 grad_norm: 5.3326 loss: 0.8531 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8531 2022/12/08 23:22:04 - mmengine - INFO - Epoch(train) [59][200/940] lr: 1.0000e-03 eta: 6:50:35 time: 0.5265 data_time: 0.0213 memory: 16095 grad_norm: 5.3426 loss: 0.8363 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8363 2022/12/08 23:22:17 - mmengine - INFO - Epoch(train) [59][220/940] lr: 1.0000e-03 eta: 6:50:23 time: 0.6689 data_time: 0.0258 memory: 16095 grad_norm: 5.3731 loss: 0.8453 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8453 2022/12/08 23:22:28 - mmengine - INFO - Epoch(train) [59][240/940] lr: 1.0000e-03 eta: 6:50:09 time: 0.5359 data_time: 0.0235 memory: 16095 grad_norm: 5.3909 loss: 0.8599 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8599 2022/12/08 23:22:41 - mmengine - INFO - Epoch(train) [59][260/940] lr: 1.0000e-03 eta: 6:49:57 time: 0.6495 data_time: 0.0236 memory: 16095 grad_norm: 5.4003 loss: 0.8040 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8040 2022/12/08 23:22:52 - mmengine - INFO - Epoch(train) [59][280/940] lr: 1.0000e-03 eta: 6:49:43 time: 0.5549 data_time: 0.0254 memory: 16095 grad_norm: 5.3117 loss: 0.8137 top1_acc: 0.8125 top5_acc: 0.8438 loss_cls: 0.8137 2022/12/08 23:23:05 - mmengine - INFO - Epoch(train) [59][300/940] lr: 1.0000e-03 eta: 6:49:31 time: 0.6492 data_time: 0.0255 memory: 16095 grad_norm: 5.3747 loss: 0.7794 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7794 2022/12/08 23:23:15 - mmengine - INFO - Epoch(train) [59][320/940] lr: 1.0000e-03 eta: 6:49:17 time: 0.5235 data_time: 0.0229 memory: 16095 grad_norm: 5.2906 loss: 0.7508 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7508 2022/12/08 23:23:29 - mmengine - INFO - Epoch(train) [59][340/940] lr: 1.0000e-03 eta: 6:49:05 time: 0.6953 data_time: 0.0235 memory: 16095 grad_norm: 5.4332 loss: 0.8938 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8938 2022/12/08 23:23:40 - mmengine - INFO - Epoch(train) [59][360/940] lr: 1.0000e-03 eta: 6:48:51 time: 0.5359 data_time: 0.0241 memory: 16095 grad_norm: 5.3066 loss: 0.7981 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.7981 2022/12/08 23:23:54 - mmengine - INFO - Epoch(train) [59][380/940] lr: 1.0000e-03 eta: 6:48:40 time: 0.6902 data_time: 0.0254 memory: 16095 grad_norm: 5.4930 loss: 0.8320 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8320 2022/12/08 23:24:06 - mmengine - INFO - Epoch(train) [59][400/940] lr: 1.0000e-03 eta: 6:48:27 time: 0.6219 data_time: 0.0358 memory: 16095 grad_norm: 5.2831 loss: 0.7096 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7096 2022/12/08 23:24:19 - mmengine - INFO - Epoch(train) [59][420/940] lr: 1.0000e-03 eta: 6:48:15 time: 0.6563 data_time: 0.0251 memory: 16095 grad_norm: 5.3783 loss: 0.7833 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7833 2022/12/08 23:24:31 - mmengine - INFO - Epoch(train) [59][440/940] lr: 1.0000e-03 eta: 6:48:02 time: 0.5654 data_time: 0.0267 memory: 16095 grad_norm: 5.4101 loss: 0.8693 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8693 2022/12/08 23:24:43 - mmengine - INFO - Epoch(train) [59][460/940] lr: 1.0000e-03 eta: 6:47:49 time: 0.6382 data_time: 0.0260 memory: 16095 grad_norm: 5.4710 loss: 0.8348 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8348 2022/12/08 23:24:54 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 23:24:54 - mmengine - INFO - Epoch(train) [59][480/940] lr: 1.0000e-03 eta: 6:47:35 time: 0.5434 data_time: 0.0256 memory: 16095 grad_norm: 5.4144 loss: 0.8035 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8035 2022/12/08 23:25:08 - mmengine - INFO - Epoch(train) [59][500/940] lr: 1.0000e-03 eta: 6:47:23 time: 0.6597 data_time: 0.0469 memory: 16095 grad_norm: 5.3081 loss: 0.8724 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8724 2022/12/08 23:25:18 - mmengine - INFO - Epoch(train) [59][520/940] lr: 1.0000e-03 eta: 6:47:10 time: 0.5338 data_time: 0.0240 memory: 16095 grad_norm: 5.3607 loss: 0.7630 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7630 2022/12/08 23:25:32 - mmengine - INFO - Epoch(train) [59][540/940] lr: 1.0000e-03 eta: 6:46:58 time: 0.6782 data_time: 0.0288 memory: 16095 grad_norm: 5.4584 loss: 0.8028 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.8028 2022/12/08 23:25:43 - mmengine - INFO - Epoch(train) [59][560/940] lr: 1.0000e-03 eta: 6:46:44 time: 0.5668 data_time: 0.0211 memory: 16095 grad_norm: 5.3824 loss: 0.8394 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8394 2022/12/08 23:25:56 - mmengine - INFO - Epoch(train) [59][580/940] lr: 1.0000e-03 eta: 6:46:32 time: 0.6510 data_time: 0.0283 memory: 16095 grad_norm: 5.4744 loss: 0.8715 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.8715 2022/12/08 23:26:08 - mmengine - INFO - Epoch(train) [59][600/940] lr: 1.0000e-03 eta: 6:46:19 time: 0.5709 data_time: 0.0245 memory: 16095 grad_norm: 5.2972 loss: 0.8029 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8029 2022/12/08 23:26:21 - mmengine - INFO - Epoch(train) [59][620/940] lr: 1.0000e-03 eta: 6:46:07 time: 0.6514 data_time: 0.0255 memory: 16095 grad_norm: 5.4171 loss: 0.8138 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 0.8138 2022/12/08 23:26:33 - mmengine - INFO - Epoch(train) [59][640/940] lr: 1.0000e-03 eta: 6:45:54 time: 0.6065 data_time: 0.0221 memory: 16095 grad_norm: 5.5597 loss: 0.9269 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.9269 2022/12/08 23:26:46 - mmengine - INFO - Epoch(train) [59][660/940] lr: 1.0000e-03 eta: 6:45:41 time: 0.6397 data_time: 0.0272 memory: 16095 grad_norm: 5.5015 loss: 0.7777 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7777 2022/12/08 23:26:56 - mmengine - INFO - Epoch(train) [59][680/940] lr: 1.0000e-03 eta: 6:45:27 time: 0.5202 data_time: 0.0225 memory: 16095 grad_norm: 5.5247 loss: 0.8765 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8765 2022/12/08 23:27:10 - mmengine - INFO - Epoch(train) [59][700/940] lr: 1.0000e-03 eta: 6:45:16 time: 0.6866 data_time: 0.0252 memory: 16095 grad_norm: 5.5184 loss: 0.8059 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8059 2022/12/08 23:27:20 - mmengine - INFO - Epoch(train) [59][720/940] lr: 1.0000e-03 eta: 6:45:02 time: 0.5336 data_time: 0.0229 memory: 16095 grad_norm: 5.5393 loss: 0.7777 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 0.7777 2022/12/08 23:27:34 - mmengine - INFO - Epoch(train) [59][740/940] lr: 1.0000e-03 eta: 6:44:50 time: 0.6754 data_time: 0.0366 memory: 16095 grad_norm: 5.4445 loss: 0.7645 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7645 2022/12/08 23:27:45 - mmengine - INFO - Epoch(train) [59][760/940] lr: 1.0000e-03 eta: 6:44:37 time: 0.5647 data_time: 0.0501 memory: 16095 grad_norm: 5.4516 loss: 0.8324 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8324 2022/12/08 23:27:58 - mmengine - INFO - Epoch(train) [59][780/940] lr: 1.0000e-03 eta: 6:44:24 time: 0.6463 data_time: 0.0283 memory: 16095 grad_norm: 5.3092 loss: 0.7783 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.7783 2022/12/08 23:28:10 - mmengine - INFO - Epoch(train) [59][800/940] lr: 1.0000e-03 eta: 6:44:11 time: 0.5803 data_time: 0.0238 memory: 16095 grad_norm: 5.5012 loss: 0.8761 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8761 2022/12/08 23:28:23 - mmengine - INFO - Epoch(train) [59][820/940] lr: 1.0000e-03 eta: 6:43:59 time: 0.6812 data_time: 0.0338 memory: 16095 grad_norm: 5.3744 loss: 0.8062 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8062 2022/12/08 23:28:34 - mmengine - INFO - Epoch(train) [59][840/940] lr: 1.0000e-03 eta: 6:43:46 time: 0.5486 data_time: 0.0240 memory: 16095 grad_norm: 5.5385 loss: 0.8210 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8210 2022/12/08 23:28:47 - mmengine - INFO - Epoch(train) [59][860/940] lr: 1.0000e-03 eta: 6:43:33 time: 0.6190 data_time: 0.0218 memory: 16095 grad_norm: 5.4117 loss: 0.7730 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7730 2022/12/08 23:28:59 - mmengine - INFO - Epoch(train) [59][880/940] lr: 1.0000e-03 eta: 6:43:20 time: 0.6025 data_time: 0.0230 memory: 16095 grad_norm: 5.4250 loss: 0.8474 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.8474 2022/12/08 23:29:12 - mmengine - INFO - Epoch(train) [59][900/940] lr: 1.0000e-03 eta: 6:43:08 time: 0.6679 data_time: 0.0268 memory: 16095 grad_norm: 5.3332 loss: 0.7461 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7461 2022/12/08 23:29:23 - mmengine - INFO - Epoch(train) [59][920/940] lr: 1.0000e-03 eta: 6:42:55 time: 0.5643 data_time: 0.0251 memory: 16095 grad_norm: 5.5324 loss: 0.8032 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8032 2022/12/08 23:29:34 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 23:29:34 - mmengine - INFO - Epoch(train) [59][940/940] lr: 1.0000e-03 eta: 6:42:41 time: 0.5389 data_time: 0.0164 memory: 16095 grad_norm: 5.8075 loss: 0.8204 top1_acc: 0.5714 top5_acc: 1.0000 loss_cls: 0.8204 2022/12/08 23:29:48 - mmengine - INFO - Epoch(val) [59][20/78] eta: 0:00:41 time: 0.7079 data_time: 0.6133 memory: 1686 2022/12/08 23:29:58 - mmengine - INFO - Epoch(val) [59][40/78] eta: 0:00:22 time: 0.4572 data_time: 0.3631 memory: 1686 2022/12/08 23:30:11 - mmengine - INFO - Epoch(val) [59][60/78] eta: 0:00:11 time: 0.6840 data_time: 0.5890 memory: 1686 2022/12/08 23:30:21 - mmengine - INFO - Epoch(val) [59][78/78] acc/top1: 0.6942 acc/top5: 0.8794 acc/mean1: 0.6941 2022/12/08 23:30:38 - mmengine - INFO - Epoch(train) [60][ 20/940] lr: 1.0000e-03 eta: 6:42:31 time: 0.8042 data_time: 0.3308 memory: 16095 grad_norm: 5.3811 loss: 0.7454 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7454 2022/12/08 23:30:48 - mmengine - INFO - Epoch(train) [60][ 40/940] lr: 1.0000e-03 eta: 6:42:17 time: 0.5367 data_time: 0.1214 memory: 16095 grad_norm: 5.3215 loss: 0.7494 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7494 2022/12/08 23:31:02 - mmengine - INFO - Epoch(train) [60][ 60/940] lr: 1.0000e-03 eta: 6:42:05 time: 0.6869 data_time: 0.0511 memory: 16095 grad_norm: 5.3533 loss: 0.7610 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7610 2022/12/08 23:31:14 - mmengine - INFO - Epoch(train) [60][ 80/940] lr: 1.0000e-03 eta: 6:41:52 time: 0.5882 data_time: 0.0207 memory: 16095 grad_norm: 5.2558 loss: 0.8073 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8073 2022/12/08 23:31:27 - mmengine - INFO - Epoch(train) [60][100/940] lr: 1.0000e-03 eta: 6:41:40 time: 0.6632 data_time: 0.0287 memory: 16095 grad_norm: 5.4459 loss: 0.8120 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8120 2022/12/08 23:31:38 - mmengine - INFO - Epoch(train) [60][120/940] lr: 1.0000e-03 eta: 6:41:27 time: 0.5399 data_time: 0.0198 memory: 16095 grad_norm: 5.3024 loss: 0.8435 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8435 2022/12/08 23:31:51 - mmengine - INFO - Epoch(train) [60][140/940] lr: 1.0000e-03 eta: 6:41:15 time: 0.6756 data_time: 0.0365 memory: 16095 grad_norm: 5.3922 loss: 0.7648 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7648 2022/12/08 23:32:02 - mmengine - INFO - Epoch(train) [60][160/940] lr: 1.0000e-03 eta: 6:41:01 time: 0.5502 data_time: 0.0203 memory: 16095 grad_norm: 5.3955 loss: 0.8970 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8970 2022/12/08 23:32:16 - mmengine - INFO - Epoch(train) [60][180/940] lr: 1.0000e-03 eta: 6:40:49 time: 0.6753 data_time: 0.0281 memory: 16095 grad_norm: 5.4604 loss: 0.8465 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8465 2022/12/08 23:32:27 - mmengine - INFO - Epoch(train) [60][200/940] lr: 1.0000e-03 eta: 6:40:36 time: 0.5630 data_time: 0.0212 memory: 16095 grad_norm: 5.4685 loss: 0.8255 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.8255 2022/12/08 23:32:41 - mmengine - INFO - Epoch(train) [60][220/940] lr: 1.0000e-03 eta: 6:40:24 time: 0.6923 data_time: 0.0274 memory: 16095 grad_norm: 5.3787 loss: 0.7587 top1_acc: 0.6562 top5_acc: 0.7812 loss_cls: 0.7587 2022/12/08 23:32:52 - mmengine - INFO - Epoch(train) [60][240/940] lr: 1.0000e-03 eta: 6:40:10 time: 0.5437 data_time: 0.0226 memory: 16095 grad_norm: 5.4577 loss: 0.8527 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8527 2022/12/08 23:33:06 - mmengine - INFO - Epoch(train) [60][260/940] lr: 1.0000e-03 eta: 6:39:59 time: 0.6819 data_time: 0.0279 memory: 16095 grad_norm: 5.3325 loss: 0.7665 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7665 2022/12/08 23:33:18 - mmengine - INFO - Epoch(train) [60][280/940] lr: 1.0000e-03 eta: 6:39:46 time: 0.6029 data_time: 0.0214 memory: 16095 grad_norm: 5.4204 loss: 0.8966 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8966 2022/12/08 23:33:30 - mmengine - INFO - Epoch(train) [60][300/940] lr: 1.0000e-03 eta: 6:39:33 time: 0.6041 data_time: 0.0277 memory: 16095 grad_norm: 5.5157 loss: 0.8419 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8419 2022/12/08 23:33:41 - mmengine - INFO - Epoch(train) [60][320/940] lr: 1.0000e-03 eta: 6:39:20 time: 0.5740 data_time: 0.0207 memory: 16095 grad_norm: 5.4214 loss: 0.7933 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7933 2022/12/08 23:33:54 - mmengine - INFO - Epoch(train) [60][340/940] lr: 1.0000e-03 eta: 6:39:07 time: 0.6467 data_time: 0.0303 memory: 16095 grad_norm: 5.5388 loss: 0.8676 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8676 2022/12/08 23:34:06 - mmengine - INFO - Epoch(train) [60][360/940] lr: 1.0000e-03 eta: 6:38:54 time: 0.5798 data_time: 0.0229 memory: 16095 grad_norm: 5.2931 loss: 0.8579 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8579 2022/12/08 23:34:19 - mmengine - INFO - Epoch(train) [60][380/940] lr: 1.0000e-03 eta: 6:38:42 time: 0.6751 data_time: 0.0244 memory: 16095 grad_norm: 5.4470 loss: 0.7388 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7388 2022/12/08 23:34:30 - mmengine - INFO - Epoch(train) [60][400/940] lr: 1.0000e-03 eta: 6:38:29 time: 0.5516 data_time: 0.0293 memory: 16095 grad_norm: 5.3867 loss: 0.8140 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8140 2022/12/08 23:34:44 - mmengine - INFO - Epoch(train) [60][420/940] lr: 1.0000e-03 eta: 6:38:17 time: 0.6741 data_time: 0.0248 memory: 16095 grad_norm: 5.5629 loss: 0.7835 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7835 2022/12/08 23:34:55 - mmengine - INFO - Epoch(train) [60][440/940] lr: 1.0000e-03 eta: 6:38:03 time: 0.5555 data_time: 0.0240 memory: 16095 grad_norm: 5.5717 loss: 0.7887 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.7887 2022/12/08 23:35:09 - mmengine - INFO - Epoch(train) [60][460/940] lr: 1.0000e-03 eta: 6:37:52 time: 0.6936 data_time: 0.0223 memory: 16095 grad_norm: 5.4477 loss: 0.8102 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8102 2022/12/08 23:35:19 - mmengine - INFO - Epoch(train) [60][480/940] lr: 1.0000e-03 eta: 6:37:38 time: 0.5308 data_time: 0.0244 memory: 16095 grad_norm: 5.4057 loss: 0.7586 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7586 2022/12/08 23:35:32 - mmengine - INFO - Epoch(train) [60][500/940] lr: 1.0000e-03 eta: 6:37:26 time: 0.6344 data_time: 0.0243 memory: 16095 grad_norm: 5.5378 loss: 0.9034 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.9034 2022/12/08 23:35:43 - mmengine - INFO - Epoch(train) [60][520/940] lr: 1.0000e-03 eta: 6:37:12 time: 0.5210 data_time: 0.0250 memory: 16095 grad_norm: 5.5764 loss: 0.8655 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.8655 2022/12/08 23:35:55 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 23:35:55 - mmengine - INFO - Epoch(train) [60][540/940] lr: 1.0000e-03 eta: 6:36:59 time: 0.6356 data_time: 0.0287 memory: 16095 grad_norm: 5.4995 loss: 0.8469 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8469 2022/12/08 23:36:07 - mmengine - INFO - Epoch(train) [60][560/940] lr: 1.0000e-03 eta: 6:36:46 time: 0.5660 data_time: 0.0342 memory: 16095 grad_norm: 5.5068 loss: 0.7707 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7707 2022/12/08 23:36:19 - mmengine - INFO - Epoch(train) [60][580/940] lr: 1.0000e-03 eta: 6:36:33 time: 0.6448 data_time: 0.0238 memory: 16095 grad_norm: 5.4184 loss: 0.8534 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8534 2022/12/08 23:36:31 - mmengine - INFO - Epoch(train) [60][600/940] lr: 1.0000e-03 eta: 6:36:20 time: 0.5657 data_time: 0.0219 memory: 16095 grad_norm: 5.5091 loss: 0.8813 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8813 2022/12/08 23:36:44 - mmengine - INFO - Epoch(train) [60][620/940] lr: 1.0000e-03 eta: 6:36:08 time: 0.6694 data_time: 0.0291 memory: 16095 grad_norm: 5.4786 loss: 0.8508 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.8508 2022/12/08 23:36:56 - mmengine - INFO - Epoch(train) [60][640/940] lr: 1.0000e-03 eta: 6:35:55 time: 0.5831 data_time: 0.0244 memory: 16095 grad_norm: 5.4286 loss: 0.7949 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7949 2022/12/08 23:37:08 - mmengine - INFO - Epoch(train) [60][660/940] lr: 1.0000e-03 eta: 6:35:42 time: 0.6134 data_time: 0.0280 memory: 16095 grad_norm: 5.4334 loss: 0.8823 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8823 2022/12/08 23:37:20 - mmengine - INFO - Epoch(train) [60][680/940] lr: 1.0000e-03 eta: 6:35:30 time: 0.6123 data_time: 0.0199 memory: 16095 grad_norm: 5.4895 loss: 0.8293 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8293 2022/12/08 23:37:32 - mmengine - INFO - Epoch(train) [60][700/940] lr: 1.0000e-03 eta: 6:35:16 time: 0.5858 data_time: 0.0265 memory: 16095 grad_norm: 5.3770 loss: 0.7538 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7538 2022/12/08 23:37:45 - mmengine - INFO - Epoch(train) [60][720/940] lr: 1.0000e-03 eta: 6:35:04 time: 0.6481 data_time: 0.0223 memory: 16095 grad_norm: 5.4670 loss: 0.7727 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7727 2022/12/08 23:37:57 - mmengine - INFO - Epoch(train) [60][740/940] lr: 1.0000e-03 eta: 6:34:51 time: 0.5994 data_time: 0.0267 memory: 16095 grad_norm: 5.4748 loss: 0.8018 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.8018 2022/12/08 23:38:10 - mmengine - INFO - Epoch(train) [60][760/940] lr: 1.0000e-03 eta: 6:34:39 time: 0.6594 data_time: 0.0221 memory: 16095 grad_norm: 5.6030 loss: 0.8198 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8198 2022/12/08 23:38:22 - mmengine - INFO - Epoch(train) [60][780/940] lr: 1.0000e-03 eta: 6:34:26 time: 0.5910 data_time: 0.0256 memory: 16095 grad_norm: 5.4289 loss: 0.8084 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.8084 2022/12/08 23:38:35 - mmengine - INFO - Epoch(train) [60][800/940] lr: 1.0000e-03 eta: 6:34:14 time: 0.6673 data_time: 0.0250 memory: 16095 grad_norm: 5.3516 loss: 0.8608 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 0.8608 2022/12/08 23:38:48 - mmengine - INFO - Epoch(train) [60][820/940] lr: 1.0000e-03 eta: 6:34:02 time: 0.6266 data_time: 0.0248 memory: 16095 grad_norm: 5.4125 loss: 0.8873 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.8873 2022/12/08 23:39:01 - mmengine - INFO - Epoch(train) [60][840/940] lr: 1.0000e-03 eta: 6:33:49 time: 0.6316 data_time: 0.0220 memory: 16095 grad_norm: 5.5528 loss: 0.8430 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8430 2022/12/08 23:39:12 - mmengine - INFO - Epoch(train) [60][860/940] lr: 1.0000e-03 eta: 6:33:36 time: 0.5493 data_time: 0.0234 memory: 16095 grad_norm: 5.4638 loss: 0.7475 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7475 2022/12/08 23:39:24 - mmengine - INFO - Epoch(train) [60][880/940] lr: 1.0000e-03 eta: 6:33:23 time: 0.6166 data_time: 0.0236 memory: 16095 grad_norm: 5.5294 loss: 0.8677 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8677 2022/12/08 23:39:35 - mmengine - INFO - Epoch(train) [60][900/940] lr: 1.0000e-03 eta: 6:33:09 time: 0.5550 data_time: 0.0252 memory: 16095 grad_norm: 5.5252 loss: 0.8139 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.8139 2022/12/08 23:39:48 - mmengine - INFO - Epoch(train) [60][920/940] lr: 1.0000e-03 eta: 6:32:57 time: 0.6363 data_time: 0.0251 memory: 16095 grad_norm: 5.5623 loss: 0.8485 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8485 2022/12/08 23:39:57 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 23:39:57 - mmengine - INFO - Epoch(train) [60][940/940] lr: 1.0000e-03 eta: 6:32:42 time: 0.4735 data_time: 0.0171 memory: 16095 grad_norm: 5.9606 loss: 0.8664 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 0.8664 2022/12/08 23:39:57 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/12/08 23:40:14 - mmengine - INFO - Epoch(val) [60][20/78] eta: 0:00:40 time: 0.7007 data_time: 0.6055 memory: 1686 2022/12/08 23:40:24 - mmengine - INFO - Epoch(val) [60][40/78] eta: 0:00:22 time: 0.4775 data_time: 0.3830 memory: 1686 2022/12/08 23:40:37 - mmengine - INFO - Epoch(val) [60][60/78] eta: 0:00:11 time: 0.6641 data_time: 0.5691 memory: 1686 2022/12/08 23:40:47 - mmengine - INFO - Epoch(val) [60][78/78] acc/top1: 0.6932 acc/top5: 0.8787 acc/mean1: 0.6931 2022/12/08 23:41:04 - mmengine - INFO - Epoch(train) [61][ 20/940] lr: 1.0000e-03 eta: 6:32:33 time: 0.8417 data_time: 0.3531 memory: 16095 grad_norm: 5.5068 loss: 0.7927 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.7927 2022/12/08 23:41:14 - mmengine - INFO - Epoch(train) [61][ 40/940] lr: 1.0000e-03 eta: 6:32:19 time: 0.5444 data_time: 0.0710 memory: 16095 grad_norm: 5.2077 loss: 0.7434 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7434 2022/12/08 23:41:28 - mmengine - INFO - Epoch(train) [61][ 60/940] lr: 1.0000e-03 eta: 6:32:08 time: 0.6918 data_time: 0.0301 memory: 16095 grad_norm: 5.4554 loss: 0.7500 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 0.7500 2022/12/08 23:41:39 - mmengine - INFO - Epoch(train) [61][ 80/940] lr: 1.0000e-03 eta: 6:31:54 time: 0.5549 data_time: 0.0530 memory: 16095 grad_norm: 5.3697 loss: 0.7935 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7935 2022/12/08 23:41:53 - mmengine - INFO - Epoch(train) [61][100/940] lr: 1.0000e-03 eta: 6:31:42 time: 0.6725 data_time: 0.1926 memory: 16095 grad_norm: 5.3495 loss: 0.8676 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8676 2022/12/08 23:42:03 - mmengine - INFO - Epoch(train) [61][120/940] lr: 1.0000e-03 eta: 6:31:28 time: 0.5265 data_time: 0.1848 memory: 16095 grad_norm: 5.3850 loss: 0.8808 top1_acc: 0.9688 top5_acc: 0.9688 loss_cls: 0.8808 2022/12/08 23:42:17 - mmengine - INFO - Epoch(train) [61][140/940] lr: 1.0000e-03 eta: 6:31:17 time: 0.6968 data_time: 0.3358 memory: 16095 grad_norm: 5.4324 loss: 0.7433 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7433 2022/12/08 23:42:28 - mmengine - INFO - Epoch(train) [61][160/940] lr: 1.0000e-03 eta: 6:31:03 time: 0.5229 data_time: 0.1403 memory: 16095 grad_norm: 5.3561 loss: 0.8317 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8317 2022/12/08 23:42:42 - mmengine - INFO - Epoch(train) [61][180/940] lr: 1.0000e-03 eta: 6:30:51 time: 0.6948 data_time: 0.2513 memory: 16095 grad_norm: 5.4271 loss: 0.6940 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.6940 2022/12/08 23:42:53 - mmengine - INFO - Epoch(train) [61][200/940] lr: 1.0000e-03 eta: 6:30:37 time: 0.5439 data_time: 0.1563 memory: 16095 grad_norm: 5.4587 loss: 0.7559 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7559 2022/12/08 23:43:07 - mmengine - INFO - Epoch(train) [61][220/940] lr: 1.0000e-03 eta: 6:30:26 time: 0.7089 data_time: 0.2373 memory: 16095 grad_norm: 5.3808 loss: 0.8031 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8031 2022/12/08 23:43:18 - mmengine - INFO - Epoch(train) [61][240/940] lr: 1.0000e-03 eta: 6:30:13 time: 0.5602 data_time: 0.0765 memory: 16095 grad_norm: 5.4564 loss: 0.8356 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.8356 2022/12/08 23:43:31 - mmengine - INFO - Epoch(train) [61][260/940] lr: 1.0000e-03 eta: 6:30:00 time: 0.6286 data_time: 0.0421 memory: 16095 grad_norm: 5.6126 loss: 0.7868 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.7868 2022/12/08 23:43:41 - mmengine - INFO - Epoch(train) [61][280/940] lr: 1.0000e-03 eta: 6:29:46 time: 0.5351 data_time: 0.1075 memory: 16095 grad_norm: 5.4830 loss: 0.8399 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.8399 2022/12/08 23:43:54 - mmengine - INFO - Epoch(train) [61][300/940] lr: 1.0000e-03 eta: 6:29:34 time: 0.6577 data_time: 0.2396 memory: 16095 grad_norm: 5.3607 loss: 0.7699 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7699 2022/12/08 23:44:06 - mmengine - INFO - Epoch(train) [61][320/940] lr: 1.0000e-03 eta: 6:29:21 time: 0.5556 data_time: 0.2137 memory: 16095 grad_norm: 5.4741 loss: 0.8979 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8979 2022/12/08 23:44:19 - mmengine - INFO - Epoch(train) [61][340/940] lr: 1.0000e-03 eta: 6:29:09 time: 0.6603 data_time: 0.2727 memory: 16095 grad_norm: 5.3750 loss: 0.6461 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6461 2022/12/08 23:44:30 - mmengine - INFO - Epoch(train) [61][360/940] lr: 1.0000e-03 eta: 6:28:55 time: 0.5499 data_time: 0.2217 memory: 16095 grad_norm: 5.5528 loss: 0.7769 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7769 2022/12/08 23:44:43 - mmengine - INFO - Epoch(train) [61][380/940] lr: 1.0000e-03 eta: 6:28:43 time: 0.6532 data_time: 0.3126 memory: 16095 grad_norm: 5.4740 loss: 0.8643 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.8643 2022/12/08 23:44:54 - mmengine - INFO - Epoch(train) [61][400/940] lr: 1.0000e-03 eta: 6:28:29 time: 0.5550 data_time: 0.2046 memory: 16095 grad_norm: 5.4433 loss: 0.7966 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7966 2022/12/08 23:45:07 - mmengine - INFO - Epoch(train) [61][420/940] lr: 1.0000e-03 eta: 6:28:17 time: 0.6629 data_time: 0.3183 memory: 16095 grad_norm: 5.4968 loss: 0.7839 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.7839 2022/12/08 23:45:19 - mmengine - INFO - Epoch(train) [61][440/940] lr: 1.0000e-03 eta: 6:28:04 time: 0.5743 data_time: 0.2199 memory: 16095 grad_norm: 5.5852 loss: 0.9734 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.9734 2022/12/08 23:45:31 - mmengine - INFO - Epoch(train) [61][460/940] lr: 1.0000e-03 eta: 6:27:51 time: 0.6084 data_time: 0.2147 memory: 16095 grad_norm: 5.4401 loss: 0.8472 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8472 2022/12/08 23:45:42 - mmengine - INFO - Epoch(train) [61][480/940] lr: 1.0000e-03 eta: 6:27:38 time: 0.5774 data_time: 0.1199 memory: 16095 grad_norm: 5.4538 loss: 0.7436 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7436 2022/12/08 23:45:56 - mmengine - INFO - Epoch(train) [61][500/940] lr: 1.0000e-03 eta: 6:27:26 time: 0.6566 data_time: 0.2862 memory: 16095 grad_norm: 5.6057 loss: 0.8018 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8018 2022/12/08 23:46:07 - mmengine - INFO - Epoch(train) [61][520/940] lr: 1.0000e-03 eta: 6:27:13 time: 0.5586 data_time: 0.2216 memory: 16095 grad_norm: 5.4359 loss: 0.7548 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.7548 2022/12/08 23:46:20 - mmengine - INFO - Epoch(train) [61][540/940] lr: 1.0000e-03 eta: 6:27:00 time: 0.6455 data_time: 0.2504 memory: 16095 grad_norm: 5.3785 loss: 0.7544 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.7544 2022/12/08 23:46:30 - mmengine - INFO - Epoch(train) [61][560/940] lr: 1.0000e-03 eta: 6:26:47 time: 0.5415 data_time: 0.1583 memory: 16095 grad_norm: 5.5388 loss: 0.8101 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8101 2022/12/08 23:46:44 - mmengine - INFO - Epoch(train) [61][580/940] lr: 1.0000e-03 eta: 6:26:35 time: 0.6566 data_time: 0.1696 memory: 16095 grad_norm: 5.5679 loss: 0.8231 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8231 2022/12/08 23:46:55 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 23:46:55 - mmengine - INFO - Epoch(train) [61][600/940] lr: 1.0000e-03 eta: 6:26:21 time: 0.5639 data_time: 0.1265 memory: 16095 grad_norm: 5.5560 loss: 0.8532 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8532 2022/12/08 23:47:08 - mmengine - INFO - Epoch(train) [61][620/940] lr: 1.0000e-03 eta: 6:26:09 time: 0.6725 data_time: 0.1370 memory: 16095 grad_norm: 5.7002 loss: 0.9275 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9275 2022/12/08 23:47:19 - mmengine - INFO - Epoch(train) [61][640/940] lr: 1.0000e-03 eta: 6:25:56 time: 0.5295 data_time: 0.0499 memory: 16095 grad_norm: 5.5484 loss: 0.8535 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8535 2022/12/08 23:47:33 - mmengine - INFO - Epoch(train) [61][660/940] lr: 1.0000e-03 eta: 6:25:44 time: 0.6978 data_time: 0.0558 memory: 16095 grad_norm: 5.5111 loss: 0.8264 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.8264 2022/12/08 23:47:44 - mmengine - INFO - Epoch(train) [61][680/940] lr: 1.0000e-03 eta: 6:25:30 time: 0.5446 data_time: 0.0237 memory: 16095 grad_norm: 5.5646 loss: 0.8063 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8063 2022/12/08 23:47:57 - mmengine - INFO - Epoch(train) [61][700/940] lr: 1.0000e-03 eta: 6:25:18 time: 0.6622 data_time: 0.0252 memory: 16095 grad_norm: 5.5573 loss: 0.7925 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7925 2022/12/08 23:48:08 - mmengine - INFO - Epoch(train) [61][720/940] lr: 1.0000e-03 eta: 6:25:05 time: 0.5642 data_time: 0.0247 memory: 16095 grad_norm: 5.5484 loss: 0.8674 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 0.8674 2022/12/08 23:48:21 - mmengine - INFO - Epoch(train) [61][740/940] lr: 1.0000e-03 eta: 6:24:53 time: 0.6520 data_time: 0.0239 memory: 16095 grad_norm: 5.5144 loss: 0.8361 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 0.8361 2022/12/08 23:48:32 - mmengine - INFO - Epoch(train) [61][760/940] lr: 1.0000e-03 eta: 6:24:39 time: 0.5339 data_time: 0.0222 memory: 16095 grad_norm: 5.3633 loss: 0.7779 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.7779 2022/12/08 23:48:46 - mmengine - INFO - Epoch(train) [61][780/940] lr: 1.0000e-03 eta: 6:24:27 time: 0.6720 data_time: 0.0243 memory: 16095 grad_norm: 5.6037 loss: 0.9058 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.9058 2022/12/08 23:48:57 - mmengine - INFO - Epoch(train) [61][800/940] lr: 1.0000e-03 eta: 6:24:14 time: 0.5775 data_time: 0.0225 memory: 16095 grad_norm: 5.6257 loss: 0.9093 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9093 2022/12/08 23:49:10 - mmengine - INFO - Epoch(train) [61][820/940] lr: 1.0000e-03 eta: 6:24:02 time: 0.6547 data_time: 0.0258 memory: 16095 grad_norm: 5.5958 loss: 0.8519 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8519 2022/12/08 23:49:21 - mmengine - INFO - Epoch(train) [61][840/940] lr: 1.0000e-03 eta: 6:23:48 time: 0.5341 data_time: 0.0256 memory: 16095 grad_norm: 5.5154 loss: 0.8754 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 0.8754 2022/12/08 23:49:34 - mmengine - INFO - Epoch(train) [61][860/940] lr: 1.0000e-03 eta: 6:23:36 time: 0.6658 data_time: 0.0274 memory: 16095 grad_norm: 5.5578 loss: 0.8779 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 0.8779 2022/12/08 23:49:45 - mmengine - INFO - Epoch(train) [61][880/940] lr: 1.0000e-03 eta: 6:23:23 time: 0.5527 data_time: 0.0250 memory: 16095 grad_norm: 5.4962 loss: 0.7874 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7874 2022/12/08 23:49:59 - mmengine - INFO - Epoch(train) [61][900/940] lr: 1.0000e-03 eta: 6:23:11 time: 0.6695 data_time: 0.0234 memory: 16095 grad_norm: 5.3597 loss: 0.7352 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.7352 2022/12/08 23:50:10 - mmengine - INFO - Epoch(train) [61][920/940] lr: 1.0000e-03 eta: 6:22:57 time: 0.5495 data_time: 0.0229 memory: 16095 grad_norm: 5.5130 loss: 0.7283 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7283 2022/12/08 23:50:21 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 23:50:21 - mmengine - INFO - Epoch(train) [61][940/940] lr: 1.0000e-03 eta: 6:22:44 time: 0.5514 data_time: 0.0185 memory: 16095 grad_norm: 6.0044 loss: 0.8208 top1_acc: 0.4286 top5_acc: 1.0000 loss_cls: 0.8208 2022/12/08 23:50:35 - mmengine - INFO - Epoch(val) [61][20/78] eta: 0:00:40 time: 0.7003 data_time: 0.6059 memory: 1686 2022/12/08 23:50:44 - mmengine - INFO - Epoch(val) [61][40/78] eta: 0:00:22 time: 0.4712 data_time: 0.3781 memory: 1686 2022/12/08 23:50:58 - mmengine - INFO - Epoch(val) [61][60/78] eta: 0:00:11 time: 0.6801 data_time: 0.5868 memory: 1686 2022/12/08 23:51:08 - mmengine - INFO - Epoch(val) [61][78/78] acc/top1: 0.6938 acc/top5: 0.8777 acc/mean1: 0.6937 2022/12/08 23:51:24 - mmengine - INFO - Epoch(train) [62][ 20/940] lr: 1.0000e-03 eta: 6:22:33 time: 0.8093 data_time: 0.4598 memory: 16095 grad_norm: 5.4616 loss: 0.7876 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.7876 2022/12/08 23:51:35 - mmengine - INFO - Epoch(train) [62][ 40/940] lr: 1.0000e-03 eta: 6:22:20 time: 0.5218 data_time: 0.1593 memory: 16095 grad_norm: 5.5388 loss: 0.9222 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.9222 2022/12/08 23:51:48 - mmengine - INFO - Epoch(train) [62][ 60/940] lr: 1.0000e-03 eta: 6:22:08 time: 0.6798 data_time: 0.1237 memory: 16095 grad_norm: 5.4518 loss: 0.7564 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7564 2022/12/08 23:51:59 - mmengine - INFO - Epoch(train) [62][ 80/940] lr: 1.0000e-03 eta: 6:21:54 time: 0.5564 data_time: 0.0402 memory: 16095 grad_norm: 5.6220 loss: 0.7613 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7613 2022/12/08 23:52:14 - mmengine - INFO - Epoch(train) [62][100/940] lr: 1.0000e-03 eta: 6:21:43 time: 0.7179 data_time: 0.0422 memory: 16095 grad_norm: 5.5291 loss: 0.7933 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7933 2022/12/08 23:52:24 - mmengine - INFO - Epoch(train) [62][120/940] lr: 1.0000e-03 eta: 6:21:29 time: 0.5325 data_time: 0.0200 memory: 16095 grad_norm: 5.3600 loss: 0.6758 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6758 2022/12/08 23:52:38 - mmengine - INFO - Epoch(train) [62][140/940] lr: 1.0000e-03 eta: 6:21:18 time: 0.7033 data_time: 0.0261 memory: 16095 grad_norm: 5.5000 loss: 0.8136 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8136 2022/12/08 23:52:50 - mmengine - INFO - Epoch(train) [62][160/940] lr: 1.0000e-03 eta: 6:21:04 time: 0.5613 data_time: 0.0202 memory: 16095 grad_norm: 5.4609 loss: 0.7900 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7900 2022/12/08 23:53:04 - mmengine - INFO - Epoch(train) [62][180/940] lr: 1.0000e-03 eta: 6:20:53 time: 0.7146 data_time: 0.0252 memory: 16095 grad_norm: 5.3633 loss: 0.7626 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7626 2022/12/08 23:53:15 - mmengine - INFO - Epoch(train) [62][200/940] lr: 1.0000e-03 eta: 6:20:39 time: 0.5475 data_time: 0.0233 memory: 16095 grad_norm: 5.4547 loss: 0.8536 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8536 2022/12/08 23:53:28 - mmengine - INFO - Epoch(train) [62][220/940] lr: 1.0000e-03 eta: 6:20:27 time: 0.6334 data_time: 0.0255 memory: 16095 grad_norm: 5.4456 loss: 0.8096 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8096 2022/12/08 23:53:38 - mmengine - INFO - Epoch(train) [62][240/940] lr: 1.0000e-03 eta: 6:20:13 time: 0.5464 data_time: 0.0260 memory: 16095 grad_norm: 5.5721 loss: 0.8861 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8861 2022/12/08 23:53:52 - mmengine - INFO - Epoch(train) [62][260/940] lr: 1.0000e-03 eta: 6:20:01 time: 0.6692 data_time: 0.0227 memory: 16095 grad_norm: 5.4469 loss: 0.7869 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7869 2022/12/08 23:54:03 - mmengine - INFO - Epoch(train) [62][280/940] lr: 1.0000e-03 eta: 6:19:48 time: 0.5624 data_time: 0.0250 memory: 16095 grad_norm: 5.5859 loss: 0.9264 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.9264 2022/12/08 23:54:16 - mmengine - INFO - Epoch(train) [62][300/940] lr: 1.0000e-03 eta: 6:19:36 time: 0.6420 data_time: 0.0255 memory: 16095 grad_norm: 5.4067 loss: 0.8461 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8461 2022/12/08 23:54:27 - mmengine - INFO - Epoch(train) [62][320/940] lr: 1.0000e-03 eta: 6:19:22 time: 0.5663 data_time: 0.0250 memory: 16095 grad_norm: 5.5532 loss: 0.7834 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7834 2022/12/08 23:54:41 - mmengine - INFO - Epoch(train) [62][340/940] lr: 1.0000e-03 eta: 6:19:11 time: 0.7102 data_time: 0.0268 memory: 16095 grad_norm: 5.5312 loss: 0.7918 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.7918 2022/12/08 23:54:52 - mmengine - INFO - Epoch(train) [62][360/940] lr: 1.0000e-03 eta: 6:18:57 time: 0.5372 data_time: 0.0234 memory: 16095 grad_norm: 5.6278 loss: 0.8823 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8823 2022/12/08 23:55:06 - mmengine - INFO - Epoch(train) [62][380/940] lr: 1.0000e-03 eta: 6:18:46 time: 0.6920 data_time: 0.0232 memory: 16095 grad_norm: 5.4860 loss: 0.8573 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8573 2022/12/08 23:55:17 - mmengine - INFO - Epoch(train) [62][400/940] lr: 1.0000e-03 eta: 6:18:32 time: 0.5578 data_time: 0.0235 memory: 16095 grad_norm: 5.4845 loss: 0.7610 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7610 2022/12/08 23:55:29 - mmengine - INFO - Epoch(train) [62][420/940] lr: 1.0000e-03 eta: 6:18:19 time: 0.5887 data_time: 0.0269 memory: 16095 grad_norm: 5.5604 loss: 0.8346 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8346 2022/12/08 23:55:41 - mmengine - INFO - Epoch(train) [62][440/940] lr: 1.0000e-03 eta: 6:18:06 time: 0.5734 data_time: 0.0280 memory: 16095 grad_norm: 5.6213 loss: 0.8160 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8160 2022/12/08 23:55:54 - mmengine - INFO - Epoch(train) [62][460/940] lr: 1.0000e-03 eta: 6:17:54 time: 0.6502 data_time: 0.0254 memory: 16095 grad_norm: 5.3290 loss: 0.7975 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7975 2022/12/08 23:56:05 - mmengine - INFO - Epoch(train) [62][480/940] lr: 1.0000e-03 eta: 6:17:41 time: 0.5693 data_time: 0.0310 memory: 16095 grad_norm: 5.5219 loss: 0.8114 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8114 2022/12/08 23:56:18 - mmengine - INFO - Epoch(train) [62][500/940] lr: 1.0000e-03 eta: 6:17:28 time: 0.6374 data_time: 0.0235 memory: 16095 grad_norm: 5.5554 loss: 0.6765 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6765 2022/12/08 23:56:29 - mmengine - INFO - Epoch(train) [62][520/940] lr: 1.0000e-03 eta: 6:17:15 time: 0.5678 data_time: 0.0239 memory: 16095 grad_norm: 5.4837 loss: 0.7948 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.7948 2022/12/08 23:56:42 - mmengine - INFO - Epoch(train) [62][540/940] lr: 1.0000e-03 eta: 6:17:03 time: 0.6479 data_time: 0.0241 memory: 16095 grad_norm: 5.5750 loss: 0.7994 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.7994 2022/12/08 23:56:54 - mmengine - INFO - Epoch(train) [62][560/940] lr: 1.0000e-03 eta: 6:16:50 time: 0.5884 data_time: 0.0300 memory: 16095 grad_norm: 5.4632 loss: 0.7696 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7696 2022/12/08 23:57:06 - mmengine - INFO - Epoch(train) [62][580/940] lr: 1.0000e-03 eta: 6:16:37 time: 0.6128 data_time: 0.0219 memory: 16095 grad_norm: 5.6751 loss: 0.9203 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.9203 2022/12/08 23:57:18 - mmengine - INFO - Epoch(train) [62][600/940] lr: 1.0000e-03 eta: 6:16:24 time: 0.6163 data_time: 0.0790 memory: 16095 grad_norm: 5.5487 loss: 0.7015 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7015 2022/12/08 23:57:31 - mmengine - INFO - Epoch(train) [62][620/940] lr: 1.0000e-03 eta: 6:16:12 time: 0.6335 data_time: 0.0233 memory: 16095 grad_norm: 5.5286 loss: 0.7991 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7991 2022/12/08 23:57:43 - mmengine - INFO - Epoch(train) [62][640/940] lr: 1.0000e-03 eta: 6:15:59 time: 0.6121 data_time: 0.0274 memory: 16095 grad_norm: 5.6626 loss: 0.7406 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7406 2022/12/08 23:57:55 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/08 23:57:55 - mmengine - INFO - Epoch(train) [62][660/940] lr: 1.0000e-03 eta: 6:15:46 time: 0.5869 data_time: 0.0242 memory: 16095 grad_norm: 5.5259 loss: 0.8814 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8814 2022/12/08 23:58:08 - mmengine - INFO - Epoch(train) [62][680/940] lr: 1.0000e-03 eta: 6:15:34 time: 0.6689 data_time: 0.0251 memory: 16095 grad_norm: 5.5376 loss: 0.7771 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7771 2022/12/08 23:58:19 - mmengine - INFO - Epoch(train) [62][700/940] lr: 1.0000e-03 eta: 6:15:21 time: 0.5515 data_time: 0.0225 memory: 16095 grad_norm: 5.5621 loss: 0.7837 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7837 2022/12/08 23:58:33 - mmengine - INFO - Epoch(train) [62][720/940] lr: 1.0000e-03 eta: 6:15:09 time: 0.6695 data_time: 0.0275 memory: 16095 grad_norm: 5.5933 loss: 0.7651 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7651 2022/12/08 23:58:44 - mmengine - INFO - Epoch(train) [62][740/940] lr: 1.0000e-03 eta: 6:14:56 time: 0.5792 data_time: 0.0228 memory: 16095 grad_norm: 5.6207 loss: 0.8626 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8626 2022/12/08 23:58:56 - mmengine - INFO - Epoch(train) [62][760/940] lr: 1.0000e-03 eta: 6:14:43 time: 0.5930 data_time: 0.0315 memory: 16095 grad_norm: 5.4558 loss: 0.8868 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 0.8868 2022/12/08 23:59:09 - mmengine - INFO - Epoch(train) [62][780/940] lr: 1.0000e-03 eta: 6:14:30 time: 0.6284 data_time: 0.0388 memory: 16095 grad_norm: 5.4813 loss: 0.8222 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 0.8222 2022/12/08 23:59:20 - mmengine - INFO - Epoch(train) [62][800/940] lr: 1.0000e-03 eta: 6:14:17 time: 0.5588 data_time: 0.0260 memory: 16095 grad_norm: 5.6878 loss: 0.8672 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8672 2022/12/08 23:59:32 - mmengine - INFO - Epoch(train) [62][820/940] lr: 1.0000e-03 eta: 6:14:04 time: 0.6203 data_time: 0.0243 memory: 16095 grad_norm: 5.4580 loss: 0.8284 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.8284 2022/12/08 23:59:44 - mmengine - INFO - Epoch(train) [62][840/940] lr: 1.0000e-03 eta: 6:13:51 time: 0.5728 data_time: 0.0254 memory: 16095 grad_norm: 5.5527 loss: 0.8214 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.8214 2022/12/08 23:59:58 - mmengine - INFO - Epoch(train) [62][860/940] lr: 1.0000e-03 eta: 6:13:40 time: 0.7214 data_time: 0.0269 memory: 16095 grad_norm: 5.6794 loss: 0.8261 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8261 2022/12/09 00:00:10 - mmengine - INFO - Epoch(train) [62][880/940] lr: 1.0000e-03 eta: 6:13:27 time: 0.5896 data_time: 0.0243 memory: 16095 grad_norm: 5.5390 loss: 0.8724 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.8724 2022/12/09 00:00:24 - mmengine - INFO - Epoch(train) [62][900/940] lr: 1.0000e-03 eta: 6:13:15 time: 0.6832 data_time: 0.0321 memory: 16095 grad_norm: 5.4216 loss: 0.7318 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.7318 2022/12/09 00:00:35 - mmengine - INFO - Epoch(train) [62][920/940] lr: 1.0000e-03 eta: 6:13:01 time: 0.5354 data_time: 0.0213 memory: 16095 grad_norm: 5.4838 loss: 0.7946 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7946 2022/12/09 00:00:45 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 00:00:45 - mmengine - INFO - Epoch(train) [62][940/940] lr: 1.0000e-03 eta: 6:12:47 time: 0.5245 data_time: 0.0169 memory: 16095 grad_norm: 6.1284 loss: 0.8250 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 0.8250 2022/12/09 00:00:59 - mmengine - INFO - Epoch(val) [62][20/78] eta: 0:00:41 time: 0.7224 data_time: 0.6292 memory: 1686 2022/12/09 00:01:08 - mmengine - INFO - Epoch(val) [62][40/78] eta: 0:00:22 time: 0.4402 data_time: 0.3471 memory: 1686 2022/12/09 00:01:22 - mmengine - INFO - Epoch(val) [62][60/78] eta: 0:00:11 time: 0.6802 data_time: 0.5841 memory: 1686 2022/12/09 00:01:33 - mmengine - INFO - Epoch(val) [62][78/78] acc/top1: 0.6942 acc/top5: 0.8784 acc/mean1: 0.6941 2022/12/09 00:01:49 - mmengine - INFO - Epoch(train) [63][ 20/940] lr: 1.0000e-03 eta: 6:12:37 time: 0.8093 data_time: 0.3356 memory: 16095 grad_norm: 5.5870 loss: 0.8325 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.8325 2022/12/09 00:01:59 - mmengine - INFO - Epoch(train) [63][ 40/940] lr: 1.0000e-03 eta: 6:12:23 time: 0.5284 data_time: 0.1659 memory: 16095 grad_norm: 5.4819 loss: 0.7842 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7842 2022/12/09 00:02:13 - mmengine - INFO - Epoch(train) [63][ 60/940] lr: 1.0000e-03 eta: 6:12:12 time: 0.6834 data_time: 0.1883 memory: 16095 grad_norm: 5.5666 loss: 0.7860 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.7860 2022/12/09 00:02:24 - mmengine - INFO - Epoch(train) [63][ 80/940] lr: 1.0000e-03 eta: 6:11:58 time: 0.5604 data_time: 0.0625 memory: 16095 grad_norm: 5.5787 loss: 0.8333 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8333 2022/12/09 00:02:38 - mmengine - INFO - Epoch(train) [63][100/940] lr: 1.0000e-03 eta: 6:11:47 time: 0.6981 data_time: 0.1441 memory: 16095 grad_norm: 5.4874 loss: 0.7215 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7215 2022/12/09 00:02:49 - mmengine - INFO - Epoch(train) [63][120/940] lr: 1.0000e-03 eta: 6:11:33 time: 0.5551 data_time: 0.1168 memory: 16095 grad_norm: 5.6405 loss: 0.7765 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7765 2022/12/09 00:03:02 - mmengine - INFO - Epoch(train) [63][140/940] lr: 1.0000e-03 eta: 6:11:21 time: 0.6553 data_time: 0.1463 memory: 16095 grad_norm: 5.5604 loss: 0.9129 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 0.9129 2022/12/09 00:03:13 - mmengine - INFO - Epoch(train) [63][160/940] lr: 1.0000e-03 eta: 6:11:07 time: 0.5358 data_time: 0.0664 memory: 16095 grad_norm: 5.6246 loss: 0.7361 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7361 2022/12/09 00:03:27 - mmengine - INFO - Epoch(train) [63][180/940] lr: 1.0000e-03 eta: 6:10:56 time: 0.7150 data_time: 0.2652 memory: 16095 grad_norm: 5.4054 loss: 0.7163 top1_acc: 0.5938 top5_acc: 0.8438 loss_cls: 0.7163 2022/12/09 00:03:38 - mmengine - INFO - Epoch(train) [63][200/940] lr: 1.0000e-03 eta: 6:10:42 time: 0.5461 data_time: 0.0878 memory: 16095 grad_norm: 5.5947 loss: 0.7920 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7920 2022/12/09 00:03:52 - mmengine - INFO - Epoch(train) [63][220/940] lr: 1.0000e-03 eta: 6:10:30 time: 0.6670 data_time: 0.1284 memory: 16095 grad_norm: 5.4328 loss: 0.8236 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8236 2022/12/09 00:04:03 - mmengine - INFO - Epoch(train) [63][240/940] lr: 1.0000e-03 eta: 6:10:17 time: 0.5490 data_time: 0.0215 memory: 16095 grad_norm: 5.4855 loss: 0.8386 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8386 2022/12/09 00:04:17 - mmengine - INFO - Epoch(train) [63][260/940] lr: 1.0000e-03 eta: 6:10:05 time: 0.6998 data_time: 0.0305 memory: 16095 grad_norm: 5.4622 loss: 0.7118 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7118 2022/12/09 00:04:28 - mmengine - INFO - Epoch(train) [63][280/940] lr: 1.0000e-03 eta: 6:09:52 time: 0.5416 data_time: 0.0214 memory: 16095 grad_norm: 5.6131 loss: 0.8782 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 0.8782 2022/12/09 00:04:40 - mmengine - INFO - Epoch(train) [63][300/940] lr: 1.0000e-03 eta: 6:09:39 time: 0.6371 data_time: 0.0286 memory: 16095 grad_norm: 5.5038 loss: 0.8431 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8431 2022/12/09 00:04:51 - mmengine - INFO - Epoch(train) [63][320/940] lr: 1.0000e-03 eta: 6:09:26 time: 0.5597 data_time: 0.0201 memory: 16095 grad_norm: 5.5249 loss: 0.7096 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7096 2022/12/09 00:05:04 - mmengine - INFO - Epoch(train) [63][340/940] lr: 1.0000e-03 eta: 6:09:13 time: 0.6192 data_time: 0.0265 memory: 16095 grad_norm: 5.5433 loss: 0.8266 top1_acc: 0.6250 top5_acc: 0.8438 loss_cls: 0.8266 2022/12/09 00:05:15 - mmengine - INFO - Epoch(train) [63][360/940] lr: 1.0000e-03 eta: 6:09:00 time: 0.5530 data_time: 0.0363 memory: 16095 grad_norm: 5.5939 loss: 0.7579 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7579 2022/12/09 00:05:29 - mmengine - INFO - Epoch(train) [63][380/940] lr: 1.0000e-03 eta: 6:08:48 time: 0.6876 data_time: 0.0279 memory: 16095 grad_norm: 5.6036 loss: 0.7557 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.7557 2022/12/09 00:05:41 - mmengine - INFO - Epoch(train) [63][400/940] lr: 1.0000e-03 eta: 6:08:36 time: 0.6195 data_time: 0.0212 memory: 16095 grad_norm: 5.4708 loss: 0.9165 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.9165 2022/12/09 00:05:53 - mmengine - INFO - Epoch(train) [63][420/940] lr: 1.0000e-03 eta: 6:08:23 time: 0.6206 data_time: 0.0280 memory: 16095 grad_norm: 5.6150 loss: 0.8578 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8578 2022/12/09 00:06:05 - mmengine - INFO - Epoch(train) [63][440/940] lr: 1.0000e-03 eta: 6:08:10 time: 0.5730 data_time: 0.0214 memory: 16095 grad_norm: 5.6520 loss: 0.8013 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8013 2022/12/09 00:06:18 - mmengine - INFO - Epoch(train) [63][460/940] lr: 1.0000e-03 eta: 6:07:58 time: 0.6573 data_time: 0.0265 memory: 16095 grad_norm: 5.6520 loss: 0.8277 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.8277 2022/12/09 00:06:29 - mmengine - INFO - Epoch(train) [63][480/940] lr: 1.0000e-03 eta: 6:07:44 time: 0.5265 data_time: 0.0251 memory: 16095 grad_norm: 5.6122 loss: 0.7977 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7977 2022/12/09 00:06:42 - mmengine - INFO - Epoch(train) [63][500/940] lr: 1.0000e-03 eta: 6:07:32 time: 0.6501 data_time: 0.0250 memory: 16095 grad_norm: 5.5212 loss: 0.7269 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7269 2022/12/09 00:06:53 - mmengine - INFO - Epoch(train) [63][520/940] lr: 1.0000e-03 eta: 6:07:19 time: 0.5685 data_time: 0.0239 memory: 16095 grad_norm: 5.6916 loss: 0.8639 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8639 2022/12/09 00:07:05 - mmengine - INFO - Epoch(train) [63][540/940] lr: 1.0000e-03 eta: 6:07:06 time: 0.6027 data_time: 0.0242 memory: 16095 grad_norm: 5.6476 loss: 0.7388 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.7388 2022/12/09 00:07:17 - mmengine - INFO - Epoch(train) [63][560/940] lr: 1.0000e-03 eta: 6:06:53 time: 0.5778 data_time: 0.0255 memory: 16095 grad_norm: 5.5995 loss: 0.8665 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.8665 2022/12/09 00:07:29 - mmengine - INFO - Epoch(train) [63][580/940] lr: 1.0000e-03 eta: 6:06:40 time: 0.6249 data_time: 0.0260 memory: 16095 grad_norm: 5.5656 loss: 0.7187 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 0.7187 2022/12/09 00:07:41 - mmengine - INFO - Epoch(train) [63][600/940] lr: 1.0000e-03 eta: 6:06:27 time: 0.6030 data_time: 0.0312 memory: 16095 grad_norm: 5.5844 loss: 0.6731 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6731 2022/12/09 00:07:54 - mmengine - INFO - Epoch(train) [63][620/940] lr: 1.0000e-03 eta: 6:06:15 time: 0.6410 data_time: 0.0289 memory: 16095 grad_norm: 5.5231 loss: 0.7830 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 0.7830 2022/12/09 00:08:06 - mmengine - INFO - Epoch(train) [63][640/940] lr: 1.0000e-03 eta: 6:06:02 time: 0.6031 data_time: 0.0230 memory: 16095 grad_norm: 5.7978 loss: 0.8205 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8205 2022/12/09 00:08:19 - mmengine - INFO - Epoch(train) [63][660/940] lr: 1.0000e-03 eta: 6:05:50 time: 0.6368 data_time: 0.0216 memory: 16095 grad_norm: 5.5966 loss: 0.7892 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7892 2022/12/09 00:08:31 - mmengine - INFO - Epoch(train) [63][680/940] lr: 1.0000e-03 eta: 6:05:37 time: 0.6034 data_time: 0.0268 memory: 16095 grad_norm: 5.4745 loss: 0.7828 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7828 2022/12/09 00:08:43 - mmengine - INFO - Epoch(train) [63][700/940] lr: 1.0000e-03 eta: 6:05:24 time: 0.6062 data_time: 0.0231 memory: 16095 grad_norm: 5.5107 loss: 0.8208 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.8208 2022/12/09 00:08:56 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 00:08:56 - mmengine - INFO - Epoch(train) [63][720/940] lr: 1.0000e-03 eta: 6:05:12 time: 0.6283 data_time: 0.0250 memory: 16095 grad_norm: 5.6244 loss: 0.7792 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7792 2022/12/09 00:09:08 - mmengine - INFO - Epoch(train) [63][740/940] lr: 1.0000e-03 eta: 6:04:59 time: 0.5960 data_time: 0.0244 memory: 16095 grad_norm: 5.5557 loss: 0.7756 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.7756 2022/12/09 00:09:20 - mmengine - INFO - Epoch(train) [63][760/940] lr: 1.0000e-03 eta: 6:04:46 time: 0.6028 data_time: 0.0292 memory: 16095 grad_norm: 5.5935 loss: 0.7648 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7648 2022/12/09 00:09:31 - mmengine - INFO - Epoch(train) [63][780/940] lr: 1.0000e-03 eta: 6:04:33 time: 0.5927 data_time: 0.0237 memory: 16095 grad_norm: 5.5756 loss: 0.8698 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8698 2022/12/09 00:09:45 - mmengine - INFO - Epoch(train) [63][800/940] lr: 1.0000e-03 eta: 6:04:21 time: 0.6578 data_time: 0.0260 memory: 16095 grad_norm: 5.3552 loss: 0.7621 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7621 2022/12/09 00:09:56 - mmengine - INFO - Epoch(train) [63][820/940] lr: 1.0000e-03 eta: 6:04:08 time: 0.5471 data_time: 0.0225 memory: 16095 grad_norm: 5.5359 loss: 0.7447 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7447 2022/12/09 00:10:08 - mmengine - INFO - Epoch(train) [63][840/940] lr: 1.0000e-03 eta: 6:03:55 time: 0.6415 data_time: 0.0260 memory: 16095 grad_norm: 5.6498 loss: 0.8702 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.8702 2022/12/09 00:10:19 - mmengine - INFO - Epoch(train) [63][860/940] lr: 1.0000e-03 eta: 6:03:42 time: 0.5507 data_time: 0.0287 memory: 16095 grad_norm: 5.5796 loss: 0.8255 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8255 2022/12/09 00:10:33 - mmengine - INFO - Epoch(train) [63][880/940] lr: 1.0000e-03 eta: 6:03:30 time: 0.6542 data_time: 0.0228 memory: 16095 grad_norm: 5.7145 loss: 0.7518 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7518 2022/12/09 00:10:44 - mmengine - INFO - Epoch(train) [63][900/940] lr: 1.0000e-03 eta: 6:03:17 time: 0.5866 data_time: 0.0247 memory: 16095 grad_norm: 5.6136 loss: 0.8633 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 0.8633 2022/12/09 00:10:58 - mmengine - INFO - Epoch(train) [63][920/940] lr: 1.0000e-03 eta: 6:03:05 time: 0.7021 data_time: 0.0225 memory: 16095 grad_norm: 5.5128 loss: 0.8286 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.8286 2022/12/09 00:11:07 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 00:11:07 - mmengine - INFO - Epoch(train) [63][940/940] lr: 1.0000e-03 eta: 6:02:50 time: 0.4304 data_time: 0.0176 memory: 16095 grad_norm: 5.9624 loss: 0.8636 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 0.8636 2022/12/09 00:11:07 - mmengine - INFO - Saving checkpoint at 63 epochs 2022/12/09 00:11:24 - mmengine - INFO - Epoch(val) [63][20/78] eta: 0:00:40 time: 0.7058 data_time: 0.6114 memory: 1686 2022/12/09 00:11:33 - mmengine - INFO - Epoch(val) [63][40/78] eta: 0:00:22 time: 0.4746 data_time: 0.3804 memory: 1686 2022/12/09 00:11:47 - mmengine - INFO - Epoch(val) [63][60/78] eta: 0:00:11 time: 0.6766 data_time: 0.5814 memory: 1686 2022/12/09 00:11:57 - mmengine - INFO - Epoch(val) [63][78/78] acc/top1: 0.6910 acc/top5: 0.8782 acc/mean1: 0.6910 2022/12/09 00:12:13 - mmengine - INFO - Epoch(train) [64][ 20/940] lr: 1.0000e-03 eta: 6:02:40 time: 0.7888 data_time: 0.3484 memory: 16095 grad_norm: 5.6648 loss: 0.8278 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8278 2022/12/09 00:12:24 - mmengine - INFO - Epoch(train) [64][ 40/940] lr: 1.0000e-03 eta: 6:02:26 time: 0.5627 data_time: 0.1608 memory: 16095 grad_norm: 5.5149 loss: 0.7349 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7349 2022/12/09 00:12:38 - mmengine - INFO - Epoch(train) [64][ 60/940] lr: 1.0000e-03 eta: 6:02:15 time: 0.6994 data_time: 0.1753 memory: 16095 grad_norm: 5.5468 loss: 0.8582 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8582 2022/12/09 00:12:49 - mmengine - INFO - Epoch(train) [64][ 80/940] lr: 1.0000e-03 eta: 6:02:01 time: 0.5511 data_time: 0.0211 memory: 16095 grad_norm: 5.5716 loss: 0.7526 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7526 2022/12/09 00:13:03 - mmengine - INFO - Epoch(train) [64][100/940] lr: 1.0000e-03 eta: 6:01:50 time: 0.6965 data_time: 0.0280 memory: 16095 grad_norm: 5.4907 loss: 0.7835 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 0.7835 2022/12/09 00:13:14 - mmengine - INFO - Epoch(train) [64][120/940] lr: 1.0000e-03 eta: 6:01:36 time: 0.5434 data_time: 0.0241 memory: 16095 grad_norm: 5.6086 loss: 0.7512 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7512 2022/12/09 00:13:26 - mmengine - INFO - Epoch(train) [64][140/940] lr: 1.0000e-03 eta: 6:01:23 time: 0.6068 data_time: 0.0328 memory: 16095 grad_norm: 5.7245 loss: 0.7470 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7470 2022/12/09 00:13:37 - mmengine - INFO - Epoch(train) [64][160/940] lr: 1.0000e-03 eta: 6:01:10 time: 0.5609 data_time: 0.0540 memory: 16095 grad_norm: 5.5583 loss: 0.7131 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7131 2022/12/09 00:13:50 - mmengine - INFO - Epoch(train) [64][180/940] lr: 1.0000e-03 eta: 6:00:58 time: 0.6516 data_time: 0.0254 memory: 16095 grad_norm: 5.4848 loss: 0.8635 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8635 2022/12/09 00:14:01 - mmengine - INFO - Epoch(train) [64][200/940] lr: 1.0000e-03 eta: 6:00:45 time: 0.5696 data_time: 0.0207 memory: 16095 grad_norm: 5.6936 loss: 0.7784 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7784 2022/12/09 00:14:15 - mmengine - INFO - Epoch(train) [64][220/940] lr: 1.0000e-03 eta: 6:00:33 time: 0.6741 data_time: 0.0313 memory: 16095 grad_norm: 5.6805 loss: 0.7189 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7189 2022/12/09 00:14:26 - mmengine - INFO - Epoch(train) [64][240/940] lr: 1.0000e-03 eta: 6:00:20 time: 0.5658 data_time: 0.0222 memory: 16095 grad_norm: 5.6619 loss: 0.9371 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 0.9371 2022/12/09 00:14:40 - mmengine - INFO - Epoch(train) [64][260/940] lr: 1.0000e-03 eta: 6:00:08 time: 0.6911 data_time: 0.0266 memory: 16095 grad_norm: 5.5529 loss: 0.8627 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8627 2022/12/09 00:14:51 - mmengine - INFO - Epoch(train) [64][280/940] lr: 1.0000e-03 eta: 5:59:54 time: 0.5428 data_time: 0.0302 memory: 16095 grad_norm: 5.5780 loss: 0.7712 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7712 2022/12/09 00:15:05 - mmengine - INFO - Epoch(train) [64][300/940] lr: 1.0000e-03 eta: 5:59:42 time: 0.6784 data_time: 0.0230 memory: 16095 grad_norm: 5.4475 loss: 0.7194 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7194 2022/12/09 00:15:15 - mmengine - INFO - Epoch(train) [64][320/940] lr: 1.0000e-03 eta: 5:59:29 time: 0.5448 data_time: 0.0242 memory: 16095 grad_norm: 5.5075 loss: 0.7285 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7285 2022/12/09 00:15:29 - mmengine - INFO - Epoch(train) [64][340/940] lr: 1.0000e-03 eta: 5:59:17 time: 0.6792 data_time: 0.0232 memory: 16095 grad_norm: 5.7020 loss: 0.7603 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7603 2022/12/09 00:15:41 - mmengine - INFO - Epoch(train) [64][360/940] lr: 1.0000e-03 eta: 5:59:04 time: 0.5785 data_time: 0.0337 memory: 16095 grad_norm: 5.4660 loss: 0.7060 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7060 2022/12/09 00:15:53 - mmengine - INFO - Epoch(train) [64][380/940] lr: 1.0000e-03 eta: 5:58:52 time: 0.6385 data_time: 0.0230 memory: 16095 grad_norm: 5.5314 loss: 0.7158 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7158 2022/12/09 00:16:04 - mmengine - INFO - Epoch(train) [64][400/940] lr: 1.0000e-03 eta: 5:58:38 time: 0.5392 data_time: 0.0237 memory: 16095 grad_norm: 5.6724 loss: 0.9092 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9092 2022/12/09 00:16:17 - mmengine - INFO - Epoch(train) [64][420/940] lr: 1.0000e-03 eta: 5:58:26 time: 0.6593 data_time: 0.0286 memory: 16095 grad_norm: 5.6359 loss: 0.8862 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.8862 2022/12/09 00:16:28 - mmengine - INFO - Epoch(train) [64][440/940] lr: 1.0000e-03 eta: 5:58:12 time: 0.5281 data_time: 0.0222 memory: 16095 grad_norm: 5.5574 loss: 0.7636 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7636 2022/12/09 00:16:41 - mmengine - INFO - Epoch(train) [64][460/940] lr: 1.0000e-03 eta: 5:58:00 time: 0.6563 data_time: 0.0234 memory: 16095 grad_norm: 5.6257 loss: 0.7981 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7981 2022/12/09 00:16:52 - mmengine - INFO - Epoch(train) [64][480/940] lr: 1.0000e-03 eta: 5:57:47 time: 0.5493 data_time: 0.0652 memory: 16095 grad_norm: 5.6312 loss: 0.8975 top1_acc: 0.5625 top5_acc: 0.9062 loss_cls: 0.8975 2022/12/09 00:17:06 - mmengine - INFO - Epoch(train) [64][500/940] lr: 1.0000e-03 eta: 5:57:35 time: 0.6841 data_time: 0.0248 memory: 16095 grad_norm: 5.6625 loss: 0.8290 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8290 2022/12/09 00:17:17 - mmengine - INFO - Epoch(train) [64][520/940] lr: 1.0000e-03 eta: 5:57:22 time: 0.5601 data_time: 0.0353 memory: 16095 grad_norm: 5.6150 loss: 0.8636 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.8636 2022/12/09 00:17:31 - mmengine - INFO - Epoch(train) [64][540/940] lr: 1.0000e-03 eta: 5:57:10 time: 0.7020 data_time: 0.0302 memory: 16095 grad_norm: 5.4936 loss: 0.7398 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 0.7398 2022/12/09 00:17:43 - mmengine - INFO - Epoch(train) [64][560/940] lr: 1.0000e-03 eta: 5:56:57 time: 0.5760 data_time: 0.0240 memory: 16095 grad_norm: 5.5435 loss: 0.7611 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7611 2022/12/09 00:17:55 - mmengine - INFO - Epoch(train) [64][580/940] lr: 1.0000e-03 eta: 5:56:45 time: 0.6449 data_time: 0.0223 memory: 16095 grad_norm: 5.5946 loss: 0.7639 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7639 2022/12/09 00:18:06 - mmengine - INFO - Epoch(train) [64][600/940] lr: 1.0000e-03 eta: 5:56:31 time: 0.5517 data_time: 0.0270 memory: 16095 grad_norm: 5.6623 loss: 0.8456 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8456 2022/12/09 00:18:19 - mmengine - INFO - Epoch(train) [64][620/940] lr: 1.0000e-03 eta: 5:56:19 time: 0.6504 data_time: 0.0239 memory: 16095 grad_norm: 5.6985 loss: 0.8038 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8038 2022/12/09 00:18:30 - mmengine - INFO - Epoch(train) [64][640/940] lr: 1.0000e-03 eta: 5:56:05 time: 0.5389 data_time: 0.0279 memory: 16095 grad_norm: 5.7227 loss: 0.7813 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.7813 2022/12/09 00:18:43 - mmengine - INFO - Epoch(train) [64][660/940] lr: 1.0000e-03 eta: 5:55:53 time: 0.6515 data_time: 0.0228 memory: 16095 grad_norm: 5.5322 loss: 0.8013 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8013 2022/12/09 00:18:55 - mmengine - INFO - Epoch(train) [64][680/940] lr: 1.0000e-03 eta: 5:55:40 time: 0.5675 data_time: 0.0640 memory: 16095 grad_norm: 5.5527 loss: 0.8086 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8086 2022/12/09 00:19:07 - mmengine - INFO - Epoch(train) [64][700/940] lr: 1.0000e-03 eta: 5:55:28 time: 0.6322 data_time: 0.0273 memory: 16095 grad_norm: 5.7482 loss: 0.7990 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7990 2022/12/09 00:19:19 - mmengine - INFO - Epoch(train) [64][720/940] lr: 1.0000e-03 eta: 5:55:14 time: 0.5653 data_time: 0.0403 memory: 16095 grad_norm: 5.6092 loss: 0.8373 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8373 2022/12/09 00:19:31 - mmengine - INFO - Epoch(train) [64][740/940] lr: 1.0000e-03 eta: 5:55:02 time: 0.6319 data_time: 0.0851 memory: 16095 grad_norm: 5.6030 loss: 0.8022 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 0.8022 2022/12/09 00:19:43 - mmengine - INFO - Epoch(train) [64][760/940] lr: 1.0000e-03 eta: 5:54:49 time: 0.5646 data_time: 0.1343 memory: 16095 grad_norm: 5.7370 loss: 0.7933 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7933 2022/12/09 00:19:54 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 00:19:54 - mmengine - INFO - Epoch(train) [64][780/940] lr: 1.0000e-03 eta: 5:54:36 time: 0.5674 data_time: 0.1344 memory: 16095 grad_norm: 5.7149 loss: 0.7502 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7502 2022/12/09 00:20:08 - mmengine - INFO - Epoch(train) [64][800/940] lr: 1.0000e-03 eta: 5:54:24 time: 0.6888 data_time: 0.2857 memory: 16095 grad_norm: 5.6777 loss: 0.7755 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7755 2022/12/09 00:20:19 - mmengine - INFO - Epoch(train) [64][820/940] lr: 1.0000e-03 eta: 5:54:11 time: 0.5792 data_time: 0.1626 memory: 16095 grad_norm: 5.5212 loss: 0.6813 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6813 2022/12/09 00:20:33 - mmengine - INFO - Epoch(train) [64][840/940] lr: 1.0000e-03 eta: 5:53:59 time: 0.6821 data_time: 0.2305 memory: 16095 grad_norm: 5.5998 loss: 0.8147 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8147 2022/12/09 00:20:45 - mmengine - INFO - Epoch(train) [64][860/940] lr: 1.0000e-03 eta: 5:53:46 time: 0.6196 data_time: 0.1098 memory: 16095 grad_norm: 5.7067 loss: 0.8301 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8301 2022/12/09 00:20:57 - mmengine - INFO - Epoch(train) [64][880/940] lr: 1.0000e-03 eta: 5:53:33 time: 0.5920 data_time: 0.1167 memory: 16095 grad_norm: 5.6578 loss: 0.7252 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7252 2022/12/09 00:21:09 - mmengine - INFO - Epoch(train) [64][900/940] lr: 1.0000e-03 eta: 5:53:20 time: 0.5704 data_time: 0.1615 memory: 16095 grad_norm: 5.5765 loss: 0.7553 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.7553 2022/12/09 00:21:21 - mmengine - INFO - Epoch(train) [64][920/940] lr: 1.0000e-03 eta: 5:53:08 time: 0.6336 data_time: 0.0827 memory: 16095 grad_norm: 5.7229 loss: 0.7414 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 0.7414 2022/12/09 00:21:31 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 00:21:31 - mmengine - INFO - Epoch(train) [64][940/940] lr: 1.0000e-03 eta: 5:52:54 time: 0.5075 data_time: 0.0215 memory: 16095 grad_norm: 6.0434 loss: 0.7722 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 0.7722 2022/12/09 00:21:46 - mmengine - INFO - Epoch(val) [64][20/78] eta: 0:00:41 time: 0.7081 data_time: 0.6135 memory: 1686 2022/12/09 00:21:55 - mmengine - INFO - Epoch(val) [64][40/78] eta: 0:00:21 time: 0.4488 data_time: 0.3553 memory: 1686 2022/12/09 00:22:08 - mmengine - INFO - Epoch(val) [64][60/78] eta: 0:00:11 time: 0.6953 data_time: 0.5997 memory: 1686 2022/12/09 00:22:19 - mmengine - INFO - Epoch(val) [64][78/78] acc/top1: 0.6934 acc/top5: 0.8774 acc/mean1: 0.6933 2022/12/09 00:22:35 - mmengine - INFO - Epoch(train) [65][ 20/940] lr: 1.0000e-03 eta: 5:52:44 time: 0.8102 data_time: 0.3521 memory: 16095 grad_norm: 5.4811 loss: 0.8016 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8016 2022/12/09 00:22:46 - mmengine - INFO - Epoch(train) [65][ 40/940] lr: 1.0000e-03 eta: 5:52:30 time: 0.5344 data_time: 0.1076 memory: 16095 grad_norm: 5.6380 loss: 0.7958 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7958 2022/12/09 00:23:00 - mmengine - INFO - Epoch(train) [65][ 60/940] lr: 1.0000e-03 eta: 5:52:18 time: 0.7119 data_time: 0.0755 memory: 16095 grad_norm: 5.6016 loss: 0.7846 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7846 2022/12/09 00:23:11 - mmengine - INFO - Epoch(train) [65][ 80/940] lr: 1.0000e-03 eta: 5:52:05 time: 0.5469 data_time: 0.0222 memory: 16095 grad_norm: 5.4978 loss: 0.7564 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7564 2022/12/09 00:23:24 - mmengine - INFO - Epoch(train) [65][100/940] lr: 1.0000e-03 eta: 5:51:53 time: 0.6360 data_time: 0.0294 memory: 16095 grad_norm: 5.5707 loss: 0.7509 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7509 2022/12/09 00:23:35 - mmengine - INFO - Epoch(train) [65][120/940] lr: 1.0000e-03 eta: 5:51:39 time: 0.5437 data_time: 0.0199 memory: 16095 grad_norm: 5.6254 loss: 0.8169 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8169 2022/12/09 00:23:48 - mmengine - INFO - Epoch(train) [65][140/940] lr: 1.0000e-03 eta: 5:51:27 time: 0.6709 data_time: 0.0278 memory: 16095 grad_norm: 5.5793 loss: 0.7707 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7707 2022/12/09 00:23:59 - mmengine - INFO - Epoch(train) [65][160/940] lr: 1.0000e-03 eta: 5:51:14 time: 0.5314 data_time: 0.0212 memory: 16095 grad_norm: 5.6413 loss: 0.7694 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7694 2022/12/09 00:24:12 - mmengine - INFO - Epoch(train) [65][180/940] lr: 1.0000e-03 eta: 5:51:02 time: 0.6700 data_time: 0.0294 memory: 16095 grad_norm: 5.6097 loss: 0.7918 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7918 2022/12/09 00:24:23 - mmengine - INFO - Epoch(train) [65][200/940] lr: 1.0000e-03 eta: 5:50:48 time: 0.5732 data_time: 0.0602 memory: 16095 grad_norm: 5.6250 loss: 0.7357 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 0.7357 2022/12/09 00:24:38 - mmengine - INFO - Epoch(train) [65][220/940] lr: 1.0000e-03 eta: 5:50:37 time: 0.7037 data_time: 0.0467 memory: 16095 grad_norm: 5.7851 loss: 0.8739 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8739 2022/12/09 00:24:49 - mmengine - INFO - Epoch(train) [65][240/940] lr: 1.0000e-03 eta: 5:50:24 time: 0.5564 data_time: 0.0215 memory: 16095 grad_norm: 5.6138 loss: 0.8404 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8404 2022/12/09 00:25:02 - mmengine - INFO - Epoch(train) [65][260/940] lr: 1.0000e-03 eta: 5:50:11 time: 0.6550 data_time: 0.0284 memory: 16095 grad_norm: 5.6904 loss: 0.8571 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8571 2022/12/09 00:25:13 - mmengine - INFO - Epoch(train) [65][280/940] lr: 1.0000e-03 eta: 5:49:58 time: 0.5385 data_time: 0.0209 memory: 16095 grad_norm: 5.4759 loss: 0.7433 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7433 2022/12/09 00:25:26 - mmengine - INFO - Epoch(train) [65][300/940] lr: 1.0000e-03 eta: 5:49:46 time: 0.6871 data_time: 0.0257 memory: 16095 grad_norm: 5.4663 loss: 0.8561 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.8561 2022/12/09 00:25:37 - mmengine - INFO - Epoch(train) [65][320/940] lr: 1.0000e-03 eta: 5:49:33 time: 0.5464 data_time: 0.0525 memory: 16095 grad_norm: 5.4212 loss: 0.7158 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7158 2022/12/09 00:25:51 - mmengine - INFO - Epoch(train) [65][340/940] lr: 1.0000e-03 eta: 5:49:21 time: 0.7021 data_time: 0.1310 memory: 16095 grad_norm: 5.4693 loss: 0.7364 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7364 2022/12/09 00:26:03 - mmengine - INFO - Epoch(train) [65][360/940] lr: 1.0000e-03 eta: 5:49:08 time: 0.5634 data_time: 0.0260 memory: 16095 grad_norm: 5.4811 loss: 0.8359 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8359 2022/12/09 00:26:16 - mmengine - INFO - Epoch(train) [65][380/940] lr: 1.0000e-03 eta: 5:48:56 time: 0.6673 data_time: 0.0259 memory: 16095 grad_norm: 5.4780 loss: 0.7864 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 0.7864 2022/12/09 00:26:27 - mmengine - INFO - Epoch(train) [65][400/940] lr: 1.0000e-03 eta: 5:48:42 time: 0.5444 data_time: 0.0393 memory: 16095 grad_norm: 5.4660 loss: 0.7228 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7228 2022/12/09 00:26:41 - mmengine - INFO - Epoch(train) [65][420/940] lr: 1.0000e-03 eta: 5:48:30 time: 0.6850 data_time: 0.0239 memory: 16095 grad_norm: 5.6684 loss: 0.7799 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.7799 2022/12/09 00:26:52 - mmengine - INFO - Epoch(train) [65][440/940] lr: 1.0000e-03 eta: 5:48:17 time: 0.5545 data_time: 0.0254 memory: 16095 grad_norm: 5.5825 loss: 0.7984 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7984 2022/12/09 00:27:06 - mmengine - INFO - Epoch(train) [65][460/940] lr: 1.0000e-03 eta: 5:48:06 time: 0.7136 data_time: 0.0250 memory: 16095 grad_norm: 5.7071 loss: 0.6545 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6545 2022/12/09 00:27:17 - mmengine - INFO - Epoch(train) [65][480/940] lr: 1.0000e-03 eta: 5:47:52 time: 0.5333 data_time: 0.0247 memory: 16095 grad_norm: 5.7568 loss: 0.7963 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7963 2022/12/09 00:27:29 - mmengine - INFO - Epoch(train) [65][500/940] lr: 1.0000e-03 eta: 5:47:40 time: 0.6411 data_time: 0.0281 memory: 16095 grad_norm: 5.4521 loss: 0.7541 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7541 2022/12/09 00:27:41 - mmengine - INFO - Epoch(train) [65][520/940] lr: 1.0000e-03 eta: 5:47:27 time: 0.5727 data_time: 0.0218 memory: 16095 grad_norm: 5.6338 loss: 0.6904 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6904 2022/12/09 00:27:54 - mmengine - INFO - Epoch(train) [65][540/940] lr: 1.0000e-03 eta: 5:47:15 time: 0.6683 data_time: 0.0267 memory: 16095 grad_norm: 5.7786 loss: 0.8788 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8788 2022/12/09 00:28:05 - mmengine - INFO - Epoch(train) [65][560/940] lr: 1.0000e-03 eta: 5:47:01 time: 0.5555 data_time: 0.0240 memory: 16095 grad_norm: 5.7878 loss: 0.8828 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8828 2022/12/09 00:28:18 - mmengine - INFO - Epoch(train) [65][580/940] lr: 1.0000e-03 eta: 5:46:49 time: 0.6291 data_time: 0.0284 memory: 16095 grad_norm: 5.7050 loss: 0.8260 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8260 2022/12/09 00:28:29 - mmengine - INFO - Epoch(train) [65][600/940] lr: 1.0000e-03 eta: 5:46:35 time: 0.5396 data_time: 0.0230 memory: 16095 grad_norm: 5.6433 loss: 0.7391 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7391 2022/12/09 00:28:42 - mmengine - INFO - Epoch(train) [65][620/940] lr: 1.0000e-03 eta: 5:46:23 time: 0.6659 data_time: 0.0246 memory: 16095 grad_norm: 5.6014 loss: 0.7378 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7378 2022/12/09 00:28:53 - mmengine - INFO - Epoch(train) [65][640/940] lr: 1.0000e-03 eta: 5:46:10 time: 0.5543 data_time: 0.0247 memory: 16095 grad_norm: 5.5816 loss: 0.7653 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.7653 2022/12/09 00:29:07 - mmengine - INFO - Epoch(train) [65][660/940] lr: 1.0000e-03 eta: 5:45:58 time: 0.7014 data_time: 0.0321 memory: 16095 grad_norm: 5.6325 loss: 0.7769 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 0.7769 2022/12/09 00:29:18 - mmengine - INFO - Epoch(train) [65][680/940] lr: 1.0000e-03 eta: 5:45:45 time: 0.5583 data_time: 0.0257 memory: 16095 grad_norm: 5.5474 loss: 0.7490 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.7490 2022/12/09 00:29:32 - mmengine - INFO - Epoch(train) [65][700/940] lr: 1.0000e-03 eta: 5:45:33 time: 0.6605 data_time: 0.0214 memory: 16095 grad_norm: 5.5708 loss: 0.7676 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7676 2022/12/09 00:29:43 - mmengine - INFO - Epoch(train) [65][720/940] lr: 1.0000e-03 eta: 5:45:20 time: 0.5645 data_time: 0.0258 memory: 16095 grad_norm: 5.6552 loss: 0.7531 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7531 2022/12/09 00:29:56 - mmengine - INFO - Epoch(train) [65][740/940] lr: 1.0000e-03 eta: 5:45:07 time: 0.6449 data_time: 0.0261 memory: 16095 grad_norm: 5.7841 loss: 0.8073 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8073 2022/12/09 00:30:07 - mmengine - INFO - Epoch(train) [65][760/940] lr: 1.0000e-03 eta: 5:44:54 time: 0.5703 data_time: 0.0277 memory: 16095 grad_norm: 5.5392 loss: 0.7496 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.7496 2022/12/09 00:30:20 - mmengine - INFO - Epoch(train) [65][780/940] lr: 1.0000e-03 eta: 5:44:42 time: 0.6557 data_time: 0.0249 memory: 16095 grad_norm: 5.6063 loss: 0.8236 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8236 2022/12/09 00:30:32 - mmengine - INFO - Epoch(train) [65][800/940] lr: 1.0000e-03 eta: 5:44:29 time: 0.5791 data_time: 0.0259 memory: 16095 grad_norm: 5.7505 loss: 0.8920 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8920 2022/12/09 00:30:45 - mmengine - INFO - Epoch(train) [65][820/940] lr: 1.0000e-03 eta: 5:44:17 time: 0.6599 data_time: 0.0223 memory: 16095 grad_norm: 5.6485 loss: 0.8425 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8425 2022/12/09 00:30:56 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 00:30:56 - mmengine - INFO - Epoch(train) [65][840/940] lr: 1.0000e-03 eta: 5:44:04 time: 0.5656 data_time: 0.0248 memory: 16095 grad_norm: 5.7040 loss: 0.8126 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 0.8126 2022/12/09 00:31:11 - mmengine - INFO - Epoch(train) [65][860/940] lr: 1.0000e-03 eta: 5:43:52 time: 0.7111 data_time: 0.0237 memory: 16095 grad_norm: 5.5839 loss: 0.7943 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7943 2022/12/09 00:31:21 - mmengine - INFO - Epoch(train) [65][880/940] lr: 1.0000e-03 eta: 5:43:39 time: 0.5252 data_time: 0.0337 memory: 16095 grad_norm: 5.6397 loss: 0.8403 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8403 2022/12/09 00:31:34 - mmengine - INFO - Epoch(train) [65][900/940] lr: 1.0000e-03 eta: 5:43:27 time: 0.6634 data_time: 0.0230 memory: 16095 grad_norm: 5.6500 loss: 0.7887 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.7887 2022/12/09 00:31:45 - mmengine - INFO - Epoch(train) [65][920/940] lr: 1.0000e-03 eta: 5:43:13 time: 0.5118 data_time: 0.0252 memory: 16095 grad_norm: 5.6804 loss: 0.8843 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8843 2022/12/09 00:31:56 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 00:31:56 - mmengine - INFO - Epoch(train) [65][940/940] lr: 1.0000e-03 eta: 5:43:00 time: 0.5916 data_time: 0.0168 memory: 16095 grad_norm: 6.1019 loss: 0.8189 top1_acc: 0.5714 top5_acc: 1.0000 loss_cls: 0.8189 2022/12/09 00:32:10 - mmengine - INFO - Epoch(val) [65][20/78] eta: 0:00:40 time: 0.6933 data_time: 0.5991 memory: 1686 2022/12/09 00:32:20 - mmengine - INFO - Epoch(val) [65][40/78] eta: 0:00:22 time: 0.4854 data_time: 0.3918 memory: 1686 2022/12/09 00:32:33 - mmengine - INFO - Epoch(val) [65][60/78] eta: 0:00:11 time: 0.6611 data_time: 0.5670 memory: 1686 2022/12/09 00:32:44 - mmengine - INFO - Epoch(val) [65][78/78] acc/top1: 0.6920 acc/top5: 0.8792 acc/mean1: 0.6919 2022/12/09 00:33:00 - mmengine - INFO - Epoch(train) [66][ 20/940] lr: 1.0000e-03 eta: 5:42:49 time: 0.8104 data_time: 0.4797 memory: 16095 grad_norm: 5.6435 loss: 0.8325 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8325 2022/12/09 00:33:11 - mmengine - INFO - Epoch(train) [66][ 40/940] lr: 1.0000e-03 eta: 5:42:36 time: 0.5637 data_time: 0.2689 memory: 16095 grad_norm: 5.6138 loss: 0.7901 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7901 2022/12/09 00:33:26 - mmengine - INFO - Epoch(train) [66][ 60/940] lr: 1.0000e-03 eta: 5:42:25 time: 0.7311 data_time: 0.4331 memory: 16095 grad_norm: 5.4467 loss: 0.6863 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.6863 2022/12/09 00:33:37 - mmengine - INFO - Epoch(train) [66][ 80/940] lr: 1.0000e-03 eta: 5:42:12 time: 0.5610 data_time: 0.2296 memory: 16095 grad_norm: 5.6084 loss: 0.7761 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 0.7761 2022/12/09 00:33:51 - mmengine - INFO - Epoch(train) [66][100/940] lr: 1.0000e-03 eta: 5:42:00 time: 0.6679 data_time: 0.3375 memory: 16095 grad_norm: 5.7902 loss: 0.8502 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8502 2022/12/09 00:34:01 - mmengine - INFO - Epoch(train) [66][120/940] lr: 1.0000e-03 eta: 5:41:46 time: 0.5417 data_time: 0.2373 memory: 16095 grad_norm: 5.6671 loss: 0.7436 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7436 2022/12/09 00:34:14 - mmengine - INFO - Epoch(train) [66][140/940] lr: 1.0000e-03 eta: 5:41:34 time: 0.6323 data_time: 0.2925 memory: 16095 grad_norm: 5.6346 loss: 0.7382 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.7382 2022/12/09 00:34:25 - mmengine - INFO - Epoch(train) [66][160/940] lr: 1.0000e-03 eta: 5:41:21 time: 0.5576 data_time: 0.2585 memory: 16095 grad_norm: 5.6344 loss: 0.7882 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7882 2022/12/09 00:34:39 - mmengine - INFO - Epoch(train) [66][180/940] lr: 1.0000e-03 eta: 5:41:09 time: 0.6742 data_time: 0.2753 memory: 16095 grad_norm: 5.5014 loss: 0.6420 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.6420 2022/12/09 00:34:50 - mmengine - INFO - Epoch(train) [66][200/940] lr: 1.0000e-03 eta: 5:40:55 time: 0.5502 data_time: 0.1623 memory: 16095 grad_norm: 5.6118 loss: 0.7899 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7899 2022/12/09 00:35:03 - mmengine - INFO - Epoch(train) [66][220/940] lr: 1.0000e-03 eta: 5:40:43 time: 0.6611 data_time: 0.1919 memory: 16095 grad_norm: 5.7086 loss: 0.8108 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8108 2022/12/09 00:35:14 - mmengine - INFO - Epoch(train) [66][240/940] lr: 1.0000e-03 eta: 5:40:30 time: 0.5460 data_time: 0.1087 memory: 16095 grad_norm: 5.7149 loss: 0.7830 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7830 2022/12/09 00:35:27 - mmengine - INFO - Epoch(train) [66][260/940] lr: 1.0000e-03 eta: 5:40:18 time: 0.6767 data_time: 0.1570 memory: 16095 grad_norm: 5.6748 loss: 0.7399 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7399 2022/12/09 00:35:38 - mmengine - INFO - Epoch(train) [66][280/940] lr: 1.0000e-03 eta: 5:40:04 time: 0.5383 data_time: 0.1734 memory: 16095 grad_norm: 5.7252 loss: 0.7740 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.7740 2022/12/09 00:35:52 - mmengine - INFO - Epoch(train) [66][300/940] lr: 1.0000e-03 eta: 5:39:52 time: 0.6728 data_time: 0.1397 memory: 16095 grad_norm: 5.6180 loss: 0.8468 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8468 2022/12/09 00:36:03 - mmengine - INFO - Epoch(train) [66][320/940] lr: 1.0000e-03 eta: 5:39:39 time: 0.5664 data_time: 0.0898 memory: 16095 grad_norm: 5.6031 loss: 0.7717 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.7717 2022/12/09 00:36:16 - mmengine - INFO - Epoch(train) [66][340/940] lr: 1.0000e-03 eta: 5:39:27 time: 0.6511 data_time: 0.1676 memory: 16095 grad_norm: 5.5885 loss: 0.7975 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7975 2022/12/09 00:36:27 - mmengine - INFO - Epoch(train) [66][360/940] lr: 1.0000e-03 eta: 5:39:14 time: 0.5615 data_time: 0.0697 memory: 16095 grad_norm: 5.6674 loss: 0.7978 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7978 2022/12/09 00:36:42 - mmengine - INFO - Epoch(train) [66][380/940] lr: 1.0000e-03 eta: 5:39:02 time: 0.7240 data_time: 0.2288 memory: 16095 grad_norm: 5.6748 loss: 0.7827 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.7827 2022/12/09 00:36:53 - mmengine - INFO - Epoch(train) [66][400/940] lr: 1.0000e-03 eta: 5:38:49 time: 0.5554 data_time: 0.2104 memory: 16095 grad_norm: 5.6483 loss: 0.8041 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.8041 2022/12/09 00:37:07 - mmengine - INFO - Epoch(train) [66][420/940] lr: 1.0000e-03 eta: 5:38:37 time: 0.6929 data_time: 0.3709 memory: 16095 grad_norm: 5.6737 loss: 0.8586 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.8586 2022/12/09 00:37:17 - mmengine - INFO - Epoch(train) [66][440/940] lr: 1.0000e-03 eta: 5:38:24 time: 0.5381 data_time: 0.2059 memory: 16095 grad_norm: 5.6939 loss: 0.7809 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7809 2022/12/09 00:37:31 - mmengine - INFO - Epoch(train) [66][460/940] lr: 1.0000e-03 eta: 5:38:12 time: 0.6835 data_time: 0.3491 memory: 16095 grad_norm: 5.8926 loss: 0.8363 top1_acc: 0.7188 top5_acc: 0.7812 loss_cls: 0.8363 2022/12/09 00:37:42 - mmengine - INFO - Epoch(train) [66][480/940] lr: 1.0000e-03 eta: 5:37:59 time: 0.5675 data_time: 0.2274 memory: 16095 grad_norm: 5.7869 loss: 0.8979 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8979 2022/12/09 00:37:55 - mmengine - INFO - Epoch(train) [66][500/940] lr: 1.0000e-03 eta: 5:37:46 time: 0.6200 data_time: 0.2897 memory: 16095 grad_norm: 5.6998 loss: 0.8405 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8405 2022/12/09 00:38:06 - mmengine - INFO - Epoch(train) [66][520/940] lr: 1.0000e-03 eta: 5:37:33 time: 0.5700 data_time: 0.2320 memory: 16095 grad_norm: 5.7886 loss: 0.8029 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8029 2022/12/09 00:38:20 - mmengine - INFO - Epoch(train) [66][540/940] lr: 1.0000e-03 eta: 5:37:21 time: 0.6761 data_time: 0.3348 memory: 16095 grad_norm: 5.6815 loss: 0.7799 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7799 2022/12/09 00:38:30 - mmengine - INFO - Epoch(train) [66][560/940] lr: 1.0000e-03 eta: 5:37:08 time: 0.5182 data_time: 0.1872 memory: 16095 grad_norm: 5.7333 loss: 0.8495 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8495 2022/12/09 00:38:43 - mmengine - INFO - Epoch(train) [66][580/940] lr: 1.0000e-03 eta: 5:36:55 time: 0.6605 data_time: 0.2375 memory: 16095 grad_norm: 5.6061 loss: 0.6599 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6599 2022/12/09 00:38:55 - mmengine - INFO - Epoch(train) [66][600/940] lr: 1.0000e-03 eta: 5:36:42 time: 0.5634 data_time: 0.2095 memory: 16095 grad_norm: 5.5415 loss: 0.7994 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7994 2022/12/09 00:39:08 - mmengine - INFO - Epoch(train) [66][620/940] lr: 1.0000e-03 eta: 5:36:30 time: 0.6618 data_time: 0.3210 memory: 16095 grad_norm: 5.8098 loss: 0.6941 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6941 2022/12/09 00:39:20 - mmengine - INFO - Epoch(train) [66][640/940] lr: 1.0000e-03 eta: 5:36:17 time: 0.5830 data_time: 0.2183 memory: 16095 grad_norm: 5.7833 loss: 0.8019 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.8019 2022/12/09 00:39:33 - mmengine - INFO - Epoch(train) [66][660/940] lr: 1.0000e-03 eta: 5:36:05 time: 0.6623 data_time: 0.2941 memory: 16095 grad_norm: 5.6776 loss: 0.7693 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7693 2022/12/09 00:39:43 - mmengine - INFO - Epoch(train) [66][680/940] lr: 1.0000e-03 eta: 5:35:51 time: 0.5057 data_time: 0.1236 memory: 16095 grad_norm: 5.7024 loss: 0.8530 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8530 2022/12/09 00:39:56 - mmengine - INFO - Epoch(train) [66][700/940] lr: 1.0000e-03 eta: 5:35:39 time: 0.6331 data_time: 0.0778 memory: 16095 grad_norm: 5.6163 loss: 0.6908 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 0.6908 2022/12/09 00:40:07 - mmengine - INFO - Epoch(train) [66][720/940] lr: 1.0000e-03 eta: 5:35:26 time: 0.5784 data_time: 0.0463 memory: 16095 grad_norm: 5.7425 loss: 0.7681 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7681 2022/12/09 00:40:22 - mmengine - INFO - Epoch(train) [66][740/940] lr: 1.0000e-03 eta: 5:35:14 time: 0.7189 data_time: 0.0482 memory: 16095 grad_norm: 5.7306 loss: 0.7858 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 0.7858 2022/12/09 00:40:31 - mmengine - INFO - Epoch(train) [66][760/940] lr: 1.0000e-03 eta: 5:35:00 time: 0.4958 data_time: 0.0213 memory: 16095 grad_norm: 5.5374 loss: 0.8530 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8530 2022/12/09 00:40:46 - mmengine - INFO - Epoch(train) [66][780/940] lr: 1.0000e-03 eta: 5:34:49 time: 0.7039 data_time: 0.0266 memory: 16095 grad_norm: 5.7604 loss: 0.7362 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7362 2022/12/09 00:40:57 - mmengine - INFO - Epoch(train) [66][800/940] lr: 1.0000e-03 eta: 5:34:36 time: 0.5727 data_time: 0.0231 memory: 16095 grad_norm: 5.8279 loss: 0.8292 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8292 2022/12/09 00:41:10 - mmengine - INFO - Epoch(train) [66][820/940] lr: 1.0000e-03 eta: 5:34:23 time: 0.6479 data_time: 0.0268 memory: 16095 grad_norm: 5.7396 loss: 0.7737 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7737 2022/12/09 00:41:21 - mmengine - INFO - Epoch(train) [66][840/940] lr: 1.0000e-03 eta: 5:34:10 time: 0.5489 data_time: 0.0203 memory: 16095 grad_norm: 5.8101 loss: 0.8820 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8820 2022/12/09 00:41:35 - mmengine - INFO - Epoch(train) [66][860/940] lr: 1.0000e-03 eta: 5:33:58 time: 0.6960 data_time: 0.0268 memory: 16095 grad_norm: 5.5318 loss: 0.7961 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7961 2022/12/09 00:41:46 - mmengine - INFO - Epoch(train) [66][880/940] lr: 1.0000e-03 eta: 5:33:45 time: 0.5662 data_time: 0.0229 memory: 16095 grad_norm: 5.6858 loss: 0.7555 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.7555 2022/12/09 00:42:00 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 00:42:00 - mmengine - INFO - Epoch(train) [66][900/940] lr: 1.0000e-03 eta: 5:33:33 time: 0.6879 data_time: 0.0250 memory: 16095 grad_norm: 5.8194 loss: 0.8155 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8155 2022/12/09 00:42:10 - mmengine - INFO - Epoch(train) [66][920/940] lr: 1.0000e-03 eta: 5:33:20 time: 0.5112 data_time: 0.0251 memory: 16095 grad_norm: 5.5211 loss: 0.7965 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7965 2022/12/09 00:42:22 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 00:42:22 - mmengine - INFO - Epoch(train) [66][940/940] lr: 1.0000e-03 eta: 5:33:07 time: 0.6115 data_time: 0.0181 memory: 16095 grad_norm: 6.2030 loss: 0.8907 top1_acc: 0.7143 top5_acc: 1.0000 loss_cls: 0.8907 2022/12/09 00:42:22 - mmengine - INFO - Saving checkpoint at 66 epochs 2022/12/09 00:42:40 - mmengine - INFO - Epoch(val) [66][20/78] eta: 0:00:41 time: 0.7115 data_time: 0.6174 memory: 1686 2022/12/09 00:42:49 - mmengine - INFO - Epoch(val) [66][40/78] eta: 0:00:22 time: 0.4473 data_time: 0.3527 memory: 1686 2022/12/09 00:43:02 - mmengine - INFO - Epoch(val) [66][60/78] eta: 0:00:11 time: 0.6786 data_time: 0.5841 memory: 1686 2022/12/09 00:43:12 - mmengine - INFO - Epoch(val) [66][78/78] acc/top1: 0.6901 acc/top5: 0.8784 acc/mean1: 0.6901 2022/12/09 00:43:28 - mmengine - INFO - Epoch(train) [67][ 20/940] lr: 1.0000e-03 eta: 5:32:57 time: 0.8256 data_time: 0.5127 memory: 16095 grad_norm: 5.5866 loss: 0.7352 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.7352 2022/12/09 00:43:39 - mmengine - INFO - Epoch(train) [67][ 40/940] lr: 1.0000e-03 eta: 5:32:43 time: 0.5398 data_time: 0.2293 memory: 16095 grad_norm: 5.6508 loss: 0.7466 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7466 2022/12/09 00:43:53 - mmengine - INFO - Epoch(train) [67][ 60/940] lr: 1.0000e-03 eta: 5:32:31 time: 0.6790 data_time: 0.3304 memory: 16095 grad_norm: 5.6965 loss: 0.8015 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8015 2022/12/09 00:44:04 - mmengine - INFO - Epoch(train) [67][ 80/940] lr: 1.0000e-03 eta: 5:32:18 time: 0.5519 data_time: 0.2150 memory: 16095 grad_norm: 5.5222 loss: 0.7248 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7248 2022/12/09 00:44:17 - mmengine - INFO - Epoch(train) [67][100/940] lr: 1.0000e-03 eta: 5:32:06 time: 0.6517 data_time: 0.2158 memory: 16095 grad_norm: 5.6792 loss: 0.7359 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7359 2022/12/09 00:44:29 - mmengine - INFO - Epoch(train) [67][120/940] lr: 1.0000e-03 eta: 5:31:53 time: 0.5915 data_time: 0.0966 memory: 16095 grad_norm: 5.6248 loss: 0.7541 top1_acc: 0.5312 top5_acc: 0.8438 loss_cls: 0.7541 2022/12/09 00:44:42 - mmengine - INFO - Epoch(train) [67][140/940] lr: 1.0000e-03 eta: 5:31:41 time: 0.6470 data_time: 0.0249 memory: 16095 grad_norm: 5.7479 loss: 0.7520 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7520 2022/12/09 00:44:53 - mmengine - INFO - Epoch(train) [67][160/940] lr: 1.0000e-03 eta: 5:31:27 time: 0.5706 data_time: 0.0252 memory: 16095 grad_norm: 5.6037 loss: 0.8076 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.8076 2022/12/09 00:45:06 - mmengine - INFO - Epoch(train) [67][180/940] lr: 1.0000e-03 eta: 5:31:15 time: 0.6663 data_time: 0.0235 memory: 16095 grad_norm: 5.5113 loss: 0.6936 top1_acc: 0.6562 top5_acc: 0.8125 loss_cls: 0.6936 2022/12/09 00:45:16 - mmengine - INFO - Epoch(train) [67][200/940] lr: 1.0000e-03 eta: 5:31:02 time: 0.5060 data_time: 0.0255 memory: 16095 grad_norm: 5.6426 loss: 0.7073 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7073 2022/12/09 00:45:30 - mmengine - INFO - Epoch(train) [67][220/940] lr: 1.0000e-03 eta: 5:30:50 time: 0.6866 data_time: 0.0233 memory: 16095 grad_norm: 5.7157 loss: 0.7903 top1_acc: 0.9688 top5_acc: 0.9688 loss_cls: 0.7903 2022/12/09 00:45:41 - mmengine - INFO - Epoch(train) [67][240/940] lr: 1.0000e-03 eta: 5:30:37 time: 0.5656 data_time: 0.0397 memory: 16095 grad_norm: 5.4981 loss: 0.7090 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7090 2022/12/09 00:45:55 - mmengine - INFO - Epoch(train) [67][260/940] lr: 1.0000e-03 eta: 5:30:25 time: 0.6749 data_time: 0.0291 memory: 16095 grad_norm: 5.8257 loss: 0.8123 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.8123 2022/12/09 00:46:06 - mmengine - INFO - Epoch(train) [67][280/940] lr: 1.0000e-03 eta: 5:30:12 time: 0.5694 data_time: 0.0290 memory: 16095 grad_norm: 5.7914 loss: 0.8120 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8120 2022/12/09 00:46:20 - mmengine - INFO - Epoch(train) [67][300/940] lr: 1.0000e-03 eta: 5:30:00 time: 0.6714 data_time: 0.0444 memory: 16095 grad_norm: 5.7878 loss: 0.8215 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8215 2022/12/09 00:46:32 - mmengine - INFO - Epoch(train) [67][320/940] lr: 1.0000e-03 eta: 5:29:47 time: 0.5876 data_time: 0.0969 memory: 16095 grad_norm: 5.7008 loss: 0.7494 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7494 2022/12/09 00:46:44 - mmengine - INFO - Epoch(train) [67][340/940] lr: 1.0000e-03 eta: 5:29:34 time: 0.6133 data_time: 0.0199 memory: 16095 grad_norm: 5.6678 loss: 0.8917 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8917 2022/12/09 00:46:55 - mmengine - INFO - Epoch(train) [67][360/940] lr: 1.0000e-03 eta: 5:29:21 time: 0.5476 data_time: 0.0376 memory: 16095 grad_norm: 5.7720 loss: 0.7713 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7713 2022/12/09 00:47:08 - mmengine - INFO - Epoch(train) [67][380/940] lr: 1.0000e-03 eta: 5:29:09 time: 0.6583 data_time: 0.0959 memory: 16095 grad_norm: 5.7472 loss: 0.7583 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7583 2022/12/09 00:47:20 - mmengine - INFO - Epoch(train) [67][400/940] lr: 1.0000e-03 eta: 5:28:56 time: 0.6179 data_time: 0.1763 memory: 16095 grad_norm: 5.8963 loss: 0.7752 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7752 2022/12/09 00:47:33 - mmengine - INFO - Epoch(train) [67][420/940] lr: 1.0000e-03 eta: 5:28:44 time: 0.6412 data_time: 0.0191 memory: 16095 grad_norm: 5.7461 loss: 0.8004 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.8004 2022/12/09 00:47:44 - mmengine - INFO - Epoch(train) [67][440/940] lr: 1.0000e-03 eta: 5:28:30 time: 0.5382 data_time: 0.0308 memory: 16095 grad_norm: 5.6685 loss: 0.8219 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8219 2022/12/09 00:47:57 - mmengine - INFO - Epoch(train) [67][460/940] lr: 1.0000e-03 eta: 5:28:18 time: 0.6746 data_time: 0.0221 memory: 16095 grad_norm: 5.6011 loss: 0.7451 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7451 2022/12/09 00:48:08 - mmengine - INFO - Epoch(train) [67][480/940] lr: 1.0000e-03 eta: 5:28:05 time: 0.5446 data_time: 0.1028 memory: 16095 grad_norm: 5.6541 loss: 0.7497 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7497 2022/12/09 00:48:21 - mmengine - INFO - Epoch(train) [67][500/940] lr: 1.0000e-03 eta: 5:27:53 time: 0.6563 data_time: 0.0331 memory: 16095 grad_norm: 5.6878 loss: 0.6881 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6881 2022/12/09 00:48:33 - mmengine - INFO - Epoch(train) [67][520/940] lr: 1.0000e-03 eta: 5:27:40 time: 0.5611 data_time: 0.0747 memory: 16095 grad_norm: 5.7216 loss: 0.7791 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7791 2022/12/09 00:48:45 - mmengine - INFO - Epoch(train) [67][540/940] lr: 1.0000e-03 eta: 5:27:27 time: 0.6338 data_time: 0.1730 memory: 16095 grad_norm: 5.6728 loss: 0.7754 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7754 2022/12/09 00:48:58 - mmengine - INFO - Epoch(train) [67][560/940] lr: 1.0000e-03 eta: 5:27:15 time: 0.6520 data_time: 0.3090 memory: 16095 grad_norm: 5.6617 loss: 0.7640 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7640 2022/12/09 00:49:10 - mmengine - INFO - Epoch(train) [67][580/940] lr: 1.0000e-03 eta: 5:27:02 time: 0.5751 data_time: 0.2362 memory: 16095 grad_norm: 5.8595 loss: 0.8159 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8159 2022/12/09 00:49:23 - mmengine - INFO - Epoch(train) [67][600/940] lr: 1.0000e-03 eta: 5:26:50 time: 0.6470 data_time: 0.3077 memory: 16095 grad_norm: 5.7081 loss: 0.8155 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.8155 2022/12/09 00:49:34 - mmengine - INFO - Epoch(train) [67][620/940] lr: 1.0000e-03 eta: 5:26:36 time: 0.5656 data_time: 0.1990 memory: 16095 grad_norm: 5.6816 loss: 0.8307 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.8307 2022/12/09 00:49:47 - mmengine - INFO - Epoch(train) [67][640/940] lr: 1.0000e-03 eta: 5:26:24 time: 0.6645 data_time: 0.3305 memory: 16095 grad_norm: 5.7402 loss: 0.6841 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6841 2022/12/09 00:49:59 - mmengine - INFO - Epoch(train) [67][660/940] lr: 1.0000e-03 eta: 5:26:11 time: 0.5871 data_time: 0.1860 memory: 16095 grad_norm: 5.7248 loss: 0.7755 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7755 2022/12/09 00:50:11 - mmengine - INFO - Epoch(train) [67][680/940] lr: 1.0000e-03 eta: 5:25:58 time: 0.5713 data_time: 0.1560 memory: 16095 grad_norm: 5.7349 loss: 0.7588 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7588 2022/12/09 00:50:22 - mmengine - INFO - Epoch(train) [67][700/940] lr: 1.0000e-03 eta: 5:25:46 time: 0.5889 data_time: 0.1870 memory: 16095 grad_norm: 5.7096 loss: 0.7669 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.7669 2022/12/09 00:50:34 - mmengine - INFO - Epoch(train) [67][720/940] lr: 1.0000e-03 eta: 5:25:33 time: 0.6004 data_time: 0.1360 memory: 16095 grad_norm: 5.8140 loss: 0.7181 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7181 2022/12/09 00:50:48 - mmengine - INFO - Epoch(train) [67][740/940] lr: 1.0000e-03 eta: 5:25:21 time: 0.6666 data_time: 0.0553 memory: 16095 grad_norm: 5.8971 loss: 0.7651 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7651 2022/12/09 00:50:59 - mmengine - INFO - Epoch(train) [67][760/940] lr: 1.0000e-03 eta: 5:25:08 time: 0.5697 data_time: 0.0255 memory: 16095 grad_norm: 5.5598 loss: 0.7364 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7364 2022/12/09 00:51:13 - mmengine - INFO - Epoch(train) [67][780/940] lr: 1.0000e-03 eta: 5:24:56 time: 0.6740 data_time: 0.0222 memory: 16095 grad_norm: 5.6651 loss: 0.6867 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6867 2022/12/09 00:51:24 - mmengine - INFO - Epoch(train) [67][800/940] lr: 1.0000e-03 eta: 5:24:43 time: 0.5797 data_time: 0.0871 memory: 16095 grad_norm: 5.7160 loss: 0.7637 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7637 2022/12/09 00:51:37 - mmengine - INFO - Epoch(train) [67][820/940] lr: 1.0000e-03 eta: 5:24:30 time: 0.6352 data_time: 0.1052 memory: 16095 grad_norm: 5.7288 loss: 0.7910 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7910 2022/12/09 00:51:49 - mmengine - INFO - Epoch(train) [67][840/940] lr: 1.0000e-03 eta: 5:24:18 time: 0.6250 data_time: 0.1669 memory: 16095 grad_norm: 5.8984 loss: 0.7936 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7936 2022/12/09 00:52:02 - mmengine - INFO - Epoch(train) [67][860/940] lr: 1.0000e-03 eta: 5:24:05 time: 0.6179 data_time: 0.0209 memory: 16095 grad_norm: 5.7775 loss: 0.7689 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7689 2022/12/09 00:52:14 - mmengine - INFO - Epoch(train) [67][880/940] lr: 1.0000e-03 eta: 5:23:52 time: 0.5859 data_time: 0.0355 memory: 16095 grad_norm: 5.8026 loss: 0.7424 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.7424 2022/12/09 00:52:26 - mmengine - INFO - Epoch(train) [67][900/940] lr: 1.0000e-03 eta: 5:23:40 time: 0.6341 data_time: 0.0850 memory: 16095 grad_norm: 5.6744 loss: 0.7348 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7348 2022/12/09 00:52:38 - mmengine - INFO - Epoch(train) [67][920/940] lr: 1.0000e-03 eta: 5:23:27 time: 0.5884 data_time: 0.1245 memory: 16095 grad_norm: 5.7265 loss: 0.8198 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8198 2022/12/09 00:52:48 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 00:52:48 - mmengine - INFO - Epoch(train) [67][940/940] lr: 1.0000e-03 eta: 5:23:13 time: 0.5071 data_time: 0.1156 memory: 16095 grad_norm: 6.2034 loss: 0.7544 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 0.7544 2022/12/09 00:53:02 - mmengine - INFO - Epoch(val) [67][20/78] eta: 0:00:40 time: 0.6978 data_time: 0.6041 memory: 1686 2022/12/09 00:53:11 - mmengine - INFO - Epoch(val) [67][40/78] eta: 0:00:22 time: 0.4618 data_time: 0.3691 memory: 1686 2022/12/09 00:53:25 - mmengine - INFO - Epoch(val) [67][60/78] eta: 0:00:11 time: 0.6998 data_time: 0.6046 memory: 1686 2022/12/09 00:53:35 - mmengine - INFO - Epoch(val) [67][78/78] acc/top1: 0.6926 acc/top5: 0.8776 acc/mean1: 0.6925 2022/12/09 00:53:52 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 00:53:52 - mmengine - INFO - Epoch(train) [68][ 20/940] lr: 1.0000e-03 eta: 5:23:03 time: 0.8046 data_time: 0.3985 memory: 16095 grad_norm: 5.5798 loss: 0.7978 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7978 2022/12/09 00:54:03 - mmengine - INFO - Epoch(train) [68][ 40/940] lr: 1.0000e-03 eta: 5:22:50 time: 0.5760 data_time: 0.1675 memory: 16095 grad_norm: 5.6030 loss: 0.7387 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7387 2022/12/09 00:54:17 - mmengine - INFO - Epoch(train) [68][ 60/940] lr: 1.0000e-03 eta: 5:22:38 time: 0.6830 data_time: 0.1285 memory: 16095 grad_norm: 5.6781 loss: 0.7983 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7983 2022/12/09 00:54:27 - mmengine - INFO - Epoch(train) [68][ 80/940] lr: 1.0000e-03 eta: 5:22:24 time: 0.5198 data_time: 0.0712 memory: 16095 grad_norm: 5.6204 loss: 0.7822 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7822 2022/12/09 00:54:40 - mmengine - INFO - Epoch(train) [68][100/940] lr: 1.0000e-03 eta: 5:22:12 time: 0.6582 data_time: 0.1602 memory: 16095 grad_norm: 5.7772 loss: 0.7487 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.7487 2022/12/09 00:54:52 - mmengine - INFO - Epoch(train) [68][120/940] lr: 1.0000e-03 eta: 5:21:59 time: 0.5733 data_time: 0.0596 memory: 16095 grad_norm: 5.7756 loss: 0.7220 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7220 2022/12/09 00:55:06 - mmengine - INFO - Epoch(train) [68][140/940] lr: 1.0000e-03 eta: 5:21:47 time: 0.7025 data_time: 0.0274 memory: 16095 grad_norm: 5.7250 loss: 0.7637 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7637 2022/12/09 00:55:16 - mmengine - INFO - Epoch(train) [68][160/940] lr: 1.0000e-03 eta: 5:21:34 time: 0.5268 data_time: 0.0221 memory: 16095 grad_norm: 5.8440 loss: 0.8103 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.8103 2022/12/09 00:55:30 - mmengine - INFO - Epoch(train) [68][180/940] lr: 1.0000e-03 eta: 5:21:22 time: 0.6712 data_time: 0.0232 memory: 16095 grad_norm: 5.6301 loss: 0.7003 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.7003 2022/12/09 00:55:40 - mmengine - INFO - Epoch(train) [68][200/940] lr: 1.0000e-03 eta: 5:21:08 time: 0.5349 data_time: 0.0274 memory: 16095 grad_norm: 5.6337 loss: 0.7520 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.7520 2022/12/09 00:55:54 - mmengine - INFO - Epoch(train) [68][220/940] lr: 1.0000e-03 eta: 5:20:56 time: 0.6755 data_time: 0.0253 memory: 16095 grad_norm: 5.5505 loss: 0.6596 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6596 2022/12/09 00:56:06 - mmengine - INFO - Epoch(train) [68][240/940] lr: 1.0000e-03 eta: 5:20:43 time: 0.5811 data_time: 0.0315 memory: 16095 grad_norm: 5.6550 loss: 0.7817 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7817 2022/12/09 00:56:20 - mmengine - INFO - Epoch(train) [68][260/940] lr: 1.0000e-03 eta: 5:20:32 time: 0.6985 data_time: 0.0225 memory: 16095 grad_norm: 5.6026 loss: 0.8118 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 0.8118 2022/12/09 00:56:31 - mmengine - INFO - Epoch(train) [68][280/940] lr: 1.0000e-03 eta: 5:20:18 time: 0.5688 data_time: 0.0247 memory: 16095 grad_norm: 5.6898 loss: 0.7673 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7673 2022/12/09 00:56:45 - mmengine - INFO - Epoch(train) [68][300/940] lr: 1.0000e-03 eta: 5:20:07 time: 0.6894 data_time: 0.0220 memory: 16095 grad_norm: 5.5880 loss: 0.7804 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7804 2022/12/09 00:56:56 - mmengine - INFO - Epoch(train) [68][320/940] lr: 1.0000e-03 eta: 5:19:54 time: 0.5625 data_time: 0.0298 memory: 16095 grad_norm: 5.8052 loss: 0.7411 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7411 2022/12/09 00:57:10 - mmengine - INFO - Epoch(train) [68][340/940] lr: 1.0000e-03 eta: 5:19:41 time: 0.6764 data_time: 0.0228 memory: 16095 grad_norm: 5.8936 loss: 0.7928 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7928 2022/12/09 00:57:20 - mmengine - INFO - Epoch(train) [68][360/940] lr: 1.0000e-03 eta: 5:19:28 time: 0.5409 data_time: 0.0236 memory: 16095 grad_norm: 5.4829 loss: 0.6946 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6946 2022/12/09 00:57:33 - mmengine - INFO - Epoch(train) [68][380/940] lr: 1.0000e-03 eta: 5:19:16 time: 0.6248 data_time: 0.0267 memory: 16095 grad_norm: 5.6767 loss: 0.7650 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7650 2022/12/09 00:57:44 - mmengine - INFO - Epoch(train) [68][400/940] lr: 1.0000e-03 eta: 5:19:03 time: 0.5758 data_time: 0.0223 memory: 16095 grad_norm: 5.6667 loss: 0.8112 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8112 2022/12/09 00:57:57 - mmengine - INFO - Epoch(train) [68][420/940] lr: 1.0000e-03 eta: 5:18:50 time: 0.6464 data_time: 0.0237 memory: 16095 grad_norm: 5.8852 loss: 0.7511 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7511 2022/12/09 00:58:10 - mmengine - INFO - Epoch(train) [68][440/940] lr: 1.0000e-03 eta: 5:18:38 time: 0.6184 data_time: 0.0244 memory: 16095 grad_norm: 5.7552 loss: 0.7935 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.7935 2022/12/09 00:58:23 - mmengine - INFO - Epoch(train) [68][460/940] lr: 1.0000e-03 eta: 5:18:25 time: 0.6399 data_time: 0.0227 memory: 16095 grad_norm: 5.6093 loss: 0.8485 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.8485 2022/12/09 00:58:34 - mmengine - INFO - Epoch(train) [68][480/940] lr: 1.0000e-03 eta: 5:18:13 time: 0.5810 data_time: 0.0264 memory: 16095 grad_norm: 5.6970 loss: 0.7505 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7505 2022/12/09 00:58:48 - mmengine - INFO - Epoch(train) [68][500/940] lr: 1.0000e-03 eta: 5:18:01 time: 0.7152 data_time: 0.0187 memory: 16095 grad_norm: 5.7730 loss: 0.9135 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.9135 2022/12/09 00:58:59 - mmengine - INFO - Epoch(train) [68][520/940] lr: 1.0000e-03 eta: 5:17:48 time: 0.5456 data_time: 0.0271 memory: 16095 grad_norm: 5.8196 loss: 0.8377 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.8377 2022/12/09 00:59:12 - mmengine - INFO - Epoch(train) [68][540/940] lr: 1.0000e-03 eta: 5:17:35 time: 0.6406 data_time: 0.0214 memory: 16095 grad_norm: 5.7580 loss: 0.7794 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7794 2022/12/09 00:59:24 - mmengine - INFO - Epoch(train) [68][560/940] lr: 1.0000e-03 eta: 5:17:22 time: 0.5856 data_time: 0.0269 memory: 16095 grad_norm: 5.7603 loss: 0.8434 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.8434 2022/12/09 00:59:37 - mmengine - INFO - Epoch(train) [68][580/940] lr: 1.0000e-03 eta: 5:17:10 time: 0.6764 data_time: 0.0223 memory: 16095 grad_norm: 5.6471 loss: 0.7603 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7603 2022/12/09 00:59:49 - mmengine - INFO - Epoch(train) [68][600/940] lr: 1.0000e-03 eta: 5:16:57 time: 0.5645 data_time: 0.0250 memory: 16095 grad_norm: 5.6979 loss: 0.7273 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7273 2022/12/09 01:00:02 - mmengine - INFO - Epoch(train) [68][620/940] lr: 1.0000e-03 eta: 5:16:45 time: 0.6680 data_time: 0.0231 memory: 16095 grad_norm: 5.8326 loss: 0.8635 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.8635 2022/12/09 01:00:13 - mmengine - INFO - Epoch(train) [68][640/940] lr: 1.0000e-03 eta: 5:16:32 time: 0.5574 data_time: 0.0248 memory: 16095 grad_norm: 5.7739 loss: 0.7206 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7206 2022/12/09 01:00:26 - mmengine - INFO - Epoch(train) [68][660/940] lr: 1.0000e-03 eta: 5:16:20 time: 0.6359 data_time: 0.0251 memory: 16095 grad_norm: 5.7575 loss: 0.7053 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.7053 2022/12/09 01:00:37 - mmengine - INFO - Epoch(train) [68][680/940] lr: 1.0000e-03 eta: 5:16:07 time: 0.5706 data_time: 0.0271 memory: 16095 grad_norm: 5.6889 loss: 0.7893 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.7893 2022/12/09 01:00:51 - mmengine - INFO - Epoch(train) [68][700/940] lr: 1.0000e-03 eta: 5:15:54 time: 0.6597 data_time: 0.0334 memory: 16095 grad_norm: 5.8185 loss: 0.7688 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7688 2022/12/09 01:01:02 - mmengine - INFO - Epoch(train) [68][720/940] lr: 1.0000e-03 eta: 5:15:41 time: 0.5523 data_time: 0.0255 memory: 16095 grad_norm: 5.8847 loss: 0.8539 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8539 2022/12/09 01:01:16 - mmengine - INFO - Epoch(train) [68][740/940] lr: 1.0000e-03 eta: 5:15:30 time: 0.7103 data_time: 0.0237 memory: 16095 grad_norm: 5.7021 loss: 0.7210 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.7210 2022/12/09 01:01:26 - mmengine - INFO - Epoch(train) [68][760/940] lr: 1.0000e-03 eta: 5:15:16 time: 0.5262 data_time: 0.0264 memory: 16095 grad_norm: 5.7238 loss: 0.7528 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7528 2022/12/09 01:01:40 - mmengine - INFO - Epoch(train) [68][780/940] lr: 1.0000e-03 eta: 5:15:04 time: 0.6594 data_time: 0.0241 memory: 16095 grad_norm: 5.6991 loss: 0.7800 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7800 2022/12/09 01:01:50 - mmengine - INFO - Epoch(train) [68][800/940] lr: 1.0000e-03 eta: 5:14:51 time: 0.5421 data_time: 0.0235 memory: 16095 grad_norm: 5.7527 loss: 0.8016 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8016 2022/12/09 01:02:03 - mmengine - INFO - Epoch(train) [68][820/940] lr: 1.0000e-03 eta: 5:14:38 time: 0.6490 data_time: 0.0237 memory: 16095 grad_norm: 5.7997 loss: 0.7021 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7021 2022/12/09 01:02:14 - mmengine - INFO - Epoch(train) [68][840/940] lr: 1.0000e-03 eta: 5:14:25 time: 0.5387 data_time: 0.0249 memory: 16095 grad_norm: 5.7157 loss: 0.7839 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7839 2022/12/09 01:02:27 - mmengine - INFO - Epoch(train) [68][860/940] lr: 1.0000e-03 eta: 5:14:13 time: 0.6364 data_time: 0.0235 memory: 16095 grad_norm: 5.6085 loss: 0.7312 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7312 2022/12/09 01:02:38 - mmengine - INFO - Epoch(train) [68][880/940] lr: 1.0000e-03 eta: 5:13:59 time: 0.5390 data_time: 0.0246 memory: 16095 grad_norm: 5.8050 loss: 0.7408 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7408 2022/12/09 01:02:51 - mmengine - INFO - Epoch(train) [68][900/940] lr: 1.0000e-03 eta: 5:13:47 time: 0.6441 data_time: 0.0232 memory: 16095 grad_norm: 5.8846 loss: 0.7472 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7472 2022/12/09 01:03:02 - mmengine - INFO - Epoch(train) [68][920/940] lr: 1.0000e-03 eta: 5:13:34 time: 0.5851 data_time: 0.0247 memory: 16095 grad_norm: 5.8788 loss: 0.8214 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8214 2022/12/09 01:03:14 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 01:03:14 - mmengine - INFO - Epoch(train) [68][940/940] lr: 1.0000e-03 eta: 5:13:21 time: 0.5993 data_time: 0.0179 memory: 16095 grad_norm: 6.3199 loss: 0.7585 top1_acc: 0.2857 top5_acc: 0.8571 loss_cls: 0.7585 2022/12/09 01:03:28 - mmengine - INFO - Epoch(val) [68][20/78] eta: 0:00:40 time: 0.6914 data_time: 0.5988 memory: 1686 2022/12/09 01:03:38 - mmengine - INFO - Epoch(val) [68][40/78] eta: 0:00:22 time: 0.4804 data_time: 0.3850 memory: 1686 2022/12/09 01:03:52 - mmengine - INFO - Epoch(val) [68][60/78] eta: 0:00:11 time: 0.6978 data_time: 0.6029 memory: 1686 2022/12/09 01:04:02 - mmengine - INFO - Epoch(val) [68][78/78] acc/top1: 0.6922 acc/top5: 0.8770 acc/mean1: 0.6921 2022/12/09 01:04:18 - mmengine - INFO - Epoch(train) [69][ 20/940] lr: 1.0000e-03 eta: 5:13:11 time: 0.7982 data_time: 0.4448 memory: 16095 grad_norm: 5.7613 loss: 0.7706 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7706 2022/12/09 01:04:29 - mmengine - INFO - Epoch(train) [69][ 40/940] lr: 1.0000e-03 eta: 5:12:57 time: 0.5570 data_time: 0.1487 memory: 16095 grad_norm: 5.6829 loss: 0.7112 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7112 2022/12/09 01:04:43 - mmengine - INFO - Epoch(train) [69][ 60/940] lr: 1.0000e-03 eta: 5:12:46 time: 0.7282 data_time: 0.0504 memory: 16095 grad_norm: 5.6539 loss: 0.7471 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7471 2022/12/09 01:04:54 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 01:04:54 - mmengine - INFO - Epoch(train) [69][ 80/940] lr: 1.0000e-03 eta: 5:12:32 time: 0.5197 data_time: 0.0470 memory: 16095 grad_norm: 5.5431 loss: 0.7914 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.7914 2022/12/09 01:05:06 - mmengine - INFO - Epoch(train) [69][100/940] lr: 1.0000e-03 eta: 5:12:20 time: 0.6266 data_time: 0.0382 memory: 16095 grad_norm: 5.7707 loss: 0.7841 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7841 2022/12/09 01:05:18 - mmengine - INFO - Epoch(train) [69][120/940] lr: 1.0000e-03 eta: 5:12:07 time: 0.5643 data_time: 0.0463 memory: 16095 grad_norm: 5.6825 loss: 0.7949 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7949 2022/12/09 01:05:31 - mmengine - INFO - Epoch(train) [69][140/940] lr: 1.0000e-03 eta: 5:11:55 time: 0.6911 data_time: 0.0365 memory: 16095 grad_norm: 5.6337 loss: 0.8227 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.8227 2022/12/09 01:05:42 - mmengine - INFO - Epoch(train) [69][160/940] lr: 1.0000e-03 eta: 5:11:42 time: 0.5496 data_time: 0.0245 memory: 16095 grad_norm: 5.7555 loss: 0.7898 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7898 2022/12/09 01:05:56 - mmengine - INFO - Epoch(train) [69][180/940] lr: 1.0000e-03 eta: 5:11:30 time: 0.6818 data_time: 0.0278 memory: 16095 grad_norm: 5.7318 loss: 0.8149 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8149 2022/12/09 01:06:07 - mmengine - INFO - Epoch(train) [69][200/940] lr: 1.0000e-03 eta: 5:11:17 time: 0.5497 data_time: 0.0265 memory: 16095 grad_norm: 5.6622 loss: 0.8720 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8720 2022/12/09 01:06:19 - mmengine - INFO - Epoch(train) [69][220/940] lr: 1.0000e-03 eta: 5:11:04 time: 0.5943 data_time: 0.0891 memory: 16095 grad_norm: 5.7939 loss: 0.7269 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7269 2022/12/09 01:06:31 - mmengine - INFO - Epoch(train) [69][240/940] lr: 1.0000e-03 eta: 5:10:51 time: 0.5892 data_time: 0.1134 memory: 16095 grad_norm: 5.7356 loss: 0.7692 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7692 2022/12/09 01:06:45 - mmengine - INFO - Epoch(train) [69][260/940] lr: 1.0000e-03 eta: 5:10:39 time: 0.6844 data_time: 0.0502 memory: 16095 grad_norm: 5.6366 loss: 0.7164 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7164 2022/12/09 01:06:56 - mmengine - INFO - Epoch(train) [69][280/940] lr: 1.0000e-03 eta: 5:10:26 time: 0.5878 data_time: 0.0385 memory: 16095 grad_norm: 5.7795 loss: 0.7878 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7878 2022/12/09 01:07:08 - mmengine - INFO - Epoch(train) [69][300/940] lr: 1.0000e-03 eta: 5:10:13 time: 0.6057 data_time: 0.0270 memory: 16095 grad_norm: 5.6578 loss: 0.7202 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7202 2022/12/09 01:07:21 - mmengine - INFO - Epoch(train) [69][320/940] lr: 1.0000e-03 eta: 5:10:01 time: 0.6372 data_time: 0.0205 memory: 16095 grad_norm: 5.8678 loss: 0.8851 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.8851 2022/12/09 01:07:32 - mmengine - INFO - Epoch(train) [69][340/940] lr: 1.0000e-03 eta: 5:09:48 time: 0.5395 data_time: 0.0262 memory: 16095 grad_norm: 5.7714 loss: 0.7227 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7227 2022/12/09 01:07:45 - mmengine - INFO - Epoch(train) [69][360/940] lr: 1.0000e-03 eta: 5:09:36 time: 0.6709 data_time: 0.0233 memory: 16095 grad_norm: 5.8330 loss: 0.8399 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 0.8399 2022/12/09 01:07:56 - mmengine - INFO - Epoch(train) [69][380/940] lr: 1.0000e-03 eta: 5:09:22 time: 0.5413 data_time: 0.0248 memory: 16095 grad_norm: 5.7787 loss: 0.7545 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7545 2022/12/09 01:08:10 - mmengine - INFO - Epoch(train) [69][400/940] lr: 1.0000e-03 eta: 5:09:11 time: 0.7048 data_time: 0.0228 memory: 16095 grad_norm: 5.6198 loss: 0.6083 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6083 2022/12/09 01:08:20 - mmengine - INFO - Epoch(train) [69][420/940] lr: 1.0000e-03 eta: 5:08:57 time: 0.5023 data_time: 0.0240 memory: 16095 grad_norm: 5.7092 loss: 0.7650 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 0.7650 2022/12/09 01:08:34 - mmengine - INFO - Epoch(train) [69][440/940] lr: 1.0000e-03 eta: 5:08:45 time: 0.6599 data_time: 0.0239 memory: 16095 grad_norm: 5.6549 loss: 0.7072 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7072 2022/12/09 01:08:46 - mmengine - INFO - Epoch(train) [69][460/940] lr: 1.0000e-03 eta: 5:08:32 time: 0.6207 data_time: 0.0237 memory: 16095 grad_norm: 5.6443 loss: 0.7885 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7885 2022/12/09 01:08:59 - mmengine - INFO - Epoch(train) [69][480/940] lr: 1.0000e-03 eta: 5:08:20 time: 0.6432 data_time: 0.0352 memory: 16095 grad_norm: 5.7842 loss: 0.7773 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7773 2022/12/09 01:09:10 - mmengine - INFO - Epoch(train) [69][500/940] lr: 1.0000e-03 eta: 5:08:07 time: 0.5762 data_time: 0.0216 memory: 16095 grad_norm: 5.8021 loss: 0.7960 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.7960 2022/12/09 01:09:23 - mmengine - INFO - Epoch(train) [69][520/940] lr: 1.0000e-03 eta: 5:07:55 time: 0.6553 data_time: 0.0264 memory: 16095 grad_norm: 5.7823 loss: 0.7657 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7657 2022/12/09 01:09:35 - mmengine - INFO - Epoch(train) [69][540/940] lr: 1.0000e-03 eta: 5:07:42 time: 0.5541 data_time: 0.0239 memory: 16095 grad_norm: 5.7800 loss: 0.7707 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.7707 2022/12/09 01:09:47 - mmengine - INFO - Epoch(train) [69][560/940] lr: 1.0000e-03 eta: 5:07:29 time: 0.6438 data_time: 0.0232 memory: 16095 grad_norm: 6.0022 loss: 0.7532 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7532 2022/12/09 01:09:59 - mmengine - INFO - Epoch(train) [69][580/940] lr: 1.0000e-03 eta: 5:07:16 time: 0.5680 data_time: 0.0242 memory: 16095 grad_norm: 5.7086 loss: 0.7600 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 0.7600 2022/12/09 01:10:11 - mmengine - INFO - Epoch(train) [69][600/940] lr: 1.0000e-03 eta: 5:07:03 time: 0.5887 data_time: 0.0256 memory: 16095 grad_norm: 5.7332 loss: 0.7172 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7172 2022/12/09 01:10:23 - mmengine - INFO - Epoch(train) [69][620/940] lr: 1.0000e-03 eta: 5:06:51 time: 0.6313 data_time: 0.0226 memory: 16095 grad_norm: 5.7607 loss: 0.7453 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7453 2022/12/09 01:10:35 - mmengine - INFO - Epoch(train) [69][640/940] lr: 1.0000e-03 eta: 5:06:38 time: 0.5964 data_time: 0.0250 memory: 16095 grad_norm: 5.7440 loss: 0.7763 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.7763 2022/12/09 01:10:48 - mmengine - INFO - Epoch(train) [69][660/940] lr: 1.0000e-03 eta: 5:06:26 time: 0.6666 data_time: 0.0257 memory: 16095 grad_norm: 5.6796 loss: 0.8410 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8410 2022/12/09 01:11:00 - mmengine - INFO - Epoch(train) [69][680/940] lr: 1.0000e-03 eta: 5:06:13 time: 0.5803 data_time: 0.0263 memory: 16095 grad_norm: 5.7514 loss: 0.7080 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7080 2022/12/09 01:11:13 - mmengine - INFO - Epoch(train) [69][700/940] lr: 1.0000e-03 eta: 5:06:01 time: 0.6345 data_time: 0.0209 memory: 16095 grad_norm: 5.6631 loss: 0.7624 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7624 2022/12/09 01:11:25 - mmengine - INFO - Epoch(train) [69][720/940] lr: 1.0000e-03 eta: 5:05:48 time: 0.5885 data_time: 0.0279 memory: 16095 grad_norm: 5.6895 loss: 0.8102 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8102 2022/12/09 01:11:38 - mmengine - INFO - Epoch(train) [69][740/940] lr: 1.0000e-03 eta: 5:05:36 time: 0.6597 data_time: 0.0228 memory: 16095 grad_norm: 5.7488 loss: 0.7276 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7276 2022/12/09 01:11:49 - mmengine - INFO - Epoch(train) [69][760/940] lr: 1.0000e-03 eta: 5:05:23 time: 0.5810 data_time: 0.0247 memory: 16095 grad_norm: 5.8776 loss: 0.7722 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.7722 2022/12/09 01:12:03 - mmengine - INFO - Epoch(train) [69][780/940] lr: 1.0000e-03 eta: 5:05:11 time: 0.6773 data_time: 0.0225 memory: 16095 grad_norm: 5.7916 loss: 0.7558 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7558 2022/12/09 01:12:14 - mmengine - INFO - Epoch(train) [69][800/940] lr: 1.0000e-03 eta: 5:04:58 time: 0.5517 data_time: 0.0250 memory: 16095 grad_norm: 5.8659 loss: 0.8078 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.8078 2022/12/09 01:12:27 - mmengine - INFO - Epoch(train) [69][820/940] lr: 1.0000e-03 eta: 5:04:45 time: 0.6566 data_time: 0.0237 memory: 16095 grad_norm: 5.8255 loss: 0.7779 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 0.7779 2022/12/09 01:12:38 - mmengine - INFO - Epoch(train) [69][840/940] lr: 1.0000e-03 eta: 5:04:32 time: 0.5554 data_time: 0.0272 memory: 16095 grad_norm: 5.7885 loss: 0.7182 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7182 2022/12/09 01:12:52 - mmengine - INFO - Epoch(train) [69][860/940] lr: 1.0000e-03 eta: 5:04:20 time: 0.6709 data_time: 0.0216 memory: 16095 grad_norm: 5.7715 loss: 0.7832 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7832 2022/12/09 01:13:02 - mmengine - INFO - Epoch(train) [69][880/940] lr: 1.0000e-03 eta: 5:04:07 time: 0.5405 data_time: 0.0353 memory: 16095 grad_norm: 5.8772 loss: 0.7510 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7510 2022/12/09 01:13:15 - mmengine - INFO - Epoch(train) [69][900/940] lr: 1.0000e-03 eta: 5:03:55 time: 0.6331 data_time: 0.0215 memory: 16095 grad_norm: 5.8137 loss: 0.8571 top1_acc: 0.6875 top5_acc: 0.9688 loss_cls: 0.8571 2022/12/09 01:13:27 - mmengine - INFO - Epoch(train) [69][920/940] lr: 1.0000e-03 eta: 5:03:42 time: 0.6027 data_time: 0.0262 memory: 16095 grad_norm: 5.8021 loss: 0.7032 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7032 2022/12/09 01:13:39 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 01:13:39 - mmengine - INFO - Epoch(train) [69][940/940] lr: 1.0000e-03 eta: 5:03:29 time: 0.5777 data_time: 0.0159 memory: 16095 grad_norm: 6.3197 loss: 0.7912 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 0.7912 2022/12/09 01:13:39 - mmengine - INFO - Saving checkpoint at 69 epochs 2022/12/09 01:13:56 - mmengine - INFO - Epoch(val) [69][20/78] eta: 0:00:40 time: 0.7016 data_time: 0.6077 memory: 1686 2022/12/09 01:14:05 - mmengine - INFO - Epoch(val) [69][40/78] eta: 0:00:22 time: 0.4722 data_time: 0.3786 memory: 1686 2022/12/09 01:14:18 - mmengine - INFO - Epoch(val) [69][60/78] eta: 0:00:11 time: 0.6636 data_time: 0.5685 memory: 1686 2022/12/09 01:14:29 - mmengine - INFO - Epoch(val) [69][78/78] acc/top1: 0.6915 acc/top5: 0.8760 acc/mean1: 0.6914 2022/12/09 01:14:45 - mmengine - INFO - Epoch(train) [70][ 20/940] lr: 1.0000e-03 eta: 5:03:18 time: 0.7879 data_time: 0.4131 memory: 16095 grad_norm: 5.6448 loss: 0.7039 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7039 2022/12/09 01:14:56 - mmengine - INFO - Epoch(train) [70][ 40/940] lr: 1.0000e-03 eta: 5:03:05 time: 0.5728 data_time: 0.2296 memory: 16095 grad_norm: 5.7208 loss: 0.7015 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7015 2022/12/09 01:15:10 - mmengine - INFO - Epoch(train) [70][ 60/940] lr: 1.0000e-03 eta: 5:02:53 time: 0.6683 data_time: 0.1847 memory: 16095 grad_norm: 5.7066 loss: 0.7568 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7568 2022/12/09 01:15:21 - mmengine - INFO - Epoch(train) [70][ 80/940] lr: 1.0000e-03 eta: 5:02:40 time: 0.5827 data_time: 0.0774 memory: 16095 grad_norm: 5.6926 loss: 0.6660 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.6660 2022/12/09 01:15:35 - mmengine - INFO - Epoch(train) [70][100/940] lr: 1.0000e-03 eta: 5:02:28 time: 0.6669 data_time: 0.0844 memory: 16095 grad_norm: 5.6479 loss: 0.7223 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7223 2022/12/09 01:15:45 - mmengine - INFO - Epoch(train) [70][120/940] lr: 1.0000e-03 eta: 5:02:15 time: 0.5373 data_time: 0.0909 memory: 16095 grad_norm: 5.7365 loss: 0.7503 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7503 2022/12/09 01:15:58 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 01:15:58 - mmengine - INFO - Epoch(train) [70][140/940] lr: 1.0000e-03 eta: 5:02:02 time: 0.6564 data_time: 0.1020 memory: 16095 grad_norm: 5.7053 loss: 0.7152 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.7152 2022/12/09 01:16:10 - mmengine - INFO - Epoch(train) [70][160/940] lr: 1.0000e-03 eta: 5:01:49 time: 0.5585 data_time: 0.0282 memory: 16095 grad_norm: 5.6660 loss: 0.7834 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.7834 2022/12/09 01:16:23 - mmengine - INFO - Epoch(train) [70][180/940] lr: 1.0000e-03 eta: 5:01:37 time: 0.6560 data_time: 0.0667 memory: 16095 grad_norm: 5.7761 loss: 0.8468 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8468 2022/12/09 01:16:34 - mmengine - INFO - Epoch(train) [70][200/940] lr: 1.0000e-03 eta: 5:01:24 time: 0.5865 data_time: 0.0468 memory: 16095 grad_norm: 5.6800 loss: 0.7195 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7195 2022/12/09 01:16:48 - mmengine - INFO - Epoch(train) [70][220/940] lr: 1.0000e-03 eta: 5:01:12 time: 0.6759 data_time: 0.0237 memory: 16095 grad_norm: 5.7962 loss: 0.7336 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7336 2022/12/09 01:16:59 - mmengine - INFO - Epoch(train) [70][240/940] lr: 1.0000e-03 eta: 5:00:59 time: 0.5739 data_time: 0.0248 memory: 16095 grad_norm: 5.9956 loss: 0.7773 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7773 2022/12/09 01:17:12 - mmengine - INFO - Epoch(train) [70][260/940] lr: 1.0000e-03 eta: 5:00:47 time: 0.6217 data_time: 0.0348 memory: 16095 grad_norm: 5.8249 loss: 0.7933 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7933 2022/12/09 01:17:23 - mmengine - INFO - Epoch(train) [70][280/940] lr: 1.0000e-03 eta: 5:00:34 time: 0.5678 data_time: 0.0223 memory: 16095 grad_norm: 5.6711 loss: 0.7343 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7343 2022/12/09 01:17:36 - mmengine - INFO - Epoch(train) [70][300/940] lr: 1.0000e-03 eta: 5:00:21 time: 0.6428 data_time: 0.0257 memory: 16095 grad_norm: 5.7336 loss: 0.6879 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6879 2022/12/09 01:17:47 - mmengine - INFO - Epoch(train) [70][320/940] lr: 1.0000e-03 eta: 5:00:08 time: 0.5578 data_time: 0.0244 memory: 16095 grad_norm: 5.8725 loss: 0.8085 top1_acc: 0.7188 top5_acc: 1.0000 loss_cls: 0.8085 2022/12/09 01:18:00 - mmengine - INFO - Epoch(train) [70][340/940] lr: 1.0000e-03 eta: 4:59:56 time: 0.6445 data_time: 0.0269 memory: 16095 grad_norm: 5.8146 loss: 0.7175 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7175 2022/12/09 01:18:12 - mmengine - INFO - Epoch(train) [70][360/940] lr: 1.0000e-03 eta: 4:59:43 time: 0.5684 data_time: 0.0236 memory: 16095 grad_norm: 5.6381 loss: 0.7002 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7002 2022/12/09 01:18:25 - mmengine - INFO - Epoch(train) [70][380/940] lr: 1.0000e-03 eta: 4:59:31 time: 0.6678 data_time: 0.0247 memory: 16095 grad_norm: 5.6037 loss: 0.6860 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6860 2022/12/09 01:18:37 - mmengine - INFO - Epoch(train) [70][400/940] lr: 1.0000e-03 eta: 4:59:18 time: 0.5846 data_time: 0.0230 memory: 16095 grad_norm: 5.7997 loss: 0.8530 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.8530 2022/12/09 01:18:50 - mmengine - INFO - Epoch(train) [70][420/940] lr: 1.0000e-03 eta: 4:59:06 time: 0.6460 data_time: 0.0231 memory: 16095 grad_norm: 5.9426 loss: 0.7972 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7972 2022/12/09 01:19:01 - mmengine - INFO - Epoch(train) [70][440/940] lr: 1.0000e-03 eta: 4:58:53 time: 0.5855 data_time: 0.0258 memory: 16095 grad_norm: 5.8699 loss: 0.6942 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6942 2022/12/09 01:19:14 - mmengine - INFO - Epoch(train) [70][460/940] lr: 1.0000e-03 eta: 4:58:40 time: 0.6378 data_time: 0.0254 memory: 16095 grad_norm: 5.7758 loss: 0.7652 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7652 2022/12/09 01:19:25 - mmengine - INFO - Epoch(train) [70][480/940] lr: 1.0000e-03 eta: 4:58:27 time: 0.5703 data_time: 0.0231 memory: 16095 grad_norm: 5.7177 loss: 0.7383 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7383 2022/12/09 01:19:39 - mmengine - INFO - Epoch(train) [70][500/940] lr: 1.0000e-03 eta: 4:58:15 time: 0.6599 data_time: 0.0250 memory: 16095 grad_norm: 5.7965 loss: 0.7684 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7684 2022/12/09 01:19:50 - mmengine - INFO - Epoch(train) [70][520/940] lr: 1.0000e-03 eta: 4:58:02 time: 0.5450 data_time: 0.0237 memory: 16095 grad_norm: 5.8088 loss: 0.7476 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7476 2022/12/09 01:20:03 - mmengine - INFO - Epoch(train) [70][540/940] lr: 1.0000e-03 eta: 4:57:50 time: 0.6546 data_time: 0.0275 memory: 16095 grad_norm: 5.5660 loss: 0.7531 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7531 2022/12/09 01:20:14 - mmengine - INFO - Epoch(train) [70][560/940] lr: 1.0000e-03 eta: 4:57:37 time: 0.5514 data_time: 0.0224 memory: 16095 grad_norm: 5.8805 loss: 0.8281 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8281 2022/12/09 01:20:28 - mmengine - INFO - Epoch(train) [70][580/940] lr: 1.0000e-03 eta: 4:57:25 time: 0.7158 data_time: 0.0286 memory: 16095 grad_norm: 5.7190 loss: 0.7054 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7054 2022/12/09 01:20:39 - mmengine - INFO - Epoch(train) [70][600/940] lr: 1.0000e-03 eta: 4:57:12 time: 0.5595 data_time: 0.0218 memory: 16095 grad_norm: 5.8310 loss: 0.7402 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7402 2022/12/09 01:20:54 - mmengine - INFO - Epoch(train) [70][620/940] lr: 1.0000e-03 eta: 4:57:00 time: 0.7232 data_time: 0.0222 memory: 16095 grad_norm: 5.8629 loss: 0.7527 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.7527 2022/12/09 01:21:04 - mmengine - INFO - Epoch(train) [70][640/940] lr: 1.0000e-03 eta: 4:56:47 time: 0.5289 data_time: 0.0232 memory: 16095 grad_norm: 5.7827 loss: 0.7103 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7103 2022/12/09 01:21:18 - mmengine - INFO - Epoch(train) [70][660/940] lr: 1.0000e-03 eta: 4:56:35 time: 0.6723 data_time: 0.0270 memory: 16095 grad_norm: 5.7529 loss: 0.7085 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7085 2022/12/09 01:21:29 - mmengine - INFO - Epoch(train) [70][680/940] lr: 1.0000e-03 eta: 4:56:22 time: 0.5790 data_time: 0.0250 memory: 16095 grad_norm: 5.8190 loss: 0.6360 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6360 2022/12/09 01:21:41 - mmengine - INFO - Epoch(train) [70][700/940] lr: 1.0000e-03 eta: 4:56:09 time: 0.5910 data_time: 0.0365 memory: 16095 grad_norm: 5.7649 loss: 0.7555 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.7555 2022/12/09 01:21:52 - mmengine - INFO - Epoch(train) [70][720/940] lr: 1.0000e-03 eta: 4:55:56 time: 0.5324 data_time: 0.0262 memory: 16095 grad_norm: 5.5928 loss: 0.6610 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6610 2022/12/09 01:22:06 - mmengine - INFO - Epoch(train) [70][740/940] lr: 1.0000e-03 eta: 4:55:44 time: 0.7017 data_time: 0.0341 memory: 16095 grad_norm: 5.7631 loss: 0.7885 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7885 2022/12/09 01:22:17 - mmengine - INFO - Epoch(train) [70][760/940] lr: 1.0000e-03 eta: 4:55:31 time: 0.5455 data_time: 0.0224 memory: 16095 grad_norm: 5.7387 loss: 0.7580 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7580 2022/12/09 01:22:29 - mmengine - INFO - Epoch(train) [70][780/940] lr: 1.0000e-03 eta: 4:55:18 time: 0.6158 data_time: 0.0236 memory: 16095 grad_norm: 5.9778 loss: 0.7326 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.7326 2022/12/09 01:22:40 - mmengine - INFO - Epoch(train) [70][800/940] lr: 1.0000e-03 eta: 4:55:05 time: 0.5567 data_time: 0.0250 memory: 16095 grad_norm: 5.8134 loss: 0.7653 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7653 2022/12/09 01:22:53 - mmengine - INFO - Epoch(train) [70][820/940] lr: 1.0000e-03 eta: 4:54:53 time: 0.6150 data_time: 0.0246 memory: 16095 grad_norm: 5.9009 loss: 0.8027 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8027 2022/12/09 01:23:04 - mmengine - INFO - Epoch(train) [70][840/940] lr: 1.0000e-03 eta: 4:54:40 time: 0.5865 data_time: 0.0239 memory: 16095 grad_norm: 5.8891 loss: 0.7188 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7188 2022/12/09 01:23:17 - mmengine - INFO - Epoch(train) [70][860/940] lr: 1.0000e-03 eta: 4:54:28 time: 0.6516 data_time: 0.0243 memory: 16095 grad_norm: 5.7812 loss: 0.7978 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 0.7978 2022/12/09 01:23:29 - mmengine - INFO - Epoch(train) [70][880/940] lr: 1.0000e-03 eta: 4:54:15 time: 0.5965 data_time: 0.0250 memory: 16095 grad_norm: 5.7621 loss: 0.7636 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7636 2022/12/09 01:23:43 - mmengine - INFO - Epoch(train) [70][900/940] lr: 1.0000e-03 eta: 4:54:03 time: 0.6907 data_time: 0.0258 memory: 16095 grad_norm: 5.7478 loss: 0.7461 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.7461 2022/12/09 01:23:54 - mmengine - INFO - Epoch(train) [70][920/940] lr: 1.0000e-03 eta: 4:53:50 time: 0.5570 data_time: 0.0218 memory: 16095 grad_norm: 5.7977 loss: 0.7687 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7687 2022/12/09 01:24:06 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 01:24:06 - mmengine - INFO - Epoch(train) [70][940/940] lr: 1.0000e-03 eta: 4:53:37 time: 0.5666 data_time: 0.0182 memory: 16095 grad_norm: 6.2426 loss: 0.8173 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 0.8173 2022/12/09 01:24:19 - mmengine - INFO - Epoch(val) [70][20/78] eta: 0:00:40 time: 0.6978 data_time: 0.6027 memory: 1686 2022/12/09 01:24:29 - mmengine - INFO - Epoch(val) [70][40/78] eta: 0:00:22 time: 0.4742 data_time: 0.3815 memory: 1686 2022/12/09 01:24:42 - mmengine - INFO - Epoch(val) [70][60/78] eta: 0:00:11 time: 0.6756 data_time: 0.5810 memory: 1686 2022/12/09 01:24:53 - mmengine - INFO - Epoch(val) [70][78/78] acc/top1: 0.6933 acc/top5: 0.8779 acc/mean1: 0.6932 2022/12/09 01:25:09 - mmengine - INFO - Epoch(train) [71][ 20/940] lr: 1.0000e-03 eta: 4:53:26 time: 0.8198 data_time: 0.4578 memory: 16095 grad_norm: 5.7285 loss: 0.7671 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7671 2022/12/09 01:25:21 - mmengine - INFO - Epoch(train) [71][ 40/940] lr: 1.0000e-03 eta: 4:53:13 time: 0.5587 data_time: 0.1087 memory: 16095 grad_norm: 5.7680 loss: 0.8280 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.8280 2022/12/09 01:25:35 - mmengine - INFO - Epoch(train) [71][ 60/940] lr: 1.0000e-03 eta: 4:53:01 time: 0.7109 data_time: 0.0276 memory: 16095 grad_norm: 5.8492 loss: 0.7572 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.7572 2022/12/09 01:25:46 - mmengine - INFO - Epoch(train) [71][ 80/940] lr: 1.0000e-03 eta: 4:52:48 time: 0.5481 data_time: 0.0206 memory: 16095 grad_norm: 5.8297 loss: 0.7381 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7381 2022/12/09 01:25:59 - mmengine - INFO - Epoch(train) [71][100/940] lr: 1.0000e-03 eta: 4:52:36 time: 0.6560 data_time: 0.0261 memory: 16095 grad_norm: 5.6507 loss: 0.6566 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6566 2022/12/09 01:26:11 - mmengine - INFO - Epoch(train) [71][120/940] lr: 1.0000e-03 eta: 4:52:23 time: 0.5835 data_time: 0.0212 memory: 16095 grad_norm: 5.6831 loss: 0.6619 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 0.6619 2022/12/09 01:26:25 - mmengine - INFO - Epoch(train) [71][140/940] lr: 1.0000e-03 eta: 4:52:11 time: 0.7095 data_time: 0.0273 memory: 16095 grad_norm: 5.9723 loss: 0.6868 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6868 2022/12/09 01:26:35 - mmengine - INFO - Epoch(train) [71][160/940] lr: 1.0000e-03 eta: 4:51:58 time: 0.5177 data_time: 0.0288 memory: 16095 grad_norm: 5.5554 loss: 0.6853 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6853 2022/12/09 01:26:49 - mmengine - INFO - Epoch(train) [71][180/940] lr: 1.0000e-03 eta: 4:51:46 time: 0.6779 data_time: 0.0258 memory: 16095 grad_norm: 5.7083 loss: 0.6608 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6608 2022/12/09 01:27:00 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 01:27:00 - mmengine - INFO - Epoch(train) [71][200/940] lr: 1.0000e-03 eta: 4:51:33 time: 0.5673 data_time: 0.0242 memory: 16095 grad_norm: 5.9346 loss: 0.7491 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7491 2022/12/09 01:27:12 - mmengine - INFO - Epoch(train) [71][220/940] lr: 1.0000e-03 eta: 4:51:20 time: 0.6145 data_time: 0.0270 memory: 16095 grad_norm: 5.7522 loss: 0.7192 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7192 2022/12/09 01:27:24 - mmengine - INFO - Epoch(train) [71][240/940] lr: 1.0000e-03 eta: 4:51:07 time: 0.5605 data_time: 0.0238 memory: 16095 grad_norm: 5.7067 loss: 0.6615 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 0.6615 2022/12/09 01:27:38 - mmengine - INFO - Epoch(train) [71][260/940] lr: 1.0000e-03 eta: 4:50:55 time: 0.7052 data_time: 0.0239 memory: 16095 grad_norm: 5.7010 loss: 0.7018 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7018 2022/12/09 01:27:48 - mmengine - INFO - Epoch(train) [71][280/940] lr: 1.0000e-03 eta: 4:50:42 time: 0.5356 data_time: 0.0258 memory: 16095 grad_norm: 5.8544 loss: 0.7251 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 0.7251 2022/12/09 01:28:01 - mmengine - INFO - Epoch(train) [71][300/940] lr: 1.0000e-03 eta: 4:50:30 time: 0.6221 data_time: 0.0260 memory: 16095 grad_norm: 5.9085 loss: 0.7854 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.7854 2022/12/09 01:28:12 - mmengine - INFO - Epoch(train) [71][320/940] lr: 1.0000e-03 eta: 4:50:17 time: 0.5768 data_time: 0.0274 memory: 16095 grad_norm: 5.8377 loss: 0.6987 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6987 2022/12/09 01:28:26 - mmengine - INFO - Epoch(train) [71][340/940] lr: 1.0000e-03 eta: 4:50:05 time: 0.6993 data_time: 0.0246 memory: 16095 grad_norm: 5.7670 loss: 0.7879 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.7879 2022/12/09 01:28:38 - mmengine - INFO - Epoch(train) [71][360/940] lr: 1.0000e-03 eta: 4:49:52 time: 0.5723 data_time: 0.0247 memory: 16095 grad_norm: 5.8416 loss: 0.7569 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7569 2022/12/09 01:28:51 - mmengine - INFO - Epoch(train) [71][380/940] lr: 1.0000e-03 eta: 4:49:39 time: 0.6440 data_time: 0.0253 memory: 16095 grad_norm: 5.7662 loss: 0.6581 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6581 2022/12/09 01:29:03 - mmengine - INFO - Epoch(train) [71][400/940] lr: 1.0000e-03 eta: 4:49:27 time: 0.6094 data_time: 0.0248 memory: 16095 grad_norm: 5.9525 loss: 0.7996 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7996 2022/12/09 01:29:16 - mmengine - INFO - Epoch(train) [71][420/940] lr: 1.0000e-03 eta: 4:49:15 time: 0.6762 data_time: 0.0239 memory: 16095 grad_norm: 5.8866 loss: 0.7471 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.7471 2022/12/09 01:29:27 - mmengine - INFO - Epoch(train) [71][440/940] lr: 1.0000e-03 eta: 4:49:02 time: 0.5395 data_time: 0.0240 memory: 16095 grad_norm: 5.8917 loss: 0.7848 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.7848 2022/12/09 01:29:39 - mmengine - INFO - Epoch(train) [71][460/940] lr: 1.0000e-03 eta: 4:48:49 time: 0.6013 data_time: 0.0258 memory: 16095 grad_norm: 5.5578 loss: 0.7571 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7571 2022/12/09 01:29:50 - mmengine - INFO - Epoch(train) [71][480/940] lr: 1.0000e-03 eta: 4:48:36 time: 0.5402 data_time: 0.0235 memory: 16095 grad_norm: 5.8427 loss: 0.7429 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 0.7429 2022/12/09 01:30:03 - mmengine - INFO - Epoch(train) [71][500/940] lr: 1.0000e-03 eta: 4:48:23 time: 0.6403 data_time: 0.0248 memory: 16095 grad_norm: 5.6865 loss: 0.6566 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6566 2022/12/09 01:30:14 - mmengine - INFO - Epoch(train) [71][520/940] lr: 1.0000e-03 eta: 4:48:10 time: 0.5702 data_time: 0.0241 memory: 16095 grad_norm: 5.8697 loss: 0.8859 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.8859 2022/12/09 01:30:27 - mmengine - INFO - Epoch(train) [71][540/940] lr: 1.0000e-03 eta: 4:47:58 time: 0.6312 data_time: 0.0267 memory: 16095 grad_norm: 5.9352 loss: 0.7108 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7108 2022/12/09 01:30:39 - mmengine - INFO - Epoch(train) [71][560/940] lr: 1.0000e-03 eta: 4:47:45 time: 0.6122 data_time: 0.0234 memory: 16095 grad_norm: 5.7292 loss: 0.7332 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7332 2022/12/09 01:30:52 - mmengine - INFO - Epoch(train) [71][580/940] lr: 1.0000e-03 eta: 4:47:33 time: 0.6231 data_time: 0.0240 memory: 16095 grad_norm: 5.8238 loss: 0.6857 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.6857 2022/12/09 01:31:04 - mmengine - INFO - Epoch(train) [71][600/940] lr: 1.0000e-03 eta: 4:47:20 time: 0.6154 data_time: 0.0237 memory: 16095 grad_norm: 5.8464 loss: 0.6979 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6979 2022/12/09 01:31:16 - mmengine - INFO - Epoch(train) [71][620/940] lr: 1.0000e-03 eta: 4:47:08 time: 0.6127 data_time: 0.0306 memory: 16095 grad_norm: 5.7816 loss: 0.7814 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7814 2022/12/09 01:31:29 - mmengine - INFO - Epoch(train) [71][640/940] lr: 1.0000e-03 eta: 4:46:55 time: 0.6332 data_time: 0.0293 memory: 16095 grad_norm: 5.8904 loss: 0.7989 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7989 2022/12/09 01:31:40 - mmengine - INFO - Epoch(train) [71][660/940] lr: 1.0000e-03 eta: 4:46:42 time: 0.5621 data_time: 0.0188 memory: 16095 grad_norm: 5.6751 loss: 0.7348 top1_acc: 0.6875 top5_acc: 0.9688 loss_cls: 0.7348 2022/12/09 01:31:53 - mmengine - INFO - Epoch(train) [71][680/940] lr: 1.0000e-03 eta: 4:46:30 time: 0.6407 data_time: 0.0284 memory: 16095 grad_norm: 5.7579 loss: 0.7097 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7097 2022/12/09 01:32:05 - mmengine - INFO - Epoch(train) [71][700/940] lr: 1.0000e-03 eta: 4:46:17 time: 0.6243 data_time: 0.0206 memory: 16095 grad_norm: 5.8666 loss: 0.8177 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8177 2022/12/09 01:32:17 - mmengine - INFO - Epoch(train) [71][720/940] lr: 1.0000e-03 eta: 4:46:04 time: 0.5680 data_time: 0.0273 memory: 16095 grad_norm: 5.8074 loss: 0.7443 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7443 2022/12/09 01:32:29 - mmengine - INFO - Epoch(train) [71][740/940] lr: 1.0000e-03 eta: 4:45:52 time: 0.6298 data_time: 0.0202 memory: 16095 grad_norm: 5.8947 loss: 0.6642 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6642 2022/12/09 01:32:41 - mmengine - INFO - Epoch(train) [71][760/940] lr: 1.0000e-03 eta: 4:45:39 time: 0.5876 data_time: 0.0355 memory: 16095 grad_norm: 5.8990 loss: 0.7701 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7701 2022/12/09 01:32:54 - mmengine - INFO - Epoch(train) [71][780/940] lr: 1.0000e-03 eta: 4:45:27 time: 0.6294 data_time: 0.0211 memory: 16095 grad_norm: 5.8714 loss: 0.7267 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7267 2022/12/09 01:33:05 - mmengine - INFO - Epoch(train) [71][800/940] lr: 1.0000e-03 eta: 4:45:14 time: 0.5552 data_time: 0.0280 memory: 16095 grad_norm: 5.8080 loss: 0.8443 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8443 2022/12/09 01:33:18 - mmengine - INFO - Epoch(train) [71][820/940] lr: 1.0000e-03 eta: 4:45:02 time: 0.6771 data_time: 0.0238 memory: 16095 grad_norm: 5.8434 loss: 0.7948 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7948 2022/12/09 01:33:29 - mmengine - INFO - Epoch(train) [71][840/940] lr: 1.0000e-03 eta: 4:44:48 time: 0.5475 data_time: 0.0274 memory: 16095 grad_norm: 5.9294 loss: 0.8283 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8283 2022/12/09 01:33:43 - mmengine - INFO - Epoch(train) [71][860/940] lr: 1.0000e-03 eta: 4:44:37 time: 0.6979 data_time: 0.0203 memory: 16095 grad_norm: 5.8287 loss: 0.7635 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7635 2022/12/09 01:33:54 - mmengine - INFO - Epoch(train) [71][880/940] lr: 1.0000e-03 eta: 4:44:23 time: 0.5326 data_time: 0.0285 memory: 16095 grad_norm: 6.0218 loss: 0.6973 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6973 2022/12/09 01:34:07 - mmengine - INFO - Epoch(train) [71][900/940] lr: 1.0000e-03 eta: 4:44:11 time: 0.6611 data_time: 0.0198 memory: 16095 grad_norm: 5.8337 loss: 0.7104 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.7104 2022/12/09 01:34:18 - mmengine - INFO - Epoch(train) [71][920/940] lr: 1.0000e-03 eta: 4:43:58 time: 0.5529 data_time: 0.0273 memory: 16095 grad_norm: 5.8127 loss: 0.6511 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6511 2022/12/09 01:34:30 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 01:34:30 - mmengine - INFO - Epoch(train) [71][940/940] lr: 1.0000e-03 eta: 4:43:45 time: 0.5803 data_time: 0.0155 memory: 16095 grad_norm: 6.4205 loss: 0.7490 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 0.7490 2022/12/09 01:34:44 - mmengine - INFO - Epoch(val) [71][20/78] eta: 0:00:40 time: 0.7001 data_time: 0.6065 memory: 1686 2022/12/09 01:34:53 - mmengine - INFO - Epoch(val) [71][40/78] eta: 0:00:22 time: 0.4663 data_time: 0.3746 memory: 1686 2022/12/09 01:35:07 - mmengine - INFO - Epoch(val) [71][60/78] eta: 0:00:11 time: 0.6930 data_time: 0.5987 memory: 1686 2022/12/09 01:35:18 - mmengine - INFO - Epoch(val) [71][78/78] acc/top1: 0.6937 acc/top5: 0.8766 acc/mean1: 0.6936 2022/12/09 01:35:35 - mmengine - INFO - Epoch(train) [72][ 20/940] lr: 1.0000e-03 eta: 4:43:35 time: 0.8454 data_time: 0.3401 memory: 16095 grad_norm: 5.9271 loss: 0.6485 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6485 2022/12/09 01:35:45 - mmengine - INFO - Epoch(train) [72][ 40/940] lr: 1.0000e-03 eta: 4:43:21 time: 0.5297 data_time: 0.0796 memory: 16095 grad_norm: 5.7529 loss: 0.8229 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8229 2022/12/09 01:35:58 - mmengine - INFO - Epoch(train) [72][ 60/940] lr: 1.0000e-03 eta: 4:43:09 time: 0.6602 data_time: 0.0866 memory: 16095 grad_norm: 5.7886 loss: 0.7839 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7839 2022/12/09 01:36:10 - mmengine - INFO - Epoch(train) [72][ 80/940] lr: 1.0000e-03 eta: 4:42:56 time: 0.5538 data_time: 0.0614 memory: 16095 grad_norm: 5.8589 loss: 0.7982 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7982 2022/12/09 01:36:22 - mmengine - INFO - Epoch(train) [72][100/940] lr: 1.0000e-03 eta: 4:42:44 time: 0.6457 data_time: 0.0272 memory: 16095 grad_norm: 5.7347 loss: 0.8201 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8201 2022/12/09 01:36:34 - mmengine - INFO - Epoch(train) [72][120/940] lr: 1.0000e-03 eta: 4:42:31 time: 0.5757 data_time: 0.0227 memory: 16095 grad_norm: 5.7174 loss: 0.7624 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7624 2022/12/09 01:36:48 - mmengine - INFO - Epoch(train) [72][140/940] lr: 1.0000e-03 eta: 4:42:19 time: 0.6917 data_time: 0.0290 memory: 16095 grad_norm: 5.6627 loss: 0.7501 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7501 2022/12/09 01:36:59 - mmengine - INFO - Epoch(train) [72][160/940] lr: 1.0000e-03 eta: 4:42:06 time: 0.5355 data_time: 0.0215 memory: 16095 grad_norm: 5.7484 loss: 0.7590 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.7590 2022/12/09 01:37:12 - mmengine - INFO - Epoch(train) [72][180/940] lr: 1.0000e-03 eta: 4:41:54 time: 0.6875 data_time: 0.0283 memory: 16095 grad_norm: 5.9837 loss: 0.8031 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.8031 2022/12/09 01:37:23 - mmengine - INFO - Epoch(train) [72][200/940] lr: 1.0000e-03 eta: 4:41:41 time: 0.5502 data_time: 0.0242 memory: 16095 grad_norm: 5.7293 loss: 0.7154 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7154 2022/12/09 01:37:37 - mmengine - INFO - Epoch(train) [72][220/940] lr: 1.0000e-03 eta: 4:41:28 time: 0.6627 data_time: 0.0298 memory: 16095 grad_norm: 5.8238 loss: 0.8121 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8121 2022/12/09 01:37:48 - mmengine - INFO - Epoch(train) [72][240/940] lr: 1.0000e-03 eta: 4:41:15 time: 0.5468 data_time: 0.0470 memory: 16095 grad_norm: 5.7973 loss: 0.7704 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7704 2022/12/09 01:38:01 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 01:38:01 - mmengine - INFO - Epoch(train) [72][260/940] lr: 1.0000e-03 eta: 4:41:03 time: 0.6761 data_time: 0.0541 memory: 16095 grad_norm: 5.8197 loss: 0.6768 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6768 2022/12/09 01:38:11 - mmengine - INFO - Epoch(train) [72][280/940] lr: 1.0000e-03 eta: 4:40:50 time: 0.4978 data_time: 0.0200 memory: 16095 grad_norm: 5.7833 loss: 0.7423 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.7423 2022/12/09 01:38:24 - mmengine - INFO - Epoch(train) [72][300/940] lr: 1.0000e-03 eta: 4:40:37 time: 0.6418 data_time: 0.0345 memory: 16095 grad_norm: 5.9147 loss: 0.7820 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7820 2022/12/09 01:38:35 - mmengine - INFO - Epoch(train) [72][320/940] lr: 1.0000e-03 eta: 4:40:24 time: 0.5737 data_time: 0.0215 memory: 16095 grad_norm: 5.7696 loss: 0.6455 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.6455 2022/12/09 01:38:49 - mmengine - INFO - Epoch(train) [72][340/940] lr: 1.0000e-03 eta: 4:40:12 time: 0.6849 data_time: 0.0303 memory: 16095 grad_norm: 5.8639 loss: 0.7568 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7568 2022/12/09 01:39:00 - mmengine - INFO - Epoch(train) [72][360/940] lr: 1.0000e-03 eta: 4:39:59 time: 0.5446 data_time: 0.0221 memory: 16095 grad_norm: 5.9214 loss: 0.7924 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7924 2022/12/09 01:39:13 - mmengine - INFO - Epoch(train) [72][380/940] lr: 1.0000e-03 eta: 4:39:47 time: 0.6274 data_time: 0.0521 memory: 16095 grad_norm: 5.9369 loss: 0.8425 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8425 2022/12/09 01:39:24 - mmengine - INFO - Epoch(train) [72][400/940] lr: 1.0000e-03 eta: 4:39:34 time: 0.5762 data_time: 0.0219 memory: 16095 grad_norm: 5.7266 loss: 0.6906 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6906 2022/12/09 01:39:37 - mmengine - INFO - Epoch(train) [72][420/940] lr: 1.0000e-03 eta: 4:39:22 time: 0.6565 data_time: 0.0274 memory: 16095 grad_norm: 5.7841 loss: 0.7024 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.7024 2022/12/09 01:39:48 - mmengine - INFO - Epoch(train) [72][440/940] lr: 1.0000e-03 eta: 4:39:09 time: 0.5548 data_time: 0.0230 memory: 16095 grad_norm: 5.9857 loss: 0.7332 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.7332 2022/12/09 01:40:02 - mmengine - INFO - Epoch(train) [72][460/940] lr: 1.0000e-03 eta: 4:38:57 time: 0.6763 data_time: 0.0269 memory: 16095 grad_norm: 5.9392 loss: 0.7587 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7587 2022/12/09 01:40:13 - mmengine - INFO - Epoch(train) [72][480/940] lr: 1.0000e-03 eta: 4:38:43 time: 0.5498 data_time: 0.0218 memory: 16095 grad_norm: 5.9498 loss: 0.6693 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6693 2022/12/09 01:40:27 - mmengine - INFO - Epoch(train) [72][500/940] lr: 1.0000e-03 eta: 4:38:32 time: 0.7169 data_time: 0.0236 memory: 16095 grad_norm: 5.8282 loss: 0.8257 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.8257 2022/12/09 01:40:39 - mmengine - INFO - Epoch(train) [72][520/940] lr: 1.0000e-03 eta: 4:38:19 time: 0.5913 data_time: 0.0229 memory: 16095 grad_norm: 5.9827 loss: 0.8188 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 0.8188 2022/12/09 01:40:52 - mmengine - INFO - Epoch(train) [72][540/940] lr: 1.0000e-03 eta: 4:38:07 time: 0.6396 data_time: 0.0249 memory: 16095 grad_norm: 5.8464 loss: 0.7400 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7400 2022/12/09 01:41:02 - mmengine - INFO - Epoch(train) [72][560/940] lr: 1.0000e-03 eta: 4:37:53 time: 0.5295 data_time: 0.0230 memory: 16095 grad_norm: 5.9467 loss: 0.7341 top1_acc: 0.7188 top5_acc: 1.0000 loss_cls: 0.7341 2022/12/09 01:41:15 - mmengine - INFO - Epoch(train) [72][580/940] lr: 1.0000e-03 eta: 4:37:41 time: 0.6275 data_time: 0.0254 memory: 16095 grad_norm: 6.0006 loss: 0.7852 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7852 2022/12/09 01:41:26 - mmengine - INFO - Epoch(train) [72][600/940] lr: 1.0000e-03 eta: 4:37:28 time: 0.5539 data_time: 0.0236 memory: 16095 grad_norm: 5.8199 loss: 0.7723 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7723 2022/12/09 01:41:40 - mmengine - INFO - Epoch(train) [72][620/940] lr: 1.0000e-03 eta: 4:37:16 time: 0.6742 data_time: 0.0260 memory: 16095 grad_norm: 6.0109 loss: 0.7442 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.7442 2022/12/09 01:41:51 - mmengine - INFO - Epoch(train) [72][640/940] lr: 1.0000e-03 eta: 4:37:03 time: 0.5941 data_time: 0.0237 memory: 16095 grad_norm: 5.9765 loss: 0.7471 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7471 2022/12/09 01:42:04 - mmengine - INFO - Epoch(train) [72][660/940] lr: 1.0000e-03 eta: 4:36:51 time: 0.6503 data_time: 0.0294 memory: 16095 grad_norm: 5.9793 loss: 0.7392 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.7392 2022/12/09 01:42:15 - mmengine - INFO - Epoch(train) [72][680/940] lr: 1.0000e-03 eta: 4:36:37 time: 0.5282 data_time: 0.0219 memory: 16095 grad_norm: 5.7596 loss: 0.7577 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7577 2022/12/09 01:42:29 - mmengine - INFO - Epoch(train) [72][700/940] lr: 1.0000e-03 eta: 4:36:25 time: 0.6798 data_time: 0.0238 memory: 16095 grad_norm: 5.8052 loss: 0.6885 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6885 2022/12/09 01:42:40 - mmengine - INFO - Epoch(train) [72][720/940] lr: 1.0000e-03 eta: 4:36:12 time: 0.5557 data_time: 0.0347 memory: 16095 grad_norm: 5.8018 loss: 0.6734 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.6734 2022/12/09 01:42:53 - mmengine - INFO - Epoch(train) [72][740/940] lr: 1.0000e-03 eta: 4:36:00 time: 0.6463 data_time: 0.0240 memory: 16095 grad_norm: 6.0812 loss: 0.7493 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.7493 2022/12/09 01:43:04 - mmengine - INFO - Epoch(train) [72][760/940] lr: 1.0000e-03 eta: 4:35:47 time: 0.5550 data_time: 0.0286 memory: 16095 grad_norm: 5.8752 loss: 0.8405 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.8405 2022/12/09 01:43:17 - mmengine - INFO - Epoch(train) [72][780/940] lr: 1.0000e-03 eta: 4:35:35 time: 0.6687 data_time: 0.0249 memory: 16095 grad_norm: 5.6805 loss: 0.7282 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7282 2022/12/09 01:43:28 - mmengine - INFO - Epoch(train) [72][800/940] lr: 1.0000e-03 eta: 4:35:22 time: 0.5495 data_time: 0.0269 memory: 16095 grad_norm: 6.1362 loss: 0.8377 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.8377 2022/12/09 01:43:41 - mmengine - INFO - Epoch(train) [72][820/940] lr: 1.0000e-03 eta: 4:35:09 time: 0.6375 data_time: 0.0206 memory: 16095 grad_norm: 5.9346 loss: 0.7336 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 0.7336 2022/12/09 01:43:52 - mmengine - INFO - Epoch(train) [72][840/940] lr: 1.0000e-03 eta: 4:34:56 time: 0.5450 data_time: 0.0294 memory: 16095 grad_norm: 6.0382 loss: 0.8410 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.8410 2022/12/09 01:44:05 - mmengine - INFO - Epoch(train) [72][860/940] lr: 1.0000e-03 eta: 4:34:44 time: 0.6711 data_time: 0.0212 memory: 16095 grad_norm: 5.9853 loss: 0.7934 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7934 2022/12/09 01:44:17 - mmengine - INFO - Epoch(train) [72][880/940] lr: 1.0000e-03 eta: 4:34:31 time: 0.5913 data_time: 0.0300 memory: 16095 grad_norm: 5.9253 loss: 0.6454 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6454 2022/12/09 01:44:31 - mmengine - INFO - Epoch(train) [72][900/940] lr: 1.0000e-03 eta: 4:34:19 time: 0.6790 data_time: 0.0321 memory: 16095 grad_norm: 5.9701 loss: 0.7384 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7384 2022/12/09 01:44:42 - mmengine - INFO - Epoch(train) [72][920/940] lr: 1.0000e-03 eta: 4:34:06 time: 0.5467 data_time: 0.0267 memory: 16095 grad_norm: 5.9882 loss: 0.7789 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.7789 2022/12/09 01:44:52 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 01:44:52 - mmengine - INFO - Epoch(train) [72][940/940] lr: 1.0000e-03 eta: 4:33:53 time: 0.5344 data_time: 0.0137 memory: 16095 grad_norm: 6.4802 loss: 0.8794 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 0.8794 2022/12/09 01:44:52 - mmengine - INFO - Saving checkpoint at 72 epochs 2022/12/09 01:45:09 - mmengine - INFO - Epoch(val) [72][20/78] eta: 0:00:41 time: 0.7073 data_time: 0.6118 memory: 1686 2022/12/09 01:45:19 - mmengine - INFO - Epoch(val) [72][40/78] eta: 0:00:22 time: 0.4732 data_time: 0.3791 memory: 1686 2022/12/09 01:45:32 - mmengine - INFO - Epoch(val) [72][60/78] eta: 0:00:11 time: 0.6779 data_time: 0.5823 memory: 1686 2022/12/09 01:45:42 - mmengine - INFO - Epoch(val) [72][78/78] acc/top1: 0.6908 acc/top5: 0.8781 acc/mean1: 0.6907 2022/12/09 01:45:58 - mmengine - INFO - Epoch(train) [73][ 20/940] lr: 1.0000e-03 eta: 4:33:42 time: 0.8133 data_time: 0.3868 memory: 16095 grad_norm: 5.7460 loss: 0.7617 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7617 2022/12/09 01:46:09 - mmengine - INFO - Epoch(train) [73][ 40/940] lr: 1.0000e-03 eta: 4:33:29 time: 0.5556 data_time: 0.0873 memory: 16095 grad_norm: 6.0167 loss: 0.8030 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8030 2022/12/09 01:46:23 - mmengine - INFO - Epoch(train) [73][ 60/940] lr: 1.0000e-03 eta: 4:33:17 time: 0.6954 data_time: 0.0600 memory: 16095 grad_norm: 5.7726 loss: 0.6445 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.6445 2022/12/09 01:46:34 - mmengine - INFO - Epoch(train) [73][ 80/940] lr: 1.0000e-03 eta: 4:33:04 time: 0.5574 data_time: 0.0216 memory: 16095 grad_norm: 5.9046 loss: 0.7333 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7333 2022/12/09 01:46:47 - mmengine - INFO - Epoch(train) [73][100/940] lr: 1.0000e-03 eta: 4:32:52 time: 0.6300 data_time: 0.0299 memory: 16095 grad_norm: 5.6732 loss: 0.8496 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8496 2022/12/09 01:46:58 - mmengine - INFO - Epoch(train) [73][120/940] lr: 1.0000e-03 eta: 4:32:39 time: 0.5567 data_time: 0.0214 memory: 16095 grad_norm: 5.8293 loss: 0.6798 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6798 2022/12/09 01:47:12 - mmengine - INFO - Epoch(train) [73][140/940] lr: 1.0000e-03 eta: 4:32:27 time: 0.6813 data_time: 0.0293 memory: 16095 grad_norm: 5.8063 loss: 0.7123 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7123 2022/12/09 01:47:23 - mmengine - INFO - Epoch(train) [73][160/940] lr: 1.0000e-03 eta: 4:32:14 time: 0.5446 data_time: 0.0203 memory: 16095 grad_norm: 5.9192 loss: 0.7413 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.7413 2022/12/09 01:47:36 - mmengine - INFO - Epoch(train) [73][180/940] lr: 1.0000e-03 eta: 4:32:01 time: 0.6605 data_time: 0.0286 memory: 16095 grad_norm: 5.8445 loss: 0.7756 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7756 2022/12/09 01:47:47 - mmengine - INFO - Epoch(train) [73][200/940] lr: 1.0000e-03 eta: 4:31:48 time: 0.5791 data_time: 0.0218 memory: 16095 grad_norm: 5.9362 loss: 0.8856 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8856 2022/12/09 01:48:00 - mmengine - INFO - Epoch(train) [73][220/940] lr: 1.0000e-03 eta: 4:31:36 time: 0.6118 data_time: 0.0284 memory: 16095 grad_norm: 5.7749 loss: 0.7180 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7180 2022/12/09 01:48:11 - mmengine - INFO - Epoch(train) [73][240/940] lr: 1.0000e-03 eta: 4:31:23 time: 0.5882 data_time: 0.0256 memory: 16095 grad_norm: 5.8986 loss: 0.6986 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6986 2022/12/09 01:48:25 - mmengine - INFO - Epoch(train) [73][260/940] lr: 1.0000e-03 eta: 4:31:11 time: 0.6675 data_time: 0.0265 memory: 16095 grad_norm: 5.9727 loss: 0.8180 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8180 2022/12/09 01:48:35 - mmengine - INFO - Epoch(train) [73][280/940] lr: 1.0000e-03 eta: 4:30:58 time: 0.5067 data_time: 0.0203 memory: 16095 grad_norm: 6.0123 loss: 0.7952 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 0.7952 2022/12/09 01:48:49 - mmengine - INFO - Epoch(train) [73][300/940] lr: 1.0000e-03 eta: 4:30:46 time: 0.6995 data_time: 0.0273 memory: 16095 grad_norm: 5.7344 loss: 0.7360 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7360 2022/12/09 01:49:01 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 01:49:01 - mmengine - INFO - Epoch(train) [73][320/940] lr: 1.0000e-03 eta: 4:30:33 time: 0.5866 data_time: 0.0220 memory: 16095 grad_norm: 5.9342 loss: 0.7496 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.7496 2022/12/09 01:49:13 - mmengine - INFO - Epoch(train) [73][340/940] lr: 1.0000e-03 eta: 4:30:20 time: 0.6358 data_time: 0.0245 memory: 16095 grad_norm: 5.7946 loss: 0.6327 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6327 2022/12/09 01:49:26 - mmengine - INFO - Epoch(train) [73][360/940] lr: 1.0000e-03 eta: 4:30:08 time: 0.6418 data_time: 0.0237 memory: 16095 grad_norm: 5.7427 loss: 0.6619 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6619 2022/12/09 01:49:38 - mmengine - INFO - Epoch(train) [73][380/940] lr: 1.0000e-03 eta: 4:29:55 time: 0.5657 data_time: 0.0259 memory: 16095 grad_norm: 5.7183 loss: 0.8108 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.8108 2022/12/09 01:49:51 - mmengine - INFO - Epoch(train) [73][400/940] lr: 1.0000e-03 eta: 4:29:43 time: 0.6628 data_time: 0.0318 memory: 16095 grad_norm: 5.7961 loss: 0.6593 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 0.6593 2022/12/09 01:50:02 - mmengine - INFO - Epoch(train) [73][420/940] lr: 1.0000e-03 eta: 4:29:30 time: 0.5633 data_time: 0.0231 memory: 16095 grad_norm: 5.9980 loss: 0.7275 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7275 2022/12/09 01:50:15 - mmengine - INFO - Epoch(train) [73][440/940] lr: 1.0000e-03 eta: 4:29:18 time: 0.6323 data_time: 0.0233 memory: 16095 grad_norm: 5.8041 loss: 0.8056 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.8056 2022/12/09 01:50:26 - mmengine - INFO - Epoch(train) [73][460/940] lr: 1.0000e-03 eta: 4:29:05 time: 0.5602 data_time: 0.0248 memory: 16095 grad_norm: 5.9183 loss: 0.7553 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7553 2022/12/09 01:50:39 - mmengine - INFO - Epoch(train) [73][480/940] lr: 1.0000e-03 eta: 4:28:52 time: 0.6429 data_time: 0.0228 memory: 16095 grad_norm: 6.0050 loss: 0.6583 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6583 2022/12/09 01:50:50 - mmengine - INFO - Epoch(train) [73][500/940] lr: 1.0000e-03 eta: 4:28:39 time: 0.5675 data_time: 0.0247 memory: 16095 grad_norm: 5.9097 loss: 0.6911 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6911 2022/12/09 01:51:03 - mmengine - INFO - Epoch(train) [73][520/940] lr: 1.0000e-03 eta: 4:28:27 time: 0.6452 data_time: 0.0229 memory: 16095 grad_norm: 5.7083 loss: 0.7380 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7380 2022/12/09 01:51:14 - mmengine - INFO - Epoch(train) [73][540/940] lr: 1.0000e-03 eta: 4:28:14 time: 0.5577 data_time: 0.0255 memory: 16095 grad_norm: 5.7301 loss: 0.7148 top1_acc: 0.8125 top5_acc: 0.8438 loss_cls: 0.7148 2022/12/09 01:51:27 - mmengine - INFO - Epoch(train) [73][560/940] lr: 1.0000e-03 eta: 4:28:02 time: 0.6509 data_time: 0.0235 memory: 16095 grad_norm: 5.7710 loss: 0.6994 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6994 2022/12/09 01:51:39 - mmengine - INFO - Epoch(train) [73][580/940] lr: 1.0000e-03 eta: 4:27:49 time: 0.5819 data_time: 0.0267 memory: 16095 grad_norm: 5.9496 loss: 0.6140 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6140 2022/12/09 01:51:53 - mmengine - INFO - Epoch(train) [73][600/940] lr: 1.0000e-03 eta: 4:27:37 time: 0.6819 data_time: 0.0225 memory: 16095 grad_norm: 5.7609 loss: 0.7186 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 0.7186 2022/12/09 01:52:04 - mmengine - INFO - Epoch(train) [73][620/940] lr: 1.0000e-03 eta: 4:27:24 time: 0.5588 data_time: 0.0261 memory: 16095 grad_norm: 5.9787 loss: 0.7318 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7318 2022/12/09 01:52:16 - mmengine - INFO - Epoch(train) [73][640/940] lr: 1.0000e-03 eta: 4:27:12 time: 0.6349 data_time: 0.0223 memory: 16095 grad_norm: 5.9126 loss: 0.7754 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7754 2022/12/09 01:52:28 - mmengine - INFO - Epoch(train) [73][660/940] lr: 1.0000e-03 eta: 4:26:59 time: 0.5655 data_time: 0.0254 memory: 16095 grad_norm: 5.9604 loss: 0.6750 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6750 2022/12/09 01:52:42 - mmengine - INFO - Epoch(train) [73][680/940] lr: 1.0000e-03 eta: 4:26:47 time: 0.6920 data_time: 0.0244 memory: 16095 grad_norm: 5.9713 loss: 0.7428 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7428 2022/12/09 01:52:53 - mmengine - INFO - Epoch(train) [73][700/940] lr: 1.0000e-03 eta: 4:26:34 time: 0.5654 data_time: 0.0260 memory: 16095 grad_norm: 5.8726 loss: 0.7681 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.7681 2022/12/09 01:53:06 - mmengine - INFO - Epoch(train) [73][720/940] lr: 1.0000e-03 eta: 4:26:21 time: 0.6386 data_time: 0.0218 memory: 16095 grad_norm: 6.0007 loss: 0.7684 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 0.7684 2022/12/09 01:53:17 - mmengine - INFO - Epoch(train) [73][740/940] lr: 1.0000e-03 eta: 4:26:09 time: 0.5803 data_time: 0.0248 memory: 16095 grad_norm: 5.9425 loss: 0.8118 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8118 2022/12/09 01:53:31 - mmengine - INFO - Epoch(train) [73][760/940] lr: 1.0000e-03 eta: 4:25:56 time: 0.6714 data_time: 0.0265 memory: 16095 grad_norm: 5.9623 loss: 0.8256 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8256 2022/12/09 01:53:41 - mmengine - INFO - Epoch(train) [73][780/940] lr: 1.0000e-03 eta: 4:25:43 time: 0.5200 data_time: 0.0237 memory: 16095 grad_norm: 6.0126 loss: 0.7868 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7868 2022/12/09 01:53:55 - mmengine - INFO - Epoch(train) [73][800/940] lr: 1.0000e-03 eta: 4:25:31 time: 0.6837 data_time: 0.0270 memory: 16095 grad_norm: 6.1468 loss: 0.7938 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7938 2022/12/09 01:54:06 - mmengine - INFO - Epoch(train) [73][820/940] lr: 1.0000e-03 eta: 4:25:18 time: 0.5810 data_time: 0.0266 memory: 16095 grad_norm: 5.9636 loss: 0.8167 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8167 2022/12/09 01:54:19 - mmengine - INFO - Epoch(train) [73][840/940] lr: 1.0000e-03 eta: 4:25:06 time: 0.6503 data_time: 0.0323 memory: 16095 grad_norm: 6.0824 loss: 0.8052 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.8052 2022/12/09 01:54:30 - mmengine - INFO - Epoch(train) [73][860/940] lr: 1.0000e-03 eta: 4:24:53 time: 0.5508 data_time: 0.0259 memory: 16095 grad_norm: 5.7864 loss: 0.7232 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7232 2022/12/09 01:54:44 - mmengine - INFO - Epoch(train) [73][880/940] lr: 1.0000e-03 eta: 4:24:41 time: 0.6827 data_time: 0.0230 memory: 16095 grad_norm: 6.0208 loss: 0.7315 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.7315 2022/12/09 01:54:55 - mmengine - INFO - Epoch(train) [73][900/940] lr: 1.0000e-03 eta: 4:24:28 time: 0.5679 data_time: 0.0255 memory: 16095 grad_norm: 5.9278 loss: 0.6923 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6923 2022/12/09 01:55:09 - mmengine - INFO - Epoch(train) [73][920/940] lr: 1.0000e-03 eta: 4:24:16 time: 0.6572 data_time: 0.0249 memory: 16095 grad_norm: 5.9751 loss: 0.7072 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7072 2022/12/09 01:55:18 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 01:55:18 - mmengine - INFO - Epoch(train) [73][940/940] lr: 1.0000e-03 eta: 4:24:02 time: 0.4530 data_time: 0.0176 memory: 16095 grad_norm: 6.3808 loss: 0.7623 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 0.7623 2022/12/09 01:55:32 - mmengine - INFO - Epoch(val) [73][20/78] eta: 0:00:41 time: 0.7095 data_time: 0.6122 memory: 1686 2022/12/09 01:55:41 - mmengine - INFO - Epoch(val) [73][40/78] eta: 0:00:22 time: 0.4685 data_time: 0.3756 memory: 1686 2022/12/09 01:55:55 - mmengine - INFO - Epoch(val) [73][60/78] eta: 0:00:11 time: 0.6646 data_time: 0.5685 memory: 1686 2022/12/09 01:56:05 - mmengine - INFO - Epoch(val) [73][78/78] acc/top1: 0.6922 acc/top5: 0.8781 acc/mean1: 0.6921 2022/12/09 01:56:22 - mmengine - INFO - Epoch(train) [74][ 20/940] lr: 1.0000e-03 eta: 4:23:51 time: 0.8092 data_time: 0.4930 memory: 16095 grad_norm: 5.7610 loss: 0.6967 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6967 2022/12/09 01:56:33 - mmengine - INFO - Epoch(train) [74][ 40/940] lr: 1.0000e-03 eta: 4:23:38 time: 0.5520 data_time: 0.1756 memory: 16095 grad_norm: 5.9002 loss: 0.8085 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8085 2022/12/09 01:56:46 - mmengine - INFO - Epoch(train) [74][ 60/940] lr: 1.0000e-03 eta: 4:23:26 time: 0.6674 data_time: 0.2862 memory: 16095 grad_norm: 5.9265 loss: 0.6740 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6740 2022/12/09 01:56:57 - mmengine - INFO - Epoch(train) [74][ 80/940] lr: 1.0000e-03 eta: 4:23:13 time: 0.5420 data_time: 0.0991 memory: 16095 grad_norm: 5.8859 loss: 0.7309 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7309 2022/12/09 01:57:10 - mmengine - INFO - Epoch(train) [74][100/940] lr: 1.0000e-03 eta: 4:23:00 time: 0.6725 data_time: 0.0905 memory: 16095 grad_norm: 5.7238 loss: 0.6386 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6386 2022/12/09 01:57:22 - mmengine - INFO - Epoch(train) [74][120/940] lr: 1.0000e-03 eta: 4:22:48 time: 0.5641 data_time: 0.0456 memory: 16095 grad_norm: 5.8311 loss: 0.7400 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 0.7400 2022/12/09 01:57:35 - mmengine - INFO - Epoch(train) [74][140/940] lr: 1.0000e-03 eta: 4:22:35 time: 0.6515 data_time: 0.1424 memory: 16095 grad_norm: 5.9351 loss: 0.6542 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6542 2022/12/09 01:57:45 - mmengine - INFO - Epoch(train) [74][160/940] lr: 1.0000e-03 eta: 4:22:22 time: 0.5353 data_time: 0.2102 memory: 16095 grad_norm: 5.8429 loss: 0.7192 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7192 2022/12/09 01:57:59 - mmengine - INFO - Epoch(train) [74][180/940] lr: 1.0000e-03 eta: 4:22:10 time: 0.6595 data_time: 0.3329 memory: 16095 grad_norm: 5.9797 loss: 0.6772 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6772 2022/12/09 01:58:10 - mmengine - INFO - Epoch(train) [74][200/940] lr: 1.0000e-03 eta: 4:21:57 time: 0.5561 data_time: 0.2476 memory: 16095 grad_norm: 6.0344 loss: 0.7326 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.7326 2022/12/09 01:58:23 - mmengine - INFO - Epoch(train) [74][220/940] lr: 1.0000e-03 eta: 4:21:45 time: 0.6654 data_time: 0.2495 memory: 16095 grad_norm: 5.8356 loss: 0.6653 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6653 2022/12/09 01:58:34 - mmengine - INFO - Epoch(train) [74][240/940] lr: 1.0000e-03 eta: 4:21:32 time: 0.5695 data_time: 0.1151 memory: 16095 grad_norm: 5.9590 loss: 0.7160 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7160 2022/12/09 01:58:48 - mmengine - INFO - Epoch(train) [74][260/940] lr: 1.0000e-03 eta: 4:21:20 time: 0.6843 data_time: 0.1298 memory: 16095 grad_norm: 5.8470 loss: 0.6393 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6393 2022/12/09 01:58:58 - mmengine - INFO - Epoch(train) [74][280/940] lr: 1.0000e-03 eta: 4:21:07 time: 0.5169 data_time: 0.0636 memory: 16095 grad_norm: 5.8116 loss: 0.7929 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7929 2022/12/09 01:59:12 - mmengine - INFO - Epoch(train) [74][300/940] lr: 1.0000e-03 eta: 4:20:54 time: 0.6785 data_time: 0.1946 memory: 16095 grad_norm: 5.9205 loss: 0.8037 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.8037 2022/12/09 01:59:23 - mmengine - INFO - Epoch(train) [74][320/940] lr: 1.0000e-03 eta: 4:20:42 time: 0.5720 data_time: 0.1191 memory: 16095 grad_norm: 6.0338 loss: 0.8551 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.8551 2022/12/09 01:59:37 - mmengine - INFO - Epoch(train) [74][340/940] lr: 1.0000e-03 eta: 4:20:29 time: 0.6529 data_time: 0.3016 memory: 16095 grad_norm: 5.8616 loss: 0.7224 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7224 2022/12/09 01:59:48 - mmengine - INFO - Epoch(train) [74][360/940] lr: 1.0000e-03 eta: 4:20:17 time: 0.5828 data_time: 0.2268 memory: 16095 grad_norm: 5.9477 loss: 0.7925 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7925 2022/12/09 02:00:02 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 02:00:02 - mmengine - INFO - Epoch(train) [74][380/940] lr: 1.0000e-03 eta: 4:20:04 time: 0.6732 data_time: 0.2000 memory: 16095 grad_norm: 5.9181 loss: 0.7471 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7471 2022/12/09 02:00:13 - mmengine - INFO - Epoch(train) [74][400/940] lr: 1.0000e-03 eta: 4:19:51 time: 0.5557 data_time: 0.1964 memory: 16095 grad_norm: 5.9630 loss: 0.7210 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7210 2022/12/09 02:00:26 - mmengine - INFO - Epoch(train) [74][420/940] lr: 1.0000e-03 eta: 4:19:39 time: 0.6494 data_time: 0.2826 memory: 16095 grad_norm: 5.8852 loss: 0.7038 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7038 2022/12/09 02:00:37 - mmengine - INFO - Epoch(train) [74][440/940] lr: 1.0000e-03 eta: 4:19:26 time: 0.5511 data_time: 0.1224 memory: 16095 grad_norm: 5.8914 loss: 0.8128 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.8128 2022/12/09 02:00:49 - mmengine - INFO - Epoch(train) [74][460/940] lr: 1.0000e-03 eta: 4:19:14 time: 0.6310 data_time: 0.2252 memory: 16095 grad_norm: 5.9237 loss: 0.7145 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.7145 2022/12/09 02:01:00 - mmengine - INFO - Epoch(train) [74][480/940] lr: 1.0000e-03 eta: 4:19:01 time: 0.5464 data_time: 0.1447 memory: 16095 grad_norm: 6.0320 loss: 0.7514 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7514 2022/12/09 02:01:14 - mmengine - INFO - Epoch(train) [74][500/940] lr: 1.0000e-03 eta: 4:18:49 time: 0.6910 data_time: 0.1995 memory: 16095 grad_norm: 5.8826 loss: 0.7517 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7517 2022/12/09 02:01:24 - mmengine - INFO - Epoch(train) [74][520/940] lr: 1.0000e-03 eta: 4:18:35 time: 0.5093 data_time: 0.0782 memory: 16095 grad_norm: 5.9576 loss: 0.7510 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7510 2022/12/09 02:01:38 - mmengine - INFO - Epoch(train) [74][540/940] lr: 1.0000e-03 eta: 4:18:23 time: 0.6908 data_time: 0.1953 memory: 16095 grad_norm: 5.9207 loss: 0.8226 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8226 2022/12/09 02:01:49 - mmengine - INFO - Epoch(train) [74][560/940] lr: 1.0000e-03 eta: 4:18:10 time: 0.5560 data_time: 0.0997 memory: 16095 grad_norm: 5.8555 loss: 0.7718 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7718 2022/12/09 02:02:03 - mmengine - INFO - Epoch(train) [74][580/940] lr: 1.0000e-03 eta: 4:17:58 time: 0.6642 data_time: 0.1371 memory: 16095 grad_norm: 6.0036 loss: 0.6843 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6843 2022/12/09 02:02:13 - mmengine - INFO - Epoch(train) [74][600/940] lr: 1.0000e-03 eta: 4:17:45 time: 0.5309 data_time: 0.0729 memory: 16095 grad_norm: 5.9529 loss: 0.7398 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7398 2022/12/09 02:02:26 - mmengine - INFO - Epoch(train) [74][620/940] lr: 1.0000e-03 eta: 4:17:33 time: 0.6470 data_time: 0.0613 memory: 16095 grad_norm: 5.8229 loss: 0.7659 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7659 2022/12/09 02:02:37 - mmengine - INFO - Epoch(train) [74][640/940] lr: 1.0000e-03 eta: 4:17:20 time: 0.5423 data_time: 0.0469 memory: 16095 grad_norm: 5.9096 loss: 0.7255 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7255 2022/12/09 02:02:50 - mmengine - INFO - Epoch(train) [74][660/940] lr: 1.0000e-03 eta: 4:17:07 time: 0.6271 data_time: 0.0256 memory: 16095 grad_norm: 5.7978 loss: 0.7151 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7151 2022/12/09 02:03:01 - mmengine - INFO - Epoch(train) [74][680/940] lr: 1.0000e-03 eta: 4:16:54 time: 0.5815 data_time: 0.0306 memory: 16095 grad_norm: 5.9264 loss: 0.7261 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7261 2022/12/09 02:03:13 - mmengine - INFO - Epoch(train) [74][700/940] lr: 1.0000e-03 eta: 4:16:42 time: 0.6034 data_time: 0.0286 memory: 16095 grad_norm: 6.0902 loss: 0.7369 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7369 2022/12/09 02:03:25 - mmengine - INFO - Epoch(train) [74][720/940] lr: 1.0000e-03 eta: 4:16:29 time: 0.5995 data_time: 0.0425 memory: 16095 grad_norm: 6.0245 loss: 0.7847 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7847 2022/12/09 02:03:39 - mmengine - INFO - Epoch(train) [74][740/940] lr: 1.0000e-03 eta: 4:16:17 time: 0.6629 data_time: 0.0450 memory: 16095 grad_norm: 6.2027 loss: 0.7898 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.7898 2022/12/09 02:03:50 - mmengine - INFO - Epoch(train) [74][760/940] lr: 1.0000e-03 eta: 4:16:04 time: 0.5696 data_time: 0.0219 memory: 16095 grad_norm: 5.8792 loss: 0.6714 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.6714 2022/12/09 02:04:03 - mmengine - INFO - Epoch(train) [74][780/940] lr: 1.0000e-03 eta: 4:15:52 time: 0.6426 data_time: 0.0292 memory: 16095 grad_norm: 6.0047 loss: 0.6699 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6699 2022/12/09 02:04:15 - mmengine - INFO - Epoch(train) [74][800/940] lr: 1.0000e-03 eta: 4:15:39 time: 0.6197 data_time: 0.0384 memory: 16095 grad_norm: 5.8966 loss: 0.7312 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7312 2022/12/09 02:04:27 - mmengine - INFO - Epoch(train) [74][820/940] lr: 1.0000e-03 eta: 4:15:26 time: 0.5632 data_time: 0.0239 memory: 16095 grad_norm: 5.9726 loss: 0.6994 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6994 2022/12/09 02:04:38 - mmengine - INFO - Epoch(train) [74][840/940] lr: 1.0000e-03 eta: 4:15:14 time: 0.5941 data_time: 0.0238 memory: 16095 grad_norm: 5.8182 loss: 0.6860 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6860 2022/12/09 02:04:52 - mmengine - INFO - Epoch(train) [74][860/940] lr: 1.0000e-03 eta: 4:15:01 time: 0.6548 data_time: 0.0722 memory: 16095 grad_norm: 5.9947 loss: 0.7453 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7453 2022/12/09 02:05:03 - mmengine - INFO - Epoch(train) [74][880/940] lr: 1.0000e-03 eta: 4:14:48 time: 0.5763 data_time: 0.0264 memory: 16095 grad_norm: 6.0382 loss: 0.7192 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7192 2022/12/09 02:05:16 - mmengine - INFO - Epoch(train) [74][900/940] lr: 1.0000e-03 eta: 4:14:36 time: 0.6492 data_time: 0.0686 memory: 16095 grad_norm: 6.1407 loss: 0.7733 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7733 2022/12/09 02:05:27 - mmengine - INFO - Epoch(train) [74][920/940] lr: 1.0000e-03 eta: 4:14:23 time: 0.5395 data_time: 0.0234 memory: 16095 grad_norm: 6.0257 loss: 0.7350 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.7350 2022/12/09 02:05:39 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 02:05:39 - mmengine - INFO - Epoch(train) [74][940/940] lr: 1.0000e-03 eta: 4:14:10 time: 0.5875 data_time: 0.0174 memory: 16095 grad_norm: 6.3074 loss: 0.8020 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8020 2022/12/09 02:05:53 - mmengine - INFO - Epoch(val) [74][20/78] eta: 0:00:41 time: 0.7155 data_time: 0.6188 memory: 1686 2022/12/09 02:06:02 - mmengine - INFO - Epoch(val) [74][40/78] eta: 0:00:22 time: 0.4627 data_time: 0.3679 memory: 1686 2022/12/09 02:06:15 - mmengine - INFO - Epoch(val) [74][60/78] eta: 0:00:11 time: 0.6659 data_time: 0.5699 memory: 1686 2022/12/09 02:06:26 - mmengine - INFO - Epoch(val) [74][78/78] acc/top1: 0.6921 acc/top5: 0.8773 acc/mean1: 0.6920 2022/12/09 02:06:42 - mmengine - INFO - Epoch(train) [75][ 20/940] lr: 1.0000e-03 eta: 4:13:59 time: 0.8081 data_time: 0.4412 memory: 16095 grad_norm: 5.8221 loss: 0.6751 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6751 2022/12/09 02:06:53 - mmengine - INFO - Epoch(train) [75][ 40/940] lr: 1.0000e-03 eta: 4:13:46 time: 0.5340 data_time: 0.1456 memory: 16095 grad_norm: 5.8266 loss: 0.6154 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6154 2022/12/09 02:07:06 - mmengine - INFO - Epoch(train) [75][ 60/940] lr: 1.0000e-03 eta: 4:13:34 time: 0.6722 data_time: 0.2025 memory: 16095 grad_norm: 5.8919 loss: 0.7370 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7370 2022/12/09 02:07:18 - mmengine - INFO - Epoch(train) [75][ 80/940] lr: 1.0000e-03 eta: 4:13:21 time: 0.5629 data_time: 0.1107 memory: 16095 grad_norm: 5.9368 loss: 0.7447 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.7447 2022/12/09 02:07:31 - mmengine - INFO - Epoch(train) [75][100/940] lr: 1.0000e-03 eta: 4:13:09 time: 0.6494 data_time: 0.2587 memory: 16095 grad_norm: 6.0428 loss: 0.7508 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.7508 2022/12/09 02:07:42 - mmengine - INFO - Epoch(train) [75][120/940] lr: 1.0000e-03 eta: 4:12:56 time: 0.5780 data_time: 0.2276 memory: 16095 grad_norm: 5.9738 loss: 0.7217 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7217 2022/12/09 02:07:56 - mmengine - INFO - Epoch(train) [75][140/940] lr: 1.0000e-03 eta: 4:12:44 time: 0.6956 data_time: 0.2357 memory: 16095 grad_norm: 5.7997 loss: 0.7482 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.7482 2022/12/09 02:08:07 - mmengine - INFO - Epoch(train) [75][160/940] lr: 1.0000e-03 eta: 4:12:31 time: 0.5422 data_time: 0.0594 memory: 16095 grad_norm: 5.8092 loss: 0.6650 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6650 2022/12/09 02:08:20 - mmengine - INFO - Epoch(train) [75][180/940] lr: 1.0000e-03 eta: 4:12:19 time: 0.6521 data_time: 0.0401 memory: 16095 grad_norm: 5.9122 loss: 0.7855 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7855 2022/12/09 02:08:31 - mmengine - INFO - Epoch(train) [75][200/940] lr: 1.0000e-03 eta: 4:12:06 time: 0.5422 data_time: 0.0208 memory: 16095 grad_norm: 5.8129 loss: 0.6996 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6996 2022/12/09 02:08:44 - mmengine - INFO - Epoch(train) [75][220/940] lr: 1.0000e-03 eta: 4:11:53 time: 0.6516 data_time: 0.0325 memory: 16095 grad_norm: 5.8177 loss: 0.7335 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7335 2022/12/09 02:08:56 - mmengine - INFO - Epoch(train) [75][240/940] lr: 1.0000e-03 eta: 4:11:41 time: 0.5996 data_time: 0.0205 memory: 16095 grad_norm: 5.7884 loss: 0.7017 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7017 2022/12/09 02:09:09 - mmengine - INFO - Epoch(train) [75][260/940] lr: 1.0000e-03 eta: 4:11:28 time: 0.6629 data_time: 0.0286 memory: 16095 grad_norm: 5.6873 loss: 0.7235 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7235 2022/12/09 02:09:21 - mmengine - INFO - Epoch(train) [75][280/940] lr: 1.0000e-03 eta: 4:11:16 time: 0.5860 data_time: 0.0204 memory: 16095 grad_norm: 5.8738 loss: 0.7475 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.7475 2022/12/09 02:09:34 - mmengine - INFO - Epoch(train) [75][300/940] lr: 1.0000e-03 eta: 4:11:03 time: 0.6647 data_time: 0.0318 memory: 16095 grad_norm: 5.9441 loss: 0.8354 top1_acc: 0.5938 top5_acc: 0.8750 loss_cls: 0.8354 2022/12/09 02:09:45 - mmengine - INFO - Epoch(train) [75][320/940] lr: 1.0000e-03 eta: 4:10:50 time: 0.5235 data_time: 0.0220 memory: 16095 grad_norm: 5.9974 loss: 0.6885 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 0.6885 2022/12/09 02:09:58 - mmengine - INFO - Epoch(train) [75][340/940] lr: 1.0000e-03 eta: 4:10:38 time: 0.6547 data_time: 0.0275 memory: 16095 grad_norm: 5.7881 loss: 0.7091 top1_acc: 0.8125 top5_acc: 0.8438 loss_cls: 0.7091 2022/12/09 02:10:09 - mmengine - INFO - Epoch(train) [75][360/940] lr: 1.0000e-03 eta: 4:10:25 time: 0.5501 data_time: 0.0210 memory: 16095 grad_norm: 5.8302 loss: 0.6973 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6973 2022/12/09 02:10:23 - mmengine - INFO - Epoch(train) [75][380/940] lr: 1.0000e-03 eta: 4:10:13 time: 0.6881 data_time: 0.0268 memory: 16095 grad_norm: 6.0408 loss: 0.6615 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.6615 2022/12/09 02:10:33 - mmengine - INFO - Epoch(train) [75][400/940] lr: 1.0000e-03 eta: 4:10:00 time: 0.5219 data_time: 0.0224 memory: 16095 grad_norm: 5.7330 loss: 0.6347 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6347 2022/12/09 02:10:46 - mmengine - INFO - Epoch(train) [75][420/940] lr: 1.0000e-03 eta: 4:09:47 time: 0.6415 data_time: 0.0353 memory: 16095 grad_norm: 6.0539 loss: 0.7917 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7917 2022/12/09 02:10:58 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 02:10:58 - mmengine - INFO - Epoch(train) [75][440/940] lr: 1.0000e-03 eta: 4:09:35 time: 0.5915 data_time: 0.0219 memory: 16095 grad_norm: 5.9243 loss: 0.6458 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6458 2022/12/09 02:11:12 - mmengine - INFO - Epoch(train) [75][460/940] lr: 1.0000e-03 eta: 4:09:23 time: 0.6949 data_time: 0.0272 memory: 16095 grad_norm: 6.0897 loss: 0.7507 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7507 2022/12/09 02:11:23 - mmengine - INFO - Epoch(train) [75][480/940] lr: 1.0000e-03 eta: 4:09:10 time: 0.5714 data_time: 0.0265 memory: 16095 grad_norm: 6.0014 loss: 0.7245 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7245 2022/12/09 02:11:36 - mmengine - INFO - Epoch(train) [75][500/940] lr: 1.0000e-03 eta: 4:08:57 time: 0.6360 data_time: 0.0263 memory: 16095 grad_norm: 5.9770 loss: 0.6775 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6775 2022/12/09 02:11:47 - mmengine - INFO - Epoch(train) [75][520/940] lr: 1.0000e-03 eta: 4:08:45 time: 0.5565 data_time: 0.0221 memory: 16095 grad_norm: 6.0130 loss: 0.8022 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.8022 2022/12/09 02:12:02 - mmengine - INFO - Epoch(train) [75][540/940] lr: 1.0000e-03 eta: 4:08:33 time: 0.7314 data_time: 0.0257 memory: 16095 grad_norm: 6.0160 loss: 0.7885 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7885 2022/12/09 02:12:13 - mmengine - INFO - Epoch(train) [75][560/940] lr: 1.0000e-03 eta: 4:08:20 time: 0.5658 data_time: 0.0227 memory: 16095 grad_norm: 5.9668 loss: 0.7077 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7077 2022/12/09 02:12:26 - mmengine - INFO - Epoch(train) [75][580/940] lr: 1.0000e-03 eta: 4:08:08 time: 0.6732 data_time: 0.0243 memory: 16095 grad_norm: 5.9748 loss: 0.7220 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7220 2022/12/09 02:12:36 - mmengine - INFO - Epoch(train) [75][600/940] lr: 1.0000e-03 eta: 4:07:54 time: 0.4961 data_time: 0.0251 memory: 16095 grad_norm: 6.0552 loss: 0.7562 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.7562 2022/12/09 02:12:49 - mmengine - INFO - Epoch(train) [75][620/940] lr: 1.0000e-03 eta: 4:07:42 time: 0.6586 data_time: 0.0238 memory: 16095 grad_norm: 5.9164 loss: 0.6965 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6965 2022/12/09 02:13:01 - mmengine - INFO - Epoch(train) [75][640/940] lr: 1.0000e-03 eta: 4:07:29 time: 0.5573 data_time: 0.0244 memory: 16095 grad_norm: 5.9747 loss: 0.7181 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7181 2022/12/09 02:13:13 - mmengine - INFO - Epoch(train) [75][660/940] lr: 1.0000e-03 eta: 4:07:17 time: 0.6170 data_time: 0.0240 memory: 16095 grad_norm: 5.9301 loss: 0.7076 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7076 2022/12/09 02:13:25 - mmengine - INFO - Epoch(train) [75][680/940] lr: 1.0000e-03 eta: 4:07:04 time: 0.5750 data_time: 0.0236 memory: 16095 grad_norm: 5.9545 loss: 0.7751 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.7751 2022/12/09 02:13:38 - mmengine - INFO - Epoch(train) [75][700/940] lr: 1.0000e-03 eta: 4:06:52 time: 0.6527 data_time: 0.0245 memory: 16095 grad_norm: 5.8930 loss: 0.7339 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7339 2022/12/09 02:13:50 - mmengine - INFO - Epoch(train) [75][720/940] lr: 1.0000e-03 eta: 4:06:39 time: 0.6445 data_time: 0.0254 memory: 16095 grad_norm: 6.1908 loss: 0.6770 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6770 2022/12/09 02:14:02 - mmengine - INFO - Epoch(train) [75][740/940] lr: 1.0000e-03 eta: 4:06:26 time: 0.5541 data_time: 0.0244 memory: 16095 grad_norm: 5.8969 loss: 0.6728 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6728 2022/12/09 02:14:15 - mmengine - INFO - Epoch(train) [75][760/940] lr: 1.0000e-03 eta: 4:06:14 time: 0.6516 data_time: 0.0252 memory: 16095 grad_norm: 6.0557 loss: 0.7890 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.7890 2022/12/09 02:14:27 - mmengine - INFO - Epoch(train) [75][780/940] lr: 1.0000e-03 eta: 4:06:01 time: 0.6042 data_time: 0.0227 memory: 16095 grad_norm: 5.8795 loss: 0.6721 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6721 2022/12/09 02:14:40 - mmengine - INFO - Epoch(train) [75][800/940] lr: 1.0000e-03 eta: 4:05:49 time: 0.6760 data_time: 0.0259 memory: 16095 grad_norm: 5.9746 loss: 0.8068 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.8068 2022/12/09 02:14:52 - mmengine - INFO - Epoch(train) [75][820/940] lr: 1.0000e-03 eta: 4:05:36 time: 0.5732 data_time: 0.0224 memory: 16095 grad_norm: 5.8939 loss: 0.7536 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7536 2022/12/09 02:15:05 - mmengine - INFO - Epoch(train) [75][840/940] lr: 1.0000e-03 eta: 4:05:24 time: 0.6506 data_time: 0.0264 memory: 16095 grad_norm: 5.8983 loss: 0.7414 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.7414 2022/12/09 02:15:15 - mmengine - INFO - Epoch(train) [75][860/940] lr: 1.0000e-03 eta: 4:05:11 time: 0.5339 data_time: 0.0325 memory: 16095 grad_norm: 5.9875 loss: 0.8125 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.8125 2022/12/09 02:15:28 - mmengine - INFO - Epoch(train) [75][880/940] lr: 1.0000e-03 eta: 4:04:59 time: 0.6129 data_time: 0.0249 memory: 16095 grad_norm: 5.9036 loss: 0.7802 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7802 2022/12/09 02:15:39 - mmengine - INFO - Epoch(train) [75][900/940] lr: 1.0000e-03 eta: 4:04:46 time: 0.5796 data_time: 0.0467 memory: 16095 grad_norm: 6.0818 loss: 0.6850 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6850 2022/12/09 02:15:52 - mmengine - INFO - Epoch(train) [75][920/940] lr: 1.0000e-03 eta: 4:04:33 time: 0.6412 data_time: 0.0692 memory: 16095 grad_norm: 5.9663 loss: 0.7080 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7080 2022/12/09 02:16:03 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 02:16:03 - mmengine - INFO - Epoch(train) [75][940/940] lr: 1.0000e-03 eta: 4:04:20 time: 0.5392 data_time: 0.1140 memory: 16095 grad_norm: 6.2873 loss: 0.7555 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 0.7555 2022/12/09 02:16:03 - mmengine - INFO - Saving checkpoint at 75 epochs 2022/12/09 02:16:20 - mmengine - INFO - Epoch(val) [75][20/78] eta: 0:00:41 time: 0.7193 data_time: 0.6242 memory: 1686 2022/12/09 02:16:30 - mmengine - INFO - Epoch(val) [75][40/78] eta: 0:00:22 time: 0.4714 data_time: 0.3777 memory: 1686 2022/12/09 02:16:43 - mmengine - INFO - Epoch(val) [75][60/78] eta: 0:00:11 time: 0.6838 data_time: 0.5888 memory: 1686 2022/12/09 02:16:53 - mmengine - INFO - Epoch(val) [75][78/78] acc/top1: 0.6913 acc/top5: 0.8772 acc/mean1: 0.6912 2022/12/09 02:17:09 - mmengine - INFO - Epoch(train) [76][ 20/940] lr: 1.0000e-03 eta: 4:04:09 time: 0.8149 data_time: 0.3284 memory: 16095 grad_norm: 5.9409 loss: 0.7539 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7539 2022/12/09 02:17:20 - mmengine - INFO - Epoch(train) [76][ 40/940] lr: 1.0000e-03 eta: 4:03:56 time: 0.5504 data_time: 0.1268 memory: 16095 grad_norm: 6.0202 loss: 0.6612 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6612 2022/12/09 02:17:33 - mmengine - INFO - Epoch(train) [76][ 60/940] lr: 1.0000e-03 eta: 4:03:44 time: 0.6499 data_time: 0.1150 memory: 16095 grad_norm: 5.9754 loss: 0.6860 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6860 2022/12/09 02:17:44 - mmengine - INFO - Epoch(train) [76][ 80/940] lr: 1.0000e-03 eta: 4:03:31 time: 0.5548 data_time: 0.0676 memory: 16095 grad_norm: 5.9683 loss: 0.6953 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6953 2022/12/09 02:17:58 - mmengine - INFO - Epoch(train) [76][100/940] lr: 1.0000e-03 eta: 4:03:19 time: 0.6906 data_time: 0.0310 memory: 16095 grad_norm: 6.0959 loss: 0.7993 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7993 2022/12/09 02:18:09 - mmengine - INFO - Epoch(train) [76][120/940] lr: 1.0000e-03 eta: 4:03:06 time: 0.5691 data_time: 0.0209 memory: 16095 grad_norm: 5.8217 loss: 0.6200 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6200 2022/12/09 02:18:23 - mmengine - INFO - Epoch(train) [76][140/940] lr: 1.0000e-03 eta: 4:02:54 time: 0.6637 data_time: 0.0279 memory: 16095 grad_norm: 5.9649 loss: 0.6434 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6434 2022/12/09 02:18:34 - mmengine - INFO - Epoch(train) [76][160/940] lr: 1.0000e-03 eta: 4:02:41 time: 0.5697 data_time: 0.0217 memory: 16095 grad_norm: 6.0443 loss: 0.7261 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7261 2022/12/09 02:18:48 - mmengine - INFO - Epoch(train) [76][180/940] lr: 1.0000e-03 eta: 4:02:29 time: 0.6839 data_time: 0.0339 memory: 16095 grad_norm: 5.9794 loss: 0.6842 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6842 2022/12/09 02:18:59 - mmengine - INFO - Epoch(train) [76][200/940] lr: 1.0000e-03 eta: 4:02:16 time: 0.5582 data_time: 0.0210 memory: 16095 grad_norm: 5.8994 loss: 0.7226 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7226 2022/12/09 02:19:12 - mmengine - INFO - Epoch(train) [76][220/940] lr: 1.0000e-03 eta: 4:02:04 time: 0.6719 data_time: 0.0270 memory: 16095 grad_norm: 6.0443 loss: 0.6929 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6929 2022/12/09 02:19:23 - mmengine - INFO - Epoch(train) [76][240/940] lr: 1.0000e-03 eta: 4:01:51 time: 0.5260 data_time: 0.0309 memory: 16095 grad_norm: 5.9714 loss: 0.7074 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.7074 2022/12/09 02:19:36 - mmengine - INFO - Epoch(train) [76][260/940] lr: 1.0000e-03 eta: 4:01:38 time: 0.6489 data_time: 0.0247 memory: 16095 grad_norm: 6.0155 loss: 0.7978 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7978 2022/12/09 02:19:48 - mmengine - INFO - Epoch(train) [76][280/940] lr: 1.0000e-03 eta: 4:01:26 time: 0.5940 data_time: 0.0242 memory: 16095 grad_norm: 5.9255 loss: 0.6889 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.6889 2022/12/09 02:20:01 - mmengine - INFO - Epoch(train) [76][300/940] lr: 1.0000e-03 eta: 4:01:13 time: 0.6432 data_time: 0.0245 memory: 16095 grad_norm: 6.0262 loss: 0.7062 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7062 2022/12/09 02:20:13 - mmengine - INFO - Epoch(train) [76][320/940] lr: 1.0000e-03 eta: 4:01:01 time: 0.6228 data_time: 0.0263 memory: 16095 grad_norm: 6.0409 loss: 0.6761 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6761 2022/12/09 02:20:26 - mmengine - INFO - Epoch(train) [76][340/940] lr: 1.0000e-03 eta: 4:00:49 time: 0.6594 data_time: 0.0250 memory: 16095 grad_norm: 5.9172 loss: 0.7176 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7176 2022/12/09 02:20:37 - mmengine - INFO - Epoch(train) [76][360/940] lr: 1.0000e-03 eta: 4:00:36 time: 0.5414 data_time: 0.0253 memory: 16095 grad_norm: 5.9453 loss: 0.6537 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6537 2022/12/09 02:20:51 - mmengine - INFO - Epoch(train) [76][380/940] lr: 1.0000e-03 eta: 4:00:24 time: 0.6822 data_time: 0.0240 memory: 16095 grad_norm: 5.9960 loss: 0.6586 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6586 2022/12/09 02:21:02 - mmengine - INFO - Epoch(train) [76][400/940] lr: 1.0000e-03 eta: 4:00:11 time: 0.5685 data_time: 0.0259 memory: 16095 grad_norm: 5.8426 loss: 0.6720 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6720 2022/12/09 02:21:15 - mmengine - INFO - Epoch(train) [76][420/940] lr: 1.0000e-03 eta: 3:59:58 time: 0.6364 data_time: 0.0225 memory: 16095 grad_norm: 5.9970 loss: 0.7139 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.7139 2022/12/09 02:21:26 - mmengine - INFO - Epoch(train) [76][440/940] lr: 1.0000e-03 eta: 3:59:45 time: 0.5407 data_time: 0.0283 memory: 16095 grad_norm: 5.9790 loss: 0.7629 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7629 2022/12/09 02:21:39 - mmengine - INFO - Epoch(train) [76][460/940] lr: 1.0000e-03 eta: 3:59:33 time: 0.6497 data_time: 0.0206 memory: 16095 grad_norm: 5.9730 loss: 0.7930 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7930 2022/12/09 02:21:50 - mmengine - INFO - Epoch(train) [76][480/940] lr: 1.0000e-03 eta: 3:59:20 time: 0.5702 data_time: 0.0281 memory: 16095 grad_norm: 6.0054 loss: 0.7783 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.7783 2022/12/09 02:22:02 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 02:22:02 - mmengine - INFO - Epoch(train) [76][500/940] lr: 1.0000e-03 eta: 3:59:08 time: 0.6031 data_time: 0.0216 memory: 16095 grad_norm: 6.0571 loss: 0.6973 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6973 2022/12/09 02:22:14 - mmengine - INFO - Epoch(train) [76][520/940] lr: 1.0000e-03 eta: 3:58:55 time: 0.5864 data_time: 0.0257 memory: 16095 grad_norm: 5.8731 loss: 0.6985 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6985 2022/12/09 02:22:27 - mmengine - INFO - Epoch(train) [76][540/940] lr: 1.0000e-03 eta: 3:58:43 time: 0.6595 data_time: 0.0221 memory: 16095 grad_norm: 6.0052 loss: 0.6833 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.6833 2022/12/09 02:22:38 - mmengine - INFO - Epoch(train) [76][560/940] lr: 1.0000e-03 eta: 3:58:30 time: 0.5344 data_time: 0.0247 memory: 16095 grad_norm: 5.9058 loss: 0.7145 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7145 2022/12/09 02:22:52 - mmengine - INFO - Epoch(train) [76][580/940] lr: 1.0000e-03 eta: 3:58:18 time: 0.7114 data_time: 0.0268 memory: 16095 grad_norm: 5.9356 loss: 0.6670 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6670 2022/12/09 02:23:03 - mmengine - INFO - Epoch(train) [76][600/940] lr: 1.0000e-03 eta: 3:58:05 time: 0.5551 data_time: 0.0249 memory: 16095 grad_norm: 6.0892 loss: 0.6927 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6927 2022/12/09 02:23:16 - mmengine - INFO - Epoch(train) [76][620/940] lr: 1.0000e-03 eta: 3:57:52 time: 0.6241 data_time: 0.0273 memory: 16095 grad_norm: 5.9491 loss: 0.7483 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7483 2022/12/09 02:23:27 - mmengine - INFO - Epoch(train) [76][640/940] lr: 1.0000e-03 eta: 3:57:40 time: 0.5795 data_time: 0.0232 memory: 16095 grad_norm: 6.0809 loss: 0.7701 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7701 2022/12/09 02:23:40 - mmengine - INFO - Epoch(train) [76][660/940] lr: 1.0000e-03 eta: 3:57:27 time: 0.6512 data_time: 0.0541 memory: 16095 grad_norm: 6.0690 loss: 0.6836 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.6836 2022/12/09 02:23:53 - mmengine - INFO - Epoch(train) [76][680/940] lr: 1.0000e-03 eta: 3:57:15 time: 0.6283 data_time: 0.0380 memory: 16095 grad_norm: 6.1214 loss: 0.7279 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7279 2022/12/09 02:24:05 - mmengine - INFO - Epoch(train) [76][700/940] lr: 1.0000e-03 eta: 3:57:02 time: 0.6293 data_time: 0.0273 memory: 16095 grad_norm: 6.0757 loss: 0.6670 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6670 2022/12/09 02:24:16 - mmengine - INFO - Epoch(train) [76][720/940] lr: 1.0000e-03 eta: 3:56:49 time: 0.5553 data_time: 0.0221 memory: 16095 grad_norm: 5.8253 loss: 0.6298 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6298 2022/12/09 02:24:29 - mmengine - INFO - Epoch(train) [76][740/940] lr: 1.0000e-03 eta: 3:56:37 time: 0.6157 data_time: 0.0261 memory: 16095 grad_norm: 5.8489 loss: 0.7544 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7544 2022/12/09 02:24:40 - mmengine - INFO - Epoch(train) [76][760/940] lr: 1.0000e-03 eta: 3:56:24 time: 0.5703 data_time: 0.0265 memory: 16095 grad_norm: 6.0248 loss: 0.6749 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.6749 2022/12/09 02:24:52 - mmengine - INFO - Epoch(train) [76][780/940] lr: 1.0000e-03 eta: 3:56:11 time: 0.5866 data_time: 0.0278 memory: 16095 grad_norm: 6.1494 loss: 0.7539 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.7539 2022/12/09 02:25:05 - mmengine - INFO - Epoch(train) [76][800/940] lr: 1.0000e-03 eta: 3:55:59 time: 0.6381 data_time: 0.0852 memory: 16095 grad_norm: 6.1225 loss: 0.7434 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7434 2022/12/09 02:25:16 - mmengine - INFO - Epoch(train) [76][820/940] lr: 1.0000e-03 eta: 3:55:46 time: 0.5773 data_time: 0.0750 memory: 16095 grad_norm: 6.1077 loss: 0.6290 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6290 2022/12/09 02:25:30 - mmengine - INFO - Epoch(train) [76][840/940] lr: 1.0000e-03 eta: 3:55:34 time: 0.6777 data_time: 0.1318 memory: 16095 grad_norm: 5.8021 loss: 0.7483 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7483 2022/12/09 02:25:40 - mmengine - INFO - Epoch(train) [76][860/940] lr: 1.0000e-03 eta: 3:55:21 time: 0.5236 data_time: 0.0468 memory: 16095 grad_norm: 6.0188 loss: 0.7516 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7516 2022/12/09 02:25:53 - mmengine - INFO - Epoch(train) [76][880/940] lr: 1.0000e-03 eta: 3:55:08 time: 0.6188 data_time: 0.1029 memory: 16095 grad_norm: 5.9716 loss: 0.7774 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7774 2022/12/09 02:26:05 - mmengine - INFO - Epoch(train) [76][900/940] lr: 1.0000e-03 eta: 3:54:56 time: 0.6204 data_time: 0.2611 memory: 16095 grad_norm: 5.9974 loss: 0.7705 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7705 2022/12/09 02:26:17 - mmengine - INFO - Epoch(train) [76][920/940] lr: 1.0000e-03 eta: 3:54:43 time: 0.5939 data_time: 0.1213 memory: 16095 grad_norm: 6.0689 loss: 0.7668 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7668 2022/12/09 02:26:26 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 02:26:26 - mmengine - INFO - Epoch(train) [76][940/940] lr: 1.0000e-03 eta: 3:54:30 time: 0.4632 data_time: 0.1339 memory: 16095 grad_norm: 6.6232 loss: 0.7889 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 0.7889 2022/12/09 02:26:40 - mmengine - INFO - Epoch(val) [76][20/78] eta: 0:00:41 time: 0.7107 data_time: 0.6160 memory: 1686 2022/12/09 02:26:50 - mmengine - INFO - Epoch(val) [76][40/78] eta: 0:00:22 time: 0.4731 data_time: 0.3793 memory: 1686 2022/12/09 02:27:04 - mmengine - INFO - Epoch(val) [76][60/78] eta: 0:00:11 time: 0.6990 data_time: 0.6033 memory: 1686 2022/12/09 02:27:14 - mmengine - INFO - Epoch(val) [76][78/78] acc/top1: 0.6907 acc/top5: 0.8768 acc/mean1: 0.6906 2022/12/09 02:27:30 - mmengine - INFO - Epoch(train) [77][ 20/940] lr: 1.0000e-03 eta: 3:54:18 time: 0.8089 data_time: 0.4873 memory: 16095 grad_norm: 5.9912 loss: 0.7299 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7299 2022/12/09 02:27:41 - mmengine - INFO - Epoch(train) [77][ 40/940] lr: 1.0000e-03 eta: 3:54:06 time: 0.5761 data_time: 0.2756 memory: 16095 grad_norm: 5.9573 loss: 0.7439 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7439 2022/12/09 02:27:54 - mmengine - INFO - Epoch(train) [77][ 60/940] lr: 1.0000e-03 eta: 3:53:53 time: 0.6514 data_time: 0.3071 memory: 16095 grad_norm: 6.0140 loss: 0.7331 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.7331 2022/12/09 02:28:05 - mmengine - INFO - Epoch(train) [77][ 80/940] lr: 1.0000e-03 eta: 3:53:40 time: 0.5293 data_time: 0.2073 memory: 16095 grad_norm: 6.0316 loss: 0.7324 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.7324 2022/12/09 02:28:19 - mmengine - INFO - Epoch(train) [77][100/940] lr: 1.0000e-03 eta: 3:53:28 time: 0.7015 data_time: 0.3952 memory: 16095 grad_norm: 5.9493 loss: 0.7862 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.7862 2022/12/09 02:28:31 - mmengine - INFO - Epoch(train) [77][120/940] lr: 1.0000e-03 eta: 3:53:16 time: 0.5717 data_time: 0.2617 memory: 16095 grad_norm: 5.8175 loss: 0.7591 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7591 2022/12/09 02:28:44 - mmengine - INFO - Epoch(train) [77][140/940] lr: 1.0000e-03 eta: 3:53:03 time: 0.6744 data_time: 0.3033 memory: 16095 grad_norm: 6.0790 loss: 0.7448 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7448 2022/12/09 02:28:54 - mmengine - INFO - Epoch(train) [77][160/940] lr: 1.0000e-03 eta: 3:52:50 time: 0.5071 data_time: 0.1394 memory: 16095 grad_norm: 5.9065 loss: 0.6900 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.6900 2022/12/09 02:29:07 - mmengine - INFO - Epoch(train) [77][180/940] lr: 1.0000e-03 eta: 3:52:38 time: 0.6522 data_time: 0.2488 memory: 16095 grad_norm: 6.0003 loss: 0.6769 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6769 2022/12/09 02:29:19 - mmengine - INFO - Epoch(train) [77][200/940] lr: 1.0000e-03 eta: 3:52:25 time: 0.5973 data_time: 0.2213 memory: 16095 grad_norm: 5.9148 loss: 0.7353 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7353 2022/12/09 02:29:32 - mmengine - INFO - Epoch(train) [77][220/940] lr: 1.0000e-03 eta: 3:52:13 time: 0.6290 data_time: 0.2312 memory: 16095 grad_norm: 6.0557 loss: 0.7205 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7205 2022/12/09 02:29:43 - mmengine - INFO - Epoch(train) [77][240/940] lr: 1.0000e-03 eta: 3:52:00 time: 0.5668 data_time: 0.2374 memory: 16095 grad_norm: 6.1536 loss: 0.7008 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.7008 2022/12/09 02:29:57 - mmengine - INFO - Epoch(train) [77][260/940] lr: 1.0000e-03 eta: 3:51:48 time: 0.6854 data_time: 0.3049 memory: 16095 grad_norm: 6.0668 loss: 0.6031 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6031 2022/12/09 02:30:08 - mmengine - INFO - Epoch(train) [77][280/940] lr: 1.0000e-03 eta: 3:51:35 time: 0.5377 data_time: 0.1652 memory: 16095 grad_norm: 5.9019 loss: 0.6517 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6517 2022/12/09 02:30:21 - mmengine - INFO - Epoch(train) [77][300/940] lr: 1.0000e-03 eta: 3:51:23 time: 0.6940 data_time: 0.2056 memory: 16095 grad_norm: 5.9139 loss: 0.5875 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.5875 2022/12/09 02:30:32 - mmengine - INFO - Epoch(train) [77][320/940] lr: 1.0000e-03 eta: 3:51:10 time: 0.5279 data_time: 0.1025 memory: 16095 grad_norm: 6.1256 loss: 0.8496 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.8496 2022/12/09 02:30:46 - mmengine - INFO - Epoch(train) [77][340/940] lr: 1.0000e-03 eta: 3:50:58 time: 0.7070 data_time: 0.0814 memory: 16095 grad_norm: 6.1270 loss: 0.7038 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7038 2022/12/09 02:30:57 - mmengine - INFO - Epoch(train) [77][360/940] lr: 1.0000e-03 eta: 3:50:45 time: 0.5544 data_time: 0.0214 memory: 16095 grad_norm: 6.1578 loss: 0.7284 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.7284 2022/12/09 02:31:10 - mmengine - INFO - Epoch(train) [77][380/940] lr: 1.0000e-03 eta: 3:50:33 time: 0.6364 data_time: 0.0408 memory: 16095 grad_norm: 5.8600 loss: 0.6744 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.6744 2022/12/09 02:31:22 - mmengine - INFO - Epoch(train) [77][400/940] lr: 1.0000e-03 eta: 3:50:20 time: 0.5734 data_time: 0.0738 memory: 16095 grad_norm: 6.0677 loss: 0.7346 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.7346 2022/12/09 02:31:35 - mmengine - INFO - Epoch(train) [77][420/940] lr: 1.0000e-03 eta: 3:50:07 time: 0.6541 data_time: 0.0272 memory: 16095 grad_norm: 5.9509 loss: 0.7178 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7178 2022/12/09 02:31:45 - mmengine - INFO - Epoch(train) [77][440/940] lr: 1.0000e-03 eta: 3:49:54 time: 0.5166 data_time: 0.0245 memory: 16095 grad_norm: 6.1127 loss: 0.8273 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.8273 2022/12/09 02:31:58 - mmengine - INFO - Epoch(train) [77][460/940] lr: 1.0000e-03 eta: 3:49:42 time: 0.6375 data_time: 0.0262 memory: 16095 grad_norm: 6.1975 loss: 0.7441 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7441 2022/12/09 02:32:09 - mmengine - INFO - Epoch(train) [77][480/940] lr: 1.0000e-03 eta: 3:49:29 time: 0.5810 data_time: 0.0257 memory: 16095 grad_norm: 6.0235 loss: 0.7260 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7260 2022/12/09 02:32:24 - mmengine - INFO - Epoch(train) [77][500/940] lr: 1.0000e-03 eta: 3:49:17 time: 0.7199 data_time: 0.0266 memory: 16095 grad_norm: 6.0035 loss: 0.7194 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7194 2022/12/09 02:32:35 - mmengine - INFO - Epoch(train) [77][520/940] lr: 1.0000e-03 eta: 3:49:04 time: 0.5609 data_time: 0.0285 memory: 16095 grad_norm: 5.9244 loss: 0.7247 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7247 2022/12/09 02:32:48 - mmengine - INFO - Epoch(train) [77][540/940] lr: 1.0000e-03 eta: 3:48:52 time: 0.6296 data_time: 0.0243 memory: 16095 grad_norm: 5.9395 loss: 0.6589 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6589 2022/12/09 02:32:59 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 02:32:59 - mmengine - INFO - Epoch(train) [77][560/940] lr: 1.0000e-03 eta: 3:48:39 time: 0.5888 data_time: 0.0228 memory: 16095 grad_norm: 6.1295 loss: 0.7405 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7405 2022/12/09 02:33:12 - mmengine - INFO - Epoch(train) [77][580/940] lr: 1.0000e-03 eta: 3:48:27 time: 0.6499 data_time: 0.0256 memory: 16095 grad_norm: 6.0509 loss: 0.6683 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6683 2022/12/09 02:33:24 - mmengine - INFO - Epoch(train) [77][600/940] lr: 1.0000e-03 eta: 3:48:14 time: 0.5689 data_time: 0.0234 memory: 16095 grad_norm: 6.1444 loss: 0.7634 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.7634 2022/12/09 02:33:36 - mmengine - INFO - Epoch(train) [77][620/940] lr: 1.0000e-03 eta: 3:48:02 time: 0.6306 data_time: 0.0270 memory: 16095 grad_norm: 6.0517 loss: 0.6833 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 0.6833 2022/12/09 02:33:48 - mmengine - INFO - Epoch(train) [77][640/940] lr: 1.0000e-03 eta: 3:47:49 time: 0.5625 data_time: 0.0240 memory: 16095 grad_norm: 6.1181 loss: 0.6861 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6861 2022/12/09 02:34:01 - mmengine - INFO - Epoch(train) [77][660/940] lr: 1.0000e-03 eta: 3:47:37 time: 0.6601 data_time: 0.0262 memory: 16095 grad_norm: 6.0928 loss: 0.7199 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7199 2022/12/09 02:34:12 - mmengine - INFO - Epoch(train) [77][680/940] lr: 1.0000e-03 eta: 3:47:24 time: 0.5710 data_time: 0.0238 memory: 16095 grad_norm: 6.1546 loss: 0.7715 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.7715 2022/12/09 02:34:25 - mmengine - INFO - Epoch(train) [77][700/940] lr: 1.0000e-03 eta: 3:47:12 time: 0.6412 data_time: 0.0344 memory: 16095 grad_norm: 6.1045 loss: 0.7372 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7372 2022/12/09 02:34:36 - mmengine - INFO - Epoch(train) [77][720/940] lr: 1.0000e-03 eta: 3:46:59 time: 0.5647 data_time: 0.0219 memory: 16095 grad_norm: 6.2453 loss: 0.7673 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7673 2022/12/09 02:34:50 - mmengine - INFO - Epoch(train) [77][740/940] lr: 1.0000e-03 eta: 3:46:47 time: 0.6850 data_time: 0.0277 memory: 16095 grad_norm: 6.1107 loss: 0.7586 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7586 2022/12/09 02:35:01 - mmengine - INFO - Epoch(train) [77][760/940] lr: 1.0000e-03 eta: 3:46:34 time: 0.5454 data_time: 0.0239 memory: 16095 grad_norm: 6.2212 loss: 0.7532 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7532 2022/12/09 02:35:14 - mmengine - INFO - Epoch(train) [77][780/940] lr: 1.0000e-03 eta: 3:46:21 time: 0.6706 data_time: 0.0247 memory: 16095 grad_norm: 6.0873 loss: 0.7294 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7294 2022/12/09 02:35:26 - mmengine - INFO - Epoch(train) [77][800/940] lr: 1.0000e-03 eta: 3:46:09 time: 0.5700 data_time: 0.0234 memory: 16095 grad_norm: 6.0933 loss: 0.6379 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6379 2022/12/09 02:35:37 - mmengine - INFO - Epoch(train) [77][820/940] lr: 1.0000e-03 eta: 3:45:56 time: 0.5770 data_time: 0.0261 memory: 16095 grad_norm: 6.0482 loss: 0.7489 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7489 2022/12/09 02:35:51 - mmengine - INFO - Epoch(train) [77][840/940] lr: 1.0000e-03 eta: 3:45:44 time: 0.6825 data_time: 0.0241 memory: 16095 grad_norm: 5.9237 loss: 0.7029 top1_acc: 0.7188 top5_acc: 0.8125 loss_cls: 0.7029 2022/12/09 02:36:02 - mmengine - INFO - Epoch(train) [77][860/940] lr: 1.0000e-03 eta: 3:45:31 time: 0.5707 data_time: 0.0264 memory: 16095 grad_norm: 5.8783 loss: 0.7459 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7459 2022/12/09 02:36:14 - mmengine - INFO - Epoch(train) [77][880/940] lr: 1.0000e-03 eta: 3:45:18 time: 0.5852 data_time: 0.0232 memory: 16095 grad_norm: 6.0789 loss: 0.8279 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.8279 2022/12/09 02:36:27 - mmengine - INFO - Epoch(train) [77][900/940] lr: 1.0000e-03 eta: 3:45:06 time: 0.6430 data_time: 0.0261 memory: 16095 grad_norm: 6.1273 loss: 0.7184 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7184 2022/12/09 02:36:38 - mmengine - INFO - Epoch(train) [77][920/940] lr: 1.0000e-03 eta: 3:44:53 time: 0.5638 data_time: 0.0235 memory: 16095 grad_norm: 6.0773 loss: 0.6673 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6673 2022/12/09 02:36:49 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 02:36:49 - mmengine - INFO - Epoch(train) [77][940/940] lr: 1.0000e-03 eta: 3:44:40 time: 0.5441 data_time: 0.0192 memory: 16095 grad_norm: 6.5777 loss: 0.8477 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 0.8477 2022/12/09 02:37:04 - mmengine - INFO - Epoch(val) [77][20/78] eta: 0:00:41 time: 0.7178 data_time: 0.6238 memory: 1686 2022/12/09 02:37:12 - mmengine - INFO - Epoch(val) [77][40/78] eta: 0:00:22 time: 0.4452 data_time: 0.3514 memory: 1686 2022/12/09 02:37:26 - mmengine - INFO - Epoch(val) [77][60/78] eta: 0:00:11 time: 0.6758 data_time: 0.5803 memory: 1686 2022/12/09 02:37:37 - mmengine - INFO - Epoch(val) [77][78/78] acc/top1: 0.6885 acc/top5: 0.8772 acc/mean1: 0.6884 2022/12/09 02:37:53 - mmengine - INFO - Epoch(train) [78][ 20/940] lr: 1.0000e-03 eta: 3:44:29 time: 0.8255 data_time: 0.4289 memory: 16095 grad_norm: 6.0576 loss: 0.7339 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7339 2022/12/09 02:38:04 - mmengine - INFO - Epoch(train) [78][ 40/940] lr: 1.0000e-03 eta: 3:44:16 time: 0.5517 data_time: 0.1720 memory: 16095 grad_norm: 5.9903 loss: 0.7215 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7215 2022/12/09 02:38:18 - mmengine - INFO - Epoch(train) [78][ 60/940] lr: 1.0000e-03 eta: 3:44:04 time: 0.6836 data_time: 0.1628 memory: 16095 grad_norm: 5.9378 loss: 0.6231 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6231 2022/12/09 02:38:29 - mmengine - INFO - Epoch(train) [78][ 80/940] lr: 1.0000e-03 eta: 3:43:51 time: 0.5554 data_time: 0.1050 memory: 16095 grad_norm: 5.9958 loss: 0.7697 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7697 2022/12/09 02:38:43 - mmengine - INFO - Epoch(train) [78][100/940] lr: 1.0000e-03 eta: 3:43:39 time: 0.7014 data_time: 0.1918 memory: 16095 grad_norm: 5.9820 loss: 0.8456 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.8456 2022/12/09 02:38:55 - mmengine - INFO - Epoch(train) [78][120/940] lr: 1.0000e-03 eta: 3:43:26 time: 0.5589 data_time: 0.0601 memory: 16095 grad_norm: 5.9868 loss: 0.6994 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6994 2022/12/09 02:39:07 - mmengine - INFO - Epoch(train) [78][140/940] lr: 1.0000e-03 eta: 3:43:14 time: 0.6355 data_time: 0.0876 memory: 16095 grad_norm: 5.9735 loss: 0.8006 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.8006 2022/12/09 02:39:18 - mmengine - INFO - Epoch(train) [78][160/940] lr: 1.0000e-03 eta: 3:43:01 time: 0.5603 data_time: 0.0217 memory: 16095 grad_norm: 6.0841 loss: 0.5900 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.5900 2022/12/09 02:39:33 - mmengine - INFO - Epoch(train) [78][180/940] lr: 1.0000e-03 eta: 3:42:49 time: 0.7030 data_time: 0.0275 memory: 16095 grad_norm: 5.8225 loss: 0.6872 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6872 2022/12/09 02:39:44 - mmengine - INFO - Epoch(train) [78][200/940] lr: 1.0000e-03 eta: 3:42:36 time: 0.5719 data_time: 0.0221 memory: 16095 grad_norm: 5.9891 loss: 0.7059 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7059 2022/12/09 02:39:57 - mmengine - INFO - Epoch(train) [78][220/940] lr: 1.0000e-03 eta: 3:42:24 time: 0.6313 data_time: 0.0246 memory: 16095 grad_norm: 5.9903 loss: 0.6249 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6249 2022/12/09 02:40:07 - mmengine - INFO - Epoch(train) [78][240/940] lr: 1.0000e-03 eta: 3:42:11 time: 0.5281 data_time: 0.0235 memory: 16095 grad_norm: 6.0720 loss: 0.7522 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7522 2022/12/09 02:40:20 - mmengine - INFO - Epoch(train) [78][260/940] lr: 1.0000e-03 eta: 3:41:58 time: 0.6332 data_time: 0.0289 memory: 16095 grad_norm: 6.2157 loss: 0.7670 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7670 2022/12/09 02:40:31 - mmengine - INFO - Epoch(train) [78][280/940] lr: 1.0000e-03 eta: 3:41:45 time: 0.5439 data_time: 0.0263 memory: 16095 grad_norm: 5.9531 loss: 0.7962 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7962 2022/12/09 02:40:45 - mmengine - INFO - Epoch(train) [78][300/940] lr: 1.0000e-03 eta: 3:41:33 time: 0.6993 data_time: 0.1123 memory: 16095 grad_norm: 6.0602 loss: 0.7500 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7500 2022/12/09 02:40:56 - mmengine - INFO - Epoch(train) [78][320/940] lr: 1.0000e-03 eta: 3:41:20 time: 0.5484 data_time: 0.0912 memory: 16095 grad_norm: 5.9937 loss: 0.6246 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6246 2022/12/09 02:41:09 - mmengine - INFO - Epoch(train) [78][340/940] lr: 1.0000e-03 eta: 3:41:08 time: 0.6758 data_time: 0.1359 memory: 16095 grad_norm: 6.0062 loss: 0.7395 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.7395 2022/12/09 02:41:19 - mmengine - INFO - Epoch(train) [78][360/940] lr: 1.0000e-03 eta: 3:40:55 time: 0.5134 data_time: 0.0639 memory: 16095 grad_norm: 6.0738 loss: 0.6409 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.6409 2022/12/09 02:41:33 - mmengine - INFO - Epoch(train) [78][380/940] lr: 1.0000e-03 eta: 3:40:43 time: 0.6709 data_time: 0.0518 memory: 16095 grad_norm: 6.0203 loss: 0.7716 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7716 2022/12/09 02:41:44 - mmengine - INFO - Epoch(train) [78][400/940] lr: 1.0000e-03 eta: 3:40:30 time: 0.5529 data_time: 0.0626 memory: 16095 grad_norm: 5.8669 loss: 0.6531 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 0.6531 2022/12/09 02:41:58 - mmengine - INFO - Epoch(train) [78][420/940] lr: 1.0000e-03 eta: 3:40:18 time: 0.6886 data_time: 0.0394 memory: 16095 grad_norm: 6.0475 loss: 0.7584 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7584 2022/12/09 02:42:08 - mmengine - INFO - Epoch(train) [78][440/940] lr: 1.0000e-03 eta: 3:40:05 time: 0.5359 data_time: 0.0209 memory: 16095 grad_norm: 6.1018 loss: 0.8019 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.8019 2022/12/09 02:42:23 - mmengine - INFO - Epoch(train) [78][460/940] lr: 1.0000e-03 eta: 3:39:53 time: 0.7069 data_time: 0.0284 memory: 16095 grad_norm: 6.0594 loss: 0.7046 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.7046 2022/12/09 02:42:34 - mmengine - INFO - Epoch(train) [78][480/940] lr: 1.0000e-03 eta: 3:39:40 time: 0.5628 data_time: 0.0228 memory: 16095 grad_norm: 6.1688 loss: 0.7151 top1_acc: 0.6250 top5_acc: 0.7812 loss_cls: 0.7151 2022/12/09 02:42:47 - mmengine - INFO - Epoch(train) [78][500/940] lr: 1.0000e-03 eta: 3:39:28 time: 0.6809 data_time: 0.0259 memory: 16095 grad_norm: 6.0357 loss: 0.6884 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6884 2022/12/09 02:42:58 - mmengine - INFO - Epoch(train) [78][520/940] lr: 1.0000e-03 eta: 3:39:15 time: 0.4999 data_time: 0.0238 memory: 16095 grad_norm: 5.9999 loss: 0.6669 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6669 2022/12/09 02:43:11 - mmengine - INFO - Epoch(train) [78][540/940] lr: 1.0000e-03 eta: 3:39:03 time: 0.6892 data_time: 0.0247 memory: 16095 grad_norm: 5.9573 loss: 0.6966 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 0.6966 2022/12/09 02:43:23 - mmengine - INFO - Epoch(train) [78][560/940] lr: 1.0000e-03 eta: 3:38:50 time: 0.5863 data_time: 0.0235 memory: 16095 grad_norm: 5.9528 loss: 0.6905 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.6905 2022/12/09 02:43:36 - mmengine - INFO - Epoch(train) [78][580/940] lr: 1.0000e-03 eta: 3:38:38 time: 0.6500 data_time: 0.0278 memory: 16095 grad_norm: 5.9648 loss: 0.7200 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7200 2022/12/09 02:43:47 - mmengine - INFO - Epoch(train) [78][600/940] lr: 1.0000e-03 eta: 3:38:25 time: 0.5727 data_time: 0.0216 memory: 16095 grad_norm: 6.1549 loss: 0.6871 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6871 2022/12/09 02:44:01 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 02:44:01 - mmengine - INFO - Epoch(train) [78][620/940] lr: 1.0000e-03 eta: 3:38:13 time: 0.6573 data_time: 0.0250 memory: 16095 grad_norm: 5.9533 loss: 0.6811 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6811 2022/12/09 02:44:12 - mmengine - INFO - Epoch(train) [78][640/940] lr: 1.0000e-03 eta: 3:38:00 time: 0.5424 data_time: 0.0235 memory: 16095 grad_norm: 5.9771 loss: 0.6847 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.6847 2022/12/09 02:44:25 - mmengine - INFO - Epoch(train) [78][660/940] lr: 1.0000e-03 eta: 3:37:48 time: 0.6642 data_time: 0.0228 memory: 16095 grad_norm: 6.0587 loss: 0.7535 top1_acc: 0.9688 top5_acc: 0.9688 loss_cls: 0.7535 2022/12/09 02:44:36 - mmengine - INFO - Epoch(train) [78][680/940] lr: 1.0000e-03 eta: 3:37:35 time: 0.5616 data_time: 0.0253 memory: 16095 grad_norm: 5.9067 loss: 0.7316 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 0.7316 2022/12/09 02:44:50 - mmengine - INFO - Epoch(train) [78][700/940] lr: 1.0000e-03 eta: 3:37:23 time: 0.6768 data_time: 0.0281 memory: 16095 grad_norm: 6.1326 loss: 0.7378 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7378 2022/12/09 02:45:01 - mmengine - INFO - Epoch(train) [78][720/940] lr: 1.0000e-03 eta: 3:37:10 time: 0.5623 data_time: 0.0256 memory: 16095 grad_norm: 5.9648 loss: 0.7343 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7343 2022/12/09 02:45:14 - mmengine - INFO - Epoch(train) [78][740/940] lr: 1.0000e-03 eta: 3:36:57 time: 0.6451 data_time: 0.0251 memory: 16095 grad_norm: 6.0131 loss: 0.7030 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7030 2022/12/09 02:45:24 - mmengine - INFO - Epoch(train) [78][760/940] lr: 1.0000e-03 eta: 3:36:44 time: 0.5352 data_time: 0.0261 memory: 16095 grad_norm: 6.1025 loss: 0.6708 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6708 2022/12/09 02:45:39 - mmengine - INFO - Epoch(train) [78][780/940] lr: 1.0000e-03 eta: 3:36:32 time: 0.7050 data_time: 0.0240 memory: 16095 grad_norm: 6.1296 loss: 0.7551 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.7551 2022/12/09 02:45:50 - mmengine - INFO - Epoch(train) [78][800/940] lr: 1.0000e-03 eta: 3:36:20 time: 0.5817 data_time: 0.0241 memory: 16095 grad_norm: 5.9602 loss: 0.6947 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.6947 2022/12/09 02:46:02 - mmengine - INFO - Epoch(train) [78][820/940] lr: 1.0000e-03 eta: 3:36:07 time: 0.6099 data_time: 0.0238 memory: 16095 grad_norm: 6.1496 loss: 0.7184 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 0.7184 2022/12/09 02:46:14 - mmengine - INFO - Epoch(train) [78][840/940] lr: 1.0000e-03 eta: 3:35:54 time: 0.5668 data_time: 0.0220 memory: 16095 grad_norm: 6.2049 loss: 0.7978 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7978 2022/12/09 02:46:26 - mmengine - INFO - Epoch(train) [78][860/940] lr: 1.0000e-03 eta: 3:35:42 time: 0.6081 data_time: 0.0252 memory: 16095 grad_norm: 6.0795 loss: 0.6826 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6826 2022/12/09 02:46:38 - mmengine - INFO - Epoch(train) [78][880/940] lr: 1.0000e-03 eta: 3:35:29 time: 0.5927 data_time: 0.0229 memory: 16095 grad_norm: 6.0997 loss: 0.6905 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6905 2022/12/09 02:46:50 - mmengine - INFO - Epoch(train) [78][900/940] lr: 1.0000e-03 eta: 3:35:17 time: 0.6007 data_time: 0.0412 memory: 16095 grad_norm: 6.0195 loss: 0.6643 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6643 2022/12/09 02:47:03 - mmengine - INFO - Epoch(train) [78][920/940] lr: 1.0000e-03 eta: 3:35:04 time: 0.6737 data_time: 0.0243 memory: 16095 grad_norm: 6.1356 loss: 0.7897 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7897 2022/12/09 02:47:14 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 02:47:14 - mmengine - INFO - Epoch(train) [78][940/940] lr: 1.0000e-03 eta: 3:34:51 time: 0.5248 data_time: 0.0175 memory: 16095 grad_norm: 6.7937 loss: 0.6903 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 0.6903 2022/12/09 02:47:14 - mmengine - INFO - Saving checkpoint at 78 epochs 2022/12/09 02:47:31 - mmengine - INFO - Epoch(val) [78][20/78] eta: 0:00:41 time: 0.7212 data_time: 0.6267 memory: 1686 2022/12/09 02:47:41 - mmengine - INFO - Epoch(val) [78][40/78] eta: 0:00:22 time: 0.4779 data_time: 0.3842 memory: 1686 2022/12/09 02:47:54 - mmengine - INFO - Epoch(val) [78][60/78] eta: 0:00:11 time: 0.6660 data_time: 0.5711 memory: 1686 2022/12/09 02:48:04 - mmengine - INFO - Epoch(val) [78][78/78] acc/top1: 0.6878 acc/top5: 0.8763 acc/mean1: 0.6877 2022/12/09 02:48:21 - mmengine - INFO - Epoch(train) [79][ 20/940] lr: 1.0000e-03 eta: 3:34:40 time: 0.8398 data_time: 0.4511 memory: 16095 grad_norm: 5.9368 loss: 0.7199 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.7199 2022/12/09 02:48:32 - mmengine - INFO - Epoch(train) [79][ 40/940] lr: 1.0000e-03 eta: 3:34:27 time: 0.5480 data_time: 0.1925 memory: 16095 grad_norm: 5.9052 loss: 0.6529 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6529 2022/12/09 02:48:46 - mmengine - INFO - Epoch(train) [79][ 60/940] lr: 1.0000e-03 eta: 3:34:15 time: 0.6771 data_time: 0.2898 memory: 16095 grad_norm: 5.8917 loss: 0.7325 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7325 2022/12/09 02:48:57 - mmengine - INFO - Epoch(train) [79][ 80/940] lr: 1.0000e-03 eta: 3:34:02 time: 0.5565 data_time: 0.2193 memory: 16095 grad_norm: 5.9306 loss: 0.5243 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.5243 2022/12/09 02:49:10 - mmengine - INFO - Epoch(train) [79][100/940] lr: 1.0000e-03 eta: 3:33:50 time: 0.6783 data_time: 0.3111 memory: 16095 grad_norm: 6.1159 loss: 0.7758 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7758 2022/12/09 02:49:21 - mmengine - INFO - Epoch(train) [79][120/940] lr: 1.0000e-03 eta: 3:33:37 time: 0.5602 data_time: 0.2065 memory: 16095 grad_norm: 6.0353 loss: 0.7224 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.7224 2022/12/09 02:49:34 - mmengine - INFO - Epoch(train) [79][140/940] lr: 1.0000e-03 eta: 3:33:25 time: 0.6395 data_time: 0.2356 memory: 16095 grad_norm: 6.0333 loss: 0.6844 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6844 2022/12/09 02:49:45 - mmengine - INFO - Epoch(train) [79][160/940] lr: 1.0000e-03 eta: 3:33:12 time: 0.5466 data_time: 0.1765 memory: 16095 grad_norm: 6.1584 loss: 0.7069 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7069 2022/12/09 02:49:59 - mmengine - INFO - Epoch(train) [79][180/940] lr: 1.0000e-03 eta: 3:33:00 time: 0.7054 data_time: 0.3076 memory: 16095 grad_norm: 6.0930 loss: 0.7237 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7237 2022/12/09 02:50:10 - mmengine - INFO - Epoch(train) [79][200/940] lr: 1.0000e-03 eta: 3:32:47 time: 0.5375 data_time: 0.0946 memory: 16095 grad_norm: 5.9653 loss: 0.6865 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6865 2022/12/09 02:50:24 - mmengine - INFO - Epoch(train) [79][220/940] lr: 1.0000e-03 eta: 3:32:35 time: 0.7142 data_time: 0.1641 memory: 16095 grad_norm: 6.1936 loss: 0.7279 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7279 2022/12/09 02:50:35 - mmengine - INFO - Epoch(train) [79][240/940] lr: 1.0000e-03 eta: 3:32:22 time: 0.5331 data_time: 0.0309 memory: 16095 grad_norm: 6.0412 loss: 0.6870 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6870 2022/12/09 02:50:48 - mmengine - INFO - Epoch(train) [79][260/940] lr: 1.0000e-03 eta: 3:32:10 time: 0.6698 data_time: 0.1121 memory: 16095 grad_norm: 6.1114 loss: 0.6953 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6953 2022/12/09 02:50:59 - mmengine - INFO - Epoch(train) [79][280/940] lr: 1.0000e-03 eta: 3:31:57 time: 0.5174 data_time: 0.0723 memory: 16095 grad_norm: 6.1816 loss: 0.7086 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7086 2022/12/09 02:51:13 - mmengine - INFO - Epoch(train) [79][300/940] lr: 1.0000e-03 eta: 3:31:45 time: 0.6957 data_time: 0.2326 memory: 16095 grad_norm: 6.1073 loss: 0.7583 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.7583 2022/12/09 02:51:24 - mmengine - INFO - Epoch(train) [79][320/940] lr: 1.0000e-03 eta: 3:31:32 time: 0.5799 data_time: 0.0945 memory: 16095 grad_norm: 6.1011 loss: 0.6813 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.6813 2022/12/09 02:51:36 - mmengine - INFO - Epoch(train) [79][340/940] lr: 1.0000e-03 eta: 3:31:19 time: 0.6059 data_time: 0.1285 memory: 16095 grad_norm: 6.1559 loss: 0.7021 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7021 2022/12/09 02:51:48 - mmengine - INFO - Epoch(train) [79][360/940] lr: 1.0000e-03 eta: 3:31:07 time: 0.5806 data_time: 0.1985 memory: 16095 grad_norm: 5.9778 loss: 0.6169 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6169 2022/12/09 02:52:01 - mmengine - INFO - Epoch(train) [79][380/940] lr: 1.0000e-03 eta: 3:30:55 time: 0.6630 data_time: 0.2218 memory: 16095 grad_norm: 6.1096 loss: 0.6873 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6873 2022/12/09 02:52:12 - mmengine - INFO - Epoch(train) [79][400/940] lr: 1.0000e-03 eta: 3:30:42 time: 0.5280 data_time: 0.1209 memory: 16095 grad_norm: 5.8609 loss: 0.6446 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6446 2022/12/09 02:52:26 - mmengine - INFO - Epoch(train) [79][420/940] lr: 1.0000e-03 eta: 3:30:30 time: 0.7076 data_time: 0.2727 memory: 16095 grad_norm: 6.0221 loss: 0.8017 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8017 2022/12/09 02:52:37 - mmengine - INFO - Epoch(train) [79][440/940] lr: 1.0000e-03 eta: 3:30:17 time: 0.5661 data_time: 0.0628 memory: 16095 grad_norm: 5.9792 loss: 0.7066 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7066 2022/12/09 02:52:50 - mmengine - INFO - Epoch(train) [79][460/940] lr: 1.0000e-03 eta: 3:30:04 time: 0.6423 data_time: 0.0266 memory: 16095 grad_norm: 6.1268 loss: 0.6668 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6668 2022/12/09 02:53:01 - mmengine - INFO - Epoch(train) [79][480/940] lr: 1.0000e-03 eta: 3:29:51 time: 0.5326 data_time: 0.0239 memory: 16095 grad_norm: 6.2345 loss: 0.7496 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.7496 2022/12/09 02:53:14 - mmengine - INFO - Epoch(train) [79][500/940] lr: 1.0000e-03 eta: 3:29:39 time: 0.6327 data_time: 0.0764 memory: 16095 grad_norm: 6.0592 loss: 0.7781 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.7781 2022/12/09 02:53:25 - mmengine - INFO - Epoch(train) [79][520/940] lr: 1.0000e-03 eta: 3:29:26 time: 0.5595 data_time: 0.0830 memory: 16095 grad_norm: 6.0668 loss: 0.6685 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6685 2022/12/09 02:53:38 - mmengine - INFO - Epoch(train) [79][540/940] lr: 1.0000e-03 eta: 3:29:14 time: 0.6527 data_time: 0.1015 memory: 16095 grad_norm: 6.2795 loss: 0.7716 top1_acc: 0.6562 top5_acc: 0.8438 loss_cls: 0.7716 2022/12/09 02:53:49 - mmengine - INFO - Epoch(train) [79][560/940] lr: 1.0000e-03 eta: 3:29:01 time: 0.5590 data_time: 0.1961 memory: 16095 grad_norm: 6.0975 loss: 0.7611 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7611 2022/12/09 02:54:02 - mmengine - INFO - Epoch(train) [79][580/940] lr: 1.0000e-03 eta: 3:28:49 time: 0.6520 data_time: 0.2779 memory: 16095 grad_norm: 6.0487 loss: 0.7863 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7863 2022/12/09 02:54:14 - mmengine - INFO - Epoch(train) [79][600/940] lr: 1.0000e-03 eta: 3:28:36 time: 0.5749 data_time: 0.2509 memory: 16095 grad_norm: 6.1818 loss: 0.8743 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.8743 2022/12/09 02:54:27 - mmengine - INFO - Epoch(train) [79][620/940] lr: 1.0000e-03 eta: 3:28:24 time: 0.6673 data_time: 0.3285 memory: 16095 grad_norm: 6.0493 loss: 0.6873 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6873 2022/12/09 02:54:38 - mmengine - INFO - Epoch(train) [79][640/940] lr: 1.0000e-03 eta: 3:28:11 time: 0.5648 data_time: 0.2479 memory: 16095 grad_norm: 5.9540 loss: 0.7364 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7364 2022/12/09 02:54:51 - mmengine - INFO - Epoch(train) [79][660/940] lr: 1.0000e-03 eta: 3:27:59 time: 0.6462 data_time: 0.3210 memory: 16095 grad_norm: 6.0557 loss: 0.6563 top1_acc: 0.9688 top5_acc: 0.9688 loss_cls: 0.6563 2022/12/09 02:55:02 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 02:55:02 - mmengine - INFO - Epoch(train) [79][680/940] lr: 1.0000e-03 eta: 3:27:46 time: 0.5361 data_time: 0.1892 memory: 16095 grad_norm: 6.1135 loss: 0.6876 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.6876 2022/12/09 02:55:15 - mmengine - INFO - Epoch(train) [79][700/940] lr: 1.0000e-03 eta: 3:27:34 time: 0.6786 data_time: 0.1098 memory: 16095 grad_norm: 6.1139 loss: 0.6123 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6123 2022/12/09 02:55:27 - mmengine - INFO - Epoch(train) [79][720/940] lr: 1.0000e-03 eta: 3:27:21 time: 0.5595 data_time: 0.0206 memory: 16095 grad_norm: 6.0875 loss: 0.7978 top1_acc: 0.5625 top5_acc: 0.7812 loss_cls: 0.7978 2022/12/09 02:55:39 - mmengine - INFO - Epoch(train) [79][740/940] lr: 1.0000e-03 eta: 3:27:08 time: 0.6262 data_time: 0.0273 memory: 16095 grad_norm: 6.2418 loss: 0.6782 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6782 2022/12/09 02:55:51 - mmengine - INFO - Epoch(train) [79][760/940] lr: 1.0000e-03 eta: 3:26:56 time: 0.5809 data_time: 0.0220 memory: 16095 grad_norm: 6.0754 loss: 0.7599 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.7599 2022/12/09 02:56:05 - mmengine - INFO - Epoch(train) [79][780/940] lr: 1.0000e-03 eta: 3:26:44 time: 0.7043 data_time: 0.0247 memory: 16095 grad_norm: 6.0794 loss: 0.6760 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6760 2022/12/09 02:56:16 - mmengine - INFO - Epoch(train) [79][800/940] lr: 1.0000e-03 eta: 3:26:31 time: 0.5427 data_time: 0.0234 memory: 16095 grad_norm: 6.1332 loss: 0.6631 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6631 2022/12/09 02:56:29 - mmengine - INFO - Epoch(train) [79][820/940] lr: 1.0000e-03 eta: 3:26:18 time: 0.6699 data_time: 0.0293 memory: 16095 grad_norm: 6.0673 loss: 0.6366 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6366 2022/12/09 02:56:40 - mmengine - INFO - Epoch(train) [79][840/940] lr: 1.0000e-03 eta: 3:26:06 time: 0.5649 data_time: 0.0214 memory: 16095 grad_norm: 6.2532 loss: 0.7524 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.7524 2022/12/09 02:56:53 - mmengine - INFO - Epoch(train) [79][860/940] lr: 1.0000e-03 eta: 3:25:53 time: 0.6490 data_time: 0.0335 memory: 16095 grad_norm: 6.1560 loss: 0.7433 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7433 2022/12/09 02:57:05 - mmengine - INFO - Epoch(train) [79][880/940] lr: 1.0000e-03 eta: 3:25:41 time: 0.5545 data_time: 0.0241 memory: 16095 grad_norm: 6.1054 loss: 0.7776 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7776 2022/12/09 02:57:17 - mmengine - INFO - Epoch(train) [79][900/940] lr: 1.0000e-03 eta: 3:25:28 time: 0.6372 data_time: 0.0233 memory: 16095 grad_norm: 6.0216 loss: 0.6582 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 0.6582 2022/12/09 02:57:28 - mmengine - INFO - Epoch(train) [79][920/940] lr: 1.0000e-03 eta: 3:25:15 time: 0.5544 data_time: 0.0239 memory: 16095 grad_norm: 6.0582 loss: 0.6048 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6048 2022/12/09 02:57:40 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 02:57:40 - mmengine - INFO - Epoch(train) [79][940/940] lr: 1.0000e-03 eta: 3:25:03 time: 0.5697 data_time: 0.0187 memory: 16095 grad_norm: 6.6797 loss: 0.8094 top1_acc: 0.4286 top5_acc: 0.4286 loss_cls: 0.8094 2022/12/09 02:57:54 - mmengine - INFO - Epoch(val) [79][20/78] eta: 0:00:40 time: 0.7050 data_time: 0.6104 memory: 1686 2022/12/09 02:58:03 - mmengine - INFO - Epoch(val) [79][40/78] eta: 0:00:22 time: 0.4635 data_time: 0.3688 memory: 1686 2022/12/09 02:58:16 - mmengine - INFO - Epoch(val) [79][60/78] eta: 0:00:10 time: 0.6626 data_time: 0.5674 memory: 1686 2022/12/09 02:58:27 - mmengine - INFO - Epoch(val) [79][78/78] acc/top1: 0.6897 acc/top5: 0.8752 acc/mean1: 0.6896 2022/12/09 02:58:43 - mmengine - INFO - Epoch(train) [80][ 20/940] lr: 1.0000e-03 eta: 3:24:51 time: 0.7979 data_time: 0.3518 memory: 16095 grad_norm: 5.9407 loss: 0.6353 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.6353 2022/12/09 02:58:55 - mmengine - INFO - Epoch(train) [80][ 40/940] lr: 1.0000e-03 eta: 3:24:38 time: 0.5698 data_time: 0.0388 memory: 16095 grad_norm: 5.9592 loss: 0.7295 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.7295 2022/12/09 02:59:08 - mmengine - INFO - Epoch(train) [80][ 60/940] lr: 1.0000e-03 eta: 3:24:26 time: 0.6882 data_time: 0.0710 memory: 16095 grad_norm: 6.0998 loss: 0.7160 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7160 2022/12/09 02:59:20 - mmengine - INFO - Epoch(train) [80][ 80/940] lr: 1.0000e-03 eta: 3:24:13 time: 0.5625 data_time: 0.0353 memory: 16095 grad_norm: 5.9616 loss: 0.7196 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7196 2022/12/09 02:59:33 - mmengine - INFO - Epoch(train) [80][100/940] lr: 1.0000e-03 eta: 3:24:01 time: 0.6710 data_time: 0.0260 memory: 16095 grad_norm: 6.0327 loss: 0.6277 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6277 2022/12/09 02:59:44 - mmengine - INFO - Epoch(train) [80][120/940] lr: 1.0000e-03 eta: 3:23:48 time: 0.5502 data_time: 0.0235 memory: 16095 grad_norm: 6.1495 loss: 0.7391 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7391 2022/12/09 02:59:58 - mmengine - INFO - Epoch(train) [80][140/940] lr: 1.0000e-03 eta: 3:23:36 time: 0.6781 data_time: 0.0247 memory: 16095 grad_norm: 6.1476 loss: 0.7637 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7637 2022/12/09 03:00:08 - mmengine - INFO - Epoch(train) [80][160/940] lr: 1.0000e-03 eta: 3:23:23 time: 0.5370 data_time: 0.0242 memory: 16095 grad_norm: 6.1414 loss: 0.7136 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7136 2022/12/09 03:00:21 - mmengine - INFO - Epoch(train) [80][180/940] lr: 1.0000e-03 eta: 3:23:11 time: 0.6502 data_time: 0.0335 memory: 16095 grad_norm: 6.0365 loss: 0.7354 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7354 2022/12/09 03:00:32 - mmengine - INFO - Epoch(train) [80][200/940] lr: 1.0000e-03 eta: 3:22:58 time: 0.5452 data_time: 0.0230 memory: 16095 grad_norm: 6.0843 loss: 0.6767 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.6767 2022/12/09 03:00:46 - mmengine - INFO - Epoch(train) [80][220/940] lr: 1.0000e-03 eta: 3:22:46 time: 0.7016 data_time: 0.1534 memory: 16095 grad_norm: 6.1917 loss: 0.7667 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7667 2022/12/09 03:00:57 - mmengine - INFO - Epoch(train) [80][240/940] lr: 1.0000e-03 eta: 3:22:33 time: 0.5509 data_time: 0.0621 memory: 16095 grad_norm: 6.1183 loss: 0.7498 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7498 2022/12/09 03:01:11 - mmengine - INFO - Epoch(train) [80][260/940] lr: 1.0000e-03 eta: 3:22:21 time: 0.6796 data_time: 0.0908 memory: 16095 grad_norm: 6.2359 loss: 0.7100 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 0.7100 2022/12/09 03:01:23 - mmengine - INFO - Epoch(train) [80][280/940] lr: 1.0000e-03 eta: 3:22:08 time: 0.6040 data_time: 0.0813 memory: 16095 grad_norm: 5.9301 loss: 0.6418 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6418 2022/12/09 03:01:36 - mmengine - INFO - Epoch(train) [80][300/940] lr: 1.0000e-03 eta: 3:21:56 time: 0.6546 data_time: 0.0268 memory: 16095 grad_norm: 6.2821 loss: 0.8120 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.8120 2022/12/09 03:01:46 - mmengine - INFO - Epoch(train) [80][320/940] lr: 1.0000e-03 eta: 3:21:43 time: 0.5098 data_time: 0.0210 memory: 16095 grad_norm: 6.1672 loss: 0.6507 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6507 2022/12/09 03:01:59 - mmengine - INFO - Epoch(train) [80][340/940] lr: 1.0000e-03 eta: 3:21:31 time: 0.6396 data_time: 0.0345 memory: 16095 grad_norm: 6.1467 loss: 0.6633 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6633 2022/12/09 03:02:11 - mmengine - INFO - Epoch(train) [80][360/940] lr: 1.0000e-03 eta: 3:21:18 time: 0.6090 data_time: 0.0218 memory: 16095 grad_norm: 6.1277 loss: 0.6646 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6646 2022/12/09 03:02:24 - mmengine - INFO - Epoch(train) [80][380/940] lr: 1.0000e-03 eta: 3:21:06 time: 0.6419 data_time: 0.0252 memory: 16095 grad_norm: 6.1414 loss: 0.6578 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6578 2022/12/09 03:02:36 - mmengine - INFO - Epoch(train) [80][400/940] lr: 1.0000e-03 eta: 3:20:53 time: 0.6041 data_time: 0.0254 memory: 16095 grad_norm: 6.0575 loss: 0.7703 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7703 2022/12/09 03:02:48 - mmengine - INFO - Epoch(train) [80][420/940] lr: 1.0000e-03 eta: 3:20:41 time: 0.6048 data_time: 0.0209 memory: 16095 grad_norm: 6.0684 loss: 0.7064 top1_acc: 0.9688 top5_acc: 0.9688 loss_cls: 0.7064 2022/12/09 03:03:00 - mmengine - INFO - Epoch(train) [80][440/940] lr: 1.0000e-03 eta: 3:20:28 time: 0.5667 data_time: 0.0246 memory: 16095 grad_norm: 6.2862 loss: 0.7912 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7912 2022/12/09 03:03:14 - mmengine - INFO - Epoch(train) [80][460/940] lr: 1.0000e-03 eta: 3:20:16 time: 0.6904 data_time: 0.0298 memory: 16095 grad_norm: 6.2060 loss: 0.6444 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6444 2022/12/09 03:03:25 - mmengine - INFO - Epoch(train) [80][480/940] lr: 1.0000e-03 eta: 3:20:03 time: 0.5779 data_time: 0.0258 memory: 16095 grad_norm: 6.1103 loss: 0.6734 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6734 2022/12/09 03:03:38 - mmengine - INFO - Epoch(train) [80][500/940] lr: 1.0000e-03 eta: 3:19:51 time: 0.6344 data_time: 0.0220 memory: 16095 grad_norm: 6.0196 loss: 0.6782 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.6782 2022/12/09 03:03:49 - mmengine - INFO - Epoch(train) [80][520/940] lr: 1.0000e-03 eta: 3:19:38 time: 0.5769 data_time: 0.0263 memory: 16095 grad_norm: 6.2364 loss: 0.7057 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7057 2022/12/09 03:04:03 - mmengine - INFO - Epoch(train) [80][540/940] lr: 1.0000e-03 eta: 3:19:26 time: 0.6713 data_time: 0.0238 memory: 16095 grad_norm: 6.1823 loss: 0.6771 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.6771 2022/12/09 03:04:14 - mmengine - INFO - Epoch(train) [80][560/940] lr: 1.0000e-03 eta: 3:19:13 time: 0.5440 data_time: 0.0257 memory: 16095 grad_norm: 6.1122 loss: 0.6946 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.6946 2022/12/09 03:04:27 - mmengine - INFO - Epoch(train) [80][580/940] lr: 1.0000e-03 eta: 3:19:00 time: 0.6500 data_time: 0.0237 memory: 16095 grad_norm: 6.1292 loss: 0.6646 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6646 2022/12/09 03:04:38 - mmengine - INFO - Epoch(train) [80][600/940] lr: 1.0000e-03 eta: 3:18:48 time: 0.5663 data_time: 0.0256 memory: 16095 grad_norm: 6.0914 loss: 0.5901 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.5901 2022/12/09 03:04:52 - mmengine - INFO - Epoch(train) [80][620/940] lr: 1.0000e-03 eta: 3:18:36 time: 0.6760 data_time: 0.0219 memory: 16095 grad_norm: 6.0462 loss: 0.7570 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.7570 2022/12/09 03:05:03 - mmengine - INFO - Epoch(train) [80][640/940] lr: 1.0000e-03 eta: 3:18:23 time: 0.5516 data_time: 0.0256 memory: 16095 grad_norm: 6.2154 loss: 0.6622 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6622 2022/12/09 03:05:17 - mmengine - INFO - Epoch(train) [80][660/940] lr: 1.0000e-03 eta: 3:18:11 time: 0.7148 data_time: 0.0254 memory: 16095 grad_norm: 6.3776 loss: 0.8140 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.8140 2022/12/09 03:05:28 - mmengine - INFO - Epoch(train) [80][680/940] lr: 1.0000e-03 eta: 3:17:58 time: 0.5513 data_time: 0.0328 memory: 16095 grad_norm: 6.1622 loss: 0.6926 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6926 2022/12/09 03:05:41 - mmengine - INFO - Epoch(train) [80][700/940] lr: 1.0000e-03 eta: 3:17:46 time: 0.6617 data_time: 0.0256 memory: 16095 grad_norm: 5.9273 loss: 0.6895 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6895 2022/12/09 03:05:53 - mmengine - INFO - Epoch(train) [80][720/940] lr: 1.0000e-03 eta: 3:17:33 time: 0.5836 data_time: 0.0255 memory: 16095 grad_norm: 6.1133 loss: 0.7139 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7139 2022/12/09 03:06:05 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 03:06:05 - mmengine - INFO - Epoch(train) [80][740/940] lr: 1.0000e-03 eta: 3:17:20 time: 0.6031 data_time: 0.0217 memory: 16095 grad_norm: 6.2905 loss: 0.6206 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6206 2022/12/09 03:06:16 - mmengine - INFO - Epoch(train) [80][760/940] lr: 1.0000e-03 eta: 3:17:08 time: 0.5598 data_time: 0.0237 memory: 16095 grad_norm: 6.3014 loss: 0.7737 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.7737 2022/12/09 03:06:29 - mmengine - INFO - Epoch(train) [80][780/940] lr: 1.0000e-03 eta: 3:16:55 time: 0.6479 data_time: 0.0256 memory: 16095 grad_norm: 6.1895 loss: 0.7509 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7509 2022/12/09 03:06:40 - mmengine - INFO - Epoch(train) [80][800/940] lr: 1.0000e-03 eta: 3:16:42 time: 0.5666 data_time: 0.0260 memory: 16095 grad_norm: 6.1087 loss: 0.6882 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6882 2022/12/09 03:06:53 - mmengine - INFO - Epoch(train) [80][820/940] lr: 1.0000e-03 eta: 3:16:30 time: 0.6228 data_time: 0.0231 memory: 16095 grad_norm: 6.2140 loss: 0.6733 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6733 2022/12/09 03:07:04 - mmengine - INFO - Epoch(train) [80][840/940] lr: 1.0000e-03 eta: 3:16:17 time: 0.5484 data_time: 0.0274 memory: 16095 grad_norm: 6.1995 loss: 0.7627 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.7627 2022/12/09 03:07:19 - mmengine - INFO - Epoch(train) [80][860/940] lr: 1.0000e-03 eta: 3:16:05 time: 0.7431 data_time: 0.0231 memory: 16095 grad_norm: 6.0742 loss: 0.7879 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7879 2022/12/09 03:07:30 - mmengine - INFO - Epoch(train) [80][880/940] lr: 1.0000e-03 eta: 3:15:52 time: 0.5489 data_time: 0.0250 memory: 16095 grad_norm: 6.2325 loss: 0.6582 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6582 2022/12/09 03:07:42 - mmengine - INFO - Epoch(train) [80][900/940] lr: 1.0000e-03 eta: 3:15:40 time: 0.6227 data_time: 0.0229 memory: 16095 grad_norm: 5.9032 loss: 0.6381 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6381 2022/12/09 03:07:53 - mmengine - INFO - Epoch(train) [80][920/940] lr: 1.0000e-03 eta: 3:15:27 time: 0.5353 data_time: 0.0283 memory: 16095 grad_norm: 6.1623 loss: 0.6741 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.6741 2022/12/09 03:08:04 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 03:08:04 - mmengine - INFO - Epoch(train) [80][940/940] lr: 1.0000e-03 eta: 3:15:14 time: 0.5651 data_time: 0.0169 memory: 16095 grad_norm: 6.6682 loss: 0.8032 top1_acc: 0.4286 top5_acc: 0.8571 loss_cls: 0.8032 2022/12/09 03:08:18 - mmengine - INFO - Epoch(val) [80][20/78] eta: 0:00:40 time: 0.7036 data_time: 0.6081 memory: 1686 2022/12/09 03:08:27 - mmengine - INFO - Epoch(val) [80][40/78] eta: 0:00:22 time: 0.4581 data_time: 0.3648 memory: 1686 2022/12/09 03:08:41 - mmengine - INFO - Epoch(val) [80][60/78] eta: 0:00:10 time: 0.6631 data_time: 0.5680 memory: 1686 2022/12/09 03:08:51 - mmengine - INFO - Epoch(val) [80][78/78] acc/top1: 0.6883 acc/top5: 0.8761 acc/mean1: 0.6881 2022/12/09 03:09:08 - mmengine - INFO - Epoch(train) [81][ 20/940] lr: 1.0000e-04 eta: 3:15:03 time: 0.8184 data_time: 0.4363 memory: 16095 grad_norm: 6.0226 loss: 0.7206 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7206 2022/12/09 03:09:19 - mmengine - INFO - Epoch(train) [81][ 40/940] lr: 1.0000e-04 eta: 3:14:50 time: 0.5397 data_time: 0.1743 memory: 16095 grad_norm: 6.0069 loss: 0.6466 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6466 2022/12/09 03:09:32 - mmengine - INFO - Epoch(train) [81][ 60/940] lr: 1.0000e-04 eta: 3:14:38 time: 0.6413 data_time: 0.2408 memory: 16095 grad_norm: 6.1190 loss: 0.7085 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.7085 2022/12/09 03:09:43 - mmengine - INFO - Epoch(train) [81][ 80/940] lr: 1.0000e-04 eta: 3:14:25 time: 0.5776 data_time: 0.2646 memory: 16095 grad_norm: 6.1849 loss: 0.6177 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6177 2022/12/09 03:09:57 - mmengine - INFO - Epoch(train) [81][100/940] lr: 1.0000e-04 eta: 3:14:13 time: 0.7130 data_time: 0.2631 memory: 16095 grad_norm: 5.8873 loss: 0.6820 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.6820 2022/12/09 03:10:07 - mmengine - INFO - Epoch(train) [81][120/940] lr: 1.0000e-04 eta: 3:14:00 time: 0.5026 data_time: 0.1087 memory: 16095 grad_norm: 6.0768 loss: 0.6878 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6878 2022/12/09 03:10:20 - mmengine - INFO - Epoch(train) [81][140/940] lr: 1.0000e-04 eta: 3:13:47 time: 0.6353 data_time: 0.2865 memory: 16095 grad_norm: 6.0297 loss: 0.7581 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7581 2022/12/09 03:10:31 - mmengine - INFO - Epoch(train) [81][160/940] lr: 1.0000e-04 eta: 3:13:35 time: 0.5693 data_time: 0.2658 memory: 16095 grad_norm: 6.0570 loss: 0.6391 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 0.6391 2022/12/09 03:10:44 - mmengine - INFO - Epoch(train) [81][180/940] lr: 1.0000e-04 eta: 3:13:22 time: 0.6462 data_time: 0.3248 memory: 16095 grad_norm: 5.9931 loss: 0.6500 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6500 2022/12/09 03:10:56 - mmengine - INFO - Epoch(train) [81][200/940] lr: 1.0000e-04 eta: 3:13:10 time: 0.5882 data_time: 0.2108 memory: 16095 grad_norm: 6.0036 loss: 0.6480 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6480 2022/12/09 03:11:09 - mmengine - INFO - Epoch(train) [81][220/940] lr: 1.0000e-04 eta: 3:12:57 time: 0.6640 data_time: 0.1044 memory: 16095 grad_norm: 5.9921 loss: 0.6504 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6504 2022/12/09 03:11:21 - mmengine - INFO - Epoch(train) [81][240/940] lr: 1.0000e-04 eta: 3:12:45 time: 0.5784 data_time: 0.0976 memory: 16095 grad_norm: 6.0034 loss: 0.7189 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7189 2022/12/09 03:11:34 - mmengine - INFO - Epoch(train) [81][260/940] lr: 1.0000e-04 eta: 3:12:32 time: 0.6325 data_time: 0.1895 memory: 16095 grad_norm: 6.1215 loss: 0.6984 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6984 2022/12/09 03:11:45 - mmengine - INFO - Epoch(train) [81][280/940] lr: 1.0000e-04 eta: 3:12:20 time: 0.5622 data_time: 0.1815 memory: 16095 grad_norm: 6.0253 loss: 0.6193 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6193 2022/12/09 03:11:58 - mmengine - INFO - Epoch(train) [81][300/940] lr: 1.0000e-04 eta: 3:12:07 time: 0.6297 data_time: 0.2830 memory: 16095 grad_norm: 5.9807 loss: 0.7123 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7123 2022/12/09 03:12:09 - mmengine - INFO - Epoch(train) [81][320/940] lr: 1.0000e-04 eta: 3:11:54 time: 0.5767 data_time: 0.1106 memory: 16095 grad_norm: 5.9579 loss: 0.6628 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6628 2022/12/09 03:12:23 - mmengine - INFO - Epoch(train) [81][340/940] lr: 1.0000e-04 eta: 3:11:42 time: 0.6738 data_time: 0.1271 memory: 16095 grad_norm: 5.9781 loss: 0.6388 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.6388 2022/12/09 03:12:34 - mmengine - INFO - Epoch(train) [81][360/940] lr: 1.0000e-04 eta: 3:11:30 time: 0.5592 data_time: 0.0684 memory: 16095 grad_norm: 5.9160 loss: 0.6287 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6287 2022/12/09 03:12:47 - mmengine - INFO - Epoch(train) [81][380/940] lr: 1.0000e-04 eta: 3:11:17 time: 0.6804 data_time: 0.0409 memory: 16095 grad_norm: 5.9674 loss: 0.7442 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7442 2022/12/09 03:12:58 - mmengine - INFO - Epoch(train) [81][400/940] lr: 1.0000e-04 eta: 3:11:04 time: 0.5291 data_time: 0.0212 memory: 16095 grad_norm: 6.2549 loss: 0.7022 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7022 2022/12/09 03:13:12 - mmengine - INFO - Epoch(train) [81][420/940] lr: 1.0000e-04 eta: 3:10:52 time: 0.6858 data_time: 0.0284 memory: 16095 grad_norm: 5.9965 loss: 0.6319 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6319 2022/12/09 03:13:23 - mmengine - INFO - Epoch(train) [81][440/940] lr: 1.0000e-04 eta: 3:10:40 time: 0.5721 data_time: 0.0234 memory: 16095 grad_norm: 6.1015 loss: 0.7497 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7497 2022/12/09 03:13:37 - mmengine - INFO - Epoch(train) [81][460/940] lr: 1.0000e-04 eta: 3:10:27 time: 0.6736 data_time: 0.0270 memory: 16095 grad_norm: 6.0810 loss: 0.6666 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6666 2022/12/09 03:13:48 - mmengine - INFO - Epoch(train) [81][480/940] lr: 1.0000e-04 eta: 3:10:15 time: 0.5627 data_time: 0.0220 memory: 16095 grad_norm: 6.1135 loss: 0.7009 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7009 2022/12/09 03:14:01 - mmengine - INFO - Epoch(train) [81][500/940] lr: 1.0000e-04 eta: 3:10:02 time: 0.6348 data_time: 0.0271 memory: 16095 grad_norm: 5.8976 loss: 0.7134 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7134 2022/12/09 03:14:11 - mmengine - INFO - Epoch(train) [81][520/940] lr: 1.0000e-04 eta: 3:09:49 time: 0.5444 data_time: 0.0234 memory: 16095 grad_norm: 5.9768 loss: 0.6218 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.6218 2022/12/09 03:14:25 - mmengine - INFO - Epoch(train) [81][540/940] lr: 1.0000e-04 eta: 3:09:37 time: 0.6797 data_time: 0.0241 memory: 16095 grad_norm: 6.0465 loss: 0.7761 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7761 2022/12/09 03:14:36 - mmengine - INFO - Epoch(train) [81][560/940] lr: 1.0000e-04 eta: 3:09:24 time: 0.5583 data_time: 0.0258 memory: 16095 grad_norm: 6.1950 loss: 0.7121 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7121 2022/12/09 03:14:49 - mmengine - INFO - Epoch(train) [81][580/940] lr: 1.0000e-04 eta: 3:09:12 time: 0.6614 data_time: 0.0259 memory: 16095 grad_norm: 6.1847 loss: 0.8207 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.8207 2022/12/09 03:15:00 - mmengine - INFO - Epoch(train) [81][600/940] lr: 1.0000e-04 eta: 3:08:59 time: 0.5403 data_time: 0.0228 memory: 16095 grad_norm: 6.1499 loss: 0.6398 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6398 2022/12/09 03:15:14 - mmengine - INFO - Epoch(train) [81][620/940] lr: 1.0000e-04 eta: 3:08:47 time: 0.7011 data_time: 0.0258 memory: 16095 grad_norm: 6.0207 loss: 0.6805 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6805 2022/12/09 03:15:24 - mmengine - INFO - Epoch(train) [81][640/940] lr: 1.0000e-04 eta: 3:08:34 time: 0.4866 data_time: 0.0222 memory: 16095 grad_norm: 6.1108 loss: 0.7107 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7107 2022/12/09 03:15:37 - mmengine - INFO - Epoch(train) [81][660/940] lr: 1.0000e-04 eta: 3:08:22 time: 0.6704 data_time: 0.0253 memory: 16095 grad_norm: 5.9227 loss: 0.6154 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.6154 2022/12/09 03:15:48 - mmengine - INFO - Epoch(train) [81][680/940] lr: 1.0000e-04 eta: 3:08:09 time: 0.5488 data_time: 0.0221 memory: 16095 grad_norm: 6.1581 loss: 0.7765 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7765 2022/12/09 03:16:01 - mmengine - INFO - Epoch(train) [81][700/940] lr: 1.0000e-04 eta: 3:07:57 time: 0.6476 data_time: 0.0237 memory: 16095 grad_norm: 6.0515 loss: 0.6770 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6770 2022/12/09 03:16:13 - mmengine - INFO - Epoch(train) [81][720/940] lr: 1.0000e-04 eta: 3:07:44 time: 0.5912 data_time: 0.0252 memory: 16095 grad_norm: 6.0408 loss: 0.7002 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7002 2022/12/09 03:16:26 - mmengine - INFO - Epoch(train) [81][740/940] lr: 1.0000e-04 eta: 3:07:32 time: 0.6251 data_time: 0.0236 memory: 16095 grad_norm: 6.0087 loss: 0.6769 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.6769 2022/12/09 03:16:37 - mmengine - INFO - Epoch(train) [81][760/940] lr: 1.0000e-04 eta: 3:07:19 time: 0.5386 data_time: 0.0253 memory: 16095 grad_norm: 6.0107 loss: 0.6499 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6499 2022/12/09 03:16:49 - mmengine - INFO - Epoch(train) [81][780/940] lr: 1.0000e-04 eta: 3:07:06 time: 0.6448 data_time: 0.0234 memory: 16095 grad_norm: 6.1264 loss: 0.7972 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7972 2022/12/09 03:17:00 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 03:17:00 - mmengine - INFO - Epoch(train) [81][800/940] lr: 1.0000e-04 eta: 3:06:53 time: 0.5395 data_time: 0.0276 memory: 16095 grad_norm: 5.9687 loss: 0.7448 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7448 2022/12/09 03:17:14 - mmengine - INFO - Epoch(train) [81][820/940] lr: 1.0000e-04 eta: 3:06:41 time: 0.6712 data_time: 0.0229 memory: 16095 grad_norm: 6.0306 loss: 0.6540 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6540 2022/12/09 03:17:25 - mmengine - INFO - Epoch(train) [81][840/940] lr: 1.0000e-04 eta: 3:06:29 time: 0.5649 data_time: 0.0355 memory: 16095 grad_norm: 6.0182 loss: 0.7236 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7236 2022/12/09 03:17:39 - mmengine - INFO - Epoch(train) [81][860/940] lr: 1.0000e-04 eta: 3:06:16 time: 0.6956 data_time: 0.0220 memory: 16095 grad_norm: 6.0281 loss: 0.7521 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7521 2022/12/09 03:17:50 - mmengine - INFO - Epoch(train) [81][880/940] lr: 1.0000e-04 eta: 3:06:04 time: 0.5624 data_time: 0.0262 memory: 16095 grad_norm: 6.1022 loss: 0.7501 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7501 2022/12/09 03:18:04 - mmengine - INFO - Epoch(train) [81][900/940] lr: 1.0000e-04 eta: 3:05:51 time: 0.6695 data_time: 0.0252 memory: 16095 grad_norm: 6.0769 loss: 0.6810 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6810 2022/12/09 03:18:15 - mmengine - INFO - Epoch(train) [81][920/940] lr: 1.0000e-04 eta: 3:05:39 time: 0.5588 data_time: 0.0243 memory: 16095 grad_norm: 5.9974 loss: 0.7085 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7085 2022/12/09 03:18:26 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 03:18:26 - mmengine - INFO - Epoch(train) [81][940/940] lr: 1.0000e-04 eta: 3:05:26 time: 0.5664 data_time: 0.0160 memory: 16095 grad_norm: 6.5055 loss: 0.6292 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 0.6292 2022/12/09 03:18:26 - mmengine - INFO - Saving checkpoint at 81 epochs 2022/12/09 03:18:43 - mmengine - INFO - Epoch(val) [81][20/78] eta: 0:00:40 time: 0.7043 data_time: 0.6104 memory: 1686 2022/12/09 03:18:53 - mmengine - INFO - Epoch(val) [81][40/78] eta: 0:00:22 time: 0.4711 data_time: 0.3765 memory: 1686 2022/12/09 03:19:06 - mmengine - INFO - Epoch(val) [81][60/78] eta: 0:00:11 time: 0.6850 data_time: 0.5899 memory: 1686 2022/12/09 03:19:16 - mmengine - INFO - Epoch(val) [81][78/78] acc/top1: 0.6906 acc/top5: 0.8771 acc/mean1: 0.6905 2022/12/09 03:19:32 - mmengine - INFO - Epoch(train) [82][ 20/940] lr: 1.0000e-04 eta: 3:05:14 time: 0.8407 data_time: 0.5241 memory: 16095 grad_norm: 6.1234 loss: 0.7029 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7029 2022/12/09 03:19:44 - mmengine - INFO - Epoch(train) [82][ 40/940] lr: 1.0000e-04 eta: 3:05:02 time: 0.5723 data_time: 0.2623 memory: 16095 grad_norm: 5.8709 loss: 0.6325 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6325 2022/12/09 03:19:58 - mmengine - INFO - Epoch(train) [82][ 60/940] lr: 1.0000e-04 eta: 3:04:50 time: 0.7230 data_time: 0.4052 memory: 16095 grad_norm: 6.0313 loss: 0.6525 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6525 2022/12/09 03:20:11 - mmengine - INFO - Epoch(train) [82][ 80/940] lr: 1.0000e-04 eta: 3:04:37 time: 0.6095 data_time: 0.2904 memory: 16095 grad_norm: 5.8799 loss: 0.6475 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6475 2022/12/09 03:20:24 - mmengine - INFO - Epoch(train) [82][100/940] lr: 1.0000e-04 eta: 3:04:25 time: 0.6629 data_time: 0.3234 memory: 16095 grad_norm: 6.1854 loss: 0.7083 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.7083 2022/12/09 03:20:35 - mmengine - INFO - Epoch(train) [82][120/940] lr: 1.0000e-04 eta: 3:04:12 time: 0.5374 data_time: 0.2154 memory: 16095 grad_norm: 5.7230 loss: 0.6012 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6012 2022/12/09 03:20:48 - mmengine - INFO - Epoch(train) [82][140/940] lr: 1.0000e-04 eta: 3:04:00 time: 0.6952 data_time: 0.3654 memory: 16095 grad_norm: 6.0718 loss: 0.6758 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.6758 2022/12/09 03:21:00 - mmengine - INFO - Epoch(train) [82][160/940] lr: 1.0000e-04 eta: 3:03:47 time: 0.5677 data_time: 0.2174 memory: 16095 grad_norm: 6.1377 loss: 0.6926 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6926 2022/12/09 03:21:12 - mmengine - INFO - Epoch(train) [82][180/940] lr: 1.0000e-04 eta: 3:03:35 time: 0.6311 data_time: 0.3079 memory: 16095 grad_norm: 6.1013 loss: 0.6362 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6362 2022/12/09 03:21:24 - mmengine - INFO - Epoch(train) [82][200/940] lr: 1.0000e-04 eta: 3:03:22 time: 0.5585 data_time: 0.2331 memory: 16095 grad_norm: 5.8991 loss: 0.5434 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.5434 2022/12/09 03:21:37 - mmengine - INFO - Epoch(train) [82][220/940] lr: 1.0000e-04 eta: 3:03:10 time: 0.6572 data_time: 0.3297 memory: 16095 grad_norm: 5.9660 loss: 0.7165 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7165 2022/12/09 03:21:48 - mmengine - INFO - Epoch(train) [82][240/940] lr: 1.0000e-04 eta: 3:02:57 time: 0.5385 data_time: 0.2077 memory: 16095 grad_norm: 5.9163 loss: 0.6082 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6082 2022/12/09 03:22:00 - mmengine - INFO - Epoch(train) [82][260/940] lr: 1.0000e-04 eta: 3:02:44 time: 0.6144 data_time: 0.2905 memory: 16095 grad_norm: 5.9542 loss: 0.6692 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.6692 2022/12/09 03:22:11 - mmengine - INFO - Epoch(train) [82][280/940] lr: 1.0000e-04 eta: 3:02:32 time: 0.5627 data_time: 0.2394 memory: 16095 grad_norm: 5.9596 loss: 0.6236 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6236 2022/12/09 03:22:24 - mmengine - INFO - Epoch(train) [82][300/940] lr: 1.0000e-04 eta: 3:02:19 time: 0.6349 data_time: 0.3042 memory: 16095 grad_norm: 6.1213 loss: 0.6345 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6345 2022/12/09 03:22:34 - mmengine - INFO - Epoch(train) [82][320/940] lr: 1.0000e-04 eta: 3:02:06 time: 0.5200 data_time: 0.1961 memory: 16095 grad_norm: 6.0716 loss: 0.5919 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.5919 2022/12/09 03:22:47 - mmengine - INFO - Epoch(train) [82][340/940] lr: 1.0000e-04 eta: 3:01:54 time: 0.6495 data_time: 0.2314 memory: 16095 grad_norm: 5.8389 loss: 0.6569 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.6569 2022/12/09 03:22:59 - mmengine - INFO - Epoch(train) [82][360/940] lr: 1.0000e-04 eta: 3:01:41 time: 0.5704 data_time: 0.1052 memory: 16095 grad_norm: 5.9456 loss: 0.6551 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6551 2022/12/09 03:23:12 - mmengine - INFO - Epoch(train) [82][380/940] lr: 1.0000e-04 eta: 3:01:29 time: 0.6656 data_time: 0.0351 memory: 16095 grad_norm: 6.0227 loss: 0.6910 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6910 2022/12/09 03:23:24 - mmengine - INFO - Epoch(train) [82][400/940] lr: 1.0000e-04 eta: 3:01:16 time: 0.5849 data_time: 0.0327 memory: 16095 grad_norm: 5.9278 loss: 0.6724 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6724 2022/12/09 03:23:36 - mmengine - INFO - Epoch(train) [82][420/940] lr: 1.0000e-04 eta: 3:01:04 time: 0.6398 data_time: 0.0346 memory: 16095 grad_norm: 6.1197 loss: 0.6383 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6383 2022/12/09 03:23:48 - mmengine - INFO - Epoch(train) [82][440/940] lr: 1.0000e-04 eta: 3:00:51 time: 0.5943 data_time: 0.0442 memory: 16095 grad_norm: 6.0486 loss: 0.6712 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.6712 2022/12/09 03:24:00 - mmengine - INFO - Epoch(train) [82][460/940] lr: 1.0000e-04 eta: 3:00:39 time: 0.6030 data_time: 0.1159 memory: 16095 grad_norm: 5.9386 loss: 0.6311 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6311 2022/12/09 03:24:12 - mmengine - INFO - Epoch(train) [82][480/940] lr: 1.0000e-04 eta: 3:00:26 time: 0.6040 data_time: 0.0329 memory: 16095 grad_norm: 6.0823 loss: 0.7155 top1_acc: 0.7188 top5_acc: 1.0000 loss_cls: 0.7155 2022/12/09 03:24:27 - mmengine - INFO - Epoch(train) [82][500/940] lr: 1.0000e-04 eta: 3:00:14 time: 0.7457 data_time: 0.0249 memory: 16095 grad_norm: 6.2256 loss: 0.7419 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.7419 2022/12/09 03:24:38 - mmengine - INFO - Epoch(train) [82][520/940] lr: 1.0000e-04 eta: 3:00:02 time: 0.5338 data_time: 0.0238 memory: 16095 grad_norm: 6.2180 loss: 0.6856 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6856 2022/12/09 03:24:51 - mmengine - INFO - Epoch(train) [82][540/940] lr: 1.0000e-04 eta: 2:59:49 time: 0.6640 data_time: 0.0241 memory: 16095 grad_norm: 5.9889 loss: 0.7020 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7020 2022/12/09 03:25:02 - mmengine - INFO - Epoch(train) [82][560/940] lr: 1.0000e-04 eta: 2:59:37 time: 0.5481 data_time: 0.0231 memory: 16095 grad_norm: 5.9387 loss: 0.6736 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6736 2022/12/09 03:25:15 - mmengine - INFO - Epoch(train) [82][580/940] lr: 1.0000e-04 eta: 2:59:24 time: 0.6447 data_time: 0.0261 memory: 16095 grad_norm: 6.0336 loss: 0.6631 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6631 2022/12/09 03:25:27 - mmengine - INFO - Epoch(train) [82][600/940] lr: 1.0000e-04 eta: 2:59:12 time: 0.6002 data_time: 0.0270 memory: 16095 grad_norm: 6.0429 loss: 0.6859 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6859 2022/12/09 03:25:41 - mmengine - INFO - Epoch(train) [82][620/940] lr: 1.0000e-04 eta: 2:58:59 time: 0.6985 data_time: 0.0222 memory: 16095 grad_norm: 5.9855 loss: 0.7078 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7078 2022/12/09 03:25:53 - mmengine - INFO - Epoch(train) [82][640/940] lr: 1.0000e-04 eta: 2:58:47 time: 0.5722 data_time: 0.0330 memory: 16095 grad_norm: 6.0653 loss: 0.6591 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6591 2022/12/09 03:26:06 - mmengine - INFO - Epoch(train) [82][660/940] lr: 1.0000e-04 eta: 2:58:35 time: 0.6582 data_time: 0.0241 memory: 16095 grad_norm: 6.2445 loss: 0.7078 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7078 2022/12/09 03:26:17 - mmengine - INFO - Epoch(train) [82][680/940] lr: 1.0000e-04 eta: 2:58:22 time: 0.5627 data_time: 0.0230 memory: 16095 grad_norm: 5.9446 loss: 0.6622 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6622 2022/12/09 03:26:31 - mmengine - INFO - Epoch(train) [82][700/940] lr: 1.0000e-04 eta: 2:58:10 time: 0.7133 data_time: 0.0251 memory: 16095 grad_norm: 5.8843 loss: 0.6805 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6805 2022/12/09 03:26:42 - mmengine - INFO - Epoch(train) [82][720/940] lr: 1.0000e-04 eta: 2:57:57 time: 0.5332 data_time: 0.0220 memory: 16095 grad_norm: 5.9488 loss: 0.7148 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7148 2022/12/09 03:26:54 - mmengine - INFO - Epoch(train) [82][740/940] lr: 1.0000e-04 eta: 2:57:44 time: 0.6203 data_time: 0.0241 memory: 16095 grad_norm: 6.3793 loss: 0.7333 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7333 2022/12/09 03:27:06 - mmengine - INFO - Epoch(train) [82][760/940] lr: 1.0000e-04 eta: 2:57:32 time: 0.5562 data_time: 0.0269 memory: 16095 grad_norm: 5.9940 loss: 0.6995 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.6995 2022/12/09 03:27:20 - mmengine - INFO - Epoch(train) [82][780/940] lr: 1.0000e-04 eta: 2:57:20 time: 0.7207 data_time: 0.0243 memory: 16095 grad_norm: 6.2042 loss: 0.6442 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.6442 2022/12/09 03:27:31 - mmengine - INFO - Epoch(train) [82][800/940] lr: 1.0000e-04 eta: 2:57:07 time: 0.5533 data_time: 0.0224 memory: 16095 grad_norm: 5.9633 loss: 0.6207 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6207 2022/12/09 03:27:45 - mmengine - INFO - Epoch(train) [82][820/940] lr: 1.0000e-04 eta: 2:56:55 time: 0.6880 data_time: 0.0252 memory: 16095 grad_norm: 6.0013 loss: 0.6791 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6791 2022/12/09 03:27:56 - mmengine - INFO - Epoch(train) [82][840/940] lr: 1.0000e-04 eta: 2:56:42 time: 0.5698 data_time: 0.0232 memory: 16095 grad_norm: 5.9719 loss: 0.7121 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.7121 2022/12/09 03:28:09 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 03:28:09 - mmengine - INFO - Epoch(train) [82][860/940] lr: 1.0000e-04 eta: 2:56:30 time: 0.6340 data_time: 0.0247 memory: 16095 grad_norm: 5.8731 loss: 0.6025 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6025 2022/12/09 03:28:21 - mmengine - INFO - Epoch(train) [82][880/940] lr: 1.0000e-04 eta: 2:56:17 time: 0.6071 data_time: 0.0236 memory: 16095 grad_norm: 6.0414 loss: 0.7182 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7182 2022/12/09 03:28:34 - mmengine - INFO - Epoch(train) [82][900/940] lr: 1.0000e-04 eta: 2:56:05 time: 0.6520 data_time: 0.0243 memory: 16095 grad_norm: 6.1113 loss: 0.6626 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.6626 2022/12/09 03:28:45 - mmengine - INFO - Epoch(train) [82][920/940] lr: 1.0000e-04 eta: 2:55:52 time: 0.5647 data_time: 0.0240 memory: 16095 grad_norm: 6.0243 loss: 0.6534 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6534 2022/12/09 03:28:56 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 03:28:56 - mmengine - INFO - Epoch(train) [82][940/940] lr: 1.0000e-04 eta: 2:55:39 time: 0.5438 data_time: 0.0175 memory: 16095 grad_norm: 6.4643 loss: 0.6897 top1_acc: 0.7143 top5_acc: 1.0000 loss_cls: 0.6897 2022/12/09 03:29:11 - mmengine - INFO - Epoch(val) [82][20/78] eta: 0:00:41 time: 0.7080 data_time: 0.6144 memory: 1686 2022/12/09 03:29:20 - mmengine - INFO - Epoch(val) [82][40/78] eta: 0:00:22 time: 0.4608 data_time: 0.3672 memory: 1686 2022/12/09 03:29:33 - mmengine - INFO - Epoch(val) [82][60/78] eta: 0:00:11 time: 0.6652 data_time: 0.5713 memory: 1686 2022/12/09 03:29:44 - mmengine - INFO - Epoch(val) [82][78/78] acc/top1: 0.6911 acc/top5: 0.8777 acc/mean1: 0.6911 2022/12/09 03:30:00 - mmengine - INFO - Epoch(train) [83][ 20/940] lr: 1.0000e-04 eta: 2:55:28 time: 0.8259 data_time: 0.4698 memory: 16095 grad_norm: 5.9590 loss: 0.6207 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.6207 2022/12/09 03:30:12 - mmengine - INFO - Epoch(train) [83][ 40/940] lr: 1.0000e-04 eta: 2:55:15 time: 0.5710 data_time: 0.2605 memory: 16095 grad_norm: 6.0765 loss: 0.7534 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7534 2022/12/09 03:30:26 - mmengine - INFO - Epoch(train) [83][ 60/940] lr: 1.0000e-04 eta: 2:55:03 time: 0.6986 data_time: 0.3808 memory: 16095 grad_norm: 5.9603 loss: 0.6306 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6306 2022/12/09 03:30:37 - mmengine - INFO - Epoch(train) [83][ 80/940] lr: 1.0000e-04 eta: 2:54:50 time: 0.5854 data_time: 0.2842 memory: 16095 grad_norm: 5.9990 loss: 0.6929 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.6929 2022/12/09 03:30:50 - mmengine - INFO - Epoch(train) [83][100/940] lr: 1.0000e-04 eta: 2:54:38 time: 0.6130 data_time: 0.2941 memory: 16095 grad_norm: 5.7543 loss: 0.6328 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6328 2022/12/09 03:31:00 - mmengine - INFO - Epoch(train) [83][120/940] lr: 1.0000e-04 eta: 2:54:25 time: 0.5398 data_time: 0.2040 memory: 16095 grad_norm: 6.0356 loss: 0.6117 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6117 2022/12/09 03:31:13 - mmengine - INFO - Epoch(train) [83][140/940] lr: 1.0000e-04 eta: 2:54:13 time: 0.6476 data_time: 0.3213 memory: 16095 grad_norm: 5.8229 loss: 0.6509 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6509 2022/12/09 03:31:25 - mmengine - INFO - Epoch(train) [83][160/940] lr: 1.0000e-04 eta: 2:54:00 time: 0.5596 data_time: 0.1919 memory: 16095 grad_norm: 5.9647 loss: 0.6607 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6607 2022/12/09 03:31:39 - mmengine - INFO - Epoch(train) [83][180/940] lr: 1.0000e-04 eta: 2:53:48 time: 0.7234 data_time: 0.2947 memory: 16095 grad_norm: 5.9956 loss: 0.6380 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6380 2022/12/09 03:31:49 - mmengine - INFO - Epoch(train) [83][200/940] lr: 1.0000e-04 eta: 2:53:35 time: 0.5074 data_time: 0.0916 memory: 16095 grad_norm: 6.0874 loss: 0.6953 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6953 2022/12/09 03:32:03 - mmengine - INFO - Epoch(train) [83][220/940] lr: 1.0000e-04 eta: 2:53:23 time: 0.6987 data_time: 0.1759 memory: 16095 grad_norm: 5.8650 loss: 0.5700 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.5700 2022/12/09 03:32:14 - mmengine - INFO - Epoch(train) [83][240/940] lr: 1.0000e-04 eta: 2:53:10 time: 0.5500 data_time: 0.0608 memory: 16095 grad_norm: 5.8671 loss: 0.7185 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7185 2022/12/09 03:32:27 - mmengine - INFO - Epoch(train) [83][260/940] lr: 1.0000e-04 eta: 2:52:58 time: 0.6555 data_time: 0.0460 memory: 16095 grad_norm: 6.0663 loss: 0.7771 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 0.7771 2022/12/09 03:32:38 - mmengine - INFO - Epoch(train) [83][280/940] lr: 1.0000e-04 eta: 2:52:45 time: 0.5408 data_time: 0.0776 memory: 16095 grad_norm: 6.0799 loss: 0.7144 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7144 2022/12/09 03:32:53 - mmengine - INFO - Epoch(train) [83][300/940] lr: 1.0000e-04 eta: 2:52:33 time: 0.7163 data_time: 0.1391 memory: 16095 grad_norm: 5.9544 loss: 0.6726 top1_acc: 0.8438 top5_acc: 0.8438 loss_cls: 0.6726 2022/12/09 03:33:03 - mmengine - INFO - Epoch(train) [83][320/940] lr: 1.0000e-04 eta: 2:52:20 time: 0.5486 data_time: 0.0243 memory: 16095 grad_norm: 6.1071 loss: 0.6431 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6431 2022/12/09 03:33:17 - mmengine - INFO - Epoch(train) [83][340/940] lr: 1.0000e-04 eta: 2:52:08 time: 0.6730 data_time: 0.0217 memory: 16095 grad_norm: 6.0113 loss: 0.7256 top1_acc: 0.6562 top5_acc: 0.9375 loss_cls: 0.7256 2022/12/09 03:33:28 - mmengine - INFO - Epoch(train) [83][360/940] lr: 1.0000e-04 eta: 2:51:55 time: 0.5306 data_time: 0.0233 memory: 16095 grad_norm: 5.9871 loss: 0.5585 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.5585 2022/12/09 03:33:41 - mmengine - INFO - Epoch(train) [83][380/940] lr: 1.0000e-04 eta: 2:51:43 time: 0.6626 data_time: 0.0321 memory: 16095 grad_norm: 5.7890 loss: 0.6048 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6048 2022/12/09 03:33:51 - mmengine - INFO - Epoch(train) [83][400/940] lr: 1.0000e-04 eta: 2:51:30 time: 0.5215 data_time: 0.0246 memory: 16095 grad_norm: 5.9954 loss: 0.7212 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7212 2022/12/09 03:34:04 - mmengine - INFO - Epoch(train) [83][420/940] lr: 1.0000e-04 eta: 2:51:17 time: 0.6325 data_time: 0.0255 memory: 16095 grad_norm: 6.0781 loss: 0.7427 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7427 2022/12/09 03:34:16 - mmengine - INFO - Epoch(train) [83][440/940] lr: 1.0000e-04 eta: 2:51:05 time: 0.6004 data_time: 0.0234 memory: 16095 grad_norm: 5.8251 loss: 0.6520 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6520 2022/12/09 03:34:28 - mmengine - INFO - Epoch(train) [83][460/940] lr: 1.0000e-04 eta: 2:50:52 time: 0.6224 data_time: 0.0249 memory: 16095 grad_norm: 5.8652 loss: 0.6489 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6489 2022/12/09 03:34:40 - mmengine - INFO - Epoch(train) [83][480/940] lr: 1.0000e-04 eta: 2:50:40 time: 0.5959 data_time: 0.0260 memory: 16095 grad_norm: 5.9532 loss: 0.7504 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7504 2022/12/09 03:34:53 - mmengine - INFO - Epoch(train) [83][500/940] lr: 1.0000e-04 eta: 2:50:27 time: 0.6325 data_time: 0.0251 memory: 16095 grad_norm: 6.0511 loss: 0.6076 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6076 2022/12/09 03:35:04 - mmengine - INFO - Epoch(train) [83][520/940] lr: 1.0000e-04 eta: 2:50:14 time: 0.5296 data_time: 0.0258 memory: 16095 grad_norm: 6.1227 loss: 0.7117 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7117 2022/12/09 03:35:18 - mmengine - INFO - Epoch(train) [83][540/940] lr: 1.0000e-04 eta: 2:50:02 time: 0.7157 data_time: 0.0229 memory: 16095 grad_norm: 6.0326 loss: 0.5981 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.5981 2022/12/09 03:35:29 - mmengine - INFO - Epoch(train) [83][560/940] lr: 1.0000e-04 eta: 2:49:50 time: 0.5585 data_time: 0.0290 memory: 16095 grad_norm: 5.9921 loss: 0.7468 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7468 2022/12/09 03:35:42 - mmengine - INFO - Epoch(train) [83][580/940] lr: 1.0000e-04 eta: 2:49:37 time: 0.6404 data_time: 0.0215 memory: 16095 grad_norm: 5.9921 loss: 0.6215 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6215 2022/12/09 03:35:54 - mmengine - INFO - Epoch(train) [83][600/940] lr: 1.0000e-04 eta: 2:49:25 time: 0.6159 data_time: 0.1055 memory: 16095 grad_norm: 6.0249 loss: 0.7416 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7416 2022/12/09 03:36:07 - mmengine - INFO - Epoch(train) [83][620/940] lr: 1.0000e-04 eta: 2:49:12 time: 0.6170 data_time: 0.1664 memory: 16095 grad_norm: 5.8942 loss: 0.7063 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.7063 2022/12/09 03:36:19 - mmengine - INFO - Epoch(train) [83][640/940] lr: 1.0000e-04 eta: 2:49:00 time: 0.6180 data_time: 0.2430 memory: 16095 grad_norm: 6.0628 loss: 0.5761 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.5761 2022/12/09 03:36:31 - mmengine - INFO - Epoch(train) [83][660/940] lr: 1.0000e-04 eta: 2:48:47 time: 0.5873 data_time: 0.1703 memory: 16095 grad_norm: 5.8633 loss: 0.5651 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.5651 2022/12/09 03:36:45 - mmengine - INFO - Epoch(train) [83][680/940] lr: 1.0000e-04 eta: 2:48:35 time: 0.6937 data_time: 0.3624 memory: 16095 grad_norm: 6.0727 loss: 0.7340 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7340 2022/12/09 03:36:55 - mmengine - INFO - Epoch(train) [83][700/940] lr: 1.0000e-04 eta: 2:48:22 time: 0.5328 data_time: 0.1890 memory: 16095 grad_norm: 6.1308 loss: 0.6732 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6732 2022/12/09 03:37:08 - mmengine - INFO - Epoch(train) [83][720/940] lr: 1.0000e-04 eta: 2:48:10 time: 0.6460 data_time: 0.2706 memory: 16095 grad_norm: 6.0202 loss: 0.7116 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7116 2022/12/09 03:37:20 - mmengine - INFO - Epoch(train) [83][740/940] lr: 1.0000e-04 eta: 2:47:57 time: 0.5967 data_time: 0.1380 memory: 16095 grad_norm: 5.9870 loss: 0.6854 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.6854 2022/12/09 03:37:32 - mmengine - INFO - Epoch(train) [83][760/940] lr: 1.0000e-04 eta: 2:47:45 time: 0.5753 data_time: 0.2065 memory: 16095 grad_norm: 5.8984 loss: 0.5385 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.5385 2022/12/09 03:37:45 - mmengine - INFO - Epoch(train) [83][780/940] lr: 1.0000e-04 eta: 2:47:32 time: 0.6549 data_time: 0.1785 memory: 16095 grad_norm: 5.9525 loss: 0.7100 top1_acc: 0.9062 top5_acc: 0.9062 loss_cls: 0.7100 2022/12/09 03:37:57 - mmengine - INFO - Epoch(train) [83][800/940] lr: 1.0000e-04 eta: 2:47:20 time: 0.5930 data_time: 0.0782 memory: 16095 grad_norm: 5.8900 loss: 0.6751 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6751 2022/12/09 03:38:09 - mmengine - INFO - Epoch(train) [83][820/940] lr: 1.0000e-04 eta: 2:47:07 time: 0.6092 data_time: 0.0804 memory: 16095 grad_norm: 5.9268 loss: 0.7214 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7214 2022/12/09 03:38:21 - mmengine - INFO - Epoch(train) [83][840/940] lr: 1.0000e-04 eta: 2:46:55 time: 0.6262 data_time: 0.1458 memory: 16095 grad_norm: 5.9709 loss: 0.6732 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6732 2022/12/09 03:38:34 - mmengine - INFO - Epoch(train) [83][860/940] lr: 1.0000e-04 eta: 2:46:42 time: 0.6215 data_time: 0.0243 memory: 16095 grad_norm: 6.1259 loss: 0.6588 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6588 2022/12/09 03:38:45 - mmengine - INFO - Epoch(train) [83][880/940] lr: 1.0000e-04 eta: 2:46:30 time: 0.5798 data_time: 0.1143 memory: 16095 grad_norm: 5.9752 loss: 0.8296 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8296 2022/12/09 03:38:57 - mmengine - INFO - Epoch(train) [83][900/940] lr: 1.0000e-04 eta: 2:46:17 time: 0.5898 data_time: 0.1669 memory: 16095 grad_norm: 6.1477 loss: 0.6151 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6151 2022/12/09 03:39:09 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 03:39:09 - mmengine - INFO - Epoch(train) [83][920/940] lr: 1.0000e-04 eta: 2:46:05 time: 0.6143 data_time: 0.1500 memory: 16095 grad_norm: 5.9476 loss: 0.5714 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.5714 2022/12/09 03:39:19 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 03:39:19 - mmengine - INFO - Epoch(train) [83][940/940] lr: 1.0000e-04 eta: 2:45:52 time: 0.4943 data_time: 0.0162 memory: 16095 grad_norm: 6.5250 loss: 0.6013 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 0.6013 2022/12/09 03:39:34 - mmengine - INFO - Epoch(val) [83][20/78] eta: 0:00:41 time: 0.7101 data_time: 0.6151 memory: 1686 2022/12/09 03:39:43 - mmengine - INFO - Epoch(val) [83][40/78] eta: 0:00:22 time: 0.4694 data_time: 0.3748 memory: 1686 2022/12/09 03:39:56 - mmengine - INFO - Epoch(val) [83][60/78] eta: 0:00:11 time: 0.6640 data_time: 0.5684 memory: 1686 2022/12/09 03:40:07 - mmengine - INFO - Epoch(val) [83][78/78] acc/top1: 0.6913 acc/top5: 0.8774 acc/mean1: 0.6912 2022/12/09 03:40:25 - mmengine - INFO - Epoch(train) [84][ 20/940] lr: 1.0000e-04 eta: 2:45:40 time: 0.8746 data_time: 0.5642 memory: 16095 grad_norm: 6.1110 loss: 0.5565 top1_acc: 0.9062 top5_acc: 0.9062 loss_cls: 0.5565 2022/12/09 03:40:36 - mmengine - INFO - Epoch(train) [84][ 40/940] lr: 1.0000e-04 eta: 2:45:27 time: 0.5516 data_time: 0.2466 memory: 16095 grad_norm: 6.1192 loss: 0.6515 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6515 2022/12/09 03:40:49 - mmengine - INFO - Epoch(train) [84][ 60/940] lr: 1.0000e-04 eta: 2:45:15 time: 0.6660 data_time: 0.2844 memory: 16095 grad_norm: 5.8718 loss: 0.6788 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6788 2022/12/09 03:41:00 - mmengine - INFO - Epoch(train) [84][ 80/940] lr: 1.0000e-04 eta: 2:45:02 time: 0.5343 data_time: 0.1497 memory: 16095 grad_norm: 5.9497 loss: 0.6351 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.6351 2022/12/09 03:41:14 - mmengine - INFO - Epoch(train) [84][100/940] lr: 1.0000e-04 eta: 2:44:50 time: 0.6986 data_time: 0.1689 memory: 16095 grad_norm: 5.9120 loss: 0.6339 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6339 2022/12/09 03:41:25 - mmengine - INFO - Epoch(train) [84][120/940] lr: 1.0000e-04 eta: 2:44:37 time: 0.5462 data_time: 0.0655 memory: 16095 grad_norm: 6.0907 loss: 0.6065 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.6065 2022/12/09 03:41:38 - mmengine - INFO - Epoch(train) [84][140/940] lr: 1.0000e-04 eta: 2:44:25 time: 0.6564 data_time: 0.0286 memory: 16095 grad_norm: 5.9809 loss: 0.6549 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.6549 2022/12/09 03:41:49 - mmengine - INFO - Epoch(train) [84][160/940] lr: 1.0000e-04 eta: 2:44:12 time: 0.5572 data_time: 0.0249 memory: 16095 grad_norm: 6.1071 loss: 0.6530 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6530 2022/12/09 03:42:02 - mmengine - INFO - Epoch(train) [84][180/940] lr: 1.0000e-04 eta: 2:44:00 time: 0.6710 data_time: 0.0466 memory: 16095 grad_norm: 6.0143 loss: 0.7301 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7301 2022/12/09 03:42:13 - mmengine - INFO - Epoch(train) [84][200/940] lr: 1.0000e-04 eta: 2:43:47 time: 0.5460 data_time: 0.0238 memory: 16095 grad_norm: 6.1050 loss: 0.7749 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 0.7749 2022/12/09 03:42:26 - mmengine - INFO - Epoch(train) [84][220/940] lr: 1.0000e-04 eta: 2:43:35 time: 0.6447 data_time: 0.0302 memory: 16095 grad_norm: 6.0573 loss: 0.6038 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6038 2022/12/09 03:42:36 - mmengine - INFO - Epoch(train) [84][240/940] lr: 1.0000e-04 eta: 2:43:22 time: 0.5137 data_time: 0.0222 memory: 16095 grad_norm: 5.9122 loss: 0.7299 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7299 2022/12/09 03:42:50 - mmengine - INFO - Epoch(train) [84][260/940] lr: 1.0000e-04 eta: 2:43:10 time: 0.6637 data_time: 0.0505 memory: 16095 grad_norm: 6.1274 loss: 0.7065 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.7065 2022/12/09 03:43:01 - mmengine - INFO - Epoch(train) [84][280/940] lr: 1.0000e-04 eta: 2:42:57 time: 0.5504 data_time: 0.0802 memory: 16095 grad_norm: 6.0261 loss: 0.6323 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.6323 2022/12/09 03:43:14 - mmengine - INFO - Epoch(train) [84][300/940] lr: 1.0000e-04 eta: 2:42:45 time: 0.6675 data_time: 0.0932 memory: 16095 grad_norm: 5.9733 loss: 0.6814 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.6814 2022/12/09 03:43:25 - mmengine - INFO - Epoch(train) [84][320/940] lr: 1.0000e-04 eta: 2:42:32 time: 0.5442 data_time: 0.0697 memory: 16095 grad_norm: 5.9722 loss: 0.7131 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7131 2022/12/09 03:43:38 - mmengine - INFO - Epoch(train) [84][340/940] lr: 1.0000e-04 eta: 2:42:20 time: 0.6648 data_time: 0.1421 memory: 16095 grad_norm: 5.8773 loss: 0.6585 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6585 2022/12/09 03:43:50 - mmengine - INFO - Epoch(train) [84][360/940] lr: 1.0000e-04 eta: 2:42:07 time: 0.5660 data_time: 0.2108 memory: 16095 grad_norm: 5.9313 loss: 0.6300 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6300 2022/12/09 03:44:03 - mmengine - INFO - Epoch(train) [84][380/940] lr: 1.0000e-04 eta: 2:41:55 time: 0.6550 data_time: 0.1823 memory: 16095 grad_norm: 6.1766 loss: 0.6061 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6061 2022/12/09 03:44:14 - mmengine - INFO - Epoch(train) [84][400/940] lr: 1.0000e-04 eta: 2:41:42 time: 0.5470 data_time: 0.1002 memory: 16095 grad_norm: 5.8863 loss: 0.6312 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6312 2022/12/09 03:44:27 - mmengine - INFO - Epoch(train) [84][420/940] lr: 1.0000e-04 eta: 2:41:30 time: 0.6829 data_time: 0.1104 memory: 16095 grad_norm: 5.9033 loss: 0.5671 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.5671 2022/12/09 03:44:38 - mmengine - INFO - Epoch(train) [84][440/940] lr: 1.0000e-04 eta: 2:41:17 time: 0.5518 data_time: 0.0374 memory: 16095 grad_norm: 5.7443 loss: 0.6175 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6175 2022/12/09 03:44:51 - mmengine - INFO - Epoch(train) [84][460/940] lr: 1.0000e-04 eta: 2:41:05 time: 0.6379 data_time: 0.1172 memory: 16095 grad_norm: 6.0105 loss: 0.6620 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6620 2022/12/09 03:45:03 - mmengine - INFO - Epoch(train) [84][480/940] lr: 1.0000e-04 eta: 2:40:52 time: 0.6137 data_time: 0.1217 memory: 16095 grad_norm: 5.9347 loss: 0.6689 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.6689 2022/12/09 03:45:17 - mmengine - INFO - Epoch(train) [84][500/940] lr: 1.0000e-04 eta: 2:40:40 time: 0.6623 data_time: 0.0262 memory: 16095 grad_norm: 5.8902 loss: 0.5537 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.5537 2022/12/09 03:45:28 - mmengine - INFO - Epoch(train) [84][520/940] lr: 1.0000e-04 eta: 2:40:27 time: 0.5726 data_time: 0.0225 memory: 16095 grad_norm: 5.9963 loss: 0.6517 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6517 2022/12/09 03:45:40 - mmengine - INFO - Epoch(train) [84][540/940] lr: 1.0000e-04 eta: 2:40:15 time: 0.6193 data_time: 0.0245 memory: 16095 grad_norm: 6.1395 loss: 0.6770 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.6770 2022/12/09 03:45:53 - mmengine - INFO - Epoch(train) [84][560/940] lr: 1.0000e-04 eta: 2:40:02 time: 0.6188 data_time: 0.0243 memory: 16095 grad_norm: 6.0502 loss: 0.6103 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6103 2022/12/09 03:46:06 - mmengine - INFO - Epoch(train) [84][580/940] lr: 1.0000e-04 eta: 2:39:50 time: 0.6512 data_time: 0.0246 memory: 16095 grad_norm: 5.8762 loss: 0.7214 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7214 2022/12/09 03:46:17 - mmengine - INFO - Epoch(train) [84][600/940] lr: 1.0000e-04 eta: 2:39:37 time: 0.5396 data_time: 0.0231 memory: 16095 grad_norm: 5.9705 loss: 0.5698 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5698 2022/12/09 03:46:31 - mmengine - INFO - Epoch(train) [84][620/940] lr: 1.0000e-04 eta: 2:39:25 time: 0.6979 data_time: 0.0234 memory: 16095 grad_norm: 5.9670 loss: 0.6899 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6899 2022/12/09 03:46:41 - mmengine - INFO - Epoch(train) [84][640/940] lr: 1.0000e-04 eta: 2:39:12 time: 0.5368 data_time: 0.0274 memory: 16095 grad_norm: 5.9260 loss: 0.6242 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.6242 2022/12/09 03:46:54 - mmengine - INFO - Epoch(train) [84][660/940] lr: 1.0000e-04 eta: 2:39:00 time: 0.6342 data_time: 0.0229 memory: 16095 grad_norm: 5.9047 loss: 0.6518 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.6518 2022/12/09 03:47:05 - mmengine - INFO - Epoch(train) [84][680/940] lr: 1.0000e-04 eta: 2:38:47 time: 0.5523 data_time: 0.0253 memory: 16095 grad_norm: 5.8856 loss: 0.6914 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6914 2022/12/09 03:47:18 - mmengine - INFO - Epoch(train) [84][700/940] lr: 1.0000e-04 eta: 2:38:35 time: 0.6609 data_time: 0.0259 memory: 16095 grad_norm: 6.0523 loss: 0.6099 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6099 2022/12/09 03:47:29 - mmengine - INFO - Epoch(train) [84][720/940] lr: 1.0000e-04 eta: 2:38:22 time: 0.5562 data_time: 0.0231 memory: 16095 grad_norm: 6.0126 loss: 0.6665 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6665 2022/12/09 03:47:42 - mmengine - INFO - Epoch(train) [84][740/940] lr: 1.0000e-04 eta: 2:38:09 time: 0.6123 data_time: 0.0270 memory: 16095 grad_norm: 6.0466 loss: 0.6480 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6480 2022/12/09 03:47:53 - mmengine - INFO - Epoch(train) [84][760/940] lr: 1.0000e-04 eta: 2:37:57 time: 0.5689 data_time: 0.0236 memory: 16095 grad_norm: 5.8781 loss: 0.6242 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6242 2022/12/09 03:48:06 - mmengine - INFO - Epoch(train) [84][780/940] lr: 1.0000e-04 eta: 2:37:44 time: 0.6450 data_time: 0.0280 memory: 16095 grad_norm: 6.0331 loss: 0.6748 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6748 2022/12/09 03:48:18 - mmengine - INFO - Epoch(train) [84][800/940] lr: 1.0000e-04 eta: 2:37:32 time: 0.6005 data_time: 0.0298 memory: 16095 grad_norm: 6.1786 loss: 0.6366 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6366 2022/12/09 03:48:31 - mmengine - INFO - Epoch(train) [84][820/940] lr: 1.0000e-04 eta: 2:37:20 time: 0.6585 data_time: 0.0272 memory: 16095 grad_norm: 5.9742 loss: 0.6356 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6356 2022/12/09 03:48:43 - mmengine - INFO - Epoch(train) [84][840/940] lr: 1.0000e-04 eta: 2:37:07 time: 0.5725 data_time: 0.0205 memory: 16095 grad_norm: 5.9838 loss: 0.7322 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 0.7322 2022/12/09 03:48:54 - mmengine - INFO - Epoch(train) [84][860/940] lr: 1.0000e-04 eta: 2:36:54 time: 0.5908 data_time: 0.0422 memory: 16095 grad_norm: 6.0040 loss: 0.6716 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6716 2022/12/09 03:49:08 - mmengine - INFO - Epoch(train) [84][880/940] lr: 1.0000e-04 eta: 2:36:42 time: 0.6806 data_time: 0.0221 memory: 16095 grad_norm: 5.9520 loss: 0.5845 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.5845 2022/12/09 03:49:19 - mmengine - INFO - Epoch(train) [84][900/940] lr: 1.0000e-04 eta: 2:36:29 time: 0.5630 data_time: 0.0798 memory: 16095 grad_norm: 6.0005 loss: 0.7222 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7222 2022/12/09 03:49:32 - mmengine - INFO - Epoch(train) [84][920/940] lr: 1.0000e-04 eta: 2:36:17 time: 0.6525 data_time: 0.0200 memory: 16095 grad_norm: 5.8550 loss: 0.6859 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6859 2022/12/09 03:49:41 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 03:49:41 - mmengine - INFO - Epoch(train) [84][940/940] lr: 1.0000e-04 eta: 2:36:04 time: 0.4358 data_time: 0.0170 memory: 16095 grad_norm: 6.4130 loss: 0.6865 top1_acc: 0.7143 top5_acc: 0.7143 loss_cls: 0.6865 2022/12/09 03:49:41 - mmengine - INFO - Saving checkpoint at 84 epochs 2022/12/09 03:49:58 - mmengine - INFO - Epoch(val) [84][20/78] eta: 0:00:40 time: 0.7065 data_time: 0.6108 memory: 1686 2022/12/09 03:50:07 - mmengine - INFO - Epoch(val) [84][40/78] eta: 0:00:22 time: 0.4624 data_time: 0.3685 memory: 1686 2022/12/09 03:50:21 - mmengine - INFO - Epoch(val) [84][60/78] eta: 0:00:10 time: 0.6604 data_time: 0.5652 memory: 1686 2022/12/09 03:50:30 - mmengine - INFO - Epoch(val) [84][78/78] acc/top1: 0.6896 acc/top5: 0.8767 acc/mean1: 0.6895 2022/12/09 03:50:47 - mmengine - INFO - Epoch(train) [85][ 20/940] lr: 1.0000e-04 eta: 2:35:52 time: 0.8137 data_time: 0.3775 memory: 16095 grad_norm: 6.0885 loss: 0.6787 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6787 2022/12/09 03:50:57 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 03:50:57 - mmengine - INFO - Epoch(train) [85][ 40/940] lr: 1.0000e-04 eta: 2:35:39 time: 0.5391 data_time: 0.1893 memory: 16095 grad_norm: 6.0461 loss: 0.6724 top1_acc: 0.6875 top5_acc: 0.9688 loss_cls: 0.6724 2022/12/09 03:51:12 - mmengine - INFO - Epoch(train) [85][ 60/940] lr: 1.0000e-04 eta: 2:35:27 time: 0.7125 data_time: 0.2296 memory: 16095 grad_norm: 6.0881 loss: 0.6728 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6728 2022/12/09 03:51:24 - mmengine - INFO - Epoch(train) [85][ 80/940] lr: 1.0000e-04 eta: 2:35:15 time: 0.5981 data_time: 0.0670 memory: 16095 grad_norm: 5.9646 loss: 0.6754 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.6754 2022/12/09 03:51:36 - mmengine - INFO - Epoch(train) [85][100/940] lr: 1.0000e-04 eta: 2:35:02 time: 0.6437 data_time: 0.0278 memory: 16095 grad_norm: 6.1625 loss: 0.5711 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.5711 2022/12/09 03:51:47 - mmengine - INFO - Epoch(train) [85][120/940] lr: 1.0000e-04 eta: 2:34:50 time: 0.5461 data_time: 0.0308 memory: 16095 grad_norm: 5.8995 loss: 0.7008 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7008 2022/12/09 03:52:01 - mmengine - INFO - Epoch(train) [85][140/940] lr: 1.0000e-04 eta: 2:34:37 time: 0.6852 data_time: 0.0278 memory: 16095 grad_norm: 6.0404 loss: 0.6890 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6890 2022/12/09 03:52:13 - mmengine - INFO - Epoch(train) [85][160/940] lr: 1.0000e-04 eta: 2:34:25 time: 0.5752 data_time: 0.0215 memory: 16095 grad_norm: 5.9017 loss: 0.6349 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6349 2022/12/09 03:52:25 - mmengine - INFO - Epoch(train) [85][180/940] lr: 1.0000e-04 eta: 2:34:12 time: 0.6135 data_time: 0.0283 memory: 16095 grad_norm: 6.0544 loss: 0.6486 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6486 2022/12/09 03:52:36 - mmengine - INFO - Epoch(train) [85][200/940] lr: 1.0000e-04 eta: 2:34:00 time: 0.5501 data_time: 0.0197 memory: 16095 grad_norm: 5.9286 loss: 0.6825 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6825 2022/12/09 03:52:49 - mmengine - INFO - Epoch(train) [85][220/940] lr: 1.0000e-04 eta: 2:33:47 time: 0.6525 data_time: 0.0266 memory: 16095 grad_norm: 6.0595 loss: 0.6216 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6216 2022/12/09 03:53:01 - mmengine - INFO - Epoch(train) [85][240/940] lr: 1.0000e-04 eta: 2:33:35 time: 0.5873 data_time: 0.0411 memory: 16095 grad_norm: 5.8438 loss: 0.6188 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6188 2022/12/09 03:53:13 - mmengine - INFO - Epoch(train) [85][260/940] lr: 1.0000e-04 eta: 2:33:22 time: 0.6233 data_time: 0.0342 memory: 16095 grad_norm: 6.0772 loss: 0.7508 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7508 2022/12/09 03:53:25 - mmengine - INFO - Epoch(train) [85][280/940] lr: 1.0000e-04 eta: 2:33:10 time: 0.6099 data_time: 0.0684 memory: 16095 grad_norm: 6.0485 loss: 0.7213 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7213 2022/12/09 03:53:39 - mmengine - INFO - Epoch(train) [85][300/940] lr: 1.0000e-04 eta: 2:32:57 time: 0.6576 data_time: 0.1369 memory: 16095 grad_norm: 6.1520 loss: 0.6547 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6547 2022/12/09 03:53:49 - mmengine - INFO - Epoch(train) [85][320/940] lr: 1.0000e-04 eta: 2:32:45 time: 0.5463 data_time: 0.1283 memory: 16095 grad_norm: 6.0423 loss: 0.7225 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7225 2022/12/09 03:54:03 - mmengine - INFO - Epoch(train) [85][340/940] lr: 1.0000e-04 eta: 2:32:32 time: 0.6670 data_time: 0.2574 memory: 16095 grad_norm: 5.9557 loss: 0.5948 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5948 2022/12/09 03:54:14 - mmengine - INFO - Epoch(train) [85][360/940] lr: 1.0000e-04 eta: 2:32:20 time: 0.5580 data_time: 0.1883 memory: 16095 grad_norm: 6.0155 loss: 0.7386 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7386 2022/12/09 03:54:27 - mmengine - INFO - Epoch(train) [85][380/940] lr: 1.0000e-04 eta: 2:32:07 time: 0.6539 data_time: 0.2590 memory: 16095 grad_norm: 5.8988 loss: 0.5966 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5966 2022/12/09 03:54:39 - mmengine - INFO - Epoch(train) [85][400/940] lr: 1.0000e-04 eta: 2:31:55 time: 0.5977 data_time: 0.1329 memory: 16095 grad_norm: 6.0562 loss: 0.6553 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6553 2022/12/09 03:54:51 - mmengine - INFO - Epoch(train) [85][420/940] lr: 1.0000e-04 eta: 2:31:42 time: 0.6023 data_time: 0.0406 memory: 16095 grad_norm: 6.1849 loss: 0.6535 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6535 2022/12/09 03:55:04 - mmengine - INFO - Epoch(train) [85][440/940] lr: 1.0000e-04 eta: 2:31:30 time: 0.6366 data_time: 0.0503 memory: 16095 grad_norm: 5.8685 loss: 0.6298 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6298 2022/12/09 03:55:16 - mmengine - INFO - Epoch(train) [85][460/940] lr: 1.0000e-04 eta: 2:31:17 time: 0.6126 data_time: 0.2037 memory: 16095 grad_norm: 6.0009 loss: 0.6595 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6595 2022/12/09 03:55:28 - mmengine - INFO - Epoch(train) [85][480/940] lr: 1.0000e-04 eta: 2:31:05 time: 0.5835 data_time: 0.1062 memory: 16095 grad_norm: 6.0575 loss: 0.6783 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.6783 2022/12/09 03:55:41 - mmengine - INFO - Epoch(train) [85][500/940] lr: 1.0000e-04 eta: 2:30:52 time: 0.6734 data_time: 0.1670 memory: 16095 grad_norm: 6.0173 loss: 0.7513 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7513 2022/12/09 03:55:53 - mmengine - INFO - Epoch(train) [85][520/940] lr: 1.0000e-04 eta: 2:30:40 time: 0.5877 data_time: 0.0863 memory: 16095 grad_norm: 5.8433 loss: 0.6676 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6676 2022/12/09 03:56:06 - mmengine - INFO - Epoch(train) [85][540/940] lr: 1.0000e-04 eta: 2:30:28 time: 0.6651 data_time: 0.0444 memory: 16095 grad_norm: 5.8759 loss: 0.6356 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6356 2022/12/09 03:56:17 - mmengine - INFO - Epoch(train) [85][560/940] lr: 1.0000e-04 eta: 2:30:15 time: 0.5465 data_time: 0.0271 memory: 16095 grad_norm: 6.1801 loss: 0.7092 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7092 2022/12/09 03:56:30 - mmengine - INFO - Epoch(train) [85][580/940] lr: 1.0000e-04 eta: 2:30:03 time: 0.6542 data_time: 0.0263 memory: 16095 grad_norm: 5.9288 loss: 0.7283 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7283 2022/12/09 03:56:41 - mmengine - INFO - Epoch(train) [85][600/940] lr: 1.0000e-04 eta: 2:29:50 time: 0.5548 data_time: 0.0270 memory: 16095 grad_norm: 6.1371 loss: 0.6986 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6986 2022/12/09 03:56:55 - mmengine - INFO - Epoch(train) [85][620/940] lr: 1.0000e-04 eta: 2:29:38 time: 0.6763 data_time: 0.0664 memory: 16095 grad_norm: 6.0208 loss: 0.6037 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6037 2022/12/09 03:57:06 - mmengine - INFO - Epoch(train) [85][640/940] lr: 1.0000e-04 eta: 2:29:25 time: 0.5602 data_time: 0.0433 memory: 16095 grad_norm: 5.9831 loss: 0.7026 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7026 2022/12/09 03:57:20 - mmengine - INFO - Epoch(train) [85][660/940] lr: 1.0000e-04 eta: 2:29:13 time: 0.7142 data_time: 0.0265 memory: 16095 grad_norm: 5.9710 loss: 0.6057 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6057 2022/12/09 03:57:31 - mmengine - INFO - Epoch(train) [85][680/940] lr: 1.0000e-04 eta: 2:29:00 time: 0.5379 data_time: 0.0237 memory: 16095 grad_norm: 6.0736 loss: 0.6812 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6812 2022/12/09 03:57:45 - mmengine - INFO - Epoch(train) [85][700/940] lr: 1.0000e-04 eta: 2:28:48 time: 0.6872 data_time: 0.0238 memory: 16095 grad_norm: 5.9996 loss: 0.6894 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6894 2022/12/09 03:57:55 - mmengine - INFO - Epoch(train) [85][720/940] lr: 1.0000e-04 eta: 2:28:35 time: 0.5144 data_time: 0.0264 memory: 16095 grad_norm: 5.8929 loss: 0.7548 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7548 2022/12/09 03:58:09 - mmengine - INFO - Epoch(train) [85][740/940] lr: 1.0000e-04 eta: 2:28:23 time: 0.6840 data_time: 0.0232 memory: 16095 grad_norm: 5.9514 loss: 0.6161 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6161 2022/12/09 03:58:20 - mmengine - INFO - Epoch(train) [85][760/940] lr: 1.0000e-04 eta: 2:28:10 time: 0.5435 data_time: 0.0261 memory: 16095 grad_norm: 6.0349 loss: 0.6996 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.6996 2022/12/09 03:58:34 - mmengine - INFO - Epoch(train) [85][780/940] lr: 1.0000e-04 eta: 2:27:58 time: 0.6965 data_time: 0.0255 memory: 16095 grad_norm: 6.1211 loss: 0.6136 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6136 2022/12/09 03:58:45 - mmengine - INFO - Epoch(train) [85][800/940] lr: 1.0000e-04 eta: 2:27:45 time: 0.5515 data_time: 0.0225 memory: 16095 grad_norm: 6.1109 loss: 0.6810 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6810 2022/12/09 03:58:59 - mmengine - INFO - Epoch(train) [85][820/940] lr: 1.0000e-04 eta: 2:27:33 time: 0.7040 data_time: 0.0233 memory: 16095 grad_norm: 6.0058 loss: 0.6605 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6605 2022/12/09 03:59:10 - mmengine - INFO - Epoch(train) [85][840/940] lr: 1.0000e-04 eta: 2:27:20 time: 0.5419 data_time: 0.0242 memory: 16095 grad_norm: 6.0959 loss: 0.7472 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.7472 2022/12/09 03:59:21 - mmengine - INFO - Epoch(train) [85][860/940] lr: 1.0000e-04 eta: 2:27:08 time: 0.5774 data_time: 0.0252 memory: 16095 grad_norm: 6.1010 loss: 0.7346 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.7346 2022/12/09 03:59:33 - mmengine - INFO - Epoch(train) [85][880/940] lr: 1.0000e-04 eta: 2:26:55 time: 0.5666 data_time: 0.0239 memory: 16095 grad_norm: 5.8644 loss: 0.6972 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6972 2022/12/09 03:59:46 - mmengine - INFO - Epoch(train) [85][900/940] lr: 1.0000e-04 eta: 2:26:43 time: 0.6790 data_time: 0.0387 memory: 16095 grad_norm: 5.9506 loss: 0.6848 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6848 2022/12/09 03:59:57 - mmengine - INFO - Epoch(train) [85][920/940] lr: 1.0000e-04 eta: 2:26:30 time: 0.5512 data_time: 0.0220 memory: 16095 grad_norm: 5.9541 loss: 0.6149 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6149 2022/12/09 04:00:08 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 04:00:08 - mmengine - INFO - Epoch(train) [85][940/940] lr: 1.0000e-04 eta: 2:26:17 time: 0.5301 data_time: 0.0170 memory: 16095 grad_norm: 6.2924 loss: 0.6818 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6818 2022/12/09 04:00:22 - mmengine - INFO - Epoch(val) [85][20/78] eta: 0:00:41 time: 0.7082 data_time: 0.6153 memory: 1686 2022/12/09 04:00:31 - mmengine - INFO - Epoch(val) [85][40/78] eta: 0:00:22 time: 0.4712 data_time: 0.3779 memory: 1686 2022/12/09 04:00:45 - mmengine - INFO - Epoch(val) [85][60/78] eta: 0:00:11 time: 0.6604 data_time: 0.5647 memory: 1686 2022/12/09 04:00:55 - mmengine - INFO - Epoch(val) [85][78/78] acc/top1: 0.6897 acc/top5: 0.8765 acc/mean1: 0.6896 2022/12/09 04:01:11 - mmengine - INFO - Epoch(train) [86][ 20/940] lr: 1.0000e-04 eta: 2:26:05 time: 0.8003 data_time: 0.4047 memory: 16095 grad_norm: 6.0138 loss: 0.6265 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6265 2022/12/09 04:01:22 - mmengine - INFO - Epoch(train) [86][ 40/940] lr: 1.0000e-04 eta: 2:25:53 time: 0.5548 data_time: 0.1217 memory: 16095 grad_norm: 6.0079 loss: 0.6967 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6967 2022/12/09 04:01:35 - mmengine - INFO - Epoch(train) [86][ 60/940] lr: 1.0000e-04 eta: 2:25:40 time: 0.6424 data_time: 0.0895 memory: 16095 grad_norm: 5.9749 loss: 0.6618 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6618 2022/12/09 04:01:47 - mmengine - INFO - Epoch(train) [86][ 80/940] lr: 1.0000e-04 eta: 2:25:28 time: 0.6096 data_time: 0.1121 memory: 16095 grad_norm: 6.1569 loss: 0.7064 top1_acc: 0.6562 top5_acc: 0.8750 loss_cls: 0.7064 2022/12/09 04:02:01 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 04:02:01 - mmengine - INFO - Epoch(train) [86][100/940] lr: 1.0000e-04 eta: 2:25:16 time: 0.6629 data_time: 0.0711 memory: 16095 grad_norm: 6.0911 loss: 0.7020 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7020 2022/12/09 04:02:12 - mmengine - INFO - Epoch(train) [86][120/940] lr: 1.0000e-04 eta: 2:25:03 time: 0.5737 data_time: 0.0308 memory: 16095 grad_norm: 6.0929 loss: 0.7094 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7094 2022/12/09 04:02:26 - mmengine - INFO - Epoch(train) [86][140/940] lr: 1.0000e-04 eta: 2:24:51 time: 0.6721 data_time: 0.0277 memory: 16095 grad_norm: 6.0490 loss: 0.7136 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7136 2022/12/09 04:02:37 - mmengine - INFO - Epoch(train) [86][160/940] lr: 1.0000e-04 eta: 2:24:38 time: 0.5590 data_time: 0.0220 memory: 16095 grad_norm: 6.0069 loss: 0.6363 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6363 2022/12/09 04:02:50 - mmengine - INFO - Epoch(train) [86][180/940] lr: 1.0000e-04 eta: 2:24:26 time: 0.6516 data_time: 0.0263 memory: 16095 grad_norm: 6.0131 loss: 0.7707 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.7707 2022/12/09 04:03:01 - mmengine - INFO - Epoch(train) [86][200/940] lr: 1.0000e-04 eta: 2:24:13 time: 0.5444 data_time: 0.0247 memory: 16095 grad_norm: 6.2032 loss: 0.7094 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.7094 2022/12/09 04:03:14 - mmengine - INFO - Epoch(train) [86][220/940] lr: 1.0000e-04 eta: 2:24:01 time: 0.6582 data_time: 0.0366 memory: 16095 grad_norm: 5.8741 loss: 0.7187 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.7187 2022/12/09 04:03:25 - mmengine - INFO - Epoch(train) [86][240/940] lr: 1.0000e-04 eta: 2:23:48 time: 0.5705 data_time: 0.0217 memory: 16095 grad_norm: 5.7947 loss: 0.7543 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.7543 2022/12/09 04:03:38 - mmengine - INFO - Epoch(train) [86][260/940] lr: 1.0000e-04 eta: 2:23:36 time: 0.6422 data_time: 0.0282 memory: 16095 grad_norm: 5.9792 loss: 0.7005 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7005 2022/12/09 04:03:50 - mmengine - INFO - Epoch(train) [86][280/940] lr: 1.0000e-04 eta: 2:23:23 time: 0.5959 data_time: 0.0220 memory: 16095 grad_norm: 5.9573 loss: 0.7751 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7751 2022/12/09 04:04:03 - mmengine - INFO - Epoch(train) [86][300/940] lr: 1.0000e-04 eta: 2:23:11 time: 0.6571 data_time: 0.0307 memory: 16095 grad_norm: 6.0207 loss: 0.6734 top1_acc: 0.6562 top5_acc: 0.9688 loss_cls: 0.6734 2022/12/09 04:04:15 - mmengine - INFO - Epoch(train) [86][320/940] lr: 1.0000e-04 eta: 2:22:58 time: 0.5821 data_time: 0.0779 memory: 16095 grad_norm: 5.9565 loss: 0.6199 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6199 2022/12/09 04:04:28 - mmengine - INFO - Epoch(train) [86][340/940] lr: 1.0000e-04 eta: 2:22:46 time: 0.6461 data_time: 0.0268 memory: 16095 grad_norm: 6.0430 loss: 0.6231 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6231 2022/12/09 04:04:39 - mmengine - INFO - Epoch(train) [86][360/940] lr: 1.0000e-04 eta: 2:22:33 time: 0.5367 data_time: 0.0238 memory: 16095 grad_norm: 5.8243 loss: 0.6551 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6551 2022/12/09 04:04:52 - mmengine - INFO - Epoch(train) [86][380/940] lr: 1.0000e-04 eta: 2:22:21 time: 0.6814 data_time: 0.0269 memory: 16095 grad_norm: 6.1168 loss: 0.5837 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.5837 2022/12/09 04:05:04 - mmengine - INFO - Epoch(train) [86][400/940] lr: 1.0000e-04 eta: 2:22:08 time: 0.5764 data_time: 0.0225 memory: 16095 grad_norm: 5.9749 loss: 0.6170 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6170 2022/12/09 04:05:17 - mmengine - INFO - Epoch(train) [86][420/940] lr: 1.0000e-04 eta: 2:21:56 time: 0.6715 data_time: 0.0270 memory: 16095 grad_norm: 5.9912 loss: 0.6318 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6318 2022/12/09 04:05:28 - mmengine - INFO - Epoch(train) [86][440/940] lr: 1.0000e-04 eta: 2:21:43 time: 0.5623 data_time: 0.0228 memory: 16095 grad_norm: 6.2323 loss: 0.7260 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7260 2022/12/09 04:05:42 - mmengine - INFO - Epoch(train) [86][460/940] lr: 1.0000e-04 eta: 2:21:31 time: 0.6678 data_time: 0.0260 memory: 16095 grad_norm: 5.8519 loss: 0.5785 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.5785 2022/12/09 04:05:53 - mmengine - INFO - Epoch(train) [86][480/940] lr: 1.0000e-04 eta: 2:21:18 time: 0.5682 data_time: 0.0233 memory: 16095 grad_norm: 6.0202 loss: 0.6607 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6607 2022/12/09 04:06:06 - mmengine - INFO - Epoch(train) [86][500/940] lr: 1.0000e-04 eta: 2:21:06 time: 0.6329 data_time: 0.0253 memory: 16095 grad_norm: 6.0202 loss: 0.6451 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6451 2022/12/09 04:06:17 - mmengine - INFO - Epoch(train) [86][520/940] lr: 1.0000e-04 eta: 2:20:53 time: 0.5652 data_time: 0.0244 memory: 16095 grad_norm: 6.0565 loss: 0.7094 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7094 2022/12/09 04:06:30 - mmengine - INFO - Epoch(train) [86][540/940] lr: 1.0000e-04 eta: 2:20:41 time: 0.6325 data_time: 0.0247 memory: 16095 grad_norm: 6.0298 loss: 0.6809 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6809 2022/12/09 04:06:41 - mmengine - INFO - Epoch(train) [86][560/940] lr: 1.0000e-04 eta: 2:20:28 time: 0.5771 data_time: 0.0246 memory: 16095 grad_norm: 5.9363 loss: 0.6832 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6832 2022/12/09 04:06:54 - mmengine - INFO - Epoch(train) [86][580/940] lr: 1.0000e-04 eta: 2:20:16 time: 0.6364 data_time: 0.0248 memory: 16095 grad_norm: 6.1494 loss: 0.6098 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6098 2022/12/09 04:07:05 - mmengine - INFO - Epoch(train) [86][600/940] lr: 1.0000e-04 eta: 2:20:03 time: 0.5670 data_time: 0.0248 memory: 16095 grad_norm: 6.1065 loss: 0.7241 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7241 2022/12/09 04:07:19 - mmengine - INFO - Epoch(train) [86][620/940] lr: 1.0000e-04 eta: 2:19:51 time: 0.6829 data_time: 0.0329 memory: 16095 grad_norm: 6.1378 loss: 0.5605 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5605 2022/12/09 04:07:31 - mmengine - INFO - Epoch(train) [86][640/940] lr: 1.0000e-04 eta: 2:19:38 time: 0.5830 data_time: 0.0245 memory: 16095 grad_norm: 6.1618 loss: 0.7791 top1_acc: 0.6875 top5_acc: 0.8438 loss_cls: 0.7791 2022/12/09 04:07:43 - mmengine - INFO - Epoch(train) [86][660/940] lr: 1.0000e-04 eta: 2:19:26 time: 0.6189 data_time: 0.0259 memory: 16095 grad_norm: 5.9801 loss: 0.6081 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6081 2022/12/09 04:07:54 - mmengine - INFO - Epoch(train) [86][680/940] lr: 1.0000e-04 eta: 2:19:13 time: 0.5645 data_time: 0.0251 memory: 16095 grad_norm: 6.0091 loss: 0.6397 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6397 2022/12/09 04:08:07 - mmengine - INFO - Epoch(train) [86][700/940] lr: 1.0000e-04 eta: 2:19:01 time: 0.6336 data_time: 0.0254 memory: 16095 grad_norm: 5.9806 loss: 0.7083 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7083 2022/12/09 04:08:18 - mmengine - INFO - Epoch(train) [86][720/940] lr: 1.0000e-04 eta: 2:18:48 time: 0.5700 data_time: 0.0238 memory: 16095 grad_norm: 5.9274 loss: 0.5561 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5561 2022/12/09 04:08:32 - mmengine - INFO - Epoch(train) [86][740/940] lr: 1.0000e-04 eta: 2:18:36 time: 0.6728 data_time: 0.0333 memory: 16095 grad_norm: 5.9848 loss: 0.6370 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6370 2022/12/09 04:08:43 - mmengine - INFO - Epoch(train) [86][760/940] lr: 1.0000e-04 eta: 2:18:23 time: 0.5626 data_time: 0.0215 memory: 16095 grad_norm: 5.9866 loss: 0.6114 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6114 2022/12/09 04:08:57 - mmengine - INFO - Epoch(train) [86][780/940] lr: 1.0000e-04 eta: 2:18:11 time: 0.6743 data_time: 0.0279 memory: 16095 grad_norm: 5.9336 loss: 0.6766 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6766 2022/12/09 04:09:08 - mmengine - INFO - Epoch(train) [86][800/940] lr: 1.0000e-04 eta: 2:17:58 time: 0.5499 data_time: 0.0237 memory: 16095 grad_norm: 6.0052 loss: 0.6190 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6190 2022/12/09 04:09:20 - mmengine - INFO - Epoch(train) [86][820/940] lr: 1.0000e-04 eta: 2:17:46 time: 0.6262 data_time: 0.0275 memory: 16095 grad_norm: 6.0205 loss: 0.5978 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.5978 2022/12/09 04:09:31 - mmengine - INFO - Epoch(train) [86][840/940] lr: 1.0000e-04 eta: 2:17:33 time: 0.5559 data_time: 0.0247 memory: 16095 grad_norm: 6.0764 loss: 0.6559 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6559 2022/12/09 04:09:44 - mmengine - INFO - Epoch(train) [86][860/940] lr: 1.0000e-04 eta: 2:17:21 time: 0.6262 data_time: 0.0277 memory: 16095 grad_norm: 6.0769 loss: 0.7573 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.7573 2022/12/09 04:09:55 - mmengine - INFO - Epoch(train) [86][880/940] lr: 1.0000e-04 eta: 2:17:08 time: 0.5736 data_time: 0.0233 memory: 16095 grad_norm: 6.0182 loss: 0.7111 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7111 2022/12/09 04:10:09 - mmengine - INFO - Epoch(train) [86][900/940] lr: 1.0000e-04 eta: 2:16:56 time: 0.6636 data_time: 0.0256 memory: 16095 grad_norm: 6.1142 loss: 0.6365 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.6365 2022/12/09 04:10:20 - mmengine - INFO - Epoch(train) [86][920/940] lr: 1.0000e-04 eta: 2:16:43 time: 0.5545 data_time: 0.0226 memory: 16095 grad_norm: 6.0737 loss: 0.7296 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.7296 2022/12/09 04:10:32 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 04:10:32 - mmengine - INFO - Epoch(train) [86][940/940] lr: 1.0000e-04 eta: 2:16:31 time: 0.5945 data_time: 0.0177 memory: 16095 grad_norm: 6.4068 loss: 0.7254 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 0.7254 2022/12/09 04:10:46 - mmengine - INFO - Epoch(val) [86][20/78] eta: 0:00:40 time: 0.6957 data_time: 0.6017 memory: 1686 2022/12/09 04:10:55 - mmengine - INFO - Epoch(val) [86][40/78] eta: 0:00:21 time: 0.4617 data_time: 0.3685 memory: 1686 2022/12/09 04:11:08 - mmengine - INFO - Epoch(val) [86][60/78] eta: 0:00:11 time: 0.6778 data_time: 0.5839 memory: 1686 2022/12/09 04:11:19 - mmengine - INFO - Epoch(val) [86][78/78] acc/top1: 0.6903 acc/top5: 0.8771 acc/mean1: 0.6902 2022/12/09 04:11:35 - mmengine - INFO - Epoch(train) [87][ 20/940] lr: 1.0000e-04 eta: 2:16:19 time: 0.8024 data_time: 0.3498 memory: 16095 grad_norm: 6.0841 loss: 0.6514 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6514 2022/12/09 04:11:46 - mmengine - INFO - Epoch(train) [87][ 40/940] lr: 1.0000e-04 eta: 2:16:06 time: 0.5487 data_time: 0.1145 memory: 16095 grad_norm: 6.1221 loss: 0.6904 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6904 2022/12/09 04:12:00 - mmengine - INFO - Epoch(train) [87][ 60/940] lr: 1.0000e-04 eta: 2:15:54 time: 0.7059 data_time: 0.1177 memory: 16095 grad_norm: 5.9580 loss: 0.6412 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6412 2022/12/09 04:12:10 - mmengine - INFO - Epoch(train) [87][ 80/940] lr: 1.0000e-04 eta: 2:15:41 time: 0.5159 data_time: 0.0204 memory: 16095 grad_norm: 6.1083 loss: 0.6136 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6136 2022/12/09 04:12:24 - mmengine - INFO - Epoch(train) [87][100/940] lr: 1.0000e-04 eta: 2:15:29 time: 0.6702 data_time: 0.0341 memory: 16095 grad_norm: 6.0314 loss: 0.5635 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.5635 2022/12/09 04:12:35 - mmengine - INFO - Epoch(train) [87][120/940] lr: 1.0000e-04 eta: 2:15:16 time: 0.5809 data_time: 0.0237 memory: 16095 grad_norm: 5.9367 loss: 0.6389 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.6389 2022/12/09 04:12:48 - mmengine - INFO - Epoch(train) [87][140/940] lr: 1.0000e-04 eta: 2:15:04 time: 0.6516 data_time: 0.0288 memory: 16095 grad_norm: 6.0554 loss: 0.5686 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.5686 2022/12/09 04:13:00 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 04:13:00 - mmengine - INFO - Epoch(train) [87][160/940] lr: 1.0000e-04 eta: 2:14:51 time: 0.5619 data_time: 0.0210 memory: 16095 grad_norm: 5.9552 loss: 0.6886 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6886 2022/12/09 04:13:14 - mmengine - INFO - Epoch(train) [87][180/940] lr: 1.0000e-04 eta: 2:14:39 time: 0.7186 data_time: 0.0272 memory: 16095 grad_norm: 5.9643 loss: 0.7233 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7233 2022/12/09 04:13:25 - mmengine - INFO - Epoch(train) [87][200/940] lr: 1.0000e-04 eta: 2:14:26 time: 0.5529 data_time: 0.0247 memory: 16095 grad_norm: 6.0677 loss: 0.5993 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.5993 2022/12/09 04:13:40 - mmengine - INFO - Epoch(train) [87][220/940] lr: 1.0000e-04 eta: 2:14:14 time: 0.7203 data_time: 0.0224 memory: 16095 grad_norm: 6.0253 loss: 0.7281 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7281 2022/12/09 04:13:51 - mmengine - INFO - Epoch(train) [87][240/940] lr: 1.0000e-04 eta: 2:14:02 time: 0.5534 data_time: 0.0244 memory: 16095 grad_norm: 6.0629 loss: 0.6472 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6472 2022/12/09 04:14:04 - mmengine - INFO - Epoch(train) [87][260/940] lr: 1.0000e-04 eta: 2:13:49 time: 0.6607 data_time: 0.0238 memory: 16095 grad_norm: 5.9850 loss: 0.5790 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.5790 2022/12/09 04:14:15 - mmengine - INFO - Epoch(train) [87][280/940] lr: 1.0000e-04 eta: 2:13:37 time: 0.5652 data_time: 0.0222 memory: 16095 grad_norm: 5.9400 loss: 0.7050 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7050 2022/12/09 04:14:28 - mmengine - INFO - Epoch(train) [87][300/940] lr: 1.0000e-04 eta: 2:13:24 time: 0.6493 data_time: 0.0240 memory: 16095 grad_norm: 6.0722 loss: 0.7204 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7204 2022/12/09 04:14:40 - mmengine - INFO - Epoch(train) [87][320/940] lr: 1.0000e-04 eta: 2:13:12 time: 0.5690 data_time: 0.0253 memory: 16095 grad_norm: 6.0826 loss: 0.6723 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.6723 2022/12/09 04:14:53 - mmengine - INFO - Epoch(train) [87][340/940] lr: 1.0000e-04 eta: 2:12:59 time: 0.6473 data_time: 0.0257 memory: 16095 grad_norm: 5.9365 loss: 0.6422 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.6422 2022/12/09 04:15:03 - mmengine - INFO - Epoch(train) [87][360/940] lr: 1.0000e-04 eta: 2:12:47 time: 0.5376 data_time: 0.0213 memory: 16095 grad_norm: 5.9980 loss: 0.5802 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5802 2022/12/09 04:15:17 - mmengine - INFO - Epoch(train) [87][380/940] lr: 1.0000e-04 eta: 2:12:34 time: 0.6688 data_time: 0.0268 memory: 16095 grad_norm: 6.0185 loss: 0.6332 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6332 2022/12/09 04:15:28 - mmengine - INFO - Epoch(train) [87][400/940] lr: 1.0000e-04 eta: 2:12:22 time: 0.5730 data_time: 0.0233 memory: 16095 grad_norm: 5.9600 loss: 0.6660 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6660 2022/12/09 04:15:41 - mmengine - INFO - Epoch(train) [87][420/940] lr: 1.0000e-04 eta: 2:12:09 time: 0.6609 data_time: 0.0232 memory: 16095 grad_norm: 6.0003 loss: 0.6360 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6360 2022/12/09 04:15:52 - mmengine - INFO - Epoch(train) [87][440/940] lr: 1.0000e-04 eta: 2:11:57 time: 0.5430 data_time: 0.0249 memory: 16095 grad_norm: 6.0837 loss: 0.6622 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.6622 2022/12/09 04:16:06 - mmengine - INFO - Epoch(train) [87][460/940] lr: 1.0000e-04 eta: 2:11:44 time: 0.6836 data_time: 0.0261 memory: 16095 grad_norm: 5.9652 loss: 0.7395 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.7395 2022/12/09 04:16:17 - mmengine - INFO - Epoch(train) [87][480/940] lr: 1.0000e-04 eta: 2:11:32 time: 0.5549 data_time: 0.0234 memory: 16095 grad_norm: 6.0765 loss: 0.5835 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.5835 2022/12/09 04:16:29 - mmengine - INFO - Epoch(train) [87][500/940] lr: 1.0000e-04 eta: 2:11:19 time: 0.6171 data_time: 0.0233 memory: 16095 grad_norm: 6.0642 loss: 0.7060 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.7060 2022/12/09 04:16:41 - mmengine - INFO - Epoch(train) [87][520/940] lr: 1.0000e-04 eta: 2:11:07 time: 0.5858 data_time: 0.0290 memory: 16095 grad_norm: 6.0062 loss: 0.6920 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6920 2022/12/09 04:16:54 - mmengine - INFO - Epoch(train) [87][540/940] lr: 1.0000e-04 eta: 2:10:54 time: 0.6540 data_time: 0.0325 memory: 16095 grad_norm: 6.0382 loss: 0.6759 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.6759 2022/12/09 04:17:05 - mmengine - INFO - Epoch(train) [87][560/940] lr: 1.0000e-04 eta: 2:10:42 time: 0.5485 data_time: 0.0255 memory: 16095 grad_norm: 5.9811 loss: 0.6428 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6428 2022/12/09 04:17:18 - mmengine - INFO - Epoch(train) [87][580/940] lr: 1.0000e-04 eta: 2:10:29 time: 0.6208 data_time: 0.0248 memory: 16095 grad_norm: 5.9608 loss: 0.6086 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6086 2022/12/09 04:17:28 - mmengine - INFO - Epoch(train) [87][600/940] lr: 1.0000e-04 eta: 2:10:16 time: 0.5377 data_time: 0.0258 memory: 16095 grad_norm: 6.1057 loss: 0.7562 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.7562 2022/12/09 04:17:41 - mmengine - INFO - Epoch(train) [87][620/940] lr: 1.0000e-04 eta: 2:10:04 time: 0.6403 data_time: 0.0241 memory: 16095 grad_norm: 6.0386 loss: 0.7189 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7189 2022/12/09 04:17:52 - mmengine - INFO - Epoch(train) [87][640/940] lr: 1.0000e-04 eta: 2:09:51 time: 0.5423 data_time: 0.0266 memory: 16095 grad_norm: 6.1405 loss: 0.6018 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6018 2022/12/09 04:18:05 - mmengine - INFO - Epoch(train) [87][660/940] lr: 1.0000e-04 eta: 2:09:39 time: 0.6710 data_time: 0.0231 memory: 16095 grad_norm: 6.0378 loss: 0.7287 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7287 2022/12/09 04:18:17 - mmengine - INFO - Epoch(train) [87][680/940] lr: 1.0000e-04 eta: 2:09:26 time: 0.5797 data_time: 0.0246 memory: 16095 grad_norm: 5.9120 loss: 0.5373 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5373 2022/12/09 04:18:30 - mmengine - INFO - Epoch(train) [87][700/940] lr: 1.0000e-04 eta: 2:09:14 time: 0.6678 data_time: 0.0272 memory: 16095 grad_norm: 6.0858 loss: 0.7275 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7275 2022/12/09 04:18:42 - mmengine - INFO - Epoch(train) [87][720/940] lr: 1.0000e-04 eta: 2:09:02 time: 0.5750 data_time: 0.0234 memory: 16095 grad_norm: 6.0798 loss: 0.7173 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7173 2022/12/09 04:18:54 - mmengine - INFO - Epoch(train) [87][740/940] lr: 1.0000e-04 eta: 2:08:49 time: 0.6049 data_time: 0.0294 memory: 16095 grad_norm: 5.9713 loss: 0.5824 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5824 2022/12/09 04:19:06 - mmengine - INFO - Epoch(train) [87][760/940] lr: 1.0000e-04 eta: 2:08:37 time: 0.6048 data_time: 0.0228 memory: 16095 grad_norm: 6.0278 loss: 0.6743 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6743 2022/12/09 04:19:18 - mmengine - INFO - Epoch(train) [87][780/940] lr: 1.0000e-04 eta: 2:08:24 time: 0.6196 data_time: 0.0317 memory: 16095 grad_norm: 6.0361 loss: 0.6766 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6766 2022/12/09 04:19:29 - mmengine - INFO - Epoch(train) [87][800/940] lr: 1.0000e-04 eta: 2:08:11 time: 0.5478 data_time: 0.0225 memory: 16095 grad_norm: 5.9055 loss: 0.6097 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.6097 2022/12/09 04:19:42 - mmengine - INFO - Epoch(train) [87][820/940] lr: 1.0000e-04 eta: 2:07:59 time: 0.6420 data_time: 0.0724 memory: 16095 grad_norm: 5.9840 loss: 0.6625 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6625 2022/12/09 04:19:54 - mmengine - INFO - Epoch(train) [87][840/940] lr: 1.0000e-04 eta: 2:07:46 time: 0.5772 data_time: 0.1323 memory: 16095 grad_norm: 6.0523 loss: 0.6284 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6284 2022/12/09 04:20:07 - mmengine - INFO - Epoch(train) [87][860/940] lr: 1.0000e-04 eta: 2:07:34 time: 0.6479 data_time: 0.0581 memory: 16095 grad_norm: 6.1596 loss: 0.6653 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.6653 2022/12/09 04:20:18 - mmengine - INFO - Epoch(train) [87][880/940] lr: 1.0000e-04 eta: 2:07:22 time: 0.5771 data_time: 0.0246 memory: 16095 grad_norm: 5.8587 loss: 0.7025 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7025 2022/12/09 04:20:32 - mmengine - INFO - Epoch(train) [87][900/940] lr: 1.0000e-04 eta: 2:07:09 time: 0.6697 data_time: 0.0225 memory: 16095 grad_norm: 6.0473 loss: 0.8171 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.8171 2022/12/09 04:20:44 - mmengine - INFO - Epoch(train) [87][920/940] lr: 1.0000e-04 eta: 2:06:57 time: 0.5896 data_time: 0.0244 memory: 16095 grad_norm: 6.0225 loss: 0.6897 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6897 2022/12/09 04:20:56 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 04:20:56 - mmengine - INFO - Epoch(train) [87][940/940] lr: 1.0000e-04 eta: 2:06:44 time: 0.6124 data_time: 0.0158 memory: 16095 grad_norm: 6.3727 loss: 0.6281 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6281 2022/12/09 04:20:56 - mmengine - INFO - Saving checkpoint at 87 epochs 2022/12/09 04:21:13 - mmengine - INFO - Epoch(val) [87][20/78] eta: 0:00:42 time: 0.7284 data_time: 0.6342 memory: 1686 2022/12/09 04:21:22 - mmengine - INFO - Epoch(val) [87][40/78] eta: 0:00:22 time: 0.4532 data_time: 0.3579 memory: 1686 2022/12/09 04:21:36 - mmengine - INFO - Epoch(val) [87][60/78] eta: 0:00:11 time: 0.6584 data_time: 0.5630 memory: 1686 2022/12/09 04:21:45 - mmengine - INFO - Epoch(val) [87][78/78] acc/top1: 0.6909 acc/top5: 0.8776 acc/mean1: 0.6908 2022/12/09 04:22:02 - mmengine - INFO - Epoch(train) [88][ 20/940] lr: 1.0000e-04 eta: 2:06:32 time: 0.8134 data_time: 0.4048 memory: 16095 grad_norm: 6.0782 loss: 0.6514 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6514 2022/12/09 04:22:13 - mmengine - INFO - Epoch(train) [88][ 40/940] lr: 1.0000e-04 eta: 2:06:20 time: 0.5598 data_time: 0.1643 memory: 16095 grad_norm: 6.0050 loss: 0.5709 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.5709 2022/12/09 04:22:27 - mmengine - INFO - Epoch(train) [88][ 60/940] lr: 1.0000e-04 eta: 2:06:08 time: 0.7134 data_time: 0.2356 memory: 16095 grad_norm: 6.1125 loss: 0.6576 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6576 2022/12/09 04:22:38 - mmengine - INFO - Epoch(train) [88][ 80/940] lr: 1.0000e-04 eta: 2:05:55 time: 0.5529 data_time: 0.1491 memory: 16095 grad_norm: 5.9318 loss: 0.6292 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 0.6292 2022/12/09 04:22:51 - mmengine - INFO - Epoch(train) [88][100/940] lr: 1.0000e-04 eta: 2:05:42 time: 0.6377 data_time: 0.1256 memory: 16095 grad_norm: 6.2022 loss: 0.6898 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6898 2022/12/09 04:23:03 - mmengine - INFO - Epoch(train) [88][120/940] lr: 1.0000e-04 eta: 2:05:30 time: 0.5735 data_time: 0.1178 memory: 16095 grad_norm: 6.0457 loss: 0.6523 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6523 2022/12/09 04:23:16 - mmengine - INFO - Epoch(train) [88][140/940] lr: 1.0000e-04 eta: 2:05:18 time: 0.6546 data_time: 0.0392 memory: 16095 grad_norm: 5.9545 loss: 0.5461 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.5461 2022/12/09 04:23:27 - mmengine - INFO - Epoch(train) [88][160/940] lr: 1.0000e-04 eta: 2:05:05 time: 0.5809 data_time: 0.1385 memory: 16095 grad_norm: 6.0085 loss: 0.6518 top1_acc: 0.7188 top5_acc: 1.0000 loss_cls: 0.6518 2022/12/09 04:23:40 - mmengine - INFO - Epoch(train) [88][180/940] lr: 1.0000e-04 eta: 2:04:53 time: 0.6458 data_time: 0.1333 memory: 16095 grad_norm: 6.0730 loss: 0.6812 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6812 2022/12/09 04:23:51 - mmengine - INFO - Epoch(train) [88][200/940] lr: 1.0000e-04 eta: 2:04:40 time: 0.5279 data_time: 0.0217 memory: 16095 grad_norm: 5.9671 loss: 0.6244 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6244 2022/12/09 04:24:05 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 04:24:05 - mmengine - INFO - Epoch(train) [88][220/940] lr: 1.0000e-04 eta: 2:04:28 time: 0.6991 data_time: 0.0403 memory: 16095 grad_norm: 5.8221 loss: 0.5576 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.5576 2022/12/09 04:24:16 - mmengine - INFO - Epoch(train) [88][240/940] lr: 1.0000e-04 eta: 2:04:15 time: 0.5540 data_time: 0.0455 memory: 16095 grad_norm: 6.0814 loss: 0.8045 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8045 2022/12/09 04:24:28 - mmengine - INFO - Epoch(train) [88][260/940] lr: 1.0000e-04 eta: 2:04:03 time: 0.6242 data_time: 0.0389 memory: 16095 grad_norm: 5.9802 loss: 0.7301 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7301 2022/12/09 04:24:41 - mmengine - INFO - Epoch(train) [88][280/940] lr: 1.0000e-04 eta: 2:03:50 time: 0.6103 data_time: 0.0214 memory: 16095 grad_norm: 5.9019 loss: 0.6080 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6080 2022/12/09 04:24:54 - mmengine - INFO - Epoch(train) [88][300/940] lr: 1.0000e-04 eta: 2:03:38 time: 0.6785 data_time: 0.0291 memory: 16095 grad_norm: 6.0879 loss: 0.7174 top1_acc: 0.7188 top5_acc: 0.8438 loss_cls: 0.7174 2022/12/09 04:25:06 - mmengine - INFO - Epoch(train) [88][320/940] lr: 1.0000e-04 eta: 2:03:25 time: 0.5858 data_time: 0.0203 memory: 16095 grad_norm: 6.1156 loss: 0.6058 top1_acc: 0.9688 top5_acc: 0.9688 loss_cls: 0.6058 2022/12/09 04:25:19 - mmengine - INFO - Epoch(train) [88][340/940] lr: 1.0000e-04 eta: 2:03:13 time: 0.6513 data_time: 0.0270 memory: 16095 grad_norm: 6.0551 loss: 0.6521 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6521 2022/12/09 04:25:30 - mmengine - INFO - Epoch(train) [88][360/940] lr: 1.0000e-04 eta: 2:03:00 time: 0.5346 data_time: 0.0290 memory: 16095 grad_norm: 6.0363 loss: 0.6729 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6729 2022/12/09 04:25:43 - mmengine - INFO - Epoch(train) [88][380/940] lr: 1.0000e-04 eta: 2:02:48 time: 0.6696 data_time: 0.0236 memory: 16095 grad_norm: 6.0143 loss: 0.6253 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6253 2022/12/09 04:25:54 - mmengine - INFO - Epoch(train) [88][400/940] lr: 1.0000e-04 eta: 2:02:35 time: 0.5508 data_time: 0.0275 memory: 16095 grad_norm: 5.9163 loss: 0.6986 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6986 2022/12/09 04:26:06 - mmengine - INFO - Epoch(train) [88][420/940] lr: 1.0000e-04 eta: 2:02:23 time: 0.6172 data_time: 0.0240 memory: 16095 grad_norm: 5.8745 loss: 0.5319 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5319 2022/12/09 04:26:17 - mmengine - INFO - Epoch(train) [88][440/940] lr: 1.0000e-04 eta: 2:02:10 time: 0.5435 data_time: 0.0277 memory: 16095 grad_norm: 5.9144 loss: 0.5883 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.5883 2022/12/09 04:26:31 - mmengine - INFO - Epoch(train) [88][460/940] lr: 1.0000e-04 eta: 2:01:58 time: 0.6933 data_time: 0.0233 memory: 16095 grad_norm: 6.2527 loss: 0.6383 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6383 2022/12/09 04:26:43 - mmengine - INFO - Epoch(train) [88][480/940] lr: 1.0000e-04 eta: 2:01:45 time: 0.5696 data_time: 0.0254 memory: 16095 grad_norm: 6.0924 loss: 0.7340 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7340 2022/12/09 04:26:56 - mmengine - INFO - Epoch(train) [88][500/940] lr: 1.0000e-04 eta: 2:01:33 time: 0.6609 data_time: 0.0240 memory: 16095 grad_norm: 5.9897 loss: 0.6305 top1_acc: 0.6875 top5_acc: 0.9688 loss_cls: 0.6305 2022/12/09 04:27:06 - mmengine - INFO - Epoch(train) [88][520/940] lr: 1.0000e-04 eta: 2:01:20 time: 0.5344 data_time: 0.0258 memory: 16095 grad_norm: 6.0843 loss: 0.6610 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6610 2022/12/09 04:27:20 - mmengine - INFO - Epoch(train) [88][540/940] lr: 1.0000e-04 eta: 2:01:08 time: 0.6608 data_time: 0.0259 memory: 16095 grad_norm: 6.0717 loss: 0.6284 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.6284 2022/12/09 04:27:31 - mmengine - INFO - Epoch(train) [88][560/940] lr: 1.0000e-04 eta: 2:00:55 time: 0.5694 data_time: 0.0219 memory: 16095 grad_norm: 5.8577 loss: 0.6328 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.6328 2022/12/09 04:27:44 - mmengine - INFO - Epoch(train) [88][580/940] lr: 1.0000e-04 eta: 2:00:43 time: 0.6337 data_time: 0.0232 memory: 16095 grad_norm: 5.9769 loss: 0.7174 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.7174 2022/12/09 04:27:55 - mmengine - INFO - Epoch(train) [88][600/940] lr: 1.0000e-04 eta: 2:00:30 time: 0.5640 data_time: 0.0256 memory: 16095 grad_norm: 5.8834 loss: 0.6137 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6137 2022/12/09 04:28:08 - mmengine - INFO - Epoch(train) [88][620/940] lr: 1.0000e-04 eta: 2:00:18 time: 0.6683 data_time: 0.0227 memory: 16095 grad_norm: 6.1237 loss: 0.6793 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6793 2022/12/09 04:28:20 - mmengine - INFO - Epoch(train) [88][640/940] lr: 1.0000e-04 eta: 2:00:05 time: 0.5671 data_time: 0.0256 memory: 16095 grad_norm: 6.0263 loss: 0.6403 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6403 2022/12/09 04:28:33 - mmengine - INFO - Epoch(train) [88][660/940] lr: 1.0000e-04 eta: 1:59:53 time: 0.6748 data_time: 0.0229 memory: 16095 grad_norm: 5.9424 loss: 0.5742 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.5742 2022/12/09 04:28:44 - mmengine - INFO - Epoch(train) [88][680/940] lr: 1.0000e-04 eta: 1:59:40 time: 0.5491 data_time: 0.0340 memory: 16095 grad_norm: 6.2003 loss: 0.6546 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6546 2022/12/09 04:28:58 - mmengine - INFO - Epoch(train) [88][700/940] lr: 1.0000e-04 eta: 1:59:28 time: 0.6870 data_time: 0.0225 memory: 16095 grad_norm: 5.9255 loss: 0.6178 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6178 2022/12/09 04:29:10 - mmengine - INFO - Epoch(train) [88][720/940] lr: 1.0000e-04 eta: 1:59:16 time: 0.6007 data_time: 0.0259 memory: 16095 grad_norm: 6.0807 loss: 0.6308 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6308 2022/12/09 04:29:23 - mmengine - INFO - Epoch(train) [88][740/940] lr: 1.0000e-04 eta: 1:59:03 time: 0.6677 data_time: 0.0217 memory: 16095 grad_norm: 6.0057 loss: 0.6321 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6321 2022/12/09 04:29:34 - mmengine - INFO - Epoch(train) [88][760/940] lr: 1.0000e-04 eta: 1:58:51 time: 0.5464 data_time: 0.0272 memory: 16095 grad_norm: 6.0738 loss: 0.6503 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.6503 2022/12/09 04:29:47 - mmengine - INFO - Epoch(train) [88][780/940] lr: 1.0000e-04 eta: 1:58:38 time: 0.6419 data_time: 0.0228 memory: 16095 grad_norm: 6.0940 loss: 0.6505 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6505 2022/12/09 04:29:58 - mmengine - INFO - Epoch(train) [88][800/940] lr: 1.0000e-04 eta: 1:58:26 time: 0.5671 data_time: 0.0262 memory: 16095 grad_norm: 5.9631 loss: 0.6340 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6340 2022/12/09 04:30:12 - mmengine - INFO - Epoch(train) [88][820/940] lr: 1.0000e-04 eta: 1:58:13 time: 0.6564 data_time: 0.0250 memory: 16095 grad_norm: 6.0277 loss: 0.6260 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6260 2022/12/09 04:30:24 - mmengine - INFO - Epoch(train) [88][840/940] lr: 1.0000e-04 eta: 1:58:01 time: 0.5989 data_time: 0.0235 memory: 16095 grad_norm: 5.9026 loss: 0.6620 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 0.6620 2022/12/09 04:30:35 - mmengine - INFO - Epoch(train) [88][860/940] lr: 1.0000e-04 eta: 1:57:48 time: 0.5820 data_time: 0.0240 memory: 16095 grad_norm: 5.9194 loss: 0.5941 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.5941 2022/12/09 04:30:47 - mmengine - INFO - Epoch(train) [88][880/940] lr: 1.0000e-04 eta: 1:57:36 time: 0.5943 data_time: 0.0258 memory: 16095 grad_norm: 6.0464 loss: 0.6388 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6388 2022/12/09 04:31:01 - mmengine - INFO - Epoch(train) [88][900/940] lr: 1.0000e-04 eta: 1:57:23 time: 0.6765 data_time: 0.0239 memory: 16095 grad_norm: 5.9470 loss: 0.6095 top1_acc: 0.9688 top5_acc: 0.9688 loss_cls: 0.6095 2022/12/09 04:31:12 - mmengine - INFO - Epoch(train) [88][920/940] lr: 1.0000e-04 eta: 1:57:11 time: 0.5655 data_time: 0.0264 memory: 16095 grad_norm: 6.0300 loss: 0.6832 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.6832 2022/12/09 04:31:21 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 04:31:21 - mmengine - INFO - Epoch(train) [88][940/940] lr: 1.0000e-04 eta: 1:56:58 time: 0.4623 data_time: 0.0175 memory: 16095 grad_norm: 6.4973 loss: 0.7214 top1_acc: 0.7143 top5_acc: 0.7143 loss_cls: 0.7214 2022/12/09 04:31:35 - mmengine - INFO - Epoch(val) [88][20/78] eta: 0:00:41 time: 0.7083 data_time: 0.6142 memory: 1686 2022/12/09 04:31:45 - mmengine - INFO - Epoch(val) [88][40/78] eta: 0:00:22 time: 0.4831 data_time: 0.3883 memory: 1686 2022/12/09 04:31:58 - mmengine - INFO - Epoch(val) [88][60/78] eta: 0:00:11 time: 0.6544 data_time: 0.5586 memory: 1686 2022/12/09 04:32:09 - mmengine - INFO - Epoch(val) [88][78/78] acc/top1: 0.6907 acc/top5: 0.8772 acc/mean1: 0.6906 2022/12/09 04:32:25 - mmengine - INFO - Epoch(train) [89][ 20/940] lr: 1.0000e-04 eta: 1:56:46 time: 0.8193 data_time: 0.4988 memory: 16095 grad_norm: 6.1872 loss: 0.6087 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 0.6087 2022/12/09 04:32:36 - mmengine - INFO - Epoch(train) [89][ 40/940] lr: 1.0000e-04 eta: 1:56:33 time: 0.5323 data_time: 0.2037 memory: 16095 grad_norm: 5.8592 loss: 0.5979 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.5979 2022/12/09 04:32:50 - mmengine - INFO - Epoch(train) [89][ 60/940] lr: 1.0000e-04 eta: 1:56:21 time: 0.7003 data_time: 0.1454 memory: 16095 grad_norm: 6.1861 loss: 0.6859 top1_acc: 0.6875 top5_acc: 0.9062 loss_cls: 0.6859 2022/12/09 04:33:01 - mmengine - INFO - Epoch(train) [89][ 80/940] lr: 1.0000e-04 eta: 1:56:08 time: 0.5623 data_time: 0.0405 memory: 16095 grad_norm: 5.9714 loss: 0.7002 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7002 2022/12/09 04:33:15 - mmengine - INFO - Epoch(train) [89][100/940] lr: 1.0000e-04 eta: 1:55:56 time: 0.6795 data_time: 0.1634 memory: 16095 grad_norm: 5.9539 loss: 0.7156 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7156 2022/12/09 04:33:26 - mmengine - INFO - Epoch(train) [89][120/940] lr: 1.0000e-04 eta: 1:55:44 time: 0.5467 data_time: 0.1675 memory: 16095 grad_norm: 6.0696 loss: 0.6974 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6974 2022/12/09 04:33:39 - mmengine - INFO - Epoch(train) [89][140/940] lr: 1.0000e-04 eta: 1:55:31 time: 0.6473 data_time: 0.2510 memory: 16095 grad_norm: 5.9783 loss: 0.5806 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.5806 2022/12/09 04:33:50 - mmengine - INFO - Epoch(train) [89][160/940] lr: 1.0000e-04 eta: 1:55:19 time: 0.5381 data_time: 0.1974 memory: 16095 grad_norm: 5.9203 loss: 0.6951 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6951 2022/12/09 04:34:03 - mmengine - INFO - Epoch(train) [89][180/940] lr: 1.0000e-04 eta: 1:55:06 time: 0.6962 data_time: 0.3554 memory: 16095 grad_norm: 6.0465 loss: 0.6291 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6291 2022/12/09 04:34:14 - mmengine - INFO - Epoch(train) [89][200/940] lr: 1.0000e-04 eta: 1:54:54 time: 0.5337 data_time: 0.2080 memory: 16095 grad_norm: 6.1275 loss: 0.6408 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 0.6408 2022/12/09 04:34:28 - mmengine - INFO - Epoch(train) [89][220/940] lr: 1.0000e-04 eta: 1:54:41 time: 0.6733 data_time: 0.3173 memory: 16095 grad_norm: 6.0567 loss: 0.6629 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6629 2022/12/09 04:34:39 - mmengine - INFO - Epoch(train) [89][240/940] lr: 1.0000e-04 eta: 1:54:29 time: 0.5618 data_time: 0.2005 memory: 16095 grad_norm: 6.0292 loss: 0.6820 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6820 2022/12/09 04:34:52 - mmengine - INFO - Epoch(train) [89][260/940] lr: 1.0000e-04 eta: 1:54:16 time: 0.6456 data_time: 0.1807 memory: 16095 grad_norm: 6.0772 loss: 0.6331 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6331 2022/12/09 04:35:03 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 04:35:03 - mmengine - INFO - Epoch(train) [89][280/940] lr: 1.0000e-04 eta: 1:54:04 time: 0.5800 data_time: 0.0878 memory: 16095 grad_norm: 6.0323 loss: 0.6293 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6293 2022/12/09 04:35:17 - mmengine - INFO - Epoch(train) [89][300/940] lr: 1.0000e-04 eta: 1:53:51 time: 0.6580 data_time: 0.0275 memory: 16095 grad_norm: 6.0662 loss: 0.7515 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7515 2022/12/09 04:35:29 - mmengine - INFO - Epoch(train) [89][320/940] lr: 1.0000e-04 eta: 1:53:39 time: 0.6305 data_time: 0.0225 memory: 16095 grad_norm: 5.9146 loss: 0.6660 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6660 2022/12/09 04:35:41 - mmengine - INFO - Epoch(train) [89][340/940] lr: 1.0000e-04 eta: 1:53:26 time: 0.5773 data_time: 0.0259 memory: 16095 grad_norm: 6.0737 loss: 0.5866 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.5866 2022/12/09 04:35:53 - mmengine - INFO - Epoch(train) [89][360/940] lr: 1.0000e-04 eta: 1:53:14 time: 0.6133 data_time: 0.0339 memory: 16095 grad_norm: 6.1967 loss: 0.7406 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7406 2022/12/09 04:36:05 - mmengine - INFO - Epoch(train) [89][380/940] lr: 1.0000e-04 eta: 1:53:01 time: 0.5998 data_time: 0.0249 memory: 16095 grad_norm: 5.9938 loss: 0.6476 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.6476 2022/12/09 04:36:18 - mmengine - INFO - Epoch(train) [89][400/940] lr: 1.0000e-04 eta: 1:52:49 time: 0.6348 data_time: 0.0224 memory: 16095 grad_norm: 5.9715 loss: 0.6269 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6269 2022/12/09 04:36:31 - mmengine - INFO - Epoch(train) [89][420/940] lr: 1.0000e-04 eta: 1:52:37 time: 0.6406 data_time: 0.0258 memory: 16095 grad_norm: 5.8048 loss: 0.6931 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6931 2022/12/09 04:36:44 - mmengine - INFO - Epoch(train) [89][440/940] lr: 1.0000e-04 eta: 1:52:24 time: 0.6546 data_time: 0.0230 memory: 16095 grad_norm: 6.0068 loss: 0.7053 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7053 2022/12/09 04:36:55 - mmengine - INFO - Epoch(train) [89][460/940] lr: 1.0000e-04 eta: 1:52:12 time: 0.5442 data_time: 0.0268 memory: 16095 grad_norm: 6.0388 loss: 0.6282 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6282 2022/12/09 04:37:08 - mmengine - INFO - Epoch(train) [89][480/940] lr: 1.0000e-04 eta: 1:51:59 time: 0.6664 data_time: 0.0239 memory: 16095 grad_norm: 6.0896 loss: 0.7356 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7356 2022/12/09 04:37:19 - mmengine - INFO - Epoch(train) [89][500/940] lr: 1.0000e-04 eta: 1:51:47 time: 0.5418 data_time: 0.0257 memory: 16095 grad_norm: 6.0503 loss: 0.7176 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7176 2022/12/09 04:37:31 - mmengine - INFO - Epoch(train) [89][520/940] lr: 1.0000e-04 eta: 1:51:34 time: 0.5953 data_time: 0.0253 memory: 16095 grad_norm: 5.9200 loss: 0.6018 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6018 2022/12/09 04:37:44 - mmengine - INFO - Epoch(train) [89][540/940] lr: 1.0000e-04 eta: 1:51:22 time: 0.6704 data_time: 0.0229 memory: 16095 grad_norm: 5.9464 loss: 0.6020 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6020 2022/12/09 04:37:56 - mmengine - INFO - Epoch(train) [89][560/940] lr: 1.0000e-04 eta: 1:51:09 time: 0.5755 data_time: 0.0239 memory: 16095 grad_norm: 5.9555 loss: 0.6297 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6297 2022/12/09 04:38:09 - mmengine - INFO - Epoch(train) [89][580/940] lr: 1.0000e-04 eta: 1:50:57 time: 0.6600 data_time: 0.0252 memory: 16095 grad_norm: 5.9664 loss: 0.5764 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5764 2022/12/09 04:38:21 - mmengine - INFO - Epoch(train) [89][600/940] lr: 1.0000e-04 eta: 1:50:44 time: 0.5929 data_time: 0.0218 memory: 16095 grad_norm: 6.1074 loss: 0.6761 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6761 2022/12/09 04:38:34 - mmengine - INFO - Epoch(train) [89][620/940] lr: 1.0000e-04 eta: 1:50:32 time: 0.6739 data_time: 0.0236 memory: 16095 grad_norm: 5.8462 loss: 0.6346 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6346 2022/12/09 04:38:45 - mmengine - INFO - Epoch(train) [89][640/940] lr: 1.0000e-04 eta: 1:50:19 time: 0.5444 data_time: 0.0246 memory: 16095 grad_norm: 6.1704 loss: 0.6711 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.6711 2022/12/09 04:38:58 - mmengine - INFO - Epoch(train) [89][660/940] lr: 1.0000e-04 eta: 1:50:07 time: 0.6579 data_time: 0.0243 memory: 16095 grad_norm: 6.0974 loss: 0.6932 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6932 2022/12/09 04:39:08 - mmengine - INFO - Epoch(train) [89][680/940] lr: 1.0000e-04 eta: 1:49:54 time: 0.5092 data_time: 0.0254 memory: 16095 grad_norm: 5.9818 loss: 0.6978 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6978 2022/12/09 04:39:21 - mmengine - INFO - Epoch(train) [89][700/940] lr: 1.0000e-04 eta: 1:49:42 time: 0.6483 data_time: 0.0234 memory: 16095 grad_norm: 6.1185 loss: 0.6502 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.6502 2022/12/09 04:39:33 - mmengine - INFO - Epoch(train) [89][720/940] lr: 1.0000e-04 eta: 1:49:29 time: 0.5657 data_time: 0.0267 memory: 16095 grad_norm: 5.8241 loss: 0.6949 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6949 2022/12/09 04:39:45 - mmengine - INFO - Epoch(train) [89][740/940] lr: 1.0000e-04 eta: 1:49:17 time: 0.6172 data_time: 0.0235 memory: 16095 grad_norm: 5.9256 loss: 0.6253 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6253 2022/12/09 04:39:58 - mmengine - INFO - Epoch(train) [89][760/940] lr: 1.0000e-04 eta: 1:49:04 time: 0.6244 data_time: 0.0270 memory: 16095 grad_norm: 5.9529 loss: 0.6460 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6460 2022/12/09 04:40:09 - mmengine - INFO - Epoch(train) [89][780/940] lr: 1.0000e-04 eta: 1:48:52 time: 0.5723 data_time: 0.0659 memory: 16095 grad_norm: 5.8441 loss: 0.6494 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6494 2022/12/09 04:40:23 - mmengine - INFO - Epoch(train) [89][800/940] lr: 1.0000e-04 eta: 1:48:40 time: 0.6902 data_time: 0.0598 memory: 16095 grad_norm: 6.0831 loss: 0.6688 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6688 2022/12/09 04:40:34 - mmengine - INFO - Epoch(train) [89][820/940] lr: 1.0000e-04 eta: 1:48:27 time: 0.5522 data_time: 0.0685 memory: 16095 grad_norm: 6.0825 loss: 0.7608 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.7608 2022/12/09 04:40:47 - mmengine - INFO - Epoch(train) [89][840/940] lr: 1.0000e-04 eta: 1:48:15 time: 0.6639 data_time: 0.1421 memory: 16095 grad_norm: 5.8435 loss: 0.6895 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6895 2022/12/09 04:40:59 - mmengine - INFO - Epoch(train) [89][860/940] lr: 1.0000e-04 eta: 1:48:02 time: 0.5761 data_time: 0.0881 memory: 16095 grad_norm: 6.0563 loss: 0.6826 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6826 2022/12/09 04:41:12 - mmengine - INFO - Epoch(train) [89][880/940] lr: 1.0000e-04 eta: 1:47:50 time: 0.6518 data_time: 0.1986 memory: 16095 grad_norm: 6.1293 loss: 0.6130 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6130 2022/12/09 04:41:23 - mmengine - INFO - Epoch(train) [89][900/940] lr: 1.0000e-04 eta: 1:47:37 time: 0.5885 data_time: 0.2688 memory: 16095 grad_norm: 6.0561 loss: 0.6970 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6970 2022/12/09 04:41:36 - mmengine - INFO - Epoch(train) [89][920/940] lr: 1.0000e-04 eta: 1:47:25 time: 0.6057 data_time: 0.2592 memory: 16095 grad_norm: 6.1982 loss: 0.7473 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7473 2022/12/09 04:41:45 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 04:41:45 - mmengine - INFO - Epoch(train) [89][940/940] lr: 1.0000e-04 eta: 1:47:12 time: 0.4743 data_time: 0.1796 memory: 16095 grad_norm: 6.4015 loss: 0.6377 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 0.6377 2022/12/09 04:41:59 - mmengine - INFO - Epoch(val) [89][20/78] eta: 0:00:40 time: 0.6969 data_time: 0.6028 memory: 1686 2022/12/09 04:42:08 - mmengine - INFO - Epoch(val) [89][40/78] eta: 0:00:22 time: 0.4613 data_time: 0.3670 memory: 1686 2022/12/09 04:42:22 - mmengine - INFO - Epoch(val) [89][60/78] eta: 0:00:11 time: 0.6752 data_time: 0.5803 memory: 1686 2022/12/09 04:42:32 - mmengine - INFO - Epoch(val) [89][78/78] acc/top1: 0.6904 acc/top5: 0.8769 acc/mean1: 0.6903 2022/12/09 04:42:49 - mmengine - INFO - Epoch(train) [90][ 20/940] lr: 1.0000e-04 eta: 1:47:00 time: 0.8445 data_time: 0.3293 memory: 16095 grad_norm: 6.0937 loss: 0.6645 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.6645 2022/12/09 04:43:00 - mmengine - INFO - Epoch(train) [90][ 40/940] lr: 1.0000e-04 eta: 1:46:47 time: 0.5466 data_time: 0.0240 memory: 16095 grad_norm: 6.0783 loss: 0.7288 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7288 2022/12/09 04:43:14 - mmengine - INFO - Epoch(train) [90][ 60/940] lr: 1.0000e-04 eta: 1:46:35 time: 0.6851 data_time: 0.0287 memory: 16095 grad_norm: 6.1618 loss: 0.6809 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6809 2022/12/09 04:43:25 - mmengine - INFO - Epoch(train) [90][ 80/940] lr: 1.0000e-04 eta: 1:46:23 time: 0.5481 data_time: 0.0208 memory: 16095 grad_norm: 6.0945 loss: 0.7046 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.7046 2022/12/09 04:43:39 - mmengine - INFO - Epoch(train) [90][100/940] lr: 1.0000e-04 eta: 1:46:10 time: 0.6969 data_time: 0.0620 memory: 16095 grad_norm: 6.1717 loss: 0.6997 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.6997 2022/12/09 04:43:50 - mmengine - INFO - Epoch(train) [90][120/940] lr: 1.0000e-04 eta: 1:45:58 time: 0.5605 data_time: 0.0452 memory: 16095 grad_norm: 6.1004 loss: 0.6510 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.6510 2022/12/09 04:44:03 - mmengine - INFO - Epoch(train) [90][140/940] lr: 1.0000e-04 eta: 1:45:45 time: 0.6202 data_time: 0.0361 memory: 16095 grad_norm: 6.2137 loss: 0.6859 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 0.6859 2022/12/09 04:44:14 - mmengine - INFO - Epoch(train) [90][160/940] lr: 1.0000e-04 eta: 1:45:33 time: 0.5776 data_time: 0.0281 memory: 16095 grad_norm: 6.0707 loss: 0.7110 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.7110 2022/12/09 04:44:28 - mmengine - INFO - Epoch(train) [90][180/940] lr: 1.0000e-04 eta: 1:45:20 time: 0.6740 data_time: 0.0554 memory: 16095 grad_norm: 6.0048 loss: 0.6871 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6871 2022/12/09 04:44:38 - mmengine - INFO - Epoch(train) [90][200/940] lr: 1.0000e-04 eta: 1:45:08 time: 0.5376 data_time: 0.0516 memory: 16095 grad_norm: 5.9785 loss: 0.6545 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.6545 2022/12/09 04:44:53 - mmengine - INFO - Epoch(train) [90][220/940] lr: 1.0000e-04 eta: 1:44:56 time: 0.7267 data_time: 0.1612 memory: 16095 grad_norm: 5.9831 loss: 0.6756 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6756 2022/12/09 04:45:03 - mmengine - INFO - Epoch(train) [90][240/940] lr: 1.0000e-04 eta: 1:44:43 time: 0.5120 data_time: 0.0552 memory: 16095 grad_norm: 6.0090 loss: 0.6490 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6490 2022/12/09 04:45:16 - mmengine - INFO - Epoch(train) [90][260/940] lr: 1.0000e-04 eta: 1:44:30 time: 0.6595 data_time: 0.0710 memory: 16095 grad_norm: 6.0358 loss: 0.6225 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6225 2022/12/09 04:45:28 - mmengine - INFO - Epoch(train) [90][280/940] lr: 1.0000e-04 eta: 1:44:18 time: 0.5857 data_time: 0.0220 memory: 16095 grad_norm: 6.0401 loss: 0.5830 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.5830 2022/12/09 04:45:41 - mmengine - INFO - Epoch(train) [90][300/940] lr: 1.0000e-04 eta: 1:44:06 time: 0.6660 data_time: 0.0591 memory: 16095 grad_norm: 6.0851 loss: 0.6513 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6513 2022/12/09 04:45:52 - mmengine - INFO - Epoch(train) [90][320/940] lr: 1.0000e-04 eta: 1:43:53 time: 0.5500 data_time: 0.1515 memory: 16095 grad_norm: 5.9882 loss: 0.6710 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.6710 2022/12/09 04:46:06 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 04:46:06 - mmengine - INFO - Epoch(train) [90][340/940] lr: 1.0000e-04 eta: 1:43:41 time: 0.6896 data_time: 0.3079 memory: 16095 grad_norm: 6.0276 loss: 0.6425 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6425 2022/12/09 04:46:18 - mmengine - INFO - Epoch(train) [90][360/940] lr: 1.0000e-04 eta: 1:43:28 time: 0.5706 data_time: 0.2451 memory: 16095 grad_norm: 6.0108 loss: 0.6079 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6079 2022/12/09 04:46:30 - mmengine - INFO - Epoch(train) [90][380/940] lr: 1.0000e-04 eta: 1:43:16 time: 0.6234 data_time: 0.2757 memory: 16095 grad_norm: 6.1194 loss: 0.7911 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.7911 2022/12/09 04:46:41 - mmengine - INFO - Epoch(train) [90][400/940] lr: 1.0000e-04 eta: 1:43:03 time: 0.5323 data_time: 0.1620 memory: 16095 grad_norm: 6.0417 loss: 0.7724 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7724 2022/12/09 04:46:54 - mmengine - INFO - Epoch(train) [90][420/940] lr: 1.0000e-04 eta: 1:42:51 time: 0.6551 data_time: 0.2590 memory: 16095 grad_norm: 5.9656 loss: 0.6473 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6473 2022/12/09 04:47:05 - mmengine - INFO - Epoch(train) [90][440/940] lr: 1.0000e-04 eta: 1:42:38 time: 0.5452 data_time: 0.2158 memory: 16095 grad_norm: 6.0534 loss: 0.6485 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.6485 2022/12/09 04:47:19 - mmengine - INFO - Epoch(train) [90][460/940] lr: 1.0000e-04 eta: 1:42:26 time: 0.7072 data_time: 0.3786 memory: 16095 grad_norm: 6.0863 loss: 0.5600 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5600 2022/12/09 04:47:29 - mmengine - INFO - Epoch(train) [90][480/940] lr: 1.0000e-04 eta: 1:42:13 time: 0.5137 data_time: 0.1766 memory: 16095 grad_norm: 6.0642 loss: 0.6010 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.6010 2022/12/09 04:47:43 - mmengine - INFO - Epoch(train) [90][500/940] lr: 1.0000e-04 eta: 1:42:01 time: 0.6677 data_time: 0.1665 memory: 16095 grad_norm: 6.0609 loss: 0.6468 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6468 2022/12/09 04:47:54 - mmengine - INFO - Epoch(train) [90][520/940] lr: 1.0000e-04 eta: 1:41:48 time: 0.5738 data_time: 0.1850 memory: 16095 grad_norm: 6.0610 loss: 0.7233 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7233 2022/12/09 04:48:07 - mmengine - INFO - Epoch(train) [90][540/940] lr: 1.0000e-04 eta: 1:41:36 time: 0.6511 data_time: 0.2875 memory: 16095 grad_norm: 6.0163 loss: 0.6258 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6258 2022/12/09 04:48:19 - mmengine - INFO - Epoch(train) [90][560/940] lr: 1.0000e-04 eta: 1:41:23 time: 0.6054 data_time: 0.2418 memory: 16095 grad_norm: 6.0296 loss: 0.7392 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7392 2022/12/09 04:48:32 - mmengine - INFO - Epoch(train) [90][580/940] lr: 1.0000e-04 eta: 1:41:11 time: 0.6597 data_time: 0.3135 memory: 16095 grad_norm: 5.9959 loss: 0.6917 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.6917 2022/12/09 04:48:44 - mmengine - INFO - Epoch(train) [90][600/940] lr: 1.0000e-04 eta: 1:40:58 time: 0.5823 data_time: 0.2375 memory: 16095 grad_norm: 5.9345 loss: 0.5866 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 0.5866 2022/12/09 04:48:56 - mmengine - INFO - Epoch(train) [90][620/940] lr: 1.0000e-04 eta: 1:40:46 time: 0.6182 data_time: 0.2649 memory: 16095 grad_norm: 6.1911 loss: 0.7104 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7104 2022/12/09 04:49:08 - mmengine - INFO - Epoch(train) [90][640/940] lr: 1.0000e-04 eta: 1:40:33 time: 0.5588 data_time: 0.1951 memory: 16095 grad_norm: 5.9739 loss: 0.5769 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.5769 2022/12/09 04:49:20 - mmengine - INFO - Epoch(train) [90][660/940] lr: 1.0000e-04 eta: 1:40:21 time: 0.6299 data_time: 0.2517 memory: 16095 grad_norm: 5.9744 loss: 0.6425 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6425 2022/12/09 04:49:31 - mmengine - INFO - Epoch(train) [90][680/940] lr: 1.0000e-04 eta: 1:40:08 time: 0.5614 data_time: 0.1642 memory: 16095 grad_norm: 5.9331 loss: 0.6030 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6030 2022/12/09 04:49:45 - mmengine - INFO - Epoch(train) [90][700/940] lr: 1.0000e-04 eta: 1:39:56 time: 0.6768 data_time: 0.2637 memory: 16095 grad_norm: 6.0719 loss: 0.7536 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.7536 2022/12/09 04:49:56 - mmengine - INFO - Epoch(train) [90][720/940] lr: 1.0000e-04 eta: 1:39:44 time: 0.5565 data_time: 0.1272 memory: 16095 grad_norm: 6.0435 loss: 0.7674 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.7674 2022/12/09 04:50:09 - mmengine - INFO - Epoch(train) [90][740/940] lr: 1.0000e-04 eta: 1:39:31 time: 0.6450 data_time: 0.2616 memory: 16095 grad_norm: 6.1318 loss: 0.6573 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6573 2022/12/09 04:50:20 - mmengine - INFO - Epoch(train) [90][760/940] lr: 1.0000e-04 eta: 1:39:19 time: 0.5587 data_time: 0.0911 memory: 16095 grad_norm: 5.9801 loss: 0.6184 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6184 2022/12/09 04:50:33 - mmengine - INFO - Epoch(train) [90][780/940] lr: 1.0000e-04 eta: 1:39:06 time: 0.6641 data_time: 0.1314 memory: 16095 grad_norm: 6.1532 loss: 0.6960 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6960 2022/12/09 04:50:45 - mmengine - INFO - Epoch(train) [90][800/940] lr: 1.0000e-04 eta: 1:38:54 time: 0.5988 data_time: 0.1304 memory: 16095 grad_norm: 5.9358 loss: 0.6344 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6344 2022/12/09 04:50:58 - mmengine - INFO - Epoch(train) [90][820/940] lr: 1.0000e-04 eta: 1:38:41 time: 0.6068 data_time: 0.2134 memory: 16095 grad_norm: 6.0959 loss: 0.6596 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6596 2022/12/09 04:51:09 - mmengine - INFO - Epoch(train) [90][840/940] lr: 1.0000e-04 eta: 1:38:29 time: 0.5722 data_time: 0.1507 memory: 16095 grad_norm: 5.9772 loss: 0.6178 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6178 2022/12/09 04:51:22 - mmengine - INFO - Epoch(train) [90][860/940] lr: 1.0000e-04 eta: 1:38:16 time: 0.6374 data_time: 0.2828 memory: 16095 grad_norm: 6.1726 loss: 0.6007 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6007 2022/12/09 04:51:33 - mmengine - INFO - Epoch(train) [90][880/940] lr: 1.0000e-04 eta: 1:38:04 time: 0.5598 data_time: 0.1760 memory: 16095 grad_norm: 5.9077 loss: 0.6150 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6150 2022/12/09 04:51:46 - mmengine - INFO - Epoch(train) [90][900/940] lr: 1.0000e-04 eta: 1:37:51 time: 0.6508 data_time: 0.3151 memory: 16095 grad_norm: 6.0690 loss: 0.7386 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.7386 2022/12/09 04:51:58 - mmengine - INFO - Epoch(train) [90][920/940] lr: 1.0000e-04 eta: 1:37:39 time: 0.5822 data_time: 0.2076 memory: 16095 grad_norm: 5.8238 loss: 0.6713 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6713 2022/12/09 04:52:08 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 04:52:08 - mmengine - INFO - Epoch(train) [90][940/940] lr: 1.0000e-04 eta: 1:37:26 time: 0.4961 data_time: 0.1772 memory: 16095 grad_norm: 6.5635 loss: 0.7374 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 0.7374 2022/12/09 04:52:08 - mmengine - INFO - Saving checkpoint at 90 epochs 2022/12/09 04:52:25 - mmengine - INFO - Epoch(val) [90][20/78] eta: 0:00:41 time: 0.7074 data_time: 0.6122 memory: 1686 2022/12/09 04:52:34 - mmengine - INFO - Epoch(val) [90][40/78] eta: 0:00:22 time: 0.4600 data_time: 0.3644 memory: 1686 2022/12/09 04:52:47 - mmengine - INFO - Epoch(val) [90][60/78] eta: 0:00:11 time: 0.6726 data_time: 0.5775 memory: 1686 2022/12/09 04:52:57 - mmengine - INFO - Epoch(val) [90][78/78] acc/top1: 0.6913 acc/top5: 0.8771 acc/mean1: 0.6912 2022/12/09 04:53:13 - mmengine - INFO - Epoch(train) [91][ 20/940] lr: 1.0000e-04 eta: 1:37:14 time: 0.8305 data_time: 0.3534 memory: 16095 grad_norm: 6.1090 loss: 0.6524 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6524 2022/12/09 04:53:24 - mmengine - INFO - Epoch(train) [91][ 40/940] lr: 1.0000e-04 eta: 1:37:01 time: 0.5500 data_time: 0.1315 memory: 16095 grad_norm: 6.0022 loss: 0.6223 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6223 2022/12/09 04:53:37 - mmengine - INFO - Epoch(train) [91][ 60/940] lr: 1.0000e-04 eta: 1:36:49 time: 0.6212 data_time: 0.2893 memory: 16095 grad_norm: 5.9267 loss: 0.6797 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.6797 2022/12/09 04:53:49 - mmengine - INFO - Epoch(train) [91][ 80/940] lr: 1.0000e-04 eta: 1:36:37 time: 0.6153 data_time: 0.2924 memory: 16095 grad_norm: 6.1649 loss: 0.6141 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6141 2022/12/09 04:54:03 - mmengine - INFO - Epoch(train) [91][100/940] lr: 1.0000e-04 eta: 1:36:24 time: 0.7099 data_time: 0.4039 memory: 16095 grad_norm: 6.0262 loss: 0.6599 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.6599 2022/12/09 04:54:14 - mmengine - INFO - Epoch(train) [91][120/940] lr: 1.0000e-04 eta: 1:36:12 time: 0.5171 data_time: 0.2039 memory: 16095 grad_norm: 5.7702 loss: 0.6017 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6017 2022/12/09 04:54:28 - mmengine - INFO - Epoch(train) [91][140/940] lr: 1.0000e-04 eta: 1:35:59 time: 0.7206 data_time: 0.4087 memory: 16095 grad_norm: 6.0969 loss: 0.6841 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6841 2022/12/09 04:54:39 - mmengine - INFO - Epoch(train) [91][160/940] lr: 1.0000e-04 eta: 1:35:47 time: 0.5481 data_time: 0.2233 memory: 16095 grad_norm: 6.1623 loss: 0.7034 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7034 2022/12/09 04:54:52 - mmengine - INFO - Epoch(train) [91][180/940] lr: 1.0000e-04 eta: 1:35:34 time: 0.6357 data_time: 0.3204 memory: 16095 grad_norm: 6.0463 loss: 0.5884 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.5884 2022/12/09 04:55:02 - mmengine - INFO - Epoch(train) [91][200/940] lr: 1.0000e-04 eta: 1:35:22 time: 0.5250 data_time: 0.2089 memory: 16095 grad_norm: 5.9798 loss: 0.6879 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6879 2022/12/09 04:55:16 - mmengine - INFO - Epoch(train) [91][220/940] lr: 1.0000e-04 eta: 1:35:10 time: 0.7052 data_time: 0.3840 memory: 16095 grad_norm: 6.0669 loss: 0.6768 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6768 2022/12/09 04:55:27 - mmengine - INFO - Epoch(train) [91][240/940] lr: 1.0000e-04 eta: 1:34:57 time: 0.5304 data_time: 0.2111 memory: 16095 grad_norm: 6.0596 loss: 0.6714 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6714 2022/12/09 04:55:40 - mmengine - INFO - Epoch(train) [91][260/940] lr: 1.0000e-04 eta: 1:34:44 time: 0.6230 data_time: 0.3019 memory: 16095 grad_norm: 6.0384 loss: 0.6282 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6282 2022/12/09 04:55:51 - mmengine - INFO - Epoch(train) [91][280/940] lr: 1.0000e-04 eta: 1:34:32 time: 0.5498 data_time: 0.2081 memory: 16095 grad_norm: 6.0616 loss: 0.6941 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6941 2022/12/09 04:56:04 - mmengine - INFO - Epoch(train) [91][300/940] lr: 1.0000e-04 eta: 1:34:20 time: 0.6659 data_time: 0.2478 memory: 16095 grad_norm: 6.0482 loss: 0.6666 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.6666 2022/12/09 04:56:15 - mmengine - INFO - Epoch(train) [91][320/940] lr: 1.0000e-04 eta: 1:34:07 time: 0.5638 data_time: 0.1438 memory: 16095 grad_norm: 5.8720 loss: 0.6058 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6058 2022/12/09 04:56:28 - mmengine - INFO - Epoch(train) [91][340/940] lr: 1.0000e-04 eta: 1:33:55 time: 0.6530 data_time: 0.1801 memory: 16095 grad_norm: 5.9116 loss: 0.6600 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6600 2022/12/09 04:56:39 - mmengine - INFO - Epoch(train) [91][360/940] lr: 1.0000e-04 eta: 1:33:42 time: 0.5615 data_time: 0.1373 memory: 16095 grad_norm: 6.0671 loss: 0.6662 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6662 2022/12/09 04:56:52 - mmengine - INFO - Epoch(train) [91][380/940] lr: 1.0000e-04 eta: 1:33:30 time: 0.6416 data_time: 0.2012 memory: 16095 grad_norm: 6.0810 loss: 0.7147 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7147 2022/12/09 04:57:04 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 04:57:04 - mmengine - INFO - Epoch(train) [91][400/940] lr: 1.0000e-04 eta: 1:33:17 time: 0.5993 data_time: 0.1107 memory: 16095 grad_norm: 6.1507 loss: 0.6303 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6303 2022/12/09 04:57:16 - mmengine - INFO - Epoch(train) [91][420/940] lr: 1.0000e-04 eta: 1:33:05 time: 0.5937 data_time: 0.1387 memory: 16095 grad_norm: 6.1045 loss: 0.6955 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6955 2022/12/09 04:57:28 - mmengine - INFO - Epoch(train) [91][440/940] lr: 1.0000e-04 eta: 1:32:52 time: 0.6148 data_time: 0.0852 memory: 16095 grad_norm: 5.9896 loss: 0.6263 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6263 2022/12/09 04:57:41 - mmengine - INFO - Epoch(train) [91][460/940] lr: 1.0000e-04 eta: 1:32:40 time: 0.6096 data_time: 0.1332 memory: 16095 grad_norm: 6.0736 loss: 0.6387 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6387 2022/12/09 04:57:53 - mmengine - INFO - Epoch(train) [91][480/940] lr: 1.0000e-04 eta: 1:32:27 time: 0.6096 data_time: 0.0246 memory: 16095 grad_norm: 6.0037 loss: 0.5967 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.5967 2022/12/09 04:58:05 - mmengine - INFO - Epoch(train) [91][500/940] lr: 1.0000e-04 eta: 1:32:15 time: 0.6253 data_time: 0.0663 memory: 16095 grad_norm: 6.0780 loss: 0.6621 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6621 2022/12/09 04:58:18 - mmengine - INFO - Epoch(train) [91][520/940] lr: 1.0000e-04 eta: 1:32:02 time: 0.6114 data_time: 0.0276 memory: 16095 grad_norm: 6.1913 loss: 0.6036 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.6036 2022/12/09 04:58:31 - mmengine - INFO - Epoch(train) [91][540/940] lr: 1.0000e-04 eta: 1:31:50 time: 0.6486 data_time: 0.0294 memory: 16095 grad_norm: 6.0467 loss: 0.5925 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5925 2022/12/09 04:58:42 - mmengine - INFO - Epoch(train) [91][560/940] lr: 1.0000e-04 eta: 1:31:37 time: 0.5526 data_time: 0.0210 memory: 16095 grad_norm: 6.0614 loss: 0.6065 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6065 2022/12/09 04:58:55 - mmengine - INFO - Epoch(train) [91][580/940] lr: 1.0000e-04 eta: 1:31:25 time: 0.6776 data_time: 0.0288 memory: 16095 grad_norm: 5.8699 loss: 0.6091 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6091 2022/12/09 04:59:06 - mmengine - INFO - Epoch(train) [91][600/940] lr: 1.0000e-04 eta: 1:31:12 time: 0.5626 data_time: 0.0219 memory: 16095 grad_norm: 6.0390 loss: 0.6650 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6650 2022/12/09 04:59:20 - mmengine - INFO - Epoch(train) [91][620/940] lr: 1.0000e-04 eta: 1:31:00 time: 0.6559 data_time: 0.0277 memory: 16095 grad_norm: 5.9526 loss: 0.6475 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6475 2022/12/09 04:59:31 - mmengine - INFO - Epoch(train) [91][640/940] lr: 1.0000e-04 eta: 1:30:48 time: 0.5728 data_time: 0.0442 memory: 16095 grad_norm: 5.8993 loss: 0.5485 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.5485 2022/12/09 04:59:44 - mmengine - INFO - Epoch(train) [91][660/940] lr: 1.0000e-04 eta: 1:30:35 time: 0.6649 data_time: 0.0377 memory: 16095 grad_norm: 5.9403 loss: 0.7377 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7377 2022/12/09 04:59:57 - mmengine - INFO - Epoch(train) [91][680/940] lr: 1.0000e-04 eta: 1:30:23 time: 0.6070 data_time: 0.0233 memory: 16095 grad_norm: 5.9379 loss: 0.5775 top1_acc: 0.9688 top5_acc: 0.9688 loss_cls: 0.5775 2022/12/09 05:00:08 - mmengine - INFO - Epoch(train) [91][700/940] lr: 1.0000e-04 eta: 1:30:10 time: 0.5970 data_time: 0.0287 memory: 16095 grad_norm: 6.0056 loss: 0.7001 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.7001 2022/12/09 05:00:20 - mmengine - INFO - Epoch(train) [91][720/940] lr: 1.0000e-04 eta: 1:29:58 time: 0.5849 data_time: 0.0207 memory: 16095 grad_norm: 5.9769 loss: 0.6160 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6160 2022/12/09 05:00:33 - mmengine - INFO - Epoch(train) [91][740/940] lr: 1.0000e-04 eta: 1:29:45 time: 0.6335 data_time: 0.0945 memory: 16095 grad_norm: 5.8742 loss: 0.6766 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6766 2022/12/09 05:00:44 - mmengine - INFO - Epoch(train) [91][760/940] lr: 1.0000e-04 eta: 1:29:33 time: 0.5812 data_time: 0.0799 memory: 16095 grad_norm: 6.1116 loss: 0.6103 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.6103 2022/12/09 05:00:58 - mmengine - INFO - Epoch(train) [91][780/940] lr: 1.0000e-04 eta: 1:29:20 time: 0.6511 data_time: 0.0369 memory: 16095 grad_norm: 6.0702 loss: 0.6936 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.6936 2022/12/09 05:01:09 - mmengine - INFO - Epoch(train) [91][800/940] lr: 1.0000e-04 eta: 1:29:08 time: 0.5981 data_time: 0.0210 memory: 16095 grad_norm: 5.9597 loss: 0.6864 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6864 2022/12/09 05:01:22 - mmengine - INFO - Epoch(train) [91][820/940] lr: 1.0000e-04 eta: 1:28:55 time: 0.6068 data_time: 0.0292 memory: 16095 grad_norm: 5.9356 loss: 0.5666 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.5666 2022/12/09 05:01:33 - mmengine - INFO - Epoch(train) [91][840/940] lr: 1.0000e-04 eta: 1:28:43 time: 0.5819 data_time: 0.0198 memory: 16095 grad_norm: 6.1271 loss: 0.6884 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6884 2022/12/09 05:01:46 - mmengine - INFO - Epoch(train) [91][860/940] lr: 1.0000e-04 eta: 1:28:31 time: 0.6384 data_time: 0.0283 memory: 16095 grad_norm: 6.0422 loss: 0.6265 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6265 2022/12/09 05:01:58 - mmengine - INFO - Epoch(train) [91][880/940] lr: 1.0000e-04 eta: 1:28:18 time: 0.5747 data_time: 0.0305 memory: 16095 grad_norm: 5.9493 loss: 0.5663 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.5663 2022/12/09 05:02:11 - mmengine - INFO - Epoch(train) [91][900/940] lr: 1.0000e-04 eta: 1:28:06 time: 0.6556 data_time: 0.0290 memory: 16095 grad_norm: 6.0775 loss: 0.5709 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.5709 2022/12/09 05:02:21 - mmengine - INFO - Epoch(train) [91][920/940] lr: 1.0000e-04 eta: 1:27:53 time: 0.5365 data_time: 0.0200 memory: 16095 grad_norm: 5.9773 loss: 0.5908 top1_acc: 0.9688 top5_acc: 0.9688 loss_cls: 0.5908 2022/12/09 05:02:33 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 05:02:33 - mmengine - INFO - Epoch(train) [91][940/940] lr: 1.0000e-04 eta: 1:27:41 time: 0.5595 data_time: 0.0173 memory: 16095 grad_norm: 6.5826 loss: 0.7101 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 0.7101 2022/12/09 05:02:47 - mmengine - INFO - Epoch(val) [91][20/78] eta: 0:00:41 time: 0.7214 data_time: 0.6273 memory: 1686 2022/12/09 05:02:56 - mmengine - INFO - Epoch(val) [91][40/78] eta: 0:00:22 time: 0.4462 data_time: 0.3527 memory: 1686 2022/12/09 05:03:10 - mmengine - INFO - Epoch(val) [91][60/78] eta: 0:00:11 time: 0.6775 data_time: 0.5822 memory: 1686 2022/12/09 05:03:20 - mmengine - INFO - Epoch(val) [91][78/78] acc/top1: 0.6915 acc/top5: 0.8768 acc/mean1: 0.6913 2022/12/09 05:03:37 - mmengine - INFO - Epoch(train) [92][ 20/940] lr: 1.0000e-04 eta: 1:27:28 time: 0.8345 data_time: 0.3871 memory: 16095 grad_norm: 5.9441 loss: 0.6570 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6570 2022/12/09 05:03:48 - mmengine - INFO - Epoch(train) [92][ 40/940] lr: 1.0000e-04 eta: 1:27:16 time: 0.5549 data_time: 0.0438 memory: 16095 grad_norm: 6.0511 loss: 0.7359 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7359 2022/12/09 05:04:02 - mmengine - INFO - Epoch(train) [92][ 60/940] lr: 1.0000e-04 eta: 1:27:04 time: 0.7011 data_time: 0.0500 memory: 16095 grad_norm: 6.0522 loss: 0.6128 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6128 2022/12/09 05:04:13 - mmengine - INFO - Epoch(train) [92][ 80/940] lr: 1.0000e-04 eta: 1:26:51 time: 0.5668 data_time: 0.0222 memory: 16095 grad_norm: 5.8942 loss: 0.6277 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6277 2022/12/09 05:04:26 - mmengine - INFO - Epoch(train) [92][100/940] lr: 1.0000e-04 eta: 1:26:39 time: 0.6258 data_time: 0.0241 memory: 16095 grad_norm: 6.0535 loss: 0.6629 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6629 2022/12/09 05:04:38 - mmengine - INFO - Epoch(train) [92][120/940] lr: 1.0000e-04 eta: 1:26:26 time: 0.6042 data_time: 0.0250 memory: 16095 grad_norm: 6.1213 loss: 0.6851 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.6851 2022/12/09 05:04:51 - mmengine - INFO - Epoch(train) [92][140/940] lr: 1.0000e-04 eta: 1:26:14 time: 0.6618 data_time: 0.0235 memory: 16095 grad_norm: 6.1831 loss: 0.6451 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6451 2022/12/09 05:05:02 - mmengine - INFO - Epoch(train) [92][160/940] lr: 1.0000e-04 eta: 1:26:01 time: 0.5301 data_time: 0.0257 memory: 16095 grad_norm: 6.1263 loss: 0.5879 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.5879 2022/12/09 05:05:15 - mmengine - INFO - Epoch(train) [92][180/940] lr: 1.0000e-04 eta: 1:25:49 time: 0.6526 data_time: 0.0260 memory: 16095 grad_norm: 5.9956 loss: 0.6927 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6927 2022/12/09 05:05:26 - mmengine - INFO - Epoch(train) [92][200/940] lr: 1.0000e-04 eta: 1:25:36 time: 0.5589 data_time: 0.0244 memory: 16095 grad_norm: 6.0335 loss: 0.7309 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.7309 2022/12/09 05:05:39 - mmengine - INFO - Epoch(train) [92][220/940] lr: 1.0000e-04 eta: 1:25:24 time: 0.6397 data_time: 0.0246 memory: 16095 grad_norm: 6.0893 loss: 0.6860 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6860 2022/12/09 05:05:50 - mmengine - INFO - Epoch(train) [92][240/940] lr: 1.0000e-04 eta: 1:25:11 time: 0.5562 data_time: 0.0323 memory: 16095 grad_norm: 5.8681 loss: 0.5985 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.5985 2022/12/09 05:06:04 - mmengine - INFO - Epoch(train) [92][260/940] lr: 1.0000e-04 eta: 1:24:59 time: 0.6808 data_time: 0.0258 memory: 16095 grad_norm: 6.2481 loss: 0.6910 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6910 2022/12/09 05:06:15 - mmengine - INFO - Epoch(train) [92][280/940] lr: 1.0000e-04 eta: 1:24:46 time: 0.5494 data_time: 0.0239 memory: 16095 grad_norm: 6.1445 loss: 0.6228 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6228 2022/12/09 05:06:28 - mmengine - INFO - Epoch(train) [92][300/940] lr: 1.0000e-04 eta: 1:24:34 time: 0.6815 data_time: 0.0284 memory: 16095 grad_norm: 6.0073 loss: 0.6140 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6140 2022/12/09 05:06:39 - mmengine - INFO - Epoch(train) [92][320/940] lr: 1.0000e-04 eta: 1:24:22 time: 0.5503 data_time: 0.0220 memory: 16095 grad_norm: 5.9593 loss: 0.5854 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.5854 2022/12/09 05:06:54 - mmengine - INFO - Epoch(train) [92][340/940] lr: 1.0000e-04 eta: 1:24:09 time: 0.7093 data_time: 0.0273 memory: 16095 grad_norm: 6.1154 loss: 0.6181 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6181 2022/12/09 05:07:05 - mmengine - INFO - Epoch(train) [92][360/940] lr: 1.0000e-04 eta: 1:23:57 time: 0.5572 data_time: 0.0223 memory: 16095 grad_norm: 6.0648 loss: 0.6892 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6892 2022/12/09 05:07:17 - mmengine - INFO - Epoch(train) [92][380/940] lr: 1.0000e-04 eta: 1:23:44 time: 0.6169 data_time: 0.0241 memory: 16095 grad_norm: 6.0719 loss: 0.6677 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6677 2022/12/09 05:07:29 - mmengine - INFO - Epoch(train) [92][400/940] lr: 1.0000e-04 eta: 1:23:32 time: 0.5857 data_time: 0.0252 memory: 16095 grad_norm: 6.2087 loss: 0.6662 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6662 2022/12/09 05:07:42 - mmengine - INFO - Epoch(train) [92][420/940] lr: 1.0000e-04 eta: 1:23:19 time: 0.6466 data_time: 0.0244 memory: 16095 grad_norm: 6.0112 loss: 0.6633 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.6633 2022/12/09 05:07:53 - mmengine - INFO - Epoch(train) [92][440/940] lr: 1.0000e-04 eta: 1:23:07 time: 0.5477 data_time: 0.0247 memory: 16095 grad_norm: 6.1839 loss: 0.7687 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7687 2022/12/09 05:08:06 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 05:08:06 - mmengine - INFO - Epoch(train) [92][460/940] lr: 1.0000e-04 eta: 1:22:54 time: 0.6732 data_time: 0.0276 memory: 16095 grad_norm: 5.9536 loss: 0.7056 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.7056 2022/12/09 05:08:18 - mmengine - INFO - Epoch(train) [92][480/940] lr: 1.0000e-04 eta: 1:22:42 time: 0.5929 data_time: 0.0224 memory: 16095 grad_norm: 5.9224 loss: 0.6342 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6342 2022/12/09 05:08:31 - mmengine - INFO - Epoch(train) [92][500/940] lr: 1.0000e-04 eta: 1:22:30 time: 0.6639 data_time: 0.0252 memory: 16095 grad_norm: 6.0000 loss: 0.7043 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7043 2022/12/09 05:08:42 - mmengine - INFO - Epoch(train) [92][520/940] lr: 1.0000e-04 eta: 1:22:17 time: 0.5551 data_time: 0.0253 memory: 16095 grad_norm: 6.1293 loss: 0.6515 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.6515 2022/12/09 05:08:55 - mmengine - INFO - Epoch(train) [92][540/940] lr: 1.0000e-04 eta: 1:22:05 time: 0.6542 data_time: 0.0239 memory: 16095 grad_norm: 6.0152 loss: 0.6554 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.6554 2022/12/09 05:09:07 - mmengine - INFO - Epoch(train) [92][560/940] lr: 1.0000e-04 eta: 1:21:52 time: 0.5779 data_time: 0.0245 memory: 16095 grad_norm: 6.0052 loss: 0.6474 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6474 2022/12/09 05:09:20 - mmengine - INFO - Epoch(train) [92][580/940] lr: 1.0000e-04 eta: 1:21:40 time: 0.6557 data_time: 0.0251 memory: 16095 grad_norm: 5.7617 loss: 0.6512 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6512 2022/12/09 05:09:31 - mmengine - INFO - Epoch(train) [92][600/940] lr: 1.0000e-04 eta: 1:21:27 time: 0.5422 data_time: 0.0244 memory: 16095 grad_norm: 6.0447 loss: 0.6302 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.6302 2022/12/09 05:09:44 - mmengine - INFO - Epoch(train) [92][620/940] lr: 1.0000e-04 eta: 1:21:15 time: 0.6235 data_time: 0.0246 memory: 16095 grad_norm: 6.0636 loss: 0.6124 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6124 2022/12/09 05:09:55 - mmengine - INFO - Epoch(train) [92][640/940] lr: 1.0000e-04 eta: 1:21:02 time: 0.5709 data_time: 0.0974 memory: 16095 grad_norm: 6.1443 loss: 0.6766 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6766 2022/12/09 05:10:07 - mmengine - INFO - Epoch(train) [92][660/940] lr: 1.0000e-04 eta: 1:20:50 time: 0.6067 data_time: 0.1042 memory: 16095 grad_norm: 5.9953 loss: 0.6820 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6820 2022/12/09 05:10:19 - mmengine - INFO - Epoch(train) [92][680/940] lr: 1.0000e-04 eta: 1:20:37 time: 0.5815 data_time: 0.0774 memory: 16095 grad_norm: 5.9392 loss: 0.6488 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6488 2022/12/09 05:10:32 - mmengine - INFO - Epoch(train) [92][700/940] lr: 1.0000e-04 eta: 1:20:25 time: 0.6764 data_time: 0.0252 memory: 16095 grad_norm: 5.9822 loss: 0.7175 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.7175 2022/12/09 05:10:43 - mmengine - INFO - Epoch(train) [92][720/940] lr: 1.0000e-04 eta: 1:20:12 time: 0.5592 data_time: 0.0256 memory: 16095 grad_norm: 6.0331 loss: 0.6070 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.6070 2022/12/09 05:10:58 - mmengine - INFO - Epoch(train) [92][740/940] lr: 1.0000e-04 eta: 1:20:00 time: 0.7096 data_time: 0.0255 memory: 16095 grad_norm: 5.9811 loss: 0.6129 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6129 2022/12/09 05:11:09 - mmengine - INFO - Epoch(train) [92][760/940] lr: 1.0000e-04 eta: 1:19:48 time: 0.5717 data_time: 0.0234 memory: 16095 grad_norm: 6.0729 loss: 0.6035 top1_acc: 0.6875 top5_acc: 0.9688 loss_cls: 0.6035 2022/12/09 05:11:22 - mmengine - INFO - Epoch(train) [92][780/940] lr: 1.0000e-04 eta: 1:19:35 time: 0.6461 data_time: 0.0233 memory: 16095 grad_norm: 5.8906 loss: 0.5837 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.5837 2022/12/09 05:11:33 - mmengine - INFO - Epoch(train) [92][800/940] lr: 1.0000e-04 eta: 1:19:23 time: 0.5525 data_time: 0.0256 memory: 16095 grad_norm: 6.1666 loss: 0.6088 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6088 2022/12/09 05:11:46 - mmengine - INFO - Epoch(train) [92][820/940] lr: 1.0000e-04 eta: 1:19:10 time: 0.6304 data_time: 0.0248 memory: 16095 grad_norm: 6.0157 loss: 0.5384 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.5384 2022/12/09 05:11:57 - mmengine - INFO - Epoch(train) [92][840/940] lr: 1.0000e-04 eta: 1:18:58 time: 0.5758 data_time: 0.0241 memory: 16095 grad_norm: 6.1213 loss: 0.6976 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6976 2022/12/09 05:12:11 - mmengine - INFO - Epoch(train) [92][860/940] lr: 1.0000e-04 eta: 1:18:45 time: 0.6831 data_time: 0.0252 memory: 16095 grad_norm: 6.1377 loss: 0.6095 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6095 2022/12/09 05:12:22 - mmengine - INFO - Epoch(train) [92][880/940] lr: 1.0000e-04 eta: 1:18:33 time: 0.5778 data_time: 0.0215 memory: 16095 grad_norm: 6.0545 loss: 0.5986 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.5986 2022/12/09 05:12:36 - mmengine - INFO - Epoch(train) [92][900/940] lr: 1.0000e-04 eta: 1:18:20 time: 0.6842 data_time: 0.0252 memory: 16095 grad_norm: 6.0136 loss: 0.5611 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.5611 2022/12/09 05:12:47 - mmengine - INFO - Epoch(train) [92][920/940] lr: 1.0000e-04 eta: 1:18:08 time: 0.5352 data_time: 0.0254 memory: 16095 grad_norm: 6.1845 loss: 0.7231 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.7231 2022/12/09 05:12:58 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 05:12:58 - mmengine - INFO - Epoch(train) [92][940/940] lr: 1.0000e-04 eta: 1:17:55 time: 0.5795 data_time: 0.0176 memory: 16095 grad_norm: 6.5539 loss: 0.6406 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 0.6406 2022/12/09 05:13:13 - mmengine - INFO - Epoch(val) [92][20/78] eta: 0:00:40 time: 0.7044 data_time: 0.6108 memory: 1686 2022/12/09 05:13:22 - mmengine - INFO - Epoch(val) [92][40/78] eta: 0:00:22 time: 0.4627 data_time: 0.3696 memory: 1686 2022/12/09 05:13:35 - mmengine - INFO - Epoch(val) [92][60/78] eta: 0:00:11 time: 0.6699 data_time: 0.5734 memory: 1686 2022/12/09 05:13:46 - mmengine - INFO - Epoch(val) [92][78/78] acc/top1: 0.6909 acc/top5: 0.8766 acc/mean1: 0.6908 2022/12/09 05:14:02 - mmengine - INFO - Epoch(train) [93][ 20/940] lr: 1.0000e-04 eta: 1:17:43 time: 0.8110 data_time: 0.3410 memory: 16095 grad_norm: 5.9391 loss: 0.6394 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6394 2022/12/09 05:14:13 - mmengine - INFO - Epoch(train) [93][ 40/940] lr: 1.0000e-04 eta: 1:17:31 time: 0.5542 data_time: 0.0763 memory: 16095 grad_norm: 6.3190 loss: 0.7255 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7255 2022/12/09 05:14:27 - mmengine - INFO - Epoch(train) [93][ 60/940] lr: 1.0000e-04 eta: 1:17:18 time: 0.6760 data_time: 0.0452 memory: 16095 grad_norm: 5.9796 loss: 0.6584 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6584 2022/12/09 05:14:37 - mmengine - INFO - Epoch(train) [93][ 80/940] lr: 1.0000e-04 eta: 1:17:06 time: 0.5168 data_time: 0.0233 memory: 16095 grad_norm: 6.0444 loss: 0.6285 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6285 2022/12/09 05:14:51 - mmengine - INFO - Epoch(train) [93][100/940] lr: 1.0000e-04 eta: 1:16:53 time: 0.6697 data_time: 0.0925 memory: 16095 grad_norm: 5.9883 loss: 0.6928 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6928 2022/12/09 05:15:02 - mmengine - INFO - Epoch(train) [93][120/940] lr: 1.0000e-04 eta: 1:16:41 time: 0.5549 data_time: 0.1268 memory: 16095 grad_norm: 6.1390 loss: 0.6789 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6789 2022/12/09 05:15:15 - mmengine - INFO - Epoch(train) [93][140/940] lr: 1.0000e-04 eta: 1:16:28 time: 0.6642 data_time: 0.2240 memory: 16095 grad_norm: 5.9974 loss: 0.6233 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 0.6233 2022/12/09 05:15:27 - mmengine - INFO - Epoch(train) [93][160/940] lr: 1.0000e-04 eta: 1:16:16 time: 0.5784 data_time: 0.1453 memory: 16095 grad_norm: 6.1145 loss: 0.7461 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.7461 2022/12/09 05:15:40 - mmengine - INFO - Epoch(train) [93][180/940] lr: 1.0000e-04 eta: 1:16:04 time: 0.6913 data_time: 0.1337 memory: 16095 grad_norm: 5.9281 loss: 0.6357 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6357 2022/12/09 05:15:52 - mmengine - INFO - Epoch(train) [93][200/940] lr: 1.0000e-04 eta: 1:15:51 time: 0.5569 data_time: 0.0248 memory: 16095 grad_norm: 5.9440 loss: 0.6184 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6184 2022/12/09 05:16:05 - mmengine - INFO - Epoch(train) [93][220/940] lr: 1.0000e-04 eta: 1:15:39 time: 0.6599 data_time: 0.0255 memory: 16095 grad_norm: 6.0594 loss: 0.5700 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5700 2022/12/09 05:16:16 - mmengine - INFO - Epoch(train) [93][240/940] lr: 1.0000e-04 eta: 1:15:26 time: 0.5388 data_time: 0.0249 memory: 16095 grad_norm: 6.0299 loss: 0.5690 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5690 2022/12/09 05:16:28 - mmengine - INFO - Epoch(train) [93][260/940] lr: 1.0000e-04 eta: 1:15:14 time: 0.6398 data_time: 0.0252 memory: 16095 grad_norm: 6.0364 loss: 0.6700 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6700 2022/12/09 05:16:39 - mmengine - INFO - Epoch(train) [93][280/940] lr: 1.0000e-04 eta: 1:15:01 time: 0.5353 data_time: 0.0234 memory: 16095 grad_norm: 6.0627 loss: 0.6853 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6853 2022/12/09 05:16:52 - mmengine - INFO - Epoch(train) [93][300/940] lr: 1.0000e-04 eta: 1:14:49 time: 0.6223 data_time: 0.0284 memory: 16095 grad_norm: 5.9289 loss: 0.6295 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6295 2022/12/09 05:17:02 - mmengine - INFO - Epoch(train) [93][320/940] lr: 1.0000e-04 eta: 1:14:36 time: 0.5333 data_time: 0.0219 memory: 16095 grad_norm: 5.8990 loss: 0.5551 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.5551 2022/12/09 05:17:15 - mmengine - INFO - Epoch(train) [93][340/940] lr: 1.0000e-04 eta: 1:14:24 time: 0.6594 data_time: 0.0328 memory: 16095 grad_norm: 5.9032 loss: 0.5881 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5881 2022/12/09 05:17:27 - mmengine - INFO - Epoch(train) [93][360/940] lr: 1.0000e-04 eta: 1:14:11 time: 0.5804 data_time: 0.0341 memory: 16095 grad_norm: 5.8328 loss: 0.6346 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6346 2022/12/09 05:17:40 - mmengine - INFO - Epoch(train) [93][380/940] lr: 1.0000e-04 eta: 1:13:59 time: 0.6713 data_time: 0.0254 memory: 16095 grad_norm: 6.1008 loss: 0.6396 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6396 2022/12/09 05:17:52 - mmengine - INFO - Epoch(train) [93][400/940] lr: 1.0000e-04 eta: 1:13:46 time: 0.5744 data_time: 0.0236 memory: 16095 grad_norm: 5.9789 loss: 0.5779 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.5779 2022/12/09 05:18:06 - mmengine - INFO - Epoch(train) [93][420/940] lr: 1.0000e-04 eta: 1:13:34 time: 0.7033 data_time: 0.0264 memory: 16095 grad_norm: 6.1334 loss: 0.7061 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7061 2022/12/09 05:18:17 - mmengine - INFO - Epoch(train) [93][440/940] lr: 1.0000e-04 eta: 1:13:22 time: 0.5546 data_time: 0.0275 memory: 16095 grad_norm: 6.0737 loss: 0.6369 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6369 2022/12/09 05:18:29 - mmengine - INFO - Epoch(train) [93][460/940] lr: 1.0000e-04 eta: 1:13:09 time: 0.5928 data_time: 0.0556 memory: 16095 grad_norm: 6.0950 loss: 0.6531 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6531 2022/12/09 05:18:42 - mmengine - INFO - Epoch(train) [93][480/940] lr: 1.0000e-04 eta: 1:12:57 time: 0.6485 data_time: 0.2831 memory: 16095 grad_norm: 6.0138 loss: 0.6981 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6981 2022/12/09 05:18:53 - mmengine - INFO - Epoch(train) [93][500/940] lr: 1.0000e-04 eta: 1:12:44 time: 0.5671 data_time: 0.1938 memory: 16095 grad_norm: 6.0605 loss: 0.7067 top1_acc: 0.9062 top5_acc: 0.9062 loss_cls: 0.7067 2022/12/09 05:19:07 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 05:19:07 - mmengine - INFO - Epoch(train) [93][520/940] lr: 1.0000e-04 eta: 1:12:32 time: 0.6615 data_time: 0.3091 memory: 16095 grad_norm: 6.1250 loss: 0.6806 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6806 2022/12/09 05:19:18 - mmengine - INFO - Epoch(train) [93][540/940] lr: 1.0000e-04 eta: 1:12:19 time: 0.5884 data_time: 0.1475 memory: 16095 grad_norm: 6.2562 loss: 0.6485 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6485 2022/12/09 05:19:31 - mmengine - INFO - Epoch(train) [93][560/940] lr: 1.0000e-04 eta: 1:12:07 time: 0.6334 data_time: 0.2934 memory: 16095 grad_norm: 5.8155 loss: 0.5715 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.5715 2022/12/09 05:19:43 - mmengine - INFO - Epoch(train) [93][580/940] lr: 1.0000e-04 eta: 1:11:54 time: 0.5920 data_time: 0.2395 memory: 16095 grad_norm: 6.1060 loss: 0.6390 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6390 2022/12/09 05:19:56 - mmengine - INFO - Epoch(train) [93][600/940] lr: 1.0000e-04 eta: 1:11:42 time: 0.6382 data_time: 0.2699 memory: 16095 grad_norm: 5.9350 loss: 0.6799 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6799 2022/12/09 05:20:08 - mmengine - INFO - Epoch(train) [93][620/940] lr: 1.0000e-04 eta: 1:11:30 time: 0.5978 data_time: 0.1389 memory: 16095 grad_norm: 6.1603 loss: 0.5962 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5962 2022/12/09 05:20:20 - mmengine - INFO - Epoch(train) [93][640/940] lr: 1.0000e-04 eta: 1:11:17 time: 0.6322 data_time: 0.2612 memory: 16095 grad_norm: 6.2158 loss: 0.7346 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.7346 2022/12/09 05:20:31 - mmengine - INFO - Epoch(train) [93][660/940] lr: 1.0000e-04 eta: 1:11:05 time: 0.5529 data_time: 0.1511 memory: 16095 grad_norm: 6.0986 loss: 0.7145 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7145 2022/12/09 05:20:44 - mmengine - INFO - Epoch(train) [93][680/940] lr: 1.0000e-04 eta: 1:10:52 time: 0.6319 data_time: 0.0632 memory: 16095 grad_norm: 6.0654 loss: 0.6796 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.6796 2022/12/09 05:20:56 - mmengine - INFO - Epoch(train) [93][700/940] lr: 1.0000e-04 eta: 1:10:40 time: 0.6249 data_time: 0.0197 memory: 16095 grad_norm: 6.0951 loss: 0.6434 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6434 2022/12/09 05:21:09 - mmengine - INFO - Epoch(train) [93][720/940] lr: 1.0000e-04 eta: 1:10:27 time: 0.6314 data_time: 0.0316 memory: 16095 grad_norm: 5.9968 loss: 0.6086 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6086 2022/12/09 05:21:21 - mmengine - INFO - Epoch(train) [93][740/940] lr: 1.0000e-04 eta: 1:10:15 time: 0.6099 data_time: 0.0202 memory: 16095 grad_norm: 5.9240 loss: 0.6395 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6395 2022/12/09 05:21:33 - mmengine - INFO - Epoch(train) [93][760/940] lr: 1.0000e-04 eta: 1:10:02 time: 0.5883 data_time: 0.0273 memory: 16095 grad_norm: 6.0681 loss: 0.6554 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6554 2022/12/09 05:21:46 - mmengine - INFO - Epoch(train) [93][780/940] lr: 1.0000e-04 eta: 1:09:50 time: 0.6414 data_time: 0.0626 memory: 16095 grad_norm: 5.9979 loss: 0.6525 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6525 2022/12/09 05:21:58 - mmengine - INFO - Epoch(train) [93][800/940] lr: 1.0000e-04 eta: 1:09:37 time: 0.6036 data_time: 0.0277 memory: 16095 grad_norm: 6.3250 loss: 0.7368 top1_acc: 0.7188 top5_acc: 1.0000 loss_cls: 0.7368 2022/12/09 05:22:10 - mmengine - INFO - Epoch(train) [93][820/940] lr: 1.0000e-04 eta: 1:09:25 time: 0.5928 data_time: 0.0205 memory: 16095 grad_norm: 6.1316 loss: 0.6069 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6069 2022/12/09 05:22:22 - mmengine - INFO - Epoch(train) [93][840/940] lr: 1.0000e-04 eta: 1:09:13 time: 0.5866 data_time: 0.0318 memory: 16095 grad_norm: 5.9691 loss: 0.6305 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6305 2022/12/09 05:22:34 - mmengine - INFO - Epoch(train) [93][860/940] lr: 1.0000e-04 eta: 1:09:00 time: 0.6212 data_time: 0.0187 memory: 16095 grad_norm: 6.0445 loss: 0.5701 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5701 2022/12/09 05:22:46 - mmengine - INFO - Epoch(train) [93][880/940] lr: 1.0000e-04 eta: 1:08:48 time: 0.6113 data_time: 0.0487 memory: 16095 grad_norm: 6.1835 loss: 0.6822 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6822 2022/12/09 05:22:59 - mmengine - INFO - Epoch(train) [93][900/940] lr: 1.0000e-04 eta: 1:08:35 time: 0.6570 data_time: 0.0239 memory: 16095 grad_norm: 5.9336 loss: 0.6334 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6334 2022/12/09 05:23:11 - mmengine - INFO - Epoch(train) [93][920/940] lr: 1.0000e-04 eta: 1:08:23 time: 0.5955 data_time: 0.0256 memory: 16095 grad_norm: 6.0963 loss: 0.7088 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.7088 2022/12/09 05:23:21 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 05:23:21 - mmengine - INFO - Epoch(train) [93][940/940] lr: 1.0000e-04 eta: 1:08:10 time: 0.5077 data_time: 0.0168 memory: 16095 grad_norm: 6.5492 loss: 0.6047 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 0.6047 2022/12/09 05:23:21 - mmengine - INFO - Saving checkpoint at 93 epochs 2022/12/09 05:23:38 - mmengine - INFO - Epoch(val) [93][20/78] eta: 0:00:40 time: 0.7036 data_time: 0.6091 memory: 1686 2022/12/09 05:23:48 - mmengine - INFO - Epoch(val) [93][40/78] eta: 0:00:22 time: 0.4813 data_time: 0.3882 memory: 1686 2022/12/09 05:24:01 - mmengine - INFO - Epoch(val) [93][60/78] eta: 0:00:11 time: 0.6538 data_time: 0.5580 memory: 1686 2022/12/09 05:24:11 - mmengine - INFO - Epoch(val) [93][78/78] acc/top1: 0.6916 acc/top5: 0.8772 acc/mean1: 0.6914 2022/12/09 05:24:27 - mmengine - INFO - Epoch(train) [94][ 20/940] lr: 1.0000e-04 eta: 1:07:58 time: 0.8115 data_time: 0.3601 memory: 16095 grad_norm: 6.0592 loss: 0.5937 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.5937 2022/12/09 05:24:39 - mmengine - INFO - Epoch(train) [94][ 40/940] lr: 1.0000e-04 eta: 1:07:46 time: 0.6076 data_time: 0.0526 memory: 16095 grad_norm: 5.9938 loss: 0.6880 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6880 2022/12/09 05:24:53 - mmengine - INFO - Epoch(train) [94][ 60/940] lr: 1.0000e-04 eta: 1:07:33 time: 0.6764 data_time: 0.0275 memory: 16095 grad_norm: 6.0395 loss: 0.6182 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6182 2022/12/09 05:25:04 - mmengine - INFO - Epoch(train) [94][ 80/940] lr: 1.0000e-04 eta: 1:07:21 time: 0.5322 data_time: 0.0224 memory: 16095 grad_norm: 6.0486 loss: 0.6753 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6753 2022/12/09 05:25:17 - mmengine - INFO - Epoch(train) [94][100/940] lr: 1.0000e-04 eta: 1:07:08 time: 0.6571 data_time: 0.0401 memory: 16095 grad_norm: 6.0601 loss: 0.6585 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.6585 2022/12/09 05:25:27 - mmengine - INFO - Epoch(train) [94][120/940] lr: 1.0000e-04 eta: 1:06:56 time: 0.4920 data_time: 0.0407 memory: 16095 grad_norm: 5.9884 loss: 0.6546 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6546 2022/12/09 05:25:40 - mmengine - INFO - Epoch(train) [94][140/940] lr: 1.0000e-04 eta: 1:06:43 time: 0.6713 data_time: 0.0913 memory: 16095 grad_norm: 6.0246 loss: 0.6918 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.6918 2022/12/09 05:25:52 - mmengine - INFO - Epoch(train) [94][160/940] lr: 1.0000e-04 eta: 1:06:31 time: 0.5880 data_time: 0.0218 memory: 16095 grad_norm: 5.9583 loss: 0.6510 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6510 2022/12/09 05:26:04 - mmengine - INFO - Epoch(train) [94][180/940] lr: 1.0000e-04 eta: 1:06:18 time: 0.6162 data_time: 0.0371 memory: 16095 grad_norm: 6.0095 loss: 0.5987 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.5987 2022/12/09 05:26:16 - mmengine - INFO - Epoch(train) [94][200/940] lr: 1.0000e-04 eta: 1:06:06 time: 0.6080 data_time: 0.1266 memory: 16095 grad_norm: 6.0230 loss: 0.6204 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6204 2022/12/09 05:26:29 - mmengine - INFO - Epoch(train) [94][220/940] lr: 1.0000e-04 eta: 1:05:53 time: 0.6220 data_time: 0.2121 memory: 16095 grad_norm: 6.0643 loss: 0.6420 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6420 2022/12/09 05:26:40 - mmengine - INFO - Epoch(train) [94][240/940] lr: 1.0000e-04 eta: 1:05:41 time: 0.5833 data_time: 0.2208 memory: 16095 grad_norm: 6.1238 loss: 0.6366 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.6366 2022/12/09 05:26:54 - mmengine - INFO - Epoch(train) [94][260/940] lr: 1.0000e-04 eta: 1:05:29 time: 0.6780 data_time: 0.3573 memory: 16095 grad_norm: 6.0229 loss: 0.6692 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6692 2022/12/09 05:27:05 - mmengine - INFO - Epoch(train) [94][280/940] lr: 1.0000e-04 eta: 1:05:16 time: 0.5615 data_time: 0.2423 memory: 16095 grad_norm: 5.9001 loss: 0.6116 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.6116 2022/12/09 05:27:19 - mmengine - INFO - Epoch(train) [94][300/940] lr: 1.0000e-04 eta: 1:05:04 time: 0.6756 data_time: 0.3403 memory: 16095 grad_norm: 6.2099 loss: 0.6782 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6782 2022/12/09 05:27:29 - mmengine - INFO - Epoch(train) [94][320/940] lr: 1.0000e-04 eta: 1:04:51 time: 0.5321 data_time: 0.1355 memory: 16095 grad_norm: 5.9976 loss: 0.5859 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 0.5859 2022/12/09 05:27:43 - mmengine - INFO - Epoch(train) [94][340/940] lr: 1.0000e-04 eta: 1:04:39 time: 0.6577 data_time: 0.2162 memory: 16095 grad_norm: 6.2175 loss: 0.7247 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7247 2022/12/09 05:27:55 - mmengine - INFO - Epoch(train) [94][360/940] lr: 1.0000e-04 eta: 1:04:26 time: 0.6285 data_time: 0.1112 memory: 16095 grad_norm: 6.0195 loss: 0.5762 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.5762 2022/12/09 05:28:08 - mmengine - INFO - Epoch(train) [94][380/940] lr: 1.0000e-04 eta: 1:04:14 time: 0.6668 data_time: 0.0375 memory: 16095 grad_norm: 6.0801 loss: 0.7313 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.7313 2022/12/09 05:28:19 - mmengine - INFO - Epoch(train) [94][400/940] lr: 1.0000e-04 eta: 1:04:01 time: 0.5244 data_time: 0.0583 memory: 16095 grad_norm: 5.9322 loss: 0.5826 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.5826 2022/12/09 05:28:34 - mmengine - INFO - Epoch(train) [94][420/940] lr: 1.0000e-04 eta: 1:03:49 time: 0.7285 data_time: 0.0779 memory: 16095 grad_norm: 5.9913 loss: 0.6151 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.6151 2022/12/09 05:28:45 - mmengine - INFO - Epoch(train) [94][440/940] lr: 1.0000e-04 eta: 1:03:37 time: 0.5528 data_time: 0.0322 memory: 16095 grad_norm: 5.8714 loss: 0.6325 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6325 2022/12/09 05:28:57 - mmengine - INFO - Epoch(train) [94][460/940] lr: 1.0000e-04 eta: 1:03:24 time: 0.6287 data_time: 0.1198 memory: 16095 grad_norm: 6.0561 loss: 0.6763 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6763 2022/12/09 05:29:08 - mmengine - INFO - Epoch(train) [94][480/940] lr: 1.0000e-04 eta: 1:03:12 time: 0.5400 data_time: 0.1364 memory: 16095 grad_norm: 6.0420 loss: 0.6539 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6539 2022/12/09 05:29:22 - mmengine - INFO - Epoch(train) [94][500/940] lr: 1.0000e-04 eta: 1:02:59 time: 0.6789 data_time: 0.3449 memory: 16095 grad_norm: 5.9726 loss: 0.6617 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6617 2022/12/09 05:29:33 - mmengine - INFO - Epoch(train) [94][520/940] lr: 1.0000e-04 eta: 1:02:47 time: 0.5695 data_time: 0.1964 memory: 16095 grad_norm: 5.9709 loss: 0.6508 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6508 2022/12/09 05:29:45 - mmengine - INFO - Epoch(train) [94][540/940] lr: 1.0000e-04 eta: 1:02:34 time: 0.6188 data_time: 0.2229 memory: 16095 grad_norm: 6.0850 loss: 0.6645 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6645 2022/12/09 05:29:56 - mmengine - INFO - Epoch(train) [94][560/940] lr: 1.0000e-04 eta: 1:02:22 time: 0.5419 data_time: 0.1479 memory: 16095 grad_norm: 6.0674 loss: 0.6430 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6430 2022/12/09 05:30:10 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 05:30:10 - mmengine - INFO - Epoch(train) [94][580/940] lr: 1.0000e-04 eta: 1:02:09 time: 0.6718 data_time: 0.3118 memory: 16095 grad_norm: 5.9714 loss: 0.7207 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7207 2022/12/09 05:30:21 - mmengine - INFO - Epoch(train) [94][600/940] lr: 1.0000e-04 eta: 1:01:57 time: 0.5556 data_time: 0.2089 memory: 16095 grad_norm: 5.9330 loss: 0.6254 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.6254 2022/12/09 05:30:35 - mmengine - INFO - Epoch(train) [94][620/940] lr: 1.0000e-04 eta: 1:01:45 time: 0.6913 data_time: 0.3119 memory: 16095 grad_norm: 5.8941 loss: 0.6531 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.6531 2022/12/09 05:30:47 - mmengine - INFO - Epoch(train) [94][640/940] lr: 1.0000e-04 eta: 1:01:32 time: 0.6017 data_time: 0.1437 memory: 16095 grad_norm: 6.0258 loss: 0.6391 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6391 2022/12/09 05:31:00 - mmengine - INFO - Epoch(train) [94][660/940] lr: 1.0000e-04 eta: 1:01:20 time: 0.6706 data_time: 0.0861 memory: 16095 grad_norm: 5.8560 loss: 0.5778 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.5778 2022/12/09 05:31:10 - mmengine - INFO - Epoch(train) [94][680/940] lr: 1.0000e-04 eta: 1:01:07 time: 0.5087 data_time: 0.0635 memory: 16095 grad_norm: 6.2387 loss: 0.6299 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 0.6299 2022/12/09 05:31:23 - mmengine - INFO - Epoch(train) [94][700/940] lr: 1.0000e-04 eta: 1:00:55 time: 0.6506 data_time: 0.1568 memory: 16095 grad_norm: 6.1074 loss: 0.6991 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6991 2022/12/09 05:31:34 - mmengine - INFO - Epoch(train) [94][720/940] lr: 1.0000e-04 eta: 1:00:42 time: 0.5578 data_time: 0.1589 memory: 16095 grad_norm: 6.0785 loss: 0.6068 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6068 2022/12/09 05:31:48 - mmengine - INFO - Epoch(train) [94][740/940] lr: 1.0000e-04 eta: 1:00:30 time: 0.6665 data_time: 0.1586 memory: 16095 grad_norm: 5.8957 loss: 0.5604 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.5604 2022/12/09 05:31:59 - mmengine - INFO - Epoch(train) [94][760/940] lr: 1.0000e-04 eta: 1:00:17 time: 0.5608 data_time: 0.0943 memory: 16095 grad_norm: 5.8987 loss: 0.6980 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6980 2022/12/09 05:32:12 - mmengine - INFO - Epoch(train) [94][780/940] lr: 1.0000e-04 eta: 1:00:05 time: 0.6432 data_time: 0.1482 memory: 16095 grad_norm: 6.0798 loss: 0.6773 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.6773 2022/12/09 05:32:23 - mmengine - INFO - Epoch(train) [94][800/940] lr: 1.0000e-04 eta: 0:59:52 time: 0.5558 data_time: 0.1010 memory: 16095 grad_norm: 6.1656 loss: 0.6630 top1_acc: 0.6562 top5_acc: 0.9688 loss_cls: 0.6630 2022/12/09 05:32:37 - mmengine - INFO - Epoch(train) [94][820/940] lr: 1.0000e-04 eta: 0:59:40 time: 0.6802 data_time: 0.0989 memory: 16095 grad_norm: 6.0487 loss: 0.6288 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6288 2022/12/09 05:32:48 - mmengine - INFO - Epoch(train) [94][840/940] lr: 1.0000e-04 eta: 0:59:28 time: 0.5522 data_time: 0.0707 memory: 16095 grad_norm: 6.1382 loss: 0.6992 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6992 2022/12/09 05:33:00 - mmengine - INFO - Epoch(train) [94][860/940] lr: 1.0000e-04 eta: 0:59:15 time: 0.6397 data_time: 0.0929 memory: 16095 grad_norm: 6.1775 loss: 0.7309 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.7309 2022/12/09 05:33:12 - mmengine - INFO - Epoch(train) [94][880/940] lr: 1.0000e-04 eta: 0:59:03 time: 0.5895 data_time: 0.0621 memory: 16095 grad_norm: 6.1679 loss: 0.6500 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6500 2022/12/09 05:33:24 - mmengine - INFO - Epoch(train) [94][900/940] lr: 1.0000e-04 eta: 0:58:50 time: 0.5941 data_time: 0.0415 memory: 16095 grad_norm: 6.1338 loss: 0.6695 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6695 2022/12/09 05:33:36 - mmengine - INFO - Epoch(train) [94][920/940] lr: 1.0000e-04 eta: 0:58:38 time: 0.6060 data_time: 0.0258 memory: 16095 grad_norm: 6.0341 loss: 0.6643 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6643 2022/12/09 05:33:47 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 05:33:47 - mmengine - INFO - Epoch(train) [94][940/940] lr: 1.0000e-04 eta: 0:58:25 time: 0.5218 data_time: 0.0182 memory: 16095 grad_norm: 6.5274 loss: 0.6582 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 0.6582 2022/12/09 05:34:01 - mmengine - INFO - Epoch(val) [94][20/78] eta: 0:00:41 time: 0.7117 data_time: 0.6172 memory: 1686 2022/12/09 05:34:10 - mmengine - INFO - Epoch(val) [94][40/78] eta: 0:00:22 time: 0.4570 data_time: 0.3622 memory: 1686 2022/12/09 05:34:24 - mmengine - INFO - Epoch(val) [94][60/78] eta: 0:00:11 time: 0.6926 data_time: 0.5968 memory: 1686 2022/12/09 05:34:34 - mmengine - INFO - Epoch(val) [94][78/78] acc/top1: 0.6908 acc/top5: 0.8770 acc/mean1: 0.6907 2022/12/09 05:34:50 - mmengine - INFO - Epoch(train) [95][ 20/940] lr: 1.0000e-04 eta: 0:58:13 time: 0.7937 data_time: 0.3573 memory: 16095 grad_norm: 5.9466 loss: 0.6201 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6201 2022/12/09 05:35:01 - mmengine - INFO - Epoch(train) [95][ 40/940] lr: 1.0000e-04 eta: 0:58:00 time: 0.5446 data_time: 0.1553 memory: 16095 grad_norm: 6.1151 loss: 0.6718 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6718 2022/12/09 05:35:15 - mmengine - INFO - Epoch(train) [95][ 60/940] lr: 1.0000e-04 eta: 0:57:48 time: 0.6868 data_time: 0.1934 memory: 16095 grad_norm: 5.9889 loss: 0.5825 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.5825 2022/12/09 05:35:26 - mmengine - INFO - Epoch(train) [95][ 80/940] lr: 1.0000e-04 eta: 0:57:36 time: 0.5484 data_time: 0.0253 memory: 16095 grad_norm: 5.9558 loss: 0.6767 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6767 2022/12/09 05:35:39 - mmengine - INFO - Epoch(train) [95][100/940] lr: 1.0000e-04 eta: 0:57:23 time: 0.6674 data_time: 0.0437 memory: 16095 grad_norm: 6.1463 loss: 0.7180 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7180 2022/12/09 05:35:50 - mmengine - INFO - Epoch(train) [95][120/940] lr: 1.0000e-04 eta: 0:57:11 time: 0.5477 data_time: 0.0330 memory: 16095 grad_norm: 5.9848 loss: 0.6012 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6012 2022/12/09 05:36:04 - mmengine - INFO - Epoch(train) [95][140/940] lr: 1.0000e-04 eta: 0:56:58 time: 0.6972 data_time: 0.0271 memory: 16095 grad_norm: 5.9813 loss: 0.6142 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6142 2022/12/09 05:36:15 - mmengine - INFO - Epoch(train) [95][160/940] lr: 1.0000e-04 eta: 0:56:46 time: 0.5526 data_time: 0.0236 memory: 16095 grad_norm: 5.9106 loss: 0.6385 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.6385 2022/12/09 05:36:29 - mmengine - INFO - Epoch(train) [95][180/940] lr: 1.0000e-04 eta: 0:56:34 time: 0.6989 data_time: 0.0279 memory: 16095 grad_norm: 5.9959 loss: 0.6325 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6325 2022/12/09 05:36:40 - mmengine - INFO - Epoch(train) [95][200/940] lr: 1.0000e-04 eta: 0:56:21 time: 0.5636 data_time: 0.0211 memory: 16095 grad_norm: 6.0731 loss: 0.6740 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6740 2022/12/09 05:36:53 - mmengine - INFO - Epoch(train) [95][220/940] lr: 1.0000e-04 eta: 0:56:09 time: 0.6374 data_time: 0.0278 memory: 16095 grad_norm: 6.0478 loss: 0.6855 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6855 2022/12/09 05:37:04 - mmengine - INFO - Epoch(train) [95][240/940] lr: 1.0000e-04 eta: 0:55:56 time: 0.5579 data_time: 0.0209 memory: 16095 grad_norm: 6.0400 loss: 0.7055 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7055 2022/12/09 05:37:18 - mmengine - INFO - Epoch(train) [95][260/940] lr: 1.0000e-04 eta: 0:55:44 time: 0.6829 data_time: 0.0279 memory: 16095 grad_norm: 5.9966 loss: 0.6385 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6385 2022/12/09 05:37:29 - mmengine - INFO - Epoch(train) [95][280/940] lr: 1.0000e-04 eta: 0:55:31 time: 0.5642 data_time: 0.0224 memory: 16095 grad_norm: 6.1616 loss: 0.7213 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.7213 2022/12/09 05:37:43 - mmengine - INFO - Epoch(train) [95][300/940] lr: 1.0000e-04 eta: 0:55:19 time: 0.6837 data_time: 0.0255 memory: 16095 grad_norm: 5.9721 loss: 0.6758 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.6758 2022/12/09 05:37:53 - mmengine - INFO - Epoch(train) [95][320/940] lr: 1.0000e-04 eta: 0:55:06 time: 0.5159 data_time: 0.0225 memory: 16095 grad_norm: 5.9262 loss: 0.5923 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.5923 2022/12/09 05:38:07 - mmengine - INFO - Epoch(train) [95][340/940] lr: 1.0000e-04 eta: 0:54:54 time: 0.6729 data_time: 0.0246 memory: 16095 grad_norm: 6.0424 loss: 0.6635 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.6635 2022/12/09 05:38:18 - mmengine - INFO - Epoch(train) [95][360/940] lr: 1.0000e-04 eta: 0:54:41 time: 0.5543 data_time: 0.0239 memory: 16095 grad_norm: 6.0857 loss: 0.6115 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6115 2022/12/09 05:38:31 - mmengine - INFO - Epoch(train) [95][380/940] lr: 1.0000e-04 eta: 0:54:29 time: 0.6832 data_time: 0.0240 memory: 16095 grad_norm: 6.0767 loss: 0.6682 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6682 2022/12/09 05:38:42 - mmengine - INFO - Epoch(train) [95][400/940] lr: 1.0000e-04 eta: 0:54:17 time: 0.5408 data_time: 0.0357 memory: 16095 grad_norm: 6.0828 loss: 0.7058 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7058 2022/12/09 05:38:55 - mmengine - INFO - Epoch(train) [95][420/940] lr: 1.0000e-04 eta: 0:54:04 time: 0.6635 data_time: 0.0249 memory: 16095 grad_norm: 6.0875 loss: 0.7166 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7166 2022/12/09 05:39:06 - mmengine - INFO - Epoch(train) [95][440/940] lr: 1.0000e-04 eta: 0:53:52 time: 0.5359 data_time: 0.0270 memory: 16095 grad_norm: 6.0166 loss: 0.7316 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7316 2022/12/09 05:39:19 - mmengine - INFO - Epoch(train) [95][460/940] lr: 1.0000e-04 eta: 0:53:39 time: 0.6263 data_time: 0.0227 memory: 16095 grad_norm: 6.0604 loss: 0.7164 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.7164 2022/12/09 05:39:30 - mmengine - INFO - Epoch(train) [95][480/940] lr: 1.0000e-04 eta: 0:53:27 time: 0.5430 data_time: 0.0261 memory: 16095 grad_norm: 6.1112 loss: 0.6350 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6350 2022/12/09 05:39:43 - mmengine - INFO - Epoch(train) [95][500/940] lr: 1.0000e-04 eta: 0:53:14 time: 0.6769 data_time: 0.0251 memory: 16095 grad_norm: 5.8923 loss: 0.6597 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6597 2022/12/09 05:39:54 - mmengine - INFO - Epoch(train) [95][520/940] lr: 1.0000e-04 eta: 0:53:02 time: 0.5678 data_time: 0.0259 memory: 16095 grad_norm: 6.0528 loss: 0.6217 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6217 2022/12/09 05:40:08 - mmengine - INFO - Epoch(train) [95][540/940] lr: 1.0000e-04 eta: 0:52:49 time: 0.6585 data_time: 0.0241 memory: 16095 grad_norm: 6.0261 loss: 0.6097 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.6097 2022/12/09 05:40:18 - mmengine - INFO - Epoch(train) [95][560/940] lr: 1.0000e-04 eta: 0:52:37 time: 0.5227 data_time: 0.0245 memory: 16095 grad_norm: 6.2748 loss: 0.6343 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.6343 2022/12/09 05:40:32 - mmengine - INFO - Epoch(train) [95][580/940] lr: 1.0000e-04 eta: 0:52:25 time: 0.6805 data_time: 0.0241 memory: 16095 grad_norm: 6.1433 loss: 0.6142 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6142 2022/12/09 05:40:43 - mmengine - INFO - Epoch(train) [95][600/940] lr: 1.0000e-04 eta: 0:52:12 time: 0.5608 data_time: 0.0264 memory: 16095 grad_norm: 6.1069 loss: 0.6073 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6073 2022/12/09 05:40:56 - mmengine - INFO - Epoch(train) [95][620/940] lr: 1.0000e-04 eta: 0:52:00 time: 0.6418 data_time: 0.0233 memory: 16095 grad_norm: 5.9407 loss: 0.7057 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7057 2022/12/09 05:41:08 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 05:41:08 - mmengine - INFO - Epoch(train) [95][640/940] lr: 1.0000e-04 eta: 0:51:47 time: 0.5905 data_time: 0.0242 memory: 16095 grad_norm: 6.0085 loss: 0.6500 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6500 2022/12/09 05:41:21 - mmengine - INFO - Epoch(train) [95][660/940] lr: 1.0000e-04 eta: 0:51:35 time: 0.6467 data_time: 0.0227 memory: 16095 grad_norm: 5.9972 loss: 0.5236 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5236 2022/12/09 05:41:32 - mmengine - INFO - Epoch(train) [95][680/940] lr: 1.0000e-04 eta: 0:51:22 time: 0.5881 data_time: 0.0280 memory: 16095 grad_norm: 6.2122 loss: 0.7178 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7178 2022/12/09 05:41:45 - mmengine - INFO - Epoch(train) [95][700/940] lr: 1.0000e-04 eta: 0:51:10 time: 0.6374 data_time: 0.0217 memory: 16095 grad_norm: 5.9163 loss: 0.6359 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6359 2022/12/09 05:41:57 - mmengine - INFO - Epoch(train) [95][720/940] lr: 1.0000e-04 eta: 0:50:57 time: 0.5996 data_time: 0.0294 memory: 16095 grad_norm: 6.0505 loss: 0.6017 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.6017 2022/12/09 05:42:10 - mmengine - INFO - Epoch(train) [95][740/940] lr: 1.0000e-04 eta: 0:50:45 time: 0.6407 data_time: 0.0271 memory: 16095 grad_norm: 6.0887 loss: 0.6620 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.6620 2022/12/09 05:42:21 - mmengine - INFO - Epoch(train) [95][760/940] lr: 1.0000e-04 eta: 0:50:32 time: 0.5589 data_time: 0.0275 memory: 16095 grad_norm: 6.0933 loss: 0.6136 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6136 2022/12/09 05:42:34 - mmengine - INFO - Epoch(train) [95][780/940] lr: 1.0000e-04 eta: 0:50:20 time: 0.6295 data_time: 0.0240 memory: 16095 grad_norm: 6.0872 loss: 0.7156 top1_acc: 0.6562 top5_acc: 0.9062 loss_cls: 0.7156 2022/12/09 05:42:45 - mmengine - INFO - Epoch(train) [95][800/940] lr: 1.0000e-04 eta: 0:50:08 time: 0.5447 data_time: 0.0289 memory: 16095 grad_norm: 6.0379 loss: 0.5639 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.5639 2022/12/09 05:42:58 - mmengine - INFO - Epoch(train) [95][820/940] lr: 1.0000e-04 eta: 0:49:55 time: 0.6846 data_time: 0.0313 memory: 16095 grad_norm: 5.9631 loss: 0.6376 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.6376 2022/12/09 05:43:09 - mmengine - INFO - Epoch(train) [95][840/940] lr: 1.0000e-04 eta: 0:49:43 time: 0.5605 data_time: 0.0288 memory: 16095 grad_norm: 6.0381 loss: 0.6802 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6802 2022/12/09 05:43:22 - mmengine - INFO - Epoch(train) [95][860/940] lr: 1.0000e-04 eta: 0:49:30 time: 0.6198 data_time: 0.0228 memory: 16095 grad_norm: 5.8764 loss: 0.6176 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6176 2022/12/09 05:43:34 - mmengine - INFO - Epoch(train) [95][880/940] lr: 1.0000e-04 eta: 0:49:18 time: 0.6050 data_time: 0.0279 memory: 16095 grad_norm: 6.0328 loss: 0.6265 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6265 2022/12/09 05:43:46 - mmengine - INFO - Epoch(train) [95][900/940] lr: 1.0000e-04 eta: 0:49:05 time: 0.6040 data_time: 0.0276 memory: 16095 grad_norm: 5.9073 loss: 0.6120 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6120 2022/12/09 05:43:59 - mmengine - INFO - Epoch(train) [95][920/940] lr: 1.0000e-04 eta: 0:48:53 time: 0.6266 data_time: 0.0245 memory: 16095 grad_norm: 5.9837 loss: 0.6615 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6615 2022/12/09 05:44:08 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 05:44:08 - mmengine - INFO - Epoch(train) [95][940/940] lr: 1.0000e-04 eta: 0:48:40 time: 0.4896 data_time: 0.0180 memory: 16095 grad_norm: 6.6718 loss: 0.6988 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 0.6988 2022/12/09 05:44:22 - mmengine - INFO - Epoch(val) [95][20/78] eta: 0:00:40 time: 0.7008 data_time: 0.6055 memory: 1686 2022/12/09 05:44:32 - mmengine - INFO - Epoch(val) [95][40/78] eta: 0:00:22 time: 0.4648 data_time: 0.3730 memory: 1686 2022/12/09 05:44:45 - mmengine - INFO - Epoch(val) [95][60/78] eta: 0:00:11 time: 0.6701 data_time: 0.5746 memory: 1686 2022/12/09 05:44:56 - mmengine - INFO - Epoch(val) [95][78/78] acc/top1: 0.6904 acc/top5: 0.8766 acc/mean1: 0.6903 2022/12/09 05:45:12 - mmengine - INFO - Epoch(train) [96][ 20/940] lr: 1.0000e-04 eta: 0:48:28 time: 0.8201 data_time: 0.4063 memory: 16095 grad_norm: 6.0382 loss: 0.5559 top1_acc: 0.7188 top5_acc: 0.9375 loss_cls: 0.5559 2022/12/09 05:45:24 - mmengine - INFO - Epoch(train) [96][ 40/940] lr: 1.0000e-04 eta: 0:48:16 time: 0.5723 data_time: 0.1418 memory: 16095 grad_norm: 6.0044 loss: 0.6007 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.6007 2022/12/09 05:45:37 - mmengine - INFO - Epoch(train) [96][ 60/940] lr: 1.0000e-04 eta: 0:48:03 time: 0.6447 data_time: 0.1324 memory: 16095 grad_norm: 5.9421 loss: 0.6552 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6552 2022/12/09 05:45:49 - mmengine - INFO - Epoch(train) [96][ 80/940] lr: 1.0000e-04 eta: 0:47:51 time: 0.5915 data_time: 0.1277 memory: 16095 grad_norm: 6.1595 loss: 0.6537 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6537 2022/12/09 05:46:02 - mmengine - INFO - Epoch(train) [96][100/940] lr: 1.0000e-04 eta: 0:47:38 time: 0.6681 data_time: 0.1129 memory: 16095 grad_norm: 6.0282 loss: 0.6335 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6335 2022/12/09 05:46:12 - mmengine - INFO - Epoch(train) [96][120/940] lr: 1.0000e-04 eta: 0:47:26 time: 0.5118 data_time: 0.0629 memory: 16095 grad_norm: 6.2291 loss: 0.6543 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6543 2022/12/09 05:46:26 - mmengine - INFO - Epoch(train) [96][140/940] lr: 1.0000e-04 eta: 0:47:14 time: 0.6898 data_time: 0.0905 memory: 16095 grad_norm: 5.9035 loss: 0.6486 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6486 2022/12/09 05:46:37 - mmengine - INFO - Epoch(train) [96][160/940] lr: 1.0000e-04 eta: 0:47:01 time: 0.5596 data_time: 0.0348 memory: 16095 grad_norm: 5.9521 loss: 0.6675 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.6675 2022/12/09 05:46:51 - mmengine - INFO - Epoch(train) [96][180/940] lr: 1.0000e-04 eta: 0:46:49 time: 0.7070 data_time: 0.0377 memory: 16095 grad_norm: 6.0366 loss: 0.5511 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5511 2022/12/09 05:47:03 - mmengine - INFO - Epoch(train) [96][200/940] lr: 1.0000e-04 eta: 0:46:36 time: 0.5720 data_time: 0.0214 memory: 16095 grad_norm: 6.0922 loss: 0.6009 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 0.6009 2022/12/09 05:47:15 - mmengine - INFO - Epoch(train) [96][220/940] lr: 1.0000e-04 eta: 0:46:24 time: 0.6369 data_time: 0.0288 memory: 16095 grad_norm: 6.1165 loss: 0.6615 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6615 2022/12/09 05:47:27 - mmengine - INFO - Epoch(train) [96][240/940] lr: 1.0000e-04 eta: 0:46:11 time: 0.5544 data_time: 0.0219 memory: 16095 grad_norm: 5.9796 loss: 0.6925 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6925 2022/12/09 05:47:40 - mmengine - INFO - Epoch(train) [96][260/940] lr: 1.0000e-04 eta: 0:45:59 time: 0.6780 data_time: 0.0647 memory: 16095 grad_norm: 6.1659 loss: 0.6518 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6518 2022/12/09 05:47:52 - mmengine - INFO - Epoch(train) [96][280/940] lr: 1.0000e-04 eta: 0:45:46 time: 0.5705 data_time: 0.0390 memory: 16095 grad_norm: 5.9293 loss: 0.6412 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6412 2022/12/09 05:48:05 - mmengine - INFO - Epoch(train) [96][300/940] lr: 1.0000e-04 eta: 0:45:34 time: 0.6731 data_time: 0.0288 memory: 16095 grad_norm: 6.1905 loss: 0.6321 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.6321 2022/12/09 05:48:16 - mmengine - INFO - Epoch(train) [96][320/940] lr: 1.0000e-04 eta: 0:45:22 time: 0.5519 data_time: 0.0203 memory: 16095 grad_norm: 6.0353 loss: 0.6447 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.6447 2022/12/09 05:48:29 - mmengine - INFO - Epoch(train) [96][340/940] lr: 1.0000e-04 eta: 0:45:09 time: 0.6228 data_time: 0.0276 memory: 16095 grad_norm: 6.1534 loss: 0.6943 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6943 2022/12/09 05:48:40 - mmengine - INFO - Epoch(train) [96][360/940] lr: 1.0000e-04 eta: 0:44:57 time: 0.5552 data_time: 0.0229 memory: 16095 grad_norm: 6.0820 loss: 0.6302 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6302 2022/12/09 05:48:53 - mmengine - INFO - Epoch(train) [96][380/940] lr: 1.0000e-04 eta: 0:44:44 time: 0.6638 data_time: 0.0245 memory: 16095 grad_norm: 5.9224 loss: 0.6472 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6472 2022/12/09 05:49:04 - mmengine - INFO - Epoch(train) [96][400/940] lr: 1.0000e-04 eta: 0:44:32 time: 0.5379 data_time: 0.0241 memory: 16095 grad_norm: 6.1278 loss: 0.6446 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6446 2022/12/09 05:49:17 - mmengine - INFO - Epoch(train) [96][420/940] lr: 1.0000e-04 eta: 0:44:19 time: 0.6427 data_time: 0.0266 memory: 16095 grad_norm: 5.9560 loss: 0.6337 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6337 2022/12/09 05:49:28 - mmengine - INFO - Epoch(train) [96][440/940] lr: 1.0000e-04 eta: 0:44:07 time: 0.5590 data_time: 0.0259 memory: 16095 grad_norm: 6.0257 loss: 0.6094 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6094 2022/12/09 05:49:41 - mmengine - INFO - Epoch(train) [96][460/940] lr: 1.0000e-04 eta: 0:43:54 time: 0.6529 data_time: 0.0240 memory: 16095 grad_norm: 5.9064 loss: 0.6535 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6535 2022/12/09 05:49:52 - mmengine - INFO - Epoch(train) [96][480/940] lr: 1.0000e-04 eta: 0:43:42 time: 0.5620 data_time: 0.0256 memory: 16095 grad_norm: 5.9896 loss: 0.6785 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6785 2022/12/09 05:50:05 - mmengine - INFO - Epoch(train) [96][500/940] lr: 1.0000e-04 eta: 0:43:30 time: 0.6594 data_time: 0.0267 memory: 16095 grad_norm: 6.2006 loss: 0.6029 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6029 2022/12/09 05:50:16 - mmengine - INFO - Epoch(train) [96][520/940] lr: 1.0000e-04 eta: 0:43:17 time: 0.5585 data_time: 0.0255 memory: 16095 grad_norm: 6.0078 loss: 0.6845 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6845 2022/12/09 05:50:29 - mmengine - INFO - Epoch(train) [96][540/940] lr: 1.0000e-04 eta: 0:43:05 time: 0.6446 data_time: 0.0237 memory: 16095 grad_norm: 6.0513 loss: 0.6520 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6520 2022/12/09 05:50:40 - mmengine - INFO - Epoch(train) [96][560/940] lr: 1.0000e-04 eta: 0:42:52 time: 0.5567 data_time: 0.0270 memory: 16095 grad_norm: 6.0285 loss: 0.6666 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.6666 2022/12/09 05:50:54 - mmengine - INFO - Epoch(train) [96][580/940] lr: 1.0000e-04 eta: 0:42:40 time: 0.6943 data_time: 0.0305 memory: 16095 grad_norm: 6.1569 loss: 0.5762 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.5762 2022/12/09 05:51:05 - mmengine - INFO - Epoch(train) [96][600/940] lr: 1.0000e-04 eta: 0:42:27 time: 0.5476 data_time: 0.0255 memory: 16095 grad_norm: 6.2397 loss: 0.6129 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6129 2022/12/09 05:51:18 - mmengine - INFO - Epoch(train) [96][620/940] lr: 1.0000e-04 eta: 0:42:15 time: 0.6540 data_time: 0.0215 memory: 16095 grad_norm: 5.9808 loss: 0.6690 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6690 2022/12/09 05:51:30 - mmengine - INFO - Epoch(train) [96][640/940] lr: 1.0000e-04 eta: 0:42:02 time: 0.5750 data_time: 0.0243 memory: 16095 grad_norm: 6.0700 loss: 0.6504 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6504 2022/12/09 05:51:44 - mmengine - INFO - Epoch(train) [96][660/940] lr: 1.0000e-04 eta: 0:41:50 time: 0.6855 data_time: 0.0254 memory: 16095 grad_norm: 6.1623 loss: 0.6146 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6146 2022/12/09 05:51:54 - mmengine - INFO - Epoch(train) [96][680/940] lr: 1.0000e-04 eta: 0:41:38 time: 0.5373 data_time: 0.0259 memory: 16095 grad_norm: 6.0582 loss: 0.6821 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6821 2022/12/09 05:52:08 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 05:52:08 - mmengine - INFO - Epoch(train) [96][700/940] lr: 1.0000e-04 eta: 0:41:25 time: 0.6825 data_time: 0.0292 memory: 16095 grad_norm: 6.0766 loss: 0.6598 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6598 2022/12/09 05:52:19 - mmengine - INFO - Epoch(train) [96][720/940] lr: 1.0000e-04 eta: 0:41:13 time: 0.5633 data_time: 0.0305 memory: 16095 grad_norm: 6.0419 loss: 0.6727 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6727 2022/12/09 05:52:32 - mmengine - INFO - Epoch(train) [96][740/940] lr: 1.0000e-04 eta: 0:41:00 time: 0.6273 data_time: 0.0292 memory: 16095 grad_norm: 6.0325 loss: 0.6520 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6520 2022/12/09 05:52:43 - mmengine - INFO - Epoch(train) [96][760/940] lr: 1.0000e-04 eta: 0:40:48 time: 0.5796 data_time: 0.0208 memory: 16095 grad_norm: 5.9841 loss: 0.6701 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.6701 2022/12/09 05:52:57 - mmengine - INFO - Epoch(train) [96][780/940] lr: 1.0000e-04 eta: 0:40:35 time: 0.6589 data_time: 0.0282 memory: 16095 grad_norm: 6.1114 loss: 0.6233 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6233 2022/12/09 05:53:07 - mmengine - INFO - Epoch(train) [96][800/940] lr: 1.0000e-04 eta: 0:40:23 time: 0.5312 data_time: 0.0229 memory: 16095 grad_norm: 6.0372 loss: 0.7149 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7149 2022/12/09 05:53:21 - mmengine - INFO - Epoch(train) [96][820/940] lr: 1.0000e-04 eta: 0:40:11 time: 0.6680 data_time: 0.0271 memory: 16095 grad_norm: 6.0903 loss: 0.6698 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6698 2022/12/09 05:53:32 - mmengine - INFO - Epoch(train) [96][840/940] lr: 1.0000e-04 eta: 0:39:58 time: 0.5439 data_time: 0.0232 memory: 16095 grad_norm: 6.0889 loss: 0.6049 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.6049 2022/12/09 05:53:45 - mmengine - INFO - Epoch(train) [96][860/940] lr: 1.0000e-04 eta: 0:39:46 time: 0.6764 data_time: 0.0274 memory: 16095 grad_norm: 5.9458 loss: 0.6313 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.6313 2022/12/09 05:53:57 - mmengine - INFO - Epoch(train) [96][880/940] lr: 1.0000e-04 eta: 0:39:33 time: 0.5851 data_time: 0.0212 memory: 16095 grad_norm: 5.9760 loss: 0.6244 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6244 2022/12/09 05:54:09 - mmengine - INFO - Epoch(train) [96][900/940] lr: 1.0000e-04 eta: 0:39:21 time: 0.6330 data_time: 0.0263 memory: 16095 grad_norm: 6.2519 loss: 0.6912 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.6912 2022/12/09 05:54:21 - mmengine - INFO - Epoch(train) [96][920/940] lr: 1.0000e-04 eta: 0:39:08 time: 0.5579 data_time: 0.0224 memory: 16095 grad_norm: 6.0633 loss: 0.6397 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6397 2022/12/09 05:54:32 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 05:54:32 - mmengine - INFO - Epoch(train) [96][940/940] lr: 1.0000e-04 eta: 0:38:56 time: 0.5534 data_time: 0.0247 memory: 16095 grad_norm: 6.6855 loss: 0.7750 top1_acc: 0.4286 top5_acc: 1.0000 loss_cls: 0.7750 2022/12/09 05:54:32 - mmengine - INFO - Saving checkpoint at 96 epochs 2022/12/09 05:54:49 - mmengine - INFO - Epoch(val) [96][20/78] eta: 0:00:41 time: 0.7127 data_time: 0.6183 memory: 1686 2022/12/09 05:54:58 - mmengine - INFO - Epoch(val) [96][40/78] eta: 0:00:22 time: 0.4564 data_time: 0.3629 memory: 1686 2022/12/09 05:55:12 - mmengine - INFO - Epoch(val) [96][60/78] eta: 0:00:11 time: 0.6919 data_time: 0.5967 memory: 1686 2022/12/09 05:55:22 - mmengine - INFO - Epoch(val) [96][78/78] acc/top1: 0.6910 acc/top5: 0.8765 acc/mean1: 0.6909 2022/12/09 05:55:39 - mmengine - INFO - Epoch(train) [97][ 20/940] lr: 1.0000e-04 eta: 0:38:44 time: 0.8328 data_time: 0.4627 memory: 16095 grad_norm: 6.0589 loss: 0.6621 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6621 2022/12/09 05:55:49 - mmengine - INFO - Epoch(train) [97][ 40/940] lr: 1.0000e-04 eta: 0:38:31 time: 0.5288 data_time: 0.1647 memory: 16095 grad_norm: 6.1156 loss: 0.6749 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6749 2022/12/09 05:56:02 - mmengine - INFO - Epoch(train) [97][ 60/940] lr: 1.0000e-04 eta: 0:38:19 time: 0.6443 data_time: 0.2701 memory: 16095 grad_norm: 5.9739 loss: 0.5907 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.5907 2022/12/09 05:56:13 - mmengine - INFO - Epoch(train) [97][ 80/940] lr: 1.0000e-04 eta: 0:38:06 time: 0.5421 data_time: 0.1665 memory: 16095 grad_norm: 5.9696 loss: 0.6414 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6414 2022/12/09 05:56:27 - mmengine - INFO - Epoch(train) [97][100/940] lr: 1.0000e-04 eta: 0:37:54 time: 0.6943 data_time: 0.2089 memory: 16095 grad_norm: 6.2089 loss: 0.6879 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.6879 2022/12/09 05:56:38 - mmengine - INFO - Epoch(train) [97][120/940] lr: 1.0000e-04 eta: 0:37:41 time: 0.5411 data_time: 0.0805 memory: 16095 grad_norm: 6.1684 loss: 0.6699 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6699 2022/12/09 05:56:52 - mmengine - INFO - Epoch(train) [97][140/940] lr: 1.0000e-04 eta: 0:37:29 time: 0.7183 data_time: 0.0388 memory: 16095 grad_norm: 6.0670 loss: 0.6105 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6105 2022/12/09 05:57:03 - mmengine - INFO - Epoch(train) [97][160/940] lr: 1.0000e-04 eta: 0:37:16 time: 0.5447 data_time: 0.0358 memory: 16095 grad_norm: 5.8270 loss: 0.7169 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7169 2022/12/09 05:57:15 - mmengine - INFO - Epoch(train) [97][180/940] lr: 1.0000e-04 eta: 0:37:04 time: 0.6168 data_time: 0.1667 memory: 16095 grad_norm: 6.1492 loss: 0.6701 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6701 2022/12/09 05:57:28 - mmengine - INFO - Epoch(train) [97][200/940] lr: 1.0000e-04 eta: 0:36:52 time: 0.6278 data_time: 0.0361 memory: 16095 grad_norm: 6.2120 loss: 0.7477 top1_acc: 0.7188 top5_acc: 1.0000 loss_cls: 0.7477 2022/12/09 05:57:40 - mmengine - INFO - Epoch(train) [97][220/940] lr: 1.0000e-04 eta: 0:36:39 time: 0.5997 data_time: 0.0309 memory: 16095 grad_norm: 5.9389 loss: 0.7137 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.7137 2022/12/09 05:57:52 - mmengine - INFO - Epoch(train) [97][240/940] lr: 1.0000e-04 eta: 0:36:27 time: 0.6220 data_time: 0.0239 memory: 16095 grad_norm: 6.0586 loss: 0.6634 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6634 2022/12/09 05:58:04 - mmengine - INFO - Epoch(train) [97][260/940] lr: 1.0000e-04 eta: 0:36:14 time: 0.5807 data_time: 0.0258 memory: 16095 grad_norm: 6.1500 loss: 0.6262 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.6262 2022/12/09 05:58:17 - mmengine - INFO - Epoch(train) [97][280/940] lr: 1.0000e-04 eta: 0:36:02 time: 0.6355 data_time: 0.0266 memory: 16095 grad_norm: 6.1312 loss: 0.6562 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.6562 2022/12/09 05:58:28 - mmengine - INFO - Epoch(train) [97][300/940] lr: 1.0000e-04 eta: 0:35:49 time: 0.5780 data_time: 0.0768 memory: 16095 grad_norm: 6.0562 loss: 0.6816 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6816 2022/12/09 05:58:40 - mmengine - INFO - Epoch(train) [97][320/940] lr: 1.0000e-04 eta: 0:35:37 time: 0.5948 data_time: 0.1012 memory: 16095 grad_norm: 6.1357 loss: 0.7377 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.7377 2022/12/09 05:58:53 - mmengine - INFO - Epoch(train) [97][340/940] lr: 1.0000e-04 eta: 0:35:25 time: 0.6371 data_time: 0.1734 memory: 16095 grad_norm: 6.0610 loss: 0.6009 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.6009 2022/12/09 05:59:05 - mmengine - INFO - Epoch(train) [97][360/940] lr: 1.0000e-04 eta: 0:35:12 time: 0.6047 data_time: 0.0466 memory: 16095 grad_norm: 5.9994 loss: 0.6894 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6894 2022/12/09 05:59:18 - mmengine - INFO - Epoch(train) [97][380/940] lr: 1.0000e-04 eta: 0:35:00 time: 0.6259 data_time: 0.1027 memory: 16095 grad_norm: 6.0268 loss: 0.6385 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6385 2022/12/09 05:59:30 - mmengine - INFO - Epoch(train) [97][400/940] lr: 1.0000e-04 eta: 0:34:47 time: 0.6312 data_time: 0.2020 memory: 16095 grad_norm: 6.0456 loss: 0.6165 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6165 2022/12/09 05:59:43 - mmengine - INFO - Epoch(train) [97][420/940] lr: 1.0000e-04 eta: 0:34:35 time: 0.6295 data_time: 0.1076 memory: 16095 grad_norm: 5.9577 loss: 0.6822 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6822 2022/12/09 05:59:55 - mmengine - INFO - Epoch(train) [97][440/940] lr: 1.0000e-04 eta: 0:34:22 time: 0.5980 data_time: 0.0992 memory: 16095 grad_norm: 6.0393 loss: 0.6799 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6799 2022/12/09 06:00:08 - mmengine - INFO - Epoch(train) [97][460/940] lr: 1.0000e-04 eta: 0:34:10 time: 0.6598 data_time: 0.0324 memory: 16095 grad_norm: 6.1440 loss: 0.6186 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6186 2022/12/09 06:00:19 - mmengine - INFO - Epoch(train) [97][480/940] lr: 1.0000e-04 eta: 0:33:58 time: 0.5533 data_time: 0.0271 memory: 16095 grad_norm: 6.1068 loss: 0.6331 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6331 2022/12/09 06:00:31 - mmengine - INFO - Epoch(train) [97][500/940] lr: 1.0000e-04 eta: 0:33:45 time: 0.6137 data_time: 0.0220 memory: 16095 grad_norm: 6.3155 loss: 0.7218 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7218 2022/12/09 06:00:43 - mmengine - INFO - Epoch(train) [97][520/940] lr: 1.0000e-04 eta: 0:33:33 time: 0.6042 data_time: 0.0278 memory: 16095 grad_norm: 6.0473 loss: 0.5537 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5537 2022/12/09 06:00:56 - mmengine - INFO - Epoch(train) [97][540/940] lr: 1.0000e-04 eta: 0:33:20 time: 0.6400 data_time: 0.0200 memory: 16095 grad_norm: 6.1017 loss: 0.6684 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6684 2022/12/09 06:01:07 - mmengine - INFO - Epoch(train) [97][560/940] lr: 1.0000e-04 eta: 0:33:08 time: 0.5580 data_time: 0.0277 memory: 16095 grad_norm: 5.9253 loss: 0.6514 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6514 2022/12/09 06:01:21 - mmengine - INFO - Epoch(train) [97][580/940] lr: 1.0000e-04 eta: 0:32:55 time: 0.6948 data_time: 0.0228 memory: 16095 grad_norm: 6.1360 loss: 0.6816 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6816 2022/12/09 06:01:32 - mmengine - INFO - Epoch(train) [97][600/940] lr: 1.0000e-04 eta: 0:32:43 time: 0.5257 data_time: 0.0259 memory: 16095 grad_norm: 6.0416 loss: 0.7451 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7451 2022/12/09 06:01:45 - mmengine - INFO - Epoch(train) [97][620/940] lr: 1.0000e-04 eta: 0:32:30 time: 0.6448 data_time: 0.0213 memory: 16095 grad_norm: 6.1081 loss: 0.5836 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5836 2022/12/09 06:01:57 - mmengine - INFO - Epoch(train) [97][640/940] lr: 1.0000e-04 eta: 0:32:18 time: 0.6369 data_time: 0.0375 memory: 16095 grad_norm: 6.0963 loss: 0.6458 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6458 2022/12/09 06:02:09 - mmengine - INFO - Epoch(train) [97][660/940] lr: 1.0000e-04 eta: 0:32:06 time: 0.5901 data_time: 0.0207 memory: 16095 grad_norm: 6.0830 loss: 0.7091 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7091 2022/12/09 06:02:22 - mmengine - INFO - Epoch(train) [97][680/940] lr: 1.0000e-04 eta: 0:31:53 time: 0.6404 data_time: 0.0293 memory: 16095 grad_norm: 6.0907 loss: 0.7458 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.7458 2022/12/09 06:02:34 - mmengine - INFO - Epoch(train) [97][700/940] lr: 1.0000e-04 eta: 0:31:41 time: 0.5820 data_time: 0.0214 memory: 16095 grad_norm: 5.9689 loss: 0.6140 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6140 2022/12/09 06:02:47 - mmengine - INFO - Epoch(train) [97][720/940] lr: 1.0000e-04 eta: 0:31:28 time: 0.6450 data_time: 0.0264 memory: 16095 grad_norm: 6.0670 loss: 0.6946 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 0.6946 2022/12/09 06:02:58 - mmengine - INFO - Epoch(train) [97][740/940] lr: 1.0000e-04 eta: 0:31:16 time: 0.5757 data_time: 0.0212 memory: 16095 grad_norm: 6.0075 loss: 0.6175 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6175 2022/12/09 06:03:11 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 06:03:11 - mmengine - INFO - Epoch(train) [97][760/940] lr: 1.0000e-04 eta: 0:31:03 time: 0.6610 data_time: 0.0236 memory: 16095 grad_norm: 6.0861 loss: 0.6892 top1_acc: 0.8438 top5_acc: 0.8750 loss_cls: 0.6892 2022/12/09 06:03:23 - mmengine - INFO - Epoch(train) [97][780/940] lr: 1.0000e-04 eta: 0:30:51 time: 0.5788 data_time: 0.0257 memory: 16095 grad_norm: 6.0202 loss: 0.6651 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6651 2022/12/09 06:03:36 - mmengine - INFO - Epoch(train) [97][800/940] lr: 1.0000e-04 eta: 0:30:39 time: 0.6570 data_time: 0.0278 memory: 16095 grad_norm: 6.0877 loss: 0.6709 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.6709 2022/12/09 06:03:47 - mmengine - INFO - Epoch(train) [97][820/940] lr: 1.0000e-04 eta: 0:30:26 time: 0.5353 data_time: 0.0246 memory: 16095 grad_norm: 6.2286 loss: 0.7163 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.7163 2022/12/09 06:03:59 - mmengine - INFO - Epoch(train) [97][840/940] lr: 1.0000e-04 eta: 0:30:14 time: 0.6333 data_time: 0.0261 memory: 16095 grad_norm: 6.0069 loss: 0.6016 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6016 2022/12/09 06:04:10 - mmengine - INFO - Epoch(train) [97][860/940] lr: 1.0000e-04 eta: 0:30:01 time: 0.5483 data_time: 0.0231 memory: 16095 grad_norm: 5.9357 loss: 0.5623 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.5623 2022/12/09 06:04:23 - mmengine - INFO - Epoch(train) [97][880/940] lr: 1.0000e-04 eta: 0:29:49 time: 0.6498 data_time: 0.0257 memory: 16095 grad_norm: 6.0499 loss: 0.6842 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6842 2022/12/09 06:04:35 - mmengine - INFO - Epoch(train) [97][900/940] lr: 1.0000e-04 eta: 0:29:36 time: 0.5760 data_time: 0.0249 memory: 16095 grad_norm: 6.1754 loss: 0.6166 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 0.6166 2022/12/09 06:04:47 - mmengine - INFO - Epoch(train) [97][920/940] lr: 1.0000e-04 eta: 0:29:24 time: 0.5980 data_time: 0.0248 memory: 16095 grad_norm: 6.1413 loss: 0.5892 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.5892 2022/12/09 06:04:58 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 06:04:58 - mmengine - INFO - Epoch(train) [97][940/940] lr: 1.0000e-04 eta: 0:29:11 time: 0.5329 data_time: 0.0351 memory: 16095 grad_norm: 6.4896 loss: 0.6141 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 0.6141 2022/12/09 06:05:12 - mmengine - INFO - Epoch(val) [97][20/78] eta: 0:00:41 time: 0.7138 data_time: 0.6182 memory: 1686 2022/12/09 06:05:21 - mmengine - INFO - Epoch(val) [97][40/78] eta: 0:00:22 time: 0.4497 data_time: 0.3560 memory: 1686 2022/12/09 06:05:35 - mmengine - INFO - Epoch(val) [97][60/78] eta: 0:00:11 time: 0.6831 data_time: 0.5886 memory: 1686 2022/12/09 06:05:45 - mmengine - INFO - Epoch(val) [97][78/78] acc/top1: 0.6906 acc/top5: 0.8767 acc/mean1: 0.6905 2022/12/09 06:06:01 - mmengine - INFO - Epoch(train) [98][ 20/940] lr: 1.0000e-04 eta: 0:28:59 time: 0.8005 data_time: 0.4226 memory: 16095 grad_norm: 5.8189 loss: 0.5947 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.5947 2022/12/09 06:06:13 - mmengine - INFO - Epoch(train) [98][ 40/940] lr: 1.0000e-04 eta: 0:28:47 time: 0.5833 data_time: 0.1421 memory: 16095 grad_norm: 6.0040 loss: 0.5953 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.5953 2022/12/09 06:06:28 - mmengine - INFO - Epoch(train) [98][ 60/940] lr: 1.0000e-04 eta: 0:28:34 time: 0.7480 data_time: 0.2096 memory: 16095 grad_norm: 6.1085 loss: 0.5815 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.5815 2022/12/09 06:06:39 - mmengine - INFO - Epoch(train) [98][ 80/940] lr: 1.0000e-04 eta: 0:28:22 time: 0.5504 data_time: 0.0228 memory: 16095 grad_norm: 5.9709 loss: 0.6417 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6417 2022/12/09 06:06:52 - mmengine - INFO - Epoch(train) [98][100/940] lr: 1.0000e-04 eta: 0:28:09 time: 0.6512 data_time: 0.0733 memory: 16095 grad_norm: 6.0272 loss: 0.6979 top1_acc: 0.7188 top5_acc: 0.9062 loss_cls: 0.6979 2022/12/09 06:07:03 - mmengine - INFO - Epoch(train) [98][120/940] lr: 1.0000e-04 eta: 0:27:57 time: 0.5641 data_time: 0.0395 memory: 16095 grad_norm: 6.0714 loss: 0.6504 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6504 2022/12/09 06:07:17 - mmengine - INFO - Epoch(train) [98][140/940] lr: 1.0000e-04 eta: 0:27:45 time: 0.6812 data_time: 0.0541 memory: 16095 grad_norm: 6.0526 loss: 0.6501 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6501 2022/12/09 06:07:27 - mmengine - INFO - Epoch(train) [98][160/940] lr: 1.0000e-04 eta: 0:27:32 time: 0.5326 data_time: 0.0318 memory: 16095 grad_norm: 6.0341 loss: 0.6784 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6784 2022/12/09 06:07:40 - mmengine - INFO - Epoch(train) [98][180/940] lr: 1.0000e-04 eta: 0:27:20 time: 0.6442 data_time: 0.0554 memory: 16095 grad_norm: 6.0518 loss: 0.6904 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6904 2022/12/09 06:07:51 - mmengine - INFO - Epoch(train) [98][200/940] lr: 1.0000e-04 eta: 0:27:07 time: 0.5400 data_time: 0.0348 memory: 16095 grad_norm: 5.9786 loss: 0.5854 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.5854 2022/12/09 06:08:05 - mmengine - INFO - Epoch(train) [98][220/940] lr: 1.0000e-04 eta: 0:26:55 time: 0.6899 data_time: 0.0562 memory: 16095 grad_norm: 6.1152 loss: 0.5819 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.5819 2022/12/09 06:08:15 - mmengine - INFO - Epoch(train) [98][240/940] lr: 1.0000e-04 eta: 0:26:42 time: 0.5289 data_time: 0.0677 memory: 16095 grad_norm: 6.2029 loss: 0.7111 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7111 2022/12/09 06:08:28 - mmengine - INFO - Epoch(train) [98][260/940] lr: 1.0000e-04 eta: 0:26:30 time: 0.6400 data_time: 0.2594 memory: 16095 grad_norm: 6.3099 loss: 0.6921 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6921 2022/12/09 06:08:39 - mmengine - INFO - Epoch(train) [98][280/940] lr: 1.0000e-04 eta: 0:26:18 time: 0.5326 data_time: 0.1654 memory: 16095 grad_norm: 6.2304 loss: 0.7263 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7263 2022/12/09 06:08:53 - mmengine - INFO - Epoch(train) [98][300/940] lr: 1.0000e-04 eta: 0:26:05 time: 0.6895 data_time: 0.2939 memory: 16095 grad_norm: 5.9481 loss: 0.6044 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.6044 2022/12/09 06:09:04 - mmengine - INFO - Epoch(train) [98][320/940] lr: 1.0000e-04 eta: 0:25:53 time: 0.5602 data_time: 0.2345 memory: 16095 grad_norm: 6.1023 loss: 0.6517 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6517 2022/12/09 06:09:17 - mmengine - INFO - Epoch(train) [98][340/940] lr: 1.0000e-04 eta: 0:25:40 time: 0.6586 data_time: 0.3247 memory: 16095 grad_norm: 6.0026 loss: 0.6202 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6202 2022/12/09 06:09:29 - mmengine - INFO - Epoch(train) [98][360/940] lr: 1.0000e-04 eta: 0:25:28 time: 0.5685 data_time: 0.1949 memory: 16095 grad_norm: 6.0138 loss: 0.4889 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.4889 2022/12/09 06:09:42 - mmengine - INFO - Epoch(train) [98][380/940] lr: 1.0000e-04 eta: 0:25:15 time: 0.6861 data_time: 0.2584 memory: 16095 grad_norm: 5.9588 loss: 0.6956 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.6956 2022/12/09 06:09:53 - mmengine - INFO - Epoch(train) [98][400/940] lr: 1.0000e-04 eta: 0:25:03 time: 0.5483 data_time: 0.1232 memory: 16095 grad_norm: 5.9848 loss: 0.6158 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.6158 2022/12/09 06:10:07 - mmengine - INFO - Epoch(train) [98][420/940] lr: 1.0000e-04 eta: 0:24:51 time: 0.6782 data_time: 0.2907 memory: 16095 grad_norm: 6.1727 loss: 0.7287 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.7287 2022/12/09 06:10:18 - mmengine - INFO - Epoch(train) [98][440/940] lr: 1.0000e-04 eta: 0:24:38 time: 0.5534 data_time: 0.1208 memory: 16095 grad_norm: 6.0506 loss: 0.7277 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.7277 2022/12/09 06:10:31 - mmengine - INFO - Epoch(train) [98][460/940] lr: 1.0000e-04 eta: 0:24:26 time: 0.6788 data_time: 0.1720 memory: 16095 grad_norm: 5.9151 loss: 0.5563 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.5563 2022/12/09 06:10:42 - mmengine - INFO - Epoch(train) [98][480/940] lr: 1.0000e-04 eta: 0:24:13 time: 0.5490 data_time: 0.1697 memory: 16095 grad_norm: 5.9551 loss: 0.5704 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.5704 2022/12/09 06:10:55 - mmengine - INFO - Epoch(train) [98][500/940] lr: 1.0000e-04 eta: 0:24:01 time: 0.6336 data_time: 0.1412 memory: 16095 grad_norm: 6.0574 loss: 0.6436 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.6436 2022/12/09 06:11:06 - mmengine - INFO - Epoch(train) [98][520/940] lr: 1.0000e-04 eta: 0:23:48 time: 0.5486 data_time: 0.0569 memory: 16095 grad_norm: 5.8976 loss: 0.5975 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.5975 2022/12/09 06:11:19 - mmengine - INFO - Epoch(train) [98][540/940] lr: 1.0000e-04 eta: 0:23:36 time: 0.6629 data_time: 0.0241 memory: 16095 grad_norm: 6.0882 loss: 0.6354 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6354 2022/12/09 06:11:30 - mmengine - INFO - Epoch(train) [98][560/940] lr: 1.0000e-04 eta: 0:23:23 time: 0.5443 data_time: 0.0245 memory: 16095 grad_norm: 6.1186 loss: 0.5887 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5887 2022/12/09 06:11:44 - mmengine - INFO - Epoch(train) [98][580/940] lr: 1.0000e-04 eta: 0:23:11 time: 0.6926 data_time: 0.0239 memory: 16095 grad_norm: 5.9893 loss: 0.5953 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.5953 2022/12/09 06:11:55 - mmengine - INFO - Epoch(train) [98][600/940] lr: 1.0000e-04 eta: 0:22:59 time: 0.5277 data_time: 0.0248 memory: 16095 grad_norm: 6.0292 loss: 0.5973 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.5973 2022/12/09 06:12:09 - mmengine - INFO - Epoch(train) [98][620/940] lr: 1.0000e-04 eta: 0:22:46 time: 0.7134 data_time: 0.0274 memory: 16095 grad_norm: 5.9687 loss: 0.6419 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6419 2022/12/09 06:12:19 - mmengine - INFO - Epoch(train) [98][640/940] lr: 1.0000e-04 eta: 0:22:34 time: 0.5259 data_time: 0.0235 memory: 16095 grad_norm: 5.9683 loss: 0.6160 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6160 2022/12/09 06:12:32 - mmengine - INFO - Epoch(train) [98][660/940] lr: 1.0000e-04 eta: 0:22:21 time: 0.6420 data_time: 0.0254 memory: 16095 grad_norm: 5.9853 loss: 0.6247 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6247 2022/12/09 06:12:44 - mmengine - INFO - Epoch(train) [98][680/940] lr: 1.0000e-04 eta: 0:22:09 time: 0.5771 data_time: 0.0260 memory: 16095 grad_norm: 6.1088 loss: 0.6817 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6817 2022/12/09 06:12:57 - mmengine - INFO - Epoch(train) [98][700/940] lr: 1.0000e-04 eta: 0:21:57 time: 0.6608 data_time: 0.0307 memory: 16095 grad_norm: 5.9788 loss: 0.6255 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6255 2022/12/09 06:13:08 - mmengine - INFO - Epoch(train) [98][720/940] lr: 1.0000e-04 eta: 0:21:44 time: 0.5532 data_time: 0.0239 memory: 16095 grad_norm: 6.1036 loss: 0.5878 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5878 2022/12/09 06:13:22 - mmengine - INFO - Epoch(train) [98][740/940] lr: 1.0000e-04 eta: 0:21:32 time: 0.6914 data_time: 0.0251 memory: 16095 grad_norm: 6.0359 loss: 0.5996 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.5996 2022/12/09 06:13:33 - mmengine - INFO - Epoch(train) [98][760/940] lr: 1.0000e-04 eta: 0:21:19 time: 0.5432 data_time: 0.0278 memory: 16095 grad_norm: 6.0980 loss: 0.6764 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6764 2022/12/09 06:13:46 - mmengine - INFO - Epoch(train) [98][780/940] lr: 1.0000e-04 eta: 0:21:07 time: 0.6626 data_time: 0.0227 memory: 16095 grad_norm: 6.0118 loss: 0.5308 top1_acc: 0.9688 top5_acc: 0.9688 loss_cls: 0.5308 2022/12/09 06:13:58 - mmengine - INFO - Epoch(train) [98][800/940] lr: 1.0000e-04 eta: 0:20:54 time: 0.5770 data_time: 0.0259 memory: 16095 grad_norm: 6.0816 loss: 0.6361 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.6361 2022/12/09 06:14:11 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 06:14:11 - mmengine - INFO - Epoch(train) [98][820/940] lr: 1.0000e-04 eta: 0:20:42 time: 0.6791 data_time: 0.0254 memory: 16095 grad_norm: 6.2465 loss: 0.7269 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.7269 2022/12/09 06:14:22 - mmengine - INFO - Epoch(train) [98][840/940] lr: 1.0000e-04 eta: 0:20:29 time: 0.5260 data_time: 0.0239 memory: 16095 grad_norm: 6.1110 loss: 0.6563 top1_acc: 0.9062 top5_acc: 0.9375 loss_cls: 0.6563 2022/12/09 06:14:36 - mmengine - INFO - Epoch(train) [98][860/940] lr: 1.0000e-04 eta: 0:20:17 time: 0.7162 data_time: 0.0259 memory: 16095 grad_norm: 6.0388 loss: 0.6799 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6799 2022/12/09 06:14:47 - mmengine - INFO - Epoch(train) [98][880/940] lr: 1.0000e-04 eta: 0:20:05 time: 0.5605 data_time: 0.0258 memory: 16095 grad_norm: 6.0964 loss: 0.6194 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6194 2022/12/09 06:15:01 - mmengine - INFO - Epoch(train) [98][900/940] lr: 1.0000e-04 eta: 0:19:52 time: 0.6734 data_time: 0.0268 memory: 16095 grad_norm: 5.9497 loss: 0.5563 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.5563 2022/12/09 06:15:12 - mmengine - INFO - Epoch(train) [98][920/940] lr: 1.0000e-04 eta: 0:19:40 time: 0.5597 data_time: 0.0241 memory: 16095 grad_norm: 6.0573 loss: 0.6744 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6744 2022/12/09 06:15:24 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 06:15:24 - mmengine - INFO - Epoch(train) [98][940/940] lr: 1.0000e-04 eta: 0:19:27 time: 0.5751 data_time: 0.0173 memory: 16095 grad_norm: 6.4556 loss: 0.6094 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 0.6094 2022/12/09 06:15:37 - mmengine - INFO - Epoch(val) [98][20/78] eta: 0:00:40 time: 0.6905 data_time: 0.5961 memory: 1686 2022/12/09 06:15:47 - mmengine - INFO - Epoch(val) [98][40/78] eta: 0:00:22 time: 0.4734 data_time: 0.3801 memory: 1686 2022/12/09 06:16:00 - mmengine - INFO - Epoch(val) [98][60/78] eta: 0:00:11 time: 0.6708 data_time: 0.5758 memory: 1686 2022/12/09 06:16:11 - mmengine - INFO - Epoch(val) [98][78/78] acc/top1: 0.6902 acc/top5: 0.8779 acc/mean1: 0.6901 2022/12/09 06:16:27 - mmengine - INFO - Epoch(train) [99][ 20/940] lr: 1.0000e-04 eta: 0:19:15 time: 0.7808 data_time: 0.3949 memory: 16095 grad_norm: 6.0659 loss: 0.6050 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.6050 2022/12/09 06:16:38 - mmengine - INFO - Epoch(train) [99][ 40/940] lr: 1.0000e-04 eta: 0:19:03 time: 0.5622 data_time: 0.2030 memory: 16095 grad_norm: 5.9641 loss: 0.6211 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6211 2022/12/09 06:16:51 - mmengine - INFO - Epoch(train) [99][ 60/940] lr: 1.0000e-04 eta: 0:18:50 time: 0.6714 data_time: 0.2836 memory: 16095 grad_norm: 6.0364 loss: 0.6559 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6559 2022/12/09 06:17:02 - mmengine - INFO - Epoch(train) [99][ 80/940] lr: 1.0000e-04 eta: 0:18:38 time: 0.5429 data_time: 0.1184 memory: 16095 grad_norm: 5.8027 loss: 0.6535 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6535 2022/12/09 06:17:16 - mmengine - INFO - Epoch(train) [99][100/940] lr: 1.0000e-04 eta: 0:18:25 time: 0.7003 data_time: 0.1793 memory: 16095 grad_norm: 6.1075 loss: 0.7254 top1_acc: 0.8125 top5_acc: 0.8438 loss_cls: 0.7254 2022/12/09 06:17:26 - mmengine - INFO - Epoch(train) [99][120/940] lr: 1.0000e-04 eta: 0:18:13 time: 0.5148 data_time: 0.0630 memory: 16095 grad_norm: 6.0927 loss: 0.6404 top1_acc: 0.9688 top5_acc: 1.0000 loss_cls: 0.6404 2022/12/09 06:17:40 - mmengine - INFO - Epoch(train) [99][140/940] lr: 1.0000e-04 eta: 0:18:00 time: 0.6772 data_time: 0.1907 memory: 16095 grad_norm: 6.0788 loss: 0.5806 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.5806 2022/12/09 06:17:51 - mmengine - INFO - Epoch(train) [99][160/940] lr: 1.0000e-04 eta: 0:17:48 time: 0.5612 data_time: 0.2054 memory: 16095 grad_norm: 6.1146 loss: 0.6983 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6983 2022/12/09 06:18:04 - mmengine - INFO - Epoch(train) [99][180/940] lr: 1.0000e-04 eta: 0:17:36 time: 0.6593 data_time: 0.2811 memory: 16095 grad_norm: 5.9843 loss: 0.6494 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.6494 2022/12/09 06:18:16 - mmengine - INFO - Epoch(train) [99][200/940] lr: 1.0000e-04 eta: 0:17:23 time: 0.5523 data_time: 0.2425 memory: 16095 grad_norm: 6.0639 loss: 0.5864 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.5864 2022/12/09 06:18:30 - mmengine - INFO - Epoch(train) [99][220/940] lr: 1.0000e-04 eta: 0:17:11 time: 0.7238 data_time: 0.4102 memory: 16095 grad_norm: 6.1882 loss: 0.6357 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6357 2022/12/09 06:18:41 - mmengine - INFO - Epoch(train) [99][240/940] lr: 1.0000e-04 eta: 0:16:58 time: 0.5438 data_time: 0.2279 memory: 16095 grad_norm: 5.9946 loss: 0.6054 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6054 2022/12/09 06:18:54 - mmengine - INFO - Epoch(train) [99][260/940] lr: 1.0000e-04 eta: 0:16:46 time: 0.6657 data_time: 0.3461 memory: 16095 grad_norm: 6.0908 loss: 0.6106 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6106 2022/12/09 06:19:05 - mmengine - INFO - Epoch(train) [99][280/940] lr: 1.0000e-04 eta: 0:16:33 time: 0.5596 data_time: 0.2354 memory: 16095 grad_norm: 6.1176 loss: 0.5885 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.5885 2022/12/09 06:19:19 - mmengine - INFO - Epoch(train) [99][300/940] lr: 1.0000e-04 eta: 0:16:21 time: 0.6747 data_time: 0.3531 memory: 16095 grad_norm: 6.0303 loss: 0.6458 top1_acc: 0.7500 top5_acc: 0.9688 loss_cls: 0.6458 2022/12/09 06:19:29 - mmengine - INFO - Epoch(train) [99][320/940] lr: 1.0000e-04 eta: 0:16:09 time: 0.5156 data_time: 0.1960 memory: 16095 grad_norm: 5.8639 loss: 0.5847 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.5847 2022/12/09 06:19:42 - mmengine - INFO - Epoch(train) [99][340/940] lr: 1.0000e-04 eta: 0:15:56 time: 0.6415 data_time: 0.2958 memory: 16095 grad_norm: 6.0050 loss: 0.5154 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.5154 2022/12/09 06:19:53 - mmengine - INFO - Epoch(train) [99][360/940] lr: 1.0000e-04 eta: 0:15:44 time: 0.5667 data_time: 0.1500 memory: 16095 grad_norm: 6.0447 loss: 0.6110 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.6110 2022/12/09 06:20:07 - mmengine - INFO - Epoch(train) [99][380/940] lr: 1.0000e-04 eta: 0:15:31 time: 0.6602 data_time: 0.1980 memory: 16095 grad_norm: 6.0267 loss: 0.7063 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.7063 2022/12/09 06:20:17 - mmengine - INFO - Epoch(train) [99][400/940] lr: 1.0000e-04 eta: 0:15:19 time: 0.5364 data_time: 0.1316 memory: 16095 grad_norm: 5.9349 loss: 0.6850 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6850 2022/12/09 06:20:32 - mmengine - INFO - Epoch(train) [99][420/940] lr: 1.0000e-04 eta: 0:15:06 time: 0.7323 data_time: 0.0706 memory: 16095 grad_norm: 6.0619 loss: 0.5591 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.5591 2022/12/09 06:20:42 - mmengine - INFO - Epoch(train) [99][440/940] lr: 1.0000e-04 eta: 0:14:54 time: 0.5163 data_time: 0.0264 memory: 16095 grad_norm: 6.0937 loss: 0.7035 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.7035 2022/12/09 06:20:56 - mmengine - INFO - Epoch(train) [99][460/940] lr: 1.0000e-04 eta: 0:14:42 time: 0.6859 data_time: 0.0272 memory: 16095 grad_norm: 5.9511 loss: 0.7025 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.7025 2022/12/09 06:21:08 - mmengine - INFO - Epoch(train) [99][480/940] lr: 1.0000e-04 eta: 0:14:29 time: 0.6033 data_time: 0.0234 memory: 16095 grad_norm: 6.0729 loss: 0.7030 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7030 2022/12/09 06:21:20 - mmengine - INFO - Epoch(train) [99][500/940] lr: 1.0000e-04 eta: 0:14:17 time: 0.6181 data_time: 0.0248 memory: 16095 grad_norm: 6.0585 loss: 0.6890 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6890 2022/12/09 06:21:31 - mmengine - INFO - Epoch(train) [99][520/940] lr: 1.0000e-04 eta: 0:14:04 time: 0.5365 data_time: 0.0257 memory: 16095 grad_norm: 5.8585 loss: 0.5916 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.5916 2022/12/09 06:21:44 - mmengine - INFO - Epoch(train) [99][540/940] lr: 1.0000e-04 eta: 0:13:52 time: 0.6444 data_time: 0.0258 memory: 16095 grad_norm: 6.4197 loss: 0.7341 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.7341 2022/12/09 06:21:55 - mmengine - INFO - Epoch(train) [99][560/940] lr: 1.0000e-04 eta: 0:13:39 time: 0.5576 data_time: 0.0268 memory: 16095 grad_norm: 5.9121 loss: 0.6645 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.6645 2022/12/09 06:22:09 - mmengine - INFO - Epoch(train) [99][580/940] lr: 1.0000e-04 eta: 0:13:27 time: 0.6727 data_time: 0.0255 memory: 16095 grad_norm: 5.9858 loss: 0.7048 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.7048 2022/12/09 06:22:21 - mmengine - INFO - Epoch(train) [99][600/940] lr: 1.0000e-04 eta: 0:13:15 time: 0.5961 data_time: 0.0232 memory: 16095 grad_norm: 5.9561 loss: 0.6153 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.6153 2022/12/09 06:22:34 - mmengine - INFO - Epoch(train) [99][620/940] lr: 1.0000e-04 eta: 0:13:02 time: 0.6862 data_time: 0.0266 memory: 16095 grad_norm: 6.0043 loss: 0.6876 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6876 2022/12/09 06:22:46 - mmengine - INFO - Epoch(train) [99][640/940] lr: 1.0000e-04 eta: 0:12:50 time: 0.5852 data_time: 0.0209 memory: 16095 grad_norm: 6.0694 loss: 0.6569 top1_acc: 0.8438 top5_acc: 0.9062 loss_cls: 0.6569 2022/12/09 06:22:59 - mmengine - INFO - Epoch(train) [99][660/940] lr: 1.0000e-04 eta: 0:12:37 time: 0.6640 data_time: 0.0261 memory: 16095 grad_norm: 6.2044 loss: 0.6579 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6579 2022/12/09 06:23:11 - mmengine - INFO - Epoch(train) [99][680/940] lr: 1.0000e-04 eta: 0:12:25 time: 0.5769 data_time: 0.0226 memory: 16095 grad_norm: 5.9882 loss: 0.6634 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6634 2022/12/09 06:23:24 - mmengine - INFO - Epoch(train) [99][700/940] lr: 1.0000e-04 eta: 0:12:12 time: 0.6525 data_time: 0.0268 memory: 16095 grad_norm: 5.9714 loss: 0.6277 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6277 2022/12/09 06:23:35 - mmengine - INFO - Epoch(train) [99][720/940] lr: 1.0000e-04 eta: 0:12:00 time: 0.5551 data_time: 0.0223 memory: 16095 grad_norm: 6.0286 loss: 0.6884 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6884 2022/12/09 06:23:48 - mmengine - INFO - Epoch(train) [99][740/940] lr: 1.0000e-04 eta: 0:11:48 time: 0.6501 data_time: 0.0283 memory: 16095 grad_norm: 6.0308 loss: 0.6786 top1_acc: 0.8750 top5_acc: 0.9688 loss_cls: 0.6786 2022/12/09 06:24:00 - mmengine - INFO - Epoch(train) [99][760/940] lr: 1.0000e-04 eta: 0:11:35 time: 0.5884 data_time: 0.0224 memory: 16095 grad_norm: 6.0413 loss: 0.7012 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.7012 2022/12/09 06:24:13 - mmengine - INFO - Epoch(train) [99][780/940] lr: 1.0000e-04 eta: 0:11:23 time: 0.6740 data_time: 0.0276 memory: 16095 grad_norm: 6.0589 loss: 0.5530 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.5530 2022/12/09 06:24:24 - mmengine - INFO - Epoch(train) [99][800/940] lr: 1.0000e-04 eta: 0:11:10 time: 0.5386 data_time: 0.0214 memory: 16095 grad_norm: 5.9397 loss: 0.6075 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6075 2022/12/09 06:24:38 - mmengine - INFO - Epoch(train) [99][820/940] lr: 1.0000e-04 eta: 0:10:58 time: 0.6724 data_time: 0.0374 memory: 16095 grad_norm: 6.2076 loss: 0.6879 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 0.6879 2022/12/09 06:24:48 - mmengine - INFO - Epoch(train) [99][840/940] lr: 1.0000e-04 eta: 0:10:46 time: 0.5306 data_time: 0.0220 memory: 16095 grad_norm: 6.0664 loss: 0.6489 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6489 2022/12/09 06:25:03 - mmengine - INFO - Epoch(train) [99][860/940] lr: 1.0000e-04 eta: 0:10:33 time: 0.7227 data_time: 0.0287 memory: 16095 grad_norm: 5.9402 loss: 0.6287 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6287 2022/12/09 06:25:13 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 06:25:13 - mmengine - INFO - Epoch(train) [99][880/940] lr: 1.0000e-04 eta: 0:10:21 time: 0.5253 data_time: 0.0201 memory: 16095 grad_norm: 5.9812 loss: 0.5292 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.5292 2022/12/09 06:25:27 - mmengine - INFO - Epoch(train) [99][900/940] lr: 1.0000e-04 eta: 0:10:08 time: 0.6734 data_time: 0.0326 memory: 16095 grad_norm: 6.2533 loss: 0.7112 top1_acc: 0.9375 top5_acc: 0.9688 loss_cls: 0.7112 2022/12/09 06:25:38 - mmengine - INFO - Epoch(train) [99][920/940] lr: 1.0000e-04 eta: 0:09:56 time: 0.5439 data_time: 0.0222 memory: 16095 grad_norm: 6.0941 loss: 0.6589 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6589 2022/12/09 06:25:49 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 06:25:49 - mmengine - INFO - Epoch(train) [99][940/940] lr: 1.0000e-04 eta: 0:09:43 time: 0.5801 data_time: 0.0171 memory: 16095 grad_norm: 6.5890 loss: 0.6464 top1_acc: 0.5714 top5_acc: 1.0000 loss_cls: 0.6464 2022/12/09 06:25:49 - mmengine - INFO - Saving checkpoint at 99 epochs 2022/12/09 06:26:06 - mmengine - INFO - Epoch(val) [99][20/78] eta: 0:00:40 time: 0.7061 data_time: 0.6109 memory: 1686 2022/12/09 06:26:16 - mmengine - INFO - Epoch(val) [99][40/78] eta: 0:00:22 time: 0.4614 data_time: 0.3677 memory: 1686 2022/12/09 06:26:29 - mmengine - INFO - Epoch(val) [99][60/78] eta: 0:00:11 time: 0.6847 data_time: 0.5899 memory: 1686 2022/12/09 06:26:39 - mmengine - INFO - Epoch(val) [99][78/78] acc/top1: 0.6920 acc/top5: 0.8784 acc/mean1: 0.6919 2022/12/09 06:26:55 - mmengine - INFO - Epoch(train) [100][ 20/940] lr: 1.0000e-04 eta: 0:09:31 time: 0.7927 data_time: 0.4274 memory: 16095 grad_norm: 6.1470 loss: 0.6893 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.6893 2022/12/09 06:27:06 - mmengine - INFO - Epoch(train) [100][ 40/940] lr: 1.0000e-04 eta: 0:09:19 time: 0.5755 data_time: 0.1799 memory: 16095 grad_norm: 6.1367 loss: 0.6708 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6708 2022/12/09 06:27:20 - mmengine - INFO - Epoch(train) [100][ 60/940] lr: 1.0000e-04 eta: 0:09:06 time: 0.7102 data_time: 0.2064 memory: 16095 grad_norm: 5.9711 loss: 0.6025 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6025 2022/12/09 06:27:32 - mmengine - INFO - Epoch(train) [100][ 80/940] lr: 1.0000e-04 eta: 0:08:54 time: 0.5624 data_time: 0.1292 memory: 16095 grad_norm: 6.0235 loss: 0.6357 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.6357 2022/12/09 06:27:44 - mmengine - INFO - Epoch(train) [100][100/940] lr: 1.0000e-04 eta: 0:08:41 time: 0.6393 data_time: 0.2155 memory: 16095 grad_norm: 6.1541 loss: 0.6705 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6705 2022/12/09 06:27:56 - mmengine - INFO - Epoch(train) [100][120/940] lr: 1.0000e-04 eta: 0:08:29 time: 0.5794 data_time: 0.2719 memory: 16095 grad_norm: 6.1269 loss: 0.5957 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 0.5957 2022/12/09 06:28:09 - mmengine - INFO - Epoch(train) [100][140/940] lr: 1.0000e-04 eta: 0:08:16 time: 0.6726 data_time: 0.3360 memory: 16095 grad_norm: 6.0752 loss: 0.6832 top1_acc: 0.7812 top5_acc: 0.8750 loss_cls: 0.6832 2022/12/09 06:28:20 - mmengine - INFO - Epoch(train) [100][160/940] lr: 1.0000e-04 eta: 0:08:04 time: 0.5503 data_time: 0.2258 memory: 16095 grad_norm: 6.0656 loss: 0.5674 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.5674 2022/12/09 06:28:34 - mmengine - INFO - Epoch(train) [100][180/940] lr: 1.0000e-04 eta: 0:07:52 time: 0.6733 data_time: 0.3443 memory: 16095 grad_norm: 6.1715 loss: 0.6609 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6609 2022/12/09 06:28:45 - mmengine - INFO - Epoch(train) [100][200/940] lr: 1.0000e-04 eta: 0:07:39 time: 0.5602 data_time: 0.2298 memory: 16095 grad_norm: 6.0709 loss: 0.5987 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.5987 2022/12/09 06:28:58 - mmengine - INFO - Epoch(train) [100][220/940] lr: 1.0000e-04 eta: 0:07:27 time: 0.6582 data_time: 0.3147 memory: 16095 grad_norm: 5.9621 loss: 0.5808 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.5808 2022/12/09 06:29:08 - mmengine - INFO - Epoch(train) [100][240/940] lr: 1.0000e-04 eta: 0:07:14 time: 0.5113 data_time: 0.1828 memory: 16095 grad_norm: 5.8796 loss: 0.6370 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6370 2022/12/09 06:29:21 - mmengine - INFO - Epoch(train) [100][260/940] lr: 1.0000e-04 eta: 0:07:02 time: 0.6349 data_time: 0.2958 memory: 16095 grad_norm: 5.9657 loss: 0.6939 top1_acc: 0.8438 top5_acc: 1.0000 loss_cls: 0.6939 2022/12/09 06:29:32 - mmengine - INFO - Epoch(train) [100][280/940] lr: 1.0000e-04 eta: 0:06:49 time: 0.5587 data_time: 0.1995 memory: 16095 grad_norm: 6.0966 loss: 0.6568 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 0.6568 2022/12/09 06:29:46 - mmengine - INFO - Epoch(train) [100][300/940] lr: 1.0000e-04 eta: 0:06:37 time: 0.6899 data_time: 0.3584 memory: 16095 grad_norm: 6.0726 loss: 0.6316 top1_acc: 0.7812 top5_acc: 0.9375 loss_cls: 0.6316 2022/12/09 06:29:58 - mmengine - INFO - Epoch(train) [100][320/940] lr: 1.0000e-04 eta: 0:06:25 time: 0.5972 data_time: 0.2729 memory: 16095 grad_norm: 6.0941 loss: 0.6718 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6718 2022/12/09 06:30:11 - mmengine - INFO - Epoch(train) [100][340/940] lr: 1.0000e-04 eta: 0:06:12 time: 0.6398 data_time: 0.3028 memory: 16095 grad_norm: 5.9842 loss: 0.6366 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6366 2022/12/09 06:30:22 - mmengine - INFO - Epoch(train) [100][360/940] lr: 1.0000e-04 eta: 0:06:00 time: 0.5625 data_time: 0.2397 memory: 16095 grad_norm: 6.1639 loss: 0.6557 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.6557 2022/12/09 06:30:36 - mmengine - INFO - Epoch(train) [100][380/940] lr: 1.0000e-04 eta: 0:05:47 time: 0.6686 data_time: 0.3401 memory: 16095 grad_norm: 6.0198 loss: 0.6611 top1_acc: 0.7812 top5_acc: 0.9688 loss_cls: 0.6611 2022/12/09 06:30:47 - mmengine - INFO - Epoch(train) [100][400/940] lr: 1.0000e-04 eta: 0:05:35 time: 0.5557 data_time: 0.2328 memory: 16095 grad_norm: 6.0706 loss: 0.6418 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6418 2022/12/09 06:31:01 - mmengine - INFO - Epoch(train) [100][420/940] lr: 1.0000e-04 eta: 0:05:22 time: 0.6986 data_time: 0.3768 memory: 16095 grad_norm: 5.9890 loss: 0.6733 top1_acc: 0.7812 top5_acc: 1.0000 loss_cls: 0.6733 2022/12/09 06:31:12 - mmengine - INFO - Epoch(train) [100][440/940] lr: 1.0000e-04 eta: 0:05:10 time: 0.5498 data_time: 0.2336 memory: 16095 grad_norm: 6.2061 loss: 0.6613 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.6613 2022/12/09 06:31:25 - mmengine - INFO - Epoch(train) [100][460/940] lr: 1.0000e-04 eta: 0:04:58 time: 0.6875 data_time: 0.3559 memory: 16095 grad_norm: 5.9773 loss: 0.5836 top1_acc: 0.7812 top5_acc: 0.8438 loss_cls: 0.5836 2022/12/09 06:31:37 - mmengine - INFO - Epoch(train) [100][480/940] lr: 1.0000e-04 eta: 0:04:45 time: 0.5599 data_time: 0.2174 memory: 16095 grad_norm: 6.1360 loss: 0.6880 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6880 2022/12/09 06:31:50 - mmengine - INFO - Epoch(train) [100][500/940] lr: 1.0000e-04 eta: 0:04:33 time: 0.6881 data_time: 0.3541 memory: 16095 grad_norm: 6.0081 loss: 0.5129 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.5129 2022/12/09 06:32:01 - mmengine - INFO - Epoch(train) [100][520/940] lr: 1.0000e-04 eta: 0:04:20 time: 0.5453 data_time: 0.2162 memory: 16095 grad_norm: 6.0304 loss: 0.7111 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.7111 2022/12/09 06:32:14 - mmengine - INFO - Epoch(train) [100][540/940] lr: 1.0000e-04 eta: 0:04:08 time: 0.6505 data_time: 0.3149 memory: 16095 grad_norm: 6.0311 loss: 0.5703 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.5703 2022/12/09 06:32:26 - mmengine - INFO - Epoch(train) [100][560/940] lr: 1.0000e-04 eta: 0:03:56 time: 0.5651 data_time: 0.2395 memory: 16095 grad_norm: 6.1310 loss: 0.6388 top1_acc: 0.8438 top5_acc: 0.9375 loss_cls: 0.6388 2022/12/09 06:32:39 - mmengine - INFO - Epoch(train) [100][580/940] lr: 1.0000e-04 eta: 0:03:43 time: 0.6563 data_time: 0.3090 memory: 16095 grad_norm: 6.1241 loss: 0.6660 top1_acc: 0.7500 top5_acc: 0.8438 loss_cls: 0.6660 2022/12/09 06:32:50 - mmengine - INFO - Epoch(train) [100][600/940] lr: 1.0000e-04 eta: 0:03:31 time: 0.5428 data_time: 0.2107 memory: 16095 grad_norm: 6.1198 loss: 0.6897 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6897 2022/12/09 06:33:02 - mmengine - INFO - Epoch(train) [100][620/940] lr: 1.0000e-04 eta: 0:03:18 time: 0.6111 data_time: 0.2697 memory: 16095 grad_norm: 6.1092 loss: 0.6414 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6414 2022/12/09 06:33:14 - mmengine - INFO - Epoch(train) [100][640/940] lr: 1.0000e-04 eta: 0:03:06 time: 0.5969 data_time: 0.2290 memory: 16095 grad_norm: 6.1421 loss: 0.6751 top1_acc: 0.7188 top5_acc: 0.9688 loss_cls: 0.6751 2022/12/09 06:33:29 - mmengine - INFO - Epoch(train) [100][660/940] lr: 1.0000e-04 eta: 0:02:53 time: 0.7471 data_time: 0.2736 memory: 16095 grad_norm: 6.0650 loss: 0.6289 top1_acc: 0.8750 top5_acc: 0.9062 loss_cls: 0.6289 2022/12/09 06:33:39 - mmengine - INFO - Epoch(train) [100][680/940] lr: 1.0000e-04 eta: 0:02:41 time: 0.5130 data_time: 0.1207 memory: 16095 grad_norm: 6.0300 loss: 0.6110 top1_acc: 0.8125 top5_acc: 0.9688 loss_cls: 0.6110 2022/12/09 06:33:52 - mmengine - INFO - Epoch(train) [100][700/940] lr: 1.0000e-04 eta: 0:02:29 time: 0.6524 data_time: 0.2115 memory: 16095 grad_norm: 6.0298 loss: 0.6082 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.6082 2022/12/09 06:34:04 - mmengine - INFO - Epoch(train) [100][720/940] lr: 1.0000e-04 eta: 0:02:16 time: 0.5984 data_time: 0.0747 memory: 16095 grad_norm: 6.0307 loss: 0.6130 top1_acc: 0.7188 top5_acc: 0.8750 loss_cls: 0.6130 2022/12/09 06:34:17 - mmengine - INFO - Epoch(train) [100][740/940] lr: 1.0000e-04 eta: 0:02:04 time: 0.6634 data_time: 0.0429 memory: 16095 grad_norm: 6.2273 loss: 0.5769 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.5769 2022/12/09 06:34:28 - mmengine - INFO - Epoch(train) [100][760/940] lr: 1.0000e-04 eta: 0:01:51 time: 0.5368 data_time: 0.0228 memory: 16095 grad_norm: 6.0934 loss: 0.6913 top1_acc: 0.8125 top5_acc: 0.9062 loss_cls: 0.6913 2022/12/09 06:34:40 - mmengine - INFO - Epoch(train) [100][780/940] lr: 1.0000e-04 eta: 0:01:39 time: 0.6132 data_time: 0.1603 memory: 16095 grad_norm: 6.2210 loss: 0.5898 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.5898 2022/12/09 06:34:53 - mmengine - INFO - Epoch(train) [100][800/940] lr: 1.0000e-04 eta: 0:01:26 time: 0.6242 data_time: 0.1684 memory: 16095 grad_norm: 6.1260 loss: 0.6573 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6573 2022/12/09 06:35:05 - mmengine - INFO - Epoch(train) [100][820/940] lr: 1.0000e-04 eta: 0:01:14 time: 0.6189 data_time: 0.1871 memory: 16095 grad_norm: 6.0617 loss: 0.6526 top1_acc: 0.8438 top5_acc: 0.9688 loss_cls: 0.6526 2022/12/09 06:35:18 - mmengine - INFO - Epoch(train) [100][840/940] lr: 1.0000e-04 eta: 0:01:02 time: 0.6247 data_time: 0.1603 memory: 16095 grad_norm: 6.1471 loss: 0.7236 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.7236 2022/12/09 06:35:30 - mmengine - INFO - Epoch(train) [100][860/940] lr: 1.0000e-04 eta: 0:00:49 time: 0.6004 data_time: 0.1408 memory: 16095 grad_norm: 6.1193 loss: 0.6882 top1_acc: 0.7500 top5_acc: 0.9062 loss_cls: 0.6882 2022/12/09 06:35:42 - mmengine - INFO - Epoch(train) [100][880/940] lr: 1.0000e-04 eta: 0:00:37 time: 0.6071 data_time: 0.1428 memory: 16095 grad_norm: 6.0571 loss: 0.6350 top1_acc: 0.9062 top5_acc: 1.0000 loss_cls: 0.6350 2022/12/09 06:35:55 - mmengine - INFO - Epoch(train) [100][900/940] lr: 1.0000e-04 eta: 0:00:24 time: 0.6334 data_time: 0.2500 memory: 16095 grad_norm: 5.9836 loss: 0.6808 top1_acc: 0.7812 top5_acc: 0.9062 loss_cls: 0.6808 2022/12/09 06:36:06 - mmengine - INFO - Epoch(train) [100][920/940] lr: 1.0000e-04 eta: 0:00:12 time: 0.5866 data_time: 0.2407 memory: 16095 grad_norm: 5.9394 loss: 0.6259 top1_acc: 0.9062 top5_acc: 0.9688 loss_cls: 0.6259 2022/12/09 06:36:19 - mmengine - INFO - Exp name: tsn_imagenet-pretrained-rn101-32x4d_8xb32-1x1x3-100e_kinetics400-rgb_20221208_125747 2022/12/09 06:36:19 - mmengine - INFO - Epoch(train) [100][940/940] lr: 1.0000e-04 eta: 0:00:00 time: 0.6334 data_time: 0.3333 memory: 16095 grad_norm: 6.6318 loss: 0.7319 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 0.7319 2022/12/09 06:36:19 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/12/09 06:36:36 - mmengine - INFO - Epoch(val) [100][20/78] eta: 0:00:41 time: 0.7150 data_time: 0.6204 memory: 1686 2022/12/09 06:36:45 - mmengine - INFO - Epoch(val) [100][40/78] eta: 0:00:22 time: 0.4603 data_time: 0.3666 memory: 1686 2022/12/09 06:36:59 - mmengine - INFO - Epoch(val) [100][60/78] eta: 0:00:11 time: 0.6749 data_time: 0.5799 memory: 1686 2022/12/09 06:37:09 - mmengine - INFO - Epoch(val) [100][78/78] acc/top1: 0.6901 acc/top5: 0.8777 acc/mean1: 0.6900